def log_training_results(trainer):
evaluator.run(train_loader)
metrics = evaluator.state.metrics
timestamp = get_readable_time()
print(timestamp + " Training set Results - Epoch: {} Avg mae: {:.2f} Avg mse: {:.2f} Avg loss: {:.2f}"
.format(trainer.state.epoch, metrics['mae'], metrics['mse'], metrics['loss']))
experiment.log_metric("epoch", trainer.state.epoch)
experiment.log_metric("train_mae", metrics['mae'])
experiment.log_metric("train_mse", metrics['mse'])
experiment.log_metric("train_loss", metrics['loss'])
experiment.log_metric("lr", get_lr(optimizer))
def log_training_results(trainer):
experiment.log_metric("epoch", trainer.state.epoch)
if not args.skip_train_eval:
evaluator_train.run(train_loader_eval)
metrics = evaluator_train.state.metrics
timestamp = get_readable_time()
print(
timestamp +
" Training set Results - Epoch: {} Avg mae: {:.2f} Avg mse: {:.2f} Avg loss: {:.2f}"
.format(trainer.state.epoch, metrics['mae'], metrics['mse'], 0)
)
# experiment.log_metric("epoch", trainer.state.epoch)
experiment.log_metric("train_mae", metrics['mae'])
experiment.log_metric("train_mse", metrics['mse'])
experiment.log_metric("lr", get_lr(optimizer))
print("batch_timer ", batch_timer.value())
print("train_timer ", train_timer.value())
experiment.log_metric("batch_timer", batch_timer.value())
experiment.log_metric("train_timer", train_timer.value())