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10nodes-results-log.txt
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10nodes-results-log.txt
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training time: 00:00:35.49
t 0: train_loss = 1.584, test_acc = 59.044
training time: 00:00:41.32
t 1: train_loss = 0.950, test_acc = 76.333
training time: 00:00:47.05
t 2: train_loss = 0.657, test_acc = 80.933
training time: 00:00:52.71
t 3: train_loss = 0.511, test_acc = 83.211
training time: 00:00:58.27
t 4: train_loss = 0.419, test_acc = 83.744
training time: 00:01:03.75
t 5: train_loss = 0.354, test_acc = 84.700
training time: 00:01:09.34
t 6: train_loss = 0.307, test_acc = 85.289
training time: 00:01:14.93
t 7: train_loss = 0.269, test_acc = 85.478
training time: 00:01:20.38
t 8: train_loss = 0.239, test_acc = 85.611
training time: 00:00:15.89
t 0: train_loss = 1.584, test_acc = 59.044
training time: 00:00:21.45
t 1: train_loss = 0.950, test_acc = 76.333
training time: 00:00:27.10
t 2: train_loss = 0.657, test_acc = 80.933
training time: 00:00:32.89
t 3: train_loss = 0.511, test_acc = 83.211
training time: 00:00:38.61
t 4: train_loss = 0.419, test_acc = 83.744
training time: 00:00:44.04
t 5: train_loss = 0.354, test_acc = 84.700
training time: 00:00:49.78
t 6: train_loss = 0.307, test_acc = 85.289
training time: 00:00:55.47
t 7: train_loss = 0.269, test_acc = 85.478
training time: 00:01:01.07
t 8: train_loss = 0.239, test_acc = 85.611
---------------
training time: 00:00:25.96
t 0: train_loss = 1.584, test_acc = 9.967
training time: 00:00:31.57
t 1: train_loss = 1.479, test_acc = 13.444
---------
training time: 00:00:37.78
t 0: train_loss = 1.584, test_acc = 59.044
--------
training time: 00:00:04.36
t 0: train_loss = 1.584, test_acc = 72.678
training time: 00:00:08.03
t 1: train_loss = 0.707, test_acc = 82.167
training time: 00:00:11.66
t 2: train_loss = 0.456, test_acc = 84.422
training time: 00:00:15.42
t 3: train_loss = 0.345, test_acc = 84.744
training time: 00:00:19.22
t 4: train_loss = 0.278, test_acc = 85.467
training time: 00:00:22.92
t 5: train_loss = 0.232, test_acc = 85.556
training time: 00:00:26.72
t 6: train_loss = 0.201, test_acc = 86.044
training time: 00:00:30.42
t 7: train_loss = 0.175, test_acc = 85.911
training time: 00:00:34.10
t 8: train_loss = 0.156, test_acc = 85.889
training time: 00:00:37.87
t 9: train_loss = 0.140, test_acc = 86.122
-------
FMNIST Startting here (mongodb)
-------
training time: 00:00:05.91
t 0: train_loss = 0.547, test_acc = 85.378
training time: 00:00:10.07
t 1: train_loss = 0.313, test_acc = 88.100
training time: 00:00:14.15
t 2: train_loss = 0.236, test_acc = 89.333
training time: 00:00:18.38
t 3: train_loss = 0.192, test_acc = 89.844
training time: 00:00:22.56
t 4: train_loss = 0.163, test_acc = 90.233
training time: 00:00:26.59
t 5: train_loss = 0.143, test_acc = 90.411
training time: 00:00:31.11
t 6: train_loss = 0.129, test_acc = 90.711
-------
FMNIST Startting here (mongodb)
-------
training time: 00:00:25.39
t 0: train_loss = 0.547, test_acc = 85.378
training time: 00:00:35.41
t 1: train_loss = 0.313, test_acc = 88.100
training time: 00:00:46.05
t 2: train_loss = 0.236, test_acc = 89.333
training time: 00:01:07.14
t 3: train_loss = 0.192, test_acc = 89.844
training time: 00:02:02.57
t 4: train_loss = 0.163, test_acc = 90.233
training time: 00:03:15.04
t 5: train_loss = 0.143, test_acc = 90.411
training time: 00:05:01.03
t 6: train_loss = 0.129, test_acc = 90.711
training time: 00:08:48.63
t 7: train_loss = 0.121, test_acc = 90.411
training time: 00:13:08.87
t 8: train_loss = 0.114, test_acc = 90.678
training time: 00:15:36.28
t 9: train_loss = 0.110, test_acc = 90.656
-------
SVHN Startting here (mongodb)
-------
training time: 00:00:39.66
t 0: train_loss = 0.273, test_acc = 90.625
training time: 00:01:09.24
t 1: train_loss = 0.097, test_acc = 94.083
training time: 00:01:42.75
t 2: train_loss = 0.061, test_acc = 94.911
training time: 00:02:55.85
t 3: train_loss = 0.046, test_acc = 95.302
training time: 00:05:16.17
t 4: train_loss = 0.038, test_acc = 95.464
training time: 00:08:47.53
t 5: train_loss = 0.032, test_acc = 95.593
training time: 00:12:29.53
t 6: train_loss = 0.028, test_acc = 95.672
training time: 00:19:27.75
t 7: train_loss = 0.025, test_acc = 95.668
training time: 00:27:11.32
t 8: train_loss = 0.022, test_acc = 95.647
training time: 00:34:08.99
t 9: train_loss = 0.020, test_acc = 95.814
-------
FMNIST cassandra
------
training time: 00:00:44.98
t 0: train_loss = 0.547, test_acc = 85.467
training time: 00:00:52.58
t 1: train_loss = 0.315, test_acc = 88.556
training time: 00:01:00.13
t 2: train_loss = 0.235, test_acc = 89.678
training time: 00:01:07.83
t 3: train_loss = 0.187, test_acc = 90.667
training time: 00:01:15.27
t 4: train_loss = 0.153, test_acc = 91.211
training time: 00:01:22.65
t 5: train_loss = 0.128, test_acc = 91.378
training time: 00:01:30.15
t 6: train_loss = 0.107, test_acc = 91.489
training time: 00:01:37.59
t 7: train_loss = 0.092, test_acc = 91.756
training time: 00:01:44.77
t 8: train_loss = 0.080, test_acc = 91.856
training time: 00:01:52.30
t 9: train_loss = 0.070, test_acc = 92.000
-----------
training time: 00:00:16.12
t 0: train_loss = 0.547, test_acc = 85.378
training time: 00:00:25.53
t 1: train_loss = 0.313, test_acc = 88.100
training time: 00:00:44.00
t 2: train_loss = 0.236, test_acc = 89.333
training time: 00:01:11.05
t 3: train_loss = 0.192, test_acc = 89.844
training time: 00:01:30.54
t 4: train_loss = 0.163, test_acc = 90.233
training time: 00:02:37.26
t 5: train_loss = 0.143, test_acc = 90.411
training time: 00:04:15.19
t 6: train_loss = 0.129, test_acc = 90.711
training time: 00:06:19.60
t 7: train_loss = 0.121, test_acc = 90.411
training time: 00:08:58.87
t 8: train_loss = 0.114, test_acc = 90.678
training time: 00:09:35.95
t 9: train_loss = 0.110, test_acc = 90.656
######################## FMNIST #################################################################
training time: 00:01:03.84
t 0: train_loss = 0.436, test_acc = 88.311
training time: 00:01:12.39
t 1: train_loss = 0.222, test_acc = 91.122
training time: 00:01:21.03
t 2: train_loss = 0.135, test_acc = 92.011