本文整理汇总了Java中org.deeplearning4j.nn.conf.NeuralNetConfiguration类的典型用法代码示例。如果您正苦于以下问题：Java NeuralNetConfiguration类的具体用法？Java NeuralNetConfiguration怎么用？Java NeuralNetConfiguration使用的例子？那么, 这里精选的类代码示例或许可以为您提供帮助。
NeuralNetConfiguration类属于org.deeplearning4j.nn.conf包，在下文中一共展示了NeuralNetConfiguration类的15个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Java代码示例。
示例1: softMaxRegression
import org.deeplearning4j.nn.conf.NeuralNetConfiguration; //导入依赖的package包/类
private static MultiLayerNetwork softMaxRegression(int seed,
int iterations, int numRows, int numColumns, int outputNum) {
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
.seed(seed)
.gradientNormalization(
GradientNormalization.ClipElementWiseAbsoluteValue)
.gradientNormalizationThreshold(1.0)
.iterations(iterations)
.momentum(0.5)
.momentumAfter(Collections.singletonMap(3, 0.9))
.optimizationAlgo(OptimizationAlgorithm.CONJUGATE_GRADIENT)
.list(1)
.layer(0,
new OutputLayer.Builder(
LossFunction.NEGATIVELOGLIKELIHOOD)
.activation("softmax")
.nIn(numColumns * numRows).nOut(outputNum)
.build()).pretrain(true).backprop(false)
.build();
MultiLayerNetwork model = new MultiLayerNetwork(conf);
return model;
}
开发者ID:PacktPublishing，项目名称:Machine-Learning-End-to-Endguide-for-Java-developers，代码行数:25，代码来源:NeuralNetworks.java
示例2: getDeepDenseLayerNetworkConfiguration
import org.deeplearning4j.nn.conf.NeuralNetConfiguration; //导入依赖的package包/类
/** Returns the network configuration, 2 hidden DenseLayers of size 50.
*/
private static MultiLayerConfiguration getDeepDenseLayerNetworkConfiguration() {
final int numHiddenNodes = 50;
return new NeuralNetConfiguration.Builder()
.seed(seed)
.iterations(iterations)
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.learningRate(learningRate)
.weightInit(WeightInit.XAVIER)
.updater(Updater.NESTEROVS).momentum(0.9)
.list()
.layer(0, new DenseLayer.Builder().nIn(numInputs).nOut(numHiddenNodes)
.activation(Activation.TANH).build())
.layer(1, new DenseLayer.Builder().nIn(numHiddenNodes).nOut(numHiddenNodes)
.activation(Activation.TANH).build())
.layer(2, new OutputLayer.Builder(LossFunctions.LossFunction.MSE)
.activation(Activation.IDENTITY)
.nIn(numHiddenNodes).nOut(numOutputs).build())
.pretrain(false).backprop(true).build();
}
示例3: getConfiguration
import org.deeplearning4j.nn.conf.NeuralNetConfiguration; //导入依赖的package包/类
protected MultiLayerConfiguration getConfiguration()
{
int hiddenLayerNodes = parameters.getHiddeLayerNodes()[0];
final RBM hiddenLayer = new RBM.Builder(RBM.HiddenUnit.RECTIFIED, RBM.VisibleUnit.GAUSSIAN)
.nIn(parameters.getInputSize()).nOut(hiddenLayerNodes).weightInit(WeightInit.XAVIER).k(1)
.activation("relu").lossFunction(LossFunctions.LossFunction.RMSE_XENT).updater(Updater.ADAGRAD)
.dropOut(0.5).build();
final OutputLayer outputLayer = new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT).nIn(hiddenLayerNodes)
.nOut(parameters.getOutputSize()).activation("softmax").build();
return new NeuralNetConfiguration.Builder().seed(parameters.getSeed()).iterations(parameters.getIterations())
.learningRate(parameters.getLearningRate()).optimizationAlgo(OptimizationAlgorithm.CONJUGATE_GRADIENT)
.l2(2e-4).regularization(true).momentum(0.9).useDropConnect(true).list(2).layer(0, hiddenLayer)
.layer(1, outputLayer).build();
}
示例4: getConfiguration
import org.deeplearning4j.nn.conf.NeuralNetConfiguration; //导入依赖的package包/类
@Override
protected MultiLayerConfiguration getConfiguration()
{
return new NeuralNetConfiguration.Builder().seed(parameters.getSeed()).iterations(parameters.getIterations())
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).learningRate(parameters.getLearningRate()).l2(0.001)
.list(4)
.layer(0,
new DenseLayer.Builder().nIn(parameters.getInputSize()).nOut(250).weightInit(WeightInit.XAVIER)
.updater(Updater.ADAGRAD).activation("relu").build())
.layer(1,
new DenseLayer.Builder().nIn(250).nOut(10).weightInit(WeightInit.XAVIER)
.updater(Updater.ADAGRAD).activation("relu").build())
.layer(2,
new DenseLayer.Builder().nIn(10).nOut(250).weightInit(WeightInit.XAVIER)
.updater(Updater.ADAGRAD).activation("relu").build())
.layer(3,
new OutputLayer.Builder().nIn(250).nOut(parameters.getInputSize()).weightInit(WeightInit.XAVIER)
.updater(Updater.ADAGRAD).activation("relu")
.lossFunction(LossFunctions.LossFunction.MSE).build())
.pretrain(false).backprop(true).build();
}
示例5: getConfiguration
import org.deeplearning4j.nn.conf.NeuralNetConfiguration; //导入依赖的package包/类
@Override
protected MultiLayerConfiguration getConfiguration()
{
return new NeuralNetConfiguration.Builder().seed(parameters.getSeed())
.gradientNormalization(GradientNormalization.ClipElementWiseAbsoluteValue)
.gradientNormalizationThreshold(1.0).iterations(parameters.getIterations()).momentum(0.5)
.momentumAfter(Collections.singletonMap(3, 0.9))
.optimizationAlgo(OptimizationAlgorithm.CONJUGATE_GRADIENT).list(4)
.layer(0,
new AutoEncoder.Builder().nIn(parameters.getInputSize()).nOut(500).weightInit(WeightInit.XAVIER)
.lossFunction(LossFunction.RMSE_XENT).corruptionLevel(0.3).build())
.layer(1, new AutoEncoder.Builder().nIn(500).nOut(250).weightInit(WeightInit.XAVIER)
.lossFunction(LossFunction.RMSE_XENT).corruptionLevel(0.3)
.build())
.layer(2,
new AutoEncoder.Builder().nIn(250).nOut(200).weightInit(WeightInit.XAVIER)
.lossFunction(LossFunction.RMSE_XENT).corruptionLevel(0.3).build())
.layer(3, new OutputLayer.Builder(LossFunction.NEGATIVELOGLIKELIHOOD).activation("softmax").nIn(200)
.nOut(parameters.getOutputSize()).build())
.pretrain(true).backprop(false).build();
}
示例6: getConfiguration
import org.deeplearning4j.nn.conf.NeuralNetConfiguration; //导入依赖的package包/类
@Override
protected MultiLayerConfiguration getConfiguration()
{
final ConvulationalNetParameters parameters = (ConvulationalNetParameters) this.parameters;
final MultiLayerConfiguration.Builder builder = new NeuralNetConfiguration.Builder().seed(parameters.getSeed())
.iterations(parameters.getIterations())
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).list(2)
.layer(0,
new ConvolutionLayer.Builder(new int[] { 1, 1 }).nIn(parameters.getInputSize()).nOut(1000)
.activation("relu").weightInit(WeightInit.RELU).build())
.layer(1,
new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT).nOut(parameters.getOutputSize())
.weightInit(WeightInit.XAVIER).activation("softmax").build())
.backprop(true).pretrain(false);
new ConvolutionLayerSetup(builder, parameters.getRows(), parameters.getColumns(), parameters.getChannels());
return builder.build();
}
示例7: getConfiguration
import org.deeplearning4j.nn.conf.NeuralNetConfiguration; //导入依赖的package包/类
@Override
protected MultiLayerConfiguration getConfiguration()
{
final ConvulationalNetParameters parameters = (ConvulationalNetParameters) this.parameters;
final MultiLayerConfiguration.Builder builder = new NeuralNetConfiguration.Builder().seed(parameters.getSeed())
.iterations(parameters.getIterations())
.gradientNormalization(GradientNormalization.RenormalizeL2PerLayer)
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).list(3)
.layer(0,
new ConvolutionLayer.Builder(10, 10).stride(2, 2).nIn(parameters.getChannels()).nOut(6)
.weightInit(WeightInit.XAVIER).activation("relu").build())
.layer(1, new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX, new int[] { 2, 2 }).build())
.layer(2, new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
.nOut(parameters.getOutputSize()).weightInit(WeightInit.XAVIER).activation("softmax").build())
.backprop(true).pretrain(false);
new ConvolutionLayerSetup(builder, parameters.getRows(), parameters.getColumns(), parameters.getChannels());
return builder.build();
}
示例8: net
import org.deeplearning4j.nn.conf.NeuralNetConfiguration; //导入依赖的package包/类
private static MultiLayerConfiguration net(int nIn, int nOut) {
return new NeuralNetConfiguration.Builder()
.seed(42)
.iterations(1)
.activation(Activation.RELU)
.weightInit(WeightInit.XAVIER)
.learningRate(0.1)
.regularization(true).l2(1e-4)
.list(
new DenseLayer.Builder().nIn(nIn).nOut(3).build(),
new DenseLayer.Builder().nIn(3).nOut(3).build(),
new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
.activation(Activation.SOFTMAX)
.nIn(3)
.nOut(nOut)
.build()
)
.build();
}
示例9: getConfiguration
import org.deeplearning4j.nn.conf.NeuralNetConfiguration; //导入依赖的package包/类
private static MultiLayerConfiguration getConfiguration(DataFrame dataset) {
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
.seed(seed)
.constrainGradientToUnitNorm(true)
.optimizationAlgo(OptimizationAlgorithm.CONJUGATE_GRADIENT)
.list(4)
.layer(0, new RBM.Builder(RBM.HiddenUnit.BINARY, RBM.VisibleUnit.BINARY)
.weightInit(WeightInit.XAVIER)
.nIn(rows * columns).nOut(600).build())
.layer(1, new RBM.Builder(RBM.HiddenUnit.BINARY, RBM.VisibleUnit.BINARY)
.weightInit(WeightInit.XAVIER)
.nIn(600).nOut(250).build())
.layer(2, new RBM.Builder(RBM.HiddenUnit.BINARY, RBM.VisibleUnit.BINARY)
.weightInit(WeightInit.XAVIER)
.nIn(250).nOut(200).build())
.layer(3, new OutputLayer.Builder(LossFunctions.LossFunction.RMSE_XENT)
.weightInit(WeightInit.XAVIER)
.activation("softmax")
.nIn(200).nOut(AUTOMATIC).build())
.pretrain(true).backprop(false)
.build();
return conf;
}
示例10: doTestMLN
import org.deeplearning4j.nn.conf.NeuralNetConfiguration; //导入依赖的package包/类
private static void doTestMLN(SparkConf sparkConf) {
JavaSparkContext sc = new JavaSparkContext(sparkConf);
try {
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().list()
.layer(0, new OutputLayer.Builder().nIn(10).nOut(10).build()).pretrain(false).backprop(true)
.build();
TrainingMaster tm = new ParameterAveragingTrainingMaster.Builder(1).build();
SparkDl4jMultiLayer sml = new SparkDl4jMultiLayer(sc, conf, tm);
} finally {
sc.stop();
}
}
示例11: testCompGraphNullLayer
import org.deeplearning4j.nn.conf.NeuralNetConfiguration; //导入依赖的package包/类
@Test
public void testCompGraphNullLayer() {
ComputationGraphConfiguration.GraphBuilder gb = new NeuralNetConfiguration.Builder()
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).updater(new Sgd(0.01))
.seed(42).miniBatch(false).l1(0.2).l2(0.2)
/* Graph Builder */
.updater(Updater.RMSPROP).graphBuilder().addInputs("in")
.addLayer("L" + 1,
new GravesLSTM.Builder().nIn(20).updater(Updater.RMSPROP).nOut(10)
.weightInit(WeightInit.XAVIER)
.dropOut(0.4).l1(0.3).activation(Activation.SIGMOID).build(),
"in")
.addLayer("output",
new RnnOutputLayer.Builder().nIn(20).nOut(10).activation(Activation.SOFTMAX)
.weightInit(WeightInit.RELU_UNIFORM).build(),
"L" + 1)
.setOutputs("output");
ComputationGraphConfiguration conf = gb.build();
ComputationGraph cg = new ComputationGraph(conf);
cg.init();
}
示例12: testBackProp
import org.deeplearning4j.nn.conf.NeuralNetConfiguration; //导入依赖的package包/类
@Test
public void testBackProp() throws Exception {
MnistDataFetcher fetcher = new MnistDataFetcher(true);
// LayerFactory layerFactory = LayerFactories.getFactory(new org.deeplearning4j.nn.conf.layers.AutoEncoder());
NeuralNetConfiguration conf = new NeuralNetConfiguration.Builder()
.optimizationAlgo(OptimizationAlgorithm.LINE_GRADIENT_DESCENT)
.updater(new Sgd(0.1))
.layer(new org.deeplearning4j.nn.conf.layers.AutoEncoder.Builder().nIn(784).nOut(600)
.corruptionLevel(0.6)
.lossFunction(LossFunctions.LossFunction.RECONSTRUCTION_CROSSENTROPY).build())
.build();
fetcher.fetch(100);
DataSet d2 = fetcher.next();
INDArray input = d2.getFeatureMatrix();
int numParams = conf.getLayer().initializer().numParams(conf);
INDArray params = Nd4j.create(1, numParams);
AutoEncoder da = (AutoEncoder) conf.getLayer().instantiate(conf, null, 0, params, true);
Gradient g = new DefaultGradient();
g.gradientForVariable().put(DefaultParamInitializer.WEIGHT_KEY, da.decode(da.activate(input)).sub(input));
}
示例13: testCNNBNActivationCombo
import org.deeplearning4j.nn.conf.NeuralNetConfiguration; //导入依赖的package包/类
@Test
public void testCNNBNActivationCombo() throws Exception {
DataSetIterator iter = new MnistDataSetIterator(2, 2);
DataSet next = iter.next();
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).seed(123)
.list()
.layer(0, new ConvolutionLayer.Builder().nIn(1).nOut(6).weightInit(WeightInit.XAVIER)
.activation(Activation.IDENTITY).build())
.layer(1, new BatchNormalization.Builder().build())
.layer(2, new ActivationLayer.Builder().activation(Activation.RELU).build())
.layer(3, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT)
.weightInit(WeightInit.XAVIER).activation(Activation.SOFTMAX).nOut(10).build())
.backprop(true).pretrain(false).setInputType(InputType.convolutionalFlat(28, 28, 1)).build();
MultiLayerNetwork network = new MultiLayerNetwork(conf);
network.init();
network.fit(next);
assertNotEquals(null, network.getLayer(0).getParam("W"));
assertNotEquals(null, network.getLayer(0).getParam("b"));
}
示例14: incompleteLRN
import org.deeplearning4j.nn.conf.NeuralNetConfiguration; //导入依赖的package包/类
public MultiLayerConfiguration.Builder incompleteLRN() {
MultiLayerConfiguration.Builder builder =
new NeuralNetConfiguration.Builder().seed(3)
.optimizationAlgo(OptimizationAlgorithm.CONJUGATE_GRADIENT).list()
.layer(0, new org.deeplearning4j.nn.conf.layers.ConvolutionLayer.Builder(
new int[] {5, 5}).nOut(6).build())
.layer(1, new org.deeplearning4j.nn.conf.layers.SubsamplingLayer.Builder(
new int[] {2, 2}).build())
.layer(2, new LocalResponseNormalization.Builder().build())
.layer(3, new org.deeplearning4j.nn.conf.layers.ConvolutionLayer.Builder(
new int[] {5, 5}).nOut(6).build())
.layer(4, new org.deeplearning4j.nn.conf.layers.SubsamplingLayer.Builder(
new int[] {2, 2}).build())
.layer(5, new org.deeplearning4j.nn.conf.layers.OutputLayer.Builder(
LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD).nOut(2)
.build());
return builder;
}
示例15: testDistributionDeserializer
import org.deeplearning4j.nn.conf.NeuralNetConfiguration; //导入依赖的package包/类
@Test
public void testDistributionDeserializer() throws Exception {
//Test current format:
Distribution[] distributions =
new Distribution[] {new NormalDistribution(3, 0.5), new UniformDistribution(-2, 1),
new GaussianDistribution(2, 1.0), new BinomialDistribution(10, 0.3)};
ObjectMapper om = NeuralNetConfiguration.mapper();
for (Distribution d : distributions) {
String json = om.writeValueAsString(d);
Distribution fromJson = om.readValue(json, Distribution.class);
assertEquals(d, fromJson);
}
}