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/*
* ******************************************************************************
* *
* *
* * 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.
* *
* * See the NOTICE file distributed with this work for additional
* * information regarding copyright ownership.
* * 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.reduce.same;
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.BaseReduceSameOp;
import org.nd4j.linalg.api.ops.impl.reduce.bp.SumBp;
import java.util.List;
@Slf4j
public class Sum extends BaseReduceSameOp {
public Sum(SameDiff sameDiff, SDVariable i_v, boolean keepDims, int[] dimensions) {
super(sameDiff, i_v, dimensions, keepDims);
}
public Sum(SameDiff sameDiff, SDVariable i_v, SDVariable i_v2, int[] dimensions) {
super(sameDiff, i_v, i_v2, dimensions);
}
public Sum() {
}
public Sum(INDArray x, int... dimensions) {
super(x, dimensions);
}
public Sum(INDArray x, INDArray z, int... dimensions) {
super(x, null, z, dimensions);
}
public Sum(INDArray x, INDArray z, boolean keepDims, int... dimensions) {
super(x, z, keepDims, dimensions);
}
public Sum(INDArray x, boolean keepDims, int... dimensions) {
this(x, null, keepDims, dimensions);
}
@Override
public int opNum() {
return 0;
}
@Override
public String opName() {
return "reduce_sum";
}
@Override
public List doDiff(List i_v1) {
//Out = sum(in)
// dL/dIn = dL/dOut * dOut/dIn
// = dL/dOut * 1
// But broadcast to shape of the input
return new SumBp(sameDiff, arg(), i_v1.get(0), keepDims, dimensions).outputs();
}
@Override
public String onnxName() {
return "Sum";
}
@Override
public String tensorflowName() {
return "Sum";
}
}