org.deeplearning4j.nn.graph.ComputationGraph Java Examples
The following examples show how to use
org.deeplearning4j.nn.graph.ComputationGraph.
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Example #1
Source File: NeuralStyleTransfer.java From Java-Machine-Learning-for-Computer-Vision with MIT License | 6 votes |
private INDArray backPropagateStyles(ComputationGraph vgg16FineTune, HashMap<String, INDArray> StyleActivationsGramMap, Map<String, INDArray> generatedActivationsMap) throws Exception { INDArray styleBackProb = Nd4j.zeros(new int[]{1, CHANNELS, HEIGHT, WIDTH}); CountDownLatch countDownLatch = new CountDownLatch(STYLE_LAYERS.length); for (String styleLayer : STYLE_LAYERS) { String[] split = styleLayer.split(","); String styleLayerName = split[0]; INDArray styleGramValues = StyleActivationsGramMap.get(styleLayerName); INDArray generatedValues = generatedActivationsMap.get(styleLayerName); double weight = Double.parseDouble(split[1]); int index = findLayerIndex(styleLayerName); executorService.execute(() -> { INDArray dStyleValues = styleCostFunction.styleContentFunctionDerivative(styleGramValues, generatedValues).transpose(); INDArray backProb = backPropagate(vgg16FineTune, dStyleValues.reshape(generatedValues.shape()), index).muli(weight); styleBackProb.addi(backProb); countDownLatch.countDown(); }); } countDownLatch.await(); return styleBackProb; }
Example #2
Source File: CenterLossOutputLayerTest.java From deeplearning4j with Apache License 2.0 | 6 votes |
private ComputationGraph getGraph(int numLabels, double lambda) { Nd4j.getRandom().setSeed(12345); ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder().seed(12345) .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT) .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").build(); ComputationGraph graph = new ComputationGraph(conf); graph.init(); return graph; }
Example #3
Source File: KerasLambdaTest.java From deeplearning4j with Apache License 2.0 | 6 votes |
@Test public void testModelLambdaLayerImport() throws Exception { KerasLayer.registerLambdaLayer("lambda_3", new ExponentialLambda()); KerasLayer.registerLambdaLayer("lambda_4", new TimesThreeLambda()); String modelPath = "modelimport/keras/examples/lambda/model_lambda.h5"; try(InputStream is = Resources.asStream(modelPath)) { File modelFile = testDir.newFile("tempModel" + System.currentTimeMillis() + ".h5"); Files.copy(is, modelFile.toPath(), StandardCopyOption.REPLACE_EXISTING); ComputationGraph model = new KerasModel().modelBuilder().modelHdf5Filename(modelFile.getAbsolutePath()) .enforceTrainingConfig(false).buildModel().getComputationGraph(); System.out.println(model.summary()); INDArray input = Nd4j.create(new int[]{10, 784}); model.output(input); } finally { KerasLayer.clearLambdaLayers(); // Clear all lambdas, so other tests aren't affected. } }
Example #4
Source File: Dl4jVGG.java From wekaDeeplearning4j with GNU General Public License v3.0 | 6 votes |
public ComputationGraph init(int numLabels, long seed, int[] shape, boolean filterMode) { ZooModel net = null; if (m_variation == VGG.VARIATION.VGG16) { net = org.deeplearning4j.zoo.model.VGG16.builder() .cacheMode(CacheMode.NONE) .workspaceMode(Preferences.WORKSPACE_MODE) .inputShape(shape) .numClasses(numLabels) .build(); } else if (m_variation == VGG.VARIATION.VGG19) { net = org.deeplearning4j.zoo.model.VGG19.builder() .cacheMode(CacheMode.NONE) .workspaceMode(Preferences.WORKSPACE_MODE) .inputShape(shape) .numClasses(numLabels) .build(); } ComputationGraph defaultNet = net.init(); return attemptToLoadWeights(net, defaultNet, seed, numLabels, filterMode); }
Example #5
Source File: AbstractZooModel.java From wekaDeeplearning4j with GNU General Public License v3.0 | 6 votes |
/** * Attempts to download weights for the given zoo model * @param zooModel Model to try download weights for * @return new ComputationGraph initialized with the given PretrainedType */ protected ComputationGraph downloadWeights(org.deeplearning4j.zoo.ZooModel zooModel) { try { log.info(String.format("Downloading %s weights", m_pretrainedType)); Object pretrained = zooModel.initPretrained(m_pretrainedType.getBackend()); if (pretrained == null) { throw new Exception("Error while initialising model"); } if (pretrained instanceof MultiLayerNetwork) { return ((MultiLayerNetwork) pretrained).toComputationGraph(); } else { return (ComputationGraph) pretrained; } } catch (Exception ex) { ex.printStackTrace(); return null; } }
Example #6
Source File: TestVertxUI.java From deeplearning4j with Apache License 2.0 | 6 votes |
@Test public void testUICompGraph() { StatsStorage ss = new InMemoryStatsStorage(); UIServer uiServer = UIServer.getInstance(); uiServer.attach(ss); ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder().graphBuilder().addInputs("in") .addLayer("L0", new DenseLayer.Builder().activation(Activation.TANH).nIn(4).nOut(4).build(), "in") .addLayer("L1", new OutputLayer.Builder().lossFunction(LossFunctions.LossFunction.MCXENT) .activation(Activation.SOFTMAX).nIn(4).nOut(3).build(), "L0") .setOutputs("L1").build(); ComputationGraph net = new ComputationGraph(conf); net.init(); net.setListeners(new StatsListener(ss), new ScoreIterationListener(1)); DataSetIterator iter = new IrisDataSetIterator(150, 150); for (int i = 0; i < 100; i++) { net.fit(iter); } }
Example #7
Source File: Dl4jMlpClassifier.java From wekaDeeplearning4j with GNU General Public License v3.0 | 6 votes |
/** * Build the multilayer network defined by the networkconfiguration and the list of layers. */ protected void createModel() throws Exception { final INDArray features = getFirstBatchFeatures(trainData); ComputationGraphConfiguration.GraphBuilder gb = netConfig.builder().seed(getSeed()).graphBuilder(); // Set ouput size final Layer lastLayer = layers[layers.length - 1]; final int nOut = trainData.numClasses(); if (lastLayer instanceof FeedForwardLayer) { ((FeedForwardLayer) lastLayer).setNOut(nOut); } if (getInstanceIterator() instanceof CnnTextEmbeddingInstanceIterator) { makeCnnTextLayerSetup(gb); } else { makeDefaultLayerSetup(gb); } gb.setInputTypes(InputType.inferInputType(features)); ComputationGraphConfiguration conf = gb.build(); ComputationGraph model = new ComputationGraph(conf); model.init(); this.model = model; }
Example #8
Source File: DL4jServlet.java From deeplearning4j with Apache License 2.0 | 6 votes |
private O process(MultiDataSet mds) { O result = null; if (parallelEnabled) { // process result result = inferenceAdapter.apply(parallelInference.output(mds.getFeatures(), mds.getFeaturesMaskArrays())); } else { synchronized (this) { if (model instanceof ComputationGraph) result = inferenceAdapter.apply(((ComputationGraph) model).output(false, mds.getFeatures(), mds.getFeaturesMaskArrays())); else if (model instanceof MultiLayerNetwork) { Preconditions.checkArgument(mds.getFeatures().length > 0 || (mds.getFeaturesMaskArrays() != null && mds.getFeaturesMaskArrays().length > 0), "Input data for MultilayerNetwork is invalid!"); result = inferenceAdapter.apply(((MultiLayerNetwork) model).output(mds.getFeatures()[0], false, mds.getFeaturesMaskArrays() != null ? mds.getFeaturesMaskArrays()[0] : null, null)); } } } return result; }
Example #9
Source File: ScoreUtil.java From deeplearning4j with Apache License 2.0 | 6 votes |
/** * Score based on the loss function * @param model the model to score with * @param testData the test data to score * @param average whether to average the score * for the whole batch or not * @return the score for the given test set */ public static double score(ComputationGraph model, MultiDataSetIterator testData, boolean average) { //TODO: do this properly taking into account division by N, L1/L2 etc double sumScore = 0.0; int totalExamples = 0; while (testData.hasNext()) { MultiDataSet ds = testData.next(); long numExamples = ds.getFeatures(0).size(0); sumScore += numExamples * model.score(ds); totalExamples += numExamples; } if (!average) return sumScore; return sumScore / totalExamples; }
Example #10
Source File: ReverseTimeSeriesVertex.java From deeplearning4j with Apache License 2.0 | 6 votes |
public ReverseTimeSeriesVertex(ComputationGraph graph, String name, int vertexIndex, String inputName, DataType dataType) { super(graph, name, vertexIndex, null, null, dataType); this.inputName = inputName; if (inputName == null) { // Don't use masks this.inputIdx = -1; } else { // Find the given input this.inputIdx = graph.getConfiguration().getNetworkInputs().indexOf(inputName); if (inputIdx == -1) throw new IllegalArgumentException("Invalid input name: \"" + inputName + "\" not found in list " + "of network inputs (" + graph.getConfiguration().getNetworkInputs() + ")"); } }
Example #11
Source File: GraphTestCase.java From jstarcraft-ai with Apache License 2.0 | 6 votes |
private ComputationGraph getOldFunction() { NeuralNetConfiguration.Builder netBuilder = new NeuralNetConfiguration.Builder(); // 设置随机种子 netBuilder.seed(6); netBuilder.setL1(l1Regularization); netBuilder.setL1Bias(l1Regularization); netBuilder.setL2(l2Regularization); netBuilder.setL2Bias(l2Regularization); netBuilder.weightInit(WeightInit.XAVIER_UNIFORM); netBuilder.updater(new Sgd(learnRatio)).optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT); GraphBuilder graphBuilder = netBuilder.graphBuilder(); graphBuilder.addInputs("leftInput", "rightInput"); graphBuilder.addLayer("leftEmbed", new EmbeddingLayer.Builder().nIn(5).nOut(5).hasBias(true).activation(Activation.IDENTITY).build(), "leftInput"); graphBuilder.addLayer("rightEmbed", new EmbeddingLayer.Builder().nIn(5).nOut(5).hasBias(true).activation(Activation.IDENTITY).build(), "rightInput"); graphBuilder.addVertex("embed", new MergeVertex(), "leftEmbed", "rightEmbed"); graphBuilder.addLayer("output", new OutputLayer.Builder(LossFunctions.LossFunction.MSE).activation(Activation.IDENTITY).nIn(10).nOut(1).build(), "embed"); graphBuilder.setOutputs("output"); ComputationGraphConfiguration configuration = graphBuilder.build(); ComputationGraph graph = new ComputationGraph(configuration); graph.init(); return graph; }
Example #12
Source File: ScoringModelTest.java From deeplearning4j with Apache License 2.0 | 5 votes |
protected Model buildComputationGraphModel(int numFeatures) throws Exception { final ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder() .graphBuilder() .addInputs("inputLayer") .addLayer("outputLayer", new OutputLayer.Builder().nIn(numFeatures).nOut(1).lossFunction(LossFunctions.LossFunction.MSE).activation(Activation.IDENTITY).build(), "inputLayer") .setOutputs("outputLayer") .build(); final ComputationGraph model = new ComputationGraph(conf); model.init(); final float[] floats = new float[numFeatures+1]; float base = 1f; for (int ii=0; ii<floats.length; ++ii) { base *= 2; floats[ii] = base; } final INDArray params = Nd4j.create(floats); model.setParams(params); return model; }
Example #13
Source File: InplaceParallelInferenceTest.java From deeplearning4j with Apache License 2.0 | 5 votes |
@Test public void testOutput_RoundRobin_1() throws Exception { int nIn = 5; val conf = new NeuralNetConfiguration.Builder() .graphBuilder() .addInputs("in") .layer("out0", new OutputLayer.Builder().nIn(nIn).nOut(4).activation(Activation.SOFTMAX).build(), "in") .layer("out1", new OutputLayer.Builder().nIn(nIn).nOut(6).activation(Activation.SOFTMAX).build(), "in") .setOutputs("out0", "out1") .build(); val net = new ComputationGraph(conf); net.init(); val pi = new ParallelInference.Builder(net) .inferenceMode(InferenceMode.INPLACE) .loadBalanceMode(LoadBalanceMode.ROUND_ROBIN) .workers(2) .build(); try { val result0 = pi.output(new INDArray[]{Nd4j.create(new double[]{1.0, 2.0, 3.0, 4.0, 5.0}, new long[]{1, 5})}, null)[0]; val result1 = pi.output(new INDArray[]{Nd4j.create(new double[]{1.0, 2.0, 3.0, 4.0, 5.0}, new long[]{1, 5})}, null)[0]; assertNotNull(result0); assertEquals(result0, result1); } finally { pi.shutdown(); } }
Example #14
Source File: PolicyTest.java From deeplearning4j with Apache License 2.0 | 5 votes |
@Test public void testACPolicy() throws Exception { ComputationGraph cg = new ComputationGraph(new NeuralNetConfiguration.Builder().seed(444).graphBuilder().addInputs("input") .addLayer("output", new OutputLayer.Builder().nOut(1).lossFunction(LossFunctions.LossFunction.XENT).activation(Activation.SIGMOID).build(), "input").setOutputs("output").build()); MultiLayerNetwork mln = new MultiLayerNetwork(new NeuralNetConfiguration.Builder().seed(555).list() .layer(0, new OutputLayer.Builder().nOut(1).lossFunction(LossFunctions.LossFunction.XENT).activation(Activation.SIGMOID).build()).build()); ACPolicy policy = new ACPolicy(new DummyAC(cg)); assertNotNull(policy.rnd); policy = new ACPolicy(new DummyAC(mln)); assertNotNull(policy.rnd); INDArray input = Nd4j.create(new double[] {1.0, 0.0}, new long[]{1,2}); for (int i = 0; i < 100; i++) { assertEquals(0, (int)policy.nextAction(input)); } input = Nd4j.create(new double[] {0.0, 1.0}, new long[]{1,2}); for (int i = 0; i < 100; i++) { assertEquals(1, (int)policy.nextAction(input)); } input = Nd4j.create(new double[] {0.1, 0.2, 0.3, 0.4}, new long[]{1, 4}); int[] count = new int[4]; for (int i = 0; i < 100; i++) { count[policy.nextAction(input)]++; } // System.out.println(count[0] + " " + count[1] + " " + count[2] + " " + count[3]); assertTrue(count[0] < 20); assertTrue(count[1] < 30); assertTrue(count[2] < 40); assertTrue(count[3] < 50); }
Example #15
Source File: DataSetLossCalculator.java From deeplearning4j with Apache License 2.0 | 5 votes |
@Override protected INDArray[] output(Model network, INDArray[] input, INDArray[] fMask, INDArray[] lMask) { if(network instanceof MultiLayerNetwork){ INDArray out = ((MultiLayerNetwork) network).output(input[0], false, get0(fMask), get0(lMask)); return new INDArray[]{out}; } else if(network instanceof ComputationGraph){ return ((ComputationGraph) network).output(false, input, fMask, lMask); } else { throw new RuntimeException("Unknown model type: " + network.getClass()); } }
Example #16
Source File: TestEarlyStoppingCompGraph.java From deeplearning4j with Apache License 2.0 | 5 votes |
@Test public void testEarlyStoppingListenersCG() { ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder() .updater(new Sgd(0.001)).weightInit(WeightInit.XAVIER) .graphBuilder() .addInputs("in") .layer("0", new OutputLayer.Builder().nIn(4).nOut(3) .activation(Activation.SOFTMAX) .lossFunction(LossFunctions.LossFunction.MCXENT).build(), "in") .setOutputs("0") .build(); ComputationGraph net = new ComputationGraph(conf); TestEarlyStopping.TestListener tl = new TestEarlyStopping.TestListener(); net.setListeners(tl); DataSetIterator irisIter = new IrisDataSetIterator(50, 150); EarlyStoppingModelSaver<ComputationGraph> saver = new InMemoryModelSaver<>(); EarlyStoppingConfiguration<ComputationGraph> esConf = new EarlyStoppingConfiguration.Builder<ComputationGraph>() .epochTerminationConditions(new MaxEpochsTerminationCondition(5)) .iterationTerminationConditions( new MaxTimeIterationTerminationCondition(1, TimeUnit.MINUTES)) .scoreCalculator(new DataSetLossCalculator(irisIter, true)).modelSaver(saver) .build(); IEarlyStoppingTrainer<ComputationGraph> trainer = new EarlyStoppingGraphTrainer(esConf, net, irisIter); trainer.fit(); assertEquals(5, tl.getCountEpochStart()); assertEquals(5, tl.getCountEpochEnd()); assertEquals(5 * 150/50, tl.getIterCount()); assertEquals(4, tl.getMaxEpochStart()); assertEquals(4, tl.getMaxEpochEnd()); }
Example #17
Source File: DLModel.java From java-ml-projects with Apache License 2.0 | 5 votes |
public String outputForImageFile(File file, int h, int w, int channels) throws IOException { NativeImageLoader loader = new NativeImageLoader(h, w, channels); INDArray img1 = loader.asMatrix(file); if (model instanceof ComputationGraph) { ((ComputationGraph) model).output(img1); } else if (model instanceof MultiLayerNetwork) { ((MultiLayerNetwork) model).output(img1); } activationsCache.clear(); return null; }
Example #18
Source File: KerasVGG.java From wekaDeeplearning4j with GNU General Public License v3.0 | 5 votes |
@Override public ComputationGraph init(int numLabels, long seed, int[] shape, boolean filterMode) { VGG vgg = new VGG(); vgg.setVariation(variation); return attemptToLoadWeights(vgg, null, seed, numLabels, filterMode); }
Example #19
Source File: ImageClassifier.java From java-ml-projects with Apache License 2.0 | 5 votes |
private INDArray getOutput(INDArray image) { if (dl4jModel instanceof MultiLayerNetwork) { MultiLayerNetwork multiLayerNetwork = (MultiLayerNetwork) dl4jModel; multiLayerNetwork.init(); return multiLayerNetwork.output(image); } else { ComputationGraph graph = (ComputationGraph) dl4jModel; graph.init(); return graph.output(image)[0]; } }
Example #20
Source File: TransferLearningVGG16.java From Java-Machine-Learning-for-Computer-Vision with MIT License | 5 votes |
private void saveProgressEveryConfiguredInterval(ComputationGraph vgg16Transfer, int iEpoch, int iIteration) throws IOException { if (iIteration % SAVING_INTERVAL == 0 && iIteration != 0) { ModelSerializer.writeModel(vgg16Transfer, new File(SAVING_PATH + iIteration + "_epoch_" + iEpoch + ".zip"), false); evalOn(vgg16Transfer, neuralNetworkTrainingData.getDevIterator(), iIteration); } }
Example #21
Source File: Dl4jResNet50.java From wekaDeeplearning4j with GNU General Public License v3.0 | 5 votes |
@Override public ComputationGraph init(int numLabels, long seed, int[] shape, boolean filterMode) { org.deeplearning4j.zoo.model.ResNet50 net = org.deeplearning4j.zoo.model.ResNet50.builder() .cacheMode(CacheMode.NONE) .workspaceMode(Preferences.WORKSPACE_MODE) .inputShape(shape) .numClasses(numLabels) .build(); ComputationGraph defaultNet = net.init(); return attemptToLoadWeights(net, defaultNet, seed, numLabels, filterMode); }
Example #22
Source File: TrainCifar10Model.java From Java-Machine-Learning-for-Computer-Vision with MIT License | 5 votes |
private void testResults(ComputationGraph cifar10, DataSetIterator testIterator, int iEpoch, String modelName) throws IOException { if (iEpoch % TEST_INTERVAL == 0) { Evaluation eval = cifar10.evaluate(testIterator); log.info(eval.stats()); testIterator.reset(); } // TestModels.TestResult test = TestModels.test(cifar10, modelName); // log.info("Test Results >> " + test); }
Example #23
Source File: KerasModelConverter.java From wekaDeeplearning4j with GNU General Public License v3.0 | 5 votes |
private static void saveH5File(File modelFile, File outputFolder) { try { INDArray testShape = Nd4j.zeros(1, 3, 224, 224); String modelName = modelFile.getName(); Method method = null; try { method = InputType.class.getMethod("setDefaultCNN2DFormat", CNN2DFormat.class); method.invoke(null, CNN2DFormat.NCHW); } catch (NoSuchMethodException ex) { System.err.println("setDefaultCNN2DFormat() not found on InputType class... " + "Are you using the custom built deeplearning4j-nn.jar?"); System.exit(1); } if (modelName.contains("EfficientNet")) { // Fixes for EfficientNet family of models testShape = Nd4j.zeros(1, 224, 224, 3); method.invoke(null, CNN2DFormat.NHWC); // We don't want the resulting .zip files to have 'Fixed' in the name, so we'll strip it off here modelName = modelName.replace("Fixed", ""); } ComputationGraph kerasModel = KerasModelImport.importKerasModelAndWeights(modelFile.getAbsolutePath()); kerasModel.feedForward(testShape, false); // e.g. ResNet50.h5 -> KerasResNet50.zip modelName = "Keras" + modelName.replace(".h5", ".zip"); String newZip = Paths.get(outputFolder.getPath(), modelName).toString(); kerasModel.save(new File(newZip)); System.out.println("Saved file " + newZip); } catch (Exception e) { System.err.println("\n\nCouldn't save " + modelFile.getName()); e.printStackTrace(); } }
Example #24
Source File: Keras1ModelConfigurationTest.java From deeplearning4j with Apache License 2.0 | 5 votes |
private void runModelConfigTest(String path) throws Exception { try(InputStream is = Resources.asStream(path)) { ComputationGraphConfiguration config = new KerasModel().modelBuilder().modelJsonInputStream(is) .enforceTrainingConfig(true).buildModel().getComputationGraphConfiguration(); ComputationGraph model = new ComputationGraph(config); model.init(); } }
Example #25
Source File: ElementWiseVertexTest.java From deeplearning4j with Apache License 2.0 | 5 votes |
@Test public void testElementWiseVertexForwardProduct() { int batchsz = 24; int featuresz = 17; ComputationGraphConfiguration cgc = new NeuralNetConfiguration.Builder().graphBuilder() .addInputs("input1", "input2", "input3") .addLayer("denselayer", new DenseLayer.Builder().nIn(featuresz).nOut(1).activation(Activation.IDENTITY) .build(), "input1") /* denselayer is not actually used, but it seems that you _need_ to have trainable parameters, otherwise, you get * Invalid shape: Requested INDArray shape [1, 0] contains dimension size values < 1 (all dimensions must be 1 or more) * at org.nd4j.linalg.factory.Nd4j.checkShapeValues(Nd4j.java:4877) * at org.nd4j.linalg.factory.Nd4j.create(Nd4j.java:4867) * at org.nd4j.linalg.factory.Nd4j.create(Nd4j.java:4820) * at org.nd4j.linalg.factory.Nd4j.create(Nd4j.java:3948) * at org.deeplearning4j.nn.graph.ComputationGraph.init(ComputationGraph.java:409) * at org.deeplearning4j.nn.graph.ComputationGraph.init(ComputationGraph.java:341) */ .addVertex("elementwiseProduct", new ElementWiseVertex(ElementWiseVertex.Op.Product), "input1", "input2", "input3") .addLayer("Product", new ActivationLayer.Builder().activation(Activation.IDENTITY).build(), "elementwiseProduct") .setOutputs("Product", "denselayer").build(); ComputationGraph cg = new ComputationGraph(cgc); cg.init(); INDArray input1 = Nd4j.rand(batchsz, featuresz); INDArray input2 = Nd4j.rand(batchsz, featuresz); INDArray input3 = Nd4j.rand(batchsz, featuresz); INDArray target = input1.dup().muli(input2).muli(input3); INDArray output = cg.output(input1, input2, input3)[0]; INDArray squared = output.sub(target.castTo(output.dataType())); double rms = squared.mul(squared).sumNumber().doubleValue(); Assert.assertEquals(0.0, rms, this.epsilon); }
Example #26
Source File: BaseStatsListener.java From deeplearning4j with Apache License 2.0 | 5 votes |
private void updateExamplesMinibatchesCounts(Model model) { ModelInfo modelInfo = getModelInfo(model); int examplesThisMinibatch = 0; if (model instanceof MultiLayerNetwork) { examplesThisMinibatch = ((MultiLayerNetwork) model).batchSize(); } else if (model instanceof ComputationGraph) { examplesThisMinibatch = ((ComputationGraph) model).batchSize(); } else if (model instanceof Layer) { examplesThisMinibatch = ((Layer) model).getInputMiniBatchSize(); } modelInfo.examplesSinceLastReport += examplesThisMinibatch; modelInfo.totalExamples += examplesThisMinibatch; modelInfo.minibatchesSinceLastReport++; modelInfo.totalMinibatches++; }
Example #27
Source File: KerasWeightSettingTests.java From deeplearning4j with Apache License 2.0 | 5 votes |
private ComputationGraph loadComputationalGraph(String modelPath, boolean training) throws Exception { File modelFile = createTempFile("temp", ".h5"); try(InputStream is = Resources.asStream(modelPath)) { Files.copy(is, modelFile.toPath(), StandardCopyOption.REPLACE_EXISTING); return new KerasModel().modelBuilder().modelHdf5Filename(modelFile.getAbsolutePath()) .enforceTrainingConfig(training).buildModel().getComputationGraph(); } }
Example #28
Source File: Dl4jXception.java From wekaDeeplearning4j with GNU General Public License v3.0 | 5 votes |
@Override public ComputationGraph init(int numLabels, long seed, int[] shape, boolean filterMode) { org.deeplearning4j.zoo.model.Xception net = org.deeplearning4j.zoo.model.Xception.builder() .cacheMode(CacheMode.NONE) .workspaceMode(Preferences.WORKSPACE_MODE) .inputShape(shape) .numClasses(numLabels) .build(); ComputationGraph defaultNet = net.init(); setRequiresPooling(true); return attemptToLoadWeights(net, defaultNet, seed, numLabels, filterMode); }
Example #29
Source File: TestModels.java From Java-Machine-Learning-for-Computer-Vision with MIT License | 5 votes |
private static INDArray getEmbeddings(ComputationGraph vgg16, File image) throws IOException { INDArray indArray = LOADER.asMatrix(image); IMAGE_PRE_PROCESSOR.preProcess(indArray); Map<String, INDArray> stringINDArrayMap = vgg16.feedForward(indArray, false); INDArray embeddings = stringINDArrayMap.get("embeddings"); return embeddings; }
Example #30
Source File: TestMasking.java From deeplearning4j with Apache License 2.0 | 5 votes |
@Test public void testRnnCnnMaskingSimple(){ int kernelSize1 = 2; int padding = 0; int cnnStride1 = 1; int channels = 1; ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder() .seed(12345) .weightInit(WeightInit.XAVIER) .convolutionMode(ConvolutionMode.Same) .graphBuilder() .addInputs("inputs") .addLayer("cnn1", new ConvolutionLayer.Builder(new int[] { kernelSize1, kernelSize1 }, new int[] { cnnStride1, cnnStride1 }, new int[] { padding, padding }) .nIn(channels) .nOut(2).build(), "inputs") .addLayer("lstm1", new LSTM.Builder().nIn(7 * 7 * 2).nOut(2).build(), "cnn1") .addLayer("output", new RnnOutputLayer.Builder(LossFunctions.LossFunction.MSE) .activation(Activation.RELU).nIn(2).nOut(2).build(), "lstm1") .setOutputs("output") .setInputTypes(InputType.recurrent(7*7, 1)) .inputPreProcessor("cnn1", new RnnToCnnPreProcessor(7, 7, channels)) .inputPreProcessor("lstm1", new CnnToRnnPreProcessor(7, 7, 2)) .build(); ComputationGraph cg = new ComputationGraph(conf); cg.init(); cg.fit(new DataSet( Nd4j.create(1, 7*7, 5), Nd4j.create(1, 2, 5), Nd4j.ones(1, 5), Nd4j.ones(1, 5))); }