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Sync public subset from Flux

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This commit is contained in:
Gitea CI
2025-10-07 11:09:55 +00:00
parent 8892d58e66
commit 35023cb7e1
30 changed files with 707 additions and 229 deletions
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24
include/modules/neural_networks/activation_functions/Activation_ReLU.h Normal file
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@@ -0,0 +1,24 @@
#pragma once
#include "./core/omp_config.h"
#include "./utils/vector.h"
#include "./utils/matrix.h"
namespace neural_networks{
template <typename T>
struct Activation_ReLU{
//utils::Matrix<T> inputs;
utils::Matrix<T> outputs;
void forward(const utils::Matrix<T>& inputs){
outputs = numerics::matclip_low(inputs, T{0});
}
};
} // end namespace neural_networks

13
include/modules/neural_networks/activation_functions/Softmax.h → include/modules/neural_networks/activation_functions/Activation_Softmax.h
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@@ -5,27 +5,30 @@
#include "./utils/vector.h"
#include "./utils/matrix.h"
#include "./numerics/max.h"
#include "./numerics/matmax.h"
#include "./numerics/matsubtract.h"
#include "./numerics/exponential.h"
#include "./numerics/matexp.h"
#include "./numerics/matdiv.h"
namespace neural_networks{
template <typename T>
struct activation_softmax{
struct Activation_Softmax{
utils::Matrix<T> exp_values;
utils::Matrix<T> probabilities;
utils::Matrix<T> outputs;
void forward(const utils::Matrix<T> inputs){
void forward(const utils::Matrix<T>& inputs){
// Get unnormalized probabilities
exp_values = numerics::matexp(numerics::matsubtract(inputs, numerics::matmax(inputs, "rows"), "col"));
exp_values = numerics::exponential(numerics::matsubtract(inputs, numerics::max(inputs, "rows"), "col"));
// Normalize them for each sample
probabilities = numerics::matdiv(exp_values, numerics::matsum(exp_values, "col"), "col");
outputs = probabilities;
}

28
include/modules/neural_networks/activation_functions/ReLU.h
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@@ -1,28 +0,0 @@
#pragma once
#include "./core/omp_config.h"
#include "./utils/vector.h"
#include "./utils/matrix.h"
#include "./utils/random.h"
namespace neural_networks{
template <typename T>
struct activation_ReLU{
utils::Matrix<T> outputs;
void forward(utils::Matrix<T> inputs){
outputs = numerics::max(inputs, T{0});
//outputs.print();
}
};
} // end namespace neural_networks