# 多层网络

### 为什么用多层网络?

MultiLayerNetwork类是Eclipse DL4J中可用的最简单的网络配置API。该类对于不需要复杂和分支的网络图的初学者或用户很有用。

### 用法

``````MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
.seed(seed)
.iterations(1)
.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();``````

``````MultiLayerConfiguration.Builder builder = new NeuralNetConfiguration.Builder()
.seed(seed)
.iterations(iterations)
.regularization(true).l2(0.0005)
.learningRate(0.01)//.biasLearningRate(0.02)
//.learningRateDecayPolicy(LearningRatePolicy.Inverse).lrPolicyDecayRate(0.001).lrPolicyPower(0.75)
.weightInit(WeightInit.XAVIER)
.updater(Updater.NESTEROVS).momentum(0.9)
.list()
.layer(0, new ConvolutionLayer.Builder(5, 5)
//nIn 与 nOut 指定深度。这里是nChannels和nOut是要应用的过滤器的数量。
.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)
//注意，在稍后的层中不需要指定nIn。
.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())
.backprop(true).pretrain(false);``````