org.nd4j.linalg.api.ops.random.compat.RandomStandardNormal Maven / Gradle / Ivy
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package org.nd4j.linalg.api.ops.random.compat;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.common.base.Preconditions;
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.factory.Nd4j;
import org.nd4j.common.util.ArrayUtil;
import java.util.Collections;
import java.util.List;
public class RandomStandardNormal extends DynamicCustomOp {
public RandomStandardNormal() {
// values are just hardcoded for this op
addTArgument(0.0, 1.0);
}
public RandomStandardNormal(SameDiff sameDiff, SDVariable[] args) {
super(null, sameDiff, args);
// values are just hardcoded for this op
addTArgument(0.0, 1.0);
}
public RandomStandardNormal(INDArray shape) {
super(null, new INDArray[]{shape},new INDArray[0]);
// values are just hardcoded for this op
addTArgument(0.0, 1.0);
}
public RandomStandardNormal(INDArray shape, INDArray output) {
super(null, new INDArray[]{shape},new INDArray[]{output});
// values are just hardcoded for this op
addTArgument(0.0, 1.0);
}
public RandomStandardNormal(long shape[]) {
this(Nd4j.create(ArrayUtil.toDouble(shape)), Nd4j.create(shape));
}
@Override
public String opName() {
return "random_normal";
}
@Override
public String tensorflowName() {
return "RandomStandardNormal";
}
@Override
public Object[] getExtraArgs() {
// FIXME: why the hell we need this?
return new Object[] {new Double(0.0), new Double(1.0)};
}
@Override
public List calculateOutputDataTypes(List inputDataTypes){
Preconditions.checkState(inputDataTypes != null && inputDataTypes.size() == 1, "Expected exactly 1 input datatype for %s, got %s", getClass(), inputDataTypes);
//Input data type specifies the shape; output data type should be any float
//TODO MAKE CONFIGUREABLE - https://github.com/eclipse/deeplearning4j/issues/6854
return Collections.singletonList(DataType.FLOAT);
}
}