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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 org.nd4j.autodiff.functions.DifferentialFunction;
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 java.util.Collections;
import java.util.List;
/**
* LogSumExp - this op returns https://en.wikipedia.org/wiki/LogSumExp
*
* @author [email protected]
*/
public class LogSumExp extends BaseAccumulation {
public LogSumExp(SameDiff sameDiff, SDVariable i_v, int[] dimensions) {
super(sameDiff, i_v, dimensions);
}
public LogSumExp(SameDiff sameDiff, SDVariable i_v, SDVariable i_v2, int[] dimensions) {
super(sameDiff, i_v, i_v2, dimensions);
}
public LogSumExp() {}
public LogSumExp(INDArray x, INDArray y, INDArray z, long n) {
super(x, y, z, n);
}
public LogSumExp(INDArray x, INDArray y, long n) {
super(x, y, n);
}
public LogSumExp(INDArray x) {
super(x);
}
public LogSumExp(INDArray x, INDArray y) {
super(x, y);
}
public LogSumExp(INDArray x, INDArray y, INDArray z) {
super(x, y, z, x.lengthLong());
}
@Override
public int opNum() {
return 19;
}
@Override
public String opName() {
return "logexpsum";
}
@Override
public List doDiff(List f1) {
//z = log(sum_i exp(x_i)) = log(s)
//dL/dx = dL/dz * dz/ds * ds/dx
//dz/ds = 1/s
SDVariable exp = f().exp(arg());
SDVariable sumExp = exp.sum(dimensions);
SDVariable gradProd = f1.get(0).div(sumExp);
SDVariable dSumExpdx = f().sumBp(arg(), gradProd, keepDims, dimensions).mul(exp);
return Collections.singletonList(dSumExpdx);
}
@Override
public String onnxName() {
return "ReduceLogSumExp";
}
@Override
public String tensorflowName() {
return "reduce_logsumexp";
}
@Override
public Type getOpType() {
return Type.REDUCE;
}
}