fix_kox
This commit is contained in:
parent
63d63f0522
commit
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5
my_runs/5/config.json
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my_runs/5/config.json
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{
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"epochs": 10,
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"learning_rate": 0.001,
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"seed": 102385107
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}
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my_runs/5/cout.txt
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my_runs/5/cout.txt
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tensor(1.9501, grad_fn=<NllLossBackward>)
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tensor(2.2223, grad_fn=<NllLossBackward>)
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tensor(2.0121, grad_fn=<NllLossBackward>)
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tensor(2.0934, grad_fn=<NllLossBackward>)
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tensor(2.1164, grad_fn=<NllLossBackward>)
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tensor(1.7837, grad_fn=<NllLossBackward>)
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tensor(1.8654, grad_fn=<NllLossBackward>)
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tensor(1.8964, grad_fn=<NllLossBackward>)
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tensor(2.0159, grad_fn=<NllLossBackward>)
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tensor(2.0181, grad_fn=<NllLossBackward>)
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tensor(1.8816, grad_fn=<NllLossBackward>)
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tensor(2.0737, grad_fn=<NllLossBackward>)
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tensor(1.9732, grad_fn=<NllLossBackward>)
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tensor(2.1428, grad_fn=<NllLossBackward>)
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tensor(1.7334, grad_fn=<NllLossBackward>)
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tensor(1.9861, grad_fn=<NllLossBackward>)
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tensor(1.7247, grad_fn=<NllLossBackward>)
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tensor(1.9532, grad_fn=<NllLossBackward>)
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tensor(2.0621, grad_fn=<NllLossBackward>)
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tensor(1.8170, grad_fn=<NllLossBackward>)
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tensor(1.9526, grad_fn=<NllLossBackward>)
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tensor(1.9936, grad_fn=<NllLossBackward>)
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tensor(1.9125, grad_fn=<NllLossBackward>)
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tensor(1.8121, grad_fn=<NllLossBackward>)
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tensor(1.9899, grad_fn=<NllLossBackward>)
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tensor(2.1121, grad_fn=<NllLossBackward>)
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tensor(2.0649, grad_fn=<NllLossBackward>)
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tensor(1.9243, grad_fn=<NllLossBackward>)
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tensor(1.8116, grad_fn=<NllLossBackward>)
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tensor(1.7532, grad_fn=<NllLossBackward>)
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tensor(1.7859, grad_fn=<NllLossBackward>)
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tensor(1.5712, grad_fn=<NllLossBackward>)
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tensor(1.8279, grad_fn=<NllLossBackward>)
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tensor(1.7469, grad_fn=<NllLossBackward>)
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tensor(1.7512, grad_fn=<NllLossBackward>)
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tensor(1.6985, grad_fn=<NllLossBackward>)
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tensor(1.7779, grad_fn=<NllLossBackward>)
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tensor(1.8746, grad_fn=<NllLossBackward>)
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tensor(1.8326, grad_fn=<NllLossBackward>)
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tensor(1.7284, grad_fn=<NllLossBackward>)
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tensor(1.5637, grad_fn=<NllLossBackward>)
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tensor(1.7761, grad_fn=<NllLossBackward>)
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tensor(1.7882, grad_fn=<NllLossBackward>)
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tensor(1.7751, grad_fn=<NllLossBackward>)
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tensor(1.7592, grad_fn=<NllLossBackward>)
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tensor(1.8005, grad_fn=<NllLossBackward>)
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tensor(1.7046, grad_fn=<NllLossBackward>)
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tensor(1.6825, grad_fn=<NllLossBackward>)
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tensor(1.6354, grad_fn=<NllLossBackward>)
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tensor(1.6500, grad_fn=<NllLossBackward>)
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tensor(1.5438, grad_fn=<NllLossBackward>)
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tensor(1.6757, grad_fn=<NllLossBackward>)
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tensor(1.5822, grad_fn=<NllLossBackward>)
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tensor(1.7962, grad_fn=<NllLossBackward>)
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tensor(1.7180, grad_fn=<NllLossBackward>)
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tensor(1.7428, grad_fn=<NllLossBackward>)
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tensor(1.5483, grad_fn=<NllLossBackward>)
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tensor(1.5469, grad_fn=<NllLossBackward>)
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tensor(1.5246, grad_fn=<NllLossBackward>)
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tensor(1.4950, grad_fn=<NllLossBackward>)
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tensor(1.4438, grad_fn=<NllLossBackward>)
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tensor(1.5273, grad_fn=<NllLossBackward>)
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tensor(1.5544, grad_fn=<NllLossBackward>)
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tensor(1.4241, grad_fn=<NllLossBackward>)
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tensor(1.4795, grad_fn=<NllLossBackward>)
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tensor(1.6521, grad_fn=<NllLossBackward>)
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tensor(1.5346, grad_fn=<NllLossBackward>)
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tensor(1.5802, grad_fn=<NllLossBackward>)
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tensor(1.5093, grad_fn=<NllLossBackward>)
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tensor(1.6289, grad_fn=<NllLossBackward>)
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tensor(1.5276, grad_fn=<NllLossBackward>)
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tensor(1.4607, grad_fn=<NllLossBackward>)
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tensor(1.4889, grad_fn=<NllLossBackward>)
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tensor(1.5309, grad_fn=<NllLossBackward>)
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tensor(1.5775, grad_fn=<NllLossBackward>)
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tensor(1.5067, grad_fn=<NllLossBackward>)
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tensor(1.5720, grad_fn=<NllLossBackward>)
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tensor(1.2021, grad_fn=<NllLossBackward>)
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tensor(1.4729, grad_fn=<NllLossBackward>)
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tensor(1.5119, grad_fn=<NllLossBackward>)
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tensor(1.5914, grad_fn=<NllLossBackward>)
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tensor(1.5150, grad_fn=<NllLossBackward>)
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tensor(1.5267, grad_fn=<NllLossBackward>)
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tensor(1.4954, grad_fn=<NllLossBackward>)
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tensor(1.4353, grad_fn=<NllLossBackward>)
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tensor(1.4683, grad_fn=<NllLossBackward>)
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tensor(1.4718, grad_fn=<NllLossBackward>)
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tensor(1.5055, grad_fn=<NllLossBackward>)
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tensor(1.4560, grad_fn=<NllLossBackward>)
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tensor(1.4269, grad_fn=<NllLossBackward>)
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tensor(1.3962, grad_fn=<NllLossBackward>)
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tensor(1.4041, grad_fn=<NllLossBackward>)
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tensor(1.3641, grad_fn=<NllLossBackward>)
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tensor(1.4375, grad_fn=<NllLossBackward>)
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tensor(1.3466, grad_fn=<NllLossBackward>)
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tensor(1.3901, grad_fn=<NllLossBackward>)
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tensor(1.3942, grad_fn=<NllLossBackward>)
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tensor(1.3253, grad_fn=<NllLossBackward>)
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tensor(1.3589, grad_fn=<NllLossBackward>)
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tensor(1.2713, grad_fn=<NllLossBackward>)
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tensor(1.3262, grad_fn=<NllLossBackward>)
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tensor(1.3965, grad_fn=<NllLossBackward>)
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tensor(1.3821, grad_fn=<NllLossBackward>)
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tensor(1.3932, grad_fn=<NllLossBackward>)
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tensor(1.2758, grad_fn=<NllLossBackward>)
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tensor(1.3762, grad_fn=<NllLossBackward>)
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tensor(1.3881, grad_fn=<NllLossBackward>)
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tensor(1.3043, grad_fn=<NllLossBackward>)
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tensor(1.2947, grad_fn=<NllLossBackward>)
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tensor(1.3123, grad_fn=<NllLossBackward>)
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tensor(1.4206, grad_fn=<NllLossBackward>)
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tensor(1.4382, grad_fn=<NllLossBackward>)
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tensor(1.3018, grad_fn=<NllLossBackward>)
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tensor(1.3518, grad_fn=<NllLossBackward>)
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tensor(1.3169, grad_fn=<NllLossBackward>)
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tensor(1.4328, grad_fn=<NllLossBackward>)
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tensor(1.1904, grad_fn=<NllLossBackward>)
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tensor(1.3218, grad_fn=<NllLossBackward>)
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tensor(1.4076, grad_fn=<NllLossBackward>)
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tensor(1.3104, grad_fn=<NllLossBackward>)
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tensor(1.3910, grad_fn=<NllLossBackward>)
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tensor(1.2078, grad_fn=<NllLossBackward>)
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tensor(1.3289, grad_fn=<NllLossBackward>)
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tensor(1.2552, grad_fn=<NllLossBackward>)
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tensor(1.2702, grad_fn=<NllLossBackward>)
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tensor(1.2306, grad_fn=<NllLossBackward>)
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tensor(1.2207, grad_fn=<NllLossBackward>)
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tensor(1.3377, grad_fn=<NllLossBackward>)
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tensor(1.2046, grad_fn=<NllLossBackward>)
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tensor(1.2382, grad_fn=<NllLossBackward>)
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tensor(1.1590, grad_fn=<NllLossBackward>)
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tensor(1.1479, grad_fn=<NllLossBackward>)
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tensor(1.1792, grad_fn=<NllLossBackward>)
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tensor(1.1465, grad_fn=<NllLossBackward>)
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tensor(1.2166, grad_fn=<NllLossBackward>)
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tensor(1.1696, grad_fn=<NllLossBackward>)
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tensor(1.1821, grad_fn=<NllLossBackward>)
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tensor(1.2314, grad_fn=<NllLossBackward>)
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tensor(1.2756, grad_fn=<NllLossBackward>)
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tensor(1.2671, grad_fn=<NllLossBackward>)
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tensor(1.2991, grad_fn=<NllLossBackward>)
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tensor(1.2198, grad_fn=<NllLossBackward>)
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tensor(1.3112, grad_fn=<NllLossBackward>)
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tensor(1.2875, grad_fn=<NllLossBackward>)
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tensor(1.1222, grad_fn=<NllLossBackward>)
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tensor(1.2184, grad_fn=<NllLossBackward>)
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tensor(1.1955, grad_fn=<NllLossBackward>)
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tensor(1.1467, grad_fn=<NllLossBackward>)
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tensor(1.2610, grad_fn=<NllLossBackward>)
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tensor(1.2439, grad_fn=<NllLossBackward>)
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tensor(1.0732, grad_fn=<NllLossBackward>)
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tensor(1.0671, grad_fn=<NllLossBackward>)
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tensor(1.1094, grad_fn=<NllLossBackward>)
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tensor(1.1906, grad_fn=<NllLossBackward>)
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tensor(1.2624, grad_fn=<NllLossBackward>)
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tensor(1.2240, grad_fn=<NllLossBackward>)
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tensor(1.2576, grad_fn=<NllLossBackward>)
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tensor(1.1523, grad_fn=<NllLossBackward>)
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tensor(1.0610, grad_fn=<NllLossBackward>)
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tensor(1.1444, grad_fn=<NllLossBackward>)
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tensor(1.0518, grad_fn=<NllLossBackward>)
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tensor(1.1127, grad_fn=<NllLossBackward>)
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tensor(1.0876, grad_fn=<NllLossBackward>)
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tensor(1.0053, grad_fn=<NllLossBackward>)
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tensor(1.1154, grad_fn=<NllLossBackward>)
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tensor(1.0077, grad_fn=<NllLossBackward>)
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tensor(1.1619, grad_fn=<NllLossBackward>)
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tensor(1.1994, grad_fn=<NllLossBackward>)
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tensor(1.2479, grad_fn=<NllLossBackward>)
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tensor(0.9788, grad_fn=<NllLossBackward>)
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tensor(1.0385, grad_fn=<NllLossBackward>)
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tensor(1.1875, grad_fn=<NllLossBackward>)
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tensor(1.1982, grad_fn=<NllLossBackward>)
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tensor(1.1858, grad_fn=<NllLossBackward>)
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tensor(1.1008, grad_fn=<NllLossBackward>)
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tensor(1.0502, grad_fn=<NllLossBackward>)
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tensor(1.0356, grad_fn=<NllLossBackward>)
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tensor(1.0291, grad_fn=<NllLossBackward>)
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tensor(1.0144, grad_fn=<NllLossBackward>)
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tensor(1.2293, grad_fn=<NllLossBackward>)
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tensor(1.2292, grad_fn=<NllLossBackward>)
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tensor(1.1344, grad_fn=<NllLossBackward>)
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tensor(1.0412, grad_fn=<NllLossBackward>)
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tensor(1.1474, grad_fn=<NllLossBackward>)
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tensor(0.9736, grad_fn=<NllLossBackward>)
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tensor(1.0998, grad_fn=<NllLossBackward>)
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tensor(1.2037, grad_fn=<NllLossBackward>)
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tensor(1.1035, grad_fn=<NllLossBackward>)
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tensor(0.9991, grad_fn=<NllLossBackward>)
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tensor(1.0947, grad_fn=<NllLossBackward>)
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tensor(1.0100, grad_fn=<NllLossBackward>)
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tensor(1.0542, grad_fn=<NllLossBackward>)
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tensor(1.1516, grad_fn=<NllLossBackward>)
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tensor(1.0869, grad_fn=<NllLossBackward>)
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tensor(1.1072, grad_fn=<NllLossBackward>)
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tensor(1.0855, grad_fn=<NllLossBackward>)
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tensor(1.0719, grad_fn=<NllLossBackward>)
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tensor(1.0497, grad_fn=<NllLossBackward>)
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tensor(1.0355, grad_fn=<NllLossBackward>)
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tensor(1.0872, grad_fn=<NllLossBackward>)
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tensor(1.0117, grad_fn=<NllLossBackward>)
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tensor(0.9536, grad_fn=<NllLossBackward>)
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tensor(1.1327, grad_fn=<NllLossBackward>)
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tensor(0.8925, grad_fn=<NllLossBackward>)
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tensor(1.1434, grad_fn=<NllLossBackward>)
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tensor(1.0641, grad_fn=<NllLossBackward>)
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tensor(1.1626, grad_fn=<NllLossBackward>)
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tensor(1.1528, grad_fn=<NllLossBackward>)
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tensor(1.3020, grad_fn=<NllLossBackward>)
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tensor(0.9304, grad_fn=<NllLossBackward>)
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tensor(1.1756, grad_fn=<NllLossBackward>)
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tensor(1.0326, grad_fn=<NllLossBackward>)
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tensor(1.1883, grad_fn=<NllLossBackward>)
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tensor(0.9641, grad_fn=<NllLossBackward>)
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tensor(1.1524, grad_fn=<NllLossBackward>)
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tensor(1.1084, grad_fn=<NllLossBackward>)
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tensor(0.9375, grad_fn=<NllLossBackward>)
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tensor(1.1257, grad_fn=<NllLossBackward>)
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tensor(0.9931, grad_fn=<NllLossBackward>)
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tensor(0.9661, grad_fn=<NllLossBackward>)
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tensor(1.0908, grad_fn=<NllLossBackward>)
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tensor(1.2453, grad_fn=<NllLossBackward>)
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tensor(1.0393, grad_fn=<NllLossBackward>)
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tensor(1.0218, grad_fn=<NllLossBackward>)
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tensor(0.9613, grad_fn=<NllLossBackward>)
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tensor(0.9365, grad_fn=<NllLossBackward>)
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tensor(1.1133, grad_fn=<NllLossBackward>)
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tensor(1.0401, grad_fn=<NllLossBackward>)
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tensor(0.9598, grad_fn=<NllLossBackward>)
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tensor(0.9377, grad_fn=<NllLossBackward>)
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tensor(1.0843, grad_fn=<NllLossBackward>)
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tensor(0.9690, grad_fn=<NllLossBackward>)
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tensor(0.9090, grad_fn=<NllLossBackward>)
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tensor(0.8713, grad_fn=<NllLossBackward>)
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tensor(1.1776, grad_fn=<NllLossBackward>)
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tensor(1.0289, grad_fn=<NllLossBackward>)
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tensor(1.1626, grad_fn=<NllLossBackward>)
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tensor(1.0496, grad_fn=<NllLossBackward>)
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tensor(1.0960, grad_fn=<NllLossBackward>)
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tensor(1.0211, grad_fn=<NllLossBackward>)
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tensor(1.0563, grad_fn=<NllLossBackward>)
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tensor(1.1636, grad_fn=<NllLossBackward>)
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tensor(1.1681, grad_fn=<NllLossBackward>)
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tensor(1.0832, grad_fn=<NllLossBackward>)
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tensor(1.0135, grad_fn=<NllLossBackward>)
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tensor(0.8882, grad_fn=<NllLossBackward>)
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tensor(0.9941, grad_fn=<NllLossBackward>)
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tensor(1.1249, grad_fn=<NllLossBackward>)
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tensor(0.9753, grad_fn=<NllLossBackward>)
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tensor(1.0903, grad_fn=<NllLossBackward>)
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tensor(0.9399, grad_fn=<NllLossBackward>)
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tensor(1.1707, grad_fn=<NllLossBackward>)
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tensor(1.0738, grad_fn=<NllLossBackward>)
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tensor(1.0737, grad_fn=<NllLossBackward>)
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tensor(0.9548, grad_fn=<NllLossBackward>)
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tensor(1.0821, grad_fn=<NllLossBackward>)
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||||||
|
tensor(0.5808, grad_fn=<NllLossBackward>)
|
||||||
|
tensor(0.7947, grad_fn=<NllLossBackward>)
|
||||||
|
tensor(0.9082, grad_fn=<NllLossBackward>)
|
||||||
|
tensor(0.7287, grad_fn=<NllLossBackward>)
|
||||||
|
tensor(0.5995, grad_fn=<NllLossBackward>)
|
||||||
|
tensor(0.9633, grad_fn=<NllLossBackward>)
|
||||||
|
tensor(0.8313, grad_fn=<NllLossBackward>)
|
||||||
|
tensor(0.7518, grad_fn=<NllLossBackward>)
|
||||||
|
tensor(0.6092, grad_fn=<NllLossBackward>)
|
||||||
|
tensor(0.9824, grad_fn=<NllLossBackward>)
|
||||||
|
tensor(0.6653, grad_fn=<NllLossBackward>)
|
||||||
|
tensor(0.8603, grad_fn=<NllLossBackward>)
|
||||||
|
tensor(0.6490, grad_fn=<NllLossBackward>)
|
||||||
|
tensor(0.6911, grad_fn=<NllLossBackward>)
|
||||||
|
tensor(0.7257, grad_fn=<NllLossBackward>)
|
||||||
|
tensor(0.7694, grad_fn=<NllLossBackward>)
|
||||||
2410
my_runs/5/metrics.json
Normal file
2410
my_runs/5/metrics.json
Normal file
File diff suppressed because it is too large
Load Diff
88
my_runs/5/run.json
Normal file
88
my_runs/5/run.json
Normal file
@ -0,0 +1,88 @@
|
|||||||
|
{
|
||||||
|
"artifacts": [],
|
||||||
|
"command": "my_main",
|
||||||
|
"experiment": {
|
||||||
|
"base_dir": "e:\\Pyton\\IUM\\ium_452627",
|
||||||
|
"dependencies": [
|
||||||
|
"numpy==1.20.0",
|
||||||
|
"pandas==1.4.1",
|
||||||
|
"sacred==0.8.4",
|
||||||
|
"torch==1.8.1+cu102",
|
||||||
|
"torchvision==0.9.1+cu102"
|
||||||
|
],
|
||||||
|
"mainfile": "sacred_train.py",
|
||||||
|
"name": "s452627",
|
||||||
|
"repositories": [],
|
||||||
|
"sources": [
|
||||||
|
[
|
||||||
|
"sacred_train.py",
|
||||||
|
"_sources\\sacred_train_58880e146636573a7d2893b734269763.py"
|
||||||
|
],
|
||||||
|
[
|
||||||
|
"zadanie1.py",
|
||||||
|
"_sources\\zadanie1_214ad86c108ac00197ed071c54ee3658.py"
|
||||||
|
]
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"heartbeat": "2023-05-11T19:47:40.525506",
|
||||||
|
"host": {
|
||||||
|
"ENV": {},
|
||||||
|
"cpu": "Intel(R) Core(TM) i7-6700K CPU @ 4.00GHz",
|
||||||
|
"gpus": {
|
||||||
|
"driver_version": "472.12",
|
||||||
|
"gpus": [
|
||||||
|
{
|
||||||
|
"model": "NVIDIA GeForce GTX 1070",
|
||||||
|
"persistence_mode": false,
|
||||||
|
"total_memory": 8192
|
||||||
|
}
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"hostname": "JAKUB-HENYK",
|
||||||
|
"os": [
|
||||||
|
"Windows",
|
||||||
|
"Windows-10-10.0.19041-SP0"
|
||||||
|
],
|
||||||
|
"python_version": "3.8.3"
|
||||||
|
},
|
||||||
|
"meta": {
|
||||||
|
"command": "my_main",
|
||||||
|
"config_updates": {},
|
||||||
|
"named_configs": [],
|
||||||
|
"options": {
|
||||||
|
"--beat-interval": null,
|
||||||
|
"--capture": null,
|
||||||
|
"--comment": null,
|
||||||
|
"--debug": false,
|
||||||
|
"--enforce_clean": false,
|
||||||
|
"--file_storage": null,
|
||||||
|
"--force": false,
|
||||||
|
"--help": false,
|
||||||
|
"--id": null,
|
||||||
|
"--loglevel": null,
|
||||||
|
"--mongo_db": null,
|
||||||
|
"--name": null,
|
||||||
|
"--pdb": false,
|
||||||
|
"--print-config": false,
|
||||||
|
"--priority": null,
|
||||||
|
"--queue": false,
|
||||||
|
"--s3": null,
|
||||||
|
"--sql": null,
|
||||||
|
"--tiny_db": null,
|
||||||
|
"--unobserved": false,
|
||||||
|
"COMMAND": null,
|
||||||
|
"UPDATE": [],
|
||||||
|
"help": false,
|
||||||
|
"with": false
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"resources": [
|
||||||
|
[
|
||||||
|
"E:\\Pyton\\IUM\\ium_452627\\Customers.csv",
|
||||||
|
"my_runs\\_resources\\Customers_6514be2808e61a30190fa6265e2352da.csv"
|
||||||
|
]
|
||||||
|
],
|
||||||
|
"result": null,
|
||||||
|
"start_time": "2023-05-11T19:47:00.196563",
|
||||||
|
"status": "RUNNING"
|
||||||
|
}
|
||||||
5
my_runs/6/config.json
Normal file
5
my_runs/6/config.json
Normal file
@ -0,0 +1,5 @@
|
|||||||
|
{
|
||||||
|
"epochs": 10,
|
||||||
|
"learning_rate": 0.001,
|
||||||
|
"seed": 562570933
|
||||||
|
}
|
||||||
0
my_runs/6/cout.txt
Normal file
0
my_runs/6/cout.txt
Normal file
1
my_runs/6/metrics.json
Normal file
1
my_runs/6/metrics.json
Normal file
@ -0,0 +1 @@
|
|||||||
|
{}
|
||||||
96
my_runs/6/run.json
Normal file
96
my_runs/6/run.json
Normal file
@ -0,0 +1,96 @@
|
|||||||
|
{
|
||||||
|
"artifacts": [],
|
||||||
|
"command": "my_main",
|
||||||
|
"experiment": {
|
||||||
|
"base_dir": "e:\\Pyton\\IUM\\ium_452627",
|
||||||
|
"dependencies": [
|
||||||
|
"numpy==1.20.0",
|
||||||
|
"pandas==1.4.1",
|
||||||
|
"sacred==0.8.4",
|
||||||
|
"torch==1.8.1+cu102",
|
||||||
|
"torchvision==0.9.1+cu102"
|
||||||
|
],
|
||||||
|
"mainfile": "sacred_train.py",
|
||||||
|
"name": "s452627",
|
||||||
|
"repositories": [],
|
||||||
|
"sources": [
|
||||||
|
[
|
||||||
|
"sacred_train.py",
|
||||||
|
"_sources\\sacred_train_ff46cdc09c67889917b2588f7c9f993f.py"
|
||||||
|
],
|
||||||
|
[
|
||||||
|
"zadanie1.py",
|
||||||
|
"_sources\\zadanie1_214ad86c108ac00197ed071c54ee3658.py"
|
||||||
|
]
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"fail_trace": [
|
||||||
|
"Traceback (most recent call last):\n",
|
||||||
|
" File \"C:\\Users\\kubak\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sacred\\config\\captured_function.py\", line 42, in captured_function\n result = wrapped(*args, **kwargs)\n",
|
||||||
|
" File \"e:/Pyton/IUM/ium_452627/sacred_train.py\", line 94, in my_main\n trainNet(_run, trainloader, criterion, optimizer, net, int(float(epochs)))\n",
|
||||||
|
" File \"e:/Pyton/IUM/ium_452627/sacred_train.py\", line 50, in trainNet\n print(loss[0])\n",
|
||||||
|
"IndexError: invalid index of a 0-dim tensor. Use `tensor.item()` in Python or `tensor.item<T>()` in C++ to convert a 0-dim tensor to a number\n"
|
||||||
|
],
|
||||||
|
"heartbeat": "2023-05-11T19:48:10.063748",
|
||||||
|
"host": {
|
||||||
|
"ENV": {},
|
||||||
|
"cpu": "Intel(R) Core(TM) i7-6700K CPU @ 4.00GHz",
|
||||||
|
"gpus": {
|
||||||
|
"driver_version": "472.12",
|
||||||
|
"gpus": [
|
||||||
|
{
|
||||||
|
"model": "NVIDIA GeForce GTX 1070",
|
||||||
|
"persistence_mode": false,
|
||||||
|
"total_memory": 8192
|
||||||
|
}
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"hostname": "JAKUB-HENYK",
|
||||||
|
"os": [
|
||||||
|
"Windows",
|
||||||
|
"Windows-10-10.0.19041-SP0"
|
||||||
|
],
|
||||||
|
"python_version": "3.8.3"
|
||||||
|
},
|
||||||
|
"meta": {
|
||||||
|
"command": "my_main",
|
||||||
|
"config_updates": {},
|
||||||
|
"named_configs": [],
|
||||||
|
"options": {
|
||||||
|
"--beat-interval": null,
|
||||||
|
"--capture": null,
|
||||||
|
"--comment": null,
|
||||||
|
"--debug": false,
|
||||||
|
"--enforce_clean": false,
|
||||||
|
"--file_storage": null,
|
||||||
|
"--force": false,
|
||||||
|
"--help": false,
|
||||||
|
"--id": null,
|
||||||
|
"--loglevel": null,
|
||||||
|
"--mongo_db": null,
|
||||||
|
"--name": null,
|
||||||
|
"--pdb": false,
|
||||||
|
"--print-config": false,
|
||||||
|
"--priority": null,
|
||||||
|
"--queue": false,
|
||||||
|
"--s3": null,
|
||||||
|
"--sql": null,
|
||||||
|
"--tiny_db": null,
|
||||||
|
"--unobserved": false,
|
||||||
|
"COMMAND": null,
|
||||||
|
"UPDATE": [],
|
||||||
|
"help": false,
|
||||||
|
"with": false
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"resources": [
|
||||||
|
[
|
||||||
|
"E:\\Pyton\\IUM\\ium_452627\\Customers.csv",
|
||||||
|
"my_runs\\_resources\\Customers_6514be2808e61a30190fa6265e2352da.csv"
|
||||||
|
]
|
||||||
|
],
|
||||||
|
"result": null,
|
||||||
|
"start_time": "2023-05-11T19:48:03.769748",
|
||||||
|
"status": "FAILED",
|
||||||
|
"stop_time": "2023-05-11T19:48:10.065747"
|
||||||
|
}
|
||||||
5
my_runs/7/config.json
Normal file
5
my_runs/7/config.json
Normal file
@ -0,0 +1,5 @@
|
|||||||
|
{
|
||||||
|
"epochs": 10,
|
||||||
|
"learning_rate": 0.001,
|
||||||
|
"seed": 486901295
|
||||||
|
}
|
||||||
400
my_runs/7/cout.txt
Normal file
400
my_runs/7/cout.txt
Normal file
@ -0,0 +1,400 @@
|
|||||||
|
1.6230660676956177
|
||||||
|
1.6820298433303833
|
||||||
|
1.7491368055343628
|
||||||
|
1.5389869213104248
|
||||||
|
1.6283531188964844
|
||||||
|
1.5094218254089355
|
||||||
|
1.7905722856521606
|
||||||
|
1.5641237497329712
|
||||||
|
1.5830129384994507
|
||||||
|
1.5243418216705322
|
||||||
|
1.519378900527954
|
||||||
|
1.6054351329803467
|
||||||
|
1.5723960399627686
|
||||||
|
1.6757309436798096
|
||||||
|
1.6402519941329956
|
||||||
|
1.5999528169631958
|
||||||
|
1.5646768808364868
|
||||||
|
1.621456503868103
|
||||||
|
1.556501030921936
|
||||||
|
1.5461764335632324
|
||||||
|
1.5640205144882202
|
||||||
|
1.5807640552520752
|
||||||
|
1.509324073791504
|
||||||
|
1.5999341011047363
|
||||||
|
1.54926598072052
|
||||||
|
1.4149173498153687
|
||||||
|
1.4550354480743408
|
||||||
|
1.4619419574737549
|
||||||
|
1.5496633052825928
|
||||||
|
1.4661115407943726
|
||||||
|
1.4423216581344604
|
||||||
|
1.4768248796463013
|
||||||
|
1.5189144611358643
|
||||||
|
1.5314130783081055
|
||||||
|
1.375200867652893
|
||||||
|
1.6448571681976318
|
||||||
|
1.4357342720031738
|
||||||
|
1.4321017265319824
|
||||||
|
1.377081036567688
|
||||||
|
1.4897136688232422
|
||||||
|
1.4930771589279175
|
||||||
|
1.384333610534668
|
||||||
|
1.4029908180236816
|
||||||
|
1.5566425323486328
|
||||||
|
1.3000067472457886
|
||||||
|
1.4866435527801514
|
||||||
|
1.4507079124450684
|
||||||
|
1.276503562927246
|
||||||
|
1.3888378143310547
|
||||||
|
1.524937391281128
|
||||||
|
1.3618971109390259
|
||||||
|
1.3161665201187134
|
||||||
|
1.5433791875839233
|
||||||
|
1.2492393255233765
|
||||||
|
1.4940097332000732
|
||||||
|
1.4125964641571045
|
||||||
|
1.4479514360427856
|
||||||
|
1.3118144273757935
|
||||||
|
1.4395060539245605
|
||||||
|
1.2881097793579102
|
||||||
|
1.3483657836914062
|
||||||
|
1.468015193939209
|
||||||
|
1.2474229335784912
|
||||||
|
1.4239823818206787
|
||||||
|
1.2277672290802002
|
||||||
|
1.274768590927124
|
||||||
|
1.2919094562530518
|
||||||
|
1.1657848358154297
|
||||||
|
1.4304120540618896
|
||||||
|
1.3109701871871948
|
||||||
|
1.3414161205291748
|
||||||
|
1.1767857074737549
|
||||||
|
1.4821016788482666
|
||||||
|
1.321860432624817
|
||||||
|
1.1414504051208496
|
||||||
|
1.3034462928771973
|
||||||
|
1.1764267683029175
|
||||||
|
1.289609670639038
|
||||||
|
1.0915460586547852
|
||||||
|
1.2168611288070679
|
||||||
|
1.29572331905365
|
||||||
|
1.2754342555999756
|
||||||
|
1.2659229040145874
|
||||||
|
1.2495107650756836
|
||||||
|
1.3686363697052002
|
||||||
|
1.39882493019104
|
||||||
|
1.2654469013214111
|
||||||
|
1.1343276500701904
|
||||||
|
1.0715656280517578
|
||||||
|
1.5835096836090088
|
||||||
|
1.024012804031372
|
||||||
|
1.2513726949691772
|
||||||
|
1.2777726650238037
|
||||||
|
1.1889636516571045
|
||||||
|
1.2051066160202026
|
||||||
|
1.2106695175170898
|
||||||
|
1.1721521615982056
|
||||||
|
1.1284140348434448
|
||||||
|
1.080606460571289
|
||||||
|
1.0519185066223145
|
||||||
|
1.0566167831420898
|
||||||
|
1.2587218284606934
|
||||||
|
0.9446205496788025
|
||||||
|
1.2454330921173096
|
||||||
|
1.1374781131744385
|
||||||
|
1.150152564048767
|
||||||
|
1.2487719058990479
|
||||||
|
1.297490119934082
|
||||||
|
1.193822979927063
|
||||||
|
1.0731717348098755
|
||||||
|
1.1139096021652222
|
||||||
|
1.1425707340240479
|
||||||
|
1.0449187755584717
|
||||||
|
1.1798356771469116
|
||||||
|
0.975722074508667
|
||||||
|
1.0425740480422974
|
||||||
|
1.0942325592041016
|
||||||
|
1.388864278793335
|
||||||
|
1.192713737487793
|
||||||
|
1.1410622596740723
|
||||||
|
0.9496395587921143
|
||||||
|
1.0701346397399902
|
||||||
|
1.1487751007080078
|
||||||
|
1.432262659072876
|
||||||
|
1.2153462171554565
|
||||||
|
1.0495221614837646
|
||||||
|
1.0923141241073608
|
||||||
|
1.1329574584960938
|
||||||
|
1.1577043533325195
|
||||||
|
1.3608773946762085
|
||||||
|
1.1734118461608887
|
||||||
|
0.9182109832763672
|
||||||
|
0.9625385999679565
|
||||||
|
1.1836010217666626
|
||||||
|
0.8289363980293274
|
||||||
|
0.9563491940498352
|
||||||
|
1.2100787162780762
|
||||||
|
0.8395973443984985
|
||||||
|
1.2490291595458984
|
||||||
|
0.8419893980026245
|
||||||
|
1.1588988304138184
|
||||||
|
1.1695365905761719
|
||||||
|
1.3074092864990234
|
||||||
|
1.2472511529922485
|
||||||
|
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0.9110106229782104
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0.7787065505981445
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0.9346214532852173
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1.0036942958831787
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1.1346150636672974
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|
1.1032440662384033
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|
0.9842561483383179
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0.6584384441375732
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0.7690529823303223
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0.9518932700157166
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0.969390869140625
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0.6292759776115417
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0.861823558807373
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1.0020227432250977
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1.0703190565109253
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1.2174514532089233
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0.9096038341522217
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|
0.9197603464126587
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|
0.8633270263671875
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|
1.0342717170715332
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|
0.8141842484474182
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|
0.8617056012153625
|
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|
0.8302423357963562
|
||||||
|
0.8497481346130371
|
||||||
|
0.871558666229248
|
||||||
|
1.1848621368408203
|
||||||
|
1.0444331169128418
|
||||||
|
1.0134308338165283
|
||||||
|
0.6316218376159668
|
||||||
|
0.8203948140144348
|
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|
0.9308501482009888
|
||||||
|
0.9720712304115295
|
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|
0.9066691398620605
|
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|
0.849102795124054
|
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|
0.9337921142578125
|
||||||
|
0.8078703284263611
|
||||||
|
0.7128146886825562
|
||||||
|
1.0422967672348022
|
||||||
|
0.9367537498474121
|
||||||
|
0.8211072087287903
|
||||||
|
0.7752896547317505
|
||||||
|
0.9761680960655212
|
||||||
|
0.6779760718345642
|
||||||
|
0.9350895881652832
|
||||||
|
0.8722249269485474
|
||||||
|
0.8311982154846191
|
||||||
|
0.905919075012207
|
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|
0.8581783771514893
|
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|
0.869439959526062
|
||||||
|
0.7472037672996521
|
||||||
|
0.7891084551811218
|
||||||
|
0.9732564687728882
|
||||||
|
1.0308506488800049
|
||||||
|
0.7775583863258362
|
||||||
|
0.7620372176170349
|
||||||
|
1.0218868255615234
|
||||||
|
0.9783582091331482
|
||||||
|
1.0991756916046143
|
||||||
|
1.1250553131103516
|
||||||
|
0.9594120383262634
|
||||||
|
0.8759898543357849
|
||||||
|
0.9254418611526489
|
||||||
|
0.8226104974746704
|
||||||
1210
my_runs/7/metrics.json
Normal file
1210
my_runs/7/metrics.json
Normal file
File diff suppressed because it is too large
Load Diff
88
my_runs/7/run.json
Normal file
88
my_runs/7/run.json
Normal file
@ -0,0 +1,88 @@
|
|||||||
|
{
|
||||||
|
"artifacts": [],
|
||||||
|
"command": "my_main",
|
||||||
|
"experiment": {
|
||||||
|
"base_dir": "e:\\Pyton\\IUM\\ium_452627",
|
||||||
|
"dependencies": [
|
||||||
|
"numpy==1.20.0",
|
||||||
|
"pandas==1.4.1",
|
||||||
|
"sacred==0.8.4",
|
||||||
|
"torch==1.8.1+cu102",
|
||||||
|
"torchvision==0.9.1+cu102"
|
||||||
|
],
|
||||||
|
"mainfile": "sacred_train.py",
|
||||||
|
"name": "s452627",
|
||||||
|
"repositories": [],
|
||||||
|
"sources": [
|
||||||
|
[
|
||||||
|
"sacred_train.py",
|
||||||
|
"_sources\\sacred_train_544504897ad356dd64cd4527a9914747.py"
|
||||||
|
],
|
||||||
|
[
|
||||||
|
"zadanie1.py",
|
||||||
|
"_sources\\zadanie1_214ad86c108ac00197ed071c54ee3658.py"
|
||||||
|
]
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"heartbeat": "2023-05-11T19:49:13.645610",
|
||||||
|
"host": {
|
||||||
|
"ENV": {},
|
||||||
|
"cpu": "Intel(R) Core(TM) i7-6700K CPU @ 4.00GHz",
|
||||||
|
"gpus": {
|
||||||
|
"driver_version": "472.12",
|
||||||
|
"gpus": [
|
||||||
|
{
|
||||||
|
"model": "NVIDIA GeForce GTX 1070",
|
||||||
|
"persistence_mode": false,
|
||||||
|
"total_memory": 8192
|
||||||
|
}
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"hostname": "JAKUB-HENYK",
|
||||||
|
"os": [
|
||||||
|
"Windows",
|
||||||
|
"Windows-10-10.0.19041-SP0"
|
||||||
|
],
|
||||||
|
"python_version": "3.8.3"
|
||||||
|
},
|
||||||
|
"meta": {
|
||||||
|
"command": "my_main",
|
||||||
|
"config_updates": {},
|
||||||
|
"named_configs": [],
|
||||||
|
"options": {
|
||||||
|
"--beat-interval": null,
|
||||||
|
"--capture": null,
|
||||||
|
"--comment": null,
|
||||||
|
"--debug": false,
|
||||||
|
"--enforce_clean": false,
|
||||||
|
"--file_storage": null,
|
||||||
|
"--force": false,
|
||||||
|
"--help": false,
|
||||||
|
"--id": null,
|
||||||
|
"--loglevel": null,
|
||||||
|
"--mongo_db": null,
|
||||||
|
"--name": null,
|
||||||
|
"--pdb": false,
|
||||||
|
"--print-config": false,
|
||||||
|
"--priority": null,
|
||||||
|
"--queue": false,
|
||||||
|
"--s3": null,
|
||||||
|
"--sql": null,
|
||||||
|
"--tiny_db": null,
|
||||||
|
"--unobserved": false,
|
||||||
|
"COMMAND": null,
|
||||||
|
"UPDATE": [],
|
||||||
|
"help": false,
|
||||||
|
"with": false
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"resources": [
|
||||||
|
[
|
||||||
|
"E:\\Pyton\\IUM\\ium_452627\\Customers.csv",
|
||||||
|
"my_runs\\_resources\\Customers_6514be2808e61a30190fa6265e2352da.csv"
|
||||||
|
]
|
||||||
|
],
|
||||||
|
"result": null,
|
||||||
|
"start_time": "2023-05-11T19:48:43.353238",
|
||||||
|
"status": "RUNNING"
|
||||||
|
}
|
||||||
@ -0,0 +1,104 @@
|
|||||||
|
#!/usr/bin/python
|
||||||
|
|
||||||
|
import pandas as pd
|
||||||
|
import numpy as np
|
||||||
|
import zadanie1 as z
|
||||||
|
import torch
|
||||||
|
import torchvision
|
||||||
|
import torchvision.transforms as transforms
|
||||||
|
import torch.nn as nn
|
||||||
|
import torch.nn.functional as F
|
||||||
|
import torch.optim as optim
|
||||||
|
from sacred import Experiment
|
||||||
|
from sacred.observers import FileStorageObserver
|
||||||
|
from sacred.observers import MongoObserver
|
||||||
|
|
||||||
|
class Net(nn.Module):
|
||||||
|
def __init__(self):
|
||||||
|
super().__init__()
|
||||||
|
#self.conv1 = nn.Conv2d(3, 6, 5)
|
||||||
|
#self.pool = nn.MaxPool2d(2, 2)
|
||||||
|
#self.conv2 = nn.Conv2d(6, 16, 5)
|
||||||
|
#self.fc1 = nn.Linear(16 * 5 * 5, 120)
|
||||||
|
#self.fc2 = nn.Linear(20, 6)
|
||||||
|
self.fc3 = nn.Linear(6, 6)
|
||||||
|
|
||||||
|
def forward(self, x):
|
||||||
|
#x = self.pool(F.relu(self.conv1(x)))
|
||||||
|
#x = self.pool(F.relu(self.conv2(x)))
|
||||||
|
#x = torch.flatten(x, 1)
|
||||||
|
#x = F.relu(self.fc1(x))
|
||||||
|
#x = F.relu(self.fc2(x))
|
||||||
|
x = self.fc3(x)
|
||||||
|
return x
|
||||||
|
|
||||||
|
|
||||||
|
def trainNet(_run, trainloader, criterion, optimizer, net, epochs=20):
|
||||||
|
for epoch in range(epochs):
|
||||||
|
|
||||||
|
for i, data in enumerate(trainloader, 0):
|
||||||
|
inputs, labels = data
|
||||||
|
|
||||||
|
labelsX = torch.Tensor([x for x in labels])
|
||||||
|
labels = labelsX.type(torch.LongTensor)
|
||||||
|
|
||||||
|
optimizer.zero_grad()
|
||||||
|
|
||||||
|
outputs = net(inputs)
|
||||||
|
|
||||||
|
loss = criterion(outputs, labels)
|
||||||
|
print(loss.item())
|
||||||
|
|
||||||
|
_run.log_scalar("training.loss", loss)
|
||||||
|
loss.backward()
|
||||||
|
optimizer.step()
|
||||||
|
|
||||||
|
print('Finished Training')
|
||||||
|
|
||||||
|
ex = Experiment("s452627", interactive=True, save_git_info=False)
|
||||||
|
ex.observers.append(FileStorageObserver('my_runs'))
|
||||||
|
#ex.observers.append(MongoObserver(url='mongodb://admin:IUM_2021@172.17.0.1:27017', db_name='sacred'))
|
||||||
|
|
||||||
|
@ex.config
|
||||||
|
def my_config():
|
||||||
|
epochs = 10
|
||||||
|
learning_rate = 0.001
|
||||||
|
|
||||||
|
@ex.automain
|
||||||
|
def my_main(epochs, learning_rate, _run):
|
||||||
|
|
||||||
|
ex.open_resource("Customers.csv", "r")
|
||||||
|
|
||||||
|
train, dev, test = z.prepareData()
|
||||||
|
|
||||||
|
batch_size = 4
|
||||||
|
|
||||||
|
trainlist = train.values.tolist()
|
||||||
|
testlist = test.values.tolist()
|
||||||
|
|
||||||
|
trainset = [[torch.Tensor(x[1:]), torch.Tensor([x[0]])] for x in trainlist]
|
||||||
|
trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size,
|
||||||
|
shuffle=True, num_workers=2)
|
||||||
|
|
||||||
|
testset = [[torch.Tensor(x[1:]), torch.Tensor([x[0]])] for x in testlist]
|
||||||
|
testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size,
|
||||||
|
shuffle=False, num_workers=2)
|
||||||
|
|
||||||
|
classes = ('male', 'female')
|
||||||
|
|
||||||
|
net = Net()
|
||||||
|
|
||||||
|
criterion = nn.CrossEntropyLoss()
|
||||||
|
optimizer = optim.SGD(net.parameters(), lr=learning_rate, momentum=0.9)
|
||||||
|
|
||||||
|
trainNet(_run, trainloader, criterion, optimizer, net, int(float(epochs)))
|
||||||
|
|
||||||
|
PATH = './cifar_net.pth'
|
||||||
|
torch.save(net.state_dict(), PATH)
|
||||||
|
|
||||||
|
ex.add_artifact("cifar_net.pth")
|
||||||
|
|
||||||
|
#if __name__ == '__main__':
|
||||||
|
|
||||||
|
#ex.run()
|
||||||
|
|
||||||
@ -0,0 +1,104 @@
|
|||||||
|
#!/usr/bin/python
|
||||||
|
|
||||||
|
import pandas as pd
|
||||||
|
import numpy as np
|
||||||
|
import zadanie1 as z
|
||||||
|
import torch
|
||||||
|
import torchvision
|
||||||
|
import torchvision.transforms as transforms
|
||||||
|
import torch.nn as nn
|
||||||
|
import torch.nn.functional as F
|
||||||
|
import torch.optim as optim
|
||||||
|
from sacred import Experiment
|
||||||
|
from sacred.observers import FileStorageObserver
|
||||||
|
from sacred.observers import MongoObserver
|
||||||
|
|
||||||
|
class Net(nn.Module):
|
||||||
|
def __init__(self):
|
||||||
|
super().__init__()
|
||||||
|
#self.conv1 = nn.Conv2d(3, 6, 5)
|
||||||
|
#self.pool = nn.MaxPool2d(2, 2)
|
||||||
|
#self.conv2 = nn.Conv2d(6, 16, 5)
|
||||||
|
#self.fc1 = nn.Linear(16 * 5 * 5, 120)
|
||||||
|
#self.fc2 = nn.Linear(20, 6)
|
||||||
|
self.fc3 = nn.Linear(6, 6)
|
||||||
|
|
||||||
|
def forward(self, x):
|
||||||
|
#x = self.pool(F.relu(self.conv1(x)))
|
||||||
|
#x = self.pool(F.relu(self.conv2(x)))
|
||||||
|
#x = torch.flatten(x, 1)
|
||||||
|
#x = F.relu(self.fc1(x))
|
||||||
|
#x = F.relu(self.fc2(x))
|
||||||
|
x = self.fc3(x)
|
||||||
|
return x
|
||||||
|
|
||||||
|
|
||||||
|
def trainNet(_run, trainloader, criterion, optimizer, net, epochs=20):
|
||||||
|
for epoch in range(epochs):
|
||||||
|
|
||||||
|
for i, data in enumerate(trainloader, 0):
|
||||||
|
inputs, labels = data
|
||||||
|
|
||||||
|
labelsX = torch.Tensor([x for x in labels])
|
||||||
|
labels = labelsX.type(torch.LongTensor)
|
||||||
|
|
||||||
|
optimizer.zero_grad()
|
||||||
|
|
||||||
|
outputs = net(inputs)
|
||||||
|
|
||||||
|
loss = criterion(outputs, labels)
|
||||||
|
print(loss)
|
||||||
|
|
||||||
|
_run.log_scalar("training.loss", loss)
|
||||||
|
loss.backward()
|
||||||
|
optimizer.step()
|
||||||
|
|
||||||
|
print('Finished Training')
|
||||||
|
|
||||||
|
ex = Experiment("s452627", interactive=True, save_git_info=False)
|
||||||
|
ex.observers.append(FileStorageObserver('my_runs'))
|
||||||
|
#ex.observers.append(MongoObserver(url='mongodb://admin:IUM_2021@172.17.0.1:27017', db_name='sacred'))
|
||||||
|
|
||||||
|
@ex.config
|
||||||
|
def my_config():
|
||||||
|
epochs = 10
|
||||||
|
learning_rate = 0.001
|
||||||
|
|
||||||
|
@ex.automain
|
||||||
|
def my_main(epochs, learning_rate, _run):
|
||||||
|
|
||||||
|
ex.open_resource("Customers.csv", "r")
|
||||||
|
|
||||||
|
train, dev, test = z.prepareData()
|
||||||
|
|
||||||
|
batch_size = 4
|
||||||
|
|
||||||
|
trainlist = train.values.tolist()
|
||||||
|
testlist = test.values.tolist()
|
||||||
|
|
||||||
|
trainset = [[torch.Tensor(x[1:]), torch.Tensor([x[0]])] for x in trainlist]
|
||||||
|
trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size,
|
||||||
|
shuffle=True, num_workers=2)
|
||||||
|
|
||||||
|
testset = [[torch.Tensor(x[1:]), torch.Tensor([x[0]])] for x in testlist]
|
||||||
|
testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size,
|
||||||
|
shuffle=False, num_workers=2)
|
||||||
|
|
||||||
|
classes = ('male', 'female')
|
||||||
|
|
||||||
|
net = Net()
|
||||||
|
|
||||||
|
criterion = nn.CrossEntropyLoss()
|
||||||
|
optimizer = optim.SGD(net.parameters(), lr=learning_rate, momentum=0.9)
|
||||||
|
|
||||||
|
trainNet(_run, trainloader, criterion, optimizer, net, int(float(epochs)))
|
||||||
|
|
||||||
|
PATH = './cifar_net.pth'
|
||||||
|
torch.save(net.state_dict(), PATH)
|
||||||
|
|
||||||
|
ex.add_artifact("cifar_net.pth")
|
||||||
|
|
||||||
|
#if __name__ == '__main__':
|
||||||
|
|
||||||
|
#ex.run()
|
||||||
|
|
||||||
@ -0,0 +1,104 @@
|
|||||||
|
#!/usr/bin/python
|
||||||
|
|
||||||
|
import pandas as pd
|
||||||
|
import numpy as np
|
||||||
|
import zadanie1 as z
|
||||||
|
import torch
|
||||||
|
import torchvision
|
||||||
|
import torchvision.transforms as transforms
|
||||||
|
import torch.nn as nn
|
||||||
|
import torch.nn.functional as F
|
||||||
|
import torch.optim as optim
|
||||||
|
from sacred import Experiment
|
||||||
|
from sacred.observers import FileStorageObserver
|
||||||
|
from sacred.observers import MongoObserver
|
||||||
|
|
||||||
|
class Net(nn.Module):
|
||||||
|
def __init__(self):
|
||||||
|
super().__init__()
|
||||||
|
#self.conv1 = nn.Conv2d(3, 6, 5)
|
||||||
|
#self.pool = nn.MaxPool2d(2, 2)
|
||||||
|
#self.conv2 = nn.Conv2d(6, 16, 5)
|
||||||
|
#self.fc1 = nn.Linear(16 * 5 * 5, 120)
|
||||||
|
#self.fc2 = nn.Linear(20, 6)
|
||||||
|
self.fc3 = nn.Linear(6, 6)
|
||||||
|
|
||||||
|
def forward(self, x):
|
||||||
|
#x = self.pool(F.relu(self.conv1(x)))
|
||||||
|
#x = self.pool(F.relu(self.conv2(x)))
|
||||||
|
#x = torch.flatten(x, 1)
|
||||||
|
#x = F.relu(self.fc1(x))
|
||||||
|
#x = F.relu(self.fc2(x))
|
||||||
|
x = self.fc3(x)
|
||||||
|
return x
|
||||||
|
|
||||||
|
|
||||||
|
def trainNet(_run, trainloader, criterion, optimizer, net, epochs=20):
|
||||||
|
for epoch in range(epochs):
|
||||||
|
|
||||||
|
for i, data in enumerate(trainloader, 0):
|
||||||
|
inputs, labels = data
|
||||||
|
|
||||||
|
labelsX = torch.Tensor([x for x in labels])
|
||||||
|
labels = labelsX.type(torch.LongTensor)
|
||||||
|
|
||||||
|
optimizer.zero_grad()
|
||||||
|
|
||||||
|
outputs = net(inputs)
|
||||||
|
|
||||||
|
loss = criterion(outputs, labels)
|
||||||
|
print(loss[0])
|
||||||
|
|
||||||
|
_run.log_scalar("training.loss", loss)
|
||||||
|
loss.backward()
|
||||||
|
optimizer.step()
|
||||||
|
|
||||||
|
print('Finished Training')
|
||||||
|
|
||||||
|
ex = Experiment("s452627", interactive=True, save_git_info=False)
|
||||||
|
ex.observers.append(FileStorageObserver('my_runs'))
|
||||||
|
#ex.observers.append(MongoObserver(url='mongodb://admin:IUM_2021@172.17.0.1:27017', db_name='sacred'))
|
||||||
|
|
||||||
|
@ex.config
|
||||||
|
def my_config():
|
||||||
|
epochs = 10
|
||||||
|
learning_rate = 0.001
|
||||||
|
|
||||||
|
@ex.automain
|
||||||
|
def my_main(epochs, learning_rate, _run):
|
||||||
|
|
||||||
|
ex.open_resource("Customers.csv", "r")
|
||||||
|
|
||||||
|
train, dev, test = z.prepareData()
|
||||||
|
|
||||||
|
batch_size = 4
|
||||||
|
|
||||||
|
trainlist = train.values.tolist()
|
||||||
|
testlist = test.values.tolist()
|
||||||
|
|
||||||
|
trainset = [[torch.Tensor(x[1:]), torch.Tensor([x[0]])] for x in trainlist]
|
||||||
|
trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size,
|
||||||
|
shuffle=True, num_workers=2)
|
||||||
|
|
||||||
|
testset = [[torch.Tensor(x[1:]), torch.Tensor([x[0]])] for x in testlist]
|
||||||
|
testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size,
|
||||||
|
shuffle=False, num_workers=2)
|
||||||
|
|
||||||
|
classes = ('male', 'female')
|
||||||
|
|
||||||
|
net = Net()
|
||||||
|
|
||||||
|
criterion = nn.CrossEntropyLoss()
|
||||||
|
optimizer = optim.SGD(net.parameters(), lr=learning_rate, momentum=0.9)
|
||||||
|
|
||||||
|
trainNet(_run, trainloader, criterion, optimizer, net, int(float(epochs)))
|
||||||
|
|
||||||
|
PATH = './cifar_net.pth'
|
||||||
|
torch.save(net.state_dict(), PATH)
|
||||||
|
|
||||||
|
ex.add_artifact("cifar_net.pth")
|
||||||
|
|
||||||
|
#if __name__ == '__main__':
|
||||||
|
|
||||||
|
#ex.run()
|
||||||
|
|
||||||
@ -47,6 +47,7 @@ def trainNet(_run, trainloader, criterion, optimizer, net, epochs=20):
|
|||||||
outputs = net(inputs)
|
outputs = net(inputs)
|
||||||
|
|
||||||
loss = criterion(outputs, labels)
|
loss = criterion(outputs, labels)
|
||||||
|
print(loss.item())
|
||||||
|
|
||||||
_run.log_scalar("training.loss", loss)
|
_run.log_scalar("training.loss", loss)
|
||||||
loss.backward()
|
loss.backward()
|
||||||
|
|||||||
Loading…
Reference in New Issue
Block a user