org.nd4j.linalg.api.ops.impl.reduce.floating.Mean 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.reduce.floating;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.BaseReduceFloatOp;
import org.nd4j.linalg.api.ops.impl.reduce.bp.MeanBp;
import java.util.List;
public class Mean extends BaseReduceFloatOp {
public Mean(SameDiff sameDiff, SDVariable i_v, boolean keepDims, int[] dimensions) {
super(sameDiff, i_v, keepDims, dimensions);
}
public Mean() {
}
public Mean(INDArray x, INDArray z, int... dimensions) {
super(x, null, z, dimensions);
}
public Mean(INDArray x, int... dimensions) {
super(x, dimensions);
}
public Mean(INDArray x, boolean keepDims, int... dimensions) {
super(x, keepDims, dimensions);
}
public Mean(INDArray x, INDArray z, boolean keepDims, int... dimensions) {
super(x, z, keepDims, dimensions);
}
@Override
public int opNum() {
return 0;
}
@Override
public String opName() {
return "reduce_mean";
}
@Override
public List doDiff(List i_v1) {
//If out = mean(in), then dL/dIn = 1/N * dL/dOut (broadcast to appropriate shape)
//Note that N differs for "along dimension" vs. "whole array" reduce cases
return new MeanBp(sameDiff, arg(), i_v1.get(0), keepDims, dimensions).outputs();
}
@Override
public String onnxName() {
return "ReduceMean";
}
@Override
public String tensorflowName() {
return "Mean";
}
}