DIGITS-CNN/cars/lr-investigations/fixed/1e-6/caffe_output.log

I0405 08:36:15.490830 30176 upgrade_proto.cpp:1082] Attempting to upgrade input file specified using deprecated 'solver_type' field (enum)': /mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210405-083613-0f14/solver.prototxt
I0405 08:36:15.491001 30176 upgrade_proto.cpp:1089] Successfully upgraded file specified using deprecated 'solver_type' field (enum) to 'type' field (string).
W0405 08:36:15.491006 30176 upgrade_proto.cpp:1091] Note that future Caffe releases will only support 'type' field (string) for a solver's type.
I0405 08:36:15.491075 30176 caffe.cpp:218] Using GPUs 1
I0405 08:36:15.506974 30176 caffe.cpp:223] GPU 1: GeForce GTX TITAN X
I0405 08:36:15.709868 30176 solver.cpp:44] Initializing solver from parameters:
test_iter: 51
test_interval: 102
base_lr: 1e-06
display: 12
max_iter: 20400
lr_policy: "fixed"
momentum: 0.9
weight_decay: 1e-08
snapshot: 102
snapshot_prefix: "snapshot"
solver_mode: GPU
device_id: 1
net: "train_val.prototxt"
train_state {
level: 0
stage: ""
}
type: "SGD"
I0405 08:36:15.710813 30176 solver.cpp:87] Creating training net from net file: train_val.prototxt
I0405 08:36:15.711489 30176 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer val-data
I0405 08:36:15.711503 30176 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy
I0405 08:36:15.711628 30176 net.cpp:51] Initializing net from parameters:
state {
phase: TRAIN
level: 0
stage: ""
}
layer {
name: "train-data"
type: "Data"
top: "data"
top: "label"
include {
phase: TRAIN
}
transform_param {
mirror: true
crop_size: 227
mean_file: "/mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210401-115716-aaf7/mean.binaryproto"
}
data_param {
source: "/mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210401-115716-aaf7/train_db"
batch_size: 128
backend: LMDB
}
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 96
kernel_size: 11
stride: 4
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "conv1"
top: "conv1"
}
layer {
name: "norm1"
type: "LRN"
bottom: "conv1"
top: "norm1"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool1"
type: "Pooling"
bottom: "norm1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 2
kernel_size: 5
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu2"
type: "ReLU"
bottom: "conv2"
top: "conv2"
}
layer {
name: "norm2"
type: "LRN"
bottom: "conv2"
top: "norm2"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool2"
type: "Pooling"
bottom: "norm2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv3"
type: "Convolution"
bottom: "pool2"
top: "conv3"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu3"
type: "ReLU"
bottom: "conv3"
top: "conv3"
}
layer {
name: "conv4"
type: "Convolution"
bottom: "conv3"
top: "conv4"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu4"
type: "ReLU"
bottom: "conv4"
top: "conv4"
}
layer {
name: "conv5"
type: "Convolution"
bottom: "conv4"
top: "conv5"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu5"
type: "ReLU"
bottom: "conv5"
top: "conv5"
}
layer {
name: "pool5"
type: "Pooling"
bottom: "conv5"
top: "pool5"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "fc6"
type: "InnerProduct"
bottom: "pool5"
top: "fc6"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 4096
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu6"
type: "ReLU"
bottom: "fc6"
top: "fc6"
}
layer {
name: "drop6"
type: "Dropout"
bottom: "fc6"
top: "fc6"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc7"
type: "InnerProduct"
bottom: "fc6"
top: "fc7"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 4096
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu7"
type: "ReLU"
bottom: "fc7"
top: "fc7"
}
layer {
name: "drop7"
type: "Dropout"
bottom: "fc7"
top: "fc7"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc8"
type: "InnerProduct"
bottom: "fc7"
top: "fc8"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 196
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "loss"
type: "SoftmaxWithLoss"
bottom: "fc8"
bottom: "label"
top: "loss"
}
I0405 08:36:15.711707 30176 layer_factory.hpp:77] Creating layer train-data
I0405 08:36:15.714818 30176 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210401-115716-aaf7/train_db
I0405 08:36:15.715068 30176 net.cpp:84] Creating Layer train-data
I0405 08:36:15.715086 30176 net.cpp:380] train-data -> data
I0405 08:36:15.715114 30176 net.cpp:380] train-data -> label
I0405 08:36:15.715128 30176 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210401-115716-aaf7/mean.binaryproto
I0405 08:36:15.720026 30176 data_layer.cpp:45] output data size: 128,3,227,227
I0405 08:36:15.864374 30176 net.cpp:122] Setting up train-data
I0405 08:36:15.864398 30176 net.cpp:129] Top shape: 128 3 227 227 (19787136)
I0405 08:36:15.864401 30176 net.cpp:129] Top shape: 128 (128)
I0405 08:36:15.864404 30176 net.cpp:137] Memory required for data: 79149056
I0405 08:36:15.864413 30176 layer_factory.hpp:77] Creating layer conv1
I0405 08:36:15.864432 30176 net.cpp:84] Creating Layer conv1
I0405 08:36:15.864437 30176 net.cpp:406] conv1 <- data
I0405 08:36:15.864449 30176 net.cpp:380] conv1 -> conv1
I0405 08:36:16.329130 30176 net.cpp:122] Setting up conv1
I0405 08:36:16.329151 30176 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0405 08:36:16.329154 30176 net.cpp:137] Memory required for data: 227833856
I0405 08:36:16.329174 30176 layer_factory.hpp:77] Creating layer relu1
I0405 08:36:16.329202 30176 net.cpp:84] Creating Layer relu1
I0405 08:36:16.329205 30176 net.cpp:406] relu1 <- conv1
I0405 08:36:16.329210 30176 net.cpp:367] relu1 -> conv1 (in-place)
I0405 08:36:16.329502 30176 net.cpp:122] Setting up relu1
I0405 08:36:16.329511 30176 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0405 08:36:16.329514 30176 net.cpp:137] Memory required for data: 376518656
I0405 08:36:16.329516 30176 layer_factory.hpp:77] Creating layer norm1
I0405 08:36:16.329524 30176 net.cpp:84] Creating Layer norm1
I0405 08:36:16.329528 30176 net.cpp:406] norm1 <- conv1
I0405 08:36:16.329551 30176 net.cpp:380] norm1 -> norm1
I0405 08:36:16.330045 30176 net.cpp:122] Setting up norm1
I0405 08:36:16.330054 30176 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0405 08:36:16.330056 30176 net.cpp:137] Memory required for data: 525203456
I0405 08:36:16.330060 30176 layer_factory.hpp:77] Creating layer pool1
I0405 08:36:16.330067 30176 net.cpp:84] Creating Layer pool1
I0405 08:36:16.330070 30176 net.cpp:406] pool1 <- norm1
I0405 08:36:16.330075 30176 net.cpp:380] pool1 -> pool1
I0405 08:36:16.330108 30176 net.cpp:122] Setting up pool1
I0405 08:36:16.330113 30176 net.cpp:129] Top shape: 128 96 27 27 (8957952)
I0405 08:36:16.330116 30176 net.cpp:137] Memory required for data: 561035264
I0405 08:36:16.330117 30176 layer_factory.hpp:77] Creating layer conv2
I0405 08:36:16.330127 30176 net.cpp:84] Creating Layer conv2
I0405 08:36:16.330130 30176 net.cpp:406] conv2 <- pool1
I0405 08:36:16.330135 30176 net.cpp:380] conv2 -> conv2
I0405 08:36:16.336133 30176 net.cpp:122] Setting up conv2
I0405 08:36:16.336154 30176 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0405 08:36:16.336158 30176 net.cpp:137] Memory required for data: 656586752
I0405 08:36:16.336169 30176 layer_factory.hpp:77] Creating layer relu2
I0405 08:36:16.336175 30176 net.cpp:84] Creating Layer relu2
I0405 08:36:16.336181 30176 net.cpp:406] relu2 <- conv2
I0405 08:36:16.336186 30176 net.cpp:367] relu2 -> conv2 (in-place)
I0405 08:36:16.336656 30176 net.cpp:122] Setting up relu2
I0405 08:36:16.336664 30176 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0405 08:36:16.336666 30176 net.cpp:137] Memory required for data: 752138240
I0405 08:36:16.336669 30176 layer_factory.hpp:77] Creating layer norm2
I0405 08:36:16.336676 30176 net.cpp:84] Creating Layer norm2
I0405 08:36:16.336679 30176 net.cpp:406] norm2 <- conv2
I0405 08:36:16.336683 30176 net.cpp:380] norm2 -> norm2
I0405 08:36:16.337020 30176 net.cpp:122] Setting up norm2
I0405 08:36:16.337028 30176 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0405 08:36:16.337030 30176 net.cpp:137] Memory required for data: 847689728
I0405 08:36:16.337033 30176 layer_factory.hpp:77] Creating layer pool2
I0405 08:36:16.337040 30176 net.cpp:84] Creating Layer pool2
I0405 08:36:16.337044 30176 net.cpp:406] pool2 <- norm2
I0405 08:36:16.337047 30176 net.cpp:380] pool2 -> pool2
I0405 08:36:16.337074 30176 net.cpp:122] Setting up pool2
I0405 08:36:16.337077 30176 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0405 08:36:16.337080 30176 net.cpp:137] Memory required for data: 869840896
I0405 08:36:16.337081 30176 layer_factory.hpp:77] Creating layer conv3
I0405 08:36:16.337092 30176 net.cpp:84] Creating Layer conv3
I0405 08:36:16.337095 30176 net.cpp:406] conv3 <- pool2
I0405 08:36:16.337098 30176 net.cpp:380] conv3 -> conv3
I0405 08:36:16.347095 30176 net.cpp:122] Setting up conv3
I0405 08:36:16.347112 30176 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0405 08:36:16.347115 30176 net.cpp:137] Memory required for data: 903067648
I0405 08:36:16.347127 30176 layer_factory.hpp:77] Creating layer relu3
I0405 08:36:16.347136 30176 net.cpp:84] Creating Layer relu3
I0405 08:36:16.347138 30176 net.cpp:406] relu3 <- conv3
I0405 08:36:16.347143 30176 net.cpp:367] relu3 -> conv3 (in-place)
I0405 08:36:16.347621 30176 net.cpp:122] Setting up relu3
I0405 08:36:16.347630 30176 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0405 08:36:16.347632 30176 net.cpp:137] Memory required for data: 936294400
I0405 08:36:16.347635 30176 layer_factory.hpp:77] Creating layer conv4
I0405 08:36:16.347645 30176 net.cpp:84] Creating Layer conv4
I0405 08:36:16.347648 30176 net.cpp:406] conv4 <- conv3
I0405 08:36:16.347653 30176 net.cpp:380] conv4 -> conv4
I0405 08:36:16.357033 30176 net.cpp:122] Setting up conv4
I0405 08:36:16.357049 30176 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0405 08:36:16.357053 30176 net.cpp:137] Memory required for data: 969521152
I0405 08:36:16.357061 30176 layer_factory.hpp:77] Creating layer relu4
I0405 08:36:16.357070 30176 net.cpp:84] Creating Layer relu4
I0405 08:36:16.357074 30176 net.cpp:406] relu4 <- conv4
I0405 08:36:16.357095 30176 net.cpp:367] relu4 -> conv4 (in-place)
I0405 08:36:16.357411 30176 net.cpp:122] Setting up relu4
I0405 08:36:16.357419 30176 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0405 08:36:16.357421 30176 net.cpp:137] Memory required for data: 1002747904
I0405 08:36:16.357424 30176 layer_factory.hpp:77] Creating layer conv5
I0405 08:36:16.357434 30176 net.cpp:84] Creating Layer conv5
I0405 08:36:16.357435 30176 net.cpp:406] conv5 <- conv4
I0405 08:36:16.357441 30176 net.cpp:380] conv5 -> conv5
I0405 08:36:16.364828 30176 net.cpp:122] Setting up conv5
I0405 08:36:16.364847 30176 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0405 08:36:16.364850 30176 net.cpp:137] Memory required for data: 1024899072
I0405 08:36:16.364862 30176 layer_factory.hpp:77] Creating layer relu5
I0405 08:36:16.364871 30176 net.cpp:84] Creating Layer relu5
I0405 08:36:16.364874 30176 net.cpp:406] relu5 <- conv5
I0405 08:36:16.364879 30176 net.cpp:367] relu5 -> conv5 (in-place)
I0405 08:36:16.365423 30176 net.cpp:122] Setting up relu5
I0405 08:36:16.365432 30176 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0405 08:36:16.365434 30176 net.cpp:137] Memory required for data: 1047050240
I0405 08:36:16.365437 30176 layer_factory.hpp:77] Creating layer pool5
I0405 08:36:16.365445 30176 net.cpp:84] Creating Layer pool5
I0405 08:36:16.365448 30176 net.cpp:406] pool5 <- conv5
I0405 08:36:16.365453 30176 net.cpp:380] pool5 -> pool5
I0405 08:36:16.365487 30176 net.cpp:122] Setting up pool5
I0405 08:36:16.365491 30176 net.cpp:129] Top shape: 128 256 6 6 (1179648)
I0405 08:36:16.365494 30176 net.cpp:137] Memory required for data: 1051768832
I0405 08:36:16.365496 30176 layer_factory.hpp:77] Creating layer fc6
I0405 08:36:16.365504 30176 net.cpp:84] Creating Layer fc6
I0405 08:36:16.365507 30176 net.cpp:406] fc6 <- pool5
I0405 08:36:16.365512 30176 net.cpp:380] fc6 -> fc6
I0405 08:36:16.702841 30176 net.cpp:122] Setting up fc6
I0405 08:36:16.702859 30176 net.cpp:129] Top shape: 128 4096 (524288)
I0405 08:36:16.702862 30176 net.cpp:137] Memory required for data: 1053865984
I0405 08:36:16.702870 30176 layer_factory.hpp:77] Creating layer relu6
I0405 08:36:16.702878 30176 net.cpp:84] Creating Layer relu6
I0405 08:36:16.702881 30176 net.cpp:406] relu6 <- fc6
I0405 08:36:16.702888 30176 net.cpp:367] relu6 -> fc6 (in-place)
I0405 08:36:16.703572 30176 net.cpp:122] Setting up relu6
I0405 08:36:16.703581 30176 net.cpp:129] Top shape: 128 4096 (524288)
I0405 08:36:16.703583 30176 net.cpp:137] Memory required for data: 1055963136
I0405 08:36:16.703586 30176 layer_factory.hpp:77] Creating layer drop6
I0405 08:36:16.703593 30176 net.cpp:84] Creating Layer drop6
I0405 08:36:16.703596 30176 net.cpp:406] drop6 <- fc6
I0405 08:36:16.703599 30176 net.cpp:367] drop6 -> fc6 (in-place)
I0405 08:36:16.703625 30176 net.cpp:122] Setting up drop6
I0405 08:36:16.703629 30176 net.cpp:129] Top shape: 128 4096 (524288)
I0405 08:36:16.703631 30176 net.cpp:137] Memory required for data: 1058060288
I0405 08:36:16.703634 30176 layer_factory.hpp:77] Creating layer fc7
I0405 08:36:16.703639 30176 net.cpp:84] Creating Layer fc7
I0405 08:36:16.703641 30176 net.cpp:406] fc7 <- fc6
I0405 08:36:16.703646 30176 net.cpp:380] fc7 -> fc7
I0405 08:36:16.851491 30176 net.cpp:122] Setting up fc7
I0405 08:36:16.851509 30176 net.cpp:129] Top shape: 128 4096 (524288)
I0405 08:36:16.851512 30176 net.cpp:137] Memory required for data: 1060157440
I0405 08:36:16.851521 30176 layer_factory.hpp:77] Creating layer relu7
I0405 08:36:16.851528 30176 net.cpp:84] Creating Layer relu7
I0405 08:36:16.851531 30176 net.cpp:406] relu7 <- fc7
I0405 08:36:16.851536 30176 net.cpp:367] relu7 -> fc7 (in-place)
I0405 08:36:16.851905 30176 net.cpp:122] Setting up relu7
I0405 08:36:16.851912 30176 net.cpp:129] Top shape: 128 4096 (524288)
I0405 08:36:16.851914 30176 net.cpp:137] Memory required for data: 1062254592
I0405 08:36:16.851917 30176 layer_factory.hpp:77] Creating layer drop7
I0405 08:36:16.851922 30176 net.cpp:84] Creating Layer drop7
I0405 08:36:16.851925 30176 net.cpp:406] drop7 <- fc7
I0405 08:36:16.851946 30176 net.cpp:367] drop7 -> fc7 (in-place)
I0405 08:36:16.851966 30176 net.cpp:122] Setting up drop7
I0405 08:36:16.851971 30176 net.cpp:129] Top shape: 128 4096 (524288)
I0405 08:36:16.851974 30176 net.cpp:137] Memory required for data: 1064351744
I0405 08:36:16.851975 30176 layer_factory.hpp:77] Creating layer fc8
I0405 08:36:16.851982 30176 net.cpp:84] Creating Layer fc8
I0405 08:36:16.851984 30176 net.cpp:406] fc8 <- fc7
I0405 08:36:16.851989 30176 net.cpp:380] fc8 -> fc8
I0405 08:36:16.859177 30176 net.cpp:122] Setting up fc8
I0405 08:36:16.859197 30176 net.cpp:129] Top shape: 128 196 (25088)
I0405 08:36:16.859200 30176 net.cpp:137] Memory required for data: 1064452096
I0405 08:36:16.859207 30176 layer_factory.hpp:77] Creating layer loss
I0405 08:36:16.859216 30176 net.cpp:84] Creating Layer loss
I0405 08:36:16.859220 30176 net.cpp:406] loss <- fc8
I0405 08:36:16.859223 30176 net.cpp:406] loss <- label
I0405 08:36:16.859228 30176 net.cpp:380] loss -> loss
I0405 08:36:16.859238 30176 layer_factory.hpp:77] Creating layer loss
I0405 08:36:16.859954 30176 net.cpp:122] Setting up loss
I0405 08:36:16.859962 30176 net.cpp:129] Top shape: (1)
I0405 08:36:16.859964 30176 net.cpp:132]     with loss weight 1
I0405 08:36:16.859987 30176 net.cpp:137] Memory required for data: 1064452100
I0405 08:36:16.859989 30176 net.cpp:198] loss needs backward computation.
I0405 08:36:16.859994 30176 net.cpp:198] fc8 needs backward computation.
I0405 08:36:16.859997 30176 net.cpp:198] drop7 needs backward computation.
I0405 08:36:16.859999 30176 net.cpp:198] relu7 needs backward computation.
I0405 08:36:16.860002 30176 net.cpp:198] fc7 needs backward computation.
I0405 08:36:16.860004 30176 net.cpp:198] drop6 needs backward computation.
I0405 08:36:16.860006 30176 net.cpp:198] relu6 needs backward computation.
I0405 08:36:16.860008 30176 net.cpp:198] fc6 needs backward computation.
I0405 08:36:16.860011 30176 net.cpp:198] pool5 needs backward computation.
I0405 08:36:16.860013 30176 net.cpp:198] relu5 needs backward computation.
I0405 08:36:16.860016 30176 net.cpp:198] conv5 needs backward computation.
I0405 08:36:16.860018 30176 net.cpp:198] relu4 needs backward computation.
I0405 08:36:16.860020 30176 net.cpp:198] conv4 needs backward computation.
I0405 08:36:16.860023 30176 net.cpp:198] relu3 needs backward computation.
I0405 08:36:16.860024 30176 net.cpp:198] conv3 needs backward computation.
I0405 08:36:16.860028 30176 net.cpp:198] pool2 needs backward computation.
I0405 08:36:16.860030 30176 net.cpp:198] norm2 needs backward computation.
I0405 08:36:16.860033 30176 net.cpp:198] relu2 needs backward computation.
I0405 08:36:16.860035 30176 net.cpp:198] conv2 needs backward computation.
I0405 08:36:16.860038 30176 net.cpp:198] pool1 needs backward computation.
I0405 08:36:16.860040 30176 net.cpp:198] norm1 needs backward computation.
I0405 08:36:16.860042 30176 net.cpp:198] relu1 needs backward computation.
I0405 08:36:16.860044 30176 net.cpp:198] conv1 needs backward computation.
I0405 08:36:16.860047 30176 net.cpp:200] train-data does not need backward computation.
I0405 08:36:16.860049 30176 net.cpp:242] This network produces output loss
I0405 08:36:16.860061 30176 net.cpp:255] Network initialization done.
I0405 08:36:16.860580 30176 solver.cpp:172] Creating test net (#0) specified by net file: train_val.prototxt
I0405 08:36:16.860607 30176 net.cpp:294] The NetState phase (1) differed from the phase (0) specified by a rule in layer train-data
I0405 08:36:16.860736 30176 net.cpp:51] Initializing net from parameters:
state {
phase: TEST
}
layer {
name: "val-data"
type: "Data"
top: "data"
top: "label"
include {
phase: TEST
}
transform_param {
crop_size: 227
mean_file: "/mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210401-115716-aaf7/mean.binaryproto"
}
data_param {
source: "/mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210401-115716-aaf7/val_db"
batch_size: 32
backend: LMDB
}
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 96
kernel_size: 11
stride: 4
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "conv1"
top: "conv1"
}
layer {
name: "norm1"
type: "LRN"
bottom: "conv1"
top: "norm1"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool1"
type: "Pooling"
bottom: "norm1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 2
kernel_size: 5
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu2"
type: "ReLU"
bottom: "conv2"
top: "conv2"
}
layer {
name: "norm2"
type: "LRN"
bottom: "conv2"
top: "norm2"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool2"
type: "Pooling"
bottom: "norm2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv3"
type: "Convolution"
bottom: "pool2"
top: "conv3"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu3"
type: "ReLU"
bottom: "conv3"
top: "conv3"
}
layer {
name: "conv4"
type: "Convolution"
bottom: "conv3"
top: "conv4"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu4"
type: "ReLU"
bottom: "conv4"
top: "conv4"
}
layer {
name: "conv5"
type: "Convolution"
bottom: "conv4"
top: "conv5"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu5"
type: "ReLU"
bottom: "conv5"
top: "conv5"
}
layer {
name: "pool5"
type: "Pooling"
bottom: "conv5"
top: "pool5"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "fc6"
type: "InnerProduct"
bottom: "pool5"
top: "fc6"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 4096
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu6"
type: "ReLU"
bottom: "fc6"
top: "fc6"
}
layer {
name: "drop6"
type: "Dropout"
bottom: "fc6"
top: "fc6"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc7"
type: "InnerProduct"
bottom: "fc6"
top: "fc7"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 4096
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu7"
type: "ReLU"
bottom: "fc7"
top: "fc7"
}
layer {
name: "drop7"
type: "Dropout"
bottom: "fc7"
top: "fc7"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc8"
type: "InnerProduct"
bottom: "fc7"
top: "fc8"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 196
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "accuracy"
type: "Accuracy"
bottom: "fc8"
bottom: "label"
top: "accuracy"
include {
phase: TEST
}
}
layer {
name: "loss"
type: "SoftmaxWithLoss"
bottom: "fc8"
bottom: "label"
top: "loss"
}
I0405 08:36:16.860833 30176 layer_factory.hpp:77] Creating layer val-data
I0405 08:36:16.863656 30176 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210401-115716-aaf7/val_db
I0405 08:36:16.863883 30176 net.cpp:84] Creating Layer val-data
I0405 08:36:16.863891 30176 net.cpp:380] val-data -> data
I0405 08:36:16.863898 30176 net.cpp:380] val-data -> label
I0405 08:36:16.863906 30176 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210401-115716-aaf7/mean.binaryproto
I0405 08:36:16.867624 30176 data_layer.cpp:45] output data size: 32,3,227,227
I0405 08:36:16.904527 30176 net.cpp:122] Setting up val-data
I0405 08:36:16.904548 30176 net.cpp:129] Top shape: 32 3 227 227 (4946784)
I0405 08:36:16.904552 30176 net.cpp:129] Top shape: 32 (32)
I0405 08:36:16.904554 30176 net.cpp:137] Memory required for data: 19787264
I0405 08:36:16.904559 30176 layer_factory.hpp:77] Creating layer label_val-data_1_split
I0405 08:36:16.904569 30176 net.cpp:84] Creating Layer label_val-data_1_split
I0405 08:36:16.904573 30176 net.cpp:406] label_val-data_1_split <- label
I0405 08:36:16.904578 30176 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_0
I0405 08:36:16.904587 30176 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_1
I0405 08:36:16.904633 30176 net.cpp:122] Setting up label_val-data_1_split
I0405 08:36:16.904636 30176 net.cpp:129] Top shape: 32 (32)
I0405 08:36:16.904639 30176 net.cpp:129] Top shape: 32 (32)
I0405 08:36:16.904641 30176 net.cpp:137] Memory required for data: 19787520
I0405 08:36:16.904644 30176 layer_factory.hpp:77] Creating layer conv1
I0405 08:36:16.904654 30176 net.cpp:84] Creating Layer conv1
I0405 08:36:16.904656 30176 net.cpp:406] conv1 <- data
I0405 08:36:16.904660 30176 net.cpp:380] conv1 -> conv1
I0405 08:36:16.907120 30176 net.cpp:122] Setting up conv1
I0405 08:36:16.907135 30176 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0405 08:36:16.907138 30176 net.cpp:137] Memory required for data: 56958720
I0405 08:36:16.907148 30176 layer_factory.hpp:77] Creating layer relu1
I0405 08:36:16.907155 30176 net.cpp:84] Creating Layer relu1
I0405 08:36:16.907158 30176 net.cpp:406] relu1 <- conv1
I0405 08:36:16.907163 30176 net.cpp:367] relu1 -> conv1 (in-place)
I0405 08:36:16.907423 30176 net.cpp:122] Setting up relu1
I0405 08:36:16.907429 30176 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0405 08:36:16.907431 30176 net.cpp:137] Memory required for data: 94129920
I0405 08:36:16.907434 30176 layer_factory.hpp:77] Creating layer norm1
I0405 08:36:16.907440 30176 net.cpp:84] Creating Layer norm1
I0405 08:36:16.907444 30176 net.cpp:406] norm1 <- conv1
I0405 08:36:16.907447 30176 net.cpp:380] norm1 -> norm1
I0405 08:36:16.907928 30176 net.cpp:122] Setting up norm1
I0405 08:36:16.907936 30176 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0405 08:36:16.907939 30176 net.cpp:137] Memory required for data: 131301120
I0405 08:36:16.907943 30176 layer_factory.hpp:77] Creating layer pool1
I0405 08:36:16.907948 30176 net.cpp:84] Creating Layer pool1
I0405 08:36:16.907950 30176 net.cpp:406] pool1 <- norm1
I0405 08:36:16.907954 30176 net.cpp:380] pool1 -> pool1
I0405 08:36:16.907981 30176 net.cpp:122] Setting up pool1
I0405 08:36:16.907986 30176 net.cpp:129] Top shape: 32 96 27 27 (2239488)
I0405 08:36:16.907989 30176 net.cpp:137] Memory required for data: 140259072
I0405 08:36:16.907990 30176 layer_factory.hpp:77] Creating layer conv2
I0405 08:36:16.907999 30176 net.cpp:84] Creating Layer conv2
I0405 08:36:16.908000 30176 net.cpp:406] conv2 <- pool1
I0405 08:36:16.908027 30176 net.cpp:380] conv2 -> conv2
I0405 08:36:16.914187 30176 net.cpp:122] Setting up conv2
I0405 08:36:16.914206 30176 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0405 08:36:16.914207 30176 net.cpp:137] Memory required for data: 164146944
I0405 08:36:16.914219 30176 layer_factory.hpp:77] Creating layer relu2
I0405 08:36:16.914227 30176 net.cpp:84] Creating Layer relu2
I0405 08:36:16.914230 30176 net.cpp:406] relu2 <- conv2
I0405 08:36:16.914237 30176 net.cpp:367] relu2 -> conv2 (in-place)
I0405 08:36:16.914783 30176 net.cpp:122] Setting up relu2
I0405 08:36:16.914790 30176 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0405 08:36:16.914793 30176 net.cpp:137] Memory required for data: 188034816
I0405 08:36:16.914795 30176 layer_factory.hpp:77] Creating layer norm2
I0405 08:36:16.914804 30176 net.cpp:84] Creating Layer norm2
I0405 08:36:16.914808 30176 net.cpp:406] norm2 <- conv2
I0405 08:36:16.914811 30176 net.cpp:380] norm2 -> norm2
I0405 08:36:16.915302 30176 net.cpp:122] Setting up norm2
I0405 08:36:16.915311 30176 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0405 08:36:16.915313 30176 net.cpp:137] Memory required for data: 211922688
I0405 08:36:16.915316 30176 layer_factory.hpp:77] Creating layer pool2
I0405 08:36:16.915323 30176 net.cpp:84] Creating Layer pool2
I0405 08:36:16.915325 30176 net.cpp:406] pool2 <- norm2
I0405 08:36:16.915329 30176 net.cpp:380] pool2 -> pool2
I0405 08:36:16.915359 30176 net.cpp:122] Setting up pool2
I0405 08:36:16.915362 30176 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0405 08:36:16.915364 30176 net.cpp:137] Memory required for data: 217460480
I0405 08:36:16.915366 30176 layer_factory.hpp:77] Creating layer conv3
I0405 08:36:16.915375 30176 net.cpp:84] Creating Layer conv3
I0405 08:36:16.915378 30176 net.cpp:406] conv3 <- pool2
I0405 08:36:16.915382 30176 net.cpp:380] conv3 -> conv3
I0405 08:36:16.925333 30176 net.cpp:122] Setting up conv3
I0405 08:36:16.925352 30176 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0405 08:36:16.925354 30176 net.cpp:137] Memory required for data: 225767168
I0405 08:36:16.925366 30176 layer_factory.hpp:77] Creating layer relu3
I0405 08:36:16.925374 30176 net.cpp:84] Creating Layer relu3
I0405 08:36:16.925377 30176 net.cpp:406] relu3 <- conv3
I0405 08:36:16.925384 30176 net.cpp:367] relu3 -> conv3 (in-place)
I0405 08:36:16.925988 30176 net.cpp:122] Setting up relu3
I0405 08:36:16.925999 30176 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0405 08:36:16.926002 30176 net.cpp:137] Memory required for data: 234073856
I0405 08:36:16.926004 30176 layer_factory.hpp:77] Creating layer conv4
I0405 08:36:16.926015 30176 net.cpp:84] Creating Layer conv4
I0405 08:36:16.926018 30176 net.cpp:406] conv4 <- conv3
I0405 08:36:16.926024 30176 net.cpp:380] conv4 -> conv4
I0405 08:36:16.937461 30176 net.cpp:122] Setting up conv4
I0405 08:36:16.937479 30176 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0405 08:36:16.937481 30176 net.cpp:137] Memory required for data: 242380544
I0405 08:36:16.937490 30176 layer_factory.hpp:77] Creating layer relu4
I0405 08:36:16.937500 30176 net.cpp:84] Creating Layer relu4
I0405 08:36:16.937502 30176 net.cpp:406] relu4 <- conv4
I0405 08:36:16.937507 30176 net.cpp:367] relu4 -> conv4 (in-place)
I0405 08:36:16.937829 30176 net.cpp:122] Setting up relu4
I0405 08:36:16.937835 30176 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0405 08:36:16.937837 30176 net.cpp:137] Memory required for data: 250687232
I0405 08:36:16.937840 30176 layer_factory.hpp:77] Creating layer conv5
I0405 08:36:16.937850 30176 net.cpp:84] Creating Layer conv5
I0405 08:36:16.937853 30176 net.cpp:406] conv5 <- conv4
I0405 08:36:16.937858 30176 net.cpp:380] conv5 -> conv5
I0405 08:36:16.953004 30176 net.cpp:122] Setting up conv5
I0405 08:36:16.953025 30176 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0405 08:36:16.953028 30176 net.cpp:137] Memory required for data: 256225024
I0405 08:36:16.953040 30176 layer_factory.hpp:77] Creating layer relu5
I0405 08:36:16.953052 30176 net.cpp:84] Creating Layer relu5
I0405 08:36:16.953055 30176 net.cpp:406] relu5 <- conv5
I0405 08:36:16.953076 30176 net.cpp:367] relu5 -> conv5 (in-place)
I0405 08:36:16.954473 30176 net.cpp:122] Setting up relu5
I0405 08:36:16.954484 30176 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0405 08:36:16.954490 30176 net.cpp:137] Memory required for data: 261762816
I0405 08:36:16.954494 30176 layer_factory.hpp:77] Creating layer pool5
I0405 08:36:16.954509 30176 net.cpp:84] Creating Layer pool5
I0405 08:36:16.954514 30176 net.cpp:406] pool5 <- conv5
I0405 08:36:16.954521 30176 net.cpp:380] pool5 -> pool5
I0405 08:36:16.954566 30176 net.cpp:122] Setting up pool5
I0405 08:36:16.954571 30176 net.cpp:129] Top shape: 32 256 6 6 (294912)
I0405 08:36:16.954573 30176 net.cpp:137] Memory required for data: 262942464
I0405 08:36:16.954576 30176 layer_factory.hpp:77] Creating layer fc6
I0405 08:36:16.954582 30176 net.cpp:84] Creating Layer fc6
I0405 08:36:16.954584 30176 net.cpp:406] fc6 <- pool5
I0405 08:36:16.954589 30176 net.cpp:380] fc6 -> fc6
I0405 08:36:17.289238 30176 net.cpp:122] Setting up fc6
I0405 08:36:17.289260 30176 net.cpp:129] Top shape: 32 4096 (131072)
I0405 08:36:17.289263 30176 net.cpp:137] Memory required for data: 263466752
I0405 08:36:17.289286 30176 layer_factory.hpp:77] Creating layer relu6
I0405 08:36:17.289294 30176 net.cpp:84] Creating Layer relu6
I0405 08:36:17.289297 30176 net.cpp:406] relu6 <- fc6
I0405 08:36:17.289302 30176 net.cpp:367] relu6 -> fc6 (in-place)
I0405 08:36:17.289986 30176 net.cpp:122] Setting up relu6
I0405 08:36:17.289996 30176 net.cpp:129] Top shape: 32 4096 (131072)
I0405 08:36:17.289999 30176 net.cpp:137] Memory required for data: 263991040
I0405 08:36:17.290001 30176 layer_factory.hpp:77] Creating layer drop6
I0405 08:36:17.290007 30176 net.cpp:84] Creating Layer drop6
I0405 08:36:17.290009 30176 net.cpp:406] drop6 <- fc6
I0405 08:36:17.290014 30176 net.cpp:367] drop6 -> fc6 (in-place)
I0405 08:36:17.290036 30176 net.cpp:122] Setting up drop6
I0405 08:36:17.290040 30176 net.cpp:129] Top shape: 32 4096 (131072)
I0405 08:36:17.290042 30176 net.cpp:137] Memory required for data: 264515328
I0405 08:36:17.290045 30176 layer_factory.hpp:77] Creating layer fc7
I0405 08:36:17.290051 30176 net.cpp:84] Creating Layer fc7
I0405 08:36:17.290053 30176 net.cpp:406] fc7 <- fc6
I0405 08:36:17.290057 30176 net.cpp:380] fc7 -> fc7
I0405 08:36:17.435891 30176 net.cpp:122] Setting up fc7
I0405 08:36:17.435912 30176 net.cpp:129] Top shape: 32 4096 (131072)
I0405 08:36:17.435915 30176 net.cpp:137] Memory required for data: 265039616
I0405 08:36:17.435925 30176 layer_factory.hpp:77] Creating layer relu7
I0405 08:36:17.435931 30176 net.cpp:84] Creating Layer relu7
I0405 08:36:17.435935 30176 net.cpp:406] relu7 <- fc7
I0405 08:36:17.435941 30176 net.cpp:367] relu7 -> fc7 (in-place)
I0405 08:36:17.436331 30176 net.cpp:122] Setting up relu7
I0405 08:36:17.436337 30176 net.cpp:129] Top shape: 32 4096 (131072)
I0405 08:36:17.436339 30176 net.cpp:137] Memory required for data: 265563904
I0405 08:36:17.436342 30176 layer_factory.hpp:77] Creating layer drop7
I0405 08:36:17.436347 30176 net.cpp:84] Creating Layer drop7
I0405 08:36:17.436349 30176 net.cpp:406] drop7 <- fc7
I0405 08:36:17.436353 30176 net.cpp:367] drop7 -> fc7 (in-place)
I0405 08:36:17.436374 30176 net.cpp:122] Setting up drop7
I0405 08:36:17.436378 30176 net.cpp:129] Top shape: 32 4096 (131072)
I0405 08:36:17.436380 30176 net.cpp:137] Memory required for data: 266088192
I0405 08:36:17.436383 30176 layer_factory.hpp:77] Creating layer fc8
I0405 08:36:17.436389 30176 net.cpp:84] Creating Layer fc8
I0405 08:36:17.436391 30176 net.cpp:406] fc8 <- fc7
I0405 08:36:17.436395 30176 net.cpp:380] fc8 -> fc8
I0405 08:36:17.443671 30176 net.cpp:122] Setting up fc8
I0405 08:36:17.443686 30176 net.cpp:129] Top shape: 32 196 (6272)
I0405 08:36:17.443688 30176 net.cpp:137] Memory required for data: 266113280
I0405 08:36:17.443696 30176 layer_factory.hpp:77] Creating layer fc8_fc8_0_split
I0405 08:36:17.443701 30176 net.cpp:84] Creating Layer fc8_fc8_0_split
I0405 08:36:17.443704 30176 net.cpp:406] fc8_fc8_0_split <- fc8
I0405 08:36:17.443729 30176 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_0
I0405 08:36:17.443735 30176 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_1
I0405 08:36:17.443768 30176 net.cpp:122] Setting up fc8_fc8_0_split
I0405 08:36:17.443773 30176 net.cpp:129] Top shape: 32 196 (6272)
I0405 08:36:17.443775 30176 net.cpp:129] Top shape: 32 196 (6272)
I0405 08:36:17.443778 30176 net.cpp:137] Memory required for data: 266163456
I0405 08:36:17.443779 30176 layer_factory.hpp:77] Creating layer accuracy
I0405 08:36:17.443784 30176 net.cpp:84] Creating Layer accuracy
I0405 08:36:17.443787 30176 net.cpp:406] accuracy <- fc8_fc8_0_split_0
I0405 08:36:17.443790 30176 net.cpp:406] accuracy <- label_val-data_1_split_0
I0405 08:36:17.443794 30176 net.cpp:380] accuracy -> accuracy
I0405 08:36:17.443800 30176 net.cpp:122] Setting up accuracy
I0405 08:36:17.443804 30176 net.cpp:129] Top shape: (1)
I0405 08:36:17.443805 30176 net.cpp:137] Memory required for data: 266163460
I0405 08:36:17.443807 30176 layer_factory.hpp:77] Creating layer loss
I0405 08:36:17.443811 30176 net.cpp:84] Creating Layer loss
I0405 08:36:17.443814 30176 net.cpp:406] loss <- fc8_fc8_0_split_1
I0405 08:36:17.443816 30176 net.cpp:406] loss <- label_val-data_1_split_1
I0405 08:36:17.443819 30176 net.cpp:380] loss -> loss
I0405 08:36:17.443825 30176 layer_factory.hpp:77] Creating layer loss
I0405 08:36:17.444468 30176 net.cpp:122] Setting up loss
I0405 08:36:17.444475 30176 net.cpp:129] Top shape: (1)
I0405 08:36:17.444478 30176 net.cpp:132]     with loss weight 1
I0405 08:36:17.444487 30176 net.cpp:137] Memory required for data: 266163464
I0405 08:36:17.444489 30176 net.cpp:198] loss needs backward computation.
I0405 08:36:17.444494 30176 net.cpp:200] accuracy does not need backward computation.
I0405 08:36:17.444496 30176 net.cpp:198] fc8_fc8_0_split needs backward computation.
I0405 08:36:17.444499 30176 net.cpp:198] fc8 needs backward computation.
I0405 08:36:17.444500 30176 net.cpp:198] drop7 needs backward computation.
I0405 08:36:17.444502 30176 net.cpp:198] relu7 needs backward computation.
I0405 08:36:17.444504 30176 net.cpp:198] fc7 needs backward computation.
I0405 08:36:17.444507 30176 net.cpp:198] drop6 needs backward computation.
I0405 08:36:17.444509 30176 net.cpp:198] relu6 needs backward computation.
I0405 08:36:17.444511 30176 net.cpp:198] fc6 needs backward computation.
I0405 08:36:17.444514 30176 net.cpp:198] pool5 needs backward computation.
I0405 08:36:17.444517 30176 net.cpp:198] relu5 needs backward computation.
I0405 08:36:17.444519 30176 net.cpp:198] conv5 needs backward computation.
I0405 08:36:17.444521 30176 net.cpp:198] relu4 needs backward computation.
I0405 08:36:17.444523 30176 net.cpp:198] conv4 needs backward computation.
I0405 08:36:17.444525 30176 net.cpp:198] relu3 needs backward computation.
I0405 08:36:17.444528 30176 net.cpp:198] conv3 needs backward computation.
I0405 08:36:17.444530 30176 net.cpp:198] pool2 needs backward computation.
I0405 08:36:17.444532 30176 net.cpp:198] norm2 needs backward computation.
I0405 08:36:17.444535 30176 net.cpp:198] relu2 needs backward computation.
I0405 08:36:17.444537 30176 net.cpp:198] conv2 needs backward computation.
I0405 08:36:17.444540 30176 net.cpp:198] pool1 needs backward computation.
I0405 08:36:17.444541 30176 net.cpp:198] norm1 needs backward computation.
I0405 08:36:17.444545 30176 net.cpp:198] relu1 needs backward computation.
I0405 08:36:17.444546 30176 net.cpp:198] conv1 needs backward computation.
I0405 08:36:17.444550 30176 net.cpp:200] label_val-data_1_split does not need backward computation.
I0405 08:36:17.444551 30176 net.cpp:200] val-data does not need backward computation.
I0405 08:36:17.444553 30176 net.cpp:242] This network produces output accuracy
I0405 08:36:17.444556 30176 net.cpp:242] This network produces output loss
I0405 08:36:17.444571 30176 net.cpp:255] Network initialization done.
I0405 08:36:17.444638 30176 solver.cpp:56] Solver scaffolding done.
I0405 08:36:17.445068 30176 caffe.cpp:248] Starting Optimization
I0405 08:36:17.445076 30176 solver.cpp:272] Solving
I0405 08:36:17.445087 30176 solver.cpp:273] Learning Rate Policy: fixed
I0405 08:36:17.446707 30176 solver.cpp:330] Iteration 0, Testing net (#0)
I0405 08:36:17.446714 30176 net.cpp:676] Ignoring source layer train-data
I0405 08:36:17.554252 30176 blocking_queue.cpp:49] Waiting for data
I0405 08:36:21.656584 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 08:36:21.704236 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 08:36:21.704262 30176 solver.cpp:397]     Test net output #1: loss = 5.27987 (* 1 = 5.27987 loss)
I0405 08:36:21.851444 30176 solver.cpp:218] Iteration 0 (9.65568e+36 iter/s, 4.40628s/12 iters), loss = 5.29764
I0405 08:36:21.853040 30176 solver.cpp:237]     Train net output #0: loss = 5.29764 (* 1 = 5.29764 loss)
I0405 08:36:21.853052 30176 sgd_solver.cpp:105] Iteration 0, lr = 1e-06
I0405 08:36:25.991724 30176 solver.cpp:218] Iteration 12 (2.89951 iter/s, 4.13863s/12 iters), loss = 5.28003
I0405 08:36:25.991770 30176 solver.cpp:237]     Train net output #0: loss = 5.28003 (* 1 = 5.28003 loss)
I0405 08:36:25.991776 30176 sgd_solver.cpp:105] Iteration 12, lr = 1e-06
I0405 08:36:31.067091 30176 solver.cpp:218] Iteration 24 (2.36441 iter/s, 5.07526s/12 iters), loss = 5.29365
I0405 08:36:31.067143 30176 solver.cpp:237]     Train net output #0: loss = 5.29365 (* 1 = 5.29365 loss)
I0405 08:36:31.067152 30176 sgd_solver.cpp:105] Iteration 24, lr = 1e-06
I0405 08:36:36.353804 30176 solver.cpp:218] Iteration 36 (2.26989 iter/s, 5.2866s/12 iters), loss = 5.28544
I0405 08:36:36.353840 30176 solver.cpp:237]     Train net output #0: loss = 5.28544 (* 1 = 5.28544 loss)
I0405 08:36:36.353845 30176 sgd_solver.cpp:105] Iteration 36, lr = 1e-06
I0405 08:36:41.779834 30176 solver.cpp:218] Iteration 48 (2.2116 iter/s, 5.42593s/12 iters), loss = 5.28481
I0405 08:36:41.779875 30176 solver.cpp:237]     Train net output #0: loss = 5.28481 (* 1 = 5.28481 loss)
I0405 08:36:41.779881 30176 sgd_solver.cpp:105] Iteration 48, lr = 1e-06
I0405 08:36:47.102527 30176 solver.cpp:218] Iteration 60 (2.25454 iter/s, 5.32258s/12 iters), loss = 5.29721
I0405 08:36:47.102702 30176 solver.cpp:237]     Train net output #0: loss = 5.29721 (* 1 = 5.29721 loss)
I0405 08:36:47.102711 30176 sgd_solver.cpp:105] Iteration 60, lr = 1e-06
I0405 08:36:52.625866 30176 solver.cpp:218] Iteration 72 (2.17269 iter/s, 5.52311s/12 iters), loss = 5.28689
I0405 08:36:52.625901 30176 solver.cpp:237]     Train net output #0: loss = 5.28689 (* 1 = 5.28689 loss)
I0405 08:36:52.625906 30176 sgd_solver.cpp:105] Iteration 72, lr = 1e-06
I0405 08:36:57.967231 30176 solver.cpp:218] Iteration 84 (2.24667 iter/s, 5.34125s/12 iters), loss = 5.28701
I0405 08:36:57.967347 30176 solver.cpp:237]     Train net output #0: loss = 5.28701 (* 1 = 5.28701 loss)
I0405 08:36:57.967357 30176 sgd_solver.cpp:105] Iteration 84, lr = 1e-06
I0405 08:37:03.165882 30176 solver.cpp:218] Iteration 96 (2.30837 iter/s, 5.19848s/12 iters), loss = 5.28576
I0405 08:37:03.165941 30176 solver.cpp:237]     Train net output #0: loss = 5.28576 (* 1 = 5.28576 loss)
I0405 08:37:03.165949 30176 sgd_solver.cpp:105] Iteration 96, lr = 1e-06
I0405 08:37:05.148838 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 08:37:05.456116 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_102.caffemodel
I0405 08:37:10.721040 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_102.solverstate
I0405 08:37:14.736212 30176 solver.cpp:330] Iteration 102, Testing net (#0)
I0405 08:37:14.736234 30176 net.cpp:676] Ignoring source layer train-data
I0405 08:37:18.949496 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 08:37:19.027282 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 08:37:19.027314 30176 solver.cpp:397]     Test net output #1: loss = 5.2804 (* 1 = 5.2804 loss)
I0405 08:37:20.976487 30176 solver.cpp:218] Iteration 108 (0.673765 iter/s, 17.8104s/12 iters), loss = 5.26655
I0405 08:37:20.976545 30176 solver.cpp:237]     Train net output #0: loss = 5.26655 (* 1 = 5.26655 loss)
I0405 08:37:20.976557 30176 sgd_solver.cpp:105] Iteration 108, lr = 1e-06
I0405 08:37:26.153815 30176 solver.cpp:218] Iteration 120 (2.31785 iter/s, 5.17721s/12 iters), loss = 5.27509
I0405 08:37:26.153861 30176 solver.cpp:237]     Train net output #0: loss = 5.27509 (* 1 = 5.27509 loss)
I0405 08:37:26.153867 30176 sgd_solver.cpp:105] Iteration 120, lr = 1e-06
I0405 08:37:31.628049 30176 solver.cpp:218] Iteration 132 (2.19213 iter/s, 5.47412s/12 iters), loss = 5.28296
I0405 08:37:31.628088 30176 solver.cpp:237]     Train net output #0: loss = 5.28296 (* 1 = 5.28296 loss)
I0405 08:37:31.628094 30176 sgd_solver.cpp:105] Iteration 132, lr = 1e-06
I0405 08:37:36.509033 30176 solver.cpp:218] Iteration 144 (2.45857 iter/s, 4.88089s/12 iters), loss = 5.27661
I0405 08:37:36.509073 30176 solver.cpp:237]     Train net output #0: loss = 5.27661 (* 1 = 5.27661 loss)
I0405 08:37:36.509078 30176 sgd_solver.cpp:105] Iteration 144, lr = 1e-06
I0405 08:37:41.477788 30176 solver.cpp:218] Iteration 156 (2.41514 iter/s, 4.96866s/12 iters), loss = 5.2901
I0405 08:37:41.477824 30176 solver.cpp:237]     Train net output #0: loss = 5.2901 (* 1 = 5.2901 loss)
I0405 08:37:41.477830 30176 sgd_solver.cpp:105] Iteration 156, lr = 1e-06
I0405 08:37:46.534834 30176 solver.cpp:218] Iteration 168 (2.37297 iter/s, 5.05695s/12 iters), loss = 5.30047
I0405 08:37:46.534876 30176 solver.cpp:237]     Train net output #0: loss = 5.30047 (* 1 = 5.30047 loss)
I0405 08:37:46.534883 30176 sgd_solver.cpp:105] Iteration 168, lr = 1e-06
I0405 08:37:51.820266 30176 solver.cpp:218] Iteration 180 (2.27044 iter/s, 5.28533s/12 iters), loss = 5.28629
I0405 08:37:51.820406 30176 solver.cpp:237]     Train net output #0: loss = 5.28629 (* 1 = 5.28629 loss)
I0405 08:37:51.820415 30176 sgd_solver.cpp:105] Iteration 180, lr = 1e-06
I0405 08:37:57.060158 30176 solver.cpp:218] Iteration 192 (2.29021 iter/s, 5.2397s/12 iters), loss = 5.29786
I0405 08:37:57.060190 30176 solver.cpp:237]     Train net output #0: loss = 5.29786 (* 1 = 5.29786 loss)
I0405 08:37:57.060195 30176 sgd_solver.cpp:105] Iteration 192, lr = 1e-06
I0405 08:38:01.264081 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 08:38:01.974514 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_204.caffemodel
I0405 08:38:05.023486 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_204.solverstate
I0405 08:38:07.327204 30176 solver.cpp:330] Iteration 204, Testing net (#0)
I0405 08:38:07.327224 30176 net.cpp:676] Ignoring source layer train-data
I0405 08:38:11.494237 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 08:38:11.617987 30176 solver.cpp:397]     Test net output #0: accuracy = 0.00735294
I0405 08:38:11.618026 30176 solver.cpp:397]     Test net output #1: loss = 5.28009 (* 1 = 5.28009 loss)
I0405 08:38:11.759014 30176 solver.cpp:218] Iteration 204 (0.8164 iter/s, 14.6987s/12 iters), loss = 5.29464
I0405 08:38:11.759053 30176 solver.cpp:237]     Train net output #0: loss = 5.29464 (* 1 = 5.29464 loss)
I0405 08:38:11.759058 30176 sgd_solver.cpp:105] Iteration 204, lr = 1e-06
I0405 08:38:16.307109 30176 solver.cpp:218] Iteration 216 (2.63852 iter/s, 4.548s/12 iters), loss = 5.277
I0405 08:38:16.307153 30176 solver.cpp:237]     Train net output #0: loss = 5.277 (* 1 = 5.277 loss)
I0405 08:38:16.307159 30176 sgd_solver.cpp:105] Iteration 216, lr = 1e-06
I0405 08:38:21.715703 30176 solver.cpp:218] Iteration 228 (2.21873 iter/s, 5.40849s/12 iters), loss = 5.27955
I0405 08:38:21.715740 30176 solver.cpp:237]     Train net output #0: loss = 5.27955 (* 1 = 5.27955 loss)
I0405 08:38:21.715745 30176 sgd_solver.cpp:105] Iteration 228, lr = 1e-06
I0405 08:38:26.910933 30176 solver.cpp:218] Iteration 240 (2.30985 iter/s, 5.19513s/12 iters), loss = 5.28498
I0405 08:38:26.911074 30176 solver.cpp:237]     Train net output #0: loss = 5.28498 (* 1 = 5.28498 loss)
I0405 08:38:26.911082 30176 sgd_solver.cpp:105] Iteration 240, lr = 1e-06
I0405 08:38:32.393828 30176 solver.cpp:218] Iteration 252 (2.1887 iter/s, 5.4827s/12 iters), loss = 5.29742
I0405 08:38:32.393867 30176 solver.cpp:237]     Train net output #0: loss = 5.29742 (* 1 = 5.29742 loss)
I0405 08:38:32.393872 30176 sgd_solver.cpp:105] Iteration 252, lr = 1e-06
I0405 08:38:37.733986 30176 solver.cpp:218] Iteration 264 (2.24717 iter/s, 5.34006s/12 iters), loss = 5.24811
I0405 08:38:37.734028 30176 solver.cpp:237]     Train net output #0: loss = 5.24811 (* 1 = 5.24811 loss)
I0405 08:38:37.734035 30176 sgd_solver.cpp:105] Iteration 264, lr = 1e-06
I0405 08:38:43.050669 30176 solver.cpp:218] Iteration 276 (2.25709 iter/s, 5.31657s/12 iters), loss = 5.28765
I0405 08:38:43.050715 30176 solver.cpp:237]     Train net output #0: loss = 5.28765 (* 1 = 5.28765 loss)
I0405 08:38:43.050721 30176 sgd_solver.cpp:105] Iteration 276, lr = 1e-06
I0405 08:38:48.326063 30176 solver.cpp:218] Iteration 288 (2.27476 iter/s, 5.27528s/12 iters), loss = 5.28564
I0405 08:38:48.326114 30176 solver.cpp:237]     Train net output #0: loss = 5.28564 (* 1 = 5.28564 loss)
I0405 08:38:48.326122 30176 sgd_solver.cpp:105] Iteration 288, lr = 1e-06
I0405 08:38:53.614816 30176 solver.cpp:218] Iteration 300 (2.26901 iter/s, 5.28865s/12 iters), loss = 5.26073
I0405 08:38:53.614850 30176 solver.cpp:237]     Train net output #0: loss = 5.26073 (* 1 = 5.26073 loss)
I0405 08:38:53.614854 30176 sgd_solver.cpp:105] Iteration 300, lr = 1e-06
I0405 08:38:54.653581 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 08:38:55.768483 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_306.caffemodel
I0405 08:38:58.933729 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_306.solverstate
I0405 08:39:03.382402 30176 solver.cpp:330] Iteration 306, Testing net (#0)
I0405 08:39:03.382421 30176 net.cpp:676] Ignoring source layer train-data
I0405 08:39:07.557405 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 08:39:07.713999 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 08:39:07.714048 30176 solver.cpp:397]     Test net output #1: loss = 5.28029 (* 1 = 5.28029 loss)
I0405 08:39:09.603847 30176 solver.cpp:218] Iteration 312 (0.750524 iter/s, 15.9888s/12 iters), loss = 5.28646
I0405 08:39:09.603891 30176 solver.cpp:237]     Train net output #0: loss = 5.28646 (* 1 = 5.28646 loss)
I0405 08:39:09.603896 30176 sgd_solver.cpp:105] Iteration 312, lr = 1e-06
I0405 08:39:14.839313 30176 solver.cpp:218] Iteration 324 (2.2921 iter/s, 5.23536s/12 iters), loss = 5.2844
I0405 08:39:14.839354 30176 solver.cpp:237]     Train net output #0: loss = 5.2844 (* 1 = 5.2844 loss)
I0405 08:39:14.839359 30176 sgd_solver.cpp:105] Iteration 324, lr = 1e-06
I0405 08:39:20.067451 30176 solver.cpp:218] Iteration 336 (2.29532 iter/s, 5.22804s/12 iters), loss = 5.28762
I0405 08:39:20.067489 30176 solver.cpp:237]     Train net output #0: loss = 5.28762 (* 1 = 5.28762 loss)
I0405 08:39:20.067494 30176 sgd_solver.cpp:105] Iteration 336, lr = 1e-06
I0405 08:39:25.401825 30176 solver.cpp:218] Iteration 348 (2.2496 iter/s, 5.33427s/12 iters), loss = 5.30256
I0405 08:39:25.401861 30176 solver.cpp:237]     Train net output #0: loss = 5.30256 (* 1 = 5.30256 loss)
I0405 08:39:25.401867 30176 sgd_solver.cpp:105] Iteration 348, lr = 1e-06
I0405 08:39:30.432145 30176 solver.cpp:218] Iteration 360 (2.38558 iter/s, 5.03022s/12 iters), loss = 5.2692
I0405 08:39:30.432260 30176 solver.cpp:237]     Train net output #0: loss = 5.2692 (* 1 = 5.2692 loss)
I0405 08:39:30.432267 30176 sgd_solver.cpp:105] Iteration 360, lr = 1e-06
I0405 08:39:35.963635 30176 solver.cpp:218] Iteration 372 (2.16947 iter/s, 5.53131s/12 iters), loss = 5.286
I0405 08:39:35.963685 30176 solver.cpp:237]     Train net output #0: loss = 5.286 (* 1 = 5.286 loss)
I0405 08:39:35.963690 30176 sgd_solver.cpp:105] Iteration 372, lr = 1e-06
I0405 08:39:41.476590 30176 solver.cpp:218] Iteration 384 (2.17673 iter/s, 5.51285s/12 iters), loss = 5.29013
I0405 08:39:41.476627 30176 solver.cpp:237]     Train net output #0: loss = 5.29013 (* 1 = 5.29013 loss)
I0405 08:39:41.476632 30176 sgd_solver.cpp:105] Iteration 384, lr = 1e-06
I0405 08:39:46.801414 30176 solver.cpp:218] Iteration 396 (2.25364 iter/s, 5.32472s/12 iters), loss = 5.29773
I0405 08:39:46.801451 30176 solver.cpp:237]     Train net output #0: loss = 5.29773 (* 1 = 5.29773 loss)
I0405 08:39:46.801457 30176 sgd_solver.cpp:105] Iteration 396, lr = 1e-06
I0405 08:39:49.977829 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 08:39:51.452158 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_408.caffemodel
I0405 08:39:54.562216 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_408.solverstate
I0405 08:39:57.056401 30176 solver.cpp:330] Iteration 408, Testing net (#0)
I0405 08:39:57.056429 30176 net.cpp:676] Ignoring source layer train-data
I0405 08:40:01.114746 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 08:40:01.320328 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 08:40:01.320364 30176 solver.cpp:397]     Test net output #1: loss = 5.2802 (* 1 = 5.2802 loss)
I0405 08:40:01.461143 30176 solver.cpp:218] Iteration 408 (0.818579 iter/s, 14.6595s/12 iters), loss = 5.30007
I0405 08:40:01.461190 30176 solver.cpp:237]     Train net output #0: loss = 5.30007 (* 1 = 5.30007 loss)
I0405 08:40:01.461197 30176 sgd_solver.cpp:105] Iteration 408, lr = 1e-06
I0405 08:40:05.584448 30176 solver.cpp:218] Iteration 420 (2.91036 iter/s, 4.12321s/12 iters), loss = 5.28467
I0405 08:40:05.584487 30176 solver.cpp:237]     Train net output #0: loss = 5.28467 (* 1 = 5.28467 loss)
I0405 08:40:05.584493 30176 sgd_solver.cpp:105] Iteration 420, lr = 1e-06
I0405 08:40:10.599378 30176 solver.cpp:218] Iteration 432 (2.3929 iter/s, 5.01483s/12 iters), loss = 5.29601
I0405 08:40:10.599421 30176 solver.cpp:237]     Train net output #0: loss = 5.29601 (* 1 = 5.29601 loss)
I0405 08:40:10.599428 30176 sgd_solver.cpp:105] Iteration 432, lr = 1e-06
I0405 08:40:15.929605 30176 solver.cpp:218] Iteration 444 (2.25136 iter/s, 5.33012s/12 iters), loss = 5.29803
I0405 08:40:15.929641 30176 solver.cpp:237]     Train net output #0: loss = 5.29803 (* 1 = 5.29803 loss)
I0405 08:40:15.929647 30176 sgd_solver.cpp:105] Iteration 444, lr = 1e-06
I0405 08:40:21.241850 30176 solver.cpp:218] Iteration 456 (2.25897 iter/s, 5.31215s/12 iters), loss = 5.29515
I0405 08:40:21.241883 30176 solver.cpp:237]     Train net output #0: loss = 5.29515 (* 1 = 5.29515 loss)
I0405 08:40:21.241888 30176 sgd_solver.cpp:105] Iteration 456, lr = 1e-06
I0405 08:40:26.414868 30176 solver.cpp:218] Iteration 468 (2.31977 iter/s, 5.17292s/12 iters), loss = 5.29724
I0405 08:40:26.414908 30176 solver.cpp:237]     Train net output #0: loss = 5.29724 (* 1 = 5.29724 loss)
I0405 08:40:26.414914 30176 sgd_solver.cpp:105] Iteration 468, lr = 1e-06
I0405 08:40:31.738507 30176 solver.cpp:218] Iteration 480 (2.25414 iter/s, 5.32353s/12 iters), loss = 5.28175
I0405 08:40:31.738601 30176 solver.cpp:237]     Train net output #0: loss = 5.28175 (* 1 = 5.28175 loss)
I0405 08:40:31.738607 30176 sgd_solver.cpp:105] Iteration 480, lr = 1e-06
I0405 08:40:36.838285 30176 solver.cpp:218] Iteration 492 (2.35312 iter/s, 5.09962s/12 iters), loss = 5.28536
I0405 08:40:36.838327 30176 solver.cpp:237]     Train net output #0: loss = 5.28536 (* 1 = 5.28536 loss)
I0405 08:40:36.838333 30176 sgd_solver.cpp:105] Iteration 492, lr = 1e-06
I0405 08:40:41.778877 30176 solver.cpp:218] Iteration 504 (2.4289 iter/s, 4.9405s/12 iters), loss = 5.27119
I0405 08:40:41.778908 30176 solver.cpp:237]     Train net output #0: loss = 5.27119 (* 1 = 5.27119 loss)
I0405 08:40:41.778913 30176 sgd_solver.cpp:105] Iteration 504, lr = 1e-06
I0405 08:40:42.008186 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 08:40:43.689592 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_510.caffemodel
I0405 08:40:46.691593 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_510.solverstate
I0405 08:40:49.038548 30176 solver.cpp:330] Iteration 510, Testing net (#0)
I0405 08:40:49.038569 30176 net.cpp:676] Ignoring source layer train-data
I0405 08:40:53.080423 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 08:40:53.317165 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 08:40:53.317214 30176 solver.cpp:397]     Test net output #1: loss = 5.27973 (* 1 = 5.27973 loss)
I0405 08:40:55.214285 30176 solver.cpp:218] Iteration 516 (0.893173 iter/s, 13.4352s/12 iters), loss = 5.28828
I0405 08:40:55.214334 30176 solver.cpp:237]     Train net output #0: loss = 5.28828 (* 1 = 5.28828 loss)
I0405 08:40:55.214341 30176 sgd_solver.cpp:105] Iteration 516, lr = 1e-06
I0405 08:41:00.574038 30176 solver.cpp:218] Iteration 528 (2.23896 iter/s, 5.35964s/12 iters), loss = 5.28808
I0405 08:41:00.574075 30176 solver.cpp:237]     Train net output #0: loss = 5.28808 (* 1 = 5.28808 loss)
I0405 08:41:00.574080 30176 sgd_solver.cpp:105] Iteration 528, lr = 1e-06
I0405 08:41:06.067396 30176 solver.cpp:218] Iteration 540 (2.1845 iter/s, 5.49326s/12 iters), loss = 5.2881
I0405 08:41:06.067566 30176 solver.cpp:237]     Train net output #0: loss = 5.2881 (* 1 = 5.2881 loss)
I0405 08:41:06.067572 30176 sgd_solver.cpp:105] Iteration 540, lr = 1e-06
I0405 08:41:11.262554 30176 solver.cpp:218] Iteration 552 (2.30995 iter/s, 5.19493s/12 iters), loss = 5.283
I0405 08:41:11.262598 30176 solver.cpp:237]     Train net output #0: loss = 5.283 (* 1 = 5.283 loss)
I0405 08:41:11.262603 30176 sgd_solver.cpp:105] Iteration 552, lr = 1e-06
I0405 08:41:16.715777 30176 solver.cpp:218] Iteration 564 (2.20058 iter/s, 5.45312s/12 iters), loss = 5.27944
I0405 08:41:16.715823 30176 solver.cpp:237]     Train net output #0: loss = 5.27944 (* 1 = 5.27944 loss)
I0405 08:41:16.715828 30176 sgd_solver.cpp:105] Iteration 564, lr = 1e-06
I0405 08:41:22.135480 30176 solver.cpp:218] Iteration 576 (2.21419 iter/s, 5.41959s/12 iters), loss = 5.29238
I0405 08:41:22.135531 30176 solver.cpp:237]     Train net output #0: loss = 5.29238 (* 1 = 5.29238 loss)
I0405 08:41:22.135538 30176 sgd_solver.cpp:105] Iteration 576, lr = 1e-06
I0405 08:41:27.439822 30176 solver.cpp:218] Iteration 588 (2.26235 iter/s, 5.30423s/12 iters), loss = 5.28739
I0405 08:41:27.439865 30176 solver.cpp:237]     Train net output #0: loss = 5.28739 (* 1 = 5.28739 loss)
I0405 08:41:27.439872 30176 sgd_solver.cpp:105] Iteration 588, lr = 1e-06
I0405 08:41:32.802119 30176 solver.cpp:218] Iteration 600 (2.23789 iter/s, 5.36219s/12 iters), loss = 5.28114
I0405 08:41:32.802167 30176 solver.cpp:237]     Train net output #0: loss = 5.28114 (* 1 = 5.28114 loss)
I0405 08:41:32.802172 30176 sgd_solver.cpp:105] Iteration 600, lr = 1e-06
I0405 08:41:35.338673 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 08:41:37.637017 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_612.caffemodel
I0405 08:41:40.639803 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_612.solverstate
I0405 08:41:43.332026 30176 solver.cpp:330] Iteration 612, Testing net (#0)
I0405 08:41:43.332044 30176 net.cpp:676] Ignoring source layer train-data
I0405 08:41:47.327217 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 08:41:47.676743 30176 solver.cpp:397]     Test net output #0: accuracy = 0.00735294
I0405 08:41:47.676770 30176 solver.cpp:397]     Test net output #1: loss = 5.2801 (* 1 = 5.2801 loss)
I0405 08:41:47.817462 30176 solver.cpp:218] Iteration 612 (0.799192 iter/s, 15.0152s/12 iters), loss = 5.29024
I0405 08:41:47.819018 30176 solver.cpp:237]     Train net output #0: loss = 5.29024 (* 1 = 5.29024 loss)
I0405 08:41:47.819025 30176 sgd_solver.cpp:105] Iteration 612, lr = 1e-06
I0405 08:41:52.312309 30176 solver.cpp:218] Iteration 624 (2.67068 iter/s, 4.49324s/12 iters), loss = 5.29074
I0405 08:41:52.312350 30176 solver.cpp:237]     Train net output #0: loss = 5.29074 (* 1 = 5.29074 loss)
I0405 08:41:52.312355 30176 sgd_solver.cpp:105] Iteration 624, lr = 1e-06
I0405 08:41:57.801115 30176 solver.cpp:218] Iteration 636 (2.18631 iter/s, 5.4887s/12 iters), loss = 5.28844
I0405 08:41:57.801159 30176 solver.cpp:237]     Train net output #0: loss = 5.28844 (* 1 = 5.28844 loss)
I0405 08:41:57.801167 30176 sgd_solver.cpp:105] Iteration 636, lr = 1e-06
I0405 08:42:03.046432 30176 solver.cpp:218] Iteration 648 (2.2878 iter/s, 5.24522s/12 iters), loss = 5.2772
I0405 08:42:03.046474 30176 solver.cpp:237]     Train net output #0: loss = 5.2772 (* 1 = 5.2772 loss)
I0405 08:42:03.046480 30176 sgd_solver.cpp:105] Iteration 648, lr = 1e-06
I0405 08:42:08.130856 30176 solver.cpp:218] Iteration 660 (2.36019 iter/s, 5.08433s/12 iters), loss = 5.29922
I0405 08:42:08.131000 30176 solver.cpp:237]     Train net output #0: loss = 5.29922 (* 1 = 5.29922 loss)
I0405 08:42:08.131008 30176 sgd_solver.cpp:105] Iteration 660, lr = 1e-06
I0405 08:42:13.129040 30176 solver.cpp:218] Iteration 672 (2.40096 iter/s, 4.99799s/12 iters), loss = 5.28455
I0405 08:42:13.129076 30176 solver.cpp:237]     Train net output #0: loss = 5.28455 (* 1 = 5.28455 loss)
I0405 08:42:13.129083 30176 sgd_solver.cpp:105] Iteration 672, lr = 1e-06
I0405 08:42:18.160262 30176 solver.cpp:218] Iteration 684 (2.38515 iter/s, 5.03113s/12 iters), loss = 5.28044
I0405 08:42:18.160303 30176 solver.cpp:237]     Train net output #0: loss = 5.28044 (* 1 = 5.28044 loss)
I0405 08:42:18.160310 30176 sgd_solver.cpp:105] Iteration 684, lr = 1e-06
I0405 08:42:18.875629 30176 blocking_queue.cpp:49] Waiting for data
I0405 08:42:23.469311 30176 solver.cpp:218] Iteration 696 (2.26034 iter/s, 5.30894s/12 iters), loss = 5.27208
I0405 08:42:23.469357 30176 solver.cpp:237]     Train net output #0: loss = 5.27208 (* 1 = 5.27208 loss)
I0405 08:42:23.469363 30176 sgd_solver.cpp:105] Iteration 696, lr = 1e-06
I0405 08:42:28.335588 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 08:42:28.748512 30176 solver.cpp:218] Iteration 708 (2.27312 iter/s, 5.27909s/12 iters), loss = 5.27451
I0405 08:42:28.748560 30176 solver.cpp:237]     Train net output #0: loss = 5.27451 (* 1 = 5.27451 loss)
I0405 08:42:28.748567 30176 sgd_solver.cpp:105] Iteration 708, lr = 1e-06
I0405 08:42:30.773269 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_714.caffemodel
I0405 08:42:34.377717 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_714.solverstate
I0405 08:42:36.682163 30176 solver.cpp:330] Iteration 714, Testing net (#0)
I0405 08:42:36.682184 30176 net.cpp:676] Ignoring source layer train-data
I0405 08:42:40.642455 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 08:42:40.956631 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 08:42:40.956666 30176 solver.cpp:397]     Test net output #1: loss = 5.28017 (* 1 = 5.28017 loss)
I0405 08:42:42.971390 30176 solver.cpp:218] Iteration 720 (0.843722 iter/s, 14.2227s/12 iters), loss = 5.28453
I0405 08:42:42.971439 30176 solver.cpp:237]     Train net output #0: loss = 5.28453 (* 1 = 5.28453 loss)
I0405 08:42:42.971446 30176 sgd_solver.cpp:105] Iteration 720, lr = 1e-06
I0405 08:42:48.175895 30176 solver.cpp:218] Iteration 732 (2.30574 iter/s, 5.2044s/12 iters), loss = 5.29254
I0405 08:42:48.175925 30176 solver.cpp:237]     Train net output #0: loss = 5.29254 (* 1 = 5.29254 loss)
I0405 08:42:48.175930 30176 sgd_solver.cpp:105] Iteration 732, lr = 1e-06
I0405 08:42:53.586302 30176 solver.cpp:218] Iteration 744 (2.21799 iter/s, 5.41031s/12 iters), loss = 5.2774
I0405 08:42:53.586341 30176 solver.cpp:237]     Train net output #0: loss = 5.2774 (* 1 = 5.2774 loss)
I0405 08:42:53.586347 30176 sgd_solver.cpp:105] Iteration 744, lr = 1e-06
I0405 08:42:58.634460 30176 solver.cpp:218] Iteration 756 (2.37715 iter/s, 5.04806s/12 iters), loss = 5.28131
I0405 08:42:58.634502 30176 solver.cpp:237]     Train net output #0: loss = 5.28131 (* 1 = 5.28131 loss)
I0405 08:42:58.634508 30176 sgd_solver.cpp:105] Iteration 756, lr = 1e-06
I0405 08:43:03.866588 30176 solver.cpp:218] Iteration 768 (2.29357 iter/s, 5.23203s/12 iters), loss = 5.29467
I0405 08:43:03.866622 30176 solver.cpp:237]     Train net output #0: loss = 5.29467 (* 1 = 5.29467 loss)
I0405 08:43:03.866627 30176 sgd_solver.cpp:105] Iteration 768, lr = 1e-06
I0405 08:43:09.071283 30176 solver.cpp:218] Iteration 780 (2.30565 iter/s, 5.2046s/12 iters), loss = 5.29716
I0405 08:43:09.071324 30176 solver.cpp:237]     Train net output #0: loss = 5.29716 (* 1 = 5.29716 loss)
I0405 08:43:09.071329 30176 sgd_solver.cpp:105] Iteration 780, lr = 1e-06
I0405 08:43:14.496970 30176 solver.cpp:218] Iteration 792 (2.21174 iter/s, 5.42559s/12 iters), loss = 5.28731
I0405 08:43:14.497074 30176 solver.cpp:237]     Train net output #0: loss = 5.28731 (* 1 = 5.28731 loss)
I0405 08:43:14.497081 30176 sgd_solver.cpp:105] Iteration 792, lr = 1e-06
I0405 08:43:19.722719 30176 solver.cpp:218] Iteration 804 (2.29639 iter/s, 5.22559s/12 iters), loss = 5.28563
I0405 08:43:19.722757 30176 solver.cpp:237]     Train net output #0: loss = 5.28563 (* 1 = 5.28563 loss)
I0405 08:43:19.722762 30176 sgd_solver.cpp:105] Iteration 804, lr = 1e-06
I0405 08:43:21.524276 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 08:43:24.436538 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_816.caffemodel
I0405 08:43:27.508184 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_816.solverstate
I0405 08:43:29.844744 30176 solver.cpp:330] Iteration 816, Testing net (#0)
I0405 08:43:29.844770 30176 net.cpp:676] Ignoring source layer train-data
I0405 08:43:33.820397 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 08:43:34.169135 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 08:43:34.169183 30176 solver.cpp:397]     Test net output #1: loss = 5.28022 (* 1 = 5.28022 loss)
I0405 08:43:34.306985 30176 solver.cpp:218] Iteration 816 (0.822815 iter/s, 14.5841s/12 iters), loss = 5.28009
I0405 08:43:34.308560 30176 solver.cpp:237]     Train net output #0: loss = 5.28009 (* 1 = 5.28009 loss)
I0405 08:43:34.308573 30176 sgd_solver.cpp:105] Iteration 816, lr = 1e-06
I0405 08:43:38.754422 30176 solver.cpp:218] Iteration 828 (2.69917 iter/s, 4.44582s/12 iters), loss = 5.28677
I0405 08:43:38.754467 30176 solver.cpp:237]     Train net output #0: loss = 5.28677 (* 1 = 5.28677 loss)
I0405 08:43:38.754474 30176 sgd_solver.cpp:105] Iteration 828, lr = 1e-06
I0405 08:43:44.114718 30176 solver.cpp:218] Iteration 840 (2.23873 iter/s, 5.36019s/12 iters), loss = 5.29105
I0405 08:43:44.114758 30176 solver.cpp:237]     Train net output #0: loss = 5.29105 (* 1 = 5.29105 loss)
I0405 08:43:44.114764 30176 sgd_solver.cpp:105] Iteration 840, lr = 1e-06
I0405 08:43:49.537885 30176 solver.cpp:218] Iteration 852 (2.21277 iter/s, 5.42306s/12 iters), loss = 5.28033
I0405 08:43:49.538035 30176 solver.cpp:237]     Train net output #0: loss = 5.28033 (* 1 = 5.28033 loss)
I0405 08:43:49.538043 30176 sgd_solver.cpp:105] Iteration 852, lr = 1e-06
I0405 08:43:54.808261 30176 solver.cpp:218] Iteration 864 (2.27697 iter/s, 5.27017s/12 iters), loss = 5.28736
I0405 08:43:54.808300 30176 solver.cpp:237]     Train net output #0: loss = 5.28736 (* 1 = 5.28736 loss)
I0405 08:43:54.808307 30176 sgd_solver.cpp:105] Iteration 864, lr = 1e-06
I0405 08:44:00.101577 30176 solver.cpp:218] Iteration 876 (2.26706 iter/s, 5.29321s/12 iters), loss = 5.29554
I0405 08:44:00.101620 30176 solver.cpp:237]     Train net output #0: loss = 5.29554 (* 1 = 5.29554 loss)
I0405 08:44:00.101626 30176 sgd_solver.cpp:105] Iteration 876, lr = 1e-06
I0405 08:44:05.326133 30176 solver.cpp:218] Iteration 888 (2.29689 iter/s, 5.22445s/12 iters), loss = 5.27935
I0405 08:44:05.326176 30176 solver.cpp:237]     Train net output #0: loss = 5.27935 (* 1 = 5.27935 loss)
I0405 08:44:05.326184 30176 sgd_solver.cpp:105] Iteration 888, lr = 1e-06
I0405 08:44:10.482420 30176 solver.cpp:218] Iteration 900 (2.3273 iter/s, 5.15619s/12 iters), loss = 5.27706
I0405 08:44:10.482456 30176 solver.cpp:237]     Train net output #0: loss = 5.27706 (* 1 = 5.27706 loss)
I0405 08:44:10.482462 30176 sgd_solver.cpp:105] Iteration 900, lr = 1e-06
I0405 08:44:14.507417 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 08:44:15.686123 30176 solver.cpp:218] Iteration 912 (2.3061 iter/s, 5.2036s/12 iters), loss = 5.28566
I0405 08:44:15.686173 30176 solver.cpp:237]     Train net output #0: loss = 5.28566 (* 1 = 5.28566 loss)
I0405 08:44:15.686180 30176 sgd_solver.cpp:105] Iteration 912, lr = 1e-06
I0405 08:44:17.672922 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_918.caffemodel
I0405 08:44:20.687986 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_918.solverstate
I0405 08:44:23.003100 30176 solver.cpp:330] Iteration 918, Testing net (#0)
I0405 08:44:23.003125 30176 net.cpp:676] Ignoring source layer train-data
I0405 08:44:27.085999 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 08:44:27.507293 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 08:44:27.507341 30176 solver.cpp:397]     Test net output #1: loss = 5.28054 (* 1 = 5.28054 loss)
I0405 08:44:29.542165 30176 solver.cpp:218] Iteration 924 (0.86606 iter/s, 13.8559s/12 iters), loss = 5.2911
I0405 08:44:29.542207 30176 solver.cpp:237]     Train net output #0: loss = 5.2911 (* 1 = 5.2911 loss)
I0405 08:44:29.542213 30176 sgd_solver.cpp:105] Iteration 924, lr = 1e-06
I0405 08:44:34.577824 30176 solver.cpp:218] Iteration 936 (2.38305 iter/s, 5.03556s/12 iters), loss = 5.27814
I0405 08:44:34.577869 30176 solver.cpp:237]     Train net output #0: loss = 5.27814 (* 1 = 5.27814 loss)
I0405 08:44:34.577877 30176 sgd_solver.cpp:105] Iteration 936, lr = 1e-06
I0405 08:44:39.461540 30176 solver.cpp:218] Iteration 948 (2.4572 iter/s, 4.88361s/12 iters), loss = 5.28289
I0405 08:44:39.461585 30176 solver.cpp:237]     Train net output #0: loss = 5.28289 (* 1 = 5.28289 loss)
I0405 08:44:39.461591 30176 sgd_solver.cpp:105] Iteration 948, lr = 1e-06
I0405 08:44:44.889156 30176 solver.cpp:218] Iteration 960 (2.21096 iter/s, 5.42751s/12 iters), loss = 5.29788
I0405 08:44:44.889199 30176 solver.cpp:237]     Train net output #0: loss = 5.29788 (* 1 = 5.29788 loss)
I0405 08:44:44.889204 30176 sgd_solver.cpp:105] Iteration 960, lr = 1e-06
I0405 08:44:50.190829 30176 solver.cpp:218] Iteration 972 (2.26348 iter/s, 5.30157s/12 iters), loss = 5.2843
I0405 08:44:50.190860 30176 solver.cpp:237]     Train net output #0: loss = 5.2843 (* 1 = 5.2843 loss)
I0405 08:44:50.190865 30176 sgd_solver.cpp:105] Iteration 972, lr = 1e-06
I0405 08:44:55.597182 30176 solver.cpp:218] Iteration 984 (2.21965 iter/s, 5.40625s/12 iters), loss = 5.27565
I0405 08:44:55.597280 30176 solver.cpp:237]     Train net output #0: loss = 5.27565 (* 1 = 5.27565 loss)
I0405 08:44:55.597287 30176 sgd_solver.cpp:105] Iteration 984, lr = 1e-06
I0405 08:45:00.670255 30176 solver.cpp:218] Iteration 996 (2.3655 iter/s, 5.07292s/12 iters), loss = 5.28178
I0405 08:45:00.670295 30176 solver.cpp:237]     Train net output #0: loss = 5.28178 (* 1 = 5.28178 loss)
I0405 08:45:00.670300 30176 sgd_solver.cpp:105] Iteration 996, lr = 1e-06
I0405 08:45:06.045264 30176 solver.cpp:218] Iteration 1008 (2.2326 iter/s, 5.37491s/12 iters), loss = 5.28463
I0405 08:45:06.045300 30176 solver.cpp:237]     Train net output #0: loss = 5.28463 (* 1 = 5.28463 loss)
I0405 08:45:06.045307 30176 sgd_solver.cpp:105] Iteration 1008, lr = 1e-06
I0405 08:45:07.126332 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 08:45:10.818804 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1020.caffemodel
I0405 08:45:14.382270 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1020.solverstate
I0405 08:45:16.786345 30176 solver.cpp:330] Iteration 1020, Testing net (#0)
I0405 08:45:16.786365 30176 net.cpp:676] Ignoring source layer train-data
I0405 08:45:20.691148 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 08:45:21.117729 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 08:45:21.117763 30176 solver.cpp:397]     Test net output #1: loss = 5.28013 (* 1 = 5.28013 loss)
I0405 08:45:21.258924 30176 solver.cpp:218] Iteration 1020 (0.788775 iter/s, 15.2135s/12 iters), loss = 5.29597
I0405 08:45:21.258972 30176 solver.cpp:237]     Train net output #0: loss = 5.29597 (* 1 = 5.29597 loss)
I0405 08:45:21.258977 30176 sgd_solver.cpp:105] Iteration 1020, lr = 1e-06
I0405 08:45:25.500290 30176 solver.cpp:218] Iteration 1032 (2.82934 iter/s, 4.24127s/12 iters), loss = 5.28955
I0405 08:45:25.500324 30176 solver.cpp:237]     Train net output #0: loss = 5.28955 (* 1 = 5.28955 loss)
I0405 08:45:25.500329 30176 sgd_solver.cpp:105] Iteration 1032, lr = 1e-06
I0405 08:45:30.864840 30176 solver.cpp:218] Iteration 1044 (2.23695 iter/s, 5.36445s/12 iters), loss = 5.29072
I0405 08:45:30.864984 30176 solver.cpp:237]     Train net output #0: loss = 5.29072 (* 1 = 5.29072 loss)
I0405 08:45:30.864991 30176 sgd_solver.cpp:105] Iteration 1044, lr = 1e-06
I0405 08:45:35.988526 30176 solver.cpp:218] Iteration 1056 (2.34216 iter/s, 5.12348s/12 iters), loss = 5.29256
I0405 08:45:35.988570 30176 solver.cpp:237]     Train net output #0: loss = 5.29256 (* 1 = 5.29256 loss)
I0405 08:45:35.988577 30176 sgd_solver.cpp:105] Iteration 1056, lr = 1e-06
I0405 08:45:41.401515 30176 solver.cpp:218] Iteration 1068 (2.21693 iter/s, 5.41288s/12 iters), loss = 5.26018
I0405 08:45:41.401561 30176 solver.cpp:237]     Train net output #0: loss = 5.26018 (* 1 = 5.26018 loss)
I0405 08:45:41.401566 30176 sgd_solver.cpp:105] Iteration 1068, lr = 1e-06
I0405 08:45:46.718798 30176 solver.cpp:218] Iteration 1080 (2.25683 iter/s, 5.31718s/12 iters), loss = 5.27965
I0405 08:45:46.718833 30176 solver.cpp:237]     Train net output #0: loss = 5.27965 (* 1 = 5.27965 loss)
I0405 08:45:46.718839 30176 sgd_solver.cpp:105] Iteration 1080, lr = 1e-06
I0405 08:45:52.017510 30176 solver.cpp:218] Iteration 1092 (2.26474 iter/s, 5.29861s/12 iters), loss = 5.30065
I0405 08:45:52.017560 30176 solver.cpp:237]     Train net output #0: loss = 5.30065 (* 1 = 5.30065 loss)
I0405 08:45:52.017566 30176 sgd_solver.cpp:105] Iteration 1092, lr = 1e-06
I0405 08:45:57.325608 30176 solver.cpp:218] Iteration 1104 (2.26074 iter/s, 5.30799s/12 iters), loss = 5.29105
I0405 08:45:57.325646 30176 solver.cpp:237]     Train net output #0: loss = 5.29105 (* 1 = 5.29105 loss)
I0405 08:45:57.325652 30176 sgd_solver.cpp:105] Iteration 1104, lr = 1e-06
I0405 08:46:00.677726 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 08:46:02.580848 30176 solver.cpp:218] Iteration 1116 (2.28348 iter/s, 5.25514s/12 iters), loss = 5.29972
I0405 08:46:02.580994 30176 solver.cpp:237]     Train net output #0: loss = 5.29972 (* 1 = 5.29972 loss)
I0405 08:46:02.581003 30176 sgd_solver.cpp:105] Iteration 1116, lr = 1e-06
I0405 08:46:04.571326 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1122.caffemodel
I0405 08:46:07.599280 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1122.solverstate
I0405 08:46:09.928042 30176 solver.cpp:330] Iteration 1122, Testing net (#0)
I0405 08:46:09.928066 30176 net.cpp:676] Ignoring source layer train-data
I0405 08:46:13.815322 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 08:46:14.284761 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 08:46:14.284796 30176 solver.cpp:397]     Test net output #1: loss = 5.2804 (* 1 = 5.2804 loss)
I0405 08:46:16.124405 30176 solver.cpp:218] Iteration 1128 (0.886048 iter/s, 13.5433s/12 iters), loss = 5.30007
I0405 08:46:16.124449 30176 solver.cpp:237]     Train net output #0: loss = 5.30007 (* 1 = 5.30007 loss)
I0405 08:46:16.124455 30176 sgd_solver.cpp:105] Iteration 1128, lr = 1e-06
I0405 08:46:21.422732 30176 solver.cpp:218] Iteration 1140 (2.26491 iter/s, 5.29822s/12 iters), loss = 5.29136
I0405 08:46:21.422794 30176 solver.cpp:237]     Train net output #0: loss = 5.29136 (* 1 = 5.29136 loss)
I0405 08:46:21.422803 30176 sgd_solver.cpp:105] Iteration 1140, lr = 1e-06
I0405 08:46:27.025053 30176 solver.cpp:218] Iteration 1152 (2.14202 iter/s, 5.60219s/12 iters), loss = 5.29263
I0405 08:46:27.025110 30176 solver.cpp:237]     Train net output #0: loss = 5.29263 (* 1 = 5.29263 loss)
I0405 08:46:27.025120 30176 sgd_solver.cpp:105] Iteration 1152, lr = 1e-06
I0405 08:46:31.981633 30176 solver.cpp:218] Iteration 1164 (2.42108 iter/s, 4.95647s/12 iters), loss = 5.29032
I0405 08:46:31.981673 30176 solver.cpp:237]     Train net output #0: loss = 5.29032 (* 1 = 5.29032 loss)
I0405 08:46:31.981678 30176 sgd_solver.cpp:105] Iteration 1164, lr = 1e-06
I0405 08:46:37.038486 30176 solver.cpp:218] Iteration 1176 (2.37306 iter/s, 5.05676s/12 iters), loss = 5.28205
I0405 08:46:37.038611 30176 solver.cpp:237]     Train net output #0: loss = 5.28205 (* 1 = 5.28205 loss)
I0405 08:46:37.038619 30176 sgd_solver.cpp:105] Iteration 1176, lr = 1e-06
I0405 08:46:42.296484 30176 solver.cpp:218] Iteration 1188 (2.28232 iter/s, 5.25781s/12 iters), loss = 5.30007
I0405 08:46:42.296521 30176 solver.cpp:237]     Train net output #0: loss = 5.30007 (* 1 = 5.30007 loss)
I0405 08:46:42.296526 30176 sgd_solver.cpp:105] Iteration 1188, lr = 1e-06
I0405 08:46:47.382961 30176 solver.cpp:218] Iteration 1200 (2.35924 iter/s, 5.08638s/12 iters), loss = 5.27993
I0405 08:46:47.382997 30176 solver.cpp:237]     Train net output #0: loss = 5.27993 (* 1 = 5.27993 loss)
I0405 08:46:47.383002 30176 sgd_solver.cpp:105] Iteration 1200, lr = 1e-06
I0405 08:46:52.597267 30176 solver.cpp:218] Iteration 1212 (2.3014 iter/s, 5.21421s/12 iters), loss = 5.28823
I0405 08:46:52.597301 30176 solver.cpp:237]     Train net output #0: loss = 5.28823 (* 1 = 5.28823 loss)
I0405 08:46:52.597307 30176 sgd_solver.cpp:105] Iteration 1212, lr = 1e-06
I0405 08:46:52.854605 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 08:46:57.035485 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1224.caffemodel
I0405 08:47:00.053282 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1224.solverstate
I0405 08:47:02.361629 30176 solver.cpp:330] Iteration 1224, Testing net (#0)
I0405 08:47:02.361651 30176 net.cpp:676] Ignoring source layer train-data
I0405 08:47:06.232228 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 08:47:06.732803 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 08:47:06.732832 30176 solver.cpp:397]     Test net output #1: loss = 5.28029 (* 1 = 5.28029 loss)
I0405 08:47:06.873191 30176 solver.cpp:218] Iteration 1224 (0.840586 iter/s, 14.2757s/12 iters), loss = 5.28697
I0405 08:47:06.873247 30176 solver.cpp:237]     Train net output #0: loss = 5.28697 (* 1 = 5.28697 loss)
I0405 08:47:06.873255 30176 sgd_solver.cpp:105] Iteration 1224, lr = 1e-06
I0405 08:47:11.176476 30176 solver.cpp:218] Iteration 1236 (2.78863 iter/s, 4.30318s/12 iters), loss = 5.28793
I0405 08:47:11.176591 30176 solver.cpp:237]     Train net output #0: loss = 5.28793 (* 1 = 5.28793 loss)
I0405 08:47:11.176601 30176 sgd_solver.cpp:105] Iteration 1236, lr = 1e-06
I0405 08:47:16.576102 30176 solver.cpp:218] Iteration 1248 (2.22245 iter/s, 5.39946s/12 iters), loss = 5.27651
I0405 08:47:16.576136 30176 solver.cpp:237]     Train net output #0: loss = 5.27651 (* 1 = 5.27651 loss)
I0405 08:47:16.576141 30176 sgd_solver.cpp:105] Iteration 1248, lr = 1e-06
I0405 08:47:21.884160 30176 solver.cpp:218] Iteration 1260 (2.26075 iter/s, 5.30796s/12 iters), loss = 5.28083
I0405 08:47:21.884194 30176 solver.cpp:237]     Train net output #0: loss = 5.28083 (* 1 = 5.28083 loss)
I0405 08:47:21.884199 30176 sgd_solver.cpp:105] Iteration 1260, lr = 1e-06
I0405 08:47:27.221545 30176 solver.cpp:218] Iteration 1272 (2.24834 iter/s, 5.33728s/12 iters), loss = 5.28987
I0405 08:47:27.221591 30176 solver.cpp:237]     Train net output #0: loss = 5.28987 (* 1 = 5.28987 loss)
I0405 08:47:27.221596 30176 sgd_solver.cpp:105] Iteration 1272, lr = 1e-06
I0405 08:47:32.530140 30176 solver.cpp:218] Iteration 1284 (2.26053 iter/s, 5.30849s/12 iters), loss = 5.2906
I0405 08:47:32.530177 30176 solver.cpp:237]     Train net output #0: loss = 5.2906 (* 1 = 5.2906 loss)
I0405 08:47:32.530182 30176 sgd_solver.cpp:105] Iteration 1284, lr = 1e-06
I0405 08:47:37.618177 30176 solver.cpp:218] Iteration 1296 (2.35852 iter/s, 5.08793s/12 iters), loss = 5.28489
I0405 08:47:37.618224 30176 solver.cpp:237]     Train net output #0: loss = 5.28489 (* 1 = 5.28489 loss)
I0405 08:47:37.618230 30176 sgd_solver.cpp:105] Iteration 1296, lr = 1e-06
I0405 08:47:42.771006 30176 solver.cpp:218] Iteration 1308 (2.32887 iter/s, 5.15272s/12 iters), loss = 5.27558
I0405 08:47:42.771131 30176 solver.cpp:237]     Train net output #0: loss = 5.27558 (* 1 = 5.27558 loss)
I0405 08:47:42.771137 30176 sgd_solver.cpp:105] Iteration 1308, lr = 1e-06
I0405 08:47:45.259415 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 08:47:47.981571 30176 solver.cpp:218] Iteration 1320 (2.3031 iter/s, 5.21038s/12 iters), loss = 5.29289
I0405 08:47:47.981611 30176 solver.cpp:237]     Train net output #0: loss = 5.29289 (* 1 = 5.29289 loss)
I0405 08:47:47.981616 30176 sgd_solver.cpp:105] Iteration 1320, lr = 1e-06
I0405 08:47:50.142297 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1326.caffemodel
I0405 08:47:53.142436 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1326.solverstate
I0405 08:47:55.455173 30176 solver.cpp:330] Iteration 1326, Testing net (#0)
I0405 08:47:55.455193 30176 net.cpp:676] Ignoring source layer train-data
I0405 08:47:59.350265 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 08:47:59.922142 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 08:47:59.922176 30176 solver.cpp:397]     Test net output #1: loss = 5.27998 (* 1 = 5.27998 loss)
I0405 08:48:01.959074 30176 solver.cpp:218] Iteration 1332 (0.858533 iter/s, 13.9773s/12 iters), loss = 5.28781
I0405 08:48:01.959110 30176 solver.cpp:237]     Train net output #0: loss = 5.28781 (* 1 = 5.28781 loss)
I0405 08:48:01.959115 30176 sgd_solver.cpp:105] Iteration 1332, lr = 1e-06
I0405 08:48:07.047449 30176 solver.cpp:218] Iteration 1344 (2.35836 iter/s, 5.08828s/12 iters), loss = 5.30365
I0405 08:48:07.047499 30176 solver.cpp:237]     Train net output #0: loss = 5.30365 (* 1 = 5.30365 loss)
I0405 08:48:07.047505 30176 sgd_solver.cpp:105] Iteration 1344, lr = 1e-06
I0405 08:48:12.334043 30176 solver.cpp:218] Iteration 1356 (2.26994 iter/s, 5.28649s/12 iters), loss = 5.29035
I0405 08:48:12.334074 30176 solver.cpp:237]     Train net output #0: loss = 5.29035 (* 1 = 5.29035 loss)
I0405 08:48:12.334080 30176 sgd_solver.cpp:105] Iteration 1356, lr = 1e-06
I0405 08:48:17.524547 30176 solver.cpp:218] Iteration 1368 (2.31195 iter/s, 5.19041s/12 iters), loss = 5.28935
I0405 08:48:17.524646 30176 solver.cpp:237]     Train net output #0: loss = 5.28935 (* 1 = 5.28935 loss)
I0405 08:48:17.524652 30176 sgd_solver.cpp:105] Iteration 1368, lr = 1e-06
I0405 08:48:18.812507 30176 blocking_queue.cpp:49] Waiting for data
I0405 08:48:22.577940 30176 solver.cpp:218] Iteration 1380 (2.37472 iter/s, 5.05323s/12 iters), loss = 5.2901
I0405 08:48:22.577983 30176 solver.cpp:237]     Train net output #0: loss = 5.2901 (* 1 = 5.2901 loss)
I0405 08:48:22.577988 30176 sgd_solver.cpp:105] Iteration 1380, lr = 1e-06
I0405 08:48:27.716840 30176 solver.cpp:218] Iteration 1392 (2.33517 iter/s, 5.1388s/12 iters), loss = 5.26488
I0405 08:48:27.716876 30176 solver.cpp:237]     Train net output #0: loss = 5.26488 (* 1 = 5.26488 loss)
I0405 08:48:27.716888 30176 sgd_solver.cpp:105] Iteration 1392, lr = 1e-06
I0405 08:48:33.207489 30176 solver.cpp:218] Iteration 1404 (2.18557 iter/s, 5.49055s/12 iters), loss = 5.27301
I0405 08:48:33.207525 30176 solver.cpp:237]     Train net output #0: loss = 5.27301 (* 1 = 5.27301 loss)
I0405 08:48:33.207531 30176 sgd_solver.cpp:105] Iteration 1404, lr = 1e-06
I0405 08:48:38.072137 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 08:48:38.466233 30176 solver.cpp:218] Iteration 1416 (2.28195 iter/s, 5.25865s/12 iters), loss = 5.28302
I0405 08:48:38.466264 30176 solver.cpp:237]     Train net output #0: loss = 5.28302 (* 1 = 5.28302 loss)
I0405 08:48:38.466269 30176 sgd_solver.cpp:105] Iteration 1416, lr = 1e-06
I0405 08:48:43.295255 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1428.caffemodel
I0405 08:48:46.294961 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1428.solverstate
I0405 08:48:48.611502 30176 solver.cpp:330] Iteration 1428, Testing net (#0)
I0405 08:48:48.612100 30176 net.cpp:676] Ignoring source layer train-data
I0405 08:48:52.280583 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 08:48:52.863168 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 08:48:52.863202 30176 solver.cpp:397]     Test net output #1: loss = 5.28019 (* 1 = 5.28019 loss)
I0405 08:48:52.997124 30176 solver.cpp:218] Iteration 1428 (0.825837 iter/s, 14.5307s/12 iters), loss = 5.27893
I0405 08:48:52.997169 30176 solver.cpp:237]     Train net output #0: loss = 5.27893 (* 1 = 5.27893 loss)
I0405 08:48:52.997176 30176 sgd_solver.cpp:105] Iteration 1428, lr = 1e-06
I0405 08:48:57.306725 30176 solver.cpp:218] Iteration 1440 (2.78454 iter/s, 4.30951s/12 iters), loss = 5.29441
I0405 08:48:57.306761 30176 solver.cpp:237]     Train net output #0: loss = 5.29441 (* 1 = 5.29441 loss)
I0405 08:48:57.306767 30176 sgd_solver.cpp:105] Iteration 1440, lr = 1e-06
I0405 08:49:02.423959 30176 solver.cpp:218] Iteration 1452 (2.34506 iter/s, 5.11714s/12 iters), loss = 5.27709
I0405 08:49:02.423997 30176 solver.cpp:237]     Train net output #0: loss = 5.27709 (* 1 = 5.27709 loss)
I0405 08:49:02.424003 30176 sgd_solver.cpp:105] Iteration 1452, lr = 1e-06
I0405 08:49:07.828488 30176 solver.cpp:218] Iteration 1464 (2.2204 iter/s, 5.40442s/12 iters), loss = 5.27019
I0405 08:49:07.828532 30176 solver.cpp:237]     Train net output #0: loss = 5.27019 (* 1 = 5.27019 loss)
I0405 08:49:07.828538 30176 sgd_solver.cpp:105] Iteration 1464, lr = 1e-06
I0405 08:49:13.169304 30176 solver.cpp:218] Iteration 1476 (2.24689 iter/s, 5.34071s/12 iters), loss = 5.27465
I0405 08:49:13.169353 30176 solver.cpp:237]     Train net output #0: loss = 5.27465 (* 1 = 5.27465 loss)
I0405 08:49:13.169360 30176 sgd_solver.cpp:105] Iteration 1476, lr = 1e-06
I0405 08:49:18.494473 30176 solver.cpp:218] Iteration 1488 (2.2535 iter/s, 5.32506s/12 iters), loss = 5.28684
I0405 08:49:18.494519 30176 solver.cpp:237]     Train net output #0: loss = 5.28684 (* 1 = 5.28684 loss)
I0405 08:49:18.494526 30176 sgd_solver.cpp:105] Iteration 1488, lr = 1e-06
I0405 08:49:23.776358 30176 solver.cpp:218] Iteration 1500 (2.27196 iter/s, 5.28178s/12 iters), loss = 5.30086
I0405 08:49:23.776458 30176 solver.cpp:237]     Train net output #0: loss = 5.30086 (* 1 = 5.30086 loss)
I0405 08:49:23.776463 30176 sgd_solver.cpp:105] Iteration 1500, lr = 1e-06
I0405 08:49:28.911120 30176 solver.cpp:218] Iteration 1512 (2.33708 iter/s, 5.1346s/12 iters), loss = 5.28784
I0405 08:49:28.911156 30176 solver.cpp:237]     Train net output #0: loss = 5.28784 (* 1 = 5.28784 loss)
I0405 08:49:28.911161 30176 sgd_solver.cpp:105] Iteration 1512, lr = 1e-06
I0405 08:49:30.722784 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 08:49:34.037190 30176 solver.cpp:218] Iteration 1524 (2.34102 iter/s, 5.12598s/12 iters), loss = 5.2694
I0405 08:49:34.037232 30176 solver.cpp:237]     Train net output #0: loss = 5.2694 (* 1 = 5.2694 loss)
I0405 08:49:34.037240 30176 sgd_solver.cpp:105] Iteration 1524, lr = 1e-06
I0405 08:49:36.113781 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1530.caffemodel
I0405 08:49:39.130651 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1530.solverstate
I0405 08:49:41.486168 30176 solver.cpp:330] Iteration 1530, Testing net (#0)
I0405 08:49:41.486191 30176 net.cpp:676] Ignoring source layer train-data
I0405 08:49:45.214941 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 08:49:45.843988 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 08:49:45.844022 30176 solver.cpp:397]     Test net output #1: loss = 5.28007 (* 1 = 5.28007 loss)
I0405 08:49:47.848970 30176 solver.cpp:218] Iteration 1536 (0.868835 iter/s, 13.8116s/12 iters), loss = 5.28723
I0405 08:49:47.849010 30176 solver.cpp:237]     Train net output #0: loss = 5.28723 (* 1 = 5.28723 loss)
I0405 08:49:47.849016 30176 sgd_solver.cpp:105] Iteration 1536, lr = 1e-06
I0405 08:49:52.993523 30176 solver.cpp:218] Iteration 1548 (2.33261 iter/s, 5.14444s/12 iters), loss = 5.29406
I0405 08:49:52.993584 30176 solver.cpp:237]     Train net output #0: loss = 5.29406 (* 1 = 5.29406 loss)
I0405 08:49:52.993593 30176 sgd_solver.cpp:105] Iteration 1548, lr = 1e-06
I0405 08:49:58.351665 30176 solver.cpp:218] Iteration 1560 (2.23963 iter/s, 5.35802s/12 iters), loss = 5.27617
I0405 08:49:58.351814 30176 solver.cpp:237]     Train net output #0: loss = 5.27617 (* 1 = 5.27617 loss)
I0405 08:49:58.351820 30176 sgd_solver.cpp:105] Iteration 1560, lr = 1e-06
I0405 08:50:03.458878 30176 solver.cpp:218] Iteration 1572 (2.34972 iter/s, 5.10699s/12 iters), loss = 5.28305
I0405 08:50:03.458932 30176 solver.cpp:237]     Train net output #0: loss = 5.28305 (* 1 = 5.28305 loss)
I0405 08:50:03.458940 30176 sgd_solver.cpp:105] Iteration 1572, lr = 1e-06
I0405 08:50:08.866189 30176 solver.cpp:218] Iteration 1584 (2.21927 iter/s, 5.40719s/12 iters), loss = 5.29078
I0405 08:50:08.866228 30176 solver.cpp:237]     Train net output #0: loss = 5.29078 (* 1 = 5.29078 loss)
I0405 08:50:08.866233 30176 sgd_solver.cpp:105] Iteration 1584, lr = 1e-06
I0405 08:50:14.117724 30176 solver.cpp:218] Iteration 1596 (2.28509 iter/s, 5.25144s/12 iters), loss = 5.27633
I0405 08:50:14.117759 30176 solver.cpp:237]     Train net output #0: loss = 5.27633 (* 1 = 5.27633 loss)
I0405 08:50:14.117764 30176 sgd_solver.cpp:105] Iteration 1596, lr = 1e-06
I0405 08:50:19.635704 30176 solver.cpp:218] Iteration 1608 (2.17475 iter/s, 5.51788s/12 iters), loss = 5.29612
I0405 08:50:19.635740 30176 solver.cpp:237]     Train net output #0: loss = 5.29612 (* 1 = 5.29612 loss)
I0405 08:50:19.635746 30176 sgd_solver.cpp:105] Iteration 1608, lr = 1e-06
I0405 08:50:23.583094 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 08:50:24.777096 30176 solver.cpp:218] Iteration 1620 (2.33404 iter/s, 5.14129s/12 iters), loss = 5.27436
I0405 08:50:24.777138 30176 solver.cpp:237]     Train net output #0: loss = 5.27436 (* 1 = 5.27436 loss)
I0405 08:50:24.777143 30176 sgd_solver.cpp:105] Iteration 1620, lr = 1e-06
I0405 08:50:29.393975 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1632.caffemodel
I0405 08:50:32.402974 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1632.solverstate
I0405 08:50:34.709936 30176 solver.cpp:330] Iteration 1632, Testing net (#0)
I0405 08:50:34.709959 30176 net.cpp:676] Ignoring source layer train-data
I0405 08:50:38.389268 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 08:50:39.058940 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 08:50:39.058970 30176 solver.cpp:397]     Test net output #1: loss = 5.28009 (* 1 = 5.28009 loss)
I0405 08:50:39.194298 30176 solver.cpp:218] Iteration 1632 (0.83235 iter/s, 14.417s/12 iters), loss = 5.28209
I0405 08:50:39.194348 30176 solver.cpp:237]     Train net output #0: loss = 5.28209 (* 1 = 5.28209 loss)
I0405 08:50:39.194356 30176 sgd_solver.cpp:105] Iteration 1632, lr = 1e-06
I0405 08:50:43.507015 30176 solver.cpp:218] Iteration 1644 (2.78254 iter/s, 4.31261s/12 iters), loss = 5.28067
I0405 08:50:43.507061 30176 solver.cpp:237]     Train net output #0: loss = 5.28067 (* 1 = 5.28067 loss)
I0405 08:50:43.507066 30176 sgd_solver.cpp:105] Iteration 1644, lr = 1e-06
I0405 08:50:48.787804 30176 solver.cpp:218] Iteration 1656 (2.27243 iter/s, 5.28069s/12 iters), loss = 5.28193
I0405 08:50:48.787840 30176 solver.cpp:237]     Train net output #0: loss = 5.28193 (* 1 = 5.28193 loss)
I0405 08:50:48.787847 30176 sgd_solver.cpp:105] Iteration 1656, lr = 1e-06
I0405 08:50:53.730100 30176 solver.cpp:218] Iteration 1668 (2.42807 iter/s, 4.9422s/12 iters), loss = 5.29169
I0405 08:50:53.730135 30176 solver.cpp:237]     Train net output #0: loss = 5.29169 (* 1 = 5.29169 loss)
I0405 08:50:53.730140 30176 sgd_solver.cpp:105] Iteration 1668, lr = 1e-06
I0405 08:50:59.138096 30176 solver.cpp:218] Iteration 1680 (2.21898 iter/s, 5.40789s/12 iters), loss = 5.28633
I0405 08:50:59.138139 30176 solver.cpp:237]     Train net output #0: loss = 5.28633 (* 1 = 5.28633 loss)
I0405 08:50:59.138144 30176 sgd_solver.cpp:105] Iteration 1680, lr = 1e-06
I0405 08:51:04.313004 30176 solver.cpp:218] Iteration 1692 (2.31893 iter/s, 5.17481s/12 iters), loss = 5.28891
I0405 08:51:04.313135 30176 solver.cpp:237]     Train net output #0: loss = 5.28891 (* 1 = 5.28891 loss)
I0405 08:51:04.313141 30176 sgd_solver.cpp:105] Iteration 1692, lr = 1e-06
I0405 08:51:09.540509 30176 solver.cpp:218] Iteration 1704 (2.29563 iter/s, 5.22732s/12 iters), loss = 5.27706
I0405 08:51:09.540545 30176 solver.cpp:237]     Train net output #0: loss = 5.27706 (* 1 = 5.27706 loss)
I0405 08:51:09.540551 30176 sgd_solver.cpp:105] Iteration 1704, lr = 1e-06
I0405 08:51:14.896965 30176 solver.cpp:218] Iteration 1716 (2.24033 iter/s, 5.35636s/12 iters), loss = 5.28497
I0405 08:51:14.897001 30176 solver.cpp:237]     Train net output #0: loss = 5.28497 (* 1 = 5.28497 loss)
I0405 08:51:14.897006 30176 sgd_solver.cpp:105] Iteration 1716, lr = 1e-06
I0405 08:51:15.936914 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 08:51:20.114298 30176 solver.cpp:218] Iteration 1728 (2.30007 iter/s, 5.21724s/12 iters), loss = 5.26982
I0405 08:51:20.114332 30176 solver.cpp:237]     Train net output #0: loss = 5.26982 (* 1 = 5.26982 loss)
I0405 08:51:20.114337 30176 sgd_solver.cpp:105] Iteration 1728, lr = 1e-06
I0405 08:51:22.150699 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1734.caffemodel
I0405 08:51:25.173880 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1734.solverstate
I0405 08:51:27.504861 30176 solver.cpp:330] Iteration 1734, Testing net (#0)
I0405 08:51:27.504890 30176 net.cpp:676] Ignoring source layer train-data
I0405 08:51:31.086457 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 08:51:31.778546 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 08:51:31.778579 30176 solver.cpp:397]     Test net output #1: loss = 5.28004 (* 1 = 5.28004 loss)
I0405 08:51:33.605259 30176 solver.cpp:218] Iteration 1740 (0.889496 iter/s, 13.4908s/12 iters), loss = 5.28894
I0405 08:51:33.605310 30176 solver.cpp:237]     Train net output #0: loss = 5.28894 (* 1 = 5.28894 loss)
I0405 08:51:33.605319 30176 sgd_solver.cpp:105] Iteration 1740, lr = 1e-06
I0405 08:51:38.879377 30176 solver.cpp:218] Iteration 1752 (2.27531 iter/s, 5.27401s/12 iters), loss = 5.28639
I0405 08:51:38.879465 30176 solver.cpp:237]     Train net output #0: loss = 5.28639 (* 1 = 5.28639 loss)
I0405 08:51:38.879472 30176 sgd_solver.cpp:105] Iteration 1752, lr = 1e-06
I0405 08:51:44.238679 30176 solver.cpp:218] Iteration 1764 (2.23916 iter/s, 5.35915s/12 iters), loss = 5.28785
I0405 08:51:44.238732 30176 solver.cpp:237]     Train net output #0: loss = 5.28785 (* 1 = 5.28785 loss)
I0405 08:51:44.238741 30176 sgd_solver.cpp:105] Iteration 1764, lr = 1e-06
I0405 08:51:49.563277 30176 solver.cpp:218] Iteration 1776 (2.25374 iter/s, 5.32448s/12 iters), loss = 5.27062
I0405 08:51:49.563323 30176 solver.cpp:237]     Train net output #0: loss = 5.27062 (* 1 = 5.27062 loss)
I0405 08:51:49.563330 30176 sgd_solver.cpp:105] Iteration 1776, lr = 1e-06
I0405 08:51:54.811457 30176 solver.cpp:218] Iteration 1788 (2.28655 iter/s, 5.24807s/12 iters), loss = 5.2796
I0405 08:51:54.811496 30176 solver.cpp:237]     Train net output #0: loss = 5.2796 (* 1 = 5.2796 loss)
I0405 08:51:54.811501 30176 sgd_solver.cpp:105] Iteration 1788, lr = 1e-06
I0405 08:51:59.686602 30176 solver.cpp:218] Iteration 1800 (2.46152 iter/s, 4.87504s/12 iters), loss = 5.2882
I0405 08:51:59.686645 30176 solver.cpp:237]     Train net output #0: loss = 5.2882 (* 1 = 5.2882 loss)
I0405 08:51:59.686650 30176 sgd_solver.cpp:105] Iteration 1800, lr = 1e-06
I0405 08:52:04.682615 30176 solver.cpp:218] Iteration 1812 (2.40196 iter/s, 4.99591s/12 iters), loss = 5.28042
I0405 08:52:04.682655 30176 solver.cpp:237]     Train net output #0: loss = 5.28042 (* 1 = 5.28042 loss)
I0405 08:52:04.682662 30176 sgd_solver.cpp:105] Iteration 1812, lr = 1e-06
I0405 08:52:07.872071 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 08:52:09.674691 30176 solver.cpp:218] Iteration 1824 (2.40386 iter/s, 4.99197s/12 iters), loss = 5.28795
I0405 08:52:09.674823 30176 solver.cpp:237]     Train net output #0: loss = 5.28795 (* 1 = 5.28795 loss)
I0405 08:52:09.674830 30176 sgd_solver.cpp:105] Iteration 1824, lr = 1e-06
I0405 08:52:14.445132 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1836.caffemodel
I0405 08:52:17.379002 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1836.solverstate
I0405 08:52:19.681327 30176 solver.cpp:330] Iteration 1836, Testing net (#0)
I0405 08:52:19.681349 30176 net.cpp:676] Ignoring source layer train-data
I0405 08:52:23.306188 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 08:52:24.047152 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 08:52:24.047178 30176 solver.cpp:397]     Test net output #1: loss = 5.28008 (* 1 = 5.28008 loss)
I0405 08:52:24.187098 30176 solver.cpp:218] Iteration 1836 (0.826894 iter/s, 14.5121s/12 iters), loss = 5.27535
I0405 08:52:24.187150 30176 solver.cpp:237]     Train net output #0: loss = 5.27535 (* 1 = 5.27535 loss)
I0405 08:52:24.187157 30176 sgd_solver.cpp:105] Iteration 1836, lr = 1e-06
I0405 08:52:28.626780 30176 solver.cpp:218] Iteration 1848 (2.70296 iter/s, 4.43958s/12 iters), loss = 5.29897
I0405 08:52:28.626818 30176 solver.cpp:237]     Train net output #0: loss = 5.29897 (* 1 = 5.29897 loss)
I0405 08:52:28.626823 30176 sgd_solver.cpp:105] Iteration 1848, lr = 1e-06
I0405 08:52:33.905802 30176 solver.cpp:218] Iteration 1860 (2.27319 iter/s, 5.27892s/12 iters), loss = 5.2762
I0405 08:52:33.905843 30176 solver.cpp:237]     Train net output #0: loss = 5.2762 (* 1 = 5.2762 loss)
I0405 08:52:33.905848 30176 sgd_solver.cpp:105] Iteration 1860, lr = 1e-06
I0405 08:52:39.232461 30176 solver.cpp:218] Iteration 1872 (2.25286 iter/s, 5.32656s/12 iters), loss = 5.2717
I0405 08:52:39.232499 30176 solver.cpp:237]     Train net output #0: loss = 5.2717 (* 1 = 5.2717 loss)
I0405 08:52:39.232506 30176 sgd_solver.cpp:105] Iteration 1872, lr = 1e-06
I0405 08:52:44.320029 30176 solver.cpp:218] Iteration 1884 (2.35874 iter/s, 5.08747s/12 iters), loss = 5.2666
I0405 08:52:44.320152 30176 solver.cpp:237]     Train net output #0: loss = 5.2666 (* 1 = 5.2666 loss)
I0405 08:52:44.320158 30176 sgd_solver.cpp:105] Iteration 1884, lr = 1e-06
I0405 08:52:49.516758 30176 solver.cpp:218] Iteration 1896 (2.30922 iter/s, 5.19655s/12 iters), loss = 5.2748
I0405 08:52:49.516803 30176 solver.cpp:237]     Train net output #0: loss = 5.2748 (* 1 = 5.2748 loss)
I0405 08:52:49.516809 30176 sgd_solver.cpp:105] Iteration 1896, lr = 1e-06
I0405 08:52:54.965581 30176 solver.cpp:218] Iteration 1908 (2.20235 iter/s, 5.44872s/12 iters), loss = 5.27831
I0405 08:52:54.965624 30176 solver.cpp:237]     Train net output #0: loss = 5.27831 (* 1 = 5.27831 loss)
I0405 08:52:54.965631 30176 sgd_solver.cpp:105] Iteration 1908, lr = 1e-06
I0405 08:53:00.230037 30176 solver.cpp:218] Iteration 1920 (2.27948 iter/s, 5.26435s/12 iters), loss = 5.27831
I0405 08:53:00.230072 30176 solver.cpp:237]     Train net output #0: loss = 5.27831 (* 1 = 5.27831 loss)
I0405 08:53:00.230077 30176 sgd_solver.cpp:105] Iteration 1920, lr = 1e-06
I0405 08:53:00.516582 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 08:53:05.614841 30176 solver.cpp:218] Iteration 1932 (2.22853 iter/s, 5.38471s/12 iters), loss = 5.28847
I0405 08:53:05.614873 30176 solver.cpp:237]     Train net output #0: loss = 5.28847 (* 1 = 5.28847 loss)
I0405 08:53:05.614878 30176 sgd_solver.cpp:105] Iteration 1932, lr = 1e-06
I0405 08:53:07.786063 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1938.caffemodel
I0405 08:53:10.819494 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1938.solverstate
I0405 08:53:13.140177 30176 solver.cpp:330] Iteration 1938, Testing net (#0)
I0405 08:53:13.140197 30176 net.cpp:676] Ignoring source layer train-data
I0405 08:53:16.816864 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 08:53:17.657665 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 08:53:17.657689 30176 solver.cpp:397]     Test net output #1: loss = 5.27992 (* 1 = 5.27992 loss)
I0405 08:53:19.538928 30176 solver.cpp:218] Iteration 1944 (0.861827 iter/s, 13.9239s/12 iters), loss = 5.29366
I0405 08:53:19.538969 30176 solver.cpp:237]     Train net output #0: loss = 5.29366 (* 1 = 5.29366 loss)
I0405 08:53:19.538975 30176 sgd_solver.cpp:105] Iteration 1944, lr = 1e-06
I0405 08:53:24.886124 30176 solver.cpp:218] Iteration 1956 (2.24421 iter/s, 5.3471s/12 iters), loss = 5.29279
I0405 08:53:24.886160 30176 solver.cpp:237]     Train net output #0: loss = 5.29279 (* 1 = 5.29279 loss)
I0405 08:53:24.886166 30176 sgd_solver.cpp:105] Iteration 1956, lr = 1e-06
I0405 08:53:30.117007 30176 solver.cpp:218] Iteration 1968 (2.29411 iter/s, 5.23078s/12 iters), loss = 5.28174
I0405 08:53:30.117044 30176 solver.cpp:237]     Train net output #0: loss = 5.28174 (* 1 = 5.28174 loss)
I0405 08:53:30.117049 30176 sgd_solver.cpp:105] Iteration 1968, lr = 1e-06
I0405 08:53:35.402850 30176 solver.cpp:218] Iteration 1980 (2.27026 iter/s, 5.28573s/12 iters), loss = 5.28027
I0405 08:53:35.402906 30176 solver.cpp:237]     Train net output #0: loss = 5.28027 (* 1 = 5.28027 loss)
I0405 08:53:35.402915 30176 sgd_solver.cpp:105] Iteration 1980, lr = 1e-06
I0405 08:53:40.746774 30176 solver.cpp:218] Iteration 1992 (2.24559 iter/s, 5.34381s/12 iters), loss = 5.29944
I0405 08:53:40.746814 30176 solver.cpp:237]     Train net output #0: loss = 5.29944 (* 1 = 5.29944 loss)
I0405 08:53:40.746821 30176 sgd_solver.cpp:105] Iteration 1992, lr = 1e-06
I0405 08:53:46.068662 30176 solver.cpp:218] Iteration 2004 (2.25489 iter/s, 5.32178s/12 iters), loss = 5.28822
I0405 08:53:46.068706 30176 solver.cpp:237]     Train net output #0: loss = 5.28822 (* 1 = 5.28822 loss)
I0405 08:53:46.068712 30176 sgd_solver.cpp:105] Iteration 2004, lr = 1e-06
I0405 08:53:51.317128 30176 solver.cpp:218] Iteration 2016 (2.28643 iter/s, 5.24836s/12 iters), loss = 5.24992
I0405 08:53:51.317214 30176 solver.cpp:237]     Train net output #0: loss = 5.24992 (* 1 = 5.24992 loss)
I0405 08:53:51.317220 30176 sgd_solver.cpp:105] Iteration 2016, lr = 1e-06
I0405 08:53:53.972990 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 08:53:56.666150 30176 solver.cpp:218] Iteration 2028 (2.24346 iter/s, 5.34888s/12 iters), loss = 5.29143
I0405 08:53:56.666188 30176 solver.cpp:237]     Train net output #0: loss = 5.29143 (* 1 = 5.29143 loss)
I0405 08:53:56.666193 30176 sgd_solver.cpp:105] Iteration 2028, lr = 1e-06
I0405 08:54:01.533766 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2040.caffemodel
I0405 08:54:06.805579 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2040.solverstate
I0405 08:54:10.163425 30176 solver.cpp:330] Iteration 2040, Testing net (#0)
I0405 08:54:10.163447 30176 net.cpp:676] Ignoring source layer train-data
I0405 08:54:13.602874 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 08:54:14.419569 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 08:54:14.419605 30176 solver.cpp:397]     Test net output #1: loss = 5.28001 (* 1 = 5.28001 loss)
I0405 08:54:14.560837 30176 solver.cpp:218] Iteration 2040 (0.670598 iter/s, 17.8945s/12 iters), loss = 5.28225
I0405 08:54:14.560886 30176 solver.cpp:237]     Train net output #0: loss = 5.28225 (* 1 = 5.28225 loss)
I0405 08:54:14.560892 30176 sgd_solver.cpp:105] Iteration 2040, lr = 1e-06
I0405 08:54:18.976438 30176 solver.cpp:218] Iteration 2052 (2.7177 iter/s, 4.4155s/12 iters), loss = 5.28532
I0405 08:54:18.976480 30176 solver.cpp:237]     Train net output #0: loss = 5.28532 (* 1 = 5.28532 loss)
I0405 08:54:18.976486 30176 sgd_solver.cpp:105] Iteration 2052, lr = 1e-06
I0405 08:54:20.682418 30176 blocking_queue.cpp:49] Waiting for data
I0405 08:54:24.254768 30176 solver.cpp:218] Iteration 2064 (2.27349 iter/s, 5.27823s/12 iters), loss = 5.2786
I0405 08:54:24.254915 30176 solver.cpp:237]     Train net output #0: loss = 5.2786 (* 1 = 5.2786 loss)
I0405 08:54:24.254922 30176 sgd_solver.cpp:105] Iteration 2064, lr = 1e-06
I0405 08:54:29.330034 30176 solver.cpp:218] Iteration 2076 (2.3645 iter/s, 5.07506s/12 iters), loss = 5.28914
I0405 08:54:29.330073 30176 solver.cpp:237]     Train net output #0: loss = 5.28914 (* 1 = 5.28914 loss)
I0405 08:54:29.330078 30176 sgd_solver.cpp:105] Iteration 2076, lr = 1e-06
I0405 08:54:34.437819 30176 solver.cpp:218] Iteration 2088 (2.3494 iter/s, 5.10769s/12 iters), loss = 5.26187
I0405 08:54:34.437855 30176 solver.cpp:237]     Train net output #0: loss = 5.26187 (* 1 = 5.26187 loss)
I0405 08:54:34.437860 30176 sgd_solver.cpp:105] Iteration 2088, lr = 1e-06
I0405 08:54:39.802497 30176 solver.cpp:218] Iteration 2100 (2.23689 iter/s, 5.36458s/12 iters), loss = 5.27099
I0405 08:54:39.802531 30176 solver.cpp:237]     Train net output #0: loss = 5.27099 (* 1 = 5.27099 loss)
I0405 08:54:39.802536 30176 sgd_solver.cpp:105] Iteration 2100, lr = 1e-06
I0405 08:54:44.768522 30176 solver.cpp:218] Iteration 2112 (2.41647 iter/s, 4.96593s/12 iters), loss = 5.28345
I0405 08:54:44.768556 30176 solver.cpp:237]     Train net output #0: loss = 5.28345 (* 1 = 5.28345 loss)
I0405 08:54:44.768561 30176 sgd_solver.cpp:105] Iteration 2112, lr = 1e-06
I0405 08:54:49.513339 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 08:54:49.868774 30176 solver.cpp:218] Iteration 2124 (2.35287 iter/s, 5.10016s/12 iters), loss = 5.28386
I0405 08:54:49.868808 30176 solver.cpp:237]     Train net output #0: loss = 5.28386 (* 1 = 5.28386 loss)
I0405 08:54:49.868813 30176 sgd_solver.cpp:105] Iteration 2124, lr = 1e-06
I0405 08:54:55.286795 30176 solver.cpp:218] Iteration 2136 (2.21487 iter/s, 5.41792s/12 iters), loss = 5.28796
I0405 08:54:55.286890 30176 solver.cpp:237]     Train net output #0: loss = 5.28796 (* 1 = 5.28796 loss)
I0405 08:54:55.286896 30176 sgd_solver.cpp:105] Iteration 2136, lr = 1e-06
I0405 08:54:57.459693 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2142.caffemodel
I0405 08:55:00.516054 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2142.solverstate
I0405 08:55:02.930344 30176 solver.cpp:330] Iteration 2142, Testing net (#0)
I0405 08:55:02.930364 30176 net.cpp:676] Ignoring source layer train-data
I0405 08:55:06.397068 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 08:55:07.274724 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 08:55:07.274755 30176 solver.cpp:397]     Test net output #1: loss = 5.28004 (* 1 = 5.28004 loss)
I0405 08:55:09.072894 30176 solver.cpp:218] Iteration 2148 (0.870457 iter/s, 13.7859s/12 iters), loss = 5.28554
I0405 08:55:09.072937 30176 solver.cpp:237]     Train net output #0: loss = 5.28554 (* 1 = 5.28554 loss)
I0405 08:55:09.072943 30176 sgd_solver.cpp:105] Iteration 2148, lr = 1e-06
I0405 08:55:14.439286 30176 solver.cpp:218] Iteration 2160 (2.23618 iter/s, 5.36628s/12 iters), loss = 5.27422
I0405 08:55:14.439330 30176 solver.cpp:237]     Train net output #0: loss = 5.27422 (* 1 = 5.27422 loss)
I0405 08:55:14.439335 30176 sgd_solver.cpp:105] Iteration 2160, lr = 1e-06
I0405 08:55:19.610427 30176 solver.cpp:218] Iteration 2172 (2.32062 iter/s, 5.17103s/12 iters), loss = 5.2754
I0405 08:55:19.610471 30176 solver.cpp:237]     Train net output #0: loss = 5.2754 (* 1 = 5.2754 loss)
I0405 08:55:19.610476 30176 sgd_solver.cpp:105] Iteration 2172, lr = 1e-06
I0405 08:55:24.810555 30176 solver.cpp:218] Iteration 2184 (2.30768 iter/s, 5.20003s/12 iters), loss = 5.29467
I0405 08:55:24.810593 30176 solver.cpp:237]     Train net output #0: loss = 5.29467 (* 1 = 5.29467 loss)
I0405 08:55:24.810601 30176 sgd_solver.cpp:105] Iteration 2184, lr = 1e-06
I0405 08:55:30.126227 30176 solver.cpp:218] Iteration 2196 (2.25752 iter/s, 5.31558s/12 iters), loss = 5.27249
I0405 08:55:30.126358 30176 solver.cpp:237]     Train net output #0: loss = 5.27249 (* 1 = 5.27249 loss)
I0405 08:55:30.126364 30176 sgd_solver.cpp:105] Iteration 2196, lr = 1e-06
I0405 08:55:35.437703 30176 solver.cpp:218] Iteration 2208 (2.25934 iter/s, 5.31129s/12 iters), loss = 5.28243
I0405 08:55:35.437742 30176 solver.cpp:237]     Train net output #0: loss = 5.28243 (* 1 = 5.28243 loss)
I0405 08:55:35.437747 30176 sgd_solver.cpp:105] Iteration 2208, lr = 1e-06
I0405 08:55:40.702567 30176 solver.cpp:218] Iteration 2220 (2.27931 iter/s, 5.26475s/12 iters), loss = 5.28025
I0405 08:55:40.702625 30176 solver.cpp:237]     Train net output #0: loss = 5.28025 (* 1 = 5.28025 loss)
I0405 08:55:40.702636 30176 sgd_solver.cpp:105] Iteration 2220, lr = 1e-06
I0405 08:55:42.625581 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 08:55:46.246039 30176 solver.cpp:218] Iteration 2232 (2.16476 iter/s, 5.54335s/12 iters), loss = 5.28012
I0405 08:55:46.246088 30176 solver.cpp:237]     Train net output #0: loss = 5.28012 (* 1 = 5.28012 loss)
I0405 08:55:46.246094 30176 sgd_solver.cpp:105] Iteration 2232, lr = 1e-06
I0405 08:55:51.041426 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2244.caffemodel
I0405 08:55:54.099673 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2244.solverstate
I0405 08:55:56.392364 30176 solver.cpp:330] Iteration 2244, Testing net (#0)
I0405 08:55:56.392382 30176 net.cpp:676] Ignoring source layer train-data
I0405 08:55:59.748065 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 08:56:00.646467 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 08:56:00.646580 30176 solver.cpp:397]     Test net output #1: loss = 5.2801 (* 1 = 5.2801 loss)
I0405 08:56:00.787128 30176 solver.cpp:218] Iteration 2244 (0.825259 iter/s, 14.5409s/12 iters), loss = 5.29182
I0405 08:56:00.787190 30176 solver.cpp:237]     Train net output #0: loss = 5.29182 (* 1 = 5.29182 loss)
I0405 08:56:00.787199 30176 sgd_solver.cpp:105] Iteration 2244, lr = 1e-06
I0405 08:56:05.041312 30176 solver.cpp:218] Iteration 2256 (2.82083 iter/s, 4.25407s/12 iters), loss = 5.28838
I0405 08:56:05.041352 30176 solver.cpp:237]     Train net output #0: loss = 5.28838 (* 1 = 5.28838 loss)
I0405 08:56:05.041359 30176 sgd_solver.cpp:105] Iteration 2256, lr = 1e-06
I0405 08:56:10.296461 30176 solver.cpp:218] Iteration 2268 (2.28352 iter/s, 5.25505s/12 iters), loss = 5.28315
I0405 08:56:10.296499 30176 solver.cpp:237]     Train net output #0: loss = 5.28315 (* 1 = 5.28315 loss)
I0405 08:56:10.296505 30176 sgd_solver.cpp:105] Iteration 2268, lr = 1e-06
I0405 08:56:15.818516 30176 solver.cpp:218] Iteration 2280 (2.17315 iter/s, 5.52195s/12 iters), loss = 5.28706
I0405 08:56:15.818560 30176 solver.cpp:237]     Train net output #0: loss = 5.28706 (* 1 = 5.28706 loss)
I0405 08:56:15.818567 30176 sgd_solver.cpp:105] Iteration 2280, lr = 1e-06
I0405 08:56:21.028020 30176 solver.cpp:218] Iteration 2292 (2.30353 iter/s, 5.2094s/12 iters), loss = 5.29024
I0405 08:56:21.028057 30176 solver.cpp:237]     Train net output #0: loss = 5.29024 (* 1 = 5.29024 loss)
I0405 08:56:21.028061 30176 sgd_solver.cpp:105] Iteration 2292, lr = 1e-06
I0405 08:56:26.072849 30176 solver.cpp:218] Iteration 2304 (2.37872 iter/s, 5.04473s/12 iters), loss = 5.28423
I0405 08:56:26.072893 30176 solver.cpp:237]     Train net output #0: loss = 5.28423 (* 1 = 5.28423 loss)
I0405 08:56:26.072901 30176 sgd_solver.cpp:105] Iteration 2304, lr = 1e-06
I0405 08:56:31.326668 30176 solver.cpp:218] Iteration 2316 (2.2841 iter/s, 5.25371s/12 iters), loss = 5.2935
I0405 08:56:31.326761 30176 solver.cpp:237]     Train net output #0: loss = 5.2935 (* 1 = 5.2935 loss)
I0405 08:56:31.326767 30176 sgd_solver.cpp:105] Iteration 2316, lr = 1e-06
I0405 08:56:35.395174 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 08:56:36.564813 30176 solver.cpp:218] Iteration 2328 (2.29095 iter/s, 5.23799s/12 iters), loss = 5.29646
I0405 08:56:36.564847 30176 solver.cpp:237]     Train net output #0: loss = 5.29646 (* 1 = 5.29646 loss)
I0405 08:56:36.564852 30176 sgd_solver.cpp:105] Iteration 2328, lr = 1e-06
I0405 08:56:41.612704 30176 solver.cpp:218] Iteration 2340 (2.37728 iter/s, 5.04779s/12 iters), loss = 5.28652
I0405 08:56:41.612748 30176 solver.cpp:237]     Train net output #0: loss = 5.28652 (* 1 = 5.28652 loss)
I0405 08:56:41.612754 30176 sgd_solver.cpp:105] Iteration 2340, lr = 1e-06
I0405 08:56:43.817497 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2346.caffemodel
I0405 08:56:49.002259 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2346.solverstate
I0405 08:56:53.020998 30176 solver.cpp:330] Iteration 2346, Testing net (#0)
I0405 08:56:53.021020 30176 net.cpp:676] Ignoring source layer train-data
I0405 08:56:56.498176 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 08:56:57.427749 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 08:56:57.427778 30176 solver.cpp:397]     Test net output #1: loss = 5.28009 (* 1 = 5.28009 loss)
I0405 08:56:59.296422 30176 solver.cpp:218] Iteration 2352 (0.678599 iter/s, 17.6835s/12 iters), loss = 5.29242
I0405 08:56:59.296465 30176 solver.cpp:237]     Train net output #0: loss = 5.29242 (* 1 = 5.29242 loss)
I0405 08:56:59.296471 30176 sgd_solver.cpp:105] Iteration 2352, lr = 1e-06
I0405 08:57:04.587026 30176 solver.cpp:218] Iteration 2364 (2.26822 iter/s, 5.2905s/12 iters), loss = 5.26729
I0405 08:57:04.587143 30176 solver.cpp:237]     Train net output #0: loss = 5.26729 (* 1 = 5.26729 loss)
I0405 08:57:04.587149 30176 sgd_solver.cpp:105] Iteration 2364, lr = 1e-06
I0405 08:57:09.791366 30176 solver.cpp:218] Iteration 2376 (2.30584 iter/s, 5.20417s/12 iters), loss = 5.29919
I0405 08:57:09.791396 30176 solver.cpp:237]     Train net output #0: loss = 5.29919 (* 1 = 5.29919 loss)
I0405 08:57:09.791401 30176 sgd_solver.cpp:105] Iteration 2376, lr = 1e-06
I0405 08:57:15.160384 30176 solver.cpp:218] Iteration 2388 (2.23509 iter/s, 5.36892s/12 iters), loss = 5.28003
I0405 08:57:15.160429 30176 solver.cpp:237]     Train net output #0: loss = 5.28003 (* 1 = 5.28003 loss)
I0405 08:57:15.160434 30176 sgd_solver.cpp:105] Iteration 2388, lr = 1e-06
I0405 08:57:20.401484 30176 solver.cpp:218] Iteration 2400 (2.28964 iter/s, 5.241s/12 iters), loss = 5.2794
I0405 08:57:20.401531 30176 solver.cpp:237]     Train net output #0: loss = 5.2794 (* 1 = 5.2794 loss)
I0405 08:57:20.401540 30176 sgd_solver.cpp:105] Iteration 2400, lr = 1e-06
I0405 08:57:25.749250 30176 solver.cpp:218] Iteration 2412 (2.24397 iter/s, 5.34766s/12 iters), loss = 5.27072
I0405 08:57:25.749276 30176 solver.cpp:237]     Train net output #0: loss = 5.27072 (* 1 = 5.27072 loss)
I0405 08:57:25.749282 30176 sgd_solver.cpp:105] Iteration 2412, lr = 1e-06
I0405 08:57:31.113323 30176 solver.cpp:218] Iteration 2424 (2.23715 iter/s, 5.36397s/12 iters), loss = 5.28331
I0405 08:57:31.113376 30176 solver.cpp:237]     Train net output #0: loss = 5.28331 (* 1 = 5.28331 loss)
I0405 08:57:31.113385 30176 sgd_solver.cpp:105] Iteration 2424, lr = 1e-06
I0405 08:57:32.280596 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 08:57:36.587458 30176 solver.cpp:218] Iteration 2436 (2.19217 iter/s, 5.47402s/12 iters), loss = 5.27582
I0405 08:57:36.587594 30176 solver.cpp:237]     Train net output #0: loss = 5.27582 (* 1 = 5.27582 loss)
I0405 08:57:36.587600 30176 sgd_solver.cpp:105] Iteration 2436, lr = 1e-06
I0405 08:57:41.406316 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2448.caffemodel
I0405 08:57:44.458204 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2448.solverstate
I0405 08:57:46.879402 30176 solver.cpp:330] Iteration 2448, Testing net (#0)
I0405 08:57:46.879422 30176 net.cpp:676] Ignoring source layer train-data
I0405 08:57:50.261194 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 08:57:51.218394 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 08:57:51.218436 30176 solver.cpp:397]     Test net output #1: loss = 5.27983 (* 1 = 5.27983 loss)
I0405 08:57:51.358134 30176 solver.cpp:218] Iteration 2448 (0.812436 iter/s, 14.7704s/12 iters), loss = 5.27399
I0405 08:57:51.358179 30176 solver.cpp:237]     Train net output #0: loss = 5.27399 (* 1 = 5.27399 loss)
I0405 08:57:51.358186 30176 sgd_solver.cpp:105] Iteration 2448, lr = 1e-06
I0405 08:57:55.693178 30176 solver.cpp:218] Iteration 2460 (2.7682 iter/s, 4.33494s/12 iters), loss = 5.29973
I0405 08:57:55.693222 30176 solver.cpp:237]     Train net output #0: loss = 5.29973 (* 1 = 5.29973 loss)
I0405 08:57:55.693228 30176 sgd_solver.cpp:105] Iteration 2460, lr = 1e-06
I0405 08:58:00.977156 30176 solver.cpp:218] Iteration 2472 (2.27106 iter/s, 5.28388s/12 iters), loss = 5.2888
I0405 08:58:00.977186 30176 solver.cpp:237]     Train net output #0: loss = 5.2888 (* 1 = 5.2888 loss)
I0405 08:58:00.977192 30176 sgd_solver.cpp:105] Iteration 2472, lr = 1e-06
I0405 08:58:06.283696 30176 solver.cpp:218] Iteration 2484 (2.2614 iter/s, 5.30645s/12 iters), loss = 5.27269
I0405 08:58:06.283735 30176 solver.cpp:237]     Train net output #0: loss = 5.27269 (* 1 = 5.27269 loss)
I0405 08:58:06.283740 30176 sgd_solver.cpp:105] Iteration 2484, lr = 1e-06
I0405 08:58:11.623168 30176 solver.cpp:218] Iteration 2496 (2.24746 iter/s, 5.33937s/12 iters), loss = 5.28564
I0405 08:58:11.623287 30176 solver.cpp:237]     Train net output #0: loss = 5.28564 (* 1 = 5.28564 loss)
I0405 08:58:11.623293 30176 sgd_solver.cpp:105] Iteration 2496, lr = 1e-06
I0405 08:58:16.855224 30176 solver.cpp:218] Iteration 2508 (2.29363 iter/s, 5.23188s/12 iters), loss = 5.28937
I0405 08:58:16.855258 30176 solver.cpp:237]     Train net output #0: loss = 5.28937 (* 1 = 5.28937 loss)
I0405 08:58:16.855263 30176 sgd_solver.cpp:105] Iteration 2508, lr = 1e-06
I0405 08:58:22.043025 30176 solver.cpp:218] Iteration 2520 (2.31316 iter/s, 5.18771s/12 iters), loss = 5.28682
I0405 08:58:22.043057 30176 solver.cpp:237]     Train net output #0: loss = 5.28682 (* 1 = 5.28682 loss)
I0405 08:58:22.043063 30176 sgd_solver.cpp:105] Iteration 2520, lr = 1e-06
I0405 08:58:25.613014 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 08:58:27.623703 30176 solver.cpp:218] Iteration 2532 (2.15032 iter/s, 5.58057s/12 iters), loss = 5.2834
I0405 08:58:27.623746 30176 solver.cpp:237]     Train net output #0: loss = 5.2834 (* 1 = 5.2834 loss)
I0405 08:58:27.623751 30176 sgd_solver.cpp:105] Iteration 2532, lr = 1e-06
I0405 08:58:32.779244 30176 solver.cpp:218] Iteration 2544 (2.32764 iter/s, 5.15545s/12 iters), loss = 5.2885
I0405 08:58:32.779270 30176 solver.cpp:237]     Train net output #0: loss = 5.2885 (* 1 = 5.2885 loss)
I0405 08:58:32.779276 30176 sgd_solver.cpp:105] Iteration 2544, lr = 1e-06
I0405 08:58:35.025979 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2550.caffemodel
I0405 08:58:37.996668 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2550.solverstate
I0405 08:58:40.333887 30176 solver.cpp:330] Iteration 2550, Testing net (#0)
I0405 08:58:40.333906 30176 net.cpp:676] Ignoring source layer train-data
I0405 08:58:43.588299 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 08:58:44.600852 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 08:58:44.600915 30176 solver.cpp:397]     Test net output #1: loss = 5.27997 (* 1 = 5.27997 loss)
I0405 08:58:46.423418 30176 solver.cpp:218] Iteration 2556 (0.879507 iter/s, 13.644s/12 iters), loss = 5.30239
I0405 08:58:46.423460 30176 solver.cpp:237]     Train net output #0: loss = 5.30239 (* 1 = 5.30239 loss)
I0405 08:58:46.423465 30176 sgd_solver.cpp:105] Iteration 2556, lr = 1e-06
I0405 08:58:51.587306 30176 solver.cpp:218] Iteration 2568 (2.32388 iter/s, 5.16379s/12 iters), loss = 5.28101
I0405 08:58:51.587349 30176 solver.cpp:237]     Train net output #0: loss = 5.28101 (* 1 = 5.28101 loss)
I0405 08:58:51.587355 30176 sgd_solver.cpp:105] Iteration 2568, lr = 1e-06
I0405 08:58:56.688359 30176 solver.cpp:218] Iteration 2580 (2.3525 iter/s, 5.10095s/12 iters), loss = 5.28746
I0405 08:58:56.688400 30176 solver.cpp:237]     Train net output #0: loss = 5.28746 (* 1 = 5.28746 loss)
I0405 08:58:56.688406 30176 sgd_solver.cpp:105] Iteration 2580, lr = 1e-06
I0405 08:59:01.799208 30176 solver.cpp:218] Iteration 2592 (2.34799 iter/s, 5.11075s/12 iters), loss = 5.26485
I0405 08:59:01.799238 30176 solver.cpp:237]     Train net output #0: loss = 5.26485 (* 1 = 5.26485 loss)
I0405 08:59:01.799243 30176 sgd_solver.cpp:105] Iteration 2592, lr = 1e-06
I0405 08:59:07.240993 30176 solver.cpp:218] Iteration 2604 (2.2052 iter/s, 5.44169s/12 iters), loss = 5.29156
I0405 08:59:07.241029 30176 solver.cpp:237]     Train net output #0: loss = 5.29156 (* 1 = 5.29156 loss)
I0405 08:59:07.241034 30176 sgd_solver.cpp:105] Iteration 2604, lr = 1e-06
I0405 08:59:12.579840 30176 solver.cpp:218] Iteration 2616 (2.24772 iter/s, 5.33875s/12 iters), loss = 5.26723
I0405 08:59:12.579879 30176 solver.cpp:237]     Train net output #0: loss = 5.26723 (* 1 = 5.26723 loss)
I0405 08:59:12.579885 30176 sgd_solver.cpp:105] Iteration 2616, lr = 1e-06
I0405 08:59:17.600595 30176 solver.cpp:218] Iteration 2628 (2.39013 iter/s, 5.02066s/12 iters), loss = 5.28686
I0405 08:59:17.600713 30176 solver.cpp:237]     Train net output #0: loss = 5.28686 (* 1 = 5.28686 loss)
I0405 08:59:17.600718 30176 sgd_solver.cpp:105] Iteration 2628, lr = 1e-06
I0405 08:59:18.016279 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 08:59:22.832046 30176 solver.cpp:218] Iteration 2640 (2.2939 iter/s, 5.23128s/12 iters), loss = 5.28891
I0405 08:59:22.832084 30176 solver.cpp:237]     Train net output #0: loss = 5.28891 (* 1 = 5.28891 loss)
I0405 08:59:22.832090 30176 sgd_solver.cpp:105] Iteration 2640, lr = 1e-06
I0405 08:59:27.318006 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2652.caffemodel
I0405 08:59:30.376332 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2652.solverstate
I0405 08:59:32.701862 30176 solver.cpp:330] Iteration 2652, Testing net (#0)
I0405 08:59:32.701885 30176 net.cpp:676] Ignoring source layer train-data
I0405 08:59:35.973443 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 08:59:37.010190 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 08:59:37.010222 30176 solver.cpp:397]     Test net output #1: loss = 5.28004 (* 1 = 5.28004 loss)
I0405 08:59:37.147547 30176 solver.cpp:218] Iteration 2652 (0.838263 iter/s, 14.3153s/12 iters), loss = 5.29786
I0405 08:59:37.147591 30176 solver.cpp:237]     Train net output #0: loss = 5.29786 (* 1 = 5.29786 loss)
I0405 08:59:37.147599 30176 sgd_solver.cpp:105] Iteration 2652, lr = 1e-06
I0405 08:59:41.583626 30176 solver.cpp:218] Iteration 2664 (2.70515 iter/s, 4.43598s/12 iters), loss = 5.28224
I0405 08:59:41.583663 30176 solver.cpp:237]     Train net output #0: loss = 5.28224 (* 1 = 5.28224 loss)
I0405 08:59:41.583669 30176 sgd_solver.cpp:105] Iteration 2664, lr = 1e-06
I0405 08:59:47.025264 30176 solver.cpp:218] Iteration 2676 (2.20526 iter/s, 5.44154s/12 iters), loss = 5.28332
I0405 08:59:47.025300 30176 solver.cpp:237]     Train net output #0: loss = 5.28332 (* 1 = 5.28332 loss)
I0405 08:59:47.025305 30176 sgd_solver.cpp:105] Iteration 2676, lr = 1e-06
I0405 08:59:52.338075 30176 solver.cpp:218] Iteration 2688 (2.25874 iter/s, 5.31271s/12 iters), loss = 5.28249
I0405 08:59:52.338207 30176 solver.cpp:237]     Train net output #0: loss = 5.28249 (* 1 = 5.28249 loss)
I0405 08:59:52.338214 30176 sgd_solver.cpp:105] Iteration 2688, lr = 1e-06
I0405 08:59:57.787528 30176 solver.cpp:218] Iteration 2700 (2.20213 iter/s, 5.44927s/12 iters), loss = 5.28662
I0405 08:59:57.787564 30176 solver.cpp:237]     Train net output #0: loss = 5.28662 (* 1 = 5.28662 loss)
I0405 08:59:57.787568 30176 sgd_solver.cpp:105] Iteration 2700, lr = 1e-06
I0405 09:00:03.090392 30176 solver.cpp:218] Iteration 2712 (2.26297 iter/s, 5.30277s/12 iters), loss = 5.29235
I0405 09:00:03.090435 30176 solver.cpp:237]     Train net output #0: loss = 5.29235 (* 1 = 5.29235 loss)
I0405 09:00:03.090440 30176 sgd_solver.cpp:105] Iteration 2712, lr = 1e-06
I0405 09:00:08.319622 30176 solver.cpp:218] Iteration 2724 (2.29484 iter/s, 5.22913s/12 iters), loss = 5.26947
I0405 09:00:08.319649 30176 solver.cpp:237]     Train net output #0: loss = 5.26947 (* 1 = 5.26947 loss)
I0405 09:00:08.319653 30176 sgd_solver.cpp:105] Iteration 2724, lr = 1e-06
I0405 09:00:11.101413 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:00:13.716159 30176 solver.cpp:218] Iteration 2736 (2.22368 iter/s, 5.39645s/12 iters), loss = 5.29145
I0405 09:00:13.716193 30176 solver.cpp:237]     Train net output #0: loss = 5.29145 (* 1 = 5.29145 loss)
I0405 09:00:13.716199 30176 sgd_solver.cpp:105] Iteration 2736, lr = 1e-06
I0405 09:00:19.601073 30176 solver.cpp:218] Iteration 2748 (2.03915 iter/s, 5.88481s/12 iters), loss = 5.28772
I0405 09:00:19.601114 30176 solver.cpp:237]     Train net output #0: loss = 5.28772 (* 1 = 5.28772 loss)
I0405 09:00:19.601121 30176 sgd_solver.cpp:105] Iteration 2748, lr = 1e-06
I0405 09:00:21.682288 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2754.caffemodel
I0405 09:00:24.741173 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2754.solverstate
I0405 09:00:27.049764 30176 solver.cpp:330] Iteration 2754, Testing net (#0)
I0405 09:00:27.049784 30176 net.cpp:676] Ignoring source layer train-data
I0405 09:00:30.016569 30176 blocking_queue.cpp:49] Waiting for data
I0405 09:00:30.246860 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:00:31.335379 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 09:00:31.335410 30176 solver.cpp:397]     Test net output #1: loss = 5.28027 (* 1 = 5.28027 loss)
I0405 09:00:33.169173 30176 solver.cpp:218] Iteration 2760 (0.884439 iter/s, 13.5679s/12 iters), loss = 5.31254
I0405 09:00:33.169207 30176 solver.cpp:237]     Train net output #0: loss = 5.31254 (* 1 = 5.31254 loss)
I0405 09:00:33.169212 30176 sgd_solver.cpp:105] Iteration 2760, lr = 1e-06
I0405 09:00:38.366269 30176 solver.cpp:218] Iteration 2772 (2.30902 iter/s, 5.197s/12 iters), loss = 5.28916
I0405 09:00:38.366305 30176 solver.cpp:237]     Train net output #0: loss = 5.28916 (* 1 = 5.28916 loss)
I0405 09:00:38.366312 30176 sgd_solver.cpp:105] Iteration 2772, lr = 1e-06
I0405 09:00:43.933835 30176 solver.cpp:218] Iteration 2784 (2.15538 iter/s, 5.56746s/12 iters), loss = 5.29242
I0405 09:00:43.933876 30176 solver.cpp:237]     Train net output #0: loss = 5.29242 (* 1 = 5.29242 loss)
I0405 09:00:43.933881 30176 sgd_solver.cpp:105] Iteration 2784, lr = 1e-06
I0405 09:00:49.291672 30176 solver.cpp:218] Iteration 2796 (2.23975 iter/s, 5.35773s/12 iters), loss = 5.29494
I0405 09:00:49.291712 30176 solver.cpp:237]     Train net output #0: loss = 5.29494 (* 1 = 5.29494 loss)
I0405 09:00:49.291716 30176 sgd_solver.cpp:105] Iteration 2796, lr = 1e-06
I0405 09:00:54.587368 30176 solver.cpp:218] Iteration 2808 (2.26604 iter/s, 5.29559s/12 iters), loss = 5.28379
I0405 09:00:54.587410 30176 solver.cpp:237]     Train net output #0: loss = 5.28379 (* 1 = 5.28379 loss)
I0405 09:00:54.587416 30176 sgd_solver.cpp:105] Iteration 2808, lr = 1e-06
I0405 09:00:59.443297 30176 solver.cpp:218] Iteration 2820 (2.47126 iter/s, 4.85582s/12 iters), loss = 5.27505
I0405 09:00:59.443390 30176 solver.cpp:237]     Train net output #0: loss = 5.27505 (* 1 = 5.27505 loss)
I0405 09:00:59.443397 30176 sgd_solver.cpp:105] Iteration 2820, lr = 1e-06
I0405 09:01:04.334515 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:01:04.671118 30176 solver.cpp:218] Iteration 2832 (2.29548 iter/s, 5.22767s/12 iters), loss = 5.28128
I0405 09:01:04.671162 30176 solver.cpp:237]     Train net output #0: loss = 5.28128 (* 1 = 5.28128 loss)
I0405 09:01:04.671169 30176 sgd_solver.cpp:105] Iteration 2832, lr = 1e-06
I0405 09:01:10.003576 30176 solver.cpp:218] Iteration 2844 (2.25041 iter/s, 5.33235s/12 iters), loss = 5.28749
I0405 09:01:10.003618 30176 solver.cpp:237]     Train net output #0: loss = 5.28749 (* 1 = 5.28749 loss)
I0405 09:01:10.003624 30176 sgd_solver.cpp:105] Iteration 2844, lr = 1e-06
I0405 09:01:14.745911 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2856.caffemodel
I0405 09:01:17.818540 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2856.solverstate
I0405 09:01:20.124826 30176 solver.cpp:330] Iteration 2856, Testing net (#0)
I0405 09:01:20.124845 30176 net.cpp:676] Ignoring source layer train-data
I0405 09:01:23.401880 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:01:24.526139 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 09:01:24.526175 30176 solver.cpp:397]     Test net output #1: loss = 5.28026 (* 1 = 5.28026 loss)
I0405 09:01:24.662425 30176 solver.cpp:218] Iteration 2856 (0.818629 iter/s, 14.6587s/12 iters), loss = 5.28598
I0405 09:01:24.662490 30176 solver.cpp:237]     Train net output #0: loss = 5.28598 (* 1 = 5.28598 loss)
I0405 09:01:24.662498 30176 sgd_solver.cpp:105] Iteration 2856, lr = 1e-06
I0405 09:01:29.062939 30176 solver.cpp:218] Iteration 2868 (2.72702 iter/s, 4.4004s/12 iters), loss = 5.27881
I0405 09:01:29.062974 30176 solver.cpp:237]     Train net output #0: loss = 5.27881 (* 1 = 5.27881 loss)
I0405 09:01:29.062979 30176 sgd_solver.cpp:105] Iteration 2868, lr = 1e-06
I0405 09:01:34.531628 30176 solver.cpp:218] Iteration 2880 (2.19435 iter/s, 5.46859s/12 iters), loss = 5.26484
I0405 09:01:34.531747 30176 solver.cpp:237]     Train net output #0: loss = 5.26484 (* 1 = 5.26484 loss)
I0405 09:01:34.531754 30176 sgd_solver.cpp:105] Iteration 2880, lr = 1e-06
I0405 09:01:39.648717 30176 solver.cpp:218] Iteration 2892 (2.34517 iter/s, 5.11691s/12 iters), loss = 5.27955
I0405 09:01:39.648757 30176 solver.cpp:237]     Train net output #0: loss = 5.27955 (* 1 = 5.27955 loss)
I0405 09:01:39.648762 30176 sgd_solver.cpp:105] Iteration 2892, lr = 1e-06
I0405 09:01:45.074492 30176 solver.cpp:218] Iteration 2904 (2.2117 iter/s, 5.42568s/12 iters), loss = 5.29062
I0405 09:01:45.074517 30176 solver.cpp:237]     Train net output #0: loss = 5.29062 (* 1 = 5.29062 loss)
I0405 09:01:45.074522 30176 sgd_solver.cpp:105] Iteration 2904, lr = 1e-06
I0405 09:01:50.521801 30176 solver.cpp:218] Iteration 2916 (2.20296 iter/s, 5.44722s/12 iters), loss = 5.28253
I0405 09:01:50.521844 30176 solver.cpp:237]     Train net output #0: loss = 5.28253 (* 1 = 5.28253 loss)
I0405 09:01:50.521852 30176 sgd_solver.cpp:105] Iteration 2916, lr = 1e-06
I0405 09:01:55.840297 30176 solver.cpp:218] Iteration 2928 (2.25632 iter/s, 5.3184s/12 iters), loss = 5.28468
I0405 09:01:55.840332 30176 solver.cpp:237]     Train net output #0: loss = 5.28468 (* 1 = 5.28468 loss)
I0405 09:01:55.840337 30176 sgd_solver.cpp:105] Iteration 2928, lr = 1e-06
I0405 09:01:57.812701 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:02:01.288861 30176 solver.cpp:218] Iteration 2940 (2.20245 iter/s, 5.44847s/12 iters), loss = 5.27518
I0405 09:02:01.288900 30176 solver.cpp:237]     Train net output #0: loss = 5.27518 (* 1 = 5.27518 loss)
I0405 09:02:01.288906 30176 sgd_solver.cpp:105] Iteration 2940, lr = 1e-06
I0405 09:02:06.692306 30176 solver.cpp:218] Iteration 2952 (2.22085 iter/s, 5.40335s/12 iters), loss = 5.2843
I0405 09:02:06.692384 30176 solver.cpp:237]     Train net output #0: loss = 5.2843 (* 1 = 5.2843 loss)
I0405 09:02:06.692389 30176 sgd_solver.cpp:105] Iteration 2952, lr = 1e-06
I0405 09:02:09.085919 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2958.caffemodel
I0405 09:02:13.923640 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2958.solverstate
I0405 09:02:16.225653 30176 solver.cpp:330] Iteration 2958, Testing net (#0)
I0405 09:02:16.225673 30176 net.cpp:676] Ignoring source layer train-data
I0405 09:02:19.415119 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:02:20.589870 30176 solver.cpp:397]     Test net output #0: accuracy = 0.00735294
I0405 09:02:20.589913 30176 solver.cpp:397]     Test net output #1: loss = 5.28002 (* 1 = 5.28002 loss)
I0405 09:02:22.435071 30176 solver.cpp:218] Iteration 2964 (0.762266 iter/s, 15.7425s/12 iters), loss = 5.29252
I0405 09:02:22.435113 30176 solver.cpp:237]     Train net output #0: loss = 5.29252 (* 1 = 5.29252 loss)
I0405 09:02:22.435120 30176 sgd_solver.cpp:105] Iteration 2964, lr = 1e-06
I0405 09:02:27.739874 30176 solver.cpp:218] Iteration 2976 (2.26215 iter/s, 5.30469s/12 iters), loss = 5.28823
I0405 09:02:27.739921 30176 solver.cpp:237]     Train net output #0: loss = 5.28823 (* 1 = 5.28823 loss)
I0405 09:02:27.739928 30176 sgd_solver.cpp:105] Iteration 2976, lr = 1e-06
I0405 09:02:33.204823 30176 solver.cpp:218] Iteration 2988 (2.19586 iter/s, 5.46484s/12 iters), loss = 5.30106
I0405 09:02:33.204855 30176 solver.cpp:237]     Train net output #0: loss = 5.30106 (* 1 = 5.30106 loss)
I0405 09:02:33.204861 30176 sgd_solver.cpp:105] Iteration 2988, lr = 1e-06
I0405 09:02:38.689330 30176 solver.cpp:218] Iteration 3000 (2.18802 iter/s, 5.48441s/12 iters), loss = 5.2813
I0405 09:02:38.689482 30176 solver.cpp:237]     Train net output #0: loss = 5.2813 (* 1 = 5.2813 loss)
I0405 09:02:38.689489 30176 sgd_solver.cpp:105] Iteration 3000, lr = 1e-06
I0405 09:02:43.867139 30176 solver.cpp:218] Iteration 3012 (2.31768 iter/s, 5.1776s/12 iters), loss = 5.28631
I0405 09:02:43.867183 30176 solver.cpp:237]     Train net output #0: loss = 5.28631 (* 1 = 5.28631 loss)
I0405 09:02:43.867189 30176 sgd_solver.cpp:105] Iteration 3012, lr = 1e-06
I0405 09:02:49.096868 30176 solver.cpp:218] Iteration 3024 (2.29462 iter/s, 5.22962s/12 iters), loss = 5.2902
I0405 09:02:49.096910 30176 solver.cpp:237]     Train net output #0: loss = 5.2902 (* 1 = 5.2902 loss)
I0405 09:02:49.096915 30176 sgd_solver.cpp:105] Iteration 3024, lr = 1e-06
I0405 09:02:53.383891 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:02:54.523458 30176 solver.cpp:218] Iteration 3036 (2.21138 iter/s, 5.42649s/12 iters), loss = 5.29059
I0405 09:02:54.523495 30176 solver.cpp:237]     Train net output #0: loss = 5.29059 (* 1 = 5.29059 loss)
I0405 09:02:54.523500 30176 sgd_solver.cpp:105] Iteration 3036, lr = 1e-06
I0405 09:02:59.661388 30176 solver.cpp:218] Iteration 3048 (2.33562 iter/s, 5.13783s/12 iters), loss = 5.28409
I0405 09:02:59.661428 30176 solver.cpp:237]     Train net output #0: loss = 5.28409 (* 1 = 5.28409 loss)
I0405 09:02:59.661434 30176 sgd_solver.cpp:105] Iteration 3048, lr = 1e-06
I0405 09:03:04.356518 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3060.caffemodel
I0405 09:03:07.347373 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3060.solverstate
I0405 09:03:09.670979 30176 solver.cpp:330] Iteration 3060, Testing net (#0)
I0405 09:03:09.671061 30176 net.cpp:676] Ignoring source layer train-data
I0405 09:03:12.805598 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:03:14.001406 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 09:03:14.001441 30176 solver.cpp:397]     Test net output #1: loss = 5.27974 (* 1 = 5.27974 loss)
I0405 09:03:14.142154 30176 solver.cpp:218] Iteration 3060 (0.828696 iter/s, 14.4806s/12 iters), loss = 5.27441
I0405 09:03:14.142218 30176 solver.cpp:237]     Train net output #0: loss = 5.27441 (* 1 = 5.27441 loss)
I0405 09:03:14.142226 30176 sgd_solver.cpp:105] Iteration 3060, lr = 1e-06
I0405 09:03:18.356174 30176 solver.cpp:218] Iteration 3072 (2.84772 iter/s, 4.2139s/12 iters), loss = 5.28075
I0405 09:03:18.356225 30176 solver.cpp:237]     Train net output #0: loss = 5.28075 (* 1 = 5.28075 loss)
I0405 09:03:18.356232 30176 sgd_solver.cpp:105] Iteration 3072, lr = 1e-06
I0405 09:03:23.723850 30176 solver.cpp:218] Iteration 3084 (2.23565 iter/s, 5.36756s/12 iters), loss = 5.29185
I0405 09:03:23.723892 30176 solver.cpp:237]     Train net output #0: loss = 5.29185 (* 1 = 5.29185 loss)
I0405 09:03:23.723897 30176 sgd_solver.cpp:105] Iteration 3084, lr = 1e-06
I0405 09:03:28.981866 30176 solver.cpp:218] Iteration 3096 (2.28228 iter/s, 5.25791s/12 iters), loss = 5.2865
I0405 09:03:28.981911 30176 solver.cpp:237]     Train net output #0: loss = 5.2865 (* 1 = 5.2865 loss)
I0405 09:03:28.981920 30176 sgd_solver.cpp:105] Iteration 3096, lr = 1e-06
I0405 09:03:34.166280 30176 solver.cpp:218] Iteration 3108 (2.31468 iter/s, 5.1843s/12 iters), loss = 5.29774
I0405 09:03:34.166329 30176 solver.cpp:237]     Train net output #0: loss = 5.29774 (* 1 = 5.29774 loss)
I0405 09:03:34.166337 30176 sgd_solver.cpp:105] Iteration 3108, lr = 1e-06
I0405 09:03:39.614261 30176 solver.cpp:218] Iteration 3120 (2.2027 iter/s, 5.44786s/12 iters), loss = 5.27077
I0405 09:03:39.614308 30176 solver.cpp:237]     Train net output #0: loss = 5.27077 (* 1 = 5.27077 loss)
I0405 09:03:39.614316 30176 sgd_solver.cpp:105] Iteration 3120, lr = 1e-06
I0405 09:03:44.620108 30176 solver.cpp:218] Iteration 3132 (2.39724 iter/s, 5.00575s/12 iters), loss = 5.27552
I0405 09:03:44.620261 30176 solver.cpp:237]     Train net output #0: loss = 5.27552 (* 1 = 5.27552 loss)
I0405 09:03:44.620270 30176 sgd_solver.cpp:105] Iteration 3132, lr = 1e-06
I0405 09:03:45.775820 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:03:49.950495 30176 solver.cpp:218] Iteration 3144 (2.25133 iter/s, 5.33018s/12 iters), loss = 5.27947
I0405 09:03:49.950533 30176 solver.cpp:237]     Train net output #0: loss = 5.27947 (* 1 = 5.27947 loss)
I0405 09:03:49.950539 30176 sgd_solver.cpp:105] Iteration 3144, lr = 1e-06
I0405 09:03:55.191320 30176 solver.cpp:218] Iteration 3156 (2.28976 iter/s, 5.24072s/12 iters), loss = 5.26704
I0405 09:03:55.191365 30176 solver.cpp:237]     Train net output #0: loss = 5.26704 (* 1 = 5.26704 loss)
I0405 09:03:55.191371 30176 sgd_solver.cpp:105] Iteration 3156, lr = 1e-06
I0405 09:03:57.216928 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3162.caffemodel
I0405 09:04:00.252357 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3162.solverstate
I0405 09:04:02.565846 30176 solver.cpp:330] Iteration 3162, Testing net (#0)
I0405 09:04:02.565863 30176 net.cpp:676] Ignoring source layer train-data
I0405 09:04:05.616900 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:04:06.861440 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 09:04:06.861475 30176 solver.cpp:397]     Test net output #1: loss = 5.28033 (* 1 = 5.28033 loss)
I0405 09:04:08.722635 30176 solver.cpp:218] Iteration 3168 (0.886844 iter/s, 13.5311s/12 iters), loss = 5.29959
I0405 09:04:08.722683 30176 solver.cpp:237]     Train net output #0: loss = 5.29959 (* 1 = 5.29959 loss)
I0405 09:04:08.722690 30176 sgd_solver.cpp:105] Iteration 3168, lr = 1e-06
I0405 09:04:14.018090 30176 solver.cpp:218] Iteration 3180 (2.26614 iter/s, 5.29535s/12 iters), loss = 5.29557
I0405 09:04:14.018119 30176 solver.cpp:237]     Train net output #0: loss = 5.29557 (* 1 = 5.29557 loss)
I0405 09:04:14.018126 30176 sgd_solver.cpp:105] Iteration 3180, lr = 1e-06
I0405 09:04:19.551728 30176 solver.cpp:218] Iteration 3192 (2.16859 iter/s, 5.53354s/12 iters), loss = 5.27772
I0405 09:04:19.551817 30176 solver.cpp:237]     Train net output #0: loss = 5.27772 (* 1 = 5.27772 loss)
I0405 09:04:19.551824 30176 sgd_solver.cpp:105] Iteration 3192, lr = 1e-06
I0405 09:04:24.883836 30176 solver.cpp:218] Iteration 3204 (2.25058 iter/s, 5.33196s/12 iters), loss = 5.2996
I0405 09:04:24.883872 30176 solver.cpp:237]     Train net output #0: loss = 5.2996 (* 1 = 5.2996 loss)
I0405 09:04:24.883877 30176 sgd_solver.cpp:105] Iteration 3204, lr = 1e-06
I0405 09:04:30.362238 30176 solver.cpp:218] Iteration 3216 (2.19046 iter/s, 5.4783s/12 iters), loss = 5.28399
I0405 09:04:30.362277 30176 solver.cpp:237]     Train net output #0: loss = 5.28399 (* 1 = 5.28399 loss)
I0405 09:04:30.362283 30176 sgd_solver.cpp:105] Iteration 3216, lr = 1e-06
I0405 09:04:35.689710 30176 solver.cpp:218] Iteration 3228 (2.25252 iter/s, 5.32737s/12 iters), loss = 5.2937
I0405 09:04:35.689750 30176 solver.cpp:237]     Train net output #0: loss = 5.2937 (* 1 = 5.2937 loss)
I0405 09:04:35.689755 30176 sgd_solver.cpp:105] Iteration 3228, lr = 1e-06
I0405 09:04:38.956032 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:04:40.945209 30176 solver.cpp:218] Iteration 3240 (2.28337 iter/s, 5.2554s/12 iters), loss = 5.28766
I0405 09:04:40.945250 30176 solver.cpp:237]     Train net output #0: loss = 5.28766 (* 1 = 5.28766 loss)
I0405 09:04:40.945256 30176 sgd_solver.cpp:105] Iteration 3240, lr = 1e-06
I0405 09:04:46.466785 30176 solver.cpp:218] Iteration 3252 (2.17333 iter/s, 5.52148s/12 iters), loss = 5.2901
I0405 09:04:46.466820 30176 solver.cpp:237]     Train net output #0: loss = 5.2901 (* 1 = 5.2901 loss)
I0405 09:04:46.466825 30176 sgd_solver.cpp:105] Iteration 3252, lr = 1e-06
I0405 09:04:51.267992 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3264.caffemodel
I0405 09:04:55.924273 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3264.solverstate
I0405 09:05:01.136003 30176 solver.cpp:330] Iteration 3264, Testing net (#0)
I0405 09:05:01.136021 30176 net.cpp:676] Ignoring source layer train-data
I0405 09:05:04.141283 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:05:05.413533 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 09:05:05.413564 30176 solver.cpp:397]     Test net output #1: loss = 5.28007 (* 1 = 5.28007 loss)
I0405 09:05:05.554634 30176 solver.cpp:218] Iteration 3264 (0.628679 iter/s, 19.0876s/12 iters), loss = 5.28557
I0405 09:05:05.554675 30176 solver.cpp:237]     Train net output #0: loss = 5.28557 (* 1 = 5.28557 loss)
I0405 09:05:05.554680 30176 sgd_solver.cpp:105] Iteration 3264, lr = 1e-06
I0405 09:05:09.684705 30176 solver.cpp:218] Iteration 3276 (2.90558 iter/s, 4.12998s/12 iters), loss = 5.28912
I0405 09:05:09.684738 30176 solver.cpp:237]     Train net output #0: loss = 5.28912 (* 1 = 5.28912 loss)
I0405 09:05:09.684743 30176 sgd_solver.cpp:105] Iteration 3276, lr = 1e-06
I0405 09:05:14.948232 30176 solver.cpp:218] Iteration 3288 (2.27988 iter/s, 5.26343s/12 iters), loss = 5.27991
I0405 09:05:14.948280 30176 solver.cpp:237]     Train net output #0: loss = 5.27991 (* 1 = 5.27991 loss)
I0405 09:05:14.948287 30176 sgd_solver.cpp:105] Iteration 3288, lr = 1e-06
I0405 09:05:20.328521 30176 solver.cpp:218] Iteration 3300 (2.23041 iter/s, 5.38017s/12 iters), loss = 5.27277
I0405 09:05:20.328568 30176 solver.cpp:237]     Train net output #0: loss = 5.27277 (* 1 = 5.27277 loss)
I0405 09:05:20.328575 30176 sgd_solver.cpp:105] Iteration 3300, lr = 1e-06
I0405 09:05:25.439970 30176 solver.cpp:218] Iteration 3312 (2.34772 iter/s, 5.11134s/12 iters), loss = 5.29716
I0405 09:05:25.440078 30176 solver.cpp:237]     Train net output #0: loss = 5.29716 (* 1 = 5.29716 loss)
I0405 09:05:25.440083 30176 sgd_solver.cpp:105] Iteration 3312, lr = 1e-06
I0405 09:05:30.612181 30176 solver.cpp:218] Iteration 3324 (2.32017 iter/s, 5.17204s/12 iters), loss = 5.2787
I0405 09:05:30.612228 30176 solver.cpp:237]     Train net output #0: loss = 5.2787 (* 1 = 5.2787 loss)
I0405 09:05:30.612236 30176 sgd_solver.cpp:105] Iteration 3324, lr = 1e-06
I0405 09:05:36.029525 30176 solver.cpp:218] Iteration 3336 (2.21515 iter/s, 5.41723s/12 iters), loss = 5.2918
I0405 09:05:36.029567 30176 solver.cpp:237]     Train net output #0: loss = 5.2918 (* 1 = 5.2918 loss)
I0405 09:05:36.029572 30176 sgd_solver.cpp:105] Iteration 3336, lr = 1e-06
I0405 09:05:36.533500 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:05:41.262920 30176 solver.cpp:218] Iteration 3348 (2.29301 iter/s, 5.2333s/12 iters), loss = 5.30188
I0405 09:05:41.262956 30176 solver.cpp:237]     Train net output #0: loss = 5.30188 (* 1 = 5.30188 loss)
I0405 09:05:41.262961 30176 sgd_solver.cpp:105] Iteration 3348, lr = 1e-06
I0405 09:05:46.356582 30176 solver.cpp:218] Iteration 3360 (2.35591 iter/s, 5.09356s/12 iters), loss = 5.30179
I0405 09:05:46.356623 30176 solver.cpp:237]     Train net output #0: loss = 5.30179 (* 1 = 5.30179 loss)
I0405 09:05:46.356628 30176 sgd_solver.cpp:105] Iteration 3360, lr = 1e-06
I0405 09:05:48.506203 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3366.caffemodel
I0405 09:05:51.545392 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3366.solverstate
I0405 09:05:54.455729 30176 solver.cpp:330] Iteration 3366, Testing net (#0)
I0405 09:05:54.455746 30176 net.cpp:676] Ignoring source layer train-data
I0405 09:05:57.641997 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:05:59.049985 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 09:05:59.050014 30176 solver.cpp:397]     Test net output #1: loss = 5.28034 (* 1 = 5.28034 loss)
I0405 09:06:00.878499 30176 solver.cpp:218] Iteration 3372 (0.826348 iter/s, 14.5217s/12 iters), loss = 5.28909
I0405 09:06:00.878561 30176 solver.cpp:237]     Train net output #0: loss = 5.28909 (* 1 = 5.28909 loss)
I0405 09:06:00.878573 30176 sgd_solver.cpp:105] Iteration 3372, lr = 1e-06
I0405 09:06:05.913213 30176 solver.cpp:218] Iteration 3384 (2.38351 iter/s, 5.0346s/12 iters), loss = 5.2812
I0405 09:06:05.913251 30176 solver.cpp:237]     Train net output #0: loss = 5.2812 (* 1 = 5.2812 loss)
I0405 09:06:05.913257 30176 sgd_solver.cpp:105] Iteration 3384, lr = 1e-06
I0405 09:06:11.365612 30176 solver.cpp:218] Iteration 3396 (2.20091 iter/s, 5.45229s/12 iters), loss = 5.28256
I0405 09:06:11.365659 30176 solver.cpp:237]     Train net output #0: loss = 5.28256 (* 1 = 5.28256 loss)
I0405 09:06:11.365667 30176 sgd_solver.cpp:105] Iteration 3396, lr = 1e-06
I0405 09:06:16.777575 30176 solver.cpp:218] Iteration 3408 (2.21735 iter/s, 5.41186s/12 iters), loss = 5.28415
I0405 09:06:16.777607 30176 solver.cpp:237]     Train net output #0: loss = 5.28415 (* 1 = 5.28415 loss)
I0405 09:06:16.777612 30176 sgd_solver.cpp:105] Iteration 3408, lr = 1e-06
I0405 09:06:22.195220 30176 solver.cpp:218] Iteration 3420 (2.21502 iter/s, 5.41755s/12 iters), loss = 5.30553
I0405 09:06:22.195261 30176 solver.cpp:237]     Train net output #0: loss = 5.30553 (* 1 = 5.30553 loss)
I0405 09:06:22.195268 30176 sgd_solver.cpp:105] Iteration 3420, lr = 1e-06
I0405 09:06:27.469202 30176 solver.cpp:218] Iteration 3432 (2.27537 iter/s, 5.27387s/12 iters), loss = 5.26718
I0405 09:06:27.469259 30176 solver.cpp:237]     Train net output #0: loss = 5.26718 (* 1 = 5.26718 loss)
I0405 09:06:27.469272 30176 sgd_solver.cpp:105] Iteration 3432, lr = 1e-06
I0405 09:06:30.161794 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:06:32.781237 30176 solver.cpp:218] Iteration 3444 (2.25907 iter/s, 5.31192s/12 iters), loss = 5.28883
I0405 09:06:32.781282 30176 solver.cpp:237]     Train net output #0: loss = 5.28883 (* 1 = 5.28883 loss)
I0405 09:06:32.781287 30176 sgd_solver.cpp:105] Iteration 3444, lr = 1e-06
I0405 09:06:37.930181 30176 solver.cpp:218] Iteration 3456 (2.33062 iter/s, 5.14884s/12 iters), loss = 5.27596
I0405 09:06:37.930223 30176 solver.cpp:237]     Train net output #0: loss = 5.27596 (* 1 = 5.27596 loss)
I0405 09:06:37.930230 30176 sgd_solver.cpp:105] Iteration 3456, lr = 1e-06
I0405 09:06:42.560319 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3468.caffemodel
I0405 09:06:45.525817 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3468.solverstate
I0405 09:06:47.836277 30176 solver.cpp:330] Iteration 3468, Testing net (#0)
I0405 09:06:47.836302 30176 net.cpp:676] Ignoring source layer train-data
I0405 09:06:48.305900 30176 blocking_queue.cpp:49] Waiting for data
I0405 09:06:50.846298 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:06:52.278239 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 09:06:52.278267 30176 solver.cpp:397]     Test net output #1: loss = 5.27993 (* 1 = 5.27993 loss)
I0405 09:06:52.419155 30176 solver.cpp:218] Iteration 3468 (0.828227 iter/s, 14.4888s/12 iters), loss = 5.30424
I0405 09:06:52.419215 30176 solver.cpp:237]     Train net output #0: loss = 5.30424 (* 1 = 5.30424 loss)
I0405 09:06:52.419224 30176 sgd_solver.cpp:105] Iteration 3468, lr = 1e-06
I0405 09:06:56.776778 30176 solver.cpp:218] Iteration 3480 (2.75386 iter/s, 4.35752s/12 iters), loss = 5.28069
I0405 09:06:56.776811 30176 solver.cpp:237]     Train net output #0: loss = 5.28069 (* 1 = 5.28069 loss)
I0405 09:06:56.776816 30176 sgd_solver.cpp:105] Iteration 3480, lr = 1e-06
I0405 09:07:01.725358 30176 solver.cpp:218] Iteration 3492 (2.42499 iter/s, 4.94848s/12 iters), loss = 5.30169
I0405 09:07:01.725541 30176 solver.cpp:237]     Train net output #0: loss = 5.30169 (* 1 = 5.30169 loss)
I0405 09:07:01.725551 30176 sgd_solver.cpp:105] Iteration 3492, lr = 1e-06
I0405 09:07:06.715871 30176 solver.cpp:218] Iteration 3504 (2.40468 iter/s, 4.99027s/12 iters), loss = 5.29029
I0405 09:07:06.715920 30176 solver.cpp:237]     Train net output #0: loss = 5.29029 (* 1 = 5.29029 loss)
I0405 09:07:06.715926 30176 sgd_solver.cpp:105] Iteration 3504, lr = 1e-06
I0405 09:07:11.950961 30176 solver.cpp:218] Iteration 3516 (2.29227 iter/s, 5.23499s/12 iters), loss = 5.27876
I0405 09:07:11.950989 30176 solver.cpp:237]     Train net output #0: loss = 5.27876 (* 1 = 5.27876 loss)
I0405 09:07:11.950994 30176 sgd_solver.cpp:105] Iteration 3516, lr = 1e-06
I0405 09:07:17.151962 30176 solver.cpp:218] Iteration 3528 (2.30729 iter/s, 5.20091s/12 iters), loss = 5.27843
I0405 09:07:17.151999 30176 solver.cpp:237]     Train net output #0: loss = 5.27843 (* 1 = 5.27843 loss)
I0405 09:07:17.152005 30176 sgd_solver.cpp:105] Iteration 3528, lr = 1e-06
I0405 09:07:22.128235 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:07:22.431604 30176 solver.cpp:218] Iteration 3540 (2.27292 iter/s, 5.27955s/12 iters), loss = 5.28712
I0405 09:07:22.431641 30176 solver.cpp:237]     Train net output #0: loss = 5.28712 (* 1 = 5.28712 loss)
I0405 09:07:22.431646 30176 sgd_solver.cpp:105] Iteration 3540, lr = 1e-06
I0405 09:07:27.805246 30176 solver.cpp:218] Iteration 3552 (2.23317 iter/s, 5.37354s/12 iters), loss = 5.2895
I0405 09:07:27.805295 30176 solver.cpp:237]     Train net output #0: loss = 5.2895 (* 1 = 5.2895 loss)
I0405 09:07:27.805301 30176 sgd_solver.cpp:105] Iteration 3552, lr = 1e-06
I0405 09:07:33.180745 30176 solver.cpp:218] Iteration 3564 (2.2324 iter/s, 5.37539s/12 iters), loss = 5.28774
I0405 09:07:33.180852 30176 solver.cpp:237]     Train net output #0: loss = 5.28774 (* 1 = 5.28774 loss)
I0405 09:07:33.180858 30176 sgd_solver.cpp:105] Iteration 3564, lr = 1e-06
I0405 09:07:35.425841 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3570.caffemodel
I0405 09:07:40.633899 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3570.solverstate
I0405 09:07:43.993537 30176 solver.cpp:330] Iteration 3570, Testing net (#0)
I0405 09:07:43.993561 30176 net.cpp:676] Ignoring source layer train-data
I0405 09:07:46.916805 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:07:48.352843 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 09:07:48.352922 30176 solver.cpp:397]     Test net output #1: loss = 5.28031 (* 1 = 5.28031 loss)
I0405 09:07:50.292178 30176 solver.cpp:218] Iteration 3576 (0.701296 iter/s, 17.1112s/12 iters), loss = 5.28204
I0405 09:07:50.292213 30176 solver.cpp:237]     Train net output #0: loss = 5.28204 (* 1 = 5.28204 loss)
I0405 09:07:50.292217 30176 sgd_solver.cpp:105] Iteration 3576, lr = 1e-06
I0405 09:07:55.736488 30176 solver.cpp:218] Iteration 3588 (2.20418 iter/s, 5.44421s/12 iters), loss = 5.27524
I0405 09:07:55.736528 30176 solver.cpp:237]     Train net output #0: loss = 5.27524 (* 1 = 5.27524 loss)
I0405 09:07:55.736534 30176 sgd_solver.cpp:105] Iteration 3588, lr = 1e-06
I0405 09:08:01.186726 30176 solver.cpp:218] Iteration 3600 (2.20178 iter/s, 5.45013s/12 iters), loss = 5.28599
I0405 09:08:01.186767 30176 solver.cpp:237]     Train net output #0: loss = 5.28599 (* 1 = 5.28599 loss)
I0405 09:08:01.186774 30176 sgd_solver.cpp:105] Iteration 3600, lr = 1e-06
I0405 09:08:06.597424 30176 solver.cpp:218] Iteration 3612 (2.21787 iter/s, 5.41059s/12 iters), loss = 5.29515
I0405 09:08:06.597558 30176 solver.cpp:237]     Train net output #0: loss = 5.29515 (* 1 = 5.29515 loss)
I0405 09:08:06.597568 30176 sgd_solver.cpp:105] Iteration 3612, lr = 1e-06
I0405 09:08:12.096478 30176 solver.cpp:218] Iteration 3624 (2.18227 iter/s, 5.49886s/12 iters), loss = 5.28478
I0405 09:08:12.096518 30176 solver.cpp:237]     Train net output #0: loss = 5.28478 (* 1 = 5.28478 loss)
I0405 09:08:12.096524 30176 sgd_solver.cpp:105] Iteration 3624, lr = 1e-06
I0405 09:08:17.245448 30176 solver.cpp:218] Iteration 3636 (2.33061 iter/s, 5.14886s/12 iters), loss = 5.28409
I0405 09:08:17.245496 30176 solver.cpp:237]     Train net output #0: loss = 5.28409 (* 1 = 5.28409 loss)
I0405 09:08:17.245502 30176 sgd_solver.cpp:105] Iteration 3636, lr = 1e-06
I0405 09:08:19.146921 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:08:22.401962 30176 solver.cpp:218] Iteration 3648 (2.3272 iter/s, 5.15641s/12 iters), loss = 5.27796
I0405 09:08:22.401999 30176 solver.cpp:237]     Train net output #0: loss = 5.27796 (* 1 = 5.27796 loss)
I0405 09:08:22.402005 30176 sgd_solver.cpp:105] Iteration 3648, lr = 1e-06
I0405 09:08:27.740417 30176 solver.cpp:218] Iteration 3660 (2.24788 iter/s, 5.33836s/12 iters), loss = 5.30447
I0405 09:08:27.740454 30176 solver.cpp:237]     Train net output #0: loss = 5.30447 (* 1 = 5.30447 loss)
I0405 09:08:27.740459 30176 sgd_solver.cpp:105] Iteration 3660, lr = 1e-06
I0405 09:08:32.476650 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3672.caffemodel
I0405 09:08:35.485435 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3672.solverstate
I0405 09:08:40.452265 30176 solver.cpp:330] Iteration 3672, Testing net (#0)
I0405 09:08:40.452337 30176 net.cpp:676] Ignoring source layer train-data
I0405 09:08:43.420508 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:08:44.849572 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 09:08:44.849598 30176 solver.cpp:397]     Test net output #1: loss = 5.28024 (* 1 = 5.28024 loss)
I0405 09:08:44.990521 30176 solver.cpp:218] Iteration 3672 (0.695656 iter/s, 17.2499s/12 iters), loss = 5.27816
I0405 09:08:44.990568 30176 solver.cpp:237]     Train net output #0: loss = 5.27816 (* 1 = 5.27816 loss)
I0405 09:08:44.990573 30176 sgd_solver.cpp:105] Iteration 3672, lr = 1e-06
I0405 09:08:49.554955 30176 solver.cpp:218] Iteration 3684 (2.62908 iter/s, 4.56434s/12 iters), loss = 5.27121
I0405 09:08:49.554987 30176 solver.cpp:237]     Train net output #0: loss = 5.27121 (* 1 = 5.27121 loss)
I0405 09:08:49.554993 30176 sgd_solver.cpp:105] Iteration 3684, lr = 1e-06
I0405 09:08:54.771476 30176 solver.cpp:218] Iteration 3696 (2.30043 iter/s, 5.21643s/12 iters), loss = 5.28907
I0405 09:08:54.771514 30176 solver.cpp:237]     Train net output #0: loss = 5.28907 (* 1 = 5.28907 loss)
I0405 09:08:54.771520 30176 sgd_solver.cpp:105] Iteration 3696, lr = 1e-06
I0405 09:09:00.124044 30176 solver.cpp:218] Iteration 3708 (2.24196 iter/s, 5.35246s/12 iters), loss = 5.2811
I0405 09:09:00.124104 30176 solver.cpp:237]     Train net output #0: loss = 5.2811 (* 1 = 5.2811 loss)
I0405 09:09:00.124112 30176 sgd_solver.cpp:105] Iteration 3708, lr = 1e-06
I0405 09:09:05.483333 30176 solver.cpp:218] Iteration 3720 (2.23915 iter/s, 5.35917s/12 iters), loss = 5.27877
I0405 09:09:05.483368 30176 solver.cpp:237]     Train net output #0: loss = 5.27877 (* 1 = 5.27877 loss)
I0405 09:09:05.483373 30176 sgd_solver.cpp:105] Iteration 3720, lr = 1e-06
I0405 09:09:10.842684 30176 solver.cpp:218] Iteration 3732 (2.23912 iter/s, 5.35925s/12 iters), loss = 5.29071
I0405 09:09:10.842783 30176 solver.cpp:237]     Train net output #0: loss = 5.29071 (* 1 = 5.29071 loss)
I0405 09:09:10.842789 30176 sgd_solver.cpp:105] Iteration 3732, lr = 1e-06
I0405 09:09:15.128280 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:09:16.267045 30176 solver.cpp:218] Iteration 3744 (2.21231 iter/s, 5.4242s/12 iters), loss = 5.29016
I0405 09:09:16.267079 30176 solver.cpp:237]     Train net output #0: loss = 5.29016 (* 1 = 5.29016 loss)
I0405 09:09:16.267084 30176 sgd_solver.cpp:105] Iteration 3744, lr = 1e-06
I0405 09:09:21.652961 30176 solver.cpp:218] Iteration 3756 (2.22807 iter/s, 5.38582s/12 iters), loss = 5.27637
I0405 09:09:21.652998 30176 solver.cpp:237]     Train net output #0: loss = 5.27637 (* 1 = 5.27637 loss)
I0405 09:09:21.653003 30176 sgd_solver.cpp:105] Iteration 3756, lr = 1e-06
I0405 09:09:26.922690 30176 solver.cpp:218] Iteration 3768 (2.2772 iter/s, 5.26964s/12 iters), loss = 5.28427
I0405 09:09:26.922721 30176 solver.cpp:237]     Train net output #0: loss = 5.28427 (* 1 = 5.28427 loss)
I0405 09:09:26.922726 30176 sgd_solver.cpp:105] Iteration 3768, lr = 1e-06
I0405 09:09:28.936491 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3774.caffemodel
I0405 09:09:31.951690 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3774.solverstate
I0405 09:09:34.248615 30176 solver.cpp:330] Iteration 3774, Testing net (#0)
I0405 09:09:34.248631 30176 net.cpp:676] Ignoring source layer train-data
I0405 09:09:37.155954 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:09:38.622731 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 09:09:38.622782 30176 solver.cpp:397]     Test net output #1: loss = 5.28008 (* 1 = 5.28008 loss)
I0405 09:09:40.716323 30176 solver.cpp:218] Iteration 3780 (0.869977 iter/s, 13.7935s/12 iters), loss = 5.2754
I0405 09:09:40.716361 30176 solver.cpp:237]     Train net output #0: loss = 5.2754 (* 1 = 5.2754 loss)
I0405 09:09:40.716365 30176 sgd_solver.cpp:105] Iteration 3780, lr = 1e-06
I0405 09:09:45.994695 30176 solver.cpp:218] Iteration 3792 (2.27347 iter/s, 5.27828s/12 iters), loss = 5.29445
I0405 09:09:45.994812 30176 solver.cpp:237]     Train net output #0: loss = 5.29445 (* 1 = 5.29445 loss)
I0405 09:09:45.994818 30176 sgd_solver.cpp:105] Iteration 3792, lr = 1e-06
I0405 09:09:51.263018 30176 solver.cpp:218] Iteration 3804 (2.27784 iter/s, 5.26814s/12 iters), loss = 5.27632
I0405 09:09:51.263060 30176 solver.cpp:237]     Train net output #0: loss = 5.27632 (* 1 = 5.27632 loss)
I0405 09:09:51.263065 30176 sgd_solver.cpp:105] Iteration 3804, lr = 1e-06
I0405 09:09:56.546164 30176 solver.cpp:218] Iteration 3816 (2.27142 iter/s, 5.28305s/12 iters), loss = 5.27276
I0405 09:09:56.546195 30176 solver.cpp:237]     Train net output #0: loss = 5.27276 (* 1 = 5.27276 loss)
I0405 09:09:56.546200 30176 sgd_solver.cpp:105] Iteration 3816, lr = 1e-06
I0405 09:10:01.544909 30176 solver.cpp:218] Iteration 3828 (2.40064 iter/s, 4.99866s/12 iters), loss = 5.26761
I0405 09:10:01.544945 30176 solver.cpp:237]     Train net output #0: loss = 5.26761 (* 1 = 5.26761 loss)
I0405 09:10:01.544950 30176 sgd_solver.cpp:105] Iteration 3828, lr = 1e-06
I0405 09:10:06.983958 30176 solver.cpp:218] Iteration 3840 (2.20631 iter/s, 5.43895s/12 iters), loss = 5.29203
I0405 09:10:06.983997 30176 solver.cpp:237]     Train net output #0: loss = 5.29203 (* 1 = 5.29203 loss)
I0405 09:10:06.984002 30176 sgd_solver.cpp:105] Iteration 3840, lr = 1e-06
I0405 09:10:08.202944 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:10:12.280287 30176 solver.cpp:218] Iteration 3852 (2.26576 iter/s, 5.29623s/12 iters), loss = 5.28755
I0405 09:10:12.280329 30176 solver.cpp:237]     Train net output #0: loss = 5.28755 (* 1 = 5.28755 loss)
I0405 09:10:12.280334 30176 sgd_solver.cpp:105] Iteration 3852, lr = 1e-06
I0405 09:10:17.429497 30176 solver.cpp:218] Iteration 3864 (2.3305 iter/s, 5.14911s/12 iters), loss = 5.29128
I0405 09:10:17.429615 30176 solver.cpp:237]     Train net output #0: loss = 5.29128 (* 1 = 5.29128 loss)
I0405 09:10:17.429625 30176 sgd_solver.cpp:105] Iteration 3864, lr = 1e-06
I0405 09:10:22.376550 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3876.caffemodel
I0405 09:10:27.587505 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3876.solverstate
I0405 09:10:30.812111 30176 solver.cpp:330] Iteration 3876, Testing net (#0)
I0405 09:10:30.812139 30176 net.cpp:676] Ignoring source layer train-data
I0405 09:10:33.672248 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:10:35.179141 30176 solver.cpp:397]     Test net output #0: accuracy = 0.00735294
I0405 09:10:35.179177 30176 solver.cpp:397]     Test net output #1: loss = 5.27976 (* 1 = 5.27976 loss)
I0405 09:10:35.313114 30176 solver.cpp:218] Iteration 3876 (0.671016 iter/s, 17.8833s/12 iters), loss = 5.26906
I0405 09:10:35.313169 30176 solver.cpp:237]     Train net output #0: loss = 5.26906 (* 1 = 5.26906 loss)
I0405 09:10:35.313175 30176 sgd_solver.cpp:105] Iteration 3876, lr = 1e-06
I0405 09:10:39.652375 30176 solver.cpp:218] Iteration 3888 (2.76551 iter/s, 4.33916s/12 iters), loss = 5.30375
I0405 09:10:39.652418 30176 solver.cpp:237]     Train net output #0: loss = 5.30375 (* 1 = 5.30375 loss)
I0405 09:10:39.652423 30176 sgd_solver.cpp:105] Iteration 3888, lr = 1e-06
I0405 09:10:44.935717 30176 solver.cpp:218] Iteration 3900 (2.27133 iter/s, 5.28324s/12 iters), loss = 5.27855
I0405 09:10:44.935750 30176 solver.cpp:237]     Train net output #0: loss = 5.27855 (* 1 = 5.27855 loss)
I0405 09:10:44.935755 30176 sgd_solver.cpp:105] Iteration 3900, lr = 1e-06
I0405 09:10:50.343379 30176 solver.cpp:218] Iteration 3912 (2.21911 iter/s, 5.40757s/12 iters), loss = 5.28214
I0405 09:10:50.343510 30176 solver.cpp:237]     Train net output #0: loss = 5.28214 (* 1 = 5.28214 loss)
I0405 09:10:50.343516 30176 sgd_solver.cpp:105] Iteration 3912, lr = 1e-06
I0405 09:10:55.845983 30176 solver.cpp:218] Iteration 3924 (2.18086 iter/s, 5.50241s/12 iters), loss = 5.28835
I0405 09:10:55.846035 30176 solver.cpp:237]     Train net output #0: loss = 5.28835 (* 1 = 5.28835 loss)
I0405 09:10:55.846043 30176 sgd_solver.cpp:105] Iteration 3924, lr = 1e-06
I0405 09:11:00.926847 30176 solver.cpp:218] Iteration 3936 (2.36185 iter/s, 5.08076s/12 iters), loss = 5.28649
I0405 09:11:00.926884 30176 solver.cpp:237]     Train net output #0: loss = 5.28649 (* 1 = 5.28649 loss)
I0405 09:11:00.926889 30176 sgd_solver.cpp:105] Iteration 3936, lr = 1e-06
I0405 09:11:04.243887 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:11:06.011961 30176 solver.cpp:218] Iteration 3948 (2.35987 iter/s, 5.08502s/12 iters), loss = 5.28988
I0405 09:11:06.011988 30176 solver.cpp:237]     Train net output #0: loss = 5.28988 (* 1 = 5.28988 loss)
I0405 09:11:06.011993 30176 sgd_solver.cpp:105] Iteration 3948, lr = 1e-06
I0405 09:11:11.349129 30176 solver.cpp:218] Iteration 3960 (2.24842 iter/s, 5.33707s/12 iters), loss = 5.26808
I0405 09:11:11.349174 30176 solver.cpp:237]     Train net output #0: loss = 5.26808 (* 1 = 5.26808 loss)
I0405 09:11:11.349179 30176 sgd_solver.cpp:105] Iteration 3960, lr = 1e-06
I0405 09:11:16.507616 30176 solver.cpp:218] Iteration 3972 (2.32631 iter/s, 5.15838s/12 iters), loss = 5.31396
I0405 09:11:16.507654 30176 solver.cpp:237]     Train net output #0: loss = 5.31396 (* 1 = 5.31396 loss)
I0405 09:11:16.507659 30176 sgd_solver.cpp:105] Iteration 3972, lr = 1e-06
I0405 09:11:18.621699 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3978.caffemodel
I0405 09:11:23.742849 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3978.solverstate
I0405 09:11:28.249045 30176 solver.cpp:330] Iteration 3978, Testing net (#0)
I0405 09:11:28.249061 30176 net.cpp:676] Ignoring source layer train-data
I0405 09:11:30.971205 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:11:32.522748 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 09:11:32.522781 30176 solver.cpp:397]     Test net output #1: loss = 5.28018 (* 1 = 5.28018 loss)
I0405 09:11:34.318929 30176 solver.cpp:218] Iteration 3984 (0.673737 iter/s, 17.8111s/12 iters), loss = 5.28992
I0405 09:11:34.318974 30176 solver.cpp:237]     Train net output #0: loss = 5.28992 (* 1 = 5.28992 loss)
I0405 09:11:34.318982 30176 sgd_solver.cpp:105] Iteration 3984, lr = 1e-06
I0405 09:11:39.189189 30176 solver.cpp:218] Iteration 3996 (2.46399 iter/s, 4.87016s/12 iters), loss = 5.29403
I0405 09:11:39.189240 30176 solver.cpp:237]     Train net output #0: loss = 5.29403 (* 1 = 5.29403 loss)
I0405 09:11:39.189249 30176 sgd_solver.cpp:105] Iteration 3996, lr = 1e-06
I0405 09:11:44.604838 30176 solver.cpp:218] Iteration 4008 (2.21585 iter/s, 5.41554s/12 iters), loss = 5.27306
I0405 09:11:44.604877 30176 solver.cpp:237]     Train net output #0: loss = 5.27306 (* 1 = 5.27306 loss)
I0405 09:11:44.604885 30176 sgd_solver.cpp:105] Iteration 4008, lr = 1e-06
I0405 09:11:49.808547 30176 solver.cpp:218] Iteration 4020 (2.30609 iter/s, 5.20361s/12 iters), loss = 5.28454
I0405 09:11:49.808584 30176 solver.cpp:237]     Train net output #0: loss = 5.28454 (* 1 = 5.28454 loss)
I0405 09:11:49.808589 30176 sgd_solver.cpp:105] Iteration 4020, lr = 1e-06
I0405 09:11:55.146394 30176 solver.cpp:218] Iteration 4032 (2.24814 iter/s, 5.33775s/12 iters), loss = 5.28097
I0405 09:11:55.146543 30176 solver.cpp:237]     Train net output #0: loss = 5.28097 (* 1 = 5.28097 loss)
I0405 09:11:55.146551 30176 sgd_solver.cpp:105] Iteration 4032, lr = 1e-06
I0405 09:12:00.511631 30176 solver.cpp:218] Iteration 4044 (2.23671 iter/s, 5.36503s/12 iters), loss = 5.28589
I0405 09:12:00.511682 30176 solver.cpp:237]     Train net output #0: loss = 5.28589 (* 1 = 5.28589 loss)
I0405 09:12:00.511693 30176 sgd_solver.cpp:105] Iteration 4044, lr = 1e-06
I0405 09:12:00.976141 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:12:05.644161 30176 solver.cpp:218] Iteration 4056 (2.33808 iter/s, 5.13242s/12 iters), loss = 5.3069
I0405 09:12:05.644201 30176 solver.cpp:237]     Train net output #0: loss = 5.3069 (* 1 = 5.3069 loss)
I0405 09:12:05.644207 30176 sgd_solver.cpp:105] Iteration 4056, lr = 1e-06
I0405 09:12:10.943954 30176 solver.cpp:218] Iteration 4068 (2.26428 iter/s, 5.29969s/12 iters), loss = 5.30099
I0405 09:12:10.943994 30176 solver.cpp:237]     Train net output #0: loss = 5.30099 (* 1 = 5.30099 loss)
I0405 09:12:10.943998 30176 sgd_solver.cpp:105] Iteration 4068, lr = 1e-06
I0405 09:12:15.550653 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4080.caffemodel
I0405 09:12:19.026130 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4080.solverstate
I0405 09:12:21.321319 30176 solver.cpp:330] Iteration 4080, Testing net (#0)
I0405 09:12:21.321339 30176 net.cpp:676] Ignoring source layer train-data
I0405 09:12:24.021167 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:12:25.592502 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 09:12:25.592660 30176 solver.cpp:397]     Test net output #1: loss = 5.27974 (* 1 = 5.27974 loss)
I0405 09:12:25.733600 30176 solver.cpp:218] Iteration 4080 (0.811388 iter/s, 14.7895s/12 iters), loss = 5.2953
I0405 09:12:25.733644 30176 solver.cpp:237]     Train net output #0: loss = 5.2953 (* 1 = 5.2953 loss)
I0405 09:12:25.733649 30176 sgd_solver.cpp:105] Iteration 4080, lr = 1e-06
I0405 09:12:30.194684 30176 solver.cpp:218] Iteration 4092 (2.68999 iter/s, 4.46099s/12 iters), loss = 5.27788
I0405 09:12:30.194720 30176 solver.cpp:237]     Train net output #0: loss = 5.27788 (* 1 = 5.27788 loss)
I0405 09:12:30.194725 30176 sgd_solver.cpp:105] Iteration 4092, lr = 1e-06
I0405 09:12:35.694453 30176 solver.cpp:218] Iteration 4104 (2.18195 iter/s, 5.49967s/12 iters), loss = 5.26623
I0405 09:12:35.694512 30176 solver.cpp:237]     Train net output #0: loss = 5.26623 (* 1 = 5.26623 loss)
I0405 09:12:35.694521 30176 sgd_solver.cpp:105] Iteration 4104, lr = 1e-06
I0405 09:12:40.941694 30176 solver.cpp:218] Iteration 4116 (2.28697 iter/s, 5.24712s/12 iters), loss = 5.2895
I0405 09:12:40.941733 30176 solver.cpp:237]     Train net output #0: loss = 5.2895 (* 1 = 5.2895 loss)
I0405 09:12:40.941738 30176 sgd_solver.cpp:105] Iteration 4116, lr = 1e-06
I0405 09:12:46.030359 30176 solver.cpp:218] Iteration 4128 (2.35823 iter/s, 5.08857s/12 iters), loss = 5.31167
I0405 09:12:46.030398 30176 solver.cpp:237]     Train net output #0: loss = 5.31167 (* 1 = 5.31167 loss)
I0405 09:12:46.030405 30176 sgd_solver.cpp:105] Iteration 4128, lr = 1e-06
I0405 09:12:51.079718 30176 solver.cpp:218] Iteration 4140 (2.37658 iter/s, 5.04926s/12 iters), loss = 5.27391
I0405 09:12:51.079764 30176 solver.cpp:237]     Train net output #0: loss = 5.27391 (* 1 = 5.27391 loss)
I0405 09:12:51.079769 30176 sgd_solver.cpp:105] Iteration 4140, lr = 1e-06
I0405 09:12:53.802038 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:12:56.261531 30176 solver.cpp:218] Iteration 4152 (2.31584 iter/s, 5.18171s/12 iters), loss = 5.29232
I0405 09:12:56.261684 30176 solver.cpp:237]     Train net output #0: loss = 5.29232 (* 1 = 5.29232 loss)
I0405 09:12:56.261691 30176 sgd_solver.cpp:105] Iteration 4152, lr = 1e-06
I0405 09:12:57.979156 30176 blocking_queue.cpp:49] Waiting for data
I0405 09:13:01.715011 30176 solver.cpp:218] Iteration 4164 (2.20051 iter/s, 5.45327s/12 iters), loss = 5.28225
I0405 09:13:01.715049 30176 solver.cpp:237]     Train net output #0: loss = 5.28225 (* 1 = 5.28225 loss)
I0405 09:13:01.715054 30176 sgd_solver.cpp:105] Iteration 4164, lr = 1e-06
I0405 09:13:06.834115 30176 solver.cpp:218] Iteration 4176 (2.3442 iter/s, 5.11901s/12 iters), loss = 5.28836
I0405 09:13:06.834152 30176 solver.cpp:237]     Train net output #0: loss = 5.28836 (* 1 = 5.28836 loss)
I0405 09:13:06.834157 30176 sgd_solver.cpp:105] Iteration 4176, lr = 1e-06
I0405 09:13:09.062624 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4182.caffemodel
I0405 09:13:13.329465 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4182.solverstate
I0405 09:13:17.802238 30176 solver.cpp:330] Iteration 4182, Testing net (#0)
I0405 09:13:17.802266 30176 net.cpp:676] Ignoring source layer train-data
I0405 09:13:20.431085 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:13:22.048156 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 09:13:22.048207 30176 solver.cpp:397]     Test net output #1: loss = 5.27963 (* 1 = 5.27963 loss)
I0405 09:13:23.916733 30176 solver.cpp:218] Iteration 4188 (0.702477 iter/s, 17.0824s/12 iters), loss = 5.28651
I0405 09:13:23.916785 30176 solver.cpp:237]     Train net output #0: loss = 5.28651 (* 1 = 5.28651 loss)
I0405 09:13:23.916791 30176 sgd_solver.cpp:105] Iteration 4188, lr = 1e-06
I0405 09:13:29.087894 30176 solver.cpp:218] Iteration 4200 (2.32061 iter/s, 5.17106s/12 iters), loss = 5.28466
I0405 09:13:29.087997 30176 solver.cpp:237]     Train net output #0: loss = 5.28466 (* 1 = 5.28466 loss)
I0405 09:13:29.088006 30176 sgd_solver.cpp:105] Iteration 4200, lr = 1e-06
I0405 09:13:34.455411 30176 solver.cpp:218] Iteration 4212 (2.23574 iter/s, 5.36736s/12 iters), loss = 5.28077
I0405 09:13:34.455442 30176 solver.cpp:237]     Train net output #0: loss = 5.28077 (* 1 = 5.28077 loss)
I0405 09:13:34.455447 30176 sgd_solver.cpp:105] Iteration 4212, lr = 1e-06
I0405 09:13:39.844799 30176 solver.cpp:218] Iteration 4224 (2.22664 iter/s, 5.3893s/12 iters), loss = 5.26672
I0405 09:13:39.844836 30176 solver.cpp:237]     Train net output #0: loss = 5.26672 (* 1 = 5.26672 loss)
I0405 09:13:39.844841 30176 sgd_solver.cpp:105] Iteration 4224, lr = 1e-06
I0405 09:13:45.200834 30176 solver.cpp:218] Iteration 4236 (2.2405 iter/s, 5.35594s/12 iters), loss = 5.28741
I0405 09:13:45.200876 30176 solver.cpp:237]     Train net output #0: loss = 5.28741 (* 1 = 5.28741 loss)
I0405 09:13:45.200886 30176 sgd_solver.cpp:105] Iteration 4236, lr = 1e-06
I0405 09:13:50.359391 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:13:50.636041 30176 solver.cpp:218] Iteration 4248 (2.20787 iter/s, 5.4351s/12 iters), loss = 5.28816
I0405 09:13:50.636085 30176 solver.cpp:237]     Train net output #0: loss = 5.28816 (* 1 = 5.28816 loss)
I0405 09:13:50.636090 30176 sgd_solver.cpp:105] Iteration 4248, lr = 1e-06
I0405 09:13:55.861820 30176 solver.cpp:218] Iteration 4260 (2.29636 iter/s, 5.22567s/12 iters), loss = 5.28287
I0405 09:13:55.861876 30176 solver.cpp:237]     Train net output #0: loss = 5.28287 (* 1 = 5.28287 loss)
I0405 09:13:55.861883 30176 sgd_solver.cpp:105] Iteration 4260, lr = 1e-06
I0405 09:14:01.149413 30176 solver.cpp:218] Iteration 4272 (2.26952 iter/s, 5.28747s/12 iters), loss = 5.27396
I0405 09:14:01.149595 30176 solver.cpp:237]     Train net output #0: loss = 5.27396 (* 1 = 5.27396 loss)
I0405 09:14:01.149605 30176 sgd_solver.cpp:105] Iteration 4272, lr = 1e-06
I0405 09:14:06.006965 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4284.caffemodel
I0405 09:14:11.263067 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4284.solverstate
I0405 09:14:15.774657 30176 solver.cpp:330] Iteration 4284, Testing net (#0)
I0405 09:14:15.774677 30176 net.cpp:676] Ignoring source layer train-data
I0405 09:14:18.395717 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:14:20.047353 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 09:14:20.047389 30176 solver.cpp:397]     Test net output #1: loss = 5.2798 (* 1 = 5.2798 loss)
I0405 09:14:20.188205 30176 solver.cpp:218] Iteration 4284 (0.630304 iter/s, 19.0384s/12 iters), loss = 5.2857
I0405 09:14:20.189771 30176 solver.cpp:237]     Train net output #0: loss = 5.2857 (* 1 = 5.2857 loss)
I0405 09:14:20.189780 30176 sgd_solver.cpp:105] Iteration 4284, lr = 1e-06
I0405 09:14:24.367316 30176 solver.cpp:218] Iteration 4296 (2.87253 iter/s, 4.1775s/12 iters), loss = 5.28042
I0405 09:14:24.367357 30176 solver.cpp:237]     Train net output #0: loss = 5.28042 (* 1 = 5.28042 loss)
I0405 09:14:24.367363 30176 sgd_solver.cpp:105] Iteration 4296, lr = 1e-06
I0405 09:14:29.829229 30176 solver.cpp:218] Iteration 4308 (2.19707 iter/s, 5.46182s/12 iters), loss = 5.28787
I0405 09:14:29.829269 30176 solver.cpp:237]     Train net output #0: loss = 5.28787 (* 1 = 5.28787 loss)
I0405 09:14:29.829274 30176 sgd_solver.cpp:105] Iteration 4308, lr = 1e-06
I0405 09:14:35.127069 30176 solver.cpp:218] Iteration 4320 (2.26512 iter/s, 5.29773s/12 iters), loss = 5.29656
I0405 09:14:35.127187 30176 solver.cpp:237]     Train net output #0: loss = 5.29656 (* 1 = 5.29656 loss)
I0405 09:14:35.127198 30176 sgd_solver.cpp:105] Iteration 4320, lr = 1e-06
I0405 09:14:40.020380 30176 solver.cpp:218] Iteration 4332 (2.45241 iter/s, 4.89314s/12 iters), loss = 5.28477
I0405 09:14:40.020419 30176 solver.cpp:237]     Train net output #0: loss = 5.28477 (* 1 = 5.28477 loss)
I0405 09:14:40.020424 30176 sgd_solver.cpp:105] Iteration 4332, lr = 1e-06
I0405 09:14:45.401576 30176 solver.cpp:218] Iteration 4344 (2.23003 iter/s, 5.3811s/12 iters), loss = 5.28812
I0405 09:14:45.401609 30176 solver.cpp:237]     Train net output #0: loss = 5.28812 (* 1 = 5.28812 loss)
I0405 09:14:45.401614 30176 sgd_solver.cpp:105] Iteration 4344, lr = 1e-06
I0405 09:14:47.415856 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:14:50.760931 30176 solver.cpp:218] Iteration 4356 (2.23912 iter/s, 5.35926s/12 iters), loss = 5.2952
I0405 09:14:50.760977 30176 solver.cpp:237]     Train net output #0: loss = 5.2952 (* 1 = 5.2952 loss)
I0405 09:14:50.760983 30176 sgd_solver.cpp:105] Iteration 4356, lr = 1e-06
I0405 09:14:55.841322 30176 solver.cpp:218] Iteration 4368 (2.36207 iter/s, 5.08029s/12 iters), loss = 5.28056
I0405 09:14:55.841370 30176 solver.cpp:237]     Train net output #0: loss = 5.28056 (* 1 = 5.28056 loss)
I0405 09:14:55.841378 30176 sgd_solver.cpp:105] Iteration 4368, lr = 1e-06
I0405 09:15:01.010612 30176 solver.cpp:218] Iteration 4380 (2.32145 iter/s, 5.16919s/12 iters), loss = 5.29233
I0405 09:15:01.010653 30176 solver.cpp:237]     Train net output #0: loss = 5.29233 (* 1 = 5.29233 loss)
I0405 09:15:01.010659 30176 sgd_solver.cpp:105] Iteration 4380, lr = 1e-06
I0405 09:15:03.210443 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4386.caffemodel
I0405 09:15:06.213357 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4386.solverstate
I0405 09:15:08.516078 30176 solver.cpp:330] Iteration 4386, Testing net (#0)
I0405 09:15:08.516101 30176 net.cpp:676] Ignoring source layer train-data
I0405 09:15:11.130659 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:15:12.829792 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 09:15:12.829826 30176 solver.cpp:397]     Test net output #1: loss = 5.27996 (* 1 = 5.27996 loss)
I0405 09:15:14.661283 30176 solver.cpp:218] Iteration 4392 (0.879089 iter/s, 13.6505s/12 iters), loss = 5.28878
I0405 09:15:14.661327 30176 solver.cpp:237]     Train net output #0: loss = 5.28878 (* 1 = 5.28878 loss)
I0405 09:15:14.661332 30176 sgd_solver.cpp:105] Iteration 4392, lr = 1e-06
I0405 09:15:20.081763 30176 solver.cpp:218] Iteration 4404 (2.21387 iter/s, 5.42038s/12 iters), loss = 5.29169
I0405 09:15:20.081802 30176 solver.cpp:237]     Train net output #0: loss = 5.29169 (* 1 = 5.29169 loss)
I0405 09:15:20.081809 30176 sgd_solver.cpp:105] Iteration 4404, lr = 1e-06
I0405 09:15:25.348600 30176 solver.cpp:218] Iteration 4416 (2.27845 iter/s, 5.26674s/12 iters), loss = 5.27978
I0405 09:15:25.348640 30176 solver.cpp:237]     Train net output #0: loss = 5.27978 (* 1 = 5.27978 loss)
I0405 09:15:25.348645 30176 sgd_solver.cpp:105] Iteration 4416, lr = 1e-06
I0405 09:15:30.817615 30176 solver.cpp:218] Iteration 4428 (2.19422 iter/s, 5.46891s/12 iters), loss = 5.30007
I0405 09:15:30.817657 30176 solver.cpp:237]     Train net output #0: loss = 5.30007 (* 1 = 5.30007 loss)
I0405 09:15:30.817662 30176 sgd_solver.cpp:105] Iteration 4428, lr = 1e-06
I0405 09:15:36.147840 30176 solver.cpp:218] Iteration 4440 (2.25135 iter/s, 5.33013s/12 iters), loss = 5.29142
I0405 09:15:36.147874 30176 solver.cpp:237]     Train net output #0: loss = 5.29142 (* 1 = 5.29142 loss)
I0405 09:15:36.147879 30176 sgd_solver.cpp:105] Iteration 4440, lr = 1e-06
I0405 09:15:40.446995 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:15:41.475770 30176 solver.cpp:218] Iteration 4452 (2.25232 iter/s, 5.32784s/12 iters), loss = 5.27869
I0405 09:15:41.475801 30176 solver.cpp:237]     Train net output #0: loss = 5.27869 (* 1 = 5.27869 loss)
I0405 09:15:41.475806 30176 sgd_solver.cpp:105] Iteration 4452, lr = 1e-06
I0405 09:15:46.772454 30176 solver.cpp:218] Iteration 4464 (2.26561 iter/s, 5.29659s/12 iters), loss = 5.28312
I0405 09:15:46.772501 30176 solver.cpp:237]     Train net output #0: loss = 5.28312 (* 1 = 5.28312 loss)
I0405 09:15:46.772507 30176 sgd_solver.cpp:105] Iteration 4464, lr = 1e-06
I0405 09:15:52.031052 30176 solver.cpp:218] Iteration 4476 (2.28203 iter/s, 5.25849s/12 iters), loss = 5.27897
I0405 09:15:52.031093 30176 solver.cpp:237]     Train net output #0: loss = 5.27897 (* 1 = 5.27897 loss)
I0405 09:15:52.031098 30176 sgd_solver.cpp:105] Iteration 4476, lr = 1e-06
I0405 09:15:56.908381 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4488.caffemodel
I0405 09:16:01.110375 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4488.solverstate
I0405 09:16:03.973436 30176 solver.cpp:330] Iteration 4488, Testing net (#0)
I0405 09:16:03.973459 30176 net.cpp:676] Ignoring source layer train-data
I0405 09:16:06.509330 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:16:08.243408 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 09:16:08.243439 30176 solver.cpp:397]     Test net output #1: loss = 5.28037 (* 1 = 5.28037 loss)
I0405 09:16:08.384438 30176 solver.cpp:218] Iteration 4488 (0.733802 iter/s, 16.3532s/12 iters), loss = 5.27819
I0405 09:16:08.384488 30176 solver.cpp:237]     Train net output #0: loss = 5.27819 (* 1 = 5.27819 loss)
I0405 09:16:08.384495 30176 sgd_solver.cpp:105] Iteration 4488, lr = 1e-06
I0405 09:16:12.847332 30176 solver.cpp:218] Iteration 4500 (2.6889 iter/s, 4.4628s/12 iters), loss = 5.30659
I0405 09:16:12.847424 30176 solver.cpp:237]     Train net output #0: loss = 5.30659 (* 1 = 5.30659 loss)
I0405 09:16:12.847430 30176 sgd_solver.cpp:105] Iteration 4500, lr = 1e-06
I0405 09:16:18.789944 30176 solver.cpp:218] Iteration 4512 (2.01937 iter/s, 5.94245s/12 iters), loss = 5.27082
I0405 09:16:18.789992 30176 solver.cpp:237]     Train net output #0: loss = 5.27082 (* 1 = 5.27082 loss)
I0405 09:16:18.790001 30176 sgd_solver.cpp:105] Iteration 4512, lr = 1e-06
I0405 09:16:24.032500 30176 solver.cpp:218] Iteration 4524 (2.28901 iter/s, 5.24245s/12 iters), loss = 5.28359
I0405 09:16:24.032541 30176 solver.cpp:237]     Train net output #0: loss = 5.28359 (* 1 = 5.28359 loss)
I0405 09:16:24.032547 30176 sgd_solver.cpp:105] Iteration 4524, lr = 1e-06
I0405 09:16:29.420353 30176 solver.cpp:218] Iteration 4536 (2.22727 iter/s, 5.38776s/12 iters), loss = 5.26934
I0405 09:16:29.420385 30176 solver.cpp:237]     Train net output #0: loss = 5.26934 (* 1 = 5.26934 loss)
I0405 09:16:29.420390 30176 sgd_solver.cpp:105] Iteration 4536, lr = 1e-06
I0405 09:16:34.460850 30176 solver.cpp:218] Iteration 4548 (2.38076 iter/s, 5.04041s/12 iters), loss = 5.29496
I0405 09:16:34.460891 30176 solver.cpp:237]     Train net output #0: loss = 5.29496 (* 1 = 5.29496 loss)
I0405 09:16:34.460897 30176 sgd_solver.cpp:105] Iteration 4548, lr = 1e-06
I0405 09:16:35.716352 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:16:39.716930 30176 solver.cpp:218] Iteration 4560 (2.28311 iter/s, 5.25599s/12 iters), loss = 5.26504
I0405 09:16:39.716969 30176 solver.cpp:237]     Train net output #0: loss = 5.26504 (* 1 = 5.26504 loss)
I0405 09:16:39.716974 30176 sgd_solver.cpp:105] Iteration 4560, lr = 1e-06
I0405 09:16:45.187467 30176 solver.cpp:218] Iteration 4572 (2.19361 iter/s, 5.47044s/12 iters), loss = 5.27658
I0405 09:16:45.187579 30176 solver.cpp:237]     Train net output #0: loss = 5.27658 (* 1 = 5.27658 loss)
I0405 09:16:45.187585 30176 sgd_solver.cpp:105] Iteration 4572, lr = 1e-06
I0405 09:16:50.170404 30176 solver.cpp:218] Iteration 4584 (2.4083 iter/s, 4.98277s/12 iters), loss = 5.28088
I0405 09:16:50.170449 30176 solver.cpp:237]     Train net output #0: loss = 5.28088 (* 1 = 5.28088 loss)
I0405 09:16:50.170455 30176 sgd_solver.cpp:105] Iteration 4584, lr = 1e-06
I0405 09:16:52.230687 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4590.caffemodel
I0405 09:16:58.116237 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4590.solverstate
I0405 09:17:02.637885 30176 solver.cpp:330] Iteration 4590, Testing net (#0)
I0405 09:17:02.637908 30176 net.cpp:676] Ignoring source layer train-data
I0405 09:17:05.253631 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:17:07.051590 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 09:17:07.051640 30176 solver.cpp:397]     Test net output #1: loss = 5.27995 (* 1 = 5.27995 loss)
I0405 09:17:08.853405 30176 solver.cpp:218] Iteration 4596 (0.642303 iter/s, 18.6828s/12 iters), loss = 5.29758
I0405 09:17:08.853463 30176 solver.cpp:237]     Train net output #0: loss = 5.29758 (* 1 = 5.29758 loss)
I0405 09:17:08.853474 30176 sgd_solver.cpp:105] Iteration 4596, lr = 1e-06
I0405 09:17:14.075949 30176 solver.cpp:218] Iteration 4608 (2.29778 iter/s, 5.22243s/12 iters), loss = 5.26828
I0405 09:17:14.075997 30176 solver.cpp:237]     Train net output #0: loss = 5.26828 (* 1 = 5.26828 loss)
I0405 09:17:14.076004 30176 sgd_solver.cpp:105] Iteration 4608, lr = 1e-06
I0405 09:17:19.596572 30176 solver.cpp:218] Iteration 4620 (2.17371 iter/s, 5.52052s/12 iters), loss = 5.28228
I0405 09:17:19.596681 30176 solver.cpp:237]     Train net output #0: loss = 5.28228 (* 1 = 5.28228 loss)
I0405 09:17:19.596688 30176 sgd_solver.cpp:105] Iteration 4620, lr = 1e-06
I0405 09:17:24.987977 30176 solver.cpp:218] Iteration 4632 (2.22583 iter/s, 5.39124s/12 iters), loss = 5.27515
I0405 09:17:24.988018 30176 solver.cpp:237]     Train net output #0: loss = 5.27515 (* 1 = 5.27515 loss)
I0405 09:17:24.988023 30176 sgd_solver.cpp:105] Iteration 4632, lr = 1e-06
I0405 09:17:30.029305 30176 solver.cpp:218] Iteration 4644 (2.38037 iter/s, 5.04123s/12 iters), loss = 5.30449
I0405 09:17:30.029345 30176 solver.cpp:237]     Train net output #0: loss = 5.30449 (* 1 = 5.30449 loss)
I0405 09:17:30.029350 30176 sgd_solver.cpp:105] Iteration 4644, lr = 1e-06
I0405 09:17:33.609699 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:17:35.373589 30176 solver.cpp:218] Iteration 4656 (2.24543 iter/s, 5.34418s/12 iters), loss = 5.2905
I0405 09:17:35.373621 30176 solver.cpp:237]     Train net output #0: loss = 5.2905 (* 1 = 5.2905 loss)
I0405 09:17:35.373626 30176 sgd_solver.cpp:105] Iteration 4656, lr = 1e-06
I0405 09:17:40.701054 30176 solver.cpp:218] Iteration 4668 (2.25251 iter/s, 5.32738s/12 iters), loss = 5.28405
I0405 09:17:40.701087 30176 solver.cpp:237]     Train net output #0: loss = 5.28405 (* 1 = 5.28405 loss)
I0405 09:17:40.701092 30176 sgd_solver.cpp:105] Iteration 4668, lr = 1e-06
I0405 09:17:45.831693 30176 solver.cpp:218] Iteration 4680 (2.33894 iter/s, 5.13054s/12 iters), loss = 5.28666
I0405 09:17:45.831749 30176 solver.cpp:237]     Train net output #0: loss = 5.28666 (* 1 = 5.28666 loss)
I0405 09:17:45.831758 30176 sgd_solver.cpp:105] Iteration 4680, lr = 1e-06
I0405 09:17:50.555619 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4692.caffemodel
I0405 09:17:53.601008 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4692.solverstate
I0405 09:17:55.900424 30176 solver.cpp:330] Iteration 4692, Testing net (#0)
I0405 09:17:55.900447 30176 net.cpp:676] Ignoring source layer train-data
I0405 09:17:58.452108 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:18:00.286265 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 09:18:00.286310 30176 solver.cpp:397]     Test net output #1: loss = 5.28011 (* 1 = 5.28011 loss)
I0405 09:18:00.427873 30176 solver.cpp:218] Iteration 4692 (0.822144 iter/s, 14.596s/12 iters), loss = 5.29285
I0405 09:18:00.427925 30176 solver.cpp:237]     Train net output #0: loss = 5.29285 (* 1 = 5.29285 loss)
I0405 09:18:00.427934 30176 sgd_solver.cpp:105] Iteration 4692, lr = 1e-06
I0405 09:18:04.813692 30176 solver.cpp:218] Iteration 4704 (2.73615 iter/s, 4.38572s/12 iters), loss = 5.27618
I0405 09:18:04.813730 30176 solver.cpp:237]     Train net output #0: loss = 5.27618 (* 1 = 5.27618 loss)
I0405 09:18:04.813735 30176 sgd_solver.cpp:105] Iteration 4704, lr = 1e-06
I0405 09:18:10.126529 30176 solver.cpp:218] Iteration 4716 (2.25872 iter/s, 5.31275s/12 iters), loss = 5.28012
I0405 09:18:10.126556 30176 solver.cpp:237]     Train net output #0: loss = 5.28012 (* 1 = 5.28012 loss)
I0405 09:18:10.126561 30176 sgd_solver.cpp:105] Iteration 4716, lr = 1e-06
I0405 09:18:15.477651 30176 solver.cpp:218] Iteration 4728 (2.24256 iter/s, 5.35103s/12 iters), loss = 5.29798
I0405 09:18:15.477690 30176 solver.cpp:237]     Train net output #0: loss = 5.29798 (* 1 = 5.29798 loss)
I0405 09:18:15.477696 30176 sgd_solver.cpp:105] Iteration 4728, lr = 1e-06
I0405 09:18:20.520586 30176 solver.cpp:218] Iteration 4740 (2.37961 iter/s, 5.04284s/12 iters), loss = 5.28527
I0405 09:18:20.520622 30176 solver.cpp:237]     Train net output #0: loss = 5.28527 (* 1 = 5.28527 loss)
I0405 09:18:20.520628 30176 sgd_solver.cpp:105] Iteration 4740, lr = 1e-06
I0405 09:18:25.604310 30176 solver.cpp:218] Iteration 4752 (2.36052 iter/s, 5.08363s/12 iters), loss = 5.29919
I0405 09:18:25.604382 30176 solver.cpp:237]     Train net output #0: loss = 5.29919 (* 1 = 5.29919 loss)
I0405 09:18:25.604388 30176 sgd_solver.cpp:105] Iteration 4752, lr = 1e-06
I0405 09:18:26.168797 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:18:30.895030 30176 solver.cpp:218] Iteration 4764 (2.26818 iter/s, 5.29059s/12 iters), loss = 5.29639
I0405 09:18:30.895074 30176 solver.cpp:237]     Train net output #0: loss = 5.29639 (* 1 = 5.29639 loss)
I0405 09:18:30.895081 30176 sgd_solver.cpp:105] Iteration 4764, lr = 1e-06
I0405 09:18:36.043212 30176 solver.cpp:218] Iteration 4776 (2.33096 iter/s, 5.14808s/12 iters), loss = 5.28603
I0405 09:18:36.043256 30176 solver.cpp:237]     Train net output #0: loss = 5.28603 (* 1 = 5.28603 loss)
I0405 09:18:36.043262 30176 sgd_solver.cpp:105] Iteration 4776, lr = 1e-06
I0405 09:18:41.307910 30176 solver.cpp:218] Iteration 4788 (2.27938 iter/s, 5.26459s/12 iters), loss = 5.27855
I0405 09:18:41.307974 30176 solver.cpp:237]     Train net output #0: loss = 5.27855 (* 1 = 5.27855 loss)
I0405 09:18:41.307984 30176 sgd_solver.cpp:105] Iteration 4788, lr = 1e-06
I0405 09:18:43.453792 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4794.caffemodel
I0405 09:18:48.554592 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4794.solverstate
I0405 09:18:51.049523 30176 solver.cpp:330] Iteration 4794, Testing net (#0)
I0405 09:18:51.049551 30176 net.cpp:676] Ignoring source layer train-data
I0405 09:18:53.456192 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:18:55.317968 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 09:18:55.318001 30176 solver.cpp:397]     Test net output #1: loss = 5.27973 (* 1 = 5.27973 loss)
I0405 09:18:57.151933 30176 solver.cpp:218] Iteration 4800 (0.757393 iter/s, 15.8438s/12 iters), loss = 5.28527
I0405 09:18:57.152042 30176 solver.cpp:237]     Train net output #0: loss = 5.28527 (* 1 = 5.28527 loss)
I0405 09:18:57.152048 30176 sgd_solver.cpp:105] Iteration 4800, lr = 1e-06
I0405 09:19:02.318259 30176 solver.cpp:218] Iteration 4812 (2.32281 iter/s, 5.16616s/12 iters), loss = 5.28028
I0405 09:19:02.318295 30176 solver.cpp:237]     Train net output #0: loss = 5.28028 (* 1 = 5.28028 loss)
I0405 09:19:02.318300 30176 sgd_solver.cpp:105] Iteration 4812, lr = 1e-06
I0405 09:19:07.529047 30176 solver.cpp:218] Iteration 4824 (2.30296 iter/s, 5.21069s/12 iters), loss = 5.29836
I0405 09:19:07.529089 30176 solver.cpp:237]     Train net output #0: loss = 5.29836 (* 1 = 5.29836 loss)
I0405 09:19:07.529098 30176 sgd_solver.cpp:105] Iteration 4824, lr = 1e-06
I0405 09:19:12.817178 30176 solver.cpp:218] Iteration 4836 (2.26927 iter/s, 5.28803s/12 iters), loss = 5.29138
I0405 09:19:12.817214 30176 solver.cpp:237]     Train net output #0: loss = 5.29138 (* 1 = 5.29138 loss)
I0405 09:19:12.817220 30176 sgd_solver.cpp:105] Iteration 4836, lr = 1e-06
I0405 09:19:14.892460 30176 blocking_queue.cpp:49] Waiting for data
I0405 09:19:18.094703 30176 solver.cpp:218] Iteration 4848 (2.27383 iter/s, 5.27743s/12 iters), loss = 5.26226
I0405 09:19:18.094743 30176 solver.cpp:237]     Train net output #0: loss = 5.26226 (* 1 = 5.26226 loss)
I0405 09:19:18.094748 30176 sgd_solver.cpp:105] Iteration 4848, lr = 1e-06
I0405 09:19:20.964414 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:19:23.393261 30176 solver.cpp:218] Iteration 4860 (2.26481 iter/s, 5.29845s/12 iters), loss = 5.27759
I0405 09:19:23.393317 30176 solver.cpp:237]     Train net output #0: loss = 5.27759 (* 1 = 5.27759 loss)
I0405 09:19:23.393326 30176 sgd_solver.cpp:105] Iteration 4860, lr = 1e-06
I0405 09:19:28.744573 30176 solver.cpp:218] Iteration 4872 (2.24249 iter/s, 5.35119s/12 iters), loss = 5.29083
I0405 09:19:28.744717 30176 solver.cpp:237]     Train net output #0: loss = 5.29083 (* 1 = 5.29083 loss)
I0405 09:19:28.744730 30176 sgd_solver.cpp:105] Iteration 4872, lr = 1e-06
I0405 09:19:34.063277 30176 solver.cpp:218] Iteration 4884 (2.25627 iter/s, 5.31851s/12 iters), loss = 5.28217
I0405 09:19:34.063311 30176 solver.cpp:237]     Train net output #0: loss = 5.28217 (* 1 = 5.28217 loss)
I0405 09:19:34.063318 30176 sgd_solver.cpp:105] Iteration 4884, lr = 1e-06
I0405 09:19:38.979517 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4896.caffemodel
I0405 09:19:44.241899 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4896.solverstate
I0405 09:19:48.740051 30176 solver.cpp:330] Iteration 4896, Testing net (#0)
I0405 09:19:48.740074 30176 net.cpp:676] Ignoring source layer train-data
I0405 09:19:51.262286 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:19:53.167230 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 09:19:53.167263 30176 solver.cpp:397]     Test net output #1: loss = 5.28011 (* 1 = 5.28011 loss)
I0405 09:19:53.307958 30176 solver.cpp:218] Iteration 4896 (0.623556 iter/s, 19.2445s/12 iters), loss = 5.28564
I0405 09:19:53.308005 30176 solver.cpp:237]     Train net output #0: loss = 5.28564 (* 1 = 5.28564 loss)
I0405 09:19:53.308013 30176 sgd_solver.cpp:105] Iteration 4896, lr = 1e-06
I0405 09:19:57.822716 30176 solver.cpp:218] Iteration 4908 (2.65801 iter/s, 4.51466s/12 iters), loss = 5.29349
I0405 09:19:57.822764 30176 solver.cpp:237]     Train net output #0: loss = 5.29349 (* 1 = 5.29349 loss)
I0405 09:19:57.822772 30176 sgd_solver.cpp:105] Iteration 4908, lr = 1e-06
I0405 09:20:03.068941 30176 solver.cpp:218] Iteration 4920 (2.2874 iter/s, 5.24612s/12 iters), loss = 5.26857
I0405 09:20:03.069072 30176 solver.cpp:237]     Train net output #0: loss = 5.26857 (* 1 = 5.26857 loss)
I0405 09:20:03.069080 30176 sgd_solver.cpp:105] Iteration 4920, lr = 1e-06
I0405 09:20:08.517325 30176 solver.cpp:218] Iteration 4932 (2.20256 iter/s, 5.4482s/12 iters), loss = 5.27391
I0405 09:20:08.517359 30176 solver.cpp:237]     Train net output #0: loss = 5.27391 (* 1 = 5.27391 loss)
I0405 09:20:08.517364 30176 sgd_solver.cpp:105] Iteration 4932, lr = 1e-06
I0405 09:20:13.945207 30176 solver.cpp:218] Iteration 4944 (2.21085 iter/s, 5.42778s/12 iters), loss = 5.27955
I0405 09:20:13.945261 30176 solver.cpp:237]     Train net output #0: loss = 5.27955 (* 1 = 5.27955 loss)
I0405 09:20:13.945271 30176 sgd_solver.cpp:105] Iteration 4944, lr = 1e-06
I0405 09:20:19.108732 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:20:19.360638 30176 solver.cpp:218] Iteration 4956 (2.21594 iter/s, 5.41532s/12 iters), loss = 5.28896
I0405 09:20:19.360674 30176 solver.cpp:237]     Train net output #0: loss = 5.28896 (* 1 = 5.28896 loss)
I0405 09:20:19.360679 30176 sgd_solver.cpp:105] Iteration 4956, lr = 1e-06
I0405 09:20:24.759202 30176 solver.cpp:218] Iteration 4968 (2.22285 iter/s, 5.39847s/12 iters), loss = 5.27828
I0405 09:20:24.759236 30176 solver.cpp:237]     Train net output #0: loss = 5.27828 (* 1 = 5.27828 loss)
I0405 09:20:24.759241 30176 sgd_solver.cpp:105] Iteration 4968, lr = 1e-06
I0405 09:20:30.210623 30176 solver.cpp:218] Iteration 4980 (2.2013 iter/s, 5.45133s/12 iters), loss = 5.2936
I0405 09:20:30.210657 30176 solver.cpp:237]     Train net output #0: loss = 5.2936 (* 1 = 5.2936 loss)
I0405 09:20:30.210662 30176 sgd_solver.cpp:105] Iteration 4980, lr = 1e-06
I0405 09:20:35.622138 30176 solver.cpp:218] Iteration 4992 (2.21753 iter/s, 5.41142s/12 iters), loss = 5.28478
I0405 09:20:35.622253 30176 solver.cpp:237]     Train net output #0: loss = 5.28478 (* 1 = 5.28478 loss)
I0405 09:20:35.622262 30176 sgd_solver.cpp:105] Iteration 4992, lr = 1e-06
I0405 09:20:37.828629 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4998.caffemodel
I0405 09:20:41.546229 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4998.solverstate
I0405 09:20:43.844077 30176 solver.cpp:330] Iteration 4998, Testing net (#0)
I0405 09:20:43.844096 30176 net.cpp:676] Ignoring source layer train-data
I0405 09:20:46.233597 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:20:48.160964 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 09:20:48.160992 30176 solver.cpp:397]     Test net output #1: loss = 5.2804 (* 1 = 5.2804 loss)
I0405 09:20:50.111682 30176 solver.cpp:218] Iteration 5004 (0.828197 iter/s, 14.4893s/12 iters), loss = 5.27303
I0405 09:20:50.111729 30176 solver.cpp:237]     Train net output #0: loss = 5.27303 (* 1 = 5.27303 loss)
I0405 09:20:50.111737 30176 sgd_solver.cpp:105] Iteration 5004, lr = 1e-06
I0405 09:20:55.290167 30176 solver.cpp:218] Iteration 5016 (2.31733 iter/s, 5.17838s/12 iters), loss = 5.28686
I0405 09:20:55.290205 30176 solver.cpp:237]     Train net output #0: loss = 5.28686 (* 1 = 5.28686 loss)
I0405 09:20:55.290210 30176 sgd_solver.cpp:105] Iteration 5016, lr = 1e-06
I0405 09:21:00.582082 30176 solver.cpp:218] Iteration 5028 (2.26765 iter/s, 5.29182s/12 iters), loss = 5.27702
I0405 09:21:00.582114 30176 solver.cpp:237]     Train net output #0: loss = 5.27702 (* 1 = 5.27702 loss)
I0405 09:21:00.582119 30176 sgd_solver.cpp:105] Iteration 5028, lr = 1e-06
I0405 09:21:05.907121 30176 solver.cpp:218] Iteration 5040 (2.25354 iter/s, 5.32495s/12 iters), loss = 5.28401
I0405 09:21:05.907280 30176 solver.cpp:237]     Train net output #0: loss = 5.28401 (* 1 = 5.28401 loss)
I0405 09:21:05.907289 30176 sgd_solver.cpp:105] Iteration 5040, lr = 1e-06
I0405 09:21:11.266566 30176 solver.cpp:218] Iteration 5052 (2.23913 iter/s, 5.35923s/12 iters), loss = 5.29094
I0405 09:21:11.266607 30176 solver.cpp:237]     Train net output #0: loss = 5.29094 (* 1 = 5.29094 loss)
I0405 09:21:11.266613 30176 sgd_solver.cpp:105] Iteration 5052, lr = 1e-06
I0405 09:21:13.269706 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:21:16.624029 30176 solver.cpp:218] Iteration 5064 (2.23991 iter/s, 5.35736s/12 iters), loss = 5.28484
I0405 09:21:16.624068 30176 solver.cpp:237]     Train net output #0: loss = 5.28484 (* 1 = 5.28484 loss)
I0405 09:21:16.624073 30176 sgd_solver.cpp:105] Iteration 5064, lr = 1e-06
I0405 09:21:21.773773 30176 solver.cpp:218] Iteration 5076 (2.33026 iter/s, 5.14964s/12 iters), loss = 5.29224
I0405 09:21:21.773828 30176 solver.cpp:237]     Train net output #0: loss = 5.29224 (* 1 = 5.29224 loss)
I0405 09:21:21.773836 30176 sgd_solver.cpp:105] Iteration 5076, lr = 1e-06
I0405 09:21:26.849486 30176 solver.cpp:218] Iteration 5088 (2.36425 iter/s, 5.0756s/12 iters), loss = 5.2967
I0405 09:21:26.849529 30176 solver.cpp:237]     Train net output #0: loss = 5.2967 (* 1 = 5.2967 loss)
I0405 09:21:26.849534 30176 sgd_solver.cpp:105] Iteration 5088, lr = 1e-06
I0405 09:21:31.584825 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5100.caffemodel
I0405 09:21:34.604120 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5100.solverstate
I0405 09:21:36.904201 30176 solver.cpp:330] Iteration 5100, Testing net (#0)
I0405 09:21:36.904279 30176 net.cpp:676] Ignoring source layer train-data
I0405 09:21:39.187685 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:21:41.158162 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 09:21:41.158193 30176 solver.cpp:397]     Test net output #1: loss = 5.28033 (* 1 = 5.28033 loss)
I0405 09:21:41.296526 30176 solver.cpp:218] Iteration 5100 (0.83063 iter/s, 14.4469s/12 iters), loss = 5.27239
I0405 09:21:41.296563 30176 solver.cpp:237]     Train net output #0: loss = 5.27239 (* 1 = 5.27239 loss)
I0405 09:21:41.296568 30176 sgd_solver.cpp:105] Iteration 5100, lr = 1e-06
I0405 09:21:45.785073 30176 solver.cpp:218] Iteration 5112 (2.67352 iter/s, 4.48846s/12 iters), loss = 5.2903
I0405 09:21:45.785109 30176 solver.cpp:237]     Train net output #0: loss = 5.2903 (* 1 = 5.2903 loss)
I0405 09:21:45.785115 30176 sgd_solver.cpp:105] Iteration 5112, lr = 1e-06
I0405 09:21:51.117302 30176 solver.cpp:218] Iteration 5124 (2.25051 iter/s, 5.33213s/12 iters), loss = 5.278
I0405 09:21:51.117344 30176 solver.cpp:237]     Train net output #0: loss = 5.278 (* 1 = 5.278 loss)
I0405 09:21:51.117349 30176 sgd_solver.cpp:105] Iteration 5124, lr = 1e-06
I0405 09:21:56.382791 30176 solver.cpp:218] Iteration 5136 (2.27903 iter/s, 5.26539s/12 iters), loss = 5.29686
I0405 09:21:56.382824 30176 solver.cpp:237]     Train net output #0: loss = 5.29686 (* 1 = 5.29686 loss)
I0405 09:21:56.382829 30176 sgd_solver.cpp:105] Iteration 5136, lr = 1e-06
I0405 09:22:01.501660 30176 solver.cpp:218] Iteration 5148 (2.34431 iter/s, 5.11879s/12 iters), loss = 5.28928
I0405 09:22:01.501690 30176 solver.cpp:237]     Train net output #0: loss = 5.28928 (* 1 = 5.28928 loss)
I0405 09:22:01.501695 30176 sgd_solver.cpp:105] Iteration 5148, lr = 1e-06
I0405 09:22:05.605108 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:22:06.602510 30176 solver.cpp:218] Iteration 5160 (2.35259 iter/s, 5.10076s/12 iters), loss = 5.27542
I0405 09:22:06.602560 30176 solver.cpp:237]     Train net output #0: loss = 5.27542 (* 1 = 5.27542 loss)
I0405 09:22:06.602566 30176 sgd_solver.cpp:105] Iteration 5160, lr = 1e-06
I0405 09:22:11.939687 30176 solver.cpp:218] Iteration 5172 (2.24843 iter/s, 5.33706s/12 iters), loss = 5.28093
I0405 09:22:11.939853 30176 solver.cpp:237]     Train net output #0: loss = 5.28093 (* 1 = 5.28093 loss)
I0405 09:22:11.939862 30176 sgd_solver.cpp:105] Iteration 5172, lr = 1e-06
I0405 09:22:17.095592 30176 solver.cpp:218] Iteration 5184 (2.32753 iter/s, 5.15568s/12 iters), loss = 5.28301
I0405 09:22:17.095634 30176 solver.cpp:237]     Train net output #0: loss = 5.28301 (* 1 = 5.28301 loss)
I0405 09:22:17.095640 30176 sgd_solver.cpp:105] Iteration 5184, lr = 1e-06
I0405 09:22:22.540966 30176 solver.cpp:218] Iteration 5196 (2.20375 iter/s, 5.44527s/12 iters), loss = 5.28035
I0405 09:22:22.541002 30176 solver.cpp:237]     Train net output #0: loss = 5.28035 (* 1 = 5.28035 loss)
I0405 09:22:22.541007 30176 sgd_solver.cpp:105] Iteration 5196, lr = 1e-06
I0405 09:22:24.741035 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5202.caffemodel
I0405 09:22:30.011976 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5202.solverstate
I0405 09:22:34.533835 30176 solver.cpp:330] Iteration 5202, Testing net (#0)
I0405 09:22:34.533857 30176 net.cpp:676] Ignoring source layer train-data
I0405 09:22:36.879352 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:22:38.904539 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 09:22:38.904573 30176 solver.cpp:397]     Test net output #1: loss = 5.2799 (* 1 = 5.2799 loss)
I0405 09:22:40.739756 30176 solver.cpp:218] Iteration 5208 (0.659392 iter/s, 18.1986s/12 iters), loss = 5.29529
I0405 09:22:40.739789 30176 solver.cpp:237]     Train net output #0: loss = 5.29529 (* 1 = 5.29529 loss)
I0405 09:22:40.739794 30176 sgd_solver.cpp:105] Iteration 5208, lr = 1e-06
I0405 09:22:46.273241 30176 solver.cpp:218] Iteration 5220 (2.16865 iter/s, 5.53339s/12 iters), loss = 5.28626
I0405 09:22:46.273334 30176 solver.cpp:237]     Train net output #0: loss = 5.28626 (* 1 = 5.28626 loss)
I0405 09:22:46.273339 30176 sgd_solver.cpp:105] Iteration 5220, lr = 1e-06
I0405 09:22:51.621964 30176 solver.cpp:218] Iteration 5232 (2.24359 iter/s, 5.34857s/12 iters), loss = 5.28689
I0405 09:22:51.622009 30176 solver.cpp:237]     Train net output #0: loss = 5.28689 (* 1 = 5.28689 loss)
I0405 09:22:51.622015 30176 sgd_solver.cpp:105] Iteration 5232, lr = 1e-06
I0405 09:22:56.977263 30176 solver.cpp:218] Iteration 5244 (2.24082 iter/s, 5.35519s/12 iters), loss = 5.27653
I0405 09:22:56.977306 30176 solver.cpp:237]     Train net output #0: loss = 5.27653 (* 1 = 5.27653 loss)
I0405 09:22:56.977313 30176 sgd_solver.cpp:105] Iteration 5244, lr = 1e-06
I0405 09:23:02.459744 30176 solver.cpp:218] Iteration 5256 (2.18883 iter/s, 5.48238s/12 iters), loss = 5.27995
I0405 09:23:02.459785 30176 solver.cpp:237]     Train net output #0: loss = 5.27995 (* 1 = 5.27995 loss)
I0405 09:23:02.459790 30176 sgd_solver.cpp:105] Iteration 5256, lr = 1e-06
I0405 09:23:03.858192 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:23:07.908748 30176 solver.cpp:218] Iteration 5268 (2.20228 iter/s, 5.4489s/12 iters), loss = 5.27889
I0405 09:23:07.908790 30176 solver.cpp:237]     Train net output #0: loss = 5.27889 (* 1 = 5.27889 loss)
I0405 09:23:07.908795 30176 sgd_solver.cpp:105] Iteration 5268, lr = 1e-06
I0405 09:23:13.362221 30176 solver.cpp:218] Iteration 5280 (2.20047 iter/s, 5.45337s/12 iters), loss = 5.28766
I0405 09:23:13.362262 30176 solver.cpp:237]     Train net output #0: loss = 5.28766 (* 1 = 5.28766 loss)
I0405 09:23:13.362267 30176 sgd_solver.cpp:105] Iteration 5280, lr = 1e-06
I0405 09:23:18.645324 30176 solver.cpp:218] Iteration 5292 (2.27144 iter/s, 5.283s/12 iters), loss = 5.29007
I0405 09:23:18.645411 30176 solver.cpp:237]     Train net output #0: loss = 5.29007 (* 1 = 5.29007 loss)
I0405 09:23:18.645417 30176 sgd_solver.cpp:105] Iteration 5292, lr = 1e-06
I0405 09:23:23.431061 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5304.caffemodel
I0405 09:23:26.457481 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5304.solverstate
I0405 09:23:28.781653 30176 solver.cpp:330] Iteration 5304, Testing net (#0)
I0405 09:23:28.781677 30176 net.cpp:676] Ignoring source layer train-data
I0405 09:23:30.983290 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:23:33.050693 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 09:23:33.050741 30176 solver.cpp:397]     Test net output #1: loss = 5.28003 (* 1 = 5.28003 loss)
I0405 09:23:33.188546 30176 solver.cpp:218] Iteration 5304 (0.825139 iter/s, 14.543s/12 iters), loss = 5.28848
I0405 09:23:33.188591 30176 solver.cpp:237]     Train net output #0: loss = 5.28848 (* 1 = 5.28848 loss)
I0405 09:23:33.188599 30176 sgd_solver.cpp:105] Iteration 5304, lr = 1e-06
I0405 09:23:37.607854 30176 solver.cpp:218] Iteration 5316 (2.71541 iter/s, 4.41922s/12 iters), loss = 5.28545
I0405 09:23:37.607888 30176 solver.cpp:237]     Train net output #0: loss = 5.28545 (* 1 = 5.28545 loss)
I0405 09:23:37.607893 30176 sgd_solver.cpp:105] Iteration 5316, lr = 1e-06
I0405 09:23:42.974638 30176 solver.cpp:218] Iteration 5328 (2.23602 iter/s, 5.36668s/12 iters), loss = 5.28702
I0405 09:23:42.974700 30176 solver.cpp:237]     Train net output #0: loss = 5.28702 (* 1 = 5.28702 loss)
I0405 09:23:42.974710 30176 sgd_solver.cpp:105] Iteration 5328, lr = 1e-06
I0405 09:23:48.330669 30176 solver.cpp:218] Iteration 5340 (2.24051 iter/s, 5.35591s/12 iters), loss = 5.28167
I0405 09:23:48.330705 30176 solver.cpp:237]     Train net output #0: loss = 5.28167 (* 1 = 5.28167 loss)
I0405 09:23:48.330710 30176 sgd_solver.cpp:105] Iteration 5340, lr = 1e-06
I0405 09:23:53.627344 30176 solver.cpp:218] Iteration 5352 (2.26561 iter/s, 5.29658s/12 iters), loss = 5.27704
I0405 09:23:53.627507 30176 solver.cpp:237]     Train net output #0: loss = 5.27704 (* 1 = 5.27704 loss)
I0405 09:23:53.627516 30176 sgd_solver.cpp:105] Iteration 5352, lr = 1e-06
I0405 09:23:57.052960 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:23:58.659302 30176 solver.cpp:218] Iteration 5364 (2.38486 iter/s, 5.03174s/12 iters), loss = 5.28634
I0405 09:23:58.659340 30176 solver.cpp:237]     Train net output #0: loss = 5.28634 (* 1 = 5.28634 loss)
I0405 09:23:58.659345 30176 sgd_solver.cpp:105] Iteration 5364, lr = 1e-06
I0405 09:24:04.012642 30176 solver.cpp:218] Iteration 5376 (2.24163 iter/s, 5.35324s/12 iters), loss = 5.2883
I0405 09:24:04.012682 30176 solver.cpp:237]     Train net output #0: loss = 5.2883 (* 1 = 5.2883 loss)
I0405 09:24:04.012688 30176 sgd_solver.cpp:105] Iteration 5376, lr = 1e-06
I0405 09:24:09.324088 30176 solver.cpp:218] Iteration 5388 (2.25931 iter/s, 5.31135s/12 iters), loss = 5.2798
I0405 09:24:09.324126 30176 solver.cpp:237]     Train net output #0: loss = 5.2798 (* 1 = 5.2798 loss)
I0405 09:24:09.324131 30176 sgd_solver.cpp:105] Iteration 5388, lr = 1e-06
I0405 09:24:14.723670 30176 solver.cpp:218] Iteration 5400 (2.22243 iter/s, 5.39948s/12 iters), loss = 5.2929
I0405 09:24:14.723713 30176 solver.cpp:237]     Train net output #0: loss = 5.2929 (* 1 = 5.2929 loss)
I0405 09:24:14.723718 30176 sgd_solver.cpp:105] Iteration 5400, lr = 1e-06
I0405 09:24:16.902999 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5406.caffemodel
I0405 09:24:19.992972 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5406.solverstate
I0405 09:24:22.429194 30176 solver.cpp:330] Iteration 5406, Testing net (#0)
I0405 09:24:22.429214 30176 net.cpp:676] Ignoring source layer train-data
I0405 09:24:24.682786 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:24:26.821007 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 09:24:26.821036 30176 solver.cpp:397]     Test net output #1: loss = 5.27989 (* 1 = 5.27989 loss)
I0405 09:24:28.611060 30176 solver.cpp:218] Iteration 5412 (0.864104 iter/s, 13.8872s/12 iters), loss = 5.27369
I0405 09:24:28.611114 30176 solver.cpp:237]     Train net output #0: loss = 5.27369 (* 1 = 5.27369 loss)
I0405 09:24:28.611121 30176 sgd_solver.cpp:105] Iteration 5412, lr = 1e-06
I0405 09:24:33.862233 30176 solver.cpp:218] Iteration 5424 (2.28525 iter/s, 5.25106s/12 iters), loss = 5.26926
I0405 09:24:33.862275 30176 solver.cpp:237]     Train net output #0: loss = 5.26926 (* 1 = 5.26926 loss)
I0405 09:24:33.862280 30176 sgd_solver.cpp:105] Iteration 5424, lr = 1e-06
I0405 09:24:39.295779 30176 solver.cpp:218] Iteration 5436 (2.20855 iter/s, 5.43344s/12 iters), loss = 5.29073
I0405 09:24:39.295821 30176 solver.cpp:237]     Train net output #0: loss = 5.29073 (* 1 = 5.29073 loss)
I0405 09:24:39.295826 30176 sgd_solver.cpp:105] Iteration 5436, lr = 1e-06
I0405 09:24:44.581544 30176 solver.cpp:218] Iteration 5448 (2.27029 iter/s, 5.28566s/12 iters), loss = 5.2919
I0405 09:24:44.581581 30176 solver.cpp:237]     Train net output #0: loss = 5.2919 (* 1 = 5.2919 loss)
I0405 09:24:44.581586 30176 sgd_solver.cpp:105] Iteration 5448, lr = 1e-06
I0405 09:24:49.877190 30176 solver.cpp:218] Iteration 5460 (2.26605 iter/s, 5.29555s/12 iters), loss = 5.29547
I0405 09:24:49.877224 30176 solver.cpp:237]     Train net output #0: loss = 5.29547 (* 1 = 5.29547 loss)
I0405 09:24:49.877229 30176 sgd_solver.cpp:105] Iteration 5460, lr = 1e-06
I0405 09:24:50.481379 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:24:55.323001 30176 solver.cpp:218] Iteration 5472 (2.20357 iter/s, 5.44572s/12 iters), loss = 5.28706
I0405 09:24:55.323138 30176 solver.cpp:237]     Train net output #0: loss = 5.28706 (* 1 = 5.28706 loss)
I0405 09:24:55.323144 30176 sgd_solver.cpp:105] Iteration 5472, lr = 1e-06
I0405 09:25:00.691406 30176 solver.cpp:218] Iteration 5484 (2.23538 iter/s, 5.36821s/12 iters), loss = 5.28972
I0405 09:25:00.691442 30176 solver.cpp:237]     Train net output #0: loss = 5.28972 (* 1 = 5.28972 loss)
I0405 09:25:00.691447 30176 sgd_solver.cpp:105] Iteration 5484, lr = 1e-06
I0405 09:25:06.316238 30176 solver.cpp:218] Iteration 5496 (2.13343 iter/s, 5.62473s/12 iters), loss = 5.2844
I0405 09:25:06.316275 30176 solver.cpp:237]     Train net output #0: loss = 5.2844 (* 1 = 5.2844 loss)
I0405 09:25:06.316280 30176 sgd_solver.cpp:105] Iteration 5496, lr = 1e-06
I0405 09:25:11.159339 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5508.caffemodel
I0405 09:25:16.406319 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5508.solverstate
I0405 09:25:21.458480 30176 solver.cpp:330] Iteration 5508, Testing net (#0)
I0405 09:25:21.458494 30176 net.cpp:676] Ignoring source layer train-data
I0405 09:25:23.704317 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:25:25.967367 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 09:25:25.967525 30176 solver.cpp:397]     Test net output #1: loss = 5.27994 (* 1 = 5.27994 loss)
I0405 09:25:26.103013 30176 solver.cpp:218] Iteration 5508 (0.606472 iter/s, 19.7866s/12 iters), loss = 5.2768
I0405 09:25:26.103067 30176 solver.cpp:237]     Train net output #0: loss = 5.2768 (* 1 = 5.2768 loss)
I0405 09:25:26.103075 30176 sgd_solver.cpp:105] Iteration 5508, lr = 1e-06
I0405 09:25:30.437376 30176 solver.cpp:218] Iteration 5520 (2.76864 iter/s, 4.33426s/12 iters), loss = 5.28941
I0405 09:25:30.437413 30176 solver.cpp:237]     Train net output #0: loss = 5.28941 (* 1 = 5.28941 loss)
I0405 09:25:30.437419 30176 sgd_solver.cpp:105] Iteration 5520, lr = 1e-06
I0405 09:25:33.069674 30176 blocking_queue.cpp:49] Waiting for data
I0405 09:25:35.744088 30176 solver.cpp:218] Iteration 5532 (2.26133 iter/s, 5.30661s/12 iters), loss = 5.29445
I0405 09:25:35.744144 30176 solver.cpp:237]     Train net output #0: loss = 5.29445 (* 1 = 5.29445 loss)
I0405 09:25:35.744153 30176 sgd_solver.cpp:105] Iteration 5532, lr = 1e-06
I0405 09:25:41.009563 30176 solver.cpp:218] Iteration 5544 (2.27905 iter/s, 5.26536s/12 iters), loss = 5.29538
I0405 09:25:41.009603 30176 solver.cpp:237]     Train net output #0: loss = 5.29538 (* 1 = 5.29538 loss)
I0405 09:25:41.009608 30176 sgd_solver.cpp:105] Iteration 5544, lr = 1e-06
I0405 09:25:46.083704 30176 solver.cpp:218] Iteration 5556 (2.36498 iter/s, 5.07404s/12 iters), loss = 5.26977
I0405 09:25:46.083745 30176 solver.cpp:237]     Train net output #0: loss = 5.26977 (* 1 = 5.26977 loss)
I0405 09:25:46.083751 30176 sgd_solver.cpp:105] Iteration 5556, lr = 1e-06
I0405 09:25:48.810130 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:25:51.257774 30176 solver.cpp:218] Iteration 5568 (2.3193 iter/s, 5.17397s/12 iters), loss = 5.28141
I0405 09:25:51.257822 30176 solver.cpp:237]     Train net output #0: loss = 5.28141 (* 1 = 5.28141 loss)
I0405 09:25:51.257828 30176 sgd_solver.cpp:105] Iteration 5568, lr = 1e-06
I0405 09:25:56.615293 30176 solver.cpp:218] Iteration 5580 (2.23989 iter/s, 5.35741s/12 iters), loss = 5.28507
I0405 09:25:56.615425 30176 solver.cpp:237]     Train net output #0: loss = 5.28507 (* 1 = 5.28507 loss)
I0405 09:25:56.615432 30176 sgd_solver.cpp:105] Iteration 5580, lr = 1e-06
I0405 09:26:01.675359 30176 solver.cpp:218] Iteration 5592 (2.3716 iter/s, 5.05988s/12 iters), loss = 5.29853
I0405 09:26:01.675398 30176 solver.cpp:237]     Train net output #0: loss = 5.29853 (* 1 = 5.29853 loss)
I0405 09:26:01.675403 30176 sgd_solver.cpp:105] Iteration 5592, lr = 1e-06
I0405 09:26:07.084200 30176 solver.cpp:218] Iteration 5604 (2.21863 iter/s, 5.40874s/12 iters), loss = 5.29491
I0405 09:26:07.084240 30176 solver.cpp:237]     Train net output #0: loss = 5.29491 (* 1 = 5.29491 loss)
I0405 09:26:07.084246 30176 sgd_solver.cpp:105] Iteration 5604, lr = 1e-06
I0405 09:26:09.222414 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5610.caffemodel
I0405 09:26:12.264645 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5610.solverstate
I0405 09:26:14.599261 30176 solver.cpp:330] Iteration 5610, Testing net (#0)
I0405 09:26:14.599280 30176 net.cpp:676] Ignoring source layer train-data
I0405 09:26:16.760593 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:26:18.940383 30176 solver.cpp:397]     Test net output #0: accuracy = 0.00735294
I0405 09:26:18.940433 30176 solver.cpp:397]     Test net output #1: loss = 5.27962 (* 1 = 5.27962 loss)
I0405 09:26:20.839679 30176 solver.cpp:218] Iteration 5616 (0.87239 iter/s, 13.7553s/12 iters), loss = 5.28406
I0405 09:26:20.839720 30176 solver.cpp:237]     Train net output #0: loss = 5.28406 (* 1 = 5.28406 loss)
I0405 09:26:20.839725 30176 sgd_solver.cpp:105] Iteration 5616, lr = 1e-06
I0405 09:26:26.208717 30176 solver.cpp:218] Iteration 5628 (2.23508 iter/s, 5.36894s/12 iters), loss = 5.29383
I0405 09:26:26.208758 30176 solver.cpp:237]     Train net output #0: loss = 5.29383 (* 1 = 5.29383 loss)
I0405 09:26:26.208763 30176 sgd_solver.cpp:105] Iteration 5628, lr = 1e-06
I0405 09:26:31.567108 30176 solver.cpp:218] Iteration 5640 (2.23952 iter/s, 5.35828s/12 iters), loss = 5.28074
I0405 09:26:31.567257 30176 solver.cpp:237]     Train net output #0: loss = 5.28074 (* 1 = 5.28074 loss)
I0405 09:26:31.567268 30176 sgd_solver.cpp:105] Iteration 5640, lr = 1e-06
I0405 09:26:36.781248 30176 solver.cpp:218] Iteration 5652 (2.30152 iter/s, 5.21394s/12 iters), loss = 5.26976
I0405 09:26:36.781288 30176 solver.cpp:237]     Train net output #0: loss = 5.26976 (* 1 = 5.26976 loss)
I0405 09:26:36.781293 30176 sgd_solver.cpp:105] Iteration 5652, lr = 1e-06
I0405 09:26:41.765049 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:26:41.984781 30176 solver.cpp:218] Iteration 5664 (2.30617 iter/s, 5.20343s/12 iters), loss = 5.29443
I0405 09:26:41.984825 30176 solver.cpp:237]     Train net output #0: loss = 5.29443 (* 1 = 5.29443 loss)
I0405 09:26:41.984830 30176 sgd_solver.cpp:105] Iteration 5664, lr = 1e-06
I0405 09:26:47.490811 30176 solver.cpp:218] Iteration 5676 (2.17947 iter/s, 5.50592s/12 iters), loss = 5.30843
I0405 09:26:47.490850 30176 solver.cpp:237]     Train net output #0: loss = 5.30843 (* 1 = 5.30843 loss)
I0405 09:26:47.490855 30176 sgd_solver.cpp:105] Iteration 5676, lr = 1e-06
I0405 09:26:52.956557 30176 solver.cpp:218] Iteration 5688 (2.19553 iter/s, 5.46564s/12 iters), loss = 5.30376
I0405 09:26:52.956609 30176 solver.cpp:237]     Train net output #0: loss = 5.30376 (* 1 = 5.30376 loss)
I0405 09:26:52.956616 30176 sgd_solver.cpp:105] Iteration 5688, lr = 1e-06
I0405 09:26:58.232879 30176 solver.cpp:218] Iteration 5700 (2.27436 iter/s, 5.27621s/12 iters), loss = 5.2784
I0405 09:26:58.232941 30176 solver.cpp:237]     Train net output #0: loss = 5.2784 (* 1 = 5.2784 loss)
I0405 09:26:58.232949 30176 sgd_solver.cpp:105] Iteration 5700, lr = 1e-06
I0405 09:27:03.044745 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5712.caffemodel
I0405 09:27:06.168500 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5712.solverstate
I0405 09:27:08.564851 30176 solver.cpp:330] Iteration 5712, Testing net (#0)
I0405 09:27:08.564869 30176 net.cpp:676] Ignoring source layer train-data
I0405 09:27:10.665248 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:27:12.869900 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 09:27:12.869948 30176 solver.cpp:397]     Test net output #1: loss = 5.27998 (* 1 = 5.27998 loss)
I0405 09:27:13.001516 30176 solver.cpp:218] Iteration 5712 (0.812543 iter/s, 14.7684s/12 iters), loss = 5.27338
I0405 09:27:13.001554 30176 solver.cpp:237]     Train net output #0: loss = 5.27338 (* 1 = 5.27338 loss)
I0405 09:27:13.001560 30176 sgd_solver.cpp:105] Iteration 5712, lr = 1e-06
I0405 09:27:17.508821 30176 solver.cpp:218] Iteration 5724 (2.6624 iter/s, 4.50722s/12 iters), loss = 5.29131
I0405 09:27:17.508862 30176 solver.cpp:237]     Train net output #0: loss = 5.29131 (* 1 = 5.29131 loss)
I0405 09:27:17.508868 30176 sgd_solver.cpp:105] Iteration 5724, lr = 1e-06
I0405 09:27:22.890291 30176 solver.cpp:218] Iteration 5736 (2.22992 iter/s, 5.38137s/12 iters), loss = 5.29125
I0405 09:27:22.890332 30176 solver.cpp:237]     Train net output #0: loss = 5.29125 (* 1 = 5.29125 loss)
I0405 09:27:22.890337 30176 sgd_solver.cpp:105] Iteration 5736, lr = 1e-06
I0405 09:27:28.194635 30176 solver.cpp:218] Iteration 5748 (2.26234 iter/s, 5.30424s/12 iters), loss = 5.28754
I0405 09:27:28.194674 30176 solver.cpp:237]     Train net output #0: loss = 5.28754 (* 1 = 5.28754 loss)
I0405 09:27:28.194679 30176 sgd_solver.cpp:105] Iteration 5748, lr = 1e-06
I0405 09:27:33.437084 30176 solver.cpp:218] Iteration 5760 (2.28905 iter/s, 5.24235s/12 iters), loss = 5.29456
I0405 09:27:33.437175 30176 solver.cpp:237]     Train net output #0: loss = 5.29456 (* 1 = 5.29456 loss)
I0405 09:27:33.437182 30176 sgd_solver.cpp:105] Iteration 5760, lr = 1e-06
I0405 09:27:35.486109 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:27:38.864477 30176 solver.cpp:218] Iteration 5772 (2.21107 iter/s, 5.42724s/12 iters), loss = 5.2856
I0405 09:27:38.864519 30176 solver.cpp:237]     Train net output #0: loss = 5.2856 (* 1 = 5.2856 loss)
I0405 09:27:38.864526 30176 sgd_solver.cpp:105] Iteration 5772, lr = 1e-06
I0405 09:27:43.971105 30176 solver.cpp:218] Iteration 5784 (2.34993 iter/s, 5.10653s/12 iters), loss = 5.29114
I0405 09:27:43.971144 30176 solver.cpp:237]     Train net output #0: loss = 5.29114 (* 1 = 5.29114 loss)
I0405 09:27:43.971150 30176 sgd_solver.cpp:105] Iteration 5784, lr = 1e-06
I0405 09:27:49.216259 30176 solver.cpp:218] Iteration 5796 (2.28787 iter/s, 5.24506s/12 iters), loss = 5.28328
I0405 09:27:49.216298 30176 solver.cpp:237]     Train net output #0: loss = 5.28328 (* 1 = 5.28328 loss)
I0405 09:27:49.216303 30176 sgd_solver.cpp:105] Iteration 5796, lr = 1e-06
I0405 09:27:54.314126 30176 solver.cpp:218] Iteration 5808 (2.35397 iter/s, 5.09777s/12 iters), loss = 5.28029
I0405 09:27:54.314172 30176 solver.cpp:237]     Train net output #0: loss = 5.28029 (* 1 = 5.28029 loss)
I0405 09:27:54.314177 30176 sgd_solver.cpp:105] Iteration 5808, lr = 1e-06
I0405 09:27:56.506664 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5814.caffemodel
I0405 09:28:01.719143 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5814.solverstate
I0405 09:28:06.218297 30176 solver.cpp:330] Iteration 5814, Testing net (#0)
I0405 09:28:06.218430 30176 net.cpp:676] Ignoring source layer train-data
I0405 09:28:08.378176 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:28:10.760180 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 09:28:10.760215 30176 solver.cpp:397]     Test net output #1: loss = 5.28017 (* 1 = 5.28017 loss)
I0405 09:28:12.732760 30176 solver.cpp:218] Iteration 5820 (0.651522 iter/s, 18.4184s/12 iters), loss = 5.27478
I0405 09:28:12.732812 30176 solver.cpp:237]     Train net output #0: loss = 5.27478 (* 1 = 5.27478 loss)
I0405 09:28:12.732820 30176 sgd_solver.cpp:105] Iteration 5820, lr = 1e-06
I0405 09:28:18.048909 30176 solver.cpp:218] Iteration 5832 (2.25732 iter/s, 5.31604s/12 iters), loss = 5.27411
I0405 09:28:18.048952 30176 solver.cpp:237]     Train net output #0: loss = 5.27411 (* 1 = 5.27411 loss)
I0405 09:28:18.048959 30176 sgd_solver.cpp:105] Iteration 5832, lr = 1e-06
I0405 09:28:23.223989 30176 solver.cpp:218] Iteration 5844 (2.31885 iter/s, 5.17498s/12 iters), loss = 5.27918
I0405 09:28:23.224030 30176 solver.cpp:237]     Train net output #0: loss = 5.27918 (* 1 = 5.27918 loss)
I0405 09:28:23.224037 30176 sgd_solver.cpp:105] Iteration 5844, lr = 1e-06
I0405 09:28:28.521080 30176 solver.cpp:218] Iteration 5856 (2.26544 iter/s, 5.29699s/12 iters), loss = 5.31494
I0405 09:28:28.521121 30176 solver.cpp:237]     Train net output #0: loss = 5.31494 (* 1 = 5.31494 loss)
I0405 09:28:28.521126 30176 sgd_solver.cpp:105] Iteration 5856, lr = 1e-06
I0405 09:28:32.589399 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:28:33.464989 30176 solver.cpp:218] Iteration 5868 (2.42728 iter/s, 4.94381s/12 iters), loss = 5.28932
I0405 09:28:33.465040 30176 solver.cpp:237]     Train net output #0: loss = 5.28932 (* 1 = 5.28932 loss)
I0405 09:28:33.465049 30176 sgd_solver.cpp:105] Iteration 5868, lr = 1e-06
I0405 09:28:38.680408 30176 solver.cpp:218] Iteration 5880 (2.30092 iter/s, 5.21531s/12 iters), loss = 5.28347
I0405 09:28:38.680534 30176 solver.cpp:237]     Train net output #0: loss = 5.28347 (* 1 = 5.28347 loss)
I0405 09:28:38.680543 30176 sgd_solver.cpp:105] Iteration 5880, lr = 1e-06
I0405 09:28:44.031211 30176 solver.cpp:218] Iteration 5892 (2.24273 iter/s, 5.35062s/12 iters), loss = 5.27897
I0405 09:28:44.031257 30176 solver.cpp:237]     Train net output #0: loss = 5.27897 (* 1 = 5.27897 loss)
I0405 09:28:44.031263 30176 sgd_solver.cpp:105] Iteration 5892, lr = 1e-06
I0405 09:28:49.238085 30176 solver.cpp:218] Iteration 5904 (2.30469 iter/s, 5.20677s/12 iters), loss = 5.28723
I0405 09:28:49.238118 30176 solver.cpp:237]     Train net output #0: loss = 5.28723 (* 1 = 5.28723 loss)
I0405 09:28:49.238123 30176 sgd_solver.cpp:105] Iteration 5904, lr = 1e-06
I0405 09:28:53.942883 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5916.caffemodel
I0405 09:28:57.024641 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5916.solverstate
I0405 09:28:59.329799 30176 solver.cpp:330] Iteration 5916, Testing net (#0)
I0405 09:28:59.329820 30176 net.cpp:676] Ignoring source layer train-data
I0405 09:29:01.326767 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:29:03.597157 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 09:29:03.597191 30176 solver.cpp:397]     Test net output #1: loss = 5.28008 (* 1 = 5.28008 loss)
I0405 09:29:03.734525 30176 solver.cpp:218] Iteration 5916 (0.827799 iter/s, 14.4963s/12 iters), loss = 5.28246
I0405 09:29:03.734578 30176 solver.cpp:237]     Train net output #0: loss = 5.28246 (* 1 = 5.28246 loss)
I0405 09:29:03.734586 30176 sgd_solver.cpp:105] Iteration 5916, lr = 1e-06
I0405 09:29:08.160346 30176 solver.cpp:218] Iteration 5928 (2.71142 iter/s, 4.42572s/12 iters), loss = 5.26347
I0405 09:29:08.160378 30176 solver.cpp:237]     Train net output #0: loss = 5.26347 (* 1 = 5.26347 loss)
I0405 09:29:08.160383 30176 sgd_solver.cpp:105] Iteration 5928, lr = 1e-06
I0405 09:29:13.601172 30176 solver.cpp:218] Iteration 5940 (2.20558 iter/s, 5.44074s/12 iters), loss = 5.27582
I0405 09:29:13.601313 30176 solver.cpp:237]     Train net output #0: loss = 5.27582 (* 1 = 5.27582 loss)
I0405 09:29:13.601320 30176 sgd_solver.cpp:105] Iteration 5940, lr = 1e-06
I0405 09:29:18.663341 30176 solver.cpp:218] Iteration 5952 (2.37062 iter/s, 5.06197s/12 iters), loss = 5.27412
I0405 09:29:18.663383 30176 solver.cpp:237]     Train net output #0: loss = 5.27412 (* 1 = 5.27412 loss)
I0405 09:29:18.663390 30176 sgd_solver.cpp:105] Iteration 5952, lr = 1e-06
I0405 09:29:23.863785 30176 solver.cpp:218] Iteration 5964 (2.30754 iter/s, 5.20034s/12 iters), loss = 5.28994
I0405 09:29:23.863829 30176 solver.cpp:237]     Train net output #0: loss = 5.28994 (* 1 = 5.28994 loss)
I0405 09:29:23.863835 30176 sgd_solver.cpp:105] Iteration 5964, lr = 1e-06
I0405 09:29:25.203608 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:29:29.171183 30176 solver.cpp:218] Iteration 5976 (2.26104 iter/s, 5.3073s/12 iters), loss = 5.2804
I0405 09:29:29.171226 30176 solver.cpp:237]     Train net output #0: loss = 5.2804 (* 1 = 5.2804 loss)
I0405 09:29:29.171234 30176 sgd_solver.cpp:105] Iteration 5976, lr = 1e-06
I0405 09:29:34.414422 30176 solver.cpp:218] Iteration 5988 (2.28871 iter/s, 5.24313s/12 iters), loss = 5.27024
I0405 09:29:34.414456 30176 solver.cpp:237]     Train net output #0: loss = 5.27024 (* 1 = 5.27024 loss)
I0405 09:29:34.414461 30176 sgd_solver.cpp:105] Iteration 5988, lr = 1e-06
I0405 09:29:39.556027 30176 solver.cpp:218] Iteration 6000 (2.33394 iter/s, 5.14151s/12 iters), loss = 5.30522
I0405 09:29:39.556073 30176 solver.cpp:237]     Train net output #0: loss = 5.30522 (* 1 = 5.30522 loss)
I0405 09:29:39.556079 30176 sgd_solver.cpp:105] Iteration 6000, lr = 1e-06
I0405 09:29:44.739899 30176 solver.cpp:218] Iteration 6012 (2.31492 iter/s, 5.18377s/12 iters), loss = 5.28393
I0405 09:29:44.739991 30176 solver.cpp:237]     Train net output #0: loss = 5.28393 (* 1 = 5.28393 loss)
I0405 09:29:44.739997 30176 sgd_solver.cpp:105] Iteration 6012, lr = 1e-06
I0405 09:29:46.857069 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6018.caffemodel
I0405 09:29:52.107235 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6018.solverstate
I0405 09:29:56.082868 30176 solver.cpp:330] Iteration 6018, Testing net (#0)
I0405 09:29:56.082888 30176 net.cpp:676] Ignoring source layer train-data
I0405 09:29:58.019279 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:30:00.340799 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 09:30:00.340832 30176 solver.cpp:397]     Test net output #1: loss = 5.28003 (* 1 = 5.28003 loss)
I0405 09:30:02.334388 30176 solver.cpp:218] Iteration 6024 (0.682042 iter/s, 17.5942s/12 iters), loss = 5.30066
I0405 09:30:02.334465 30176 solver.cpp:237]     Train net output #0: loss = 5.30066 (* 1 = 5.30066 loss)
I0405 09:30:02.334481 30176 sgd_solver.cpp:105] Iteration 6024, lr = 1e-06
I0405 09:30:07.534376 30176 solver.cpp:218] Iteration 6036 (2.30775 iter/s, 5.19986s/12 iters), loss = 5.3121
I0405 09:30:07.534417 30176 solver.cpp:237]     Train net output #0: loss = 5.3121 (* 1 = 5.3121 loss)
I0405 09:30:07.534422 30176 sgd_solver.cpp:105] Iteration 6036, lr = 1e-06
I0405 09:30:12.891362 30176 solver.cpp:218] Iteration 6048 (2.24011 iter/s, 5.35689s/12 iters), loss = 5.27567
I0405 09:30:12.891405 30176 solver.cpp:237]     Train net output #0: loss = 5.27567 (* 1 = 5.27567 loss)
I0405 09:30:12.891412 30176 sgd_solver.cpp:105] Iteration 6048, lr = 1e-06
I0405 09:30:18.324595 30176 solver.cpp:218] Iteration 6060 (2.20867 iter/s, 5.43313s/12 iters), loss = 5.28153
I0405 09:30:18.324702 30176 solver.cpp:237]     Train net output #0: loss = 5.28153 (* 1 = 5.28153 loss)
I0405 09:30:18.324707 30176 sgd_solver.cpp:105] Iteration 6060, lr = 1e-06
I0405 09:30:22.212375 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:30:23.853629 30176 solver.cpp:218] Iteration 6072 (2.17043 iter/s, 5.52887s/12 iters), loss = 5.28683
I0405 09:30:23.853673 30176 solver.cpp:237]     Train net output #0: loss = 5.28683 (* 1 = 5.28683 loss)
I0405 09:30:23.853678 30176 sgd_solver.cpp:105] Iteration 6072, lr = 1e-06
I0405 09:30:29.183997 30176 solver.cpp:218] Iteration 6084 (2.25129 iter/s, 5.33027s/12 iters), loss = 5.28723
I0405 09:30:29.184037 30176 solver.cpp:237]     Train net output #0: loss = 5.28723 (* 1 = 5.28723 loss)
I0405 09:30:29.184043 30176 sgd_solver.cpp:105] Iteration 6084, lr = 1e-06
I0405 09:30:34.438329 30176 solver.cpp:218] Iteration 6096 (2.28387 iter/s, 5.25424s/12 iters), loss = 5.27626
I0405 09:30:34.438367 30176 solver.cpp:237]     Train net output #0: loss = 5.27626 (* 1 = 5.27626 loss)
I0405 09:30:34.438371 30176 sgd_solver.cpp:105] Iteration 6096, lr = 1e-06
I0405 09:30:39.981477 30176 solver.cpp:218] Iteration 6108 (2.16487 iter/s, 5.54305s/12 iters), loss = 5.27596
I0405 09:30:39.981520 30176 solver.cpp:237]     Train net output #0: loss = 5.27596 (* 1 = 5.27596 loss)
I0405 09:30:39.981525 30176 sgd_solver.cpp:105] Iteration 6108, lr = 1e-06
I0405 09:30:45.012077 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6120.caffemodel
I0405 09:30:50.009860 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6120.solverstate
I0405 09:30:53.160449 30176 solver.cpp:330] Iteration 6120, Testing net (#0)
I0405 09:30:53.160471 30176 net.cpp:676] Ignoring source layer train-data
I0405 09:30:55.068681 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:30:57.422961 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 09:30:57.422996 30176 solver.cpp:397]     Test net output #1: loss = 5.28038 (* 1 = 5.28038 loss)
I0405 09:30:57.562460 30176 solver.cpp:218] Iteration 6120 (0.682563 iter/s, 17.5808s/12 iters), loss = 5.28154
I0405 09:30:57.562505 30176 solver.cpp:237]     Train net output #0: loss = 5.28154 (* 1 = 5.28154 loss)
I0405 09:30:57.562510 30176 sgd_solver.cpp:105] Iteration 6120, lr = 1e-06
I0405 09:31:02.091490 30176 solver.cpp:218] Iteration 6132 (2.64963 iter/s, 4.52894s/12 iters), loss = 5.27613
I0405 09:31:02.091533 30176 solver.cpp:237]     Train net output #0: loss = 5.27613 (* 1 = 5.27613 loss)
I0405 09:31:02.091539 30176 sgd_solver.cpp:105] Iteration 6132, lr = 1e-06
I0405 09:31:07.488257 30176 solver.cpp:218] Iteration 6144 (2.22359 iter/s, 5.39667s/12 iters), loss = 5.30079
I0405 09:31:07.488293 30176 solver.cpp:237]     Train net output #0: loss = 5.30079 (* 1 = 5.30079 loss)
I0405 09:31:07.488299 30176 sgd_solver.cpp:105] Iteration 6144, lr = 1e-06
I0405 09:31:12.766902 30176 solver.cpp:218] Iteration 6156 (2.27335 iter/s, 5.27855s/12 iters), loss = 5.28469
I0405 09:31:12.766937 30176 solver.cpp:237]     Train net output #0: loss = 5.28469 (* 1 = 5.28469 loss)
I0405 09:31:12.766942 30176 sgd_solver.cpp:105] Iteration 6156, lr = 1e-06
I0405 09:31:18.057230 30176 solver.cpp:218] Iteration 6168 (2.26833 iter/s, 5.29024s/12 iters), loss = 5.27844
I0405 09:31:18.057262 30176 solver.cpp:237]     Train net output #0: loss = 5.27844 (* 1 = 5.27844 loss)
I0405 09:31:18.057267 30176 sgd_solver.cpp:105] Iteration 6168, lr = 1e-06
I0405 09:31:18.789891 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:31:23.766893 30176 solver.cpp:218] Iteration 6180 (2.10173 iter/s, 5.70957s/12 iters), loss = 5.28749
I0405 09:31:23.767004 30176 solver.cpp:237]     Train net output #0: loss = 5.28749 (* 1 = 5.28749 loss)
I0405 09:31:23.767011 30176 sgd_solver.cpp:105] Iteration 6180, lr = 1e-06
I0405 09:31:29.231351 30176 solver.cpp:218] Iteration 6192 (2.19607 iter/s, 5.4643s/12 iters), loss = 5.29658
I0405 09:31:29.231382 30176 solver.cpp:237]     Train net output #0: loss = 5.29658 (* 1 = 5.29658 loss)
I0405 09:31:29.231387 30176 sgd_solver.cpp:105] Iteration 6192, lr = 1e-06
I0405 09:31:34.576429 30176 solver.cpp:218] Iteration 6204 (2.24509 iter/s, 5.34499s/12 iters), loss = 5.28277
I0405 09:31:34.576468 30176 solver.cpp:237]     Train net output #0: loss = 5.28277 (* 1 = 5.28277 loss)
I0405 09:31:34.576475 30176 sgd_solver.cpp:105] Iteration 6204, lr = 1e-06
I0405 09:31:40.095324 30176 solver.cpp:218] Iteration 6216 (2.17439 iter/s, 5.5188s/12 iters), loss = 5.2858
I0405 09:31:40.095361 30176 solver.cpp:237]     Train net output #0: loss = 5.2858 (* 1 = 5.2858 loss)
I0405 09:31:40.095367 30176 sgd_solver.cpp:105] Iteration 6216, lr = 1e-06
I0405 09:31:42.302551 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6222.caffemodel
I0405 09:31:45.358692 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6222.solverstate
I0405 09:31:47.703208 30176 solver.cpp:330] Iteration 6222, Testing net (#0)
I0405 09:31:47.703234 30176 net.cpp:676] Ignoring source layer train-data
I0405 09:31:49.584331 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:31:50.824126 30176 blocking_queue.cpp:49] Waiting for data
I0405 09:31:51.968775 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 09:31:51.968808 30176 solver.cpp:397]     Test net output #1: loss = 5.28006 (* 1 = 5.28006 loss)
I0405 09:31:53.987257 30176 solver.cpp:218] Iteration 6228 (0.863821 iter/s, 13.8918s/12 iters), loss = 5.28581
I0405 09:31:53.987399 30176 solver.cpp:237]     Train net output #0: loss = 5.28581 (* 1 = 5.28581 loss)
I0405 09:31:53.987407 30176 sgd_solver.cpp:105] Iteration 6228, lr = 1e-06
I0405 09:31:59.118656 30176 solver.cpp:218] Iteration 6240 (2.33863 iter/s, 5.13121s/12 iters), loss = 5.29098
I0405 09:31:59.118695 30176 solver.cpp:237]     Train net output #0: loss = 5.29098 (* 1 = 5.29098 loss)
I0405 09:31:59.118700 30176 sgd_solver.cpp:105] Iteration 6240, lr = 1e-06
I0405 09:32:04.430168 30176 solver.cpp:218] Iteration 6252 (2.25928 iter/s, 5.31142s/12 iters), loss = 5.30384
I0405 09:32:04.430220 30176 solver.cpp:237]     Train net output #0: loss = 5.30384 (* 1 = 5.30384 loss)
I0405 09:32:04.430231 30176 sgd_solver.cpp:105] Iteration 6252, lr = 1e-06
I0405 09:32:09.886952 30176 solver.cpp:218] Iteration 6264 (2.19914 iter/s, 5.45667s/12 iters), loss = 5.27105
I0405 09:32:09.886997 30176 solver.cpp:237]     Train net output #0: loss = 5.27105 (* 1 = 5.27105 loss)
I0405 09:32:09.887004 30176 sgd_solver.cpp:105] Iteration 6264, lr = 1e-06
I0405 09:32:12.802973 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:32:15.359097 30176 solver.cpp:218] Iteration 6276 (2.19296 iter/s, 5.47205s/12 iters), loss = 5.29073
I0405 09:32:15.359133 30176 solver.cpp:237]     Train net output #0: loss = 5.29073 (* 1 = 5.29073 loss)
I0405 09:32:15.359138 30176 sgd_solver.cpp:105] Iteration 6276, lr = 1e-06
I0405 09:32:20.798897 30176 solver.cpp:218] Iteration 6288 (2.206 iter/s, 5.43971s/12 iters), loss = 5.28672
I0405 09:32:20.798929 30176 solver.cpp:237]     Train net output #0: loss = 5.28672 (* 1 = 5.28672 loss)
I0405 09:32:20.798935 30176 sgd_solver.cpp:105] Iteration 6288, lr = 1e-06
I0405 09:32:26.338784 30176 solver.cpp:218] Iteration 6300 (2.16615 iter/s, 5.53979s/12 iters), loss = 5.28005
I0405 09:32:26.338923 30176 solver.cpp:237]     Train net output #0: loss = 5.28005 (* 1 = 5.28005 loss)
I0405 09:32:26.338932 30176 sgd_solver.cpp:105] Iteration 6300, lr = 1e-06
I0405 09:32:31.769591 30176 solver.cpp:218] Iteration 6312 (2.20969 iter/s, 5.43062s/12 iters), loss = 5.3011
I0405 09:32:31.769632 30176 solver.cpp:237]     Train net output #0: loss = 5.3011 (* 1 = 5.3011 loss)
I0405 09:32:31.769639 30176 sgd_solver.cpp:105] Iteration 6312, lr = 1e-06
I0405 09:32:36.700708 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6324.caffemodel
I0405 09:32:39.803776 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6324.solverstate
I0405 09:32:42.147123 30176 solver.cpp:330] Iteration 6324, Testing net (#0)
I0405 09:32:42.147152 30176 net.cpp:676] Ignoring source layer train-data
I0405 09:32:44.072875 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:32:46.465498 30176 solver.cpp:397]     Test net output #0: accuracy = 0.00735294
I0405 09:32:46.465540 30176 solver.cpp:397]     Test net output #1: loss = 5.27964 (* 1 = 5.27964 loss)
I0405 09:32:46.605748 30176 solver.cpp:218] Iteration 6324 (0.808845 iter/s, 14.836s/12 iters), loss = 5.27586
I0405 09:32:46.605810 30176 solver.cpp:237]     Train net output #0: loss = 5.27586 (* 1 = 5.27586 loss)
I0405 09:32:46.605819 30176 sgd_solver.cpp:105] Iteration 6324, lr = 1e-06
I0405 09:32:51.136638 30176 solver.cpp:218] Iteration 6336 (2.64855 iter/s, 4.53078s/12 iters), loss = 5.2749
I0405 09:32:51.136680 30176 solver.cpp:237]     Train net output #0: loss = 5.2749 (* 1 = 5.2749 loss)
I0405 09:32:51.136688 30176 sgd_solver.cpp:105] Iteration 6336, lr = 1e-06
I0405 09:32:56.661525 30176 solver.cpp:218] Iteration 6348 (2.17203 iter/s, 5.52479s/12 iters), loss = 5.274
I0405 09:32:56.661674 30176 solver.cpp:237]     Train net output #0: loss = 5.274 (* 1 = 5.274 loss)
I0405 09:32:56.661684 30176 sgd_solver.cpp:105] Iteration 6348, lr = 1e-06
I0405 09:33:01.951318 30176 solver.cpp:218] Iteration 6360 (2.26861 iter/s, 5.28959s/12 iters), loss = 5.27077
I0405 09:33:01.951364 30176 solver.cpp:237]     Train net output #0: loss = 5.27077 (* 1 = 5.27077 loss)
I0405 09:33:01.951372 30176 sgd_solver.cpp:105] Iteration 6360, lr = 1e-06
I0405 09:33:07.113323 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:33:07.305361 30176 solver.cpp:218] Iteration 6372 (2.24134 iter/s, 5.35394s/12 iters), loss = 5.2808
I0405 09:33:07.305395 30176 solver.cpp:237]     Train net output #0: loss = 5.2808 (* 1 = 5.2808 loss)
I0405 09:33:07.305402 30176 sgd_solver.cpp:105] Iteration 6372, lr = 1e-06
I0405 09:33:12.674628 30176 solver.cpp:218] Iteration 6384 (2.23498 iter/s, 5.36918s/12 iters), loss = 5.2854
I0405 09:33:12.674660 30176 solver.cpp:237]     Train net output #0: loss = 5.2854 (* 1 = 5.2854 loss)
I0405 09:33:12.674665 30176 sgd_solver.cpp:105] Iteration 6384, lr = 1e-06
I0405 09:33:18.187366 30176 solver.cpp:218] Iteration 6396 (2.17681 iter/s, 5.51264s/12 iters), loss = 5.28769
I0405 09:33:18.187412 30176 solver.cpp:237]     Train net output #0: loss = 5.28769 (* 1 = 5.28769 loss)
I0405 09:33:18.187420 30176 sgd_solver.cpp:105] Iteration 6396, lr = 1e-06
I0405 09:33:23.654071 30176 solver.cpp:218] Iteration 6408 (2.19515 iter/s, 5.4666s/12 iters), loss = 5.28409
I0405 09:33:23.654115 30176 solver.cpp:237]     Train net output #0: loss = 5.28409 (* 1 = 5.28409 loss)
I0405 09:33:23.654124 30176 sgd_solver.cpp:105] Iteration 6408, lr = 1e-06
I0405 09:33:29.205107 30176 solver.cpp:218] Iteration 6420 (2.1618 iter/s, 5.55094s/12 iters), loss = 5.2859
I0405 09:33:29.205222 30176 solver.cpp:237]     Train net output #0: loss = 5.2859 (* 1 = 5.2859 loss)
I0405 09:33:29.205230 30176 sgd_solver.cpp:105] Iteration 6420, lr = 1e-06
I0405 09:33:31.415779 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6426.caffemodel
I0405 09:33:34.534554 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6426.solverstate
I0405 09:33:36.876650 30176 solver.cpp:330] Iteration 6426, Testing net (#0)
I0405 09:33:36.876682 30176 net.cpp:676] Ignoring source layer train-data
I0405 09:33:38.758762 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:33:41.209669 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 09:33:41.209702 30176 solver.cpp:397]     Test net output #1: loss = 5.27994 (* 1 = 5.27994 loss)
I0405 09:33:43.244668 30176 solver.cpp:218] Iteration 6432 (0.854742 iter/s, 14.0393s/12 iters), loss = 5.29129
I0405 09:33:43.244710 30176 solver.cpp:237]     Train net output #0: loss = 5.29129 (* 1 = 5.29129 loss)
I0405 09:33:43.244717 30176 sgd_solver.cpp:105] Iteration 6432, lr = 1e-06
I0405 09:33:48.607601 30176 solver.cpp:218] Iteration 6444 (2.23762 iter/s, 5.36283s/12 iters), loss = 5.29374
I0405 09:33:48.607635 30176 solver.cpp:237]     Train net output #0: loss = 5.29374 (* 1 = 5.29374 loss)
I0405 09:33:48.607641 30176 sgd_solver.cpp:105] Iteration 6444, lr = 1e-06
I0405 09:33:53.926533 30176 solver.cpp:218] Iteration 6456 (2.25613 iter/s, 5.31884s/12 iters), loss = 5.29834
I0405 09:33:53.926565 30176 solver.cpp:237]     Train net output #0: loss = 5.29834 (* 1 = 5.29834 loss)
I0405 09:33:53.926570 30176 sgd_solver.cpp:105] Iteration 6456, lr = 1e-06
I0405 09:33:59.428812 30176 solver.cpp:218] Iteration 6468 (2.18095 iter/s, 5.50219s/12 iters), loss = 5.28186
I0405 09:33:59.428997 30176 solver.cpp:237]     Train net output #0: loss = 5.28186 (* 1 = 5.28186 loss)
I0405 09:33:59.429003 30176 sgd_solver.cpp:105] Iteration 6468, lr = 1e-06
I0405 09:34:01.678360 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:34:05.080992 30176 solver.cpp:218] Iteration 6480 (2.12317 iter/s, 5.65194s/12 iters), loss = 5.28463
I0405 09:34:05.081034 30176 solver.cpp:237]     Train net output #0: loss = 5.28463 (* 1 = 5.28463 loss)
I0405 09:34:05.081040 30176 sgd_solver.cpp:105] Iteration 6480, lr = 1e-06
I0405 09:34:10.449298 30176 solver.cpp:218] Iteration 6492 (2.23539 iter/s, 5.3682s/12 iters), loss = 5.29678
I0405 09:34:10.449344 30176 solver.cpp:237]     Train net output #0: loss = 5.29678 (* 1 = 5.29678 loss)
I0405 09:34:10.449352 30176 sgd_solver.cpp:105] Iteration 6492, lr = 1e-06
I0405 09:34:15.969411 30176 solver.cpp:218] Iteration 6504 (2.17391 iter/s, 5.52001s/12 iters), loss = 5.29067
I0405 09:34:15.969449 30176 solver.cpp:237]     Train net output #0: loss = 5.29067 (* 1 = 5.29067 loss)
I0405 09:34:15.969453 30176 sgd_solver.cpp:105] Iteration 6504, lr = 1e-06
I0405 09:34:21.292060 30176 solver.cpp:218] Iteration 6516 (2.25456 iter/s, 5.32255s/12 iters), loss = 5.2865
I0405 09:34:21.292101 30176 solver.cpp:237]     Train net output #0: loss = 5.2865 (* 1 = 5.2865 loss)
I0405 09:34:21.292109 30176 sgd_solver.cpp:105] Iteration 6516, lr = 1e-06
I0405 09:34:26.292543 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6528.caffemodel
I0405 09:34:29.371796 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6528.solverstate
I0405 09:34:31.720252 30176 solver.cpp:330] Iteration 6528, Testing net (#0)
I0405 09:34:31.720366 30176 net.cpp:676] Ignoring source layer train-data
I0405 09:34:33.519441 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:34:36.001588 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 09:34:36.001636 30176 solver.cpp:397]     Test net output #1: loss = 5.27994 (* 1 = 5.27994 loss)
I0405 09:34:36.142884 30176 solver.cpp:218] Iteration 6528 (0.808046 iter/s, 14.8506s/12 iters), loss = 5.2724
I0405 09:34:36.142935 30176 solver.cpp:237]     Train net output #0: loss = 5.2724 (* 1 = 5.2724 loss)
I0405 09:34:36.142941 30176 sgd_solver.cpp:105] Iteration 6528, lr = 1e-06
I0405 09:34:40.456244 30176 solver.cpp:218] Iteration 6540 (2.78212 iter/s, 4.31326s/12 iters), loss = 5.28885
I0405 09:34:40.456280 30176 solver.cpp:237]     Train net output #0: loss = 5.28885 (* 1 = 5.28885 loss)
I0405 09:34:40.456285 30176 sgd_solver.cpp:105] Iteration 6540, lr = 1e-06
I0405 09:34:46.006183 30176 solver.cpp:218] Iteration 6552 (2.16222 iter/s, 5.54984s/12 iters), loss = 5.30033
I0405 09:34:46.006227 30176 solver.cpp:237]     Train net output #0: loss = 5.30033 (* 1 = 5.30033 loss)
I0405 09:34:46.006235 30176 sgd_solver.cpp:105] Iteration 6552, lr = 1e-06
I0405 09:34:51.365504 30176 solver.cpp:218] Iteration 6564 (2.23913 iter/s, 5.35922s/12 iters), loss = 5.30956
I0405 09:34:51.365542 30176 solver.cpp:237]     Train net output #0: loss = 5.30956 (* 1 = 5.30956 loss)
I0405 09:34:51.365548 30176 sgd_solver.cpp:105] Iteration 6564, lr = 1e-06
I0405 09:34:56.125113 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:34:56.992147 30176 solver.cpp:218] Iteration 6576 (2.13275 iter/s, 5.62654s/12 iters), loss = 5.29645
I0405 09:34:56.992187 30176 solver.cpp:237]     Train net output #0: loss = 5.29645 (* 1 = 5.29645 loss)
I0405 09:34:56.992192 30176 sgd_solver.cpp:105] Iteration 6576, lr = 1e-06
I0405 09:35:02.387924 30176 solver.cpp:218] Iteration 6588 (2.224 iter/s, 5.39568s/12 iters), loss = 5.29834
I0405 09:35:02.388067 30176 solver.cpp:237]     Train net output #0: loss = 5.29834 (* 1 = 5.29834 loss)
I0405 09:35:02.388074 30176 sgd_solver.cpp:105] Iteration 6588, lr = 1e-06
I0405 09:35:07.811036 30176 solver.cpp:218] Iteration 6600 (2.21283 iter/s, 5.42291s/12 iters), loss = 5.27509
I0405 09:35:07.811085 30176 solver.cpp:237]     Train net output #0: loss = 5.27509 (* 1 = 5.27509 loss)
I0405 09:35:07.811094 30176 sgd_solver.cpp:105] Iteration 6600, lr = 1e-06
I0405 09:35:13.327194 30176 solver.cpp:218] Iteration 6612 (2.17547 iter/s, 5.51606s/12 iters), loss = 5.27777
I0405 09:35:13.327231 30176 solver.cpp:237]     Train net output #0: loss = 5.27777 (* 1 = 5.27777 loss)
I0405 09:35:13.327239 30176 sgd_solver.cpp:105] Iteration 6612, lr = 1e-06
I0405 09:35:18.759893 30176 solver.cpp:218] Iteration 6624 (2.20889 iter/s, 5.4326s/12 iters), loss = 5.28466
I0405 09:35:18.759941 30176 solver.cpp:237]     Train net output #0: loss = 5.28466 (* 1 = 5.28466 loss)
I0405 09:35:18.759948 30176 sgd_solver.cpp:105] Iteration 6624, lr = 1e-06
I0405 09:35:20.767841 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6630.caffemodel
I0405 09:35:23.837095 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6630.solverstate
I0405 09:35:26.160966 30176 solver.cpp:330] Iteration 6630, Testing net (#0)
I0405 09:35:26.160993 30176 net.cpp:676] Ignoring source layer train-data
I0405 09:35:28.083339 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:35:30.600029 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 09:35:30.600078 30176 solver.cpp:397]     Test net output #1: loss = 5.27998 (* 1 = 5.27998 loss)
I0405 09:35:32.584403 30176 solver.cpp:218] Iteration 6636 (0.868035 iter/s, 13.8243s/12 iters), loss = 5.28383
I0405 09:35:32.584532 30176 solver.cpp:237]     Train net output #0: loss = 5.28383 (* 1 = 5.28383 loss)
I0405 09:35:32.584538 30176 sgd_solver.cpp:105] Iteration 6636, lr = 1e-06
I0405 09:35:37.458448 30176 solver.cpp:218] Iteration 6648 (2.46211 iter/s, 4.87387s/12 iters), loss = 5.28511
I0405 09:35:37.458480 30176 solver.cpp:237]     Train net output #0: loss = 5.28511 (* 1 = 5.28511 loss)
I0405 09:35:37.458487 30176 sgd_solver.cpp:105] Iteration 6648, lr = 1e-06
I0405 09:35:42.862726 30176 solver.cpp:218] Iteration 6660 (2.2205 iter/s, 5.40418s/12 iters), loss = 5.27157
I0405 09:35:42.862776 30176 solver.cpp:237]     Train net output #0: loss = 5.27157 (* 1 = 5.27157 loss)
I0405 09:35:42.862783 30176 sgd_solver.cpp:105] Iteration 6660, lr = 1e-06
I0405 09:35:48.324998 30176 solver.cpp:218] Iteration 6672 (2.19693 iter/s, 5.46217s/12 iters), loss = 5.27359
I0405 09:35:48.325035 30176 solver.cpp:237]     Train net output #0: loss = 5.27359 (* 1 = 5.27359 loss)
I0405 09:35:48.325042 30176 sgd_solver.cpp:105] Iteration 6672, lr = 1e-06
I0405 09:35:49.813712 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:35:53.828680 30176 solver.cpp:218] Iteration 6684 (2.1804 iter/s, 5.50358s/12 iters), loss = 5.27392
I0405 09:35:53.828724 30176 solver.cpp:237]     Train net output #0: loss = 5.27392 (* 1 = 5.27392 loss)
I0405 09:35:53.828732 30176 sgd_solver.cpp:105] Iteration 6684, lr = 1e-06
I0405 09:35:59.376530 30176 solver.cpp:218] Iteration 6696 (2.16304 iter/s, 5.54775s/12 iters), loss = 5.28136
I0405 09:35:59.376561 30176 solver.cpp:237]     Train net output #0: loss = 5.28136 (* 1 = 5.28136 loss)
I0405 09:35:59.376567 30176 sgd_solver.cpp:105] Iteration 6696, lr = 1e-06
I0405 09:36:04.916028 30176 solver.cpp:218] Iteration 6708 (2.1663 iter/s, 5.53941s/12 iters), loss = 5.28668
I0405 09:36:04.916158 30176 solver.cpp:237]     Train net output #0: loss = 5.28668 (* 1 = 5.28668 loss)
I0405 09:36:04.916167 30176 sgd_solver.cpp:105] Iteration 6708, lr = 1e-06
I0405 09:36:10.428767 30176 solver.cpp:218] Iteration 6720 (2.17685 iter/s, 5.51255s/12 iters), loss = 5.28571
I0405 09:36:10.428808 30176 solver.cpp:237]     Train net output #0: loss = 5.28571 (* 1 = 5.28571 loss)
I0405 09:36:10.428813 30176 sgd_solver.cpp:105] Iteration 6720, lr = 1e-06
I0405 09:36:15.378918 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6732.caffemodel
I0405 09:36:18.522200 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6732.solverstate
I0405 09:36:20.873988 30176 solver.cpp:330] Iteration 6732, Testing net (#0)
I0405 09:36:20.874018 30176 net.cpp:676] Ignoring source layer train-data
I0405 09:36:22.610599 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:36:25.317188 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 09:36:25.317232 30176 solver.cpp:397]     Test net output #1: loss = 5.2802 (* 1 = 5.2802 loss)
I0405 09:36:25.447712 30176 solver.cpp:218] Iteration 6732 (0.799001 iter/s, 15.0188s/12 iters), loss = 5.29154
I0405 09:36:25.447762 30176 solver.cpp:237]     Train net output #0: loss = 5.29154 (* 1 = 5.29154 loss)
I0405 09:36:25.447768 30176 sgd_solver.cpp:105] Iteration 6732, lr = 1e-06
I0405 09:36:29.886435 30176 solver.cpp:218] Iteration 6744 (2.70354 iter/s, 4.43862s/12 iters), loss = 5.31065
I0405 09:36:29.886476 30176 solver.cpp:237]     Train net output #0: loss = 5.31065 (* 1 = 5.31065 loss)
I0405 09:36:29.886483 30176 sgd_solver.cpp:105] Iteration 6744, lr = 1e-06
I0405 09:36:35.369324 30176 solver.cpp:218] Iteration 6756 (2.18866 iter/s, 5.48279s/12 iters), loss = 5.27021
I0405 09:36:35.369467 30176 solver.cpp:237]     Train net output #0: loss = 5.27021 (* 1 = 5.27021 loss)
I0405 09:36:35.369474 30176 sgd_solver.cpp:105] Iteration 6756, lr = 1e-06
I0405 09:36:40.910781 30176 solver.cpp:218] Iteration 6768 (2.16557 iter/s, 5.54125s/12 iters), loss = 5.30006
I0405 09:36:40.910826 30176 solver.cpp:237]     Train net output #0: loss = 5.30006 (* 1 = 5.30006 loss)
I0405 09:36:40.910833 30176 sgd_solver.cpp:105] Iteration 6768, lr = 1e-06
I0405 09:36:44.678133 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:36:46.357956 30176 solver.cpp:218] Iteration 6780 (2.20302 iter/s, 5.44707s/12 iters), loss = 5.2811
I0405 09:36:46.357997 30176 solver.cpp:237]     Train net output #0: loss = 5.2811 (* 1 = 5.2811 loss)
I0405 09:36:46.358004 30176 sgd_solver.cpp:105] Iteration 6780, lr = 1e-06
I0405 09:36:51.828482 30176 solver.cpp:218] Iteration 6792 (2.19361 iter/s, 5.47043s/12 iters), loss = 5.28639
I0405 09:36:51.828518 30176 solver.cpp:237]     Train net output #0: loss = 5.28639 (* 1 = 5.28639 loss)
I0405 09:36:51.828526 30176 sgd_solver.cpp:105] Iteration 6792, lr = 1e-06
I0405 09:36:57.335963 30176 solver.cpp:218] Iteration 6804 (2.17889 iter/s, 5.50739s/12 iters), loss = 5.29717
I0405 09:36:57.335999 30176 solver.cpp:237]     Train net output #0: loss = 5.29717 (* 1 = 5.29717 loss)
I0405 09:36:57.336004 30176 sgd_solver.cpp:105] Iteration 6804, lr = 1e-06
I0405 09:37:02.669576 30176 solver.cpp:218] Iteration 6816 (2.24992 iter/s, 5.33352s/12 iters), loss = 5.2978
I0405 09:37:02.669618 30176 solver.cpp:237]     Train net output #0: loss = 5.2978 (* 1 = 5.2978 loss)
I0405 09:37:02.669625 30176 sgd_solver.cpp:105] Iteration 6816, lr = 1e-06
I0405 09:37:08.193509 30176 solver.cpp:218] Iteration 6828 (2.17241 iter/s, 5.52383s/12 iters), loss = 5.27226
I0405 09:37:08.193620 30176 solver.cpp:237]     Train net output #0: loss = 5.27226 (* 1 = 5.27226 loss)
I0405 09:37:08.193629 30176 sgd_solver.cpp:105] Iteration 6828, lr = 1e-06
I0405 09:37:10.335896 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6834.caffemodel
I0405 09:37:13.479452 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6834.solverstate
I0405 09:37:15.870429 30176 solver.cpp:330] Iteration 6834, Testing net (#0)
I0405 09:37:15.870451 30176 net.cpp:676] Ignoring source layer train-data
I0405 09:37:17.583757 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:37:20.175454 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 09:37:20.175488 30176 solver.cpp:397]     Test net output #1: loss = 5.27991 (* 1 = 5.27991 loss)
I0405 09:37:22.239531 30176 solver.cpp:218] Iteration 6840 (0.854349 iter/s, 14.0458s/12 iters), loss = 5.26335
I0405 09:37:22.239567 30176 solver.cpp:237]     Train net output #0: loss = 5.26335 (* 1 = 5.26335 loss)
I0405 09:37:22.239573 30176 sgd_solver.cpp:105] Iteration 6840, lr = 1e-06
I0405 09:37:27.637265 30176 solver.cpp:218] Iteration 6852 (2.22319 iter/s, 5.39764s/12 iters), loss = 5.30415
I0405 09:37:27.637303 30176 solver.cpp:237]     Train net output #0: loss = 5.30415 (* 1 = 5.30415 loss)
I0405 09:37:27.637308 30176 sgd_solver.cpp:105] Iteration 6852, lr = 1e-06
I0405 09:37:33.028198 30176 solver.cpp:218] Iteration 6864 (2.226 iter/s, 5.39083s/12 iters), loss = 5.28688
I0405 09:37:33.028230 30176 solver.cpp:237]     Train net output #0: loss = 5.28688 (* 1 = 5.28688 loss)
I0405 09:37:33.028235 30176 sgd_solver.cpp:105] Iteration 6864, lr = 1e-06
I0405 09:37:38.454221 30176 solver.cpp:218] Iteration 6876 (2.2116 iter/s, 5.42593s/12 iters), loss = 5.28535
I0405 09:37:38.454378 30176 solver.cpp:237]     Train net output #0: loss = 5.28535 (* 1 = 5.28535 loss)
I0405 09:37:38.454388 30176 sgd_solver.cpp:105] Iteration 6876, lr = 1e-06
I0405 09:37:39.091238 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:37:43.620632 30176 solver.cpp:218] Iteration 6888 (2.32279 iter/s, 5.1662s/12 iters), loss = 5.29676
I0405 09:37:43.620671 30176 solver.cpp:237]     Train net output #0: loss = 5.29676 (* 1 = 5.29676 loss)
I0405 09:37:43.620676 30176 sgd_solver.cpp:105] Iteration 6888, lr = 1e-06
I0405 09:37:49.091917 30176 solver.cpp:218] Iteration 6900 (2.19331 iter/s, 5.47118s/12 iters), loss = 5.29431
I0405 09:37:49.091980 30176 solver.cpp:237]     Train net output #0: loss = 5.29431 (* 1 = 5.29431 loss)
I0405 09:37:49.091987 30176 sgd_solver.cpp:105] Iteration 6900, lr = 1e-06
I0405 09:37:54.642995 30176 solver.cpp:218] Iteration 6912 (2.16178 iter/s, 5.55097s/12 iters), loss = 5.27979
I0405 09:37:54.643036 30176 solver.cpp:237]     Train net output #0: loss = 5.27979 (* 1 = 5.27979 loss)
I0405 09:37:54.643041 30176 sgd_solver.cpp:105] Iteration 6912, lr = 1e-06
I0405 09:38:00.011312 30176 solver.cpp:218] Iteration 6924 (2.23538 iter/s, 5.36822s/12 iters), loss = 5.28031
I0405 09:38:00.011350 30176 solver.cpp:237]     Train net output #0: loss = 5.28031 (* 1 = 5.28031 loss)
I0405 09:38:00.011355 30176 sgd_solver.cpp:105] Iteration 6924, lr = 1e-06
I0405 09:38:04.782109 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6936.caffemodel
I0405 09:38:07.859305 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6936.solverstate
I0405 09:38:10.192793 30176 solver.cpp:330] Iteration 6936, Testing net (#0)
I0405 09:38:10.192934 30176 net.cpp:676] Ignoring source layer train-data
I0405 09:38:10.843422 30176 blocking_queue.cpp:49] Waiting for data
I0405 09:38:11.880949 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:38:14.509608 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 09:38:14.509654 30176 solver.cpp:397]     Test net output #1: loss = 5.28006 (* 1 = 5.28006 loss)
I0405 09:38:14.646039 30176 solver.cpp:218] Iteration 6936 (0.819977 iter/s, 14.6346s/12 iters), loss = 5.29029
I0405 09:38:14.646090 30176 solver.cpp:237]     Train net output #0: loss = 5.29029 (* 1 = 5.29029 loss)
I0405 09:38:14.646096 30176 sgd_solver.cpp:105] Iteration 6936, lr = 1e-06
I0405 09:38:19.060252 30176 solver.cpp:218] Iteration 6948 (2.71855 iter/s, 4.41411s/12 iters), loss = 5.27638
I0405 09:38:19.060297 30176 solver.cpp:237]     Train net output #0: loss = 5.27638 (* 1 = 5.27638 loss)
I0405 09:38:19.060303 30176 sgd_solver.cpp:105] Iteration 6948, lr = 1e-06
I0405 09:38:24.555683 30176 solver.cpp:218] Iteration 6960 (2.18368 iter/s, 5.49532s/12 iters), loss = 5.29424
I0405 09:38:24.555721 30176 solver.cpp:237]     Train net output #0: loss = 5.29424 (* 1 = 5.29424 loss)
I0405 09:38:24.555728 30176 sgd_solver.cpp:105] Iteration 6960, lr = 1e-06
I0405 09:38:30.077994 30176 solver.cpp:218] Iteration 6972 (2.17304 iter/s, 5.52222s/12 iters), loss = 5.27576
I0405 09:38:30.078030 30176 solver.cpp:237]     Train net output #0: loss = 5.27576 (* 1 = 5.27576 loss)
I0405 09:38:30.078037 30176 sgd_solver.cpp:105] Iteration 6972, lr = 1e-06
I0405 09:38:33.029933 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:38:35.560127 30176 solver.cpp:218] Iteration 6984 (2.18897 iter/s, 5.48204s/12 iters), loss = 5.28
I0405 09:38:35.560164 30176 solver.cpp:237]     Train net output #0: loss = 5.28 (* 1 = 5.28 loss)
I0405 09:38:35.560169 30176 sgd_solver.cpp:105] Iteration 6984, lr = 1e-06
I0405 09:38:40.927353 30176 solver.cpp:218] Iteration 6996 (2.23583 iter/s, 5.36713s/12 iters), loss = 5.28581
I0405 09:38:40.927526 30176 solver.cpp:237]     Train net output #0: loss = 5.28581 (* 1 = 5.28581 loss)
I0405 09:38:40.927536 30176 sgd_solver.cpp:105] Iteration 6996, lr = 1e-06
I0405 09:38:46.413952 30176 solver.cpp:218] Iteration 7008 (2.18724 iter/s, 5.48637s/12 iters), loss = 5.28636
I0405 09:38:46.413997 30176 solver.cpp:237]     Train net output #0: loss = 5.28636 (* 1 = 5.28636 loss)
I0405 09:38:46.414003 30176 sgd_solver.cpp:105] Iteration 7008, lr = 1e-06
I0405 09:38:51.476606 30176 solver.cpp:218] Iteration 7020 (2.37034 iter/s, 5.06256s/12 iters), loss = 5.27761
I0405 09:38:51.476644 30176 solver.cpp:237]     Train net output #0: loss = 5.27761 (* 1 = 5.27761 loss)
I0405 09:38:51.476651 30176 sgd_solver.cpp:105] Iteration 7020, lr = 1e-06
I0405 09:38:56.951579 30176 solver.cpp:218] Iteration 7032 (2.19183 iter/s, 5.47487s/12 iters), loss = 5.29165
I0405 09:38:56.951634 30176 solver.cpp:237]     Train net output #0: loss = 5.29165 (* 1 = 5.29165 loss)
I0405 09:38:56.951642 30176 sgd_solver.cpp:105] Iteration 7032, lr = 1e-06
I0405 09:38:59.040791 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7038.caffemodel
I0405 09:39:02.181715 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7038.solverstate
I0405 09:39:04.550451 30176 solver.cpp:330] Iteration 7038, Testing net (#0)
I0405 09:39:04.550479 30176 net.cpp:676] Ignoring source layer train-data
I0405 09:39:06.168267 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:39:08.846009 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 09:39:08.846058 30176 solver.cpp:397]     Test net output #1: loss = 5.28009 (* 1 = 5.28009 loss)
I0405 09:39:10.751052 30176 solver.cpp:218] Iteration 7044 (0.86961 iter/s, 13.7993s/12 iters), loss = 5.2951
I0405 09:39:10.751103 30176 solver.cpp:237]     Train net output #0: loss = 5.2951 (* 1 = 5.2951 loss)
I0405 09:39:10.751111 30176 sgd_solver.cpp:105] Iteration 7044, lr = 1e-06
I0405 09:39:16.235849 30176 solver.cpp:218] Iteration 7056 (2.18791 iter/s, 5.48469s/12 iters), loss = 5.26828
I0405 09:39:16.235968 30176 solver.cpp:237]     Train net output #0: loss = 5.26828 (* 1 = 5.26828 loss)
I0405 09:39:16.235975 30176 sgd_solver.cpp:105] Iteration 7056, lr = 1e-06
I0405 09:39:21.775411 30176 solver.cpp:218] Iteration 7068 (2.16631 iter/s, 5.53938s/12 iters), loss = 5.2891
I0405 09:39:21.775465 30176 solver.cpp:237]     Train net output #0: loss = 5.2891 (* 1 = 5.2891 loss)
I0405 09:39:21.775475 30176 sgd_solver.cpp:105] Iteration 7068, lr = 1e-06
I0405 09:39:27.079969 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:39:27.245275 30176 solver.cpp:218] Iteration 7080 (2.19388 iter/s, 5.46975s/12 iters), loss = 5.283
I0405 09:39:27.245322 30176 solver.cpp:237]     Train net output #0: loss = 5.283 (* 1 = 5.283 loss)
I0405 09:39:27.245329 30176 sgd_solver.cpp:105] Iteration 7080, lr = 1e-06
I0405 09:39:32.770751 30176 solver.cpp:218] Iteration 7092 (2.1718 iter/s, 5.52537s/12 iters), loss = 5.27838
I0405 09:39:32.770793 30176 solver.cpp:237]     Train net output #0: loss = 5.27838 (* 1 = 5.27838 loss)
I0405 09:39:32.770799 30176 sgd_solver.cpp:105] Iteration 7092, lr = 1e-06
I0405 09:39:38.229997 30176 solver.cpp:218] Iteration 7104 (2.19815 iter/s, 5.45914s/12 iters), loss = 5.27067
I0405 09:39:38.230039 30176 solver.cpp:237]     Train net output #0: loss = 5.27067 (* 1 = 5.27067 loss)
I0405 09:39:38.230047 30176 sgd_solver.cpp:105] Iteration 7104, lr = 1e-06
I0405 09:39:43.743376 30176 solver.cpp:218] Iteration 7116 (2.17657 iter/s, 5.51327s/12 iters), loss = 5.27256
I0405 09:39:43.743424 30176 solver.cpp:237]     Train net output #0: loss = 5.27256 (* 1 = 5.27256 loss)
I0405 09:39:43.743432 30176 sgd_solver.cpp:105] Iteration 7116, lr = 1e-06
I0405 09:39:49.158097 30176 solver.cpp:218] Iteration 7128 (2.21622 iter/s, 5.41461s/12 iters), loss = 5.26704
I0405 09:39:49.158229 30176 solver.cpp:237]     Train net output #0: loss = 5.26704 (* 1 = 5.26704 loss)
I0405 09:39:49.158236 30176 sgd_solver.cpp:105] Iteration 7128, lr = 1e-06
I0405 09:39:53.922327 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7140.caffemodel
I0405 09:39:56.949720 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7140.solverstate
I0405 09:39:59.251881 30176 solver.cpp:330] Iteration 7140, Testing net (#0)
I0405 09:39:59.251901 30176 net.cpp:676] Ignoring source layer train-data
I0405 09:40:00.829355 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:40:03.536860 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 09:40:03.536928 30176 solver.cpp:397]     Test net output #1: loss = 5.27998 (* 1 = 5.27998 loss)
I0405 09:40:03.675318 30176 solver.cpp:218] Iteration 7140 (0.826621 iter/s, 14.5169s/12 iters), loss = 5.28547
I0405 09:40:03.675390 30176 solver.cpp:237]     Train net output #0: loss = 5.28547 (* 1 = 5.28547 loss)
I0405 09:40:03.675405 30176 sgd_solver.cpp:105] Iteration 7140, lr = 1e-06
I0405 09:40:08.187939 30176 solver.cpp:218] Iteration 7152 (2.65928 iter/s, 4.5125s/12 iters), loss = 5.28374
I0405 09:40:08.187984 30176 solver.cpp:237]     Train net output #0: loss = 5.28374 (* 1 = 5.28374 loss)
I0405 09:40:08.187990 30176 sgd_solver.cpp:105] Iteration 7152, lr = 1e-06
I0405 09:40:13.455256 30176 solver.cpp:218] Iteration 7164 (2.27825 iter/s, 5.2672s/12 iters), loss = 5.28298
I0405 09:40:13.455307 30176 solver.cpp:237]     Train net output #0: loss = 5.28298 (* 1 = 5.28298 loss)
I0405 09:40:13.455314 30176 sgd_solver.cpp:105] Iteration 7164, lr = 1e-06
I0405 09:40:18.709632 30176 solver.cpp:218] Iteration 7176 (2.28386 iter/s, 5.25427s/12 iters), loss = 5.29347
I0405 09:40:18.709679 30176 solver.cpp:237]     Train net output #0: loss = 5.29347 (* 1 = 5.29347 loss)
I0405 09:40:18.709686 30176 sgd_solver.cpp:105] Iteration 7176, lr = 1e-06
I0405 09:40:21.031772 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:40:24.117955 30176 solver.cpp:218] Iteration 7188 (2.21885 iter/s, 5.40821s/12 iters), loss = 5.28585
I0405 09:40:24.118000 30176 solver.cpp:237]     Train net output #0: loss = 5.28585 (* 1 = 5.28585 loss)
I0405 09:40:24.118005 30176 sgd_solver.cpp:105] Iteration 7188, lr = 1e-06
I0405 09:40:29.269098 30176 solver.cpp:218] Iteration 7200 (2.32963 iter/s, 5.15103s/12 iters), loss = 5.28175
I0405 09:40:29.269138 30176 solver.cpp:237]     Train net output #0: loss = 5.28175 (* 1 = 5.28175 loss)
I0405 09:40:29.269145 30176 sgd_solver.cpp:105] Iteration 7200, lr = 1e-06
I0405 09:40:34.522406 30176 solver.cpp:218] Iteration 7212 (2.28432 iter/s, 5.25321s/12 iters), loss = 5.30009
I0405 09:40:34.522450 30176 solver.cpp:237]     Train net output #0: loss = 5.30009 (* 1 = 5.30009 loss)
I0405 09:40:34.522456 30176 sgd_solver.cpp:105] Iteration 7212, lr = 1e-06
I0405 09:40:39.710364 30176 solver.cpp:218] Iteration 7224 (2.3131 iter/s, 5.18785s/12 iters), loss = 5.28217
I0405 09:40:39.710404 30176 solver.cpp:237]     Train net output #0: loss = 5.28217 (* 1 = 5.28217 loss)
I0405 09:40:39.710410 30176 sgd_solver.cpp:105] Iteration 7224, lr = 1e-06
I0405 09:40:44.708698 30176 solver.cpp:218] Iteration 7236 (2.40085 iter/s, 4.99823s/12 iters), loss = 5.27469
I0405 09:40:44.708740 30176 solver.cpp:237]     Train net output #0: loss = 5.27469 (* 1 = 5.27469 loss)
I0405 09:40:44.708746 30176 sgd_solver.cpp:105] Iteration 7236, lr = 1e-06
I0405 09:40:46.822095 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7242.caffemodel
I0405 09:40:49.807893 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7242.solverstate
I0405 09:40:52.230572 30176 solver.cpp:330] Iteration 7242, Testing net (#0)
I0405 09:40:52.230724 30176 net.cpp:676] Ignoring source layer train-data
I0405 09:40:53.732421 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:40:56.535101 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 09:40:56.535151 30176 solver.cpp:397]     Test net output #1: loss = 5.28016 (* 1 = 5.28016 loss)
I0405 09:40:58.453676 30176 solver.cpp:218] Iteration 7248 (0.873057 iter/s, 13.7448s/12 iters), loss = 5.27832
I0405 09:40:58.453722 30176 solver.cpp:237]     Train net output #0: loss = 5.27832 (* 1 = 5.27832 loss)
I0405 09:40:58.453729 30176 sgd_solver.cpp:105] Iteration 7248, lr = 1e-06
I0405 09:41:03.636706 30176 solver.cpp:218] Iteration 7260 (2.31529 iter/s, 5.18293s/12 iters), loss = 5.27602
I0405 09:41:03.636745 30176 solver.cpp:237]     Train net output #0: loss = 5.27602 (* 1 = 5.27602 loss)
I0405 09:41:03.636751 30176 sgd_solver.cpp:105] Iteration 7260, lr = 1e-06
I0405 09:41:08.939549 30176 solver.cpp:218] Iteration 7272 (2.26298 iter/s, 5.30275s/12 iters), loss = 5.30745
I0405 09:41:08.939580 30176 solver.cpp:237]     Train net output #0: loss = 5.30745 (* 1 = 5.30745 loss)
I0405 09:41:08.939585 30176 sgd_solver.cpp:105] Iteration 7272, lr = 1e-06
I0405 09:41:13.604168 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:41:14.442236 30176 solver.cpp:218] Iteration 7284 (2.18079 iter/s, 5.50259s/12 iters), loss = 5.28541
I0405 09:41:14.442277 30176 solver.cpp:237]     Train net output #0: loss = 5.28541 (* 1 = 5.28541 loss)
I0405 09:41:14.442282 30176 sgd_solver.cpp:105] Iteration 7284, lr = 1e-06
I0405 09:41:19.820199 30176 solver.cpp:218] Iteration 7296 (2.23137 iter/s, 5.37786s/12 iters), loss = 5.29232
I0405 09:41:19.820240 30176 solver.cpp:237]     Train net output #0: loss = 5.29232 (* 1 = 5.29232 loss)
I0405 09:41:19.820245 30176 sgd_solver.cpp:105] Iteration 7296, lr = 1e-06
I0405 09:41:25.244915 30176 solver.cpp:218] Iteration 7308 (2.21214 iter/s, 5.42461s/12 iters), loss = 5.26326
I0405 09:41:25.245023 30176 solver.cpp:237]     Train net output #0: loss = 5.26326 (* 1 = 5.26326 loss)
I0405 09:41:25.245030 30176 sgd_solver.cpp:105] Iteration 7308, lr = 1e-06
I0405 09:41:30.532428 30176 solver.cpp:218] Iteration 7320 (2.26957 iter/s, 5.28734s/12 iters), loss = 5.27042
I0405 09:41:30.532480 30176 solver.cpp:237]     Train net output #0: loss = 5.27042 (* 1 = 5.27042 loss)
I0405 09:41:30.532487 30176 sgd_solver.cpp:105] Iteration 7320, lr = 1e-06
I0405 09:41:35.958490 30176 solver.cpp:218] Iteration 7332 (2.2116 iter/s, 5.42595s/12 iters), loss = 5.29733
I0405 09:41:35.958541 30176 solver.cpp:237]     Train net output #0: loss = 5.29733 (* 1 = 5.29733 loss)
I0405 09:41:35.958547 30176 sgd_solver.cpp:105] Iteration 7332, lr = 1e-06
I0405 09:41:40.793046 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7344.caffemodel
I0405 09:41:43.863618 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7344.solverstate
I0405 09:41:46.199349 30176 solver.cpp:330] Iteration 7344, Testing net (#0)
I0405 09:41:46.199371 30176 net.cpp:676] Ignoring source layer train-data
I0405 09:41:47.777503 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:41:50.578644 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 09:41:50.578691 30176 solver.cpp:397]     Test net output #1: loss = 5.28022 (* 1 = 5.28022 loss)
I0405 09:41:50.717779 30176 solver.cpp:218] Iteration 7344 (0.813058 iter/s, 14.7591s/12 iters), loss = 5.29246
I0405 09:41:50.717842 30176 solver.cpp:237]     Train net output #0: loss = 5.29246 (* 1 = 5.29246 loss)
I0405 09:41:50.717851 30176 sgd_solver.cpp:105] Iteration 7344, lr = 1e-06
I0405 09:41:55.284919 30176 solver.cpp:218] Iteration 7356 (2.62753 iter/s, 4.56702s/12 iters), loss = 5.28555
I0405 09:41:55.285065 30176 solver.cpp:237]     Train net output #0: loss = 5.28555 (* 1 = 5.28555 loss)
I0405 09:41:55.285074 30176 sgd_solver.cpp:105] Iteration 7356, lr = 1e-06
I0405 09:42:00.533797 30176 solver.cpp:218] Iteration 7368 (2.28629 iter/s, 5.24867s/12 iters), loss = 5.26177
I0405 09:42:00.533838 30176 solver.cpp:237]     Train net output #0: loss = 5.26177 (* 1 = 5.26177 loss)
I0405 09:42:00.533843 30176 sgd_solver.cpp:105] Iteration 7368, lr = 1e-06
I0405 09:42:05.481309 30176 solver.cpp:218] Iteration 7380 (2.42551 iter/s, 4.94741s/12 iters), loss = 5.26249
I0405 09:42:05.481364 30176 solver.cpp:237]     Train net output #0: loss = 5.26249 (* 1 = 5.26249 loss)
I0405 09:42:05.481374 30176 sgd_solver.cpp:105] Iteration 7380, lr = 1e-06
I0405 09:42:06.973258 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:42:10.927484 30176 solver.cpp:218] Iteration 7392 (2.20343 iter/s, 5.44605s/12 iters), loss = 5.26136
I0405 09:42:10.927533 30176 solver.cpp:237]     Train net output #0: loss = 5.26136 (* 1 = 5.26136 loss)
I0405 09:42:10.927541 30176 sgd_solver.cpp:105] Iteration 7392, lr = 1e-06
I0405 09:42:16.138801 30176 solver.cpp:218] Iteration 7404 (2.30273 iter/s, 5.2112s/12 iters), loss = 5.28843
I0405 09:42:16.138859 30176 solver.cpp:237]     Train net output #0: loss = 5.28843 (* 1 = 5.28843 loss)
I0405 09:42:16.138867 30176 sgd_solver.cpp:105] Iteration 7404, lr = 1e-06
I0405 09:42:21.253156 30176 solver.cpp:218] Iteration 7416 (2.34639 iter/s, 5.11424s/12 iters), loss = 5.29223
I0405 09:42:21.253196 30176 solver.cpp:237]     Train net output #0: loss = 5.29223 (* 1 = 5.29223 loss)
I0405 09:42:21.253201 30176 sgd_solver.cpp:105] Iteration 7416, lr = 1e-06
I0405 09:42:26.639032 30176 solver.cpp:218] Iteration 7428 (2.22809 iter/s, 5.38577s/12 iters), loss = 5.29687
I0405 09:42:26.639205 30176 solver.cpp:237]     Train net output #0: loss = 5.29687 (* 1 = 5.29687 loss)
I0405 09:42:26.639214 30176 sgd_solver.cpp:105] Iteration 7428, lr = 1e-06
I0405 09:42:32.091190 30176 solver.cpp:218] Iteration 7440 (2.20106 iter/s, 5.45193s/12 iters), loss = 5.29258
I0405 09:42:32.091248 30176 solver.cpp:237]     Train net output #0: loss = 5.29258 (* 1 = 5.29258 loss)
I0405 09:42:32.091260 30176 sgd_solver.cpp:105] Iteration 7440, lr = 1e-06
I0405 09:42:33.990953 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7446.caffemodel
I0405 09:42:37.031669 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7446.solverstate
I0405 09:42:39.345242 30176 solver.cpp:330] Iteration 7446, Testing net (#0)
I0405 09:42:39.345268 30176 net.cpp:676] Ignoring source layer train-data
I0405 09:42:40.847712 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:42:43.736956 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 09:42:43.736989 30176 solver.cpp:397]     Test net output #1: loss = 5.28011 (* 1 = 5.28011 loss)
I0405 09:42:45.548331 30176 solver.cpp:218] Iteration 7452 (0.891732 iter/s, 13.457s/12 iters), loss = 5.30091
I0405 09:42:45.548377 30176 solver.cpp:237]     Train net output #0: loss = 5.30091 (* 1 = 5.30091 loss)
I0405 09:42:45.548382 30176 sgd_solver.cpp:105] Iteration 7452, lr = 1e-06
I0405 09:42:50.897907 30176 solver.cpp:218] Iteration 7464 (2.24321 iter/s, 5.34947s/12 iters), loss = 5.26512
I0405 09:42:50.897946 30176 solver.cpp:237]     Train net output #0: loss = 5.26512 (* 1 = 5.26512 loss)
I0405 09:42:50.897951 30176 sgd_solver.cpp:105] Iteration 7464, lr = 1e-06
I0405 09:42:56.170926 30176 solver.cpp:218] Iteration 7476 (2.27578 iter/s, 5.27292s/12 iters), loss = 5.28933
I0405 09:42:56.170964 30176 solver.cpp:237]     Train net output #0: loss = 5.28933 (* 1 = 5.28933 loss)
I0405 09:42:56.170969 30176 sgd_solver.cpp:105] Iteration 7476, lr = 1e-06
I0405 09:42:59.816236 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:43:01.493445 30176 solver.cpp:218] Iteration 7488 (2.25461 iter/s, 5.32242s/12 iters), loss = 5.28411
I0405 09:43:01.493481 30176 solver.cpp:237]     Train net output #0: loss = 5.28411 (* 1 = 5.28411 loss)
I0405 09:43:01.493487 30176 sgd_solver.cpp:105] Iteration 7488, lr = 1e-06
I0405 09:43:06.796109 30176 solver.cpp:218] Iteration 7500 (2.26306 iter/s, 5.30256s/12 iters), loss = 5.27409
I0405 09:43:06.796152 30176 solver.cpp:237]     Train net output #0: loss = 5.27409 (* 1 = 5.27409 loss)
I0405 09:43:06.796159 30176 sgd_solver.cpp:105] Iteration 7500, lr = 1e-06
I0405 09:43:12.266335 30176 solver.cpp:218] Iteration 7512 (2.19373 iter/s, 5.47012s/12 iters), loss = 5.2786
I0405 09:43:12.266382 30176 solver.cpp:237]     Train net output #0: loss = 5.2786 (* 1 = 5.2786 loss)
I0405 09:43:12.266388 30176 sgd_solver.cpp:105] Iteration 7512, lr = 1e-06
I0405 09:43:17.630960 30176 solver.cpp:218] Iteration 7524 (2.23692 iter/s, 5.36451s/12 iters), loss = 5.28601
I0405 09:43:17.631006 30176 solver.cpp:237]     Train net output #0: loss = 5.28601 (* 1 = 5.28601 loss)
I0405 09:43:17.631011 30176 sgd_solver.cpp:105] Iteration 7524, lr = 1e-06
I0405 09:43:23.057235 30176 solver.cpp:218] Iteration 7536 (2.21151 iter/s, 5.42617s/12 iters), loss = 5.2695
I0405 09:43:23.057281 30176 solver.cpp:237]     Train net output #0: loss = 5.2695 (* 1 = 5.2695 loss)
I0405 09:43:23.057287 30176 sgd_solver.cpp:105] Iteration 7536, lr = 1e-06
I0405 09:43:27.845937 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7548.caffemodel
I0405 09:43:31.019515 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7548.solverstate
I0405 09:43:33.344163 30176 solver.cpp:330] Iteration 7548, Testing net (#0)
I0405 09:43:33.344184 30176 net.cpp:676] Ignoring source layer train-data
I0405 09:43:34.746938 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:43:37.654742 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 09:43:37.654791 30176 solver.cpp:397]     Test net output #1: loss = 5.28002 (* 1 = 5.28002 loss)
I0405 09:43:37.792119 30176 solver.cpp:218] Iteration 7548 (0.814404 iter/s, 14.7347s/12 iters), loss = 5.27693
I0405 09:43:37.792192 30176 solver.cpp:237]     Train net output #0: loss = 5.27693 (* 1 = 5.27693 loss)
I0405 09:43:37.792201 30176 sgd_solver.cpp:105] Iteration 7548, lr = 1e-06
I0405 09:43:42.101640 30176 solver.cpp:218] Iteration 7560 (2.78461 iter/s, 4.3094s/12 iters), loss = 5.30951
I0405 09:43:42.101672 30176 solver.cpp:237]     Train net output #0: loss = 5.30951 (* 1 = 5.30951 loss)
I0405 09:43:42.101678 30176 sgd_solver.cpp:105] Iteration 7560, lr = 1e-06
I0405 09:43:47.529373 30176 solver.cpp:218] Iteration 7572 (2.21091 iter/s, 5.42763s/12 iters), loss = 5.28071
I0405 09:43:47.529430 30176 solver.cpp:237]     Train net output #0: loss = 5.28071 (* 1 = 5.28071 loss)
I0405 09:43:47.529440 30176 sgd_solver.cpp:105] Iteration 7572, lr = 1e-06
I0405 09:43:53.017463 30176 solver.cpp:218] Iteration 7584 (2.1866 iter/s, 5.48798s/12 iters), loss = 5.29257
I0405 09:43:53.017498 30176 solver.cpp:237]     Train net output #0: loss = 5.29257 (* 1 = 5.29257 loss)
I0405 09:43:53.017503 30176 sgd_solver.cpp:105] Iteration 7584, lr = 1e-06
I0405 09:43:53.709152 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:43:58.431849 30176 solver.cpp:218] Iteration 7596 (2.21636 iter/s, 5.41429s/12 iters), loss = 5.29996
I0405 09:43:58.431890 30176 solver.cpp:237]     Train net output #0: loss = 5.29996 (* 1 = 5.29996 loss)
I0405 09:43:58.431896 30176 sgd_solver.cpp:105] Iteration 7596, lr = 1e-06
I0405 09:44:03.866348 30176 solver.cpp:218] Iteration 7608 (2.20816 iter/s, 5.43439s/12 iters), loss = 5.29771
I0405 09:44:03.866451 30176 solver.cpp:237]     Train net output #0: loss = 5.29771 (* 1 = 5.29771 loss)
I0405 09:44:03.866457 30176 sgd_solver.cpp:105] Iteration 7608, lr = 1e-06
I0405 09:44:09.086006 30176 solver.cpp:218] Iteration 7620 (2.29907 iter/s, 5.2195s/12 iters), loss = 5.27261
I0405 09:44:09.086043 30176 solver.cpp:237]     Train net output #0: loss = 5.27261 (* 1 = 5.27261 loss)
I0405 09:44:09.086050 30176 sgd_solver.cpp:105] Iteration 7620, lr = 1e-06
I0405 09:44:11.758121 30176 blocking_queue.cpp:49] Waiting for data
I0405 09:44:14.286818 30176 solver.cpp:218] Iteration 7632 (2.30738 iter/s, 5.20071s/12 iters), loss = 5.29409
I0405 09:44:14.286870 30176 solver.cpp:237]     Train net output #0: loss = 5.29409 (* 1 = 5.29409 loss)
I0405 09:44:14.286876 30176 sgd_solver.cpp:105] Iteration 7632, lr = 1e-06
I0405 09:44:19.487788 30176 solver.cpp:218] Iteration 7644 (2.30731 iter/s, 5.20086s/12 iters), loss = 5.27434
I0405 09:44:19.487831 30176 solver.cpp:237]     Train net output #0: loss = 5.27434 (* 1 = 5.27434 loss)
I0405 09:44:19.487836 30176 sgd_solver.cpp:105] Iteration 7644, lr = 1e-06
I0405 09:44:21.493969 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7650.caffemodel
I0405 09:44:24.550688 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7650.solverstate
I0405 09:44:26.852942 30176 solver.cpp:330] Iteration 7650, Testing net (#0)
I0405 09:44:26.852967 30176 net.cpp:676] Ignoring source layer train-data
I0405 09:44:28.256160 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:44:31.203999 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 09:44:31.204035 30176 solver.cpp:397]     Test net output #1: loss = 5.28022 (* 1 = 5.28022 loss)
I0405 09:44:33.274188 30176 solver.cpp:218] Iteration 7656 (0.870434 iter/s, 13.7862s/12 iters), loss = 5.29178
I0405 09:44:33.274226 30176 solver.cpp:237]     Train net output #0: loss = 5.29178 (* 1 = 5.29178 loss)
I0405 09:44:33.274231 30176 sgd_solver.cpp:105] Iteration 7656, lr = 1e-06
I0405 09:44:38.633857 30176 solver.cpp:218] Iteration 7668 (2.23899 iter/s, 5.35956s/12 iters), loss = 5.31151
I0405 09:44:38.634002 30176 solver.cpp:237]     Train net output #0: loss = 5.31151 (* 1 = 5.31151 loss)
I0405 09:44:38.634011 30176 sgd_solver.cpp:105] Iteration 7668, lr = 1e-06
I0405 09:44:43.933074 30176 solver.cpp:218] Iteration 7680 (2.26457 iter/s, 5.29901s/12 iters), loss = 5.28441
I0405 09:44:43.933117 30176 solver.cpp:237]     Train net output #0: loss = 5.28441 (* 1 = 5.28441 loss)
I0405 09:44:43.933123 30176 sgd_solver.cpp:105] Iteration 7680, lr = 1e-06
I0405 09:44:46.695549 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:44:48.976114 30176 solver.cpp:218] Iteration 7692 (2.37957 iter/s, 5.04293s/12 iters), loss = 5.2979
I0405 09:44:48.976161 30176 solver.cpp:237]     Train net output #0: loss = 5.2979 (* 1 = 5.2979 loss)
I0405 09:44:48.976166 30176 sgd_solver.cpp:105] Iteration 7692, lr = 1e-06
I0405 09:44:54.321190 30176 solver.cpp:218] Iteration 7704 (2.2451 iter/s, 5.34496s/12 iters), loss = 5.28727
I0405 09:44:54.321238 30176 solver.cpp:237]     Train net output #0: loss = 5.28727 (* 1 = 5.28727 loss)
I0405 09:44:54.321245 30176 sgd_solver.cpp:105] Iteration 7704, lr = 1e-06
I0405 09:44:59.602218 30176 solver.cpp:218] Iteration 7716 (2.27233 iter/s, 5.28092s/12 iters), loss = 5.28849
I0405 09:44:59.602260 30176 solver.cpp:237]     Train net output #0: loss = 5.28849 (* 1 = 5.28849 loss)
I0405 09:44:59.602265 30176 sgd_solver.cpp:105] Iteration 7716, lr = 1e-06
I0405 09:45:04.992967 30176 solver.cpp:218] Iteration 7728 (2.22608 iter/s, 5.39064s/12 iters), loss = 5.28664
I0405 09:45:04.993005 30176 solver.cpp:237]     Train net output #0: loss = 5.28664 (* 1 = 5.28664 loss)
I0405 09:45:04.993010 30176 sgd_solver.cpp:105] Iteration 7728, lr = 1e-06
I0405 09:45:10.438874 30176 solver.cpp:218] Iteration 7740 (2.20353 iter/s, 5.4458s/12 iters), loss = 5.27962
I0405 09:45:10.439005 30176 solver.cpp:237]     Train net output #0: loss = 5.27962 (* 1 = 5.27962 loss)
I0405 09:45:10.439011 30176 sgd_solver.cpp:105] Iteration 7740, lr = 1e-06
I0405 09:45:15.313158 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7752.caffemodel
I0405 09:45:18.355563 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7752.solverstate
I0405 09:45:20.690572 30176 solver.cpp:330] Iteration 7752, Testing net (#0)
I0405 09:45:20.690588 30176 net.cpp:676] Ignoring source layer train-data
I0405 09:45:22.060708 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:45:25.265822 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 09:45:25.265857 30176 solver.cpp:397]     Test net output #1: loss = 5.27987 (* 1 = 5.27987 loss)
I0405 09:45:25.406519 30176 solver.cpp:218] Iteration 7752 (0.801744 iter/s, 14.9674s/12 iters), loss = 5.30011
I0405 09:45:25.406571 30176 solver.cpp:237]     Train net output #0: loss = 5.30011 (* 1 = 5.30011 loss)
I0405 09:45:25.406577 30176 sgd_solver.cpp:105] Iteration 7752, lr = 1e-06
I0405 09:45:29.707973 30176 solver.cpp:218] Iteration 7764 (2.78982 iter/s, 4.30136s/12 iters), loss = 5.28299
I0405 09:45:29.708009 30176 solver.cpp:237]     Train net output #0: loss = 5.28299 (* 1 = 5.28299 loss)
I0405 09:45:29.708014 30176 sgd_solver.cpp:105] Iteration 7764, lr = 1e-06
I0405 09:45:34.783367 30176 solver.cpp:218] Iteration 7776 (2.36439 iter/s, 5.07529s/12 iters), loss = 5.2747
I0405 09:45:34.783421 30176 solver.cpp:237]     Train net output #0: loss = 5.2747 (* 1 = 5.2747 loss)
I0405 09:45:34.783432 30176 sgd_solver.cpp:105] Iteration 7776, lr = 1e-06
I0405 09:45:40.154842 30176 solver.cpp:218] Iteration 7788 (2.23407 iter/s, 5.37136s/12 iters), loss = 5.29561
I0405 09:45:40.154881 30176 solver.cpp:237]     Train net output #0: loss = 5.29561 (* 1 = 5.29561 loss)
I0405 09:45:40.154886 30176 sgd_solver.cpp:105] Iteration 7788, lr = 1e-06
I0405 09:45:40.161577 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:45:45.558391 30176 solver.cpp:218] Iteration 7800 (2.2208 iter/s, 5.40345s/12 iters), loss = 5.29648
I0405 09:45:45.558513 30176 solver.cpp:237]     Train net output #0: loss = 5.29648 (* 1 = 5.29648 loss)
I0405 09:45:45.558521 30176 sgd_solver.cpp:105] Iteration 7800, lr = 1e-06
I0405 09:45:50.866681 30176 solver.cpp:218] Iteration 7812 (2.26069 iter/s, 5.30811s/12 iters), loss = 5.28634
I0405 09:45:50.866722 30176 solver.cpp:237]     Train net output #0: loss = 5.28634 (* 1 = 5.28634 loss)
I0405 09:45:50.866727 30176 sgd_solver.cpp:105] Iteration 7812, lr = 1e-06
I0405 09:45:56.089862 30176 solver.cpp:218] Iteration 7824 (2.2975 iter/s, 5.22308s/12 iters), loss = 5.27997
I0405 09:45:56.089905 30176 solver.cpp:237]     Train net output #0: loss = 5.27997 (* 1 = 5.27997 loss)
I0405 09:45:56.089912 30176 sgd_solver.cpp:105] Iteration 7824, lr = 1e-06
I0405 09:46:01.477000 30176 solver.cpp:218] Iteration 7836 (2.22757 iter/s, 5.38703s/12 iters), loss = 5.27857
I0405 09:46:01.477046 30176 solver.cpp:237]     Train net output #0: loss = 5.27857 (* 1 = 5.27857 loss)
I0405 09:46:01.477051 30176 sgd_solver.cpp:105] Iteration 7836, lr = 1e-06
I0405 09:46:06.698179 30176 solver.cpp:218] Iteration 7848 (2.29838 iter/s, 5.22107s/12 iters), loss = 5.27749
I0405 09:46:06.698221 30176 solver.cpp:237]     Train net output #0: loss = 5.27749 (* 1 = 5.27749 loss)
I0405 09:46:06.698227 30176 sgd_solver.cpp:105] Iteration 7848, lr = 1e-06
I0405 09:46:08.687436 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7854.caffemodel
I0405 09:46:11.688195 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7854.solverstate
I0405 09:46:14.015887 30176 solver.cpp:330] Iteration 7854, Testing net (#0)
I0405 09:46:14.015916 30176 net.cpp:676] Ignoring source layer train-data
I0405 09:46:15.259918 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:46:18.420485 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 09:46:18.420617 30176 solver.cpp:397]     Test net output #1: loss = 5.28003 (* 1 = 5.28003 loss)
I0405 09:46:20.318670 30176 solver.cpp:218] Iteration 7860 (0.881037 iter/s, 13.6203s/12 iters), loss = 5.27549
I0405 09:46:20.318712 30176 solver.cpp:237]     Train net output #0: loss = 5.27549 (* 1 = 5.27549 loss)
I0405 09:46:20.318718 30176 sgd_solver.cpp:105] Iteration 7860, lr = 1e-06
I0405 09:46:25.578027 30176 solver.cpp:218] Iteration 7872 (2.28169 iter/s, 5.25925s/12 iters), loss = 5.29333
I0405 09:46:25.584252 30176 solver.cpp:237]     Train net output #0: loss = 5.29333 (* 1 = 5.29333 loss)
I0405 09:46:25.584272 30176 sgd_solver.cpp:105] Iteration 7872, lr = 1e-06
I0405 09:46:30.765173 30176 solver.cpp:218] Iteration 7884 (2.31621 iter/s, 5.18088s/12 iters), loss = 5.28296
I0405 09:46:30.765205 30176 solver.cpp:237]     Train net output #0: loss = 5.28296 (* 1 = 5.28296 loss)
I0405 09:46:30.765210 30176 sgd_solver.cpp:105] Iteration 7884, lr = 1e-06
I0405 09:46:33.043936 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:46:36.126296 30176 solver.cpp:218] Iteration 7896 (2.23838 iter/s, 5.36102s/12 iters), loss = 5.28496
I0405 09:46:36.126343 30176 solver.cpp:237]     Train net output #0: loss = 5.28496 (* 1 = 5.28496 loss)
I0405 09:46:36.126350 30176 sgd_solver.cpp:105] Iteration 7896, lr = 1e-06
I0405 09:46:41.188964 30176 solver.cpp:218] Iteration 7908 (2.37034 iter/s, 5.06256s/12 iters), loss = 5.28371
I0405 09:46:41.189004 30176 solver.cpp:237]     Train net output #0: loss = 5.28371 (* 1 = 5.28371 loss)
I0405 09:46:41.189009 30176 sgd_solver.cpp:105] Iteration 7908, lr = 1e-06
I0405 09:46:46.110817 30176 solver.cpp:218] Iteration 7920 (2.43815 iter/s, 4.92176s/12 iters), loss = 5.29499
I0405 09:46:46.110857 30176 solver.cpp:237]     Train net output #0: loss = 5.29499 (* 1 = 5.29499 loss)
I0405 09:46:46.110862 30176 sgd_solver.cpp:105] Iteration 7920, lr = 1e-06
I0405 09:46:51.522768 30176 solver.cpp:218] Iteration 7932 (2.21736 iter/s, 5.41185s/12 iters), loss = 5.29165
I0405 09:46:51.522886 30176 solver.cpp:237]     Train net output #0: loss = 5.29165 (* 1 = 5.29165 loss)
I0405 09:46:51.522892 30176 sgd_solver.cpp:105] Iteration 7932, lr = 1e-06
I0405 09:46:56.801676 30176 solver.cpp:218] Iteration 7944 (2.27327 iter/s, 5.27873s/12 iters), loss = 5.26875
I0405 09:46:56.801719 30176 solver.cpp:237]     Train net output #0: loss = 5.26875 (* 1 = 5.26875 loss)
I0405 09:46:56.801726 30176 sgd_solver.cpp:105] Iteration 7944, lr = 1e-06
I0405 09:47:01.459203 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7956.caffemodel
I0405 09:47:04.450268 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7956.solverstate
I0405 09:47:06.749996 30176 solver.cpp:330] Iteration 7956, Testing net (#0)
I0405 09:47:06.750015 30176 net.cpp:676] Ignoring source layer train-data
I0405 09:47:07.970115 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:47:11.052356 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 09:47:11.052397 30176 solver.cpp:397]     Test net output #1: loss = 5.28002 (* 1 = 5.28002 loss)
I0405 09:47:11.192126 30176 solver.cpp:218] Iteration 7956 (0.833897 iter/s, 14.3903s/12 iters), loss = 5.28656
I0405 09:47:11.192178 30176 solver.cpp:237]     Train net output #0: loss = 5.28656 (* 1 = 5.28656 loss)
I0405 09:47:11.192186 30176 sgd_solver.cpp:105] Iteration 7956, lr = 1e-06
I0405 09:47:15.551476 30176 solver.cpp:218] Iteration 7968 (2.75277 iter/s, 4.35924s/12 iters), loss = 5.30292
I0405 09:47:15.551522 30176 solver.cpp:237]     Train net output #0: loss = 5.30292 (* 1 = 5.30292 loss)
I0405 09:47:15.551530 30176 sgd_solver.cpp:105] Iteration 7968, lr = 1e-06
I0405 09:47:20.756371 30176 solver.cpp:218] Iteration 7980 (2.30557 iter/s, 5.20479s/12 iters), loss = 5.30822
I0405 09:47:20.756412 30176 solver.cpp:237]     Train net output #0: loss = 5.30822 (* 1 = 5.30822 loss)
I0405 09:47:20.756417 30176 sgd_solver.cpp:105] Iteration 7980, lr = 1e-06
I0405 09:47:25.198370 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:47:25.987780 30176 solver.cpp:218] Iteration 7992 (2.29388 iter/s, 5.23131s/12 iters), loss = 5.28469
I0405 09:47:25.987821 30176 solver.cpp:237]     Train net output #0: loss = 5.28469 (* 1 = 5.28469 loss)
I0405 09:47:25.987826 30176 sgd_solver.cpp:105] Iteration 7992, lr = 1e-06
I0405 09:47:31.219614 30176 solver.cpp:218] Iteration 8004 (2.2937 iter/s, 5.23173s/12 iters), loss = 5.28873
I0405 09:47:31.219650 30176 solver.cpp:237]     Train net output #0: loss = 5.28873 (* 1 = 5.28873 loss)
I0405 09:47:31.219656 30176 sgd_solver.cpp:105] Iteration 8004, lr = 1e-06
I0405 09:47:36.582710 30176 solver.cpp:218] Iteration 8016 (2.23756 iter/s, 5.363s/12 iters), loss = 5.27183
I0405 09:47:36.582751 30176 solver.cpp:237]     Train net output #0: loss = 5.27183 (* 1 = 5.27183 loss)
I0405 09:47:36.582756 30176 sgd_solver.cpp:105] Iteration 8016, lr = 1e-06
I0405 09:47:41.827116 30176 solver.cpp:218] Iteration 8028 (2.2882 iter/s, 5.2443s/12 iters), loss = 5.287
I0405 09:47:41.827159 30176 solver.cpp:237]     Train net output #0: loss = 5.287 (* 1 = 5.287 loss)
I0405 09:47:41.827168 30176 sgd_solver.cpp:105] Iteration 8028, lr = 1e-06
I0405 09:47:47.129930 30176 solver.cpp:218] Iteration 8040 (2.263 iter/s, 5.3027s/12 iters), loss = 5.28062
I0405 09:47:47.129992 30176 solver.cpp:237]     Train net output #0: loss = 5.28062 (* 1 = 5.28062 loss)
I0405 09:47:47.130002 30176 sgd_solver.cpp:105] Iteration 8040, lr = 1e-06
I0405 09:47:52.307011 30176 solver.cpp:218] Iteration 8052 (2.31796 iter/s, 5.17696s/12 iters), loss = 5.29088
I0405 09:47:52.307052 30176 solver.cpp:237]     Train net output #0: loss = 5.29088 (* 1 = 5.29088 loss)
I0405 09:47:52.307058 30176 sgd_solver.cpp:105] Iteration 8052, lr = 1e-06
I0405 09:47:54.428440 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8058.caffemodel
I0405 09:47:57.403192 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8058.solverstate
I0405 09:47:59.742549 30176 solver.cpp:330] Iteration 8058, Testing net (#0)
I0405 09:47:59.742578 30176 net.cpp:676] Ignoring source layer train-data
I0405 09:48:00.970965 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:48:04.118824 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 09:48:04.118858 30176 solver.cpp:397]     Test net output #1: loss = 5.27979 (* 1 = 5.27979 loss)
I0405 09:48:05.932025 30176 solver.cpp:218] Iteration 8064 (0.880745 iter/s, 13.6248s/12 iters), loss = 5.29548
I0405 09:48:05.932078 30176 solver.cpp:237]     Train net output #0: loss = 5.29548 (* 1 = 5.29548 loss)
I0405 09:48:05.932085 30176 sgd_solver.cpp:105] Iteration 8064, lr = 1e-06
I0405 09:48:11.102635 30176 solver.cpp:218] Iteration 8076 (2.32086 iter/s, 5.1705s/12 iters), loss = 5.27544
I0405 09:48:11.102674 30176 solver.cpp:237]     Train net output #0: loss = 5.27544 (* 1 = 5.27544 loss)
I0405 09:48:11.102679 30176 sgd_solver.cpp:105] Iteration 8076, lr = 1e-06
I0405 09:48:16.620712 30176 solver.cpp:218] Iteration 8088 (2.17471 iter/s, 5.51797s/12 iters), loss = 5.28813
I0405 09:48:16.620749 30176 solver.cpp:237]     Train net output #0: loss = 5.28813 (* 1 = 5.28813 loss)
I0405 09:48:16.620754 30176 sgd_solver.cpp:105] Iteration 8088, lr = 1e-06
I0405 09:48:18.082549 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:48:21.771714 30176 solver.cpp:218] Iteration 8100 (2.32969 iter/s, 5.1509s/12 iters), loss = 5.27109
I0405 09:48:21.771764 30176 solver.cpp:237]     Train net output #0: loss = 5.27109 (* 1 = 5.27109 loss)
I0405 09:48:21.771770 30176 sgd_solver.cpp:105] Iteration 8100, lr = 1e-06
I0405 09:48:27.088205 30176 solver.cpp:218] Iteration 8112 (2.25717 iter/s, 5.31638s/12 iters), loss = 5.26552
I0405 09:48:27.088246 30176 solver.cpp:237]     Train net output #0: loss = 5.26552 (* 1 = 5.26552 loss)
I0405 09:48:27.088253 30176 sgd_solver.cpp:105] Iteration 8112, lr = 1e-06
I0405 09:48:32.509436 30176 solver.cpp:218] Iteration 8124 (2.21356 iter/s, 5.42113s/12 iters), loss = 5.28908
I0405 09:48:32.509534 30176 solver.cpp:237]     Train net output #0: loss = 5.28908 (* 1 = 5.28908 loss)
I0405 09:48:32.509541 30176 sgd_solver.cpp:105] Iteration 8124, lr = 1e-06
I0405 09:48:37.587463 30176 solver.cpp:218] Iteration 8136 (2.36319 iter/s, 5.07787s/12 iters), loss = 5.28668
I0405 09:48:37.587499 30176 solver.cpp:237]     Train net output #0: loss = 5.28668 (* 1 = 5.28668 loss)
I0405 09:48:37.587505 30176 sgd_solver.cpp:105] Iteration 8136, lr = 1e-06
I0405 09:48:42.780066 30176 solver.cpp:218] Iteration 8148 (2.31102 iter/s, 5.19251s/12 iters), loss = 5.28942
I0405 09:48:42.780112 30176 solver.cpp:237]     Train net output #0: loss = 5.28942 (* 1 = 5.28942 loss)
I0405 09:48:42.780119 30176 sgd_solver.cpp:105] Iteration 8148, lr = 1e-06
I0405 09:48:47.350380 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8160.caffemodel
I0405 09:48:50.355922 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8160.solverstate
I0405 09:48:52.648229 30176 solver.cpp:330] Iteration 8160, Testing net (#0)
I0405 09:48:52.648247 30176 net.cpp:676] Ignoring source layer train-data
I0405 09:48:53.777058 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:48:57.068514 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 09:48:57.068562 30176 solver.cpp:397]     Test net output #1: loss = 5.27994 (* 1 = 5.27994 loss)
I0405 09:48:57.209636 30176 solver.cpp:218] Iteration 8160 (0.831636 iter/s, 14.4294s/12 iters), loss = 5.29906
I0405 09:48:57.209679 30176 solver.cpp:237]     Train net output #0: loss = 5.29906 (* 1 = 5.29906 loss)
I0405 09:48:57.209684 30176 sgd_solver.cpp:105] Iteration 8160, lr = 1e-06
I0405 09:49:01.570274 30176 solver.cpp:218] Iteration 8172 (2.75195 iter/s, 4.36054s/12 iters), loss = 5.27236
I0405 09:49:01.570314 30176 solver.cpp:237]     Train net output #0: loss = 5.27236 (* 1 = 5.27236 loss)
I0405 09:49:01.570320 30176 sgd_solver.cpp:105] Iteration 8172, lr = 1e-06
I0405 09:49:06.999655 30176 solver.cpp:218] Iteration 8184 (2.21024 iter/s, 5.42928s/12 iters), loss = 5.27658
I0405 09:49:06.999780 30176 solver.cpp:237]     Train net output #0: loss = 5.27658 (* 1 = 5.27658 loss)
I0405 09:49:06.999788 30176 sgd_solver.cpp:105] Iteration 8184, lr = 1e-06
I0405 09:49:10.780094 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:49:12.212935 30176 solver.cpp:218] Iteration 8196 (2.3019 iter/s, 5.21309s/12 iters), loss = 5.26869
I0405 09:49:12.212985 30176 solver.cpp:237]     Train net output #0: loss = 5.26869 (* 1 = 5.26869 loss)
I0405 09:49:12.212990 30176 sgd_solver.cpp:105] Iteration 8196, lr = 1e-06
I0405 09:49:17.184829 30176 solver.cpp:218] Iteration 8208 (2.41362 iter/s, 4.97178s/12 iters), loss = 5.27024
I0405 09:49:17.184878 30176 solver.cpp:237]     Train net output #0: loss = 5.27024 (* 1 = 5.27024 loss)
I0405 09:49:17.184897 30176 sgd_solver.cpp:105] Iteration 8208, lr = 1e-06
I0405 09:49:22.529882 30176 solver.cpp:218] Iteration 8220 (2.24511 iter/s, 5.34494s/12 iters), loss = 5.29811
I0405 09:49:22.529923 30176 solver.cpp:237]     Train net output #0: loss = 5.29811 (* 1 = 5.29811 loss)
I0405 09:49:22.529929 30176 sgd_solver.cpp:105] Iteration 8220, lr = 1e-06
I0405 09:49:27.784929 30176 solver.cpp:218] Iteration 8232 (2.28357 iter/s, 5.25494s/12 iters), loss = 5.29624
I0405 09:49:27.784972 30176 solver.cpp:237]     Train net output #0: loss = 5.29624 (* 1 = 5.29624 loss)
I0405 09:49:27.784978 30176 sgd_solver.cpp:105] Iteration 8232, lr = 1e-06
I0405 09:49:33.058539 30176 solver.cpp:218] Iteration 8244 (2.27553 iter/s, 5.2735s/12 iters), loss = 5.28441
I0405 09:49:33.058588 30176 solver.cpp:237]     Train net output #0: loss = 5.28441 (* 1 = 5.28441 loss)
I0405 09:49:33.058595 30176 sgd_solver.cpp:105] Iteration 8244, lr = 1e-06
I0405 09:49:37.982486 30176 solver.cpp:218] Iteration 8256 (2.43712 iter/s, 4.92384s/12 iters), loss = 5.25374
I0405 09:49:37.982614 30176 solver.cpp:237]     Train net output #0: loss = 5.25374 (* 1 = 5.25374 loss)
I0405 09:49:37.982621 30176 sgd_solver.cpp:105] Iteration 8256, lr = 1e-06
I0405 09:49:40.070652 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8262.caffemodel
I0405 09:49:43.069851 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8262.solverstate
I0405 09:49:45.367085 30176 solver.cpp:330] Iteration 8262, Testing net (#0)
I0405 09:49:45.367102 30176 net.cpp:676] Ignoring source layer train-data
I0405 09:49:46.519268 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:49:49.699916 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 09:49:49.699946 30176 solver.cpp:397]     Test net output #1: loss = 5.27961 (* 1 = 5.27961 loss)
I0405 09:49:51.612951 30176 solver.cpp:218] Iteration 8268 (0.880398 iter/s, 13.6302s/12 iters), loss = 5.30421
I0405 09:49:51.612998 30176 solver.cpp:237]     Train net output #0: loss = 5.30421 (* 1 = 5.30421 loss)
I0405 09:49:51.613003 30176 sgd_solver.cpp:105] Iteration 8268, lr = 1e-06
I0405 09:49:56.806773 30176 solver.cpp:218] Iteration 8280 (2.31049 iter/s, 5.19371s/12 iters), loss = 5.2889
I0405 09:49:56.806816 30176 solver.cpp:237]     Train net output #0: loss = 5.2889 (* 1 = 5.2889 loss)
I0405 09:49:56.806823 30176 sgd_solver.cpp:105] Iteration 8280, lr = 1e-06
I0405 09:50:02.075268 30176 solver.cpp:218] Iteration 8292 (2.27774 iter/s, 5.26839s/12 iters), loss = 5.2881
I0405 09:50:02.075318 30176 solver.cpp:237]     Train net output #0: loss = 5.2881 (* 1 = 5.2881 loss)
I0405 09:50:02.075325 30176 sgd_solver.cpp:105] Iteration 8292, lr = 1e-06
I0405 09:50:02.747264 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:50:07.139657 30176 solver.cpp:218] Iteration 8304 (2.36954 iter/s, 5.06427s/12 iters), loss = 5.30054
I0405 09:50:07.139704 30176 solver.cpp:237]     Train net output #0: loss = 5.30054 (* 1 = 5.30054 loss)
I0405 09:50:07.139711 30176 sgd_solver.cpp:105] Iteration 8304, lr = 1e-06
I0405 09:50:10.118592 30176 blocking_queue.cpp:49] Waiting for data
I0405 09:50:12.455447 30176 solver.cpp:218] Iteration 8316 (2.25747 iter/s, 5.31568s/12 iters), loss = 5.28991
I0405 09:50:12.455495 30176 solver.cpp:237]     Train net output #0: loss = 5.28991 (* 1 = 5.28991 loss)
I0405 09:50:12.455502 30176 sgd_solver.cpp:105] Iteration 8316, lr = 1e-06
I0405 09:50:17.671675 30176 solver.cpp:218] Iteration 8328 (2.30056 iter/s, 5.21612s/12 iters), loss = 5.29302
I0405 09:50:17.671717 30176 solver.cpp:237]     Train net output #0: loss = 5.29302 (* 1 = 5.29302 loss)
I0405 09:50:17.671723 30176 sgd_solver.cpp:105] Iteration 8328, lr = 1e-06
I0405 09:50:23.091647 30176 solver.cpp:218] Iteration 8340 (2.21408 iter/s, 5.41986s/12 iters), loss = 5.29053
I0405 09:50:23.091688 30176 solver.cpp:237]     Train net output #0: loss = 5.29053 (* 1 = 5.29053 loss)
I0405 09:50:23.091693 30176 sgd_solver.cpp:105] Iteration 8340, lr = 1e-06
I0405 09:50:28.473090 30176 solver.cpp:218] Iteration 8352 (2.22993 iter/s, 5.38134s/12 iters), loss = 5.27726
I0405 09:50:28.473125 30176 solver.cpp:237]     Train net output #0: loss = 5.27726 (* 1 = 5.27726 loss)
I0405 09:50:28.473130 30176 sgd_solver.cpp:105] Iteration 8352, lr = 1e-06
I0405 09:50:33.143858 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8364.caffemodel
I0405 09:50:36.063760 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8364.solverstate
I0405 09:50:38.357517 30176 solver.cpp:330] Iteration 8364, Testing net (#0)
I0405 09:50:38.357540 30176 net.cpp:676] Ignoring source layer train-data
I0405 09:50:39.486825 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:50:42.709225 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 09:50:42.709364 30176 solver.cpp:397]     Test net output #1: loss = 5.27991 (* 1 = 5.27991 loss)
I0405 09:50:42.844511 30176 solver.cpp:218] Iteration 8364 (0.835001 iter/s, 14.3712s/12 iters), loss = 5.28311
I0405 09:50:42.844573 30176 solver.cpp:237]     Train net output #0: loss = 5.28311 (* 1 = 5.28311 loss)
I0405 09:50:42.844581 30176 sgd_solver.cpp:105] Iteration 8364, lr = 1e-06
I0405 09:50:47.274662 30176 solver.cpp:218] Iteration 8376 (2.70878 iter/s, 4.43003s/12 iters), loss = 5.28986
I0405 09:50:47.274706 30176 solver.cpp:237]     Train net output #0: loss = 5.28986 (* 1 = 5.28986 loss)
I0405 09:50:47.274713 30176 sgd_solver.cpp:105] Iteration 8376, lr = 1e-06
I0405 09:50:52.595219 30176 solver.cpp:218] Iteration 8388 (2.25545 iter/s, 5.32045s/12 iters), loss = 5.29681
I0405 09:50:52.595257 30176 solver.cpp:237]     Train net output #0: loss = 5.29681 (* 1 = 5.29681 loss)
I0405 09:50:52.595263 30176 sgd_solver.cpp:105] Iteration 8388, lr = 1e-06
I0405 09:50:55.402926 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:50:57.774964 30176 solver.cpp:218] Iteration 8400 (2.31676 iter/s, 5.17965s/12 iters), loss = 5.28945
I0405 09:50:57.775007 30176 solver.cpp:237]     Train net output #0: loss = 5.28945 (* 1 = 5.28945 loss)
I0405 09:50:57.775012 30176 sgd_solver.cpp:105] Iteration 8400, lr = 1e-06
I0405 09:51:02.874402 30176 solver.cpp:218] Iteration 8412 (2.35325 iter/s, 5.09933s/12 iters), loss = 5.28119
I0405 09:51:02.874449 30176 solver.cpp:237]     Train net output #0: loss = 5.28119 (* 1 = 5.28119 loss)
I0405 09:51:02.874455 30176 sgd_solver.cpp:105] Iteration 8412, lr = 1e-06
I0405 09:51:08.158190 30176 solver.cpp:218] Iteration 8424 (2.27115 iter/s, 5.28367s/12 iters), loss = 5.29141
I0405 09:51:08.158241 30176 solver.cpp:237]     Train net output #0: loss = 5.29141 (* 1 = 5.29141 loss)
I0405 09:51:08.158248 30176 sgd_solver.cpp:105] Iteration 8424, lr = 1e-06
I0405 09:51:13.470995 30176 solver.cpp:218] Iteration 8436 (2.25874 iter/s, 5.31269s/12 iters), loss = 5.29511
I0405 09:51:13.471143 30176 solver.cpp:237]     Train net output #0: loss = 5.29511 (* 1 = 5.29511 loss)
I0405 09:51:13.471149 30176 sgd_solver.cpp:105] Iteration 8436, lr = 1e-06
I0405 09:51:18.755151 30176 solver.cpp:218] Iteration 8448 (2.27103 iter/s, 5.28395s/12 iters), loss = 5.29936
I0405 09:51:18.755201 30176 solver.cpp:237]     Train net output #0: loss = 5.29936 (* 1 = 5.29936 loss)
I0405 09:51:18.755208 30176 sgd_solver.cpp:105] Iteration 8448, lr = 1e-06
I0405 09:51:23.897125 30176 solver.cpp:218] Iteration 8460 (2.33379 iter/s, 5.14185s/12 iters), loss = 5.29428
I0405 09:51:23.897179 30176 solver.cpp:237]     Train net output #0: loss = 5.29428 (* 1 = 5.29428 loss)
I0405 09:51:23.897187 30176 sgd_solver.cpp:105] Iteration 8460, lr = 1e-06
I0405 09:51:26.201661 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8466.caffemodel
I0405 09:51:29.082643 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8466.solverstate
I0405 09:51:31.377876 30176 solver.cpp:330] Iteration 8466, Testing net (#0)
I0405 09:51:31.377894 30176 net.cpp:676] Ignoring source layer train-data
I0405 09:51:32.403771 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:51:35.668319 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 09:51:35.668350 30176 solver.cpp:397]     Test net output #1: loss = 5.27987 (* 1 = 5.27987 loss)
I0405 09:51:37.548794 30176 solver.cpp:218] Iteration 8472 (0.879026 iter/s, 13.6515s/12 iters), loss = 5.27783
I0405 09:51:37.548838 30176 solver.cpp:237]     Train net output #0: loss = 5.27783 (* 1 = 5.27783 loss)
I0405 09:51:37.548844 30176 sgd_solver.cpp:105] Iteration 8472, lr = 1e-06
I0405 09:51:42.960933 30176 solver.cpp:218] Iteration 8484 (2.21728 iter/s, 5.41203s/12 iters), loss = 5.28029
I0405 09:51:42.960976 30176 solver.cpp:237]     Train net output #0: loss = 5.28029 (* 1 = 5.28029 loss)
I0405 09:51:42.960981 30176 sgd_solver.cpp:105] Iteration 8484, lr = 1e-06
I0405 09:51:48.395706 30176 solver.cpp:218] Iteration 8496 (2.20805 iter/s, 5.43466s/12 iters), loss = 5.29443
I0405 09:51:48.395869 30176 solver.cpp:237]     Train net output #0: loss = 5.29443 (* 1 = 5.29443 loss)
I0405 09:51:48.395880 30176 sgd_solver.cpp:105] Iteration 8496, lr = 1e-06
I0405 09:51:48.414136 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:51:53.855057 30176 solver.cpp:218] Iteration 8508 (2.19816 iter/s, 5.45912s/12 iters), loss = 5.27955
I0405 09:51:53.855119 30176 solver.cpp:237]     Train net output #0: loss = 5.27955 (* 1 = 5.27955 loss)
I0405 09:51:53.855129 30176 sgd_solver.cpp:105] Iteration 8508, lr = 1e-06
I0405 09:51:59.212080 30176 solver.cpp:218] Iteration 8520 (2.2401 iter/s, 5.35689s/12 iters), loss = 5.26843
I0405 09:51:59.212126 30176 solver.cpp:237]     Train net output #0: loss = 5.26843 (* 1 = 5.26843 loss)
I0405 09:51:59.212131 30176 sgd_solver.cpp:105] Iteration 8520, lr = 1e-06
I0405 09:52:04.524624 30176 solver.cpp:218] Iteration 8532 (2.25885 iter/s, 5.31244s/12 iters), loss = 5.26692
I0405 09:52:04.524662 30176 solver.cpp:237]     Train net output #0: loss = 5.26692 (* 1 = 5.26692 loss)
I0405 09:52:04.524667 30176 sgd_solver.cpp:105] Iteration 8532, lr = 1e-06
I0405 09:52:09.692934 30176 solver.cpp:218] Iteration 8544 (2.32189 iter/s, 5.16821s/12 iters), loss = 5.28513
I0405 09:52:09.692975 30176 solver.cpp:237]     Train net output #0: loss = 5.28513 (* 1 = 5.28513 loss)
I0405 09:52:09.692981 30176 sgd_solver.cpp:105] Iteration 8544, lr = 1e-06
I0405 09:52:14.827649 30176 solver.cpp:218] Iteration 8556 (2.33708 iter/s, 5.13461s/12 iters), loss = 5.28139
I0405 09:52:14.827692 30176 solver.cpp:237]     Train net output #0: loss = 5.28139 (* 1 = 5.28139 loss)
I0405 09:52:14.827698 30176 sgd_solver.cpp:105] Iteration 8556, lr = 1e-06
I0405 09:52:19.652585 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8568.caffemodel
I0405 09:52:22.568655 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8568.solverstate
I0405 09:52:24.864465 30176 solver.cpp:330] Iteration 8568, Testing net (#0)
I0405 09:52:24.864486 30176 net.cpp:676] Ignoring source layer train-data
I0405 09:52:25.851230 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:52:29.218765 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 09:52:29.218804 30176 solver.cpp:397]     Test net output #1: loss = 5.27985 (* 1 = 5.27985 loss)
I0405 09:52:29.356968 30176 solver.cpp:218] Iteration 8568 (0.825927 iter/s, 14.5291s/12 iters), loss = 5.29302
I0405 09:52:29.357012 30176 solver.cpp:237]     Train net output #0: loss = 5.29302 (* 1 = 5.29302 loss)
I0405 09:52:29.357017 30176 sgd_solver.cpp:105] Iteration 8568, lr = 1e-06
I0405 09:52:33.655079 30176 solver.cpp:218] Iteration 8580 (2.79198 iter/s, 4.29802s/12 iters), loss = 5.2827
I0405 09:52:33.655114 30176 solver.cpp:237]     Train net output #0: loss = 5.2827 (* 1 = 5.2827 loss)
I0405 09:52:33.655118 30176 sgd_solver.cpp:105] Iteration 8580, lr = 1e-06
I0405 09:52:38.830536 30176 solver.cpp:218] Iteration 8592 (2.31868 iter/s, 5.17536s/12 iters), loss = 5.28135
I0405 09:52:38.830574 30176 solver.cpp:237]     Train net output #0: loss = 5.28135 (* 1 = 5.28135 loss)
I0405 09:52:38.830580 30176 sgd_solver.cpp:105] Iteration 8592, lr = 1e-06
I0405 09:52:40.956292 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:52:43.901196 30176 solver.cpp:218] Iteration 8604 (2.36661 iter/s, 5.07055s/12 iters), loss = 5.29282
I0405 09:52:43.901244 30176 solver.cpp:237]     Train net output #0: loss = 5.29282 (* 1 = 5.29282 loss)
I0405 09:52:43.901252 30176 sgd_solver.cpp:105] Iteration 8604, lr = 1e-06
I0405 09:52:49.141852 30176 solver.cpp:218] Iteration 8616 (2.28984 iter/s, 5.24055s/12 iters), loss = 5.28476
I0405 09:52:49.141892 30176 solver.cpp:237]     Train net output #0: loss = 5.28476 (* 1 = 5.28476 loss)
I0405 09:52:49.141898 30176 sgd_solver.cpp:105] Iteration 8616, lr = 1e-06
I0405 09:52:54.351398 30176 solver.cpp:218] Iteration 8628 (2.30351 iter/s, 5.20944s/12 iters), loss = 5.28415
I0405 09:52:54.351519 30176 solver.cpp:237]     Train net output #0: loss = 5.28415 (* 1 = 5.28415 loss)
I0405 09:52:54.351526 30176 sgd_solver.cpp:105] Iteration 8628, lr = 1e-06
I0405 09:52:59.479233 30176 solver.cpp:218] Iteration 8640 (2.34025 iter/s, 5.12765s/12 iters), loss = 5.27307
I0405 09:52:59.479291 30176 solver.cpp:237]     Train net output #0: loss = 5.27307 (* 1 = 5.27307 loss)
I0405 09:52:59.479300 30176 sgd_solver.cpp:105] Iteration 8640, lr = 1e-06
I0405 09:53:04.472685 30176 solver.cpp:218] Iteration 8652 (2.4032 iter/s, 4.99334s/12 iters), loss = 5.2761
I0405 09:53:04.472724 30176 solver.cpp:237]     Train net output #0: loss = 5.2761 (* 1 = 5.2761 loss)
I0405 09:53:04.472730 30176 sgd_solver.cpp:105] Iteration 8652, lr = 1e-06
I0405 09:53:09.761895 30176 solver.cpp:218] Iteration 8664 (2.26881 iter/s, 5.28911s/12 iters), loss = 5.29132
I0405 09:53:09.761936 30176 solver.cpp:237]     Train net output #0: loss = 5.29132 (* 1 = 5.29132 loss)
I0405 09:53:09.761942 30176 sgd_solver.cpp:105] Iteration 8664, lr = 1e-06
I0405 09:53:11.937861 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8670.caffemodel
I0405 09:53:15.000434 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8670.solverstate
I0405 09:53:17.335675 30176 solver.cpp:330] Iteration 8670, Testing net (#0)
I0405 09:53:17.335696 30176 net.cpp:676] Ignoring source layer train-data
I0405 09:53:18.287266 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:53:21.818838 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 09:53:21.818871 30176 solver.cpp:397]     Test net output #1: loss = 5.27996 (* 1 = 5.27996 loss)
I0405 09:53:23.678270 30176 solver.cpp:218] Iteration 8676 (0.862305 iter/s, 13.9162s/12 iters), loss = 5.29456
I0405 09:53:23.678320 30176 solver.cpp:237]     Train net output #0: loss = 5.29456 (* 1 = 5.29456 loss)
I0405 09:53:23.678328 30176 sgd_solver.cpp:105] Iteration 8676, lr = 1e-06
I0405 09:53:28.914767 30176 solver.cpp:218] Iteration 8688 (2.29166 iter/s, 5.23638s/12 iters), loss = 5.31113
I0405 09:53:28.914886 30176 solver.cpp:237]     Train net output #0: loss = 5.31113 (* 1 = 5.31113 loss)
I0405 09:53:28.914892 30176 sgd_solver.cpp:105] Iteration 8688, lr = 1e-06
I0405 09:53:33.565102 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:53:34.329448 30176 solver.cpp:218] Iteration 8700 (2.21627 iter/s, 5.4145s/12 iters), loss = 5.28883
I0405 09:53:34.329488 30176 solver.cpp:237]     Train net output #0: loss = 5.28883 (* 1 = 5.28883 loss)
I0405 09:53:34.329493 30176 sgd_solver.cpp:105] Iteration 8700, lr = 1e-06
I0405 09:53:39.708494 30176 solver.cpp:218] Iteration 8712 (2.23092 iter/s, 5.37894s/12 iters), loss = 5.27968
I0405 09:53:39.708539 30176 solver.cpp:237]     Train net output #0: loss = 5.27968 (* 1 = 5.27968 loss)
I0405 09:53:39.708544 30176 sgd_solver.cpp:105] Iteration 8712, lr = 1e-06
I0405 09:53:45.057821 30176 solver.cpp:218] Iteration 8724 (2.24332 iter/s, 5.34922s/12 iters), loss = 5.27281
I0405 09:53:45.057863 30176 solver.cpp:237]     Train net output #0: loss = 5.27281 (* 1 = 5.27281 loss)
I0405 09:53:45.057870 30176 sgd_solver.cpp:105] Iteration 8724, lr = 1e-06
I0405 09:53:50.399085 30176 solver.cpp:218] Iteration 8736 (2.2467 iter/s, 5.34116s/12 iters), loss = 5.28638
I0405 09:53:50.399125 30176 solver.cpp:237]     Train net output #0: loss = 5.28638 (* 1 = 5.28638 loss)
I0405 09:53:50.399132 30176 sgd_solver.cpp:105] Iteration 8736, lr = 1e-06
I0405 09:53:55.699170 30176 solver.cpp:218] Iteration 8748 (2.26416 iter/s, 5.29998s/12 iters), loss = 5.28438
I0405 09:53:55.699213 30176 solver.cpp:237]     Train net output #0: loss = 5.28438 (* 1 = 5.28438 loss)
I0405 09:53:55.699218 30176 sgd_solver.cpp:105] Iteration 8748, lr = 1e-06
I0405 09:54:01.099768 30176 solver.cpp:218] Iteration 8760 (2.22202 iter/s, 5.4005s/12 iters), loss = 5.28432
I0405 09:54:01.099860 30176 solver.cpp:237]     Train net output #0: loss = 5.28432 (* 1 = 5.28432 loss)
I0405 09:54:01.099866 30176 sgd_solver.cpp:105] Iteration 8760, lr = 1e-06
I0405 09:54:06.043637 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8772.caffemodel
I0405 09:54:09.034808 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8772.solverstate
I0405 09:54:11.346966 30176 solver.cpp:330] Iteration 8772, Testing net (#0)
I0405 09:54:11.346990 30176 net.cpp:676] Ignoring source layer train-data
I0405 09:54:12.246332 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:54:15.718047 30176 solver.cpp:397]     Test net output #0: accuracy = 0.00735294
I0405 09:54:15.718077 30176 solver.cpp:397]     Test net output #1: loss = 5.27962 (* 1 = 5.27962 loss)
I0405 09:54:15.858817 30176 solver.cpp:218] Iteration 8772 (0.813074 iter/s, 14.7588s/12 iters), loss = 5.28585
I0405 09:54:15.858886 30176 solver.cpp:237]     Train net output #0: loss = 5.28585 (* 1 = 5.28585 loss)
I0405 09:54:15.858893 30176 sgd_solver.cpp:105] Iteration 8772, lr = 1e-06
I0405 09:54:20.033154 30176 solver.cpp:218] Iteration 8784 (2.87479 iter/s, 4.17421s/12 iters), loss = 5.28896
I0405 09:54:20.033205 30176 solver.cpp:237]     Train net output #0: loss = 5.28896 (* 1 = 5.28896 loss)
I0405 09:54:20.033210 30176 sgd_solver.cpp:105] Iteration 8784, lr = 1e-06
I0405 09:54:25.242229 30176 solver.cpp:218] Iteration 8796 (2.30372 iter/s, 5.20896s/12 iters), loss = 5.27343
I0405 09:54:25.242272 30176 solver.cpp:237]     Train net output #0: loss = 5.27343 (* 1 = 5.27343 loss)
I0405 09:54:25.242278 30176 sgd_solver.cpp:105] Iteration 8796, lr = 1e-06
I0405 09:54:26.836851 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:54:30.425341 30176 solver.cpp:218] Iteration 8808 (2.31526 iter/s, 5.18301s/12 iters), loss = 5.26468
I0405 09:54:30.425379 30176 solver.cpp:237]     Train net output #0: loss = 5.26468 (* 1 = 5.26468 loss)
I0405 09:54:30.425384 30176 sgd_solver.cpp:105] Iteration 8808, lr = 1e-06
I0405 09:54:35.721166 30176 solver.cpp:218] Iteration 8820 (2.26598 iter/s, 5.29572s/12 iters), loss = 5.27494
I0405 09:54:35.721315 30176 solver.cpp:237]     Train net output #0: loss = 5.27494 (* 1 = 5.27494 loss)
I0405 09:54:35.721324 30176 sgd_solver.cpp:105] Iteration 8820, lr = 1e-06
I0405 09:54:41.012603 30176 solver.cpp:218] Iteration 8832 (2.2679 iter/s, 5.29123s/12 iters), loss = 5.28867
I0405 09:54:41.012653 30176 solver.cpp:237]     Train net output #0: loss = 5.28867 (* 1 = 5.28867 loss)
I0405 09:54:41.012662 30176 sgd_solver.cpp:105] Iteration 8832, lr = 1e-06
I0405 09:54:46.221143 30176 solver.cpp:218] Iteration 8844 (2.30396 iter/s, 5.20843s/12 iters), loss = 5.27798
I0405 09:54:46.221189 30176 solver.cpp:237]     Train net output #0: loss = 5.27798 (* 1 = 5.27798 loss)
I0405 09:54:46.221194 30176 sgd_solver.cpp:105] Iteration 8844, lr = 1e-06
I0405 09:54:51.567272 30176 solver.cpp:218] Iteration 8856 (2.24466 iter/s, 5.34602s/12 iters), loss = 5.28173
I0405 09:54:51.567318 30176 solver.cpp:237]     Train net output #0: loss = 5.28173 (* 1 = 5.28173 loss)
I0405 09:54:51.567325 30176 sgd_solver.cpp:105] Iteration 8856, lr = 1e-06
I0405 09:54:56.817656 30176 solver.cpp:218] Iteration 8868 (2.2856 iter/s, 5.25027s/12 iters), loss = 5.30773
I0405 09:54:56.817703 30176 solver.cpp:237]     Train net output #0: loss = 5.30773 (* 1 = 5.30773 loss)
I0405 09:54:56.817710 30176 sgd_solver.cpp:105] Iteration 8868, lr = 1e-06
I0405 09:54:58.852672 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8874.caffemodel
I0405 09:55:01.924190 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8874.solverstate
I0405 09:55:04.265009 30176 solver.cpp:330] Iteration 8874, Testing net (#0)
I0405 09:55:04.265029 30176 net.cpp:676] Ignoring source layer train-data
I0405 09:55:05.287092 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:55:09.059741 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 09:55:09.059851 30176 solver.cpp:397]     Test net output #1: loss = 5.27992 (* 1 = 5.27992 loss)
I0405 09:55:10.982954 30176 solver.cpp:218] Iteration 8880 (0.847152 iter/s, 14.1651s/12 iters), loss = 5.26923
I0405 09:55:10.983000 30176 solver.cpp:237]     Train net output #0: loss = 5.26923 (* 1 = 5.26923 loss)
I0405 09:55:10.983006 30176 sgd_solver.cpp:105] Iteration 8880, lr = 1e-06
I0405 09:55:16.710955 30176 solver.cpp:218] Iteration 8892 (2.09501 iter/s, 5.72789s/12 iters), loss = 5.29554
I0405 09:55:16.710999 30176 solver.cpp:237]     Train net output #0: loss = 5.29554 (* 1 = 5.29554 loss)
I0405 09:55:16.711004 30176 sgd_solver.cpp:105] Iteration 8892, lr = 1e-06
I0405 09:55:20.551622 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:55:22.217495 30176 solver.cpp:218] Iteration 8904 (2.17927 iter/s, 5.50643s/12 iters), loss = 5.282
I0405 09:55:22.217535 30176 solver.cpp:237]     Train net output #0: loss = 5.282 (* 1 = 5.282 loss)
I0405 09:55:22.217540 30176 sgd_solver.cpp:105] Iteration 8904, lr = 1e-06
I0405 09:55:27.723731 30176 solver.cpp:218] Iteration 8916 (2.17939 iter/s, 5.50613s/12 iters), loss = 5.27441
I0405 09:55:27.723774 30176 solver.cpp:237]     Train net output #0: loss = 5.27441 (* 1 = 5.27441 loss)
I0405 09:55:27.723780 30176 sgd_solver.cpp:105] Iteration 8916, lr = 1e-06
I0405 09:55:33.283334 30176 solver.cpp:218] Iteration 8928 (2.15847 iter/s, 5.55949s/12 iters), loss = 5.29532
I0405 09:55:33.283380 30176 solver.cpp:237]     Train net output #0: loss = 5.29532 (* 1 = 5.29532 loss)
I0405 09:55:33.283386 30176 sgd_solver.cpp:105] Iteration 8928, lr = 1e-06
I0405 09:55:38.925997 30176 solver.cpp:218] Iteration 8940 (2.1267 iter/s, 5.64255s/12 iters), loss = 5.28771
I0405 09:55:38.926043 30176 solver.cpp:237]     Train net output #0: loss = 5.28771 (* 1 = 5.28771 loss)
I0405 09:55:38.926049 30176 sgd_solver.cpp:105] Iteration 8940, lr = 1e-06
I0405 09:55:44.611495 30176 solver.cpp:218] Iteration 8952 (2.11067 iter/s, 5.68539s/12 iters), loss = 5.2689
I0405 09:55:44.611609 30176 solver.cpp:237]     Train net output #0: loss = 5.2689 (* 1 = 5.2689 loss)
I0405 09:55:44.611615 30176 sgd_solver.cpp:105] Iteration 8952, lr = 1e-06
I0405 09:55:50.067802 30176 solver.cpp:218] Iteration 8964 (2.19936 iter/s, 5.45612s/12 iters), loss = 5.27097
I0405 09:55:50.067854 30176 solver.cpp:237]     Train net output #0: loss = 5.27097 (* 1 = 5.27097 loss)
I0405 09:55:50.067862 30176 sgd_solver.cpp:105] Iteration 8964, lr = 1e-06
I0405 09:55:54.653223 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8976.caffemodel
I0405 09:55:57.685487 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8976.solverstate
I0405 09:56:00.028362 30176 solver.cpp:330] Iteration 8976, Testing net (#0)
I0405 09:56:00.028384 30176 net.cpp:676] Ignoring source layer train-data
I0405 09:56:01.002931 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:56:04.639374 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 09:56:04.639407 30176 solver.cpp:397]     Test net output #1: loss = 5.28008 (* 1 = 5.28008 loss)
I0405 09:56:04.779134 30176 solver.cpp:218] Iteration 8976 (0.815709 iter/s, 14.7111s/12 iters), loss = 5.30666
I0405 09:56:04.779181 30176 solver.cpp:237]     Train net output #0: loss = 5.30666 (* 1 = 5.30666 loss)
I0405 09:56:04.779188 30176 sgd_solver.cpp:105] Iteration 8976, lr = 1e-06
I0405 09:56:09.367985 30176 solver.cpp:218] Iteration 8988 (2.61509 iter/s, 4.58874s/12 iters), loss = 5.29211
I0405 09:56:09.368032 30176 solver.cpp:237]     Train net output #0: loss = 5.29211 (* 1 = 5.29211 loss)
I0405 09:56:09.368037 30176 sgd_solver.cpp:105] Iteration 8988, lr = 1e-06
I0405 09:56:12.991693 30176 blocking_queue.cpp:49] Waiting for data
I0405 09:56:14.828960 30176 solver.cpp:218] Iteration 9000 (2.19745 iter/s, 5.46087s/12 iters), loss = 5.27142
I0405 09:56:14.829042 30176 solver.cpp:237]     Train net output #0: loss = 5.27142 (* 1 = 5.27142 loss)
I0405 09:56:14.829049 30176 sgd_solver.cpp:105] Iteration 9000, lr = 1e-06
I0405 09:56:15.603576 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:56:20.251777 30176 solver.cpp:218] Iteration 9012 (2.21293 iter/s, 5.42267s/12 iters), loss = 5.28919
I0405 09:56:20.251828 30176 solver.cpp:237]     Train net output #0: loss = 5.28919 (* 1 = 5.28919 loss)
I0405 09:56:20.251835 30176 sgd_solver.cpp:105] Iteration 9012, lr = 1e-06
I0405 09:56:25.616870 30176 solver.cpp:218] Iteration 9024 (2.23673 iter/s, 5.36498s/12 iters), loss = 5.27638
I0405 09:56:25.616925 30176 solver.cpp:237]     Train net output #0: loss = 5.27638 (* 1 = 5.27638 loss)
I0405 09:56:25.616930 30176 sgd_solver.cpp:105] Iteration 9024, lr = 1e-06
I0405 09:56:31.052170 30176 solver.cpp:218] Iteration 9036 (2.20784 iter/s, 5.43518s/12 iters), loss = 5.28375
I0405 09:56:31.052212 30176 solver.cpp:237]     Train net output #0: loss = 5.28375 (* 1 = 5.28375 loss)
I0405 09:56:31.052217 30176 sgd_solver.cpp:105] Iteration 9036, lr = 1e-06
I0405 09:56:36.548095 30176 solver.cpp:218] Iteration 9048 (2.18348 iter/s, 5.49582s/12 iters), loss = 5.28197
I0405 09:56:36.548141 30176 solver.cpp:237]     Train net output #0: loss = 5.28197 (* 1 = 5.28197 loss)
I0405 09:56:36.548148 30176 sgd_solver.cpp:105] Iteration 9048, lr = 1e-06
I0405 09:56:42.108999 30176 solver.cpp:218] Iteration 9060 (2.15796 iter/s, 5.5608s/12 iters), loss = 5.28342
I0405 09:56:42.109041 30176 solver.cpp:237]     Train net output #0: loss = 5.28342 (* 1 = 5.28342 loss)
I0405 09:56:42.109046 30176 sgd_solver.cpp:105] Iteration 9060, lr = 1e-06
I0405 09:56:47.568271 30176 solver.cpp:218] Iteration 9072 (2.20321 iter/s, 5.44659s/12 iters), loss = 5.28778
I0405 09:56:47.568413 30176 solver.cpp:237]     Train net output #0: loss = 5.28778 (* 1 = 5.28778 loss)
I0405 09:56:47.568423 30176 sgd_solver.cpp:105] Iteration 9072, lr = 1e-06
I0405 09:56:49.708385 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9078.caffemodel
I0405 09:56:53.288801 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9078.solverstate
I0405 09:56:55.593660 30176 solver.cpp:330] Iteration 9078, Testing net (#0)
I0405 09:56:55.593679 30176 net.cpp:676] Ignoring source layer train-data
I0405 09:56:56.495077 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:57:00.398926 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 09:57:00.398959 30176 solver.cpp:397]     Test net output #1: loss = 5.28012 (* 1 = 5.28012 loss)
I0405 09:57:02.364081 30176 solver.cpp:218] Iteration 9084 (0.811056 iter/s, 14.7955s/12 iters), loss = 5.30904
I0405 09:57:02.364135 30176 solver.cpp:237]     Train net output #0: loss = 5.30904 (* 1 = 5.30904 loss)
I0405 09:57:02.364145 30176 sgd_solver.cpp:105] Iteration 9084, lr = 1e-06
I0405 09:57:08.009011 30176 solver.cpp:218] Iteration 9096 (2.12585 iter/s, 5.64481s/12 iters), loss = 5.29426
I0405 09:57:08.009054 30176 solver.cpp:237]     Train net output #0: loss = 5.29426 (* 1 = 5.29426 loss)
I0405 09:57:08.009060 30176 sgd_solver.cpp:105] Iteration 9096, lr = 1e-06
I0405 09:57:11.349853 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:57:13.600394 30176 solver.cpp:218] Iteration 9108 (2.1462 iter/s, 5.59127s/12 iters), loss = 5.27275
I0405 09:57:13.600494 30176 solver.cpp:237]     Train net output #0: loss = 5.27275 (* 1 = 5.27275 loss)
I0405 09:57:13.600507 30176 sgd_solver.cpp:105] Iteration 9108, lr = 1e-06
I0405 09:57:18.988063 30176 solver.cpp:218] Iteration 9120 (2.22737 iter/s, 5.38752s/12 iters), loss = 5.27949
I0405 09:57:18.988173 30176 solver.cpp:237]     Train net output #0: loss = 5.27949 (* 1 = 5.27949 loss)
I0405 09:57:18.988181 30176 sgd_solver.cpp:105] Iteration 9120, lr = 1e-06
I0405 09:57:24.552901 30176 solver.cpp:218] Iteration 9132 (2.15646 iter/s, 5.56466s/12 iters), loss = 5.28751
I0405 09:57:24.552943 30176 solver.cpp:237]     Train net output #0: loss = 5.28751 (* 1 = 5.28751 loss)
I0405 09:57:24.552949 30176 sgd_solver.cpp:105] Iteration 9132, lr = 1e-06
I0405 09:57:29.876643 30176 solver.cpp:218] Iteration 9144 (2.2541 iter/s, 5.32364s/12 iters), loss = 5.29445
I0405 09:57:29.876694 30176 solver.cpp:237]     Train net output #0: loss = 5.29445 (* 1 = 5.29445 loss)
I0405 09:57:29.876701 30176 sgd_solver.cpp:105] Iteration 9144, lr = 1e-06
I0405 09:57:35.349659 30176 solver.cpp:218] Iteration 9156 (2.19262 iter/s, 5.4729s/12 iters), loss = 5.28759
I0405 09:57:35.349714 30176 solver.cpp:237]     Train net output #0: loss = 5.28759 (* 1 = 5.28759 loss)
I0405 09:57:35.349723 30176 sgd_solver.cpp:105] Iteration 9156, lr = 1e-06
I0405 09:57:40.785337 30176 solver.cpp:218] Iteration 9168 (2.20768 iter/s, 5.43557s/12 iters), loss = 5.28906
I0405 09:57:40.785380 30176 solver.cpp:237]     Train net output #0: loss = 5.28906 (* 1 = 5.28906 loss)
I0405 09:57:40.785385 30176 sgd_solver.cpp:105] Iteration 9168, lr = 1e-06
I0405 09:57:45.790774 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9180.caffemodel
I0405 09:57:48.805701 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9180.solverstate
I0405 09:57:51.127507 30176 solver.cpp:330] Iteration 9180, Testing net (#0)
I0405 09:57:51.129699 30176 net.cpp:676] Ignoring source layer train-data
I0405 09:57:51.953361 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:57:55.691902 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 09:57:55.691943 30176 solver.cpp:397]     Test net output #1: loss = 5.28001 (* 1 = 5.28001 loss)
I0405 09:57:55.832370 30176 solver.cpp:218] Iteration 9180 (0.797509 iter/s, 15.0469s/12 iters), loss = 5.28155
I0405 09:57:55.832423 30176 solver.cpp:237]     Train net output #0: loss = 5.28155 (* 1 = 5.28155 loss)
I0405 09:57:55.832432 30176 sgd_solver.cpp:105] Iteration 9180, lr = 1e-06
I0405 09:58:00.475412 30176 solver.cpp:218] Iteration 9192 (2.58457 iter/s, 4.64294s/12 iters), loss = 5.28126
I0405 09:58:00.475463 30176 solver.cpp:237]     Train net output #0: loss = 5.28126 (* 1 = 5.28126 loss)
I0405 09:58:00.475471 30176 sgd_solver.cpp:105] Iteration 9192, lr = 1e-06
I0405 09:58:05.822377 30176 solver.cpp:218] Iteration 9204 (2.24431 iter/s, 5.34686s/12 iters), loss = 5.2879
I0405 09:58:05.822422 30176 solver.cpp:237]     Train net output #0: loss = 5.2879 (* 1 = 5.2879 loss)
I0405 09:58:05.822427 30176 sgd_solver.cpp:105] Iteration 9204, lr = 1e-06
I0405 09:58:05.884675 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:58:11.334503 30176 solver.cpp:218] Iteration 9216 (2.17706 iter/s, 5.51202s/12 iters), loss = 5.29411
I0405 09:58:11.334556 30176 solver.cpp:237]     Train net output #0: loss = 5.29411 (* 1 = 5.29411 loss)
I0405 09:58:11.334566 30176 sgd_solver.cpp:105] Iteration 9216, lr = 1e-06
I0405 09:58:16.886099 30176 solver.cpp:218] Iteration 9228 (2.16158 iter/s, 5.55149s/12 iters), loss = 5.28498
I0405 09:58:16.886147 30176 solver.cpp:237]     Train net output #0: loss = 5.28498 (* 1 = 5.28498 loss)
I0405 09:58:16.886152 30176 sgd_solver.cpp:105] Iteration 9228, lr = 1e-06
I0405 09:58:22.289963 30176 solver.cpp:218] Iteration 9240 (2.22068 iter/s, 5.40376s/12 iters), loss = 5.26567
I0405 09:58:22.290060 30176 solver.cpp:237]     Train net output #0: loss = 5.26567 (* 1 = 5.26567 loss)
I0405 09:58:22.290066 30176 sgd_solver.cpp:105] Iteration 9240, lr = 1e-06
I0405 09:58:27.943686 30176 solver.cpp:218] Iteration 9252 (2.12255 iter/s, 5.65357s/12 iters), loss = 5.27186
I0405 09:58:27.943734 30176 solver.cpp:237]     Train net output #0: loss = 5.27186 (* 1 = 5.27186 loss)
I0405 09:58:27.943742 30176 sgd_solver.cpp:105] Iteration 9252, lr = 1e-06
I0405 09:58:33.491696 30176 solver.cpp:218] Iteration 9264 (2.16298 iter/s, 5.54791s/12 iters), loss = 5.27358
I0405 09:58:33.491744 30176 solver.cpp:237]     Train net output #0: loss = 5.27358 (* 1 = 5.27358 loss)
I0405 09:58:33.491752 30176 sgd_solver.cpp:105] Iteration 9264, lr = 1e-06
I0405 09:58:38.899600 30176 solver.cpp:218] Iteration 9276 (2.21902 iter/s, 5.4078s/12 iters), loss = 5.29777
I0405 09:58:38.899638 30176 solver.cpp:237]     Train net output #0: loss = 5.29777 (* 1 = 5.29777 loss)
I0405 09:58:38.899643 30176 sgd_solver.cpp:105] Iteration 9276, lr = 1e-06
I0405 09:58:41.146239 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9282.caffemodel
I0405 09:58:44.192463 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9282.solverstate
I0405 09:58:46.507932 30176 solver.cpp:330] Iteration 9282, Testing net (#0)
I0405 09:58:46.507954 30176 net.cpp:676] Ignoring source layer train-data
I0405 09:58:47.329433 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:58:51.221617 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 09:58:51.221657 30176 solver.cpp:397]     Test net output #1: loss = 5.27999 (* 1 = 5.27999 loss)
I0405 09:58:53.217916 30176 solver.cpp:218] Iteration 9288 (0.838097 iter/s, 14.3182s/12 iters), loss = 5.28755
I0405 09:58:53.218044 30176 solver.cpp:237]     Train net output #0: loss = 5.28755 (* 1 = 5.28755 loss)
I0405 09:58:53.218050 30176 sgd_solver.cpp:105] Iteration 9288, lr = 1e-06
I0405 09:58:58.657164 30176 solver.cpp:218] Iteration 9300 (2.20626 iter/s, 5.43906s/12 iters), loss = 5.28434
I0405 09:58:58.657214 30176 solver.cpp:237]     Train net output #0: loss = 5.28434 (* 1 = 5.28434 loss)
I0405 09:58:58.657222 30176 sgd_solver.cpp:105] Iteration 9300, lr = 1e-06
I0405 09:59:01.107101 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:59:04.103942 30176 solver.cpp:218] Iteration 9312 (2.20318 iter/s, 5.44667s/12 iters), loss = 5.29549
I0405 09:59:04.103998 30176 solver.cpp:237]     Train net output #0: loss = 5.29549 (* 1 = 5.29549 loss)
I0405 09:59:04.104007 30176 sgd_solver.cpp:105] Iteration 9312, lr = 1e-06
I0405 09:59:09.408931 30176 solver.cpp:218] Iteration 9324 (2.26207 iter/s, 5.30488s/12 iters), loss = 5.28019
I0405 09:59:09.408973 30176 solver.cpp:237]     Train net output #0: loss = 5.28019 (* 1 = 5.28019 loss)
I0405 09:59:09.408979 30176 sgd_solver.cpp:105] Iteration 9324, lr = 1e-06
I0405 09:59:14.759007 30176 solver.cpp:218] Iteration 9336 (2.243 iter/s, 5.34997s/12 iters), loss = 5.29533
I0405 09:59:14.759061 30176 solver.cpp:237]     Train net output #0: loss = 5.29533 (* 1 = 5.29533 loss)
I0405 09:59:14.759069 30176 sgd_solver.cpp:105] Iteration 9336, lr = 1e-06
I0405 09:59:20.055423 30176 solver.cpp:218] Iteration 9348 (2.26573 iter/s, 5.29631s/12 iters), loss = 5.27894
I0405 09:59:20.055459 30176 solver.cpp:237]     Train net output #0: loss = 5.27894 (* 1 = 5.27894 loss)
I0405 09:59:20.055464 30176 sgd_solver.cpp:105] Iteration 9348, lr = 1e-06
I0405 09:59:25.601132 30176 solver.cpp:218] Iteration 9360 (2.16387 iter/s, 5.54561s/12 iters), loss = 5.27251
I0405 09:59:25.601241 30176 solver.cpp:237]     Train net output #0: loss = 5.27251 (* 1 = 5.27251 loss)
I0405 09:59:25.601249 30176 sgd_solver.cpp:105] Iteration 9360, lr = 1e-06
I0405 09:59:30.889786 30176 solver.cpp:218] Iteration 9372 (2.26953 iter/s, 5.28744s/12 iters), loss = 5.28894
I0405 09:59:30.889830 30176 solver.cpp:237]     Train net output #0: loss = 5.28894 (* 1 = 5.28894 loss)
I0405 09:59:30.889837 30176 sgd_solver.cpp:105] Iteration 9372, lr = 1e-06
I0405 09:59:35.743568 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9384.caffemodel
I0405 09:59:38.801275 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9384.solverstate
I0405 09:59:41.134874 30176 solver.cpp:330] Iteration 9384, Testing net (#0)
I0405 09:59:41.134897 30176 net.cpp:676] Ignoring source layer train-data
I0405 09:59:41.849694 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:59:45.818639 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 09:59:45.818681 30176 solver.cpp:397]     Test net output #1: loss = 5.27978 (* 1 = 5.27978 loss)
I0405 09:59:45.959924 30176 solver.cpp:218] Iteration 9384 (0.796285 iter/s, 15.07s/12 iters), loss = 5.28704
I0405 09:59:45.961505 30176 solver.cpp:237]     Train net output #0: loss = 5.28704 (* 1 = 5.28704 loss)
I0405 09:59:45.961516 30176 sgd_solver.cpp:105] Iteration 9384, lr = 1e-06
I0405 09:59:50.651912 30176 solver.cpp:218] Iteration 9396 (2.55844 iter/s, 4.69036s/12 iters), loss = 5.31052
I0405 09:59:50.651955 30176 solver.cpp:237]     Train net output #0: loss = 5.31052 (* 1 = 5.31052 loss)
I0405 09:59:50.651960 30176 sgd_solver.cpp:105] Iteration 9396, lr = 1e-06
I0405 09:59:55.268847 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 09:59:56.043435 30176 solver.cpp:218] Iteration 9408 (2.22576 iter/s, 5.39142s/12 iters), loss = 5.28986
I0405 09:59:56.043538 30176 solver.cpp:237]     Train net output #0: loss = 5.28986 (* 1 = 5.28986 loss)
I0405 09:59:56.043545 30176 sgd_solver.cpp:105] Iteration 9408, lr = 1e-06
I0405 10:00:01.555929 30176 solver.cpp:218] Iteration 9420 (2.17694 iter/s, 5.51233s/12 iters), loss = 5.27255
I0405 10:00:01.555972 30176 solver.cpp:237]     Train net output #0: loss = 5.27255 (* 1 = 5.27255 loss)
I0405 10:00:01.555977 30176 sgd_solver.cpp:105] Iteration 9420, lr = 1e-06
I0405 10:00:07.098615 30176 solver.cpp:218] Iteration 9432 (2.16506 iter/s, 5.54258s/12 iters), loss = 5.28844
I0405 10:00:07.098666 30176 solver.cpp:237]     Train net output #0: loss = 5.28844 (* 1 = 5.28844 loss)
I0405 10:00:07.098675 30176 sgd_solver.cpp:105] Iteration 9432, lr = 1e-06
I0405 10:00:12.367053 30176 solver.cpp:218] Iteration 9444 (2.27776 iter/s, 5.26833s/12 iters), loss = 5.27693
I0405 10:00:12.367094 30176 solver.cpp:237]     Train net output #0: loss = 5.27693 (* 1 = 5.27693 loss)
I0405 10:00:12.367100 30176 sgd_solver.cpp:105] Iteration 9444, lr = 1e-06
I0405 10:00:17.959781 30176 solver.cpp:218] Iteration 9456 (2.14568 iter/s, 5.59263s/12 iters), loss = 5.28393
I0405 10:00:17.959827 30176 solver.cpp:237]     Train net output #0: loss = 5.28393 (* 1 = 5.28393 loss)
I0405 10:00:17.959834 30176 sgd_solver.cpp:105] Iteration 9456, lr = 1e-06
I0405 10:00:23.450067 30176 solver.cpp:218] Iteration 9468 (2.18572 iter/s, 5.49018s/12 iters), loss = 5.28011
I0405 10:00:23.450110 30176 solver.cpp:237]     Train net output #0: loss = 5.28011 (* 1 = 5.28011 loss)
I0405 10:00:23.450117 30176 sgd_solver.cpp:105] Iteration 9468, lr = 1e-06
I0405 10:00:28.809424 30176 solver.cpp:218] Iteration 9480 (2.23911 iter/s, 5.35926s/12 iters), loss = 5.28212
I0405 10:00:28.809559 30176 solver.cpp:237]     Train net output #0: loss = 5.28212 (* 1 = 5.28212 loss)
I0405 10:00:28.809566 30176 sgd_solver.cpp:105] Iteration 9480, lr = 1e-06
I0405 10:00:31.062700 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9486.caffemodel
I0405 10:00:34.108111 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9486.solverstate
I0405 10:00:36.412768 30176 solver.cpp:330] Iteration 9486, Testing net (#0)
I0405 10:00:36.412792 30176 net.cpp:676] Ignoring source layer train-data
I0405 10:00:37.062173 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:00:40.854233 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 10:00:40.854266 30176 solver.cpp:397]     Test net output #1: loss = 5.28016 (* 1 = 5.28016 loss)
I0405 10:00:42.872685 30176 solver.cpp:218] Iteration 9492 (0.853302 iter/s, 14.063s/12 iters), loss = 5.27628
I0405 10:00:42.872730 30176 solver.cpp:237]     Train net output #0: loss = 5.27628 (* 1 = 5.27628 loss)
I0405 10:00:42.872737 30176 sgd_solver.cpp:105] Iteration 9492, lr = 1e-06
I0405 10:00:48.185567 30176 solver.cpp:218] Iteration 9504 (2.25871 iter/s, 5.31277s/12 iters), loss = 5.27327
I0405 10:00:48.185621 30176 solver.cpp:237]     Train net output #0: loss = 5.27327 (* 1 = 5.27327 loss)
I0405 10:00:48.185629 30176 sgd_solver.cpp:105] Iteration 9504, lr = 1e-06
I0405 10:00:49.779932 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:00:53.651223 30176 solver.cpp:218] Iteration 9516 (2.19557 iter/s, 5.46555s/12 iters), loss = 5.28121
I0405 10:00:53.651271 30176 solver.cpp:237]     Train net output #0: loss = 5.28121 (* 1 = 5.28121 loss)
I0405 10:00:53.651279 30176 sgd_solver.cpp:105] Iteration 9516, lr = 1e-06
I0405 10:00:58.877885 30176 solver.cpp:218] Iteration 9528 (2.29597 iter/s, 5.22656s/12 iters), loss = 5.28832
I0405 10:00:58.878006 30176 solver.cpp:237]     Train net output #0: loss = 5.28832 (* 1 = 5.28832 loss)
I0405 10:00:58.878015 30176 sgd_solver.cpp:105] Iteration 9528, lr = 1e-06
I0405 10:01:04.393836 30176 solver.cpp:218] Iteration 9540 (2.17558 iter/s, 5.51577s/12 iters), loss = 5.29589
I0405 10:01:04.393896 30176 solver.cpp:237]     Train net output #0: loss = 5.29589 (* 1 = 5.29589 loss)
I0405 10:01:04.393905 30176 sgd_solver.cpp:105] Iteration 9540, lr = 1e-06
I0405 10:01:10.001098 30176 solver.cpp:218] Iteration 9552 (2.14013 iter/s, 5.60714s/12 iters), loss = 5.27703
I0405 10:01:10.001154 30176 solver.cpp:237]     Train net output #0: loss = 5.27703 (* 1 = 5.27703 loss)
I0405 10:01:10.001163 30176 sgd_solver.cpp:105] Iteration 9552, lr = 1e-06
I0405 10:01:15.209587 30176 solver.cpp:218] Iteration 9564 (2.30398 iter/s, 5.20838s/12 iters), loss = 5.27485
I0405 10:01:15.209650 30176 solver.cpp:237]     Train net output #0: loss = 5.27485 (* 1 = 5.27485 loss)
I0405 10:01:15.209658 30176 sgd_solver.cpp:105] Iteration 9564, lr = 1e-06
I0405 10:01:20.756423 30176 solver.cpp:218] Iteration 9576 (2.16344 iter/s, 5.54673s/12 iters), loss = 5.29267
I0405 10:01:20.756464 30176 solver.cpp:237]     Train net output #0: loss = 5.29267 (* 1 = 5.29267 loss)
I0405 10:01:20.756469 30176 sgd_solver.cpp:105] Iteration 9576, lr = 1e-06
I0405 10:01:25.570770 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9588.caffemodel
I0405 10:01:28.658720 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9588.solverstate
I0405 10:01:30.976179 30176 solver.cpp:330] Iteration 9588, Testing net (#0)
I0405 10:01:30.976302 30176 net.cpp:676] Ignoring source layer train-data
I0405 10:01:31.602895 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:01:35.469635 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 10:01:35.469663 30176 solver.cpp:397]     Test net output #1: loss = 5.27994 (* 1 = 5.27994 loss)
I0405 10:01:35.610544 30176 solver.cpp:218] Iteration 9588 (0.807866 iter/s, 14.854s/12 iters), loss = 5.26796
I0405 10:01:35.610617 30176 solver.cpp:237]     Train net output #0: loss = 5.26796 (* 1 = 5.26796 loss)
I0405 10:01:35.610626 30176 sgd_solver.cpp:105] Iteration 9588, lr = 1e-06
I0405 10:01:39.996289 30176 solver.cpp:218] Iteration 9600 (2.73621 iter/s, 4.38562s/12 iters), loss = 5.29179
I0405 10:01:39.996331 30176 solver.cpp:237]     Train net output #0: loss = 5.29179 (* 1 = 5.29179 loss)
I0405 10:01:39.996336 30176 sgd_solver.cpp:105] Iteration 9600, lr = 1e-06
I0405 10:01:43.743021 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:01:45.355393 30176 solver.cpp:218] Iteration 9612 (2.23922 iter/s, 5.35901s/12 iters), loss = 5.2866
I0405 10:01:45.355446 30176 solver.cpp:237]     Train net output #0: loss = 5.2866 (* 1 = 5.2866 loss)
I0405 10:01:45.355455 30176 sgd_solver.cpp:105] Iteration 9612, lr = 1e-06
I0405 10:01:50.940547 30176 solver.cpp:218] Iteration 9624 (2.1486 iter/s, 5.58504s/12 iters), loss = 5.27807
I0405 10:01:50.940596 30176 solver.cpp:237]     Train net output #0: loss = 5.27807 (* 1 = 5.27807 loss)
I0405 10:01:50.940601 30176 sgd_solver.cpp:105] Iteration 9624, lr = 1e-06
I0405 10:01:56.261919 30176 solver.cpp:218] Iteration 9636 (2.2551 iter/s, 5.32127s/12 iters), loss = 5.27764
I0405 10:01:56.261957 30176 solver.cpp:237]     Train net output #0: loss = 5.27764 (* 1 = 5.27764 loss)
I0405 10:01:56.261962 30176 sgd_solver.cpp:105] Iteration 9636, lr = 1e-06
I0405 10:02:01.536465 30176 solver.cpp:218] Iteration 9648 (2.27512 iter/s, 5.27445s/12 iters), loss = 5.28984
I0405 10:02:01.536550 30176 solver.cpp:237]     Train net output #0: loss = 5.28984 (* 1 = 5.28984 loss)
I0405 10:02:01.536556 30176 sgd_solver.cpp:105] Iteration 9648, lr = 1e-06
I0405 10:02:07.079061 30176 solver.cpp:218] Iteration 9660 (2.1651 iter/s, 5.54246s/12 iters), loss = 5.27805
I0405 10:02:07.079107 30176 solver.cpp:237]     Train net output #0: loss = 5.27805 (* 1 = 5.27805 loss)
I0405 10:02:07.079115 30176 sgd_solver.cpp:105] Iteration 9660, lr = 1e-06
I0405 10:02:12.569077 30176 solver.cpp:218] Iteration 9672 (2.18583 iter/s, 5.48991s/12 iters), loss = 5.26535
I0405 10:02:12.569133 30176 solver.cpp:237]     Train net output #0: loss = 5.26535 (* 1 = 5.26535 loss)
I0405 10:02:12.569141 30176 sgd_solver.cpp:105] Iteration 9672, lr = 1e-06
I0405 10:02:18.054250 30176 solver.cpp:218] Iteration 9684 (2.18776 iter/s, 5.48506s/12 iters), loss = 5.30003
I0405 10:02:18.054303 30176 solver.cpp:237]     Train net output #0: loss = 5.30003 (* 1 = 5.30003 loss)
I0405 10:02:18.054311 30176 sgd_solver.cpp:105] Iteration 9684, lr = 1e-06
I0405 10:02:20.188902 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9690.caffemodel
I0405 10:02:23.181031 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9690.solverstate
I0405 10:02:25.503762 30176 solver.cpp:330] Iteration 9690, Testing net (#0)
I0405 10:02:25.503787 30176 net.cpp:676] Ignoring source layer train-data
I0405 10:02:26.049072 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:02:28.999557 30176 blocking_queue.cpp:49] Waiting for data
I0405 10:02:29.979414 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 10:02:29.979452 30176 solver.cpp:397]     Test net output #1: loss = 5.27993 (* 1 = 5.27993 loss)
I0405 10:02:31.979425 30176 solver.cpp:218] Iteration 9696 (0.861759 iter/s, 13.925s/12 iters), loss = 5.28506
I0405 10:02:31.979598 30176 solver.cpp:237]     Train net output #0: loss = 5.28506 (* 1 = 5.28506 loss)
I0405 10:02:31.979607 30176 sgd_solver.cpp:105] Iteration 9696, lr = 1e-06
I0405 10:02:37.204106 30176 solver.cpp:218] Iteration 9708 (2.29689 iter/s, 5.22446s/12 iters), loss = 5.28466
I0405 10:02:37.204147 30176 solver.cpp:237]     Train net output #0: loss = 5.28466 (* 1 = 5.28466 loss)
I0405 10:02:37.204154 30176 sgd_solver.cpp:105] Iteration 9708, lr = 1e-06
I0405 10:02:37.963569 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:02:42.491564 30176 solver.cpp:218] Iteration 9720 (2.26956 iter/s, 5.28736s/12 iters), loss = 5.29134
I0405 10:02:42.491612 30176 solver.cpp:237]     Train net output #0: loss = 5.29134 (* 1 = 5.29134 loss)
I0405 10:02:42.491619 30176 sgd_solver.cpp:105] Iteration 9720, lr = 1e-06
I0405 10:02:47.727632 30176 solver.cpp:218] Iteration 9732 (2.29184 iter/s, 5.23597s/12 iters), loss = 5.28489
I0405 10:02:47.727670 30176 solver.cpp:237]     Train net output #0: loss = 5.28489 (* 1 = 5.28489 loss)
I0405 10:02:47.727676 30176 sgd_solver.cpp:105] Iteration 9732, lr = 1e-06
I0405 10:02:53.231403 30176 solver.cpp:218] Iteration 9744 (2.18036 iter/s, 5.50367s/12 iters), loss = 5.28116
I0405 10:02:53.231464 30176 solver.cpp:237]     Train net output #0: loss = 5.28116 (* 1 = 5.28116 loss)
I0405 10:02:53.231475 30176 sgd_solver.cpp:105] Iteration 9744, lr = 1e-06
I0405 10:02:58.426658 30176 solver.cpp:218] Iteration 9756 (2.30985 iter/s, 5.19514s/12 iters), loss = 5.2844
I0405 10:02:58.426709 30176 solver.cpp:237]     Train net output #0: loss = 5.2844 (* 1 = 5.2844 loss)
I0405 10:02:58.426717 30176 sgd_solver.cpp:105] Iteration 9756, lr = 1e-06
I0405 10:03:03.795701 30176 solver.cpp:218] Iteration 9768 (2.23508 iter/s, 5.36893s/12 iters), loss = 5.28096
I0405 10:03:03.795830 30176 solver.cpp:237]     Train net output #0: loss = 5.28096 (* 1 = 5.28096 loss)
I0405 10:03:03.795840 30176 sgd_solver.cpp:105] Iteration 9768, lr = 1e-06
I0405 10:03:09.150367 30176 solver.cpp:218] Iteration 9780 (2.24111 iter/s, 5.35448s/12 iters), loss = 5.27587
I0405 10:03:09.150421 30176 solver.cpp:237]     Train net output #0: loss = 5.27587 (* 1 = 5.27587 loss)
I0405 10:03:09.150430 30176 sgd_solver.cpp:105] Iteration 9780, lr = 1e-06
I0405 10:03:14.202874 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9792.caffemodel
I0405 10:03:17.269731 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9792.solverstate
I0405 10:03:19.605078 30176 solver.cpp:330] Iteration 9792, Testing net (#0)
I0405 10:03:19.605098 30176 net.cpp:676] Ignoring source layer train-data
I0405 10:03:20.228153 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:03:24.412211 30176 solver.cpp:397]     Test net output #0: accuracy = 0.00735294
I0405 10:03:24.412246 30176 solver.cpp:397]     Test net output #1: loss = 5.27975 (* 1 = 5.27975 loss)
I0405 10:03:24.553187 30176 solver.cpp:218] Iteration 9792 (0.779087 iter/s, 15.4026s/12 iters), loss = 5.29694
I0405 10:03:24.553236 30176 solver.cpp:237]     Train net output #0: loss = 5.29694 (* 1 = 5.29694 loss)
I0405 10:03:24.553243 30176 sgd_solver.cpp:105] Iteration 9792, lr = 1e-06
I0405 10:03:29.205178 30176 solver.cpp:218] Iteration 9804 (2.5796 iter/s, 4.65189s/12 iters), loss = 5.2963
I0405 10:03:29.205219 30176 solver.cpp:237]     Train net output #0: loss = 5.2963 (* 1 = 5.2963 loss)
I0405 10:03:29.205225 30176 sgd_solver.cpp:105] Iteration 9804, lr = 1e-06
I0405 10:03:32.244745 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:03:34.468921 30176 solver.cpp:218] Iteration 9816 (2.27979 iter/s, 5.26365s/12 iters), loss = 5.27934
I0405 10:03:34.469053 30176 solver.cpp:237]     Train net output #0: loss = 5.27934 (* 1 = 5.27934 loss)
I0405 10:03:34.469060 30176 sgd_solver.cpp:105] Iteration 9816, lr = 1e-06
I0405 10:03:39.877894 30176 solver.cpp:218] Iteration 9828 (2.21861 iter/s, 5.40879s/12 iters), loss = 5.28881
I0405 10:03:39.877940 30176 solver.cpp:237]     Train net output #0: loss = 5.28881 (* 1 = 5.28881 loss)
I0405 10:03:39.877946 30176 sgd_solver.cpp:105] Iteration 9828, lr = 1e-06
I0405 10:03:45.343861 30176 solver.cpp:218] Iteration 9840 (2.19544 iter/s, 5.46587s/12 iters), loss = 5.28335
I0405 10:03:45.343901 30176 solver.cpp:237]     Train net output #0: loss = 5.28335 (* 1 = 5.28335 loss)
I0405 10:03:45.343906 30176 sgd_solver.cpp:105] Iteration 9840, lr = 1e-06
I0405 10:03:50.681005 30176 solver.cpp:218] Iteration 9852 (2.24843 iter/s, 5.33705s/12 iters), loss = 5.29039
I0405 10:03:50.681057 30176 solver.cpp:237]     Train net output #0: loss = 5.29039 (* 1 = 5.29039 loss)
I0405 10:03:50.681066 30176 sgd_solver.cpp:105] Iteration 9852, lr = 1e-06
I0405 10:03:55.974234 30176 solver.cpp:218] Iteration 9864 (2.26709 iter/s, 5.29312s/12 iters), loss = 5.29244
I0405 10:03:55.974279 30176 solver.cpp:237]     Train net output #0: loss = 5.29244 (* 1 = 5.29244 loss)
I0405 10:03:55.974284 30176 sgd_solver.cpp:105] Iteration 9864, lr = 1e-06
I0405 10:04:01.141618 30176 solver.cpp:218] Iteration 9876 (2.3223 iter/s, 5.16729s/12 iters), loss = 5.29752
I0405 10:04:01.141660 30176 solver.cpp:237]     Train net output #0: loss = 5.29752 (* 1 = 5.29752 loss)
I0405 10:04:01.141665 30176 sgd_solver.cpp:105] Iteration 9876, lr = 1e-06
I0405 10:04:06.532142 30176 solver.cpp:218] Iteration 9888 (2.22617 iter/s, 5.39043s/12 iters), loss = 5.29553
I0405 10:04:06.532244 30176 solver.cpp:237]     Train net output #0: loss = 5.29553 (* 1 = 5.29553 loss)
I0405 10:04:06.532251 30176 sgd_solver.cpp:105] Iteration 9888, lr = 1e-06
I0405 10:04:08.756520 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9894.caffemodel
I0405 10:04:11.801733 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9894.solverstate
I0405 10:04:14.127712 30176 solver.cpp:330] Iteration 9894, Testing net (#0)
I0405 10:04:14.127730 30176 net.cpp:676] Ignoring source layer train-data
I0405 10:04:14.643203 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:04:18.683162 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 10:04:18.683197 30176 solver.cpp:397]     Test net output #1: loss = 5.28 (* 1 = 5.28 loss)
I0405 10:04:20.785650 30176 solver.cpp:218] Iteration 9900 (0.841911 iter/s, 14.2533s/12 iters), loss = 5.30631
I0405 10:04:20.785691 30176 solver.cpp:237]     Train net output #0: loss = 5.30631 (* 1 = 5.30631 loss)
I0405 10:04:20.785696 30176 sgd_solver.cpp:105] Iteration 9900, lr = 1e-06
I0405 10:04:26.124096 30176 solver.cpp:218] Iteration 9912 (2.24789 iter/s, 5.33835s/12 iters), loss = 5.28274
I0405 10:04:26.124146 30176 solver.cpp:237]     Train net output #0: loss = 5.28274 (* 1 = 5.28274 loss)
I0405 10:04:26.124157 30176 sgd_solver.cpp:105] Iteration 9912, lr = 1e-06
I0405 10:04:26.229543 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:04:31.639057 30176 solver.cpp:218] Iteration 9924 (2.17594 iter/s, 5.51486s/12 iters), loss = 5.29217
I0405 10:04:31.639099 30176 solver.cpp:237]     Train net output #0: loss = 5.29217 (* 1 = 5.29217 loss)
I0405 10:04:31.639104 30176 sgd_solver.cpp:105] Iteration 9924, lr = 1e-06
I0405 10:04:37.062543 30176 solver.cpp:218] Iteration 9936 (2.21264 iter/s, 5.42338s/12 iters), loss = 5.29522
I0405 10:04:37.062664 30176 solver.cpp:237]     Train net output #0: loss = 5.29522 (* 1 = 5.29522 loss)
I0405 10:04:37.062669 30176 sgd_solver.cpp:105] Iteration 9936, lr = 1e-06
I0405 10:04:42.340864 30176 solver.cpp:218] Iteration 9948 (2.27353 iter/s, 5.27815s/12 iters), loss = 5.26815
I0405 10:04:42.340925 30176 solver.cpp:237]     Train net output #0: loss = 5.26815 (* 1 = 5.26815 loss)
I0405 10:04:42.340935 30176 sgd_solver.cpp:105] Iteration 9948, lr = 1e-06
I0405 10:04:47.911181 30176 solver.cpp:218] Iteration 9960 (2.15432 iter/s, 5.57021s/12 iters), loss = 5.26542
I0405 10:04:47.911227 30176 solver.cpp:237]     Train net output #0: loss = 5.26542 (* 1 = 5.26542 loss)
I0405 10:04:47.911232 30176 sgd_solver.cpp:105] Iteration 9960, lr = 1e-06
I0405 10:04:53.034567 30176 solver.cpp:218] Iteration 9972 (2.34225 iter/s, 5.12328s/12 iters), loss = 5.26906
I0405 10:04:53.034622 30176 solver.cpp:237]     Train net output #0: loss = 5.26906 (* 1 = 5.26906 loss)
I0405 10:04:53.034631 30176 sgd_solver.cpp:105] Iteration 9972, lr = 1e-06
I0405 10:04:58.297978 30176 solver.cpp:218] Iteration 9984 (2.27994 iter/s, 5.26331s/12 iters), loss = 5.27302
I0405 10:04:58.298022 30176 solver.cpp:237]     Train net output #0: loss = 5.27302 (* 1 = 5.27302 loss)
I0405 10:04:58.298029 30176 sgd_solver.cpp:105] Iteration 9984, lr = 1e-06
I0405 10:05:03.088212 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9996.caffemodel
I0405 10:05:06.929841 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9996.solverstate
I0405 10:05:09.235095 30176 solver.cpp:330] Iteration 9996, Testing net (#0)
I0405 10:05:09.235194 30176 net.cpp:676] Ignoring source layer train-data
I0405 10:05:09.665683 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:05:13.761337 30176 solver.cpp:397]     Test net output #0: accuracy = 0.00735294
I0405 10:05:13.761370 30176 solver.cpp:397]     Test net output #1: loss = 5.27984 (* 1 = 5.27984 loss)
I0405 10:05:13.902240 30176 solver.cpp:218] Iteration 9996 (0.769029 iter/s, 15.6041s/12 iters), loss = 5.29121
I0405 10:05:13.902295 30176 solver.cpp:237]     Train net output #0: loss = 5.29121 (* 1 = 5.29121 loss)
I0405 10:05:13.902303 30176 sgd_solver.cpp:105] Iteration 9996, lr = 1e-06
I0405 10:05:18.283388 30176 solver.cpp:218] Iteration 10008 (2.73907 iter/s, 4.38105s/12 iters), loss = 5.28767
I0405 10:05:18.283433 30176 solver.cpp:237]     Train net output #0: loss = 5.28767 (* 1 = 5.28767 loss)
I0405 10:05:18.283440 30176 sgd_solver.cpp:105] Iteration 10008, lr = 1e-06
I0405 10:05:20.753991 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:05:23.849434 30176 solver.cpp:218] Iteration 10020 (2.15597 iter/s, 5.56595s/12 iters), loss = 5.30071
I0405 10:05:23.849476 30176 solver.cpp:237]     Train net output #0: loss = 5.30071 (* 1 = 5.30071 loss)
I0405 10:05:23.849481 30176 sgd_solver.cpp:105] Iteration 10020, lr = 1e-06
I0405 10:05:29.223975 30176 solver.cpp:218] Iteration 10032 (2.23279 iter/s, 5.37444s/12 iters), loss = 5.26993
I0405 10:05:29.224018 30176 solver.cpp:237]     Train net output #0: loss = 5.26993 (* 1 = 5.26993 loss)
I0405 10:05:29.224025 30176 sgd_solver.cpp:105] Iteration 10032, lr = 1e-06
I0405 10:05:34.554595 30176 solver.cpp:218] Iteration 10044 (2.25119 iter/s, 5.33051s/12 iters), loss = 5.29041
I0405 10:05:34.554653 30176 solver.cpp:237]     Train net output #0: loss = 5.29041 (* 1 = 5.29041 loss)
I0405 10:05:34.554661 30176 sgd_solver.cpp:105] Iteration 10044, lr = 1e-06
I0405 10:05:39.770402 30176 solver.cpp:218] Iteration 10056 (2.30075 iter/s, 5.21569s/12 iters), loss = 5.29644
I0405 10:05:39.770539 30176 solver.cpp:237]     Train net output #0: loss = 5.29644 (* 1 = 5.29644 loss)
I0405 10:05:39.770550 30176 sgd_solver.cpp:105] Iteration 10056, lr = 1e-06
I0405 10:05:45.102309 30176 solver.cpp:218] Iteration 10068 (2.25068 iter/s, 5.33171s/12 iters), loss = 5.2742
I0405 10:05:45.102372 30176 solver.cpp:237]     Train net output #0: loss = 5.2742 (* 1 = 5.2742 loss)
I0405 10:05:45.102381 30176 sgd_solver.cpp:105] Iteration 10068, lr = 1e-06
I0405 10:05:50.612740 30176 solver.cpp:218] Iteration 10080 (2.17773 iter/s, 5.51032s/12 iters), loss = 5.28119
I0405 10:05:50.612776 30176 solver.cpp:237]     Train net output #0: loss = 5.28119 (* 1 = 5.28119 loss)
I0405 10:05:50.612782 30176 sgd_solver.cpp:105] Iteration 10080, lr = 1e-06
I0405 10:05:55.977003 30176 solver.cpp:218] Iteration 10092 (2.23706 iter/s, 5.36417s/12 iters), loss = 5.30006
I0405 10:05:55.977043 30176 solver.cpp:237]     Train net output #0: loss = 5.30006 (* 1 = 5.30006 loss)
I0405 10:05:55.977049 30176 sgd_solver.cpp:105] Iteration 10092, lr = 1e-06
I0405 10:05:58.106513 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10098.caffemodel
I0405 10:06:01.188589 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10098.solverstate
I0405 10:06:03.485721 30176 solver.cpp:330] Iteration 10098, Testing net (#0)
I0405 10:06:03.485743 30176 net.cpp:676] Ignoring source layer train-data
I0405 10:06:04.021468 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:06:08.425793 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 10:06:08.425828 30176 solver.cpp:397]     Test net output #1: loss = 5.28 (* 1 = 5.28 loss)
I0405 10:06:10.368580 30176 solver.cpp:218] Iteration 10104 (0.83383 iter/s, 14.3914s/12 iters), loss = 5.29748
I0405 10:06:10.368746 30176 solver.cpp:237]     Train net output #0: loss = 5.29748 (* 1 = 5.29748 loss)
I0405 10:06:10.368755 30176 sgd_solver.cpp:105] Iteration 10104, lr = 1e-06
I0405 10:06:15.435094 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:06:16.142745 30176 solver.cpp:218] Iteration 10116 (2.0783 iter/s, 5.77395s/12 iters), loss = 5.27351
I0405 10:06:16.142783 30176 solver.cpp:237]     Train net output #0: loss = 5.27351 (* 1 = 5.27351 loss)
I0405 10:06:16.142788 30176 sgd_solver.cpp:105] Iteration 10116, lr = 1e-06
I0405 10:06:21.454056 30176 solver.cpp:218] Iteration 10128 (2.25937 iter/s, 5.31122s/12 iters), loss = 5.28009
I0405 10:06:21.454098 30176 solver.cpp:237]     Train net output #0: loss = 5.28009 (* 1 = 5.28009 loss)
I0405 10:06:21.454104 30176 sgd_solver.cpp:105] Iteration 10128, lr = 1e-06
I0405 10:06:26.935600 30176 solver.cpp:218] Iteration 10140 (2.1892 iter/s, 5.48145s/12 iters), loss = 5.27236
I0405 10:06:26.935650 30176 solver.cpp:237]     Train net output #0: loss = 5.27236 (* 1 = 5.27236 loss)
I0405 10:06:26.935657 30176 sgd_solver.cpp:105] Iteration 10140, lr = 1e-06
I0405 10:06:32.338244 30176 solver.cpp:218] Iteration 10152 (2.22118 iter/s, 5.40254s/12 iters), loss = 5.27867
I0405 10:06:32.338290 30176 solver.cpp:237]     Train net output #0: loss = 5.27867 (* 1 = 5.27867 loss)
I0405 10:06:32.338297 30176 sgd_solver.cpp:105] Iteration 10152, lr = 1e-06
I0405 10:06:37.582044 30176 solver.cpp:218] Iteration 10164 (2.28846 iter/s, 5.2437s/12 iters), loss = 5.27921
I0405 10:06:37.582087 30176 solver.cpp:237]     Train net output #0: loss = 5.27921 (* 1 = 5.27921 loss)
I0405 10:06:37.582093 30176 sgd_solver.cpp:105] Iteration 10164, lr = 1e-06
I0405 10:06:42.912681 30176 solver.cpp:218] Iteration 10176 (2.25118 iter/s, 5.33054s/12 iters), loss = 5.28626
I0405 10:06:42.912806 30176 solver.cpp:237]     Train net output #0: loss = 5.28626 (* 1 = 5.28626 loss)
I0405 10:06:42.912814 30176 sgd_solver.cpp:105] Iteration 10176, lr = 1e-06
I0405 10:06:48.350751 30176 solver.cpp:218] Iteration 10188 (2.20674 iter/s, 5.43789s/12 iters), loss = 5.28906
I0405 10:06:48.350801 30176 solver.cpp:237]     Train net output #0: loss = 5.28906 (* 1 = 5.28906 loss)
I0405 10:06:48.350811 30176 sgd_solver.cpp:105] Iteration 10188, lr = 1e-06
I0405 10:06:53.140564 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10200.caffemodel
I0405 10:06:56.145468 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10200.solverstate
I0405 10:06:59.217111 30176 solver.cpp:330] Iteration 10200, Testing net (#0)
I0405 10:06:59.217135 30176 net.cpp:676] Ignoring source layer train-data
I0405 10:06:59.620118 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:07:03.905019 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 10:07:03.905059 30176 solver.cpp:397]     Test net output #1: loss = 5.27973 (* 1 = 5.27973 loss)
I0405 10:07:04.046104 30176 solver.cpp:218] Iteration 10200 (0.764566 iter/s, 15.6952s/12 iters), loss = 5.29353
I0405 10:07:04.046155 30176 solver.cpp:237]     Train net output #0: loss = 5.29353 (* 1 = 5.29353 loss)
I0405 10:07:04.046164 30176 sgd_solver.cpp:105] Iteration 10200, lr = 1e-06
I0405 10:07:08.519698 30176 solver.cpp:218] Iteration 10212 (2.68246 iter/s, 4.4735s/12 iters), loss = 5.28156
I0405 10:07:08.519734 30176 solver.cpp:237]     Train net output #0: loss = 5.28156 (* 1 = 5.28156 loss)
I0405 10:07:08.519739 30176 sgd_solver.cpp:105] Iteration 10212, lr = 1e-06
I0405 10:07:10.133296 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:07:13.898715 30176 solver.cpp:218] Iteration 10224 (2.23093 iter/s, 5.37892s/12 iters), loss = 5.2798
I0405 10:07:13.898857 30176 solver.cpp:237]     Train net output #0: loss = 5.2798 (* 1 = 5.2798 loss)
I0405 10:07:13.898867 30176 sgd_solver.cpp:105] Iteration 10224, lr = 1e-06
I0405 10:07:19.195988 30176 solver.cpp:218] Iteration 10236 (2.2654 iter/s, 5.29707s/12 iters), loss = 5.27842
I0405 10:07:19.196050 30176 solver.cpp:237]     Train net output #0: loss = 5.27842 (* 1 = 5.27842 loss)
I0405 10:07:19.196059 30176 sgd_solver.cpp:105] Iteration 10236, lr = 1e-06
I0405 10:07:24.674002 30176 solver.cpp:218] Iteration 10248 (2.19062 iter/s, 5.4779s/12 iters), loss = 5.27576
I0405 10:07:24.674044 30176 solver.cpp:237]     Train net output #0: loss = 5.27576 (* 1 = 5.27576 loss)
I0405 10:07:24.674050 30176 sgd_solver.cpp:105] Iteration 10248, lr = 1e-06
I0405 10:07:29.981390 30176 solver.cpp:218] Iteration 10260 (2.26104 iter/s, 5.30729s/12 iters), loss = 5.27768
I0405 10:07:29.981451 30176 solver.cpp:237]     Train net output #0: loss = 5.27768 (* 1 = 5.27768 loss)
I0405 10:07:29.981459 30176 sgd_solver.cpp:105] Iteration 10260, lr = 1e-06
I0405 10:07:35.243711 30176 solver.cpp:218] Iteration 10272 (2.28041 iter/s, 5.26221s/12 iters), loss = 5.27851
I0405 10:07:35.243762 30176 solver.cpp:237]     Train net output #0: loss = 5.27851 (* 1 = 5.27851 loss)
I0405 10:07:35.243769 30176 sgd_solver.cpp:105] Iteration 10272, lr = 1e-06
I0405 10:07:40.595046 30176 solver.cpp:218] Iteration 10284 (2.24247 iter/s, 5.35123s/12 iters), loss = 5.28937
I0405 10:07:40.595098 30176 solver.cpp:237]     Train net output #0: loss = 5.28937 (* 1 = 5.28937 loss)
I0405 10:07:40.595106 30176 sgd_solver.cpp:105] Iteration 10284, lr = 1e-06
I0405 10:07:45.787786 30176 solver.cpp:218] Iteration 10296 (2.31096 iter/s, 5.19264s/12 iters), loss = 5.27561
I0405 10:07:45.787885 30176 solver.cpp:237]     Train net output #0: loss = 5.27561 (* 1 = 5.27561 loss)
I0405 10:07:45.787892 30176 sgd_solver.cpp:105] Iteration 10296, lr = 1e-06
I0405 10:07:47.874935 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10302.caffemodel
I0405 10:07:50.691442 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10302.solverstate
I0405 10:07:53.014151 30176 solver.cpp:330] Iteration 10302, Testing net (#0)
I0405 10:07:53.014173 30176 net.cpp:676] Ignoring source layer train-data
I0405 10:07:53.362115 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:07:57.461618 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 10:07:57.461652 30176 solver.cpp:397]     Test net output #1: loss = 5.28012 (* 1 = 5.28012 loss)
I0405 10:07:59.393396 30176 solver.cpp:218] Iteration 10308 (0.882003 iter/s, 13.6054s/12 iters), loss = 5.29065
I0405 10:07:59.393455 30176 solver.cpp:237]     Train net output #0: loss = 5.29065 (* 1 = 5.29065 loss)
I0405 10:07:59.393463 30176 sgd_solver.cpp:105] Iteration 10308, lr = 1e-06
I0405 10:08:03.310665 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:08:04.963935 30176 solver.cpp:218] Iteration 10320 (2.15423 iter/s, 5.57043s/12 iters), loss = 5.28906
I0405 10:08:04.963981 30176 solver.cpp:237]     Train net output #0: loss = 5.28906 (* 1 = 5.28906 loss)
I0405 10:08:04.963986 30176 sgd_solver.cpp:105] Iteration 10320, lr = 1e-06
I0405 10:08:10.452713 30176 solver.cpp:218] Iteration 10332 (2.18632 iter/s, 5.48868s/12 iters), loss = 5.28045
I0405 10:08:10.452755 30176 solver.cpp:237]     Train net output #0: loss = 5.28045 (* 1 = 5.28045 loss)
I0405 10:08:10.452760 30176 sgd_solver.cpp:105] Iteration 10332, lr = 1e-06
I0405 10:08:15.842312 30176 solver.cpp:218] Iteration 10344 (2.22655 iter/s, 5.3895s/12 iters), loss = 5.29279
I0405 10:08:15.844929 30176 solver.cpp:237]     Train net output #0: loss = 5.29279 (* 1 = 5.29279 loss)
I0405 10:08:15.844939 30176 sgd_solver.cpp:105] Iteration 10344, lr = 1e-06
I0405 10:08:21.161357 30176 solver.cpp:218] Iteration 10356 (2.25717 iter/s, 5.31638s/12 iters), loss = 5.2986
I0405 10:08:21.161398 30176 solver.cpp:237]     Train net output #0: loss = 5.2986 (* 1 = 5.2986 loss)
I0405 10:08:21.161403 30176 sgd_solver.cpp:105] Iteration 10356, lr = 1e-06
I0405 10:08:26.524363 30176 solver.cpp:218] Iteration 10368 (2.23759 iter/s, 5.3629s/12 iters), loss = 5.27915
I0405 10:08:26.524413 30176 solver.cpp:237]     Train net output #0: loss = 5.27915 (* 1 = 5.27915 loss)
I0405 10:08:26.524421 30176 sgd_solver.cpp:105] Iteration 10368, lr = 1e-06
I0405 10:08:31.895993 30176 solver.cpp:218] Iteration 10380 (2.234 iter/s, 5.37152s/12 iters), loss = 5.27646
I0405 10:08:31.896054 30176 solver.cpp:237]     Train net output #0: loss = 5.27646 (* 1 = 5.27646 loss)
I0405 10:08:31.896064 30176 sgd_solver.cpp:105] Iteration 10380, lr = 1e-06
I0405 10:08:37.066838 30176 solver.cpp:218] Iteration 10392 (2.32075 iter/s, 5.17074s/12 iters), loss = 5.28906
I0405 10:08:37.066881 30176 solver.cpp:237]     Train net output #0: loss = 5.28906 (* 1 = 5.28906 loss)
I0405 10:08:37.066887 30176 sgd_solver.cpp:105] Iteration 10392, lr = 1e-06
I0405 10:08:41.823860 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10404.caffemodel
I0405 10:08:44.839143 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10404.solverstate
I0405 10:08:47.168152 30176 solver.cpp:330] Iteration 10404, Testing net (#0)
I0405 10:08:47.168215 30176 net.cpp:676] Ignoring source layer train-data
I0405 10:08:47.498860 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:08:47.994977 30176 blocking_queue.cpp:49] Waiting for data
I0405 10:08:51.811524 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 10:08:51.811561 30176 solver.cpp:397]     Test net output #1: loss = 5.28014 (* 1 = 5.28014 loss)
I0405 10:08:51.960834 30176 solver.cpp:218] Iteration 10404 (0.805703 iter/s, 14.8938s/12 iters), loss = 5.28833
I0405 10:08:51.960902 30176 solver.cpp:237]     Train net output #0: loss = 5.28833 (* 1 = 5.28833 loss)
I0405 10:08:51.960911 30176 sgd_solver.cpp:105] Iteration 10404, lr = 1e-06
I0405 10:08:56.245409 30176 solver.cpp:218] Iteration 10416 (2.80082 iter/s, 4.28446s/12 iters), loss = 5.28139
I0405 10:08:56.245453 30176 solver.cpp:237]     Train net output #0: loss = 5.28139 (* 1 = 5.28139 loss)
I0405 10:08:56.245460 30176 sgd_solver.cpp:105] Iteration 10416, lr = 1e-06
I0405 10:08:57.172015 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:09:01.552003 30176 solver.cpp:218] Iteration 10428 (2.26138 iter/s, 5.3065s/12 iters), loss = 5.28061
I0405 10:09:01.552043 30176 solver.cpp:237]     Train net output #0: loss = 5.28061 (* 1 = 5.28061 loss)
I0405 10:09:01.552048 30176 sgd_solver.cpp:105] Iteration 10428, lr = 1e-06
I0405 10:09:06.868983 30176 solver.cpp:218] Iteration 10440 (2.25696 iter/s, 5.31688s/12 iters), loss = 5.2764
I0405 10:09:06.869032 30176 solver.cpp:237]     Train net output #0: loss = 5.2764 (* 1 = 5.2764 loss)
I0405 10:09:06.869037 30176 sgd_solver.cpp:105] Iteration 10440, lr = 1e-06
I0405 10:09:12.345137 30176 solver.cpp:218] Iteration 10452 (2.19136 iter/s, 5.47605s/12 iters), loss = 5.2833
I0405 10:09:12.345182 30176 solver.cpp:237]     Train net output #0: loss = 5.2833 (* 1 = 5.2833 loss)
I0405 10:09:12.345188 30176 sgd_solver.cpp:105] Iteration 10452, lr = 1e-06
I0405 10:09:17.692898 30176 solver.cpp:218] Iteration 10464 (2.24398 iter/s, 5.34765s/12 iters), loss = 5.27892
I0405 10:09:17.693061 30176 solver.cpp:237]     Train net output #0: loss = 5.27892 (* 1 = 5.27892 loss)
I0405 10:09:17.693070 30176 sgd_solver.cpp:105] Iteration 10464, lr = 1e-06
I0405 10:09:23.320027 30176 solver.cpp:218] Iteration 10476 (2.13261 iter/s, 5.62691s/12 iters), loss = 5.27837
I0405 10:09:23.320086 30176 solver.cpp:237]     Train net output #0: loss = 5.27837 (* 1 = 5.27837 loss)
I0405 10:09:23.320094 30176 sgd_solver.cpp:105] Iteration 10476, lr = 1e-06
I0405 10:09:28.806452 30176 solver.cpp:218] Iteration 10488 (2.18726 iter/s, 5.48632s/12 iters), loss = 5.28328
I0405 10:09:28.806490 30176 solver.cpp:237]     Train net output #0: loss = 5.28328 (* 1 = 5.28328 loss)
I0405 10:09:28.806495 30176 sgd_solver.cpp:105] Iteration 10488, lr = 1e-06
I0405 10:09:34.128314 30176 solver.cpp:218] Iteration 10500 (2.25489 iter/s, 5.32177s/12 iters), loss = 5.3
I0405 10:09:34.128355 30176 solver.cpp:237]     Train net output #0: loss = 5.3 (* 1 = 5.3 loss)
I0405 10:09:34.128360 30176 sgd_solver.cpp:105] Iteration 10500, lr = 1e-06
I0405 10:09:36.396673 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10506.caffemodel
I0405 10:09:39.404170 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10506.solverstate
I0405 10:09:41.706672 30176 solver.cpp:330] Iteration 10506, Testing net (#0)
I0405 10:09:41.706691 30176 net.cpp:676] Ignoring source layer train-data
I0405 10:09:41.990393 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:09:46.183699 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 10:09:46.183737 30176 solver.cpp:397]     Test net output #1: loss = 5.27962 (* 1 = 5.27962 loss)
I0405 10:09:48.264909 30176 solver.cpp:218] Iteration 10512 (0.84887 iter/s, 14.1364s/12 iters), loss = 5.29597
I0405 10:09:48.265008 30176 solver.cpp:237]     Train net output #0: loss = 5.29597 (* 1 = 5.29597 loss)
I0405 10:09:48.265014 30176 sgd_solver.cpp:105] Iteration 10512, lr = 1e-06
I0405 10:09:51.373626 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:09:53.497264 30176 solver.cpp:218] Iteration 10524 (2.29349 iter/s, 5.2322s/12 iters), loss = 5.30412
I0405 10:09:53.497306 30176 solver.cpp:237]     Train net output #0: loss = 5.30412 (* 1 = 5.30412 loss)
I0405 10:09:53.497311 30176 sgd_solver.cpp:105] Iteration 10524, lr = 1e-06
I0405 10:09:58.958901 30176 solver.cpp:218] Iteration 10536 (2.19718 iter/s, 5.46154s/12 iters), loss = 5.2874
I0405 10:09:58.958950 30176 solver.cpp:237]     Train net output #0: loss = 5.2874 (* 1 = 5.2874 loss)
I0405 10:09:58.958958 30176 sgd_solver.cpp:105] Iteration 10536, lr = 1e-06
I0405 10:10:04.576612 30176 solver.cpp:218] Iteration 10548 (2.13614 iter/s, 5.61761s/12 iters), loss = 5.28445
I0405 10:10:04.576653 30176 solver.cpp:237]     Train net output #0: loss = 5.28445 (* 1 = 5.28445 loss)
I0405 10:10:04.576658 30176 sgd_solver.cpp:105] Iteration 10548, lr = 1e-06
I0405 10:10:10.132237 30176 solver.cpp:218] Iteration 10560 (2.16001 iter/s, 5.55553s/12 iters), loss = 5.2948
I0405 10:10:10.132279 30176 solver.cpp:237]     Train net output #0: loss = 5.2948 (* 1 = 5.2948 loss)
I0405 10:10:10.132287 30176 sgd_solver.cpp:105] Iteration 10560, lr = 1e-06
I0405 10:10:15.483011 30176 solver.cpp:218] Iteration 10572 (2.24271 iter/s, 5.35067s/12 iters), loss = 5.29019
I0405 10:10:15.483067 30176 solver.cpp:237]     Train net output #0: loss = 5.29019 (* 1 = 5.29019 loss)
I0405 10:10:15.483072 30176 sgd_solver.cpp:105] Iteration 10572, lr = 1e-06
I0405 10:10:20.933488 30176 solver.cpp:218] Iteration 10584 (2.20169 iter/s, 5.45037s/12 iters), loss = 5.28322
I0405 10:10:20.933641 30176 solver.cpp:237]     Train net output #0: loss = 5.28322 (* 1 = 5.28322 loss)
I0405 10:10:20.933647 30176 sgd_solver.cpp:105] Iteration 10584, lr = 1e-06
I0405 10:10:26.365492 30176 solver.cpp:218] Iteration 10596 (2.20921 iter/s, 5.4318s/12 iters), loss = 5.26845
I0405 10:10:26.365531 30176 solver.cpp:237]     Train net output #0: loss = 5.26845 (* 1 = 5.26845 loss)
I0405 10:10:26.365537 30176 sgd_solver.cpp:105] Iteration 10596, lr = 1e-06
I0405 10:10:31.311668 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10608.caffemodel
I0405 10:10:34.453832 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10608.solverstate
I0405 10:10:36.784219 30176 solver.cpp:330] Iteration 10608, Testing net (#0)
I0405 10:10:36.784243 30176 net.cpp:676] Ignoring source layer train-data
I0405 10:10:37.011812 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:10:41.340961 30176 solver.cpp:397]     Test net output #0: accuracy = 0.00735294
I0405 10:10:41.340996 30176 solver.cpp:397]     Test net output #1: loss = 5.27988 (* 1 = 5.27988 loss)
I0405 10:10:41.481762 30176 solver.cpp:218] Iteration 10608 (0.793855 iter/s, 15.1161s/12 iters), loss = 5.28797
I0405 10:10:41.481802 30176 solver.cpp:237]     Train net output #0: loss = 5.28797 (* 1 = 5.28797 loss)
I0405 10:10:41.481807 30176 sgd_solver.cpp:105] Iteration 10608, lr = 1e-06
I0405 10:10:45.993719 30176 solver.cpp:218] Iteration 10620 (2.65965 iter/s, 4.51186s/12 iters), loss = 5.29519
I0405 10:10:45.993773 30176 solver.cpp:237]     Train net output #0: loss = 5.29519 (* 1 = 5.29519 loss)
I0405 10:10:45.993780 30176 sgd_solver.cpp:105] Iteration 10620, lr = 1e-06
I0405 10:10:46.112229 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:10:51.208387 30176 solver.cpp:218] Iteration 10632 (2.30125 iter/s, 5.21457s/12 iters), loss = 5.28702
I0405 10:10:51.208498 30176 solver.cpp:237]     Train net output #0: loss = 5.28702 (* 1 = 5.28702 loss)
I0405 10:10:51.208504 30176 sgd_solver.cpp:105] Iteration 10632, lr = 1e-06
I0405 10:10:56.612129 30176 solver.cpp:218] Iteration 10644 (2.22075 iter/s, 5.40358s/12 iters), loss = 5.29171
I0405 10:10:56.612172 30176 solver.cpp:237]     Train net output #0: loss = 5.29171 (* 1 = 5.29171 loss)
I0405 10:10:56.612179 30176 sgd_solver.cpp:105] Iteration 10644, lr = 1e-06
I0405 10:11:01.676940 30176 solver.cpp:218] Iteration 10656 (2.36933 iter/s, 5.06472s/12 iters), loss = 5.27563
I0405 10:11:01.676981 30176 solver.cpp:237]     Train net output #0: loss = 5.27563 (* 1 = 5.27563 loss)
I0405 10:11:01.676986 30176 sgd_solver.cpp:105] Iteration 10656, lr = 1e-06
I0405 10:11:06.838735 30176 solver.cpp:218] Iteration 10668 (2.32482 iter/s, 5.1617s/12 iters), loss = 5.26458
I0405 10:11:06.838779 30176 solver.cpp:237]     Train net output #0: loss = 5.26458 (* 1 = 5.26458 loss)
I0405 10:11:06.838784 30176 sgd_solver.cpp:105] Iteration 10668, lr = 1e-06
I0405 10:11:12.504274 30176 solver.cpp:218] Iteration 10680 (2.11811 iter/s, 5.66544s/12 iters), loss = 5.2817
I0405 10:11:12.504318 30176 solver.cpp:237]     Train net output #0: loss = 5.2817 (* 1 = 5.2817 loss)
I0405 10:11:12.504324 30176 sgd_solver.cpp:105] Iteration 10680, lr = 1e-06
I0405 10:11:17.911124 30176 solver.cpp:218] Iteration 10692 (2.21945 iter/s, 5.40675s/12 iters), loss = 5.27607
I0405 10:11:17.911168 30176 solver.cpp:237]     Train net output #0: loss = 5.27607 (* 1 = 5.27607 loss)
I0405 10:11:17.911175 30176 sgd_solver.cpp:105] Iteration 10692, lr = 1e-06
I0405 10:11:23.441643 30176 solver.cpp:218] Iteration 10704 (2.16982 iter/s, 5.53042s/12 iters), loss = 5.28825
I0405 10:11:23.441776 30176 solver.cpp:237]     Train net output #0: loss = 5.28825 (* 1 = 5.28825 loss)
I0405 10:11:23.441783 30176 sgd_solver.cpp:105] Iteration 10704, lr = 1e-06
I0405 10:11:25.656121 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10710.caffemodel
I0405 10:11:28.730808 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10710.solverstate
I0405 10:11:31.070647 30176 solver.cpp:330] Iteration 10710, Testing net (#0)
I0405 10:11:31.070668 30176 net.cpp:676] Ignoring source layer train-data
I0405 10:11:31.255607 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:11:35.582540 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 10:11:35.582578 30176 solver.cpp:397]     Test net output #1: loss = 5.27993 (* 1 = 5.27993 loss)
I0405 10:11:37.534834 30176 solver.cpp:218] Iteration 10716 (0.851489 iter/s, 14.093s/12 iters), loss = 5.27427
I0405 10:11:37.534878 30176 solver.cpp:237]     Train net output #0: loss = 5.27427 (* 1 = 5.27427 loss)
I0405 10:11:37.534883 30176 sgd_solver.cpp:105] Iteration 10716, lr = 1e-06
I0405 10:11:39.966601 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:11:42.808099 30176 solver.cpp:218] Iteration 10728 (2.27567 iter/s, 5.27316s/12 iters), loss = 5.29602
I0405 10:11:42.808151 30176 solver.cpp:237]     Train net output #0: loss = 5.29602 (* 1 = 5.29602 loss)
I0405 10:11:42.808158 30176 sgd_solver.cpp:105] Iteration 10728, lr = 1e-06
I0405 10:11:48.233618 30176 solver.cpp:218] Iteration 10740 (2.21181 iter/s, 5.42542s/12 iters), loss = 5.28464
I0405 10:11:48.233669 30176 solver.cpp:237]     Train net output #0: loss = 5.28464 (* 1 = 5.28464 loss)
I0405 10:11:48.233676 30176 sgd_solver.cpp:105] Iteration 10740, lr = 1e-06
I0405 10:11:53.613695 30176 solver.cpp:218] Iteration 10752 (2.2305 iter/s, 5.37997s/12 iters), loss = 5.27815
I0405 10:11:53.628949 30176 solver.cpp:237]     Train net output #0: loss = 5.27815 (* 1 = 5.27815 loss)
I0405 10:11:53.628962 30176 sgd_solver.cpp:105] Iteration 10752, lr = 1e-06
I0405 10:11:59.052873 30176 solver.cpp:218] Iteration 10764 (2.21244 iter/s, 5.42388s/12 iters), loss = 5.27485
I0405 10:11:59.052942 30176 solver.cpp:237]     Train net output #0: loss = 5.27485 (* 1 = 5.27485 loss)
I0405 10:11:59.052951 30176 sgd_solver.cpp:105] Iteration 10764, lr = 1e-06
I0405 10:12:04.612659 30176 solver.cpp:218] Iteration 10776 (2.1584 iter/s, 5.55967s/12 iters), loss = 5.28445
I0405 10:12:04.612700 30176 solver.cpp:237]     Train net output #0: loss = 5.28445 (* 1 = 5.28445 loss)
I0405 10:12:04.612706 30176 sgd_solver.cpp:105] Iteration 10776, lr = 1e-06
I0405 10:12:10.157508 30176 solver.cpp:218] Iteration 10788 (2.16421 iter/s, 5.54475s/12 iters), loss = 5.27017
I0405 10:12:10.157562 30176 solver.cpp:237]     Train net output #0: loss = 5.27017 (* 1 = 5.27017 loss)
I0405 10:12:10.157572 30176 sgd_solver.cpp:105] Iteration 10788, lr = 1e-06
I0405 10:12:15.807188 30176 solver.cpp:218] Iteration 10800 (2.12405 iter/s, 5.64957s/12 iters), loss = 5.29676
I0405 10:12:15.807240 30176 solver.cpp:237]     Train net output #0: loss = 5.29676 (* 1 = 5.29676 loss)
I0405 10:12:15.807247 30176 sgd_solver.cpp:105] Iteration 10800, lr = 1e-06
I0405 10:12:20.766966 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10812.caffemodel
I0405 10:12:23.786304 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10812.solverstate
I0405 10:12:26.106946 30176 solver.cpp:330] Iteration 10812, Testing net (#0)
I0405 10:12:26.106967 30176 net.cpp:676] Ignoring source layer train-data
I0405 10:12:26.247977 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:12:30.603480 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 10:12:30.603518 30176 solver.cpp:397]     Test net output #1: loss = 5.28004 (* 1 = 5.28004 loss)
I0405 10:12:30.744801 30176 solver.cpp:218] Iteration 10812 (0.803351 iter/s, 14.9374s/12 iters), loss = 5.30528
I0405 10:12:30.746392 30176 solver.cpp:237]     Train net output #0: loss = 5.30528 (* 1 = 5.30528 loss)
I0405 10:12:30.746417 30176 sgd_solver.cpp:105] Iteration 10812, lr = 1e-06
I0405 10:12:34.696326 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:12:35.292896 30176 solver.cpp:218] Iteration 10824 (2.63941 iter/s, 4.54647s/12 iters), loss = 5.29814
I0405 10:12:35.292948 30176 solver.cpp:237]     Train net output #0: loss = 5.29814 (* 1 = 5.29814 loss)
I0405 10:12:35.292955 30176 sgd_solver.cpp:105] Iteration 10824, lr = 1e-06
I0405 10:12:40.782945 30176 solver.cpp:218] Iteration 10836 (2.18581 iter/s, 5.48995s/12 iters), loss = 5.27937
I0405 10:12:40.782994 30176 solver.cpp:237]     Train net output #0: loss = 5.27937 (* 1 = 5.27937 loss)
I0405 10:12:40.783001 30176 sgd_solver.cpp:105] Iteration 10836, lr = 1e-06
I0405 10:12:46.182054 30176 solver.cpp:218] Iteration 10848 (2.22263 iter/s, 5.39901s/12 iters), loss = 5.2737
I0405 10:12:46.182111 30176 solver.cpp:237]     Train net output #0: loss = 5.2737 (* 1 = 5.2737 loss)
I0405 10:12:46.182119 30176 sgd_solver.cpp:105] Iteration 10848, lr = 1e-06
I0405 10:12:51.644826 30176 solver.cpp:218] Iteration 10860 (2.19673 iter/s, 5.46266s/12 iters), loss = 5.28935
I0405 10:12:51.644867 30176 solver.cpp:237]     Train net output #0: loss = 5.28935 (* 1 = 5.28935 loss)
I0405 10:12:51.644872 30176 sgd_solver.cpp:105] Iteration 10860, lr = 1e-06
I0405 10:12:57.004554 30176 solver.cpp:218] Iteration 10872 (2.23896 iter/s, 5.35963s/12 iters), loss = 5.27987
I0405 10:12:57.004688 30176 solver.cpp:237]     Train net output #0: loss = 5.27987 (* 1 = 5.27987 loss)
I0405 10:12:57.004694 30176 sgd_solver.cpp:105] Iteration 10872, lr = 1e-06
I0405 10:13:02.437361 30176 solver.cpp:218] Iteration 10884 (2.20888 iter/s, 5.43262s/12 iters), loss = 5.28699
I0405 10:13:02.437400 30176 solver.cpp:237]     Train net output #0: loss = 5.28699 (* 1 = 5.28699 loss)
I0405 10:13:02.437405 30176 sgd_solver.cpp:105] Iteration 10884, lr = 1e-06
I0405 10:13:07.919093 30176 solver.cpp:218] Iteration 10896 (2.18913 iter/s, 5.48163s/12 iters), loss = 5.27819
I0405 10:13:07.919154 30176 solver.cpp:237]     Train net output #0: loss = 5.27819 (* 1 = 5.27819 loss)
I0405 10:13:07.919163 30176 sgd_solver.cpp:105] Iteration 10896, lr = 1e-06
I0405 10:13:13.206275 30176 solver.cpp:218] Iteration 10908 (2.26969 iter/s, 5.28708s/12 iters), loss = 5.28018
I0405 10:13:13.206313 30176 solver.cpp:237]     Train net output #0: loss = 5.28018 (* 1 = 5.28018 loss)
I0405 10:13:13.206319 30176 sgd_solver.cpp:105] Iteration 10908, lr = 1e-06
I0405 10:13:15.160429 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10914.caffemodel
I0405 10:13:18.358265 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10914.solverstate
I0405 10:13:20.728679 30176 solver.cpp:330] Iteration 10914, Testing net (#0)
I0405 10:13:20.728703 30176 net.cpp:676] Ignoring source layer train-data
I0405 10:13:20.808910 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:13:25.264562 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 10:13:25.264596 30176 solver.cpp:397]     Test net output #1: loss = 5.28004 (* 1 = 5.28004 loss)
I0405 10:13:27.262506 30176 solver.cpp:218] Iteration 10920 (0.853723 iter/s, 14.0561s/12 iters), loss = 5.28121
I0405 10:13:27.262636 30176 solver.cpp:237]     Train net output #0: loss = 5.28121 (* 1 = 5.28121 loss)
I0405 10:13:27.262645 30176 sgd_solver.cpp:105] Iteration 10920, lr = 1e-06
I0405 10:13:28.926970 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:13:32.845502 30176 solver.cpp:218] Iteration 10932 (2.14945 iter/s, 5.58281s/12 iters), loss = 5.26209
I0405 10:13:32.845553 30176 solver.cpp:237]     Train net output #0: loss = 5.26209 (* 1 = 5.26209 loss)
I0405 10:13:32.845561 30176 sgd_solver.cpp:105] Iteration 10932, lr = 1e-06
I0405 10:13:38.244269 30176 solver.cpp:218] Iteration 10944 (2.22277 iter/s, 5.39866s/12 iters), loss = 5.27904
I0405 10:13:38.244310 30176 solver.cpp:237]     Train net output #0: loss = 5.27904 (* 1 = 5.27904 loss)
I0405 10:13:38.244315 30176 sgd_solver.cpp:105] Iteration 10944, lr = 1e-06
I0405 10:13:43.513469 30176 solver.cpp:218] Iteration 10956 (2.27743 iter/s, 5.2691s/12 iters), loss = 5.28953
I0405 10:13:43.513525 30176 solver.cpp:237]     Train net output #0: loss = 5.28953 (* 1 = 5.28953 loss)
I0405 10:13:43.513535 30176 sgd_solver.cpp:105] Iteration 10956, lr = 1e-06
I0405 10:13:48.755733 30176 solver.cpp:218] Iteration 10968 (2.28913 iter/s, 5.24216s/12 iters), loss = 5.27607
I0405 10:13:48.755774 30176 solver.cpp:237]     Train net output #0: loss = 5.27607 (* 1 = 5.27607 loss)
I0405 10:13:48.755780 30176 sgd_solver.cpp:105] Iteration 10968, lr = 1e-06
I0405 10:13:54.245184 30176 solver.cpp:218] Iteration 10980 (2.18605 iter/s, 5.48936s/12 iters), loss = 5.29054
I0405 10:13:54.245220 30176 solver.cpp:237]     Train net output #0: loss = 5.29054 (* 1 = 5.29054 loss)
I0405 10:13:54.245226 30176 sgd_solver.cpp:105] Iteration 10980, lr = 1e-06
I0405 10:13:59.661041 30176 solver.cpp:218] Iteration 10992 (2.21575 iter/s, 5.41577s/12 iters), loss = 5.2934
I0405 10:13:59.661161 30176 solver.cpp:237]     Train net output #0: loss = 5.2934 (* 1 = 5.2934 loss)
I0405 10:13:59.661168 30176 sgd_solver.cpp:105] Iteration 10992, lr = 1e-06
I0405 10:14:05.212961 30176 solver.cpp:218] Iteration 11004 (2.16148 iter/s, 5.55174s/12 iters), loss = 5.282
I0405 10:14:05.213019 30176 solver.cpp:237]     Train net output #0: loss = 5.282 (* 1 = 5.282 loss)
I0405 10:14:05.213028 30176 sgd_solver.cpp:105] Iteration 11004, lr = 1e-06
I0405 10:14:10.030966 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_11016.caffemodel
I0405 10:14:13.158385 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_11016.solverstate
I0405 10:14:15.454900 30176 solver.cpp:330] Iteration 11016, Testing net (#0)
I0405 10:14:15.454919 30176 net.cpp:676] Ignoring source layer train-data
I0405 10:14:15.505075 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:14:20.192706 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 10:14:20.192742 30176 solver.cpp:397]     Test net output #1: loss = 5.27972 (* 1 = 5.27972 loss)
I0405 10:14:20.333812 30176 solver.cpp:218] Iteration 11016 (0.793616 iter/s, 15.1207s/12 iters), loss = 5.28523
I0405 10:14:20.333871 30176 solver.cpp:237]     Train net output #0: loss = 5.28523 (* 1 = 5.28523 loss)
I0405 10:14:20.333879 30176 sgd_solver.cpp:105] Iteration 11016, lr = 1e-06
I0405 10:14:20.897497 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:14:23.589092 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:14:24.909832 30176 solver.cpp:218] Iteration 11028 (2.62243 iter/s, 4.57591s/12 iters), loss = 5.28635
I0405 10:14:24.909885 30176 solver.cpp:237]     Train net output #0: loss = 5.28635 (* 1 = 5.28635 loss)
I0405 10:14:24.909893 30176 sgd_solver.cpp:105] Iteration 11028, lr = 1e-06
I0405 10:14:30.358397 30176 solver.cpp:218] Iteration 11040 (2.20246 iter/s, 5.44846s/12 iters), loss = 5.27404
I0405 10:14:30.358500 30176 solver.cpp:237]     Train net output #0: loss = 5.27404 (* 1 = 5.27404 loss)
I0405 10:14:30.358506 30176 sgd_solver.cpp:105] Iteration 11040, lr = 1e-06
I0405 10:14:35.599949 30176 solver.cpp:218] Iteration 11052 (2.28947 iter/s, 5.24139s/12 iters), loss = 5.27544
I0405 10:14:35.600001 30176 solver.cpp:237]     Train net output #0: loss = 5.27544 (* 1 = 5.27544 loss)
I0405 10:14:35.600010 30176 sgd_solver.cpp:105] Iteration 11052, lr = 1e-06
I0405 10:14:41.010304 30176 solver.cpp:218] Iteration 11064 (2.21801 iter/s, 5.41025s/12 iters), loss = 5.27668
I0405 10:14:41.010342 30176 solver.cpp:237]     Train net output #0: loss = 5.27668 (* 1 = 5.27668 loss)
I0405 10:14:41.010349 30176 sgd_solver.cpp:105] Iteration 11064, lr = 1e-06
I0405 10:14:46.180653 30176 solver.cpp:218] Iteration 11076 (2.32097 iter/s, 5.17026s/12 iters), loss = 5.30365
I0405 10:14:46.180696 30176 solver.cpp:237]     Train net output #0: loss = 5.30365 (* 1 = 5.30365 loss)
I0405 10:14:46.180703 30176 sgd_solver.cpp:105] Iteration 11076, lr = 1e-06
I0405 10:14:51.661301 30176 solver.cpp:218] Iteration 11088 (2.18956 iter/s, 5.48055s/12 iters), loss = 5.27681
I0405 10:14:51.661345 30176 solver.cpp:237]     Train net output #0: loss = 5.27681 (* 1 = 5.27681 loss)
I0405 10:14:51.661350 30176 sgd_solver.cpp:105] Iteration 11088, lr = 1e-06
I0405 10:14:55.236027 30176 blocking_queue.cpp:49] Waiting for data
I0405 10:14:57.123440 30176 solver.cpp:218] Iteration 11100 (2.19698 iter/s, 5.46204s/12 iters), loss = 5.29152
I0405 10:14:57.123492 30176 solver.cpp:237]     Train net output #0: loss = 5.29152 (* 1 = 5.29152 loss)
I0405 10:14:57.123500 30176 sgd_solver.cpp:105] Iteration 11100, lr = 1e-06
I0405 10:15:02.331387 30176 solver.cpp:218] Iteration 11112 (2.30422 iter/s, 5.20784s/12 iters), loss = 5.28795
I0405 10:15:02.331542 30176 solver.cpp:237]     Train net output #0: loss = 5.28795 (* 1 = 5.28795 loss)
I0405 10:15:02.331550 30176 sgd_solver.cpp:105] Iteration 11112, lr = 1e-06
I0405 10:15:04.420516 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_11118.caffemodel
I0405 10:15:07.507623 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_11118.solverstate
I0405 10:15:10.195904 30176 solver.cpp:330] Iteration 11118, Testing net (#0)
I0405 10:15:10.195921 30176 net.cpp:676] Ignoring source layer train-data
I0405 10:15:14.687760 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 10:15:14.687800 30176 solver.cpp:397]     Test net output #1: loss = 5.27987 (* 1 = 5.27987 loss)
I0405 10:15:15.399130 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:15:16.567813 30176 solver.cpp:218] Iteration 11124 (0.842924 iter/s, 14.2362s/12 iters), loss = 5.27999
I0405 10:15:16.567859 30176 solver.cpp:237]     Train net output #0: loss = 5.27999 (* 1 = 5.27999 loss)
I0405 10:15:16.567865 30176 sgd_solver.cpp:105] Iteration 11124, lr = 1e-06
I0405 10:15:17.519269 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:15:21.966154 30176 solver.cpp:218] Iteration 11136 (2.22295 iter/s, 5.39824s/12 iters), loss = 5.29166
I0405 10:15:21.966192 30176 solver.cpp:237]     Train net output #0: loss = 5.29166 (* 1 = 5.29166 loss)
I0405 10:15:21.966197 30176 sgd_solver.cpp:105] Iteration 11136, lr = 1e-06
I0405 10:15:27.270574 30176 solver.cpp:218] Iteration 11148 (2.26231 iter/s, 5.30432s/12 iters), loss = 5.28018
I0405 10:15:27.270628 30176 solver.cpp:237]     Train net output #0: loss = 5.28018 (* 1 = 5.28018 loss)
I0405 10:15:27.270637 30176 sgd_solver.cpp:105] Iteration 11148, lr = 1e-06
I0405 10:15:32.573730 30176 solver.cpp:218] Iteration 11160 (2.26285 iter/s, 5.30305s/12 iters), loss = 5.28353
I0405 10:15:32.573832 30176 solver.cpp:237]     Train net output #0: loss = 5.28353 (* 1 = 5.28353 loss)
I0405 10:15:32.573838 30176 sgd_solver.cpp:105] Iteration 11160, lr = 1e-06
I0405 10:15:38.077361 30176 solver.cpp:218] Iteration 11172 (2.18044 iter/s, 5.50348s/12 iters), loss = 5.28329
I0405 10:15:38.077399 30176 solver.cpp:237]     Train net output #0: loss = 5.28329 (* 1 = 5.28329 loss)
I0405 10:15:38.077404 30176 sgd_solver.cpp:105] Iteration 11172, lr = 1e-06
I0405 10:15:43.569824 30176 solver.cpp:218] Iteration 11184 (2.18485 iter/s, 5.49237s/12 iters), loss = 5.27119
I0405 10:15:43.569880 30176 solver.cpp:237]     Train net output #0: loss = 5.27119 (* 1 = 5.27119 loss)
I0405 10:15:43.569888 30176 sgd_solver.cpp:105] Iteration 11184, lr = 1e-06
I0405 10:15:48.656131 30176 solver.cpp:218] Iteration 11196 (2.35933 iter/s, 5.0862s/12 iters), loss = 5.28319
I0405 10:15:48.656193 30176 solver.cpp:237]     Train net output #0: loss = 5.28319 (* 1 = 5.28319 loss)
I0405 10:15:48.656203 30176 sgd_solver.cpp:105] Iteration 11196, lr = 1e-06
I0405 10:15:54.055033 30176 solver.cpp:218] Iteration 11208 (2.22272 iter/s, 5.39879s/12 iters), loss = 5.29684
I0405 10:15:54.055075 30176 solver.cpp:237]     Train net output #0: loss = 5.29684 (* 1 = 5.29684 loss)
I0405 10:15:54.055081 30176 sgd_solver.cpp:105] Iteration 11208, lr = 1e-06
I0405 10:15:58.924815 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_11220.caffemodel
I0405 10:16:02.109880 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_11220.solverstate
I0405 10:16:05.190184 30176 solver.cpp:330] Iteration 11220, Testing net (#0)
I0405 10:16:05.190244 30176 net.cpp:676] Ignoring source layer train-data
I0405 10:16:09.803225 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 10:16:09.803267 30176 solver.cpp:397]     Test net output #1: loss = 5.27997 (* 1 = 5.27997 loss)
I0405 10:16:09.944200 30176 solver.cpp:218] Iteration 11220 (0.75524 iter/s, 15.889s/12 iters), loss = 5.28207
I0405 10:16:09.944248 30176 solver.cpp:237]     Train net output #0: loss = 5.28207 (* 1 = 5.28207 loss)
I0405 10:16:09.944255 30176 sgd_solver.cpp:105] Iteration 11220, lr = 1e-06
I0405 10:16:10.319403 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:16:12.310961 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:16:14.352651 30176 solver.cpp:218] Iteration 11232 (2.7221 iter/s, 4.40836s/12 iters), loss = 5.3042
I0405 10:16:14.352694 30176 solver.cpp:237]     Train net output #0: loss = 5.3042 (* 1 = 5.3042 loss)
I0405 10:16:14.352699 30176 sgd_solver.cpp:105] Iteration 11232, lr = 1e-06
I0405 10:16:19.542445 30176 solver.cpp:218] Iteration 11244 (2.31228 iter/s, 5.18969s/12 iters), loss = 5.29421
I0405 10:16:19.542501 30176 solver.cpp:237]     Train net output #0: loss = 5.29421 (* 1 = 5.29421 loss)
I0405 10:16:19.542510 30176 sgd_solver.cpp:105] Iteration 11244, lr = 1e-06
I0405 10:16:24.992127 30176 solver.cpp:218] Iteration 11256 (2.20201 iter/s, 5.44957s/12 iters), loss = 5.29304
I0405 10:16:24.992172 30176 solver.cpp:237]     Train net output #0: loss = 5.29304 (* 1 = 5.29304 loss)
I0405 10:16:24.992178 30176 sgd_solver.cpp:105] Iteration 11256, lr = 1e-06
I0405 10:16:30.286044 30176 solver.cpp:218] Iteration 11268 (2.26679 iter/s, 5.29382s/12 iters), loss = 5.29903
I0405 10:16:30.286082 30176 solver.cpp:237]     Train net output #0: loss = 5.29903 (* 1 = 5.29903 loss)
I0405 10:16:30.286087 30176 sgd_solver.cpp:105] Iteration 11268, lr = 1e-06
I0405 10:16:35.531535 30176 solver.cpp:218] Iteration 11280 (2.28772 iter/s, 5.24539s/12 iters), loss = 5.28536
I0405 10:16:35.531715 30176 solver.cpp:237]     Train net output #0: loss = 5.28536 (* 1 = 5.28536 loss)
I0405 10:16:35.531724 30176 sgd_solver.cpp:105] Iteration 11280, lr = 1e-06
I0405 10:16:40.852874 30176 solver.cpp:218] Iteration 11292 (2.25517 iter/s, 5.32111s/12 iters), loss = 5.29738
I0405 10:16:40.852931 30176 solver.cpp:237]     Train net output #0: loss = 5.29738 (* 1 = 5.29738 loss)
I0405 10:16:40.852939 30176 sgd_solver.cpp:105] Iteration 11292, lr = 1e-06
I0405 10:16:46.292181 30176 solver.cpp:218] Iteration 11304 (2.20621 iter/s, 5.4392s/12 iters), loss = 5.28536
I0405 10:16:46.292218 30176 solver.cpp:237]     Train net output #0: loss = 5.28536 (* 1 = 5.28536 loss)
I0405 10:16:46.292223 30176 sgd_solver.cpp:105] Iteration 11304, lr = 1e-06
I0405 10:16:51.970968 30176 solver.cpp:218] Iteration 11316 (2.11316 iter/s, 5.67869s/12 iters), loss = 5.29243
I0405 10:16:51.971014 30176 solver.cpp:237]     Train net output #0: loss = 5.29243 (* 1 = 5.29243 loss)
I0405 10:16:51.971019 30176 sgd_solver.cpp:105] Iteration 11316, lr = 1e-06
I0405 10:16:54.149299 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_11322.caffemodel
I0405 10:16:57.181262 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_11322.solverstate
I0405 10:16:59.492252 30176 solver.cpp:330] Iteration 11322, Testing net (#0)
I0405 10:16:59.492271 30176 net.cpp:676] Ignoring source layer train-data
I0405 10:17:04.070277 30176 solver.cpp:397]     Test net output #0: accuracy = 0.00735294
I0405 10:17:04.070325 30176 solver.cpp:397]     Test net output #1: loss = 5.27965 (* 1 = 5.27965 loss)
I0405 10:17:04.467618 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:17:05.957007 30176 solver.cpp:218] Iteration 11328 (0.858008 iter/s, 13.9859s/12 iters), loss = 5.27911
I0405 10:17:05.957319 30176 solver.cpp:237]     Train net output #0: loss = 5.27911 (* 1 = 5.27911 loss)
I0405 10:17:05.957326 30176 sgd_solver.cpp:105] Iteration 11328, lr = 1e-06
I0405 10:17:06.103433 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:17:11.329208 30176 solver.cpp:218] Iteration 11340 (2.23388 iter/s, 5.37183s/12 iters), loss = 5.29743
I0405 10:17:11.329259 30176 solver.cpp:237]     Train net output #0: loss = 5.29743 (* 1 = 5.29743 loss)
I0405 10:17:11.329267 30176 sgd_solver.cpp:105] Iteration 11340, lr = 1e-06
I0405 10:17:16.735026 30176 solver.cpp:218] Iteration 11352 (2.21987 iter/s, 5.40571s/12 iters), loss = 5.27576
I0405 10:17:16.735085 30176 solver.cpp:237]     Train net output #0: loss = 5.27576 (* 1 = 5.27576 loss)
I0405 10:17:16.735095 30176 sgd_solver.cpp:105] Iteration 11352, lr = 1e-06
I0405 10:17:22.284396 30176 solver.cpp:218] Iteration 11364 (2.16245 iter/s, 5.54926s/12 iters), loss = 5.2836
I0405 10:17:22.284446 30176 solver.cpp:237]     Train net output #0: loss = 5.2836 (* 1 = 5.2836 loss)
I0405 10:17:22.284454 30176 sgd_solver.cpp:105] Iteration 11364, lr = 1e-06
I0405 10:17:27.622489 30176 solver.cpp:218] Iteration 11376 (2.24804 iter/s, 5.33799s/12 iters), loss = 5.27776
I0405 10:17:27.622539 30176 solver.cpp:237]     Train net output #0: loss = 5.27776 (* 1 = 5.27776 loss)
I0405 10:17:27.622546 30176 sgd_solver.cpp:105] Iteration 11376, lr = 1e-06
I0405 10:17:32.947883 30176 solver.cpp:218] Iteration 11388 (2.2534 iter/s, 5.32529s/12 iters), loss = 5.27184
I0405 10:17:32.947926 30176 solver.cpp:237]     Train net output #0: loss = 5.27184 (* 1 = 5.27184 loss)
I0405 10:17:32.947933 30176 sgd_solver.cpp:105] Iteration 11388, lr = 1e-06
I0405 10:17:38.208638 30176 solver.cpp:218] Iteration 11400 (2.28109 iter/s, 5.26065s/12 iters), loss = 5.28314
I0405 10:17:38.208819 30176 solver.cpp:237]     Train net output #0: loss = 5.28314 (* 1 = 5.28314 loss)
I0405 10:17:38.208828 30176 sgd_solver.cpp:105] Iteration 11400, lr = 1e-06
I0405 10:17:43.612203 30176 solver.cpp:218] Iteration 11412 (2.22085 iter/s, 5.40333s/12 iters), loss = 5.27958
I0405 10:17:43.612258 30176 solver.cpp:237]     Train net output #0: loss = 5.27958 (* 1 = 5.27958 loss)
I0405 10:17:43.612267 30176 sgd_solver.cpp:105] Iteration 11412, lr = 1e-06
I0405 10:17:48.595217 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_11424.caffemodel
I0405 10:17:51.647056 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_11424.solverstate
I0405 10:17:53.984406 30176 solver.cpp:330] Iteration 11424, Testing net (#0)
I0405 10:17:53.984432 30176 net.cpp:676] Ignoring source layer train-data
I0405 10:17:58.634094 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 10:17:58.634126 30176 solver.cpp:397]     Test net output #1: loss = 5.27984 (* 1 = 5.27984 loss)
I0405 10:17:58.774821 30176 solver.cpp:218] Iteration 11424 (0.791429 iter/s, 15.1624s/12 iters), loss = 5.29354
I0405 10:17:58.774864 30176 solver.cpp:237]     Train net output #0: loss = 5.29354 (* 1 = 5.29354 loss)
I0405 10:17:58.774869 30176 sgd_solver.cpp:105] Iteration 11424, lr = 1e-06
I0405 10:17:59.084035 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:18:00.376292 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:18:03.160732 30176 solver.cpp:218] Iteration 11436 (2.7361 iter/s, 4.38581s/12 iters), loss = 5.2894
I0405 10:18:03.160785 30176 solver.cpp:237]     Train net output #0: loss = 5.2894 (* 1 = 5.2894 loss)
I0405 10:18:03.160794 30176 sgd_solver.cpp:105] Iteration 11436, lr = 1e-06
I0405 10:18:08.495908 30176 solver.cpp:218] Iteration 11448 (2.24927 iter/s, 5.33506s/12 iters), loss = 5.27509
I0405 10:18:08.496027 30176 solver.cpp:237]     Train net output #0: loss = 5.27509 (* 1 = 5.27509 loss)
I0405 10:18:08.496035 30176 sgd_solver.cpp:105] Iteration 11448, lr = 1e-06
I0405 10:18:13.915789 30176 solver.cpp:218] Iteration 11460 (2.21414 iter/s, 5.41971s/12 iters), loss = 5.28792
I0405 10:18:13.915838 30176 solver.cpp:237]     Train net output #0: loss = 5.28792 (* 1 = 5.28792 loss)
I0405 10:18:13.915846 30176 sgd_solver.cpp:105] Iteration 11460, lr = 1e-06
I0405 10:18:19.404022 30176 solver.cpp:218] Iteration 11472 (2.18654 iter/s, 5.48813s/12 iters), loss = 5.28887
I0405 10:18:19.404076 30176 solver.cpp:237]     Train net output #0: loss = 5.28887 (* 1 = 5.28887 loss)
I0405 10:18:19.404084 30176 sgd_solver.cpp:105] Iteration 11472, lr = 1e-06
I0405 10:18:24.798300 30176 solver.cpp:218] Iteration 11484 (2.22463 iter/s, 5.39416s/12 iters), loss = 5.27868
I0405 10:18:24.798357 30176 solver.cpp:237]     Train net output #0: loss = 5.27868 (* 1 = 5.27868 loss)
I0405 10:18:24.798364 30176 sgd_solver.cpp:105] Iteration 11484, lr = 1e-06
I0405 10:18:29.859688 30176 solver.cpp:218] Iteration 11496 (2.37094 iter/s, 5.06128s/12 iters), loss = 5.27111
I0405 10:18:29.859730 30176 solver.cpp:237]     Train net output #0: loss = 5.27111 (* 1 = 5.27111 loss)
I0405 10:18:29.859735 30176 sgd_solver.cpp:105] Iteration 11496, lr = 1e-06
I0405 10:18:35.373014 30176 solver.cpp:218] Iteration 11508 (2.17658 iter/s, 5.51323s/12 iters), loss = 5.29159
I0405 10:18:35.373072 30176 solver.cpp:237]     Train net output #0: loss = 5.29159 (* 1 = 5.29159 loss)
I0405 10:18:35.373081 30176 sgd_solver.cpp:105] Iteration 11508, lr = 1e-06
I0405 10:18:40.765910 30176 solver.cpp:218] Iteration 11520 (2.2252 iter/s, 5.39278s/12 iters), loss = 5.30567
I0405 10:18:40.766077 30176 solver.cpp:237]     Train net output #0: loss = 5.30567 (* 1 = 5.30567 loss)
I0405 10:18:40.766084 30176 sgd_solver.cpp:105] Iteration 11520, lr = 1e-06
I0405 10:18:42.955029 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_11526.caffemodel
I0405 10:18:46.001551 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_11526.solverstate
I0405 10:18:48.315973 30176 solver.cpp:330] Iteration 11526, Testing net (#0)
I0405 10:18:48.315994 30176 net.cpp:676] Ignoring source layer train-data
I0405 10:18:52.911505 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 10:18:52.911559 30176 solver.cpp:397]     Test net output #1: loss = 5.28021 (* 1 = 5.28021 loss)
I0405 10:18:53.020295 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:18:54.289209 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:18:54.861585 30176 solver.cpp:218] Iteration 11532 (0.851341 iter/s, 14.0954s/12 iters), loss = 5.28256
I0405 10:18:54.861635 30176 solver.cpp:237]     Train net output #0: loss = 5.28256 (* 1 = 5.28256 loss)
I0405 10:18:54.861642 30176 sgd_solver.cpp:105] Iteration 11532, lr = 1e-06
I0405 10:19:00.242411 30176 solver.cpp:218] Iteration 11544 (2.23018 iter/s, 5.38072s/12 iters), loss = 5.27859
I0405 10:19:00.242453 30176 solver.cpp:237]     Train net output #0: loss = 5.27859 (* 1 = 5.27859 loss)
I0405 10:19:00.242458 30176 sgd_solver.cpp:105] Iteration 11544, lr = 1e-06
I0405 10:19:05.213014 30176 solver.cpp:218] Iteration 11556 (2.41424 iter/s, 4.97051s/12 iters), loss = 5.27962
I0405 10:19:05.213063 30176 solver.cpp:237]     Train net output #0: loss = 5.27962 (* 1 = 5.27962 loss)
I0405 10:19:05.213070 30176 sgd_solver.cpp:105] Iteration 11556, lr = 1e-06
I0405 10:19:10.440954 30176 solver.cpp:218] Iteration 11568 (2.2954 iter/s, 5.22784s/12 iters), loss = 5.27421
I0405 10:19:10.440994 30176 solver.cpp:237]     Train net output #0: loss = 5.27421 (* 1 = 5.27421 loss)
I0405 10:19:10.440999 30176 sgd_solver.cpp:105] Iteration 11568, lr = 1e-06
I0405 10:19:15.868873 30176 solver.cpp:218] Iteration 11580 (2.21083 iter/s, 5.42782s/12 iters), loss = 5.27879
I0405 10:19:15.869022 30176 solver.cpp:237]     Train net output #0: loss = 5.27879 (* 1 = 5.27879 loss)
I0405 10:19:15.869032 30176 sgd_solver.cpp:105] Iteration 11580, lr = 1e-06
I0405 10:19:21.212580 30176 solver.cpp:218] Iteration 11592 (2.24571 iter/s, 5.34351s/12 iters), loss = 5.27471
I0405 10:19:21.212625 30176 solver.cpp:237]     Train net output #0: loss = 5.27471 (* 1 = 5.27471 loss)
I0405 10:19:21.212631 30176 sgd_solver.cpp:105] Iteration 11592, lr = 1e-06
I0405 10:19:26.626017 30176 solver.cpp:218] Iteration 11604 (2.21675 iter/s, 5.41333s/12 iters), loss = 5.29503
I0405 10:19:26.626085 30176 solver.cpp:237]     Train net output #0: loss = 5.29503 (* 1 = 5.29503 loss)
I0405 10:19:26.626092 30176 sgd_solver.cpp:105] Iteration 11604, lr = 1e-06
I0405 10:19:31.947459 30176 solver.cpp:218] Iteration 11616 (2.25508 iter/s, 5.32132s/12 iters), loss = 5.27936
I0405 10:19:31.947502 30176 solver.cpp:237]     Train net output #0: loss = 5.27936 (* 1 = 5.27936 loss)
I0405 10:19:31.947508 30176 sgd_solver.cpp:105] Iteration 11616, lr = 1e-06
I0405 10:19:36.762104 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_11628.caffemodel
I0405 10:19:39.821502 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_11628.solverstate
I0405 10:19:42.236351 30176 solver.cpp:330] Iteration 11628, Testing net (#0)
I0405 10:19:42.236368 30176 net.cpp:676] Ignoring source layer train-data
I0405 10:19:46.674614 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 10:19:46.674805 30176 solver.cpp:397]     Test net output #1: loss = 5.27979 (* 1 = 5.27979 loss)
I0405 10:19:46.778553 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:19:46.829988 30176 solver.cpp:218] Iteration 11628 (0.806323 iter/s, 14.8824s/12 iters), loss = 5.27358
I0405 10:19:46.830039 30176 solver.cpp:237]     Train net output #0: loss = 5.27358 (* 1 = 5.27358 loss)
I0405 10:19:46.830045 30176 sgd_solver.cpp:105] Iteration 11628, lr = 1e-06
I0405 10:19:47.532685 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:19:51.189348 30176 solver.cpp:218] Iteration 11640 (2.75276 iter/s, 4.35926s/12 iters), loss = 5.27022
I0405 10:19:51.189419 30176 solver.cpp:237]     Train net output #0: loss = 5.27022 (* 1 = 5.27022 loss)
I0405 10:19:51.189429 30176 sgd_solver.cpp:105] Iteration 11640, lr = 1e-06
I0405 10:19:56.699839 30176 solver.cpp:218] Iteration 11652 (2.17771 iter/s, 5.51037s/12 iters), loss = 5.28582
I0405 10:19:56.699878 30176 solver.cpp:237]     Train net output #0: loss = 5.28582 (* 1 = 5.28582 loss)
I0405 10:19:56.699883 30176 sgd_solver.cpp:105] Iteration 11652, lr = 1e-06
I0405 10:20:01.807664 30176 solver.cpp:218] Iteration 11664 (2.34938 iter/s, 5.10773s/12 iters), loss = 5.26904
I0405 10:20:01.807713 30176 solver.cpp:237]     Train net output #0: loss = 5.26904 (* 1 = 5.26904 loss)
I0405 10:20:01.807721 30176 sgd_solver.cpp:105] Iteration 11664, lr = 1e-06
I0405 10:20:07.055564 30176 solver.cpp:218] Iteration 11676 (2.28667 iter/s, 5.2478s/12 iters), loss = 5.27212
I0405 10:20:07.055606 30176 solver.cpp:237]     Train net output #0: loss = 5.27212 (* 1 = 5.27212 loss)
I0405 10:20:07.055611 30176 sgd_solver.cpp:105] Iteration 11676, lr = 1e-06
I0405 10:20:12.347404 30176 solver.cpp:218] Iteration 11688 (2.26768 iter/s, 5.29175s/12 iters), loss = 5.30002
I0405 10:20:12.347442 30176 solver.cpp:237]     Train net output #0: loss = 5.30002 (* 1 = 5.30002 loss)
I0405 10:20:12.347447 30176 sgd_solver.cpp:105] Iteration 11688, lr = 1e-06
I0405 10:20:17.659369 30176 solver.cpp:218] Iteration 11700 (2.25909 iter/s, 5.31187s/12 iters), loss = 5.28955
I0405 10:20:17.659459 30176 solver.cpp:237]     Train net output #0: loss = 5.28955 (* 1 = 5.28955 loss)
I0405 10:20:17.659466 30176 sgd_solver.cpp:105] Iteration 11700, lr = 1e-06
I0405 10:20:23.439338 30176 solver.cpp:218] Iteration 11712 (2.07619 iter/s, 5.77982s/12 iters), loss = 5.28205
I0405 10:20:23.439385 30176 solver.cpp:237]     Train net output #0: loss = 5.28205 (* 1 = 5.28205 loss)
I0405 10:20:23.439391 30176 sgd_solver.cpp:105] Iteration 11712, lr = 1e-06
I0405 10:20:28.990859 30176 solver.cpp:218] Iteration 11724 (2.16161 iter/s, 5.55142s/12 iters), loss = 5.28623
I0405 10:20:28.990906 30176 solver.cpp:237]     Train net output #0: loss = 5.28623 (* 1 = 5.28623 loss)
I0405 10:20:28.990912 30176 sgd_solver.cpp:105] Iteration 11724, lr = 1e-06
I0405 10:20:31.202575 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_11730.caffemodel
I0405 10:20:34.299557 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_11730.solverstate
I0405 10:20:36.611765 30176 solver.cpp:330] Iteration 11730, Testing net (#0)
I0405 10:20:36.611793 30176 net.cpp:676] Ignoring source layer train-data
I0405 10:20:41.152421 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:20:41.182583 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 10:20:41.182617 30176 solver.cpp:397]     Test net output #1: loss = 5.27975 (* 1 = 5.27975 loss)
I0405 10:20:41.848738 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:20:43.124892 30176 solver.cpp:218] Iteration 11736 (0.849025 iter/s, 14.1339s/12 iters), loss = 5.2959
I0405 10:20:43.124950 30176 solver.cpp:237]     Train net output #0: loss = 5.2959 (* 1 = 5.2959 loss)
I0405 10:20:43.124958 30176 sgd_solver.cpp:105] Iteration 11736, lr = 1e-06
I0405 10:20:48.637964 30176 solver.cpp:218] Iteration 11748 (2.17669 iter/s, 5.51296s/12 iters), loss = 5.28853
I0405 10:20:48.638078 30176 solver.cpp:237]     Train net output #0: loss = 5.28853 (* 1 = 5.28853 loss)
I0405 10:20:48.638087 30176 sgd_solver.cpp:105] Iteration 11748, lr = 1e-06
I0405 10:20:53.856693 30176 solver.cpp:218] Iteration 11760 (2.29948 iter/s, 5.21856s/12 iters), loss = 5.28433
I0405 10:20:53.856734 30176 solver.cpp:237]     Train net output #0: loss = 5.28433 (* 1 = 5.28433 loss)
I0405 10:20:53.856739 30176 sgd_solver.cpp:105] Iteration 11760, lr = 1e-06
I0405 10:20:59.071919 30176 solver.cpp:218] Iteration 11772 (2.301 iter/s, 5.21513s/12 iters), loss = 5.29139
I0405 10:20:59.071970 30176 solver.cpp:237]     Train net output #0: loss = 5.29139 (* 1 = 5.29139 loss)
I0405 10:20:59.071979 30176 sgd_solver.cpp:105] Iteration 11772, lr = 1e-06
I0405 10:21:02.945520 30176 blocking_queue.cpp:49] Waiting for data
I0405 10:21:04.477154 30176 solver.cpp:218] Iteration 11784 (2.22011 iter/s, 5.40513s/12 iters), loss = 5.31122
I0405 10:21:04.477207 30176 solver.cpp:237]     Train net output #0: loss = 5.31122 (* 1 = 5.31122 loss)
I0405 10:21:04.477216 30176 sgd_solver.cpp:105] Iteration 11784, lr = 1e-06
I0405 10:21:10.003150 30176 solver.cpp:218] Iteration 11796 (2.1716 iter/s, 5.52589s/12 iters), loss = 5.2717
I0405 10:21:10.003198 30176 solver.cpp:237]     Train net output #0: loss = 5.2717 (* 1 = 5.2717 loss)
I0405 10:21:10.003206 30176 sgd_solver.cpp:105] Iteration 11796, lr = 1e-06
I0405 10:21:15.299990 30176 solver.cpp:218] Iteration 11808 (2.26555 iter/s, 5.29674s/12 iters), loss = 5.28467
I0405 10:21:15.300047 30176 solver.cpp:237]     Train net output #0: loss = 5.28467 (* 1 = 5.28467 loss)
I0405 10:21:15.300055 30176 sgd_solver.cpp:105] Iteration 11808, lr = 1e-06
I0405 10:21:20.903261 30176 solver.cpp:218] Iteration 11820 (2.14165 iter/s, 5.60316s/12 iters), loss = 5.28236
I0405 10:21:20.903383 30176 solver.cpp:237]     Train net output #0: loss = 5.28236 (* 1 = 5.28236 loss)
I0405 10:21:20.903391 30176 sgd_solver.cpp:105] Iteration 11820, lr = 1e-06
I0405 10:21:25.754650 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_11832.caffemodel
I0405 10:21:28.770473 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_11832.solverstate
I0405 10:21:31.065899 30176 solver.cpp:330] Iteration 11832, Testing net (#0)
I0405 10:21:31.065917 30176 net.cpp:676] Ignoring source layer train-data
I0405 10:21:35.573228 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:21:35.635355 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 10:21:35.635394 30176 solver.cpp:397]     Test net output #1: loss = 5.27982 (* 1 = 5.27982 loss)
I0405 10:21:35.772316 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:21:35.783012 30176 solver.cpp:218] Iteration 11832 (0.806478 iter/s, 14.8795s/12 iters), loss = 5.26939
I0405 10:21:35.783073 30176 solver.cpp:237]     Train net output #0: loss = 5.26939 (* 1 = 5.26939 loss)
I0405 10:21:35.783080 30176 sgd_solver.cpp:105] Iteration 11832, lr = 1e-06
I0405 10:21:40.326279 30176 solver.cpp:218] Iteration 11844 (2.64133 iter/s, 4.54316s/12 iters), loss = 5.27909
I0405 10:21:40.326316 30176 solver.cpp:237]     Train net output #0: loss = 5.27909 (* 1 = 5.27909 loss)
I0405 10:21:40.326323 30176 sgd_solver.cpp:105] Iteration 11844, lr = 1e-06
I0405 10:21:45.525174 30176 solver.cpp:218] Iteration 11856 (2.30822 iter/s, 5.1988s/12 iters), loss = 5.27293
I0405 10:21:45.525228 30176 solver.cpp:237]     Train net output #0: loss = 5.27293 (* 1 = 5.27293 loss)
I0405 10:21:45.525235 30176 sgd_solver.cpp:105] Iteration 11856, lr = 1e-06
I0405 10:21:50.901705 30176 solver.cpp:218] Iteration 11868 (2.23197 iter/s, 5.37642s/12 iters), loss = 5.27993
I0405 10:21:50.901753 30176 solver.cpp:237]     Train net output #0: loss = 5.27993 (* 1 = 5.27993 loss)
I0405 10:21:50.901760 30176 sgd_solver.cpp:105] Iteration 11868, lr = 1e-06
I0405 10:21:56.113231 30176 solver.cpp:218] Iteration 11880 (2.30263 iter/s, 5.21143s/12 iters), loss = 5.28924
I0405 10:21:56.113348 30176 solver.cpp:237]     Train net output #0: loss = 5.28924 (* 1 = 5.28924 loss)
I0405 10:21:56.113356 30176 sgd_solver.cpp:105] Iteration 11880, lr = 1e-06
I0405 10:22:01.465111 30176 solver.cpp:218] Iteration 11892 (2.24227 iter/s, 5.35171s/12 iters), loss = 5.27404
I0405 10:22:01.465170 30176 solver.cpp:237]     Train net output #0: loss = 5.27404 (* 1 = 5.27404 loss)
I0405 10:22:01.465180 30176 sgd_solver.cpp:105] Iteration 11892, lr = 1e-06
I0405 10:22:06.853160 30176 solver.cpp:218] Iteration 11904 (2.2272 iter/s, 5.38793s/12 iters), loss = 5.27443
I0405 10:22:06.853219 30176 solver.cpp:237]     Train net output #0: loss = 5.27443 (* 1 = 5.27443 loss)
I0405 10:22:06.853229 30176 sgd_solver.cpp:105] Iteration 11904, lr = 1e-06
I0405 10:22:12.055616 30176 solver.cpp:218] Iteration 11916 (2.30665 iter/s, 5.20234s/12 iters), loss = 5.29051
I0405 10:22:12.055670 30176 solver.cpp:237]     Train net output #0: loss = 5.29051 (* 1 = 5.29051 loss)
I0405 10:22:12.055680 30176 sgd_solver.cpp:105] Iteration 11916, lr = 1e-06
I0405 10:22:17.518878 30176 solver.cpp:218] Iteration 11928 (2.19653 iter/s, 5.46315s/12 iters), loss = 5.29338
I0405 10:22:17.518929 30176 solver.cpp:237]     Train net output #0: loss = 5.29338 (* 1 = 5.29338 loss)
I0405 10:22:17.518935 30176 sgd_solver.cpp:105] Iteration 11928, lr = 1e-06
I0405 10:22:19.773134 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_11934.caffemodel
I0405 10:22:20.797699 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:22:22.859869 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_11934.solverstate
I0405 10:22:25.156265 30176 solver.cpp:330] Iteration 11934, Testing net (#0)
I0405 10:22:25.156286 30176 net.cpp:676] Ignoring source layer train-data
I0405 10:22:29.610241 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:22:29.716178 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 10:22:29.716224 30176 solver.cpp:397]     Test net output #1: loss = 5.2801 (* 1 = 5.2801 loss)
I0405 10:22:31.840214 30176 solver.cpp:218] Iteration 11940 (0.83792 iter/s, 14.3212s/12 iters), loss = 5.29623
I0405 10:22:31.840268 30176 solver.cpp:237]     Train net output #0: loss = 5.29623 (* 1 = 5.29623 loss)
I0405 10:22:31.840276 30176 sgd_solver.cpp:105] Iteration 11940, lr = 1e-06
I0405 10:22:37.401896 30176 solver.cpp:218] Iteration 11952 (2.15766 iter/s, 5.56158s/12 iters), loss = 5.29095
I0405 10:22:37.401944 30176 solver.cpp:237]     Train net output #0: loss = 5.29095 (* 1 = 5.29095 loss)
I0405 10:22:37.401953 30176 sgd_solver.cpp:105] Iteration 11952, lr = 1e-06
I0405 10:22:42.915277 30176 solver.cpp:218] Iteration 11964 (2.17656 iter/s, 5.51327s/12 iters), loss = 5.28179
I0405 10:22:42.915333 30176 solver.cpp:237]     Train net output #0: loss = 5.28179 (* 1 = 5.28179 loss)
I0405 10:22:42.915341 30176 sgd_solver.cpp:105] Iteration 11964, lr = 1e-06
I0405 10:22:48.331475 30176 solver.cpp:218] Iteration 11976 (2.21562 iter/s, 5.41609s/12 iters), loss = 5.29755
I0405 10:22:48.331524 30176 solver.cpp:237]     Train net output #0: loss = 5.29755 (* 1 = 5.29755 loss)
I0405 10:22:48.331532 30176 sgd_solver.cpp:105] Iteration 11976, lr = 1e-06
I0405 10:22:53.824244 30176 solver.cpp:218] Iteration 11988 (2.18473 iter/s, 5.49266s/12 iters), loss = 5.27917
I0405 10:22:53.824286 30176 solver.cpp:237]     Train net output #0: loss = 5.27917 (* 1 = 5.27917 loss)
I0405 10:22:53.824291 30176 sgd_solver.cpp:105] Iteration 11988, lr = 1e-06
I0405 10:22:59.251809 30176 solver.cpp:218] Iteration 12000 (2.21098 iter/s, 5.42747s/12 iters), loss = 5.27915
I0405 10:22:59.251852 30176 solver.cpp:237]     Train net output #0: loss = 5.27915 (* 1 = 5.27915 loss)
I0405 10:22:59.251857 30176 sgd_solver.cpp:105] Iteration 12000, lr = 1e-06
I0405 10:23:04.762681 30176 solver.cpp:218] Iteration 12012 (2.17755 iter/s, 5.51077s/12 iters), loss = 5.28449
I0405 10:23:04.762877 30176 solver.cpp:237]     Train net output #0: loss = 5.28449 (* 1 = 5.28449 loss)
I0405 10:23:04.762889 30176 sgd_solver.cpp:105] Iteration 12012, lr = 1e-06
I0405 10:23:10.173637 30176 solver.cpp:218] Iteration 12024 (2.21782 iter/s, 5.41071s/12 iters), loss = 5.28304
I0405 10:23:10.173694 30176 solver.cpp:237]     Train net output #0: loss = 5.28304 (* 1 = 5.28304 loss)
I0405 10:23:10.173702 30176 sgd_solver.cpp:105] Iteration 12024, lr = 1e-06
I0405 10:23:14.924074 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_12036.caffemodel
I0405 10:23:15.733400 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:23:18.026046 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_12036.solverstate
I0405 10:23:20.350826 30176 solver.cpp:330] Iteration 12036, Testing net (#0)
I0405 10:23:20.350845 30176 net.cpp:676] Ignoring source layer train-data
I0405 10:23:24.773602 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:23:24.929102 30176 solver.cpp:397]     Test net output #0: accuracy = 0.00735294
I0405 10:23:24.929142 30176 solver.cpp:397]     Test net output #1: loss = 5.27984 (* 1 = 5.27984 loss)
I0405 10:23:25.070098 30176 solver.cpp:218] Iteration 12036 (0.80557 iter/s, 14.8963s/12 iters), loss = 5.28332
I0405 10:23:25.070163 30176 solver.cpp:237]     Train net output #0: loss = 5.28332 (* 1 = 5.28332 loss)
I0405 10:23:25.070168 30176 sgd_solver.cpp:105] Iteration 12036, lr = 1e-06
I0405 10:23:29.726320 30176 solver.cpp:218] Iteration 12048 (2.57725 iter/s, 4.65612s/12 iters), loss = 5.28888
I0405 10:23:29.726362 30176 solver.cpp:237]     Train net output #0: loss = 5.28888 (* 1 = 5.28888 loss)
I0405 10:23:29.726368 30176 sgd_solver.cpp:105] Iteration 12048, lr = 1e-06
I0405 10:23:34.981637 30176 solver.cpp:218] Iteration 12060 (2.28345 iter/s, 5.25521s/12 iters), loss = 5.27259
I0405 10:23:34.981743 30176 solver.cpp:237]     Train net output #0: loss = 5.27259 (* 1 = 5.27259 loss)
I0405 10:23:34.981752 30176 sgd_solver.cpp:105] Iteration 12060, lr = 1e-06
I0405 10:23:40.400486 30176 solver.cpp:218] Iteration 12072 (2.21456 iter/s, 5.41869s/12 iters), loss = 5.27773
I0405 10:23:40.400547 30176 solver.cpp:237]     Train net output #0: loss = 5.27773 (* 1 = 5.27773 loss)
I0405 10:23:40.400555 30176 sgd_solver.cpp:105] Iteration 12072, lr = 1e-06
I0405 10:23:45.797379 30176 solver.cpp:218] Iteration 12084 (2.22355 iter/s, 5.39679s/12 iters), loss = 5.28085
I0405 10:23:45.797417 30176 solver.cpp:237]     Train net output #0: loss = 5.28085 (* 1 = 5.28085 loss)
I0405 10:23:45.797422 30176 sgd_solver.cpp:105] Iteration 12084, lr = 1e-06
I0405 10:23:51.102550 30176 solver.cpp:218] Iteration 12096 (2.26198 iter/s, 5.30508s/12 iters), loss = 5.29536
I0405 10:23:51.102596 30176 solver.cpp:237]     Train net output #0: loss = 5.29536 (* 1 = 5.29536 loss)
I0405 10:23:51.102602 30176 sgd_solver.cpp:105] Iteration 12096, lr = 1e-06
I0405 10:23:56.585209 30176 solver.cpp:218] Iteration 12108 (2.18876 iter/s, 5.48255s/12 iters), loss = 5.29347
I0405 10:23:56.585265 30176 solver.cpp:237]     Train net output #0: loss = 5.29347 (* 1 = 5.29347 loss)
I0405 10:23:56.585273 30176 sgd_solver.cpp:105] Iteration 12108, lr = 1e-06
I0405 10:24:02.061466 30176 solver.cpp:218] Iteration 12120 (2.19132 iter/s, 5.47615s/12 iters), loss = 5.26812
I0405 10:24:02.061508 30176 solver.cpp:237]     Train net output #0: loss = 5.26812 (* 1 = 5.26812 loss)
I0405 10:24:02.061514 30176 sgd_solver.cpp:105] Iteration 12120, lr = 1e-06
I0405 10:24:07.666097 30176 solver.cpp:218] Iteration 12132 (2.14113 iter/s, 5.60453s/12 iters), loss = 5.26366
I0405 10:24:07.666254 30176 solver.cpp:237]     Train net output #0: loss = 5.26366 (* 1 = 5.26366 loss)
I0405 10:24:07.666263 30176 sgd_solver.cpp:105] Iteration 12132, lr = 1e-06
I0405 10:24:09.953615 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_12138.caffemodel
I0405 10:24:10.292068 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:24:13.093261 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_12138.solverstate
I0405 10:24:15.819674 30176 solver.cpp:330] Iteration 12138, Testing net (#0)
I0405 10:24:15.819696 30176 net.cpp:676] Ignoring source layer train-data
I0405 10:24:20.331161 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:24:20.551347 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 10:24:20.551378 30176 solver.cpp:397]     Test net output #1: loss = 5.2801 (* 1 = 5.2801 loss)
I0405 10:24:22.659124 30176 solver.cpp:218] Iteration 12144 (0.800387 iter/s, 14.9928s/12 iters), loss = 5.29525
I0405 10:24:22.659183 30176 solver.cpp:237]     Train net output #0: loss = 5.29525 (* 1 = 5.29525 loss)
I0405 10:24:22.659193 30176 sgd_solver.cpp:105] Iteration 12144, lr = 1e-06
I0405 10:24:28.231087 30176 solver.cpp:218] Iteration 12156 (2.15368 iter/s, 5.57185s/12 iters), loss = 5.26709
I0405 10:24:28.231155 30176 solver.cpp:237]     Train net output #0: loss = 5.26709 (* 1 = 5.26709 loss)
I0405 10:24:28.231163 30176 sgd_solver.cpp:105] Iteration 12156, lr = 1e-06
I0405 10:24:33.690310 30176 solver.cpp:218] Iteration 12168 (2.19816 iter/s, 5.4591s/12 iters), loss = 5.29213
I0405 10:24:33.690371 30176 solver.cpp:237]     Train net output #0: loss = 5.29213 (* 1 = 5.29213 loss)
I0405 10:24:33.690378 30176 sgd_solver.cpp:105] Iteration 12168, lr = 1e-06
I0405 10:24:39.220546 30176 solver.cpp:218] Iteration 12180 (2.16993 iter/s, 5.53012s/12 iters), loss = 5.28347
I0405 10:24:39.220674 30176 solver.cpp:237]     Train net output #0: loss = 5.28347 (* 1 = 5.28347 loss)
I0405 10:24:39.220682 30176 sgd_solver.cpp:105] Iteration 12180, lr = 1e-06
I0405 10:24:44.724587 30176 solver.cpp:218] Iteration 12192 (2.18029 iter/s, 5.50386s/12 iters), loss = 5.26972
I0405 10:24:44.724644 30176 solver.cpp:237]     Train net output #0: loss = 5.26972 (* 1 = 5.26972 loss)
I0405 10:24:44.724653 30176 sgd_solver.cpp:105] Iteration 12192, lr = 1e-06
I0405 10:24:50.365105 30176 solver.cpp:218] Iteration 12204 (2.12751 iter/s, 5.6404s/12 iters), loss = 5.28518
I0405 10:24:50.365155 30176 solver.cpp:237]     Train net output #0: loss = 5.28518 (* 1 = 5.28518 loss)
I0405 10:24:50.365164 30176 sgd_solver.cpp:105] Iteration 12204, lr = 1e-06
I0405 10:24:55.666553 30176 solver.cpp:218] Iteration 12216 (2.26358 iter/s, 5.30134s/12 iters), loss = 5.27729
I0405 10:24:55.666602 30176 solver.cpp:237]     Train net output #0: loss = 5.27729 (* 1 = 5.27729 loss)
I0405 10:24:55.666610 30176 sgd_solver.cpp:105] Iteration 12216, lr = 1e-06
I0405 10:25:01.161556 30176 solver.cpp:218] Iteration 12228 (2.18384 iter/s, 5.4949s/12 iters), loss = 5.31069
I0405 10:25:01.161600 30176 solver.cpp:237]     Train net output #0: loss = 5.31069 (* 1 = 5.31069 loss)
I0405 10:25:01.161605 30176 sgd_solver.cpp:105] Iteration 12228, lr = 1e-06
I0405 10:25:05.729447 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:25:05.910658 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_12240.caffemodel
I0405 10:25:09.043385 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_12240.solverstate
I0405 10:25:11.519749 30176 solver.cpp:330] Iteration 12240, Testing net (#0)
I0405 10:25:11.519834 30176 net.cpp:676] Ignoring source layer train-data
I0405 10:25:15.908663 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:25:16.160323 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 10:25:16.160362 30176 solver.cpp:397]     Test net output #1: loss = 5.2797 (* 1 = 5.2797 loss)
I0405 10:25:16.301527 30176 solver.cpp:218] Iteration 12240 (0.792612 iter/s, 15.1398s/12 iters), loss = 5.30946
I0405 10:25:16.301573 30176 solver.cpp:237]     Train net output #0: loss = 5.30946 (* 1 = 5.30946 loss)
I0405 10:25:16.301578 30176 sgd_solver.cpp:105] Iteration 12240, lr = 1e-06
I0405 10:25:21.041612 30176 solver.cpp:218] Iteration 12252 (2.53165 iter/s, 4.73999s/12 iters), loss = 5.26836
I0405 10:25:21.041663 30176 solver.cpp:237]     Train net output #0: loss = 5.26836 (* 1 = 5.26836 loss)
I0405 10:25:21.041671 30176 sgd_solver.cpp:105] Iteration 12252, lr = 1e-06
I0405 10:25:26.583523 30176 solver.cpp:218] Iteration 12264 (2.16536 iter/s, 5.5418s/12 iters), loss = 5.29093
I0405 10:25:26.583565 30176 solver.cpp:237]     Train net output #0: loss = 5.29093 (* 1 = 5.29093 loss)
I0405 10:25:26.583570 30176 sgd_solver.cpp:105] Iteration 12264, lr = 1e-06
I0405 10:25:32.041160 30176 solver.cpp:218] Iteration 12276 (2.19879 iter/s, 5.45754s/12 iters), loss = 5.28065
I0405 10:25:32.041213 30176 solver.cpp:237]     Train net output #0: loss = 5.28065 (* 1 = 5.28065 loss)
I0405 10:25:32.041220 30176 sgd_solver.cpp:105] Iteration 12276, lr = 1e-06
I0405 10:25:37.388475 30176 solver.cpp:218] Iteration 12288 (2.24416 iter/s, 5.34721s/12 iters), loss = 5.26904
I0405 10:25:37.388530 30176 solver.cpp:237]     Train net output #0: loss = 5.26904 (* 1 = 5.26904 loss)
I0405 10:25:37.388538 30176 sgd_solver.cpp:105] Iteration 12288, lr = 1e-06
I0405 10:25:42.803993 30176 solver.cpp:218] Iteration 12300 (2.2159 iter/s, 5.41541s/12 iters), loss = 5.29015
I0405 10:25:42.804126 30176 solver.cpp:237]     Train net output #0: loss = 5.29015 (* 1 = 5.29015 loss)
I0405 10:25:42.804132 30176 sgd_solver.cpp:105] Iteration 12300, lr = 1e-06
I0405 10:25:48.261795 30176 solver.cpp:218] Iteration 12312 (2.19876 iter/s, 5.45762s/12 iters), loss = 5.28954
I0405 10:25:48.261845 30176 solver.cpp:237]     Train net output #0: loss = 5.28954 (* 1 = 5.28954 loss)
I0405 10:25:48.261853 30176 sgd_solver.cpp:105] Iteration 12312, lr = 1e-06
I0405 10:25:53.583282 30176 solver.cpp:218] Iteration 12324 (2.25505 iter/s, 5.32139s/12 iters), loss = 5.28089
I0405 10:25:53.583321 30176 solver.cpp:237]     Train net output #0: loss = 5.28089 (* 1 = 5.28089 loss)
I0405 10:25:53.583328 30176 sgd_solver.cpp:105] Iteration 12324, lr = 1e-06
I0405 10:25:58.957058 30176 solver.cpp:218] Iteration 12336 (2.23311 iter/s, 5.37368s/12 iters), loss = 5.27889
I0405 10:25:58.957119 30176 solver.cpp:237]     Train net output #0: loss = 5.27889 (* 1 = 5.27889 loss)
I0405 10:25:58.957127 30176 sgd_solver.cpp:105] Iteration 12336, lr = 1e-06
I0405 10:26:00.776646 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:26:01.205600 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_12342.caffemodel
I0405 10:26:04.142652 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_12342.solverstate
I0405 10:26:06.464059 30176 solver.cpp:330] Iteration 12342, Testing net (#0)
I0405 10:26:06.464084 30176 net.cpp:676] Ignoring source layer train-data
I0405 10:26:10.852496 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:26:11.104975 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 10:26:11.105016 30176 solver.cpp:397]     Test net output #1: loss = 5.2796 (* 1 = 5.2796 loss)
I0405 10:26:13.157143 30176 solver.cpp:218] Iteration 12348 (0.845075 iter/s, 14.1999s/12 iters), loss = 5.2865
I0405 10:26:13.157238 30176 solver.cpp:237]     Train net output #0: loss = 5.2865 (* 1 = 5.2865 loss)
I0405 10:26:13.157244 30176 sgd_solver.cpp:105] Iteration 12348, lr = 1e-06
I0405 10:26:18.597303 30176 solver.cpp:218] Iteration 12360 (2.20588 iter/s, 5.44001s/12 iters), loss = 5.27485
I0405 10:26:18.597353 30176 solver.cpp:237]     Train net output #0: loss = 5.27485 (* 1 = 5.27485 loss)
I0405 10:26:18.597362 30176 sgd_solver.cpp:105] Iteration 12360, lr = 1e-06
I0405 10:26:24.010777 30176 solver.cpp:218] Iteration 12372 (2.21673 iter/s, 5.41337s/12 iters), loss = 5.27945
I0405 10:26:24.010826 30176 solver.cpp:237]     Train net output #0: loss = 5.27945 (* 1 = 5.27945 loss)
I0405 10:26:24.010834 30176 sgd_solver.cpp:105] Iteration 12372, lr = 1e-06
I0405 10:26:29.526985 30176 solver.cpp:218] Iteration 12384 (2.17545 iter/s, 5.51611s/12 iters), loss = 5.27117
I0405 10:26:29.527024 30176 solver.cpp:237]     Train net output #0: loss = 5.27117 (* 1 = 5.27117 loss)
I0405 10:26:29.527030 30176 sgd_solver.cpp:105] Iteration 12384, lr = 1e-06
I0405 10:26:34.909198 30176 solver.cpp:218] Iteration 12396 (2.22961 iter/s, 5.38211s/12 iters), loss = 5.27516
I0405 10:26:34.909255 30176 solver.cpp:237]     Train net output #0: loss = 5.27516 (* 1 = 5.27516 loss)
I0405 10:26:34.909263 30176 sgd_solver.cpp:105] Iteration 12396, lr = 1e-06
I0405 10:26:40.517352 30176 solver.cpp:218] Iteration 12408 (2.13978 iter/s, 5.60804s/12 iters), loss = 5.28546
I0405 10:26:40.517405 30176 solver.cpp:237]     Train net output #0: loss = 5.28546 (* 1 = 5.28546 loss)
I0405 10:26:40.517411 30176 sgd_solver.cpp:105] Iteration 12408, lr = 1e-06
I0405 10:26:45.804620 30176 solver.cpp:218] Iteration 12420 (2.26965 iter/s, 5.28717s/12 iters), loss = 5.26858
I0405 10:26:45.804769 30176 solver.cpp:237]     Train net output #0: loss = 5.26858 (* 1 = 5.26858 loss)
I0405 10:26:45.804775 30176 sgd_solver.cpp:105] Iteration 12420, lr = 1e-06
I0405 10:26:51.009526 30176 solver.cpp:218] Iteration 12432 (2.30561 iter/s, 5.20471s/12 iters), loss = 5.26393
I0405 10:26:51.009565 30176 solver.cpp:237]     Train net output #0: loss = 5.26393 (* 1 = 5.26393 loss)
I0405 10:26:51.009572 30176 sgd_solver.cpp:105] Iteration 12432, lr = 1e-06
I0405 10:26:55.359037 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:26:56.128418 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_12444.caffemodel
I0405 10:26:59.178362 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_12444.solverstate
I0405 10:27:01.494733 30176 solver.cpp:330] Iteration 12444, Testing net (#0)
I0405 10:27:01.494758 30176 net.cpp:676] Ignoring source layer train-data
I0405 10:27:05.809010 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:27:06.106554 30176 solver.cpp:397]     Test net output #0: accuracy = 0.00735294
I0405 10:27:06.106588 30176 solver.cpp:397]     Test net output #1: loss = 5.2797 (* 1 = 5.2797 loss)
I0405 10:27:06.251406 30176 solver.cpp:218] Iteration 12444 (0.787313 iter/s, 15.2417s/12 iters), loss = 5.29252
I0405 10:27:06.251446 30176 solver.cpp:237]     Train net output #0: loss = 5.29252 (* 1 = 5.29252 loss)
I0405 10:27:06.251451 30176 sgd_solver.cpp:105] Iteration 12444, lr = 1e-06
I0405 10:27:10.872609 30176 solver.cpp:218] Iteration 12456 (2.59678 iter/s, 4.62111s/12 iters), loss = 5.28078
I0405 10:27:10.872654 30176 solver.cpp:237]     Train net output #0: loss = 5.28078 (* 1 = 5.28078 loss)
I0405 10:27:10.872659 30176 sgd_solver.cpp:105] Iteration 12456, lr = 1e-06
I0405 10:27:15.249475 30176 blocking_queue.cpp:49] Waiting for data
I0405 10:27:16.137048 30176 solver.cpp:218] Iteration 12468 (2.27949 iter/s, 5.26434s/12 iters), loss = 5.29095
I0405 10:27:16.137135 30176 solver.cpp:237]     Train net output #0: loss = 5.29095 (* 1 = 5.29095 loss)
I0405 10:27:16.137141 30176 sgd_solver.cpp:105] Iteration 12468, lr = 1e-06
I0405 10:27:21.312949 30176 solver.cpp:218] Iteration 12480 (2.3185 iter/s, 5.17577s/12 iters), loss = 5.28541
I0405 10:27:21.312999 30176 solver.cpp:237]     Train net output #0: loss = 5.28541 (* 1 = 5.28541 loss)
I0405 10:27:21.313006 30176 sgd_solver.cpp:105] Iteration 12480, lr = 1e-06
I0405 10:27:27.010805 30176 solver.cpp:218] Iteration 12492 (2.10609 iter/s, 5.69775s/12 iters), loss = 5.29333
I0405 10:27:27.010845 30176 solver.cpp:237]     Train net output #0: loss = 5.29333 (* 1 = 5.29333 loss)
I0405 10:27:27.010851 30176 sgd_solver.cpp:105] Iteration 12492, lr = 1e-06
I0405 10:27:32.835249 30176 solver.cpp:218] Iteration 12504 (2.06032 iter/s, 5.82434s/12 iters), loss = 5.26894
I0405 10:27:32.835301 30176 solver.cpp:237]     Train net output #0: loss = 5.26894 (* 1 = 5.26894 loss)
I0405 10:27:32.835306 30176 sgd_solver.cpp:105] Iteration 12504, lr = 1e-06
I0405 10:27:38.139283 30176 solver.cpp:218] Iteration 12516 (2.26247 iter/s, 5.30393s/12 iters), loss = 5.29001
I0405 10:27:38.139330 30176 solver.cpp:237]     Train net output #0: loss = 5.29001 (* 1 = 5.29001 loss)
I0405 10:27:38.139336 30176 sgd_solver.cpp:105] Iteration 12516, lr = 1e-06
I0405 10:27:43.368217 30176 solver.cpp:218] Iteration 12528 (2.29497 iter/s, 5.22883s/12 iters), loss = 5.26468
I0405 10:27:43.368258 30176 solver.cpp:237]     Train net output #0: loss = 5.26468 (* 1 = 5.26468 loss)
I0405 10:27:43.368263 30176 sgd_solver.cpp:105] Iteration 12528, lr = 1e-06
I0405 10:27:48.561574 30176 solver.cpp:218] Iteration 12540 (2.31069 iter/s, 5.19326s/12 iters), loss = 5.28162
I0405 10:27:48.561717 30176 solver.cpp:237]     Train net output #0: loss = 5.28162 (* 1 = 5.28162 loss)
I0405 10:27:48.561722 30176 sgd_solver.cpp:105] Iteration 12540, lr = 1e-06
I0405 10:27:49.630988 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:27:50.681555 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_12546.caffemodel
I0405 10:27:53.691416 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_12546.solverstate
I0405 10:27:55.996892 30176 solver.cpp:330] Iteration 12546, Testing net (#0)
I0405 10:27:55.996910 30176 net.cpp:676] Ignoring source layer train-data
I0405 10:28:00.304240 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:28:00.666013 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 10:28:00.666045 30176 solver.cpp:397]     Test net output #1: loss = 5.27988 (* 1 = 5.27988 loss)
I0405 10:28:02.464941 30176 solver.cpp:218] Iteration 12552 (0.863116 iter/s, 13.9031s/12 iters), loss = 5.28199
I0405 10:28:02.464979 30176 solver.cpp:237]     Train net output #0: loss = 5.28199 (* 1 = 5.28199 loss)
I0405 10:28:02.464985 30176 sgd_solver.cpp:105] Iteration 12552, lr = 1e-06
I0405 10:28:07.911644 30176 solver.cpp:218] Iteration 12564 (2.20321 iter/s, 5.44661s/12 iters), loss = 5.28668
I0405 10:28:07.911687 30176 solver.cpp:237]     Train net output #0: loss = 5.28668 (* 1 = 5.28668 loss)
I0405 10:28:07.911693 30176 sgd_solver.cpp:105] Iteration 12564, lr = 1e-06
I0405 10:28:13.366677 30176 solver.cpp:218] Iteration 12576 (2.19984 iter/s, 5.45493s/12 iters), loss = 5.28455
I0405 10:28:13.366719 30176 solver.cpp:237]     Train net output #0: loss = 5.28455 (* 1 = 5.28455 loss)
I0405 10:28:13.366724 30176 sgd_solver.cpp:105] Iteration 12576, lr = 1e-06
I0405 10:28:18.668090 30176 solver.cpp:218] Iteration 12588 (2.26359 iter/s, 5.30132s/12 iters), loss = 5.29866
I0405 10:28:18.669059 30176 solver.cpp:237]     Train net output #0: loss = 5.29866 (* 1 = 5.29866 loss)
I0405 10:28:18.669070 30176 sgd_solver.cpp:105] Iteration 12588, lr = 1e-06
I0405 10:28:24.306497 30176 solver.cpp:218] Iteration 12600 (2.12865 iter/s, 5.63738s/12 iters), loss = 5.27623
I0405 10:28:24.306550 30176 solver.cpp:237]     Train net output #0: loss = 5.27623 (* 1 = 5.27623 loss)
I0405 10:28:24.306558 30176 sgd_solver.cpp:105] Iteration 12600, lr = 1e-06
I0405 10:28:29.651718 30176 solver.cpp:218] Iteration 12612 (2.24504 iter/s, 5.34512s/12 iters), loss = 5.28005
I0405 10:28:29.651768 30176 solver.cpp:237]     Train net output #0: loss = 5.28005 (* 1 = 5.28005 loss)
I0405 10:28:29.651777 30176 sgd_solver.cpp:105] Iteration 12612, lr = 1e-06
I0405 10:28:34.865607 30176 solver.cpp:218] Iteration 12624 (2.30159 iter/s, 5.21379s/12 iters), loss = 5.30076
I0405 10:28:34.865664 30176 solver.cpp:237]     Train net output #0: loss = 5.30076 (* 1 = 5.30076 loss)
I0405 10:28:34.865672 30176 sgd_solver.cpp:105] Iteration 12624, lr = 1e-06
I0405 10:28:40.011490 30176 solver.cpp:218] Iteration 12636 (2.33201 iter/s, 5.14577s/12 iters), loss = 5.30611
I0405 10:28:40.011548 30176 solver.cpp:237]     Train net output #0: loss = 5.30611 (* 1 = 5.30611 loss)
I0405 10:28:40.011556 30176 sgd_solver.cpp:105] Iteration 12636, lr = 1e-06
I0405 10:28:43.477842 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:28:45.051262 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_12648.caffemodel
I0405 10:28:48.118284 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_12648.solverstate
I0405 10:28:50.462944 30176 solver.cpp:330] Iteration 12648, Testing net (#0)
I0405 10:28:50.463039 30176 net.cpp:676] Ignoring source layer train-data
I0405 10:28:54.666081 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:28:55.089704 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 10:28:55.089735 30176 solver.cpp:397]     Test net output #1: loss = 5.28 (* 1 = 5.28 loss)
I0405 10:28:55.231036 30176 solver.cpp:218] Iteration 12648 (0.788469 iter/s, 15.2194s/12 iters), loss = 5.28816
I0405 10:28:55.231081 30176 solver.cpp:237]     Train net output #0: loss = 5.28816 (* 1 = 5.28816 loss)
I0405 10:28:55.231087 30176 sgd_solver.cpp:105] Iteration 12648, lr = 1e-06
I0405 10:28:59.728930 30176 solver.cpp:218] Iteration 12660 (2.66797 iter/s, 4.4978s/12 iters), loss = 5.28603
I0405 10:28:59.728976 30176 solver.cpp:237]     Train net output #0: loss = 5.28603 (* 1 = 5.28603 loss)
I0405 10:28:59.728981 30176 sgd_solver.cpp:105] Iteration 12660, lr = 1e-06
I0405 10:29:04.982991 30176 solver.cpp:218] Iteration 12672 (2.28399 iter/s, 5.25396s/12 iters), loss = 5.29394
I0405 10:29:04.983043 30176 solver.cpp:237]     Train net output #0: loss = 5.29394 (* 1 = 5.29394 loss)
I0405 10:29:04.983052 30176 sgd_solver.cpp:105] Iteration 12672, lr = 1e-06
I0405 10:29:10.440325 30176 solver.cpp:218] Iteration 12684 (2.19892 iter/s, 5.45723s/12 iters), loss = 5.27895
I0405 10:29:10.440374 30176 solver.cpp:237]     Train net output #0: loss = 5.27895 (* 1 = 5.27895 loss)
I0405 10:29:10.440382 30176 sgd_solver.cpp:105] Iteration 12684, lr = 1e-06
I0405 10:29:16.077582 30176 solver.cpp:218] Iteration 12696 (2.12873 iter/s, 5.63715s/12 iters), loss = 5.27422
I0405 10:29:16.077632 30176 solver.cpp:237]     Train net output #0: loss = 5.27422 (* 1 = 5.27422 loss)
I0405 10:29:16.077639 30176 sgd_solver.cpp:105] Iteration 12696, lr = 1e-06
I0405 10:29:21.298743 30176 solver.cpp:218] Iteration 12708 (2.29838 iter/s, 5.22106s/12 iters), loss = 5.28449
I0405 10:29:21.298871 30176 solver.cpp:237]     Train net output #0: loss = 5.28449 (* 1 = 5.28449 loss)
I0405 10:29:21.298880 30176 sgd_solver.cpp:105] Iteration 12708, lr = 1e-06
I0405 10:29:26.696516 30176 solver.cpp:218] Iteration 12720 (2.22321 iter/s, 5.3976s/12 iters), loss = 5.27625
I0405 10:29:26.696557 30176 solver.cpp:237]     Train net output #0: loss = 5.27625 (* 1 = 5.27625 loss)
I0405 10:29:26.696563 30176 sgd_solver.cpp:105] Iteration 12720, lr = 1e-06
I0405 10:29:31.935775 30176 solver.cpp:218] Iteration 12732 (2.29044 iter/s, 5.23916s/12 iters), loss = 5.28597
I0405 10:29:31.935822 30176 solver.cpp:237]     Train net output #0: loss = 5.28597 (* 1 = 5.28597 loss)
I0405 10:29:31.935827 30176 sgd_solver.cpp:105] Iteration 12732, lr = 1e-06
I0405 10:29:37.113938 30176 solver.cpp:218] Iteration 12744 (2.31747 iter/s, 5.17806s/12 iters), loss = 5.27551
I0405 10:29:37.113986 30176 solver.cpp:237]     Train net output #0: loss = 5.27551 (* 1 = 5.27551 loss)
I0405 10:29:37.113991 30176 sgd_solver.cpp:105] Iteration 12744, lr = 1e-06
I0405 10:29:37.328989 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:29:39.332089 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_12750.caffemodel
I0405 10:29:42.429021 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_12750.solverstate
I0405 10:29:44.763255 30176 solver.cpp:330] Iteration 12750, Testing net (#0)
I0405 10:29:44.763284 30176 net.cpp:676] Ignoring source layer train-data
I0405 10:29:48.985950 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:29:49.392853 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 10:29:49.392895 30176 solver.cpp:397]     Test net output #1: loss = 5.28017 (* 1 = 5.28017 loss)
I0405 10:29:51.387719 30176 solver.cpp:218] Iteration 12756 (0.840712 iter/s, 14.2736s/12 iters), loss = 5.28693
I0405 10:29:51.387882 30176 solver.cpp:237]     Train net output #0: loss = 5.28693 (* 1 = 5.28693 loss)
I0405 10:29:51.387892 30176 sgd_solver.cpp:105] Iteration 12756, lr = 1e-06
I0405 10:29:57.018698 30176 solver.cpp:218] Iteration 12768 (2.13115 iter/s, 5.63076s/12 iters), loss = 5.29422
I0405 10:29:57.018759 30176 solver.cpp:237]     Train net output #0: loss = 5.29422 (* 1 = 5.29422 loss)
I0405 10:29:57.018767 30176 sgd_solver.cpp:105] Iteration 12768, lr = 1e-06
I0405 10:30:02.616173 30176 solver.cpp:218] Iteration 12780 (2.14387 iter/s, 5.59736s/12 iters), loss = 5.28207
I0405 10:30:02.616227 30176 solver.cpp:237]     Train net output #0: loss = 5.28207 (* 1 = 5.28207 loss)
I0405 10:30:02.616235 30176 sgd_solver.cpp:105] Iteration 12780, lr = 1e-06
I0405 10:30:07.769064 30176 solver.cpp:218] Iteration 12792 (2.32884 iter/s, 5.15279s/12 iters), loss = 5.27434
I0405 10:30:07.769119 30176 solver.cpp:237]     Train net output #0: loss = 5.27434 (* 1 = 5.27434 loss)
I0405 10:30:07.769126 30176 sgd_solver.cpp:105] Iteration 12792, lr = 1e-06
I0405 10:30:13.232534 30176 solver.cpp:218] Iteration 12804 (2.19645 iter/s, 5.46337s/12 iters), loss = 5.2754
I0405 10:30:13.232586 30176 solver.cpp:237]     Train net output #0: loss = 5.2754 (* 1 = 5.2754 loss)
I0405 10:30:13.232595 30176 sgd_solver.cpp:105] Iteration 12804, lr = 1e-06
I0405 10:30:18.684191 30176 solver.cpp:218] Iteration 12816 (2.20121 iter/s, 5.45155s/12 iters), loss = 5.30201
I0405 10:30:18.684231 30176 solver.cpp:237]     Train net output #0: loss = 5.30201 (* 1 = 5.30201 loss)
I0405 10:30:18.684235 30176 sgd_solver.cpp:105] Iteration 12816, lr = 1e-06
I0405 10:30:24.168790 30176 solver.cpp:218] Iteration 12828 (2.18798 iter/s, 5.4845s/12 iters), loss = 5.27986
I0405 10:30:24.168898 30176 solver.cpp:237]     Train net output #0: loss = 5.27986 (* 1 = 5.27986 loss)
I0405 10:30:24.168905 30176 sgd_solver.cpp:105] Iteration 12828, lr = 1e-06
I0405 10:30:29.543068 30176 solver.cpp:218] Iteration 12840 (2.23292 iter/s, 5.37412s/12 iters), loss = 5.27326
I0405 10:30:29.543116 30176 solver.cpp:237]     Train net output #0: loss = 5.27326 (* 1 = 5.27326 loss)
I0405 10:30:29.543124 30176 sgd_solver.cpp:105] Iteration 12840, lr = 1e-06
I0405 10:30:32.132450 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:30:34.547857 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_12852.caffemodel
I0405 10:30:37.603432 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_12852.solverstate
I0405 10:30:39.923280 30176 solver.cpp:330] Iteration 12852, Testing net (#0)
I0405 10:30:39.923306 30176 net.cpp:676] Ignoring source layer train-data
I0405 10:30:44.221284 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:30:44.760942 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 10:30:44.760972 30176 solver.cpp:397]     Test net output #1: loss = 5.27988 (* 1 = 5.27988 loss)
I0405 10:30:44.901901 30176 solver.cpp:218] Iteration 12852 (0.781318 iter/s, 15.3587s/12 iters), loss = 5.29148
I0405 10:30:44.901963 30176 solver.cpp:237]     Train net output #0: loss = 5.29148 (* 1 = 5.29148 loss)
I0405 10:30:44.901973 30176 sgd_solver.cpp:105] Iteration 12852, lr = 1e-06
I0405 10:30:49.203511 30176 solver.cpp:218] Iteration 12864 (2.78972 iter/s, 4.3015s/12 iters), loss = 5.28909
I0405 10:30:49.203558 30176 solver.cpp:237]     Train net output #0: loss = 5.28909 (* 1 = 5.28909 loss)
I0405 10:30:49.203564 30176 sgd_solver.cpp:105] Iteration 12864, lr = 1e-06
I0405 10:30:54.608290 30176 solver.cpp:218] Iteration 12876 (2.2203 iter/s, 5.40468s/12 iters), loss = 5.29691
I0405 10:30:54.608374 30176 solver.cpp:237]     Train net output #0: loss = 5.29691 (* 1 = 5.29691 loss)
I0405 10:30:54.608381 30176 sgd_solver.cpp:105] Iteration 12876, lr = 1e-06
I0405 10:30:59.823179 30176 solver.cpp:218] Iteration 12888 (2.30117 iter/s, 5.21475s/12 iters), loss = 5.2702
I0405 10:30:59.823235 30176 solver.cpp:237]     Train net output #0: loss = 5.2702 (* 1 = 5.2702 loss)
I0405 10:30:59.823243 30176 sgd_solver.cpp:105] Iteration 12888, lr = 1e-06
I0405 10:31:05.365160 30176 solver.cpp:218] Iteration 12900 (2.16533 iter/s, 5.54187s/12 iters), loss = 5.28231
I0405 10:31:05.365217 30176 solver.cpp:237]     Train net output #0: loss = 5.28231 (* 1 = 5.28231 loss)
I0405 10:31:05.365226 30176 sgd_solver.cpp:105] Iteration 12900, lr = 1e-06
I0405 10:31:10.784544 30176 solver.cpp:218] Iteration 12912 (2.21432 iter/s, 5.41927s/12 iters), loss = 5.29008
I0405 10:31:10.784597 30176 solver.cpp:237]     Train net output #0: loss = 5.29008 (* 1 = 5.29008 loss)
I0405 10:31:10.784605 30176 sgd_solver.cpp:105] Iteration 12912, lr = 1e-06
I0405 10:31:16.055950 30176 solver.cpp:218] Iteration 12924 (2.27648 iter/s, 5.2713s/12 iters), loss = 5.29419
I0405 10:31:16.055990 30176 solver.cpp:237]     Train net output #0: loss = 5.29419 (* 1 = 5.29419 loss)
I0405 10:31:16.055995 30176 sgd_solver.cpp:105] Iteration 12924, lr = 1e-06
I0405 10:31:21.606114 30176 solver.cpp:218] Iteration 12936 (2.16214 iter/s, 5.55007s/12 iters), loss = 5.30401
I0405 10:31:21.606163 30176 solver.cpp:237]     Train net output #0: loss = 5.30401 (* 1 = 5.30401 loss)
I0405 10:31:21.606171 30176 sgd_solver.cpp:105] Iteration 12936, lr = 1e-06
I0405 10:31:26.400527 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:31:26.950800 30176 solver.cpp:218] Iteration 12948 (2.24527 iter/s, 5.34458s/12 iters), loss = 5.29117
I0405 10:31:26.950858 30176 solver.cpp:237]     Train net output #0: loss = 5.29117 (* 1 = 5.29117 loss)
I0405 10:31:26.950868 30176 sgd_solver.cpp:105] Iteration 12948, lr = 1e-06
I0405 10:31:29.289413 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_12954.caffemodel
I0405 10:31:32.283078 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_12954.solverstate
I0405 10:31:34.607224 30176 solver.cpp:330] Iteration 12954, Testing net (#0)
I0405 10:31:34.607249 30176 net.cpp:676] Ignoring source layer train-data
I0405 10:31:38.582986 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:31:39.067414 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 10:31:39.067451 30176 solver.cpp:397]     Test net output #1: loss = 5.28019 (* 1 = 5.28019 loss)
I0405 10:31:41.170923 30176 solver.cpp:218] Iteration 12960 (0.843885 iter/s, 14.22s/12 iters), loss = 5.2771
I0405 10:31:41.170977 30176 solver.cpp:237]     Train net output #0: loss = 5.2771 (* 1 = 5.2771 loss)
I0405 10:31:41.170985 30176 sgd_solver.cpp:105] Iteration 12960, lr = 1e-06
I0405 10:31:46.647011 30176 solver.cpp:218] Iteration 12972 (2.19139 iter/s, 5.47598s/12 iters), loss = 5.2915
I0405 10:31:46.647053 30176 solver.cpp:237]     Train net output #0: loss = 5.2915 (* 1 = 5.2915 loss)
I0405 10:31:46.647058 30176 sgd_solver.cpp:105] Iteration 12972, lr = 1e-06
I0405 10:31:51.825341 30176 solver.cpp:218] Iteration 12984 (2.31739 iter/s, 5.17823s/12 iters), loss = 5.28702
I0405 10:31:51.825393 30176 solver.cpp:237]     Train net output #0: loss = 5.28702 (* 1 = 5.28702 loss)
I0405 10:31:51.825402 30176 sgd_solver.cpp:105] Iteration 12984, lr = 1e-06
I0405 10:31:57.256642 30176 solver.cpp:218] Iteration 12996 (2.20946 iter/s, 5.4312s/12 iters), loss = 5.29038
I0405 10:31:57.256748 30176 solver.cpp:237]     Train net output #0: loss = 5.29038 (* 1 = 5.29038 loss)
I0405 10:31:57.256757 30176 sgd_solver.cpp:105] Iteration 12996, lr = 1e-06
I0405 10:32:02.618566 30176 solver.cpp:218] Iteration 13008 (2.23807 iter/s, 5.36176s/12 iters), loss = 5.28167
I0405 10:32:02.618618 30176 solver.cpp:237]     Train net output #0: loss = 5.28167 (* 1 = 5.28167 loss)
I0405 10:32:02.618625 30176 sgd_solver.cpp:105] Iteration 13008, lr = 1e-06
I0405 10:32:08.130213 30176 solver.cpp:218] Iteration 13020 (2.17725 iter/s, 5.51155s/12 iters), loss = 5.28254
I0405 10:32:08.130261 30176 solver.cpp:237]     Train net output #0: loss = 5.28254 (* 1 = 5.28254 loss)
I0405 10:32:08.130268 30176 sgd_solver.cpp:105] Iteration 13020, lr = 1e-06
I0405 10:32:13.590960 30176 solver.cpp:218] Iteration 13032 (2.19754 iter/s, 5.46065s/12 iters), loss = 5.27924
I0405 10:32:13.591003 30176 solver.cpp:237]     Train net output #0: loss = 5.27924 (* 1 = 5.27924 loss)
I0405 10:32:13.591009 30176 sgd_solver.cpp:105] Iteration 13032, lr = 1e-06
I0405 10:32:18.880929 30176 solver.cpp:218] Iteration 13044 (2.26849 iter/s, 5.28987s/12 iters), loss = 5.28573
I0405 10:32:18.880975 30176 solver.cpp:237]     Train net output #0: loss = 5.28573 (* 1 = 5.28573 loss)
I0405 10:32:18.880980 30176 sgd_solver.cpp:105] Iteration 13044, lr = 1e-06
I0405 10:32:20.895623 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:32:23.957494 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_13056.caffemodel
I0405 10:32:27.123577 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_13056.solverstate
I0405 10:32:29.436895 30176 solver.cpp:330] Iteration 13056, Testing net (#0)
I0405 10:32:29.437007 30176 net.cpp:676] Ignoring source layer train-data
I0405 10:32:33.408097 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:32:33.940455 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 10:32:33.940498 30176 solver.cpp:397]     Test net output #1: loss = 5.27977 (* 1 = 5.27977 loss)
I0405 10:32:34.082196 30176 solver.cpp:218] Iteration 13056 (0.789417 iter/s, 15.2011s/12 iters), loss = 5.27299
I0405 10:32:34.082252 30176 solver.cpp:237]     Train net output #0: loss = 5.27299 (* 1 = 5.27299 loss)
I0405 10:32:34.082260 30176 sgd_solver.cpp:105] Iteration 13056, lr = 1e-06
I0405 10:32:38.287413 30176 solver.cpp:218] Iteration 13068 (2.85367 iter/s, 4.20512s/12 iters), loss = 5.29543
I0405 10:32:38.287462 30176 solver.cpp:237]     Train net output #0: loss = 5.29543 (* 1 = 5.29543 loss)
I0405 10:32:38.287469 30176 sgd_solver.cpp:105] Iteration 13068, lr = 1e-06
I0405 10:32:43.972146 30176 solver.cpp:218] Iteration 13080 (2.11096 iter/s, 5.68463s/12 iters), loss = 5.28742
I0405 10:32:43.972199 30176 solver.cpp:237]     Train net output #0: loss = 5.28742 (* 1 = 5.28742 loss)
I0405 10:32:43.972208 30176 sgd_solver.cpp:105] Iteration 13080, lr = 1e-06
I0405 10:32:49.372220 30176 solver.cpp:218] Iteration 13092 (2.22224 iter/s, 5.39997s/12 iters), loss = 5.26684
I0405 10:32:49.372268 30176 solver.cpp:237]     Train net output #0: loss = 5.26684 (* 1 = 5.26684 loss)
I0405 10:32:49.372275 30176 sgd_solver.cpp:105] Iteration 13092, lr = 1e-06
I0405 10:32:54.896517 30176 solver.cpp:218] Iteration 13104 (2.17227 iter/s, 5.52419s/12 iters), loss = 5.2865
I0405 10:32:54.896570 30176 solver.cpp:237]     Train net output #0: loss = 5.2865 (* 1 = 5.2865 loss)
I0405 10:32:54.896577 30176 sgd_solver.cpp:105] Iteration 13104, lr = 1e-06
I0405 10:33:00.065187 30176 solver.cpp:218] Iteration 13116 (2.32173 iter/s, 5.16856s/12 iters), loss = 5.28565
I0405 10:33:00.065336 30176 solver.cpp:237]     Train net output #0: loss = 5.28565 (* 1 = 5.28565 loss)
I0405 10:33:00.065346 30176 sgd_solver.cpp:105] Iteration 13116, lr = 1e-06
I0405 10:33:05.460537 30176 solver.cpp:218] Iteration 13128 (2.22422 iter/s, 5.39515s/12 iters), loss = 5.27939
I0405 10:33:05.460592 30176 solver.cpp:237]     Train net output #0: loss = 5.27939 (* 1 = 5.27939 loss)
I0405 10:33:05.460599 30176 sgd_solver.cpp:105] Iteration 13128, lr = 1e-06
I0405 10:33:10.782753 30176 solver.cpp:218] Iteration 13140 (2.25475 iter/s, 5.32211s/12 iters), loss = 5.27945
I0405 10:33:10.782815 30176 solver.cpp:237]     Train net output #0: loss = 5.27945 (* 1 = 5.27945 loss)
I0405 10:33:10.782824 30176 sgd_solver.cpp:105] Iteration 13140, lr = 1e-06
I0405 10:33:14.970341 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:33:16.281402 30176 solver.cpp:218] Iteration 13152 (2.1824 iter/s, 5.49854s/12 iters), loss = 5.28251
I0405 10:33:16.281440 30176 solver.cpp:237]     Train net output #0: loss = 5.28251 (* 1 = 5.28251 loss)
I0405 10:33:16.281445 30176 sgd_solver.cpp:105] Iteration 13152, lr = 1e-06
I0405 10:33:18.493166 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_13158.caffemodel
I0405 10:33:21.548316 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_13158.solverstate
I0405 10:33:23.870445 30176 solver.cpp:330] Iteration 13158, Testing net (#0)
I0405 10:33:23.870466 30176 net.cpp:676] Ignoring source layer train-data
I0405 10:33:27.637440 30176 blocking_queue.cpp:49] Waiting for data
I0405 10:33:27.924643 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:33:28.492130 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 10:33:28.492166 30176 solver.cpp:397]     Test net output #1: loss = 5.27993 (* 1 = 5.27993 loss)
I0405 10:33:30.566349 30176 solver.cpp:218] Iteration 13164 (0.840054 iter/s, 14.2848s/12 iters), loss = 5.28731
I0405 10:33:30.566509 30176 solver.cpp:237]     Train net output #0: loss = 5.28731 (* 1 = 5.28731 loss)
I0405 10:33:30.566519 30176 sgd_solver.cpp:105] Iteration 13164, lr = 1e-06
I0405 10:33:36.081377 30176 solver.cpp:218] Iteration 13176 (2.17596 iter/s, 5.51481s/12 iters), loss = 5.28696
I0405 10:33:36.081444 30176 solver.cpp:237]     Train net output #0: loss = 5.28696 (* 1 = 5.28696 loss)
I0405 10:33:36.081454 30176 sgd_solver.cpp:105] Iteration 13176, lr = 1e-06
I0405 10:33:41.338382 30176 solver.cpp:218] Iteration 13188 (2.28272 iter/s, 5.25688s/12 iters), loss = 5.27857
I0405 10:33:41.338428 30176 solver.cpp:237]     Train net output #0: loss = 5.27857 (* 1 = 5.27857 loss)
I0405 10:33:41.338433 30176 sgd_solver.cpp:105] Iteration 13188, lr = 1e-06
I0405 10:33:46.713589 30176 solver.cpp:218] Iteration 13200 (2.23251 iter/s, 5.37511s/12 iters), loss = 5.29413
I0405 10:33:46.713629 30176 solver.cpp:237]     Train net output #0: loss = 5.29413 (* 1 = 5.29413 loss)
I0405 10:33:46.713634 30176 sgd_solver.cpp:105] Iteration 13200, lr = 1e-06
I0405 10:33:51.700083 30176 solver.cpp:218] Iteration 13212 (2.40655 iter/s, 4.9864s/12 iters), loss = 5.2749
I0405 10:33:51.700139 30176 solver.cpp:237]     Train net output #0: loss = 5.2749 (* 1 = 5.2749 loss)
I0405 10:33:51.700148 30176 sgd_solver.cpp:105] Iteration 13212, lr = 1e-06
I0405 10:33:57.047662 30176 solver.cpp:218] Iteration 13224 (2.24405 iter/s, 5.34748s/12 iters), loss = 5.28902
I0405 10:33:57.047711 30176 solver.cpp:237]     Train net output #0: loss = 5.28902 (* 1 = 5.28902 loss)
I0405 10:33:57.047719 30176 sgd_solver.cpp:105] Iteration 13224, lr = 1e-06
I0405 10:34:02.354120 30176 solver.cpp:218] Iteration 13236 (2.26144 iter/s, 5.30635s/12 iters), loss = 5.29022
I0405 10:34:02.354235 30176 solver.cpp:237]     Train net output #0: loss = 5.29022 (* 1 = 5.29022 loss)
I0405 10:34:02.354243 30176 sgd_solver.cpp:105] Iteration 13236, lr = 1e-06
I0405 10:34:07.706621 30176 solver.cpp:218] Iteration 13248 (2.24201 iter/s, 5.35233s/12 iters), loss = 5.2874
I0405 10:34:07.706671 30176 solver.cpp:237]     Train net output #0: loss = 5.2874 (* 1 = 5.2874 loss)
I0405 10:34:07.706679 30176 sgd_solver.cpp:105] Iteration 13248, lr = 1e-06
I0405 10:34:08.776024 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:34:12.676662 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_13260.caffemodel
I0405 10:34:16.413164 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_13260.solverstate
I0405 10:34:19.516434 30176 solver.cpp:330] Iteration 13260, Testing net (#0)
I0405 10:34:19.516454 30176 net.cpp:676] Ignoring source layer train-data
I0405 10:34:23.620841 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:34:24.361037 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 10:34:24.361078 30176 solver.cpp:397]     Test net output #1: loss = 5.27975 (* 1 = 5.27975 loss)
I0405 10:34:24.503665 30176 solver.cpp:218] Iteration 13260 (0.714419 iter/s, 16.7969s/12 iters), loss = 5.2731
I0405 10:34:24.503716 30176 solver.cpp:237]     Train net output #0: loss = 5.2731 (* 1 = 5.2731 loss)
I0405 10:34:24.503724 30176 sgd_solver.cpp:105] Iteration 13260, lr = 1e-06
I0405 10:34:29.043295 30176 solver.cpp:218] Iteration 13272 (2.64344 iter/s, 4.53953s/12 iters), loss = 5.28104
I0405 10:34:29.043354 30176 solver.cpp:237]     Train net output #0: loss = 5.28104 (* 1 = 5.28104 loss)
I0405 10:34:29.043362 30176 sgd_solver.cpp:105] Iteration 13272, lr = 1e-06
I0405 10:34:34.654618 30176 solver.cpp:218] Iteration 13284 (2.13858 iter/s, 5.61121s/12 iters), loss = 5.29158
I0405 10:34:34.654779 30176 solver.cpp:237]     Train net output #0: loss = 5.29158 (* 1 = 5.29158 loss)
I0405 10:34:34.654791 30176 sgd_solver.cpp:105] Iteration 13284, lr = 1e-06
I0405 10:34:39.973289 30176 solver.cpp:218] Iteration 13296 (2.25629 iter/s, 5.31846s/12 iters), loss = 5.28859
I0405 10:34:39.973341 30176 solver.cpp:237]     Train net output #0: loss = 5.28859 (* 1 = 5.28859 loss)
I0405 10:34:39.973349 30176 sgd_solver.cpp:105] Iteration 13296, lr = 1e-06
I0405 10:34:45.247666 30176 solver.cpp:218] Iteration 13308 (2.2752 iter/s, 5.27427s/12 iters), loss = 5.28023
I0405 10:34:45.247722 30176 solver.cpp:237]     Train net output #0: loss = 5.28023 (* 1 = 5.28023 loss)
I0405 10:34:45.247730 30176 sgd_solver.cpp:105] Iteration 13308, lr = 1e-06
I0405 10:34:50.667358 30176 solver.cpp:218] Iteration 13320 (2.21419 iter/s, 5.41958s/12 iters), loss = 5.27221
I0405 10:34:50.667408 30176 solver.cpp:237]     Train net output #0: loss = 5.27221 (* 1 = 5.27221 loss)
I0405 10:34:50.667416 30176 sgd_solver.cpp:105] Iteration 13320, lr = 1e-06
I0405 10:34:56.286897 30176 solver.cpp:218] Iteration 13332 (2.13545 iter/s, 5.61944s/12 iters), loss = 5.29453
I0405 10:34:56.286940 30176 solver.cpp:237]     Train net output #0: loss = 5.29453 (* 1 = 5.29453 loss)
I0405 10:34:56.286945 30176 sgd_solver.cpp:105] Iteration 13332, lr = 1e-06
I0405 10:35:01.559569 30176 solver.cpp:218] Iteration 13344 (2.27593 iter/s, 5.27257s/12 iters), loss = 5.29283
I0405 10:35:01.565766 30176 solver.cpp:237]     Train net output #0: loss = 5.29283 (* 1 = 5.29283 loss)
I0405 10:35:01.565779 30176 sgd_solver.cpp:105] Iteration 13344, lr = 1e-06
I0405 10:35:04.971006 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:35:07.027463 30176 solver.cpp:218] Iteration 13356 (2.19714 iter/s, 5.46165s/12 iters), loss = 5.29832
I0405 10:35:07.027519 30176 solver.cpp:237]     Train net output #0: loss = 5.29832 (* 1 = 5.29832 loss)
I0405 10:35:07.027529 30176 sgd_solver.cpp:105] Iteration 13356, lr = 1e-06
I0405 10:35:09.306602 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_13362.caffemodel
I0405 10:35:12.324348 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_13362.solverstate
I0405 10:35:14.633505 30176 solver.cpp:330] Iteration 13362, Testing net (#0)
I0405 10:35:14.633525 30176 net.cpp:676] Ignoring source layer train-data
I0405 10:35:18.425340 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:35:19.072721 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 10:35:19.072752 30176 solver.cpp:397]     Test net output #1: loss = 5.27982 (* 1 = 5.27982 loss)
I0405 10:35:21.032043 30176 solver.cpp:218] Iteration 13368 (0.856873 iter/s, 14.0044s/12 iters), loss = 5.29022
I0405 10:35:21.032100 30176 solver.cpp:237]     Train net output #0: loss = 5.29022 (* 1 = 5.29022 loss)
I0405 10:35:21.032109 30176 sgd_solver.cpp:105] Iteration 13368, lr = 1e-06
I0405 10:35:26.613245 30176 solver.cpp:218] Iteration 13380 (2.15012 iter/s, 5.58109s/12 iters), loss = 5.27347
I0405 10:35:26.613296 30176 solver.cpp:237]     Train net output #0: loss = 5.27347 (* 1 = 5.27347 loss)
I0405 10:35:26.613304 30176 sgd_solver.cpp:105] Iteration 13380, lr = 1e-06
I0405 10:35:31.933879 30176 solver.cpp:218] Iteration 13392 (2.25542 iter/s, 5.32053s/12 iters), loss = 5.29283
I0405 10:35:31.933940 30176 solver.cpp:237]     Train net output #0: loss = 5.29283 (* 1 = 5.29283 loss)
I0405 10:35:31.933951 30176 sgd_solver.cpp:105] Iteration 13392, lr = 1e-06
I0405 10:35:37.400482 30176 solver.cpp:218] Iteration 13404 (2.19519 iter/s, 5.46649s/12 iters), loss = 5.28203
I0405 10:35:37.400611 30176 solver.cpp:237]     Train net output #0: loss = 5.28203 (* 1 = 5.28203 loss)
I0405 10:35:37.400617 30176 sgd_solver.cpp:105] Iteration 13404, lr = 1e-06
I0405 10:35:42.861322 30176 solver.cpp:218] Iteration 13416 (2.19754 iter/s, 5.46065s/12 iters), loss = 5.28852
I0405 10:35:42.861382 30176 solver.cpp:237]     Train net output #0: loss = 5.28852 (* 1 = 5.28852 loss)
I0405 10:35:42.861392 30176 sgd_solver.cpp:105] Iteration 13416, lr = 1e-06
I0405 10:35:48.164783 30176 solver.cpp:218] Iteration 13428 (2.26272 iter/s, 5.30334s/12 iters), loss = 5.28313
I0405 10:35:48.164841 30176 solver.cpp:237]     Train net output #0: loss = 5.28313 (* 1 = 5.28313 loss)
I0405 10:35:48.164849 30176 sgd_solver.cpp:105] Iteration 13428, lr = 1e-06
I0405 10:35:53.736915 30176 solver.cpp:218] Iteration 13440 (2.15363 iter/s, 5.572s/12 iters), loss = 5.27316
I0405 10:35:53.736963 30176 solver.cpp:237]     Train net output #0: loss = 5.27316 (* 1 = 5.27316 loss)
I0405 10:35:53.736970 30176 sgd_solver.cpp:105] Iteration 13440, lr = 1e-06
I0405 10:35:59.215327 30176 solver.cpp:218] Iteration 13452 (2.19046 iter/s, 5.47831s/12 iters), loss = 5.2841
I0405 10:35:59.215378 30176 solver.cpp:237]     Train net output #0: loss = 5.2841 (* 1 = 5.2841 loss)
I0405 10:35:59.215386 30176 sgd_solver.cpp:105] Iteration 13452, lr = 1e-06
I0405 10:35:59.388002 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:36:03.869626 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_13464.caffemodel
I0405 10:36:07.064620 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_13464.solverstate
I0405 10:36:09.355204 30176 solver.cpp:330] Iteration 13464, Testing net (#0)
I0405 10:36:09.355329 30176 net.cpp:676] Ignoring source layer train-data
I0405 10:36:13.181519 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:36:13.858971 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 10:36:13.859006 30176 solver.cpp:397]     Test net output #1: loss = 5.27971 (* 1 = 5.27971 loss)
I0405 10:36:14.001811 30176 solver.cpp:218] Iteration 13464 (0.811561 iter/s, 14.7863s/12 iters), loss = 5.28682
I0405 10:36:14.001863 30176 solver.cpp:237]     Train net output #0: loss = 5.28682 (* 1 = 5.28682 loss)
I0405 10:36:14.001869 30176 sgd_solver.cpp:105] Iteration 13464, lr = 1e-06
I0405 10:36:18.220301 30176 solver.cpp:218] Iteration 13476 (2.84469 iter/s, 4.21839s/12 iters), loss = 5.28018
I0405 10:36:18.220364 30176 solver.cpp:237]     Train net output #0: loss = 5.28018 (* 1 = 5.28018 loss)
I0405 10:36:18.220373 30176 sgd_solver.cpp:105] Iteration 13476, lr = 1e-06
I0405 10:36:23.463023 30176 solver.cpp:218] Iteration 13488 (2.28894 iter/s, 5.24261s/12 iters), loss = 5.26578
I0405 10:36:23.463075 30176 solver.cpp:237]     Train net output #0: loss = 5.26578 (* 1 = 5.26578 loss)
I0405 10:36:23.463083 30176 sgd_solver.cpp:105] Iteration 13488, lr = 1e-06
I0405 10:36:28.898149 30176 solver.cpp:218] Iteration 13500 (2.20791 iter/s, 5.43501s/12 iters), loss = 5.27879
I0405 10:36:28.898206 30176 solver.cpp:237]     Train net output #0: loss = 5.27879 (* 1 = 5.27879 loss)
I0405 10:36:28.898216 30176 sgd_solver.cpp:105] Iteration 13500, lr = 1e-06
I0405 10:36:34.258405 30176 solver.cpp:218] Iteration 13512 (2.23875 iter/s, 5.36015s/12 iters), loss = 5.2946
I0405 10:36:34.258458 30176 solver.cpp:237]     Train net output #0: loss = 5.2946 (* 1 = 5.2946 loss)
I0405 10:36:34.258466 30176 sgd_solver.cpp:105] Iteration 13512, lr = 1e-06
I0405 10:36:39.628631 30176 solver.cpp:218] Iteration 13524 (2.23459 iter/s, 5.37012s/12 iters), loss = 5.28052
I0405 10:36:39.628732 30176 solver.cpp:237]     Train net output #0: loss = 5.28052 (* 1 = 5.28052 loss)
I0405 10:36:39.628739 30176 sgd_solver.cpp:105] Iteration 13524, lr = 1e-06
I0405 10:36:44.892406 30176 solver.cpp:218] Iteration 13536 (2.2798 iter/s, 5.26362s/12 iters), loss = 5.28053
I0405 10:36:44.892444 30176 solver.cpp:237]     Train net output #0: loss = 5.28053 (* 1 = 5.28053 loss)
I0405 10:36:44.892450 30176 sgd_solver.cpp:105] Iteration 13536, lr = 1e-06
I0405 10:36:50.218844 30176 solver.cpp:218] Iteration 13548 (2.25295 iter/s, 5.32634s/12 iters), loss = 5.28121
I0405 10:36:50.218900 30176 solver.cpp:237]     Train net output #0: loss = 5.28121 (* 1 = 5.28121 loss)
I0405 10:36:50.218909 30176 sgd_solver.cpp:105] Iteration 13548, lr = 1e-06
I0405 10:36:52.798463 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:36:55.674414 30176 solver.cpp:218] Iteration 13560 (2.19963 iter/s, 5.45546s/12 iters), loss = 5.28275
I0405 10:36:55.674485 30176 solver.cpp:237]     Train net output #0: loss = 5.28275 (* 1 = 5.28275 loss)
I0405 10:36:55.674496 30176 sgd_solver.cpp:105] Iteration 13560, lr = 1e-06
I0405 10:36:57.992174 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_13566.caffemodel
I0405 10:37:01.099296 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_13566.solverstate
I0405 10:37:03.389489 30176 solver.cpp:330] Iteration 13566, Testing net (#0)
I0405 10:37:03.389511 30176 net.cpp:676] Ignoring source layer train-data
I0405 10:37:07.115870 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:37:07.867866 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 10:37:07.867902 30176 solver.cpp:397]     Test net output #1: loss = 5.27978 (* 1 = 5.27978 loss)
I0405 10:37:09.978955 30176 solver.cpp:218] Iteration 13572 (0.838905 iter/s, 14.3044s/12 iters), loss = 5.2733
I0405 10:37:09.979075 30176 solver.cpp:237]     Train net output #0: loss = 5.2733 (* 1 = 5.2733 loss)
I0405 10:37:09.979082 30176 sgd_solver.cpp:105] Iteration 13572, lr = 1e-06
I0405 10:37:15.346303 30176 solver.cpp:218] Iteration 13584 (2.23581 iter/s, 5.36717s/12 iters), loss = 5.2945
I0405 10:37:15.346344 30176 solver.cpp:237]     Train net output #0: loss = 5.2945 (* 1 = 5.2945 loss)
I0405 10:37:15.346349 30176 sgd_solver.cpp:105] Iteration 13584, lr = 1e-06
I0405 10:37:20.814257 30176 solver.cpp:218] Iteration 13596 (2.19464 iter/s, 5.46786s/12 iters), loss = 5.28036
I0405 10:37:20.814332 30176 solver.cpp:237]     Train net output #0: loss = 5.28036 (* 1 = 5.28036 loss)
I0405 10:37:20.814339 30176 sgd_solver.cpp:105] Iteration 13596, lr = 1e-06
I0405 10:37:26.144948 30176 solver.cpp:218] Iteration 13608 (2.25117 iter/s, 5.33056s/12 iters), loss = 5.28156
I0405 10:37:26.144994 30176 solver.cpp:237]     Train net output #0: loss = 5.28156 (* 1 = 5.28156 loss)
I0405 10:37:26.145001 30176 sgd_solver.cpp:105] Iteration 13608, lr = 1e-06
I0405 10:37:31.778213 30176 solver.cpp:218] Iteration 13620 (2.13024 iter/s, 5.63317s/12 iters), loss = 5.29292
I0405 10:37:31.778252 30176 solver.cpp:237]     Train net output #0: loss = 5.29292 (* 1 = 5.29292 loss)
I0405 10:37:31.778257 30176 sgd_solver.cpp:105] Iteration 13620, lr = 1e-06
I0405 10:37:37.286141 30176 solver.cpp:218] Iteration 13632 (2.17871 iter/s, 5.50783s/12 iters), loss = 5.28457
I0405 10:37:37.286180 30176 solver.cpp:237]     Train net output #0: loss = 5.28457 (* 1 = 5.28457 loss)
I0405 10:37:37.286185 30176 sgd_solver.cpp:105] Iteration 13632, lr = 1e-06
I0405 10:37:42.366369 30176 solver.cpp:218] Iteration 13644 (2.36215 iter/s, 5.08013s/12 iters), loss = 5.27869
I0405 10:37:42.366570 30176 solver.cpp:237]     Train net output #0: loss = 5.27869 (* 1 = 5.27869 loss)
I0405 10:37:42.366580 30176 sgd_solver.cpp:105] Iteration 13644, lr = 1e-06
I0405 10:37:47.361871 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:37:47.775936 30176 solver.cpp:218] Iteration 13656 (2.21839 iter/s, 5.40932s/12 iters), loss = 5.28634
I0405 10:37:47.775977 30176 solver.cpp:237]     Train net output #0: loss = 5.28634 (* 1 = 5.28634 loss)
I0405 10:37:47.775982 30176 sgd_solver.cpp:105] Iteration 13656, lr = 1e-06
I0405 10:37:52.752637 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_13668.caffemodel
I0405 10:37:55.829762 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_13668.solverstate
I0405 10:37:58.198352 30176 solver.cpp:330] Iteration 13668, Testing net (#0)
I0405 10:37:58.198376 30176 net.cpp:676] Ignoring source layer train-data
I0405 10:38:01.942227 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:38:02.705193 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 10:38:02.705232 30176 solver.cpp:397]     Test net output #1: loss = 5.27968 (* 1 = 5.27968 loss)
I0405 10:38:02.847901 30176 solver.cpp:218] Iteration 13668 (0.796189 iter/s, 15.0718s/12 iters), loss = 5.26129
I0405 10:38:02.847949 30176 solver.cpp:237]     Train net output #0: loss = 5.26129 (* 1 = 5.26129 loss)
I0405 10:38:02.847954 30176 sgd_solver.cpp:105] Iteration 13668, lr = 1e-06
I0405 10:38:07.568858 30176 solver.cpp:218] Iteration 13680 (2.54191 iter/s, 4.72086s/12 iters), loss = 5.30068
I0405 10:38:07.568920 30176 solver.cpp:237]     Train net output #0: loss = 5.30068 (* 1 = 5.30068 loss)
I0405 10:38:07.568929 30176 sgd_solver.cpp:105] Iteration 13680, lr = 1e-06
I0405 10:38:13.316275 30176 solver.cpp:218] Iteration 13692 (2.08794 iter/s, 5.7473s/12 iters), loss = 5.28792
I0405 10:38:13.316395 30176 solver.cpp:237]     Train net output #0: loss = 5.28792 (* 1 = 5.28792 loss)
I0405 10:38:13.316402 30176 sgd_solver.cpp:105] Iteration 13692, lr = 1e-06
I0405 10:38:18.495841 30176 solver.cpp:218] Iteration 13704 (2.31687 iter/s, 5.17939s/12 iters), loss = 5.28867
I0405 10:38:18.495895 30176 solver.cpp:237]     Train net output #0: loss = 5.28867 (* 1 = 5.28867 loss)
I0405 10:38:18.495903 30176 sgd_solver.cpp:105] Iteration 13704, lr = 1e-06
I0405 10:38:23.888348 30176 solver.cpp:218] Iteration 13716 (2.22536 iter/s, 5.3924s/12 iters), loss = 5.29424
I0405 10:38:23.888391 30176 solver.cpp:237]     Train net output #0: loss = 5.29424 (* 1 = 5.29424 loss)
I0405 10:38:23.888396 30176 sgd_solver.cpp:105] Iteration 13716, lr = 1e-06
I0405 10:38:29.620618 30176 solver.cpp:218] Iteration 13728 (2.09345 iter/s, 5.73216s/12 iters), loss = 5.29172
I0405 10:38:29.620678 30176 solver.cpp:237]     Train net output #0: loss = 5.29172 (* 1 = 5.29172 loss)
I0405 10:38:29.620687 30176 sgd_solver.cpp:105] Iteration 13728, lr = 1e-06
I0405 10:38:35.280910 30176 solver.cpp:218] Iteration 13740 (2.12008 iter/s, 5.66017s/12 iters), loss = 5.28216
I0405 10:38:35.280952 30176 solver.cpp:237]     Train net output #0: loss = 5.28216 (* 1 = 5.28216 loss)
I0405 10:38:35.280957 30176 sgd_solver.cpp:105] Iteration 13740, lr = 1e-06
I0405 10:38:40.619817 30176 solver.cpp:218] Iteration 13752 (2.24769 iter/s, 5.33881s/12 iters), loss = 5.28782
I0405 10:38:40.619868 30176 solver.cpp:237]     Train net output #0: loss = 5.28782 (* 1 = 5.28782 loss)
I0405 10:38:40.619877 30176 sgd_solver.cpp:105] Iteration 13752, lr = 1e-06
I0405 10:38:42.514861 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:38:45.950664 30176 solver.cpp:218] Iteration 13764 (2.25109 iter/s, 5.33074s/12 iters), loss = 5.28523
I0405 10:38:45.950785 30176 solver.cpp:237]     Train net output #0: loss = 5.28523 (* 1 = 5.28523 loss)
I0405 10:38:45.950794 30176 sgd_solver.cpp:105] Iteration 13764, lr = 1e-06
I0405 10:38:48.096082 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_13770.caffemodel
I0405 10:38:51.233530 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_13770.solverstate
I0405 10:38:53.631316 30176 solver.cpp:330] Iteration 13770, Testing net (#0)
I0405 10:38:53.631340 30176 net.cpp:676] Ignoring source layer train-data
I0405 10:38:57.229579 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:38:58.035593 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 10:38:58.035624 30176 solver.cpp:397]     Test net output #1: loss = 5.27974 (* 1 = 5.27974 loss)
I0405 10:39:00.013414 30176 solver.cpp:218] Iteration 13776 (0.853332 iter/s, 14.0625s/12 iters), loss = 5.28351
I0405 10:39:00.013469 30176 solver.cpp:237]     Train net output #0: loss = 5.28351 (* 1 = 5.28351 loss)
I0405 10:39:00.013478 30176 sgd_solver.cpp:105] Iteration 13776, lr = 1e-06
I0405 10:39:05.387142 30176 solver.cpp:218] Iteration 13788 (2.23313 iter/s, 5.37361s/12 iters), loss = 5.2771
I0405 10:39:05.387197 30176 solver.cpp:237]     Train net output #0: loss = 5.2771 (* 1 = 5.2771 loss)
I0405 10:39:05.387205 30176 sgd_solver.cpp:105] Iteration 13788, lr = 1e-06
I0405 10:39:10.817083 30176 solver.cpp:218] Iteration 13800 (2.21001 iter/s, 5.42984s/12 iters), loss = 5.27843
I0405 10:39:10.817124 30176 solver.cpp:237]     Train net output #0: loss = 5.27843 (* 1 = 5.27843 loss)
I0405 10:39:10.817129 30176 sgd_solver.cpp:105] Iteration 13800, lr = 1e-06
I0405 10:39:16.329527 30176 solver.cpp:218] Iteration 13812 (2.17693 iter/s, 5.51235s/12 iters), loss = 5.2958
I0405 10:39:16.329650 30176 solver.cpp:237]     Train net output #0: loss = 5.2958 (* 1 = 5.2958 loss)
I0405 10:39:16.329658 30176 sgd_solver.cpp:105] Iteration 13812, lr = 1e-06
I0405 10:39:21.696969 30176 solver.cpp:218] Iteration 13824 (2.23577 iter/s, 5.36727s/12 iters), loss = 5.30164
I0405 10:39:21.697012 30176 solver.cpp:237]     Train net output #0: loss = 5.30164 (* 1 = 5.30164 loss)
I0405 10:39:21.697018 30176 sgd_solver.cpp:105] Iteration 13824, lr = 1e-06
I0405 10:39:26.933693 30176 solver.cpp:218] Iteration 13836 (2.29155 iter/s, 5.23663s/12 iters), loss = 5.27178
I0405 10:39:26.933751 30176 solver.cpp:237]     Train net output #0: loss = 5.27178 (* 1 = 5.27178 loss)
I0405 10:39:26.933760 30176 sgd_solver.cpp:105] Iteration 13836, lr = 1e-06
I0405 10:39:32.197413 30176 solver.cpp:218] Iteration 13848 (2.2798 iter/s, 5.26361s/12 iters), loss = 5.27359
I0405 10:39:32.197468 30176 solver.cpp:237]     Train net output #0: loss = 5.27359 (* 1 = 5.27359 loss)
I0405 10:39:32.197476 30176 sgd_solver.cpp:105] Iteration 13848, lr = 1e-06
I0405 10:39:36.444059 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:39:37.781368 30176 solver.cpp:218] Iteration 13860 (2.14905 iter/s, 5.58386s/12 iters), loss = 5.29003
I0405 10:39:37.781405 30176 solver.cpp:237]     Train net output #0: loss = 5.29003 (* 1 = 5.29003 loss)
I0405 10:39:37.781411 30176 sgd_solver.cpp:105] Iteration 13860, lr = 1e-06
I0405 10:39:42.646620 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_13872.caffemodel
I0405 10:39:45.729789 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_13872.solverstate
I0405 10:39:48.046782 30176 solver.cpp:330] Iteration 13872, Testing net (#0)
I0405 10:39:48.046864 30176 net.cpp:676] Ignoring source layer train-data
I0405 10:39:49.055181 30176 blocking_queue.cpp:49] Waiting for data
I0405 10:39:51.725874 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:39:52.659862 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 10:39:52.659893 30176 solver.cpp:397]     Test net output #1: loss = 5.2798 (* 1 = 5.2798 loss)
I0405 10:39:52.800935 30176 solver.cpp:218] Iteration 13872 (0.798966 iter/s, 15.0194s/12 iters), loss = 5.27951
I0405 10:39:52.800981 30176 solver.cpp:237]     Train net output #0: loss = 5.27951 (* 1 = 5.27951 loss)
I0405 10:39:52.800987 30176 sgd_solver.cpp:105] Iteration 13872, lr = 1e-06
I0405 10:39:57.154983 30176 solver.cpp:218] Iteration 13884 (2.75611 iter/s, 4.35396s/12 iters), loss = 5.29308
I0405 10:39:57.155021 30176 solver.cpp:237]     Train net output #0: loss = 5.29308 (* 1 = 5.29308 loss)
I0405 10:39:57.155026 30176 sgd_solver.cpp:105] Iteration 13884, lr = 1e-06
I0405 10:40:02.247285 30176 solver.cpp:218] Iteration 13896 (2.35654 iter/s, 5.0922s/12 iters), loss = 5.29828
I0405 10:40:02.247344 30176 solver.cpp:237]     Train net output #0: loss = 5.29828 (* 1 = 5.29828 loss)
I0405 10:40:02.247354 30176 sgd_solver.cpp:105] Iteration 13896, lr = 1e-06
I0405 10:40:07.730180 30176 solver.cpp:218] Iteration 13908 (2.18867 iter/s, 5.48279s/12 iters), loss = 5.30583
I0405 10:40:07.730226 30176 solver.cpp:237]     Train net output #0: loss = 5.30583 (* 1 = 5.30583 loss)
I0405 10:40:07.730232 30176 sgd_solver.cpp:105] Iteration 13908, lr = 1e-06
I0405 10:40:13.325613 30176 solver.cpp:218] Iteration 13920 (2.14464 iter/s, 5.59534s/12 iters), loss = 5.26929
I0405 10:40:13.325654 30176 solver.cpp:237]     Train net output #0: loss = 5.26929 (* 1 = 5.26929 loss)
I0405 10:40:13.325659 30176 sgd_solver.cpp:105] Iteration 13920, lr = 1e-06
I0405 10:40:18.810619 30176 solver.cpp:218] Iteration 13932 (2.18782 iter/s, 5.4849s/12 iters), loss = 5.27592
I0405 10:40:18.810781 30176 solver.cpp:237]     Train net output #0: loss = 5.27592 (* 1 = 5.27592 loss)
I0405 10:40:18.810793 30176 sgd_solver.cpp:105] Iteration 13932, lr = 1e-06
I0405 10:40:24.243429 30176 solver.cpp:218] Iteration 13944 (2.20889 iter/s, 5.4326s/12 iters), loss = 5.26233
I0405 10:40:24.243480 30176 solver.cpp:237]     Train net output #0: loss = 5.26233 (* 1 = 5.26233 loss)
I0405 10:40:24.243489 30176 sgd_solver.cpp:105] Iteration 13944, lr = 1e-06
I0405 10:40:29.750473 30176 solver.cpp:218] Iteration 13956 (2.17907 iter/s, 5.50694s/12 iters), loss = 5.27245
I0405 10:40:29.750514 30176 solver.cpp:237]     Train net output #0: loss = 5.27245 (* 1 = 5.27245 loss)
I0405 10:40:29.750519 30176 sgd_solver.cpp:105] Iteration 13956, lr = 1e-06
I0405 10:40:30.853741 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:40:35.202468 30176 solver.cpp:218] Iteration 13968 (2.20107 iter/s, 5.4519s/12 iters), loss = 5.28593
I0405 10:40:35.202510 30176 solver.cpp:237]     Train net output #0: loss = 5.28593 (* 1 = 5.28593 loss)
I0405 10:40:35.202515 30176 sgd_solver.cpp:105] Iteration 13968, lr = 1e-06
I0405 10:40:37.501940 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_13974.caffemodel
I0405 10:40:40.597775 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_13974.solverstate
I0405 10:40:42.898411 30176 solver.cpp:330] Iteration 13974, Testing net (#0)
I0405 10:40:42.898430 30176 net.cpp:676] Ignoring source layer train-data
I0405 10:40:46.436303 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:40:47.348177 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 10:40:47.348217 30176 solver.cpp:397]     Test net output #1: loss = 5.27981 (* 1 = 5.27981 loss)
I0405 10:40:49.225333 30176 solver.cpp:218] Iteration 13980 (0.855754 iter/s, 14.0227s/12 iters), loss = 5.27266
I0405 10:40:49.225462 30176 solver.cpp:237]     Train net output #0: loss = 5.27266 (* 1 = 5.27266 loss)
I0405 10:40:49.225472 30176 sgd_solver.cpp:105] Iteration 13980, lr = 1e-06
I0405 10:40:54.539973 30176 solver.cpp:218] Iteration 13992 (2.25799 iter/s, 5.31446s/12 iters), loss = 5.27124
I0405 10:40:54.540015 30176 solver.cpp:237]     Train net output #0: loss = 5.27124 (* 1 = 5.27124 loss)
I0405 10:40:54.540021 30176 sgd_solver.cpp:105] Iteration 13992, lr = 1e-06
I0405 10:41:00.030699 30176 solver.cpp:218] Iteration 14004 (2.18554 iter/s, 5.49063s/12 iters), loss = 5.28463
I0405 10:41:00.030755 30176 solver.cpp:237]     Train net output #0: loss = 5.28463 (* 1 = 5.28463 loss)
I0405 10:41:00.030761 30176 sgd_solver.cpp:105] Iteration 14004, lr = 1e-06
I0405 10:41:05.420511 30176 solver.cpp:218] Iteration 14016 (2.22647 iter/s, 5.3897s/12 iters), loss = 5.26021
I0405 10:41:05.420557 30176 solver.cpp:237]     Train net output #0: loss = 5.26021 (* 1 = 5.26021 loss)
I0405 10:41:05.420562 30176 sgd_solver.cpp:105] Iteration 14016, lr = 1e-06
I0405 10:41:10.561539 30176 solver.cpp:218] Iteration 14028 (2.33421 iter/s, 5.14093s/12 iters), loss = 5.28552
I0405 10:41:10.561579 30176 solver.cpp:237]     Train net output #0: loss = 5.28552 (* 1 = 5.28552 loss)
I0405 10:41:10.561584 30176 sgd_solver.cpp:105] Iteration 14028, lr = 1e-06
I0405 10:41:16.130802 30176 solver.cpp:218] Iteration 14040 (2.15472 iter/s, 5.56917s/12 iters), loss = 5.28669
I0405 10:41:16.130848 30176 solver.cpp:237]     Train net output #0: loss = 5.28669 (* 1 = 5.28669 loss)
I0405 10:41:16.130853 30176 sgd_solver.cpp:105] Iteration 14040, lr = 1e-06
I0405 10:41:21.510746 30176 solver.cpp:218] Iteration 14052 (2.23061 iter/s, 5.37969s/12 iters), loss = 5.30157
I0405 10:41:21.510862 30176 solver.cpp:237]     Train net output #0: loss = 5.30157 (* 1 = 5.30157 loss)
I0405 10:41:21.510871 30176 sgd_solver.cpp:105] Iteration 14052, lr = 1e-06
I0405 10:41:24.976893 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:41:26.880129 30176 solver.cpp:218] Iteration 14064 (2.23496 iter/s, 5.36922s/12 iters), loss = 5.29935
I0405 10:41:26.880179 30176 solver.cpp:237]     Train net output #0: loss = 5.29935 (* 1 = 5.29935 loss)
I0405 10:41:26.880187 30176 sgd_solver.cpp:105] Iteration 14064, lr = 1e-06
I0405 10:41:31.805567 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_14076.caffemodel
I0405 10:41:34.944613 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_14076.solverstate
I0405 10:41:37.286665 30176 solver.cpp:330] Iteration 14076, Testing net (#0)
I0405 10:41:37.286689 30176 net.cpp:676] Ignoring source layer train-data
I0405 10:41:40.739492 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:41:41.739178 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 10:41:41.739217 30176 solver.cpp:397]     Test net output #1: loss = 5.27962 (* 1 = 5.27962 loss)
I0405 10:41:41.880359 30176 solver.cpp:218] Iteration 14076 (0.799996 iter/s, 15.0001s/12 iters), loss = 5.28761
I0405 10:41:41.880414 30176 solver.cpp:237]     Train net output #0: loss = 5.28761 (* 1 = 5.28761 loss)
I0405 10:41:41.880420 30176 sgd_solver.cpp:105] Iteration 14076, lr = 1e-06
I0405 10:41:46.373466 30176 solver.cpp:218] Iteration 14088 (2.67082 iter/s, 4.49301s/12 iters), loss = 5.29487
I0405 10:41:46.373523 30176 solver.cpp:237]     Train net output #0: loss = 5.29487 (* 1 = 5.29487 loss)
I0405 10:41:46.373531 30176 sgd_solver.cpp:105] Iteration 14088, lr = 1e-06
I0405 10:41:51.857405 30176 solver.cpp:218] Iteration 14100 (2.18825 iter/s, 5.48383s/12 iters), loss = 5.28602
I0405 10:41:51.857514 30176 solver.cpp:237]     Train net output #0: loss = 5.28602 (* 1 = 5.28602 loss)
I0405 10:41:51.857522 30176 sgd_solver.cpp:105] Iteration 14100, lr = 1e-06
I0405 10:41:57.140291 30176 solver.cpp:218] Iteration 14112 (2.27155 iter/s, 5.28273s/12 iters), loss = 5.27793
I0405 10:41:57.140333 30176 solver.cpp:237]     Train net output #0: loss = 5.27793 (* 1 = 5.27793 loss)
I0405 10:41:57.140338 30176 sgd_solver.cpp:105] Iteration 14112, lr = 1e-06
I0405 10:42:02.539022 30176 solver.cpp:218] Iteration 14124 (2.22279 iter/s, 5.39863s/12 iters), loss = 5.27581
I0405 10:42:02.539089 30176 solver.cpp:237]     Train net output #0: loss = 5.27581 (* 1 = 5.27581 loss)
I0405 10:42:02.539098 30176 sgd_solver.cpp:105] Iteration 14124, lr = 1e-06
I0405 10:42:07.951122 30176 solver.cpp:218] Iteration 14136 (2.2173 iter/s, 5.41198s/12 iters), loss = 5.28117
I0405 10:42:07.951162 30176 solver.cpp:237]     Train net output #0: loss = 5.28117 (* 1 = 5.28117 loss)
I0405 10:42:07.951167 30176 sgd_solver.cpp:105] Iteration 14136, lr = 1e-06
I0405 10:42:13.349851 30176 solver.cpp:218] Iteration 14148 (2.22278 iter/s, 5.39864s/12 iters), loss = 5.27719
I0405 10:42:13.349890 30176 solver.cpp:237]     Train net output #0: loss = 5.27719 (* 1 = 5.27719 loss)
I0405 10:42:13.349895 30176 sgd_solver.cpp:105] Iteration 14148, lr = 1e-06
I0405 10:42:18.764698 30176 solver.cpp:218] Iteration 14160 (2.21617 iter/s, 5.41475s/12 iters), loss = 5.27874
I0405 10:42:18.764746 30176 solver.cpp:237]     Train net output #0: loss = 5.27874 (* 1 = 5.27874 loss)
I0405 10:42:18.764753 30176 sgd_solver.cpp:105] Iteration 14160, lr = 1e-06
I0405 10:42:19.045090 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:42:24.116509 30176 solver.cpp:218] Iteration 14172 (2.24227 iter/s, 5.35171s/12 iters), loss = 5.29405
I0405 10:42:24.116612 30176 solver.cpp:237]     Train net output #0: loss = 5.29405 (* 1 = 5.29405 loss)
I0405 10:42:24.116618 30176 sgd_solver.cpp:105] Iteration 14172, lr = 1e-06
I0405 10:42:26.236200 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_14178.caffemodel
I0405 10:42:29.314211 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_14178.solverstate
I0405 10:42:31.623266 30176 solver.cpp:330] Iteration 14178, Testing net (#0)
I0405 10:42:31.623291 30176 net.cpp:676] Ignoring source layer train-data
I0405 10:42:35.273224 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:42:36.273974 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 10:42:36.274013 30176 solver.cpp:397]     Test net output #1: loss = 5.27976 (* 1 = 5.27976 loss)
I0405 10:42:38.155283 30176 solver.cpp:218] Iteration 14184 (0.854788 iter/s, 14.0386s/12 iters), loss = 5.27631
I0405 10:42:38.155323 30176 solver.cpp:237]     Train net output #0: loss = 5.27631 (* 1 = 5.27631 loss)
I0405 10:42:38.155328 30176 sgd_solver.cpp:105] Iteration 14184, lr = 1e-06
I0405 10:42:43.220769 30176 solver.cpp:218] Iteration 14196 (2.36902 iter/s, 5.06539s/12 iters), loss = 5.28262
I0405 10:42:43.220824 30176 solver.cpp:237]     Train net output #0: loss = 5.28262 (* 1 = 5.28262 loss)
I0405 10:42:43.220831 30176 sgd_solver.cpp:105] Iteration 14196, lr = 1e-06
I0405 10:42:48.929111 30176 solver.cpp:218] Iteration 14208 (2.10223 iter/s, 5.70823s/12 iters), loss = 5.2788
I0405 10:42:48.929165 30176 solver.cpp:237]     Train net output #0: loss = 5.2788 (* 1 = 5.2788 loss)
I0405 10:42:48.929172 30176 sgd_solver.cpp:105] Iteration 14208, lr = 1e-06
I0405 10:42:54.307265 30176 solver.cpp:218] Iteration 14220 (2.23129 iter/s, 5.37805s/12 iters), loss = 5.28014
I0405 10:42:54.307397 30176 solver.cpp:237]     Train net output #0: loss = 5.28014 (* 1 = 5.28014 loss)
I0405 10:42:54.307406 30176 sgd_solver.cpp:105] Iteration 14220, lr = 1e-06
I0405 10:42:59.669725 30176 solver.cpp:218] Iteration 14232 (2.23785 iter/s, 5.36228s/12 iters), loss = 5.28096
I0405 10:42:59.669782 30176 solver.cpp:237]     Train net output #0: loss = 5.28096 (* 1 = 5.28096 loss)
I0405 10:42:59.669790 30176 sgd_solver.cpp:105] Iteration 14232, lr = 1e-06
I0405 10:43:05.252806 30176 solver.cpp:218] Iteration 14244 (2.14939 iter/s, 5.58297s/12 iters), loss = 5.2855
I0405 10:43:05.252859 30176 solver.cpp:237]     Train net output #0: loss = 5.2855 (* 1 = 5.2855 loss)
I0405 10:43:05.252864 30176 sgd_solver.cpp:105] Iteration 14244, lr = 1e-06
I0405 10:43:10.384912 30176 solver.cpp:218] Iteration 14256 (2.33827 iter/s, 5.132s/12 iters), loss = 5.26866
I0405 10:43:10.384955 30176 solver.cpp:237]     Train net output #0: loss = 5.26866 (* 1 = 5.26866 loss)
I0405 10:43:10.384961 30176 sgd_solver.cpp:105] Iteration 14256, lr = 1e-06
I0405 10:43:13.084324 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:43:15.855760 30176 solver.cpp:218] Iteration 14268 (2.19348 iter/s, 5.47075s/12 iters), loss = 5.29583
I0405 10:43:15.855805 30176 solver.cpp:237]     Train net output #0: loss = 5.29583 (* 1 = 5.29583 loss)
I0405 10:43:15.855810 30176 sgd_solver.cpp:105] Iteration 14268, lr = 1e-06
I0405 10:43:20.598901 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_14280.caffemodel
I0405 10:43:24.740438 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_14280.solverstate
I0405 10:43:27.069631 30176 solver.cpp:330] Iteration 14280, Testing net (#0)
I0405 10:43:27.069650 30176 net.cpp:676] Ignoring source layer train-data
I0405 10:43:30.750813 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:43:31.935205 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 10:43:31.935243 30176 solver.cpp:397]     Test net output #1: loss = 5.27964 (* 1 = 5.27964 loss)
I0405 10:43:32.078310 30176 solver.cpp:218] Iteration 14280 (0.739719 iter/s, 16.2224s/12 iters), loss = 5.28169
I0405 10:43:32.078367 30176 solver.cpp:237]     Train net output #0: loss = 5.28169 (* 1 = 5.28169 loss)
I0405 10:43:32.078375 30176 sgd_solver.cpp:105] Iteration 14280, lr = 1e-06
I0405 10:43:36.445796 30176 solver.cpp:218] Iteration 14292 (2.74764 iter/s, 4.36739s/12 iters), loss = 5.28323
I0405 10:43:36.445835 30176 solver.cpp:237]     Train net output #0: loss = 5.28323 (* 1 = 5.28323 loss)
I0405 10:43:36.445842 30176 sgd_solver.cpp:105] Iteration 14292, lr = 1e-06
I0405 10:43:41.856453 30176 solver.cpp:218] Iteration 14304 (2.21788 iter/s, 5.41056s/12 iters), loss = 5.29066
I0405 10:43:41.856498 30176 solver.cpp:237]     Train net output #0: loss = 5.29066 (* 1 = 5.29066 loss)
I0405 10:43:41.856503 30176 sgd_solver.cpp:105] Iteration 14304, lr = 1e-06
I0405 10:43:47.103845 30176 solver.cpp:218] Iteration 14316 (2.28689 iter/s, 5.24729s/12 iters), loss = 5.30038
I0405 10:43:47.103906 30176 solver.cpp:237]     Train net output #0: loss = 5.30038 (* 1 = 5.30038 loss)
I0405 10:43:47.103914 30176 sgd_solver.cpp:105] Iteration 14316, lr = 1e-06
I0405 10:43:52.635912 30176 solver.cpp:218] Iteration 14328 (2.16921 iter/s, 5.53196s/12 iters), loss = 5.29318
I0405 10:43:52.635953 30176 solver.cpp:237]     Train net output #0: loss = 5.29318 (* 1 = 5.29318 loss)
I0405 10:43:52.635958 30176 sgd_solver.cpp:105] Iteration 14328, lr = 1e-06
I0405 10:43:57.685554 30176 solver.cpp:218] Iteration 14340 (2.37645 iter/s, 5.04955s/12 iters), loss = 5.26835
I0405 10:43:57.685670 30176 solver.cpp:237]     Train net output #0: loss = 5.26835 (* 1 = 5.26835 loss)
I0405 10:43:57.685678 30176 sgd_solver.cpp:105] Iteration 14340, lr = 1e-06
I0405 10:44:03.091367 30176 solver.cpp:218] Iteration 14352 (2.2199 iter/s, 5.40565s/12 iters), loss = 5.27891
I0405 10:44:03.091418 30176 solver.cpp:237]     Train net output #0: loss = 5.27891 (* 1 = 5.27891 loss)
I0405 10:44:03.091425 30176 sgd_solver.cpp:105] Iteration 14352, lr = 1e-06
I0405 10:44:08.128713 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:44:08.514799 30176 solver.cpp:218] Iteration 14364 (2.21266 iter/s, 5.42333s/12 iters), loss = 5.28205
I0405 10:44:08.514863 30176 solver.cpp:237]     Train net output #0: loss = 5.28205 (* 1 = 5.28205 loss)
I0405 10:44:08.514873 30176 sgd_solver.cpp:105] Iteration 14364, lr = 1e-06
I0405 10:44:13.904479 30176 solver.cpp:218] Iteration 14376 (2.22653 iter/s, 5.38954s/12 iters), loss = 5.27407
I0405 10:44:13.904538 30176 solver.cpp:237]     Train net output #0: loss = 5.27407 (* 1 = 5.27407 loss)
I0405 10:44:13.904546 30176 sgd_solver.cpp:105] Iteration 14376, lr = 1e-06
I0405 10:44:16.191082 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_14382.caffemodel
I0405 10:44:19.247311 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_14382.solverstate
I0405 10:44:21.617128 30176 solver.cpp:330] Iteration 14382, Testing net (#0)
I0405 10:44:21.617146 30176 net.cpp:676] Ignoring source layer train-data
I0405 10:44:25.103299 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:44:26.150390 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 10:44:26.150425 30176 solver.cpp:397]     Test net output #1: loss = 5.27978 (* 1 = 5.27978 loss)
I0405 10:44:28.237198 30176 solver.cpp:218] Iteration 14388 (0.837255 iter/s, 14.3326s/12 iters), loss = 5.27541
I0405 10:44:28.237303 30176 solver.cpp:237]     Train net output #0: loss = 5.27541 (* 1 = 5.27541 loss)
I0405 10:44:28.237309 30176 sgd_solver.cpp:105] Iteration 14388, lr = 1e-06
I0405 10:44:33.637728 30176 solver.cpp:218] Iteration 14400 (2.22207 iter/s, 5.40038s/12 iters), loss = 5.28514
I0405 10:44:33.637771 30176 solver.cpp:237]     Train net output #0: loss = 5.28514 (* 1 = 5.28514 loss)
I0405 10:44:33.637776 30176 sgd_solver.cpp:105] Iteration 14400, lr = 1e-06
I0405 10:44:38.970851 30176 solver.cpp:218] Iteration 14412 (2.25013 iter/s, 5.33303s/12 iters), loss = 5.27576
I0405 10:44:38.970906 30176 solver.cpp:237]     Train net output #0: loss = 5.27576 (* 1 = 5.27576 loss)
I0405 10:44:38.970916 30176 sgd_solver.cpp:105] Iteration 14412, lr = 1e-06
I0405 10:44:44.371026 30176 solver.cpp:218] Iteration 14424 (2.22219 iter/s, 5.40007s/12 iters), loss = 5.27722
I0405 10:44:44.371081 30176 solver.cpp:237]     Train net output #0: loss = 5.27722 (* 1 = 5.27722 loss)
I0405 10:44:44.371089 30176 sgd_solver.cpp:105] Iteration 14424, lr = 1e-06
I0405 10:44:49.567378 30176 solver.cpp:218] Iteration 14436 (2.30936 iter/s, 5.19624s/12 iters), loss = 5.28683
I0405 10:44:49.567426 30176 solver.cpp:237]     Train net output #0: loss = 5.28683 (* 1 = 5.28683 loss)
I0405 10:44:49.567433 30176 sgd_solver.cpp:105] Iteration 14436, lr = 1e-06
I0405 10:44:54.873121 30176 solver.cpp:218] Iteration 14448 (2.26174 iter/s, 5.30564s/12 iters), loss = 5.28733
I0405 10:44:54.873163 30176 solver.cpp:237]     Train net output #0: loss = 5.28733 (* 1 = 5.28733 loss)
I0405 10:44:54.873168 30176 sgd_solver.cpp:105] Iteration 14448, lr = 1e-06
I0405 10:44:59.922760 30176 solver.cpp:218] Iteration 14460 (2.37645 iter/s, 5.04954s/12 iters), loss = 5.27987
I0405 10:44:59.922930 30176 solver.cpp:237]     Train net output #0: loss = 5.27987 (* 1 = 5.27987 loss)
I0405 10:44:59.922940 30176 sgd_solver.cpp:105] Iteration 14460, lr = 1e-06
I0405 10:45:01.860617 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:45:05.395771 30176 solver.cpp:218] Iteration 14472 (2.19266 iter/s, 5.47279s/12 iters), loss = 5.27839
I0405 10:45:05.395813 30176 solver.cpp:237]     Train net output #0: loss = 5.27839 (* 1 = 5.27839 loss)
I0405 10:45:05.395818 30176 sgd_solver.cpp:105] Iteration 14472, lr = 1e-06
I0405 10:45:09.969008 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_14484.caffemodel
I0405 10:45:13.009886 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_14484.solverstate
I0405 10:45:15.319402 30176 solver.cpp:330] Iteration 14484, Testing net (#0)
I0405 10:45:15.319423 30176 net.cpp:676] Ignoring source layer train-data
I0405 10:45:18.714543 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:45:19.863190 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 10:45:19.863230 30176 solver.cpp:397]     Test net output #1: loss = 5.2799 (* 1 = 5.2799 loss)
I0405 10:45:20.007599 30176 solver.cpp:218] Iteration 14484 (0.821261 iter/s, 14.6117s/12 iters), loss = 5.27157
I0405 10:45:20.009174 30176 solver.cpp:237]     Train net output #0: loss = 5.27157 (* 1 = 5.27157 loss)
I0405 10:45:20.009186 30176 sgd_solver.cpp:105] Iteration 14484, lr = 1e-06
I0405 10:45:24.783128 30176 solver.cpp:218] Iteration 14496 (2.51366 iter/s, 4.77392s/12 iters), loss = 5.28958
I0405 10:45:24.783167 30176 solver.cpp:237]     Train net output #0: loss = 5.28958 (* 1 = 5.28958 loss)
I0405 10:45:24.783172 30176 sgd_solver.cpp:105] Iteration 14496, lr = 1e-06
I0405 10:45:30.272135 30176 solver.cpp:218] Iteration 14508 (2.18623 iter/s, 5.48891s/12 iters), loss = 5.27346
I0405 10:45:30.272246 30176 solver.cpp:237]     Train net output #0: loss = 5.27346 (* 1 = 5.27346 loss)
I0405 10:45:30.272253 30176 sgd_solver.cpp:105] Iteration 14508, lr = 1e-06
I0405 10:45:35.638464 30176 solver.cpp:218] Iteration 14520 (2.23623 iter/s, 5.36617s/12 iters), loss = 5.27864
I0405 10:45:35.638525 30176 solver.cpp:237]     Train net output #0: loss = 5.27864 (* 1 = 5.27864 loss)
I0405 10:45:35.638535 30176 sgd_solver.cpp:105] Iteration 14520, lr = 1e-06
I0405 10:45:41.115854 30176 solver.cpp:218] Iteration 14532 (2.19087 iter/s, 5.47728s/12 iters), loss = 5.29491
I0405 10:45:41.115909 30176 solver.cpp:237]     Train net output #0: loss = 5.29491 (* 1 = 5.29491 loss)
I0405 10:45:41.115917 30176 sgd_solver.cpp:105] Iteration 14532, lr = 1e-06
I0405 10:45:46.532022 30176 solver.cpp:218] Iteration 14544 (2.21563 iter/s, 5.41606s/12 iters), loss = 5.29332
I0405 10:45:46.532078 30176 solver.cpp:237]     Train net output #0: loss = 5.29332 (* 1 = 5.29332 loss)
I0405 10:45:46.532086 30176 sgd_solver.cpp:105] Iteration 14544, lr = 1e-06
I0405 10:45:51.987558 30176 solver.cpp:218] Iteration 14556 (2.19965 iter/s, 5.45542s/12 iters), loss = 5.2888
I0405 10:45:51.987622 30176 solver.cpp:237]     Train net output #0: loss = 5.2888 (* 1 = 5.2888 loss)
I0405 10:45:51.987630 30176 sgd_solver.cpp:105] Iteration 14556, lr = 1e-06
I0405 10:45:56.238898 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:45:56.495920 30176 blocking_queue.cpp:49] Waiting for data
I0405 10:45:57.393371 30176 solver.cpp:218] Iteration 14568 (2.21988 iter/s, 5.4057s/12 iters), loss = 5.30184
I0405 10:45:57.393419 30176 solver.cpp:237]     Train net output #0: loss = 5.30184 (* 1 = 5.30184 loss)
I0405 10:45:57.393427 30176 sgd_solver.cpp:105] Iteration 14568, lr = 1e-06
I0405 10:46:02.630075 30176 solver.cpp:218] Iteration 14580 (2.29156 iter/s, 5.23661s/12 iters), loss = 5.2759
I0405 10:46:02.630192 30176 solver.cpp:237]     Train net output #0: loss = 5.2759 (* 1 = 5.2759 loss)
I0405 10:46:02.630198 30176 sgd_solver.cpp:105] Iteration 14580, lr = 1e-06
I0405 10:46:04.752460 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_14586.caffemodel
I0405 10:46:07.775956 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_14586.solverstate
I0405 10:46:10.080960 30176 solver.cpp:330] Iteration 14586, Testing net (#0)
I0405 10:46:10.080978 30176 net.cpp:676] Ignoring source layer train-data
I0405 10:46:13.479662 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:46:14.580855 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 10:46:14.580899 30176 solver.cpp:397]     Test net output #1: loss = 5.27989 (* 1 = 5.27989 loss)
I0405 10:46:16.527022 30176 solver.cpp:218] Iteration 14592 (0.863513 iter/s, 13.8967s/12 iters), loss = 5.28387
I0405 10:46:16.527068 30176 solver.cpp:237]     Train net output #0: loss = 5.28387 (* 1 = 5.28387 loss)
I0405 10:46:16.527076 30176 sgd_solver.cpp:105] Iteration 14592, lr = 1e-06
I0405 10:46:21.623003 30176 solver.cpp:218] Iteration 14604 (2.35484 iter/s, 5.09588s/12 iters), loss = 5.26724
I0405 10:46:21.623060 30176 solver.cpp:237]     Train net output #0: loss = 5.26724 (* 1 = 5.26724 loss)
I0405 10:46:21.623070 30176 sgd_solver.cpp:105] Iteration 14604, lr = 1e-06
I0405 10:46:26.993793 30176 solver.cpp:218] Iteration 14616 (2.23435 iter/s, 5.37069s/12 iters), loss = 5.30517
I0405 10:46:26.993834 30176 solver.cpp:237]     Train net output #0: loss = 5.30517 (* 1 = 5.30517 loss)
I0405 10:46:26.993839 30176 sgd_solver.cpp:105] Iteration 14616, lr = 1e-06
I0405 10:46:32.596608 30176 solver.cpp:218] Iteration 14628 (2.14182 iter/s, 5.60272s/12 iters), loss = 5.28398
I0405 10:46:32.596650 30176 solver.cpp:237]     Train net output #0: loss = 5.28398 (* 1 = 5.28398 loss)
I0405 10:46:32.596655 30176 sgd_solver.cpp:105] Iteration 14628, lr = 1e-06
I0405 10:46:38.063748 30176 solver.cpp:218] Iteration 14640 (2.19497 iter/s, 5.46705s/12 iters), loss = 5.28014
I0405 10:46:38.063841 30176 solver.cpp:237]     Train net output #0: loss = 5.28014 (* 1 = 5.28014 loss)
I0405 10:46:38.063848 30176 sgd_solver.cpp:105] Iteration 14640, lr = 1e-06
I0405 10:46:43.392822 30176 solver.cpp:218] Iteration 14652 (2.25186 iter/s, 5.32893s/12 iters), loss = 5.27263
I0405 10:46:43.392869 30176 solver.cpp:237]     Train net output #0: loss = 5.27263 (* 1 = 5.27263 loss)
I0405 10:46:43.392876 30176 sgd_solver.cpp:105] Iteration 14652, lr = 1e-06
I0405 10:46:48.652683 30176 solver.cpp:218] Iteration 14664 (2.28147 iter/s, 5.25976s/12 iters), loss = 5.29074
I0405 10:46:48.652724 30176 solver.cpp:237]     Train net output #0: loss = 5.29074 (* 1 = 5.29074 loss)
I0405 10:46:48.652730 30176 sgd_solver.cpp:105] Iteration 14664, lr = 1e-06
I0405 10:46:49.777211 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:46:53.943117 30176 solver.cpp:218] Iteration 14676 (2.26828 iter/s, 5.29034s/12 iters), loss = 5.27057
I0405 10:46:53.943156 30176 solver.cpp:237]     Train net output #0: loss = 5.27057 (* 1 = 5.27057 loss)
I0405 10:46:53.943162 30176 sgd_solver.cpp:105] Iteration 14676, lr = 1e-06
I0405 10:46:58.784900 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_14688.caffemodel
I0405 10:47:01.893059 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_14688.solverstate
I0405 10:47:04.228088 30176 solver.cpp:330] Iteration 14688, Testing net (#0)
I0405 10:47:04.228116 30176 net.cpp:676] Ignoring source layer train-data
I0405 10:47:07.542150 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:47:08.726330 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 10:47:08.726428 30176 solver.cpp:397]     Test net output #1: loss = 5.27993 (* 1 = 5.27993 loss)
I0405 10:47:08.867267 30176 solver.cpp:218] Iteration 14688 (0.804074 iter/s, 14.924s/12 iters), loss = 5.27408
I0405 10:47:08.867307 30176 solver.cpp:237]     Train net output #0: loss = 5.27408 (* 1 = 5.27408 loss)
I0405 10:47:08.867313 30176 sgd_solver.cpp:105] Iteration 14688, lr = 1e-06
I0405 10:47:13.243559 30176 solver.cpp:218] Iteration 14700 (2.7421 iter/s, 4.3762s/12 iters), loss = 5.28458
I0405 10:47:13.243621 30176 solver.cpp:237]     Train net output #0: loss = 5.28458 (* 1 = 5.28458 loss)
I0405 10:47:13.243630 30176 sgd_solver.cpp:105] Iteration 14700, lr = 1e-06
I0405 10:47:18.621093 30176 solver.cpp:218] Iteration 14712 (2.23155 iter/s, 5.37742s/12 iters), loss = 5.28477
I0405 10:47:18.621150 30176 solver.cpp:237]     Train net output #0: loss = 5.28477 (* 1 = 5.28477 loss)
I0405 10:47:18.621157 30176 sgd_solver.cpp:105] Iteration 14712, lr = 1e-06
I0405 10:47:24.158717 30176 solver.cpp:218] Iteration 14724 (2.16703 iter/s, 5.53752s/12 iters), loss = 5.28244
I0405 10:47:24.158756 30176 solver.cpp:237]     Train net output #0: loss = 5.28244 (* 1 = 5.28244 loss)
I0405 10:47:24.158761 30176 sgd_solver.cpp:105] Iteration 14724, lr = 1e-06
I0405 10:47:29.460939 30176 solver.cpp:218] Iteration 14736 (2.26324 iter/s, 5.30213s/12 iters), loss = 5.2831
I0405 10:47:29.460986 30176 solver.cpp:237]     Train net output #0: loss = 5.2831 (* 1 = 5.2831 loss)
I0405 10:47:29.460994 30176 sgd_solver.cpp:105] Iteration 14736, lr = 1e-06
I0405 10:47:35.100961 30176 solver.cpp:218] Iteration 14748 (2.12769 iter/s, 5.63992s/12 iters), loss = 5.28788
I0405 10:47:35.101014 30176 solver.cpp:237]     Train net output #0: loss = 5.28788 (* 1 = 5.28788 loss)
I0405 10:47:35.101022 30176 sgd_solver.cpp:105] Iteration 14748, lr = 1e-06
I0405 10:47:40.638002 30176 solver.cpp:218] Iteration 14760 (2.16726 iter/s, 5.53694s/12 iters), loss = 5.29156
I0405 10:47:40.638095 30176 solver.cpp:237]     Train net output #0: loss = 5.29156 (* 1 = 5.29156 loss)
I0405 10:47:40.638101 30176 sgd_solver.cpp:105] Iteration 14760, lr = 1e-06
I0405 10:47:44.085876 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:47:45.897006 30176 solver.cpp:218] Iteration 14772 (2.28186 iter/s, 5.25886s/12 iters), loss = 5.28698
I0405 10:47:45.897045 30176 solver.cpp:237]     Train net output #0: loss = 5.28698 (* 1 = 5.28698 loss)
I0405 10:47:45.897050 30176 sgd_solver.cpp:105] Iteration 14772, lr = 1e-06
I0405 10:47:50.927256 30176 solver.cpp:218] Iteration 14784 (2.38561 iter/s, 5.03016s/12 iters), loss = 5.28448
I0405 10:47:50.927312 30176 solver.cpp:237]     Train net output #0: loss = 5.28448 (* 1 = 5.28448 loss)
I0405 10:47:50.927318 30176 sgd_solver.cpp:105] Iteration 14784, lr = 1e-06
I0405 10:47:53.177268 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_14790.caffemodel
I0405 10:47:56.243122 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_14790.solverstate
I0405 10:47:58.620497 30176 solver.cpp:330] Iteration 14790, Testing net (#0)
I0405 10:47:58.620517 30176 net.cpp:676] Ignoring source layer train-data
I0405 10:48:01.800482 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:48:03.016638 30176 solver.cpp:397]     Test net output #0: accuracy = 0.00735294
I0405 10:48:03.016671 30176 solver.cpp:397]     Test net output #1: loss = 5.27948 (* 1 = 5.27948 loss)
I0405 10:48:05.015805 30176 solver.cpp:218] Iteration 14796 (0.851765 iter/s, 14.0884s/12 iters), loss = 5.28333
I0405 10:48:05.015861 30176 solver.cpp:237]     Train net output #0: loss = 5.28333 (* 1 = 5.28333 loss)
I0405 10:48:05.015868 30176 sgd_solver.cpp:105] Iteration 14796, lr = 1e-06
I0405 10:48:10.490092 30176 solver.cpp:218] Iteration 14808 (2.19211 iter/s, 5.47418s/12 iters), loss = 5.27624
I0405 10:48:10.490134 30176 solver.cpp:237]     Train net output #0: loss = 5.27624 (* 1 = 5.27624 loss)
I0405 10:48:10.490139 30176 sgd_solver.cpp:105] Iteration 14808, lr = 1e-06
I0405 10:48:16.043304 30176 solver.cpp:218] Iteration 14820 (2.16095 iter/s, 5.55312s/12 iters), loss = 5.2745
I0405 10:48:16.043421 30176 solver.cpp:237]     Train net output #0: loss = 5.2745 (* 1 = 5.2745 loss)
I0405 10:48:16.043427 30176 sgd_solver.cpp:105] Iteration 14820, lr = 1e-06
I0405 10:48:21.260280 30176 solver.cpp:218] Iteration 14832 (2.30026 iter/s, 5.21681s/12 iters), loss = 5.28058
I0405 10:48:21.260327 30176 solver.cpp:237]     Train net output #0: loss = 5.28058 (* 1 = 5.28058 loss)
I0405 10:48:21.260334 30176 sgd_solver.cpp:105] Iteration 14832, lr = 1e-06
I0405 10:48:26.624243 30176 solver.cpp:218] Iteration 14844 (2.23719 iter/s, 5.36387s/12 iters), loss = 5.27613
I0405 10:48:26.624280 30176 solver.cpp:237]     Train net output #0: loss = 5.27613 (* 1 = 5.27613 loss)
I0405 10:48:26.624285 30176 sgd_solver.cpp:105] Iteration 14844, lr = 1e-06
I0405 10:48:32.281877 30176 solver.cpp:218] Iteration 14856 (2.12107 iter/s, 5.65753s/12 iters), loss = 5.27843
I0405 10:48:32.281917 30176 solver.cpp:237]     Train net output #0: loss = 5.27843 (* 1 = 5.27843 loss)
I0405 10:48:32.281924 30176 sgd_solver.cpp:105] Iteration 14856, lr = 1e-06
I0405 10:48:37.820456 30176 solver.cpp:218] Iteration 14868 (2.16666 iter/s, 5.53848s/12 iters), loss = 5.29224
I0405 10:48:37.820511 30176 solver.cpp:237]     Train net output #0: loss = 5.29224 (* 1 = 5.29224 loss)
I0405 10:48:37.820520 30176 sgd_solver.cpp:105] Iteration 14868, lr = 1e-06
I0405 10:48:38.110352 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:48:43.069103 30176 solver.cpp:218] Iteration 14880 (2.28635 iter/s, 5.24854s/12 iters), loss = 5.28717
I0405 10:48:43.069154 30176 solver.cpp:237]     Train net output #0: loss = 5.28717 (* 1 = 5.28717 loss)
I0405 10:48:43.069161 30176 sgd_solver.cpp:105] Iteration 14880, lr = 1e-06
I0405 10:48:47.926676 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_14892.caffemodel
I0405 10:48:51.161187 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_14892.solverstate
I0405 10:48:53.465838 30176 solver.cpp:330] Iteration 14892, Testing net (#0)
I0405 10:48:53.465857 30176 net.cpp:676] Ignoring source layer train-data
I0405 10:48:56.761405 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:48:58.036454 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 10:48:58.036501 30176 solver.cpp:397]     Test net output #1: loss = 5.27979 (* 1 = 5.27979 loss)
I0405 10:48:58.177320 30176 solver.cpp:218] Iteration 14892 (0.794278 iter/s, 15.1081s/12 iters), loss = 5.29979
I0405 10:48:58.178889 30176 solver.cpp:237]     Train net output #0: loss = 5.29979 (* 1 = 5.29979 loss)
I0405 10:48:58.178900 30176 sgd_solver.cpp:105] Iteration 14892, lr = 1e-06
I0405 10:49:02.643745 30176 solver.cpp:218] Iteration 14904 (2.68768 iter/s, 4.46482s/12 iters), loss = 5.29057
I0405 10:49:02.643795 30176 solver.cpp:237]     Train net output #0: loss = 5.29057 (* 1 = 5.29057 loss)
I0405 10:49:02.643821 30176 sgd_solver.cpp:105] Iteration 14904, lr = 1e-06
I0405 10:49:08.297170 30176 solver.cpp:218] Iteration 14916 (2.12265 iter/s, 5.65332s/12 iters), loss = 5.27008
I0405 10:49:08.297219 30176 solver.cpp:237]     Train net output #0: loss = 5.27008 (* 1 = 5.27008 loss)
I0405 10:49:08.297224 30176 sgd_solver.cpp:105] Iteration 14916, lr = 1e-06
I0405 10:49:13.444617 30176 solver.cpp:218] Iteration 14928 (2.3313 iter/s, 5.14735s/12 iters), loss = 5.28103
I0405 10:49:13.444658 30176 solver.cpp:237]     Train net output #0: loss = 5.28103 (* 1 = 5.28103 loss)
I0405 10:49:13.444665 30176 sgd_solver.cpp:105] Iteration 14928, lr = 1e-06
I0405 10:49:18.864156 30176 solver.cpp:218] Iteration 14940 (2.21425 iter/s, 5.41945s/12 iters), loss = 5.28905
I0405 10:49:18.864262 30176 solver.cpp:237]     Train net output #0: loss = 5.28905 (* 1 = 5.28905 loss)
I0405 10:49:18.864270 30176 sgd_solver.cpp:105] Iteration 14940, lr = 1e-06
I0405 10:49:24.093945 30176 solver.cpp:218] Iteration 14952 (2.29461 iter/s, 5.22964s/12 iters), loss = 5.28542
I0405 10:49:24.093998 30176 solver.cpp:237]     Train net output #0: loss = 5.28542 (* 1 = 5.28542 loss)
I0405 10:49:24.094007 30176 sgd_solver.cpp:105] Iteration 14952, lr = 1e-06
I0405 10:49:29.522333 30176 solver.cpp:218] Iteration 14964 (2.21064 iter/s, 5.42828s/12 iters), loss = 5.28549
I0405 10:49:29.522389 30176 solver.cpp:237]     Train net output #0: loss = 5.28549 (* 1 = 5.28549 loss)
I0405 10:49:29.522399 30176 sgd_solver.cpp:105] Iteration 14964, lr = 1e-06
I0405 10:49:32.466315 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:49:35.270188 30176 solver.cpp:218] Iteration 14976 (2.08778 iter/s, 5.74774s/12 iters), loss = 5.28232
I0405 10:49:35.270238 30176 solver.cpp:237]     Train net output #0: loss = 5.28232 (* 1 = 5.28232 loss)
I0405 10:49:35.270247 30176 sgd_solver.cpp:105] Iteration 14976, lr = 1e-06
I0405 10:49:40.924803 30176 solver.cpp:218] Iteration 14988 (2.1222 iter/s, 5.65451s/12 iters), loss = 5.28821
I0405 10:49:40.924845 30176 solver.cpp:237]     Train net output #0: loss = 5.28821 (* 1 = 5.28821 loss)
I0405 10:49:40.924850 30176 sgd_solver.cpp:105] Iteration 14988, lr = 1e-06
I0405 10:49:43.175287 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_14994.caffemodel
I0405 10:49:46.277976 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_14994.solverstate
I0405 10:49:48.597340 30176 solver.cpp:330] Iteration 14994, Testing net (#0)
I0405 10:49:48.597358 30176 net.cpp:676] Ignoring source layer train-data
I0405 10:49:51.805116 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:49:53.226280 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 10:49:53.226316 30176 solver.cpp:397]     Test net output #1: loss = 5.27992 (* 1 = 5.27992 loss)
I0405 10:49:55.099005 30176 solver.cpp:218] Iteration 15000 (0.846618 iter/s, 14.174s/12 iters), loss = 5.30274
I0405 10:49:55.099071 30176 solver.cpp:237]     Train net output #0: loss = 5.30274 (* 1 = 5.30274 loss)
I0405 10:49:55.099081 30176 sgd_solver.cpp:105] Iteration 15000, lr = 1e-06
I0405 10:50:00.710119 30176 solver.cpp:218] Iteration 15012 (2.13865 iter/s, 5.611s/12 iters), loss = 5.28387
I0405 10:50:00.710158 30176 solver.cpp:237]     Train net output #0: loss = 5.28387 (* 1 = 5.28387 loss)
I0405 10:50:00.710163 30176 sgd_solver.cpp:105] Iteration 15012, lr = 1e-06
I0405 10:50:06.173722 30176 solver.cpp:218] Iteration 15024 (2.19639 iter/s, 5.4635s/12 iters), loss = 5.28948
I0405 10:50:06.173789 30176 solver.cpp:237]     Train net output #0: loss = 5.28948 (* 1 = 5.28948 loss)
I0405 10:50:06.173799 30176 sgd_solver.cpp:105] Iteration 15024, lr = 1e-06
I0405 10:50:11.629300 30176 solver.cpp:218] Iteration 15036 (2.19963 iter/s, 5.45546s/12 iters), loss = 5.28528
I0405 10:50:11.629361 30176 solver.cpp:237]     Train net output #0: loss = 5.28528 (* 1 = 5.28528 loss)
I0405 10:50:11.629370 30176 sgd_solver.cpp:105] Iteration 15036, lr = 1e-06
I0405 10:50:17.071880 30176 solver.cpp:218] Iteration 15048 (2.20488 iter/s, 5.44246s/12 iters), loss = 5.27704
I0405 10:50:17.071940 30176 solver.cpp:237]     Train net output #0: loss = 5.27704 (* 1 = 5.27704 loss)
I0405 10:50:17.071949 30176 sgd_solver.cpp:105] Iteration 15048, lr = 1e-06
I0405 10:50:22.689067 30176 solver.cpp:218] Iteration 15060 (2.13634 iter/s, 5.61708s/12 iters), loss = 5.29092
I0405 10:50:22.689159 30176 solver.cpp:237]     Train net output #0: loss = 5.29092 (* 1 = 5.29092 loss)
I0405 10:50:22.689165 30176 sgd_solver.cpp:105] Iteration 15060, lr = 1e-06
I0405 10:50:27.440505 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:50:27.797387 30176 solver.cpp:218] Iteration 15072 (2.34917 iter/s, 5.10818s/12 iters), loss = 5.27046
I0405 10:50:27.797436 30176 solver.cpp:237]     Train net output #0: loss = 5.27046 (* 1 = 5.27046 loss)
I0405 10:50:27.797443 30176 sgd_solver.cpp:105] Iteration 15072, lr = 1e-06
I0405 10:50:33.276644 30176 solver.cpp:218] Iteration 15084 (2.19012 iter/s, 5.47916s/12 iters), loss = 5.26606
I0405 10:50:33.276684 30176 solver.cpp:237]     Train net output #0: loss = 5.26606 (* 1 = 5.26606 loss)
I0405 10:50:33.276690 30176 sgd_solver.cpp:105] Iteration 15084, lr = 1e-06
I0405 10:50:37.984150 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_15096.caffemodel
I0405 10:50:41.027643 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_15096.solverstate
I0405 10:50:43.404688 30176 solver.cpp:330] Iteration 15096, Testing net (#0)
I0405 10:50:43.404711 30176 net.cpp:676] Ignoring source layer train-data
I0405 10:50:46.519343 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:50:47.841567 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 10:50:47.841605 30176 solver.cpp:397]     Test net output #1: loss = 5.28007 (* 1 = 5.28007 loss)
I0405 10:50:47.982688 30176 solver.cpp:218] Iteration 15096 (0.816 iter/s, 14.7059s/12 iters), loss = 5.29487
I0405 10:50:47.982734 30176 solver.cpp:237]     Train net output #0: loss = 5.29487 (* 1 = 5.29487 loss)
I0405 10:50:47.982739 30176 sgd_solver.cpp:105] Iteration 15096, lr = 1e-06
I0405 10:50:52.403637 30176 solver.cpp:218] Iteration 15108 (2.71441 iter/s, 4.42085s/12 iters), loss = 5.27912
I0405 10:50:52.403690 30176 solver.cpp:237]     Train net output #0: loss = 5.27912 (* 1 = 5.27912 loss)
I0405 10:50:52.403697 30176 sgd_solver.cpp:105] Iteration 15108, lr = 1e-06
I0405 10:50:57.417356 30176 solver.cpp:218] Iteration 15120 (2.39348 iter/s, 5.01361s/12 iters), loss = 5.27735
I0405 10:50:57.417515 30176 solver.cpp:237]     Train net output #0: loss = 5.27735 (* 1 = 5.27735 loss)
I0405 10:50:57.417523 30176 sgd_solver.cpp:105] Iteration 15120, lr = 1e-06
I0405 10:51:02.913792 30176 solver.cpp:218] Iteration 15132 (2.18332 iter/s, 5.49622s/12 iters), loss = 5.2838
I0405 10:51:02.913848 30176 solver.cpp:237]     Train net output #0: loss = 5.2838 (* 1 = 5.2838 loss)
I0405 10:51:02.913856 30176 sgd_solver.cpp:105] Iteration 15132, lr = 1e-06
I0405 10:51:08.370223 30176 solver.cpp:218] Iteration 15144 (2.19928 iter/s, 5.45632s/12 iters), loss = 5.28037
I0405 10:51:08.370270 30176 solver.cpp:237]     Train net output #0: loss = 5.28037 (* 1 = 5.28037 loss)
I0405 10:51:08.370275 30176 sgd_solver.cpp:105] Iteration 15144, lr = 1e-06
I0405 10:51:13.702050 30176 solver.cpp:218] Iteration 15156 (2.25068 iter/s, 5.33173s/12 iters), loss = 5.2894
I0405 10:51:13.702087 30176 solver.cpp:237]     Train net output #0: loss = 5.2894 (* 1 = 5.2894 loss)
I0405 10:51:13.702093 30176 sgd_solver.cpp:105] Iteration 15156, lr = 1e-06
I0405 10:51:19.078840 30176 solver.cpp:218] Iteration 15168 (2.23185 iter/s, 5.3767s/12 iters), loss = 5.28391
I0405 10:51:19.078882 30176 solver.cpp:237]     Train net output #0: loss = 5.28391 (* 1 = 5.28391 loss)
I0405 10:51:19.078888 30176 sgd_solver.cpp:105] Iteration 15168, lr = 1e-06
I0405 10:51:20.949882 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:51:24.419533 30176 solver.cpp:218] Iteration 15180 (2.24694 iter/s, 5.34059s/12 iters), loss = 5.29123
I0405 10:51:24.419593 30176 solver.cpp:237]     Train net output #0: loss = 5.29123 (* 1 = 5.29123 loss)
I0405 10:51:24.419601 30176 sgd_solver.cpp:105] Iteration 15180, lr = 1e-06
I0405 10:51:29.719274 30176 solver.cpp:218] Iteration 15192 (2.26431 iter/s, 5.29963s/12 iters), loss = 5.28314
I0405 10:51:29.719612 30176 solver.cpp:237]     Train net output #0: loss = 5.28314 (* 1 = 5.28314 loss)
I0405 10:51:29.719619 30176 sgd_solver.cpp:105] Iteration 15192, lr = 1e-06
I0405 10:51:31.719327 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_15198.caffemodel
I0405 10:51:34.843865 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_15198.solverstate
I0405 10:51:37.165621 30176 solver.cpp:330] Iteration 15198, Testing net (#0)
I0405 10:51:37.165642 30176 net.cpp:676] Ignoring source layer train-data
I0405 10:51:40.273635 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:51:41.811798 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 10:51:41.811828 30176 solver.cpp:397]     Test net output #1: loss = 5.27984 (* 1 = 5.27984 loss)
I0405 10:51:43.810825 30176 solver.cpp:218] Iteration 15204 (0.851602 iter/s, 14.0911s/12 iters), loss = 5.28726
I0405 10:51:43.810896 30176 solver.cpp:237]     Train net output #0: loss = 5.28726 (* 1 = 5.28726 loss)
I0405 10:51:43.810904 30176 sgd_solver.cpp:105] Iteration 15204, lr = 1e-06
I0405 10:51:49.392720 30176 solver.cpp:218] Iteration 15216 (2.14985 iter/s, 5.58178s/12 iters), loss = 5.26662
I0405 10:51:49.392760 30176 solver.cpp:237]     Train net output #0: loss = 5.26662 (* 1 = 5.26662 loss)
I0405 10:51:49.392765 30176 sgd_solver.cpp:105] Iteration 15216, lr = 1e-06
I0405 10:51:54.690034 30176 solver.cpp:218] Iteration 15228 (2.26534 iter/s, 5.29722s/12 iters), loss = 5.3005
I0405 10:51:54.690074 30176 solver.cpp:237]     Train net output #0: loss = 5.3005 (* 1 = 5.3005 loss)
I0405 10:51:54.690080 30176 sgd_solver.cpp:105] Iteration 15228, lr = 1e-06
I0405 10:51:59.975502 30176 solver.cpp:218] Iteration 15240 (2.27042 iter/s, 5.28537s/12 iters), loss = 5.28754
I0405 10:51:59.975647 30176 solver.cpp:237]     Train net output #0: loss = 5.28754 (* 1 = 5.28754 loss)
I0405 10:51:59.975653 30176 sgd_solver.cpp:105] Iteration 15240, lr = 1e-06
I0405 10:52:05.140565 30176 blocking_queue.cpp:49] Waiting for data
I0405 10:52:05.678318 30176 solver.cpp:218] Iteration 15252 (2.1043 iter/s, 5.70262s/12 iters), loss = 5.2824
I0405 10:52:05.678359 30176 solver.cpp:237]     Train net output #0: loss = 5.2824 (* 1 = 5.2824 loss)
I0405 10:52:05.678364 30176 sgd_solver.cpp:105] Iteration 15252, lr = 1e-06
I0405 10:52:10.814010 30176 solver.cpp:218] Iteration 15264 (2.33663 iter/s, 5.1356s/12 iters), loss = 5.28718
I0405 10:52:10.814054 30176 solver.cpp:237]     Train net output #0: loss = 5.28718 (* 1 = 5.28718 loss)
I0405 10:52:10.814061 30176 sgd_solver.cpp:105] Iteration 15264, lr = 1e-06
I0405 10:52:15.070461 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:52:16.278234 30176 solver.cpp:218] Iteration 15276 (2.19614 iter/s, 5.46412s/12 iters), loss = 5.29027
I0405 10:52:16.278275 30176 solver.cpp:237]     Train net output #0: loss = 5.29027 (* 1 = 5.29027 loss)
I0405 10:52:16.278281 30176 sgd_solver.cpp:105] Iteration 15276, lr = 1e-06
I0405 10:52:21.621107 30176 solver.cpp:218] Iteration 15288 (2.24602 iter/s, 5.34278s/12 iters), loss = 5.28964
I0405 10:52:21.621145 30176 solver.cpp:237]     Train net output #0: loss = 5.28964 (* 1 = 5.28964 loss)
I0405 10:52:21.621150 30176 sgd_solver.cpp:105] Iteration 15288, lr = 1e-06
I0405 10:52:26.518600 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_15300.caffemodel
I0405 10:52:30.890668 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_15300.solverstate
I0405 10:52:33.262368 30176 solver.cpp:330] Iteration 15300, Testing net (#0)
I0405 10:52:33.262389 30176 net.cpp:676] Ignoring source layer train-data
I0405 10:52:36.514896 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:52:37.929103 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 10:52:37.929137 30176 solver.cpp:397]     Test net output #1: loss = 5.27966 (* 1 = 5.27966 loss)
I0405 10:52:38.065923 30176 solver.cpp:218] Iteration 15300 (0.729721 iter/s, 16.4447s/12 iters), loss = 5.28442
I0405 10:52:38.065977 30176 solver.cpp:237]     Train net output #0: loss = 5.28442 (* 1 = 5.28442 loss)
I0405 10:52:38.065985 30176 sgd_solver.cpp:105] Iteration 15300, lr = 1e-06
I0405 10:52:42.554545 30176 solver.cpp:218] Iteration 15312 (2.67349 iter/s, 4.48852s/12 iters), loss = 5.27619
I0405 10:52:42.554602 30176 solver.cpp:237]     Train net output #0: loss = 5.27619 (* 1 = 5.27619 loss)
I0405 10:52:42.554611 30176 sgd_solver.cpp:105] Iteration 15312, lr = 1e-06
I0405 10:52:48.066531 30176 solver.cpp:218] Iteration 15324 (2.17712 iter/s, 5.51188s/12 iters), loss = 5.30211
I0405 10:52:48.066582 30176 solver.cpp:237]     Train net output #0: loss = 5.30211 (* 1 = 5.30211 loss)
I0405 10:52:48.066591 30176 sgd_solver.cpp:105] Iteration 15324, lr = 1e-06
I0405 10:52:53.421435 30176 solver.cpp:218] Iteration 15336 (2.24125 iter/s, 5.35417s/12 iters), loss = 5.28005
I0405 10:52:53.421480 30176 solver.cpp:237]     Train net output #0: loss = 5.28005 (* 1 = 5.28005 loss)
I0405 10:52:53.421489 30176 sgd_solver.cpp:105] Iteration 15336, lr = 1e-06
I0405 10:52:58.714495 30176 solver.cpp:218] Iteration 15348 (2.26716 iter/s, 5.29296s/12 iters), loss = 5.27574
I0405 10:52:58.714538 30176 solver.cpp:237]     Train net output #0: loss = 5.27574 (* 1 = 5.27574 loss)
I0405 10:52:58.714543 30176 sgd_solver.cpp:105] Iteration 15348, lr = 1e-06
I0405 10:53:04.114559 30176 solver.cpp:218] Iteration 15360 (2.22224 iter/s, 5.39996s/12 iters), loss = 5.2735
I0405 10:53:04.114734 30176 solver.cpp:237]     Train net output #0: loss = 5.2735 (* 1 = 5.2735 loss)
I0405 10:53:04.114743 30176 sgd_solver.cpp:105] Iteration 15360, lr = 1e-06
I0405 10:53:09.590694 30176 solver.cpp:218] Iteration 15372 (2.19142 iter/s, 5.47591s/12 iters), loss = 5.2917
I0405 10:53:09.590739 30176 solver.cpp:237]     Train net output #0: loss = 5.2917 (* 1 = 5.2917 loss)
I0405 10:53:09.590744 30176 sgd_solver.cpp:105] Iteration 15372, lr = 1e-06
I0405 10:53:10.658170 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:53:15.051388 30176 solver.cpp:218] Iteration 15384 (2.19756 iter/s, 5.4606s/12 iters), loss = 5.26851
I0405 10:53:15.051434 30176 solver.cpp:237]     Train net output #0: loss = 5.26851 (* 1 = 5.26851 loss)
I0405 10:53:15.051440 30176 sgd_solver.cpp:105] Iteration 15384, lr = 1e-06
I0405 10:53:20.491056 30176 solver.cpp:218] Iteration 15396 (2.20606 iter/s, 5.43956s/12 iters), loss = 5.28081
I0405 10:53:20.491117 30176 solver.cpp:237]     Train net output #0: loss = 5.28081 (* 1 = 5.28081 loss)
I0405 10:53:20.491125 30176 sgd_solver.cpp:105] Iteration 15396, lr = 1e-06
I0405 10:53:22.567015 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_15402.caffemodel
I0405 10:53:25.644098 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_15402.solverstate
I0405 10:53:27.948479 30176 solver.cpp:330] Iteration 15402, Testing net (#0)
I0405 10:53:27.948503 30176 net.cpp:676] Ignoring source layer train-data
I0405 10:53:31.020680 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:53:32.427448 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 10:53:32.427489 30176 solver.cpp:397]     Test net output #1: loss = 5.28015 (* 1 = 5.28015 loss)
I0405 10:53:34.456230 30176 solver.cpp:218] Iteration 15408 (0.859291 iter/s, 13.965s/12 iters), loss = 5.2908
I0405 10:53:34.456348 30176 solver.cpp:237]     Train net output #0: loss = 5.2908 (* 1 = 5.2908 loss)
I0405 10:53:34.456357 30176 sgd_solver.cpp:105] Iteration 15408, lr = 1e-06
I0405 10:53:40.074123 30176 solver.cpp:218] Iteration 15420 (2.1361 iter/s, 5.61772s/12 iters), loss = 5.28996
I0405 10:53:40.074179 30176 solver.cpp:237]     Train net output #0: loss = 5.28996 (* 1 = 5.28996 loss)
I0405 10:53:40.074188 30176 sgd_solver.cpp:105] Iteration 15420, lr = 1e-06
I0405 10:53:45.394449 30176 solver.cpp:218] Iteration 15432 (2.25555 iter/s, 5.32022s/12 iters), loss = 5.27656
I0405 10:53:45.394501 30176 solver.cpp:237]     Train net output #0: loss = 5.27656 (* 1 = 5.27656 loss)
I0405 10:53:45.394510 30176 sgd_solver.cpp:105] Iteration 15432, lr = 1e-06
I0405 10:53:50.858726 30176 solver.cpp:218] Iteration 15444 (2.19613 iter/s, 5.46417s/12 iters), loss = 5.26485
I0405 10:53:50.858779 30176 solver.cpp:237]     Train net output #0: loss = 5.26485 (* 1 = 5.26485 loss)
I0405 10:53:50.858788 30176 sgd_solver.cpp:105] Iteration 15444, lr = 1e-06
I0405 10:53:56.300542 30176 solver.cpp:218] Iteration 15456 (2.20519 iter/s, 5.44171s/12 iters), loss = 5.28224
I0405 10:53:56.300585 30176 solver.cpp:237]     Train net output #0: loss = 5.28224 (* 1 = 5.28224 loss)
I0405 10:53:56.300590 30176 sgd_solver.cpp:105] Iteration 15456, lr = 1e-06
I0405 10:54:01.550570 30176 solver.cpp:218] Iteration 15468 (2.28574 iter/s, 5.24994s/12 iters), loss = 5.27675
I0405 10:54:01.550607 30176 solver.cpp:237]     Train net output #0: loss = 5.27675 (* 1 = 5.27675 loss)
I0405 10:54:01.550613 30176 sgd_solver.cpp:105] Iteration 15468, lr = 1e-06
I0405 10:54:04.959372 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:54:06.847728 30176 solver.cpp:218] Iteration 15480 (2.26541 iter/s, 5.29706s/12 iters), loss = 5.30354
I0405 10:54:06.847780 30176 solver.cpp:237]     Train net output #0: loss = 5.30354 (* 1 = 5.30354 loss)
I0405 10:54:06.847788 30176 sgd_solver.cpp:105] Iteration 15480, lr = 1e-06
I0405 10:54:12.360132 30176 solver.cpp:218] Iteration 15492 (2.17695 iter/s, 5.5123s/12 iters), loss = 5.27923
I0405 10:54:12.360188 30176 solver.cpp:237]     Train net output #0: loss = 5.27923 (* 1 = 5.27923 loss)
I0405 10:54:12.360194 30176 sgd_solver.cpp:105] Iteration 15492, lr = 1e-06
I0405 10:54:17.374078 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_15504.caffemodel
I0405 10:54:20.423485 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_15504.solverstate
I0405 10:54:22.799103 30176 solver.cpp:330] Iteration 15504, Testing net (#0)
I0405 10:54:22.799129 30176 net.cpp:676] Ignoring source layer train-data
I0405 10:54:25.886605 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:54:27.418522 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 10:54:27.418557 30176 solver.cpp:397]     Test net output #1: loss = 5.27993 (* 1 = 5.27993 loss)
I0405 10:54:27.559314 30176 solver.cpp:218] Iteration 15504 (0.789524 iter/s, 15.199s/12 iters), loss = 5.28141
I0405 10:54:27.559348 30176 solver.cpp:237]     Train net output #0: loss = 5.28141 (* 1 = 5.28141 loss)
I0405 10:54:27.559353 30176 sgd_solver.cpp:105] Iteration 15504, lr = 1e-06
I0405 10:54:32.144510 30176 solver.cpp:218] Iteration 15516 (2.61717 iter/s, 4.58511s/12 iters), loss = 5.2783
I0405 10:54:32.144564 30176 solver.cpp:237]     Train net output #0: loss = 5.2783 (* 1 = 5.2783 loss)
I0405 10:54:32.144573 30176 sgd_solver.cpp:105] Iteration 15516, lr = 1e-06
I0405 10:54:37.606413 30176 solver.cpp:218] Iteration 15528 (2.19708 iter/s, 5.46179s/12 iters), loss = 5.27791
I0405 10:54:37.606529 30176 solver.cpp:237]     Train net output #0: loss = 5.27791 (* 1 = 5.27791 loss)
I0405 10:54:37.606539 30176 sgd_solver.cpp:105] Iteration 15528, lr = 1e-06
I0405 10:54:43.153240 30176 solver.cpp:218] Iteration 15540 (2.16347 iter/s, 5.54665s/12 iters), loss = 5.27691
I0405 10:54:43.153295 30176 solver.cpp:237]     Train net output #0: loss = 5.27691 (* 1 = 5.27691 loss)
I0405 10:54:43.153303 30176 sgd_solver.cpp:105] Iteration 15540, lr = 1e-06
I0405 10:54:48.511332 30176 solver.cpp:218] Iteration 15552 (2.23965 iter/s, 5.35799s/12 iters), loss = 5.28919
I0405 10:54:48.511387 30176 solver.cpp:237]     Train net output #0: loss = 5.28919 (* 1 = 5.28919 loss)
I0405 10:54:48.511396 30176 sgd_solver.cpp:105] Iteration 15552, lr = 1e-06
I0405 10:54:53.884392 30176 solver.cpp:218] Iteration 15564 (2.23341 iter/s, 5.37295s/12 iters), loss = 5.27649
I0405 10:54:53.884438 30176 solver.cpp:237]     Train net output #0: loss = 5.27649 (* 1 = 5.27649 loss)
I0405 10:54:53.884443 30176 sgd_solver.cpp:105] Iteration 15564, lr = 1e-06
I0405 10:54:59.235819 30176 solver.cpp:218] Iteration 15576 (2.24244 iter/s, 5.35132s/12 iters), loss = 5.28181
I0405 10:54:59.235878 30176 solver.cpp:237]     Train net output #0: loss = 5.28181 (* 1 = 5.28181 loss)
I0405 10:54:59.235888 30176 sgd_solver.cpp:105] Iteration 15576, lr = 1e-06
I0405 10:54:59.708909 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:55:04.776901 30176 solver.cpp:218] Iteration 15588 (2.16569 iter/s, 5.54096s/12 iters), loss = 5.27878
I0405 10:55:04.776944 30176 solver.cpp:237]     Train net output #0: loss = 5.27878 (* 1 = 5.27878 loss)
I0405 10:55:04.776950 30176 sgd_solver.cpp:105] Iteration 15588, lr = 1e-06
I0405 10:55:10.143115 30176 solver.cpp:218] Iteration 15600 (2.23625 iter/s, 5.36612s/12 iters), loss = 5.29104
I0405 10:55:10.143252 30176 solver.cpp:237]     Train net output #0: loss = 5.29104 (* 1 = 5.29104 loss)
I0405 10:55:10.143260 30176 sgd_solver.cpp:105] Iteration 15600, lr = 1e-06
I0405 10:55:12.192708 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_15606.caffemodel
I0405 10:55:15.292868 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_15606.solverstate
I0405 10:55:17.595491 30176 solver.cpp:330] Iteration 15606, Testing net (#0)
I0405 10:55:17.595510 30176 net.cpp:676] Ignoring source layer train-data
I0405 10:55:20.523099 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:55:22.018580 30176 solver.cpp:397]     Test net output #0: accuracy = 0.00735294
I0405 10:55:22.018620 30176 solver.cpp:397]     Test net output #1: loss = 5.27955 (* 1 = 5.27955 loss)
I0405 10:55:23.969233 30176 solver.cpp:218] Iteration 15612 (0.867938 iter/s, 13.8259s/12 iters), loss = 5.28814
I0405 10:55:23.969285 30176 solver.cpp:237]     Train net output #0: loss = 5.28814 (* 1 = 5.28814 loss)
I0405 10:55:23.969290 30176 sgd_solver.cpp:105] Iteration 15612, lr = 1e-06
I0405 10:55:29.405467 30176 solver.cpp:218] Iteration 15624 (2.20745 iter/s, 5.43613s/12 iters), loss = 5.28036
I0405 10:55:29.411672 30176 solver.cpp:237]     Train net output #0: loss = 5.28036 (* 1 = 5.28036 loss)
I0405 10:55:29.411687 30176 sgd_solver.cpp:105] Iteration 15624, lr = 1e-06
I0405 10:55:34.939142 30176 solver.cpp:218] Iteration 15636 (2.17099 iter/s, 5.52743s/12 iters), loss = 5.28689
I0405 10:55:34.939198 30176 solver.cpp:237]     Train net output #0: loss = 5.28689 (* 1 = 5.28689 loss)
I0405 10:55:34.939205 30176 sgd_solver.cpp:105] Iteration 15636, lr = 1e-06
I0405 10:55:40.389348 30176 solver.cpp:218] Iteration 15648 (2.2018 iter/s, 5.45009s/12 iters), loss = 5.30158
I0405 10:55:40.389479 30176 solver.cpp:237]     Train net output #0: loss = 5.30158 (* 1 = 5.30158 loss)
I0405 10:55:40.389492 30176 sgd_solver.cpp:105] Iteration 15648, lr = 1e-06
I0405 10:55:45.684228 30176 solver.cpp:218] Iteration 15660 (2.26642 iter/s, 5.2947s/12 iters), loss = 5.27865
I0405 10:55:45.684276 30176 solver.cpp:237]     Train net output #0: loss = 5.27865 (* 1 = 5.27865 loss)
I0405 10:55:45.684283 30176 sgd_solver.cpp:105] Iteration 15660, lr = 1e-06
I0405 10:55:51.121855 30176 solver.cpp:218] Iteration 15672 (2.20689 iter/s, 5.43752s/12 iters), loss = 5.26744
I0405 10:55:51.121909 30176 solver.cpp:237]     Train net output #0: loss = 5.26744 (* 1 = 5.26744 loss)
I0405 10:55:51.121917 30176 sgd_solver.cpp:105] Iteration 15672, lr = 1e-06
I0405 10:55:53.973814 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:55:56.698527 30176 solver.cpp:218] Iteration 15684 (2.15186 iter/s, 5.57656s/12 iters), loss = 5.27947
I0405 10:55:56.698580 30176 solver.cpp:237]     Train net output #0: loss = 5.27947 (* 1 = 5.27947 loss)
I0405 10:55:56.698586 30176 sgd_solver.cpp:105] Iteration 15684, lr = 1e-06
I0405 10:56:02.112210 30176 solver.cpp:218] Iteration 15696 (2.21665 iter/s, 5.41358s/12 iters), loss = 5.2776
I0405 10:56:02.112251 30176 solver.cpp:237]     Train net output #0: loss = 5.2776 (* 1 = 5.2776 loss)
I0405 10:56:02.112257 30176 sgd_solver.cpp:105] Iteration 15696, lr = 1e-06
I0405 10:56:07.052301 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_15708.caffemodel
I0405 10:56:10.270785 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_15708.solverstate
I0405 10:56:12.601222 30176 solver.cpp:330] Iteration 15708, Testing net (#0)
I0405 10:56:12.601295 30176 net.cpp:676] Ignoring source layer train-data
I0405 10:56:15.466727 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:56:17.109488 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 10:56:17.109519 30176 solver.cpp:397]     Test net output #1: loss = 5.27965 (* 1 = 5.27965 loss)
I0405 10:56:17.250411 30176 solver.cpp:218] Iteration 15708 (0.792705 iter/s, 15.138s/12 iters), loss = 5.31021
I0405 10:56:17.251991 30176 solver.cpp:237]     Train net output #0: loss = 5.31021 (* 1 = 5.31021 loss)
I0405 10:56:17.252002 30176 sgd_solver.cpp:105] Iteration 15708, lr = 1e-06
I0405 10:56:21.684773 30176 solver.cpp:218] Iteration 15720 (2.70713 iter/s, 4.43274s/12 iters), loss = 5.27371
I0405 10:56:21.684824 30176 solver.cpp:237]     Train net output #0: loss = 5.27371 (* 1 = 5.27371 loss)
I0405 10:56:21.684831 30176 sgd_solver.cpp:105] Iteration 15720, lr = 1e-06
I0405 10:56:27.225803 30176 solver.cpp:218] Iteration 15732 (2.1657 iter/s, 5.54093s/12 iters), loss = 5.3022
I0405 10:56:27.225843 30176 solver.cpp:237]     Train net output #0: loss = 5.3022 (* 1 = 5.3022 loss)
I0405 10:56:27.225848 30176 sgd_solver.cpp:105] Iteration 15732, lr = 1e-06
I0405 10:56:32.634790 30176 solver.cpp:218] Iteration 15744 (2.21857 iter/s, 5.40889s/12 iters), loss = 5.27838
I0405 10:56:32.634835 30176 solver.cpp:237]     Train net output #0: loss = 5.27838 (* 1 = 5.27838 loss)
I0405 10:56:32.634841 30176 sgd_solver.cpp:105] Iteration 15744, lr = 1e-06
I0405 10:56:38.105331 30176 solver.cpp:218] Iteration 15756 (2.19361 iter/s, 5.47044s/12 iters), loss = 5.26775
I0405 10:56:38.105373 30176 solver.cpp:237]     Train net output #0: loss = 5.26775 (* 1 = 5.26775 loss)
I0405 10:56:38.105379 30176 sgd_solver.cpp:105] Iteration 15756, lr = 1e-06
I0405 10:56:43.817929 30176 solver.cpp:218] Iteration 15768 (2.10066 iter/s, 5.7125s/12 iters), loss = 5.27446
I0405 10:56:43.818099 30176 solver.cpp:237]     Train net output #0: loss = 5.27446 (* 1 = 5.27446 loss)
I0405 10:56:43.818109 30176 sgd_solver.cpp:105] Iteration 15768, lr = 1e-06
I0405 10:56:49.159375 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:56:49.503885 30176 solver.cpp:218] Iteration 15780 (2.11054 iter/s, 5.68574s/12 iters), loss = 5.29271
I0405 10:56:49.503926 30176 solver.cpp:237]     Train net output #0: loss = 5.29271 (* 1 = 5.29271 loss)
I0405 10:56:49.503931 30176 sgd_solver.cpp:105] Iteration 15780, lr = 1e-06
I0405 10:56:54.854972 30176 solver.cpp:218] Iteration 15792 (2.24257 iter/s, 5.35099s/12 iters), loss = 5.2685
I0405 10:56:54.855031 30176 solver.cpp:237]     Train net output #0: loss = 5.2685 (* 1 = 5.2685 loss)
I0405 10:56:54.855038 30176 sgd_solver.cpp:105] Iteration 15792, lr = 1e-06
I0405 10:57:00.277401 30176 solver.cpp:218] Iteration 15804 (2.21308 iter/s, 5.42231s/12 iters), loss = 5.26537
I0405 10:57:00.277456 30176 solver.cpp:237]     Train net output #0: loss = 5.26537 (* 1 = 5.26537 loss)
I0405 10:57:00.277464 30176 sgd_solver.cpp:105] Iteration 15804, lr = 1e-06
I0405 10:57:02.444685 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_15810.caffemodel
I0405 10:57:05.566402 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_15810.solverstate
I0405 10:57:07.892045 30176 solver.cpp:330] Iteration 15810, Testing net (#0)
I0405 10:57:07.892064 30176 net.cpp:676] Ignoring source layer train-data
I0405 10:57:10.641995 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:57:12.374186 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 10:57:12.374223 30176 solver.cpp:397]     Test net output #1: loss = 5.2798 (* 1 = 5.2798 loss)
I0405 10:57:14.421334 30176 solver.cpp:218] Iteration 15816 (0.84843 iter/s, 14.1438s/12 iters), loss = 5.27378
I0405 10:57:14.421427 30176 solver.cpp:237]     Train net output #0: loss = 5.27378 (* 1 = 5.27378 loss)
I0405 10:57:14.421434 30176 sgd_solver.cpp:105] Iteration 15816, lr = 1e-06
I0405 10:57:20.188997 30176 solver.cpp:218] Iteration 15828 (2.08062 iter/s, 5.76751s/12 iters), loss = 5.27511
I0405 10:57:20.189038 30176 solver.cpp:237]     Train net output #0: loss = 5.27511 (* 1 = 5.27511 loss)
I0405 10:57:20.189043 30176 sgd_solver.cpp:105] Iteration 15828, lr = 1e-06
I0405 10:57:25.463027 30176 solver.cpp:218] Iteration 15840 (2.27534 iter/s, 5.27393s/12 iters), loss = 5.28551
I0405 10:57:25.463071 30176 solver.cpp:237]     Train net output #0: loss = 5.28551 (* 1 = 5.28551 loss)
I0405 10:57:25.463078 30176 sgd_solver.cpp:105] Iteration 15840, lr = 1e-06
I0405 10:57:30.658114 30176 solver.cpp:218] Iteration 15852 (2.30992 iter/s, 5.19499s/12 iters), loss = 5.28075
I0405 10:57:30.658156 30176 solver.cpp:237]     Train net output #0: loss = 5.28075 (* 1 = 5.28075 loss)
I0405 10:57:30.658161 30176 sgd_solver.cpp:105] Iteration 15852, lr = 1e-06
I0405 10:57:36.006654 30176 solver.cpp:218] Iteration 15864 (2.24364 iter/s, 5.34844s/12 iters), loss = 5.29487
I0405 10:57:36.006695 30176 solver.cpp:237]     Train net output #0: loss = 5.29487 (* 1 = 5.29487 loss)
I0405 10:57:36.006700 30176 sgd_solver.cpp:105] Iteration 15864, lr = 1e-06
I0405 10:57:41.623234 30176 solver.cpp:218] Iteration 15876 (2.13657 iter/s, 5.61648s/12 iters), loss = 5.28008
I0405 10:57:41.623283 30176 solver.cpp:237]     Train net output #0: loss = 5.28008 (* 1 = 5.28008 loss)
I0405 10:57:41.623291 30176 sgd_solver.cpp:105] Iteration 15876, lr = 1e-06
I0405 10:57:43.576645 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:57:47.043329 30176 solver.cpp:218] Iteration 15888 (2.21403 iter/s, 5.41999s/12 iters), loss = 5.28871
I0405 10:57:47.045207 30176 solver.cpp:237]     Train net output #0: loss = 5.28871 (* 1 = 5.28871 loss)
I0405 10:57:47.045215 30176 sgd_solver.cpp:105] Iteration 15888, lr = 1e-06
I0405 10:57:52.592216 30176 solver.cpp:218] Iteration 15900 (2.16335 iter/s, 5.54696s/12 iters), loss = 5.29277
I0405 10:57:52.592260 30176 solver.cpp:237]     Train net output #0: loss = 5.29277 (* 1 = 5.29277 loss)
I0405 10:57:52.592267 30176 sgd_solver.cpp:105] Iteration 15900, lr = 1e-06
I0405 10:57:57.430020 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_15912.caffemodel
I0405 10:58:00.507426 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_15912.solverstate
I0405 10:58:02.805121 30176 solver.cpp:330] Iteration 15912, Testing net (#0)
I0405 10:58:02.805141 30176 net.cpp:676] Ignoring source layer train-data
I0405 10:58:05.785168 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:58:07.498636 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 10:58:07.498669 30176 solver.cpp:397]     Test net output #1: loss = 5.27936 (* 1 = 5.27936 loss)
I0405 10:58:07.639597 30176 solver.cpp:218] Iteration 15912 (0.79749 iter/s, 15.0472s/12 iters), loss = 5.279
I0405 10:58:07.639660 30176 solver.cpp:237]     Train net output #0: loss = 5.279 (* 1 = 5.279 loss)
I0405 10:58:07.639668 30176 sgd_solver.cpp:105] Iteration 15912, lr = 1e-06
I0405 10:58:12.140730 30176 solver.cpp:218] Iteration 15924 (2.66606 iter/s, 4.50102s/12 iters), loss = 5.27307
I0405 10:58:12.140775 30176 solver.cpp:237]     Train net output #0: loss = 5.27307 (* 1 = 5.27307 loss)
I0405 10:58:12.140780 30176 sgd_solver.cpp:105] Iteration 15924, lr = 1e-06
I0405 10:58:17.514406 30176 solver.cpp:218] Iteration 15936 (2.23315 iter/s, 5.37357s/12 iters), loss = 5.30263
I0405 10:58:17.514541 30176 solver.cpp:237]     Train net output #0: loss = 5.30263 (* 1 = 5.30263 loss)
I0405 10:58:17.514549 30176 sgd_solver.cpp:105] Iteration 15936, lr = 1e-06
I0405 10:58:17.514744 30176 blocking_queue.cpp:49] Waiting for data
I0405 10:58:22.785471 30176 solver.cpp:218] Iteration 15948 (2.27666 iter/s, 5.27088s/12 iters), loss = 5.28577
I0405 10:58:22.785516 30176 solver.cpp:237]     Train net output #0: loss = 5.28577 (* 1 = 5.28577 loss)
I0405 10:58:22.785521 30176 sgd_solver.cpp:105] Iteration 15948, lr = 1e-06
I0405 10:58:28.059870 30176 solver.cpp:218] Iteration 15960 (2.27518 iter/s, 5.2743s/12 iters), loss = 5.28408
I0405 10:58:28.059922 30176 solver.cpp:237]     Train net output #0: loss = 5.28408 (* 1 = 5.28408 loss)
I0405 10:58:28.059931 30176 sgd_solver.cpp:105] Iteration 15960, lr = 1e-06
I0405 10:58:33.236693 30176 solver.cpp:218] Iteration 15972 (2.31807 iter/s, 5.17672s/12 iters), loss = 5.28137
I0405 10:58:33.236744 30176 solver.cpp:237]     Train net output #0: loss = 5.28137 (* 1 = 5.28137 loss)
I0405 10:58:33.236750 30176 sgd_solver.cpp:105] Iteration 15972, lr = 1e-06
I0405 10:58:37.628396 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:58:38.769438 30176 solver.cpp:218] Iteration 15984 (2.16895 iter/s, 5.53264s/12 iters), loss = 5.28846
I0405 10:58:38.769486 30176 solver.cpp:237]     Train net output #0: loss = 5.28846 (* 1 = 5.28846 loss)
I0405 10:58:38.769491 30176 sgd_solver.cpp:105] Iteration 15984, lr = 1e-06
I0405 10:58:43.842528 30176 solver.cpp:218] Iteration 15996 (2.36547 iter/s, 5.07298s/12 iters), loss = 5.28909
I0405 10:58:43.842581 30176 solver.cpp:237]     Train net output #0: loss = 5.28909 (* 1 = 5.28909 loss)
I0405 10:58:43.842589 30176 sgd_solver.cpp:105] Iteration 15996, lr = 1e-06
I0405 10:58:48.948635 30176 solver.cpp:218] Iteration 16008 (2.35017 iter/s, 5.106s/12 iters), loss = 5.28496
I0405 10:58:48.948761 30176 solver.cpp:237]     Train net output #0: loss = 5.28496 (* 1 = 5.28496 loss)
I0405 10:58:48.948768 30176 sgd_solver.cpp:105] Iteration 16008, lr = 1e-06
I0405 10:58:51.109632 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_16014.caffemodel
I0405 10:58:54.226013 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_16014.solverstate
I0405 10:58:56.528414 30176 solver.cpp:330] Iteration 16014, Testing net (#0)
I0405 10:58:56.528436 30176 net.cpp:676] Ignoring source layer train-data
I0405 10:58:59.190124 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:59:00.903134 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 10:59:00.903174 30176 solver.cpp:397]     Test net output #1: loss = 5.27961 (* 1 = 5.27961 loss)
I0405 10:59:02.732125 30176 solver.cpp:218] Iteration 16020 (0.870622 iter/s, 13.7833s/12 iters), loss = 5.28128
I0405 10:59:02.732168 30176 solver.cpp:237]     Train net output #0: loss = 5.28128 (* 1 = 5.28128 loss)
I0405 10:59:02.732173 30176 sgd_solver.cpp:105] Iteration 16020, lr = 1e-06
I0405 10:59:08.180007 30176 solver.cpp:218] Iteration 16032 (2.20273 iter/s, 5.44778s/12 iters), loss = 5.28723
I0405 10:59:08.180052 30176 solver.cpp:237]     Train net output #0: loss = 5.28723 (* 1 = 5.28723 loss)
I0405 10:59:08.180056 30176 sgd_solver.cpp:105] Iteration 16032, lr = 1e-06
I0405 10:59:13.562816 30176 solver.cpp:218] Iteration 16044 (2.22936 iter/s, 5.38271s/12 iters), loss = 5.28201
I0405 10:59:13.562867 30176 solver.cpp:237]     Train net output #0: loss = 5.28201 (* 1 = 5.28201 loss)
I0405 10:59:13.562876 30176 sgd_solver.cpp:105] Iteration 16044, lr = 1e-06
I0405 10:59:18.948626 30176 solver.cpp:218] Iteration 16056 (2.22812 iter/s, 5.3857s/12 iters), loss = 5.27595
I0405 10:59:18.948690 30176 solver.cpp:237]     Train net output #0: loss = 5.27595 (* 1 = 5.27595 loss)
I0405 10:59:18.948769 30176 sgd_solver.cpp:105] Iteration 16056, lr = 1e-06
I0405 10:59:24.236104 30176 solver.cpp:218] Iteration 16068 (2.26957 iter/s, 5.28736s/12 iters), loss = 5.2689
I0405 10:59:24.236158 30176 solver.cpp:237]     Train net output #0: loss = 5.2689 (* 1 = 5.2689 loss)
I0405 10:59:24.236166 30176 sgd_solver.cpp:105] Iteration 16068, lr = 1e-06
I0405 10:59:29.383332 30176 solver.cpp:218] Iteration 16080 (2.3314 iter/s, 5.14712s/12 iters), loss = 5.29286
I0405 10:59:29.383378 30176 solver.cpp:237]     Train net output #0: loss = 5.29286 (* 1 = 5.29286 loss)
I0405 10:59:29.383383 30176 sgd_solver.cpp:105] Iteration 16080, lr = 1e-06
I0405 10:59:30.555032 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:59:34.970510 30176 solver.cpp:218] Iteration 16092 (2.14782 iter/s, 5.58707s/12 iters), loss = 5.29027
I0405 10:59:34.970568 30176 solver.cpp:237]     Train net output #0: loss = 5.29027 (* 1 = 5.29027 loss)
I0405 10:59:34.970574 30176 sgd_solver.cpp:105] Iteration 16092, lr = 1e-06
I0405 10:59:40.404433 30176 solver.cpp:218] Iteration 16104 (2.20839 iter/s, 5.43381s/12 iters), loss = 5.27681
I0405 10:59:40.404490 30176 solver.cpp:237]     Train net output #0: loss = 5.27681 (* 1 = 5.27681 loss)
I0405 10:59:40.404498 30176 sgd_solver.cpp:105] Iteration 16104, lr = 1e-06
I0405 10:59:45.482964 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_16116.caffemodel
I0405 10:59:48.536775 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_16116.solverstate
I0405 10:59:50.884403 30176 solver.cpp:330] Iteration 16116, Testing net (#0)
I0405 10:59:50.884552 30176 net.cpp:676] Ignoring source layer train-data
I0405 10:59:53.797920 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 10:59:55.519948 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 10:59:55.519984 30176 solver.cpp:397]     Test net output #1: loss = 5.27946 (* 1 = 5.27946 loss)
I0405 10:59:55.660966 30176 solver.cpp:218] Iteration 16116 (0.786557 iter/s, 15.2564s/12 iters), loss = 5.298
I0405 10:59:55.661026 30176 solver.cpp:237]     Train net output #0: loss = 5.298 (* 1 = 5.298 loss)
I0405 10:59:55.661036 30176 sgd_solver.cpp:105] Iteration 16116, lr = 1e-06
I0405 11:00:00.143604 30176 solver.cpp:218] Iteration 16128 (2.67706 iter/s, 4.48253s/12 iters), loss = 5.29141
I0405 11:00:00.143646 30176 solver.cpp:237]     Train net output #0: loss = 5.29141 (* 1 = 5.29141 loss)
I0405 11:00:00.143651 30176 sgd_solver.cpp:105] Iteration 16128, lr = 1e-06
I0405 11:00:05.662151 30176 solver.cpp:218] Iteration 16140 (2.17452 iter/s, 5.51845s/12 iters), loss = 5.28346
I0405 11:00:05.662194 30176 solver.cpp:237]     Train net output #0: loss = 5.28346 (* 1 = 5.28346 loss)
I0405 11:00:05.662199 30176 sgd_solver.cpp:105] Iteration 16140, lr = 1e-06
I0405 11:00:11.065495 30176 solver.cpp:218] Iteration 16152 (2.22089 iter/s, 5.40324s/12 iters), loss = 5.26989
I0405 11:00:11.065546 30176 solver.cpp:237]     Train net output #0: loss = 5.26989 (* 1 = 5.26989 loss)
I0405 11:00:11.065555 30176 sgd_solver.cpp:105] Iteration 16152, lr = 1e-06
I0405 11:00:16.484974 30176 solver.cpp:218] Iteration 16164 (2.21428 iter/s, 5.41937s/12 iters), loss = 5.28154
I0405 11:00:16.485019 30176 solver.cpp:237]     Train net output #0: loss = 5.28154 (* 1 = 5.28154 loss)
I0405 11:00:16.485024 30176 sgd_solver.cpp:105] Iteration 16164, lr = 1e-06
I0405 11:00:21.999110 30176 solver.cpp:218] Iteration 16176 (2.17626 iter/s, 5.51404s/12 iters), loss = 5.2991
I0405 11:00:21.999207 30176 solver.cpp:237]     Train net output #0: loss = 5.2991 (* 1 = 5.2991 loss)
I0405 11:00:21.999213 30176 sgd_solver.cpp:105] Iteration 16176, lr = 1e-06
I0405 11:00:25.452364 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 11:00:27.411753 30176 solver.cpp:218] Iteration 16188 (2.21709 iter/s, 5.41249s/12 iters), loss = 5.27881
I0405 11:00:27.411798 30176 solver.cpp:237]     Train net output #0: loss = 5.27881 (* 1 = 5.27881 loss)
I0405 11:00:27.411803 30176 sgd_solver.cpp:105] Iteration 16188, lr = 1e-06
I0405 11:00:32.930058 30176 solver.cpp:218] Iteration 16200 (2.17462 iter/s, 5.5182s/12 iters), loss = 5.29303
I0405 11:00:32.930104 30176 solver.cpp:237]     Train net output #0: loss = 5.29303 (* 1 = 5.29303 loss)
I0405 11:00:32.930111 30176 sgd_solver.cpp:105] Iteration 16200, lr = 1e-06
I0405 11:00:38.130574 30176 solver.cpp:218] Iteration 16212 (2.30751 iter/s, 5.20042s/12 iters), loss = 5.28936
I0405 11:00:38.130623 30176 solver.cpp:237]     Train net output #0: loss = 5.28936 (* 1 = 5.28936 loss)
I0405 11:00:38.130630 30176 sgd_solver.cpp:105] Iteration 16212, lr = 1e-06
I0405 11:00:40.246385 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_16218.caffemodel
I0405 11:00:43.385937 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_16218.solverstate
I0405 11:00:45.698886 30176 solver.cpp:330] Iteration 16218, Testing net (#0)
I0405 11:00:45.698905 30176 net.cpp:676] Ignoring source layer train-data
I0405 11:00:48.377077 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 11:00:50.275131 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 11:00:50.275166 30176 solver.cpp:397]     Test net output #1: loss = 5.27994 (* 1 = 5.27994 loss)
I0405 11:00:52.319267 30176 solver.cpp:218] Iteration 16224 (0.845754 iter/s, 14.1885s/12 iters), loss = 5.27849
I0405 11:00:52.319437 30176 solver.cpp:237]     Train net output #0: loss = 5.27849 (* 1 = 5.27849 loss)
I0405 11:00:52.319447 30176 sgd_solver.cpp:105] Iteration 16224, lr = 1e-06
I0405 11:00:57.993306 30176 solver.cpp:218] Iteration 16236 (2.11498 iter/s, 5.67382s/12 iters), loss = 5.28987
I0405 11:00:57.993350 30176 solver.cpp:237]     Train net output #0: loss = 5.28987 (* 1 = 5.28987 loss)
I0405 11:00:57.993355 30176 sgd_solver.cpp:105] Iteration 16236, lr = 1e-06
I0405 11:01:03.355751 30176 solver.cpp:218] Iteration 16248 (2.23783 iter/s, 5.36234s/12 iters), loss = 5.28091
I0405 11:01:03.355808 30176 solver.cpp:237]     Train net output #0: loss = 5.28091 (* 1 = 5.28091 loss)
I0405 11:01:03.355816 30176 sgd_solver.cpp:105] Iteration 16248, lr = 1e-06
I0405 11:01:08.826905 30176 solver.cpp:218] Iteration 16260 (2.19337 iter/s, 5.47104s/12 iters), loss = 5.29786
I0405 11:01:08.826954 30176 solver.cpp:237]     Train net output #0: loss = 5.29786 (* 1 = 5.29786 loss)
I0405 11:01:08.826961 30176 sgd_solver.cpp:105] Iteration 16260, lr = 1e-06
I0405 11:01:13.894142 30176 solver.cpp:218] Iteration 16272 (2.3682 iter/s, 5.06714s/12 iters), loss = 5.27399
I0405 11:01:13.894193 30176 solver.cpp:237]     Train net output #0: loss = 5.27399 (* 1 = 5.27399 loss)
I0405 11:01:13.894202 30176 sgd_solver.cpp:105] Iteration 16272, lr = 1e-06
I0405 11:01:19.296406 30176 solver.cpp:218] Iteration 16284 (2.22134 iter/s, 5.40215s/12 iters), loss = 5.2912
I0405 11:01:19.296473 30176 solver.cpp:237]     Train net output #0: loss = 5.2912 (* 1 = 5.2912 loss)
I0405 11:01:19.296483 30176 sgd_solver.cpp:105] Iteration 16284, lr = 1e-06
I0405 11:01:19.821252 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 11:01:24.743489 30176 solver.cpp:218] Iteration 16296 (2.20306 iter/s, 5.44697s/12 iters), loss = 5.30474
I0405 11:01:24.743593 30176 solver.cpp:237]     Train net output #0: loss = 5.30474 (* 1 = 5.30474 loss)
I0405 11:01:24.743599 30176 sgd_solver.cpp:105] Iteration 16296, lr = 1e-06
I0405 11:01:30.182816 30176 solver.cpp:218] Iteration 16308 (2.20622 iter/s, 5.43917s/12 iters), loss = 5.30013
I0405 11:01:30.182855 30176 solver.cpp:237]     Train net output #0: loss = 5.30013 (* 1 = 5.30013 loss)
I0405 11:01:30.182862 30176 sgd_solver.cpp:105] Iteration 16308, lr = 1e-06
I0405 11:01:35.097158 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_16320.caffemodel
I0405 11:01:38.282213 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_16320.solverstate
I0405 11:01:40.577428 30176 solver.cpp:330] Iteration 16320, Testing net (#0)
I0405 11:01:40.577446 30176 net.cpp:676] Ignoring source layer train-data
I0405 11:01:43.386334 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 11:01:45.554437 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 11:01:45.554473 30176 solver.cpp:397]     Test net output #1: loss = 5.27989 (* 1 = 5.27989 loss)
I0405 11:01:45.695165 30176 solver.cpp:218] Iteration 16320 (0.773585 iter/s, 15.5122s/12 iters), loss = 5.27986
I0405 11:01:45.695214 30176 solver.cpp:237]     Train net output #0: loss = 5.27986 (* 1 = 5.27986 loss)
I0405 11:01:45.695221 30176 sgd_solver.cpp:105] Iteration 16320, lr = 1e-06
I0405 11:01:50.293001 30176 solver.cpp:218] Iteration 16332 (2.60998 iter/s, 4.59774s/12 iters), loss = 5.28372
I0405 11:01:50.293052 30176 solver.cpp:237]     Train net output #0: loss = 5.28372 (* 1 = 5.28372 loss)
I0405 11:01:50.293061 30176 sgd_solver.cpp:105] Iteration 16332, lr = 1e-06
I0405 11:01:55.626430 30176 solver.cpp:218] Iteration 16344 (2.25 iter/s, 5.33332s/12 iters), loss = 5.29189
I0405 11:01:55.626540 30176 solver.cpp:237]     Train net output #0: loss = 5.29189 (* 1 = 5.29189 loss)
I0405 11:01:55.626549 30176 sgd_solver.cpp:105] Iteration 16344, lr = 1e-06
I0405 11:02:00.993362 30176 solver.cpp:218] Iteration 16356 (2.23598 iter/s, 5.36677s/12 iters), loss = 5.29358
I0405 11:02:00.993430 30176 solver.cpp:237]     Train net output #0: loss = 5.29358 (* 1 = 5.29358 loss)
I0405 11:02:00.993439 30176 sgd_solver.cpp:105] Iteration 16356, lr = 1e-06
I0405 11:02:06.471076 30176 solver.cpp:218] Iteration 16368 (2.19074 iter/s, 5.4776s/12 iters), loss = 5.29133
I0405 11:02:06.471115 30176 solver.cpp:237]     Train net output #0: loss = 5.29133 (* 1 = 5.29133 loss)
I0405 11:02:06.471120 30176 sgd_solver.cpp:105] Iteration 16368, lr = 1e-06
I0405 11:02:11.714923 30176 solver.cpp:218] Iteration 16380 (2.28844 iter/s, 5.24375s/12 iters), loss = 5.27049
I0405 11:02:11.714965 30176 solver.cpp:237]     Train net output #0: loss = 5.27049 (* 1 = 5.27049 loss)
I0405 11:02:11.714970 30176 sgd_solver.cpp:105] Iteration 16380, lr = 1e-06
I0405 11:02:14.723268 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 11:02:17.541548 30176 solver.cpp:218] Iteration 16392 (2.05955 iter/s, 5.82652s/12 iters), loss = 5.28634
I0405 11:02:17.541604 30176 solver.cpp:237]     Train net output #0: loss = 5.28634 (* 1 = 5.28634 loss)
I0405 11:02:17.541612 30176 sgd_solver.cpp:105] Iteration 16392, lr = 1e-06
I0405 11:02:22.951278 30176 solver.cpp:218] Iteration 16404 (2.21827 iter/s, 5.40962s/12 iters), loss = 5.28894
I0405 11:02:22.951336 30176 solver.cpp:237]     Train net output #0: loss = 5.28894 (* 1 = 5.28894 loss)
I0405 11:02:22.951344 30176 sgd_solver.cpp:105] Iteration 16404, lr = 1e-06
I0405 11:02:28.392634 30176 solver.cpp:218] Iteration 16416 (2.20538 iter/s, 5.44124s/12 iters), loss = 5.29911
I0405 11:02:28.392807 30176 solver.cpp:237]     Train net output #0: loss = 5.29911 (* 1 = 5.29911 loss)
I0405 11:02:28.392817 30176 sgd_solver.cpp:105] Iteration 16416, lr = 1e-06
I0405 11:02:30.557633 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_16422.caffemodel
I0405 11:02:33.632608 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_16422.solverstate
I0405 11:02:37.173243 30176 solver.cpp:330] Iteration 16422, Testing net (#0)
I0405 11:02:37.173267 30176 net.cpp:676] Ignoring source layer train-data
I0405 11:02:39.787317 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 11:02:41.598912 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 11:02:41.598948 30176 solver.cpp:397]     Test net output #1: loss = 5.27999 (* 1 = 5.27999 loss)
I0405 11:02:43.569658 30176 solver.cpp:218] Iteration 16428 (0.790684 iter/s, 15.1767s/12 iters), loss = 5.29346
I0405 11:02:43.569722 30176 solver.cpp:237]     Train net output #0: loss = 5.29346 (* 1 = 5.29346 loss)
I0405 11:02:43.569731 30176 sgd_solver.cpp:105] Iteration 16428, lr = 1e-06
I0405 11:02:49.434535 30176 solver.cpp:218] Iteration 16440 (2.04612 iter/s, 5.86475s/12 iters), loss = 5.28128
I0405 11:02:49.434590 30176 solver.cpp:237]     Train net output #0: loss = 5.28128 (* 1 = 5.28128 loss)
I0405 11:02:49.434600 30176 sgd_solver.cpp:105] Iteration 16440, lr = 1e-06
I0405 11:02:54.742640 30176 solver.cpp:218] Iteration 16452 (2.26074 iter/s, 5.308s/12 iters), loss = 5.27627
I0405 11:02:54.742679 30176 solver.cpp:237]     Train net output #0: loss = 5.27627 (* 1 = 5.27627 loss)
I0405 11:02:54.742686 30176 sgd_solver.cpp:105] Iteration 16452, lr = 1e-06
I0405 11:03:00.362319 30176 solver.cpp:218] Iteration 16464 (2.13539 iter/s, 5.61958s/12 iters), loss = 5.27391
I0405 11:03:00.362462 30176 solver.cpp:237]     Train net output #0: loss = 5.27391 (* 1 = 5.27391 loss)
I0405 11:03:00.362470 30176 sgd_solver.cpp:105] Iteration 16464, lr = 1e-06
I0405 11:03:05.864604 30176 solver.cpp:218] Iteration 16476 (2.18099 iter/s, 5.50209s/12 iters), loss = 5.27491
I0405 11:03:05.864660 30176 solver.cpp:237]     Train net output #0: loss = 5.27491 (* 1 = 5.27491 loss)
I0405 11:03:05.864667 30176 sgd_solver.cpp:105] Iteration 16476, lr = 1e-06
I0405 11:03:11.059123 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 11:03:11.362629 30176 solver.cpp:218] Iteration 16488 (2.18265 iter/s, 5.49791s/12 iters), loss = 5.29609
I0405 11:03:11.362682 30176 solver.cpp:237]     Train net output #0: loss = 5.29609 (* 1 = 5.29609 loss)
I0405 11:03:11.362691 30176 sgd_solver.cpp:105] Iteration 16488, lr = 1e-06
I0405 11:03:16.794375 30176 solver.cpp:218] Iteration 16500 (2.20928 iter/s, 5.43164s/12 iters), loss = 5.28379
I0405 11:03:16.794411 30176 solver.cpp:237]     Train net output #0: loss = 5.28379 (* 1 = 5.28379 loss)
I0405 11:03:16.794416 30176 sgd_solver.cpp:105] Iteration 16500, lr = 1e-06
I0405 11:03:22.235299 30176 solver.cpp:218] Iteration 16512 (2.20554 iter/s, 5.44083s/12 iters), loss = 5.29134
I0405 11:03:22.235338 30176 solver.cpp:237]     Train net output #0: loss = 5.29134 (* 1 = 5.29134 loss)
I0405 11:03:22.235344 30176 sgd_solver.cpp:105] Iteration 16512, lr = 1e-06
I0405 11:03:27.181550 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_16524.caffemodel
I0405 11:03:30.302788 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_16524.solverstate
I0405 11:03:32.601038 30176 solver.cpp:330] Iteration 16524, Testing net (#0)
I0405 11:03:32.601135 30176 net.cpp:676] Ignoring source layer train-data
I0405 11:03:35.164322 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 11:03:37.195809 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 11:03:37.195843 30176 solver.cpp:397]     Test net output #1: loss = 5.2797 (* 1 = 5.2797 loss)
I0405 11:03:37.337970 30176 solver.cpp:218] Iteration 16524 (0.79457 iter/s, 15.1025s/12 iters), loss = 5.28727
I0405 11:03:37.338034 30176 solver.cpp:237]     Train net output #0: loss = 5.28727 (* 1 = 5.28727 loss)
I0405 11:03:37.338043 30176 sgd_solver.cpp:105] Iteration 16524, lr = 1e-06
I0405 11:03:41.944037 30176 solver.cpp:218] Iteration 16536 (2.60532 iter/s, 4.60596s/12 iters), loss = 5.28098
I0405 11:03:41.944092 30176 solver.cpp:237]     Train net output #0: loss = 5.28098 (* 1 = 5.28098 loss)
I0405 11:03:41.944100 30176 sgd_solver.cpp:105] Iteration 16536, lr = 1e-06
I0405 11:03:47.497808 30176 solver.cpp:218] Iteration 16548 (2.16074 iter/s, 5.55366s/12 iters), loss = 5.28405
I0405 11:03:47.497859 30176 solver.cpp:237]     Train net output #0: loss = 5.28405 (* 1 = 5.28405 loss)
I0405 11:03:47.497866 30176 sgd_solver.cpp:105] Iteration 16548, lr = 1e-06
I0405 11:03:52.936964 30176 solver.cpp:218] Iteration 16560 (2.20627 iter/s, 5.43905s/12 iters), loss = 5.28177
I0405 11:03:52.937009 30176 solver.cpp:237]     Train net output #0: loss = 5.28177 (* 1 = 5.28177 loss)
I0405 11:03:52.937016 30176 sgd_solver.cpp:105] Iteration 16560, lr = 1e-06
I0405 11:03:58.497615 30176 solver.cpp:218] Iteration 16572 (2.15806 iter/s, 5.56055s/12 iters), loss = 5.28798
I0405 11:03:58.497663 30176 solver.cpp:237]     Train net output #0: loss = 5.28798 (* 1 = 5.28798 loss)
I0405 11:03:58.497669 30176 sgd_solver.cpp:105] Iteration 16572, lr = 1e-06
I0405 11:04:03.733584 30176 solver.cpp:218] Iteration 16584 (2.29188 iter/s, 5.23587s/12 iters), loss = 5.28622
I0405 11:04:03.733714 30176 solver.cpp:237]     Train net output #0: loss = 5.28622 (* 1 = 5.28622 loss)
I0405 11:04:03.733724 30176 sgd_solver.cpp:105] Iteration 16584, lr = 1e-06
I0405 11:04:05.533449 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 11:04:08.996860 30176 solver.cpp:218] Iteration 16596 (2.28003 iter/s, 5.2631s/12 iters), loss = 5.28276
I0405 11:04:08.996901 30176 solver.cpp:237]     Train net output #0: loss = 5.28276 (* 1 = 5.28276 loss)
I0405 11:04:08.996906 30176 sgd_solver.cpp:105] Iteration 16596, lr = 1e-06
I0405 11:04:14.183538 30176 solver.cpp:218] Iteration 16608 (2.31366 iter/s, 5.18659s/12 iters), loss = 5.2849
I0405 11:04:14.183584 30176 solver.cpp:237]     Train net output #0: loss = 5.2849 (* 1 = 5.2849 loss)
I0405 11:04:14.183589 30176 sgd_solver.cpp:105] Iteration 16608, lr = 1e-06
I0405 11:04:19.428604 30176 solver.cpp:218] Iteration 16620 (2.28791 iter/s, 5.24497s/12 iters), loss = 5.29672
I0405 11:04:19.428653 30176 solver.cpp:237]     Train net output #0: loss = 5.29672 (* 1 = 5.29672 loss)
I0405 11:04:19.428659 30176 sgd_solver.cpp:105] Iteration 16620, lr = 1e-06
I0405 11:04:21.525609 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_16626.caffemodel
I0405 11:04:24.618582 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_16626.solverstate
I0405 11:04:26.923614 30176 solver.cpp:330] Iteration 16626, Testing net (#0)
I0405 11:04:26.923638 30176 net.cpp:676] Ignoring source layer train-data
I0405 11:04:29.375936 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 11:04:30.672998 30176 blocking_queue.cpp:49] Waiting for data
I0405 11:04:31.290758 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 11:04:31.290786 30176 solver.cpp:397]     Test net output #1: loss = 5.2795 (* 1 = 5.2795 loss)
I0405 11:04:33.238212 30176 solver.cpp:218] Iteration 16632 (0.86897 iter/s, 13.8095s/12 iters), loss = 5.2757
I0405 11:04:33.238260 30176 solver.cpp:237]     Train net output #0: loss = 5.2757 (* 1 = 5.2757 loss)
I0405 11:04:33.238266 30176 sgd_solver.cpp:105] Iteration 16632, lr = 1e-06
I0405 11:04:38.606556 30176 solver.cpp:218] Iteration 16644 (2.23537 iter/s, 5.36824s/12 iters), loss = 5.28055
I0405 11:04:38.606739 30176 solver.cpp:237]     Train net output #0: loss = 5.28055 (* 1 = 5.28055 loss)
I0405 11:04:38.606750 30176 sgd_solver.cpp:105] Iteration 16644, lr = 1e-06
I0405 11:04:43.916563 30176 solver.cpp:218] Iteration 16656 (2.25998 iter/s, 5.30978s/12 iters), loss = 5.27563
I0405 11:04:43.916604 30176 solver.cpp:237]     Train net output #0: loss = 5.27563 (* 1 = 5.27563 loss)
I0405 11:04:43.916682 30176 sgd_solver.cpp:105] Iteration 16656, lr = 1e-06
I0405 11:04:49.233620 30176 solver.cpp:218] Iteration 16668 (2.25693 iter/s, 5.31696s/12 iters), loss = 5.28614
I0405 11:04:49.233661 30176 solver.cpp:237]     Train net output #0: loss = 5.28614 (* 1 = 5.28614 loss)
I0405 11:04:49.233667 30176 sgd_solver.cpp:105] Iteration 16668, lr = 1e-06
I0405 11:04:54.656939 30176 solver.cpp:218] Iteration 16680 (2.21271 iter/s, 5.42322s/12 iters), loss = 5.28246
I0405 11:04:54.656993 30176 solver.cpp:237]     Train net output #0: loss = 5.28246 (* 1 = 5.28246 loss)
I0405 11:04:54.657001 30176 sgd_solver.cpp:105] Iteration 16680, lr = 1e-06
I0405 11:04:58.662750 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 11:04:59.748596 30176 solver.cpp:218] Iteration 16692 (2.35685 iter/s, 5.09155s/12 iters), loss = 5.28103
I0405 11:04:59.748649 30176 solver.cpp:237]     Train net output #0: loss = 5.28103 (* 1 = 5.28103 loss)
I0405 11:04:59.748657 30176 sgd_solver.cpp:105] Iteration 16692, lr = 1e-06
I0405 11:05:04.752928 30176 solver.cpp:218] Iteration 16704 (2.39797 iter/s, 5.00423s/12 iters), loss = 5.28069
I0405 11:05:04.752976 30176 solver.cpp:237]     Train net output #0: loss = 5.28069 (* 1 = 5.28069 loss)
I0405 11:05:04.752985 30176 sgd_solver.cpp:105] Iteration 16704, lr = 1e-06
I0405 11:05:09.916043 30176 solver.cpp:218] Iteration 16716 (2.32422 iter/s, 5.16302s/12 iters), loss = 5.28349
I0405 11:05:09.916146 30176 solver.cpp:237]     Train net output #0: loss = 5.28349 (* 1 = 5.28349 loss)
I0405 11:05:09.916154 30176 sgd_solver.cpp:105] Iteration 16716, lr = 1e-06
I0405 11:05:14.711983 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_16728.caffemodel
I0405 11:05:17.717444 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_16728.solverstate
I0405 11:05:20.022974 30176 solver.cpp:330] Iteration 16728, Testing net (#0)
I0405 11:05:20.023001 30176 net.cpp:676] Ignoring source layer train-data
I0405 11:05:22.424129 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 11:05:24.339715 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 11:05:24.339764 30176 solver.cpp:397]     Test net output #1: loss = 5.27988 (* 1 = 5.27988 loss)
I0405 11:05:24.482125 30176 solver.cpp:218] Iteration 16728 (0.823844 iter/s, 14.5659s/12 iters), loss = 5.29469
I0405 11:05:24.482178 30176 solver.cpp:237]     Train net output #0: loss = 5.29469 (* 1 = 5.29469 loss)
I0405 11:05:24.482185 30176 sgd_solver.cpp:105] Iteration 16728, lr = 1e-06
I0405 11:05:28.849627 30176 solver.cpp:218] Iteration 16740 (2.74763 iter/s, 4.3674s/12 iters), loss = 5.30106
I0405 11:05:28.849674 30176 solver.cpp:237]     Train net output #0: loss = 5.30106 (* 1 = 5.30106 loss)
I0405 11:05:28.849682 30176 sgd_solver.cpp:105] Iteration 16740, lr = 1e-06
I0405 11:05:34.054224 30176 solver.cpp:218] Iteration 16752 (2.3057 iter/s, 5.20449s/12 iters), loss = 5.28318
I0405 11:05:34.054281 30176 solver.cpp:237]     Train net output #0: loss = 5.28318 (* 1 = 5.28318 loss)
I0405 11:05:34.054289 30176 sgd_solver.cpp:105] Iteration 16752, lr = 1e-06
I0405 11:05:39.297355 30176 solver.cpp:218] Iteration 16764 (2.28875 iter/s, 5.24303s/12 iters), loss = 5.28642
I0405 11:05:39.297449 30176 solver.cpp:237]     Train net output #0: loss = 5.28642 (* 1 = 5.28642 loss)
I0405 11:05:39.297457 30176 sgd_solver.cpp:105] Iteration 16764, lr = 1e-06
I0405 11:05:44.497107 30176 solver.cpp:218] Iteration 16776 (2.30787 iter/s, 5.19961s/12 iters), loss = 5.26205
I0405 11:05:44.497262 30176 solver.cpp:237]     Train net output #0: loss = 5.26205 (* 1 = 5.26205 loss)
I0405 11:05:44.497270 30176 sgd_solver.cpp:105] Iteration 16776, lr = 1e-06
I0405 11:05:49.813616 30176 solver.cpp:218] Iteration 16788 (2.25721 iter/s, 5.31631s/12 iters), loss = 5.28547
I0405 11:05:49.813654 30176 solver.cpp:237]     Train net output #0: loss = 5.28547 (* 1 = 5.28547 loss)
I0405 11:05:49.813659 30176 sgd_solver.cpp:105] Iteration 16788, lr = 1e-06
I0405 11:05:50.853734 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 11:05:55.074236 30176 solver.cpp:218] Iteration 16800 (2.28114 iter/s, 5.26053s/12 iters), loss = 5.27657
I0405 11:05:55.074296 30176 solver.cpp:237]     Train net output #0: loss = 5.27657 (* 1 = 5.27657 loss)
I0405 11:05:55.074306 30176 sgd_solver.cpp:105] Iteration 16800, lr = 1e-06
I0405 11:06:00.585932 30176 solver.cpp:218] Iteration 16812 (2.17723 iter/s, 5.51159s/12 iters), loss = 5.27413
I0405 11:06:00.585980 30176 solver.cpp:237]     Train net output #0: loss = 5.27413 (* 1 = 5.27413 loss)
I0405 11:06:00.585988 30176 sgd_solver.cpp:105] Iteration 16812, lr = 1e-06
I0405 11:06:05.852550 30176 solver.cpp:218] Iteration 16824 (2.27855 iter/s, 5.26652s/12 iters), loss = 5.27663
I0405 11:06:05.852603 30176 solver.cpp:237]     Train net output #0: loss = 5.27663 (* 1 = 5.27663 loss)
I0405 11:06:05.852610 30176 sgd_solver.cpp:105] Iteration 16824, lr = 1e-06
I0405 11:06:08.006705 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_16830.caffemodel
I0405 11:06:11.018044 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_16830.solverstate
I0405 11:06:13.325593 30176 solver.cpp:330] Iteration 16830, Testing net (#0)
I0405 11:06:13.325611 30176 net.cpp:676] Ignoring source layer train-data
I0405 11:06:15.721593 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 11:06:17.736637 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 11:06:17.736665 30176 solver.cpp:397]     Test net output #1: loss = 5.28006 (* 1 = 5.28006 loss)
I0405 11:06:19.620798 30176 solver.cpp:218] Iteration 16836 (0.871581 iter/s, 13.7681s/12 iters), loss = 5.29794
I0405 11:06:19.620846 30176 solver.cpp:237]     Train net output #0: loss = 5.29794 (* 1 = 5.29794 loss)
I0405 11:06:19.620851 30176 sgd_solver.cpp:105] Iteration 16836, lr = 1e-06
I0405 11:06:24.891594 30176 solver.cpp:218] Iteration 16848 (2.27674 iter/s, 5.27069s/12 iters), loss = 5.27215
I0405 11:06:24.891647 30176 solver.cpp:237]     Train net output #0: loss = 5.27215 (* 1 = 5.27215 loss)
I0405 11:06:24.891654 30176 sgd_solver.cpp:105] Iteration 16848, lr = 1e-06
I0405 11:06:29.839872 30176 solver.cpp:218] Iteration 16860 (2.42513 iter/s, 4.94818s/12 iters), loss = 5.27346
I0405 11:06:29.839912 30176 solver.cpp:237]     Train net output #0: loss = 5.27346 (* 1 = 5.27346 loss)
I0405 11:06:29.839917 30176 sgd_solver.cpp:105] Iteration 16860, lr = 1e-06
I0405 11:06:35.312422 30176 solver.cpp:218] Iteration 16872 (2.1928 iter/s, 5.47246s/12 iters), loss = 5.2852
I0405 11:06:35.312467 30176 solver.cpp:237]     Train net output #0: loss = 5.2852 (* 1 = 5.2852 loss)
I0405 11:06:35.312472 30176 sgd_solver.cpp:105] Iteration 16872, lr = 1e-06
I0405 11:06:40.681288 30176 solver.cpp:218] Iteration 16884 (2.23515 iter/s, 5.36877s/12 iters), loss = 5.28024
I0405 11:06:40.681331 30176 solver.cpp:237]     Train net output #0: loss = 5.28024 (* 1 = 5.28024 loss)
I0405 11:06:40.681337 30176 sgd_solver.cpp:105] Iteration 16884, lr = 1e-06
I0405 11:06:44.045748 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 11:06:45.803809 30176 solver.cpp:218] Iteration 16896 (2.34264 iter/s, 5.12243s/12 iters), loss = 5.29207
I0405 11:06:45.803936 30176 solver.cpp:237]     Train net output #0: loss = 5.29207 (* 1 = 5.29207 loss)
I0405 11:06:45.803941 30176 sgd_solver.cpp:105] Iteration 16896, lr = 1e-06
I0405 11:06:51.144249 30176 solver.cpp:218] Iteration 16908 (2.24708 iter/s, 5.34026s/12 iters), loss = 5.29328
I0405 11:06:51.144292 30176 solver.cpp:237]     Train net output #0: loss = 5.29328 (* 1 = 5.29328 loss)
I0405 11:06:51.144299 30176 sgd_solver.cpp:105] Iteration 16908, lr = 1e-06
I0405 11:06:56.387301 30176 solver.cpp:218] Iteration 16920 (2.28879 iter/s, 5.24296s/12 iters), loss = 5.28957
I0405 11:06:56.387344 30176 solver.cpp:237]     Train net output #0: loss = 5.28957 (* 1 = 5.28957 loss)
I0405 11:06:56.387351 30176 sgd_solver.cpp:105] Iteration 16920, lr = 1e-06
I0405 11:07:01.016230 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_16932.caffemodel
I0405 11:07:04.029193 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_16932.solverstate
I0405 11:07:06.360334 30176 solver.cpp:330] Iteration 16932, Testing net (#0)
I0405 11:07:06.360352 30176 net.cpp:676] Ignoring source layer train-data
I0405 11:07:08.868108 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 11:07:10.883436 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 11:07:10.883476 30176 solver.cpp:397]     Test net output #1: loss = 5.27992 (* 1 = 5.27992 loss)
I0405 11:07:11.024376 30176 solver.cpp:218] Iteration 16932 (0.819845 iter/s, 14.6369s/12 iters), loss = 5.2773
I0405 11:07:11.025940 30176 solver.cpp:237]     Train net output #0: loss = 5.2773 (* 1 = 5.2773 loss)
I0405 11:07:11.025955 30176 sgd_solver.cpp:105] Iteration 16932, lr = 1e-06
I0405 11:07:15.540171 30176 solver.cpp:218] Iteration 16944 (2.65828 iter/s, 4.51419s/12 iters), loss = 5.27445
I0405 11:07:15.540220 30176 solver.cpp:237]     Train net output #0: loss = 5.27445 (* 1 = 5.27445 loss)
I0405 11:07:15.540225 30176 sgd_solver.cpp:105] Iteration 16944, lr = 1e-06
I0405 11:07:20.614321 30176 solver.cpp:218] Iteration 16956 (2.36497 iter/s, 5.07405s/12 iters), loss = 5.27974
I0405 11:07:20.614405 30176 solver.cpp:237]     Train net output #0: loss = 5.27974 (* 1 = 5.27974 loss)
I0405 11:07:20.614411 30176 sgd_solver.cpp:105] Iteration 16956, lr = 1e-06
I0405 11:07:25.976440 30176 solver.cpp:218] Iteration 16968 (2.23798 iter/s, 5.36198s/12 iters), loss = 5.28202
I0405 11:07:25.976492 30176 solver.cpp:237]     Train net output #0: loss = 5.28202 (* 1 = 5.28202 loss)
I0405 11:07:25.976500 30176 sgd_solver.cpp:105] Iteration 16968, lr = 1e-06
I0405 11:07:31.181246 30176 solver.cpp:218] Iteration 16980 (2.30561 iter/s, 5.2047s/12 iters), loss = 5.28432
I0405 11:07:31.181284 30176 solver.cpp:237]     Train net output #0: loss = 5.28432 (* 1 = 5.28432 loss)
I0405 11:07:31.181290 30176 sgd_solver.cpp:105] Iteration 16980, lr = 1e-06
I0405 11:07:36.550696 30176 solver.cpp:218] Iteration 16992 (2.2349 iter/s, 5.36936s/12 iters), loss = 5.27929
I0405 11:07:36.550736 30176 solver.cpp:237]     Train net output #0: loss = 5.27929 (* 1 = 5.27929 loss)
I0405 11:07:36.550741 30176 sgd_solver.cpp:105] Iteration 16992, lr = 1e-06
I0405 11:07:36.967900 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 11:07:41.621469 30176 solver.cpp:218] Iteration 17004 (2.36655 iter/s, 5.07068s/12 iters), loss = 5.30379
I0405 11:07:41.621511 30176 solver.cpp:237]     Train net output #0: loss = 5.30379 (* 1 = 5.30379 loss)
I0405 11:07:41.621516 30176 sgd_solver.cpp:105] Iteration 17004, lr = 1e-06
I0405 11:07:46.766372 30176 solver.cpp:218] Iteration 17016 (2.33245 iter/s, 5.14481s/12 iters), loss = 5.29175
I0405 11:07:46.766420 30176 solver.cpp:237]     Train net output #0: loss = 5.29175 (* 1 = 5.29175 loss)
I0405 11:07:46.766425 30176 sgd_solver.cpp:105] Iteration 17016, lr = 1e-06
I0405 11:07:52.013568 30176 solver.cpp:218] Iteration 17028 (2.28698 iter/s, 5.24709s/12 iters), loss = 5.2963
I0405 11:07:52.013689 30176 solver.cpp:237]     Train net output #0: loss = 5.2963 (* 1 = 5.2963 loss)
I0405 11:07:52.013696 30176 sgd_solver.cpp:105] Iteration 17028, lr = 1e-06
I0405 11:07:54.148902 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_17034.caffemodel
I0405 11:07:57.183531 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_17034.solverstate
I0405 11:07:59.492713 30176 solver.cpp:330] Iteration 17034, Testing net (#0)
I0405 11:07:59.492733 30176 net.cpp:676] Ignoring source layer train-data
I0405 11:08:01.777698 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 11:08:03.827587 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 11:08:03.827630 30176 solver.cpp:397]     Test net output #1: loss = 5.27984 (* 1 = 5.27984 loss)
I0405 11:08:05.714212 30176 solver.cpp:218] Iteration 17040 (0.875886 iter/s, 13.7004s/12 iters), loss = 5.29083
I0405 11:08:05.714262 30176 solver.cpp:237]     Train net output #0: loss = 5.29083 (* 1 = 5.29083 loss)
I0405 11:08:05.714267 30176 sgd_solver.cpp:105] Iteration 17040, lr = 1e-06
I0405 11:08:10.972807 30176 solver.cpp:218] Iteration 17052 (2.28202 iter/s, 5.25849s/12 iters), loss = 5.29731
I0405 11:08:10.972864 30176 solver.cpp:237]     Train net output #0: loss = 5.29731 (* 1 = 5.29731 loss)
I0405 11:08:10.972872 30176 sgd_solver.cpp:105] Iteration 17052, lr = 1e-06
I0405 11:08:16.223405 30176 solver.cpp:218] Iteration 17064 (2.2855 iter/s, 5.25049s/12 iters), loss = 5.29153
I0405 11:08:16.223448 30176 solver.cpp:237]     Train net output #0: loss = 5.29153 (* 1 = 5.29153 loss)
I0405 11:08:16.223453 30176 sgd_solver.cpp:105] Iteration 17064, lr = 1e-06
I0405 11:08:21.579998 30176 solver.cpp:218] Iteration 17076 (2.24027 iter/s, 5.3565s/12 iters), loss = 5.29475
I0405 11:08:21.580041 30176 solver.cpp:237]     Train net output #0: loss = 5.29475 (* 1 = 5.29475 loss)
I0405 11:08:21.580046 30176 sgd_solver.cpp:105] Iteration 17076, lr = 1e-06
I0405 11:08:26.729449 30176 solver.cpp:218] Iteration 17088 (2.33039 iter/s, 5.14936s/12 iters), loss = 5.26661
I0405 11:08:26.729544 30176 solver.cpp:237]     Train net output #0: loss = 5.26661 (* 1 = 5.26661 loss)
I0405 11:08:26.729550 30176 sgd_solver.cpp:105] Iteration 17088, lr = 1e-06
I0405 11:08:29.485062 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 11:08:32.028852 30176 solver.cpp:218] Iteration 17100 (2.26447 iter/s, 5.29925s/12 iters), loss = 5.2794
I0405 11:08:32.028898 30176 solver.cpp:237]     Train net output #0: loss = 5.2794 (* 1 = 5.2794 loss)
I0405 11:08:32.028904 30176 sgd_solver.cpp:105] Iteration 17100, lr = 1e-06
I0405 11:08:37.298647 30176 solver.cpp:218] Iteration 17112 (2.27717 iter/s, 5.2697s/12 iters), loss = 5.27964
I0405 11:08:37.298689 30176 solver.cpp:237]     Train net output #0: loss = 5.27964 (* 1 = 5.27964 loss)
I0405 11:08:37.298696 30176 sgd_solver.cpp:105] Iteration 17112, lr = 1e-06
I0405 11:08:42.562038 30176 solver.cpp:218] Iteration 17124 (2.27994 iter/s, 5.26329s/12 iters), loss = 5.28304
I0405 11:08:42.562083 30176 solver.cpp:237]     Train net output #0: loss = 5.28304 (* 1 = 5.28304 loss)
I0405 11:08:42.562088 30176 sgd_solver.cpp:105] Iteration 17124, lr = 1e-06
I0405 11:08:47.555008 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_17136.caffemodel
I0405 11:08:50.653430 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_17136.solverstate
I0405 11:08:52.966348 30176 solver.cpp:330] Iteration 17136, Testing net (#0)
I0405 11:08:52.966368 30176 net.cpp:676] Ignoring source layer train-data
I0405 11:08:55.202702 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 11:08:57.280822 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 11:08:57.281199 30176 solver.cpp:397]     Test net output #1: loss = 5.27969 (* 1 = 5.27969 loss)
I0405 11:08:57.421964 30176 solver.cpp:218] Iteration 17136 (0.80755 iter/s, 14.8598s/12 iters), loss = 5.28826
I0405 11:08:57.422020 30176 solver.cpp:237]     Train net output #0: loss = 5.28826 (* 1 = 5.28826 loss)
I0405 11:08:57.422029 30176 sgd_solver.cpp:105] Iteration 17136, lr = 1e-06
I0405 11:09:01.853199 30176 solver.cpp:218] Iteration 17148 (2.70811 iter/s, 4.43113s/12 iters), loss = 5.30024
I0405 11:09:01.853251 30176 solver.cpp:237]     Train net output #0: loss = 5.30024 (* 1 = 5.30024 loss)
I0405 11:09:01.853260 30176 sgd_solver.cpp:105] Iteration 17148, lr = 1e-06
I0405 11:09:07.390018 30176 solver.cpp:218] Iteration 17160 (2.16735 iter/s, 5.53671s/12 iters), loss = 5.27287
I0405 11:09:07.390071 30176 solver.cpp:237]     Train net output #0: loss = 5.27287 (* 1 = 5.27287 loss)
I0405 11:09:07.390079 30176 sgd_solver.cpp:105] Iteration 17160, lr = 1e-06
I0405 11:09:12.818181 30176 solver.cpp:218] Iteration 17172 (2.21073 iter/s, 5.42806s/12 iters), loss = 5.28209
I0405 11:09:12.818217 30176 solver.cpp:237]     Train net output #0: loss = 5.28209 (* 1 = 5.28209 loss)
I0405 11:09:12.818223 30176 sgd_solver.cpp:105] Iteration 17172, lr = 1e-06
I0405 11:09:18.297996 30176 solver.cpp:218] Iteration 17184 (2.18989 iter/s, 5.47973s/12 iters), loss = 5.29952
I0405 11:09:18.298039 30176 solver.cpp:237]     Train net output #0: loss = 5.29952 (* 1 = 5.29952 loss)
I0405 11:09:18.298044 30176 sgd_solver.cpp:105] Iteration 17184, lr = 1e-06
I0405 11:09:23.164063 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 11:09:23.440114 30176 solver.cpp:218] Iteration 17196 (2.33371 iter/s, 5.14202s/12 iters), loss = 5.29283
I0405 11:09:23.440153 30176 solver.cpp:237]     Train net output #0: loss = 5.29283 (* 1 = 5.29283 loss)
I0405 11:09:23.440158 30176 sgd_solver.cpp:105] Iteration 17196, lr = 1e-06
I0405 11:09:28.764595 30176 solver.cpp:218] Iteration 17208 (2.25378 iter/s, 5.32439s/12 iters), loss = 5.29312
I0405 11:09:28.764678 30176 solver.cpp:237]     Train net output #0: loss = 5.29312 (* 1 = 5.29312 loss)
I0405 11:09:28.764683 30176 sgd_solver.cpp:105] Iteration 17208, lr = 1e-06
I0405 11:09:33.991935 30176 solver.cpp:218] Iteration 17220 (2.29568 iter/s, 5.22721s/12 iters), loss = 5.26822
I0405 11:09:33.991972 30176 solver.cpp:237]     Train net output #0: loss = 5.26822 (* 1 = 5.26822 loss)
I0405 11:09:33.991977 30176 sgd_solver.cpp:105] Iteration 17220, lr = 1e-06
I0405 11:09:39.292546 30176 solver.cpp:218] Iteration 17232 (2.26393 iter/s, 5.30051s/12 iters), loss = 5.28074
I0405 11:09:39.292593 30176 solver.cpp:237]     Train net output #0: loss = 5.28074 (* 1 = 5.28074 loss)
I0405 11:09:39.292601 30176 sgd_solver.cpp:105] Iteration 17232, lr = 1e-06
I0405 11:09:41.440557 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_17238.caffemodel
I0405 11:09:44.501740 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_17238.solverstate
I0405 11:09:46.818794 30176 solver.cpp:330] Iteration 17238, Testing net (#0)
I0405 11:09:46.818818 30176 net.cpp:676] Ignoring source layer train-data
I0405 11:09:49.182062 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 11:09:51.381155 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 11:09:51.381184 30176 solver.cpp:397]     Test net output #1: loss = 5.27968 (* 1 = 5.27968 loss)
I0405 11:09:53.280066 30176 solver.cpp:218] Iteration 17244 (0.857917 iter/s, 13.9874s/12 iters), loss = 5.28189
I0405 11:09:53.280112 30176 solver.cpp:237]     Train net output #0: loss = 5.28189 (* 1 = 5.28189 loss)
I0405 11:09:53.280118 30176 sgd_solver.cpp:105] Iteration 17244, lr = 1e-06
I0405 11:09:58.430711 30176 solver.cpp:218] Iteration 17256 (2.32985 iter/s, 5.15055s/12 iters), loss = 5.28727
I0405 11:09:58.430750 30176 solver.cpp:237]     Train net output #0: loss = 5.28727 (* 1 = 5.28727 loss)
I0405 11:09:58.430757 30176 sgd_solver.cpp:105] Iteration 17256, lr = 1e-06
I0405 11:10:03.466372 30176 solver.cpp:218] Iteration 17268 (2.38305 iter/s, 5.03557s/12 iters), loss = 5.2783
I0405 11:10:03.466831 30176 solver.cpp:237]     Train net output #0: loss = 5.2783 (* 1 = 5.2783 loss)
I0405 11:10:03.466841 30176 sgd_solver.cpp:105] Iteration 17268, lr = 1e-06
I0405 11:10:08.819118 30176 solver.cpp:218] Iteration 17280 (2.24205 iter/s, 5.35224s/12 iters), loss = 5.27076
I0405 11:10:08.819172 30176 solver.cpp:237]     Train net output #0: loss = 5.27076 (* 1 = 5.27076 loss)
I0405 11:10:08.819182 30176 sgd_solver.cpp:105] Iteration 17280, lr = 1e-06
I0405 11:10:14.170374 30176 solver.cpp:218] Iteration 17292 (2.24251 iter/s, 5.35115s/12 iters), loss = 5.29037
I0405 11:10:14.170415 30176 solver.cpp:237]     Train net output #0: loss = 5.29037 (* 1 = 5.29037 loss)
I0405 11:10:14.170421 30176 sgd_solver.cpp:105] Iteration 17292, lr = 1e-06
I0405 11:10:16.171404 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 11:10:19.536897 30176 solver.cpp:218] Iteration 17304 (2.23613 iter/s, 5.36642s/12 iters), loss = 5.28018
I0405 11:10:19.536959 30176 solver.cpp:237]     Train net output #0: loss = 5.28018 (* 1 = 5.28018 loss)
I0405 11:10:19.536968 30176 sgd_solver.cpp:105] Iteration 17304, lr = 1e-06
I0405 11:10:24.730435 30176 solver.cpp:218] Iteration 17316 (2.31061 iter/s, 5.19343s/12 iters), loss = 5.27719
I0405 11:10:24.730475 30176 solver.cpp:237]     Train net output #0: loss = 5.27719 (* 1 = 5.27719 loss)
I0405 11:10:24.730481 30176 sgd_solver.cpp:105] Iteration 17316, lr = 1e-06
I0405 11:10:29.795177 30176 solver.cpp:218] Iteration 17328 (2.36936 iter/s, 5.06465s/12 iters), loss = 5.27888
I0405 11:10:29.795214 30176 solver.cpp:237]     Train net output #0: loss = 5.27888 (* 1 = 5.27888 loss)
I0405 11:10:29.795220 30176 sgd_solver.cpp:105] Iteration 17328, lr = 1e-06
I0405 11:10:34.704351 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_17340.caffemodel
I0405 11:10:38.065814 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_17340.solverstate
I0405 11:10:40.440202 30176 solver.cpp:330] Iteration 17340, Testing net (#0)
I0405 11:10:40.440223 30176 net.cpp:676] Ignoring source layer train-data
I0405 11:10:41.532405 30176 blocking_queue.cpp:49] Waiting for data
I0405 11:10:42.585440 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 11:10:44.791730 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 11:10:44.791766 30176 solver.cpp:397]     Test net output #1: loss = 5.27958 (* 1 = 5.27958 loss)
I0405 11:10:44.932776 30176 solver.cpp:218] Iteration 17340 (0.792736 iter/s, 15.1374s/12 iters), loss = 5.28451
I0405 11:10:44.932814 30176 solver.cpp:237]     Train net output #0: loss = 5.28451 (* 1 = 5.28451 loss)
I0405 11:10:44.932821 30176 sgd_solver.cpp:105] Iteration 17340, lr = 1e-06
I0405 11:10:49.441829 30176 solver.cpp:218] Iteration 17352 (2.66136 iter/s, 4.50897s/12 iters), loss = 5.27334
I0405 11:10:49.441882 30176 solver.cpp:237]     Train net output #0: loss = 5.27334 (* 1 = 5.27334 loss)
I0405 11:10:49.441891 30176 sgd_solver.cpp:105] Iteration 17352, lr = 1e-06
I0405 11:10:54.647228 30176 solver.cpp:218] Iteration 17364 (2.30534 iter/s, 5.2053s/12 iters), loss = 5.27805
I0405 11:10:54.647266 30176 solver.cpp:237]     Train net output #0: loss = 5.27805 (* 1 = 5.27805 loss)
I0405 11:10:54.647271 30176 sgd_solver.cpp:105] Iteration 17364, lr = 1e-06
I0405 11:10:59.889412 30176 solver.cpp:218] Iteration 17376 (2.28916 iter/s, 5.24209s/12 iters), loss = 5.2798
I0405 11:10:59.889451 30176 solver.cpp:237]     Train net output #0: loss = 5.2798 (* 1 = 5.2798 loss)
I0405 11:10:59.889456 30176 sgd_solver.cpp:105] Iteration 17376, lr = 1e-06
I0405 11:11:04.867107 30176 solver.cpp:218] Iteration 17388 (2.41079 iter/s, 4.97761s/12 iters), loss = 5.29076
I0405 11:11:04.867218 30176 solver.cpp:237]     Train net output #0: loss = 5.29076 (* 1 = 5.29076 loss)
I0405 11:11:04.867225 30176 sgd_solver.cpp:105] Iteration 17388, lr = 1e-06
I0405 11:11:09.100641 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 11:11:10.123010 30176 solver.cpp:218] Iteration 17400 (2.28321 iter/s, 5.25575s/12 iters), loss = 5.28702
I0405 11:11:10.123046 30176 solver.cpp:237]     Train net output #0: loss = 5.28702 (* 1 = 5.28702 loss)
I0405 11:11:10.123051 30176 sgd_solver.cpp:105] Iteration 17400, lr = 1e-06
I0405 11:11:15.489418 30176 solver.cpp:218] Iteration 17412 (2.23617 iter/s, 5.36631s/12 iters), loss = 5.28468
I0405 11:11:15.489473 30176 solver.cpp:237]     Train net output #0: loss = 5.28468 (* 1 = 5.28468 loss)
I0405 11:11:15.489482 30176 sgd_solver.cpp:105] Iteration 17412, lr = 1e-06
I0405 11:11:20.703917 30176 solver.cpp:218] Iteration 17424 (2.30132 iter/s, 5.2144s/12 iters), loss = 5.26642
I0405 11:11:20.703953 30176 solver.cpp:237]     Train net output #0: loss = 5.26642 (* 1 = 5.26642 loss)
I0405 11:11:20.703958 30176 sgd_solver.cpp:105] Iteration 17424, lr = 1e-06
I0405 11:11:25.768146 30176 solver.cpp:218] Iteration 17436 (2.3696 iter/s, 5.06414s/12 iters), loss = 5.27383
I0405 11:11:25.768193 30176 solver.cpp:237]     Train net output #0: loss = 5.27383 (* 1 = 5.27383 loss)
I0405 11:11:25.768198 30176 sgd_solver.cpp:105] Iteration 17436, lr = 1e-06
I0405 11:11:27.818940 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_17442.caffemodel
I0405 11:11:30.860245 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_17442.solverstate
I0405 11:11:33.547466 30176 solver.cpp:330] Iteration 17442, Testing net (#0)
I0405 11:11:33.547487 30176 net.cpp:676] Ignoring source layer train-data
I0405 11:11:35.670889 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 11:11:37.850344 30176 solver.cpp:397]     Test net output #0: accuracy = 0.00735294
I0405 11:11:37.850378 30176 solver.cpp:397]     Test net output #1: loss = 5.2795 (* 1 = 5.2795 loss)
I0405 11:11:39.806154 30176 solver.cpp:218] Iteration 17448 (0.854832 iter/s, 14.0378s/12 iters), loss = 5.30205
I0405 11:11:39.806205 30176 solver.cpp:237]     Train net output #0: loss = 5.30205 (* 1 = 5.30205 loss)
I0405 11:11:39.806213 30176 sgd_solver.cpp:105] Iteration 17448, lr = 1e-06
I0405 11:11:45.133985 30176 solver.cpp:218] Iteration 17460 (2.25237 iter/s, 5.32773s/12 iters), loss = 5.27999
I0405 11:11:45.134024 30176 solver.cpp:237]     Train net output #0: loss = 5.27999 (* 1 = 5.27999 loss)
I0405 11:11:45.134030 30176 sgd_solver.cpp:105] Iteration 17460, lr = 1e-06
I0405 11:11:50.399127 30176 solver.cpp:218] Iteration 17472 (2.27918 iter/s, 5.26504s/12 iters), loss = 5.28624
I0405 11:11:50.399169 30176 solver.cpp:237]     Train net output #0: loss = 5.28624 (* 1 = 5.28624 loss)
I0405 11:11:50.399174 30176 sgd_solver.cpp:105] Iteration 17472, lr = 1e-06
I0405 11:11:55.822144 30176 solver.cpp:218] Iteration 17484 (2.21283 iter/s, 5.42292s/12 iters), loss = 5.27032
I0405 11:11:55.822208 30176 solver.cpp:237]     Train net output #0: loss = 5.27032 (* 1 = 5.27032 loss)
I0405 11:11:55.822218 30176 sgd_solver.cpp:105] Iteration 17484, lr = 1e-06
I0405 11:12:01.131494 30176 solver.cpp:218] Iteration 17496 (2.26021 iter/s, 5.30924s/12 iters), loss = 5.28024
I0405 11:12:01.131543 30176 solver.cpp:237]     Train net output #0: loss = 5.28024 (* 1 = 5.28024 loss)
I0405 11:12:01.131551 30176 sgd_solver.cpp:105] Iteration 17496, lr = 1e-06
I0405 11:12:02.470172 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 11:12:06.479809 30176 solver.cpp:218] Iteration 17508 (2.24374 iter/s, 5.34821s/12 iters), loss = 5.29241
I0405 11:12:06.479926 30176 solver.cpp:237]     Train net output #0: loss = 5.29241 (* 1 = 5.29241 loss)
I0405 11:12:06.479938 30176 sgd_solver.cpp:105] Iteration 17508, lr = 1e-06
I0405 11:12:11.685221 30176 solver.cpp:218] Iteration 17520 (2.30536 iter/s, 5.20525s/12 iters), loss = 5.2775
I0405 11:12:11.685258 30176 solver.cpp:237]     Train net output #0: loss = 5.2775 (* 1 = 5.2775 loss)
I0405 11:12:11.685264 30176 sgd_solver.cpp:105] Iteration 17520, lr = 1e-06
I0405 11:12:16.945502 30176 solver.cpp:218] Iteration 17532 (2.28129 iter/s, 5.26019s/12 iters), loss = 5.2747
I0405 11:12:16.945542 30176 solver.cpp:237]     Train net output #0: loss = 5.2747 (* 1 = 5.2747 loss)
I0405 11:12:16.945549 30176 sgd_solver.cpp:105] Iteration 17532, lr = 1e-06
I0405 11:12:21.557113 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_17544.caffemodel
I0405 11:12:24.604717 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_17544.solverstate
I0405 11:12:26.951293 30176 solver.cpp:330] Iteration 17544, Testing net (#0)
I0405 11:12:26.951313 30176 net.cpp:676] Ignoring source layer train-data
I0405 11:12:29.090924 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 11:12:31.345242 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 11:12:31.345280 30176 solver.cpp:397]     Test net output #1: loss = 5.27988 (* 1 = 5.27988 loss)
I0405 11:12:31.486232 30176 solver.cpp:218] Iteration 17544 (0.825277 iter/s, 14.5406s/12 iters), loss = 5.29173
I0405 11:12:31.486287 30176 solver.cpp:237]     Train net output #0: loss = 5.29173 (* 1 = 5.29173 loss)
I0405 11:12:31.486296 30176 sgd_solver.cpp:105] Iteration 17544, lr = 1e-06
I0405 11:12:36.014139 30176 solver.cpp:218] Iteration 17556 (2.65029 iter/s, 4.52781s/12 iters), loss = 5.27843
I0405 11:12:36.014184 30176 solver.cpp:237]     Train net output #0: loss = 5.27843 (* 1 = 5.27843 loss)
I0405 11:12:36.014191 30176 sgd_solver.cpp:105] Iteration 17556, lr = 1e-06
I0405 11:12:41.108916 30176 solver.cpp:218] Iteration 17568 (2.3554 iter/s, 5.09468s/12 iters), loss = 5.28356
I0405 11:12:41.109067 30176 solver.cpp:237]     Train net output #0: loss = 5.28356 (* 1 = 5.28356 loss)
I0405 11:12:41.109076 30176 sgd_solver.cpp:105] Iteration 17568, lr = 1e-06
I0405 11:12:46.313406 30176 solver.cpp:218] Iteration 17580 (2.30579 iter/s, 5.20429s/12 iters), loss = 5.27639
I0405 11:12:46.313449 30176 solver.cpp:237]     Train net output #0: loss = 5.27639 (* 1 = 5.27639 loss)
I0405 11:12:46.313454 30176 sgd_solver.cpp:105] Iteration 17580, lr = 1e-06
I0405 11:12:51.787346 30176 solver.cpp:218] Iteration 17592 (2.19225 iter/s, 5.47384s/12 iters), loss = 5.29517
I0405 11:12:51.787405 30176 solver.cpp:237]     Train net output #0: loss = 5.29517 (* 1 = 5.29517 loss)
I0405 11:12:51.787412 30176 sgd_solver.cpp:105] Iteration 17592, lr = 1e-06
I0405 11:12:55.330727 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 11:12:56.982446 30176 solver.cpp:218] Iteration 17604 (2.30992 iter/s, 5.19499s/12 iters), loss = 5.28533
I0405 11:12:56.982487 30176 solver.cpp:237]     Train net output #0: loss = 5.28533 (* 1 = 5.28533 loss)
I0405 11:12:56.982493 30176 sgd_solver.cpp:105] Iteration 17604, lr = 1e-06
I0405 11:13:02.277557 30176 solver.cpp:218] Iteration 17616 (2.26628 iter/s, 5.29502s/12 iters), loss = 5.28335
I0405 11:13:02.277599 30176 solver.cpp:237]     Train net output #0: loss = 5.28335 (* 1 = 5.28335 loss)
I0405 11:13:02.277606 30176 sgd_solver.cpp:105] Iteration 17616, lr = 1e-06
I0405 11:13:07.573738 30176 solver.cpp:218] Iteration 17628 (2.26583 iter/s, 5.29608s/12 iters), loss = 5.28146
I0405 11:13:07.573803 30176 solver.cpp:237]     Train net output #0: loss = 5.28146 (* 1 = 5.28146 loss)
I0405 11:13:07.573813 30176 sgd_solver.cpp:105] Iteration 17628, lr = 1e-06
I0405 11:13:12.762578 30176 solver.cpp:218] Iteration 17640 (2.31271 iter/s, 5.18872s/12 iters), loss = 5.29682
I0405 11:13:12.762697 30176 solver.cpp:237]     Train net output #0: loss = 5.29682 (* 1 = 5.29682 loss)
I0405 11:13:12.762706 30176 sgd_solver.cpp:105] Iteration 17640, lr = 1e-06
I0405 11:13:14.838873 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_17646.caffemodel
I0405 11:13:17.939636 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_17646.solverstate
I0405 11:13:20.269026 30176 solver.cpp:330] Iteration 17646, Testing net (#0)
I0405 11:13:20.269047 30176 net.cpp:676] Ignoring source layer train-data
I0405 11:13:22.336370 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 11:13:24.618384 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 11:13:24.618413 30176 solver.cpp:397]     Test net output #1: loss = 5.27988 (* 1 = 5.27988 loss)
I0405 11:13:26.472653 30176 solver.cpp:218] Iteration 17652 (0.875283 iter/s, 13.7099s/12 iters), loss = 5.27974
I0405 11:13:26.472693 30176 solver.cpp:237]     Train net output #0: loss = 5.27974 (* 1 = 5.27974 loss)
I0405 11:13:26.472699 30176 sgd_solver.cpp:105] Iteration 17652, lr = 1e-06
I0405 11:13:31.562712 30176 solver.cpp:218] Iteration 17664 (2.35758 iter/s, 5.08996s/12 iters), loss = 5.27301
I0405 11:13:31.562759 30176 solver.cpp:237]     Train net output #0: loss = 5.27301 (* 1 = 5.27301 loss)
I0405 11:13:31.562765 30176 sgd_solver.cpp:105] Iteration 17664, lr = 1e-06
I0405 11:13:36.575062 30176 solver.cpp:218] Iteration 17676 (2.39413 iter/s, 5.01225s/12 iters), loss = 5.30555
I0405 11:13:36.575103 30176 solver.cpp:237]     Train net output #0: loss = 5.30555 (* 1 = 5.30555 loss)
I0405 11:13:36.575109 30176 sgd_solver.cpp:105] Iteration 17676, lr = 1e-06
I0405 11:13:41.877210 30176 solver.cpp:218] Iteration 17688 (2.26327 iter/s, 5.30205s/12 iters), loss = 5.2857
I0405 11:13:41.877251 30176 solver.cpp:237]     Train net output #0: loss = 5.2857 (* 1 = 5.2857 loss)
I0405 11:13:41.877256 30176 sgd_solver.cpp:105] Iteration 17688, lr = 1e-06
I0405 11:13:47.131446 30176 solver.cpp:218] Iteration 17700 (2.28391 iter/s, 5.25414s/12 iters), loss = 5.29393
I0405 11:13:47.131624 30176 solver.cpp:237]     Train net output #0: loss = 5.29393 (* 1 = 5.29393 loss)
I0405 11:13:47.131633 30176 sgd_solver.cpp:105] Iteration 17700, lr = 1e-06
I0405 11:13:47.686015 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 11:13:52.490985 30176 solver.cpp:218] Iteration 17712 (2.23909 iter/s, 5.35931s/12 iters), loss = 5.28531
I0405 11:13:52.491024 30176 solver.cpp:237]     Train net output #0: loss = 5.28531 (* 1 = 5.28531 loss)
I0405 11:13:52.491029 30176 sgd_solver.cpp:105] Iteration 17712, lr = 1e-06
I0405 11:13:57.683343 30176 solver.cpp:218] Iteration 17724 (2.31113 iter/s, 5.19227s/12 iters), loss = 5.28884
I0405 11:13:57.683396 30176 solver.cpp:237]     Train net output #0: loss = 5.28884 (* 1 = 5.28884 loss)
I0405 11:13:57.683405 30176 sgd_solver.cpp:105] Iteration 17724, lr = 1e-06
I0405 11:14:03.029264 30176 solver.cpp:218] Iteration 17736 (2.24475 iter/s, 5.34582s/12 iters), loss = 5.2798
I0405 11:14:03.029309 30176 solver.cpp:237]     Train net output #0: loss = 5.2798 (* 1 = 5.2798 loss)
I0405 11:14:03.029314 30176 sgd_solver.cpp:105] Iteration 17736, lr = 1e-06
I0405 11:14:07.876585 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_17748.caffemodel
I0405 11:14:10.944802 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_17748.solverstate
I0405 11:14:13.254015 30176 solver.cpp:330] Iteration 17748, Testing net (#0)
I0405 11:14:13.254034 30176 net.cpp:676] Ignoring source layer train-data
I0405 11:14:15.352159 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 11:14:17.671551 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 11:14:17.671680 30176 solver.cpp:397]     Test net output #1: loss = 5.27979 (* 1 = 5.27979 loss)
I0405 11:14:17.812750 30176 solver.cpp:218] Iteration 17748 (0.811725 iter/s, 14.7833s/12 iters), loss = 5.28663
I0405 11:14:17.812813 30176 solver.cpp:237]     Train net output #0: loss = 5.28663 (* 1 = 5.28663 loss)
I0405 11:14:17.812820 30176 sgd_solver.cpp:105] Iteration 17748, lr = 1e-06
I0405 11:14:22.244992 30176 solver.cpp:218] Iteration 17760 (2.7075 iter/s, 4.43214s/12 iters), loss = 5.28176
I0405 11:14:22.245047 30176 solver.cpp:237]     Train net output #0: loss = 5.28176 (* 1 = 5.28176 loss)
I0405 11:14:22.245061 30176 sgd_solver.cpp:105] Iteration 17760, lr = 1e-06
I0405 11:14:27.493350 30176 solver.cpp:218] Iteration 17772 (2.28648 iter/s, 5.24825s/12 iters), loss = 5.2839
I0405 11:14:27.493391 30176 solver.cpp:237]     Train net output #0: loss = 5.2839 (* 1 = 5.2839 loss)
I0405 11:14:27.493396 30176 sgd_solver.cpp:105] Iteration 17772, lr = 1e-06
I0405 11:14:32.790218 30176 solver.cpp:218] Iteration 17784 (2.26553 iter/s, 5.29678s/12 iters), loss = 5.28878
I0405 11:14:32.790257 30176 solver.cpp:237]     Train net output #0: loss = 5.28878 (* 1 = 5.28878 loss)
I0405 11:14:32.790263 30176 sgd_solver.cpp:105] Iteration 17784, lr = 1e-06
I0405 11:14:38.110617 30176 solver.cpp:218] Iteration 17796 (2.25551 iter/s, 5.32031s/12 iters), loss = 5.25449
I0405 11:14:38.110657 30176 solver.cpp:237]     Train net output #0: loss = 5.25449 (* 1 = 5.25449 loss)
I0405 11:14:38.110662 30176 sgd_solver.cpp:105] Iteration 17796, lr = 1e-06
I0405 11:14:40.950493 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 11:14:43.515599 30176 solver.cpp:218] Iteration 17808 (2.22022 iter/s, 5.40488s/12 iters), loss = 5.27589
I0405 11:14:43.515652 30176 solver.cpp:237]     Train net output #0: loss = 5.27589 (* 1 = 5.27589 loss)
I0405 11:14:43.515661 30176 sgd_solver.cpp:105] Iteration 17808, lr = 1e-06
I0405 11:14:48.924021 30176 solver.cpp:218] Iteration 17820 (2.21881 iter/s, 5.40832s/12 iters), loss = 5.28331
I0405 11:14:48.924156 30176 solver.cpp:237]     Train net output #0: loss = 5.28331 (* 1 = 5.28331 loss)
I0405 11:14:48.924162 30176 sgd_solver.cpp:105] Iteration 17820, lr = 1e-06
I0405 11:14:54.139103 30176 solver.cpp:218] Iteration 17832 (2.3011 iter/s, 5.2149s/12 iters), loss = 5.27989
I0405 11:14:54.139147 30176 solver.cpp:237]     Train net output #0: loss = 5.27989 (* 1 = 5.27989 loss)
I0405 11:14:54.139151 30176 sgd_solver.cpp:105] Iteration 17832, lr = 1e-06
I0405 11:14:59.504487 30176 solver.cpp:218] Iteration 17844 (2.2366 iter/s, 5.36528s/12 iters), loss = 5.30625
I0405 11:14:59.504529 30176 solver.cpp:237]     Train net output #0: loss = 5.30625 (* 1 = 5.30625 loss)
I0405 11:14:59.504536 30176 sgd_solver.cpp:105] Iteration 17844, lr = 1e-06
I0405 11:15:01.590356 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_17850.caffemodel
I0405 11:15:04.701925 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_17850.solverstate
I0405 11:15:07.004895 30176 solver.cpp:330] Iteration 17850, Testing net (#0)
I0405 11:15:07.004916 30176 net.cpp:676] Ignoring source layer train-data
I0405 11:15:08.937775 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 11:15:11.282383 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 11:15:11.282424 30176 solver.cpp:397]     Test net output #1: loss = 5.27996 (* 1 = 5.27996 loss)
I0405 11:15:13.175308 30176 solver.cpp:218] Iteration 17856 (0.877792 iter/s, 13.6707s/12 iters), loss = 5.28474
I0405 11:15:13.175349 30176 solver.cpp:237]     Train net output #0: loss = 5.28474 (* 1 = 5.28474 loss)
I0405 11:15:13.175355 30176 sgd_solver.cpp:105] Iteration 17856, lr = 1e-06
I0405 11:15:18.192701 30176 solver.cpp:218] Iteration 17868 (2.39173 iter/s, 5.0173s/12 iters), loss = 5.2773
I0405 11:15:18.192744 30176 solver.cpp:237]     Train net output #0: loss = 5.2773 (* 1 = 5.2773 loss)
I0405 11:15:18.192750 30176 sgd_solver.cpp:105] Iteration 17868, lr = 1e-06
I0405 11:15:23.315129 30176 solver.cpp:218] Iteration 17880 (2.34268 iter/s, 5.12233s/12 iters), loss = 5.28291
I0405 11:15:23.315248 30176 solver.cpp:237]     Train net output #0: loss = 5.28291 (* 1 = 5.28291 loss)
I0405 11:15:23.315256 30176 sgd_solver.cpp:105] Iteration 17880, lr = 1e-06
I0405 11:15:28.531697 30176 solver.cpp:218] Iteration 17892 (2.30044 iter/s, 5.2164s/12 iters), loss = 5.28513
I0405 11:15:28.531738 30176 solver.cpp:237]     Train net output #0: loss = 5.28513 (* 1 = 5.28513 loss)
I0405 11:15:28.531744 30176 sgd_solver.cpp:105] Iteration 17892, lr = 1e-06
I0405 11:15:33.397131 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 11:15:33.646597 30176 solver.cpp:218] Iteration 17904 (2.34613 iter/s, 5.11481s/12 iters), loss = 5.28374
I0405 11:15:33.646641 30176 solver.cpp:237]     Train net output #0: loss = 5.28374 (* 1 = 5.28374 loss)
I0405 11:15:33.646646 30176 sgd_solver.cpp:105] Iteration 17904, lr = 1e-06
I0405 11:15:38.790767 30176 solver.cpp:218] Iteration 17916 (2.33281 iter/s, 5.14401s/12 iters), loss = 5.28684
I0405 11:15:38.790822 30176 solver.cpp:237]     Train net output #0: loss = 5.28684 (* 1 = 5.28684 loss)
I0405 11:15:38.790830 30176 sgd_solver.cpp:105] Iteration 17916, lr = 1e-06
I0405 11:15:44.070750 30176 solver.cpp:218] Iteration 17928 (2.27278 iter/s, 5.27988s/12 iters), loss = 5.28946
I0405 11:15:44.070798 30176 solver.cpp:237]     Train net output #0: loss = 5.28946 (* 1 = 5.28946 loss)
I0405 11:15:44.070804 30176 sgd_solver.cpp:105] Iteration 17928, lr = 1e-06
I0405 11:15:49.401223 30176 solver.cpp:218] Iteration 17940 (2.25125 iter/s, 5.33037s/12 iters), loss = 5.28725
I0405 11:15:49.401273 30176 solver.cpp:237]     Train net output #0: loss = 5.28725 (* 1 = 5.28725 loss)
I0405 11:15:49.401283 30176 sgd_solver.cpp:105] Iteration 17940, lr = 1e-06
I0405 11:15:54.050053 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_17952.caffemodel
I0405 11:15:57.204552 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_17952.solverstate
I0405 11:15:59.525903 30176 solver.cpp:330] Iteration 17952, Testing net (#0)
I0405 11:15:59.525929 30176 net.cpp:676] Ignoring source layer train-data
I0405 11:16:01.487028 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 11:16:03.860117 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 11:16:03.860157 30176 solver.cpp:397]     Test net output #1: loss = 5.27972 (* 1 = 5.27972 loss)
I0405 11:16:04.001011 30176 solver.cpp:218] Iteration 17952 (0.821939 iter/s, 14.5996s/12 iters), loss = 5.29184
I0405 11:16:04.002574 30176 solver.cpp:237]     Train net output #0: loss = 5.29184 (* 1 = 5.29184 loss)
I0405 11:16:04.002586 30176 sgd_solver.cpp:105] Iteration 17952, lr = 1e-06
I0405 11:16:08.048504 30176 solver.cpp:218] Iteration 17964 (2.96597 iter/s, 4.04589s/12 iters), loss = 5.29657
I0405 11:16:08.048565 30176 solver.cpp:237]     Train net output #0: loss = 5.29657 (* 1 = 5.29657 loss)
I0405 11:16:08.048574 30176 sgd_solver.cpp:105] Iteration 17964, lr = 1e-06
I0405 11:16:13.400019 30176 solver.cpp:218] Iteration 17976 (2.2424 iter/s, 5.35141s/12 iters), loss = 5.30027
I0405 11:16:13.400061 30176 solver.cpp:237]     Train net output #0: loss = 5.30027 (* 1 = 5.30027 loss)
I0405 11:16:13.400066 30176 sgd_solver.cpp:105] Iteration 17976, lr = 1e-06
I0405 11:16:18.623209 30176 solver.cpp:218] Iteration 17988 (2.29749 iter/s, 5.2231s/12 iters), loss = 5.28265
I0405 11:16:18.623246 30176 solver.cpp:237]     Train net output #0: loss = 5.28265 (* 1 = 5.28265 loss)
I0405 11:16:18.623251 30176 sgd_solver.cpp:105] Iteration 17988, lr = 1e-06
I0405 11:16:23.851843 30176 solver.cpp:218] Iteration 18000 (2.2951 iter/s, 5.22854s/12 iters), loss = 5.28976
I0405 11:16:23.851898 30176 solver.cpp:237]     Train net output #0: loss = 5.28976 (* 1 = 5.28976 loss)
I0405 11:16:23.851905 30176 sgd_solver.cpp:105] Iteration 18000, lr = 1e-06
I0405 11:16:25.996764 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 11:16:29.247203 30176 solver.cpp:218] Iteration 18012 (2.22418 iter/s, 5.39526s/12 iters), loss = 5.28881
I0405 11:16:29.247251 30176 solver.cpp:237]     Train net output #0: loss = 5.28881 (* 1 = 5.28881 loss)
I0405 11:16:29.247256 30176 sgd_solver.cpp:105] Iteration 18012, lr = 1e-06
I0405 11:16:34.341120 30176 solver.cpp:218] Iteration 18024 (2.3558 iter/s, 5.09382s/12 iters), loss = 5.27927
I0405 11:16:34.341181 30176 solver.cpp:237]     Train net output #0: loss = 5.27927 (* 1 = 5.27927 loss)
I0405 11:16:34.341189 30176 sgd_solver.cpp:105] Iteration 18024, lr = 1e-06
I0405 11:16:39.660295 30176 solver.cpp:218] Iteration 18036 (2.25604 iter/s, 5.31906s/12 iters), loss = 5.29683
I0405 11:16:39.660338 30176 solver.cpp:237]     Train net output #0: loss = 5.29683 (* 1 = 5.29683 loss)
I0405 11:16:39.660343 30176 sgd_solver.cpp:105] Iteration 18036, lr = 1e-06
I0405 11:16:39.660534 30176 blocking_queue.cpp:49] Waiting for data
I0405 11:16:44.866117 30176 solver.cpp:218] Iteration 18048 (2.30515 iter/s, 5.20573s/12 iters), loss = 5.28001
I0405 11:16:44.866163 30176 solver.cpp:237]     Train net output #0: loss = 5.28001 (* 1 = 5.28001 loss)
I0405 11:16:44.866170 30176 sgd_solver.cpp:105] Iteration 18048, lr = 1e-06
I0405 11:16:46.839766 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_18054.caffemodel
I0405 11:16:49.859599 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_18054.solverstate
I0405 11:16:52.172119 30176 solver.cpp:330] Iteration 18054, Testing net (#0)
I0405 11:16:52.172139 30176 net.cpp:676] Ignoring source layer train-data
I0405 11:16:54.077237 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 11:16:56.488942 30176 solver.cpp:397]     Test net output #0: accuracy = 0.00735294
I0405 11:16:56.489112 30176 solver.cpp:397]     Test net output #1: loss = 5.27966 (* 1 = 5.27966 loss)
I0405 11:16:58.394099 30176 solver.cpp:218] Iteration 18060 (0.887061 iter/s, 13.5278s/12 iters), loss = 5.27025
I0405 11:16:58.394156 30176 solver.cpp:237]     Train net output #0: loss = 5.27025 (* 1 = 5.27025 loss)
I0405 11:16:58.394165 30176 sgd_solver.cpp:105] Iteration 18060, lr = 1e-06
I0405 11:17:03.508895 30176 solver.cpp:218] Iteration 18072 (2.34619 iter/s, 5.11468s/12 iters), loss = 5.27726
I0405 11:17:03.508945 30176 solver.cpp:237]     Train net output #0: loss = 5.27726 (* 1 = 5.27726 loss)
I0405 11:17:03.508952 30176 sgd_solver.cpp:105] Iteration 18072, lr = 1e-06
I0405 11:17:08.754477 30176 solver.cpp:218] Iteration 18084 (2.28768 iter/s, 5.24548s/12 iters), loss = 5.2856
I0405 11:17:08.754518 30176 solver.cpp:237]     Train net output #0: loss = 5.2856 (* 1 = 5.2856 loss)
I0405 11:17:08.754524 30176 sgd_solver.cpp:105] Iteration 18084, lr = 1e-06
I0405 11:17:14.159919 30176 solver.cpp:218] Iteration 18096 (2.22002 iter/s, 5.40535s/12 iters), loss = 5.28297
I0405 11:17:14.159955 30176 solver.cpp:237]     Train net output #0: loss = 5.28297 (* 1 = 5.28297 loss)
I0405 11:17:14.159960 30176 sgd_solver.cpp:105] Iteration 18096, lr = 1e-06
I0405 11:17:18.482856 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 11:17:19.534195 30176 solver.cpp:218] Iteration 18108 (2.2329 iter/s, 5.37418s/12 iters), loss = 5.28348
I0405 11:17:19.534255 30176 solver.cpp:237]     Train net output #0: loss = 5.28348 (* 1 = 5.28348 loss)
I0405 11:17:19.534261 30176 sgd_solver.cpp:105] Iteration 18108, lr = 1e-06
I0405 11:17:24.802887 30176 solver.cpp:218] Iteration 18120 (2.27765 iter/s, 5.26859s/12 iters), loss = 5.28029
I0405 11:17:24.802925 30176 solver.cpp:237]     Train net output #0: loss = 5.28029 (* 1 = 5.28029 loss)
I0405 11:17:24.802930 30176 sgd_solver.cpp:105] Iteration 18120, lr = 1e-06
I0405 11:17:29.837440 30176 solver.cpp:218] Iteration 18132 (2.38357 iter/s, 5.03446s/12 iters), loss = 5.29436
I0405 11:17:29.837594 30176 solver.cpp:237]     Train net output #0: loss = 5.29436 (* 1 = 5.29436 loss)
I0405 11:17:29.837600 30176 sgd_solver.cpp:105] Iteration 18132, lr = 1e-06
I0405 11:17:35.030130 30176 solver.cpp:218] Iteration 18144 (2.31103 iter/s, 5.19249s/12 iters), loss = 5.28556
I0405 11:17:35.030176 30176 solver.cpp:237]     Train net output #0: loss = 5.28556 (* 1 = 5.28556 loss)
I0405 11:17:35.030182 30176 sgd_solver.cpp:105] Iteration 18144, lr = 1e-06
I0405 11:17:39.864854 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_18156.caffemodel
I0405 11:17:42.950779 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_18156.solverstate
I0405 11:17:45.250299 30176 solver.cpp:330] Iteration 18156, Testing net (#0)
I0405 11:17:45.250319 30176 net.cpp:676] Ignoring source layer train-data
I0405 11:17:47.313078 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 11:17:49.947185 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 11:17:49.947228 30176 solver.cpp:397]     Test net output #1: loss = 5.27967 (* 1 = 5.27967 loss)
I0405 11:17:50.088201 30176 solver.cpp:218] Iteration 18156 (0.796924 iter/s, 15.0579s/12 iters), loss = 5.29488
I0405 11:17:50.088250 30176 solver.cpp:237]     Train net output #0: loss = 5.29488 (* 1 = 5.29488 loss)
I0405 11:17:50.088258 30176 sgd_solver.cpp:105] Iteration 18156, lr = 1e-06
I0405 11:17:54.459805 30176 solver.cpp:218] Iteration 18168 (2.74505 iter/s, 4.37151s/12 iters), loss = 5.27225
I0405 11:17:54.459847 30176 solver.cpp:237]     Train net output #0: loss = 5.27225 (* 1 = 5.27225 loss)
I0405 11:17:54.459852 30176 sgd_solver.cpp:105] Iteration 18168, lr = 1e-06
I0405 11:17:59.836779 30176 solver.cpp:218] Iteration 18180 (2.23178 iter/s, 5.37688s/12 iters), loss = 5.28743
I0405 11:17:59.836822 30176 solver.cpp:237]     Train net output #0: loss = 5.28743 (* 1 = 5.28743 loss)
I0405 11:17:59.836828 30176 sgd_solver.cpp:105] Iteration 18180, lr = 1e-06
I0405 11:18:05.285003 30176 solver.cpp:218] Iteration 18192 (2.20259 iter/s, 5.44812s/12 iters), loss = 5.27086
I0405 11:18:05.286953 30176 solver.cpp:237]     Train net output #0: loss = 5.27086 (* 1 = 5.27086 loss)
I0405 11:18:05.286960 30176 sgd_solver.cpp:105] Iteration 18192, lr = 1e-06
I0405 11:18:10.571436 30176 solver.cpp:218] Iteration 18204 (2.27082 iter/s, 5.28443s/12 iters), loss = 5.27982
I0405 11:18:10.571483 30176 solver.cpp:237]     Train net output #0: loss = 5.27982 (* 1 = 5.27982 loss)
I0405 11:18:10.571491 30176 sgd_solver.cpp:105] Iteration 18204, lr = 1e-06
I0405 11:18:11.938719 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 11:18:15.924284 30176 solver.cpp:218] Iteration 18216 (2.24184 iter/s, 5.35274s/12 iters), loss = 5.28036
I0405 11:18:15.924347 30176 solver.cpp:237]     Train net output #0: loss = 5.28036 (* 1 = 5.28036 loss)
I0405 11:18:15.924356 30176 sgd_solver.cpp:105] Iteration 18216, lr = 1e-06
I0405 11:18:21.267778 30176 solver.cpp:218] Iteration 18228 (2.24577 iter/s, 5.34338s/12 iters), loss = 5.29074
I0405 11:18:21.267819 30176 solver.cpp:237]     Train net output #0: loss = 5.29074 (* 1 = 5.29074 loss)
I0405 11:18:21.267824 30176 sgd_solver.cpp:105] Iteration 18228, lr = 1e-06
I0405 11:18:26.430368 30176 solver.cpp:218] Iteration 18240 (2.32446 iter/s, 5.1625s/12 iters), loss = 5.2939
I0405 11:18:26.430409 30176 solver.cpp:237]     Train net output #0: loss = 5.2939 (* 1 = 5.2939 loss)
I0405 11:18:26.430415 30176 sgd_solver.cpp:105] Iteration 18240, lr = 1e-06
I0405 11:18:31.858819 30176 solver.cpp:218] Iteration 18252 (2.21061 iter/s, 5.42835s/12 iters), loss = 5.29356
I0405 11:18:31.858861 30176 solver.cpp:237]     Train net output #0: loss = 5.29356 (* 1 = 5.29356 loss)
I0405 11:18:31.858866 30176 sgd_solver.cpp:105] Iteration 18252, lr = 1e-06
I0405 11:18:33.927605 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_18258.caffemodel
I0405 11:18:36.941498 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_18258.solverstate
I0405 11:18:39.887951 30176 solver.cpp:330] Iteration 18258, Testing net (#0)
I0405 11:18:39.887971 30176 net.cpp:676] Ignoring source layer train-data
I0405 11:18:41.733794 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 11:18:44.233777 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 11:18:44.233814 30176 solver.cpp:397]     Test net output #1: loss = 5.27985 (* 1 = 5.27985 loss)
I0405 11:18:46.092224 30176 solver.cpp:218] Iteration 18264 (0.843097 iter/s, 14.2332s/12 iters), loss = 5.28003
I0405 11:18:46.092280 30176 solver.cpp:237]     Train net output #0: loss = 5.28003 (* 1 = 5.28003 loss)
I0405 11:18:46.092289 30176 sgd_solver.cpp:105] Iteration 18264, lr = 1e-06
I0405 11:18:51.223595 30176 solver.cpp:218] Iteration 18276 (2.33861 iter/s, 5.13126s/12 iters), loss = 5.29178
I0405 11:18:51.223644 30176 solver.cpp:237]     Train net output #0: loss = 5.29178 (* 1 = 5.29178 loss)
I0405 11:18:51.223650 30176 sgd_solver.cpp:105] Iteration 18276, lr = 1e-06
I0405 11:18:56.285645 30176 solver.cpp:218] Iteration 18288 (2.37063 iter/s, 5.06195s/12 iters), loss = 5.28582
I0405 11:18:56.285699 30176 solver.cpp:237]     Train net output #0: loss = 5.28582 (* 1 = 5.28582 loss)
I0405 11:18:56.285706 30176 sgd_solver.cpp:105] Iteration 18288, lr = 1e-06
I0405 11:19:01.400866 30176 solver.cpp:218] Iteration 18300 (2.34599 iter/s, 5.11511s/12 iters), loss = 5.28999
I0405 11:19:01.400928 30176 solver.cpp:237]     Train net output #0: loss = 5.28999 (* 1 = 5.28999 loss)
I0405 11:19:01.400935 30176 sgd_solver.cpp:105] Iteration 18300, lr = 1e-06
I0405 11:19:04.838804 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 11:19:06.457434 30176 solver.cpp:218] Iteration 18312 (2.3732 iter/s, 5.05646s/12 iters), loss = 5.27728
I0405 11:19:06.457474 30176 solver.cpp:237]     Train net output #0: loss = 5.27728 (* 1 = 5.27728 loss)
I0405 11:19:06.457479 30176 sgd_solver.cpp:105] Iteration 18312, lr = 1e-06
I0405 11:19:11.880304 30176 solver.cpp:218] Iteration 18324 (2.21289 iter/s, 5.42277s/12 iters), loss = 5.29087
I0405 11:19:11.880434 30176 solver.cpp:237]     Train net output #0: loss = 5.29087 (* 1 = 5.29087 loss)
I0405 11:19:11.880440 30176 sgd_solver.cpp:105] Iteration 18324, lr = 1e-06
I0405 11:19:17.006434 30176 solver.cpp:218] Iteration 18336 (2.34103 iter/s, 5.12595s/12 iters), loss = 5.28152
I0405 11:19:17.006474 30176 solver.cpp:237]     Train net output #0: loss = 5.28152 (* 1 = 5.28152 loss)
I0405 11:19:17.006479 30176 sgd_solver.cpp:105] Iteration 18336, lr = 1e-06
I0405 11:19:22.434794 30176 solver.cpp:218] Iteration 18348 (2.21065 iter/s, 5.42827s/12 iters), loss = 5.27085
I0405 11:19:22.434837 30176 solver.cpp:237]     Train net output #0: loss = 5.27085 (* 1 = 5.27085 loss)
I0405 11:19:22.434842 30176 sgd_solver.cpp:105] Iteration 18348, lr = 1e-06
I0405 11:19:26.961944 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_18360.caffemodel
I0405 11:19:29.996697 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_18360.solverstate
I0405 11:19:33.092346 30176 solver.cpp:330] Iteration 18360, Testing net (#0)
I0405 11:19:33.092370 30176 net.cpp:676] Ignoring source layer train-data
I0405 11:19:34.842519 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 11:19:37.385815 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 11:19:37.385849 30176 solver.cpp:397]     Test net output #1: loss = 5.2796 (* 1 = 5.2796 loss)
I0405 11:19:37.526773 30176 solver.cpp:218] Iteration 18360 (0.795133 iter/s, 15.0918s/12 iters), loss = 5.26853
I0405 11:19:37.526834 30176 solver.cpp:237]     Train net output #0: loss = 5.26853 (* 1 = 5.26853 loss)
I0405 11:19:37.526844 30176 sgd_solver.cpp:105] Iteration 18360, lr = 1e-06
I0405 11:19:42.106092 30176 solver.cpp:218] Iteration 18372 (2.62054 iter/s, 4.57922s/12 iters), loss = 5.27499
I0405 11:19:42.106194 30176 solver.cpp:237]     Train net output #0: loss = 5.27499 (* 1 = 5.27499 loss)
I0405 11:19:42.106199 30176 sgd_solver.cpp:105] Iteration 18372, lr = 1e-06
I0405 11:19:47.104128 30176 solver.cpp:218] Iteration 18384 (2.40102 iter/s, 4.99788s/12 iters), loss = 5.28563
I0405 11:19:47.104183 30176 solver.cpp:237]     Train net output #0: loss = 5.28563 (* 1 = 5.28563 loss)
I0405 11:19:47.104192 30176 sgd_solver.cpp:105] Iteration 18384, lr = 1e-06
I0405 11:19:52.475939 30176 solver.cpp:218] Iteration 18396 (2.23393 iter/s, 5.37171s/12 iters), loss = 5.27795
I0405 11:19:52.475975 30176 solver.cpp:237]     Train net output #0: loss = 5.27795 (* 1 = 5.27795 loss)
I0405 11:19:52.475981 30176 sgd_solver.cpp:105] Iteration 18396, lr = 1e-06
I0405 11:19:57.862572 30176 solver.cpp:218] Iteration 18408 (2.22777 iter/s, 5.38654s/12 iters), loss = 5.29846
I0405 11:19:57.862623 30176 solver.cpp:237]     Train net output #0: loss = 5.29846 (* 1 = 5.29846 loss)
I0405 11:19:57.862629 30176 sgd_solver.cpp:105] Iteration 18408, lr = 1e-06
I0405 11:19:58.471681 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 11:20:03.065353 30176 solver.cpp:218] Iteration 18420 (2.3065 iter/s, 5.20268s/12 iters), loss = 5.29202
I0405 11:20:03.065400 30176 solver.cpp:237]     Train net output #0: loss = 5.29202 (* 1 = 5.29202 loss)
I0405 11:20:03.065405 30176 sgd_solver.cpp:105] Iteration 18420, lr = 1e-06
I0405 11:20:08.389041 30176 solver.cpp:218] Iteration 18432 (2.25412 iter/s, 5.32358s/12 iters), loss = 5.27722
I0405 11:20:08.389101 30176 solver.cpp:237]     Train net output #0: loss = 5.27722 (* 1 = 5.27722 loss)
I0405 11:20:08.389111 30176 sgd_solver.cpp:105] Iteration 18432, lr = 1e-06
I0405 11:20:13.757915 30176 solver.cpp:218] Iteration 18444 (2.23515 iter/s, 5.36876s/12 iters), loss = 5.28936
I0405 11:20:13.758042 30176 solver.cpp:237]     Train net output #0: loss = 5.28936 (* 1 = 5.28936 loss)
I0405 11:20:13.758049 30176 sgd_solver.cpp:105] Iteration 18444, lr = 1e-06
I0405 11:20:19.109441 30176 solver.cpp:218] Iteration 18456 (2.24243 iter/s, 5.35135s/12 iters), loss = 5.27972
I0405 11:20:19.109484 30176 solver.cpp:237]     Train net output #0: loss = 5.27972 (* 1 = 5.27972 loss)
I0405 11:20:19.109489 30176 sgd_solver.cpp:105] Iteration 18456, lr = 1e-06
I0405 11:20:21.321231 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_18462.caffemodel
I0405 11:20:24.290289 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_18462.solverstate
I0405 11:20:26.586778 30176 solver.cpp:330] Iteration 18462, Testing net (#0)
I0405 11:20:26.586796 30176 net.cpp:676] Ignoring source layer train-data
I0405 11:20:28.332355 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 11:20:30.921886 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 11:20:30.921922 30176 solver.cpp:397]     Test net output #1: loss = 5.27993 (* 1 = 5.27993 loss)
I0405 11:20:33.048524 30176 solver.cpp:218] Iteration 18468 (0.860898 iter/s, 13.9389s/12 iters), loss = 5.28183
I0405 11:20:33.048563 30176 solver.cpp:237]     Train net output #0: loss = 5.28183 (* 1 = 5.28183 loss)
I0405 11:20:33.048568 30176 sgd_solver.cpp:105] Iteration 18468, lr = 1e-06
I0405 11:20:38.392812 30176 solver.cpp:218] Iteration 18480 (2.24543 iter/s, 5.34419s/12 iters), loss = 5.28308
I0405 11:20:38.392855 30176 solver.cpp:237]     Train net output #0: loss = 5.28308 (* 1 = 5.28308 loss)
I0405 11:20:38.392863 30176 sgd_solver.cpp:105] Iteration 18480, lr = 1e-06
I0405 11:20:43.686453 30176 solver.cpp:218] Iteration 18492 (2.26691 iter/s, 5.29354s/12 iters), loss = 5.29008
I0405 11:20:43.686501 30176 solver.cpp:237]     Train net output #0: loss = 5.29008 (* 1 = 5.29008 loss)
I0405 11:20:43.686509 30176 sgd_solver.cpp:105] Iteration 18492, lr = 1e-06
I0405 11:20:48.884562 30176 solver.cpp:218] Iteration 18504 (2.30858 iter/s, 5.198s/12 iters), loss = 5.26684
I0405 11:20:48.884727 30176 solver.cpp:237]     Train net output #0: loss = 5.26684 (* 1 = 5.26684 loss)
I0405 11:20:48.884737 30176 sgd_solver.cpp:105] Iteration 18504, lr = 1e-06
I0405 11:20:51.744387 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 11:20:54.269320 30176 solver.cpp:218] Iteration 18516 (2.2286 iter/s, 5.38455s/12 iters), loss = 5.29205
I0405 11:20:54.269379 30176 solver.cpp:237]     Train net output #0: loss = 5.29205 (* 1 = 5.29205 loss)
I0405 11:20:54.269389 30176 sgd_solver.cpp:105] Iteration 18516, lr = 1e-06
I0405 11:20:59.576124 30176 solver.cpp:218] Iteration 18528 (2.2613 iter/s, 5.30669s/12 iters), loss = 5.28043
I0405 11:20:59.576184 30176 solver.cpp:237]     Train net output #0: loss = 5.28043 (* 1 = 5.28043 loss)
I0405 11:20:59.576192 30176 sgd_solver.cpp:105] Iteration 18528, lr = 1e-06
I0405 11:21:04.759491 30176 solver.cpp:218] Iteration 18540 (2.31515 iter/s, 5.18326s/12 iters), loss = 5.28698
I0405 11:21:04.759531 30176 solver.cpp:237]     Train net output #0: loss = 5.28698 (* 1 = 5.28698 loss)
I0405 11:21:04.759536 30176 sgd_solver.cpp:105] Iteration 18540, lr = 1e-06
I0405 11:21:10.101858 30176 solver.cpp:218] Iteration 18552 (2.24623 iter/s, 5.34227s/12 iters), loss = 5.28515
I0405 11:21:10.101894 30176 solver.cpp:237]     Train net output #0: loss = 5.28515 (* 1 = 5.28515 loss)
I0405 11:21:10.101899 30176 sgd_solver.cpp:105] Iteration 18552, lr = 1e-06
I0405 11:21:14.828506 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_18564.caffemodel
I0405 11:21:17.869339 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_18564.solverstate
I0405 11:21:20.171051 30176 solver.cpp:330] Iteration 18564, Testing net (#0)
I0405 11:21:20.171173 30176 net.cpp:676] Ignoring source layer train-data
I0405 11:21:21.881319 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 11:21:24.558087 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 11:21:24.558123 30176 solver.cpp:397]     Test net output #1: loss = 5.27968 (* 1 = 5.27968 loss)
I0405 11:21:24.698632 30176 solver.cpp:218] Iteration 18564 (0.822108 iter/s, 14.5966s/12 iters), loss = 5.29906
I0405 11:21:24.698674 30176 solver.cpp:237]     Train net output #0: loss = 5.29906 (* 1 = 5.29906 loss)
I0405 11:21:24.698680 30176 sgd_solver.cpp:105] Iteration 18564, lr = 1e-06
I0405 11:21:29.097373 30176 solver.cpp:218] Iteration 18576 (2.72811 iter/s, 4.39865s/12 iters), loss = 5.28931
I0405 11:21:29.097417 30176 solver.cpp:237]     Train net output #0: loss = 5.28931 (* 1 = 5.28931 loss)
I0405 11:21:29.097425 30176 sgd_solver.cpp:105] Iteration 18576, lr = 1e-06
I0405 11:21:34.442822 30176 solver.cpp:218] Iteration 18588 (2.24494 iter/s, 5.34535s/12 iters), loss = 5.29105
I0405 11:21:34.442862 30176 solver.cpp:237]     Train net output #0: loss = 5.29105 (* 1 = 5.29105 loss)
I0405 11:21:34.442867 30176 sgd_solver.cpp:105] Iteration 18588, lr = 1e-06
I0405 11:21:39.810833 30176 solver.cpp:218] Iteration 18600 (2.23551 iter/s, 5.36791s/12 iters), loss = 5.29717
I0405 11:21:39.810887 30176 solver.cpp:237]     Train net output #0: loss = 5.29717 (* 1 = 5.29717 loss)
I0405 11:21:39.810896 30176 sgd_solver.cpp:105] Iteration 18600, lr = 1e-06
I0405 11:21:45.214628 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 11:21:45.434054 30176 solver.cpp:218] Iteration 18612 (2.13405 iter/s, 5.62311s/12 iters), loss = 5.28693
I0405 11:21:45.434094 30176 solver.cpp:237]     Train net output #0: loss = 5.28693 (* 1 = 5.28693 loss)
I0405 11:21:45.434100 30176 sgd_solver.cpp:105] Iteration 18612, lr = 1e-06
I0405 11:21:50.821117 30176 solver.cpp:218] Iteration 18624 (2.2276 iter/s, 5.38696s/12 iters), loss = 5.27695
I0405 11:21:50.821254 30176 solver.cpp:237]     Train net output #0: loss = 5.27695 (* 1 = 5.27695 loss)
I0405 11:21:50.821264 30176 sgd_solver.cpp:105] Iteration 18624, lr = 1e-06
I0405 11:21:56.261251 30176 solver.cpp:218] Iteration 18636 (2.2059 iter/s, 5.43995s/12 iters), loss = 5.28884
I0405 11:21:56.261304 30176 solver.cpp:237]     Train net output #0: loss = 5.28884 (* 1 = 5.28884 loss)
I0405 11:21:56.261312 30176 sgd_solver.cpp:105] Iteration 18636, lr = 1e-06
I0405 11:22:01.429445 30176 solver.cpp:218] Iteration 18648 (2.32194 iter/s, 5.16809s/12 iters), loss = 5.27681
I0405 11:22:01.429484 30176 solver.cpp:237]     Train net output #0: loss = 5.27681 (* 1 = 5.27681 loss)
I0405 11:22:01.429491 30176 sgd_solver.cpp:105] Iteration 18648, lr = 1e-06
I0405 11:22:06.718686 30176 solver.cpp:218] Iteration 18660 (2.2688 iter/s, 5.28915s/12 iters), loss = 5.27786
I0405 11:22:06.718732 30176 solver.cpp:237]     Train net output #0: loss = 5.27786 (* 1 = 5.27786 loss)
I0405 11:22:06.718740 30176 sgd_solver.cpp:105] Iteration 18660, lr = 1e-06
I0405 11:22:08.866832 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_18666.caffemodel
I0405 11:22:11.833992 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_18666.solverstate
I0405 11:22:14.135291 30176 solver.cpp:330] Iteration 18666, Testing net (#0)
I0405 11:22:14.135313 30176 net.cpp:676] Ignoring source layer train-data
I0405 11:22:15.771579 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 11:22:18.411542 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 11:22:18.411587 30176 solver.cpp:397]     Test net output #1: loss = 5.27973 (* 1 = 5.27973 loss)
I0405 11:22:20.449421 30176 solver.cpp:218] Iteration 18672 (0.873962 iter/s, 13.7306s/12 iters), loss = 5.2863
I0405 11:22:20.449465 30176 solver.cpp:237]     Train net output #0: loss = 5.2863 (* 1 = 5.2863 loss)
I0405 11:22:20.449470 30176 sgd_solver.cpp:105] Iteration 18672, lr = 1e-06
I0405 11:22:25.813946 30176 solver.cpp:218] Iteration 18684 (2.23696 iter/s, 5.36443s/12 iters), loss = 5.28135
I0405 11:22:25.814067 30176 solver.cpp:237]     Train net output #0: loss = 5.28135 (* 1 = 5.28135 loss)
I0405 11:22:25.814074 30176 sgd_solver.cpp:105] Iteration 18684, lr = 1e-06
I0405 11:22:31.121330 30176 solver.cpp:218] Iteration 18696 (2.26108 iter/s, 5.30721s/12 iters), loss = 5.28464
I0405 11:22:31.121368 30176 solver.cpp:237]     Train net output #0: loss = 5.28464 (* 1 = 5.28464 loss)
I0405 11:22:31.121374 30176 sgd_solver.cpp:105] Iteration 18696, lr = 1e-06
I0405 11:22:36.434932 30176 solver.cpp:218] Iteration 18708 (2.2584 iter/s, 5.3135s/12 iters), loss = 5.2772
I0405 11:22:36.434976 30176 solver.cpp:237]     Train net output #0: loss = 5.2772 (* 1 = 5.2772 loss)
I0405 11:22:36.434981 30176 sgd_solver.cpp:105] Iteration 18708, lr = 1e-06
I0405 11:22:38.477193 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 11:22:41.950371 30176 solver.cpp:218] Iteration 18720 (2.17575 iter/s, 5.51534s/12 iters), loss = 5.27245
I0405 11:22:41.950412 30176 solver.cpp:237]     Train net output #0: loss = 5.27245 (* 1 = 5.27245 loss)
I0405 11:22:41.950417 30176 sgd_solver.cpp:105] Iteration 18720, lr = 1e-06
I0405 11:22:42.303100 30176 blocking_queue.cpp:49] Waiting for data
I0405 11:22:47.332829 30176 solver.cpp:218] Iteration 18732 (2.22951 iter/s, 5.38236s/12 iters), loss = 5.28084
I0405 11:22:47.332897 30176 solver.cpp:237]     Train net output #0: loss = 5.28084 (* 1 = 5.28084 loss)
I0405 11:22:47.332906 30176 sgd_solver.cpp:105] Iteration 18732, lr = 1e-06
I0405 11:22:52.715186 30176 solver.cpp:218] Iteration 18744 (2.22955 iter/s, 5.38224s/12 iters), loss = 5.30037
I0405 11:22:52.715229 30176 solver.cpp:237]     Train net output #0: loss = 5.30037 (* 1 = 5.30037 loss)
I0405 11:22:52.715234 30176 sgd_solver.cpp:105] Iteration 18744, lr = 1e-06
I0405 11:22:58.052057 30176 solver.cpp:218] Iteration 18756 (2.24855 iter/s, 5.33677s/12 iters), loss = 5.29455
I0405 11:22:58.052155 30176 solver.cpp:237]     Train net output #0: loss = 5.29455 (* 1 = 5.29455 loss)
I0405 11:22:58.052160 30176 sgd_solver.cpp:105] Iteration 18756, lr = 1e-06
I0405 11:23:02.769829 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_18768.caffemodel
I0405 11:23:05.777398 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_18768.solverstate
I0405 11:23:08.080247 30176 solver.cpp:330] Iteration 18768, Testing net (#0)
I0405 11:23:08.080266 30176 net.cpp:676] Ignoring source layer train-data
I0405 11:23:09.667857 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 11:23:12.458581 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 11:23:12.458611 30176 solver.cpp:397]     Test net output #1: loss = 5.27987 (* 1 = 5.27987 loss)
I0405 11:23:12.599607 30176 solver.cpp:218] Iteration 18768 (0.824893 iter/s, 14.5473s/12 iters), loss = 5.27474
I0405 11:23:12.599653 30176 solver.cpp:237]     Train net output #0: loss = 5.27474 (* 1 = 5.27474 loss)
I0405 11:23:12.599659 30176 sgd_solver.cpp:105] Iteration 18768, lr = 1e-06
I0405 11:23:17.058068 30176 solver.cpp:218] Iteration 18780 (2.69157 iter/s, 4.45837s/12 iters), loss = 5.27155
I0405 11:23:17.058110 30176 solver.cpp:237]     Train net output #0: loss = 5.27155 (* 1 = 5.27155 loss)
I0405 11:23:17.058116 30176 sgd_solver.cpp:105] Iteration 18780, lr = 1e-06
I0405 11:23:22.294025 30176 solver.cpp:218] Iteration 18792 (2.29189 iter/s, 5.23586s/12 iters), loss = 5.28927
I0405 11:23:22.294067 30176 solver.cpp:237]     Train net output #0: loss = 5.28927 (* 1 = 5.28927 loss)
I0405 11:23:22.294072 30176 sgd_solver.cpp:105] Iteration 18792, lr = 1e-06
I0405 11:23:27.573699 30176 solver.cpp:218] Iteration 18804 (2.27291 iter/s, 5.27958s/12 iters), loss = 5.29565
I0405 11:23:27.573747 30176 solver.cpp:237]     Train net output #0: loss = 5.29565 (* 1 = 5.29565 loss)
I0405 11:23:27.573755 30176 sgd_solver.cpp:105] Iteration 18804, lr = 1e-06
I0405 11:23:31.730806 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 11:23:32.601935 30176 solver.cpp:218] Iteration 18816 (2.38657 iter/s, 5.02814s/12 iters), loss = 5.29247
I0405 11:23:32.601974 30176 solver.cpp:237]     Train net output #0: loss = 5.29247 (* 1 = 5.29247 loss)
I0405 11:23:32.601979 30176 sgd_solver.cpp:105] Iteration 18816, lr = 1e-06
I0405 11:23:37.902617 30176 solver.cpp:218] Iteration 18828 (2.2639 iter/s, 5.30059s/12 iters), loss = 5.28129
I0405 11:23:37.902671 30176 solver.cpp:237]     Train net output #0: loss = 5.28129 (* 1 = 5.28129 loss)
I0405 11:23:37.902680 30176 sgd_solver.cpp:105] Iteration 18828, lr = 1e-06
I0405 11:23:43.166986 30176 solver.cpp:218] Iteration 18840 (2.27952 iter/s, 5.26427s/12 iters), loss = 5.2627
I0405 11:23:43.167026 30176 solver.cpp:237]     Train net output #0: loss = 5.2627 (* 1 = 5.2627 loss)
I0405 11:23:43.167032 30176 sgd_solver.cpp:105] Iteration 18840, lr = 1e-06
I0405 11:23:48.390493 30176 solver.cpp:218] Iteration 18852 (2.29735 iter/s, 5.22341s/12 iters), loss = 5.2877
I0405 11:23:48.390552 30176 solver.cpp:237]     Train net output #0: loss = 5.2877 (* 1 = 5.2877 loss)
I0405 11:23:48.390559 30176 sgd_solver.cpp:105] Iteration 18852, lr = 1e-06
I0405 11:23:53.720510 30176 solver.cpp:218] Iteration 18864 (2.25145 iter/s, 5.3299s/12 iters), loss = 5.28657
I0405 11:23:53.720573 30176 solver.cpp:237]     Train net output #0: loss = 5.28657 (* 1 = 5.28657 loss)
I0405 11:23:53.720582 30176 sgd_solver.cpp:105] Iteration 18864, lr = 1e-06
I0405 11:23:55.879142 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_18870.caffemodel
I0405 11:23:58.960589 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_18870.solverstate
I0405 11:24:01.286550 30176 solver.cpp:330] Iteration 18870, Testing net (#0)
I0405 11:24:01.286571 30176 net.cpp:676] Ignoring source layer train-data
I0405 11:24:02.844799 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 11:24:05.661278 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 11:24:05.661324 30176 solver.cpp:397]     Test net output #1: loss = 5.2798 (* 1 = 5.2798 loss)
I0405 11:24:07.627427 30176 solver.cpp:218] Iteration 18876 (0.862891 iter/s, 13.9067s/12 iters), loss = 5.28668
I0405 11:24:07.627472 30176 solver.cpp:237]     Train net output #0: loss = 5.28668 (* 1 = 5.28668 loss)
I0405 11:24:07.627477 30176 sgd_solver.cpp:105] Iteration 18876, lr = 1e-06
I0405 11:24:12.872747 30176 solver.cpp:218] Iteration 18888 (2.28779 iter/s, 5.24523s/12 iters), loss = 5.28841
I0405 11:24:12.872783 30176 solver.cpp:237]     Train net output #0: loss = 5.28841 (* 1 = 5.28841 loss)
I0405 11:24:12.872789 30176 sgd_solver.cpp:105] Iteration 18888, lr = 1e-06
I0405 11:24:17.927757 30176 solver.cpp:218] Iteration 18900 (2.37393 iter/s, 5.05491s/12 iters), loss = 5.277
I0405 11:24:17.927811 30176 solver.cpp:237]     Train net output #0: loss = 5.277 (* 1 = 5.277 loss)
I0405 11:24:17.927819 30176 sgd_solver.cpp:105] Iteration 18900, lr = 1e-06
I0405 11:24:23.243124 30176 solver.cpp:218] Iteration 18912 (2.25765 iter/s, 5.31525s/12 iters), loss = 5.28955
I0405 11:24:23.243185 30176 solver.cpp:237]     Train net output #0: loss = 5.28955 (* 1 = 5.28955 loss)
I0405 11:24:23.243196 30176 sgd_solver.cpp:105] Iteration 18912, lr = 1e-06
I0405 11:24:24.591251 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 11:24:28.574115 30176 solver.cpp:218] Iteration 18924 (2.25103 iter/s, 5.33088s/12 iters), loss = 5.28872
I0405 11:24:28.574158 30176 solver.cpp:237]     Train net output #0: loss = 5.28872 (* 1 = 5.28872 loss)
I0405 11:24:28.574164 30176 sgd_solver.cpp:105] Iteration 18924, lr = 1e-06
I0405 11:24:33.847895 30176 solver.cpp:218] Iteration 18936 (2.27545 iter/s, 5.27368s/12 iters), loss = 5.28735
I0405 11:24:33.848016 30176 solver.cpp:237]     Train net output #0: loss = 5.28735 (* 1 = 5.28735 loss)
I0405 11:24:33.848022 30176 sgd_solver.cpp:105] Iteration 18936, lr = 1e-06
I0405 11:24:39.289064 30176 solver.cpp:218] Iteration 18948 (2.20548 iter/s, 5.44099s/12 iters), loss = 5.29506
I0405 11:24:39.289105 30176 solver.cpp:237]     Train net output #0: loss = 5.29506 (* 1 = 5.29506 loss)
I0405 11:24:39.289111 30176 sgd_solver.cpp:105] Iteration 18948, lr = 1e-06
I0405 11:24:44.598310 30176 solver.cpp:218] Iteration 18960 (2.26025 iter/s, 5.30915s/12 iters), loss = 5.29476
I0405 11:24:44.598376 30176 solver.cpp:237]     Train net output #0: loss = 5.29476 (* 1 = 5.29476 loss)
I0405 11:24:44.598385 30176 sgd_solver.cpp:105] Iteration 18960, lr = 1e-06
I0405 11:24:49.369597 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_18972.caffemodel
I0405 11:24:52.371322 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_18972.solverstate
I0405 11:24:54.670874 30176 solver.cpp:330] Iteration 18972, Testing net (#0)
I0405 11:24:54.670894 30176 net.cpp:676] Ignoring source layer train-data
I0405 11:24:56.371713 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 11:24:59.236018 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 11:24:59.236052 30176 solver.cpp:397]     Test net output #1: loss = 5.2797 (* 1 = 5.2797 loss)
I0405 11:24:59.377863 30176 solver.cpp:218] Iteration 18972 (0.811943 iter/s, 14.7794s/12 iters), loss = 5.2912
I0405 11:24:59.377918 30176 solver.cpp:237]     Train net output #0: loss = 5.2912 (* 1 = 5.2912 loss)
I0405 11:24:59.377924 30176 sgd_solver.cpp:105] Iteration 18972, lr = 1e-06
I0405 11:25:03.870242 30176 solver.cpp:218] Iteration 18984 (2.67125 iter/s, 4.49227s/12 iters), loss = 5.29115
I0405 11:25:03.870362 30176 solver.cpp:237]     Train net output #0: loss = 5.29115 (* 1 = 5.29115 loss)
I0405 11:25:03.870369 30176 sgd_solver.cpp:105] Iteration 18984, lr = 1e-06
I0405 11:25:09.131276 30176 solver.cpp:218] Iteration 18996 (2.281 iter/s, 5.26086s/12 iters), loss = 5.27021
I0405 11:25:09.131336 30176 solver.cpp:237]     Train net output #0: loss = 5.27021 (* 1 = 5.27021 loss)
I0405 11:25:09.131345 30176 sgd_solver.cpp:105] Iteration 18996, lr = 1e-06
I0405 11:25:14.299371 30176 solver.cpp:218] Iteration 19008 (2.32199 iter/s, 5.16797s/12 iters), loss = 5.2861
I0405 11:25:14.299448 30176 solver.cpp:237]     Train net output #0: loss = 5.2861 (* 1 = 5.2861 loss)
I0405 11:25:14.299460 30176 sgd_solver.cpp:105] Iteration 19008, lr = 1e-06
I0405 11:25:17.864787 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 11:25:19.460520 30176 solver.cpp:218] Iteration 19020 (2.32512 iter/s, 5.16102s/12 iters), loss = 5.26439
I0405 11:25:19.460572 30176 solver.cpp:237]     Train net output #0: loss = 5.26439 (* 1 = 5.26439 loss)
I0405 11:25:19.460579 30176 sgd_solver.cpp:105] Iteration 19020, lr = 1e-06
I0405 11:25:24.447361 30176 solver.cpp:218] Iteration 19032 (2.40638 iter/s, 4.98674s/12 iters), loss = 5.27541
I0405 11:25:24.447412 30176 solver.cpp:237]     Train net output #0: loss = 5.27541 (* 1 = 5.27541 loss)
I0405 11:25:24.447419 30176 sgd_solver.cpp:105] Iteration 19032, lr = 1e-06
I0405 11:25:29.882644 30176 solver.cpp:218] Iteration 19044 (2.20784 iter/s, 5.43518s/12 iters), loss = 5.30084
I0405 11:25:29.882683 30176 solver.cpp:237]     Train net output #0: loss = 5.30084 (* 1 = 5.30084 loss)
I0405 11:25:29.882688 30176 sgd_solver.cpp:105] Iteration 19044, lr = 1e-06
I0405 11:25:34.835243 30176 solver.cpp:218] Iteration 19056 (2.42302 iter/s, 4.95251s/12 iters), loss = 5.28961
I0405 11:25:34.835376 30176 solver.cpp:237]     Train net output #0: loss = 5.28961 (* 1 = 5.28961 loss)
I0405 11:25:34.835386 30176 sgd_solver.cpp:105] Iteration 19056, lr = 1e-06
I0405 11:25:40.035800 30176 solver.cpp:218] Iteration 19068 (2.30753 iter/s, 5.20038s/12 iters), loss = 5.28135
I0405 11:25:40.035847 30176 solver.cpp:237]     Train net output #0: loss = 5.28135 (* 1 = 5.28135 loss)
I0405 11:25:40.035853 30176 sgd_solver.cpp:105] Iteration 19068, lr = 1e-06
I0405 11:25:42.256906 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_19074.caffemodel
I0405 11:25:45.264258 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_19074.solverstate
I0405 11:25:47.572438 30176 solver.cpp:330] Iteration 19074, Testing net (#0)
I0405 11:25:47.572466 30176 net.cpp:676] Ignoring source layer train-data
I0405 11:25:49.103621 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 11:25:51.939571 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 11:25:51.939615 30176 solver.cpp:397]     Test net output #1: loss = 5.28001 (* 1 = 5.28001 loss)
I0405 11:25:53.798709 30176 solver.cpp:218] Iteration 19080 (0.871919 iter/s, 13.7628s/12 iters), loss = 5.26294
I0405 11:25:53.798748 30176 solver.cpp:237]     Train net output #0: loss = 5.26294 (* 1 = 5.26294 loss)
I0405 11:25:53.798753 30176 sgd_solver.cpp:105] Iteration 19080, lr = 1e-06
I0405 11:25:59.200474 30176 solver.cpp:218] Iteration 19092 (2.22154 iter/s, 5.40167s/12 iters), loss = 5.28207
I0405 11:25:59.200518 30176 solver.cpp:237]     Train net output #0: loss = 5.28207 (* 1 = 5.28207 loss)
I0405 11:25:59.200523 30176 sgd_solver.cpp:105] Iteration 19092, lr = 1e-06
I0405 11:26:04.545943 30176 solver.cpp:218] Iteration 19104 (2.24494 iter/s, 5.34537s/12 iters), loss = 5.28332
I0405 11:26:04.545998 30176 solver.cpp:237]     Train net output #0: loss = 5.28332 (* 1 = 5.28332 loss)
I0405 11:26:04.546006 30176 sgd_solver.cpp:105] Iteration 19104, lr = 1e-06
I0405 11:26:09.879694 30176 solver.cpp:218] Iteration 19116 (2.24987 iter/s, 5.33365s/12 iters), loss = 5.27367
I0405 11:26:09.880326 30176 solver.cpp:237]     Train net output #0: loss = 5.27367 (* 1 = 5.27367 loss)
I0405 11:26:09.880333 30176 sgd_solver.cpp:105] Iteration 19116, lr = 1e-06
I0405 11:26:10.430459 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 11:26:15.126113 30176 solver.cpp:218] Iteration 19128 (2.28758 iter/s, 5.24573s/12 iters), loss = 5.28438
I0405 11:26:15.126178 30176 solver.cpp:237]     Train net output #0: loss = 5.28438 (* 1 = 5.28438 loss)
I0405 11:26:15.126188 30176 sgd_solver.cpp:105] Iteration 19128, lr = 1e-06
I0405 11:26:20.412473 30176 solver.cpp:218] Iteration 19140 (2.27004 iter/s, 5.28624s/12 iters), loss = 5.29489
I0405 11:26:20.412514 30176 solver.cpp:237]     Train net output #0: loss = 5.29489 (* 1 = 5.29489 loss)
I0405 11:26:20.412520 30176 sgd_solver.cpp:105] Iteration 19140, lr = 1e-06
I0405 11:26:25.463208 30176 solver.cpp:218] Iteration 19152 (2.37594 iter/s, 5.05064s/12 iters), loss = 5.28253
I0405 11:26:25.463245 30176 solver.cpp:237]     Train net output #0: loss = 5.28253 (* 1 = 5.28253 loss)
I0405 11:26:25.463250 30176 sgd_solver.cpp:105] Iteration 19152, lr = 1e-06
I0405 11:26:30.867470 30176 solver.cpp:218] Iteration 19164 (2.22051 iter/s, 5.40417s/12 iters), loss = 5.27748
I0405 11:26:30.867516 30176 solver.cpp:237]     Train net output #0: loss = 5.27748 (* 1 = 5.27748 loss)
I0405 11:26:30.867522 30176 sgd_solver.cpp:105] Iteration 19164, lr = 1e-06
I0405 11:26:35.347177 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_19176.caffemodel
I0405 11:26:38.432312 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_19176.solverstate
I0405 11:26:41.520778 30176 solver.cpp:330] Iteration 19176, Testing net (#0)
I0405 11:26:41.520875 30176 net.cpp:676] Ignoring source layer train-data
I0405 11:26:43.115200 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 11:26:46.002229 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 11:26:46.002274 30176 solver.cpp:397]     Test net output #1: loss = 5.27996 (* 1 = 5.27996 loss)
I0405 11:26:46.143193 30176 solver.cpp:218] Iteration 19176 (0.785569 iter/s, 15.2755s/12 iters), loss = 5.27308
I0405 11:26:46.143254 30176 solver.cpp:237]     Train net output #0: loss = 5.27308 (* 1 = 5.27308 loss)
I0405 11:26:46.143262 30176 sgd_solver.cpp:105] Iteration 19176, lr = 1e-06
I0405 11:26:50.504096 30176 solver.cpp:218] Iteration 19188 (2.75179 iter/s, 4.3608s/12 iters), loss = 5.30687
I0405 11:26:50.504148 30176 solver.cpp:237]     Train net output #0: loss = 5.30687 (* 1 = 5.30687 loss)
I0405 11:26:50.504156 30176 sgd_solver.cpp:105] Iteration 19188, lr = 1e-06
I0405 11:26:55.675923 30176 solver.cpp:218] Iteration 19200 (2.32031 iter/s, 5.17172s/12 iters), loss = 5.29161
I0405 11:26:55.675977 30176 solver.cpp:237]     Train net output #0: loss = 5.29161 (* 1 = 5.29161 loss)
I0405 11:26:55.675985 30176 sgd_solver.cpp:105] Iteration 19200, lr = 1e-06
I0405 11:27:00.972426 30176 solver.cpp:218] Iteration 19212 (2.26569 iter/s, 5.29639s/12 iters), loss = 5.27089
I0405 11:27:00.972470 30176 solver.cpp:237]     Train net output #0: loss = 5.27089 (* 1 = 5.27089 loss)
I0405 11:27:00.972477 30176 sgd_solver.cpp:105] Iteration 19212, lr = 1e-06
I0405 11:27:03.879281 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 11:27:06.287664 30176 solver.cpp:218] Iteration 19224 (2.2577 iter/s, 5.31514s/12 iters), loss = 5.28491
I0405 11:27:06.287704 30176 solver.cpp:237]     Train net output #0: loss = 5.28491 (* 1 = 5.28491 loss)
I0405 11:27:06.287710 30176 sgd_solver.cpp:105] Iteration 19224, lr = 1e-06
I0405 11:27:11.511762 30176 solver.cpp:218] Iteration 19236 (2.29709 iter/s, 5.224s/12 iters), loss = 5.2941
I0405 11:27:11.511831 30176 solver.cpp:237]     Train net output #0: loss = 5.2941 (* 1 = 5.2941 loss)
I0405 11:27:11.511840 30176 sgd_solver.cpp:105] Iteration 19236, lr = 1e-06
I0405 11:27:16.851140 30176 solver.cpp:218] Iteration 19248 (2.2475 iter/s, 5.33926s/12 iters), loss = 5.28588
I0405 11:27:16.851269 30176 solver.cpp:237]     Train net output #0: loss = 5.28588 (* 1 = 5.28588 loss)
I0405 11:27:16.851275 30176 sgd_solver.cpp:105] Iteration 19248, lr = 1e-06
I0405 11:27:22.071849 30176 solver.cpp:218] Iteration 19260 (2.29862 iter/s, 5.22053s/12 iters), loss = 5.29399
I0405 11:27:22.071887 30176 solver.cpp:237]     Train net output #0: loss = 5.29399 (* 1 = 5.29399 loss)
I0405 11:27:22.071892 30176 sgd_solver.cpp:105] Iteration 19260, lr = 1e-06
I0405 11:27:27.551323 30176 solver.cpp:218] Iteration 19272 (2.19003 iter/s, 5.47937s/12 iters), loss = 5.30097
I0405 11:27:27.551389 30176 solver.cpp:237]     Train net output #0: loss = 5.30097 (* 1 = 5.30097 loss)
I0405 11:27:27.551398 30176 sgd_solver.cpp:105] Iteration 19272, lr = 1e-06
I0405 11:27:29.649485 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_19278.caffemodel
I0405 11:27:33.410691 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_19278.solverstate
I0405 11:27:35.717581 30176 solver.cpp:330] Iteration 19278, Testing net (#0)
I0405 11:27:35.717602 30176 net.cpp:676] Ignoring source layer train-data
I0405 11:27:37.124122 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 11:27:40.029930 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 11:27:40.029966 30176 solver.cpp:397]     Test net output #1: loss = 5.27982 (* 1 = 5.27982 loss)
I0405 11:27:41.973016 30176 solver.cpp:218] Iteration 19284 (0.832091 iter/s, 14.4215s/12 iters), loss = 5.29172
I0405 11:27:41.973059 30176 solver.cpp:237]     Train net output #0: loss = 5.29172 (* 1 = 5.29172 loss)
I0405 11:27:41.973064 30176 sgd_solver.cpp:105] Iteration 19284, lr = 1e-06
I0405 11:27:47.369907 30176 solver.cpp:218] Iteration 19296 (2.22354 iter/s, 5.39679s/12 iters), loss = 5.26277
I0405 11:27:47.370054 30176 solver.cpp:237]     Train net output #0: loss = 5.26277 (* 1 = 5.26277 loss)
I0405 11:27:47.370062 30176 sgd_solver.cpp:105] Iteration 19296, lr = 1e-06
I0405 11:27:52.561034 30176 solver.cpp:218] Iteration 19308 (2.31172 iter/s, 5.19094s/12 iters), loss = 5.29255
I0405 11:27:52.561072 30176 solver.cpp:237]     Train net output #0: loss = 5.29255 (* 1 = 5.29255 loss)
I0405 11:27:52.561077 30176 sgd_solver.cpp:105] Iteration 19308, lr = 1e-06
I0405 11:27:57.463742 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 11:27:57.656378 30176 solver.cpp:218] Iteration 19320 (2.35514 iter/s, 5.09525s/12 iters), loss = 5.27801
I0405 11:27:57.656430 30176 solver.cpp:237]     Train net output #0: loss = 5.27801 (* 1 = 5.27801 loss)
I0405 11:27:57.656437 30176 sgd_solver.cpp:105] Iteration 19320, lr = 1e-06
I0405 11:28:03.053421 30176 solver.cpp:218] Iteration 19332 (2.22348 iter/s, 5.39693s/12 iters), loss = 5.27772
I0405 11:28:03.053463 30176 solver.cpp:237]     Train net output #0: loss = 5.27772 (* 1 = 5.27772 loss)
I0405 11:28:03.053469 30176 sgd_solver.cpp:105] Iteration 19332, lr = 1e-06
I0405 11:28:08.402470 30176 solver.cpp:218] Iteration 19344 (2.24343 iter/s, 5.34895s/12 iters), loss = 5.28328
I0405 11:28:08.402510 30176 solver.cpp:237]     Train net output #0: loss = 5.28328 (* 1 = 5.28328 loss)
I0405 11:28:08.402516 30176 sgd_solver.cpp:105] Iteration 19344, lr = 1e-06
I0405 11:28:13.691640 30176 solver.cpp:218] Iteration 19356 (2.26883 iter/s, 5.28908s/12 iters), loss = 5.26781
I0405 11:28:13.691681 30176 solver.cpp:237]     Train net output #0: loss = 5.26781 (* 1 = 5.26781 loss)
I0405 11:28:13.691686 30176 sgd_solver.cpp:105] Iteration 19356, lr = 1e-06
I0405 11:28:19.128372 30176 solver.cpp:218] Iteration 19368 (2.20725 iter/s, 5.43663s/12 iters), loss = 5.27518
I0405 11:28:19.128509 30176 solver.cpp:237]     Train net output #0: loss = 5.27518 (* 1 = 5.27518 loss)
I0405 11:28:19.128516 30176 sgd_solver.cpp:105] Iteration 19368, lr = 1e-06
I0405 11:28:23.871953 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_19380.caffemodel
I0405 11:28:26.869305 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_19380.solverstate
I0405 11:28:29.183774 30176 solver.cpp:330] Iteration 19380, Testing net (#0)
I0405 11:28:29.183795 30176 net.cpp:676] Ignoring source layer train-data
I0405 11:28:30.540824 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 11:28:33.482270 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 11:28:33.482306 30176 solver.cpp:397]     Test net output #1: loss = 5.28 (* 1 = 5.28 loss)
I0405 11:28:33.622853 30176 solver.cpp:218] Iteration 19380 (0.827916 iter/s, 14.4942s/12 iters), loss = 5.27843
I0405 11:28:33.622912 30176 solver.cpp:237]     Train net output #0: loss = 5.27843 (* 1 = 5.27843 loss)
I0405 11:28:33.622920 30176 sgd_solver.cpp:105] Iteration 19380, lr = 1e-06
I0405 11:28:38.012259 30176 solver.cpp:218] Iteration 19392 (2.73391 iter/s, 4.38931s/12 iters), loss = 5.26975
I0405 11:28:38.012313 30176 solver.cpp:237]     Train net output #0: loss = 5.26975 (* 1 = 5.26975 loss)
I0405 11:28:38.012320 30176 sgd_solver.cpp:105] Iteration 19392, lr = 1e-06
I0405 11:28:43.386081 30176 solver.cpp:218] Iteration 19404 (2.23309 iter/s, 5.37371s/12 iters), loss = 5.28421
I0405 11:28:43.386123 30176 solver.cpp:237]     Train net output #0: loss = 5.28421 (* 1 = 5.28421 loss)
I0405 11:28:43.386128 30176 sgd_solver.cpp:105] Iteration 19404, lr = 1e-06
I0405 11:28:44.202675 30176 blocking_queue.cpp:49] Waiting for data
I0405 11:28:48.865731 30176 solver.cpp:218] Iteration 19416 (2.18996 iter/s, 5.47955s/12 iters), loss = 5.29268
I0405 11:28:48.865774 30176 solver.cpp:237]     Train net output #0: loss = 5.29268 (* 1 = 5.29268 loss)
I0405 11:28:48.865782 30176 sgd_solver.cpp:105] Iteration 19416, lr = 1e-06
I0405 11:28:50.962502 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 11:28:54.335373 30176 solver.cpp:218] Iteration 19428 (2.19397 iter/s, 5.46954s/12 iters), loss = 5.28267
I0405 11:28:54.335412 30176 solver.cpp:237]     Train net output #0: loss = 5.28267 (* 1 = 5.28267 loss)
I0405 11:28:54.335417 30176 sgd_solver.cpp:105] Iteration 19428, lr = 1e-06
I0405 11:28:59.592108 30176 solver.cpp:218] Iteration 19440 (2.28283 iter/s, 5.25664s/12 iters), loss = 5.2804
I0405 11:28:59.592149 30176 solver.cpp:237]     Train net output #0: loss = 5.2804 (* 1 = 5.2804 loss)
I0405 11:28:59.592154 30176 sgd_solver.cpp:105] Iteration 19440, lr = 1e-06
I0405 11:29:04.922854 30176 solver.cpp:218] Iteration 19452 (2.25113 iter/s, 5.33065s/12 iters), loss = 5.28051
I0405 11:29:04.922891 30176 solver.cpp:237]     Train net output #0: loss = 5.28051 (* 1 = 5.28051 loss)
I0405 11:29:04.922897 30176 sgd_solver.cpp:105] Iteration 19452, lr = 1e-06
I0405 11:29:10.187304 30176 solver.cpp:218] Iteration 19464 (2.27948 iter/s, 5.26436s/12 iters), loss = 5.27566
I0405 11:29:10.187345 30176 solver.cpp:237]     Train net output #0: loss = 5.27566 (* 1 = 5.27566 loss)
I0405 11:29:10.187351 30176 sgd_solver.cpp:105] Iteration 19464, lr = 1e-06
I0405 11:29:15.599686 30176 solver.cpp:218] Iteration 19476 (2.21718 iter/s, 5.41228s/12 iters), loss = 5.26092
I0405 11:29:15.599737 30176 solver.cpp:237]     Train net output #0: loss = 5.26092 (* 1 = 5.26092 loss)
I0405 11:29:15.599745 30176 sgd_solver.cpp:105] Iteration 19476, lr = 1e-06
I0405 11:29:17.650135 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_19482.caffemodel
I0405 11:29:20.653434 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_19482.solverstate
I0405 11:29:22.954152 30176 solver.cpp:330] Iteration 19482, Testing net (#0)
I0405 11:29:22.954242 30176 net.cpp:676] Ignoring source layer train-data
I0405 11:29:24.274523 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 11:29:27.233644 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 11:29:27.233680 30176 solver.cpp:397]     Test net output #1: loss = 5.27962 (* 1 = 5.27962 loss)
I0405 11:29:28.907806 30176 solver.cpp:218] Iteration 19488 (0.901716 iter/s, 13.308s/12 iters), loss = 5.26881
I0405 11:29:28.907845 30176 solver.cpp:237]     Train net output #0: loss = 5.26881 (* 1 = 5.26881 loss)
I0405 11:29:28.907850 30176 sgd_solver.cpp:105] Iteration 19488, lr = 1e-06
I0405 11:29:34.150787 30176 solver.cpp:218] Iteration 19500 (2.28882 iter/s, 5.24289s/12 iters), loss = 5.2961
I0405 11:29:34.150828 30176 solver.cpp:237]     Train net output #0: loss = 5.2961 (* 1 = 5.2961 loss)
I0405 11:29:34.150835 30176 sgd_solver.cpp:105] Iteration 19500, lr = 1e-06
I0405 11:29:39.436853 30176 solver.cpp:218] Iteration 19512 (2.27016 iter/s, 5.28596s/12 iters), loss = 5.29508
I0405 11:29:39.436918 30176 solver.cpp:237]     Train net output #0: loss = 5.29508 (* 1 = 5.29508 loss)
I0405 11:29:39.436925 30176 sgd_solver.cpp:105] Iteration 19512, lr = 1e-06
I0405 11:29:43.905835 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 11:29:44.773741 30176 solver.cpp:218] Iteration 19524 (2.24855 iter/s, 5.33677s/12 iters), loss = 5.28957
I0405 11:29:44.773782 30176 solver.cpp:237]     Train net output #0: loss = 5.28957 (* 1 = 5.28957 loss)
I0405 11:29:44.773787 30176 sgd_solver.cpp:105] Iteration 19524, lr = 1e-06
I0405 11:29:50.143756 30176 solver.cpp:218] Iteration 19536 (2.23467 iter/s, 5.36992s/12 iters), loss = 5.27469
I0405 11:29:50.143796 30176 solver.cpp:237]     Train net output #0: loss = 5.27469 (* 1 = 5.27469 loss)
I0405 11:29:50.143802 30176 sgd_solver.cpp:105] Iteration 19536, lr = 1e-06
I0405 11:29:55.492842 30176 solver.cpp:218] Iteration 19548 (2.24341 iter/s, 5.34899s/12 iters), loss = 5.28896
I0405 11:29:55.492952 30176 solver.cpp:237]     Train net output #0: loss = 5.28896 (* 1 = 5.28896 loss)
I0405 11:29:55.492961 30176 sgd_solver.cpp:105] Iteration 19548, lr = 1e-06
I0405 11:30:00.976397 30176 solver.cpp:218] Iteration 19560 (2.18843 iter/s, 5.48339s/12 iters), loss = 5.28208
I0405 11:30:00.976439 30176 solver.cpp:237]     Train net output #0: loss = 5.28208 (* 1 = 5.28208 loss)
I0405 11:30:00.976444 30176 sgd_solver.cpp:105] Iteration 19560, lr = 1e-06
I0405 11:30:06.107165 30176 solver.cpp:218] Iteration 19572 (2.33887 iter/s, 5.13068s/12 iters), loss = 5.27682
I0405 11:30:06.107203 30176 solver.cpp:237]     Train net output #0: loss = 5.27682 (* 1 = 5.27682 loss)
I0405 11:30:06.107208 30176 sgd_solver.cpp:105] Iteration 19572, lr = 1e-06
I0405 11:30:11.026886 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_19584.caffemodel
I0405 11:30:14.044173 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_19584.solverstate
I0405 11:30:16.364856 30176 solver.cpp:330] Iteration 19584, Testing net (#0)
I0405 11:30:16.364879 30176 net.cpp:676] Ignoring source layer train-data
I0405 11:30:17.650182 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 11:30:20.750918 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 11:30:20.750954 30176 solver.cpp:397]     Test net output #1: loss = 5.27979 (* 1 = 5.27979 loss)
I0405 11:30:20.891549 30176 solver.cpp:218] Iteration 19584 (0.811677 iter/s, 14.7842s/12 iters), loss = 5.26974
I0405 11:30:20.891633 30176 solver.cpp:237]     Train net output #0: loss = 5.26974 (* 1 = 5.26974 loss)
I0405 11:30:20.891646 30176 sgd_solver.cpp:105] Iteration 19584, lr = 1e-06
I0405 11:30:25.188948 30176 solver.cpp:218] Iteration 19596 (2.79247 iter/s, 4.29727s/12 iters), loss = 5.2956
I0405 11:30:25.189004 30176 solver.cpp:237]     Train net output #0: loss = 5.2956 (* 1 = 5.2956 loss)
I0405 11:30:25.189013 30176 sgd_solver.cpp:105] Iteration 19596, lr = 1e-06
I0405 11:30:30.403828 30176 solver.cpp:218] Iteration 19608 (2.30116 iter/s, 5.21477s/12 iters), loss = 5.2564
I0405 11:30:30.403952 30176 solver.cpp:237]     Train net output #0: loss = 5.2564 (* 1 = 5.2564 loss)
I0405 11:30:30.403959 30176 sgd_solver.cpp:105] Iteration 19608, lr = 1e-06
I0405 11:30:35.586931 30176 solver.cpp:218] Iteration 19620 (2.3153 iter/s, 5.18292s/12 iters), loss = 5.28253
I0405 11:30:35.586985 30176 solver.cpp:237]     Train net output #0: loss = 5.28253 (* 1 = 5.28253 loss)
I0405 11:30:35.586994 30176 sgd_solver.cpp:105] Iteration 19620, lr = 1e-06
I0405 11:30:37.024726 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 11:30:40.953068 30176 solver.cpp:218] Iteration 19632 (2.23629 iter/s, 5.36603s/12 iters), loss = 5.26889
I0405 11:30:40.953121 30176 solver.cpp:237]     Train net output #0: loss = 5.26889 (* 1 = 5.26889 loss)
I0405 11:30:40.953130 30176 sgd_solver.cpp:105] Iteration 19632, lr = 1e-06
I0405 11:30:46.316555 30176 solver.cpp:218] Iteration 19644 (2.2374 iter/s, 5.36338s/12 iters), loss = 5.2776
I0405 11:30:46.316606 30176 solver.cpp:237]     Train net output #0: loss = 5.2776 (* 1 = 5.2776 loss)
I0405 11:30:46.316615 30176 sgd_solver.cpp:105] Iteration 19644, lr = 1e-06
I0405 11:30:51.474337 30176 solver.cpp:218] Iteration 19656 (2.32663 iter/s, 5.15768s/12 iters), loss = 5.27884
I0405 11:30:51.474381 30176 solver.cpp:237]     Train net output #0: loss = 5.27884 (* 1 = 5.27884 loss)
I0405 11:30:51.474386 30176 sgd_solver.cpp:105] Iteration 19656, lr = 1e-06
I0405 11:30:56.504391 30176 solver.cpp:218] Iteration 19668 (2.38571 iter/s, 5.02995s/12 iters), loss = 5.28168
I0405 11:30:56.504443 30176 solver.cpp:237]     Train net output #0: loss = 5.28168 (* 1 = 5.28168 loss)
I0405 11:30:56.504452 30176 sgd_solver.cpp:105] Iteration 19668, lr = 1e-06
I0405 11:31:01.731096 30176 solver.cpp:218] Iteration 19680 (2.29595 iter/s, 5.2266s/12 iters), loss = 5.28218
I0405 11:31:01.731197 30176 solver.cpp:237]     Train net output #0: loss = 5.28218 (* 1 = 5.28218 loss)
I0405 11:31:01.731204 30176 sgd_solver.cpp:105] Iteration 19680, lr = 1e-06
I0405 11:31:03.764071 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_19686.caffemodel
I0405 11:31:06.846583 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_19686.solverstate
I0405 11:31:09.158295 30176 solver.cpp:330] Iteration 19686, Testing net (#0)
I0405 11:31:09.158318 30176 net.cpp:676] Ignoring source layer train-data
I0405 11:31:10.410207 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 11:31:13.573530 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 11:31:13.573560 30176 solver.cpp:397]     Test net output #1: loss = 5.27977 (* 1 = 5.27977 loss)
I0405 11:31:15.574888 30176 solver.cpp:218] Iteration 19692 (0.866828 iter/s, 13.8436s/12 iters), loss = 5.29055
I0405 11:31:15.574932 30176 solver.cpp:237]     Train net output #0: loss = 5.29055 (* 1 = 5.29055 loss)
I0405 11:31:15.574939 30176 sgd_solver.cpp:105] Iteration 19692, lr = 1e-06
I0405 11:31:20.800256 30176 solver.cpp:218] Iteration 19704 (2.29653 iter/s, 5.22527s/12 iters), loss = 5.27506
I0405 11:31:20.800293 30176 solver.cpp:237]     Train net output #0: loss = 5.27506 (* 1 = 5.27506 loss)
I0405 11:31:20.800298 30176 sgd_solver.cpp:105] Iteration 19704, lr = 1e-06
I0405 11:31:26.153945 30176 solver.cpp:218] Iteration 19716 (2.24149 iter/s, 5.35359s/12 iters), loss = 5.2839
I0405 11:31:26.153987 30176 solver.cpp:237]     Train net output #0: loss = 5.2839 (* 1 = 5.2839 loss)
I0405 11:31:26.153992 30176 sgd_solver.cpp:105] Iteration 19716, lr = 1e-06
I0405 11:31:29.843557 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 11:31:31.564379 30176 solver.cpp:218] Iteration 19728 (2.21798 iter/s, 5.41033s/12 iters), loss = 5.27677
I0405 11:31:31.564424 30176 solver.cpp:237]     Train net output #0: loss = 5.27677 (* 1 = 5.27677 loss)
I0405 11:31:31.564429 30176 sgd_solver.cpp:105] Iteration 19728, lr = 1e-06
I0405 11:31:36.801728 30176 solver.cpp:218] Iteration 19740 (2.29128 iter/s, 5.23725s/12 iters), loss = 5.28734
I0405 11:31:36.801869 30176 solver.cpp:237]     Train net output #0: loss = 5.28734 (* 1 = 5.28734 loss)
I0405 11:31:36.801878 30176 sgd_solver.cpp:105] Iteration 19740, lr = 1e-06
I0405 11:31:42.248087 30176 solver.cpp:218] Iteration 19752 (2.20339 iter/s, 5.44616s/12 iters), loss = 5.29211
I0405 11:31:42.248138 30176 solver.cpp:237]     Train net output #0: loss = 5.29211 (* 1 = 5.29211 loss)
I0405 11:31:42.248147 30176 sgd_solver.cpp:105] Iteration 19752, lr = 1e-06
I0405 11:31:47.603237 30176 solver.cpp:218] Iteration 19764 (2.24088 iter/s, 5.35504s/12 iters), loss = 5.28401
I0405 11:31:47.603298 30176 solver.cpp:237]     Train net output #0: loss = 5.28401 (* 1 = 5.28401 loss)
I0405 11:31:47.603308 30176 sgd_solver.cpp:105] Iteration 19764, lr = 1e-06
I0405 11:31:52.950047 30176 solver.cpp:218] Iteration 19776 (2.24438 iter/s, 5.3467s/12 iters), loss = 5.27599
I0405 11:31:52.950088 30176 solver.cpp:237]     Train net output #0: loss = 5.27599 (* 1 = 5.27599 loss)
I0405 11:31:52.950093 30176 sgd_solver.cpp:105] Iteration 19776, lr = 1e-06
I0405 11:31:57.722096 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_19788.caffemodel
I0405 11:32:00.776194 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_19788.solverstate
I0405 11:32:03.092689 30176 solver.cpp:330] Iteration 19788, Testing net (#0)
I0405 11:32:03.092712 30176 net.cpp:676] Ignoring source layer train-data
I0405 11:32:04.318038 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 11:32:07.422499 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 11:32:07.422621 30176 solver.cpp:397]     Test net output #1: loss = 5.27978 (* 1 = 5.27978 loss)
I0405 11:32:07.563827 30176 solver.cpp:218] Iteration 19788 (0.821152 iter/s, 14.6136s/12 iters), loss = 5.2755
I0405 11:32:07.563886 30176 solver.cpp:237]     Train net output #0: loss = 5.2755 (* 1 = 5.2755 loss)
I0405 11:32:07.563895 30176 sgd_solver.cpp:105] Iteration 19788, lr = 1e-06
I0405 11:32:11.995343 30176 solver.cpp:218] Iteration 19800 (2.70794 iter/s, 4.43141s/12 iters), loss = 5.29639
I0405 11:32:11.995388 30176 solver.cpp:237]     Train net output #0: loss = 5.29639 (* 1 = 5.29639 loss)
I0405 11:32:11.995393 30176 sgd_solver.cpp:105] Iteration 19800, lr = 1e-06
I0405 11:32:17.328691 30176 solver.cpp:218] Iteration 19812 (2.25004 iter/s, 5.33325s/12 iters), loss = 5.27534
I0405 11:32:17.328733 30176 solver.cpp:237]     Train net output #0: loss = 5.27534 (* 1 = 5.27534 loss)
I0405 11:32:17.328739 30176 sgd_solver.cpp:105] Iteration 19812, lr = 1e-06
I0405 11:32:22.767117 30176 solver.cpp:218] Iteration 19824 (2.20656 iter/s, 5.43833s/12 iters), loss = 5.29276
I0405 11:32:22.767172 30176 solver.cpp:237]     Train net output #0: loss = 5.29276 (* 1 = 5.29276 loss)
I0405 11:32:22.767181 30176 sgd_solver.cpp:105] Iteration 19824, lr = 1e-06
I0405 11:32:23.404125 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 11:32:28.193460 30176 solver.cpp:218] Iteration 19836 (2.21148 iter/s, 5.42623s/12 iters), loss = 5.29563
I0405 11:32:28.193506 30176 solver.cpp:237]     Train net output #0: loss = 5.29563 (* 1 = 5.29563 loss)
I0405 11:32:28.193511 30176 sgd_solver.cpp:105] Iteration 19836, lr = 1e-06
I0405 11:32:33.257205 30176 solver.cpp:218] Iteration 19848 (2.36983 iter/s, 5.06365s/12 iters), loss = 5.28867
I0405 11:32:33.257243 30176 solver.cpp:237]     Train net output #0: loss = 5.28867 (* 1 = 5.28867 loss)
I0405 11:32:33.257248 30176 sgd_solver.cpp:105] Iteration 19848, lr = 1e-06
I0405 11:32:38.634424 30176 solver.cpp:218] Iteration 19860 (2.23168 iter/s, 5.37712s/12 iters), loss = 5.28284
I0405 11:32:38.634552 30176 solver.cpp:237]     Train net output #0: loss = 5.28284 (* 1 = 5.28284 loss)
I0405 11:32:38.634559 30176 sgd_solver.cpp:105] Iteration 19860, lr = 1e-06
I0405 11:32:44.100417 30176 solver.cpp:218] Iteration 19872 (2.19547 iter/s, 5.46581s/12 iters), loss = 5.27026
I0405 11:32:44.100455 30176 solver.cpp:237]     Train net output #0: loss = 5.27026 (* 1 = 5.27026 loss)
I0405 11:32:44.100461 30176 sgd_solver.cpp:105] Iteration 19872, lr = 1e-06
I0405 11:32:49.162926 30176 solver.cpp:218] Iteration 19884 (2.37041 iter/s, 5.06241s/12 iters), loss = 5.27925
I0405 11:32:49.162971 30176 solver.cpp:237]     Train net output #0: loss = 5.27925 (* 1 = 5.27925 loss)
I0405 11:32:49.162976 30176 sgd_solver.cpp:105] Iteration 19884, lr = 1e-06
I0405 11:32:51.369748 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_19890.caffemodel
I0405 11:32:54.406028 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_19890.solverstate
I0405 11:32:56.715401 30176 solver.cpp:330] Iteration 19890, Testing net (#0)
I0405 11:32:56.715425 30176 net.cpp:676] Ignoring source layer train-data
I0405 11:32:57.864727 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 11:33:01.005775 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 11:33:01.005811 30176 solver.cpp:397]     Test net output #1: loss = 5.27964 (* 1 = 5.27964 loss)
I0405 11:33:03.089031 30176 solver.cpp:218] Iteration 19896 (0.861701 iter/s, 13.9259s/12 iters), loss = 5.29483
I0405 11:33:03.089071 30176 solver.cpp:237]     Train net output #0: loss = 5.29483 (* 1 = 5.29483 loss)
I0405 11:33:03.089076 30176 sgd_solver.cpp:105] Iteration 19896, lr = 1e-06
I0405 11:33:08.286753 30176 solver.cpp:218] Iteration 19908 (2.30875 iter/s, 5.19762s/12 iters), loss = 5.29959
I0405 11:33:08.286809 30176 solver.cpp:237]     Train net output #0: loss = 5.29959 (* 1 = 5.29959 loss)
I0405 11:33:08.286818 30176 sgd_solver.cpp:105] Iteration 19908, lr = 1e-06
I0405 11:33:13.548671 30176 solver.cpp:218] Iteration 19920 (2.28059 iter/s, 5.26181s/12 iters), loss = 5.27482
I0405 11:33:13.548813 30176 solver.cpp:237]     Train net output #0: loss = 5.27482 (* 1 = 5.27482 loss)
I0405 11:33:13.548821 30176 sgd_solver.cpp:105] Iteration 19920, lr = 1e-06
I0405 11:33:16.330921 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 11:33:18.752065 30176 solver.cpp:218] Iteration 19932 (2.30627 iter/s, 5.2032s/12 iters), loss = 5.27766
I0405 11:33:18.752110 30176 solver.cpp:237]     Train net output #0: loss = 5.27766 (* 1 = 5.27766 loss)
I0405 11:33:18.752117 30176 sgd_solver.cpp:105] Iteration 19932, lr = 1e-06
I0405 11:33:24.076294 30176 solver.cpp:218] Iteration 19944 (2.25389 iter/s, 5.32413s/12 iters), loss = 5.28192
I0405 11:33:24.076339 30176 solver.cpp:237]     Train net output #0: loss = 5.28192 (* 1 = 5.28192 loss)
I0405 11:33:24.076344 30176 sgd_solver.cpp:105] Iteration 19944, lr = 1e-06
I0405 11:33:29.434605 30176 solver.cpp:218] Iteration 19956 (2.23955 iter/s, 5.35821s/12 iters), loss = 5.28619
I0405 11:33:29.434646 30176 solver.cpp:237]     Train net output #0: loss = 5.28619 (* 1 = 5.28619 loss)
I0405 11:33:29.434653 30176 sgd_solver.cpp:105] Iteration 19956, lr = 1e-06
I0405 11:33:34.421311 30176 solver.cpp:218] Iteration 19968 (2.40645 iter/s, 4.98661s/12 iters), loss = 5.28174
I0405 11:33:34.421362 30176 solver.cpp:237]     Train net output #0: loss = 5.28174 (* 1 = 5.28174 loss)
I0405 11:33:34.421370 30176 sgd_solver.cpp:105] Iteration 19968, lr = 1e-06
I0405 11:33:39.457033 30176 solver.cpp:218] Iteration 19980 (2.38302 iter/s, 5.03562s/12 iters), loss = 5.28903
I0405 11:33:39.457077 30176 solver.cpp:237]     Train net output #0: loss = 5.28903 (* 1 = 5.28903 loss)
I0405 11:33:39.457082 30176 sgd_solver.cpp:105] Iteration 19980, lr = 1e-06
I0405 11:33:44.358086 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_19992.caffemodel
I0405 11:33:47.387066 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_19992.solverstate
I0405 11:33:49.684463 30176 solver.cpp:330] Iteration 19992, Testing net (#0)
I0405 11:33:49.684481 30176 net.cpp:676] Ignoring source layer train-data
I0405 11:33:50.798854 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 11:33:54.027424 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 11:33:54.027457 30176 solver.cpp:397]     Test net output #1: loss = 5.27963 (* 1 = 5.27963 loss)
I0405 11:33:54.168313 30176 solver.cpp:218] Iteration 19992 (0.81571 iter/s, 14.7111s/12 iters), loss = 5.28093
I0405 11:33:54.169875 30176 solver.cpp:237]     Train net output #0: loss = 5.28093 (* 1 = 5.28093 loss)
I0405 11:33:54.169888 30176 sgd_solver.cpp:105] Iteration 19992, lr = 1e-06
I0405 11:33:58.363153 30176 solver.cpp:218] Iteration 20004 (2.86175 iter/s, 4.19324s/12 iters), loss = 5.27133
I0405 11:33:58.363190 30176 solver.cpp:237]     Train net output #0: loss = 5.27133 (* 1 = 5.27133 loss)
I0405 11:33:58.363196 30176 sgd_solver.cpp:105] Iteration 20004, lr = 1e-06
I0405 11:34:03.358429 30176 solver.cpp:218] Iteration 20016 (2.40231 iter/s, 4.99518s/12 iters), loss = 5.27117
I0405 11:34:03.358480 30176 solver.cpp:237]     Train net output #0: loss = 5.27117 (* 1 = 5.27117 loss)
I0405 11:34:03.358489 30176 sgd_solver.cpp:105] Iteration 20016, lr = 1e-06
I0405 11:34:08.468261 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 11:34:08.633260 30176 solver.cpp:218] Iteration 20028 (2.275 iter/s, 5.27472s/12 iters), loss = 5.28485
I0405 11:34:08.633311 30176 solver.cpp:237]     Train net output #0: loss = 5.28485 (* 1 = 5.28485 loss)
I0405 11:34:08.633318 30176 sgd_solver.cpp:105] Iteration 20028, lr = 1e-06
I0405 11:34:14.121112 30176 solver.cpp:218] Iteration 20040 (2.18669 iter/s, 5.48775s/12 iters), loss = 5.27316
I0405 11:34:14.121155 30176 solver.cpp:237]     Train net output #0: loss = 5.27316 (* 1 = 5.27316 loss)
I0405 11:34:14.121160 30176 sgd_solver.cpp:105] Iteration 20040, lr = 1e-06
I0405 11:34:19.179265 30176 solver.cpp:218] Iteration 20052 (2.37245 iter/s, 5.05806s/12 iters), loss = 5.28649
I0405 11:34:19.179373 30176 solver.cpp:237]     Train net output #0: loss = 5.28649 (* 1 = 5.28649 loss)
I0405 11:34:19.179383 30176 sgd_solver.cpp:105] Iteration 20052, lr = 1e-06
I0405 11:34:24.186359 30176 solver.cpp:218] Iteration 20064 (2.39667 iter/s, 5.00694s/12 iters), loss = 5.27837
I0405 11:34:24.186395 30176 solver.cpp:237]     Train net output #0: loss = 5.27837 (* 1 = 5.27837 loss)
I0405 11:34:24.186400 30176 sgd_solver.cpp:105] Iteration 20064, lr = 1e-06
I0405 11:34:29.208803 30176 solver.cpp:218] Iteration 20076 (2.38932 iter/s, 5.02236s/12 iters), loss = 5.27966
I0405 11:34:29.208838 30176 solver.cpp:237]     Train net output #0: loss = 5.27966 (* 1 = 5.27966 loss)
I0405 11:34:29.208843 30176 sgd_solver.cpp:105] Iteration 20076, lr = 1e-06
I0405 11:34:34.353277 30176 solver.cpp:218] Iteration 20088 (2.33264 iter/s, 5.14439s/12 iters), loss = 5.27692
I0405 11:34:34.353319 30176 solver.cpp:237]     Train net output #0: loss = 5.27692 (* 1 = 5.27692 loss)
I0405 11:34:34.353324 30176 sgd_solver.cpp:105] Iteration 20088, lr = 1e-06
I0405 11:34:36.471680 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_20094.caffemodel
I0405 11:34:39.499667 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_20094.solverstate
I0405 11:34:42.602164 30176 solver.cpp:330] Iteration 20094, Testing net (#0)
I0405 11:34:42.602185 30176 net.cpp:676] Ignoring source layer train-data
I0405 11:34:43.758810 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 11:34:46.503207 30176 blocking_queue.cpp:49] Waiting for data
I0405 11:34:46.979440 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 11:34:46.979480 30176 solver.cpp:397]     Test net output #1: loss = 5.27953 (* 1 = 5.27953 loss)
I0405 11:34:49.065511 30176 solver.cpp:218] Iteration 20100 (0.815657 iter/s, 14.7121s/12 iters), loss = 5.2779
I0405 11:34:49.065553 30176 solver.cpp:237]     Train net output #0: loss = 5.2779 (* 1 = 5.2779 loss)
I0405 11:34:49.065558 30176 sgd_solver.cpp:105] Iteration 20100, lr = 1e-06
I0405 11:34:54.186199 30176 solver.cpp:218] Iteration 20112 (2.34348 iter/s, 5.12059s/12 iters), loss = 5.28477
I0405 11:34:54.186347 30176 solver.cpp:237]     Train net output #0: loss = 5.28477 (* 1 = 5.28477 loss)
I0405 11:34:54.186357 30176 sgd_solver.cpp:105] Iteration 20112, lr = 1e-06
I0405 11:34:59.258384 30176 solver.cpp:218] Iteration 20124 (2.36594 iter/s, 5.07199s/12 iters), loss = 5.27973
I0405 11:34:59.258424 30176 solver.cpp:237]     Train net output #0: loss = 5.27973 (* 1 = 5.27973 loss)
I0405 11:34:59.258430 30176 sgd_solver.cpp:105] Iteration 20124, lr = 1e-06
I0405 11:35:01.355038 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 11:35:04.384284 30176 solver.cpp:218] Iteration 20136 (2.3411 iter/s, 5.1258s/12 iters), loss = 5.28594
I0405 11:35:04.384330 30176 solver.cpp:237]     Train net output #0: loss = 5.28594 (* 1 = 5.28594 loss)
I0405 11:35:04.384335 30176 sgd_solver.cpp:105] Iteration 20136, lr = 1e-06
I0405 11:35:09.800743 30176 solver.cpp:218] Iteration 20148 (2.21551 iter/s, 5.41636s/12 iters), loss = 5.28356
I0405 11:35:09.800781 30176 solver.cpp:237]     Train net output #0: loss = 5.28356 (* 1 = 5.28356 loss)
I0405 11:35:09.800786 30176 sgd_solver.cpp:105] Iteration 20148, lr = 1e-06
I0405 11:35:15.102427 30176 solver.cpp:218] Iteration 20160 (2.26347 iter/s, 5.30159s/12 iters), loss = 5.29349
I0405 11:35:15.102481 30176 solver.cpp:237]     Train net output #0: loss = 5.29349 (* 1 = 5.29349 loss)
I0405 11:35:15.102489 30176 sgd_solver.cpp:105] Iteration 20160, lr = 1e-06
I0405 11:35:20.304656 30176 solver.cpp:218] Iteration 20172 (2.30675 iter/s, 5.20212s/12 iters), loss = 5.26905
I0405 11:35:20.304708 30176 solver.cpp:237]     Train net output #0: loss = 5.26905 (* 1 = 5.26905 loss)
I0405 11:35:20.304715 30176 sgd_solver.cpp:105] Iteration 20172, lr = 1e-06
I0405 11:35:25.508288 30176 solver.cpp:218] Iteration 20184 (2.30613 iter/s, 5.20353s/12 iters), loss = 5.27597
I0405 11:35:25.508406 30176 solver.cpp:237]     Train net output #0: loss = 5.27597 (* 1 = 5.27597 loss)
I0405 11:35:25.508414 30176 sgd_solver.cpp:105] Iteration 20184, lr = 1e-06
I0405 11:35:30.328975 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_20196.caffemodel
I0405 11:35:33.325520 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_20196.solverstate
I0405 11:35:35.613806 30176 solver.cpp:330] Iteration 20196, Testing net (#0)
I0405 11:35:35.613829 30176 net.cpp:676] Ignoring source layer train-data
I0405 11:35:36.693507 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 11:35:39.971272 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 11:35:39.971309 30176 solver.cpp:397]     Test net output #1: loss = 5.27971 (* 1 = 5.27971 loss)
I0405 11:35:40.111858 30176 solver.cpp:218] Iteration 20196 (0.82173 iter/s, 14.6033s/12 iters), loss = 5.28287
I0405 11:35:40.111913 30176 solver.cpp:237]     Train net output #0: loss = 5.28287 (* 1 = 5.28287 loss)
I0405 11:35:40.111922 30176 sgd_solver.cpp:105] Iteration 20196, lr = 1e-06
I0405 11:35:44.443918 30176 solver.cpp:218] Iteration 20208 (2.77011 iter/s, 4.33196s/12 iters), loss = 5.28672
I0405 11:35:44.443974 30176 solver.cpp:237]     Train net output #0: loss = 5.28672 (* 1 = 5.28672 loss)
I0405 11:35:44.443982 30176 sgd_solver.cpp:105] Iteration 20208, lr = 1e-06
I0405 11:35:49.701786 30176 solver.cpp:218] Iteration 20220 (2.28234 iter/s, 5.25776s/12 iters), loss = 5.2963
I0405 11:35:49.701822 30176 solver.cpp:237]     Train net output #0: loss = 5.2963 (* 1 = 5.2963 loss)
I0405 11:35:49.701828 30176 sgd_solver.cpp:105] Iteration 20220, lr = 1e-06
I0405 11:35:54.252156 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 11:35:55.076567 30176 solver.cpp:218] Iteration 20232 (2.23269 iter/s, 5.37469s/12 iters), loss = 5.26852
I0405 11:35:55.076608 30176 solver.cpp:237]     Train net output #0: loss = 5.26852 (* 1 = 5.26852 loss)
I0405 11:35:55.076613 30176 sgd_solver.cpp:105] Iteration 20232, lr = 1e-06
I0405 11:36:00.475906 30176 solver.cpp:218] Iteration 20244 (2.22253 iter/s, 5.39924s/12 iters), loss = 5.29282
I0405 11:36:00.476037 30176 solver.cpp:237]     Train net output #0: loss = 5.29282 (* 1 = 5.29282 loss)
I0405 11:36:00.476043 30176 sgd_solver.cpp:105] Iteration 20244, lr = 1e-06
I0405 11:36:05.735666 30176 solver.cpp:218] Iteration 20256 (2.28155 iter/s, 5.25957s/12 iters), loss = 5.27718
I0405 11:36:05.735714 30176 solver.cpp:237]     Train net output #0: loss = 5.27718 (* 1 = 5.27718 loss)
I0405 11:36:05.735723 30176 sgd_solver.cpp:105] Iteration 20256, lr = 1e-06
I0405 11:36:10.914849 30176 solver.cpp:218] Iteration 20268 (2.31701 iter/s, 5.17908s/12 iters), loss = 5.28189
I0405 11:36:10.914896 30176 solver.cpp:237]     Train net output #0: loss = 5.28189 (* 1 = 5.28189 loss)
I0405 11:36:10.914901 30176 sgd_solver.cpp:105] Iteration 20268, lr = 1e-06
I0405 11:36:16.338717 30176 solver.cpp:218] Iteration 20280 (2.21249 iter/s, 5.42376s/12 iters), loss = 5.28969
I0405 11:36:16.338814 30176 solver.cpp:237]     Train net output #0: loss = 5.28969 (* 1 = 5.28969 loss)
I0405 11:36:16.338821 30176 sgd_solver.cpp:105] Iteration 20280, lr = 1e-06
I0405 11:36:21.648257 30176 solver.cpp:218] Iteration 20292 (2.26015 iter/s, 5.30939s/12 iters), loss = 5.27908
I0405 11:36:21.648295 30176 solver.cpp:237]     Train net output #0: loss = 5.27908 (* 1 = 5.27908 loss)
I0405 11:36:21.648300 30176 sgd_solver.cpp:105] Iteration 20292, lr = 1e-06
I0405 11:36:23.810391 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_20298.caffemodel
I0405 11:36:26.902665 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_20298.solverstate
I0405 11:36:29.226950 30176 solver.cpp:330] Iteration 20298, Testing net (#0)
I0405 11:36:29.226974 30176 net.cpp:676] Ignoring source layer train-data
I0405 11:36:30.317545 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 11:36:33.824569 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 11:36:33.824659 30176 solver.cpp:397]     Test net output #1: loss = 5.27969 (* 1 = 5.27969 loss)
I0405 11:36:35.829288 30176 solver.cpp:218] Iteration 20304 (0.84621 iter/s, 14.1809s/12 iters), loss = 5.29428
I0405 11:36:35.829327 30176 solver.cpp:237]     Train net output #0: loss = 5.29428 (* 1 = 5.29428 loss)
I0405 11:36:35.829334 30176 sgd_solver.cpp:105] Iteration 20304, lr = 1e-06
I0405 11:36:41.149638 30176 solver.cpp:218] Iteration 20316 (2.25553 iter/s, 5.32025s/12 iters), loss = 5.28404
I0405 11:36:41.149693 30176 solver.cpp:237]     Train net output #0: loss = 5.28404 (* 1 = 5.28404 loss)
I0405 11:36:41.149701 30176 sgd_solver.cpp:105] Iteration 20316, lr = 1e-06
I0405 11:36:46.125365 30176 solver.cpp:218] Iteration 20328 (2.41176 iter/s, 4.97562s/12 iters), loss = 5.27728
I0405 11:36:46.125414 30176 solver.cpp:237]     Train net output #0: loss = 5.27728 (* 1 = 5.27728 loss)
I0405 11:36:46.125422 30176 sgd_solver.cpp:105] Iteration 20328, lr = 1e-06
I0405 11:36:47.477339 30195 data_layer.cpp:73] Restarting data prefetching from start.
I0405 11:36:51.207774 30176 solver.cpp:218] Iteration 20340 (2.36113 iter/s, 5.0823s/12 iters), loss = 5.27416
I0405 11:36:51.207829 30176 solver.cpp:237]     Train net output #0: loss = 5.27416 (* 1 = 5.27416 loss)
I0405 11:36:51.207837 30176 sgd_solver.cpp:105] Iteration 20340, lr = 1e-06
I0405 11:36:56.268452 30176 solver.cpp:218] Iteration 20352 (2.37128 iter/s, 5.06057s/12 iters), loss = 5.27845
I0405 11:36:56.268498 30176 solver.cpp:237]     Train net output #0: loss = 5.27845 (* 1 = 5.27845 loss)
I0405 11:36:56.268504 30176 sgd_solver.cpp:105] Iteration 20352, lr = 1e-06
I0405 11:37:01.452035 30176 solver.cpp:218] Iteration 20364 (2.31505 iter/s, 5.18348s/12 iters), loss = 5.29649
I0405 11:37:01.452072 30176 solver.cpp:237]     Train net output #0: loss = 5.29649 (* 1 = 5.29649 loss)
I0405 11:37:01.452078 30176 sgd_solver.cpp:105] Iteration 20364, lr = 1e-06
I0405 11:37:06.756037 30176 solver.cpp:218] Iteration 20376 (2.26248 iter/s, 5.30391s/12 iters), loss = 5.28045
I0405 11:37:06.756196 30176 solver.cpp:237]     Train net output #0: loss = 5.28045 (* 1 = 5.28045 loss)
I0405 11:37:06.756204 30176 sgd_solver.cpp:105] Iteration 20376, lr = 1e-06
I0405 11:37:12.130364 30176 solver.cpp:218] Iteration 20388 (2.23292 iter/s, 5.37412s/12 iters), loss = 5.29743
I0405 11:37:12.130406 30176 solver.cpp:237]     Train net output #0: loss = 5.29743 (* 1 = 5.29743 loss)
I0405 11:37:12.130411 30176 sgd_solver.cpp:105] Iteration 20388, lr = 1e-06
I0405 11:37:16.806418 30176 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_20400.caffemodel
I0405 11:37:19.807677 30176 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_20400.solverstate
I0405 11:37:22.160482 30176 solver.cpp:310] Iteration 20400, loss = 5.29113
I0405 11:37:22.160513 30176 solver.cpp:330] Iteration 20400, Testing net (#0)
I0405 11:37:22.160519 30176 net.cpp:676] Ignoring source layer train-data
I0405 11:37:23.223743 30221 data_layer.cpp:73] Restarting data prefetching from start.
I0405 11:37:26.549371 30176 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0405 11:37:26.549410 30176 solver.cpp:397]     Test net output #1: loss = 5.2796 (* 1 = 5.2796 loss)
I0405 11:37:26.549417 30176 solver.cpp:315] Optimization Done.
I0405 11:37:26.549422 30176 caffe.cpp:259] Optimization Done.