switched to new Sigmoid(), PlusNode::BackpropTo() still not TensorView
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c7b5c5f2d3
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96cde8ea48
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@ -123,6 +123,7 @@ namespace Microsoft { namespace MSR { namespace CNTK {
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virtual void /*ComputationNode::*/ForwardProp(const FrameRange & fr) override
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{
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#ifdef ENABLE_TENSORVIEW
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static int c = 0; if (c++ == 0) { fprintf(stderr, "#PLUS#\n"); }
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size_t rank = DetermineElementwiseTensorRank();
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auto result = ValueTensorFor(rank, fr);
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auto input0 = Input(0)->ValueTensorFor(rank, fr.AllowBroadcast());
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@ -50,7 +50,7 @@ namespace Microsoft { namespace MSR { namespace CNTK {
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template<class ElemType>
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DECL ElemType Sigmoid(ElemType z)
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{
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#if 1 // BUGBUG: Numerically bad. But if I don't use this, results change.
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#if 0 // BUGBUG: Numerically bad. But if I don't use this, results change.
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ElemType negElem = -z;
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ElemType e = exp_(negElem);
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@ -1,3 +1,4 @@
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# Sigmoid non-linearity
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DNNSigmoidLayer(inDim, outDim, x, parmScale) = [
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W = Parameter(outDim, inDim, init="uniform", initValueScale=parmScale)
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b = Parameter(outDim, 1, init="uniform", initValueScale=parmScale)
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@ -6,6 +7,7 @@ DNNSigmoidLayer(inDim, outDim, x, parmScale) = [
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y = Sigmoid(z)
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]
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# no non-linearity, as input for SoftMax
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DNNLayer(inDim, outDim, x, parmScale) = [
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W = Parameter(outDim, inDim, init="uniform", initValueScale=parmScale)
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b = Parameter(outDim, 1, init="uniform", initValueScale=parmScale)
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@ -13,6 +15,7 @@ DNNLayer(inDim, outDim, x, parmScale) = [
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z = Plus(t, b)
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]
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# ReLU non-linearity
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ConvReLULayer(inp, outMap, inWCount, kW, kH, hStride, vStride, wScale, bValue) = [
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convW = Parameter(outMap, inWCount, init="uniform", initValueScale=wScale)
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conv = Convolution(convW, inp, kW, kH, outMap, hStride, vStride, zeroPadding=false)
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