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Add divergence to logs

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Divergence is the difference between training and validation to
allow a clear value to indicate the difference between the two
in the logs.
This commit is contained in:
rockerBOO
2025-01-12 13:18:26 -05:00
parent 264167fa16
commit 4c61adc996
1 changed files with 10 additions and 3 deletions
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13
train_network.py
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@@ -1418,14 +1418,16 @@ class NetworkTrainer:
if is_tracking:
logs = {
"loss/validation/step/current": current_loss,
"loss/validation/step_current": current_loss,
"val_step": (epoch * validation_steps) + val_step,
}
accelerator.log(logs, step=global_step)
if is_tracking:
loss_validation_divergence = val_step_loss_recorder.moving_average - loss_recorder.moving_average
logs = {
"loss/validation/step/average": val_step_loss_recorder.moving_average,
"loss/validation/step_average": val_step_loss_recorder.moving_average,
"loss/validation/step_divergence": loss_validation_divergence,
}
accelerator.log(logs, step=global_step)
@@ -1485,7 +1487,12 @@ class NetworkTrainer:
if is_tracking:
avr_loss: float = val_epoch_loss_recorder.moving_average
logs = {"loss/validation/epoch_average": avr_loss, "epoch": epoch + 1}
loss_validation_divergence = val_step_loss_recorder.moving_average - avr_loss
logs = {
"loss/validation/epoch_average": avr_loss,
"loss/validation/epoch_divergence": loss_validation_divergence,
"epoch": epoch + 1
}
accelerator.log(logs, step=global_step)
# END OF EPOCH