org.deeplearning4j.arbiter.data.MnistDataProvider Maven / Gradle / Ivy
package org.deeplearning4j.arbiter.data;
import lombok.Data;
import lombok.NoArgsConstructor;
import org.deeplearning4j.arbiter.optimize.api.data.DataProvider;
import org.deeplearning4j.datasets.iterator.impl.MnistDataSetIterator;
import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
import org.nd4j.linalg.dataset.api.iterator.MultipleEpochsIterator;
import org.nd4j.shade.jackson.annotation.JsonProperty;
import java.io.IOException;
import java.util.Map;
import java.util.Random;
/**
*
* MnistDataProvider - a DataProvider for the MNIST data set, with configurable number of epochs, batch size
* and RNG seed
*
* @author Alex Black
*/
@Data
@NoArgsConstructor
public class MnistDataProvider implements DataProvider{
private int numEpochs;
private int batchSize;
private int rngSeed;
public MnistDataProvider(int numEpochs, int batchSize){
this(numEpochs, batchSize, new Random().nextInt());
}
public MnistDataProvider(@JsonProperty("numEpochs") int numEpochs, @JsonProperty("batchSize") int batchSize,
@JsonProperty("rngSeed") int rngSeed) {
this.numEpochs = numEpochs;
this.batchSize = batchSize;
this.rngSeed = rngSeed;
}
@Override
public Object trainData(Map dataParameters) {
try {
return new MultipleEpochsIterator(numEpochs, new MnistDataSetIterator(batchSize, true, rngSeed));
} catch (IOException e) {
throw new RuntimeException(e);
}
}
@Override
public Object testData(Map dataParameters) {
try {
return new MnistDataSetIterator(batchSize, false, 12345);
} catch (IOException e) {
throw new RuntimeException(e);
}
}
@Override
public Class getDataType() {
return DataSetIterator.class;
}
}