Deep-Learning-with-PyTorch

Graphing training metrics with TensorBoard
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Along the top of the browser window, you should see the orange header. The right
side of the header has the typical widgets for settings, a link to the GitHub repository,
and the like. We can ignore those for now. The left side of the header has items for the
data types we’ve provided. You should have at least the following:
• Scalars (the default tab)
• Histograms
• Precision-Recall Curves (shown as PR Curves)
You might see Distributions as well as the second UI tab (to the right of Scalars in figure
11.10). We won’t use or discuss those here. Make sure you’ve selected Scalars by
clicking it.
On the left is a set of controls for display options, as well as a list of runs that are
present. The smoothing option can be useful if you have particularly noisy data; it will
calm things down so that you can pick out the overall trend. The original nonsmoothed
data will still be visible in the background as a faded line in the same color.
Figure 11.11 shows this, although it might be difficult to discern when printed in black
and white.
Depending on how many times you’ve run the training script, you might have multiple
runs to select from. With too many runs being rendered, the graphs can get
overly noisy, so don’t hesitate to deselect runs that aren’t of interest at the moment.
If you want to permanently remove a run, the data can be deleted from disk while
TensorBoard is running. You can do this to get rid of experiments that crashed, had
SmOothed
trend lines
Raw Data
ploTted
Figure 11.11
The TensorBoard sidebar with Smoothing set to 0.6 and two runs selected for display