using-deeplearning4j-to-distinguish-between-cats-and-dogs.2.kt

using-deeplearning4j-to-distinguish-between-cats-and-dogs.2.kt

// 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()

                .layer(0, DenseLayer.Builder()
                        .nIn(1000) // Number of input datapoints.
                        .nOut(1000) // Number of output datapoints.
                        .activation(ActivationReLU()) // Activation function.
                        .weightInit(WeightInit.XAVIER) // Weight initialization.
                        .build()
                )
                .layer(1, OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
                        .nIn(1000)
                        .nOut(2)
                        .activation(ActivationSoftmax())
                        .weightInit(WeightInit.XAVIER)
                        .build()
                )
                .pretrain(false).backprop(true)
                .build()

        val model = MultiLayerNetwork(conf)
        model.init()

// define the answeres
val cat = Nd4j.create(doubleArrayOf(1.0,0.0))
val dog = Nd4j.create(doubleArrayOf(0.0,1.0))

// catLabels and dogLabels hold the input labels from the training set in a list
val trainingData = catLabels.map { DataSet(it, cat) } + dogLabels.map { DataSet(it, dog) }

// don't show dogs and cats one after another
Collections.shuffle(trainingData)

// feed all data to the model
model.fit(ListDataSetIterator(trainingData))