Examples with Shape ai.djl.ndarray.types.Shape used on opensource projects

Example 1 with Shape

use of ai.djl.ndarray.types.Shape in project djl-demo by deepjavalibrary.

the class CSVDataset method encodeData.

/**
 * Convert the URL string to NDArray encoded form
 *
 * @param manager NDManager for NDArray context
 * @param url URL in string format
 */
private NDArray encodeData(NDManager manager, String url) {
    NDArray encoded = manager.zeros(new Shape(alphabets.size(), FEATURE_LENGTH));
    char[] arrayText = url.toCharArray();
    for (int i = 0; i < url.length(); i++) {
        if (i > FEATURE_LENGTH) {
            break;
        }
        if (alphabetsIndex.containsKey(arrayText[i])) {
            encoded.set(new NDIndex(alphabetsIndex.get(arrayText[i]), i), 1);
        }
    }
    return encoded;
}

Example 2 with Shape

use of ai.djl.ndarray.types.Shape in project djl-demo by deepjavalibrary.

the class Training method main.

public static void main(String[] args) throws IOException, TranslateException {
    // the location to save the model
    Path modelDir = Paths.get("models");
    // create ImageFolder dataset from directory
    ImageFolder dataset = initDataset("ut-zap50k-images-square");
    // Split the dataset set into training dataset and validate dataset
    RandomAccessDataset[] datasets = dataset.randomSplit(8, 2);
    // set loss function, which seeks to minimize errors
    // loss function evaluates model's predictions against the correct answer (during training)
    // higher numbers are bad - means model performed poorly; indicates more errors; want to
    // minimize errors (loss)
    Loss loss = Loss.softmaxCrossEntropyLoss();
    // setting training parameters (ie hyperparameters)
    TrainingConfig config = setupTrainingConfig(loss);
    try (// empty model instance to hold patterns
    Model model = Models.getModel();
        Trainer trainer = model.newTrainer(config)) {
        // metrics collect and report key performance indicators, like accuracy
        trainer.setMetrics(new Metrics());
        Shape inputShape = new Shape(1, 3, Models.IMAGE_HEIGHT, Models.IMAGE_HEIGHT);
        // initialize trainer with proper input shape
        trainer.initialize(inputShape);
        // find the patterns in data
        EasyTrain.fit(trainer, EPOCHS, datasets[0], datasets[1]);
        // set model properties
        TrainingResult result = trainer.getTrainingResult();
        model.setProperty("Epoch", String.valueOf(EPOCHS));
        model.setProperty("Accuracy", String.format("%.5f", result.getValidateEvaluation("Accuracy")));
        model.setProperty("Loss", String.format("%.5f", result.getValidateLoss()));
        // save the model after done training for inference later
        // model saved as shoeclassifier-0000.params
        model.save(modelDir, Models.MODEL_NAME);
        // save labels into model directory
        Models.saveSynset(modelDir, dataset.getSynset());
    }
}

Example 3 with Shape

use of ai.djl.ndarray.types.Shape in project djl-demo by deepjavalibrary.

the class FaceDetectionTranslator method processInput.

@Override
public NDList processInput(TranslatorContext ctx, Image input) {
    width = input.getWidth();
    height = input.getHeight();
    NDArray array = input.toNDArray(ctx.getNDManager(), Image.Flag.COLOR);
    // HWC -> CHW RGB -> BGR
    array = array.transpose(2, 0, 1).flip(0);
    // The network by default takes float32
    if (!array.getDataType().equals(DataType.FLOAT32)) {
        array = array.toType(DataType.FLOAT32, false);
    }
    NDArray mean = ctx.getNDManager().create(new float[] { 104f, 117f, 123f }, new Shape(3, 1, 1));
    array = array.sub(mean);
    NDList list = new NDList(array);
    return list;
}

Example 4 with Shape

use of ai.djl.ndarray.types.Shape in project djl by deepjavalibrary.

the class CsvDataset method toNDList.

protected NDList toNDList(NDManager manager, CSVRecord record, List<Feature> selected) {
    DynamicBuffer bb = new DynamicBuffer();
    for (Feature feature : selected) {
        String name = feature.getName();
        String value = record.get(name);
        feature.featurizer.featurize(bb, value);
    }
    FloatBuffer buf = bb.getBuffer();
    return new NDList(manager.create(buf, new Shape(bb.getLength())));
}

Example 5 with Shape

use of ai.djl.ndarray.types.Shape in project djl by deepjavalibrary.

the class AirfoilRandomAccess method toNDList.

/**
 * {@inheritDoc}
 */
@Override
protected NDList toNDList(NDManager manager, CSVRecord record, List<Feature> selected) {
    int length = selected.size();
    ByteBuffer bb = manager.allocateDirect(length * 4);
    FloatBuffer buf = bb.asFloatBuffer();
    int index = 0;
    for (Feature feature : selected) {
        String name = feature.getName();
        float value = Float.parseFloat(record.get(name));
        if (normalize) {
            value = (value - mean.get(name)) / std.get(name);
        }
        buf.put(value);
        ++index;
    }
    buf.rewind();
    return new NDList(manager.create(buf, new Shape(length)));
}