Dependency version upgrades + small SameDiff fix (#405)
* #8861 Training evaluation on variables not required for loss fix Signed-off-by: Alex Black <blacka101@gmail.com> * Dependency version updates flagged by dependabot Signed-off-by: Alex Black <blacka101@gmail.com>master
Alex Black
GitHub
parent
8f765c80ff
commit
f5f77df846
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@ -132,7 +132,7 @@ public abstract class AbstractSession<T, O> {
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Preconditions.checkState(!variables.isEmpty() || !requiredActivations.isEmpty(), "Variables to perform forward pass for must not be empty");
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if (requiredActivations == null)
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requiredActivations = Collections.emptyList();
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requiredActivations = Collections.emptySet();
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if (at == null)
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at = At.defaultAt();
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@ -75,7 +75,7 @@ public class TrainingSession extends InferenceSession {
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this.listeners = filtered.isEmpty() ? null : filtered;
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}
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List<String> requiredActivations = new ArrayList<>();
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Set<String> requiredActivations = new HashSet<>();
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gradVarToVarMap = new HashMap<>(); //Key: gradient variable. Value: variable that the key is gradient for
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for (String s : paramsToTrain) {
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Preconditions.checkState(sameDiff.hasVariable(s), "SameDiff instance does not have a variable with name \"%s\"", s);
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@ -95,6 +95,12 @@ public class TrainingSession extends InferenceSession {
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gradVarToVarMap.put(grad.name(), s);
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}
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//Also add evaluations - in case we want to evaluate something that isn't required to determine loss
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// (hence wouldn't normally be calculated)
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if(config.getTrainEvaluations() != null){
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requiredActivations.addAll(config.getTrainEvaluations().keySet());
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}
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//Set up losses
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lossVarsToLossIdx = new LinkedHashMap<>();
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List<String> lossVars;
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@ -16,6 +16,7 @@
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package org.nd4j.autodiff.samediff;
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import static org.junit.Assert.assertEquals;
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import static org.junit.Assert.assertTrue;
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import java.util.Collections;
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@ -35,6 +36,7 @@ import org.nd4j.linalg.api.ndarray.INDArray;
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import org.nd4j.linalg.dataset.DataSet;
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import org.nd4j.linalg.dataset.IrisDataSetIterator;
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import org.nd4j.linalg.dataset.MultiDataSet;
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import org.nd4j.linalg.dataset.adapter.SingletonDataSetIterator;
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import org.nd4j.linalg.dataset.adapter.SingletonMultiDataSetIterator;
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import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
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import org.nd4j.linalg.dataset.api.preprocessor.NormalizerStandardize;
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@ -341,6 +343,39 @@ public class SameDiffTrainingTest extends BaseNd4jTest {
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History history = sd.fit(new SingletonMultiDataSetIterator(mds), 1);
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}
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@Test
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public void testTrainingEvalVarNotReqForLoss(){
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//If a variable is not required for the loss - normally it won't be calculated
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//But we want to make sure it IS calculated here - so we can perform evaluation on it
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SameDiff sd = SameDiff.create();
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SDVariable in = sd.placeHolder("in", DataType.FLOAT, -1, 4);
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SDVariable label = sd.placeHolder("label", DataType.FLOAT, -1, 3);
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SDVariable w = sd.var("w", Nd4j.rand(DataType.FLOAT, 4, 3));
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SDVariable z = in.mmul(w);
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SDVariable out = sd.nn.softmax("softmax", z);
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SDVariable loss = sd.loss.logLoss("loss", label, out);
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SDVariable notRequiredForLoss = sd.nn.softmax("notRequiredForLoss", z);
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sd.setTrainingConfig(TrainingConfig.builder()
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.updater(new Adam(0.001))
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.dataSetFeatureMapping("in")
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.dataSetLabelMapping("label")
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.trainEvaluation("notRequiredForLoss", 0, new Evaluation())
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.build());
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// sd.setListeners(new ScoreListener(1));
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DataSet ds = new DataSet(Nd4j.rand(DataType.FLOAT, 3, 4), Nd4j.createFromArray(new float[][]{{1,0,0}, {0,1,0}, {0,0,1}}));
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History h = sd.fit()
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.train(new SingletonDataSetIterator(ds), 4)
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.exec();
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List<Double> l = h.trainingEval(Evaluation.Metric.ACCURACY);
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assertEquals(4, l.size());
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}
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@Override
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public char ordering() {
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@ -40,7 +40,7 @@
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<dependency>
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<groupId>com.mchange</groupId>
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<artifactId>c3p0</artifactId>
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<version>0.9.5-pre5</version>
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<version>0.9.5.4</version>
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</dependency>
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<dependency>
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<groupId>org.nd4j</groupId>
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@ -121,7 +121,7 @@
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<dependency>
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<groupId>javax.activation</groupId>
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<artifactId>activation</artifactId>
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<version>1.1</version>
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<version>1.1.1</version>
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</dependency>
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<dependency>
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10
pom.xml
10
pom.xml
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@ -271,7 +271,7 @@
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<jodah.typetools.version>0.5.0</jodah.typetools.version>
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<freemarker.version>2.3.23</freemarker.version>
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<geoip2.version>2.8.1</geoip2.version>
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<stream.analytics.version>2.7.0</stream.analytics.version>
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<stream.analytics.version>2.9.8</stream.analytics.version>
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<opencsv.version>2.3</opencsv.version>
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<tdigest.version>3.2</tdigest.version>
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<jtransforms.version>3.1</jtransforms.version>
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@ -325,14 +325,14 @@
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<commons-collections.version>3.2.2</commons-collections.version>
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<commons-collections4.version>4.1</commons-collections4.version>
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<spark.version>2.4.3</spark.version>
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<spark.version>2.4.5</spark.version>
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<spark.major.version>2</spark.major.version>
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<args4j.version>2.0.29</args4j.version>
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<slf4j.version>1.7.21</slf4j.version>
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<junit.version>4.12</junit.version>
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<logback.version>1.2.3</logback.version>
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<jackson.version>2.10.1</jackson.version>
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<jackson.databind.version>2.10.1</jackson.databind.version>
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<jackson.databind.version>2.10.3</jackson.databind.version>
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<shaded.snakeyaml.version>1.24</shaded.snakeyaml.version>
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<geo.jackson.version>2.8.7</geo.jackson.version>
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<lombok.version>1.18.12</lombok.version>
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@ -353,7 +353,7 @@
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<gson.version>2.8.0</gson.version>
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<fbs.version>1.2.0-3f79e055</fbs.version>
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<threadly.version>4.10.0</threadly.version>
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<vertx.version>3.8.3</vertx.version>
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<vertx.version>3.9.0</vertx.version>
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<flatbuffers.version>1.10.0</flatbuffers.version>
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<grpc.version>1.14.0</grpc.version>
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@ -368,7 +368,7 @@
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<maven-compiler-plugin.version>3.7.0</maven-compiler-plugin.version>
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<maven-scala-plugin.version>3.3.1</maven-scala-plugin.version>
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<maven-resources-plugin.version>3.0.1</maven-resources-plugin.version>
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<sbt-compiler-maven-plugin.version>1.0.0-beta8</sbt-compiler-maven-plugin.version>
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<sbt-compiler-maven-plugin.version>1.0.0</sbt-compiler-maven-plugin.version>
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<maven-git-commit-plugin.version>2.2.2</maven-git-commit-plugin.version>
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<maven-git-commit-id-plugin.version>${maven-git-commit-plugin.version}
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</maven-git-commit-id-plugin.version>
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