9bbe253810
- CMakeLists.txt with libtorch, GoogleTest, GoogleBenchmark, OpenMP, pybind11 - Header files: config, controller, population, fitness, evolution, spectral, oscillation, telemetry, optimizer - Source implementations: controller (full micro-MLP forward pass, mutation, crossover), fitness (Welford's algorithm), oscillation (DFT), spectral (SVD rank), optimizer (sign-SGD stub) - Tests: controller, population, fitness, optimizer (Google Test) - Benchmarks: evolve throughput, optimizer step (Google Benchmark) - Examples: simple optimization, PyTorch/libtorch integration - Python extension: pybind11 bindings with setup.py - README with architecture diagram and build instructions
62 lines
1.6 KiB
C++
62 lines
1.6 KiB
C++
/**
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* @file pytorch_integration.cpp
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* @brief Example: train a small neural network with FCES via libtorch.
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*/
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#include <iostream>
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#include <torch/torch.h>
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#include "fces/optimizer.hpp"
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struct TinyNet : torch::nn::Module {
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torch::nn::Linear fc1{nullptr}, fc2{nullptr};
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TinyNet() {
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fc1 = register_module("fc1", torch::nn::Linear(10, 32));
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fc2 = register_module("fc2", torch::nn::Linear(32, 1));
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}
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torch::Tensor forward(torch::Tensor x) {
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x = torch::relu(fc1->forward(x));
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return fc2->forward(x);
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}
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};
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int main() {
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auto model = std::make_shared<TinyNet>();
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std::vector<torch::Tensor> params;
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for (auto& p : model->parameters()) params.push_back(p);
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fces::FCESOptimizer optimizer(
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params,
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fces::FCESConfig{}
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.set_lr(1.6e-3f)
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.set_population_size(200)
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.set_total_steps(1000)
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);
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// Generate synthetic regression data
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auto x_train = torch::randn({100, 10});
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auto y_train = torch::sin(x_train.sum(1, true));
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for (int epoch = 0; epoch < 100; ++epoch) {
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optimizer.zero_grad();
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auto pred = model->forward(x_train);
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auto loss = torch::mse_loss(pred, y_train);
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loss.backward();
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optimizer.step();
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optimizer.update_fitness(loss.item<float>());
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if (epoch % 10 == 0) {
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std::cout << "Epoch " << epoch
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<< " | Loss: " << loss.item<float>() << std::endl;
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}
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}
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std::cout << "\nTraining complete. Final loss: "
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<< torch::mse_loss(model->forward(x_train), y_train).item<float>()
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<< std::endl;
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return 0;
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}
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