org.nd4j.linalg.api.ops.impl.reduce.floating.NormMax Maven / Gradle / Ivy
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* ******************************************************************************
* *
* *
* * 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.
* *
* * See the NOTICE file distributed with this work for additional
* * information regarding copyright ownership.
* * 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.reduce.floating;
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.BaseReduceFloatOp;
import org.nd4j.linalg.api.ops.impl.reduce.bp.NormMaxBp;
import org.nd4j.linalg.ops.transforms.Transforms;
import java.util.List;
/**
* The max absolute value
*
* @author Adam Gibson
*/
public class NormMax extends BaseReduceFloatOp {
public NormMax(SameDiff sameDiff, SDVariable i_v, boolean keepDims, int[] dimensions) {
super(sameDiff, i_v, dimensions, keepDims);
}
public NormMax(SameDiff sameDiff, SDVariable i_v, SDVariable i_v2, int[] dimensions) {
super(sameDiff, i_v, i_v2, dimensions);
}
public NormMax(SameDiff sameDiff, SDVariable input, int[] dimensions, boolean keepDims) {
super(sameDiff, input, dimensions, keepDims);
}
public NormMax(SameDiff sameDiff, SDVariable input, int... dimensions) {
super(sameDiff, input, dimensions);
}
public NormMax(SameDiff sameDiff, SDVariable i_v, boolean keepDims, SDVariable dimensions) {
super(sameDiff, i_v, keepDims, dimensions);
}
public NormMax(SameDiff sameDiff, SDVariable i_v, SDVariable i_v2, SDVariable dimensions) {
super(sameDiff, i_v, i_v2, dimensions);
}
public NormMax(SameDiff sameDiff, SDVariable input, SDVariable dimensions, boolean keepDims) {
super(sameDiff, input, dimensions, keepDims);
}
public NormMax(SameDiff sameDiff, SDVariable input, SDVariable dimensions) {
super(sameDiff, input, dimensions);
}
public NormMax(INDArray input, INDArray output, boolean keepDims, int... dimensions) {
super(input, output, keepDims, dimensions);
}
public NormMax(INDArray x, INDArray y, INDArray z, int... dimensions) {
super(x, y, z, dimensions);
}
public NormMax() {
}
public NormMax(INDArray x, INDArray z, int... dimensions) {
super(x, null, z, dimensions);
}
public NormMax(INDArray x, int... dimensions) {
super(x, dimensions);
}
public NormMax(INDArray x, boolean keepDims, int... dimensions) {
super(x, keepDims, dimensions);
}
public NormMax(INDArray in, INDArray indArray, boolean keepDims) {
super(in,indArray,keepDims);
}
public NormMax(INDArray x, INDArray y, INDArray z, boolean keepDims, int... dimensions) {
super(x, y, z, keepDims, dimensions);
}
public NormMax(INDArray in, int[] dimensions, boolean keepDims) {
super(in,keepDims,dimensions);
}
@Override
public INDArray noOp() {
return Transforms.abs(x());
}
@Override
public int opNum() {
return 4;
}
@Override
public String opName() {
return "reduce_normmax";
}
@Override
public List doDiff(List grad) {
//maxnorm(in) = max_i |x_i|
//d maxnorm(in)/dx = 0 if x_i is not the max, or d|x|/dx otherwise
return new NormMaxBp(sameDiff, arg(), grad.get(0), keepDims, dimensions).outputs();
}
@Override
public String onnxName(){
throw new NoOpNameFoundException("No onnx op opName found for " + opName());
}
@Override
public String tensorflowName() {
throw new NoOpNameFoundException("No tensorflow op opName found for " + opName());
}
}