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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.functions.DifferentialFunctionFactory;
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.BaseAccumulation;
import java.util.Collections;
import java.util.List;
/**
* Entropy Op - returns the entropy (information gain, or uncertainty of a random variable).
* -sum(x * log(x))
*
* @author [email protected]
*/
public class Entropy extends BaseAccumulation {
public Entropy(SameDiff sameDiff, SDVariable i_v, int[] dimensions) {
super(sameDiff, i_v, dimensions);
}
public Entropy() {}
public Entropy(INDArray x, INDArray y, INDArray z, long n) {
super(x, y, z, n);
}
public Entropy(INDArray x, INDArray y, long n) {
super(x, y, n);
}
public Entropy(INDArray x) {
super(x);
}
public Entropy(INDArray x, INDArray y) {
super(x, y);
}
public Entropy(INDArray x, INDArray y, INDArray z) {
super(x, y, z, x.lengthLong());
}
@Override
public int opNum() {
return 16;
}
@Override
public String opName() {
return "entropy";
}
@Override
public String onnxName() {
throw new NoOpNameFoundException("No onnx op opName found for " + opName());
}
@Override
public String tensorflowName() {
return "entropy";
}
@Override
public Type getOpType() {
return Type.REDUCE;
}
@Override
public List doDiff(List f1) {
//dL/dx = dL/dOut * dOut/dIn
//out = -sum(x*log(x))
// let z = x * log(x)
//Then we can do sumBp(z, -dL/dOut)
//Note d/dx(x*log(x)) = log(x)+1
return grad(f(), arg(), f1.get(0), dimensions);
}
public static List grad(DifferentialFunctionFactory f, SDVariable arg, SDVariable grad, int[] dimensions){
SDVariable logx = f.log(arg);
SDVariable xLogX = arg.mul(logx);
SDVariable sumBp = f.sumBp(xLogX, grad.neg(), false, dimensions);
return Collections.singletonList(sumBp.mul(logx.add(1.0)));
}
}