i try save model after traing,but i got error after training mnist data,follow example here:
https://pytorch.org/cppdocs/frontend.html 1
here is my error:
D:\yangxiaodong\VSProject\libtorch-win-shared-with-deps-latest\libtorch\include\torch\csrc\api\include\torch/serialize.h(39): error C2679: binary ‘<<’ : no operator found which takes a right-hand operand of type ‘const Value’ (or there is no acceptable conversion)
here is my code:
#include <torch/torch.h>
#include <torch/script.h>
#include 
#include 
#include 
#include <vector
#include <opencv2/core.hpp>
#include <opencv2/highgui.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/imgcodecs.hpp>
struct Net : torch::nn::Module {
Net() {
// Construct and register two Linear submodules.
fc1 = register_module(“fc1”, torch::nn::Linear(784, 64));
fc2 = register_module(“fc2”, torch::nn::Linear(64, 32));
fc3 = register_module(“fc3”, torch::nn::Linear(32, 10));
}
// Implement the Net's algorithm.
torch::Tensor forward(torch::Tensor x) {
	// Use one of many tensor manipulation functions.
	x = torch::relu(fc1->forward(x.reshape({ x.size(0), 784 })));
	x = torch::dropout(x, /*p=*/0.5, /*train=*/is_training());
	x = torch::relu(fc2->forward(x));
	x = torch::log_softmax(fc3->forward(x), /*dim=*/1);
	return x;
}

// Use one of many "standard library" modules.
torch::nn::Linear fc1{ nullptr }, fc2{ nullptr }, fc3{ nullptr };

};
int main() {
// Create a new Net.
auto net = std::make_shared();
// Create a multi-threaded data loader for the MNIST dataset.
auto data_loader = torch::data::make_data_loader(
	torch::data::datasets::MNIST("./data").map(
		torch::data::transforms::Stack<>()),
	/*batch_size=*/64);

// Instantiate an SGD optimization algorithm to update our Net's parameters.
torch::optim::SGD optimizer(net->parameters(), /*lr=*/0.01);

for (size_t epoch = 1; epoch <= 10; ++epoch) {
	size_t batch_index = 0;
	// Iterate the data loader to yield batches from the dataset.
	for (auto& batch : *data_loader) {
		// Reset gradients.
		optimizer.zero_grad();
		// Execute the model on the input data.
		torch::Tensor prediction = net->forward(batch.data);
		// Compute a loss value to judge the prediction of our model.
		torch::Tensor loss = torch::nll_loss(prediction, batch.target);
		// Compute gradients of the loss w.r.t. the parameters of our model.
		loss.backward();
		// Update the parameters based on the calculated gradients.
		optimizer.step();
		// Output the loss and checkpoint every 100 batches.
		if (++batch_index % 100 == 0) {
			std::cout << "Epoch: " << epoch << " | Batch: " << batch_index
				<< " | Loss: " << loss.item<float>() << std::endl;
			// Serialize your model periodically as a checkpoint.
                            //here i got an error
			torch::save(net, "d:/net.pt");
		}
	}
}

}