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renamed ErrorPrediction to ClassificationError

This commit is contained in:
Frank Seide 2016-08-22 10:30:17 -07:00
Родитель 050a84035f
Коммит 9bd57387bb
7 изменённых файлов: 38 добавлений и 37 удалений
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2
Source/ActionsLib/NetworkDescriptionLanguage.cpp
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@ -158,6 +158,7 @@ bool CheckFunction(std::string& p_nodeType, bool* allowUndeterminedVariable)
else if (EqualInsensitive(nodeType, OperationNameOf(CRFNode), L"CRF")) ret = true;
#endif
else if (EqualInsensitive(nodeType, OperationNameOf(ClassBasedCrossEntropyWithSoftmaxNode), L"CBCEWithSM")) ret = true;
else if (EqualInsensitive(nodeType, OperationNameOf(ClassificationErrorNode), L"ErrorPrediction")) ret = true;
else if (EqualInsensitive(nodeType, OperationNameOf(EqualNode))) ret = true;
else if (EqualInsensitive(nodeType, OperationNameOf(GreaterEqualNode))) ret = true;
else if (EqualInsensitive(nodeType, OperationNameOf(GreaterNode))) ret = true;
@ -177,7 +178,6 @@ bool CheckFunction(std::string& p_nodeType, bool* allowUndeterminedVariable)
else if (EqualInsensitive(nodeType, OperationNameOf(DropoutNode))) ret = true;
else if (EqualInsensitive(nodeType, OperationNameOf(DummyCriterionNode), L"DummyCriterion")) ret = true;
else if (EqualInsensitive(nodeType, OperationNameOf(ElementTimesNode))) ret = true;
else if (EqualInsensitive(nodeType, OperationNameOf(ErrorPredictionNode), L"ClassificationError")) ret = true;
else if (EqualInsensitive(nodeType, OperationNameOf(ExpNode))) ret = true;
else if (EqualInsensitive(nodeType, OperationNameOf(FloorNode))) ret = true;
else if (EqualInsensitive(nodeType, OperationNameOf(FutureValueNode))) ret = true;

15
Source/ActionsLib/SimpleNetworkBuilder.cpp
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@ -1751,10 +1751,10 @@ shared_ptr<ComputationNode<ElemType>> SimpleNetworkBuilder<ElemType>::AddTrainAn
// output = builder.SquareError(label, tinput, (evalNodeName == L"")?L"EvalSquareError":evalNodeName);
output = builder.Logistic(label, tinput, (evalNodeName == L"") ? L"Logistic" : evalNodeName);
break;
case EvalCriterion::ErrorPrediction:
case EvalCriterion::ClassificationError:
if (matrix != nullptr && tinput == input)
tinput = builder.Times(matrix, input);
output = builder.ErrorPrediction(label, tinput, (evalNodeName == L"") ? L"EvalErrorPrediction" : evalNodeName);
output = builder.ClassificationError(label, tinput, (evalNodeName == L"") ? L"EvalClassificationError" : evalNodeName);
break;
#ifdef COMING_SOON
case EvalCriterion::CRF:
@ -1785,23 +1785,26 @@ template class SimpleNetworkBuilder<double>;
TrainingCriterion ParseTrainingCriterionString(wstring s)
{
if (EqualCI(s, L"crossEntropyWithSoftmax")) return TrainingCriterion::CrossEntropyWithSoftmax;
else if (EqualCI(s, L"sequenceWithSoftmax")) return TrainingCriterion::SequenceWithSoftmax;
else if (EqualCI(s, L"squareError")) return TrainingCriterion::SquareError;
else if (EqualCI(s, L"logistic")) return TrainingCriterion::Logistic;
else if (EqualCI(s, L"noiseContrastiveEstimation")) return TrainingCriterion::NCECrossEntropyWithSoftmax;
// legacy/deprecated
else if (EqualCI(s, L"classCrossEntropyWithSoftmax")) return TrainingCriterion::ClassCrossEntropyWithSoftmax;
else if (EqualCI(s, L"sequenceWithSoftmax")) return TrainingCriterion::SequenceWithSoftmax;
else LogicError("trainingCriterion: Invalid trainingCriterion value. Valid values are (crossEntropyWithSoftmax | squareError | logistic | classCrossEntropyWithSoftmax| sequenceWithSoftmax)");
}
EvalCriterion ParseEvalCriterionString(wstring s)
{
if (EqualCI(s, L"errorPrediction")) return EvalCriterion::ErrorPrediction;
if (EqualCI(s, L"classificationError")) return EvalCriterion::ClassificationError;
else if (EqualCI(s, L"crossEntropyWithSoftmax")) return EvalCriterion::CrossEntropyWithSoftmax;
else if (EqualCI(s, L"sequenceWithSoftmax")) return EvalCriterion::SequenceWithSoftmax;
else if (EqualCI(s, L"classCrossEntropyWithSoftmax")) return EvalCriterion::ClassCrossEntropyWithSoftmax;
else if (EqualCI(s, L"logistic")) return EvalCriterion::Logistic;
else if (EqualCI(s, L"noiseContrastiveEstimation")) return EvalCriterion::NCECrossEntropyWithSoftmax;
else if (EqualCI(s, L"squareError")) return EvalCriterion::SquareError;
// legacy/deprecated
else if (EqualCI(s, L"classCrossEntropyWithSoftmax")) return EvalCriterion::ClassCrossEntropyWithSoftmax;
else if (EqualCI(s, L"sequenceWithSoftmax")) return EvalCriterion::SequenceWithSoftmax;
else if (EqualCI(s, L"errorPrediction")) return EvalCriterion::ClassificationError;
else LogicError("evalCriterion: Invalid trainingCriterion value. Valid values are (errorPrediction | crossEntropyWithSoftmax | squareError | logistic | sequenceWithSoftmax)");
}

9
Source/CNTK/BrainScript/CNTKCoreLib/CNTK.core.bs
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@ -243,7 +243,7 @@ CrossEntropyWithSoftmax = CNTK2.CrossEntropyWithSoftmax
Dropout = CNTK2.Dropout
ElementTimes = CNTK2.ElementTimes
ElementDivide = CNTK2.ElementDivide
ErrorPrediction = CNTK2.ErrorPrediction
ClassificationError = CNTK2.ClassificationError
Exp = CNTK2.Exp
Floor = CNTK2.Floor
Log = CNTK2.Log
@ -377,11 +377,12 @@ CNTK2 = [
// No changes here - we said the default input would be the label sequence here, against which the
// empirical sequence is compared to. Keeping this for now.
CrossEntropyWithSoftmax(_, outProbVectorSequence, tag='') = new ComputationNode [ operation = 'CrossEntropyWithSoftmax' ; inputs = (_ : outProbVectorSequence) /*plus the function args*/ ]
ErrorPrediction(_, outVectorSequence, topN=1, tag='') = new ComputationNode [ operation = 'ErrorPrediction' ; inputs = if topN == 1 then (_ : outVectorSequence) else (_ : outVectorSequence : Constant (topN)) /*plus the function args*/ ]
ClassificationError(_, outVectorSequence, topN=1, tag='') = new ComputationNode [ operation = 'ClassificationError' ; inputs = if topN == 1 then (_ : outVectorSequence) else (_ : outVectorSequence : Constant (topN)) /*plus the function args*/ ]
ErrorPrediction = ClassificationError # legacy
# TODO: replace with this (need to deal with topN thing):
# (_new will be removed once the change is made)
CrossEntropyWithSoftmax_new (L, z, tag='') = Minus (ReduceLogSum (z), TransposeTimes (L, z), tag=tag)
ErrorPrediction_new (L, z, tag='') = Minus (BS.Constants.One, TransposeTimes (L, Hardmax (z)), tag=tag)
ClassificationError_new (L, z, tag='') = Minus (BS.Constants.One, TransposeTimes (L, Hardmax (z)), tag=tag)
// 12. Comparison nodes
Less(_, y, tag='') = new ComputationNode [ operation = 'Less' ; inputs = (_ : y) /*plus the function args*/ ]
@ -464,7 +465,7 @@ MaxUnpooling(unpoolInput, poolInput, kernelDims, stride=1, autoPadding = true, l
MaxPooling(input, windowWidth, windowHeight, horizontalSubsample, verticalSubsample, imageLayout='CHW', tag='') = new ComputationNode [ operation = 'MaxPooling' ; inputs = input /*plus the function args*/ ]
AveragePooling(input, windowWidth, windowHeight, horizontalSubsample, verticalSubsample, imageLayout='CHW', tag='') = new ComputationNode [ operation = 'AveragePooling' ; inputs = input /*plus the function args*/ ]
ColumnwiseCrossProduct = KhatriRaoProduct // deprecated
ClassificationError = ErrorPrediction
ErrorPrediction = ClassificationError # legacy name
Delay = PastValue
BatchNormalization(input, scale, bias, runMean, runInvStdDev, spatial, normalizationTimeConstant = 0, blendTimeConstant = 0, epsilon = 0.00001, useCntkEngine = true, imageLayout='CHW', tag='') = new ComputationNode [ operation = 'BatchNormalization' ; inputs = (input : scale : bias : runMean : runInvStdDev) /*plus the function args*/ ]

19
Source/ComputationNetworkLib/ComputationNetworkBuilder.cpp
Просмотреть файл

@ -40,13 +40,8 @@ static shared_ptr<ComputationNode<ElemType>> CreateStandardNode(const std::wstri
#endif
if (nodeType == OperationNameOf(AbsNode)) return New<AbsNode<ElemType>>(forward<_Types>(_Args)...);
else if (nodeType == OperationNameOf(ClassBasedCrossEntropyWithSoftmaxNode))return New<ClassBasedCrossEntropyWithSoftmaxNode<ElemType>>(forward<_Types>(_Args)...);
else if (nodeType == OperationNameOf(ClassificationErrorNode)) return New<ClassificationErrorNode<ElemType>>(forward<_Types>(_Args)...);
else if (nodeType == OperationNameOf(ClipNode)) return New<ClipNode<ElemType>>(forward<_Types>(_Args)...);
else if (nodeType == OperationNameOf(EqualNode)) return New<EqualNode<ElemType>>(forward<_Types>(_Args)...);
else if (nodeType == OperationNameOf(GreaterEqualNode)) return New<GreaterEqualNode<ElemType>>(forward<_Types>(_Args)...);
else if (nodeType == OperationNameOf(GreaterNode)) return New<GreaterNode<ElemType>>(forward<_Types>(_Args)...);
else if (nodeType == OperationNameOf(LessEqualNode)) return New<LessEqualNode<ElemType>>(forward<_Types>(_Args)...);
else if (nodeType == OperationNameOf(LessNode)) return New<LessNode<ElemType>>(forward<_Types>(_Args)...);
else if (nodeType == OperationNameOf(NotEqualNode)) return New<NotEqualNode<ElemType>>(forward<_Types>(_Args)...);
else if (nodeType == OperationNameOf(CosDistanceNode)) return New<CosDistanceNode<ElemType>>(forward<_Types>(_Args)...);
else if (nodeType == OperationNameOf(CosDistanceWithNegativeSamplesNode)) return New<CosDistanceWithNegativeSamplesNode<ElemType>>(forward<_Types>(_Args)...);
else if (nodeType == OperationNameOf(CosineNode)) return New<CosineNode<ElemType>>(forward<_Types>(_Args)...);
@ -59,7 +54,7 @@ static shared_ptr<ComputationNode<ElemType>> CreateStandardNode(const std::wstri
else if (nodeType == OperationNameOf(DynamicAxisNode)) return New<DynamicAxisNode<ElemType>>(forward<_Types>(_Args)...);
else if (nodeType == OperationNameOf(ElementTimesNode)) return New<ElementTimesNode<ElemType>>(forward<_Types>(_Args)...);
else if (nodeType == OperationNameOf(EnvironmentInputNode)) return New<EnvironmentInputNode<ElemType>>(forward<_Types>(_Args)...);
else if (nodeType == OperationNameOf(ErrorPredictionNode)) return New<ErrorPredictionNode<ElemType>>(forward<_Types>(_Args)...);
else if (nodeType == OperationNameOf(EqualNode)) return New<EqualNode<ElemType>>(forward<_Types>(_Args)...);
else if (nodeType == OperationNameOf(ExpNode)) return New<ExpNode<ElemType>>(forward<_Types>(_Args)...);
else if (nodeType == OperationNameOf(FloorNode)) return New<FloorNode<ElemType>>(forward<_Types>(_Args)...);
else if (nodeType == OperationNameOf(FutureValueNode)) return New<FutureValueNode<ElemType>>(forward<_Types>(_Args)...);
@ -67,10 +62,14 @@ static shared_ptr<ComputationNode<ElemType>> CreateStandardNode(const std::wstri
#ifdef COMING_SOON
else if (nodeType == OperationNameOf(GMMLogLikelihoodNode)) return New<GMMLogLikelihoodNode<ElemType>>(forward<_Types>(_Args)...);
#endif
else if (nodeType == OperationNameOf(GreaterEqualNode)) return New<GreaterEqualNode<ElemType>>(forward<_Types>(_Args)...);
else if (nodeType == OperationNameOf(GreaterNode)) return New<GreaterNode<ElemType>>(forward<_Types>(_Args)...);
else if (nodeType == OperationNameOf(HardmaxNode)) return New<HardmaxNode<ElemType>>(forward<_Types>(_Args)...);
else if (nodeType == OperationNameOf(IfNode)) return New<IfNode<ElemType>>(forward<_Types>(_Args)...);
else if (nodeType == OperationNameOf(InvStdDevNode)) return New<InvStdDevNode<ElemType>>(forward<_Types>(_Args)...);
else if (nodeType == OperationNameOf(KhatriRaoProductNode)) return New<KhatriRaoProductNode<ElemType>>(forward<_Types>(_Args)...);
else if (nodeType == OperationNameOf(LessEqualNode)) return New<LessEqualNode<ElemType>>(forward<_Types>(_Args)...);
else if (nodeType == OperationNameOf(LessNode)) return New<LessNode<ElemType>>(forward<_Types>(_Args)...);
else if (nodeType == OperationNameOf(LogNode)) return New<LogNode<ElemType>>(forward<_Types>(_Args)...);
else if (nodeType == OperationNameOf(LogPlusNode)) return New<LogPlusNode<ElemType>>(forward<_Types>(_Args)...);
else if (nodeType == OperationNameOf(LogSoftmaxNode)) return New<LogSoftmaxNode<ElemType>>(forward<_Types>(_Args)...);
@ -80,6 +79,7 @@ static shared_ptr<ComputationNode<ElemType>> CreateStandardNode(const std::wstri
else if (nodeType == OperationNameOf(MeanNode)) return New<MeanNode<ElemType>>(forward<_Types>(_Args)...);
else if (nodeType == OperationNameOf(MinusNode)) return New<MinusNode<ElemType>>(forward<_Types>(_Args)...);
else if (nodeType == OperationNameOf(NegateNode)) return New<NegateNode<ElemType>>(forward<_Types>(_Args)...);
else if (nodeType == OperationNameOf(NotEqualNode)) return New<NotEqualNode<ElemType>>(forward<_Types>(_Args)...);
else if (nodeType == OperationNameOf(NoiseContrastiveEstimationNode)) return New<NoiseContrastiveEstimationNode<ElemType>>(forward<_Types>(_Args)...);
else if (nodeType == OperationNameOf(PackedIndexNode)) return New<PackedIndexNode<ElemType>>(forward<_Types>(_Args)...);
else if (nodeType == OperationNameOf(PastValueNode)) return New<PastValueNode<ElemType>>(forward<_Types>(_Args)...);
@ -119,6 +119,7 @@ static shared_ptr<ComputationNode<ElemType>> CreateStandardNode(const std::wstri
else if (nodeType == OperationNameOf(WhereNode)) return New<WhereNode<ElemType>>(forward<_Types>(_Args)...);
// legacy names we also support for back compat of model-files
else if (nodeType == L"ColumnElementTimes") return New<ElementTimesNode<ElemType>>(forward<_Types>(_Args)...);
else if (nodeType == L"ErrorPrediction") return New<ClassificationErrorNode<ElemType>>(forward<_Types>(_Args)...);
else if (nodeType == L"Delay") return New<PastValueNode<ElemType>>(forward<_Types>(_Args)...);
// TODO: DiagTimes is also an alias of ElementTimes; current separate implementation is unnecessary.
else if (nodeType == L"PerDimMeanVarNormalizationNode") return New<PerDimMeanVarNormalizationNode<ElemType>>(forward<_Types>(_Args)...);
@ -368,9 +369,9 @@ shared_ptr<ComputationNode<ElemType>> ComputationNetworkBuilder<ElemType>::Avera
}
template <class ElemType>
shared_ptr<ComputationNode<ElemType>> ComputationNetworkBuilder<ElemType>::ErrorPrediction(const ComputationNodePtr a, const ComputationNodePtr b, const std::wstring nodeName)
shared_ptr<ComputationNode<ElemType>> ComputationNetworkBuilder<ElemType>::ClassificationError(const ComputationNodePtr a, const ComputationNodePtr b, const std::wstring nodeName)
{
return net.AddNodeToNetAndAttachInputs(New<ErrorPredictionNode<ElemType>>(net.GetDeviceId(), nodeName), { a, b });
return net.AddNodeToNetAndAttachInputs(New<ClassificationErrorNode<ElemType>>(net.GetDeviceId(), nodeName), { a, b });
}
template <class ElemType>

26
Source/ComputationNetworkLib/EvaluationNodes.h
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@ -18,24 +18,20 @@
namespace Microsoft { namespace MSR { namespace CNTK {
// -----------------------------------------------------------------------
// ErrorPredictionNode (label, prediction) or ErrorPredictionNode (prediction, label)
// ClassificationErrorNode (label, prediction) or ClassificationErrorNode (prediction, label)
// Performs classification and error counting.
// Result is an error rate, lower = better.
// -----------------------------------------------------------------------
template <class ElemType>
class ErrorPredictionNode : public ComputationNodeNonLooping /*ComputationNode*/<ElemType>
class ClassificationErrorNode : public ComputationNodeNonLooping /*ComputationNode*/<ElemType>
{
typedef ComputationNodeNonLooping<ElemType> Base;
UsingComputationNodeMembersBoilerplate;
static const std::wstring TypeName()
{
return L"ErrorPrediction";
}
typedef ComputationNodeNonLooping<ElemType> Base; UsingComputationNodeMembersBoilerplate;
static const std::wstring TypeName() { return L"ClassificationError"; }
public:
DeclareConstructorFromConfig(ErrorPredictionNode);
ErrorPredictionNode(DEVICEID_TYPE deviceId, const wstring& name)
DeclareConstructorFromConfig(ClassificationErrorNode);
ClassificationErrorNode(DEVICEID_TYPE deviceId, const wstring& name)
: Base(deviceId, name)
{
}
@ -63,10 +59,10 @@ public:
MaskMissingColumnsToZero(*m_maxIndexes1, Input(1)->GetMBLayout(), fr);
Value().AssignNumOfDiff(*m_maxIndexes0, *m_maxIndexes1, m_topK > 1);
#if NANCHECK
Value().HasNan("ErrorPrediction");
Value().HasNan("ClassificationError");
#endif
#if DUMPOUTPUT
Value().Print("ErrorPredictionNode");
Value().Print("ClassificationErrorNode");
#endif
}
@ -100,7 +96,7 @@ public:
Base::CopyTo(nodeP, newName, flags);
if (flags & CopyNodeFlags::copyNodeValue)
{
auto node = dynamic_pointer_cast<ErrorPredictionNode<ElemType>>(nodeP);
auto node = dynamic_pointer_cast<ClassificationErrorNode<ElemType>>(nodeP);
node->m_maxIndexes0->SetValue(*m_maxIndexes0);
node->m_maxIndexes1->SetValue(*m_maxIndexes1);
node->m_maxValues->SetValue(*m_maxValues);
@ -131,8 +127,8 @@ private:
int m_topK;
};
template class ErrorPredictionNode<float>;
template class ErrorPredictionNode<double>;
template class ClassificationErrorNode<float>;
template class ClassificationErrorNode<double>;
#ifdef COMING_SOON

2
Tests/EndToEndTests/Text/SequenceClassification/Config/seqcla.cntk
Просмотреть файл

@ -50,7 +50,7 @@ Train=[
# training criteria
ce = CrossEntropyWithSoftmax (labels, zp) // this is the training objective
err = ErrorPrediction (labels, zp) // this also gets tracked
err = ClassificationError (labels, zp) // this also gets tracked
# connect to system
featureNodes = (features)

2
Tutorials/ImageHandsOn/ImageHandsOn.cntk
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@ -39,7 +39,7 @@ TrainConvNet = {
# connect to system
ce = CrossEntropyWithSoftmax (labels, z)
errs = ErrorPrediction (labels, z)
errs = ClassificationError (labels, z)
featureNodes = (features)
labelNodes = (labels)