| using namespace neuroflow; | |
| int main() { | |
| try { | |
| std::cout << "Full forward test..." << std::endl; | |
| NeuroFlowModel::Config cfg; | |
| cfg.input_dim = 64; | |
| cfg.hidden_dim = 32; | |
| cfg.output_dim = 5; | |
| cfg.memory_slots = 8; | |
| cfg.memory_dim = 16; | |
| cfg.num_layers = 1; | |
| cfg.num_associations = 2; | |
| cfg.use_mla = false; | |
| NeuroFlowModel model(cfg); | |
| Tensor input({2, cfg.input_dim}); | |
| for (size_t i = 0; i < input.numel(); ++i) input.as_fp32()[i] = 0.1f * i; | |
| std::cout << "Calling full forward..." << std::endl; | |
| auto output = model.forward(input, nullptr, false, false); | |
| std::cout << "Output shape: [" << output.output.shape_[0] << ", " << output.output.shape_[1] << "]" << std::endl; | |
| std::cout << "Success!" << std::endl; | |
| return 0; | |
| } catch (const std::exception& e) { | |
| std::cout << "Error: " << e.what() << std::endl; | |
| return 1; | |
| } | |
| } | |