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TMVA::DNN::TCpu< AReal > Member List

This is the complete list of members for TMVA::DNN::TCpu< AReal >, including all inherited members.

ActivationDescriptor_t typedefTMVA::DNN::TCpu< AReal >
ActivationFunctionBackward(Tensor_t &dX, const Tensor_t &Y, const Tensor_t &dY, const Tensor_t &X, EActivationFunction activFunct, const ActivationDescriptor_t activationDescr, const Scalar_t alpha=1, const Scalar_t beta=0)TMVA::DNN::TCpu< AReal >static
ActivationFunctionForward(Tensor_t &X, EActivationFunction activFunct, const ActivationDescriptor_t activationDescr, const double coef=0.0, const Scalar_t alpha=1, const Scalar_t beta=0)TMVA::DNN::TCpu< AReal >static
AdamUpdate(Matrix_t &A, const Matrix_t &M, const Matrix_t &V, Scalar_t alpha, Scalar_t eps)TMVA::DNN::TCpu< AReal >static
AdamUpdateFirstMom(Matrix_t &A, const Matrix_t &B, Scalar_t beta)TMVA::DNN::TCpu< AReal >static
AdamUpdateSecondMom(Matrix_t &A, const Matrix_t &B, Scalar_t beta)TMVA::DNN::TCpu< AReal >static
AddConvBiases(Matrix_t &output, const Matrix_t &biases)TMVA::DNN::TCpu< AReal >static
AddL1RegularizationGradients(Matrix_t &A, const Matrix_t &W, Scalar_t weightDecay)TMVA::DNN::TCpu< AReal >static
AddL2RegularizationGradients(Matrix_t &A, const Matrix_t &W, Scalar_t weightDecay)TMVA::DNN::TCpu< AReal >static
AddRowWise(Matrix_t &output, const Matrix_t &biases)TMVA::DNN::TCpu< AReal >static
AddRowWise(Tensor_t &output, const Matrix_t &biases)TMVA::DNN::TCpu< AReal >inlinestatic
AlgorithmBackward_t typedefTMVA::DNN::TCpu< AReal >
AlgorithmDataType_t typedefTMVA::DNN::TCpu< AReal >
AlgorithmForward_t typedefTMVA::DNN::TCpu< AReal >
AlgorithmHelper_t typedefTMVA::DNN::TCpu< AReal >
AlmostEquals(const Matrix_t &A, const Matrix_t &B, double epsilon=0.1)TMVA::DNN::TCpu< AReal >static
Backward(Tensor_t &activationGradientsBackward, Matrix_t &weightGradients, Matrix_t &biasGradients, const Tensor_t &df, const Tensor_t &activationGradients, const Matrix_t &weights, const Tensor_t &activationBackward)TMVA::DNN::TCpu< AReal >static
BatchNormLayerBackward(int axis, const Tensor_t &x, const Tensor_t &dy, Tensor_t &dx, Matrix_t &gamma, Matrix_t &dgamma, Matrix_t &dbeta, const Matrix_t &mean, const Matrix_t &variance, const Matrix_t &iVariance, Scalar_t epsilon, const TensorDescriptor_t &)TMVA::DNN::TCpu< AReal >static
BatchNormLayerForwardInference(int axis, const Tensor_t &x, Matrix_t &gamma, Matrix_t &beta, Tensor_t &y, const Matrix_t &runningMeans, const Matrix_t &runningVars, Scalar_t epsilon, const TensorDescriptor_t &)TMVA::DNN::TCpu< AReal >static
BatchNormLayerForwardTraining(int axis, const Tensor_t &x, Tensor_t &y, Matrix_t &gamma, Matrix_t &beta, Matrix_t &mean, Matrix_t &, Matrix_t &iVariance, Matrix_t &runningMeans, Matrix_t &runningVars, Scalar_t nTrainedBatches, Scalar_t momentum, Scalar_t epsilon, const TensorDescriptor_t &bnParDescriptor)TMVA::DNN::TCpu< AReal >static
BatchNormLayerReshapeTensor(int axis, const Tensor_t &x)TMVA::DNN::TCpu< AReal >static
BNormDescriptors_t typedefTMVA::DNN::TCpu< AReal >
BNormLayer_t typedefTMVA::DNN::TCpu< AReal >
CalculateConvActivationGradients(Tensor_t &activationGradientsBackward, const Tensor_t &df, const Matrix_t &weights, size_t batchSize, size_t inputHeight, size_t inputWidth, size_t depth, size_t height, size_t width, size_t filterDepth, size_t filterHeight, size_t filterWidth)TMVA::DNN::TCpu< AReal >static
CalculateConvBiasGradients(Matrix_t &biasGradients, const Tensor_t &df, size_t batchSize, size_t depth, size_t nLocalViews)TMVA::DNN::TCpu< AReal >static
CalculateConvWeightGradients(Matrix_t &weightGradients, const Tensor_t &df, const Tensor_t &activations_backward, size_t batchSize, size_t inputHeight, size_t inputWidth, size_t depth, size_t height, size_t width, size_t filterDepth, size_t filterHeight, size_t filterWidth, size_t nLocalViews)TMVA::DNN::TCpu< AReal >static
calculateDimension(size_t imgDim, size_t fltDim, size_t padding, size_t stride)TMVA::DNN::TCpu< AReal >static
ConstAdd(Matrix_t &A, Scalar_t beta)TMVA::DNN::TCpu< AReal >static
ConstMult(Matrix_t &A, Scalar_t beta)TMVA::DNN::TCpu< AReal >static
ConvDescriptors_t typedefTMVA::DNN::TCpu< AReal >
ConvLayer_t typedefTMVA::DNN::TCpu< AReal >
ConvLayerBackward(Tensor_t &activationGradientsBackward, Matrix_t &weightGradients, Matrix_t &biasGradients, Tensor_t &df, Tensor_t &activationGradients, const Matrix_t &weights, const Tensor_t &activationBackward, const Tensor_t &outputTensor, EActivationFunction activFunc, const ConvDescriptors_t &, ConvWorkspace_t &, size_t batchSize, size_t inputHeight, size_t inputWidth, size_t depth, size_t height, size_t width, size_t filterDepth, size_t filterHeight, size_t filterWidth, size_t nLocalViews)TMVA::DNN::TCpu< AReal >static
ConvLayerForward(Tensor_t &output, Tensor_t &inputActivationFunc, const Tensor_t &input, const Matrix_t &weights, const Matrix_t &biases, const DNN::CNN::TConvParams &params, EActivationFunction activFunc, Tensor_t &, const ConvDescriptors_t &, ConvWorkspace_t &)TMVA::DNN::TCpu< AReal >static
ConvolutionDescriptor_t typedefTMVA::DNN::TCpu< AReal >
ConvWorkspace_t typedefTMVA::DNN::TCpu< AReal >
Copy(Matrix_t &B, const Matrix_t &A)TMVA::DNN::TCpu< AReal >static
Copy(Tensor_t &A, const Tensor_t &B)TMVA::DNN::TCpu< AReal >static
CopyDiffArch(Matrix_t &B, const AMatrix_t &A)TMVA::DNN::TCpu< AReal >static
CopyDiffArch(Tensor_t &A, const ATensor_t &B)TMVA::DNN::TCpu< AReal >static
CopyDiffArch(std::vector< Matrix_t > &A, const std::vector< AMatrix_t > &B)TMVA::DNN::TCpu< AReal >static
CopyDiffArch(std::vector< TCpuMatrix< AReal >> &A, const std::vector< AMatrix_t > &B)TMVA::DNN::TCpu< AReal >
CreateTensor(size_t n, size_t c, size_t h, size_t w)TMVA::DNN::TCpu< AReal >inlinestatic
CreateTensor(DeviceBuffer_t buffer, size_t n, size_t c, size_t h, size_t w)TMVA::DNN::TCpu< AReal >inlinestatic
CreateTensor(size_t b, size_t t, size_t w)TMVA::DNN::TCpu< AReal >inlinestatic
CreateTensor(DeviceBuffer_t buffer, size_t b, size_t t, size_t w)TMVA::DNN::TCpu< AReal >inlinestatic
CreateWeightTensors(std::vector< Matrix_t > &newWeights, const std::vector< Matrix_t > &weights)TMVA::DNN::TCpu< AReal >inlinestatic
CrossEntropy(const Matrix_t &Y, const Matrix_t &output, const Matrix_t &weights)TMVA::DNN::TCpu< AReal >static
CrossEntropyGradients(Matrix_t &dY, const Matrix_t &Y, const Matrix_t &output, const Matrix_t &weights)TMVA::DNN::TCpu< AReal >static
Deflatten(Tensor_t &A, const Tensor_t &B)TMVA::DNN::TCpu< AReal >static
DeviceBuffer_t typedefTMVA::DNN::TCpu< AReal >
Downsample(Tensor_t &A, Tensor_t &B, const Tensor_t &C, const PoolingDescriptors_t &, PoolingWorkspace_t &, size_t imgHeight, size_t imgWidth, size_t fltHeight, size_t fltWidth, size_t strideRows, size_t strideCols)TMVA::DNN::TCpu< AReal >static
DropoutBackward(Tensor_t &, TDescriptors *, TWorkspace *)TMVA::DNN::TCpu< AReal >inlinestatic
DropoutDescriptor_t typedefTMVA::DNN::TCpu< AReal >
DropoutForward(Tensor_t &A, TDescriptors *descriptors, TWorkspace *workspace, Scalar_t p)TMVA::DNN::TCpu< AReal >static
DropoutForward(Matrix_t &A, Scalar_t p)TMVA::DNN::TCpu< AReal >inlinestatic
EmptyDescriptor_t typedefTMVA::DNN::TCpu< AReal >
FastTanh(Tensor_t &B)TMVA::DNN::TCpu< AReal >static
FastTanhDerivative(Tensor_t &B, const Tensor_t &A)TMVA::DNN::TCpu< AReal >static
fgRandomGenTMVA::DNN::TCpu< AReal >privatestatic
FilterDescriptor_t typedefTMVA::DNN::TCpu< AReal >
Flatten(Tensor_t &A, const Tensor_t &B)TMVA::DNN::TCpu< AReal >static
FreeConvWorkspace(TWorkspace *&)TMVA::DNN::TCpu< AReal >inlinestatic
FreePoolDropoutWorkspace(TWorkspace *&)TMVA::DNN::TCpu< AReal >inlinestatic
FreeRNNWorkspace(TWorkspace *&)TMVA::DNN::TCpu< AReal >inlinestatic
Gauss(Tensor_t &B)TMVA::DNN::TCpu< AReal >static
GaussDerivative(Tensor_t &B, const Tensor_t &A)TMVA::DNN::TCpu< AReal >static
GenLayer_t typedefTMVA::DNN::TCpu< AReal >
GetRandomGenerator()TMVA::DNN::TCpu< AReal >static
GetTensorLayout()TMVA::DNN::TCpu< AReal >inlinestatic
GRULayerBackward(TCpuMatrix< Scalar_t > &state_gradients_backward, TCpuMatrix< Scalar_t > &reset_weight_gradients, TCpuMatrix< Scalar_t > &update_weight_gradients, TCpuMatrix< Scalar_t > &candidate_weight_gradients, TCpuMatrix< Scalar_t > &reset_state_weight_gradients, TCpuMatrix< Scalar_t > &update_state_weight_gradients, TCpuMatrix< Scalar_t > &candidate_state_weight_gradients, TCpuMatrix< Scalar_t > &reset_bias_gradients, TCpuMatrix< Scalar_t > &update_bias_gradients, TCpuMatrix< Scalar_t > &candidate_bias_gradients, TCpuMatrix< Scalar_t > &dr, TCpuMatrix< Scalar_t > &du, TCpuMatrix< Scalar_t > &dc, const TCpuMatrix< Scalar_t > &precStateActivations, const TCpuMatrix< Scalar_t > &fReset, const TCpuMatrix< Scalar_t > &fUpdate, const TCpuMatrix< Scalar_t > &fCandidate, const TCpuMatrix< Scalar_t > &weights_reset, const TCpuMatrix< Scalar_t > &weights_update, const TCpuMatrix< Scalar_t > &weights_candidate, const TCpuMatrix< Scalar_t > &weights_reset_state, const TCpuMatrix< Scalar_t > &weights_update_state, const TCpuMatrix< Scalar_t > &weights_candidate_state, const TCpuMatrix< Scalar_t > &input, TCpuMatrix< Scalar_t > &input_gradient, bool resetGateAfter)TMVA::DNN::TCpu< AReal >inlinestatic
Hadamard(Tensor_t &A, const Tensor_t &B)TMVA::DNN::TCpu< AReal >static
Hadamard(Matrix_t &A, const Matrix_t &B)TMVA::DNN::TCpu< AReal >static
HostBuffer_t typedefTMVA::DNN::TCpu< AReal >
IdentityDerivative(Tensor_t &B, const Tensor_t &A)TMVA::DNN::TCpu< AReal >static
Im2col(Matrix_t &A, const Matrix_t &B, size_t imgHeight, size_t imgWidth, size_t fltHeight, size_t fltWidth, size_t strideRows, size_t strideCols, size_t zeroPaddingHeight, size_t zeroPaddingWidth)TMVA::DNN::TCpu< AReal >static
Im2colFast(Matrix_t &A, const Matrix_t &B, const std::vector< int > &V)TMVA::DNN::TCpu< AReal >static
Im2colIndices(std::vector< int > &V, const Matrix_t &B, size_t nLocalViews, size_t imgHeight, size_t imgWidth, size_t fltHeight, size_t fltWidth, size_t strideRows, size_t strideCols, size_t zeroPaddingHeight, size_t zeroPaddingWidth)TMVA::DNN::TCpu< AReal >static
InitializeActivationDescriptor(ActivationDescriptor_t &, EActivationFunction, double=0.0)TMVA::DNN::TCpu< AReal >inlinestatic
InitializeBNormDescriptors(TDescriptors *&, BNormLayer_t *)TMVA::DNN::TCpu< AReal >inlinestatic
InitializeConvDescriptors(TDescriptors *&, ConvLayer_t *)TMVA::DNN::TCpu< AReal >inlinestatic
InitializeConvWorkspace(TWorkspace *&, TDescriptors *&, const DNN::CNN::TConvParams &, ConvLayer_t *)TMVA::DNN::TCpu< AReal >inlinestatic
InitializeGauss(Matrix_t &A)TMVA::DNN::TCpu< AReal >static
InitializeGlorotNormal(Matrix_t &A)TMVA::DNN::TCpu< AReal >static
InitializeGlorotUniform(Matrix_t &A)TMVA::DNN::TCpu< AReal >static
InitializeGRUDescriptors(TDescriptors *&, GenLayer_t *)TMVA::DNN::TCpu< AReal >inlinestatic
InitializeGRUTensors(GenLayer_t *)TMVA::DNN::TCpu< AReal >inlinestatic
InitializeGRUWorkspace(TWorkspace *&, TDescriptors *&, GenLayer_t *)TMVA::DNN::TCpu< AReal >inlinestatic
InitializeIdentity(Matrix_t &A)TMVA::DNN::TCpu< AReal >static
InitializeLSTMDescriptors(TDescriptors *&, GenLayer_t *)TMVA::DNN::TCpu< AReal >inlinestatic
InitializeLSTMTensors(GenLayer_t *)TMVA::DNN::TCpu< AReal >inlinestatic
InitializeLSTMWorkspace(TWorkspace *&, TDescriptors *&, GenLayer_t *)TMVA::DNN::TCpu< AReal >inlinestatic
InitializePoolDescriptors(TDescriptors *&, PoolingLayer_t *)TMVA::DNN::TCpu< AReal >inlinestatic
InitializePoolDropoutWorkspace(TWorkspace *&, TDescriptors *&, const DNN::CNN::TConvParams &, PoolingLayer_t *)TMVA::DNN::TCpu< AReal >inlinestatic
InitializeRNNDescriptors(TDescriptors *&, GenLayer_t *)TMVA::DNN::TCpu< AReal >inlinestatic
InitializeRNNTensors(GenLayer_t *)TMVA::DNN::TCpu< AReal >inlinestatic
InitializeRNNWorkspace(TWorkspace *&, TDescriptors *&, GenLayer_t *)TMVA::DNN::TCpu< AReal >inlinestatic
InitializeUniform(Matrix_t &A)TMVA::DNN::TCpu< AReal >static
InitializeZero(Matrix_t &A)TMVA::DNN::TCpu< AReal >static
InitializeZero(Tensor_t &A)TMVA::DNN::TCpu< AReal >static
IsCudnn()TMVA::DNN::TCpu< AReal >inlinestatic
L1Regularization(const Matrix_t &W)TMVA::DNN::TCpu< AReal >static
L2Regularization(const Matrix_t &W)TMVA::DNN::TCpu< AReal >static
LSTMLayerBackward(TCpuMatrix< Scalar_t > &state_gradients_backward, TCpuMatrix< Scalar_t > &cell_gradients_backward, TCpuMatrix< Scalar_t > &input_weight_gradients, TCpuMatrix< Scalar_t > &forget_weight_gradients, TCpuMatrix< Scalar_t > &candidate_weight_gradients, TCpuMatrix< Scalar_t > &output_weight_gradients, TCpuMatrix< Scalar_t > &input_state_weight_gradients, TCpuMatrix< Scalar_t > &forget_state_weight_gradients, TCpuMatrix< Scalar_t > &candidate_state_weight_gradients, TCpuMatrix< Scalar_t > &output_state_weight_gradients, TCpuMatrix< Scalar_t > &input_bias_gradients, TCpuMatrix< Scalar_t > &forget_bias_gradients, TCpuMatrix< Scalar_t > &candidate_bias_gradients, TCpuMatrix< Scalar_t > &output_bias_gradients, TCpuMatrix< Scalar_t > &di, TCpuMatrix< Scalar_t > &df, TCpuMatrix< Scalar_t > &dc, TCpuMatrix< Scalar_t > &dout, const TCpuMatrix< Scalar_t > &precStateActivations, const TCpuMatrix< Scalar_t > &precCellActivations, const TCpuMatrix< Scalar_t > &fInput, const TCpuMatrix< Scalar_t > &fForget, const TCpuMatrix< Scalar_t > &fCandidate, const TCpuMatrix< Scalar_t > &fOutput, const TCpuMatrix< Scalar_t > &weights_input, const TCpuMatrix< Scalar_t > &weights_forget, const TCpuMatrix< Scalar_t > &weights_candidate, const TCpuMatrix< Scalar_t > &weights_output, const TCpuMatrix< Scalar_t > &weights_input_state, const TCpuMatrix< Scalar_t > &weights_forget_state, const TCpuMatrix< Scalar_t > &weights_candidate_state, const TCpuMatrix< Scalar_t > &weights_output_state, const TCpuMatrix< Scalar_t > &input, TCpuMatrix< Scalar_t > &input_gradient, TCpuMatrix< Scalar_t > &cell_gradient, TCpuMatrix< Scalar_t > &cell_tanh)TMVA::DNN::TCpu< AReal >inlinestatic
Matrix_t typedefTMVA::DNN::TCpu< AReal >
MaxPoolLayerBackward(Tensor_t &activationGradientsBackward, const Tensor_t &activationGradients, const Tensor_t &indexMatrix, const Tensor_t &, const Tensor_t &, const PoolingDescriptors_t &, PoolingWorkspace_t &, size_t imgHeight, size_t imgWidth, size_t fltHeight, size_t fltWidth, size_t strideRows, size_t strideCols, size_t nLocalViews)TMVA::DNN::TCpu< AReal >static
MeanSquaredError(const Matrix_t &Y, const Matrix_t &output, const Matrix_t &weights)TMVA::DNN::TCpu< AReal >static
MeanSquaredErrorGradients(Matrix_t &dY, const Matrix_t &Y, const Matrix_t &output, const Matrix_t &weights)TMVA::DNN::TCpu< AReal >static
Multiply(Matrix_t &C, const Matrix_t &A, const Matrix_t &B)TMVA::DNN::TCpu< AReal >static
MultiplyTranspose(Matrix_t &output, const Matrix_t &input, const Matrix_t &weights)TMVA::DNN::TCpu< AReal >static
MultiplyTranspose(Tensor_t &output, const Tensor_t &input, const Matrix_t &weights)TMVA::DNN::TCpu< AReal >inlinestatic
PoolingDescriptor_t typedefTMVA::DNN::TCpu< AReal >
PoolingDescriptors_t typedefTMVA::DNN::TCpu< AReal >
PoolingLayer_t typedefTMVA::DNN::TCpu< AReal >
PoolingWorkspace_t typedefTMVA::DNN::TCpu< AReal >
PrepareInternals(Tensor_t &)TMVA::DNN::TCpu< AReal >inlinestatic
PrintTensor(const Tensor_t &A, const std::string name="Cpu-tensor", bool truncate=false)TMVA::DNN::TCpu< AReal >static
Rearrange(Tensor_t &out, const Tensor_t &in)TMVA::DNN::TCpu< AReal >static
ReciprocalElementWise(Matrix_t &A)TMVA::DNN::TCpu< AReal >static
RecurrentDescriptor_t typedefTMVA::DNN::TCpu< AReal >
RecurrentLayerBackward(Matrix_t &state_gradients_backward, Matrix_t &input_weight_gradients, Matrix_t &state_weight_gradients, Matrix_t &bias_gradients, Matrix_t &df, const Matrix_t &state, const Matrix_t &weights_input, const Matrix_t &weights_state, const Matrix_t &input, Matrix_t &input_gradient)TMVA::DNN::TCpu< AReal >static
ReduceTensorDescriptor_t typedefTMVA::DNN::TCpu< AReal >
ReleaseBNormDescriptors(TDescriptors *&)TMVA::DNN::TCpu< AReal >inlinestatic
ReleaseConvDescriptors(TDescriptors *&)TMVA::DNN::TCpu< AReal >inlinestatic
ReleaseDescriptor(ActivationDescriptor_t &)TMVA::DNN::TCpu< AReal >inlinestatic
ReleasePoolDescriptors(TDescriptors *&)TMVA::DNN::TCpu< AReal >inlinestatic
ReleaseRNNDescriptors(TDescriptors *&)TMVA::DNN::TCpu< AReal >inlinestatic
Relu(Tensor_t &B)TMVA::DNN::TCpu< AReal >static
ReluDerivative(Tensor_t &B, const Tensor_t &A)TMVA::DNN::TCpu< AReal >static
Reshape(Matrix_t &A, const Matrix_t &B)TMVA::DNN::TCpu< AReal >static
RNNBackward(const Tensor_t &, const Matrix_t &, const Matrix_t &, const Tensor_t &, const Tensor_t &, const Matrix_t &, const Matrix_t &, const Tensor_t &, Tensor_t &, Matrix_t &, Matrix_t &, Tensor_t &, const RNNDescriptors_t &, RNNWorkspace_t &)TMVA::DNN::TCpu< AReal >inlinestatic
RNNDescriptors_t typedefTMVA::DNN::TCpu< AReal >
RNNForward(const Tensor_t &, const Matrix_t &, const Matrix_t &, const Matrix_t &, Tensor_t &, Matrix_t &, Matrix_t &, const RNNDescriptors_t &, RNNWorkspace_t &, bool)TMVA::DNN::TCpu< AReal >inlinestatic
RNNWorkspace_t typedefTMVA::DNN::TCpu< AReal >
RotateWeights(Matrix_t &A, const Matrix_t &B, size_t filterDepth, size_t filterHeight, size_t filterWidth, size_t numFilters)TMVA::DNN::TCpu< AReal >static
Scalar_t typedefTMVA::DNN::TCpu< AReal >
ScaleAdd(Matrix_t &A, const Matrix_t &B, Scalar_t beta=1.0)TMVA::DNN::TCpu< AReal >static
ScaleAdd(Tensor_t &A, const Tensor_t &B, Scalar_t beta=1.0)TMVA::DNN::TCpu< AReal >static
SetRandomSeed(size_t seed)TMVA::DNN::TCpu< AReal >static
Sigmoid(Tensor_t &B)TMVA::DNN::TCpu< AReal >static
Sigmoid(Matrix_t &YHat, const Matrix_t &)TMVA::DNN::TCpu< AReal >static
SigmoidDerivative(Tensor_t &B, const Tensor_t &A)TMVA::DNN::TCpu< AReal >static
Softmax(Matrix_t &YHat, const Matrix_t &)TMVA::DNN::TCpu< AReal >static
SoftmaxCrossEntropy(const Matrix_t &Y, const Matrix_t &output, const Matrix_t &weights)TMVA::DNN::TCpu< AReal >static
SoftmaxCrossEntropyGradients(Matrix_t &dY, const Matrix_t &Y, const Matrix_t &output, const Matrix_t &weights)TMVA::DNN::TCpu< AReal >static
SoftSign(Tensor_t &B)TMVA::DNN::TCpu< AReal >static
SoftSignDerivative(Tensor_t &B, const Tensor_t &A)TMVA::DNN::TCpu< AReal >static
SqrtElementWise(Matrix_t &A)TMVA::DNN::TCpu< AReal >static
SquareElementWise(Matrix_t &A)TMVA::DNN::TCpu< AReal >static
Sum(const Matrix_t &A)TMVA::DNN::TCpu< AReal >static
SumColumns(Matrix_t &B, const Matrix_t &A, Scalar_t alpha=1.0, Scalar_t beta=0.)TMVA::DNN::TCpu< AReal >static
SymmetricRelu(Tensor_t &B)TMVA::DNN::TCpu< AReal >static
SymmetricReluDerivative(Tensor_t &B, const Tensor_t &A)TMVA::DNN::TCpu< AReal >static
Tanh(Tensor_t &B)TMVA::DNN::TCpu< AReal >static
TanhDerivative(Tensor_t &B, const Tensor_t &A)TMVA::DNN::TCpu< AReal >static
Tensor_t typedefTMVA::DNN::TCpu< AReal >
TensorDescriptor_t typedefTMVA::DNN::TCpu< AReal >
TransposeMultiply(Matrix_t &output, const Matrix_t &input, const Matrix_t &Weights, Scalar_t alpha=1.0, Scalar_t beta=0.)TMVA::DNN::TCpu< AReal >static