moved DiagTimes() to DeprecatedNodes.h, since it is redundant (ElementTimes) and should no longer be used;
removed a log message from SeqCla's required test patterns as they are no longer being generated
This commit is contained in:
Frank Seide
Родитель
5d6785fdac
Коммит
0e34934f78
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@ -346,11 +346,11 @@ CNTK2 = [
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Tanh(_, tag='') = new ComputationNode [ operation = 'Tanh' ; inputs = _ /*plus the function args*/ ]
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// 6. Reductions
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ReduceSum (_, axis=None, tag='') = new ComputationNode [ operation = 'ReduceElements' ; inputs = _ ; axis = if BS.Constants.IsNone (axis) then 0 else axis reductionOp = "Sum" /*plus the function args*/ ]
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ReduceLogSum(_, axis=None, tag='') = new ComputationNode [ operation = 'ReduceElements' ; inputs = _ ; axis = if BS.Constants.IsNone (axis) then 0 else axis reductionOp = "LogSum" /*plus the function args*/ ]
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ReduceMin (_, axis=None, tag='') = new ComputationNode [ operation = 'ReduceElements' ; inputs = _ ; axis = if BS.Constants.IsNone (axis) then 0 else axis reductionOp = "Min" /*plus the function args*/ ]
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ReduceMax (_, axis=None, tag='') = new ComputationNode [ operation = 'ReduceElements' ; inputs = _ ; axis = if BS.Constants.IsNone (axis) then 0 else axis reductionOp = "Max" /*plus the function args*/ ]
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#ReduceMean (_, axis=None, tag='') = new ComputationNode [ operation = 'ReduceElements' ; inputs = _ ; axis = if BS.Constants.IsNone (axis) then 0 else axis reductionOp = "Mean" /*plus the function args*/ ]
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ReduceSum (_, axis=None, tag='') = new ComputationNode [ operation = 'ReduceElements' ; inputs = _ ; axis = if BS.Constants.IsNone (axis) then 0 else axis ; reductionOp = "Sum" /*plus the function args*/ ]
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ReduceLogSum(_, axis=None, tag='') = new ComputationNode [ operation = 'ReduceElements' ; inputs = _ ; axis = if BS.Constants.IsNone (axis) then 0 else axis ; reductionOp = "LogSum" /*plus the function args*/ ]
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ReduceMin (_, axis=None, tag='') = new ComputationNode [ operation = 'ReduceElements' ; inputs = _ ; axis = if BS.Constants.IsNone (axis) then 0 else axis ; reductionOp = "Min" /*plus the function args*/ ]
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ReduceMax (_, axis=None, tag='') = new ComputationNode [ operation = 'ReduceElements' ; inputs = _ ; axis = if BS.Constants.IsNone (axis) then 0 else axis ; reductionOp = "Max" /*plus the function args*/ ]
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#ReduceMean (_, axis=None, tag='') = new ComputationNode [ operation = 'ReduceElements' ; inputs = _ ; axis = if BS.Constants.IsNone (axis) then 0 else axis ; reductionOp = "Mean" /*plus the function args*/ ]
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// 7. Control flow (if, composite etc.)
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// None so far
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@ -166,4 +166,128 @@ public:
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template class PerDimMeanVarNormalizationNode<float>;
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template class PerDimMeanVarNormalizationNode<double>;
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// -----------------------------------------------------------------------
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// DiagTimesNode (vector representing the diagonal of a square matrix, data)
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// Deprecated because can be implemented with ElementTimes.
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// -----------------------------------------------------------------------
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template <class ElemType>
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class DiagTimesNode : public ComputationNode<ElemType>, public NumInputs<2>
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{
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typedef ComputationNode<ElemType> Base; UsingComputationNodeMembersBoilerplate;
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static const std::wstring TypeName() { return L"DiagTimes"; }
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public:
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DeclareConstructorFromConfigWithNumInputs(DiagTimesNode);
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DiagTimesNode(DEVICEID_TYPE deviceId, const wstring& name)
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: Base(deviceId, name)
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{
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}
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virtual void /*ComputationNode::*/ BackpropTo(const size_t inputIndex, const FrameRange& fr) override
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{
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if (inputIndex == 0) // left derivative
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{
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Matrix<ElemType> sliceOutputGrad = MaskedGradientFor(fr); // use Masked- version since this is reducing over frames
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Matrix<ElemType> sliceInput1Value = Input(1)->MaskedValueFor(fr);
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m_innerproduct->AssignInnerProductOf(sliceOutputGrad, sliceInput1Value, false);
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Input(0)->GradientAsMatrix() += *m_innerproduct;
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}
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else // right derivative
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{
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Matrix<ElemType> sliceOutputGrad = GradientFor(fr);
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Matrix<ElemType> sliceInput1Grad = Input(1)->GradientFor(fr);
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m_rightGradient->SetValue(sliceOutputGrad);
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m_rightGradient->ColumnElementMultiplyWith(Input(0)->ValueAsMatrix());
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sliceInput1Grad += *m_rightGradient;
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}
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}
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virtual bool OutputUsedInComputingInputNodesGradients() const override
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{
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// The DiagTimesNode does not require its output value for computing
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// the gradients of its input nodes
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return false;
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}
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virtual void /*ComputationNode::*/ ForwardProp(const FrameRange& fr) override
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{
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Matrix<ElemType> sliceInput1Value = Input(1)->ValueFor(fr);
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Matrix<ElemType> sliceOutputValue = ValueFor(fr);
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sliceOutputValue.AssignValuesOf(sliceInput1Value);
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sliceOutputValue.ColumnElementMultiplyWith(Input(0)->ValueAsMatrix());
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}
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virtual void /*ComputationNodeBase::*/ Validate(bool isFinalValidationPass) override
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{
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Base::Validate(isFinalValidationPass);
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InferMBLayoutFromInputsForStandardCase(isFinalValidationPass);
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size_t rows0 = Input(0)->GetAsMatrixNumRows();
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size_t rows1 = Input(1)->HasMBLayout() ? Input(1)->GetSampleMatrixNumRows() : Input(1)->GetAsMatrixNumRows();
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// if dimension not specified we assume two operands' dimensions should match
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Input(0)->ValidateInferInputDimsFrom(TensorShape(rows1));
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if (Input(1)->HasMBLayout())
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{
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// infer rows1 as rows0
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Input(1)->ValidateInferInputDimsFrom(TensorShape(rows0));
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SetDims(TensorShape(rows0), true);
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}
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else // multiplying two straight matrices
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{
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size_t cols1 = Input(1)->GetAsMatrixNumCols();
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// infer rows1 as rows0
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Input(1)->ValidateInferInputDimsFrom(TensorShape(rows0, cols1));
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SetDims(TensorShape(rows0, cols1), false);
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}
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// update after inference
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rows0 = Input(0)->GetAsMatrixNumRows();
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rows1 = Input(1)->HasMBLayout() ? Input(1)->GetSampleMatrixNumRows() : Input(1)->GetAsMatrixNumRows();
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if (isFinalValidationPass && rows0 != rows1)
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InvalidArgument("The inner matrix dimension in the %ls %ls operation does not match (%d vs. %d).", NodeName().c_str(), OperationName().c_str(), (int) rows1, (int) rows0);
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size_t cols0 = Input(0)->GetAsMatrixNumCols();
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if (isFinalValidationPass && cols0 != 1)
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InvalidArgument("The first matrix should be a column vector representing the diagonal of a square matrix in the DiagTimes operation.");
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SetDims(Input(1));
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}
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virtual void CopyTo(ComputationNodeBasePtr nodeP, const std::wstring& newName, const CopyNodeFlags flags) const override
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{
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Base::CopyTo(nodeP, newName, flags);
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if (flags & CopyNodeFlags::copyNodeValue)
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{
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auto node = dynamic_pointer_cast<DiagTimesNode<ElemType>>(nodeP);
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node->m_innerproduct->SetValue(*m_innerproduct);
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node->m_rightGradient->SetValue(*m_rightGradient);
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}
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}
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// request matrices that are needed for gradient computation
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virtual void RequestMatricesBeforeBackprop(MatrixPool& matrixPool)
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{
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Base::RequestMatricesBeforeBackprop(matrixPool);
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RequestMatrixFromPool(m_innerproduct, matrixPool);
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RequestMatrixFromPool(m_rightGradient, matrixPool);
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}
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// release gradient and temp matrices that no longer needed after all the children's gradients are computed.
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virtual void ReleaseMatricesAfterBackprop(MatrixPool& matrixPool)
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{
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Base::ReleaseMatricesAfterBackprop(matrixPool);
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ReleaseMatrixToPool(m_innerproduct, matrixPool);
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ReleaseMatrixToPool(m_rightGradient, matrixPool);
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}
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private:
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shared_ptr<Matrix<ElemType>> m_innerproduct;
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shared_ptr<Matrix<ElemType>> m_rightGradient;
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};
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template class DiagTimesNode<float>;
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template class DiagTimesNode<double>;
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}}}
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@ -570,130 +570,6 @@ public:
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template class TransposeTimesNode<float>;
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template class TransposeTimesNode<double>;
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// -----------------------------------------------------------------------
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// DiagTimesNode (vector representing the diagonal of a square matrix, data)
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// TODO: Deprecate and move to DeprecatedNodes.h. (Can be implemented with ElementTimes.)
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// -----------------------------------------------------------------------
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template <class ElemType>
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class DiagTimesNode : public ComputationNode<ElemType>, public NumInputs<2>
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{
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typedef ComputationNode<ElemType> Base; UsingComputationNodeMembersBoilerplate;
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static const std::wstring TypeName() { return L"DiagTimes"; }
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public:
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DeclareConstructorFromConfigWithNumInputs(DiagTimesNode);
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DiagTimesNode(DEVICEID_TYPE deviceId, const wstring& name)
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: Base(deviceId, name)
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{
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}
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virtual void /*ComputationNode::*/ BackpropTo(const size_t inputIndex, const FrameRange& fr) override
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{
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if (inputIndex == 0) // left derivative
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{
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Matrix<ElemType> sliceOutputGrad = MaskedGradientFor(fr); // use Masked- version since this is reducing over frames
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Matrix<ElemType> sliceInput1Value = Input(1)->MaskedValueFor(fr);
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m_innerproduct->AssignInnerProductOf(sliceOutputGrad, sliceInput1Value, false);
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Input(0)->GradientAsMatrix() += *m_innerproduct;
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}
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else // right derivative
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{
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Matrix<ElemType> sliceOutputGrad = GradientFor(fr);
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Matrix<ElemType> sliceInput1Grad = Input(1)->GradientFor(fr);
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m_rightGradient->SetValue(sliceOutputGrad);
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m_rightGradient->ColumnElementMultiplyWith(Input(0)->ValueAsMatrix());
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sliceInput1Grad += *m_rightGradient;
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}
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}
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virtual bool OutputUsedInComputingInputNodesGradients() const override
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{
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// The DiagTimesNode does not require its output value for computing
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// the gradients of its input nodes
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return false;
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}
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virtual void /*ComputationNode::*/ ForwardProp(const FrameRange& fr) override
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{
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Matrix<ElemType> sliceInput1Value = Input(1)->ValueFor(fr);
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Matrix<ElemType> sliceOutputValue = ValueFor(fr);
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sliceOutputValue.AssignValuesOf(sliceInput1Value);
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sliceOutputValue.ColumnElementMultiplyWith(Input(0)->ValueAsMatrix());
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}
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virtual void /*ComputationNodeBase::*/ Validate(bool isFinalValidationPass) override
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{
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Base::Validate(isFinalValidationPass);
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InferMBLayoutFromInputsForStandardCase(isFinalValidationPass);
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size_t rows0 = Input(0)->GetAsMatrixNumRows();
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size_t rows1 = Input(1)->HasMBLayout() ? Input(1)->GetSampleMatrixNumRows() : Input(1)->GetAsMatrixNumRows();
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// if dimension not specified we assume two operands' dimensions should match
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Input(0)->ValidateInferInputDimsFrom(TensorShape(rows1));
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if (Input(1)->HasMBLayout())
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{
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// infer rows1 as rows0
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Input(1)->ValidateInferInputDimsFrom(TensorShape(rows0));
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SetDims(TensorShape(rows0), true);
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}
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else // multiplying two straight matrices
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{
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size_t cols1 = Input(1)->GetAsMatrixNumCols();
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// infer rows1 as rows0
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Input(1)->ValidateInferInputDimsFrom(TensorShape(rows0, cols1));
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SetDims(TensorShape(rows0, cols1), false);
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}
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// update after inference
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rows0 = Input(0)->GetAsMatrixNumRows();
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rows1 = Input(1)->HasMBLayout() ? Input(1)->GetSampleMatrixNumRows() : Input(1)->GetAsMatrixNumRows();
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if (isFinalValidationPass && rows0 != rows1)
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InvalidArgument("The inner matrix dimension in the %ls %ls operation does not match (%d vs. %d).", NodeName().c_str(), OperationName().c_str(), (int) rows1, (int) rows0);
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size_t cols0 = Input(0)->GetAsMatrixNumCols();
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if (isFinalValidationPass && cols0 != 1)
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InvalidArgument("The first matrix should be a column vector representing the diagonal of a square matrix in the DiagTimes operation.");
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SetDims(Input(1));
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}
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virtual void CopyTo(ComputationNodeBasePtr nodeP, const std::wstring& newName, const CopyNodeFlags flags) const override
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{
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Base::CopyTo(nodeP, newName, flags);
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if (flags & CopyNodeFlags::copyNodeValue)
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{
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auto node = dynamic_pointer_cast<DiagTimesNode<ElemType>>(nodeP);
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node->m_innerproduct->SetValue(*m_innerproduct);
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node->m_rightGradient->SetValue(*m_rightGradient);
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}
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}
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// request matrices that are needed for gradient computation
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virtual void RequestMatricesBeforeBackprop(MatrixPool& matrixPool)
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{
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Base::RequestMatricesBeforeBackprop(matrixPool);
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RequestMatrixFromPool(m_innerproduct, matrixPool);
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RequestMatrixFromPool(m_rightGradient, matrixPool);
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}
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// release gradient and temp matrices that no longer needed after all the children's gradients are computed.
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virtual void ReleaseMatricesAfterBackprop(MatrixPool& matrixPool)
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{
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Base::ReleaseMatricesAfterBackprop(matrixPool);
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ReleaseMatrixToPool(m_innerproduct, matrixPool);
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ReleaseMatrixToPool(m_rightGradient, matrixPool);
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}
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private:
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shared_ptr<Matrix<ElemType>> m_innerproduct;
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shared_ptr<Matrix<ElemType>> m_rightGradient;
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};
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template class DiagTimesNode<float>;
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template class DiagTimesNode<double>;
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// -----------------------------------------------------------------------
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// SumElementsNode (input)
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// Sums up all elements in the input across all samples into a single scalar.
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@ -10,11 +10,12 @@ testCases:
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patterns:
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- __COMPLETED__
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Must train epochs in exactly same order and parameters:
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patterns:
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- Starting Epoch {{integer}}
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- learning rate per sample = {{float}}
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- momentum = {{float}}
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# Note: These log messages have been removed.
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# Must train epochs in exactly same order and parameters:
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# patterns:
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# - Starting Epoch {{integer}}
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# - learning rate per sample = {{float}}
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# - momentum = {{float}}
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Epochs must be finished with expected results:
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patterns:
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