template<typename AReal = Real_t>
class TMVA::DNN::TCpu< AReal >
The TCpu architecture class.
Low-level interface class for multi-threaded CPU architectures. Contains as public types the declaration of the scalar, matrix and data loader types for this architecture as well as the remaining functions in the low-level interface in the form of static members.
Definition at line 43 of file Cpu.h.
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static void | 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) |
| Forward propagation in the Convolutional layer. More...
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Low-level functions required for the forward propagation of activations through the network.
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static void | MultiplyTranspose (TCpuMatrix< Scalar_t > &output, const TCpuMatrix< Scalar_t > &input, const TCpuMatrix< Scalar_t > &weights) |
| Matrix-multiply input with the transpose of and write the results into output . More...
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static void | AddRowWise (TCpuMatrix< Scalar_t > &output, const TCpuMatrix< Scalar_t > &biases) |
| Add the vectors biases row-wise to the matrix output. More...
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Low-level functions required for the forward propagation of activations through the network.
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static void | 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) |
| Perform the complete backward propagation step. More...
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static Matrix_t & | 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) |
| Backward pass for Recurrent Networks. More...
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static void | ScaleAdd (TCpuMatrix< Scalar_t > &A, const TCpuMatrix< Scalar_t > &B, Scalar_t beta=1.0) |
| Adds a the elements in matrix B scaled by c to the elements in the matrix A. More...
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static void | Copy (TCpuMatrix< Scalar_t > &B, const TCpuMatrix< Scalar_t > &A) |
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template<typename AMatrix_t > |
static void | CopyDiffArch (TCpuMatrix< Scalar_t > &B, const AMatrix_t &A) |
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static void | ScaleAdd (std::vector< TCpuMatrix< Scalar_t >> &A, const std::vector< TCpuMatrix< Scalar_t >> &B, Scalar_t beta=1.0) |
| Above functions extended to vectors. More...
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static void | Copy (std::vector< TCpuMatrix< Scalar_t >> &A, const std::vector< TCpuMatrix< Scalar_t >> &B) |
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template<typename AMatrix_t > |
static void | CopyDiffArch (std::vector< TCpuMatrix< Scalar_t >> &A, const std::vector< AMatrix_t > &B) |
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For each activation function, the low-level interface contains two routines.
One that applies the acitvation function to a matrix and one that evaluate the derivatives of the activation function at the elements of a given matrix and writes the results into the result matrix.
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static void | IdentityDerivative (TCpuMatrix< Scalar_t > &B, const TCpuMatrix< Scalar_t > &A) |
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static void | Relu (TCpuMatrix< Scalar_t > &B) |
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static void | ReluDerivative (TCpuMatrix< Scalar_t > &B, const TCpuMatrix< Scalar_t > &A) |
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static void | Sigmoid (TCpuMatrix< Scalar_t > &B) |
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static void | SigmoidDerivative (TCpuMatrix< Scalar_t > &B, const TCpuMatrix< Scalar_t > &A) |
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static void | Tanh (TCpuMatrix< Scalar_t > &B) |
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static void | TanhDerivative (TCpuMatrix< Scalar_t > &B, const TCpuMatrix< Scalar_t > &A) |
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static void | SymmetricRelu (TCpuMatrix< Scalar_t > &B) |
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static void | SymmetricReluDerivative (TCpuMatrix< Scalar_t > &B, const TCpuMatrix< Scalar_t > &A) |
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static void | SoftSign (TCpuMatrix< Scalar_t > &B) |
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static void | SoftSignDerivative (TCpuMatrix< Scalar_t > &B, const TCpuMatrix< Scalar_t > &A) |
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static void | Gauss (TCpuMatrix< Scalar_t > &B) |
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static void | GaussDerivative (TCpuMatrix< Scalar_t > &B, const TCpuMatrix< Scalar_t > &A) |
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Loss functions compute a scalar value given the output of the network for a given training input and the expected network prediction Y that quantifies the quality of the prediction.
For each function also a routing that computes the gradients (suffixed by Gradients) must be provided for the starting of the backpropagation algorithm.
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static Scalar_t | MeanSquaredError (const TCpuMatrix< Scalar_t > &Y, const TCpuMatrix< Scalar_t > &output, const TCpuMatrix< Scalar_t > &weights) |
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static void | MeanSquaredErrorGradients (TCpuMatrix< Scalar_t > &dY, const TCpuMatrix< Scalar_t > &Y, const TCpuMatrix< Scalar_t > &output, const TCpuMatrix< Scalar_t > &weights) |
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static Scalar_t | CrossEntropy (const TCpuMatrix< Scalar_t > &Y, const TCpuMatrix< Scalar_t > &output, const TCpuMatrix< Scalar_t > &weights) |
| Sigmoid transformation is implicitly applied, thus output should hold the linear activations of the last layer in the net. More...
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static void | CrossEntropyGradients (TCpuMatrix< Scalar_t > &dY, const TCpuMatrix< Scalar_t > &Y, const TCpuMatrix< Scalar_t > &output, const TCpuMatrix< Scalar_t > &weights) |
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static Scalar_t | SoftmaxCrossEntropy (const TCpuMatrix< Scalar_t > &Y, const TCpuMatrix< Scalar_t > &output, const TCpuMatrix< Scalar_t > &weights) |
| Softmax transformation is implicitly applied, thus output should hold the linear activations of the last layer in the net. More...
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static void | SoftmaxCrossEntropyGradients (TCpuMatrix< Scalar_t > &dY, const TCpuMatrix< Scalar_t > &Y, const TCpuMatrix< Scalar_t > &output, const TCpuMatrix< Scalar_t > &weights) |
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Output functions transform the activations output of the output layer in the network to a valid prediction YHat for the desired usage of the network, e.g.
the identity function for regression or the sigmoid transformation for two-class classification.
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static void | Sigmoid (TCpuMatrix< Scalar_t > &YHat, const TCpuMatrix< Scalar_t > &) |
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static void | Softmax (TCpuMatrix< Scalar_t > &YHat, const TCpuMatrix< Scalar_t > &) |
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For each regularization type two functions are required, one named <Type>Regularization that evaluates the corresponding regularization functional for a given weight matrix and the Add<Type>RegularizationGradients , that adds the regularization component in the gradients to the provided matrix.
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static Scalar_t | L1Regularization (const TCpuMatrix< Scalar_t > &W) |
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static void | AddL1RegularizationGradients (TCpuMatrix< Scalar_t > &A, const TCpuMatrix< Scalar_t > &W, Scalar_t weightDecay) |
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static Scalar_t | L2Regularization (const TCpuMatrix< Scalar_t > &W) |
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static void | AddL2RegularizationGradients (TCpuMatrix< Scalar_t > &A, const TCpuMatrix< Scalar_t > &W, Scalar_t weightDecay) |
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For each initialization method, one function in the low-level interface is provided.
The naming scheme is
Initialize<Type>
for a given initialization method Type.
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static void | InitializeGauss (TCpuMatrix< Scalar_t > &A) |
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static void | InitializeUniform (TCpuMatrix< Scalar_t > &A) |
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static void | InitializeIdentity (TCpuMatrix< Scalar_t > &A) |
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static void | InitializeZero (TCpuMatrix< Scalar_t > &A) |
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static void | InitializeGlorotNormal (TCpuMatrix< Scalar_t > &A) |
| Truncated normal initialization (Glorot, called also Xavier normal) The values are sample with a normal distribution with stddev = sqrt(2/N_input + N_output) and values larger than 2 * stddev are discarded See Glorot & Bengio, AISTATS 2010 - http://jmlr.org/proceedings/papers/v9/glorot10a/glorot10a.pdf. More...
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static void | InitializeGlorotUniform (TCpuMatrix< Scalar_t > &A) |
| Sample from a uniform distribution in range [ -lim,+lim] where lim = sqrt(6/N_in+N_out). More...
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static TRandom & | GetRandomGenerator () |
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static void | SetRandomSeed (size_t seed) |
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static void | Dropout (TCpuMatrix< Scalar_t > &A, Scalar_t p) |
| Apply dropout with activation probability p to the given matrix A and scale the result by reciprocal of p . More...
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static void | 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) |
| Transform the matrix B in local view format, suitable for convolution, and store it in matrix A. More...
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static void | 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) |
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static void | Im2colFast (TCpuMatrix< AReal > &A, const TCpuMatrix< AReal > &B, const std::vector< int > &V) |
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static void | RotateWeights (TCpuMatrix< AReal > &A, const TCpuMatrix< AReal > &B, size_t filterDepth, size_t filterHeight, size_t filterWidth, size_t numFilters) |
| Rotates the matrix B , which is representing a weights, and stores them in the matrix A . More...
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static void | AddConvBiases (TCpuMatrix< Scalar_t > &output, const TCpuMatrix< Scalar_t > &biases) |
| Add the biases in the Convolutional Layer. More...
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static void | 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) |
| Perform the complete backward propagation step in a Convolutional Layer. More...
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static void | 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) |
| Utility function for calculating the activation gradients of the layer before the convolutional layer. More...
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static void | 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) |
| Utility function for calculating the weight gradients of the convolutional layer. More...
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static void | CalculateConvBiasGradients (TCpuMatrix< Scalar_t > &biasGradients, const std::vector< TCpuMatrix< Scalar_t >> &df, size_t batchSize, size_t depth, size_t nLocalViews) |
| Utility function for calculating the bias gradients of the convolutional layer. More...
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static void | 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) |
| Downsample the matrix C to the matrix A , using max operation, such that the winning indices are stored in matrix B . More...
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static void | 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) |
| Perform the complete backward propagation step in a Pooling Layer. More...
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static void | Reshape (TCpuMatrix< AReal > &A, const TCpuMatrix< AReal > &B) |
| Transform the matrix B to a matrix with different dimensions A . More...
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static void | Flatten (TCpuMatrix< AReal > &A, const std::vector< TCpuMatrix< AReal >> &B, size_t size, size_t nRows, size_t nCols) |
| Flattens the tensor B , such that each matrix, is stretched in one row, resulting with a matrix A . More...
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static void | Deflatten (std::vector< TCpuMatrix< AReal >> &A, const TCpuMatrix< AReal > &B, size_t index, size_t nRows, size_t nCols) |
| Transforms each row of B to a matrix and stores it in the tensor B . More...
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static void | Rearrange (std::vector< TCpuMatrix< AReal >> &out, const std::vector< TCpuMatrix< AReal >> &in) |
| Rearrage data accoring to time fill B x T x D out with T x B x D matrix in. More...
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Additional arithmetic on CUDA matrices used to implement the low-level interface.
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static void | Multiply (TCpuMatrix< Scalar_t > &C, const TCpuMatrix< Scalar_t > &A, const TCpuMatrix< Scalar_t > &B) |
| Standard multiplication of two matrices A and B with the result being written into C. More...
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static void | TransposeMultiply (TCpuMatrix< Scalar_t > &output, const TCpuMatrix< Scalar_t > &input, const TCpuMatrix< Scalar_t > &Weights, Scalar_t alpha=1.0, Scalar_t beta=0.) |
| Matrix multiplication of two matrices A and B^T (transposed) with the result being written into C. More...
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static void | Hadamard (TCpuMatrix< Scalar_t > &A, const TCpuMatrix< Scalar_t > &B) |
| In-place Hadamard (element-wise) product of matrices A and B with the result being written into A . More...
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static void | SumColumns (TCpuMatrix< Scalar_t > &B, const TCpuMatrix< Scalar_t > &A, Scalar_t alpha=1.0, Scalar_t beta=0.) |
| Sum columns of (m x n) matrixx A and write the results into the first m elements in A . More...
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static Scalar_t | Sum (const TCpuMatrix< Scalar_t > &A) |
| Compute the sum of all elements in A . More...
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