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/*******************************************************************************
* 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.accum;
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.BaseAccumulation;
import org.nd4j.linalg.api.shape.Shape;
import java.util.Arrays;
import java.util.Collections;
import java.util.List;
/**
* Sum the components
*
* @author Adam Gibson
*/
@Slf4j
public class Sum extends BaseAccumulation {
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, INDArray y, INDArray z, long n) {
super(x, y, z, n);
}
public Sum(INDArray x, INDArray y, long n) {
super(x, y, n);
}
public Sum(INDArray x) {
super(x);
}
public Sum(INDArray x, INDArray y) {
super(x, y);
}
public Sum(INDArray x, INDArray y, INDArray z) {
super(x, y, z, x.lengthLong());
}
public Sum(INDArray x, INDArray y, INDArray z, boolean newFormat, boolean keepDims, int[] dimensions) {
super(x, y, z, newFormat, keepDims, dimensions);
}
@Override
public int opNum() {
return 1;
}
@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 Collections.singletonList(f().sumBp(arg(), i_v1.get(0), keepDims, dimensions));
}
@Override
public String onnxName() {
return "Sum";
}
@Override
public String tensorflowName() {
return "Sum";
}
@Override
public Type getOpType() {
return Type.REDUCE;
}
}