org.nd4j.linalg.api.ops.impl.transforms.MatrixDeterminant Maven / Gradle / Ivy
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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;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.linalg.api.ops.DynamicCustomOp;
import org.nd4j.linalg.factory.Nd4j;
import java.util.Collections;
import java.util.List;
/**
* Matrix Determinant op
*
* Given input with shape [..., N, N] output the determinant for each sub-matrix.
*
* @author Alex Black
*/
public class MatrixDeterminant extends DynamicCustomOp {
public MatrixDeterminant() {
//
}
public MatrixDeterminant(SameDiff sameDiff, SDVariable in, boolean inPlace) {
super(null, sameDiff, new SDVariable[]{in}, inPlace);
}
@Override
public String opName() {
return "matrix_determinant";
}
@Override
public String tensorflowName() {
return "MatrixDeterminant";
}
@Override
public List doDiff(List i_v) {
//Derivative of matrix determinant
//From: Matrix Cookbook - Petersen & Pedersen
// z=det(X) then dz/dx = z * tr(X^-1)
SDVariable transpose = f().matrixInverse(arg());
SDVariable trace = f().diagPart(transpose).sum(-1);
SDVariable dOutdIn = outputVariable().mul(trace);
return Collections.singletonList(i_v.get(0).mul(dOutdIn));
}
}