Function test_simple_linear_regression_with_mult_prev

dlib/test/dnn.cpp:3392–3432

// ----------------------------------------------------------------------------------------

    void test_simple_linear_regression_with_mult_prev()
    {
        srand(1234);
        print_spinner();
        const int num_samples = 1000;
        ::std::vector<matrix<double>> x(num_samples);
        ::std::vector<float> y(num_samples);
        const float true_slope = 2.0;
        for ( int ii = 0; ii < num_samples; ++ii )
        {
            const double val = static_cast<double>(ii-500)/100;
            matrix<double> tmp(1,1);
            tmp = val;
            x[ii] = tmp;
            y[ii] = ( true_slope*static_cast<float>(val*val));
        }

        randomize_samples(x,y);

        using net_type = loss_mean_squared<fc<1, mult_prev1<fc<2,tag1<fc<2,input<matrix<double>>>>>>>>;
        net_type net;
        sgd defsolver(0,0.9);
        dnn_trainer<net_type> trainer(net, defsolver);
        trainer.set_learning_rate(1e-5);
        trainer.set_min_learning_rate(1e-11);
        trainer.set_mini_batch_size(50);
        trainer.set_max_num_epochs(2000);
        trainer.train(x, y);

        running_stats<double> rs;
        for (size_t i = 0; i < x.size(); ++i)
        {
            double val = y[i];
            double out = net(x[i]);
            rs.add(std::abs(val-out));
        }
        dlog << LINFO << "rs.mean(): " << rs.mean();
        dlog << LINFO << "rs.stddev(): " << rs.stddev();
        dlog << LINFO << "rs.max(): " << rs.max();
        DLIB_TEST(rs.mean() < 0.1);
    }

// ----------------------------------------------------------------------------------------