org.nd4j.linalg.api.ops.impl.layers.convolution.Upsampling2dDerivative Maven / Gradle / Ivy
/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
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.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.DynamicCustomOp;
import java.util.List;
/**
* UpsamplingDerivative operation
*/
@Slf4j
public class Upsampling2dDerivative extends DynamicCustomOp {
protected boolean nchw;
protected int scaleH;
protected int scaleW;
public Upsampling2dDerivative() {}
public Upsampling2dDerivative(SameDiff sameDiff, SDVariable input, SDVariable gradient, boolean nchw, int scaleH, int scaleW) {
super(null, sameDiff, new SDVariable[]{input, gradient});
this.nchw = nchw;
this.scaleH = scaleH;
this.scaleW = scaleW;
addIArgument(scaleH);
addIArgument(scaleW);
addIArgument(nchw ? 1 : 0);
}
@Override
public String opName() {
return "upsampling2d_bp";
}
@Override
public List doDiff(List f1) {
throw new UnsupportedOperationException("Unable to take derivative of derivative.");
}
}