Deep belief Network

Deep belief Network
3 Example
3.1 Test data
Follow the previous example data, or create a new image recognition data.

3.2 DBN Instances
Example 2 (reading a fixed sample: From the classical optimization algorithm test function sphere Model) ***********//

2 Reading Sample Data

Logger.getRootLogger.setLevel (Level.warn)

Valdata_path = "/user/huangmeiling/deeplearn/data1"

Valexamples =www.ycyc66.cn/sc.textfile (Data_path). Cache ()

Valtrain_d1 =www.zhenlyule.cn Examples.map {line =

Valf1 = Line.split ("\ t")

Valf =f1.map (f =>www.egouyule.cn 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.www.zhenloyl88.cn map (f = (f._2, f._3))

valopts = Array (100.0,20.0,0.0)

3 Set up training parameters, establish DBN model

Valdbnmodel =new DBN ().

SetSize (Array (5, 7)).

Setlayer (2).

Setmomentum (0.1).

Setalpha (1.0).

Dbntrain (Train_d, opts)

4 DBN Model converted to NN model

Valmynn =dbnmodel.www.yghrcp88.cn dbnunfoldtonn (1)

valnnopts = Array (100.0,50.0,0.0)

Valnumexamples =train_d.count ()

println (S "Numexamples =www.huacairen88.cn $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 totmpw1www.jyz521.com/. rows-1) {

For (J <-0 totmpw1.cols-1) {

Print (TMPW1 (i,j) + "\ T")

}

println ()

}

Valnnmodel =new www.ludingyule66.cn neuralnet ().

SetSize (mynn._1).

Setlayer (mynn._2).

Setactivation_function ("Sigm").

Setoutput_function ("Sigm").

SETINITW (Mynn._3).

Nntrain (Train_d, nnopts)

5 NN model test

Valnnforecast =nnmodel.www.yyzx66.cn/predict (Train_d)

Valnnerror =nnmodel.loss (Nnforecast)

println (S "nnerror = $NNerror.")

VALPRINTF1 =nnforecast.map (f = (www.myqunliphoto.com/f.label.data (0), F.predict_label.data (0)). Take (200)

PRINTLN ("forecast result--Actual value: Predicted value: Error")

for (i <-0 untilprintf1.length)

println (PRINTF1 (i). _1 + "\ T" +PRINTF1 (i). _2 + "\ T" + (PRINTF1 (i). _2-PRINTF1 (i). _1))

Deep belief Network