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Configuration error
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3a2e5f0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 | """Default training callbacks.
Mirrors notebook cell 22 (``EarlyStopping(patience=3, restore_best_weights=True)``)
and adds Phase-2 hooks (``ModelCheckpoint``, ``CSVLogger``) that the trainer
will use. Each callback is created by a tiny factory so callers don't have to
import TF for the names.
"""
from __future__ import annotations
from pathlib import Path
from captioning.config.schema import AppConfig
def default_callbacks(
config: AppConfig,
*,
output_dir: str | Path | None = None,
):
"""Return the list of callbacks ``Trainer.fit`` will pass to ``model.fit``.
Args:
config: App config (uses ``train.early_stopping_patience``).
output_dir: If provided, ``ModelCheckpoint`` writes ``best.h5`` and
``CSVLogger`` writes ``training_log.csv`` here. Notebook does
neither — these are Phase-1b improvements layered on top of the
parity baseline. They run *before* parity is exercised because
adding a callback does not change loss values, only emits files.
Returns:
A list of ``tf.keras.callbacks.Callback`` instances.
"""
import tensorflow as tf
callbacks = [
tf.keras.callbacks.EarlyStopping(
patience=config.train.early_stopping_patience,
restore_best_weights=True,
),
]
if output_dir is not None:
out = Path(output_dir)
out.mkdir(parents=True, exist_ok=True)
callbacks += [
tf.keras.callbacks.ModelCheckpoint(
filepath=str(out / "best.h5"),
save_weights_only=True,
save_best_only=True,
monitor="val_loss",
),
tf.keras.callbacks.CSVLogger(str(out / "training_log.csv")),
]
return callbacks
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