Net(int numIn, int numOut, int numHid, const size_t hid_count=1) {
		first = register_parameter("inputW", torch::randn({numIn, numHid}))/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::randn({numHid, numHid}))/numHid;
		last = register_parameter("outputW", torch::randn({numHid, numOut}))/numOut;
	}

When i am Initing the module (and in extent it’s underling parameters/tensors) I do not specify anything about biases, what is the default behavior
I have been searching in the docks the other and could find an answer
you were probably  reffering to the bias of sigmoid?
anyway i think i need to go back to drawing board after reading more