23 lines
4.9 KiB
Plaintext
23 lines
4.9 KiB
Plaintext
Final best results (dictionary):
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{'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}}, 'one_fifth': {'csv': {'path': '/home/judson/Neural-Networks-in-GNC/inverted_pendulum/training/base_loss_learning_rate_sweep/one_fifth/lr_0.250', 'csv_loss': 0.08940213173627853, 'constant_loss': 2.5548017024993896}, 'constant': {'path': '/home/judson/Neural-Networks-in-GNC/inverted_pendulum/training/base_loss_learning_rate_sweep/one_fifth/lr_0.100', 'csv_loss': 0.09396396577358246, 'constant_loss': 2.5306591987609863}}, 'three': {'csv': {'path': '/home/judson/Neural-Networks-in-GNC/inverted_pendulum/training/base_loss_learning_rate_sweep/three/lr_0.100', 'csv_loss': 0.09003690630197525, 'constant_loss': 2.5991272926330566}, 'constant': {'path': '/home/judson/Neural-Networks-in-GNC/inverted_pendulum/training/base_loss_learning_rate_sweep/three/lr_0.100', 'csv_loss': 0.09003690630197525, 'constant_loss': 2.5991272926330566}}, 'five': {'csv': {'path': '/home/judson/Neural-Networks-in-GNC/inverted_pendulum/training/base_loss_learning_rate_sweep/five/lr_0.200', 'csv_loss': 0.2009778916835785, 'constant_loss': 3.64280366897583}, 'constant': {'path': '/home/judson/Neural-Networks-in-GNC/inverted_pendulum/training/base_loss_learning_rate_sweep/five/lr_0.125', 'csv_loss': 0.20845991373062134, 'constant_loss': 3.589925527572632}}, 'one_third': {'csv': {'path': '/home/judson/Neural-Networks-in-GNC/inverted_pendulum/training/base_loss_learning_rate_sweep/one_third/lr_0.250', 'csv_loss': 0.0854221060872078, 'constant_loss': 2.506823778152466}, 'constant': {'path': '/home/judson/Neural-Networks-in-GNC/inverted_pendulum/training/base_loss_learning_rate_sweep/one_third/lr_0.250', 'csv_loss': 0.0854221060872078, 'constant_loss': 2.506823778152466}}, '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}}}
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Summary Table:
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Function Name Candidate Learning Rate CSV Loss Constant Loss
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one CSV 0.100 0.078672 2.539019
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Constant 0.100 0.078672 2.539019
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one_fourth CSV 0.300 0.088760 2.531947
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Constant 0.250 0.091723 2.528850
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four CSV 0.200 0.129314 2.997689
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Constant 0.200 0.129314 2.997689
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one_fifth CSV 0.250 0.089402 2.554802
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Constant 0.100 0.093964 2.530659
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three CSV 0.100 0.090037 2.599127
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Constant 0.100 0.090037 2.599127
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five CSV 0.200 0.200978 3.642804
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Constant 0.125 0.208460 3.589926
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one_third CSV 0.250 0.085422 2.506824
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Constant 0.250 0.085422 2.506824
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two CSV 0.100 0.076783 2.558541
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Constant 0.100 0.076783 2.558541
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one_half CSV 0.200 0.086204 2.541422
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Constant 0.200 0.086204 2.541422 |