new NDL for QuickE2E model now compiles (now fails due to mismatching node names for Input)

MachineLearning/CNTK/ExperimentalNetworkBuilder.cpp

@@ -40,8 +40,8 @@ namespace Microsoft { namespace MSR { namespace BS {
         L"Fac(n) = if n > 1 then Fac(n-1) * n else 1 \n"
         ;
 
-    wstring computationNodes =      // BUGBUG: optional args not working yet, some scope problem causing a circular reference
-        L"Parameter(rows, cols, needGradient = true, init = 'uniform'/*|fixedValueor|gaussian|fromFile*/, initValueScale = 1, value = 0, initFromFilePath = '', tag='') = new ComputationNode [ operation = 'LearnableParameter' /*plus the function args*/ ]\n"
+    wstring computationNodes =
+        L"Parameter(rows, cols, needGradient = true, init = 'uniform'/*|fixedValue|gaussian|fromFile*/, initValueScale = 1, value = 0, initFromFilePath = '', tag='') = new ComputationNode [ operation = 'LearnableParameter' /*plus the function args*/ ]\n"
         L"Input(rows, cols, tag='feature') = new ComputationNode [ operation = 'Input' /*plus the function args*/ ]\n"
         // ^^ already works; vv not yet working
         L"Mean(z, tag='') = new ComputationNode [ operation = 'Mean' ; inputs = z /* ; tag = tag */ ]\n"
@@ -57,8 +57,8 @@ namespace Microsoft { namespace MSR { namespace BS {
         L"ErrorPrediction(labels, outZ, tag='') = new ComputationNode [ operation = 'ErrorPrediction' ; inputs = labels:outZ /* ; tag = tag */ ]\n"
         ;
 
-    wstring commonMacros =  // TODO: rename rows and cols to inDim and outDim or vice versa, whichever it is
-        L"BFF(in, rows, cols) = [ B = Parameter(rows, 1/*init = fixedvalue, value = 0*/) ; W = Parameter(rows, cols) ; z = /*W*in+B*/Log(in) ] \n" // TODO: fix this once we got the ComputationNode type connected correctly
+    wstring commonMacros =
+        L"BFF(in, rows, cols) = [ B = Parameter(rows, 1, init = 'fixedValue', value = 0) ; W = Parameter(rows, cols) ; z = W*in+B ] \n"
         L"SBFF(in, rows, cols) = [ Eh = Sigmoid(BFF(in, rows, cols).z) ] \n "
         L"MeanVarNorm(feat) = PerDimMeanVarNormalization(feat, Mean(feat), InvStdDev(feat)) \n"
         L"LogPrior(labels) = Log(Mean(labels)) \n"

Tests/Speech/QuickE2E/cntk.config

@@ -27,13 +27,13 @@ speechTrain=[
 
         applyMeanVarNorm=true
 
-        numHiddenLayers = Length(layerSizes)-1
-        features = Input(layerSizes[0], tag='feature') ; labels = Input(layerSizes[Length(layerSizes)-1], tag='label')
+        L = Length(layerSizes)-1    // number of model layers
+        features = Input(layerSizes[0], 1, tag='feature') ; labels = Input(layerSizes[Length(layerSizes)-1], 1, tag='label')
         featNorm = if applyMeanVarNorm
                    then MeanVarNorm(features)
                    else features
-        layers = array[1..numHiddenLayers] (layer => if layer > 1 then SBFF(layers[layer-1].Eh, layerSizes[layer], layerSizes[layer-1]) else SBFF(featNorm, layerSizes[layer], layerSizes[layer-1]))
-        outLayer = BFF(layers[numHiddenLayers].Eh, labelDim, hiddenDim)
+        layers = array[1..L-1] (layer => if layer > 1 then SBFF(layers[layer-1].Eh, layerSizes[layer], layerSizes[layer-1]) else SBFF(featNorm, layerSizes[layer], layerSizes[layer-1]))
+        outLayer = BFF(layers[L-1].Eh, layerSizes[L], layerSizes[L-1])
         outZ = outLayer.z
         CE = if trainingCriterion == 'CE'
              then CrossEntropyWithSoftmax(labels, outZ, tag='criterion')
@@ -42,6 +42,7 @@ speechTrain=[
               ErrorPrediction(labels, outZ, tag='eval')
               else Fail('unknown evalCriterion ' + evalCriterion)
         logPrior = LogPrior(labels)
+        // TODO: how to add a tag to an infix operation?
         ScaledLogLikelihood = outZ - logPrior
     ]