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org.nd4j.linalg.api.ops.impl.transforms.LogSoftMax Maven / Gradle / Ivy
/*-
*
* * Copyright 2015 Skymind,Inc.
* *
* * Licensed under the Apache License, Version 2.0 (the "License");
* * you may not use this file except in compliance with the License.
* * You may obtain a copy of the License at
* *
* * http://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.
*
*
*/
package org.nd4j.linalg.api.ops.impl.transforms;
import org.apache.commons.math3.util.FastMath;
import org.nd4j.linalg.api.complex.IComplexNumber;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.BaseTransformOp;
import org.nd4j.linalg.api.ops.Op;
import org.nd4j.linalg.factory.Nd4j;
/**
* Log(softmax(X))
* @author Alex Black
*/
public class LogSoftMax extends BaseTransformOp {
public LogSoftMax() {}
public LogSoftMax(INDArray x, INDArray z) {
super(x, z);
}
public LogSoftMax(INDArray x, INDArray z, long n) {
super(x, z, n);
}
public LogSoftMax(INDArray x, INDArray y, INDArray z, long n) {
super(x, y, z, n);
}
public LogSoftMax(INDArray x, INDArray y, INDArray z) {
super(x, y, z, x.lengthLong());
}
public LogSoftMax(INDArray x) {
super(x);
}
@Override
public int opNum() {
return 40;
}
@Override
public String name() {
return "logsoftmax";
}
@Override
public IComplexNumber op(IComplexNumber origin, double other) {
return origin;
}
@Override
public IComplexNumber op(IComplexNumber origin, float other) {
return origin;
}
@Override
public IComplexNumber op(IComplexNumber origin, IComplexNumber other) {
return origin;
}
@Override
public float op(float origin, float other) {
return origin;
}
@Override
public double op(double origin, double other) {
return origin;
}
@Override
public double op(double origin) {
return origin;
}
@Override
public float op(float origin) {
return origin;
}
@Override
public IComplexNumber op(IComplexNumber origin) {
return origin;
}
@Override
public Op opForDimension(int index, int dimension) {
INDArray xAlongDimension = x.vectorAlongDimension(index, dimension);
if (y() != null)
return new LogSoftMax(xAlongDimension, y.vectorAlongDimension(index, dimension),
z.vectorAlongDimension(index, dimension), xAlongDimension.length());
else
return new LogSoftMax(xAlongDimension, z.vectorAlongDimension(index, dimension), xAlongDimension.length());
}
@Override
public Op opForDimension(int index, int... dimension) {
INDArray xAlongDimension = x.tensorAlongDimension(index, dimension);
if (y() != null)
return new LogSoftMax(xAlongDimension, y.tensorAlongDimension(index, dimension),
z.tensorAlongDimension(index, dimension), xAlongDimension.length());
else
return new LogSoftMax(xAlongDimension, z.tensorAlongDimension(index, dimension), xAlongDimension.length());
}
@Override
public void exec() {
exec(1);
}
@Override
public boolean isExecSpecial() {
return true;
}
@Override
public void exec(int... dimensions) {
if (dimensions[0] != 1)
throw new IllegalArgumentException("Only supports row wise calculations");
if (x.isMatrix()) {
INDArray rowMax = x.max(1);
INDArray xMinusRowMax = x.subColumnVector(rowMax);
INDArray expXMinusRowMax = Nd4j.getExecutioner().execAndReturn(new Exp(xMinusRowMax.dup()));
INDArray logRowSumExp = expXMinusRowMax.sum(1);
Nd4j.getExecutioner().exec(new Log(logRowSumExp));
INDArray logsoftmax = xMinusRowMax.subiColumnVector(logRowSumExp);
if (this.z != null)
z.assign(logsoftmax);
else
this.z = logsoftmax;
} else if (x.isVector()) {
double max = x.maxNumber().doubleValue();
INDArray xMinusMax = x.sub(max);
INDArray expXMinusMax = Nd4j.getExecutioner().execAndReturn(new Exp(xMinusMax.dup()));
double logRowSumExp = FastMath.log(expXMinusMax.sumNumber().doubleValue());
INDArray logsoftmax = xMinusMax.subi(logRowSumExp);
if (this.z != null)
z.assign(logsoftmax);
else
this.z = logsoftmax;
}
}
}