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Add labels to examples due to zoo deprecation, update usage to reflect new behavior with getRow
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‎tensorflow-keras-import-examples/src/main/java/org/deeplearning4j/modelimportexamples/tf/advanced/mobilenet/ImportMobileNetExample.java‎

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import org.apache.commons.io.FilenameUtils;
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import org.datavec.image.loader.ImageLoader;
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import org.deeplearning4j.zoo.util.imagenet.ImageNetLabels;
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import org.deeplearning4j.modelimportexamples.util.imagenet.ImageNetLabels;
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import org.nd4j.autodiff.samediff.SameDiff;
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import org.nd4j.common.resources.Downloader;
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import org.nd4j.linalg.api.ndarray.INDArray;

‎tensorflow-keras-import-examples/src/main/java/org/deeplearning4j/modelimportexamples/util/BaseLabels.java‎

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/*
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* ******************************************************************************
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* *
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* *
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* * This program and the accompanying materials are made available under the
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* * terms of the Apache License, Version 2.0 which is available at
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* * https://www.apache.org/licenses/LICENSE-2.0.
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* *
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* * See the NOTICE file distributed with this work for additional
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* * information regarding copyright ownership.
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* * Unless required by applicable law or agreed to in writing, software
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* * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
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* * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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* * License for the specific language governing permissions and limitations
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* * under the License.
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* *
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* * SPDX-License-Identifier: Apache-2.0
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* *****************************************************************************
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*/
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package org.deeplearning4j.modelimportexamples.util;
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import org.deeplearning4j.common.resources.DL4JResources;
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import org.deeplearning4j.common.resources.ResourceType;
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import org.deeplearning4j.zoo.util.ClassPrediction;
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import org.deeplearning4j.zoo.util.Labels;
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import org.nd4j.common.base.Preconditions;
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import org.nd4j.common.resources.Downloader;
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import org.nd4j.linalg.api.ndarray.INDArray;
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import org.nd4j.linalg.factory.Nd4j;
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import java.io.*;
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import java.net.URL;
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import java.util.ArrayList;
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import java.util.List;
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import java.util.Scanner;
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public abstract class BaseLabels implements Labels {
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protected ArrayList<String> labels;
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/** Override {@link #getLabels()} when using this constructor. */
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protected BaseLabels() throws IOException {
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this.labels = getLabels();
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}
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/**
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* No need to override anything with this constructor.
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*
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* @param textResource name of a resource containing labels as a list in a text file.
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* @throws IOException
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*/
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protected BaseLabels(String textResource) throws IOException {
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this.labels = getLabels(textResource);
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}
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/**
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* Override to return labels when not calling {@link #BaseLabels(String)}.
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*/
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protected ArrayList<String> getLabels() throws IOException {
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return null;
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}
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/**
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* Returns labels based on the text file resource.
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*/
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protected ArrayList<String> getLabels(String textResource) throws IOException {
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ArrayList<String> labels = new ArrayList<>();
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File resourceFile = getResourceFile(); //Download if required
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try (InputStream is = new BufferedInputStream(new FileInputStream(resourceFile)); Scanner s = new Scanner(is)) {
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while (s.hasNextLine()) {
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labels.add(s.nextLine());
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}
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}
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return labels;
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}
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@Override
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public String getLabel(int n) {
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Preconditions.checkArgument(n >= 0 && n < labels.size(), "Invalid index: %s. Must be in range" +
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"0 <= n < %s", n, labels.size());
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return labels.get(n);
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}
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@Override
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public List<List<ClassPrediction>> decodePredictions(INDArray predictions, int n) {
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if(predictions.rank() == 1){
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//Reshape 1d edge case to [1, nClasses] 2d
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predictions = predictions.reshape(1, predictions.length());
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}
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Preconditions.checkState(predictions.size(1) == labels.size(), "Invalid input array:" +
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" expected array with size(1) equal to numLabels (%s), got array with shape %s", labels.size(), predictions.shape());
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long rows = predictions.size(0);
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long cols = predictions.size(1);
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if (predictions.isColumnVectorOrScalar()) {
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predictions = predictions.ravel();
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rows = (int) predictions.size(0);
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cols = (int) predictions.size(1);
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}
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List<List<ClassPrediction>> descriptions = new ArrayList<>();
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for (int batch = 0; batch < rows; batch++) {
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INDArray result = predictions.getRow(batch, true);
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result = Nd4j.vstack(Nd4j.linspace(result.dataType(), 0, cols, 1).reshape(1,cols), result);
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result = Nd4j.sortColumns(result, 1, false);
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List<ClassPrediction> current = new ArrayList<>();
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for (int i = 0; i < n; i++) {
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int label = result.getInt(0, i);
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double prob = result.getDouble(1, i);
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current.add(new ClassPrediction(label, getLabel(label), prob));
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}
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descriptions.add(current);
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}
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return descriptions;
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}
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/**
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* @return URL of the resource to download
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*/
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protected abstract URL getURL();
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/**
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* @return Name of the resource (used for inferring local storage parent directory)
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*/
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protected abstract String resourceName();
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/**
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* @return MD5 of the resource at getURL()
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*/
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protected abstract String resourceMD5();
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/**
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* Download the resource at getURL() to the local resource directory, and return the local copy as a File
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*
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* @return File of the local resource
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*/
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protected File getResourceFile() {
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URL url = getURL();
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String urlString = url.toString();
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String filename = urlString.substring(urlString.lastIndexOf('/')+1);
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File resourceDir = DL4JResources.getDirectory(ResourceType.RESOURCE, resourceName());
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File localFile = new File(resourceDir, filename);
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String expMD5 = resourceMD5();
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if(localFile.exists()) {
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try{
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//empty string means ignore the MD5
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if(Downloader.checkMD5OfFile(expMD5, localFile)) {
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return localFile;
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}
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} catch (IOException e){
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//Ignore
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}
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//MD5 failed
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localFile.delete();
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}
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//Download
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try {
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Downloader.download(resourceName(), url, localFile, expMD5, 3);
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} catch (IOException e){
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throw new RuntimeException("Error downloading labels",e);
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}
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return localFile;
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}
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}

‎tensorflow-keras-import-examples/src/main/java/org/deeplearning4j/modelimportexamples/util/ClassPrediction.java‎

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/*
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* ******************************************************************************
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* *
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* *
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* * This program and the accompanying materials are made available under the
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* * terms of the Apache License, Version 2.0 which is available at
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* * https://www.apache.org/licenses/LICENSE-2.0.
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* *
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* * See the NOTICE file distributed with this work for additional
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* * information regarding copyright ownership.
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* * Unless required by applicable law or agreed to in writing, software
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* * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
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* * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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* * License for the specific language governing permissions and limitations
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* * under the License.
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* *
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* * SPDX-License-Identifier: Apache-2.0
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* *****************************************************************************
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*/
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package org.deeplearning4j.modelimportexamples.util;
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import lombok.AllArgsConstructor;
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import lombok.Data;
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import java.util.Objects;
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public class ClassPrediction {
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private int number;
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private String label;
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@Override
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public boolean equals(Object o) {
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if (this == o) return true;
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if (o == null || getClass() != o.getClass()) return false;
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ClassPrediction that = (ClassPrediction) o;
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return number == that.number && Double.compare(that.probability, probability) == 0 && Objects.equals(label, that.label);
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}
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@Override
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public int hashCode() {
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return Objects.hash(number, label, probability);
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}
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public int getNumber() {
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return number;
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}
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public void setNumber(int number) {
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this.number = number;
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}
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public String getLabel() {
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return label;
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}
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public void setLabel(String label) {
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this.label = label;
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}
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public double getProbability() {
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return probability;
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}
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public void setProbability(double probability) {
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this.probability = probability;
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}
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private double probability;
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@Override
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public String toString() {
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return "ClassPrediction(number=" + number + ",label=" + label + ",probability=" + probability + ")";
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}
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}

‎tensorflow-keras-import-examples/src/main/java/org/deeplearning4j/modelimportexamples/util/Labels.java‎

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/*
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* ******************************************************************************
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* *
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* *
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* * This program and the accompanying materials are made available under the
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* * terms of the Apache License, Version 2.0 which is available at
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* * https://www.apache.org/licenses/LICENSE-2.0.
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* *
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* * See the NOTICE file distributed with this work for additional
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* * information regarding copyright ownership.
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* * Unless required by applicable law or agreed to in writing, software
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* * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
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* * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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* * License for the specific language governing permissions and limitations
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* * under the License.
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* *
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* * SPDX-License-Identifier: Apache-2.0
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* *****************************************************************************
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*/
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package org.deeplearning4j.modelimportexamples.util;
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import org.deeplearning4j.zoo.util.ClassPrediction;
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import org.nd4j.linalg.api.ndarray.INDArray;
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import java.util.List;
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public interface Labels {
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/**
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* Returns the description of the nth class from the classes of a dataset.
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* @param n
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* @return label description
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*/
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String getLabel(int n);
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/**
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* Given predictions from the trained model this method will return a list
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* of the top n matches and the respective probabilities.
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* @param predictions raw
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* @return decoded predictions
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*/
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List<List<ClassPrediction>> decodePredictions(INDArray predictions, int n);
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}

‎tensorflow-keras-import-examples/src/main/java/org/deeplearning4j/modelimportexamples/util/darknet/COCOLabels.java‎

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/*
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* ******************************************************************************
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* *
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* *
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* * This program and the accompanying materials are made available under the
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* * terms of the Apache License, Version 2.0 which is available at
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* * https://www.apache.org/licenses/LICENSE-2.0.
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* *
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* * See the NOTICE file distributed with this work for additional
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* * information regarding copyright ownership.
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* * Unless required by applicable law or agreed to in writing, software
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* * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
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* * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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* * License for the specific language governing permissions and limitations
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* * under the License.
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* *
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* * SPDX-License-Identifier: Apache-2.0
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* *****************************************************************************
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*/
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package org.deeplearning4j.modelimportexamples.util.darknet;
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import org.deeplearning4j.common.resources.DL4JResources;
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import org.deeplearning4j.zoo.util.BaseLabels;
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import java.io.IOException;
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import java.net.MalformedURLException;
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import java.net.URL;
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public class COCOLabels extends BaseLabels {
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public COCOLabels() throws IOException {
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super("coco.names");
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}
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@Override
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protected URL getURL() {
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try {
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return DL4JResources.getURL("resources/darknet/coco.names");
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} catch (MalformedURLException e){
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throw new RuntimeException(e);
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}
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}
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@Override
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protected String resourceName() {
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return "darknet";
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}
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@Override
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protected String resourceMD5() {
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return "4caf6834300c8b2ff19964b36e54d637";
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}
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}

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