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Improves softmax function for standard ml

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xavier dupré 2019-12-02 11:44:46 +01:00 committed by Changming Sun
parent ec88f6d8d6
commit c42148a0c3
1 changed files with 11 additions and 17 deletions
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28
onnxruntime/core/providers/cpu/ml/ml_common.h
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@ -251,7 +251,6 @@ static inline float sigmoid_probability(float score, float proba, float probb) {
}
static inline void ComputeSoftmax(std::vector<float>& values) {
std::vector<float> newscores;
// compute exp with negative number to be numerically stable
float v_max = -std::numeric_limits<float>::max();
for (float value : values) {
@ -259,19 +258,16 @@ static inline void ComputeSoftmax(std::vector<float>& values) {
v_max = value;
}
float this_sum = 0.f;
for (float value : values) {
float val2 = std::exp(value - v_max);
this_sum += val2;
newscores.push_back(val2);
}
for (int64_t k = 0; k < static_cast<int64_t>(values.size()); k++) {
values[k] = newscores[k] / this_sum;
for (float& value : values) {
value = std::exp(value - v_max);
this_sum += value;
}
for (float& value : values)
value /= this_sum;
}
//this function skips zero values (since exp(0) is non zero)
static inline void ComputeSoftmaxZero(std::vector<float>& values) {
std::vector<float> newscores;
// compute exp with negative number to be numerically stable
float v_max = -std::numeric_limits<float>::max();
for (float value : values) {
@ -280,18 +276,16 @@ static inline void ComputeSoftmaxZero(std::vector<float>& values) {
}
float exp_neg_v_max = std::exp(-v_max);
float this_sum = 0.f;
for (float value : values) {
for (float& value : values) {
if (value > 0.0000001f || value < -0.0000001f) {
float val2 = std::exp(value - v_max);
this_sum += val2;
newscores.push_back(val2);
value = std::exp(value - v_max);
this_sum += value;
} else {
newscores.push_back(value * exp_neg_v_max);
value *= exp_neg_v_max;
}
}
for (int64_t k = 0; k < static_cast<int64_t>(values.size()); k++) {
values[k] = newscores[k] / this_sum;
}
for (float& value : values)
value /= this_sum;
}
template <typename T>