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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.transforms.gradient;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.imports.NoOpNameFoundException;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.BaseGradientOp;
import org.nd4j.linalg.api.ops.impl.broadcast.BroadcastSubOp;
import org.nd4j.linalg.api.ops.impl.transforms.arithmetic.OldMulOp;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.ops.transforms.Transforms;
import java.util.Arrays;
import java.util.List;
/**
*
*/
public class LogSoftMaxDerivative extends BaseGradientOp {
public LogSoftMaxDerivative(SameDiff sameDiff, SDVariable i_v1, SDVariable i_v2) {
super(sameDiff, i_v1, i_v2);
}
public LogSoftMaxDerivative(SameDiff sameDiff, SDVariable i_v1, SDVariable i_v2, boolean inPlace) {
super(sameDiff, i_v1, i_v2, inPlace);
}
public LogSoftMaxDerivative(INDArray x, INDArray z) {
super(x, z);
}
public LogSoftMaxDerivative() {
}
public LogSoftMaxDerivative(INDArray x, INDArray z, long n) {
super(x, z, n);
}
public LogSoftMaxDerivative(INDArray x, INDArray y, INDArray z) {
super(x, y, z, z.lengthLong());
}
public LogSoftMaxDerivative(INDArray x) {
super(x);
}
public LogSoftMaxDerivative(INDArray indArray, INDArray indArray1, INDArray indArray2, int length) {
super(indArray,indArray1,indArray2,length);
}
/**
* An op number
*
* @return
*/
@Override
public int opNum() {
return 0;
}
/**
* The opName of this operation
*
* @return the opName of this operation
*/
@Override
public String opName() {
return "logsoftmaxderivative";
}
@Override
public String onnxName() {
throw new NoOpNameFoundException("No onnx op opName found for " + opName());
}
@Override
public String tensorflowName() {
throw new NoOpNameFoundException("No tensorflow op opName found for " + opName());
}
@Override
public void exec() {
//TODO add dimension arg. For now: hardcoded along dimension 1...
//Out = log(softmax(x)) = l(s(x))
//dL/dIn = dL/dOut * dOut/dIn = dL/dOut * dl(s(x))/ds(x) * ds(x)/dx
// = (softmax deriv) * 1/s(x)
INDArray softmax = Transforms.softmax(x, true);
Nd4j.getExecutioner().exec(new SoftMaxDerivative(x,y,z));
z.divi(softmax);
}
@Override
public void exec(int... dimensions) {
super.exec(dimensions);
}
@Override
public List doDiff(List i_v) {
return Arrays.asList(f().sub(i_v.get(0),f().sum(f().exp(larg()), false,1)));
}
}