本文整理汇总了Java中org.deeplearning4j.nn.api.Model.score方法的典型用法代码示例。如果您正苦于以下问题:Java Model.score方法的具体用法?Java Model.score怎么用?Java Model.score使用的例子?那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类org.deeplearning4j.nn.api.Model
的用法示例。
在下文中一共展示了Model.score方法的10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: iterationDone
import org.deeplearning4j.nn.api.Model; //导入方法依赖的package包/类
@Override
public void iterationDone(Model model, int iteration, int epoch) {
//Check per-iteration termination conditions
double latestScore = model.score();
trainer.setLatestScore(latestScore);
for (IterationTerminationCondition c : esConfig.getIterationTerminationConditions()) {
if (c.terminate(latestScore)) {
trainer.setTermination(true);
trainer.setTerminationReason(c);
break;
}
}
if (trainer.getTermination()) {
// use built-in kill switch to stop fit operation
wrapper.stopFit();
}
trainer.incrementIteration();
}
示例2: iterationDone
import org.deeplearning4j.nn.api.Model; //导入方法依赖的package包/类
@Override
public void iterationDone(Model model, int iteration) {
if(m_printIterations <= 0)
m_printIterations = 1;
if(m_iterCount % m_printIterations == 0) {
invoke();
double result = model.score();
m_progressBar.printProgress("Iteration: " + m_iterCount + ", Score: " + result);
}
m_iterCount++;
}
示例3: iterationDone
import org.deeplearning4j.nn.api.Model; //导入方法依赖的package包/类
@Override
public void iterationDone (Model model,
int iteration)
{
iterCount++;
if ((iterCount % constants.listenerPeriod.getValue()) == 0) {
invoke();
final double score = model.score();
final int count = (int) iterCount;
logger.info(String.format("Score at iteration %d is %.5f", count, score));
display(epoch, count, score);
}
}
示例4: iterationDone
import org.deeplearning4j.nn.api.Model; //导入方法依赖的package包/类
@Override
public void iterationDone(Model model, int iteration, int epoch) {
if (++iterationCount % frequency == 0) {
double score = model.score();
scoreVsIter.add(new Pair<>(iterationCount, score));
}
}
示例5: iterationDone
import org.deeplearning4j.nn.api.Model; //导入方法依赖的package包/类
@Override
public void iterationDone(Model model, int iteration, int epoch) {
if (printIterations <= 0)
printIterations = 1;
if (iteration % printIterations == 0) {
double score = model.score();
log.info("Score at iteration {} is {}", iteration, score);
}
}
示例6: testSphereFnMultipleStepsHelper
import org.deeplearning4j.nn.api.Model; //导入方法依赖的package包/类
private static void testSphereFnMultipleStepsHelper(OptimizationAlgorithm oa, int nOptIter,
int maxNumLineSearchIter) {
double[] scores = new double[nOptIter + 1];
for (int i = 0; i <= nOptIter; i++) {
Random rng = new DefaultRandom(12345L);
org.nd4j.linalg.api.rng.distribution.Distribution dist =
new org.nd4j.linalg.api.rng.distribution.impl.UniformDistribution(rng, -10, 10);
NeuralNetConfiguration conf = new NeuralNetConfiguration.Builder()
.maxNumLineSearchIterations(maxNumLineSearchIter).updater(new Sgd(0.1))
.layer(new DenseLayer.Builder().nIn(1).nOut(1).build()).build();
conf.addVariable("W"); //Normally done by ParamInitializers, but obviously that isn't done here
Model m = new SphereFunctionModel(100, dist, conf);
if (i == 0) {
m.computeGradientAndScore();
scores[0] = m.score(); //Before optimization
} else {
ConvexOptimizer opt = getOptimizer(oa, conf, m);
for( int j=0; j<100; j++ ) {
opt.optimize();
}
m.computeGradientAndScore();
scores[i] = m.score();
assertTrue(!Double.isNaN(scores[i]) && !Double.isInfinite(scores[i]));
}
}
if (PRINT_OPT_RESULTS) {
System.out.println("Multiple optimization iterations (" + nOptIter
+ " opt. iter.) score vs iteration, maxNumLineSearchIter=" + maxNumLineSearchIter + ": "
+ oa);
System.out.println(Arrays.toString(scores));
}
for (int i = 1; i < scores.length; i++) {
assertTrue(scores[i] <= scores[i - 1]);
}
assertTrue(scores[scores.length - 1] < 1.0); //Very easy function, expect score ~= 0 with any reasonable number of steps/numLineSearchIter
}
示例7: testRastriginFnMultipleStepsHelper
import org.deeplearning4j.nn.api.Model; //导入方法依赖的package包/类
private static void testRastriginFnMultipleStepsHelper(OptimizationAlgorithm oa, int nOptIter,
int maxNumLineSearchIter) {
double[] scores = new double[nOptIter + 1];
for (int i = 0; i <= nOptIter; i++) {
NeuralNetConfiguration conf = new NeuralNetConfiguration.Builder()
.maxNumLineSearchIterations(maxNumLineSearchIter).miniBatch(false)
.updater(new AdaGrad(1e-2))
.layer(new DenseLayer.Builder().nIn(1).nOut(1).build()).build();
conf.addVariable("W"); //Normally done by ParamInitializers, but obviously that isn't done here
Model m = new RastriginFunctionModel(10, conf);
int nParams = m.numParams();
if (i == 0) {
m.computeGradientAndScore();
scores[0] = m.score(); //Before optimization
} else {
ConvexOptimizer opt = getOptimizer(oa, conf, m);
opt.getUpdater().setStateViewArray((Layer) m, Nd4j.create(new int[] {1, nParams}, 'c'), true);
opt.optimize();
m.computeGradientAndScore();
scores[i] = m.score();
assertTrue(!Double.isNaN(scores[i]) && !Double.isInfinite(scores[i]));
}
}
if (PRINT_OPT_RESULTS) {
System.out.println("Rastrigin: Multiple optimization iterations (" + nOptIter
+ " opt. iter.) score vs iteration, maxNumLineSearchIter=" + maxNumLineSearchIter + ": "
+ oa);
System.out.println(Arrays.toString(scores));
}
for (int i = 1; i < scores.length; i++) {
if (i == 1) {
assertTrue(scores[i] <= scores[i - 1]); //Require at least one step of improvement
} else {
assertTrue(scores[i] <= scores[i - 1]);
}
}
}
示例8: testSphereFnOptHelper
import org.deeplearning4j.nn.api.Model; //导入方法依赖的package包/类
public void testSphereFnOptHelper(OptimizationAlgorithm oa, int numLineSearchIter, int nDimensions) {
if (PRINT_OPT_RESULTS)
System.out.println("---------\n Alg= " + oa + ", nIter= " + numLineSearchIter + ", nDimensions= "
+ nDimensions);
NeuralNetConfiguration conf = new NeuralNetConfiguration.Builder().maxNumLineSearchIterations(numLineSearchIter)
.updater(new Sgd(1e-2))
.layer(new DenseLayer.Builder().nIn(1).nOut(1).build()).build();
conf.addVariable("W"); //Normally done by ParamInitializers, but obviously that isn't done here
Random rng = new DefaultRandom(12345L);
org.nd4j.linalg.api.rng.distribution.Distribution dist =
new org.nd4j.linalg.api.rng.distribution.impl.UniformDistribution(rng, -10, 10);
Model m = new SphereFunctionModel(nDimensions, dist, conf);
m.computeGradientAndScore();
double scoreBefore = m.score();
assertTrue(!Double.isNaN(scoreBefore) && !Double.isInfinite(scoreBefore));
if (PRINT_OPT_RESULTS) {
System.out.println("Before:");
System.out.println(scoreBefore);
System.out.println(m.params());
}
ConvexOptimizer opt = getOptimizer(oa, conf, m);
opt.setupSearchState(m.gradientAndScore());
for( int i=0; i<100; i++ ) {
opt.optimize();
}
m.computeGradientAndScore();
double scoreAfter = m.score();
assertTrue(!Double.isNaN(scoreAfter) && !Double.isInfinite(scoreAfter));
if (PRINT_OPT_RESULTS) {
System.out.println("After:");
System.out.println(scoreAfter);
System.out.println(m.params());
}
//Expected behaviour after optimization:
//(a) score is better (lower) after optimization.
//(b) Parameters are closer to minimum after optimization (TODO)
assertTrue("Score did not improve after optimization (b= " + scoreBefore + " ,a= " + scoreAfter + ")",
scoreAfter < scoreBefore);
}
示例9: testRosenbrockFnMultipleStepsHelper
import org.deeplearning4j.nn.api.Model; //导入方法依赖的package包/类
private static void testRosenbrockFnMultipleStepsHelper(OptimizationAlgorithm oa, int nOptIter,
int maxNumLineSearchIter) {
double[] scores = new double[nOptIter + 1];
for (int i = 0; i <= nOptIter; i++) {
NeuralNetConfiguration conf = new NeuralNetConfiguration.Builder()
.maxNumLineSearchIterations(maxNumLineSearchIter)
.updater(new Sgd(1e-1))
.stepFunction(new org.deeplearning4j.nn.conf.stepfunctions.NegativeDefaultStepFunction())
.layer(new DenseLayer.Builder().nIn(1).nOut(1).build())
.build();
conf.addVariable("W"); //Normally done by ParamInitializers, but obviously that isn't done here
Model m = new RosenbrockFunctionModel(100, conf);
if (i == 0) {
m.computeGradientAndScore();
scores[0] = m.score(); //Before optimization
} else {
ConvexOptimizer opt = getOptimizer(oa, conf, m);
opt.optimize();
m.computeGradientAndScore();
scores[i] = m.score();
assertTrue("NaN or infinite score: " + scores[i],
!Double.isNaN(scores[i]) && !Double.isInfinite(scores[i]));
}
}
if (PRINT_OPT_RESULTS) {
System.out.println("Rosenbrock: Multiple optimization iterations ( " + nOptIter
+ " opt. iter.) score vs iteration, maxNumLineSearchIter= " + maxNumLineSearchIter + ": "
+ oa);
System.out.println(Arrays.toString(scores));
}
for (int i = 1; i < scores.length; i++) {
if (i == 1) {
assertTrue(scores[i] < scores[i - 1]); //Require at least one step of improvement
} else {
assertTrue(scores[i] <= scores[i - 1]);
}
}
}
示例10: ModelAndGradient
import org.deeplearning4j.nn.api.Model; //导入方法依赖的package包/类
public ModelAndGradient(Model model) {
model.computeGradientAndScore();
this.gradients = model.gradient().gradientForVariable();
this.parameters = model.paramTable();
this.score = model.score();
}