4
我開始使用deeplearning4j,我試圖創建一個簡單的神經網絡。用deeplearning4j訓練簡單的神經網絡
我想近似功能sin(x)/x
。這在理論上應該是可能的,只有一個隱藏層。
首先我創建一個模擬數據集(x,y)
,然後嘗試使用具有20個隱藏節點和S形激活函數的神經網絡來近似函數。不幸的是,用NN y_est
估算的價值甚至沒有接近實際價值y
。
我想知道錯誤在哪裏。
這是我當前的代碼:
package org.deeplearning4j.examples.myexamples
import org.deeplearning4j.nn.api.OptimizationAlgorithm
import org.deeplearning4j.nn.conf.{ MultiLayerConfiguration, NeuralNetConfiguration }
import org.deeplearning4j.nn.conf.layers.OutputLayer
import org.deeplearning4j.nn.conf.layers.DenseLayer
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork
import org.deeplearning4j.nn.weights.WeightInit
import org.deeplearning4j.optimize.listeners.ScoreIterationListener
import org.nd4j.linalg.api.ops.impl.transforms.Sin
import org.nd4j.linalg.dataset.DataSet
import org.nd4j.linalg.factory.Nd4j
import org.nd4j.linalg.lossfunctions.LossFunctions.LossFunction
import org.nd4j.linalg.api.ndarray.INDArray
import scalax.chart.api._
import breeze.linalg.linspace
/**
* Created by donbeo on 16/10/15.
*/
package object MyExample1 {
def main(args: Array[String]) = {
def plotXY(x:INDArray, y:INDArray):Unit = {
val dataPlot = for(i <- 0 to y.length()-1) yield (x.getFloat(i), y.getFloat(i))
val chart = XYLineChart(dataPlot)
chart.show()
}
val nSamples = 500
val xMin = -4
val xMax = 4
val x0 = linspace(xMin, xMax, nSamples)
val y0 = breeze.numerics.sin(x0)/x0
val x = Nd4j.create(x0.toArray).reshape(nSamples, 1)
val y = Nd4j.create(y0.toArray).reshape(nSamples, 1)
plotXY(x, y)
val numInputs = 1
val numOutputs = 1
val numHiddenNodes = 20
val seed = 123
val iterations = 100
val conf = new NeuralNetConfiguration.Builder()
.seed(seed)
.iterations(iterations)
.optimizationAlgo(OptimizationAlgorithm.LBFGS)
.list(2)
.layer(0, new DenseLayer.Builder().nIn(numInputs).nOut(numHiddenNodes)
.activation("sigmoid")
.weightInit(WeightInit.XAVIER)
.build())
.layer(1, new OutputLayer.Builder(LossFunction.MSE)
.weightInit(WeightInit.XAVIER)
.activation("identity")
.nIn(numHiddenNodes).nOut(numOutputs).build())
.build()
val dataSet = new DataSet(x, y)
val network: MultiLayerNetwork = new MultiLayerNetwork(conf)
network.init()
network.setListeners(new ScoreIterationListener(1))
network.fit(dataSet)
val y_est = network.output(x)
plotXY(x, y_est)
}
}
你能釋放你的sbt文件嗎?我得到一個錯誤,因爲我說圖層不是「org.deeplearning4j.nn.conf」的一部分,我懷疑這是因爲缺少一個我沒有聲明的庫依賴項。 – stian