switched to new Sigmoid(), PlusNode::BackpropTo() still not TensorView

Source/ComputationNetworkLib/LinearAlgebraNodes.h

@@ -123,6 +123,7 @@ namespace Microsoft { namespace MSR { namespace CNTK {
         virtual void /*ComputationNode::*/ForwardProp(const FrameRange & fr) override  
         {
 #ifdef ENABLE_TENSORVIEW
+            static int c = 0; if (c++ == 0) { fprintf(stderr, "#PLUS#\n"); }
             size_t rank = DetermineElementwiseTensorRank();
             auto result =           ValueTensorFor(rank, fr);
             auto input0 = Input(0)->ValueTensorFor(rank, fr.AllowBroadcast());

Source/Math/TensorOps.h

@@ -50,7 +50,7 @@ namespace Microsoft { namespace MSR { namespace CNTK {
     template<class ElemType>
     DECL ElemType Sigmoid(ElemType z)
     {
-#if 1   // BUGBUG: Numerically bad. But if I don't use this, results change.
+#if 0   // BUGBUG: Numerically bad. But if I don't use this, results change.
         ElemType negElem = -z;
         ElemType e = exp_(negElem);
 

Tests/EndToEndTests/Image/QuickE2E/Macros.ndl

@@ -1,3 +1,4 @@
+# Sigmoid non-linearity
 DNNSigmoidLayer(inDim, outDim, x, parmScale) = [
     W = Parameter(outDim, inDim, init="uniform", initValueScale=parmScale) 
     b = Parameter(outDim, 1,     init="uniform", initValueScale=parmScale) 
@@ -6,6 +7,7 @@ DNNSigmoidLayer(inDim, outDim, x, parmScale) = [
     y = Sigmoid(z)
 ]
 
+# no non-linearity, as input for SoftMax
 DNNLayer(inDim, outDim, x, parmScale) = [
     W = Parameter(outDim, inDim, init="uniform", initValueScale=parmScale)
     b = Parameter(outDim, 1,     init="uniform", initValueScale=parmScale)
@@ -13,6 +15,7 @@ DNNLayer(inDim, outDim, x, parmScale) = [
     z = Plus(t, b)
 ]
 
+# ReLU non-linearity
 ConvReLULayer(inp, outMap, inWCount, kW, kH, hStride, vStride, wScale, bValue) = [
     convW = Parameter(outMap, inWCount, init="uniform", initValueScale=wScale)
     conv = Convolution(convW, inp, kW, kH, outMap, hStride, vStride, zeroPadding=false)