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org.nd4j.linalg.api.ops.impl.shape.ConfusionMatrix Maven / Gradle / Ivy
/*******************************************************************************
* 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.shape;
import lombok.NonNull;
import lombok.val;
import org.apache.commons.lang3.NotImplementedException;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.base.Preconditions;
import org.nd4j.imports.descriptors.properties.PropertyMapping;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.DynamicCustomOp;
import org.tensorflow.framework.AttrValue;
import org.tensorflow.framework.GraphDef;
import org.tensorflow.framework.NodeDef;
import java.util.*;
/**
*
*/
public class ConfusionMatrix extends DynamicCustomOp {
public static final DataType DEFAULT_DTYPE = DataType.INT;
private DataType outputType = DEFAULT_DTYPE;
public ConfusionMatrix(){
}
public ConfusionMatrix(@NonNull INDArray labels, @NonNull INDArray predicted, @NonNull DataType dataType){
super(new INDArray[]{labels, predicted}, null);
this.outputType = dataType;
}
public ConfusionMatrix(@NonNull INDArray labels, @NonNull INDArray predicted, int numClasses){
this(labels, predicted, numClasses, DEFAULT_DTYPE);
}
public ConfusionMatrix(@NonNull INDArray labels, @NonNull INDArray predicted, INDArray weights) {
this(labels, predicted, weights, null);
}
public ConfusionMatrix(@NonNull INDArray labels, @NonNull INDArray predicted, INDArray weights, Integer numClasses) {
this(labels, predicted, weights, numClasses, DEFAULT_DTYPE);
}
public ConfusionMatrix(@NonNull INDArray labels, @NonNull INDArray predicted, Integer numClasses, @NonNull DataType dataType) {
this(labels, predicted, null, numClasses, dataType);
}
public ConfusionMatrix(@NonNull INDArray labels, @NonNull INDArray predicted, INDArray weights, Integer numClasses, @NonNull DataType dataType) {
super(wrapFilterNull(labels, predicted, weights), null);
this.outputType = dataType;
if(numClasses != null) {
addIArgument(numClasses);
}
}
public ConfusionMatrix(SameDiff sameDiff, SDVariable labels, SDVariable pred, DataType dataType){
super(null, sameDiff, new SDVariable[]{labels, pred});
this.outputType = dataType;
}
public ConfusionMatrix(SameDiff sameDiff, SDVariable labels, SDVariable pred, SDVariable weights){
super(null, sameDiff, new SDVariable[]{labels, pred, weights});
}
public ConfusionMatrix(SameDiff sameDiff, SDVariable labels, SDVariable pred, Integer numClasses){
super(null, sameDiff, new SDVariable[]{labels, pred});
addIArgument(numClasses);
}
public ConfusionMatrix(SameDiff sameDiff, SDVariable labels, SDVariable pred, Integer numClasses, SDVariable weights){
super(null, sameDiff, new SDVariable[]{labels, pred, weights});
if(numClasses != null) {
addIArgument(numClasses);
}
}
@Override
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map attributesForNode, GraphDef graph) {
super.initFromTensorFlow(nodeDef, initWith, attributesForNode, graph);
//Looks like this is implemented in practice using a large collection of discrete ops - not single TF import op?
}
@Override
public String opName() {
return "confusion_matrix";
}
@Override
public String tensorflowName() {
return "ConfusionMatrix";
}
@Override
public List doDiff(List i_v){
return Arrays.asList(sameDiff.zerosLike(arg(0)), sameDiff.zerosLike(arg(1)));
}
@Override
public List calculateOutputDataTypes(List dataTypes){
return Collections.singletonList(outputType);
}
}