![]() |
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 |