mirror of https://github.com/alibaba/MNN.git
59 lines
1.7 KiB
C++
59 lines
1.7 KiB
C++
//
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// linearRegression.cpp
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// MNN
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//
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// Created by MNN on 2019/11/22.
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// Copyright © 2018, Alibaba Group Holding Limited
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//
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#include <MNN/expr/ExprCreator.hpp>
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#include <random>
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#include "DemoUnit.hpp"
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#include "SGD.hpp"
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using namespace MNN::Express;
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using namespace MNN::Train;
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std::random_device gRandom;
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class LinearRegress : public DemoUnit {
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public:
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virtual int run(int argc, const char* argv[]) override {
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VARP w = _TrainableParam(0.3f, {}, NHWC);
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VARP b = _TrainableParam(0.1f, {}, NHWC);
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std::shared_ptr<SGD> opt(new SGD);
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opt->append({w, b});
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opt->setLearningRate(0.1f);
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const int number = 10;
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const int limit = 300;
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for (int i = 0; i < limit; ++i) {
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VARP x = _Input({number}, NHWC);
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// Fill x
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auto xPtr = x->writeMap<float>();
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for (int v = 0; v < number; ++v) {
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xPtr[v] = (gRandom() % 10000) / 10000.0f;
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}
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VARP label = _Input({number}, NHWC);
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// Fill label
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auto ptr = label->writeMap<float>();
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for (int v = 0; v < number; ++v) {
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ptr[v] = xPtr[v] * 0.8f + 0.7f;
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}
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VARP y = x * w + b;
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VARP diff = y - label;
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VARP loss = (diff * diff).mean({});
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if (i == limit - 1) {
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MNN_PRINT("w = %f, b = %f, Target w = 0.8f, Target b = 0.7f\n", w->readMap<float>()[0],
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b->readMap<float>()[0]);
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Variable::save({y}, "linear.mnn");
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} else {
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opt->step(loss);
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}
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}
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return 0;
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}
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};
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DemoUnitSetRegister(LinearRegress, "LinearRegress");
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