Example usage for org.deeplearning4j.nn.conf NeuralNetConfiguration fromJson

Introduction

In this page you can find the example usage for org.deeplearning4j.nn.conf NeuralNetConfiguration fromJson.

Prototype

public static NeuralNetConfiguration fromJson(String json) 

Source Link

Document

Create a neural net configuration from json

Usage

From source file:org.knime.ext.dl4j.base.util.DLModelPortObjectUtils.java

License:Open Source License

/**
 * Loads a {@link DLModelPortObject} from the specified {@link ZipInputStream}. Supports both deserialization of old
 * and new format./*w ww .ja v  a2 s. co m*/
 *
 * @param inStream the stream to load from
 * @return the loaded {@link DLModelPortObject}
 * @throws IOException
 */
@SuppressWarnings("resource")
public static DLModelPortObject loadPortFromZip(final ZipInputStream inStream) throws IOException {
    final List<Layer> layers = new ArrayList<>();

    //old model format
    INDArray mln_params = null;
    MultiLayerConfiguration mln_config = null;
    org.deeplearning4j.nn.api.Updater updater = null;

    //new model format
    boolean mlnLoaded = false;
    boolean cgLoaded = false;
    MultiLayerNetwork mlnFromModelSerializer = null;
    ComputationGraph cgFromModelSerializer = null;

    ZipEntry entry;

    while ((entry = inStream.getNextEntry()) != null) {
        // read layers
        if (entry.getName().matches("layer[0123456789]+")) {
            final String read = readStringFromZipStream(inStream);
            Layer l = NeuralNetConfiguration.fromJson(read).getLayer();

            if (l instanceof BaseLayer) {
                BaseLayer bl = (BaseLayer) l;
                /* Compatibility issue between dl4j 0.6 and 0.8 due to API change. Activations changed from
                 * Strings to an interface. Therefore, if a model was saved with 0.6 the corresponding member
                 * of the layer object will contain null after 'NeuralNetConfiguration.fromJson'. Old method to
                 * retrieve String representation of the activation function was removed. Therefore, we parse
                 * the old activation from the json ourself and map it to the new Activation. */
                if (bl.getActivationFn() == null) {
                    Optional<Activation> layerActivation = DL4JVersionUtils.parseLayerActivationFromJson(read);

                    if (layerActivation.isPresent()) {
                        bl.setActivationFn(layerActivation.get().getActivationFunction());
                    }
                }
            }

            layers.add(l);

            // directly read MultiLayerNetwork, new format
        } else if (entry.getName().matches("mln_model")) {
            //stream must not be closed, ModelSerializer tries to close the stream
            CloseShieldInputStream shieldIs = new CloseShieldInputStream(inStream);
            mlnFromModelSerializer = ModelSerializer.restoreMultiLayerNetwork(shieldIs, true);
            mlnLoaded = true;

            // directly read MultiLayerNetwork, new format
        } else if (entry.getName().matches("cg_model")) {
            //stream must not be closed, ModelSerializer tries to close the stream
            CloseShieldInputStream shieldIs = new CloseShieldInputStream(inStream);
            cgFromModelSerializer = ModelSerializer.restoreComputationGraph(shieldIs, true);
            cgLoaded = true;

            // read MultilayerNetworkConfig, old format
        } else if (entry.getName().matches("mln_config")) {

            final String read = readStringFromZipStream(inStream);
            mln_config = MultiLayerConfiguration.fromJson(read.toString());

            // read params, old format
        } else if (entry.getName().matches("mln_params")) {
            try {
                mln_params = Nd4j.read(inStream);
            } catch (Exception e) {
                throw new IOException("Could not load network parameters. Please re-execute the Node.", e);
            }

            // read updater, old format
        } else if (entry.getName().matches("mln_updater")) {
            // stream must not be closed, even if an exception is thrown, because the wrapped stream must stay open
            final IgnoreIDObjectInputStream ois = new IgnoreIDObjectInputStream(inStream);
            try {
                updater = (org.deeplearning4j.nn.api.Updater) ois.readObject();
            } catch (final ClassNotFoundException e) {
                throw new IOException("Problem with updater loading: " + e.getMessage(), e);
            }
        }
    }

    if (mlnLoaded) {
        assert (!cgLoaded);
        return new DLModelPortObject(layers, mlnFromModelSerializer, null);
    } else if (cgLoaded) {
        assert (!mlnLoaded);
        return new DLModelPortObject(layers, cgFromModelSerializer, null);
    } else {
        return new DLModelPortObject(layers, buildMln(mln_config, updater, mln_params), null);
    }
}

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