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Updated examples

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This commit is contained in:
Alejandro Saucedo 2021-02-28 17:07:17 +00:00
parent 63e220a8a4
commit 4fddf74ca7
11 changed files with 408 additions and 405 deletions
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101
examples/godot_logistic_regression/gdnative_shared/src/KomputeModelML.cpp
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@ -33,54 +33,41 @@ void KomputeModelML::train(Array yArr, Array xIArr, Array xJArr) {
uint32_t ITERATIONS = 100;
float learningRate = 0.1;
std::shared_ptr<kp::Tensor> xI{ new kp::Tensor(xIData) };
std::shared_ptr<kp::Tensor> xJ{ new kp::Tensor(xJData) };
std::shared_ptr<kp::Tensor> y{ new kp::Tensor(yData) };
std::shared_ptr<kp::Tensor> wIn{ new kp::Tensor({ 0.001, 0.001 }) };
std::shared_ptr<kp::Tensor> wOutI{ new kp::Tensor(zerosData) };
std::shared_ptr<kp::Tensor> wOutJ{ new kp::Tensor(zerosData) };
std::shared_ptr<kp::Tensor> bIn{ new kp::Tensor({ 0 }) };
std::shared_ptr<kp::Tensor> bOut{ new kp::Tensor(zerosData) };
std::shared_ptr<kp::Tensor> lOut{ new kp::Tensor(zerosData) };
std::vector<std::shared_ptr<kp::Tensor>> params = { xI, xJ, y,
wIn, wOutI, wOutJ,
bIn, bOut, lOut };
{
kp::Manager mgr;
std::shared_ptr<kp::Tensor> xI = mgr.tensor(xIData);
std::shared_ptr<kp::Tensor> xJ = mgr.tensor(xJData);
std::shared_ptr<kp::Tensor> y = mgr.tensor(yData);
std::shared_ptr<kp::Tensor> wIn = mgr.tensor({ 0.001, 0.001 });
std::shared_ptr<kp::Tensor> wOutI = mgr.tensor(zerosData);
std::shared_ptr<kp::Tensor> wOutJ = mgr.tensor(zerosData);
std::shared_ptr<kp::Tensor> bIn = mgr.tensor({ 0 });
std::shared_ptr<kp::Tensor> bOut = mgr.tensor(zerosData);
std::shared_ptr<kp::Tensor> lOut = mgr.tensor(zerosData);
std::vector<std::shared_ptr<kp::Tensor>> params = { xI, xJ, y,
wIn, wOutI, wOutJ,
bIn, bOut, lOut };
{
mgr.rebuild(params);
std::vector<uint32_t> spirv(
(uint32_t*)kp::shader_data::shaders_glsl_logisticregression_comp_spv,
(uint32_t*)(kp::shader_data::shaders_glsl_logisticregression_comp_spv
+ kp::shader_data::shaders_glsl_logisticregression_comp_spv_len));
std::shared_ptr<kp::Sequence> sq = mgr.sequence();
std::shared_ptr<kp::Algorithm> algo = mgr.algorithm(params, spirv);
// Record op algo base
sq->begin();
mgr.sequence()->eval<kp::OpTensorSyncDevice>(params);
sq->record<kp::OpTensorSyncDevice>({ wIn, bIn });
#ifdef KOMPUTE_ANDROID_SHADER_FROM_STRING
// Newer versions of Android are able to use shaderc to read raw string
sq->record<kp::OpAlgoCreate>(
params, std::vector<char>(LR_SHADER.begin(), LR_SHADER.end()));
#else
// Older versions of Android require the SPIRV binary directly
sq->record<kp::OpAlgoCreate>(
params, std::vector<char>(
kp::shader_data::shaders_glsl_logisticregression_comp_spv,
kp::shader_data::shaders_glsl_logisticregression_comp_spv
+ kp::shader_data::shaders_glsl_logisticregression_comp_spv_len
));
#endif
sq->record<kp::OpTensorSyncLocal>({ wOutI, wOutJ, bOut, lOut });
sq->end();
std::shared_ptr<kp::Sequence> sq = mgr.sequence()
->record<kp::OpTensorSyncDevice>({ wIn, bIn })
->record<kp::OpAlgoDispatch>(algo)
->record<kp::OpTensorSyncLocal>({ wOutI, wOutJ, bOut, lOut });
// Iterate across all expected iterations
for (size_t i = 0; i < ITERATIONS; i++) {
@ -94,15 +81,15 @@ void KomputeModelML::train(Array yArr, Array xIArr, Array xJArr) {
}
}
}
KP_LOG_INFO("RESULT: <<<<<<<<<<<<<<<<<<<");
KP_LOG_INFO(wIn->data()[0]);
KP_LOG_INFO(wIn->data()[1]);
KP_LOG_INFO(bIn->data()[0]);
this->mWeights = wIn;
this->mBias = bIn;
}
KP_LOG_INFO("RESULT: <<<<<<<<<<<<<<<<<<<");
KP_LOG_INFO(wIn->data()[0]);
KP_LOG_INFO(wIn->data()[1]);
KP_LOG_INFO(bIn->data()[0]);
this->mWeights = kp::Tensor(wIn->data());
this->mBias = kp::Tensor(bIn->data());
}
Array KomputeModelML::predict(Array xI, Array xJ) {
@ -116,9 +103,9 @@ Array KomputeModelML::predict(Array xI, Array xJ) {
for (size_t i = 0; i < xI.size(); i++) {
float xIVal = xI[i];
float xJVal = xJ[i];
float result = (xIVal * this->mWeights.data()[0]
+ xJVal * this->mWeights.data()[1]
+ this->mBias.data()[0]);
float result = (xIVal * this->mWeights->data()[0]
+ xJVal * this->mWeights->data()[1]
+ this->mBias->data()[0]);
// Instead of using sigmoid we'll just return full numbers
Variant var = result > 0 ? 1 : 0;
@ -131,15 +118,15 @@ Array KomputeModelML::predict(Array xI, Array xJ) {
Array KomputeModelML::get_params() {
Array retArray;
KP_LOG_INFO(this->mWeights.size() + this->mBias.size());
KP_LOG_INFO(this->mWeights->size() + this->mBias->size());
if(this->mWeights.size() + this->mBias.size() == 0) {
if(this->mWeights->size() + this->mBias->size() == 0) {
return retArray;
}
retArray.push_back(this->mWeights.data()[0]);
retArray.push_back(this->mWeights.data()[1]);
retArray.push_back(this->mBias.data()[0]);
retArray.push_back(this->mWeights->data()[0]);
retArray.push_back(this->mWeights->data()[1]);
retArray.push_back(this->mBias->data()[0]);
retArray.push_back(99.0);
return retArray;