Simple and sequential network configuration.
The MultiLayerNetwork class is the simplest network configuration API available in Eclipse Deeplearning4j. This class is useful for beginners or users who do not need a complex and branched network graph.
You will not want to use MultiLayerNetwork configuration if you are creating complex loss functions, using graph vertices, or doing advanced training such as a triplet network. This includes popular complex networks such as InceptionV4.
The example below shows how to build a simple linear classifier using DenseLayer (a basic multiperceptron layer).
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
.seed(seed)
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.learningRate(learningRate)
.updater(Updater.NESTEROVS).momentum(0.9)
.list()
.layer(0, new DenseLayer.Builder().nIn(numInputs).nOut(numHiddenNodes)
.weightInit(WeightInit.XAVIER)
.activation("relu")
.build())
.layer(1, new OutputLayer.Builder(LossFunction.NEGATIVELOGLIKELIHOOD)
.weightInit(WeightInit.XAVIER)
.activation("softmax").weightInit(WeightInit.XAVIER)
.nIn(numHiddenNodes).nOut(numOutputs).build())
.pretrain(false).backprop(true).build();You can also create convolutional configurations:
MultiLayerConfiguration.Builder builder = new NeuralNetConfiguration.Builder()
.seed(seed)
.regularization(true).l2(0.0005)
.learningRate(0.01)
.weightInit(WeightInit.XAVIER)
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.updater(Updater.NESTEROVS).momentum(0.9)
.list()
.layer(0, new ConvolutionLayer.Builder(5, 5)
//nIn and nOut specify depth. nIn here is the nChannels and nOut is the number of filters to be applied
.nIn(nChannels)
.stride(1, 1)
.nOut(20)
.activation("identity")
.build())
.layer(1, new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX)
.kernelSize(2,2)
.stride(2,2)
.build())
.layer(2, new ConvolutionLayer.Builder(5, 5)
//Note that nIn need not be specified in later layers
.stride(1, 1)
.nOut(50)
.activation("identity")
.build())
.layer(3, new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX)
.kernelSize(2,2)
.stride(2,2)
.build())
.layer(4, new DenseLayer.Builder().activation("relu")
.nOut(500).build())
.layer(5, new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
.nOut(outputNum)
.activation("softmax")
.build());RetroSearch is an open source project built by @garambo | Open a GitHub Issue
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