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/*-
*
* * 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.nd4j.linalg.api.complex.IComplexNDArray;
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.api.ops.TransformOp;
import org.nd4j.linalg.factory.Nd4j;
/**
* Soft max function
* row_maxes is a row vector (max for each row)
* row_maxes = rowmaxes(input)
* diff = exp(input - max) / diff.rowSums()
* Outputs a probability distribution.
* Note that this is a parameterized model and requires
* the sum and max for the vector being calculated
*
* @author Adam Gibson
*/
public class SoftMax extends BaseTransformOp {
public SoftMax() {}
public SoftMax(INDArray x, INDArray z) {
super(x, z);
}
public SoftMax(INDArray x, INDArray z, long n) {
super(x, z, n);
}
public SoftMax(INDArray x, INDArray y, INDArray z, long n) {
super(x, y, z, n);
}
public SoftMax(INDArray x, INDArray y, INDArray z) {
super(x, y, z, x.lengthLong());
}
public SoftMax(INDArray x) {
super(x);
}
@Override
public int opNum() {
return 38;
}
@Override
public boolean isExecSpecial() {
return true;
}
@Override
public String name() {
return "softmax";
}
@Override
public IComplexNumber op(IComplexNumber origin, double other) {
IComplexNDArray arr = (IComplexNDArray) y;
if (numProcessed >= Integer.MAX_VALUE)
throw new IllegalArgumentException("Number of processed elements can not be >= Integer.MAX_VALUE");
IComplexNumber ret = arr.getComplex((int) numProcessed);
numProcessed++;
return ret;
}
@Override
public IComplexNumber op(IComplexNumber origin, float other) {
IComplexNDArray arr = (IComplexNDArray) y;
if (numProcessed >= Integer.MAX_VALUE)
throw new IllegalArgumentException("Number of processed elements can not be >= Integer.MAX_VALUE");
IComplexNumber ret = arr.getComplex((int) numProcessed);
numProcessed++;
return ret;
}
@Override
public IComplexNumber op(IComplexNumber origin, IComplexNumber other) {
IComplexNDArray arr = (IComplexNDArray) y;
if (numProcessed >= Integer.MAX_VALUE)
throw new IllegalArgumentException("Number of processed elements can not be >= Integer.MAX_VALUE");
IComplexNumber ret = arr.getComplex((int) numProcessed);
numProcessed++;
return ret;
}
@Override
public float op(float origin, float other) {
float ret = other;
numProcessed++;
return ret;
}
@Override
public double op(double origin, double other) {
double ret = other;
numProcessed++;
return ret;
}
@Override
public double op(double origin) {
if (numProcessed >= Integer.MAX_VALUE)
throw new IllegalArgumentException("Number of processed elements can not be >= Integer.MAX_VALUE");
double ret = y.getDouble((int) numProcessed);
numProcessed++;
return ret;
}
@Override
public float op(float origin) {
if (numProcessed >= Integer.MAX_VALUE)
throw new IllegalArgumentException("Number of processed elements can not be >= Integer.MAX_VALUE");
float ret = (y.getFloat((int) numProcessed));
numProcessed++;
return ret;
}
@Override
public IComplexNumber op(IComplexNumber origin) {
IComplexNDArray arr = (IComplexNDArray) y;
if (numProcessed >= Integer.MAX_VALUE)
throw new IllegalArgumentException("Number of processed elements can not be >= Integer.MAX_VALUE");
IComplexNumber ret = arr.getComplex((int) numProcessed);
numProcessed++;
return ret;
}
@Override
public TransformOp derivative() {
return new SoftMaxDerivative(x, y, z, n);
}
@Override
public Op opForDimension(int index, int dimension) {
INDArray xAlongDimension = x.vectorAlongDimension(index, dimension);
if (y() != null)
return new SoftMax(xAlongDimension, y.vectorAlongDimension(index, dimension),
z.vectorAlongDimension(index, dimension), xAlongDimension.length());
else
return new SoftMax(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 SoftMax(xAlongDimension, y.tensorAlongDimension(index, dimension),
z.tensorAlongDimension(index, dimension), xAlongDimension.length());
else
return new SoftMax(xAlongDimension, z.tensorAlongDimension(index, dimension), xAlongDimension.length());
}
@Override
public void exec() {
exec(1);
}
@Override
public void init(INDArray x, INDArray y, INDArray z, long n) {
super.init(x, y, z, n);
passThrough = true;
}
@Override
public void exec(int... dimensions) {
if (dimensions[0] != 1)
throw new IllegalArgumentException("Only supports row wise calculations");
if (x.isMatrix()) {
INDArray maxAlongDimension = x.max(dimensions);
if (!maxAlongDimension.isVector() && !maxAlongDimension.isScalar())
throw new IllegalStateException("Max along dimension for input must either be a row vector or scalar");
INDArray xMinusMax = x.subColumnVector(maxAlongDimension);
INDArray exp;
if (z != null) {
exp = Nd4j.getExecutioner().execAndReturn(new Exp(xMinusMax, z));
} else {
exp = Nd4j.getExecutioner().execAndReturn(new Exp(xMinusMax));
}
INDArray sum = exp.sum(dimensions);
exp.diviColumnVector(sum);
if (z == null)
z = exp;
} else if (x.isVector()) {
double max = x.maxNumber().doubleValue();
INDArray exp;
if (z != null) {
exp = Nd4j.getExecutioner().execAndReturn(new Exp(x.sub(max), z));
} else {
exp = Nd4j.getExecutioner().execAndReturn(new Exp(x.sub(max)));
}
exp.divi(exp.sumNumber().doubleValue());
this.z = exp;
}
}
}
