library("rplatypus")
spec <- platypus_spec(
data = segmentation_data("train", "valid", colormap = binary_colormap),
models = list(u_net("vessels", input_shape = c(256, 256))),
output_dir = "runs"
)
fit <- platypus_fit(spec)
record <- read_record("runs/vessels")
record$pyplatypus # the engine that produced it
head(record$history) # one row per epoch
record$specification # enough to run it againRead what a run wrote down about itself
Description
platypus_fit() leaves a record beside the weights when the specification named an output_dir: the whole specification, whatever the run worked out for itself, the epoch-by-epoch history, and the version of the engine that produced it. This reads one back.
Usage
read_record(path)
Arguments
path
|
The run.json to read, or the directory holding it - so both read_record(“out/my_model”) and read_record(“out/my_model/run.json”) work.
|
Details
Named to match read_record() in the engine, and read_images() and read_masks() here.
Value
A platypus_record: a list with written, pyplatypus, model, specification, derived and history. Everything is plain R data, because a record describes what happened rather than being a live thing - except history, which comes back as a data frame with one row per epoch.
That frame is not quite what training_history() returns, and the difference is worth knowing before you rbind() them: a record belongs to one model, whose name is the top-level model field, so the frame has no model column. training_history() spans every model in the fit and needs one. Every other column is the same.
Why you would want it
The specification in the record is a thing you can run again, not a description of a run. And for a detector whose anchors were fitted rather than named, derived$anchors is the only copy outside a weights sidecar - so a record is how a result stays reproducible after the session that produced it has gone.
It does not start Python
Deliberately. Everything else in this package asks the engine; this reads a text file, and a first call to the engine provisions an environment of several gigabytes. A record you cannot read without the thing that wrote it is worth less than one you can, so this needs only jsonlite, and works on a machine that has never had Python.
See Also
training_history() for the same numbers from a fit in the current session, and export_weights(), whose sidecar answers a different question - what the weights are for, rather than what the run was.