本文整理汇总了Java中org.nd4j.linalg.lossfunctions.LossFunctions.LossFunction类的典型用法代码示例。如果您正苦于以下问题：Java LossFunction类的具体用法？Java LossFunction怎么用？Java LossFunction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。
LossFunction类属于org.nd4j.linalg.lossfunctions.LossFunctions包，在下文中一共展示了LossFunction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.nd4j.linalg.lossfunctions.LossFunctions.LossFunction; //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: getConfiguration
import org.nd4j.linalg.lossfunctions.LossFunctions.LossFunction; //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3: testSetParams
import org.nd4j.linalg.lossfunctions.LossFunctions.LossFunction; //导入依赖的package包/类
@Test
public void testSetParams() {
NeuralNetConfiguration conf = new NeuralNetConfiguration.Builder()
.optimizationAlgo(OptimizationAlgorithm.LINE_GRADIENT_DESCENT)
.updater(new Sgd(1e-1))
.layer(new org.deeplearning4j.nn.conf.layers.OutputLayer.Builder().nIn(4).nOut(3)
.weightInit(WeightInit.ZERO).activation(Activation.SOFTMAX)
.lossFunction(LossFunctions.LossFunction.MCXENT).build())
.build();
int numParams = conf.getLayer().initializer().numParams(conf);
INDArray params = Nd4j.create(1, numParams);
OutputLayer l = (OutputLayer) conf.getLayer().instantiate(conf,
Collections.<IterationListener>singletonList(new ScoreIterationListener(1)), 0, params, true);
params = l.params();
l.setParams(params);
assertEquals(params, l.params());
}
示例4: getGraph
import org.nd4j.linalg.lossfunctions.LossFunctions.LossFunction; //导入依赖的package包/类
private ComputationGraph getGraph(int numLabels, double lambda) {
Nd4j.getRandom().setSeed(12345);
ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder().seed(12345)
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.weightInit(WeightInit.DISTRIBUTION).dist(new NormalDistribution(0, 1)).updater(new NoOp())
.graphBuilder().addInputs("input1")
.addLayer("l1", new DenseLayer.Builder().nIn(4).nOut(5).activation(Activation.RELU).build(),
"input1")
.addLayer("lossLayer", new CenterLossOutputLayer.Builder()
.lossFunction(LossFunctions.LossFunction.MCXENT).nIn(5).nOut(numLabels)
.lambda(lambda).activation(Activation.SOFTMAX).build(), "l1")
.setOutputs("lossLayer").pretrain(false).backprop(true).build();
ComputationGraph graph = new ComputationGraph(conf);
graph.init();
return graph;
}
示例5: getIrisMLPSimpleConfig
import org.nd4j.linalg.lossfunctions.LossFunctions.LossFunction; //导入依赖的package包/类
/** Very simple back-prop config set up for Iris.
* Learning Rate = 0.1
* No regularization, no Adagrad, no momentum etc. One iteration.
*/
private static MultiLayerConfiguration getIrisMLPSimpleConfig(int[] hiddenLayerSizes,
Activation activationFunction) {
NeuralNetConfiguration.ListBuilder lb = new NeuralNetConfiguration.Builder().updater(new Sgd(0.1))
.seed(12345L).list();
for (int i = 0; i < hiddenLayerSizes.length; i++) {
int nIn = (i == 0 ? 4 : hiddenLayerSizes[i - 1]);
lb.layer(i, new DenseLayer.Builder().nIn(nIn).nOut(hiddenLayerSizes[i]).weightInit(WeightInit.XAVIER)
.activation(activationFunction).build());
}
lb.layer(hiddenLayerSizes.length,
new OutputLayer.Builder(LossFunction.MCXENT).nIn(hiddenLayerSizes[hiddenLayerSizes.length - 1])
.nOut(3).weightInit(WeightInit.XAVIER)
.activation(activationFunction.equals(Activation.IDENTITY) ? Activation.IDENTITY
: Activation.SOFTMAX)
.build());
lb.pretrain(false).backprop(true);
return lb.build();
}
示例6: testRnnTimeStepWithPreprocessor
import org.nd4j.linalg.lossfunctions.LossFunctions.LossFunction; //导入依赖的package包/类
@Test
public void testRnnTimeStepWithPreprocessor() {
MultiLayerConfiguration conf =
new NeuralNetConfiguration.Builder()
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.list()
.layer(0, new org.deeplearning4j.nn.conf.layers.GravesLSTM.Builder().nIn(10)
.nOut(10).activation(Activation.TANH).build())
.layer(1, new org.deeplearning4j.nn.conf.layers.GravesLSTM.Builder().nIn(10)
.nOut(10).activation(Activation.TANH).build())
.layer(2, new RnnOutputLayer.Builder(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nIn(10).nOut(10).build())
.inputPreProcessor(0, new FeedForwardToRnnPreProcessor()).pretrain(false)
.backprop(true).build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
INDArray in = Nd4j.rand(1, 10);
net.rnnTimeStep(in);
}
示例7: testRnnTimeStepWithPreprocessorGraph
import org.nd4j.linalg.lossfunctions.LossFunctions.LossFunction; //导入依赖的package包/类
@Test
public void testRnnTimeStepWithPreprocessorGraph() {
ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder()
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.graphBuilder().addInputs("in")
.addLayer("0", new org.deeplearning4j.nn.conf.layers.GravesLSTM.Builder().nIn(10).nOut(10)
.activation(Activation.TANH).build(), "in")
.addLayer("1", new org.deeplearning4j.nn.conf.layers.GravesLSTM.Builder().nIn(10).nOut(10)
.activation(Activation.TANH).build(), "0")
.addLayer("2", new RnnOutputLayer.Builder(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nIn(10).nOut(10).build(), "1")
.setOutputs("2").inputPreProcessor("0", new FeedForwardToRnnPreProcessor()).pretrain(false)
.backprop(true).build();
ComputationGraph net = new ComputationGraph(conf);
net.init();
INDArray in = Nd4j.rand(1, 10);
net.rnnTimeStep(in);
}
示例8: getConfiguration
import org.nd4j.linalg.lossfunctions.LossFunctions.LossFunction; //导入依赖的package包/类
@Override
protected MultiLayerConfiguration getConfiguration()
{
final int[] hiddenLayerNodes = parameters.getHiddeLayerNodes();
final int nLayers = hiddenLayerNodes.length + 1;
final ListBuilder list = new NeuralNetConfiguration.Builder()
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.iterations(parameters.getIterations()).learningRate(parameters.getLearningRate()).rmsDecay(0.95)
.seed(parameters.getSeed()).regularization(true).l2(0.001).list(nLayers).pretrain(false).backprop(true);
for (int i = 0; i < nLayers; i++)
{
int nIn;
if (i == 0)
{
nIn = parameters.getInputSize();
}
else
{
nIn = hiddenLayerNodes[i - 1];
}
if (i < nLayers - 1)
{
final GravesLSTM layer = new GravesLSTM.Builder().nIn(nIn).nOut(hiddenLayerNodes[i])
.updater(Updater.RMSPROP).activation("tanh").weightInit(WeightInit.DISTRIBUTION)
.dist(new UniformDistribution(-0.08, 0.08)).build();
list.layer(i, layer);
}
else
{
final RnnOutputLayer outputLayer = new RnnOutputLayer.Builder(LossFunction.MCXENT).activation("softmax")
.updater(Updater.RMSPROP).nIn(hiddenLayerNodes[1]).nOut(parameters.getOutputSize())
.weightInit(WeightInit.DISTRIBUTION).dist(new UniformDistribution(-0.08, 0.08)).build();
list.layer(i, outputLayer);
}
}
return list.build();
}
示例9: testL2
import org.nd4j.linalg.lossfunctions.LossFunctions.LossFunction; //导入依赖的package包/类
@Test
public void testL2() {
LossFunction[] lossFunctionList = {LossFunction.MSE, LossFunction.L1, LossFunction.EXPLL, LossFunction.XENT,
LossFunction.MCXENT, LossFunction.SQUARED_LOSS, LossFunction.RECONSTRUCTION_CROSSENTROPY,
LossFunction.NEGATIVELOGLIKELIHOOD, LossFunction.COSINE_PROXIMITY, LossFunction.HINGE,
LossFunction.SQUARED_HINGE, LossFunction.KL_DIVERGENCE, LossFunction.MEAN_ABSOLUTE_ERROR,
LossFunction.L2, LossFunction.MEAN_ABSOLUTE_PERCENTAGE_ERROR,
LossFunction.MEAN_SQUARED_LOGARITHMIC_ERROR, LossFunction.POISSON};
testLossFunctions(lossFunctionList);
}
示例10: testEmbeddingLayerSimple
import org.nd4j.linalg.lossfunctions.LossFunctions.LossFunction; //导入依赖的package包/类
@Test
public void testEmbeddingLayerSimple() {
Random r = new Random(12345);
int nExamples = 5;
INDArray input = Nd4j.zeros(nExamples, 1);
INDArray labels = Nd4j.zeros(nExamples, 3);
for (int i = 0; i < nExamples; i++) {
input.putScalar(i, r.nextInt(4));
labels.putScalar(new int[] {i, r.nextInt(3)}, 1.0);
}
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().l2(0.2).l1(0.1)
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).seed(12345L)
.list().layer(0,
new EmbeddingLayer.Builder().nIn(4).nOut(3).weightInit(WeightInit.XAVIER)
.dist(new NormalDistribution(0, 1))
.updater(new NoOp()).activation(
Activation.TANH)
.build())
.layer(1, new OutputLayer.Builder(LossFunction.MCXENT).nIn(3).nOut(3)
.weightInit(WeightInit.XAVIER).dist(new NormalDistribution(0, 1))
.updater(new NoOp()).activation(Activation.SOFTMAX).build())
.pretrain(false).backprop(true).build();
MultiLayerNetwork mln = new MultiLayerNetwork(conf);
mln.init();
if (PRINT_RESULTS) {
System.out.println("testEmbeddingLayerSimple");
for (int j = 0; j < mln.getnLayers(); j++)
System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
}
boolean gradOK = GradientCheckUtil.checkGradients(mln, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, input, labels);
String msg = "testEmbeddingLayerSimple";
assertTrue(msg, gradOK);
}
示例11: testIris2
import org.nd4j.linalg.lossfunctions.LossFunctions.LossFunction; //导入依赖的package包/类
@Test
public void testIris2() {
NeuralNetConfiguration conf = new NeuralNetConfiguration.Builder()
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.updater(new Sgd(1e-1))
.layer(new org.deeplearning4j.nn.conf.layers.OutputLayer.Builder().nIn(4).nOut(3)
.weightInit(WeightInit.XAVIER).activation(Activation.SOFTMAX)
.lossFunction(LossFunctions.LossFunction.MCXENT).build())
.build();
int numParams = conf.getLayer().initializer().numParams(conf);
INDArray params = Nd4j.create(1, numParams);
OutputLayer l = (OutputLayer) conf.getLayer().instantiate(conf,
Collections.<IterationListener>singletonList(new ScoreIterationListener(1)), 0, params, true);
l.setBackpropGradientsViewArray(Nd4j.create(1, params.length()));
DataSetIterator iter = new IrisDataSetIterator(150, 150);
DataSet next = iter.next();
next.shuffle();
SplitTestAndTrain trainTest = next.splitTestAndTrain(110);
trainTest.getTrain().normalizeZeroMeanZeroUnitVariance();
for( int i=0; i<10; i++ ) {
l.fit(trainTest.getTrain());
}
DataSet test = trainTest.getTest();
test.normalizeZeroMeanZeroUnitVariance();
Evaluation eval = new Evaluation();
INDArray output = l.output(test.getFeatureMatrix());
eval.eval(test.getLabels(), output);
log.info("Score " + eval.stats());
}
示例12: testWeightsDifferent
import org.nd4j.linalg.lossfunctions.LossFunctions.LossFunction; //导入依赖的package包/类
@Test
public void testWeightsDifferent() {
Nd4j.MAX_ELEMENTS_PER_SLICE = Integer.MAX_VALUE;
Nd4j.MAX_SLICES_TO_PRINT = Integer.MAX_VALUE;
NeuralNetConfiguration conf = new NeuralNetConfiguration.Builder()
.miniBatch(false).seed(123)
.updater(new AdaGrad(1e-1))
.layer(new org.deeplearning4j.nn.conf.layers.OutputLayer.Builder().nIn(4).nOut(3)
.weightInit(WeightInit.XAVIER)
.lossFunction(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
.activation(Activation.SOFTMAX).build())
.build();
int numParams = conf.getLayer().initializer().numParams(conf);
INDArray params = Nd4j.create(1, numParams);
OutputLayer o = (OutputLayer) conf.getLayer().instantiate(conf, null, 0, params, true);
o.setBackpropGradientsViewArray(Nd4j.create(1, params.length()));
int numSamples = 150;
int batchSize = 150;
DataSetIterator iter = new IrisDataSetIterator(batchSize, numSamples);
DataSet iris = iter.next(); // Loads data into generator and format consumable for NN
iris.normalizeZeroMeanZeroUnitVariance();
o.setListeners(new ScoreIterationListener(1));
SplitTestAndTrain t = iris.splitTestAndTrain(0.8);
for( int i=0; i<1000; i++ ){
o.fit(t.getTrain());
}
log.info("Evaluate model....");
Evaluation eval = new Evaluation(3);
eval.eval(t.getTest().getLabels(), o.output(t.getTest().getFeatureMatrix(), true));
log.info(eval.stats());
}
示例13: testBinary
import org.nd4j.linalg.lossfunctions.LossFunctions.LossFunction; //导入依赖的package包/类
@Test
public void testBinary() {
Nd4j.MAX_ELEMENTS_PER_SLICE = Integer.MAX_VALUE;
Nd4j.MAX_SLICES_TO_PRINT = Integer.MAX_VALUE;
DataBuffer.Type initialType = Nd4j.dataType();
DataTypeUtil.setDTypeForContext(DataBuffer.Type.DOUBLE);
INDArray data = Nd4j.create(new double[][] {{1, 1, 1, 0, 0, 0}, {1, 0, 1, 0, 0, 0}, {1, 1, 1, 0, 0, 0},
{0, 0, 1, 1, 1, 0}, {0, 0, 1, 1, 0, 0}, {0, 0, 1, 1, 1, 0}});
INDArray data2 = Nd4j.create(new double[][] {{1, 0}, {1, 0}, {1, 0}, {0, 1}, {0, 1}, {0, 1}});
DataSet dataset = new DataSet(data, data2);
NeuralNetConfiguration conf = new NeuralNetConfiguration.Builder()
.seed(123)
.updater(new Sgd(1e-2))
.layer(new org.deeplearning4j.nn.conf.layers.OutputLayer.Builder().nIn(6).nOut(2)
.weightInit(WeightInit.ZERO).activation(Activation.SOFTMAX)
.lossFunction(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD).build())
.build();
int numParams = conf.getLayer().initializer().numParams(conf);
INDArray params = Nd4j.create(1, numParams);
OutputLayer o = (OutputLayer) conf.getLayer().instantiate(conf, null, 0, params, true);
o.setBackpropGradientsViewArray(Nd4j.create(1, params.length()));
o.setListeners(new ScoreIterationListener(1));
for( int i=0; i<200; i++ ) {
o.fit(dataset);
}
DataTypeUtil.setDTypeForContext(initialType);
}
示例14: testIris
import org.nd4j.linalg.lossfunctions.LossFunctions.LossFunction; //导入依赖的package包/类
@Test
public void testIris() {
NeuralNetConfiguration conf = new NeuralNetConfiguration.Builder()
.optimizationAlgo(OptimizationAlgorithm.LINE_GRADIENT_DESCENT).updater(new Sgd(1e-1))
.layer(new org.deeplearning4j.nn.conf.layers.OutputLayer.Builder().nIn(4).nOut(3)
.weightInit(WeightInit.XAVIER).activation(Activation.SOFTMAX)
.lossFunction(LossFunctions.LossFunction.MCXENT).build())
.build();
int numParams = conf.getLayer().initializer().numParams(conf);
INDArray params = Nd4j.create(1, numParams);
OutputLayer l = (OutputLayer) conf.getLayer().instantiate(conf,
Collections.<IterationListener>singletonList(new ScoreIterationListener(1)), 0, params, true);
l.setBackpropGradientsViewArray(Nd4j.create(1, params.length()));
DataSetIterator iter = new IrisDataSetIterator(150, 150);
DataSet next = iter.next();
next.shuffle();
SplitTestAndTrain trainTest = next.splitTestAndTrain(110);
trainTest.getTrain().normalizeZeroMeanZeroUnitVariance();
for( int i=0; i<5; i++ ) {
l.fit(trainTest.getTrain());
}
DataSet test = trainTest.getTest();
test.normalizeZeroMeanZeroUnitVariance();
Evaluation eval = new Evaluation();
INDArray output = l.output(test.getFeatureMatrix());
eval.eval(test.getLabels(), output);
log.info("Score " + eval.stats());
}
示例15: testCnnOutputLayerSoftmax
import org.nd4j.linalg.lossfunctions.LossFunctions.LossFunction; //导入依赖的package包/类
@Test
public void testCnnOutputLayerSoftmax(){
//Check that softmax is applied depth-wise
MultiLayerConfiguration conf =
new NeuralNetConfiguration.Builder().seed(12345L)
.updater(new NoOp())
.convolutionMode(ConvolutionMode.Same)
.list()
.layer(new ConvolutionLayer.Builder().nIn(3).nOut(4).activation(Activation.IDENTITY)
.weightInit(WeightInit.DISTRIBUTION).dist(new NormalDistribution(0, 1.0))
.updater(new NoOp()).build())
.layer(new CnnLossLayer.Builder(LossFunction.MSE)
.activation(Activation.SOFTMAX)
.build())
.build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
INDArray in = Nd4j.rand(new int[]{2,3,4,5});
INDArray out = net.output(in);
double min = out.minNumber().doubleValue();
double max = out.maxNumber().doubleValue();
assertTrue(min >= 0 && max <= 1.0);
INDArray sum = out.sum(1);
assertEquals(Nd4j.ones(2,4,5), sum);
}