Inverted-Pendulum-Neural-Ne.../analysis/best_base_loss_learning_rate_sweep.txt

15 lines
2.7 KiB
Plaintext

Final best results (dictionary):
{'one': {'csv': {'path': '/home/judson/Neural-Networks-in-GNC/inverted_pendulum/training/base_loss_learning_rate_sweep/one/lr_0.100', 'csv_loss': 0.07867201417684555, 'constant_loss': 2.5390186309814453}, 'constant': {'path': '/home/judson/Neural-Networks-in-GNC/inverted_pendulum/training/base_loss_learning_rate_sweep/one/lr_0.100', 'csv_loss': 0.07867201417684555, 'constant_loss': 2.5390186309814453}}, 'one_fourth': {'csv': {'path': '/home/judson/Neural-Networks-in-GNC/inverted_pendulum/training/base_loss_learning_rate_sweep/one_fourth/lr_0.300', 'csv_loss': 0.08876045793294907, 'constant_loss': 2.5319466590881348}, 'constant': {'path': '/home/judson/Neural-Networks-in-GNC/inverted_pendulum/training/base_loss_learning_rate_sweep/one_fourth/lr_0.250', 'csv_loss': 0.09172269701957703, 'constant_loss': 2.5288496017456055}}, 'four': {'csv': {'path': '/home/judson/Neural-Networks-in-GNC/inverted_pendulum/training/base_loss_learning_rate_sweep/four/lr_0.200', 'csv_loss': 0.1293140947818756, 'constant_loss': 2.9976892471313477}, 'constant': {'path': '/home/judson/Neural-Networks-in-GNC/inverted_pendulum/training/base_loss_learning_rate_sweep/four/lr_0.200', 'csv_loss': 0.1293140947818756, 'constant_loss': 2.9976892471313477}}, 'two': {'csv': {'path': '/home/judson/Neural-Networks-in-GNC/inverted_pendulum/training/base_loss_learning_rate_sweep/two/lr_0.100', 'csv_loss': 0.07678339630365372, 'constant_loss': 2.5585412979125977}, 'constant': {'path': '/home/judson/Neural-Networks-in-GNC/inverted_pendulum/training/base_loss_learning_rate_sweep/two/lr_0.100', 'csv_loss': 0.07678339630365372, 'constant_loss': 2.5585412979125977}}, 'one_half': {'csv': {'path': '/home/judson/Neural-Networks-in-GNC/inverted_pendulum/training/base_loss_learning_rate_sweep/one_half/lr_0.200', 'csv_loss': 0.08620432019233704, 'constant_loss': 2.541421890258789}, 'constant': {'path': '/home/judson/Neural-Networks-in-GNC/inverted_pendulum/training/base_loss_learning_rate_sweep/one_half/lr_0.200', 'csv_loss': 0.08620432019233704, 'constant_loss': 2.541421890258789}}}
Summary Table:
Function Name Candidate Learning Rate CSV Loss Constant Loss
one CSV 0.100 0.078672 2.539019
Constant 0.100 0.078672 2.539019
one_fourth CSV 0.300 0.088760 2.531947
Constant 0.250 0.091723 2.528850
four CSV 0.200 0.129314 2.997689
Constant 0.200 0.129314 2.997689
two CSV 0.100 0.076783 2.558541
Constant 0.100 0.076783 2.558541
one_half CSV 0.200 0.086204 2.541422
Constant 0.200 0.086204 2.541422