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MLPMnistSingleLayerEvaluate.java
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MLPMnistSingleLayerEvaluate.java
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package org.deeplearning4j.feedforward.mnist;
import org.deeplearning4j.datasets.iterator.impl.MnistDataSetIterator;
import org.deeplearning4j.eval.Evaluation;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.jetbrains.annotations.NotNull;
import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import java.io.File;
import java.io.IOException;
import java.nio.file.Paths;
public class MLPMnistSingleLayerEvaluate {
private static Logger log = LoggerFactory.getLogger(MLPMnistSingleLayerEvaluate.class);
private String modelFilename;
private final String targetDir;
public MLPMnistSingleLayerEvaluate(String modelFilename, String targetDir) {
this.modelFilename = modelFilename;
this.targetDir = targetDir;
}
void execute(int batchSize, int rngSeed) throws IOException {
log.info("Evaluate model....");
log.info(String.format("BatchSize: %d", batchSize));
DataSetIterator mnistTestSet = new MnistDataSetIterator(batchSize, false, rngSeed);
if (! new File(getModelFilename()).exists()) {
log.error(String.format("Model file %s does not exists", getModelFilename()));
log.error("Re-run with the correct path specified with --input-dir");
log.error("Aborting...");
System.exit(-1);
}
log.info(String.format("Loading saved model: %s", getModelFilename()));
MultiLayerNetwork model = MultiLayerNetwork.load(new File(getModelFilename()), false);
Evaluation eval = model.evaluate(mnistTestSet);
log.info(eval.stats());
log.info("Finished evaluating model....");
}
@NotNull
String getModelFilename() {
return Paths.get(targetDir, modelFilename).toString();
}
}