m1=[
    # constants defined
    # Sample, Hidden, and Label dimensions
    SDim=784
    HDim=256
    LDim=10

    macro(test)
    {
        local=test
    }

    features=Input(SDim, tag="feature")
    labels=Input(LDim, tag="label")

    # compute mean/stddev for mean/stddev normalization
    meanVal = Mean(features);
    stddev=InvStdDev(features)
    normInput=PerDimMeanVarNormalization(features, meanVal, stddev)

    # Layer operations
    L1 = RBFF(normInput, HDim, SDim)
    L2 = RBFF(L1, HDim, HDim)
    L3 = RBFF(L2, HDim, HDim)
    CE = SMBFF(L3, LDim, HDim, labels, tag="criterion")
    Err=ClassificationError(labels, CE.BFF.FF.P, tag="evaluation")

    # rootNodes defined here
    OutputNodes=(CE.BFF.FF.P)
]

SetDefaultModel(m1)
Dump(m1, "c:\temp\dump1.txt", includeData = true)
LoadNDLSnippet(m2, "C:\dev\CNTK\MachineLearning\cn\NdlScript.txt")
Copy(L3.RL, L4.RL)
#add a layer to a 3-layer network, copy all nodes in layer 3 to layer 4
# all strings that start with L3* will be copied to L4* along with internal connections
#if L4* nodes exist they will be replaced, if not, they will be created
Copy(L3*,L4*)

#hook up layer 4 to the previous and next layers
# name[#] is how inputs are identified
SetInput(CE.*.T, 1, L4.RL);
SetInput(L4.*.T, 1, L3.RL);
Dump(m1, "c:\temp\dump1.1.txt")

#now lock down the first 3 layers learnable parameters
SetProperty(L*.W, ComputeGradient, false);
SetProperty(L*.B, ComputeGradient, false);
SaveModel(m1, "C:\temp\mnist\cntkdebug2.dnn", format=cntk)
Dump(m1, "c:\temp\dump1.2.txt")

#Remove the second layer and mean variance normalization
Remove(L2.*, meanVal, stddev, normInput)
#fixup the links
SetInput(L3.*.T, 1, L1.RL)
SetInput(L1.*.T, 1, features)
Dump(m1, "c:\temp\dump1.3.txt")

#Add mean variance using in-line NDL 
meanVal = Mean(features)
invstdVal = InvStdDev(features)
inputVal = PerDimMeanVarNormalization(features,meanVal,invstdVal)

SetInput(L1.*.T, 1, inputVal)
Dump(m1, "c:\temp\dump2.txt")
SetProperty(features, Feature, true) # make sure the features node has the feature property
Dump(m1, "c:\temp\dump2.5.txt")
SaveModel(m1, "C:\temp\mnist\cntkdebug3.dnn", format=cntk)

#replace rectified linear with Sigmoid
Rename(L*.RL,L*.S)
Dump(m1, "c:\temp\dumpRename.txt")
Rename(L*.S,L*.RL)

#add a new sigmoid hidden layer
HDIM=256
L2=SBFF(L1.RL,HDIM,HDIM, init="uniform")
SetInput(L3.*.T, 1, L2.S)

Dump(m1, "c:\temp\dump3.txt")

#newly defined layer has no weight or bias values
#so copy them from another model
m2=LoadModel("C:\temp\mnist\cntkdebug2.dnn", format=cntk);
Copy(m2.L2.*.W, L2.*.W, copy=value)
Copy(m2.L2.*.B, L2.*.B, copy=value)
Dump(m1, "c:\temp\dump4.txt")
#now link the layer in
#SetInput(L2.*.T, 1, L1.RL)

Dump(m1, "c:\temp\dump5.txt")
SaveModel(m1, "C:\temp\mnist\cntkdebug4.dnn", format=cntk)