Method do_train

src/core/test/train.cpp:60–98

}

void TestTrain::do_train(SymbolVar dev_w_updated, const char* type) {
    int iter = 0;
    float err;
    auto update_err = [&]() {
        err = 0;
        auto p0 = host_w->ptr<float>(), p1 = host_w_truth->ptr<float>();
        for (size_t i = 0; i < DIM; i++) {
            auto d = p0[i] - p1[i];
            err += d * d;
        }
        err = sqrt(err / DIM);
        mgb_log("iter %d: lr=%.2e err=%.5f", iter, learning_rate->get<float>(), err);
    };
    auto copy_w = [&](DeviceTensorND& data) {
        if (iter % 20 == 0) {
            host_w->comp_node(data.comp_node());
            host_w->copy_from_fixlayout(data).sync();
            update_err();
        }
    };
    auto func = graph->compile({{dev_w_updated, copy_w}});

    func->to_json()->writeto_fpath(output_file(ssprintf("train-%s.json", type)));

    learning_rate->set<float>(-0.3 / NR_DATA);
    update_err();
    ASSERT_GE(err, 1);
    while (iter < 100) {
        iter++;
        func->execute();
    }

    ASSERT_LE(err, 1e-3);
    if (expected_err == -1) {
        expected_err = err;
    } else {
        MGB_ASSERT_FLOAT_EQ(err, expected_err);
    }
}

TEST_F(TestTrain, SimpleLinearRegression) {
    SymbolVar dev_w = opr::SharedDeviceTensor::make(*graph, *host_w, {"w"}),