keras_model_import.txt

public class YoloKerasModelImport {
	public static int nBoxes = 5;
	public static double [][] priorBoxes = {{0.57273, 0.677385}, {1.87446, 2.06253} ,
		{3.33843, 5.47434}, {7.88282, 3.52778}, {9.77052, 9.16828}};

	private static long seed;
	private static WorkspaceMode workspaceMode;

	public static void main(String[] args)
			throws IOException, UnsupportedKerasConfigurationException, InvalidKerasConfigurationException {
		String pretrainedModelPath = "/home/project/data/tiny_yolov2.h5";
    KerasLayer.registerCustomLayer("Lambda", KerasSpaceToDepth.class);
		ComputationGraph graph = KerasModelImport.importKerasModelAndWeights(pretrainedModelPath,false);
		INDArray priors = Nd4j.create(priorBoxes);

		FineTuneConfiguration fineTuneConf = new FineTuneConfiguration.Builder().seed(seed)
				.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
				.gradientNormalization(GradientNormalization.RenormalizeL2PerLayer).gradientNormalizationThreshold(1.0)
				.updater(new Adam.Builder().learningRate(1e-3).build()).l2(0.00001).activation(Activation.IDENTITY)
				.trainingWorkspaceMode(workspaceMode).inferenceWorkspaceMode(workspaceMode).build();

		ComputationGraph model = new TransferLearning.GraphBuilder(graph).fineTuneConfiguration(fineTuneConf)
				.addLayer("outputs", new Yolo2OutputLayer.Builder().boundingBoxPriors(priors).build(), "conv2d_9")
				.setOutputs("outputs").build();

		System.out.println(model.summary(InputType.convolutional(224, 224, 3)));

		ModelSerializer.writeModel(model, "/home/project/data/tiny_yolov2.zip", false);

	}

}