Java tutorial
/* * * * 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; } } }