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Reference Guide |
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Reference Guide |
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This is the complete list of members for TMVA::DNN::TReference< AReal >, including all inherited members.
| AdamUpdate(TMatrixT< AReal > &A, const TMatrixT< AReal > &M, const TMatrixT< AReal > &V, AReal alpha, AReal eps) | TMVA::DNN::TReference< AReal > | static |
| AdamUpdateFirstMom(TMatrixT< AReal > &A, const TMatrixT< AReal > &B, AReal beta) | TMVA::DNN::TReference< AReal > | static |
| AdamUpdateSecondMom(TMatrixT< AReal > &A, const TMatrixT< AReal > &B, AReal beta) | TMVA::DNN::TReference< AReal > | static |
| AddBiases(TMatrixT< AReal > &A, const TMatrixT< AReal > &biases) | TMVA::DNN::TReference< AReal > | static |
| AddConvBiases(TMatrixT< AReal > &output, const TMatrixT< AReal > &biases) | TMVA::DNN::TReference< AReal > | static |
| AddL1RegularizationGradients(TMatrixT< AReal > &A, const TMatrixT< AReal > &W, AReal weightDecay) | TMVA::DNN::TReference< AReal > | static |
| AddL2RegularizationGradients(TMatrixT< AReal > &A, const TMatrixT< AReal > &W, AReal weightDecay) | TMVA::DNN::TReference< AReal > | static |
| AddRowWise(TMatrixT< Scalar_t > &output, const TMatrixT< Scalar_t > &biases) | TMVA::DNN::TReference< AReal > | static |
| Backward(TMatrixT< Scalar_t > &activationGradientsBackward, TMatrixT< Scalar_t > &weightGradients, TMatrixT< Scalar_t > &biasGradients, TMatrixT< Scalar_t > &df, const TMatrixT< Scalar_t > &activationGradients, const TMatrixT< Scalar_t > &weights, const TMatrixT< Scalar_t > &activationBackward) | TMVA::DNN::TReference< AReal > | static |
| ConstAdd(TMatrixT< AReal > &A, AReal beta) | TMVA::DNN::TReference< AReal > | static |
| ConstMult(TMatrixT< AReal > &A, AReal beta) | TMVA::DNN::TReference< AReal > | static |
| ConvLayerBackward(std::vector< TMatrixT< AReal > > &, TMatrixT< AReal > &, TMatrixT< AReal > &, std::vector< TMatrixT< AReal > > &, const std::vector< TMatrixT< AReal > > &, const TMatrixT< AReal > &, const std::vector< TMatrixT< AReal > > &, size_t, size_t, size_t, size_t, size_t, size_t, size_t, size_t, size_t, size_t) | TMVA::DNN::TReference< AReal > | inlinestatic |
| ConvLayerForward(std::vector< TMatrixT< AReal > > &, std::vector< TMatrixT< AReal > > &, const std::vector< TMatrixT< AReal > > &, const TMatrixT< AReal > &, const TMatrixT< AReal > &, const DNN::CNN::TConvParams &, EActivationFunction, std::vector< TMatrixT< AReal > > &) | TMVA::DNN::TReference< AReal > | inlinestatic |
| Copy(TMatrixT< Scalar_t > &A, const TMatrixT< Scalar_t > &B) | TMVA::DNN::TReference< AReal > | static |
| Copy(std::vector< TMatrixT< Scalar_t > > &A, const std::vector< TMatrixT< Scalar_t > > &B) | TMVA::DNN::TReference< AReal > | static |
| CopyDiffArch(TMatrixT< Scalar_t > &A, const AMatrix_t &B) | TMVA::DNN::TReference< AReal > | static |
| CopyDiffArch(std::vector< TMatrixT< Scalar_t > > &A, const std::vector< AMatrix_t > &B) | TMVA::DNN::TReference< AReal > | static |
| CorruptInput(TMatrixT< AReal > &input, TMatrixT< AReal > &corruptedInput, AReal corruptionLevel) | TMVA::DNN::TReference< AReal > | static |
| CrossEntropy(const TMatrixT< AReal > &Y, const TMatrixT< AReal > &output, const TMatrixT< AReal > &weights) | TMVA::DNN::TReference< AReal > | static |
| CrossEntropyGradients(TMatrixT< AReal > &dY, const TMatrixT< AReal > &Y, const TMatrixT< AReal > &output, const TMatrixT< AReal > &weights) | TMVA::DNN::TReference< AReal > | static |
| Deflatten(std::vector< TMatrixT< AReal > > &A, const TMatrixT< Scalar_t > &B, size_t index, size_t nRows, size_t nCols) | TMVA::DNN::TReference< AReal > | static |
| Downsample(TMatrixT< AReal > &A, TMatrixT< AReal > &B, const TMatrixT< AReal > &C, size_t imgHeight, size_t imgWidth, size_t fltHeight, size_t fltWidth, size_t strideRows, size_t strideCols) | TMVA::DNN::TReference< AReal > | static |
| DropoutForward(Tensor_t &A, TDescriptors *descriptors, TWorkspace *workspace, Scalar_t p) | TMVA::DNN::TReference< AReal > | static |
| DropoutForward(Matrix_t &A, Scalar_t p) | TMVA::DNN::TReference< AReal > | inlinestatic |
| EncodeInput(TMatrixT< AReal > &input, TMatrixT< AReal > &compressedInput, TMatrixT< AReal > &Weights) | TMVA::DNN::TReference< AReal > | static |
| FastTanh(Tensor_t &B) | TMVA::DNN::TReference< AReal > | inlinestatic |
| FastTanhDerivative(Tensor_t &B, const Tensor_t &A) | TMVA::DNN::TReference< AReal > | inlinestatic |
| fgRandomGen | TMVA::DNN::TReference< AReal > | privatestatic |
| Flatten(TMatrixT< AReal > &A, const std::vector< TMatrixT< AReal > > &B, size_t size, size_t nRows, size_t nCols) | TMVA::DNN::TReference< AReal > | static |
| ForwardLogReg(TMatrixT< AReal > &input, TMatrixT< AReal > &p, TMatrixT< AReal > &fWeights) | TMVA::DNN::TReference< AReal > | static |
| Gauss(TMatrixT< AReal > &B) | TMVA::DNN::TReference< AReal > | inlinestatic |
| GaussDerivative(TMatrixT< AReal > &B, const TMatrixT< AReal > &A) | TMVA::DNN::TReference< AReal > | inlinestatic |
| GetRandomGenerator() | TMVA::DNN::TReference< AReal > | static |
| GRULayerBackward(TMatrixT< Scalar_t > &state_gradients_backward, TMatrixT< Scalar_t > &reset_weight_gradients, TMatrixT< Scalar_t > &update_weight_gradients, TMatrixT< Scalar_t > &candidate_weight_gradients, TMatrixT< Scalar_t > &reset_state_weight_gradients, TMatrixT< Scalar_t > &update_state_weight_gradients, TMatrixT< Scalar_t > &candidate_state_weight_gradients, TMatrixT< Scalar_t > &reset_bias_gradients, TMatrixT< Scalar_t > &update_bias_gradients, TMatrixT< Scalar_t > &candidate_bias_gradients, TMatrixT< Scalar_t > &dr, TMatrixT< Scalar_t > &du, TMatrixT< Scalar_t > &dc, const TMatrixT< Scalar_t > &precStateActivations, const TMatrixT< Scalar_t > &fReset, const TMatrixT< Scalar_t > &fUpdate, const TMatrixT< Scalar_t > &fCandidate, const TMatrixT< Scalar_t > &weights_reset, const TMatrixT< Scalar_t > &weights_update, const TMatrixT< Scalar_t > &weights_candidate, const TMatrixT< Scalar_t > &weights_reset_state, const TMatrixT< Scalar_t > &weights_update_state, const TMatrixT< Scalar_t > &weights_candidate_state, const TMatrixT< Scalar_t > &input, TMatrixT< Scalar_t > &input_gradient) | TMVA::DNN::TReference< AReal > | static |
| Hadamard(TMatrixT< AReal > &A, const TMatrixT< AReal > &B) | TMVA::DNN::TReference< AReal > | static |
| Identity(TMatrixT< AReal > &B) | TMVA::DNN::TReference< AReal > | static |
| IdentityDerivative(TMatrixT< AReal > &B, const TMatrixT< AReal > &A) | TMVA::DNN::TReference< AReal > | static |
| Im2col(TMatrixT< AReal > &A, const TMatrixT< AReal > &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::TReference< AReal > | static |
| Im2colFast(TMatrixT< AReal > &, const TMatrixT< AReal > &, const std::vector< int > &) | TMVA::DNN::TReference< AReal > | inlinestatic |
| Im2colIndices(std::vector< int > &, const TMatrixT< AReal > &, size_t, size_t, size_t, size_t, size_t, size_t, size_t, size_t, size_t) | TMVA::DNN::TReference< AReal > | inlinestatic |
| InitializeGauss(TMatrixT< AReal > &A) | TMVA::DNN::TReference< AReal > | static |
| InitializeGlorotNormal(TMatrixT< AReal > &A) | TMVA::DNN::TReference< AReal > | static |
| InitializeGlorotUniform(TMatrixT< AReal > &A) | TMVA::DNN::TReference< AReal > | static |
| InitializeIdentity(TMatrixT< AReal > &A) | TMVA::DNN::TReference< AReal > | static |
| InitializeUniform(TMatrixT< AReal > &A) | TMVA::DNN::TReference< AReal > | static |
| InitializeZero(TMatrixT< AReal > &A) | TMVA::DNN::TReference< AReal > | static |
| L1Regularization(const TMatrixT< AReal > &W) | TMVA::DNN::TReference< AReal > | static |
| L2Regularization(const TMatrixT< AReal > &W) | TMVA::DNN::TReference< AReal > | static |
| LSTMLayerBackward(TMatrixT< Scalar_t > &state_gradients_backward, TMatrixT< Scalar_t > &cell_gradients_backward, TMatrixT< Scalar_t > &input_weight_gradients, TMatrixT< Scalar_t > &forget_weight_gradients, TMatrixT< Scalar_t > &candidate_weight_gradients, TMatrixT< Scalar_t > &output_weight_gradients, TMatrixT< Scalar_t > &input_state_weight_gradients, TMatrixT< Scalar_t > &forget_state_weight_gradients, TMatrixT< Scalar_t > &candidate_state_weight_gradients, TMatrixT< Scalar_t > &output_state_weight_gradients, TMatrixT< Scalar_t > &input_bias_gradients, TMatrixT< Scalar_t > &forget_bias_gradients, TMatrixT< Scalar_t > &candidate_bias_gradients, TMatrixT< Scalar_t > &output_bias_gradients, TMatrixT< Scalar_t > &di, TMatrixT< Scalar_t > &df, TMatrixT< Scalar_t > &dc, TMatrixT< Scalar_t > &dout, const TMatrixT< Scalar_t > &precStateActivations, const TMatrixT< Scalar_t > &precCellActivations, const TMatrixT< Scalar_t > &fInput, const TMatrixT< Scalar_t > &fForget, const TMatrixT< Scalar_t > &fCandidate, const TMatrixT< Scalar_t > &fOutput, const TMatrixT< Scalar_t > &weights_input, const TMatrixT< Scalar_t > &weights_forget, const TMatrixT< Scalar_t > &weights_candidate, const TMatrixT< Scalar_t > &weights_output, const TMatrixT< Scalar_t > &weights_input_state, const TMatrixT< Scalar_t > &weights_forget_state, const TMatrixT< Scalar_t > &weights_candidate_state, const TMatrixT< Scalar_t > &weights_output_state, const TMatrixT< Scalar_t > &input, TMatrixT< Scalar_t > &input_gradient, TMatrixT< Scalar_t > &cell_gradient, TMatrixT< Scalar_t > &cell_tanh) | TMVA::DNN::TReference< AReal > | static |
| Matrix_t typedef | TMVA::DNN::TReference< AReal > | |
| MaxPoolLayerBackward(TMatrixT< AReal > &activationGradientsBackward, const TMatrixT< AReal > &activationGradients, const TMatrixT< AReal > &indexMatrix, size_t imgHeight, size_t imgWidth, size_t fltHeight, size_t fltWidth, size_t strideRows, size_t strideCol, size_t nLocalViews) | TMVA::DNN::TReference< AReal > | static |
| MeanSquaredError(const TMatrixT< AReal > &Y, const TMatrixT< AReal > &output, const TMatrixT< AReal > &weights) | TMVA::DNN::TReference< AReal > | static |
| MeanSquaredErrorGradients(TMatrixT< AReal > &dY, const TMatrixT< AReal > &Y, const TMatrixT< AReal > &output, const TMatrixT< AReal > &weights) | TMVA::DNN::TReference< AReal > | static |
| MultiplyTranspose(TMatrixT< Scalar_t > &output, const TMatrixT< Scalar_t > &input, const TMatrixT< Scalar_t > &weights) | TMVA::DNN::TReference< AReal > | static |
| PrepareInternals(std::vector< TMatrixT< AReal > > &) | TMVA::DNN::TReference< AReal > | inlinestatic |
| Rearrange(std::vector< TMatrixT< AReal > > &out, const std::vector< TMatrixT< AReal > > &in) | TMVA::DNN::TReference< AReal > | static |
| ReciprocalElementWise(TMatrixT< AReal > &A) | TMVA::DNN::TReference< AReal > | static |
| ReconstructInput(TMatrixT< AReal > &compressedInput, TMatrixT< AReal > &reconstructedInput, TMatrixT< AReal > &fWeights) | TMVA::DNN::TReference< AReal > | static |
| RecurrentLayerBackward(TMatrixT< Scalar_t > &state_gradients_backward, TMatrixT< Scalar_t > &input_weight_gradients, TMatrixT< Scalar_t > &state_weight_gradients, TMatrixT< Scalar_t > &bias_gradients, TMatrixT< Scalar_t > &df, const TMatrixT< Scalar_t > &state, const TMatrixT< Scalar_t > &weights_input, const TMatrixT< Scalar_t > &weights_state, const TMatrixT< Scalar_t > &input, TMatrixT< Scalar_t > &input_gradient) | TMVA::DNN::TReference< AReal > | static |
| Relu(TMatrixT< AReal > &B) | TMVA::DNN::TReference< AReal > | static |
| ReluDerivative(TMatrixT< AReal > &B, const TMatrixT< AReal > &A) | TMVA::DNN::TReference< AReal > | inlinestatic |
| Reshape(TMatrixT< AReal > &A, const TMatrixT< AReal > &B) | TMVA::DNN::TReference< AReal > | static |
| RotateWeights(TMatrixT< AReal > &A, const TMatrixT< AReal > &B, size_t filterDepth, size_t filterHeight, size_t filterWidth, size_t numFilters) | TMVA::DNN::TReference< AReal > | static |
| Scalar_t typedef | TMVA::DNN::TReference< AReal > | |
| ScaleAdd(TMatrixT< Scalar_t > &A, const TMatrixT< Scalar_t > &B, Scalar_t beta=1.0) | TMVA::DNN::TReference< AReal > | static |
| ScaleAdd(std::vector< TMatrixT< Scalar_t > > &A, const std::vector< TMatrixT< Scalar_t > > &B, Scalar_t beta=1.0) | TMVA::DNN::TReference< AReal > | static |
| SetRandomSeed(size_t seed) | TMVA::DNN::TReference< AReal > | static |
| Sigmoid(TMatrixT< AReal > &B) | TMVA::DNN::TReference< AReal > | static |
| Sigmoid(TMatrixT< AReal > &YHat, const TMatrixT< AReal > &) | TMVA::DNN::TReference< AReal > | static |
| SigmoidDerivative(TMatrixT< AReal > &B, const TMatrixT< AReal > &A) | TMVA::DNN::TReference< AReal > | inlinestatic |
| Softmax(TMatrixT< AReal > &YHat, const TMatrixT< AReal > &) | TMVA::DNN::TReference< AReal > | static |
| SoftmaxAE(TMatrixT< AReal > &A) | TMVA::DNN::TReference< AReal > | static |
| SoftmaxCrossEntropy(const TMatrixT< AReal > &Y, const TMatrixT< AReal > &output, const TMatrixT< AReal > &weights) | TMVA::DNN::TReference< AReal > | static |
| SoftmaxCrossEntropyGradients(TMatrixT< AReal > &dY, const TMatrixT< AReal > &Y, const TMatrixT< AReal > &output, const TMatrixT< AReal > &weights) | TMVA::DNN::TReference< AReal > | static |
| SoftSign(TMatrixT< AReal > &B) | TMVA::DNN::TReference< AReal > | inlinestatic |
| SoftSignDerivative(TMatrixT< AReal > &B, const TMatrixT< AReal > &A) | TMVA::DNN::TReference< AReal > | inlinestatic |
| SqrtElementWise(TMatrixT< AReal > &A) | TMVA::DNN::TReference< AReal > | static |
| SquareElementWise(TMatrixT< AReal > &A) | TMVA::DNN::TReference< AReal > | static |
| SumColumns(TMatrixT< AReal > &B, const TMatrixT< AReal > &A) | TMVA::DNN::TReference< AReal > | static |
| SymmetricRelu(TMatrixT< AReal > &B) | TMVA::DNN::TReference< AReal > | inlinestatic |
| SymmetricReluDerivative(TMatrixT< AReal > &B, const TMatrixT< AReal > &A) | TMVA::DNN::TReference< AReal > | inlinestatic |
| Tanh(TMatrixT< AReal > &B) | TMVA::DNN::TReference< AReal > | inlinestatic |
| TanhDerivative(TMatrixT< AReal > &B, const TMatrixT< AReal > &A) | TMVA::DNN::TReference< AReal > | inlinestatic |
| Tensor_t typedef | TMVA::DNN::TReference< AReal > | |
| UpdateParams(TMatrixT< AReal > &x, TMatrixT< AReal > &tildeX, TMatrixT< AReal > &y, TMatrixT< AReal > &z, TMatrixT< AReal > &fVBiases, TMatrixT< AReal > &fHBiases, TMatrixT< AReal > &fWeights, TMatrixT< AReal > &VBiasError, TMatrixT< AReal > &HBiasError, AReal learningRate, size_t fBatchSize) | TMVA::DNN::TReference< AReal > | static |
| UpdateParamsLogReg(TMatrixT< AReal > &input, TMatrixT< AReal > &output, TMatrixT< AReal > &difference, TMatrixT< AReal > &p, TMatrixT< AReal > &fWeights, TMatrixT< AReal > &fBiases, AReal learningRate, size_t fBatchSize) | TMVA::DNN::TReference< AReal > | static |
