First, let's import the necessary modules. PyTorch modules are quite straight forward. You can build a fully functional neural network using Tensor computation alone, but this is not what this article is about. We'll make use of the more powerful and convenient torch.nn, For those of you interested in knowing how to do this from'scratch scratch', visit this fantastic PyTorch official tutorial by Jeremy Howard.

When an epoch ends, we'll calculate the epoch duration and the run duration(up to this epoch, not the final run duration unless for the last epoch of the run). We'll calculate the total loss and accuracy for this epoch, then export the loss, accuracy, weights/biases, gradients we recorded into Tensor Board. For ease of tracking within the Jupyter Notebook, we also created an OrderedDict object results and put all our run data(loss, accuracy, run count, epoch count, run duration, epoch duration, all hyperparameters) into it. Then we'll use Pandas to read it in and display it in a neat table format.