Tracker
Tracks loss, accuracy and model weights during model.fit()
__getitem__(self, key)
special
¶
- key=
train | val
then return respectiveTrackingValues
object - key=
metrics
then return a dictionary of metrics - key=
loss
then return a dictionary of losses
Parameters:
Name | Type | Description | Default |
---|---|---|---|
key |
str |
train, val, metrics or loss |
required |
Returns:
Type | Description |
---|---|
|
reset(self)
¶
Resets epochs, logs and train & val TrackingValues
.
track_loss(self, 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(self, 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 |
Last update:
October 3, 2021