DIGITS-CNN/cars/lr-investigations/fixed/1e-2/caffe_output.log

I0406 07:07:41.118609  5226 upgrade_proto.cpp:1082] Attempting to upgrade input file specified using deprecated 'solver_type' field (enum)': /mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210406-070739-2500/solver.prototxt
I0406 07:07:41.118760  5226 upgrade_proto.cpp:1089] Successfully upgraded file specified using deprecated 'solver_type' field (enum) to 'type' field (string).
W0406 07:07:41.118765  5226 upgrade_proto.cpp:1091] Note that future Caffe releases will only support 'type' field (string) for a solver's type.
I0406 07:07:41.118825  5226 caffe.cpp:218] Using GPUs 0
I0406 07:07:41.143887  5226 caffe.cpp:223] GPU 0: GeForce GTX TITAN X
I0406 07:07:41.395608  5226 solver.cpp:44] Initializing solver from parameters:
test_iter: 51
test_interval: 102
base_lr: 0.01
display: 12
max_iter: 20400
lr_policy: "fixed"
momentum: 0.9
weight_decay: 0.0001
snapshot: 102
snapshot_prefix: "snapshot"
solver_mode: GPU
device_id: 0
net: "train_val.prototxt"
train_state {
level: 0
stage: ""
}
type: "SGD"
I0406 07:07:41.396505  5226 solver.cpp:87] Creating training net from net file: train_val.prototxt
I0406 07:07:41.397136  5226 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer val-data
I0406 07:07:41.397150  5226 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy
I0406 07:07:41.397270  5226 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"
}
I0406 07:07:41.397347  5226 layer_factory.hpp:77] Creating layer train-data
I0406 07:07:41.421499  5226 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210401-115716-aaf7/train_db
I0406 07:07:41.421733  5226 net.cpp:84] Creating Layer train-data
I0406 07:07:41.421748  5226 net.cpp:380] train-data -> data
I0406 07:07:41.421768  5226 net.cpp:380] train-data -> label
I0406 07:07:41.421779  5226 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210401-115716-aaf7/mean.binaryproto
I0406 07:07:41.462285  5226 data_layer.cpp:45] output data size: 128,3,227,227
I0406 07:07:41.609122  5226 net.cpp:122] Setting up train-data
I0406 07:07:41.609144  5226 net.cpp:129] Top shape: 128 3 227 227 (19787136)
I0406 07:07:41.609148  5226 net.cpp:129] Top shape: 128 (128)
I0406 07:07:41.609151  5226 net.cpp:137] Memory required for data: 79149056
I0406 07:07:41.609158  5226 layer_factory.hpp:77] Creating layer conv1
I0406 07:07:41.609176  5226 net.cpp:84] Creating Layer conv1
I0406 07:07:41.609181  5226 net.cpp:406] conv1 <- data
I0406 07:07:41.609192  5226 net.cpp:380] conv1 -> conv1
I0406 07:07:42.050434  5226 net.cpp:122] Setting up conv1
I0406 07:07:42.050458  5226 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0406 07:07:42.050462  5226 net.cpp:137] Memory required for data: 227833856
I0406 07:07:42.050479  5226 layer_factory.hpp:77] Creating layer relu1
I0406 07:07:42.050489  5226 net.cpp:84] Creating Layer relu1
I0406 07:07:42.050493  5226 net.cpp:406] relu1 <- conv1
I0406 07:07:42.050496  5226 net.cpp:367] relu1 -> conv1 (in-place)
I0406 07:07:42.050762  5226 net.cpp:122] Setting up relu1
I0406 07:07:42.050770  5226 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0406 07:07:42.050772  5226 net.cpp:137] Memory required for data: 376518656
I0406 07:07:42.050776  5226 layer_factory.hpp:77] Creating layer norm1
I0406 07:07:42.050783  5226 net.cpp:84] Creating Layer norm1
I0406 07:07:42.050786  5226 net.cpp:406] norm1 <- conv1
I0406 07:07:42.050817  5226 net.cpp:380] norm1 -> norm1
I0406 07:07:42.051363  5226 net.cpp:122] Setting up norm1
I0406 07:07:42.051373  5226 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0406 07:07:42.051375  5226 net.cpp:137] Memory required for data: 525203456
I0406 07:07:42.051378  5226 layer_factory.hpp:77] Creating layer pool1
I0406 07:07:42.051384  5226 net.cpp:84] Creating Layer pool1
I0406 07:07:42.051388  5226 net.cpp:406] pool1 <- norm1
I0406 07:07:42.051391  5226 net.cpp:380] pool1 -> pool1
I0406 07:07:42.051424  5226 net.cpp:122] Setting up pool1
I0406 07:07:42.051429  5226 net.cpp:129] Top shape: 128 96 27 27 (8957952)
I0406 07:07:42.051431  5226 net.cpp:137] Memory required for data: 561035264
I0406 07:07:42.051434  5226 layer_factory.hpp:77] Creating layer conv2
I0406 07:07:42.051443  5226 net.cpp:84] Creating Layer conv2
I0406 07:07:42.051445  5226 net.cpp:406] conv2 <- pool1
I0406 07:07:42.051450  5226 net.cpp:380] conv2 -> conv2
I0406 07:07:42.058259  5226 net.cpp:122] Setting up conv2
I0406 07:07:42.058277  5226 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0406 07:07:42.058280  5226 net.cpp:137] Memory required for data: 656586752
I0406 07:07:42.058290  5226 layer_factory.hpp:77] Creating layer relu2
I0406 07:07:42.058297  5226 net.cpp:84] Creating Layer relu2
I0406 07:07:42.058301  5226 net.cpp:406] relu2 <- conv2
I0406 07:07:42.058305  5226 net.cpp:367] relu2 -> conv2 (in-place)
I0406 07:07:42.058734  5226 net.cpp:122] Setting up relu2
I0406 07:07:42.058743  5226 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0406 07:07:42.058745  5226 net.cpp:137] Memory required for data: 752138240
I0406 07:07:42.058748  5226 layer_factory.hpp:77] Creating layer norm2
I0406 07:07:42.058755  5226 net.cpp:84] Creating Layer norm2
I0406 07:07:42.058758  5226 net.cpp:406] norm2 <- conv2
I0406 07:07:42.058763  5226 net.cpp:380] norm2 -> norm2
I0406 07:07:42.059024  5226 net.cpp:122] Setting up norm2
I0406 07:07:42.059032  5226 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0406 07:07:42.059034  5226 net.cpp:137] Memory required for data: 847689728
I0406 07:07:42.059037  5226 layer_factory.hpp:77] Creating layer pool2
I0406 07:07:42.059044  5226 net.cpp:84] Creating Layer pool2
I0406 07:07:42.059046  5226 net.cpp:406] pool2 <- norm2
I0406 07:07:42.059051  5226 net.cpp:380] pool2 -> pool2
I0406 07:07:42.059075  5226 net.cpp:122] Setting up pool2
I0406 07:07:42.059079  5226 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0406 07:07:42.059082  5226 net.cpp:137] Memory required for data: 869840896
I0406 07:07:42.059083  5226 layer_factory.hpp:77] Creating layer conv3
I0406 07:07:42.059092  5226 net.cpp:84] Creating Layer conv3
I0406 07:07:42.059094  5226 net.cpp:406] conv3 <- pool2
I0406 07:07:42.059098  5226 net.cpp:380] conv3 -> conv3
I0406 07:07:42.068939  5226 net.cpp:122] Setting up conv3
I0406 07:07:42.068958  5226 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0406 07:07:42.068959  5226 net.cpp:137] Memory required for data: 903067648
I0406 07:07:42.068970  5226 layer_factory.hpp:77] Creating layer relu3
I0406 07:07:42.068979  5226 net.cpp:84] Creating Layer relu3
I0406 07:07:42.068981  5226 net.cpp:406] relu3 <- conv3
I0406 07:07:42.068986  5226 net.cpp:367] relu3 -> conv3 (in-place)
I0406 07:07:42.069411  5226 net.cpp:122] Setting up relu3
I0406 07:07:42.069420  5226 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0406 07:07:42.069423  5226 net.cpp:137] Memory required for data: 936294400
I0406 07:07:42.069425  5226 layer_factory.hpp:77] Creating layer conv4
I0406 07:07:42.069435  5226 net.cpp:84] Creating Layer conv4
I0406 07:07:42.069437  5226 net.cpp:406] conv4 <- conv3
I0406 07:07:42.069442  5226 net.cpp:380] conv4 -> conv4
I0406 07:07:42.078717  5226 net.cpp:122] Setting up conv4
I0406 07:07:42.078732  5226 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0406 07:07:42.078735  5226 net.cpp:137] Memory required for data: 969521152
I0406 07:07:42.078742  5226 layer_factory.hpp:77] Creating layer relu4
I0406 07:07:42.078750  5226 net.cpp:84] Creating Layer relu4
I0406 07:07:42.078753  5226 net.cpp:406] relu4 <- conv4
I0406 07:07:42.078776  5226 net.cpp:367] relu4 -> conv4 (in-place)
I0406 07:07:42.079092  5226 net.cpp:122] Setting up relu4
I0406 07:07:42.079100  5226 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0406 07:07:42.079102  5226 net.cpp:137] Memory required for data: 1002747904
I0406 07:07:42.079105  5226 layer_factory.hpp:77] Creating layer conv5
I0406 07:07:42.079115  5226 net.cpp:84] Creating Layer conv5
I0406 07:07:42.079118  5226 net.cpp:406] conv5 <- conv4
I0406 07:07:42.079123  5226 net.cpp:380] conv5 -> conv5
I0406 07:07:42.086643  5226 net.cpp:122] Setting up conv5
I0406 07:07:42.086659  5226 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0406 07:07:42.086663  5226 net.cpp:137] Memory required for data: 1024899072
I0406 07:07:42.086673  5226 layer_factory.hpp:77] Creating layer relu5
I0406 07:07:42.086683  5226 net.cpp:84] Creating Layer relu5
I0406 07:07:42.086685  5226 net.cpp:406] relu5 <- conv5
I0406 07:07:42.086691  5226 net.cpp:367] relu5 -> conv5 (in-place)
I0406 07:07:42.087175  5226 net.cpp:122] Setting up relu5
I0406 07:07:42.087183  5226 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0406 07:07:42.087186  5226 net.cpp:137] Memory required for data: 1047050240
I0406 07:07:42.087188  5226 layer_factory.hpp:77] Creating layer pool5
I0406 07:07:42.087194  5226 net.cpp:84] Creating Layer pool5
I0406 07:07:42.087196  5226 net.cpp:406] pool5 <- conv5
I0406 07:07:42.087203  5226 net.cpp:380] pool5 -> pool5
I0406 07:07:42.087235  5226 net.cpp:122] Setting up pool5
I0406 07:07:42.087239  5226 net.cpp:129] Top shape: 128 256 6 6 (1179648)
I0406 07:07:42.087241  5226 net.cpp:137] Memory required for data: 1051768832
I0406 07:07:42.087244  5226 layer_factory.hpp:77] Creating layer fc6
I0406 07:07:42.087253  5226 net.cpp:84] Creating Layer fc6
I0406 07:07:42.087255  5226 net.cpp:406] fc6 <- pool5
I0406 07:07:42.087260  5226 net.cpp:380] fc6 -> fc6
I0406 07:07:42.420754  5226 net.cpp:122] Setting up fc6
I0406 07:07:42.420773  5226 net.cpp:129] Top shape: 128 4096 (524288)
I0406 07:07:42.420775  5226 net.cpp:137] Memory required for data: 1053865984
I0406 07:07:42.420783  5226 layer_factory.hpp:77] Creating layer relu6
I0406 07:07:42.420792  5226 net.cpp:84] Creating Layer relu6
I0406 07:07:42.420795  5226 net.cpp:406] relu6 <- fc6
I0406 07:07:42.420800  5226 net.cpp:367] relu6 -> fc6 (in-place)
I0406 07:07:42.421501  5226 net.cpp:122] Setting up relu6
I0406 07:07:42.421511  5226 net.cpp:129] Top shape: 128 4096 (524288)
I0406 07:07:42.421514  5226 net.cpp:137] Memory required for data: 1055963136
I0406 07:07:42.421515  5226 layer_factory.hpp:77] Creating layer drop6
I0406 07:07:42.421521  5226 net.cpp:84] Creating Layer drop6
I0406 07:07:42.421525  5226 net.cpp:406] drop6 <- fc6
I0406 07:07:42.421530  5226 net.cpp:367] drop6 -> fc6 (in-place)
I0406 07:07:42.421553  5226 net.cpp:122] Setting up drop6
I0406 07:07:42.421557  5226 net.cpp:129] Top shape: 128 4096 (524288)
I0406 07:07:42.421559  5226 net.cpp:137] Memory required for data: 1058060288
I0406 07:07:42.421561  5226 layer_factory.hpp:77] Creating layer fc7
I0406 07:07:42.421572  5226 net.cpp:84] Creating Layer fc7
I0406 07:07:42.421574  5226 net.cpp:406] fc7 <- fc6
I0406 07:07:42.421579  5226 net.cpp:380] fc7 -> fc7
I0406 07:07:42.570488  5226 net.cpp:122] Setting up fc7
I0406 07:07:42.570508  5226 net.cpp:129] Top shape: 128 4096 (524288)
I0406 07:07:42.570510  5226 net.cpp:137] Memory required for data: 1060157440
I0406 07:07:42.570518  5226 layer_factory.hpp:77] Creating layer relu7
I0406 07:07:42.570526  5226 net.cpp:84] Creating Layer relu7
I0406 07:07:42.570529  5226 net.cpp:406] relu7 <- fc7
I0406 07:07:42.570534  5226 net.cpp:367] relu7 -> fc7 (in-place)
I0406 07:07:42.570909  5226 net.cpp:122] Setting up relu7
I0406 07:07:42.570919  5226 net.cpp:129] Top shape: 128 4096 (524288)
I0406 07:07:42.570920  5226 net.cpp:137] Memory required for data: 1062254592
I0406 07:07:42.570922  5226 layer_factory.hpp:77] Creating layer drop7
I0406 07:07:42.570928  5226 net.cpp:84] Creating Layer drop7
I0406 07:07:42.570930  5226 net.cpp:406] drop7 <- fc7
I0406 07:07:42.570952  5226 net.cpp:367] drop7 -> fc7 (in-place)
I0406 07:07:42.570974  5226 net.cpp:122] Setting up drop7
I0406 07:07:42.570978  5226 net.cpp:129] Top shape: 128 4096 (524288)
I0406 07:07:42.570981  5226 net.cpp:137] Memory required for data: 1064351744
I0406 07:07:42.570982  5226 layer_factory.hpp:77] Creating layer fc8
I0406 07:07:42.570989  5226 net.cpp:84] Creating Layer fc8
I0406 07:07:42.570991  5226 net.cpp:406] fc8 <- fc7
I0406 07:07:42.570995  5226 net.cpp:380] fc8 -> fc8
I0406 07:07:42.578217  5226 net.cpp:122] Setting up fc8
I0406 07:07:42.578231  5226 net.cpp:129] Top shape: 128 196 (25088)
I0406 07:07:42.578234  5226 net.cpp:137] Memory required for data: 1064452096
I0406 07:07:42.578241  5226 layer_factory.hpp:77] Creating layer loss
I0406 07:07:42.578248  5226 net.cpp:84] Creating Layer loss
I0406 07:07:42.578250  5226 net.cpp:406] loss <- fc8
I0406 07:07:42.578255  5226 net.cpp:406] loss <- label
I0406 07:07:42.578261  5226 net.cpp:380] loss -> loss
I0406 07:07:42.578270  5226 layer_factory.hpp:77] Creating layer loss
I0406 07:07:42.578953  5226 net.cpp:122] Setting up loss
I0406 07:07:42.578960  5226 net.cpp:129] Top shape: (1)
I0406 07:07:42.578963  5226 net.cpp:132]     with loss weight 1
I0406 07:07:42.578985  5226 net.cpp:137] Memory required for data: 1064452100
I0406 07:07:42.578989  5226 net.cpp:198] loss needs backward computation.
I0406 07:07:42.578994  5226 net.cpp:198] fc8 needs backward computation.
I0406 07:07:42.578996  5226 net.cpp:198] drop7 needs backward computation.
I0406 07:07:42.578999  5226 net.cpp:198] relu7 needs backward computation.
I0406 07:07:42.579000  5226 net.cpp:198] fc7 needs backward computation.
I0406 07:07:42.579003  5226 net.cpp:198] drop6 needs backward computation.
I0406 07:07:42.579005  5226 net.cpp:198] relu6 needs backward computation.
I0406 07:07:42.579008  5226 net.cpp:198] fc6 needs backward computation.
I0406 07:07:42.579011  5226 net.cpp:198] pool5 needs backward computation.
I0406 07:07:42.579013  5226 net.cpp:198] relu5 needs backward computation.
I0406 07:07:42.579015  5226 net.cpp:198] conv5 needs backward computation.
I0406 07:07:42.579018  5226 net.cpp:198] relu4 needs backward computation.
I0406 07:07:42.579020  5226 net.cpp:198] conv4 needs backward computation.
I0406 07:07:42.579023  5226 net.cpp:198] relu3 needs backward computation.
I0406 07:07:42.579025  5226 net.cpp:198] conv3 needs backward computation.
I0406 07:07:42.579027  5226 net.cpp:198] pool2 needs backward computation.
I0406 07:07:42.579030  5226 net.cpp:198] norm2 needs backward computation.
I0406 07:07:42.579032  5226 net.cpp:198] relu2 needs backward computation.
I0406 07:07:42.579035  5226 net.cpp:198] conv2 needs backward computation.
I0406 07:07:42.579037  5226 net.cpp:198] pool1 needs backward computation.
I0406 07:07:42.579039  5226 net.cpp:198] norm1 needs backward computation.
I0406 07:07:42.579042  5226 net.cpp:198] relu1 needs backward computation.
I0406 07:07:42.579044  5226 net.cpp:198] conv1 needs backward computation.
I0406 07:07:42.579047  5226 net.cpp:200] train-data does not need backward computation.
I0406 07:07:42.579049  5226 net.cpp:242] This network produces output loss
I0406 07:07:42.579061  5226 net.cpp:255] Network initialization done.
I0406 07:07:42.579543  5226 solver.cpp:172] Creating test net (#0) specified by net file: train_val.prototxt
I0406 07:07:42.579573  5226 net.cpp:294] The NetState phase (1) differed from the phase (0) specified by a rule in layer train-data
I0406 07:07:42.579708  5226 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"
}
I0406 07:07:42.579808  5226 layer_factory.hpp:77] Creating layer val-data
I0406 07:07:42.582653  5226 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210401-115716-aaf7/val_db
I0406 07:07:42.582876  5226 net.cpp:84] Creating Layer val-data
I0406 07:07:42.582885  5226 net.cpp:380] val-data -> data
I0406 07:07:42.582892  5226 net.cpp:380] val-data -> label
I0406 07:07:42.582899  5226 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210401-115716-aaf7/mean.binaryproto
I0406 07:07:42.586594  5226 data_layer.cpp:45] output data size: 32,3,227,227
I0406 07:07:42.623560  5226 net.cpp:122] Setting up val-data
I0406 07:07:42.623580  5226 net.cpp:129] Top shape: 32 3 227 227 (4946784)
I0406 07:07:42.623584  5226 net.cpp:129] Top shape: 32 (32)
I0406 07:07:42.623585  5226 net.cpp:137] Memory required for data: 19787264
I0406 07:07:42.623590  5226 layer_factory.hpp:77] Creating layer label_val-data_1_split
I0406 07:07:42.623601  5226 net.cpp:84] Creating Layer label_val-data_1_split
I0406 07:07:42.623605  5226 net.cpp:406] label_val-data_1_split <- label
I0406 07:07:42.623610  5226 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_0
I0406 07:07:42.623618  5226 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_1
I0406 07:07:42.623661  5226 net.cpp:122] Setting up label_val-data_1_split
I0406 07:07:42.623664  5226 net.cpp:129] Top shape: 32 (32)
I0406 07:07:42.623667  5226 net.cpp:129] Top shape: 32 (32)
I0406 07:07:42.623668  5226 net.cpp:137] Memory required for data: 19787520
I0406 07:07:42.623672  5226 layer_factory.hpp:77] Creating layer conv1
I0406 07:07:42.623682  5226 net.cpp:84] Creating Layer conv1
I0406 07:07:42.623683  5226 net.cpp:406] conv1 <- data
I0406 07:07:42.623687  5226 net.cpp:380] conv1 -> conv1
I0406 07:07:42.632581  5226 net.cpp:122] Setting up conv1
I0406 07:07:42.632593  5226 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0406 07:07:42.632596  5226 net.cpp:137] Memory required for data: 56958720
I0406 07:07:42.632604  5226 layer_factory.hpp:77] Creating layer relu1
I0406 07:07:42.632611  5226 net.cpp:84] Creating Layer relu1
I0406 07:07:42.632613  5226 net.cpp:406] relu1 <- conv1
I0406 07:07:42.632617  5226 net.cpp:367] relu1 -> conv1 (in-place)
I0406 07:07:42.632874  5226 net.cpp:122] Setting up relu1
I0406 07:07:42.632889  5226 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0406 07:07:42.632891  5226 net.cpp:137] Memory required for data: 94129920
I0406 07:07:42.632894  5226 layer_factory.hpp:77] Creating layer norm1
I0406 07:07:42.632901  5226 net.cpp:84] Creating Layer norm1
I0406 07:07:42.632903  5226 net.cpp:406] norm1 <- conv1
I0406 07:07:42.632908  5226 net.cpp:380] norm1 -> norm1
I0406 07:07:42.639832  5226 net.cpp:122] Setting up norm1
I0406 07:07:42.639847  5226 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0406 07:07:42.639851  5226 net.cpp:137] Memory required for data: 131301120
I0406 07:07:42.639856  5226 layer_factory.hpp:77] Creating layer pool1
I0406 07:07:42.639864  5226 net.cpp:84] Creating Layer pool1
I0406 07:07:42.639869  5226 net.cpp:406] pool1 <- norm1
I0406 07:07:42.639875  5226 net.cpp:380] pool1 -> pool1
I0406 07:07:42.639914  5226 net.cpp:122] Setting up pool1
I0406 07:07:42.639920  5226 net.cpp:129] Top shape: 32 96 27 27 (2239488)
I0406 07:07:42.639923  5226 net.cpp:137] Memory required for data: 140259072
I0406 07:07:42.639927  5226 layer_factory.hpp:77] Creating layer conv2
I0406 07:07:42.639938  5226 net.cpp:84] Creating Layer conv2
I0406 07:07:42.639942  5226 net.cpp:406] conv2 <- pool1
I0406 07:07:42.639977  5226 net.cpp:380] conv2 -> conv2
I0406 07:07:42.652181  5226 net.cpp:122] Setting up conv2
I0406 07:07:42.652201  5226 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0406 07:07:42.652205  5226 net.cpp:137] Memory required for data: 164146944
I0406 07:07:42.652220  5226 layer_factory.hpp:77] Creating layer relu2
I0406 07:07:42.652233  5226 net.cpp:84] Creating Layer relu2
I0406 07:07:42.652238  5226 net.cpp:406] relu2 <- conv2
I0406 07:07:42.652245  5226 net.cpp:367] relu2 -> conv2 (in-place)
I0406 07:07:42.652994  5226 net.cpp:122] Setting up relu2
I0406 07:07:42.653007  5226 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0406 07:07:42.653010  5226 net.cpp:137] Memory required for data: 188034816
I0406 07:07:42.653014  5226 layer_factory.hpp:77] Creating layer norm2
I0406 07:07:42.653030  5226 net.cpp:84] Creating Layer norm2
I0406 07:07:42.653034  5226 net.cpp:406] norm2 <- conv2
I0406 07:07:42.653041  5226 net.cpp:380] norm2 -> norm2
I0406 07:07:42.653803  5226 net.cpp:122] Setting up norm2
I0406 07:07:42.653816  5226 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0406 07:07:42.653820  5226 net.cpp:137] Memory required for data: 211922688
I0406 07:07:42.653825  5226 layer_factory.hpp:77] Creating layer pool2
I0406 07:07:42.653831  5226 net.cpp:84] Creating Layer pool2
I0406 07:07:42.653836  5226 net.cpp:406] pool2 <- norm2
I0406 07:07:42.653843  5226 net.cpp:380] pool2 -> pool2
I0406 07:07:42.653882  5226 net.cpp:122] Setting up pool2
I0406 07:07:42.653889  5226 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0406 07:07:42.653892  5226 net.cpp:137] Memory required for data: 217460480
I0406 07:07:42.653896  5226 layer_factory.hpp:77] Creating layer conv3
I0406 07:07:42.653910  5226 net.cpp:84] Creating Layer conv3
I0406 07:07:42.653914  5226 net.cpp:406] conv3 <- pool2
I0406 07:07:42.653921  5226 net.cpp:380] conv3 -> conv3
I0406 07:07:42.669800  5226 net.cpp:122] Setting up conv3
I0406 07:07:42.669822  5226 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0406 07:07:42.669826  5226 net.cpp:137] Memory required for data: 225767168
I0406 07:07:42.669840  5226 layer_factory.hpp:77] Creating layer relu3
I0406 07:07:42.669857  5226 net.cpp:84] Creating Layer relu3
I0406 07:07:42.669862  5226 net.cpp:406] relu3 <- conv3
I0406 07:07:42.669868  5226 net.cpp:367] relu3 -> conv3 (in-place)
I0406 07:07:42.670576  5226 net.cpp:122] Setting up relu3
I0406 07:07:42.670593  5226 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0406 07:07:42.670596  5226 net.cpp:137] Memory required for data: 234073856
I0406 07:07:42.670600  5226 layer_factory.hpp:77] Creating layer conv4
I0406 07:07:42.670612  5226 net.cpp:84] Creating Layer conv4
I0406 07:07:42.670617  5226 net.cpp:406] conv4 <- conv3
I0406 07:07:42.670624  5226 net.cpp:380] conv4 -> conv4
I0406 07:07:42.684892  5226 net.cpp:122] Setting up conv4
I0406 07:07:42.684912  5226 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0406 07:07:42.684916  5226 net.cpp:137] Memory required for data: 242380544
I0406 07:07:42.684926  5226 layer_factory.hpp:77] Creating layer relu4
I0406 07:07:42.684937  5226 net.cpp:84] Creating Layer relu4
I0406 07:07:42.684940  5226 net.cpp:406] relu4 <- conv4
I0406 07:07:42.684949  5226 net.cpp:367] relu4 -> conv4 (in-place)
I0406 07:07:42.685421  5226 net.cpp:122] Setting up relu4
I0406 07:07:42.685432  5226 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0406 07:07:42.685436  5226 net.cpp:137] Memory required for data: 250687232
I0406 07:07:42.685441  5226 layer_factory.hpp:77] Creating layer conv5
I0406 07:07:42.685453  5226 net.cpp:84] Creating Layer conv5
I0406 07:07:42.685457  5226 net.cpp:406] conv5 <- conv4
I0406 07:07:42.685464  5226 net.cpp:380] conv5 -> conv5
I0406 07:07:42.697454  5226 net.cpp:122] Setting up conv5
I0406 07:07:42.697470  5226 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0406 07:07:42.697474  5226 net.cpp:137] Memory required for data: 256225024
I0406 07:07:42.697485  5226 layer_factory.hpp:77] Creating layer relu5
I0406 07:07:42.697495  5226 net.cpp:84] Creating Layer relu5
I0406 07:07:42.697499  5226 net.cpp:406] relu5 <- conv5
I0406 07:07:42.697523  5226 net.cpp:367] relu5 -> conv5 (in-place)
I0406 07:07:42.698045  5226 net.cpp:122] Setting up relu5
I0406 07:07:42.698055  5226 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0406 07:07:42.698056  5226 net.cpp:137] Memory required for data: 261762816
I0406 07:07:42.698060  5226 layer_factory.hpp:77] Creating layer pool5
I0406 07:07:42.698069  5226 net.cpp:84] Creating Layer pool5
I0406 07:07:42.698071  5226 net.cpp:406] pool5 <- conv5
I0406 07:07:42.698076  5226 net.cpp:380] pool5 -> pool5
I0406 07:07:42.698112  5226 net.cpp:122] Setting up pool5
I0406 07:07:42.698115  5226 net.cpp:129] Top shape: 32 256 6 6 (294912)
I0406 07:07:42.698117  5226 net.cpp:137] Memory required for data: 262942464
I0406 07:07:42.698119  5226 layer_factory.hpp:77] Creating layer fc6
I0406 07:07:42.698125  5226 net.cpp:84] Creating Layer fc6
I0406 07:07:42.698129  5226 net.cpp:406] fc6 <- pool5
I0406 07:07:42.698132  5226 net.cpp:380] fc6 -> fc6
I0406 07:07:43.029364  5226 net.cpp:122] Setting up fc6
I0406 07:07:43.029381  5226 net.cpp:129] Top shape: 32 4096 (131072)
I0406 07:07:43.029383  5226 net.cpp:137] Memory required for data: 263466752
I0406 07:07:43.029392  5226 layer_factory.hpp:77] Creating layer relu6
I0406 07:07:43.029400  5226 net.cpp:84] Creating Layer relu6
I0406 07:07:43.029403  5226 net.cpp:406] relu6 <- fc6
I0406 07:07:43.029408  5226 net.cpp:367] relu6 -> fc6 (in-place)
I0406 07:07:43.031992  5226 net.cpp:122] Setting up relu6
I0406 07:07:43.032001  5226 net.cpp:129] Top shape: 32 4096 (131072)
I0406 07:07:43.032003  5226 net.cpp:137] Memory required for data: 263991040
I0406 07:07:43.032006  5226 layer_factory.hpp:77] Creating layer drop6
I0406 07:07:43.032012  5226 net.cpp:84] Creating Layer drop6
I0406 07:07:43.032014  5226 net.cpp:406] drop6 <- fc6
I0406 07:07:43.032019  5226 net.cpp:367] drop6 -> fc6 (in-place)
I0406 07:07:43.032043  5226 net.cpp:122] Setting up drop6
I0406 07:07:43.032047  5226 net.cpp:129] Top shape: 32 4096 (131072)
I0406 07:07:43.032050  5226 net.cpp:137] Memory required for data: 264515328
I0406 07:07:43.032052  5226 layer_factory.hpp:77] Creating layer fc7
I0406 07:07:43.032058  5226 net.cpp:84] Creating Layer fc7
I0406 07:07:43.032060  5226 net.cpp:406] fc7 <- fc6
I0406 07:07:43.032065  5226 net.cpp:380] fc7 -> fc7
I0406 07:07:43.178071  5226 net.cpp:122] Setting up fc7
I0406 07:07:43.178089  5226 net.cpp:129] Top shape: 32 4096 (131072)
I0406 07:07:43.178092  5226 net.cpp:137] Memory required for data: 265039616
I0406 07:07:43.178099  5226 layer_factory.hpp:77] Creating layer relu7
I0406 07:07:43.178107  5226 net.cpp:84] Creating Layer relu7
I0406 07:07:43.178110  5226 net.cpp:406] relu7 <- fc7
I0406 07:07:43.178117  5226 net.cpp:367] relu7 -> fc7 (in-place)
I0406 07:07:43.178485  5226 net.cpp:122] Setting up relu7
I0406 07:07:43.178493  5226 net.cpp:129] Top shape: 32 4096 (131072)
I0406 07:07:43.178495  5226 net.cpp:137] Memory required for data: 265563904
I0406 07:07:43.178498  5226 layer_factory.hpp:77] Creating layer drop7
I0406 07:07:43.178504  5226 net.cpp:84] Creating Layer drop7
I0406 07:07:43.178506  5226 net.cpp:406] drop7 <- fc7
I0406 07:07:43.178510  5226 net.cpp:367] drop7 -> fc7 (in-place)
I0406 07:07:43.178531  5226 net.cpp:122] Setting up drop7
I0406 07:07:43.178535  5226 net.cpp:129] Top shape: 32 4096 (131072)
I0406 07:07:43.178537  5226 net.cpp:137] Memory required for data: 266088192
I0406 07:07:43.178539  5226 layer_factory.hpp:77] Creating layer fc8
I0406 07:07:43.178545  5226 net.cpp:84] Creating Layer fc8
I0406 07:07:43.178547  5226 net.cpp:406] fc8 <- fc7
I0406 07:07:43.178551  5226 net.cpp:380] fc8 -> fc8
I0406 07:07:43.185705  5226 net.cpp:122] Setting up fc8
I0406 07:07:43.185714  5226 net.cpp:129] Top shape: 32 196 (6272)
I0406 07:07:43.185715  5226 net.cpp:137] Memory required for data: 266113280
I0406 07:07:43.185720  5226 layer_factory.hpp:77] Creating layer fc8_fc8_0_split
I0406 07:07:43.185725  5226 net.cpp:84] Creating Layer fc8_fc8_0_split
I0406 07:07:43.185729  5226 net.cpp:406] fc8_fc8_0_split <- fc8
I0406 07:07:43.185751  5226 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_0
I0406 07:07:43.185757  5226 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_1
I0406 07:07:43.185784  5226 net.cpp:122] Setting up fc8_fc8_0_split
I0406 07:07:43.185788  5226 net.cpp:129] Top shape: 32 196 (6272)
I0406 07:07:43.185791  5226 net.cpp:129] Top shape: 32 196 (6272)
I0406 07:07:43.185792  5226 net.cpp:137] Memory required for data: 266163456
I0406 07:07:43.185794  5226 layer_factory.hpp:77] Creating layer accuracy
I0406 07:07:43.185801  5226 net.cpp:84] Creating Layer accuracy
I0406 07:07:43.185803  5226 net.cpp:406] accuracy <- fc8_fc8_0_split_0
I0406 07:07:43.185806  5226 net.cpp:406] accuracy <- label_val-data_1_split_0
I0406 07:07:43.185809  5226 net.cpp:380] accuracy -> accuracy
I0406 07:07:43.185815  5226 net.cpp:122] Setting up accuracy
I0406 07:07:43.185818  5226 net.cpp:129] Top shape: (1)
I0406 07:07:43.185819  5226 net.cpp:137] Memory required for data: 266163460
I0406 07:07:43.185822  5226 layer_factory.hpp:77] Creating layer loss
I0406 07:07:43.185827  5226 net.cpp:84] Creating Layer loss
I0406 07:07:43.185828  5226 net.cpp:406] loss <- fc8_fc8_0_split_1
I0406 07:07:43.185832  5226 net.cpp:406] loss <- label_val-data_1_split_1
I0406 07:07:43.185834  5226 net.cpp:380] loss -> loss
I0406 07:07:43.185839  5226 layer_factory.hpp:77] Creating layer loss
I0406 07:07:43.186419  5226 net.cpp:122] Setting up loss
I0406 07:07:43.186427  5226 net.cpp:129] Top shape: (1)
I0406 07:07:43.186429  5226 net.cpp:132]     with loss weight 1
I0406 07:07:43.186437  5226 net.cpp:137] Memory required for data: 266163464
I0406 07:07:43.186439  5226 net.cpp:198] loss needs backward computation.
I0406 07:07:43.186444  5226 net.cpp:200] accuracy does not need backward computation.
I0406 07:07:43.186446  5226 net.cpp:198] fc8_fc8_0_split needs backward computation.
I0406 07:07:43.186448  5226 net.cpp:198] fc8 needs backward computation.
I0406 07:07:43.186450  5226 net.cpp:198] drop7 needs backward computation.
I0406 07:07:43.186452  5226 net.cpp:198] relu7 needs backward computation.
I0406 07:07:43.186455  5226 net.cpp:198] fc7 needs backward computation.
I0406 07:07:43.186456  5226 net.cpp:198] drop6 needs backward computation.
I0406 07:07:43.186458  5226 net.cpp:198] relu6 needs backward computation.
I0406 07:07:43.186460  5226 net.cpp:198] fc6 needs backward computation.
I0406 07:07:43.186463  5226 net.cpp:198] pool5 needs backward computation.
I0406 07:07:43.186466  5226 net.cpp:198] relu5 needs backward computation.
I0406 07:07:43.186468  5226 net.cpp:198] conv5 needs backward computation.
I0406 07:07:43.186470  5226 net.cpp:198] relu4 needs backward computation.
I0406 07:07:43.186472  5226 net.cpp:198] conv4 needs backward computation.
I0406 07:07:43.186475  5226 net.cpp:198] relu3 needs backward computation.
I0406 07:07:43.186477  5226 net.cpp:198] conv3 needs backward computation.
I0406 07:07:43.186480  5226 net.cpp:198] pool2 needs backward computation.
I0406 07:07:43.186483  5226 net.cpp:198] norm2 needs backward computation.
I0406 07:07:43.186486  5226 net.cpp:198] relu2 needs backward computation.
I0406 07:07:43.186487  5226 net.cpp:198] conv2 needs backward computation.
I0406 07:07:43.186489  5226 net.cpp:198] pool1 needs backward computation.
I0406 07:07:43.186492  5226 net.cpp:198] norm1 needs backward computation.
I0406 07:07:43.186494  5226 net.cpp:198] relu1 needs backward computation.
I0406 07:07:43.186496  5226 net.cpp:198] conv1 needs backward computation.
I0406 07:07:43.186498  5226 net.cpp:200] label_val-data_1_split does not need backward computation.
I0406 07:07:43.186501  5226 net.cpp:200] val-data does not need backward computation.
I0406 07:07:43.186503  5226 net.cpp:242] This network produces output accuracy
I0406 07:07:43.186506  5226 net.cpp:242] This network produces output loss
I0406 07:07:43.186520  5226 net.cpp:255] Network initialization done.
I0406 07:07:43.186589  5226 solver.cpp:56] Solver scaffolding done.
I0406 07:07:43.186964  5226 caffe.cpp:248] Starting Optimization
I0406 07:07:43.186972  5226 solver.cpp:272] Solving
I0406 07:07:43.186982  5226 solver.cpp:273] Learning Rate Policy: fixed
I0406 07:07:43.188912  5226 solver.cpp:330] Iteration 0, Testing net (#0)
I0406 07:07:43.188921  5226 net.cpp:676] Ignoring source layer train-data
I0406 07:07:43.290103  5226 blocking_queue.cpp:49] Waiting for data
I0406 07:07:47.569265  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:07:47.617841  5226 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 07:07:47.617877  5226 solver.cpp:397]     Test net output #1: loss = 5.28124 (* 1 = 5.28124 loss)
I0406 07:07:47.764514  5226 solver.cpp:218] Iteration 0 (1.16182e+36 iter/s, 4.57746s/12 iters), loss = 5.31153
I0406 07:07:47.766070  5226 solver.cpp:237]     Train net output #0: loss = 5.31153 (* 1 = 5.31153 loss)
I0406 07:07:47.766080  5226 sgd_solver.cpp:105] Iteration 0, lr = 0.01
I0406 07:07:51.554159  5226 solver.cpp:218] Iteration 12 (3.16785 iter/s, 3.78805s/12 iters), loss = 5.28726
I0406 07:07:51.554189  5226 solver.cpp:237]     Train net output #0: loss = 5.28726 (* 1 = 5.28726 loss)
I0406 07:07:51.554195  5226 sgd_solver.cpp:105] Iteration 12, lr = 0.01
I0406 07:07:56.632417  5226 solver.cpp:218] Iteration 24 (2.36305 iter/s, 5.07818s/12 iters), loss = 5.27883
I0406 07:07:56.632458  5226 solver.cpp:237]     Train net output #0: loss = 5.27883 (* 1 = 5.27883 loss)
I0406 07:07:56.632463  5226 sgd_solver.cpp:105] Iteration 24, lr = 0.01
I0406 07:08:01.914950  5226 solver.cpp:218] Iteration 36 (2.27167 iter/s, 5.28245s/12 iters), loss = 5.28964
I0406 07:08:01.914988  5226 solver.cpp:237]     Train net output #0: loss = 5.28964 (* 1 = 5.28964 loss)
I0406 07:08:01.914994  5226 sgd_solver.cpp:105] Iteration 36, lr = 0.01
I0406 07:08:07.229256  5226 solver.cpp:218] Iteration 48 (2.25809 iter/s, 5.31423s/12 iters), loss = 5.30546
I0406 07:08:07.229291  5226 solver.cpp:237]     Train net output #0: loss = 5.30546 (* 1 = 5.30546 loss)
I0406 07:08:07.229296  5226 sgd_solver.cpp:105] Iteration 48, lr = 0.01
I0406 07:08:12.734804  5226 solver.cpp:218] Iteration 60 (2.17965 iter/s, 5.50547s/12 iters), loss = 5.26713
I0406 07:08:12.734925  5226 solver.cpp:237]     Train net output #0: loss = 5.26713 (* 1 = 5.26713 loss)
I0406 07:08:12.734932  5226 sgd_solver.cpp:105] Iteration 60, lr = 0.01
I0406 07:08:17.932544  5226 solver.cpp:218] Iteration 72 (2.30877 iter/s, 5.19757s/12 iters), loss = 5.32175
I0406 07:08:17.932590  5226 solver.cpp:237]     Train net output #0: loss = 5.32175 (* 1 = 5.32175 loss)
I0406 07:08:17.932600  5226 sgd_solver.cpp:105] Iteration 72, lr = 0.01
I0406 07:08:23.362987  5226 solver.cpp:218] Iteration 84 (2.2098 iter/s, 5.43035s/12 iters), loss = 5.29407
I0406 07:08:23.363035  5226 solver.cpp:237]     Train net output #0: loss = 5.29407 (* 1 = 5.29407 loss)
I0406 07:08:23.363044  5226 sgd_solver.cpp:105] Iteration 84, lr = 0.01
I0406 07:08:28.821043  5226 solver.cpp:218] Iteration 96 (2.19863 iter/s, 5.45796s/12 iters), loss = 5.27596
I0406 07:08:28.821087  5226 solver.cpp:237]     Train net output #0: loss = 5.27596 (* 1 = 5.27596 loss)
I0406 07:08:28.821095  5226 sgd_solver.cpp:105] Iteration 96, lr = 0.01
I0406 07:08:30.688700  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:08:31.006948  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_102.caffemodel
I0406 07:08:34.077026  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_102.solverstate
I0406 07:08:36.370083  5226 solver.cpp:330] Iteration 102, Testing net (#0)
I0406 07:08:36.370102  5226 net.cpp:676] Ignoring source layer train-data
I0406 07:08:40.734298  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:08:40.823169  5226 solver.cpp:397]     Test net output #0: accuracy = 0.00612745
I0406 07:08:40.823205  5226 solver.cpp:397]     Test net output #1: loss = 5.29375 (* 1 = 5.29375 loss)
I0406 07:08:42.808393  5226 solver.cpp:218] Iteration 108 (0.857927 iter/s, 13.9872s/12 iters), loss = 5.28957
I0406 07:08:42.808533  5226 solver.cpp:237]     Train net output #0: loss = 5.28957 (* 1 = 5.28957 loss)
I0406 07:08:42.808539  5226 sgd_solver.cpp:105] Iteration 108, lr = 0.01
I0406 07:08:47.928736  5226 solver.cpp:218] Iteration 120 (2.34368 iter/s, 5.12016s/12 iters), loss = 5.27194
I0406 07:08:47.928768  5226 solver.cpp:237]     Train net output #0: loss = 5.27194 (* 1 = 5.27194 loss)
I0406 07:08:47.928773  5226 sgd_solver.cpp:105] Iteration 120, lr = 0.01
I0406 07:08:53.277961  5226 solver.cpp:218] Iteration 132 (2.24335 iter/s, 5.34914s/12 iters), loss = 5.28037
I0406 07:08:53.277999  5226 solver.cpp:237]     Train net output #0: loss = 5.28037 (* 1 = 5.28037 loss)
I0406 07:08:53.278005  5226 sgd_solver.cpp:105] Iteration 132, lr = 0.01
I0406 07:08:58.564335  5226 solver.cpp:218] Iteration 144 (2.27003 iter/s, 5.28628s/12 iters), loss = 5.26267
I0406 07:08:58.564371  5226 solver.cpp:237]     Train net output #0: loss = 5.26267 (* 1 = 5.26267 loss)
I0406 07:08:58.564376  5226 sgd_solver.cpp:105] Iteration 144, lr = 0.01
I0406 07:09:03.603242  5226 solver.cpp:218] Iteration 156 (2.38151 iter/s, 5.03882s/12 iters), loss = 5.29929
I0406 07:09:03.603279  5226 solver.cpp:237]     Train net output #0: loss = 5.29929 (* 1 = 5.29929 loss)
I0406 07:09:03.603286  5226 sgd_solver.cpp:105] Iteration 156, lr = 0.01
I0406 07:09:08.798015  5226 solver.cpp:218] Iteration 168 (2.31006 iter/s, 5.19468s/12 iters), loss = 5.26474
I0406 07:09:08.798054  5226 solver.cpp:237]     Train net output #0: loss = 5.26474 (* 1 = 5.26474 loss)
I0406 07:09:08.798059  5226 sgd_solver.cpp:105] Iteration 168, lr = 0.01
I0406 07:09:14.158486  5226 solver.cpp:218] Iteration 180 (2.23865 iter/s, 5.36038s/12 iters), loss = 5.29722
I0406 07:09:14.158578  5226 solver.cpp:237]     Train net output #0: loss = 5.29722 (* 1 = 5.29722 loss)
I0406 07:09:14.158586  5226 sgd_solver.cpp:105] Iteration 180, lr = 0.01
I0406 07:09:19.540362  5226 solver.cpp:218] Iteration 192 (2.22976 iter/s, 5.38173s/12 iters), loss = 5.20534
I0406 07:09:19.540397  5226 solver.cpp:237]     Train net output #0: loss = 5.20534 (* 1 = 5.20534 loss)
I0406 07:09:19.540402  5226 sgd_solver.cpp:105] Iteration 192, lr = 0.01
I0406 07:09:23.686969  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:09:24.396731  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_204.caffemodel
I0406 07:09:27.482285  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_204.solverstate
I0406 07:09:29.789227  5226 solver.cpp:330] Iteration 204, Testing net (#0)
I0406 07:09:29.789250  5226 net.cpp:676] Ignoring source layer train-data
I0406 07:09:33.984045  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:09:34.107908  5226 solver.cpp:397]     Test net output #0: accuracy = 0.0104167
I0406 07:09:34.107944  5226 solver.cpp:397]     Test net output #1: loss = 5.21406 (* 1 = 5.21406 loss)
I0406 07:09:34.251067  5226 solver.cpp:218] Iteration 204 (0.815741 iter/s, 14.7106s/12 iters), loss = 5.15916
I0406 07:09:34.252631  5226 solver.cpp:237]     Train net output #0: loss = 5.15916 (* 1 = 5.15916 loss)
I0406 07:09:34.252645  5226 sgd_solver.cpp:105] Iteration 204, lr = 0.01
I0406 07:09:38.743819  5226 solver.cpp:218] Iteration 216 (2.67192 iter/s, 4.49114s/12 iters), loss = 5.22728
I0406 07:09:38.743860  5226 solver.cpp:237]     Train net output #0: loss = 5.22728 (* 1 = 5.22728 loss)
I0406 07:09:38.743865  5226 sgd_solver.cpp:105] Iteration 216, lr = 0.01
I0406 07:09:44.088773  5226 solver.cpp:218] Iteration 228 (2.24515 iter/s, 5.34486s/12 iters), loss = 5.22056
I0406 07:09:44.088812  5226 solver.cpp:237]     Train net output #0: loss = 5.22056 (* 1 = 5.22056 loss)
I0406 07:09:44.088819  5226 sgd_solver.cpp:105] Iteration 228, lr = 0.01
I0406 07:09:49.379869  5226 solver.cpp:218] Iteration 240 (2.268 iter/s, 5.291s/12 iters), loss = 5.18359
I0406 07:09:49.380000  5226 solver.cpp:237]     Train net output #0: loss = 5.18359 (* 1 = 5.18359 loss)
I0406 07:09:49.380007  5226 sgd_solver.cpp:105] Iteration 240, lr = 0.01
I0406 07:09:54.555052  5226 solver.cpp:218] Iteration 252 (2.31884 iter/s, 5.175s/12 iters), loss = 5.2487
I0406 07:09:54.555094  5226 solver.cpp:237]     Train net output #0: loss = 5.2487 (* 1 = 5.2487 loss)
I0406 07:09:54.555099  5226 sgd_solver.cpp:105] Iteration 252, lr = 0.01
I0406 07:09:59.578614  5226 solver.cpp:218] Iteration 264 (2.38879 iter/s, 5.02346s/12 iters), loss = 5.14489
I0406 07:09:59.578672  5226 solver.cpp:237]     Train net output #0: loss = 5.14489 (* 1 = 5.14489 loss)
I0406 07:09:59.578681  5226 sgd_solver.cpp:105] Iteration 264, lr = 0.01
I0406 07:10:04.863327  5226 solver.cpp:218] Iteration 276 (2.27075 iter/s, 5.28461s/12 iters), loss = 5.12572
I0406 07:10:04.863373  5226 solver.cpp:237]     Train net output #0: loss = 5.12572 (* 1 = 5.12572 loss)
I0406 07:10:04.863379  5226 sgd_solver.cpp:105] Iteration 276, lr = 0.01
I0406 07:10:10.183627  5226 solver.cpp:218] Iteration 288 (2.25555 iter/s, 5.3202s/12 iters), loss = 5.16231
I0406 07:10:10.183665  5226 solver.cpp:237]     Train net output #0: loss = 5.16231 (* 1 = 5.16231 loss)
I0406 07:10:10.183670  5226 sgd_solver.cpp:105] Iteration 288, lr = 0.01
I0406 07:10:15.424337  5226 solver.cpp:218] Iteration 300 (2.2898 iter/s, 5.24062s/12 iters), loss = 5.24297
I0406 07:10:15.424374  5226 solver.cpp:237]     Train net output #0: loss = 5.24297 (* 1 = 5.24297 loss)
I0406 07:10:15.424379  5226 sgd_solver.cpp:105] Iteration 300, lr = 0.01
I0406 07:10:16.467451  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:10:17.586361  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_306.caffemodel
I0406 07:10:20.719285  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_306.solverstate
I0406 07:10:23.047116  5226 solver.cpp:330] Iteration 306, Testing net (#0)
I0406 07:10:23.047137  5226 net.cpp:676] Ignoring source layer train-data
I0406 07:10:27.161191  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:10:27.317126  5226 solver.cpp:397]     Test net output #0: accuracy = 0.0104167
I0406 07:10:27.317157  5226 solver.cpp:397]     Test net output #1: loss = 5.16469 (* 1 = 5.16469 loss)
I0406 07:10:29.220289  5226 solver.cpp:218] Iteration 312 (0.869829 iter/s, 13.7958s/12 iters), loss = 5.15112
I0406 07:10:29.220326  5226 solver.cpp:237]     Train net output #0: loss = 5.15112 (* 1 = 5.15112 loss)
I0406 07:10:29.220332  5226 sgd_solver.cpp:105] Iteration 312, lr = 0.01
I0406 07:10:34.662358  5226 solver.cpp:218] Iteration 324 (2.20508 iter/s, 5.44198s/12 iters), loss = 5.23607
I0406 07:10:34.662395  5226 solver.cpp:237]     Train net output #0: loss = 5.23607 (* 1 = 5.23607 loss)
I0406 07:10:34.662400  5226 sgd_solver.cpp:105] Iteration 324, lr = 0.01
I0406 07:10:40.053938  5226 solver.cpp:218] Iteration 336 (2.22573 iter/s, 5.39149s/12 iters), loss = 5.15472
I0406 07:10:40.053977  5226 solver.cpp:237]     Train net output #0: loss = 5.15472 (* 1 = 5.15472 loss)
I0406 07:10:40.053982  5226 sgd_solver.cpp:105] Iteration 336, lr = 0.01
I0406 07:10:45.245923  5226 solver.cpp:218] Iteration 348 (2.31129 iter/s, 5.1919s/12 iters), loss = 5.11753
I0406 07:10:45.245961  5226 solver.cpp:237]     Train net output #0: loss = 5.11753 (* 1 = 5.11753 loss)
I0406 07:10:45.245967  5226 sgd_solver.cpp:105] Iteration 348, lr = 0.01
I0406 07:10:50.659412  5226 solver.cpp:218] Iteration 360 (2.21672 iter/s, 5.41341s/12 iters), loss = 5.19051
I0406 07:10:50.659442  5226 solver.cpp:237]     Train net output #0: loss = 5.19051 (* 1 = 5.19051 loss)
I0406 07:10:50.659448  5226 sgd_solver.cpp:105] Iteration 360, lr = 0.01
I0406 07:10:56.060766  5226 solver.cpp:218] Iteration 372 (2.2217 iter/s, 5.40127s/12 iters), loss = 5.1577
I0406 07:10:56.060873  5226 solver.cpp:237]     Train net output #0: loss = 5.1577 (* 1 = 5.1577 loss)
I0406 07:10:56.060889  5226 sgd_solver.cpp:105] Iteration 372, lr = 0.01
I0406 07:11:01.440582  5226 solver.cpp:218] Iteration 384 (2.23063 iter/s, 5.37965s/12 iters), loss = 5.20289
I0406 07:11:01.440636  5226 solver.cpp:237]     Train net output #0: loss = 5.20289 (* 1 = 5.20289 loss)
I0406 07:11:01.440649  5226 sgd_solver.cpp:105] Iteration 384, lr = 0.01
I0406 07:11:06.635514  5226 solver.cpp:218] Iteration 396 (2.30998 iter/s, 5.19484s/12 iters), loss = 5.13047
I0406 07:11:06.635550  5226 solver.cpp:237]     Train net output #0: loss = 5.13047 (* 1 = 5.13047 loss)
I0406 07:11:06.635555  5226 sgd_solver.cpp:105] Iteration 396, lr = 0.01
I0406 07:11:09.925401  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:11:11.458490  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_408.caffemodel
I0406 07:11:14.464010  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_408.solverstate
I0406 07:11:16.765609  5226 solver.cpp:330] Iteration 408, Testing net (#0)
I0406 07:11:16.765626  5226 net.cpp:676] Ignoring source layer train-data
I0406 07:11:20.906955  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:11:21.115682  5226 solver.cpp:397]     Test net output #0: accuracy = 0.0147059
I0406 07:11:21.115717  5226 solver.cpp:397]     Test net output #1: loss = 5.12747 (* 1 = 5.12747 loss)
I0406 07:11:21.258468  5226 solver.cpp:218] Iteration 408 (0.820636 iter/s, 14.6228s/12 iters), loss = 5.12815
I0406 07:11:21.258514  5226 solver.cpp:237]     Train net output #0: loss = 5.12815 (* 1 = 5.12815 loss)
I0406 07:11:21.258522  5226 sgd_solver.cpp:105] Iteration 408, lr = 0.01
I0406 07:11:25.609107  5226 solver.cpp:218] Iteration 420 (2.75827 iter/s, 4.35055s/12 iters), loss = 5.093
I0406 07:11:25.609150  5226 solver.cpp:237]     Train net output #0: loss = 5.093 (* 1 = 5.093 loss)
I0406 07:11:25.609156  5226 sgd_solver.cpp:105] Iteration 420, lr = 0.01
I0406 07:11:30.603524  5226 solver.cpp:218] Iteration 432 (2.40273 iter/s, 4.99433s/12 iters), loss = 5.04658
I0406 07:11:30.603638  5226 solver.cpp:237]     Train net output #0: loss = 5.04658 (* 1 = 5.04658 loss)
I0406 07:11:30.603644  5226 sgd_solver.cpp:105] Iteration 432, lr = 0.01
I0406 07:11:35.849427  5226 solver.cpp:218] Iteration 444 (2.28757 iter/s, 5.24575s/12 iters), loss = 5.11776
I0406 07:11:35.849462  5226 solver.cpp:237]     Train net output #0: loss = 5.11776 (* 1 = 5.11776 loss)
I0406 07:11:35.849467  5226 sgd_solver.cpp:105] Iteration 444, lr = 0.01
I0406 07:11:41.185731  5226 solver.cpp:218] Iteration 456 (2.24878 iter/s, 5.33622s/12 iters), loss = 5.15248
I0406 07:11:41.185772  5226 solver.cpp:237]     Train net output #0: loss = 5.15248 (* 1 = 5.15248 loss)
I0406 07:11:41.185779  5226 sgd_solver.cpp:105] Iteration 456, lr = 0.01
I0406 07:11:46.489115  5226 solver.cpp:218] Iteration 468 (2.26274 iter/s, 5.3033s/12 iters), loss = 5.08704
I0406 07:11:46.489152  5226 solver.cpp:237]     Train net output #0: loss = 5.08704 (* 1 = 5.08704 loss)
I0406 07:11:46.489157  5226 sgd_solver.cpp:105] Iteration 468, lr = 0.01
I0406 07:11:51.581387  5226 solver.cpp:218] Iteration 480 (2.35655 iter/s, 5.09219s/12 iters), loss = 5.00496
I0406 07:11:51.581419  5226 solver.cpp:237]     Train net output #0: loss = 5.00496 (* 1 = 5.00496 loss)
I0406 07:11:51.581424  5226 sgd_solver.cpp:105] Iteration 480, lr = 0.01
I0406 07:11:56.632428  5226 solver.cpp:218] Iteration 492 (2.37579 iter/s, 5.05096s/12 iters), loss = 5.11756
I0406 07:11:56.632467  5226 solver.cpp:237]     Train net output #0: loss = 5.11756 (* 1 = 5.11756 loss)
I0406 07:11:56.632472  5226 sgd_solver.cpp:105] Iteration 492, lr = 0.01
I0406 07:12:01.945720  5226 solver.cpp:218] Iteration 504 (2.25852 iter/s, 5.3132s/12 iters), loss = 5.04789
I0406 07:12:01.945844  5226 solver.cpp:237]     Train net output #0: loss = 5.04789 (* 1 = 5.04789 loss)
I0406 07:12:01.945853  5226 sgd_solver.cpp:105] Iteration 504, lr = 0.01
I0406 07:12:02.175249  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:12:04.093015  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_510.caffemodel
I0406 07:12:07.163301  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_510.solverstate
I0406 07:12:09.464279  5226 solver.cpp:330] Iteration 510, Testing net (#0)
I0406 07:12:09.464299  5226 net.cpp:676] Ignoring source layer train-data
I0406 07:12:13.557585  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:12:13.801589  5226 solver.cpp:397]     Test net output #0: accuracy = 0.0214461
I0406 07:12:13.801616  5226 solver.cpp:397]     Test net output #1: loss = 5.05785 (* 1 = 5.05785 loss)
I0406 07:12:15.738641  5226 solver.cpp:218] Iteration 516 (0.870026 iter/s, 13.7927s/12 iters), loss = 5.09813
I0406 07:12:15.738682  5226 solver.cpp:237]     Train net output #0: loss = 5.09813 (* 1 = 5.09813 loss)
I0406 07:12:15.738687  5226 sgd_solver.cpp:105] Iteration 516, lr = 0.01
I0406 07:12:21.254230  5226 solver.cpp:218] Iteration 528 (2.17569 iter/s, 5.5155s/12 iters), loss = 5.16702
I0406 07:12:21.254266  5226 solver.cpp:237]     Train net output #0: loss = 5.16702 (* 1 = 5.16702 loss)
I0406 07:12:21.254271  5226 sgd_solver.cpp:105] Iteration 528, lr = 0.01
I0406 07:12:26.438645  5226 solver.cpp:218] Iteration 540 (2.31467 iter/s, 5.18433s/12 iters), loss = 5.01441
I0406 07:12:26.438683  5226 solver.cpp:237]     Train net output #0: loss = 5.01441 (* 1 = 5.01441 loss)
I0406 07:12:26.438688  5226 sgd_solver.cpp:105] Iteration 540, lr = 0.01
I0406 07:12:31.549919  5226 solver.cpp:218] Iteration 552 (2.34779 iter/s, 5.11119s/12 iters), loss = 5.1222
I0406 07:12:31.549957  5226 solver.cpp:237]     Train net output #0: loss = 5.1222 (* 1 = 5.1222 loss)
I0406 07:12:31.549962  5226 sgd_solver.cpp:105] Iteration 552, lr = 0.01
I0406 07:12:37.008781  5226 solver.cpp:218] Iteration 564 (2.1983 iter/s, 5.45877s/12 iters), loss = 5.0354
I0406 07:12:37.008937  5226 solver.cpp:237]     Train net output #0: loss = 5.0354 (* 1 = 5.0354 loss)
I0406 07:12:37.008946  5226 sgd_solver.cpp:105] Iteration 564, lr = 0.01
I0406 07:12:42.297931  5226 solver.cpp:218] Iteration 576 (2.26888 iter/s, 5.28895s/12 iters), loss = 4.90123
I0406 07:12:42.297973  5226 solver.cpp:237]     Train net output #0: loss = 4.90123 (* 1 = 4.90123 loss)
I0406 07:12:42.297979  5226 sgd_solver.cpp:105] Iteration 576, lr = 0.01
I0406 07:12:47.422849  5226 solver.cpp:218] Iteration 588 (2.34154 iter/s, 5.12483s/12 iters), loss = 4.99606
I0406 07:12:47.422886  5226 solver.cpp:237]     Train net output #0: loss = 4.99606 (* 1 = 4.99606 loss)
I0406 07:12:47.422891  5226 sgd_solver.cpp:105] Iteration 588, lr = 0.01
I0406 07:12:52.957031  5226 solver.cpp:218] Iteration 600 (2.16838 iter/s, 5.5341s/12 iters), loss = 5.05287
I0406 07:12:52.957079  5226 solver.cpp:237]     Train net output #0: loss = 5.05287 (* 1 = 5.05287 loss)
I0406 07:12:52.957087  5226 sgd_solver.cpp:105] Iteration 600, lr = 0.01
I0406 07:12:55.310144  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:12:57.693874  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_612.caffemodel
I0406 07:13:00.691838  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_612.solverstate
I0406 07:13:02.985356  5226 solver.cpp:330] Iteration 612, Testing net (#0)
I0406 07:13:02.985375  5226 net.cpp:676] Ignoring source layer train-data
I0406 07:13:07.023017  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:13:07.305249  5226 solver.cpp:397]     Test net output #0: accuracy = 0.0232843
I0406 07:13:07.305276  5226 solver.cpp:397]     Test net output #1: loss = 5.0075 (* 1 = 5.0075 loss)
I0406 07:13:07.446513  5226 solver.cpp:218] Iteration 612 (0.828195 iter/s, 14.4893s/12 iters), loss = 4.98406
I0406 07:13:07.446553  5226 solver.cpp:237]     Train net output #0: loss = 4.98406 (* 1 = 4.98406 loss)
I0406 07:13:07.446558  5226 sgd_solver.cpp:105] Iteration 612, lr = 0.01
I0406 07:13:11.916764  5226 solver.cpp:218] Iteration 624 (2.68446 iter/s, 4.47017s/12 iters), loss = 4.91334
I0406 07:13:11.916800  5226 solver.cpp:237]     Train net output #0: loss = 4.91334 (* 1 = 4.91334 loss)
I0406 07:13:11.916805  5226 sgd_solver.cpp:105] Iteration 624, lr = 0.01
I0406 07:13:17.145615  5226 solver.cpp:218] Iteration 636 (2.295 iter/s, 5.22876s/12 iters), loss = 5.01883
I0406 07:13:17.145668  5226 solver.cpp:237]     Train net output #0: loss = 5.01883 (* 1 = 5.01883 loss)
I0406 07:13:17.145679  5226 sgd_solver.cpp:105] Iteration 636, lr = 0.01
I0406 07:13:22.233357  5226 solver.cpp:218] Iteration 648 (2.35866 iter/s, 5.08764s/12 iters), loss = 4.85558
I0406 07:13:22.233395  5226 solver.cpp:237]     Train net output #0: loss = 4.85558 (* 1 = 4.85558 loss)
I0406 07:13:22.233402  5226 sgd_solver.cpp:105] Iteration 648, lr = 0.01
I0406 07:13:27.531522  5226 solver.cpp:218] Iteration 660 (2.26498 iter/s, 5.29807s/12 iters), loss = 4.93355
I0406 07:13:27.531569  5226 solver.cpp:237]     Train net output #0: loss = 4.93355 (* 1 = 4.93355 loss)
I0406 07:13:27.531586  5226 sgd_solver.cpp:105] Iteration 660, lr = 0.01
I0406 07:13:32.876922  5226 solver.cpp:218] Iteration 672 (2.24496 iter/s, 5.34531s/12 iters), loss = 5.00926
I0406 07:13:32.876958  5226 solver.cpp:237]     Train net output #0: loss = 5.00926 (* 1 = 5.00926 loss)
I0406 07:13:32.876965  5226 sgd_solver.cpp:105] Iteration 672, lr = 0.01
I0406 07:13:38.009531  5226 solver.cpp:218] Iteration 684 (2.33803 iter/s, 5.13253s/12 iters), loss = 4.87837
I0406 07:13:38.009645  5226 solver.cpp:237]     Train net output #0: loss = 4.87837 (* 1 = 4.87837 loss)
I0406 07:13:38.009651  5226 sgd_solver.cpp:105] Iteration 684, lr = 0.01
I0406 07:13:38.772302  5226 blocking_queue.cpp:49] Waiting for data
I0406 07:13:43.268584  5226 solver.cpp:218] Iteration 696 (2.28185 iter/s, 5.25889s/12 iters), loss = 4.82162
I0406 07:13:43.268623  5226 solver.cpp:237]     Train net output #0: loss = 4.82162 (* 1 = 4.82162 loss)
I0406 07:13:43.268628  5226 sgd_solver.cpp:105] Iteration 696, lr = 0.01
I0406 07:13:48.136029  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:13:48.567907  5226 solver.cpp:218] Iteration 708 (2.26448 iter/s, 5.29924s/12 iters), loss = 4.99635
I0406 07:13:48.567945  5226 solver.cpp:237]     Train net output #0: loss = 4.99635 (* 1 = 4.99635 loss)
I0406 07:13:48.567950  5226 sgd_solver.cpp:105] Iteration 708, lr = 0.01
I0406 07:13:50.559027  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_714.caffemodel
I0406 07:13:53.547910  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_714.solverstate
I0406 07:13:55.844179  5226 solver.cpp:330] Iteration 714, Testing net (#0)
I0406 07:13:55.844198  5226 net.cpp:676] Ignoring source layer train-data
I0406 07:13:59.964818  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:14:00.278504  5226 solver.cpp:397]     Test net output #0: accuracy = 0.0330882
I0406 07:14:00.278537  5226 solver.cpp:397]     Test net output #1: loss = 4.94868 (* 1 = 4.94868 loss)
I0406 07:14:02.088179  5226 solver.cpp:218] Iteration 720 (0.887565 iter/s, 13.5201s/12 iters), loss = 4.89392
I0406 07:14:02.088219  5226 solver.cpp:237]     Train net output #0: loss = 4.89392 (* 1 = 4.89392 loss)
I0406 07:14:02.088224  5226 sgd_solver.cpp:105] Iteration 720, lr = 0.01
I0406 07:14:07.110406  5226 solver.cpp:218] Iteration 732 (2.38942 iter/s, 5.02214s/12 iters), loss = 4.81985
I0406 07:14:07.110441  5226 solver.cpp:237]     Train net output #0: loss = 4.81985 (* 1 = 4.81985 loss)
I0406 07:14:07.110446  5226 sgd_solver.cpp:105] Iteration 732, lr = 0.01
I0406 07:14:12.401988  5226 solver.cpp:218] Iteration 744 (2.26779 iter/s, 5.2915s/12 iters), loss = 4.83772
I0406 07:14:12.402101  5226 solver.cpp:237]     Train net output #0: loss = 4.83772 (* 1 = 4.83772 loss)
I0406 07:14:12.402107  5226 sgd_solver.cpp:105] Iteration 744, lr = 0.01
I0406 07:14:17.662477  5226 solver.cpp:218] Iteration 756 (2.28123 iter/s, 5.26033s/12 iters), loss = 4.68698
I0406 07:14:17.662514  5226 solver.cpp:237]     Train net output #0: loss = 4.68698 (* 1 = 4.68698 loss)
I0406 07:14:17.662519  5226 sgd_solver.cpp:105] Iteration 756, lr = 0.01
I0406 07:14:22.994112  5226 solver.cpp:218] Iteration 768 (2.25075 iter/s, 5.33155s/12 iters), loss = 4.84255
I0406 07:14:22.994148  5226 solver.cpp:237]     Train net output #0: loss = 4.84255 (* 1 = 4.84255 loss)
I0406 07:14:22.994153  5226 sgd_solver.cpp:105] Iteration 768, lr = 0.01
I0406 07:14:28.278244  5226 solver.cpp:218] Iteration 780 (2.27099 iter/s, 5.28404s/12 iters), loss = 4.83813
I0406 07:14:28.278290  5226 solver.cpp:237]     Train net output #0: loss = 4.83813 (* 1 = 4.83813 loss)
I0406 07:14:28.278298  5226 sgd_solver.cpp:105] Iteration 780, lr = 0.01
I0406 07:14:33.531306  5226 solver.cpp:218] Iteration 792 (2.28442 iter/s, 5.25297s/12 iters), loss = 4.94625
I0406 07:14:33.531344  5226 solver.cpp:237]     Train net output #0: loss = 4.94625 (* 1 = 4.94625 loss)
I0406 07:14:33.531350  5226 sgd_solver.cpp:105] Iteration 792, lr = 0.01
I0406 07:14:38.829330  5226 solver.cpp:218] Iteration 804 (2.26503 iter/s, 5.29794s/12 iters), loss = 4.88964
I0406 07:14:38.829366  5226 solver.cpp:237]     Train net output #0: loss = 4.88964 (* 1 = 4.88964 loss)
I0406 07:14:38.829372  5226 sgd_solver.cpp:105] Iteration 804, lr = 0.01
I0406 07:14:40.611006  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:14:43.559803  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_816.caffemodel
I0406 07:14:46.568624  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_816.solverstate
I0406 07:14:48.891126  5226 solver.cpp:330] Iteration 816, Testing net (#0)
I0406 07:14:48.891149  5226 net.cpp:676] Ignoring source layer train-data
I0406 07:14:52.916862  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:14:53.263047  5226 solver.cpp:397]     Test net output #0: accuracy = 0.0471814
I0406 07:14:53.263080  5226 solver.cpp:397]     Test net output #1: loss = 4.81925 (* 1 = 4.81925 loss)
I0406 07:14:53.401698  5226 solver.cpp:218] Iteration 816 (0.823484 iter/s, 14.5722s/12 iters), loss = 4.82417
I0406 07:14:53.403260  5226 solver.cpp:237]     Train net output #0: loss = 4.82417 (* 1 = 4.82417 loss)
I0406 07:14:53.403273  5226 sgd_solver.cpp:105] Iteration 816, lr = 0.01
I0406 07:14:57.870254  5226 solver.cpp:218] Iteration 828 (2.68639 iter/s, 4.46696s/12 iters), loss = 4.73788
I0406 07:14:57.870301  5226 solver.cpp:237]     Train net output #0: loss = 4.73788 (* 1 = 4.73788 loss)
I0406 07:14:57.870309  5226 sgd_solver.cpp:105] Iteration 828, lr = 0.01
I0406 07:15:03.000788  5226 solver.cpp:218] Iteration 840 (2.33898 iter/s, 5.13044s/12 iters), loss = 4.64403
I0406 07:15:03.000824  5226 solver.cpp:237]     Train net output #0: loss = 4.64403 (* 1 = 4.64403 loss)
I0406 07:15:03.000829  5226 sgd_solver.cpp:105] Iteration 840, lr = 0.01
I0406 07:15:08.395009  5226 solver.cpp:218] Iteration 852 (2.22464 iter/s, 5.39413s/12 iters), loss = 4.7397
I0406 07:15:08.395047  5226 solver.cpp:237]     Train net output #0: loss = 4.7397 (* 1 = 4.7397 loss)
I0406 07:15:08.395053  5226 sgd_solver.cpp:105] Iteration 852, lr = 0.01
I0406 07:15:13.831234  5226 solver.cpp:218] Iteration 864 (2.20745 iter/s, 5.43613s/12 iters), loss = 4.67168
I0406 07:15:13.831343  5226 solver.cpp:237]     Train net output #0: loss = 4.67168 (* 1 = 4.67168 loss)
I0406 07:15:13.831351  5226 sgd_solver.cpp:105] Iteration 864, lr = 0.01
I0406 07:15:19.099928  5226 solver.cpp:218] Iteration 876 (2.27767 iter/s, 5.26854s/12 iters), loss = 4.74518
I0406 07:15:19.099965  5226 solver.cpp:237]     Train net output #0: loss = 4.74518 (* 1 = 4.74518 loss)
I0406 07:15:19.099970  5226 sgd_solver.cpp:105] Iteration 876, lr = 0.01
I0406 07:15:24.342908  5226 solver.cpp:218] Iteration 888 (2.28881 iter/s, 5.24289s/12 iters), loss = 4.78204
I0406 07:15:24.342947  5226 solver.cpp:237]     Train net output #0: loss = 4.78204 (* 1 = 4.78204 loss)
I0406 07:15:24.342952  5226 sgd_solver.cpp:105] Iteration 888, lr = 0.01
I0406 07:15:29.420219  5226 solver.cpp:218] Iteration 900 (2.36349 iter/s, 5.07723s/12 iters), loss = 4.7116
I0406 07:15:29.420266  5226 solver.cpp:237]     Train net output #0: loss = 4.7116 (* 1 = 4.7116 loss)
I0406 07:15:29.420274  5226 sgd_solver.cpp:105] Iteration 900, lr = 0.01
I0406 07:15:33.550194  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:15:34.800318  5226 solver.cpp:218] Iteration 912 (2.23048 iter/s, 5.38001s/12 iters), loss = 4.67847
I0406 07:15:34.800351  5226 solver.cpp:237]     Train net output #0: loss = 4.67847 (* 1 = 4.67847 loss)
I0406 07:15:34.800356  5226 sgd_solver.cpp:105] Iteration 912, lr = 0.01
I0406 07:15:36.759296  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_918.caffemodel
I0406 07:15:39.769183  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_918.solverstate
I0406 07:15:42.064821  5226 solver.cpp:330] Iteration 918, Testing net (#0)
I0406 07:15:42.064839  5226 net.cpp:676] Ignoring source layer train-data
I0406 07:15:45.926584  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:15:46.319641  5226 solver.cpp:397]     Test net output #0: accuracy = 0.0435049
I0406 07:15:46.319679  5226 solver.cpp:397]     Test net output #1: loss = 4.65492 (* 1 = 4.65492 loss)
I0406 07:15:48.186735  5226 solver.cpp:218] Iteration 924 (0.89644 iter/s, 13.3863s/12 iters), loss = 4.57518
I0406 07:15:48.186770  5226 solver.cpp:237]     Train net output #0: loss = 4.57518 (* 1 = 4.57518 loss)
I0406 07:15:48.186776  5226 sgd_solver.cpp:105] Iteration 924, lr = 0.01
I0406 07:15:53.646442  5226 solver.cpp:218] Iteration 936 (2.19796 iter/s, 5.45962s/12 iters), loss = 4.62903
I0406 07:15:53.646489  5226 solver.cpp:237]     Train net output #0: loss = 4.62903 (* 1 = 4.62903 loss)
I0406 07:15:53.646497  5226 sgd_solver.cpp:105] Iteration 936, lr = 0.01
I0406 07:15:58.949519  5226 solver.cpp:218] Iteration 948 (2.26288 iter/s, 5.30298s/12 iters), loss = 4.65031
I0406 07:15:58.949558  5226 solver.cpp:237]     Train net output #0: loss = 4.65031 (* 1 = 4.65031 loss)
I0406 07:15:58.949563  5226 sgd_solver.cpp:105] Iteration 948, lr = 0.01
I0406 07:16:04.350941  5226 solver.cpp:218] Iteration 960 (2.22167 iter/s, 5.40133s/12 iters), loss = 4.67815
I0406 07:16:04.350975  5226 solver.cpp:237]     Train net output #0: loss = 4.67815 (* 1 = 4.67815 loss)
I0406 07:16:04.350980  5226 sgd_solver.cpp:105] Iteration 960, lr = 0.01
I0406 07:16:09.718039  5226 solver.cpp:218] Iteration 972 (2.23588 iter/s, 5.36701s/12 iters), loss = 4.47251
I0406 07:16:09.718076  5226 solver.cpp:237]     Train net output #0: loss = 4.47251 (* 1 = 4.47251 loss)
I0406 07:16:09.718082  5226 sgd_solver.cpp:105] Iteration 972, lr = 0.01
I0406 07:16:15.170527  5226 solver.cpp:218] Iteration 984 (2.20087 iter/s, 5.4524s/12 iters), loss = 4.48967
I0406 07:16:15.170567  5226 solver.cpp:237]     Train net output #0: loss = 4.48967 (* 1 = 4.48967 loss)
I0406 07:16:15.170573  5226 sgd_solver.cpp:105] Iteration 984, lr = 0.01
I0406 07:16:20.369804  5226 solver.cpp:218] Iteration 996 (2.30805 iter/s, 5.19919s/12 iters), loss = 4.51546
I0406 07:16:20.369899  5226 solver.cpp:237]     Train net output #0: loss = 4.51546 (* 1 = 4.51546 loss)
I0406 07:16:20.369906  5226 sgd_solver.cpp:105] Iteration 996, lr = 0.01
I0406 07:16:25.632683  5226 solver.cpp:218] Iteration 1008 (2.28018 iter/s, 5.26273s/12 iters), loss = 4.56256
I0406 07:16:25.632732  5226 solver.cpp:237]     Train net output #0: loss = 4.56256 (* 1 = 4.56256 loss)
I0406 07:16:25.632741  5226 sgd_solver.cpp:105] Iteration 1008, lr = 0.01
I0406 07:16:26.735141  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:16:30.381785  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1020.caffemodel
I0406 07:16:33.392508  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1020.solverstate
I0406 07:16:35.701859  5226 solver.cpp:330] Iteration 1020, Testing net (#0)
I0406 07:16:35.701879  5226 net.cpp:676] Ignoring source layer train-data
I0406 07:16:39.570278  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:16:39.992810  5226 solver.cpp:397]     Test net output #0: accuracy = 0.0606618
I0406 07:16:39.992843  5226 solver.cpp:397]     Test net output #1: loss = 4.50936 (* 1 = 4.50936 loss)
I0406 07:16:40.129634  5226 solver.cpp:218] Iteration 1020 (0.827769 iter/s, 14.4968s/12 iters), loss = 4.46469
I0406 07:16:40.129676  5226 solver.cpp:237]     Train net output #0: loss = 4.46469 (* 1 = 4.46469 loss)
I0406 07:16:40.129683  5226 sgd_solver.cpp:105] Iteration 1020, lr = 0.01
I0406 07:16:44.591004  5226 solver.cpp:218] Iteration 1032 (2.68981 iter/s, 4.46129s/12 iters), loss = 4.75816
I0406 07:16:44.591042  5226 solver.cpp:237]     Train net output #0: loss = 4.75816 (* 1 = 4.75816 loss)
I0406 07:16:44.591048  5226 sgd_solver.cpp:105] Iteration 1032, lr = 0.01
I0406 07:16:49.912254  5226 solver.cpp:218] Iteration 1044 (2.25515 iter/s, 5.32116s/12 iters), loss = 4.60476
I0406 07:16:49.912292  5226 solver.cpp:237]     Train net output #0: loss = 4.60476 (* 1 = 4.60476 loss)
I0406 07:16:49.912297  5226 sgd_solver.cpp:105] Iteration 1044, lr = 0.01
I0406 07:16:55.320588  5226 solver.cpp:218] Iteration 1056 (2.21883 iter/s, 5.40825s/12 iters), loss = 4.46903
I0406 07:16:55.320719  5226 solver.cpp:237]     Train net output #0: loss = 4.46903 (* 1 = 4.46903 loss)
I0406 07:16:55.320724  5226 sgd_solver.cpp:105] Iteration 1056, lr = 0.01
I0406 07:17:00.592834  5226 solver.cpp:218] Iteration 1068 (2.27615 iter/s, 5.27207s/12 iters), loss = 4.37665
I0406 07:17:00.592880  5226 solver.cpp:237]     Train net output #0: loss = 4.37665 (* 1 = 4.37665 loss)
I0406 07:17:00.592893  5226 sgd_solver.cpp:105] Iteration 1068, lr = 0.01
I0406 07:17:05.919016  5226 solver.cpp:218] Iteration 1080 (2.25306 iter/s, 5.32609s/12 iters), loss = 4.16893
I0406 07:17:05.919055  5226 solver.cpp:237]     Train net output #0: loss = 4.16893 (* 1 = 4.16893 loss)
I0406 07:17:05.919060  5226 sgd_solver.cpp:105] Iteration 1080, lr = 0.01
I0406 07:17:11.199740  5226 solver.cpp:218] Iteration 1092 (2.27245 iter/s, 5.28064s/12 iters), loss = 4.47528
I0406 07:17:11.199777  5226 solver.cpp:237]     Train net output #0: loss = 4.47528 (* 1 = 4.47528 loss)
I0406 07:17:11.199782  5226 sgd_solver.cpp:105] Iteration 1092, lr = 0.01
I0406 07:17:16.439673  5226 solver.cpp:218] Iteration 1104 (2.29014 iter/s, 5.23985s/12 iters), loss = 4.36203
I0406 07:17:16.439711  5226 solver.cpp:237]     Train net output #0: loss = 4.36203 (* 1 = 4.36203 loss)
I0406 07:17:16.439716  5226 sgd_solver.cpp:105] Iteration 1104, lr = 0.01
I0406 07:17:19.734458  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:17:21.766516  5226 solver.cpp:218] Iteration 1116 (2.25278 iter/s, 5.32676s/12 iters), loss = 4.36978
I0406 07:17:21.766553  5226 solver.cpp:237]     Train net output #0: loss = 4.36978 (* 1 = 4.36978 loss)
I0406 07:17:21.766558  5226 sgd_solver.cpp:105] Iteration 1116, lr = 0.01
I0406 07:17:23.851191  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1122.caffemodel
I0406 07:17:26.857003  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1122.solverstate
I0406 07:17:29.156157  5226 solver.cpp:330] Iteration 1122, Testing net (#0)
I0406 07:17:29.156175  5226 net.cpp:676] Ignoring source layer train-data
I0406 07:17:32.953393  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:17:33.422466  5226 solver.cpp:397]     Test net output #0: accuracy = 0.09375
I0406 07:17:33.422498  5226 solver.cpp:397]     Test net output #1: loss = 4.32848 (* 1 = 4.32848 loss)
I0406 07:17:35.250597  5226 solver.cpp:218] Iteration 1128 (0.889947 iter/s, 13.4839s/12 iters), loss = 4.14385
I0406 07:17:35.250631  5226 solver.cpp:237]     Train net output #0: loss = 4.14385 (* 1 = 4.14385 loss)
I0406 07:17:35.250636  5226 sgd_solver.cpp:105] Iteration 1128, lr = 0.01
I0406 07:17:40.446559  5226 solver.cpp:218] Iteration 1140 (2.30952 iter/s, 5.19588s/12 iters), loss = 4.32845
I0406 07:17:40.446599  5226 solver.cpp:237]     Train net output #0: loss = 4.32845 (* 1 = 4.32845 loss)
I0406 07:17:40.446605  5226 sgd_solver.cpp:105] Iteration 1140, lr = 0.01
I0406 07:17:45.603284  5226 solver.cpp:218] Iteration 1152 (2.3271 iter/s, 5.15664s/12 iters), loss = 4.45601
I0406 07:17:45.603320  5226 solver.cpp:237]     Train net output #0: loss = 4.45601 (* 1 = 4.45601 loss)
I0406 07:17:45.603327  5226 sgd_solver.cpp:105] Iteration 1152, lr = 0.01
I0406 07:17:50.869220  5226 solver.cpp:218] Iteration 1164 (2.27883 iter/s, 5.26585s/12 iters), loss = 4.39584
I0406 07:17:50.869256  5226 solver.cpp:237]     Train net output #0: loss = 4.39584 (* 1 = 4.39584 loss)
I0406 07:17:50.869261  5226 sgd_solver.cpp:105] Iteration 1164, lr = 0.01
I0406 07:17:56.115993  5226 solver.cpp:218] Iteration 1176 (2.28716 iter/s, 5.24668s/12 iters), loss = 4.23961
I0406 07:17:56.116034  5226 solver.cpp:237]     Train net output #0: loss = 4.23961 (* 1 = 4.23961 loss)
I0406 07:17:56.116039  5226 sgd_solver.cpp:105] Iteration 1176, lr = 0.01
I0406 07:18:01.324441  5226 solver.cpp:218] Iteration 1188 (2.30399 iter/s, 5.20836s/12 iters), loss = 4.30155
I0406 07:18:01.324575  5226 solver.cpp:237]     Train net output #0: loss = 4.30155 (* 1 = 4.30155 loss)
I0406 07:18:01.324582  5226 sgd_solver.cpp:105] Iteration 1188, lr = 0.01
I0406 07:18:06.534363  5226 solver.cpp:218] Iteration 1200 (2.30338 iter/s, 5.20974s/12 iters), loss = 4.11412
I0406 07:18:06.534400  5226 solver.cpp:237]     Train net output #0: loss = 4.11412 (* 1 = 4.11412 loss)
I0406 07:18:06.534406  5226 sgd_solver.cpp:105] Iteration 1200, lr = 0.01
I0406 07:18:11.857005  5226 solver.cpp:218] Iteration 1212 (2.25456 iter/s, 5.32255s/12 iters), loss = 4.28069
I0406 07:18:11.857041  5226 solver.cpp:237]     Train net output #0: loss = 4.28069 (* 1 = 4.28069 loss)
I0406 07:18:11.857048  5226 sgd_solver.cpp:105] Iteration 1212, lr = 0.01
I0406 07:18:12.123777  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:18:16.567703  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1224.caffemodel
I0406 07:18:20.569676  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1224.solverstate
I0406 07:18:22.869602  5226 solver.cpp:330] Iteration 1224, Testing net (#0)
I0406 07:18:22.869621  5226 net.cpp:676] Ignoring source layer train-data
I0406 07:18:26.712373  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:18:27.216188  5226 solver.cpp:397]     Test net output #0: accuracy = 0.0968137
I0406 07:18:27.216219  5226 solver.cpp:397]     Test net output #1: loss = 4.26293 (* 1 = 4.26293 loss)
I0406 07:18:27.353430  5226 solver.cpp:218] Iteration 1224 (0.774379 iter/s, 15.4963s/12 iters), loss = 4.17032
I0406 07:18:27.353466  5226 solver.cpp:237]     Train net output #0: loss = 4.17032 (* 1 = 4.17032 loss)
I0406 07:18:27.353471  5226 sgd_solver.cpp:105] Iteration 1224, lr = 0.01
I0406 07:18:31.746218  5226 solver.cpp:218] Iteration 1236 (2.7318 iter/s, 4.39271s/12 iters), loss = 4.337
I0406 07:18:31.746299  5226 solver.cpp:237]     Train net output #0: loss = 4.337 (* 1 = 4.337 loss)
I0406 07:18:31.746305  5226 sgd_solver.cpp:105] Iteration 1236, lr = 0.01
I0406 07:18:37.014269  5226 solver.cpp:218] Iteration 1248 (2.27794 iter/s, 5.26792s/12 iters), loss = 4.01575
I0406 07:18:37.014307  5226 solver.cpp:237]     Train net output #0: loss = 4.01575 (* 1 = 4.01575 loss)
I0406 07:18:37.014312  5226 sgd_solver.cpp:105] Iteration 1248, lr = 0.01
I0406 07:18:42.187361  5226 solver.cpp:218] Iteration 1260 (2.31974 iter/s, 5.173s/12 iters), loss = 4.23174
I0406 07:18:42.187414  5226 solver.cpp:237]     Train net output #0: loss = 4.23174 (* 1 = 4.23174 loss)
I0406 07:18:42.187423  5226 sgd_solver.cpp:105] Iteration 1260, lr = 0.01
I0406 07:18:47.607379  5226 solver.cpp:218] Iteration 1272 (2.21406 iter/s, 5.41992s/12 iters), loss = 4.21252
I0406 07:18:47.607419  5226 solver.cpp:237]     Train net output #0: loss = 4.21252 (* 1 = 4.21252 loss)
I0406 07:18:47.607425  5226 sgd_solver.cpp:105] Iteration 1272, lr = 0.01
I0406 07:18:52.843992  5226 solver.cpp:218] Iteration 1284 (2.2916 iter/s, 5.23652s/12 iters), loss = 4.10824
I0406 07:18:52.844030  5226 solver.cpp:237]     Train net output #0: loss = 4.10824 (* 1 = 4.10824 loss)
I0406 07:18:52.844036  5226 sgd_solver.cpp:105] Iteration 1284, lr = 0.01
I0406 07:18:58.077533  5226 solver.cpp:218] Iteration 1296 (2.29294 iter/s, 5.23346s/12 iters), loss = 4.13007
I0406 07:18:58.077571  5226 solver.cpp:237]     Train net output #0: loss = 4.13007 (* 1 = 4.13007 loss)
I0406 07:18:58.077577  5226 sgd_solver.cpp:105] Iteration 1296, lr = 0.01
I0406 07:19:03.148684  5226 solver.cpp:218] Iteration 1308 (2.36637 iter/s, 5.07107s/12 iters), loss = 4.24438
I0406 07:19:03.148825  5226 solver.cpp:237]     Train net output #0: loss = 4.24438 (* 1 = 4.24438 loss)
I0406 07:19:03.148831  5226 sgd_solver.cpp:105] Iteration 1308, lr = 0.01
I0406 07:19:05.525895  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:19:08.181069  5226 solver.cpp:218] Iteration 1320 (2.38464 iter/s, 5.0322s/12 iters), loss = 4.06937
I0406 07:19:08.181105  5226 solver.cpp:237]     Train net output #0: loss = 4.06937 (* 1 = 4.06937 loss)
I0406 07:19:08.181110  5226 sgd_solver.cpp:105] Iteration 1320, lr = 0.01
I0406 07:19:10.296854  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1326.caffemodel
I0406 07:19:13.301491  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1326.solverstate
I0406 07:19:15.619369  5226 solver.cpp:330] Iteration 1326, Testing net (#0)
I0406 07:19:15.619387  5226 net.cpp:676] Ignoring source layer train-data
I0406 07:19:19.403321  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:19:19.952392  5226 solver.cpp:397]     Test net output #0: accuracy = 0.107843
I0406 07:19:19.952419  5226 solver.cpp:397]     Test net output #1: loss = 4.07016 (* 1 = 4.07016 loss)
I0406 07:19:21.803865  5226 solver.cpp:218] Iteration 1332 (0.880885 iter/s, 13.6227s/12 iters), loss = 3.95342
I0406 07:19:21.803905  5226 solver.cpp:237]     Train net output #0: loss = 3.95342 (* 1 = 3.95342 loss)
I0406 07:19:21.803910  5226 sgd_solver.cpp:105] Iteration 1332, lr = 0.01
I0406 07:19:27.465436  5226 solver.cpp:218] Iteration 1344 (2.11959 iter/s, 5.66148s/12 iters), loss = 4.07274
I0406 07:19:27.465473  5226 solver.cpp:237]     Train net output #0: loss = 4.07274 (* 1 = 4.07274 loss)
I0406 07:19:27.465478  5226 sgd_solver.cpp:105] Iteration 1344, lr = 0.01
I0406 07:19:32.821652  5226 solver.cpp:218] Iteration 1356 (2.24042 iter/s, 5.35613s/12 iters), loss = 3.89633
I0406 07:19:32.821687  5226 solver.cpp:237]     Train net output #0: loss = 3.89633 (* 1 = 3.89633 loss)
I0406 07:19:32.821692  5226 sgd_solver.cpp:105] Iteration 1356, lr = 0.01
I0406 07:19:38.148391  5226 solver.cpp:218] Iteration 1368 (2.25282 iter/s, 5.32666s/12 iters), loss = 3.79107
I0406 07:19:38.148489  5226 solver.cpp:237]     Train net output #0: loss = 3.79107 (* 1 = 3.79107 loss)
I0406 07:19:38.148496  5226 sgd_solver.cpp:105] Iteration 1368, lr = 0.01
I0406 07:19:39.411227  5226 blocking_queue.cpp:49] Waiting for data
I0406 07:19:43.461465  5226 solver.cpp:218] Iteration 1380 (2.25864 iter/s, 5.31292s/12 iters), loss = 3.95237
I0406 07:19:43.461511  5226 solver.cpp:237]     Train net output #0: loss = 3.95237 (* 1 = 3.95237 loss)
I0406 07:19:43.461519  5226 sgd_solver.cpp:105] Iteration 1380, lr = 0.01
I0406 07:19:48.507176  5226 solver.cpp:218] Iteration 1392 (2.3783 iter/s, 5.04562s/12 iters), loss = 3.79814
I0406 07:19:48.507210  5226 solver.cpp:237]     Train net output #0: loss = 3.79814 (* 1 = 3.79814 loss)
I0406 07:19:48.507215  5226 sgd_solver.cpp:105] Iteration 1392, lr = 0.01
I0406 07:19:53.712174  5226 solver.cpp:218] Iteration 1404 (2.30551 iter/s, 5.20492s/12 iters), loss = 3.86488
I0406 07:19:53.712210  5226 solver.cpp:237]     Train net output #0: loss = 3.86488 (* 1 = 3.86488 loss)
I0406 07:19:53.712215  5226 sgd_solver.cpp:105] Iteration 1404, lr = 0.01
I0406 07:19:58.415917  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:19:58.799854  5226 solver.cpp:218] Iteration 1416 (2.35868 iter/s, 5.0876s/12 iters), loss = 4.04274
I0406 07:19:58.799891  5226 solver.cpp:237]     Train net output #0: loss = 4.04274 (* 1 = 4.04274 loss)
I0406 07:19:58.799897  5226 sgd_solver.cpp:105] Iteration 1416, lr = 0.01
I0406 07:20:03.587568  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1428.caffemodel
I0406 07:20:06.554714  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1428.solverstate
I0406 07:20:08.849576  5226 solver.cpp:330] Iteration 1428, Testing net (#0)
I0406 07:20:08.849684  5226 net.cpp:676] Ignoring source layer train-data
I0406 07:20:12.575773  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:20:13.159407  5226 solver.cpp:397]     Test net output #0: accuracy = 0.136642
I0406 07:20:13.159436  5226 solver.cpp:397]     Test net output #1: loss = 3.89282 (* 1 = 3.89282 loss)
I0406 07:20:13.300719  5226 solver.cpp:218] Iteration 1428 (0.827545 iter/s, 14.5007s/12 iters), loss = 4.00985
I0406 07:20:13.300758  5226 solver.cpp:237]     Train net output #0: loss = 4.00985 (* 1 = 4.00985 loss)
I0406 07:20:13.300763  5226 sgd_solver.cpp:105] Iteration 1428, lr = 0.01
I0406 07:20:17.768146  5226 solver.cpp:218] Iteration 1440 (2.68616 iter/s, 4.46735s/12 iters), loss = 3.82675
I0406 07:20:17.768182  5226 solver.cpp:237]     Train net output #0: loss = 3.82675 (* 1 = 3.82675 loss)
I0406 07:20:17.768187  5226 sgd_solver.cpp:105] Iteration 1440, lr = 0.01
I0406 07:20:23.102926  5226 solver.cpp:218] Iteration 1452 (2.24943 iter/s, 5.33469s/12 iters), loss = 3.65307
I0406 07:20:23.102962  5226 solver.cpp:237]     Train net output #0: loss = 3.65307 (* 1 = 3.65307 loss)
I0406 07:20:23.102967  5226 sgd_solver.cpp:105] Iteration 1452, lr = 0.01
I0406 07:20:28.438781  5226 solver.cpp:218] Iteration 1464 (2.24897 iter/s, 5.33577s/12 iters), loss = 3.64242
I0406 07:20:28.438835  5226 solver.cpp:237]     Train net output #0: loss = 3.64242 (* 1 = 3.64242 loss)
I0406 07:20:28.438844  5226 sgd_solver.cpp:105] Iteration 1464, lr = 0.01
I0406 07:20:33.709247  5226 solver.cpp:218] Iteration 1476 (2.27688 iter/s, 5.27036s/12 iters), loss = 3.78536
I0406 07:20:33.709285  5226 solver.cpp:237]     Train net output #0: loss = 3.78536 (* 1 = 3.78536 loss)
I0406 07:20:33.709291  5226 sgd_solver.cpp:105] Iteration 1476, lr = 0.01
I0406 07:20:38.739935  5226 solver.cpp:218] Iteration 1488 (2.3854 iter/s, 5.03061s/12 iters), loss = 3.87757
I0406 07:20:38.739976  5226 solver.cpp:237]     Train net output #0: loss = 3.87757 (* 1 = 3.87757 loss)
I0406 07:20:38.739982  5226 sgd_solver.cpp:105] Iteration 1488, lr = 0.01
I0406 07:20:44.015082  5226 solver.cpp:218] Iteration 1500 (2.27486 iter/s, 5.27506s/12 iters), loss = 3.84732
I0406 07:20:44.015189  5226 solver.cpp:237]     Train net output #0: loss = 3.84732 (* 1 = 3.84732 loss)
I0406 07:20:44.015195  5226 sgd_solver.cpp:105] Iteration 1500, lr = 0.01
I0406 07:20:49.435204  5226 solver.cpp:218] Iteration 1512 (2.21404 iter/s, 5.41996s/12 iters), loss = 3.61355
I0406 07:20:49.435242  5226 solver.cpp:237]     Train net output #0: loss = 3.61355 (* 1 = 3.61355 loss)
I0406 07:20:49.435247  5226 sgd_solver.cpp:105] Iteration 1512, lr = 0.01
I0406 07:20:51.351210  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:20:54.696135  5226 solver.cpp:218] Iteration 1524 (2.281 iter/s, 5.26084s/12 iters), loss = 3.61983
I0406 07:20:54.696171  5226 solver.cpp:237]     Train net output #0: loss = 3.61983 (* 1 = 3.61983 loss)
I0406 07:20:54.696177  5226 sgd_solver.cpp:105] Iteration 1524, lr = 0.01
I0406 07:20:56.702572  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1530.caffemodel
I0406 07:20:59.719856  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1530.solverstate
I0406 07:21:02.838994  5226 solver.cpp:330] Iteration 1530, Testing net (#0)
I0406 07:21:02.839013  5226 net.cpp:676] Ignoring source layer train-data
I0406 07:21:06.563899  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:21:07.211084  5226 solver.cpp:397]     Test net output #0: accuracy = 0.137255
I0406 07:21:07.211119  5226 solver.cpp:397]     Test net output #1: loss = 3.81117 (* 1 = 3.81117 loss)
I0406 07:21:08.951932  5226 solver.cpp:218] Iteration 1536 (0.841771 iter/s, 14.2557s/12 iters), loss = 3.3157
I0406 07:21:08.951975  5226 solver.cpp:237]     Train net output #0: loss = 3.3157 (* 1 = 3.3157 loss)
I0406 07:21:08.951982  5226 sgd_solver.cpp:105] Iteration 1536, lr = 0.01
I0406 07:21:14.235147  5226 solver.cpp:218] Iteration 1548 (2.27138 iter/s, 5.28312s/12 iters), loss = 3.61828
I0406 07:21:14.235270  5226 solver.cpp:237]     Train net output #0: loss = 3.61828 (* 1 = 3.61828 loss)
I0406 07:21:14.235276  5226 sgd_solver.cpp:105] Iteration 1548, lr = 0.01
I0406 07:21:19.633162  5226 solver.cpp:218] Iteration 1560 (2.22311 iter/s, 5.39785s/12 iters), loss = 3.59642
I0406 07:21:19.633196  5226 solver.cpp:237]     Train net output #0: loss = 3.59642 (* 1 = 3.59642 loss)
I0406 07:21:19.633203  5226 sgd_solver.cpp:105] Iteration 1560, lr = 0.01
I0406 07:21:24.682137  5226 solver.cpp:218] Iteration 1572 (2.37676 iter/s, 5.04889s/12 iters), loss = 3.60166
I0406 07:21:24.682173  5226 solver.cpp:237]     Train net output #0: loss = 3.60166 (* 1 = 3.60166 loss)
I0406 07:21:24.682179  5226 sgd_solver.cpp:105] Iteration 1572, lr = 0.01
I0406 07:21:29.874276  5226 solver.cpp:218] Iteration 1584 (2.31122 iter/s, 5.19205s/12 iters), loss = 3.56729
I0406 07:21:29.874315  5226 solver.cpp:237]     Train net output #0: loss = 3.56729 (* 1 = 3.56729 loss)
I0406 07:21:29.874320  5226 sgd_solver.cpp:105] Iteration 1584, lr = 0.01
I0406 07:21:35.271520  5226 solver.cpp:218] Iteration 1596 (2.2234 iter/s, 5.39715s/12 iters), loss = 3.52012
I0406 07:21:35.271575  5226 solver.cpp:237]     Train net output #0: loss = 3.52012 (* 1 = 3.52012 loss)
I0406 07:21:35.271584  5226 sgd_solver.cpp:105] Iteration 1596, lr = 0.01
I0406 07:21:40.616628  5226 solver.cpp:218] Iteration 1608 (2.24509 iter/s, 5.34501s/12 iters), loss = 3.19874
I0406 07:21:40.616667  5226 solver.cpp:237]     Train net output #0: loss = 3.19874 (* 1 = 3.19874 loss)
I0406 07:21:40.616672  5226 sgd_solver.cpp:105] Iteration 1608, lr = 0.01
I0406 07:21:44.679390  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:21:45.899791  5226 solver.cpp:218] Iteration 1620 (2.27141 iter/s, 5.28307s/12 iters), loss = 3.49699
I0406 07:21:45.899833  5226 solver.cpp:237]     Train net output #0: loss = 3.49699 (* 1 = 3.49699 loss)
I0406 07:21:45.899839  5226 sgd_solver.cpp:105] Iteration 1620, lr = 0.01
I0406 07:21:50.518654  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1632.caffemodel
I0406 07:21:53.557855  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1632.solverstate
I0406 07:21:55.876688  5226 solver.cpp:330] Iteration 1632, Testing net (#0)
I0406 07:21:55.876708  5226 net.cpp:676] Ignoring source layer train-data
I0406 07:21:59.634577  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:22:00.334913  5226 solver.cpp:397]     Test net output #0: accuracy = 0.159926
I0406 07:22:00.334941  5226 solver.cpp:397]     Test net output #1: loss = 3.62449 (* 1 = 3.62449 loss)
I0406 07:22:00.469847  5226 solver.cpp:218] Iteration 1632 (0.823615 iter/s, 14.5699s/12 iters), loss = 3.51187
I0406 07:22:00.469897  5226 solver.cpp:237]     Train net output #0: loss = 3.51187 (* 1 = 3.51187 loss)
I0406 07:22:00.469905  5226 sgd_solver.cpp:105] Iteration 1632, lr = 0.01
I0406 07:22:04.779532  5226 solver.cpp:218] Iteration 1644 (2.78449 iter/s, 4.30959s/12 iters), loss = 3.80759
I0406 07:22:04.779585  5226 solver.cpp:237]     Train net output #0: loss = 3.80759 (* 1 = 3.80759 loss)
I0406 07:22:04.779595  5226 sgd_solver.cpp:105] Iteration 1644, lr = 0.01
I0406 07:22:10.212400  5226 solver.cpp:218] Iteration 1656 (2.20882 iter/s, 5.43276s/12 iters), loss = 3.4897
I0406 07:22:10.212438  5226 solver.cpp:237]     Train net output #0: loss = 3.4897 (* 1 = 3.4897 loss)
I0406 07:22:10.212445  5226 sgd_solver.cpp:105] Iteration 1656, lr = 0.01
I0406 07:22:15.386605  5226 solver.cpp:218] Iteration 1668 (2.31924 iter/s, 5.17412s/12 iters), loss = 3.34191
I0406 07:22:15.386731  5226 solver.cpp:237]     Train net output #0: loss = 3.34191 (* 1 = 3.34191 loss)
I0406 07:22:15.386739  5226 sgd_solver.cpp:105] Iteration 1668, lr = 0.01
I0406 07:22:20.414834  5226 solver.cpp:218] Iteration 1680 (2.38661 iter/s, 5.02806s/12 iters), loss = 3.44416
I0406 07:22:20.414873  5226 solver.cpp:237]     Train net output #0: loss = 3.44416 (* 1 = 3.44416 loss)
I0406 07:22:20.414880  5226 sgd_solver.cpp:105] Iteration 1680, lr = 0.01
I0406 07:22:25.622228  5226 solver.cpp:218] Iteration 1692 (2.30446 iter/s, 5.2073s/12 iters), loss = 3.32771
I0406 07:22:25.622267  5226 solver.cpp:237]     Train net output #0: loss = 3.32771 (* 1 = 3.32771 loss)
I0406 07:22:25.622272  5226 sgd_solver.cpp:105] Iteration 1692, lr = 0.01
I0406 07:22:30.817466  5226 solver.cpp:218] Iteration 1704 (2.30985 iter/s, 5.19515s/12 iters), loss = 3.08907
I0406 07:22:30.817505  5226 solver.cpp:237]     Train net output #0: loss = 3.08907 (* 1 = 3.08907 loss)
I0406 07:22:30.817510  5226 sgd_solver.cpp:105] Iteration 1704, lr = 0.01
I0406 07:22:36.112320  5226 solver.cpp:218] Iteration 1716 (2.26639 iter/s, 5.29477s/12 iters), loss = 3.45159
I0406 07:22:36.112361  5226 solver.cpp:237]     Train net output #0: loss = 3.45159 (* 1 = 3.45159 loss)
I0406 07:22:36.112366  5226 sgd_solver.cpp:105] Iteration 1716, lr = 0.01
I0406 07:22:37.144914  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:22:41.211019  5226 solver.cpp:218] Iteration 1728 (2.35358 iter/s, 5.09861s/12 iters), loss = 3.23265
I0406 07:22:41.211058  5226 solver.cpp:237]     Train net output #0: loss = 3.23265 (* 1 = 3.23265 loss)
I0406 07:22:41.211064  5226 sgd_solver.cpp:105] Iteration 1728, lr = 0.01
I0406 07:22:43.223479  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1734.caffemodel
I0406 07:22:46.239655  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1734.solverstate
I0406 07:22:48.545070  5226 solver.cpp:330] Iteration 1734, Testing net (#0)
I0406 07:22:48.545089  5226 net.cpp:676] Ignoring source layer train-data
I0406 07:22:52.158030  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:22:52.851001  5226 solver.cpp:397]     Test net output #0: accuracy = 0.166054
I0406 07:22:52.851029  5226 solver.cpp:397]     Test net output #1: loss = 3.62371 (* 1 = 3.62371 loss)
I0406 07:22:54.710803  5226 solver.cpp:218] Iteration 1740 (0.888912 iter/s, 13.4996s/12 iters), loss = 3.39027
I0406 07:22:54.710841  5226 solver.cpp:237]     Train net output #0: loss = 3.39027 (* 1 = 3.39027 loss)
I0406 07:22:54.710846  5226 sgd_solver.cpp:105] Iteration 1740, lr = 0.01
I0406 07:23:00.007306  5226 solver.cpp:218] Iteration 1752 (2.26568 iter/s, 5.29642s/12 iters), loss = 3.40304
I0406 07:23:00.007344  5226 solver.cpp:237]     Train net output #0: loss = 3.40304 (* 1 = 3.40304 loss)
I0406 07:23:00.007349  5226 sgd_solver.cpp:105] Iteration 1752, lr = 0.01
I0406 07:23:05.110262  5226 solver.cpp:218] Iteration 1764 (2.35162 iter/s, 5.10286s/12 iters), loss = 3.41792
I0406 07:23:05.110311  5226 solver.cpp:237]     Train net output #0: loss = 3.41792 (* 1 = 3.41792 loss)
I0406 07:23:05.110318  5226 sgd_solver.cpp:105] Iteration 1764, lr = 0.01
I0406 07:23:10.326303  5226 solver.cpp:218] Iteration 1776 (2.30064 iter/s, 5.21595s/12 iters), loss = 3.0336
I0406 07:23:10.326349  5226 solver.cpp:237]     Train net output #0: loss = 3.0336 (* 1 = 3.0336 loss)
I0406 07:23:10.326356  5226 sgd_solver.cpp:105] Iteration 1776, lr = 0.01
I0406 07:23:15.651890  5226 solver.cpp:218] Iteration 1788 (2.25331 iter/s, 5.32549s/12 iters), loss = 2.82969
I0406 07:23:15.651929  5226 solver.cpp:237]     Train net output #0: loss = 2.82969 (* 1 = 2.82969 loss)
I0406 07:23:15.651935  5226 sgd_solver.cpp:105] Iteration 1788, lr = 0.01
I0406 07:23:20.944195  5226 solver.cpp:218] Iteration 1800 (2.26748 iter/s, 5.29222s/12 iters), loss = 3.18035
I0406 07:23:20.944288  5226 solver.cpp:237]     Train net output #0: loss = 3.18035 (* 1 = 3.18035 loss)
I0406 07:23:20.944294  5226 sgd_solver.cpp:105] Iteration 1800, lr = 0.01
I0406 07:23:26.238842  5226 solver.cpp:218] Iteration 1812 (2.2665 iter/s, 5.29451s/12 iters), loss = 3.10267
I0406 07:23:26.238878  5226 solver.cpp:237]     Train net output #0: loss = 3.10267 (* 1 = 3.10267 loss)
I0406 07:23:26.238883  5226 sgd_solver.cpp:105] Iteration 1812, lr = 0.01
I0406 07:23:29.655158  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:23:31.563151  5226 solver.cpp:218] Iteration 1824 (2.25385 iter/s, 5.32422s/12 iters), loss = 3.45772
I0406 07:23:31.563189  5226 solver.cpp:237]     Train net output #0: loss = 3.45772 (* 1 = 3.45772 loss)
I0406 07:23:31.563194  5226 sgd_solver.cpp:105] Iteration 1824, lr = 0.01
I0406 07:23:36.043905  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1836.caffemodel
I0406 07:23:39.049489  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1836.solverstate
I0406 07:23:41.366464  5226 solver.cpp:330] Iteration 1836, Testing net (#0)
I0406 07:23:41.366488  5226 net.cpp:676] Ignoring source layer train-data
I0406 07:23:44.915056  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:23:45.670183  5226 solver.cpp:397]     Test net output #0: accuracy = 0.196691
I0406 07:23:45.670212  5226 solver.cpp:397]     Test net output #1: loss = 3.45419 (* 1 = 3.45419 loss)
I0406 07:23:45.809367  5226 solver.cpp:218] Iteration 1836 (0.842338 iter/s, 14.2461s/12 iters), loss = 2.91034
I0406 07:23:45.809428  5226 solver.cpp:237]     Train net output #0: loss = 2.91034 (* 1 = 2.91034 loss)
I0406 07:23:45.809437  5226 sgd_solver.cpp:105] Iteration 1836, lr = 0.01
I0406 07:23:50.173804  5226 solver.cpp:218] Iteration 1848 (2.74956 iter/s, 4.36433s/12 iters), loss = 3.07219
I0406 07:23:50.173846  5226 solver.cpp:237]     Train net output #0: loss = 3.07219 (* 1 = 3.07219 loss)
I0406 07:23:50.173852  5226 sgd_solver.cpp:105] Iteration 1848, lr = 0.01
I0406 07:23:55.371817  5226 solver.cpp:218] Iteration 1860 (2.30861 iter/s, 5.19793s/12 iters), loss = 3.34904
I0406 07:23:55.371928  5226 solver.cpp:237]     Train net output #0: loss = 3.34904 (* 1 = 3.34904 loss)
I0406 07:23:55.371934  5226 sgd_solver.cpp:105] Iteration 1860, lr = 0.01
I0406 07:24:00.669976  5226 solver.cpp:218] Iteration 1872 (2.26501 iter/s, 5.298s/12 iters), loss = 3.02528
I0406 07:24:00.670012  5226 solver.cpp:237]     Train net output #0: loss = 3.02528 (* 1 = 3.02528 loss)
I0406 07:24:00.670017  5226 sgd_solver.cpp:105] Iteration 1872, lr = 0.01
I0406 07:24:05.628597  5226 solver.cpp:218] Iteration 1884 (2.42007 iter/s, 4.95853s/12 iters), loss = 3.29507
I0406 07:24:05.628648  5226 solver.cpp:237]     Train net output #0: loss = 3.29507 (* 1 = 3.29507 loss)
I0406 07:24:05.628659  5226 sgd_solver.cpp:105] Iteration 1884, lr = 0.01
I0406 07:24:10.858410  5226 solver.cpp:218] Iteration 1896 (2.29458 iter/s, 5.22971s/12 iters), loss = 3.04579
I0406 07:24:10.858456  5226 solver.cpp:237]     Train net output #0: loss = 3.04579 (* 1 = 3.04579 loss)
I0406 07:24:10.858464  5226 sgd_solver.cpp:105] Iteration 1896, lr = 0.01
I0406 07:24:16.128111  5226 solver.cpp:218] Iteration 1908 (2.27721 iter/s, 5.26961s/12 iters), loss = 2.9373
I0406 07:24:16.128149  5226 solver.cpp:237]     Train net output #0: loss = 2.9373 (* 1 = 2.9373 loss)
I0406 07:24:16.128154  5226 sgd_solver.cpp:105] Iteration 1908, lr = 0.01
I0406 07:24:21.435420  5226 solver.cpp:218] Iteration 1920 (2.26107 iter/s, 5.30723s/12 iters), loss = 2.82193
I0406 07:24:21.435458  5226 solver.cpp:237]     Train net output #0: loss = 2.82193 (* 1 = 2.82193 loss)
I0406 07:24:21.435463  5226 sgd_solver.cpp:105] Iteration 1920, lr = 0.01
I0406 07:24:21.722877  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:24:26.553185  5226 solver.cpp:218] Iteration 1932 (2.34481 iter/s, 5.11768s/12 iters), loss = 2.82071
I0406 07:24:26.553285  5226 solver.cpp:237]     Train net output #0: loss = 2.82071 (* 1 = 2.82071 loss)
I0406 07:24:26.553292  5226 sgd_solver.cpp:105] Iteration 1932, lr = 0.01
I0406 07:24:28.549466  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1938.caffemodel
I0406 07:24:31.572197  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1938.solverstate
I0406 07:24:33.917404  5226 solver.cpp:330] Iteration 1938, Testing net (#0)
I0406 07:24:33.917428  5226 net.cpp:676] Ignoring source layer train-data
I0406 07:24:37.581090  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:24:38.371762  5226 solver.cpp:397]     Test net output #0: accuracy = 0.218137
I0406 07:24:38.371789  5226 solver.cpp:397]     Test net output #1: loss = 3.36926 (* 1 = 3.36926 loss)
I0406 07:24:40.250432  5226 solver.cpp:218] Iteration 1944 (0.876101 iter/s, 13.697s/12 iters), loss = 2.95375
I0406 07:24:40.250474  5226 solver.cpp:237]     Train net output #0: loss = 2.95375 (* 1 = 2.95375 loss)
I0406 07:24:40.250480  5226 sgd_solver.cpp:105] Iteration 1944, lr = 0.01
I0406 07:24:45.720738  5226 solver.cpp:218] Iteration 1956 (2.1937 iter/s, 5.47022s/12 iters), loss = 2.84667
I0406 07:24:45.720775  5226 solver.cpp:237]     Train net output #0: loss = 2.84667 (* 1 = 2.84667 loss)
I0406 07:24:45.720782  5226 sgd_solver.cpp:105] Iteration 1956, lr = 0.01
I0406 07:24:50.905449  5226 solver.cpp:218] Iteration 1968 (2.31454 iter/s, 5.18462s/12 iters), loss = 3.18985
I0406 07:24:50.905489  5226 solver.cpp:237]     Train net output #0: loss = 3.18985 (* 1 = 3.18985 loss)
I0406 07:24:50.905494  5226 sgd_solver.cpp:105] Iteration 1968, lr = 0.01
I0406 07:24:55.894114  5226 solver.cpp:218] Iteration 1980 (2.40549 iter/s, 4.98858s/12 iters), loss = 3.0115
I0406 07:24:55.894151  5226 solver.cpp:237]     Train net output #0: loss = 3.0115 (* 1 = 3.0115 loss)
I0406 07:24:55.894157  5226 sgd_solver.cpp:105] Iteration 1980, lr = 0.01
I0406 07:25:01.179332  5226 solver.cpp:218] Iteration 1992 (2.27052 iter/s, 5.28513s/12 iters), loss = 3.05164
I0406 07:25:01.179481  5226 solver.cpp:237]     Train net output #0: loss = 3.05164 (* 1 = 3.05164 loss)
I0406 07:25:01.179489  5226 sgd_solver.cpp:105] Iteration 1992, lr = 0.01
I0406 07:25:06.602145  5226 solver.cpp:218] Iteration 2004 (2.21295 iter/s, 5.42261s/12 iters), loss = 3.15305
I0406 07:25:06.602186  5226 solver.cpp:237]     Train net output #0: loss = 3.15305 (* 1 = 3.15305 loss)
I0406 07:25:06.602193  5226 sgd_solver.cpp:105] Iteration 2004, lr = 0.01
I0406 07:25:11.963425  5226 solver.cpp:218] Iteration 2016 (2.23831 iter/s, 5.3612s/12 iters), loss = 3.19823
I0406 07:25:11.963457  5226 solver.cpp:237]     Train net output #0: loss = 3.19823 (* 1 = 3.19823 loss)
I0406 07:25:11.963462  5226 sgd_solver.cpp:105] Iteration 2016, lr = 0.01
I0406 07:25:14.509896  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:25:17.207149  5226 solver.cpp:218] Iteration 2028 (2.28849 iter/s, 5.24364s/12 iters), loss = 3.12019
I0406 07:25:17.207185  5226 solver.cpp:237]     Train net output #0: loss = 3.12019 (* 1 = 3.12019 loss)
I0406 07:25:17.207190  5226 sgd_solver.cpp:105] Iteration 2028, lr = 0.01
I0406 07:25:22.115345  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2040.caffemodel
I0406 07:25:25.148134  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2040.solverstate
I0406 07:25:27.448951  5226 solver.cpp:330] Iteration 2040, Testing net (#0)
I0406 07:25:27.448969  5226 net.cpp:676] Ignoring source layer train-data
I0406 07:25:30.911226  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:25:31.728209  5226 solver.cpp:397]     Test net output #0: accuracy = 0.222426
I0406 07:25:31.728292  5226 solver.cpp:397]     Test net output #1: loss = 3.39818 (* 1 = 3.39818 loss)
I0406 07:25:31.866468  5226 solver.cpp:218] Iteration 2040 (0.8186 iter/s, 14.6592s/12 iters), loss = 3.13997
I0406 07:25:31.866508  5226 solver.cpp:237]     Train net output #0: loss = 3.13997 (* 1 = 3.13997 loss)
I0406 07:25:31.866514  5226 sgd_solver.cpp:105] Iteration 2040, lr = 0.01
I0406 07:25:36.104786  5226 solver.cpp:218] Iteration 2052 (2.83137 iter/s, 4.23823s/12 iters), loss = 3.01645
I0406 07:25:36.104827  5226 solver.cpp:237]     Train net output #0: loss = 3.01645 (* 1 = 3.01645 loss)
I0406 07:25:36.104831  5226 sgd_solver.cpp:105] Iteration 2052, lr = 0.01
I0406 07:25:37.801060  5226 blocking_queue.cpp:49] Waiting for data
I0406 07:25:41.465049  5226 solver.cpp:218] Iteration 2064 (2.23873 iter/s, 5.36017s/12 iters), loss = 2.50134
I0406 07:25:41.465085  5226 solver.cpp:237]     Train net output #0: loss = 2.50134 (* 1 = 2.50134 loss)
I0406 07:25:41.465090  5226 sgd_solver.cpp:105] Iteration 2064, lr = 0.01
I0406 07:25:46.810606  5226 solver.cpp:218] Iteration 2076 (2.24489 iter/s, 5.34547s/12 iters), loss = 2.76902
I0406 07:25:46.810645  5226 solver.cpp:237]     Train net output #0: loss = 2.76902 (* 1 = 2.76902 loss)
I0406 07:25:46.810652  5226 sgd_solver.cpp:105] Iteration 2076, lr = 0.01
I0406 07:25:52.052099  5226 solver.cpp:218] Iteration 2088 (2.28946 iter/s, 5.24141s/12 iters), loss = 3.00441
I0406 07:25:52.052136  5226 solver.cpp:237]     Train net output #0: loss = 3.00441 (* 1 = 3.00441 loss)
I0406 07:25:52.052141  5226 sgd_solver.cpp:105] Iteration 2088, lr = 0.01
I0406 07:25:57.265309  5226 solver.cpp:218] Iteration 2100 (2.30188 iter/s, 5.21312s/12 iters), loss = 2.76112
I0406 07:25:57.265347  5226 solver.cpp:237]     Train net output #0: loss = 2.76112 (* 1 = 2.76112 loss)
I0406 07:25:57.265352  5226 sgd_solver.cpp:105] Iteration 2100, lr = 0.01
I0406 07:26:02.500305  5226 solver.cpp:218] Iteration 2112 (2.2923 iter/s, 5.23491s/12 iters), loss = 2.6685
I0406 07:26:02.500427  5226 solver.cpp:237]     Train net output #0: loss = 2.6685 (* 1 = 2.6685 loss)
I0406 07:26:02.500434  5226 sgd_solver.cpp:105] Iteration 2112, lr = 0.01
I0406 07:26:07.365566  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:26:07.726665  5226 solver.cpp:218] Iteration 2124 (2.29613 iter/s, 5.22619s/12 iters), loss = 2.82303
I0406 07:26:07.726701  5226 solver.cpp:237]     Train net output #0: loss = 2.82303 (* 1 = 2.82303 loss)
I0406 07:26:07.726708  5226 sgd_solver.cpp:105] Iteration 2124, lr = 0.01
I0406 07:26:13.073390  5226 solver.cpp:218] Iteration 2136 (2.2444 iter/s, 5.34664s/12 iters), loss = 2.98934
I0406 07:26:13.073433  5226 solver.cpp:237]     Train net output #0: loss = 2.98934 (* 1 = 2.98934 loss)
I0406 07:26:13.073441  5226 sgd_solver.cpp:105] Iteration 2136, lr = 0.01
I0406 07:26:15.170984  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2142.caffemodel
I0406 07:26:18.223866  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2142.solverstate
I0406 07:26:20.526892  5226 solver.cpp:330] Iteration 2142, Testing net (#0)
I0406 07:26:20.526911  5226 net.cpp:676] Ignoring source layer train-data
I0406 07:26:24.024772  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:26:24.873953  5226 solver.cpp:397]     Test net output #0: accuracy = 0.235907
I0406 07:26:24.873989  5226 solver.cpp:397]     Test net output #1: loss = 3.27825 (* 1 = 3.27825 loss)
I0406 07:26:26.736681  5226 solver.cpp:218] Iteration 2148 (0.878275 iter/s, 13.6631s/12 iters), loss = 2.80053
I0406 07:26:26.736730  5226 solver.cpp:237]     Train net output #0: loss = 2.80053 (* 1 = 2.80053 loss)
I0406 07:26:26.736737  5226 sgd_solver.cpp:105] Iteration 2148, lr = 0.01
I0406 07:26:32.328927  5226 solver.cpp:218] Iteration 2160 (2.14587 iter/s, 5.59214s/12 iters), loss = 2.48772
I0406 07:26:32.328969  5226 solver.cpp:237]     Train net output #0: loss = 2.48772 (* 1 = 2.48772 loss)
I0406 07:26:32.328975  5226 sgd_solver.cpp:105] Iteration 2160, lr = 0.01
I0406 07:26:37.600306  5226 solver.cpp:218] Iteration 2172 (2.27648 iter/s, 5.27129s/12 iters), loss = 2.69526
I0406 07:26:37.600389  5226 solver.cpp:237]     Train net output #0: loss = 2.69526 (* 1 = 2.69526 loss)
I0406 07:26:37.600397  5226 sgd_solver.cpp:105] Iteration 2172, lr = 0.01
I0406 07:26:42.859814  5226 solver.cpp:218] Iteration 2184 (2.28164 iter/s, 5.25937s/12 iters), loss = 2.78594
I0406 07:26:42.859869  5226 solver.cpp:237]     Train net output #0: loss = 2.78594 (* 1 = 2.78594 loss)
I0406 07:26:42.859879  5226 sgd_solver.cpp:105] Iteration 2184, lr = 0.01
I0406 07:26:48.213620  5226 solver.cpp:218] Iteration 2196 (2.24144 iter/s, 5.35371s/12 iters), loss = 2.66253
I0406 07:26:48.213654  5226 solver.cpp:237]     Train net output #0: loss = 2.66253 (* 1 = 2.66253 loss)
I0406 07:26:48.213660  5226 sgd_solver.cpp:105] Iteration 2196, lr = 0.01
I0406 07:26:53.524387  5226 solver.cpp:218] Iteration 2208 (2.2596 iter/s, 5.31068s/12 iters), loss = 2.77228
I0406 07:26:53.524435  5226 solver.cpp:237]     Train net output #0: loss = 2.77228 (* 1 = 2.77228 loss)
I0406 07:26:53.524441  5226 sgd_solver.cpp:105] Iteration 2208, lr = 0.01
I0406 07:26:58.809394  5226 solver.cpp:218] Iteration 2220 (2.27061 iter/s, 5.28491s/12 iters), loss = 2.52364
I0406 07:26:58.809432  5226 solver.cpp:237]     Train net output #0: loss = 2.52364 (* 1 = 2.52364 loss)
I0406 07:26:58.809437  5226 sgd_solver.cpp:105] Iteration 2220, lr = 0.01
I0406 07:27:00.768290  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:27:04.126536  5226 solver.cpp:218] Iteration 2232 (2.25689 iter/s, 5.31705s/12 iters), loss = 2.71301
I0406 07:27:04.126575  5226 solver.cpp:237]     Train net output #0: loss = 2.71301 (* 1 = 2.71301 loss)
I0406 07:27:04.126581  5226 sgd_solver.cpp:105] Iteration 2232, lr = 0.01
I0406 07:27:08.991061  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2244.caffemodel
I0406 07:27:11.995447  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2244.solverstate
I0406 07:27:14.296033  5226 solver.cpp:330] Iteration 2244, Testing net (#0)
I0406 07:27:14.296052  5226 net.cpp:676] Ignoring source layer train-data
I0406 07:27:17.663213  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:27:18.575387  5226 solver.cpp:397]     Test net output #0: accuracy = 0.268382
I0406 07:27:18.575423  5226 solver.cpp:397]     Test net output #1: loss = 3.14014 (* 1 = 3.14014 loss)
I0406 07:27:18.720084  5226 solver.cpp:218] Iteration 2244 (0.82229 iter/s, 14.5934s/12 iters), loss = 2.32883
I0406 07:27:18.720144  5226 solver.cpp:237]     Train net output #0: loss = 2.32883 (* 1 = 2.32883 loss)
I0406 07:27:18.720152  5226 sgd_solver.cpp:105] Iteration 2244, lr = 0.01
I0406 07:27:23.158629  5226 solver.cpp:218] Iteration 2256 (2.70366 iter/s, 4.43844s/12 iters), loss = 2.44244
I0406 07:27:23.158671  5226 solver.cpp:237]     Train net output #0: loss = 2.44244 (* 1 = 2.44244 loss)
I0406 07:27:23.158677  5226 sgd_solver.cpp:105] Iteration 2256, lr = 0.01
I0406 07:27:28.498594  5226 solver.cpp:218] Iteration 2268 (2.24724 iter/s, 5.33988s/12 iters), loss = 2.4724
I0406 07:27:28.498631  5226 solver.cpp:237]     Train net output #0: loss = 2.4724 (* 1 = 2.4724 loss)
I0406 07:27:28.498636  5226 sgd_solver.cpp:105] Iteration 2268, lr = 0.01
I0406 07:27:33.714381  5226 solver.cpp:218] Iteration 2280 (2.30075 iter/s, 5.2157s/12 iters), loss = 2.83676
I0406 07:27:33.714419  5226 solver.cpp:237]     Train net output #0: loss = 2.83676 (* 1 = 2.83676 loss)
I0406 07:27:33.714426  5226 sgd_solver.cpp:105] Iteration 2280, lr = 0.01
I0406 07:27:38.969461  5226 solver.cpp:218] Iteration 2292 (2.28354 iter/s, 5.255s/12 iters), loss = 2.6109
I0406 07:27:38.969496  5226 solver.cpp:237]     Train net output #0: loss = 2.6109 (* 1 = 2.6109 loss)
I0406 07:27:38.969501  5226 sgd_solver.cpp:105] Iteration 2292, lr = 0.01
I0406 07:27:44.438954  5226 solver.cpp:218] Iteration 2304 (2.19402 iter/s, 5.46941s/12 iters), loss = 2.65751
I0406 07:27:44.439044  5226 solver.cpp:237]     Train net output #0: loss = 2.65751 (* 1 = 2.65751 loss)
I0406 07:27:44.439050  5226 sgd_solver.cpp:105] Iteration 2304, lr = 0.01
I0406 07:27:49.814252  5226 solver.cpp:218] Iteration 2316 (2.23249 iter/s, 5.37516s/12 iters), loss = 2.2846
I0406 07:27:49.814291  5226 solver.cpp:237]     Train net output #0: loss = 2.2846 (* 1 = 2.2846 loss)
I0406 07:27:49.814353  5226 sgd_solver.cpp:105] Iteration 2316, lr = 0.01
I0406 07:27:53.953617  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:27:55.141860  5226 solver.cpp:218] Iteration 2328 (2.25246 iter/s, 5.32752s/12 iters), loss = 2.64037
I0406 07:27:55.141896  5226 solver.cpp:237]     Train net output #0: loss = 2.64037 (* 1 = 2.64037 loss)
I0406 07:27:55.141902  5226 sgd_solver.cpp:105] Iteration 2328, lr = 0.01
I0406 07:28:00.500648  5226 solver.cpp:218] Iteration 2340 (2.23935 iter/s, 5.35871s/12 iters), loss = 2.31318
I0406 07:28:00.500684  5226 solver.cpp:237]     Train net output #0: loss = 2.31318 (* 1 = 2.31318 loss)
I0406 07:28:00.500689  5226 sgd_solver.cpp:105] Iteration 2340, lr = 0.01
I0406 07:28:02.730651  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2346.caffemodel
I0406 07:28:05.641223  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2346.solverstate
I0406 07:28:07.975626  5226 solver.cpp:330] Iteration 2346, Testing net (#0)
I0406 07:28:07.975651  5226 net.cpp:676] Ignoring source layer train-data
I0406 07:28:11.335269  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:28:12.359982  5226 solver.cpp:397]     Test net output #0: accuracy = 0.254289
I0406 07:28:12.360018  5226 solver.cpp:397]     Test net output #1: loss = 3.21678 (* 1 = 3.21678 loss)
I0406 07:28:14.193176  5226 solver.cpp:218] Iteration 2352 (0.8764 iter/s, 13.6924s/12 iters), loss = 2.73386
I0406 07:28:14.193230  5226 solver.cpp:237]     Train net output #0: loss = 2.73386 (* 1 = 2.73386 loss)
I0406 07:28:14.193238  5226 sgd_solver.cpp:105] Iteration 2352, lr = 0.01
I0406 07:28:19.665506  5226 solver.cpp:218] Iteration 2364 (2.19289 iter/s, 5.47223s/12 iters), loss = 2.60615
I0406 07:28:19.665616  5226 solver.cpp:237]     Train net output #0: loss = 2.60615 (* 1 = 2.60615 loss)
I0406 07:28:19.665622  5226 sgd_solver.cpp:105] Iteration 2364, lr = 0.01
I0406 07:28:24.866171  5226 solver.cpp:218] Iteration 2376 (2.30747 iter/s, 5.20051s/12 iters), loss = 2.92253
I0406 07:28:24.866207  5226 solver.cpp:237]     Train net output #0: loss = 2.92253 (* 1 = 2.92253 loss)
I0406 07:28:24.866214  5226 sgd_solver.cpp:105] Iteration 2376, lr = 0.01
I0406 07:28:30.185734  5226 solver.cpp:218] Iteration 2388 (2.25586 iter/s, 5.31948s/12 iters), loss = 2.55916
I0406 07:28:30.185770  5226 solver.cpp:237]     Train net output #0: loss = 2.55916 (* 1 = 2.55916 loss)
I0406 07:28:30.185776  5226 sgd_solver.cpp:105] Iteration 2388, lr = 0.01
I0406 07:28:35.486991  5226 solver.cpp:218] Iteration 2400 (2.26365 iter/s, 5.30118s/12 iters), loss = 2.36989
I0406 07:28:35.487022  5226 solver.cpp:237]     Train net output #0: loss = 2.36989 (* 1 = 2.36989 loss)
I0406 07:28:35.487026  5226 sgd_solver.cpp:105] Iteration 2400, lr = 0.01
I0406 07:28:40.825139  5226 solver.cpp:218] Iteration 2412 (2.24801 iter/s, 5.33806s/12 iters), loss = 2.48392
I0406 07:28:40.825176  5226 solver.cpp:237]     Train net output #0: loss = 2.48392 (* 1 = 2.48392 loss)
I0406 07:28:40.825182  5226 sgd_solver.cpp:105] Iteration 2412, lr = 0.01
I0406 07:28:46.020892  5226 solver.cpp:218] Iteration 2424 (2.30962 iter/s, 5.19566s/12 iters), loss = 2.50994
I0406 07:28:46.020947  5226 solver.cpp:237]     Train net output #0: loss = 2.50994 (* 1 = 2.50994 loss)
I0406 07:28:46.020956  5226 sgd_solver.cpp:105] Iteration 2424, lr = 0.01
I0406 07:28:47.138969  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:28:51.407351  5226 solver.cpp:218] Iteration 2436 (2.22785 iter/s, 5.38635s/12 iters), loss = 2.52547
I0406 07:28:51.407461  5226 solver.cpp:237]     Train net output #0: loss = 2.52547 (* 1 = 2.52547 loss)
I0406 07:28:51.407470  5226 sgd_solver.cpp:105] Iteration 2436, lr = 0.01
I0406 07:28:56.247745  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2448.caffemodel
I0406 07:28:59.258486  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2448.solverstate
I0406 07:29:01.557282  5226 solver.cpp:330] Iteration 2448, Testing net (#0)
I0406 07:29:01.557301  5226 net.cpp:676] Ignoring source layer train-data
I0406 07:29:04.882333  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:29:05.892383  5226 solver.cpp:397]     Test net output #0: accuracy = 0.27451
I0406 07:29:05.892410  5226 solver.cpp:397]     Test net output #1: loss = 3.11446 (* 1 = 3.11446 loss)
I0406 07:29:06.021387  5226 solver.cpp:218] Iteration 2448 (0.821141 iter/s, 14.6138s/12 iters), loss = 2.68962
I0406 07:29:06.021448  5226 solver.cpp:237]     Train net output #0: loss = 2.68962 (* 1 = 2.68962 loss)
I0406 07:29:06.021458  5226 sgd_solver.cpp:105] Iteration 2448, lr = 0.01
I0406 07:29:10.255609  5226 solver.cpp:218] Iteration 2460 (2.83412 iter/s, 4.23412s/12 iters), loss = 2.72334
I0406 07:29:10.255646  5226 solver.cpp:237]     Train net output #0: loss = 2.72334 (* 1 = 2.72334 loss)
I0406 07:29:10.255651  5226 sgd_solver.cpp:105] Iteration 2460, lr = 0.01
I0406 07:29:15.613543  5226 solver.cpp:218] Iteration 2472 (2.2397 iter/s, 5.35785s/12 iters), loss = 2.38164
I0406 07:29:15.613584  5226 solver.cpp:237]     Train net output #0: loss = 2.38164 (* 1 = 2.38164 loss)
I0406 07:29:15.613590  5226 sgd_solver.cpp:105] Iteration 2472, lr = 0.01
I0406 07:29:21.038807  5226 solver.cpp:218] Iteration 2484 (2.21191 iter/s, 5.42517s/12 iters), loss = 2.1673
I0406 07:29:21.038854  5226 solver.cpp:237]     Train net output #0: loss = 2.1673 (* 1 = 2.1673 loss)
I0406 07:29:21.038862  5226 sgd_solver.cpp:105] Iteration 2484, lr = 0.01
I0406 07:29:26.320931  5226 solver.cpp:218] Iteration 2496 (2.27185 iter/s, 5.28203s/12 iters), loss = 1.98013
I0406 07:29:26.321045  5226 solver.cpp:237]     Train net output #0: loss = 1.98013 (* 1 = 1.98013 loss)
I0406 07:29:26.321053  5226 sgd_solver.cpp:105] Iteration 2496, lr = 0.01
I0406 07:29:31.672318  5226 solver.cpp:218] Iteration 2508 (2.24248 iter/s, 5.35123s/12 iters), loss = 2.52243
I0406 07:29:31.672354  5226 solver.cpp:237]     Train net output #0: loss = 2.52243 (* 1 = 2.52243 loss)
I0406 07:29:31.672360  5226 sgd_solver.cpp:105] Iteration 2508, lr = 0.01
I0406 07:29:36.850450  5226 solver.cpp:218] Iteration 2520 (2.31748 iter/s, 5.17804s/12 iters), loss = 2.14708
I0406 07:29:36.850503  5226 solver.cpp:237]     Train net output #0: loss = 2.14708 (* 1 = 2.14708 loss)
I0406 07:29:36.850512  5226 sgd_solver.cpp:105] Iteration 2520, lr = 0.01
I0406 07:29:40.193704  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:29:42.157704  5226 solver.cpp:218] Iteration 2532 (2.2611 iter/s, 5.30715s/12 iters), loss = 2.4574
I0406 07:29:42.157748  5226 solver.cpp:237]     Train net output #0: loss = 2.4574 (* 1 = 2.4574 loss)
I0406 07:29:42.157755  5226 sgd_solver.cpp:105] Iteration 2532, lr = 0.01
I0406 07:29:47.303402  5226 solver.cpp:218] Iteration 2544 (2.33209 iter/s, 5.1456s/12 iters), loss = 2.39513
I0406 07:29:47.303438  5226 solver.cpp:237]     Train net output #0: loss = 2.39513 (* 1 = 2.39513 loss)
I0406 07:29:47.303445  5226 sgd_solver.cpp:105] Iteration 2544, lr = 0.01
I0406 07:29:49.364004  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2550.caffemodel
I0406 07:29:52.394331  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2550.solverstate
I0406 07:29:54.721765  5226 solver.cpp:330] Iteration 2550, Testing net (#0)
I0406 07:29:54.721783  5226 net.cpp:676] Ignoring source layer train-data
I0406 07:29:57.980566  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:29:58.983136  5226 solver.cpp:397]     Test net output #0: accuracy = 0.276348
I0406 07:29:58.983170  5226 solver.cpp:397]     Test net output #1: loss = 3.06047 (* 1 = 3.06047 loss)
I0406 07:30:00.849547  5226 solver.cpp:218] Iteration 2556 (0.88587 iter/s, 13.546s/12 iters), loss = 2.15346
I0406 07:30:00.849598  5226 solver.cpp:237]     Train net output #0: loss = 2.15346 (* 1 = 2.15346 loss)
I0406 07:30:00.849606  5226 sgd_solver.cpp:105] Iteration 2556, lr = 0.01
I0406 07:30:05.706590  5226 solver.cpp:218] Iteration 2568 (2.47069 iter/s, 4.85695s/12 iters), loss = 1.99634
I0406 07:30:05.706638  5226 solver.cpp:237]     Train net output #0: loss = 1.99634 (* 1 = 1.99634 loss)
I0406 07:30:05.706646  5226 sgd_solver.cpp:105] Iteration 2568, lr = 0.01
I0406 07:30:11.101498  5226 solver.cpp:218] Iteration 2580 (2.22436 iter/s, 5.39481s/12 iters), loss = 2.33898
I0406 07:30:11.101536  5226 solver.cpp:237]     Train net output #0: loss = 2.33898 (* 1 = 2.33898 loss)
I0406 07:30:11.101541  5226 sgd_solver.cpp:105] Iteration 2580, lr = 0.01
I0406 07:30:16.395548  5226 solver.cpp:218] Iteration 2592 (2.26673 iter/s, 5.29396s/12 iters), loss = 2.23086
I0406 07:30:16.395584  5226 solver.cpp:237]     Train net output #0: loss = 2.23086 (* 1 = 2.23086 loss)
I0406 07:30:16.395589  5226 sgd_solver.cpp:105] Iteration 2592, lr = 0.01
I0406 07:30:21.637420  5226 solver.cpp:218] Iteration 2604 (2.2893 iter/s, 5.24178s/12 iters), loss = 2.30116
I0406 07:30:21.637461  5226 solver.cpp:237]     Train net output #0: loss = 2.30116 (* 1 = 2.30116 loss)
I0406 07:30:21.637467  5226 sgd_solver.cpp:105] Iteration 2604, lr = 0.01
I0406 07:30:26.795922  5226 solver.cpp:218] Iteration 2616 (2.3263 iter/s, 5.15842s/12 iters), loss = 1.97845
I0406 07:30:26.795961  5226 solver.cpp:237]     Train net output #0: loss = 1.97845 (* 1 = 1.97845 loss)
I0406 07:30:26.795967  5226 sgd_solver.cpp:105] Iteration 2616, lr = 0.01
I0406 07:30:31.858918  5226 solver.cpp:218] Iteration 2628 (2.37018 iter/s, 5.06291s/12 iters), loss = 2.1863
I0406 07:30:31.859036  5226 solver.cpp:237]     Train net output #0: loss = 2.1863 (* 1 = 2.1863 loss)
I0406 07:30:31.859042  5226 sgd_solver.cpp:105] Iteration 2628, lr = 0.01
I0406 07:30:32.334007  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:30:37.258980  5226 solver.cpp:218] Iteration 2640 (2.22226 iter/s, 5.3999s/12 iters), loss = 2.19801
I0406 07:30:37.259021  5226 solver.cpp:237]     Train net output #0: loss = 2.19801 (* 1 = 2.19801 loss)
I0406 07:30:37.259027  5226 sgd_solver.cpp:105] Iteration 2640, lr = 0.01
I0406 07:30:42.010056  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2652.caffemodel
I0406 07:30:45.043380  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2652.solverstate
I0406 07:30:47.344377  5226 solver.cpp:330] Iteration 2652, Testing net (#0)
I0406 07:30:47.344396  5226 net.cpp:676] Ignoring source layer train-data
I0406 07:30:50.558706  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:30:51.592180  5226 solver.cpp:397]     Test net output #0: accuracy = 0.287377
I0406 07:30:51.592211  5226 solver.cpp:397]     Test net output #1: loss = 3.16481 (* 1 = 3.16481 loss)
I0406 07:30:51.727890  5226 solver.cpp:218] Iteration 2652 (0.829373 iter/s, 14.4688s/12 iters), loss = 2.57762
I0406 07:30:51.727926  5226 solver.cpp:237]     Train net output #0: loss = 2.57762 (* 1 = 2.57762 loss)
I0406 07:30:51.727931  5226 sgd_solver.cpp:105] Iteration 2652, lr = 0.01
I0406 07:30:55.859660  5226 solver.cpp:218] Iteration 2664 (2.90438 iter/s, 4.13169s/12 iters), loss = 1.93033
I0406 07:30:55.859692  5226 solver.cpp:237]     Train net output #0: loss = 1.93033 (* 1 = 1.93033 loss)
I0406 07:30:55.859699  5226 sgd_solver.cpp:105] Iteration 2664, lr = 0.01
I0406 07:31:01.036332  5226 solver.cpp:218] Iteration 2676 (2.31813 iter/s, 5.17659s/12 iters), loss = 2.23358
I0406 07:31:01.036371  5226 solver.cpp:237]     Train net output #0: loss = 2.23358 (* 1 = 2.23358 loss)
I0406 07:31:01.036378  5226 sgd_solver.cpp:105] Iteration 2676, lr = 0.01
I0406 07:31:06.379825  5226 solver.cpp:218] Iteration 2688 (2.24576 iter/s, 5.3434s/12 iters), loss = 1.98046
I0406 07:31:06.379911  5226 solver.cpp:237]     Train net output #0: loss = 1.98046 (* 1 = 1.98046 loss)
I0406 07:31:06.379918  5226 sgd_solver.cpp:105] Iteration 2688, lr = 0.01
I0406 07:31:11.525281  5226 solver.cpp:218] Iteration 2700 (2.33222 iter/s, 5.14532s/12 iters), loss = 2.46887
I0406 07:31:11.525326  5226 solver.cpp:237]     Train net output #0: loss = 2.46887 (* 1 = 2.46887 loss)
I0406 07:31:11.525333  5226 sgd_solver.cpp:105] Iteration 2700, lr = 0.01
I0406 07:31:16.678699  5226 solver.cpp:218] Iteration 2712 (2.32859 iter/s, 5.15333s/12 iters), loss = 2.32403
I0406 07:31:16.678737  5226 solver.cpp:237]     Train net output #0: loss = 2.32403 (* 1 = 2.32403 loss)
I0406 07:31:16.678742  5226 sgd_solver.cpp:105] Iteration 2712, lr = 0.01
I0406 07:31:21.784691  5226 solver.cpp:218] Iteration 2724 (2.35022 iter/s, 5.10591s/12 iters), loss = 2.30202
I0406 07:31:21.784734  5226 solver.cpp:237]     Train net output #0: loss = 2.30202 (* 1 = 2.30202 loss)
I0406 07:31:21.784739  5226 sgd_solver.cpp:105] Iteration 2724, lr = 0.01
I0406 07:31:24.576135  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:31:27.131541  5226 solver.cpp:218] Iteration 2736 (2.24435 iter/s, 5.34676s/12 iters), loss = 2.27352
I0406 07:31:27.131577  5226 solver.cpp:237]     Train net output #0: loss = 2.27352 (* 1 = 2.27352 loss)
I0406 07:31:27.131582  5226 sgd_solver.cpp:105] Iteration 2736, lr = 0.01
I0406 07:31:32.480998  5226 solver.cpp:218] Iteration 2748 (2.24326 iter/s, 5.34937s/12 iters), loss = 2.13631
I0406 07:31:32.481035  5226 solver.cpp:237]     Train net output #0: loss = 2.13631 (* 1 = 2.13631 loss)
I0406 07:31:32.481041  5226 sgd_solver.cpp:105] Iteration 2748, lr = 0.01
I0406 07:31:34.543975  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2754.caffemodel
I0406 07:31:37.569041  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2754.solverstate
I0406 07:31:39.866463  5226 solver.cpp:330] Iteration 2754, Testing net (#0)
I0406 07:31:39.866482  5226 net.cpp:676] Ignoring source layer train-data
I0406 07:31:42.831398  5226 blocking_queue.cpp:49] Waiting for data
I0406 07:31:43.061197  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:31:44.152637  5226 solver.cpp:397]     Test net output #0: accuracy = 0.284926
I0406 07:31:44.152662  5226 solver.cpp:397]     Test net output #1: loss = 3.00838 (* 1 = 3.00838 loss)
I0406 07:31:46.126389  5226 solver.cpp:218] Iteration 2760 (0.879426 iter/s, 13.6453s/12 iters), loss = 2.31702
I0406 07:31:46.126423  5226 solver.cpp:237]     Train net output #0: loss = 2.31702 (* 1 = 2.31702 loss)
I0406 07:31:46.126428  5226 sgd_solver.cpp:105] Iteration 2760, lr = 0.01
I0406 07:31:51.299409  5226 solver.cpp:218] Iteration 2772 (2.31977 iter/s, 5.17293s/12 iters), loss = 1.89611
I0406 07:31:51.299463  5226 solver.cpp:237]     Train net output #0: loss = 1.89611 (* 1 = 1.89611 loss)
I0406 07:31:51.299472  5226 sgd_solver.cpp:105] Iteration 2772, lr = 0.01
I0406 07:31:56.570516  5226 solver.cpp:218] Iteration 2784 (2.2766 iter/s, 5.27101s/12 iters), loss = 2.19765
I0406 07:31:56.570554  5226 solver.cpp:237]     Train net output #0: loss = 2.19765 (* 1 = 2.19765 loss)
I0406 07:31:56.570559  5226 sgd_solver.cpp:105] Iteration 2784, lr = 0.01
I0406 07:32:01.878157  5226 solver.cpp:218] Iteration 2796 (2.26093 iter/s, 5.30756s/12 iters), loss = 2.23022
I0406 07:32:01.878194  5226 solver.cpp:237]     Train net output #0: loss = 2.23022 (* 1 = 2.23022 loss)
I0406 07:32:01.878199  5226 sgd_solver.cpp:105] Iteration 2796, lr = 0.01
I0406 07:32:07.075815  5226 solver.cpp:218] Iteration 2808 (2.30877 iter/s, 5.19757s/12 iters), loss = 2.08422
I0406 07:32:07.075855  5226 solver.cpp:237]     Train net output #0: loss = 2.08422 (* 1 = 2.08422 loss)
I0406 07:32:07.075860  5226 sgd_solver.cpp:105] Iteration 2808, lr = 0.01
I0406 07:32:12.279300  5226 solver.cpp:218] Iteration 2820 (2.30618 iter/s, 5.2034s/12 iters), loss = 1.9555
I0406 07:32:12.279402  5226 solver.cpp:237]     Train net output #0: loss = 1.9555 (* 1 = 1.9555 loss)
I0406 07:32:12.279409  5226 sgd_solver.cpp:105] Iteration 2820, lr = 0.01
I0406 07:32:17.223429  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:32:17.552944  5226 solver.cpp:218] Iteration 2832 (2.27553 iter/s, 5.27349s/12 iters), loss = 2.25999
I0406 07:32:17.552980  5226 solver.cpp:237]     Train net output #0: loss = 2.25999 (* 1 = 2.25999 loss)
I0406 07:32:17.552985  5226 sgd_solver.cpp:105] Iteration 2832, lr = 0.01
I0406 07:32:22.575731  5226 solver.cpp:218] Iteration 2844 (2.38915 iter/s, 5.0227s/12 iters), loss = 2.27501
I0406 07:32:22.575768  5226 solver.cpp:237]     Train net output #0: loss = 2.27501 (* 1 = 2.27501 loss)
I0406 07:32:22.575773  5226 sgd_solver.cpp:105] Iteration 2844, lr = 0.01
I0406 07:32:26.959570  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2856.caffemodel
I0406 07:32:30.000635  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2856.solverstate
I0406 07:32:32.305446  5226 solver.cpp:330] Iteration 2856, Testing net (#0)
I0406 07:32:32.305464  5226 net.cpp:676] Ignoring source layer train-data
I0406 07:32:35.485757  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:32:36.598892  5226 solver.cpp:397]     Test net output #0: accuracy = 0.311887
I0406 07:32:36.598923  5226 solver.cpp:397]     Test net output #1: loss = 2.9559 (* 1 = 2.9559 loss)
I0406 07:32:36.737751  5226 solver.cpp:218] Iteration 2856 (0.847345 iter/s, 14.1619s/12 iters), loss = 2.06675
I0406 07:32:36.737797  5226 solver.cpp:237]     Train net output #0: loss = 2.06675 (* 1 = 2.06675 loss)
I0406 07:32:36.737803  5226 sgd_solver.cpp:105] Iteration 2856, lr = 0.01
I0406 07:32:40.949191  5226 solver.cpp:218] Iteration 2868 (2.84944 iter/s, 4.21136s/12 iters), loss = 1.91832
I0406 07:32:40.949226  5226 solver.cpp:237]     Train net output #0: loss = 1.91832 (* 1 = 1.91832 loss)
I0406 07:32:40.949232  5226 sgd_solver.cpp:105] Iteration 2868, lr = 0.01
I0406 07:32:46.101409  5226 solver.cpp:218] Iteration 2880 (2.32913 iter/s, 5.15214s/12 iters), loss = 2.00836
I0406 07:32:46.101533  5226 solver.cpp:237]     Train net output #0: loss = 2.00836 (* 1 = 2.00836 loss)
I0406 07:32:46.101539  5226 sgd_solver.cpp:105] Iteration 2880, lr = 0.01
I0406 07:32:51.267841  5226 solver.cpp:218] Iteration 2892 (2.32276 iter/s, 5.16626s/12 iters), loss = 2.01934
I0406 07:32:51.267881  5226 solver.cpp:237]     Train net output #0: loss = 2.01934 (* 1 = 2.01934 loss)
I0406 07:32:51.267887  5226 sgd_solver.cpp:105] Iteration 2892, lr = 0.01
I0406 07:32:56.607414  5226 solver.cpp:218] Iteration 2904 (2.24741 iter/s, 5.33949s/12 iters), loss = 2.12261
I0406 07:32:56.607448  5226 solver.cpp:237]     Train net output #0: loss = 2.12261 (* 1 = 2.12261 loss)
I0406 07:32:56.607455  5226 sgd_solver.cpp:105] Iteration 2904, lr = 0.01
I0406 07:33:01.597326  5226 solver.cpp:218] Iteration 2916 (2.40489 iter/s, 4.98983s/12 iters), loss = 1.88956
I0406 07:33:01.597362  5226 solver.cpp:237]     Train net output #0: loss = 1.88956 (* 1 = 1.88956 loss)
I0406 07:33:01.597368  5226 sgd_solver.cpp:105] Iteration 2916, lr = 0.01
I0406 07:33:06.687352  5226 solver.cpp:218] Iteration 2928 (2.35759 iter/s, 5.08994s/12 iters), loss = 1.97696
I0406 07:33:06.687402  5226 solver.cpp:237]     Train net output #0: loss = 1.97696 (* 1 = 1.97696 loss)
I0406 07:33:06.687409  5226 sgd_solver.cpp:105] Iteration 2928, lr = 0.01
I0406 07:33:08.535871  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:33:11.995040  5226 solver.cpp:218] Iteration 2940 (2.26091 iter/s, 5.30759s/12 iters), loss = 1.71754
I0406 07:33:11.995079  5226 solver.cpp:237]     Train net output #0: loss = 1.71754 (* 1 = 1.71754 loss)
I0406 07:33:11.995083  5226 sgd_solver.cpp:105] Iteration 2940, lr = 0.01
I0406 07:33:17.293838  5226 solver.cpp:218] Iteration 2952 (2.2647 iter/s, 5.29871s/12 iters), loss = 1.85602
I0406 07:33:17.293977  5226 solver.cpp:237]     Train net output #0: loss = 1.85602 (* 1 = 1.85602 loss)
I0406 07:33:17.293985  5226 sgd_solver.cpp:105] Iteration 2952, lr = 0.01
I0406 07:33:19.428295  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2958.caffemodel
I0406 07:33:22.450825  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2958.solverstate
I0406 07:33:24.749099  5226 solver.cpp:330] Iteration 2958, Testing net (#0)
I0406 07:33:24.749117  5226 net.cpp:676] Ignoring source layer train-data
I0406 07:33:27.948948  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:33:29.112268  5226 solver.cpp:397]     Test net output #0: accuracy = 0.308211
I0406 07:33:29.112304  5226 solver.cpp:397]     Test net output #1: loss = 2.96295 (* 1 = 2.96295 loss)
I0406 07:33:30.993541  5226 solver.cpp:218] Iteration 2964 (0.875947 iter/s, 13.6995s/12 iters), loss = 1.87735
I0406 07:33:30.993585  5226 solver.cpp:237]     Train net output #0: loss = 1.87735 (* 1 = 1.87735 loss)
I0406 07:33:30.993592  5226 sgd_solver.cpp:105] Iteration 2964, lr = 0.01
I0406 07:33:36.381143  5226 solver.cpp:218] Iteration 2976 (2.22737 iter/s, 5.38751s/12 iters), loss = 1.70259
I0406 07:33:36.381181  5226 solver.cpp:237]     Train net output #0: loss = 1.70259 (* 1 = 1.70259 loss)
I0406 07:33:36.381189  5226 sgd_solver.cpp:105] Iteration 2976, lr = 0.01
I0406 07:33:41.511652  5226 solver.cpp:218] Iteration 2988 (2.33899 iter/s, 5.13043s/12 iters), loss = 1.99707
I0406 07:33:41.511690  5226 solver.cpp:237]     Train net output #0: loss = 1.99707 (* 1 = 1.99707 loss)
I0406 07:33:41.511695  5226 sgd_solver.cpp:105] Iteration 2988, lr = 0.01
I0406 07:33:46.866042  5226 solver.cpp:218] Iteration 3000 (2.24119 iter/s, 5.3543s/12 iters), loss = 1.91498
I0406 07:33:46.866081  5226 solver.cpp:237]     Train net output #0: loss = 1.91498 (* 1 = 1.91498 loss)
I0406 07:33:46.866087  5226 sgd_solver.cpp:105] Iteration 3000, lr = 0.01
I0406 07:33:52.113684  5226 solver.cpp:218] Iteration 3012 (2.28678 iter/s, 5.24755s/12 iters), loss = 2.10747
I0406 07:33:52.113832  5226 solver.cpp:237]     Train net output #0: loss = 2.10747 (* 1 = 2.10747 loss)
I0406 07:33:52.113842  5226 sgd_solver.cpp:105] Iteration 3012, lr = 0.01
I0406 07:33:57.337056  5226 solver.cpp:218] Iteration 3024 (2.29746 iter/s, 5.22317s/12 iters), loss = 1.86945
I0406 07:33:57.337092  5226 solver.cpp:237]     Train net output #0: loss = 1.86945 (* 1 = 1.86945 loss)
I0406 07:33:57.337097  5226 sgd_solver.cpp:105] Iteration 3024, lr = 0.01
I0406 07:34:01.640444  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:34:02.837633  5226 solver.cpp:218] Iteration 3036 (2.18162 iter/s, 5.50049s/12 iters), loss = 1.74254
I0406 07:34:02.837669  5226 solver.cpp:237]     Train net output #0: loss = 1.74254 (* 1 = 1.74254 loss)
I0406 07:34:02.837675  5226 sgd_solver.cpp:105] Iteration 3036, lr = 0.01
I0406 07:34:07.857492  5226 solver.cpp:218] Iteration 3048 (2.39054 iter/s, 5.01978s/12 iters), loss = 1.80254
I0406 07:34:07.857532  5226 solver.cpp:237]     Train net output #0: loss = 1.80254 (* 1 = 1.80254 loss)
I0406 07:34:07.857537  5226 sgd_solver.cpp:105] Iteration 3048, lr = 0.01
I0406 07:34:12.515982  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3060.caffemodel
I0406 07:34:15.508728  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3060.solverstate
I0406 07:34:17.811239  5226 solver.cpp:330] Iteration 3060, Testing net (#0)
I0406 07:34:17.811260  5226 net.cpp:676] Ignoring source layer train-data
I0406 07:34:20.986883  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:34:22.195775  5226 solver.cpp:397]     Test net output #0: accuracy = 0.308824
I0406 07:34:22.195890  5226 solver.cpp:397]     Test net output #1: loss = 3.05209 (* 1 = 3.05209 loss)
I0406 07:34:22.331161  5226 solver.cpp:218] Iteration 3060 (0.8291 iter/s, 14.4735s/12 iters), loss = 1.5061
I0406 07:34:22.331208  5226 solver.cpp:237]     Train net output #0: loss = 1.5061 (* 1 = 1.5061 loss)
I0406 07:34:22.331216  5226 sgd_solver.cpp:105] Iteration 3060, lr = 0.01
I0406 07:34:26.661587  5226 solver.cpp:218] Iteration 3072 (2.77115 iter/s, 4.33034s/12 iters), loss = 1.92765
I0406 07:34:26.661623  5226 solver.cpp:237]     Train net output #0: loss = 1.92765 (* 1 = 1.92765 loss)
I0406 07:34:26.661629  5226 sgd_solver.cpp:105] Iteration 3072, lr = 0.01
I0406 07:34:31.796916  5226 solver.cpp:218] Iteration 3084 (2.33679 iter/s, 5.13525s/12 iters), loss = 1.89066
I0406 07:34:31.796952  5226 solver.cpp:237]     Train net output #0: loss = 1.89066 (* 1 = 1.89066 loss)
I0406 07:34:31.796957  5226 sgd_solver.cpp:105] Iteration 3084, lr = 0.01
I0406 07:34:37.040153  5226 solver.cpp:218] Iteration 3096 (2.2887 iter/s, 5.24315s/12 iters), loss = 1.98083
I0406 07:34:37.040190  5226 solver.cpp:237]     Train net output #0: loss = 1.98083 (* 1 = 1.98083 loss)
I0406 07:34:37.040196  5226 sgd_solver.cpp:105] Iteration 3096, lr = 0.01
I0406 07:34:42.300650  5226 solver.cpp:218] Iteration 3108 (2.28119 iter/s, 5.26041s/12 iters), loss = 1.60416
I0406 07:34:42.300690  5226 solver.cpp:237]     Train net output #0: loss = 1.60416 (* 1 = 1.60416 loss)
I0406 07:34:42.300695  5226 sgd_solver.cpp:105] Iteration 3108, lr = 0.01
I0406 07:34:47.732422  5226 solver.cpp:218] Iteration 3120 (2.20926 iter/s, 5.43168s/12 iters), loss = 2.16043
I0406 07:34:47.732462  5226 solver.cpp:237]     Train net output #0: loss = 2.16043 (* 1 = 2.16043 loss)
I0406 07:34:47.732470  5226 sgd_solver.cpp:105] Iteration 3120, lr = 0.01
I0406 07:34:53.001641  5226 solver.cpp:218] Iteration 3132 (2.27742 iter/s, 5.26913s/12 iters), loss = 2.18725
I0406 07:34:53.001803  5226 solver.cpp:237]     Train net output #0: loss = 2.18725 (* 1 = 2.18725 loss)
I0406 07:34:53.001811  5226 sgd_solver.cpp:105] Iteration 3132, lr = 0.01
I0406 07:34:54.149668  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:34:58.375219  5226 solver.cpp:218] Iteration 3144 (2.23324 iter/s, 5.37337s/12 iters), loss = 1.74044
I0406 07:34:58.375275  5226 solver.cpp:237]     Train net output #0: loss = 1.74044 (* 1 = 1.74044 loss)
I0406 07:34:58.375284  5226 sgd_solver.cpp:105] Iteration 3144, lr = 0.01
I0406 07:35:03.671880  5226 solver.cpp:218] Iteration 3156 (2.26562 iter/s, 5.29655s/12 iters), loss = 2.13622
I0406 07:35:03.671918  5226 solver.cpp:237]     Train net output #0: loss = 2.13622 (* 1 = 2.13622 loss)
I0406 07:35:03.671924  5226 sgd_solver.cpp:105] Iteration 3156, lr = 0.01
I0406 07:35:05.815239  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3162.caffemodel
I0406 07:35:08.885056  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3162.solverstate
I0406 07:35:11.279619  5226 solver.cpp:330] Iteration 3162, Testing net (#0)
I0406 07:35:11.279635  5226 net.cpp:676] Ignoring source layer train-data
I0406 07:35:14.323442  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:35:15.565133  5226 solver.cpp:397]     Test net output #0: accuracy = 0.305147
I0406 07:35:15.565165  5226 solver.cpp:397]     Test net output #1: loss = 2.99407 (* 1 = 2.99407 loss)
I0406 07:35:17.398216  5226 solver.cpp:218] Iteration 3168 (0.874241 iter/s, 13.7262s/12 iters), loss = 1.77368
I0406 07:35:17.398257  5226 solver.cpp:237]     Train net output #0: loss = 1.77368 (* 1 = 1.77368 loss)
I0406 07:35:17.398263  5226 sgd_solver.cpp:105] Iteration 3168, lr = 0.01
I0406 07:35:22.641556  5226 solver.cpp:218] Iteration 3180 (2.28866 iter/s, 5.24325s/12 iters), loss = 2.07338
I0406 07:35:22.641598  5226 solver.cpp:237]     Train net output #0: loss = 2.07338 (* 1 = 2.07338 loss)
I0406 07:35:22.641606  5226 sgd_solver.cpp:105] Iteration 3180, lr = 0.01
I0406 07:35:27.987327  5226 solver.cpp:218] Iteration 3192 (2.2448 iter/s, 5.34568s/12 iters), loss = 1.81821
I0406 07:35:27.987432  5226 solver.cpp:237]     Train net output #0: loss = 1.81821 (* 1 = 1.81821 loss)
I0406 07:35:27.987439  5226 sgd_solver.cpp:105] Iteration 3192, lr = 0.01
I0406 07:35:33.374217  5226 solver.cpp:218] Iteration 3204 (2.2277 iter/s, 5.38673s/12 iters), loss = 1.6746
I0406 07:35:33.374261  5226 solver.cpp:237]     Train net output #0: loss = 1.6746 (* 1 = 1.6746 loss)
I0406 07:35:33.374269  5226 sgd_solver.cpp:105] Iteration 3204, lr = 0.01
I0406 07:35:38.747400  5226 solver.cpp:218] Iteration 3216 (2.23335 iter/s, 5.37309s/12 iters), loss = 2.12646
I0406 07:35:38.747438  5226 solver.cpp:237]     Train net output #0: loss = 2.12646 (* 1 = 2.12646 loss)
I0406 07:35:38.747443  5226 sgd_solver.cpp:105] Iteration 3216, lr = 0.01
I0406 07:35:44.132916  5226 solver.cpp:218] Iteration 3228 (2.22824 iter/s, 5.38542s/12 iters), loss = 2.00402
I0406 07:35:44.132962  5226 solver.cpp:237]     Train net output #0: loss = 2.00402 (* 1 = 2.00402 loss)
I0406 07:35:44.132970  5226 sgd_solver.cpp:105] Iteration 3228, lr = 0.01
I0406 07:35:47.387502  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:35:49.317137  5226 solver.cpp:218] Iteration 3240 (2.31476 iter/s, 5.18413s/12 iters), loss = 1.80821
I0406 07:35:49.317175  5226 solver.cpp:237]     Train net output #0: loss = 1.80821 (* 1 = 1.80821 loss)
I0406 07:35:49.317181  5226 sgd_solver.cpp:105] Iteration 3240, lr = 0.01
I0406 07:35:54.582360  5226 solver.cpp:218] Iteration 3252 (2.27914 iter/s, 5.26514s/12 iters), loss = 2.28704
I0406 07:35:54.582397  5226 solver.cpp:237]     Train net output #0: loss = 2.28704 (* 1 = 2.28704 loss)
I0406 07:35:54.582402  5226 sgd_solver.cpp:105] Iteration 3252, lr = 0.01
I0406 07:35:59.110524  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3264.caffemodel
I0406 07:36:02.172075  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3264.solverstate
I0406 07:36:04.502154  5226 solver.cpp:330] Iteration 3264, Testing net (#0)
I0406 07:36:04.502174  5226 net.cpp:676] Ignoring source layer train-data
I0406 07:36:07.572098  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:36:08.926558  5226 solver.cpp:397]     Test net output #0: accuracy = 0.310049
I0406 07:36:08.926586  5226 solver.cpp:397]     Test net output #1: loss = 3.08834 (* 1 = 3.08834 loss)
I0406 07:36:09.061693  5226 solver.cpp:218] Iteration 3264 (0.828776 iter/s, 14.4792s/12 iters), loss = 1.64652
I0406 07:36:09.061743  5226 solver.cpp:237]     Train net output #0: loss = 1.64652 (* 1 = 1.64652 loss)
I0406 07:36:09.061750  5226 sgd_solver.cpp:105] Iteration 3264, lr = 0.01
I0406 07:36:13.503286  5226 solver.cpp:218] Iteration 3276 (2.70179 iter/s, 4.4415s/12 iters), loss = 1.44034
I0406 07:36:13.503330  5226 solver.cpp:237]     Train net output #0: loss = 1.44034 (* 1 = 1.44034 loss)
I0406 07:36:13.503336  5226 sgd_solver.cpp:105] Iteration 3276, lr = 0.01
I0406 07:36:18.739557  5226 solver.cpp:218] Iteration 3288 (2.29175 iter/s, 5.23618s/12 iters), loss = 1.78925
I0406 07:36:18.739593  5226 solver.cpp:237]     Train net output #0: loss = 1.78925 (* 1 = 1.78925 loss)
I0406 07:36:18.739598  5226 sgd_solver.cpp:105] Iteration 3288, lr = 0.01
I0406 07:36:24.141005  5226 solver.cpp:218] Iteration 3300 (2.22166 iter/s, 5.40136s/12 iters), loss = 1.79829
I0406 07:36:24.141041  5226 solver.cpp:237]     Train net output #0: loss = 1.79829 (* 1 = 1.79829 loss)
I0406 07:36:24.141047  5226 sgd_solver.cpp:105] Iteration 3300, lr = 0.01
I0406 07:36:29.120118  5226 solver.cpp:218] Iteration 3312 (2.41011 iter/s, 4.97903s/12 iters), loss = 2.0169
I0406 07:36:29.120219  5226 solver.cpp:237]     Train net output #0: loss = 2.0169 (* 1 = 2.0169 loss)
I0406 07:36:29.120229  5226 sgd_solver.cpp:105] Iteration 3312, lr = 0.01
I0406 07:36:34.114833  5226 solver.cpp:218] Iteration 3324 (2.40261 iter/s, 4.99457s/12 iters), loss = 2.23304
I0406 07:36:34.114884  5226 solver.cpp:237]     Train net output #0: loss = 2.23304 (* 1 = 2.23304 loss)
I0406 07:36:34.114893  5226 sgd_solver.cpp:105] Iteration 3324, lr = 0.01
I0406 07:36:39.476953  5226 solver.cpp:218] Iteration 3336 (2.23796 iter/s, 5.36202s/12 iters), loss = 1.75603
I0406 07:36:39.477003  5226 solver.cpp:237]     Train net output #0: loss = 1.75603 (* 1 = 1.75603 loss)
I0406 07:36:39.477011  5226 sgd_solver.cpp:105] Iteration 3336, lr = 0.01
I0406 07:36:39.984163  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:36:44.852927  5226 solver.cpp:218] Iteration 3348 (2.23219 iter/s, 5.37587s/12 iters), loss = 1.97806
I0406 07:36:44.852977  5226 solver.cpp:237]     Train net output #0: loss = 1.97806 (* 1 = 1.97806 loss)
I0406 07:36:44.852986  5226 sgd_solver.cpp:105] Iteration 3348, lr = 0.01
I0406 07:36:50.265210  5226 solver.cpp:218] Iteration 3360 (2.21722 iter/s, 5.41219s/12 iters), loss = 1.63585
I0406 07:36:50.265255  5226 solver.cpp:237]     Train net output #0: loss = 1.63585 (* 1 = 1.63585 loss)
I0406 07:36:50.265264  5226 sgd_solver.cpp:105] Iteration 3360, lr = 0.01
I0406 07:36:52.381248  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3366.caffemodel
I0406 07:36:55.435907  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3366.solverstate
I0406 07:36:57.745326  5226 solver.cpp:330] Iteration 3366, Testing net (#0)
I0406 07:36:57.745347  5226 net.cpp:676] Ignoring source layer train-data
I0406 07:37:00.871713  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:37:02.224436  5226 solver.cpp:397]     Test net output #0: accuracy = 0.300858
I0406 07:37:02.224472  5226 solver.cpp:397]     Test net output #1: loss = 3.06763 (* 1 = 3.06763 loss)
I0406 07:37:04.113633  5226 solver.cpp:218] Iteration 3372 (0.866534 iter/s, 13.8483s/12 iters), loss = 1.54664
I0406 07:37:04.113669  5226 solver.cpp:237]     Train net output #0: loss = 1.54664 (* 1 = 1.54664 loss)
I0406 07:37:04.113674  5226 sgd_solver.cpp:105] Iteration 3372, lr = 0.01
I0406 07:37:09.114207  5226 solver.cpp:218] Iteration 3384 (2.39976 iter/s, 5.00049s/12 iters), loss = 1.59694
I0406 07:37:09.114243  5226 solver.cpp:237]     Train net output #0: loss = 1.59694 (* 1 = 1.59694 loss)
I0406 07:37:09.114249  5226 sgd_solver.cpp:105] Iteration 3384, lr = 0.01
I0406 07:37:14.394776  5226 solver.cpp:218] Iteration 3396 (2.27252 iter/s, 5.28048s/12 iters), loss = 1.97977
I0406 07:37:14.394827  5226 solver.cpp:237]     Train net output #0: loss = 1.97977 (* 1 = 1.97977 loss)
I0406 07:37:14.394835  5226 sgd_solver.cpp:105] Iteration 3396, lr = 0.01
I0406 07:37:19.644085  5226 solver.cpp:218] Iteration 3408 (2.28606 iter/s, 5.24921s/12 iters), loss = 1.46625
I0406 07:37:19.644121  5226 solver.cpp:237]     Train net output #0: loss = 1.46625 (* 1 = 1.46625 loss)
I0406 07:37:19.644126  5226 sgd_solver.cpp:105] Iteration 3408, lr = 0.01
I0406 07:37:25.090657  5226 solver.cpp:218] Iteration 3420 (2.20326 iter/s, 5.44649s/12 iters), loss = 1.89754
I0406 07:37:25.090692  5226 solver.cpp:237]     Train net output #0: loss = 1.89754 (* 1 = 1.89754 loss)
I0406 07:37:25.090698  5226 sgd_solver.cpp:105] Iteration 3420, lr = 0.01
I0406 07:37:30.405474  5226 solver.cpp:218] Iteration 3432 (2.25788 iter/s, 5.31473s/12 iters), loss = 2.00795
I0406 07:37:30.405512  5226 solver.cpp:237]     Train net output #0: loss = 2.00795 (* 1 = 2.00795 loss)
I0406 07:37:30.405519  5226 sgd_solver.cpp:105] Iteration 3432, lr = 0.01
I0406 07:37:33.222360  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:37:35.736797  5226 solver.cpp:218] Iteration 3444 (2.25088 iter/s, 5.33124s/12 iters), loss = 1.57833
I0406 07:37:35.736832  5226 solver.cpp:237]     Train net output #0: loss = 1.57833 (* 1 = 1.57833 loss)
I0406 07:37:35.736837  5226 sgd_solver.cpp:105] Iteration 3444, lr = 0.01
I0406 07:37:41.088246  5226 solver.cpp:218] Iteration 3456 (2.24242 iter/s, 5.35136s/12 iters), loss = 1.89692
I0406 07:37:41.088284  5226 solver.cpp:237]     Train net output #0: loss = 1.89692 (* 1 = 1.89692 loss)
I0406 07:37:41.088289  5226 sgd_solver.cpp:105] Iteration 3456, lr = 0.01
I0406 07:37:45.926414  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3468.caffemodel
I0406 07:37:48.977488  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3468.solverstate
I0406 07:37:51.276590  5226 solver.cpp:330] Iteration 3468, Testing net (#0)
I0406 07:37:51.276609  5226 net.cpp:676] Ignoring source layer train-data
I0406 07:37:51.689401  5226 blocking_queue.cpp:49] Waiting for data
I0406 07:37:54.217684  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:37:55.568193  5226 solver.cpp:397]     Test net output #0: accuracy = 0.311274
I0406 07:37:55.568226  5226 solver.cpp:397]     Test net output #1: loss = 3.06279 (* 1 = 3.06279 loss)
I0406 07:37:55.708770  5226 solver.cpp:218] Iteration 3468 (0.820772 iter/s, 14.6204s/12 iters), loss = 1.84632
I0406 07:37:55.708812  5226 solver.cpp:237]     Train net output #0: loss = 1.84632 (* 1 = 1.84632 loss)
I0406 07:37:55.708817  5226 sgd_solver.cpp:105] Iteration 3468, lr = 0.01
I0406 07:37:59.940122  5226 solver.cpp:218] Iteration 3480 (2.83609 iter/s, 4.23118s/12 iters), loss = 1.86877
I0406 07:37:59.940165  5226 solver.cpp:237]     Train net output #0: loss = 1.86877 (* 1 = 1.86877 loss)
I0406 07:37:59.940171  5226 sgd_solver.cpp:105] Iteration 3480, lr = 0.01
I0406 07:38:04.944483  5226 solver.cpp:218] Iteration 3492 (2.39795 iter/s, 5.00427s/12 iters), loss = 1.77182
I0406 07:38:04.944667  5226 solver.cpp:237]     Train net output #0: loss = 1.77182 (* 1 = 1.77182 loss)
I0406 07:38:04.944681  5226 sgd_solver.cpp:105] Iteration 3492, lr = 0.01
I0406 07:38:10.229126  5226 solver.cpp:218] Iteration 3504 (2.27083 iter/s, 5.28442s/12 iters), loss = 1.53097
I0406 07:38:10.229166  5226 solver.cpp:237]     Train net output #0: loss = 1.53097 (* 1 = 1.53097 loss)
I0406 07:38:10.229172  5226 sgd_solver.cpp:105] Iteration 3504, lr = 0.01
I0406 07:38:15.509979  5226 solver.cpp:218] Iteration 3516 (2.2724 iter/s, 5.28076s/12 iters), loss = 1.72546
I0406 07:38:15.510016  5226 solver.cpp:237]     Train net output #0: loss = 1.72546 (* 1 = 1.72546 loss)
I0406 07:38:15.510022  5226 sgd_solver.cpp:105] Iteration 3516, lr = 0.01
I0406 07:38:20.753340  5226 solver.cpp:218] Iteration 3528 (2.28865 iter/s, 5.24328s/12 iters), loss = 1.6793
I0406 07:38:20.753377  5226 solver.cpp:237]     Train net output #0: loss = 1.6793 (* 1 = 1.6793 loss)
I0406 07:38:20.753382  5226 sgd_solver.cpp:105] Iteration 3528, lr = 0.01
I0406 07:38:25.631577  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:38:25.934854  5226 solver.cpp:218] Iteration 3540 (2.31596 iter/s, 5.18143s/12 iters), loss = 1.20465
I0406 07:38:25.934892  5226 solver.cpp:237]     Train net output #0: loss = 1.20465 (* 1 = 1.20465 loss)
I0406 07:38:25.934897  5226 sgd_solver.cpp:105] Iteration 3540, lr = 0.01
I0406 07:38:31.109880  5226 solver.cpp:218] Iteration 3552 (2.31887 iter/s, 5.17494s/12 iters), loss = 1.6886
I0406 07:38:31.109915  5226 solver.cpp:237]     Train net output #0: loss = 1.6886 (* 1 = 1.6886 loss)
I0406 07:38:31.109920  5226 sgd_solver.cpp:105] Iteration 3552, lr = 0.01
I0406 07:38:36.230836  5226 solver.cpp:218] Iteration 3564 (2.34335 iter/s, 5.12087s/12 iters), loss = 1.82824
I0406 07:38:36.231294  5226 solver.cpp:237]     Train net output #0: loss = 1.82824 (* 1 = 1.82824 loss)
I0406 07:38:36.231302  5226 sgd_solver.cpp:105] Iteration 3564, lr = 0.01
I0406 07:38:38.438297  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3570.caffemodel
I0406 07:38:41.458721  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3570.solverstate
I0406 07:38:43.769591  5226 solver.cpp:330] Iteration 3570, Testing net (#0)
I0406 07:38:43.769611  5226 net.cpp:676] Ignoring source layer train-data
I0406 07:38:46.681118  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:38:48.068444  5226 solver.cpp:397]     Test net output #0: accuracy = 0.306985
I0406 07:38:48.068475  5226 solver.cpp:397]     Test net output #1: loss = 3.07257 (* 1 = 3.07257 loss)
I0406 07:38:49.824061  5226 solver.cpp:218] Iteration 3576 (0.882829 iter/s, 13.5927s/12 iters), loss = 1.46884
I0406 07:38:49.824111  5226 solver.cpp:237]     Train net output #0: loss = 1.46884 (* 1 = 1.46884 loss)
I0406 07:38:49.824120  5226 sgd_solver.cpp:105] Iteration 3576, lr = 0.01
I0406 07:38:55.115608  5226 solver.cpp:218] Iteration 3588 (2.26781 iter/s, 5.29145s/12 iters), loss = 1.8096
I0406 07:38:55.115656  5226 solver.cpp:237]     Train net output #0: loss = 1.8096 (* 1 = 1.8096 loss)
I0406 07:38:55.115664  5226 sgd_solver.cpp:105] Iteration 3588, lr = 0.01
I0406 07:39:00.417953  5226 solver.cpp:218] Iteration 3600 (2.26319 iter/s, 5.30225s/12 iters), loss = 1.55499
I0406 07:39:00.417995  5226 solver.cpp:237]     Train net output #0: loss = 1.55499 (* 1 = 1.55499 loss)
I0406 07:39:00.418002  5226 sgd_solver.cpp:105] Iteration 3600, lr = 0.01
I0406 07:39:05.712479  5226 solver.cpp:218] Iteration 3612 (2.26653 iter/s, 5.29444s/12 iters), loss = 1.75943
I0406 07:39:05.712522  5226 solver.cpp:237]     Train net output #0: loss = 1.75943 (* 1 = 1.75943 loss)
I0406 07:39:05.712527  5226 sgd_solver.cpp:105] Iteration 3612, lr = 0.01
I0406 07:39:11.019515  5226 solver.cpp:218] Iteration 3624 (2.26119 iter/s, 5.30695s/12 iters), loss = 1.62137
I0406 07:39:11.019640  5226 solver.cpp:237]     Train net output #0: loss = 1.62137 (* 1 = 1.62137 loss)
I0406 07:39:11.019647  5226 sgd_solver.cpp:105] Iteration 3624, lr = 0.01
I0406 07:39:16.083143  5226 solver.cpp:218] Iteration 3636 (2.36992 iter/s, 5.06346s/12 iters), loss = 1.40349
I0406 07:39:16.083178  5226 solver.cpp:237]     Train net output #0: loss = 1.40349 (* 1 = 1.40349 loss)
I0406 07:39:16.083184  5226 sgd_solver.cpp:105] Iteration 3636, lr = 0.01
I0406 07:39:18.021706  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:39:21.380954  5226 solver.cpp:218] Iteration 3648 (2.26512 iter/s, 5.29772s/12 iters), loss = 1.63095
I0406 07:39:21.381002  5226 solver.cpp:237]     Train net output #0: loss = 1.63095 (* 1 = 1.63095 loss)
I0406 07:39:21.381008  5226 sgd_solver.cpp:105] Iteration 3648, lr = 0.01
I0406 07:39:26.652264  5226 solver.cpp:218] Iteration 3660 (2.27651 iter/s, 5.27122s/12 iters), loss = 1.59956
I0406 07:39:26.652299  5226 solver.cpp:237]     Train net output #0: loss = 1.59956 (* 1 = 1.59956 loss)
I0406 07:39:26.652304  5226 sgd_solver.cpp:105] Iteration 3660, lr = 0.01
I0406 07:39:31.426250  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3672.caffemodel
I0406 07:39:34.459728  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3672.solverstate
I0406 07:39:36.755369  5226 solver.cpp:330] Iteration 3672, Testing net (#0)
I0406 07:39:36.755388  5226 net.cpp:676] Ignoring source layer train-data
I0406 07:39:39.599421  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:39:41.027675  5226 solver.cpp:397]     Test net output #0: accuracy = 0.321691
I0406 07:39:41.027793  5226 solver.cpp:397]     Test net output #1: loss = 3.10042 (* 1 = 3.10042 loss)
I0406 07:39:41.159737  5226 solver.cpp:218] Iteration 3672 (0.827168 iter/s, 14.5073s/12 iters), loss = 1.53173
I0406 07:39:41.159788  5226 solver.cpp:237]     Train net output #0: loss = 1.53173 (* 1 = 1.53173 loss)
I0406 07:39:41.159795  5226 sgd_solver.cpp:105] Iteration 3672, lr = 0.01
I0406 07:39:45.523808  5226 solver.cpp:218] Iteration 3684 (2.74979 iter/s, 4.36398s/12 iters), loss = 1.54747
I0406 07:39:45.523846  5226 solver.cpp:237]     Train net output #0: loss = 1.54747 (* 1 = 1.54747 loss)
I0406 07:39:45.523851  5226 sgd_solver.cpp:105] Iteration 3684, lr = 0.01
I0406 07:39:50.635200  5226 solver.cpp:218] Iteration 3696 (2.34774 iter/s, 5.1113s/12 iters), loss = 1.81616
I0406 07:39:50.635237  5226 solver.cpp:237]     Train net output #0: loss = 1.81616 (* 1 = 1.81616 loss)
I0406 07:39:50.635242  5226 sgd_solver.cpp:105] Iteration 3696, lr = 0.01
I0406 07:39:55.909107  5226 solver.cpp:218] Iteration 3708 (2.27539 iter/s, 5.27382s/12 iters), loss = 1.42184
I0406 07:39:55.909147  5226 solver.cpp:237]     Train net output #0: loss = 1.42184 (* 1 = 1.42184 loss)
I0406 07:39:55.909153  5226 sgd_solver.cpp:105] Iteration 3708, lr = 0.01
I0406 07:40:01.064651  5226 solver.cpp:218] Iteration 3720 (2.32763 iter/s, 5.15546s/12 iters), loss = 1.58978
I0406 07:40:01.064697  5226 solver.cpp:237]     Train net output #0: loss = 1.58978 (* 1 = 1.58978 loss)
I0406 07:40:01.064704  5226 sgd_solver.cpp:105] Iteration 3720, lr = 0.01
I0406 07:40:06.113077  5226 solver.cpp:218] Iteration 3732 (2.37702 iter/s, 5.04834s/12 iters), loss = 1.78033
I0406 07:40:06.113116  5226 solver.cpp:237]     Train net output #0: loss = 1.78033 (* 1 = 1.78033 loss)
I0406 07:40:06.113121  5226 sgd_solver.cpp:105] Iteration 3732, lr = 0.01
I0406 07:40:10.241977  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:40:11.325973  5226 solver.cpp:218] Iteration 3744 (2.30202 iter/s, 5.21281s/12 iters), loss = 1.46165
I0406 07:40:11.326054  5226 solver.cpp:237]     Train net output #0: loss = 1.46165 (* 1 = 1.46165 loss)
I0406 07:40:11.326061  5226 sgd_solver.cpp:105] Iteration 3744, lr = 0.01
I0406 07:40:16.703722  5226 solver.cpp:218] Iteration 3756 (2.23147 iter/s, 5.37762s/12 iters), loss = 1.61511
I0406 07:40:16.703764  5226 solver.cpp:237]     Train net output #0: loss = 1.61511 (* 1 = 1.61511 loss)
I0406 07:40:16.703771  5226 sgd_solver.cpp:105] Iteration 3756, lr = 0.01
I0406 07:40:21.900269  5226 solver.cpp:218] Iteration 3768 (2.30927 iter/s, 5.19645s/12 iters), loss = 1.73678
I0406 07:40:21.900315  5226 solver.cpp:237]     Train net output #0: loss = 1.73678 (* 1 = 1.73678 loss)
I0406 07:40:21.900323  5226 sgd_solver.cpp:105] Iteration 3768, lr = 0.01
I0406 07:40:23.910573  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3774.caffemodel
I0406 07:40:26.926476  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3774.solverstate
I0406 07:40:29.257396  5226 solver.cpp:330] Iteration 3774, Testing net (#0)
I0406 07:40:29.257422  5226 net.cpp:676] Ignoring source layer train-data
I0406 07:40:32.103924  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:40:33.564268  5226 solver.cpp:397]     Test net output #0: accuracy = 0.300858
I0406 07:40:33.564303  5226 solver.cpp:397]     Test net output #1: loss = 3.14698 (* 1 = 3.14698 loss)
I0406 07:40:35.401221  5226 solver.cpp:218] Iteration 3780 (0.888835 iter/s, 13.5008s/12 iters), loss = 1.7147
I0406 07:40:35.401252  5226 solver.cpp:237]     Train net output #0: loss = 1.7147 (* 1 = 1.7147 loss)
I0406 07:40:35.401258  5226 sgd_solver.cpp:105] Iteration 3780, lr = 0.01
I0406 07:40:40.299185  5226 solver.cpp:218] Iteration 3792 (2.45004 iter/s, 4.89789s/12 iters), loss = 1.82895
I0406 07:40:40.299217  5226 solver.cpp:237]     Train net output #0: loss = 1.82895 (* 1 = 1.82895 loss)
I0406 07:40:40.299222  5226 sgd_solver.cpp:105] Iteration 3792, lr = 0.01
I0406 07:40:45.502756  5226 solver.cpp:218] Iteration 3804 (2.30615 iter/s, 5.20349s/12 iters), loss = 1.64243
I0406 07:40:45.502888  5226 solver.cpp:237]     Train net output #0: loss = 1.64243 (* 1 = 1.64243 loss)
I0406 07:40:45.502897  5226 sgd_solver.cpp:105] Iteration 3804, lr = 0.01
I0406 07:40:50.853632  5226 solver.cpp:218] Iteration 3816 (2.2427 iter/s, 5.3507s/12 iters), loss = 1.60145
I0406 07:40:50.853683  5226 solver.cpp:237]     Train net output #0: loss = 1.60145 (* 1 = 1.60145 loss)
I0406 07:40:50.853691  5226 sgd_solver.cpp:105] Iteration 3816, lr = 0.01
I0406 07:40:56.027886  5226 solver.cpp:218] Iteration 3828 (2.31922 iter/s, 5.17415s/12 iters), loss = 1.59594
I0406 07:40:56.027938  5226 solver.cpp:237]     Train net output #0: loss = 1.59594 (* 1 = 1.59594 loss)
I0406 07:40:56.027948  5226 sgd_solver.cpp:105] Iteration 3828, lr = 0.01
I0406 07:41:01.357242  5226 solver.cpp:218] Iteration 3840 (2.25172 iter/s, 5.32926s/12 iters), loss = 1.38014
I0406 07:41:01.357282  5226 solver.cpp:237]     Train net output #0: loss = 1.38014 (* 1 = 1.38014 loss)
I0406 07:41:01.357288  5226 sgd_solver.cpp:105] Iteration 3840, lr = 0.01
I0406 07:41:02.562899  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:41:06.764495  5226 solver.cpp:218] Iteration 3852 (2.21928 iter/s, 5.40716s/12 iters), loss = 1.30779
I0406 07:41:06.764533  5226 solver.cpp:237]     Train net output #0: loss = 1.30779 (* 1 = 1.30779 loss)
I0406 07:41:06.764537  5226 sgd_solver.cpp:105] Iteration 3852, lr = 0.01
I0406 07:41:12.159624  5226 solver.cpp:218] Iteration 3864 (2.22426 iter/s, 5.39504s/12 iters), loss = 1.6621
I0406 07:41:12.159663  5226 solver.cpp:237]     Train net output #0: loss = 1.6621 (* 1 = 1.6621 loss)
I0406 07:41:12.159668  5226 sgd_solver.cpp:105] Iteration 3864, lr = 0.01
I0406 07:41:16.761612  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3876.caffemodel
I0406 07:41:19.845855  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3876.solverstate
I0406 07:41:22.159665  5226 solver.cpp:330] Iteration 3876, Testing net (#0)
I0406 07:41:22.159687  5226 net.cpp:676] Ignoring source layer train-data
I0406 07:41:25.036998  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:41:26.558998  5226 solver.cpp:397]     Test net output #0: accuracy = 0.335172
I0406 07:41:26.559031  5226 solver.cpp:397]     Test net output #1: loss = 2.88753 (* 1 = 2.88753 loss)
I0406 07:41:26.694090  5226 solver.cpp:218] Iteration 3876 (0.825632 iter/s, 14.5343s/12 iters), loss = 1.29414
I0406 07:41:26.694139  5226 solver.cpp:237]     Train net output #0: loss = 1.29414 (* 1 = 1.29414 loss)
I0406 07:41:26.694145  5226 sgd_solver.cpp:105] Iteration 3876, lr = 0.01
I0406 07:41:30.824424  5226 solver.cpp:218] Iteration 3888 (2.9054 iter/s, 4.13024s/12 iters), loss = 1.28747
I0406 07:41:30.824476  5226 solver.cpp:237]     Train net output #0: loss = 1.28747 (* 1 = 1.28747 loss)
I0406 07:41:30.824483  5226 sgd_solver.cpp:105] Iteration 3888, lr = 0.01
I0406 07:41:36.169155  5226 solver.cpp:218] Iteration 3900 (2.24524 iter/s, 5.34463s/12 iters), loss = 1.49082
I0406 07:41:36.169193  5226 solver.cpp:237]     Train net output #0: loss = 1.49082 (* 1 = 1.49082 loss)
I0406 07:41:36.169198  5226 sgd_solver.cpp:105] Iteration 3900, lr = 0.01
I0406 07:41:41.501305  5226 solver.cpp:218] Iteration 3912 (2.25054 iter/s, 5.33206s/12 iters), loss = 1.72229
I0406 07:41:41.501343  5226 solver.cpp:237]     Train net output #0: loss = 1.72229 (* 1 = 1.72229 loss)
I0406 07:41:41.501348  5226 sgd_solver.cpp:105] Iteration 3912, lr = 0.01
I0406 07:41:46.855024  5226 solver.cpp:218] Iteration 3924 (2.24147 iter/s, 5.35363s/12 iters), loss = 1.9069
I0406 07:41:46.855161  5226 solver.cpp:237]     Train net output #0: loss = 1.9069 (* 1 = 1.9069 loss)
I0406 07:41:46.855168  5226 sgd_solver.cpp:105] Iteration 3924, lr = 0.01
I0406 07:41:51.942207  5226 solver.cpp:218] Iteration 3936 (2.35895 iter/s, 5.08701s/12 iters), loss = 1.21786
I0406 07:41:51.942242  5226 solver.cpp:237]     Train net output #0: loss = 1.21786 (* 1 = 1.21786 loss)
I0406 07:41:51.942248  5226 sgd_solver.cpp:105] Iteration 3936, lr = 0.01
I0406 07:41:55.467344  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:41:57.211980  5226 solver.cpp:218] Iteration 3948 (2.27718 iter/s, 5.26969s/12 iters), loss = 1.3895
I0406 07:41:57.212018  5226 solver.cpp:237]     Train net output #0: loss = 1.3895 (* 1 = 1.3895 loss)
I0406 07:41:57.212023  5226 sgd_solver.cpp:105] Iteration 3948, lr = 0.01
I0406 07:42:02.648705  5226 solver.cpp:218] Iteration 3960 (2.20725 iter/s, 5.43664s/12 iters), loss = 1.33313
I0406 07:42:02.648741  5226 solver.cpp:237]     Train net output #0: loss = 1.33313 (* 1 = 1.33313 loss)
I0406 07:42:02.648746  5226 sgd_solver.cpp:105] Iteration 3960, lr = 0.01
I0406 07:42:08.008857  5226 solver.cpp:218] Iteration 3972 (2.23878 iter/s, 5.36007s/12 iters), loss = 1.31996
I0406 07:42:08.008898  5226 solver.cpp:237]     Train net output #0: loss = 1.31996 (* 1 = 1.31996 loss)
I0406 07:42:08.008903  5226 sgd_solver.cpp:105] Iteration 3972, lr = 0.01
I0406 07:42:10.309165  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3978.caffemodel
I0406 07:42:13.227551  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3978.solverstate
I0406 07:42:15.533095  5226 solver.cpp:330] Iteration 3978, Testing net (#0)
I0406 07:42:15.533119  5226 net.cpp:676] Ignoring source layer train-data
I0406 07:42:18.339995  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:42:19.900418  5226 solver.cpp:397]     Test net output #0: accuracy = 0.322304
I0406 07:42:19.900445  5226 solver.cpp:397]     Test net output #1: loss = 3.00782 (* 1 = 3.00782 loss)
I0406 07:42:21.714197  5226 solver.cpp:218] Iteration 3984 (0.875581 iter/s, 13.7052s/12 iters), loss = 1.47955
I0406 07:42:21.714257  5226 solver.cpp:237]     Train net output #0: loss = 1.47955 (* 1 = 1.47955 loss)
I0406 07:42:21.714263  5226 sgd_solver.cpp:105] Iteration 3984, lr = 0.01
I0406 07:42:26.971159  5226 solver.cpp:218] Iteration 3996 (2.28273 iter/s, 5.25686s/12 iters), loss = 1.57538
I0406 07:42:26.971204  5226 solver.cpp:237]     Train net output #0: loss = 1.57538 (* 1 = 1.57538 loss)
I0406 07:42:26.971211  5226 sgd_solver.cpp:105] Iteration 3996, lr = 0.01
I0406 07:42:32.329207  5226 solver.cpp:218] Iteration 4008 (2.23966 iter/s, 5.35795s/12 iters), loss = 1.16564
I0406 07:42:32.329242  5226 solver.cpp:237]     Train net output #0: loss = 1.16564 (* 1 = 1.16564 loss)
I0406 07:42:32.329248  5226 sgd_solver.cpp:105] Iteration 4008, lr = 0.01
I0406 07:42:37.623857  5226 solver.cpp:218] Iteration 4020 (2.26648 iter/s, 5.29456s/12 iters), loss = 1.41902
I0406 07:42:37.623896  5226 solver.cpp:237]     Train net output #0: loss = 1.41902 (* 1 = 1.41902 loss)
I0406 07:42:37.623901  5226 sgd_solver.cpp:105] Iteration 4020, lr = 0.01
I0406 07:42:42.963275  5226 solver.cpp:218] Iteration 4032 (2.24747 iter/s, 5.33933s/12 iters), loss = 1.48964
I0406 07:42:42.963333  5226 solver.cpp:237]     Train net output #0: loss = 1.48964 (* 1 = 1.48964 loss)
I0406 07:42:42.963346  5226 sgd_solver.cpp:105] Iteration 4032, lr = 0.01
I0406 07:42:48.142302  5226 solver.cpp:218] Iteration 4044 (2.31708 iter/s, 5.17893s/12 iters), loss = 1.55566
I0406 07:42:48.142343  5226 solver.cpp:237]     Train net output #0: loss = 1.55566 (* 1 = 1.55566 loss)
I0406 07:42:48.142349  5226 sgd_solver.cpp:105] Iteration 4044, lr = 0.01
I0406 07:42:48.676218  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:42:53.273089  5226 solver.cpp:218] Iteration 4056 (2.33886 iter/s, 5.1307s/12 iters), loss = 1.4053
I0406 07:42:53.273125  5226 solver.cpp:237]     Train net output #0: loss = 1.4053 (* 1 = 1.4053 loss)
I0406 07:42:53.273130  5226 sgd_solver.cpp:105] Iteration 4056, lr = 0.01
I0406 07:42:58.448902  5226 solver.cpp:218] Iteration 4068 (2.31852 iter/s, 5.17572s/12 iters), loss = 1.49174
I0406 07:42:58.448940  5226 solver.cpp:237]     Train net output #0: loss = 1.49174 (* 1 = 1.49174 loss)
I0406 07:42:58.448946  5226 sgd_solver.cpp:105] Iteration 4068, lr = 0.01
I0406 07:43:03.149427  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4080.caffemodel
I0406 07:43:06.157959  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4080.solverstate
I0406 07:43:08.465924  5226 solver.cpp:330] Iteration 4080, Testing net (#0)
I0406 07:43:08.465942  5226 net.cpp:676] Ignoring source layer train-data
I0406 07:43:11.173875  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:43:12.757417  5226 solver.cpp:397]     Test net output #0: accuracy = 0.327206
I0406 07:43:12.757441  5226 solver.cpp:397]     Test net output #1: loss = 3.06441 (* 1 = 3.06441 loss)
I0406 07:43:12.893946  5226 solver.cpp:218] Iteration 4080 (0.830743 iter/s, 14.4449s/12 iters), loss = 1.47637
I0406 07:43:12.893985  5226 solver.cpp:237]     Train net output #0: loss = 1.47637 (* 1 = 1.47637 loss)
I0406 07:43:12.893990  5226 sgd_solver.cpp:105] Iteration 4080, lr = 0.01
I0406 07:43:17.437860  5226 solver.cpp:218] Iteration 4092 (2.64094 iter/s, 4.54383s/12 iters), loss = 1.70891
I0406 07:43:17.437896  5226 solver.cpp:237]     Train net output #0: loss = 1.70891 (* 1 = 1.70891 loss)
I0406 07:43:17.437902  5226 sgd_solver.cpp:105] Iteration 4092, lr = 0.01
I0406 07:43:23.010995  5226 solver.cpp:218] Iteration 4104 (2.15322 iter/s, 5.57305s/12 iters), loss = 1.0637
I0406 07:43:23.011080  5226 solver.cpp:237]     Train net output #0: loss = 1.0637 (* 1 = 1.0637 loss)
I0406 07:43:23.011085  5226 sgd_solver.cpp:105] Iteration 4104, lr = 0.01
I0406 07:43:28.396471  5226 solver.cpp:218] Iteration 4116 (2.22827 iter/s, 5.38534s/12 iters), loss = 1.2998
I0406 07:43:28.396512  5226 solver.cpp:237]     Train net output #0: loss = 1.2998 (* 1 = 1.2998 loss)
I0406 07:43:28.396518  5226 sgd_solver.cpp:105] Iteration 4116, lr = 0.01
I0406 07:43:33.727180  5226 solver.cpp:218] Iteration 4128 (2.25115 iter/s, 5.33062s/12 iters), loss = 1.54419
I0406 07:43:33.727221  5226 solver.cpp:237]     Train net output #0: loss = 1.54419 (* 1 = 1.54419 loss)
I0406 07:43:33.727227  5226 sgd_solver.cpp:105] Iteration 4128, lr = 0.01
I0406 07:43:38.791496  5226 solver.cpp:218] Iteration 4140 (2.36956 iter/s, 5.06423s/12 iters), loss = 1.35332
I0406 07:43:38.791532  5226 solver.cpp:237]     Train net output #0: loss = 1.35332 (* 1 = 1.35332 loss)
I0406 07:43:38.791536  5226 sgd_solver.cpp:105] Iteration 4140, lr = 0.01
I0406 07:43:41.476444  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:43:43.946408  5226 solver.cpp:218] Iteration 4152 (2.32791 iter/s, 5.15483s/12 iters), loss = 1.5944
I0406 07:43:43.946444  5226 solver.cpp:237]     Train net output #0: loss = 1.5944 (* 1 = 1.5944 loss)
I0406 07:43:43.946449  5226 sgd_solver.cpp:105] Iteration 4152, lr = 0.01
I0406 07:43:45.588577  5226 blocking_queue.cpp:49] Waiting for data
I0406 07:43:49.212473  5226 solver.cpp:218] Iteration 4164 (2.27878 iter/s, 5.26598s/12 iters), loss = 1.41644
I0406 07:43:49.212519  5226 solver.cpp:237]     Train net output #0: loss = 1.41644 (* 1 = 1.41644 loss)
I0406 07:43:49.212527  5226 sgd_solver.cpp:105] Iteration 4164, lr = 0.01
I0406 07:43:54.415076  5226 solver.cpp:218] Iteration 4176 (2.30658 iter/s, 5.20251s/12 iters), loss = 1.51725
I0406 07:43:54.415203  5226 solver.cpp:237]     Train net output #0: loss = 1.51725 (* 1 = 1.51725 loss)
I0406 07:43:54.415211  5226 sgd_solver.cpp:105] Iteration 4176, lr = 0.01
I0406 07:43:56.462532  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4182.caffemodel
I0406 07:43:59.492276  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4182.solverstate
I0406 07:44:01.831262  5226 solver.cpp:330] Iteration 4182, Testing net (#0)
I0406 07:44:01.831282  5226 net.cpp:676] Ignoring source layer train-data
I0406 07:44:04.516916  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:44:06.203562  5226 solver.cpp:397]     Test net output #0: accuracy = 0.324755
I0406 07:44:06.203596  5226 solver.cpp:397]     Test net output #1: loss = 3.08177 (* 1 = 3.08177 loss)
I0406 07:44:07.972090  5226 solver.cpp:218] Iteration 4188 (0.885165 iter/s, 13.5568s/12 iters), loss = 1.28694
I0406 07:44:07.972131  5226 solver.cpp:237]     Train net output #0: loss = 1.28694 (* 1 = 1.28694 loss)
I0406 07:44:07.972136  5226 sgd_solver.cpp:105] Iteration 4188, lr = 0.01
I0406 07:44:13.320312  5226 solver.cpp:218] Iteration 4200 (2.24378 iter/s, 5.34813s/12 iters), loss = 1.2066
I0406 07:44:13.320348  5226 solver.cpp:237]     Train net output #0: loss = 1.2066 (* 1 = 1.2066 loss)
I0406 07:44:13.320353  5226 sgd_solver.cpp:105] Iteration 4200, lr = 0.01
I0406 07:44:18.788738  5226 solver.cpp:218] Iteration 4212 (2.19445 iter/s, 5.46834s/12 iters), loss = 1.17914
I0406 07:44:18.788771  5226 solver.cpp:237]     Train net output #0: loss = 1.17914 (* 1 = 1.17914 loss)
I0406 07:44:18.788777  5226 sgd_solver.cpp:105] Iteration 4212, lr = 0.01
I0406 07:44:24.056030  5226 solver.cpp:218] Iteration 4224 (2.27825 iter/s, 5.26721s/12 iters), loss = 1.17233
I0406 07:44:24.056072  5226 solver.cpp:237]     Train net output #0: loss = 1.17233 (* 1 = 1.17233 loss)
I0406 07:44:24.056078  5226 sgd_solver.cpp:105] Iteration 4224, lr = 0.01
I0406 07:44:29.446471  5226 solver.cpp:218] Iteration 4236 (2.2262 iter/s, 5.39034s/12 iters), loss = 1.39547
I0406 07:44:29.446555  5226 solver.cpp:237]     Train net output #0: loss = 1.39547 (* 1 = 1.39547 loss)
I0406 07:44:29.446564  5226 sgd_solver.cpp:105] Iteration 4236, lr = 0.01
I0406 07:44:34.450280  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:44:34.726584  5226 solver.cpp:218] Iteration 4248 (2.27274 iter/s, 5.27998s/12 iters), loss = 1.26418
I0406 07:44:34.726629  5226 solver.cpp:237]     Train net output #0: loss = 1.26418 (* 1 = 1.26418 loss)
I0406 07:44:34.726635  5226 sgd_solver.cpp:105] Iteration 4248, lr = 0.01
I0406 07:44:40.007169  5226 solver.cpp:218] Iteration 4260 (2.27252 iter/s, 5.28049s/12 iters), loss = 1.43957
I0406 07:44:40.007222  5226 solver.cpp:237]     Train net output #0: loss = 1.43957 (* 1 = 1.43957 loss)
I0406 07:44:40.007231  5226 sgd_solver.cpp:105] Iteration 4260, lr = 0.01
I0406 07:44:45.340837  5226 solver.cpp:218] Iteration 4272 (2.2499 iter/s, 5.33357s/12 iters), loss = 1.323
I0406 07:44:45.340876  5226 solver.cpp:237]     Train net output #0: loss = 1.323 (* 1 = 1.323 loss)
I0406 07:44:45.340886  5226 sgd_solver.cpp:105] Iteration 4272, lr = 0.01
I0406 07:44:50.123627  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4284.caffemodel
I0406 07:44:53.206562  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4284.solverstate
I0406 07:44:55.546898  5226 solver.cpp:330] Iteration 4284, Testing net (#0)
I0406 07:44:55.546921  5226 net.cpp:676] Ignoring source layer train-data
I0406 07:44:58.198887  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:44:59.849579  5226 solver.cpp:397]     Test net output #0: accuracy = 0.333946
I0406 07:44:59.849733  5226 solver.cpp:397]     Test net output #1: loss = 2.985 (* 1 = 2.985 loss)
I0406 07:44:59.982801  5226 solver.cpp:218] Iteration 4284 (0.819571 iter/s, 14.6418s/12 iters), loss = 1.43728
I0406 07:44:59.982842  5226 solver.cpp:237]     Train net output #0: loss = 1.43728 (* 1 = 1.43728 loss)
I0406 07:44:59.982848  5226 sgd_solver.cpp:105] Iteration 4284, lr = 0.01
I0406 07:45:04.194146  5226 solver.cpp:218] Iteration 4296 (2.84951 iter/s, 4.21125s/12 iters), loss = 1.46934
I0406 07:45:04.194213  5226 solver.cpp:237]     Train net output #0: loss = 1.46934 (* 1 = 1.46934 loss)
I0406 07:45:04.194226  5226 sgd_solver.cpp:105] Iteration 4296, lr = 0.01
I0406 07:45:09.485049  5226 solver.cpp:218] Iteration 4308 (2.26809 iter/s, 5.29079s/12 iters), loss = 1.10036
I0406 07:45:09.485085  5226 solver.cpp:237]     Train net output #0: loss = 1.10036 (* 1 = 1.10036 loss)
I0406 07:45:09.485091  5226 sgd_solver.cpp:105] Iteration 4308, lr = 0.01
I0406 07:45:14.804729  5226 solver.cpp:218] Iteration 4320 (2.25581 iter/s, 5.3196s/12 iters), loss = 1.11731
I0406 07:45:14.804762  5226 solver.cpp:237]     Train net output #0: loss = 1.11731 (* 1 = 1.11731 loss)
I0406 07:45:14.804769  5226 sgd_solver.cpp:105] Iteration 4320, lr = 0.01
I0406 07:45:19.954612  5226 solver.cpp:218] Iteration 4332 (2.33019 iter/s, 5.14979s/12 iters), loss = 0.934898
I0406 07:45:19.954659  5226 solver.cpp:237]     Train net output #0: loss = 0.934898 (* 1 = 0.934898 loss)
I0406 07:45:19.954668  5226 sgd_solver.cpp:105] Iteration 4332, lr = 0.01
I0406 07:45:25.265318  5226 solver.cpp:218] Iteration 4344 (2.25963 iter/s, 5.31061s/12 iters), loss = 1.36034
I0406 07:45:25.265369  5226 solver.cpp:237]     Train net output #0: loss = 1.36034 (* 1 = 1.36034 loss)
I0406 07:45:25.265377  5226 sgd_solver.cpp:105] Iteration 4344, lr = 0.01
I0406 07:45:27.202687  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:45:30.424093  5226 solver.cpp:218] Iteration 4356 (2.32618 iter/s, 5.15868s/12 iters), loss = 1.06496
I0406 07:45:30.424628  5226 solver.cpp:237]     Train net output #0: loss = 1.06496 (* 1 = 1.06496 loss)
I0406 07:45:30.424636  5226 sgd_solver.cpp:105] Iteration 4356, lr = 0.01
I0406 07:45:35.784809  5226 solver.cpp:218] Iteration 4368 (2.23875 iter/s, 5.36014s/12 iters), loss = 1.36264
I0406 07:45:35.784843  5226 solver.cpp:237]     Train net output #0: loss = 1.36264 (* 1 = 1.36264 loss)
I0406 07:45:35.784849  5226 sgd_solver.cpp:105] Iteration 4368, lr = 0.01
I0406 07:45:41.000325  5226 solver.cpp:218] Iteration 4380 (2.30086 iter/s, 5.21544s/12 iters), loss = 1.48893
I0406 07:45:41.000365  5226 solver.cpp:237]     Train net output #0: loss = 1.48893 (* 1 = 1.48893 loss)
I0406 07:45:41.000371  5226 sgd_solver.cpp:105] Iteration 4380, lr = 0.01
I0406 07:45:43.195434  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4386.caffemodel
I0406 07:45:46.197104  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4386.solverstate
I0406 07:45:48.496718  5226 solver.cpp:330] Iteration 4386, Testing net (#0)
I0406 07:45:48.496735  5226 net.cpp:676] Ignoring source layer train-data
I0406 07:45:51.056607  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:45:52.756392  5226 solver.cpp:397]     Test net output #0: accuracy = 0.328431
I0406 07:45:52.756439  5226 solver.cpp:397]     Test net output #1: loss = 2.9974 (* 1 = 2.9974 loss)
I0406 07:45:54.592645  5226 solver.cpp:218] Iteration 4392 (0.882861 iter/s, 13.5922s/12 iters), loss = 1.34584
I0406 07:45:54.592703  5226 solver.cpp:237]     Train net output #0: loss = 1.34584 (* 1 = 1.34584 loss)
I0406 07:45:54.592712  5226 sgd_solver.cpp:105] Iteration 4392, lr = 0.01
I0406 07:45:59.929893  5226 solver.cpp:218] Iteration 4404 (2.2484 iter/s, 5.33714s/12 iters), loss = 1.61502
I0406 07:45:59.929946  5226 solver.cpp:237]     Train net output #0: loss = 1.61502 (* 1 = 1.61502 loss)
I0406 07:45:59.929955  5226 sgd_solver.cpp:105] Iteration 4404, lr = 0.01
I0406 07:46:05.188912  5226 solver.cpp:218] Iteration 4416 (2.28184 iter/s, 5.25892s/12 iters), loss = 1.2401
I0406 07:46:05.189079  5226 solver.cpp:237]     Train net output #0: loss = 1.2401 (* 1 = 1.2401 loss)
I0406 07:46:05.189088  5226 sgd_solver.cpp:105] Iteration 4416, lr = 0.01
I0406 07:46:10.543936  5226 solver.cpp:218] Iteration 4428 (2.24097 iter/s, 5.35481s/12 iters), loss = 1.37075
I0406 07:46:10.543972  5226 solver.cpp:237]     Train net output #0: loss = 1.37075 (* 1 = 1.37075 loss)
I0406 07:46:10.543977  5226 sgd_solver.cpp:105] Iteration 4428, lr = 0.01
I0406 07:46:15.717097  5226 solver.cpp:218] Iteration 4440 (2.3197 iter/s, 5.17308s/12 iters), loss = 1.19256
I0406 07:46:15.717135  5226 solver.cpp:237]     Train net output #0: loss = 1.19256 (* 1 = 1.19256 loss)
I0406 07:46:15.717140  5226 sgd_solver.cpp:105] Iteration 4440, lr = 0.01
I0406 07:46:20.013849  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:46:21.126646  5226 solver.cpp:218] Iteration 4452 (2.21834 iter/s, 5.40946s/12 iters), loss = 1.46465
I0406 07:46:21.126682  5226 solver.cpp:237]     Train net output #0: loss = 1.46465 (* 1 = 1.46465 loss)
I0406 07:46:21.126688  5226 sgd_solver.cpp:105] Iteration 4452, lr = 0.01
I0406 07:46:26.508162  5226 solver.cpp:218] Iteration 4464 (2.22989 iter/s, 5.38143s/12 iters), loss = 1.21948
I0406 07:46:26.508199  5226 solver.cpp:237]     Train net output #0: loss = 1.21948 (* 1 = 1.21948 loss)
I0406 07:46:26.508204  5226 sgd_solver.cpp:105] Iteration 4464, lr = 0.01
I0406 07:46:31.598654  5226 solver.cpp:218] Iteration 4476 (2.35737 iter/s, 5.09041s/12 iters), loss = 1.38957
I0406 07:46:31.598690  5226 solver.cpp:237]     Train net output #0: loss = 1.38957 (* 1 = 1.38957 loss)
I0406 07:46:31.598695  5226 sgd_solver.cpp:105] Iteration 4476, lr = 0.01
I0406 07:46:36.164391  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4488.caffemodel
I0406 07:46:39.195258  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4488.solverstate
I0406 07:46:41.496449  5226 solver.cpp:330] Iteration 4488, Testing net (#0)
I0406 07:46:41.496469  5226 net.cpp:676] Ignoring source layer train-data
I0406 07:46:44.070693  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:46:45.857641  5226 solver.cpp:397]     Test net output #0: accuracy = 0.313113
I0406 07:46:45.857677  5226 solver.cpp:397]     Test net output #1: loss = 3.21875 (* 1 = 3.21875 loss)
I0406 07:46:45.994784  5226 solver.cpp:218] Iteration 4488 (0.833566 iter/s, 14.396s/12 iters), loss = 1.40557
I0406 07:46:45.994833  5226 solver.cpp:237]     Train net output #0: loss = 1.40557 (* 1 = 1.40557 loss)
I0406 07:46:45.994843  5226 sgd_solver.cpp:105] Iteration 4488, lr = 0.01
I0406 07:46:50.418093  5226 solver.cpp:218] Iteration 4500 (2.71296 iter/s, 4.42322s/12 iters), loss = 1.31592
I0406 07:46:50.418135  5226 solver.cpp:237]     Train net output #0: loss = 1.31592 (* 1 = 1.31592 loss)
I0406 07:46:50.418141  5226 sgd_solver.cpp:105] Iteration 4500, lr = 0.01
I0406 07:46:55.890537  5226 solver.cpp:218] Iteration 4512 (2.19284 iter/s, 5.47235s/12 iters), loss = 1.1564
I0406 07:46:55.890573  5226 solver.cpp:237]     Train net output #0: loss = 1.1564 (* 1 = 1.1564 loss)
I0406 07:46:55.890579  5226 sgd_solver.cpp:105] Iteration 4512, lr = 0.01
I0406 07:47:01.150938  5226 solver.cpp:218] Iteration 4524 (2.28123 iter/s, 5.26031s/12 iters), loss = 1.20819
I0406 07:47:01.150975  5226 solver.cpp:237]     Train net output #0: loss = 1.20819 (* 1 = 1.20819 loss)
I0406 07:47:01.150980  5226 sgd_solver.cpp:105] Iteration 4524, lr = 0.01
I0406 07:47:06.358862  5226 solver.cpp:218] Iteration 4536 (2.30422 iter/s, 5.20784s/12 iters), loss = 1.46608
I0406 07:47:06.359016  5226 solver.cpp:237]     Train net output #0: loss = 1.46608 (* 1 = 1.46608 loss)
I0406 07:47:06.359025  5226 sgd_solver.cpp:105] Iteration 4536, lr = 0.01
I0406 07:47:11.272907  5226 solver.cpp:218] Iteration 4548 (2.44208 iter/s, 4.91385s/12 iters), loss = 1.52902
I0406 07:47:11.272943  5226 solver.cpp:237]     Train net output #0: loss = 1.52902 (* 1 = 1.52902 loss)
I0406 07:47:11.272949  5226 sgd_solver.cpp:105] Iteration 4548, lr = 0.01
I0406 07:47:12.659642  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:47:16.570510  5226 solver.cpp:218] Iteration 4560 (2.26521 iter/s, 5.29752s/12 iters), loss = 1.1028
I0406 07:47:16.570549  5226 solver.cpp:237]     Train net output #0: loss = 1.1028 (* 1 = 1.1028 loss)
I0406 07:47:16.570554  5226 sgd_solver.cpp:105] Iteration 4560, lr = 0.01
I0406 07:47:21.749428  5226 solver.cpp:218] Iteration 4572 (2.31713 iter/s, 5.17883s/12 iters), loss = 1.30269
I0406 07:47:21.749469  5226 solver.cpp:237]     Train net output #0: loss = 1.30269 (* 1 = 1.30269 loss)
I0406 07:47:21.749473  5226 sgd_solver.cpp:105] Iteration 4572, lr = 0.01
I0406 07:47:27.030598  5226 solver.cpp:218] Iteration 4584 (2.27226 iter/s, 5.28108s/12 iters), loss = 1.34215
I0406 07:47:27.030634  5226 solver.cpp:237]     Train net output #0: loss = 1.34215 (* 1 = 1.34215 loss)
I0406 07:47:27.030640  5226 sgd_solver.cpp:105] Iteration 4584, lr = 0.01
I0406 07:47:29.269740  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4590.caffemodel
I0406 07:47:32.235402  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4590.solverstate
I0406 07:47:34.538337  5226 solver.cpp:330] Iteration 4590, Testing net (#0)
I0406 07:47:34.538357  5226 net.cpp:676] Ignoring source layer train-data
I0406 07:47:37.032946  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:47:38.879949  5226 solver.cpp:397]     Test net output #0: accuracy = 0.328431
I0406 07:47:38.879983  5226 solver.cpp:397]     Test net output #1: loss = 2.97265 (* 1 = 2.97265 loss)
I0406 07:47:40.945192  5226 solver.cpp:218] Iteration 4596 (0.862413 iter/s, 13.9144s/12 iters), loss = 1.18053
I0406 07:47:40.945243  5226 solver.cpp:237]     Train net output #0: loss = 1.18053 (* 1 = 1.18053 loss)
I0406 07:47:40.945251  5226 sgd_solver.cpp:105] Iteration 4596, lr = 0.01
I0406 07:47:46.331529  5226 solver.cpp:218] Iteration 4608 (2.2279 iter/s, 5.38624s/12 iters), loss = 0.950801
I0406 07:47:46.331580  5226 solver.cpp:237]     Train net output #0: loss = 0.950801 (* 1 = 0.950801 loss)
I0406 07:47:46.331589  5226 sgd_solver.cpp:105] Iteration 4608, lr = 0.01
I0406 07:47:51.763516  5226 solver.cpp:218] Iteration 4620 (2.20918 iter/s, 5.43189s/12 iters), loss = 1.30758
I0406 07:47:51.763554  5226 solver.cpp:237]     Train net output #0: loss = 1.30758 (* 1 = 1.30758 loss)
I0406 07:47:51.763561  5226 sgd_solver.cpp:105] Iteration 4620, lr = 0.01
I0406 07:47:57.007606  5226 solver.cpp:218] Iteration 4632 (2.28833 iter/s, 5.244s/12 iters), loss = 1.3201
I0406 07:47:57.007660  5226 solver.cpp:237]     Train net output #0: loss = 1.3201 (* 1 = 1.3201 loss)
I0406 07:47:57.007668  5226 sgd_solver.cpp:105] Iteration 4632, lr = 0.01
I0406 07:48:02.299629  5226 solver.cpp:218] Iteration 4644 (2.26761 iter/s, 5.29192s/12 iters), loss = 1.49239
I0406 07:48:02.299664  5226 solver.cpp:237]     Train net output #0: loss = 1.49239 (* 1 = 1.49239 loss)
I0406 07:48:02.299670  5226 sgd_solver.cpp:105] Iteration 4644, lr = 0.01
I0406 07:48:05.837280  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:48:07.581893  5226 solver.cpp:218] Iteration 4656 (2.27179 iter/s, 5.28218s/12 iters), loss = 1.3102
I0406 07:48:07.582015  5226 solver.cpp:237]     Train net output #0: loss = 1.3102 (* 1 = 1.3102 loss)
I0406 07:48:07.582020  5226 sgd_solver.cpp:105] Iteration 4656, lr = 0.01
I0406 07:48:12.990911  5226 solver.cpp:218] Iteration 4668 (2.21859 iter/s, 5.40885s/12 iters), loss = 1.36358
I0406 07:48:12.990952  5226 solver.cpp:237]     Train net output #0: loss = 1.36358 (* 1 = 1.36358 loss)
I0406 07:48:12.990957  5226 sgd_solver.cpp:105] Iteration 4668, lr = 0.01
I0406 07:48:18.295114  5226 solver.cpp:218] Iteration 4680 (2.26239 iter/s, 5.30411s/12 iters), loss = 1.43451
I0406 07:48:18.295151  5226 solver.cpp:237]     Train net output #0: loss = 1.43451 (* 1 = 1.43451 loss)
I0406 07:48:18.295156  5226 sgd_solver.cpp:105] Iteration 4680, lr = 0.01
I0406 07:48:23.088085  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4692.caffemodel
I0406 07:48:26.172093  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4692.solverstate
I0406 07:48:28.474866  5226 solver.cpp:330] Iteration 4692, Testing net (#0)
I0406 07:48:28.474884  5226 net.cpp:676] Ignoring source layer train-data
I0406 07:48:31.003047  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:48:32.812199  5226 solver.cpp:397]     Test net output #0: accuracy = 0.322304
I0406 07:48:32.812232  5226 solver.cpp:397]     Test net output #1: loss = 3.10933 (* 1 = 3.10933 loss)
I0406 07:48:32.953163  5226 solver.cpp:218] Iteration 4692 (0.818671 iter/s, 14.6579s/12 iters), loss = 0.981167
I0406 07:48:32.953212  5226 solver.cpp:237]     Train net output #0: loss = 0.981167 (* 1 = 0.981167 loss)
I0406 07:48:32.953220  5226 sgd_solver.cpp:105] Iteration 4692, lr = 0.01
I0406 07:48:37.042342  5226 solver.cpp:218] Iteration 4704 (2.93464 iter/s, 4.08908s/12 iters), loss = 1.28855
I0406 07:48:37.042387  5226 solver.cpp:237]     Train net output #0: loss = 1.28855 (* 1 = 1.28855 loss)
I0406 07:48:37.042392  5226 sgd_solver.cpp:105] Iteration 4704, lr = 0.01
I0406 07:48:42.288740  5226 solver.cpp:218] Iteration 4716 (2.28733 iter/s, 5.2463s/12 iters), loss = 1.11105
I0406 07:48:42.288861  5226 solver.cpp:237]     Train net output #0: loss = 1.11105 (* 1 = 1.11105 loss)
I0406 07:48:42.288867  5226 sgd_solver.cpp:105] Iteration 4716, lr = 0.01
I0406 07:48:47.453068  5226 solver.cpp:218] Iteration 4728 (2.32371 iter/s, 5.16416s/12 iters), loss = 1.32187
I0406 07:48:47.453100  5226 solver.cpp:237]     Train net output #0: loss = 1.32187 (* 1 = 1.32187 loss)
I0406 07:48:47.453106  5226 sgd_solver.cpp:105] Iteration 4728, lr = 0.01
I0406 07:48:52.675240  5226 solver.cpp:218] Iteration 4740 (2.29793 iter/s, 5.2221s/12 iters), loss = 1.43182
I0406 07:48:52.675268  5226 solver.cpp:237]     Train net output #0: loss = 1.43182 (* 1 = 1.43182 loss)
I0406 07:48:52.675273  5226 sgd_solver.cpp:105] Iteration 4740, lr = 0.01
I0406 07:48:57.944686  5226 solver.cpp:218] Iteration 4752 (2.27731 iter/s, 5.26937s/12 iters), loss = 1.32995
I0406 07:48:57.944734  5226 solver.cpp:237]     Train net output #0: loss = 1.32995 (* 1 = 1.32995 loss)
I0406 07:48:57.944741  5226 sgd_solver.cpp:105] Iteration 4752, lr = 0.01
I0406 07:48:58.504801  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:49:03.316118  5226 solver.cpp:218] Iteration 4764 (2.23408 iter/s, 5.37133s/12 iters), loss = 1.32814
I0406 07:49:03.316164  5226 solver.cpp:237]     Train net output #0: loss = 1.32814 (* 1 = 1.32814 loss)
I0406 07:49:03.316174  5226 sgd_solver.cpp:105] Iteration 4764, lr = 0.01
I0406 07:49:08.534395  5226 solver.cpp:218] Iteration 4776 (2.29965 iter/s, 5.21818s/12 iters), loss = 1.42889
I0406 07:49:08.534432  5226 solver.cpp:237]     Train net output #0: loss = 1.42889 (* 1 = 1.42889 loss)
I0406 07:49:08.534437  5226 sgd_solver.cpp:105] Iteration 4776, lr = 0.01
I0406 07:49:13.869709  5226 solver.cpp:218] Iteration 4788 (2.2492 iter/s, 5.33523s/12 iters), loss = 1.29923
I0406 07:49:13.869825  5226 solver.cpp:237]     Train net output #0: loss = 1.29923 (* 1 = 1.29923 loss)
I0406 07:49:13.869834  5226 sgd_solver.cpp:105] Iteration 4788, lr = 0.01
I0406 07:49:16.005098  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4794.caffemodel
I0406 07:49:19.011186  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4794.solverstate
I0406 07:49:21.332480  5226 solver.cpp:330] Iteration 4794, Testing net (#0)
I0406 07:49:21.332502  5226 net.cpp:676] Ignoring source layer train-data
I0406 07:49:23.731123  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:49:25.586869  5226 solver.cpp:397]     Test net output #0: accuracy = 0.324142
I0406 07:49:25.586905  5226 solver.cpp:397]     Test net output #1: loss = 3.05492 (* 1 = 3.05492 loss)
I0406 07:49:27.579016  5226 solver.cpp:218] Iteration 4800 (0.875331 iter/s, 13.7091s/12 iters), loss = 1.11227
I0406 07:49:27.579053  5226 solver.cpp:237]     Train net output #0: loss = 1.11227 (* 1 = 1.11227 loss)
I0406 07:49:27.579058  5226 sgd_solver.cpp:105] Iteration 4800, lr = 0.01
I0406 07:49:32.750728  5226 solver.cpp:218] Iteration 4812 (2.32035 iter/s, 5.17162s/12 iters), loss = 1.2966
I0406 07:49:32.750764  5226 solver.cpp:237]     Train net output #0: loss = 1.2966 (* 1 = 1.2966 loss)
I0406 07:49:32.750771  5226 sgd_solver.cpp:105] Iteration 4812, lr = 0.01
I0406 07:49:37.887214  5226 solver.cpp:218] Iteration 4824 (2.33627 iter/s, 5.1364s/12 iters), loss = 1.13206
I0406 07:49:37.887262  5226 solver.cpp:237]     Train net output #0: loss = 1.13206 (* 1 = 1.13206 loss)
I0406 07:49:37.887270  5226 sgd_solver.cpp:105] Iteration 4824, lr = 0.01
I0406 07:49:43.078636  5226 solver.cpp:218] Iteration 4836 (2.31155 iter/s, 5.19133s/12 iters), loss = 0.997291
I0406 07:49:43.078673  5226 solver.cpp:237]     Train net output #0: loss = 0.997291 (* 1 = 0.997291 loss)
I0406 07:49:43.078678  5226 sgd_solver.cpp:105] Iteration 4836, lr = 0.01
I0406 07:49:45.341719  5226 blocking_queue.cpp:49] Waiting for data
I0406 07:49:48.468688  5226 solver.cpp:218] Iteration 4848 (2.22636 iter/s, 5.38996s/12 iters), loss = 1.74306
I0406 07:49:48.468725  5226 solver.cpp:237]     Train net output #0: loss = 1.74306 (* 1 = 1.74306 loss)
I0406 07:49:48.468730  5226 sgd_solver.cpp:105] Iteration 4848, lr = 0.01
I0406 07:49:51.164045  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:49:53.547287  5226 solver.cpp:218] Iteration 4860 (2.3629 iter/s, 5.07851s/12 iters), loss = 1.10049
I0406 07:49:53.547325  5226 solver.cpp:237]     Train net output #0: loss = 1.10049 (* 1 = 1.10049 loss)
I0406 07:49:53.547330  5226 sgd_solver.cpp:105] Iteration 4860, lr = 0.01
I0406 07:49:58.577877  5226 solver.cpp:218] Iteration 4872 (2.38545 iter/s, 5.0305s/12 iters), loss = 1.40458
I0406 07:49:58.577913  5226 solver.cpp:237]     Train net output #0: loss = 1.40458 (* 1 = 1.40458 loss)
I0406 07:49:58.577919  5226 sgd_solver.cpp:105] Iteration 4872, lr = 0.01
I0406 07:50:03.783627  5226 solver.cpp:218] Iteration 4884 (2.30518 iter/s, 5.20567s/12 iters), loss = 1.23893
I0406 07:50:03.783663  5226 solver.cpp:237]     Train net output #0: loss = 1.23893 (* 1 = 1.23893 loss)
I0406 07:50:03.783668  5226 sgd_solver.cpp:105] Iteration 4884, lr = 0.01
I0406 07:50:08.589906  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4896.caffemodel
I0406 07:50:11.617627  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4896.solverstate
I0406 07:50:13.915295  5226 solver.cpp:330] Iteration 4896, Testing net (#0)
I0406 07:50:13.915314  5226 net.cpp:676] Ignoring source layer train-data
I0406 07:50:16.306262  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:50:18.199350  5226 solver.cpp:397]     Test net output #0: accuracy = 0.318015
I0406 07:50:18.199384  5226 solver.cpp:397]     Test net output #1: loss = 3.18566 (* 1 = 3.18566 loss)
I0406 07:50:18.340287  5226 solver.cpp:218] Iteration 4896 (0.824373 iter/s, 14.5565s/12 iters), loss = 1.30596
I0406 07:50:18.340335  5226 solver.cpp:237]     Train net output #0: loss = 1.30596 (* 1 = 1.30596 loss)
I0406 07:50:18.340343  5226 sgd_solver.cpp:105] Iteration 4896, lr = 0.01
I0406 07:50:22.697813  5226 solver.cpp:218] Iteration 4908 (2.75391 iter/s, 4.35744s/12 iters), loss = 1.08516
I0406 07:50:22.697849  5226 solver.cpp:237]     Train net output #0: loss = 1.08516 (* 1 = 1.08516 loss)
I0406 07:50:22.697854  5226 sgd_solver.cpp:105] Iteration 4908, lr = 0.01
I0406 07:50:27.925341  5226 solver.cpp:218] Iteration 4920 (2.29558 iter/s, 5.22745s/12 iters), loss = 1.15898
I0406 07:50:27.925379  5226 solver.cpp:237]     Train net output #0: loss = 1.15898 (* 1 = 1.15898 loss)
I0406 07:50:27.925384  5226 sgd_solver.cpp:105] Iteration 4920, lr = 0.01
I0406 07:50:33.311020  5226 solver.cpp:218] Iteration 4932 (2.22817 iter/s, 5.38559s/12 iters), loss = 1.06388
I0406 07:50:33.311055  5226 solver.cpp:237]     Train net output #0: loss = 1.06388 (* 1 = 1.06388 loss)
I0406 07:50:33.311060  5226 sgd_solver.cpp:105] Iteration 4932, lr = 0.01
I0406 07:50:38.662585  5226 solver.cpp:218] Iteration 4944 (2.24237 iter/s, 5.35148s/12 iters), loss = 1.10959
I0406 07:50:38.662622  5226 solver.cpp:237]     Train net output #0: loss = 1.10959 (* 1 = 1.10959 loss)
I0406 07:50:38.662627  5226 sgd_solver.cpp:105] Iteration 4944, lr = 0.01
I0406 07:50:43.623112  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:50:43.871531  5226 solver.cpp:218] Iteration 4956 (2.30377 iter/s, 5.20886s/12 iters), loss = 1.37315
I0406 07:50:43.871572  5226 solver.cpp:237]     Train net output #0: loss = 1.37315 (* 1 = 1.37315 loss)
I0406 07:50:43.871580  5226 sgd_solver.cpp:105] Iteration 4956, lr = 0.01
I0406 07:50:49.059175  5226 solver.cpp:218] Iteration 4968 (2.31323 iter/s, 5.18755s/12 iters), loss = 1.07251
I0406 07:50:49.059303  5226 solver.cpp:237]     Train net output #0: loss = 1.07251 (* 1 = 1.07251 loss)
I0406 07:50:49.059312  5226 sgd_solver.cpp:105] Iteration 4968, lr = 0.01
I0406 07:50:54.432083  5226 solver.cpp:218] Iteration 4980 (2.2335 iter/s, 5.37273s/12 iters), loss = 1.22782
I0406 07:50:54.432122  5226 solver.cpp:237]     Train net output #0: loss = 1.22782 (* 1 = 1.22782 loss)
I0406 07:50:54.432127  5226 sgd_solver.cpp:105] Iteration 4980, lr = 0.01
I0406 07:50:59.575440  5226 solver.cpp:218] Iteration 4992 (2.33315 iter/s, 5.14327s/12 iters), loss = 1.52138
I0406 07:50:59.575486  5226 solver.cpp:237]     Train net output #0: loss = 1.52138 (* 1 = 1.52138 loss)
I0406 07:50:59.575495  5226 sgd_solver.cpp:105] Iteration 4992, lr = 0.01
I0406 07:51:01.859560  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4998.caffemodel
I0406 07:51:04.856849  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4998.solverstate
I0406 07:51:08.376473  5226 solver.cpp:330] Iteration 4998, Testing net (#0)
I0406 07:51:08.376492  5226 net.cpp:676] Ignoring source layer train-data
I0406 07:51:10.747936  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:51:12.676488  5226 solver.cpp:397]     Test net output #0: accuracy = 0.344363
I0406 07:51:12.676518  5226 solver.cpp:397]     Test net output #1: loss = 3.11989 (* 1 = 3.11989 loss)
I0406 07:51:14.786027  5226 solver.cpp:218] Iteration 5004 (0.788932 iter/s, 15.2104s/12 iters), loss = 1.57642
I0406 07:51:14.786064  5226 solver.cpp:237]     Train net output #0: loss = 1.57642 (* 1 = 1.57642 loss)
I0406 07:51:14.786069  5226 sgd_solver.cpp:105] Iteration 5004, lr = 0.01
I0406 07:51:20.009764  5226 solver.cpp:218] Iteration 5016 (2.29724 iter/s, 5.22365s/12 iters), loss = 1.19052
I0406 07:51:20.009902  5226 solver.cpp:237]     Train net output #0: loss = 1.19052 (* 1 = 1.19052 loss)
I0406 07:51:20.009912  5226 sgd_solver.cpp:105] Iteration 5016, lr = 0.01
I0406 07:51:25.383754  5226 solver.cpp:218] Iteration 5028 (2.23305 iter/s, 5.37381s/12 iters), loss = 1.14189
I0406 07:51:25.383810  5226 solver.cpp:237]     Train net output #0: loss = 1.14189 (* 1 = 1.14189 loss)
I0406 07:51:25.383818  5226 sgd_solver.cpp:105] Iteration 5028, lr = 0.01
I0406 07:51:30.635444  5226 solver.cpp:218] Iteration 5040 (2.28502 iter/s, 5.25159s/12 iters), loss = 1.32997
I0406 07:51:30.635479  5226 solver.cpp:237]     Train net output #0: loss = 1.32997 (* 1 = 1.32997 loss)
I0406 07:51:30.635484  5226 sgd_solver.cpp:105] Iteration 5040, lr = 0.01
I0406 07:51:35.896574  5226 solver.cpp:218] Iteration 5052 (2.28091 iter/s, 5.26105s/12 iters), loss = 0.935736
I0406 07:51:35.896610  5226 solver.cpp:237]     Train net output #0: loss = 0.935736 (* 1 = 0.935736 loss)
I0406 07:51:35.896615  5226 sgd_solver.cpp:105] Iteration 5052, lr = 0.01
I0406 07:51:37.912302  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:51:40.925587  5226 solver.cpp:218] Iteration 5064 (2.38619 iter/s, 5.02893s/12 iters), loss = 1.33313
I0406 07:51:40.925623  5226 solver.cpp:237]     Train net output #0: loss = 1.33313 (* 1 = 1.33313 loss)
I0406 07:51:40.925629  5226 sgd_solver.cpp:105] Iteration 5064, lr = 0.01
I0406 07:51:46.135932  5226 solver.cpp:218] Iteration 5076 (2.30315 iter/s, 5.21025s/12 iters), loss = 1.14384
I0406 07:51:46.135982  5226 solver.cpp:237]     Train net output #0: loss = 1.14384 (* 1 = 1.14384 loss)
I0406 07:51:46.135989  5226 sgd_solver.cpp:105] Iteration 5076, lr = 0.01
I0406 07:51:51.299016  5226 solver.cpp:218] Iteration 5088 (2.32423 iter/s, 5.16299s/12 iters), loss = 1.36084
I0406 07:51:51.299127  5226 solver.cpp:237]     Train net output #0: loss = 1.36084 (* 1 = 1.36084 loss)
I0406 07:51:51.299134  5226 sgd_solver.cpp:105] Iteration 5088, lr = 0.01
I0406 07:51:55.968628  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5100.caffemodel
I0406 07:51:58.899629  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5100.solverstate
I0406 07:52:01.277495  5226 solver.cpp:330] Iteration 5100, Testing net (#0)
I0406 07:52:01.277518  5226 net.cpp:676] Ignoring source layer train-data
I0406 07:52:03.613283  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:52:05.679862  5226 solver.cpp:397]     Test net output #0: accuracy = 0.346814
I0406 07:52:05.679894  5226 solver.cpp:397]     Test net output #1: loss = 3.0375 (* 1 = 3.0375 loss)
I0406 07:52:05.816800  5226 solver.cpp:218] Iteration 5100 (0.826585 iter/s, 14.5176s/12 iters), loss = 0.861127
I0406 07:52:05.816870  5226 solver.cpp:237]     Train net output #0: loss = 0.861127 (* 1 = 0.861127 loss)
I0406 07:52:05.816879  5226 sgd_solver.cpp:105] Iteration 5100, lr = 0.01
I0406 07:52:10.214721  5226 solver.cpp:218] Iteration 5112 (2.72863 iter/s, 4.39781s/12 iters), loss = 1.16427
I0406 07:52:10.214761  5226 solver.cpp:237]     Train net output #0: loss = 1.16427 (* 1 = 1.16427 loss)
I0406 07:52:10.214768  5226 sgd_solver.cpp:105] Iteration 5112, lr = 0.01
I0406 07:52:15.449792  5226 solver.cpp:218] Iteration 5124 (2.29227 iter/s, 5.23499s/12 iters), loss = 1.01348
I0406 07:52:15.449826  5226 solver.cpp:237]     Train net output #0: loss = 1.01348 (* 1 = 1.01348 loss)
I0406 07:52:15.449831  5226 sgd_solver.cpp:105] Iteration 5124, lr = 0.01
I0406 07:52:20.839573  5226 solver.cpp:218] Iteration 5136 (2.22647 iter/s, 5.3897s/12 iters), loss = 1.17138
I0406 07:52:20.839609  5226 solver.cpp:237]     Train net output #0: loss = 1.17138 (* 1 = 1.17138 loss)
I0406 07:52:20.839615  5226 sgd_solver.cpp:105] Iteration 5136, lr = 0.01
I0406 07:52:26.101734  5226 solver.cpp:218] Iteration 5148 (2.28047 iter/s, 5.26207s/12 iters), loss = 1.29594
I0406 07:52:26.101835  5226 solver.cpp:237]     Train net output #0: loss = 1.29594 (* 1 = 1.29594 loss)
I0406 07:52:26.101841  5226 sgd_solver.cpp:105] Iteration 5148, lr = 0.01
I0406 07:52:30.450764  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:52:31.408334  5226 solver.cpp:218] Iteration 5160 (2.2614 iter/s, 5.30645s/12 iters), loss = 1.16029
I0406 07:52:31.408377  5226 solver.cpp:237]     Train net output #0: loss = 1.16029 (* 1 = 1.16029 loss)
I0406 07:52:31.408385  5226 sgd_solver.cpp:105] Iteration 5160, lr = 0.01
I0406 07:52:36.593050  5226 solver.cpp:218] Iteration 5172 (2.31453 iter/s, 5.18463s/12 iters), loss = 1.15684
I0406 07:52:36.593087  5226 solver.cpp:237]     Train net output #0: loss = 1.15684 (* 1 = 1.15684 loss)
I0406 07:52:36.593092  5226 sgd_solver.cpp:105] Iteration 5172, lr = 0.01
I0406 07:52:42.041817  5226 solver.cpp:218] Iteration 5184 (2.20237 iter/s, 5.44868s/12 iters), loss = 1.26785
I0406 07:52:42.041857  5226 solver.cpp:237]     Train net output #0: loss = 1.26785 (* 1 = 1.26785 loss)
I0406 07:52:42.041862  5226 sgd_solver.cpp:105] Iteration 5184, lr = 0.01
I0406 07:52:47.323772  5226 solver.cpp:218] Iteration 5196 (2.27192 iter/s, 5.28187s/12 iters), loss = 1.09619
I0406 07:52:47.323805  5226 solver.cpp:237]     Train net output #0: loss = 1.09619 (* 1 = 1.09619 loss)
I0406 07:52:47.323812  5226 sgd_solver.cpp:105] Iteration 5196, lr = 0.01
I0406 07:52:49.493296  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5202.caffemodel
I0406 07:52:52.527582  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5202.solverstate
I0406 07:52:54.836535  5226 solver.cpp:330] Iteration 5202, Testing net (#0)
I0406 07:52:54.836553  5226 net.cpp:676] Ignoring source layer train-data
I0406 07:52:57.153734  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:52:59.167555  5226 solver.cpp:397]     Test net output #0: accuracy = 0.35049
I0406 07:52:59.167588  5226 solver.cpp:397]     Test net output #1: loss = 2.91695 (* 1 = 2.91695 loss)
I0406 07:53:00.998950  5226 solver.cpp:218] Iteration 5208 (0.877511 iter/s, 13.675s/12 iters), loss = 1.74033
I0406 07:53:00.998994  5226 solver.cpp:237]     Train net output #0: loss = 1.74033 (* 1 = 1.74033 loss)
I0406 07:53:00.999001  5226 sgd_solver.cpp:105] Iteration 5208, lr = 0.01
I0406 07:53:06.284822  5226 solver.cpp:218] Iteration 5220 (2.27025 iter/s, 5.28577s/12 iters), loss = 1.03007
I0406 07:53:06.284878  5226 solver.cpp:237]     Train net output #0: loss = 1.03007 (* 1 = 1.03007 loss)
I0406 07:53:06.284893  5226 sgd_solver.cpp:105] Iteration 5220, lr = 0.01
I0406 07:53:11.703543  5226 solver.cpp:218] Iteration 5232 (2.21459 iter/s, 5.41862s/12 iters), loss = 1.47741
I0406 07:53:11.703581  5226 solver.cpp:237]     Train net output #0: loss = 1.47741 (* 1 = 1.47741 loss)
I0406 07:53:11.703586  5226 sgd_solver.cpp:105] Iteration 5232, lr = 0.01
I0406 07:53:17.181150  5226 solver.cpp:218] Iteration 5244 (2.19077 iter/s, 5.47752s/12 iters), loss = 1.09277
I0406 07:53:17.181188  5226 solver.cpp:237]     Train net output #0: loss = 1.09277 (* 1 = 1.09277 loss)
I0406 07:53:17.181195  5226 sgd_solver.cpp:105] Iteration 5244, lr = 0.01
I0406 07:53:22.675824  5226 solver.cpp:218] Iteration 5256 (2.18397 iter/s, 5.49459s/12 iters), loss = 1.53562
I0406 07:53:22.675861  5226 solver.cpp:237]     Train net output #0: loss = 1.53562 (* 1 = 1.53562 loss)
I0406 07:53:22.675868  5226 sgd_solver.cpp:105] Iteration 5256, lr = 0.01
I0406 07:53:24.091971  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:53:28.068123  5226 solver.cpp:218] Iteration 5268 (2.22543 iter/s, 5.39221s/12 iters), loss = 0.990537
I0406 07:53:28.068215  5226 solver.cpp:237]     Train net output #0: loss = 0.990537 (* 1 = 0.990537 loss)
I0406 07:53:28.068224  5226 sgd_solver.cpp:105] Iteration 5268, lr = 0.01
I0406 07:53:33.424564  5226 solver.cpp:218] Iteration 5280 (2.24035 iter/s, 5.35631s/12 iters), loss = 1.52296
I0406 07:53:33.424605  5226 solver.cpp:237]     Train net output #0: loss = 1.52296 (* 1 = 1.52296 loss)
I0406 07:53:33.424612  5226 sgd_solver.cpp:105] Iteration 5280, lr = 0.01
I0406 07:53:38.622931  5226 solver.cpp:218] Iteration 5292 (2.30846 iter/s, 5.19828s/12 iters), loss = 1.00433
I0406 07:53:38.622969  5226 solver.cpp:237]     Train net output #0: loss = 1.00433 (* 1 = 1.00433 loss)
I0406 07:53:38.622975  5226 sgd_solver.cpp:105] Iteration 5292, lr = 0.01
I0406 07:53:43.351786  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5304.caffemodel
I0406 07:53:46.348980  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5304.solverstate
I0406 07:53:48.667948  5226 solver.cpp:330] Iteration 5304, Testing net (#0)
I0406 07:53:48.667969  5226 net.cpp:676] Ignoring source layer train-data
I0406 07:53:50.925163  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:53:52.988348  5226 solver.cpp:397]     Test net output #0: accuracy = 0.354779
I0406 07:53:52.988379  5226 solver.cpp:397]     Test net output #1: loss = 3.04381 (* 1 = 3.04381 loss)
I0406 07:53:53.129091  5226 solver.cpp:218] Iteration 5304 (0.827243 iter/s, 14.506s/12 iters), loss = 1.08349
I0406 07:53:53.129135  5226 solver.cpp:237]     Train net output #0: loss = 1.08349 (* 1 = 1.08349 loss)
I0406 07:53:53.129142  5226 sgd_solver.cpp:105] Iteration 5304, lr = 0.01
I0406 07:53:57.527644  5226 solver.cpp:218] Iteration 5316 (2.72822 iter/s, 4.39847s/12 iters), loss = 0.944097
I0406 07:53:57.527681  5226 solver.cpp:237]     Train net output #0: loss = 0.944097 (* 1 = 0.944097 loss)
I0406 07:53:57.527686  5226 sgd_solver.cpp:105] Iteration 5316, lr = 0.01
I0406 07:54:02.780608  5226 solver.cpp:218] Iteration 5328 (2.28446 iter/s, 5.25288s/12 iters), loss = 1.18152
I0406 07:54:02.780743  5226 solver.cpp:237]     Train net output #0: loss = 1.18152 (* 1 = 1.18152 loss)
I0406 07:54:02.780750  5226 sgd_solver.cpp:105] Iteration 5328, lr = 0.01
I0406 07:54:08.022302  5226 solver.cpp:218] Iteration 5340 (2.28942 iter/s, 5.24151s/12 iters), loss = 1.12153
I0406 07:54:08.022338  5226 solver.cpp:237]     Train net output #0: loss = 1.12153 (* 1 = 1.12153 loss)
I0406 07:54:08.022344  5226 sgd_solver.cpp:105] Iteration 5340, lr = 0.01
I0406 07:54:13.409651  5226 solver.cpp:218] Iteration 5352 (2.22748 iter/s, 5.38726s/12 iters), loss = 1.08565
I0406 07:54:13.409687  5226 solver.cpp:237]     Train net output #0: loss = 1.08565 (* 1 = 1.08565 loss)
I0406 07:54:13.409693  5226 sgd_solver.cpp:105] Iteration 5352, lr = 0.01
I0406 07:54:16.865134  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:54:18.572110  5226 solver.cpp:218] Iteration 5364 (2.32451 iter/s, 5.16237s/12 iters), loss = 1.32962
I0406 07:54:18.572147  5226 solver.cpp:237]     Train net output #0: loss = 1.32962 (* 1 = 1.32962 loss)
I0406 07:54:18.572152  5226 sgd_solver.cpp:105] Iteration 5364, lr = 0.01
I0406 07:54:23.935873  5226 solver.cpp:218] Iteration 5376 (2.23727 iter/s, 5.36367s/12 iters), loss = 1.30589
I0406 07:54:23.935910  5226 solver.cpp:237]     Train net output #0: loss = 1.30589 (* 1 = 1.30589 loss)
I0406 07:54:23.935915  5226 sgd_solver.cpp:105] Iteration 5376, lr = 0.01
I0406 07:54:29.298303  5226 solver.cpp:218] Iteration 5388 (2.23783 iter/s, 5.36234s/12 iters), loss = 0.812986
I0406 07:54:29.298343  5226 solver.cpp:237]     Train net output #0: loss = 0.812986 (* 1 = 0.812986 loss)
I0406 07:54:29.298348  5226 sgd_solver.cpp:105] Iteration 5388, lr = 0.01
I0406 07:54:34.488752  5226 solver.cpp:218] Iteration 5400 (2.31197 iter/s, 5.19037s/12 iters), loss = 1.15998
I0406 07:54:34.488857  5226 solver.cpp:237]     Train net output #0: loss = 1.15998 (* 1 = 1.15998 loss)
I0406 07:54:34.488863  5226 sgd_solver.cpp:105] Iteration 5400, lr = 0.01
I0406 07:54:36.517652  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5406.caffemodel
I0406 07:54:39.576598  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5406.solverstate
I0406 07:54:41.876677  5226 solver.cpp:330] Iteration 5406, Testing net (#0)
I0406 07:54:41.876695  5226 net.cpp:676] Ignoring source layer train-data
I0406 07:54:44.064191  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:54:46.192556  5226 solver.cpp:397]     Test net output #0: accuracy = 0.334559
I0406 07:54:46.192591  5226 solver.cpp:397]     Test net output #1: loss = 3.06266 (* 1 = 3.06266 loss)
I0406 07:54:47.929920  5226 solver.cpp:218] Iteration 5412 (0.892793 iter/s, 13.441s/12 iters), loss = 1.11387
I0406 07:54:47.929975  5226 solver.cpp:237]     Train net output #0: loss = 1.11387 (* 1 = 1.11387 loss)
I0406 07:54:47.929983  5226 sgd_solver.cpp:105] Iteration 5412, lr = 0.01
I0406 07:54:53.384435  5226 solver.cpp:218] Iteration 5424 (2.20005 iter/s, 5.45441s/12 iters), loss = 1.02624
I0406 07:54:53.384474  5226 solver.cpp:237]     Train net output #0: loss = 1.02624 (* 1 = 1.02624 loss)
I0406 07:54:53.384480  5226 sgd_solver.cpp:105] Iteration 5424, lr = 0.01
I0406 07:54:58.695457  5226 solver.cpp:218] Iteration 5436 (2.25949 iter/s, 5.31093s/12 iters), loss = 1.27154
I0406 07:54:58.695503  5226 solver.cpp:237]     Train net output #0: loss = 1.27154 (* 1 = 1.27154 loss)
I0406 07:54:58.695511  5226 sgd_solver.cpp:105] Iteration 5436, lr = 0.01
I0406 07:55:03.908650  5226 solver.cpp:218] Iteration 5448 (2.30189 iter/s, 5.2131s/12 iters), loss = 1.4067
I0406 07:55:03.908695  5226 solver.cpp:237]     Train net output #0: loss = 1.4067 (* 1 = 1.4067 loss)
I0406 07:55:03.908704  5226 sgd_solver.cpp:105] Iteration 5448, lr = 0.01
I0406 07:55:09.126276  5226 solver.cpp:218] Iteration 5460 (2.29993 iter/s, 5.21754s/12 iters), loss = 1.03105
I0406 07:55:09.126396  5226 solver.cpp:237]     Train net output #0: loss = 1.03105 (* 1 = 1.03105 loss)
I0406 07:55:09.126403  5226 sgd_solver.cpp:105] Iteration 5460, lr = 0.01
I0406 07:55:09.676029  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:55:14.406342  5226 solver.cpp:218] Iteration 5472 (2.27277 iter/s, 5.27989s/12 iters), loss = 1.14117
I0406 07:55:14.406391  5226 solver.cpp:237]     Train net output #0: loss = 1.14117 (* 1 = 1.14117 loss)
I0406 07:55:14.406399  5226 sgd_solver.cpp:105] Iteration 5472, lr = 0.01
I0406 07:55:19.547338  5226 solver.cpp:218] Iteration 5484 (2.33422 iter/s, 5.1409s/12 iters), loss = 1.30119
I0406 07:55:19.547377  5226 solver.cpp:237]     Train net output #0: loss = 1.30119 (* 1 = 1.30119 loss)
I0406 07:55:19.547384  5226 sgd_solver.cpp:105] Iteration 5484, lr = 0.01
I0406 07:55:24.599706  5226 solver.cpp:218] Iteration 5496 (2.37516 iter/s, 5.05228s/12 iters), loss = 1.03323
I0406 07:55:24.599747  5226 solver.cpp:237]     Train net output #0: loss = 1.03323 (* 1 = 1.03323 loss)
I0406 07:55:24.599753  5226 sgd_solver.cpp:105] Iteration 5496, lr = 0.01
I0406 07:55:29.300906  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5508.caffemodel
I0406 07:55:32.312181  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5508.solverstate
I0406 07:55:34.642812  5226 solver.cpp:330] Iteration 5508, Testing net (#0)
I0406 07:55:34.642838  5226 net.cpp:676] Ignoring source layer train-data
I0406 07:55:36.871526  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:55:39.030405  5226 solver.cpp:397]     Test net output #0: accuracy = 0.334559
I0406 07:55:39.030437  5226 solver.cpp:397]     Test net output #1: loss = 3.07989 (* 1 = 3.07989 loss)
I0406 07:55:39.171162  5226 solver.cpp:218] Iteration 5508 (0.823536 iter/s, 14.5713s/12 iters), loss = 1.02905
I0406 07:55:39.171241  5226 solver.cpp:237]     Train net output #0: loss = 1.02905 (* 1 = 1.02905 loss)
I0406 07:55:39.171249  5226 sgd_solver.cpp:105] Iteration 5508, lr = 0.01
I0406 07:55:43.412154  5226 solver.cpp:218] Iteration 5520 (2.82961 iter/s, 4.24087s/12 iters), loss = 1.17859
I0406 07:55:43.412194  5226 solver.cpp:237]     Train net output #0: loss = 1.17859 (* 1 = 1.17859 loss)
I0406 07:55:43.412199  5226 sgd_solver.cpp:105] Iteration 5520, lr = 0.01
I0406 07:55:45.742982  5226 blocking_queue.cpp:49] Waiting for data
I0406 07:55:48.512619  5226 solver.cpp:218] Iteration 5532 (2.35277 iter/s, 5.10038s/12 iters), loss = 0.951761
I0406 07:55:48.512670  5226 solver.cpp:237]     Train net output #0: loss = 0.951761 (* 1 = 0.951761 loss)
I0406 07:55:48.512677  5226 sgd_solver.cpp:105] Iteration 5532, lr = 0.01
I0406 07:55:53.811420  5226 solver.cpp:218] Iteration 5544 (2.26471 iter/s, 5.2987s/12 iters), loss = 0.972357
I0406 07:55:53.811457  5226 solver.cpp:237]     Train net output #0: loss = 0.972357 (* 1 = 0.972357 loss)
I0406 07:55:53.811462  5226 sgd_solver.cpp:105] Iteration 5544, lr = 0.01
I0406 07:55:59.233681  5226 solver.cpp:218] Iteration 5556 (2.21313 iter/s, 5.42218s/12 iters), loss = 1.21051
I0406 07:55:59.233721  5226 solver.cpp:237]     Train net output #0: loss = 1.21051 (* 1 = 1.21051 loss)
I0406 07:55:59.233726  5226 sgd_solver.cpp:105] Iteration 5556, lr = 0.01
I0406 07:56:02.053340  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:56:04.480522  5226 solver.cpp:218] Iteration 5568 (2.28713 iter/s, 5.24676s/12 iters), loss = 1.04115
I0406 07:56:04.480557  5226 solver.cpp:237]     Train net output #0: loss = 1.04115 (* 1 = 1.04115 loss)
I0406 07:56:04.480562  5226 sgd_solver.cpp:105] Iteration 5568, lr = 0.01
I0406 07:56:09.755160  5226 solver.cpp:218] Iteration 5580 (2.27507 iter/s, 5.27455s/12 iters), loss = 1.30793
I0406 07:56:09.755280  5226 solver.cpp:237]     Train net output #0: loss = 1.30793 (* 1 = 1.30793 loss)
I0406 07:56:09.755288  5226 sgd_solver.cpp:105] Iteration 5580, lr = 0.01
I0406 07:56:14.976517  5226 solver.cpp:218] Iteration 5592 (2.29832 iter/s, 5.22119s/12 iters), loss = 0.838414
I0406 07:56:14.976554  5226 solver.cpp:237]     Train net output #0: loss = 0.838414 (* 1 = 0.838414 loss)
I0406 07:56:14.976560  5226 sgd_solver.cpp:105] Iteration 5592, lr = 0.01
I0406 07:56:20.090829  5226 solver.cpp:218] Iteration 5604 (2.34639 iter/s, 5.11423s/12 iters), loss = 1.10552
I0406 07:56:20.090863  5226 solver.cpp:237]     Train net output #0: loss = 1.10552 (* 1 = 1.10552 loss)
I0406 07:56:20.090868  5226 sgd_solver.cpp:105] Iteration 5604, lr = 0.01
I0406 07:56:22.290532  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5610.caffemodel
I0406 07:56:25.311790  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5610.solverstate
I0406 07:56:27.677520  5226 solver.cpp:330] Iteration 5610, Testing net (#0)
I0406 07:56:27.677542  5226 net.cpp:676] Ignoring source layer train-data
I0406 07:56:29.842396  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:56:32.061404  5226 solver.cpp:397]     Test net output #0: accuracy = 0.334559
I0406 07:56:32.061434  5226 solver.cpp:397]     Test net output #1: loss = 3.17719 (* 1 = 3.17719 loss)
I0406 07:56:33.922237  5226 solver.cpp:218] Iteration 5616 (0.867599 iter/s, 13.8313s/12 iters), loss = 1.44505
I0406 07:56:33.922276  5226 solver.cpp:237]     Train net output #0: loss = 1.44505 (* 1 = 1.44505 loss)
I0406 07:56:33.922281  5226 sgd_solver.cpp:105] Iteration 5616, lr = 0.01
I0406 07:56:39.288908  5226 solver.cpp:218] Iteration 5628 (2.23606 iter/s, 5.36659s/12 iters), loss = 0.971849
I0406 07:56:39.288946  5226 solver.cpp:237]     Train net output #0: loss = 0.971849 (* 1 = 0.971849 loss)
I0406 07:56:39.288952  5226 sgd_solver.cpp:105] Iteration 5628, lr = 0.01
I0406 07:56:44.606580  5226 solver.cpp:218] Iteration 5640 (2.25666 iter/s, 5.31759s/12 iters), loss = 0.906122
I0406 07:56:44.606673  5226 solver.cpp:237]     Train net output #0: loss = 0.906122 (* 1 = 0.906122 loss)
I0406 07:56:44.606679  5226 sgd_solver.cpp:105] Iteration 5640, lr = 0.01
I0406 07:56:49.992328  5226 solver.cpp:218] Iteration 5652 (2.22816 iter/s, 5.38561s/12 iters), loss = 1.24248
I0406 07:56:49.992367  5226 solver.cpp:237]     Train net output #0: loss = 1.24248 (* 1 = 1.24248 loss)
I0406 07:56:49.992372  5226 sgd_solver.cpp:105] Iteration 5652, lr = 0.01
I0406 07:56:55.100517  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:56:55.319990  5226 solver.cpp:218] Iteration 5664 (2.25243 iter/s, 5.32757s/12 iters), loss = 1.10681
I0406 07:56:55.320027  5226 solver.cpp:237]     Train net output #0: loss = 1.10681 (* 1 = 1.10681 loss)
I0406 07:56:55.320034  5226 sgd_solver.cpp:105] Iteration 5664, lr = 0.01
I0406 07:57:00.502889  5226 solver.cpp:218] Iteration 5676 (2.31534 iter/s, 5.18281s/12 iters), loss = 1.09994
I0406 07:57:00.502928  5226 solver.cpp:237]     Train net output #0: loss = 1.09994 (* 1 = 1.09994 loss)
I0406 07:57:00.502933  5226 sgd_solver.cpp:105] Iteration 5676, lr = 0.01
I0406 07:57:05.777714  5226 solver.cpp:218] Iteration 5688 (2.275 iter/s, 5.27474s/12 iters), loss = 1.40267
I0406 07:57:05.777753  5226 solver.cpp:237]     Train net output #0: loss = 1.40267 (* 1 = 1.40267 loss)
I0406 07:57:05.777758  5226 sgd_solver.cpp:105] Iteration 5688, lr = 0.01
I0406 07:57:11.109906  5226 solver.cpp:218] Iteration 5700 (2.25052 iter/s, 5.33211s/12 iters), loss = 1.38615
I0406 07:57:11.109942  5226 solver.cpp:237]     Train net output #0: loss = 1.38615 (* 1 = 1.38615 loss)
I0406 07:57:11.109948  5226 sgd_solver.cpp:105] Iteration 5700, lr = 0.01
I0406 07:57:15.860585  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5712.caffemodel
I0406 07:57:18.809514  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5712.solverstate
I0406 07:57:21.145617  5226 solver.cpp:330] Iteration 5712, Testing net (#0)
I0406 07:57:21.145648  5226 net.cpp:676] Ignoring source layer train-data
I0406 07:57:23.282548  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:57:25.585129  5226 solver.cpp:397]     Test net output #0: accuracy = 0.35049
I0406 07:57:25.585160  5226 solver.cpp:397]     Test net output #1: loss = 3.0728 (* 1 = 3.0728 loss)
I0406 07:57:25.726086  5226 solver.cpp:218] Iteration 5712 (0.821016 iter/s, 14.616s/12 iters), loss = 1.39695
I0406 07:57:25.726125  5226 solver.cpp:237]     Train net output #0: loss = 1.39695 (* 1 = 1.39695 loss)
I0406 07:57:25.726131  5226 sgd_solver.cpp:105] Iteration 5712, lr = 0.01
I0406 07:57:30.086277  5226 solver.cpp:218] Iteration 5724 (2.75223 iter/s, 4.3601s/12 iters), loss = 1.34678
I0406 07:57:30.086318  5226 solver.cpp:237]     Train net output #0: loss = 1.34678 (* 1 = 1.34678 loss)
I0406 07:57:30.086323  5226 sgd_solver.cpp:105] Iteration 5724, lr = 0.01
I0406 07:57:35.027580  5226 solver.cpp:218] Iteration 5736 (2.42855 iter/s, 4.94122s/12 iters), loss = 1.1855
I0406 07:57:35.027617  5226 solver.cpp:237]     Train net output #0: loss = 1.1855 (* 1 = 1.1855 loss)
I0406 07:57:35.027622  5226 sgd_solver.cpp:105] Iteration 5736, lr = 0.01
I0406 07:57:40.478971  5226 solver.cpp:218] Iteration 5748 (2.20131 iter/s, 5.4513s/12 iters), loss = 1.15293
I0406 07:57:40.479007  5226 solver.cpp:237]     Train net output #0: loss = 1.15293 (* 1 = 1.15293 loss)
I0406 07:57:40.479012  5226 sgd_solver.cpp:105] Iteration 5748, lr = 0.01
I0406 07:57:45.576488  5226 solver.cpp:218] Iteration 5760 (2.35413 iter/s, 5.09743s/12 iters), loss = 0.949511
I0406 07:57:45.576532  5226 solver.cpp:237]     Train net output #0: loss = 0.949511 (* 1 = 0.949511 loss)
I0406 07:57:45.576537  5226 sgd_solver.cpp:105] Iteration 5760, lr = 0.01
I0406 07:57:47.594240  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:57:50.879992  5226 solver.cpp:218] Iteration 5772 (2.2627 iter/s, 5.30341s/12 iters), loss = 0.99732
I0406 07:57:50.880030  5226 solver.cpp:237]     Train net output #0: loss = 0.99732 (* 1 = 0.99732 loss)
I0406 07:57:50.880036  5226 sgd_solver.cpp:105] Iteration 5772, lr = 0.01
I0406 07:57:56.168972  5226 solver.cpp:218] Iteration 5784 (2.2689 iter/s, 5.2889s/12 iters), loss = 1.24832
I0406 07:57:56.169008  5226 solver.cpp:237]     Train net output #0: loss = 1.24832 (* 1 = 1.24832 loss)
I0406 07:57:56.169013  5226 sgd_solver.cpp:105] Iteration 5784, lr = 0.01
I0406 07:58:01.455111  5226 solver.cpp:218] Iteration 5796 (2.27012 iter/s, 5.28606s/12 iters), loss = 1.11876
I0406 07:58:01.455149  5226 solver.cpp:237]     Train net output #0: loss = 1.11876 (* 1 = 1.11876 loss)
I0406 07:58:01.455154  5226 sgd_solver.cpp:105] Iteration 5796, lr = 0.01
I0406 07:58:06.537432  5226 solver.cpp:218] Iteration 5808 (2.36117 iter/s, 5.08224s/12 iters), loss = 1.09874
I0406 07:58:06.537472  5226 solver.cpp:237]     Train net output #0: loss = 1.09874 (* 1 = 1.09874 loss)
I0406 07:58:06.537478  5226 sgd_solver.cpp:105] Iteration 5808, lr = 0.01
I0406 07:58:08.592327  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5814.caffemodel
I0406 07:58:11.616477  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5814.solverstate
I0406 07:58:13.934227  5226 solver.cpp:330] Iteration 5814, Testing net (#0)
I0406 07:58:13.934252  5226 net.cpp:676] Ignoring source layer train-data
I0406 07:58:15.990747  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:58:18.278738  5226 solver.cpp:397]     Test net output #0: accuracy = 0.318015
I0406 07:58:18.278872  5226 solver.cpp:397]     Test net output #1: loss = 3.12658 (* 1 = 3.12658 loss)
I0406 07:58:20.190635  5226 solver.cpp:218] Iteration 5820 (0.878924 iter/s, 13.6531s/12 iters), loss = 1.30857
I0406 07:58:20.190675  5226 solver.cpp:237]     Train net output #0: loss = 1.30857 (* 1 = 1.30857 loss)
I0406 07:58:20.190685  5226 sgd_solver.cpp:105] Iteration 5820, lr = 0.01
I0406 07:58:25.484483  5226 solver.cpp:218] Iteration 5832 (2.26682 iter/s, 5.29376s/12 iters), loss = 1.00455
I0406 07:58:25.484529  5226 solver.cpp:237]     Train net output #0: loss = 1.00455 (* 1 = 1.00455 loss)
I0406 07:58:25.484536  5226 sgd_solver.cpp:105] Iteration 5832, lr = 0.01
I0406 07:58:30.603885  5226 solver.cpp:218] Iteration 5844 (2.34407 iter/s, 5.11931s/12 iters), loss = 1.32372
I0406 07:58:30.603924  5226 solver.cpp:237]     Train net output #0: loss = 1.32372 (* 1 = 1.32372 loss)
I0406 07:58:30.603929  5226 sgd_solver.cpp:105] Iteration 5844, lr = 0.01
I0406 07:58:35.937963  5226 solver.cpp:218] Iteration 5856 (2.24972 iter/s, 5.33399s/12 iters), loss = 1.39658
I0406 07:58:35.938001  5226 solver.cpp:237]     Train net output #0: loss = 1.39658 (* 1 = 1.39658 loss)
I0406 07:58:35.938006  5226 sgd_solver.cpp:105] Iteration 5856, lr = 0.01
I0406 07:58:40.245982  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:58:41.125120  5226 solver.cpp:218] Iteration 5868 (2.31344 iter/s, 5.18707s/12 iters), loss = 1.18406
I0406 07:58:41.125157  5226 solver.cpp:237]     Train net output #0: loss = 1.18406 (* 1 = 1.18406 loss)
I0406 07:58:41.125164  5226 sgd_solver.cpp:105] Iteration 5868, lr = 0.01
I0406 07:58:46.382045  5226 solver.cpp:218] Iteration 5880 (2.28274 iter/s, 5.25684s/12 iters), loss = 1.24425
I0406 07:58:46.382082  5226 solver.cpp:237]     Train net output #0: loss = 1.24425 (* 1 = 1.24425 loss)
I0406 07:58:46.382087  5226 sgd_solver.cpp:105] Iteration 5880, lr = 0.01
I0406 07:58:51.854719  5226 solver.cpp:218] Iteration 5892 (2.19275 iter/s, 5.47259s/12 iters), loss = 1.18952
I0406 07:58:51.854820  5226 solver.cpp:237]     Train net output #0: loss = 1.18952 (* 1 = 1.18952 loss)
I0406 07:58:51.854825  5226 sgd_solver.cpp:105] Iteration 5892, lr = 0.01
I0406 07:58:57.209131  5226 solver.cpp:218] Iteration 5904 (2.2412 iter/s, 5.35426s/12 iters), loss = 0.900855
I0406 07:58:57.209168  5226 solver.cpp:237]     Train net output #0: loss = 0.900855 (* 1 = 0.900855 loss)
I0406 07:58:57.209174  5226 sgd_solver.cpp:105] Iteration 5904, lr = 0.01
I0406 07:59:02.069214  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5916.caffemodel
I0406 07:59:05.094641  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5916.solverstate
I0406 07:59:07.398937  5226 solver.cpp:330] Iteration 5916, Testing net (#0)
I0406 07:59:07.398957  5226 net.cpp:676] Ignoring source layer train-data
I0406 07:59:09.425787  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:59:11.757273  5226 solver.cpp:397]     Test net output #0: accuracy = 0.316176
I0406 07:59:11.757319  5226 solver.cpp:397]     Test net output #1: loss = 3.25887 (* 1 = 3.25887 loss)
I0406 07:59:11.886507  5226 solver.cpp:218] Iteration 5916 (0.817593 iter/s, 14.6772s/12 iters), loss = 1.1649
I0406 07:59:11.886559  5226 solver.cpp:237]     Train net output #0: loss = 1.1649 (* 1 = 1.1649 loss)
I0406 07:59:11.886567  5226 sgd_solver.cpp:105] Iteration 5916, lr = 0.01
I0406 07:59:16.450744  5226 solver.cpp:218] Iteration 5928 (2.62919 iter/s, 4.56414s/12 iters), loss = 0.993896
I0406 07:59:16.450778  5226 solver.cpp:237]     Train net output #0: loss = 0.993896 (* 1 = 0.993896 loss)
I0406 07:59:16.450784  5226 sgd_solver.cpp:105] Iteration 5928, lr = 0.01
I0406 07:59:21.859767  5226 solver.cpp:218] Iteration 5940 (2.21855 iter/s, 5.40894s/12 iters), loss = 1.29924
I0406 07:59:21.859846  5226 solver.cpp:237]     Train net output #0: loss = 1.29924 (* 1 = 1.29924 loss)
I0406 07:59:21.859853  5226 sgd_solver.cpp:105] Iteration 5940, lr = 0.01
I0406 07:59:26.928897  5226 solver.cpp:218] Iteration 5952 (2.36733 iter/s, 5.069s/12 iters), loss = 1.43333
I0406 07:59:26.928936  5226 solver.cpp:237]     Train net output #0: loss = 1.43333 (* 1 = 1.43333 loss)
I0406 07:59:26.928942  5226 sgd_solver.cpp:105] Iteration 5952, lr = 0.01
I0406 07:59:32.220765  5226 solver.cpp:218] Iteration 5964 (2.26767 iter/s, 5.29178s/12 iters), loss = 1.31222
I0406 07:59:32.220800  5226 solver.cpp:237]     Train net output #0: loss = 1.31222 (* 1 = 1.31222 loss)
I0406 07:59:32.220805  5226 sgd_solver.cpp:105] Iteration 5964, lr = 0.01
I0406 07:59:33.680019  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:59:37.511986  5226 solver.cpp:218] Iteration 5976 (2.26794 iter/s, 5.29114s/12 iters), loss = 0.987962
I0406 07:59:37.512027  5226 solver.cpp:237]     Train net output #0: loss = 0.987962 (* 1 = 0.987962 loss)
I0406 07:59:37.512033  5226 sgd_solver.cpp:105] Iteration 5976, lr = 0.01
I0406 07:59:42.967118  5226 solver.cpp:218] Iteration 5988 (2.1998 iter/s, 5.45504s/12 iters), loss = 0.953547
I0406 07:59:42.967173  5226 solver.cpp:237]     Train net output #0: loss = 0.953547 (* 1 = 0.953547 loss)
I0406 07:59:42.967182  5226 sgd_solver.cpp:105] Iteration 5988, lr = 0.01
I0406 07:59:48.254253  5226 solver.cpp:218] Iteration 6000 (2.26971 iter/s, 5.28703s/12 iters), loss = 1.1414
I0406 07:59:48.254300  5226 solver.cpp:237]     Train net output #0: loss = 1.1414 (* 1 = 1.1414 loss)
I0406 07:59:48.254307  5226 sgd_solver.cpp:105] Iteration 6000, lr = 0.01
I0406 07:59:53.455752  5226 solver.cpp:218] Iteration 6012 (2.30707 iter/s, 5.2014s/12 iters), loss = 1.10675
I0406 07:59:53.456156  5226 solver.cpp:237]     Train net output #0: loss = 1.10675 (* 1 = 1.10675 loss)
I0406 07:59:53.456166  5226 sgd_solver.cpp:105] Iteration 6012, lr = 0.01
I0406 07:59:55.603401  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6018.caffemodel
I0406 07:59:58.593271  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6018.solverstate
I0406 08:00:00.905632  5226 solver.cpp:330] Iteration 6018, Testing net (#0)
I0406 08:00:00.905651  5226 net.cpp:676] Ignoring source layer train-data
I0406 08:00:02.871464  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:00:05.298772  5226 solver.cpp:397]     Test net output #0: accuracy = 0.311274
I0406 08:00:05.298804  5226 solver.cpp:397]     Test net output #1: loss = 3.28846 (* 1 = 3.28846 loss)
I0406 08:00:07.238652  5226 solver.cpp:218] Iteration 6024 (0.870676 iter/s, 13.7824s/12 iters), loss = 0.95152
I0406 08:00:07.238687  5226 solver.cpp:237]     Train net output #0: loss = 0.95152 (* 1 = 0.95152 loss)
I0406 08:00:07.238693  5226 sgd_solver.cpp:105] Iteration 6024, lr = 0.01
I0406 08:00:12.680160  5226 solver.cpp:218] Iteration 6036 (2.20531 iter/s, 5.44142s/12 iters), loss = 1.35103
I0406 08:00:12.680195  5226 solver.cpp:237]     Train net output #0: loss = 1.35103 (* 1 = 1.35103 loss)
I0406 08:00:12.680200  5226 sgd_solver.cpp:105] Iteration 6036, lr = 0.01
I0406 08:00:18.061496  5226 solver.cpp:218] Iteration 6048 (2.22996 iter/s, 5.38125s/12 iters), loss = 1.21086
I0406 08:00:18.061534  5226 solver.cpp:237]     Train net output #0: loss = 1.21086 (* 1 = 1.21086 loss)
I0406 08:00:18.061540  5226 sgd_solver.cpp:105] Iteration 6048, lr = 0.01
I0406 08:00:23.017666  5226 solver.cpp:218] Iteration 6060 (2.42127 iter/s, 4.95608s/12 iters), loss = 1.25787
I0406 08:00:23.017705  5226 solver.cpp:237]     Train net output #0: loss = 1.25787 (* 1 = 1.25787 loss)
I0406 08:00:23.017711  5226 sgd_solver.cpp:105] Iteration 6060, lr = 0.01
I0406 08:00:26.749205  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:00:28.324869  5226 solver.cpp:218] Iteration 6072 (2.26112 iter/s, 5.30711s/12 iters), loss = 1.127
I0406 08:00:28.324923  5226 solver.cpp:237]     Train net output #0: loss = 1.127 (* 1 = 1.127 loss)
I0406 08:00:28.324931  5226 sgd_solver.cpp:105] Iteration 6072, lr = 0.01
I0406 08:00:33.528533  5226 solver.cpp:218] Iteration 6084 (2.30611 iter/s, 5.20356s/12 iters), loss = 1.00436
I0406 08:00:33.528582  5226 solver.cpp:237]     Train net output #0: loss = 1.00436 (* 1 = 1.00436 loss)
I0406 08:00:33.528591  5226 sgd_solver.cpp:105] Iteration 6084, lr = 0.01
I0406 08:00:38.765733  5226 solver.cpp:218] Iteration 6096 (2.29134 iter/s, 5.2371s/12 iters), loss = 0.84998
I0406 08:00:38.765790  5226 solver.cpp:237]     Train net output #0: loss = 0.84998 (* 1 = 0.84998 loss)
I0406 08:00:38.765799  5226 sgd_solver.cpp:105] Iteration 6096, lr = 0.01
I0406 08:00:44.034705  5226 solver.cpp:218] Iteration 6108 (2.27753 iter/s, 5.26887s/12 iters), loss = 1.26552
I0406 08:00:44.034760  5226 solver.cpp:237]     Train net output #0: loss = 1.26552 (* 1 = 1.26552 loss)
I0406 08:00:44.034768  5226 sgd_solver.cpp:105] Iteration 6108, lr = 0.01
I0406 08:00:48.858479  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6120.caffemodel
I0406 08:00:51.841418  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6120.solverstate
I0406 08:00:54.135193  5226 solver.cpp:330] Iteration 6120, Testing net (#0)
I0406 08:00:54.135213  5226 net.cpp:676] Ignoring source layer train-data
I0406 08:00:56.050230  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:00:58.405539  5226 solver.cpp:397]     Test net output #0: accuracy = 0.321691
I0406 08:00:58.405665  5226 solver.cpp:397]     Test net output #1: loss = 3.23157 (* 1 = 3.23157 loss)
I0406 08:00:58.545665  5226 solver.cpp:218] Iteration 6120 (0.82697 iter/s, 14.5108s/12 iters), loss = 1.00214
I0406 08:00:58.545711  5226 solver.cpp:237]     Train net output #0: loss = 1.00214 (* 1 = 1.00214 loss)
I0406 08:00:58.545719  5226 sgd_solver.cpp:105] Iteration 6120, lr = 0.01
I0406 08:01:03.116647  5226 solver.cpp:218] Iteration 6132 (2.62531 iter/s, 4.5709s/12 iters), loss = 1.36882
I0406 08:01:03.116683  5226 solver.cpp:237]     Train net output #0: loss = 1.36882 (* 1 = 1.36882 loss)
I0406 08:01:03.116688  5226 sgd_solver.cpp:105] Iteration 6132, lr = 0.01
I0406 08:01:08.448292  5226 solver.cpp:218] Iteration 6144 (2.25075 iter/s, 5.33156s/12 iters), loss = 0.890568
I0406 08:01:08.448330  5226 solver.cpp:237]     Train net output #0: loss = 0.890568 (* 1 = 0.890568 loss)
I0406 08:01:08.448335  5226 sgd_solver.cpp:105] Iteration 6144, lr = 0.01
I0406 08:01:13.513116  5226 solver.cpp:218] Iteration 6156 (2.36932 iter/s, 5.06474s/12 iters), loss = 1.2117
I0406 08:01:13.513159  5226 solver.cpp:237]     Train net output #0: loss = 1.2117 (* 1 = 1.2117 loss)
I0406 08:01:13.513164  5226 sgd_solver.cpp:105] Iteration 6156, lr = 0.01
I0406 08:01:18.960605  5226 solver.cpp:218] Iteration 6168 (2.20289 iter/s, 5.44739s/12 iters), loss = 1.02632
I0406 08:01:18.960654  5226 solver.cpp:237]     Train net output #0: loss = 1.02632 (* 1 = 1.02632 loss)
I0406 08:01:18.960664  5226 sgd_solver.cpp:105] Iteration 6168, lr = 0.01
I0406 08:01:19.535945  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:01:24.060144  5226 solver.cpp:218] Iteration 6180 (2.3532 iter/s, 5.09945s/12 iters), loss = 1.03014
I0406 08:01:24.060179  5226 solver.cpp:237]     Train net output #0: loss = 1.03014 (* 1 = 1.03014 loss)
I0406 08:01:24.060184  5226 sgd_solver.cpp:105] Iteration 6180, lr = 0.01
I0406 08:01:29.351596  5226 solver.cpp:218] Iteration 6192 (2.26785 iter/s, 5.29136s/12 iters), loss = 1.10981
I0406 08:01:29.352939  5226 solver.cpp:237]     Train net output #0: loss = 1.10981 (* 1 = 1.10981 loss)
I0406 08:01:29.352952  5226 sgd_solver.cpp:105] Iteration 6192, lr = 0.01
I0406 08:01:34.426587  5226 solver.cpp:218] Iteration 6204 (2.36518 iter/s, 5.0736s/12 iters), loss = 1.01274
I0406 08:01:34.426626  5226 solver.cpp:237]     Train net output #0: loss = 1.01274 (* 1 = 1.01274 loss)
I0406 08:01:34.426632  5226 sgd_solver.cpp:105] Iteration 6204, lr = 0.01
I0406 08:01:39.719352  5226 solver.cpp:218] Iteration 6216 (2.26728 iter/s, 5.29268s/12 iters), loss = 1.49139
I0406 08:01:39.719386  5226 solver.cpp:237]     Train net output #0: loss = 1.49139 (* 1 = 1.49139 loss)
I0406 08:01:39.719391  5226 sgd_solver.cpp:105] Iteration 6216, lr = 0.01
I0406 08:01:41.993844  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6222.caffemodel
I0406 08:01:44.933007  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6222.solverstate
I0406 08:01:47.288713  5226 solver.cpp:330] Iteration 6222, Testing net (#0)
I0406 08:01:47.288736  5226 net.cpp:676] Ignoring source layer train-data
I0406 08:01:49.157588  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:01:50.411355  5226 blocking_queue.cpp:49] Waiting for data
I0406 08:01:51.560547  5226 solver.cpp:397]     Test net output #0: accuracy = 0.320466
I0406 08:01:51.560597  5226 solver.cpp:397]     Test net output #1: loss = 3.17218 (* 1 = 3.17218 loss)
I0406 08:01:53.476820  5226 solver.cpp:218] Iteration 6228 (0.872262 iter/s, 13.7573s/12 iters), loss = 1.21917
I0406 08:01:53.476859  5226 solver.cpp:237]     Train net output #0: loss = 1.21917 (* 1 = 1.21917 loss)
I0406 08:01:53.476866  5226 sgd_solver.cpp:105] Iteration 6228, lr = 0.01
I0406 08:01:58.920570  5226 solver.cpp:218] Iteration 6240 (2.2044 iter/s, 5.44366s/12 iters), loss = 1.03296
I0406 08:01:58.920608  5226 solver.cpp:237]     Train net output #0: loss = 1.03296 (* 1 = 1.03296 loss)
I0406 08:01:58.920614  5226 sgd_solver.cpp:105] Iteration 6240, lr = 0.01
I0406 08:02:04.259903  5226 solver.cpp:218] Iteration 6252 (2.24751 iter/s, 5.33925s/12 iters), loss = 1.21089
I0406 08:02:04.260027  5226 solver.cpp:237]     Train net output #0: loss = 1.21089 (* 1 = 1.21089 loss)
I0406 08:02:04.260035  5226 sgd_solver.cpp:105] Iteration 6252, lr = 0.01
I0406 08:02:09.588688  5226 solver.cpp:218] Iteration 6264 (2.252 iter/s, 5.32861s/12 iters), loss = 1.431
I0406 08:02:09.588734  5226 solver.cpp:237]     Train net output #0: loss = 1.431 (* 1 = 1.431 loss)
I0406 08:02:09.588742  5226 sgd_solver.cpp:105] Iteration 6264, lr = 0.01
I0406 08:02:12.515545  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:02:15.001204  5226 solver.cpp:218] Iteration 6276 (2.21712 iter/s, 5.41242s/12 iters), loss = 1.16491
I0406 08:02:15.001240  5226 solver.cpp:237]     Train net output #0: loss = 1.16491 (* 1 = 1.16491 loss)
I0406 08:02:15.001245  5226 sgd_solver.cpp:105] Iteration 6276, lr = 0.01
I0406 08:02:20.155663  5226 solver.cpp:218] Iteration 6288 (2.32812 iter/s, 5.15438s/12 iters), loss = 1.01929
I0406 08:02:20.155700  5226 solver.cpp:237]     Train net output #0: loss = 1.01929 (* 1 = 1.01929 loss)
I0406 08:02:20.155705  5226 sgd_solver.cpp:105] Iteration 6288, lr = 0.01
I0406 08:02:25.515905  5226 solver.cpp:218] Iteration 6300 (2.23874 iter/s, 5.36016s/12 iters), loss = 1.25234
I0406 08:02:25.515945  5226 solver.cpp:237]     Train net output #0: loss = 1.25234 (* 1 = 1.25234 loss)
I0406 08:02:25.515950  5226 sgd_solver.cpp:105] Iteration 6300, lr = 0.01
I0406 08:02:30.896199  5226 solver.cpp:218] Iteration 6312 (2.2304 iter/s, 5.38021s/12 iters), loss = 1.21727
I0406 08:02:30.896234  5226 solver.cpp:237]     Train net output #0: loss = 1.21727 (* 1 = 1.21727 loss)
I0406 08:02:30.896239  5226 sgd_solver.cpp:105] Iteration 6312, lr = 0.01
I0406 08:02:35.585449  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6324.caffemodel
I0406 08:02:38.565816  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6324.solverstate
I0406 08:02:40.891275  5226 solver.cpp:330] Iteration 6324, Testing net (#0)
I0406 08:02:40.891300  5226 net.cpp:676] Ignoring source layer train-data
I0406 08:02:42.803297  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:02:45.275792  5226 solver.cpp:397]     Test net output #0: accuracy = 0.3125
I0406 08:02:45.275820  5226 solver.cpp:397]     Test net output #1: loss = 3.16637 (* 1 = 3.16637 loss)
I0406 08:02:45.416664  5226 solver.cpp:218] Iteration 6324 (0.826428 iter/s, 14.5203s/12 iters), loss = 1.13985
I0406 08:02:45.416723  5226 solver.cpp:237]     Train net output #0: loss = 1.13985 (* 1 = 1.13985 loss)
I0406 08:02:45.416728  5226 sgd_solver.cpp:105] Iteration 6324, lr = 0.01
I0406 08:02:49.747298  5226 solver.cpp:218] Iteration 6336 (2.77102 iter/s, 4.33053s/12 iters), loss = 1.094
I0406 08:02:49.747346  5226 solver.cpp:237]     Train net output #0: loss = 1.094 (* 1 = 1.094 loss)
I0406 08:02:49.747354  5226 sgd_solver.cpp:105] Iteration 6336, lr = 0.01
I0406 08:02:55.001536  5226 solver.cpp:218] Iteration 6348 (2.28391 iter/s, 5.25414s/12 iters), loss = 0.723623
I0406 08:02:55.001574  5226 solver.cpp:237]     Train net output #0: loss = 0.723623 (* 1 = 0.723623 loss)
I0406 08:02:55.001579  5226 sgd_solver.cpp:105] Iteration 6348, lr = 0.01
I0406 08:03:00.353610  5226 solver.cpp:218] Iteration 6360 (2.24216 iter/s, 5.35198s/12 iters), loss = 1.13863
I0406 08:03:00.353646  5226 solver.cpp:237]     Train net output #0: loss = 1.13863 (* 1 = 1.13863 loss)
I0406 08:03:00.353652  5226 sgd_solver.cpp:105] Iteration 6360, lr = 0.01
I0406 08:03:05.346380  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:03:05.539719  5226 solver.cpp:218] Iteration 6372 (2.31391 iter/s, 5.18602s/12 iters), loss = 1.26286
I0406 08:03:05.539757  5226 solver.cpp:237]     Train net output #0: loss = 1.26286 (* 1 = 1.26286 loss)
I0406 08:03:05.539762  5226 sgd_solver.cpp:105] Iteration 6372, lr = 0.01
I0406 08:03:10.735517  5226 solver.cpp:218] Iteration 6384 (2.3096 iter/s, 5.19571s/12 iters), loss = 1.30597
I0406 08:03:10.735639  5226 solver.cpp:237]     Train net output #0: loss = 1.30597 (* 1 = 1.30597 loss)
I0406 08:03:10.735646  5226 sgd_solver.cpp:105] Iteration 6384, lr = 0.01
I0406 08:03:15.914579  5226 solver.cpp:218] Iteration 6396 (2.3171 iter/s, 5.17889s/12 iters), loss = 0.972872
I0406 08:03:15.914623  5226 solver.cpp:237]     Train net output #0: loss = 0.972872 (* 1 = 0.972872 loss)
I0406 08:03:15.914631  5226 sgd_solver.cpp:105] Iteration 6396, lr = 0.01
I0406 08:03:21.281440  5226 solver.cpp:218] Iteration 6408 (2.23598 iter/s, 5.36677s/12 iters), loss = 1.09385
I0406 08:03:21.281477  5226 solver.cpp:237]     Train net output #0: loss = 1.09385 (* 1 = 1.09385 loss)
I0406 08:03:21.281483  5226 sgd_solver.cpp:105] Iteration 6408, lr = 0.01
I0406 08:03:26.441359  5226 solver.cpp:218] Iteration 6420 (2.32566 iter/s, 5.15983s/12 iters), loss = 1.15708
I0406 08:03:26.441396  5226 solver.cpp:237]     Train net output #0: loss = 1.15708 (* 1 = 1.15708 loss)
I0406 08:03:26.441402  5226 sgd_solver.cpp:105] Iteration 6420, lr = 0.01
I0406 08:03:28.528931  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6426.caffemodel
I0406 08:03:31.528875  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6426.solverstate
I0406 08:03:33.888156  5226 solver.cpp:330] Iteration 6426, Testing net (#0)
I0406 08:03:33.888176  5226 net.cpp:676] Ignoring source layer train-data
I0406 08:03:35.741004  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:03:38.222579  5226 solver.cpp:397]     Test net output #0: accuracy = 0.318627
I0406 08:03:38.222615  5226 solver.cpp:397]     Test net output #1: loss = 3.15036 (* 1 = 3.15036 loss)
I0406 08:03:40.174453  5226 solver.cpp:218] Iteration 6432 (0.87381 iter/s, 13.733s/12 iters), loss = 1.36721
I0406 08:03:40.174492  5226 solver.cpp:237]     Train net output #0: loss = 1.36721 (* 1 = 1.36721 loss)
I0406 08:03:40.174499  5226 sgd_solver.cpp:105] Iteration 6432, lr = 0.01
I0406 08:03:45.636816  5226 solver.cpp:218] Iteration 6444 (2.19689 iter/s, 5.46228s/12 iters), loss = 1.13854
I0406 08:03:45.636914  5226 solver.cpp:237]     Train net output #0: loss = 1.13854 (* 1 = 1.13854 loss)
I0406 08:03:45.636920  5226 sgd_solver.cpp:105] Iteration 6444, lr = 0.01
I0406 08:03:50.932246  5226 solver.cpp:218] Iteration 6456 (2.26616 iter/s, 5.29529s/12 iters), loss = 0.948543
I0406 08:03:50.932288  5226 solver.cpp:237]     Train net output #0: loss = 0.948543 (* 1 = 0.948543 loss)
I0406 08:03:50.932297  5226 sgd_solver.cpp:105] Iteration 6456, lr = 0.01
I0406 08:03:55.976052  5226 solver.cpp:218] Iteration 6468 (2.3792 iter/s, 5.04372s/12 iters), loss = 1.34141
I0406 08:03:55.976083  5226 solver.cpp:237]     Train net output #0: loss = 1.34141 (* 1 = 1.34141 loss)
I0406 08:03:55.976087  5226 sgd_solver.cpp:105] Iteration 6468, lr = 0.01
I0406 08:03:57.976577  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:04:01.294920  5226 solver.cpp:218] Iteration 6480 (2.25615 iter/s, 5.31879s/12 iters), loss = 1.08114
I0406 08:04:01.294961  5226 solver.cpp:237]     Train net output #0: loss = 1.08114 (* 1 = 1.08114 loss)
I0406 08:04:01.294966  5226 sgd_solver.cpp:105] Iteration 6480, lr = 0.01
I0406 08:04:06.480978  5226 solver.cpp:218] Iteration 6492 (2.31394 iter/s, 5.18597s/12 iters), loss = 1.17164
I0406 08:04:06.481015  5226 solver.cpp:237]     Train net output #0: loss = 1.17164 (* 1 = 1.17164 loss)
I0406 08:04:06.481020  5226 sgd_solver.cpp:105] Iteration 6492, lr = 0.01
I0406 08:04:11.807209  5226 solver.cpp:218] Iteration 6504 (2.25303 iter/s, 5.32615s/12 iters), loss = 1.14135
I0406 08:04:11.807245  5226 solver.cpp:237]     Train net output #0: loss = 1.14135 (* 1 = 1.14135 loss)
I0406 08:04:11.807250  5226 sgd_solver.cpp:105] Iteration 6504, lr = 0.01
I0406 08:04:17.034530  5226 solver.cpp:218] Iteration 6516 (2.29567 iter/s, 5.22723s/12 iters), loss = 0.916203
I0406 08:04:17.034685  5226 solver.cpp:237]     Train net output #0: loss = 0.916203 (* 1 = 0.916203 loss)
I0406 08:04:17.034696  5226 sgd_solver.cpp:105] Iteration 6516, lr = 0.01
I0406 08:04:21.614195  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6528.caffemodel
I0406 08:04:24.644872  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6528.solverstate
I0406 08:04:26.945621  5226 solver.cpp:330] Iteration 6528, Testing net (#0)
I0406 08:04:26.945639  5226 net.cpp:676] Ignoring source layer train-data
I0406 08:04:28.751978  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:04:31.288625  5226 solver.cpp:397]     Test net output #0: accuracy = 0.327819
I0406 08:04:31.288661  5226 solver.cpp:397]     Test net output #1: loss = 3.11253 (* 1 = 3.11253 loss)
I0406 08:04:31.429592  5226 solver.cpp:218] Iteration 6528 (0.833634 iter/s, 14.3948s/12 iters), loss = 0.993294
I0406 08:04:31.429632  5226 solver.cpp:237]     Train net output #0: loss = 0.993294 (* 1 = 0.993294 loss)
I0406 08:04:31.429637  5226 sgd_solver.cpp:105] Iteration 6528, lr = 0.01
I0406 08:04:35.823329  5226 solver.cpp:218] Iteration 6540 (2.73121 iter/s, 4.39366s/12 iters), loss = 1.01667
I0406 08:04:35.823362  5226 solver.cpp:237]     Train net output #0: loss = 1.01667 (* 1 = 1.01667 loss)
I0406 08:04:35.823367  5226 sgd_solver.cpp:105] Iteration 6540, lr = 0.01
I0406 08:04:40.962194  5226 solver.cpp:218] Iteration 6552 (2.33518 iter/s, 5.13878s/12 iters), loss = 1.38059
I0406 08:04:40.962230  5226 solver.cpp:237]     Train net output #0: loss = 1.38059 (* 1 = 1.38059 loss)
I0406 08:04:40.962239  5226 sgd_solver.cpp:105] Iteration 6552, lr = 0.01
I0406 08:04:46.304085  5226 solver.cpp:218] Iteration 6564 (2.24643 iter/s, 5.3418s/12 iters), loss = 1.35795
I0406 08:04:46.304126  5226 solver.cpp:237]     Train net output #0: loss = 1.35795 (* 1 = 1.35795 loss)
I0406 08:04:46.304131  5226 sgd_solver.cpp:105] Iteration 6564, lr = 0.01
I0406 08:04:50.683212  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:04:51.539520  5226 solver.cpp:218] Iteration 6576 (2.29211 iter/s, 5.23534s/12 iters), loss = 1.04343
I0406 08:04:51.539566  5226 solver.cpp:237]     Train net output #0: loss = 1.04343 (* 1 = 1.04343 loss)
I0406 08:04:51.539574  5226 sgd_solver.cpp:105] Iteration 6576, lr = 0.01
I0406 08:04:56.781828  5226 solver.cpp:218] Iteration 6588 (2.28911 iter/s, 5.24222s/12 iters), loss = 1.20892
I0406 08:04:56.781864  5226 solver.cpp:237]     Train net output #0: loss = 1.20892 (* 1 = 1.20892 loss)
I0406 08:04:56.781870  5226 sgd_solver.cpp:105] Iteration 6588, lr = 0.01
I0406 08:05:02.096091  5226 solver.cpp:218] Iteration 6600 (2.25811 iter/s, 5.31418s/12 iters), loss = 0.967889
I0406 08:05:02.096130  5226 solver.cpp:237]     Train net output #0: loss = 0.967889 (* 1 = 0.967889 loss)
I0406 08:05:02.096135  5226 sgd_solver.cpp:105] Iteration 6600, lr = 0.01
I0406 08:05:07.149248  5226 solver.cpp:218] Iteration 6612 (2.37479 iter/s, 5.05308s/12 iters), loss = 1.01034
I0406 08:05:07.149286  5226 solver.cpp:237]     Train net output #0: loss = 1.01034 (* 1 = 1.01034 loss)
I0406 08:05:07.149291  5226 sgd_solver.cpp:105] Iteration 6612, lr = 0.01
I0406 08:05:12.447765  5226 solver.cpp:218] Iteration 6624 (2.26482 iter/s, 5.29844s/12 iters), loss = 0.972174
I0406 08:05:12.447798  5226 solver.cpp:237]     Train net output #0: loss = 0.972174 (* 1 = 0.972174 loss)
I0406 08:05:12.447803  5226 sgd_solver.cpp:105] Iteration 6624, lr = 0.01
I0406 08:05:14.589771  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6630.caffemodel
I0406 08:05:17.576299  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6630.solverstate
I0406 08:05:19.872946  5226 solver.cpp:330] Iteration 6630, Testing net (#0)
I0406 08:05:19.872964  5226 net.cpp:676] Ignoring source layer train-data
I0406 08:05:21.664875  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:05:24.205204  5226 solver.cpp:397]     Test net output #0: accuracy = 0.332721
I0406 08:05:24.205240  5226 solver.cpp:397]     Test net output #1: loss = 3.27978 (* 1 = 3.27978 loss)
I0406 08:05:26.305229  5226 solver.cpp:218] Iteration 6636 (0.865967 iter/s, 13.8573s/12 iters), loss = 1.41617
I0406 08:05:26.305269  5226 solver.cpp:237]     Train net output #0: loss = 1.41617 (* 1 = 1.41617 loss)
I0406 08:05:26.305274  5226 sgd_solver.cpp:105] Iteration 6636, lr = 0.01
I0406 08:05:31.669795  5226 solver.cpp:218] Iteration 6648 (2.23693 iter/s, 5.36448s/12 iters), loss = 1.31826
I0406 08:05:31.669833  5226 solver.cpp:237]     Train net output #0: loss = 1.31826 (* 1 = 1.31826 loss)
I0406 08:05:31.669838  5226 sgd_solver.cpp:105] Iteration 6648, lr = 0.01
I0406 08:05:37.038064  5226 solver.cpp:218] Iteration 6660 (2.23539 iter/s, 5.36818s/12 iters), loss = 1.51053
I0406 08:05:37.038102  5226 solver.cpp:237]     Train net output #0: loss = 1.51053 (* 1 = 1.51053 loss)
I0406 08:05:37.038107  5226 sgd_solver.cpp:105] Iteration 6660, lr = 0.01
I0406 08:05:42.339327  5226 solver.cpp:218] Iteration 6672 (2.26365 iter/s, 5.30118s/12 iters), loss = 1.0244
I0406 08:05:42.339363  5226 solver.cpp:237]     Train net output #0: loss = 1.0244 (* 1 = 1.0244 loss)
I0406 08:05:42.339370  5226 sgd_solver.cpp:105] Iteration 6672, lr = 0.01
I0406 08:05:43.782169  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:05:47.655052  5226 solver.cpp:218] Iteration 6684 (2.25749 iter/s, 5.31564s/12 iters), loss = 1.07668
I0406 08:05:47.655086  5226 solver.cpp:237]     Train net output #0: loss = 1.07668 (* 1 = 1.07668 loss)
I0406 08:05:47.655092  5226 sgd_solver.cpp:105] Iteration 6684, lr = 0.01
I0406 08:05:53.036336  5226 solver.cpp:218] Iteration 6696 (2.22999 iter/s, 5.3812s/12 iters), loss = 1.07263
I0406 08:05:53.036449  5226 solver.cpp:237]     Train net output #0: loss = 1.07263 (* 1 = 1.07263 loss)
I0406 08:05:53.036458  5226 sgd_solver.cpp:105] Iteration 6696, lr = 0.01
I0406 08:05:58.173512  5226 solver.cpp:218] Iteration 6708 (2.33598 iter/s, 5.13702s/12 iters), loss = 1.03074
I0406 08:05:58.173549  5226 solver.cpp:237]     Train net output #0: loss = 1.03074 (* 1 = 1.03074 loss)
I0406 08:05:58.173555  5226 sgd_solver.cpp:105] Iteration 6708, lr = 0.01
I0406 08:06:03.381147  5226 solver.cpp:218] Iteration 6720 (2.30434 iter/s, 5.20755s/12 iters), loss = 1.52513
I0406 08:06:03.381181  5226 solver.cpp:237]     Train net output #0: loss = 1.52513 (* 1 = 1.52513 loss)
I0406 08:06:03.381186  5226 sgd_solver.cpp:105] Iteration 6720, lr = 0.01
I0406 08:06:07.999007  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6732.caffemodel
I0406 08:06:10.954542  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6732.solverstate
I0406 08:06:13.259570  5226 solver.cpp:330] Iteration 6732, Testing net (#0)
I0406 08:06:13.259593  5226 net.cpp:676] Ignoring source layer train-data
I0406 08:06:14.961577  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:06:17.580265  5226 solver.cpp:397]     Test net output #0: accuracy = 0.322304
I0406 08:06:17.580300  5226 solver.cpp:397]     Test net output #1: loss = 3.12752 (* 1 = 3.12752 loss)
I0406 08:06:17.717653  5226 solver.cpp:218] Iteration 6732 (0.837032 iter/s, 14.3364s/12 iters), loss = 1.19235
I0406 08:06:17.717700  5226 solver.cpp:237]     Train net output #0: loss = 1.19235 (* 1 = 1.19235 loss)
I0406 08:06:17.717707  5226 sgd_solver.cpp:105] Iteration 6732, lr = 0.01
I0406 08:06:21.960300  5226 solver.cpp:218] Iteration 6744 (2.82848 iter/s, 4.24256s/12 iters), loss = 1.33845
I0406 08:06:21.960342  5226 solver.cpp:237]     Train net output #0: loss = 1.33845 (* 1 = 1.33845 loss)
I0406 08:06:21.960348  5226 sgd_solver.cpp:105] Iteration 6744, lr = 0.01
I0406 08:06:26.987522  5226 solver.cpp:218] Iteration 6756 (2.38705 iter/s, 5.02713s/12 iters), loss = 1.57507
I0406 08:06:26.987633  5226 solver.cpp:237]     Train net output #0: loss = 1.57507 (* 1 = 1.57507 loss)
I0406 08:06:26.987640  5226 sgd_solver.cpp:105] Iteration 6756, lr = 0.01
I0406 08:06:32.089697  5226 solver.cpp:218] Iteration 6768 (2.35201 iter/s, 5.10202s/12 iters), loss = 0.941016
I0406 08:06:32.089735  5226 solver.cpp:237]     Train net output #0: loss = 0.941016 (* 1 = 0.941016 loss)
I0406 08:06:32.089740  5226 sgd_solver.cpp:105] Iteration 6768, lr = 0.01
I0406 08:06:35.689278  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:06:37.366729  5226 solver.cpp:218] Iteration 6780 (2.27404 iter/s, 5.27695s/12 iters), loss = 0.895397
I0406 08:06:37.366777  5226 solver.cpp:237]     Train net output #0: loss = 0.895397 (* 1 = 0.895397 loss)
I0406 08:06:37.366784  5226 sgd_solver.cpp:105] Iteration 6780, lr = 0.01
I0406 08:06:42.427404  5226 solver.cpp:218] Iteration 6792 (2.37127 iter/s, 5.06058s/12 iters), loss = 1.33512
I0406 08:06:42.427443  5226 solver.cpp:237]     Train net output #0: loss = 1.33512 (* 1 = 1.33512 loss)
I0406 08:06:42.427449  5226 sgd_solver.cpp:105] Iteration 6792, lr = 0.01
I0406 08:06:47.726462  5226 solver.cpp:218] Iteration 6804 (2.26459 iter/s, 5.29898s/12 iters), loss = 1.34361
I0406 08:06:47.726511  5226 solver.cpp:237]     Train net output #0: loss = 1.34361 (* 1 = 1.34361 loss)
I0406 08:06:47.726517  5226 sgd_solver.cpp:105] Iteration 6804, lr = 0.01
I0406 08:06:53.007879  5226 solver.cpp:218] Iteration 6816 (2.27216 iter/s, 5.28132s/12 iters), loss = 1.00271
I0406 08:06:53.007916  5226 solver.cpp:237]     Train net output #0: loss = 1.00271 (* 1 = 1.00271 loss)
I0406 08:06:53.007922  5226 sgd_solver.cpp:105] Iteration 6816, lr = 0.01
I0406 08:06:58.346593  5226 solver.cpp:218] Iteration 6828 (2.24777 iter/s, 5.33863s/12 iters), loss = 1.15559
I0406 08:06:58.346735  5226 solver.cpp:237]     Train net output #0: loss = 1.15559 (* 1 = 1.15559 loss)
I0406 08:06:58.346741  5226 sgd_solver.cpp:105] Iteration 6828, lr = 0.01
I0406 08:07:00.377619  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6834.caffemodel
I0406 08:07:03.360368  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6834.solverstate
I0406 08:07:05.658643  5226 solver.cpp:330] Iteration 6834, Testing net (#0)
I0406 08:07:05.658660  5226 net.cpp:676] Ignoring source layer train-data
I0406 08:07:07.297152  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:07:09.947367  5226 solver.cpp:397]     Test net output #0: accuracy = 0.314338
I0406 08:07:09.947407  5226 solver.cpp:397]     Test net output #1: loss = 3.26802 (* 1 = 3.26802 loss)
I0406 08:07:11.825042  5226 solver.cpp:218] Iteration 6840 (0.890326 iter/s, 13.4782s/12 iters), loss = 1.1371
I0406 08:07:11.825080  5226 solver.cpp:237]     Train net output #0: loss = 1.1371 (* 1 = 1.1371 loss)
I0406 08:07:11.825085  5226 sgd_solver.cpp:105] Iteration 6840, lr = 0.01
I0406 08:07:17.130761  5226 solver.cpp:218] Iteration 6852 (2.26175 iter/s, 5.30563s/12 iters), loss = 0.999021
I0406 08:07:17.130802  5226 solver.cpp:237]     Train net output #0: loss = 0.999021 (* 1 = 0.999021 loss)
I0406 08:07:17.130810  5226 sgd_solver.cpp:105] Iteration 6852, lr = 0.01
I0406 08:07:22.479013  5226 solver.cpp:218] Iteration 6864 (2.24376 iter/s, 5.34816s/12 iters), loss = 0.994479
I0406 08:07:22.479053  5226 solver.cpp:237]     Train net output #0: loss = 0.994479 (* 1 = 0.994479 loss)
I0406 08:07:22.479058  5226 sgd_solver.cpp:105] Iteration 6864, lr = 0.01
I0406 08:07:27.873414  5226 solver.cpp:218] Iteration 6876 (2.22456 iter/s, 5.39432s/12 iters), loss = 1.32687
I0406 08:07:27.873446  5226 solver.cpp:237]     Train net output #0: loss = 1.32687 (* 1 = 1.32687 loss)
I0406 08:07:27.873451  5226 sgd_solver.cpp:105] Iteration 6876, lr = 0.01
I0406 08:07:28.508788  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:07:33.189221  5226 solver.cpp:218] Iteration 6888 (2.25745 iter/s, 5.31573s/12 iters), loss = 1.28745
I0406 08:07:33.189258  5226 solver.cpp:237]     Train net output #0: loss = 1.28745 (* 1 = 1.28745 loss)
I0406 08:07:33.189265  5226 sgd_solver.cpp:105] Iteration 6888, lr = 0.01
I0406 08:07:38.147213  5226 solver.cpp:218] Iteration 6900 (2.42038 iter/s, 4.95791s/12 iters), loss = 1.21176
I0406 08:07:38.147249  5226 solver.cpp:237]     Train net output #0: loss = 1.21176 (* 1 = 1.21176 loss)
I0406 08:07:38.147254  5226 sgd_solver.cpp:105] Iteration 6900, lr = 0.01
I0406 08:07:43.341907  5226 solver.cpp:218] Iteration 6912 (2.31009 iter/s, 5.19461s/12 iters), loss = 1.22255
I0406 08:07:43.341950  5226 solver.cpp:237]     Train net output #0: loss = 1.22255 (* 1 = 1.22255 loss)
I0406 08:07:43.341959  5226 sgd_solver.cpp:105] Iteration 6912, lr = 0.01
I0406 08:07:48.705101  5226 solver.cpp:218] Iteration 6924 (2.23751 iter/s, 5.3631s/12 iters), loss = 1.01774
I0406 08:07:48.705137  5226 solver.cpp:237]     Train net output #0: loss = 1.01774 (* 1 = 1.01774 loss)
I0406 08:07:48.705143  5226 sgd_solver.cpp:105] Iteration 6924, lr = 0.01
I0406 08:07:53.320048  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6936.caffemodel
I0406 08:07:56.327132  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6936.solverstate
I0406 08:07:58.689376  5226 solver.cpp:330] Iteration 6936, Testing net (#0)
I0406 08:07:58.689456  5226 net.cpp:676] Ignoring source layer train-data
I0406 08:07:59.246944  5226 blocking_queue.cpp:49] Waiting for data
I0406 08:08:00.275157  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:08:02.948769  5226 solver.cpp:397]     Test net output #0: accuracy = 0.341299
I0406 08:08:02.948796  5226 solver.cpp:397]     Test net output #1: loss = 3.09619 (* 1 = 3.09619 loss)
I0406 08:08:03.087630  5226 solver.cpp:218] Iteration 6936 (0.834353 iter/s, 14.3824s/12 iters), loss = 1.22934
I0406 08:08:03.087675  5226 solver.cpp:237]     Train net output #0: loss = 1.22934 (* 1 = 1.22934 loss)
I0406 08:08:03.087682  5226 sgd_solver.cpp:105] Iteration 6936, lr = 0.01
I0406 08:08:07.308470  5226 solver.cpp:218] Iteration 6948 (2.84309 iter/s, 4.22076s/12 iters), loss = 1.4935
I0406 08:08:07.308504  5226 solver.cpp:237]     Train net output #0: loss = 1.4935 (* 1 = 1.4935 loss)
I0406 08:08:07.308509  5226 sgd_solver.cpp:105] Iteration 6948, lr = 0.01
I0406 08:08:12.525612  5226 solver.cpp:218] Iteration 6960 (2.30014 iter/s, 5.21706s/12 iters), loss = 1.23067
I0406 08:08:12.525645  5226 solver.cpp:237]     Train net output #0: loss = 1.23067 (* 1 = 1.23067 loss)
I0406 08:08:12.525650  5226 sgd_solver.cpp:105] Iteration 6960, lr = 0.01
I0406 08:08:17.686228  5226 solver.cpp:218] Iteration 6972 (2.32534 iter/s, 5.16054s/12 iters), loss = 1.23891
I0406 08:08:17.686265  5226 solver.cpp:237]     Train net output #0: loss = 1.23891 (* 1 = 1.23891 loss)
I0406 08:08:17.686270  5226 sgd_solver.cpp:105] Iteration 6972, lr = 0.01
I0406 08:08:20.577445  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:08:23.017037  5226 solver.cpp:218] Iteration 6984 (2.2511 iter/s, 5.33072s/12 iters), loss = 0.945253
I0406 08:08:23.017076  5226 solver.cpp:237]     Train net output #0: loss = 0.945253 (* 1 = 0.945253 loss)
I0406 08:08:23.017082  5226 sgd_solver.cpp:105] Iteration 6984, lr = 0.01
I0406 08:08:28.356206  5226 solver.cpp:218] Iteration 6996 (2.24758 iter/s, 5.33908s/12 iters), loss = 1.25337
I0406 08:08:28.356243  5226 solver.cpp:237]     Train net output #0: loss = 1.25337 (* 1 = 1.25337 loss)
I0406 08:08:28.356249  5226 sgd_solver.cpp:105] Iteration 6996, lr = 0.01
I0406 08:08:33.688304  5226 solver.cpp:218] Iteration 7008 (2.25056 iter/s, 5.33201s/12 iters), loss = 1.02682
I0406 08:08:33.688447  5226 solver.cpp:237]     Train net output #0: loss = 1.02682 (* 1 = 1.02682 loss)
I0406 08:08:33.688457  5226 sgd_solver.cpp:105] Iteration 7008, lr = 0.01
I0406 08:08:38.870728  5226 solver.cpp:218] Iteration 7020 (2.3156 iter/s, 5.18224s/12 iters), loss = 1.3142
I0406 08:08:38.870764  5226 solver.cpp:237]     Train net output #0: loss = 1.3142 (* 1 = 1.3142 loss)
I0406 08:08:38.870771  5226 sgd_solver.cpp:105] Iteration 7020, lr = 0.01
I0406 08:08:44.129168  5226 solver.cpp:218] Iteration 7032 (2.28208 iter/s, 5.25836s/12 iters), loss = 1.28904
I0406 08:08:44.129204  5226 solver.cpp:237]     Train net output #0: loss = 1.28904 (* 1 = 1.28904 loss)
I0406 08:08:44.129215  5226 sgd_solver.cpp:105] Iteration 7032, lr = 0.01
I0406 08:08:46.368189  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7038.caffemodel
I0406 08:08:49.431262  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7038.solverstate
I0406 08:08:51.748904  5226 solver.cpp:330] Iteration 7038, Testing net (#0)
I0406 08:08:51.748929  5226 net.cpp:676] Ignoring source layer train-data
I0406 08:08:53.341706  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:08:56.077013  5226 solver.cpp:397]     Test net output #0: accuracy = 0.309436
I0406 08:08:56.077042  5226 solver.cpp:397]     Test net output #1: loss = 3.14588 (* 1 = 3.14588 loss)
I0406 08:08:57.839123  5226 solver.cpp:218] Iteration 7044 (0.875285 iter/s, 13.7098s/12 iters), loss = 1.21017
I0406 08:08:57.839164  5226 solver.cpp:237]     Train net output #0: loss = 1.21017 (* 1 = 1.21017 loss)
I0406 08:08:57.839169  5226 sgd_solver.cpp:105] Iteration 7044, lr = 0.01
I0406 08:09:02.927242  5226 solver.cpp:218] Iteration 7056 (2.35848 iter/s, 5.08803s/12 iters), loss = 1.42301
I0406 08:09:02.927281  5226 solver.cpp:237]     Train net output #0: loss = 1.42301 (* 1 = 1.42301 loss)
I0406 08:09:02.927286  5226 sgd_solver.cpp:105] Iteration 7056, lr = 0.01
I0406 08:09:08.095134  5226 solver.cpp:218] Iteration 7068 (2.32207 iter/s, 5.1678s/12 iters), loss = 0.962161
I0406 08:09:08.095234  5226 solver.cpp:237]     Train net output #0: loss = 0.962161 (* 1 = 0.962161 loss)
I0406 08:09:08.095240  5226 sgd_solver.cpp:105] Iteration 7068, lr = 0.01
I0406 08:09:13.355532  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:09:13.520596  5226 solver.cpp:218] Iteration 7080 (2.21186 iter/s, 5.42531s/12 iters), loss = 1.23596
I0406 08:09:13.520645  5226 solver.cpp:237]     Train net output #0: loss = 1.23596 (* 1 = 1.23596 loss)
I0406 08:09:13.520654  5226 sgd_solver.cpp:105] Iteration 7080, lr = 0.01
I0406 08:09:18.901111  5226 solver.cpp:218] Iteration 7092 (2.23031 iter/s, 5.38041s/12 iters), loss = 1.1704
I0406 08:09:18.901152  5226 solver.cpp:237]     Train net output #0: loss = 1.1704 (* 1 = 1.1704 loss)
I0406 08:09:18.901157  5226 sgd_solver.cpp:105] Iteration 7092, lr = 0.01
I0406 08:09:23.974205  5226 solver.cpp:218] Iteration 7104 (2.36546 iter/s, 5.07301s/12 iters), loss = 1.0578
I0406 08:09:23.974242  5226 solver.cpp:237]     Train net output #0: loss = 1.0578 (* 1 = 1.0578 loss)
I0406 08:09:23.974247  5226 sgd_solver.cpp:105] Iteration 7104, lr = 0.01
I0406 08:09:29.278834  5226 solver.cpp:218] Iteration 7116 (2.26221 iter/s, 5.30455s/12 iters), loss = 1.60422
I0406 08:09:29.278874  5226 solver.cpp:237]     Train net output #0: loss = 1.60422 (* 1 = 1.60422 loss)
I0406 08:09:29.278880  5226 sgd_solver.cpp:105] Iteration 7116, lr = 0.01
I0406 08:09:34.620187  5226 solver.cpp:218] Iteration 7128 (2.24666 iter/s, 5.34126s/12 iters), loss = 1.3641
I0406 08:09:34.620226  5226 solver.cpp:237]     Train net output #0: loss = 1.3641 (* 1 = 1.3641 loss)
I0406 08:09:34.620231  5226 sgd_solver.cpp:105] Iteration 7128, lr = 0.01
I0406 08:09:39.466658  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7140.caffemodel
I0406 08:09:42.522153  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7140.solverstate
I0406 08:09:44.835888  5226 solver.cpp:330] Iteration 7140, Testing net (#0)
I0406 08:09:44.835912  5226 net.cpp:676] Ignoring source layer train-data
I0406 08:09:46.393167  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:09:49.217873  5226 solver.cpp:397]     Test net output #0: accuracy = 0.31924
I0406 08:09:49.217914  5226 solver.cpp:397]     Test net output #1: loss = 3.29152 (* 1 = 3.29152 loss)
I0406 08:09:49.353264  5226 solver.cpp:218] Iteration 7140 (0.814502 iter/s, 14.7329s/12 iters), loss = 0.985333
I0406 08:09:49.353322  5226 solver.cpp:237]     Train net output #0: loss = 0.985333 (* 1 = 0.985333 loss)
I0406 08:09:49.353329  5226 sgd_solver.cpp:105] Iteration 7140, lr = 0.01
I0406 08:09:53.676674  5226 solver.cpp:218] Iteration 7152 (2.77565 iter/s, 4.32331s/12 iters), loss = 1.24605
I0406 08:09:53.676712  5226 solver.cpp:237]     Train net output #0: loss = 1.24605 (* 1 = 1.24605 loss)
I0406 08:09:53.676717  5226 sgd_solver.cpp:105] Iteration 7152, lr = 0.01
I0406 08:09:59.005640  5226 solver.cpp:218] Iteration 7164 (2.25188 iter/s, 5.32888s/12 iters), loss = 1.33262
I0406 08:09:59.005684  5226 solver.cpp:237]     Train net output #0: loss = 1.33262 (* 1 = 1.33262 loss)
I0406 08:09:59.005692  5226 sgd_solver.cpp:105] Iteration 7164, lr = 0.01
I0406 08:10:03.890460  5226 solver.cpp:218] Iteration 7176 (2.45664 iter/s, 4.88473s/12 iters), loss = 1.1438
I0406 08:10:03.890501  5226 solver.cpp:237]     Train net output #0: loss = 1.1438 (* 1 = 1.1438 loss)
I0406 08:10:03.890506  5226 sgd_solver.cpp:105] Iteration 7176, lr = 0.01
I0406 08:10:06.190948  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:10:09.356307  5226 solver.cpp:218] Iteration 7188 (2.19549 iter/s, 5.46575s/12 iters), loss = 1.03663
I0406 08:10:09.356357  5226 solver.cpp:237]     Train net output #0: loss = 1.03663 (* 1 = 1.03663 loss)
I0406 08:10:09.356366  5226 sgd_solver.cpp:105] Iteration 7188, lr = 0.01
I0406 08:10:14.413187  5226 solver.cpp:218] Iteration 7200 (2.37305 iter/s, 5.05678s/12 iters), loss = 1.25818
I0406 08:10:14.414992  5226 solver.cpp:237]     Train net output #0: loss = 1.25818 (* 1 = 1.25818 loss)
I0406 08:10:14.415004  5226 sgd_solver.cpp:105] Iteration 7200, lr = 0.01
I0406 08:10:19.600986  5226 solver.cpp:218] Iteration 7212 (2.31394 iter/s, 5.18595s/12 iters), loss = 1.43975
I0406 08:10:19.601034  5226 solver.cpp:237]     Train net output #0: loss = 1.43975 (* 1 = 1.43975 loss)
I0406 08:10:19.601042  5226 sgd_solver.cpp:105] Iteration 7212, lr = 0.01
I0406 08:10:24.784196  5226 solver.cpp:218] Iteration 7224 (2.31521 iter/s, 5.18312s/12 iters), loss = 1.30981
I0406 08:10:24.784233  5226 solver.cpp:237]     Train net output #0: loss = 1.30981 (* 1 = 1.30981 loss)
I0406 08:10:24.784238  5226 sgd_solver.cpp:105] Iteration 7224, lr = 0.01
I0406 08:10:30.095067  5226 solver.cpp:218] Iteration 7236 (2.25956 iter/s, 5.31078s/12 iters), loss = 1.27126
I0406 08:10:30.095122  5226 solver.cpp:237]     Train net output #0: loss = 1.27126 (* 1 = 1.27126 loss)
I0406 08:10:30.095130  5226 sgd_solver.cpp:105] Iteration 7236, lr = 0.01
I0406 08:10:32.099313  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7242.caffemodel
I0406 08:10:35.172044  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7242.solverstate
I0406 08:10:37.478842  5226 solver.cpp:330] Iteration 7242, Testing net (#0)
I0406 08:10:37.478864  5226 net.cpp:676] Ignoring source layer train-data
I0406 08:10:39.029176  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:10:42.007102  5226 solver.cpp:397]     Test net output #0: accuracy = 0.310662
I0406 08:10:42.007138  5226 solver.cpp:397]     Test net output #1: loss = 3.26105 (* 1 = 3.26105 loss)
I0406 08:10:43.886049  5226 solver.cpp:218] Iteration 7248 (0.870143 iter/s, 13.7908s/12 iters), loss = 1.45115
I0406 08:10:43.886091  5226 solver.cpp:237]     Train net output #0: loss = 1.45115 (* 1 = 1.45115 loss)
I0406 08:10:43.886097  5226 sgd_solver.cpp:105] Iteration 7248, lr = 0.01
I0406 08:10:49.166452  5226 solver.cpp:218] Iteration 7260 (2.27259 iter/s, 5.28031s/12 iters), loss = 1.67387
I0406 08:10:49.166627  5226 solver.cpp:237]     Train net output #0: loss = 1.67387 (* 1 = 1.67387 loss)
I0406 08:10:49.166636  5226 sgd_solver.cpp:105] Iteration 7260, lr = 0.01
I0406 08:10:54.493541  5226 solver.cpp:218] Iteration 7272 (2.25273 iter/s, 5.32687s/12 iters), loss = 1.45325
I0406 08:10:54.493592  5226 solver.cpp:237]     Train net output #0: loss = 1.45325 (* 1 = 1.45325 loss)
I0406 08:10:54.493599  5226 sgd_solver.cpp:105] Iteration 7272, lr = 0.01
I0406 08:10:59.118005  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:10:59.931618  5226 solver.cpp:218] Iteration 7284 (2.2067 iter/s, 5.43798s/12 iters), loss = 1.31641
I0406 08:10:59.931658  5226 solver.cpp:237]     Train net output #0: loss = 1.31641 (* 1 = 1.31641 loss)
I0406 08:10:59.931663  5226 sgd_solver.cpp:105] Iteration 7284, lr = 0.01
I0406 08:11:05.341847  5226 solver.cpp:218] Iteration 7296 (2.21806 iter/s, 5.41014s/12 iters), loss = 1.38925
I0406 08:11:05.341897  5226 solver.cpp:237]     Train net output #0: loss = 1.38925 (* 1 = 1.38925 loss)
I0406 08:11:05.341905  5226 sgd_solver.cpp:105] Iteration 7296, lr = 0.01
I0406 08:11:10.749009  5226 solver.cpp:218] Iteration 7308 (2.21932 iter/s, 5.40707s/12 iters), loss = 1.29207
I0406 08:11:10.749047  5226 solver.cpp:237]     Train net output #0: loss = 1.29207 (* 1 = 1.29207 loss)
I0406 08:11:10.749051  5226 sgd_solver.cpp:105] Iteration 7308, lr = 0.01
I0406 08:11:15.941305  5226 solver.cpp:218] Iteration 7320 (2.31115 iter/s, 5.19221s/12 iters), loss = 1.12175
I0406 08:11:15.941344  5226 solver.cpp:237]     Train net output #0: loss = 1.12175 (* 1 = 1.12175 loss)
I0406 08:11:15.941349  5226 sgd_solver.cpp:105] Iteration 7320, lr = 0.01
I0406 08:11:21.434329  5226 solver.cpp:218] Iteration 7332 (2.18463 iter/s, 5.49293s/12 iters), loss = 1.25404
I0406 08:11:21.434439  5226 solver.cpp:237]     Train net output #0: loss = 1.25404 (* 1 = 1.25404 loss)
I0406 08:11:21.434449  5226 sgd_solver.cpp:105] Iteration 7332, lr = 0.01
I0406 08:11:26.261636  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7344.caffemodel
I0406 08:11:29.305686  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7344.solverstate
I0406 08:11:31.607465  5226 solver.cpp:330] Iteration 7344, Testing net (#0)
I0406 08:11:31.607483  5226 net.cpp:676] Ignoring source layer train-data
I0406 08:11:33.081739  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:11:36.082605  5226 solver.cpp:397]     Test net output #0: accuracy = 0.29473
I0406 08:11:36.082641  5226 solver.cpp:397]     Test net output #1: loss = 3.27043 (* 1 = 3.27043 loss)
I0406 08:11:36.219113  5226 solver.cpp:218] Iteration 7344 (0.811657 iter/s, 14.7846s/12 iters), loss = 1.17824
I0406 08:11:36.219177  5226 solver.cpp:237]     Train net output #0: loss = 1.17824 (* 1 = 1.17824 loss)
I0406 08:11:36.219187  5226 sgd_solver.cpp:105] Iteration 7344, lr = 0.01
I0406 08:11:40.517383  5226 solver.cpp:218] Iteration 7356 (2.79189 iter/s, 4.29817s/12 iters), loss = 1.36358
I0406 08:11:40.517423  5226 solver.cpp:237]     Train net output #0: loss = 1.36358 (* 1 = 1.36358 loss)
I0406 08:11:40.517428  5226 sgd_solver.cpp:105] Iteration 7356, lr = 0.01
I0406 08:11:45.894979  5226 solver.cpp:218] Iteration 7368 (2.23152 iter/s, 5.37751s/12 iters), loss = 1.61712
I0406 08:11:45.895026  5226 solver.cpp:237]     Train net output #0: loss = 1.61712 (* 1 = 1.61712 loss)
I0406 08:11:45.895033  5226 sgd_solver.cpp:105] Iteration 7368, lr = 0.01
I0406 08:11:51.193125  5226 solver.cpp:218] Iteration 7380 (2.26498 iter/s, 5.29805s/12 iters), loss = 1.38603
I0406 08:11:51.193164  5226 solver.cpp:237]     Train net output #0: loss = 1.38603 (* 1 = 1.38603 loss)
I0406 08:11:51.193169  5226 sgd_solver.cpp:105] Iteration 7380, lr = 0.01
I0406 08:11:52.611935  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:11:56.585515  5226 solver.cpp:218] Iteration 7392 (2.2254 iter/s, 5.3923s/12 iters), loss = 1.13427
I0406 08:11:56.585556  5226 solver.cpp:237]     Train net output #0: loss = 1.13427 (* 1 = 1.13427 loss)
I0406 08:11:56.585561  5226 sgd_solver.cpp:105] Iteration 7392, lr = 0.01
I0406 08:12:01.761031  5226 solver.cpp:218] Iteration 7404 (2.31865 iter/s, 5.17543s/12 iters), loss = 1.19668
I0406 08:12:01.761083  5226 solver.cpp:237]     Train net output #0: loss = 1.19668 (* 1 = 1.19668 loss)
I0406 08:12:01.761092  5226 sgd_solver.cpp:105] Iteration 7404, lr = 0.01
I0406 08:12:07.039425  5226 solver.cpp:218] Iteration 7416 (2.27346 iter/s, 5.27829s/12 iters), loss = 1.50726
I0406 08:12:07.039474  5226 solver.cpp:237]     Train net output #0: loss = 1.50726 (* 1 = 1.50726 loss)
I0406 08:12:07.039482  5226 sgd_solver.cpp:105] Iteration 7416, lr = 0.01
I0406 08:12:12.017783  5226 solver.cpp:218] Iteration 7428 (2.41048 iter/s, 4.97827s/12 iters), loss = 1.38178
I0406 08:12:12.017823  5226 solver.cpp:237]     Train net output #0: loss = 1.38178 (* 1 = 1.38178 loss)
I0406 08:12:12.017828  5226 sgd_solver.cpp:105] Iteration 7428, lr = 0.01
I0406 08:12:17.424818  5226 solver.cpp:218] Iteration 7440 (2.21937 iter/s, 5.40694s/12 iters), loss = 1.7034
I0406 08:12:17.424871  5226 solver.cpp:237]     Train net output #0: loss = 1.7034 (* 1 = 1.7034 loss)
I0406 08:12:17.424880  5226 sgd_solver.cpp:105] Iteration 7440, lr = 0.01
I0406 08:12:19.448189  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7446.caffemodel
I0406 08:12:22.457324  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7446.solverstate
I0406 08:12:24.791182  5226 solver.cpp:330] Iteration 7446, Testing net (#0)
I0406 08:12:24.791249  5226 net.cpp:676] Ignoring source layer train-data
I0406 08:12:26.255015  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:12:29.123417  5226 solver.cpp:397]     Test net output #0: accuracy = 0.306985
I0406 08:12:29.123456  5226 solver.cpp:397]     Test net output #1: loss = 3.25548 (* 1 = 3.25548 loss)
I0406 08:12:31.111665  5226 solver.cpp:218] Iteration 7452 (0.876764 iter/s, 13.6867s/12 iters), loss = 1.26331
I0406 08:12:31.111721  5226 solver.cpp:237]     Train net output #0: loss = 1.26331 (* 1 = 1.26331 loss)
I0406 08:12:31.111728  5226 sgd_solver.cpp:105] Iteration 7452, lr = 0.01
I0406 08:12:36.485019  5226 solver.cpp:218] Iteration 7464 (2.23328 iter/s, 5.37325s/12 iters), loss = 1.48205
I0406 08:12:36.485072  5226 solver.cpp:237]     Train net output #0: loss = 1.48205 (* 1 = 1.48205 loss)
I0406 08:12:36.485081  5226 sgd_solver.cpp:105] Iteration 7464, lr = 0.01
I0406 08:12:41.842254  5226 solver.cpp:218] Iteration 7476 (2.24 iter/s, 5.35714s/12 iters), loss = 0.994821
I0406 08:12:41.842296  5226 solver.cpp:237]     Train net output #0: loss = 0.994821 (* 1 = 0.994821 loss)
I0406 08:12:41.842301  5226 sgd_solver.cpp:105] Iteration 7476, lr = 0.01
I0406 08:12:45.407567  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:12:46.985603  5226 solver.cpp:218] Iteration 7488 (2.33315 iter/s, 5.14326s/12 iters), loss = 1.03441
I0406 08:12:46.985646  5226 solver.cpp:237]     Train net output #0: loss = 1.03441 (* 1 = 1.03441 loss)
I0406 08:12:46.985651  5226 sgd_solver.cpp:105] Iteration 7488, lr = 0.01
I0406 08:12:52.286597  5226 solver.cpp:218] Iteration 7500 (2.26377 iter/s, 5.3009s/12 iters), loss = 1.299
I0406 08:12:52.286648  5226 solver.cpp:237]     Train net output #0: loss = 1.299 (* 1 = 1.299 loss)
I0406 08:12:52.286656  5226 sgd_solver.cpp:105] Iteration 7500, lr = 0.01
I0406 08:12:57.802179  5226 solver.cpp:218] Iteration 7512 (2.17569 iter/s, 5.51548s/12 iters), loss = 1.20659
I0406 08:12:57.802301  5226 solver.cpp:237]     Train net output #0: loss = 1.20659 (* 1 = 1.20659 loss)
I0406 08:12:57.802309  5226 sgd_solver.cpp:105] Iteration 7512, lr = 0.01
I0406 08:13:03.131002  5226 solver.cpp:218] Iteration 7524 (2.25198 iter/s, 5.32865s/12 iters), loss = 1.3829
I0406 08:13:03.131054  5226 solver.cpp:237]     Train net output #0: loss = 1.3829 (* 1 = 1.3829 loss)
I0406 08:13:03.131062  5226 sgd_solver.cpp:105] Iteration 7524, lr = 0.01
I0406 08:13:08.331003  5226 solver.cpp:218] Iteration 7536 (2.30774 iter/s, 5.1999s/12 iters), loss = 1.15164
I0406 08:13:08.331045  5226 solver.cpp:237]     Train net output #0: loss = 1.15164 (* 1 = 1.15164 loss)
I0406 08:13:08.331051  5226 sgd_solver.cpp:105] Iteration 7536, lr = 0.01
I0406 08:13:12.836421  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7548.caffemodel
I0406 08:13:15.798626  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7548.solverstate
I0406 08:13:18.113656  5226 solver.cpp:330] Iteration 7548, Testing net (#0)
I0406 08:13:18.113675  5226 net.cpp:676] Ignoring source layer train-data
I0406 08:13:19.476248  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:13:22.370656  5226 solver.cpp:397]     Test net output #0: accuracy = 0.307598
I0406 08:13:22.370687  5226 solver.cpp:397]     Test net output #1: loss = 3.36759 (* 1 = 3.36759 loss)
I0406 08:13:22.511624  5226 solver.cpp:218] Iteration 7548 (0.846234 iter/s, 14.1805s/12 iters), loss = 1.37236
I0406 08:13:22.511674  5226 solver.cpp:237]     Train net output #0: loss = 1.37236 (* 1 = 1.37236 loss)
I0406 08:13:22.511682  5226 sgd_solver.cpp:105] Iteration 7548, lr = 0.01
I0406 08:13:27.104182  5226 solver.cpp:218] Iteration 7560 (2.61298 iter/s, 4.59246s/12 iters), loss = 1.25314
I0406 08:13:27.104228  5226 solver.cpp:237]     Train net output #0: loss = 1.25314 (* 1 = 1.25314 loss)
I0406 08:13:27.104238  5226 sgd_solver.cpp:105] Iteration 7560, lr = 0.01
I0406 08:13:32.443171  5226 solver.cpp:218] Iteration 7572 (2.24766 iter/s, 5.33889s/12 iters), loss = 1.41166
I0406 08:13:32.443291  5226 solver.cpp:237]     Train net output #0: loss = 1.41166 (* 1 = 1.41166 loss)
I0406 08:13:32.443300  5226 sgd_solver.cpp:105] Iteration 7572, lr = 0.01
I0406 08:13:37.781738  5226 solver.cpp:218] Iteration 7584 (2.24786 iter/s, 5.3384s/12 iters), loss = 1.33473
I0406 08:13:37.781798  5226 solver.cpp:237]     Train net output #0: loss = 1.33473 (* 1 = 1.33473 loss)
I0406 08:13:37.781806  5226 sgd_solver.cpp:105] Iteration 7584, lr = 0.01
I0406 08:13:38.446951  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:13:43.156009  5226 solver.cpp:218] Iteration 7596 (2.23291 iter/s, 5.37416s/12 iters), loss = 1.11536
I0406 08:13:43.156059  5226 solver.cpp:237]     Train net output #0: loss = 1.11536 (* 1 = 1.11536 loss)
I0406 08:13:43.156067  5226 sgd_solver.cpp:105] Iteration 7596, lr = 0.01
I0406 08:13:48.149021  5226 solver.cpp:218] Iteration 7608 (2.40341 iter/s, 4.99292s/12 iters), loss = 0.837079
I0406 08:13:48.149066  5226 solver.cpp:237]     Train net output #0: loss = 0.837079 (* 1 = 0.837079 loss)
I0406 08:13:48.149073  5226 sgd_solver.cpp:105] Iteration 7608, lr = 0.01
I0406 08:13:53.595036  5226 solver.cpp:218] Iteration 7620 (2.20349 iter/s, 5.44592s/12 iters), loss = 1.64403
I0406 08:13:53.595089  5226 solver.cpp:237]     Train net output #0: loss = 1.64403 (* 1 = 1.64403 loss)
I0406 08:13:53.595098  5226 sgd_solver.cpp:105] Iteration 7620, lr = 0.01
I0406 08:13:56.020975  5226 blocking_queue.cpp:49] Waiting for data
I0406 08:13:58.505017  5226 solver.cpp:218] Iteration 7632 (2.44405 iter/s, 4.90988s/12 iters), loss = 1.34494
I0406 08:13:58.505072  5226 solver.cpp:237]     Train net output #0: loss = 1.34494 (* 1 = 1.34494 loss)
I0406 08:13:58.505080  5226 sgd_solver.cpp:105] Iteration 7632, lr = 0.01
I0406 08:14:03.942833  5226 solver.cpp:218] Iteration 7644 (2.20681 iter/s, 5.43771s/12 iters), loss = 1.4641
I0406 08:14:03.942976  5226 solver.cpp:237]     Train net output #0: loss = 1.4641 (* 1 = 1.4641 loss)
I0406 08:14:03.942986  5226 sgd_solver.cpp:105] Iteration 7644, lr = 0.01
I0406 08:14:06.053140  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7650.caffemodel
I0406 08:14:09.180851  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7650.solverstate
I0406 08:14:11.543313  5226 solver.cpp:330] Iteration 7650, Testing net (#0)
I0406 08:14:11.543334  5226 net.cpp:676] Ignoring source layer train-data
I0406 08:14:12.895263  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:14:15.912137  5226 solver.cpp:397]     Test net output #0: accuracy = 0.310049
I0406 08:14:15.912173  5226 solver.cpp:397]     Test net output #1: loss = 3.32535 (* 1 = 3.32535 loss)
I0406 08:14:17.779772  5226 solver.cpp:218] Iteration 7656 (0.867259 iter/s, 13.8367s/12 iters), loss = 1.0255
I0406 08:14:17.779811  5226 solver.cpp:237]     Train net output #0: loss = 1.0255 (* 1 = 1.0255 loss)
I0406 08:14:17.779817  5226 sgd_solver.cpp:105] Iteration 7656, lr = 0.01
I0406 08:14:23.064656  5226 solver.cpp:218] Iteration 7668 (2.27066 iter/s, 5.2848s/12 iters), loss = 1.17211
I0406 08:14:23.064697  5226 solver.cpp:237]     Train net output #0: loss = 1.17211 (* 1 = 1.17211 loss)
I0406 08:14:23.064702  5226 sgd_solver.cpp:105] Iteration 7668, lr = 0.01
I0406 08:14:28.443432  5226 solver.cpp:218] Iteration 7680 (2.23103 iter/s, 5.37868s/12 iters), loss = 0.77815
I0406 08:14:28.443476  5226 solver.cpp:237]     Train net output #0: loss = 0.77815 (* 1 = 0.77815 loss)
I0406 08:14:28.443485  5226 sgd_solver.cpp:105] Iteration 7680, lr = 0.01
I0406 08:14:31.310232  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:14:33.717849  5226 solver.cpp:218] Iteration 7692 (2.27517 iter/s, 5.27432s/12 iters), loss = 1.04241
I0406 08:14:33.717888  5226 solver.cpp:237]     Train net output #0: loss = 1.04241 (* 1 = 1.04241 loss)
I0406 08:14:33.717895  5226 sgd_solver.cpp:105] Iteration 7692, lr = 0.01
I0406 08:14:39.017709  5226 solver.cpp:218] Iteration 7704 (2.26425 iter/s, 5.29977s/12 iters), loss = 1.16704
I0406 08:14:39.017829  5226 solver.cpp:237]     Train net output #0: loss = 1.16704 (* 1 = 1.16704 loss)
I0406 08:14:39.017838  5226 sgd_solver.cpp:105] Iteration 7704, lr = 0.01
I0406 08:14:44.279841  5226 solver.cpp:218] Iteration 7716 (2.28052 iter/s, 5.26196s/12 iters), loss = 1.18875
I0406 08:14:44.279894  5226 solver.cpp:237]     Train net output #0: loss = 1.18875 (* 1 = 1.18875 loss)
I0406 08:14:44.279903  5226 sgd_solver.cpp:105] Iteration 7716, lr = 0.01
I0406 08:14:49.630910  5226 solver.cpp:218] Iteration 7728 (2.24258 iter/s, 5.35097s/12 iters), loss = 1.34568
I0406 08:14:49.630949  5226 solver.cpp:237]     Train net output #0: loss = 1.34568 (* 1 = 1.34568 loss)
I0406 08:14:49.630954  5226 sgd_solver.cpp:105] Iteration 7728, lr = 0.01
I0406 08:14:54.879954  5226 solver.cpp:218] Iteration 7740 (2.28617 iter/s, 5.24895s/12 iters), loss = 1.15676
I0406 08:14:54.880013  5226 solver.cpp:237]     Train net output #0: loss = 1.15676 (* 1 = 1.15676 loss)
I0406 08:14:54.880023  5226 sgd_solver.cpp:105] Iteration 7740, lr = 0.01
I0406 08:14:59.726756  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7752.caffemodel
I0406 08:15:02.754528  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7752.solverstate
I0406 08:15:05.071619  5226 solver.cpp:330] Iteration 7752, Testing net (#0)
I0406 08:15:05.071642  5226 net.cpp:676] Ignoring source layer train-data
I0406 08:15:06.454967  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:15:09.446283  5226 solver.cpp:397]     Test net output #0: accuracy = 0.300858
I0406 08:15:09.446379  5226 solver.cpp:397]     Test net output #1: loss = 3.31845 (* 1 = 3.31845 loss)
I0406 08:15:09.587191  5226 solver.cpp:218] Iteration 7752 (0.815934 iter/s, 14.7071s/12 iters), loss = 1.67956
I0406 08:15:09.587258  5226 solver.cpp:237]     Train net output #0: loss = 1.67956 (* 1 = 1.67956 loss)
I0406 08:15:09.587266  5226 sgd_solver.cpp:105] Iteration 7752, lr = 0.01
I0406 08:15:14.159837  5226 solver.cpp:218] Iteration 7764 (2.62437 iter/s, 4.57253s/12 iters), loss = 1.16685
I0406 08:15:14.159886  5226 solver.cpp:237]     Train net output #0: loss = 1.16685 (* 1 = 1.16685 loss)
I0406 08:15:14.159895  5226 sgd_solver.cpp:105] Iteration 7764, lr = 0.01
I0406 08:15:19.173151  5226 solver.cpp:218] Iteration 7776 (2.39367 iter/s, 5.01322s/12 iters), loss = 1.31995
I0406 08:15:19.173197  5226 solver.cpp:237]     Train net output #0: loss = 1.31995 (* 1 = 1.31995 loss)
I0406 08:15:19.173204  5226 sgd_solver.cpp:105] Iteration 7776, lr = 0.01
I0406 08:15:24.414840  5226 solver.cpp:218] Iteration 7788 (2.28938 iter/s, 5.24159s/12 iters), loss = 1.42039
I0406 08:15:24.414888  5226 solver.cpp:237]     Train net output #0: loss = 1.42039 (* 1 = 1.42039 loss)
I0406 08:15:24.414896  5226 sgd_solver.cpp:105] Iteration 7788, lr = 0.01
I0406 08:15:24.421326  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:15:29.833966  5226 solver.cpp:218] Iteration 7800 (2.21442 iter/s, 5.41903s/12 iters), loss = 0.994502
I0406 08:15:29.834007  5226 solver.cpp:237]     Train net output #0: loss = 0.994502 (* 1 = 0.994502 loss)
I0406 08:15:29.834014  5226 sgd_solver.cpp:105] Iteration 7800, lr = 0.01
I0406 08:15:35.034857  5226 solver.cpp:218] Iteration 7812 (2.30734 iter/s, 5.2008s/12 iters), loss = 1.02511
I0406 08:15:35.034896  5226 solver.cpp:237]     Train net output #0: loss = 1.02511 (* 1 = 1.02511 loss)
I0406 08:15:35.034901  5226 sgd_solver.cpp:105] Iteration 7812, lr = 0.01
I0406 08:15:40.142539  5226 solver.cpp:218] Iteration 7824 (2.34944 iter/s, 5.10759s/12 iters), loss = 1.25256
I0406 08:15:40.142664  5226 solver.cpp:237]     Train net output #0: loss = 1.25256 (* 1 = 1.25256 loss)
I0406 08:15:40.142670  5226 sgd_solver.cpp:105] Iteration 7824, lr = 0.01
I0406 08:15:45.343688  5226 solver.cpp:218] Iteration 7836 (2.30726 iter/s, 5.20097s/12 iters), loss = 1.59217
I0406 08:15:45.343725  5226 solver.cpp:237]     Train net output #0: loss = 1.59217 (* 1 = 1.59217 loss)
I0406 08:15:45.343731  5226 sgd_solver.cpp:105] Iteration 7836, lr = 0.01
I0406 08:15:50.478739  5226 solver.cpp:218] Iteration 7848 (2.33692 iter/s, 5.13496s/12 iters), loss = 1.28422
I0406 08:15:50.478787  5226 solver.cpp:237]     Train net output #0: loss = 1.28422 (* 1 = 1.28422 loss)
I0406 08:15:50.478796  5226 sgd_solver.cpp:105] Iteration 7848, lr = 0.01
I0406 08:15:52.602519  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7854.caffemodel
I0406 08:15:55.569321  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7854.solverstate
I0406 08:15:57.954066  5226 solver.cpp:330] Iteration 7854, Testing net (#0)
I0406 08:15:57.954085  5226 net.cpp:676] Ignoring source layer train-data
I0406 08:15:59.338569  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:16:02.464704  5226 solver.cpp:397]     Test net output #0: accuracy = 0.311274
I0406 08:16:02.464740  5226 solver.cpp:397]     Test net output #1: loss = 3.37465 (* 1 = 3.37465 loss)
I0406 08:16:04.451552  5226 solver.cpp:218] Iteration 7860 (0.85882 iter/s, 13.9727s/12 iters), loss = 1.3159
I0406 08:16:04.451591  5226 solver.cpp:237]     Train net output #0: loss = 1.3159 (* 1 = 1.3159 loss)
I0406 08:16:04.451596  5226 sgd_solver.cpp:105] Iteration 7860, lr = 0.01
I0406 08:16:09.770318  5226 solver.cpp:218] Iteration 7872 (2.2562 iter/s, 5.31867s/12 iters), loss = 0.957102
I0406 08:16:09.770368  5226 solver.cpp:237]     Train net output #0: loss = 0.957102 (* 1 = 0.957102 loss)
I0406 08:16:09.770375  5226 sgd_solver.cpp:105] Iteration 7872, lr = 0.01
I0406 08:16:14.976897  5226 solver.cpp:218] Iteration 7884 (2.30482 iter/s, 5.20647s/12 iters), loss = 0.968111
I0406 08:16:14.977003  5226 solver.cpp:237]     Train net output #0: loss = 0.968111 (* 1 = 0.968111 loss)
I0406 08:16:14.977010  5226 sgd_solver.cpp:105] Iteration 7884, lr = 0.01
I0406 08:16:17.292248  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:16:20.365130  5226 solver.cpp:218] Iteration 7896 (2.22714 iter/s, 5.38808s/12 iters), loss = 1.17807
I0406 08:16:20.365170  5226 solver.cpp:237]     Train net output #0: loss = 1.17807 (* 1 = 1.17807 loss)
I0406 08:16:20.365176  5226 sgd_solver.cpp:105] Iteration 7896, lr = 0.01
I0406 08:16:25.706619  5226 solver.cpp:218] Iteration 7908 (2.2466 iter/s, 5.3414s/12 iters), loss = 1.23866
I0406 08:16:25.706658  5226 solver.cpp:237]     Train net output #0: loss = 1.23866 (* 1 = 1.23866 loss)
I0406 08:16:25.706663  5226 sgd_solver.cpp:105] Iteration 7908, lr = 0.01
I0406 08:16:30.865837  5226 solver.cpp:218] Iteration 7920 (2.32598 iter/s, 5.15913s/12 iters), loss = 1.48775
I0406 08:16:30.865890  5226 solver.cpp:237]     Train net output #0: loss = 1.48775 (* 1 = 1.48775 loss)
I0406 08:16:30.865898  5226 sgd_solver.cpp:105] Iteration 7920, lr = 0.01
I0406 08:16:36.146890  5226 solver.cpp:218] Iteration 7932 (2.27232 iter/s, 5.28095s/12 iters), loss = 1.09502
I0406 08:16:36.146929  5226 solver.cpp:237]     Train net output #0: loss = 1.09502 (* 1 = 1.09502 loss)
I0406 08:16:36.146935  5226 sgd_solver.cpp:105] Iteration 7932, lr = 0.01
I0406 08:16:41.468587  5226 solver.cpp:218] Iteration 7944 (2.25496 iter/s, 5.32161s/12 iters), loss = 1.32744
I0406 08:16:41.468643  5226 solver.cpp:237]     Train net output #0: loss = 1.32744 (* 1 = 1.32744 loss)
I0406 08:16:41.468652  5226 sgd_solver.cpp:105] Iteration 7944, lr = 0.01
I0406 08:16:46.070426  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7956.caffemodel
I0406 08:16:49.052668  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7956.solverstate
I0406 08:16:51.362301  5226 solver.cpp:330] Iteration 7956, Testing net (#0)
I0406 08:16:51.362320  5226 net.cpp:676] Ignoring source layer train-data
I0406 08:16:52.631026  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:16:55.792701  5226 solver.cpp:397]     Test net output #0: accuracy = 0.284926
I0406 08:16:55.792737  5226 solver.cpp:397]     Test net output #1: loss = 3.43636 (* 1 = 3.43636 loss)
I0406 08:16:55.933223  5226 solver.cpp:218] Iteration 7956 (0.829619 iter/s, 14.4645s/12 iters), loss = 1.40492
I0406 08:16:55.933270  5226 solver.cpp:237]     Train net output #0: loss = 1.40492 (* 1 = 1.40492 loss)
I0406 08:16:55.933279  5226 sgd_solver.cpp:105] Iteration 7956, lr = 0.01
I0406 08:17:00.406278  5226 solver.cpp:218] Iteration 7968 (2.68278 iter/s, 4.47296s/12 iters), loss = 1.70144
I0406 08:17:00.406316  5226 solver.cpp:237]     Train net output #0: loss = 1.70144 (* 1 = 1.70144 loss)
I0406 08:17:00.406322  5226 sgd_solver.cpp:105] Iteration 7968, lr = 0.01
I0406 08:17:05.543663  5226 solver.cpp:218] Iteration 7980 (2.33586 iter/s, 5.1373s/12 iters), loss = 1.09255
I0406 08:17:05.543707  5226 solver.cpp:237]     Train net output #0: loss = 1.09255 (* 1 = 1.09255 loss)
I0406 08:17:05.543715  5226 sgd_solver.cpp:105] Iteration 7980, lr = 0.01
I0406 08:17:10.028910  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:17:10.816298  5226 solver.cpp:218] Iteration 7992 (2.27594 iter/s, 5.27254s/12 iters), loss = 0.917287
I0406 08:17:10.816349  5226 solver.cpp:237]     Train net output #0: loss = 0.917287 (* 1 = 0.917287 loss)
I0406 08:17:10.816359  5226 sgd_solver.cpp:105] Iteration 7992, lr = 0.01
I0406 08:17:16.058558  5226 solver.cpp:218] Iteration 8004 (2.28913 iter/s, 5.24216s/12 iters), loss = 1.26003
I0406 08:17:16.058596  5226 solver.cpp:237]     Train net output #0: loss = 1.26003 (* 1 = 1.26003 loss)
I0406 08:17:16.058602  5226 sgd_solver.cpp:105] Iteration 8004, lr = 0.01
I0406 08:17:21.362241  5226 solver.cpp:218] Iteration 8016 (2.26262 iter/s, 5.30359s/12 iters), loss = 1.2735
I0406 08:17:21.362334  5226 solver.cpp:237]     Train net output #0: loss = 1.2735 (* 1 = 1.2735 loss)
I0406 08:17:21.362341  5226 sgd_solver.cpp:105] Iteration 8016, lr = 0.01
I0406 08:17:26.656569  5226 solver.cpp:218] Iteration 8028 (2.26664 iter/s, 5.29419s/12 iters), loss = 1.02471
I0406 08:17:26.656608  5226 solver.cpp:237]     Train net output #0: loss = 1.02471 (* 1 = 1.02471 loss)
I0406 08:17:26.656615  5226 sgd_solver.cpp:105] Iteration 8028, lr = 0.01
I0406 08:17:32.000152  5226 solver.cpp:218] Iteration 8040 (2.24572 iter/s, 5.34349s/12 iters), loss = 1.22829
I0406 08:17:32.000207  5226 solver.cpp:237]     Train net output #0: loss = 1.22829 (* 1 = 1.22829 loss)
I0406 08:17:32.000216  5226 sgd_solver.cpp:105] Iteration 8040, lr = 0.01
I0406 08:17:37.124398  5226 solver.cpp:218] Iteration 8052 (2.34186 iter/s, 5.12414s/12 iters), loss = 1.16577
I0406 08:17:37.130602  5226 solver.cpp:237]     Train net output #0: loss = 1.16577 (* 1 = 1.16577 loss)
I0406 08:17:37.130621  5226 sgd_solver.cpp:105] Iteration 8052, lr = 0.01
I0406 08:17:39.280782  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8058.caffemodel
I0406 08:17:42.320255  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8058.solverstate
I0406 08:17:44.614873  5226 solver.cpp:330] Iteration 8058, Testing net (#0)
I0406 08:17:44.614890  5226 net.cpp:676] Ignoring source layer train-data
I0406 08:17:45.845966  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:17:48.951957  5226 solver.cpp:397]     Test net output #0: accuracy = 0.321078
I0406 08:17:48.951992  5226 solver.cpp:397]     Test net output #1: loss = 3.33008 (* 1 = 3.33008 loss)
I0406 08:17:50.895759  5226 solver.cpp:218] Iteration 8064 (0.871772 iter/s, 13.7651s/12 iters), loss = 1.08551
I0406 08:17:50.895797  5226 solver.cpp:237]     Train net output #0: loss = 1.08551 (* 1 = 1.08551 loss)
I0406 08:17:50.895802  5226 sgd_solver.cpp:105] Iteration 8064, lr = 0.01
I0406 08:17:56.124384  5226 solver.cpp:218] Iteration 8076 (2.2951 iter/s, 5.22854s/12 iters), loss = 1.28771
I0406 08:17:56.124514  5226 solver.cpp:237]     Train net output #0: loss = 1.28771 (* 1 = 1.28771 loss)
I0406 08:17:56.124523  5226 sgd_solver.cpp:105] Iteration 8076, lr = 0.01
I0406 08:18:01.484093  5226 solver.cpp:218] Iteration 8088 (2.239 iter/s, 5.35953s/12 iters), loss = 1.1622
I0406 08:18:01.484143  5226 solver.cpp:237]     Train net output #0: loss = 1.1622 (* 1 = 1.1622 loss)
I0406 08:18:01.484151  5226 sgd_solver.cpp:105] Iteration 8088, lr = 0.01
I0406 08:18:02.977277  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:18:06.672235  5226 solver.cpp:218] Iteration 8100 (2.31301 iter/s, 5.18805s/12 iters), loss = 1.53249
I0406 08:18:06.672273  5226 solver.cpp:237]     Train net output #0: loss = 1.53249 (* 1 = 1.53249 loss)
I0406 08:18:06.672279  5226 sgd_solver.cpp:105] Iteration 8100, lr = 0.01
I0406 08:18:11.852867  5226 solver.cpp:218] Iteration 8112 (2.31636 iter/s, 5.18055s/12 iters), loss = 1.47226
I0406 08:18:11.852910  5226 solver.cpp:237]     Train net output #0: loss = 1.47226 (* 1 = 1.47226 loss)
I0406 08:18:11.852917  5226 sgd_solver.cpp:105] Iteration 8112, lr = 0.01
I0406 08:18:17.242426  5226 solver.cpp:218] Iteration 8124 (2.22657 iter/s, 5.38946s/12 iters), loss = 0.932258
I0406 08:18:17.242475  5226 solver.cpp:237]     Train net output #0: loss = 0.932258 (* 1 = 0.932258 loss)
I0406 08:18:17.242482  5226 sgd_solver.cpp:105] Iteration 8124, lr = 0.01
I0406 08:18:22.506244  5226 solver.cpp:218] Iteration 8136 (2.27975 iter/s, 5.26372s/12 iters), loss = 1.23263
I0406 08:18:22.506281  5226 solver.cpp:237]     Train net output #0: loss = 1.23263 (* 1 = 1.23263 loss)
I0406 08:18:22.506289  5226 sgd_solver.cpp:105] Iteration 8136, lr = 0.01
I0406 08:18:27.659448  5226 solver.cpp:218] Iteration 8148 (2.32869 iter/s, 5.15312s/12 iters), loss = 1.13365
I0406 08:18:27.659556  5226 solver.cpp:237]     Train net output #0: loss = 1.13365 (* 1 = 1.13365 loss)
I0406 08:18:27.659564  5226 sgd_solver.cpp:105] Iteration 8148, lr = 0.01
I0406 08:18:32.456318  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8160.caffemodel
I0406 08:18:35.396615  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8160.solverstate
I0406 08:18:38.212797  5226 solver.cpp:330] Iteration 8160, Testing net (#0)
I0406 08:18:38.212815  5226 net.cpp:676] Ignoring source layer train-data
I0406 08:18:39.357486  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:18:42.547910  5226 solver.cpp:397]     Test net output #0: accuracy = 0.293505
I0406 08:18:42.547950  5226 solver.cpp:397]     Test net output #1: loss = 3.32601 (* 1 = 3.32601 loss)
I0406 08:18:42.690167  5226 solver.cpp:218] Iteration 8160 (0.798377 iter/s, 15.0305s/12 iters), loss = 1.36887
I0406 08:18:42.690229  5226 solver.cpp:237]     Train net output #0: loss = 1.36887 (* 1 = 1.36887 loss)
I0406 08:18:42.690237  5226 sgd_solver.cpp:105] Iteration 8160, lr = 0.01
I0406 08:18:47.007655  5226 solver.cpp:218] Iteration 8172 (2.77946 iter/s, 4.31738s/12 iters), loss = 1.04864
I0406 08:18:47.007694  5226 solver.cpp:237]     Train net output #0: loss = 1.04864 (* 1 = 1.04864 loss)
I0406 08:18:47.007699  5226 sgd_solver.cpp:105] Iteration 8172, lr = 0.01
I0406 08:18:52.347029  5226 solver.cpp:218] Iteration 8184 (2.24749 iter/s, 5.33929s/12 iters), loss = 1.419
I0406 08:18:52.347066  5226 solver.cpp:237]     Train net output #0: loss = 1.419 (* 1 = 1.419 loss)
I0406 08:18:52.347071  5226 sgd_solver.cpp:105] Iteration 8184, lr = 0.01
I0406 08:18:56.222564  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:18:57.685130  5226 solver.cpp:218] Iteration 8196 (2.24803 iter/s, 5.33801s/12 iters), loss = 1.50788
I0406 08:18:57.685292  5226 solver.cpp:237]     Train net output #0: loss = 1.50788 (* 1 = 1.50788 loss)
I0406 08:18:57.685302  5226 sgd_solver.cpp:105] Iteration 8196, lr = 0.01
I0406 08:19:02.840390  5226 solver.cpp:218] Iteration 8208 (2.32781 iter/s, 5.15505s/12 iters), loss = 1.13445
I0406 08:19:02.840437  5226 solver.cpp:237]     Train net output #0: loss = 1.13445 (* 1 = 1.13445 loss)
I0406 08:19:02.840445  5226 sgd_solver.cpp:105] Iteration 8208, lr = 0.01
I0406 08:19:08.220216  5226 solver.cpp:218] Iteration 8220 (2.2306 iter/s, 5.37972s/12 iters), loss = 1.22965
I0406 08:19:08.220269  5226 solver.cpp:237]     Train net output #0: loss = 1.22965 (* 1 = 1.22965 loss)
I0406 08:19:08.220278  5226 sgd_solver.cpp:105] Iteration 8220, lr = 0.01
I0406 08:19:13.686934  5226 solver.cpp:218] Iteration 8232 (2.19514 iter/s, 5.46661s/12 iters), loss = 1.01847
I0406 08:19:13.686986  5226 solver.cpp:237]     Train net output #0: loss = 1.01847 (* 1 = 1.01847 loss)
I0406 08:19:13.686995  5226 sgd_solver.cpp:105] Iteration 8232, lr = 0.01
I0406 08:19:19.153795  5226 solver.cpp:218] Iteration 8244 (2.19509 iter/s, 5.46676s/12 iters), loss = 1.13078
I0406 08:19:19.153841  5226 solver.cpp:237]     Train net output #0: loss = 1.13078 (* 1 = 1.13078 loss)
I0406 08:19:19.153848  5226 sgd_solver.cpp:105] Iteration 8244, lr = 0.01
I0406 08:19:24.106479  5226 solver.cpp:218] Iteration 8256 (2.42298 iter/s, 4.95259s/12 iters), loss = 1.16417
I0406 08:19:24.106530  5226 solver.cpp:237]     Train net output #0: loss = 1.16417 (* 1 = 1.16417 loss)
I0406 08:19:24.106537  5226 sgd_solver.cpp:105] Iteration 8256, lr = 0.01
I0406 08:19:26.105307  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8262.caffemodel
I0406 08:19:29.191109  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8262.solverstate
I0406 08:19:31.504225  5226 solver.cpp:330] Iteration 8262, Testing net (#0)
I0406 08:19:31.504245  5226 net.cpp:676] Ignoring source layer train-data
I0406 08:19:32.660403  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:19:35.859583  5226 solver.cpp:397]     Test net output #0: accuracy = 0.303309
I0406 08:19:35.859609  5226 solver.cpp:397]     Test net output #1: loss = 3.36301 (* 1 = 3.36301 loss)
I0406 08:19:37.807512  5226 solver.cpp:218] Iteration 8268 (0.875857 iter/s, 13.7009s/12 iters), loss = 1.33566
I0406 08:19:37.807565  5226 solver.cpp:237]     Train net output #0: loss = 1.33566 (* 1 = 1.33566 loss)
I0406 08:19:37.807574  5226 sgd_solver.cpp:105] Iteration 8268, lr = 0.01
I0406 08:19:43.053740  5226 solver.cpp:218] Iteration 8280 (2.2874 iter/s, 5.24612s/12 iters), loss = 1.01566
I0406 08:19:43.053781  5226 solver.cpp:237]     Train net output #0: loss = 1.01566 (* 1 = 1.01566 loss)
I0406 08:19:43.053786  5226 sgd_solver.cpp:105] Iteration 8280, lr = 0.01
I0406 08:19:48.235564  5226 solver.cpp:218] Iteration 8292 (2.31583 iter/s, 5.18174s/12 iters), loss = 1.13512
I0406 08:19:48.235601  5226 solver.cpp:237]     Train net output #0: loss = 1.13512 (* 1 = 1.13512 loss)
I0406 08:19:48.235607  5226 sgd_solver.cpp:105] Iteration 8292, lr = 0.01
I0406 08:19:48.839686  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:19:53.308055  5226 solver.cpp:218] Iteration 8304 (2.36574 iter/s, 5.0724s/12 iters), loss = 1.47649
I0406 08:19:53.308095  5226 solver.cpp:237]     Train net output #0: loss = 1.47649 (* 1 = 1.47649 loss)
I0406 08:19:53.308100  5226 sgd_solver.cpp:105] Iteration 8304, lr = 0.01
I0406 08:19:56.178107  5226 blocking_queue.cpp:49] Waiting for data
I0406 08:19:58.527791  5226 solver.cpp:218] Iteration 8316 (2.29901 iter/s, 5.21964s/12 iters), loss = 1.12538
I0406 08:19:58.527833  5226 solver.cpp:237]     Train net output #0: loss = 1.12538 (* 1 = 1.12538 loss)
I0406 08:19:58.527839  5226 sgd_solver.cpp:105] Iteration 8316, lr = 0.01
I0406 08:20:03.590566  5226 solver.cpp:218] Iteration 8328 (2.37028 iter/s, 5.06268s/12 iters), loss = 1.67171
I0406 08:20:03.590728  5226 solver.cpp:237]     Train net output #0: loss = 1.67171 (* 1 = 1.67171 loss)
I0406 08:20:03.590737  5226 sgd_solver.cpp:105] Iteration 8328, lr = 0.01
I0406 08:20:08.857568  5226 solver.cpp:218] Iteration 8340 (2.27843 iter/s, 5.26679s/12 iters), loss = 1.3362
I0406 08:20:08.857612  5226 solver.cpp:237]     Train net output #0: loss = 1.3362 (* 1 = 1.3362 loss)
I0406 08:20:08.857618  5226 sgd_solver.cpp:105] Iteration 8340, lr = 0.01
I0406 08:20:13.979979  5226 solver.cpp:218] Iteration 8352 (2.34269 iter/s, 5.12232s/12 iters), loss = 1.17571
I0406 08:20:13.980020  5226 solver.cpp:237]     Train net output #0: loss = 1.17571 (* 1 = 1.17571 loss)
I0406 08:20:13.980026  5226 sgd_solver.cpp:105] Iteration 8352, lr = 0.01
I0406 08:20:18.751482  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8364.caffemodel
I0406 08:20:21.770217  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8364.solverstate
I0406 08:20:24.081646  5226 solver.cpp:330] Iteration 8364, Testing net (#0)
I0406 08:20:24.081665  5226 net.cpp:676] Ignoring source layer train-data
I0406 08:20:25.175899  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:20:28.435474  5226 solver.cpp:397]     Test net output #0: accuracy = 0.30576
I0406 08:20:28.435505  5226 solver.cpp:397]     Test net output #1: loss = 3.3445 (* 1 = 3.3445 loss)
I0406 08:20:28.576171  5226 solver.cpp:218] Iteration 8364 (0.822141 iter/s, 14.596s/12 iters), loss = 1.7042
I0406 08:20:28.576225  5226 solver.cpp:237]     Train net output #0: loss = 1.7042 (* 1 = 1.7042 loss)
I0406 08:20:28.576232  5226 sgd_solver.cpp:105] Iteration 8364, lr = 0.01
I0406 08:20:32.880053  5226 solver.cpp:218] Iteration 8376 (2.78825 iter/s, 4.30378s/12 iters), loss = 1.22799
I0406 08:20:32.880102  5226 solver.cpp:237]     Train net output #0: loss = 1.22799 (* 1 = 1.22799 loss)
I0406 08:20:32.880110  5226 sgd_solver.cpp:105] Iteration 8376, lr = 0.01
I0406 08:20:38.252595  5226 solver.cpp:218] Iteration 8388 (2.23362 iter/s, 5.37244s/12 iters), loss = 1.61775
I0406 08:20:38.252692  5226 solver.cpp:237]     Train net output #0: loss = 1.61775 (* 1 = 1.61775 loss)
I0406 08:20:38.252698  5226 sgd_solver.cpp:105] Iteration 8388, lr = 0.01
I0406 08:20:41.176440  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:20:43.463783  5226 solver.cpp:218] Iteration 8400 (2.3028 iter/s, 5.21104s/12 iters), loss = 1.59125
I0406 08:20:43.463824  5226 solver.cpp:237]     Train net output #0: loss = 1.59125 (* 1 = 1.59125 loss)
I0406 08:20:43.463829  5226 sgd_solver.cpp:105] Iteration 8400, lr = 0.01
I0406 08:20:48.681504  5226 solver.cpp:218] Iteration 8412 (2.29989 iter/s, 5.21763s/12 iters), loss = 1.36855
I0406 08:20:48.681541  5226 solver.cpp:237]     Train net output #0: loss = 1.36855 (* 1 = 1.36855 loss)
I0406 08:20:48.681546  5226 sgd_solver.cpp:105] Iteration 8412, lr = 0.01
I0406 08:20:54.009905  5226 solver.cpp:218] Iteration 8424 (2.25212 iter/s, 5.32831s/12 iters), loss = 1.62781
I0406 08:20:54.009948  5226 solver.cpp:237]     Train net output #0: loss = 1.62781 (* 1 = 1.62781 loss)
I0406 08:20:54.009953  5226 sgd_solver.cpp:105] Iteration 8424, lr = 0.01
I0406 08:20:59.372435  5226 solver.cpp:218] Iteration 8436 (2.23779 iter/s, 5.36244s/12 iters), loss = 1.44612
I0406 08:20:59.372488  5226 solver.cpp:237]     Train net output #0: loss = 1.44612 (* 1 = 1.44612 loss)
I0406 08:20:59.372496  5226 sgd_solver.cpp:105] Iteration 8436, lr = 0.01
I0406 08:21:04.682574  5226 solver.cpp:218] Iteration 8448 (2.25987 iter/s, 5.31004s/12 iters), loss = 1.05502
I0406 08:21:04.682619  5226 solver.cpp:237]     Train net output #0: loss = 1.05502 (* 1 = 1.05502 loss)
I0406 08:21:04.682627  5226 sgd_solver.cpp:105] Iteration 8448, lr = 0.01
I0406 08:21:09.927034  5226 solver.cpp:218] Iteration 8460 (2.28817 iter/s, 5.24436s/12 iters), loss = 1.53199
I0406 08:21:09.927155  5226 solver.cpp:237]     Train net output #0: loss = 1.53199 (* 1 = 1.53199 loss)
I0406 08:21:09.927162  5226 sgd_solver.cpp:105] Iteration 8460, lr = 0.01
I0406 08:21:12.165880  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8466.caffemodel
I0406 08:21:15.118239  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8466.solverstate
I0406 08:21:17.431269  5226 solver.cpp:330] Iteration 8466, Testing net (#0)
I0406 08:21:17.431293  5226 net.cpp:676] Ignoring source layer train-data
I0406 08:21:18.559489  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:21:21.874444  5226 solver.cpp:397]     Test net output #0: accuracy = 0.308211
I0406 08:21:21.874486  5226 solver.cpp:397]     Test net output #1: loss = 3.2345 (* 1 = 3.2345 loss)
I0406 08:21:23.809402  5226 solver.cpp:218] Iteration 8472 (0.86442 iter/s, 13.8821s/12 iters), loss = 1.3732
I0406 08:21:23.809445  5226 solver.cpp:237]     Train net output #0: loss = 1.3732 (* 1 = 1.3732 loss)
I0406 08:21:23.809451  5226 sgd_solver.cpp:105] Iteration 8472, lr = 0.01
I0406 08:21:29.215873  5226 solver.cpp:218] Iteration 8484 (2.2196 iter/s, 5.40638s/12 iters), loss = 1.30494
I0406 08:21:29.215911  5226 solver.cpp:237]     Train net output #0: loss = 1.30494 (* 1 = 1.30494 loss)
I0406 08:21:29.215917  5226 sgd_solver.cpp:105] Iteration 8484, lr = 0.01
I0406 08:21:34.598186  5226 solver.cpp:218] Iteration 8496 (2.22956 iter/s, 5.38222s/12 iters), loss = 1.22598
I0406 08:21:34.598224  5226 solver.cpp:237]     Train net output #0: loss = 1.22598 (* 1 = 1.22598 loss)
I0406 08:21:34.598230  5226 sgd_solver.cpp:105] Iteration 8496, lr = 0.01
I0406 08:21:34.644646  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:21:39.863821  5226 solver.cpp:218] Iteration 8508 (2.27897 iter/s, 5.26554s/12 iters), loss = 1.02827
I0406 08:21:39.863857  5226 solver.cpp:237]     Train net output #0: loss = 1.02827 (* 1 = 1.02827 loss)
I0406 08:21:39.863862  5226 sgd_solver.cpp:105] Iteration 8508, lr = 0.01
I0406 08:21:45.076678  5226 solver.cpp:218] Iteration 8520 (2.30204 iter/s, 5.21277s/12 iters), loss = 0.969367
I0406 08:21:45.076758  5226 solver.cpp:237]     Train net output #0: loss = 0.969367 (* 1 = 0.969367 loss)
I0406 08:21:45.076764  5226 sgd_solver.cpp:105] Iteration 8520, lr = 0.01
I0406 08:21:50.271962  5226 solver.cpp:218] Iteration 8532 (2.30985 iter/s, 5.19515s/12 iters), loss = 0.872375
I0406 08:21:50.272001  5226 solver.cpp:237]     Train net output #0: loss = 0.872375 (* 1 = 0.872375 loss)
I0406 08:21:50.272006  5226 sgd_solver.cpp:105] Iteration 8532, lr = 0.01
I0406 08:21:55.517562  5226 solver.cpp:218] Iteration 8544 (2.28767 iter/s, 5.24551s/12 iters), loss = 1.03351
I0406 08:21:55.517602  5226 solver.cpp:237]     Train net output #0: loss = 1.03351 (* 1 = 1.03351 loss)
I0406 08:21:55.517608  5226 sgd_solver.cpp:105] Iteration 8544, lr = 0.01
I0406 08:22:00.844668  5226 solver.cpp:218] Iteration 8556 (2.25267 iter/s, 5.32701s/12 iters), loss = 1.33246
I0406 08:22:00.844705  5226 solver.cpp:237]     Train net output #0: loss = 1.33246 (* 1 = 1.33246 loss)
I0406 08:22:00.844712  5226 sgd_solver.cpp:105] Iteration 8556, lr = 0.01
I0406 08:22:05.432304  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8568.caffemodel
I0406 08:22:08.544708  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8568.solverstate
I0406 08:22:10.849048  5226 solver.cpp:330] Iteration 8568, Testing net (#0)
I0406 08:22:10.849066  5226 net.cpp:676] Ignoring source layer train-data
I0406 08:22:11.845108  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:22:15.296181  5226 solver.cpp:397]     Test net output #0: accuracy = 0.289828
I0406 08:22:15.296315  5226 solver.cpp:397]     Test net output #1: loss = 3.37105 (* 1 = 3.37105 loss)
I0406 08:22:15.436966  5226 solver.cpp:218] Iteration 8568 (0.82236 iter/s, 14.5921s/12 iters), loss = 1.46079
I0406 08:22:15.438546  5226 solver.cpp:237]     Train net output #0: loss = 1.46079 (* 1 = 1.46079 loss)
I0406 08:22:15.438557  5226 sgd_solver.cpp:105] Iteration 8568, lr = 0.01
I0406 08:22:19.766515  5226 solver.cpp:218] Iteration 8580 (2.77269 iter/s, 4.32793s/12 iters), loss = 1.31069
I0406 08:22:19.766559  5226 solver.cpp:237]     Train net output #0: loss = 1.31069 (* 1 = 1.31069 loss)
I0406 08:22:19.766566  5226 sgd_solver.cpp:105] Iteration 8580, lr = 0.01
I0406 08:22:25.097669  5226 solver.cpp:218] Iteration 8592 (2.25096 iter/s, 5.33106s/12 iters), loss = 1.54103
I0406 08:22:25.097707  5226 solver.cpp:237]     Train net output #0: loss = 1.54103 (* 1 = 1.54103 loss)
I0406 08:22:25.097712  5226 sgd_solver.cpp:105] Iteration 8592, lr = 0.01
I0406 08:22:27.464597  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:22:30.577822  5226 solver.cpp:218] Iteration 8604 (2.18976 iter/s, 5.48006s/12 iters), loss = 1.08005
I0406 08:22:30.577862  5226 solver.cpp:237]     Train net output #0: loss = 1.08005 (* 1 = 1.08005 loss)
I0406 08:22:30.577867  5226 sgd_solver.cpp:105] Iteration 8604, lr = 0.01
I0406 08:22:35.865615  5226 solver.cpp:218] Iteration 8616 (2.26942 iter/s, 5.2877s/12 iters), loss = 1.39324
I0406 08:22:35.865654  5226 solver.cpp:237]     Train net output #0: loss = 1.39324 (* 1 = 1.39324 loss)
I0406 08:22:35.865659  5226 sgd_solver.cpp:105] Iteration 8616, lr = 0.01
I0406 08:22:41.075958  5226 solver.cpp:218] Iteration 8628 (2.30315 iter/s, 5.21025s/12 iters), loss = 1.53359
I0406 08:22:41.076004  5226 solver.cpp:237]     Train net output #0: loss = 1.53359 (* 1 = 1.53359 loss)
I0406 08:22:41.076012  5226 sgd_solver.cpp:105] Iteration 8628, lr = 0.01
I0406 08:22:46.318447  5226 solver.cpp:218] Iteration 8640 (2.28903 iter/s, 5.2424s/12 iters), loss = 1.19249
I0406 08:22:46.318537  5226 solver.cpp:237]     Train net output #0: loss = 1.19249 (* 1 = 1.19249 loss)
I0406 08:22:46.318543  5226 sgd_solver.cpp:105] Iteration 8640, lr = 0.01
I0406 08:22:51.447912  5226 solver.cpp:218] Iteration 8652 (2.33949 iter/s, 5.12932s/12 iters), loss = 1.35614
I0406 08:22:51.447960  5226 solver.cpp:237]     Train net output #0: loss = 1.35614 (* 1 = 1.35614 loss)
I0406 08:22:51.447968  5226 sgd_solver.cpp:105] Iteration 8652, lr = 0.01
I0406 08:22:56.760874  5226 solver.cpp:218] Iteration 8664 (2.25867 iter/s, 5.31287s/12 iters), loss = 1.21559
I0406 08:22:56.760915  5226 solver.cpp:237]     Train net output #0: loss = 1.21559 (* 1 = 1.21559 loss)
I0406 08:22:56.760919  5226 sgd_solver.cpp:105] Iteration 8664, lr = 0.01
I0406 08:22:59.023504  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8670.caffemodel
I0406 08:23:02.107959  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8670.solverstate
I0406 08:23:04.428999  5226 solver.cpp:330] Iteration 8670, Testing net (#0)
I0406 08:23:04.429023  5226 net.cpp:676] Ignoring source layer train-data
I0406 08:23:05.438325  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:23:08.869032  5226 solver.cpp:397]     Test net output #0: accuracy = 0.296569
I0406 08:23:08.869065  5226 solver.cpp:397]     Test net output #1: loss = 3.35544 (* 1 = 3.35544 loss)
I0406 08:23:10.761075  5226 solver.cpp:218] Iteration 8676 (0.85714 iter/s, 14s/12 iters), loss = 1.42445
I0406 08:23:10.761126  5226 solver.cpp:237]     Train net output #0: loss = 1.42445 (* 1 = 1.42445 loss)
I0406 08:23:10.761134  5226 sgd_solver.cpp:105] Iteration 8676, lr = 0.01
I0406 08:23:16.192761  5226 solver.cpp:218] Iteration 8688 (2.2093 iter/s, 5.43158s/12 iters), loss = 1.24362
I0406 08:23:16.192809  5226 solver.cpp:237]     Train net output #0: loss = 1.24362 (* 1 = 1.24362 loss)
I0406 08:23:16.192817  5226 sgd_solver.cpp:105] Iteration 8688, lr = 0.01
I0406 08:23:20.748147  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:23:21.526031  5226 solver.cpp:218] Iteration 8700 (2.25007 iter/s, 5.33317s/12 iters), loss = 1.19184
I0406 08:23:21.526077  5226 solver.cpp:237]     Train net output #0: loss = 1.19184 (* 1 = 1.19184 loss)
I0406 08:23:21.526085  5226 sgd_solver.cpp:105] Iteration 8700, lr = 0.01
I0406 08:23:26.649806  5226 solver.cpp:218] Iteration 8712 (2.34207 iter/s, 5.12368s/12 iters), loss = 1.61335
I0406 08:23:26.649843  5226 solver.cpp:237]     Train net output #0: loss = 1.61335 (* 1 = 1.61335 loss)
I0406 08:23:26.649848  5226 sgd_solver.cpp:105] Iteration 8712, lr = 0.01
I0406 08:23:31.873006  5226 solver.cpp:218] Iteration 8724 (2.29748 iter/s, 5.22311s/12 iters), loss = 1.33385
I0406 08:23:31.873044  5226 solver.cpp:237]     Train net output #0: loss = 1.33385 (* 1 = 1.33385 loss)
I0406 08:23:31.873049  5226 sgd_solver.cpp:105] Iteration 8724, lr = 0.01
I0406 08:23:37.007655  5226 solver.cpp:218] Iteration 8736 (2.3371 iter/s, 5.13456s/12 iters), loss = 1.41583
I0406 08:23:37.007699  5226 solver.cpp:237]     Train net output #0: loss = 1.41583 (* 1 = 1.41583 loss)
I0406 08:23:37.007706  5226 sgd_solver.cpp:105] Iteration 8736, lr = 0.01
I0406 08:23:42.131088  5226 solver.cpp:218] Iteration 8748 (2.34222 iter/s, 5.12334s/12 iters), loss = 1.62163
I0406 08:23:42.131135  5226 solver.cpp:237]     Train net output #0: loss = 1.62163 (* 1 = 1.62163 loss)
I0406 08:23:42.131143  5226 sgd_solver.cpp:105] Iteration 8748, lr = 0.01
I0406 08:23:47.326074  5226 solver.cpp:218] Iteration 8760 (2.30996 iter/s, 5.19489s/12 iters), loss = 1.13432
I0406 08:23:47.326122  5226 solver.cpp:237]     Train net output #0: loss = 1.13432 (* 1 = 1.13432 loss)
I0406 08:23:47.326129  5226 sgd_solver.cpp:105] Iteration 8760, lr = 0.01
I0406 08:23:51.880928  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8772.caffemodel
I0406 08:23:54.911284  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8772.solverstate
I0406 08:23:57.210294  5226 solver.cpp:330] Iteration 8772, Testing net (#0)
I0406 08:23:57.210312  5226 net.cpp:676] Ignoring source layer train-data
I0406 08:23:58.161995  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:24:01.664041  5226 solver.cpp:397]     Test net output #0: accuracy = 0.295343
I0406 08:24:01.664077  5226 solver.cpp:397]     Test net output #1: loss = 3.29914 (* 1 = 3.29914 loss)
I0406 08:24:01.804873  5226 solver.cpp:218] Iteration 8772 (0.828807 iter/s, 14.4786s/12 iters), loss = 1.01154
I0406 08:24:01.804920  5226 solver.cpp:237]     Train net output #0: loss = 1.01154 (* 1 = 1.01154 loss)
I0406 08:24:01.804926  5226 sgd_solver.cpp:105] Iteration 8772, lr = 0.01
I0406 08:24:06.319221  5226 solver.cpp:218] Iteration 8784 (2.65825 iter/s, 4.51425s/12 iters), loss = 1.20669
I0406 08:24:06.319273  5226 solver.cpp:237]     Train net output #0: loss = 1.20669 (* 1 = 1.20669 loss)
I0406 08:24:06.319283  5226 sgd_solver.cpp:105] Iteration 8784, lr = 0.01
I0406 08:24:11.599531  5226 solver.cpp:218] Iteration 8796 (2.27264 iter/s, 5.28021s/12 iters), loss = 1.46906
I0406 08:24:11.599570  5226 solver.cpp:237]     Train net output #0: loss = 1.46906 (* 1 = 1.46906 loss)
I0406 08:24:11.599575  5226 sgd_solver.cpp:105] Iteration 8796, lr = 0.01
I0406 08:24:13.049666  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:24:16.838194  5226 solver.cpp:218] Iteration 8808 (2.2907 iter/s, 5.23857s/12 iters), loss = 1.01466
I0406 08:24:16.838235  5226 solver.cpp:237]     Train net output #0: loss = 1.01466 (* 1 = 1.01466 loss)
I0406 08:24:16.838241  5226 sgd_solver.cpp:105] Iteration 8808, lr = 0.01
I0406 08:24:22.317852  5226 solver.cpp:218] Iteration 8820 (2.18995 iter/s, 5.47957s/12 iters), loss = 1.11431
I0406 08:24:22.317983  5226 solver.cpp:237]     Train net output #0: loss = 1.11431 (* 1 = 1.11431 loss)
I0406 08:24:22.317989  5226 sgd_solver.cpp:105] Iteration 8820, lr = 0.01
I0406 08:24:27.517925  5226 solver.cpp:218] Iteration 8832 (2.30774 iter/s, 5.19989s/12 iters), loss = 1.28947
I0406 08:24:27.517963  5226 solver.cpp:237]     Train net output #0: loss = 1.28947 (* 1 = 1.28947 loss)
I0406 08:24:27.517969  5226 sgd_solver.cpp:105] Iteration 8832, lr = 0.01
I0406 08:24:32.827482  5226 solver.cpp:218] Iteration 8844 (2.26011 iter/s, 5.30946s/12 iters), loss = 1.53667
I0406 08:24:32.827524  5226 solver.cpp:237]     Train net output #0: loss = 1.53667 (* 1 = 1.53667 loss)
I0406 08:24:32.827530  5226 sgd_solver.cpp:105] Iteration 8844, lr = 0.01
I0406 08:24:38.063324  5226 solver.cpp:218] Iteration 8856 (2.29193 iter/s, 5.23575s/12 iters), loss = 1.54275
I0406 08:24:38.063371  5226 solver.cpp:237]     Train net output #0: loss = 1.54275 (* 1 = 1.54275 loss)
I0406 08:24:38.063380  5226 sgd_solver.cpp:105] Iteration 8856, lr = 0.01
I0406 08:24:43.386808  5226 solver.cpp:218] Iteration 8868 (2.2542 iter/s, 5.32339s/12 iters), loss = 1.45895
I0406 08:24:43.386845  5226 solver.cpp:237]     Train net output #0: loss = 1.45895 (* 1 = 1.45895 loss)
I0406 08:24:43.386852  5226 sgd_solver.cpp:105] Iteration 8868, lr = 0.01
I0406 08:24:45.474002  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8874.caffemodel
I0406 08:24:48.603260  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8874.solverstate
I0406 08:24:50.954578  5226 solver.cpp:330] Iteration 8874, Testing net (#0)
I0406 08:24:50.954597  5226 net.cpp:676] Ignoring source layer train-data
I0406 08:24:51.817083  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:24:55.422986  5226 solver.cpp:397]     Test net output #0: accuracy = 0.287377
I0406 08:24:55.423090  5226 solver.cpp:397]     Test net output #1: loss = 3.28457 (* 1 = 3.28457 loss)
I0406 08:24:57.304378  5226 solver.cpp:218] Iteration 8880 (0.862228 iter/s, 13.9174s/12 iters), loss = 1.31665
I0406 08:24:57.304438  5226 solver.cpp:237]     Train net output #0: loss = 1.31665 (* 1 = 1.31665 loss)
I0406 08:24:57.304447  5226 sgd_solver.cpp:105] Iteration 8880, lr = 0.01
I0406 08:25:02.558235  5226 solver.cpp:218] Iteration 8892 (2.2841 iter/s, 5.25372s/12 iters), loss = 1.17505
I0406 08:25:02.558275  5226 solver.cpp:237]     Train net output #0: loss = 1.17505 (* 1 = 1.17505 loss)
I0406 08:25:02.558280  5226 sgd_solver.cpp:105] Iteration 8892, lr = 0.01
I0406 08:25:06.364585  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:25:07.931510  5226 solver.cpp:218] Iteration 8904 (2.23331 iter/s, 5.37318s/12 iters), loss = 1.23806
I0406 08:25:07.931560  5226 solver.cpp:237]     Train net output #0: loss = 1.23806 (* 1 = 1.23806 loss)
I0406 08:25:07.931567  5226 sgd_solver.cpp:105] Iteration 8904, lr = 0.01
I0406 08:25:13.275257  5226 solver.cpp:218] Iteration 8916 (2.24566 iter/s, 5.34365s/12 iters), loss = 1.27448
I0406 08:25:13.275298  5226 solver.cpp:237]     Train net output #0: loss = 1.27448 (* 1 = 1.27448 loss)
I0406 08:25:13.275305  5226 sgd_solver.cpp:105] Iteration 8916, lr = 0.01
I0406 08:25:18.636299  5226 solver.cpp:218] Iteration 8928 (2.23841 iter/s, 5.36095s/12 iters), loss = 1.43343
I0406 08:25:18.636348  5226 solver.cpp:237]     Train net output #0: loss = 1.43343 (* 1 = 1.43343 loss)
I0406 08:25:18.636356  5226 sgd_solver.cpp:105] Iteration 8928, lr = 0.01
I0406 08:25:23.838407  5226 solver.cpp:218] Iteration 8940 (2.3068 iter/s, 5.202s/12 iters), loss = 1.55373
I0406 08:25:23.838454  5226 solver.cpp:237]     Train net output #0: loss = 1.55373 (* 1 = 1.55373 loss)
I0406 08:25:23.838461  5226 sgd_solver.cpp:105] Iteration 8940, lr = 0.01
I0406 08:25:29.048878  5226 solver.cpp:218] Iteration 8952 (2.3031 iter/s, 5.21037s/12 iters), loss = 1.40744
I0406 08:25:29.049011  5226 solver.cpp:237]     Train net output #0: loss = 1.40744 (* 1 = 1.40744 loss)
I0406 08:25:29.049018  5226 sgd_solver.cpp:105] Iteration 8952, lr = 0.01
I0406 08:25:34.345983  5226 solver.cpp:218] Iteration 8964 (2.26547 iter/s, 5.29692s/12 iters), loss = 0.961194
I0406 08:25:34.346020  5226 solver.cpp:237]     Train net output #0: loss = 0.961194 (* 1 = 0.961194 loss)
I0406 08:25:34.346026  5226 sgd_solver.cpp:105] Iteration 8964, lr = 0.01
I0406 08:25:39.175132  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8976.caffemodel
I0406 08:25:42.219225  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8976.solverstate
I0406 08:25:44.718933  5226 solver.cpp:330] Iteration 8976, Testing net (#0)
I0406 08:25:44.718953  5226 net.cpp:676] Ignoring source layer train-data
I0406 08:25:45.611171  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:25:49.078719  5226 solver.cpp:397]     Test net output #0: accuracy = 0.280024
I0406 08:25:49.078747  5226 solver.cpp:397]     Test net output #1: loss = 3.40016 (* 1 = 3.40016 loss)
I0406 08:25:49.219588  5226 solver.cpp:218] Iteration 8976 (0.806807 iter/s, 14.8735s/12 iters), loss = 1.36654
I0406 08:25:49.219655  5226 solver.cpp:237]     Train net output #0: loss = 1.36654 (* 1 = 1.36654 loss)
I0406 08:25:49.219664  5226 sgd_solver.cpp:105] Iteration 8976, lr = 0.01
I0406 08:25:53.602304  5226 solver.cpp:218] Iteration 8988 (2.7381 iter/s, 4.3826s/12 iters), loss = 1.43849
I0406 08:25:53.602360  5226 solver.cpp:237]     Train net output #0: loss = 1.43849 (* 1 = 1.43849 loss)
I0406 08:25:53.602370  5226 sgd_solver.cpp:105] Iteration 8988, lr = 0.01
I0406 08:25:57.016037  5226 blocking_queue.cpp:49] Waiting for data
I0406 08:25:58.854282  5226 solver.cpp:218] Iteration 9000 (2.2849 iter/s, 5.25188s/12 iters), loss = 1.54834
I0406 08:25:58.854321  5226 solver.cpp:237]     Train net output #0: loss = 1.54834 (* 1 = 1.54834 loss)
I0406 08:25:58.854326  5226 sgd_solver.cpp:105] Iteration 9000, lr = 0.01
I0406 08:25:59.499992  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:26:04.221757  5226 solver.cpp:218] Iteration 9012 (2.23572 iter/s, 5.36739s/12 iters), loss = 1.87543
I0406 08:26:04.221793  5226 solver.cpp:237]     Train net output #0: loss = 1.87543 (* 1 = 1.87543 loss)
I0406 08:26:04.221798  5226 sgd_solver.cpp:105] Iteration 9012, lr = 0.01
I0406 08:26:09.609632  5226 solver.cpp:218] Iteration 9024 (2.22726 iter/s, 5.38778s/12 iters), loss = 1.52809
I0406 08:26:09.609670  5226 solver.cpp:237]     Train net output #0: loss = 1.52809 (* 1 = 1.52809 loss)
I0406 08:26:09.609676  5226 sgd_solver.cpp:105] Iteration 9024, lr = 0.01
I0406 08:26:15.034828  5226 solver.cpp:218] Iteration 9036 (2.21194 iter/s, 5.42511s/12 iters), loss = 1.32086
I0406 08:26:15.034868  5226 solver.cpp:237]     Train net output #0: loss = 1.32086 (* 1 = 1.32086 loss)
I0406 08:26:15.034873  5226 sgd_solver.cpp:105] Iteration 9036, lr = 0.01
I0406 08:26:20.295110  5226 solver.cpp:218] Iteration 9048 (2.28129 iter/s, 5.26019s/12 iters), loss = 1.13095
I0406 08:26:20.295154  5226 solver.cpp:237]     Train net output #0: loss = 1.13095 (* 1 = 1.13095 loss)
I0406 08:26:20.295162  5226 sgd_solver.cpp:105] Iteration 9048, lr = 0.01
I0406 08:26:25.525097  5226 solver.cpp:218] Iteration 9060 (2.2945 iter/s, 5.22989s/12 iters), loss = 1.66655
I0406 08:26:25.525144  5226 solver.cpp:237]     Train net output #0: loss = 1.66655 (* 1 = 1.66655 loss)
I0406 08:26:25.525151  5226 sgd_solver.cpp:105] Iteration 9060, lr = 0.01
I0406 08:26:30.859648  5226 solver.cpp:218] Iteration 9072 (2.24953 iter/s, 5.33446s/12 iters), loss = 1.40013
I0406 08:26:30.859763  5226 solver.cpp:237]     Train net output #0: loss = 1.40013 (* 1 = 1.40013 loss)
I0406 08:26:30.859769  5226 sgd_solver.cpp:105] Iteration 9072, lr = 0.01
I0406 08:26:32.805733  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9078.caffemodel
I0406 08:26:35.853482  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9078.solverstate
I0406 08:26:38.156133  5226 solver.cpp:330] Iteration 9078, Testing net (#0)
I0406 08:26:38.156153  5226 net.cpp:676] Ignoring source layer train-data
I0406 08:26:38.973809  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:26:42.509582  5226 solver.cpp:397]     Test net output #0: accuracy = 0.246936
I0406 08:26:42.509619  5226 solver.cpp:397]     Test net output #1: loss = 3.52659 (* 1 = 3.52659 loss)
I0406 08:26:44.306329  5226 solver.cpp:218] Iteration 9084 (0.892428 iter/s, 13.4465s/12 iters), loss = 1.38034
I0406 08:26:44.306368  5226 solver.cpp:237]     Train net output #0: loss = 1.38034 (* 1 = 1.38034 loss)
I0406 08:26:44.306375  5226 sgd_solver.cpp:105] Iteration 9084, lr = 0.01
I0406 08:26:49.823982  5226 solver.cpp:218] Iteration 9096 (2.17487 iter/s, 5.51756s/12 iters), loss = 1.23829
I0406 08:26:49.824038  5226 solver.cpp:237]     Train net output #0: loss = 1.23829 (* 1 = 1.23829 loss)
I0406 08:26:49.824046  5226 sgd_solver.cpp:105] Iteration 9096, lr = 0.01
I0406 08:26:52.848687  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:26:55.106520  5226 solver.cpp:218] Iteration 9108 (2.27168 iter/s, 5.28244s/12 iters), loss = 1.44825
I0406 08:26:55.106575  5226 solver.cpp:237]     Train net output #0: loss = 1.44825 (* 1 = 1.44825 loss)
I0406 08:26:55.106583  5226 sgd_solver.cpp:105] Iteration 9108, lr = 0.01
I0406 08:27:00.443120  5226 solver.cpp:218] Iteration 9120 (2.24867 iter/s, 5.3365s/12 iters), loss = 1.13138
I0406 08:27:00.443158  5226 solver.cpp:237]     Train net output #0: loss = 1.13138 (* 1 = 1.13138 loss)
I0406 08:27:00.443164  5226 sgd_solver.cpp:105] Iteration 9120, lr = 0.01
I0406 08:27:05.884299  5226 solver.cpp:218] Iteration 9132 (2.20544 iter/s, 5.44109s/12 iters), loss = 0.973152
I0406 08:27:05.884421  5226 solver.cpp:237]     Train net output #0: loss = 0.973152 (* 1 = 0.973152 loss)
I0406 08:27:05.884430  5226 sgd_solver.cpp:105] Iteration 9132, lr = 0.01
I0406 08:27:11.239425  5226 solver.cpp:218] Iteration 9144 (2.24091 iter/s, 5.35496s/12 iters), loss = 1.50677
I0406 08:27:11.239462  5226 solver.cpp:237]     Train net output #0: loss = 1.50677 (* 1 = 1.50677 loss)
I0406 08:27:11.239467  5226 sgd_solver.cpp:105] Iteration 9144, lr = 0.01
I0406 08:27:16.430109  5226 solver.cpp:218] Iteration 9156 (2.31187 iter/s, 5.1906s/12 iters), loss = 1.5159
I0406 08:27:16.430161  5226 solver.cpp:237]     Train net output #0: loss = 1.5159 (* 1 = 1.5159 loss)
I0406 08:27:16.430171  5226 sgd_solver.cpp:105] Iteration 9156, lr = 0.01
I0406 08:27:21.577827  5226 solver.cpp:218] Iteration 9168 (2.33117 iter/s, 5.14762s/12 iters), loss = 1.49927
I0406 08:27:21.577860  5226 solver.cpp:237]     Train net output #0: loss = 1.49927 (* 1 = 1.49927 loss)
I0406 08:27:21.577865  5226 sgd_solver.cpp:105] Iteration 9168, lr = 0.01
I0406 08:27:26.326304  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9180.caffemodel
I0406 08:27:29.316359  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9180.solverstate
I0406 08:27:31.619693  5226 solver.cpp:330] Iteration 9180, Testing net (#0)
I0406 08:27:31.619717  5226 net.cpp:676] Ignoring source layer train-data
I0406 08:27:32.450837  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:27:35.960037  5226 solver.cpp:397]     Test net output #0: accuracy = 0.292892
I0406 08:27:35.960134  5226 solver.cpp:397]     Test net output #1: loss = 3.30455 (* 1 = 3.30455 loss)
I0406 08:27:36.100899  5226 solver.cpp:218] Iteration 9180 (0.82628 iter/s, 14.5229s/12 iters), loss = 1.15912
I0406 08:27:36.100951  5226 solver.cpp:237]     Train net output #0: loss = 1.15912 (* 1 = 1.15912 loss)
I0406 08:27:36.100960  5226 sgd_solver.cpp:105] Iteration 9180, lr = 0.01
I0406 08:27:40.214835  5226 solver.cpp:218] Iteration 9192 (2.91698 iter/s, 4.11384s/12 iters), loss = 1.58883
I0406 08:27:40.214876  5226 solver.cpp:237]     Train net output #0: loss = 1.58883 (* 1 = 1.58883 loss)
I0406 08:27:40.214881  5226 sgd_solver.cpp:105] Iteration 9192, lr = 0.01
I0406 08:27:45.708592  5226 solver.cpp:218] Iteration 9204 (2.18434 iter/s, 5.49366s/12 iters), loss = 1.2873
I0406 08:27:45.708642  5226 solver.cpp:237]     Train net output #0: loss = 1.2873 (* 1 = 1.2873 loss)
I0406 08:27:45.708649  5226 sgd_solver.cpp:105] Iteration 9204, lr = 0.01
I0406 08:27:45.774080  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:27:51.150384  5226 solver.cpp:218] Iteration 9216 (2.2052 iter/s, 5.44169s/12 iters), loss = 1.74755
I0406 08:27:51.150420  5226 solver.cpp:237]     Train net output #0: loss = 1.74755 (* 1 = 1.74755 loss)
I0406 08:27:51.150426  5226 sgd_solver.cpp:105] Iteration 9216, lr = 0.01
I0406 08:27:56.439889  5226 solver.cpp:218] Iteration 9228 (2.26868 iter/s, 5.28942s/12 iters), loss = 1.54323
I0406 08:27:56.439924  5226 solver.cpp:237]     Train net output #0: loss = 1.54323 (* 1 = 1.54323 loss)
I0406 08:27:56.439929  5226 sgd_solver.cpp:105] Iteration 9228, lr = 0.01
I0406 08:28:01.622162  5226 solver.cpp:218] Iteration 9240 (2.31563 iter/s, 5.18218s/12 iters), loss = 1.46492
I0406 08:28:01.622211  5226 solver.cpp:237]     Train net output #0: loss = 1.46492 (* 1 = 1.46492 loss)
I0406 08:28:01.622220  5226 sgd_solver.cpp:105] Iteration 9240, lr = 0.01
I0406 08:28:07.080965  5226 solver.cpp:218] Iteration 9252 (2.19833 iter/s, 5.4587s/12 iters), loss = 1.4019
I0406 08:28:07.081100  5226 solver.cpp:237]     Train net output #0: loss = 1.4019 (* 1 = 1.4019 loss)
I0406 08:28:07.081110  5226 sgd_solver.cpp:105] Iteration 9252, lr = 0.01
I0406 08:28:12.546842  5226 solver.cpp:218] Iteration 9264 (2.19551 iter/s, 5.46569s/12 iters), loss = 1.83513
I0406 08:28:12.546890  5226 solver.cpp:237]     Train net output #0: loss = 1.83513 (* 1 = 1.83513 loss)
I0406 08:28:12.546897  5226 sgd_solver.cpp:105] Iteration 9264, lr = 0.01
I0406 08:28:17.914494  5226 solver.cpp:218] Iteration 9276 (2.23566 iter/s, 5.36755s/12 iters), loss = 1.45986
I0406 08:28:17.914536  5226 solver.cpp:237]     Train net output #0: loss = 1.45986 (* 1 = 1.45986 loss)
I0406 08:28:17.914542  5226 sgd_solver.cpp:105] Iteration 9276, lr = 0.01
I0406 08:28:20.012820  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9282.caffemodel
I0406 08:28:23.039324  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9282.solverstate
I0406 08:28:25.430354  5226 solver.cpp:330] Iteration 9282, Testing net (#0)
I0406 08:28:25.430374  5226 net.cpp:676] Ignoring source layer train-data
I0406 08:28:26.184340  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:28:29.806226  5226 solver.cpp:397]     Test net output #0: accuracy = 0.301471
I0406 08:28:29.806262  5226 solver.cpp:397]     Test net output #1: loss = 3.29412 (* 1 = 3.29412 loss)
I0406 08:28:31.821305  5226 solver.cpp:218] Iteration 9288 (0.862896 iter/s, 13.9067s/12 iters), loss = 1.31509
I0406 08:28:31.821344  5226 solver.cpp:237]     Train net output #0: loss = 1.31509 (* 1 = 1.31509 loss)
I0406 08:28:31.821349  5226 sgd_solver.cpp:105] Iteration 9288, lr = 0.01
I0406 08:28:36.970638  5226 solver.cpp:218] Iteration 9300 (2.33044 iter/s, 5.14924s/12 iters), loss = 1.36213
I0406 08:28:36.970691  5226 solver.cpp:237]     Train net output #0: loss = 1.36213 (* 1 = 1.36213 loss)
I0406 08:28:36.970700  5226 sgd_solver.cpp:105] Iteration 9300, lr = 0.01
I0406 08:28:39.367906  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:28:42.149413  5226 solver.cpp:218] Iteration 9312 (2.3172 iter/s, 5.17867s/12 iters), loss = 1.16864
I0406 08:28:42.149452  5226 solver.cpp:237]     Train net output #0: loss = 1.16864 (* 1 = 1.16864 loss)
I0406 08:28:42.149459  5226 sgd_solver.cpp:105] Iteration 9312, lr = 0.01
I0406 08:28:47.328627  5226 solver.cpp:218] Iteration 9324 (2.31699 iter/s, 5.17912s/12 iters), loss = 1.48885
I0406 08:28:47.328673  5226 solver.cpp:237]     Train net output #0: loss = 1.48885 (* 1 = 1.48885 loss)
I0406 08:28:47.328681  5226 sgd_solver.cpp:105] Iteration 9324, lr = 0.01
I0406 08:28:52.523437  5226 solver.cpp:218] Iteration 9336 (2.31004 iter/s, 5.19472s/12 iters), loss = 1.22772
I0406 08:28:52.523484  5226 solver.cpp:237]     Train net output #0: loss = 1.22772 (* 1 = 1.22772 loss)
I0406 08:28:52.523491  5226 sgd_solver.cpp:105] Iteration 9336, lr = 0.01
I0406 08:28:57.718976  5226 solver.cpp:218] Iteration 9348 (2.30972 iter/s, 5.19544s/12 iters), loss = 1.28443
I0406 08:28:57.719019  5226 solver.cpp:237]     Train net output #0: loss = 1.28443 (* 1 = 1.28443 loss)
I0406 08:28:57.719025  5226 sgd_solver.cpp:105] Iteration 9348, lr = 0.01
I0406 08:29:03.153055  5226 solver.cpp:218] Iteration 9360 (2.20832 iter/s, 5.43398s/12 iters), loss = 1.51051
I0406 08:29:03.153106  5226 solver.cpp:237]     Train net output #0: loss = 1.51051 (* 1 = 1.51051 loss)
I0406 08:29:03.153115  5226 sgd_solver.cpp:105] Iteration 9360, lr = 0.01
I0406 08:29:08.455766  5226 solver.cpp:218] Iteration 9372 (2.26304 iter/s, 5.30261s/12 iters), loss = 1.55222
I0406 08:29:08.455814  5226 solver.cpp:237]     Train net output #0: loss = 1.55222 (* 1 = 1.55222 loss)
I0406 08:29:08.455822  5226 sgd_solver.cpp:105] Iteration 9372, lr = 0.01
I0406 08:29:13.133136  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9384.caffemodel
I0406 08:29:16.171054  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9384.solverstate
I0406 08:29:18.481362  5226 solver.cpp:330] Iteration 9384, Testing net (#0)
I0406 08:29:18.481381  5226 net.cpp:676] Ignoring source layer train-data
I0406 08:29:19.179045  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:29:22.868168  5226 solver.cpp:397]     Test net output #0: accuracy = 0.286152
I0406 08:29:22.868202  5226 solver.cpp:397]     Test net output #1: loss = 3.39326 (* 1 = 3.39326 loss)
I0406 08:29:23.009371  5226 solver.cpp:218] Iteration 9384 (0.824547 iter/s, 14.5535s/12 iters), loss = 1.73139
I0406 08:29:23.009418  5226 solver.cpp:237]     Train net output #0: loss = 1.73139 (* 1 = 1.73139 loss)
I0406 08:29:23.009424  5226 sgd_solver.cpp:105] Iteration 9384, lr = 0.01
I0406 08:29:27.324363  5226 solver.cpp:218] Iteration 9396 (2.78106 iter/s, 4.3149s/12 iters), loss = 1.73471
I0406 08:29:27.324400  5226 solver.cpp:237]     Train net output #0: loss = 1.73471 (* 1 = 1.73471 loss)
I0406 08:29:27.324405  5226 sgd_solver.cpp:105] Iteration 9396, lr = 0.01
I0406 08:29:31.807945  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:29:32.553489  5226 solver.cpp:218] Iteration 9408 (2.29488 iter/s, 5.22904s/12 iters), loss = 1.55081
I0406 08:29:32.553529  5226 solver.cpp:237]     Train net output #0: loss = 1.55081 (* 1 = 1.55081 loss)
I0406 08:29:32.553535  5226 sgd_solver.cpp:105] Iteration 9408, lr = 0.01
I0406 08:29:37.864786  5226 solver.cpp:218] Iteration 9420 (2.25938 iter/s, 5.3112s/12 iters), loss = 1.48133
I0406 08:29:37.864835  5226 solver.cpp:237]     Train net output #0: loss = 1.48133 (* 1 = 1.48133 loss)
I0406 08:29:37.864842  5226 sgd_solver.cpp:105] Iteration 9420, lr = 0.01
I0406 08:29:43.224578  5226 solver.cpp:218] Iteration 9432 (2.23893 iter/s, 5.35969s/12 iters), loss = 0.953196
I0406 08:29:43.224678  5226 solver.cpp:237]     Train net output #0: loss = 0.953196 (* 1 = 0.953196 loss)
I0406 08:29:43.224686  5226 sgd_solver.cpp:105] Iteration 9432, lr = 0.01
I0406 08:29:48.270864  5226 solver.cpp:218] Iteration 9444 (2.37805 iter/s, 5.04614s/12 iters), loss = 1.42891
I0406 08:29:48.270901  5226 solver.cpp:237]     Train net output #0: loss = 1.42891 (* 1 = 1.42891 loss)
I0406 08:29:48.270906  5226 sgd_solver.cpp:105] Iteration 9444, lr = 0.01
I0406 08:29:53.638362  5226 solver.cpp:218] Iteration 9456 (2.23572 iter/s, 5.3674s/12 iters), loss = 1.31815
I0406 08:29:53.638417  5226 solver.cpp:237]     Train net output #0: loss = 1.31815 (* 1 = 1.31815 loss)
I0406 08:29:53.638423  5226 sgd_solver.cpp:105] Iteration 9456, lr = 0.01
I0406 08:29:58.813033  5226 solver.cpp:218] Iteration 9468 (2.31903 iter/s, 5.17457s/12 iters), loss = 1.34019
I0406 08:29:58.813071  5226 solver.cpp:237]     Train net output #0: loss = 1.34019 (* 1 = 1.34019 loss)
I0406 08:29:58.813076  5226 sgd_solver.cpp:105] Iteration 9468, lr = 0.01
I0406 08:30:03.873073  5226 solver.cpp:218] Iteration 9480 (2.37157 iter/s, 5.05995s/12 iters), loss = 1.8316
I0406 08:30:03.873124  5226 solver.cpp:237]     Train net output #0: loss = 1.8316 (* 1 = 1.8316 loss)
I0406 08:30:03.873132  5226 sgd_solver.cpp:105] Iteration 9480, lr = 0.01
I0406 08:30:06.080991  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9486.caffemodel
I0406 08:30:09.298688  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9486.solverstate
I0406 08:30:11.599360  5226 solver.cpp:330] Iteration 9486, Testing net (#0)
I0406 08:30:11.599380  5226 net.cpp:676] Ignoring source layer train-data
I0406 08:30:12.215423  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:30:15.869436  5226 solver.cpp:397]     Test net output #0: accuracy = 0.270221
I0406 08:30:15.869556  5226 solver.cpp:397]     Test net output #1: loss = 3.41308 (* 1 = 3.41308 loss)
I0406 08:30:17.733644  5226 solver.cpp:218] Iteration 9492 (0.865775 iter/s, 13.8604s/12 iters), loss = 1.60063
I0406 08:30:17.733700  5226 solver.cpp:237]     Train net output #0: loss = 1.60063 (* 1 = 1.60063 loss)
I0406 08:30:17.733708  5226 sgd_solver.cpp:105] Iteration 9492, lr = 0.01
I0406 08:30:23.115406  5226 solver.cpp:218] Iteration 9504 (2.22979 iter/s, 5.38166s/12 iters), loss = 1.48874
I0406 08:30:23.115442  5226 solver.cpp:237]     Train net output #0: loss = 1.48874 (* 1 = 1.48874 loss)
I0406 08:30:23.115447  5226 sgd_solver.cpp:105] Iteration 9504, lr = 0.01
I0406 08:30:24.630280  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:30:28.336930  5226 solver.cpp:218] Iteration 9516 (2.29822 iter/s, 5.22143s/12 iters), loss = 1.67417
I0406 08:30:28.336979  5226 solver.cpp:237]     Train net output #0: loss = 1.67417 (* 1 = 1.67417 loss)
I0406 08:30:28.336987  5226 sgd_solver.cpp:105] Iteration 9516, lr = 0.01
I0406 08:30:33.732908  5226 solver.cpp:218] Iteration 9528 (2.22392 iter/s, 5.39587s/12 iters), loss = 1.43106
I0406 08:30:33.732962  5226 solver.cpp:237]     Train net output #0: loss = 1.43106 (* 1 = 1.43106 loss)
I0406 08:30:33.732971  5226 sgd_solver.cpp:105] Iteration 9528, lr = 0.01
I0406 08:30:38.984933  5226 solver.cpp:218] Iteration 9540 (2.28488 iter/s, 5.25192s/12 iters), loss = 1.24924
I0406 08:30:38.984992  5226 solver.cpp:237]     Train net output #0: loss = 1.24924 (* 1 = 1.24924 loss)
I0406 08:30:38.985002  5226 sgd_solver.cpp:105] Iteration 9540, lr = 0.01
I0406 08:30:44.345671  5226 solver.cpp:218] Iteration 9552 (2.23854 iter/s, 5.36063s/12 iters), loss = 1.43932
I0406 08:30:44.345710  5226 solver.cpp:237]     Train net output #0: loss = 1.43932 (* 1 = 1.43932 loss)
I0406 08:30:44.345715  5226 sgd_solver.cpp:105] Iteration 9552, lr = 0.01
I0406 08:30:49.688896  5226 solver.cpp:218] Iteration 9564 (2.24587 iter/s, 5.34313s/12 iters), loss = 1.66909
I0406 08:30:49.688990  5226 solver.cpp:237]     Train net output #0: loss = 1.66909 (* 1 = 1.66909 loss)
I0406 08:30:49.688997  5226 sgd_solver.cpp:105] Iteration 9564, lr = 0.01
I0406 08:30:54.941321  5226 solver.cpp:218] Iteration 9576 (2.28472 iter/s, 5.25228s/12 iters), loss = 1.59738
I0406 08:30:54.941373  5226 solver.cpp:237]     Train net output #0: loss = 1.59738 (* 1 = 1.59738 loss)
I0406 08:30:54.941381  5226 sgd_solver.cpp:105] Iteration 9576, lr = 0.01
I0406 08:30:59.671604  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9588.caffemodel
I0406 08:31:02.695439  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9588.solverstate
I0406 08:31:05.054455  5226 solver.cpp:330] Iteration 9588, Testing net (#0)
I0406 08:31:05.054474  5226 net.cpp:676] Ignoring source layer train-data
I0406 08:31:05.720475  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:31:09.432772  5226 solver.cpp:397]     Test net output #0: accuracy = 0.276348
I0406 08:31:09.432806  5226 solver.cpp:397]     Test net output #1: loss = 3.42001 (* 1 = 3.42001 loss)
I0406 08:31:09.573529  5226 solver.cpp:218] Iteration 9588 (0.820118 iter/s, 14.632s/12 iters), loss = 1.895
I0406 08:31:09.575094  5226 solver.cpp:237]     Train net output #0: loss = 1.895 (* 1 = 1.895 loss)
I0406 08:31:09.575109  5226 sgd_solver.cpp:105] Iteration 9588, lr = 0.01
I0406 08:31:13.813021  5226 solver.cpp:218] Iteration 9600 (2.8316 iter/s, 4.23789s/12 iters), loss = 1.58854
I0406 08:31:13.813076  5226 solver.cpp:237]     Train net output #0: loss = 1.58854 (* 1 = 1.58854 loss)
I0406 08:31:13.813084  5226 sgd_solver.cpp:105] Iteration 9600, lr = 0.01
I0406 08:31:17.642505  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:31:19.181569  5226 solver.cpp:218] Iteration 9612 (2.23528 iter/s, 5.36844s/12 iters), loss = 1.91278
I0406 08:31:19.181607  5226 solver.cpp:237]     Train net output #0: loss = 1.91278 (* 1 = 1.91278 loss)
I0406 08:31:19.181613  5226 sgd_solver.cpp:105] Iteration 9612, lr = 0.01
I0406 08:31:24.401324  5226 solver.cpp:218] Iteration 9624 (2.299 iter/s, 5.21967s/12 iters), loss = 1.60281
I0406 08:31:24.401437  5226 solver.cpp:237]     Train net output #0: loss = 1.60281 (* 1 = 1.60281 loss)
I0406 08:31:24.401444  5226 sgd_solver.cpp:105] Iteration 9624, lr = 0.01
I0406 08:31:29.839982  5226 solver.cpp:218] Iteration 9636 (2.20649 iter/s, 5.43849s/12 iters), loss = 1.48788
I0406 08:31:29.840021  5226 solver.cpp:237]     Train net output #0: loss = 1.48788 (* 1 = 1.48788 loss)
I0406 08:31:29.840029  5226 sgd_solver.cpp:105] Iteration 9636, lr = 0.01
I0406 08:31:35.069733  5226 solver.cpp:218] Iteration 9648 (2.2946 iter/s, 5.22966s/12 iters), loss = 1.39629
I0406 08:31:35.069773  5226 solver.cpp:237]     Train net output #0: loss = 1.39629 (* 1 = 1.39629 loss)
I0406 08:31:35.069778  5226 sgd_solver.cpp:105] Iteration 9648, lr = 0.01
I0406 08:31:40.405241  5226 solver.cpp:218] Iteration 9660 (2.24912 iter/s, 5.33541s/12 iters), loss = 1.48703
I0406 08:31:40.405300  5226 solver.cpp:237]     Train net output #0: loss = 1.48703 (* 1 = 1.48703 loss)
I0406 08:31:40.405310  5226 sgd_solver.cpp:105] Iteration 9660, lr = 0.01
I0406 08:31:45.568725  5226 solver.cpp:218] Iteration 9672 (2.32406 iter/s, 5.16337s/12 iters), loss = 1.14586
I0406 08:31:45.568773  5226 solver.cpp:237]     Train net output #0: loss = 1.14586 (* 1 = 1.14586 loss)
I0406 08:31:45.568781  5226 sgd_solver.cpp:105] Iteration 9672, lr = 0.01
I0406 08:31:50.872825  5226 solver.cpp:218] Iteration 9684 (2.26244 iter/s, 5.304s/12 iters), loss = 1.49319
I0406 08:31:50.872871  5226 solver.cpp:237]     Train net output #0: loss = 1.49319 (* 1 = 1.49319 loss)
I0406 08:31:50.872879  5226 sgd_solver.cpp:105] Iteration 9684, lr = 0.01
I0406 08:31:52.969470  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9690.caffemodel
I0406 08:31:56.136780  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9690.solverstate
I0406 08:31:58.432479  5226 solver.cpp:330] Iteration 9690, Testing net (#0)
I0406 08:31:58.432549  5226 net.cpp:676] Ignoring source layer train-data
I0406 08:31:59.059414  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:32:01.822795  5226 blocking_queue.cpp:49] Waiting for data
I0406 08:32:02.801328  5226 solver.cpp:397]     Test net output #0: accuracy = 0.275735
I0406 08:32:02.801362  5226 solver.cpp:397]     Test net output #1: loss = 3.38087 (* 1 = 3.38087 loss)
I0406 08:32:04.627833  5226 solver.cpp:218] Iteration 9696 (0.872419 iter/s, 13.7549s/12 iters), loss = 1.349
I0406 08:32:04.627876  5226 solver.cpp:237]     Train net output #0: loss = 1.349 (* 1 = 1.349 loss)
I0406 08:32:04.627882  5226 sgd_solver.cpp:105] Iteration 9696, lr = 0.01
I0406 08:32:09.903945  5226 solver.cpp:218] Iteration 9708 (2.27444 iter/s, 5.27602s/12 iters), loss = 1.55017
I0406 08:32:09.903993  5226 solver.cpp:237]     Train net output #0: loss = 1.55017 (* 1 = 1.55017 loss)
I0406 08:32:09.904001  5226 sgd_solver.cpp:105] Iteration 9708, lr = 0.01
I0406 08:32:10.642725  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:32:15.227866  5226 solver.cpp:218] Iteration 9720 (2.25402 iter/s, 5.32382s/12 iters), loss = 1.75784
I0406 08:32:15.227916  5226 solver.cpp:237]     Train net output #0: loss = 1.75784 (* 1 = 1.75784 loss)
I0406 08:32:15.227924  5226 sgd_solver.cpp:105] Iteration 9720, lr = 0.01
I0406 08:32:20.313910  5226 solver.cpp:218] Iteration 9732 (2.35944 iter/s, 5.08595s/12 iters), loss = 2.01695
I0406 08:32:20.313946  5226 solver.cpp:237]     Train net output #0: loss = 2.01695 (* 1 = 2.01695 loss)
I0406 08:32:20.313952  5226 sgd_solver.cpp:105] Iteration 9732, lr = 0.01
I0406 08:32:25.612366  5226 solver.cpp:218] Iteration 9744 (2.26485 iter/s, 5.29837s/12 iters), loss = 1.6915
I0406 08:32:25.612427  5226 solver.cpp:237]     Train net output #0: loss = 1.6915 (* 1 = 1.6915 loss)
I0406 08:32:25.612435  5226 sgd_solver.cpp:105] Iteration 9744, lr = 0.01
I0406 08:32:30.965961  5226 solver.cpp:218] Iteration 9756 (2.24153 iter/s, 5.35349s/12 iters), loss = 1.7613
I0406 08:32:30.966101  5226 solver.cpp:237]     Train net output #0: loss = 1.7613 (* 1 = 1.7613 loss)
I0406 08:32:30.966109  5226 sgd_solver.cpp:105] Iteration 9756, lr = 0.01
I0406 08:32:36.327805  5226 solver.cpp:218] Iteration 9768 (2.23811 iter/s, 5.36166s/12 iters), loss = 1.23214
I0406 08:32:36.327841  5226 solver.cpp:237]     Train net output #0: loss = 1.23214 (* 1 = 1.23214 loss)
I0406 08:32:36.327847  5226 sgd_solver.cpp:105] Iteration 9768, lr = 0.01
I0406 08:32:41.593578  5226 solver.cpp:218] Iteration 9780 (2.27891 iter/s, 5.26568s/12 iters), loss = 1.73946
I0406 08:32:41.593628  5226 solver.cpp:237]     Train net output #0: loss = 1.73946 (* 1 = 1.73946 loss)
I0406 08:32:41.593636  5226 sgd_solver.cpp:105] Iteration 9780, lr = 0.01
I0406 08:32:46.312091  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9792.caffemodel
I0406 08:32:49.400135  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9792.solverstate
I0406 08:32:51.721503  5226 solver.cpp:330] Iteration 9792, Testing net (#0)
I0406 08:32:51.721521  5226 net.cpp:676] Ignoring source layer train-data
I0406 08:32:52.277993  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:32:56.079967  5226 solver.cpp:397]     Test net output #0: accuracy = 0.264093
I0406 08:32:56.080003  5226 solver.cpp:397]     Test net output #1: loss = 3.50177 (* 1 = 3.50177 loss)
I0406 08:32:56.220778  5226 solver.cpp:218] Iteration 9792 (0.820398 iter/s, 14.627s/12 iters), loss = 1.34782
I0406 08:32:56.220829  5226 solver.cpp:237]     Train net output #0: loss = 1.34782 (* 1 = 1.34782 loss)
I0406 08:32:56.220835  5226 sgd_solver.cpp:105] Iteration 9792, lr = 0.01
I0406 08:33:00.565987  5226 solver.cpp:218] Iteration 9804 (2.76172 iter/s, 4.34511s/12 iters), loss = 1.58499
I0406 08:33:00.566027  5226 solver.cpp:237]     Train net output #0: loss = 1.58499 (* 1 = 1.58499 loss)
I0406 08:33:00.566032  5226 sgd_solver.cpp:105] Iteration 9804, lr = 0.01
I0406 08:33:03.602797  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:33:05.811208  5226 solver.cpp:218] Iteration 9816 (2.28784 iter/s, 5.24513s/12 iters), loss = 1.88589
I0406 08:33:05.811262  5226 solver.cpp:237]     Train net output #0: loss = 1.88589 (* 1 = 1.88589 loss)
I0406 08:33:05.811272  5226 sgd_solver.cpp:105] Iteration 9816, lr = 0.01
I0406 08:33:11.164916  5226 solver.cpp:218] Iteration 9828 (2.24148 iter/s, 5.3536s/12 iters), loss = 1.70971
I0406 08:33:11.164968  5226 solver.cpp:237]     Train net output #0: loss = 1.70971 (* 1 = 1.70971 loss)
I0406 08:33:11.164976  5226 sgd_solver.cpp:105] Iteration 9828, lr = 0.01
I0406 08:33:16.474483  5226 solver.cpp:218] Iteration 9840 (2.26011 iter/s, 5.30947s/12 iters), loss = 1.56853
I0406 08:33:16.474527  5226 solver.cpp:237]     Train net output #0: loss = 1.56853 (* 1 = 1.56853 loss)
I0406 08:33:16.474534  5226 sgd_solver.cpp:105] Iteration 9840, lr = 0.01
I0406 08:33:21.834518  5226 solver.cpp:218] Iteration 9852 (2.23883 iter/s, 5.35994s/12 iters), loss = 1.5757
I0406 08:33:21.834558  5226 solver.cpp:237]     Train net output #0: loss = 1.5757 (* 1 = 1.5757 loss)
I0406 08:33:21.834563  5226 sgd_solver.cpp:105] Iteration 9852, lr = 0.01
I0406 08:33:26.956693  5226 solver.cpp:218] Iteration 9864 (2.34279 iter/s, 5.12209s/12 iters), loss = 1.86951
I0406 08:33:26.956749  5226 solver.cpp:237]     Train net output #0: loss = 1.86951 (* 1 = 1.86951 loss)
I0406 08:33:26.956758  5226 sgd_solver.cpp:105] Iteration 9864, lr = 0.01
I0406 08:33:32.317757  5226 solver.cpp:218] Iteration 9876 (2.2384 iter/s, 5.36096s/12 iters), loss = 1.49468
I0406 08:33:32.317795  5226 solver.cpp:237]     Train net output #0: loss = 1.49468 (* 1 = 1.49468 loss)
I0406 08:33:32.317801  5226 sgd_solver.cpp:105] Iteration 9876, lr = 0.01
I0406 08:33:37.657953  5226 solver.cpp:218] Iteration 9888 (2.24715 iter/s, 5.34011s/12 iters), loss = 1.41868
I0406 08:33:37.658116  5226 solver.cpp:237]     Train net output #0: loss = 1.41868 (* 1 = 1.41868 loss)
I0406 08:33:37.658126  5226 sgd_solver.cpp:105] Iteration 9888, lr = 0.01
I0406 08:33:39.751976  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9894.caffemodel
I0406 08:33:42.752720  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9894.solverstate
I0406 08:33:45.813772  5226 solver.cpp:330] Iteration 9894, Testing net (#0)
I0406 08:33:45.813793  5226 net.cpp:676] Ignoring source layer train-data
I0406 08:33:46.316042  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:33:50.134459  5226 solver.cpp:397]     Test net output #0: accuracy = 0.273284
I0406 08:33:50.134510  5226 solver.cpp:397]     Test net output #1: loss = 3.50507 (* 1 = 3.50507 loss)
I0406 08:33:52.166311  5226 solver.cpp:218] Iteration 9900 (0.827124 iter/s, 14.5081s/12 iters), loss = 1.30334
I0406 08:33:52.166349  5226 solver.cpp:237]     Train net output #0: loss = 1.30334 (* 1 = 1.30334 loss)
I0406 08:33:52.166354  5226 sgd_solver.cpp:105] Iteration 9900, lr = 0.01
I0406 08:33:57.361596  5226 solver.cpp:218] Iteration 9912 (2.30983 iter/s, 5.19519s/12 iters), loss = 1.7553
I0406 08:33:57.361646  5226 solver.cpp:237]     Train net output #0: loss = 1.7553 (* 1 = 1.7553 loss)
I0406 08:33:57.361654  5226 sgd_solver.cpp:105] Iteration 9912, lr = 0.01
I0406 08:33:57.449906  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:34:02.791034  5226 solver.cpp:218] Iteration 9924 (2.21021 iter/s, 5.42934s/12 iters), loss = 1.54744
I0406 08:34:02.791072  5226 solver.cpp:237]     Train net output #0: loss = 1.54744 (* 1 = 1.54744 loss)
I0406 08:34:02.791079  5226 sgd_solver.cpp:105] Iteration 9924, lr = 0.01
I0406 08:34:08.035404  5226 solver.cpp:218] Iteration 9936 (2.28821 iter/s, 5.24428s/12 iters), loss = 1.45356
I0406 08:34:08.035495  5226 solver.cpp:237]     Train net output #0: loss = 1.45356 (* 1 = 1.45356 loss)
I0406 08:34:08.035501  5226 sgd_solver.cpp:105] Iteration 9936, lr = 0.01
I0406 08:34:13.312279  5226 solver.cpp:218] Iteration 9948 (2.27413 iter/s, 5.27674s/12 iters), loss = 1.64733
I0406 08:34:13.312317  5226 solver.cpp:237]     Train net output #0: loss = 1.64733 (* 1 = 1.64733 loss)
I0406 08:34:13.312323  5226 sgd_solver.cpp:105] Iteration 9948, lr = 0.01
I0406 08:34:18.680207  5226 solver.cpp:218] Iteration 9960 (2.23554 iter/s, 5.36783s/12 iters), loss = 1.78467
I0406 08:34:18.680254  5226 solver.cpp:237]     Train net output #0: loss = 1.78467 (* 1 = 1.78467 loss)
I0406 08:34:18.680263  5226 sgd_solver.cpp:105] Iteration 9960, lr = 0.01
I0406 08:34:23.975453  5226 solver.cpp:218] Iteration 9972 (2.26623 iter/s, 5.29515s/12 iters), loss = 1.94637
I0406 08:34:23.975509  5226 solver.cpp:237]     Train net output #0: loss = 1.94637 (* 1 = 1.94637 loss)
I0406 08:34:23.975518  5226 sgd_solver.cpp:105] Iteration 9972, lr = 0.01
I0406 08:34:29.131028  5226 solver.cpp:218] Iteration 9984 (2.32762 iter/s, 5.15548s/12 iters), loss = 1.94742
I0406 08:34:29.131072  5226 solver.cpp:237]     Train net output #0: loss = 1.94742 (* 1 = 1.94742 loss)
I0406 08:34:29.131076  5226 sgd_solver.cpp:105] Iteration 9984, lr = 0.01
I0406 08:34:33.926730  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9996.caffemodel
I0406 08:34:37.073827  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9996.solverstate
I0406 08:34:39.367050  5226 solver.cpp:330] Iteration 9996, Testing net (#0)
I0406 08:34:39.367133  5226 net.cpp:676] Ignoring source layer train-data
I0406 08:34:39.800668  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:34:43.639911  5226 solver.cpp:397]     Test net output #0: accuracy = 0.260417
I0406 08:34:43.639955  5226 solver.cpp:397]     Test net output #1: loss = 3.46497 (* 1 = 3.46497 loss)
I0406 08:34:43.780814  5226 solver.cpp:218] Iteration 9996 (0.819133 iter/s, 14.6496s/12 iters), loss = 1.68447
I0406 08:34:43.780856  5226 solver.cpp:237]     Train net output #0: loss = 1.68447 (* 1 = 1.68447 loss)
I0406 08:34:43.780861  5226 sgd_solver.cpp:105] Iteration 9996, lr = 0.01
I0406 08:34:48.034957  5226 solver.cpp:218] Iteration 10008 (2.82084 iter/s, 4.25406s/12 iters), loss = 1.74634
I0406 08:34:48.035009  5226 solver.cpp:237]     Train net output #0: loss = 1.74634 (* 1 = 1.74634 loss)
I0406 08:34:48.035018  5226 sgd_solver.cpp:105] Iteration 10008, lr = 0.01
I0406 08:34:50.300285  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:34:53.279476  5226 solver.cpp:218] Iteration 10020 (2.28815 iter/s, 5.24441s/12 iters), loss = 1.62686
I0406 08:34:53.279531  5226 solver.cpp:237]     Train net output #0: loss = 1.62686 (* 1 = 1.62686 loss)
I0406 08:34:53.279539  5226 sgd_solver.cpp:105] Iteration 10020, lr = 0.01
I0406 08:34:58.671222  5226 solver.cpp:218] Iteration 10032 (2.22567 iter/s, 5.39164s/12 iters), loss = 1.57996
I0406 08:34:58.671262  5226 solver.cpp:237]     Train net output #0: loss = 1.57996 (* 1 = 1.57996 loss)
I0406 08:34:58.671267  5226 sgd_solver.cpp:105] Iteration 10032, lr = 0.01
I0406 08:35:04.085789  5226 solver.cpp:218] Iteration 10044 (2.21628 iter/s, 5.41448s/12 iters), loss = 1.86189
I0406 08:35:04.085840  5226 solver.cpp:237]     Train net output #0: loss = 1.86189 (* 1 = 1.86189 loss)
I0406 08:35:04.085850  5226 sgd_solver.cpp:105] Iteration 10044, lr = 0.01
I0406 08:35:09.488727  5226 solver.cpp:218] Iteration 10056 (2.22106 iter/s, 5.40284s/12 iters), loss = 1.87702
I0406 08:35:09.488847  5226 solver.cpp:237]     Train net output #0: loss = 1.87702 (* 1 = 1.87702 loss)
I0406 08:35:09.488859  5226 sgd_solver.cpp:105] Iteration 10056, lr = 0.01
I0406 08:35:14.887974  5226 solver.cpp:218] Iteration 10068 (2.2226 iter/s, 5.39909s/12 iters), loss = 1.53069
I0406 08:35:14.888012  5226 solver.cpp:237]     Train net output #0: loss = 1.53069 (* 1 = 1.53069 loss)
I0406 08:35:14.888017  5226 sgd_solver.cpp:105] Iteration 10068, lr = 0.01
I0406 08:35:20.271958  5226 solver.cpp:218] Iteration 10080 (2.22887 iter/s, 5.3839s/12 iters), loss = 1.6349
I0406 08:35:20.271993  5226 solver.cpp:237]     Train net output #0: loss = 1.6349 (* 1 = 1.6349 loss)
I0406 08:35:20.271999  5226 sgd_solver.cpp:105] Iteration 10080, lr = 0.01
I0406 08:35:25.413131  5226 solver.cpp:218] Iteration 10092 (2.33414 iter/s, 5.14108s/12 iters), loss = 1.93234
I0406 08:35:25.413184  5226 solver.cpp:237]     Train net output #0: loss = 1.93234 (* 1 = 1.93234 loss)
I0406 08:35:25.413193  5226 sgd_solver.cpp:105] Iteration 10092, lr = 0.01
I0406 08:35:27.548415  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10098.caffemodel
I0406 08:35:30.562490  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10098.solverstate
I0406 08:35:32.862757  5226 solver.cpp:330] Iteration 10098, Testing net (#0)
I0406 08:35:32.862776  5226 net.cpp:676] Ignoring source layer train-data
I0406 08:35:33.317839  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:35:37.332799  5226 solver.cpp:397]     Test net output #0: accuracy = 0.268382
I0406 08:35:37.332841  5226 solver.cpp:397]     Test net output #1: loss = 3.44057 (* 1 = 3.44057 loss)
I0406 08:35:39.297561  5226 solver.cpp:218] Iteration 10104 (0.864287 iter/s, 13.8843s/12 iters), loss = 1.70096
I0406 08:35:39.297621  5226 solver.cpp:237]     Train net output #0: loss = 1.70096 (* 1 = 1.70096 loss)
I0406 08:35:39.297631  5226 sgd_solver.cpp:105] Iteration 10104, lr = 0.01
I0406 08:35:43.851413  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:35:44.583045  5226 solver.cpp:218] Iteration 10116 (2.27041 iter/s, 5.28538s/12 iters), loss = 1.69603
I0406 08:35:44.583082  5226 solver.cpp:237]     Train net output #0: loss = 1.69603 (* 1 = 1.69603 loss)
I0406 08:35:44.583087  5226 sgd_solver.cpp:105] Iteration 10116, lr = 0.01
I0406 08:35:49.797902  5226 solver.cpp:218] Iteration 10128 (2.30116 iter/s, 5.21477s/12 iters), loss = 1.49449
I0406 08:35:49.797945  5226 solver.cpp:237]     Train net output #0: loss = 1.49449 (* 1 = 1.49449 loss)
I0406 08:35:49.797950  5226 sgd_solver.cpp:105] Iteration 10128, lr = 0.01
I0406 08:35:54.929328  5226 solver.cpp:218] Iteration 10140 (2.33857 iter/s, 5.13134s/12 iters), loss = 1.64734
I0406 08:35:54.929385  5226 solver.cpp:237]     Train net output #0: loss = 1.64734 (* 1 = 1.64734 loss)
I0406 08:35:54.929394  5226 sgd_solver.cpp:105] Iteration 10140, lr = 0.01
I0406 08:36:00.394415  5226 solver.cpp:218] Iteration 10152 (2.1958 iter/s, 5.46498s/12 iters), loss = 1.75535
I0406 08:36:00.394462  5226 solver.cpp:237]     Train net output #0: loss = 1.75535 (* 1 = 1.75535 loss)
I0406 08:36:00.394470  5226 sgd_solver.cpp:105] Iteration 10152, lr = 0.01
I0406 08:36:05.424531  5226 solver.cpp:218] Iteration 10164 (2.38568 iter/s, 5.03002s/12 iters), loss = 1.24466
I0406 08:36:05.424578  5226 solver.cpp:237]     Train net output #0: loss = 1.24466 (* 1 = 1.24466 loss)
I0406 08:36:05.424587  5226 sgd_solver.cpp:105] Iteration 10164, lr = 0.01
I0406 08:36:10.652312  5226 solver.cpp:218] Iteration 10176 (2.29547 iter/s, 5.22768s/12 iters), loss = 1.6235
I0406 08:36:10.652350  5226 solver.cpp:237]     Train net output #0: loss = 1.6235 (* 1 = 1.6235 loss)
I0406 08:36:10.652356  5226 sgd_solver.cpp:105] Iteration 10176, lr = 0.01
I0406 08:36:15.961638  5226 solver.cpp:218] Iteration 10188 (2.26021 iter/s, 5.30923s/12 iters), loss = 1.82726
I0406 08:36:15.961767  5226 solver.cpp:237]     Train net output #0: loss = 1.82726 (* 1 = 1.82726 loss)
I0406 08:36:15.961777  5226 sgd_solver.cpp:105] Iteration 10188, lr = 0.01
I0406 08:36:20.776013  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10200.caffemodel
I0406 08:36:23.805336  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10200.solverstate
I0406 08:36:26.123360  5226 solver.cpp:330] Iteration 10200, Testing net (#0)
I0406 08:36:26.123378  5226 net.cpp:676] Ignoring source layer train-data
I0406 08:36:26.476485  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:36:30.430084  5226 solver.cpp:397]     Test net output #0: accuracy = 0.270221
I0406 08:36:30.430120  5226 solver.cpp:397]     Test net output #1: loss = 3.58745 (* 1 = 3.58745 loss)
I0406 08:36:30.569666  5226 solver.cpp:218] Iteration 10200 (0.821479 iter/s, 14.6078s/12 iters), loss = 1.93206
I0406 08:36:30.569710  5226 solver.cpp:237]     Train net output #0: loss = 1.93206 (* 1 = 1.93206 loss)
I0406 08:36:30.569716  5226 sgd_solver.cpp:105] Iteration 10200, lr = 0.01
I0406 08:36:34.813839  5226 solver.cpp:218] Iteration 10212 (2.82746 iter/s, 4.24409s/12 iters), loss = 2.19496
I0406 08:36:34.813879  5226 solver.cpp:237]     Train net output #0: loss = 2.19496 (* 1 = 2.19496 loss)
I0406 08:36:34.813885  5226 sgd_solver.cpp:105] Iteration 10212, lr = 0.01
I0406 08:36:36.356267  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:36:39.954640  5226 solver.cpp:218] Iteration 10224 (2.33431 iter/s, 5.14071s/12 iters), loss = 1.41348
I0406 08:36:39.954684  5226 solver.cpp:237]     Train net output #0: loss = 1.41348 (* 1 = 1.41348 loss)
I0406 08:36:39.954690  5226 sgd_solver.cpp:105] Iteration 10224, lr = 0.01
I0406 08:36:45.002214  5226 solver.cpp:218] Iteration 10236 (2.37742 iter/s, 5.04748s/12 iters), loss = 1.49571
I0406 08:36:45.002250  5226 solver.cpp:237]     Train net output #0: loss = 1.49571 (* 1 = 1.49571 loss)
I0406 08:36:45.002256  5226 sgd_solver.cpp:105] Iteration 10236, lr = 0.01
I0406 08:36:50.467960  5226 solver.cpp:218] Iteration 10248 (2.19553 iter/s, 5.46566s/12 iters), loss = 1.40195
I0406 08:36:50.468083  5226 solver.cpp:237]     Train net output #0: loss = 1.40195 (* 1 = 1.40195 loss)
I0406 08:36:50.468091  5226 sgd_solver.cpp:105] Iteration 10248, lr = 0.01
I0406 08:36:55.790956  5226 solver.cpp:218] Iteration 10260 (2.25444 iter/s, 5.32282s/12 iters), loss = 1.14686
I0406 08:36:55.790992  5226 solver.cpp:237]     Train net output #0: loss = 1.14686 (* 1 = 1.14686 loss)
I0406 08:36:55.790998  5226 sgd_solver.cpp:105] Iteration 10260, lr = 0.01
I0406 08:37:00.900602  5226 solver.cpp:218] Iteration 10272 (2.34854 iter/s, 5.10956s/12 iters), loss = 1.57964
I0406 08:37:00.900640  5226 solver.cpp:237]     Train net output #0: loss = 1.57964 (* 1 = 1.57964 loss)
I0406 08:37:00.900645  5226 sgd_solver.cpp:105] Iteration 10272, lr = 0.01
I0406 08:37:06.268011  5226 solver.cpp:218] Iteration 10284 (2.23575 iter/s, 5.36732s/12 iters), loss = 1.40208
I0406 08:37:06.268059  5226 solver.cpp:237]     Train net output #0: loss = 1.40208 (* 1 = 1.40208 loss)
I0406 08:37:06.268067  5226 sgd_solver.cpp:105] Iteration 10284, lr = 0.01
I0406 08:37:11.390699  5226 solver.cpp:218] Iteration 10296 (2.34256 iter/s, 5.1226s/12 iters), loss = 1.60574
I0406 08:37:11.390738  5226 solver.cpp:237]     Train net output #0: loss = 1.60574 (* 1 = 1.60574 loss)
I0406 08:37:11.390743  5226 sgd_solver.cpp:105] Iteration 10296, lr = 0.01
I0406 08:37:13.649708  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10302.caffemodel
I0406 08:37:16.615386  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10302.solverstate
I0406 08:37:18.906425  5226 solver.cpp:330] Iteration 10302, Testing net (#0)
I0406 08:37:18.906445  5226 net.cpp:676] Ignoring source layer train-data
I0406 08:37:19.243513  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:37:23.222255  5226 solver.cpp:397]     Test net output #0: accuracy = 0.261642
I0406 08:37:23.222365  5226 solver.cpp:397]     Test net output #1: loss = 3.59857 (* 1 = 3.59857 loss)
I0406 08:37:25.137217  5226 solver.cpp:218] Iteration 10308 (0.872957 iter/s, 13.7464s/12 iters), loss = 1.85736
I0406 08:37:25.137271  5226 solver.cpp:237]     Train net output #0: loss = 1.85736 (* 1 = 1.85736 loss)
I0406 08:37:25.137279  5226 sgd_solver.cpp:105] Iteration 10308, lr = 0.01
I0406 08:37:28.938298  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:37:30.399622  5226 solver.cpp:218] Iteration 10320 (2.28037 iter/s, 5.2623s/12 iters), loss = 1.88133
I0406 08:37:30.399667  5226 solver.cpp:237]     Train net output #0: loss = 1.88133 (* 1 = 1.88133 loss)
I0406 08:37:30.399672  5226 sgd_solver.cpp:105] Iteration 10320, lr = 0.01
I0406 08:37:35.725788  5226 solver.cpp:218] Iteration 10332 (2.25307 iter/s, 5.32607s/12 iters), loss = 1.63204
I0406 08:37:35.725832  5226 solver.cpp:237]     Train net output #0: loss = 1.63204 (* 1 = 1.63204 loss)
I0406 08:37:35.725841  5226 sgd_solver.cpp:105] Iteration 10332, lr = 0.01
I0406 08:37:40.949409  5226 solver.cpp:218] Iteration 10344 (2.2973 iter/s, 5.22353s/12 iters), loss = 1.3453
I0406 08:37:40.949448  5226 solver.cpp:237]     Train net output #0: loss = 1.3453 (* 1 = 1.3453 loss)
I0406 08:37:40.949453  5226 sgd_solver.cpp:105] Iteration 10344, lr = 0.01
I0406 08:37:46.076819  5226 solver.cpp:218] Iteration 10356 (2.3404 iter/s, 5.12733s/12 iters), loss = 1.53305
I0406 08:37:46.076866  5226 solver.cpp:237]     Train net output #0: loss = 1.53305 (* 1 = 1.53305 loss)
I0406 08:37:46.076874  5226 sgd_solver.cpp:105] Iteration 10356, lr = 0.01
I0406 08:37:51.391896  5226 solver.cpp:218] Iteration 10368 (2.25777 iter/s, 5.31499s/12 iters), loss = 1.70293
I0406 08:37:51.391933  5226 solver.cpp:237]     Train net output #0: loss = 1.70293 (* 1 = 1.70293 loss)
I0406 08:37:51.391938  5226 sgd_solver.cpp:105] Iteration 10368, lr = 0.01
I0406 08:37:56.631486  5226 solver.cpp:218] Iteration 10380 (2.29029 iter/s, 5.23951s/12 iters), loss = 1.74211
I0406 08:37:56.631611  5226 solver.cpp:237]     Train net output #0: loss = 1.74211 (* 1 = 1.74211 loss)
I0406 08:37:56.631618  5226 sgd_solver.cpp:105] Iteration 10380, lr = 0.01
I0406 08:38:01.971520  5226 solver.cpp:218] Iteration 10392 (2.24725 iter/s, 5.33986s/12 iters), loss = 1.65588
I0406 08:38:01.971561  5226 solver.cpp:237]     Train net output #0: loss = 1.65588 (* 1 = 1.65588 loss)
I0406 08:38:01.971566  5226 sgd_solver.cpp:105] Iteration 10392, lr = 0.01
I0406 08:38:06.758473  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10404.caffemodel
I0406 08:38:09.749413  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10404.solverstate
I0406 08:38:12.047278  5226 solver.cpp:330] Iteration 10404, Testing net (#0)
I0406 08:38:12.047297  5226 net.cpp:676] Ignoring source layer train-data
I0406 08:38:12.317400  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:38:12.785883  5226 blocking_queue.cpp:49] Waiting for data
I0406 08:38:16.482206  5226 solver.cpp:397]     Test net output #0: accuracy = 0.255515
I0406 08:38:16.482240  5226 solver.cpp:397]     Test net output #1: loss = 3.54629 (* 1 = 3.54629 loss)
I0406 08:38:16.621681  5226 solver.cpp:218] Iteration 10404 (0.819112 iter/s, 14.65s/12 iters), loss = 1.90986
I0406 08:38:16.621717  5226 solver.cpp:237]     Train net output #0: loss = 1.90986 (* 1 = 1.90986 loss)
I0406 08:38:16.621722  5226 sgd_solver.cpp:105] Iteration 10404, lr = 0.01
I0406 08:38:20.849551  5226 solver.cpp:218] Iteration 10416 (2.83836 iter/s, 4.22779s/12 iters), loss = 1.73078
I0406 08:38:20.849591  5226 solver.cpp:237]     Train net output #0: loss = 1.73078 (* 1 = 1.73078 loss)
I0406 08:38:20.849596  5226 sgd_solver.cpp:105] Iteration 10416, lr = 0.01
I0406 08:38:21.710225  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:38:25.670889  5226 solver.cpp:218] Iteration 10428 (2.48898 iter/s, 4.82126s/12 iters), loss = 1.58328
I0406 08:38:25.670934  5226 solver.cpp:237]     Train net output #0: loss = 1.58328 (* 1 = 1.58328 loss)
I0406 08:38:25.670943  5226 sgd_solver.cpp:105] Iteration 10428, lr = 0.01
I0406 08:38:30.948463  5226 solver.cpp:218] Iteration 10440 (2.27381 iter/s, 5.27748s/12 iters), loss = 1.66798
I0406 08:38:30.948541  5226 solver.cpp:237]     Train net output #0: loss = 1.66798 (* 1 = 1.66798 loss)
I0406 08:38:30.948547  5226 sgd_solver.cpp:105] Iteration 10440, lr = 0.01
I0406 08:38:36.333077  5226 solver.cpp:218] Iteration 10452 (2.22863 iter/s, 5.38448s/12 iters), loss = 2.03843
I0406 08:38:36.333137  5226 solver.cpp:237]     Train net output #0: loss = 2.03843 (* 1 = 2.03843 loss)
I0406 08:38:36.333148  5226 sgd_solver.cpp:105] Iteration 10452, lr = 0.01
I0406 08:38:41.628131  5226 solver.cpp:218] Iteration 10464 (2.26631 iter/s, 5.29495s/12 iters), loss = 1.72326
I0406 08:38:41.628178  5226 solver.cpp:237]     Train net output #0: loss = 1.72326 (* 1 = 1.72326 loss)
I0406 08:38:41.628186  5226 sgd_solver.cpp:105] Iteration 10464, lr = 0.01
I0406 08:38:47.037623  5226 solver.cpp:218] Iteration 10476 (2.21836 iter/s, 5.4094s/12 iters), loss = 1.83683
I0406 08:38:47.037667  5226 solver.cpp:237]     Train net output #0: loss = 1.83683 (* 1 = 1.83683 loss)
I0406 08:38:47.037674  5226 sgd_solver.cpp:105] Iteration 10476, lr = 0.01
I0406 08:38:52.382683  5226 solver.cpp:218] Iteration 10488 (2.2451 iter/s, 5.34496s/12 iters), loss = 1.4834
I0406 08:38:52.382733  5226 solver.cpp:237]     Train net output #0: loss = 1.4834 (* 1 = 1.4834 loss)
I0406 08:38:52.382740  5226 sgd_solver.cpp:105] Iteration 10488, lr = 0.01
I0406 08:38:57.787982  5226 solver.cpp:218] Iteration 10500 (2.22008 iter/s, 5.4052s/12 iters), loss = 1.48883
I0406 08:38:57.788039  5226 solver.cpp:237]     Train net output #0: loss = 1.48883 (* 1 = 1.48883 loss)
I0406 08:38:57.788048  5226 sgd_solver.cpp:105] Iteration 10500, lr = 0.01
I0406 08:39:00.011535  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10506.caffemodel
I0406 08:39:03.046933  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10506.solverstate
I0406 08:39:05.357987  5226 solver.cpp:330] Iteration 10506, Testing net (#0)
I0406 08:39:05.358011  5226 net.cpp:676] Ignoring source layer train-data
I0406 08:39:05.625165  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:39:09.882001  5226 solver.cpp:397]     Test net output #0: accuracy = 0.254289
I0406 08:39:09.882036  5226 solver.cpp:397]     Test net output #1: loss = 3.62608 (* 1 = 3.62608 loss)
I0406 08:39:11.853427  5226 solver.cpp:218] Iteration 10512 (0.853164 iter/s, 14.0653s/12 iters), loss = 1.74583
I0406 08:39:11.853468  5226 solver.cpp:237]     Train net output #0: loss = 1.74583 (* 1 = 1.74583 loss)
I0406 08:39:11.853473  5226 sgd_solver.cpp:105] Iteration 10512, lr = 0.01
I0406 08:39:14.999733  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:39:17.143844  5226 solver.cpp:218] Iteration 10524 (2.26829 iter/s, 5.29033s/12 iters), loss = 1.81615
I0406 08:39:17.143884  5226 solver.cpp:237]     Train net output #0: loss = 1.81615 (* 1 = 1.81615 loss)
I0406 08:39:17.143889  5226 sgd_solver.cpp:105] Iteration 10524, lr = 0.01
I0406 08:39:22.617177  5226 solver.cpp:218] Iteration 10536 (2.19248 iter/s, 5.47324s/12 iters), loss = 1.72172
I0406 08:39:22.617221  5226 solver.cpp:237]     Train net output #0: loss = 1.72172 (* 1 = 1.72172 loss)
I0406 08:39:22.617229  5226 sgd_solver.cpp:105] Iteration 10536, lr = 0.01
I0406 08:39:27.872583  5226 solver.cpp:218] Iteration 10548 (2.2834 iter/s, 5.25532s/12 iters), loss = 1.74302
I0406 08:39:27.872620  5226 solver.cpp:237]     Train net output #0: loss = 1.74302 (* 1 = 1.74302 loss)
I0406 08:39:27.872625  5226 sgd_solver.cpp:105] Iteration 10548, lr = 0.01
I0406 08:39:33.100697  5226 solver.cpp:218] Iteration 10560 (2.29532 iter/s, 5.22802s/12 iters), loss = 1.79315
I0406 08:39:33.101461  5226 solver.cpp:237]     Train net output #0: loss = 1.79315 (* 1 = 1.79315 loss)
I0406 08:39:33.101473  5226 sgd_solver.cpp:105] Iteration 10560, lr = 0.01
I0406 08:39:38.445492  5226 solver.cpp:218] Iteration 10572 (2.24551 iter/s, 5.34399s/12 iters), loss = 2.16118
I0406 08:39:38.445546  5226 solver.cpp:237]     Train net output #0: loss = 2.16118 (* 1 = 2.16118 loss)
I0406 08:39:38.445555  5226 sgd_solver.cpp:105] Iteration 10572, lr = 0.01
I0406 08:39:43.749366  5226 solver.cpp:218] Iteration 10584 (2.26254 iter/s, 5.30378s/12 iters), loss = 1.95264
I0406 08:39:43.749403  5226 solver.cpp:237]     Train net output #0: loss = 1.95264 (* 1 = 1.95264 loss)
I0406 08:39:43.749408  5226 sgd_solver.cpp:105] Iteration 10584, lr = 0.01
I0406 08:39:49.129091  5226 solver.cpp:218] Iteration 10596 (2.23063 iter/s, 5.37964s/12 iters), loss = 2.51427
I0406 08:39:49.129139  5226 solver.cpp:237]     Train net output #0: loss = 2.51427 (* 1 = 2.51427 loss)
I0406 08:39:49.129148  5226 sgd_solver.cpp:105] Iteration 10596, lr = 0.01
I0406 08:39:53.912124  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10608.caffemodel
I0406 08:39:56.931996  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10608.solverstate
I0406 08:39:59.255475  5226 solver.cpp:330] Iteration 10608, Testing net (#0)
I0406 08:39:59.255494  5226 net.cpp:676] Ignoring source layer train-data
I0406 08:39:59.501508  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:40:03.556175  5226 solver.cpp:397]     Test net output #0: accuracy = 0.270221
I0406 08:40:03.556327  5226 solver.cpp:397]     Test net output #1: loss = 3.54479 (* 1 = 3.54479 loss)
I0406 08:40:03.693523  5226 solver.cpp:218] Iteration 10608 (0.823933 iter/s, 14.5643s/12 iters), loss = 1.53705
I0406 08:40:03.693574  5226 solver.cpp:237]     Train net output #0: loss = 1.53705 (* 1 = 1.53705 loss)
I0406 08:40:03.693581  5226 sgd_solver.cpp:105] Iteration 10608, lr = 0.01
I0406 08:40:08.023671  5226 solver.cpp:218] Iteration 10620 (2.77133 iter/s, 4.33006s/12 iters), loss = 1.8692
I0406 08:40:08.023712  5226 solver.cpp:237]     Train net output #0: loss = 1.8692 (* 1 = 1.8692 loss)
I0406 08:40:08.023718  5226 sgd_solver.cpp:105] Iteration 10620, lr = 0.01
I0406 08:40:08.141373  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:40:13.297097  5226 solver.cpp:218] Iteration 10632 (2.2756 iter/s, 5.27334s/12 iters), loss = 1.73755
I0406 08:40:13.297135  5226 solver.cpp:237]     Train net output #0: loss = 1.73755 (* 1 = 1.73755 loss)
I0406 08:40:13.297142  5226 sgd_solver.cpp:105] Iteration 10632, lr = 0.01
I0406 08:40:18.450507  5226 solver.cpp:218] Iteration 10644 (2.3286 iter/s, 5.15332s/12 iters), loss = 1.62462
I0406 08:40:18.450557  5226 solver.cpp:237]     Train net output #0: loss = 1.62462 (* 1 = 1.62462 loss)
I0406 08:40:18.450564  5226 sgd_solver.cpp:105] Iteration 10644, lr = 0.01
I0406 08:40:23.664012  5226 solver.cpp:218] Iteration 10656 (2.30175 iter/s, 5.21341s/12 iters), loss = 1.54483
I0406 08:40:23.664055  5226 solver.cpp:237]     Train net output #0: loss = 1.54483 (* 1 = 1.54483 loss)
I0406 08:40:23.664063  5226 sgd_solver.cpp:105] Iteration 10656, lr = 0.01
I0406 08:40:28.982415  5226 solver.cpp:218] Iteration 10668 (2.25636 iter/s, 5.31831s/12 iters), loss = 1.7805
I0406 08:40:28.982462  5226 solver.cpp:237]     Train net output #0: loss = 1.7805 (* 1 = 1.7805 loss)
I0406 08:40:28.982470  5226 sgd_solver.cpp:105] Iteration 10668, lr = 0.01
I0406 08:40:34.328495  5226 solver.cpp:218] Iteration 10680 (2.24467 iter/s, 5.34599s/12 iters), loss = 1.70867
I0406 08:40:34.328591  5226 solver.cpp:237]     Train net output #0: loss = 1.70867 (* 1 = 1.70867 loss)
I0406 08:40:34.328598  5226 sgd_solver.cpp:105] Iteration 10680, lr = 0.01
I0406 08:40:39.805409  5226 solver.cpp:218] Iteration 10692 (2.19107 iter/s, 5.47677s/12 iters), loss = 1.9874
I0406 08:40:39.805449  5226 solver.cpp:237]     Train net output #0: loss = 1.9874 (* 1 = 1.9874 loss)
I0406 08:40:39.805454  5226 sgd_solver.cpp:105] Iteration 10692, lr = 0.01
I0406 08:40:44.997350  5226 solver.cpp:218] Iteration 10704 (2.31131 iter/s, 5.19185s/12 iters), loss = 1.60257
I0406 08:40:44.997406  5226 solver.cpp:237]     Train net output #0: loss = 1.60257 (* 1 = 1.60257 loss)
I0406 08:40:44.997416  5226 sgd_solver.cpp:105] Iteration 10704, lr = 0.01
I0406 08:40:47.177985  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10710.caffemodel
I0406 08:40:50.182122  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10710.solverstate
I0406 08:40:53.441059  5226 solver.cpp:330] Iteration 10710, Testing net (#0)
I0406 08:40:53.441082  5226 net.cpp:676] Ignoring source layer train-data
I0406 08:40:53.630146  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:40:58.103081  5226 solver.cpp:397]     Test net output #0: accuracy = 0.230392
I0406 08:40:58.103122  5226 solver.cpp:397]     Test net output #1: loss = 3.62848 (* 1 = 3.62848 loss)
I0406 08:40:59.937433  5226 solver.cpp:218] Iteration 10716 (0.803217 iter/s, 14.9399s/12 iters), loss = 1.57672
I0406 08:40:59.937482  5226 solver.cpp:237]     Train net output #0: loss = 1.57672 (* 1 = 1.57672 loss)
I0406 08:40:59.937490  5226 sgd_solver.cpp:105] Iteration 10716, lr = 0.01
I0406 08:41:02.272807  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:41:05.108578  5226 solver.cpp:218] Iteration 10728 (2.32061 iter/s, 5.17105s/12 iters), loss = 1.84219
I0406 08:41:05.108713  5226 solver.cpp:237]     Train net output #0: loss = 1.84219 (* 1 = 1.84219 loss)
I0406 08:41:05.108723  5226 sgd_solver.cpp:105] Iteration 10728, lr = 0.01
I0406 08:41:10.398701  5226 solver.cpp:218] Iteration 10740 (2.26846 iter/s, 5.28994s/12 iters), loss = 1.62112
I0406 08:41:10.398741  5226 solver.cpp:237]     Train net output #0: loss = 1.62112 (* 1 = 1.62112 loss)
I0406 08:41:10.398746  5226 sgd_solver.cpp:105] Iteration 10740, lr = 0.01
I0406 08:41:15.464021  5226 solver.cpp:218] Iteration 10752 (2.36909 iter/s, 5.06523s/12 iters), loss = 1.58704
I0406 08:41:15.464063  5226 solver.cpp:237]     Train net output #0: loss = 1.58704 (* 1 = 1.58704 loss)
I0406 08:41:15.464069  5226 sgd_solver.cpp:105] Iteration 10752, lr = 0.01
I0406 08:41:20.793922  5226 solver.cpp:218] Iteration 10764 (2.25149 iter/s, 5.32981s/12 iters), loss = 1.18151
I0406 08:41:20.793962  5226 solver.cpp:237]     Train net output #0: loss = 1.18151 (* 1 = 1.18151 loss)
I0406 08:41:20.793967  5226 sgd_solver.cpp:105] Iteration 10764, lr = 0.01
I0406 08:41:26.183423  5226 solver.cpp:218] Iteration 10776 (2.22659 iter/s, 5.38941s/12 iters), loss = 1.65825
I0406 08:41:26.183481  5226 solver.cpp:237]     Train net output #0: loss = 1.65825 (* 1 = 1.65825 loss)
I0406 08:41:26.183490  5226 sgd_solver.cpp:105] Iteration 10776, lr = 0.01
I0406 08:41:31.258934  5226 solver.cpp:218] Iteration 10788 (2.36434 iter/s, 5.07541s/12 iters), loss = 1.19596
I0406 08:41:31.258982  5226 solver.cpp:237]     Train net output #0: loss = 1.19596 (* 1 = 1.19596 loss)
I0406 08:41:31.258992  5226 sgd_solver.cpp:105] Iteration 10788, lr = 0.01
I0406 08:41:36.438233  5226 solver.cpp:218] Iteration 10800 (2.31696 iter/s, 5.17921s/12 iters), loss = 2.1086
I0406 08:41:36.438359  5226 solver.cpp:237]     Train net output #0: loss = 2.1086 (* 1 = 2.1086 loss)
I0406 08:41:36.438366  5226 sgd_solver.cpp:105] Iteration 10800, lr = 0.01
I0406 08:41:41.298038  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10812.caffemodel
I0406 08:41:45.068032  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10812.solverstate
I0406 08:41:47.371978  5226 solver.cpp:330] Iteration 10812, Testing net (#0)
I0406 08:41:47.371996  5226 net.cpp:676] Ignoring source layer train-data
I0406 08:41:47.500787  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:41:51.732612  5226 solver.cpp:397]     Test net output #0: accuracy = 0.257966
I0406 08:41:51.732650  5226 solver.cpp:397]     Test net output #1: loss = 3.56116 (* 1 = 3.56116 loss)
I0406 08:41:51.873582  5226 solver.cpp:218] Iteration 10812 (0.777448 iter/s, 15.4351s/12 iters), loss = 1.68942
I0406 08:41:51.873622  5226 solver.cpp:237]     Train net output #0: loss = 1.68942 (* 1 = 1.68942 loss)
I0406 08:41:51.873627  5226 sgd_solver.cpp:105] Iteration 10812, lr = 0.01
I0406 08:41:55.542743  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:41:56.219074  5226 solver.cpp:218] Iteration 10824 (2.76154 iter/s, 4.34541s/12 iters), loss = 2.08939
I0406 08:41:56.219115  5226 solver.cpp:237]     Train net output #0: loss = 2.08939 (* 1 = 2.08939 loss)
I0406 08:41:56.219120  5226 sgd_solver.cpp:105] Iteration 10824, lr = 0.01
I0406 08:42:01.497376  5226 solver.cpp:218] Iteration 10836 (2.2735 iter/s, 5.27821s/12 iters), loss = 2.09398
I0406 08:42:01.497418  5226 solver.cpp:237]     Train net output #0: loss = 2.09398 (* 1 = 2.09398 loss)
I0406 08:42:01.497423  5226 sgd_solver.cpp:105] Iteration 10836, lr = 0.01
I0406 08:42:06.766347  5226 solver.cpp:218] Iteration 10848 (2.27752 iter/s, 5.26888s/12 iters), loss = 1.72878
I0406 08:42:06.766434  5226 solver.cpp:237]     Train net output #0: loss = 1.72878 (* 1 = 1.72878 loss)
I0406 08:42:06.766441  5226 sgd_solver.cpp:105] Iteration 10848, lr = 0.01
I0406 08:42:12.049877  5226 solver.cpp:218] Iteration 10860 (2.27127 iter/s, 5.2834s/12 iters), loss = 1.93531
I0406 08:42:12.049913  5226 solver.cpp:237]     Train net output #0: loss = 1.93531 (* 1 = 1.93531 loss)
I0406 08:42:12.049919  5226 sgd_solver.cpp:105] Iteration 10860, lr = 0.01
I0406 08:42:16.892480  5226 solver.cpp:218] Iteration 10872 (2.47805 iter/s, 4.84252s/12 iters), loss = 1.53606
I0406 08:42:16.892524  5226 solver.cpp:237]     Train net output #0: loss = 1.53606 (* 1 = 1.53606 loss)
I0406 08:42:16.892531  5226 sgd_solver.cpp:105] Iteration 10872, lr = 0.01
I0406 08:42:22.101153  5226 solver.cpp:218] Iteration 10884 (2.30389 iter/s, 5.20858s/12 iters), loss = 1.62082
I0406 08:42:22.101191  5226 solver.cpp:237]     Train net output #0: loss = 1.62082 (* 1 = 1.62082 loss)
I0406 08:42:22.101197  5226 sgd_solver.cpp:105] Iteration 10884, lr = 0.01
I0406 08:42:27.305347  5226 solver.cpp:218] Iteration 10896 (2.30587 iter/s, 5.20411s/12 iters), loss = 2.1832
I0406 08:42:27.305385  5226 solver.cpp:237]     Train net output #0: loss = 2.1832 (* 1 = 2.1832 loss)
I0406 08:42:27.305390  5226 sgd_solver.cpp:105] Iteration 10896, lr = 0.01
I0406 08:42:32.669461  5226 solver.cpp:218] Iteration 10908 (2.23712 iter/s, 5.36403s/12 iters), loss = 1.96805
I0406 08:42:32.669504  5226 solver.cpp:237]     Train net output #0: loss = 1.96805 (* 1 = 1.96805 loss)
I0406 08:42:32.669509  5226 sgd_solver.cpp:105] Iteration 10908, lr = 0.01
I0406 08:42:34.717959  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10914.caffemodel
I0406 08:42:37.724551  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10914.solverstate
I0406 08:42:40.040124  5226 solver.cpp:330] Iteration 10914, Testing net (#0)
I0406 08:42:40.040143  5226 net.cpp:676] Ignoring source layer train-data
I0406 08:42:40.158378  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:42:44.537072  5226 solver.cpp:397]     Test net output #0: accuracy = 0.240809
I0406 08:42:44.537106  5226 solver.cpp:397]     Test net output #1: loss = 3.59737 (* 1 = 3.59737 loss)
I0406 08:42:46.492769  5226 solver.cpp:218] Iteration 10920 (0.868108 iter/s, 13.8232s/12 iters), loss = 1.98199
I0406 08:42:46.492821  5226 solver.cpp:237]     Train net output #0: loss = 1.98199 (* 1 = 1.98199 loss)
I0406 08:42:46.492830  5226 sgd_solver.cpp:105] Iteration 10920, lr = 0.01
I0406 08:42:47.963548  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:42:51.677886  5226 solver.cpp:218] Iteration 10932 (2.31436 iter/s, 5.18502s/12 iters), loss = 1.66489
I0406 08:42:51.677933  5226 solver.cpp:237]     Train net output #0: loss = 1.66489 (* 1 = 1.66489 loss)
I0406 08:42:51.677940  5226 sgd_solver.cpp:105] Iteration 10932, lr = 0.01
I0406 08:42:57.063853  5226 solver.cpp:218] Iteration 10944 (2.22805 iter/s, 5.38587s/12 iters), loss = 1.75694
I0406 08:42:57.063907  5226 solver.cpp:237]     Train net output #0: loss = 1.75694 (* 1 = 1.75694 loss)
I0406 08:42:57.063915  5226 sgd_solver.cpp:105] Iteration 10944, lr = 0.01
I0406 08:43:02.306483  5226 solver.cpp:218] Iteration 10956 (2.28897 iter/s, 5.24253s/12 iters), loss = 1.41838
I0406 08:43:02.306535  5226 solver.cpp:237]     Train net output #0: loss = 1.41838 (* 1 = 1.41838 loss)
I0406 08:43:02.306543  5226 sgd_solver.cpp:105] Iteration 10956, lr = 0.01
I0406 08:43:07.500488  5226 solver.cpp:218] Iteration 10968 (2.3104 iter/s, 5.19391s/12 iters), loss = 1.98088
I0406 08:43:07.500526  5226 solver.cpp:237]     Train net output #0: loss = 1.98088 (* 1 = 1.98088 loss)
I0406 08:43:07.500531  5226 sgd_solver.cpp:105] Iteration 10968, lr = 0.01
I0406 08:43:12.795881  5226 solver.cpp:218] Iteration 10980 (2.26616 iter/s, 5.29531s/12 iters), loss = 1.6785
I0406 08:43:12.795967  5226 solver.cpp:237]     Train net output #0: loss = 1.6785 (* 1 = 1.6785 loss)
I0406 08:43:12.795974  5226 sgd_solver.cpp:105] Iteration 10980, lr = 0.01
I0406 08:43:17.961589  5226 solver.cpp:218] Iteration 10992 (2.32307 iter/s, 5.16558s/12 iters), loss = 2.04665
I0406 08:43:17.961625  5226 solver.cpp:237]     Train net output #0: loss = 2.04665 (* 1 = 2.04665 loss)
I0406 08:43:17.961632  5226 sgd_solver.cpp:105] Iteration 10992, lr = 0.01
I0406 08:43:23.511693  5226 solver.cpp:218] Iteration 11004 (2.16216 iter/s, 5.55002s/12 iters), loss = 1.69658
I0406 08:43:23.511732  5226 solver.cpp:237]     Train net output #0: loss = 1.69658 (* 1 = 1.69658 loss)
I0406 08:43:23.511737  5226 sgd_solver.cpp:105] Iteration 11004, lr = 0.01
I0406 08:43:28.172616  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_11016.caffemodel
I0406 08:43:31.239485  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_11016.solverstate
I0406 08:43:33.551215  5226 solver.cpp:330] Iteration 11016, Testing net (#0)
I0406 08:43:33.551239  5226 net.cpp:676] Ignoring source layer train-data
I0406 08:43:33.605902  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:43:37.941057  5226 solver.cpp:397]     Test net output #0: accuracy = 0.262868
I0406 08:43:37.941087  5226 solver.cpp:397]     Test net output #1: loss = 3.57288 (* 1 = 3.57288 loss)
I0406 08:43:38.082005  5226 solver.cpp:218] Iteration 11016 (0.8236 iter/s, 14.5702s/12 iters), loss = 1.74997
I0406 08:43:38.082054  5226 solver.cpp:237]     Train net output #0: loss = 1.74997 (* 1 = 1.74997 loss)
I0406 08:43:38.082062  5226 sgd_solver.cpp:105] Iteration 11016, lr = 0.01
I0406 08:43:38.635910  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:43:41.148227  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:43:42.475380  5226 solver.cpp:218] Iteration 11028 (2.73144 iter/s, 4.39328s/12 iters), loss = 2.35406
I0406 08:43:42.475436  5226 solver.cpp:237]     Train net output #0: loss = 2.35406 (* 1 = 2.35406 loss)
I0406 08:43:42.475445  5226 sgd_solver.cpp:105] Iteration 11028, lr = 0.01
I0406 08:43:47.879287  5226 solver.cpp:218] Iteration 11040 (2.22066 iter/s, 5.4038s/12 iters), loss = 1.90254
I0406 08:43:47.879431  5226 solver.cpp:237]     Train net output #0: loss = 1.90254 (* 1 = 1.90254 loss)
I0406 08:43:47.879441  5226 sgd_solver.cpp:105] Iteration 11040, lr = 0.01
I0406 08:43:53.166824  5226 solver.cpp:218] Iteration 11052 (2.26957 iter/s, 5.28734s/12 iters), loss = 2.12704
I0406 08:43:53.166877  5226 solver.cpp:237]     Train net output #0: loss = 2.12704 (* 1 = 2.12704 loss)
I0406 08:43:53.166887  5226 sgd_solver.cpp:105] Iteration 11052, lr = 0.01
I0406 08:43:58.449493  5226 solver.cpp:218] Iteration 11064 (2.27162 iter/s, 5.28257s/12 iters), loss = 1.83219
I0406 08:43:58.449535  5226 solver.cpp:237]     Train net output #0: loss = 1.83219 (* 1 = 1.83219 loss)
I0406 08:43:58.449540  5226 sgd_solver.cpp:105] Iteration 11064, lr = 0.01
I0406 08:44:03.714269  5226 solver.cpp:218] Iteration 11076 (2.27934 iter/s, 5.26468s/12 iters), loss = 1.55809
I0406 08:44:03.714316  5226 solver.cpp:237]     Train net output #0: loss = 1.55809 (* 1 = 1.55809 loss)
I0406 08:44:03.714324  5226 sgd_solver.cpp:105] Iteration 11076, lr = 0.01
I0406 08:44:09.132321  5226 solver.cpp:218] Iteration 11088 (2.21486 iter/s, 5.41795s/12 iters), loss = 1.88972
I0406 08:44:09.132369  5226 solver.cpp:237]     Train net output #0: loss = 1.88972 (* 1 = 1.88972 loss)
I0406 08:44:09.132376  5226 sgd_solver.cpp:105] Iteration 11088, lr = 0.01
I0406 08:44:12.499819  5226 blocking_queue.cpp:49] Waiting for data
I0406 08:44:14.290684  5226 solver.cpp:218] Iteration 11100 (2.32636 iter/s, 5.15827s/12 iters), loss = 1.67094
I0406 08:44:14.290722  5226 solver.cpp:237]     Train net output #0: loss = 1.67094 (* 1 = 1.67094 loss)
I0406 08:44:14.290727  5226 sgd_solver.cpp:105] Iteration 11100, lr = 0.01
I0406 08:44:19.393898  5226 solver.cpp:218] Iteration 11112 (2.3515 iter/s, 5.10313s/12 iters), loss = 1.78406
I0406 08:44:19.394153  5226 solver.cpp:237]     Train net output #0: loss = 1.78406 (* 1 = 1.78406 loss)
I0406 08:44:19.394162  5226 sgd_solver.cpp:105] Iteration 11112, lr = 0.01
I0406 08:44:21.521761  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_11118.caffemodel
I0406 08:44:24.534940  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_11118.solverstate
I0406 08:44:26.852931  5226 solver.cpp:330] Iteration 11118, Testing net (#0)
I0406 08:44:26.852949  5226 net.cpp:676] Ignoring source layer train-data
I0406 08:44:31.170395  5226 solver.cpp:397]     Test net output #0: accuracy = 0.246324
I0406 08:44:31.170440  5226 solver.cpp:397]     Test net output #1: loss = 3.55621 (* 1 = 3.55621 loss)
I0406 08:44:31.713189  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:44:33.030786  5226 solver.cpp:218] Iteration 11124 (0.879989 iter/s, 13.6365s/12 iters), loss = 1.94776
I0406 08:44:33.030838  5226 solver.cpp:237]     Train net output #0: loss = 1.94776 (* 1 = 1.94776 loss)
I0406 08:44:33.030845  5226 sgd_solver.cpp:105] Iteration 11124, lr = 0.01
I0406 08:44:34.016922  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:44:38.369143  5226 solver.cpp:218] Iteration 11136 (2.24792 iter/s, 5.33826s/12 iters), loss = 2.20724
I0406 08:44:38.369192  5226 solver.cpp:237]     Train net output #0: loss = 2.20724 (* 1 = 2.20724 loss)
I0406 08:44:38.369201  5226 sgd_solver.cpp:105] Iteration 11136, lr = 0.01
I0406 08:44:43.549877  5226 solver.cpp:218] Iteration 11148 (2.31632 iter/s, 5.18064s/12 iters), loss = 1.75799
I0406 08:44:43.549916  5226 solver.cpp:237]     Train net output #0: loss = 1.75799 (* 1 = 1.75799 loss)
I0406 08:44:43.549921  5226 sgd_solver.cpp:105] Iteration 11148, lr = 0.01
I0406 08:44:48.743196  5226 solver.cpp:218] Iteration 11160 (2.3107 iter/s, 5.19323s/12 iters), loss = 1.93383
I0406 08:44:48.743242  5226 solver.cpp:237]     Train net output #0: loss = 1.93383 (* 1 = 1.93383 loss)
I0406 08:44:48.743250  5226 sgd_solver.cpp:105] Iteration 11160, lr = 0.01
I0406 08:44:54.212308  5226 solver.cpp:218] Iteration 11172 (2.19418 iter/s, 5.46902s/12 iters), loss = 1.63516
I0406 08:44:54.212432  5226 solver.cpp:237]     Train net output #0: loss = 1.63516 (* 1 = 1.63516 loss)
I0406 08:44:54.212440  5226 sgd_solver.cpp:105] Iteration 11172, lr = 0.01
I0406 08:44:59.539041  5226 solver.cpp:218] Iteration 11184 (2.25286 iter/s, 5.32655s/12 iters), loss = 1.94287
I0406 08:44:59.539088  5226 solver.cpp:237]     Train net output #0: loss = 1.94287 (* 1 = 1.94287 loss)
I0406 08:44:59.539096  5226 sgd_solver.cpp:105] Iteration 11184, lr = 0.01
I0406 08:45:04.441113  5226 solver.cpp:218] Iteration 11196 (2.44799 iter/s, 4.90198s/12 iters), loss = 1.70559
I0406 08:45:04.441150  5226 solver.cpp:237]     Train net output #0: loss = 1.70559 (* 1 = 1.70559 loss)
I0406 08:45:04.441156  5226 sgd_solver.cpp:105] Iteration 11196, lr = 0.01
I0406 08:45:09.635489  5226 solver.cpp:218] Iteration 11208 (2.31023 iter/s, 5.19429s/12 iters), loss = 1.97047
I0406 08:45:09.635529  5226 solver.cpp:237]     Train net output #0: loss = 1.97047 (* 1 = 1.97047 loss)
I0406 08:45:09.635535  5226 sgd_solver.cpp:105] Iteration 11208, lr = 0.01
I0406 08:45:14.473090  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_11220.caffemodel
I0406 08:45:17.451676  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_11220.solverstate
I0406 08:45:19.750473  5226 solver.cpp:330] Iteration 11220, Testing net (#0)
I0406 08:45:19.750494  5226 net.cpp:676] Ignoring source layer train-data
I0406 08:45:24.186167  5226 solver.cpp:397]     Test net output #0: accuracy = 0.262868
I0406 08:45:24.186194  5226 solver.cpp:397]     Test net output #1: loss = 3.56059 (* 1 = 3.56059 loss)
I0406 08:45:24.325489  5226 solver.cpp:218] Iteration 11220 (0.81689 iter/s, 14.6899s/12 iters), loss = 1.89074
I0406 08:45:24.325567  5226 solver.cpp:237]     Train net output #0: loss = 1.89074 (* 1 = 1.89074 loss)
I0406 08:45:24.325573  5226 sgd_solver.cpp:105] Iteration 11220, lr = 0.01
I0406 08:45:24.670397  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:45:26.515039  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:45:28.635885  5226 solver.cpp:218] Iteration 11232 (2.78405 iter/s, 4.31027s/12 iters), loss = 1.76823
I0406 08:45:28.635924  5226 solver.cpp:237]     Train net output #0: loss = 1.76823 (* 1 = 1.76823 loss)
I0406 08:45:28.635929  5226 sgd_solver.cpp:105] Iteration 11232, lr = 0.01
I0406 08:45:34.088584  5226 solver.cpp:218] Iteration 11244 (2.20078 iter/s, 5.45261s/12 iters), loss = 1.71553
I0406 08:45:34.088642  5226 solver.cpp:237]     Train net output #0: loss = 1.71553 (* 1 = 1.71553 loss)
I0406 08:45:34.088651  5226 sgd_solver.cpp:105] Iteration 11244, lr = 0.01
I0406 08:45:39.444531  5226 solver.cpp:218] Iteration 11256 (2.24054 iter/s, 5.35584s/12 iters), loss = 2.09982
I0406 08:45:39.444581  5226 solver.cpp:237]     Train net output #0: loss = 2.09982 (* 1 = 2.09982 loss)
I0406 08:45:39.444896  5226 sgd_solver.cpp:105] Iteration 11256, lr = 0.01
I0406 08:45:44.417891  5226 solver.cpp:218] Iteration 11268 (2.4129 iter/s, 4.97327s/12 iters), loss = 1.95018
I0406 08:45:44.417939  5226 solver.cpp:237]     Train net output #0: loss = 1.95018 (* 1 = 1.95018 loss)
I0406 08:45:44.417948  5226 sgd_solver.cpp:105] Iteration 11268, lr = 0.01
I0406 08:45:49.744841  5226 solver.cpp:218] Iteration 11280 (2.25274 iter/s, 5.32685s/12 iters), loss = 1.56122
I0406 08:45:49.744895  5226 solver.cpp:237]     Train net output #0: loss = 1.56122 (* 1 = 1.56122 loss)
I0406 08:45:49.744904  5226 sgd_solver.cpp:105] Iteration 11280, lr = 0.01
I0406 08:45:54.977285  5226 solver.cpp:218] Iteration 11292 (2.29342 iter/s, 5.23235s/12 iters), loss = 1.91252
I0406 08:45:54.977445  5226 solver.cpp:237]     Train net output #0: loss = 1.91252 (* 1 = 1.91252 loss)
I0406 08:45:54.977452  5226 sgd_solver.cpp:105] Iteration 11292, lr = 0.01
I0406 08:46:00.304841  5226 solver.cpp:218] Iteration 11304 (2.25253 iter/s, 5.32735s/12 iters), loss = 1.74319
I0406 08:46:00.304904  5226 solver.cpp:237]     Train net output #0: loss = 1.74319 (* 1 = 1.74319 loss)
I0406 08:46:00.304913  5226 sgd_solver.cpp:105] Iteration 11304, lr = 0.01
I0406 08:46:05.650084  5226 solver.cpp:218] Iteration 11316 (2.24503 iter/s, 5.34514s/12 iters), loss = 1.84395
I0406 08:46:05.650125  5226 solver.cpp:237]     Train net output #0: loss = 1.84395 (* 1 = 1.84395 loss)
I0406 08:46:05.650130  5226 sgd_solver.cpp:105] Iteration 11316, lr = 0.01
I0406 08:46:07.815052  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_11322.caffemodel
I0406 08:46:10.831476  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_11322.solverstate
I0406 08:46:13.159904  5226 solver.cpp:330] Iteration 11322, Testing net (#0)
I0406 08:46:13.159925  5226 net.cpp:676] Ignoring source layer train-data
I0406 08:46:17.661759  5226 solver.cpp:397]     Test net output #0: accuracy = 0.240809
I0406 08:46:17.661789  5226 solver.cpp:397]     Test net output #1: loss = 3.58143 (* 1 = 3.58143 loss)
I0406 08:46:18.092340  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:46:19.601292  5226 solver.cpp:218] Iteration 11328 (0.860149 iter/s, 13.9511s/12 iters), loss = 1.7194
I0406 08:46:19.601342  5226 solver.cpp:237]     Train net output #0: loss = 1.7194 (* 1 = 1.7194 loss)
I0406 08:46:19.601353  5226 sgd_solver.cpp:105] Iteration 11328, lr = 0.01
I0406 08:46:19.746233  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:46:24.676730  5226 solver.cpp:218] Iteration 11340 (2.36437 iter/s, 5.07534s/12 iters), loss = 1.47063
I0406 08:46:24.676769  5226 solver.cpp:237]     Train net output #0: loss = 1.47063 (* 1 = 1.47063 loss)
I0406 08:46:24.676774  5226 sgd_solver.cpp:105] Iteration 11340, lr = 0.01
I0406 08:46:29.999202  5226 solver.cpp:218] Iteration 11352 (2.25463 iter/s, 5.32238s/12 iters), loss = 1.51013
I0406 08:46:29.999294  5226 solver.cpp:237]     Train net output #0: loss = 1.51013 (* 1 = 1.51013 loss)
I0406 08:46:29.999300  5226 sgd_solver.cpp:105] Iteration 11352, lr = 0.01
I0406 08:46:35.487938  5226 solver.cpp:218] Iteration 11364 (2.18635 iter/s, 5.4886s/12 iters), loss = 1.51933
I0406 08:46:35.487978  5226 solver.cpp:237]     Train net output #0: loss = 1.51933 (* 1 = 1.51933 loss)
I0406 08:46:35.487983  5226 sgd_solver.cpp:105] Iteration 11364, lr = 0.01
I0406 08:46:40.406492  5226 solver.cpp:218] Iteration 11376 (2.43978 iter/s, 4.91847s/12 iters), loss = 2.08425
I0406 08:46:40.406528  5226 solver.cpp:237]     Train net output #0: loss = 2.08425 (* 1 = 2.08425 loss)
I0406 08:46:40.406535  5226 sgd_solver.cpp:105] Iteration 11376, lr = 0.01
I0406 08:46:45.670711  5226 solver.cpp:218] Iteration 11388 (2.27958 iter/s, 5.26413s/12 iters), loss = 1.78707
I0406 08:46:45.670749  5226 solver.cpp:237]     Train net output #0: loss = 1.78707 (* 1 = 1.78707 loss)
I0406 08:46:45.670754  5226 sgd_solver.cpp:105] Iteration 11388, lr = 0.01
I0406 08:46:51.078462  5226 solver.cpp:218] Iteration 11400 (2.21907 iter/s, 5.40766s/12 iters), loss = 2.40512
I0406 08:46:51.078505  5226 solver.cpp:237]     Train net output #0: loss = 2.40512 (* 1 = 2.40512 loss)
I0406 08:46:51.078510  5226 sgd_solver.cpp:105] Iteration 11400, lr = 0.01
I0406 08:46:56.327033  5226 solver.cpp:218] Iteration 11412 (2.28638 iter/s, 5.24848s/12 iters), loss = 2.22505
I0406 08:46:56.327080  5226 solver.cpp:237]     Train net output #0: loss = 2.22505 (* 1 = 2.22505 loss)
I0406 08:46:56.327086  5226 sgd_solver.cpp:105] Iteration 11412, lr = 0.01
I0406 08:47:01.181751  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_11424.caffemodel
I0406 08:47:04.233780  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_11424.solverstate
I0406 08:47:06.530071  5226 solver.cpp:330] Iteration 11424, Testing net (#0)
I0406 08:47:06.530094  5226 net.cpp:676] Ignoring source layer train-data
I0406 08:47:10.898622  5226 solver.cpp:397]     Test net output #0: accuracy = 0.230392
I0406 08:47:10.898651  5226 solver.cpp:397]     Test net output #1: loss = 3.71164 (* 1 = 3.71164 loss)
I0406 08:47:11.038820  5226 solver.cpp:218] Iteration 11424 (0.815681 iter/s, 14.7116s/12 iters), loss = 2.23305
I0406 08:47:11.038858  5226 solver.cpp:237]     Train net output #0: loss = 2.23305 (* 1 = 2.23305 loss)
I0406 08:47:11.038863  5226 sgd_solver.cpp:105] Iteration 11424, lr = 0.01
I0406 08:47:11.324560  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:47:12.549612  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:47:15.332767  5226 solver.cpp:218] Iteration 11436 (2.79469 iter/s, 4.29386s/12 iters), loss = 1.91544
I0406 08:47:15.332818  5226 solver.cpp:237]     Train net output #0: loss = 1.91544 (* 1 = 1.91544 loss)
I0406 08:47:15.332825  5226 sgd_solver.cpp:105] Iteration 11436, lr = 0.01
I0406 08:47:20.470675  5226 solver.cpp:218] Iteration 11448 (2.33562 iter/s, 5.13781s/12 iters), loss = 2.01688
I0406 08:47:20.470715  5226 solver.cpp:237]     Train net output #0: loss = 2.01688 (* 1 = 2.01688 loss)
I0406 08:47:20.470722  5226 sgd_solver.cpp:105] Iteration 11448, lr = 0.01
I0406 08:47:25.780690  5226 solver.cpp:218] Iteration 11460 (2.25992 iter/s, 5.30992s/12 iters), loss = 1.84641
I0406 08:47:25.780743  5226 solver.cpp:237]     Train net output #0: loss = 1.84641 (* 1 = 1.84641 loss)
I0406 08:47:25.780752  5226 sgd_solver.cpp:105] Iteration 11460, lr = 0.01
I0406 08:47:31.148954  5226 solver.cpp:218] Iteration 11472 (2.2354 iter/s, 5.36817s/12 iters), loss = 1.58496
I0406 08:47:31.148993  5226 solver.cpp:237]     Train net output #0: loss = 1.58496 (* 1 = 1.58496 loss)
I0406 08:47:31.148998  5226 sgd_solver.cpp:105] Iteration 11472, lr = 0.01
I0406 08:47:36.368921  5226 solver.cpp:218] Iteration 11484 (2.2989 iter/s, 5.21988s/12 iters), loss = 1.80257
I0406 08:47:36.369017  5226 solver.cpp:237]     Train net output #0: loss = 1.80257 (* 1 = 1.80257 loss)
I0406 08:47:36.369024  5226 sgd_solver.cpp:105] Iteration 11484, lr = 0.01
I0406 08:47:41.360534  5226 solver.cpp:218] Iteration 11496 (2.4041 iter/s, 4.99146s/12 iters), loss = 2.08389
I0406 08:47:41.360582  5226 solver.cpp:237]     Train net output #0: loss = 2.08389 (* 1 = 2.08389 loss)
I0406 08:47:41.360590  5226 sgd_solver.cpp:105] Iteration 11496, lr = 0.01
I0406 08:47:46.717453  5226 solver.cpp:218] Iteration 11508 (2.24013 iter/s, 5.35682s/12 iters), loss = 1.9439
I0406 08:47:46.717491  5226 solver.cpp:237]     Train net output #0: loss = 1.9439 (* 1 = 1.9439 loss)
I0406 08:47:46.717496  5226 sgd_solver.cpp:105] Iteration 11508, lr = 0.01
I0406 08:47:52.029862  5226 solver.cpp:218] Iteration 11520 (2.2589 iter/s, 5.31233s/12 iters), loss = 1.89792
I0406 08:47:52.029902  5226 solver.cpp:237]     Train net output #0: loss = 1.89792 (* 1 = 1.89792 loss)
I0406 08:47:52.029907  5226 sgd_solver.cpp:105] Iteration 11520, lr = 0.01
I0406 08:47:54.225944  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_11526.caffemodel
I0406 08:47:57.290717  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_11526.solverstate
I0406 08:47:59.603242  5226 solver.cpp:330] Iteration 11526, Testing net (#0)
I0406 08:47:59.603261  5226 net.cpp:676] Ignoring source layer train-data
I0406 08:48:03.912118  5226 solver.cpp:397]     Test net output #0: accuracy = 0.23652
I0406 08:48:03.912156  5226 solver.cpp:397]     Test net output #1: loss = 3.67689 (* 1 = 3.67689 loss)
I0406 08:48:04.013293  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:48:05.171188  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:48:05.726559  5226 solver.cpp:218] Iteration 11532 (0.876132 iter/s, 13.6966s/12 iters), loss = 2.81239
I0406 08:48:05.726611  5226 solver.cpp:237]     Train net output #0: loss = 2.81239 (* 1 = 2.81239 loss)
I0406 08:48:05.726619  5226 sgd_solver.cpp:105] Iteration 11532, lr = 0.01
I0406 08:48:10.882457  5226 solver.cpp:218] Iteration 11544 (2.32748 iter/s, 5.1558s/12 iters), loss = 2.0758
I0406 08:48:10.882588  5226 solver.cpp:237]     Train net output #0: loss = 2.0758 (* 1 = 2.0758 loss)
I0406 08:48:10.882594  5226 sgd_solver.cpp:105] Iteration 11544, lr = 0.01
I0406 08:48:15.955760  5226 solver.cpp:218] Iteration 11556 (2.3654 iter/s, 5.07313s/12 iters), loss = 2.28721
I0406 08:48:15.955796  5226 solver.cpp:237]     Train net output #0: loss = 2.28721 (* 1 = 2.28721 loss)
I0406 08:48:15.955801  5226 sgd_solver.cpp:105] Iteration 11556, lr = 0.01
I0406 08:48:21.323837  5226 solver.cpp:218] Iteration 11568 (2.23547 iter/s, 5.36799s/12 iters), loss = 2.00153
I0406 08:48:21.323875  5226 solver.cpp:237]     Train net output #0: loss = 2.00153 (* 1 = 2.00153 loss)
I0406 08:48:21.323881  5226 sgd_solver.cpp:105] Iteration 11568, lr = 0.01
I0406 08:48:26.642590  5226 solver.cpp:218] Iteration 11580 (2.2562 iter/s, 5.31867s/12 iters), loss = 2.18757
I0406 08:48:26.642630  5226 solver.cpp:237]     Train net output #0: loss = 2.18757 (* 1 = 2.18757 loss)
I0406 08:48:26.642635  5226 sgd_solver.cpp:105] Iteration 11580, lr = 0.01
I0406 08:48:31.888741  5226 solver.cpp:218] Iteration 11592 (2.28743 iter/s, 5.24606s/12 iters), loss = 1.78886
I0406 08:48:31.888792  5226 solver.cpp:237]     Train net output #0: loss = 1.78886 (* 1 = 1.78886 loss)
I0406 08:48:31.888799  5226 sgd_solver.cpp:105] Iteration 11592, lr = 0.01
I0406 08:48:37.106726  5226 solver.cpp:218] Iteration 11604 (2.29978 iter/s, 5.21789s/12 iters), loss = 1.63513
I0406 08:48:37.106775  5226 solver.cpp:237]     Train net output #0: loss = 1.63513 (* 1 = 1.63513 loss)
I0406 08:48:37.106782  5226 sgd_solver.cpp:105] Iteration 11604, lr = 0.01
I0406 08:48:42.493919  5226 solver.cpp:218] Iteration 11616 (2.22754 iter/s, 5.3871s/12 iters), loss = 2.03324
I0406 08:48:42.494065  5226 solver.cpp:237]     Train net output #0: loss = 2.03324 (* 1 = 2.03324 loss)
I0406 08:48:42.494073  5226 sgd_solver.cpp:105] Iteration 11616, lr = 0.01
I0406 08:48:47.447613  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_11628.caffemodel
I0406 08:48:50.383462  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_11628.solverstate
I0406 08:48:52.709919  5226 solver.cpp:330] Iteration 11628, Testing net (#0)
I0406 08:48:52.709939  5226 net.cpp:676] Ignoring source layer train-data
I0406 08:48:57.166172  5226 solver.cpp:397]     Test net output #0: accuracy = 0.242034
I0406 08:48:57.166209  5226 solver.cpp:397]     Test net output #1: loss = 3.67389 (* 1 = 3.67389 loss)
I0406 08:48:57.241222  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:48:57.301640  5226 solver.cpp:218] Iteration 11628 (0.810401 iter/s, 14.8075s/12 iters), loss = 1.84521
I0406 08:48:57.301681  5226 solver.cpp:237]     Train net output #0: loss = 1.84521 (* 1 = 1.84521 loss)
I0406 08:48:57.301687  5226 sgd_solver.cpp:105] Iteration 11628, lr = 0.01
I0406 08:48:58.103940  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:49:01.750751  5226 solver.cpp:218] Iteration 11640 (2.69722 iter/s, 4.44903s/12 iters), loss = 1.84684
I0406 08:49:01.750809  5226 solver.cpp:237]     Train net output #0: loss = 1.84684 (* 1 = 1.84684 loss)
I0406 08:49:01.750818  5226 sgd_solver.cpp:105] Iteration 11640, lr = 0.01
I0406 08:49:06.927625  5226 solver.cpp:218] Iteration 11652 (2.31805 iter/s, 5.17677s/12 iters), loss = 1.57954
I0406 08:49:06.927683  5226 solver.cpp:237]     Train net output #0: loss = 1.57954 (* 1 = 1.57954 loss)
I0406 08:49:06.927692  5226 sgd_solver.cpp:105] Iteration 11652, lr = 0.01
I0406 08:49:12.229154  5226 solver.cpp:218] Iteration 11664 (2.26354 iter/s, 5.30143s/12 iters), loss = 1.72684
I0406 08:49:12.229195  5226 solver.cpp:237]     Train net output #0: loss = 1.72684 (* 1 = 1.72684 loss)
I0406 08:49:12.229200  5226 sgd_solver.cpp:105] Iteration 11664, lr = 0.01
I0406 08:49:17.606823  5226 solver.cpp:218] Iteration 11676 (2.23149 iter/s, 5.37758s/12 iters), loss = 1.56391
I0406 08:49:17.606952  5226 solver.cpp:237]     Train net output #0: loss = 1.56391 (* 1 = 1.56391 loss)
I0406 08:49:17.606959  5226 sgd_solver.cpp:105] Iteration 11676, lr = 0.01
I0406 08:49:22.938372  5226 solver.cpp:218] Iteration 11688 (2.25083 iter/s, 5.33137s/12 iters), loss = 1.78094
I0406 08:49:22.938426  5226 solver.cpp:237]     Train net output #0: loss = 1.78094 (* 1 = 1.78094 loss)
I0406 08:49:22.938434  5226 sgd_solver.cpp:105] Iteration 11688, lr = 0.01
I0406 08:49:28.175235  5226 solver.cpp:218] Iteration 11700 (2.29149 iter/s, 5.23676s/12 iters), loss = 1.97296
I0406 08:49:28.175285  5226 solver.cpp:237]     Train net output #0: loss = 1.97296 (* 1 = 1.97296 loss)
I0406 08:49:28.175293  5226 sgd_solver.cpp:105] Iteration 11700, lr = 0.01
I0406 08:49:33.530195  5226 solver.cpp:218] Iteration 11712 (2.24095 iter/s, 5.35487s/12 iters), loss = 1.85167
I0406 08:49:33.530232  5226 solver.cpp:237]     Train net output #0: loss = 1.85167 (* 1 = 1.85167 loss)
I0406 08:49:33.530237  5226 sgd_solver.cpp:105] Iteration 11712, lr = 0.01
I0406 08:49:38.937616  5226 solver.cpp:218] Iteration 11724 (2.21921 iter/s, 5.40734s/12 iters), loss = 1.89333
I0406 08:49:38.937652  5226 solver.cpp:237]     Train net output #0: loss = 1.89333 (* 1 = 1.89333 loss)
I0406 08:49:38.937659  5226 sgd_solver.cpp:105] Iteration 11724, lr = 0.01
I0406 08:49:41.065572  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_11730.caffemodel
I0406 08:49:44.520797  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_11730.solverstate
I0406 08:49:47.031922  5226 solver.cpp:330] Iteration 11730, Testing net (#0)
I0406 08:49:47.031944  5226 net.cpp:676] Ignoring source layer train-data
I0406 08:49:51.289775  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:49:51.319869  5226 solver.cpp:397]     Test net output #0: accuracy = 0.213848
I0406 08:49:51.319909  5226 solver.cpp:397]     Test net output #1: loss = 3.78413 (* 1 = 3.78413 loss)
I0406 08:49:51.936784  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:49:53.183463  5226 solver.cpp:218] Iteration 11736 (0.842359 iter/s, 14.2457s/12 iters), loss = 2.17403
I0406 08:49:53.183506  5226 solver.cpp:237]     Train net output #0: loss = 2.17403 (* 1 = 2.17403 loss)
I0406 08:49:53.183511  5226 sgd_solver.cpp:105] Iteration 11736, lr = 0.01
I0406 08:49:58.525866  5226 solver.cpp:218] Iteration 11748 (2.24622 iter/s, 5.34231s/12 iters), loss = 2.08566
I0406 08:49:58.525923  5226 solver.cpp:237]     Train net output #0: loss = 2.08566 (* 1 = 2.08566 loss)
I0406 08:49:58.525931  5226 sgd_solver.cpp:105] Iteration 11748, lr = 0.01
I0406 08:50:03.896023  5226 solver.cpp:218] Iteration 11760 (2.23462 iter/s, 5.37005s/12 iters), loss = 1.71655
I0406 08:50:03.896062  5226 solver.cpp:237]     Train net output #0: loss = 1.71655 (* 1 = 1.71655 loss)
I0406 08:50:03.896068  5226 sgd_solver.cpp:105] Iteration 11760, lr = 0.01
I0406 08:50:09.187372  5226 solver.cpp:218] Iteration 11772 (2.26789 iter/s, 5.29126s/12 iters), loss = 1.73329
I0406 08:50:09.187413  5226 solver.cpp:237]     Train net output #0: loss = 1.73329 (* 1 = 1.73329 loss)
I0406 08:50:09.187418  5226 sgd_solver.cpp:105] Iteration 11772, lr = 0.01
I0406 08:50:13.149471  5226 blocking_queue.cpp:49] Waiting for data
I0406 08:50:14.576890  5226 solver.cpp:218] Iteration 11784 (2.22658 iter/s, 5.38942s/12 iters), loss = 1.95888
I0406 08:50:14.576946  5226 solver.cpp:237]     Train net output #0: loss = 1.95888 (* 1 = 1.95888 loss)
I0406 08:50:14.576956  5226 sgd_solver.cpp:105] Iteration 11784, lr = 0.01
I0406 08:50:19.932400  5226 solver.cpp:218] Iteration 11796 (2.24073 iter/s, 5.35541s/12 iters), loss = 1.69777
I0406 08:50:19.932440  5226 solver.cpp:237]     Train net output #0: loss = 1.69777 (* 1 = 1.69777 loss)
I0406 08:50:19.932446  5226 sgd_solver.cpp:105] Iteration 11796, lr = 0.01
I0406 08:50:25.074579  5226 solver.cpp:218] Iteration 11808 (2.33368 iter/s, 5.14209s/12 iters), loss = 1.52417
I0406 08:50:25.074707  5226 solver.cpp:237]     Train net output #0: loss = 1.52417 (* 1 = 1.52417 loss)
I0406 08:50:25.074713  5226 sgd_solver.cpp:105] Iteration 11808, lr = 0.01
I0406 08:50:30.474642  5226 solver.cpp:218] Iteration 11820 (2.22227 iter/s, 5.3999s/12 iters), loss = 1.84007
I0406 08:50:30.474678  5226 solver.cpp:237]     Train net output #0: loss = 1.84007 (* 1 = 1.84007 loss)
I0406 08:50:30.474682  5226 sgd_solver.cpp:105] Iteration 11820, lr = 0.01
I0406 08:50:35.161226  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_11832.caffemodel
I0406 08:50:38.176852  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_11832.solverstate
I0406 08:50:40.475224  5226 solver.cpp:330] Iteration 11832, Testing net (#0)
I0406 08:50:40.475242  5226 net.cpp:676] Ignoring source layer train-data
I0406 08:50:44.763263  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:50:44.826130  5226 solver.cpp:397]     Test net output #0: accuracy = 0.259804
I0406 08:50:44.826164  5226 solver.cpp:397]     Test net output #1: loss = 3.6715 (* 1 = 3.6715 loss)
I0406 08:50:44.966567  5226 solver.cpp:218] Iteration 11832 (0.828056 iter/s, 14.4918s/12 iters), loss = 1.53108
I0406 08:50:44.966635  5226 solver.cpp:237]     Train net output #0: loss = 1.53108 (* 1 = 1.53108 loss)
I0406 08:50:44.966645  5226 sgd_solver.cpp:105] Iteration 11832, lr = 0.01
I0406 08:50:45.064098  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:50:49.490244  5226 solver.cpp:218] Iteration 11844 (2.65277 iter/s, 4.52357s/12 iters), loss = 2.03592
I0406 08:50:49.490284  5226 solver.cpp:237]     Train net output #0: loss = 2.03592 (* 1 = 2.03592 loss)
I0406 08:50:49.490289  5226 sgd_solver.cpp:105] Iteration 11844, lr = 0.01
I0406 08:50:54.916782  5226 solver.cpp:218] Iteration 11856 (2.21139 iter/s, 5.42645s/12 iters), loss = 1.60784
I0406 08:50:54.916819  5226 solver.cpp:237]     Train net output #0: loss = 1.60784 (* 1 = 1.60784 loss)
I0406 08:50:54.916824  5226 sgd_solver.cpp:105] Iteration 11856, lr = 0.01
I0406 08:51:00.330348  5226 solver.cpp:218] Iteration 11868 (2.21669 iter/s, 5.41348s/12 iters), loss = 1.80591
I0406 08:51:00.330443  5226 solver.cpp:237]     Train net output #0: loss = 1.80591 (* 1 = 1.80591 loss)
I0406 08:51:00.330451  5226 sgd_solver.cpp:105] Iteration 11868, lr = 0.01
I0406 08:51:05.456890  5226 solver.cpp:218] Iteration 11880 (2.34083 iter/s, 5.1264s/12 iters), loss = 1.42798
I0406 08:51:05.456930  5226 solver.cpp:237]     Train net output #0: loss = 1.42798 (* 1 = 1.42798 loss)
I0406 08:51:05.456936  5226 sgd_solver.cpp:105] Iteration 11880, lr = 0.01
I0406 08:51:10.642530  5226 solver.cpp:218] Iteration 11892 (2.31412 iter/s, 5.18555s/12 iters), loss = 1.89454
I0406 08:51:10.642570  5226 solver.cpp:237]     Train net output #0: loss = 1.89454 (* 1 = 1.89454 loss)
I0406 08:51:10.642575  5226 sgd_solver.cpp:105] Iteration 11892, lr = 0.01
I0406 08:51:16.020606  5226 solver.cpp:218] Iteration 11904 (2.23132 iter/s, 5.37799s/12 iters), loss = 1.938
I0406 08:51:16.020654  5226 solver.cpp:237]     Train net output #0: loss = 1.938 (* 1 = 1.938 loss)
I0406 08:51:16.020661  5226 sgd_solver.cpp:105] Iteration 11904, lr = 0.01
I0406 08:51:21.348755  5226 solver.cpp:218] Iteration 11916 (2.25223 iter/s, 5.32806s/12 iters), loss = 2.01526
I0406 08:51:21.348794  5226 solver.cpp:237]     Train net output #0: loss = 2.01526 (* 1 = 2.01526 loss)
I0406 08:51:21.348800  5226 sgd_solver.cpp:105] Iteration 11916, lr = 0.01
I0406 08:51:26.541290  5226 solver.cpp:218] Iteration 11928 (2.31105 iter/s, 5.19245s/12 iters), loss = 1.84794
I0406 08:51:26.541338  5226 solver.cpp:237]     Train net output #0: loss = 1.84794 (* 1 = 1.84794 loss)
I0406 08:51:26.541347  5226 sgd_solver.cpp:105] Iteration 11928, lr = 0.01
I0406 08:51:28.814744  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_11934.caffemodel
I0406 08:51:29.604965  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:51:31.819617  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_11934.solverstate
I0406 08:51:34.119446  5226 solver.cpp:330] Iteration 11934, Testing net (#0)
I0406 08:51:34.119465  5226 net.cpp:676] Ignoring source layer train-data
I0406 08:51:38.428683  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:51:38.534747  5226 solver.cpp:397]     Test net output #0: accuracy = 0.223652
I0406 08:51:38.534783  5226 solver.cpp:397]     Test net output #1: loss = 3.74559 (* 1 = 3.74559 loss)
I0406 08:51:40.436434  5226 solver.cpp:218] Iteration 11940 (0.86362 iter/s, 13.895s/12 iters), loss = 2.19051
I0406 08:51:40.436487  5226 solver.cpp:237]     Train net output #0: loss = 2.19051 (* 1 = 2.19051 loss)
I0406 08:51:40.436496  5226 sgd_solver.cpp:105] Iteration 11940, lr = 0.01
I0406 08:51:45.733759  5226 solver.cpp:218] Iteration 11952 (2.26534 iter/s, 5.29723s/12 iters), loss = 2.09312
I0406 08:51:45.733798  5226 solver.cpp:237]     Train net output #0: loss = 2.09312 (* 1 = 2.09312 loss)
I0406 08:51:45.733804  5226 sgd_solver.cpp:105] Iteration 11952, lr = 0.01
I0406 08:51:51.125803  5226 solver.cpp:218] Iteration 11964 (2.22554 iter/s, 5.39195s/12 iters), loss = 1.97578
I0406 08:51:51.125855  5226 solver.cpp:237]     Train net output #0: loss = 1.97578 (* 1 = 1.97578 loss)
I0406 08:51:51.125864  5226 sgd_solver.cpp:105] Iteration 11964, lr = 0.01
I0406 08:51:56.505972  5226 solver.cpp:218] Iteration 11976 (2.23045 iter/s, 5.38007s/12 iters), loss = 1.82206
I0406 08:51:56.506022  5226 solver.cpp:237]     Train net output #0: loss = 1.82206 (* 1 = 1.82206 loss)
I0406 08:51:56.506031  5226 sgd_solver.cpp:105] Iteration 11976, lr = 0.01
I0406 08:52:01.846727  5226 solver.cpp:218] Iteration 11988 (2.24692 iter/s, 5.34065s/12 iters), loss = 2.07828
I0406 08:52:01.846848  5226 solver.cpp:237]     Train net output #0: loss = 2.07828 (* 1 = 2.07828 loss)
I0406 08:52:01.846858  5226 sgd_solver.cpp:105] Iteration 11988, lr = 0.01
I0406 08:52:07.132922  5226 solver.cpp:218] Iteration 12000 (2.27013 iter/s, 5.28603s/12 iters), loss = 2.26341
I0406 08:52:07.132962  5226 solver.cpp:237]     Train net output #0: loss = 2.26341 (* 1 = 2.26341 loss)
I0406 08:52:07.132967  5226 sgd_solver.cpp:105] Iteration 12000, lr = 0.01
I0406 08:52:12.355908  5226 solver.cpp:218] Iteration 12012 (2.29758 iter/s, 5.2229s/12 iters), loss = 2.15603
I0406 08:52:12.355957  5226 solver.cpp:237]     Train net output #0: loss = 2.15603 (* 1 = 2.15603 loss)
I0406 08:52:12.355965  5226 sgd_solver.cpp:105] Iteration 12012, lr = 0.01
I0406 08:52:17.347137  5226 solver.cpp:218] Iteration 12024 (2.40426 iter/s, 4.99113s/12 iters), loss = 2.04246
I0406 08:52:17.347198  5226 solver.cpp:237]     Train net output #0: loss = 2.04246 (* 1 = 2.04246 loss)
I0406 08:52:17.347206  5226 sgd_solver.cpp:105] Iteration 12024, lr = 0.01
I0406 08:52:22.152793  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_12036.caffemodel
I0406 08:52:22.776340  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:52:25.182013  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_12036.solverstate
I0406 08:52:27.483173  5226 solver.cpp:330] Iteration 12036, Testing net (#0)
I0406 08:52:27.483194  5226 net.cpp:676] Ignoring source layer train-data
I0406 08:52:31.621233  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:52:31.758476  5226 solver.cpp:397]     Test net output #0: accuracy = 0.227941
I0406 08:52:31.758510  5226 solver.cpp:397]     Test net output #1: loss = 3.71568 (* 1 = 3.71568 loss)
I0406 08:52:31.897584  5226 solver.cpp:218] Iteration 12036 (0.824726 iter/s, 14.5503s/12 iters), loss = 1.87633
I0406 08:52:31.899139  5226 solver.cpp:237]     Train net output #0: loss = 1.87633 (* 1 = 1.87633 loss)
I0406 08:52:31.899150  5226 sgd_solver.cpp:105] Iteration 12036, lr = 0.01
I0406 08:52:36.128262  5226 solver.cpp:218] Iteration 12048 (2.83749 iter/s, 4.22909s/12 iters), loss = 1.6342
I0406 08:52:36.128304  5226 solver.cpp:237]     Train net output #0: loss = 1.6342 (* 1 = 1.6342 loss)
I0406 08:52:36.128312  5226 sgd_solver.cpp:105] Iteration 12048, lr = 0.01
I0406 08:52:41.390291  5226 solver.cpp:218] Iteration 12060 (2.28053 iter/s, 5.26194s/12 iters), loss = 2.14986
I0406 08:52:41.390333  5226 solver.cpp:237]     Train net output #0: loss = 2.14986 (* 1 = 2.14986 loss)
I0406 08:52:41.390339  5226 sgd_solver.cpp:105] Iteration 12060, lr = 0.01
I0406 08:52:46.698346  5226 solver.cpp:218] Iteration 12072 (2.26075 iter/s, 5.30797s/12 iters), loss = 1.63935
I0406 08:52:46.698393  5226 solver.cpp:237]     Train net output #0: loss = 1.63935 (* 1 = 1.63935 loss)
I0406 08:52:46.698401  5226 sgd_solver.cpp:105] Iteration 12072, lr = 0.01
I0406 08:52:52.049711  5226 solver.cpp:218] Iteration 12084 (2.24246 iter/s, 5.35127s/12 iters), loss = 2.53069
I0406 08:52:52.049758  5226 solver.cpp:237]     Train net output #0: loss = 2.53069 (* 1 = 2.53069 loss)
I0406 08:52:52.049767  5226 sgd_solver.cpp:105] Iteration 12084, lr = 0.01
I0406 08:52:57.129184  5226 solver.cpp:218] Iteration 12096 (2.3625 iter/s, 5.07937s/12 iters), loss = 1.98032
I0406 08:52:57.129236  5226 solver.cpp:237]     Train net output #0: loss = 1.98032 (* 1 = 1.98032 loss)
I0406 08:52:57.129245  5226 sgd_solver.cpp:105] Iteration 12096, lr = 0.01
I0406 08:53:02.560909  5226 solver.cpp:218] Iteration 12108 (2.20928 iter/s, 5.43163s/12 iters), loss = 2.2319
I0406 08:53:02.560999  5226 solver.cpp:237]     Train net output #0: loss = 2.2319 (* 1 = 2.2319 loss)
I0406 08:53:02.561007  5226 sgd_solver.cpp:105] Iteration 12108, lr = 0.01
I0406 08:53:07.703745  5226 solver.cpp:218] Iteration 12120 (2.3334 iter/s, 5.1427s/12 iters), loss = 2.10387
I0406 08:53:07.703783  5226 solver.cpp:237]     Train net output #0: loss = 2.10387 (* 1 = 2.10387 loss)
I0406 08:53:07.703788  5226 sgd_solver.cpp:105] Iteration 12120, lr = 0.01
I0406 08:53:13.020109  5226 solver.cpp:218] Iteration 12132 (2.25722 iter/s, 5.31627s/12 iters), loss = 2.05641
I0406 08:53:13.020170  5226 solver.cpp:237]     Train net output #0: loss = 2.05641 (* 1 = 2.05641 loss)
I0406 08:53:13.020179  5226 sgd_solver.cpp:105] Iteration 12132, lr = 0.01
I0406 08:53:15.178459  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_12138.caffemodel
I0406 08:53:15.515172  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:53:18.268491  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_12138.solverstate
I0406 08:53:20.576864  5226 solver.cpp:330] Iteration 12138, Testing net (#0)
I0406 08:53:20.576890  5226 net.cpp:676] Ignoring source layer train-data
I0406 08:53:24.799331  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:53:24.971081  5226 solver.cpp:397]     Test net output #0: accuracy = 0.19424
I0406 08:53:24.971118  5226 solver.cpp:397]     Test net output #1: loss = 3.99001 (* 1 = 3.99001 loss)
I0406 08:53:26.742281  5226 solver.cpp:218] Iteration 12144 (0.874507 iter/s, 13.722s/12 iters), loss = 1.99688
I0406 08:53:26.742321  5226 solver.cpp:237]     Train net output #0: loss = 1.99688 (* 1 = 1.99688 loss)
I0406 08:53:26.742326  5226 sgd_solver.cpp:105] Iteration 12144, lr = 0.01
I0406 08:53:31.936920  5226 solver.cpp:218] Iteration 12156 (2.31011 iter/s, 5.19455s/12 iters), loss = 2.5073
I0406 08:53:31.936959  5226 solver.cpp:237]     Train net output #0: loss = 2.5073 (* 1 = 2.5073 loss)
I0406 08:53:31.936965  5226 sgd_solver.cpp:105] Iteration 12156, lr = 0.01
I0406 08:53:37.194880  5226 solver.cpp:218] Iteration 12168 (2.28229 iter/s, 5.25787s/12 iters), loss = 2.20415
I0406 08:53:37.195035  5226 solver.cpp:237]     Train net output #0: loss = 2.20415 (* 1 = 2.20415 loss)
I0406 08:53:37.195044  5226 sgd_solver.cpp:105] Iteration 12168, lr = 0.01
I0406 08:53:42.546789  5226 solver.cpp:218] Iteration 12180 (2.24227 iter/s, 5.35171s/12 iters), loss = 2.0271
I0406 08:53:42.546830  5226 solver.cpp:237]     Train net output #0: loss = 2.0271 (* 1 = 2.0271 loss)
I0406 08:53:42.546835  5226 sgd_solver.cpp:105] Iteration 12180, lr = 0.01
I0406 08:53:47.811519  5226 solver.cpp:218] Iteration 12192 (2.27936 iter/s, 5.26464s/12 iters), loss = 1.98259
I0406 08:53:47.811561  5226 solver.cpp:237]     Train net output #0: loss = 1.98259 (* 1 = 1.98259 loss)
I0406 08:53:47.811568  5226 sgd_solver.cpp:105] Iteration 12192, lr = 0.01
I0406 08:53:53.320174  5226 solver.cpp:218] Iteration 12204 (2.17843 iter/s, 5.50856s/12 iters), loss = 1.6622
I0406 08:53:53.320225  5226 solver.cpp:237]     Train net output #0: loss = 1.6622 (* 1 = 1.6622 loss)
I0406 08:53:53.320235  5226 sgd_solver.cpp:105] Iteration 12204, lr = 0.01
I0406 08:53:58.705425  5226 solver.cpp:218] Iteration 12216 (2.22835 iter/s, 5.38515s/12 iters), loss = 2.35338
I0406 08:53:58.705467  5226 solver.cpp:237]     Train net output #0: loss = 2.35338 (* 1 = 2.35338 loss)
I0406 08:53:58.705474  5226 sgd_solver.cpp:105] Iteration 12216, lr = 0.01
I0406 08:54:04.009377  5226 solver.cpp:218] Iteration 12228 (2.2625 iter/s, 5.30386s/12 iters), loss = 1.78302
I0406 08:54:04.009424  5226 solver.cpp:237]     Train net output #0: loss = 1.78302 (* 1 = 1.78302 loss)
I0406 08:54:04.009431  5226 sgd_solver.cpp:105] Iteration 12228, lr = 0.01
I0406 08:54:08.534565  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:54:08.671589  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_12240.caffemodel
I0406 08:54:11.702525  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_12240.solverstate
I0406 08:54:14.013617  5226 solver.cpp:330] Iteration 12240, Testing net (#0)
I0406 08:54:14.013639  5226 net.cpp:676] Ignoring source layer train-data
I0406 08:54:18.138761  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:54:18.363211  5226 solver.cpp:397]     Test net output #0: accuracy = 0.242034
I0406 08:54:18.363252  5226 solver.cpp:397]     Test net output #1: loss = 3.67446 (* 1 = 3.67446 loss)
I0406 08:54:18.497666  5226 solver.cpp:218] Iteration 12240 (0.828264 iter/s, 14.4881s/12 iters), loss = 2.28348
I0406 08:54:18.508846  5226 solver.cpp:237]     Train net output #0: loss = 2.28348 (* 1 = 2.28348 loss)
I0406 08:54:18.508870  5226 sgd_solver.cpp:105] Iteration 12240, lr = 0.01
I0406 08:54:22.789860  5226 solver.cpp:218] Iteration 12252 (2.80308 iter/s, 4.281s/12 iters), loss = 1.40058
I0406 08:54:22.789913  5226 solver.cpp:237]     Train net output #0: loss = 1.40058 (* 1 = 1.40058 loss)
I0406 08:54:22.789919  5226 sgd_solver.cpp:105] Iteration 12252, lr = 0.01
I0406 08:54:28.061539  5226 solver.cpp:218] Iteration 12264 (2.27636 iter/s, 5.27158s/12 iters), loss = 1.86653
I0406 08:54:28.061580  5226 solver.cpp:237]     Train net output #0: loss = 1.86653 (* 1 = 1.86653 loss)
I0406 08:54:28.061585  5226 sgd_solver.cpp:105] Iteration 12264, lr = 0.01
I0406 08:54:33.430259  5226 solver.cpp:218] Iteration 12276 (2.23521 iter/s, 5.36863s/12 iters), loss = 2.33644
I0406 08:54:33.430307  5226 solver.cpp:237]     Train net output #0: loss = 2.33644 (* 1 = 2.33644 loss)
I0406 08:54:33.430316  5226 sgd_solver.cpp:105] Iteration 12276, lr = 0.01
I0406 08:54:38.601475  5226 solver.cpp:218] Iteration 12288 (2.32058 iter/s, 5.17112s/12 iters), loss = 1.94653
I0406 08:54:38.601619  5226 solver.cpp:237]     Train net output #0: loss = 1.94653 (* 1 = 1.94653 loss)
I0406 08:54:38.601626  5226 sgd_solver.cpp:105] Iteration 12288, lr = 0.01
I0406 08:54:43.950040  5226 solver.cpp:218] Iteration 12300 (2.24367 iter/s, 5.34837s/12 iters), loss = 1.7984
I0406 08:54:43.950094  5226 solver.cpp:237]     Train net output #0: loss = 1.7984 (* 1 = 1.7984 loss)
I0406 08:54:43.950103  5226 sgd_solver.cpp:105] Iteration 12300, lr = 0.01
I0406 08:54:49.299345  5226 solver.cpp:218] Iteration 12312 (2.24332 iter/s, 5.34921s/12 iters), loss = 2.4692
I0406 08:54:49.305567  5226 solver.cpp:237]     Train net output #0: loss = 2.4692 (* 1 = 2.4692 loss)
I0406 08:54:49.305585  5226 sgd_solver.cpp:105] Iteration 12312, lr = 0.01
I0406 08:54:54.498692  5226 solver.cpp:218] Iteration 12324 (2.31076 iter/s, 5.19309s/12 iters), loss = 2.18558
I0406 08:54:54.498742  5226 solver.cpp:237]     Train net output #0: loss = 2.18558 (* 1 = 2.18558 loss)
I0406 08:54:54.498751  5226 sgd_solver.cpp:105] Iteration 12324, lr = 0.01
I0406 08:54:59.726038  5226 solver.cpp:218] Iteration 12336 (2.29566 iter/s, 5.22725s/12 iters), loss = 1.80057
I0406 08:54:59.726084  5226 solver.cpp:237]     Train net output #0: loss = 1.80057 (* 1 = 1.80057 loss)
I0406 08:54:59.726092  5226 sgd_solver.cpp:105] Iteration 12336, lr = 0.01
I0406 08:55:01.381563  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:55:01.715597  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_12342.caffemodel
I0406 08:55:04.741385  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_12342.solverstate
I0406 08:55:07.049391  5226 solver.cpp:330] Iteration 12342, Testing net (#0)
I0406 08:55:07.049412  5226 net.cpp:676] Ignoring source layer train-data
I0406 08:55:11.206604  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:55:11.459978  5226 solver.cpp:397]     Test net output #0: accuracy = 0.210784
I0406 08:55:11.460011  5226 solver.cpp:397]     Test net output #1: loss = 3.72294 (* 1 = 3.72294 loss)
I0406 08:55:13.343698  5226 solver.cpp:218] Iteration 12348 (0.881218 iter/s, 13.6175s/12 iters), loss = 2.02862
I0406 08:55:13.343737  5226 solver.cpp:237]     Train net output #0: loss = 2.02862 (* 1 = 2.02862 loss)
I0406 08:55:13.343744  5226 sgd_solver.cpp:105] Iteration 12348, lr = 0.01
I0406 08:55:18.647181  5226 solver.cpp:218] Iteration 12360 (2.2627 iter/s, 5.3034s/12 iters), loss = 1.78042
I0406 08:55:18.647222  5226 solver.cpp:237]     Train net output #0: loss = 1.78042 (* 1 = 1.78042 loss)
I0406 08:55:18.647228  5226 sgd_solver.cpp:105] Iteration 12360, lr = 0.01
I0406 08:55:23.925997  5226 solver.cpp:218] Iteration 12372 (2.27327 iter/s, 5.27873s/12 iters), loss = 1.92886
I0406 08:55:23.926033  5226 solver.cpp:237]     Train net output #0: loss = 1.92886 (* 1 = 1.92886 loss)
I0406 08:55:23.926038  5226 sgd_solver.cpp:105] Iteration 12372, lr = 0.01
I0406 08:55:29.264027  5226 solver.cpp:218] Iteration 12384 (2.24806 iter/s, 5.33795s/12 iters), loss = 1.66089
I0406 08:55:29.264065  5226 solver.cpp:237]     Train net output #0: loss = 1.66089 (* 1 = 1.66089 loss)
I0406 08:55:29.264070  5226 sgd_solver.cpp:105] Iteration 12384, lr = 0.01
I0406 08:55:34.621311  5226 solver.cpp:218] Iteration 12396 (2.23998 iter/s, 5.3572s/12 iters), loss = 1.8297
I0406 08:55:34.621346  5226 solver.cpp:237]     Train net output #0: loss = 1.8297 (* 1 = 1.8297 loss)
I0406 08:55:34.621352  5226 sgd_solver.cpp:105] Iteration 12396, lr = 0.01
I0406 08:55:39.919286  5226 solver.cpp:218] Iteration 12408 (2.26505 iter/s, 5.29789s/12 iters), loss = 2.10458
I0406 08:55:39.919325  5226 solver.cpp:237]     Train net output #0: loss = 2.10458 (* 1 = 2.10458 loss)
I0406 08:55:39.919330  5226 sgd_solver.cpp:105] Iteration 12408, lr = 0.01
I0406 08:55:45.282624  5226 solver.cpp:218] Iteration 12420 (2.23745 iter/s, 5.36325s/12 iters), loss = 1.9067
I0406 08:55:45.282791  5226 solver.cpp:237]     Train net output #0: loss = 1.9067 (* 1 = 1.9067 loss)
I0406 08:55:45.282800  5226 sgd_solver.cpp:105] Iteration 12420, lr = 0.01
I0406 08:55:50.699417  5226 solver.cpp:218] Iteration 12432 (2.21542 iter/s, 5.41658s/12 iters), loss = 2.26223
I0406 08:55:50.699455  5226 solver.cpp:237]     Train net output #0: loss = 2.26223 (* 1 = 2.26223 loss)
I0406 08:55:50.699461  5226 sgd_solver.cpp:105] Iteration 12432, lr = 0.01
I0406 08:55:54.801519  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:55:55.537760  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_12444.caffemodel
I0406 08:55:58.541608  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_12444.solverstate
I0406 08:56:00.839605  5226 solver.cpp:330] Iteration 12444, Testing net (#0)
I0406 08:56:00.839622  5226 net.cpp:676] Ignoring source layer train-data
I0406 08:56:04.903287  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:56:05.200853  5226 solver.cpp:397]     Test net output #0: accuracy = 0.240196
I0406 08:56:05.200899  5226 solver.cpp:397]     Test net output #1: loss = 3.71219 (* 1 = 3.71219 loss)
I0406 08:56:05.337018  5226 solver.cpp:218] Iteration 12444 (0.819815 iter/s, 14.6375s/12 iters), loss = 2.07233
I0406 08:56:05.338582  5226 solver.cpp:237]     Train net output #0: loss = 2.07233 (* 1 = 2.07233 loss)
I0406 08:56:05.338595  5226 sgd_solver.cpp:105] Iteration 12444, lr = 0.01
I0406 08:56:09.628427  5226 solver.cpp:218] Iteration 12456 (2.79733 iter/s, 4.28981s/12 iters), loss = 2.00891
I0406 08:56:09.628466  5226 solver.cpp:237]     Train net output #0: loss = 2.00891 (* 1 = 2.00891 loss)
I0406 08:56:09.628473  5226 sgd_solver.cpp:105] Iteration 12456, lr = 0.01
I0406 08:56:13.858142  5226 blocking_queue.cpp:49] Waiting for data
I0406 08:56:14.926502  5226 solver.cpp:218] Iteration 12468 (2.26501 iter/s, 5.29798s/12 iters), loss = 1.96054
I0406 08:56:14.926553  5226 solver.cpp:237]     Train net output #0: loss = 1.96054 (* 1 = 1.96054 loss)
I0406 08:56:14.926561  5226 sgd_solver.cpp:105] Iteration 12468, lr = 0.01
I0406 08:56:20.228616  5226 solver.cpp:218] Iteration 12480 (2.26329 iter/s, 5.30202s/12 iters), loss = 2.04537
I0406 08:56:20.228696  5226 solver.cpp:237]     Train net output #0: loss = 2.04537 (* 1 = 2.04537 loss)
I0406 08:56:20.228703  5226 sgd_solver.cpp:105] Iteration 12480, lr = 0.01
I0406 08:56:25.330332  5226 solver.cpp:218] Iteration 12492 (2.35221 iter/s, 5.10159s/12 iters), loss = 1.93161
I0406 08:56:25.330382  5226 solver.cpp:237]     Train net output #0: loss = 1.93161 (* 1 = 1.93161 loss)
I0406 08:56:25.330391  5226 sgd_solver.cpp:105] Iteration 12492, lr = 0.01
I0406 08:56:30.625582  5226 solver.cpp:218] Iteration 12504 (2.26622 iter/s, 5.29515s/12 iters), loss = 2.0567
I0406 08:56:30.625633  5226 solver.cpp:237]     Train net output #0: loss = 2.0567 (* 1 = 2.0567 loss)
I0406 08:56:30.625640  5226 sgd_solver.cpp:105] Iteration 12504, lr = 0.01
I0406 08:56:35.653348  5226 solver.cpp:218] Iteration 12516 (2.38679 iter/s, 5.02767s/12 iters), loss = 1.86373
I0406 08:56:35.653388  5226 solver.cpp:237]     Train net output #0: loss = 1.86373 (* 1 = 1.86373 loss)
I0406 08:56:35.653393  5226 sgd_solver.cpp:105] Iteration 12516, lr = 0.01
I0406 08:56:40.862766  5226 solver.cpp:218] Iteration 12528 (2.30356 iter/s, 5.20933s/12 iters), loss = 1.92142
I0406 08:56:40.862807  5226 solver.cpp:237]     Train net output #0: loss = 1.92142 (* 1 = 1.92142 loss)
I0406 08:56:40.862812  5226 sgd_solver.cpp:105] Iteration 12528, lr = 0.01
I0406 08:56:46.062629  5226 solver.cpp:218] Iteration 12540 (2.30779 iter/s, 5.19977s/12 iters), loss = 2.30835
I0406 08:56:46.062669  5226 solver.cpp:237]     Train net output #0: loss = 2.30835 (* 1 = 2.30835 loss)
I0406 08:56:46.062674  5226 sgd_solver.cpp:105] Iteration 12540, lr = 0.01
I0406 08:56:46.984927  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:56:48.059630  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_12546.caffemodel
I0406 08:56:51.104948  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_12546.solverstate
I0406 08:56:53.420801  5226 solver.cpp:330] Iteration 12546, Testing net (#0)
I0406 08:56:53.420821  5226 net.cpp:676] Ignoring source layer train-data
I0406 08:56:57.460188  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:56:57.812516  5226 solver.cpp:397]     Test net output #0: accuracy = 0.199755
I0406 08:56:57.812552  5226 solver.cpp:397]     Test net output #1: loss = 3.89406 (* 1 = 3.89406 loss)
I0406 08:56:59.699388  5226 solver.cpp:218] Iteration 12552 (0.879984 iter/s, 13.6366s/12 iters), loss = 2.40158
I0406 08:56:59.699425  5226 solver.cpp:237]     Train net output #0: loss = 2.40158 (* 1 = 2.40158 loss)
I0406 08:56:59.699431  5226 sgd_solver.cpp:105] Iteration 12552, lr = 0.01
I0406 08:57:04.692265  5226 solver.cpp:218] Iteration 12564 (2.40346 iter/s, 4.9928s/12 iters), loss = 1.93384
I0406 08:57:04.692302  5226 solver.cpp:237]     Train net output #0: loss = 1.93384 (* 1 = 1.93384 loss)
I0406 08:57:04.692307  5226 sgd_solver.cpp:105] Iteration 12564, lr = 0.01
I0406 08:57:09.812453  5226 solver.cpp:218] Iteration 12576 (2.3437 iter/s, 5.1201s/12 iters), loss = 2.54624
I0406 08:57:09.812507  5226 solver.cpp:237]     Train net output #0: loss = 2.54624 (* 1 = 2.54624 loss)
I0406 08:57:09.812516  5226 sgd_solver.cpp:105] Iteration 12576, lr = 0.01
I0406 08:57:15.190047  5226 solver.cpp:218] Iteration 12588 (2.23152 iter/s, 5.37749s/12 iters), loss = 2.34897
I0406 08:57:15.190084  5226 solver.cpp:237]     Train net output #0: loss = 2.34897 (* 1 = 2.34897 loss)
I0406 08:57:15.190090  5226 sgd_solver.cpp:105] Iteration 12588, lr = 0.01
I0406 08:57:20.326215  5226 solver.cpp:218] Iteration 12600 (2.33641 iter/s, 5.13608s/12 iters), loss = 1.67807
I0406 08:57:20.326261  5226 solver.cpp:237]     Train net output #0: loss = 1.67807 (* 1 = 1.67807 loss)
I0406 08:57:20.326269  5226 sgd_solver.cpp:105] Iteration 12600, lr = 0.01
I0406 08:57:25.650372  5226 solver.cpp:218] Iteration 12612 (2.25392 iter/s, 5.32406s/12 iters), loss = 1.93017
I0406 08:57:25.650496  5226 solver.cpp:237]     Train net output #0: loss = 1.93017 (* 1 = 1.93017 loss)
I0406 08:57:25.650507  5226 sgd_solver.cpp:105] Iteration 12612, lr = 0.01
I0406 08:57:30.959216  5226 solver.cpp:218] Iteration 12624 (2.26045 iter/s, 5.30867s/12 iters), loss = 2.17282
I0406 08:57:30.959260  5226 solver.cpp:237]     Train net output #0: loss = 2.17282 (* 1 = 2.17282 loss)
I0406 08:57:30.959267  5226 sgd_solver.cpp:105] Iteration 12624, lr = 0.01
I0406 08:57:36.210465  5226 solver.cpp:218] Iteration 12636 (2.28521 iter/s, 5.25115s/12 iters), loss = 1.70949
I0406 08:57:36.210505  5226 solver.cpp:237]     Train net output #0: loss = 1.70949 (* 1 = 1.70949 loss)
I0406 08:57:36.210510  5226 sgd_solver.cpp:105] Iteration 12636, lr = 0.01
I0406 08:57:39.295540  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:57:40.936054  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_12648.caffemodel
I0406 08:57:44.759793  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_12648.solverstate
I0406 08:57:47.094141  5226 solver.cpp:330] Iteration 12648, Testing net (#0)
I0406 08:57:47.094159  5226 net.cpp:676] Ignoring source layer train-data
I0406 08:57:51.291172  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:57:51.681748  5226 solver.cpp:397]     Test net output #0: accuracy = 0.210172
I0406 08:57:51.681774  5226 solver.cpp:397]     Test net output #1: loss = 3.81345 (* 1 = 3.81345 loss)
I0406 08:57:51.822319  5226 solver.cpp:218] Iteration 12648 (0.768654 iter/s, 15.6117s/12 iters), loss = 1.82351
I0406 08:57:51.822367  5226 solver.cpp:237]     Train net output #0: loss = 1.82351 (* 1 = 1.82351 loss)
I0406 08:57:51.822374  5226 sgd_solver.cpp:105] Iteration 12648, lr = 0.01
I0406 08:57:55.986485  5226 solver.cpp:218] Iteration 12660 (2.88179 iter/s, 4.16408s/12 iters), loss = 1.79833
I0406 08:57:55.986598  5226 solver.cpp:237]     Train net output #0: loss = 1.79833 (* 1 = 1.79833 loss)
I0406 08:57:55.986604  5226 sgd_solver.cpp:105] Iteration 12660, lr = 0.01
I0406 08:58:01.163935  5226 solver.cpp:218] Iteration 12672 (2.31781 iter/s, 5.17729s/12 iters), loss = 2.13792
I0406 08:58:01.163982  5226 solver.cpp:237]     Train net output #0: loss = 2.13792 (* 1 = 2.13792 loss)
I0406 08:58:01.163990  5226 sgd_solver.cpp:105] Iteration 12672, lr = 0.01
I0406 08:58:06.449580  5226 solver.cpp:218] Iteration 12684 (2.27034 iter/s, 5.28555s/12 iters), loss = 2.03929
I0406 08:58:06.449621  5226 solver.cpp:237]     Train net output #0: loss = 2.03929 (* 1 = 2.03929 loss)
I0406 08:58:06.449626  5226 sgd_solver.cpp:105] Iteration 12684, lr = 0.01
I0406 08:58:11.833459  5226 solver.cpp:218] Iteration 12696 (2.22891 iter/s, 5.38379s/12 iters), loss = 2.30356
I0406 08:58:11.833506  5226 solver.cpp:237]     Train net output #0: loss = 2.30356 (* 1 = 2.30356 loss)
I0406 08:58:11.833514  5226 sgd_solver.cpp:105] Iteration 12696, lr = 0.01
I0406 08:58:17.075721  5226 solver.cpp:218] Iteration 12708 (2.28913 iter/s, 5.24216s/12 iters), loss = 2.05397
I0406 08:58:17.075762  5226 solver.cpp:237]     Train net output #0: loss = 2.05397 (* 1 = 2.05397 loss)
I0406 08:58:17.075769  5226 sgd_solver.cpp:105] Iteration 12708, lr = 0.01
I0406 08:58:22.359730  5226 solver.cpp:218] Iteration 12720 (2.27104 iter/s, 5.28392s/12 iters), loss = 2.47962
I0406 08:58:22.359769  5226 solver.cpp:237]     Train net output #0: loss = 2.47962 (* 1 = 2.47962 loss)
I0406 08:58:22.359776  5226 sgd_solver.cpp:105] Iteration 12720, lr = 0.01
I0406 08:58:27.598197  5226 solver.cpp:218] Iteration 12732 (2.29078 iter/s, 5.23838s/12 iters), loss = 2.39226
I0406 08:58:27.598467  5226 solver.cpp:237]     Train net output #0: loss = 2.39226 (* 1 = 2.39226 loss)
I0406 08:58:27.598474  5226 sgd_solver.cpp:105] Iteration 12732, lr = 0.01
I0406 08:58:32.732507  5226 solver.cpp:218] Iteration 12744 (2.33736 iter/s, 5.13399s/12 iters), loss = 2.38341
I0406 08:58:32.732549  5226 solver.cpp:237]     Train net output #0: loss = 2.38341 (* 1 = 2.38341 loss)
I0406 08:58:32.732556  5226 sgd_solver.cpp:105] Iteration 12744, lr = 0.01
I0406 08:58:32.933468  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:58:34.807253  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_12750.caffemodel
I0406 08:58:37.836956  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_12750.solverstate
I0406 08:58:40.152251  5226 solver.cpp:330] Iteration 12750, Testing net (#0)
I0406 08:58:40.152271  5226 net.cpp:676] Ignoring source layer train-data
I0406 08:58:44.336971  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:58:44.754827  5226 solver.cpp:397]     Test net output #0: accuracy = 0.206495
I0406 08:58:44.754853  5226 solver.cpp:397]     Test net output #1: loss = 3.89849 (* 1 = 3.89849 loss)
I0406 08:58:46.635798  5226 solver.cpp:218] Iteration 12756 (0.863114 iter/s, 13.9032s/12 iters), loss = 2.03739
I0406 08:58:46.635843  5226 solver.cpp:237]     Train net output #0: loss = 2.03739 (* 1 = 2.03739 loss)
I0406 08:58:46.635849  5226 sgd_solver.cpp:105] Iteration 12756, lr = 0.01
I0406 08:58:52.024415  5226 solver.cpp:218] Iteration 12768 (2.22696 iter/s, 5.38852s/12 iters), loss = 1.84004
I0406 08:58:52.024462  5226 solver.cpp:237]     Train net output #0: loss = 1.84004 (* 1 = 1.84004 loss)
I0406 08:58:52.024471  5226 sgd_solver.cpp:105] Iteration 12768, lr = 0.01
I0406 08:58:57.289966  5226 solver.cpp:218] Iteration 12780 (2.27901 iter/s, 5.26545s/12 iters), loss = 1.90052
I0406 08:58:57.290017  5226 solver.cpp:237]     Train net output #0: loss = 1.90052 (* 1 = 1.90052 loss)
I0406 08:58:57.290026  5226 sgd_solver.cpp:105] Iteration 12780, lr = 0.01
I0406 08:59:02.556270  5226 solver.cpp:218] Iteration 12792 (2.27868 iter/s, 5.26621s/12 iters), loss = 2.2727
I0406 08:59:02.556380  5226 solver.cpp:237]     Train net output #0: loss = 2.2727 (* 1 = 2.2727 loss)
I0406 08:59:02.556387  5226 sgd_solver.cpp:105] Iteration 12792, lr = 0.01
I0406 08:59:08.030645  5226 solver.cpp:218] Iteration 12804 (2.1921 iter/s, 5.47421s/12 iters), loss = 1.71102
I0406 08:59:08.030687  5226 solver.cpp:237]     Train net output #0: loss = 1.71102 (* 1 = 1.71102 loss)
I0406 08:59:08.030692  5226 sgd_solver.cpp:105] Iteration 12804, lr = 0.01
I0406 08:59:13.457456  5226 solver.cpp:218] Iteration 12816 (2.21128 iter/s, 5.42672s/12 iters), loss = 1.67122
I0406 08:59:13.457513  5226 solver.cpp:237]     Train net output #0: loss = 1.67122 (* 1 = 1.67122 loss)
I0406 08:59:13.457521  5226 sgd_solver.cpp:105] Iteration 12816, lr = 0.01
I0406 08:59:18.853266  5226 solver.cpp:218] Iteration 12828 (2.22399 iter/s, 5.39571s/12 iters), loss = 2.01368
I0406 08:59:18.853302  5226 solver.cpp:237]     Train net output #0: loss = 2.01368 (* 1 = 2.01368 loss)
I0406 08:59:18.853307  5226 sgd_solver.cpp:105] Iteration 12828, lr = 0.01
I0406 08:59:24.041209  5226 solver.cpp:218] Iteration 12840 (2.31309 iter/s, 5.18786s/12 iters), loss = 2.36339
I0406 08:59:24.041255  5226 solver.cpp:237]     Train net output #0: loss = 2.36339 (* 1 = 2.36339 loss)
I0406 08:59:24.041261  5226 sgd_solver.cpp:105] Iteration 12840, lr = 0.01
I0406 08:59:26.539247  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:59:28.799561  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_12852.caffemodel
I0406 08:59:31.975042  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_12852.solverstate
I0406 08:59:34.300062  5226 solver.cpp:330] Iteration 12852, Testing net (#0)
I0406 08:59:34.300978  5226 net.cpp:676] Ignoring source layer train-data
I0406 08:59:38.344899  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:59:38.850325  5226 solver.cpp:397]     Test net output #0: accuracy = 0.228554
I0406 08:59:38.850356  5226 solver.cpp:397]     Test net output #1: loss = 3.8433 (* 1 = 3.8433 loss)
I0406 08:59:38.991447  5226 solver.cpp:218] Iteration 12852 (0.802671 iter/s, 14.9501s/12 iters), loss = 2.05897
I0406 08:59:38.991494  5226 solver.cpp:237]     Train net output #0: loss = 2.05897 (* 1 = 2.05897 loss)
I0406 08:59:38.991503  5226 sgd_solver.cpp:105] Iteration 12852, lr = 0.01
I0406 08:59:43.207427  5226 solver.cpp:218] Iteration 12864 (2.84637 iter/s, 4.21589s/12 iters), loss = 2.38214
I0406 08:59:43.207466  5226 solver.cpp:237]     Train net output #0: loss = 2.38214 (* 1 = 2.38214 loss)
I0406 08:59:43.207473  5226 sgd_solver.cpp:105] Iteration 12864, lr = 0.01
I0406 08:59:48.704298  5226 solver.cpp:218] Iteration 12876 (2.18309 iter/s, 5.49678s/12 iters), loss = 2.61828
I0406 08:59:48.704345  5226 solver.cpp:237]     Train net output #0: loss = 2.61828 (* 1 = 2.61828 loss)
I0406 08:59:48.704353  5226 sgd_solver.cpp:105] Iteration 12876, lr = 0.01
I0406 08:59:54.035287  5226 solver.cpp:218] Iteration 12888 (2.25103 iter/s, 5.33089s/12 iters), loss = 2.34082
I0406 08:59:54.035337  5226 solver.cpp:237]     Train net output #0: loss = 2.34082 (* 1 = 2.34082 loss)
I0406 08:59:54.035346  5226 sgd_solver.cpp:105] Iteration 12888, lr = 0.01
I0406 08:59:59.464380  5226 solver.cpp:218] Iteration 12900 (2.21035 iter/s, 5.42899s/12 iters), loss = 2.30015
I0406 08:59:59.464437  5226 solver.cpp:237]     Train net output #0: loss = 2.30015 (* 1 = 2.30015 loss)
I0406 08:59:59.464444  5226 sgd_solver.cpp:105] Iteration 12900, lr = 0.01
I0406 09:00:04.838977  5226 solver.cpp:218] Iteration 12912 (2.23277 iter/s, 5.3745s/12 iters), loss = 2.0681
I0406 09:00:04.839087  5226 solver.cpp:237]     Train net output #0: loss = 2.0681 (* 1 = 2.0681 loss)
I0406 09:00:04.839094  5226 sgd_solver.cpp:105] Iteration 12912, lr = 0.01
I0406 09:00:10.167570  5226 solver.cpp:218] Iteration 12924 (2.25207 iter/s, 5.32843s/12 iters), loss = 1.97818
I0406 09:00:10.167621  5226 solver.cpp:237]     Train net output #0: loss = 1.97818 (* 1 = 1.97818 loss)
I0406 09:00:10.167630  5226 sgd_solver.cpp:105] Iteration 12924, lr = 0.01
I0406 09:00:15.536777  5226 solver.cpp:218] Iteration 12936 (2.23501 iter/s, 5.36911s/12 iters), loss = 1.9848
I0406 09:00:15.536829  5226 solver.cpp:237]     Train net output #0: loss = 1.9848 (* 1 = 1.9848 loss)
I0406 09:00:15.536837  5226 sgd_solver.cpp:105] Iteration 12936, lr = 0.01
I0406 09:00:20.264463  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:00:20.856742  5226 solver.cpp:218] Iteration 12948 (2.2557 iter/s, 5.31987s/12 iters), loss = 2.13956
I0406 09:00:20.856799  5226 solver.cpp:237]     Train net output #0: loss = 2.13956 (* 1 = 2.13956 loss)
I0406 09:00:20.856808  5226 sgd_solver.cpp:105] Iteration 12948, lr = 0.01
I0406 09:00:23.122495  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_12954.caffemodel
I0406 09:00:26.181989  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_12954.solverstate
I0406 09:00:28.496124  5226 solver.cpp:330] Iteration 12954, Testing net (#0)
I0406 09:00:28.496143  5226 net.cpp:676] Ignoring source layer train-data
I0406 09:00:32.423219  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:00:32.975376  5226 solver.cpp:397]     Test net output #0: accuracy = 0.226716
I0406 09:00:32.975414  5226 solver.cpp:397]     Test net output #1: loss = 3.83009 (* 1 = 3.83009 loss)
I0406 09:00:34.929587  5226 solver.cpp:218] Iteration 12960 (0.852716 iter/s, 14.0727s/12 iters), loss = 2.02595
I0406 09:00:34.929744  5226 solver.cpp:237]     Train net output #0: loss = 2.02595 (* 1 = 2.02595 loss)
I0406 09:00:34.929754  5226 sgd_solver.cpp:105] Iteration 12960, lr = 0.01
I0406 09:00:40.274217  5226 solver.cpp:218] Iteration 12972 (2.24533 iter/s, 5.34443s/12 iters), loss = 1.85272
I0406 09:00:40.274266  5226 solver.cpp:237]     Train net output #0: loss = 1.85272 (* 1 = 1.85272 loss)
I0406 09:00:40.274276  5226 sgd_solver.cpp:105] Iteration 12972, lr = 0.01
I0406 09:00:45.675988  5226 solver.cpp:218] Iteration 12984 (2.22154 iter/s, 5.40167s/12 iters), loss = 1.9954
I0406 09:00:45.676039  5226 solver.cpp:237]     Train net output #0: loss = 1.9954 (* 1 = 1.9954 loss)
I0406 09:00:45.676048  5226 sgd_solver.cpp:105] Iteration 12984, lr = 0.01
I0406 09:00:50.995613  5226 solver.cpp:218] Iteration 12996 (2.25584 iter/s, 5.31953s/12 iters), loss = 1.96171
I0406 09:00:50.995654  5226 solver.cpp:237]     Train net output #0: loss = 1.96171 (* 1 = 1.96171 loss)
I0406 09:00:50.995659  5226 sgd_solver.cpp:105] Iteration 12996, lr = 0.01
I0406 09:00:56.387122  5226 solver.cpp:218] Iteration 13008 (2.22576 iter/s, 5.39142s/12 iters), loss = 2.28275
I0406 09:00:56.387176  5226 solver.cpp:237]     Train net output #0: loss = 2.28275 (* 1 = 2.28275 loss)
I0406 09:00:56.387184  5226 sgd_solver.cpp:105] Iteration 13008, lr = 0.01
I0406 09:01:01.810196  5226 solver.cpp:218] Iteration 13020 (2.21281 iter/s, 5.42297s/12 iters), loss = 2.59514
I0406 09:01:01.810236  5226 solver.cpp:237]     Train net output #0: loss = 2.59514 (* 1 = 2.59514 loss)
I0406 09:01:01.810242  5226 sgd_solver.cpp:105] Iteration 13020, lr = 0.01
I0406 09:01:06.956879  5226 solver.cpp:218] Iteration 13032 (2.33164 iter/s, 5.1466s/12 iters), loss = 2.2502
I0406 09:01:06.956995  5226 solver.cpp:237]     Train net output #0: loss = 2.2502 (* 1 = 2.2502 loss)
I0406 09:01:06.957001  5226 sgd_solver.cpp:105] Iteration 13032, lr = 0.01
I0406 09:01:12.347457  5226 solver.cpp:218] Iteration 13044 (2.22617 iter/s, 5.39042s/12 iters), loss = 2.58961
I0406 09:01:12.347492  5226 solver.cpp:237]     Train net output #0: loss = 2.58961 (* 1 = 2.58961 loss)
I0406 09:01:12.347497  5226 sgd_solver.cpp:105] Iteration 13044, lr = 0.01
I0406 09:01:14.220393  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:01:17.242931  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_13056.caffemodel
I0406 09:01:20.306138  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_13056.solverstate
I0406 09:01:22.603654  5226 solver.cpp:330] Iteration 13056, Testing net (#0)
I0406 09:01:22.603675  5226 net.cpp:676] Ignoring source layer train-data
I0406 09:01:26.578012  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:01:27.131258  5226 solver.cpp:397]     Test net output #0: accuracy = 0.219975
I0406 09:01:27.131283  5226 solver.cpp:397]     Test net output #1: loss = 3.81154 (* 1 = 3.81154 loss)
I0406 09:01:27.271960  5226 solver.cpp:218] Iteration 13056 (0.804055 iter/s, 14.9244s/12 iters), loss = 2.36838
I0406 09:01:27.271998  5226 solver.cpp:237]     Train net output #0: loss = 2.36838 (* 1 = 2.36838 loss)
I0406 09:01:27.272003  5226 sgd_solver.cpp:105] Iteration 13056, lr = 0.01
I0406 09:01:31.507488  5226 solver.cpp:218] Iteration 13068 (2.83323 iter/s, 4.23545s/12 iters), loss = 1.82797
I0406 09:01:31.507526  5226 solver.cpp:237]     Train net output #0: loss = 1.82797 (* 1 = 1.82797 loss)
I0406 09:01:31.507531  5226 sgd_solver.cpp:105] Iteration 13068, lr = 0.01
I0406 09:01:37.022451  5226 solver.cpp:218] Iteration 13080 (2.17593 iter/s, 5.51487s/12 iters), loss = 1.78876
I0406 09:01:37.022584  5226 solver.cpp:237]     Train net output #0: loss = 1.78876 (* 1 = 1.78876 loss)
I0406 09:01:37.022593  5226 sgd_solver.cpp:105] Iteration 13080, lr = 0.01
I0406 09:01:42.455754  5226 solver.cpp:218] Iteration 13092 (2.20867 iter/s, 5.43313s/12 iters), loss = 1.90038
I0406 09:01:42.455793  5226 solver.cpp:237]     Train net output #0: loss = 1.90038 (* 1 = 1.90038 loss)
I0406 09:01:42.455799  5226 sgd_solver.cpp:105] Iteration 13092, lr = 0.01
I0406 09:01:47.490206  5226 solver.cpp:218] Iteration 13104 (2.38362 iter/s, 5.03436s/12 iters), loss = 2.18204
I0406 09:01:47.490247  5226 solver.cpp:237]     Train net output #0: loss = 2.18204 (* 1 = 2.18204 loss)
I0406 09:01:47.490254  5226 sgd_solver.cpp:105] Iteration 13104, lr = 0.01
I0406 09:01:52.895884  5226 solver.cpp:218] Iteration 13116 (2.21992 iter/s, 5.40559s/12 iters), loss = 1.95494
I0406 09:01:52.895920  5226 solver.cpp:237]     Train net output #0: loss = 1.95494 (* 1 = 1.95494 loss)
I0406 09:01:52.895926  5226 sgd_solver.cpp:105] Iteration 13116, lr = 0.01
I0406 09:01:58.123517  5226 solver.cpp:218] Iteration 13128 (2.29553 iter/s, 5.22755s/12 iters), loss = 2.51104
I0406 09:01:58.123569  5226 solver.cpp:237]     Train net output #0: loss = 2.51104 (* 1 = 2.51104 loss)
I0406 09:01:58.123577  5226 sgd_solver.cpp:105] Iteration 13128, lr = 0.01
I0406 09:02:03.396598  5226 solver.cpp:218] Iteration 13140 (2.27575 iter/s, 5.27298s/12 iters), loss = 2.20705
I0406 09:02:03.396648  5226 solver.cpp:237]     Train net output #0: loss = 2.20705 (* 1 = 2.20705 loss)
I0406 09:02:03.396656  5226 sgd_solver.cpp:105] Iteration 13140, lr = 0.01
I0406 09:02:07.565629  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:02:08.811480  5226 solver.cpp:218] Iteration 13152 (2.21616 iter/s, 5.41478s/12 iters), loss = 2.71286
I0406 09:02:08.811535  5226 solver.cpp:237]     Train net output #0: loss = 2.71286 (* 1 = 2.71286 loss)
I0406 09:02:08.811545  5226 sgd_solver.cpp:105] Iteration 13152, lr = 0.01
I0406 09:02:10.970021  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_13158.caffemodel
I0406 09:02:13.991351  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_13158.solverstate
I0406 09:02:16.299675  5226 solver.cpp:330] Iteration 13158, Testing net (#0)
I0406 09:02:16.299695  5226 net.cpp:676] Ignoring source layer train-data
I0406 09:02:19.897347  5226 blocking_queue.cpp:49] Waiting for data
I0406 09:02:20.122828  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:02:20.685557  5226 solver.cpp:397]     Test net output #0: accuracy = 0.215686
I0406 09:02:20.685595  5226 solver.cpp:397]     Test net output #1: loss = 3.76541 (* 1 = 3.76541 loss)
I0406 09:02:22.625780  5226 solver.cpp:218] Iteration 13164 (0.868675 iter/s, 13.8141s/12 iters), loss = 2.09108
I0406 09:02:22.625819  5226 solver.cpp:237]     Train net output #0: loss = 2.09108 (* 1 = 2.09108 loss)
I0406 09:02:22.625825  5226 sgd_solver.cpp:105] Iteration 13164, lr = 0.01
I0406 09:02:27.822975  5226 solver.cpp:218] Iteration 13176 (2.30898 iter/s, 5.1971s/12 iters), loss = 1.87697
I0406 09:02:27.823022  5226 solver.cpp:237]     Train net output #0: loss = 1.87697 (* 1 = 1.87697 loss)
I0406 09:02:27.823030  5226 sgd_solver.cpp:105] Iteration 13176, lr = 0.01
I0406 09:02:33.051116  5226 solver.cpp:218] Iteration 13188 (2.29531 iter/s, 5.22804s/12 iters), loss = 1.73069
I0406 09:02:33.051175  5226 solver.cpp:237]     Train net output #0: loss = 1.73069 (* 1 = 1.73069 loss)
I0406 09:02:33.051184  5226 sgd_solver.cpp:105] Iteration 13188, lr = 0.01
I0406 09:02:38.401268  5226 solver.cpp:218] Iteration 13200 (2.24297 iter/s, 5.35005s/12 iters), loss = 2.105
I0406 09:02:38.403254  5226 solver.cpp:237]     Train net output #0: loss = 2.105 (* 1 = 2.105 loss)
I0406 09:02:38.403265  5226 sgd_solver.cpp:105] Iteration 13200, lr = 0.01
I0406 09:02:43.525346  5226 solver.cpp:218] Iteration 13212 (2.34281 iter/s, 5.12205s/12 iters), loss = 1.99243
I0406 09:02:43.525393  5226 solver.cpp:237]     Train net output #0: loss = 1.99243 (* 1 = 1.99243 loss)
I0406 09:02:43.525400  5226 sgd_solver.cpp:105] Iteration 13212, lr = 0.01
I0406 09:02:48.898905  5226 solver.cpp:218] Iteration 13224 (2.2332 iter/s, 5.37347s/12 iters), loss = 2.5856
I0406 09:02:48.898943  5226 solver.cpp:237]     Train net output #0: loss = 2.5856 (* 1 = 2.5856 loss)
I0406 09:02:48.898948  5226 sgd_solver.cpp:105] Iteration 13224, lr = 0.01
I0406 09:02:54.243583  5226 solver.cpp:218] Iteration 13236 (2.24526 iter/s, 5.34459s/12 iters), loss = 2.48104
I0406 09:02:54.243635  5226 solver.cpp:237]     Train net output #0: loss = 2.48104 (* 1 = 2.48104 loss)
I0406 09:02:54.243643  5226 sgd_solver.cpp:105] Iteration 13236, lr = 0.01
I0406 09:02:59.618660  5226 solver.cpp:218] Iteration 13248 (2.23257 iter/s, 5.37498s/12 iters), loss = 2.18527
I0406 09:02:59.618697  5226 solver.cpp:237]     Train net output #0: loss = 2.18527 (* 1 = 2.18527 loss)
I0406 09:02:59.618702  5226 sgd_solver.cpp:105] Iteration 13248, lr = 0.01
I0406 09:03:00.657335  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:03:04.438789  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_13260.caffemodel
I0406 09:03:08.596582  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_13260.solverstate
I0406 09:03:11.041599  5226 solver.cpp:330] Iteration 13260, Testing net (#0)
I0406 09:03:11.041617  5226 net.cpp:676] Ignoring source layer train-data
I0406 09:03:14.718336  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:03:15.336138  5226 solver.cpp:397]     Test net output #0: accuracy = 0.193627
I0406 09:03:15.336166  5226 solver.cpp:397]     Test net output #1: loss = 3.99448 (* 1 = 3.99448 loss)
I0406 09:03:15.476671  5226 solver.cpp:218] Iteration 13260 (0.756722 iter/s, 15.8579s/12 iters), loss = 2.17935
I0406 09:03:15.476711  5226 solver.cpp:237]     Train net output #0: loss = 2.17935 (* 1 = 2.17935 loss)
I0406 09:03:15.476716  5226 sgd_solver.cpp:105] Iteration 13260, lr = 0.01
I0406 09:03:19.759227  5226 solver.cpp:218] Iteration 13272 (2.80212 iter/s, 4.28247s/12 iters), loss = 2.30286
I0406 09:03:19.759276  5226 solver.cpp:237]     Train net output #0: loss = 2.30286 (* 1 = 2.30286 loss)
I0406 09:03:19.759284  5226 sgd_solver.cpp:105] Iteration 13272, lr = 0.01
I0406 09:03:25.018007  5226 solver.cpp:218] Iteration 13284 (2.28194 iter/s, 5.25868s/12 iters), loss = 2.85717
I0406 09:03:25.018060  5226 solver.cpp:237]     Train net output #0: loss = 2.85717 (* 1 = 2.85717 loss)
I0406 09:03:25.018069  5226 sgd_solver.cpp:105] Iteration 13284, lr = 0.01
I0406 09:03:30.364535  5226 solver.cpp:218] Iteration 13296 (2.24449 iter/s, 5.34643s/12 iters), loss = 2.16949
I0406 09:03:30.364570  5226 solver.cpp:237]     Train net output #0: loss = 2.16949 (* 1 = 2.16949 loss)
I0406 09:03:30.364575  5226 sgd_solver.cpp:105] Iteration 13296, lr = 0.01
I0406 09:03:35.632984  5226 solver.cpp:218] Iteration 13308 (2.27775 iter/s, 5.26837s/12 iters), loss = 1.94612
I0406 09:03:35.633021  5226 solver.cpp:237]     Train net output #0: loss = 1.94612 (* 1 = 1.94612 loss)
I0406 09:03:35.633026  5226 sgd_solver.cpp:105] Iteration 13308, lr = 0.01
I0406 09:03:40.697268  5226 solver.cpp:218] Iteration 13320 (2.36957 iter/s, 5.0642s/12 iters), loss = 2.41577
I0406 09:03:40.697402  5226 solver.cpp:237]     Train net output #0: loss = 2.41577 (* 1 = 2.41577 loss)
I0406 09:03:40.697408  5226 sgd_solver.cpp:105] Iteration 13320, lr = 0.01
I0406 09:03:46.089785  5226 solver.cpp:218] Iteration 13332 (2.22538 iter/s, 5.39233s/12 iters), loss = 2.12187
I0406 09:03:46.089835  5226 solver.cpp:237]     Train net output #0: loss = 2.12187 (* 1 = 2.12187 loss)
I0406 09:03:46.089843  5226 sgd_solver.cpp:105] Iteration 13332, lr = 0.01
I0406 09:03:51.420637  5226 solver.cpp:218] Iteration 13344 (2.25109 iter/s, 5.33074s/12 iters), loss = 2.26784
I0406 09:03:51.420689  5226 solver.cpp:237]     Train net output #0: loss = 2.26784 (* 1 = 2.26784 loss)
I0406 09:03:51.420699  5226 sgd_solver.cpp:105] Iteration 13344, lr = 0.01
I0406 09:03:54.765344  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:03:56.772083  5226 solver.cpp:218] Iteration 13356 (2.24243 iter/s, 5.35134s/12 iters), loss = 1.95577
I0406 09:03:56.772119  5226 solver.cpp:237]     Train net output #0: loss = 1.95577 (* 1 = 1.95577 loss)
I0406 09:03:56.772125  5226 sgd_solver.cpp:105] Iteration 13356, lr = 0.01
I0406 09:03:58.874276  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_13362.caffemodel
I0406 09:04:01.904891  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_13362.solverstate
I0406 09:04:04.220733  5226 solver.cpp:330] Iteration 13362, Testing net (#0)
I0406 09:04:04.220752  5226 net.cpp:676] Ignoring source layer train-data
I0406 09:04:08.101148  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:04:08.783672  5226 solver.cpp:397]     Test net output #0: accuracy = 0.197917
I0406 09:04:08.783699  5226 solver.cpp:397]     Test net output #1: loss = 3.89871 (* 1 = 3.89871 loss)
I0406 09:04:10.765869  5226 solver.cpp:218] Iteration 13368 (0.857532 iter/s, 13.9936s/12 iters), loss = 2.30211
I0406 09:04:10.765964  5226 solver.cpp:237]     Train net output #0: loss = 2.30211 (* 1 = 2.30211 loss)
I0406 09:04:10.765969  5226 sgd_solver.cpp:105] Iteration 13368, lr = 0.01
I0406 09:04:16.125847  5226 solver.cpp:218] Iteration 13380 (2.23888 iter/s, 5.35983s/12 iters), loss = 2.62597
I0406 09:04:16.125897  5226 solver.cpp:237]     Train net output #0: loss = 2.62597 (* 1 = 2.62597 loss)
I0406 09:04:16.125905  5226 sgd_solver.cpp:105] Iteration 13380, lr = 0.01
I0406 09:04:21.463968  5226 solver.cpp:218] Iteration 13392 (2.24802 iter/s, 5.33802s/12 iters), loss = 2.61026
I0406 09:04:21.464023  5226 solver.cpp:237]     Train net output #0: loss = 2.61026 (* 1 = 2.61026 loss)
I0406 09:04:21.464032  5226 sgd_solver.cpp:105] Iteration 13392, lr = 0.01
I0406 09:04:26.816376  5226 solver.cpp:218] Iteration 13404 (2.24203 iter/s, 5.3523s/12 iters), loss = 2.43207
I0406 09:04:26.816421  5226 solver.cpp:237]     Train net output #0: loss = 2.43207 (* 1 = 2.43207 loss)
I0406 09:04:26.816427  5226 sgd_solver.cpp:105] Iteration 13404, lr = 0.01
I0406 09:04:32.042165  5226 solver.cpp:218] Iteration 13416 (2.29635 iter/s, 5.22569s/12 iters), loss = 2.22829
I0406 09:04:32.042222  5226 solver.cpp:237]     Train net output #0: loss = 2.22829 (* 1 = 2.22829 loss)
I0406 09:04:32.042230  5226 sgd_solver.cpp:105] Iteration 13416, lr = 0.01
I0406 09:04:37.348088  5226 solver.cpp:218] Iteration 13428 (2.26167 iter/s, 5.30582s/12 iters), loss = 2.20421
I0406 09:04:37.348135  5226 solver.cpp:237]     Train net output #0: loss = 2.20421 (* 1 = 2.20421 loss)
I0406 09:04:37.348145  5226 sgd_solver.cpp:105] Iteration 13428, lr = 0.01
I0406 09:04:42.691124  5226 solver.cpp:218] Iteration 13440 (2.24595 iter/s, 5.34294s/12 iters), loss = 2.17978
I0406 09:04:42.691224  5226 solver.cpp:237]     Train net output #0: loss = 2.17978 (* 1 = 2.17978 loss)
I0406 09:04:42.691231  5226 sgd_solver.cpp:105] Iteration 13440, lr = 0.01
I0406 09:04:47.783449  5226 solver.cpp:218] Iteration 13452 (2.35655 iter/s, 5.09218s/12 iters), loss = 2.74603
I0406 09:04:47.783488  5226 solver.cpp:237]     Train net output #0: loss = 2.74603 (* 1 = 2.74603 loss)
I0406 09:04:47.783493  5226 sgd_solver.cpp:105] Iteration 13452, lr = 0.01
I0406 09:04:47.994081  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:04:52.495338  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_13464.caffemodel
I0406 09:04:55.508199  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_13464.solverstate
I0406 09:04:57.821684  5226 solver.cpp:330] Iteration 13464, Testing net (#0)
I0406 09:04:57.821707  5226 net.cpp:676] Ignoring source layer train-data
I0406 09:05:01.583039  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:05:02.279616  5226 solver.cpp:397]     Test net output #0: accuracy = 0.213848
I0406 09:05:02.279667  5226 solver.cpp:397]     Test net output #1: loss = 3.81124 (* 1 = 3.81124 loss)
I0406 09:05:02.417029  5226 solver.cpp:218] Iteration 13464 (0.82004 iter/s, 14.6334s/12 iters), loss = 2.2499
I0406 09:05:02.417075  5226 solver.cpp:237]     Train net output #0: loss = 2.2499 (* 1 = 2.2499 loss)
I0406 09:05:02.417081  5226 sgd_solver.cpp:105] Iteration 13464, lr = 0.01
I0406 09:05:06.669373  5226 solver.cpp:218] Iteration 13476 (2.82203 iter/s, 4.25226s/12 iters), loss = 1.69158
I0406 09:05:06.669414  5226 solver.cpp:237]     Train net output #0: loss = 1.69158 (* 1 = 1.69158 loss)
I0406 09:05:06.669418  5226 sgd_solver.cpp:105] Iteration 13476, lr = 0.01
I0406 09:05:11.885988  5226 solver.cpp:218] Iteration 13488 (2.30038 iter/s, 5.21652s/12 iters), loss = 2.443
I0406 09:05:11.886026  5226 solver.cpp:237]     Train net output #0: loss = 2.443 (* 1 = 2.443 loss)
I0406 09:05:11.886031  5226 sgd_solver.cpp:105] Iteration 13488, lr = 0.01
I0406 09:05:16.765650  5226 solver.cpp:218] Iteration 13500 (2.45923 iter/s, 4.87958s/12 iters), loss = 2.49768
I0406 09:05:16.765754  5226 solver.cpp:237]     Train net output #0: loss = 2.49768 (* 1 = 2.49768 loss)
I0406 09:05:16.765761  5226 sgd_solver.cpp:105] Iteration 13500, lr = 0.01
I0406 09:05:22.051703  5226 solver.cpp:218] Iteration 13512 (2.27019 iter/s, 5.2859s/12 iters), loss = 2.15436
I0406 09:05:22.051751  5226 solver.cpp:237]     Train net output #0: loss = 2.15436 (* 1 = 2.15436 loss)
I0406 09:05:22.051759  5226 sgd_solver.cpp:105] Iteration 13512, lr = 0.01
I0406 09:05:27.413342  5226 solver.cpp:218] Iteration 13524 (2.23816 iter/s, 5.36155s/12 iters), loss = 1.98547
I0406 09:05:27.413379  5226 solver.cpp:237]     Train net output #0: loss = 1.98547 (* 1 = 1.98547 loss)
I0406 09:05:27.413388  5226 sgd_solver.cpp:105] Iteration 13524, lr = 0.01
I0406 09:05:32.665179  5226 solver.cpp:218] Iteration 13536 (2.28495 iter/s, 5.25175s/12 iters), loss = 2.20417
I0406 09:05:32.665231  5226 solver.cpp:237]     Train net output #0: loss = 2.20417 (* 1 = 2.20417 loss)
I0406 09:05:32.665236  5226 sgd_solver.cpp:105] Iteration 13536, lr = 0.01
I0406 09:05:38.094023  5226 solver.cpp:218] Iteration 13548 (2.21046 iter/s, 5.42874s/12 iters), loss = 2.3796
I0406 09:05:38.094071  5226 solver.cpp:237]     Train net output #0: loss = 2.3796 (* 1 = 2.3796 loss)
I0406 09:05:38.094080  5226 sgd_solver.cpp:105] Iteration 13548, lr = 0.01
I0406 09:05:40.548434  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:05:43.338945  5226 solver.cpp:218] Iteration 13560 (2.28797 iter/s, 5.24483s/12 iters), loss = 1.73444
I0406 09:05:43.338985  5226 solver.cpp:237]     Train net output #0: loss = 1.73444 (* 1 = 1.73444 loss)
I0406 09:05:43.338991  5226 sgd_solver.cpp:105] Iteration 13560, lr = 0.01
I0406 09:05:45.381743  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_13566.caffemodel
I0406 09:05:48.490562  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_13566.solverstate
I0406 09:05:50.800822  5226 solver.cpp:330] Iteration 13566, Testing net (#0)
I0406 09:05:50.800843  5226 net.cpp:676] Ignoring source layer train-data
I0406 09:05:54.394147  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:05:55.115319  5226 solver.cpp:397]     Test net output #0: accuracy = 0.221814
I0406 09:05:55.115351  5226 solver.cpp:397]     Test net output #1: loss = 3.82109 (* 1 = 3.82109 loss)
I0406 09:05:56.926829  5226 solver.cpp:218] Iteration 13572 (0.883149 iter/s, 13.5877s/12 iters), loss = 2.14733
I0406 09:05:56.926867  5226 solver.cpp:237]     Train net output #0: loss = 2.14733 (* 1 = 2.14733 loss)
I0406 09:05:56.926872  5226 sgd_solver.cpp:105] Iteration 13572, lr = 0.01
I0406 09:06:02.222796  5226 solver.cpp:218] Iteration 13584 (2.26591 iter/s, 5.29588s/12 iters), loss = 2.42594
I0406 09:06:02.222851  5226 solver.cpp:237]     Train net output #0: loss = 2.42594 (* 1 = 2.42594 loss)
I0406 09:06:02.222860  5226 sgd_solver.cpp:105] Iteration 13584, lr = 0.01
I0406 09:06:07.441560  5226 solver.cpp:218] Iteration 13596 (2.29944 iter/s, 5.21866s/12 iters), loss = 2.05399
I0406 09:06:07.441614  5226 solver.cpp:237]     Train net output #0: loss = 2.05399 (* 1 = 2.05399 loss)
I0406 09:06:07.441622  5226 sgd_solver.cpp:105] Iteration 13596, lr = 0.01
I0406 09:06:12.749780  5226 solver.cpp:218] Iteration 13608 (2.26069 iter/s, 5.30812s/12 iters), loss = 2.33492
I0406 09:06:12.749816  5226 solver.cpp:237]     Train net output #0: loss = 2.33492 (* 1 = 2.33492 loss)
I0406 09:06:12.749821  5226 sgd_solver.cpp:105] Iteration 13608, lr = 0.01
I0406 09:06:17.960744  5226 solver.cpp:218] Iteration 13620 (2.30287 iter/s, 5.21088s/12 iters), loss = 2.19593
I0406 09:06:17.960783  5226 solver.cpp:237]     Train net output #0: loss = 2.19593 (* 1 = 2.19593 loss)
I0406 09:06:17.960788  5226 sgd_solver.cpp:105] Iteration 13620, lr = 0.01
I0406 09:06:23.237072  5226 solver.cpp:218] Iteration 13632 (2.27435 iter/s, 5.27624s/12 iters), loss = 1.59323
I0406 09:06:23.237193  5226 solver.cpp:237]     Train net output #0: loss = 1.59323 (* 1 = 1.59323 loss)
I0406 09:06:23.237200  5226 sgd_solver.cpp:105] Iteration 13632, lr = 0.01
I0406 09:06:28.348304  5226 solver.cpp:218] Iteration 13644 (2.34785 iter/s, 5.11107s/12 iters), loss = 1.97531
I0406 09:06:28.348345  5226 solver.cpp:237]     Train net output #0: loss = 1.97531 (* 1 = 1.97531 loss)
I0406 09:06:28.348349  5226 sgd_solver.cpp:105] Iteration 13644, lr = 0.01
I0406 09:06:33.132251  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:06:33.557981  5226 solver.cpp:218] Iteration 13656 (2.30345 iter/s, 5.20959s/12 iters), loss = 2.3836
I0406 09:06:33.558020  5226 solver.cpp:237]     Train net output #0: loss = 2.3836 (* 1 = 2.3836 loss)
I0406 09:06:33.558027  5226 sgd_solver.cpp:105] Iteration 13656, lr = 0.01
I0406 09:06:38.320755  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_13668.caffemodel
I0406 09:06:41.643426  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_13668.solverstate
I0406 09:06:44.027274  5226 solver.cpp:330] Iteration 13668, Testing net (#0)
I0406 09:06:44.027292  5226 net.cpp:676] Ignoring source layer train-data
I0406 09:06:47.896504  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:06:48.651468  5226 solver.cpp:397]     Test net output #0: accuracy = 0.205882
I0406 09:06:48.651504  5226 solver.cpp:397]     Test net output #1: loss = 3.93181 (* 1 = 3.93181 loss)
I0406 09:06:48.789579  5226 solver.cpp:218] Iteration 13668 (0.787844 iter/s, 15.2314s/12 iters), loss = 2.39534
I0406 09:06:48.789636  5226 solver.cpp:237]     Train net output #0: loss = 2.39534 (* 1 = 2.39534 loss)
I0406 09:06:48.789642  5226 sgd_solver.cpp:105] Iteration 13668, lr = 0.01
I0406 09:06:53.043021  5226 solver.cpp:218] Iteration 13680 (2.82131 iter/s, 4.25334s/12 iters), loss = 2.39708
I0406 09:06:53.043062  5226 solver.cpp:237]     Train net output #0: loss = 2.39708 (* 1 = 2.39708 loss)
I0406 09:06:53.043068  5226 sgd_solver.cpp:105] Iteration 13680, lr = 0.01
I0406 09:06:58.556622  5226 solver.cpp:218] Iteration 13692 (2.17647 iter/s, 5.5135s/12 iters), loss = 2.40788
I0406 09:06:58.560961  5226 solver.cpp:237]     Train net output #0: loss = 2.40788 (* 1 = 2.40788 loss)
I0406 09:06:58.560977  5226 sgd_solver.cpp:105] Iteration 13692, lr = 0.01
I0406 09:07:03.825335  5226 solver.cpp:218] Iteration 13704 (2.27949 iter/s, 5.26435s/12 iters), loss = 2.02432
I0406 09:07:03.825373  5226 solver.cpp:237]     Train net output #0: loss = 2.02432 (* 1 = 2.02432 loss)
I0406 09:07:03.825378  5226 sgd_solver.cpp:105] Iteration 13704, lr = 0.01
I0406 09:07:09.164023  5226 solver.cpp:218] Iteration 13716 (2.24778 iter/s, 5.3386s/12 iters), loss = 2.33532
I0406 09:07:09.164063  5226 solver.cpp:237]     Train net output #0: loss = 2.33532 (* 1 = 2.33532 loss)
I0406 09:07:09.164068  5226 sgd_solver.cpp:105] Iteration 13716, lr = 0.01
I0406 09:07:14.518608  5226 solver.cpp:218] Iteration 13728 (2.24111 iter/s, 5.3545s/12 iters), loss = 2.23236
I0406 09:07:14.518647  5226 solver.cpp:237]     Train net output #0: loss = 2.23236 (* 1 = 2.23236 loss)
I0406 09:07:14.518652  5226 sgd_solver.cpp:105] Iteration 13728, lr = 0.01
I0406 09:07:19.845876  5226 solver.cpp:218] Iteration 13740 (2.2526 iter/s, 5.32718s/12 iters), loss = 2.40155
I0406 09:07:19.845927  5226 solver.cpp:237]     Train net output #0: loss = 2.40155 (* 1 = 2.40155 loss)
I0406 09:07:19.845935  5226 sgd_solver.cpp:105] Iteration 13740, lr = 0.01
I0406 09:07:25.187639  5226 solver.cpp:218] Iteration 13752 (2.24649 iter/s, 5.34166s/12 iters), loss = 2.35743
I0406 09:07:25.187695  5226 solver.cpp:237]     Train net output #0: loss = 2.35743 (* 1 = 2.35743 loss)
I0406 09:07:25.187705  5226 sgd_solver.cpp:105] Iteration 13752, lr = 0.01
I0406 09:07:27.066740  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:07:30.522723  5226 solver.cpp:218] Iteration 13764 (2.24931 iter/s, 5.33497s/12 iters), loss = 1.79331
I0406 09:07:30.522908  5226 solver.cpp:237]     Train net output #0: loss = 1.79331 (* 1 = 1.79331 loss)
I0406 09:07:30.522917  5226 sgd_solver.cpp:105] Iteration 13764, lr = 0.01
I0406 09:07:32.513229  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_13770.caffemodel
I0406 09:07:35.577848  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_13770.solverstate
I0406 09:07:37.903879  5226 solver.cpp:330] Iteration 13770, Testing net (#0)
I0406 09:07:37.903898  5226 net.cpp:676] Ignoring source layer train-data
I0406 09:07:41.456990  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:07:42.288944  5226 solver.cpp:397]     Test net output #0: accuracy = 0.199142
I0406 09:07:42.288972  5226 solver.cpp:397]     Test net output #1: loss = 3.95267 (* 1 = 3.95267 loss)
I0406 09:07:44.128840  5226 solver.cpp:218] Iteration 13776 (0.881974 iter/s, 13.6058s/12 iters), loss = 2.28799
I0406 09:07:44.128880  5226 solver.cpp:237]     Train net output #0: loss = 2.28799 (* 1 = 2.28799 loss)
I0406 09:07:44.128893  5226 sgd_solver.cpp:105] Iteration 13776, lr = 0.01
I0406 09:07:49.357278  5226 solver.cpp:218] Iteration 13788 (2.29518 iter/s, 5.22835s/12 iters), loss = 2.19909
I0406 09:07:49.357331  5226 solver.cpp:237]     Train net output #0: loss = 2.19909 (* 1 = 2.19909 loss)
I0406 09:07:49.357342  5226 sgd_solver.cpp:105] Iteration 13788, lr = 0.01
I0406 09:07:54.596021  5226 solver.cpp:218] Iteration 13800 (2.29067 iter/s, 5.23864s/12 iters), loss = 2.34205
I0406 09:07:54.596071  5226 solver.cpp:237]     Train net output #0: loss = 2.34205 (* 1 = 2.34205 loss)
I0406 09:07:54.596078  5226 sgd_solver.cpp:105] Iteration 13800, lr = 0.01
I0406 09:07:59.938014  5226 solver.cpp:218] Iteration 13812 (2.24639 iter/s, 5.3419s/12 iters), loss = 2.15847
I0406 09:07:59.938055  5226 solver.cpp:237]     Train net output #0: loss = 2.15847 (* 1 = 2.15847 loss)
I0406 09:07:59.938060  5226 sgd_solver.cpp:105] Iteration 13812, lr = 0.01
I0406 09:08:05.393174  5226 solver.cpp:218] Iteration 13824 (2.19979 iter/s, 5.45506s/12 iters), loss = 2.41394
I0406 09:08:05.393296  5226 solver.cpp:237]     Train net output #0: loss = 2.41394 (* 1 = 2.41394 loss)
I0406 09:08:05.393309  5226 sgd_solver.cpp:105] Iteration 13824, lr = 0.01
I0406 09:08:10.583961  5226 solver.cpp:218] Iteration 13836 (2.31186 iter/s, 5.19062s/12 iters), loss = 2.4472
I0406 09:08:10.584013  5226 solver.cpp:237]     Train net output #0: loss = 2.4472 (* 1 = 2.4472 loss)
I0406 09:08:10.584020  5226 sgd_solver.cpp:105] Iteration 13836, lr = 0.01
I0406 09:08:15.764874  5226 solver.cpp:218] Iteration 13848 (2.31624 iter/s, 5.18082s/12 iters), loss = 2.31541
I0406 09:08:15.764940  5226 solver.cpp:237]     Train net output #0: loss = 2.31541 (* 1 = 2.31541 loss)
I0406 09:08:15.764953  5226 sgd_solver.cpp:105] Iteration 13848, lr = 0.01
I0406 09:08:19.718096  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:08:20.926285  5226 solver.cpp:218] Iteration 13860 (2.325 iter/s, 5.1613s/12 iters), loss = 2.08953
I0406 09:08:20.926326  5226 solver.cpp:237]     Train net output #0: loss = 2.08953 (* 1 = 2.08953 loss)
I0406 09:08:20.926331  5226 sgd_solver.cpp:105] Iteration 13860, lr = 0.01
I0406 09:08:25.692596  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_13872.caffemodel
I0406 09:08:28.748028  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_13872.solverstate
I0406 09:08:31.056689  5226 solver.cpp:330] Iteration 13872, Testing net (#0)
I0406 09:08:31.056710  5226 net.cpp:676] Ignoring source layer train-data
I0406 09:08:31.971725  5226 blocking_queue.cpp:49] Waiting for data
I0406 09:08:34.499881  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:08:35.333228  5226 solver.cpp:397]     Test net output #0: accuracy = 0.22549
I0406 09:08:35.333256  5226 solver.cpp:397]     Test net output #1: loss = 3.83533 (* 1 = 3.83533 loss)
I0406 09:08:35.474154  5226 solver.cpp:218] Iteration 13872 (0.824871 iter/s, 14.5477s/12 iters), loss = 2.11235
I0406 09:08:35.474295  5226 solver.cpp:237]     Train net output #0: loss = 2.11235 (* 1 = 2.11235 loss)
I0406 09:08:35.474304  5226 sgd_solver.cpp:105] Iteration 13872, lr = 0.01
I0406 09:08:39.734441  5226 solver.cpp:218] Iteration 13884 (2.81683 iter/s, 4.26011s/12 iters), loss = 1.84818
I0406 09:08:39.734483  5226 solver.cpp:237]     Train net output #0: loss = 1.84818 (* 1 = 1.84818 loss)
I0406 09:08:39.734488  5226 sgd_solver.cpp:105] Iteration 13884, lr = 0.01
I0406 09:08:44.962476  5226 solver.cpp:218] Iteration 13896 (2.29536 iter/s, 5.22794s/12 iters), loss = 1.96291
I0406 09:08:44.962527  5226 solver.cpp:237]     Train net output #0: loss = 1.96291 (* 1 = 1.96291 loss)
I0406 09:08:44.962536  5226 sgd_solver.cpp:105] Iteration 13896, lr = 0.01
I0406 09:08:50.325089  5226 solver.cpp:218] Iteration 13908 (2.23776 iter/s, 5.36251s/12 iters), loss = 1.94858
I0406 09:08:50.325130  5226 solver.cpp:237]     Train net output #0: loss = 1.94858 (* 1 = 1.94858 loss)
I0406 09:08:50.325135  5226 sgd_solver.cpp:105] Iteration 13908, lr = 0.01
I0406 09:08:55.592846  5226 solver.cpp:218] Iteration 13920 (2.27805 iter/s, 5.26767s/12 iters), loss = 1.95682
I0406 09:08:55.592890  5226 solver.cpp:237]     Train net output #0: loss = 1.95682 (* 1 = 1.95682 loss)
I0406 09:08:55.592896  5226 sgd_solver.cpp:105] Iteration 13920, lr = 0.01
I0406 09:09:00.912190  5226 solver.cpp:218] Iteration 13932 (2.25595 iter/s, 5.31926s/12 iters), loss = 2.67654
I0406 09:09:00.912236  5226 solver.cpp:237]     Train net output #0: loss = 2.67654 (* 1 = 2.67654 loss)
I0406 09:09:00.912243  5226 sgd_solver.cpp:105] Iteration 13932, lr = 0.01
I0406 09:09:06.248520  5226 solver.cpp:218] Iteration 13944 (2.24878 iter/s, 5.33624s/12 iters), loss = 2.03481
I0406 09:09:06.248631  5226 solver.cpp:237]     Train net output #0: loss = 2.03481 (* 1 = 2.03481 loss)
I0406 09:09:06.248636  5226 sgd_solver.cpp:105] Iteration 13944, lr = 0.01
I0406 09:09:11.415364  5226 solver.cpp:218] Iteration 13956 (2.32257 iter/s, 5.16669s/12 iters), loss = 2.15872
I0406 09:09:11.415412  5226 solver.cpp:237]     Train net output #0: loss = 2.15872 (* 1 = 2.15872 loss)
I0406 09:09:11.415421  5226 sgd_solver.cpp:105] Iteration 13956, lr = 0.01
I0406 09:09:12.509493  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:09:16.900771  5226 solver.cpp:218] Iteration 13968 (2.18766 iter/s, 5.48531s/12 iters), loss = 2.22588
I0406 09:09:16.900818  5226 solver.cpp:237]     Train net output #0: loss = 2.22588 (* 1 = 2.22588 loss)
I0406 09:09:16.900825  5226 sgd_solver.cpp:105] Iteration 13968, lr = 0.01
I0406 09:09:18.990226  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_13974.caffemodel
I0406 09:09:21.985267  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_13974.solverstate
I0406 09:09:24.302176  5226 solver.cpp:330] Iteration 13974, Testing net (#0)
I0406 09:09:24.302197  5226 net.cpp:676] Ignoring source layer train-data
I0406 09:09:27.825876  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:09:28.753934  5226 solver.cpp:397]     Test net output #0: accuracy = 0.227941
I0406 09:09:28.753971  5226 solver.cpp:397]     Test net output #1: loss = 3.92318 (* 1 = 3.92318 loss)
I0406 09:09:30.565485  5226 solver.cpp:218] Iteration 13980 (0.878184 iter/s, 13.6646s/12 iters), loss = 2.24545
I0406 09:09:30.565527  5226 solver.cpp:237]     Train net output #0: loss = 2.24545 (* 1 = 2.24545 loss)
I0406 09:09:30.565533  5226 sgd_solver.cpp:105] Iteration 13980, lr = 0.01
I0406 09:09:35.854341  5226 solver.cpp:218] Iteration 13992 (2.26896 iter/s, 5.28876s/12 iters), loss = 2.71619
I0406 09:09:35.854389  5226 solver.cpp:237]     Train net output #0: loss = 2.71619 (* 1 = 2.71619 loss)
I0406 09:09:35.854396  5226 sgd_solver.cpp:105] Iteration 13992, lr = 0.01
I0406 09:09:41.050032  5226 solver.cpp:218] Iteration 14004 (2.30965 iter/s, 5.1956s/12 iters), loss = 2.34296
I0406 09:09:41.050182  5226 solver.cpp:237]     Train net output #0: loss = 2.34296 (* 1 = 2.34296 loss)
I0406 09:09:41.050191  5226 sgd_solver.cpp:105] Iteration 14004, lr = 0.01
I0406 09:09:46.450433  5226 solver.cpp:218] Iteration 14016 (2.22214 iter/s, 5.4002s/12 iters), loss = 2.04413
I0406 09:09:46.450479  5226 solver.cpp:237]     Train net output #0: loss = 2.04413 (* 1 = 2.04413 loss)
I0406 09:09:46.450486  5226 sgd_solver.cpp:105] Iteration 14016, lr = 0.01
I0406 09:09:51.604075  5226 solver.cpp:218] Iteration 14028 (2.32849 iter/s, 5.15355s/12 iters), loss = 2.12931
I0406 09:09:51.604115  5226 solver.cpp:237]     Train net output #0: loss = 2.12931 (* 1 = 2.12931 loss)
I0406 09:09:51.604120  5226 sgd_solver.cpp:105] Iteration 14028, lr = 0.01
I0406 09:09:56.836527  5226 solver.cpp:218] Iteration 14040 (2.29342 iter/s, 5.23236s/12 iters), loss = 2.28087
I0406 09:09:56.836572  5226 solver.cpp:237]     Train net output #0: loss = 2.28087 (* 1 = 2.28087 loss)
I0406 09:09:56.836578  5226 sgd_solver.cpp:105] Iteration 14040, lr = 0.01
I0406 09:10:02.146047  5226 solver.cpp:218] Iteration 14052 (2.26013 iter/s, 5.30942s/12 iters), loss = 2.83542
I0406 09:10:02.146100  5226 solver.cpp:237]     Train net output #0: loss = 2.83542 (* 1 = 2.83542 loss)
I0406 09:10:02.146108  5226 sgd_solver.cpp:105] Iteration 14052, lr = 0.01
I0406 09:10:05.410022  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:10:07.317672  5226 solver.cpp:218] Iteration 14064 (2.3204 iter/s, 5.17153s/12 iters), loss = 2.37138
I0406 09:10:07.317711  5226 solver.cpp:237]     Train net output #0: loss = 2.37138 (* 1 = 2.37138 loss)
I0406 09:10:07.317716  5226 sgd_solver.cpp:105] Iteration 14064, lr = 0.01
I0406 09:10:12.190904  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_14076.caffemodel
I0406 09:10:15.142534  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_14076.solverstate
I0406 09:10:17.439829  5226 solver.cpp:330] Iteration 14076, Testing net (#0)
I0406 09:10:17.439859  5226 net.cpp:676] Ignoring source layer train-data
I0406 09:10:21.125290  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:10:22.044435  5226 solver.cpp:397]     Test net output #0: accuracy = 0.208946
I0406 09:10:22.044471  5226 solver.cpp:397]     Test net output #1: loss = 3.99557 (* 1 = 3.99557 loss)
I0406 09:10:22.186200  5226 solver.cpp:218] Iteration 14076 (0.807082 iter/s, 14.8684s/12 iters), loss = 2.35432
I0406 09:10:22.186252  5226 solver.cpp:237]     Train net output #0: loss = 2.35432 (* 1 = 2.35432 loss)
I0406 09:10:22.186259  5226 sgd_solver.cpp:105] Iteration 14076, lr = 0.01
I0406 09:10:26.680339  5226 solver.cpp:218] Iteration 14088 (2.6702 iter/s, 4.49404s/12 iters), loss = 2.1479
I0406 09:10:26.680382  5226 solver.cpp:237]     Train net output #0: loss = 2.1479 (* 1 = 2.1479 loss)
I0406 09:10:26.680387  5226 sgd_solver.cpp:105] Iteration 14088, lr = 0.01
I0406 09:10:32.234671  5226 solver.cpp:218] Iteration 14100 (2.16051 iter/s, 5.55424s/12 iters), loss = 2.17677
I0406 09:10:32.234707  5226 solver.cpp:237]     Train net output #0: loss = 2.17677 (* 1 = 2.17677 loss)
I0406 09:10:32.234714  5226 sgd_solver.cpp:105] Iteration 14100, lr = 0.01
I0406 09:10:37.562297  5226 solver.cpp:218] Iteration 14112 (2.25245 iter/s, 5.32754s/12 iters), loss = 2.77335
I0406 09:10:37.562351  5226 solver.cpp:237]     Train net output #0: loss = 2.77335 (* 1 = 2.77335 loss)
I0406 09:10:37.562361  5226 sgd_solver.cpp:105] Iteration 14112, lr = 0.01
I0406 09:10:42.872081  5226 solver.cpp:218] Iteration 14124 (2.26002 iter/s, 5.30969s/12 iters), loss = 2.2758
I0406 09:10:42.872303  5226 solver.cpp:237]     Train net output #0: loss = 2.2758 (* 1 = 2.2758 loss)
I0406 09:10:42.872309  5226 sgd_solver.cpp:105] Iteration 14124, lr = 0.01
I0406 09:10:47.989812  5226 solver.cpp:218] Iteration 14136 (2.34491 iter/s, 5.11746s/12 iters), loss = 2.23622
I0406 09:10:47.989864  5226 solver.cpp:237]     Train net output #0: loss = 2.23622 (* 1 = 2.23622 loss)
I0406 09:10:47.989873  5226 sgd_solver.cpp:105] Iteration 14136, lr = 0.01
I0406 09:10:53.362319  5226 solver.cpp:218] Iteration 14148 (2.23364 iter/s, 5.37241s/12 iters), loss = 2.61299
I0406 09:10:53.362360  5226 solver.cpp:237]     Train net output #0: loss = 2.61299 (* 1 = 2.61299 loss)
I0406 09:10:53.362365  5226 sgd_solver.cpp:105] Iteration 14148, lr = 0.01
I0406 09:10:58.696686  5226 solver.cpp:218] Iteration 14160 (2.2496 iter/s, 5.33428s/12 iters), loss = 2.44028
I0406 09:10:58.696727  5226 solver.cpp:237]     Train net output #0: loss = 2.44028 (* 1 = 2.44028 loss)
I0406 09:10:58.696732  5226 sgd_solver.cpp:105] Iteration 14160, lr = 0.01
I0406 09:10:58.955461  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:11:03.876199  5226 solver.cpp:218] Iteration 14172 (2.31686 iter/s, 5.17942s/12 iters), loss = 2.32228
I0406 09:11:03.876245  5226 solver.cpp:237]     Train net output #0: loss = 2.32228 (* 1 = 2.32228 loss)
I0406 09:11:03.876252  5226 sgd_solver.cpp:105] Iteration 14172, lr = 0.01
I0406 09:11:05.972968  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_14178.caffemodel
I0406 09:11:09.039983  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_14178.solverstate
I0406 09:11:11.338547  5226 solver.cpp:330] Iteration 14178, Testing net (#0)
I0406 09:11:11.338567  5226 net.cpp:676] Ignoring source layer train-data
I0406 09:11:14.804132  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:11:15.748437  5226 solver.cpp:397]     Test net output #0: accuracy = 0.183824
I0406 09:11:15.748471  5226 solver.cpp:397]     Test net output #1: loss = 4.07589 (* 1 = 4.07589 loss)
I0406 09:11:17.532730  5226 solver.cpp:218] Iteration 14184 (0.87871 iter/s, 13.6564s/12 iters), loss = 1.95331
I0406 09:11:17.532781  5226 solver.cpp:237]     Train net output #0: loss = 1.95331 (* 1 = 1.95331 loss)
I0406 09:11:17.532788  5226 sgd_solver.cpp:105] Iteration 14184, lr = 0.01
I0406 09:11:22.770311  5226 solver.cpp:218] Iteration 14196 (2.29118 iter/s, 5.23749s/12 iters), loss = 2.34385
I0406 09:11:22.770347  5226 solver.cpp:237]     Train net output #0: loss = 2.34385 (* 1 = 2.34385 loss)
I0406 09:11:22.770352  5226 sgd_solver.cpp:105] Iteration 14196, lr = 0.01
I0406 09:11:27.762193  5226 solver.cpp:218] Iteration 14208 (2.40394 iter/s, 4.9918s/12 iters), loss = 2.73436
I0406 09:11:27.762228  5226 solver.cpp:237]     Train net output #0: loss = 2.73436 (* 1 = 2.73436 loss)
I0406 09:11:27.762233  5226 sgd_solver.cpp:105] Iteration 14208, lr = 0.01
I0406 09:11:32.953739  5226 solver.cpp:218] Iteration 14220 (2.31149 iter/s, 5.19146s/12 iters), loss = 2.25962
I0406 09:11:32.953791  5226 solver.cpp:237]     Train net output #0: loss = 2.25962 (* 1 = 2.25962 loss)
I0406 09:11:32.953796  5226 sgd_solver.cpp:105] Iteration 14220, lr = 0.01
I0406 09:11:38.132721  5226 solver.cpp:218] Iteration 14232 (2.31711 iter/s, 5.17887s/12 iters), loss = 2.4327
I0406 09:11:38.132777  5226 solver.cpp:237]     Train net output #0: loss = 2.4327 (* 1 = 2.4327 loss)
I0406 09:11:38.132786  5226 sgd_solver.cpp:105] Iteration 14232, lr = 0.01
I0406 09:11:43.345237  5226 solver.cpp:218] Iteration 14244 (2.3022 iter/s, 5.21241s/12 iters), loss = 2.42727
I0406 09:11:43.345273  5226 solver.cpp:237]     Train net output #0: loss = 2.42727 (* 1 = 2.42727 loss)
I0406 09:11:43.345278  5226 sgd_solver.cpp:105] Iteration 14244, lr = 0.01
I0406 09:11:48.614328  5226 solver.cpp:218] Iteration 14256 (2.27747 iter/s, 5.26901s/12 iters), loss = 2.05933
I0406 09:11:48.614446  5226 solver.cpp:237]     Train net output #0: loss = 2.05933 (* 1 = 2.05933 loss)
I0406 09:11:48.614452  5226 sgd_solver.cpp:105] Iteration 14256, lr = 0.01
I0406 09:11:51.097167  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:11:53.835638  5226 solver.cpp:218] Iteration 14268 (2.29835 iter/s, 5.22114s/12 iters), loss = 2.7042
I0406 09:11:53.835687  5226 solver.cpp:237]     Train net output #0: loss = 2.7042 (* 1 = 2.7042 loss)
I0406 09:11:53.835695  5226 sgd_solver.cpp:105] Iteration 14268, lr = 0.01
I0406 09:11:58.653678  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_14280.caffemodel
I0406 09:12:01.705651  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_14280.solverstate
I0406 09:12:04.021034  5226 solver.cpp:330] Iteration 14280, Testing net (#0)
I0406 09:12:04.021054  5226 net.cpp:676] Ignoring source layer train-data
I0406 09:12:07.416474  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:12:08.399963  5226 solver.cpp:397]     Test net output #0: accuracy = 0.202819
I0406 09:12:08.399998  5226 solver.cpp:397]     Test net output #1: loss = 3.98414 (* 1 = 3.98414 loss)
I0406 09:12:08.540485  5226 solver.cpp:218] Iteration 14280 (0.816066 iter/s, 14.7047s/12 iters), loss = 2.41422
I0406 09:12:08.540524  5226 solver.cpp:237]     Train net output #0: loss = 2.41422 (* 1 = 2.41422 loss)
I0406 09:12:08.540529  5226 sgd_solver.cpp:105] Iteration 14280, lr = 0.01
I0406 09:12:12.849885  5226 solver.cpp:218] Iteration 14292 (2.78467 iter/s, 4.30931s/12 iters), loss = 2.07011
I0406 09:12:12.849941  5226 solver.cpp:237]     Train net output #0: loss = 2.07011 (* 1 = 2.07011 loss)
I0406 09:12:12.849951  5226 sgd_solver.cpp:105] Iteration 14292, lr = 0.01
I0406 09:12:18.045759  5226 solver.cpp:218] Iteration 14304 (2.30957 iter/s, 5.19577s/12 iters), loss = 2.31967
I0406 09:12:18.045817  5226 solver.cpp:237]     Train net output #0: loss = 2.31967 (* 1 = 2.31967 loss)
I0406 09:12:18.045826  5226 sgd_solver.cpp:105] Iteration 14304, lr = 0.01
I0406 09:12:23.363026  5226 solver.cpp:218] Iteration 14316 (2.25684 iter/s, 5.31716s/12 iters), loss = 2.21341
I0406 09:12:23.363126  5226 solver.cpp:237]     Train net output #0: loss = 2.21341 (* 1 = 2.21341 loss)
I0406 09:12:23.363133  5226 sgd_solver.cpp:105] Iteration 14316, lr = 0.01
I0406 09:12:28.760185  5226 solver.cpp:218] Iteration 14328 (2.22345 iter/s, 5.39701s/12 iters), loss = 2.02363
I0406 09:12:28.760222  5226 solver.cpp:237]     Train net output #0: loss = 2.02363 (* 1 = 2.02363 loss)
I0406 09:12:28.760227  5226 sgd_solver.cpp:105] Iteration 14328, lr = 0.01
I0406 09:12:33.790174  5226 solver.cpp:218] Iteration 14340 (2.38573 iter/s, 5.0299s/12 iters), loss = 2.54744
I0406 09:12:33.790230  5226 solver.cpp:237]     Train net output #0: loss = 2.54744 (* 1 = 2.54744 loss)
I0406 09:12:33.790237  5226 sgd_solver.cpp:105] Iteration 14340, lr = 0.01
I0406 09:12:39.134004  5226 solver.cpp:218] Iteration 14352 (2.24562 iter/s, 5.34373s/12 iters), loss = 1.9294
I0406 09:12:39.134039  5226 solver.cpp:237]     Train net output #0: loss = 1.9294 (* 1 = 1.9294 loss)
I0406 09:12:39.134045  5226 sgd_solver.cpp:105] Iteration 14352, lr = 0.01
I0406 09:12:43.991971  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:12:44.376108  5226 solver.cpp:218] Iteration 14364 (2.28919 iter/s, 5.24202s/12 iters), loss = 2.35754
I0406 09:12:44.376143  5226 solver.cpp:237]     Train net output #0: loss = 2.35754 (* 1 = 2.35754 loss)
I0406 09:12:44.376148  5226 sgd_solver.cpp:105] Iteration 14364, lr = 0.01
I0406 09:12:49.595145  5226 solver.cpp:218] Iteration 14376 (2.29931 iter/s, 5.21895s/12 iters), loss = 2.16566
I0406 09:12:49.595201  5226 solver.cpp:237]     Train net output #0: loss = 2.16566 (* 1 = 2.16566 loss)
I0406 09:12:49.595211  5226 sgd_solver.cpp:105] Iteration 14376, lr = 0.01
I0406 09:12:51.574071  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_14382.caffemodel
I0406 09:12:54.612736  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_14382.solverstate
I0406 09:12:56.929548  5226 solver.cpp:330] Iteration 14382, Testing net (#0)
I0406 09:12:56.929567  5226 net.cpp:676] Ignoring source layer train-data
I0406 09:13:00.257303  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:13:01.287714  5226 solver.cpp:397]     Test net output #0: accuracy = 0.186275
I0406 09:13:01.287750  5226 solver.cpp:397]     Test net output #1: loss = 3.96429 (* 1 = 3.96429 loss)
I0406 09:13:03.142288  5226 solver.cpp:218] Iteration 14388 (0.885805 iter/s, 13.547s/12 iters), loss = 2.94084
I0406 09:13:03.142347  5226 solver.cpp:237]     Train net output #0: loss = 2.94084 (* 1 = 2.94084 loss)
I0406 09:13:03.142356  5226 sgd_solver.cpp:105] Iteration 14388, lr = 0.01
I0406 09:13:08.341923  5226 solver.cpp:218] Iteration 14400 (2.3079 iter/s, 5.19953s/12 iters), loss = 2.35891
I0406 09:13:08.341962  5226 solver.cpp:237]     Train net output #0: loss = 2.35891 (* 1 = 2.35891 loss)
I0406 09:13:08.341969  5226 sgd_solver.cpp:105] Iteration 14400, lr = 0.01
I0406 09:13:13.585629  5226 solver.cpp:218] Iteration 14412 (2.2885 iter/s, 5.24362s/12 iters), loss = 2.00138
I0406 09:13:13.585667  5226 solver.cpp:237]     Train net output #0: loss = 2.00138 (* 1 = 2.00138 loss)
I0406 09:13:13.585672  5226 sgd_solver.cpp:105] Iteration 14412, lr = 0.01
I0406 09:13:19.070232  5226 solver.cpp:218] Iteration 14424 (2.18798 iter/s, 5.48451s/12 iters), loss = 2.44084
I0406 09:13:19.070281  5226 solver.cpp:237]     Train net output #0: loss = 2.44084 (* 1 = 2.44084 loss)
I0406 09:13:19.070288  5226 sgd_solver.cpp:105] Iteration 14424, lr = 0.01
I0406 09:13:23.964534  5226 solver.cpp:218] Iteration 14436 (2.45188 iter/s, 4.89421s/12 iters), loss = 2.31559
I0406 09:13:23.964574  5226 solver.cpp:237]     Train net output #0: loss = 2.31559 (* 1 = 2.31559 loss)
I0406 09:13:23.964581  5226 sgd_solver.cpp:105] Iteration 14436, lr = 0.01
I0406 09:13:29.241680  5226 solver.cpp:218] Iteration 14448 (2.27399 iter/s, 5.27706s/12 iters), loss = 2.0162
I0406 09:13:29.241775  5226 solver.cpp:237]     Train net output #0: loss = 2.0162 (* 1 = 2.0162 loss)
I0406 09:13:29.241782  5226 sgd_solver.cpp:105] Iteration 14448, lr = 0.01
I0406 09:13:34.563563  5226 solver.cpp:218] Iteration 14460 (2.2549 iter/s, 5.32174s/12 iters), loss = 2.97605
I0406 09:13:34.563612  5226 solver.cpp:237]     Train net output #0: loss = 2.97605 (* 1 = 2.97605 loss)
I0406 09:13:34.563621  5226 sgd_solver.cpp:105] Iteration 14460, lr = 0.01
I0406 09:13:36.455925  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:13:39.933153  5226 solver.cpp:218] Iteration 14472 (2.23485 iter/s, 5.3695s/12 iters), loss = 2.18069
I0406 09:13:39.933193  5226 solver.cpp:237]     Train net output #0: loss = 2.18069 (* 1 = 2.18069 loss)
I0406 09:13:39.933198  5226 sgd_solver.cpp:105] Iteration 14472, lr = 0.01
I0406 09:13:44.687784  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_14484.caffemodel
I0406 09:13:47.770009  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_14484.solverstate
I0406 09:13:50.092334  5226 solver.cpp:330] Iteration 14484, Testing net (#0)
I0406 09:13:50.092353  5226 net.cpp:676] Ignoring source layer train-data
I0406 09:13:53.543886  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:13:54.630252  5226 solver.cpp:397]     Test net output #0: accuracy = 0.21201
I0406 09:13:54.630288  5226 solver.cpp:397]     Test net output #1: loss = 3.96919 (* 1 = 3.96919 loss)
I0406 09:13:54.771375  5226 solver.cpp:218] Iteration 14484 (0.80873 iter/s, 14.8381s/12 iters), loss = 1.81004
I0406 09:13:54.772941  5226 solver.cpp:237]     Train net output #0: loss = 1.81004 (* 1 = 1.81004 loss)
I0406 09:13:54.772955  5226 sgd_solver.cpp:105] Iteration 14484, lr = 0.01
I0406 09:13:59.139078  5226 solver.cpp:218] Iteration 14496 (2.74844 iter/s, 4.36611s/12 iters), loss = 2.10455
I0406 09:13:59.139120  5226 solver.cpp:237]     Train net output #0: loss = 2.10455 (* 1 = 2.10455 loss)
I0406 09:13:59.139127  5226 sgd_solver.cpp:105] Iteration 14496, lr = 0.01
I0406 09:14:04.435921  5226 solver.cpp:218] Iteration 14508 (2.26554 iter/s, 5.29675s/12 iters), loss = 1.72575
I0406 09:14:04.436141  5226 solver.cpp:237]     Train net output #0: loss = 1.72575 (* 1 = 1.72575 loss)
I0406 09:14:04.436147  5226 sgd_solver.cpp:105] Iteration 14508, lr = 0.01
I0406 09:14:09.510411  5226 solver.cpp:218] Iteration 14520 (2.36489 iter/s, 5.07422s/12 iters), loss = 2.38361
I0406 09:14:09.510458  5226 solver.cpp:237]     Train net output #0: loss = 2.38361 (* 1 = 2.38361 loss)
I0406 09:14:09.510466  5226 sgd_solver.cpp:105] Iteration 14520, lr = 0.01
I0406 09:14:14.804949  5226 solver.cpp:218] Iteration 14532 (2.26653 iter/s, 5.29444s/12 iters), loss = 2.61797
I0406 09:14:14.805265  5226 solver.cpp:237]     Train net output #0: loss = 2.61797 (* 1 = 2.61797 loss)
I0406 09:14:14.805277  5226 sgd_solver.cpp:105] Iteration 14532, lr = 0.01
I0406 09:14:20.027874  5226 solver.cpp:218] Iteration 14544 (2.29772 iter/s, 5.22257s/12 iters), loss = 2.36757
I0406 09:14:20.027911  5226 solver.cpp:237]     Train net output #0: loss = 2.36757 (* 1 = 2.36757 loss)
I0406 09:14:20.027917  5226 sgd_solver.cpp:105] Iteration 14544, lr = 0.01
I0406 09:14:25.457240  5226 solver.cpp:218] Iteration 14556 (2.21024 iter/s, 5.42928s/12 iters), loss = 2.39384
I0406 09:14:25.457293  5226 solver.cpp:237]     Train net output #0: loss = 2.39384 (* 1 = 2.39384 loss)
I0406 09:14:25.457302  5226 sgd_solver.cpp:105] Iteration 14556, lr = 0.01
I0406 09:14:29.470706  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:14:29.715241  5226 blocking_queue.cpp:49] Waiting for data
I0406 09:14:30.603055  5226 solver.cpp:218] Iteration 14568 (2.33204 iter/s, 5.14572s/12 iters), loss = 2.43081
I0406 09:14:30.603096  5226 solver.cpp:237]     Train net output #0: loss = 2.43081 (* 1 = 2.43081 loss)
I0406 09:14:30.603101  5226 sgd_solver.cpp:105] Iteration 14568, lr = 0.01
I0406 09:14:35.699571  5226 solver.cpp:218] Iteration 14580 (2.35459 iter/s, 5.09643s/12 iters), loss = 2.08512
I0406 09:14:35.699673  5226 solver.cpp:237]     Train net output #0: loss = 2.08512 (* 1 = 2.08512 loss)
I0406 09:14:35.699679  5226 sgd_solver.cpp:105] Iteration 14580, lr = 0.01
I0406 09:14:37.686326  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_14586.caffemodel
I0406 09:14:40.951568  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_14586.solverstate
I0406 09:14:43.353724  5226 solver.cpp:330] Iteration 14586, Testing net (#0)
I0406 09:14:43.353744  5226 net.cpp:676] Ignoring source layer train-data
I0406 09:14:46.618400  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:14:47.730461  5226 solver.cpp:397]     Test net output #0: accuracy = 0.20527
I0406 09:14:47.730495  5226 solver.cpp:397]     Test net output #1: loss = 3.96151 (* 1 = 3.96151 loss)
I0406 09:14:49.622854  5226 solver.cpp:218] Iteration 14592 (0.861878 iter/s, 13.9231s/12 iters), loss = 1.99538
I0406 09:14:49.622896  5226 solver.cpp:237]     Train net output #0: loss = 1.99538 (* 1 = 1.99538 loss)
I0406 09:14:49.622902  5226 sgd_solver.cpp:105] Iteration 14592, lr = 0.01
I0406 09:14:54.920375  5226 solver.cpp:218] Iteration 14604 (2.26525 iter/s, 5.29743s/12 iters), loss = 2.3153
I0406 09:14:54.920415  5226 solver.cpp:237]     Train net output #0: loss = 2.3153 (* 1 = 2.3153 loss)
I0406 09:14:54.920420  5226 sgd_solver.cpp:105] Iteration 14604, lr = 0.01
I0406 09:15:00.224246  5226 solver.cpp:218] Iteration 14616 (2.26253 iter/s, 5.30379s/12 iters), loss = 3.94873
I0406 09:15:00.224283  5226 solver.cpp:237]     Train net output #0: loss = 3.94873 (* 1 = 3.94873 loss)
I0406 09:15:00.224289  5226 sgd_solver.cpp:105] Iteration 14616, lr = 0.01
I0406 09:15:05.285670  5226 solver.cpp:218] Iteration 14628 (2.37091 iter/s, 5.06134s/12 iters), loss = 2.2769
I0406 09:15:05.285714  5226 solver.cpp:237]     Train net output #0: loss = 2.2769 (* 1 = 2.2769 loss)
I0406 09:15:05.285722  5226 sgd_solver.cpp:105] Iteration 14628, lr = 0.01
I0406 09:15:10.546106  5226 solver.cpp:218] Iteration 14640 (2.28122 iter/s, 5.26034s/12 iters), loss = 1.92349
I0406 09:15:10.546252  5226 solver.cpp:237]     Train net output #0: loss = 1.92349 (* 1 = 1.92349 loss)
I0406 09:15:10.546259  5226 sgd_solver.cpp:105] Iteration 14640, lr = 0.01
I0406 09:15:15.747860  5226 solver.cpp:218] Iteration 14652 (2.307 iter/s, 5.20156s/12 iters), loss = 2.65605
I0406 09:15:15.747902  5226 solver.cpp:237]     Train net output #0: loss = 2.65605 (* 1 = 2.65605 loss)
I0406 09:15:15.747910  5226 sgd_solver.cpp:105] Iteration 14652, lr = 0.01
I0406 09:15:21.098929  5226 solver.cpp:218] Iteration 14664 (2.24258 iter/s, 5.35098s/12 iters), loss = 2.44919
I0406 09:15:21.098969  5226 solver.cpp:237]     Train net output #0: loss = 2.44919 (* 1 = 2.44919 loss)
I0406 09:15:21.098975  5226 sgd_solver.cpp:105] Iteration 14664, lr = 0.01
I0406 09:15:22.196290  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:15:26.346282  5226 solver.cpp:218] Iteration 14676 (2.2869 iter/s, 5.24727s/12 iters), loss = 2.45059
I0406 09:15:26.346321  5226 solver.cpp:237]     Train net output #0: loss = 2.45059 (* 1 = 2.45059 loss)
I0406 09:15:26.346326  5226 sgd_solver.cpp:105] Iteration 14676, lr = 0.01
I0406 09:15:30.952576  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_14688.caffemodel
I0406 09:15:34.012209  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_14688.solverstate
I0406 09:15:36.319867  5226 solver.cpp:330] Iteration 14688, Testing net (#0)
I0406 09:15:36.319886  5226 net.cpp:676] Ignoring source layer train-data
I0406 09:15:39.660727  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:15:40.849191  5226 solver.cpp:397]     Test net output #0: accuracy = 0.186275
I0406 09:15:40.849269  5226 solver.cpp:397]     Test net output #1: loss = 3.9224 (* 1 = 3.9224 loss)
I0406 09:15:40.989961  5226 solver.cpp:218] Iteration 14688 (0.819474 iter/s, 14.6435s/12 iters), loss = 2.23342
I0406 09:15:40.989997  5226 solver.cpp:237]     Train net output #0: loss = 2.23342 (* 1 = 2.23342 loss)
I0406 09:15:40.990002  5226 sgd_solver.cpp:105] Iteration 14688, lr = 0.01
I0406 09:15:45.192642  5226 solver.cpp:218] Iteration 14700 (2.85538 iter/s, 4.2026s/12 iters), loss = 2.71329
I0406 09:15:45.192680  5226 solver.cpp:237]     Train net output #0: loss = 2.71329 (* 1 = 2.71329 loss)
I0406 09:15:45.192685  5226 sgd_solver.cpp:105] Iteration 14700, lr = 0.01
I0406 09:15:50.747629  5226 solver.cpp:218] Iteration 14712 (2.16026 iter/s, 5.5549s/12 iters), loss = 3.03435
I0406 09:15:50.747668  5226 solver.cpp:237]     Train net output #0: loss = 3.03435 (* 1 = 3.03435 loss)
I0406 09:15:50.747674  5226 sgd_solver.cpp:105] Iteration 14712, lr = 0.01
I0406 09:15:55.900198  5226 solver.cpp:218] Iteration 14724 (2.32897 iter/s, 5.15248s/12 iters), loss = 2.28689
I0406 09:15:55.900234  5226 solver.cpp:237]     Train net output #0: loss = 2.28689 (* 1 = 2.28689 loss)
I0406 09:15:55.900240  5226 sgd_solver.cpp:105] Iteration 14724, lr = 0.01
I0406 09:16:01.137030  5226 solver.cpp:218] Iteration 14736 (2.2915 iter/s, 5.23674s/12 iters), loss = 2.14654
I0406 09:16:01.137079  5226 solver.cpp:237]     Train net output #0: loss = 2.14654 (* 1 = 2.14654 loss)
I0406 09:16:01.137085  5226 sgd_solver.cpp:105] Iteration 14736, lr = 0.01
I0406 09:16:06.478664  5226 solver.cpp:218] Iteration 14748 (2.24654 iter/s, 5.34154s/12 iters), loss = 2.59674
I0406 09:16:06.478706  5226 solver.cpp:237]     Train net output #0: loss = 2.59674 (* 1 = 2.59674 loss)
I0406 09:16:06.478713  5226 sgd_solver.cpp:105] Iteration 14748, lr = 0.01
I0406 09:16:11.556572  5226 solver.cpp:218] Iteration 14760 (2.36322 iter/s, 5.07782s/12 iters), loss = 2.39623
I0406 09:16:11.556695  5226 solver.cpp:237]     Train net output #0: loss = 2.39623 (* 1 = 2.39623 loss)
I0406 09:16:11.556702  5226 sgd_solver.cpp:105] Iteration 14760, lr = 0.01
I0406 09:16:14.906227  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:16:16.736292  5226 solver.cpp:218] Iteration 14772 (2.3168 iter/s, 5.17955s/12 iters), loss = 2.43255
I0406 09:16:16.736330  5226 solver.cpp:237]     Train net output #0: loss = 2.43255 (* 1 = 2.43255 loss)
I0406 09:16:16.736335  5226 sgd_solver.cpp:105] Iteration 14772, lr = 0.01
I0406 09:16:21.715226  5226 solver.cpp:218] Iteration 14784 (2.4102 iter/s, 4.97884s/12 iters), loss = 1.70921
I0406 09:16:21.715283  5226 solver.cpp:237]     Train net output #0: loss = 1.70921 (* 1 = 1.70921 loss)
I0406 09:16:21.715291  5226 sgd_solver.cpp:105] Iteration 14784, lr = 0.01
I0406 09:16:23.955961  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_14790.caffemodel
I0406 09:16:26.951361  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_14790.solverstate
I0406 09:16:29.268870  5226 solver.cpp:330] Iteration 14790, Testing net (#0)
I0406 09:16:29.268900  5226 net.cpp:676] Ignoring source layer train-data
I0406 09:16:32.498005  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:16:33.700686  5226 solver.cpp:397]     Test net output #0: accuracy = 0.207108
I0406 09:16:33.700718  5226 solver.cpp:397]     Test net output #1: loss = 3.95197 (* 1 = 3.95197 loss)
I0406 09:16:35.555115  5226 solver.cpp:218] Iteration 14796 (0.867068 iter/s, 13.8397s/12 iters), loss = 2.19746
I0406 09:16:35.555148  5226 solver.cpp:237]     Train net output #0: loss = 2.19746 (* 1 = 2.19746 loss)
I0406 09:16:35.555153  5226 sgd_solver.cpp:105] Iteration 14796, lr = 0.01
I0406 09:16:40.647352  5226 solver.cpp:218] Iteration 14808 (2.35657 iter/s, 5.09216s/12 iters), loss = 2.64486
I0406 09:16:40.647392  5226 solver.cpp:237]     Train net output #0: loss = 2.64486 (* 1 = 2.64486 loss)
I0406 09:16:40.647397  5226 sgd_solver.cpp:105] Iteration 14808, lr = 0.01
I0406 09:16:45.768039  5226 solver.cpp:218] Iteration 14820 (2.34348 iter/s, 5.12059s/12 iters), loss = 2.31862
I0406 09:16:45.768158  5226 solver.cpp:237]     Train net output #0: loss = 2.31862 (* 1 = 2.31862 loss)
I0406 09:16:45.768167  5226 sgd_solver.cpp:105] Iteration 14820, lr = 0.01
I0406 09:16:50.991766  5226 solver.cpp:218] Iteration 14832 (2.29728 iter/s, 5.22356s/12 iters), loss = 1.9846
I0406 09:16:50.991802  5226 solver.cpp:237]     Train net output #0: loss = 1.9846 (* 1 = 1.9846 loss)
I0406 09:16:50.991808  5226 sgd_solver.cpp:105] Iteration 14832, lr = 0.01
I0406 09:16:56.397054  5226 solver.cpp:218] Iteration 14844 (2.22008 iter/s, 5.4052s/12 iters), loss = 2.37669
I0406 09:16:56.397090  5226 solver.cpp:237]     Train net output #0: loss = 2.37669 (* 1 = 2.37669 loss)
I0406 09:16:56.397095  5226 sgd_solver.cpp:105] Iteration 14844, lr = 0.01
I0406 09:17:01.716208  5226 solver.cpp:218] Iteration 14856 (2.25603 iter/s, 5.31907s/12 iters), loss = 2.46445
I0406 09:17:01.716255  5226 solver.cpp:237]     Train net output #0: loss = 2.46445 (* 1 = 2.46445 loss)
I0406 09:17:01.716260  5226 sgd_solver.cpp:105] Iteration 14856, lr = 0.01
I0406 09:17:06.970147  5226 solver.cpp:218] Iteration 14868 (2.28404 iter/s, 5.25385s/12 iters), loss = 2.61024
I0406 09:17:06.970188  5226 solver.cpp:237]     Train net output #0: loss = 2.61024 (* 1 = 2.61024 loss)
I0406 09:17:06.970193  5226 sgd_solver.cpp:105] Iteration 14868, lr = 0.01
I0406 09:17:07.268007  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:17:12.335629  5226 solver.cpp:218] Iteration 14880 (2.23656 iter/s, 5.36539s/12 iters), loss = 2.44326
I0406 09:17:12.335671  5226 solver.cpp:237]     Train net output #0: loss = 2.44326 (* 1 = 2.44326 loss)
I0406 09:17:12.335680  5226 sgd_solver.cpp:105] Iteration 14880, lr = 0.01
I0406 09:17:17.105007  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_14892.caffemodel
I0406 09:17:20.123386  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_14892.solverstate
I0406 09:17:22.431665  5226 solver.cpp:330] Iteration 14892, Testing net (#0)
I0406 09:17:22.431684  5226 net.cpp:676] Ignoring source layer train-data
I0406 09:17:25.541198  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:17:26.833526  5226 solver.cpp:397]     Test net output #0: accuracy = 0.196691
I0406 09:17:26.833560  5226 solver.cpp:397]     Test net output #1: loss = 3.94578 (* 1 = 3.94578 loss)
I0406 09:17:26.968325  5226 solver.cpp:218] Iteration 14892 (0.820089 iter/s, 14.6326s/12 iters), loss = 2.20364
I0406 09:17:26.968370  5226 solver.cpp:237]     Train net output #0: loss = 2.20364 (* 1 = 2.20364 loss)
I0406 09:17:26.968377  5226 sgd_solver.cpp:105] Iteration 14892, lr = 0.01
I0406 09:17:31.358001  5226 solver.cpp:218] Iteration 14904 (2.73374 iter/s, 4.38959s/12 iters), loss = 2.41026
I0406 09:17:31.358039  5226 solver.cpp:237]     Train net output #0: loss = 2.41026 (* 1 = 2.41026 loss)
I0406 09:17:31.358045  5226 sgd_solver.cpp:105] Iteration 14904, lr = 0.01
I0406 09:17:36.484997  5226 solver.cpp:218] Iteration 14916 (2.34059 iter/s, 5.12691s/12 iters), loss = 2.39415
I0406 09:17:36.485051  5226 solver.cpp:237]     Train net output #0: loss = 2.39415 (* 1 = 2.39415 loss)
I0406 09:17:36.485059  5226 sgd_solver.cpp:105] Iteration 14916, lr = 0.01
I0406 09:17:41.760682  5226 solver.cpp:218] Iteration 14928 (2.27463 iter/s, 5.27558s/12 iters), loss = 2.1506
I0406 09:17:41.760735  5226 solver.cpp:237]     Train net output #0: loss = 2.1506 (* 1 = 2.1506 loss)
I0406 09:17:41.760746  5226 sgd_solver.cpp:105] Iteration 14928, lr = 0.01
I0406 09:17:46.966197  5226 solver.cpp:218] Iteration 14940 (2.30529 iter/s, 5.20541s/12 iters), loss = 2.83912
I0406 09:17:46.966245  5226 solver.cpp:237]     Train net output #0: loss = 2.83912 (* 1 = 2.83912 loss)
I0406 09:17:46.966253  5226 sgd_solver.cpp:105] Iteration 14940, lr = 0.01
I0406 09:17:52.316517  5226 solver.cpp:218] Iteration 14952 (2.24289 iter/s, 5.35023s/12 iters), loss = 2.47864
I0406 09:17:52.316603  5226 solver.cpp:237]     Train net output #0: loss = 2.47864 (* 1 = 2.47864 loss)
I0406 09:17:52.316612  5226 sgd_solver.cpp:105] Iteration 14952, lr = 0.01
I0406 09:17:57.693423  5226 solver.cpp:218] Iteration 14964 (2.23182 iter/s, 5.37677s/12 iters), loss = 2.31652
I0406 09:17:57.693459  5226 solver.cpp:237]     Train net output #0: loss = 2.31652 (* 1 = 2.31652 loss)
I0406 09:17:57.693465  5226 sgd_solver.cpp:105] Iteration 14964, lr = 0.01
I0406 09:18:00.402683  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:18:03.090602  5226 solver.cpp:218] Iteration 14976 (2.22342 iter/s, 5.39709s/12 iters), loss = 2.91963
I0406 09:18:03.090662  5226 solver.cpp:237]     Train net output #0: loss = 2.91963 (* 1 = 2.91963 loss)
I0406 09:18:03.090670  5226 sgd_solver.cpp:105] Iteration 14976, lr = 0.01
I0406 09:18:08.442405  5226 solver.cpp:218] Iteration 14988 (2.24228 iter/s, 5.3517s/12 iters), loss = 2.49171
I0406 09:18:08.442445  5226 solver.cpp:237]     Train net output #0: loss = 2.49171 (* 1 = 2.49171 loss)
I0406 09:18:08.442451  5226 sgd_solver.cpp:105] Iteration 14988, lr = 0.01
I0406 09:18:10.659570  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_14994.caffemodel
I0406 09:18:13.696950  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_14994.solverstate
I0406 09:18:16.008486  5226 solver.cpp:330] Iteration 14994, Testing net (#0)
I0406 09:18:16.008504  5226 net.cpp:676] Ignoring source layer train-data
I0406 09:18:19.016944  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:18:20.351589  5226 solver.cpp:397]     Test net output #0: accuracy = 0.186887
I0406 09:18:20.351624  5226 solver.cpp:397]     Test net output #1: loss = 4.0286 (* 1 = 4.0286 loss)
I0406 09:18:22.124931  5226 solver.cpp:218] Iteration 15000 (0.877039 iter/s, 13.6824s/12 iters), loss = 2.57134
I0406 09:18:22.124965  5226 solver.cpp:237]     Train net output #0: loss = 2.57134 (* 1 = 2.57134 loss)
I0406 09:18:22.124971  5226 sgd_solver.cpp:105] Iteration 15000, lr = 0.01
I0406 09:18:27.247741  5226 solver.cpp:218] Iteration 15012 (2.3425 iter/s, 5.12273s/12 iters), loss = 2.50652
I0406 09:18:27.247875  5226 solver.cpp:237]     Train net output #0: loss = 2.50652 (* 1 = 2.50652 loss)
I0406 09:18:27.247885  5226 sgd_solver.cpp:105] Iteration 15012, lr = 0.01
I0406 09:18:32.524755  5226 solver.cpp:218] Iteration 15024 (2.27409 iter/s, 5.27683s/12 iters), loss = 2.14104
I0406 09:18:32.524796  5226 solver.cpp:237]     Train net output #0: loss = 2.14104 (* 1 = 2.14104 loss)
I0406 09:18:32.524802  5226 sgd_solver.cpp:105] Iteration 15024, lr = 0.01
I0406 09:18:37.882902  5226 solver.cpp:218] Iteration 15036 (2.23961 iter/s, 5.35806s/12 iters), loss = 2.29173
I0406 09:18:37.882938  5226 solver.cpp:237]     Train net output #0: loss = 2.29173 (* 1 = 2.29173 loss)
I0406 09:18:37.882943  5226 sgd_solver.cpp:105] Iteration 15036, lr = 0.01
I0406 09:18:43.310381  5226 solver.cpp:218] Iteration 15048 (2.21101 iter/s, 5.42739s/12 iters), loss = 2.10765
I0406 09:18:43.310436  5226 solver.cpp:237]     Train net output #0: loss = 2.10765 (* 1 = 2.10765 loss)
I0406 09:18:43.310444  5226 sgd_solver.cpp:105] Iteration 15048, lr = 0.01
I0406 09:18:48.697759  5226 solver.cpp:218] Iteration 15060 (2.22747 iter/s, 5.38727s/12 iters), loss = 2.27025
I0406 09:18:48.697804  5226 solver.cpp:237]     Train net output #0: loss = 2.27025 (* 1 = 2.27025 loss)
I0406 09:18:48.697813  5226 sgd_solver.cpp:105] Iteration 15060, lr = 0.01
I0406 09:18:53.620030  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:18:53.987581  5226 solver.cpp:218] Iteration 15072 (2.26855 iter/s, 5.28973s/12 iters), loss = 2.44537
I0406 09:18:53.987619  5226 solver.cpp:237]     Train net output #0: loss = 2.44537 (* 1 = 2.44537 loss)
I0406 09:18:53.987625  5226 sgd_solver.cpp:105] Iteration 15072, lr = 0.01
I0406 09:18:59.247442  5226 solver.cpp:218] Iteration 15084 (2.28147 iter/s, 5.25977s/12 iters), loss = 2.55182
I0406 09:18:59.247560  5226 solver.cpp:237]     Train net output #0: loss = 2.55182 (* 1 = 2.55182 loss)
I0406 09:18:59.247568  5226 sgd_solver.cpp:105] Iteration 15084, lr = 0.01
I0406 09:19:04.022390  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_15096.caffemodel
I0406 09:19:07.022927  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_15096.solverstate
I0406 09:19:09.320590  5226 solver.cpp:330] Iteration 15096, Testing net (#0)
I0406 09:19:09.320609  5226 net.cpp:676] Ignoring source layer train-data
I0406 09:19:12.397886  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:19:13.749411  5226 solver.cpp:397]     Test net output #0: accuracy = 0.172794
I0406 09:19:13.749449  5226 solver.cpp:397]     Test net output #1: loss = 4.14015 (* 1 = 4.14015 loss)
I0406 09:19:13.889973  5226 solver.cpp:218] Iteration 15096 (0.819542 iter/s, 14.6423s/12 iters), loss = 2.67633
I0406 09:19:13.890013  5226 solver.cpp:237]     Train net output #0: loss = 2.67633 (* 1 = 2.67633 loss)
I0406 09:19:13.890018  5226 sgd_solver.cpp:105] Iteration 15096, lr = 0.01
I0406 09:19:18.247953  5226 solver.cpp:218] Iteration 15108 (2.75362 iter/s, 4.35789s/12 iters), loss = 2.38707
I0406 09:19:18.248005  5226 solver.cpp:237]     Train net output #0: loss = 2.38707 (* 1 = 2.38707 loss)
I0406 09:19:18.248013  5226 sgd_solver.cpp:105] Iteration 15108, lr = 0.01
I0406 09:19:23.507370  5226 solver.cpp:218] Iteration 15120 (2.28167 iter/s, 5.25931s/12 iters), loss = 2.49154
I0406 09:19:23.507416  5226 solver.cpp:237]     Train net output #0: loss = 2.49154 (* 1 = 2.49154 loss)
I0406 09:19:23.507422  5226 sgd_solver.cpp:105] Iteration 15120, lr = 0.01
I0406 09:19:28.733196  5226 solver.cpp:218] Iteration 15132 (2.29633 iter/s, 5.22573s/12 iters), loss = 2.67081
I0406 09:19:28.733233  5226 solver.cpp:237]     Train net output #0: loss = 2.67081 (* 1 = 2.67081 loss)
I0406 09:19:28.733239  5226 sgd_solver.cpp:105] Iteration 15132, lr = 0.01
I0406 09:19:33.988365  5226 solver.cpp:218] Iteration 15144 (2.2835 iter/s, 5.25508s/12 iters), loss = 2.34375
I0406 09:19:33.988521  5226 solver.cpp:237]     Train net output #0: loss = 2.34375 (* 1 = 2.34375 loss)
I0406 09:19:33.988531  5226 sgd_solver.cpp:105] Iteration 15144, lr = 0.01
I0406 09:19:39.321030  5226 solver.cpp:218] Iteration 15156 (2.25037 iter/s, 5.33247s/12 iters), loss = 2.33162
I0406 09:19:39.321069  5226 solver.cpp:237]     Train net output #0: loss = 2.33162 (* 1 = 2.33162 loss)
I0406 09:19:39.321074  5226 sgd_solver.cpp:105] Iteration 15156, lr = 0.01
I0406 09:19:44.450824  5226 solver.cpp:218] Iteration 15168 (2.33932 iter/s, 5.1297s/12 iters), loss = 2.21661
I0406 09:19:44.450866  5226 solver.cpp:237]     Train net output #0: loss = 2.21661 (* 1 = 2.21661 loss)
I0406 09:19:44.450875  5226 sgd_solver.cpp:105] Iteration 15168, lr = 0.01
I0406 09:19:46.396755  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:19:49.702390  5226 solver.cpp:218] Iteration 15180 (2.28507 iter/s, 5.25147s/12 iters), loss = 2.24728
I0406 09:19:49.702442  5226 solver.cpp:237]     Train net output #0: loss = 2.24728 (* 1 = 2.24728 loss)
I0406 09:19:49.702450  5226 sgd_solver.cpp:105] Iteration 15180, lr = 0.01
I0406 09:19:55.229676  5226 solver.cpp:218] Iteration 15192 (2.17109 iter/s, 5.52718s/12 iters), loss = 2.48743
I0406 09:19:55.229720  5226 solver.cpp:237]     Train net output #0: loss = 2.48743 (* 1 = 2.48743 loss)
I0406 09:19:55.229728  5226 sgd_solver.cpp:105] Iteration 15192, lr = 0.01
I0406 09:19:57.417174  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_15198.caffemodel
I0406 09:20:00.501956  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_15198.solverstate
I0406 09:20:02.801457  5226 solver.cpp:330] Iteration 15198, Testing net (#0)
I0406 09:20:02.801477  5226 net.cpp:676] Ignoring source layer train-data
I0406 09:20:05.848068  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:20:07.345039  5226 solver.cpp:397]     Test net output #0: accuracy = 0.220588
I0406 09:20:07.345068  5226 solver.cpp:397]     Test net output #1: loss = 3.85039 (* 1 = 3.85039 loss)
I0406 09:20:09.301525  5226 solver.cpp:218] Iteration 15204 (0.852775 iter/s, 14.0717s/12 iters), loss = 2.9643
I0406 09:20:09.301564  5226 solver.cpp:237]     Train net output #0: loss = 2.9643 (* 1 = 2.9643 loss)
I0406 09:20:09.301570  5226 sgd_solver.cpp:105] Iteration 15204, lr = 0.01
I0406 09:20:14.589720  5226 solver.cpp:218] Iteration 15216 (2.26924 iter/s, 5.2881s/12 iters), loss = 2.35505
I0406 09:20:14.589766  5226 solver.cpp:237]     Train net output #0: loss = 2.35505 (* 1 = 2.35505 loss)
I0406 09:20:14.589774  5226 sgd_solver.cpp:105] Iteration 15216, lr = 0.01
I0406 09:20:19.901767  5226 solver.cpp:218] Iteration 15228 (2.25906 iter/s, 5.31195s/12 iters), loss = 2.2205
I0406 09:20:19.901806  5226 solver.cpp:237]     Train net output #0: loss = 2.2205 (* 1 = 2.2205 loss)
I0406 09:20:19.901811  5226 sgd_solver.cpp:105] Iteration 15228, lr = 0.01
I0406 09:20:25.255931  5226 solver.cpp:218] Iteration 15240 (2.24128 iter/s, 5.35408s/12 iters), loss = 2.34903
I0406 09:20:25.255972  5226 solver.cpp:237]     Train net output #0: loss = 2.34903 (* 1 = 2.34903 loss)
I0406 09:20:25.255977  5226 sgd_solver.cpp:105] Iteration 15240, lr = 0.01
I0406 09:20:30.022627  5226 blocking_queue.cpp:49] Waiting for data
I0406 09:20:30.556344  5226 solver.cpp:218] Iteration 15252 (2.26401 iter/s, 5.30032s/12 iters), loss = 2.69788
I0406 09:20:30.556396  5226 solver.cpp:237]     Train net output #0: loss = 2.69788 (* 1 = 2.69788 loss)
I0406 09:20:30.556403  5226 sgd_solver.cpp:105] Iteration 15252, lr = 0.01
I0406 09:20:35.737082  5226 solver.cpp:218] Iteration 15264 (2.31631 iter/s, 5.18065s/12 iters), loss = 2.11559
I0406 09:20:35.737118  5226 solver.cpp:237]     Train net output #0: loss = 2.11559 (* 1 = 2.11559 loss)
I0406 09:20:35.737123  5226 sgd_solver.cpp:105] Iteration 15264, lr = 0.01
I0406 09:20:39.957659  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:20:41.120762  5226 solver.cpp:218] Iteration 15276 (2.229 iter/s, 5.38359s/12 iters), loss = 2.74078
I0406 09:20:41.120810  5226 solver.cpp:237]     Train net output #0: loss = 2.74078 (* 1 = 2.74078 loss)
I0406 09:20:41.120816  5226 sgd_solver.cpp:105] Iteration 15276, lr = 0.01
I0406 09:20:46.350329  5226 solver.cpp:218] Iteration 15288 (2.29469 iter/s, 5.22947s/12 iters), loss = 2.18095
I0406 09:20:46.350394  5226 solver.cpp:237]     Train net output #0: loss = 2.18095 (* 1 = 2.18095 loss)
I0406 09:20:46.350404  5226 sgd_solver.cpp:105] Iteration 15288, lr = 0.01
I0406 09:20:51.213483  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_15300.caffemodel
I0406 09:20:54.249163  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_15300.solverstate
I0406 09:20:56.560293  5226 solver.cpp:330] Iteration 15300, Testing net (#0)
I0406 09:20:56.560314  5226 net.cpp:676] Ignoring source layer train-data
I0406 09:20:59.546308  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:21:00.911293  5226 solver.cpp:397]     Test net output #0: accuracy = 0.186887
I0406 09:21:00.911329  5226 solver.cpp:397]     Test net output #1: loss = 4.08283 (* 1 = 4.08283 loss)
I0406 09:21:01.047930  5226 solver.cpp:218] Iteration 15300 (0.816469 iter/s, 14.6974s/12 iters), loss = 2.78283
I0406 09:21:01.047977  5226 solver.cpp:237]     Train net output #0: loss = 2.78283 (* 1 = 2.78283 loss)
I0406 09:21:01.047984  5226 sgd_solver.cpp:105] Iteration 15300, lr = 0.01
I0406 09:21:05.214291  5226 solver.cpp:218] Iteration 15312 (2.88027 iter/s, 4.16628s/12 iters), loss = 2.17308
I0406 09:21:05.214331  5226 solver.cpp:237]     Train net output #0: loss = 2.17308 (* 1 = 2.17308 loss)
I0406 09:21:05.214337  5226 sgd_solver.cpp:105] Iteration 15312, lr = 0.01
I0406 09:21:10.382927  5226 solver.cpp:218] Iteration 15324 (2.32173 iter/s, 5.16855s/12 iters), loss = 2.40783
I0406 09:21:10.383041  5226 solver.cpp:237]     Train net output #0: loss = 2.40783 (* 1 = 2.40783 loss)
I0406 09:21:10.383051  5226 sgd_solver.cpp:105] Iteration 15324, lr = 0.01
I0406 09:21:15.723915  5226 solver.cpp:218] Iteration 15336 (2.24685 iter/s, 5.34082s/12 iters), loss = 2.73631
I0406 09:21:15.723965  5226 solver.cpp:237]     Train net output #0: loss = 2.73631 (* 1 = 2.73631 loss)
I0406 09:21:15.723973  5226 sgd_solver.cpp:105] Iteration 15336, lr = 0.01
I0406 09:21:21.042649  5226 solver.cpp:218] Iteration 15348 (2.25622 iter/s, 5.31864s/12 iters), loss = 2.58036
I0406 09:21:21.042690  5226 solver.cpp:237]     Train net output #0: loss = 2.58036 (* 1 = 2.58036 loss)
I0406 09:21:21.042695  5226 sgd_solver.cpp:105] Iteration 15348, lr = 0.01
I0406 09:21:26.416411  5226 solver.cpp:218] Iteration 15360 (2.23311 iter/s, 5.37367s/12 iters), loss = 2.95746
I0406 09:21:26.416460  5226 solver.cpp:237]     Train net output #0: loss = 2.95746 (* 1 = 2.95746 loss)
I0406 09:21:26.416465  5226 sgd_solver.cpp:105] Iteration 15360, lr = 0.01
I0406 09:21:31.639911  5226 solver.cpp:218] Iteration 15372 (2.29735 iter/s, 5.22341s/12 iters), loss = 2.75997
I0406 09:21:31.639948  5226 solver.cpp:237]     Train net output #0: loss = 2.75997 (* 1 = 2.75997 loss)
I0406 09:21:31.639953  5226 sgd_solver.cpp:105] Iteration 15372, lr = 0.01
I0406 09:21:32.775804  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:21:37.087393  5226 solver.cpp:218] Iteration 15384 (2.20289 iter/s, 5.44739s/12 iters), loss = 2.73533
I0406 09:21:37.087451  5226 solver.cpp:237]     Train net output #0: loss = 2.73533 (* 1 = 2.73533 loss)
I0406 09:21:37.087460  5226 sgd_solver.cpp:105] Iteration 15384, lr = 0.01
I0406 09:21:42.339174  5226 solver.cpp:218] Iteration 15396 (2.28499 iter/s, 5.25167s/12 iters), loss = 2.5165
I0406 09:21:42.339334  5226 solver.cpp:237]     Train net output #0: loss = 2.5165 (* 1 = 2.5165 loss)
I0406 09:21:42.339344  5226 sgd_solver.cpp:105] Iteration 15396, lr = 0.01
I0406 09:21:44.460127  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_15402.caffemodel
I0406 09:21:47.484427  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_15402.solverstate
I0406 09:21:49.812829  5226 solver.cpp:330] Iteration 15402, Testing net (#0)
I0406 09:21:49.812846  5226 net.cpp:676] Ignoring source layer train-data
I0406 09:21:52.724072  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:21:54.157788  5226 solver.cpp:397]     Test net output #0: accuracy = 0.179534
I0406 09:21:54.157826  5226 solver.cpp:397]     Test net output #1: loss = 4.16302 (* 1 = 4.16302 loss)
I0406 09:21:55.918220  5226 solver.cpp:218] Iteration 15408 (0.883731 iter/s, 13.5788s/12 iters), loss = 2.98151
I0406 09:21:55.918257  5226 solver.cpp:237]     Train net output #0: loss = 2.98151 (* 1 = 2.98151 loss)
I0406 09:21:55.918263  5226 sgd_solver.cpp:105] Iteration 15408, lr = 0.01
I0406 09:22:01.181650  5226 solver.cpp:218] Iteration 15420 (2.27992 iter/s, 5.26334s/12 iters), loss = 2.73224
I0406 09:22:01.181697  5226 solver.cpp:237]     Train net output #0: loss = 2.73224 (* 1 = 2.73224 loss)
I0406 09:22:01.181705  5226 sgd_solver.cpp:105] Iteration 15420, lr = 0.01
I0406 09:22:06.605823  5226 solver.cpp:218] Iteration 15432 (2.21236 iter/s, 5.42408s/12 iters), loss = 2.54501
I0406 09:22:06.605860  5226 solver.cpp:237]     Train net output #0: loss = 2.54501 (* 1 = 2.54501 loss)
I0406 09:22:06.605866  5226 sgd_solver.cpp:105] Iteration 15432, lr = 0.01
I0406 09:22:11.951035  5226 solver.cpp:218] Iteration 15444 (2.24504 iter/s, 5.34513s/12 iters), loss = 2.64866
I0406 09:22:11.951073  5226 solver.cpp:237]     Train net output #0: loss = 2.64866 (* 1 = 2.64866 loss)
I0406 09:22:11.951078  5226 sgd_solver.cpp:105] Iteration 15444, lr = 0.01
I0406 09:22:17.229717  5226 solver.cpp:218] Iteration 15456 (2.27333 iter/s, 5.27859s/12 iters), loss = 2.65273
I0406 09:22:17.229821  5226 solver.cpp:237]     Train net output #0: loss = 2.65273 (* 1 = 2.65273 loss)
I0406 09:22:17.229830  5226 sgd_solver.cpp:105] Iteration 15456, lr = 0.01
I0406 09:22:22.430706  5226 solver.cpp:218] Iteration 15468 (2.30732 iter/s, 5.20084s/12 iters), loss = 2.24809
I0406 09:22:22.430760  5226 solver.cpp:237]     Train net output #0: loss = 2.24809 (* 1 = 2.24809 loss)
I0406 09:22:22.430768  5226 sgd_solver.cpp:105] Iteration 15468, lr = 0.01
I0406 09:22:25.821961  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:22:27.789016  5226 solver.cpp:218] Iteration 15480 (2.23955 iter/s, 5.35821s/12 iters), loss = 3.06516
I0406 09:22:27.789068  5226 solver.cpp:237]     Train net output #0: loss = 3.06516 (* 1 = 3.06516 loss)
I0406 09:22:27.789077  5226 sgd_solver.cpp:105] Iteration 15480, lr = 0.01
I0406 09:22:33.179409  5226 solver.cpp:218] Iteration 15492 (2.22622 iter/s, 5.39029s/12 iters), loss = 2.2507
I0406 09:22:33.179453  5226 solver.cpp:237]     Train net output #0: loss = 2.2507 (* 1 = 2.2507 loss)
I0406 09:22:33.179459  5226 sgd_solver.cpp:105] Iteration 15492, lr = 0.01
I0406 09:22:38.117730  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_15504.caffemodel
I0406 09:22:41.886471  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_15504.solverstate
I0406 09:22:44.186674  5226 solver.cpp:330] Iteration 15504, Testing net (#0)
I0406 09:22:44.186693  5226 net.cpp:676] Ignoring source layer train-data
I0406 09:22:47.243474  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:22:48.715207  5226 solver.cpp:397]     Test net output #0: accuracy = 0.175245
I0406 09:22:48.715241  5226 solver.cpp:397]     Test net output #1: loss = 4.11399 (* 1 = 4.11399 loss)
I0406 09:22:48.855852  5226 solver.cpp:218] Iteration 15504 (0.765487 iter/s, 15.6763s/12 iters), loss = 2.26472
I0406 09:22:48.855919  5226 solver.cpp:237]     Train net output #0: loss = 2.26472 (* 1 = 2.26472 loss)
I0406 09:22:48.855927  5226 sgd_solver.cpp:105] Iteration 15504, lr = 0.01
I0406 09:22:53.148175  5226 solver.cpp:218] Iteration 15516 (2.79576 iter/s, 4.29222s/12 iters), loss = 2.68273
I0406 09:22:53.148226  5226 solver.cpp:237]     Train net output #0: loss = 2.68273 (* 1 = 2.68273 loss)
I0406 09:22:53.148233  5226 sgd_solver.cpp:105] Iteration 15516, lr = 0.01
I0406 09:22:58.472012  5226 solver.cpp:218] Iteration 15528 (2.25406 iter/s, 5.32374s/12 iters), loss = 3.27756
I0406 09:22:58.472060  5226 solver.cpp:237]     Train net output #0: loss = 3.27756 (* 1 = 3.27756 loss)
I0406 09:22:58.472066  5226 sgd_solver.cpp:105] Iteration 15528, lr = 0.01
I0406 09:23:03.689270  5226 solver.cpp:218] Iteration 15540 (2.3001 iter/s, 5.21716s/12 iters), loss = 2.70905
I0406 09:23:03.689306  5226 solver.cpp:237]     Train net output #0: loss = 2.70905 (* 1 = 2.70905 loss)
I0406 09:23:03.689311  5226 sgd_solver.cpp:105] Iteration 15540, lr = 0.01
I0406 09:23:08.780079  5226 solver.cpp:218] Iteration 15552 (2.35723 iter/s, 5.09072s/12 iters), loss = 3.03579
I0406 09:23:08.780122  5226 solver.cpp:237]     Train net output #0: loss = 3.03579 (* 1 = 3.03579 loss)
I0406 09:23:08.780128  5226 sgd_solver.cpp:105] Iteration 15552, lr = 0.01
I0406 09:23:14.082496  5226 solver.cpp:218] Iteration 15564 (2.26316 iter/s, 5.30233s/12 iters), loss = 2.88521
I0406 09:23:14.082535  5226 solver.cpp:237]     Train net output #0: loss = 2.88521 (* 1 = 2.88521 loss)
I0406 09:23:14.082540  5226 sgd_solver.cpp:105] Iteration 15564, lr = 0.01
I0406 09:23:19.063846  5226 solver.cpp:218] Iteration 15576 (2.40903 iter/s, 4.98127s/12 iters), loss = 2.58066
I0406 09:23:19.063937  5226 solver.cpp:237]     Train net output #0: loss = 2.58066 (* 1 = 2.58066 loss)
I0406 09:23:19.063943  5226 sgd_solver.cpp:105] Iteration 15576, lr = 0.01
I0406 09:23:19.517916  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:23:24.326666  5226 solver.cpp:218] Iteration 15588 (2.28021 iter/s, 5.26268s/12 iters), loss = 2.96647
I0406 09:23:24.326706  5226 solver.cpp:237]     Train net output #0: loss = 2.96647 (* 1 = 2.96647 loss)
I0406 09:23:24.326711  5226 sgd_solver.cpp:105] Iteration 15588, lr = 0.01
I0406 09:23:29.554989  5226 solver.cpp:218] Iteration 15600 (2.29523 iter/s, 5.22823s/12 iters), loss = 2.60254
I0406 09:23:29.555037  5226 solver.cpp:237]     Train net output #0: loss = 2.60254 (* 1 = 2.60254 loss)
I0406 09:23:29.555045  5226 sgd_solver.cpp:105] Iteration 15600, lr = 0.01
I0406 09:23:31.653286  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_15606.caffemodel
I0406 09:23:34.696998  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_15606.solverstate
I0406 09:23:37.014838  5226 solver.cpp:330] Iteration 15606, Testing net (#0)
I0406 09:23:37.014863  5226 net.cpp:676] Ignoring source layer train-data
I0406 09:23:40.060835  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:23:41.554417  5226 solver.cpp:397]     Test net output #0: accuracy = 0.175245
I0406 09:23:41.554456  5226 solver.cpp:397]     Test net output #1: loss = 4.13645 (* 1 = 4.13645 loss)
I0406 09:23:43.566253  5226 solver.cpp:218] Iteration 15612 (0.856463 iter/s, 14.0111s/12 iters), loss = 2.80721
I0406 09:23:43.566291  5226 solver.cpp:237]     Train net output #0: loss = 2.80721 (* 1 = 2.80721 loss)
I0406 09:23:43.566296  5226 sgd_solver.cpp:105] Iteration 15612, lr = 0.01
I0406 09:23:48.929411  5226 solver.cpp:218] Iteration 15624 (2.23753 iter/s, 5.36306s/12 iters), loss = 2.46183
I0406 09:23:48.929459  5226 solver.cpp:237]     Train net output #0: loss = 2.46183 (* 1 = 2.46183 loss)
I0406 09:23:48.929467  5226 sgd_solver.cpp:105] Iteration 15624, lr = 0.01
I0406 09:23:54.320904  5226 solver.cpp:218] Iteration 15636 (2.22577 iter/s, 5.39139s/12 iters), loss = 2.79031
I0406 09:23:54.321048  5226 solver.cpp:237]     Train net output #0: loss = 2.79031 (* 1 = 2.79031 loss)
I0406 09:23:54.321058  5226 sgd_solver.cpp:105] Iteration 15636, lr = 0.01
I0406 09:23:59.483031  5226 solver.cpp:218] Iteration 15648 (2.32471 iter/s, 5.16194s/12 iters), loss = 2.364
I0406 09:23:59.483067  5226 solver.cpp:237]     Train net output #0: loss = 2.364 (* 1 = 2.364 loss)
I0406 09:23:59.483072  5226 sgd_solver.cpp:105] Iteration 15648, lr = 0.01
I0406 09:24:04.723836  5226 solver.cpp:218] Iteration 15660 (2.28976 iter/s, 5.24072s/12 iters), loss = 2.516
I0406 09:24:04.723882  5226 solver.cpp:237]     Train net output #0: loss = 2.516 (* 1 = 2.516 loss)
I0406 09:24:04.723891  5226 sgd_solver.cpp:105] Iteration 15660, lr = 0.01
I0406 09:24:10.012141  5226 solver.cpp:218] Iteration 15672 (2.2692 iter/s, 5.28821s/12 iters), loss = 2.47083
I0406 09:24:10.012179  5226 solver.cpp:237]     Train net output #0: loss = 2.47083 (* 1 = 2.47083 loss)
I0406 09:24:10.012184  5226 sgd_solver.cpp:105] Iteration 15672, lr = 0.01
I0406 09:24:12.794190  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:24:15.433780  5226 solver.cpp:218] Iteration 15684 (2.21339 iter/s, 5.42155s/12 iters), loss = 2.50643
I0406 09:24:15.433818  5226 solver.cpp:237]     Train net output #0: loss = 2.50643 (* 1 = 2.50643 loss)
I0406 09:24:15.433823  5226 sgd_solver.cpp:105] Iteration 15684, lr = 0.01
I0406 09:24:20.868903  5226 solver.cpp:218] Iteration 15696 (2.2079 iter/s, 5.43503s/12 iters), loss = 2.96342
I0406 09:24:20.868942  5226 solver.cpp:237]     Train net output #0: loss = 2.96342 (* 1 = 2.96342 loss)
I0406 09:24:20.868949  5226 sgd_solver.cpp:105] Iteration 15696, lr = 0.01
I0406 09:24:25.595543  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_15708.caffemodel
I0406 09:24:28.685792  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_15708.solverstate
I0406 09:24:31.021780  5226 solver.cpp:330] Iteration 15708, Testing net (#0)
I0406 09:24:31.021806  5226 net.cpp:676] Ignoring source layer train-data
I0406 09:24:33.884191  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:24:35.513459  5226 solver.cpp:397]     Test net output #0: accuracy = 0.179534
I0406 09:24:35.513496  5226 solver.cpp:397]     Test net output #1: loss = 4.13492 (* 1 = 4.13492 loss)
I0406 09:24:35.653733  5226 solver.cpp:218] Iteration 15708 (0.811651 iter/s, 14.7847s/12 iters), loss = 2.78042
I0406 09:24:35.653789  5226 solver.cpp:237]     Train net output #0: loss = 2.78042 (* 1 = 2.78042 loss)
I0406 09:24:35.653798  5226 sgd_solver.cpp:105] Iteration 15708, lr = 0.01
I0406 09:24:39.828651  5226 solver.cpp:218] Iteration 15720 (2.87437 iter/s, 4.17483s/12 iters), loss = 2.57444
I0406 09:24:39.828707  5226 solver.cpp:237]     Train net output #0: loss = 2.57444 (* 1 = 2.57444 loss)
I0406 09:24:39.828717  5226 sgd_solver.cpp:105] Iteration 15720, lr = 0.01
I0406 09:24:45.107062  5226 solver.cpp:218] Iteration 15732 (2.27346 iter/s, 5.27831s/12 iters), loss = 2.91971
I0406 09:24:45.107103  5226 solver.cpp:237]     Train net output #0: loss = 2.91971 (* 1 = 2.91971 loss)
I0406 09:24:45.107108  5226 sgd_solver.cpp:105] Iteration 15732, lr = 0.01
I0406 09:24:50.298753  5226 solver.cpp:218] Iteration 15744 (2.31143 iter/s, 5.1916s/12 iters), loss = 2.88435
I0406 09:24:50.298816  5226 solver.cpp:237]     Train net output #0: loss = 2.88435 (* 1 = 2.88435 loss)
I0406 09:24:50.298827  5226 sgd_solver.cpp:105] Iteration 15744, lr = 0.01
I0406 09:24:55.691529  5226 solver.cpp:218] Iteration 15756 (2.22524 iter/s, 5.39266s/12 iters), loss = 2.47781
I0406 09:24:55.691637  5226 solver.cpp:237]     Train net output #0: loss = 2.47781 (* 1 = 2.47781 loss)
I0406 09:24:55.691648  5226 sgd_solver.cpp:105] Iteration 15756, lr = 0.01
I0406 09:25:00.901211  5226 solver.cpp:218] Iteration 15768 (2.30347 iter/s, 5.20953s/12 iters), loss = 2.61691
I0406 09:25:00.901263  5226 solver.cpp:237]     Train net output #0: loss = 2.61691 (* 1 = 2.61691 loss)
I0406 09:25:00.901271  5226 sgd_solver.cpp:105] Iteration 15768, lr = 0.01
I0406 09:25:05.842293  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:25:06.171766  5226 solver.cpp:218] Iteration 15780 (2.27685 iter/s, 5.27045s/12 iters), loss = 2.44525
I0406 09:25:06.171813  5226 solver.cpp:237]     Train net output #0: loss = 2.44525 (* 1 = 2.44525 loss)
I0406 09:25:06.171819  5226 sgd_solver.cpp:105] Iteration 15780, lr = 0.01
I0406 09:25:11.286185  5226 solver.cpp:218] Iteration 15792 (2.34635 iter/s, 5.11433s/12 iters), loss = 2.42336
I0406 09:25:11.286224  5226 solver.cpp:237]     Train net output #0: loss = 2.42336 (* 1 = 2.42336 loss)
I0406 09:25:11.286229  5226 sgd_solver.cpp:105] Iteration 15792, lr = 0.01
I0406 09:25:16.488040  5226 solver.cpp:218] Iteration 15804 (2.30691 iter/s, 5.20177s/12 iters), loss = 2.65934
I0406 09:25:16.488080  5226 solver.cpp:237]     Train net output #0: loss = 2.65934 (* 1 = 2.65934 loss)
I0406 09:25:16.488086  5226 sgd_solver.cpp:105] Iteration 15804, lr = 0.01
I0406 09:25:18.625020  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_15810.caffemodel
I0406 09:25:22.178592  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_15810.solverstate
I0406 09:25:25.489333  5226 solver.cpp:330] Iteration 15810, Testing net (#0)
I0406 09:25:25.489358  5226 net.cpp:676] Ignoring source layer train-data
I0406 09:25:28.430613  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:25:30.061458  5226 solver.cpp:397]     Test net output #0: accuracy = 0.189951
I0406 09:25:30.061494  5226 solver.cpp:397]     Test net output #1: loss = 4.02824 (* 1 = 4.02824 loss)
I0406 09:25:32.001039  5226 solver.cpp:218] Iteration 15816 (0.773552 iter/s, 15.5128s/12 iters), loss = 2.58526
I0406 09:25:32.001080  5226 solver.cpp:237]     Train net output #0: loss = 2.58526 (* 1 = 2.58526 loss)
I0406 09:25:32.001085  5226 sgd_solver.cpp:105] Iteration 15816, lr = 0.01
I0406 09:25:37.270018  5226 solver.cpp:218] Iteration 15828 (2.27752 iter/s, 5.26889s/12 iters), loss = 2.4471
I0406 09:25:37.270069  5226 solver.cpp:237]     Train net output #0: loss = 2.4471 (* 1 = 2.4471 loss)
I0406 09:25:37.270077  5226 sgd_solver.cpp:105] Iteration 15828, lr = 0.01
I0406 09:25:42.790802  5226 solver.cpp:218] Iteration 15840 (2.17364 iter/s, 5.52069s/12 iters), loss = 2.22162
I0406 09:25:42.790840  5226 solver.cpp:237]     Train net output #0: loss = 2.22162 (* 1 = 2.22162 loss)
I0406 09:25:42.790846  5226 sgd_solver.cpp:105] Iteration 15840, lr = 0.01
I0406 09:25:48.082165  5226 solver.cpp:218] Iteration 15852 (2.26789 iter/s, 5.29127s/12 iters), loss = 2.47819
I0406 09:25:48.082213  5226 solver.cpp:237]     Train net output #0: loss = 2.47819 (* 1 = 2.47819 loss)
I0406 09:25:48.082223  5226 sgd_solver.cpp:105] Iteration 15852, lr = 0.01
I0406 09:25:53.189289  5226 solver.cpp:218] Iteration 15864 (2.3497 iter/s, 5.10703s/12 iters), loss = 2.40515
I0406 09:25:53.189330  5226 solver.cpp:237]     Train net output #0: loss = 2.40515 (* 1 = 2.40515 loss)
I0406 09:25:53.189335  5226 sgd_solver.cpp:105] Iteration 15864, lr = 0.01
I0406 09:25:58.372988  5226 solver.cpp:218] Iteration 15876 (2.31499 iter/s, 5.18361s/12 iters), loss = 2.54443
I0406 09:25:58.373042  5226 solver.cpp:237]     Train net output #0: loss = 2.54443 (* 1 = 2.54443 loss)
I0406 09:25:58.373051  5226 sgd_solver.cpp:105] Iteration 15876, lr = 0.01
I0406 09:26:00.325109  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:26:03.781400  5226 solver.cpp:218] Iteration 15888 (2.21881 iter/s, 5.40831s/12 iters), loss = 2.13872
I0406 09:26:03.781440  5226 solver.cpp:237]     Train net output #0: loss = 2.13872 (* 1 = 2.13872 loss)
I0406 09:26:03.781445  5226 sgd_solver.cpp:105] Iteration 15888, lr = 0.01
I0406 09:26:08.996037  5226 solver.cpp:218] Iteration 15900 (2.30125 iter/s, 5.21455s/12 iters), loss = 2.4455
I0406 09:26:08.996074  5226 solver.cpp:237]     Train net output #0: loss = 2.4455 (* 1 = 2.4455 loss)
I0406 09:26:08.996080  5226 sgd_solver.cpp:105] Iteration 15900, lr = 0.01
I0406 09:26:13.553409  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_15912.caffemodel
I0406 09:26:16.570116  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_15912.solverstate
I0406 09:26:18.882848  5226 solver.cpp:330] Iteration 15912, Testing net (#0)
I0406 09:26:18.882871  5226 net.cpp:676] Ignoring source layer train-data
I0406 09:26:21.599714  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:26:23.344414  5226 solver.cpp:397]     Test net output #0: accuracy = 0.183211
I0406 09:26:23.344462  5226 solver.cpp:397]     Test net output #1: loss = 4.08466 (* 1 = 4.08466 loss)
I0406 09:26:23.485219  5226 solver.cpp:218] Iteration 15912 (0.828212 iter/s, 14.489s/12 iters), loss = 2.62416
I0406 09:26:23.485285  5226 solver.cpp:237]     Train net output #0: loss = 2.62416 (* 1 = 2.62416 loss)
I0406 09:26:23.485291  5226 sgd_solver.cpp:105] Iteration 15912, lr = 0.01
I0406 09:26:27.983083  5226 solver.cpp:218] Iteration 15924 (2.668 iter/s, 4.49776s/12 iters), loss = 2.52435
I0406 09:26:27.983119  5226 solver.cpp:237]     Train net output #0: loss = 2.52435 (* 1 = 2.52435 loss)
I0406 09:26:27.983124  5226 sgd_solver.cpp:105] Iteration 15924, lr = 0.01
I0406 09:26:33.121044  5226 solver.cpp:218] Iteration 15936 (2.3356 iter/s, 5.13787s/12 iters), loss = 2.92511
I0406 09:26:33.121196  5226 solver.cpp:237]     Train net output #0: loss = 2.92511 (* 1 = 2.92511 loss)
I0406 09:26:33.121207  5226 sgd_solver.cpp:105] Iteration 15936, lr = 0.01
I0406 09:26:33.121475  5226 blocking_queue.cpp:49] Waiting for data
I0406 09:26:38.272338  5226 solver.cpp:218] Iteration 15948 (2.3296 iter/s, 5.1511s/12 iters), loss = 2.54054
I0406 09:26:38.272387  5226 solver.cpp:237]     Train net output #0: loss = 2.54054 (* 1 = 2.54054 loss)
I0406 09:26:38.272395  5226 sgd_solver.cpp:105] Iteration 15948, lr = 0.01
I0406 09:26:43.181797  5226 solver.cpp:218] Iteration 15960 (2.44431 iter/s, 4.90937s/12 iters), loss = 2.45221
I0406 09:26:43.181831  5226 solver.cpp:237]     Train net output #0: loss = 2.45221 (* 1 = 2.45221 loss)
I0406 09:26:43.181836  5226 sgd_solver.cpp:105] Iteration 15960, lr = 0.01
I0406 09:26:48.453789  5226 solver.cpp:218] Iteration 15972 (2.27622 iter/s, 5.27191s/12 iters), loss = 2.25819
I0406 09:26:48.453842  5226 solver.cpp:237]     Train net output #0: loss = 2.25819 (* 1 = 2.25819 loss)
I0406 09:26:48.453850  5226 sgd_solver.cpp:105] Iteration 15972, lr = 0.01
I0406 09:26:52.694708  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:26:53.893669  5226 solver.cpp:218] Iteration 15984 (2.20597 iter/s, 5.43978s/12 iters), loss = 2.6535
I0406 09:26:53.893718  5226 solver.cpp:237]     Train net output #0: loss = 2.6535 (* 1 = 2.6535 loss)
I0406 09:26:53.893725  5226 sgd_solver.cpp:105] Iteration 15984, lr = 0.01
I0406 09:26:59.242192  5226 solver.cpp:218] Iteration 15996 (2.24365 iter/s, 5.34843s/12 iters), loss = 2.31555
I0406 09:26:59.242229  5226 solver.cpp:237]     Train net output #0: loss = 2.31555 (* 1 = 2.31555 loss)
I0406 09:26:59.242235  5226 sgd_solver.cpp:105] Iteration 15996, lr = 0.01
I0406 09:27:04.434032  5226 solver.cpp:218] Iteration 16008 (2.31136 iter/s, 5.19175s/12 iters), loss = 2.09383
I0406 09:27:04.434135  5226 solver.cpp:237]     Train net output #0: loss = 2.09383 (* 1 = 2.09383 loss)
I0406 09:27:04.434141  5226 sgd_solver.cpp:105] Iteration 16008, lr = 0.01
I0406 09:27:06.611964  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_16014.caffemodel
I0406 09:27:09.559796  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_16014.solverstate
I0406 09:27:11.905735  5226 solver.cpp:330] Iteration 16014, Testing net (#0)
I0406 09:27:11.905755  5226 net.cpp:676] Ignoring source layer train-data
I0406 09:27:14.549826  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:27:16.242141  5226 solver.cpp:397]     Test net output #0: accuracy = 0.175858
I0406 09:27:16.242172  5226 solver.cpp:397]     Test net output #1: loss = 4.11342 (* 1 = 4.11342 loss)
I0406 09:27:18.265439  5226 solver.cpp:218] Iteration 16020 (0.867603 iter/s, 13.8312s/12 iters), loss = 2.28053
I0406 09:27:18.265480  5226 solver.cpp:237]     Train net output #0: loss = 2.28053 (* 1 = 2.28053 loss)
I0406 09:27:18.265484  5226 sgd_solver.cpp:105] Iteration 16020, lr = 0.01
I0406 09:27:23.520648  5226 solver.cpp:218] Iteration 16032 (2.28349 iter/s, 5.25512s/12 iters), loss = 2.53497
I0406 09:27:23.520699  5226 solver.cpp:237]     Train net output #0: loss = 2.53497 (* 1 = 2.53497 loss)
I0406 09:27:23.520706  5226 sgd_solver.cpp:105] Iteration 16032, lr = 0.01
I0406 09:27:28.923040  5226 solver.cpp:218] Iteration 16044 (2.22128 iter/s, 5.4023s/12 iters), loss = 2.16649
I0406 09:27:28.923077  5226 solver.cpp:237]     Train net output #0: loss = 2.16649 (* 1 = 2.16649 loss)
I0406 09:27:28.923084  5226 sgd_solver.cpp:105] Iteration 16044, lr = 0.01
I0406 09:27:34.145128  5226 solver.cpp:218] Iteration 16056 (2.29797 iter/s, 5.222s/12 iters), loss = 2.12509
I0406 09:27:34.145176  5226 solver.cpp:237]     Train net output #0: loss = 2.12509 (* 1 = 2.12509 loss)
I0406 09:27:34.145184  5226 sgd_solver.cpp:105] Iteration 16056, lr = 0.01
I0406 09:27:39.536896  5226 solver.cpp:218] Iteration 16068 (2.22566 iter/s, 5.39167s/12 iters), loss = 2.24504
I0406 09:27:39.537031  5226 solver.cpp:237]     Train net output #0: loss = 2.24504 (* 1 = 2.24504 loss)
I0406 09:27:39.537039  5226 sgd_solver.cpp:105] Iteration 16068, lr = 0.01
I0406 09:27:44.818182  5226 solver.cpp:218] Iteration 16080 (2.27225 iter/s, 5.2811s/12 iters), loss = 2.47746
I0406 09:27:44.818235  5226 solver.cpp:237]     Train net output #0: loss = 2.47746 (* 1 = 2.47746 loss)
I0406 09:27:44.818244  5226 sgd_solver.cpp:105] Iteration 16080, lr = 0.01
I0406 09:27:45.955237  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:27:50.237565  5226 solver.cpp:218] Iteration 16092 (2.21432 iter/s, 5.41928s/12 iters), loss = 2.80119
I0406 09:27:50.237604  5226 solver.cpp:237]     Train net output #0: loss = 2.80119 (* 1 = 2.80119 loss)
I0406 09:27:50.237610  5226 sgd_solver.cpp:105] Iteration 16092, lr = 0.01
I0406 09:27:55.725623  5226 solver.cpp:218] Iteration 16104 (2.1866 iter/s, 5.48797s/12 iters), loss = 2.4888
I0406 09:27:55.725662  5226 solver.cpp:237]     Train net output #0: loss = 2.4888 (* 1 = 2.4888 loss)
I0406 09:27:55.725667  5226 sgd_solver.cpp:105] Iteration 16104, lr = 0.01
I0406 09:28:00.595273  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_16116.caffemodel
I0406 09:28:03.664355  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_16116.solverstate
I0406 09:28:05.961359  5226 solver.cpp:330] Iteration 16116, Testing net (#0)
I0406 09:28:05.961377  5226 net.cpp:676] Ignoring source layer train-data
I0406 09:28:08.829941  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:28:10.649439  5226 solver.cpp:397]     Test net output #0: accuracy = 0.192402
I0406 09:28:10.649514  5226 solver.cpp:397]     Test net output #1: loss = 3.99475 (* 1 = 3.99475 loss)
I0406 09:28:10.784075  5226 solver.cpp:218] Iteration 16116 (0.796903 iter/s, 15.0583s/12 iters), loss = 2.7622
I0406 09:28:10.785636  5226 solver.cpp:237]     Train net output #0: loss = 2.7622 (* 1 = 2.7622 loss)
I0406 09:28:10.785651  5226 sgd_solver.cpp:105] Iteration 16116, lr = 0.01
I0406 09:28:15.193295  5226 solver.cpp:218] Iteration 16128 (2.72256 iter/s, 4.40762s/12 iters), loss = 2.56036
I0406 09:28:15.193333  5226 solver.cpp:237]     Train net output #0: loss = 2.56036 (* 1 = 2.56036 loss)
I0406 09:28:15.193339  5226 sgd_solver.cpp:105] Iteration 16128, lr = 0.01
I0406 09:28:20.784226  5226 solver.cpp:218] Iteration 16140 (2.14637 iter/s, 5.59084s/12 iters), loss = 2.95733
I0406 09:28:20.784261  5226 solver.cpp:237]     Train net output #0: loss = 2.95733 (* 1 = 2.95733 loss)
I0406 09:28:20.784266  5226 sgd_solver.cpp:105] Iteration 16140, lr = 0.01
I0406 09:28:26.447095  5226 solver.cpp:218] Iteration 16152 (2.1191 iter/s, 5.66278s/12 iters), loss = 2.48393
I0406 09:28:26.447144  5226 solver.cpp:237]     Train net output #0: loss = 2.48393 (* 1 = 2.48393 loss)
I0406 09:28:26.447151  5226 sgd_solver.cpp:105] Iteration 16152, lr = 0.01
I0406 09:28:32.172912  5226 solver.cpp:218] Iteration 16164 (2.09581 iter/s, 5.72571s/12 iters), loss = 2.54062
I0406 09:28:32.172950  5226 solver.cpp:237]     Train net output #0: loss = 2.54062 (* 1 = 2.54062 loss)
I0406 09:28:32.172956  5226 sgd_solver.cpp:105] Iteration 16164, lr = 0.01
I0406 09:28:37.882836  5226 solver.cpp:218] Iteration 16176 (2.10164 iter/s, 5.70983s/12 iters), loss = 2.63387
I0406 09:28:37.882884  5226 solver.cpp:237]     Train net output #0: loss = 2.63387 (* 1 = 2.63387 loss)
I0406 09:28:37.882891  5226 sgd_solver.cpp:105] Iteration 16176, lr = 0.01
I0406 09:28:41.187204  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:28:43.320613  5226 solver.cpp:218] Iteration 16188 (2.20682 iter/s, 5.43768s/12 iters), loss = 3.08065
I0406 09:28:43.320663  5226 solver.cpp:237]     Train net output #0: loss = 3.08065 (* 1 = 3.08065 loss)
I0406 09:28:43.320672  5226 sgd_solver.cpp:105] Iteration 16188, lr = 0.01
I0406 09:28:48.868044  5226 solver.cpp:218] Iteration 16200 (2.1632 iter/s, 5.54733s/12 iters), loss = 2.41528
I0406 09:28:48.868093  5226 solver.cpp:237]     Train net output #0: loss = 2.41528 (* 1 = 2.41528 loss)
I0406 09:28:48.868100  5226 sgd_solver.cpp:105] Iteration 16200, lr = 0.01
I0406 09:28:54.500289  5226 solver.cpp:218] Iteration 16212 (2.13063 iter/s, 5.63214s/12 iters), loss = 2.35922
I0406 09:28:54.506508  5226 solver.cpp:237]     Train net output #0: loss = 2.35922 (* 1 = 2.35922 loss)
I0406 09:28:54.506528  5226 sgd_solver.cpp:105] Iteration 16212, lr = 0.01
I0406 09:28:56.807871  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_16218.caffemodel
I0406 09:29:00.080796  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_16218.solverstate
I0406 09:29:02.375846  5226 solver.cpp:330] Iteration 16218, Testing net (#0)
I0406 09:29:02.375869  5226 net.cpp:676] Ignoring source layer train-data
I0406 09:29:05.283919  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:29:07.255527  5226 solver.cpp:397]     Test net output #0: accuracy = 0.165441
I0406 09:29:07.255573  5226 solver.cpp:397]     Test net output #1: loss = 4.11181 (* 1 = 4.11181 loss)
I0406 09:29:09.287992  5226 solver.cpp:218] Iteration 16224 (0.811831 iter/s, 14.7814s/12 iters), loss = 2.15621
I0406 09:29:09.288041  5226 solver.cpp:237]     Train net output #0: loss = 2.15621 (* 1 = 2.15621 loss)
I0406 09:29:09.288049  5226 sgd_solver.cpp:105] Iteration 16224, lr = 0.01
I0406 09:29:14.942277  5226 solver.cpp:218] Iteration 16236 (2.12232 iter/s, 5.65419s/12 iters), loss = 2.31498
I0406 09:29:14.942384  5226 solver.cpp:237]     Train net output #0: loss = 2.31498 (* 1 = 2.31498 loss)
I0406 09:29:14.942394  5226 sgd_solver.cpp:105] Iteration 16236, lr = 0.01
I0406 09:29:20.515019  5226 solver.cpp:218] Iteration 16248 (2.1534 iter/s, 5.57259s/12 iters), loss = 2.68945
I0406 09:29:20.515064  5226 solver.cpp:237]     Train net output #0: loss = 2.68945 (* 1 = 2.68945 loss)
I0406 09:29:20.515072  5226 sgd_solver.cpp:105] Iteration 16248, lr = 0.01
I0406 09:29:26.049319  5226 solver.cpp:218] Iteration 16260 (2.16841 iter/s, 5.534s/12 iters), loss = 2.55721
I0406 09:29:26.049367  5226 solver.cpp:237]     Train net output #0: loss = 2.55721 (* 1 = 2.55721 loss)
I0406 09:29:26.049374  5226 sgd_solver.cpp:105] Iteration 16260, lr = 0.01
I0406 09:29:31.664438  5226 solver.cpp:218] Iteration 16272 (2.13712 iter/s, 5.61502s/12 iters), loss = 2.33488
I0406 09:29:31.664479  5226 solver.cpp:237]     Train net output #0: loss = 2.33488 (* 1 = 2.33488 loss)
I0406 09:29:31.664484  5226 sgd_solver.cpp:105] Iteration 16272, lr = 0.01
I0406 09:29:37.314996  5226 solver.cpp:218] Iteration 16284 (2.12372 iter/s, 5.65046s/12 iters), loss = 2.37757
I0406 09:29:37.315050  5226 solver.cpp:237]     Train net output #0: loss = 2.37757 (* 1 = 2.37757 loss)
I0406 09:29:37.315059  5226 sgd_solver.cpp:105] Iteration 16284, lr = 0.01
I0406 09:29:37.840929  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:29:43.282966  5226 solver.cpp:218] Iteration 16296 (2.01077 iter/s, 5.96787s/12 iters), loss = 2.91481
I0406 09:29:43.283004  5226 solver.cpp:237]     Train net output #0: loss = 2.91481 (* 1 = 2.91481 loss)
I0406 09:29:43.283010  5226 sgd_solver.cpp:105] Iteration 16296, lr = 0.01
I0406 09:29:48.870033  5226 solver.cpp:218] Iteration 16308 (2.14787 iter/s, 5.58693s/12 iters), loss = 2.85057
I0406 09:29:48.870216  5226 solver.cpp:237]     Train net output #0: loss = 2.85057 (* 1 = 2.85057 loss)
I0406 09:29:48.870226  5226 sgd_solver.cpp:105] Iteration 16308, lr = 0.01
I0406 09:29:54.265199  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_16320.caffemodel
I0406 09:29:57.426184  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_16320.solverstate
I0406 09:29:59.833500  5226 solver.cpp:330] Iteration 16320, Testing net (#0)
I0406 09:29:59.833529  5226 net.cpp:676] Ignoring source layer train-data
I0406 09:30:02.847280  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:30:04.648279  5226 solver.cpp:397]     Test net output #0: accuracy = 0.153186
I0406 09:30:04.648316  5226 solver.cpp:397]     Test net output #1: loss = 4.10669 (* 1 = 4.10669 loss)
I0406 09:30:04.788470  5226 solver.cpp:218] Iteration 16320 (0.753857 iter/s, 15.9181s/12 iters), loss = 2.56875
I0406 09:30:04.788522  5226 solver.cpp:237]     Train net output #0: loss = 2.56875 (* 1 = 2.56875 loss)
I0406 09:30:04.788529  5226 sgd_solver.cpp:105] Iteration 16320, lr = 0.01
I0406 09:30:09.277542  5226 solver.cpp:218] Iteration 16332 (2.67322 iter/s, 4.48898s/12 iters), loss = 2.33193
I0406 09:30:09.277601  5226 solver.cpp:237]     Train net output #0: loss = 2.33193 (* 1 = 2.33193 loss)
I0406 09:30:09.277611  5226 sgd_solver.cpp:105] Iteration 16332, lr = 0.01
I0406 09:30:14.888790  5226 solver.cpp:218] Iteration 16344 (2.1386 iter/s, 5.61114s/12 iters), loss = 2.56185
I0406 09:30:14.888829  5226 solver.cpp:237]     Train net output #0: loss = 2.56185 (* 1 = 2.56185 loss)
I0406 09:30:14.888834  5226 sgd_solver.cpp:105] Iteration 16344, lr = 0.01
I0406 09:30:20.303822  5226 solver.cpp:218] Iteration 16356 (2.21609 iter/s, 5.41494s/12 iters), loss = 2.94448
I0406 09:30:20.303939  5226 solver.cpp:237]     Train net output #0: loss = 2.94448 (* 1 = 2.94448 loss)
I0406 09:30:20.303951  5226 sgd_solver.cpp:105] Iteration 16356, lr = 0.01
I0406 09:30:26.114989  5226 solver.cpp:218] Iteration 16368 (2.06505 iter/s, 5.811s/12 iters), loss = 2.92951
I0406 09:30:26.115039  5226 solver.cpp:237]     Train net output #0: loss = 2.92951 (* 1 = 2.92951 loss)
I0406 09:30:26.115047  5226 sgd_solver.cpp:105] Iteration 16368, lr = 0.01
I0406 09:30:31.776924  5226 solver.cpp:218] Iteration 16380 (2.11945 iter/s, 5.66184s/12 iters), loss = 3.13383
I0406 09:30:31.776962  5226 solver.cpp:237]     Train net output #0: loss = 3.13383 (* 1 = 3.13383 loss)
I0406 09:30:31.776968  5226 sgd_solver.cpp:105] Iteration 16380, lr = 0.01
I0406 09:30:34.733310  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:30:37.509531  5226 solver.cpp:218] Iteration 16392 (2.09332 iter/s, 5.73252s/12 iters), loss = 2.46985
I0406 09:30:37.509572  5226 solver.cpp:237]     Train net output #0: loss = 2.46985 (* 1 = 2.46985 loss)
I0406 09:30:37.509577  5226 sgd_solver.cpp:105] Iteration 16392, lr = 0.01
I0406 09:30:43.187196  5226 solver.cpp:218] Iteration 16404 (2.11358 iter/s, 5.67757s/12 iters), loss = 2.23007
I0406 09:30:43.187242  5226 solver.cpp:237]     Train net output #0: loss = 2.23007 (* 1 = 2.23007 loss)
I0406 09:30:43.187252  5226 sgd_solver.cpp:105] Iteration 16404, lr = 0.01
I0406 09:30:48.800408  5226 solver.cpp:218] Iteration 16416 (2.13785 iter/s, 5.61312s/12 iters), loss = 2.45317
I0406 09:30:48.800459  5226 solver.cpp:237]     Train net output #0: loss = 2.45317 (* 1 = 2.45317 loss)
I0406 09:30:48.800469  5226 sgd_solver.cpp:105] Iteration 16416, lr = 0.01
I0406 09:30:51.342778  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_16422.caffemodel
I0406 09:30:54.561311  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_16422.solverstate
I0406 09:30:56.969089  5226 solver.cpp:330] Iteration 16422, Testing net (#0)
I0406 09:30:56.969106  5226 net.cpp:676] Ignoring source layer train-data
I0406 09:30:59.958966  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:31:01.891489  5226 solver.cpp:397]     Test net output #0: accuracy = 0.172181
I0406 09:31:01.891527  5226 solver.cpp:397]     Test net output #1: loss = 4.19572 (* 1 = 4.19572 loss)
I0406 09:31:03.790828  5226 solver.cpp:218] Iteration 16428 (0.80052 iter/s, 14.9903s/12 iters), loss = 3.18151
I0406 09:31:03.790881  5226 solver.cpp:237]     Train net output #0: loss = 3.18151 (* 1 = 3.18151 loss)
I0406 09:31:03.790891  5226 sgd_solver.cpp:105] Iteration 16428, lr = 0.01
I0406 09:31:09.088055  5226 solver.cpp:218] Iteration 16440 (2.26538 iter/s, 5.29712s/12 iters), loss = 2.36082
I0406 09:31:09.088104  5226 solver.cpp:237]     Train net output #0: loss = 2.36082 (* 1 = 2.36082 loss)
I0406 09:31:09.088111  5226 sgd_solver.cpp:105] Iteration 16440, lr = 0.01
I0406 09:31:14.630079  5226 solver.cpp:218] Iteration 16452 (2.16538 iter/s, 5.54175s/12 iters), loss = 2.34356
I0406 09:31:14.630134  5226 solver.cpp:237]     Train net output #0: loss = 2.34356 (* 1 = 2.34356 loss)
I0406 09:31:14.630146  5226 sgd_solver.cpp:105] Iteration 16452, lr = 0.01
I0406 09:31:20.147900  5226 solver.cpp:218] Iteration 16464 (2.17481 iter/s, 5.51771s/12 iters), loss = 1.83815
I0406 09:31:20.147950  5226 solver.cpp:237]     Train net output #0: loss = 1.83815 (* 1 = 1.83815 loss)
I0406 09:31:20.147958  5226 sgd_solver.cpp:105] Iteration 16464, lr = 0.01
I0406 09:31:25.491257  5226 solver.cpp:218] Iteration 16476 (2.24582 iter/s, 5.34325s/12 iters), loss = 2.64603
I0406 09:31:25.491374  5226 solver.cpp:237]     Train net output #0: loss = 2.64603 (* 1 = 2.64603 loss)
I0406 09:31:25.491385  5226 sgd_solver.cpp:105] Iteration 16476, lr = 0.01
I0406 09:31:30.937216  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:31:31.239321  5226 solver.cpp:218] Iteration 16488 (2.08772 iter/s, 5.7479s/12 iters), loss = 2.93763
I0406 09:31:31.239357  5226 solver.cpp:237]     Train net output #0: loss = 2.93763 (* 1 = 2.93763 loss)
I0406 09:31:31.239362  5226 sgd_solver.cpp:105] Iteration 16488, lr = 0.01
I0406 09:31:36.937832  5226 solver.cpp:218] Iteration 16500 (2.10585 iter/s, 5.69842s/12 iters), loss = 2.73476
I0406 09:31:36.937886  5226 solver.cpp:237]     Train net output #0: loss = 2.73476 (* 1 = 2.73476 loss)
I0406 09:31:36.937894  5226 sgd_solver.cpp:105] Iteration 16500, lr = 0.01
I0406 09:31:42.717741  5226 solver.cpp:218] Iteration 16512 (2.0762 iter/s, 5.7798s/12 iters), loss = 2.10951
I0406 09:31:42.717789  5226 solver.cpp:237]     Train net output #0: loss = 2.10951 (* 1 = 2.10951 loss)
I0406 09:31:42.717798  5226 sgd_solver.cpp:105] Iteration 16512, lr = 0.01
I0406 09:31:47.854550  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_16524.caffemodel
I0406 09:31:50.970706  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_16524.solverstate
I0406 09:31:53.339844  5226 solver.cpp:330] Iteration 16524, Testing net (#0)
I0406 09:31:53.339865  5226 net.cpp:676] Ignoring source layer train-data
I0406 09:31:56.025452  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:31:58.217926  5226 solver.cpp:397]     Test net output #0: accuracy = 0.169118
I0406 09:31:58.217952  5226 solver.cpp:397]     Test net output #1: loss = 4.11092 (* 1 = 4.11092 loss)
I0406 09:31:58.361522  5226 solver.cpp:218] Iteration 16524 (0.767086 iter/s, 15.6436s/12 iters), loss = 2.64053
I0406 09:31:58.361572  5226 solver.cpp:237]     Train net output #0: loss = 2.64053 (* 1 = 2.64053 loss)
I0406 09:31:58.361579  5226 sgd_solver.cpp:105] Iteration 16524, lr = 0.01
I0406 09:32:02.910775  5226 solver.cpp:218] Iteration 16536 (2.63785 iter/s, 4.54916s/12 iters), loss = 2.09279
I0406 09:32:02.910821  5226 solver.cpp:237]     Train net output #0: loss = 2.09279 (* 1 = 2.09279 loss)
I0406 09:32:02.910830  5226 sgd_solver.cpp:105] Iteration 16536, lr = 0.01
I0406 09:32:08.656677  5226 solver.cpp:218] Iteration 16548 (2.08851 iter/s, 5.74572s/12 iters), loss = 2.44516
I0406 09:32:08.656728  5226 solver.cpp:237]     Train net output #0: loss = 2.44516 (* 1 = 2.44516 loss)
I0406 09:32:08.656734  5226 sgd_solver.cpp:105] Iteration 16548, lr = 0.01
I0406 09:32:14.274456  5226 solver.cpp:218] Iteration 16560 (2.13611 iter/s, 5.61768s/12 iters), loss = 2.54443
I0406 09:32:14.274497  5226 solver.cpp:237]     Train net output #0: loss = 2.54443 (* 1 = 2.54443 loss)
I0406 09:32:14.274502  5226 sgd_solver.cpp:105] Iteration 16560, lr = 0.01
I0406 09:32:19.852234  5226 solver.cpp:218] Iteration 16572 (2.15143 iter/s, 5.57769s/12 iters), loss = 2.59858
I0406 09:32:19.852274  5226 solver.cpp:237]     Train net output #0: loss = 2.59858 (* 1 = 2.59858 loss)
I0406 09:32:19.852279  5226 sgd_solver.cpp:105] Iteration 16572, lr = 0.01
I0406 09:32:25.674960  5226 solver.cpp:218] Iteration 16584 (2.06093 iter/s, 5.82263s/12 iters), loss = 2.65429
I0406 09:32:25.675009  5226 solver.cpp:237]     Train net output #0: loss = 2.65429 (* 1 = 2.65429 loss)
I0406 09:32:25.675017  5226 sgd_solver.cpp:105] Iteration 16584, lr = 0.01
I0406 09:32:27.852551  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:32:31.481380  5226 solver.cpp:218] Iteration 16596 (2.06671 iter/s, 5.80632s/12 iters), loss = 2.42607
I0406 09:32:31.481438  5226 solver.cpp:237]     Train net output #0: loss = 2.42607 (* 1 = 2.42607 loss)
I0406 09:32:31.481448  5226 sgd_solver.cpp:105] Iteration 16596, lr = 0.01
I0406 09:32:37.138252  5226 solver.cpp:218] Iteration 16608 (2.12135 iter/s, 5.65677s/12 iters), loss = 2.49398
I0406 09:32:37.138291  5226 solver.cpp:237]     Train net output #0: loss = 2.49398 (* 1 = 2.49398 loss)
I0406 09:32:37.138296  5226 sgd_solver.cpp:105] Iteration 16608, lr = 0.01
I0406 09:32:42.953439  5226 solver.cpp:218] Iteration 16620 (2.0636 iter/s, 5.81509s/12 iters), loss = 2.7301
I0406 09:32:42.953492  5226 solver.cpp:237]     Train net output #0: loss = 2.7301 (* 1 = 2.7301 loss)
I0406 09:32:42.953500  5226 sgd_solver.cpp:105] Iteration 16620, lr = 0.01
I0406 09:32:45.127641  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_16626.caffemodel
I0406 09:32:49.070082  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_16626.solverstate
I0406 09:32:51.479532  5226 solver.cpp:330] Iteration 16626, Testing net (#0)
I0406 09:32:51.479550  5226 net.cpp:676] Ignoring source layer train-data
I0406 09:32:54.278700  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:32:55.733291  5226 blocking_queue.cpp:49] Waiting for data
I0406 09:32:56.470927  5226 solver.cpp:397]     Test net output #0: accuracy = 0.167279
I0406 09:32:56.470960  5226 solver.cpp:397]     Test net output #1: loss = 4.1523 (* 1 = 4.1523 loss)
I0406 09:32:58.396672  5226 solver.cpp:218] Iteration 16632 (0.777048 iter/s, 15.4431s/12 iters), loss = 2.21863
I0406 09:32:58.398195  5226 solver.cpp:237]     Train net output #0: loss = 2.21863 (* 1 = 2.21863 loss)
I0406 09:32:58.398211  5226 sgd_solver.cpp:105] Iteration 16632, lr = 0.01
I0406 09:33:04.069341  5226 solver.cpp:218] Iteration 16644 (2.11599 iter/s, 5.67111s/12 iters), loss = 2.2941
I0406 09:33:04.069380  5226 solver.cpp:237]     Train net output #0: loss = 2.2941 (* 1 = 2.2941 loss)
I0406 09:33:04.069386  5226 sgd_solver.cpp:105] Iteration 16644, lr = 0.01
I0406 09:33:09.934734  5226 solver.cpp:218] Iteration 16656 (2.04593 iter/s, 5.8653s/12 iters), loss = 1.89636
I0406 09:33:09.934779  5226 solver.cpp:237]     Train net output #0: loss = 1.89636 (* 1 = 1.89636 loss)
I0406 09:33:09.934787  5226 sgd_solver.cpp:105] Iteration 16656, lr = 0.01
I0406 09:33:15.585810  5226 solver.cpp:218] Iteration 16668 (2.12353 iter/s, 5.65098s/12 iters), loss = 2.40289
I0406 09:33:15.585858  5226 solver.cpp:237]     Train net output #0: loss = 2.40289 (* 1 = 2.40289 loss)
I0406 09:33:15.585866  5226 sgd_solver.cpp:105] Iteration 16668, lr = 0.01
I0406 09:33:20.888341  5226 solver.cpp:218] Iteration 16680 (2.26311 iter/s, 5.30244s/12 iters), loss = 2.3542
I0406 09:33:20.888377  5226 solver.cpp:237]     Train net output #0: loss = 2.3542 (* 1 = 2.3542 loss)
I0406 09:33:20.888382  5226 sgd_solver.cpp:105] Iteration 16680, lr = 0.01
I0406 09:33:25.343715  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:33:26.509707  5226 solver.cpp:218] Iteration 16692 (2.13475 iter/s, 5.62128s/12 iters), loss = 2.99205
I0406 09:33:26.515930  5226 solver.cpp:237]     Train net output #0: loss = 2.99205 (* 1 = 2.99205 loss)
I0406 09:33:26.515947  5226 sgd_solver.cpp:105] Iteration 16692, lr = 0.01
I0406 09:33:32.121196  5226 solver.cpp:218] Iteration 16704 (2.14086 iter/s, 5.60523s/12 iters), loss = 2.66269
I0406 09:33:32.121381  5226 solver.cpp:237]     Train net output #0: loss = 2.66269 (* 1 = 2.66269 loss)
I0406 09:33:32.121393  5226 sgd_solver.cpp:105] Iteration 16704, lr = 0.01
I0406 09:33:37.883632  5226 solver.cpp:218] Iteration 16716 (2.08255 iter/s, 5.76217s/12 iters), loss = 2.70606
I0406 09:33:37.883687  5226 solver.cpp:237]     Train net output #0: loss = 2.70606 (* 1 = 2.70606 loss)
I0406 09:33:37.883695  5226 sgd_solver.cpp:105] Iteration 16716, lr = 0.01
I0406 09:33:42.890591  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_16728.caffemodel
I0406 09:33:46.102897  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_16728.solverstate
I0406 09:33:48.429646  5226 solver.cpp:330] Iteration 16728, Testing net (#0)
I0406 09:33:48.429666  5226 net.cpp:676] Ignoring source layer train-data
I0406 09:33:51.269460  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:33:53.218272  5226 solver.cpp:397]     Test net output #0: accuracy = 0.202819
I0406 09:33:53.218307  5226 solver.cpp:397]     Test net output #1: loss = 4.02315 (* 1 = 4.02315 loss)
I0406 09:33:53.348867  5226 solver.cpp:218] Iteration 16728 (0.775942 iter/s, 15.4651s/12 iters), loss = 2.55238
I0406 09:33:53.348917  5226 solver.cpp:237]     Train net output #0: loss = 2.55238 (* 1 = 2.55238 loss)
I0406 09:33:53.348925  5226 sgd_solver.cpp:105] Iteration 16728, lr = 0.01
I0406 09:33:58.225090  5226 solver.cpp:218] Iteration 16740 (2.46097 iter/s, 4.87613s/12 iters), loss = 2.19731
I0406 09:33:58.225147  5226 solver.cpp:237]     Train net output #0: loss = 2.19731 (* 1 = 2.19731 loss)
I0406 09:33:58.225157  5226 sgd_solver.cpp:105] Iteration 16740, lr = 0.01
I0406 09:34:04.144661  5226 solver.cpp:218] Iteration 16752 (2.02721 iter/s, 5.91947s/12 iters), loss = 2.38449
I0406 09:34:04.144774  5226 solver.cpp:237]     Train net output #0: loss = 2.38449 (* 1 = 2.38449 loss)
I0406 09:34:04.144784  5226 sgd_solver.cpp:105] Iteration 16752, lr = 0.01
I0406 09:34:09.579020  5226 solver.cpp:218] Iteration 16764 (2.20824 iter/s, 5.4342s/12 iters), loss = 2.6434
I0406 09:34:09.579064  5226 solver.cpp:237]     Train net output #0: loss = 2.6434 (* 1 = 2.6434 loss)
I0406 09:34:09.579071  5226 sgd_solver.cpp:105] Iteration 16764, lr = 0.01
I0406 09:34:15.014813  5226 solver.cpp:218] Iteration 16776 (2.20763 iter/s, 5.4357s/12 iters), loss = 2.96203
I0406 09:34:15.014851  5226 solver.cpp:237]     Train net output #0: loss = 2.96203 (* 1 = 2.96203 loss)
I0406 09:34:15.014856  5226 sgd_solver.cpp:105] Iteration 16776, lr = 0.01
I0406 09:34:20.651443  5226 solver.cpp:218] Iteration 16788 (2.12897 iter/s, 5.63654s/12 iters), loss = 2.26781
I0406 09:34:20.651497  5226 solver.cpp:237]     Train net output #0: loss = 2.26781 (* 1 = 2.26781 loss)
I0406 09:34:20.651506  5226 sgd_solver.cpp:105] Iteration 16788, lr = 0.01
I0406 09:34:21.892722  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:34:26.436740  5226 solver.cpp:218] Iteration 16800 (2.07426 iter/s, 5.78519s/12 iters), loss = 2.64273
I0406 09:34:26.442966  5226 solver.cpp:237]     Train net output #0: loss = 2.64273 (* 1 = 2.64273 loss)
I0406 09:34:26.442988  5226 sgd_solver.cpp:105] Iteration 16800, lr = 0.01
I0406 09:34:32.254184  5226 solver.cpp:218] Iteration 16812 (2.06498 iter/s, 5.81118s/12 iters), loss = 2.42553
I0406 09:34:32.254233  5226 solver.cpp:237]     Train net output #0: loss = 2.42553 (* 1 = 2.42553 loss)
I0406 09:34:32.254243  5226 sgd_solver.cpp:105] Iteration 16812, lr = 0.01
I0406 09:34:37.731284  5226 solver.cpp:218] Iteration 16824 (2.191 iter/s, 5.47696s/12 iters), loss = 2.73933
I0406 09:34:37.731446  5226 solver.cpp:237]     Train net output #0: loss = 2.73933 (* 1 = 2.73933 loss)
I0406 09:34:37.731454  5226 sgd_solver.cpp:105] Iteration 16824, lr = 0.01
I0406 09:34:39.879523  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_16830.caffemodel
I0406 09:34:42.924937  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_16830.solverstate
I0406 09:34:45.260212  5226 solver.cpp:330] Iteration 16830, Testing net (#0)
I0406 09:34:45.260237  5226 net.cpp:676] Ignoring source layer train-data
I0406 09:34:47.935811  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:34:50.216485  5226 solver.cpp:397]     Test net output #0: accuracy = 0.161765
I0406 09:34:50.216511  5226 solver.cpp:397]     Test net output #1: loss = 4.21224 (* 1 = 4.21224 loss)
I0406 09:34:52.167304  5226 solver.cpp:218] Iteration 16836 (0.831269 iter/s, 14.4358s/12 iters), loss = 2.99011
I0406 09:34:52.167353  5226 solver.cpp:237]     Train net output #0: loss = 2.99011 (* 1 = 2.99011 loss)
I0406 09:34:52.167358  5226 sgd_solver.cpp:105] Iteration 16836, lr = 0.01
I0406 09:34:57.731110  5226 solver.cpp:218] Iteration 16848 (2.15684 iter/s, 5.5637s/12 iters), loss = 2.67494
I0406 09:34:57.731156  5226 solver.cpp:237]     Train net output #0: loss = 2.67494 (* 1 = 2.67494 loss)
I0406 09:34:57.731164  5226 sgd_solver.cpp:105] Iteration 16848, lr = 0.01
I0406 09:35:03.158469  5226 solver.cpp:218] Iteration 16860 (2.21106 iter/s, 5.42726s/12 iters), loss = 2.42591
I0406 09:35:03.158517  5226 solver.cpp:237]     Train net output #0: loss = 2.42591 (* 1 = 2.42591 loss)
I0406 09:35:03.158526  5226 sgd_solver.cpp:105] Iteration 16860, lr = 0.01
I0406 09:35:08.689682  5226 solver.cpp:218] Iteration 16872 (2.16954 iter/s, 5.53113s/12 iters), loss = 2.9865
I0406 09:35:08.689777  5226 solver.cpp:237]     Train net output #0: loss = 2.9865 (* 1 = 2.9865 loss)
I0406 09:35:08.689785  5226 sgd_solver.cpp:105] Iteration 16872, lr = 0.01
I0406 09:35:14.223196  5226 solver.cpp:218] Iteration 16884 (2.16866 iter/s, 5.53337s/12 iters), loss = 2.26302
I0406 09:35:14.223246  5226 solver.cpp:237]     Train net output #0: loss = 2.26302 (* 1 = 2.26302 loss)
I0406 09:35:14.223255  5226 sgd_solver.cpp:105] Iteration 16884, lr = 0.01
I0406 09:35:18.036418  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:35:19.994814  5226 solver.cpp:218] Iteration 16896 (2.07918 iter/s, 5.77151s/12 iters), loss = 2.89087
I0406 09:35:19.994863  5226 solver.cpp:237]     Train net output #0: loss = 2.89087 (* 1 = 2.89087 loss)
I0406 09:35:19.994872  5226 sgd_solver.cpp:105] Iteration 16896, lr = 0.01
I0406 09:35:25.494658  5226 solver.cpp:218] Iteration 16908 (2.18192 iter/s, 5.49973s/12 iters), loss = 2.29341
I0406 09:35:25.494705  5226 solver.cpp:237]     Train net output #0: loss = 2.29341 (* 1 = 2.29341 loss)
I0406 09:35:25.494715  5226 sgd_solver.cpp:105] Iteration 16908, lr = 0.01
I0406 09:35:31.193148  5226 solver.cpp:218] Iteration 16920 (2.10586 iter/s, 5.69839s/12 iters), loss = 2.6235
I0406 09:35:31.193195  5226 solver.cpp:237]     Train net output #0: loss = 2.6235 (* 1 = 2.6235 loss)
I0406 09:35:31.193203  5226 sgd_solver.cpp:105] Iteration 16920, lr = 0.01
I0406 09:35:36.222177  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_16932.caffemodel
I0406 09:35:39.301908  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_16932.solverstate
I0406 09:35:41.610008  5226 solver.cpp:330] Iteration 16932, Testing net (#0)
I0406 09:35:41.610025  5226 net.cpp:676] Ignoring source layer train-data
I0406 09:35:44.274611  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:35:46.581073  5226 solver.cpp:397]     Test net output #0: accuracy = 0.179534
I0406 09:35:46.581110  5226 solver.cpp:397]     Test net output #1: loss = 4.11929 (* 1 = 4.11929 loss)
I0406 09:35:46.717133  5226 solver.cpp:218] Iteration 16932 (0.773006 iter/s, 15.5238s/12 iters), loss = 2.70538
I0406 09:35:46.717183  5226 solver.cpp:237]     Train net output #0: loss = 2.70538 (* 1 = 2.70538 loss)
I0406 09:35:46.717191  5226 sgd_solver.cpp:105] Iteration 16932, lr = 0.01
I0406 09:35:51.336233  5226 solver.cpp:218] Iteration 16944 (2.59796 iter/s, 4.619s/12 iters), loss = 2.58273
I0406 09:35:51.336282  5226 solver.cpp:237]     Train net output #0: loss = 2.58273 (* 1 = 2.58273 loss)
I0406 09:35:51.336293  5226 sgd_solver.cpp:105] Iteration 16944, lr = 0.01
I0406 09:35:56.939513  5226 solver.cpp:218] Iteration 16956 (2.14164 iter/s, 5.60318s/12 iters), loss = 2.86786
I0406 09:35:56.939565  5226 solver.cpp:237]     Train net output #0: loss = 2.86786 (* 1 = 2.86786 loss)
I0406 09:35:56.939574  5226 sgd_solver.cpp:105] Iteration 16956, lr = 0.01
I0406 09:36:02.742823  5226 solver.cpp:218] Iteration 16968 (2.06782 iter/s, 5.8032s/12 iters), loss = 2.36557
I0406 09:36:02.742877  5226 solver.cpp:237]     Train net output #0: loss = 2.36557 (* 1 = 2.36557 loss)
I0406 09:36:02.742884  5226 sgd_solver.cpp:105] Iteration 16968, lr = 0.01
I0406 09:36:08.606348  5226 solver.cpp:218] Iteration 16980 (2.04659 iter/s, 5.86342s/12 iters), loss = 2.72038
I0406 09:36:08.606396  5226 solver.cpp:237]     Train net output #0: loss = 2.72038 (* 1 = 2.72038 loss)
I0406 09:36:08.606403  5226 sgd_solver.cpp:105] Iteration 16980, lr = 0.01
I0406 09:36:14.171015  5226 solver.cpp:218] Iteration 16992 (2.15656 iter/s, 5.56441s/12 iters), loss = 3.18683
I0406 09:36:14.171133  5226 solver.cpp:237]     Train net output #0: loss = 3.18683 (* 1 = 3.18683 loss)
I0406 09:36:14.171142  5226 sgd_solver.cpp:105] Iteration 16992, lr = 0.01
I0406 09:36:14.638798  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:36:19.679415  5226 solver.cpp:218] Iteration 17004 (2.17856 iter/s, 5.50823s/12 iters), loss = 2.47616
I0406 09:36:19.679463  5226 solver.cpp:237]     Train net output #0: loss = 2.47616 (* 1 = 2.47616 loss)
I0406 09:36:19.679471  5226 sgd_solver.cpp:105] Iteration 17004, lr = 0.01
I0406 09:36:25.376852  5226 solver.cpp:218] Iteration 17016 (2.10625 iter/s, 5.69733s/12 iters), loss = 2.57479
I0406 09:36:25.376921  5226 solver.cpp:237]     Train net output #0: loss = 2.57479 (* 1 = 2.57479 loss)
I0406 09:36:25.376929  5226 sgd_solver.cpp:105] Iteration 17016, lr = 0.01
I0406 09:36:31.233106  5226 solver.cpp:218] Iteration 17028 (2.04913 iter/s, 5.85614s/12 iters), loss = 2.88804
I0406 09:36:31.233141  5226 solver.cpp:237]     Train net output #0: loss = 2.88804 (* 1 = 2.88804 loss)
I0406 09:36:31.233147  5226 sgd_solver.cpp:105] Iteration 17028, lr = 0.01
I0406 09:36:33.404342  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_17034.caffemodel
I0406 09:36:36.647012  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_17034.solverstate
I0406 09:36:39.246140  5226 solver.cpp:330] Iteration 17034, Testing net (#0)
I0406 09:36:39.246160  5226 net.cpp:676] Ignoring source layer train-data
I0406 09:36:41.766379  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:36:44.265537  5226 solver.cpp:397]     Test net output #0: accuracy = 0.16299
I0406 09:36:44.265625  5226 solver.cpp:397]     Test net output #1: loss = 4.21995 (* 1 = 4.21995 loss)
I0406 09:36:46.347651  5226 solver.cpp:218] Iteration 17040 (0.793945 iter/s, 15.1144s/12 iters), loss = 2.37067
I0406 09:36:46.352939  5226 solver.cpp:237]     Train net output #0: loss = 2.37067 (* 1 = 2.37067 loss)
I0406 09:36:46.352959  5226 sgd_solver.cpp:105] Iteration 17040, lr = 0.01
I0406 09:36:52.245440  5226 solver.cpp:218] Iteration 17052 (2.0365 iter/s, 5.89247s/12 iters), loss = 2.20378
I0406 09:36:52.245486  5226 solver.cpp:237]     Train net output #0: loss = 2.20378 (* 1 = 2.20378 loss)
I0406 09:36:52.245494  5226 sgd_solver.cpp:105] Iteration 17052, lr = 0.01
I0406 09:36:57.807592  5226 solver.cpp:218] Iteration 17064 (2.15748 iter/s, 5.56206s/12 iters), loss = 2.47126
I0406 09:36:57.807641  5226 solver.cpp:237]     Train net output #0: loss = 2.47126 (* 1 = 2.47126 loss)
I0406 09:36:57.807648  5226 sgd_solver.cpp:105] Iteration 17064, lr = 0.01
I0406 09:37:03.242210  5226 solver.cpp:218] Iteration 17076 (2.20811 iter/s, 5.43452s/12 iters), loss = 2.51492
I0406 09:37:03.242252  5226 solver.cpp:237]     Train net output #0: loss = 2.51492 (* 1 = 2.51492 loss)
I0406 09:37:03.242259  5226 sgd_solver.cpp:105] Iteration 17076, lr = 0.01
I0406 09:37:08.610327  5226 solver.cpp:218] Iteration 17088 (2.23546 iter/s, 5.36803s/12 iters), loss = 2.83734
I0406 09:37:08.610363  5226 solver.cpp:237]     Train net output #0: loss = 2.83734 (* 1 = 2.83734 loss)
I0406 09:37:08.610369  5226 sgd_solver.cpp:105] Iteration 17088, lr = 0.01
I0406 09:37:11.652858  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:37:14.167340  5226 solver.cpp:218] Iteration 17100 (2.15947 iter/s, 5.55692s/12 iters), loss = 2.34522
I0406 09:37:14.167379  5226 solver.cpp:237]     Train net output #0: loss = 2.34522 (* 1 = 2.34522 loss)
I0406 09:37:14.167385  5226 sgd_solver.cpp:105] Iteration 17100, lr = 0.01
I0406 09:37:19.485165  5226 solver.cpp:218] Iteration 17112 (2.2566 iter/s, 5.31774s/12 iters), loss = 2.60643
I0406 09:37:19.485324  5226 solver.cpp:237]     Train net output #0: loss = 2.60643 (* 1 = 2.60643 loss)
I0406 09:37:19.485333  5226 sgd_solver.cpp:105] Iteration 17112, lr = 0.01
I0406 09:37:25.069727  5226 solver.cpp:218] Iteration 17124 (2.14888 iter/s, 5.58431s/12 iters), loss = 2.55089
I0406 09:37:25.069777  5226 solver.cpp:237]     Train net output #0: loss = 2.55089 (* 1 = 2.55089 loss)
I0406 09:37:25.069784  5226 sgd_solver.cpp:105] Iteration 17124, lr = 0.01
I0406 09:37:30.427904  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_17136.caffemodel
I0406 09:37:33.598701  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_17136.solverstate
I0406 09:37:35.929870  5226 solver.cpp:330] Iteration 17136, Testing net (#0)
I0406 09:37:35.929891  5226 net.cpp:676] Ignoring source layer train-data
I0406 09:37:38.582072  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:37:40.948179  5226 solver.cpp:397]     Test net output #0: accuracy = 0.191176
I0406 09:37:40.948215  5226 solver.cpp:397]     Test net output #1: loss = 4.05446 (* 1 = 4.05446 loss)
I0406 09:37:41.080511  5226 solver.cpp:218] Iteration 17136 (0.749503 iter/s, 16.0106s/12 iters), loss = 2.55505
I0406 09:37:41.080564  5226 solver.cpp:237]     Train net output #0: loss = 2.55505 (* 1 = 2.55505 loss)
I0406 09:37:41.080571  5226 sgd_solver.cpp:105] Iteration 17136, lr = 0.01
I0406 09:37:45.866030  5226 solver.cpp:218] Iteration 17148 (2.50762 iter/s, 4.78542s/12 iters), loss = 2.87978
I0406 09:37:45.866082  5226 solver.cpp:237]     Train net output #0: loss = 2.87978 (* 1 = 2.87978 loss)
I0406 09:37:45.866091  5226 sgd_solver.cpp:105] Iteration 17148, lr = 0.01
I0406 09:37:51.538857  5226 solver.cpp:218] Iteration 17160 (2.11539 iter/s, 5.67272s/12 iters), loss = 2.34037
I0406 09:37:51.539177  5226 solver.cpp:237]     Train net output #0: loss = 2.34037 (* 1 = 2.34037 loss)
I0406 09:37:51.539187  5226 sgd_solver.cpp:105] Iteration 17160, lr = 0.01
I0406 09:37:57.113538  5226 solver.cpp:218] Iteration 17172 (2.15273 iter/s, 5.57431s/12 iters), loss = 1.82207
I0406 09:37:57.113579  5226 solver.cpp:237]     Train net output #0: loss = 1.82207 (* 1 = 1.82207 loss)
I0406 09:37:57.113584  5226 sgd_solver.cpp:105] Iteration 17172, lr = 0.01
I0406 09:38:03.035051  5226 solver.cpp:218] Iteration 17184 (2.02654 iter/s, 5.92141s/12 iters), loss = 2.83974
I0406 09:38:03.035102  5226 solver.cpp:237]     Train net output #0: loss = 2.83974 (* 1 = 2.83974 loss)
I0406 09:38:03.035113  5226 sgd_solver.cpp:105] Iteration 17184, lr = 0.01
I0406 09:38:08.331537  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:38:08.619920  5226 solver.cpp:218] Iteration 17196 (2.1487 iter/s, 5.58477s/12 iters), loss = 3.0153
I0406 09:38:08.619969  5226 solver.cpp:237]     Train net output #0: loss = 3.0153 (* 1 = 3.0153 loss)
I0406 09:38:08.619980  5226 sgd_solver.cpp:105] Iteration 17196, lr = 0.01
I0406 09:38:14.214838  5226 solver.cpp:218] Iteration 17208 (2.14484 iter/s, 5.59482s/12 iters), loss = 2.6266
I0406 09:38:14.214887  5226 solver.cpp:237]     Train net output #0: loss = 2.6266 (* 1 = 2.6266 loss)
I0406 09:38:14.214895  5226 sgd_solver.cpp:105] Iteration 17208, lr = 0.01
I0406 09:38:19.997965  5226 solver.cpp:218] Iteration 17220 (2.07504 iter/s, 5.78303s/12 iters), loss = 2.96225
I0406 09:38:19.998005  5226 solver.cpp:237]     Train net output #0: loss = 2.96225 (* 1 = 2.96225 loss)
I0406 09:38:19.998010  5226 sgd_solver.cpp:105] Iteration 17220, lr = 0.01
I0406 09:38:25.590109  5226 solver.cpp:218] Iteration 17232 (2.1459 iter/s, 5.59205s/12 iters), loss = 2.98649
I0406 09:38:25.590284  5226 solver.cpp:237]     Train net output #0: loss = 2.98649 (* 1 = 2.98649 loss)
I0406 09:38:25.590296  5226 sgd_solver.cpp:105] Iteration 17232, lr = 0.01
I0406 09:38:27.937952  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_17238.caffemodel
I0406 09:38:31.036271  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_17238.solverstate
I0406 09:38:33.343325  5226 solver.cpp:330] Iteration 17238, Testing net (#0)
I0406 09:38:33.343348  5226 net.cpp:676] Ignoring source layer train-data
I0406 09:38:35.849823  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:38:38.255728  5226 solver.cpp:397]     Test net output #0: accuracy = 0.147059
I0406 09:38:38.255766  5226 solver.cpp:397]     Test net output #1: loss = 4.31391 (* 1 = 4.31391 loss)
I0406 09:38:40.310954  5226 solver.cpp:218] Iteration 17244 (0.815188 iter/s, 14.7205s/12 iters), loss = 2.32494
I0406 09:38:40.310992  5226 solver.cpp:237]     Train net output #0: loss = 2.32494 (* 1 = 2.32494 loss)
I0406 09:38:40.310998  5226 sgd_solver.cpp:105] Iteration 17244, lr = 0.01
I0406 09:38:46.042059  5226 solver.cpp:218] Iteration 17256 (2.09387 iter/s, 5.73101s/12 iters), loss = 2.72251
I0406 09:38:46.042109  5226 solver.cpp:237]     Train net output #0: loss = 2.72251 (* 1 = 2.72251 loss)
I0406 09:38:46.042116  5226 sgd_solver.cpp:105] Iteration 17256, lr = 0.01
I0406 09:38:51.863628  5226 solver.cpp:218] Iteration 17268 (2.06134 iter/s, 5.82147s/12 iters), loss = 2.9911
I0406 09:38:51.863667  5226 solver.cpp:237]     Train net output #0: loss = 2.9911 (* 1 = 2.9911 loss)
I0406 09:38:51.863672  5226 sgd_solver.cpp:105] Iteration 17268, lr = 0.01
I0406 09:38:57.512382  5226 solver.cpp:218] Iteration 17280 (2.1244 iter/s, 5.64867s/12 iters), loss = 2.57827
I0406 09:38:57.512476  5226 solver.cpp:237]     Train net output #0: loss = 2.57827 (* 1 = 2.57827 loss)
I0406 09:38:57.512485  5226 sgd_solver.cpp:105] Iteration 17280, lr = 0.01
I0406 09:39:03.168375  5226 solver.cpp:218] Iteration 17292 (2.12171 iter/s, 5.6558s/12 iters), loss = 2.88501
I0406 09:39:03.168431  5226 solver.cpp:237]     Train net output #0: loss = 2.88501 (* 1 = 2.88501 loss)
I0406 09:39:03.168440  5226 sgd_solver.cpp:105] Iteration 17292, lr = 0.01
I0406 09:39:05.353889  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:39:09.009541  5226 solver.cpp:218] Iteration 17304 (2.05442 iter/s, 5.84106s/12 iters), loss = 2.35144
I0406 09:39:09.009588  5226 solver.cpp:237]     Train net output #0: loss = 2.35144 (* 1 = 2.35144 loss)
I0406 09:39:09.009594  5226 sgd_solver.cpp:105] Iteration 17304, lr = 0.01
I0406 09:39:14.392957  5226 solver.cpp:218] Iteration 17316 (2.22911 iter/s, 5.38332s/12 iters), loss = 2.58688
I0406 09:39:14.393007  5226 solver.cpp:237]     Train net output #0: loss = 2.58688 (* 1 = 2.58688 loss)
I0406 09:39:14.393015  5226 sgd_solver.cpp:105] Iteration 17316, lr = 0.01
I0406 09:39:20.129498  5226 solver.cpp:218] Iteration 17328 (2.09189 iter/s, 5.73644s/12 iters), loss = 2.35575
I0406 09:39:20.129536  5226 solver.cpp:237]     Train net output #0: loss = 2.35575 (* 1 = 2.35575 loss)
I0406 09:39:20.129541  5226 sgd_solver.cpp:105] Iteration 17328, lr = 0.01
I0406 09:39:25.106586  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_17340.caffemodel
I0406 09:39:28.809131  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_17340.solverstate
I0406 09:39:32.086956  5226 solver.cpp:330] Iteration 17340, Testing net (#0)
I0406 09:39:32.086977  5226 net.cpp:676] Ignoring source layer train-data
I0406 09:39:33.291972  5226 blocking_queue.cpp:49] Waiting for data
I0406 09:39:34.595803  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:39:37.039052  5226 solver.cpp:397]     Test net output #0: accuracy = 0.177696
I0406 09:39:37.039090  5226 solver.cpp:397]     Test net output #1: loss = 4.11346 (* 1 = 4.11346 loss)
I0406 09:39:37.180872  5226 solver.cpp:218] Iteration 17340 (0.703762 iter/s, 17.0512s/12 iters), loss = 2.49296
I0406 09:39:37.180927  5226 solver.cpp:237]     Train net output #0: loss = 2.49296 (* 1 = 2.49296 loss)
I0406 09:39:37.180934  5226 sgd_solver.cpp:105] Iteration 17340, lr = 0.01
I0406 09:39:41.820314  5226 solver.cpp:218] Iteration 17352 (2.58657 iter/s, 4.63934s/12 iters), loss = 2.5964
I0406 09:39:41.820354  5226 solver.cpp:237]     Train net output #0: loss = 2.5964 (* 1 = 2.5964 loss)
I0406 09:39:41.820360  5226 sgd_solver.cpp:105] Iteration 17352, lr = 0.01
I0406 09:39:47.570683  5226 solver.cpp:218] Iteration 17364 (2.08686 iter/s, 5.75028s/12 iters), loss = 3.09626
I0406 09:39:47.570719  5226 solver.cpp:237]     Train net output #0: loss = 3.09626 (* 1 = 3.09626 loss)
I0406 09:39:47.570724  5226 sgd_solver.cpp:105] Iteration 17364, lr = 0.01
I0406 09:39:53.394436  5226 solver.cpp:218] Iteration 17376 (2.06056 iter/s, 5.82366s/12 iters), loss = 3.01261
I0406 09:39:53.394482  5226 solver.cpp:237]     Train net output #0: loss = 3.01261 (* 1 = 3.01261 loss)
I0406 09:39:53.394490  5226 sgd_solver.cpp:105] Iteration 17376, lr = 0.01
I0406 09:39:58.968819  5226 solver.cpp:218] Iteration 17388 (2.15274 iter/s, 5.57428s/12 iters), loss = 2.8225
I0406 09:39:58.968945  5226 solver.cpp:237]     Train net output #0: loss = 2.8225 (* 1 = 2.8225 loss)
I0406 09:39:58.968953  5226 sgd_solver.cpp:105] Iteration 17388, lr = 0.01
I0406 09:40:03.167459  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:40:04.281426  5226 solver.cpp:218] Iteration 17400 (2.25889 iter/s, 5.31236s/12 iters), loss = 2.58022
I0406 09:40:04.281474  5226 solver.cpp:237]     Train net output #0: loss = 2.58022 (* 1 = 2.58022 loss)
I0406 09:40:04.281482  5226 sgd_solver.cpp:105] Iteration 17400, lr = 0.01
I0406 09:40:10.085455  5226 solver.cpp:218] Iteration 17412 (2.06756 iter/s, 5.80393s/12 iters), loss = 2.47987
I0406 09:40:10.085494  5226 solver.cpp:237]     Train net output #0: loss = 2.47987 (* 1 = 2.47987 loss)
I0406 09:40:10.085500  5226 sgd_solver.cpp:105] Iteration 17412, lr = 0.01
I0406 09:40:15.592054  5226 solver.cpp:218] Iteration 17424 (2.17924 iter/s, 5.50651s/12 iters), loss = 2.34384
I0406 09:40:15.592103  5226 solver.cpp:237]     Train net output #0: loss = 2.34384 (* 1 = 2.34384 loss)
I0406 09:40:15.592111  5226 sgd_solver.cpp:105] Iteration 17424, lr = 0.01
I0406 09:40:21.275457  5226 solver.cpp:218] Iteration 17436 (2.11145 iter/s, 5.6833s/12 iters), loss = 2.36691
I0406 09:40:21.275506  5226 solver.cpp:237]     Train net output #0: loss = 2.36691 (* 1 = 2.36691 loss)
I0406 09:40:21.275513  5226 sgd_solver.cpp:105] Iteration 17436, lr = 0.01
I0406 09:40:23.631533  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_17442.caffemodel
I0406 09:40:26.797827  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_17442.solverstate
I0406 09:40:29.124644  5226 solver.cpp:330] Iteration 17442, Testing net (#0)
I0406 09:40:29.124733  5226 net.cpp:676] Ignoring source layer train-data
I0406 09:40:31.525674  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:40:33.914196  5226 solver.cpp:397]     Test net output #0: accuracy = 0.18076
I0406 09:40:33.914232  5226 solver.cpp:397]     Test net output #1: loss = 4.08108 (* 1 = 4.08108 loss)
I0406 09:40:35.803243  5226 solver.cpp:218] Iteration 17448 (0.826012 iter/s, 14.5276s/12 iters), loss = 2.74822
I0406 09:40:35.803297  5226 solver.cpp:237]     Train net output #0: loss = 2.74822 (* 1 = 2.74822 loss)
I0406 09:40:35.803306  5226 sgd_solver.cpp:105] Iteration 17448, lr = 0.01
I0406 09:40:41.419297  5226 solver.cpp:218] Iteration 17460 (2.13677 iter/s, 5.61595s/12 iters), loss = 2.78272
I0406 09:40:41.419353  5226 solver.cpp:237]     Train net output #0: loss = 2.78272 (* 1 = 2.78272 loss)
I0406 09:40:41.419361  5226 sgd_solver.cpp:105] Iteration 17460, lr = 0.01
I0406 09:40:47.009218  5226 solver.cpp:218] Iteration 17472 (2.14676 iter/s, 5.58982s/12 iters), loss = 2.55214
I0406 09:40:47.009258  5226 solver.cpp:237]     Train net output #0: loss = 2.55214 (* 1 = 2.55214 loss)
I0406 09:40:47.009263  5226 sgd_solver.cpp:105] Iteration 17472, lr = 0.01
I0406 09:40:52.788568  5226 solver.cpp:218] Iteration 17484 (2.07639 iter/s, 5.77926s/12 iters), loss = 2.87774
I0406 09:40:52.788615  5226 solver.cpp:237]     Train net output #0: loss = 2.87774 (* 1 = 2.87774 loss)
I0406 09:40:52.788625  5226 sgd_solver.cpp:105] Iteration 17484, lr = 0.01
I0406 09:40:58.089170  5226 solver.cpp:218] Iteration 17496 (2.26393 iter/s, 5.30051s/12 iters), loss = 2.46048
I0406 09:40:58.089208  5226 solver.cpp:237]     Train net output #0: loss = 2.46048 (* 1 = 2.46048 loss)
I0406 09:40:58.089213  5226 sgd_solver.cpp:105] Iteration 17496, lr = 0.01
I0406 09:40:59.556021  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:41:03.845177  5226 solver.cpp:218] Iteration 17508 (2.08481 iter/s, 5.75591s/12 iters), loss = 2.50711
I0406 09:41:03.845230  5226 solver.cpp:237]     Train net output #0: loss = 2.50711 (* 1 = 2.50711 loss)
I0406 09:41:03.845240  5226 sgd_solver.cpp:105] Iteration 17508, lr = 0.01
I0406 09:41:09.743705  5226 solver.cpp:218] Iteration 17520 (2.03444 iter/s, 5.89843s/12 iters), loss = 3.28702
I0406 09:41:09.743748  5226 solver.cpp:237]     Train net output #0: loss = 3.28702 (* 1 = 3.28702 loss)
I0406 09:41:09.743754  5226 sgd_solver.cpp:105] Iteration 17520, lr = 0.01
I0406 09:41:15.334728  5226 solver.cpp:218] Iteration 17532 (2.14633 iter/s, 5.59093s/12 iters), loss = 2.5581
I0406 09:41:15.334772  5226 solver.cpp:237]     Train net output #0: loss = 2.5581 (* 1 = 2.5581 loss)
I0406 09:41:15.334780  5226 sgd_solver.cpp:105] Iteration 17532, lr = 0.01
I0406 09:41:20.443552  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_17544.caffemodel
I0406 09:41:23.565848  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_17544.solverstate
I0406 09:41:25.899669  5226 solver.cpp:330] Iteration 17544, Testing net (#0)
I0406 09:41:25.899690  5226 net.cpp:676] Ignoring source layer train-data
I0406 09:41:28.103119  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:41:30.590183  5226 solver.cpp:397]     Test net output #0: accuracy = 0.16973
I0406 09:41:30.590278  5226 solver.cpp:397]     Test net output #1: loss = 4.19862 (* 1 = 4.19862 loss)
I0406 09:41:30.732620  5226 solver.cpp:218] Iteration 17544 (0.779335 iter/s, 15.3977s/12 iters), loss = 2.6258
I0406 09:41:30.732661  5226 solver.cpp:237]     Train net output #0: loss = 2.6258 (* 1 = 2.6258 loss)
I0406 09:41:30.732667  5226 sgd_solver.cpp:105] Iteration 17544, lr = 0.01
I0406 09:41:35.509629  5226 solver.cpp:218] Iteration 17556 (2.51208 iter/s, 4.77691s/12 iters), loss = 2.36219
I0406 09:41:35.509680  5226 solver.cpp:237]     Train net output #0: loss = 2.36219 (* 1 = 2.36219 loss)
I0406 09:41:35.509688  5226 sgd_solver.cpp:105] Iteration 17556, lr = 0.01
I0406 09:41:41.038118  5226 solver.cpp:218] Iteration 17568 (2.17062 iter/s, 5.52838s/12 iters), loss = 2.63459
I0406 09:41:41.038172  5226 solver.cpp:237]     Train net output #0: loss = 2.63459 (* 1 = 2.63459 loss)
I0406 09:41:41.038179  5226 sgd_solver.cpp:105] Iteration 17568, lr = 0.01
I0406 09:41:46.357728  5226 solver.cpp:218] Iteration 17580 (2.25585 iter/s, 5.31951s/12 iters), loss = 2.49699
I0406 09:41:46.357781  5226 solver.cpp:237]     Train net output #0: loss = 2.49699 (* 1 = 2.49699 loss)
I0406 09:41:46.357791  5226 sgd_solver.cpp:105] Iteration 17580, lr = 0.01
I0406 09:41:51.956640  5226 solver.cpp:218] Iteration 17592 (2.14331 iter/s, 5.59881s/12 iters), loss = 2.92978
I0406 09:41:51.956688  5226 solver.cpp:237]     Train net output #0: loss = 2.92978 (* 1 = 2.92978 loss)
I0406 09:41:51.956696  5226 sgd_solver.cpp:105] Iteration 17592, lr = 0.01
I0406 09:41:55.978201  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:41:57.912783  5226 solver.cpp:218] Iteration 17604 (2.01476 iter/s, 5.95604s/12 iters), loss = 2.82676
I0406 09:41:57.912840  5226 solver.cpp:237]     Train net output #0: loss = 2.82676 (* 1 = 2.82676 loss)
I0406 09:41:57.912850  5226 sgd_solver.cpp:105] Iteration 17604, lr = 0.01
I0406 09:42:03.658324  5226 solver.cpp:218] Iteration 17616 (2.08861 iter/s, 5.74544s/12 iters), loss = 2.90723
I0406 09:42:03.658469  5226 solver.cpp:237]     Train net output #0: loss = 2.90723 (* 1 = 2.90723 loss)
I0406 09:42:03.658480  5226 sgd_solver.cpp:105] Iteration 17616, lr = 0.01
I0406 09:42:09.091034  5226 solver.cpp:218] Iteration 17628 (2.20892 iter/s, 5.43251s/12 iters), loss = 2.54831
I0406 09:42:09.091084  5226 solver.cpp:237]     Train net output #0: loss = 2.54831 (* 1 = 2.54831 loss)
I0406 09:42:09.091091  5226 sgd_solver.cpp:105] Iteration 17628, lr = 0.01
I0406 09:42:14.881716  5226 solver.cpp:218] Iteration 17640 (2.07233 iter/s, 5.79058s/12 iters), loss = 2.67157
I0406 09:42:14.881767  5226 solver.cpp:237]     Train net output #0: loss = 2.67157 (* 1 = 2.67157 loss)
I0406 09:42:14.881774  5226 sgd_solver.cpp:105] Iteration 17640, lr = 0.01
I0406 09:42:17.205209  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_17646.caffemodel
I0406 09:42:21.559206  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_17646.solverstate
I0406 09:42:25.024138  5226 solver.cpp:330] Iteration 17646, Testing net (#0)
I0406 09:42:25.024155  5226 net.cpp:676] Ignoring source layer train-data
I0406 09:42:27.448318  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:42:29.877810  5226 solver.cpp:397]     Test net output #0: accuracy = 0.164828
I0406 09:42:29.877846  5226 solver.cpp:397]     Test net output #1: loss = 4.17957 (* 1 = 4.17957 loss)
I0406 09:42:31.970463  5226 solver.cpp:218] Iteration 17652 (0.702224 iter/s, 17.0886s/12 iters), loss = 2.66953
I0406 09:42:31.970516  5226 solver.cpp:237]     Train net output #0: loss = 2.66953 (* 1 = 2.66953 loss)
I0406 09:42:31.970525  5226 sgd_solver.cpp:105] Iteration 17652, lr = 0.01
I0406 09:42:37.527065  5226 solver.cpp:218] Iteration 17664 (2.15963 iter/s, 5.5565s/12 iters), loss = 2.8272
I0406 09:42:37.527209  5226 solver.cpp:237]     Train net output #0: loss = 2.8272 (* 1 = 2.8272 loss)
I0406 09:42:37.527217  5226 sgd_solver.cpp:105] Iteration 17664, lr = 0.01
I0406 09:42:43.192404  5226 solver.cpp:218] Iteration 17676 (2.11822 iter/s, 5.66515s/12 iters), loss = 2.84847
I0406 09:42:43.192459  5226 solver.cpp:237]     Train net output #0: loss = 2.84847 (* 1 = 2.84847 loss)
I0406 09:42:43.192468  5226 sgd_solver.cpp:105] Iteration 17676, lr = 0.01
I0406 09:42:48.638018  5226 solver.cpp:218] Iteration 17688 (2.20365 iter/s, 5.44551s/12 iters), loss = 2.73282
I0406 09:42:48.638067  5226 solver.cpp:237]     Train net output #0: loss = 2.73282 (* 1 = 2.73282 loss)
I0406 09:42:48.638075  5226 sgd_solver.cpp:105] Iteration 17688, lr = 0.01
I0406 09:42:54.344620  5226 solver.cpp:218] Iteration 17700 (2.10286 iter/s, 5.7065s/12 iters), loss = 2.72733
I0406 09:42:54.344671  5226 solver.cpp:237]     Train net output #0: loss = 2.72733 (* 1 = 2.72733 loss)
I0406 09:42:54.344679  5226 sgd_solver.cpp:105] Iteration 17700, lr = 0.01
I0406 09:42:54.978006  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:43:00.075264  5226 solver.cpp:218] Iteration 17712 (2.09404 iter/s, 5.73054s/12 iters), loss = 2.45168
I0406 09:43:00.075321  5226 solver.cpp:237]     Train net output #0: loss = 2.45168 (* 1 = 2.45168 loss)
I0406 09:43:00.075330  5226 sgd_solver.cpp:105] Iteration 17712, lr = 0.01
I0406 09:43:05.775935  5226 solver.cpp:218] Iteration 17724 (2.10505 iter/s, 5.70057s/12 iters), loss = 2.6602
I0406 09:43:05.775977  5226 solver.cpp:237]     Train net output #0: loss = 2.6602 (* 1 = 2.6602 loss)
I0406 09:43:05.775982  5226 sgd_solver.cpp:105] Iteration 17724, lr = 0.01
I0406 09:43:11.458418  5226 solver.cpp:218] Iteration 17736 (2.11179 iter/s, 5.68239s/12 iters), loss = 2.49153
I0406 09:43:11.458571  5226 solver.cpp:237]     Train net output #0: loss = 2.49153 (* 1 = 2.49153 loss)
I0406 09:43:11.458580  5226 sgd_solver.cpp:105] Iteration 17736, lr = 0.01
I0406 09:43:16.625604  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_17748.caffemodel
I0406 09:43:19.796114  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_17748.solverstate
I0406 09:43:23.001834  5226 solver.cpp:330] Iteration 17748, Testing net (#0)
I0406 09:43:23.001857  5226 net.cpp:676] Ignoring source layer train-data
I0406 09:43:25.183641  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:43:27.665089  5226 solver.cpp:397]     Test net output #0: accuracy = 0.18076
I0406 09:43:27.665128  5226 solver.cpp:397]     Test net output #1: loss = 4.09905 (* 1 = 4.09905 loss)
I0406 09:43:27.799749  5226 solver.cpp:218] Iteration 17748 (0.734346 iter/s, 16.3411s/12 iters), loss = 2.44148
I0406 09:43:27.804927  5226 solver.cpp:237]     Train net output #0: loss = 2.44148 (* 1 = 2.44148 loss)
I0406 09:43:27.804949  5226 sgd_solver.cpp:105] Iteration 17748, lr = 0.01
I0406 09:43:32.584304  5226 solver.cpp:218] Iteration 17760 (2.5108 iter/s, 4.77935s/12 iters), loss = 2.87457
I0406 09:43:32.584342  5226 solver.cpp:237]     Train net output #0: loss = 2.87457 (* 1 = 2.87457 loss)
I0406 09:43:32.584347  5226 sgd_solver.cpp:105] Iteration 17760, lr = 0.01
I0406 09:43:38.103519  5226 solver.cpp:218] Iteration 17772 (2.17426 iter/s, 5.51913s/12 iters), loss = 2.29188
I0406 09:43:38.103559  5226 solver.cpp:237]     Train net output #0: loss = 2.29188 (* 1 = 2.29188 loss)
I0406 09:43:38.103564  5226 sgd_solver.cpp:105] Iteration 17772, lr = 0.01
I0406 09:43:43.797216  5226 solver.cpp:218] Iteration 17784 (2.10763 iter/s, 5.69361s/12 iters), loss = 2.96446
I0406 09:43:43.797338  5226 solver.cpp:237]     Train net output #0: loss = 2.96446 (* 1 = 2.96446 loss)
I0406 09:43:43.797343  5226 sgd_solver.cpp:105] Iteration 17784, lr = 0.01
I0406 09:43:49.245136  5226 solver.cpp:218] Iteration 17796 (2.20274 iter/s, 5.44775s/12 iters), loss = 2.7726
I0406 09:43:49.245188  5226 solver.cpp:237]     Train net output #0: loss = 2.7726 (* 1 = 2.7726 loss)
I0406 09:43:49.245199  5226 sgd_solver.cpp:105] Iteration 17796, lr = 0.01
I0406 09:43:52.279206  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:43:54.981252  5226 solver.cpp:218] Iteration 17808 (2.09205 iter/s, 5.73601s/12 iters), loss = 2.39674
I0406 09:43:54.981302  5226 solver.cpp:237]     Train net output #0: loss = 2.39674 (* 1 = 2.39674 loss)
I0406 09:43:54.981309  5226 sgd_solver.cpp:105] Iteration 17808, lr = 0.01
I0406 09:44:00.669909  5226 solver.cpp:218] Iteration 17820 (2.1095 iter/s, 5.68856s/12 iters), loss = 2.37164
I0406 09:44:00.669946  5226 solver.cpp:237]     Train net output #0: loss = 2.37164 (* 1 = 2.37164 loss)
I0406 09:44:00.669951  5226 sgd_solver.cpp:105] Iteration 17820, lr = 0.01
I0406 09:44:06.489933  5226 solver.cpp:218] Iteration 17832 (2.06188 iter/s, 5.81992s/12 iters), loss = 2.66357
I0406 09:44:06.489987  5226 solver.cpp:237]     Train net output #0: loss = 2.66357 (* 1 = 2.66357 loss)
I0406 09:44:06.489996  5226 sgd_solver.cpp:105] Iteration 17832, lr = 0.01
I0406 09:44:12.069243  5226 solver.cpp:218] Iteration 17844 (2.15084 iter/s, 5.57921s/12 iters), loss = 2.5385
I0406 09:44:12.069300  5226 solver.cpp:237]     Train net output #0: loss = 2.5385 (* 1 = 2.5385 loss)
I0406 09:44:12.069308  5226 sgd_solver.cpp:105] Iteration 17844, lr = 0.01
I0406 09:44:14.220479  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_17850.caffemodel
I0406 09:44:17.476472  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_17850.solverstate
I0406 09:44:20.665552  5226 solver.cpp:330] Iteration 17850, Testing net (#0)
I0406 09:44:20.665571  5226 net.cpp:676] Ignoring source layer train-data
I0406 09:44:22.897233  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:44:25.493468  5226 solver.cpp:397]     Test net output #0: accuracy = 0.150123
I0406 09:44:25.493507  5226 solver.cpp:397]     Test net output #1: loss = 4.28018 (* 1 = 4.28018 loss)
I0406 09:44:27.499778  5226 solver.cpp:218] Iteration 17856 (0.777687 iter/s, 15.4304s/12 iters), loss = 2.42105
I0406 09:44:27.499830  5226 solver.cpp:237]     Train net output #0: loss = 2.42105 (* 1 = 2.42105 loss)
I0406 09:44:27.499840  5226 sgd_solver.cpp:105] Iteration 17856, lr = 0.01
I0406 09:44:33.018020  5226 solver.cpp:218] Iteration 17868 (2.17464 iter/s, 5.51815s/12 iters), loss = 2.36433
I0406 09:44:33.018066  5226 solver.cpp:237]     Train net output #0: loss = 2.36433 (* 1 = 2.36433 loss)
I0406 09:44:33.018074  5226 sgd_solver.cpp:105] Iteration 17868, lr = 0.01
I0406 09:44:38.647756  5226 solver.cpp:218] Iteration 17880 (2.13158 iter/s, 5.62963s/12 iters), loss = 3.07001
I0406 09:44:38.647810  5226 solver.cpp:237]     Train net output #0: loss = 3.07001 (* 1 = 3.07001 loss)
I0406 09:44:38.647819  5226 sgd_solver.cpp:105] Iteration 17880, lr = 0.01
I0406 09:44:44.034554  5226 solver.cpp:218] Iteration 17892 (2.22771 iter/s, 5.3867s/12 iters), loss = 2.85333
I0406 09:44:44.034605  5226 solver.cpp:237]     Train net output #0: loss = 2.85333 (* 1 = 2.85333 loss)
I0406 09:44:44.034616  5226 sgd_solver.cpp:105] Iteration 17892, lr = 0.01
I0406 09:44:49.361501  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:44:49.670512  5226 solver.cpp:218] Iteration 17904 (2.12922 iter/s, 5.63586s/12 iters), loss = 2.99441
I0406 09:44:49.670562  5226 solver.cpp:237]     Train net output #0: loss = 2.99441 (* 1 = 2.99441 loss)
I0406 09:44:49.670569  5226 sgd_solver.cpp:105] Iteration 17904, lr = 0.01
I0406 09:44:55.390885  5226 solver.cpp:218] Iteration 17916 (2.09782 iter/s, 5.72022s/12 iters), loss = 2.85584
I0406 09:44:55.390933  5226 solver.cpp:237]     Train net output #0: loss = 2.85584 (* 1 = 2.85584 loss)
I0406 09:44:55.390941  5226 sgd_solver.cpp:105] Iteration 17916, lr = 0.01
I0406 09:45:01.098598  5226 solver.cpp:218] Iteration 17928 (2.10245 iter/s, 5.70762s/12 iters), loss = 2.64041
I0406 09:45:01.098647  5226 solver.cpp:237]     Train net output #0: loss = 2.64041 (* 1 = 2.64041 loss)
I0406 09:45:01.098655  5226 sgd_solver.cpp:105] Iteration 17928, lr = 0.01
I0406 09:45:06.719770  5226 solver.cpp:218] Iteration 17940 (2.13482 iter/s, 5.62107s/12 iters), loss = 2.74439
I0406 09:45:06.719822  5226 solver.cpp:237]     Train net output #0: loss = 2.74439 (* 1 = 2.74439 loss)
I0406 09:45:06.719830  5226 sgd_solver.cpp:105] Iteration 17940, lr = 0.01
I0406 09:45:11.672817  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_17952.caffemodel
I0406 09:45:14.797783  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_17952.solverstate
I0406 09:45:17.644163  5226 solver.cpp:330] Iteration 17952, Testing net (#0)
I0406 09:45:17.644187  5226 net.cpp:676] Ignoring source layer train-data
I0406 09:45:19.912022  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:45:22.637588  5226 solver.cpp:397]     Test net output #0: accuracy = 0.173407
I0406 09:45:22.637622  5226 solver.cpp:397]     Test net output #1: loss = 4.1446 (* 1 = 4.1446 loss)
I0406 09:45:22.810892  5226 solver.cpp:218] Iteration 17952 (0.74576 iter/s, 16.091s/12 iters), loss = 3.14317
I0406 09:45:22.812471  5226 solver.cpp:237]     Train net output #0: loss = 3.14317 (* 1 = 3.14317 loss)
I0406 09:45:22.812486  5226 sgd_solver.cpp:105] Iteration 17952, lr = 0.01
I0406 09:45:27.159741  5226 solver.cpp:218] Iteration 17964 (2.76037 iter/s, 4.34724s/12 iters), loss = 2.29988
I0406 09:45:27.159785  5226 solver.cpp:237]     Train net output #0: loss = 2.29988 (* 1 = 2.29988 loss)
I0406 09:45:27.159792  5226 sgd_solver.cpp:105] Iteration 17964, lr = 0.01
I0406 09:45:32.843408  5226 solver.cpp:218] Iteration 17976 (2.11135 iter/s, 5.68357s/12 iters), loss = 2.84172
I0406 09:45:32.843456  5226 solver.cpp:237]     Train net output #0: loss = 2.84172 (* 1 = 2.84172 loss)
I0406 09:45:32.843464  5226 sgd_solver.cpp:105] Iteration 17976, lr = 0.01
I0406 09:45:38.705754  5226 solver.cpp:218] Iteration 17988 (2.047 iter/s, 5.86224s/12 iters), loss = 2.48493
I0406 09:45:38.705799  5226 solver.cpp:237]     Train net output #0: loss = 2.48493 (* 1 = 2.48493 loss)
I0406 09:45:38.705807  5226 sgd_solver.cpp:105] Iteration 17988, lr = 0.01
I0406 09:45:44.316476  5226 solver.cpp:218] Iteration 18000 (2.1388 iter/s, 5.61063s/12 iters), loss = 2.77124
I0406 09:45:44.316524  5226 solver.cpp:237]     Train net output #0: loss = 2.77124 (* 1 = 2.77124 loss)
I0406 09:45:44.316530  5226 sgd_solver.cpp:105] Iteration 18000, lr = 0.01
I0406 09:45:46.298668  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:45:49.905369  5226 solver.cpp:218] Iteration 18012 (2.14715 iter/s, 5.5888s/12 iters), loss = 2.40614
I0406 09:45:49.905408  5226 solver.cpp:237]     Train net output #0: loss = 2.40614 (* 1 = 2.40614 loss)
I0406 09:45:49.905413  5226 sgd_solver.cpp:105] Iteration 18012, lr = 0.01
I0406 09:45:55.460817  5226 solver.cpp:218] Iteration 18024 (2.16007 iter/s, 5.55536s/12 iters), loss = 3.00819
I0406 09:45:55.460912  5226 solver.cpp:237]     Train net output #0: loss = 3.00819 (* 1 = 3.00819 loss)
I0406 09:45:55.460918  5226 sgd_solver.cpp:105] Iteration 18024, lr = 0.01
I0406 09:46:01.024089  5226 solver.cpp:218] Iteration 18036 (2.15706 iter/s, 5.56313s/12 iters), loss = 2.86129
I0406 09:46:01.024142  5226 solver.cpp:237]     Train net output #0: loss = 2.86129 (* 1 = 2.86129 loss)
I0406 09:46:01.024148  5226 sgd_solver.cpp:105] Iteration 18036, lr = 0.01
I0406 09:46:01.024410  5226 blocking_queue.cpp:49] Waiting for data
I0406 09:46:06.534756  5226 solver.cpp:218] Iteration 18048 (2.17763 iter/s, 5.51057s/12 iters), loss = 2.72902
I0406 09:46:06.534806  5226 solver.cpp:237]     Train net output #0: loss = 2.72902 (* 1 = 2.72902 loss)
I0406 09:46:06.534814  5226 sgd_solver.cpp:105] Iteration 18048, lr = 0.01
I0406 09:46:08.666837  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_18054.caffemodel
I0406 09:46:11.846040  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_18054.solverstate
I0406 09:46:14.187741  5226 solver.cpp:330] Iteration 18054, Testing net (#0)
I0406 09:46:14.187764  5226 net.cpp:676] Ignoring source layer train-data
I0406 09:46:16.292692  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:46:19.177889  5226 solver.cpp:397]     Test net output #0: accuracy = 0.17402
I0406 09:46:19.177927  5226 solver.cpp:397]     Test net output #1: loss = 4.12615 (* 1 = 4.12615 loss)
I0406 09:46:21.263212  5226 solver.cpp:218] Iteration 18060 (0.814758 iter/s, 14.7283s/12 iters), loss = 3.00604
I0406 09:46:21.263268  5226 solver.cpp:237]     Train net output #0: loss = 3.00604 (* 1 = 3.00604 loss)
I0406 09:46:21.263278  5226 sgd_solver.cpp:105] Iteration 18060, lr = 0.01
I0406 09:46:26.970561  5226 solver.cpp:218] Iteration 18072 (2.10259 iter/s, 5.70724s/12 iters), loss = 2.908
I0406 09:46:26.970734  5226 solver.cpp:237]     Train net output #0: loss = 2.908 (* 1 = 2.908 loss)
I0406 09:46:26.970746  5226 sgd_solver.cpp:105] Iteration 18072, lr = 0.01
I0406 09:46:32.481107  5226 solver.cpp:218] Iteration 18084 (2.17773 iter/s, 5.51033s/12 iters), loss = 3.11263
I0406 09:46:32.481148  5226 solver.cpp:237]     Train net output #0: loss = 3.11263 (* 1 = 3.11263 loss)
I0406 09:46:32.481153  5226 sgd_solver.cpp:105] Iteration 18084, lr = 0.01
I0406 09:46:37.988409  5226 solver.cpp:218] Iteration 18096 (2.17896 iter/s, 5.50721s/12 iters), loss = 2.71296
I0406 09:46:37.988445  5226 solver.cpp:237]     Train net output #0: loss = 2.71296 (* 1 = 2.71296 loss)
I0406 09:46:37.988451  5226 sgd_solver.cpp:105] Iteration 18096, lr = 0.01
I0406 09:46:42.274039  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:46:43.411275  5226 solver.cpp:218] Iteration 18108 (2.21289 iter/s, 5.42278s/12 iters), loss = 3.14407
I0406 09:46:43.411327  5226 solver.cpp:237]     Train net output #0: loss = 3.14407 (* 1 = 3.14407 loss)
I0406 09:46:43.411336  5226 sgd_solver.cpp:105] Iteration 18108, lr = 0.01
I0406 09:46:49.133911  5226 solver.cpp:218] Iteration 18120 (2.09697 iter/s, 5.72253s/12 iters), loss = 2.93491
I0406 09:46:49.133958  5226 solver.cpp:237]     Train net output #0: loss = 2.93491 (* 1 = 2.93491 loss)
I0406 09:46:49.133966  5226 sgd_solver.cpp:105] Iteration 18120, lr = 0.01
I0406 09:46:54.562464  5226 solver.cpp:218] Iteration 18132 (2.21059 iter/s, 5.42841s/12 iters), loss = 3.30812
I0406 09:46:54.562516  5226 solver.cpp:237]     Train net output #0: loss = 3.30812 (* 1 = 3.30812 loss)
I0406 09:46:54.562526  5226 sgd_solver.cpp:105] Iteration 18132, lr = 0.01
I0406 09:47:00.502377  5226 solver.cpp:218] Iteration 18144 (2.02027 iter/s, 5.93981s/12 iters), loss = 2.33789
I0406 09:47:00.502491  5226 solver.cpp:237]     Train net output #0: loss = 2.33789 (* 1 = 2.33789 loss)
I0406 09:47:00.502498  5226 sgd_solver.cpp:105] Iteration 18144, lr = 0.01
I0406 09:47:05.533103  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_18156.caffemodel
I0406 09:47:08.666015  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_18156.solverstate
I0406 09:47:12.214016  5226 solver.cpp:330] Iteration 18156, Testing net (#0)
I0406 09:47:12.214040  5226 net.cpp:676] Ignoring source layer train-data
I0406 09:47:14.216341  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:47:16.972262  5226 solver.cpp:397]     Test net output #0: accuracy = 0.139093
I0406 09:47:16.972298  5226 solver.cpp:397]     Test net output #1: loss = 4.27292 (* 1 = 4.27292 loss)
I0406 09:47:17.118527  5226 solver.cpp:218] Iteration 18156 (0.722199 iter/s, 16.6159s/12 iters), loss = 2.86034
I0406 09:47:17.118567  5226 solver.cpp:237]     Train net output #0: loss = 2.86034 (* 1 = 2.86034 loss)
I0406 09:47:17.118573  5226 sgd_solver.cpp:105] Iteration 18156, lr = 0.01
I0406 09:47:21.930510  5226 solver.cpp:218] Iteration 18168 (2.49385 iter/s, 4.81184s/12 iters), loss = 2.69061
I0406 09:47:21.930557  5226 solver.cpp:237]     Train net output #0: loss = 2.69061 (* 1 = 2.69061 loss)
I0406 09:47:21.930565  5226 sgd_solver.cpp:105] Iteration 18168, lr = 0.01
I0406 09:47:27.801985  5226 solver.cpp:218] Iteration 18180 (2.04381 iter/s, 5.87137s/12 iters), loss = 2.9345
I0406 09:47:27.802031  5226 solver.cpp:237]     Train net output #0: loss = 2.9345 (* 1 = 2.9345 loss)
I0406 09:47:27.802039  5226 sgd_solver.cpp:105] Iteration 18180, lr = 0.01
I0406 09:47:33.652521  5226 solver.cpp:218] Iteration 18192 (2.05113 iter/s, 5.85044s/12 iters), loss = 2.59265
I0406 09:47:33.652631  5226 solver.cpp:237]     Train net output #0: loss = 2.59265 (* 1 = 2.59265 loss)
I0406 09:47:33.652638  5226 sgd_solver.cpp:105] Iteration 18192, lr = 0.01
I0406 09:47:39.501034  5226 solver.cpp:218] Iteration 18204 (2.05186 iter/s, 5.84835s/12 iters), loss = 2.48892
I0406 09:47:39.501085  5226 solver.cpp:237]     Train net output #0: loss = 2.48892 (* 1 = 2.48892 loss)
I0406 09:47:39.501094  5226 sgd_solver.cpp:105] Iteration 18204, lr = 0.01
I0406 09:47:40.780498  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:47:44.769239  5226 solver.cpp:218] Iteration 18216 (2.27786 iter/s, 5.26811s/12 iters), loss = 2.83364
I0406 09:47:44.769287  5226 solver.cpp:237]     Train net output #0: loss = 2.83364 (* 1 = 2.83364 loss)
I0406 09:47:44.769295  5226 sgd_solver.cpp:105] Iteration 18216, lr = 0.01
I0406 09:47:50.581367  5226 solver.cpp:218] Iteration 18228 (2.06468 iter/s, 5.81203s/12 iters), loss = 3.19854
I0406 09:47:50.581427  5226 solver.cpp:237]     Train net output #0: loss = 3.19854 (* 1 = 3.19854 loss)
I0406 09:47:50.581436  5226 sgd_solver.cpp:105] Iteration 18228, lr = 0.01
I0406 09:47:56.336108  5226 solver.cpp:218] Iteration 18240 (2.08528 iter/s, 5.75463s/12 iters), loss = 2.73519
I0406 09:47:56.336158  5226 solver.cpp:237]     Train net output #0: loss = 2.73519 (* 1 = 2.73519 loss)
I0406 09:47:56.336164  5226 sgd_solver.cpp:105] Iteration 18240, lr = 0.01
I0406 09:48:02.105165  5226 solver.cpp:218] Iteration 18252 (2.0801 iter/s, 5.76896s/12 iters), loss = 2.92738
I0406 09:48:02.105206  5226 solver.cpp:237]     Train net output #0: loss = 2.92738 (* 1 = 2.92738 loss)
I0406 09:48:02.105211  5226 sgd_solver.cpp:105] Iteration 18252, lr = 0.01
I0406 09:48:04.437423  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_18258.caffemodel
I0406 09:48:07.635370  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_18258.solverstate
I0406 09:48:10.733316  5226 solver.cpp:330] Iteration 18258, Testing net (#0)
I0406 09:48:10.733337  5226 net.cpp:676] Ignoring source layer train-data
I0406 09:48:12.767627  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:48:15.403087  5226 solver.cpp:397]     Test net output #0: accuracy = 0.173407
I0406 09:48:15.403117  5226 solver.cpp:397]     Test net output #1: loss = 4.19531 (* 1 = 4.19531 loss)
I0406 09:48:17.299959  5226 solver.cpp:218] Iteration 18264 (0.789752 iter/s, 15.1946s/12 iters), loss = 2.40677
I0406 09:48:17.300009  5226 solver.cpp:237]     Train net output #0: loss = 2.40677 (* 1 = 2.40677 loss)
I0406 09:48:17.300017  5226 sgd_solver.cpp:105] Iteration 18264, lr = 0.01
I0406 09:48:23.098910  5226 solver.cpp:218] Iteration 18276 (2.06938 iter/s, 5.79885s/12 iters), loss = 2.47545
I0406 09:48:23.098953  5226 solver.cpp:237]     Train net output #0: loss = 2.47545 (* 1 = 2.47545 loss)
I0406 09:48:23.098958  5226 sgd_solver.cpp:105] Iteration 18276, lr = 0.01
I0406 09:48:28.724840  5226 solver.cpp:218] Iteration 18288 (2.13302 iter/s, 5.62583s/12 iters), loss = 2.91271
I0406 09:48:28.724900  5226 solver.cpp:237]     Train net output #0: loss = 2.91271 (* 1 = 2.91271 loss)
I0406 09:48:28.724908  5226 sgd_solver.cpp:105] Iteration 18288, lr = 0.01
I0406 09:48:34.601153  5226 solver.cpp:218] Iteration 18300 (2.04213 iter/s, 5.87621s/12 iters), loss = 2.48929
I0406 09:48:34.601267  5226 solver.cpp:237]     Train net output #0: loss = 2.48929 (* 1 = 2.48929 loss)
I0406 09:48:34.601276  5226 sgd_solver.cpp:105] Iteration 18300, lr = 0.01
I0406 09:48:38.165952  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:48:39.846613  5226 solver.cpp:218] Iteration 18312 (2.28776 iter/s, 5.2453s/12 iters), loss = 2.75617
I0406 09:48:39.846655  5226 solver.cpp:237]     Train net output #0: loss = 2.75617 (* 1 = 2.75617 loss)
I0406 09:48:39.846662  5226 sgd_solver.cpp:105] Iteration 18312, lr = 0.01
I0406 09:48:45.696408  5226 solver.cpp:218] Iteration 18324 (2.05139 iter/s, 5.8497s/12 iters), loss = 2.58896
I0406 09:48:45.696458  5226 solver.cpp:237]     Train net output #0: loss = 2.58896 (* 1 = 2.58896 loss)
I0406 09:48:45.696468  5226 sgd_solver.cpp:105] Iteration 18324, lr = 0.01
I0406 09:48:51.285014  5226 solver.cpp:218] Iteration 18336 (2.14726 iter/s, 5.58851s/12 iters), loss = 2.33807
I0406 09:48:51.285054  5226 solver.cpp:237]     Train net output #0: loss = 2.33807 (* 1 = 2.33807 loss)
I0406 09:48:51.285059  5226 sgd_solver.cpp:105] Iteration 18336, lr = 0.01
I0406 09:48:56.978015  5226 solver.cpp:218] Iteration 18348 (2.10789 iter/s, 5.69291s/12 iters), loss = 2.7505
I0406 09:48:56.978067  5226 solver.cpp:237]     Train net output #0: loss = 2.7505 (* 1 = 2.7505 loss)
I0406 09:48:56.978075  5226 sgd_solver.cpp:105] Iteration 18348, lr = 0.01
I0406 09:49:01.800734  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_18360.caffemodel
I0406 09:49:04.967018  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_18360.solverstate
I0406 09:49:07.329947  5226 solver.cpp:330] Iteration 18360, Testing net (#0)
I0406 09:49:07.329973  5226 net.cpp:676] Ignoring source layer train-data
I0406 09:49:09.247212  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:49:12.172071  5226 solver.cpp:397]     Test net output #0: accuracy = 0.147059
I0406 09:49:12.172102  5226 solver.cpp:397]     Test net output #1: loss = 4.25444 (* 1 = 4.25444 loss)
I0406 09:49:12.318850  5226 solver.cpp:218] Iteration 18360 (0.782234 iter/s, 15.3407s/12 iters), loss = 3.18243
I0406 09:49:12.318897  5226 solver.cpp:237]     Train net output #0: loss = 3.18243 (* 1 = 3.18243 loss)
I0406 09:49:12.318905  5226 sgd_solver.cpp:105] Iteration 18360, lr = 0.01
I0406 09:49:16.935120  5226 solver.cpp:218] Iteration 18372 (2.59955 iter/s, 4.61618s/12 iters), loss = 2.61853
I0406 09:49:16.935166  5226 solver.cpp:237]     Train net output #0: loss = 2.61853 (* 1 = 2.61853 loss)
I0406 09:49:16.935174  5226 sgd_solver.cpp:105] Iteration 18372, lr = 0.01
I0406 09:49:22.504688  5226 solver.cpp:218] Iteration 18384 (2.1546 iter/s, 5.56947s/12 iters), loss = 2.93539
I0406 09:49:22.504736  5226 solver.cpp:237]     Train net output #0: loss = 2.93539 (* 1 = 2.93539 loss)
I0406 09:49:22.504745  5226 sgd_solver.cpp:105] Iteration 18384, lr = 0.01
I0406 09:49:28.151232  5226 solver.cpp:218] Iteration 18396 (2.12523 iter/s, 5.64644s/12 iters), loss = 2.97436
I0406 09:49:28.151281  5226 solver.cpp:237]     Train net output #0: loss = 2.97436 (* 1 = 2.97436 loss)
I0406 09:49:28.151289  5226 sgd_solver.cpp:105] Iteration 18396, lr = 0.01
I0406 09:49:33.820475  5226 solver.cpp:218] Iteration 18408 (2.11674 iter/s, 5.6691s/12 iters), loss = 2.53136
I0406 09:49:33.820524  5226 solver.cpp:237]     Train net output #0: loss = 2.53136 (* 1 = 2.53136 loss)
I0406 09:49:33.820533  5226 sgd_solver.cpp:105] Iteration 18408, lr = 0.01
I0406 09:49:34.504357  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:49:39.332718  5226 solver.cpp:218] Iteration 18420 (2.17701 iter/s, 5.51215s/12 iters), loss = 3.38627
I0406 09:49:39.332814  5226 solver.cpp:237]     Train net output #0: loss = 3.38627 (* 1 = 3.38627 loss)
I0406 09:49:39.332821  5226 sgd_solver.cpp:105] Iteration 18420, lr = 0.01
I0406 09:49:45.152784  5226 solver.cpp:218] Iteration 18432 (2.06189 iter/s, 5.81991s/12 iters), loss = 2.90654
I0406 09:49:45.152834  5226 solver.cpp:237]     Train net output #0: loss = 2.90654 (* 1 = 2.90654 loss)
I0406 09:49:45.152842  5226 sgd_solver.cpp:105] Iteration 18432, lr = 0.01
I0406 09:49:51.120024  5226 solver.cpp:218] Iteration 18444 (2.01101 iter/s, 5.96714s/12 iters), loss = 2.96142
I0406 09:49:51.120061  5226 solver.cpp:237]     Train net output #0: loss = 2.96142 (* 1 = 2.96142 loss)
I0406 09:49:51.120067  5226 sgd_solver.cpp:105] Iteration 18444, lr = 0.01
I0406 09:49:56.531935  5226 solver.cpp:218] Iteration 18456 (2.21737 iter/s, 5.41182s/12 iters), loss = 2.9578
I0406 09:49:56.531991  5226 solver.cpp:237]     Train net output #0: loss = 2.9578 (* 1 = 2.9578 loss)
I0406 09:49:56.532001  5226 sgd_solver.cpp:105] Iteration 18456, lr = 0.01
I0406 09:49:58.817632  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_18462.caffemodel
I0406 09:50:01.912143  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_18462.solverstate
I0406 09:50:04.320299  5226 solver.cpp:330] Iteration 18462, Testing net (#0)
I0406 09:50:04.320322  5226 net.cpp:676] Ignoring source layer train-data
I0406 09:50:06.446846  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:50:09.369204  5226 solver.cpp:397]     Test net output #0: accuracy = 0.14277
I0406 09:50:09.369320  5226 solver.cpp:397]     Test net output #1: loss = 4.3483 (* 1 = 4.3483 loss)
I0406 09:50:11.520042  5226 solver.cpp:218] Iteration 18468 (0.800643 iter/s, 14.988s/12 iters), loss = 2.92021
I0406 09:50:11.520078  5226 solver.cpp:237]     Train net output #0: loss = 2.92021 (* 1 = 2.92021 loss)
I0406 09:50:11.520084  5226 sgd_solver.cpp:105] Iteration 18468, lr = 0.01
I0406 09:50:17.148154  5226 solver.cpp:218] Iteration 18480 (2.1322 iter/s, 5.62799s/12 iters), loss = 2.09946
I0406 09:50:17.148192  5226 solver.cpp:237]     Train net output #0: loss = 2.09946 (* 1 = 2.09946 loss)
I0406 09:50:17.148198  5226 sgd_solver.cpp:105] Iteration 18480, lr = 0.01
I0406 09:50:22.918964  5226 solver.cpp:218] Iteration 18492 (2.07946 iter/s, 5.77072s/12 iters), loss = 2.91972
I0406 09:50:22.919000  5226 solver.cpp:237]     Train net output #0: loss = 2.91972 (* 1 = 2.91972 loss)
I0406 09:50:22.919006  5226 sgd_solver.cpp:105] Iteration 18492, lr = 0.01
I0406 09:50:28.322741  5226 solver.cpp:218] Iteration 18504 (2.22071 iter/s, 5.40369s/12 iters), loss = 2.6155
I0406 09:50:28.322782  5226 solver.cpp:237]     Train net output #0: loss = 2.6155 (* 1 = 2.6155 loss)
I0406 09:50:28.322788  5226 sgd_solver.cpp:105] Iteration 18504, lr = 0.01
I0406 09:50:31.406450  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:50:33.979977  5226 solver.cpp:218] Iteration 18516 (2.12121 iter/s, 5.65715s/12 iters), loss = 2.52292
I0406 09:50:33.980015  5226 solver.cpp:237]     Train net output #0: loss = 2.52292 (* 1 = 2.52292 loss)
I0406 09:50:33.980019  5226 sgd_solver.cpp:105] Iteration 18516, lr = 0.01
I0406 09:50:39.558295  5226 solver.cpp:218] Iteration 18528 (2.15122 iter/s, 5.57823s/12 iters), loss = 2.75023
I0406 09:50:39.558429  5226 solver.cpp:237]     Train net output #0: loss = 2.75023 (* 1 = 2.75023 loss)
I0406 09:50:39.558440  5226 sgd_solver.cpp:105] Iteration 18528, lr = 0.01
I0406 09:50:45.211774  5226 solver.cpp:218] Iteration 18540 (2.12266 iter/s, 5.65329s/12 iters), loss = 2.36854
I0406 09:50:45.211825  5226 solver.cpp:237]     Train net output #0: loss = 2.36854 (* 1 = 2.36854 loss)
I0406 09:50:45.211833  5226 sgd_solver.cpp:105] Iteration 18540, lr = 0.01
I0406 09:50:50.958618  5226 solver.cpp:218] Iteration 18552 (2.08814 iter/s, 5.74674s/12 iters), loss = 2.65339
I0406 09:50:50.958657  5226 solver.cpp:237]     Train net output #0: loss = 2.65339 (* 1 = 2.65339 loss)
I0406 09:50:50.958662  5226 sgd_solver.cpp:105] Iteration 18552, lr = 0.01
I0406 09:50:56.086372  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_18564.caffemodel
I0406 09:50:59.246240  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_18564.solverstate
I0406 09:51:01.598513  5226 solver.cpp:330] Iteration 18564, Testing net (#0)
I0406 09:51:01.598533  5226 net.cpp:676] Ignoring source layer train-data
I0406 09:51:03.717177  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:51:06.516773  5226 solver.cpp:397]     Test net output #0: accuracy = 0.150735
I0406 09:51:06.516808  5226 solver.cpp:397]     Test net output #1: loss = 4.2536 (* 1 = 4.2536 loss)
I0406 09:51:06.660565  5226 solver.cpp:218] Iteration 18564 (0.764244 iter/s, 15.7018s/12 iters), loss = 2.60679
I0406 09:51:06.660615  5226 solver.cpp:237]     Train net output #0: loss = 2.60679 (* 1 = 2.60679 loss)
I0406 09:51:06.660622  5226 sgd_solver.cpp:105] Iteration 18564, lr = 0.01
I0406 09:51:11.341235  5226 solver.cpp:218] Iteration 18576 (2.56379 iter/s, 4.68058s/12 iters), loss = 2.42891
I0406 09:51:11.341364  5226 solver.cpp:237]     Train net output #0: loss = 2.42891 (* 1 = 2.42891 loss)
I0406 09:51:11.341373  5226 sgd_solver.cpp:105] Iteration 18576, lr = 0.01
I0406 09:51:16.923838  5226 solver.cpp:218] Iteration 18588 (2.1496 iter/s, 5.58243s/12 iters), loss = 2.71232
I0406 09:51:16.923894  5226 solver.cpp:237]     Train net output #0: loss = 2.71232 (* 1 = 2.71232 loss)
I0406 09:51:16.923902  5226 sgd_solver.cpp:105] Iteration 18588, lr = 0.01
I0406 09:51:22.183441  5226 solver.cpp:218] Iteration 18600 (2.28159 iter/s, 5.2595s/12 iters), loss = 2.8243
I0406 09:51:22.183481  5226 solver.cpp:237]     Train net output #0: loss = 2.8243 (* 1 = 2.8243 loss)
I0406 09:51:22.183487  5226 sgd_solver.cpp:105] Iteration 18600, lr = 0.01
I0406 09:51:27.247663  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:51:27.467072  5226 solver.cpp:218] Iteration 18612 (2.2712 iter/s, 5.28354s/12 iters), loss = 2.77226
I0406 09:51:27.467131  5226 solver.cpp:237]     Train net output #0: loss = 2.77226 (* 1 = 2.77226 loss)
I0406 09:51:27.467140  5226 sgd_solver.cpp:105] Iteration 18612, lr = 0.01
I0406 09:51:32.820556  5226 solver.cpp:218] Iteration 18624 (2.24157 iter/s, 5.35338s/12 iters), loss = 2.97261
I0406 09:51:32.820595  5226 solver.cpp:237]     Train net output #0: loss = 2.97261 (* 1 = 2.97261 loss)
I0406 09:51:32.820601  5226 sgd_solver.cpp:105] Iteration 18624, lr = 0.01
I0406 09:51:38.232952  5226 solver.cpp:218] Iteration 18636 (2.21717 iter/s, 5.4123s/12 iters), loss = 2.11176
I0406 09:51:38.233001  5226 solver.cpp:237]     Train net output #0: loss = 2.11176 (* 1 = 2.11176 loss)
I0406 09:51:38.233011  5226 sgd_solver.cpp:105] Iteration 18636, lr = 0.01
I0406 09:51:43.320188  5226 solver.cpp:218] Iteration 18648 (2.35889 iter/s, 5.08714s/12 iters), loss = 2.73249
I0406 09:51:43.320327  5226 solver.cpp:237]     Train net output #0: loss = 2.73249 (* 1 = 2.73249 loss)
I0406 09:51:43.320333  5226 sgd_solver.cpp:105] Iteration 18648, lr = 0.01
I0406 09:51:48.694944  5226 solver.cpp:218] Iteration 18660 (2.23274 iter/s, 5.37457s/12 iters), loss = 2.76798
I0406 09:51:48.694980  5226 solver.cpp:237]     Train net output #0: loss = 2.76798 (* 1 = 2.76798 loss)
I0406 09:51:48.694986  5226 sgd_solver.cpp:105] Iteration 18660, lr = 0.01
I0406 09:51:50.936535  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_18666.caffemodel
I0406 09:51:53.880911  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_18666.solverstate
I0406 09:51:56.192927  5226 solver.cpp:330] Iteration 18666, Testing net (#0)
I0406 09:51:56.192951  5226 net.cpp:676] Ignoring source layer train-data
I0406 09:51:57.929293  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:52:00.614500  5226 solver.cpp:397]     Test net output #0: accuracy = 0.168505
I0406 09:52:00.614533  5226 solver.cpp:397]     Test net output #1: loss = 4.29495 (* 1 = 4.29495 loss)
I0406 09:52:02.608079  5226 solver.cpp:218] Iteration 18672 (0.862503 iter/s, 13.913s/12 iters), loss = 2.53058
I0406 09:52:02.608134  5226 solver.cpp:237]     Train net output #0: loss = 2.53058 (* 1 = 2.53058 loss)
I0406 09:52:02.608144  5226 sgd_solver.cpp:105] Iteration 18672, lr = 0.01
I0406 09:52:07.690367  5226 solver.cpp:218] Iteration 18684 (2.36119 iter/s, 5.08219s/12 iters), loss = 2.62814
I0406 09:52:07.690407  5226 solver.cpp:237]     Train net output #0: loss = 2.62814 (* 1 = 2.62814 loss)
I0406 09:52:07.690412  5226 sgd_solver.cpp:105] Iteration 18684, lr = 0.01
I0406 09:52:12.886394  5226 solver.cpp:218] Iteration 18696 (2.3095 iter/s, 5.19594s/12 iters), loss = 2.3242
I0406 09:52:12.886431  5226 solver.cpp:237]     Train net output #0: loss = 2.3242 (* 1 = 2.3242 loss)
I0406 09:52:12.886436  5226 sgd_solver.cpp:105] Iteration 18696, lr = 0.01
I0406 09:52:18.100653  5226 solver.cpp:218] Iteration 18708 (2.30142 iter/s, 5.21417s/12 iters), loss = 2.65449
I0406 09:52:18.100817  5226 solver.cpp:237]     Train net output #0: loss = 2.65449 (* 1 = 2.65449 loss)
I0406 09:52:18.100826  5226 sgd_solver.cpp:105] Iteration 18708, lr = 0.01
I0406 09:52:20.133059  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:52:23.469902  5226 solver.cpp:218] Iteration 18720 (2.23503 iter/s, 5.36905s/12 iters), loss = 2.86272
I0406 09:52:23.469935  5226 solver.cpp:237]     Train net output #0: loss = 2.86272 (* 1 = 2.86272 loss)
I0406 09:52:23.469941  5226 sgd_solver.cpp:105] Iteration 18720, lr = 0.01
I0406 09:52:23.835044  5226 blocking_queue.cpp:49] Waiting for data
I0406 09:52:28.891965  5226 solver.cpp:218] Iteration 18732 (2.21321 iter/s, 5.42198s/12 iters), loss = 3.0482
I0406 09:52:28.892014  5226 solver.cpp:237]     Train net output #0: loss = 3.0482 (* 1 = 3.0482 loss)
I0406 09:52:28.892024  5226 sgd_solver.cpp:105] Iteration 18732, lr = 0.01
I0406 09:52:34.206876  5226 solver.cpp:218] Iteration 18744 (2.25784 iter/s, 5.31482s/12 iters), loss = 2.91395
I0406 09:52:34.206933  5226 solver.cpp:237]     Train net output #0: loss = 2.91395 (* 1 = 2.91395 loss)
I0406 09:52:34.206941  5226 sgd_solver.cpp:105] Iteration 18744, lr = 0.01
I0406 09:52:39.583050  5226 solver.cpp:218] Iteration 18756 (2.23211 iter/s, 5.37607s/12 iters), loss = 2.73775
I0406 09:52:39.583086  5226 solver.cpp:237]     Train net output #0: loss = 2.73775 (* 1 = 2.73775 loss)
I0406 09:52:39.583091  5226 sgd_solver.cpp:105] Iteration 18756, lr = 0.01
I0406 09:52:44.217043  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_18768.caffemodel
I0406 09:52:47.200744  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_18768.solverstate
I0406 09:52:49.504376  5226 solver.cpp:330] Iteration 18768, Testing net (#0)
I0406 09:52:49.504472  5226 net.cpp:676] Ignoring source layer train-data
I0406 09:52:51.314322  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:52:54.057606  5226 solver.cpp:397]     Test net output #0: accuracy = 0.160539
I0406 09:52:54.057642  5226 solver.cpp:397]     Test net output #1: loss = 4.18962 (* 1 = 4.18962 loss)
I0406 09:52:54.198197  5226 solver.cpp:218] Iteration 18768 (0.821074 iter/s, 14.615s/12 iters), loss = 2.57012
I0406 09:52:54.198249  5226 solver.cpp:237]     Train net output #0: loss = 2.57012 (* 1 = 2.57012 loss)
I0406 09:52:54.198257  5226 sgd_solver.cpp:105] Iteration 18768, lr = 0.01
I0406 09:52:58.684337  5226 solver.cpp:218] Iteration 18780 (2.67496 iter/s, 4.48604s/12 iters), loss = 3.12243
I0406 09:52:58.684391  5226 solver.cpp:237]     Train net output #0: loss = 3.12243 (* 1 = 3.12243 loss)
I0406 09:52:58.684401  5226 sgd_solver.cpp:105] Iteration 18780, lr = 0.01
I0406 09:53:03.669354  5226 solver.cpp:218] Iteration 18792 (2.40726 iter/s, 4.98492s/12 iters), loss = 3.04408
I0406 09:53:03.669395  5226 solver.cpp:237]     Train net output #0: loss = 3.04408 (* 1 = 3.04408 loss)
I0406 09:53:03.669400  5226 sgd_solver.cpp:105] Iteration 18792, lr = 0.01
I0406 09:53:08.985697  5226 solver.cpp:218] Iteration 18804 (2.25723 iter/s, 5.31625s/12 iters), loss = 3.1562
I0406 09:53:08.985735  5226 solver.cpp:237]     Train net output #0: loss = 3.1562 (* 1 = 3.1562 loss)
I0406 09:53:08.985740  5226 sgd_solver.cpp:105] Iteration 18804, lr = 0.01
I0406 09:53:13.379719  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:53:14.256330  5226 solver.cpp:218] Iteration 18816 (2.2768 iter/s, 5.27055s/12 iters), loss = 2.61566
I0406 09:53:14.256367  5226 solver.cpp:237]     Train net output #0: loss = 2.61566 (* 1 = 2.61566 loss)
I0406 09:53:14.256372  5226 sgd_solver.cpp:105] Iteration 18816, lr = 0.01
I0406 09:53:19.445952  5226 solver.cpp:218] Iteration 18828 (2.31235 iter/s, 5.18954s/12 iters), loss = 3.05859
I0406 09:53:19.445993  5226 solver.cpp:237]     Train net output #0: loss = 3.05859 (* 1 = 3.05859 loss)
I0406 09:53:19.445999  5226 sgd_solver.cpp:105] Iteration 18828, lr = 0.01
I0406 09:53:24.810252  5226 solver.cpp:218] Iteration 18840 (2.23705 iter/s, 5.36421s/12 iters), loss = 2.46742
I0406 09:53:24.810375  5226 solver.cpp:237]     Train net output #0: loss = 2.46742 (* 1 = 2.46742 loss)
I0406 09:53:24.810384  5226 sgd_solver.cpp:105] Iteration 18840, lr = 0.01
I0406 09:53:30.164876  5226 solver.cpp:218] Iteration 18852 (2.24112 iter/s, 5.35446s/12 iters), loss = 2.69689
I0406 09:53:30.164934  5226 solver.cpp:237]     Train net output #0: loss = 2.69689 (* 1 = 2.69689 loss)
I0406 09:53:30.164942  5226 sgd_solver.cpp:105] Iteration 18852, lr = 0.01
I0406 09:53:35.764940  5226 solver.cpp:218] Iteration 18864 (2.14287 iter/s, 5.59996s/12 iters), loss = 2.58727
I0406 09:53:35.764981  5226 solver.cpp:237]     Train net output #0: loss = 2.58727 (* 1 = 2.58727 loss)
I0406 09:53:35.764986  5226 sgd_solver.cpp:105] Iteration 18864, lr = 0.01
I0406 09:53:37.993623  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_18870.caffemodel
I0406 09:53:41.036381  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_18870.solverstate
I0406 09:53:43.337386  5226 solver.cpp:330] Iteration 18870, Testing net (#0)
I0406 09:53:43.337405  5226 net.cpp:676] Ignoring source layer train-data
I0406 09:53:44.917209  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:53:47.726856  5226 solver.cpp:397]     Test net output #0: accuracy = 0.160539
I0406 09:53:47.726883  5226 solver.cpp:397]     Test net output #1: loss = 4.28674 (* 1 = 4.28674 loss)
I0406 09:53:49.493155  5226 solver.cpp:218] Iteration 18876 (0.874121 iter/s, 13.7281s/12 iters), loss = 2.61103
I0406 09:53:49.493208  5226 solver.cpp:237]     Train net output #0: loss = 2.61103 (* 1 = 2.61103 loss)
I0406 09:53:49.493216  5226 sgd_solver.cpp:105] Iteration 18876, lr = 0.01
I0406 09:53:54.832093  5226 solver.cpp:218] Iteration 18888 (2.24768 iter/s, 5.33884s/12 iters), loss = 2.96768
I0406 09:53:54.832211  5226 solver.cpp:237]     Train net output #0: loss = 2.96768 (* 1 = 2.96768 loss)
I0406 09:53:54.832217  5226 sgd_solver.cpp:105] Iteration 18888, lr = 0.01
I0406 09:53:59.930821  5226 solver.cpp:218] Iteration 18900 (2.3536 iter/s, 5.09856s/12 iters), loss = 3.17591
I0406 09:53:59.930861  5226 solver.cpp:237]     Train net output #0: loss = 3.17591 (* 1 = 3.17591 loss)
I0406 09:53:59.930866  5226 sgd_solver.cpp:105] Iteration 18900, lr = 0.01
I0406 09:54:05.211365  5226 solver.cpp:218] Iteration 18912 (2.27253 iter/s, 5.28046s/12 iters), loss = 2.8022
I0406 09:54:05.211405  5226 solver.cpp:237]     Train net output #0: loss = 2.8022 (* 1 = 2.8022 loss)
I0406 09:54:05.211410  5226 sgd_solver.cpp:105] Iteration 18912, lr = 0.01
I0406 09:54:06.656607  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:54:10.640969  5226 solver.cpp:218] Iteration 18924 (2.21014 iter/s, 5.42951s/12 iters), loss = 2.35531
I0406 09:54:10.641024  5226 solver.cpp:237]     Train net output #0: loss = 2.35531 (* 1 = 2.35531 loss)
I0406 09:54:10.641033  5226 sgd_solver.cpp:105] Iteration 18924, lr = 0.01
I0406 09:54:15.817670  5226 solver.cpp:218] Iteration 18936 (2.31812 iter/s, 5.17661s/12 iters), loss = 2.43513
I0406 09:54:15.817715  5226 solver.cpp:237]     Train net output #0: loss = 2.43513 (* 1 = 2.43513 loss)
I0406 09:54:15.817724  5226 sgd_solver.cpp:105] Iteration 18936, lr = 0.01
I0406 09:54:21.219298  5226 solver.cpp:218] Iteration 18948 (2.22159 iter/s, 5.40154s/12 iters), loss = 2.18946
I0406 09:54:21.219345  5226 solver.cpp:237]     Train net output #0: loss = 2.18946 (* 1 = 2.18946 loss)
I0406 09:54:21.219352  5226 sgd_solver.cpp:105] Iteration 18948, lr = 0.01
I0406 09:54:26.653434  5226 solver.cpp:218] Iteration 18960 (2.2083 iter/s, 5.43404s/12 iters), loss = 2.9931
I0406 09:54:26.653564  5226 solver.cpp:237]     Train net output #0: loss = 2.9931 (* 1 = 2.9931 loss)
I0406 09:54:26.653575  5226 sgd_solver.cpp:105] Iteration 18960, lr = 0.01
I0406 09:54:31.512817  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_18972.caffemodel
I0406 09:54:34.557161  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_18972.solverstate
I0406 09:54:36.871708  5226 solver.cpp:330] Iteration 18972, Testing net (#0)
I0406 09:54:36.871728  5226 net.cpp:676] Ignoring source layer train-data
I0406 09:54:38.538091  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:54:41.308542  5226 solver.cpp:397]     Test net output #0: accuracy = 0.140319
I0406 09:54:41.308574  5226 solver.cpp:397]     Test net output #1: loss = 4.34298 (* 1 = 4.34298 loss)
I0406 09:54:41.449098  5226 solver.cpp:218] Iteration 18972 (0.811061 iter/s, 14.7954s/12 iters), loss = 2.47181
I0406 09:54:41.449138  5226 solver.cpp:237]     Train net output #0: loss = 2.47181 (* 1 = 2.47181 loss)
I0406 09:54:41.449144  5226 sgd_solver.cpp:105] Iteration 18972, lr = 0.01
I0406 09:54:45.870874  5226 solver.cpp:218] Iteration 18984 (2.7139 iter/s, 4.42169s/12 iters), loss = 2.4347
I0406 09:54:45.870921  5226 solver.cpp:237]     Train net output #0: loss = 2.4347 (* 1 = 2.4347 loss)
I0406 09:54:45.870929  5226 sgd_solver.cpp:105] Iteration 18984, lr = 0.01
I0406 09:54:51.296736  5226 solver.cpp:218] Iteration 18996 (2.21167 iter/s, 5.42576s/12 iters), loss = 2.43389
I0406 09:54:51.296795  5226 solver.cpp:237]     Train net output #0: loss = 2.43389 (* 1 = 2.43389 loss)
I0406 09:54:51.296805  5226 sgd_solver.cpp:105] Iteration 18996, lr = 0.01
I0406 09:54:56.524983  5226 solver.cpp:218] Iteration 19008 (2.29527 iter/s, 5.22815s/12 iters), loss = 2.51239
I0406 09:54:56.525022  5226 solver.cpp:237]     Train net output #0: loss = 2.51239 (* 1 = 2.51239 loss)
I0406 09:54:56.525027  5226 sgd_solver.cpp:105] Iteration 19008, lr = 0.01
I0406 09:55:00.243628  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:55:01.752319  5226 solver.cpp:218] Iteration 19020 (2.29566 iter/s, 5.22725s/12 iters), loss = 2.84622
I0406 09:55:01.752362  5226 solver.cpp:237]     Train net output #0: loss = 2.84622 (* 1 = 2.84622 loss)
I0406 09:55:01.752368  5226 sgd_solver.cpp:105] Iteration 19020, lr = 0.01
I0406 09:55:07.149793  5226 solver.cpp:218] Iteration 19032 (2.2233 iter/s, 5.39738s/12 iters), loss = 2.81705
I0406 09:55:07.149832  5226 solver.cpp:237]     Train net output #0: loss = 2.81705 (* 1 = 2.81705 loss)
I0406 09:55:07.149837  5226 sgd_solver.cpp:105] Iteration 19032, lr = 0.01
I0406 09:55:12.421520  5226 solver.cpp:218] Iteration 19044 (2.27633 iter/s, 5.27164s/12 iters), loss = 2.51426
I0406 09:55:12.421567  5226 solver.cpp:237]     Train net output #0: loss = 2.51426 (* 1 = 2.51426 loss)
I0406 09:55:12.421576  5226 sgd_solver.cpp:105] Iteration 19044, lr = 0.01
I0406 09:55:17.797022  5226 solver.cpp:218] Iteration 19056 (2.2324 iter/s, 5.37538s/12 iters), loss = 2.23711
I0406 09:55:17.797060  5226 solver.cpp:237]     Train net output #0: loss = 2.23711 (* 1 = 2.23711 loss)
I0406 09:55:17.797065  5226 sgd_solver.cpp:105] Iteration 19056, lr = 0.01
I0406 09:55:23.108865  5226 solver.cpp:218] Iteration 19068 (2.25914 iter/s, 5.31175s/12 iters), loss = 2.54198
I0406 09:55:23.108924  5226 solver.cpp:237]     Train net output #0: loss = 2.54198 (* 1 = 2.54198 loss)
I0406 09:55:23.108932  5226 sgd_solver.cpp:105] Iteration 19068, lr = 0.01
I0406 09:55:25.266986  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_19074.caffemodel
I0406 09:55:28.334154  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_19074.solverstate
I0406 09:55:30.646607  5226 solver.cpp:330] Iteration 19074, Testing net (#0)
I0406 09:55:30.646693  5226 net.cpp:676] Ignoring source layer train-data
I0406 09:55:32.183472  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:55:35.186436  5226 solver.cpp:397]     Test net output #0: accuracy = 0.181985
I0406 09:55:35.186471  5226 solver.cpp:397]     Test net output #1: loss = 4.14378 (* 1 = 4.14378 loss)
I0406 09:55:36.995689  5226 solver.cpp:218] Iteration 19080 (0.864138 iter/s, 13.8867s/12 iters), loss = 2.66738
I0406 09:55:36.995740  5226 solver.cpp:237]     Train net output #0: loss = 2.66738 (* 1 = 2.66738 loss)
I0406 09:55:36.995749  5226 sgd_solver.cpp:105] Iteration 19080, lr = 0.01
I0406 09:55:46.395162  5226 solver.cpp:218] Iteration 19092 (1.27669 iter/s, 9.39934s/12 iters), loss = 2.26242
I0406 09:55:46.395226  5226 solver.cpp:237]     Train net output #0: loss = 2.26242 (* 1 = 2.26242 loss)
I0406 09:55:46.395234  5226 sgd_solver.cpp:105] Iteration 19092, lr = 0.01
I0406 09:55:54.763990  5226 solver.cpp:218] Iteration 19104 (1.43391 iter/s, 8.3687s/12 iters), loss = 2.51026
I0406 09:55:54.764039  5226 solver.cpp:237]     Train net output #0: loss = 2.51026 (* 1 = 2.51026 loss)
I0406 09:55:54.764047  5226 sgd_solver.cpp:105] Iteration 19104, lr = 0.01
I0406 09:56:03.312409  5226 solver.cpp:218] Iteration 19116 (1.40379 iter/s, 8.54829s/12 iters), loss = 2.756
I0406 09:56:03.324959  5226 solver.cpp:237]     Train net output #0: loss = 2.756 (* 1 = 2.756 loss)
I0406 09:56:03.324971  5226 sgd_solver.cpp:105] Iteration 19116, lr = 0.01
I0406 09:56:04.214323  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:56:10.854852  5226 solver.cpp:218] Iteration 19128 (1.59366 iter/s, 7.52984s/12 iters), loss = 2.74721
I0406 09:56:10.854902  5226 solver.cpp:237]     Train net output #0: loss = 2.74721 (* 1 = 2.74721 loss)
I0406 09:56:10.854909  5226 sgd_solver.cpp:105] Iteration 19128, lr = 0.01
I0406 09:56:16.023772  5226 solver.cpp:218] Iteration 19140 (2.32161 iter/s, 5.16882s/12 iters), loss = 3.16032
I0406 09:56:16.023808  5226 solver.cpp:237]     Train net output #0: loss = 3.16032 (* 1 = 3.16032 loss)
I0406 09:56:16.023813  5226 sgd_solver.cpp:105] Iteration 19140, lr = 0.01
I0406 09:56:21.082613  5226 solver.cpp:218] Iteration 19152 (2.37213 iter/s, 5.05875s/12 iters), loss = 2.77754
I0406 09:56:21.082662  5226 solver.cpp:237]     Train net output #0: loss = 2.77754 (* 1 = 2.77754 loss)
I0406 09:56:21.082670  5226 sgd_solver.cpp:105] Iteration 19152, lr = 0.01
I0406 09:56:26.236485  5226 solver.cpp:218] Iteration 19164 (2.32839 iter/s, 5.15378s/12 iters), loss = 2.4267
I0406 09:56:26.236526  5226 solver.cpp:237]     Train net output #0: loss = 2.4267 (* 1 = 2.4267 loss)
I0406 09:56:26.236531  5226 sgd_solver.cpp:105] Iteration 19164, lr = 0.01
I0406 09:56:30.834393  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_19176.caffemodel
I0406 09:56:33.869146  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_19176.solverstate
I0406 09:56:36.187420  5226 solver.cpp:330] Iteration 19176, Testing net (#0)
I0406 09:56:36.187439  5226 net.cpp:676] Ignoring source layer train-data
I0406 09:56:37.823941  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:56:40.828379  5226 solver.cpp:397]     Test net output #0: accuracy = 0.154412
I0406 09:56:40.828408  5226 solver.cpp:397]     Test net output #1: loss = 4.27568 (* 1 = 4.27568 loss)
I0406 09:56:40.970129  5226 solver.cpp:218] Iteration 19176 (0.81447 iter/s, 14.7335s/12 iters), loss = 2.65843
I0406 09:56:40.971714  5226 solver.cpp:237]     Train net output #0: loss = 2.65843 (* 1 = 2.65843 loss)
I0406 09:56:40.971724  5226 sgd_solver.cpp:105] Iteration 19176, lr = 0.01
I0406 09:56:45.339912  5226 solver.cpp:218] Iteration 19188 (2.74715 iter/s, 4.36816s/12 iters), loss = 2.61225
I0406 09:56:45.339965  5226 solver.cpp:237]     Train net output #0: loss = 2.61225 (* 1 = 2.61225 loss)
I0406 09:56:45.339973  5226 sgd_solver.cpp:105] Iteration 19188, lr = 0.01
I0406 09:56:50.495851  5226 solver.cpp:218] Iteration 19200 (2.32746 iter/s, 5.15584s/12 iters), loss = 2.87117
I0406 09:56:50.495898  5226 solver.cpp:237]     Train net output #0: loss = 2.87117 (* 1 = 2.87117 loss)
I0406 09:56:50.495906  5226 sgd_solver.cpp:105] Iteration 19200, lr = 0.01
I0406 09:56:55.760635  5226 solver.cpp:218] Iteration 19212 (2.27934 iter/s, 5.26469s/12 iters), loss = 2.69491
I0406 09:56:55.760676  5226 solver.cpp:237]     Train net output #0: loss = 2.69491 (* 1 = 2.69491 loss)
I0406 09:56:55.760682  5226 sgd_solver.cpp:105] Iteration 19212, lr = 0.01
I0406 09:56:58.665115  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:57:01.137848  5226 solver.cpp:218] Iteration 19224 (2.23168 iter/s, 5.37712s/12 iters), loss = 2.80032
I0406 09:57:01.137903  5226 solver.cpp:237]     Train net output #0: loss = 2.80032 (* 1 = 2.80032 loss)
I0406 09:57:01.137912  5226 sgd_solver.cpp:105] Iteration 19224, lr = 0.01
I0406 09:57:06.427059  5226 solver.cpp:218] Iteration 19236 (2.26881 iter/s, 5.28911s/12 iters), loss = 2.46941
I0406 09:57:06.427202  5226 solver.cpp:237]     Train net output #0: loss = 2.46941 (* 1 = 2.46941 loss)
I0406 09:57:06.427212  5226 sgd_solver.cpp:105] Iteration 19236, lr = 0.01
I0406 09:57:11.696939  5226 solver.cpp:218] Iteration 19248 (2.27717 iter/s, 5.26969s/12 iters), loss = 2.76649
I0406 09:57:11.696977  5226 solver.cpp:237]     Train net output #0: loss = 2.76649 (* 1 = 2.76649 loss)
I0406 09:57:11.696983  5226 sgd_solver.cpp:105] Iteration 19248, lr = 0.01
I0406 09:57:17.008790  5226 solver.cpp:218] Iteration 19260 (2.25914 iter/s, 5.31176s/12 iters), loss = 2.79233
I0406 09:57:17.008836  5226 solver.cpp:237]     Train net output #0: loss = 2.79233 (* 1 = 2.79233 loss)
I0406 09:57:17.008844  5226 sgd_solver.cpp:105] Iteration 19260, lr = 0.01
I0406 09:57:22.378641  5226 solver.cpp:218] Iteration 19272 (2.23474 iter/s, 5.36976s/12 iters), loss = 2.12638
I0406 09:57:22.378679  5226 solver.cpp:237]     Train net output #0: loss = 2.12638 (* 1 = 2.12638 loss)
I0406 09:57:22.378684  5226 sgd_solver.cpp:105] Iteration 19272, lr = 0.01
I0406 09:57:24.549031  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_19278.caffemodel
I0406 09:57:27.734087  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_19278.solverstate
I0406 09:57:30.550173  5226 solver.cpp:330] Iteration 19278, Testing net (#0)
I0406 09:57:30.550197  5226 net.cpp:676] Ignoring source layer train-data
I0406 09:57:32.041424  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:57:35.055517  5226 solver.cpp:397]     Test net output #0: accuracy = 0.147672
I0406 09:57:35.055554  5226 solver.cpp:397]     Test net output #1: loss = 4.29293 (* 1 = 4.29293 loss)
I0406 09:57:36.900943  5226 solver.cpp:218] Iteration 19284 (0.826323 iter/s, 14.5222s/12 iters), loss = 2.55369
I0406 09:57:36.901058  5226 solver.cpp:237]     Train net output #0: loss = 2.55369 (* 1 = 2.55369 loss)
I0406 09:57:36.901064  5226 sgd_solver.cpp:105] Iteration 19284, lr = 0.01
I0406 09:57:42.110467  5226 solver.cpp:218] Iteration 19296 (2.30354 iter/s, 5.20936s/12 iters), loss = 2.61398
I0406 09:57:42.110505  5226 solver.cpp:237]     Train net output #0: loss = 2.61398 (* 1 = 2.61398 loss)
I0406 09:57:42.110510  5226 sgd_solver.cpp:105] Iteration 19296, lr = 0.01
I0406 09:57:47.332265  5226 solver.cpp:218] Iteration 19308 (2.2981 iter/s, 5.22171s/12 iters), loss = 3.00053
I0406 09:57:47.332305  5226 solver.cpp:237]     Train net output #0: loss = 3.00053 (* 1 = 3.00053 loss)
I0406 09:57:47.332310  5226 sgd_solver.cpp:105] Iteration 19308, lr = 0.01
I0406 09:57:52.428169  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:57:52.621233  5226 solver.cpp:218] Iteration 19320 (2.26891 iter/s, 5.28888s/12 iters), loss = 2.60861
I0406 09:57:52.621273  5226 solver.cpp:237]     Train net output #0: loss = 2.60861 (* 1 = 2.60861 loss)
I0406 09:57:52.621277  5226 sgd_solver.cpp:105] Iteration 19320, lr = 0.01
I0406 09:57:58.161604  5226 solver.cpp:218] Iteration 19332 (2.16596 iter/s, 5.54028s/12 iters), loss = 2.86653
I0406 09:57:58.161660  5226 solver.cpp:237]     Train net output #0: loss = 2.86653 (* 1 = 2.86653 loss)
I0406 09:57:58.161669  5226 sgd_solver.cpp:105] Iteration 19332, lr = 0.01
I0406 09:58:03.448232  5226 solver.cpp:218] Iteration 19344 (2.26992 iter/s, 5.28653s/12 iters), loss = 3.0863
I0406 09:58:03.448288  5226 solver.cpp:237]     Train net output #0: loss = 3.0863 (* 1 = 3.0863 loss)
I0406 09:58:03.448297  5226 sgd_solver.cpp:105] Iteration 19344, lr = 0.01
I0406 09:58:08.817152  5226 solver.cpp:218] Iteration 19356 (2.23513 iter/s, 5.36882s/12 iters), loss = 3.54207
I0406 09:58:08.817243  5226 solver.cpp:237]     Train net output #0: loss = 3.54207 (* 1 = 3.54207 loss)
I0406 09:58:08.817250  5226 sgd_solver.cpp:105] Iteration 19356, lr = 0.01
I0406 09:58:14.051836  5226 solver.cpp:218] Iteration 19368 (2.29248 iter/s, 5.2345s/12 iters), loss = 3.14982
I0406 09:58:14.051882  5226 solver.cpp:237]     Train net output #0: loss = 3.14982 (* 1 = 3.14982 loss)
I0406 09:58:14.051889  5226 sgd_solver.cpp:105] Iteration 19368, lr = 0.01
I0406 09:58:18.897032  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_19380.caffemodel
I0406 09:58:21.959190  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_19380.solverstate
I0406 09:58:24.272936  5226 solver.cpp:330] Iteration 19380, Testing net (#0)
I0406 09:58:24.272958  5226 net.cpp:676] Ignoring source layer train-data
I0406 09:58:25.640415  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:58:28.567831  5226 solver.cpp:397]     Test net output #0: accuracy = 0.162377
I0406 09:58:28.567864  5226 solver.cpp:397]     Test net output #1: loss = 4.26514 (* 1 = 4.26514 loss)
I0406 09:58:28.708443  5226 solver.cpp:218] Iteration 19380 (0.818751 iter/s, 14.6565s/12 iters), loss = 2.70815
I0406 09:58:28.708480  5226 solver.cpp:237]     Train net output #0: loss = 2.70815 (* 1 = 2.70815 loss)
I0406 09:58:28.708487  5226 sgd_solver.cpp:105] Iteration 19380, lr = 0.01
I0406 09:58:33.107223  5226 solver.cpp:218] Iteration 19392 (2.72808 iter/s, 4.3987s/12 iters), loss = 2.92216
I0406 09:58:33.107264  5226 solver.cpp:237]     Train net output #0: loss = 2.92216 (* 1 = 2.92216 loss)
I0406 09:58:33.107270  5226 sgd_solver.cpp:105] Iteration 19392, lr = 0.01
I0406 09:58:38.612675  5226 solver.cpp:218] Iteration 19404 (2.17969 iter/s, 5.50536s/12 iters), loss = 2.81593
I0406 09:58:38.612725  5226 solver.cpp:237]     Train net output #0: loss = 2.81593 (* 1 = 2.81593 loss)
I0406 09:58:38.612733  5226 sgd_solver.cpp:105] Iteration 19404, lr = 0.01
I0406 09:58:39.407426  5226 blocking_queue.cpp:49] Waiting for data
I0406 09:58:43.780370  5226 solver.cpp:218] Iteration 19416 (2.32216 iter/s, 5.1676s/12 iters), loss = 2.86767
I0406 09:58:43.780407  5226 solver.cpp:237]     Train net output #0: loss = 2.86767 (* 1 = 2.86767 loss)
I0406 09:58:43.780412  5226 sgd_solver.cpp:105] Iteration 19416, lr = 0.01
I0406 09:58:45.892205  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:58:49.135965  5226 solver.cpp:218] Iteration 19428 (2.24068 iter/s, 5.35551s/12 iters), loss = 2.91555
I0406 09:58:49.136013  5226 solver.cpp:237]     Train net output #0: loss = 2.91555 (* 1 = 2.91555 loss)
I0406 09:58:49.136020  5226 sgd_solver.cpp:105] Iteration 19428, lr = 0.01
I0406 09:58:54.453687  5226 solver.cpp:218] Iteration 19440 (2.25665 iter/s, 5.31763s/12 iters), loss = 2.48498
I0406 09:58:54.453729  5226 solver.cpp:237]     Train net output #0: loss = 2.48498 (* 1 = 2.48498 loss)
I0406 09:58:54.453735  5226 sgd_solver.cpp:105] Iteration 19440, lr = 0.01
I0406 09:58:59.676683  5226 solver.cpp:218] Iteration 19452 (2.29757 iter/s, 5.22291s/12 iters), loss = 2.7795
I0406 09:58:59.676719  5226 solver.cpp:237]     Train net output #0: loss = 2.7795 (* 1 = 2.7795 loss)
I0406 09:58:59.676725  5226 sgd_solver.cpp:105] Iteration 19452, lr = 0.01
I0406 09:59:05.138038  5226 solver.cpp:218] Iteration 19464 (2.19729 iter/s, 5.46127s/12 iters), loss = 3.36903
I0406 09:59:05.138077  5226 solver.cpp:237]     Train net output #0: loss = 3.36903 (* 1 = 3.36903 loss)
I0406 09:59:05.138082  5226 sgd_solver.cpp:105] Iteration 19464, lr = 0.01
I0406 09:59:10.583138  5226 solver.cpp:218] Iteration 19476 (2.20385 iter/s, 5.44501s/12 iters), loss = 2.70699
I0406 09:59:10.583237  5226 solver.cpp:237]     Train net output #0: loss = 2.70699 (* 1 = 2.70699 loss)
I0406 09:59:10.583243  5226 sgd_solver.cpp:105] Iteration 19476, lr = 0.01
I0406 09:59:12.816386  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_19482.caffemodel
I0406 09:59:17.359834  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_19482.solverstate
I0406 09:59:21.091992  5226 solver.cpp:330] Iteration 19482, Testing net (#0)
I0406 09:59:21.092016  5226 net.cpp:676] Ignoring source layer train-data
I0406 09:59:22.522255  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:59:25.505995  5226 solver.cpp:397]     Test net output #0: accuracy = 0.145833
I0406 09:59:25.506029  5226 solver.cpp:397]     Test net output #1: loss = 4.29541 (* 1 = 4.29541 loss)
I0406 09:59:27.425757  5226 solver.cpp:218] Iteration 19488 (0.712487 iter/s, 16.8424s/12 iters), loss = 2.67586
I0406 09:59:27.425809  5226 solver.cpp:237]     Train net output #0: loss = 2.67586 (* 1 = 2.67586 loss)
I0406 09:59:27.425819  5226 sgd_solver.cpp:105] Iteration 19488, lr = 0.01
I0406 09:59:32.769673  5226 solver.cpp:218] Iteration 19500 (2.24559 iter/s, 5.34382s/12 iters), loss = 2.838
I0406 09:59:32.769714  5226 solver.cpp:237]     Train net output #0: loss = 2.838 (* 1 = 2.838 loss)
I0406 09:59:32.769721  5226 sgd_solver.cpp:105] Iteration 19500, lr = 0.01
I0406 09:59:38.277453  5226 solver.cpp:218] Iteration 19512 (2.17877 iter/s, 5.50769s/12 iters), loss = 2.52217
I0406 09:59:38.277494  5226 solver.cpp:237]     Train net output #0: loss = 2.52217 (* 1 = 2.52217 loss)
I0406 09:59:38.277500  5226 sgd_solver.cpp:105] Iteration 19512, lr = 0.01
I0406 09:59:42.738787  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:59:43.664301  5226 solver.cpp:218] Iteration 19524 (2.22769 iter/s, 5.38676s/12 iters), loss = 2.15964
I0406 09:59:43.664353  5226 solver.cpp:237]     Train net output #0: loss = 2.15964 (* 1 = 2.15964 loss)
I0406 09:59:43.664363  5226 sgd_solver.cpp:105] Iteration 19524, lr = 0.01
I0406 09:59:49.126029  5226 solver.cpp:218] Iteration 19536 (2.19715 iter/s, 5.46162s/12 iters), loss = 2.33063
I0406 09:59:49.126098  5226 solver.cpp:237]     Train net output #0: loss = 2.33063 (* 1 = 2.33063 loss)
I0406 09:59:49.126109  5226 sgd_solver.cpp:105] Iteration 19536, lr = 0.01
I0406 09:59:54.579237  5226 solver.cpp:218] Iteration 19548 (2.20059 iter/s, 5.45309s/12 iters), loss = 2.25702
I0406 09:59:54.579290  5226 solver.cpp:237]     Train net output #0: loss = 2.25702 (* 1 = 2.25702 loss)
I0406 09:59:54.579298  5226 sgd_solver.cpp:105] Iteration 19548, lr = 0.01
I0406 09:59:59.938196  5226 solver.cpp:218] Iteration 19560 (2.23928 iter/s, 5.35886s/12 iters), loss = 2.56183
I0406 09:59:59.938241  5226 solver.cpp:237]     Train net output #0: loss = 2.56183 (* 1 = 2.56183 loss)
I0406 09:59:59.938249  5226 sgd_solver.cpp:105] Iteration 19560, lr = 0.01
I0406 10:00:05.521911  5226 solver.cpp:218] Iteration 19572 (2.14914 iter/s, 5.58362s/12 iters), loss = 2.52675
I0406 10:00:05.521948  5226 solver.cpp:237]     Train net output #0: loss = 2.52675 (* 1 = 2.52675 loss)
I0406 10:00:05.521955  5226 sgd_solver.cpp:105] Iteration 19572, lr = 0.01
I0406 10:00:10.219933  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_19584.caffemodel
I0406 10:00:13.370455  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_19584.solverstate
I0406 10:00:16.436864  5226 solver.cpp:330] Iteration 19584, Testing net (#0)
I0406 10:00:16.436890  5226 net.cpp:676] Ignoring source layer train-data
I0406 10:00:17.859496  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 10:00:21.042773  5226 solver.cpp:397]     Test net output #0: accuracy = 0.180147
I0406 10:00:21.042809  5226 solver.cpp:397]     Test net output #1: loss = 4.1592 (* 1 = 4.1592 loss)
I0406 10:00:21.187947  5226 solver.cpp:218] Iteration 19584 (0.765995 iter/s, 15.6659s/12 iters), loss = 2.55748
I0406 10:00:21.187988  5226 solver.cpp:237]     Train net output #0: loss = 2.55748 (* 1 = 2.55748 loss)
I0406 10:00:21.187992  5226 sgd_solver.cpp:105] Iteration 19584, lr = 0.01
I0406 10:00:25.739646  5226 solver.cpp:218] Iteration 19596 (2.63643 iter/s, 4.55161s/12 iters), loss = 2.67315
I0406 10:00:25.739693  5226 solver.cpp:237]     Train net output #0: loss = 2.67315 (* 1 = 2.67315 loss)
I0406 10:00:25.739701  5226 sgd_solver.cpp:105] Iteration 19596, lr = 0.01
I0406 10:00:31.497824  5226 solver.cpp:218] Iteration 19608 (2.08403 iter/s, 5.75808s/12 iters), loss = 2.80414
I0406 10:00:31.497865  5226 solver.cpp:237]     Train net output #0: loss = 2.80414 (* 1 = 2.80414 loss)
I0406 10:00:31.497870  5226 sgd_solver.cpp:105] Iteration 19608, lr = 0.01
I0406 10:00:37.089128  5226 solver.cpp:218] Iteration 19620 (2.14622 iter/s, 5.59121s/12 iters), loss = 2.99022
I0406 10:00:37.089169  5226 solver.cpp:237]     Train net output #0: loss = 2.99022 (* 1 = 2.99022 loss)
I0406 10:00:37.089174  5226 sgd_solver.cpp:105] Iteration 19620, lr = 0.01
I0406 10:00:38.773588  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 10:00:42.826838  5226 solver.cpp:218] Iteration 19632 (2.09146 iter/s, 5.73762s/12 iters), loss = 2.47468
I0406 10:00:42.826886  5226 solver.cpp:237]     Train net output #0: loss = 2.47468 (* 1 = 2.47468 loss)
I0406 10:00:42.826894  5226 sgd_solver.cpp:105] Iteration 19632, lr = 0.01
I0406 10:00:48.278543  5226 solver.cpp:218] Iteration 19644 (2.20119 iter/s, 5.45161s/12 iters), loss = 2.7661
I0406 10:00:48.278720  5226 solver.cpp:237]     Train net output #0: loss = 2.7661 (* 1 = 2.7661 loss)
I0406 10:00:48.278729  5226 sgd_solver.cpp:105] Iteration 19644, lr = 0.01
I0406 10:00:53.589474  5226 solver.cpp:218] Iteration 19656 (2.25959 iter/s, 5.31071s/12 iters), loss = 2.61602
I0406 10:00:53.589524  5226 solver.cpp:237]     Train net output #0: loss = 2.61602 (* 1 = 2.61602 loss)
I0406 10:00:53.589531  5226 sgd_solver.cpp:105] Iteration 19656, lr = 0.01
I0406 10:00:58.988624  5226 solver.cpp:218] Iteration 19668 (2.22261 iter/s, 5.39905s/12 iters), loss = 2.82244
I0406 10:00:58.988672  5226 solver.cpp:237]     Train net output #0: loss = 2.82244 (* 1 = 2.82244 loss)
I0406 10:00:58.988679  5226 sgd_solver.cpp:105] Iteration 19668, lr = 0.01
I0406 10:01:04.245191  5226 solver.cpp:218] Iteration 19680 (2.2829 iter/s, 5.25647s/12 iters), loss = 2.45953
I0406 10:01:04.245242  5226 solver.cpp:237]     Train net output #0: loss = 2.45953 (* 1 = 2.45953 loss)
I0406 10:01:04.245250  5226 sgd_solver.cpp:105] Iteration 19680, lr = 0.01
I0406 10:01:06.639740  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_19686.caffemodel
I0406 10:01:09.652125  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_19686.solverstate
I0406 10:01:13.198583  5226 solver.cpp:330] Iteration 19686, Testing net (#0)
I0406 10:01:13.198604  5226 net.cpp:676] Ignoring source layer train-data
I0406 10:01:14.521813  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 10:01:17.851245  5226 solver.cpp:397]     Test net output #0: accuracy = 0.174632
I0406 10:01:17.851282  5226 solver.cpp:397]     Test net output #1: loss = 4.19452 (* 1 = 4.19452 loss)
I0406 10:01:19.787679  5226 solver.cpp:218] Iteration 19692 (0.772085 iter/s, 15.5423s/12 iters), loss = 2.93832
I0406 10:01:19.787804  5226 solver.cpp:237]     Train net output #0: loss = 2.93832 (* 1 = 2.93832 loss)
I0406 10:01:19.787817  5226 sgd_solver.cpp:105] Iteration 19692, lr = 0.01
I0406 10:01:25.043987  5226 solver.cpp:218] Iteration 19704 (2.28304 iter/s, 5.25614s/12 iters), loss = 2.85992
I0406 10:01:25.044035  5226 solver.cpp:237]     Train net output #0: loss = 2.85992 (* 1 = 2.85992 loss)
I0406 10:01:25.044042  5226 sgd_solver.cpp:105] Iteration 19704, lr = 0.01
I0406 10:01:30.476754  5226 solver.cpp:218] Iteration 19716 (2.20886 iter/s, 5.43267s/12 iters), loss = 2.76936
I0406 10:01:30.476807  5226 solver.cpp:237]     Train net output #0: loss = 2.76936 (* 1 = 2.76936 loss)
I0406 10:01:30.476815  5226 sgd_solver.cpp:105] Iteration 19716, lr = 0.01
I0406 10:01:34.316648  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 10:01:36.029168  5226 solver.cpp:218] Iteration 19728 (2.16126 iter/s, 5.55232s/12 iters), loss = 2.64841
I0406 10:01:36.029207  5226 solver.cpp:237]     Train net output #0: loss = 2.64841 (* 1 = 2.64841 loss)
I0406 10:01:36.029218  5226 sgd_solver.cpp:105] Iteration 19728, lr = 0.01
I0406 10:01:41.592639  5226 solver.cpp:218] Iteration 19740 (2.15696 iter/s, 5.56337s/12 iters), loss = 2.73363
I0406 10:01:41.592691  5226 solver.cpp:237]     Train net output #0: loss = 2.73363 (* 1 = 2.73363 loss)
I0406 10:01:41.592700  5226 sgd_solver.cpp:105] Iteration 19740, lr = 0.01
I0406 10:01:47.095300  5226 solver.cpp:218] Iteration 19752 (2.1808 iter/s, 5.50256s/12 iters), loss = 2.18588
I0406 10:01:47.095358  5226 solver.cpp:237]     Train net output #0: loss = 2.18588 (* 1 = 2.18588 loss)
I0406 10:01:47.095368  5226 sgd_solver.cpp:105] Iteration 19752, lr = 0.01
I0406 10:01:52.475334  5226 solver.cpp:218] Iteration 19764 (2.23051 iter/s, 5.37993s/12 iters), loss = 2.68636
I0406 10:01:52.475489  5226 solver.cpp:237]     Train net output #0: loss = 2.68636 (* 1 = 2.68636 loss)
I0406 10:01:52.475498  5226 sgd_solver.cpp:105] Iteration 19764, lr = 0.01
I0406 10:01:58.304586  5226 solver.cpp:218] Iteration 19776 (2.05866 iter/s, 5.82905s/12 iters), loss = 2.37485
I0406 10:01:58.304636  5226 solver.cpp:237]     Train net output #0: loss = 2.37485 (* 1 = 2.37485 loss)
I0406 10:01:58.304643  5226 sgd_solver.cpp:105] Iteration 19776, lr = 0.01
I0406 10:02:03.341866  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_19788.caffemodel
I0406 10:02:06.477465  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_19788.solverstate
I0406 10:02:10.218739  5226 solver.cpp:330] Iteration 19788, Testing net (#0)
I0406 10:02:10.218761  5226 net.cpp:676] Ignoring source layer train-data
I0406 10:02:11.487207  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 10:02:14.822784  5226 solver.cpp:397]     Test net output #0: accuracy = 0.164216
I0406 10:02:14.822819  5226 solver.cpp:397]     Test net output #1: loss = 4.23622 (* 1 = 4.23622 loss)
I0406 10:02:14.960054  5226 solver.cpp:218] Iteration 19788 (0.720491 iter/s, 16.6553s/12 iters), loss = 2.22507
I0406 10:02:14.960114  5226 solver.cpp:237]     Train net output #0: loss = 2.22507 (* 1 = 2.22507 loss)
I0406 10:02:14.960122  5226 sgd_solver.cpp:105] Iteration 19788, lr = 0.01
I0406 10:02:19.360641  5226 solver.cpp:218] Iteration 19800 (2.72697 iter/s, 4.40048s/12 iters), loss = 2.69649
I0406 10:02:19.360697  5226 solver.cpp:237]     Train net output #0: loss = 2.69649 (* 1 = 2.69649 loss)
I0406 10:02:19.360707  5226 sgd_solver.cpp:105] Iteration 19800, lr = 0.01
I0406 10:02:24.752535  5226 solver.cpp:218] Iteration 19812 (2.22561 iter/s, 5.39179s/12 iters), loss = 2.933
I0406 10:02:24.752658  5226 solver.cpp:237]     Train net output #0: loss = 2.933 (* 1 = 2.933 loss)
I0406 10:02:24.752671  5226 sgd_solver.cpp:105] Iteration 19812, lr = 0.01
I0406 10:02:30.157891  5226 solver.cpp:218] Iteration 19824 (2.22009 iter/s, 5.40519s/12 iters), loss = 2.82492
I0406 10:02:30.157929  5226 solver.cpp:237]     Train net output #0: loss = 2.82492 (* 1 = 2.82492 loss)
I0406 10:02:30.157934  5226 sgd_solver.cpp:105] Iteration 19824, lr = 0.01
I0406 10:02:30.838217  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 10:02:35.788676  5226 solver.cpp:218] Iteration 19836 (2.13117 iter/s, 5.6307s/12 iters), loss = 2.92864
I0406 10:02:35.788715  5226 solver.cpp:237]     Train net output #0: loss = 2.92864 (* 1 = 2.92864 loss)
I0406 10:02:35.788720  5226 sgd_solver.cpp:105] Iteration 19836, lr = 0.01
I0406 10:02:41.293627  5226 solver.cpp:218] Iteration 19848 (2.17989 iter/s, 5.50486s/12 iters), loss = 2.98564
I0406 10:02:41.293673  5226 solver.cpp:237]     Train net output #0: loss = 2.98564 (* 1 = 2.98564 loss)
I0406 10:02:41.293681  5226 sgd_solver.cpp:105] Iteration 19848, lr = 0.01
I0406 10:02:46.821861  5226 solver.cpp:218] Iteration 19860 (2.17071 iter/s, 5.52814s/12 iters), loss = 2.59119
I0406 10:02:46.821916  5226 solver.cpp:237]     Train net output #0: loss = 2.59119 (* 1 = 2.59119 loss)
I0406 10:02:46.821925  5226 sgd_solver.cpp:105] Iteration 19860, lr = 0.01
I0406 10:02:52.340847  5226 solver.cpp:218] Iteration 19872 (2.17435 iter/s, 5.51888s/12 iters), loss = 2.7598
I0406 10:02:52.340909  5226 solver.cpp:237]     Train net output #0: loss = 2.7598 (* 1 = 2.7598 loss)
I0406 10:02:52.340919  5226 sgd_solver.cpp:105] Iteration 19872, lr = 0.01
I0406 10:02:57.715147  5226 solver.cpp:218] Iteration 19884 (2.2329 iter/s, 5.37419s/12 iters), loss = 2.81648
I0406 10:02:57.715274  5226 solver.cpp:237]     Train net output #0: loss = 2.81648 (* 1 = 2.81648 loss)
I0406 10:02:57.715283  5226 sgd_solver.cpp:105] Iteration 19884, lr = 0.01
I0406 10:03:00.006028  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_19890.caffemodel
I0406 10:03:03.048367  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_19890.solverstate
I0406 10:03:06.431736  5226 solver.cpp:330] Iteration 19890, Testing net (#0)
I0406 10:03:06.431761  5226 net.cpp:676] Ignoring source layer train-data
I0406 10:03:07.632391  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 10:03:11.090910  5226 solver.cpp:397]     Test net output #0: accuracy = 0.163603
I0406 10:03:11.090945  5226 solver.cpp:397]     Test net output #1: loss = 4.29033 (* 1 = 4.29033 loss)
I0406 10:03:12.970165  5226 solver.cpp:218] Iteration 19896 (0.786638 iter/s, 15.2548s/12 iters), loss = 2.81393
I0406 10:03:12.970223  5226 solver.cpp:237]     Train net output #0: loss = 2.81393 (* 1 = 2.81393 loss)
I0406 10:03:12.970232  5226 sgd_solver.cpp:105] Iteration 19896, lr = 0.01
I0406 10:03:18.350425  5226 solver.cpp:218] Iteration 19908 (2.23042 iter/s, 5.38015s/12 iters), loss = 2.29048
I0406 10:03:18.350466  5226 solver.cpp:237]     Train net output #0: loss = 2.29048 (* 1 = 2.29048 loss)
I0406 10:03:18.350471  5226 sgd_solver.cpp:105] Iteration 19908, lr = 0.01
I0406 10:03:23.800796  5226 solver.cpp:218] Iteration 19920 (2.20172 iter/s, 5.45028s/12 iters), loss = 2.59675
I0406 10:03:23.800833  5226 solver.cpp:237]     Train net output #0: loss = 2.59675 (* 1 = 2.59675 loss)
I0406 10:03:23.800838  5226 sgd_solver.cpp:105] Iteration 19920, lr = 0.01
I0406 10:03:26.722972  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 10:03:29.197059  5226 solver.cpp:218] Iteration 19932 (2.2238 iter/s, 5.39618s/12 iters), loss = 2.73143
I0406 10:03:29.197175  5226 solver.cpp:237]     Train net output #0: loss = 2.73143 (* 1 = 2.73143 loss)
I0406 10:03:29.197181  5226 sgd_solver.cpp:105] Iteration 19932, lr = 0.01
I0406 10:03:34.904337  5226 solver.cpp:218] Iteration 19944 (2.10264 iter/s, 5.70711s/12 iters), loss = 3.0777
I0406 10:03:34.904386  5226 solver.cpp:237]     Train net output #0: loss = 3.0777 (* 1 = 3.0777 loss)
I0406 10:03:34.904392  5226 sgd_solver.cpp:105] Iteration 19944, lr = 0.01
I0406 10:03:40.273808  5226 solver.cpp:218] Iteration 19956 (2.2349 iter/s, 5.36938s/12 iters), loss = 2.64861
I0406 10:03:40.273856  5226 solver.cpp:237]     Train net output #0: loss = 2.64861 (* 1 = 2.64861 loss)
I0406 10:03:40.273864  5226 sgd_solver.cpp:105] Iteration 19956, lr = 0.01
I0406 10:03:45.456115  5226 solver.cpp:218] Iteration 19968 (2.31562 iter/s, 5.1822s/12 iters), loss = 2.64935
I0406 10:03:45.456171  5226 solver.cpp:237]     Train net output #0: loss = 2.64935 (* 1 = 2.64935 loss)
I0406 10:03:45.456182  5226 sgd_solver.cpp:105] Iteration 19968, lr = 0.01
I0406 10:03:50.800523  5226 solver.cpp:218] Iteration 19980 (2.24538 iter/s, 5.34431s/12 iters), loss = 2.70214
I0406 10:03:50.800575  5226 solver.cpp:237]     Train net output #0: loss = 2.70214 (* 1 = 2.70214 loss)
I0406 10:03:50.800583  5226 sgd_solver.cpp:105] Iteration 19980, lr = 0.01
I0406 10:03:55.829727  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_19992.caffemodel
I0406 10:03:58.849689  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_19992.solverstate
I0406 10:04:01.469600  5226 solver.cpp:330] Iteration 19992, Testing net (#0)
I0406 10:04:01.469662  5226 net.cpp:676] Ignoring source layer train-data
I0406 10:04:02.646924  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 10:04:05.841603  5226 solver.cpp:397]     Test net output #0: accuracy = 0.167892
I0406 10:04:05.841639  5226 solver.cpp:397]     Test net output #1: loss = 4.1912 (* 1 = 4.1912 loss)
I0406 10:04:05.979552  5226 solver.cpp:218] Iteration 19992 (0.790572 iter/s, 15.1789s/12 iters), loss = 3.25358
I0406 10:04:05.979607  5226 solver.cpp:237]     Train net output #0: loss = 3.25358 (* 1 = 3.25358 loss)
I0406 10:04:05.979615  5226 sgd_solver.cpp:105] Iteration 19992, lr = 0.01
I0406 10:04:10.333920  5226 solver.cpp:218] Iteration 20004 (2.75591 iter/s, 4.35427s/12 iters), loss = 2.75259
I0406 10:04:10.333961  5226 solver.cpp:237]     Train net output #0: loss = 2.75259 (* 1 = 2.75259 loss)
I0406 10:04:10.333966  5226 sgd_solver.cpp:105] Iteration 20004, lr = 0.01
I0406 10:04:15.942981  5226 solver.cpp:218] Iteration 20016 (2.13943 iter/s, 5.60896s/12 iters), loss = 2.79368
I0406 10:04:15.943027  5226 solver.cpp:237]     Train net output #0: loss = 2.79368 (* 1 = 2.79368 loss)
I0406 10:04:15.943035  5226 sgd_solver.cpp:105] Iteration 20016, lr = 0.01
I0406 10:04:21.229457  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 10:04:21.394726  5226 solver.cpp:218] Iteration 20028 (2.20117 iter/s, 5.45165s/12 iters), loss = 2.57669
I0406 10:04:21.394781  5226 solver.cpp:237]     Train net output #0: loss = 2.57669 (* 1 = 2.57669 loss)
I0406 10:04:21.394790  5226 sgd_solver.cpp:105] Iteration 20028, lr = 0.01
I0406 10:04:26.973359  5226 solver.cpp:218] Iteration 20040 (2.1511 iter/s, 5.57853s/12 iters), loss = 2.99062
I0406 10:04:26.973420  5226 solver.cpp:237]     Train net output #0: loss = 2.99062 (* 1 = 2.99062 loss)
I0406 10:04:26.973428  5226 sgd_solver.cpp:105] Iteration 20040, lr = 0.01
I0406 10:04:32.324486  5226 solver.cpp:218] Iteration 20052 (2.24256 iter/s, 5.35103s/12 iters), loss = 2.56087
I0406 10:04:32.324627  5226 solver.cpp:237]     Train net output #0: loss = 2.56087 (* 1 = 2.56087 loss)
I0406 10:04:32.324636  5226 sgd_solver.cpp:105] Iteration 20052, lr = 0.01
I0406 10:04:40.948962  5226 solver.cpp:218] Iteration 20064 (1.39179 iter/s, 8.62201s/12 iters), loss = 2.90471
I0406 10:04:40.949019  5226 solver.cpp:237]     Train net output #0: loss = 2.90471 (* 1 = 2.90471 loss)
I0406 10:04:40.949028  5226 sgd_solver.cpp:105] Iteration 20064, lr = 0.01
I0406 10:04:50.913915  5226 solver.cpp:218] Iteration 20076 (1.20457 iter/s, 9.96204s/12 iters), loss = 2.82843
I0406 10:04:50.913975  5226 solver.cpp:237]     Train net output #0: loss = 2.82843 (* 1 = 2.82843 loss)
I0406 10:04:50.913983  5226 sgd_solver.cpp:105] Iteration 20076, lr = 0.01
I0406 10:04:58.682845  5226 solver.cpp:218] Iteration 20088 (1.54464 iter/s, 7.76881s/12 iters), loss = 2.53284
I0406 10:04:58.684938  5226 solver.cpp:237]     Train net output #0: loss = 2.53284 (* 1 = 2.53284 loss)
I0406 10:04:58.684958  5226 sgd_solver.cpp:105] Iteration 20088, lr = 0.01
I0406 10:05:01.583570  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_20094.caffemodel
I0406 10:05:06.201537  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_20094.solverstate
I0406 10:05:09.618099  5226 solver.cpp:330] Iteration 20094, Testing net (#0)
I0406 10:05:09.618127  5226 net.cpp:676] Ignoring source layer train-data
I0406 10:05:12.187541  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 10:05:17.569209  5226 blocking_queue.cpp:49] Waiting for data
I0406 10:05:18.524080  5226 solver.cpp:397]     Test net output #0: accuracy = 0.14277
I0406 10:05:18.524119  5226 solver.cpp:397]     Test net output #1: loss = 4.42466 (* 1 = 4.42466 loss)
I0406 10:05:21.724939  5226 solver.cpp:218] Iteration 20100 (0.520925 iter/s, 23.036s/12 iters), loss = 2.85385
I0406 10:05:21.725000  5226 solver.cpp:237]     Train net output #0: loss = 2.85385 (* 1 = 2.85385 loss)
I0406 10:05:21.725008  5226 sgd_solver.cpp:105] Iteration 20100, lr = 0.01
I0406 10:05:30.065737  5226 solver.cpp:218] Iteration 20112 (1.43873 iter/s, 8.34066s/12 iters), loss = 2.83841
I0406 10:05:30.065793  5226 solver.cpp:237]     Train net output #0: loss = 2.83841 (* 1 = 2.83841 loss)
I0406 10:05:30.065802  5226 sgd_solver.cpp:105] Iteration 20112, lr = 0.01
I0406 10:05:37.080931  5226 solver.cpp:218] Iteration 20124 (1.71301 iter/s, 7.00522s/12 iters), loss = 2.47944
I0406 10:05:37.081384  5226 solver.cpp:237]     Train net output #0: loss = 2.47944 (* 1 = 2.47944 loss)
I0406 10:05:37.081394  5226 sgd_solver.cpp:105] Iteration 20124, lr = 0.01
I0406 10:05:40.017272  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 10:05:43.877558  5226 solver.cpp:218] Iteration 20136 (1.76571 iter/s, 6.79612s/12 iters), loss = 2.62401
I0406 10:05:43.877617  5226 solver.cpp:237]     Train net output #0: loss = 2.62401 (* 1 = 2.62401 loss)
I0406 10:05:43.877626  5226 sgd_solver.cpp:105] Iteration 20136, lr = 0.01
I0406 10:05:50.228932  5226 solver.cpp:218] Iteration 20148 (1.89371 iter/s, 6.33678s/12 iters), loss = 2.69378
I0406 10:05:50.228986  5226 solver.cpp:237]     Train net output #0: loss = 2.69378 (* 1 = 2.69378 loss)
I0406 10:05:50.228996  5226 sgd_solver.cpp:105] Iteration 20148, lr = 0.01
I0406 10:05:56.992928  5226 solver.cpp:218] Iteration 20160 (1.77583 iter/s, 6.7574s/12 iters), loss = 2.29788
I0406 10:05:56.992980  5226 solver.cpp:237]     Train net output #0: loss = 2.29788 (* 1 = 2.29788 loss)
I0406 10:05:56.992988  5226 sgd_solver.cpp:105] Iteration 20160, lr = 0.01
I0406 10:06:04.076555  5226 solver.cpp:218] Iteration 20172 (1.69407 iter/s, 7.08351s/12 iters), loss = 2.44717
I0406 10:06:04.076604  5226 solver.cpp:237]     Train net output #0: loss = 2.44717 (* 1 = 2.44717 loss)
I0406 10:06:04.076612  5226 sgd_solver.cpp:105] Iteration 20172, lr = 0.01
I0406 10:06:10.535975  5226 solver.cpp:218] Iteration 20184 (1.85778 iter/s, 6.45931s/12 iters), loss = 2.75424
I0406 10:06:10.536149  5226 solver.cpp:237]     Train net output #0: loss = 2.75424 (* 1 = 2.75424 loss)
I0406 10:06:10.536160  5226 sgd_solver.cpp:105] Iteration 20184, lr = 0.01
I0406 10:06:16.711539  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_20196.caffemodel
I0406 10:06:20.339686  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_20196.solverstate
I0406 10:06:23.778192  5226 solver.cpp:330] Iteration 20196, Testing net (#0)
I0406 10:06:23.778215  5226 net.cpp:676] Ignoring source layer train-data
I0406 10:06:25.327039  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 10:06:28.911279  5226 solver.cpp:397]     Test net output #0: accuracy = 0.168505
I0406 10:06:28.911314  5226 solver.cpp:397]     Test net output #1: loss = 4.23951 (* 1 = 4.23951 loss)
I0406 10:06:29.051805  5226 solver.cpp:218] Iteration 20196 (0.648104 iter/s, 18.5155s/12 iters), loss = 2.20535
I0406 10:06:29.051856  5226 solver.cpp:237]     Train net output #0: loss = 2.20535 (* 1 = 2.20535 loss)
I0406 10:06:29.051864  5226 sgd_solver.cpp:105] Iteration 20196, lr = 0.01
I0406 10:06:33.950533  5226 solver.cpp:218] Iteration 20208 (2.44966 iter/s, 4.89863s/12 iters), loss = 2.79625
I0406 10:06:33.950587  5226 solver.cpp:237]     Train net output #0: loss = 2.79625 (* 1 = 2.79625 loss)
I0406 10:06:33.950596  5226 sgd_solver.cpp:105] Iteration 20208, lr = 0.01
I0406 10:06:39.542714  5226 solver.cpp:218] Iteration 20220 (2.14589 iter/s, 5.59208s/12 iters), loss = 2.83598
I0406 10:06:39.542754  5226 solver.cpp:237]     Train net output #0: loss = 2.83598 (* 1 = 2.83598 loss)
I0406 10:06:39.542760  5226 sgd_solver.cpp:105] Iteration 20220, lr = 0.01
I0406 10:06:45.237886  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 10:06:47.852932  5226 solver.cpp:218] Iteration 20232 (1.44406 iter/s, 8.30992s/12 iters), loss = 2.70664
I0406 10:06:47.852983  5226 solver.cpp:237]     Train net output #0: loss = 2.70664 (* 1 = 2.70664 loss)
I0406 10:06:47.852993  5226 sgd_solver.cpp:105] Iteration 20232, lr = 0.01
I0406 10:06:58.563769  5226 solver.cpp:218] Iteration 20244 (1.12043 iter/s, 10.7101s/12 iters), loss = 2.64755
I0406 10:06:58.563823  5226 solver.cpp:237]     Train net output #0: loss = 2.64755 (* 1 = 2.64755 loss)
I0406 10:06:58.563830  5226 sgd_solver.cpp:105] Iteration 20244, lr = 0.01
I0406 10:07:11.468931  5226 solver.cpp:218] Iteration 20256 (0.930074 iter/s, 12.9022s/12 iters), loss = 2.33334
I0406 10:07:11.468987  5226 solver.cpp:237]     Train net output #0: loss = 2.33334 (* 1 = 2.33334 loss)
I0406 10:07:11.468994  5226 sgd_solver.cpp:105] Iteration 20256, lr = 0.01
I0406 10:07:23.288929  5226 solver.cpp:218] Iteration 20268 (1.01529 iter/s, 11.8193s/12 iters), loss = 2.78814
I0406 10:07:23.289147  5226 solver.cpp:237]     Train net output #0: loss = 2.78814 (* 1 = 2.78814 loss)
I0406 10:07:23.289156  5226 sgd_solver.cpp:105] Iteration 20268, lr = 0.01
I0406 10:07:30.058640  5226 solver.cpp:218] Iteration 20280 (1.77267 iter/s, 6.76944s/12 iters), loss = 2.64901
I0406 10:07:30.058686  5226 solver.cpp:237]     Train net output #0: loss = 2.64901 (* 1 = 2.64901 loss)
I0406 10:07:30.058692  5226 sgd_solver.cpp:105] Iteration 20280, lr = 0.01
I0406 10:07:36.943645  5226 solver.cpp:218] Iteration 20292 (1.74296 iter/s, 6.88485s/12 iters), loss = 2.75297
I0406 10:07:36.943696  5226 solver.cpp:237]     Train net output #0: loss = 2.75297 (* 1 = 2.75297 loss)
I0406 10:07:36.943704  5226 sgd_solver.cpp:105] Iteration 20292, lr = 0.01
I0406 10:07:39.623667  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_20298.caffemodel
I0406 10:07:43.239082  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_20298.solverstate
I0406 10:07:48.326705  5226 solver.cpp:330] Iteration 20298, Testing net (#0)
I0406 10:07:48.326730  5226 net.cpp:676] Ignoring source layer train-data
I0406 10:07:49.678532  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 10:07:54.613147  5226 solver.cpp:397]     Test net output #0: accuracy = 0.151348
I0406 10:07:54.613283  5226 solver.cpp:397]     Test net output #1: loss = 4.2782 (* 1 = 4.2782 loss)
I0406 10:07:56.934345  5226 solver.cpp:218] Iteration 20304 (0.600285 iter/s, 19.9905s/12 iters), loss = 2.91154
I0406 10:07:56.934396  5226 solver.cpp:237]     Train net output #0: loss = 2.91154 (* 1 = 2.91154 loss)
I0406 10:07:56.934403  5226 sgd_solver.cpp:105] Iteration 20304, lr = 0.01
I0406 10:08:03.890859  5226 solver.cpp:218] Iteration 20316 (1.72503 iter/s, 6.9564s/12 iters), loss = 2.29866
I0406 10:08:03.890918  5226 solver.cpp:237]     Train net output #0: loss = 2.29866 (* 1 = 2.29866 loss)
I0406 10:08:03.890926  5226 sgd_solver.cpp:105] Iteration 20316, lr = 0.01
I0406 10:08:10.638542  5226 solver.cpp:218] Iteration 20328 (1.77842 iter/s, 6.74756s/12 iters), loss = 2.68673
I0406 10:08:10.638592  5226 solver.cpp:237]     Train net output #0: loss = 2.68673 (* 1 = 2.68673 loss)
I0406 10:08:10.638600  5226 sgd_solver.cpp:105] Iteration 20328, lr = 0.01
I0406 10:08:12.460681  5252 data_layer.cpp:73] Restarting data prefetching from start.
I0406 10:08:17.238793  5226 solver.cpp:218] Iteration 20340 (1.81814 iter/s, 6.60014s/12 iters), loss = 2.39676
I0406 10:08:17.258757  5226 solver.cpp:237]     Train net output #0: loss = 2.39676 (* 1 = 2.39676 loss)
I0406 10:08:17.258780  5226 sgd_solver.cpp:105] Iteration 20340, lr = 0.01
I0406 10:08:23.880574  5226 solver.cpp:218] Iteration 20352 (1.8122 iter/s, 6.62178s/12 iters), loss = 2.60019
I0406 10:08:23.880630  5226 solver.cpp:237]     Train net output #0: loss = 2.60019 (* 1 = 2.60019 loss)
I0406 10:08:23.880637  5226 sgd_solver.cpp:105] Iteration 20352, lr = 0.01
I0406 10:08:30.384938  5226 solver.cpp:218] Iteration 20364 (1.84796 iter/s, 6.49363s/12 iters), loss = 3.03568
I0406 10:08:30.385072  5226 solver.cpp:237]     Train net output #0: loss = 3.03568 (* 1 = 3.03568 loss)
I0406 10:08:30.385082  5226 sgd_solver.cpp:105] Iteration 20364, lr = 0.01
I0406 10:08:36.180506  5226 solver.cpp:218] Iteration 20376 (2.07061 iter/s, 5.79538s/12 iters), loss = 2.34857
I0406 10:08:36.180553  5226 solver.cpp:237]     Train net output #0: loss = 2.34857 (* 1 = 2.34857 loss)
I0406 10:08:36.180559  5226 sgd_solver.cpp:105] Iteration 20376, lr = 0.01
I0406 10:08:41.693197  5226 solver.cpp:218] Iteration 20388 (2.17683 iter/s, 5.51259s/12 iters), loss = 2.17682
I0406 10:08:41.693245  5226 solver.cpp:237]     Train net output #0: loss = 2.17682 (* 1 = 2.17682 loss)
I0406 10:08:41.693253  5226 sgd_solver.cpp:105] Iteration 20388, lr = 0.01
I0406 10:08:46.666807  5226 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_20400.caffemodel
I0406 10:08:49.926069  5226 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_20400.solverstate
I0406 10:08:54.345849  5226 solver.cpp:310] Iteration 20400, loss = 2.60572
I0406 10:08:54.345885  5226 solver.cpp:330] Iteration 20400, Testing net (#0)
I0406 10:08:54.345890  5226 net.cpp:676] Ignoring source layer train-data
I0406 10:08:55.404270  5283 data_layer.cpp:73] Restarting data prefetching from start.
I0406 10:08:58.876803  5226 solver.cpp:397]     Test net output #0: accuracy = 0.174632
I0406 10:08:58.876838  5226 solver.cpp:397]     Test net output #1: loss = 4.15245 (* 1 = 4.15245 loss)
I0406 10:08:58.876844  5226 solver.cpp:315] Optimization Done.
I0406 10:08:58.876847  5226 caffe.cpp:259] Optimization Done.