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Merge pull request #4956 from deeplearning4j/ab_reflections2
Remove reflections use from JSON format
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deeplearning4j-core/src/test/java/org/deeplearning4j/regressiontest/RegressionTest100a.java
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| package org.deeplearning4j.regressiontest; | ||
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| import org.deeplearning4j.BaseDL4JTest; | ||
| import org.deeplearning4j.nn.conf.BackpropType; | ||
| import org.deeplearning4j.nn.conf.ConvolutionMode; | ||
| import org.deeplearning4j.nn.conf.NeuralNetConfiguration; | ||
| import org.deeplearning4j.nn.conf.graph.LayerVertex; | ||
| import org.deeplearning4j.nn.conf.layers.ConvolutionLayer; | ||
| import org.deeplearning4j.nn.conf.layers.DenseLayer; | ||
| import org.deeplearning4j.nn.conf.layers.GravesLSTM; | ||
| import org.deeplearning4j.nn.conf.layers.RnnOutputLayer; | ||
| import org.deeplearning4j.nn.conf.layers.variational.VariationalAutoencoder; | ||
| import org.deeplearning4j.nn.conf.serde.legacyformat.LegacyLayerDeserializer; | ||
| import org.deeplearning4j.nn.graph.ComputationGraph; | ||
| import org.deeplearning4j.nn.multilayer.MultiLayerNetwork; | ||
| import org.deeplearning4j.nn.weights.WeightInit; | ||
| import org.deeplearning4j.regressiontest.customlayer100a.CustomLayer; | ||
| import org.deeplearning4j.util.ModelSerializer; | ||
| import org.junit.Test; | ||
| import org.nd4j.linalg.activations.impl.*; | ||
| import org.nd4j.linalg.api.ndarray.INDArray; | ||
| import org.nd4j.linalg.factory.Nd4j; | ||
| import org.nd4j.linalg.io.ClassPathResource; | ||
| import org.nd4j.linalg.learning.config.Adam; | ||
| import org.nd4j.linalg.learning.config.RmsProp; | ||
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| import java.io.DataInputStream; | ||
| import java.io.File; | ||
| import java.io.FileInputStream; | ||
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| import static org.junit.Assert.*; | ||
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| public class RegressionTest100a extends BaseDL4JTest { | ||
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| @Test | ||
| public void testCustomLayer() throws Exception { | ||
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| File f = new ClassPathResource("regression_testing/100a/CustomLayerExample_100a.bin").getTempFileFromArchive(); | ||
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| try { | ||
| MultiLayerNetwork.load(f, true); | ||
| fail("Expected exception"); | ||
| } catch (Exception e){ | ||
| String msg = e.getMessage(); | ||
| assertTrue(msg, msg.contains("NeuralNetConfiguration.registerLegacyCustomClassesForJSON")); | ||
| } | ||
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| NeuralNetConfiguration.registerLegacyCustomClassesForJSON(CustomLayer.class); | ||
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| MultiLayerNetwork net = MultiLayerNetwork.load(f, true); | ||
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| DenseLayer l0 = (DenseLayer) net.getLayer(0).conf().getLayer(); | ||
| assertEquals(new ActivationTanH(), l0.getActivationFn()); | ||
| assertEquals(0.03, l0.getL2(), 1e-6); | ||
| assertEquals(new RmsProp(0.95), l0.getIUpdater()); | ||
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| CustomLayer l1 = (CustomLayer) net.getLayer(1).conf().getLayer(); | ||
| assertEquals(new ActivationTanH(), l1.getActivationFn()); | ||
| assertEquals(new ActivationSigmoid(), l1.getSecondActivationFunction()); | ||
| assertEquals(new RmsProp(0.95), l1.getIUpdater()); | ||
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| INDArray outExp; | ||
| File f2 = new ClassPathResource("regression_testing/100a/CustomLayerExample_Output_100a.bin").getTempFileFromArchive(); | ||
| try(DataInputStream dis = new DataInputStream(new FileInputStream(f2))){ | ||
| outExp = Nd4j.read(dis); | ||
| } | ||
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| INDArray in; | ||
| File f3 = new ClassPathResource("regression_testing/100a/CustomLayerExample_Input_100a.bin").getTempFileFromArchive(); | ||
| try(DataInputStream dis = new DataInputStream(new FileInputStream(f3))){ | ||
| in = Nd4j.read(dis); | ||
| } | ||
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| INDArray outAct = net.output(in); | ||
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| assertEquals(outExp, outAct); | ||
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| //Check graph | ||
| f = new ClassPathResource("regression_testing/100a/CustomLayerExample_Graph_100a.bin").getTempFileFromArchive(); | ||
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| //Deregister custom class: | ||
| new LegacyLayerDeserializer().getLegacyNamesMap().remove("CustomLayer"); | ||
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| try { | ||
| ComputationGraph.load(f, true); | ||
| fail("Expected exception"); | ||
| } catch (Exception e){ | ||
| String msg = e.getMessage(); | ||
| assertTrue(msg, msg.contains("NeuralNetConfiguration.registerLegacyCustomClassesForJSON")); | ||
| } | ||
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| NeuralNetConfiguration.registerLegacyCustomClassesForJSON(CustomLayer.class); | ||
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| ComputationGraph graph = ComputationGraph.load(f, true); | ||
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| f2 = new ClassPathResource("regression_testing/100a/CustomLayerExample_Graph_Output_100a.bin").getTempFileFromArchive(); | ||
| try(DataInputStream dis = new DataInputStream(new FileInputStream(f2))){ | ||
| outExp = Nd4j.read(dis); | ||
| } | ||
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| outAct = graph.outputSingle(in); | ||
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| assertEquals(outExp, outAct); | ||
| } | ||
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| @Test | ||
| public void testGravesLSTM() throws Exception { | ||
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| File f = new ClassPathResource("regression_testing/100a/GravesLSTMCharModelingExample_100a.bin").getTempFileFromArchive(); | ||
| MultiLayerNetwork net = MultiLayerNetwork.load(f, true); | ||
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| GravesLSTM l0 = (GravesLSTM) net.getLayer(0).conf().getLayer(); | ||
| assertEquals(new ActivationTanH(), l0.getActivationFn()); | ||
| assertEquals(200, l0.getNOut()); | ||
| assertEquals(WeightInit.XAVIER, l0.getWeightInit()); | ||
| assertEquals(0.001, l0.getL2(), 1e-6); | ||
| assertEquals(new RmsProp(0.1), l0.getIUpdater()); | ||
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| GravesLSTM l1 = (GravesLSTM) net.getLayer(1).conf().getLayer(); | ||
| assertEquals(new ActivationTanH(), l1.getActivationFn()); | ||
| assertEquals(200, l1.getNOut()); | ||
| assertEquals(WeightInit.XAVIER, l1.getWeightInit()); | ||
| assertEquals(0.001, l1.getL2(), 1e-6); | ||
| assertEquals(new RmsProp(0.1), l1.getIUpdater()); | ||
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| RnnOutputLayer l2 = (RnnOutputLayer) net.getLayer(2).conf().getLayer(); | ||
| assertEquals(new ActivationSoftmax(), l2.getActivationFn()); | ||
| assertEquals(77, l2.getNOut()); | ||
| assertEquals(WeightInit.XAVIER, l2.getWeightInit()); | ||
| assertEquals(0.001, l0.getL2(), 1e-6); | ||
| assertEquals(new RmsProp(0.1), l0.getIUpdater()); | ||
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| assertEquals(BackpropType.TruncatedBPTT, net.getLayerWiseConfigurations().getBackpropType()); | ||
| assertEquals(50, net.getLayerWiseConfigurations().getTbpttBackLength()); | ||
| assertEquals(50, net.getLayerWiseConfigurations().getTbpttFwdLength()); | ||
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| INDArray outExp; | ||
| File f2 = new ClassPathResource("regression_testing/100a/GravesLSTMCharModelingExample_Output_100a.bin").getTempFileFromArchive(); | ||
| try(DataInputStream dis = new DataInputStream(new FileInputStream(f2))){ | ||
| outExp = Nd4j.read(dis); | ||
| } | ||
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| INDArray in; | ||
| File f3 = new ClassPathResource("regression_testing/100a/GravesLSTMCharModelingExample_Input_100a.bin").getTempFileFromArchive(); | ||
| try(DataInputStream dis = new DataInputStream(new FileInputStream(f3))){ | ||
| in = Nd4j.read(dis); | ||
| } | ||
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| INDArray outAct = net.output(in); | ||
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| assertEquals(outExp, outAct); | ||
| } | ||
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| @Test | ||
| public void testVae() throws Exception { | ||
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| File f = new ClassPathResource("regression_testing/100a/VaeMNISTAnomaly_100a.bin").getTempFileFromArchive(); | ||
| MultiLayerNetwork net = MultiLayerNetwork.load(f, true); | ||
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| VariationalAutoencoder l0 = (VariationalAutoencoder) net.getLayer(0).conf().getLayer(); | ||
| assertEquals(new ActivationLReLU(), l0.getActivationFn()); | ||
| assertEquals(32, l0.getNOut()); | ||
| assertArrayEquals(new int[]{256, 256}, l0.getEncoderLayerSizes()); | ||
| assertArrayEquals(new int[]{256, 256}, l0.getDecoderLayerSizes()); | ||
| assertEquals(WeightInit.XAVIER, l0.getWeightInit()); | ||
| assertEquals(1e-4, l0.getL2(), 1e-6); | ||
| assertEquals(new Adam(0.05), l0.getIUpdater()); | ||
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| INDArray outExp; | ||
| File f2 = new ClassPathResource("regression_testing/100a/VaeMNISTAnomaly_Output_100a.bin").getTempFileFromArchive(); | ||
| try(DataInputStream dis = new DataInputStream(new FileInputStream(f2))){ | ||
| outExp = Nd4j.read(dis); | ||
| } | ||
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| INDArray in; | ||
| File f3 = new ClassPathResource("regression_testing/100a/VaeMNISTAnomaly_Input_100a.bin").getTempFileFromArchive(); | ||
| try(DataInputStream dis = new DataInputStream(new FileInputStream(f3))){ | ||
| in = Nd4j.read(dis); | ||
| } | ||
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| INDArray outAct = net.output(in); | ||
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| assertEquals(outExp, outAct); | ||
| } | ||
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| @Test | ||
| public void testYoloHouseNumber() throws Exception { | ||
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| File f = new ClassPathResource("regression_testing/100a/HouseNumberDetection_100a.bin").getTempFileFromArchive(); | ||
| ComputationGraph net = ComputationGraph.load(f, true); | ||
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| int nBoxes = 5; | ||
| int nClasses = 10; | ||
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| ConvolutionLayer cl = (ConvolutionLayer)((LayerVertex)net.getConfiguration().getVertices().get("convolution2d_9")).getLayerConf().getLayer(); | ||
| assertEquals(nBoxes * (5 + nClasses), cl.getNOut()); | ||
| assertEquals(new ActivationIdentity(), cl.getActivationFn()); | ||
| assertEquals(ConvolutionMode.Same, cl.getConvolutionMode()); | ||
| assertEquals(WeightInit.XAVIER, cl.getWeightInit()); | ||
| assertArrayEquals(new int[]{1,1}, cl.getKernelSize()); | ||
| assertArrayEquals(new int[]{1,1}, cl.getKernelSize()); | ||
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| INDArray outExp; | ||
| File f2 = new ClassPathResource("regression_testing/100a/HouseNumberDetection_Output_100a.bin").getTempFileFromArchive(); | ||
| try(DataInputStream dis = new DataInputStream(new FileInputStream(f2))){ | ||
| outExp = Nd4j.read(dis); | ||
| } | ||
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| INDArray in; | ||
| File f3 = new ClassPathResource("regression_testing/100a/HouseNumberDetection_Input_100a.bin").getTempFileFromArchive(); | ||
| try(DataInputStream dis = new DataInputStream(new FileInputStream(f3))){ | ||
| in = Nd4j.read(dis); | ||
| } | ||
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| INDArray outAct = net.outputSingle(in); | ||
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| assertEquals(outExp, outAct); | ||
| } | ||
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| } |
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