add explanation on tuning results
@@ -397,6 +397,10 @@ def train(config, data_folder, learning_rate=0.0001):
min_val_loss_epoch = monitor_epoch
model_state = model.state_dict()
print(monitor_epoch, min_val_loss_epoch, min_val_loss)
+ logging.info(
+ "Monitor epoch: %d Min Validation Epoch: %d Loss : %.3f" % (
+ monitor_epoch, min_val_loss_epoch, min_val_loss)
+ )
if monitor_epoch - min_val_loss_epoch > config['training']['stop_patience']:
logging.info("Saving model ...")
# Save the name with validation loss.
