This is the complete list of members for TMVA::DNN::TCpu< AReal >, including all inherited members.
ActivationDescriptor_t typedef | TMVA::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 typedef | TMVA::DNN::TCpu< AReal > | |
AlgorithmDataType_t typedef | TMVA::DNN::TCpu< AReal > | |
AlgorithmForward_t typedef | TMVA::DNN::TCpu< AReal > | |
AlgorithmHelper_t typedef | TMVA::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 typedef | TMVA::DNN::TCpu< AReal > | |
BNormLayer_t typedef | TMVA::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 typedef | TMVA::DNN::TCpu< AReal > | |
ConvLayer_t typedef | TMVA::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 ¶ms, EActivationFunction activFunc, Tensor_t &, const ConvDescriptors_t &, ConvWorkspace_t &) | TMVA::DNN::TCpu< AReal > | static |
ConvolutionDescriptor_t typedef | TMVA::DNN::TCpu< AReal > | |
ConvWorkspace_t typedef | TMVA::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 |
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 typedef | TMVA::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 typedef | TMVA::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 typedef | TMVA::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 |
fgRandomGen | TMVA::DNN::TCpu< AReal > | privatestatic |
FilterDescriptor_t typedef | TMVA::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 typedef | TMVA::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 typedef | TMVA::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 typedef | TMVA::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 typedef | TMVA::DNN::TCpu< AReal > | |
PoolingDescriptors_t typedef | TMVA::DNN::TCpu< AReal > | |
PoolingLayer_t typedef | TMVA::DNN::TCpu< AReal > | |
PoolingWorkspace_t typedef | TMVA::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 typedef | TMVA::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 typedef | TMVA::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 typedef | TMVA::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 typedef | TMVA::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 typedef | TMVA::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 typedef | TMVA::DNN::TCpu< AReal > | |
TensorDescriptor_t typedef | TMVA::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 |