Function test_softmax_all

dlib/test/dnn.cpp:248–297

    }

    void test_softmax_all()
    {
        using namespace dlib::tt;
        print_spinner();
        const long nr = 3;
        const long nc = 3;
        resizable_tensor src(5,5,nr,nc), dest(5,5,nr,nc), gradient_input(5,5,nr,nc);
        tt::tensor_rand rnd;
        rnd.fill_uniform(src);
        rnd.fill_uniform(dest);
        // fill like this as a test of the assignment operator.
        gradient_input = matrix_cast<float>(gaussian_randm(5,5*nr*nc, 2));



        auto grad_src = [&](long idx) {
            auto f = [&](float eps) {
                const float old = src.host()[idx];
                src.host()[idx] += eps;
                tt::softmax_all(dest, src);
                float result = dot(gradient_input, dest);
                src.host()[idx] = old;
                return result;
            };
            const float eps = 0.01;
            return (f(+eps)-f(-eps))/(2*eps);
        };

        resizable_tensor src_grad;
        src_grad.copy_size(src);
        src_grad = 0;

        tt::softmax_all(dest, src);
        softmax_all_gradient(src_grad, dest, gradient_input);

        auto grad_error = compare_gradients(src_grad, grad_src);
        dlog << LINFO << "src error: " << grad_error;
        DLIB_TEST(grad_error < 0.001);

#ifdef DLIB_USE_CUDA
        resizable_tensor src1 = src;
        resizable_tensor src2 = src;
        resizable_tensor dest1, dest2;
        dest1.copy_size(src);
        dest2.copy_size(src);
        cuda::softmax_all(dest1, src1);
        cpu::softmax_all(dest2, src2);
        DLIB_TEST_MSG(max(abs(mat(dest1)-mat(dest2))) < 1e-5, max(abs(mat(dest1)-mat(dest2))));
#endif
    }

    void test_mish()
    {