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alexnet-imagenet-scratch.log
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alexnet-imagenet-scratch.log
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[32m[1104 10:59:06 @logger.py:59][0m Argv: tw-imagenet-alexnet.py --gpu 0,1,2,3 --t 0.049
[32m[1104 10:59:06 @utils.py:111][0m TENSORPACK_DATASET not set, using /raid/chenzhuo/dev/projects/binarynet/tensorpack/dataflow/dataset for dataset.
[32m[1104 10:59:13 @multigpu.py:49][0m Training a model of 4 tower
[32m[1104 10:59:13 @multigpu.py:57][0m Building graph for training tower 0...
[32m[1104 10:59:13 @_common.py:61][0m conv0 input: [None, 224, 224, 3]
[32m[1104 10:59:13 @_common.py:69][0m conv0 output: [None, 54, 54, 96]
[32m[1104 10:59:13 @_common.py:61][0m conv1 input: [None, 54, 54, 96]
[32m[1104 10:59:13 @_common.py:69][0m conv1 output: [None, 54, 54, 256]
[32m[1104 10:59:13 @_common.py:61][0m pool1 input: [None, 54, 54, 256]
[32m[1104 10:59:13 @_common.py:69][0m pool1 output: [None, 27, 27, 256]
[32m[1104 10:59:13 @_common.py:61][0m conv2 input: [None, 27, 27, 256]
[32m[1104 10:59:13 @_common.py:69][0m conv2 output: [None, 27, 27, 384]
[32m[1104 10:59:13 @_common.py:61][0m pool2 input: [None, 27, 27, 384]
[32m[1104 10:59:13 @_common.py:69][0m pool2 output: [None, 14, 14, 384]
[32m[1104 10:59:13 @_common.py:61][0m conv3 input: [None, 14, 14, 384]
[32m[1104 10:59:13 @_common.py:69][0m conv3 output: [None, 14, 14, 384]
[32m[1104 10:59:13 @_common.py:61][0m conv4 input: [None, 14, 14, 384]
[32m[1104 10:59:14 @_common.py:69][0m conv4 output: [None, 14, 14, 256]
[32m[1104 10:59:14 @_common.py:61][0m pool4 input: [None, 14, 14, 256]
[32m[1104 10:59:14 @_common.py:69][0m pool4 output: [None, 6, 6, 256]
[32m[1104 10:59:14 @_common.py:61][0m fc0 input: [None, 6, 6, 256]
[32m[1104 10:59:14 @_common.py:69][0m fc0 output: [None, 4096]
[32m[1104 10:59:14 @_common.py:61][0m fc1 input: [None, 4096]
[32m[1104 10:59:14 @_common.py:69][0m fc1 output: [None, 4096]
[32m[1104 10:59:14 @_common.py:61][0m fct input: [None, 4096]
[32m[1104 10:59:14 @_common.py:69][0m fct output: [None, 1000]
[32m[1104 10:59:14 @regularize.py:17][0m Apply regularizer for fc0/W:0
[32m[1104 10:59:14 @regularize.py:17][0m Apply regularizer for fc0/Wp:0
[32m[1104 10:59:14 @regularize.py:17][0m Apply regularizer for fc0/Wn:0
[32m[1104 10:59:14 @regularize.py:17][0m Apply regularizer for fc1/W:0
[32m[1104 10:59:14 @regularize.py:17][0m Apply regularizer for fc1/Wp:0
[32m[1104 10:59:14 @regularize.py:17][0m Apply regularizer for fc1/Wn:0
[32m[1104 10:59:14 @regularize.py:17][0m Apply regularizer for fct/W:0
[32m[1104 10:59:15 @multigpu.py:57][0m Building graph for training tower 1...
[32m[1104 10:59:16 @multigpu.py:57][0m Building graph for training tower 2...
[32m[1104 10:59:17 @multigpu.py:57][0m Building graph for training tower 3...
[32m[1104 10:59:19 @modelutils.py:22][0m Model Parameters:
conv0/W:0: shape=[12, 12, 3, 96], dim=41472
conv1/W:0: shape=[5, 5, 48, 256], dim=307200
conv1/Wp:0: shape=[], dim=1
conv1/Wn:0: shape=[], dim=1
bn1/beta:0: shape=[256], dim=256
bn1/gamma:0: shape=[256], dim=256
conv2/W:0: shape=[3, 3, 256, 384], dim=884736
conv2/Wp:0: shape=[], dim=1
conv2/Wn:0: shape=[], dim=1
bn2/beta:0: shape=[384], dim=384
bn2/gamma:0: shape=[384], dim=384
conv3/W:0: shape=[3, 3, 192, 384], dim=663552
conv3/Wp:0: shape=[], dim=1
conv3/Wn:0: shape=[], dim=1
bn3/beta:0: shape=[384], dim=384
bn3/gamma:0: shape=[384], dim=384
conv4/W:0: shape=[3, 3, 192, 256], dim=442368
conv4/Wp:0: shape=[], dim=1
conv4/Wn:0: shape=[], dim=1
bn4/beta:0: shape=[256], dim=256
bn4/gamma:0: shape=[256], dim=256
fc0/W:0: shape=[9216, 4096], dim=37748736
fc0/Wp:0: shape=[], dim=1
fc0/Wn:0: shape=[], dim=1
bnfc0/beta:0: shape=[4096], dim=4096
bnfc0/gamma:0: shape=[4096], dim=4096
fc1/W:0: shape=[4096, 4096], dim=16777216
fc1/Wp:0: shape=[], dim=1
fc1/Wn:0: shape=[], dim=1
bnfc1/beta:0: shape=[4096], dim=4096
bnfc1/gamma:0: shape=[4096], dim=4096
fct/W:0: shape=[4096, 1000], dim=4096000
fct/b:0: shape=[1000], dim=1000
Total param=60981236 (232.624954 MB assuming all float32)
[32m[1104 10:59:19 @base.py:109][0m Setup callbacks ...
[32m[1104 10:59:19 @common.py:51][0m [ModelSaver] tower0/bn1/mean/EMA:0 renamed to bn1/mean/EMA:0 when saving model.
[32m[1104 10:59:19 @common.py:51][0m [ModelSaver] tower0/bn1/variance/EMA:0 renamed to bn1/variance/EMA:0 when saving model.
[32m[1104 10:59:19 @common.py:51][0m [ModelSaver] tower0/bn2/mean/EMA:0 renamed to bn2/mean/EMA:0 when saving model.
[32m[1104 10:59:19 @common.py:51][0m [ModelSaver] tower0/bn2/variance/EMA:0 renamed to bn2/variance/EMA:0 when saving model.
[32m[1104 10:59:19 @common.py:51][0m [ModelSaver] tower0/bn3/mean/EMA:0 renamed to bn3/mean/EMA:0 when saving model.
[32m[1104 10:59:19 @common.py:51][0m [ModelSaver] tower0/bn3/variance/EMA:0 renamed to bn3/variance/EMA:0 when saving model.
[32m[1104 10:59:19 @common.py:51][0m [ModelSaver] tower0/bn4/mean/EMA:0 renamed to bn4/mean/EMA:0 when saving model.
[32m[1104 10:59:19 @common.py:51][0m [ModelSaver] tower0/bn4/variance/EMA:0 renamed to bn4/variance/EMA:0 when saving model.
[32m[1104 10:59:19 @common.py:51][0m [ModelSaver] tower0/bnfc0/mean/EMA:0 renamed to bnfc0/mean/EMA:0 when saving model.
[32m[1104 10:59:19 @common.py:51][0m [ModelSaver] tower0/bnfc0/variance/EMA:0 renamed to bnfc0/variance/EMA:0 when saving model.
[32m[1104 10:59:19 @common.py:51][0m [ModelSaver] tower0/bnfc1/mean/EMA:0 renamed to bnfc1/mean/EMA:0 when saving model.
[32m[1104 10:59:19 @common.py:51][0m [ModelSaver] tower0/bnfc1/variance/EMA:0 renamed to bnfc1/variance/EMA:0 when saving model.
[32m[1104 10:59:19 @common.py:55][0m [ModelSaver] Variable tower1/bn1/mean/EMA:0 won't be saved due to an alternative in a different tower
[32m[1104 10:59:19 @common.py:55][0m [ModelSaver] Variable tower1/bn1/variance/EMA:0 won't be saved due to an alternative in a different tower
[32m[1104 10:59:19 @common.py:55][0m [ModelSaver] Variable tower1/bn2/mean/EMA:0 won't be saved due to an alternative in a different tower
[32m[1104 10:59:19 @common.py:55][0m [ModelSaver] Variable tower1/bn2/variance/EMA:0 won't be saved due to an alternative in a different tower
[32m[1104 10:59:19 @common.py:55][0m [ModelSaver] Variable tower1/bn3/mean/EMA:0 won't be saved due to an alternative in a different tower
[32m[1104 10:59:19 @common.py:55][0m [ModelSaver] Variable tower1/bn3/variance/EMA:0 won't be saved due to an alternative in a different tower
[32m[1104 10:59:19 @common.py:55][0m [ModelSaver] Variable tower1/bn4/mean/EMA:0 won't be saved due to an alternative in a different tower
[32m[1104 10:59:19 @common.py:55][0m [ModelSaver] Variable tower1/bn4/variance/EMA:0 won't be saved due to an alternative in a different tower
[32m[1104 10:59:19 @common.py:55][0m [ModelSaver] Variable tower1/bnfc0/mean/EMA:0 won't be saved due to an alternative in a different tower
[32m[1104 10:59:19 @common.py:55][0m [ModelSaver] Variable tower1/bnfc0/variance/EMA:0 won't be saved due to an alternative in a different tower
[32m[1104 10:59:19 @common.py:55][0m [ModelSaver] Variable tower1/bnfc1/mean/EMA:0 won't be saved due to an alternative in a different tower
[32m[1104 10:59:19 @common.py:55][0m [ModelSaver] Variable tower1/bnfc1/variance/EMA:0 won't be saved due to an alternative in a different tower
[32m[1104 10:59:19 @common.py:55][0m [ModelSaver] Variable tower2/bn1/mean/EMA:0 won't be saved due to an alternative in a different tower
[32m[1104 10:59:19 @common.py:55][0m [ModelSaver] Variable tower2/bn1/variance/EMA:0 won't be saved due to an alternative in a different tower
[32m[1104 10:59:19 @common.py:55][0m [ModelSaver] Variable tower2/bn2/mean/EMA:0 won't be saved due to an alternative in a different tower
[32m[1104 10:59:19 @common.py:55][0m [ModelSaver] Variable tower2/bn2/variance/EMA:0 won't be saved due to an alternative in a different tower
[32m[1104 10:59:19 @common.py:55][0m [ModelSaver] Variable tower2/bn3/mean/EMA:0 won't be saved due to an alternative in a different tower
[32m[1104 10:59:19 @common.py:55][0m [ModelSaver] Variable tower2/bn3/variance/EMA:0 won't be saved due to an alternative in a different tower
[32m[1104 10:59:19 @common.py:55][0m [ModelSaver] Variable tower2/bn4/mean/EMA:0 won't be saved due to an alternative in a different tower
[32m[1104 10:59:19 @common.py:55][0m [ModelSaver] Variable tower2/bn4/variance/EMA:0 won't be saved due to an alternative in a different tower
[32m[1104 10:59:19 @common.py:55][0m [ModelSaver] Variable tower2/bnfc0/mean/EMA:0 won't be saved due to an alternative in a different tower
[32m[1104 10:59:19 @common.py:55][0m [ModelSaver] Variable tower2/bnfc0/variance/EMA:0 won't be saved due to an alternative in a different tower
[32m[1104 10:59:19 @common.py:55][0m [ModelSaver] Variable tower2/bnfc1/mean/EMA:0 won't be saved due to an alternative in a different tower
[32m[1104 10:59:19 @common.py:55][0m [ModelSaver] Variable tower2/bnfc1/variance/EMA:0 won't be saved due to an alternative in a different tower
[32m[1104 10:59:19 @common.py:55][0m [ModelSaver] Variable tower3/bn1/mean/EMA:0 won't be saved due to an alternative in a different tower
[32m[1104 10:59:19 @common.py:55][0m [ModelSaver] Variable tower3/bn1/variance/EMA:0 won't be saved due to an alternative in a different tower
[32m[1104 10:59:19 @common.py:55][0m [ModelSaver] Variable tower3/bn2/mean/EMA:0 won't be saved due to an alternative in a different tower
[32m[1104 10:59:19 @common.py:55][0m [ModelSaver] Variable tower3/bn2/variance/EMA:0 won't be saved due to an alternative in a different tower
[32m[1104 10:59:19 @common.py:55][0m [ModelSaver] Variable tower3/bn3/mean/EMA:0 won't be saved due to an alternative in a different tower
[32m[1104 10:59:19 @common.py:55][0m [ModelSaver] Variable tower3/bn3/variance/EMA:0 won't be saved due to an alternative in a different tower
[32m[1104 10:59:19 @common.py:55][0m [ModelSaver] Variable tower3/bn4/mean/EMA:0 won't be saved due to an alternative in a different tower
[32m[1104 10:59:19 @common.py:55][0m [ModelSaver] Variable tower3/bn4/variance/EMA:0 won't be saved due to an alternative in a different tower
[32m[1104 10:59:19 @common.py:55][0m [ModelSaver] Variable tower3/bnfc0/mean/EMA:0 won't be saved due to an alternative in a different tower
[32m[1104 10:59:19 @common.py:55][0m [ModelSaver] Variable tower3/bnfc0/variance/EMA:0 won't be saved due to an alternative in a different tower
[32m[1104 10:59:19 @common.py:55][0m [ModelSaver] Variable tower3/bnfc1/mean/EMA:0 won't be saved due to an alternative in a different tower
[32m[1104 10:59:19 @common.py:55][0m [ModelSaver] Variable tower3/bnfc1/variance/EMA:0 won't be saved due to an alternative in a different tower
[32m[1104 10:59:19 @common.py:51][0m [ModelSaver] tower0/train-error-top1/EMA:0 renamed to train-error-top1/EMA:0 when saving model.
[32m[1104 10:59:19 @common.py:51][0m [ModelSaver] tower0/train-error-top5/EMA:0 renamed to train-error-top5/EMA:0 when saving model.
[32m[1104 10:59:19 @common.py:51][0m [ModelSaver] tower0/cross_entropy_loss/EMA:0 renamed to cross_entropy_loss/EMA:0 when saving model.
[32m[1104 10:59:19 @common.py:51][0m [ModelSaver] tower0/AddN/EMA:0 renamed to AddN/EMA:0 when saving model.
[32m[1104 10:59:19 @common.py:51][0m [ModelSaver] tower0/cost/EMA:0 renamed to cost/EMA:0 when saving model.
[32m[1104 10:59:20 @base.py:111][0m Building graph for predictor tower 0...
[32m[1104 10:59:28 @base.py:114][0m Initializing graph variables ...
[32m[1104 10:59:34 @base.py:156][0m Starting all threads & procs ...
[32m[1104 10:59:34 @base.py:123][0m Start training with global_step=0
[32m[1104 11:41:09 @stat.py:82][0m AddN: 0.035644
[32m[1104 11:41:09 @stat.py:82][0m conv0/W/rms: 0.01462
[32m[1104 11:41:09 @stat.py:82][0m conv1/W/rms: 0.018677
[32m[1104 11:41:09 @stat.py:82][0m conv1/Wn:0: 1.0162
[32m[1104 11:41:09 @stat.py:82][0m conv1/Wp:0: 0.98382
[32m[1104 11:41:09 @stat.py:82][0m conv2/W/rms: 0.020424
[32m[1104 11:41:09 @stat.py:82][0m conv2/Wn:0: 1.0109
[32m[1104 11:41:09 @stat.py:82][0m conv2/Wp:0: 0.98919
[32m[1104 11:41:09 @stat.py:82][0m conv3/W/rms: 0.021506
[32m[1104 11:41:09 @stat.py:82][0m conv3/Wn:0: 1.0045
[32m[1104 11:41:09 @stat.py:82][0m conv3/Wp:0: 0.99556
[32m[1104 11:41:09 @stat.py:82][0m conv4/W/rms: 0.02424
[32m[1104 11:41:09 @stat.py:82][0m conv4/Wn:0: 1.0489
[32m[1104 11:41:09 @stat.py:82][0m conv4/Wp:0: 0.95057
[32m[1104 11:41:09 @stat.py:82][0m cost: 4.1642
[32m[1104 11:41:09 @stat.py:82][0m cross_entropy_loss: 4.1286
[32m[1104 11:41:09 @stat.py:82][0m fc0/W/rms: 0.012232
[32m[1104 11:41:09 @stat.py:82][0m fc0/Wn:0: 1.0027
[32m[1104 11:41:09 @stat.py:82][0m fc0/Wp:0: 0.99734
[32m[1104 11:41:09 @stat.py:82][0m fc1/W/rms: 0.019172
[32m[1104 11:41:09 @stat.py:82][0m fc1/Wn:0: 1.0359
[32m[1104 11:41:09 @stat.py:82][0m fc1/Wp:0: 0.96372
[32m[1104 11:41:09 @stat.py:82][0m fct/W/rms: 0.0242
[32m[1104 11:41:09 @stat.py:82][0m learning_rate: 0.0001
[32m[1104 11:41:09 @stat.py:82][0m train-error-top1: 0.79742
[32m[1104 11:41:09 @stat.py:82][0m train-error-top5: 0.58614
[32m[1104 11:41:09 @stat.py:82][0m val-error-top1: 0.79472
[32m[1104 11:41:09 @stat.py:82][0m val-error-top5: 0.5894
[32m[1104 11:41:09 @stat.py:82][0m validation_cost: 4.1606
[32m[1104 11:41:09 @group.py:40][0m Callbacks took 553.188 sec in total. InferenceRunner: 535.844sec
[32m[1104 11:41:09 @timer.py:46][0m Epoch 1 (global_step 10000) finished, time:2494.77sec.
[32m[1104 12:21:38 @stat.py:82][0m AddN: 0.027831
[32m[1104 12:21:38 @stat.py:82][0m conv0/W/rms: 0.017409
[32m[1104 12:21:38 @stat.py:82][0m conv1/W/rms: 0.021886
[32m[1104 12:21:38 @stat.py:82][0m conv1/Wn:0: 1.0255
[32m[1104 12:21:38 @stat.py:82][0m conv1/Wp:0: 0.97462
[32m[1104 12:21:38 @stat.py:82][0m conv2/W/rms: 0.02266
[32m[1104 12:21:38 @stat.py:82][0m conv2/Wn:0: 1.0165
[32m[1104 12:21:38 @stat.py:82][0m conv2/Wp:0: 0.98361
[32m[1104 12:21:38 @stat.py:82][0m conv3/W/rms: 0.023661
[32m[1104 12:21:38 @stat.py:82][0m conv3/Wn:0: 1.0058
[32m[1104 12:21:38 @stat.py:82][0m conv3/Wp:0: 0.99435
[32m[1104 12:21:38 @stat.py:82][0m conv4/W/rms: 0.026559
[32m[1104 12:21:38 @stat.py:82][0m conv4/Wn:0: 1.0717
[32m[1104 12:21:38 @stat.py:82][0m conv4/Wp:0: 0.92776
[32m[1104 12:21:38 @stat.py:82][0m cost: 3.4354
[32m[1104 12:21:38 @stat.py:82][0m cross_entropy_loss: 3.4076
[32m[1104 12:21:38 @stat.py:82][0m fc0/W/rms: 0.0098889
[32m[1104 12:21:38 @stat.py:82][0m fc0/Wn:0: 1.0047
[32m[1104 12:21:38 @stat.py:82][0m fc0/Wp:0: 0.99542
[32m[1104 12:21:38 @stat.py:82][0m fc1/W/rms: 0.016355
[32m[1104 12:21:38 @stat.py:82][0m fc1/Wn:0: 1.0427
[32m[1104 12:21:38 @stat.py:82][0m fc1/Wp:0: 0.95691
[32m[1104 12:21:38 @stat.py:82][0m fct/W/rms: 0.026724
[32m[1104 12:21:38 @stat.py:82][0m learning_rate: 0.0001
[32m[1104 12:21:38 @stat.py:82][0m train-error-top1: 0.69398
[32m[1104 12:21:38 @stat.py:82][0m train-error-top5: 0.4553
[32m[1104 12:21:38 @stat.py:82][0m val-error-top1: 0.71576
[32m[1104 12:21:38 @stat.py:82][0m val-error-top5: 0.48532
[32m[1104 12:21:38 @stat.py:82][0m validation_cost: 3.6145
[32m[1104 12:21:38 @group.py:40][0m Callbacks took 509.681 sec in total. InferenceRunner: 503.545sec
[32m[1104 12:21:38 @timer.py:46][0m Epoch 2 (global_step 20000) finished, time:2429.81sec.
[32m[1104 13:03:14 @stat.py:82][0m AddN: 0.023315
[32m[1104 13:03:14 @stat.py:82][0m conv0/W/rms: 0.019874
[32m[1104 13:03:14 @stat.py:82][0m conv1/W/rms: 0.025353
[32m[1104 13:03:14 @stat.py:82][0m conv1/Wn:0: 1.0363
[32m[1104 13:03:14 @stat.py:82][0m conv1/Wp:0: 0.96381
[32m[1104 13:03:14 @stat.py:82][0m conv2/W/rms: 0.024899
[32m[1104 13:03:14 @stat.py:82][0m conv2/Wn:0: 1.0218
[32m[1104 13:03:14 @stat.py:82][0m conv2/Wp:0: 0.97838
[32m[1104 13:03:14 @stat.py:82][0m conv3/W/rms: 0.025768
[32m[1104 13:03:14 @stat.py:82][0m conv3/Wn:0: 1.0063
[32m[1104 13:03:14 @stat.py:82][0m conv3/Wp:0: 0.99392
[32m[1104 13:03:14 @stat.py:82][0m conv4/W/rms: 0.028883
[32m[1104 13:03:14 @stat.py:82][0m conv4/Wn:0: 1.0875
[32m[1104 13:03:14 @stat.py:82][0m conv4/Wp:0: 0.912
[32m[1104 13:03:14 @stat.py:82][0m cost: 3.0624
[32m[1104 13:03:14 @stat.py:82][0m cross_entropy_loss: 3.0391
[32m[1104 13:03:14 @stat.py:82][0m fc0/W/rms: 0.0080825
[32m[1104 13:03:14 @stat.py:82][0m fc0/Wn:0: 1.0062
[32m[1104 13:03:14 @stat.py:82][0m fc0/Wp:0: 0.99403
[32m[1104 13:03:14 @stat.py:82][0m fc1/W/rms: 0.014056
[32m[1104 13:03:14 @stat.py:82][0m fc1/Wn:0: 1.0462
[32m[1104 13:03:14 @stat.py:82][0m fc1/Wp:0: 0.95345
[32m[1104 13:03:14 @stat.py:82][0m fct/W/rms: 0.029348
[32m[1104 13:03:14 @stat.py:82][0m learning_rate: 0.0001
[32m[1104 13:03:14 @stat.py:82][0m train-error-top1: 0.63913
[32m[1104 13:03:14 @stat.py:82][0m train-error-top5: 0.4004
[32m[1104 13:03:14 @stat.py:82][0m val-error-top1: 0.66156
[32m[1104 13:03:14 @stat.py:82][0m val-error-top5: 0.41888
[32m[1104 13:03:14 @stat.py:82][0m validation_cost: 3.2349
[32m[1104 13:03:14 @group.py:40][0m Callbacks took 578.983 sec in total. InferenceRunner: 572.465sec
[32m[1104 13:03:14 @timer.py:46][0m Epoch 3 (global_step 30000) finished, time:2495.78sec.
[32m[1104 13:44:33 @stat.py:82][0m AddN: 0.021096
[32m[1104 13:44:33 @stat.py:82][0m conv0/W/rms: 0.022078
[32m[1104 13:44:33 @stat.py:82][0m conv1/W/rms: 0.028927
[32m[1104 13:44:33 @stat.py:82][0m conv1/Wn:0: 1.0486
[32m[1104 13:44:33 @stat.py:82][0m conv1/Wp:0: 0.95159
[32m[1104 13:44:33 @stat.py:82][0m conv2/W/rms: 0.027098
[32m[1104 13:44:33 @stat.py:82][0m conv2/Wn:0: 1.0263
[32m[1104 13:44:33 @stat.py:82][0m conv2/Wp:0: 0.97401
[32m[1104 13:44:33 @stat.py:82][0m conv3/W/rms: 0.027826
[32m[1104 13:44:33 @stat.py:82][0m conv3/Wn:0: 1.0075
[32m[1104 13:44:33 @stat.py:82][0m conv3/Wp:0: 0.99281
[32m[1104 13:44:33 @stat.py:82][0m conv4/W/rms: 0.031188
[32m[1104 13:44:33 @stat.py:82][0m conv4/Wn:0: 1.1001
[32m[1104 13:44:33 @stat.py:82][0m conv4/Wp:0: 0.89941
[32m[1104 13:44:33 @stat.py:82][0m cost: 2.7934
[32m[1104 13:44:33 @stat.py:82][0m cross_entropy_loss: 2.7723
[32m[1104 13:44:33 @stat.py:82][0m fc0/W/rms: 0.0067618
[32m[1104 13:44:33 @stat.py:82][0m fc0/Wn:0: 1.0099
[32m[1104 13:44:33 @stat.py:82][0m fc0/Wp:0: 0.99039
[32m[1104 13:44:33 @stat.py:82][0m fc1/W/rms: 0.012231
[32m[1104 13:44:33 @stat.py:82][0m fc1/Wn:0: 1.053
[32m[1104 13:44:33 @stat.py:82][0m fc1/Wp:0: 0.94662
[32m[1104 13:44:33 @stat.py:82][0m fct/W/rms: 0.031998
[32m[1104 13:44:33 @stat.py:82][0m learning_rate: 0.0001
[32m[1104 13:44:33 @stat.py:82][0m train-error-top1: 0.59545
[32m[1104 13:44:33 @stat.py:82][0m train-error-top5: 0.35875
[32m[1104 13:44:33 @stat.py:82][0m val-error-top1: 0.63232
[32m[1104 13:44:33 @stat.py:82][0m val-error-top5: 0.38766
[32m[1104 13:44:33 @stat.py:82][0m validation_cost: 3.0404
[32m[1104 13:44:33 @group.py:40][0m Callbacks took 565.381 sec in total. InferenceRunner: 557.690sec
[32m[1104 13:44:33 @timer.py:46][0m Epoch 4 (global_step 40000) finished, time:2478.55sec.
[32m[1104 14:25:53 @stat.py:82][0m AddN: 0.020462
[32m[1104 14:25:53 @stat.py:82][0m conv0/W/rms: 0.024056
[32m[1104 14:25:53 @stat.py:82][0m conv1/W/rms: 0.032502
[32m[1104 14:25:53 @stat.py:82][0m conv1/Wn:0: 1.0635
[32m[1104 14:25:53 @stat.py:82][0m conv1/Wp:0: 0.93679
[32m[1104 14:25:53 @stat.py:82][0m conv2/W/rms: 0.029261
[32m[1104 14:25:53 @stat.py:82][0m conv2/Wn:0: 1.031
[32m[1104 14:25:53 @stat.py:82][0m conv2/Wp:0: 0.96937
[32m[1104 14:25:53 @stat.py:82][0m conv3/W/rms: 0.029842
[32m[1104 14:25:53 @stat.py:82][0m conv3/Wn:0: 1.0103
[32m[1104 14:25:53 @stat.py:82][0m conv3/Wp:0: 0.99016
[32m[1104 14:25:53 @stat.py:82][0m conv4/W/rms: 0.033496
[32m[1104 14:25:53 @stat.py:82][0m conv4/Wn:0: 1.107
[32m[1104 14:25:53 @stat.py:82][0m conv4/Wp:0: 0.89261
[32m[1104 14:25:53 @stat.py:82][0m cost: 2.7022
[32m[1104 14:25:53 @stat.py:82][0m cross_entropy_loss: 2.6817
[32m[1104 14:25:53 @stat.py:82][0m fc0/W/rms: 0.0058756
[32m[1104 14:25:53 @stat.py:82][0m fc0/Wn:0: 1.0143
[32m[1104 14:25:53 @stat.py:82][0m fc0/Wp:0: 0.98605
[32m[1104 14:25:53 @stat.py:82][0m fc1/W/rms: 0.010826
[32m[1104 14:25:53 @stat.py:82][0m fc1/Wn:0: 1.0621
[32m[1104 14:25:53 @stat.py:82][0m fc1/Wp:0: 0.93753
[32m[1104 14:25:53 @stat.py:82][0m fct/W/rms: 0.034626
[32m[1104 14:25:53 @stat.py:82][0m learning_rate: 0.0001
[32m[1104 14:25:53 @stat.py:82][0m train-error-top1: 0.58132
[32m[1104 14:25:53 @stat.py:82][0m train-error-top5: 0.33273
[32m[1104 14:25:53 @stat.py:82][0m val-error-top1: 0.59342
[32m[1104 14:25:53 @stat.py:82][0m val-error-top5: 0.34858
[32m[1104 14:25:53 @stat.py:82][0m validation_cost: 2.8072
[32m[1104 14:25:53 @group.py:40][0m Callbacks took 569.981 sec in total. InferenceRunner: 563.666sec
[32m[1104 14:25:53 @timer.py:46][0m Epoch 5 (global_step 50000) finished, time:2480.01sec.
[32m[1104 15:07:09 @stat.py:82][0m AddN: 0.020922
[32m[1104 15:07:09 @stat.py:82][0m conv0/W/rms: 0.025871
[32m[1104 15:07:09 @stat.py:82][0m conv1/W/rms: 0.036027
[32m[1104 15:07:09 @stat.py:82][0m conv1/Wn:0: 1.0768
[32m[1104 15:07:09 @stat.py:82][0m conv1/Wp:0: 0.92346
[32m[1104 15:07:09 @stat.py:82][0m conv2/W/rms: 0.031403
[32m[1104 15:07:09 @stat.py:82][0m conv2/Wn:0: 1.0362
[32m[1104 15:07:09 @stat.py:82][0m conv2/Wp:0: 0.9643
[32m[1104 15:07:09 @stat.py:82][0m conv3/W/rms: 0.031828
[32m[1104 15:07:09 @stat.py:82][0m conv3/Wn:0: 1.0128
[32m[1104 15:07:09 @stat.py:82][0m conv3/Wp:0: 0.9877
[32m[1104 15:07:09 @stat.py:82][0m conv4/W/rms: 0.035801
[32m[1104 15:07:09 @stat.py:82][0m conv4/Wn:0: 1.1134
[32m[1104 15:07:09 @stat.py:82][0m conv4/Wp:0: 0.88633
[32m[1104 15:07:09 @stat.py:82][0m cost: 2.4908
[32m[1104 15:07:09 @stat.py:82][0m cross_entropy_loss: 2.4699
[32m[1104 15:07:09 @stat.py:82][0m fc0/W/rms: 0.00535
[32m[1104 15:07:09 @stat.py:82][0m fc0/Wn:0: 1.0205
[32m[1104 15:07:09 @stat.py:82][0m fc0/Wp:0: 0.97993
[32m[1104 15:07:09 @stat.py:82][0m fc1/W/rms: 0.0097863
[32m[1104 15:07:09 @stat.py:82][0m fc1/Wn:0: 1.0662
[32m[1104 15:07:09 @stat.py:82][0m fc1/Wp:0: 0.93344
[32m[1104 15:07:09 @stat.py:82][0m fct/W/rms: 0.037241
[32m[1104 15:07:09 @stat.py:82][0m learning_rate: 0.0001
[32m[1104 15:07:09 @stat.py:82][0m train-error-top1: 0.54501
[32m[1104 15:07:09 @stat.py:82][0m train-error-top5: 0.2997
[32m[1104 15:07:09 @stat.py:82][0m val-error-top1: 0.5798
[32m[1104 15:07:09 @stat.py:82][0m val-error-top5: 0.33412
[32m[1104 15:07:09 @stat.py:82][0m validation_cost: 2.7339
[32m[1104 15:07:09 @group.py:40][0m Callbacks took 567.392 sec in total. InferenceRunner: 558.577sec
[32m[1104 15:07:09 @timer.py:46][0m Epoch 6 (global_step 60000) finished, time:2476.53sec.
[32m[1104 15:48:29 @stat.py:82][0m AddN: 0.022103
[32m[1104 15:48:29 @stat.py:82][0m conv0/W/rms: 0.027569
[32m[1104 15:48:29 @stat.py:82][0m conv1/W/rms: 0.039488
[32m[1104 15:48:29 @stat.py:82][0m conv1/Wn:0: 1.0913
[32m[1104 15:48:29 @stat.py:82][0m conv1/Wp:0: 0.90903
[32m[1104 15:48:29 @stat.py:82][0m conv2/W/rms: 0.033504
[32m[1104 15:48:29 @stat.py:82][0m conv2/Wn:0: 1.0411
[32m[1104 15:48:29 @stat.py:82][0m conv2/Wp:0: 0.95948
[32m[1104 15:48:29 @stat.py:82][0m conv3/W/rms: 0.033769
[32m[1104 15:48:29 @stat.py:82][0m conv3/Wn:0: 1.015
[32m[1104 15:48:29 @stat.py:82][0m conv3/Wp:0: 0.98558
[32m[1104 15:48:29 @stat.py:82][0m conv4/W/rms: 0.038076
[32m[1104 15:48:29 @stat.py:82][0m conv4/Wn:0: 1.117
[32m[1104 15:48:29 @stat.py:82][0m conv4/Wp:0: 0.8828
[32m[1104 15:48:29 @stat.py:82][0m cost: 2.4242
[32m[1104 15:48:29 @stat.py:82][0m cross_entropy_loss: 2.402
[32m[1104 15:48:29 @stat.py:82][0m fc0/W/rms: 0.0050567
[32m[1104 15:48:29 @stat.py:82][0m fc0/Wn:0: 1.0274
[32m[1104 15:48:29 @stat.py:82][0m fc0/Wp:0: 0.97305
[32m[1104 15:48:29 @stat.py:82][0m fc1/W/rms: 0.0090586
[32m[1104 15:48:29 @stat.py:82][0m fc1/Wn:0: 1.0698
[32m[1104 15:48:29 @stat.py:82][0m fc1/Wp:0: 0.9298
[32m[1104 15:48:29 @stat.py:82][0m fct/W/rms: 0.039841
[32m[1104 15:48:29 @stat.py:82][0m learning_rate: 0.0001
[32m[1104 15:48:29 @stat.py:82][0m train-error-top1: 0.53756
[32m[1104 15:48:29 @stat.py:82][0m train-error-top5: 0.28834
[32m[1104 15:48:29 @stat.py:82][0m val-error-top1: 0.5755
[32m[1104 15:48:29 @stat.py:82][0m val-error-top5: 0.32696
[32m[1104 15:48:29 @stat.py:82][0m validation_cost: 2.7088
[32m[1104 15:48:29 @group.py:40][0m Callbacks took 571.041 sec in total. InferenceRunner: 565.473sec
[32m[1104 15:48:29 @timer.py:46][0m Epoch 7 (global_step 70000) finished, time:2479.42sec.
[32m[1104 16:30:09 @stat.py:82][0m AddN: 0.023798
[32m[1104 16:30:09 @stat.py:82][0m conv0/W/rms: 0.029154
[32m[1104 16:30:09 @stat.py:82][0m conv1/W/rms: 0.042875
[32m[1104 16:30:09 @stat.py:82][0m conv1/Wn:0: 1.1059
[32m[1104 16:30:09 @stat.py:82][0m conv1/Wp:0: 0.89453
[32m[1104 16:30:09 @stat.py:82][0m conv2/W/rms: 0.035562
[32m[1104 16:30:09 @stat.py:82][0m conv2/Wn:0: 1.0437
[32m[1104 16:30:09 @stat.py:82][0m conv2/Wp:0: 0.9569
[32m[1104 16:30:09 @stat.py:82][0m conv3/W/rms: 0.035686
[32m[1104 16:30:09 @stat.py:82][0m conv3/Wn:0: 1.0154
[32m[1104 16:30:09 @stat.py:82][0m conv3/Wp:0: 0.98526
[32m[1104 16:30:09 @stat.py:82][0m conv4/W/rms: 0.040337
[32m[1104 16:30:10 @stat.py:82][0m conv4/Wn:0: 1.1198
[32m[1104 16:30:10 @stat.py:82][0m conv4/Wp:0: 0.88005
[32m[1104 16:30:10 @stat.py:82][0m cost: 2.3196
[32m[1104 16:30:10 @stat.py:82][0m cross_entropy_loss: 2.2958
[32m[1104 16:30:10 @stat.py:82][0m fc0/W/rms: 0.0049423
[32m[1104 16:30:10 @stat.py:82][0m fc0/Wn:0: 1.0346
[32m[1104 16:30:10 @stat.py:82][0m fc0/Wp:0: 0.96589
[32m[1104 16:30:10 @stat.py:82][0m fc1/W/rms: 0.0085827
[32m[1104 16:30:10 @stat.py:82][0m fc1/Wn:0: 1.0729
[32m[1104 16:30:10 @stat.py:82][0m fc1/Wp:0: 0.92669
[32m[1104 16:30:10 @stat.py:82][0m fct/W/rms: 0.042404
[32m[1104 16:30:10 @stat.py:82][0m learning_rate: 0.0001
[32m[1104 16:30:10 @stat.py:82][0m train-error-top1: 0.51381
[32m[1104 16:30:10 @stat.py:82][0m train-error-top5: 0.27326
[32m[1104 16:30:10 @stat.py:82][0m val-error-top1: 0.56468
[32m[1104 16:30:10 @stat.py:82][0m val-error-top5: 0.31874
[32m[1104 16:30:10 @stat.py:82][0m validation_cost: 2.6664
[32m[1104 16:30:10 @group.py:40][0m Callbacks took 587.861 sec in total. InferenceRunner: 582.302sec
[32m[1104 16:30:10 @timer.py:46][0m Epoch 8 (global_step 80000) finished, time:2500.91sec.
[32m[1104 17:11:53 @stat.py:82][0m AddN: 0.025847
[32m[1104 17:11:53 @stat.py:82][0m conv0/W/rms: 0.030652
[32m[1104 17:11:53 @stat.py:82][0m conv1/W/rms: 0.046175
[32m[1104 17:11:53 @stat.py:82][0m conv1/Wn:0: 1.1194
[32m[1104 17:11:53 @stat.py:82][0m conv1/Wp:0: 0.881
[32m[1104 17:11:53 @stat.py:82][0m conv2/W/rms: 0.037585
[32m[1104 17:11:53 @stat.py:82][0m conv2/Wn:0: 1.0466
[32m[1104 17:11:53 @stat.py:82][0m conv2/Wp:0: 0.95412
[32m[1104 17:11:53 @stat.py:82][0m conv3/W/rms: 0.037573
[32m[1104 17:11:53 @stat.py:82][0m conv3/Wn:0: 1.0186
[32m[1104 17:11:53 @stat.py:82][0m conv3/Wp:0: 0.98218
[32m[1104 17:11:53 @stat.py:82][0m conv4/W/rms: 0.042585
[32m[1104 17:11:53 @stat.py:82][0m conv4/Wn:0: 1.1231
[32m[1104 17:11:53 @stat.py:82][0m conv4/Wp:0: 0.87682
[32m[1104 17:11:53 @stat.py:82][0m cost: 2.2691
[32m[1104 17:11:53 @stat.py:82][0m cross_entropy_loss: 2.2432
[32m[1104 17:11:53 @stat.py:82][0m fc0/W/rms: 0.0049195
[32m[1104 17:11:53 @stat.py:82][0m fc0/Wn:0: 1.0421
[32m[1104 17:11:53 @stat.py:82][0m fc0/Wp:0: 0.95849
[32m[1104 17:11:53 @stat.py:82][0m fc1/W/rms: 0.0083094
[32m[1104 17:11:53 @stat.py:82][0m fc1/Wn:0: 1.0793
[32m[1104 17:11:53 @stat.py:82][0m fc1/Wp:0: 0.92024
[32m[1104 17:11:53 @stat.py:82][0m fct/W/rms: 0.044938
[32m[1104 17:11:53 @stat.py:82][0m learning_rate: 0.0001
[32m[1104 17:11:53 @stat.py:82][0m train-error-top1: 0.50453
[32m[1104 17:11:53 @stat.py:82][0m train-error-top5: 0.2641
[32m[1104 17:11:53 @stat.py:82][0m val-error-top1: 0.558
[32m[1104 17:11:53 @stat.py:82][0m val-error-top5: 0.31072
[32m[1104 17:11:53 @stat.py:82][0m validation_cost: 2.6175
[32m[1104 17:11:53 @group.py:40][0m Callbacks took 591.247 sec in total. InferenceRunner: 583.190sec
[32m[1104 17:11:53 @timer.py:46][0m Epoch 9 (global_step 90000) finished, time:2503.55sec.
[32m[1104 17:53:21 @stat.py:82][0m AddN: 0.028167
[32m[1104 17:53:21 @stat.py:82][0m conv0/W/rms: 0.032066
[32m[1104 17:53:21 @stat.py:82][0m conv1/W/rms: 0.049423
[32m[1104 17:53:21 @stat.py:82][0m conv1/Wn:0: 1.1331
[32m[1104 17:53:21 @stat.py:82][0m conv1/Wp:0: 0.8674
[32m[1104 17:53:21 @stat.py:82][0m conv2/W/rms: 0.039578
[32m[1104 17:53:21 @stat.py:82][0m conv2/Wn:0: 1.051
[32m[1104 17:53:21 @stat.py:82][0m conv2/Wp:0: 0.94978
[32m[1104 17:53:21 @stat.py:82][0m conv3/W/rms: 0.039433
[32m[1104 17:53:21 @stat.py:82][0m conv3/Wn:0: 1.0217
[32m[1104 17:53:21 @stat.py:82][0m conv3/Wp:0: 0.97918
[32m[1104 17:53:21 @stat.py:82][0m conv4/W/rms: 0.044795
[32m[1104 17:53:21 @stat.py:82][0m conv4/Wn:0: 1.1259
[32m[1104 17:53:21 @stat.py:82][0m conv4/Wp:0: 0.87418
[32m[1104 17:53:21 @stat.py:82][0m cost: 2.2163
[32m[1104 17:53:21 @stat.py:82][0m cross_entropy_loss: 2.1881
[32m[1104 17:53:21 @stat.py:82][0m fc0/W/rms: 0.0049591
[32m[1104 17:53:21 @stat.py:82][0m fc0/Wn:0: 1.0495
[32m[1104 17:53:21 @stat.py:82][0m fc0/Wp:0: 0.95109
[32m[1104 17:53:21 @stat.py:82][0m fc1/W/rms: 0.0081832
[32m[1104 17:53:21 @stat.py:82][0m fc1/Wn:0: 1.0843
[32m[1104 17:53:21 @stat.py:82][0m fc1/Wp:0: 0.9152
[32m[1104 17:53:21 @stat.py:82][0m fct/W/rms: 0.047446
[32m[1104 17:53:21 @stat.py:82][0m learning_rate: 0.0001
[32m[1104 17:53:21 @stat.py:82][0m train-error-top1: 0.48771
[32m[1104 17:53:21 @stat.py:82][0m train-error-top5: 0.25562
[32m[1104 17:53:21 @stat.py:82][0m val-error-top1: 0.55344
[32m[1104 17:53:21 @stat.py:82][0m val-error-top5: 0.30626
[32m[1104 17:53:21 @stat.py:82][0m validation_cost: 2.6148
[32m[1104 17:53:21 @group.py:40][0m Callbacks took 575.831 sec in total. InferenceRunner: 569.965sec
[32m[1104 17:53:21 @timer.py:46][0m Epoch 10 (global_step 100000) finished, time:2488.20sec.
[32m[1104 18:34:42 @stat.py:82][0m AddN: 0.030733
[32m[1104 18:34:42 @stat.py:82][0m conv0/W/rms: 0.033419
[32m[1104 18:34:42 @stat.py:82][0m conv1/W/rms: 0.052628
[32m[1104 18:34:42 @stat.py:82][0m conv1/Wn:0: 1.1462
[32m[1104 18:34:42 @stat.py:82][0m conv1/Wp:0: 0.85434
[32m[1104 18:34:42 @stat.py:82][0m conv2/W/rms: 0.041546
[32m[1104 18:34:42 @stat.py:82][0m conv2/Wn:0: 1.0549
[32m[1104 18:34:42 @stat.py:82][0m conv2/Wp:0: 0.94603
[32m[1104 18:34:42 @stat.py:82][0m conv3/W/rms: 0.04125
[32m[1104 18:34:42 @stat.py:82][0m conv3/Wn:0: 1.0232
[32m[1104 18:34:42 @stat.py:82][0m conv3/Wp:0: 0.97779
[32m[1104 18:34:42 @stat.py:82][0m conv4/W/rms: 0.046964
[32m[1104 18:34:42 @stat.py:82][0m conv4/Wn:0: 1.1292
[32m[1104 18:34:42 @stat.py:82][0m conv4/Wp:0: 0.87091
[32m[1104 18:34:42 @stat.py:82][0m cost: 2.2325
[32m[1104 18:34:42 @stat.py:82][0m cross_entropy_loss: 2.2018
[32m[1104 18:34:42 @stat.py:82][0m fc0/W/rms: 0.0050722
[32m[1104 18:34:42 @stat.py:82][0m fc0/Wn:0: 1.0571
[32m[1104 18:34:42 @stat.py:82][0m fc0/Wp:0: 0.94349
[32m[1104 18:34:42 @stat.py:82][0m fc1/W/rms: 0.0081743
[32m[1104 18:34:42 @stat.py:82][0m fc1/Wn:0: 1.0906
[32m[1104 18:34:42 @stat.py:82][0m fc1/Wp:0: 0.90883
[32m[1104 18:34:42 @stat.py:82][0m fct/W/rms: 0.04992
[32m[1104 18:34:42 @stat.py:82][0m learning_rate: 0.0001
[32m[1104 18:34:42 @stat.py:82][0m train-error-top1: 0.49397
[32m[1104 18:34:42 @stat.py:82][0m train-error-top5: 0.26293
[32m[1104 18:34:42 @stat.py:82][0m val-error-top1: 0.5389
[32m[1104 18:34:42 @stat.py:82][0m val-error-top5: 0.29192
[32m[1104 18:34:42 @stat.py:82][0m validation_cost: 2.5388
[32m[1104 18:34:42 @group.py:40][0m Callbacks took 569.622 sec in total. InferenceRunner: 561.232sec
[32m[1104 18:34:42 @timer.py:46][0m Epoch 11 (global_step 110000) finished, time:2480.32sec.
[32m[1104 19:15:52 @stat.py:82][0m AddN: 0.033482
[32m[1104 19:15:52 @stat.py:82][0m conv0/W/rms: 0.034726
[32m[1104 19:15:52 @stat.py:82][0m conv1/W/rms: 0.055766
[32m[1104 19:15:52 @stat.py:82][0m conv1/Wn:0: 1.1591
[32m[1104 19:15:52 @stat.py:82][0m conv1/Wp:0: 0.84147
[32m[1104 19:15:52 @stat.py:82][0m conv2/W/rms: 0.043489
[32m[1104 19:15:52 @stat.py:82][0m conv2/Wn:0: 1.0584
[32m[1104 19:15:52 @stat.py:82][0m conv2/Wp:0: 0.94266
[32m[1104 19:15:52 @stat.py:82][0m conv3/W/rms: 0.043061
[32m[1104 19:15:52 @stat.py:82][0m conv3/Wn:0: 1.0257
[32m[1104 19:15:52 @stat.py:82][0m conv3/Wp:0: 0.97538
[32m[1104 19:15:52 @stat.py:82][0m conv4/W/rms: 0.049118
[32m[1104 19:15:52 @stat.py:82][0m conv4/Wn:0: 1.1296
[32m[1104 19:15:52 @stat.py:82][0m conv4/Wp:0: 0.87067
[32m[1104 19:15:52 @stat.py:82][0m cost: 2.1459
[32m[1104 19:15:52 @stat.py:82][0m cross_entropy_loss: 2.1124
[32m[1104 19:15:52 @stat.py:82][0m fc0/W/rms: 0.0052164
[32m[1104 19:15:52 @stat.py:82][0m fc0/Wn:0: 1.0637
[32m[1104 19:15:52 @stat.py:82][0m fc0/Wp:0: 0.93695
[32m[1104 19:15:52 @stat.py:82][0m fc1/W/rms: 0.0082326
[32m[1104 19:15:52 @stat.py:82][0m fc1/Wn:0: 1.0904
[32m[1104 19:15:52 @stat.py:82][0m fc1/Wp:0: 0.90898
[32m[1104 19:15:52 @stat.py:82][0m fct/W/rms: 0.052375
[32m[1104 19:15:52 @stat.py:82][0m learning_rate: 0.0001
[32m[1104 19:15:52 @stat.py:82][0m train-error-top1: 0.48166
[32m[1104 19:15:52 @stat.py:82][0m train-error-top5: 0.24533
[32m[1104 19:15:52 @stat.py:82][0m val-error-top1: 0.53352
[32m[1104 19:15:52 @stat.py:82][0m val-error-top5: 0.28658
[32m[1104 19:15:52 @stat.py:82][0m validation_cost: 2.4855
[32m[1104 19:15:52 @group.py:40][0m Callbacks took 563.052 sec in total. InferenceRunner: 557.554sec
[32m[1104 19:15:52 @timer.py:46][0m Epoch 12 (global_step 120000) finished, time:2469.98sec.
[32m[1104 19:57:09 @stat.py:82][0m AddN: 0.03645
[32m[1104 19:57:09 @stat.py:82][0m conv0/W/rms: 0.035965
[32m[1104 19:57:09 @stat.py:82][0m conv1/W/rms: 0.058853
[32m[1104 19:57:09 @stat.py:82][0m conv1/Wn:0: 1.1713
[32m[1104 19:57:09 @stat.py:82][0m conv1/Wp:0: 0.82936
[32m[1104 19:57:09 @stat.py:82][0m conv2/W/rms: 0.045406
[32m[1104 19:57:09 @stat.py:82][0m conv2/Wn:0: 1.0613
[32m[1104 19:57:09 @stat.py:82][0m conv2/Wp:0: 0.93979
[32m[1104 19:57:09 @stat.py:82][0m conv3/W/rms: 0.044853
[32m[1104 19:57:09 @stat.py:82][0m conv3/Wn:0: 1.0281
[32m[1104 19:57:09 @stat.py:82][0m conv3/Wp:0: 0.97304
[32m[1104 19:57:09 @stat.py:82][0m conv4/W/rms: 0.051261
[32m[1104 19:57:09 @stat.py:82][0m conv4/Wn:0: 1.13
[32m[1104 19:57:09 @stat.py:82][0m conv4/Wp:0: 0.87034
[32m[1104 19:57:09 @stat.py:82][0m cost: 2.0695
[32m[1104 19:57:09 @stat.py:82][0m cross_entropy_loss: 2.0331
[32m[1104 19:57:09 @stat.py:82][0m fc0/W/rms: 0.0053653
[32m[1104 19:57:09 @stat.py:82][0m fc0/Wn:0: 1.0704
[32m[1104 19:57:09 @stat.py:82][0m fc0/Wp:0: 0.93037
[32m[1104 19:57:09 @stat.py:82][0m fc1/W/rms: 0.0083762
[32m[1104 19:57:09 @stat.py:82][0m fc1/Wn:0: 1.0948
[32m[1104 19:57:09 @stat.py:82][0m fc1/Wp:0: 0.90457
[32m[1104 19:57:09 @stat.py:82][0m fct/W/rms: 0.054853
[32m[1104 19:57:09 @stat.py:82][0m learning_rate: 0.0001
[32m[1104 19:57:09 @stat.py:82][0m train-error-top1: 0.47062
[32m[1104 19:57:09 @stat.py:82][0m train-error-top5: 0.22694
[32m[1104 19:57:09 @stat.py:82][0m val-error-top1: 0.53614
[32m[1104 19:57:09 @stat.py:82][0m val-error-top5: 0.28972
[32m[1104 19:57:09 @stat.py:82][0m validation_cost: 2.5075
[32m[1104 19:57:09 @group.py:40][0m Callbacks took 568.689 sec in total. InferenceRunner: 562.529sec
[32m[1104 19:57:09 @timer.py:46][0m Epoch 13 (global_step 130000) finished, time:2477.89sec.
[32m[1104 20:38:54 @stat.py:82][0m AddN: 0.039568
[32m[1104 20:38:54 @stat.py:82][0m conv0/W/rms: 0.037174
[32m[1104 20:38:54 @stat.py:82][0m conv1/W/rms: 0.061902
[32m[1104 20:38:54 @stat.py:82][0m conv1/Wn:0: 1.1814
[32m[1104 20:38:54 @stat.py:82][0m conv1/Wp:0: 0.81927
[32m[1104 20:38:54 @stat.py:82][0m conv2/W/rms: 0.047302
[32m[1104 20:38:54 @stat.py:82][0m conv2/Wn:0: 1.0653
[32m[1104 20:38:54 @stat.py:82][0m conv2/Wp:0: 0.93594
[32m[1104 20:38:54 @stat.py:82][0m conv3/W/rms: 0.046628
[32m[1104 20:38:54 @stat.py:82][0m conv3/Wn:0: 1.0318
[32m[1104 20:38:54 @stat.py:82][0m conv3/Wp:0: 0.96941
[32m[1104 20:38:54 @stat.py:82][0m conv4/W/rms: 0.053378
[32m[1104 20:38:54 @stat.py:82][0m conv4/Wn:0: 1.1322
[32m[1104 20:38:54 @stat.py:82][0m conv4/Wp:0: 0.86818
[32m[1104 20:38:54 @stat.py:82][0m cost: 2.0272
[32m[1104 20:38:54 @stat.py:82][0m cross_entropy_loss: 1.9877
[32m[1104 20:38:54 @stat.py:82][0m fc0/W/rms: 0.0055646
[32m[1104 20:38:54 @stat.py:82][0m fc0/Wn:0: 1.079
[32m[1104 20:38:54 @stat.py:82][0m fc0/Wp:0: 0.92176
[32m[1104 20:38:54 @stat.py:82][0m fc1/W/rms: 0.0085698
[32m[1104 20:38:54 @stat.py:82][0m fc1/Wn:0: 1.0961
[32m[1104 20:38:54 @stat.py:82][0m fc1/Wp:0: 0.90318
[32m[1104 20:38:54 @stat.py:82][0m fct/W/rms: 0.057271
[32m[1104 20:38:54 @stat.py:82][0m learning_rate: 0.0001
[32m[1104 20:38:54 @stat.py:82][0m train-error-top1: 0.45419
[32m[1104 20:38:54 @stat.py:82][0m train-error-top5: 0.22347
[32m[1104 20:38:54 @stat.py:82][0m val-error-top1: 0.52078
[32m[1104 20:38:54 @stat.py:82][0m val-error-top5: 0.27866
[32m[1104 20:38:54 @stat.py:82][0m validation_cost: 2.4492
[32m[1104 20:38:54 @group.py:40][0m Callbacks took 594.596 sec in total. InferenceRunner: 586.964sec
[32m[1104 20:38:54 @timer.py:46][0m Epoch 14 (global_step 140000) finished, time:2504.13sec.
[32m[1104 21:19:54 @stat.py:82][0m AddN: 0.042742
[32m[1104 21:19:54 @stat.py:82][0m conv0/W/rms: 0.038351
[32m[1104 21:19:54 @stat.py:82][0m conv1/W/rms: 0.064933
[32m[1104 21:19:54 @stat.py:82][0m conv1/Wn:0: 1.1915
[32m[1104 21:19:54 @stat.py:82][0m conv1/Wp:0: 0.8093
[32m[1104 21:19:54 @stat.py:82][0m conv2/W/rms: 0.049182
[32m[1104 21:19:54 @stat.py:82][0m conv2/Wn:0: 1.0682
[32m[1104 21:19:54 @stat.py:82][0m conv2/Wp:0: 0.93307
[32m[1104 21:19:54 @stat.py:82][0m conv3/W/rms: 0.048391
[32m[1104 21:19:54 @stat.py:82][0m conv3/Wn:0: 1.0333
[32m[1104 21:19:54 @stat.py:82][0m conv3/Wp:0: 0.96805
[32m[1104 21:19:54 @stat.py:82][0m conv4/W/rms: 0.055486
[32m[1104 21:19:54 @stat.py:82][0m conv4/Wn:0: 1.1333
[32m[1104 21:19:54 @stat.py:82][0m conv4/Wp:0: 0.86719
[32m[1104 21:19:54 @stat.py:82][0m cost: 2.0319
[32m[1104 21:19:54 @stat.py:82][0m cross_entropy_loss: 1.9891
[32m[1104 21:19:54 @stat.py:82][0m fc0/W/rms: 0.005715
[32m[1104 21:19:54 @stat.py:82][0m fc0/Wn:0: 1.084
[32m[1104 21:19:54 @stat.py:82][0m fc0/Wp:0: 0.91681
[32m[1104 21:19:54 @stat.py:82][0m fc1/W/rms: 0.0087783
[32m[1104 21:19:54 @stat.py:82][0m fc1/Wn:0: 1.0954
[32m[1104 21:19:54 @stat.py:82][0m fc1/Wp:0: 0.9039
[32m[1104 21:19:54 @stat.py:82][0m fct/W/rms: 0.059659
[32m[1104 21:19:54 @stat.py:82][0m learning_rate: 0.0001
[32m[1104 21:19:54 @stat.py:82][0m train-error-top1: 0.4544
[32m[1104 21:19:54 @stat.py:82][0m train-error-top5: 0.2172
[32m[1104 21:19:54 @stat.py:82][0m val-error-top1: 0.52382
[32m[1104 21:19:54 @stat.py:82][0m val-error-top5: 0.27846
[32m[1104 21:19:54 @stat.py:82][0m validation_cost: 2.483
[32m[1104 21:19:54 @group.py:40][0m Callbacks took 549.690 sec in total. InferenceRunner: 541.155sec
[32m[1104 21:19:54 @timer.py:46][0m Epoch 15 (global_step 150000) finished, time:2460.14sec.
[32m[1104 22:00:40 @stat.py:82][0m AddN: 0.046005
[32m[1104 22:00:40 @stat.py:82][0m conv0/W/rms: 0.039498
[32m[1104 22:00:40 @stat.py:82][0m conv1/W/rms: 0.067887
[32m[1104 22:00:40 @stat.py:82][0m conv1/Wn:0: 1.1996
[32m[1104 22:00:40 @stat.py:82][0m conv1/Wp:0: 0.80123
[32m[1104 22:00:40 @stat.py:82][0m conv2/W/rms: 0.051046
[32m[1104 22:00:40 @stat.py:82][0m conv2/Wn:0: 1.0702
[32m[1104 22:00:40 @stat.py:82][0m conv2/Wp:0: 0.93116
[32m[1104 22:00:40 @stat.py:82][0m conv3/W/rms: 0.05013
[32m[1104 22:00:40 @stat.py:82][0m conv3/Wn:0: 1.0352
[32m[1104 22:00:40 @stat.py:82][0m conv3/Wp:0: 0.96617
[32m[1104 22:00:40 @stat.py:82][0m conv4/W/rms: 0.057563
[32m[1104 22:00:40 @stat.py:82][0m conv4/Wn:0: 1.1345
[32m[1104 22:00:40 @stat.py:82][0m conv4/Wp:0: 0.86607
[32m[1104 22:00:40 @stat.py:82][0m cost: 2.0175
[32m[1104 22:00:40 @stat.py:82][0m cross_entropy_loss: 1.9715
[32m[1104 22:00:40 @stat.py:82][0m fc0/W/rms: 0.0058955
[32m[1104 22:00:40 @stat.py:82][0m fc0/Wn:0: 1.09
[32m[1104 22:00:40 @stat.py:82][0m fc0/Wp:0: 0.91084
[32m[1104 22:00:40 @stat.py:82][0m fc1/W/rms: 0.0089695
[32m[1104 22:00:40 @stat.py:82][0m fc1/Wn:0: 1.0955
[32m[1104 22:00:40 @stat.py:82][0m fc1/Wp:0: 0.90379
[32m[1104 22:00:40 @stat.py:82][0m fct/W/rms: 0.062008
[32m[1104 22:00:40 @stat.py:82][0m learning_rate: 0.0001
[32m[1104 22:00:40 @stat.py:82][0m train-error-top1: 0.45303
[32m[1104 22:00:40 @stat.py:82][0m train-error-top5: 0.22101
[32m[1104 22:00:40 @stat.py:82][0m val-error-top1: 0.50942
[32m[1104 22:00:40 @stat.py:82][0m val-error-top5: 0.26766
[32m[1104 22:00:40 @stat.py:82][0m validation_cost: 2.3926
[32m[1104 22:00:40 @group.py:40][0m Callbacks took 530.995 sec in total. InferenceRunner: 524.939sec
[32m[1104 22:00:40 @timer.py:46][0m Epoch 16 (global_step 160000) finished, time:2446.71sec.
[32m[1104 22:41:34 @stat.py:82][0m AddN: 0.049372
[32m[1104 22:41:34 @stat.py:82][0m conv0/W/rms: 0.040618
[32m[1104 22:41:34 @stat.py:82][0m conv1/W/rms: 0.070828
[32m[1104 22:41:34 @stat.py:82][0m conv1/Wn:0: 1.2084
[32m[1104 22:41:34 @stat.py:82][0m conv1/Wp:0: 0.7925
[32m[1104 22:41:34 @stat.py:82][0m conv2/W/rms: 0.052894
[32m[1104 22:41:34 @stat.py:82][0m conv2/Wn:0: 1.0726
[32m[1104 22:41:34 @stat.py:82][0m conv2/Wp:0: 0.92883
[32m[1104 22:41:34 @stat.py:82][0m conv3/W/rms: 0.051859
[32m[1104 22:41:34 @stat.py:82][0m conv3/Wn:0: 1.0381
[32m[1104 22:41:34 @stat.py:82][0m conv3/Wp:0: 0.96339
[32m[1104 22:41:34 @stat.py:82][0m conv4/W/rms: 0.059641
[32m[1104 22:41:34 @stat.py:82][0m conv4/Wn:0: 1.1339
[32m[1104 22:41:34 @stat.py:82][0m conv4/Wp:0: 0.86675
[32m[1104 22:41:34 @stat.py:82][0m cost: 1.939
[32m[1104 22:41:34 @stat.py:82][0m cross_entropy_loss: 1.8896
[32m[1104 22:41:34 @stat.py:82][0m fc0/W/rms: 0.0060655
[32m[1104 22:41:34 @stat.py:82][0m fc0/Wn:0: 1.0952
[32m[1104 22:41:34 @stat.py:82][0m fc0/Wp:0: 0.90575
[32m[1104 22:41:34 @stat.py:82][0m fc1/W/rms: 0.0091793
[32m[1104 22:41:34 @stat.py:82][0m fc1/Wn:0: 1.0976
[32m[1104 22:41:34 @stat.py:82][0m fc1/Wp:0: 0.90157
[32m[1104 22:41:34 @stat.py:82][0m fct/W/rms: 0.06434
[32m[1104 22:41:34 @stat.py:82][0m learning_rate: 0.0001
[32m[1104 22:41:34 @stat.py:82][0m train-error-top1: 0.43044
[32m[1104 22:41:34 @stat.py:82][0m train-error-top5: 0.20702
[32m[1104 22:41:34 @stat.py:82][0m val-error-top1: 0.51442
[32m[1104 22:41:34 @stat.py:82][0m val-error-top5: 0.27076
[32m[1104 22:41:34 @stat.py:82][0m validation_cost: 2.4324
[32m[1104 22:41:34 @group.py:40][0m Callbacks took 541.894 sec in total. InferenceRunner: 536.046sec
[32m[1104 22:41:34 @timer.py:46][0m Epoch 17 (global_step 170000) finished, time:2453.17sec.
[32m[1104 23:22:43 @stat.py:82][0m AddN: 0.052874
[32m[1104 23:22:43 @stat.py:82][0m conv0/W/rms: 0.041695
[32m[1104 23:22:43 @stat.py:82][0m conv1/W/rms: 0.073771
[32m[1104 23:22:43 @stat.py:82][0m conv1/Wn:0: 1.2184
[32m[1104 23:22:43 @stat.py:82][0m conv1/Wp:0: 0.78261
[32m[1104 23:22:43 @stat.py:82][0m conv2/W/rms: 0.054704
[32m[1104 23:22:43 @stat.py:82][0m conv2/Wn:0: 1.0758
[32m[1104 23:22:43 @stat.py:82][0m conv2/Wp:0: 0.92575
[32m[1104 23:22:43 @stat.py:82][0m conv3/W/rms: 0.053575
[32m[1104 23:22:43 @stat.py:82][0m conv3/Wn:0: 1.0398
[32m[1104 23:22:43 @stat.py:82][0m conv3/Wp:0: 0.96182
[32m[1104 23:22:43 @stat.py:82][0m conv4/W/rms: 0.061684
[32m[1104 23:22:43 @stat.py:82][0m conv4/Wn:0: 1.1334
[32m[1104 23:22:43 @stat.py:82][0m conv4/Wp:0: 0.86741
[32m[1104 23:22:43 @stat.py:82][0m cost: 1.9416
[32m[1104 23:22:43 @stat.py:82][0m cross_entropy_loss: 1.8888
[32m[1104 23:22:43 @stat.py:82][0m fc0/W/rms: 0.0062646
[32m[1104 23:22:43 @stat.py:82][0m fc0/Wn:0: 1.1006
[32m[1104 23:22:43 @stat.py:82][0m fc0/Wp:0: 0.90034
[32m[1104 23:22:43 @stat.py:82][0m fc1/W/rms: 0.0094027
[32m[1104 23:22:43 @stat.py:82][0m fc1/Wn:0: 1.095
[32m[1104 23:22:43 @stat.py:82][0m fc1/Wp:0: 0.90416
[32m[1104 23:22:43 @stat.py:82][0m fct/W/rms: 0.06665
[32m[1104 23:22:43 @stat.py:82][0m learning_rate: 0.0001
[32m[1104 23:22:43 @stat.py:82][0m train-error-top1: 0.4389
[32m[1104 23:22:43 @stat.py:82][0m train-error-top5: 0.20655
[32m[1104 23:22:43 @stat.py:82][0m val-error-top1: 0.5081
[32m[1104 23:22:43 @stat.py:82][0m val-error-top5: 0.26798
[32m[1104 23:22:43 @stat.py:82][0m validation_cost: 2.4191
[32m[1104 23:22:43 @group.py:40][0m Callbacks took 555.697 sec in total. InferenceRunner: 548.595sec
[32m[1104 23:22:43 @timer.py:46][0m Epoch 18 (global_step 180000) finished, time:2469.43sec.
[32m[1105 00:04:19 @stat.py:82][0m AddN: 0.056342
[32m[1105 00:04:19 @stat.py:82][0m conv0/W/rms: 0.042745
[32m[1105 00:04:19 @stat.py:82][0m conv1/W/rms: 0.076639
[32m[1105 00:04:19 @stat.py:82][0m conv1/Wn:0: 1.2259
[32m[1105 00:04:19 @stat.py:82][0m conv1/Wp:0: 0.77518
[32m[1105 00:04:19 @stat.py:82][0m conv2/W/rms: 0.056507
[32m[1105 00:04:19 @stat.py:82][0m conv2/Wn:0: 1.0774
[32m[1105 00:04:19 @stat.py:82][0m conv2/Wp:0: 0.9243
[32m[1105 00:04:19 @stat.py:82][0m conv3/W/rms: 0.055284
[32m[1105 00:04:19 @stat.py:82][0m conv3/Wn:0: 1.0428
[32m[1105 00:04:19 @stat.py:82][0m conv3/Wp:0: 0.95887
[32m[1105 00:04:19 @stat.py:82][0m conv4/W/rms: 0.063718
[32m[1105 00:04:19 @stat.py:82][0m conv4/Wn:0: 1.1361
[32m[1105 00:04:19 @stat.py:82][0m conv4/Wp:0: 0.86478
[32m[1105 00:04:19 @stat.py:82][0m cost: 1.9341
[32m[1105 00:04:19 @stat.py:82][0m cross_entropy_loss: 1.8777
[32m[1105 00:04:19 @stat.py:82][0m fc0/W/rms: 0.0063555
[32m[1105 00:04:19 @stat.py:82][0m fc0/Wn:0: 1.1029
[32m[1105 00:04:19 @stat.py:82][0m fc0/Wp:0: 0.89813
[32m[1105 00:04:19 @stat.py:82][0m fc1/W/rms: 0.0096275
[32m[1105 00:04:19 @stat.py:82][0m fc1/Wn:0: 1.0971
[32m[1105 00:04:19 @stat.py:82][0m fc1/Wp:0: 0.90205
[32m[1105 00:04:19 @stat.py:82][0m fct/W/rms: 0.06894
[32m[1105 00:04:19 @stat.py:82][0m learning_rate: 0.0001
[32m[1105 00:04:19 @stat.py:82][0m train-error-top1: 0.44567
[32m[1105 00:04:19 @stat.py:82][0m train-error-top5: 0.20702
[32m[1105 00:04:19 @stat.py:82][0m val-error-top1: 0.5105
[32m[1105 00:04:19 @stat.py:82][0m val-error-top5: 0.26708
[32m[1105 00:04:19 @stat.py:82][0m validation_cost: 2.4169
[32m[1105 00:04:19 @group.py:40][0m Callbacks took 579.476 sec in total. InferenceRunner: 572.484sec
[32m[1105 00:04:19 @timer.py:46][0m Epoch 19 (global_step 190000) finished, time:2495.57sec.
[32m[1105 00:46:03 @stat.py:82][0m AddN: 0.059905
[32m[1105 00:46:03 @stat.py:82][0m conv0/W/rms: 0.043767
[32m[1105 00:46:03 @stat.py:82][0m conv1/W/rms: 0.079482
[32m[1105 00:46:03 @stat.py:82][0m conv1/Wn:0: 1.2333
[32m[1105 00:46:03 @stat.py:82][0m conv1/Wp:0: 0.76784
[32m[1105 00:46:03 @stat.py:82][0m conv2/W/rms: 0.058301
[32m[1105 00:46:03 @stat.py:82][0m conv2/Wn:0: 1.0782
[32m[1105 00:46:03 @stat.py:82][0m conv2/Wp:0: 0.92358
[32m[1105 00:46:03 @stat.py:82][0m conv3/W/rms: 0.056963
[32m[1105 00:46:03 @stat.py:82][0m conv3/Wn:0: 1.0434
[32m[1105 00:46:03 @stat.py:82][0m conv3/Wp:0: 0.95833
[32m[1105 00:46:03 @stat.py:82][0m conv4/W/rms: 0.065753
[32m[1105 00:46:03 @stat.py:82][0m conv4/Wn:0: 1.1362
[32m[1105 00:46:03 @stat.py:82][0m conv4/Wp:0: 0.86472
[32m[1105 00:46:03 @stat.py:82][0m cost: 1.8951
[32m[1105 00:46:03 @stat.py:82][0m cross_entropy_loss: 1.8352
[32m[1105 00:46:03 @stat.py:82][0m fc0/W/rms: 0.0064413
[32m[1105 00:46:03 @stat.py:82][0m fc0/Wn:0: 1.1056
[32m[1105 00:46:03 @stat.py:82][0m fc0/Wp:0: 0.89551
[32m[1105 00:46:03 @stat.py:82][0m fc1/W/rms: 0.0098669
[32m[1105 00:46:03 @stat.py:82][0m fc1/Wn:0: 1.0958
[32m[1105 00:46:03 @stat.py:82][0m fc1/Wp:0: 0.90335
[32m[1105 00:46:03 @stat.py:82][0m fct/W/rms: 0.071214
[32m[1105 00:46:03 @stat.py:82][0m learning_rate: 0.0001
[32m[1105 00:46:03 @stat.py:82][0m train-error-top1: 0.42909
[32m[1105 00:46:03 @stat.py:82][0m train-error-top5: 0.20035
[32m[1105 00:46:03 @stat.py:82][0m val-error-top1: 0.4991
[32m[1105 00:46:03 @stat.py:82][0m val-error-top5: 0.2599
[32m[1105 00:46:03 @stat.py:82][0m validation_cost: 2.3818
[32m[1105 00:46:03 @group.py:40][0m Callbacks took 592.999 sec in total. InferenceRunner: 587.464sec
[32m[1105 00:46:03 @timer.py:46][0m Epoch 20 (global_step 200000) finished, time:2504.76sec.
[32m[1105 01:27:51 @stat.py:82][0m AddN: 0.063469
[32m[1105 01:27:51 @stat.py:82][0m conv0/W/rms: 0.044775
[32m[1105 01:27:51 @stat.py:82][0m conv1/W/rms: 0.082317
[32m[1105 01:27:51 @stat.py:82][0m conv1/Wn:0: 1.2402
[32m[1105 01:27:51 @stat.py:82][0m conv1/Wp:0: 0.76109
[32m[1105 01:27:51 @stat.py:82][0m conv2/W/rms: 0.060073
[32m[1105 01:27:51 @stat.py:82][0m conv2/Wn:0: 1.0809
[32m[1105 01:27:51 @stat.py:82][0m conv2/Wp:0: 0.92093
[32m[1105 01:27:51 @stat.py:82][0m conv3/W/rms: 0.058631
[32m[1105 01:27:51 @stat.py:82][0m conv3/Wn:0: 1.0454
[32m[1105 01:27:51 @stat.py:82][0m conv3/Wp:0: 0.95642
[32m[1105 01:27:51 @stat.py:82][0m conv4/W/rms: 0.067767
[32m[1105 01:27:51 @stat.py:82][0m conv4/Wn:0: 1.1331
[32m[1105 01:27:51 @stat.py:82][0m conv4/Wp:0: 0.86793
[32m[1105 01:27:51 @stat.py:82][0m cost: 1.9102
[32m[1105 01:27:51 @stat.py:82][0m cross_entropy_loss: 1.8468
[32m[1105 01:27:51 @stat.py:82][0m fc0/W/rms: 0.0065003
[32m[1105 01:27:51 @stat.py:82][0m fc0/Wn:0: 1.1097
[32m[1105 01:27:51 @stat.py:82][0m fc0/Wp:0: 0.89141
[32m[1105 01:27:51 @stat.py:82][0m fc1/W/rms: 0.010066
[32m[1105 01:27:51 @stat.py:82][0m fc1/Wn:0: 1.0955
[32m[1105 01:27:51 @stat.py:82][0m fc1/Wp:0: 0.90357
[32m[1105 01:27:51 @stat.py:82][0m fct/W/rms: 0.073461
[32m[1105 01:27:51 @stat.py:82][0m learning_rate: 0.0001
[32m[1105 01:27:51 @stat.py:82][0m train-error-top1: 0.42931
[32m[1105 01:27:51 @stat.py:82][0m train-error-top5: 0.20122
[32m[1105 01:27:51 @stat.py:82][0m val-error-top1: 0.50382
[32m[1105 01:27:51 @stat.py:82][0m val-error-top5: 0.26096
[32m[1105 01:27:51 @stat.py:82][0m validation_cost: 2.415
[32m[1105 01:27:51 @group.py:40][0m Callbacks took 595.320 sec in total. InferenceRunner: 589.559sec
[32m[1105 01:27:51 @timer.py:46][0m Epoch 21 (global_step 210000) finished, time:2507.80sec.
[32m[1105 02:09:02 @stat.py:82][0m AddN: 0.067216
[32m[1105 02:09:02 @stat.py:82][0m conv0/W/rms: 0.045765
[32m[1105 02:09:02 @stat.py:82][0m conv1/W/rms: 0.08512
[32m[1105 02:09:02 @stat.py:82][0m conv1/Wn:0: 1.2454
[32m[1105 02:09:02 @stat.py:82][0m conv1/Wp:0: 0.7559
[32m[1105 02:09:02 @stat.py:82][0m conv2/W/rms: 0.061832
[32m[1105 02:09:02 @stat.py:82][0m conv2/Wn:0: 1.0825
[32m[1105 02:09:02 @stat.py:82][0m conv2/Wp:0: 0.91943
[32m[1105 02:09:02 @stat.py:82][0m conv3/W/rms: 0.060282
[32m[1105 02:09:02 @stat.py:82][0m conv3/Wn:0: 1.0487
[32m[1105 02:09:02 @stat.py:82][0m conv3/Wp:0: 0.95323
[32m[1105 02:09:02 @stat.py:82][0m conv4/W/rms: 0.069762
[32m[1105 02:09:02 @stat.py:82][0m conv4/Wn:0: 1.1335
[32m[1105 02:09:02 @stat.py:82][0m conv4/Wp:0: 0.86768
[32m[1105 02:09:02 @stat.py:82][0m cost: 1.8484
[32m[1105 02:09:02 @stat.py:82][0m cross_entropy_loss: 1.7812
[32m[1105 02:09:02 @stat.py:82][0m fc0/W/rms: 0.0066157
[32m[1105 02:09:02 @stat.py:82][0m fc0/Wn:0: 1.1126
[32m[1105 02:09:02 @stat.py:82][0m fc0/Wp:0: 0.88864
[32m[1105 02:09:02 @stat.py:82][0m fc1/W/rms: 0.010315
[32m[1105 02:09:02 @stat.py:82][0m fc1/Wn:0: 1.0968
[32m[1105 02:09:02 @stat.py:82][0m fc1/Wp:0: 0.9023
[32m[1105 02:09:02 @stat.py:82][0m fct/W/rms: 0.075681
[32m[1105 02:09:02 @stat.py:82][0m learning_rate: 0.0001
[32m[1105 02:09:02 @stat.py:82][0m train-error-top1: 0.42022
[32m[1105 02:09:02 @stat.py:82][0m train-error-top5: 0.19281
[32m[1105 02:09:02 @stat.py:82][0m val-error-top1: 0.49698
[32m[1105 02:09:02 @stat.py:82][0m val-error-top5: 0.25454
[32m[1105 02:09:02 @stat.py:82][0m validation_cost: 2.3823
[32m[1105 02:09:02 @group.py:40][0m Callbacks took 567.268 sec in total. InferenceRunner: 559.362sec
[32m[1105 02:09:02 @timer.py:46][0m Epoch 22 (global_step 220000) finished, time:2470.55sec.
[32m[1105 02:50:06 @stat.py:82][0m AddN: 0.071104
[32m[1105 02:50:06 @stat.py:82][0m conv0/W/rms: 0.046732
[32m[1105 02:50:06 @stat.py:82][0m conv1/W/rms: 0.087931
[32m[1105 02:50:06 @stat.py:82][0m conv1/Wn:0: 1.2511
[32m[1105 02:50:06 @stat.py:82][0m conv1/Wp:0: 0.75036
[32m[1105 02:50:06 @stat.py:82][0m conv2/W/rms: 0.063574
[32m[1105 02:50:06 @stat.py:82][0m conv2/Wn:0: 1.0858
[32m[1105 02:50:06 @stat.py:82][0m conv2/Wp:0: 0.91618
[32m[1105 02:50:06 @stat.py:82][0m conv3/W/rms: 0.061936
[32m[1105 02:50:06 @stat.py:82][0m conv3/Wn:0: 1.049
[32m[1105 02:50:06 @stat.py:82][0m conv3/Wp:0: 0.95296
[32m[1105 02:50:06 @stat.py:82][0m conv4/W/rms: 0.071765
[32m[1105 02:50:06 @stat.py:82][0m conv4/Wn:0: 1.1338
[32m[1105 02:50:06 @stat.py:82][0m conv4/Wp:0: 0.86747
[32m[1105 02:50:06 @stat.py:82][0m cost: 1.8658
[32m[1105 02:50:06 @stat.py:82][0m cross_entropy_loss: 1.7947
[32m[1105 02:50:06 @stat.py:82][0m fc0/W/rms: 0.0067573
[32m[1105 02:50:06 @stat.py:82][0m fc0/Wn:0: 1.1161
[32m[1105 02:50:06 @stat.py:82][0m fc0/Wp:0: 0.88512
[32m[1105 02:50:06 @stat.py:82][0m fc1/W/rms: 0.010603
[32m[1105 02:50:06 @stat.py:82][0m fc1/Wn:0: 1.0986
[32m[1105 02:50:06 @stat.py:82][0m fc1/Wp:0: 0.9004
[32m[1105 02:50:06 @stat.py:82][0m fct/W/rms: 0.077879
[32m[1105 02:50:06 @stat.py:82][0m learning_rate: 0.0001
[32m[1105 02:50:06 @stat.py:82][0m train-error-top1: 0.41721
[32m[1105 02:50:06 @stat.py:82][0m train-error-top5: 0.19569
[32m[1105 02:50:06 @stat.py:82][0m val-error-top1: 0.49952
[32m[1105 02:50:06 @stat.py:82][0m val-error-top5: 0.25978
[32m[1105 02:50:06 @stat.py:82][0m validation_cost: 2.3976
[32m[1105 02:50:06 @group.py:40][0m Callbacks took 568.501 sec in total. InferenceRunner: 559.860sec
[32m[1105 02:50:06 @timer.py:46][0m Epoch 23 (global_step 230000) finished, time:2464.11sec.
[32m[1105 03:31:20 @stat.py:82][0m AddN: 0.074966
[32m[1105 03:31:20 @stat.py:82][0m conv0/W/rms: 0.047681
[32m[1105 03:31:20 @stat.py:82][0m conv1/W/rms: 0.09073
[32m[1105 03:31:20 @stat.py:82][0m conv1/Wn:0: 1.2551
[32m[1105 03:31:20 @stat.py:82][0m conv1/Wp:0: 0.74644
[32m[1105 03:31:20 @stat.py:82][0m conv2/W/rms: 0.065326
[32m[1105 03:31:20 @stat.py:82][0m conv2/Wn:0: 1.0862
[32m[1105 03:31:20 @stat.py:82][0m conv2/Wp:0: 0.91596
[32m[1105 03:31:20 @stat.py:82][0m conv3/W/rms: 0.063561
[32m[1105 03:31:20 @stat.py:82][0m conv3/Wn:0: 1.0492
[32m[1105 03:31:20 @stat.py:82][0m conv3/Wp:0: 0.95288
[32m[1105 03:31:20 @stat.py:82][0m conv4/W/rms: 0.073739
[32m[1105 03:31:20 @stat.py:82][0m conv4/Wn:0: 1.1364
[32m[1105 03:31:20 @stat.py:82][0m conv4/Wp:0: 0.86497
[32m[1105 03:31:20 @stat.py:82][0m cost: 1.8197
[32m[1105 03:31:20 @stat.py:82][0m cross_entropy_loss: 1.7447
[32m[1105 03:31:20 @stat.py:82][0m fc0/W/rms: 0.0068734
[32m[1105 03:31:20 @stat.py:82][0m fc0/Wn:0: 1.1182
[32m[1105 03:31:20 @stat.py:82][0m fc0/Wp:0: 0.88306
[32m[1105 03:31:20 @stat.py:82][0m fc1/W/rms: 0.010833
[32m[1105 03:31:20 @stat.py:82][0m fc1/Wn:0: 1.095
[32m[1105 03:31:20 @stat.py:82][0m fc1/Wp:0: 0.90403
[32m[1105 03:31:20 @stat.py:82][0m fct/W/rms: 0.080048
[32m[1105 03:31:20 @stat.py:82][0m learning_rate: 0.0001
[32m[1105 03:31:20 @stat.py:82][0m train-error-top1: 0.41579
[32m[1105 03:31:20 @stat.py:82][0m train-error-top5: 0.18901
[32m[1105 03:31:20 @stat.py:82][0m val-error-top1: 0.50458
[32m[1105 03:31:20 @stat.py:82][0m val-error-top5: 0.2618
[32m[1105 03:31:20 @stat.py:82][0m validation_cost: 2.4433
[32m[1105 03:31:20 @group.py:40][0m Callbacks took 575.993 sec in total. InferenceRunner: 568.904sec
[32m[1105 03:31:20 @timer.py:46][0m Epoch 24 (global_step 240000) finished, time:2474.54sec.
[32m[1105 04:12:33 @stat.py:82][0m AddN: 0.078994
[32m[1105 04:12:33 @stat.py:82][0m conv0/W/rms: 0.048608
[32m[1105 04:12:33 @stat.py:82][0m conv1/W/rms: 0.093523
[32m[1105 04:12:33 @stat.py:82][0m conv1/Wn:0: 1.2597
[32m[1105 04:12:33 @stat.py:82][0m conv1/Wp:0: 0.74191
[32m[1105 04:12:33 @stat.py:82][0m conv2/W/rms: 0.067079
[32m[1105 04:12:33 @stat.py:82][0m conv2/Wn:0: 1.0891
[32m[1105 04:12:33 @stat.py:82][0m conv2/Wp:0: 0.91316
[32m[1105 04:12:33 @stat.py:82][0m conv3/W/rms: 0.065202
[32m[1105 04:12:33 @stat.py:82][0m conv3/Wn:0: 1.051
[32m[1105 04:12:33 @stat.py:82][0m conv3/Wp:0: 0.95123
[32m[1105 04:12:33 @stat.py:82][0m conv4/W/rms: 0.07569
[32m[1105 04:12:33 @stat.py:82][0m conv4/Wn:0: 1.1352
[32m[1105 04:12:33 @stat.py:82][0m conv4/Wp:0: 0.86619
[32m[1105 04:12:33 @stat.py:82][0m cost: 1.7895
[32m[1105 04:12:33 @stat.py:82][0m cross_entropy_loss: 1.7105
[32m[1105 04:12:33 @stat.py:82][0m fc0/W/rms: 0.0070055
[32m[1105 04:12:33 @stat.py:82][0m fc0/Wn:0: 1.1208
[32m[1105 04:12:33 @stat.py:82][0m fc0/Wp:0: 0.88058
[32m[1105 04:12:33 @stat.py:82][0m fc1/W/rms: 0.011001
[32m[1105 04:12:33 @stat.py:82][0m fc1/Wn:0: 1.0886
[32m[1105 04:12:33 @stat.py:82][0m fc1/Wp:0: 0.91039
[32m[1105 04:12:33 @stat.py:82][0m fct/W/rms: 0.082275
[32m[1105 04:12:33 @stat.py:82][0m learning_rate: 0.0001
[32m[1105 04:12:33 @stat.py:82][0m train-error-top1: 0.4033
[32m[1105 04:12:33 @stat.py:82][0m train-error-top5: 0.17732
[32m[1105 04:12:33 @stat.py:82][0m val-error-top1: 0.50054
[32m[1105 04:12:33 @stat.py:82][0m val-error-top5: 0.26248
[32m[1105 04:12:33 @stat.py:82][0m validation_cost: 2.4358
[32m[1105 04:12:33 @group.py:40][0m Callbacks took 569.389 sec in total. InferenceRunner: 561.158sec
[32m[1105 04:12:33 @timer.py:46][0m Epoch 25 (global_step 250000) finished, time:2472.61sec.
[32m[1105 04:54:04 @stat.py:82][0m AddN: 0.083026
[32m[1105 04:54:04 @stat.py:82][0m conv0/W/rms: 0.049528
[32m[1105 04:54:04 @stat.py:82][0m conv1/W/rms: 0.096284
[32m[1105 04:54:04 @stat.py:82][0m conv1/Wn:0: 1.2635
[32m[1105 04:54:04 @stat.py:82][0m conv1/Wp:0: 0.73816
[32m[1105 04:54:04 @stat.py:82][0m conv2/W/rms: 0.068816
[32m[1105 04:54:04 @stat.py:82][0m conv2/Wn:0: 1.089
[32m[1105 04:54:04 @stat.py:82][0m conv2/Wp:0: 0.91332
[32m[1105 04:54:04 @stat.py:82][0m conv3/W/rms: 0.066837
[32m[1105 04:54:04 @stat.py:82][0m conv3/Wn:0: 1.0528
[32m[1105 04:54:04 @stat.py:82][0m conv3/Wp:0: 0.94948
[32m[1105 04:54:04 @stat.py:82][0m conv4/W/rms: 0.077651
[32m[1105 04:54:04 @stat.py:82][0m conv4/Wn:0: 1.1344
[32m[1105 04:54:04 @stat.py:82][0m conv4/Wp:0: 0.86716
[32m[1105 04:54:04 @stat.py:82][0m cost: 1.7824
[32m[1105 04:54:04 @stat.py:82][0m cross_entropy_loss: 1.6994
[32m[1105 04:54:04 @stat.py:82][0m fc0/W/rms: 0.0071374
[32m[1105 04:54:04 @stat.py:82][0m fc0/Wn:0: 1.1243
[32m[1105 04:54:04 @stat.py:82][0m fc0/Wp:0: 0.8771
[32m[1105 04:54:04 @stat.py:82][0m fc1/W/rms: 0.011189
[32m[1105 04:54:04 @stat.py:82][0m fc1/Wn:0: 1.0934
[32m[1105 04:54:04 @stat.py:82][0m fc1/Wp:0: 0.90554
[32m[1105 04:54:04 @stat.py:82][0m fct/W/rms: 0.084431
[32m[1105 04:54:04 @stat.py:82][0m learning_rate: 0.0001
[32m[1105 04:54:04 @stat.py:82][0m train-error-top1: 0.39971
[32m[1105 04:54:04 @stat.py:82][0m train-error-top5: 0.17561
[32m[1105 04:54:04 @stat.py:82][0m val-error-top1: 0.496
[32m[1105 04:54:04 @stat.py:82][0m val-error-top5: 0.25544
[32m[1105 04:54:04 @stat.py:82][0m validation_cost: 2.3949
[32m[1105 04:54:04 @group.py:40][0m Callbacks took 590.565 sec in total. InferenceRunner: 582.931sec
[32m[1105 04:54:04 @timer.py:46][0m Epoch 26 (global_step 260000) finished, time:2491.24sec.
[32m[1105 05:35:04 @stat.py:82][0m AddN: 0.086959
[32m[1105 05:35:04 @stat.py:82][0m conv0/W/rms: 0.050419
[32m[1105 05:35:04 @stat.py:82][0m conv1/W/rms: 0.09904
[32m[1105 05:35:04 @stat.py:82][0m conv1/Wn:0: 1.268
[32m[1105 05:35:04 @stat.py:82][0m conv1/Wp:0: 0.73379
[32m[1105 05:35:04 @stat.py:82][0m conv2/W/rms: 0.070543
[32m[1105 05:35:04 @stat.py:82][0m conv2/Wn:0: 1.0916
[32m[1105 05:35:04 @stat.py:82][0m conv2/Wp:0: 0.91081
[32m[1105 05:35:04 @stat.py:82][0m conv3/W/rms: 0.068449
[32m[1105 05:35:04 @stat.py:82][0m conv3/Wn:0: 1.0548
[32m[1105 05:35:04 @stat.py:82][0m conv3/Wp:0: 0.94753
[32m[1105 05:35:04 @stat.py:82][0m conv4/W/rms: 0.079599
[32m[1105 05:35:04 @stat.py:82][0m conv4/Wn:0: 1.1369
[32m[1105 05:35:04 @stat.py:82][0m conv4/Wp:0: 0.86468
[32m[1105 05:35:04 @stat.py:82][0m cost: 1.7813
[32m[1105 05:35:04 @stat.py:82][0m cross_entropy_loss: 1.6943
[32m[1105 05:35:04 @stat.py:82][0m fc0/W/rms: 0.0071836
[32m[1105 05:35:04 @stat.py:82][0m fc0/Wn:0: 1.1242
[32m[1105 05:35:04 @stat.py:82][0m fc0/Wp:0: 0.87724
[32m[1105 05:35:04 @stat.py:82][0m fc1/W/rms: 0.011346
[32m[1105 05:35:04 @stat.py:82][0m fc1/Wn:0: 1.0887
[32m[1105 05:35:04 @stat.py:82][0m fc1/Wp:0: 0.91022
[32m[1105 05:35:04 @stat.py:82][0m fct/W/rms: 0.086556
[32m[1105 05:35:04 @stat.py:82][0m learning_rate: 0.0001
[32m[1105 05:35:04 @stat.py:82][0m train-error-top1: 0.40348
[32m[1105 05:35:04 @stat.py:82][0m train-error-top5: 0.18033
[32m[1105 05:35:04 @stat.py:82][0m val-error-top1: 0.4944
[32m[1105 05:35:04 @stat.py:82][0m val-error-top5: 0.2554
[32m[1105 05:35:04 @stat.py:82][0m validation_cost: 2.4035
[32m[1105 05:35:04 @group.py:40][0m Callbacks took 564.699 sec in total. InferenceRunner: 557.073sec
[32m[1105 05:35:04 @timer.py:46][0m Epoch 27 (global_step 270000) finished, time:2459.90sec.
[32m[1105 06:16:14 @stat.py:82][0m AddN: 0.090916
[32m[1105 06:16:14 @stat.py:82][0m conv0/W/rms: 0.051295
[32m[1105 06:16:14 @stat.py:82][0m conv1/W/rms: 0.10181
[32m[1105 06:16:14 @stat.py:82][0m conv1/Wn:0: 1.2712
[32m[1105 06:16:14 @stat.py:82][0m conv1/Wp:0: 0.73071
[32m[1105 06:16:14 @stat.py:82][0m conv2/W/rms: 0.072262
[32m[1105 06:16:14 @stat.py:82][0m conv2/Wn:0: 1.0933
[32m[1105 06:16:14 @stat.py:82][0m conv2/Wp:0: 0.90924
[32m[1105 06:16:14 @stat.py:82][0m conv3/W/rms: 0.070036
[32m[1105 06:16:14 @stat.py:82][0m conv3/Wn:0: 1.0569
[32m[1105 06:16:14 @stat.py:82][0m conv3/Wp:0: 0.94555
[32m[1105 06:16:14 @stat.py:82][0m conv4/W/rms: 0.08151
[32m[1105 06:16:14 @stat.py:82][0m conv4/Wn:0: 1.1374
[32m[1105 06:16:14 @stat.py:82][0m conv4/Wp:0: 0.86431
[32m[1105 06:16:14 @stat.py:82][0m cost: 1.8069
[32m[1105 06:16:14 @stat.py:82][0m cross_entropy_loss: 1.716
[32m[1105 06:16:14 @stat.py:82][0m fc0/W/rms: 0.0072361
[32m[1105 06:16:14 @stat.py:82][0m fc0/Wn:0: 1.1258
[32m[1105 06:16:14 @stat.py:82][0m fc0/Wp:0: 0.87577
[32m[1105 06:16:14 @stat.py:82][0m fc1/W/rms: 0.011462
[32m[1105 06:16:14 @stat.py:82][0m fc1/Wn:0: 1.0911
[32m[1105 06:16:14 @stat.py:82][0m fc1/Wp:0: 0.90782
[32m[1105 06:16:14 @stat.py:82][0m fct/W/rms: 0.088662
[32m[1105 06:16:14 @stat.py:82][0m learning_rate: 0.0001
[32m[1105 06:16:14 @stat.py:82][0m train-error-top1: 0.40953
[32m[1105 06:16:14 @stat.py:82][0m train-error-top5: 0.18202
[32m[1105 06:16:14 @stat.py:82][0m val-error-top1: 0.4901
[32m[1105 06:16:14 @stat.py:82][0m val-error-top5: 0.251
[32m[1105 06:16:14 @stat.py:82][0m validation_cost: 2.3807
[32m[1105 06:16:14 @group.py:40][0m Callbacks took 577.701 sec in total. InferenceRunner: 570.275sec
[32m[1105 06:16:14 @timer.py:46][0m Epoch 28 (global_step 280000) finished, time:2470.26sec.
[32m[1105 06:57:22 @stat.py:82][0m AddN: 0.094997
[32m[1105 06:57:22 @stat.py:82][0m conv0/W/rms: 0.05216
[32m[1105 06:57:22 @stat.py:82][0m conv1/W/rms: 0.10453