本文整理汇总了Java中org.deeplearning4j.nn.conf.MultiLayerConfiguration类的典型用法代码示例。如果您正苦于以下问题：Java MultiLayerConfiguration类的具体用法？Java MultiLayerConfiguration怎么用？Java MultiLayerConfiguration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。
MultiLayerConfiguration类属于org.deeplearning4j.nn.conf包，在下文中一共展示了MultiLayerConfiguration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.MultiLayerConfiguration; //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.MultiLayerConfiguration; //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.MultiLayerConfiguration; //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.MultiLayerConfiguration; //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.MultiLayerConfiguration; //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.MultiLayerConfiguration; //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.MultiLayerConfiguration; //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.MultiLayerConfiguration; //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.MultiLayerConfiguration; //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: testLayerName
import org.deeplearning4j.nn.conf.MultiLayerConfiguration; //导入依赖的package包/类
@Test
public void testLayerName() {
String name1 = "genisys";
String name2 = "bill";
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().list()
.layer(0, new DenseLayer.Builder().nIn(2).nOut(2).name(name1).build())
.layer(1, new DenseLayer.Builder().nIn(2).nOut(2).name(name2).build()).build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
assertEquals(name1, conf.getConf(0).getLayer().getLayerName());
assertEquals(name2, conf.getConf(1).getLayer().getLayerName());
}
示例11: testUpdaterAdamParamsLayerwiseOverride
import org.deeplearning4j.nn.conf.MultiLayerConfiguration; //导入依赖的package包/类
@Test
public void testUpdaterAdamParamsLayerwiseOverride() {
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
.updater(new Adam(1.0, 0.5, 0.5, 1e-8))
.list()
.layer(0, new DenseLayer.Builder().nIn(2).nOut(2).build())
.layer(1, new DenseLayer.Builder().nIn(2).nOut(2).updater(new Adam(1.0, 0.6, 0.7, 1e-8)).build())
.build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
assertEquals(0.5, ((Adam) ((BaseLayer) conf.getConf(0).getLayer()).getIUpdater()).getBeta1(), 0.0);
assertEquals(0.6, ((Adam) ((BaseLayer) conf.getConf(1).getLayer()).getIUpdater()).getBeta1(), 0.0);
assertEquals(0.5, ((Adam) ((BaseLayer) conf.getConf(0).getLayer()).getIUpdater()).getBeta2(), 0.0);
assertEquals(0.7, ((Adam) ((BaseLayer) conf.getConf(1).getLayer()).getIUpdater()).getBeta2(), 0.0);
}
示例12: getNetwork
import org.deeplearning4j.nn.conf.MultiLayerConfiguration; //导入依赖的package包/类
private MultiLayerNetwork getNetwork() {
int nIn = 5;
int nOut = 6;
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().seed(12345).l1(0.01).l2(0.01)
.updater(new Sgd(0.1)).activation(Activation.TANH).weightInit(WeightInit.XAVIER).list()
.layer(0, new DenseLayer.Builder().nIn(nIn).nOut(20).build())
.layer(1, new DenseLayer.Builder().nIn(20).nOut(30).build()).layer(2, new OutputLayer.Builder()
.lossFunction(LossFunctions.LossFunction.MSE).nIn(30).nOut(nOut).build())
.build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
return net;
}
示例13: testSeparableConv2D
import org.deeplearning4j.nn.conf.MultiLayerConfiguration; //导入依赖的package包/类
@Test
public void testSeparableConv2D() {
MultiLayerConfiguration.Builder builder = new NeuralNetConfiguration.Builder().list()
.layer( new SeparableConvolution2D.Builder(2, 2)
.depthMultiplier(2)
.padding(0, 0)
.stride(2, 2).nIn(1).nOut(3).build()) //(28-2+0)/2+1 = 14
.layer( new SubsamplingLayer.Builder().kernelSize(2, 2).padding(1, 1).stride(2, 2).build()) //(14-2+2)/2+1 = 8 -> 8x8x3
.layer(2, new OutputLayer.Builder().nOut(3).build())
.setInputType(InputType.convolutional(28, 28, 1));
MultiLayerConfiguration conf = builder.build();
assertNotNull(conf.getInputPreProcess(2));
assertTrue(conf.getInputPreProcess(2) instanceof CnnToFeedForwardPreProcessor);
CnnToFeedForwardPreProcessor proc = (CnnToFeedForwardPreProcessor) conf.getInputPreProcess(2);
assertEquals(8, proc.getInputHeight());
assertEquals(8, proc.getInputWidth());
assertEquals(3, proc.getNumChannels());
assertEquals(8 * 8 * 3, ((FeedForwardLayer) conf.getConf(2).getLayer()).getNIn());
}
示例14: getDenseMLNConfig
import org.deeplearning4j.nn.conf.MultiLayerConfiguration; //导入依赖的package包/类
private static MultiLayerNetwork getDenseMLNConfig(boolean backprop, boolean pretrain) {
int numInputs = 4;
int outputNum = 3;
long seed = 6;
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().seed(seed)
.updater(new Sgd(1e-3)).l1(0.3).l2(1e-3).list()
.layer(0, new org.deeplearning4j.nn.conf.layers.DenseLayer.Builder().nIn(numInputs).nOut(3)
.activation(Activation.TANH).weightInit(WeightInit.XAVIER).build())
.layer(1, new org.deeplearning4j.nn.conf.layers.DenseLayer.Builder().nIn(3).nOut(2)
.activation(Activation.TANH).weightInit(WeightInit.XAVIER).build())
.layer(2, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT)
.weightInit(WeightInit.XAVIER).nIn(2).nOut(outputNum).build())
.backprop(backprop).pretrain(pretrain).build();
MultiLayerNetwork model = new MultiLayerNetwork(conf);
model.init();
return model;
}
示例15: incompleteLRN
import org.deeplearning4j.nn.conf.MultiLayerConfiguration; //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;
}