Why do 2 identical Neural Network fowarding a different result

3

Your problem is caused by dividing the result of ‘register_parameter’ → it creates different tensor which is then assigned to member variable (thus can’t be affected from outside the module by editing ‘.parameters()’)
the part → “/numHid”:

first = register_parameter("inputW", torch::rand({numIn, numHid}))/numHid;

Full example using approach from: How to copy parameters:

struct NetImpl : torch::nn::Module {
	NetImpl(int numIn, int numOut, int numHid, const size_t hid_count = 1) {
		assert(hid_count >= 1);
		first = register_parameter("inputW", torch::rand({ numIn, numHid }));
		middle = new torch::Tensor[hid_count - 1];
		for (int i = 1; i != hid_count; i++)
			middle[i] = register_parameter("hidW" + std::to_string(i), torch::rand({ numHid, numHid }));
		last = register_parameter("outputW", torch::rand({ numHid, numOut }));
		h_c = hid_count;
		n_h = numHid;
	}
	torch::Tensor forward(torch::Tensor input)
	{
		torch::Tensor output_layer, h;
		h = (torch::mm(input, first));
		for (int i = 1; i != h_c; i++)
			h = torch::sigmoid(torch::mm(h, middle[i]));
		output_layer = (torch::mm(h, last));
		return output_layer;
	}
	torch::Tensor first, last, *middle;
	size_t h_c, n_h;
};

TORCH_MODULE(Net);

void main(int argc, char** argv)
{
	try
	{
		const int input_nodes = 20, output_nodes = 10, hidden_nodes = 2, hidden_count = 1;

		Net nn0(input_nodes, output_nodes, hidden_nodes, hidden_count);
		Net nn1(input_nodes, output_nodes, hidden_nodes, hidden_count);

		{
			torch::autograd::GradMode::set_enabled(false);
			auto src = nn0->named_parameters(true /*recurse*/);
			auto dst = nn1->named_parameters(true /*recurse*/);

			for (auto& val : src)
			{
				auto name = val.key();
				auto* t = dst.find(name);
				if (t != nullptr)
				{
					t->copy_(val.value());
				}
			}

			torch::autograd::GradMode::set_enabled(true);
		}

		const torch::Tensor input = torch::ones({ 1,input_nodes });

		std::cout << "Diff: " << nn0->forward(input) - nn1->forward(input) << std::endl;
	}
	catch (const c10::Error& e)
	{
		std::cout << e.what() << std::endl;
	}
	catch (const std::runtime_error& e)
	{
		std::cout << e.what() << std::endl;
	}
	
	system("PAUSE");
}