lukasjapan

// load vgg16 model from model zoo
val model = VGG16().initPretrained(PretrainedType.IMAGENET) as ComputationGraph

// restore the new model that was saved to a file
val catsdogsModel = ModelSerializer.restoreMultiLayerNetwork(javaClass.getResource("/catdogmodel.dl4j").openStream())

// get vgg16 labels
val image = FileInputStream("input.png")
val input = NativeImageLoader(224, 224, 3).asMatrix(image).also { VGG16ImagePreProcessor().transform(it) }
val vgg16labels = vgg16model.outputSingle(input)

lukasjapan

// designing the model - mostly the same as shown at https://deeplearning4j.org/mnist-for-beginners
val conf = NeuralNetConfiguration.Builder()
                .seed(ThreadLocalRandom.current().nextLong())
                .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
                .iterations(1)
                .learningRate(0.006)
                .updater(Nesterovs(0.9))
                .regularization(true).l2(1e-4)
                .list()

lukasjapan

val model = VGG16().initPretrained(PretrainedType.IMAGENET) as ComputationGraph
val image = FileInputStream("input.png")
val input = NativeImageLoader(224, 224, 3).asMatrix(image).also { VGG16ImagePreProcessor().transform(it) }
val output = model.outputSingle(input)