Added logistic regression example

examples/linear_regression/src/Main.cpp

@@ -0,0 +1,77 @@
+
+#include <iostream>
+#include <memory>
+#include <vector>
+
+#include "kompute/Kompute.hpp"
+
+int main()
+{
+#if KOMPUTE_ENABLE_SPDLOG
+    spdlog::set_level(
+      static_cast<spdlog::level::level_enum>(SPDLOG_ACTIVE_LEVEL));
+#endif
+
+    uint32_t ITERATIONS = 100;
+    float learningRate = 0.1;
+
+    std::shared_ptr<kp::Tensor> xI{ new kp::Tensor({ 0, 1, 1, 1, 1 }) };
+    std::shared_ptr<kp::Tensor> xJ{ new kp::Tensor({ 0, 0, 0, 1, 1 }) };
+
+    std::shared_ptr<kp::Tensor> y{ new kp::Tensor({ 0, 0, 0, 1, 1 }) };
+
+    std::shared_ptr<kp::Tensor> wIn{ new kp::Tensor({ 0.001, 0.001 }) };
+    std::shared_ptr<kp::Tensor> wOutI{ new kp::Tensor({ 0, 0, 0, 0, 0 }) };
+    std::shared_ptr<kp::Tensor> wOutJ{ new kp::Tensor({ 0, 0, 0, 0, 0 }) };
+
+    std::shared_ptr<kp::Tensor> bIn{ new kp::Tensor({ 0 }) };
+    std::shared_ptr<kp::Tensor> bOut{ new kp::Tensor({ 0, 0, 0, 0, 0 }) };
+
+    std::shared_ptr<kp::Tensor> lOut{ new kp::Tensor({ 0, 0, 0, 0, 0 }) };
+
+    std::vector<std::shared_ptr<kp::Tensor>> params = { xI,  xJ,    y,
+                                                        wIn, wOutI, wOutJ,
+                                                        bIn, bOut,  lOut };
+
+    kp::Manager mgr;
+
+    std::weak_ptr<kp::Sequence> sqWeakPtr = mgr.getOrCreateManagedSequence("createTensors");
+    std::shared_ptr<kp::Sequence> sq = sqWeakPtr.lock();
+
+    sq->begin();
+
+    sq->record<kp::OpTensorCreate>(params);
+
+    sq->end();
+    sq->eval();
+
+    // Record op algo base
+    sq->begin();
+
+    sq->record<kp::OpTensorSyncDevice>({ wIn, bIn });
+
+    sq->record<kp::OpAlgoBase<>>(
+      params, "shaders/glsl/logistic_regression.comp");
+
+    sq->record<kp::OpTensorSyncLocal>({ wOutI, wOutJ, bOut, lOut });
+
+    sq->end();
+
+    // Iterate across all expected iterations
+    for (size_t i = 0; i < ITERATIONS; i++) {
+
+        sq->eval();
+
+        for (size_t j = 0; j < bOut->size(); j++) {
+            wIn->data()[0] -= learningRate * wOutI->data()[j];
+            wIn->data()[1] -= learningRate * wOutJ->data()[j];
+            bIn->data()[0] -= learningRate * bOut->data()[j];
+        }
+    }
+
+    std::cout << "RESULTS" << std::endl;
+    std::cout << "w1: " << wIn->data()[0] << std::endl;
+    std::cout << "w2: " << wIn->data()[1] << std::endl;
+    std::cout << "b: " << bIn->data()[0] << std::endl;
+}
+