Tracker

Bases: BaseTracker

Tracks loss, accuracy and model weights during model.fit()

__getitem__(key)

1. key= train | val then return respective TrackingValues object
2. key=metrics then return a dictionary of metrics
3. key=loss then return a dictionary of losses

Parameters:

Name	Type	Description	Default
key	str	train, val, metrics or loss	required

Returns:

Type	Description
	TrackingValues or a Dictionary

reset()

Resets epochs, logs and train & val TrackingValues.

track_loss(loss, mode)

Tracks loss by adding to Tracker.logs and maintaining average loss in a single Epoch with TrackingValues. Update loss with TrackingValues.update_loss(loss) which is called with TrainEvalCallback at *_step_end.

Parameters:

Name	Type	Description	Default
loss	float	Step Loss	required
mode	str	can be train | val	required

track_metrics(metric, mode)

Tracks metrics by adding to Tracker.logs and maintaining average metric in a single Epoch with TrackingValues. Update metrics with TrackingValues.update_metrics(metrics) which is called with TrainEvalCallback at *_step_end.

Parameters:

Name	Type	Description	Default
metric	Dict[str, float]	Step metric	required
mode	str	can be train | val	required

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Last update: October 3, 2021