| 8 | #include "test_logistic_regression_shader.hpp" |
| 9 | |
| 10 | TEST(TestLogisticRegression, TestMainLogisticRegression) |
| 11 | { |
| 12 | |
| 13 | uint32_t ITERATIONS = 100; |
| 14 | float learningRate = 0.1; |
| 15 | |
| 16 | { |
| 17 | kp::Manager mgr; |
| 18 | |
| 19 | std::shared_ptr<kp::TensorT<float>> xI = mgr.tensor({ 0, 1, 1, 1, 1 }); |
| 20 | std::shared_ptr<kp::TensorT<float>> xJ = mgr.tensor({ 0, 0, 0, 1, 1 }); |
| 21 | |
| 22 | std::shared_ptr<kp::TensorT<float>> y = mgr.tensor({ 0, 0, 0, 1, 1 }); |
| 23 | |
| 24 | std::shared_ptr<kp::TensorT<float>> wIn = mgr.tensor({ 0.001, 0.001 }); |
| 25 | std::shared_ptr<kp::TensorT<float>> wOutI = |
| 26 | mgr.tensor({ 0, 0, 0, 0, 0 }); |
| 27 | std::shared_ptr<kp::TensorT<float>> wOutJ = |
| 28 | mgr.tensor({ 0, 0, 0, 0, 0 }); |
| 29 | |
| 30 | std::shared_ptr<kp::TensorT<float>> bIn = mgr.tensor({ 0 }); |
| 31 | std::shared_ptr<kp::TensorT<float>> bOut = |
| 32 | mgr.tensor({ 0, 0, 0, 0, 0 }); |
| 33 | |
| 34 | std::shared_ptr<kp::TensorT<float>> lOut = |
| 35 | mgr.tensor({ 0, 0, 0, 0, 0 }); |
| 36 | |
| 37 | std::vector<std::shared_ptr<kp::Tensor>> params = { xI, xJ, y, |
| 38 | wIn, wOutI, wOutJ, |
| 39 | bIn, bOut, lOut }; |
| 40 | |
| 41 | mgr.sequence()->eval<kp::OpTensorSyncDevice>(params); |
| 42 | |
| 43 | std::vector<uint32_t> spirv2{ 0x1, 0x2 }; |
| 44 | |
| 45 | std::vector<uint32_t> spirv( |
| 46 | kp::TEST_LOGISTIC_REGRESSION_SHADER_COMP_SPV.begin(), |
| 47 | kp::TEST_LOGISTIC_REGRESSION_SHADER_COMP_SPV.end()); |
| 48 | |
| 49 | std::shared_ptr<kp::Algorithm> algorithm = mgr.algorithm( |
| 50 | params, spirv, kp::Workgroup({ 5 }), std::vector<float>({ 5.0 })); |
| 51 | |
| 52 | std::shared_ptr<kp::Sequence> sq = |
| 53 | mgr.sequence() |
| 54 | ->record<kp::OpTensorSyncDevice>({ wIn, bIn }) |
| 55 | ->record<kp::OpAlgoDispatch>(algorithm) |
| 56 | ->record<kp::OpTensorSyncLocal>({ wOutI, wOutJ, bOut, lOut }); |
| 57 | |
| 58 | // Iterate across all expected iterations |
| 59 | for (size_t i = 0; i < ITERATIONS; i++) { |
| 60 | sq->eval(); |
| 61 | |
| 62 | for (size_t j = 0; j < bOut->size(); j++) { |
| 63 | wIn->data()[0] -= learningRate * wOutI->data()[j]; |
| 64 | wIn->data()[1] -= learningRate * wOutJ->data()[j]; |
| 65 | bIn->data()[0] -= learningRate * bOut->data()[j]; |
| 66 | } |
| 67 | } |