Slides from a talk at the Seattle Java User Group on Oct 17, 2017. Source code at https://github.com/ejain/deeplabel.

8. import org.apache.tika.metadata.Metadata;
import org.apache.tika.metadata.TikaCoreProperties;
import org.apache.tika.parser.ParseContext;
import org.apache.tika.parser.jpeg.JpegParser;
InputStream stream = new BufferedInputStream(new FileInputStream(path.toFile()))
new JpegParser().parse(stream, new DefaultHandler(), metadata, new ParseContext());
String caption = metadata.get(TikaCoreProperties.DESCRIPTION);

9. wordart.com

12. https://becominghuman.ai/cheat-sheets-for-ai-neural-networks-machine-learning-deep-learning-big-data-678c51b4b463

13. https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf

16. AlexNet model = new AlexNet(numLabels, seed, iterations);

17. log.info("Loading data from {}...", imagePath);
PathLabelGenerator labelMaker = new ImageDescriptionLabelGenerator("Trail");
BalancedPathFilter pathFilter = new BalancedPathFilter(rand, NativeImageLoader.ALLOWED_FORMATS, labelMaker);
FileSplit fileSplit = new FileSplit(imagePath, NativeImageLoader.ALLOWED_FORMATS, rand);
InputSplit[] inputSplit = fileSplit.sample(pathFilter, splitTrainTest, 1 - splitTrainTest);
InputSplit trainingData = inputSplit[0];
InputSplit testingData = inputSplit[1];
log.info("Building model...");
AlexNet model = new AlexNet(numLabels, seed, iterations);
model.setInputShape(new int[][] {{ channels, width, height }});
MultiLayerNetwork network = model.init();
log.info("Training model using {} samples...", trainingData.length());
ImageRecordReader recordReader = new ImageRecordReader(height, width, channels, labelMaker);
recordReader.initialize(trainingData, null);
DataSetIterator dataIter = new RecordReaderDataSetIterator(recordReader, batchSize, 1, numLabels);
scaler.fit(dataIter);
DataNormalization scaler = new ImagePreProcessingScaler(0, 1);
dataIter.setPreProcessor(scaler);
MultipleEpochsIterator trainIter = new MultipleEpochsIterator(epochs, dataIter);
network.fit(trainIter);
log.info("Evaluating model using {} samples...", testingData.length());
recordReader.initialize(testingData);
dataIter = new RecordReaderDataSetIterator(recordReader, batchSize, 1, numLabels);
scaler.fit(dataIter);
dataIter.setPreProcessor(scaler);
Evaluation eval = network.evaluate(dataIter);
log.info(eval.stats(true));
log.info("Saving model to {}...", modelPath);
ModelSerializer.writeModel(network, modelPath, true);

18. 14:57:44.074 INFO ImageModelBuilder - Loading data from imagesphotos-1000...
14:57:57.518 INFO ImageModelBuilder - Building model...
14:57:57.550 INFO org.nd4j.linalg.factory.Nd4jBackend - Loaded [CpuBackend] backend
14:57:58.398 INFO org.nd4j.nativeblas.NativeOpsHolder - Number of threads used for NativeOps: 2
14:57:58.724 INFO org.nd4j.nativeblas.Nd4jBlas - Number of threads used for BLAS: 2
14:57:58.726 INFO o.n.l.a.o.e.DefaultOpExecutioner - Backend used: [CPU]; OS: [Windows 10]
14:57:58.726 INFO o.n.l.a.o.e.DefaultOpExecutioner - Cores: [4]; Memory: [10.7GB];
14:57:58.726 INFO o.n.l.a.o.e.DefaultOpExecutioner - Blas vendor: [OPENBLAS]
14:58:03.013 INFO o.d.nn.multilayer.MultiLayerNetwork - Starting MultiLayerNetwork with WorkspaceModes
set to [training: SINGLE; inference: SINGLE]
14:58:04.717 INFO ImageModelBuilder - Training model using 354 samples...
14:58:14.191 INFO o.d.d.i.MultipleEpochsIterator - Epoch 1, number of batches completed 2
14:58:18.025 INFO o.d.o.l.ScoreIterationListener - Score at iteration 0 is 0.8973041230682596
14:58:20.517 INFO o.d.d.i.MultipleEpochsIterator - Epoch 2, number of batches completed 2
14:58:20.937 INFO o.d.o.l.ScoreIterationListener - Score at iteration 1 is 1.3137047898462706
14:58:24.958 INFO o.d.o.l.ScoreIterationListener - Score at iteration 2 is 0.8076716838671805
14:58:26.688 INFO o.d.o.l.ScoreIterationListener - Score at iteration 3 is 1.592903741321036
14:58:26.719 INFO o.d.d.i.MultipleEpochsIterator - Epoch 3, number of batches completed 2
14:58:30.798 INFO o.d.o.l.ScoreIterationListener - Score at iteration 4 is 0.8828689747742253
14:58:32.682 INFO o.d.o.l.ScoreIterationListener - Score at iteration 5 is 1.2827695432226482
14:58:33.259 INFO o.d.d.i.MultipleEpochsIterator - Epoch 4, number of batches completed 2
14:58:36.885 INFO o.d.o.l.ScoreIterationListener - Score at iteration 6 is 0.8240061319768378
14:58:38.641 INFO o.d.o.l.ScoreIterationListener - Score at iteration 7 is 1.4599294093575166
14:58:39.405 INFO o.d.d.i.MultipleEpochsIterator - Epoch 5, number of batches completed 2
14:58:42.013 INFO o.d.o.l.ScoreIterationListener - Score at iteration 8 is 0.8207273637625443
14:58:43.364 INFO o.d.o.l.ScoreIterationListener - Score at iteration 9 is 1.3621771943792143
14:59:21.842 INFO ImageModelBuilder - Evaluating model using 88 samples...
14:59:24.084 INFO ImageModelBuilder -
Examples labeled as not_trail classified by model as not_trail: 44 times
Examples labeled as trail classified by model as not_trail: 44 times
==========================Scores========================================
# of classes: 2
Accuracy: 0.5000
Precision: 0.5000
Recall: 0.5000
F1 Score: 0.0000
========================================================================
14:59:24.084 INFO ImageModelBuilder - Saving model to modelsalexnet-1000-dev.net...

19. ==========================Scores========================================
# of classes: 2
Accuracy: 0.5036
Precision: 0.7510
Recall: 0.5030
F1 Score: 0.6685
========================================================================

22. MultiLayerNetwork network = ModelSerializer.restoreMultiLayerNetwork(modelPath.toFile());
NativeImageLoader loader = new NativeImageLoader(100, 100, 1);
INDArray image = loader.asMatrix(imagePath.toFile());
DataNormalization scaler = new ImagePreProcessingScaler(0, 1);
scaler.transform(image);
INDArray output = network.output(loadImage(imagePath));
log.info("{} -> {}", imagePath, output.getDouble(0));

24. trail

26. trail

28. NOT trail

30. NOT trail

32. trail

34. trail

36. trail