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Add divergence to logs
Divergence is the difference between training and validation to allow a clear value to indicate the difference between the two in the logs.
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@@ -1418,14 +1418,16 @@ class NetworkTrainer:
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if is_tracking:
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if is_tracking:
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logs = {
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logs = {
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"loss/validation/step/current": current_loss,
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"loss/validation/step_current": current_loss,
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"val_step": (epoch * validation_steps) + val_step,
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"val_step": (epoch * validation_steps) + val_step,
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}
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}
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accelerator.log(logs, step=global_step)
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accelerator.log(logs, step=global_step)
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if is_tracking:
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if is_tracking:
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loss_validation_divergence = val_step_loss_recorder.moving_average - loss_recorder.moving_average
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logs = {
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logs = {
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"loss/validation/step/average": val_step_loss_recorder.moving_average,
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"loss/validation/step_average": val_step_loss_recorder.moving_average,
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"loss/validation/step_divergence": loss_validation_divergence,
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}
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}
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accelerator.log(logs, step=global_step)
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accelerator.log(logs, step=global_step)
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@@ -1485,7 +1487,12 @@ class NetworkTrainer:
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if is_tracking:
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if is_tracking:
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avr_loss: float = val_epoch_loss_recorder.moving_average
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avr_loss: float = val_epoch_loss_recorder.moving_average
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logs = {"loss/validation/epoch_average": avr_loss, "epoch": epoch + 1}
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loss_validation_divergence = val_step_loss_recorder.moving_average - avr_loss
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logs = {
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"loss/validation/epoch_average": avr_loss,
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"loss/validation/epoch_divergence": loss_validation_divergence,
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"epoch": epoch + 1
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}
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accelerator.log(logs, step=global_step)
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accelerator.log(logs, step=global_step)
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# END OF EPOCH
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# END OF EPOCH
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