org.nd4j.linalg.api.ops.random.custom.DistributionUniform Maven / Gradle / Ivy
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package org.nd4j.linalg.api.ops.random.custom;
import lombok.extern.slf4j.Slf4j;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.common.base.Preconditions;
import org.nd4j.imports.graphmapper.tf.TFGraphMapper;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.DynamicCustomOp;
import org.nd4j.linalg.exception.ND4JIllegalStateException;
import org.tensorflow.framework.AttrValue;
import org.tensorflow.framework.GraphDef;
import org.tensorflow.framework.NodeDef;
import java.util.Arrays;
import java.util.Collections;
import java.util.List;
import java.util.Map;
@Slf4j
public class DistributionUniform extends DynamicCustomOp {
private double min = 0.0;
private double max = 1.0;
private DataType dataType;
public DistributionUniform() {
//
}
public DistributionUniform(SameDiff sd, SDVariable shape, double min, double max) {
this(sd, shape, min, max, null);
}
public DistributionUniform(SameDiff sd, SDVariable shape, double min, double max, DataType dataType){
super(null, sd, new SDVariable[]{shape});
Preconditions.checkState(min <= max, "Minimum (%s) must be <= max (%s)", min, max);
Preconditions.checkState(dataType == null || dataType.isNumerical(), "Only numerical datatypes can be used with DistributionUniform - rquested output datatype: %s", dataType);
this.dataType = dataType;
this.min = min;
this.max = max;
addArgs();
}
public DistributionUniform(INDArray shape, INDArray out, double min, double max) {
this(shape, out, min, max, null);
}
public DistributionUniform(INDArray shape, INDArray out, double min, double max, DataType dataType){
super(null, new INDArray[]{shape}, new INDArray[]{out}, Arrays.asList(min, max), (List)null);
this.min = min;
this.max = max;
this.dataType = dataType;
}
@Override
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map attributesForNode, GraphDef graph) {
AttrValue vDtype = attributesForNode.get("dtype");
AttrValue vTout = attributesForNode.get("Tout");
if (vDtype == null && vTout == null) {
throw new ND4JIllegalStateException("Unable to find output data type for node " + nodeDef.getName());
}
AttrValue v = vDtype == null ? vTout : vDtype;
dataType = TFGraphMapper.convertType(v.getType());
addIArgument(dataType.toInt());
addTArgument(0.0, 1.0); //TF version is hardcoded 0 to 1
}
protected void addArgs() {
tArguments.clear();
addTArgument(min, max);
if(dataType != null){
iArguments.clear();
addIArgument(dataType.toInt());
}
}
@Override
public String opName() {
return "randomuniform";
}
@Override
public String[] tensorflowNames() {
return new String[]{"RandomUniform","RandomUniformInt"};
}
@Override
public List doDiff(List gradients){
return Collections.singletonList(sameDiff.zerosLike(arg()));
}
@Override
public List calculateOutputDataTypes(List inputDataTypes){
Preconditions.checkState(inputDataTypes != null /*&& inputDataTypes.size() == 1*/, "Expected input datatypes for %s, got %s", getClass(), inputDataTypes);
//Input data type specifies the shape
if(dataType != null){
return Collections.singletonList(dataType);
}
return Collections.singletonList(DataType.FLOAT);
}
}