Plot the trajectory of a Keras model fit.

Source: R/FitHistoryPlot.R

Plot a history of model fit performance over the number of training epochs.

plot_Keras_fit_trajectory(
  d,
  title,
  ...,
  epoch_name = "epoch",
  lossname = "loss",
  loss_pretty_name = "minus binary cross entropy",
  perfname = "acc",
  perf_pretty_name = "accuracy",
  pick_metric = loss_pretty_name,
  fliploss = TRUE,
  discount_rate = NULL,
  draw_ribbon = FALSE,
  val_color = "#d95f02",
  train_color = "#1b9e77",
  pick_color = "#e6ab02"
)

Arguments

d

data frame to get values from.
title

character title for plot.
...

force later arguments to be bound by name
epoch_name

name for epoch or trajectory column.
lossname

name of training loss column (default 'loss')
loss_pretty_name

name for loss on graph (default 'minus binary cross entropy')
perfname

name of training performance column (default 'acc')
perf_pretty_name

name for performance metric on graph (default 'accuracy')
pick_metric

character: metric to maximize (NULL for no pick line - default loss_pretty_name)
fliploss

flip the loss so that "larger is better"? (default TRUE)
discount_rate

numeric: what fraction of over-fit to subtract from validation performance.
draw_ribbon

present the difference in training and validation performance as a ribbon rather than two curves? (default FALSE)
val_color

color for validation performance curve
train_color

color for training performance curve
pick_color

color for indicating optimal stopping point

Value

ggplot2 plot

Details

Assumes a performance matrix that carries information for both training and validation loss, and an additional training and validation performance metric, in the format that a Keras history object returns.

By default, flips the loss so that better performance is larger for both the loss and the performance metric, and then draws a vertical line at the minimum validation loss (maximum flipped validation loss). If you choose not to flip the loss, you should not use the loss as the pick_metric.

The example below gives a fit plot for a history report from Keras R package. Please see https://winvector.github.io/FluidData/PlotExample/KerasPerfPlot.html for some details.

See also

plot_fit_trajectory

Examples


# example data (from Keras)
d <- data.frame(
  val_loss = c(0.3769818, 0.2996994, 0.2963943, 0.2779052, 0.2842501),
  val_acc  = c(0.8722000, 0.8895000, 0.8822000, 0.8899000, 0.8861000),
  loss     = c(0.5067290, 0.3002033, 0.2165675, 0.1738829, 0.1410933),
  acc      = c(0.7852000, 0.9040000, 0.9303333, 0.9428000, 0.9545333) )

plt <- plot_Keras_fit_trajectory(
  d,
  title = "model performance by epoch, dataset, and measure")

suppressWarnings(print(plt)) # too few points for loess

#> `geom_smooth()` using formula 'y ~ x'
#> `geom_smooth()` using formula 'y ~ x'