Spark MLlib Deep Learning Deep Belief Network (深度学习-深度信念网络)2.3

Spark MLlib Deep Learning Deep Belief Network (深度学习-深度信念网络)2.3

http://blog.csdn.net/sunbow0

第二章Deep Belief Network (深度信念网络)

3实例

3.1 测试数据

按照上例数据，或者新建图片识别数据。

3.2 DBN实例

//****************例2（读取固定样本:来源于经典优化算法测试函数Sphere
Model）***********//

//2 读取样本数据

Logger.getRootLogger.setLevel(Level.WARN)

valdata_path ="/user/huangmeiling/deeplearn/data1"

valexamples =sc.textFile(data_path).cache()

valtrain_d1 =examples.map { line =>

valf1 = line.split("\t")

valf =f1.map(f =>f.toDouble)

valid =f(0)

valy = Array(f(1))

valx =f.slice(2,f.length)

(id, new BDM(1,y.length,y),new
BDM(1,x.length,x))

}

valtrain_d =train_d1.map(f => (f._2,
f._3))

valopts = Array(100.0,20.0,0.0)

//3 设置训练参数，建立DBN模型

valDBNmodel =new DBN().

setSize(Array(5,
7)).

setLayer(2).

setMomentum(0.1).

setAlpha(1.0).

DBNtrain(train_d,
opts)

//4 DBN模型转化为NN模型

valmynn =DBNmodel.dbnunfoldtonn(1)

valnnopts = Array(100.0,50.0,0.0)

valnumExamples =train_d.count()

println(s"numExamples = $numExamples.")

println(mynn._2)

for (i <-0 tomynn._1.length
-1) {

print(mynn._1(i) +"\t")

}

println()

println("mynn_W1")

valtmpw1 =mynn._3(0)

for (i <-0 totmpw1.rows
-1) {

for (j <-0 totmpw1.cols
-1) {

print(tmpw1(i,j) +"\t")

}

println()

}

valNNmodel =new NeuralNet().

setSize(mynn._1).

setLayer(mynn._2).

setActivation_function("sigm").

setOutput_function("sigm").

setInitW(mynn._3).

NNtrain(train_d,
nnopts)

//5 NN模型测试

valNNforecast =NNmodel.predict(train_d)

valNNerror =NNmodel.Loss(NNforecast)

println(s"NNerror = $NNerror.")

valprintf1 =NNforecast.map(f => (f.label.data(0),
f.predict_label.data(0))).take(200)

println("预测结果——实际值：预测值：误差")

for (i <-0 untilprintf1.length)

println(printf1(i)._1 +"\t"
+printf1(i)._2 +"\t"
+ (printf1(i)._2 -printf1(i)._1))
转载请注明出处：


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