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[1005 22:02:15 @logger.py:73] Argv: /XXX/tensorpack/examples/ResNet/./cifar10-resnet.py --gpu 0,1
[1005 22:02:15 @cifar.py:32] Found cifar10 data in /XXX/data/cifar10_data.
[1005 22:02:17 @parallel.py:186] [MultiProcessPrefetchData] Will fork a dataflow more than one times. This assumes the datapoints are i.i.d.
[1005 22:02:17 @cifar.py:32] Found cifar10 data in /XXX/data/cifar10_data.
[1005 22:02:18 @training.py:51] [DataParallel] Training a model of 2 towers.
[1005 22:02:18 @interface.py:34] Automatically applying QueueInput on the DataFlow.
[1005 22:02:18 @interface.py:46] Automatically applying StagingInput on the DataFlow.
[1005 22:02:18 @input_source.py:220] Setting up the queue 'QueueInput/input_queue' for CPU prefetching ...
[1005 22:02:18 @training.py:113] Building graph for training tower 0 ...
[1005 22:02:21 @registry.py:121] conv0 input: [None, 3, 32, 32]
[1005 22:02:21 @registry.py:129] conv0 output: [None, 16, 32, 32]
[1005 22:02:21 @registry.py:121] res1.0/conv1 input: [None, 16, 32, 32]
[1005 22:02:21 @registry.py:129] res1.0/conv1 output: [None, 16, 32, 32]
[1005 22:02:21 @registry.py:121] res1.0/conv2 input: [None, 16, 32, 32]
[1005 22:02:21 @registry.py:129] res1.0/conv2 output: [None, 16, 32, 32]
[1005 22:02:21 @registry.py:121] res1.1/conv1 input: [None, 16, 32, 32]
[1005 22:02:21 @registry.py:129] res1.1/conv1 output: [None, 16, 32, 32]
[1005 22:02:21 @registry.py:121] res1.1/conv2 input: [None, 16, 32, 32]
[1005 22:02:21 @registry.py:129] res1.1/conv2 output: [None, 16, 32, 32]
[1005 22:02:21 @registry.py:121] res1.2/conv1 input: [None, 16, 32, 32]
[1005 22:02:21 @registry.py:129] res1.2/conv1 output: [None, 16, 32, 32]
[1005 22:02:21 @registry.py:121] res1.2/conv2 input: [None, 16, 32, 32]
[1005 22:02:21 @registry.py:129] res1.2/conv2 output: [None, 16, 32, 32]
[1005 22:02:22 @registry.py:121] res1.3/conv1 input: [None, 16, 32, 32]
[1005 22:02:22 @registry.py:129] res1.3/conv1 output: [None, 16, 32, 32]
[1005 22:02:22 @registry.py:121] res1.3/conv2 input: [None, 16, 32, 32]
[1005 22:02:22 @registry.py:129] res1.3/conv2 output: [None, 16, 32, 32]
[1005 22:02:22 @registry.py:121] res1.4/conv1 input: [None, 16, 32, 32]
[1005 22:02:22 @registry.py:129] res1.4/conv1 output: [None, 16, 32, 32]
[1005 22:02:22 @registry.py:121] res1.4/conv2 input: [None, 16, 32, 32]
[1005 22:02:22 @registry.py:129] res1.4/conv2 output: [None, 16, 32, 32]
[1005 22:02:22 @registry.py:121] res1.5/conv1 input: [None, 16, 32, 32]
[1005 22:02:22 @registry.py:129] res1.5/conv1 output: [None, 16, 32, 32]
[1005 22:02:22 @registry.py:121] res1.5/conv2 input: [None, 16, 32, 32]
[1005 22:02:22 @registry.py:129] res1.5/conv2 output: [None, 16, 32, 32]
[1005 22:02:22 @registry.py:121] res1.6/conv1 input: [None, 16, 32, 32]
[1005 22:02:22 @registry.py:129] res1.6/conv1 output: [None, 16, 32, 32]
[1005 22:02:22 @registry.py:121] res1.6/conv2 input: [None, 16, 32, 32]
[1005 22:02:22 @registry.py:129] res1.6/conv2 output: [None, 16, 32, 32]
[1005 22:02:22 @registry.py:121] res1.7/conv1 input: [None, 16, 32, 32]
[1005 22:02:22 @registry.py:129] res1.7/conv1 output: [None, 16, 32, 32]
[1005 22:02:22 @registry.py:121] res1.7/conv2 input: [None, 16, 32, 32]
[1005 22:02:22 @registry.py:129] res1.7/conv2 output: [None, 16, 32, 32]
[1005 22:02:22 @registry.py:121] res1.8/conv1 input: [None, 16, 32, 32]
[1005 22:02:22 @registry.py:129] res1.8/conv1 output: [None, 16, 32, 32]
[1005 22:02:22 @registry.py:121] res1.8/conv2 input: [None, 16, 32, 32]
[1005 22:02:22 @registry.py:129] res1.8/conv2 output: [None, 16, 32, 32]
[1005 22:02:22 @registry.py:121] res1.9/conv1 input: [None, 16, 32, 32]
[1005 22:02:22 @registry.py:129] res1.9/conv1 output: [None, 16, 32, 32]
[1005 22:02:22 @registry.py:121] res1.9/conv2 input: [None, 16, 32, 32]
[1005 22:02:22 @registry.py:129] res1.9/conv2 output: [None, 16, 32, 32]
[1005 22:02:22 @registry.py:121] res1.10/conv1 input: [None, 16, 32, 32]
[1005 22:02:22 @registry.py:129] res1.10/conv1 output: [None, 16, 32, 32]
[1005 22:02:22 @registry.py:121] res1.10/conv2 input: [None, 16, 32, 32]
[1005 22:02:22 @registry.py:129] res1.10/conv2 output: [None, 16, 32, 32]
[1005 22:02:22 @registry.py:121] res1.11/conv1 input: [None, 16, 32, 32]
[1005 22:02:22 @registry.py:129] res1.11/conv1 output: [None, 16, 32, 32]
[1005 22:02:22 @registry.py:121] res1.11/conv2 input: [None, 16, 32, 32]
[1005 22:02:22 @registry.py:129] res1.11/conv2 output: [None, 16, 32, 32]
[1005 22:02:22 @registry.py:121] res1.12/conv1 input: [None, 16, 32, 32]
[1005 22:02:22 @registry.py:129] res1.12/conv1 output: [None, 16, 32, 32]
[1005 22:02:22 @registry.py:121] res1.12/conv2 input: [None, 16, 32, 32]
[1005 22:02:22 @registry.py:129] res1.12/conv2 output: [None, 16, 32, 32]
[1005 22:02:22 @registry.py:121] res1.13/conv1 input: [None, 16, 32, 32]
[1005 22:02:22 @registry.py:129] res1.13/conv1 output: [None, 16, 32, 32]
[1005 22:02:22 @registry.py:121] res1.13/conv2 input: [None, 16, 32, 32]
[1005 22:02:22 @registry.py:129] res1.13/conv2 output: [None, 16, 32, 32]
[1005 22:02:22 @registry.py:121] res1.14/conv1 input: [None, 16, 32, 32]
[1005 22:02:23 @registry.py:129] res1.14/conv1 output: [None, 16, 32, 32]
[1005 22:02:23 @registry.py:121] res1.14/conv2 input: [None, 16, 32, 32]
[1005 22:02:23 @registry.py:129] res1.14/conv2 output: [None, 16, 32, 32]
[1005 22:02:23 @registry.py:121] res1.15/conv1 input: [None, 16, 32, 32]
[1005 22:02:23 @registry.py:129] res1.15/conv1 output: [None, 16, 32, 32]
[1005 22:02:23 @registry.py:121] res1.15/conv2 input: [None, 16, 32, 32]
[1005 22:02:23 @registry.py:129] res1.15/conv2 output: [None, 16, 32, 32]
[1005 22:02:23 @registry.py:121] res1.16/conv1 input: [None, 16, 32, 32]
[1005 22:02:23 @registry.py:129] res1.16/conv1 output: [None, 16, 32, 32]
[1005 22:02:23 @registry.py:121] res1.16/conv2 input: [None, 16, 32, 32]
[1005 22:02:23 @registry.py:129] res1.16/conv2 output: [None, 16, 32, 32]
[1005 22:02:23 @registry.py:121] res1.17/conv1 input: [None, 16, 32, 32]
[1005 22:02:23 @registry.py:129] res1.17/conv1 output: [None, 16, 32, 32]
[1005 22:02:23 @registry.py:121] res1.17/conv2 input: [None, 16, 32, 32]
[1005 22:02:23 @registry.py:129] res1.17/conv2 output: [None, 16, 32, 32]
[1005 22:02:23 @registry.py:121] res2.0/conv1 input: [None, 16, 32, 32]
[1005 22:02:23 @registry.py:129] res2.0/conv1 output: [None, 32, 16, 16]
[1005 22:02:23 @registry.py:121] res2.0/conv2 input: [None, 32, 16, 16]
[1005 22:02:23 @registry.py:129] res2.0/conv2 output: [None, 32, 16, 16]
[1005 22:02:23 @registry.py:121] res2.0/pool input: [None, 16, 32, 32]
[1005 22:02:23 @registry.py:129] res2.0/pool output: [None, 16, 16, 16]
[1005 22:02:23 @registry.py:121] res2.1/conv1 input: [None, 32, 16, 16]
[1005 22:02:23 @registry.py:129] res2.1/conv1 output: [None, 32, 16, 16]
[1005 22:02:23 @registry.py:121] res2.1/conv2 input: [None, 32, 16, 16]
[1005 22:02:23 @registry.py:129] res2.1/conv2 output: [None, 32, 16, 16]
[1005 22:02:23 @registry.py:121] res2.2/conv1 input: [None, 32, 16, 16]
[1005 22:02:23 @registry.py:129] res2.2/conv1 output: [None, 32, 16, 16]
[1005 22:02:23 @registry.py:121] res2.2/conv2 input: [None, 32, 16, 16]
[1005 22:02:23 @registry.py:129] res2.2/conv2 output: [None, 32, 16, 16]
[1005 22:02:23 @registry.py:121] res2.3/conv1 input: [None, 32, 16, 16]
[1005 22:02:23 @registry.py:129] res2.3/conv1 output: [None, 32, 16, 16]
[1005 22:02:23 @registry.py:121] res2.3/conv2 input: [None, 32, 16, 16]
[1005 22:02:23 @registry.py:129] res2.3/conv2 output: [None, 32, 16, 16]
[1005 22:02:23 @registry.py:121] res2.4/conv1 input: [None, 32, 16, 16]
[1005 22:02:23 @registry.py:129] res2.4/conv1 output: [None, 32, 16, 16]
[1005 22:02:23 @registry.py:121] res2.4/conv2 input: [None, 32, 16, 16]
[1005 22:02:23 @registry.py:129] res2.4/conv2 output: [None, 32, 16, 16]
[1005 22:02:23 @registry.py:121] res2.5/conv1 input: [None, 32, 16, 16]
[1005 22:02:23 @registry.py:129] res2.5/conv1 output: [None, 32, 16, 16]
[1005 22:02:23 @registry.py:121] res2.5/conv2 input: [None, 32, 16, 16]
[1005 22:02:23 @registry.py:129] res2.5/conv2 output: [None, 32, 16, 16]
[1005 22:02:23 @registry.py:121] res2.6/conv1 input: [None, 32, 16, 16]
[1005 22:02:23 @registry.py:129] res2.6/conv1 output: [None, 32, 16, 16]
[1005 22:02:23 @registry.py:121] res2.6/conv2 input: [None, 32, 16, 16]
[1005 22:02:23 @registry.py:129] res2.6/conv2 output: [None, 32, 16, 16]
[1005 22:02:23 @registry.py:121] res2.7/conv1 input: [None, 32, 16, 16]
[1005 22:02:24 @registry.py:129] res2.7/conv1 output: [None, 32, 16, 16]
[1005 22:02:24 @registry.py:121] res2.7/conv2 input: [None, 32, 16, 16]
[1005 22:02:24 @registry.py:129] res2.7/conv2 output: [None, 32, 16, 16]
[1005 22:02:24 @registry.py:121] res2.8/conv1 input: [None, 32, 16, 16]
[1005 22:02:24 @registry.py:129] res2.8/conv1 output: [None, 32, 16, 16]
[1005 22:02:24 @registry.py:121] res2.8/conv2 input: [None, 32, 16, 16]
[1005 22:02:24 @registry.py:129] res2.8/conv2 output: [None, 32, 16, 16]
[1005 22:02:24 @registry.py:121] res2.9/conv1 input: [None, 32, 16, 16]
[1005 22:02:24 @registry.py:129] res2.9/conv1 output: [None, 32, 16, 16]
[1005 22:02:24 @registry.py:121] res2.9/conv2 input: [None, 32, 16, 16]
[1005 22:02:24 @registry.py:129] res2.9/conv2 output: [None, 32, 16, 16]
[1005 22:02:24 @registry.py:121] res2.10/conv1 input: [None, 32, 16, 16]
[1005 22:02:24 @registry.py:129] res2.10/conv1 output: [None, 32, 16, 16]
[1005 22:02:24 @registry.py:121] res2.10/conv2 input: [None, 32, 16, 16]
[1005 22:02:24 @registry.py:129] res2.10/conv2 output: [None, 32, 16, 16]
[1005 22:02:24 @registry.py:121] res2.11/conv1 input: [None, 32, 16, 16]
[1005 22:02:24 @registry.py:129] res2.11/conv1 output: [None, 32, 16, 16]
[1005 22:02:24 @registry.py:121] res2.11/conv2 input: [None, 32, 16, 16]
[1005 22:02:24 @registry.py:129] res2.11/conv2 output: [None, 32, 16, 16]
[1005 22:02:24 @registry.py:121] res2.12/conv1 input: [None, 32, 16, 16]
[1005 22:02:24 @registry.py:129] res2.12/conv1 output: [None, 32, 16, 16]
[1005 22:02:24 @registry.py:121] res2.12/conv2 input: [None, 32, 16, 16]
[1005 22:02:24 @registry.py:129] res2.12/conv2 output: [None, 32, 16, 16]
[1005 22:02:24 @registry.py:121] res2.13/conv1 input: [None, 32, 16, 16]
[1005 22:02:24 @registry.py:129] res2.13/conv1 output: [None, 32, 16, 16]
[1005 22:02:24 @registry.py:121] res2.13/conv2 input: [None, 32, 16, 16]
[1005 22:02:24 @registry.py:129] res2.13/conv2 output: [None, 32, 16, 16]
[1005 22:02:24 @registry.py:121] res2.14/conv1 input: [None, 32, 16, 16]
[1005 22:02:24 @registry.py:129] res2.14/conv1 output: [None, 32, 16, 16]
[1005 22:02:24 @registry.py:121] res2.14/conv2 input: [None, 32, 16, 16]
[1005 22:02:24 @registry.py:129] res2.14/conv2 output: [None, 32, 16, 16]
[1005 22:02:24 @registry.py:121] res2.15/conv1 input: [None, 32, 16, 16]
[1005 22:02:24 @registry.py:129] res2.15/conv1 output: [None, 32, 16, 16]
[1005 22:02:24 @registry.py:121] res2.15/conv2 input: [None, 32, 16, 16]
[1005 22:02:24 @registry.py:129] res2.15/conv2 output: [None, 32, 16, 16]
[1005 22:02:24 @registry.py:121] res2.16/conv1 input: [None, 32, 16, 16]
[1005 22:02:24 @registry.py:129] res2.16/conv1 output: [None, 32, 16, 16]
[1005 22:02:24 @registry.py:121] res2.16/conv2 input: [None, 32, 16, 16]
[1005 22:02:24 @registry.py:129] res2.16/conv2 output: [None, 32, 16, 16]
[1005 22:02:24 @registry.py:121] res2.17/conv1 input: [None, 32, 16, 16]
[1005 22:02:24 @registry.py:129] res2.17/conv1 output: [None, 32, 16, 16]
[1005 22:02:24 @registry.py:121] res2.17/conv2 input: [None, 32, 16, 16]
[1005 22:02:24 @registry.py:129] res2.17/conv2 output: [None, 32, 16, 16]
[1005 22:02:24 @registry.py:121] res3.0/conv1 input: [None, 32, 16, 16]
[1005 22:02:24 @registry.py:129] res3.0/conv1 output: [None, 64, 8, 8]
[1005 22:02:24 @registry.py:121] res3.0/conv2 input: [None, 64, 8, 8]
[1005 22:02:24 @registry.py:129] res3.0/conv2 output: [None, 64, 8, 8]
[1005 22:02:24 @registry.py:121] res3.0/pool input: [None, 32, 16, 16]
[1005 22:02:24 @registry.py:129] res3.0/pool output: [None, 32, 8, 8]
[1005 22:02:24 @registry.py:121] res3.1/conv1 input: [None, 64, 8, 8]
[1005 22:02:24 @registry.py:129] res3.1/conv1 output: [None, 64, 8, 8]
[1005 22:02:24 @registry.py:121] res3.1/conv2 input: [None, 64, 8, 8]
[1005 22:02:24 @registry.py:129] res3.1/conv2 output: [None, 64, 8, 8]
[1005 22:02:25 @registry.py:121] res3.2/conv1 input: [None, 64, 8, 8]
[1005 22:02:25 @registry.py:129] res3.2/conv1 output: [None, 64, 8, 8]
[1005 22:02:25 @registry.py:121] res3.2/conv2 input: [None, 64, 8, 8]
[1005 22:02:25 @registry.py:129] res3.2/conv2 output: [None, 64, 8, 8]
[1005 22:02:25 @registry.py:121] res3.3/conv1 input: [None, 64, 8, 8]
[1005 22:02:25 @registry.py:129] res3.3/conv1 output: [None, 64, 8, 8]
[1005 22:02:25 @registry.py:121] res3.3/conv2 input: [None, 64, 8, 8]
[1005 22:02:25 @registry.py:129] res3.3/conv2 output: [None, 64, 8, 8]
[1005 22:02:25 @registry.py:121] res3.4/conv1 input: [None, 64, 8, 8]
[1005 22:02:25 @registry.py:129] res3.4/conv1 output: [None, 64, 8, 8]
[1005 22:02:25 @registry.py:121] res3.4/conv2 input: [None, 64, 8, 8]
[1005 22:02:25 @registry.py:129] res3.4/conv2 output: [None, 64, 8, 8]
[1005 22:02:25 @registry.py:121] res3.5/conv1 input: [None, 64, 8, 8]
[1005 22:02:25 @registry.py:129] res3.5/conv1 output: [None, 64, 8, 8]
[1005 22:02:25 @registry.py:121] res3.5/conv2 input: [None, 64, 8, 8]
[1005 22:02:25 @registry.py:129] res3.5/conv2 output: [None, 64, 8, 8]
[1005 22:02:25 @registry.py:121] res3.6/conv1 input: [None, 64, 8, 8]
[1005 22:02:25 @registry.py:129] res3.6/conv1 output: [None, 64, 8, 8]
[1005 22:02:25 @registry.py:121] res3.6/conv2 input: [None, 64, 8, 8]
[1005 22:02:25 @registry.py:129] res3.6/conv2 output: [None, 64, 8, 8]
[1005 22:02:25 @registry.py:121] res3.7/conv1 input: [None, 64, 8, 8]
[1005 22:02:25 @registry.py:129] res3.7/conv1 output: [None, 64, 8, 8]
[1005 22:02:25 @registry.py:121] res3.7/conv2 input: [None, 64, 8, 8]
[1005 22:02:25 @registry.py:129] res3.7/conv2 output: [None, 64, 8, 8]
[1005 22:02:25 @registry.py:121] res3.8/conv1 input: [None, 64, 8, 8]
[1005 22:02:25 @registry.py:129] res3.8/conv1 output: [None, 64, 8, 8]
[1005 22:02:25 @registry.py:121] res3.8/conv2 input: [None, 64, 8, 8]
[1005 22:02:25 @registry.py:129] res3.8/conv2 output: [None, 64, 8, 8]
[1005 22:02:25 @registry.py:121] res3.9/conv1 input: [None, 64, 8, 8]
[1005 22:02:25 @registry.py:129] res3.9/conv1 output: [None, 64, 8, 8]
[1005 22:02:25 @registry.py:121] res3.9/conv2 input: [None, 64, 8, 8]
[1005 22:02:25 @registry.py:129] res3.9/conv2 output: [None, 64, 8, 8]
[1005 22:02:25 @registry.py:121] res3.10/conv1 input: [None, 64, 8, 8]
[1005 22:02:25 @registry.py:129] res3.10/conv1 output: [None, 64, 8, 8]
[1005 22:02:25 @registry.py:121] res3.10/conv2 input: [None, 64, 8, 8]
[1005 22:02:25 @registry.py:129] res3.10/conv2 output: [None, 64, 8, 8]
[1005 22:02:25 @registry.py:121] res3.11/conv1 input: [None, 64, 8, 8]
[1005 22:02:25 @registry.py:129] res3.11/conv1 output: [None, 64, 8, 8]
[1005 22:02:25 @registry.py:121] res3.11/conv2 input: [None, 64, 8, 8]
[1005 22:02:25 @registry.py:129] res3.11/conv2 output: [None, 64, 8, 8]
[1005 22:02:25 @registry.py:121] res3.12/conv1 input: [None, 64, 8, 8]
[1005 22:02:25 @registry.py:129] res3.12/conv1 output: [None, 64, 8, 8]
[1005 22:02:25 @registry.py:121] res3.12/conv2 input: [None, 64, 8, 8]
[1005 22:02:25 @registry.py:129] res3.12/conv2 output: [None, 64, 8, 8]
[1005 22:02:26 @registry.py:121] res3.13/conv1 input: [None, 64, 8, 8]
[1005 22:02:26 @registry.py:129] res3.13/conv1 output: [None, 64, 8, 8]
[1005 22:02:26 @registry.py:121] res3.13/conv2 input: [None, 64, 8, 8]
[1005 22:02:26 @registry.py:129] res3.13/conv2 output: [None, 64, 8, 8]
[1005 22:02:26 @registry.py:121] res3.14/conv1 input: [None, 64, 8, 8]
[1005 22:02:26 @registry.py:129] res3.14/conv1 output: [None, 64, 8, 8]
[1005 22:02:26 @registry.py:121] res3.14/conv2 input: [None, 64, 8, 8]
[1005 22:02:26 @registry.py:129] res3.14/conv2 output: [None, 64, 8, 8]
[1005 22:02:26 @registry.py:121] res3.15/conv1 input: [None, 64, 8, 8]
[1005 22:02:26 @registry.py:129] res3.15/conv1 output: [None, 64, 8, 8]
[1005 22:02:26 @registry.py:121] res3.15/conv2 input: [None, 64, 8, 8]
[1005 22:02:26 @registry.py:129] res3.15/conv2 output: [None, 64, 8, 8]
[1005 22:02:26 @registry.py:121] res3.16/conv1 input: [None, 64, 8, 8]
[1005 22:02:26 @registry.py:129] res3.16/conv1 output: [None, 64, 8, 8]
[1005 22:02:26 @registry.py:121] res3.16/conv2 input: [None, 64, 8, 8]
[1005 22:02:26 @registry.py:129] res3.16/conv2 output: [None, 64, 8, 8]
[1005 22:02:26 @registry.py:121] res3.17/conv1 input: [None, 64, 8, 8]
[1005 22:02:26 @registry.py:129] res3.17/conv1 output: [None, 64, 8, 8]
[1005 22:02:26 @registry.py:121] res3.17/conv2 input: [None, 64, 8, 8]
[1005 22:02:26 @registry.py:129] res3.17/conv2 output: [None, 64, 8, 8]
[1005 22:02:26 @registry.py:121] gap input: [None, 64, 8, 8]
[1005 22:02:26 @registry.py:129] gap output: [None, 64]
[1005 22:02:26 @registry.py:121] linear input: [None, 64]
[1005 22:02:26 @registry.py:129] linear output: [None, 10]
[1005 22:02:26 @regularize.py:90] regularize_cost() found 110 variables to regularize.
[1005 22:02:26 @regularize.py:19] The following tensors will be regularized: conv0/W:0, res1.0/conv1/W:0, res1.0/conv2/W:0, res1.1/conv1/W:0, res1.1/conv2/W:0, res1.2/conv1/W:0, res1.2/conv2/W:0, res1.3/conv1/W:0, res1.3/conv2/W:0, res1.4/conv1/W:0, res1.4/conv2/W:0, res1.5/conv1/W:0, res1.5/conv2/W:0, res1.6/conv1/W:0, res1.6/conv2/W:0, res1.7/conv1/W:0, res1.7/conv2/W:0, res1.8/conv1/W:0, res1.8/conv2/W:0, res1.9/conv1/W:0, res1.9/conv2/W:0, res1.10/conv1/W:0, res1.10/conv2/W:0, res1.11/conv1/W:0, res1.11/conv2/W:0, res1.12/conv1/W:0, res1.12/conv2/W:0, res1.13/conv1/W:0, res1.13/conv2/W:0, res1.14/conv1/W:0, res1.14/conv2/W:0, res1.15/conv1/W:0, res1.15/conv2/W:0, res1.16/conv1/W:0, res1.16/conv2/W:0, res1.17/conv1/W:0, res1.17/conv2/W:0, res2.0/conv1/W:0, res2.0/conv2/W:0, res2.1/conv1/W:0, res2.1/conv2/W:0, res2.2/conv1/W:0, res2.2/conv2/W:0, res2.3/conv1/W:0, res2.3/conv2/W:0, res2.4/conv1/W:0, res2.4/conv2/W:0, res2.5/conv1/W:0, res2.5/conv2/W:0, res2.6/conv1/W:0, res2.6/conv2/W:0, res2.7/conv1/W:0, res2.7/conv2/W:0, res2.8/conv1/W:0, res2.8/conv2/W:0, res2.9/conv1/W:0, res2.9/conv2/W:0, res2.10/conv1/W:0, res2.10/conv2/W:0, res2.11/conv1/W:0, res2.11/conv2/W:0, res2.12/conv1/W:0, res2.12/conv2/W:0, res2.13/conv1/W:0, res2.13/conv2/W:0, res2.14/conv1/W:0, res2.14/conv2/W:0, res2.15/conv1/W:0, res2.15/conv2/W:0, res2.16/conv1/W:0, res2.16/conv2/W:0, res2.17/conv1/W:0, res2.17/conv2/W:0, res3.0/conv1/W:0, res3.0/conv2/W:0, res3.1/conv1/W:0, res3.1/conv2/W:0, res3.2/conv1/W:0, res3.2/conv2/W:0, res3.3/conv1/W:0, res3.3/conv2/W:0, res3.4/conv1/W:0, res3.4/conv2/W:0, res3.5/conv1/W:0, res3.5/conv2/W:0, res3.6/conv1/W:0, res3.6/conv2/W:0, res3.7/conv1/W:0, res3.7/conv2/W:0, res3.8/conv1/W:0, res3.8/conv2/W:0, res3.9/conv1/W:0, res3.9/conv2/W:0, res3.10/conv1/W:0, res3.10/conv2/W:0, res3.11/conv1/W:0, res3.11/conv2/W:0, res3.12/conv1/W:0, res3.12/conv2/W:0, res3.13/conv1/W:0, res3.13/conv2/W:0, res3.14/conv1/W:0, res3.14/conv2/W:0, res3.15/conv1/W:0, res3.15/conv2/W:0, res3.16/conv1/W:0, res3.16/conv2/W:0, res3.17/conv1/W:0, res3.17/conv2/W:0, linear/W:0
[1005 22:02:38 @training.py:113] Building graph for training tower 1 ...
[1005 22:02:42 @regularize.py:90] regularize_cost() found 110 variables to regularize.
[1005 22:02:57 @model_utils.py:64] Trainable Variables:
name shape dim device
--------------------- -------------- ----- ------------------------------
conv0/W:0 [3, 3, 3, 16] 432 /replica:0/task:0/device:GPU:0
conv0/beta:0 [16] 16 /replica:0/task:0/device:GPU:1
conv0/gamma:0 [16] 16 /replica:0/task:0/device:GPU:1
res1.0/conv1/W:0 [3, 3, 16, 16] 2304 /replica:0/task:0/device:GPU:1
res1.0/conv1/beta:0 [16] 16 /replica:0/task:0/device:GPU:0
res1.0/conv1/gamma:0 [16] 16 /replica:0/task:0/device:GPU:0
res1.0/conv2/W:0 [3, 3, 16, 16] 2304 /replica:0/task:0/device:GPU:0
res1.1/beta:0 [16] 16 /replica:0/task:0/device:GPU:1
res1.1/gamma:0 [16] 16 /replica:0/task:0/device:GPU:1
res1.1/conv1/W:0 [3, 3, 16, 16] 2304 /replica:0/task:0/device:GPU:1
res1.1/conv1/beta:0 [16] 16 /replica:0/task:0/device:GPU:0
res1.1/conv1/gamma:0 [16] 16 /replica:0/task:0/device:GPU:0
res1.1/conv2/W:0 [3, 3, 16, 16] 2304 /replica:0/task:0/device:GPU:0
res1.2/beta:0 [16] 16 /replica:0/task:0/device:GPU:1
res1.2/gamma:0 [16] 16 /replica:0/task:0/device:GPU:1
res1.2/conv1/W:0 [3, 3, 16, 16] 2304 /replica:0/task:0/device:GPU:1
res1.2/conv1/beta:0 [16] 16 /replica:0/task:0/device:GPU:0
res1.2/conv1/gamma:0 [16] 16 /replica:0/task:0/device:GPU:0
res1.2/conv2/W:0 [3, 3, 16, 16] 2304 /replica:0/task:0/device:GPU:0
res1.3/beta:0 [16] 16 /replica:0/task:0/device:GPU:1
res1.3/gamma:0 [16] 16 /replica:0/task:0/device:GPU:1
res1.3/conv1/W:0 [3, 3, 16, 16] 2304 /replica:0/task:0/device:GPU:1
res1.3/conv1/beta:0 [16] 16 /replica:0/task:0/device:GPU:0
res1.3/conv1/gamma:0 [16] 16 /replica:0/task:0/device:GPU:0
res1.3/conv2/W:0 [3, 3, 16, 16] 2304 /replica:0/task:0/device:GPU:0
res1.4/beta:0 [16] 16 /replica:0/task:0/device:GPU:1
res1.4/gamma:0 [16] 16 /replica:0/task:0/device:GPU:1
res1.4/conv1/W:0 [3, 3, 16, 16] 2304 /replica:0/task:0/device:GPU:1
res1.4/conv1/beta:0 [16] 16 /replica:0/task:0/device:GPU:0
res1.4/conv1/gamma:0 [16] 16 /replica:0/task:0/device:GPU:0
res1.4/conv2/W:0 [3, 3, 16, 16] 2304 /replica:0/task:0/device:GPU:0
res1.5/beta:0 [16] 16 /replica:0/task:0/device:GPU:1
res1.5/gamma:0 [16] 16 /replica:0/task:0/device:GPU:1
res1.5/conv1/W:0 [3, 3, 16, 16] 2304 /replica:0/task:0/device:GPU:1
res1.5/conv1/beta:0 [16] 16 /replica:0/task:0/device:GPU:0
res1.5/conv1/gamma:0 [16] 16 /replica:0/task:0/device:GPU:0
res1.5/conv2/W:0 [3, 3, 16, 16] 2304 /replica:0/task:0/device:GPU:0
res1.6/beta:0 [16] 16 /replica:0/task:0/device:GPU:1
res1.6/gamma:0 [16] 16 /replica:0/task:0/device:GPU:1
res1.6/conv1/W:0 [3, 3, 16, 16] 2304 /replica:0/task:0/device:GPU:1
res1.6/conv1/beta:0 [16] 16 /replica:0/task:0/device:GPU:0
res1.6/conv1/gamma:0 [16] 16 /replica:0/task:0/device:GPU:0
res1.6/conv2/W:0 [3, 3, 16, 16] 2304 /replica:0/task:0/device:GPU:0
res1.7/beta:0 [16] 16 /replica:0/task:0/device:GPU:1
res1.7/gamma:0 [16] 16 /replica:0/task:0/device:GPU:1
res1.7/conv1/W:0 [3, 3, 16, 16] 2304 /replica:0/task:0/device:GPU:1
res1.7/conv1/beta:0 [16] 16 /replica:0/task:0/device:GPU:0
res1.7/conv1/gamma:0 [16] 16 /replica:0/task:0/device:GPU:0
res1.7/conv2/W:0 [3, 3, 16, 16] 2304 /replica:0/task:0/device:GPU:0
res1.8/beta:0 [16] 16 /replica:0/task:0/device:GPU:1
res1.8/gamma:0 [16] 16 /replica:0/task:0/device:GPU:1
res1.8/conv1/W:0 [3, 3, 16, 16] 2304 /replica:0/task:0/device:GPU:1
res1.8/conv1/beta:0 [16] 16 /replica:0/task:0/device:GPU:0
res1.8/conv1/gamma:0 [16] 16 /replica:0/task:0/device:GPU:0
res1.8/conv2/W:0 [3, 3, 16, 16] 2304 /replica:0/task:0/device:GPU:0
res1.9/beta:0 [16] 16 /replica:0/task:0/device:GPU:1
res1.9/gamma:0 [16] 16 /replica:0/task:0/device:GPU:1
res1.9/conv1/W:0 [3, 3, 16, 16] 2304 /replica:0/task:0/device:GPU:1
res1.9/conv1/beta:0 [16] 16 /replica:0/task:0/device:GPU:0
res1.9/conv1/gamma:0 [16] 16 /replica:0/task:0/device:GPU:0
res1.9/conv2/W:0 [3, 3, 16, 16] 2304 /replica:0/task:0/device:GPU:0
res1.10/beta:0 [16] 16 /replica:0/task:0/device:GPU:1
res1.10/gamma:0 [16] 16 /replica:0/task:0/device:GPU:1
res1.10/conv1/W:0 [3, 3, 16, 16] 2304 /replica:0/task:0/device:GPU:1
res1.10/conv1/beta:0 [16] 16 /replica:0/task:0/device:GPU:0
res1.10/conv1/gamma:0 [16] 16 /replica:0/task:0/device:GPU:0
res1.10/conv2/W:0 [3, 3, 16, 16] 2304 /replica:0/task:0/device:GPU:0
res1.11/beta:0 [16] 16 /replica:0/task:0/device:GPU:1
res1.11/gamma:0 [16] 16 /replica:0/task:0/device:GPU:1
res1.11/conv1/W:0 [3, 3, 16, 16] 2304 /replica:0/task:0/device:GPU:1
res1.11/conv1/beta:0 [16] 16 /replica:0/task:0/device:GPU:0
res1.11/conv1/gamma:0 [16] 16 /replica:0/task:0/device:GPU:0
res1.11/conv2/W:0 [3, 3, 16, 16] 2304 /replica:0/task:0/device:GPU:0
res1.12/beta:0 [16] 16 /replica:0/task:0/device:GPU:1
res1.12/gamma:0 [16] 16 /replica:0/task:0/device:GPU:1
res1.12/conv1/W:0 [3, 3, 16, 16] 2304 /replica:0/task:0/device:GPU:1
res1.12/conv1/beta:0 [16] 16 /replica:0/task:0/device:GPU:0
res1.12/conv1/gamma:0 [16] 16 /replica:0/task:0/device:GPU:0
res1.12/conv2/W:0 [3, 3, 16, 16] 2304 /replica:0/task:0/device:GPU:0
res1.13/beta:0 [16] 16 /replica:0/task:0/device:GPU:1
res1.13/gamma:0 [16] 16 /replica:0/task:0/device:GPU:1
res1.13/conv1/W:0 [3, 3, 16, 16] 2304 /replica:0/task:0/device:GPU:1
res1.13/conv1/beta:0 [16] 16 /replica:0/task:0/device:GPU:0
res1.13/conv1/gamma:0 [16] 16 /replica:0/task:0/device:GPU:0
res1.13/conv2/W:0 [3, 3, 16, 16] 2304 /replica:0/task:0/device:GPU:0
res1.14/beta:0 [16] 16 /replica:0/task:0/device:GPU:1
res1.14/gamma:0 [16] 16 /replica:0/task:0/device:GPU:1
res1.14/conv1/W:0 [3, 3, 16, 16] 2304 /replica:0/task:0/device:GPU:1
res1.14/conv1/beta:0 [16] 16 /replica:0/task:0/device:GPU:0
res1.14/conv1/gamma:0 [16] 16 /replica:0/task:0/device:GPU:0
res1.14/conv2/W:0 [3, 3, 16, 16] 2304 /replica:0/task:0/device:GPU:0
res1.15/beta:0 [16] 16 /replica:0/task:0/device:GPU:1
res1.15/gamma:0 [16] 16 /replica:0/task:0/device:GPU:1
res1.15/conv1/W:0 [3, 3, 16, 16] 2304 /replica:0/task:0/device:GPU:1
res1.15/conv1/beta:0 [16] 16 /replica:0/task:0/device:GPU:0
res1.15/conv1/gamma:0 [16] 16 /replica:0/task:0/device:GPU:0
res1.15/conv2/W:0 [3, 3, 16, 16] 2304 /replica:0/task:0/device:GPU:0
res1.16/beta:0 [16] 16 /replica:0/task:0/device:GPU:1
res1.16/gamma:0 [16] 16 /replica:0/task:0/device:GPU:1
res1.16/conv1/W:0 [3, 3, 16, 16] 2304 /replica:0/task:0/device:GPU:1
res1.16/conv1/beta:0 [16] 16 /replica:0/task:0/device:GPU:0
res1.16/conv1/gamma:0 [16] 16 /replica:0/task:0/device:GPU:0
res1.16/conv2/W:0 [3, 3, 16, 16] 2304 /replica:0/task:0/device:GPU:0
res1.17/beta:0 [16] 16 /replica:0/task:0/device:GPU:1
res1.17/gamma:0 [16] 16 /replica:0/task:0/device:GPU:1
res1.17/conv1/W:0 [3, 3, 16, 16] 2304 /replica:0/task:0/device:GPU:1
res1.17/conv1/beta:0 [16] 16 /replica:0/task:0/device:GPU:0
res1.17/conv1/gamma:0 [16] 16 /replica:0/task:0/device:GPU:0
res1.17/conv2/W:0 [3, 3, 16, 16] 2304 /replica:0/task:0/device:GPU:0
res2.0/beta:0 [16] 16 /replica:0/task:0/device:GPU:1
res2.0/gamma:0 [16] 16 /replica:0/task:0/device:GPU:1
res2.0/conv1/W:0 [3, 3, 16, 32] 4608 /replica:0/task:0/device:GPU:1
res2.0/conv1/beta:0 [32] 32 /replica:0/task:0/device:GPU:0
res2.0/conv1/gamma:0 [32] 32 /replica:0/task:0/device:GPU:0
res2.0/conv2/W:0 [3, 3, 32, 32] 9216 /replica:0/task:0/device:GPU:0
res2.1/beta:0 [32] 32 /replica:0/task:0/device:GPU:1
res2.1/gamma:0 [32] 32 /replica:0/task:0/device:GPU:1
res2.1/conv1/W:0 [3, 3, 32, 32] 9216 /replica:0/task:0/device:GPU:1
res2.1/conv1/beta:0 [32] 32 /replica:0/task:0/device:GPU:0
res2.1/conv1/gamma:0 [32] 32 /replica:0/task:0/device:GPU:0
res2.1/conv2/W:0 [3, 3, 32, 32] 9216 /replica:0/task:0/device:GPU:0
res2.2/beta:0 [32] 32 /replica:0/task:0/device:GPU:1
res2.2/gamma:0 [32] 32 /replica:0/task:0/device:GPU:1
res2.2/conv1/W:0 [3, 3, 32, 32] 9216 /replica:0/task:0/device:GPU:1
res2.2/conv1/beta:0 [32] 32 /replica:0/task:0/device:GPU:0
res2.2/conv1/gamma:0 [32] 32 /replica:0/task:0/device:GPU:0
res2.2/conv2/W:0 [3, 3, 32, 32] 9216 /replica:0/task:0/device:GPU:0
res2.3/beta:0 [32] 32 /replica:0/task:0/device:GPU:1
res2.3/gamma:0 [32] 32 /replica:0/task:0/device:GPU:1
res2.3/conv1/W:0 [3, 3, 32, 32] 9216 /replica:0/task:0/device:GPU:1
res2.3/conv1/beta:0 [32] 32 /replica:0/task:0/device:GPU:0
res2.3/conv1/gamma:0 [32] 32 /replica:0/task:0/device:GPU:0
res2.3/conv2/W:0 [3, 3, 32, 32] 9216 /replica:0/task:0/device:GPU:0
res2.4/beta:0 [32] 32 /replica:0/task:0/device:GPU:1
res2.4/gamma:0 [32] 32 /replica:0/task:0/device:GPU:1
res2.4/conv1/W:0 [3, 3, 32, 32] 9216 /replica:0/task:0/device:GPU:1
res2.4/conv1/beta:0 [32] 32 /replica:0/task:0/device:GPU:0
res2.4/conv1/gamma:0 [32] 32 /replica:0/task:0/device:GPU:0
res2.4/conv2/W:0 [3, 3, 32, 32] 9216 /replica:0/task:0/device:GPU:0
res2.5/beta:0 [32] 32 /replica:0/task:0/device:GPU:1
res2.5/gamma:0 [32] 32 /replica:0/task:0/device:GPU:1
res2.5/conv1/W:0 [3, 3, 32, 32] 9216 /replica:0/task:0/device:GPU:1
res2.5/conv1/beta:0 [32] 32 /replica:0/task:0/device:GPU:0
res2.5/conv1/gamma:0 [32] 32 /replica:0/task:0/device:GPU:0
res2.5/conv2/W:0 [3, 3, 32, 32] 9216 /replica:0/task:0/device:GPU:0
res2.6/beta:0 [32] 32 /replica:0/task:0/device:GPU:1
res2.6/gamma:0 [32] 32 /replica:0/task:0/device:GPU:1
res2.6/conv1/W:0 [3, 3, 32, 32] 9216 /replica:0/task:0/device:GPU:1
res2.6/conv1/beta:0 [32] 32 /replica:0/task:0/device:GPU:0
res2.6/conv1/gamma:0 [32] 32 /replica:0/task:0/device:GPU:0
res2.6/conv2/W:0 [3, 3, 32, 32] 9216 /replica:0/task:0/device:GPU:0
res2.7/beta:0 [32] 32 /replica:0/task:0/device:GPU:1
res2.7/gamma:0 [32] 32 /replica:0/task:0/device:GPU:1
res2.7/conv1/W:0 [3, 3, 32, 32] 9216 /replica:0/task:0/device:GPU:1
res2.7/conv1/beta:0 [32] 32 /replica:0/task:0/device:GPU:0
res2.7/conv1/gamma:0 [32] 32 /replica:0/task:0/device:GPU:0
res2.7/conv2/W:0 [3, 3, 32, 32] 9216 /replica:0/task:0/device:GPU:0
res2.8/beta:0 [32] 32 /replica:0/task:0/device:GPU:1
res2.8/gamma:0 [32] 32 /replica:0/task:0/device:GPU:1
res2.8/conv1/W:0 [3, 3, 32, 32] 9216 /replica:0/task:0/device:GPU:1
res2.8/conv1/beta:0 [32] 32 /replica:0/task:0/device:GPU:0
res2.8/conv1/gamma:0 [32] 32 /replica:0/task:0/device:GPU:0
res2.8/conv2/W:0 [3, 3, 32, 32] 9216 /replica:0/task:0/device:GPU:0
res2.9/beta:0 [32] 32 /replica:0/task:0/device:GPU:1
res2.9/gamma:0 [32] 32 /replica:0/task:0/device:GPU:1
res2.9/conv1/W:0 [3, 3, 32, 32] 9216 /replica:0/task:0/device:GPU:1
res2.9/conv1/beta:0 [32] 32 /replica:0/task:0/device:GPU:0
res2.9/conv1/gamma:0 [32] 32 /replica:0/task:0/device:GPU:0
res2.9/conv2/W:0 [3, 3, 32, 32] 9216 /replica:0/task:0/device:GPU:0
res2.10/beta:0 [32] 32 /replica:0/task:0/device:GPU:1
res2.10/gamma:0 [32] 32 /replica:0/task:0/device:GPU:1
res2.10/conv1/W:0 [3, 3, 32, 32] 9216 /replica:0/task:0/device:GPU:1
res2.10/conv1/beta:0 [32] 32 /replica:0/task:0/device:GPU:0
res2.10/conv1/gamma:0 [32] 32 /replica:0/task:0/device:GPU:0
res2.10/conv2/W:0 [3, 3, 32, 32] 9216 /replica:0/task:0/device:GPU:0
res2.11/beta:0 [32] 32 /replica:0/task:0/device:GPU:1
res2.11/gamma:0 [32] 32 /replica:0/task:0/device:GPU:1
res2.11/conv1/W:0 [3, 3, 32, 32] 9216 /replica:0/task:0/device:GPU:1
res2.11/conv1/beta:0 [32] 32 /replica:0/task:0/device:GPU:0
res2.11/conv1/gamma:0 [32] 32 /replica:0/task:0/device:GPU:0
res2.11/conv2/W:0 [3, 3, 32, 32] 9216 /replica:0/task:0/device:GPU:0
res2.12/beta:0 [32] 32 /replica:0/task:0/device:GPU:1
res2.12/gamma:0 [32] 32 /replica:0/task:0/device:GPU:1
res2.12/conv1/W:0 [3, 3, 32, 32] 9216 /replica:0/task:0/device:GPU:1
res2.12/conv1/beta:0 [32] 32 /replica:0/task:0/device:GPU:0
res2.12/conv1/gamma:0 [32] 32 /replica:0/task:0/device:GPU:0
res2.12/conv2/W:0 [3, 3, 32, 32] 9216 /replica:0/task:0/device:GPU:0
res2.13/beta:0 [32] 32 /replica:0/task:0/device:GPU:1
res2.13/gamma:0 [32] 32 /replica:0/task:0/device:GPU:1
res2.13/conv1/W:0 [3, 3, 32, 32] 9216 /replica:0/task:0/device:GPU:1
res2.13/conv1/beta:0 [32] 32 /replica:0/task:0/device:GPU:0
res2.13/conv1/gamma:0 [32] 32 /replica:0/task:0/device:GPU:0
res2.13/conv2/W:0 [3, 3, 32, 32] 9216 /replica:0/task:0/device:GPU:0
res2.14/beta:0 [32] 32 /replica:0/task:0/device:GPU:1
res2.14/gamma:0 [32] 32 /replica:0/task:0/device:GPU:1
res2.14/conv1/W:0 [3, 3, 32, 32] 9216 /replica:0/task:0/device:GPU:1
res2.14/conv1/beta:0 [32] 32 /replica:0/task:0/device:GPU:0
res2.14/conv1/gamma:0 [32] 32 /replica:0/task:0/device:GPU:0
res2.14/conv2/W:0 [3, 3, 32, 32] 9216 /replica:0/task:0/device:GPU:0
res2.15/beta:0 [32] 32 /replica:0/task:0/device:GPU:1
res2.15/gamma:0 [32] 32 /replica:0/task:0/device:GPU:1
res2.15/conv1/W:0 [3, 3, 32, 32] 9216 /replica:0/task:0/device:GPU:1
res2.15/conv1/beta:0 [32] 32 /replica:0/task:0/device:GPU:0
res2.15/conv1/gamma:0 [32] 32 /replica:0/task:0/device:GPU:0
res2.15/conv2/W:0 [3, 3, 32, 32] 9216 /replica:0/task:0/device:GPU:0
res2.16/beta:0 [32] 32 /replica:0/task:0/device:GPU:1
res2.16/gamma:0 [32] 32 /replica:0/task:0/device:GPU:1
res2.16/conv1/W:0 [3, 3, 32, 32] 9216 /replica:0/task:0/device:GPU:1
res2.16/conv1/beta:0 [32] 32 /replica:0/task:0/device:GPU:0
res2.16/conv1/gamma:0 [32] 32 /replica:0/task:0/device:GPU:0
res2.16/conv2/W:0 [3, 3, 32, 32] 9216 /replica:0/task:0/device:GPU:0
res2.17/beta:0 [32] 32 /replica:0/task:0/device:GPU:1
res2.17/gamma:0 [32] 32 /replica:0/task:0/device:GPU:1
res2.17/conv1/W:0 [3, 3, 32, 32] 9216 /replica:0/task:0/device:GPU:1
res2.17/conv1/beta:0 [32] 32 /replica:0/task:0/device:GPU:0
res2.17/conv1/gamma:0 [32] 32 /replica:0/task:0/device:GPU:0
res2.17/conv2/W:0 [3, 3, 32, 32] 9216 /replica:0/task:0/device:GPU:0
res3.0/beta:0 [32] 32 /replica:0/task:0/device:GPU:1
res3.0/gamma:0 [32] 32 /replica:0/task:0/device:GPU:1
res3.0/conv1/W:0 [3, 3, 32, 64] 18432 /replica:0/task:0/device:GPU:1
res3.0/conv1/beta:0 [64] 64 /replica:0/task:0/device:GPU:0
res3.0/conv1/gamma:0 [64] 64 /replica:0/task:0/device:GPU:0
res3.0/conv2/W:0 [3, 3, 64, 64] 36864 /replica:0/task:0/device:GPU:0
res3.1/beta:0 [64] 64 /replica:0/task:0/device:GPU:1
res3.1/gamma:0 [64] 64 /replica:0/task:0/device:GPU:1
res3.1/conv1/W:0 [3, 3, 64, 64] 36864 /replica:0/task:0/device:GPU:1
res3.1/conv1/beta:0 [64] 64 /replica:0/task:0/device:GPU:0
res3.1/conv1/gamma:0 [64] 64 /replica:0/task:0/device:GPU:0
res3.1/conv2/W:0 [3, 3, 64, 64] 36864 /replica:0/task:0/device:GPU:0
res3.2/beta:0 [64] 64 /replica:0/task:0/device:GPU:1
res3.2/gamma:0 [64] 64 /replica:0/task:0/device:GPU:1
res3.2/conv1/W:0 [3, 3, 64, 64] 36864 /replica:0/task:0/device:GPU:1
res3.2/conv1/beta:0 [64] 64 /replica:0/task:0/device:GPU:0
res3.2/conv1/gamma:0 [64] 64 /replica:0/task:0/device:GPU:0
res3.2/conv2/W:0 [3, 3, 64, 64] 36864 /replica:0/task:0/device:GPU:0
res3.3/beta:0 [64] 64 /replica:0/task:0/device:GPU:1
res3.3/gamma:0 [64] 64 /replica:0/task:0/device:GPU:1
res3.3/conv1/W:0 [3, 3, 64, 64] 36864 /replica:0/task:0/device:GPU:1
res3.3/conv1/beta:0 [64] 64 /replica:0/task:0/device:GPU:0
res3.3/conv1/gamma:0 [64] 64 /replica:0/task:0/device:GPU:0
res3.3/conv2/W:0 [3, 3, 64, 64] 36864 /replica:0/task:0/device:GPU:0
res3.4/beta:0 [64] 64 /replica:0/task:0/device:GPU:1
res3.4/gamma:0 [64] 64 /replica:0/task:0/device:GPU:1
res3.4/conv1/W:0 [3, 3, 64, 64] 36864 /replica:0/task:0/device:GPU:1
res3.4/conv1/beta:0 [64] 64 /replica:0/task:0/device:GPU:0
res3.4/conv1/gamma:0 [64] 64 /replica:0/task:0/device:GPU:0
res3.4/conv2/W:0 [3, 3, 64, 64] 36864 /replica:0/task:0/device:GPU:0
res3.5/beta:0 [64] 64 /replica:0/task:0/device:GPU:1
res3.5/gamma:0 [64] 64 /replica:0/task:0/device:GPU:1
res3.5/conv1/W:0 [3, 3, 64, 64] 36864 /replica:0/task:0/device:GPU:1
res3.5/conv1/beta:0 [64] 64 /replica:0/task:0/device:GPU:0
res3.5/conv1/gamma:0 [64] 64 /replica:0/task:0/device:GPU:0
res3.5/conv2/W:0 [3, 3, 64, 64] 36864 /replica:0/task:0/device:GPU:0
res3.6/beta:0 [64] 64 /replica:0/task:0/device:GPU:1
res3.6/gamma:0 [64] 64 /replica:0/task:0/device:GPU:1
res3.6/conv1/W:0 [3, 3, 64, 64] 36864 /replica:0/task:0/device:GPU:1
res3.6/conv1/beta:0 [64] 64 /replica:0/task:0/device:GPU:0
res3.6/conv1/gamma:0 [64] 64 /replica:0/task:0/device:GPU:0
res3.6/conv2/W:0 [3, 3, 64, 64] 36864 /replica:0/task:0/device:GPU:0
res3.7/beta:0 [64] 64 /replica:0/task:0/device:GPU:1
res3.7/gamma:0 [64] 64 /replica:0/task:0/device:GPU:1
res3.7/conv1/W:0 [3, 3, 64, 64] 36864 /replica:0/task:0/device:GPU:1
res3.7/conv1/beta:0 [64] 64 /replica:0/task:0/device:GPU:0
res3.7/conv1/gamma:0 [64] 64 /replica:0/task:0/device:GPU:0
res3.7/conv2/W:0 [3, 3, 64, 64] 36864 /replica:0/task:0/device:GPU:0
res3.8/beta:0 [64] 64 /replica:0/task:0/device:GPU:1
res3.8/gamma:0 [64] 64 /replica:0/task:0/device:GPU:1
res3.8/conv1/W:0 [3, 3, 64, 64] 36864 /replica:0/task:0/device:GPU:1
res3.8/conv1/beta:0 [64] 64 /replica:0/task:0/device:GPU:0
res3.8/conv1/gamma:0 [64] 64 /replica:0/task:0/device:GPU:0
res3.8/conv2/W:0 [3, 3, 64, 64] 36864 /replica:0/task:0/device:GPU:0
res3.9/beta:0 [64] 64 /replica:0/task:0/device:GPU:1
res3.9/gamma:0 [64] 64 /replica:0/task:0/device:GPU:1
res3.9/conv1/W:0 [3, 3, 64, 64] 36864 /replica:0/task:0/device:GPU:1
res3.9/conv1/beta:0 [64] 64 /replica:0/task:0/device:GPU:0
res3.9/conv1/gamma:0 [64] 64 /replica:0/task:0/device:GPU:0
res3.9/conv2/W:0 [3, 3, 64, 64] 36864 /replica:0/task:0/device:GPU:0
res3.10/beta:0 [64] 64 /replica:0/task:0/device:GPU:1
res3.10/gamma:0 [64] 64 /replica:0/task:0/device:GPU:1
res3.10/conv1/W:0 [3, 3, 64, 64] 36864 /replica:0/task:0/device:GPU:1
res3.10/conv1/beta:0 [64] 64 /replica:0/task:0/device:GPU:0
res3.10/conv1/gamma:0 [64] 64 /replica:0/task:0/device:GPU:0
res3.10/conv2/W:0 [3, 3, 64, 64] 36864 /replica:0/task:0/device:GPU:0
res3.11/beta:0 [64] 64 /replica:0/task:0/device:GPU:1
res3.11/gamma:0 [64] 64 /replica:0/task:0/device:GPU:1
res3.11/conv1/W:0 [3, 3, 64, 64] 36864 /replica:0/task:0/device:GPU:1
res3.11/conv1/beta:0 [64] 64 /replica:0/task:0/device:GPU:0
res3.11/conv1/gamma:0 [64] 64 /replica:0/task:0/device:GPU:0
res3.11/conv2/W:0 [3, 3, 64, 64] 36864 /replica:0/task:0/device:GPU:0
res3.12/beta:0 [64] 64 /replica:0/task:0/device:GPU:1
res3.12/gamma:0 [64] 64 /replica:0/task:0/device:GPU:1
res3.12/conv1/W:0 [3, 3, 64, 64] 36864 /replica:0/task:0/device:GPU:1
res3.12/conv1/beta:0 [64] 64 /replica:0/task:0/device:GPU:0
res3.12/conv1/gamma:0 [64] 64 /replica:0/task:0/device:GPU:0
res3.12/conv2/W:0 [3, 3, 64, 64] 36864 /replica:0/task:0/device:GPU:0
res3.13/beta:0 [64] 64 /replica:0/task:0/device:GPU:1
res3.13/gamma:0 [64] 64 /replica:0/task:0/device:GPU:1
res3.13/conv1/W:0 [3, 3, 64, 64] 36864 /replica:0/task:0/device:GPU:1
res3.13/conv1/beta:0 [64] 64 /replica:0/task:0/device:GPU:0
res3.13/conv1/gamma:0 [64] 64 /replica:0/task:0/device:GPU:0
res3.13/conv2/W:0 [3, 3, 64, 64] 36864 /replica:0/task:0/device:GPU:0
res3.14/beta:0 [64] 64 /replica:0/task:0/device:GPU:1
res3.14/gamma:0 [64] 64 /replica:0/task:0/device:GPU:1
res3.14/conv1/W:0 [3, 3, 64, 64] 36864 /replica:0/task:0/device:GPU:1
res3.14/conv1/beta:0 [64] 64 /replica:0/task:0/device:GPU:0
res3.14/conv1/gamma:0 [64] 64 /replica:0/task:0/device:GPU:0
res3.14/conv2/W:0 [3, 3, 64, 64] 36864 /replica:0/task:0/device:GPU:0
res3.15/beta:0 [64] 64 /replica:0/task:0/device:GPU:1
res3.15/gamma:0 [64] 64 /replica:0/task:0/device:GPU:1
res3.15/conv1/W:0 [3, 3, 64, 64] 36864 /replica:0/task:0/device:GPU:1
res3.15/conv1/beta:0 [64] 64 /replica:0/task:0/device:GPU:0
res3.15/conv1/gamma:0 [64] 64 /replica:0/task:0/device:GPU:0
res3.15/conv2/W:0 [3, 3, 64, 64] 36864 /replica:0/task:0/device:GPU:0
res3.16/beta:0 [64] 64 /replica:0/task:0/device:GPU:1
res3.16/gamma:0 [64] 64 /replica:0/task:0/device:GPU:1
res3.16/conv1/W:0 [3, 3, 64, 64] 36864 /replica:0/task:0/device:GPU:1
res3.16/conv1/beta:0 [64] 64 /replica:0/task:0/device:GPU:0
res3.16/conv1/gamma:0 [64] 64 /replica:0/task:0/device:GPU:0
res3.16/conv2/W:0 [3, 3, 64, 64] 36864 /replica:0/task:0/device:GPU:0
res3.17/beta:0 [64] 64 /replica:0/task:0/device:GPU:1
res3.17/gamma:0 [64] 64 /replica:0/task:0/device:GPU:1
res3.17/conv1/W:0 [3, 3, 64, 64] 36864 /replica:0/task:0/device:GPU:1
res3.17/conv1/beta:0 [64] 64 /replica:0/task:0/device:GPU:0
res3.17/conv1/gamma:0 [64] 64 /replica:0/task:0/device:GPU:0
res3.17/conv2/W:0 [3, 3, 64, 64] 36864 /replica:0/task:0/device:GPU:0
bnlast/beta:0 [64] 64 /replica:0/task:0/device:GPU:1
bnlast/gamma:0 [64] 64 /replica:0/task:0/device:GPU:1
linear/W:0 [64, 10] 640 /replica:0/task:0/device:GPU:1
linear/b:0 [10] 10 /replica:0/task:0/device:GPU:1
Total #vars=329, #params=1727962, size=6.59MB
[1005 22:02:57 @base.py:187] Setup callbacks graph ...
[1005 22:02:57 @inference_runner.py:152] [InferenceRunner] Building tower 'InferenceTower' on device /gpu:0 ...
[1005 22:03:00 @summary.py:38] Maintain moving average summary of 3 tensors in collection MOVING_SUMMARY_OPS.
[1005 22:03:00 @summary.py:75] Summarizing collection 'summaries' of size 114.
[1005 22:03:08 @base.py:205] Creating the session ...
[1005 22:03:14 @base.py:211] Initializing the session ...
[1005 22:03:14 @base.py:218] Graph Finalized.
[1005 22:03:15 @concurrency.py:37] Starting EnqueueThread QueueInput/input_queue ...
[1005 22:03:19 @inference_runner.py:101] [InferenceRunner] Will eval 79 iterations
[1005 22:03:20 @base.py:250] Start Epoch 1 ...
[1005 22:03:20 @input_source.py:551] Pre-filling StagingArea ...
[1005 22:03:21 @input_source.py:555] 1 element was put into StagingArea on each tower.
[1005 22:05:57 @base.py:260] Epoch 1 (global_step 390) finished, time:2 minutes 36 seconds.
[1005 22:05:57 @saver.py:77] Model saved to train_log/GN-cifar/model-390.
[1005 22:06:02 @param.py:157] [HyperParamSetter] At global_step=390, learning_rate will change to 0.10000000
[1005 22:06:02 @monitor.py:456] QueueInput/queue_size: 49.969
[1005 22:06:02 @monitor.py:456] cross_entropy_loss: 1.6717
[1005 22:06:02 @monitor.py:456] train_error: 0.62602
[1005 22:06:02 @monitor.py:456] validation_cost: 1.9773
[1005 22:06:02 @monitor.py:456] validation_error: 0.6589
[1005 22:06:02 @monitor.py:456] wd_cost: 0.78951
[1005 22:06:02 @group.py:48] Callbacks took 5.571 sec in total. InferenceRunner: 4.68 seconds
[1005 22:06:02 @base.py:250] Start Epoch 2 ...
[1005 22:06:55 @base.py:260] Epoch 2 (global_step 780) finished, time:52.4 seconds.
[1005 22:06:55 @saver.py:77] Model saved to train_log/GN-cifar/model-780.
[1005 22:06:57 @monitor.py:456] QueueInput/queue_size: 49.933
[1005 22:06:57 @monitor.py:456] cross_entropy_loss: 1.8494
[1005 22:06:57 @monitor.py:456] train_error: 0.69078
[1005 22:06:57 @monitor.py:456] validation_cost: 1.9092
[1005 22:06:57 @monitor.py:456] validation_error: 0.7032
[1005 22:06:57 @monitor.py:456] wd_cost: 0.69225
[1005 22:06:57 @base.py:250] Start Epoch 3 ...
[1005 22:07:49 @base.py:260] Epoch 3 (global_step 1170) finished, time:51.6 seconds.
[1005 22:07:49 @saver.py:77] Model saved to train_log/GN-cifar/model-1170.
[1005 22:07:51 @monitor.py:456] QueueInput/queue_size: 49.222
[1005 22:07:51 @monitor.py:456] cross_entropy_loss: 1.559
[1005 22:07:51 @monitor.py:456] train_error: 0.57625
[1005 22:07:51 @monitor.py:456] validation_cost: 1.6629
[1005 22:07:51 @monitor.py:456] validation_error: 0.5898
[1005 22:07:51 @monitor.py:456] wd_cost: 0.60618
[1005 22:07:51 @base.py:250] Start Epoch 4 ...
[1005 22:08:43 @base.py:260] Epoch 4 (global_step 1560) finished, time:52 seconds.
[1005 22:08:43 @saver.py:77] Model saved to train_log/GN-cifar/model-1560.
[1005 22:08:46 @monitor.py:456] QueueInput/queue_size: 49.242
[1005 22:08:46 @monitor.py:456] cross_entropy_loss: 1.3054
[1005 22:08:46 @monitor.py:456] train_error: 0.47518
[1005 22:08:46 @monitor.py:456] validation_cost: 1.4184
[1005 22:08:46 @monitor.py:456] validation_error: 0.5156
[1005 22:08:46 @monitor.py:456] wd_cost: 0.53863
[1005 22:08:46 @base.py:250] Start Epoch 5 ...
[1005 22:09:38 @base.py:260] Epoch 5 (global_step 1950) finished, time:52.6 seconds.
[1005 22:09:38 @saver.py:77] Model saved to train_log/GN-cifar/model-1950.
[1005 22:09:41 @monitor.py:456] QueueInput/queue_size: 49.359
[1005 22:09:41 @monitor.py:456] cross_entropy_loss: 1.1172
[1005 22:09:41 @monitor.py:456] train_error: 0.40438
[1005 22:09:41 @monitor.py:456] validation_cost: 1.1914
[1005 22:09:41 @monitor.py:456] validation_error: 0.4328
[1005 22:09:41 @monitor.py:456] wd_cost: 0.48473
[1005 22:09:41 @base.py:250] Start Epoch 6 ...
[1005 22:10:33 @base.py:260] Epoch 6 (global_step 2340) finished, time:52 seconds.
[1005 22:10:33 @saver.py:77] Model saved to train_log/GN-cifar/model-2340.
[1005 22:10:35 @monitor.py:456] QueueInput/queue_size: 49.93
[1005 22:10:35 @monitor.py:456] cross_entropy_loss: 0.95915
[1005 22:10:35 @monitor.py:456] train_error: 0.34203
[1005 22:10:35 @monitor.py:456] validation_cost: 1.1296
[1005 22:10:35 @monitor.py:456] validation_error: 0.3963
[1005 22:10:35 @monitor.py:456] wd_cost: 0.44283
[1005 22:10:35 @base.py:250] Start Epoch 7 ...
[1005 22:11:27 @base.py:260] Epoch 7 (global_step 2730) finished, time:51.8 seconds.
[1005 22:11:27 @saver.py:77] Model saved to train_log/GN-cifar/model-2730.
[1005 22:11:29 @monitor.py:456] QueueInput/queue_size: 49.801
[1005 22:11:29 @monitor.py:456] cross_entropy_loss: 0.83328
[1005 22:11:29 @monitor.py:456] train_error: 0.29602
[1005 22:11:29 @monitor.py:456] validation_cost: 0.88453
[1005 22:11:29 @monitor.py:456] validation_error: 0.3118
[1005 22:11:29 @monitor.py:456] wd_cost: 0.40976
[1005 22:11:29 @base.py:250] Start Epoch 8 ...
[1005 22:12:22 @base.py:260] Epoch 8 (global_step 3120) finished, time:52.3 seconds.
[1005 22:12:22 @saver.py:77] Model saved to train_log/GN-cifar/model-3120.
[1005 22:12:24 @monitor.py:456] QueueInput/queue_size: 49.992
[1005 22:12:24 @monitor.py:456] cross_entropy_loss: 0.68815
[1005 22:12:24 @monitor.py:456] train_error: 0.24074
[1005 22:12:24 @monitor.py:456] validation_cost: 0.78764
[1005 22:12:24 @monitor.py:456] validation_error: 0.2667
[1005 22:12:24 @monitor.py:456] wd_cost: 0.3815
[1005 22:12:24 @base.py:250] Start Epoch 9 ...
[1005 22:13:17 @base.py:260] Epoch 9 (global_step 3510) finished, time:52.7 seconds.
[1005 22:13:17 @saver.py:77] Model saved to train_log/GN-cifar/model-3510.
[1005 22:13:19 @monitor.py:456] QueueInput/queue_size: 49.99
[1005 22:13:19 @monitor.py:456] cross_entropy_loss: 0.59951
[1005 22:13:19 @monitor.py:456] train_error: 0.21586
[1005 22:13:19 @monitor.py:456] validation_cost: 0.66493
[1005 22:13:19 @monitor.py:456] validation_error: 0.2324
[1005 22:13:19 @monitor.py:456] wd_cost: 0.35653
[1005 22:13:19 @base.py:250] Start Epoch 10 ...
[1005 22:14:11 @base.py:260] Epoch 10 (global_step 3900) finished, time:51.8 seconds.
[1005 22:14:11 @saver.py:77] Model saved to train_log/GN-cifar/model-3900.
[1005 22:14:13 @monitor.py:456] QueueInput/queue_size: 49.219
[1005 22:14:13 @monitor.py:456] cross_entropy_loss: 0.55457
[1005 22:14:13 @monitor.py:456] train_error: 0.19468
[1005 22:14:13 @monitor.py:456] validation_cost: 0.69008
[1005 22:14:13 @monitor.py:456] validation_error: 0.2376
[1005 22:14:13 @monitor.py:456] wd_cost: 0.33601
[1005 22:14:13 @base.py:250] Start Epoch 11 ...
[1005 22:15:05 @base.py:260] Epoch 11 (global_step 4290) finished, time:52 seconds.
[1005 22:15:05 @saver.py:77] Model saved to train_log/GN-cifar/model-4290.
[1005 22:15:08 @monitor.py:456] QueueInput/queue_size: 49.733
[1005 22:15:08 @monitor.py:456] cross_entropy_loss: 0.46893
[1005 22:15:08 @monitor.py:456] train_error: 0.15775
[1005 22:15:08 @monitor.py:456] validation_cost: 0.55856
[1005 22:15:08 @monitor.py:456] validation_error: 0.193
[1005 22:15:08 @monitor.py:456] wd_cost: 0.31819
[1005 22:15:08 @base.py:250] Start Epoch 12 ...
[1005 22:16:00 @base.py:260] Epoch 12 (global_step 4680) finished, time:52.1 seconds.
[1005 22:16:00 @saver.py:77] Model saved to train_log/GN-cifar/model-4680.
[1005 22:16:02 @monitor.py:456] QueueInput/queue_size: 49.607
[1005 22:16:02 @monitor.py:456] cross_entropy_loss: 0.46838
[1005 22:16:02 @monitor.py:456] train_error: 0.15891
[1005 22:16:02 @monitor.py:456] validation_cost: 0.55695
[1005 22:16:02 @monitor.py:456] validation_error: 0.1916
[1005 22:16:02 @monitor.py:456] wd_cost: 0.30296
[1005 22:16:02 @base.py:250] Start Epoch 13 ...
[1005 22:16:55 @base.py:260] Epoch 13 (global_step 5070) finished, time:52.3 seconds.
[1005 22:16:55 @saver.py:77] Model saved to train_log/GN-cifar/model-5070.
[1005 22:16:57 @monitor.py:456] QueueInput/queue_size: 49.281
[1005 22:16:57 @monitor.py:456] cross_entropy_loss: 0.42584
[1005 22:16:57 @monitor.py:456] train_error: 0.14538
[1005 22:16:57 @monitor.py:456] validation_cost: 0.58209
[1005 22:16:57 @monitor.py:456] validation_error: 0.1951
[1005 22:16:57 @monitor.py:456] wd_cost: 0.29042
[1005 22:16:57 @base.py:250] Start Epoch 14 ...
[1005 22:17:49 @base.py:260] Epoch 14 (global_step 5460) finished, time:52.1 seconds.
[1005 22:17:49 @saver.py:77] Model saved to train_log/GN-cifar/model-5460.
[1005 22:17:52 @monitor.py:456] QueueInput/queue_size: 49.953
[1005 22:17:52 @monitor.py:456] cross_entropy_loss: 0.3854
[1005 22:17:52 @monitor.py:456] train_error: 0.13848
[1005 22:17:52 @monitor.py:456] validation_cost: 0.47786
[1005 22:17:52 @monitor.py:456] validation_error: 0.1631
[1005 22:17:52 @monitor.py:456] wd_cost: 0.27963
[1005 22:17:52 @base.py:250] Start Epoch 15 ...
[1005 22:18:43 @base.py:260] Epoch 15 (global_step 5850) finished, time:51.7 seconds.
[1005 22:18:43 @saver.py:77] Model saved to train_log/GN-cifar/model-5850.
[1005 22:18:46 @monitor.py:456] QueueInput/queue_size: 49.748
[1005 22:18:46 @monitor.py:456] cross_entropy_loss: 0.36245
[1005 22:18:46 @monitor.py:456] train_error: 0.122
[1005 22:18:46 @monitor.py:456] validation_cost: 0.48098
[1005 22:18:46 @monitor.py:456] validation_error: 0.1634
[1005 22:18:46 @monitor.py:456] wd_cost: 0.2706
[1005 22:18:46 @base.py:250] Start Epoch 16 ...
[1005 22:19:38 @base.py:260] Epoch 16 (global_step 6240) finished, time:52 seconds.
[1005 22:19:38 @saver.py:77] Model saved to train_log/GN-cifar/model-6240.
[1005 22:19:40 @monitor.py:456] QueueInput/queue_size: 49.5
[1005 22:19:40 @monitor.py:456] cross_entropy_loss: 0.35263
[1005 22:19:40 @monitor.py:456] train_error: 0.11684
[1005 22:19:40 @monitor.py:456] validation_cost: 0.46868
[1005 22:19:40 @monitor.py:456] validation_error: 0.1564
[1005 22:19:40 @monitor.py:456] wd_cost: 0.26315
[1005 22:19:40 @base.py:250] Start Epoch 17 ...
[1005 22:20:32 @base.py:260] Epoch 17 (global_step 6630) finished, time:51.8 seconds.
[1005 22:20:32 @saver.py:77] Model saved to train_log/GN-cifar/model-6630.
[1005 22:20:34 @monitor.py:456] QueueInput/queue_size: 50
[1005 22:20:34 @monitor.py:456] cross_entropy_loss: 0.32331
[1005 22:20:34 @monitor.py:456] train_error: 0.11332
[1005 22:20:34 @monitor.py:456] validation_cost: 0.46325
[1005 22:20:34 @monitor.py:456] validation_error: 0.1544
[1005 22:20:34 @monitor.py:456] wd_cost: 0.25767
[1005 22:20:34 @base.py:250] Start Epoch 18 ...
[1005 22:21:27 @base.py:260] Epoch 18 (global_step 7020) finished, time:52.3 seconds.
[1005 22:21:27 @saver.py:77] Model saved to train_log/GN-cifar/model-7020.
[1005 22:21:29 @monitor.py:456] QueueInput/queue_size: 49.828
[1005 22:21:29 @monitor.py:456] cross_entropy_loss: 0.3187
[1005 22:21:29 @monitor.py:456] train_error: 0.11077
[1005 22:21:29 @monitor.py:456] validation_cost: 0.49189
[1005 22:21:29 @monitor.py:456] validation_error: 0.1662
[1005 22:21:29 @monitor.py:456] wd_cost: 0.2524
[1005 22:21:29 @base.py:250] Start Epoch 19 ...
[1005 22:22:21 @base.py:260] Epoch 19 (global_step 7410) finished, time:52 seconds.
[1005 22:22:21 @saver.py:77] Model saved to train_log/GN-cifar/model-7410.
[1005 22:22:24 @monitor.py:456] QueueInput/queue_size: 49.406
[1005 22:22:24 @monitor.py:456] cross_entropy_loss: 0.31823
[1005 22:22:24 @monitor.py:456] train_error: 0.1146
[1005 22:22:24 @monitor.py:456] validation_cost: 0.43812
[1005 22:22:24 @monitor.py:456] validation_error: 0.1468
[1005 22:22:24 @monitor.py:456] wd_cost: 0.25003
[1005 22:22:24 @base.py:250] Start Epoch 20 ...
[1005 22:23:15 @base.py:260] Epoch 20 (global_step 7800) finished, time:51.8 seconds.
[1005 22:23:15 @saver.py:77] Model saved to train_log/GN-cifar/model-7800.
[1005 22:23:18 @monitor.py:456] QueueInput/queue_size: 49.662
[1005 22:23:18 @monitor.py:456] cross_entropy_loss: 0.30801
[1005 22:23:18 @monitor.py:456] train_error: 0.10043
[1005 22:23:18 @monitor.py:456] validation_cost: 0.49956
[1005 22:23:18 @monitor.py:456] validation_error: 0.1626
[1005 22:23:18 @monitor.py:456] wd_cost: 0.2468
[1005 22:23:18 @base.py:250] Start Epoch 21 ...
[1005 22:24:10 @base.py:260] Epoch 21 (global_step 8190) finished, time:52.4 seconds.
[1005 22:24:10 @saver.py:77] Model saved to train_log/GN-cifar/model-8190.
[1005 22:24:13 @monitor.py:456] QueueInput/queue_size: 49.5
[1005 22:24:13 @monitor.py:456] cross_entropy_loss: 0.27746
[1005 22:24:13 @monitor.py:456] train_error: 0.097825
[1005 22:24:13 @monitor.py:456] validation_cost: 0.4484
[1005 22:24:13 @monitor.py:456] validation_error: 0.1469
[1005 22:24:13 @monitor.py:456] wd_cost: 0.24536
[1005 22:24:13 @base.py:250] Start Epoch 22 ...
[1005 22:25:05 @base.py:260] Epoch 22 (global_step 8580) finished, time:52.1 seconds.
[1005 22:25:05 @saver.py:77] Model saved to train_log/GN-cifar/model-8580.
[1005 22:25:07 @monitor.py:456] QueueInput/queue_size: 49.999
[1005 22:25:07 @monitor.py:456] cross_entropy_loss: 0.26903
[1005 22:25:07 @monitor.py:456] train_error: 0.091692
[1005 22:25:07 @monitor.py:456] validation_cost: 0.46003
[1005 22:25:07 @monitor.py:456] validation_error: 0.1511
[1005 22:25:07 @monitor.py:456] wd_cost: 0.24281
[1005 22:25:07 @base.py:250] Start Epoch 23 ...
[1005 22:25:59 @base.py:260] Epoch 23 (global_step 8970) finished, time:52 seconds.
[1005 22:25:59 @saver.py:77] Model saved to train_log/GN-cifar/model-8970.
[1005 22:26:02 @monitor.py:456] QueueInput/queue_size: 50
[1005 22:26:02 @monitor.py:456] cross_entropy_loss: 0.23315
[1005 22:26:02 @monitor.py:456] train_error: 0.081824
[1005 22:26:02 @monitor.py:456] validation_cost: 0.3735
[1005 22:26:02 @monitor.py:456] validation_error: 0.1257
[1005 22:26:02 @monitor.py:456] wd_cost: 0.2423
[1005 22:26:02 @base.py:250] Start Epoch 24 ...
[1005 22:26:54 @base.py:260] Epoch 24 (global_step 9360) finished, time:52 seconds.
[1005 22:26:54 @saver.py:77] Model saved to train_log/GN-cifar/model-9360.
[1005 22:26:56 @monitor.py:456] QueueInput/queue_size: 49.085
[1005 22:26:56 @monitor.py:456] cross_entropy_loss: 0.25434
[1005 22:26:56 @monitor.py:456] train_error: 0.088251
[1005 22:26:56 @monitor.py:456] validation_cost: 0.44658
[1005 22:26:56 @monitor.py:456] validation_error: 0.1425
[1005 22:26:56 @monitor.py:456] wd_cost: 0.24171
[1005 22:26:56 @base.py:250] Start Epoch 25 ...
[1005 22:27:48 @base.py:260] Epoch 25 (global_step 9750) finished, time:52.1 seconds.
[1005 22:27:48 @saver.py:77] Model saved to train_log/GN-cifar/model-9750.
[1005 22:27:51 @monitor.py:456] QueueInput/queue_size: 49.433
[1005 22:27:51 @monitor.py:456] cross_entropy_loss: 0.22657
[1005 22:27:51 @monitor.py:456] train_error: 0.078193
[1005 22:27:51 @monitor.py:456] validation_cost: 0.40224
[1005 22:27:51 @monitor.py:456] validation_error: 0.1277
[1005 22:27:51 @monitor.py:456] wd_cost: 0.24256
[1005 22:27:51 @base.py:250] Start Epoch 26 ...
[1005 22:28:42 @base.py:260] Epoch 26 (global_step 10140) finished, time:51.8 seconds.
[1005 22:28:42 @saver.py:77] Model saved to train_log/GN-cifar/model-10140.
[1005 22:28:45 @monitor.py:456] QueueInput/queue_size: 49.741
[1005 22:28:45 @monitor.py:456] cross_entropy_loss: 0.24681
[1005 22:28:45 @monitor.py:456] train_error: 0.082912
[1005 22:28:45 @monitor.py:456] validation_cost: 0.38942
[1005 22:28:45 @monitor.py:456] validation_error: 0.1341
[1005 22:28:45 @monitor.py:456] wd_cost: 0.24073
[1005 22:28:45 @base.py:250] Start Epoch 27 ...
[1005 22:29:37 @base.py:260] Epoch 27 (global_step 10530) finished, time:52 seconds.
[1005 22:29:37 @saver.py:77] Model saved to train_log/GN-cifar/model-10530.
[1005 22:29:39 @monitor.py:456] QueueInput/queue_size: 49.996
[1005 22:29:39 @monitor.py:456] cross_entropy_loss: 0.21896
[1005 22:29:39 @monitor.py:456] train_error: 0.074041
[1005 22:29:39 @monitor.py:456] validation_cost: 0.40517
[1005 22:29:39 @monitor.py:456] validation_error: 0.1292
[1005 22:29:39 @monitor.py:456] wd_cost: 0.24056
[1005 22:29:39 @base.py:250] Start Epoch 28 ...
[1005 22:30:32 @base.py:260] Epoch 28 (global_step 10920) finished, time:52.4 seconds.
[1005 22:30:32 @saver.py:77] Model saved to train_log/GN-cifar/model-10920.
[1005 22:30:34 @monitor.py:456] QueueInput/queue_size: 49.375
[1005 22:30:34 @monitor.py:456] cross_entropy_loss: 0.23153
[1005 22:30:34 @monitor.py:456] train_error: 0.081052
[1005 22:30:34 @monitor.py:456] validation_cost: 0.40627
[1005 22:30:34 @monitor.py:456] validation_error: 0.13
[1005 22:30:34 @monitor.py:456] wd_cost: 0.24055
[1005 22:30:34 @base.py:250] Start Epoch 29 ...
[1005 22:31:26 @base.py:260] Epoch 29 (global_step 11310) finished, time:52 seconds.
[1005 22:31:26 @saver.py:77] Model saved to train_log/GN-cifar/model-11310.
[1005 22:31:29 @monitor.py:456] QueueInput/queue_size: 49.937
[1005 22:31:29 @monitor.py:456] cross_entropy_loss: 0.22215
[1005 22:31:29 @monitor.py:456] train_error: 0.076885
[1005 22:31:29 @monitor.py:456] validation_cost: 0.37814
[1005 22:31:29 @monitor.py:456] validation_error: 0.1251
[1005 22:31:29 @monitor.py:456] wd_cost: 0.24104
[1005 22:31:29 @base.py:250] Start Epoch 30 ...
[1005 22:32:21 @base.py:260] Epoch 30 (global_step 11700) finished, time:52.5 seconds.
[1005 22:32:21 @saver.py:77] Model saved to train_log/GN-cifar/model-11700.
[1005 22:32:23 @monitor.py:456] QueueInput/queue_size: 49.937
[1005 22:32:23 @monitor.py:456] cross_entropy_loss: 0.21523
[1005 22:32:23 @monitor.py:456] train_error: 0.075131
[1005 22:32:23 @monitor.py:456] validation_cost: 0.4789
[1005 22:32:23 @monitor.py:456] validation_error: 0.1457
[1005 22:32:23 @monitor.py:456] wd_cost: 0.24173
[1005 22:32:23 @base.py:250] Start Epoch 31 ...
[1005 22:33:15 @base.py:260] Epoch 31 (global_step 12090) finished, time:51.5 seconds.
[1005 22:33:15 @saver.py:77] Model saved to train_log/GN-cifar/model-12090.
[1005 22:33:17 @monitor.py:456] QueueInput/queue_size: 49.98
[1005 22:33:17 @monitor.py:456] cross_entropy_loss: 0.23474
[1005 22:33:17 @monitor.py:456] train_error: 0.084759
[1005 22:33:17 @monitor.py:456] validation_cost: 0.36924
[1005 22:33:17 @monitor.py:456] validation_error: 0.1216
[1005 22:33:17 @monitor.py:456] wd_cost: 0.24219
[1005 22:33:17 @base.py:250] Start Epoch 32 ...
[1005 22:34:10 @base.py:260] Epoch 32 (global_step 12480) finished, time:52.5 seconds.
[1005 22:34:10 @saver.py:77] Model saved to train_log/GN-cifar/model-12480.
[1005 22:34:12 @monitor.py:456] QueueInput/queue_size: 49.919
[1005 22:34:12 @monitor.py:456] cross_entropy_loss: 0.19071
[1005 22:34:12 @monitor.py:456] train_error: 0.06696
[1005 22:34:12 @monitor.py:456] validation_cost: 0.36532
[1005 22:34:12 @monitor.py:456] validation_error: 0.1196
[1005 22:34:12 @monitor.py:456] wd_cost: 0.24367
[1005 22:34:12 @base.py:250] Start Epoch 33 ...
[1005 22:35:05 @base.py:260] Epoch 33 (global_step 12870) finished, time:52.4 seconds.
[1005 22:35:05 @saver.py:77] Model saved to train_log/GN-cifar/model-12870.
[1005 22:35:07 @monitor.py:456] QueueInput/queue_size: 49.717
[1005 22:35:07 @monitor.py:456] cross_entropy_loss: 0.21025
[1005 22:35:07 @monitor.py:456] train_error: 0.072234
[1005 22:35:07 @monitor.py:456] validation_cost: 0.36231
[1005 22:35:07 @monitor.py:456] validation_error: 0.1194
[1005 22:35:07 @monitor.py:456] wd_cost: 0.24491
[1005 22:35:07 @base.py:250] Start Epoch 34 ...
[1005 22:35:59 @base.py:260] Epoch 34 (global_step 13260) finished, time:51.9 seconds.
[1005 22:35:59 @saver.py:77] Model saved to train_log/GN-cifar/model-13260.
[1005 22:36:01 @monitor.py:456] QueueInput/queue_size: 49.718
[1005 22:36:01 @monitor.py:456] cross_entropy_loss: 0.19437
[1005 22:36:01 @monitor.py:456] train_error: 0.068861
[1005 22:36:01 @monitor.py:456] validation_cost: 0.41659
[1005 22:36:01 @monitor.py:456] validation_error: 0.1316
[1005 22:36:01 @monitor.py:456] wd_cost: 0.24587
[1005 22:36:01 @base.py:250] Start Epoch 35 ...
[1005 22:36:53 @base.py:260] Epoch 35 (global_step 13650) finished, time:51.7 seconds.
[1005 22:36:53 @saver.py:77] Model saved to train_log/GN-cifar/model-13650.
[1005 22:36:56 @monitor.py:456] QueueInput/queue_size: 49.246
[1005 22:36:56 @monitor.py:456] cross_entropy_loss: 0.21091
[1005 22:36:56 @monitor.py:456] train_error: 0.074879
[1005 22:36:56 @monitor.py:456] validation_cost: 0.36238
[1005 22:36:56 @monitor.py:456] validation_error: 0.1152
[1005 22:36:56 @monitor.py:456] wd_cost: 0.24744
[1005 22:36:56 @base.py:250] Start Epoch 36 ...
[1005 22:37:48 @base.py:260] Epoch 36 (global_step 14040) finished, time:52 seconds.
[1005 22:37:48 @saver.py:77] Model saved to train_log/GN-cifar/model-14040.
[1005 22:37:50 @monitor.py:456] QueueInput/queue_size: 49.968
[1005 22:37:50 @monitor.py:456] cross_entropy_loss: 0.18303
[1005 22:37:50 @monitor.py:456] train_error: 0.065078
[1005 22:37:50 @monitor.py:456] validation_cost: 0.36554
[1005 22:37:50 @monitor.py:456] validation_error: 0.1186
[1005 22:37:50 @monitor.py:456] wd_cost: 0.24712
[1005 22:37:50 @base.py:250] Start Epoch 37 ...
[1005 22:38:43 @base.py:260] Epoch 37 (global_step 14430) finished, time:52.4 seconds.
[1005 22:38:43 @saver.py:77] Model saved to train_log/GN-cifar/model-14430.
[1005 22:38:45 @monitor.py:456] QueueInput/queue_size: 49.501
[1005 22:38:45 @monitor.py:456] cross_entropy_loss: 0.18171
[1005 22:38:45 @monitor.py:456] train_error: 0.060151
[1005 22:38:45 @monitor.py:456] validation_cost: 0.43371
[1005 22:38:45 @monitor.py:456] validation_error: 0.1338
[1005 22:38:45 @monitor.py:456] wd_cost: 0.24859
[1005 22:38:45 @base.py:250] Start Epoch 38 ...
[1005 22:39:37 @base.py:260] Epoch 38 (global_step 14820) finished, time:52 seconds.
[1005 22:39:37 @saver.py:77] Model saved to train_log/GN-cifar/model-14820.
[1005 22:39:39 @monitor.py:456] QueueInput/queue_size: 49.983
[1005 22:39:39 @monitor.py:456] cross_entropy_loss: 0.17533
[1005 22:39:39 @monitor.py:456] train_error: 0.057241
[1005 22:39:39 @monitor.py:456] validation_cost: 0.34672
[1005 22:39:39 @monitor.py:456] validation_error: 0.1111
[1005 22:39:39 @monitor.py:456] wd_cost: 0.24876
[1005 22:39:39 @base.py:250] Start Epoch 39 ...
[1005 22:40:31 @base.py:260] Epoch 39 (global_step 15210) finished, time:51.9 seconds.
[1005 22:40:31 @saver.py:77] Model saved to train_log/GN-cifar/model-15210.
[1005 22:40:34 @monitor.py:456] QueueInput/queue_size: 49.993
[1005 22:40:34 @monitor.py:456] cross_entropy_loss: 0.17383
[1005 22:40:34 @monitor.py:456] train_error: 0.065585
[1005 22:40:34 @monitor.py:456] validation_cost: 0.40029
[1005 22:40:34 @monitor.py:456] validation_error: 0.1196
[1005 22:40:34 @monitor.py:456] wd_cost: 0.24939
[1005 22:40:34 @base.py:250] Start Epoch 40 ...
[1005 22:41:26 @base.py:260] Epoch 40 (global_step 15600) finished, time:52.5 seconds.
[1005 22:41:26 @saver.py:77] Model saved to train_log/GN-cifar/model-15600.
[1005 22:41:29 @monitor.py:456] QueueInput/queue_size: 49.96
[1005 22:41:29 @monitor.py:456] cross_entropy_loss: 0.16968
[1005 22:41:29 @monitor.py:456] train_error: 0.056697
[1005 22:41:29 @monitor.py:456] validation_cost: 0.41944
[1005 22:41:29 @monitor.py:456] validation_error: 0.128
[1005 22:41:29 @monitor.py:456] wd_cost: 0.2503
[1005 22:41:29 @base.py:250] Start Epoch 41 ...
[1005 22:42:21 @base.py:260] Epoch 41 (global_step 15990) finished, time:52.3 seconds.
[1005 22:42:21 @saver.py:77] Model saved to train_log/GN-cifar/model-15990.
[1005 22:42:23 @monitor.py:456] QueueInput/queue_size: 49.975
[1005 22:42:23 @monitor.py:456] cross_entropy_loss: 0.16786
[1005 22:42:23 @monitor.py:456] train_error: 0.058263
[1005 22:42:23 @monitor.py:456] validation_cost: 0.38693
[1005 22:42:23 @monitor.py:456] validation_error: 0.1198
[1005 22:42:23 @monitor.py:456] wd_cost: 0.25138
[1005 22:42:23 @base.py:250] Start Epoch 42 ...
[1005 22:43:16 @base.py:260] Epoch 42 (global_step 16380) finished, time:52.2 seconds.
[1005 22:43:16 @saver.py:77] Model saved to train_log/GN-cifar/model-16380.
[1005 22:43:18 @monitor.py:456] QueueInput/queue_size: 49.244
[1005 22:43:18 @monitor.py:456] cross_entropy_loss: 0.1539
[1005 22:43:18 @monitor.py:456] train_error: 0.050431
[1005 22:43:18 @monitor.py:456] validation_cost: 0.41259
[1005 22:43:18 @monitor.py:456] validation_error: 0.1298
[1005 22:43:18 @monitor.py:456] wd_cost: 0.25253
[1005 22:43:18 @base.py:250] Start Epoch 43 ...
[1005 22:44:10 @base.py:260] Epoch 43 (global_step 16770) finished, time:51.9 seconds.
[1005 22:44:10 @saver.py:77] Model saved to train_log/GN-cifar/model-16770.
[1005 22:44:12 @monitor.py:456] QueueInput/queue_size: 49.969
[1005 22:44:12 @monitor.py:456] cross_entropy_loss: 0.16553
[1005 22:44:12 @monitor.py:456] train_error: 0.060787
[1005 22:44:12 @monitor.py:456] validation_cost: 0.44662
[1005 22:44:12 @monitor.py:456] validation_error: 0.139
[1005 22:44:12 @monitor.py:456] wd_cost: 0.2526
[1005 22:44:12 @base.py:250] Start Epoch 44 ...
[1005 22:45:04 @base.py:260] Epoch 44 (global_step 17160) finished, time:51.9 seconds.
[1005 22:45:04 @saver.py:77] Model saved to train_log/GN-cifar/model-17160.
[1005 22:45:07 @monitor.py:456] QueueInput/queue_size: 49.733
[1005 22:45:07 @monitor.py:456] cross_entropy_loss: 0.15965
[1005 22:45:07 @monitor.py:456] train_error: 0.057
[1005 22:45:07 @monitor.py:456] validation_cost: 0.35644
[1005 22:45:07 @monitor.py:456] validation_error: 0.1148
[1005 22:45:07 @monitor.py:456] wd_cost: 0.25289
[1005 22:45:07 @base.py:250] Start Epoch 45 ...
[1005 22:45:59 @base.py:260] Epoch 45 (global_step 17550) finished, time:52.1 seconds.
[1005 22:45:59 @saver.py:77] Model saved to train_log/GN-cifar/model-17550.
[1005 22:46:01 @monitor.py:456] QueueInput/queue_size: 49.984
[1005 22:46:01 @monitor.py:456] cross_entropy_loss: 0.1684
[1005 22:46:01 @monitor.py:456] train_error: 0.057105
[1005 22:46:01 @monitor.py:456] validation_cost: 0.39319
[1005 22:46:01 @monitor.py:456] validation_error: 0.1254
[1005 22:46:01 @monitor.py:456] wd_cost: 0.25423
[1005 22:46:01 @base.py:250] Start Epoch 46 ...
[1005 22:46:53 @base.py:260] Epoch 46 (global_step 17940) finished, time:51.9 seconds.
[1005 22:46:53 @saver.py:77] Model saved to train_log/GN-cifar/model-17940.
[1005 22:46:56 @monitor.py:456] QueueInput/queue_size: 49.623
[1005 22:46:56 @monitor.py:456] cross_entropy_loss: 0.14711
[1005 22:46:56 @monitor.py:456] train_error: 0.054302
[1005 22:46:56 @monitor.py:456] validation_cost: 0.4098
[1005 22:46:56 @monitor.py:456] validation_error: 0.125
[1005 22:46:56 @monitor.py:456] wd_cost: 0.25407
[1005 22:46:56 @base.py:250] Start Epoch 47 ...