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ROOT 6.14/05 Reference Guide |
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ROOT 6.14/05 Reference Guide |
This is the complete list of members for TMVA::DNN::TCpu< AReal >, including all inherited members.
| AddConvBiases(TCpuMatrix< Scalar_t > &output, const TCpuMatrix< Scalar_t > &biases) | TMVA::DNN::TCpu< AReal > | static |
| AddL1RegularizationGradients(TCpuMatrix< Scalar_t > &A, const TCpuMatrix< Scalar_t > &W, Scalar_t weightDecay) | TMVA::DNN::TCpu< AReal > | static |
| AddL2RegularizationGradients(TCpuMatrix< Scalar_t > &A, const TCpuMatrix< Scalar_t > &W, Scalar_t weightDecay) | TMVA::DNN::TCpu< AReal > | static |
| AddRowWise(TCpuMatrix< Scalar_t > &output, const TCpuMatrix< Scalar_t > &biases) | TMVA::DNN::TCpu< AReal > | static |
| Backward(TCpuMatrix< Scalar_t > &activationGradientsBackward, TCpuMatrix< Scalar_t > &weightGradients, TCpuMatrix< Scalar_t > &biasGradients, TCpuMatrix< Scalar_t > &df, const TCpuMatrix< Scalar_t > &activationGradients, const TCpuMatrix< Scalar_t > &weights, const TCpuMatrix< Scalar_t > &activationBackward) | TMVA::DNN::TCpu< AReal > | static |
| CalculateConvActivationGradients(std::vector< TCpuMatrix< Scalar_t >> &activationGradientsBackward, const std::vector< TCpuMatrix< Scalar_t >> &df, const TCpuMatrix< Scalar_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(TCpuMatrix< Scalar_t > &biasGradients, const std::vector< TCpuMatrix< Scalar_t >> &df, size_t batchSize, size_t depth, size_t nLocalViews) | TMVA::DNN::TCpu< AReal > | static |
| CalculateConvWeightGradients(TCpuMatrix< Scalar_t > &weightGradients, const std::vector< TCpuMatrix< Scalar_t >> &df, const std::vector< TCpuMatrix< Scalar_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 |
| ConvLayerBackward(std::vector< TCpuMatrix< Scalar_t >> &activationGradientsBackward, TCpuMatrix< Scalar_t > &weightGradients, TCpuMatrix< Scalar_t > &biasGradients, std::vector< TCpuMatrix< Scalar_t >> &df, const std::vector< TCpuMatrix< Scalar_t >> &activationGradients, const TCpuMatrix< Scalar_t > &weights, const std::vector< TCpuMatrix< Scalar_t >> &activationBackward, 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(std::vector< TCpuMatrix< Scalar_t >> &output, std::vector< TCpuMatrix< Scalar_t >> &derivatives, const std::vector< TCpuMatrix< Scalar_t >> &input, const TCpuMatrix< Scalar_t > &weights, const TCpuMatrix< Scalar_t > &biases, EActivationFunction func, const std::vector< int > &vIndices, size_t nlocalViews, size_t nlocalViewPixels, Scalar_t dropoutProbability, bool applyDropout) | TMVA::DNN::TCpu< AReal > | static |
| Copy(TCpuMatrix< Scalar_t > &B, const TCpuMatrix< Scalar_t > &A) | TMVA::DNN::TCpu< AReal > | static |
| Copy(std::vector< TCpuMatrix< Scalar_t >> &A, const std::vector< TCpuMatrix< Scalar_t >> &B) | TMVA::DNN::TCpu< AReal > | static |
| CopyDiffArch(TCpuMatrix< Scalar_t > &B, const AMatrix_t &A) | TMVA::DNN::TCpu< AReal > | static |
| CopyDiffArch(std::vector< TCpuMatrix< Scalar_t >> &A, const std::vector< AMatrix_t > &B) | TMVA::DNN::TCpu< AReal > | static |
| CopyDiffArch(TCpuMatrix< Real_t > &B, const AMatrix_t &A) | TMVA::DNN::TCpu< AReal > | |
| CopyDiffArch(std::vector< TCpuMatrix< Real_t >> &B, const std::vector< AMatrix_t > &A) | TMVA::DNN::TCpu< AReal > | |
| CrossEntropy(const TCpuMatrix< Scalar_t > &Y, const TCpuMatrix< Scalar_t > &output, const TCpuMatrix< Scalar_t > &weights) | TMVA::DNN::TCpu< AReal > | static |
| CrossEntropyGradients(TCpuMatrix< Scalar_t > &dY, const TCpuMatrix< Scalar_t > &Y, const TCpuMatrix< Scalar_t > &output, const TCpuMatrix< Scalar_t > &weights) | TMVA::DNN::TCpu< AReal > | static |
| Deflatten(std::vector< TCpuMatrix< AReal >> &A, const TCpuMatrix< AReal > &B, size_t index, size_t nRows, size_t nCols) | TMVA::DNN::TCpu< AReal > | static |
| DeviceBuffer_t typedef | TMVA::DNN::TCpu< AReal > | |
| Downsample(TCpuMatrix< AReal > &A, TCpuMatrix< AReal > &B, const TCpuMatrix< AReal > &C, size_t imgHeight, size_t imgWidth, size_t fltHeight, size_t fltWidth, size_t strideRows, size_t strideCols) | TMVA::DNN::TCpu< AReal > | static |
| Dropout(TCpuMatrix< Scalar_t > &A, Scalar_t p) | TMVA::DNN::TCpu< AReal > | static |
| fgRandomGen | TMVA::DNN::TCpu< AReal > | privatestatic |
| Flatten(TCpuMatrix< AReal > &A, const std::vector< TCpuMatrix< AReal >> &B, size_t size, size_t nRows, size_t nCols) | TMVA::DNN::TCpu< AReal > | static |
| Gauss(TCpuMatrix< Scalar_t > &B) | TMVA::DNN::TCpu< AReal > | static |
| GaussDerivative(TCpuMatrix< Scalar_t > &B, const TCpuMatrix< Scalar_t > &A) | TMVA::DNN::TCpu< AReal > | static |
| GetRandomGenerator() | TMVA::DNN::TCpu< AReal > | static |
| Hadamard(TCpuMatrix< Scalar_t > &A, const TCpuMatrix< Scalar_t > &B) | TMVA::DNN::TCpu< AReal > | static |
| HostBuffer_t typedef | TMVA::DNN::TCpu< AReal > | |
| IdentityDerivative(TCpuMatrix< Scalar_t > &B, const TCpuMatrix< Scalar_t > &A) | TMVA::DNN::TCpu< AReal > | static |
| Im2col(TCpuMatrix< AReal > &A, const TCpuMatrix< 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::TCpu< AReal > | static |
| Im2colFast(TCpuMatrix< AReal > &A, const TCpuMatrix< AReal > &B, const std::vector< int > &V) | TMVA::DNN::TCpu< AReal > | static |
| Im2colIndices(std::vector< int > &V, const TCpuMatrix< AReal > &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 |
| InitializeGauss(TCpuMatrix< Scalar_t > &A) | TMVA::DNN::TCpu< AReal > | static |
| InitializeGlorotNormal(TCpuMatrix< Scalar_t > &A) | TMVA::DNN::TCpu< AReal > | static |
| InitializeGlorotUniform(TCpuMatrix< Scalar_t > &A) | TMVA::DNN::TCpu< AReal > | static |
| InitializeIdentity(TCpuMatrix< Scalar_t > &A) | TMVA::DNN::TCpu< AReal > | static |
| InitializeUniform(TCpuMatrix< Scalar_t > &A) | TMVA::DNN::TCpu< AReal > | static |
| InitializeZero(TCpuMatrix< Scalar_t > &A) | TMVA::DNN::TCpu< AReal > | static |
| L1Regularization(const TCpuMatrix< Scalar_t > &W) | TMVA::DNN::TCpu< AReal > | static |
| L2Regularization(const TCpuMatrix< Scalar_t > &W) | TMVA::DNN::TCpu< AReal > | static |
| Matrix_t typedef | TMVA::DNN::TCpu< AReal > | |
| MaxPoolLayerBackward(std::vector< TCpuMatrix< AReal >> &activationGradientsBackward, const std::vector< TCpuMatrix< AReal >> &activationGradients, const std::vector< TCpuMatrix< AReal >> &indexMatrix, size_t batchSize, size_t depth, size_t nLocalViews) | TMVA::DNN::TCpu< AReal > | static |
| MeanSquaredError(const TCpuMatrix< Scalar_t > &Y, const TCpuMatrix< Scalar_t > &output, const TCpuMatrix< Scalar_t > &weights) | TMVA::DNN::TCpu< AReal > | static |
| MeanSquaredErrorGradients(TCpuMatrix< Scalar_t > &dY, const TCpuMatrix< Scalar_t > &Y, const TCpuMatrix< Scalar_t > &output, const TCpuMatrix< Scalar_t > &weights) | TMVA::DNN::TCpu< AReal > | static |
| Multiply(TCpuMatrix< Scalar_t > &C, const TCpuMatrix< Scalar_t > &A, const TCpuMatrix< Scalar_t > &B) | TMVA::DNN::TCpu< AReal > | static |
| MultiplyTranspose(TCpuMatrix< Scalar_t > &output, const TCpuMatrix< Scalar_t > &input, const TCpuMatrix< Scalar_t > &weights) | TMVA::DNN::TCpu< AReal > | static |
| Rearrange(std::vector< TCpuMatrix< AReal >> &out, const std::vector< TCpuMatrix< AReal >> &in) | TMVA::DNN::TCpu< AReal > | static |
| RecurrentLayerBackward(TCpuMatrix< Scalar_t > &state_gradients_backward, TCpuMatrix< Scalar_t > &input_weight_gradients, TCpuMatrix< Scalar_t > &state_weight_gradients, TCpuMatrix< Scalar_t > &bias_gradients, TCpuMatrix< Scalar_t > &df, const TCpuMatrix< Scalar_t > &state, const TCpuMatrix< Scalar_t > &weights_input, const TCpuMatrix< Scalar_t > &weights_state, const TCpuMatrix< Scalar_t > &input, TCpuMatrix< Scalar_t > &input_gradient) | TMVA::DNN::TCpu< AReal > | static |
| Relu(TCpuMatrix< Scalar_t > &B) | TMVA::DNN::TCpu< AReal > | static |
| ReluDerivative(TCpuMatrix< Scalar_t > &B, const TCpuMatrix< Scalar_t > &A) | TMVA::DNN::TCpu< AReal > | static |
| Reshape(TCpuMatrix< AReal > &A, const TCpuMatrix< AReal > &B) | TMVA::DNN::TCpu< AReal > | static |
| RotateWeights(TCpuMatrix< AReal > &A, const TCpuMatrix< AReal > &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(TCpuMatrix< Scalar_t > &A, const TCpuMatrix< Scalar_t > &B, Scalar_t beta=1.0) | TMVA::DNN::TCpu< AReal > | static |
| ScaleAdd(std::vector< TCpuMatrix< Scalar_t >> &A, const std::vector< TCpuMatrix< Scalar_t >> &B, Scalar_t beta=1.0) | TMVA::DNN::TCpu< AReal > | static |
| SetRandomSeed(size_t seed) | TMVA::DNN::TCpu< AReal > | static |
| Sigmoid(TCpuMatrix< Scalar_t > &B) | TMVA::DNN::TCpu< AReal > | static |
| Sigmoid(TCpuMatrix< Scalar_t > &YHat, const TCpuMatrix< Scalar_t > &) | TMVA::DNN::TCpu< AReal > | static |
| SigmoidDerivative(TCpuMatrix< Scalar_t > &B, const TCpuMatrix< Scalar_t > &A) | TMVA::DNN::TCpu< AReal > | static |
| Softmax(TCpuMatrix< Scalar_t > &YHat, const TCpuMatrix< Scalar_t > &) | TMVA::DNN::TCpu< AReal > | static |
| SoftmaxCrossEntropy(const TCpuMatrix< Scalar_t > &Y, const TCpuMatrix< Scalar_t > &output, const TCpuMatrix< Scalar_t > &weights) | TMVA::DNN::TCpu< AReal > | static |
| SoftmaxCrossEntropyGradients(TCpuMatrix< Scalar_t > &dY, const TCpuMatrix< Scalar_t > &Y, const TCpuMatrix< Scalar_t > &output, const TCpuMatrix< Scalar_t > &weights) | TMVA::DNN::TCpu< AReal > | static |
| SoftSign(TCpuMatrix< Scalar_t > &B) | TMVA::DNN::TCpu< AReal > | static |
| SoftSignDerivative(TCpuMatrix< Scalar_t > &B, const TCpuMatrix< Scalar_t > &A) | TMVA::DNN::TCpu< AReal > | static |
| Sum(const TCpuMatrix< Scalar_t > &A) | TMVA::DNN::TCpu< AReal > | static |
| SumColumns(TCpuMatrix< Scalar_t > &B, const TCpuMatrix< Scalar_t > &A, Scalar_t alpha=1.0, Scalar_t beta=0.) | TMVA::DNN::TCpu< AReal > | static |
| SymmetricRelu(TCpuMatrix< Scalar_t > &B) | TMVA::DNN::TCpu< AReal > | static |
| SymmetricReluDerivative(TCpuMatrix< Scalar_t > &B, const TCpuMatrix< Scalar_t > &A) | TMVA::DNN::TCpu< AReal > | static |
| Tanh(TCpuMatrix< Scalar_t > &B) | TMVA::DNN::TCpu< AReal > | static |
| TanhDerivative(TCpuMatrix< Scalar_t > &B, const TCpuMatrix< Scalar_t > &A) | TMVA::DNN::TCpu< AReal > | static |
| TransposeMultiply(TCpuMatrix< Scalar_t > &output, const TCpuMatrix< Scalar_t > &input, const TCpuMatrix< Scalar_t > &Weights, Scalar_t alpha=1.0, Scalar_t beta=0.) | TMVA::DNN::TCpu< AReal > | static |
ROOT 6.14/05 - Reference Guide Generated on Fri Nov 2 2018 11:01:25 (GVA Time) using Doxygen 1.8.13.