82 lines
3.1 KiB
Markdown
82 lines
3.1 KiB
Markdown
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---
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title: Multilayer Network
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short_title: Multilayer Network
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description: Simple and sequential network configuration.
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category: Models
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weight: 3
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---
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## Why use MultiLayerNetwork?
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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.
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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.
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## Usage
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The example below shows how to build a simple linear classifier using `DenseLayer` (a basic multiperceptron layer).
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```java
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MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
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.seed(seed)
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.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
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.learningRate(learningRate)
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.updater(Updater.NESTEROVS).momentum(0.9)
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.list()
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.layer(0, new DenseLayer.Builder().nIn(numInputs).nOut(numHiddenNodes)
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.weightInit(WeightInit.XAVIER)
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.activation("relu")
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.build())
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.layer(1, new OutputLayer.Builder(LossFunction.NEGATIVELOGLIKELIHOOD)
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.weightInit(WeightInit.XAVIER)
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.activation("softmax").weightInit(WeightInit.XAVIER)
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.nIn(numHiddenNodes).nOut(numOutputs).build())
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.pretrain(false).backprop(true).build();
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```
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You can also create convolutional configurations:
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```java
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MultiLayerConfiguration.Builder builder = new NeuralNetConfiguration.Builder()
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.seed(seed)
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.regularization(true).l2(0.0005)
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.learningRate(0.01)//.biasLearningRate(0.02)
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//.learningRateDecayPolicy(LearningRatePolicy.Inverse).lrPolicyDecayRate(0.001).lrPolicyPower(0.75)
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.weightInit(WeightInit.XAVIER)
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.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
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.updater(Updater.NESTEROVS).momentum(0.9)
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.list()
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.layer(0, new ConvolutionLayer.Builder(5, 5)
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//nIn and nOut specify depth. nIn here is the nChannels and nOut is the number of filters to be applied
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.nIn(nChannels)
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.stride(1, 1)
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.nOut(20)
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.activation("identity")
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.build())
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.layer(1, new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX)
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.kernelSize(2,2)
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.stride(2,2)
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.build())
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.layer(2, new ConvolutionLayer.Builder(5, 5)
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//Note that nIn need not be specified in later layers
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.stride(1, 1)
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.nOut(50)
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.activation("identity")
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.build())
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.layer(3, new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX)
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.kernelSize(2,2)
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.stride(2,2)
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.build())
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.layer(4, new DenseLayer.Builder().activation("relu")
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.nOut(500).build())
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.layer(5, new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
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.nOut(outputNum)
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.activation("softmax")
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.build())
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.backprop(true).pretrain(false);
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```
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## API
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{{autogenerated}}
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