Better visualization with respect to hparams

When doing hyperparameter tuning, i'll often have tensorboards that look like this:

which technically has all the information from my tuning run. However, it's hard to parse and use. Ideally, I would be able to:

• Label curves by hparam settings (where I can define which hparams to display).
• Let different hparam choices set the line formatting.

As an example for the latter point: suppose I had a hyperparameter sweep which included different learning rates and different batch sizes. I would love to be able to understand the effect of both by, say:

• Coloring curves with the same learning rates the same color.
• Choosing a different linestyle (solid, dotted, dashed, etc.) for each batch size.
  There may be different ways to provide multiple dimensions of curve sorting, IANAE. However I believe having more visualization options can help make tensorboards more useful.

Start by reviewing the existing hyperparameter-tuning visualization described in the issue and the linked example. Define how users would select hyperparameters for curve labels, colors, and line styles, then identify the relevant visualization entry points and tests in the repository. Done means tuning curves can be distinguished across multiple hyperparameter dimensions.