This post pertains to performing multivariate (predicting more than 1 output) regression in PyTorch. Say we are predicting N-variables, so our output layer has N nodes. The objective function used is the MSE objective function.
cost_func = nn.MSELoss()
When training, we supply 2 arguments to cost_func(arg_1, arg_2), where arg_1 is the labels of the training samples, and arg_2 is the output of the forward pass. Should arg_1 and arg_2 bot have N columns, or should they be cast to 1 column tensors?