view_run_metrics: View metrics for a training run

In tfruns: Training Run Tools for 'TensorFlow'

View metrics for a training run

Description

Interactive D3 visualization of metrics for a training run. Metrics will be displayed in the RStudio Viewer (if available), otherwise will be displayed in an external web browser.

Usage

view_run_metrics(metrics)

update_run_metrics(viewer, metrics)

Arguments

metrics	Data frame containing run metrics
viewer	Viewer object returned from view_run_metrics().

Metrics Data Frame

Metrics should be passed as a data frame with one column for each metric. If the metrics are not yet complete (e.g. only metrics for the first several epochs are provided) then metrics in yet to be completed epochs should use NA as their values. For example:

data.frame':	30 obs. of  4 variables:
$ loss    : num  0.423 0.201 NA NA NA ...
$ acc     : num  0.873 0.942 NA NA NA ...
$ val_loss: num  0.174 0.121 NA NA NA ...
$ val_acc : num  0.949 0.964 NA NA NA ...

If both metrics and validation metrics are provided, you should preface the name of the validation metric with "val_" (e.g. for a metric named "loss" provide validation metrics in "val_loss"). This indicates that the metrics are related which is useful e.g. when plotting metrics.

Realtime Updates

Metrics can be updated in real-time by calling the update_run_metrics() with the run viewer instance returned from view_run_metrics(). For example:

# view metrics
viewer <- view_run_metrics(metrics)

# update with new metrics
update_run_metrics(viewer, updated_metrics)

Note

Metrics named "acc" or "accuracy" will automatically use 1.0 as the maximum value on their y-axis scale.

See Also

write_run_metrics

tfruns documentation built on May 29, 2024, 7:31 a.m.