documentation/reference/math__utilities_8h_source.html

//   OpenNN: Open Neural Networks Library

//   www.opennn.net

//

//   M A T H   U T I L I T I E S   H E A D E R

//

//   Artificial Intelligence Techniques SL

//   artelnics@artelnics.com


#pragma once


#include "pch.h"

#include "tensor_utilities.h"

#include "enum_map.h"

#include "operators.h"


namespace opennn

{

void pad(const TensorView& input, TensorView& output);


void bound(const TensorView& input, const TensorView& lower_bounds, const TensorView& upper_bounds, TensorView& output);

void bound_cpu(const TensorView& input, const TensorView& lower_bounds, const TensorView& upper_bounds, TensorView& output);

#ifdef OPENNN_HAS_CUDA

void bound_gpu(const TensorView& input, const TensorView& lower_bounds, const TensorView& upper_bounds, TensorView& output);

#endif


void scale(const TensorView& input,

           const TensorView& minimums, const TensorView& maximums,

           const TensorView& means, const TensorView& standard_deviations,

           const TensorView& scalers,

           float min_range, float max_range,

           TensorView& output);

void scale_cpu(const TensorView& input,

               const TensorView& minimums, const TensorView& maximums,

               const TensorView& means, const TensorView& standard_deviations,

               const TensorView& scalers,

               float min_range, float max_range,

               TensorView& output);

#ifdef OPENNN_HAS_CUDA

void scale_gpu(const TensorView& input,

               const TensorView& minimums, const TensorView& maximums,

               const TensorView& means, const TensorView& standard_deviations,

               const TensorView& scalers,

               float min_range, float max_range,

               TensorView& output);

#endif


void unscale(const TensorView& input,

             const TensorView& minimums, const TensorView& maximums,

             const TensorView& means, const TensorView& standard_deviations,

             const TensorView& scalers,

             float min_range, float max_range,

             TensorView& output);

void unscale_cpu(const TensorView& input,

                 const TensorView& minimums, const TensorView& maximums,

                 const TensorView& means, const TensorView& standard_deviations,

                 const TensorView& scalers,

                 float min_range, float max_range,

                 TensorView& output);

#ifdef OPENNN_HAS_CUDA

void unscale_gpu(const TensorView& input,

                 const TensorView& minimums, const TensorView& maximums,

                 const TensorView& means, const TensorView& standard_deviations,

                 const TensorView& scalers,

                 float min_range, float max_range,

                 TensorView& output);

#endif


void copy(const TensorView& source, TensorView& destination);

void copy_cpu(const TensorView& source, TensorView& destination);

#ifdef OPENNN_HAS_CUDA

void copy_gpu(const TensorView& source, TensorView& destination);

#endif


void add(const TensorView& input_1, const TensorView& input_2, TensorView& output);

void add_cpu(const TensorView& input_1, const TensorView& input_2, TensorView& output);

#ifdef OPENNN_HAS_CUDA

void add_gpu(const TensorView& input_1, const TensorView& input_2, TensorView& output);

#endif


void multiply(const TensorView& input_a, bool transpose_a, const TensorView& input_b, bool transpose_b, TensorView& output, float alpha = 1.0f, float beta = 0.0f);

void multiply_cpu(const TensorView& input_a, bool transpose_a, const TensorView& input_b, bool transpose_b, TensorView& output, float alpha = 1.0f, float beta = 0.0f);

#ifdef OPENNN_HAS_CUDA

void multiply_gpu(const TensorView& input_a, bool transpose_a, const TensorView& input_b, bool transpose_b, TensorView& output, float alpha = 1.0f, float beta = 0.0f);

#endif


void softmax(TensorView& output);

void softmax_cpu(TensorView& output);

#ifdef OPENNN_HAS_CUDA

void softmax_gpu(TensorView& output);

#endif

void max_pooling_3d_forward(const TensorView& input, TensorView& output, TensorView& maximal_indices, bool is_training);

void max_pooling_3d_forward_cpu(const TensorView& input, TensorView& output, TensorView& maximal_indices, bool is_training);

#ifdef OPENNN_HAS_CUDA

void max_pooling_3d_forward_gpu(const TensorView& input, TensorView& output, TensorView& maximal_indices, bool is_training);

#endif


void average_pooling_3d_forward(const TensorView& input, TensorView& output);

void average_pooling_3d_forward_cpu(const TensorView& input, TensorView& output);

#ifdef OPENNN_HAS_CUDA

void average_pooling_3d_forward_gpu(const TensorView& input, TensorView& output);

#endif


void max_pooling_3d_backward(const TensorView& maximal_indices, const TensorView& output_delta, TensorView& input_delta);

void max_pooling_3d_backward_cpu(const TensorView& maximal_indices, const TensorView& output_delta, TensorView& input_delta);

#ifdef OPENNN_HAS_CUDA

void max_pooling_3d_backward_gpu(const TensorView& maximal_indices, const TensorView& output_delta, TensorView& input_delta);

#endif


void average_pooling_3d_backward(const TensorView& input, const TensorView& output_delta, TensorView& input_delta);

void average_pooling_3d_backward_cpu(const TensorView& input, const TensorView& output_delta, TensorView& input_delta);

#ifdef OPENNN_HAS_CUDA

void average_pooling_3d_backward_gpu(const TensorView& input, const TensorView& output_delta, TensorView& input_delta);

#endif

void split_heads(const TensorView& source, TensorView& destination);

void split_heads_cpu(const TensorView& source, TensorView& destination);

#ifdef OPENNN_HAS_CUDA

void split_heads_gpu(const TensorView& source, TensorView& destination);

#endif


void merge_heads(const TensorView& source, TensorView& destination);

void merge_heads_cpu(const TensorView& source, TensorView& destination);

#ifdef OPENNN_HAS_CUDA

void merge_heads_gpu(const TensorView& source, TensorView& destination);

#endif


}


// OpenNN: Open Neural Networks Library.

// Copyright(C) 2005-2026 Artificial Intelligence Techniques, SL.

// Licensed under the GNU Lesser General Public License v2.1 or later.

enum_map.h

opennn
Definition adaptive_moment_estimation.h:14

opennn::copy
void copy(const TensorView &source, TensorView &destination)
Copies the contents of source into destination, dispatching to CPU or GPU as needed.

opennn::unscale
void unscale(const TensorView &input, const TensorView &minimums, const TensorView &maximums, const TensorView &means, const TensorView &standard_deviations, const TensorView &scalers, float min_range, float max_range, TensorView &output)
Inverse of scale(); reconstructs original values from a previously scaled tensor.

opennn::softmax
void softmax(TensorView &output)
Applies softmax in place along the trailing dimension of output.

opennn::copy_cpu
void copy_cpu(const TensorView &source, TensorView &destination)
CPU implementation of copy().

opennn::multiply_cpu
void multiply_cpu(const TensorView &input_a, bool transpose_a, const TensorView &input_b, bool transpose_b, TensorView &output, float alpha=1.0f, float beta=0.0f)
CPU implementation of multiply().

opennn::bound
void bound(const TensorView &input, const TensorView &lower_bounds, const TensorView &upper_bounds, TensorView &output)
Clamps each element of input to the [lower_bounds, upper_bounds] range.

opennn::bound_cpu
void bound_cpu(const TensorView &input, const TensorView &lower_bounds, const TensorView &upper_bounds, TensorView &output)
CPU implementation of bound().

opennn::unscale_cpu
void unscale_cpu(const TensorView &input, const TensorView &minimums, const TensorView &maximums, const TensorView &means, const TensorView &standard_deviations, const TensorView &scalers, float min_range, float max_range, TensorView &output)
CPU implementation of unscale().

opennn::max_pooling_3d_backward_cpu
void max_pooling_3d_backward_cpu(const TensorView &maximal_indices, const TensorView &output_delta, TensorView &input_delta)
CPU implementation of max_pooling_3d_backward().

opennn::pad
void pad(const TensorView &input, TensorView &output)
Pads the input tensor and writes the result into output.

opennn::maximal_indices
VectorI maximal_indices(const VectorR &, Index)
Indices of the n largest elements of a vector.

opennn::max_pooling_3d_backward
void max_pooling_3d_backward(const TensorView &maximal_indices, const TensorView &output_delta, TensorView &input_delta)
Backward pass for 3D max pooling; routes gradients to argmax positions.

opennn::merge_heads
void merge_heads(const TensorView &source, TensorView &destination)
Inverse of split_heads(); merges per-head tensors back into a single representation.

opennn::merge_heads_cpu
void merge_heads_cpu(const TensorView &source, TensorView &destination)
CPU implementation of merge_heads().

opennn::average_pooling_3d_forward_cpu
void average_pooling_3d_forward_cpu(const TensorView &input, TensorView &output)
CPU implementation of average_pooling_3d_forward().

opennn::average_pooling_3d_backward_cpu
void average_pooling_3d_backward_cpu(const TensorView &input, const TensorView &output_delta, TensorView &input_delta)
CPU implementation of average_pooling_3d_backward().

opennn::average_pooling_3d_forward
void average_pooling_3d_forward(const TensorView &input, TensorView &output)
Forward pass of 3D average pooling.

opennn::add
void add(const TensorView &input_1, const TensorView &input_2, TensorView &output)
Element-wise addition: output = input_1 + input_2.

opennn::multiply
void multiply(const TensorView &input_a, bool transpose_a, const TensorView &input_b, bool transpose_b, TensorView &output, float alpha=1.0f, float beta=0.0f)
General matrix multiply: output = alpha * op(input_a) * op(input_b) + beta * output.

opennn::max_pooling_3d_forward
void max_pooling_3d_forward(const TensorView &input, TensorView &output, TensorView &maximal_indices, bool is_training)
Forward pass of 3D max pooling; records argmax positions when training.

opennn::scale_cpu
void scale_cpu(const TensorView &input, const TensorView &minimums, const TensorView &maximums, const TensorView &means, const TensorView &standard_deviations, const TensorView &scalers, float min_range, float max_range, TensorView &output)
CPU implementation of scale().

opennn::split_heads
void split_heads(const TensorView &source, TensorView &destination)
Reshapes a multi-head attention tensor by splitting the last axis into heads.

opennn::split_heads_cpu
void split_heads_cpu(const TensorView &source, TensorView &destination)
CPU implementation of split_heads().

opennn::softmax_cpu
void softmax_cpu(TensorView &output)
CPU implementation of softmax().

opennn::scale
void scale(const TensorView &input, const TensorView &minimums, const TensorView &maximums, const TensorView &means, const TensorView &standard_deviations, const TensorView &scalers, float min_range, float max_range, TensorView &output)
Applies per-feature scaling (mean/std, min/max, or other scalers) to a tensor.

opennn::max_pooling_3d_forward_cpu
void max_pooling_3d_forward_cpu(const TensorView &input, TensorView &output, TensorView &maximal_indices, bool is_training)
CPU implementation of max_pooling_3d_forward().

opennn::average_pooling_3d_backward
void average_pooling_3d_backward(const TensorView &input, const TensorView &output_delta, TensorView &input_delta)
Backward pass for 3D average pooling.

opennn::add_cpu
void add_cpu(const TensorView &input_1, const TensorView &input_2, TensorView &output)
CPU implementation of add().

operators.h

pch.h

opennn::TensorView
Non-owning view over a tensor: pointer, shape, and data type with rich reshape helpers.
Definition tensor_utilities.h:293

tensor_utilities.h