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ROOT
Reference Guide |
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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 Tensor_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 |
