Tune hyperparameters using training flags
Run all combinations of the specifed training flags. The number of
combinations can be reduced by specifying the sample parameter, which
will result in a random sample of the flag combinations being run.
tuning_run(
file = "train.R",
context = "local",
config = Sys.getenv("R_CONFIG_ACTIVE", unset = "default"),
flags = NULL,
sample = NULL,
properties = NULL,
runs_dir = getOption("tfruns.runs_dir", "runs"),
artifacts_dir = getwd(),
echo = TRUE,
confirm = interactive(),
envir = parent.frame(),
encoding = getOption("encoding")
)file |
Path to training script (defaults to "train.R") |
context |
Run context (defaults to "local") |
config |
The configuration to use. Defaults to the active configuration
for the current environment (as specified by the |
flags |
Either a named list with flag values (multiple values can be
provided for each flag) or a data frame that contains pre-generated
combinations of flags (e.g. via |
sample |
Sampling rate for flag combinations (defaults to running all combinations). |
properties |
Named character vector with run properties. Properties are
additional metadata about the run which will be subsequently available via
|
runs_dir |
Directory containing runs. Defaults to "runs" beneath the
current working directory (or to the value of the |
artifacts_dir |
Directory to capture created and modified files within.
Pass |
echo |
Print expressions within training script |
confirm |
Confirm before executing tuning run. |
envir |
The environment in which the script should be evaluated |
encoding |
The encoding of the training script; see |
Data frame with summary of all training runs performed during tuning.
## Not run:
library(tfruns)
# using a list as input to the flags argument
runs <- tuning_run(
system.file("examples/mnist_mlp/mnist_mlp.R", package = "tfruns"),
flags = list(
dropout1 = c(0.2, 0.3, 0.4),
dropout2 = c(0.2, 0.3, 0.4)
)
)
runs[order(runs$eval_acc, decreasing = TRUE), ]
# using a data frame as input to the flags argument
# resulting in the same combinations above, but remove those
# where the combined dropout rate exceeds 1
grid <- expand.grid(
dropout1 = c(0.2, 0.3, 0.4),
dropout2 = c(0.2, 0.3, 0.4)
)
grid$combined_droput <- grid$dropout1 + grid$dropout2
grid <- grid[grid$combined_droput <= 1, ]
runs <- tuning_run(
system.file("examples/mnist_mlp/mnist_mlp.R", package = "tfruns"),
flags = grid[, c("dropout1", "dropout2")]
)
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