org.nd4j.linalg.api.ops.impl.layers.convolution.Conv2DDerivative 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 Conv2DDerivative extends Conv2D {
@Builder(builderMethodName = "derivativeBuilder")
public Conv2DDerivative(SameDiff sameDiff, SDVariable[] inputFunctions, Conv2DConfig config) {
super(sameDiff, inputFunctions, config);
}
public Conv2DDerivative() {}
@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 String[] tensorflowNames() {
throw new NoOpNameFoundException("No op name found for backwards");
}
@Override
public String opName() {
return "conv2d_bp";
}
@Override
public List doDiff(List f1) {
throw new UnsupportedOperationException("Unable to take derivative of derivative.");
}
@Override
public int getNumOutputs(){
if(args().length == 4){
return 3; //Includes bias
} else {
return 2; //No bias - only input + weight grads
}
}
@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