org.nd4j.linalg.api.ops.impl.layers.convolution.SConv2DDerivative Maven / Gradle / Ivy
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package org.nd4j.linalg.api.ops.impl.layers.convolution;
import lombok.Builder;
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.NoOpNameFoundException;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ops.impl.layers.convolution.config.Conv2DConfig;
import java.util.ArrayList;
import java.util.List;
@Slf4j
public class SConv2DDerivative extends SConv2D {
@Builder(builderMethodName = "sDerviativeBuilder")
public SConv2DDerivative(SameDiff sameDiff, SDVariable[] inputFunctions, Conv2DConfig conv2DConfig) {
super(sameDiff, inputFunctions, conv2DConfig);
}
public SConv2DDerivative() {}
@Override
public String opName() {
return "sconv2d_bp";
}
@Override
public String[] tensorflowNames() {
throw new NoOpNameFoundException("No op name found for backwards");
}
@Override
public String onnxName() {
throw new NoOpNameFoundException("No op name found for backwards.");
}
@Override
public String tensorflowName() {
throw new NoOpNameFoundException("No op name found for backwards");
}
@Override
public List doDiff(List f1) {
throw new UnsupportedOperationException("Unable to take derivative of derivative.");
}
@Override
public int getNumOutputs(){
//Inputs: in, gradAtOutput, weightsDepth, optional weightsPoint, optional weightsBias 3 req, 2 optional
//Outputs: gradAtInput, gradWD, optional gradWP, optional gradB 2 req, 2 optional
SDVariable[] args = args();
return args.length - 1;
}
@Override
public List calculateOutputDataTypes(List inputDataTypes){
int n = args().length; //Original inputs + gradient at
Preconditions.checkState(inputDataTypes != null && inputDataTypes.size() == n, "Expected %s input data types for %s, got %s", n, getClass(), inputDataTypes);
List out = new ArrayList<>(n-1);
for( int i=0; i