chore: import upstream snapshot with attribution

2026-07-13 13:33:03 +08:00
commit 5b57521aa1
8226 changed files with 3425766 additions and 0 deletions
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+//
+//  linearRegression.cpp
+//  MNN
+//
+//  Created by MNN on 2019/11/22.
+//  Copyright © 2018, Alibaba Group Holding Limited
+//
+
+#include <MNN/expr/ExprCreator.hpp>
+#include <random>
+#include "DemoUnit.hpp"
+#include "SGD.hpp"
+using namespace MNN::Express;
+using namespace MNN::Train;
+std::random_device gRandom;
+class LinearRegress : public DemoUnit {
+public:
+    virtual int run(int argc, const char* argv[]) override {
+        VARP w = _TrainableParam(0.3f, {}, NHWC);
+        VARP b = _TrainableParam(0.1f, {}, NHWC);
+        std::shared_ptr<Module> _m(Module::createEmpty({w, b}));
+        std::shared_ptr<SGD> opt(new SGD(_m));
+        opt->setLearningRate(0.1f);
+
+        const int number = 10;
+        const int limit  = 300;
+        for (int i = 0; i < limit; ++i) {
+            VARP x = _Input({number}, NHWC);
+            // Fill x
+            auto xPtr = x->writeMap<float>();
+            for (int v = 0; v < number; ++v) {
+                xPtr[v] = (gRandom() % 10000) / 10000.0f;
+            }
+            VARP label = _Input({number}, NHWC);
+            // Fill label
+            auto ptr = label->writeMap<float>();
+            for (int v = 0; v < number; ++v) {
+                ptr[v] = xPtr[v] * 0.8f + 0.7f;
+            }
+            VARP y = x * w + b;
+
+            VARP diff = y - label;
+            VARP loss = (diff * diff).mean({});
+
+            if (i == limit - 1) {
+                MNN_PRINT("w = %f, b = %f, Target w = 0.8f, Target b = 0.7f\n", w->readMap<float>()[0],
+                          b->readMap<float>()[0]);
+                Variable::save({y}, "linear.mnn");
+            } else {
+                opt->step(loss);
+            }
+        }
+        return 0;
+    }
+};
+
+DemoUnitSetRegister(LinearRegress, "LinearRegress");