org.nd4j.linalg.api.ops.impl.transforms.custom.Standardize Maven / Gradle / Ivy
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* * terms of the Apache License, Version 2.0 which is available at
* * https://www.apache.org/licenses/LICENSE-2.0.
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package org.nd4j.linalg.api.ops.impl.transforms.custom;
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 java.util.Collections;
import java.util.List;
public class Standardize extends DynamicCustomOp {
public Standardize(SameDiff sameDiff, SDVariable i_v, int... dimensions) {
super(null, sameDiff, new SDVariable[]{i_v}, false);
setDimensions(dimensions);
}
public Standardize(INDArray input, int... dimensions){
this(input, null, dimensions);
}
public Standardize(INDArray input, INDArray result, int... dimensions){
super("standardize", new INDArray[]{input},wrapOrNull(result));
setDimensions(dimensions);
}
public Standardize() {
}
@Override
public void setDimensions(int[] dimensions) {
Preconditions.checkArgument(dimensions != null, "Standardize: You have to provide dimensions");
Preconditions.checkArgument(dimensions.length > 0, "Standardize: You have to provide dimensions");
this.dimensions = dimensions;
addIArgument(dimensions);
}
@Override
public String opName() {
return "standardize";
}
@Override
public List doDiff(List grad) {
return new StandardizeBp(sameDiff, arg(0), grad.get(0), dimensions).outputs();
}
@Override
public List calculateOutputDataTypes(List dataTypes){
Preconditions.checkState(dataTypes != null && dataTypes.size() == 1, "Expected exactly 1 input datatype for %s, got %s", getClass(), dataTypes);
Preconditions.checkState(dataTypes.get(0).isFPType(), "Input must be a floating point type, got %s", dataTypes.get(0));
return Collections.singletonList(dataTypes.get(0));
}
}