Let's start by generating the training data. The following code shows how we can do this:
torch::DeviceType device = torch::cuda::is_available() ? torch::DeviceType::CUDA : torch::DeviceType::CPU; std::random_device rd; std::mt19937 re(rd()); std::uniform_real_distribution<float> dist(-0.1f, 0.1f); // generate data size_t n = 1000; torch::Tensor x; torch::Tensor y; { std::vector<float> values(n); std::iota(values.begin(), values.end(), 0); std::shuffle(values.begin(), values.end(), re); std::vector<torch::Tensor> x_vec(n); std::vector<torch::Tensor> y_vec(n); for (size_t i = 0; i < n; ++i) { x_vec[i] = torch::tensor( values[i], torch::dtype(torch::kFloat).device(device).requires_grad(false)); y_vec[i] = ...
