use of org.nd4j.linalg.io.ClassPathResource in project deeplearning4j by deeplearning4j.
the class RegressionTest060 method regressionTestMLP1.
@Test
public void regressionTestMLP1() throws Exception {
File f = new ClassPathResource("regression_testing/060/060_ModelSerializer_Regression_MLP_1.zip").getTempFileFromArchive();
MultiLayerNetwork net = ModelSerializer.restoreMultiLayerNetwork(f, true);
MultiLayerConfiguration conf = net.getLayerWiseConfigurations();
assertEquals(2, conf.getConfs().size());
assertTrue(conf.isBackprop());
assertFalse(conf.isPretrain());
DenseLayer l0 = (DenseLayer) conf.getConf(0).getLayer();
assertEquals("relu", l0.getActivationFn().toString());
assertEquals(3, l0.getNIn());
assertEquals(4, l0.getNOut());
assertEquals(WeightInit.XAVIER, l0.getWeightInit());
assertEquals(Updater.NESTEROVS, l0.getUpdater());
assertEquals(0.9, l0.getMomentum(), 1e-6);
assertEquals(0.15, l0.getLearningRate(), 1e-6);
OutputLayer l1 = (OutputLayer) conf.getConf(1).getLayer();
assertEquals("softmax", l1.getActivationFn().toString());
assertEquals(LossFunctions.LossFunction.MCXENT, l1.getLossFunction());
assertTrue(l1.getLossFn() instanceof LossMCXENT);
assertEquals(4, l1.getNIn());
assertEquals(5, l1.getNOut());
assertEquals(WeightInit.XAVIER, l1.getWeightInit());
assertEquals(Updater.NESTEROVS, l1.getUpdater());
assertEquals(0.9, l1.getMomentum(), 1e-6);
assertEquals(0.15, l1.getLearningRate(), 1e-6);
int numParams = net.numParams();
assertEquals(Nd4j.linspace(1, numParams, numParams), net.params());
int updaterSize = net.getUpdater().stateSizeForLayer(net);
assertEquals(Nd4j.linspace(1, updaterSize, updaterSize), net.getUpdater().getStateViewArray());
}
use of org.nd4j.linalg.io.ClassPathResource in project deeplearning4j by deeplearning4j.
the class KerasModelConfigurationTest method importKerasConvnetTheanoConfigTest.
@Test
public void importKerasConvnetTheanoConfigTest() throws Exception {
ClassPathResource configResource = new ClassPathResource("modelimport/keras/configs/cnn_th_config.json", KerasModelConfigurationTest.class.getClassLoader());
MultiLayerConfiguration config = new KerasModel.ModelBuilder().modelJsonInputStream(configResource.getInputStream()).enforceTrainingConfig(true).buildSequential().getMultiLayerConfiguration();
MultiLayerNetwork model = new MultiLayerNetwork(config);
model.init();
}
use of org.nd4j.linalg.io.ClassPathResource in project deeplearning4j by deeplearning4j.
the class KerasModelConfigurationTest method importKerasMlpSequentialConfigTest.
@Test
public void importKerasMlpSequentialConfigTest() throws Exception {
ClassPathResource configResource = new ClassPathResource("modelimport/keras/configs/mlp_config.json", KerasModelConfigurationTest.class.getClassLoader());
MultiLayerConfiguration config = new KerasModel.ModelBuilder().modelJsonInputStream(configResource.getInputStream()).enforceTrainingConfig(true).buildSequential().getMultiLayerConfiguration();
MultiLayerNetwork model = new MultiLayerNetwork(config);
model.init();
}
use of org.nd4j.linalg.io.ClassPathResource in project deeplearning4j by deeplearning4j.
the class KerasModelConfigurationTest method importKerasMlpModelMultilossConfigTest.
@Test
public void importKerasMlpModelMultilossConfigTest() throws Exception {
ClassPathResource configResource = new ClassPathResource("modelimport/keras/configs/mlp_fapi_multiloss_config.json", KerasModelConfigurationTest.class.getClassLoader());
ComputationGraphConfiguration config = new KerasModel.ModelBuilder().modelJsonInputStream(configResource.getInputStream()).enforceTrainingConfig(true).buildModel().getComputationGraphConfiguration();
ComputationGraph model = new ComputationGraph(config);
model.init();
}
use of org.nd4j.linalg.io.ClassPathResource in project deeplearning4j by deeplearning4j.
the class KerasModelConfigurationTest method importKerasMlpModelConfigTest.
@Test
public void importKerasMlpModelConfigTest() throws Exception {
ClassPathResource configResource = new ClassPathResource("modelimport/keras/configs/mlp_fapi_config.json", KerasModelConfigurationTest.class.getClassLoader());
ComputationGraphConfiguration config = new KerasModel.ModelBuilder().modelJsonInputStream(configResource.getInputStream()).enforceTrainingConfig(true).buildModel().getComputationGraphConfiguration();
ComputationGraph model = new ComputationGraph(config);
model.init();
}
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