Examples with ActivationSwish ml.shifu.shifu.core.dtrain.nn.ActivationSwish used on opensource projects

Example 1 with ActivationSwish

use of ml.shifu.shifu.core.dtrain.nn.ActivationSwish in project shifu by ShifuML.

the class ActivationSwishTest method testDerive.

@Test
public void testDerive() {
    ActivationSwish swish = new ActivationSwish();
    // Test activtion function
    double[] swishInputValue = { -1d, 0, 1d };
    double[] truth = { -1 / (1 + Math.exp(1)), 0, 1 / (1 + Math.exp(-1)) };
    double error = 0;
    swish.activationFunction(swishInputValue, 0, 3);
    for (int i = 0; i < 3; i++) {
        error += Math.abs(truth[i] - swishInputValue[i]);
    }
    Assert.assertTrue(error < 1E-6);
    // Test derivative function
    double d0 = swish.derivativeFunction(0.0, 0.0);
    Assert.assertTrue(Math.abs(d0 - 0.5) < 1E-6);
    double d1 = swish.derivativeFunction(1, 0.0);
    double d11 = (1 + 2 * Math.exp(-1)) / Math.pow(1 + Math.exp(-1), 2);
    Assert.assertTrue(Math.abs(d1 - d11) < 1E-6);
    double dn1 = swish.derivativeFunction(-1, 0.0);
    double dn11 = 1 / Math.pow(1 + Math.exp(1), 2);
    Assert.assertTrue(Math.abs(dn1 - dn11) < 1E-6);
}

Example 2 with ActivationSwish

use of ml.shifu.shifu.core.dtrain.nn.ActivationSwish in project shifu by ShifuML.

the class PersistBasicFloatNetwork method readNetwork.

public BasicFloatNetwork readNetwork(final DataInput in) throws IOException {
    final BasicFloatNetwork result = new BasicFloatNetwork();
    final FlatNetwork flat = new FlatNetwork();
    // read properties
    Map<String, String> properties = new HashMap<String, String>();
    int size = in.readInt();
    for (int i = 0; i < size; i++) {
        properties.put(ml.shifu.shifu.core.dtrain.StringUtils.readString(in), ml.shifu.shifu.core.dtrain.StringUtils.readString(in));
    }
    result.getProperties().putAll(properties);
    // read fields
    flat.setBeginTraining(in.readInt());
    flat.setConnectionLimit(in.readDouble());
    flat.setContextTargetOffset(readIntArray(in));
    flat.setContextTargetSize(readIntArray(in));
    flat.setEndTraining(in.readInt());
    flat.setHasContext(in.readBoolean());
    flat.setInputCount(in.readInt());
    flat.setLayerCounts(readIntArray(in));
    flat.setLayerFeedCounts(readIntArray(in));
    flat.setLayerContextCount(readIntArray(in));
    flat.setLayerIndex(readIntArray(in));
    flat.setLayerOutput(readDoubleArray(in));
    flat.setOutputCount(in.readInt());
    flat.setLayerSums(new double[flat.getLayerOutput().length]);
    flat.setWeightIndex(readIntArray(in));
    flat.setWeights(readDoubleArray(in));
    flat.setBiasActivation(readDoubleArray(in));
    // read activations
    flat.setActivationFunctions(new ActivationFunction[flat.getLayerCounts().length]);
    int acSize = in.readInt();
    for (int i = 0; i < acSize; i++) {
        String name = ml.shifu.shifu.core.dtrain.StringUtils.readString(in);
        if (name.equals("ActivationReLU")) {
            name = ActivationReLU.class.getName();
        } else if (name.equals("ActivationLeakyReLU")) {
            name = ActivationLeakyReLU.class.getName();
        } else if (name.equals("ActivationSwish")) {
            name = ActivationSwish.class.getName();
        } else if (name.equals("ActivationPTANH")) {
            name = ActivationPTANH.class.getName();
        } else {
            name = "org.encog.engine.network.activation." + name;
        }
        ActivationFunction af = null;
        try {
            final Class<?> clazz = Class.forName(name);
            af = (ActivationFunction) clazz.newInstance();
        } catch (final ClassNotFoundException e) {
            throw new PersistError(e);
        } catch (final InstantiationException e) {
            throw new PersistError(e);
        } catch (final IllegalAccessException e) {
            throw new PersistError(e);
        }
        double[] params = readDoubleArray(in);
        for (int j = 0; j < params.length; j++) {
            af.setParam(j, params[j]);
        }
        flat.getActivationFunctions()[i] = af;
    }
    // read subset
    int subsetSize = in.readInt();
    Set<Integer> featureList = new HashSet<Integer>();
    for (int i = 0; i < subsetSize; i++) {
        featureList.add(in.readInt());
    }
    result.setFeatureSet(featureList);
    result.getStructure().setFlat(flat);
    return result;
}