"""CABS Gauge."""
import warnings
from cockpit.instruments.utils_instruments import check_data, create_basic_plot
[docs]def cabs_gauge(self, fig, gridspec):
"""CABS gauge, showing the CABS rule versus iteration.
The batch size trades-off more accurate gradient approximations with longer
computation. The `CABS criterion <https://arxiv.org/abs/1612.05086>`_ describes
the optimal batch size under certain assumptions.
The instruments shows the suggested batch size (and an exponential weighted
average) over the course of training, according to
- `Balles, L., Romero, J., & Hennig, P.,
Coupling adaptive batch sizes with learning rates (2017).
<https://arxiv.org/abs/1612.05086>`_
**Preview**
.. image:: ../../_static/instrument_previews/CABS.png
:alt: Preview CABS Gauge
**Requires**
This instrument requires data from the :class:`~cockpit.quantities.CABS`
quantity class.
Args:
self (CockpitPlotter): The cockpit plotter requesting this instrument.
fig (matplotlib.figure.Figure): Figure of the Cockpit.
gridspec (matplotlib.gridspec.GridSpec): GridSpec where the instrument should be
placed
"""
# Plot Trace vs iteration
title = "CABS"
# Check if the required data is available, else skip this instrument
requires = ["CABS"]
plot_possible = check_data(self.tracking_data, requires)
if not plot_possible:
if self.debug:
warnings.warn(
"Couldn't get the required data for the " + title + " instrument",
stacklevel=1,
)
return
plot_args = {
"x": "iteration",
"y": "CABS",
"data": self.tracking_data,
"x_scale": "symlog" if self.show_log_iter else "linear",
"y_scale": "linear",
"cmap": self.cmap,
"EMA": "y",
"EMA_alpha": self.EMA_alpha,
"EMA_cmap": self.cmap2,
"title": title,
"xlim": "tight",
"ylim": None,
"fontweight": "bold",
"facecolor": self.bg_color_instruments,
}
ax = fig.add_subplot(gridspec)
create_basic_plot(**plot_args, ax=ax)