I0408 15:34:45.928066 27257 upgrade_proto.cpp:1082] Attempting to upgrade input file specified using deprecated 'solver_type' field (enum)': /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210408-153444-5890/solver.prototxt
I0408 15:34:45.928289 27257 upgrade_proto.cpp:1089] Successfully upgraded file specified using deprecated 'solver_type' field (enum) to 'type' field (string).
W0408 15:34:45.928299 27257 upgrade_proto.cpp:1091] Note that future Caffe releases will only support 'type' field (string) for a solver's type.
I0408 15:34:45.928391 27257 caffe.cpp:218] Using GPUs 2
I0408 15:34:45.951186 27257 caffe.cpp:223] GPU 2: GeForce GTX 1080 Ti
I0408 15:34:46.248340 27257 solver.cpp:44] Initializing solver from parameters:
test_iter: 51
test_interval: 102
base_lr: 0.01
display: 12
max_iter: 10200
lr_policy: "exp"
gamma: 0.99650931
momentum: 0.9
weight_decay: 0.0001
snapshot: 102
snapshot_prefix: "snapshot"
solver_mode: GPU
device_id: 2
net: "train_val.prototxt"
train_state {
level: 0
stage: ""
}
type: "SGD"
I0408 15:34:46.286235 27257 solver.cpp:87] Creating training net from net file: train_val.prototxt
I0408 15:34:46.286885 27257 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer val-data
I0408 15:34:46.286900 27257 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy
I0408 15:34:46.287050 27257 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-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto"
}
data_param {
source: "/mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/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"
}
I0408 15:34:46.287137 27257 layer_factory.hpp:77] Creating layer train-data
I0408 15:34:46.288661 27257 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/train_db
I0408 15:34:46.288874 27257 net.cpp:84] Creating Layer train-data
I0408 15:34:46.288885 27257 net.cpp:380] train-data -> data
I0408 15:34:46.288905 27257 net.cpp:380] train-data -> label
I0408 15:34:46.288918 27257 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto
I0408 15:34:46.293511 27257 data_layer.cpp:45] output data size: 128,3,227,227
I0408 15:34:46.414856 27257 net.cpp:122] Setting up train-data
I0408 15:34:46.414880 27257 net.cpp:129] Top shape: 128 3 227 227 (19787136)
I0408 15:34:46.414885 27257 net.cpp:129] Top shape: 128 (128)
I0408 15:34:46.414889 27257 net.cpp:137] Memory required for data: 79149056
I0408 15:34:46.414898 27257 layer_factory.hpp:77] Creating layer conv1
I0408 15:34:46.414921 27257 net.cpp:84] Creating Layer conv1
I0408 15:34:46.414927 27257 net.cpp:406] conv1 <- data
I0408 15:34:46.414939 27257 net.cpp:380] conv1 -> conv1
I0408 15:34:46.982251 27257 net.cpp:122] Setting up conv1
I0408 15:34:46.982273 27257 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0408 15:34:46.982277 27257 net.cpp:137] Memory required for data: 227833856
I0408 15:34:46.982296 27257 layer_factory.hpp:77] Creating layer relu1
I0408 15:34:46.982307 27257 net.cpp:84] Creating Layer relu1
I0408 15:34:46.982311 27257 net.cpp:406] relu1 <- conv1
I0408 15:34:46.982318 27257 net.cpp:367] relu1 -> conv1 (in-place)
I0408 15:34:46.982604 27257 net.cpp:122] Setting up relu1
I0408 15:34:46.982611 27257 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0408 15:34:46.982615 27257 net.cpp:137] Memory required for data: 376518656
I0408 15:34:46.982620 27257 layer_factory.hpp:77] Creating layer norm1
I0408 15:34:46.982628 27257 net.cpp:84] Creating Layer norm1
I0408 15:34:46.982631 27257 net.cpp:406] norm1 <- conv1
I0408 15:34:46.982656 27257 net.cpp:380] norm1 -> norm1
I0408 15:34:46.983094 27257 net.cpp:122] Setting up norm1
I0408 15:34:46.983104 27257 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0408 15:34:46.983108 27257 net.cpp:137] Memory required for data: 525203456
I0408 15:34:46.983111 27257 layer_factory.hpp:77] Creating layer pool1
I0408 15:34:46.983119 27257 net.cpp:84] Creating Layer pool1
I0408 15:34:46.983124 27257 net.cpp:406] pool1 <- norm1
I0408 15:34:46.983129 27257 net.cpp:380] pool1 -> pool1
I0408 15:34:46.983165 27257 net.cpp:122] Setting up pool1
I0408 15:34:46.983171 27257 net.cpp:129] Top shape: 128 96 27 27 (8957952)
I0408 15:34:46.983175 27257 net.cpp:137] Memory required for data: 561035264
I0408 15:34:46.983177 27257 layer_factory.hpp:77] Creating layer conv2
I0408 15:34:46.983188 27257 net.cpp:84] Creating Layer conv2
I0408 15:34:46.983192 27257 net.cpp:406] conv2 <- pool1
I0408 15:34:46.983197 27257 net.cpp:380] conv2 -> conv2
I0408 15:34:46.992434 27257 net.cpp:122] Setting up conv2
I0408 15:34:46.992450 27257 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0408 15:34:46.992453 27257 net.cpp:137] Memory required for data: 656586752
I0408 15:34:46.992463 27257 layer_factory.hpp:77] Creating layer relu2
I0408 15:34:46.992471 27257 net.cpp:84] Creating Layer relu2
I0408 15:34:46.992475 27257 net.cpp:406] relu2 <- conv2
I0408 15:34:46.992481 27257 net.cpp:367] relu2 -> conv2 (in-place)
I0408 15:34:46.992905 27257 net.cpp:122] Setting up relu2
I0408 15:34:46.992914 27257 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0408 15:34:46.992918 27257 net.cpp:137] Memory required for data: 752138240
I0408 15:34:46.992921 27257 layer_factory.hpp:77] Creating layer norm2
I0408 15:34:46.992928 27257 net.cpp:84] Creating Layer norm2
I0408 15:34:46.992933 27257 net.cpp:406] norm2 <- conv2
I0408 15:34:46.992938 27257 net.cpp:380] norm2 -> norm2
I0408 15:34:46.993227 27257 net.cpp:122] Setting up norm2
I0408 15:34:46.993235 27257 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0408 15:34:46.993238 27257 net.cpp:137] Memory required for data: 847689728
I0408 15:34:46.993242 27257 layer_factory.hpp:77] Creating layer pool2
I0408 15:34:46.993249 27257 net.cpp:84] Creating Layer pool2
I0408 15:34:46.993253 27257 net.cpp:406] pool2 <- norm2
I0408 15:34:46.993258 27257 net.cpp:380] pool2 -> pool2
I0408 15:34:46.993285 27257 net.cpp:122] Setting up pool2
I0408 15:34:46.993290 27257 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0408 15:34:46.993294 27257 net.cpp:137] Memory required for data: 869840896
I0408 15:34:46.993296 27257 layer_factory.hpp:77] Creating layer conv3
I0408 15:34:46.993305 27257 net.cpp:84] Creating Layer conv3
I0408 15:34:46.993309 27257 net.cpp:406] conv3 <- pool2
I0408 15:34:46.993314 27257 net.cpp:380] conv3 -> conv3
I0408 15:34:47.003067 27257 net.cpp:122] Setting up conv3
I0408 15:34:47.003080 27257 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0408 15:34:47.003084 27257 net.cpp:137] Memory required for data: 903067648
I0408 15:34:47.003094 27257 layer_factory.hpp:77] Creating layer relu3
I0408 15:34:47.003101 27257 net.cpp:84] Creating Layer relu3
I0408 15:34:47.003105 27257 net.cpp:406] relu3 <- conv3
I0408 15:34:47.003110 27257 net.cpp:367] relu3 -> conv3 (in-place)
I0408 15:34:47.003528 27257 net.cpp:122] Setting up relu3
I0408 15:34:47.003537 27257 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0408 15:34:47.003540 27257 net.cpp:137] Memory required for data: 936294400
I0408 15:34:47.003545 27257 layer_factory.hpp:77] Creating layer conv4
I0408 15:34:47.003553 27257 net.cpp:84] Creating Layer conv4
I0408 15:34:47.003557 27257 net.cpp:406] conv4 <- conv3
I0408 15:34:47.003563 27257 net.cpp:380] conv4 -> conv4
I0408 15:34:47.014341 27257 net.cpp:122] Setting up conv4
I0408 15:34:47.014358 27257 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0408 15:34:47.014361 27257 net.cpp:137] Memory required for data: 969521152
I0408 15:34:47.014369 27257 layer_factory.hpp:77] Creating layer relu4
I0408 15:34:47.014376 27257 net.cpp:84] Creating Layer relu4
I0408 15:34:47.014395 27257 net.cpp:406] relu4 <- conv4
I0408 15:34:47.014402 27257 net.cpp:367] relu4 -> conv4 (in-place)
I0408 15:34:47.014683 27257 net.cpp:122] Setting up relu4
I0408 15:34:47.014691 27257 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0408 15:34:47.014695 27257 net.cpp:137] Memory required for data: 1002747904
I0408 15:34:47.014699 27257 layer_factory.hpp:77] Creating layer conv5
I0408 15:34:47.014708 27257 net.cpp:84] Creating Layer conv5
I0408 15:34:47.014712 27257 net.cpp:406] conv5 <- conv4
I0408 15:34:47.014719 27257 net.cpp:380] conv5 -> conv5
I0408 15:34:47.022753 27257 net.cpp:122] Setting up conv5
I0408 15:34:47.022768 27257 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0408 15:34:47.022773 27257 net.cpp:137] Memory required for data: 1024899072
I0408 15:34:47.022783 27257 layer_factory.hpp:77] Creating layer relu5
I0408 15:34:47.022790 27257 net.cpp:84] Creating Layer relu5
I0408 15:34:47.022794 27257 net.cpp:406] relu5 <- conv5
I0408 15:34:47.022802 27257 net.cpp:367] relu5 -> conv5 (in-place)
I0408 15:34:47.023283 27257 net.cpp:122] Setting up relu5
I0408 15:34:47.023291 27257 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0408 15:34:47.023294 27257 net.cpp:137] Memory required for data: 1047050240
I0408 15:34:47.023298 27257 layer_factory.hpp:77] Creating layer pool5
I0408 15:34:47.023306 27257 net.cpp:84] Creating Layer pool5
I0408 15:34:47.023310 27257 net.cpp:406] pool5 <- conv5
I0408 15:34:47.023315 27257 net.cpp:380] pool5 -> pool5
I0408 15:34:47.023353 27257 net.cpp:122] Setting up pool5
I0408 15:34:47.023360 27257 net.cpp:129] Top shape: 128 256 6 6 (1179648)
I0408 15:34:47.023362 27257 net.cpp:137] Memory required for data: 1051768832
I0408 15:34:47.023366 27257 layer_factory.hpp:77] Creating layer fc6
I0408 15:34:47.023375 27257 net.cpp:84] Creating Layer fc6
I0408 15:34:47.023378 27257 net.cpp:406] fc6 <- pool5
I0408 15:34:47.023386 27257 net.cpp:380] fc6 -> fc6
I0408 15:34:47.376443 27257 net.cpp:122] Setting up fc6
I0408 15:34:47.376462 27257 net.cpp:129] Top shape: 128 4096 (524288)
I0408 15:34:47.376466 27257 net.cpp:137] Memory required for data: 1053865984
I0408 15:34:47.376475 27257 layer_factory.hpp:77] Creating layer relu6
I0408 15:34:47.376485 27257 net.cpp:84] Creating Layer relu6
I0408 15:34:47.376490 27257 net.cpp:406] relu6 <- fc6
I0408 15:34:47.376497 27257 net.cpp:367] relu6 -> fc6 (in-place)
I0408 15:34:47.377110 27257 net.cpp:122] Setting up relu6
I0408 15:34:47.377120 27257 net.cpp:129] Top shape: 128 4096 (524288)
I0408 15:34:47.377122 27257 net.cpp:137] Memory required for data: 1055963136
I0408 15:34:47.377126 27257 layer_factory.hpp:77] Creating layer drop6
I0408 15:34:47.377135 27257 net.cpp:84] Creating Layer drop6
I0408 15:34:47.377137 27257 net.cpp:406] drop6 <- fc6
I0408 15:34:47.377143 27257 net.cpp:367] drop6 -> fc6 (in-place)
I0408 15:34:47.377171 27257 net.cpp:122] Setting up drop6
I0408 15:34:47.377175 27257 net.cpp:129] Top shape: 128 4096 (524288)
I0408 15:34:47.377178 27257 net.cpp:137] Memory required for data: 1058060288
I0408 15:34:47.377182 27257 layer_factory.hpp:77] Creating layer fc7
I0408 15:34:47.377189 27257 net.cpp:84] Creating Layer fc7
I0408 15:34:47.377192 27257 net.cpp:406] fc7 <- fc6
I0408 15:34:47.377198 27257 net.cpp:380] fc7 -> fc7
I0408 15:34:47.535156 27257 net.cpp:122] Setting up fc7
I0408 15:34:47.535176 27257 net.cpp:129] Top shape: 128 4096 (524288)
I0408 15:34:47.535179 27257 net.cpp:137] Memory required for data: 1060157440
I0408 15:34:47.535189 27257 layer_factory.hpp:77] Creating layer relu7
I0408 15:34:47.535198 27257 net.cpp:84] Creating Layer relu7
I0408 15:34:47.535202 27257 net.cpp:406] relu7 <- fc7
I0408 15:34:47.535209 27257 net.cpp:367] relu7 -> fc7 (in-place)
I0408 15:34:47.535825 27257 net.cpp:122] Setting up relu7
I0408 15:34:47.535836 27257 net.cpp:129] Top shape: 128 4096 (524288)
I0408 15:34:47.535840 27257 net.cpp:137] Memory required for data: 1062254592
I0408 15:34:47.535843 27257 layer_factory.hpp:77] Creating layer drop7
I0408 15:34:47.535849 27257 net.cpp:84] Creating Layer drop7
I0408 15:34:47.535871 27257 net.cpp:406] drop7 <- fc7
I0408 15:34:47.535876 27257 net.cpp:367] drop7 -> fc7 (in-place)
I0408 15:34:47.535902 27257 net.cpp:122] Setting up drop7
I0408 15:34:47.535907 27257 net.cpp:129] Top shape: 128 4096 (524288)
I0408 15:34:47.535910 27257 net.cpp:137] Memory required for data: 1064351744
I0408 15:34:47.535913 27257 layer_factory.hpp:77] Creating layer fc8
I0408 15:34:47.535921 27257 net.cpp:84] Creating Layer fc8
I0408 15:34:47.535924 27257 net.cpp:406] fc8 <- fc7
I0408 15:34:47.535930 27257 net.cpp:380] fc8 -> fc8
I0408 15:34:47.543583 27257 net.cpp:122] Setting up fc8
I0408 15:34:47.543594 27257 net.cpp:129] Top shape: 128 196 (25088)
I0408 15:34:47.543597 27257 net.cpp:137] Memory required for data: 1064452096
I0408 15:34:47.543604 27257 layer_factory.hpp:77] Creating layer loss
I0408 15:34:47.543612 27257 net.cpp:84] Creating Layer loss
I0408 15:34:47.543617 27257 net.cpp:406] loss <- fc8
I0408 15:34:47.543622 27257 net.cpp:406] loss <- label
I0408 15:34:47.543627 27257 net.cpp:380] loss -> loss
I0408 15:34:47.543637 27257 layer_factory.hpp:77] Creating layer loss
I0408 15:34:47.544246 27257 net.cpp:122] Setting up loss
I0408 15:34:47.544255 27257 net.cpp:129] Top shape: (1)
I0408 15:34:47.544258 27257 net.cpp:132]     with loss weight 1
I0408 15:34:47.544275 27257 net.cpp:137] Memory required for data: 1064452100
I0408 15:34:47.544279 27257 net.cpp:198] loss needs backward computation.
I0408 15:34:47.544286 27257 net.cpp:198] fc8 needs backward computation.
I0408 15:34:47.544291 27257 net.cpp:198] drop7 needs backward computation.
I0408 15:34:47.544294 27257 net.cpp:198] relu7 needs backward computation.
I0408 15:34:47.544298 27257 net.cpp:198] fc7 needs backward computation.
I0408 15:34:47.544302 27257 net.cpp:198] drop6 needs backward computation.
I0408 15:34:47.544306 27257 net.cpp:198] relu6 needs backward computation.
I0408 15:34:47.544309 27257 net.cpp:198] fc6 needs backward computation.
I0408 15:34:47.544314 27257 net.cpp:198] pool5 needs backward computation.
I0408 15:34:47.544317 27257 net.cpp:198] relu5 needs backward computation.
I0408 15:34:47.544322 27257 net.cpp:198] conv5 needs backward computation.
I0408 15:34:47.544325 27257 net.cpp:198] relu4 needs backward computation.
I0408 15:34:47.544329 27257 net.cpp:198] conv4 needs backward computation.
I0408 15:34:47.544333 27257 net.cpp:198] relu3 needs backward computation.
I0408 15:34:47.544337 27257 net.cpp:198] conv3 needs backward computation.
I0408 15:34:47.544340 27257 net.cpp:198] pool2 needs backward computation.
I0408 15:34:47.544345 27257 net.cpp:198] norm2 needs backward computation.
I0408 15:34:47.544348 27257 net.cpp:198] relu2 needs backward computation.
I0408 15:34:47.544353 27257 net.cpp:198] conv2 needs backward computation.
I0408 15:34:47.544356 27257 net.cpp:198] pool1 needs backward computation.
I0408 15:34:47.544360 27257 net.cpp:198] norm1 needs backward computation.
I0408 15:34:47.544364 27257 net.cpp:198] relu1 needs backward computation.
I0408 15:34:47.544368 27257 net.cpp:198] conv1 needs backward computation.
I0408 15:34:47.544373 27257 net.cpp:200] train-data does not need backward computation.
I0408 15:34:47.544375 27257 net.cpp:242] This network produces output loss
I0408 15:34:47.544390 27257 net.cpp:255] Network initialization done.
I0408 15:34:47.544932 27257 solver.cpp:172] Creating test net (#0) specified by net file: train_val.prototxt
I0408 15:34:47.544962 27257 net.cpp:294] The NetState phase (1) differed from the phase (0) specified by a rule in layer train-data
I0408 15:34:47.545100 27257 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-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto"
}
data_param {
source: "/mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/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"
}
I0408 15:34:47.545197 27257 layer_factory.hpp:77] Creating layer val-data
I0408 15:34:47.547108 27257 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/val_db
I0408 15:34:47.547333 27257 net.cpp:84] Creating Layer val-data
I0408 15:34:47.547343 27257 net.cpp:380] val-data -> data
I0408 15:34:47.547353 27257 net.cpp:380] val-data -> label
I0408 15:34:47.547358 27257 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto
I0408 15:34:47.551267 27257 data_layer.cpp:45] output data size: 32,3,227,227
I0408 15:34:47.588660 27257 net.cpp:122] Setting up val-data
I0408 15:34:47.588678 27257 net.cpp:129] Top shape: 32 3 227 227 (4946784)
I0408 15:34:47.588683 27257 net.cpp:129] Top shape: 32 (32)
I0408 15:34:47.588686 27257 net.cpp:137] Memory required for data: 19787264
I0408 15:34:47.588692 27257 layer_factory.hpp:77] Creating layer label_val-data_1_split
I0408 15:34:47.588706 27257 net.cpp:84] Creating Layer label_val-data_1_split
I0408 15:34:47.588709 27257 net.cpp:406] label_val-data_1_split <- label
I0408 15:34:47.588717 27257 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_0
I0408 15:34:47.588726 27257 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_1
I0408 15:34:47.588771 27257 net.cpp:122] Setting up label_val-data_1_split
I0408 15:34:47.588778 27257 net.cpp:129] Top shape: 32 (32)
I0408 15:34:47.588781 27257 net.cpp:129] Top shape: 32 (32)
I0408 15:34:47.588784 27257 net.cpp:137] Memory required for data: 19787520
I0408 15:34:47.588788 27257 layer_factory.hpp:77] Creating layer conv1
I0408 15:34:47.588798 27257 net.cpp:84] Creating Layer conv1
I0408 15:34:47.588802 27257 net.cpp:406] conv1 <- data
I0408 15:34:47.588807 27257 net.cpp:380] conv1 -> conv1
I0408 15:34:47.590862 27257 net.cpp:122] Setting up conv1
I0408 15:34:47.590873 27257 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0408 15:34:47.590876 27257 net.cpp:137] Memory required for data: 56958720
I0408 15:34:47.590886 27257 layer_factory.hpp:77] Creating layer relu1
I0408 15:34:47.590893 27257 net.cpp:84] Creating Layer relu1
I0408 15:34:47.590896 27257 net.cpp:406] relu1 <- conv1
I0408 15:34:47.590901 27257 net.cpp:367] relu1 -> conv1 (in-place)
I0408 15:34:47.591192 27257 net.cpp:122] Setting up relu1
I0408 15:34:47.591200 27257 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0408 15:34:47.591203 27257 net.cpp:137] Memory required for data: 94129920
I0408 15:34:47.591207 27257 layer_factory.hpp:77] Creating layer norm1
I0408 15:34:47.591215 27257 net.cpp:84] Creating Layer norm1
I0408 15:34:47.591219 27257 net.cpp:406] norm1 <- conv1
I0408 15:34:47.591224 27257 net.cpp:380] norm1 -> norm1
I0408 15:34:47.591677 27257 net.cpp:122] Setting up norm1
I0408 15:34:47.591686 27257 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0408 15:34:47.591689 27257 net.cpp:137] Memory required for data: 131301120
I0408 15:34:47.591693 27257 layer_factory.hpp:77] Creating layer pool1
I0408 15:34:47.591699 27257 net.cpp:84] Creating Layer pool1
I0408 15:34:47.591703 27257 net.cpp:406] pool1 <- norm1
I0408 15:34:47.591708 27257 net.cpp:380] pool1 -> pool1
I0408 15:34:47.591737 27257 net.cpp:122] Setting up pool1
I0408 15:34:47.591742 27257 net.cpp:129] Top shape: 32 96 27 27 (2239488)
I0408 15:34:47.591744 27257 net.cpp:137] Memory required for data: 140259072
I0408 15:34:47.591747 27257 layer_factory.hpp:77] Creating layer conv2
I0408 15:34:47.591755 27257 net.cpp:84] Creating Layer conv2
I0408 15:34:47.591758 27257 net.cpp:406] conv2 <- pool1
I0408 15:34:47.591781 27257 net.cpp:380] conv2 -> conv2
I0408 15:34:47.600204 27257 net.cpp:122] Setting up conv2
I0408 15:34:47.600220 27257 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0408 15:34:47.600224 27257 net.cpp:137] Memory required for data: 164146944
I0408 15:34:47.600234 27257 layer_factory.hpp:77] Creating layer relu2
I0408 15:34:47.600244 27257 net.cpp:84] Creating Layer relu2
I0408 15:34:47.600247 27257 net.cpp:406] relu2 <- conv2
I0408 15:34:47.600252 27257 net.cpp:367] relu2 -> conv2 (in-place)
I0408 15:34:47.600749 27257 net.cpp:122] Setting up relu2
I0408 15:34:47.600757 27257 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0408 15:34:47.600761 27257 net.cpp:137] Memory required for data: 188034816
I0408 15:34:47.600764 27257 layer_factory.hpp:77] Creating layer norm2
I0408 15:34:47.600775 27257 net.cpp:84] Creating Layer norm2
I0408 15:34:47.600778 27257 net.cpp:406] norm2 <- conv2
I0408 15:34:47.600783 27257 net.cpp:380] norm2 -> norm2
I0408 15:34:47.601294 27257 net.cpp:122] Setting up norm2
I0408 15:34:47.601305 27257 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0408 15:34:47.601310 27257 net.cpp:137] Memory required for data: 211922688
I0408 15:34:47.601312 27257 layer_factory.hpp:77] Creating layer pool2
I0408 15:34:47.601320 27257 net.cpp:84] Creating Layer pool2
I0408 15:34:47.601323 27257 net.cpp:406] pool2 <- norm2
I0408 15:34:47.601328 27257 net.cpp:380] pool2 -> pool2
I0408 15:34:47.601361 27257 net.cpp:122] Setting up pool2
I0408 15:34:47.601366 27257 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0408 15:34:47.601369 27257 net.cpp:137] Memory required for data: 217460480
I0408 15:34:47.601372 27257 layer_factory.hpp:77] Creating layer conv3
I0408 15:34:47.601382 27257 net.cpp:84] Creating Layer conv3
I0408 15:34:47.601385 27257 net.cpp:406] conv3 <- pool2
I0408 15:34:47.601392 27257 net.cpp:380] conv3 -> conv3
I0408 15:34:47.612288 27257 net.cpp:122] Setting up conv3
I0408 15:34:47.612306 27257 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0408 15:34:47.612309 27257 net.cpp:137] Memory required for data: 225767168
I0408 15:34:47.612321 27257 layer_factory.hpp:77] Creating layer relu3
I0408 15:34:47.612330 27257 net.cpp:84] Creating Layer relu3
I0408 15:34:47.612334 27257 net.cpp:406] relu3 <- conv3
I0408 15:34:47.612340 27257 net.cpp:367] relu3 -> conv3 (in-place)
I0408 15:34:47.612848 27257 net.cpp:122] Setting up relu3
I0408 15:34:47.612856 27257 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0408 15:34:47.612860 27257 net.cpp:137] Memory required for data: 234073856
I0408 15:34:47.612864 27257 layer_factory.hpp:77] Creating layer conv4
I0408 15:34:47.612874 27257 net.cpp:84] Creating Layer conv4
I0408 15:34:47.612879 27257 net.cpp:406] conv4 <- conv3
I0408 15:34:47.612885 27257 net.cpp:380] conv4 -> conv4
I0408 15:34:47.622290 27257 net.cpp:122] Setting up conv4
I0408 15:34:47.622304 27257 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0408 15:34:47.622308 27257 net.cpp:137] Memory required for data: 242380544
I0408 15:34:47.622315 27257 layer_factory.hpp:77] Creating layer relu4
I0408 15:34:47.622323 27257 net.cpp:84] Creating Layer relu4
I0408 15:34:47.622326 27257 net.cpp:406] relu4 <- conv4
I0408 15:34:47.622334 27257 net.cpp:367] relu4 -> conv4 (in-place)
I0408 15:34:47.622675 27257 net.cpp:122] Setting up relu4
I0408 15:34:47.622685 27257 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0408 15:34:47.622689 27257 net.cpp:137] Memory required for data: 250687232
I0408 15:34:47.622692 27257 layer_factory.hpp:77] Creating layer conv5
I0408 15:34:47.622701 27257 net.cpp:84] Creating Layer conv5
I0408 15:34:47.622705 27257 net.cpp:406] conv5 <- conv4
I0408 15:34:47.622712 27257 net.cpp:380] conv5 -> conv5
I0408 15:34:47.631213 27257 net.cpp:122] Setting up conv5
I0408 15:34:47.631232 27257 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0408 15:34:47.631235 27257 net.cpp:137] Memory required for data: 256225024
I0408 15:34:47.631248 27257 layer_factory.hpp:77] Creating layer relu5
I0408 15:34:47.631256 27257 net.cpp:84] Creating Layer relu5
I0408 15:34:47.631261 27257 net.cpp:406] relu5 <- conv5
I0408 15:34:47.631285 27257 net.cpp:367] relu5 -> conv5 (in-place)
I0408 15:34:47.631774 27257 net.cpp:122] Setting up relu5
I0408 15:34:47.631784 27257 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0408 15:34:47.631788 27257 net.cpp:137] Memory required for data: 261762816
I0408 15:34:47.631793 27257 layer_factory.hpp:77] Creating layer pool5
I0408 15:34:47.631803 27257 net.cpp:84] Creating Layer pool5
I0408 15:34:47.631808 27257 net.cpp:406] pool5 <- conv5
I0408 15:34:47.631812 27257 net.cpp:380] pool5 -> pool5
I0408 15:34:47.631850 27257 net.cpp:122] Setting up pool5
I0408 15:34:47.631856 27257 net.cpp:129] Top shape: 32 256 6 6 (294912)
I0408 15:34:47.631860 27257 net.cpp:137] Memory required for data: 262942464
I0408 15:34:47.631865 27257 layer_factory.hpp:77] Creating layer fc6
I0408 15:34:47.631872 27257 net.cpp:84] Creating Layer fc6
I0408 15:34:47.631876 27257 net.cpp:406] fc6 <- pool5
I0408 15:34:47.631882 27257 net.cpp:380] fc6 -> fc6
I0408 15:34:47.986665 27257 net.cpp:122] Setting up fc6
I0408 15:34:47.986685 27257 net.cpp:129] Top shape: 32 4096 (131072)
I0408 15:34:47.986690 27257 net.cpp:137] Memory required for data: 263466752
I0408 15:34:47.986697 27257 layer_factory.hpp:77] Creating layer relu6
I0408 15:34:47.986707 27257 net.cpp:84] Creating Layer relu6
I0408 15:34:47.986711 27257 net.cpp:406] relu6 <- fc6
I0408 15:34:47.986717 27257 net.cpp:367] relu6 -> fc6 (in-place)
I0408 15:34:47.987557 27257 net.cpp:122] Setting up relu6
I0408 15:34:47.987566 27257 net.cpp:129] Top shape: 32 4096 (131072)
I0408 15:34:47.987569 27257 net.cpp:137] Memory required for data: 263991040
I0408 15:34:47.987573 27257 layer_factory.hpp:77] Creating layer drop6
I0408 15:34:47.987581 27257 net.cpp:84] Creating Layer drop6
I0408 15:34:47.987584 27257 net.cpp:406] drop6 <- fc6
I0408 15:34:47.987591 27257 net.cpp:367] drop6 -> fc6 (in-place)
I0408 15:34:47.987617 27257 net.cpp:122] Setting up drop6
I0408 15:34:47.987622 27257 net.cpp:129] Top shape: 32 4096 (131072)
I0408 15:34:47.987627 27257 net.cpp:137] Memory required for data: 264515328
I0408 15:34:47.987629 27257 layer_factory.hpp:77] Creating layer fc7
I0408 15:34:47.987637 27257 net.cpp:84] Creating Layer fc7
I0408 15:34:47.987639 27257 net.cpp:406] fc7 <- fc6
I0408 15:34:47.987645 27257 net.cpp:380] fc7 -> fc7
I0408 15:34:48.144430 27257 net.cpp:122] Setting up fc7
I0408 15:34:48.144451 27257 net.cpp:129] Top shape: 32 4096 (131072)
I0408 15:34:48.144455 27257 net.cpp:137] Memory required for data: 265039616
I0408 15:34:48.144464 27257 layer_factory.hpp:77] Creating layer relu7
I0408 15:34:48.144474 27257 net.cpp:84] Creating Layer relu7
I0408 15:34:48.144477 27257 net.cpp:406] relu7 <- fc7
I0408 15:34:48.144486 27257 net.cpp:367] relu7 -> fc7 (in-place)
I0408 15:34:48.144901 27257 net.cpp:122] Setting up relu7
I0408 15:34:48.144910 27257 net.cpp:129] Top shape: 32 4096 (131072)
I0408 15:34:48.144914 27257 net.cpp:137] Memory required for data: 265563904
I0408 15:34:48.144918 27257 layer_factory.hpp:77] Creating layer drop7
I0408 15:34:48.144924 27257 net.cpp:84] Creating Layer drop7
I0408 15:34:48.144928 27257 net.cpp:406] drop7 <- fc7
I0408 15:34:48.144933 27257 net.cpp:367] drop7 -> fc7 (in-place)
I0408 15:34:48.144956 27257 net.cpp:122] Setting up drop7
I0408 15:34:48.144961 27257 net.cpp:129] Top shape: 32 4096 (131072)
I0408 15:34:48.144964 27257 net.cpp:137] Memory required for data: 266088192
I0408 15:34:48.144968 27257 layer_factory.hpp:77] Creating layer fc8
I0408 15:34:48.144976 27257 net.cpp:84] Creating Layer fc8
I0408 15:34:48.144980 27257 net.cpp:406] fc8 <- fc7
I0408 15:34:48.144985 27257 net.cpp:380] fc8 -> fc8
I0408 15:34:48.152676 27257 net.cpp:122] Setting up fc8
I0408 15:34:48.152688 27257 net.cpp:129] Top shape: 32 196 (6272)
I0408 15:34:48.152691 27257 net.cpp:137] Memory required for data: 266113280
I0408 15:34:48.152698 27257 layer_factory.hpp:77] Creating layer fc8_fc8_0_split
I0408 15:34:48.152704 27257 net.cpp:84] Creating Layer fc8_fc8_0_split
I0408 15:34:48.152709 27257 net.cpp:406] fc8_fc8_0_split <- fc8
I0408 15:34:48.152735 27257 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_0
I0408 15:34:48.152745 27257 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_1
I0408 15:34:48.152776 27257 net.cpp:122] Setting up fc8_fc8_0_split
I0408 15:34:48.152781 27257 net.cpp:129] Top shape: 32 196 (6272)
I0408 15:34:48.152786 27257 net.cpp:129] Top shape: 32 196 (6272)
I0408 15:34:48.152788 27257 net.cpp:137] Memory required for data: 266163456
I0408 15:34:48.152792 27257 layer_factory.hpp:77] Creating layer accuracy
I0408 15:34:48.152799 27257 net.cpp:84] Creating Layer accuracy
I0408 15:34:48.152802 27257 net.cpp:406] accuracy <- fc8_fc8_0_split_0
I0408 15:34:48.152807 27257 net.cpp:406] accuracy <- label_val-data_1_split_0
I0408 15:34:48.152812 27257 net.cpp:380] accuracy -> accuracy
I0408 15:34:48.152820 27257 net.cpp:122] Setting up accuracy
I0408 15:34:48.152824 27257 net.cpp:129] Top shape: (1)
I0408 15:34:48.152827 27257 net.cpp:137] Memory required for data: 266163460
I0408 15:34:48.152830 27257 layer_factory.hpp:77] Creating layer loss
I0408 15:34:48.152837 27257 net.cpp:84] Creating Layer loss
I0408 15:34:48.152839 27257 net.cpp:406] loss <- fc8_fc8_0_split_1
I0408 15:34:48.152843 27257 net.cpp:406] loss <- label_val-data_1_split_1
I0408 15:34:48.152848 27257 net.cpp:380] loss -> loss
I0408 15:34:48.152854 27257 layer_factory.hpp:77] Creating layer loss
I0408 15:34:48.153445 27257 net.cpp:122] Setting up loss
I0408 15:34:48.153455 27257 net.cpp:129] Top shape: (1)
I0408 15:34:48.153458 27257 net.cpp:132]     with loss weight 1
I0408 15:34:48.153468 27257 net.cpp:137] Memory required for data: 266163464
I0408 15:34:48.153472 27257 net.cpp:198] loss needs backward computation.
I0408 15:34:48.153476 27257 net.cpp:200] accuracy does not need backward computation.
I0408 15:34:48.153481 27257 net.cpp:198] fc8_fc8_0_split needs backward computation.
I0408 15:34:48.153484 27257 net.cpp:198] fc8 needs backward computation.
I0408 15:34:48.153487 27257 net.cpp:198] drop7 needs backward computation.
I0408 15:34:48.153491 27257 net.cpp:198] relu7 needs backward computation.
I0408 15:34:48.153493 27257 net.cpp:198] fc7 needs backward computation.
I0408 15:34:48.153497 27257 net.cpp:198] drop6 needs backward computation.
I0408 15:34:48.153501 27257 net.cpp:198] relu6 needs backward computation.
I0408 15:34:48.153504 27257 net.cpp:198] fc6 needs backward computation.
I0408 15:34:48.153508 27257 net.cpp:198] pool5 needs backward computation.
I0408 15:34:48.153512 27257 net.cpp:198] relu5 needs backward computation.
I0408 15:34:48.153515 27257 net.cpp:198] conv5 needs backward computation.
I0408 15:34:48.153519 27257 net.cpp:198] relu4 needs backward computation.
I0408 15:34:48.153522 27257 net.cpp:198] conv4 needs backward computation.
I0408 15:34:48.153527 27257 net.cpp:198] relu3 needs backward computation.
I0408 15:34:48.153529 27257 net.cpp:198] conv3 needs backward computation.
I0408 15:34:48.153533 27257 net.cpp:198] pool2 needs backward computation.
I0408 15:34:48.153537 27257 net.cpp:198] norm2 needs backward computation.
I0408 15:34:48.153542 27257 net.cpp:198] relu2 needs backward computation.
I0408 15:34:48.153544 27257 net.cpp:198] conv2 needs backward computation.
I0408 15:34:48.153548 27257 net.cpp:198] pool1 needs backward computation.
I0408 15:34:48.153551 27257 net.cpp:198] norm1 needs backward computation.
I0408 15:34:48.153555 27257 net.cpp:198] relu1 needs backward computation.
I0408 15:34:48.153559 27257 net.cpp:198] conv1 needs backward computation.
I0408 15:34:48.153563 27257 net.cpp:200] label_val-data_1_split does not need backward computation.
I0408 15:34:48.153568 27257 net.cpp:200] val-data does not need backward computation.
I0408 15:34:48.153570 27257 net.cpp:242] This network produces output accuracy
I0408 15:34:48.153574 27257 net.cpp:242] This network produces output loss
I0408 15:34:48.153591 27257 net.cpp:255] Network initialization done.
I0408 15:34:48.153659 27257 solver.cpp:56] Solver scaffolding done.
I0408 15:34:48.154083 27257 caffe.cpp:248] Starting Optimization
I0408 15:34:48.154093 27257 solver.cpp:272] Solving
I0408 15:34:48.154462 27257 solver.cpp:273] Learning Rate Policy: exp
I0408 15:34:48.156306 27257 solver.cpp:330] Iteration 0, Testing net (#0)
I0408 15:34:48.156317 27257 net.cpp:676] Ignoring source layer train-data
I0408 15:34:48.245720 27257 blocking_queue.cpp:49] Waiting for data
I0408 15:34:52.539978 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:34:52.584707 27257 solver.cpp:397]     Test net output #0: accuracy = 0.00367647
I0408 15:34:52.584753 27257 solver.cpp:397]     Test net output #1: loss = 5.27862 (* 1 = 5.27862 loss)
I0408 15:34:52.683163 27257 solver.cpp:218] Iteration 0 (0 iter/s, 4.52849s/12 iters), loss = 5.27479
I0408 15:34:52.684712 27257 solver.cpp:237]     Train net output #0: loss = 5.27479 (* 1 = 5.27479 loss)
I0408 15:34:52.684732 27257 sgd_solver.cpp:105] Iteration 0, lr = 0.01
I0408 15:34:56.617851 27257 solver.cpp:218] Iteration 12 (3.05112 iter/s, 3.93299s/12 iters), loss = 5.27669
I0408 15:34:56.617883 27257 solver.cpp:237]     Train net output #0: loss = 5.27669 (* 1 = 5.27669 loss)
I0408 15:34:56.617890 27257 sgd_solver.cpp:105] Iteration 12, lr = 0.00958907
I0408 15:35:01.623180 27257 solver.cpp:218] Iteration 24 (2.39755 iter/s, 5.00511s/12 iters), loss = 5.2944
I0408 15:35:01.623208 27257 solver.cpp:237]     Train net output #0: loss = 5.2944 (* 1 = 5.2944 loss)
I0408 15:35:01.623215 27257 sgd_solver.cpp:105] Iteration 24, lr = 0.00919502
I0408 15:35:06.521008 27257 solver.cpp:218] Iteration 36 (2.45017 iter/s, 4.89761s/12 iters), loss = 5.30128
I0408 15:35:06.521061 27257 solver.cpp:237]     Train net output #0: loss = 5.30128 (* 1 = 5.30128 loss)
I0408 15:35:06.521075 27257 sgd_solver.cpp:105] Iteration 36, lr = 0.00881717
I0408 15:35:11.409262 27257 solver.cpp:218] Iteration 48 (2.45498 iter/s, 4.88802s/12 iters), loss = 5.32378
I0408 15:35:11.409304 27257 solver.cpp:237]     Train net output #0: loss = 5.32378 (* 1 = 5.32378 loss)
I0408 15:35:11.409312 27257 sgd_solver.cpp:105] Iteration 48, lr = 0.00845484
I0408 15:35:16.316352 27257 solver.cpp:218] Iteration 60 (2.44555 iter/s, 4.90686s/12 iters), loss = 5.2864
I0408 15:35:16.316498 27257 solver.cpp:237]     Train net output #0: loss = 5.2864 (* 1 = 5.2864 loss)
I0408 15:35:16.316509 27257 sgd_solver.cpp:105] Iteration 60, lr = 0.0081074
I0408 15:35:21.185464 27257 solver.cpp:218] Iteration 72 (2.46468 iter/s, 4.86878s/12 iters), loss = 5.29263
I0408 15:35:21.185515 27257 solver.cpp:237]     Train net output #0: loss = 5.29263 (* 1 = 5.29263 loss)
I0408 15:35:21.185528 27257 sgd_solver.cpp:105] Iteration 72, lr = 0.00777424
I0408 15:35:26.153786 27257 solver.cpp:218] Iteration 84 (2.41542 iter/s, 4.96808s/12 iters), loss = 5.29702
I0408 15:35:26.153836 27257 solver.cpp:237]     Train net output #0: loss = 5.29702 (* 1 = 5.29702 loss)
I0408 15:35:26.153846 27257 sgd_solver.cpp:105] Iteration 84, lr = 0.00745477
I0408 15:35:31.201620 27257 solver.cpp:218] Iteration 96 (2.37737 iter/s, 5.04759s/12 iters), loss = 5.31764
I0408 15:35:31.201665 27257 solver.cpp:237]     Train net output #0: loss = 5.31764 (* 1 = 5.31764 loss)
I0408 15:35:31.201676 27257 sgd_solver.cpp:105] Iteration 96, lr = 0.00714843
I0408 15:35:32.937534 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:35:33.290911 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_102.caffemodel
I0408 15:35:38.479984 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_102.solverstate
I0408 15:35:42.885617 27257 solver.cpp:330] Iteration 102, Testing net (#0)
I0408 15:35:42.885643 27257 net.cpp:676] Ignoring source layer train-data
I0408 15:35:47.317698 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:35:47.394867 27257 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 15:35:47.394915 27257 solver.cpp:397]     Test net output #1: loss = 5.28706 (* 1 = 5.28706 loss)
I0408 15:35:49.208010 27257 solver.cpp:218] Iteration 108 (0.666457 iter/s, 18.0057s/12 iters), loss = 5.30687
I0408 15:35:49.208053 27257 solver.cpp:237]     Train net output #0: loss = 5.30687 (* 1 = 5.30687 loss)
I0408 15:35:49.208062 27257 sgd_solver.cpp:105] Iteration 108, lr = 0.00685468
I0408 15:35:54.167109 27257 solver.cpp:218] Iteration 120 (2.41991 iter/s, 4.95886s/12 iters), loss = 5.27053
I0408 15:35:54.167152 27257 solver.cpp:237]     Train net output #0: loss = 5.27053 (* 1 = 5.27053 loss)
I0408 15:35:54.167163 27257 sgd_solver.cpp:105] Iteration 120, lr = 0.006573
I0408 15:35:59.172093 27257 solver.cpp:218] Iteration 132 (2.39773 iter/s, 5.00474s/12 iters), loss = 5.24709
I0408 15:35:59.172148 27257 solver.cpp:237]     Train net output #0: loss = 5.24709 (* 1 = 5.24709 loss)
I0408 15:35:59.172161 27257 sgd_solver.cpp:105] Iteration 132, lr = 0.00630289
I0408 15:36:04.186899 27257 solver.cpp:218] Iteration 144 (2.39303 iter/s, 5.01455s/12 iters), loss = 5.2908
I0408 15:36:04.186946 27257 solver.cpp:237]     Train net output #0: loss = 5.2908 (* 1 = 5.2908 loss)
I0408 15:36:04.186957 27257 sgd_solver.cpp:105] Iteration 144, lr = 0.00604388
I0408 15:36:09.186424 27257 solver.cpp:218] Iteration 156 (2.40035 iter/s, 4.99928s/12 iters), loss = 5.24528
I0408 15:36:09.186480 27257 solver.cpp:237]     Train net output #0: loss = 5.24528 (* 1 = 5.24528 loss)
I0408 15:36:09.186492 27257 sgd_solver.cpp:105] Iteration 156, lr = 0.00579552
I0408 15:36:14.190274 27257 solver.cpp:218] Iteration 168 (2.39828 iter/s, 5.00359s/12 iters), loss = 5.23398
I0408 15:36:14.190335 27257 solver.cpp:237]     Train net output #0: loss = 5.23398 (* 1 = 5.23398 loss)
I0408 15:36:14.190346 27257 sgd_solver.cpp:105] Iteration 168, lr = 0.00555736
I0408 15:36:19.064386 27257 solver.cpp:218] Iteration 180 (2.46211 iter/s, 4.87386s/12 iters), loss = 5.14622
I0408 15:36:19.064477 27257 solver.cpp:237]     Train net output #0: loss = 5.14622 (* 1 = 5.14622 loss)
I0408 15:36:19.064486 27257 sgd_solver.cpp:105] Iteration 180, lr = 0.00532899
I0408 15:36:23.998411 27257 solver.cpp:218] Iteration 192 (2.43223 iter/s, 4.93374s/12 iters), loss = 5.25523
I0408 15:36:23.998450 27257 solver.cpp:237]     Train net output #0: loss = 5.25523 (* 1 = 5.25523 loss)
I0408 15:36:23.998461 27257 sgd_solver.cpp:105] Iteration 192, lr = 0.00511001
I0408 15:36:27.835398 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:36:28.491403 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_204.caffemodel
I0408 15:36:35.611292 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_204.solverstate
I0408 15:36:40.837368 27257 solver.cpp:330] Iteration 204, Testing net (#0)
I0408 15:36:40.837391 27257 net.cpp:676] Ignoring source layer train-data
I0408 15:36:45.176982 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:36:45.299994 27257 solver.cpp:397]     Test net output #0: accuracy = 0.00735294
I0408 15:36:45.300032 27257 solver.cpp:397]     Test net output #1: loss = 5.19443 (* 1 = 5.19443 loss)
I0408 15:36:45.390074 27257 solver.cpp:218] Iteration 204 (0.560989 iter/s, 21.3908s/12 iters), loss = 5.1149
I0408 15:36:45.390115 27257 solver.cpp:237]     Train net output #0: loss = 5.1149 (* 1 = 5.1149 loss)
I0408 15:36:45.390127 27257 sgd_solver.cpp:105] Iteration 204, lr = 0.00490002
I0408 15:36:49.724212 27257 solver.cpp:218] Iteration 216 (2.76886 iter/s, 4.33392s/12 iters), loss = 5.15064
I0408 15:36:49.724341 27257 solver.cpp:237]     Train net output #0: loss = 5.15064 (* 1 = 5.15064 loss)
I0408 15:36:49.724354 27257 sgd_solver.cpp:105] Iteration 216, lr = 0.00469866
I0408 15:36:54.702558 27257 solver.cpp:218] Iteration 228 (2.4106 iter/s, 4.97802s/12 iters), loss = 5.18485
I0408 15:36:54.702616 27257 solver.cpp:237]     Train net output #0: loss = 5.18485 (* 1 = 5.18485 loss)
I0408 15:36:54.702630 27257 sgd_solver.cpp:105] Iteration 228, lr = 0.00450558
I0408 15:36:59.575560 27257 solver.cpp:218] Iteration 240 (2.46268 iter/s, 4.87275s/12 iters), loss = 5.23855
I0408 15:36:59.575614 27257 solver.cpp:237]     Train net output #0: loss = 5.23855 (* 1 = 5.23855 loss)
I0408 15:36:59.575626 27257 sgd_solver.cpp:105] Iteration 240, lr = 0.00432043
I0408 15:37:04.604301 27257 solver.cpp:218] Iteration 252 (2.38641 iter/s, 5.02848s/12 iters), loss = 5.13536
I0408 15:37:04.604357 27257 solver.cpp:237]     Train net output #0: loss = 5.13536 (* 1 = 5.13536 loss)
I0408 15:37:04.604369 27257 sgd_solver.cpp:105] Iteration 252, lr = 0.00414289
I0408 15:37:09.585570 27257 solver.cpp:218] Iteration 264 (2.40915 iter/s, 4.98101s/12 iters), loss = 5.25194
I0408 15:37:09.585619 27257 solver.cpp:237]     Train net output #0: loss = 5.25194 (* 1 = 5.25194 loss)
I0408 15:37:09.585633 27257 sgd_solver.cpp:105] Iteration 264, lr = 0.00397264
I0408 15:37:14.540634 27257 solver.cpp:218] Iteration 276 (2.42189 iter/s, 4.95481s/12 iters), loss = 5.17217
I0408 15:37:14.540680 27257 solver.cpp:237]     Train net output #0: loss = 5.17217 (* 1 = 5.17217 loss)
I0408 15:37:14.540689 27257 sgd_solver.cpp:105] Iteration 276, lr = 0.00380939
I0408 15:37:19.649441 27257 solver.cpp:218] Iteration 288 (2.349 iter/s, 5.10855s/12 iters), loss = 5.04788
I0408 15:37:19.649485 27257 solver.cpp:237]     Train net output #0: loss = 5.04788 (* 1 = 5.04788 loss)
I0408 15:37:19.649497 27257 sgd_solver.cpp:105] Iteration 288, lr = 0.00365285
I0408 15:37:24.652395 27257 solver.cpp:218] Iteration 300 (2.3987 iter/s, 5.00271s/12 iters), loss = 5.17544
I0408 15:37:24.702081 27257 solver.cpp:237]     Train net output #0: loss = 5.17544 (* 1 = 5.17544 loss)
I0408 15:37:24.702105 27257 sgd_solver.cpp:105] Iteration 300, lr = 0.00350275
I0408 15:37:25.812702 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:37:26.852787 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_306.caffemodel
I0408 15:37:32.353132 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_306.solverstate
I0408 15:37:35.594544 27257 solver.cpp:330] Iteration 306, Testing net (#0)
I0408 15:37:35.594570 27257 net.cpp:676] Ignoring source layer train-data
I0408 15:37:39.896721 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:37:40.054448 27257 solver.cpp:397]     Test net output #0: accuracy = 0.00735294
I0408 15:37:40.054489 27257 solver.cpp:397]     Test net output #1: loss = 5.15737 (* 1 = 5.15737 loss)
I0408 15:37:42.034953 27257 solver.cpp:218] Iteration 312 (0.692352 iter/s, 17.3322s/12 iters), loss = 5.12871
I0408 15:37:42.035002 27257 solver.cpp:237]     Train net output #0: loss = 5.12871 (* 1 = 5.12871 loss)
I0408 15:37:42.035012 27257 sgd_solver.cpp:105] Iteration 312, lr = 0.00335881
I0408 15:37:47.062868 27257 solver.cpp:218] Iteration 324 (2.3868 iter/s, 5.02766s/12 iters), loss = 5.18643
I0408 15:37:47.062916 27257 solver.cpp:237]     Train net output #0: loss = 5.18643 (* 1 = 5.18643 loss)
I0408 15:37:47.062927 27257 sgd_solver.cpp:105] Iteration 324, lr = 0.00322078
I0408 15:37:52.293121 27257 solver.cpp:218] Iteration 336 (2.29446 iter/s, 5.23s/12 iters), loss = 5.14557
I0408 15:37:52.293157 27257 solver.cpp:237]     Train net output #0: loss = 5.14557 (* 1 = 5.14557 loss)
I0408 15:37:52.293164 27257 sgd_solver.cpp:105] Iteration 336, lr = 0.00308843
I0408 15:37:57.282210 27257 solver.cpp:218] Iteration 348 (2.40537 iter/s, 4.98885s/12 iters), loss = 5.13983
I0408 15:37:57.282335 27257 solver.cpp:237]     Train net output #0: loss = 5.13983 (* 1 = 5.13983 loss)
I0408 15:37:57.282348 27257 sgd_solver.cpp:105] Iteration 348, lr = 0.00296152
I0408 15:38:02.307569 27257 solver.cpp:218] Iteration 360 (2.38804 iter/s, 5.02503s/12 iters), loss = 5.20784
I0408 15:38:02.307619 27257 solver.cpp:237]     Train net output #0: loss = 5.20784 (* 1 = 5.20784 loss)
I0408 15:38:02.307631 27257 sgd_solver.cpp:105] Iteration 360, lr = 0.00283982
I0408 15:38:07.297788 27257 solver.cpp:218] Iteration 372 (2.40483 iter/s, 4.98996s/12 iters), loss = 5.12476
I0408 15:38:07.297833 27257 solver.cpp:237]     Train net output #0: loss = 5.12476 (* 1 = 5.12476 loss)
I0408 15:38:07.297843 27257 sgd_solver.cpp:105] Iteration 372, lr = 0.00272312
I0408 15:38:12.326256 27257 solver.cpp:218] Iteration 384 (2.38653 iter/s, 5.02822s/12 iters), loss = 5.12628
I0408 15:38:12.326303 27257 solver.cpp:237]     Train net output #0: loss = 5.12628 (* 1 = 5.12628 loss)
I0408 15:38:12.326314 27257 sgd_solver.cpp:105] Iteration 384, lr = 0.00261122
I0408 15:38:17.322769 27257 solver.cpp:218] Iteration 396 (2.4018 iter/s, 4.99626s/12 iters), loss = 5.09293
I0408 15:38:17.322821 27257 solver.cpp:237]     Train net output #0: loss = 5.09293 (* 1 = 5.09293 loss)
I0408 15:38:17.322831 27257 sgd_solver.cpp:105] Iteration 396, lr = 0.00250391
I0408 15:38:20.401841 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:38:21.792338 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_408.caffemodel
I0408 15:38:26.915113 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_408.solverstate
I0408 15:38:30.751431 27257 solver.cpp:330] Iteration 408, Testing net (#0)
I0408 15:38:30.751546 27257 net.cpp:676] Ignoring source layer train-data
I0408 15:38:35.120728 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:38:35.324259 27257 solver.cpp:397]     Test net output #0: accuracy = 0.0110294
I0408 15:38:35.324295 27257 solver.cpp:397]     Test net output #1: loss = 5.14209 (* 1 = 5.14209 loss)
I0408 15:38:35.414556 27257 solver.cpp:218] Iteration 408 (0.663312 iter/s, 18.091s/12 iters), loss = 5.21104
I0408 15:38:35.414594 27257 solver.cpp:237]     Train net output #0: loss = 5.21104 (* 1 = 5.21104 loss)
I0408 15:38:35.414603 27257 sgd_solver.cpp:105] Iteration 408, lr = 0.00240102
I0408 15:38:39.584920 27257 solver.cpp:218] Iteration 420 (2.87759 iter/s, 4.17015s/12 iters), loss = 5.21388
I0408 15:38:39.584971 27257 solver.cpp:237]     Train net output #0: loss = 5.21388 (* 1 = 5.21388 loss)
I0408 15:38:39.584985 27257 sgd_solver.cpp:105] Iteration 420, lr = 0.00230235
I0408 15:38:44.583678 27257 solver.cpp:218] Iteration 432 (2.40072 iter/s, 4.99851s/12 iters), loss = 5.17066
I0408 15:38:44.583721 27257 solver.cpp:237]     Train net output #0: loss = 5.17066 (* 1 = 5.17066 loss)
I0408 15:38:44.583730 27257 sgd_solver.cpp:105] Iteration 432, lr = 0.00220774
I0408 15:38:49.617923 27257 solver.cpp:218] Iteration 444 (2.38379 iter/s, 5.034s/12 iters), loss = 5.08991
I0408 15:38:49.617976 27257 solver.cpp:237]     Train net output #0: loss = 5.08991 (* 1 = 5.08991 loss)
I0408 15:38:49.617986 27257 sgd_solver.cpp:105] Iteration 444, lr = 0.00211702
I0408 15:38:54.641191 27257 solver.cpp:218] Iteration 456 (2.38901 iter/s, 5.02301s/12 iters), loss = 5.14268
I0408 15:38:54.641250 27257 solver.cpp:237]     Train net output #0: loss = 5.14268 (* 1 = 5.14268 loss)
I0408 15:38:54.641263 27257 sgd_solver.cpp:105] Iteration 456, lr = 0.00203002
I0408 15:38:59.717983 27257 solver.cpp:218] Iteration 468 (2.36382 iter/s, 5.07652s/12 iters), loss = 5.12468
I0408 15:38:59.718027 27257 solver.cpp:237]     Train net output #0: loss = 5.12468 (* 1 = 5.12468 loss)
I0408 15:38:59.718039 27257 sgd_solver.cpp:105] Iteration 468, lr = 0.0019466
I0408 15:39:05.111238 27257 solver.cpp:218] Iteration 480 (2.22511 iter/s, 5.393s/12 iters), loss = 5.10545
I0408 15:39:05.111299 27257 solver.cpp:237]     Train net output #0: loss = 5.10545 (* 1 = 5.10545 loss)
I0408 15:39:05.111308 27257 sgd_solver.cpp:105] Iteration 480, lr = 0.00186661
I0408 15:39:10.227691 27257 solver.cpp:218] Iteration 492 (2.3455 iter/s, 5.11618s/12 iters), loss = 5.12577
I0408 15:39:10.227733 27257 solver.cpp:237]     Train net output #0: loss = 5.12577 (* 1 = 5.12577 loss)
I0408 15:39:10.227746 27257 sgd_solver.cpp:105] Iteration 492, lr = 0.00178991
I0408 15:39:15.227891 27257 solver.cpp:218] Iteration 504 (2.40002 iter/s, 4.99996s/12 iters), loss = 5.15966
I0408 15:39:15.227931 27257 solver.cpp:237]     Train net output #0: loss = 5.15966 (* 1 = 5.15966 loss)
I0408 15:39:15.227939 27257 sgd_solver.cpp:105] Iteration 504, lr = 0.00171635
I0408 15:39:15.490896 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:39:17.300048 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_510.caffemodel
I0408 15:39:23.087808 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_510.solverstate
I0408 15:39:25.719069 27257 solver.cpp:330] Iteration 510, Testing net (#0)
I0408 15:39:25.719094 27257 net.cpp:676] Ignoring source layer train-data
I0408 15:39:30.111968 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:39:30.352978 27257 solver.cpp:397]     Test net output #0: accuracy = 0.0128676
I0408 15:39:30.353029 27257 solver.cpp:397]     Test net output #1: loss = 5.1227 (* 1 = 5.1227 loss)
I0408 15:39:32.303934 27257 solver.cpp:218] Iteration 516 (0.702768 iter/s, 17.0753s/12 iters), loss = 5.10316
I0408 15:39:32.303987 27257 solver.cpp:237]     Train net output #0: loss = 5.10316 (* 1 = 5.10316 loss)
I0408 15:39:32.303998 27257 sgd_solver.cpp:105] Iteration 516, lr = 0.00164582
I0408 15:39:37.398000 27257 solver.cpp:218] Iteration 528 (2.3558 iter/s, 5.09381s/12 iters), loss = 5.15744
I0408 15:39:37.398144 27257 solver.cpp:237]     Train net output #0: loss = 5.15744 (* 1 = 5.15744 loss)
I0408 15:39:37.398157 27257 sgd_solver.cpp:105] Iteration 528, lr = 0.00157819
I0408 15:39:42.542023 27257 solver.cpp:218] Iteration 540 (2.33296 iter/s, 5.14367s/12 iters), loss = 5.0741
I0408 15:39:42.542071 27257 solver.cpp:237]     Train net output #0: loss = 5.0741 (* 1 = 5.0741 loss)
I0408 15:39:42.542083 27257 sgd_solver.cpp:105] Iteration 540, lr = 0.00151334
I0408 15:39:47.592451 27257 solver.cpp:218] Iteration 552 (2.37616 iter/s, 5.05016s/12 iters), loss = 5.09687
I0408 15:39:47.592515 27257 solver.cpp:237]     Train net output #0: loss = 5.09687 (* 1 = 5.09687 loss)
I0408 15:39:47.592528 27257 sgd_solver.cpp:105] Iteration 552, lr = 0.00145115
I0408 15:39:52.507609 27257 solver.cpp:218] Iteration 564 (2.44156 iter/s, 4.9149s/12 iters), loss = 5.09309
I0408 15:39:52.507654 27257 solver.cpp:237]     Train net output #0: loss = 5.09309 (* 1 = 5.09309 loss)
I0408 15:39:52.507665 27257 sgd_solver.cpp:105] Iteration 564, lr = 0.00139152
I0408 15:39:57.586175 27257 solver.cpp:218] Iteration 576 (2.36299 iter/s, 5.07831s/12 iters), loss = 5.09514
I0408 15:39:57.586227 27257 solver.cpp:237]     Train net output #0: loss = 5.09514 (* 1 = 5.09514 loss)
I0408 15:39:57.586249 27257 sgd_solver.cpp:105] Iteration 576, lr = 0.00133433
I0408 15:40:03.015106 27257 solver.cpp:218] Iteration 588 (2.21049 iter/s, 5.42866s/12 iters), loss = 5.04682
I0408 15:40:03.015151 27257 solver.cpp:237]     Train net output #0: loss = 5.04682 (* 1 = 5.04682 loss)
I0408 15:40:03.015163 27257 sgd_solver.cpp:105] Iteration 588, lr = 0.0012795
I0408 15:40:08.017616 27257 solver.cpp:218] Iteration 600 (2.39891 iter/s, 5.00226s/12 iters), loss = 5.08686
I0408 15:40:08.017735 27257 solver.cpp:237]     Train net output #0: loss = 5.08686 (* 1 = 5.08686 loss)
I0408 15:40:08.017747 27257 sgd_solver.cpp:105] Iteration 600, lr = 0.00122692
I0408 15:40:10.415321 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:40:12.530850 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_612.caffemodel
I0408 15:40:19.547387 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_612.solverstate
I0408 15:40:22.448627 27257 solver.cpp:330] Iteration 612, Testing net (#0)
I0408 15:40:22.448653 27257 net.cpp:676] Ignoring source layer train-data
I0408 15:40:26.576244 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:40:26.863744 27257 solver.cpp:397]     Test net output #0: accuracy = 0.0134804
I0408 15:40:26.863791 27257 solver.cpp:397]     Test net output #1: loss = 5.10126 (* 1 = 5.10126 loss)
I0408 15:40:26.953325 27257 solver.cpp:218] Iteration 612 (0.633752 iter/s, 18.9349s/12 iters), loss = 5.11089
I0408 15:40:26.953378 27257 solver.cpp:237]     Train net output #0: loss = 5.11089 (* 1 = 5.11089 loss)
I0408 15:40:26.953389 27257 sgd_solver.cpp:105] Iteration 612, lr = 0.0011765
I0408 15:40:31.296449 27257 solver.cpp:218] Iteration 624 (2.76314 iter/s, 4.34289s/12 iters), loss = 5.12859
I0408 15:40:31.296505 27257 solver.cpp:237]     Train net output #0: loss = 5.12859 (* 1 = 5.12859 loss)
I0408 15:40:31.296517 27257 sgd_solver.cpp:105] Iteration 624, lr = 0.00112816
I0408 15:40:36.256603 27257 solver.cpp:218] Iteration 636 (2.4194 iter/s, 4.9599s/12 iters), loss = 5.0111
I0408 15:40:36.256649 27257 solver.cpp:237]     Train net output #0: loss = 5.0111 (* 1 = 5.0111 loss)
I0408 15:40:36.256660 27257 sgd_solver.cpp:105] Iteration 636, lr = 0.0010818
I0408 15:40:41.291782 27257 solver.cpp:218] Iteration 648 (2.38335 iter/s, 5.03493s/12 iters), loss = 5.10484
I0408 15:40:41.291919 27257 solver.cpp:237]     Train net output #0: loss = 5.10484 (* 1 = 5.10484 loss)
I0408 15:40:41.291932 27257 sgd_solver.cpp:105] Iteration 648, lr = 0.00103734
I0408 15:40:46.383949 27257 solver.cpp:218] Iteration 660 (2.35672 iter/s, 5.09182s/12 iters), loss = 5.07304
I0408 15:40:46.383996 27257 solver.cpp:237]     Train net output #0: loss = 5.07304 (* 1 = 5.07304 loss)
I0408 15:40:46.384007 27257 sgd_solver.cpp:105] Iteration 660, lr = 0.000994716
I0408 15:40:51.671388 27257 solver.cpp:218] Iteration 672 (2.26964 iter/s, 5.28718s/12 iters), loss = 5.05731
I0408 15:40:51.671444 27257 solver.cpp:237]     Train net output #0: loss = 5.05731 (* 1 = 5.05731 loss)
I0408 15:40:51.671456 27257 sgd_solver.cpp:105] Iteration 672, lr = 0.00095384
I0408 15:40:56.828477 27257 solver.cpp:218] Iteration 684 (2.32701 iter/s, 5.15683s/12 iters), loss = 4.93511
I0408 15:40:56.828521 27257 solver.cpp:237]     Train net output #0: loss = 4.93511 (* 1 = 4.93511 loss)
I0408 15:40:56.828533 27257 sgd_solver.cpp:105] Iteration 684, lr = 0.000914643
I0408 15:40:57.621930 27257 blocking_queue.cpp:49] Waiting for data
I0408 15:41:01.842818 27257 solver.cpp:218] Iteration 696 (2.39325 iter/s, 5.01409s/12 iters), loss = 5.09474
I0408 15:41:01.842861 27257 solver.cpp:237]     Train net output #0: loss = 5.09474 (* 1 = 5.09474 loss)
I0408 15:41:01.842873 27257 sgd_solver.cpp:105] Iteration 696, lr = 0.000877057
I0408 15:41:06.468576 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:41:06.843959 27257 solver.cpp:218] Iteration 708 (2.39957 iter/s, 5.0009s/12 iters), loss = 5.14442
I0408 15:41:06.843998 27257 solver.cpp:237]     Train net output #0: loss = 5.14442 (* 1 = 5.14442 loss)
I0408 15:41:06.844007 27257 sgd_solver.cpp:105] Iteration 708, lr = 0.000841016
I0408 15:41:08.880686 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_714.caffemodel
I0408 15:41:12.741170 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_714.solverstate
I0408 15:41:15.136365 27257 solver.cpp:330] Iteration 714, Testing net (#0)
I0408 15:41:15.136386 27257 net.cpp:676] Ignoring source layer train-data
I0408 15:41:19.258025 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:41:19.577401 27257 solver.cpp:397]     Test net output #0: accuracy = 0.0183824
I0408 15:41:19.577447 27257 solver.cpp:397]     Test net output #1: loss = 5.07425 (* 1 = 5.07425 loss)
I0408 15:41:21.434100 27257 solver.cpp:218] Iteration 720 (0.822507 iter/s, 14.5895s/12 iters), loss = 5.10858
I0408 15:41:21.434147 27257 solver.cpp:237]     Train net output #0: loss = 5.10858 (* 1 = 5.10858 loss)
I0408 15:41:21.434157 27257 sgd_solver.cpp:105] Iteration 720, lr = 0.000806456
I0408 15:41:26.415542 27257 solver.cpp:218] Iteration 732 (2.40906 iter/s, 4.98119s/12 iters), loss = 5.04345
I0408 15:41:26.415588 27257 solver.cpp:237]     Train net output #0: loss = 5.04345 (* 1 = 5.04345 loss)
I0408 15:41:26.415601 27257 sgd_solver.cpp:105] Iteration 732, lr = 0.000773316
I0408 15:41:31.367712 27257 solver.cpp:218] Iteration 744 (2.4233 iter/s, 4.95192s/12 iters), loss = 5.03532
I0408 15:41:31.367759 27257 solver.cpp:237]     Train net output #0: loss = 5.03532 (* 1 = 5.03532 loss)
I0408 15:41:31.367771 27257 sgd_solver.cpp:105] Iteration 744, lr = 0.000741538
I0408 15:41:36.238631 27257 solver.cpp:218] Iteration 756 (2.46373 iter/s, 4.87067s/12 iters), loss = 5.04359
I0408 15:41:36.238687 27257 solver.cpp:237]     Train net output #0: loss = 5.04359 (* 1 = 5.04359 loss)
I0408 15:41:36.238699 27257 sgd_solver.cpp:105] Iteration 756, lr = 0.000711066
I0408 15:41:41.378744 27257 solver.cpp:218] Iteration 768 (2.3347 iter/s, 5.13985s/12 iters), loss = 5.11277
I0408 15:41:41.378789 27257 solver.cpp:237]     Train net output #0: loss = 5.11277 (* 1 = 5.11277 loss)
I0408 15:41:41.378801 27257 sgd_solver.cpp:105] Iteration 768, lr = 0.000681846
I0408 15:41:46.397085 27257 solver.cpp:218] Iteration 780 (2.39135 iter/s, 5.01809s/12 iters), loss = 5.0782
I0408 15:41:46.397269 27257 solver.cpp:237]     Train net output #0: loss = 5.0782 (* 1 = 5.0782 loss)
I0408 15:41:46.397284 27257 sgd_solver.cpp:105] Iteration 780, lr = 0.000653826
I0408 15:41:51.359091 27257 solver.cpp:218] Iteration 792 (2.41856 iter/s, 4.96163s/12 iters), loss = 4.95848
I0408 15:41:51.359145 27257 solver.cpp:237]     Train net output #0: loss = 4.95848 (* 1 = 4.95848 loss)
I0408 15:41:51.359159 27257 sgd_solver.cpp:105] Iteration 792, lr = 0.000626958
I0408 15:41:56.523984 27257 solver.cpp:218] Iteration 804 (2.3235 iter/s, 5.16463s/12 iters), loss = 5.02789
I0408 15:41:56.524037 27257 solver.cpp:237]     Train net output #0: loss = 5.02789 (* 1 = 5.02789 loss)
I0408 15:41:56.524049 27257 sgd_solver.cpp:105] Iteration 804, lr = 0.000601195
I0408 15:41:58.270468 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:42:01.052358 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_816.caffemodel
I0408 15:42:08.459982 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_816.solverstate
I0408 15:42:11.187871 27257 solver.cpp:330] Iteration 816, Testing net (#0)
I0408 15:42:11.187897 27257 net.cpp:676] Ignoring source layer train-data
I0408 15:42:15.277989 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:42:15.633323 27257 solver.cpp:397]     Test net output #0: accuracy = 0.0196078
I0408 15:42:15.633355 27257 solver.cpp:397]     Test net output #1: loss = 5.04714 (* 1 = 5.04714 loss)
I0408 15:42:15.724164 27257 solver.cpp:218] Iteration 816 (0.62502 iter/s, 19.1994s/12 iters), loss = 5.0193
I0408 15:42:15.724217 27257 solver.cpp:237]     Train net output #0: loss = 5.0193 (* 1 = 5.0193 loss)
I0408 15:42:15.724231 27257 sgd_solver.cpp:105] Iteration 816, lr = 0.00057649
I0408 15:42:19.876171 27257 solver.cpp:218] Iteration 828 (2.89033 iter/s, 4.15178s/12 iters), loss = 5.15866
I0408 15:42:19.876298 27257 solver.cpp:237]     Train net output #0: loss = 5.15866 (* 1 = 5.15866 loss)
I0408 15:42:19.876312 27257 sgd_solver.cpp:105] Iteration 828, lr = 0.0005528
I0408 15:42:24.855996 27257 solver.cpp:218] Iteration 840 (2.40988 iter/s, 4.97949s/12 iters), loss = 4.9917
I0408 15:42:24.856051 27257 solver.cpp:237]     Train net output #0: loss = 4.9917 (* 1 = 4.9917 loss)
I0408 15:42:24.856065 27257 sgd_solver.cpp:105] Iteration 840, lr = 0.000530083
I0408 15:42:30.149317 27257 solver.cpp:218] Iteration 852 (2.26712 iter/s, 5.29305s/12 iters), loss = 5
I0408 15:42:30.149353 27257 solver.cpp:237]     Train net output #0: loss = 5 (* 1 = 5 loss)
I0408 15:42:30.149360 27257 sgd_solver.cpp:105] Iteration 852, lr = 0.0005083
I0408 15:42:35.255861 27257 solver.cpp:218] Iteration 864 (2.35004 iter/s, 5.10629s/12 iters), loss = 5.04301
I0408 15:42:35.255913 27257 solver.cpp:237]     Train net output #0: loss = 5.04301 (* 1 = 5.04301 loss)
I0408 15:42:35.255925 27257 sgd_solver.cpp:105] Iteration 864, lr = 0.000487413
I0408 15:42:40.321818 27257 solver.cpp:218] Iteration 876 (2.36887 iter/s, 5.0657s/12 iters), loss = 5.0643
I0408 15:42:40.321866 27257 solver.cpp:237]     Train net output #0: loss = 5.0643 (* 1 = 5.0643 loss)
I0408 15:42:40.321877 27257 sgd_solver.cpp:105] Iteration 876, lr = 0.000467383
I0408 15:42:45.352877 27257 solver.cpp:218] Iteration 888 (2.3853 iter/s, 5.03081s/12 iters), loss = 4.90649
I0408 15:42:45.352931 27257 solver.cpp:237]     Train net output #0: loss = 4.90649 (* 1 = 4.90649 loss)
I0408 15:42:45.352941 27257 sgd_solver.cpp:105] Iteration 888, lr = 0.000448177
I0408 15:42:50.424113 27257 solver.cpp:218] Iteration 900 (2.36641 iter/s, 5.07098s/12 iters), loss = 5.06128
I0408 15:42:50.424244 27257 solver.cpp:237]     Train net output #0: loss = 5.06128 (* 1 = 5.06128 loss)
I0408 15:42:50.424257 27257 sgd_solver.cpp:105] Iteration 900, lr = 0.00042976
I0408 15:42:54.316752 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:42:55.439412 27257 solver.cpp:218] Iteration 912 (2.39284 iter/s, 5.01496s/12 iters), loss = 4.90481
I0408 15:42:55.439468 27257 solver.cpp:237]     Train net output #0: loss = 4.90481 (* 1 = 4.90481 loss)
I0408 15:42:55.439481 27257 sgd_solver.cpp:105] Iteration 912, lr = 0.0004121
I0408 15:42:57.539474 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_918.caffemodel
I0408 15:43:05.104136 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_918.solverstate
I0408 15:43:08.126418 27257 solver.cpp:330] Iteration 918, Testing net (#0)
I0408 15:43:08.126441 27257 net.cpp:676] Ignoring source layer train-data
I0408 15:43:12.191604 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:43:12.592460 27257 solver.cpp:397]     Test net output #0: accuracy = 0.0214461
I0408 15:43:12.592506 27257 solver.cpp:397]     Test net output #1: loss = 5.02372 (* 1 = 5.02372 loss)
I0408 15:43:14.509852 27257 solver.cpp:218] Iteration 924 (0.629272 iter/s, 19.0696s/12 iters), loss = 5.06017
I0408 15:43:14.509907 27257 solver.cpp:237]     Train net output #0: loss = 5.06017 (* 1 = 5.06017 loss)
I0408 15:43:14.509919 27257 sgd_solver.cpp:105] Iteration 924, lr = 0.000395165
I0408 15:43:19.511274 27257 solver.cpp:218] Iteration 936 (2.39944 iter/s, 5.00116s/12 iters), loss = 5.06432
I0408 15:43:19.511325 27257 solver.cpp:237]     Train net output #0: loss = 5.06432 (* 1 = 5.06432 loss)
I0408 15:43:19.511337 27257 sgd_solver.cpp:105] Iteration 936, lr = 0.000378926
I0408 15:43:24.505925 27257 solver.cpp:218] Iteration 948 (2.40269 iter/s, 4.9944s/12 iters), loss = 4.99497
I0408 15:43:24.506013 27257 solver.cpp:237]     Train net output #0: loss = 4.99497 (* 1 = 4.99497 loss)
I0408 15:43:24.506023 27257 sgd_solver.cpp:105] Iteration 948, lr = 0.000363355
I0408 15:43:29.506567 27257 solver.cpp:218] Iteration 960 (2.39983 iter/s, 5.00035s/12 iters), loss = 4.97092
I0408 15:43:29.506613 27257 solver.cpp:237]     Train net output #0: loss = 4.97092 (* 1 = 4.97092 loss)
I0408 15:43:29.506623 27257 sgd_solver.cpp:105] Iteration 960, lr = 0.000348424
I0408 15:43:34.558101 27257 solver.cpp:218] Iteration 972 (2.37563 iter/s, 5.05128s/12 iters), loss = 4.97357
I0408 15:43:34.558156 27257 solver.cpp:237]     Train net output #0: loss = 4.97357 (* 1 = 4.97357 loss)
I0408 15:43:34.558169 27257 sgd_solver.cpp:105] Iteration 972, lr = 0.000334106
I0408 15:43:39.559315 27257 solver.cpp:218] Iteration 984 (2.39954 iter/s, 5.00096s/12 iters), loss = 5.04537
I0408 15:43:39.559353 27257 solver.cpp:237]     Train net output #0: loss = 5.04537 (* 1 = 5.04537 loss)
I0408 15:43:39.559361 27257 sgd_solver.cpp:105] Iteration 984, lr = 0.000320376
I0408 15:43:44.542299 27257 solver.cpp:218] Iteration 996 (2.40831 iter/s, 4.98274s/12 iters), loss = 4.88811
I0408 15:43:44.542344 27257 solver.cpp:237]     Train net output #0: loss = 4.88811 (* 1 = 4.88811 loss)
I0408 15:43:44.542356 27257 sgd_solver.cpp:105] Iteration 996, lr = 0.000307211
I0408 15:43:49.614439 27257 solver.cpp:218] Iteration 1008 (2.36598 iter/s, 5.07189s/12 iters), loss = 5.05448
I0408 15:43:49.614488 27257 solver.cpp:237]     Train net output #0: loss = 5.05448 (* 1 = 5.05448 loss)
I0408 15:43:49.614501 27257 sgd_solver.cpp:105] Iteration 1008, lr = 0.000294587
I0408 15:43:50.650677 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:43:54.138914 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1020.caffemodel
I0408 15:44:03.687189 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1020.solverstate
I0408 15:44:07.042047 27257 solver.cpp:330] Iteration 1020, Testing net (#0)
I0408 15:44:07.042075 27257 net.cpp:676] Ignoring source layer train-data
I0408 15:44:11.073537 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:44:11.505887 27257 solver.cpp:397]     Test net output #0: accuracy = 0.0226716
I0408 15:44:11.505936 27257 solver.cpp:397]     Test net output #1: loss = 5.00609 (* 1 = 5.00609 loss)
I0408 15:44:11.596338 27257 solver.cpp:218] Iteration 1020 (0.545926 iter/s, 21.981s/12 iters), loss = 4.94042
I0408 15:44:11.596388 27257 solver.cpp:237]     Train net output #0: loss = 4.94042 (* 1 = 4.94042 loss)
I0408 15:44:11.596400 27257 sgd_solver.cpp:105] Iteration 1020, lr = 0.000282481
I0408 15:44:15.680364 27257 solver.cpp:218] Iteration 1032 (2.93843 iter/s, 4.08381s/12 iters), loss = 5.00595
I0408 15:44:15.680410 27257 solver.cpp:237]     Train net output #0: loss = 5.00595 (* 1 = 5.00595 loss)
I0408 15:44:15.680423 27257 sgd_solver.cpp:105] Iteration 1032, lr = 0.000270873
I0408 15:44:20.651906 27257 solver.cpp:218] Iteration 1044 (2.41386 iter/s, 4.97129s/12 iters), loss = 5.00492
I0408 15:44:20.651960 27257 solver.cpp:237]     Train net output #0: loss = 5.00492 (* 1 = 5.00492 loss)
I0408 15:44:20.651973 27257 sgd_solver.cpp:105] Iteration 1044, lr = 0.000259742
I0408 15:44:25.560799 27257 solver.cpp:218] Iteration 1056 (2.44467 iter/s, 4.90864s/12 iters), loss = 5.08698
I0408 15:44:25.560853 27257 solver.cpp:237]     Train net output #0: loss = 5.08698 (* 1 = 5.08698 loss)
I0408 15:44:25.560863 27257 sgd_solver.cpp:105] Iteration 1056, lr = 0.000249068
I0408 15:44:30.555474 27257 solver.cpp:218] Iteration 1068 (2.40268 iter/s, 4.99442s/12 iters), loss = 5.03233
I0408 15:44:30.555522 27257 solver.cpp:237]     Train net output #0: loss = 5.03233 (* 1 = 5.03233 loss)
I0408 15:44:30.555532 27257 sgd_solver.cpp:105] Iteration 1068, lr = 0.000238833
I0408 15:44:35.531553 27257 solver.cpp:218] Iteration 1080 (2.41166 iter/s, 4.97583s/12 iters), loss = 4.88281
I0408 15:44:35.531682 27257 solver.cpp:237]     Train net output #0: loss = 4.88281 (* 1 = 4.88281 loss)
I0408 15:44:35.531695 27257 sgd_solver.cpp:105] Iteration 1080, lr = 0.000229019
I0408 15:44:40.555238 27257 solver.cpp:218] Iteration 1092 (2.38884 iter/s, 5.02335s/12 iters), loss = 4.97214
I0408 15:44:40.555290 27257 solver.cpp:237]     Train net output #0: loss = 4.97214 (* 1 = 4.97214 loss)
I0408 15:44:40.555302 27257 sgd_solver.cpp:105] Iteration 1092, lr = 0.000219608
I0408 15:44:45.547915 27257 solver.cpp:218] Iteration 1104 (2.40364 iter/s, 4.99242s/12 iters), loss = 4.96411
I0408 15:44:45.547971 27257 solver.cpp:237]     Train net output #0: loss = 4.96411 (* 1 = 4.96411 loss)
I0408 15:44:45.547984 27257 sgd_solver.cpp:105] Iteration 1104, lr = 0.000210583
I0408 15:44:48.683210 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:44:50.529129 27257 solver.cpp:218] Iteration 1116 (2.40918 iter/s, 4.98096s/12 iters), loss = 5.03045
I0408 15:44:50.529172 27257 solver.cpp:237]     Train net output #0: loss = 5.03045 (* 1 = 5.03045 loss)
I0408 15:44:50.529181 27257 sgd_solver.cpp:105] Iteration 1116, lr = 0.00020193
I0408 15:44:52.549170 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1122.caffemodel
I0408 15:44:58.890311 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1122.solverstate
I0408 15:45:02.582398 27257 solver.cpp:330] Iteration 1122, Testing net (#0)
I0408 15:45:02.582437 27257 net.cpp:676] Ignoring source layer train-data
I0408 15:45:06.630889 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:45:07.107926 27257 solver.cpp:397]     Test net output #0: accuracy = 0.0245098
I0408 15:45:07.107975 27257 solver.cpp:397]     Test net output #1: loss = 4.99577 (* 1 = 4.99577 loss)
I0408 15:45:09.098763 27257 solver.cpp:218] Iteration 1128 (0.646243 iter/s, 18.5689s/12 iters), loss = 5.10887
I0408 15:45:09.098803 27257 solver.cpp:237]     Train net output #0: loss = 5.10887 (* 1 = 5.10887 loss)
I0408 15:45:09.098811 27257 sgd_solver.cpp:105] Iteration 1128, lr = 0.000193632
I0408 15:45:14.223453 27257 solver.cpp:218] Iteration 1140 (2.34172 iter/s, 5.12444s/12 iters), loss = 5.06474
I0408 15:45:14.223496 27257 solver.cpp:237]     Train net output #0: loss = 5.06474 (* 1 = 5.06474 loss)
I0408 15:45:14.223505 27257 sgd_solver.cpp:105] Iteration 1140, lr = 0.000185675
I0408 15:45:19.193370 27257 solver.cpp:218] Iteration 1152 (2.41465 iter/s, 4.96967s/12 iters), loss = 4.94893
I0408 15:45:19.193430 27257 solver.cpp:237]     Train net output #0: loss = 4.94893 (* 1 = 4.94893 loss)
I0408 15:45:19.193442 27257 sgd_solver.cpp:105] Iteration 1152, lr = 0.000178045
I0408 15:45:24.234506 27257 solver.cpp:218] Iteration 1164 (2.38054 iter/s, 5.04087s/12 iters), loss = 4.95839
I0408 15:45:24.234553 27257 solver.cpp:237]     Train net output #0: loss = 4.95839 (* 1 = 4.95839 loss)
I0408 15:45:24.234563 27257 sgd_solver.cpp:105] Iteration 1164, lr = 0.000170728
I0408 15:45:29.161720 27257 solver.cpp:218] Iteration 1176 (2.43558 iter/s, 4.92696s/12 iters), loss = 4.93946
I0408 15:45:29.161777 27257 solver.cpp:237]     Train net output #0: loss = 4.93946 (* 1 = 4.93946 loss)
I0408 15:45:29.161789 27257 sgd_solver.cpp:105] Iteration 1176, lr = 0.000163712
I0408 15:45:34.215548 27257 solver.cpp:218] Iteration 1188 (2.37456 iter/s, 5.05357s/12 iters), loss = 4.9869
I0408 15:45:34.215592 27257 solver.cpp:237]     Train net output #0: loss = 4.9869 (* 1 = 4.9869 loss)
I0408 15:45:34.215601 27257 sgd_solver.cpp:105] Iteration 1188, lr = 0.000156985
I0408 15:45:39.241067 27257 solver.cpp:218] Iteration 1200 (2.38793 iter/s, 5.02527s/12 iters), loss = 5.03307
I0408 15:45:39.241215 27257 solver.cpp:237]     Train net output #0: loss = 5.03307 (* 1 = 5.03307 loss)
I0408 15:45:39.241230 27257 sgd_solver.cpp:105] Iteration 1200, lr = 0.000150534
I0408 15:45:44.386229 27257 solver.cpp:218] Iteration 1212 (2.33245 iter/s, 5.1448s/12 iters), loss = 5.0391
I0408 15:45:44.386299 27257 solver.cpp:237]     Train net output #0: loss = 5.0391 (* 1 = 5.0391 loss)
I0408 15:45:44.386324 27257 sgd_solver.cpp:105] Iteration 1212, lr = 0.000144348
I0408 15:45:44.678611 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:45:48.914099 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1224.caffemodel
I0408 15:45:55.602048 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1224.solverstate
I0408 15:45:59.277216 27257 solver.cpp:330] Iteration 1224, Testing net (#0)
I0408 15:45:59.277243 27257 net.cpp:676] Ignoring source layer train-data
I0408 15:46:03.371492 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:46:03.883476 27257 solver.cpp:397]     Test net output #0: accuracy = 0.0269608
I0408 15:46:03.883522 27257 solver.cpp:397]     Test net output #1: loss = 4.98536 (* 1 = 4.98536 loss)
I0408 15:46:03.973896 27257 solver.cpp:218] Iteration 1224 (0.612656 iter/s, 19.5868s/12 iters), loss = 4.93302
I0408 15:46:03.973944 27257 solver.cpp:237]     Train net output #0: loss = 4.93302 (* 1 = 4.93302 loss)
I0408 15:46:03.973973 27257 sgd_solver.cpp:105] Iteration 1224, lr = 0.000138416
I0408 15:46:08.463707 27257 solver.cpp:218] Iteration 1236 (2.67286 iter/s, 4.48958s/12 iters), loss = 5.04266
I0408 15:46:08.463753 27257 solver.cpp:237]     Train net output #0: loss = 5.04266 (* 1 = 5.04266 loss)
I0408 15:46:08.463765 27257 sgd_solver.cpp:105] Iteration 1236, lr = 0.000132728
I0408 15:46:13.834359 27257 solver.cpp:218] Iteration 1248 (2.23448 iter/s, 5.37039s/12 iters), loss = 4.94496
I0408 15:46:13.834470 27257 solver.cpp:237]     Train net output #0: loss = 4.94496 (* 1 = 4.94496 loss)
I0408 15:46:13.834483 27257 sgd_solver.cpp:105] Iteration 1248, lr = 0.000127274
I0408 15:46:18.876313 27257 solver.cpp:218] Iteration 1260 (2.38018 iter/s, 5.04164s/12 iters), loss = 4.95434
I0408 15:46:18.876358 27257 solver.cpp:237]     Train net output #0: loss = 4.95434 (* 1 = 4.95434 loss)
I0408 15:46:18.876368 27257 sgd_solver.cpp:105] Iteration 1260, lr = 0.000122044
I0408 15:46:23.860352 27257 solver.cpp:218] Iteration 1272 (2.40781 iter/s, 4.98379s/12 iters), loss = 4.86796
I0408 15:46:23.860399 27257 solver.cpp:237]     Train net output #0: loss = 4.86796 (* 1 = 4.86796 loss)
I0408 15:46:23.860410 27257 sgd_solver.cpp:105] Iteration 1272, lr = 0.000117029
I0408 15:46:28.760159 27257 solver.cpp:218] Iteration 1284 (2.4492 iter/s, 4.89956s/12 iters), loss = 4.98694
I0408 15:46:28.760205 27257 solver.cpp:237]     Train net output #0: loss = 4.98694 (* 1 = 4.98694 loss)
I0408 15:46:28.760213 27257 sgd_solver.cpp:105] Iteration 1284, lr = 0.00011222
I0408 15:46:33.867359 27257 solver.cpp:218] Iteration 1296 (2.34974 iter/s, 5.10694s/12 iters), loss = 4.87087
I0408 15:46:33.867415 27257 solver.cpp:237]     Train net output #0: loss = 4.87087 (* 1 = 4.87087 loss)
I0408 15:46:33.867429 27257 sgd_solver.cpp:105] Iteration 1296, lr = 0.000107608
I0408 15:46:38.891790 27257 solver.cpp:218] Iteration 1308 (2.38845 iter/s, 5.02417s/12 iters), loss = 4.9431
I0408 15:46:38.891842 27257 solver.cpp:237]     Train net output #0: loss = 4.9431 (* 1 = 4.9431 loss)
I0408 15:46:38.891853 27257 sgd_solver.cpp:105] Iteration 1308, lr = 0.000103186
I0408 15:46:41.418164 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:46:43.865624 27257 solver.cpp:218] Iteration 1320 (2.41275 iter/s, 4.97357s/12 iters), loss = 4.92822
I0408 15:46:43.865773 27257 solver.cpp:237]     Train net output #0: loss = 4.92822 (* 1 = 4.92822 loss)
I0408 15:46:43.865788 27257 sgd_solver.cpp:105] Iteration 1320, lr = 9.89459e-05
I0408 15:46:45.889689 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1326.caffemodel
I0408 15:46:50.865203 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1326.solverstate
I0408 15:46:57.657538 27257 solver.cpp:330] Iteration 1326, Testing net (#0)
I0408 15:46:57.657568 27257 net.cpp:676] Ignoring source layer train-data
I0408 15:47:01.463919 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:47:02.019860 27257 solver.cpp:397]     Test net output #0: accuracy = 0.026348
I0408 15:47:02.019906 27257 solver.cpp:397]     Test net output #1: loss = 4.98144 (* 1 = 4.98144 loss)
I0408 15:47:04.009948 27257 solver.cpp:218] Iteration 1332 (0.595729 iter/s, 20.1434s/12 iters), loss = 4.902
I0408 15:47:04.010015 27257 solver.cpp:237]     Train net output #0: loss = 4.902 (* 1 = 4.902 loss)
I0408 15:47:04.010026 27257 sgd_solver.cpp:105] Iteration 1332, lr = 9.48799e-05
I0408 15:47:08.950651 27257 solver.cpp:218] Iteration 1344 (2.42894 iter/s, 4.94043s/12 iters), loss = 4.9171
I0408 15:47:08.950704 27257 solver.cpp:237]     Train net output #0: loss = 4.9171 (* 1 = 4.9171 loss)
I0408 15:47:08.950717 27257 sgd_solver.cpp:105] Iteration 1344, lr = 9.0981e-05
I0408 15:47:13.945278 27257 solver.cpp:218] Iteration 1356 (2.4027 iter/s, 4.99437s/12 iters), loss = 5.00138
I0408 15:47:13.945390 27257 solver.cpp:237]     Train net output #0: loss = 5.00138 (* 1 = 5.00138 loss)
I0408 15:47:13.945403 27257 sgd_solver.cpp:105] Iteration 1356, lr = 8.72423e-05
I0408 15:47:18.956260 27257 solver.cpp:218] Iteration 1368 (2.39489 iter/s, 5.01067s/12 iters), loss = 4.98611
I0408 15:47:18.956307 27257 solver.cpp:237]     Train net output #0: loss = 4.98611 (* 1 = 4.98611 loss)
I0408 15:47:18.956317 27257 sgd_solver.cpp:105] Iteration 1368, lr = 8.36572e-05
I0408 15:47:20.158205 27257 blocking_queue.cpp:49] Waiting for data
I0408 15:47:23.933125 27257 solver.cpp:218] Iteration 1380 (2.41128 iter/s, 4.97661s/12 iters), loss = 4.90887
I0408 15:47:23.933177 27257 solver.cpp:237]     Train net output #0: loss = 4.90887 (* 1 = 4.90887 loss)
I0408 15:47:23.933190 27257 sgd_solver.cpp:105] Iteration 1380, lr = 8.02194e-05
I0408 15:47:28.978385 27257 solver.cpp:218] Iteration 1392 (2.37859 iter/s, 5.045s/12 iters), loss = 4.74893
I0408 15:47:28.978432 27257 solver.cpp:237]     Train net output #0: loss = 4.74893 (* 1 = 4.74893 loss)
I0408 15:47:28.978443 27257 sgd_solver.cpp:105] Iteration 1392, lr = 7.6923e-05
I0408 15:47:34.044008 27257 solver.cpp:218] Iteration 1404 (2.36903 iter/s, 5.06537s/12 iters), loss = 4.91067
I0408 15:47:34.044055 27257 solver.cpp:237]     Train net output #0: loss = 4.91067 (* 1 = 4.91067 loss)
I0408 15:47:34.044067 27257 sgd_solver.cpp:105] Iteration 1404, lr = 7.37619e-05
I0408 15:47:38.865523 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:47:39.216830 27257 solver.cpp:218] Iteration 1416 (2.31993 iter/s, 5.17256s/12 iters), loss = 5.08905
I0408 15:47:39.216887 27257 solver.cpp:237]     Train net output #0: loss = 5.08905 (* 1 = 5.08905 loss)
I0408 15:47:39.216900 27257 sgd_solver.cpp:105] Iteration 1416, lr = 7.07308e-05
I0408 15:47:43.735435 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1428.caffemodel
I0408 15:47:48.335165 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1428.solverstate
I0408 15:47:56.360196 27257 solver.cpp:330] Iteration 1428, Testing net (#0)
I0408 15:47:56.360229 27257 net.cpp:676] Ignoring source layer train-data
I0408 15:48:00.226580 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:48:00.816709 27257 solver.cpp:397]     Test net output #0: accuracy = 0.026348
I0408 15:48:00.816758 27257 solver.cpp:397]     Test net output #1: loss = 4.97525 (* 1 = 4.97525 loss)
I0408 15:48:00.907163 27257 solver.cpp:218] Iteration 1428 (0.553265 iter/s, 21.6894s/12 iters), loss = 5.10387
I0408 15:48:00.907203 27257 solver.cpp:237]     Train net output #0: loss = 5.10387 (* 1 = 5.10387 loss)
I0408 15:48:00.907215 27257 sgd_solver.cpp:105] Iteration 1428, lr = 6.78243e-05
I0408 15:48:05.528654 27257 solver.cpp:218] Iteration 1440 (2.5967 iter/s, 4.62126s/12 iters), loss = 4.94767
I0408 15:48:05.528708 27257 solver.cpp:237]     Train net output #0: loss = 4.94767 (* 1 = 4.94767 loss)
I0408 15:48:05.528720 27257 sgd_solver.cpp:105] Iteration 1440, lr = 6.50371e-05
I0408 15:48:10.744442 27257 solver.cpp:218] Iteration 1452 (2.30083 iter/s, 5.21552s/12 iters), loss = 4.90953
I0408 15:48:10.744490 27257 solver.cpp:237]     Train net output #0: loss = 4.90953 (* 1 = 4.90953 loss)
I0408 15:48:10.744503 27257 sgd_solver.cpp:105] Iteration 1452, lr = 6.23645e-05
I0408 15:48:16.196585 27257 solver.cpp:218] Iteration 1464 (2.20108 iter/s, 5.45187s/12 iters), loss = 4.96903
I0408 15:48:16.196630 27257 solver.cpp:237]     Train net output #0: loss = 4.96903 (* 1 = 4.96903 loss)
I0408 15:48:16.196640 27257 sgd_solver.cpp:105] Iteration 1464, lr = 5.98018e-05
I0408 15:48:21.246515 27257 solver.cpp:218] Iteration 1476 (2.37639 iter/s, 5.04967s/12 iters), loss = 5.00097
I0408 15:48:21.246646 27257 solver.cpp:237]     Train net output #0: loss = 5.00097 (* 1 = 5.00097 loss)
I0408 15:48:21.246660 27257 sgd_solver.cpp:105] Iteration 1476, lr = 5.73443e-05
I0408 15:48:26.243788 27257 solver.cpp:218] Iteration 1488 (2.40147 iter/s, 4.99694s/12 iters), loss = 4.98146
I0408 15:48:26.243845 27257 solver.cpp:237]     Train net output #0: loss = 4.98146 (* 1 = 4.98146 loss)
I0408 15:48:26.243857 27257 sgd_solver.cpp:105] Iteration 1488, lr = 5.49878e-05
I0408 15:48:31.205922 27257 solver.cpp:218] Iteration 1500 (2.41844 iter/s, 4.96188s/12 iters), loss = 4.81421
I0408 15:48:31.205986 27257 solver.cpp:237]     Train net output #0: loss = 4.81421 (* 1 = 4.81421 loss)
I0408 15:48:31.205999 27257 sgd_solver.cpp:105] Iteration 1500, lr = 5.27282e-05
I0408 15:48:36.191339 27257 solver.cpp:218] Iteration 1512 (2.40715 iter/s, 4.98515s/12 iters), loss = 5.01728
I0408 15:48:36.191388 27257 solver.cpp:237]     Train net output #0: loss = 5.01728 (* 1 = 5.01728 loss)
I0408 15:48:36.191399 27257 sgd_solver.cpp:105] Iteration 1512, lr = 5.05614e-05
I0408 15:48:37.965972 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:48:41.221741 27257 solver.cpp:218] Iteration 1524 (2.38562 iter/s, 5.03014s/12 iters), loss = 5.0083
I0408 15:48:41.221793 27257 solver.cpp:237]     Train net output #0: loss = 5.0083 (* 1 = 5.0083 loss)
I0408 15:48:41.221807 27257 sgd_solver.cpp:105] Iteration 1524, lr = 4.84837e-05
I0408 15:48:43.281489 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1530.caffemodel
I0408 15:48:46.263980 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1530.solverstate
I0408 15:48:49.787951 27257 solver.cpp:330] Iteration 1530, Testing net (#0)
I0408 15:48:49.787978 27257 net.cpp:676] Ignoring source layer train-data
I0408 15:48:53.589605 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:48:54.226128 27257 solver.cpp:397]     Test net output #0: accuracy = 0.026348
I0408 15:48:54.226176 27257 solver.cpp:397]     Test net output #1: loss = 4.97527 (* 1 = 4.97527 loss)
I0408 15:48:56.178073 27257 solver.cpp:218] Iteration 1536 (0.802371 iter/s, 14.9557s/12 iters), loss = 5.03528
I0408 15:48:56.178144 27257 solver.cpp:237]     Train net output #0: loss = 5.03528 (* 1 = 5.03528 loss)
I0408 15:48:56.178158 27257 sgd_solver.cpp:105] Iteration 1536, lr = 4.64913e-05
I0408 15:49:01.227526 27257 solver.cpp:218] Iteration 1548 (2.37662 iter/s, 5.04918s/12 iters), loss = 4.87663
I0408 15:49:01.227576 27257 solver.cpp:237]     Train net output #0: loss = 4.87663 (* 1 = 4.87663 loss)
I0408 15:49:01.227588 27257 sgd_solver.cpp:105] Iteration 1548, lr = 4.45809e-05
I0408 15:49:06.220046 27257 solver.cpp:218] Iteration 1560 (2.40372 iter/s, 4.99226s/12 iters), loss = 4.93967
I0408 15:49:06.220095 27257 solver.cpp:237]     Train net output #0: loss = 4.93967 (* 1 = 4.93967 loss)
I0408 15:49:06.220108 27257 sgd_solver.cpp:105] Iteration 1560, lr = 4.27489e-05
I0408 15:49:11.322223 27257 solver.cpp:218] Iteration 1572 (2.35206 iter/s, 5.10191s/12 iters), loss = 4.99052
I0408 15:49:11.322278 27257 solver.cpp:237]     Train net output #0: loss = 4.99052 (* 1 = 4.99052 loss)
I0408 15:49:11.322289 27257 sgd_solver.cpp:105] Iteration 1572, lr = 4.09922e-05
I0408 15:49:16.292928 27257 solver.cpp:218] Iteration 1584 (2.41427 iter/s, 4.97045s/12 iters), loss = 5.04121
I0408 15:49:16.292980 27257 solver.cpp:237]     Train net output #0: loss = 5.04121 (* 1 = 5.04121 loss)
I0408 15:49:16.292992 27257 sgd_solver.cpp:105] Iteration 1584, lr = 3.93077e-05
I0408 15:49:21.365224 27257 solver.cpp:218] Iteration 1596 (2.36591 iter/s, 5.07204s/12 iters), loss = 4.88286
I0408 15:49:21.365273 27257 solver.cpp:237]     Train net output #0: loss = 4.88286 (* 1 = 4.88286 loss)
I0408 15:49:21.365285 27257 sgd_solver.cpp:105] Iteration 1596, lr = 3.76924e-05
I0408 15:49:26.410344 27257 solver.cpp:218] Iteration 1608 (2.37866 iter/s, 5.04486s/12 iters), loss = 5.02884
I0408 15:49:26.410468 27257 solver.cpp:237]     Train net output #0: loss = 5.02884 (* 1 = 5.02884 loss)
I0408 15:49:26.410481 27257 sgd_solver.cpp:105] Iteration 1608, lr = 3.61435e-05
I0408 15:49:30.368433 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:49:31.448539 27257 solver.cpp:218] Iteration 1620 (2.38196 iter/s, 5.03786s/12 iters), loss = 4.82273
I0408 15:49:31.448588 27257 solver.cpp:237]     Train net output #0: loss = 4.82273 (* 1 = 4.82273 loss)
I0408 15:49:31.448601 27257 sgd_solver.cpp:105] Iteration 1620, lr = 3.46582e-05
I0408 15:49:35.952297 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1632.caffemodel
I0408 15:49:38.945441 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1632.solverstate
I0408 15:49:41.278787 27257 solver.cpp:330] Iteration 1632, Testing net (#0)
I0408 15:49:41.278815 27257 net.cpp:676] Ignoring source layer train-data
I0408 15:49:45.174398 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:49:45.887455 27257 solver.cpp:397]     Test net output #0: accuracy = 0.026348
I0408 15:49:45.887503 27257 solver.cpp:397]     Test net output #1: loss = 4.9734 (* 1 = 4.9734 loss)
I0408 15:49:45.977995 27257 solver.cpp:218] Iteration 1632 (0.825943 iter/s, 14.5288s/12 iters), loss = 5.06388
I0408 15:49:45.978049 27257 solver.cpp:237]     Train net output #0: loss = 5.06388 (* 1 = 5.06388 loss)
I0408 15:49:45.978061 27257 sgd_solver.cpp:105] Iteration 1632, lr = 3.3234e-05
I0408 15:49:50.249598 27257 solver.cpp:218] Iteration 1644 (2.8094 iter/s, 4.27138s/12 iters), loss = 4.98286
I0408 15:49:50.249640 27257 solver.cpp:237]     Train net output #0: loss = 4.98286 (* 1 = 4.98286 loss)
I0408 15:49:50.249651 27257 sgd_solver.cpp:105] Iteration 1644, lr = 3.18683e-05
I0408 15:49:55.303848 27257 solver.cpp:218] Iteration 1656 (2.37436 iter/s, 5.054s/12 iters), loss = 4.97129
I0408 15:49:55.303898 27257 solver.cpp:237]     Train net output #0: loss = 4.97129 (* 1 = 4.97129 loss)
I0408 15:49:55.303910 27257 sgd_solver.cpp:105] Iteration 1656, lr = 3.05587e-05
I0408 15:50:00.371891 27257 solver.cpp:218] Iteration 1668 (2.3679 iter/s, 5.06779s/12 iters), loss = 4.8775
I0408 15:50:00.372037 27257 solver.cpp:237]     Train net output #0: loss = 4.8775 (* 1 = 4.8775 loss)
I0408 15:50:00.372051 27257 sgd_solver.cpp:105] Iteration 1668, lr = 2.9303e-05
I0408 15:50:05.386389 27257 solver.cpp:218] Iteration 1680 (2.39323 iter/s, 5.01415s/12 iters), loss = 4.92434
I0408 15:50:05.386442 27257 solver.cpp:237]     Train net output #0: loss = 4.92434 (* 1 = 4.92434 loss)
I0408 15:50:05.386456 27257 sgd_solver.cpp:105] Iteration 1680, lr = 2.80988e-05
I0408 15:50:10.402611 27257 solver.cpp:218] Iteration 1692 (2.39236 iter/s, 5.01597s/12 iters), loss = 5.02387
I0408 15:50:10.402662 27257 solver.cpp:237]     Train net output #0: loss = 5.02387 (* 1 = 5.02387 loss)
I0408 15:50:10.402673 27257 sgd_solver.cpp:105] Iteration 1692, lr = 2.69442e-05
I0408 15:50:15.357678 27257 solver.cpp:218] Iteration 1704 (2.42189 iter/s, 4.95482s/12 iters), loss = 4.81421
I0408 15:50:15.357726 27257 solver.cpp:237]     Train net output #0: loss = 4.81421 (* 1 = 4.81421 loss)
I0408 15:50:15.357738 27257 sgd_solver.cpp:105] Iteration 1704, lr = 2.58369e-05
I0408 15:50:20.334445 27257 solver.cpp:218] Iteration 1716 (2.41133 iter/s, 4.97652s/12 iters), loss = 4.98344
I0408 15:50:20.334487 27257 solver.cpp:237]     Train net output #0: loss = 4.98344 (* 1 = 4.98344 loss)
I0408 15:50:20.334496 27257 sgd_solver.cpp:105] Iteration 1716, lr = 2.47752e-05
I0408 15:50:21.397259 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:50:25.378430 27257 solver.cpp:218] Iteration 1728 (2.37919 iter/s, 5.04374s/12 iters), loss = 4.92697
I0408 15:50:25.378471 27257 solver.cpp:237]     Train net output #0: loss = 4.92697 (* 1 = 4.92697 loss)
I0408 15:50:25.378480 27257 sgd_solver.cpp:105] Iteration 1728, lr = 2.37571e-05
I0408 15:50:27.374820 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1734.caffemodel
I0408 15:50:31.342469 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1734.solverstate
I0408 15:50:33.658190 27257 solver.cpp:330] Iteration 1734, Testing net (#0)
I0408 15:50:33.658212 27257 net.cpp:676] Ignoring source layer train-data
I0408 15:50:37.589499 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:50:38.296407 27257 solver.cpp:397]     Test net output #0: accuracy = 0.0275735
I0408 15:50:38.296455 27257 solver.cpp:397]     Test net output #1: loss = 4.97335 (* 1 = 4.97335 loss)
I0408 15:50:40.163158 27257 solver.cpp:218] Iteration 1740 (0.811682 iter/s, 14.7841s/12 iters), loss = 4.99912
I0408 15:50:40.163210 27257 solver.cpp:237]     Train net output #0: loss = 4.99912 (* 1 = 4.99912 loss)
I0408 15:50:40.163223 27257 sgd_solver.cpp:105] Iteration 1740, lr = 2.27809e-05
I0408 15:50:44.957585 27257 solver.cpp:218] Iteration 1752 (2.50304 iter/s, 4.79418s/12 iters), loss = 4.94518
I0408 15:50:44.957643 27257 solver.cpp:237]     Train net output #0: loss = 4.94518 (* 1 = 4.94518 loss)
I0408 15:50:44.957655 27257 sgd_solver.cpp:105] Iteration 1752, lr = 2.18447e-05
I0408 15:50:49.847916 27257 solver.cpp:218] Iteration 1764 (2.45395 iter/s, 4.89008s/12 iters), loss = 4.93281
I0408 15:50:49.847951 27257 solver.cpp:237]     Train net output #0: loss = 4.93281 (* 1 = 4.93281 loss)
I0408 15:50:49.847959 27257 sgd_solver.cpp:105] Iteration 1764, lr = 2.0947e-05
I0408 15:50:54.795787 27257 solver.cpp:218] Iteration 1776 (2.4254 iter/s, 4.94763s/12 iters), loss = 5.02357
I0408 15:50:54.795830 27257 solver.cpp:237]     Train net output #0: loss = 5.02357 (* 1 = 5.02357 loss)
I0408 15:50:54.795841 27257 sgd_solver.cpp:105] Iteration 1776, lr = 2.00863e-05
I0408 15:50:59.774678 27257 solver.cpp:218] Iteration 1788 (2.4103 iter/s, 4.97864s/12 iters), loss = 4.95197
I0408 15:50:59.774724 27257 solver.cpp:237]     Train net output #0: loss = 4.95197 (* 1 = 4.95197 loss)
I0408 15:50:59.774740 27257 sgd_solver.cpp:105] Iteration 1788, lr = 1.92608e-05
I0408 15:51:04.765480 27257 solver.cpp:218] Iteration 1800 (2.40454 iter/s, 4.99055s/12 iters), loss = 4.8607
I0408 15:51:04.765655 27257 solver.cpp:237]     Train net output #0: loss = 4.8607 (* 1 = 4.8607 loss)
I0408 15:51:04.765676 27257 sgd_solver.cpp:105] Iteration 1800, lr = 1.84694e-05
I0408 15:51:09.810017 27257 solver.cpp:218] Iteration 1812 (2.37898 iter/s, 5.04417s/12 iters), loss = 4.97339
I0408 15:51:09.810060 27257 solver.cpp:237]     Train net output #0: loss = 4.97339 (* 1 = 4.97339 loss)
I0408 15:51:09.810071 27257 sgd_solver.cpp:105] Iteration 1812, lr = 1.77104e-05
I0408 15:51:12.970420 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:51:14.928959 27257 solver.cpp:218] Iteration 1824 (2.34435 iter/s, 5.11869s/12 iters), loss = 4.93465
I0408 15:51:14.929006 27257 solver.cpp:237]     Train net output #0: loss = 4.93465 (* 1 = 4.93465 loss)
I0408 15:51:14.929018 27257 sgd_solver.cpp:105] Iteration 1824, lr = 1.69826e-05
I0408 15:51:19.610292 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1836.caffemodel
I0408 15:51:22.679270 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1836.solverstate
I0408 15:51:25.016357 27257 solver.cpp:330] Iteration 1836, Testing net (#0)
I0408 15:51:25.016384 27257 net.cpp:676] Ignoring source layer train-data
I0408 15:51:28.858126 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:51:29.668045 27257 solver.cpp:397]     Test net output #0: accuracy = 0.026348
I0408 15:51:29.668079 27257 solver.cpp:397]     Test net output #1: loss = 4.96888 (* 1 = 4.96888 loss)
I0408 15:51:29.757830 27257 solver.cpp:218] Iteration 1836 (0.809267 iter/s, 14.8282s/12 iters), loss = 5.05345
I0408 15:51:29.757880 27257 solver.cpp:237]     Train net output #0: loss = 5.05345 (* 1 = 5.05345 loss)
I0408 15:51:29.757891 27257 sgd_solver.cpp:105] Iteration 1836, lr = 1.62847e-05
I0408 15:51:34.064558 27257 solver.cpp:218] Iteration 1848 (2.78649 iter/s, 4.30649s/12 iters), loss = 5.04042
I0408 15:51:34.064602 27257 solver.cpp:237]     Train net output #0: loss = 5.04042 (* 1 = 5.04042 loss)
I0408 15:51:34.064612 27257 sgd_solver.cpp:105] Iteration 1848, lr = 1.56155e-05
I0408 15:51:39.119746 27257 solver.cpp:218] Iteration 1860 (2.37392 iter/s, 5.05494s/12 iters), loss = 4.95258
I0408 15:51:39.119832 27257 solver.cpp:237]     Train net output #0: loss = 4.95258 (* 1 = 4.95258 loss)
I0408 15:51:39.119841 27257 sgd_solver.cpp:105] Iteration 1860, lr = 1.49738e-05
I0408 15:51:44.116070 27257 solver.cpp:218] Iteration 1872 (2.4019 iter/s, 4.99604s/12 iters), loss = 4.93783
I0408 15:51:44.116107 27257 solver.cpp:237]     Train net output #0: loss = 4.93783 (* 1 = 4.93783 loss)
I0408 15:51:44.116115 27257 sgd_solver.cpp:105] Iteration 1872, lr = 1.43585e-05
I0408 15:51:49.155417 27257 solver.cpp:218] Iteration 1884 (2.38138 iter/s, 5.0391s/12 iters), loss = 4.95372
I0408 15:51:49.155457 27257 solver.cpp:237]     Train net output #0: loss = 4.95372 (* 1 = 4.95372 loss)
I0408 15:51:49.155467 27257 sgd_solver.cpp:105] Iteration 1884, lr = 1.37685e-05
I0408 15:51:54.178440 27257 solver.cpp:218] Iteration 1896 (2.38912 iter/s, 5.02278s/12 iters), loss = 4.97431
I0408 15:51:54.178484 27257 solver.cpp:237]     Train net output #0: loss = 4.97431 (* 1 = 4.97431 loss)
I0408 15:51:54.178496 27257 sgd_solver.cpp:105] Iteration 1896, lr = 1.32027e-05
I0408 15:51:59.233917 27257 solver.cpp:218] Iteration 1908 (2.37378 iter/s, 5.05523s/12 iters), loss = 5.00984
I0408 15:51:59.233974 27257 solver.cpp:237]     Train net output #0: loss = 5.00984 (* 1 = 5.00984 loss)
I0408 15:51:59.233987 27257 sgd_solver.cpp:105] Iteration 1908, lr = 1.26601e-05
I0408 15:52:04.264868 27257 solver.cpp:218] Iteration 1920 (2.38536 iter/s, 5.03069s/12 iters), loss = 5.05077
I0408 15:52:04.264919 27257 solver.cpp:237]     Train net output #0: loss = 5.05077 (* 1 = 5.05077 loss)
I0408 15:52:04.264930 27257 sgd_solver.cpp:105] Iteration 1920, lr = 1.21399e-05
I0408 15:52:04.577024 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:52:09.369612 27257 solver.cpp:218] Iteration 1932 (2.35087 iter/s, 5.10448s/12 iters), loss = 4.85904
I0408 15:52:09.369729 27257 solver.cpp:237]     Train net output #0: loss = 4.85904 (* 1 = 4.85904 loss)
I0408 15:52:09.369742 27257 sgd_solver.cpp:105] Iteration 1932, lr = 1.1641e-05
I0408 15:52:11.578819 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1938.caffemodel
I0408 15:52:14.700053 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1938.solverstate
I0408 15:52:17.071537 27257 solver.cpp:330] Iteration 1938, Testing net (#0)
I0408 15:52:17.071564 27257 net.cpp:676] Ignoring source layer train-data
I0408 15:52:20.752230 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:52:21.536826 27257 solver.cpp:397]     Test net output #0: accuracy = 0.0257353
I0408 15:52:21.536876 27257 solver.cpp:397]     Test net output #1: loss = 4.97238 (* 1 = 4.97238 loss)
I0408 15:52:23.522392 27257 solver.cpp:218] Iteration 1944 (0.84793 iter/s, 14.1521s/12 iters), loss = 4.95196
I0408 15:52:23.522426 27257 solver.cpp:237]     Train net output #0: loss = 4.95196 (* 1 = 4.95196 loss)
I0408 15:52:23.522435 27257 sgd_solver.cpp:105] Iteration 1944, lr = 1.11627e-05
I0408 15:52:28.612114 27257 solver.cpp:218] Iteration 1956 (2.35781 iter/s, 5.08948s/12 iters), loss = 4.92274
I0408 15:52:28.612165 27257 solver.cpp:237]     Train net output #0: loss = 4.92274 (* 1 = 4.92274 loss)
I0408 15:52:28.612177 27257 sgd_solver.cpp:105] Iteration 1956, lr = 1.0704e-05
I0408 15:52:33.735723 27257 solver.cpp:218] Iteration 1968 (2.34222 iter/s, 5.12335s/12 iters), loss = 4.89329
I0408 15:52:33.735775 27257 solver.cpp:237]     Train net output #0: loss = 4.89329 (* 1 = 4.89329 loss)
I0408 15:52:33.735787 27257 sgd_solver.cpp:105] Iteration 1968, lr = 1.02641e-05
I0408 15:52:38.693392 27257 solver.cpp:218] Iteration 1980 (2.42062 iter/s, 4.95741s/12 iters), loss = 4.83569
I0408 15:52:38.693440 27257 solver.cpp:237]     Train net output #0: loss = 4.83569 (* 1 = 4.83569 loss)
I0408 15:52:38.693451 27257 sgd_solver.cpp:105] Iteration 1980, lr = 9.84231e-06
I0408 15:52:44.101598 27257 solver.cpp:218] Iteration 1992 (2.21896 iter/s, 5.40794s/12 iters), loss = 5.03488
I0408 15:52:44.101711 27257 solver.cpp:237]     Train net output #0: loss = 5.03488 (* 1 = 5.03488 loss)
I0408 15:52:44.101724 27257 sgd_solver.cpp:105] Iteration 1992, lr = 9.43785e-06
I0408 15:52:49.280887 27257 solver.cpp:218] Iteration 2004 (2.31706 iter/s, 5.17897s/12 iters), loss = 4.84784
I0408 15:52:49.280931 27257 solver.cpp:237]     Train net output #0: loss = 4.84784 (* 1 = 4.84784 loss)
I0408 15:52:49.280941 27257 sgd_solver.cpp:105] Iteration 2004, lr = 9.05002e-06
I0408 15:52:54.315289 27257 solver.cpp:218] Iteration 2016 (2.38372 iter/s, 5.03415s/12 iters), loss = 4.93212
I0408 15:52:54.315333 27257 solver.cpp:237]     Train net output #0: loss = 4.93212 (* 1 = 4.93212 loss)
I0408 15:52:54.315344 27257 sgd_solver.cpp:105] Iteration 2016, lr = 8.67812e-06
I0408 15:52:56.852160 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:52:59.331915 27257 solver.cpp:218] Iteration 2028 (2.39216 iter/s, 5.01638s/12 iters), loss = 4.85911
I0408 15:52:59.331964 27257 solver.cpp:237]     Train net output #0: loss = 4.85911 (* 1 = 4.85911 loss)
I0408 15:52:59.331975 27257 sgd_solver.cpp:105] Iteration 2028, lr = 8.32151e-06
I0408 15:53:03.977528 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2040.caffemodel
I0408 15:53:06.995626 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2040.solverstate
I0408 15:53:09.316494 27257 solver.cpp:330] Iteration 2040, Testing net (#0)
I0408 15:53:09.316519 27257 net.cpp:676] Ignoring source layer train-data
I0408 15:53:12.881862 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:53:13.837849 27257 solver.cpp:397]     Test net output #0: accuracy = 0.028799
I0408 15:53:13.837896 27257 solver.cpp:397]     Test net output #1: loss = 4.96656 (* 1 = 4.96656 loss)
I0408 15:53:13.926172 27257 solver.cpp:218] Iteration 2040 (0.822276 iter/s, 14.5936s/12 iters), loss = 4.93732
I0408 15:53:13.926229 27257 solver.cpp:237]     Train net output #0: loss = 4.93732 (* 1 = 4.93732 loss)
I0408 15:53:13.926239 27257 sgd_solver.cpp:105] Iteration 2040, lr = 7.97955e-06
I0408 15:53:18.258965 27257 solver.cpp:218] Iteration 2052 (2.76973 iter/s, 4.33256s/12 iters), loss = 4.9282
I0408 15:53:18.259114 27257 solver.cpp:237]     Train net output #0: loss = 4.9282 (* 1 = 4.9282 loss)
I0408 15:53:18.259127 27257 sgd_solver.cpp:105] Iteration 2052, lr = 7.65165e-06
I0408 15:53:19.854233 27257 blocking_queue.cpp:49] Waiting for data
I0408 15:53:23.386797 27257 solver.cpp:218] Iteration 2064 (2.34034 iter/s, 5.12747s/12 iters), loss = 4.93693
I0408 15:53:23.386847 27257 solver.cpp:237]     Train net output #0: loss = 4.93693 (* 1 = 4.93693 loss)
I0408 15:53:23.386859 27257 sgd_solver.cpp:105] Iteration 2064, lr = 7.33722e-06
I0408 15:53:28.484285 27257 solver.cpp:218] Iteration 2076 (2.35422 iter/s, 5.09723s/12 iters), loss = 4.94308
I0408 15:53:28.484340 27257 solver.cpp:237]     Train net output #0: loss = 4.94308 (* 1 = 4.94308 loss)
I0408 15:53:28.484352 27257 sgd_solver.cpp:105] Iteration 2076, lr = 7.03571e-06
I0408 15:53:33.494976 27257 solver.cpp:218] Iteration 2088 (2.395 iter/s, 5.01043s/12 iters), loss = 4.83835
I0408 15:53:33.495021 27257 solver.cpp:237]     Train net output #0: loss = 4.83835 (* 1 = 4.83835 loss)
I0408 15:53:33.495030 27257 sgd_solver.cpp:105] Iteration 2088, lr = 6.74658e-06
I0408 15:53:38.542591 27257 solver.cpp:218] Iteration 2100 (2.37748 iter/s, 5.04736s/12 iters), loss = 4.75588
I0408 15:53:38.542637 27257 solver.cpp:237]     Train net output #0: loss = 4.75588 (* 1 = 4.75588 loss)
I0408 15:53:38.542647 27257 sgd_solver.cpp:105] Iteration 2100, lr = 6.46934e-06
I0408 15:53:43.559628 27257 solver.cpp:218] Iteration 2112 (2.39197 iter/s, 5.01678s/12 iters), loss = 4.94042
I0408 15:53:43.559667 27257 solver.cpp:237]     Train net output #0: loss = 4.94042 (* 1 = 4.94042 loss)
I0408 15:53:43.559675 27257 sgd_solver.cpp:105] Iteration 2112, lr = 6.2035e-06
I0408 15:53:48.246152 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:53:48.565248 27257 solver.cpp:218] Iteration 2124 (2.39743 iter/s, 5.00536s/12 iters), loss = 5.0069
I0408 15:53:48.565364 27257 solver.cpp:237]     Train net output #0: loss = 5.0069 (* 1 = 5.0069 loss)
I0408 15:53:48.565377 27257 sgd_solver.cpp:105] Iteration 2124, lr = 5.94858e-06
I0408 15:53:53.570394 27257 solver.cpp:218] Iteration 2136 (2.39768 iter/s, 5.00483s/12 iters), loss = 5.06671
I0408 15:53:53.570436 27257 solver.cpp:237]     Train net output #0: loss = 5.06671 (* 1 = 5.06671 loss)
I0408 15:53:53.570447 27257 sgd_solver.cpp:105] Iteration 2136, lr = 5.70413e-06
I0408 15:53:55.530930 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2142.caffemodel
I0408 15:54:01.986840 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2142.solverstate
I0408 15:54:04.315361 27257 solver.cpp:330] Iteration 2142, Testing net (#0)
I0408 15:54:04.315390 27257 net.cpp:676] Ignoring source layer train-data
I0408 15:54:07.972620 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:54:08.938918 27257 solver.cpp:397]     Test net output #0: accuracy = 0.0281863
I0408 15:54:08.938957 27257 solver.cpp:397]     Test net output #1: loss = 4.97108 (* 1 = 4.97108 loss)
I0408 15:54:10.866196 27257 solver.cpp:218] Iteration 2148 (0.693839 iter/s, 17.2951s/12 iters), loss = 4.90737
I0408 15:54:10.866258 27257 solver.cpp:237]     Train net output #0: loss = 4.90737 (* 1 = 4.90737 loss)
I0408 15:54:10.866268 27257 sgd_solver.cpp:105] Iteration 2148, lr = 5.46973e-06
I0408 15:54:15.803418 27257 solver.cpp:218] Iteration 2160 (2.43065 iter/s, 4.93695s/12 iters), loss = 4.87647
I0408 15:54:15.803478 27257 solver.cpp:237]     Train net output #0: loss = 4.87647 (* 1 = 4.87647 loss)
I0408 15:54:15.803489 27257 sgd_solver.cpp:105] Iteration 2160, lr = 5.24496e-06
I0408 15:54:20.826336 27257 solver.cpp:218] Iteration 2172 (2.38918 iter/s, 5.02265s/12 iters), loss = 4.96651
I0408 15:54:20.826504 27257 solver.cpp:237]     Train net output #0: loss = 4.96651 (* 1 = 4.96651 loss)
I0408 15:54:20.826519 27257 sgd_solver.cpp:105] Iteration 2172, lr = 5.02943e-06
I0408 15:54:25.832877 27257 solver.cpp:218] Iteration 2184 (2.39704 iter/s, 5.00617s/12 iters), loss = 4.939
I0408 15:54:25.832921 27257 solver.cpp:237]     Train net output #0: loss = 4.939 (* 1 = 4.939 loss)
I0408 15:54:25.832932 27257 sgd_solver.cpp:105] Iteration 2184, lr = 4.82275e-06
I0408 15:54:30.858114 27257 solver.cpp:218] Iteration 2196 (2.38807 iter/s, 5.02498s/12 iters), loss = 4.99944
I0408 15:54:30.858155 27257 solver.cpp:237]     Train net output #0: loss = 4.99944 (* 1 = 4.99944 loss)
I0408 15:54:30.858165 27257 sgd_solver.cpp:105] Iteration 2196, lr = 4.62457e-06
I0408 15:54:35.901499 27257 solver.cpp:218] Iteration 2208 (2.37947 iter/s, 5.04313s/12 iters), loss = 4.86874
I0408 15:54:35.901554 27257 solver.cpp:237]     Train net output #0: loss = 4.86874 (* 1 = 4.86874 loss)
I0408 15:54:35.901566 27257 sgd_solver.cpp:105] Iteration 2208, lr = 4.43453e-06
I0408 15:54:40.887599 27257 solver.cpp:218] Iteration 2220 (2.40681 iter/s, 4.98584s/12 iters), loss = 4.98636
I0408 15:54:40.887643 27257 solver.cpp:237]     Train net output #0: loss = 4.98636 (* 1 = 4.98636 loss)
I0408 15:54:40.887653 27257 sgd_solver.cpp:105] Iteration 2220, lr = 4.2523e-06
I0408 15:54:42.715523 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:54:45.919721 27257 solver.cpp:218] Iteration 2232 (2.3848 iter/s, 5.03186s/12 iters), loss = 5.00028
I0408 15:54:45.919778 27257 solver.cpp:237]     Train net output #0: loss = 5.00028 (* 1 = 5.00028 loss)
I0408 15:54:45.919790 27257 sgd_solver.cpp:105] Iteration 2232, lr = 4.07756e-06
I0408 15:54:50.463398 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2244.caffemodel
I0408 15:54:58.771744 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2244.solverstate
I0408 15:55:02.480594 27257 solver.cpp:330] Iteration 2244, Testing net (#0)
I0408 15:55:02.480620 27257 net.cpp:676] Ignoring source layer train-data
I0408 15:55:06.046135 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:55:06.954133 27257 solver.cpp:397]     Test net output #0: accuracy = 0.0275735
I0408 15:55:06.954180 27257 solver.cpp:397]     Test net output #1: loss = 4.96827 (* 1 = 4.96827 loss)
I0408 15:55:07.044561 27257 solver.cpp:218] Iteration 2244 (0.568075 iter/s, 21.124s/12 iters), loss = 5.02333
I0408 15:55:07.044611 27257 solver.cpp:237]     Train net output #0: loss = 5.02333 (* 1 = 5.02333 loss)
I0408 15:55:07.044623 27257 sgd_solver.cpp:105] Iteration 2244, lr = 3.91e-06
I0408 15:55:11.351584 27257 solver.cpp:218] Iteration 2256 (2.78629 iter/s, 4.30679s/12 iters), loss = 4.91081
I0408 15:55:11.351631 27257 solver.cpp:237]     Train net output #0: loss = 4.91081 (* 1 = 4.91081 loss)
I0408 15:55:11.351644 27257 sgd_solver.cpp:105] Iteration 2256, lr = 3.74932e-06
I0408 15:55:16.343024 27257 solver.cpp:218] Iteration 2268 (2.40424 iter/s, 4.99118s/12 iters), loss = 4.86751
I0408 15:55:16.343075 27257 solver.cpp:237]     Train net output #0: loss = 4.86751 (* 1 = 4.86751 loss)
I0408 15:55:16.343086 27257 sgd_solver.cpp:105] Iteration 2268, lr = 3.59525e-06
I0408 15:55:21.298102 27257 solver.cpp:218] Iteration 2280 (2.42188 iter/s, 4.95482s/12 iters), loss = 5.00705
I0408 15:55:21.298158 27257 solver.cpp:237]     Train net output #0: loss = 5.00705 (* 1 = 5.00705 loss)
I0408 15:55:21.298171 27257 sgd_solver.cpp:105] Iteration 2280, lr = 3.44751e-06
I0408 15:55:26.408149 27257 solver.cpp:218] Iteration 2292 (2.34844 iter/s, 5.10978s/12 iters), loss = 5.01412
I0408 15:55:26.408200 27257 solver.cpp:237]     Train net output #0: loss = 5.01412 (* 1 = 5.01412 loss)
I0408 15:55:26.408213 27257 sgd_solver.cpp:105] Iteration 2292, lr = 3.30584e-06
I0408 15:55:31.414317 27257 solver.cpp:218] Iteration 2304 (2.39716 iter/s, 5.00592s/12 iters), loss = 4.94299
I0408 15:55:31.414433 27257 solver.cpp:237]     Train net output #0: loss = 4.94299 (* 1 = 4.94299 loss)
I0408 15:55:31.414448 27257 sgd_solver.cpp:105] Iteration 2304, lr = 3.16999e-06
I0408 15:55:36.447436 27257 solver.cpp:218] Iteration 2316 (2.38436 iter/s, 5.0328s/12 iters), loss = 4.96377
I0408 15:55:36.447484 27257 solver.cpp:237]     Train net output #0: loss = 4.96377 (* 1 = 4.96377 loss)
I0408 15:55:36.447494 27257 sgd_solver.cpp:105] Iteration 2316, lr = 3.03973e-06
I0408 15:55:40.405591 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:55:41.444500 27257 solver.cpp:218] Iteration 2328 (2.40153 iter/s, 4.9968s/12 iters), loss = 4.80967
I0408 15:55:41.444553 27257 solver.cpp:237]     Train net output #0: loss = 4.80967 (* 1 = 4.80967 loss)
I0408 15:55:41.444566 27257 sgd_solver.cpp:105] Iteration 2328, lr = 2.91481e-06
I0408 15:55:46.492228 27257 solver.cpp:218] Iteration 2340 (2.37743 iter/s, 5.04747s/12 iters), loss = 5.00645
I0408 15:55:46.492274 27257 solver.cpp:237]     Train net output #0: loss = 5.00645 (* 1 = 5.00645 loss)
I0408 15:55:46.492286 27257 sgd_solver.cpp:105] Iteration 2340, lr = 2.79503e-06
I0408 15:55:48.510659 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2346.caffemodel
I0408 15:55:55.831715 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2346.solverstate
I0408 15:55:58.582458 27257 solver.cpp:330] Iteration 2346, Testing net (#0)
I0408 15:55:58.582477 27257 net.cpp:676] Ignoring source layer train-data
I0408 15:56:02.113425 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:56:03.055737 27257 solver.cpp:397]     Test net output #0: accuracy = 0.0275735
I0408 15:56:03.055788 27257 solver.cpp:397]     Test net output #1: loss = 4.97031 (* 1 = 4.97031 loss)
I0408 15:56:04.972229 27257 solver.cpp:218] Iteration 2352 (0.649378 iter/s, 18.4792s/12 iters), loss = 5.02898
I0408 15:56:04.972287 27257 solver.cpp:237]     Train net output #0: loss = 5.02898 (* 1 = 5.02898 loss)
I0408 15:56:04.972302 27257 sgd_solver.cpp:105] Iteration 2352, lr = 2.68018e-06
I0408 15:56:09.944651 27257 solver.cpp:218] Iteration 2364 (2.41344 iter/s, 4.97216s/12 iters), loss = 4.83857
I0408 15:56:09.944705 27257 solver.cpp:237]     Train net output #0: loss = 4.83857 (* 1 = 4.83857 loss)
I0408 15:56:09.944720 27257 sgd_solver.cpp:105] Iteration 2364, lr = 2.57004e-06
I0408 15:56:15.021288 27257 solver.cpp:218] Iteration 2376 (2.36389 iter/s, 5.07638s/12 iters), loss = 4.90144
I0408 15:56:15.021340 27257 solver.cpp:237]     Train net output #0: loss = 4.90144 (* 1 = 4.90144 loss)
I0408 15:56:15.021353 27257 sgd_solver.cpp:105] Iteration 2376, lr = 2.46443e-06
I0408 15:56:20.097460 27257 solver.cpp:218] Iteration 2388 (2.36411 iter/s, 5.07591s/12 iters), loss = 4.92724
I0408 15:56:20.097514 27257 solver.cpp:237]     Train net output #0: loss = 4.92724 (* 1 = 4.92724 loss)
I0408 15:56:20.097527 27257 sgd_solver.cpp:105] Iteration 2388, lr = 2.36316e-06
I0408 15:56:25.144078 27257 solver.cpp:218] Iteration 2400 (2.37795 iter/s, 5.04636s/12 iters), loss = 5.07093
I0408 15:56:25.144125 27257 solver.cpp:237]     Train net output #0: loss = 5.07093 (* 1 = 5.07093 loss)
I0408 15:56:25.144134 27257 sgd_solver.cpp:105] Iteration 2400, lr = 2.26605e-06
I0408 15:56:30.175154 27257 solver.cpp:218] Iteration 2412 (2.38529 iter/s, 5.03083s/12 iters), loss = 4.83498
I0408 15:56:30.175192 27257 solver.cpp:237]     Train net output #0: loss = 4.83498 (* 1 = 4.83498 loss)
I0408 15:56:30.175201 27257 sgd_solver.cpp:105] Iteration 2412, lr = 2.17293e-06
I0408 15:56:35.239917 27257 solver.cpp:218] Iteration 2424 (2.36943 iter/s, 5.06452s/12 iters), loss = 5.0007
I0408 15:56:35.240038 27257 solver.cpp:237]     Train net output #0: loss = 5.0007 (* 1 = 5.0007 loss)
I0408 15:56:35.240049 27257 sgd_solver.cpp:105] Iteration 2424, lr = 2.08363e-06
I0408 15:56:36.337500 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:56:40.299861 27257 solver.cpp:218] Iteration 2436 (2.37172 iter/s, 5.05961s/12 iters), loss = 4.92971
I0408 15:56:40.299911 27257 solver.cpp:237]     Train net output #0: loss = 4.92971 (* 1 = 4.92971 loss)
I0408 15:56:40.299922 27257 sgd_solver.cpp:105] Iteration 2436, lr = 1.99801e-06
I0408 15:56:44.935887 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2448.caffemodel
I0408 15:56:50.376013 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2448.solverstate
I0408 15:56:55.269650 27257 solver.cpp:330] Iteration 2448, Testing net (#0)
I0408 15:56:55.269677 27257 net.cpp:676] Ignoring source layer train-data
I0408 15:56:58.764874 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:56:59.742915 27257 solver.cpp:397]     Test net output #0: accuracy = 0.0275735
I0408 15:56:59.742961 27257 solver.cpp:397]     Test net output #1: loss = 4.96641 (* 1 = 4.96641 loss)
I0408 15:56:59.833317 27257 solver.cpp:218] Iteration 2448 (0.614356 iter/s, 19.5326s/12 iters), loss = 4.89671
I0408 15:56:59.833365 27257 solver.cpp:237]     Train net output #0: loss = 4.89671 (* 1 = 4.89671 loss)
I0408 15:56:59.833377 27257 sgd_solver.cpp:105] Iteration 2448, lr = 1.91591e-06
I0408 15:57:04.351018 27257 solver.cpp:218] Iteration 2460 (2.65636 iter/s, 4.51746s/12 iters), loss = 4.95361
I0408 15:57:04.351069 27257 solver.cpp:237]     Train net output #0: loss = 4.95361 (* 1 = 4.95361 loss)
I0408 15:57:04.351083 27257 sgd_solver.cpp:105] Iteration 2460, lr = 1.83718e-06
I0408 15:57:09.314286 27257 solver.cpp:218] Iteration 2472 (2.41789 iter/s, 4.96301s/12 iters), loss = 4.9627
I0408 15:57:09.314401 27257 solver.cpp:237]     Train net output #0: loss = 4.9627 (* 1 = 4.9627 loss)
I0408 15:57:09.314415 27257 sgd_solver.cpp:105] Iteration 2472, lr = 1.76168e-06
I0408 15:57:14.343163 27257 solver.cpp:218] Iteration 2484 (2.38637 iter/s, 5.02855s/12 iters), loss = 4.97103
I0408 15:57:14.343216 27257 solver.cpp:237]     Train net output #0: loss = 4.97103 (* 1 = 4.97103 loss)
I0408 15:57:14.343228 27257 sgd_solver.cpp:105] Iteration 2484, lr = 1.68929e-06
I0408 15:57:19.329317 27257 solver.cpp:218] Iteration 2496 (2.40679 iter/s, 4.9859s/12 iters), loss = 5.01133
I0408 15:57:19.329375 27257 solver.cpp:237]     Train net output #0: loss = 5.01133 (* 1 = 5.01133 loss)
I0408 15:57:19.329391 27257 sgd_solver.cpp:105] Iteration 2496, lr = 1.61987e-06
I0408 15:57:24.381633 27257 solver.cpp:218] Iteration 2508 (2.37527 iter/s, 5.05206s/12 iters), loss = 4.92711
I0408 15:57:24.381680 27257 solver.cpp:237]     Train net output #0: loss = 4.92711 (* 1 = 4.92711 loss)
I0408 15:57:24.381691 27257 sgd_solver.cpp:105] Iteration 2508, lr = 1.5533e-06
I0408 15:57:29.556557 27257 solver.cpp:218] Iteration 2520 (2.31899 iter/s, 5.17466s/12 iters), loss = 4.9326
I0408 15:57:29.556612 27257 solver.cpp:237]     Train net output #0: loss = 4.9326 (* 1 = 4.9326 loss)
I0408 15:57:29.556624 27257 sgd_solver.cpp:105] Iteration 2520, lr = 1.48947e-06
I0408 15:57:32.758443 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:57:34.549868 27257 solver.cpp:218] Iteration 2532 (2.40334 iter/s, 4.99305s/12 iters), loss = 4.9797
I0408 15:57:34.549909 27257 solver.cpp:237]     Train net output #0: loss = 4.9797 (* 1 = 4.9797 loss)
I0408 15:57:34.549921 27257 sgd_solver.cpp:105] Iteration 2532, lr = 1.42826e-06
I0408 15:57:39.598918 27257 solver.cpp:218] Iteration 2544 (2.3768 iter/s, 5.0488s/12 iters), loss = 5.01809
I0408 15:57:39.599041 27257 solver.cpp:237]     Train net output #0: loss = 5.01809 (* 1 = 5.01809 loss)
I0408 15:57:39.599056 27257 sgd_solver.cpp:105] Iteration 2544, lr = 1.36957e-06
I0408 15:57:41.792222 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2550.caffemodel
I0408 15:57:46.293275 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2550.solverstate
I0408 15:57:48.776718 27257 solver.cpp:330] Iteration 2550, Testing net (#0)
I0408 15:57:48.776743 27257 net.cpp:676] Ignoring source layer train-data
I0408 15:57:52.139003 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:57:53.162273 27257 solver.cpp:397]     Test net output #0: accuracy = 0.0281863
I0408 15:57:53.162322 27257 solver.cpp:397]     Test net output #1: loss = 4.96928 (* 1 = 4.96928 loss)
I0408 15:57:55.116057 27257 solver.cpp:218] Iteration 2556 (0.773375 iter/s, 15.5164s/12 iters), loss = 5.03307
I0408 15:57:55.116109 27257 solver.cpp:237]     Train net output #0: loss = 5.03307 (* 1 = 5.03307 loss)
I0408 15:57:55.116122 27257 sgd_solver.cpp:105] Iteration 2556, lr = 1.31329e-06
I0408 15:58:00.356163 27257 solver.cpp:218] Iteration 2568 (2.29015 iter/s, 5.23984s/12 iters), loss = 4.90873
I0408 15:58:00.356213 27257 solver.cpp:237]     Train net output #0: loss = 4.90873 (* 1 = 4.90873 loss)
I0408 15:58:00.356225 27257 sgd_solver.cpp:105] Iteration 2568, lr = 1.25932e-06
I0408 15:58:05.392586 27257 solver.cpp:218] Iteration 2580 (2.38277 iter/s, 5.03617s/12 iters), loss = 4.93937
I0408 15:58:05.392640 27257 solver.cpp:237]     Train net output #0: loss = 4.93937 (* 1 = 4.93937 loss)
I0408 15:58:05.392652 27257 sgd_solver.cpp:105] Iteration 2580, lr = 1.20757e-06
I0408 15:58:10.438933 27257 solver.cpp:218] Iteration 2592 (2.37808 iter/s, 5.04609s/12 iters), loss = 4.96243
I0408 15:58:10.439014 27257 solver.cpp:237]     Train net output #0: loss = 4.96243 (* 1 = 4.96243 loss)
I0408 15:58:10.439028 27257 sgd_solver.cpp:105] Iteration 2592, lr = 1.15795e-06
I0408 15:58:15.705813 27257 solver.cpp:218] Iteration 2604 (2.27852 iter/s, 5.26659s/12 iters), loss = 4.98273
I0408 15:58:15.705875 27257 solver.cpp:237]     Train net output #0: loss = 4.98273 (* 1 = 4.98273 loss)
I0408 15:58:15.705888 27257 sgd_solver.cpp:105] Iteration 2604, lr = 1.11037e-06
I0408 15:58:20.664754 27257 solver.cpp:218] Iteration 2616 (2.41999 iter/s, 4.95869s/12 iters), loss = 4.97081
I0408 15:58:20.664808 27257 solver.cpp:237]     Train net output #0: loss = 4.97081 (* 1 = 4.97081 loss)
I0408 15:58:20.664820 27257 sgd_solver.cpp:105] Iteration 2616, lr = 1.06474e-06
I0408 15:58:25.674525 27257 solver.cpp:218] Iteration 2628 (2.39544 iter/s, 5.00951s/12 iters), loss = 5.00719
I0408 15:58:25.674577 27257 solver.cpp:237]     Train net output #0: loss = 5.00719 (* 1 = 5.00719 loss)
I0408 15:58:25.674589 27257 sgd_solver.cpp:105] Iteration 2628, lr = 1.02099e-06
I0408 15:58:26.124810 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:58:30.692119 27257 solver.cpp:218] Iteration 2640 (2.39171 iter/s, 5.01734s/12 iters), loss = 4.85626
I0408 15:58:30.692167 27257 solver.cpp:237]     Train net output #0: loss = 4.85626 (* 1 = 4.85626 loss)
I0408 15:58:30.692178 27257 sgd_solver.cpp:105] Iteration 2640, lr = 9.7903e-07
I0408 15:58:35.254523 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2652.caffemodel
I0408 15:58:39.728119 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2652.solverstate
I0408 15:58:42.452011 27257 solver.cpp:330] Iteration 2652, Testing net (#0)
I0408 15:58:42.452062 27257 net.cpp:676] Ignoring source layer train-data
I0408 15:58:45.824546 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:58:46.884212 27257 solver.cpp:397]     Test net output #0: accuracy = 0.0275735
I0408 15:58:46.884263 27257 solver.cpp:397]     Test net output #1: loss = 4.97089 (* 1 = 4.97089 loss)
I0408 15:58:46.974838 27257 solver.cpp:218] Iteration 2652 (0.737009 iter/s, 16.282s/12 iters), loss = 5.06718
I0408 15:58:46.974895 27257 solver.cpp:237]     Train net output #0: loss = 5.06718 (* 1 = 5.06718 loss)
I0408 15:58:46.974907 27257 sgd_solver.cpp:105] Iteration 2652, lr = 9.38798e-07
I0408 15:58:51.264590 27257 solver.cpp:218] Iteration 2664 (2.79752 iter/s, 4.28951s/12 iters), loss = 4.9276
I0408 15:58:51.264640 27257 solver.cpp:237]     Train net output #0: loss = 4.9276 (* 1 = 4.9276 loss)
I0408 15:58:51.264653 27257 sgd_solver.cpp:105] Iteration 2664, lr = 9.0022e-07
I0408 15:58:56.264554 27257 solver.cpp:218] Iteration 2676 (2.40014 iter/s, 4.99972s/12 iters), loss = 4.90008
I0408 15:58:56.264596 27257 solver.cpp:237]     Train net output #0: loss = 4.90008 (* 1 = 4.90008 loss)
I0408 15:58:56.264605 27257 sgd_solver.cpp:105] Iteration 2676, lr = 8.63227e-07
I0408 15:59:01.304023 27257 solver.cpp:218] Iteration 2688 (2.38132 iter/s, 5.03922s/12 iters), loss = 4.89193
I0408 15:59:01.304072 27257 solver.cpp:237]     Train net output #0: loss = 4.89193 (* 1 = 4.89193 loss)
I0408 15:59:01.304085 27257 sgd_solver.cpp:105] Iteration 2688, lr = 8.27754e-07
I0408 15:59:06.280553 27257 solver.cpp:218] Iteration 2700 (2.41144 iter/s, 4.97628s/12 iters), loss = 4.92245
I0408 15:59:06.280591 27257 solver.cpp:237]     Train net output #0: loss = 4.92245 (* 1 = 4.92245 loss)
I0408 15:59:06.280598 27257 sgd_solver.cpp:105] Iteration 2700, lr = 7.93739e-07
I0408 15:59:11.355127 27257 solver.cpp:218] Iteration 2712 (2.36485 iter/s, 5.07433s/12 iters), loss = 4.88539
I0408 15:59:11.355180 27257 solver.cpp:237]     Train net output #0: loss = 4.88539 (* 1 = 4.88539 loss)
I0408 15:59:11.355190 27257 sgd_solver.cpp:105] Iteration 2712, lr = 7.61121e-07
I0408 15:59:16.291445 27257 solver.cpp:218] Iteration 2724 (2.43109 iter/s, 4.93606s/12 iters), loss = 4.92537
I0408 15:59:16.291577 27257 solver.cpp:237]     Train net output #0: loss = 4.92537 (* 1 = 4.92537 loss)
I0408 15:59:16.291589 27257 sgd_solver.cpp:105] Iteration 2724, lr = 7.29844e-07
I0408 15:59:18.843364 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:59:21.292943 27257 solver.cpp:218] Iteration 2736 (2.39944 iter/s, 5.00116s/12 iters), loss = 4.86544
I0408 15:59:21.292990 27257 solver.cpp:237]     Train net output #0: loss = 4.86544 (* 1 = 4.86544 loss)
I0408 15:59:21.293001 27257 sgd_solver.cpp:105] Iteration 2736, lr = 6.99853e-07
I0408 15:59:26.231231 27257 solver.cpp:218] Iteration 2748 (2.43011 iter/s, 4.93804s/12 iters), loss = 4.95742
I0408 15:59:26.231271 27257 solver.cpp:237]     Train net output #0: loss = 4.95742 (* 1 = 4.95742 loss)
I0408 15:59:26.231279 27257 sgd_solver.cpp:105] Iteration 2748, lr = 6.71093e-07
I0408 15:59:28.277473 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2754.caffemodel
I0408 15:59:32.995350 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2754.solverstate
I0408 15:59:36.974885 27257 solver.cpp:330] Iteration 2754, Testing net (#0)
I0408 15:59:36.974912 27257 net.cpp:676] Ignoring source layer train-data
I0408 15:59:39.971673 27257 blocking_queue.cpp:49] Waiting for data
I0408 15:59:40.207643 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:59:41.311904 27257 solver.cpp:397]     Test net output #0: accuracy = 0.0275735
I0408 15:59:41.311949 27257 solver.cpp:397]     Test net output #1: loss = 4.96999 (* 1 = 4.96999 loss)
I0408 15:59:43.316520 27257 solver.cpp:218] Iteration 2760 (0.702388 iter/s, 17.0846s/12 iters), loss = 4.8759
I0408 15:59:43.316568 27257 solver.cpp:237]     Train net output #0: loss = 4.8759 (* 1 = 4.8759 loss)
I0408 15:59:43.316581 27257 sgd_solver.cpp:105] Iteration 2760, lr = 6.43516e-07
I0408 15:59:48.449590 27257 solver.cpp:218] Iteration 2772 (2.3379 iter/s, 5.13281s/12 iters), loss = 4.95607
I0408 15:59:48.449695 27257 solver.cpp:237]     Train net output #0: loss = 4.95607 (* 1 = 4.95607 loss)
I0408 15:59:48.449707 27257 sgd_solver.cpp:105] Iteration 2772, lr = 6.17072e-07
I0408 15:59:53.486110 27257 solver.cpp:218] Iteration 2784 (2.38275 iter/s, 5.03621s/12 iters), loss = 4.9298
I0408 15:59:53.486155 27257 solver.cpp:237]     Train net output #0: loss = 4.9298 (* 1 = 4.9298 loss)
I0408 15:59:53.486163 27257 sgd_solver.cpp:105] Iteration 2784, lr = 5.91714e-07
I0408 15:59:58.501019 27257 solver.cpp:218] Iteration 2796 (2.39299 iter/s, 5.01465s/12 iters), loss = 4.80368
I0408 15:59:58.501070 27257 solver.cpp:237]     Train net output #0: loss = 4.80368 (* 1 = 4.80368 loss)
I0408 15:59:58.501081 27257 sgd_solver.cpp:105] Iteration 2796, lr = 5.67399e-07
I0408 16:00:03.519783 27257 solver.cpp:218] Iteration 2808 (2.39115 iter/s, 5.01851s/12 iters), loss = 4.73893
I0408 16:00:03.519824 27257 solver.cpp:237]     Train net output #0: loss = 4.73893 (* 1 = 4.73893 loss)
I0408 16:00:03.519834 27257 sgd_solver.cpp:105] Iteration 2808, lr = 5.44082e-07
I0408 16:00:08.619516 27257 solver.cpp:218] Iteration 2820 (2.35318 iter/s, 5.09948s/12 iters), loss = 4.90954
I0408 16:00:08.619565 27257 solver.cpp:237]     Train net output #0: loss = 4.90954 (* 1 = 4.90954 loss)
I0408 16:00:08.619577 27257 sgd_solver.cpp:105] Iteration 2820, lr = 5.21724e-07
I0408 16:00:13.318862 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:00:13.604396 27257 solver.cpp:218] Iteration 2832 (2.4074 iter/s, 4.98463s/12 iters), loss = 5.06478
I0408 16:00:13.604445 27257 solver.cpp:237]     Train net output #0: loss = 5.06478 (* 1 = 5.06478 loss)
I0408 16:00:13.604460 27257 sgd_solver.cpp:105] Iteration 2832, lr = 5.00285e-07
I0408 16:00:18.631330 27257 solver.cpp:218] Iteration 2844 (2.38726 iter/s, 5.02668s/12 iters), loss = 5.04364
I0408 16:00:18.631469 27257 solver.cpp:237]     Train net output #0: loss = 5.04364 (* 1 = 5.04364 loss)
I0408 16:00:18.631482 27257 sgd_solver.cpp:105] Iteration 2844, lr = 4.79727e-07
I0408 16:00:23.178892 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2856.caffemodel
I0408 16:00:26.462214 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2856.solverstate
I0408 16:00:30.101001 27257 solver.cpp:330] Iteration 2856, Testing net (#0)
I0408 16:00:30.101022 27257 net.cpp:676] Ignoring source layer train-data
I0408 16:00:33.486503 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:00:34.625913 27257 solver.cpp:397]     Test net output #0: accuracy = 0.0275735
I0408 16:00:34.625952 27257 solver.cpp:397]     Test net output #1: loss = 4.97216 (* 1 = 4.97216 loss)
I0408 16:00:34.716329 27257 solver.cpp:218] Iteration 2856 (0.746072 iter/s, 16.0842s/12 iters), loss = 4.79556
I0408 16:00:34.716379 27257 solver.cpp:237]     Train net output #0: loss = 4.79556 (* 1 = 4.79556 loss)
I0408 16:00:34.716388 27257 sgd_solver.cpp:105] Iteration 2856, lr = 4.60013e-07
I0408 16:00:39.022560 27257 solver.cpp:218] Iteration 2868 (2.78681 iter/s, 4.30601s/12 iters), loss = 4.90004
I0408 16:00:39.022604 27257 solver.cpp:237]     Train net output #0: loss = 4.90004 (* 1 = 4.90004 loss)
I0408 16:00:39.022615 27257 sgd_solver.cpp:105] Iteration 2868, lr = 4.41109e-07
I0408 16:00:44.052561 27257 solver.cpp:218] Iteration 2880 (2.3858 iter/s, 5.02975s/12 iters), loss = 4.96641
I0408 16:00:44.052599 27257 solver.cpp:237]     Train net output #0: loss = 4.96641 (* 1 = 4.96641 loss)
I0408 16:00:44.052608 27257 sgd_solver.cpp:105] Iteration 2880, lr = 4.22983e-07
I0408 16:00:49.097749 27257 solver.cpp:218] Iteration 2892 (2.37862 iter/s, 5.04494s/12 iters), loss = 4.9342
I0408 16:00:49.097829 27257 solver.cpp:237]     Train net output #0: loss = 4.9342 (* 1 = 4.9342 loss)
I0408 16:00:49.097837 27257 sgd_solver.cpp:105] Iteration 2892, lr = 4.05601e-07
I0408 16:00:54.109079 27257 solver.cpp:218] Iteration 2904 (2.39471 iter/s, 5.01104s/12 iters), loss = 5.0315
I0408 16:00:54.109138 27257 solver.cpp:237]     Train net output #0: loss = 5.0315 (* 1 = 5.0315 loss)
I0408 16:00:54.109151 27257 sgd_solver.cpp:105] Iteration 2904, lr = 3.88934e-07
I0408 16:00:59.119119 27257 solver.cpp:218] Iteration 2916 (2.39531 iter/s, 5.00978s/12 iters), loss = 4.85663
I0408 16:00:59.119160 27257 solver.cpp:237]     Train net output #0: loss = 4.85663 (* 1 = 4.85663 loss)
I0408 16:00:59.119169 27257 sgd_solver.cpp:105] Iteration 2916, lr = 3.72951e-07
I0408 16:01:04.139976 27257 solver.cpp:218] Iteration 2928 (2.39015 iter/s, 5.02061s/12 iters), loss = 5.09683
I0408 16:01:04.140034 27257 solver.cpp:237]     Train net output #0: loss = 5.09683 (* 1 = 5.09683 loss)
I0408 16:01:04.140051 27257 sgd_solver.cpp:105] Iteration 2928, lr = 3.57625e-07
I0408 16:01:05.997248 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:01:09.190542 27257 solver.cpp:218] Iteration 2940 (2.37609 iter/s, 5.05031s/12 iters), loss = 4.98603
I0408 16:01:09.190580 27257 solver.cpp:237]     Train net output #0: loss = 4.98603 (* 1 = 4.98603 loss)
I0408 16:01:09.190589 27257 sgd_solver.cpp:105] Iteration 2940, lr = 3.42929e-07
I0408 16:01:14.178498 27257 solver.cpp:218] Iteration 2952 (2.40591 iter/s, 4.98771s/12 iters), loss = 4.98893
I0408 16:01:14.178545 27257 solver.cpp:237]     Train net output #0: loss = 4.98893 (* 1 = 4.98893 loss)
I0408 16:01:14.178555 27257 sgd_solver.cpp:105] Iteration 2952, lr = 3.28837e-07
I0408 16:01:16.191988 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2958.caffemodel
I0408 16:01:19.122645 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2958.solverstate
I0408 16:01:22.881498 27257 solver.cpp:330] Iteration 2958, Testing net (#0)
I0408 16:01:22.881520 27257 net.cpp:676] Ignoring source layer train-data
I0408 16:01:26.380026 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:01:27.859921 27257 solver.cpp:397]     Test net output #0: accuracy = 0.0275735
I0408 16:01:27.859968 27257 solver.cpp:397]     Test net output #1: loss = 4.96587 (* 1 = 4.96587 loss)
I0408 16:01:29.833730 27257 solver.cpp:218] Iteration 2964 (0.766549 iter/s, 15.6546s/12 iters), loss = 4.89137
I0408 16:01:29.833786 27257 solver.cpp:237]     Train net output #0: loss = 4.89137 (* 1 = 4.89137 loss)
I0408 16:01:29.833797 27257 sgd_solver.cpp:105] Iteration 2964, lr = 3.15324e-07
I0408 16:01:34.867725 27257 solver.cpp:218] Iteration 2976 (2.38392 iter/s, 5.03373s/12 iters), loss = 4.88638
I0408 16:01:34.867772 27257 solver.cpp:237]     Train net output #0: loss = 4.88638 (* 1 = 4.88638 loss)
I0408 16:01:34.867782 27257 sgd_solver.cpp:105] Iteration 2976, lr = 3.02366e-07
I0408 16:01:39.882925 27257 solver.cpp:218] Iteration 2988 (2.39285 iter/s, 5.01494s/12 iters), loss = 5.02777
I0408 16:01:39.882978 27257 solver.cpp:237]     Train net output #0: loss = 5.02777 (* 1 = 5.02777 loss)
I0408 16:01:39.882990 27257 sgd_solver.cpp:105] Iteration 2988, lr = 2.89941e-07
I0408 16:01:44.965119 27257 solver.cpp:218] Iteration 3000 (2.36131 iter/s, 5.08193s/12 iters), loss = 4.98617
I0408 16:01:44.965173 27257 solver.cpp:237]     Train net output #0: loss = 4.98617 (* 1 = 4.98617 loss)
I0408 16:01:44.965185 27257 sgd_solver.cpp:105] Iteration 3000, lr = 2.78026e-07
I0408 16:01:50.033102 27257 solver.cpp:218] Iteration 3012 (2.36793 iter/s, 5.06773s/12 iters), loss = 4.88701
I0408 16:01:50.033181 27257 solver.cpp:237]     Train net output #0: loss = 4.88701 (* 1 = 4.88701 loss)
I0408 16:01:50.033193 27257 sgd_solver.cpp:105] Iteration 3012, lr = 2.66601e-07
I0408 16:01:55.098553 27257 solver.cpp:218] Iteration 3024 (2.36912 iter/s, 5.06517s/12 iters), loss = 4.91213
I0408 16:01:55.098600 27257 solver.cpp:237]     Train net output #0: loss = 4.91213 (* 1 = 4.91213 loss)
I0408 16:01:55.098611 27257 sgd_solver.cpp:105] Iteration 3024, lr = 2.55646e-07
I0408 16:01:59.143043 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:02:00.240620 27257 solver.cpp:218] Iteration 3036 (2.33381 iter/s, 5.1418s/12 iters), loss = 4.84885
I0408 16:02:00.240675 27257 solver.cpp:237]     Train net output #0: loss = 4.84885 (* 1 = 4.84885 loss)
I0408 16:02:00.240686 27257 sgd_solver.cpp:105] Iteration 3036, lr = 2.45141e-07
I0408 16:02:05.714309 27257 solver.cpp:218] Iteration 3048 (2.19242 iter/s, 5.47341s/12 iters), loss = 4.99697
I0408 16:02:05.714362 27257 solver.cpp:237]     Train net output #0: loss = 4.99697 (* 1 = 4.99697 loss)
I0408 16:02:05.714375 27257 sgd_solver.cpp:105] Iteration 3048, lr = 2.35067e-07
I0408 16:02:10.469663 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3060.caffemodel
I0408 16:02:14.276937 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3060.solverstate
I0408 16:02:19.084466 27257 solver.cpp:330] Iteration 3060, Testing net (#0)
I0408 16:02:19.084493 27257 net.cpp:676] Ignoring source layer train-data
I0408 16:02:22.391337 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:02:23.713887 27257 solver.cpp:397]     Test net output #0: accuracy = 0.028799
I0408 16:02:23.713935 27257 solver.cpp:397]     Test net output #1: loss = 4.96565 (* 1 = 4.96565 loss)
I0408 16:02:23.804520 27257 solver.cpp:218] Iteration 3060 (0.66337 iter/s, 18.0894s/12 iters), loss = 4.97175
I0408 16:02:23.804590 27257 solver.cpp:237]     Train net output #0: loss = 4.97175 (* 1 = 4.97175 loss)
I0408 16:02:23.804605 27257 sgd_solver.cpp:105] Iteration 3060, lr = 2.25407e-07
I0408 16:02:28.032821 27257 solver.cpp:218] Iteration 3072 (2.83818 iter/s, 4.22806s/12 iters), loss = 4.82815
I0408 16:02:28.032860 27257 solver.cpp:237]     Train net output #0: loss = 4.82815 (* 1 = 4.82815 loss)
I0408 16:02:28.032871 27257 sgd_solver.cpp:105] Iteration 3072, lr = 2.16145e-07
I0408 16:02:33.077757 27257 solver.cpp:218] Iteration 3084 (2.37874 iter/s, 5.04469s/12 iters), loss = 4.88058
I0408 16:02:33.077795 27257 solver.cpp:237]     Train net output #0: loss = 4.88058 (* 1 = 4.88058 loss)
I0408 16:02:33.077802 27257 sgd_solver.cpp:105] Iteration 3084, lr = 2.07262e-07
I0408 16:02:38.054693 27257 solver.cpp:218] Iteration 3096 (2.41124 iter/s, 4.97669s/12 iters), loss = 4.93658
I0408 16:02:38.054747 27257 solver.cpp:237]     Train net output #0: loss = 4.93658 (* 1 = 4.93658 loss)
I0408 16:02:38.054759 27257 sgd_solver.cpp:105] Iteration 3096, lr = 1.98745e-07
I0408 16:02:43.242899 27257 solver.cpp:218] Iteration 3108 (2.31306 iter/s, 5.18794s/12 iters), loss = 4.9791
I0408 16:02:43.242946 27257 solver.cpp:237]     Train net output #0: loss = 4.9791 (* 1 = 4.9791 loss)
I0408 16:02:43.242959 27257 sgd_solver.cpp:105] Iteration 3108, lr = 1.90578e-07
I0408 16:02:48.260270 27257 solver.cpp:218] Iteration 3120 (2.39181 iter/s, 5.01712s/12 iters), loss = 4.82442
I0408 16:02:48.260318 27257 solver.cpp:237]     Train net output #0: loss = 4.82442 (* 1 = 4.82442 loss)
I0408 16:02:48.260329 27257 sgd_solver.cpp:105] Iteration 3120, lr = 1.82747e-07
I0408 16:02:53.315023 27257 solver.cpp:218] Iteration 3132 (2.37412 iter/s, 5.0545s/12 iters), loss = 5.01939
I0408 16:02:53.315138 27257 solver.cpp:237]     Train net output #0: loss = 5.01939 (* 1 = 5.01939 loss)
I0408 16:02:53.315148 27257 sgd_solver.cpp:105] Iteration 3132, lr = 1.75237e-07
I0408 16:02:54.423032 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:02:58.353546 27257 solver.cpp:218] Iteration 3144 (2.3818 iter/s, 5.0382s/12 iters), loss = 4.88529
I0408 16:02:58.353591 27257 solver.cpp:237]     Train net output #0: loss = 4.88529 (* 1 = 4.88529 loss)
I0408 16:02:58.353602 27257 sgd_solver.cpp:105] Iteration 3144, lr = 1.68036e-07
I0408 16:03:03.373127 27257 solver.cpp:218] Iteration 3156 (2.39076 iter/s, 5.01933s/12 iters), loss = 5.00869
I0408 16:03:03.373172 27257 solver.cpp:237]     Train net output #0: loss = 5.00869 (* 1 = 5.00869 loss)
I0408 16:03:03.373181 27257 sgd_solver.cpp:105] Iteration 3156, lr = 1.61131e-07
I0408 16:03:05.449788 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3162.caffemodel
I0408 16:03:08.508688 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3162.solverstate
I0408 16:03:12.833223 27257 solver.cpp:330] Iteration 3162, Testing net (#0)
I0408 16:03:12.833251 27257 net.cpp:676] Ignoring source layer train-data
I0408 16:03:16.032132 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:03:17.300447 27257 solver.cpp:397]     Test net output #0: accuracy = 0.0275735
I0408 16:03:17.300494 27257 solver.cpp:397]     Test net output #1: loss = 4.96756 (* 1 = 4.96756 loss)
I0408 16:03:19.299794 27257 solver.cpp:218] Iteration 3168 (0.753485 iter/s, 15.926s/12 iters), loss = 5.05795
I0408 16:03:19.299849 27257 solver.cpp:237]     Train net output #0: loss = 5.05795 (* 1 = 5.05795 loss)
I0408 16:03:19.299861 27257 sgd_solver.cpp:105] Iteration 3168, lr = 1.54509e-07
I0408 16:03:24.470880 27257 solver.cpp:218] Iteration 3180 (2.32072 iter/s, 5.17082s/12 iters), loss = 5.00366
I0408 16:03:24.471045 27257 solver.cpp:237]     Train net output #0: loss = 5.00366 (* 1 = 5.00366 loss)
I0408 16:03:24.471058 27257 sgd_solver.cpp:105] Iteration 3180, lr = 1.4816e-07
I0408 16:03:29.540771 27257 solver.cpp:218] Iteration 3192 (2.36709 iter/s, 5.06952s/12 iters), loss = 4.93962
I0408 16:03:29.540824 27257 solver.cpp:237]     Train net output #0: loss = 4.93962 (* 1 = 4.93962 loss)
I0408 16:03:29.540836 27257 sgd_solver.cpp:105] Iteration 3192, lr = 1.42072e-07
I0408 16:03:34.512673 27257 solver.cpp:218] Iteration 3204 (2.41369 iter/s, 4.97164s/12 iters), loss = 4.92067
I0408 16:03:34.512725 27257 solver.cpp:237]     Train net output #0: loss = 4.92067 (* 1 = 4.92067 loss)
I0408 16:03:34.512735 27257 sgd_solver.cpp:105] Iteration 3204, lr = 1.36234e-07
I0408 16:03:39.531033 27257 solver.cpp:218] Iteration 3216 (2.39134 iter/s, 5.01811s/12 iters), loss = 5.02776
I0408 16:03:39.531076 27257 solver.cpp:237]     Train net output #0: loss = 5.02776 (* 1 = 5.02776 loss)
I0408 16:03:39.531088 27257 sgd_solver.cpp:105] Iteration 3216, lr = 1.30635e-07
I0408 16:03:44.577971 27257 solver.cpp:218] Iteration 3228 (2.3778 iter/s, 5.04668s/12 iters), loss = 4.9555
I0408 16:03:44.578011 27257 solver.cpp:237]     Train net output #0: loss = 4.9555 (* 1 = 4.9555 loss)
I0408 16:03:44.578018 27257 sgd_solver.cpp:105] Iteration 3228, lr = 1.25267e-07
I0408 16:03:47.857697 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:03:49.618227 27257 solver.cpp:218] Iteration 3240 (2.38095 iter/s, 5.04001s/12 iters), loss = 5.00874
I0408 16:03:49.618275 27257 solver.cpp:237]     Train net output #0: loss = 5.00874 (* 1 = 5.00874 loss)
I0408 16:03:49.618286 27257 sgd_solver.cpp:105] Iteration 3240, lr = 1.20119e-07
I0408 16:03:54.643513 27257 solver.cpp:218] Iteration 3252 (2.38804 iter/s, 5.02504s/12 iters), loss = 5.05511
I0408 16:03:54.643623 27257 solver.cpp:237]     Train net output #0: loss = 5.05511 (* 1 = 5.05511 loss)
I0408 16:03:54.643633 27257 sgd_solver.cpp:105] Iteration 3252, lr = 1.15183e-07
I0408 16:03:59.172596 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3264.caffemodel
I0408 16:04:02.139089 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3264.solverstate
I0408 16:04:05.825155 27257 solver.cpp:330] Iteration 3264, Testing net (#0)
I0408 16:04:05.825176 27257 net.cpp:676] Ignoring source layer train-data
I0408 16:04:09.020294 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:04:10.361096 27257 solver.cpp:397]     Test net output #0: accuracy = 0.0275735
I0408 16:04:10.361145 27257 solver.cpp:397]     Test net output #1: loss = 4.96934 (* 1 = 4.96934 loss)
I0408 16:04:10.451603 27257 solver.cpp:218] Iteration 3264 (0.75914 iter/s, 15.8074s/12 iters), loss = 5.08502
I0408 16:04:10.451653 27257 solver.cpp:237]     Train net output #0: loss = 5.08502 (* 1 = 5.08502 loss)
I0408 16:04:10.451665 27257 sgd_solver.cpp:105] Iteration 3264, lr = 1.1045e-07
I0408 16:04:14.958988 27257 solver.cpp:218] Iteration 3276 (2.66244 iter/s, 4.50715s/12 iters), loss = 4.88171
I0408 16:04:14.959033 27257 solver.cpp:237]     Train net output #0: loss = 4.88171 (* 1 = 4.88171 loss)
I0408 16:04:14.959041 27257 sgd_solver.cpp:105] Iteration 3276, lr = 1.05911e-07
I0408 16:04:20.222555 27257 solver.cpp:218] Iteration 3288 (2.27994 iter/s, 5.26331s/12 iters), loss = 4.91775
I0408 16:04:20.222599 27257 solver.cpp:237]     Train net output #0: loss = 4.91775 (* 1 = 4.91775 loss)
I0408 16:04:20.222610 27257 sgd_solver.cpp:105] Iteration 3288, lr = 1.01559e-07
I0408 16:04:25.223932 27257 solver.cpp:218] Iteration 3300 (2.39946 iter/s, 5.00112s/12 iters), loss = 4.91979
I0408 16:04:25.224089 27257 solver.cpp:237]     Train net output #0: loss = 4.91979 (* 1 = 4.91979 loss)
I0408 16:04:25.224103 27257 sgd_solver.cpp:105] Iteration 3300, lr = 9.73856e-08
I0408 16:04:30.258396 27257 solver.cpp:218] Iteration 3312 (2.38374 iter/s, 5.03411s/12 iters), loss = 5.00028
I0408 16:04:30.258441 27257 solver.cpp:237]     Train net output #0: loss = 5.00028 (* 1 = 5.00028 loss)
I0408 16:04:30.258452 27257 sgd_solver.cpp:105] Iteration 3312, lr = 9.33837e-08
I0408 16:04:35.283797 27257 solver.cpp:218] Iteration 3324 (2.38799 iter/s, 5.02515s/12 iters), loss = 5.03752
I0408 16:04:35.283843 27257 solver.cpp:237]     Train net output #0: loss = 5.03752 (* 1 = 5.03752 loss)
I0408 16:04:35.283855 27257 sgd_solver.cpp:105] Iteration 3324, lr = 8.95463e-08
I0408 16:04:40.323261 27257 solver.cpp:218] Iteration 3336 (2.38133 iter/s, 5.03921s/12 iters), loss = 5.07727
I0408 16:04:40.323299 27257 solver.cpp:237]     Train net output #0: loss = 5.07727 (* 1 = 5.07727 loss)
I0408 16:04:40.323308 27257 sgd_solver.cpp:105] Iteration 3336, lr = 8.58665e-08
I0408 16:04:40.803515 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:04:45.421564 27257 solver.cpp:218] Iteration 3348 (2.35384 iter/s, 5.09805s/12 iters), loss = 4.91054
I0408 16:04:45.421623 27257 solver.cpp:237]     Train net output #0: loss = 4.91054 (* 1 = 4.91054 loss)
I0408 16:04:45.421636 27257 sgd_solver.cpp:105] Iteration 3348, lr = 8.2338e-08
I0408 16:04:50.455627 27257 solver.cpp:218] Iteration 3360 (2.38389 iter/s, 5.0338s/12 iters), loss = 5.0248
I0408 16:04:50.455672 27257 solver.cpp:237]     Train net output #0: loss = 5.0248 (* 1 = 5.0248 loss)
I0408 16:04:50.455682 27257 sgd_solver.cpp:105] Iteration 3360, lr = 7.89544e-08
I0408 16:04:52.501416 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3366.caffemodel
I0408 16:04:55.520396 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3366.solverstate
I0408 16:04:57.958508 27257 solver.cpp:330] Iteration 3366, Testing net (#0)
I0408 16:04:57.958535 27257 net.cpp:676] Ignoring source layer train-data
I0408 16:05:01.089231 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:05:02.432273 27257 solver.cpp:397]     Test net output #0: accuracy = 0.0275735
I0408 16:05:02.432320 27257 solver.cpp:397]     Test net output #1: loss = 4.9695 (* 1 = 4.9695 loss)
I0408 16:05:04.377099 27257 solver.cpp:218] Iteration 3372 (0.862015 iter/s, 13.9209s/12 iters), loss = 4.93235
I0408 16:05:04.377157 27257 solver.cpp:237]     Train net output #0: loss = 4.93235 (* 1 = 4.93235 loss)
I0408 16:05:04.377169 27257 sgd_solver.cpp:105] Iteration 3372, lr = 7.57099e-08
I0408 16:05:09.397586 27257 solver.cpp:218] Iteration 3384 (2.39033 iter/s, 5.02023s/12 iters), loss = 4.87245
I0408 16:05:09.397634 27257 solver.cpp:237]     Train net output #0: loss = 4.87245 (* 1 = 4.87245 loss)
I0408 16:05:09.397644 27257 sgd_solver.cpp:105] Iteration 3384, lr = 7.25988e-08
I0408 16:05:14.431437 27257 solver.cpp:218] Iteration 3396 (2.38398 iter/s, 5.03359s/12 iters), loss = 4.85853
I0408 16:05:14.431489 27257 solver.cpp:237]     Train net output #0: loss = 4.85853 (* 1 = 4.85853 loss)
I0408 16:05:14.431501 27257 sgd_solver.cpp:105] Iteration 3396, lr = 6.96154e-08
I0408 16:05:19.464247 27257 solver.cpp:218] Iteration 3408 (2.38448 iter/s, 5.03255s/12 iters), loss = 4.93369
I0408 16:05:19.464299 27257 solver.cpp:237]     Train net output #0: loss = 4.93369 (* 1 = 4.93369 loss)
I0408 16:05:19.464311 27257 sgd_solver.cpp:105] Iteration 3408, lr = 6.67547e-08
I0408 16:05:24.454923 27257 solver.cpp:218] Iteration 3420 (2.40461 iter/s, 4.99042s/12 iters), loss = 4.88285
I0408 16:05:24.454960 27257 solver.cpp:237]     Train net output #0: loss = 4.88285 (* 1 = 4.88285 loss)
I0408 16:05:24.454968 27257 sgd_solver.cpp:105] Iteration 3420, lr = 6.40115e-08
I0408 16:05:29.508111 27257 solver.cpp:218] Iteration 3432 (2.37485 iter/s, 5.05294s/12 iters), loss = 4.97289
I0408 16:05:29.508225 27257 solver.cpp:237]     Train net output #0: loss = 4.97289 (* 1 = 4.97289 loss)
I0408 16:05:29.508237 27257 sgd_solver.cpp:105] Iteration 3432, lr = 6.13811e-08
I0408 16:05:32.156392 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:05:34.558177 27257 solver.cpp:218] Iteration 3444 (2.37636 iter/s, 5.04975s/12 iters), loss = 4.84625
I0408 16:05:34.558229 27257 solver.cpp:237]     Train net output #0: loss = 4.84625 (* 1 = 4.84625 loss)
I0408 16:05:34.558241 27257 sgd_solver.cpp:105] Iteration 3444, lr = 5.88587e-08
I0408 16:05:39.607565 27257 solver.cpp:218] Iteration 3456 (2.37665 iter/s, 5.04913s/12 iters), loss = 4.93599
I0408 16:05:39.607610 27257 solver.cpp:237]     Train net output #0: loss = 4.93599 (* 1 = 4.93599 loss)
I0408 16:05:39.607618 27257 sgd_solver.cpp:105] Iteration 3456, lr = 5.644e-08
I0408 16:05:44.226102 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3468.caffemodel
I0408 16:05:47.234186 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3468.solverstate
I0408 16:05:49.598484 27257 solver.cpp:330] Iteration 3468, Testing net (#0)
I0408 16:05:49.598510 27257 net.cpp:676] Ignoring source layer train-data
I0408 16:05:50.161592 27257 blocking_queue.cpp:49] Waiting for data
I0408 16:05:52.780570 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:05:54.162955 27257 solver.cpp:397]     Test net output #0: accuracy = 0.0281863
I0408 16:05:54.162982 27257 solver.cpp:397]     Test net output #1: loss = 4.96721 (* 1 = 4.96721 loss)
I0408 16:05:54.252182 27257 solver.cpp:218] Iteration 3468 (0.819448 iter/s, 14.644s/12 iters), loss = 4.89229
I0408 16:05:54.252226 27257 solver.cpp:237]     Train net output #0: loss = 4.89229 (* 1 = 4.89229 loss)
I0408 16:05:54.252236 27257 sgd_solver.cpp:105] Iteration 3468, lr = 5.41207e-08
I0408 16:05:58.365526 27257 solver.cpp:218] Iteration 3480 (2.91749 iter/s, 4.11312s/12 iters), loss = 4.98568
I0408 16:05:58.365576 27257 solver.cpp:237]     Train net output #0: loss = 4.98568 (* 1 = 4.98568 loss)
I0408 16:05:58.365586 27257 sgd_solver.cpp:105] Iteration 3480, lr = 5.18967e-08
I0408 16:06:03.416131 27257 solver.cpp:218] Iteration 3492 (2.37607 iter/s, 5.05035s/12 iters), loss = 4.90967
I0408 16:06:03.416230 27257 solver.cpp:237]     Train net output #0: loss = 4.90967 (* 1 = 4.90967 loss)
I0408 16:06:03.416244 27257 sgd_solver.cpp:105] Iteration 3492, lr = 4.97641e-08
I0408 16:06:08.469610 27257 solver.cpp:218] Iteration 3504 (2.37475 iter/s, 5.05317s/12 iters), loss = 4.88277
I0408 16:06:08.469655 27257 solver.cpp:237]     Train net output #0: loss = 4.88277 (* 1 = 4.88277 loss)
I0408 16:06:08.469666 27257 sgd_solver.cpp:105] Iteration 3504, lr = 4.77191e-08
I0408 16:06:13.411000 27257 solver.cpp:218] Iteration 3516 (2.42859 iter/s, 4.94114s/12 iters), loss = 4.72995
I0408 16:06:13.411036 27257 solver.cpp:237]     Train net output #0: loss = 4.72995 (* 1 = 4.72995 loss)
I0408 16:06:13.411043 27257 sgd_solver.cpp:105] Iteration 3516, lr = 4.57582e-08
I0408 16:06:18.404213 27257 solver.cpp:218] Iteration 3528 (2.40338 iter/s, 4.99297s/12 iters), loss = 4.93889
I0408 16:06:18.404259 27257 solver.cpp:237]     Train net output #0: loss = 4.93889 (* 1 = 4.93889 loss)
I0408 16:06:18.404271 27257 sgd_solver.cpp:105] Iteration 3528, lr = 4.38779e-08
I0408 16:06:23.154150 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:06:23.414528 27257 solver.cpp:218] Iteration 3540 (2.39518 iter/s, 5.01006s/12 iters), loss = 5.00525
I0408 16:06:23.414587 27257 solver.cpp:237]     Train net output #0: loss = 5.00525 (* 1 = 5.00525 loss)
I0408 16:06:23.414599 27257 sgd_solver.cpp:105] Iteration 3540, lr = 4.20748e-08
I0408 16:06:28.423605 27257 solver.cpp:218] Iteration 3552 (2.39578 iter/s, 5.00882s/12 iters), loss = 5.02368
I0408 16:06:28.423648 27257 solver.cpp:237]     Train net output #0: loss = 5.02368 (* 1 = 5.02368 loss)
I0408 16:06:28.423658 27257 sgd_solver.cpp:105] Iteration 3552, lr = 4.03458e-08
I0408 16:06:33.431006 27257 solver.cpp:218] Iteration 3564 (2.39657 iter/s, 5.00715s/12 iters), loss = 4.8811
I0408 16:06:33.431177 27257 solver.cpp:237]     Train net output #0: loss = 4.8811 (* 1 = 4.8811 loss)
I0408 16:06:33.431195 27257 sgd_solver.cpp:105] Iteration 3564, lr = 3.86878e-08
I0408 16:06:35.449437 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3570.caffemodel
I0408 16:06:38.500954 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3570.solverstate
I0408 16:06:40.949211 27257 solver.cpp:330] Iteration 3570, Testing net (#0)
I0408 16:06:40.949236 27257 net.cpp:676] Ignoring source layer train-data
I0408 16:06:44.055552 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:06:45.474766 27257 solver.cpp:397]     Test net output #0: accuracy = 0.0275735
I0408 16:06:45.474813 27257 solver.cpp:397]     Test net output #1: loss = 4.97036 (* 1 = 4.97036 loss)
I0408 16:06:47.280287 27257 solver.cpp:218] Iteration 3576 (0.866515 iter/s, 13.8486s/12 iters), loss = 4.918
I0408 16:06:47.280333 27257 solver.cpp:237]     Train net output #0: loss = 4.918 (* 1 = 4.918 loss)
I0408 16:06:47.280342 27257 sgd_solver.cpp:105] Iteration 3576, lr = 3.7098e-08
I0408 16:06:52.322691 27257 solver.cpp:218] Iteration 3588 (2.37994 iter/s, 5.04215s/12 iters), loss = 4.98643
I0408 16:06:52.322734 27257 solver.cpp:237]     Train net output #0: loss = 4.98643 (* 1 = 4.98643 loss)
I0408 16:06:52.322743 27257 sgd_solver.cpp:105] Iteration 3588, lr = 3.55735e-08
I0408 16:06:57.373281 27257 solver.cpp:218] Iteration 3600 (2.37608 iter/s, 5.05034s/12 iters), loss = 4.94675
I0408 16:06:57.373327 27257 solver.cpp:237]     Train net output #0: loss = 4.94675 (* 1 = 4.94675 loss)
I0408 16:06:57.373338 27257 sgd_solver.cpp:105] Iteration 3600, lr = 3.41117e-08
I0408 16:07:02.416548 27257 solver.cpp:218] Iteration 3612 (2.37953 iter/s, 5.04301s/12 iters), loss = 4.98113
I0408 16:07:02.416600 27257 solver.cpp:237]     Train net output #0: loss = 4.98113 (* 1 = 4.98113 loss)
I0408 16:07:02.416612 27257 sgd_solver.cpp:105] Iteration 3612, lr = 3.27099e-08
I0408 16:07:07.462827 27257 solver.cpp:218] Iteration 3624 (2.37811 iter/s, 5.04602s/12 iters), loss = 4.89515
I0408 16:07:07.462921 27257 solver.cpp:237]     Train net output #0: loss = 4.89515 (* 1 = 4.89515 loss)
I0408 16:07:07.462931 27257 sgd_solver.cpp:105] Iteration 3624, lr = 3.13658e-08
I0408 16:07:12.458173 27257 solver.cpp:218] Iteration 3636 (2.40238 iter/s, 4.99504s/12 iters), loss = 5.03731
I0408 16:07:12.458238 27257 solver.cpp:237]     Train net output #0: loss = 5.03731 (* 1 = 5.03731 loss)
I0408 16:07:12.458248 27257 sgd_solver.cpp:105] Iteration 3636, lr = 3.00769e-08
I0408 16:07:14.364750 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:07:17.467195 27257 solver.cpp:218] Iteration 3648 (2.39581 iter/s, 5.00875s/12 iters), loss = 5.00272
I0408 16:07:17.467244 27257 solver.cpp:237]     Train net output #0: loss = 5.00272 (* 1 = 5.00272 loss)
I0408 16:07:17.467255 27257 sgd_solver.cpp:105] Iteration 3648, lr = 2.88409e-08
I0408 16:07:22.533254 27257 solver.cpp:218] Iteration 3660 (2.36883 iter/s, 5.0658s/12 iters), loss = 4.98754
I0408 16:07:22.533301 27257 solver.cpp:237]     Train net output #0: loss = 4.98754 (* 1 = 4.98754 loss)
I0408 16:07:22.533313 27257 sgd_solver.cpp:105] Iteration 3660, lr = 2.76557e-08
I0408 16:07:27.091369 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3672.caffemodel
I0408 16:07:30.098624 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3672.solverstate
I0408 16:07:33.460054 27257 solver.cpp:330] Iteration 3672, Testing net (#0)
I0408 16:07:33.460081 27257 net.cpp:676] Ignoring source layer train-data
I0408 16:07:36.422816 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:07:37.891294 27257 solver.cpp:397]     Test net output #0: accuracy = 0.0275735
I0408 16:07:37.891376 27257 solver.cpp:397]     Test net output #1: loss = 4.96787 (* 1 = 4.96787 loss)
I0408 16:07:37.981781 27257 solver.cpp:218] Iteration 3672 (0.776806 iter/s, 15.4479s/12 iters), loss = 4.89207
I0408 16:07:37.981837 27257 solver.cpp:237]     Train net output #0: loss = 4.89207 (* 1 = 4.89207 loss)
I0408 16:07:37.981848 27257 sgd_solver.cpp:105] Iteration 3672, lr = 2.65193e-08
I0408 16:07:42.231833 27257 solver.cpp:218] Iteration 3684 (2.82365 iter/s, 4.24982s/12 iters), loss = 4.97188
I0408 16:07:42.231889 27257 solver.cpp:237]     Train net output #0: loss = 4.97188 (* 1 = 4.97188 loss)
I0408 16:07:42.231900 27257 sgd_solver.cpp:105] Iteration 3684, lr = 2.54295e-08
I0408 16:07:47.320924 27257 solver.cpp:218] Iteration 3696 (2.35811 iter/s, 5.08883s/12 iters), loss = 4.97212
I0408 16:07:47.320967 27257 solver.cpp:237]     Train net output #0: loss = 4.97212 (* 1 = 4.97212 loss)
I0408 16:07:47.320976 27257 sgd_solver.cpp:105] Iteration 3696, lr = 2.43845e-08
I0408 16:07:52.719738 27257 solver.cpp:218] Iteration 3708 (2.22282 iter/s, 5.39855s/12 iters), loss = 4.96007
I0408 16:07:52.719779 27257 solver.cpp:237]     Train net output #0: loss = 4.96007 (* 1 = 4.96007 loss)
I0408 16:07:52.719789 27257 sgd_solver.cpp:105] Iteration 3708, lr = 2.33825e-08
I0408 16:07:57.722801 27257 solver.cpp:218] Iteration 3720 (2.39865 iter/s, 5.00282s/12 iters), loss = 4.8703
I0408 16:07:57.722843 27257 solver.cpp:237]     Train net output #0: loss = 4.8703 (* 1 = 4.8703 loss)
I0408 16:07:57.722852 27257 sgd_solver.cpp:105] Iteration 3720, lr = 2.24216e-08
I0408 16:08:02.716507 27257 solver.cpp:218] Iteration 3732 (2.40314 iter/s, 4.99346s/12 iters), loss = 4.9521
I0408 16:08:02.716552 27257 solver.cpp:237]     Train net output #0: loss = 4.9521 (* 1 = 4.9521 loss)
I0408 16:08:02.716562 27257 sgd_solver.cpp:105] Iteration 3732, lr = 2.15002e-08
I0408 16:08:06.758651 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:08:07.766604 27257 solver.cpp:218] Iteration 3744 (2.37631 iter/s, 5.04985s/12 iters), loss = 4.77461
I0408 16:08:07.766645 27257 solver.cpp:237]     Train net output #0: loss = 4.77461 (* 1 = 4.77461 loss)
I0408 16:08:07.766654 27257 sgd_solver.cpp:105] Iteration 3744, lr = 2.06167e-08
I0408 16:08:12.920083 27257 solver.cpp:218] Iteration 3756 (2.32864 iter/s, 5.15323s/12 iters), loss = 4.97388
I0408 16:08:12.920194 27257 solver.cpp:237]     Train net output #0: loss = 4.97388 (* 1 = 4.97388 loss)
I0408 16:08:12.920205 27257 sgd_solver.cpp:105] Iteration 3756, lr = 1.97695e-08
I0408 16:08:17.962568 27257 solver.cpp:218] Iteration 3768 (2.37993 iter/s, 5.04217s/12 iters), loss = 4.98179
I0408 16:08:17.962610 27257 solver.cpp:237]     Train net output #0: loss = 4.98179 (* 1 = 4.98179 loss)
I0408 16:08:17.962621 27257 sgd_solver.cpp:105] Iteration 3768, lr = 1.89571e-08
I0408 16:08:20.024267 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3774.caffemodel
I0408 16:08:24.786671 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3774.solverstate
I0408 16:08:27.117683 27257 solver.cpp:330] Iteration 3774, Testing net (#0)
I0408 16:08:27.117708 27257 net.cpp:676] Ignoring source layer train-data
I0408 16:08:30.346769 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:08:31.843706 27257 solver.cpp:397]     Test net output #0: accuracy = 0.0275735
I0408 16:08:31.843750 27257 solver.cpp:397]     Test net output #1: loss = 4.97018 (* 1 = 4.97018 loss)
I0408 16:08:33.631363 27257 solver.cpp:218] Iteration 3780 (0.765885 iter/s, 15.6681s/12 iters), loss = 4.90851
I0408 16:08:33.631413 27257 solver.cpp:237]     Train net output #0: loss = 4.90851 (* 1 = 4.90851 loss)
I0408 16:08:33.631424 27257 sgd_solver.cpp:105] Iteration 3780, lr = 1.81781e-08
I0408 16:08:38.712705 27257 solver.cpp:218] Iteration 3792 (2.3617 iter/s, 5.08109s/12 iters), loss = 4.95985
I0408 16:08:38.712744 27257 solver.cpp:237]     Train net output #0: loss = 4.95985 (* 1 = 4.95985 loss)
I0408 16:08:38.712754 27257 sgd_solver.cpp:105] Iteration 3792, lr = 1.74311e-08
I0408 16:08:44.169812 27257 solver.cpp:218] Iteration 3804 (2.19908 iter/s, 5.45684s/12 iters), loss = 4.96101
I0408 16:08:44.169930 27257 solver.cpp:237]     Train net output #0: loss = 4.96101 (* 1 = 4.96101 loss)
I0408 16:08:44.169945 27257 sgd_solver.cpp:105] Iteration 3804, lr = 1.67148e-08
I0408 16:08:49.393800 27257 solver.cpp:218] Iteration 3816 (2.29724 iter/s, 5.22366s/12 iters), loss = 5.01251
I0408 16:08:49.393852 27257 solver.cpp:237]     Train net output #0: loss = 5.01251 (* 1 = 5.01251 loss)
I0408 16:08:49.393864 27257 sgd_solver.cpp:105] Iteration 3816, lr = 1.60279e-08
I0408 16:08:54.367600 27257 solver.cpp:218] Iteration 3828 (2.41277 iter/s, 4.97354s/12 iters), loss = 4.82062
I0408 16:08:54.367656 27257 solver.cpp:237]     Train net output #0: loss = 4.82062 (* 1 = 4.82062 loss)
I0408 16:08:54.367668 27257 sgd_solver.cpp:105] Iteration 3828, lr = 1.53693e-08
I0408 16:08:59.374976 27257 solver.cpp:218] Iteration 3840 (2.39659 iter/s, 5.00711s/12 iters), loss = 4.98558
I0408 16:08:59.375030 27257 solver.cpp:237]     Train net output #0: loss = 4.98558 (* 1 = 4.98558 loss)
I0408 16:08:59.375042 27257 sgd_solver.cpp:105] Iteration 3840, lr = 1.47377e-08
I0408 16:09:00.510401 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:09:04.425781 27257 solver.cpp:218] Iteration 3852 (2.37598 iter/s, 5.05055s/12 iters), loss = 4.94195
I0408 16:09:04.425815 27257 solver.cpp:237]     Train net output #0: loss = 4.94195 (* 1 = 4.94195 loss)
I0408 16:09:04.425824 27257 sgd_solver.cpp:105] Iteration 3852, lr = 1.41321e-08
I0408 16:09:09.372009 27257 solver.cpp:218] Iteration 3864 (2.42621 iter/s, 4.94599s/12 iters), loss = 4.97727
I0408 16:09:09.372051 27257 solver.cpp:237]     Train net output #0: loss = 4.97727 (* 1 = 4.97727 loss)
I0408 16:09:09.372059 27257 sgd_solver.cpp:105] Iteration 3864, lr = 1.35514e-08
I0408 16:09:13.959465 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3876.caffemodel
I0408 16:09:19.014158 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3876.solverstate
I0408 16:09:22.846794 27257 solver.cpp:330] Iteration 3876, Testing net (#0)
I0408 16:09:22.846817 27257 net.cpp:676] Ignoring source layer train-data
I0408 16:09:25.972002 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:09:27.516546 27257 solver.cpp:397]     Test net output #0: accuracy = 0.0275735
I0408 16:09:27.516592 27257 solver.cpp:397]     Test net output #1: loss = 4.97035 (* 1 = 4.97035 loss)
I0408 16:09:27.607076 27257 solver.cpp:218] Iteration 3876 (0.6581 iter/s, 18.2343s/12 iters), loss = 5.04657
I0408 16:09:27.607125 27257 solver.cpp:237]     Train net output #0: loss = 5.04657 (* 1 = 5.04657 loss)
I0408 16:09:27.607137 27257 sgd_solver.cpp:105] Iteration 3876, lr = 1.29945e-08
I0408 16:09:31.820976 27257 solver.cpp:218] Iteration 3888 (2.84787 iter/s, 4.21368s/12 iters), loss = 4.95878
I0408 16:09:31.821027 27257 solver.cpp:237]     Train net output #0: loss = 4.95878 (* 1 = 4.95878 loss)
I0408 16:09:31.821038 27257 sgd_solver.cpp:105] Iteration 3888, lr = 1.24605e-08
I0408 16:09:36.825937 27257 solver.cpp:218] Iteration 3900 (2.39774 iter/s, 5.0047s/12 iters), loss = 4.98632
I0408 16:09:36.826006 27257 solver.cpp:237]     Train net output #0: loss = 4.98632 (* 1 = 4.98632 loss)
I0408 16:09:36.826017 27257 sgd_solver.cpp:105] Iteration 3900, lr = 1.19485e-08
I0408 16:09:41.812849 27257 solver.cpp:218] Iteration 3912 (2.40643 iter/s, 4.98664s/12 iters), loss = 4.86288
I0408 16:09:41.812902 27257 solver.cpp:237]     Train net output #0: loss = 4.86288 (* 1 = 4.86288 loss)
I0408 16:09:41.812914 27257 sgd_solver.cpp:105] Iteration 3912, lr = 1.14575e-08
I0408 16:09:46.892532 27257 solver.cpp:218] Iteration 3924 (2.36247 iter/s, 5.07942s/12 iters), loss = 5.01829
I0408 16:09:46.892586 27257 solver.cpp:237]     Train net output #0: loss = 5.01829 (* 1 = 5.01829 loss)
I0408 16:09:46.892598 27257 sgd_solver.cpp:105] Iteration 3924, lr = 1.09866e-08
I0408 16:09:51.921810 27257 solver.cpp:218] Iteration 3936 (2.38615 iter/s, 5.02901s/12 iters), loss = 4.97839
I0408 16:09:51.921921 27257 solver.cpp:237]     Train net output #0: loss = 4.97839 (* 1 = 4.97839 loss)
I0408 16:09:51.921934 27257 sgd_solver.cpp:105] Iteration 3936, lr = 1.05352e-08
I0408 16:09:55.299613 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:09:56.936573 27257 solver.cpp:218] Iteration 3948 (2.39308 iter/s, 5.01445s/12 iters), loss = 5.04272
I0408 16:09:56.936617 27257 solver.cpp:237]     Train net output #0: loss = 5.04272 (* 1 = 5.04272 loss)
I0408 16:09:56.936628 27257 sgd_solver.cpp:105] Iteration 3948, lr = 1.01022e-08
I0408 16:10:01.995292 27257 solver.cpp:218] Iteration 3960 (2.37226 iter/s, 5.05847s/12 iters), loss = 4.99551
I0408 16:10:01.995338 27257 solver.cpp:237]     Train net output #0: loss = 4.99551 (* 1 = 4.99551 loss)
I0408 16:10:01.995350 27257 sgd_solver.cpp:105] Iteration 3960, lr = 9.6871e-09
I0408 16:10:07.087110 27257 solver.cpp:218] Iteration 3972 (2.35684 iter/s, 5.09156s/12 iters), loss = 5.05536
I0408 16:10:07.087162 27257 solver.cpp:237]     Train net output #0: loss = 5.05536 (* 1 = 5.05536 loss)
I0408 16:10:07.087174 27257 sgd_solver.cpp:105] Iteration 3972, lr = 9.28902e-09
I0408 16:10:09.178622 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3978.caffemodel
I0408 16:10:12.199743 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3978.solverstate
I0408 16:10:14.850999 27257 solver.cpp:330] Iteration 3978, Testing net (#0)
I0408 16:10:14.851027 27257 net.cpp:676] Ignoring source layer train-data
I0408 16:10:17.702580 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:10:19.280706 27257 solver.cpp:397]     Test net output #0: accuracy = 0.0281863
I0408 16:10:19.280755 27257 solver.cpp:397]     Test net output #1: loss = 4.96813 (* 1 = 4.96813 loss)
I0408 16:10:21.086728 27257 solver.cpp:218] Iteration 3984 (0.857203 iter/s, 13.999s/12 iters), loss = 4.88959
I0408 16:10:21.086786 27257 solver.cpp:237]     Train net output #0: loss = 4.88959 (* 1 = 4.88959 loss)
I0408 16:10:21.086800 27257 sgd_solver.cpp:105] Iteration 3984, lr = 8.90731e-09
I0408 16:10:26.098162 27257 solver.cpp:218] Iteration 3996 (2.39465 iter/s, 5.01118s/12 iters), loss = 4.94771
I0408 16:10:26.098302 27257 solver.cpp:237]     Train net output #0: loss = 4.94771 (* 1 = 4.94771 loss)
I0408 16:10:26.098312 27257 sgd_solver.cpp:105] Iteration 3996, lr = 8.54128e-09
I0408 16:10:31.052676 27257 solver.cpp:218] Iteration 4008 (2.4222 iter/s, 4.95417s/12 iters), loss = 4.95163
I0408 16:10:31.052728 27257 solver.cpp:237]     Train net output #0: loss = 4.95163 (* 1 = 4.95163 loss)
I0408 16:10:31.052740 27257 sgd_solver.cpp:105] Iteration 4008, lr = 8.19029e-09
I0408 16:10:36.168910 27257 solver.cpp:218] Iteration 4020 (2.3456 iter/s, 5.11597s/12 iters), loss = 5.0211
I0408 16:10:36.168962 27257 solver.cpp:237]     Train net output #0: loss = 5.0211 (* 1 = 5.0211 loss)
I0408 16:10:36.168977 27257 sgd_solver.cpp:105] Iteration 4020, lr = 7.85372e-09
I0408 16:10:41.232136 27257 solver.cpp:218] Iteration 4032 (2.37015 iter/s, 5.06296s/12 iters), loss = 5.00952
I0408 16:10:41.232193 27257 solver.cpp:237]     Train net output #0: loss = 5.00952 (* 1 = 5.00952 loss)
I0408 16:10:41.232205 27257 sgd_solver.cpp:105] Iteration 4032, lr = 7.53099e-09
I0408 16:10:46.179281 27257 solver.cpp:218] Iteration 4044 (2.42577 iter/s, 4.94688s/12 iters), loss = 5.03242
I0408 16:10:46.179332 27257 solver.cpp:237]     Train net output #0: loss = 5.03242 (* 1 = 5.03242 loss)
I0408 16:10:46.179342 27257 sgd_solver.cpp:105] Iteration 4044, lr = 7.22151e-09
I0408 16:10:46.671474 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:10:51.242805 27257 solver.cpp:218] Iteration 4056 (2.37001 iter/s, 5.06326s/12 iters), loss = 4.9688
I0408 16:10:51.242849 27257 solver.cpp:237]     Train net output #0: loss = 4.9688 (* 1 = 4.9688 loss)
I0408 16:10:51.242861 27257 sgd_solver.cpp:105] Iteration 4056, lr = 6.92476e-09
I0408 16:10:56.270273 27257 solver.cpp:218] Iteration 4068 (2.387 iter/s, 5.02722s/12 iters), loss = 5.00712
I0408 16:10:56.270368 27257 solver.cpp:237]     Train net output #0: loss = 5.00712 (* 1 = 5.00712 loss)
I0408 16:10:56.270377 27257 sgd_solver.cpp:105] Iteration 4068, lr = 6.6402e-09
I0408 16:11:00.805979 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4080.caffemodel
I0408 16:11:04.753237 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4080.solverstate
I0408 16:11:07.094183 27257 solver.cpp:330] Iteration 4080, Testing net (#0)
I0408 16:11:07.094211 27257 net.cpp:676] Ignoring source layer train-data
I0408 16:11:09.928326 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:11:11.545348 27257 solver.cpp:397]     Test net output #0: accuracy = 0.0275735
I0408 16:11:11.545394 27257 solver.cpp:397]     Test net output #1: loss = 4.97104 (* 1 = 4.97104 loss)
I0408 16:11:11.635691 27257 solver.cpp:218] Iteration 4080 (0.78101 iter/s, 15.3647s/12 iters), loss = 4.94227
I0408 16:11:11.635743 27257 solver.cpp:237]     Train net output #0: loss = 4.94227 (* 1 = 4.94227 loss)
I0408 16:11:11.635756 27257 sgd_solver.cpp:105] Iteration 4080, lr = 6.36733e-09
I0408 16:11:15.997134 27257 solver.cpp:218] Iteration 4092 (2.75153 iter/s, 4.36121s/12 iters), loss = 4.82414
I0408 16:11:15.997180 27257 solver.cpp:237]     Train net output #0: loss = 4.82414 (* 1 = 4.82414 loss)
I0408 16:11:15.997191 27257 sgd_solver.cpp:105] Iteration 4092, lr = 6.10567e-09
I0408 16:11:20.974861 27257 solver.cpp:218] Iteration 4104 (2.41086 iter/s, 4.97747s/12 iters), loss = 4.8762
I0408 16:11:20.974912 27257 solver.cpp:237]     Train net output #0: loss = 4.8762 (* 1 = 4.8762 loss)
I0408 16:11:20.974925 27257 sgd_solver.cpp:105] Iteration 4104, lr = 5.85477e-09
I0408 16:11:25.978328 27257 solver.cpp:218] Iteration 4116 (2.39846 iter/s, 5.00321s/12 iters), loss = 4.98111
I0408 16:11:25.978370 27257 solver.cpp:237]     Train net output #0: loss = 4.98111 (* 1 = 4.98111 loss)
I0408 16:11:25.978380 27257 sgd_solver.cpp:105] Iteration 4116, lr = 5.61418e-09
I0408 16:11:30.952018 27257 solver.cpp:218] Iteration 4128 (2.41282 iter/s, 4.97344s/12 iters), loss = 4.92036
I0408 16:11:30.955075 27257 solver.cpp:237]     Train net output #0: loss = 4.92036 (* 1 = 4.92036 loss)
I0408 16:11:30.955086 27257 sgd_solver.cpp:105] Iteration 4128, lr = 5.38347e-09
I0408 16:11:35.937269 27257 solver.cpp:218] Iteration 4140 (2.40867 iter/s, 4.98199s/12 iters), loss = 4.98736
I0408 16:11:35.937309 27257 solver.cpp:237]     Train net output #0: loss = 4.98736 (* 1 = 4.98736 loss)
I0408 16:11:35.937317 27257 sgd_solver.cpp:105] Iteration 4140, lr = 5.16225e-09
I0408 16:11:38.404724 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:11:40.748723 27257 solver.cpp:218] Iteration 4152 (2.49418 iter/s, 4.81121s/12 iters), loss = 4.85663
I0408 16:11:40.748790 27257 solver.cpp:237]     Train net output #0: loss = 4.85663 (* 1 = 4.85663 loss)
I0408 16:11:40.748806 27257 sgd_solver.cpp:105] Iteration 4152, lr = 4.95011e-09
I0408 16:11:42.312703 27257 blocking_queue.cpp:49] Waiting for data
I0408 16:11:45.553755 27257 solver.cpp:218] Iteration 4164 (2.49752 iter/s, 4.80477s/12 iters), loss = 4.83274
I0408 16:11:45.553800 27257 solver.cpp:237]     Train net output #0: loss = 4.83274 (* 1 = 4.83274 loss)
I0408 16:11:45.553809 27257 sgd_solver.cpp:105] Iteration 4164, lr = 4.7467e-09
I0408 16:11:50.472507 27257 solver.cpp:218] Iteration 4176 (2.43977 iter/s, 4.9185s/12 iters), loss = 4.89641
I0408 16:11:50.472560 27257 solver.cpp:237]     Train net output #0: loss = 4.89641 (* 1 = 4.89641 loss)
I0408 16:11:50.472573 27257 sgd_solver.cpp:105] Iteration 4176, lr = 4.55164e-09
I0408 16:11:52.433387 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4182.caffemodel
I0408 16:11:56.421808 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4182.solverstate
I0408 16:11:58.798594 27257 solver.cpp:330] Iteration 4182, Testing net (#0)
I0408 16:11:58.798619 27257 net.cpp:676] Ignoring source layer train-data
I0408 16:12:01.607568 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:12:03.271687 27257 solver.cpp:397]     Test net output #0: accuracy = 0.0281863
I0408 16:12:03.271735 27257 solver.cpp:397]     Test net output #1: loss = 4.97092 (* 1 = 4.97092 loss)
I0408 16:12:05.281761 27257 solver.cpp:218] Iteration 4188 (0.810339 iter/s, 14.8086s/12 iters), loss = 4.93767
I0408 16:12:05.281816 27257 solver.cpp:237]     Train net output #0: loss = 4.93767 (* 1 = 4.93767 loss)
I0408 16:12:05.281829 27257 sgd_solver.cpp:105] Iteration 4188, lr = 4.3646e-09
I0408 16:12:10.309248 27257 solver.cpp:218] Iteration 4200 (2.387 iter/s, 5.02723s/12 iters), loss = 4.91535
I0408 16:12:10.309286 27257 solver.cpp:237]     Train net output #0: loss = 4.91535 (* 1 = 4.91535 loss)
I0408 16:12:10.309293 27257 sgd_solver.cpp:105] Iteration 4200, lr = 4.18524e-09
I0408 16:12:15.331553 27257 solver.cpp:218] Iteration 4212 (2.38946 iter/s, 5.02206s/12 iters), loss = 4.91222
I0408 16:12:15.331609 27257 solver.cpp:237]     Train net output #0: loss = 4.91222 (* 1 = 4.91222 loss)
I0408 16:12:15.331621 27257 sgd_solver.cpp:105] Iteration 4212, lr = 4.01326e-09
I0408 16:12:20.369910 27257 solver.cpp:218] Iteration 4224 (2.38185 iter/s, 5.03809s/12 iters), loss = 4.78051
I0408 16:12:20.369974 27257 solver.cpp:237]     Train net output #0: loss = 4.78051 (* 1 = 4.78051 loss)
I0408 16:12:20.369983 27257 sgd_solver.cpp:105] Iteration 4224, lr = 3.84834e-09
I0408 16:12:25.429664 27257 solver.cpp:218] Iteration 4236 (2.37178 iter/s, 5.0595s/12 iters), loss = 4.90871
I0408 16:12:25.429704 27257 solver.cpp:237]     Train net output #0: loss = 4.90871 (* 1 = 4.90871 loss)
I0408 16:12:25.429713 27257 sgd_solver.cpp:105] Iteration 4236, lr = 3.6902e-09
I0408 16:12:30.210858 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:12:30.437129 27257 solver.cpp:218] Iteration 4248 (2.39654 iter/s, 5.00721s/12 iters), loss = 5.04675
I0408 16:12:30.437186 27257 solver.cpp:237]     Train net output #0: loss = 5.04675 (* 1 = 5.04675 loss)
I0408 16:12:30.437198 27257 sgd_solver.cpp:105] Iteration 4248, lr = 3.53856e-09
I0408 16:12:35.469635 27257 solver.cpp:218] Iteration 4260 (2.38462 iter/s, 5.03225s/12 iters), loss = 4.98472
I0408 16:12:35.469779 27257 solver.cpp:237]     Train net output #0: loss = 4.98472 (* 1 = 4.98472 loss)
I0408 16:12:35.469792 27257 sgd_solver.cpp:105] Iteration 4260, lr = 3.39314e-09
I0408 16:12:40.404362 27257 solver.cpp:218] Iteration 4272 (2.43191 iter/s, 4.93438s/12 iters), loss = 4.84107
I0408 16:12:40.404420 27257 solver.cpp:237]     Train net output #0: loss = 4.84107 (* 1 = 4.84107 loss)
I0408 16:12:40.404435 27257 sgd_solver.cpp:105] Iteration 4272, lr = 3.25371e-09
I0408 16:12:44.910619 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4284.caffemodel
I0408 16:12:48.012212 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4284.solverstate
I0408 16:12:51.180121 27257 solver.cpp:330] Iteration 4284, Testing net (#0)
I0408 16:12:51.180145 27257 net.cpp:676] Ignoring source layer train-data
I0408 16:12:53.956029 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:12:55.655100 27257 solver.cpp:397]     Test net output #0: accuracy = 0.0281863
I0408 16:12:55.655146 27257 solver.cpp:397]     Test net output #1: loss = 4.96957 (* 1 = 4.96957 loss)
I0408 16:12:55.745853 27257 solver.cpp:218] Iteration 4284 (0.782226 iter/s, 15.3408s/12 iters), loss = 4.90055
I0408 16:12:55.745923 27257 solver.cpp:237]     Train net output #0: loss = 4.90055 (* 1 = 4.90055 loss)
I0408 16:12:55.745937 27257 sgd_solver.cpp:105] Iteration 4284, lr = 3.12e-09
I0408 16:12:59.975077 27257 solver.cpp:218] Iteration 4296 (2.83756 iter/s, 4.22898s/12 iters), loss = 5.00122
I0408 16:12:59.975137 27257 solver.cpp:237]     Train net output #0: loss = 5.00122 (* 1 = 5.00122 loss)
I0408 16:12:59.975150 27257 sgd_solver.cpp:105] Iteration 4296, lr = 2.99179e-09
I0408 16:13:05.411664 27257 solver.cpp:218] Iteration 4308 (2.20738 iter/s, 5.43631s/12 iters), loss = 4.94671
I0408 16:13:05.411713 27257 solver.cpp:237]     Train net output #0: loss = 4.94671 (* 1 = 4.94671 loss)
I0408 16:13:05.411728 27257 sgd_solver.cpp:105] Iteration 4308, lr = 2.86885e-09
I0408 16:13:10.785317 27257 solver.cpp:218] Iteration 4320 (2.23323 iter/s, 5.37339s/12 iters), loss = 5.00645
I0408 16:13:10.785450 27257 solver.cpp:237]     Train net output #0: loss = 5.00645 (* 1 = 5.00645 loss)
I0408 16:13:10.785461 27257 sgd_solver.cpp:105] Iteration 4320, lr = 2.75096e-09
I0408 16:13:16.038295 27257 solver.cpp:218] Iteration 4332 (2.28457 iter/s, 5.25262s/12 iters), loss = 4.94333
I0408 16:13:16.038349 27257 solver.cpp:237]     Train net output #0: loss = 4.94333 (* 1 = 4.94333 loss)
I0408 16:13:16.038360 27257 sgd_solver.cpp:105] Iteration 4332, lr = 2.63791e-09
I0408 16:13:21.030524 27257 solver.cpp:218] Iteration 4344 (2.40386 iter/s, 4.99198s/12 iters), loss = 4.9958
I0408 16:13:21.030562 27257 solver.cpp:237]     Train net output #0: loss = 4.9958 (* 1 = 4.9958 loss)
I0408 16:13:21.030571 27257 sgd_solver.cpp:105] Iteration 4344, lr = 2.52951e-09
I0408 16:13:22.940901 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:13:26.067703 27257 solver.cpp:218] Iteration 4356 (2.3824 iter/s, 5.03693s/12 iters), loss = 4.97965
I0408 16:13:26.067746 27257 solver.cpp:237]     Train net output #0: loss = 4.97965 (* 1 = 4.97965 loss)
I0408 16:13:26.067757 27257 sgd_solver.cpp:105] Iteration 4356, lr = 2.42557e-09
I0408 16:13:31.036998 27257 solver.cpp:218] Iteration 4368 (2.41495 iter/s, 4.96904s/12 iters), loss = 5.01939
I0408 16:13:31.037055 27257 solver.cpp:237]     Train net output #0: loss = 5.01939 (* 1 = 5.01939 loss)
I0408 16:13:31.037066 27257 sgd_solver.cpp:105] Iteration 4368, lr = 2.32589e-09
I0408 16:13:36.059350 27257 solver.cpp:218] Iteration 4380 (2.38944 iter/s, 5.02209s/12 iters), loss = 4.93766
I0408 16:13:36.059396 27257 solver.cpp:237]     Train net output #0: loss = 4.93766 (* 1 = 4.93766 loss)
I0408 16:13:36.059407 27257 sgd_solver.cpp:105] Iteration 4380, lr = 2.23031e-09
I0408 16:13:38.041890 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4386.caffemodel
I0408 16:13:41.072995 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4386.solverstate
I0408 16:13:43.403878 27257 solver.cpp:330] Iteration 4386, Testing net (#0)
I0408 16:13:43.403905 27257 net.cpp:676] Ignoring source layer train-data
I0408 16:13:46.136276 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:13:47.876191 27257 solver.cpp:397]     Test net output #0: accuracy = 0.0281863
I0408 16:13:47.876238 27257 solver.cpp:397]     Test net output #1: loss = 4.96807 (* 1 = 4.96807 loss)
I0408 16:13:49.721278 27257 solver.cpp:218] Iteration 4392 (0.87839 iter/s, 13.6614s/12 iters), loss = 4.91918
I0408 16:13:49.721318 27257 solver.cpp:237]     Train net output #0: loss = 4.91918 (* 1 = 4.91918 loss)
I0408 16:13:49.721325 27257 sgd_solver.cpp:105] Iteration 4392, lr = 2.13866e-09
I0408 16:13:54.729995 27257 solver.cpp:218] Iteration 4404 (2.39595 iter/s, 5.00846s/12 iters), loss = 4.93684
I0408 16:13:54.730042 27257 solver.cpp:237]     Train net output #0: loss = 4.93684 (* 1 = 4.93684 loss)
I0408 16:13:54.730054 27257 sgd_solver.cpp:105] Iteration 4404, lr = 2.05078e-09
I0408 16:13:59.673045 27257 solver.cpp:218] Iteration 4416 (2.42777 iter/s, 4.9428s/12 iters), loss = 4.98269
I0408 16:13:59.673094 27257 solver.cpp:237]     Train net output #0: loss = 4.98269 (* 1 = 4.98269 loss)
I0408 16:13:59.673105 27257 sgd_solver.cpp:105] Iteration 4416, lr = 1.9665e-09
I0408 16:14:04.991668 27257 solver.cpp:218] Iteration 4428 (2.25634 iter/s, 5.31835s/12 iters), loss = 4.8382
I0408 16:14:04.991720 27257 solver.cpp:237]     Train net output #0: loss = 4.8382 (* 1 = 4.8382 loss)
I0408 16:14:04.991732 27257 sgd_solver.cpp:105] Iteration 4428, lr = 1.88569e-09
I0408 16:14:10.152832 27257 solver.cpp:218] Iteration 4440 (2.32518 iter/s, 5.1609s/12 iters), loss = 4.9211
I0408 16:14:10.152890 27257 solver.cpp:237]     Train net output #0: loss = 4.9211 (* 1 = 4.9211 loss)
I0408 16:14:10.152902 27257 sgd_solver.cpp:105] Iteration 4440, lr = 1.8082e-09
I0408 16:14:14.221482 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:14:15.198594 27257 solver.cpp:218] Iteration 4452 (2.37836 iter/s, 5.0455s/12 iters), loss = 4.82734
I0408 16:14:15.198639 27257 solver.cpp:237]     Train net output #0: loss = 4.82734 (* 1 = 4.82734 loss)
I0408 16:14:15.198648 27257 sgd_solver.cpp:105] Iteration 4452, lr = 1.7339e-09
I0408 16:14:20.280480 27257 solver.cpp:218] Iteration 4464 (2.36145 iter/s, 5.08163s/12 iters), loss = 4.91441
I0408 16:14:20.280532 27257 solver.cpp:237]     Train net output #0: loss = 4.91441 (* 1 = 4.91441 loss)
I0408 16:14:20.280544 27257 sgd_solver.cpp:105] Iteration 4464, lr = 1.66265e-09
I0408 16:14:25.274204 27257 solver.cpp:218] Iteration 4476 (2.40314 iter/s, 4.99347s/12 iters), loss = 4.9489
I0408 16:14:25.274258 27257 solver.cpp:237]     Train net output #0: loss = 4.9489 (* 1 = 4.9489 loss)
I0408 16:14:25.274269 27257 sgd_solver.cpp:105] Iteration 4476, lr = 1.59432e-09
I0408 16:14:29.920936 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4488.caffemodel
I0408 16:14:32.892755 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4488.solverstate
I0408 16:14:35.232759 27257 solver.cpp:330] Iteration 4488, Testing net (#0)
I0408 16:14:35.232781 27257 net.cpp:676] Ignoring source layer train-data
I0408 16:14:37.919407 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:14:39.692698 27257 solver.cpp:397]     Test net output #0: accuracy = 0.0275735
I0408 16:14:39.692732 27257 solver.cpp:397]     Test net output #1: loss = 4.96916 (* 1 = 4.96916 loss)
I0408 16:14:39.782959 27257 solver.cpp:218] Iteration 4488 (0.827123 iter/s, 14.5081s/12 iters), loss = 4.901
I0408 16:14:39.783012 27257 solver.cpp:237]     Train net output #0: loss = 4.901 (* 1 = 4.901 loss)
I0408 16:14:39.783023 27257 sgd_solver.cpp:105] Iteration 4488, lr = 1.52881e-09
I0408 16:14:44.298622 27257 solver.cpp:218] Iteration 4500 (2.65756 iter/s, 4.51542s/12 iters), loss = 4.82339
I0408 16:14:44.298738 27257 solver.cpp:237]     Train net output #0: loss = 4.82339 (* 1 = 4.82339 loss)
I0408 16:14:44.298750 27257 sgd_solver.cpp:105] Iteration 4500, lr = 1.46598e-09
I0408 16:14:49.323932 27257 solver.cpp:218] Iteration 4512 (2.38806 iter/s, 5.02499s/12 iters), loss = 4.912
I0408 16:14:49.323987 27257 solver.cpp:237]     Train net output #0: loss = 4.912 (* 1 = 4.912 loss)
I0408 16:14:49.323998 27257 sgd_solver.cpp:105] Iteration 4512, lr = 1.40574e-09
I0408 16:14:54.332545 27257 solver.cpp:218] Iteration 4524 (2.396 iter/s, 5.00835s/12 iters), loss = 5.06685
I0408 16:14:54.332597 27257 solver.cpp:237]     Train net output #0: loss = 5.06685 (* 1 = 5.06685 loss)
I0408 16:14:54.332609 27257 sgd_solver.cpp:105] Iteration 4524, lr = 1.34798e-09
I0408 16:14:59.359674 27257 solver.cpp:218] Iteration 4536 (2.38717 iter/s, 5.02688s/12 iters), loss = 4.91783
I0408 16:14:59.359719 27257 solver.cpp:237]     Train net output #0: loss = 4.91783 (* 1 = 4.91783 loss)
I0408 16:14:59.359732 27257 sgd_solver.cpp:105] Iteration 4536, lr = 1.29258e-09
I0408 16:15:04.435513 27257 solver.cpp:218] Iteration 4548 (2.36426 iter/s, 5.07559s/12 iters), loss = 4.93676
I0408 16:15:04.435560 27257 solver.cpp:237]     Train net output #0: loss = 4.93676 (* 1 = 4.93676 loss)
I0408 16:15:04.435571 27257 sgd_solver.cpp:105] Iteration 4548, lr = 1.23947e-09
I0408 16:15:05.664286 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:15:09.429894 27257 solver.cpp:218] Iteration 4560 (2.40282 iter/s, 4.99413s/12 iters), loss = 4.90213
I0408 16:15:09.429940 27257 solver.cpp:237]     Train net output #0: loss = 4.90213 (* 1 = 4.90213 loss)
I0408 16:15:09.429951 27257 sgd_solver.cpp:105] Iteration 4560, lr = 1.18853e-09
I0408 16:15:14.452354 27257 solver.cpp:218] Iteration 4572 (2.38939 iter/s, 5.0222s/12 iters), loss = 5.06119
I0408 16:15:14.452452 27257 solver.cpp:237]     Train net output #0: loss = 5.06119 (* 1 = 5.06119 loss)
I0408 16:15:14.452463 27257 sgd_solver.cpp:105] Iteration 4572, lr = 1.13969e-09
I0408 16:15:19.520203 27257 solver.cpp:218] Iteration 4584 (2.36801 iter/s, 5.06755s/12 iters), loss = 5.05202
I0408 16:15:19.520251 27257 solver.cpp:237]     Train net output #0: loss = 5.05202 (* 1 = 5.05202 loss)
I0408 16:15:19.520263 27257 sgd_solver.cpp:105] Iteration 4584, lr = 1.09286e-09
I0408 16:15:21.548064 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4590.caffemodel
I0408 16:15:24.988211 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4590.solverstate
I0408 16:15:29.075120 27257 solver.cpp:330] Iteration 4590, Testing net (#0)
I0408 16:15:29.075147 27257 net.cpp:676] Ignoring source layer train-data
I0408 16:15:31.729727 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:15:33.550168 27257 solver.cpp:397]     Test net output #0: accuracy = 0.0275735
I0408 16:15:33.550210 27257 solver.cpp:397]     Test net output #1: loss = 4.96689 (* 1 = 4.96689 loss)
I0408 16:15:35.364223 27257 solver.cpp:218] Iteration 4596 (0.757416 iter/s, 15.8433s/12 iters), loss = 4.98149
I0408 16:15:35.364289 27257 solver.cpp:237]     Train net output #0: loss = 4.98149 (* 1 = 4.98149 loss)
I0408 16:15:35.364302 27257 sgd_solver.cpp:105] Iteration 4596, lr = 1.04795e-09
I0408 16:15:40.320909 27257 solver.cpp:218] Iteration 4608 (2.42111 iter/s, 4.95641s/12 iters), loss = 4.97982
I0408 16:15:40.320974 27257 solver.cpp:237]     Train net output #0: loss = 4.97982 (* 1 = 4.97982 loss)
I0408 16:15:40.320987 27257 sgd_solver.cpp:105] Iteration 4608, lr = 1.00489e-09
I0408 16:15:45.298789 27257 solver.cpp:218] Iteration 4620 (2.41079 iter/s, 4.97762s/12 iters), loss = 4.9354
I0408 16:15:45.298878 27257 solver.cpp:237]     Train net output #0: loss = 4.9354 (* 1 = 4.9354 loss)
I0408 16:15:45.298888 27257 sgd_solver.cpp:105] Iteration 4620, lr = 9.63591e-10
I0408 16:15:50.499426 27257 solver.cpp:218] Iteration 4632 (2.30754 iter/s, 5.20034s/12 iters), loss = 4.93263
I0408 16:15:50.499472 27257 solver.cpp:237]     Train net output #0: loss = 4.93263 (* 1 = 4.93263 loss)
I0408 16:15:50.499485 27257 sgd_solver.cpp:105] Iteration 4632, lr = 9.23994e-10
I0408 16:15:55.527024 27257 solver.cpp:218] Iteration 4644 (2.38695 iter/s, 5.02734s/12 iters), loss = 4.951
I0408 16:15:55.527078 27257 solver.cpp:237]     Train net output #0: loss = 4.951 (* 1 = 4.951 loss)
I0408 16:15:55.527091 27257 sgd_solver.cpp:105] Iteration 4644, lr = 8.86024e-10
I0408 16:15:58.877755 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:16:00.496417 27257 solver.cpp:218] Iteration 4656 (2.41491 iter/s, 4.96914s/12 iters), loss = 4.97804
I0408 16:16:00.496467 27257 solver.cpp:237]     Train net output #0: loss = 4.97804 (* 1 = 4.97804 loss)
I0408 16:16:00.496480 27257 sgd_solver.cpp:105] Iteration 4656, lr = 8.49614e-10
I0408 16:16:05.500458 27257 solver.cpp:218] Iteration 4668 (2.39819 iter/s, 5.00378s/12 iters), loss = 5.01236
I0408 16:16:05.500514 27257 solver.cpp:237]     Train net output #0: loss = 5.01236 (* 1 = 5.01236 loss)
I0408 16:16:05.500526 27257 sgd_solver.cpp:105] Iteration 4668, lr = 8.14701e-10
I0408 16:16:10.832053 27257 solver.cpp:218] Iteration 4680 (2.25085 iter/s, 5.33132s/12 iters), loss = 5.03201
I0408 16:16:10.832108 27257 solver.cpp:237]     Train net output #0: loss = 5.03201 (* 1 = 5.03201 loss)
I0408 16:16:10.832119 27257 sgd_solver.cpp:105] Iteration 4680, lr = 7.81222e-10
I0408 16:16:15.460064 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4692.caffemodel
I0408 16:16:18.525597 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4692.solverstate
I0408 16:16:20.861090 27257 solver.cpp:330] Iteration 4692, Testing net (#0)
I0408 16:16:20.861119 27257 net.cpp:676] Ignoring source layer train-data
I0408 16:16:23.478235 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:16:25.332496 27257 solver.cpp:397]     Test net output #0: accuracy = 0.0275735
I0408 16:16:25.332543 27257 solver.cpp:397]     Test net output #1: loss = 4.96872 (* 1 = 4.96872 loss)
I0408 16:16:25.422909 27257 solver.cpp:218] Iteration 4692 (0.822468 iter/s, 14.5902s/12 iters), loss = 4.94677
I0408 16:16:25.422955 27257 solver.cpp:237]     Train net output #0: loss = 4.94677 (* 1 = 4.94677 loss)
I0408 16:16:25.422964 27257 sgd_solver.cpp:105] Iteration 4692, lr = 7.49119e-10
I0408 16:16:29.628363 27257 solver.cpp:218] Iteration 4704 (2.85359 iter/s, 4.20523s/12 iters), loss = 4.95217
I0408 16:16:29.628413 27257 solver.cpp:237]     Train net output #0: loss = 4.95217 (* 1 = 4.95217 loss)
I0408 16:16:29.628425 27257 sgd_solver.cpp:105] Iteration 4704, lr = 7.18335e-10
I0408 16:16:34.624707 27257 solver.cpp:218] Iteration 4716 (2.40188 iter/s, 4.99609s/12 iters), loss = 4.93137
I0408 16:16:34.624752 27257 solver.cpp:237]     Train net output #0: loss = 4.93137 (* 1 = 4.93137 loss)
I0408 16:16:34.624763 27257 sgd_solver.cpp:105] Iteration 4716, lr = 6.88816e-10
I0408 16:16:39.879432 27257 solver.cpp:218] Iteration 4728 (2.28377 iter/s, 5.25447s/12 iters), loss = 5.05855
I0408 16:16:39.879479 27257 solver.cpp:237]     Train net output #0: loss = 5.05855 (* 1 = 5.05855 loss)
I0408 16:16:39.879490 27257 sgd_solver.cpp:105] Iteration 4728, lr = 6.60511e-10
I0408 16:16:44.865288 27257 solver.cpp:218] Iteration 4740 (2.40693 iter/s, 4.9856s/12 iters), loss = 4.97187
I0408 16:16:44.865334 27257 solver.cpp:237]     Train net output #0: loss = 4.97187 (* 1 = 4.97187 loss)
I0408 16:16:44.865345 27257 sgd_solver.cpp:105] Iteration 4740, lr = 6.33368e-10
I0408 16:16:49.868772 27257 solver.cpp:218] Iteration 4752 (2.39845 iter/s, 5.00324s/12 iters), loss = 4.97963
I0408 16:16:49.868911 27257 solver.cpp:237]     Train net output #0: loss = 4.97963 (* 1 = 4.97963 loss)
I0408 16:16:49.868925 27257 sgd_solver.cpp:105] Iteration 4752, lr = 6.07341e-10
I0408 16:16:50.399987 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:16:54.852699 27257 solver.cpp:218] Iteration 4764 (2.4079 iter/s, 4.98359s/12 iters), loss = 4.91717
I0408 16:16:54.852746 27257 solver.cpp:237]     Train net output #0: loss = 4.91717 (* 1 = 4.91717 loss)
I0408 16:16:54.852756 27257 sgd_solver.cpp:105] Iteration 4764, lr = 5.82383e-10
I0408 16:16:59.894204 27257 solver.cpp:218] Iteration 4776 (2.38036 iter/s, 5.04125s/12 iters), loss = 4.93377
I0408 16:16:59.894254 27257 solver.cpp:237]     Train net output #0: loss = 4.93377 (* 1 = 4.93377 loss)
I0408 16:16:59.894268 27257 sgd_solver.cpp:105] Iteration 4776, lr = 5.58451e-10
I0408 16:17:04.905563 27257 solver.cpp:218] Iteration 4788 (2.39468 iter/s, 5.01111s/12 iters), loss = 4.92749
I0408 16:17:04.905601 27257 solver.cpp:237]     Train net output #0: loss = 4.92749 (* 1 = 4.92749 loss)
I0408 16:17:04.905609 27257 sgd_solver.cpp:105] Iteration 4788, lr = 5.35503e-10
I0408 16:17:06.930135 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4794.caffemodel
I0408 16:17:09.967164 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4794.solverstate
I0408 16:17:12.624492 27257 solver.cpp:330] Iteration 4794, Testing net (#0)
I0408 16:17:12.624517 27257 net.cpp:676] Ignoring source layer train-data
I0408 16:17:15.187182 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:17:17.085501 27257 solver.cpp:397]     Test net output #0: accuracy = 0.0281863
I0408 16:17:17.085548 27257 solver.cpp:397]     Test net output #1: loss = 4.97058 (* 1 = 4.97058 loss)
I0408 16:17:18.900193 27257 solver.cpp:218] Iteration 4800 (0.857508 iter/s, 13.994s/12 iters), loss = 4.85974
I0408 16:17:18.900245 27257 solver.cpp:237]     Train net output #0: loss = 4.85974 (* 1 = 4.85974 loss)
I0408 16:17:18.900257 27257 sgd_solver.cpp:105] Iteration 4800, lr = 5.13497e-10
I0408 16:17:23.834895 27257 solver.cpp:218] Iteration 4812 (2.43188 iter/s, 4.93445s/12 iters), loss = 4.91284
I0408 16:17:23.834997 27257 solver.cpp:237]     Train net output #0: loss = 4.91284 (* 1 = 4.91284 loss)
I0408 16:17:23.835007 27257 sgd_solver.cpp:105] Iteration 4812, lr = 4.92396e-10
I0408 16:17:28.764616 27257 solver.cpp:218] Iteration 4824 (2.43437 iter/s, 4.92941s/12 iters), loss = 4.92799
I0408 16:17:28.764680 27257 solver.cpp:237]     Train net output #0: loss = 4.92799 (* 1 = 4.92799 loss)
I0408 16:17:28.764693 27257 sgd_solver.cpp:105] Iteration 4824, lr = 4.72162e-10
I0408 16:17:33.691179 27257 solver.cpp:218] Iteration 4836 (2.4359 iter/s, 4.9263s/12 iters), loss = 4.89457
I0408 16:17:33.691226 27257 solver.cpp:237]     Train net output #0: loss = 4.89457 (* 1 = 4.89457 loss)
I0408 16:17:33.691238 27257 sgd_solver.cpp:105] Iteration 4836, lr = 4.52759e-10
I0408 16:17:35.699411 27257 blocking_queue.cpp:49] Waiting for data
I0408 16:17:38.627528 27257 solver.cpp:218] Iteration 4848 (2.43107 iter/s, 4.9361s/12 iters), loss = 4.96137
I0408 16:17:38.627574 27257 solver.cpp:237]     Train net output #0: loss = 4.96137 (* 1 = 4.96137 loss)
I0408 16:17:38.627585 27257 sgd_solver.cpp:105] Iteration 4848, lr = 4.34153e-10
I0408 16:17:41.252213 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:17:43.566388 27257 solver.cpp:218] Iteration 4860 (2.42983 iter/s, 4.93862s/12 iters), loss = 4.84169
I0408 16:17:43.566438 27257 solver.cpp:237]     Train net output #0: loss = 4.84169 (* 1 = 4.84169 loss)
I0408 16:17:43.566449 27257 sgd_solver.cpp:105] Iteration 4860, lr = 4.16313e-10
I0408 16:17:48.503887 27257 solver.cpp:218] Iteration 4872 (2.4305 iter/s, 4.93725s/12 iters), loss = 4.85702
I0408 16:17:48.503933 27257 solver.cpp:237]     Train net output #0: loss = 4.85702 (* 1 = 4.85702 loss)
I0408 16:17:48.503943 27257 sgd_solver.cpp:105] Iteration 4872, lr = 3.99205e-10
I0408 16:17:53.510392 27257 solver.cpp:218] Iteration 4884 (2.397 iter/s, 5.00626s/12 iters), loss = 4.91709
I0408 16:17:53.510426 27257 solver.cpp:237]     Train net output #0: loss = 4.91709 (* 1 = 4.91709 loss)
I0408 16:17:53.510433 27257 sgd_solver.cpp:105] Iteration 4884, lr = 3.828e-10
I0408 16:17:58.294421 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4896.caffemodel
I0408 16:18:01.314669 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4896.solverstate
I0408 16:18:03.642047 27257 solver.cpp:330] Iteration 4896, Testing net (#0)
I0408 16:18:03.642071 27257 net.cpp:676] Ignoring source layer train-data
I0408 16:18:06.179832 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:18:08.112807 27257 solver.cpp:397]     Test net output #0: accuracy = 0.0275735
I0408 16:18:08.112848 27257 solver.cpp:397]     Test net output #1: loss = 4.96875 (* 1 = 4.96875 loss)
I0408 16:18:08.203346 27257 solver.cpp:218] Iteration 4896 (0.816752 iter/s, 14.6923s/12 iters), loss = 4.93672
I0408 16:18:08.203392 27257 solver.cpp:237]     Train net output #0: loss = 4.93672 (* 1 = 4.93672 loss)
I0408 16:18:08.203402 27257 sgd_solver.cpp:105] Iteration 4896, lr = 3.6707e-10
I0408 16:18:12.430974 27257 solver.cpp:218] Iteration 4908 (2.83862 iter/s, 4.22741s/12 iters), loss = 4.96902
I0408 16:18:12.431016 27257 solver.cpp:237]     Train net output #0: loss = 4.96902 (* 1 = 4.96902 loss)
I0408 16:18:12.431025 27257 sgd_solver.cpp:105] Iteration 4908, lr = 3.51986e-10
I0408 16:18:17.466511 27257 solver.cpp:218] Iteration 4920 (2.38318 iter/s, 5.03529s/12 iters), loss = 4.9017
I0408 16:18:17.466545 27257 solver.cpp:237]     Train net output #0: loss = 4.9017 (* 1 = 4.9017 loss)
I0408 16:18:17.466554 27257 sgd_solver.cpp:105] Iteration 4920, lr = 3.37521e-10
I0408 16:18:22.512141 27257 solver.cpp:218] Iteration 4932 (2.37842 iter/s, 5.04538s/12 iters), loss = 4.89805
I0408 16:18:22.512202 27257 solver.cpp:237]     Train net output #0: loss = 4.89805 (* 1 = 4.89805 loss)
I0408 16:18:22.512212 27257 sgd_solver.cpp:105] Iteration 4932, lr = 3.23652e-10
I0408 16:18:27.525133 27257 solver.cpp:218] Iteration 4944 (2.39391 iter/s, 5.01273s/12 iters), loss = 4.96608
I0408 16:18:27.525180 27257 solver.cpp:237]     Train net output #0: loss = 4.96608 (* 1 = 4.96608 loss)
I0408 16:18:27.525192 27257 sgd_solver.cpp:105] Iteration 4944, lr = 3.10352e-10
I0408 16:18:32.355350 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:18:32.554081 27257 solver.cpp:218] Iteration 4956 (2.3863 iter/s, 5.0287s/12 iters), loss = 5.02734
I0408 16:18:32.554126 27257 solver.cpp:237]     Train net output #0: loss = 5.02734 (* 1 = 5.02734 loss)
I0408 16:18:32.554137 27257 sgd_solver.cpp:105] Iteration 4956, lr = 2.97598e-10
I0408 16:18:37.547755 27257 solver.cpp:218] Iteration 4968 (2.40316 iter/s, 4.99343s/12 iters), loss = 5.04395
I0408 16:18:37.547798 27257 solver.cpp:237]     Train net output #0: loss = 5.04395 (* 1 = 5.04395 loss)
I0408 16:18:37.547811 27257 sgd_solver.cpp:105] Iteration 4968, lr = 2.85369e-10
I0408 16:18:42.691337 27257 solver.cpp:218] Iteration 4980 (2.33312 iter/s, 5.14333s/12 iters), loss = 4.79017
I0408 16:18:42.691380 27257 solver.cpp:237]     Train net output #0: loss = 4.79017 (* 1 = 4.79017 loss)
I0408 16:18:42.691390 27257 sgd_solver.cpp:105] Iteration 4980, lr = 2.73642e-10
I0408 16:18:47.937702 27257 solver.cpp:218] Iteration 4992 (2.28741 iter/s, 5.24611s/12 iters), loss = 4.89607
I0408 16:18:47.937737 27257 solver.cpp:237]     Train net output #0: loss = 4.89607 (* 1 = 4.89607 loss)
I0408 16:18:47.937747 27257 sgd_solver.cpp:105] Iteration 4992, lr = 2.62397e-10
I0408 16:18:49.984633 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4998.caffemodel
I0408 16:18:53.041036 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4998.solverstate
I0408 16:18:55.346724 27257 solver.cpp:330] Iteration 4998, Testing net (#0)
I0408 16:18:55.346746 27257 net.cpp:676] Ignoring source layer train-data
I0408 16:18:57.767482 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:18:59.929672 27257 solver.cpp:397]     Test net output #0: accuracy = 0.0275735
I0408 16:18:59.929700 27257 solver.cpp:397]     Test net output #1: loss = 4.97068 (* 1 = 4.97068 loss)
I0408 16:19:01.901131 27257 solver.cpp:218] Iteration 5004 (0.859424 iter/s, 13.9628s/12 iters), loss = 5.02367
I0408 16:19:01.901178 27257 solver.cpp:237]     Train net output #0: loss = 5.02367 (* 1 = 5.02367 loss)
I0408 16:19:01.901190 27257 sgd_solver.cpp:105] Iteration 5004, lr = 2.51615e-10
I0408 16:19:06.838408 27257 solver.cpp:218] Iteration 5016 (2.43061 iter/s, 4.93703s/12 iters), loss = 4.87585
I0408 16:19:06.838572 27257 solver.cpp:237]     Train net output #0: loss = 4.87585 (* 1 = 4.87585 loss)
I0408 16:19:06.838587 27257 sgd_solver.cpp:105] Iteration 5016, lr = 2.41275e-10
I0408 16:19:11.919813 27257 solver.cpp:218] Iteration 5028 (2.36172 iter/s, 5.08104s/12 iters), loss = 4.9809
I0408 16:19:11.919862 27257 solver.cpp:237]     Train net output #0: loss = 4.9809 (* 1 = 4.9809 loss)
I0408 16:19:11.919873 27257 sgd_solver.cpp:105] Iteration 5028, lr = 2.3136e-10
I0408 16:19:17.006887 27257 solver.cpp:218] Iteration 5040 (2.35904 iter/s, 5.08682s/12 iters), loss = 4.91193
I0408 16:19:17.006935 27257 solver.cpp:237]     Train net output #0: loss = 4.91193 (* 1 = 4.91193 loss)
I0408 16:19:17.006947 27257 sgd_solver.cpp:105] Iteration 5040, lr = 2.21853e-10
I0408 16:19:22.429986 27257 solver.cpp:218] Iteration 5052 (2.21286 iter/s, 5.42283s/12 iters), loss = 5.07523
I0408 16:19:22.430032 27257 solver.cpp:237]     Train net output #0: loss = 5.07523 (* 1 = 5.07523 loss)
I0408 16:19:22.430043 27257 sgd_solver.cpp:105] Iteration 5052, lr = 2.12736e-10
I0408 16:19:24.373406 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:19:27.440253 27257 solver.cpp:218] Iteration 5064 (2.3952 iter/s, 5.01002s/12 iters), loss = 4.9703
I0408 16:19:27.440299 27257 solver.cpp:237]     Train net output #0: loss = 4.9703 (* 1 = 4.9703 loss)
I0408 16:19:27.440311 27257 sgd_solver.cpp:105] Iteration 5064, lr = 2.03994e-10
I0408 16:19:32.505228 27257 solver.cpp:218] Iteration 5076 (2.36933 iter/s, 5.06472s/12 iters), loss = 4.93028
I0408 16:19:32.505276 27257 solver.cpp:237]     Train net output #0: loss = 4.93028 (* 1 = 4.93028 loss)
I0408 16:19:32.505288 27257 sgd_solver.cpp:105] Iteration 5076, lr = 1.95611e-10
I0408 16:19:37.535085 27257 solver.cpp:218] Iteration 5088 (2.38587 iter/s, 5.0296s/12 iters), loss = 4.84013
I0408 16:19:37.535195 27257 solver.cpp:237]     Train net output #0: loss = 4.84013 (* 1 = 4.84013 loss)
I0408 16:19:37.535208 27257 sgd_solver.cpp:105] Iteration 5088, lr = 1.87573e-10
I0408 16:19:42.118491 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5100.caffemodel
I0408 16:19:45.806835 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5100.solverstate
I0408 16:19:48.133131 27257 solver.cpp:330] Iteration 5100, Testing net (#0)
I0408 16:19:48.133154 27257 net.cpp:676] Ignoring source layer train-data
I0408 16:19:50.596504 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:19:52.610549 27257 solver.cpp:397]     Test net output #0: accuracy = 0.0275735
I0408 16:19:52.610595 27257 solver.cpp:397]     Test net output #1: loss = 4.96935 (* 1 = 4.96935 loss)
I0408 16:19:52.700944 27257 solver.cpp:218] Iteration 5100 (0.791278 iter/s, 15.1653s/12 iters), loss = 4.96642
I0408 16:19:52.700992 27257 solver.cpp:237]     Train net output #0: loss = 4.96642 (* 1 = 4.96642 loss)
I0408 16:19:52.701004 27257 sgd_solver.cpp:105] Iteration 5100, lr = 1.79865e-10
I0408 16:19:57.105129 27257 solver.cpp:218] Iteration 5112 (2.72479 iter/s, 4.40401s/12 iters), loss = 4.95401
I0408 16:19:57.105172 27257 solver.cpp:237]     Train net output #0: loss = 4.95401 (* 1 = 4.95401 loss)
I0408 16:19:57.105183 27257 sgd_solver.cpp:105] Iteration 5112, lr = 1.72474e-10
I0408 16:20:02.115659 27257 solver.cpp:218] Iteration 5124 (2.39505 iter/s, 5.01034s/12 iters), loss = 4.96165
I0408 16:20:02.115706 27257 solver.cpp:237]     Train net output #0: loss = 4.96165 (* 1 = 4.96165 loss)
I0408 16:20:02.115718 27257 sgd_solver.cpp:105] Iteration 5124, lr = 1.65386e-10
I0408 16:20:07.070935 27257 solver.cpp:218] Iteration 5136 (2.42175 iter/s, 4.95509s/12 iters), loss = 4.94503
I0408 16:20:07.070981 27257 solver.cpp:237]     Train net output #0: loss = 4.94503 (* 1 = 4.94503 loss)
I0408 16:20:07.070992 27257 sgd_solver.cpp:105] Iteration 5136, lr = 1.5859e-10
I0408 16:20:12.059240 27257 solver.cpp:218] Iteration 5148 (2.40572 iter/s, 4.98812s/12 iters), loss = 5.00146
I0408 16:20:12.059382 27257 solver.cpp:237]     Train net output #0: loss = 5.00146 (* 1 = 5.00146 loss)
I0408 16:20:12.059396 27257 sgd_solver.cpp:105] Iteration 5148, lr = 1.52073e-10
I0408 16:20:16.140733 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:20:17.079254 27257 solver.cpp:218] Iteration 5160 (2.39057 iter/s, 5.01973s/12 iters), loss = 4.87059
I0408 16:20:17.079305 27257 solver.cpp:237]     Train net output #0: loss = 4.87059 (* 1 = 4.87059 loss)
I0408 16:20:17.079319 27257 sgd_solver.cpp:105] Iteration 5160, lr = 1.45824e-10
I0408 16:20:22.096942 27257 solver.cpp:218] Iteration 5172 (2.39163 iter/s, 5.01749s/12 iters), loss = 4.92856
I0408 16:20:22.096985 27257 solver.cpp:237]     Train net output #0: loss = 4.92856 (* 1 = 4.92856 loss)
I0408 16:20:22.096995 27257 sgd_solver.cpp:105] Iteration 5172, lr = 1.39831e-10
I0408 16:20:27.114568 27257 solver.cpp:218] Iteration 5184 (2.39166 iter/s, 5.01744s/12 iters), loss = 4.97727
I0408 16:20:27.114612 27257 solver.cpp:237]     Train net output #0: loss = 4.97727 (* 1 = 4.97727 loss)
I0408 16:20:27.114624 27257 sgd_solver.cpp:105] Iteration 5184, lr = 1.34085e-10
I0408 16:20:32.119952 27257 solver.cpp:218] Iteration 5196 (2.39751 iter/s, 5.0052s/12 iters), loss = 4.94071
I0408 16:20:32.119994 27257 solver.cpp:237]     Train net output #0: loss = 4.94071 (* 1 = 4.94071 loss)
I0408 16:20:32.120005 27257 sgd_solver.cpp:105] Iteration 5196, lr = 1.28575e-10
I0408 16:20:34.162788 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5202.caffemodel
I0408 16:20:37.192545 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5202.solverstate
I0408 16:20:39.512465 27257 solver.cpp:330] Iteration 5202, Testing net (#0)
I0408 16:20:39.512490 27257 net.cpp:676] Ignoring source layer train-data
I0408 16:20:41.855680 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:20:44.022662 27257 solver.cpp:397]     Test net output #0: accuracy = 0.0275735
I0408 16:20:44.022819 27257 solver.cpp:397]     Test net output #1: loss = 4.96721 (* 1 = 4.96721 loss)
I0408 16:20:45.905721 27257 solver.cpp:218] Iteration 5208 (0.87049 iter/s, 13.7853s/12 iters), loss = 4.81396
I0408 16:20:45.905766 27257 solver.cpp:237]     Train net output #0: loss = 4.81396 (* 1 = 4.81396 loss)
I0408 16:20:45.905778 27257 sgd_solver.cpp:105] Iteration 5208, lr = 1.23292e-10
I0408 16:20:50.916683 27257 solver.cpp:218] Iteration 5220 (2.39484 iter/s, 5.01077s/12 iters), loss = 4.90529
I0408 16:20:50.916743 27257 solver.cpp:237]     Train net output #0: loss = 4.90529 (* 1 = 4.90529 loss)
I0408 16:20:50.916754 27257 sgd_solver.cpp:105] Iteration 5220, lr = 1.18225e-10
I0408 16:20:55.931586 27257 solver.cpp:218] Iteration 5232 (2.39297 iter/s, 5.01469s/12 iters), loss = 5.02489
I0408 16:20:55.931632 27257 solver.cpp:237]     Train net output #0: loss = 5.02489 (* 1 = 5.02489 loss)
I0408 16:20:55.931644 27257 sgd_solver.cpp:105] Iteration 5232, lr = 1.13367e-10
I0408 16:21:00.917040 27257 solver.cpp:218] Iteration 5244 (2.40709 iter/s, 4.98526s/12 iters), loss = 4.8518
I0408 16:21:00.917074 27257 solver.cpp:237]     Train net output #0: loss = 4.8518 (* 1 = 4.8518 loss)
I0408 16:21:00.917083 27257 sgd_solver.cpp:105] Iteration 5244, lr = 1.08708e-10
I0408 16:21:05.940414 27257 solver.cpp:218] Iteration 5256 (2.38892 iter/s, 5.02319s/12 iters), loss = 4.98315
I0408 16:21:05.940457 27257 solver.cpp:237]     Train net output #0: loss = 4.98315 (* 1 = 4.98315 loss)
I0408 16:21:05.940469 27257 sgd_solver.cpp:105] Iteration 5256, lr = 1.04241e-10
I0408 16:21:07.194187 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:21:10.920470 27257 solver.cpp:218] Iteration 5268 (2.40971 iter/s, 4.97986s/12 iters), loss = 4.90413
I0408 16:21:10.920516 27257 solver.cpp:237]     Train net output #0: loss = 4.90413 (* 1 = 4.90413 loss)
I0408 16:21:10.920528 27257 sgd_solver.cpp:105] Iteration 5268, lr = 9.99575e-11
I0408 16:21:15.876971 27257 solver.cpp:218] Iteration 5280 (2.42116 iter/s, 4.95631s/12 iters), loss = 5.07345
I0408 16:21:15.877076 27257 solver.cpp:237]     Train net output #0: loss = 5.07345 (* 1 = 5.07345 loss)
I0408 16:21:15.877089 27257 sgd_solver.cpp:105] Iteration 5280, lr = 9.58499e-11
I0408 16:21:20.818522 27257 solver.cpp:218] Iteration 5292 (2.42851 iter/s, 4.9413s/12 iters), loss = 5.07388
I0408 16:21:20.818559 27257 solver.cpp:237]     Train net output #0: loss = 5.07388 (* 1 = 5.07388 loss)
I0408 16:21:20.818568 27257 sgd_solver.cpp:105] Iteration 5292, lr = 9.19111e-11
I0408 16:21:25.226963 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5304.caffemodel
I0408 16:21:28.296079 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5304.solverstate
I0408 16:21:30.609287 27257 solver.cpp:330] Iteration 5304, Testing net (#0)
I0408 16:21:30.609309 27257 net.cpp:676] Ignoring source layer train-data
I0408 16:21:32.857915 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:21:34.953521 27257 solver.cpp:397]     Test net output #0: accuracy = 0.0281863
I0408 16:21:34.953564 27257 solver.cpp:397]     Test net output #1: loss = 4.97021 (* 1 = 4.97021 loss)
I0408 16:21:35.043864 27257 solver.cpp:218] Iteration 5304 (0.843592 iter/s, 14.2249s/12 iters), loss = 4.96447
I0408 16:21:35.043915 27257 solver.cpp:237]     Train net output #0: loss = 4.96447 (* 1 = 4.96447 loss)
I0408 16:21:35.043926 27257 sgd_solver.cpp:105] Iteration 5304, lr = 8.81342e-11
I0408 16:21:39.248546 27257 solver.cpp:218] Iteration 5316 (2.85408 iter/s, 4.2045s/12 iters), loss = 4.97892
I0408 16:21:39.248590 27257 solver.cpp:237]     Train net output #0: loss = 4.97892 (* 1 = 4.97892 loss)
I0408 16:21:39.248602 27257 sgd_solver.cpp:105] Iteration 5316, lr = 8.45125e-11
I0408 16:21:44.159785 27257 solver.cpp:218] Iteration 5328 (2.44347 iter/s, 4.91104s/12 iters), loss = 4.93485
I0408 16:21:44.159832 27257 solver.cpp:237]     Train net output #0: loss = 4.93485 (* 1 = 4.93485 loss)
I0408 16:21:44.159844 27257 sgd_solver.cpp:105] Iteration 5328, lr = 8.10396e-11
I0408 16:21:49.108743 27257 solver.cpp:218] Iteration 5340 (2.42485 iter/s, 4.94876s/12 iters), loss = 4.94074
I0408 16:21:49.112059 27257 solver.cpp:237]     Train net output #0: loss = 4.94074 (* 1 = 4.94074 loss)
I0408 16:21:49.112072 27257 sgd_solver.cpp:105] Iteration 5340, lr = 7.77094e-11
I0408 16:21:54.136775 27257 solver.cpp:218] Iteration 5352 (2.38827 iter/s, 5.02456s/12 iters), loss = 4.97016
I0408 16:21:54.136824 27257 solver.cpp:237]     Train net output #0: loss = 4.97016 (* 1 = 4.97016 loss)
I0408 16:21:54.136834 27257 sgd_solver.cpp:105] Iteration 5352, lr = 7.4516e-11
I0408 16:21:57.580374 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:21:59.155508 27257 solver.cpp:218] Iteration 5364 (2.39114 iter/s, 5.01853s/12 iters), loss = 4.90241
I0408 16:21:59.155555 27257 solver.cpp:237]     Train net output #0: loss = 4.90241 (* 1 = 4.90241 loss)
I0408 16:21:59.155566 27257 sgd_solver.cpp:105] Iteration 5364, lr = 7.14539e-11
I0408 16:22:04.146176 27257 solver.cpp:218] Iteration 5376 (2.40459 iter/s, 4.99047s/12 iters), loss = 4.95999
I0408 16:22:04.146225 27257 solver.cpp:237]     Train net output #0: loss = 4.95999 (* 1 = 4.95999 loss)
I0408 16:22:04.146236 27257 sgd_solver.cpp:105] Iteration 5376, lr = 6.85176e-11
I0408 16:22:09.188244 27257 solver.cpp:218] Iteration 5388 (2.38007 iter/s, 5.04187s/12 iters), loss = 5.00496
I0408 16:22:09.188284 27257 solver.cpp:237]     Train net output #0: loss = 5.00496 (* 1 = 5.00496 loss)
I0408 16:22:09.188293 27257 sgd_solver.cpp:105] Iteration 5388, lr = 6.5702e-11
I0408 16:22:14.185822 27257 solver.cpp:218] Iteration 5400 (2.40126 iter/s, 4.99738s/12 iters), loss = 4.95212
I0408 16:22:14.185858 27257 solver.cpp:237]     Train net output #0: loss = 4.95212 (* 1 = 4.95212 loss)
I0408 16:22:14.185868 27257 sgd_solver.cpp:105] Iteration 5400, lr = 6.30021e-11
I0408 16:22:16.241830 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5406.caffemodel
I0408 16:22:19.297199 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5406.solverstate
I0408 16:22:21.631217 27257 solver.cpp:330] Iteration 5406, Testing net (#0)
I0408 16:22:21.631238 27257 net.cpp:676] Ignoring source layer train-data
I0408 16:22:23.926597 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:22:26.057603 27257 solver.cpp:397]     Test net output #0: accuracy = 0.0281863
I0408 16:22:26.057641 27257 solver.cpp:397]     Test net output #1: loss = 4.96717 (* 1 = 4.96717 loss)
I0408 16:22:27.958376 27257 solver.cpp:218] Iteration 5412 (0.871326 iter/s, 13.7721s/12 iters), loss = 4.91347
I0408 16:22:27.958411 27257 solver.cpp:237]     Train net output #0: loss = 4.91347 (* 1 = 4.91347 loss)
I0408 16:22:27.958420 27257 sgd_solver.cpp:105] Iteration 5412, lr = 6.04131e-11
I0408 16:22:33.382354 27257 solver.cpp:218] Iteration 5424 (2.21248 iter/s, 5.42377s/12 iters), loss = 4.88546
I0408 16:22:33.382397 27257 solver.cpp:237]     Train net output #0: loss = 4.88546 (* 1 = 4.88546 loss)
I0408 16:22:33.382408 27257 sgd_solver.cpp:105] Iteration 5424, lr = 5.79306e-11
I0408 16:22:38.418618 27257 solver.cpp:218] Iteration 5436 (2.38281 iter/s, 5.03606s/12 iters), loss = 5.05593
I0408 16:22:38.418659 27257 solver.cpp:237]     Train net output #0: loss = 5.05593 (* 1 = 5.05593 loss)
I0408 16:22:38.418670 27257 sgd_solver.cpp:105] Iteration 5436, lr = 5.555e-11
I0408 16:22:43.402968 27257 solver.cpp:218] Iteration 5448 (2.40763 iter/s, 4.98415s/12 iters), loss = 4.93387
I0408 16:22:43.403017 27257 solver.cpp:237]     Train net output #0: loss = 4.93387 (* 1 = 4.93387 loss)
I0408 16:22:43.403028 27257 sgd_solver.cpp:105] Iteration 5448, lr = 5.32673e-11
I0408 16:22:48.435099 27257 solver.cpp:218] Iteration 5460 (2.38477 iter/s, 5.03192s/12 iters), loss = 4.99723
I0408 16:22:48.435148 27257 solver.cpp:237]     Train net output #0: loss = 4.99723 (* 1 = 4.99723 loss)
I0408 16:22:48.435160 27257 sgd_solver.cpp:105] Iteration 5460, lr = 5.10783e-11
I0408 16:22:48.991896 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:22:53.465885 27257 solver.cpp:218] Iteration 5472 (2.38541 iter/s, 5.03058s/12 iters), loss = 4.90175
I0408 16:22:53.466049 27257 solver.cpp:237]     Train net output #0: loss = 4.90175 (* 1 = 4.90175 loss)
I0408 16:22:53.466064 27257 sgd_solver.cpp:105] Iteration 5472, lr = 4.89794e-11
I0408 16:22:58.383759 27257 solver.cpp:218] Iteration 5484 (2.44023 iter/s, 4.91756s/12 iters), loss = 5.00206
I0408 16:22:58.383805 27257 solver.cpp:237]     Train net output #0: loss = 5.00206 (* 1 = 5.00206 loss)
I0408 16:22:58.383816 27257 sgd_solver.cpp:105] Iteration 5484, lr = 4.69666e-11
I0408 16:23:03.294068 27257 solver.cpp:218] Iteration 5496 (2.44394 iter/s, 4.91011s/12 iters), loss = 4.89756
I0408 16:23:03.294117 27257 solver.cpp:237]     Train net output #0: loss = 4.89756 (* 1 = 4.89756 loss)
I0408 16:23:03.294129 27257 sgd_solver.cpp:105] Iteration 5496, lr = 4.50366e-11
I0408 16:23:07.868672 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5508.caffemodel
I0408 16:23:10.864080 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5508.solverstate
I0408 16:23:13.177364 27257 solver.cpp:330] Iteration 5508, Testing net (#0)
I0408 16:23:13.177386 27257 net.cpp:676] Ignoring source layer train-data
I0408 16:23:15.458636 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:23:17.635056 27257 solver.cpp:397]     Test net output #0: accuracy = 0.0275735
I0408 16:23:17.635093 27257 solver.cpp:397]     Test net output #1: loss = 4.9724 (* 1 = 4.9724 loss)
I0408 16:23:17.725301 27257 solver.cpp:218] Iteration 5508 (0.831558 iter/s, 14.4307s/12 iters), loss = 4.87109
I0408 16:23:17.725350 27257 solver.cpp:237]     Train net output #0: loss = 4.87109 (* 1 = 4.87109 loss)
I0408 16:23:17.725361 27257 sgd_solver.cpp:105] Iteration 5508, lr = 4.31859e-11
I0408 16:23:22.083647 27257 solver.cpp:218] Iteration 5520 (2.75346 iter/s, 4.35816s/12 iters), loss = 4.90541
I0408 16:23:22.083693 27257 solver.cpp:237]     Train net output #0: loss = 4.90541 (* 1 = 4.90541 loss)
I0408 16:23:22.083704 27257 sgd_solver.cpp:105] Iteration 5520, lr = 4.14113e-11
I0408 16:23:24.735882 27257 blocking_queue.cpp:49] Waiting for data
I0408 16:23:27.297564 27257 solver.cpp:218] Iteration 5532 (2.30163 iter/s, 5.2137s/12 iters), loss = 4.9233
I0408 16:23:27.297606 27257 solver.cpp:237]     Train net output #0: loss = 4.9233 (* 1 = 4.9233 loss)
I0408 16:23:27.297616 27257 sgd_solver.cpp:105] Iteration 5532, lr = 3.97095e-11
I0408 16:23:32.289175 27257 solver.cpp:218] Iteration 5544 (2.40413 iter/s, 4.99141s/12 iters), loss = 4.90385
I0408 16:23:32.289224 27257 solver.cpp:237]     Train net output #0: loss = 4.90385 (* 1 = 4.90385 loss)
I0408 16:23:32.289237 27257 sgd_solver.cpp:105] Iteration 5544, lr = 3.80777e-11
I0408 16:23:37.258694 27257 solver.cpp:218] Iteration 5556 (2.41482 iter/s, 4.96931s/12 iters), loss = 4.90399
I0408 16:23:37.258741 27257 solver.cpp:237]     Train net output #0: loss = 4.90399 (* 1 = 4.90399 loss)
I0408 16:23:37.258752 27257 sgd_solver.cpp:105] Iteration 5556, lr = 3.6513e-11
I0408 16:23:39.966627 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:23:42.304548 27257 solver.cpp:218] Iteration 5568 (2.37829 iter/s, 5.04564s/12 iters), loss = 4.85677
I0408 16:23:42.304612 27257 solver.cpp:237]     Train net output #0: loss = 4.85677 (* 1 = 4.85677 loss)
I0408 16:23:42.304631 27257 sgd_solver.cpp:105] Iteration 5568, lr = 3.50126e-11
I0408 16:23:47.222266 27257 solver.cpp:218] Iteration 5580 (2.44026 iter/s, 4.9175s/12 iters), loss = 4.89667
I0408 16:23:47.222311 27257 solver.cpp:237]     Train net output #0: loss = 4.89667 (* 1 = 4.89667 loss)
I0408 16:23:47.222322 27257 sgd_solver.cpp:105] Iteration 5580, lr = 3.35738e-11
I0408 16:23:52.225543 27257 solver.cpp:218] Iteration 5592 (2.39853 iter/s, 5.00307s/12 iters), loss = 4.8585
I0408 16:23:52.225592 27257 solver.cpp:237]     Train net output #0: loss = 4.8585 (* 1 = 4.8585 loss)
I0408 16:23:52.225605 27257 sgd_solver.cpp:105] Iteration 5592, lr = 3.21941e-11
I0408 16:23:57.230895 27257 solver.cpp:218] Iteration 5604 (2.39753 iter/s, 5.00514s/12 iters), loss = 4.9374
I0408 16:23:57.231034 27257 solver.cpp:237]     Train net output #0: loss = 4.9374 (* 1 = 4.9374 loss)
I0408 16:23:57.231047 27257 sgd_solver.cpp:105] Iteration 5604, lr = 3.08712e-11
I0408 16:23:59.268397 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5610.caffemodel
I0408 16:24:02.249012 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5610.solverstate
I0408 16:24:04.625950 27257 solver.cpp:330] Iteration 5610, Testing net (#0)
I0408 16:24:04.625995 27257 net.cpp:676] Ignoring source layer train-data
I0408 16:24:06.860833 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:24:09.140625 27257 solver.cpp:397]     Test net output #0: accuracy = 0.0275735
I0408 16:24:09.140672 27257 solver.cpp:397]     Test net output #1: loss = 4.96946 (* 1 = 4.96946 loss)
I0408 16:24:11.131330 27257 solver.cpp:218] Iteration 5616 (0.863318 iter/s, 13.8999s/12 iters), loss = 4.89286
I0408 16:24:11.131376 27257 solver.cpp:237]     Train net output #0: loss = 4.89286 (* 1 = 4.89286 loss)
I0408 16:24:11.131387 27257 sgd_solver.cpp:105] Iteration 5616, lr = 2.96026e-11
I0408 16:24:16.186419 27257 solver.cpp:218] Iteration 5628 (2.37394 iter/s, 5.05488s/12 iters), loss = 4.9825
I0408 16:24:16.186470 27257 solver.cpp:237]     Train net output #0: loss = 4.9825 (* 1 = 4.9825 loss)
I0408 16:24:16.186482 27257 sgd_solver.cpp:105] Iteration 5628, lr = 2.83861e-11
I0408 16:24:21.134145 27257 solver.cpp:218] Iteration 5640 (2.42546 iter/s, 4.94752s/12 iters), loss = 4.96237
I0408 16:24:21.134194 27257 solver.cpp:237]     Train net output #0: loss = 4.96237 (* 1 = 4.96237 loss)
I0408 16:24:21.134207 27257 sgd_solver.cpp:105] Iteration 5640, lr = 2.72196e-11
I0408 16:24:26.195763 27257 solver.cpp:218] Iteration 5652 (2.37088 iter/s, 5.06141s/12 iters), loss = 5.0096
I0408 16:24:26.195809 27257 solver.cpp:237]     Train net output #0: loss = 5.0096 (* 1 = 5.0096 loss)
I0408 16:24:26.195820 27257 sgd_solver.cpp:105] Iteration 5652, lr = 2.61011e-11
I0408 16:24:31.064707 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:24:31.229375 27257 solver.cpp:218] Iteration 5664 (2.38408 iter/s, 5.0334s/12 iters), loss = 4.9797
I0408 16:24:31.229427 27257 solver.cpp:237]     Train net output #0: loss = 4.9797 (* 1 = 4.9797 loss)
I0408 16:24:31.229439 27257 sgd_solver.cpp:105] Iteration 5664, lr = 2.50285e-11
I0408 16:24:36.297317 27257 solver.cpp:218] Iteration 5676 (2.36793 iter/s, 5.06773s/12 iters), loss = 5.00156
I0408 16:24:36.297363 27257 solver.cpp:237]     Train net output #0: loss = 5.00156 (* 1 = 5.00156 loss)
I0408 16:24:36.297375 27257 sgd_solver.cpp:105] Iteration 5676, lr = 2.4e-11
I0408 16:24:41.307476 27257 solver.cpp:218] Iteration 5688 (2.39523 iter/s, 5.00995s/12 iters), loss = 4.79657
I0408 16:24:41.307520 27257 solver.cpp:237]     Train net output #0: loss = 4.79657 (* 1 = 4.79657 loss)
I0408 16:24:41.307533 27257 sgd_solver.cpp:105] Iteration 5688, lr = 2.30138e-11
I0408 16:24:46.281488 27257 solver.cpp:218] Iteration 5700 (2.41264 iter/s, 4.97381s/12 iters), loss = 4.88548
I0408 16:24:46.281530 27257 solver.cpp:237]     Train net output #0: loss = 4.88548 (* 1 = 4.88548 loss)
I0408 16:24:46.281541 27257 sgd_solver.cpp:105] Iteration 5700, lr = 2.2068e-11
I0408 16:24:50.852946 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5712.caffemodel
I0408 16:24:53.893867 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5712.solverstate
I0408 16:24:56.200520 27257 solver.cpp:330] Iteration 5712, Testing net (#0)
I0408 16:24:56.200541 27257 net.cpp:676] Ignoring source layer train-data
I0408 16:24:58.300981 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:25:00.548101 27257 solver.cpp:397]     Test net output #0: accuracy = 0.0281863
I0408 16:25:00.548137 27257 solver.cpp:397]     Test net output #1: loss = 4.96793 (* 1 = 4.96793 loss)
I0408 16:25:00.638859 27257 solver.cpp:218] Iteration 5712 (0.835837 iter/s, 14.3569s/12 iters), loss = 4.93778
I0408 16:25:00.638895 27257 solver.cpp:237]     Train net output #0: loss = 4.93778 (* 1 = 4.93778 loss)
I0408 16:25:00.638906 27257 sgd_solver.cpp:105] Iteration 5712, lr = 2.11612e-11
I0408 16:25:04.791883 27257 solver.cpp:218] Iteration 5724 (2.88958 iter/s, 4.15285s/12 iters), loss = 4.9036
I0408 16:25:04.792390 27257 solver.cpp:237]     Train net output #0: loss = 4.9036 (* 1 = 4.9036 loss)
I0408 16:25:04.792407 27257 sgd_solver.cpp:105] Iteration 5724, lr = 2.02916e-11
I0408 16:25:09.785094 27257 solver.cpp:218] Iteration 5736 (2.40359 iter/s, 4.99254s/12 iters), loss = 4.9839
I0408 16:25:09.785145 27257 solver.cpp:237]     Train net output #0: loss = 4.9839 (* 1 = 4.9839 loss)
I0408 16:25:09.785157 27257 sgd_solver.cpp:105] Iteration 5736, lr = 1.94578e-11
I0408 16:25:14.787842 27257 solver.cpp:218] Iteration 5748 (2.39878 iter/s, 5.00254s/12 iters), loss = 4.90128
I0408 16:25:14.787880 27257 solver.cpp:237]     Train net output #0: loss = 4.90128 (* 1 = 4.90128 loss)
I0408 16:25:14.787889 27257 sgd_solver.cpp:105] Iteration 5748, lr = 1.86582e-11
I0408 16:25:19.811921 27257 solver.cpp:218] Iteration 5760 (2.38859 iter/s, 5.02388s/12 iters), loss = 5.03145
I0408 16:25:19.811957 27257 solver.cpp:237]     Train net output #0: loss = 5.03145 (* 1 = 5.03145 loss)
I0408 16:25:19.811966 27257 sgd_solver.cpp:105] Iteration 5760, lr = 1.78914e-11
I0408 16:25:21.798883 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:25:24.793979 27257 solver.cpp:218] Iteration 5772 (2.40875 iter/s, 4.98184s/12 iters), loss = 4.99398
I0408 16:25:24.794029 27257 solver.cpp:237]     Train net output #0: loss = 4.99398 (* 1 = 4.99398 loss)
I0408 16:25:24.794056 27257 sgd_solver.cpp:105] Iteration 5772, lr = 1.71562e-11
I0408 16:25:29.774809 27257 solver.cpp:218] Iteration 5784 (2.40934 iter/s, 4.98061s/12 iters), loss = 4.94999
I0408 16:25:29.774854 27257 solver.cpp:237]     Train net output #0: loss = 4.94999 (* 1 = 4.94999 loss)
I0408 16:25:29.774866 27257 sgd_solver.cpp:105] Iteration 5784, lr = 1.64512e-11
I0408 16:25:34.794260 27257 solver.cpp:218] Iteration 5796 (2.3908 iter/s, 5.01924s/12 iters), loss = 4.92305
I0408 16:25:34.807016 27257 solver.cpp:237]     Train net output #0: loss = 4.92305 (* 1 = 4.92305 loss)
I0408 16:25:34.807027 27257 sgd_solver.cpp:105] Iteration 5796, lr = 1.57752e-11
I0408 16:25:39.790223 27257 solver.cpp:218] Iteration 5808 (2.40817 iter/s, 4.98304s/12 iters), loss = 5.01875
I0408 16:25:39.790277 27257 solver.cpp:237]     Train net output #0: loss = 5.01875 (* 1 = 5.01875 loss)
I0408 16:25:39.790289 27257 sgd_solver.cpp:105] Iteration 5808, lr = 1.51269e-11
I0408 16:25:41.806840 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5814.caffemodel
I0408 16:25:45.633141 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5814.solverstate
I0408 16:25:47.963241 27257 solver.cpp:330] Iteration 5814, Testing net (#0)
I0408 16:25:47.963266 27257 net.cpp:676] Ignoring source layer train-data
I0408 16:25:50.136147 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:25:52.426148 27257 solver.cpp:397]     Test net output #0: accuracy = 0.0275735
I0408 16:25:52.426198 27257 solver.cpp:397]     Test net output #1: loss = 4.97123 (* 1 = 4.97123 loss)
I0408 16:25:54.290496 27257 solver.cpp:218] Iteration 5820 (0.8276 iter/s, 14.4998s/12 iters), loss = 4.96602
I0408 16:25:54.290544 27257 solver.cpp:237]     Train net output #0: loss = 4.96602 (* 1 = 4.96602 loss)
I0408 16:25:54.290555 27257 sgd_solver.cpp:105] Iteration 5820, lr = 1.45053e-11
I0408 16:25:59.257751 27257 solver.cpp:218] Iteration 5832 (2.41593 iter/s, 4.96704s/12 iters), loss = 4.95427
I0408 16:25:59.257798 27257 solver.cpp:237]     Train net output #0: loss = 4.95427 (* 1 = 4.95427 loss)
I0408 16:25:59.257810 27257 sgd_solver.cpp:105] Iteration 5832, lr = 1.39092e-11
I0408 16:26:04.305184 27257 solver.cpp:218] Iteration 5844 (2.37755 iter/s, 5.04721s/12 iters), loss = 4.85183
I0408 16:26:04.305228 27257 solver.cpp:237]     Train net output #0: loss = 4.85183 (* 1 = 4.85183 loss)
I0408 16:26:04.305240 27257 sgd_solver.cpp:105] Iteration 5844, lr = 1.33377e-11
I0408 16:26:09.340660 27257 solver.cpp:218] Iteration 5856 (2.38319 iter/s, 5.03526s/12 iters), loss = 4.99181
I0408 16:26:09.340793 27257 solver.cpp:237]     Train net output #0: loss = 4.99181 (* 1 = 4.99181 loss)
I0408 16:26:09.340809 27257 sgd_solver.cpp:105] Iteration 5856, lr = 1.27896e-11
I0408 16:26:13.560763 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:26:14.379155 27257 solver.cpp:218] Iteration 5868 (2.3818 iter/s, 5.0382s/12 iters), loss = 4.9358
I0408 16:26:14.379201 27257 solver.cpp:237]     Train net output #0: loss = 4.9358 (* 1 = 4.9358 loss)
I0408 16:26:14.379215 27257 sgd_solver.cpp:105] Iteration 5868, lr = 1.2264e-11
I0408 16:26:19.472704 27257 solver.cpp:218] Iteration 5880 (2.35602 iter/s, 5.09333s/12 iters), loss = 4.94114
I0408 16:26:19.472746 27257 solver.cpp:237]     Train net output #0: loss = 4.94114 (* 1 = 4.94114 loss)
I0408 16:26:19.472756 27257 sgd_solver.cpp:105] Iteration 5880, lr = 1.176e-11
I0408 16:26:24.490634 27257 solver.cpp:218] Iteration 5892 (2.39152 iter/s, 5.01772s/12 iters), loss = 4.99763
I0408 16:26:24.490681 27257 solver.cpp:237]     Train net output #0: loss = 4.99763 (* 1 = 4.99763 loss)
I0408 16:26:24.490693 27257 sgd_solver.cpp:105] Iteration 5892, lr = 1.12768e-11
I0408 16:26:29.542764 27257 solver.cpp:218] Iteration 5904 (2.37534 iter/s, 5.05191s/12 iters), loss = 4.85567
I0408 16:26:29.542809 27257 solver.cpp:237]     Train net output #0: loss = 4.85567 (* 1 = 4.85567 loss)
I0408 16:26:29.542819 27257 sgd_solver.cpp:105] Iteration 5904, lr = 1.08134e-11
I0408 16:26:34.076660 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5916.caffemodel
I0408 16:26:37.117708 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5916.solverstate
I0408 16:26:39.447201 27257 solver.cpp:330] Iteration 5916, Testing net (#0)
I0408 16:26:39.447252 27257 net.cpp:676] Ignoring source layer train-data
I0408 16:26:41.581292 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:26:43.907274 27257 solver.cpp:397]     Test net output #0: accuracy = 0.0281863
I0408 16:26:43.907320 27257 solver.cpp:397]     Test net output #1: loss = 4.96709 (* 1 = 4.96709 loss)
I0408 16:26:43.997872 27257 solver.cpp:218] Iteration 5916 (0.830186 iter/s, 14.4546s/12 iters), loss = 4.84161
I0408 16:26:43.997910 27257 solver.cpp:237]     Train net output #0: loss = 4.84161 (* 1 = 4.84161 loss)
I0408 16:26:43.997920 27257 sgd_solver.cpp:105] Iteration 5916, lr = 1.0369e-11
I0408 16:26:48.383273 27257 solver.cpp:218] Iteration 5928 (2.73647 iter/s, 4.38521s/12 iters), loss = 4.94252
I0408 16:26:48.383327 27257 solver.cpp:237]     Train net output #0: loss = 4.94252 (* 1 = 4.94252 loss)
I0408 16:26:48.383338 27257 sgd_solver.cpp:105] Iteration 5928, lr = 9.94293e-12
I0408 16:26:53.456125 27257 solver.cpp:218] Iteration 5940 (2.36564 iter/s, 5.07263s/12 iters), loss = 5.10598
I0408 16:26:53.456167 27257 solver.cpp:237]     Train net output #0: loss = 5.10598 (* 1 = 5.10598 loss)
I0408 16:26:53.456179 27257 sgd_solver.cpp:105] Iteration 5940, lr = 9.53434e-12
I0408 16:26:58.468878 27257 solver.cpp:218] Iteration 5952 (2.39399 iter/s, 5.01254s/12 iters), loss = 4.86745
I0408 16:26:58.468911 27257 solver.cpp:237]     Train net output #0: loss = 4.86745 (* 1 = 4.86745 loss)
I0408 16:26:58.468919 27257 sgd_solver.cpp:105] Iteration 5952, lr = 9.14254e-12
I0408 16:27:03.467551 27257 solver.cpp:218] Iteration 5964 (2.40074 iter/s, 4.99847s/12 iters), loss = 4.95094
I0408 16:27:03.467595 27257 solver.cpp:237]     Train net output #0: loss = 4.95094 (* 1 = 4.95094 loss)
I0408 16:27:03.467607 27257 sgd_solver.cpp:105] Iteration 5964, lr = 8.76684e-12
I0408 16:27:04.801303 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:27:08.477892 27257 solver.cpp:218] Iteration 5976 (2.39515 iter/s, 5.01013s/12 iters), loss = 4.88215
I0408 16:27:08.477939 27257 solver.cpp:237]     Train net output #0: loss = 4.88215 (* 1 = 4.88215 loss)
I0408 16:27:08.477952 27257 sgd_solver.cpp:105] Iteration 5976, lr = 8.40659e-12
I0408 16:27:13.440618 27257 solver.cpp:218] Iteration 5988 (2.41813 iter/s, 4.96251s/12 iters), loss = 5.02875
I0408 16:27:13.440754 27257 solver.cpp:237]     Train net output #0: loss = 5.02875 (* 1 = 5.02875 loss)
I0408 16:27:13.440768 27257 sgd_solver.cpp:105] Iteration 5988, lr = 8.06113e-12
I0408 16:27:18.483734 27257 solver.cpp:218] Iteration 6000 (2.37963 iter/s, 5.04281s/12 iters), loss = 5.02641
I0408 16:27:18.483779 27257 solver.cpp:237]     Train net output #0: loss = 5.02641 (* 1 = 5.02641 loss)
I0408 16:27:18.483791 27257 sgd_solver.cpp:105] Iteration 6000, lr = 7.72987e-12
I0408 16:27:23.485915 27257 solver.cpp:218] Iteration 6012 (2.39906 iter/s, 5.00196s/12 iters), loss = 4.89176
I0408 16:27:23.485970 27257 solver.cpp:237]     Train net output #0: loss = 4.89176 (* 1 = 4.89176 loss)
I0408 16:27:23.485983 27257 sgd_solver.cpp:105] Iteration 6012, lr = 7.41223e-12
I0408 16:27:25.513196 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6018.caffemodel
I0408 16:27:28.565454 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6018.solverstate
I0408 16:27:30.948725 27257 solver.cpp:330] Iteration 6018, Testing net (#0)
I0408 16:27:30.948752 27257 net.cpp:676] Ignoring source layer train-data
I0408 16:27:33.070120 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:27:35.480376 27257 solver.cpp:397]     Test net output #0: accuracy = 0.0275735
I0408 16:27:35.480424 27257 solver.cpp:397]     Test net output #1: loss = 4.96938 (* 1 = 4.96938 loss)
I0408 16:27:37.470996 27257 solver.cpp:218] Iteration 6024 (0.858088 iter/s, 13.9846s/12 iters), loss = 4.97884
I0408 16:27:37.471045 27257 solver.cpp:237]     Train net output #0: loss = 4.97884 (* 1 = 4.97884 loss)
I0408 16:27:37.471056 27257 sgd_solver.cpp:105] Iteration 6024, lr = 7.10763e-12
I0408 16:27:42.724040 27257 solver.cpp:218] Iteration 6036 (2.28449 iter/s, 5.25281s/12 iters), loss = 4.80025
I0408 16:27:42.724087 27257 solver.cpp:237]     Train net output #0: loss = 4.80025 (* 1 = 4.80025 loss)
I0408 16:27:42.724099 27257 sgd_solver.cpp:105] Iteration 6036, lr = 6.81556e-12
I0408 16:27:47.734864 27257 solver.cpp:218] Iteration 6048 (2.39492 iter/s, 5.0106s/12 iters), loss = 4.94907
I0408 16:27:47.734970 27257 solver.cpp:237]     Train net output #0: loss = 4.94907 (* 1 = 4.94907 loss)
I0408 16:27:47.734983 27257 sgd_solver.cpp:105] Iteration 6048, lr = 6.53548e-12
I0408 16:27:52.770645 27257 solver.cpp:218] Iteration 6060 (2.38308 iter/s, 5.0355s/12 iters), loss = 4.90182
I0408 16:27:52.770692 27257 solver.cpp:237]     Train net output #0: loss = 4.90182 (* 1 = 4.90182 loss)
I0408 16:27:52.770704 27257 sgd_solver.cpp:105] Iteration 6060, lr = 6.26692e-12
I0408 16:27:56.257431 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:27:57.800211 27257 solver.cpp:218] Iteration 6072 (2.386 iter/s, 5.02935s/12 iters), loss = 4.9445
I0408 16:27:57.800256 27257 solver.cpp:237]     Train net output #0: loss = 4.9445 (* 1 = 4.9445 loss)
I0408 16:27:57.800268 27257 sgd_solver.cpp:105] Iteration 6072, lr = 6.00939e-12
I0408 16:28:02.809202 27257 solver.cpp:218] Iteration 6084 (2.39579 iter/s, 5.00878s/12 iters), loss = 4.97786
I0408 16:28:02.809240 27257 solver.cpp:237]     Train net output #0: loss = 4.97786 (* 1 = 4.97786 loss)
I0408 16:28:02.809248 27257 sgd_solver.cpp:105] Iteration 6084, lr = 5.76244e-12
I0408 16:28:07.822855 27257 solver.cpp:218] Iteration 6096 (2.39357 iter/s, 5.01344s/12 iters), loss = 4.97642
I0408 16:28:07.822899 27257 solver.cpp:237]     Train net output #0: loss = 4.97642 (* 1 = 4.97642 loss)
I0408 16:28:07.822911 27257 sgd_solver.cpp:105] Iteration 6096, lr = 5.52565e-12
I0408 16:28:12.829564 27257 solver.cpp:218] Iteration 6108 (2.39689 iter/s, 5.00649s/12 iters), loss = 4.95444
I0408 16:28:12.829614 27257 solver.cpp:237]     Train net output #0: loss = 4.95444 (* 1 = 4.95444 loss)
I0408 16:28:12.829627 27257 sgd_solver.cpp:105] Iteration 6108, lr = 5.29858e-12
I0408 16:28:17.395328 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6120.caffemodel
I0408 16:28:20.414122 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6120.solverstate
I0408 16:28:22.748637 27257 solver.cpp:330] Iteration 6120, Testing net (#0)
I0408 16:28:22.748663 27257 net.cpp:676] Ignoring source layer train-data
I0408 16:28:24.781208 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:28:27.183681 27257 solver.cpp:397]     Test net output #0: accuracy = 0.0275735
I0408 16:28:27.183728 27257 solver.cpp:397]     Test net output #1: loss = 4.96881 (* 1 = 4.96881 loss)
I0408 16:28:27.274281 27257 solver.cpp:218] Iteration 6120 (0.830784 iter/s, 14.4442s/12 iters), loss = 4.94181
I0408 16:28:27.274333 27257 solver.cpp:237]     Train net output #0: loss = 4.94181 (* 1 = 4.94181 loss)
I0408 16:28:27.274344 27257 sgd_solver.cpp:105] Iteration 6120, lr = 5.08084e-12
I0408 16:28:31.807906 27257 solver.cpp:218] Iteration 6132 (2.64701 iter/s, 4.53342s/12 iters), loss = 4.91289
I0408 16:28:31.807950 27257 solver.cpp:237]     Train net output #0: loss = 4.91289 (* 1 = 4.91289 loss)
I0408 16:28:31.807961 27257 sgd_solver.cpp:105] Iteration 6132, lr = 4.87205e-12
I0408 16:28:36.918610 27257 solver.cpp:218] Iteration 6144 (2.34812 iter/s, 5.11048s/12 iters), loss = 5.05953
I0408 16:28:36.918666 27257 solver.cpp:237]     Train net output #0: loss = 5.05953 (* 1 = 5.05953 loss)
I0408 16:28:36.918682 27257 sgd_solver.cpp:105] Iteration 6144, lr = 4.67185e-12
I0408 16:28:41.855667 27257 solver.cpp:218] Iteration 6156 (2.43071 iter/s, 4.93683s/12 iters), loss = 4.90015
I0408 16:28:41.855715 27257 solver.cpp:237]     Train net output #0: loss = 4.90015 (* 1 = 4.90015 loss)
I0408 16:28:41.855727 27257 sgd_solver.cpp:105] Iteration 6156, lr = 4.47986e-12
I0408 16:28:46.862510 27257 solver.cpp:218] Iteration 6168 (2.39682 iter/s, 5.00662s/12 iters), loss = 4.98516
I0408 16:28:46.862551 27257 solver.cpp:237]     Train net output #0: loss = 4.98516 (* 1 = 4.98516 loss)
I0408 16:28:46.862561 27257 sgd_solver.cpp:105] Iteration 6168, lr = 4.29577e-12
I0408 16:28:47.455926 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:28:51.862291 27257 solver.cpp:218] Iteration 6180 (2.40021 iter/s, 4.99956s/12 iters), loss = 4.93417
I0408 16:28:51.862365 27257 solver.cpp:237]     Train net output #0: loss = 4.93417 (* 1 = 4.93417 loss)
I0408 16:28:51.862375 27257 sgd_solver.cpp:105] Iteration 6180, lr = 4.11924e-12
I0408 16:28:56.877040 27257 solver.cpp:218] Iteration 6192 (2.39306 iter/s, 5.0145s/12 iters), loss = 5.00094
I0408 16:28:56.877094 27257 solver.cpp:237]     Train net output #0: loss = 5.00094 (* 1 = 5.00094 loss)
I0408 16:28:56.877108 27257 sgd_solver.cpp:105] Iteration 6192, lr = 3.94997e-12
I0408 16:29:01.917001 27257 solver.cpp:218] Iteration 6204 (2.38108 iter/s, 5.03974s/12 iters), loss = 4.86343
I0408 16:29:01.917045 27257 solver.cpp:237]     Train net output #0: loss = 4.86343 (* 1 = 4.86343 loss)
I0408 16:29:01.917057 27257 sgd_solver.cpp:105] Iteration 6204, lr = 3.78765e-12
I0408 16:29:06.930243 27257 solver.cpp:218] Iteration 6216 (2.39377 iter/s, 5.01302s/12 iters), loss = 4.79577
I0408 16:29:06.930289 27257 solver.cpp:237]     Train net output #0: loss = 4.79577 (* 1 = 4.79577 loss)
I0408 16:29:06.930301 27257 sgd_solver.cpp:105] Iteration 6216, lr = 3.63201e-12
I0408 16:29:08.991566 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6222.caffemodel
I0408 16:29:12.001232 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6222.solverstate
I0408 16:29:14.334620 27257 solver.cpp:330] Iteration 6222, Testing net (#0)
I0408 16:29:14.334646 27257 net.cpp:676] Ignoring source layer train-data
I0408 16:29:16.319079 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:29:17.665274 27257 blocking_queue.cpp:49] Waiting for data
I0408 16:29:18.908510 27257 solver.cpp:397]     Test net output #0: accuracy = 0.0275735
I0408 16:29:18.908550 27257 solver.cpp:397]     Test net output #1: loss = 4.97021 (* 1 = 4.97021 loss)
I0408 16:29:20.856420 27257 solver.cpp:218] Iteration 6228 (0.861718 iter/s, 13.9257s/12 iters), loss = 4.90703
I0408 16:29:20.856469 27257 solver.cpp:237]     Train net output #0: loss = 4.90703 (* 1 = 4.90703 loss)
I0408 16:29:20.856480 27257 sgd_solver.cpp:105] Iteration 6228, lr = 3.48275e-12
I0408 16:29:25.940784 27257 solver.cpp:218] Iteration 6240 (2.36028 iter/s, 5.08414s/12 iters), loss = 4.98304
I0408 16:29:25.940898 27257 solver.cpp:237]     Train net output #0: loss = 4.98304 (* 1 = 4.98304 loss)
I0408 16:29:25.940908 27257 sgd_solver.cpp:105] Iteration 6240, lr = 3.33964e-12
I0408 16:29:30.969858 27257 solver.cpp:218] Iteration 6252 (2.38626 iter/s, 5.02878s/12 iters), loss = 4.94891
I0408 16:29:30.969907 27257 solver.cpp:237]     Train net output #0: loss = 4.94891 (* 1 = 4.94891 loss)
I0408 16:29:30.969918 27257 sgd_solver.cpp:105] Iteration 6252, lr = 3.2024e-12
I0408 16:29:36.057665 27257 solver.cpp:218] Iteration 6264 (2.35869 iter/s, 5.08758s/12 iters), loss = 4.95762
I0408 16:29:36.057709 27257 solver.cpp:237]     Train net output #0: loss = 4.95762 (* 1 = 4.95762 loss)
I0408 16:29:36.057721 27257 sgd_solver.cpp:105] Iteration 6264, lr = 3.0708e-12
I0408 16:29:38.812634 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:29:41.093921 27257 solver.cpp:218] Iteration 6276 (2.38283 iter/s, 5.03603s/12 iters), loss = 4.95547
I0408 16:29:41.093982 27257 solver.cpp:237]     Train net output #0: loss = 4.95547 (* 1 = 4.95547 loss)
I0408 16:29:41.093994 27257 sgd_solver.cpp:105] Iteration 6276, lr = 2.94461e-12
I0408 16:29:46.119504 27257 solver.cpp:218] Iteration 6288 (2.38789 iter/s, 5.02535s/12 iters), loss = 4.88391
I0408 16:29:46.119546 27257 solver.cpp:237]     Train net output #0: loss = 4.88391 (* 1 = 4.88391 loss)
I0408 16:29:46.119557 27257 sgd_solver.cpp:105] Iteration 6288, lr = 2.82361e-12
I0408 16:29:51.381019 27257 solver.cpp:218] Iteration 6300 (2.28081 iter/s, 5.26129s/12 iters), loss = 4.83294
I0408 16:29:51.381062 27257 solver.cpp:237]     Train net output #0: loss = 4.83294 (* 1 = 4.83294 loss)
I0408 16:29:51.381074 27257 sgd_solver.cpp:105] Iteration 6300, lr = 2.70758e-12
I0408 16:29:56.329835 27257 solver.cpp:218] Iteration 6312 (2.42493 iter/s, 4.9486s/12 iters), loss = 4.87031
I0408 16:29:56.329937 27257 solver.cpp:237]     Train net output #0: loss = 4.87031 (* 1 = 4.87031 loss)
I0408 16:29:56.329950 27257 sgd_solver.cpp:105] Iteration 6312, lr = 2.59631e-12
I0408 16:30:00.861224 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6324.caffemodel
I0408 16:30:03.892670 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6324.solverstate
I0408 16:30:06.627993 27257 solver.cpp:330] Iteration 6324, Testing net (#0)
I0408 16:30:06.628013 27257 net.cpp:676] Ignoring source layer train-data
I0408 16:30:08.616464 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:30:11.098742 27257 solver.cpp:397]     Test net output #0: accuracy = 0.0281863
I0408 16:30:11.098786 27257 solver.cpp:397]     Test net output #1: loss = 4.96802 (* 1 = 4.96802 loss)
I0408 16:30:11.189404 27257 solver.cpp:218] Iteration 6324 (0.807593 iter/s, 14.859s/12 iters), loss = 4.90845
I0408 16:30:11.189458 27257 solver.cpp:237]     Train net output #0: loss = 4.90845 (* 1 = 4.90845 loss)
I0408 16:30:11.189469 27257 sgd_solver.cpp:105] Iteration 6324, lr = 2.48962e-12
I0408 16:30:15.368607 27257 solver.cpp:218] Iteration 6336 (2.8715 iter/s, 4.179s/12 iters), loss = 4.95824
I0408 16:30:15.368643 27257 solver.cpp:237]     Train net output #0: loss = 4.95824 (* 1 = 4.95824 loss)
I0408 16:30:15.368652 27257 sgd_solver.cpp:105] Iteration 6336, lr = 2.38732e-12
I0408 16:30:20.387655 27257 solver.cpp:218] Iteration 6348 (2.391 iter/s, 5.01883s/12 iters), loss = 4.9963
I0408 16:30:20.387704 27257 solver.cpp:237]     Train net output #0: loss = 4.9963 (* 1 = 4.9963 loss)
I0408 16:30:20.387715 27257 sgd_solver.cpp:105] Iteration 6348, lr = 2.28921e-12
I0408 16:30:25.408210 27257 solver.cpp:218] Iteration 6360 (2.39028 iter/s, 5.02033s/12 iters), loss = 4.96673
I0408 16:30:25.408247 27257 solver.cpp:237]     Train net output #0: loss = 4.96673 (* 1 = 4.96673 loss)
I0408 16:30:25.408257 27257 sgd_solver.cpp:105] Iteration 6360, lr = 2.19514e-12
I0408 16:30:30.262027 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:30:30.400527 27257 solver.cpp:218] Iteration 6372 (2.4038 iter/s, 4.9921s/12 iters), loss = 4.98659
I0408 16:30:30.400563 27257 solver.cpp:237]     Train net output #0: loss = 4.98659 (* 1 = 4.98659 loss)
I0408 16:30:30.400570 27257 sgd_solver.cpp:105] Iteration 6372, lr = 2.10494e-12
I0408 16:30:35.417950 27257 solver.cpp:218] Iteration 6384 (2.39177 iter/s, 5.01721s/12 iters), loss = 5.01396
I0408 16:30:35.417994 27257 solver.cpp:237]     Train net output #0: loss = 5.01396 (* 1 = 5.01396 loss)
I0408 16:30:35.418004 27257 sgd_solver.cpp:105] Iteration 6384, lr = 2.01844e-12
I0408 16:30:40.446652 27257 solver.cpp:218] Iteration 6396 (2.38641 iter/s, 5.02847s/12 iters), loss = 4.86842
I0408 16:30:40.446712 27257 solver.cpp:237]     Train net output #0: loss = 4.86842 (* 1 = 4.86842 loss)
I0408 16:30:40.446724 27257 sgd_solver.cpp:105] Iteration 6396, lr = 1.93549e-12
I0408 16:30:45.496207 27257 solver.cpp:218] Iteration 6408 (2.37656 iter/s, 5.04932s/12 iters), loss = 4.9009
I0408 16:30:45.496256 27257 solver.cpp:237]     Train net output #0: loss = 4.9009 (* 1 = 4.9009 loss)
I0408 16:30:45.496269 27257 sgd_solver.cpp:105] Iteration 6408, lr = 1.85596e-12
I0408 16:30:50.556644 27257 solver.cpp:218] Iteration 6420 (2.37144 iter/s, 5.06021s/12 iters), loss = 4.94037
I0408 16:30:50.556690 27257 solver.cpp:237]     Train net output #0: loss = 4.94037 (* 1 = 4.94037 loss)
I0408 16:30:50.556702 27257 sgd_solver.cpp:105] Iteration 6420, lr = 1.77969e-12
I0408 16:30:52.535483 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6426.caffemodel
I0408 16:30:56.653875 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6426.solverstate
I0408 16:30:58.982671 27257 solver.cpp:330] Iteration 6426, Testing net (#0)
I0408 16:30:58.982698 27257 net.cpp:676] Ignoring source layer train-data
I0408 16:31:00.916060 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:31:03.446010 27257 solver.cpp:397]     Test net output #0: accuracy = 0.0275735
I0408 16:31:03.446038 27257 solver.cpp:397]     Test net output #1: loss = 4.9701 (* 1 = 4.9701 loss)
I0408 16:31:05.194947 27257 solver.cpp:218] Iteration 6432 (0.819798 iter/s, 14.6378s/12 iters), loss = 4.88243
I0408 16:31:05.194999 27257 solver.cpp:237]     Train net output #0: loss = 4.88243 (* 1 = 4.88243 loss)
I0408 16:31:05.195011 27257 sgd_solver.cpp:105] Iteration 6432, lr = 1.70656e-12
I0408 16:31:10.223837 27257 solver.cpp:218] Iteration 6444 (2.38632 iter/s, 5.02866s/12 iters), loss = 4.93451
I0408 16:31:10.223887 27257 solver.cpp:237]     Train net output #0: loss = 4.93451 (* 1 = 4.93451 loss)
I0408 16:31:10.223899 27257 sgd_solver.cpp:105] Iteration 6444, lr = 1.63643e-12
I0408 16:31:15.193320 27257 solver.cpp:218] Iteration 6456 (2.41485 iter/s, 4.96926s/12 iters), loss = 5.0223
I0408 16:31:15.193363 27257 solver.cpp:237]     Train net output #0: loss = 5.0223 (* 1 = 5.0223 loss)
I0408 16:31:15.193374 27257 sgd_solver.cpp:105] Iteration 6456, lr = 1.56918e-12
I0408 16:31:20.150499 27257 solver.cpp:218] Iteration 6468 (2.42084 iter/s, 4.95696s/12 iters), loss = 5.04799
I0408 16:31:20.150543 27257 solver.cpp:237]     Train net output #0: loss = 5.04799 (* 1 = 5.04799 loss)
I0408 16:31:20.150555 27257 sgd_solver.cpp:105] Iteration 6468, lr = 1.5047e-12
I0408 16:31:22.123224 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:31:25.092751 27257 solver.cpp:218] Iteration 6480 (2.42815 iter/s, 4.94203s/12 iters), loss = 4.95421
I0408 16:31:25.092797 27257 solver.cpp:237]     Train net output #0: loss = 4.95421 (* 1 = 4.95421 loss)
I0408 16:31:25.092808 27257 sgd_solver.cpp:105] Iteration 6480, lr = 1.44287e-12
I0408 16:31:30.127748 27257 solver.cpp:218] Iteration 6492 (2.38342 iter/s, 5.03477s/12 iters), loss = 4.86639
I0408 16:31:30.127784 27257 solver.cpp:237]     Train net output #0: loss = 4.86639 (* 1 = 4.86639 loss)
I0408 16:31:30.127791 27257 sgd_solver.cpp:105] Iteration 6492, lr = 1.38357e-12
I0408 16:31:35.082207 27257 solver.cpp:218] Iteration 6504 (2.42216 iter/s, 4.95425s/12 iters), loss = 4.85511
I0408 16:31:35.082348 27257 solver.cpp:237]     Train net output #0: loss = 4.85511 (* 1 = 4.85511 loss)
I0408 16:31:35.082363 27257 sgd_solver.cpp:105] Iteration 6504, lr = 1.32672e-12
I0408 16:31:39.989493 27257 solver.cpp:218] Iteration 6516 (2.4455 iter/s, 4.90698s/12 iters), loss = 4.98139
I0408 16:31:39.989539 27257 solver.cpp:237]     Train net output #0: loss = 4.98139 (* 1 = 4.98139 loss)
I0408 16:31:39.989552 27257 sgd_solver.cpp:105] Iteration 6516, lr = 1.2722e-12
I0408 16:31:44.544095 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6528.caffemodel
I0408 16:31:47.583566 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6528.solverstate
I0408 16:31:49.882256 27257 solver.cpp:330] Iteration 6528, Testing net (#0)
I0408 16:31:49.882277 27257 net.cpp:676] Ignoring source layer train-data
I0408 16:31:51.772900 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:31:54.334106 27257 solver.cpp:397]     Test net output #0: accuracy = 0.0275735
I0408 16:31:54.334152 27257 solver.cpp:397]     Test net output #1: loss = 4.97163 (* 1 = 4.97163 loss)
I0408 16:31:54.424810 27257 solver.cpp:218] Iteration 6528 (0.831326 iter/s, 14.4348s/12 iters), loss = 4.97076
I0408 16:31:54.424865 27257 solver.cpp:237]     Train net output #0: loss = 4.97076 (* 1 = 4.97076 loss)
I0408 16:31:54.424877 27257 sgd_solver.cpp:105] Iteration 6528, lr = 1.21992e-12
I0408 16:31:58.912010 27257 solver.cpp:218] Iteration 6540 (2.6744 iter/s, 4.48699s/12 iters), loss = 4.89091
I0408 16:31:58.912058 27257 solver.cpp:237]     Train net output #0: loss = 4.89091 (* 1 = 4.89091 loss)
I0408 16:31:58.912070 27257 sgd_solver.cpp:105] Iteration 6540, lr = 1.16979e-12
I0408 16:32:03.963992 27257 solver.cpp:218] Iteration 6552 (2.37541 iter/s, 5.05176s/12 iters), loss = 4.90292
I0408 16:32:03.964026 27257 solver.cpp:237]     Train net output #0: loss = 4.90292 (* 1 = 4.90292 loss)
I0408 16:32:03.964035 27257 sgd_solver.cpp:105] Iteration 6552, lr = 1.12172e-12
I0408 16:32:08.894528 27257 solver.cpp:218] Iteration 6564 (2.43392 iter/s, 4.93032s/12 iters), loss = 4.96598
I0408 16:32:08.894667 27257 solver.cpp:237]     Train net output #0: loss = 4.96598 (* 1 = 4.96598 loss)
I0408 16:32:08.894680 27257 sgd_solver.cpp:105] Iteration 6564, lr = 1.07562e-12
I0408 16:32:13.136072 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:32:13.901881 27257 solver.cpp:218] Iteration 6576 (2.39663 iter/s, 5.00704s/12 iters), loss = 4.92673
I0408 16:32:13.901928 27257 solver.cpp:237]     Train net output #0: loss = 4.92673 (* 1 = 4.92673 loss)
I0408 16:32:13.901939 27257 sgd_solver.cpp:105] Iteration 6576, lr = 1.03142e-12
I0408 16:32:18.913130 27257 solver.cpp:218] Iteration 6588 (2.39472 iter/s, 5.01102s/12 iters), loss = 4.8927
I0408 16:32:18.913174 27257 solver.cpp:237]     Train net output #0: loss = 4.8927 (* 1 = 4.8927 loss)
I0408 16:32:18.913187 27257 sgd_solver.cpp:105] Iteration 6588, lr = 9.89039e-13
I0408 16:32:23.934495 27257 solver.cpp:218] Iteration 6600 (2.3899 iter/s, 5.02114s/12 iters), loss = 4.95721
I0408 16:32:23.934543 27257 solver.cpp:237]     Train net output #0: loss = 4.95721 (* 1 = 4.95721 loss)
I0408 16:32:23.934556 27257 sgd_solver.cpp:105] Iteration 6600, lr = 9.48396e-13
I0408 16:32:28.918179 27257 solver.cpp:218] Iteration 6612 (2.40797 iter/s, 4.98345s/12 iters), loss = 4.87676
I0408 16:32:28.918229 27257 solver.cpp:237]     Train net output #0: loss = 4.87676 (* 1 = 4.87676 loss)
I0408 16:32:28.918241 27257 sgd_solver.cpp:105] Iteration 6612, lr = 9.09423e-13
I0408 16:32:34.026278 27257 solver.cpp:218] Iteration 6624 (2.34932 iter/s, 5.10787s/12 iters), loss = 4.83864
I0408 16:32:34.026312 27257 solver.cpp:237]     Train net output #0: loss = 4.83864 (* 1 = 4.83864 loss)
I0408 16:32:34.026320 27257 sgd_solver.cpp:105] Iteration 6624, lr = 8.72052e-13
I0408 16:32:36.048292 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6630.caffemodel
I0408 16:32:39.113149 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6630.solverstate
I0408 16:32:41.439904 27257 solver.cpp:330] Iteration 6630, Testing net (#0)
I0408 16:32:41.439932 27257 net.cpp:676] Ignoring source layer train-data
I0408 16:32:43.300736 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:32:45.895826 27257 solver.cpp:397]     Test net output #0: accuracy = 0.0275735
I0408 16:32:45.895871 27257 solver.cpp:397]     Test net output #1: loss = 4.9689 (* 1 = 4.9689 loss)
I0408 16:32:47.852952 27257 solver.cpp:218] Iteration 6636 (0.86792 iter/s, 13.8262s/12 iters), loss = 4.9424
I0408 16:32:47.853000 27257 solver.cpp:237]     Train net output #0: loss = 4.9424 (* 1 = 4.9424 loss)
I0408 16:32:47.853010 27257 sgd_solver.cpp:105] Iteration 6636, lr = 8.36216e-13
I0408 16:32:52.886986 27257 solver.cpp:218] Iteration 6648 (2.38388 iter/s, 5.0338s/12 iters), loss = 5.07322
I0408 16:32:52.887046 27257 solver.cpp:237]     Train net output #0: loss = 5.07322 (* 1 = 5.07322 loss)
I0408 16:32:52.887060 27257 sgd_solver.cpp:105] Iteration 6648, lr = 8.01853e-13
I0408 16:32:57.789335 27257 solver.cpp:218] Iteration 6660 (2.44792 iter/s, 4.90212s/12 iters), loss = 4.94209
I0408 16:32:57.789378 27257 solver.cpp:237]     Train net output #0: loss = 4.94209 (* 1 = 4.94209 loss)
I0408 16:32:57.789388 27257 sgd_solver.cpp:105] Iteration 6660, lr = 7.68903e-13
I0408 16:33:02.803237 27257 solver.cpp:218] Iteration 6672 (2.39345 iter/s, 5.01368s/12 iters), loss = 4.97386
I0408 16:33:02.803287 27257 solver.cpp:237]     Train net output #0: loss = 4.97386 (* 1 = 4.97386 loss)
I0408 16:33:02.803298 27257 sgd_solver.cpp:105] Iteration 6672, lr = 7.37306e-13
I0408 16:33:04.176167 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:33:07.835703 27257 solver.cpp:218] Iteration 6684 (2.38463 iter/s, 5.03223s/12 iters), loss = 4.94916
I0408 16:33:07.835745 27257 solver.cpp:237]     Train net output #0: loss = 4.94916 (* 1 = 4.94916 loss)
I0408 16:33:07.835755 27257 sgd_solver.cpp:105] Iteration 6684, lr = 7.07007e-13
I0408 16:33:12.837365 27257 solver.cpp:218] Iteration 6696 (2.39931 iter/s, 5.00143s/12 iters), loss = 5.09324
I0408 16:33:12.837472 27257 solver.cpp:237]     Train net output #0: loss = 5.09324 (* 1 = 5.09324 loss)
I0408 16:33:12.837486 27257 sgd_solver.cpp:105] Iteration 6696, lr = 6.77954e-13
I0408 16:33:17.879042 27257 solver.cpp:218] Iteration 6708 (2.3803 iter/s, 5.04139s/12 iters), loss = 5.05749
I0408 16:33:17.879081 27257 solver.cpp:237]     Train net output #0: loss = 5.05749 (* 1 = 5.05749 loss)
I0408 16:33:17.879089 27257 sgd_solver.cpp:105] Iteration 6708, lr = 6.50095e-13
I0408 16:33:22.845816 27257 solver.cpp:218] Iteration 6720 (2.41616 iter/s, 4.96656s/12 iters), loss = 4.9205
I0408 16:33:22.845850 27257 solver.cpp:237]     Train net output #0: loss = 4.9205 (* 1 = 4.9205 loss)
I0408 16:33:22.845858 27257 sgd_solver.cpp:105] Iteration 6720, lr = 6.2338e-13
I0408 16:33:27.408916 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6732.caffemodel
I0408 16:33:30.502781 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6732.solverstate
I0408 16:33:32.854212 27257 solver.cpp:330] Iteration 6732, Testing net (#0)
I0408 16:33:32.854241 27257 net.cpp:676] Ignoring source layer train-data
I0408 16:33:34.688370 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:33:37.325582 27257 solver.cpp:397]     Test net output #0: accuracy = 0.028799
I0408 16:33:37.325629 27257 solver.cpp:397]     Test net output #1: loss = 4.9688 (* 1 = 4.9688 loss)
I0408 16:33:37.416242 27257 solver.cpp:218] Iteration 6732 (0.823617 iter/s, 14.5699s/12 iters), loss = 4.97307
I0408 16:33:37.416291 27257 solver.cpp:237]     Train net output #0: loss = 4.97307 (* 1 = 4.97307 loss)
I0408 16:33:37.416302 27257 sgd_solver.cpp:105] Iteration 6732, lr = 5.97763e-13
I0408 16:33:41.735944 27257 solver.cpp:218] Iteration 6744 (2.7781 iter/s, 4.31949s/12 iters), loss = 4.90535
I0408 16:33:41.735989 27257 solver.cpp:237]     Train net output #0: loss = 4.90535 (* 1 = 4.90535 loss)
I0408 16:33:41.736001 27257 sgd_solver.cpp:105] Iteration 6744, lr = 5.73199e-13
I0408 16:33:46.783674 27257 solver.cpp:218] Iteration 6756 (2.37741 iter/s, 5.0475s/12 iters), loss = 4.9069
I0408 16:33:46.783828 27257 solver.cpp:237]     Train net output #0: loss = 4.9069 (* 1 = 4.9069 loss)
I0408 16:33:46.783841 27257 sgd_solver.cpp:105] Iteration 6756, lr = 5.49645e-13
I0408 16:33:51.819564 27257 solver.cpp:218] Iteration 6768 (2.38305 iter/s, 5.03556s/12 iters), loss = 4.96622
I0408 16:33:51.819608 27257 solver.cpp:237]     Train net output #0: loss = 4.96622 (* 1 = 4.96622 loss)
I0408 16:33:51.819619 27257 sgd_solver.cpp:105] Iteration 6768, lr = 5.27058e-13
I0408 16:33:55.558996 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:33:57.208549 27257 solver.cpp:218] Iteration 6780 (2.22686 iter/s, 5.38875s/12 iters), loss = 5.00655
I0408 16:33:57.208597 27257 solver.cpp:237]     Train net output #0: loss = 5.00655 (* 1 = 5.00655 loss)
I0408 16:33:57.208608 27257 sgd_solver.cpp:105] Iteration 6780, lr = 5.05399e-13
I0408 16:34:02.294358 27257 solver.cpp:218] Iteration 6792 (2.35962 iter/s, 5.08557s/12 iters), loss = 4.99052
I0408 16:34:02.294407 27257 solver.cpp:237]     Train net output #0: loss = 4.99052 (* 1 = 4.99052 loss)
I0408 16:34:02.294420 27257 sgd_solver.cpp:105] Iteration 6792, lr = 4.84631e-13
I0408 16:34:07.305464 27257 solver.cpp:218] Iteration 6804 (2.39479 iter/s, 5.01087s/12 iters), loss = 5.00698
I0408 16:34:07.305511 27257 solver.cpp:237]     Train net output #0: loss = 5.00698 (* 1 = 5.00698 loss)
I0408 16:34:07.305523 27257 sgd_solver.cpp:105] Iteration 6804, lr = 4.64716e-13
I0408 16:34:12.330091 27257 solver.cpp:218] Iteration 6816 (2.38835 iter/s, 5.02439s/12 iters), loss = 4.92999
I0408 16:34:12.330148 27257 solver.cpp:237]     Train net output #0: loss = 4.92999 (* 1 = 4.92999 loss)
I0408 16:34:12.330163 27257 sgd_solver.cpp:105] Iteration 6816, lr = 4.45619e-13
I0408 16:34:17.286666 27257 solver.cpp:218] Iteration 6828 (2.42114 iter/s, 4.95634s/12 iters), loss = 4.96731
I0408 16:34:17.286772 27257 solver.cpp:237]     Train net output #0: loss = 4.96731 (* 1 = 4.96731 loss)
I0408 16:34:17.286785 27257 sgd_solver.cpp:105] Iteration 6828, lr = 4.27307e-13
I0408 16:34:19.332589 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6834.caffemodel
I0408 16:34:22.366509 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6834.solverstate
I0408 16:34:24.666921 27257 solver.cpp:330] Iteration 6834, Testing net (#0)
I0408 16:34:24.666946 27257 net.cpp:676] Ignoring source layer train-data
I0408 16:34:26.464629 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:34:29.138657 27257 solver.cpp:397]     Test net output #0: accuracy = 0.0275735
I0408 16:34:29.138705 27257 solver.cpp:397]     Test net output #1: loss = 4.9704 (* 1 = 4.9704 loss)
I0408 16:34:31.006120 27257 solver.cpp:218] Iteration 6840 (0.874707 iter/s, 13.7189s/12 iters), loss = 4.93493
I0408 16:34:31.006165 27257 solver.cpp:237]     Train net output #0: loss = 4.93493 (* 1 = 4.93493 loss)
I0408 16:34:31.006175 27257 sgd_solver.cpp:105] Iteration 6840, lr = 4.09748e-13
I0408 16:34:35.992218 27257 solver.cpp:218] Iteration 6852 (2.4068 iter/s, 4.98587s/12 iters), loss = 5.13363
I0408 16:34:35.992266 27257 solver.cpp:237]     Train net output #0: loss = 5.13363 (* 1 = 5.13363 loss)
I0408 16:34:35.992278 27257 sgd_solver.cpp:105] Iteration 6852, lr = 3.9291e-13
I0408 16:34:40.950913 27257 solver.cpp:218] Iteration 6864 (2.4201 iter/s, 4.95847s/12 iters), loss = 4.94671
I0408 16:34:40.950963 27257 solver.cpp:237]     Train net output #0: loss = 4.94671 (* 1 = 4.94671 loss)
I0408 16:34:40.950974 27257 sgd_solver.cpp:105] Iteration 6864, lr = 3.76764e-13
I0408 16:34:45.943692 27257 solver.cpp:218] Iteration 6876 (2.40358 iter/s, 4.99255s/12 iters), loss = 4.97161
I0408 16:34:45.943742 27257 solver.cpp:237]     Train net output #0: loss = 4.97161 (* 1 = 4.97161 loss)
I0408 16:34:45.943753 27257 sgd_solver.cpp:105] Iteration 6876, lr = 3.61281e-13
I0408 16:34:46.563009 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:34:50.953773 27257 solver.cpp:218] Iteration 6888 (2.39528 iter/s, 5.00985s/12 iters), loss = 4.90783
I0408 16:34:50.953894 27257 solver.cpp:237]     Train net output #0: loss = 4.90783 (* 1 = 4.90783 loss)
I0408 16:34:50.953907 27257 sgd_solver.cpp:105] Iteration 6888, lr = 3.46435e-13
I0408 16:34:55.974893 27257 solver.cpp:218] Iteration 6900 (2.39005 iter/s, 5.02082s/12 iters), loss = 5.00945
I0408 16:34:55.974951 27257 solver.cpp:237]     Train net output #0: loss = 5.00945 (* 1 = 5.00945 loss)
I0408 16:34:55.974959 27257 sgd_solver.cpp:105] Iteration 6900, lr = 3.32199e-13
I0408 16:35:00.986124 27257 solver.cpp:218] Iteration 6912 (2.39474 iter/s, 5.01099s/12 iters), loss = 4.82847
I0408 16:35:00.986265 27257 solver.cpp:237]     Train net output #0: loss = 4.82847 (* 1 = 4.82847 loss)
I0408 16:35:00.986276 27257 sgd_solver.cpp:105] Iteration 6912, lr = 3.18548e-13
I0408 16:35:05.981638 27257 solver.cpp:218] Iteration 6924 (2.40226 iter/s, 4.99529s/12 iters), loss = 4.84179
I0408 16:35:05.981670 27257 solver.cpp:237]     Train net output #0: loss = 4.84179 (* 1 = 4.84179 loss)
I0408 16:35:05.981679 27257 sgd_solver.cpp:105] Iteration 6924, lr = 3.05458e-13
I0408 16:35:10.473573 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6936.caffemodel
I0408 16:35:13.521345 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6936.solverstate
I0408 16:35:15.898123 27257 solver.cpp:330] Iteration 6936, Testing net (#0)
I0408 16:35:15.898152 27257 net.cpp:676] Ignoring source layer train-data
I0408 16:35:16.555872 27257 blocking_queue.cpp:49] Waiting for data
I0408 16:35:17.643921 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:35:20.362224 27257 solver.cpp:397]     Test net output #0: accuracy = 0.0275735
I0408 16:35:20.362270 27257 solver.cpp:397]     Test net output #1: loss = 4.97164 (* 1 = 4.97164 loss)
I0408 16:35:20.452813 27257 solver.cpp:218] Iteration 6936 (0.829266 iter/s, 14.4706s/12 iters), loss = 4.90616
I0408 16:35:20.452864 27257 solver.cpp:237]     Train net output #0: loss = 4.90616 (* 1 = 4.90616 loss)
I0408 16:35:20.452874 27257 sgd_solver.cpp:105] Iteration 6936, lr = 2.92905e-13
I0408 16:35:24.694653 27257 solver.cpp:218] Iteration 6948 (2.8291 iter/s, 4.24163s/12 iters), loss = 4.93777
I0408 16:35:24.694763 27257 solver.cpp:237]     Train net output #0: loss = 4.93777 (* 1 = 4.93777 loss)
I0408 16:35:24.694777 27257 sgd_solver.cpp:105] Iteration 6948, lr = 2.80869e-13
I0408 16:35:29.770772 27257 solver.cpp:218] Iteration 6960 (2.36415 iter/s, 5.07582s/12 iters), loss = 4.99756
I0408 16:35:29.770823 27257 solver.cpp:237]     Train net output #0: loss = 4.99756 (* 1 = 4.99756 loss)
I0408 16:35:29.770834 27257 sgd_solver.cpp:105] Iteration 6960, lr = 2.69327e-13
I0408 16:35:34.660773 27257 solver.cpp:218] Iteration 6972 (2.4541 iter/s, 4.88977s/12 iters), loss = 4.96714
I0408 16:35:34.660810 27257 solver.cpp:237]     Train net output #0: loss = 4.96714 (* 1 = 4.96714 loss)
I0408 16:35:34.660820 27257 sgd_solver.cpp:105] Iteration 6972, lr = 2.58259e-13
I0408 16:35:37.425601 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:35:39.681246 27257 solver.cpp:218] Iteration 6984 (2.39032 iter/s, 5.02025s/12 iters), loss = 4.91259
I0408 16:35:39.681291 27257 solver.cpp:237]     Train net output #0: loss = 4.91259 (* 1 = 4.91259 loss)
I0408 16:35:39.681303 27257 sgd_solver.cpp:105] Iteration 6984, lr = 2.47647e-13
I0408 16:35:44.945780 27257 solver.cpp:218] Iteration 6996 (2.27951 iter/s, 5.2643s/12 iters), loss = 4.86538
I0408 16:35:44.945828 27257 solver.cpp:237]     Train net output #0: loss = 4.86538 (* 1 = 4.86538 loss)
I0408 16:35:44.945840 27257 sgd_solver.cpp:105] Iteration 6996, lr = 2.3747e-13
I0408 16:35:50.274644 27257 solver.cpp:218] Iteration 7008 (2.25199 iter/s, 5.32862s/12 iters), loss = 4.85158
I0408 16:35:50.274690 27257 solver.cpp:237]     Train net output #0: loss = 4.85158 (* 1 = 4.85158 loss)
I0408 16:35:50.274701 27257 sgd_solver.cpp:105] Iteration 7008, lr = 2.27712e-13
I0408 16:35:55.176257 27257 solver.cpp:218] Iteration 7020 (2.44829 iter/s, 4.90139s/12 iters), loss = 4.89033
I0408 16:35:55.176393 27257 solver.cpp:237]     Train net output #0: loss = 4.89033 (* 1 = 4.89033 loss)
I0408 16:35:55.176405 27257 sgd_solver.cpp:105] Iteration 7020, lr = 2.18354e-13
I0408 16:36:00.220424 27257 solver.cpp:218] Iteration 7032 (2.37914 iter/s, 5.04385s/12 iters), loss = 4.87689
I0408 16:36:00.220468 27257 solver.cpp:237]     Train net output #0: loss = 4.87689 (* 1 = 4.87689 loss)
I0408 16:36:00.220480 27257 sgd_solver.cpp:105] Iteration 7032, lr = 2.09381e-13
I0408 16:36:02.268992 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7038.caffemodel
I0408 16:36:05.431566 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7038.solverstate
I0408 16:36:08.906689 27257 solver.cpp:330] Iteration 7038, Testing net (#0)
I0408 16:36:08.906716 27257 net.cpp:676] Ignoring source layer train-data
I0408 16:36:10.621750 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:36:13.374610 27257 solver.cpp:397]     Test net output #0: accuracy = 0.028799
I0408 16:36:13.374657 27257 solver.cpp:397]     Test net output #1: loss = 4.96897 (* 1 = 4.96897 loss)
I0408 16:36:15.262255 27257 solver.cpp:218] Iteration 7044 (0.797806 iter/s, 15.0413s/12 iters), loss = 4.92542
I0408 16:36:15.262303 27257 solver.cpp:237]     Train net output #0: loss = 4.92542 (* 1 = 4.92542 loss)
I0408 16:36:15.262315 27257 sgd_solver.cpp:105] Iteration 7044, lr = 2.00777e-13
I0408 16:36:20.290259 27257 solver.cpp:218] Iteration 7056 (2.38674 iter/s, 5.02777s/12 iters), loss = 5.02629
I0408 16:36:20.290307 27257 solver.cpp:237]     Train net output #0: loss = 5.02629 (* 1 = 5.02629 loss)
I0408 16:36:20.290318 27257 sgd_solver.cpp:105] Iteration 7056, lr = 1.92527e-13
I0408 16:36:25.267695 27257 solver.cpp:218] Iteration 7068 (2.41099 iter/s, 4.9772s/12 iters), loss = 4.95618
I0408 16:36:25.267798 27257 solver.cpp:237]     Train net output #0: loss = 4.95618 (* 1 = 4.95618 loss)
I0408 16:36:25.267812 27257 sgd_solver.cpp:105] Iteration 7068, lr = 1.84615e-13
I0408 16:36:30.217124 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:36:30.326735 27257 solver.cpp:218] Iteration 7080 (2.37213 iter/s, 5.05875s/12 iters), loss = 4.954
I0408 16:36:30.326777 27257 solver.cpp:237]     Train net output #0: loss = 4.954 (* 1 = 4.954 loss)
I0408 16:36:30.326788 27257 sgd_solver.cpp:105] Iteration 7080, lr = 1.77029e-13
I0408 16:36:35.341547 27257 solver.cpp:218] Iteration 7092 (2.39302 iter/s, 5.01458s/12 iters), loss = 5.0046
I0408 16:36:35.341590 27257 solver.cpp:237]     Train net output #0: loss = 5.0046 (* 1 = 5.0046 loss)
I0408 16:36:35.341600 27257 sgd_solver.cpp:105] Iteration 7092, lr = 1.69754e-13
I0408 16:36:40.296067 27257 solver.cpp:218] Iteration 7104 (2.42214 iter/s, 4.95429s/12 iters), loss = 4.8585
I0408 16:36:40.296116 27257 solver.cpp:237]     Train net output #0: loss = 4.8585 (* 1 = 4.8585 loss)
I0408 16:36:40.296128 27257 sgd_solver.cpp:105] Iteration 7104, lr = 1.62778e-13
I0408 16:36:45.334550 27257 solver.cpp:218] Iteration 7116 (2.38178 iter/s, 5.03825s/12 iters), loss = 4.87204
I0408 16:36:45.334597 27257 solver.cpp:237]     Train net output #0: loss = 4.87204 (* 1 = 4.87204 loss)
I0408 16:36:45.334609 27257 sgd_solver.cpp:105] Iteration 7116, lr = 1.56089e-13
I0408 16:36:50.331952 27257 solver.cpp:218] Iteration 7128 (2.40136 iter/s, 4.99717s/12 iters), loss = 4.99004
I0408 16:36:50.332006 27257 solver.cpp:237]     Train net output #0: loss = 4.99004 (* 1 = 4.99004 loss)
I0408 16:36:50.332020 27257 sgd_solver.cpp:105] Iteration 7128, lr = 1.49675e-13
I0408 16:36:54.888475 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7140.caffemodel
I0408 16:36:58.022539 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7140.solverstate
I0408 16:37:00.350972 27257 solver.cpp:330] Iteration 7140, Testing net (#0)
I0408 16:37:00.350992 27257 net.cpp:676] Ignoring source layer train-data
I0408 16:37:01.917277 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:37:04.715478 27257 solver.cpp:397]     Test net output #0: accuracy = 0.0275735
I0408 16:37:04.715524 27257 solver.cpp:397]     Test net output #1: loss = 4.9708 (* 1 = 4.9708 loss)
I0408 16:37:04.803318 27257 solver.cpp:218] Iteration 7140 (0.829257 iter/s, 14.4708s/12 iters), loss = 4.85019
I0408 16:37:04.803371 27257 solver.cpp:237]     Train net output #0: loss = 4.85019 (* 1 = 4.85019 loss)
I0408 16:37:04.803383 27257 sgd_solver.cpp:105] Iteration 7140, lr = 1.43524e-13
I0408 16:37:09.356034 27257 solver.cpp:218] Iteration 7152 (2.63592 iter/s, 4.5525s/12 iters), loss = 4.93389
I0408 16:37:09.356073 27257 solver.cpp:237]     Train net output #0: loss = 4.93389 (* 1 = 4.93389 loss)
I0408 16:37:09.356081 27257 sgd_solver.cpp:105] Iteration 7152, lr = 1.37626e-13
I0408 16:37:14.480410 27257 solver.cpp:218] Iteration 7164 (2.34185 iter/s, 5.12415s/12 iters), loss = 4.98262
I0408 16:37:14.480445 27257 solver.cpp:237]     Train net output #0: loss = 4.98262 (* 1 = 4.98262 loss)
I0408 16:37:14.480454 27257 sgd_solver.cpp:105] Iteration 7164, lr = 1.31971e-13
I0408 16:37:19.483848 27257 solver.cpp:218] Iteration 7176 (2.39846 iter/s, 5.00322s/12 iters), loss = 5.07846
I0408 16:37:19.483882 27257 solver.cpp:237]     Train net output #0: loss = 5.07846 (* 1 = 5.07846 loss)
I0408 16:37:19.483891 27257 sgd_solver.cpp:105] Iteration 7176, lr = 1.26548e-13
I0408 16:37:21.588443 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:37:24.484022 27257 solver.cpp:218] Iteration 7188 (2.40002 iter/s, 4.99996s/12 iters), loss = 4.92088
I0408 16:37:24.484055 27257 solver.cpp:237]     Train net output #0: loss = 4.92088 (* 1 = 4.92088 loss)
I0408 16:37:24.484063 27257 sgd_solver.cpp:105] Iteration 7188, lr = 1.21347e-13
I0408 16:37:29.453642 27257 solver.cpp:218] Iteration 7200 (2.41479 iter/s, 4.96939s/12 iters), loss = 4.92121
I0408 16:37:29.453749 27257 solver.cpp:237]     Train net output #0: loss = 4.92121 (* 1 = 4.92121 loss)
I0408 16:37:29.453766 27257 sgd_solver.cpp:105] Iteration 7200, lr = 1.16361e-13
I0408 16:37:34.469256 27257 solver.cpp:218] Iteration 7212 (2.39267 iter/s, 5.01533s/12 iters), loss = 4.8803
I0408 16:37:34.469295 27257 solver.cpp:237]     Train net output #0: loss = 4.8803 (* 1 = 4.8803 loss)
I0408 16:37:34.469305 27257 sgd_solver.cpp:105] Iteration 7212, lr = 1.11579e-13
I0408 16:37:39.481194 27257 solver.cpp:218] Iteration 7224 (2.39439 iter/s, 5.01171s/12 iters), loss = 4.95906
I0408 16:37:39.481233 27257 solver.cpp:237]     Train net output #0: loss = 4.95906 (* 1 = 4.95906 loss)
I0408 16:37:39.481242 27257 sgd_solver.cpp:105] Iteration 7224, lr = 1.06994e-13
I0408 16:37:44.504362 27257 solver.cpp:218] Iteration 7236 (2.38904 iter/s, 5.02294s/12 iters), loss = 4.99381
I0408 16:37:44.504415 27257 solver.cpp:237]     Train net output #0: loss = 4.99381 (* 1 = 4.99381 loss)
I0408 16:37:44.504427 27257 sgd_solver.cpp:105] Iteration 7236, lr = 1.02597e-13
I0408 16:37:46.493681 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7242.caffemodel
I0408 16:37:49.724072 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7242.solverstate
I0408 16:37:52.054039 27257 solver.cpp:330] Iteration 7242, Testing net (#0)
I0408 16:37:52.054061 27257 net.cpp:676] Ignoring source layer train-data
I0408 16:37:53.621009 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:37:56.511996 27257 solver.cpp:397]     Test net output #0: accuracy = 0.0275735
I0408 16:37:56.512042 27257 solver.cpp:397]     Test net output #1: loss = 4.9706 (* 1 = 4.9706 loss)
I0408 16:37:58.512610 27257 solver.cpp:218] Iteration 7248 (0.856672 iter/s, 14.0077s/12 iters), loss = 4.92042
I0408 16:37:58.512655 27257 solver.cpp:237]     Train net output #0: loss = 4.92042 (* 1 = 4.92042 loss)
I0408 16:37:58.512667 27257 sgd_solver.cpp:105] Iteration 7248, lr = 9.83812e-14
I0408 16:38:03.989447 27257 solver.cpp:218] Iteration 7260 (2.19115 iter/s, 5.47658s/12 iters), loss = 4.84748
I0408 16:38:03.989600 27257 solver.cpp:237]     Train net output #0: loss = 4.84748 (* 1 = 4.84748 loss)
I0408 16:38:03.989619 27257 sgd_solver.cpp:105] Iteration 7260, lr = 9.43384e-14
I0408 16:38:09.121433 27257 solver.cpp:218] Iteration 7272 (2.33843 iter/s, 5.13165s/12 iters), loss = 4.99641
I0408 16:38:09.121480 27257 solver.cpp:237]     Train net output #0: loss = 4.99641 (* 1 = 4.99641 loss)
I0408 16:38:09.121491 27257 sgd_solver.cpp:105] Iteration 7272, lr = 9.04617e-14
I0408 16:38:13.406268 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:38:14.159638 27257 solver.cpp:218] Iteration 7284 (2.38191 iter/s, 5.03797s/12 iters), loss = 4.94222
I0408 16:38:14.159687 27257 solver.cpp:237]     Train net output #0: loss = 4.94222 (* 1 = 4.94222 loss)
I0408 16:38:14.159698 27257 sgd_solver.cpp:105] Iteration 7284, lr = 8.67444e-14
I0408 16:38:19.223803 27257 solver.cpp:218] Iteration 7296 (2.3697 iter/s, 5.06393s/12 iters), loss = 4.94294
I0408 16:38:19.223851 27257 solver.cpp:237]     Train net output #0: loss = 4.94294 (* 1 = 4.94294 loss)
I0408 16:38:19.223863 27257 sgd_solver.cpp:105] Iteration 7296, lr = 8.31797e-14
I0408 16:38:24.276902 27257 solver.cpp:218] Iteration 7308 (2.37489 iter/s, 5.05286s/12 iters), loss = 4.95296
I0408 16:38:24.276944 27257 solver.cpp:237]     Train net output #0: loss = 4.95296 (* 1 = 4.95296 loss)
I0408 16:38:24.276955 27257 sgd_solver.cpp:105] Iteration 7308, lr = 7.97616e-14
I0408 16:38:29.267109 27257 solver.cpp:218] Iteration 7320 (2.40482 iter/s, 4.98998s/12 iters), loss = 4.91802
I0408 16:38:29.267155 27257 solver.cpp:237]     Train net output #0: loss = 4.91802 (* 1 = 4.91802 loss)
I0408 16:38:29.267168 27257 sgd_solver.cpp:105] Iteration 7320, lr = 7.64839e-14
I0408 16:38:34.324133 27257 solver.cpp:218] Iteration 7332 (2.37305 iter/s, 5.05679s/12 iters), loss = 4.92181
I0408 16:38:34.324246 27257 solver.cpp:237]     Train net output #0: loss = 4.92181 (* 1 = 4.92181 loss)
I0408 16:38:34.324259 27257 sgd_solver.cpp:105] Iteration 7332, lr = 7.3341e-14
I0408 16:38:38.900820 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7344.caffemodel
I0408 16:38:45.510969 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7344.solverstate
I0408 16:38:49.483750 27257 solver.cpp:330] Iteration 7344, Testing net (#0)
I0408 16:38:49.483779 27257 net.cpp:676] Ignoring source layer train-data
I0408 16:38:51.062366 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:38:53.934252 27257 solver.cpp:397]     Test net output #0: accuracy = 0.0275735
I0408 16:38:53.934296 27257 solver.cpp:397]     Test net output #1: loss = 4.96815 (* 1 = 4.96815 loss)
I0408 16:38:54.024938 27257 solver.cpp:218] Iteration 7344 (0.609137 iter/s, 19.7s/12 iters), loss = 4.94926
I0408 16:38:54.024989 27257 solver.cpp:237]     Train net output #0: loss = 4.94926 (* 1 = 4.94926 loss)
I0408 16:38:54.025000 27257 sgd_solver.cpp:105] Iteration 7344, lr = 7.03271e-14
I0408 16:38:58.386967 27257 solver.cpp:218] Iteration 7356 (2.75115 iter/s, 4.36181s/12 iters), loss = 5.0934
I0408 16:38:58.387014 27257 solver.cpp:237]     Train net output #0: loss = 5.0934 (* 1 = 5.0934 loss)
I0408 16:38:58.387027 27257 sgd_solver.cpp:105] Iteration 7356, lr = 6.74372e-14
I0408 16:39:03.371723 27257 solver.cpp:218] Iteration 7368 (2.40745 iter/s, 4.98452s/12 iters), loss = 4.95791
I0408 16:39:03.371775 27257 solver.cpp:237]     Train net output #0: loss = 4.95791 (* 1 = 4.95791 loss)
I0408 16:39:03.371788 27257 sgd_solver.cpp:105] Iteration 7368, lr = 6.4666e-14
I0408 16:39:08.340085 27257 solver.cpp:218] Iteration 7380 (2.4154 iter/s, 4.96812s/12 iters), loss = 4.95424
I0408 16:39:08.340240 27257 solver.cpp:237]     Train net output #0: loss = 4.95424 (* 1 = 4.95424 loss)
I0408 16:39:08.340253 27257 sgd_solver.cpp:105] Iteration 7380, lr = 6.20086e-14
I0408 16:39:09.743377 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:39:13.347023 27257 solver.cpp:218] Iteration 7392 (2.39684 iter/s, 5.0066s/12 iters), loss = 4.97164
I0408 16:39:13.347069 27257 solver.cpp:237]     Train net output #0: loss = 4.97164 (* 1 = 4.97164 loss)
I0408 16:39:13.347079 27257 sgd_solver.cpp:105] Iteration 7392, lr = 5.94605e-14
I0408 16:39:18.310231 27257 solver.cpp:218] Iteration 7404 (2.41791 iter/s, 4.96297s/12 iters), loss = 5.00104
I0408 16:39:18.310293 27257 solver.cpp:237]     Train net output #0: loss = 5.00104 (* 1 = 5.00104 loss)
I0408 16:39:18.310309 27257 sgd_solver.cpp:105] Iteration 7404, lr = 5.7017e-14
I0408 16:39:23.337196 27257 solver.cpp:218] Iteration 7416 (2.38725 iter/s, 5.02671s/12 iters), loss = 5.04128
I0408 16:39:23.337245 27257 solver.cpp:237]     Train net output #0: loss = 5.04128 (* 1 = 5.04128 loss)
I0408 16:39:23.337257 27257 sgd_solver.cpp:105] Iteration 7416, lr = 5.4674e-14
I0408 16:39:28.368417 27257 solver.cpp:218] Iteration 7428 (2.38522 iter/s, 5.03098s/12 iters), loss = 4.88664
I0408 16:39:28.368461 27257 solver.cpp:237]     Train net output #0: loss = 4.88664 (* 1 = 4.88664 loss)
I0408 16:39:28.368474 27257 sgd_solver.cpp:105] Iteration 7428, lr = 5.24273e-14
I0408 16:39:33.361927 27257 solver.cpp:218] Iteration 7440 (2.40323 iter/s, 4.99327s/12 iters), loss = 4.99796
I0408 16:39:33.361995 27257 solver.cpp:237]     Train net output #0: loss = 4.99796 (* 1 = 4.99796 loss)
I0408 16:39:33.362010 27257 sgd_solver.cpp:105] Iteration 7440, lr = 5.02729e-14
I0408 16:39:35.390952 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7446.caffemodel
I0408 16:39:38.484207 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7446.solverstate
I0408 16:39:40.808866 27257 solver.cpp:330] Iteration 7446, Testing net (#0)
I0408 16:39:40.808894 27257 net.cpp:676] Ignoring source layer train-data
I0408 16:39:42.337663 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:39:45.309563 27257 solver.cpp:397]     Test net output #0: accuracy = 0.0281863
I0408 16:39:45.309612 27257 solver.cpp:397]     Test net output #1: loss = 4.97031 (* 1 = 4.97031 loss)
I0408 16:39:47.261930 27257 solver.cpp:218] Iteration 7452 (0.863345 iter/s, 13.8994s/12 iters), loss = 4.95141
I0408 16:39:47.262001 27257 solver.cpp:237]     Train net output #0: loss = 4.95141 (* 1 = 4.95141 loss)
I0408 16:39:47.262013 27257 sgd_solver.cpp:105] Iteration 7452, lr = 4.8207e-14
I0408 16:39:52.290496 27257 solver.cpp:218] Iteration 7464 (2.38649 iter/s, 5.0283s/12 iters), loss = 4.8112
I0408 16:39:52.290540 27257 solver.cpp:237]     Train net output #0: loss = 4.8112 (* 1 = 4.8112 loss)
I0408 16:39:52.290552 27257 sgd_solver.cpp:105] Iteration 7464, lr = 4.6226e-14
I0408 16:39:57.329260 27257 solver.cpp:218] Iteration 7476 (2.38165 iter/s, 5.03853s/12 iters), loss = 5.03583
I0408 16:39:57.329305 27257 solver.cpp:237]     Train net output #0: loss = 5.03583 (* 1 = 5.03583 loss)
I0408 16:39:57.329317 27257 sgd_solver.cpp:105] Iteration 7476, lr = 4.43264e-14
I0408 16:40:00.791548 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:40:02.269335 27257 solver.cpp:218] Iteration 7488 (2.42923 iter/s, 4.93984s/12 iters), loss = 4.90768
I0408 16:40:02.269379 27257 solver.cpp:237]     Train net output #0: loss = 4.90768 (* 1 = 4.90768 loss)
I0408 16:40:02.269392 27257 sgd_solver.cpp:105] Iteration 7488, lr = 4.25049e-14
I0408 16:40:07.280061 27257 solver.cpp:218] Iteration 7500 (2.39498 iter/s, 5.01049s/12 iters), loss = 5.00033
I0408 16:40:07.280104 27257 solver.cpp:237]     Train net output #0: loss = 5.00033 (* 1 = 5.00033 loss)
I0408 16:40:07.280117 27257 sgd_solver.cpp:105] Iteration 7500, lr = 4.07582e-14
I0408 16:40:12.184631 27257 solver.cpp:218] Iteration 7512 (2.44681 iter/s, 4.90435s/12 iters), loss = 4.94635
I0408 16:40:12.187772 27257 solver.cpp:237]     Train net output #0: loss = 4.94635 (* 1 = 4.94635 loss)
I0408 16:40:12.187784 27257 sgd_solver.cpp:105] Iteration 7512, lr = 3.90834e-14
I0408 16:40:17.172341 27257 solver.cpp:218] Iteration 7524 (2.40752 iter/s, 4.98438s/12 iters), loss = 4.96445
I0408 16:40:17.172385 27257 solver.cpp:237]     Train net output #0: loss = 4.96445 (* 1 = 4.96445 loss)
I0408 16:40:17.172397 27257 sgd_solver.cpp:105] Iteration 7524, lr = 3.74773e-14
I0408 16:40:22.155393 27257 solver.cpp:218] Iteration 7536 (2.40827 iter/s, 4.98283s/12 iters), loss = 4.88584
I0408 16:40:22.155429 27257 solver.cpp:237]     Train net output #0: loss = 4.88584 (* 1 = 4.88584 loss)
I0408 16:40:22.155437 27257 sgd_solver.cpp:105] Iteration 7536, lr = 3.59372e-14
I0408 16:40:26.790132 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7548.caffemodel
I0408 16:40:31.129529 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7548.solverstate
I0408 16:40:33.837774 27257 solver.cpp:330] Iteration 7548, Testing net (#0)
I0408 16:40:33.837792 27257 net.cpp:676] Ignoring source layer train-data
I0408 16:40:35.369127 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:40:38.322508 27257 solver.cpp:397]     Test net output #0: accuracy = 0.0275735
I0408 16:40:38.322556 27257 solver.cpp:397]     Test net output #1: loss = 4.96496 (* 1 = 4.96496 loss)
I0408 16:40:38.413049 27257 solver.cpp:218] Iteration 7548 (0.738142 iter/s, 16.257s/12 iters), loss = 4.90131
I0408 16:40:38.413100 27257 solver.cpp:237]     Train net output #0: loss = 4.90131 (* 1 = 4.90131 loss)
I0408 16:40:38.413110 27257 sgd_solver.cpp:105] Iteration 7548, lr = 3.44604e-14
I0408 16:40:42.778615 27257 solver.cpp:218] Iteration 7560 (2.74892 iter/s, 4.36535s/12 iters), loss = 5.05195
I0408 16:40:42.778695 27257 solver.cpp:237]     Train net output #0: loss = 5.05195 (* 1 = 5.05195 loss)
I0408 16:40:42.778708 27257 sgd_solver.cpp:105] Iteration 7560, lr = 3.30443e-14
I0408 16:40:47.794343 27257 solver.cpp:218] Iteration 7572 (2.3926 iter/s, 5.01546s/12 iters), loss = 4.905
I0408 16:40:47.794390 27257 solver.cpp:237]     Train net output #0: loss = 4.905 (* 1 = 4.905 loss)
I0408 16:40:47.794401 27257 sgd_solver.cpp:105] Iteration 7572, lr = 3.16864e-14
I0408 16:40:52.763665 27257 solver.cpp:218] Iteration 7584 (2.41493 iter/s, 4.96909s/12 iters), loss = 4.9401
I0408 16:40:52.763708 27257 solver.cpp:237]     Train net output #0: loss = 4.9401 (* 1 = 4.9401 loss)
I0408 16:40:52.763720 27257 sgd_solver.cpp:105] Iteration 7584, lr = 3.03843e-14
I0408 16:40:53.415417 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:40:57.800767 27257 solver.cpp:218] Iteration 7596 (2.38243 iter/s, 5.03687s/12 iters), loss = 4.95402
I0408 16:40:57.800814 27257 solver.cpp:237]     Train net output #0: loss = 4.95402 (* 1 = 4.95402 loss)
I0408 16:40:57.800827 27257 sgd_solver.cpp:105] Iteration 7596, lr = 2.91358e-14
I0408 16:41:02.774006 27257 solver.cpp:218] Iteration 7608 (2.41303 iter/s, 4.973s/12 iters), loss = 4.99307
I0408 16:41:02.774053 27257 solver.cpp:237]     Train net output #0: loss = 4.99307 (* 1 = 4.99307 loss)
I0408 16:41:02.774065 27257 sgd_solver.cpp:105] Iteration 7608, lr = 2.79385e-14
I0408 16:41:07.731607 27257 solver.cpp:218] Iteration 7620 (2.42064 iter/s, 4.95737s/12 iters), loss = 4.8741
I0408 16:41:07.731642 27257 solver.cpp:237]     Train net output #0: loss = 4.8741 (* 1 = 4.8741 loss)
I0408 16:41:07.731649 27257 sgd_solver.cpp:105] Iteration 7620, lr = 2.67904e-14
I0408 16:41:10.178195 27257 blocking_queue.cpp:49] Waiting for data
I0408 16:41:12.764282 27257 solver.cpp:218] Iteration 7632 (2.38453 iter/s, 5.03245s/12 iters), loss = 4.82718
I0408 16:41:12.764331 27257 solver.cpp:237]     Train net output #0: loss = 4.82718 (* 1 = 4.82718 loss)
I0408 16:41:12.764343 27257 sgd_solver.cpp:105] Iteration 7632, lr = 2.56895e-14
I0408 16:41:17.846092 27257 solver.cpp:218] Iteration 7644 (2.36148 iter/s, 5.08156s/12 iters), loss = 4.96034
I0408 16:41:17.846221 27257 solver.cpp:237]     Train net output #0: loss = 4.96034 (* 1 = 4.96034 loss)
I0408 16:41:17.846235 27257 sgd_solver.cpp:105] Iteration 7644, lr = 2.46338e-14
I0408 16:41:19.901427 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7650.caffemodel
I0408 16:41:23.213559 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7650.solverstate
I0408 16:41:25.607610 27257 solver.cpp:330] Iteration 7650, Testing net (#0)
I0408 16:41:25.607635 27257 net.cpp:676] Ignoring source layer train-data
I0408 16:41:26.938588 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:41:29.934500 27257 solver.cpp:397]     Test net output #0: accuracy = 0.0281863
I0408 16:41:29.934546 27257 solver.cpp:397]     Test net output #1: loss = 4.96516 (* 1 = 4.96516 loss)
I0408 16:41:31.925119 27257 solver.cpp:218] Iteration 7656 (0.85237 iter/s, 14.0784s/12 iters), loss = 4.90714
I0408 16:41:31.925158 27257 solver.cpp:237]     Train net output #0: loss = 4.90714 (* 1 = 4.90714 loss)
I0408 16:41:31.925168 27257 sgd_solver.cpp:105] Iteration 7656, lr = 2.36215e-14
I0408 16:41:36.969710 27257 solver.cpp:218] Iteration 7668 (2.37889 iter/s, 5.04436s/12 iters), loss = 4.99902
I0408 16:41:36.969758 27257 solver.cpp:237]     Train net output #0: loss = 4.99902 (* 1 = 4.99902 loss)
I0408 16:41:36.969770 27257 sgd_solver.cpp:105] Iteration 7668, lr = 2.26508e-14
I0408 16:41:41.979604 27257 solver.cpp:218] Iteration 7680 (2.39537 iter/s, 5.00966s/12 iters), loss = 4.87754
I0408 16:41:41.979650 27257 solver.cpp:237]     Train net output #0: loss = 4.87754 (* 1 = 4.87754 loss)
I0408 16:41:41.979662 27257 sgd_solver.cpp:105] Iteration 7680, lr = 2.172e-14
I0408 16:41:44.774593 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:41:46.994768 27257 solver.cpp:218] Iteration 7692 (2.39286 iter/s, 5.01493s/12 iters), loss = 4.95097
I0408 16:41:46.994815 27257 solver.cpp:237]     Train net output #0: loss = 4.95097 (* 1 = 4.95097 loss)
I0408 16:41:46.994827 27257 sgd_solver.cpp:105] Iteration 7692, lr = 2.08275e-14
I0408 16:41:52.024271 27257 solver.cpp:218] Iteration 7704 (2.38603 iter/s, 5.02927s/12 iters), loss = 4.88123
I0408 16:41:52.024377 27257 solver.cpp:237]     Train net output #0: loss = 4.88123 (* 1 = 4.88123 loss)
I0408 16:41:52.024391 27257 sgd_solver.cpp:105] Iteration 7704, lr = 1.99716e-14
I0408 16:41:57.026160 27257 solver.cpp:218] Iteration 7716 (2.39923 iter/s, 5.0016s/12 iters), loss = 4.90726
I0408 16:41:57.026206 27257 solver.cpp:237]     Train net output #0: loss = 4.90726 (* 1 = 4.90726 loss)
I0408 16:41:57.026217 27257 sgd_solver.cpp:105] Iteration 7716, lr = 1.91509e-14
I0408 16:42:02.049293 27257 solver.cpp:218] Iteration 7728 (2.38906 iter/s, 5.0229s/12 iters), loss = 4.94197
I0408 16:42:02.049340 27257 solver.cpp:237]     Train net output #0: loss = 4.94197 (* 1 = 4.94197 loss)
I0408 16:42:02.049352 27257 sgd_solver.cpp:105] Iteration 7728, lr = 1.83639e-14
I0408 16:42:07.093089 27257 solver.cpp:218] Iteration 7740 (2.37927 iter/s, 5.04356s/12 iters), loss = 4.9537
I0408 16:42:07.093139 27257 solver.cpp:237]     Train net output #0: loss = 4.9537 (* 1 = 4.9537 loss)
I0408 16:42:07.093151 27257 sgd_solver.cpp:105] Iteration 7740, lr = 1.76093e-14
I0408 16:42:11.592063 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7752.caffemodel
I0408 16:42:16.007678 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7752.solverstate
I0408 16:42:18.345911 27257 solver.cpp:330] Iteration 7752, Testing net (#0)
I0408 16:42:18.345937 27257 net.cpp:676] Ignoring source layer train-data
I0408 16:42:19.778551 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:42:22.809830 27257 solver.cpp:397]     Test net output #0: accuracy = 0.0275735
I0408 16:42:22.809973 27257 solver.cpp:397]     Test net output #1: loss = 4.97122 (* 1 = 4.97122 loss)
I0408 16:42:22.900413 27257 solver.cpp:218] Iteration 7752 (0.759172 iter/s, 15.8067s/12 iters), loss = 4.95543
I0408 16:42:22.900470 27257 solver.cpp:237]     Train net output #0: loss = 4.95543 (* 1 = 4.95543 loss)
I0408 16:42:22.900485 27257 sgd_solver.cpp:105] Iteration 7752, lr = 1.68857e-14
I0408 16:42:27.265815 27257 solver.cpp:218] Iteration 7764 (2.74903 iter/s, 4.36518s/12 iters), loss = 4.96419
I0408 16:42:27.265862 27257 solver.cpp:237]     Train net output #0: loss = 4.96419 (* 1 = 4.96419 loss)
I0408 16:42:27.265875 27257 sgd_solver.cpp:105] Iteration 7764, lr = 1.61918e-14
I0408 16:42:32.282768 27257 solver.cpp:218] Iteration 7776 (2.392 iter/s, 5.01672s/12 iters), loss = 5.0492
I0408 16:42:32.282809 27257 solver.cpp:237]     Train net output #0: loss = 5.0492 (* 1 = 5.0492 loss)
I0408 16:42:32.282821 27257 sgd_solver.cpp:105] Iteration 7776, lr = 1.55264e-14
I0408 16:42:37.284008 27257 solver.cpp:218] Iteration 7788 (2.39952 iter/s, 5.00101s/12 iters), loss = 4.95272
I0408 16:42:37.284050 27257 solver.cpp:237]     Train net output #0: loss = 4.95272 (* 1 = 4.95272 loss)
I0408 16:42:37.284060 27257 sgd_solver.cpp:105] Iteration 7788, lr = 1.48884e-14
I0408 16:42:37.295379 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:42:42.261803 27257 solver.cpp:218] Iteration 7800 (2.41082 iter/s, 4.97756s/12 iters), loss = 4.97677
I0408 16:42:42.261850 27257 solver.cpp:237]     Train net output #0: loss = 4.97677 (* 1 = 4.97677 loss)
I0408 16:42:42.261862 27257 sgd_solver.cpp:105] Iteration 7800, lr = 1.42766e-14
I0408 16:42:47.262651 27257 solver.cpp:218] Iteration 7812 (2.39971 iter/s, 5.00061s/12 iters), loss = 4.8226
I0408 16:42:47.262696 27257 solver.cpp:237]     Train net output #0: loss = 4.8226 (* 1 = 4.8226 loss)
I0408 16:42:47.262707 27257 sgd_solver.cpp:105] Iteration 7812, lr = 1.36899e-14
I0408 16:42:52.290907 27257 solver.cpp:218] Iteration 7824 (2.38662 iter/s, 5.02802s/12 iters), loss = 4.87914
I0408 16:42:52.290949 27257 solver.cpp:237]     Train net output #0: loss = 4.87914 (* 1 = 4.87914 loss)
I0408 16:42:52.290961 27257 sgd_solver.cpp:105] Iteration 7824, lr = 1.31273e-14
I0408 16:42:57.307282 27257 solver.cpp:218] Iteration 7836 (2.39228 iter/s, 5.01614s/12 iters), loss = 5.02497
I0408 16:42:57.307399 27257 solver.cpp:237]     Train net output #0: loss = 5.02497 (* 1 = 5.02497 loss)
I0408 16:42:57.307412 27257 sgd_solver.cpp:105] Iteration 7836, lr = 1.25879e-14
I0408 16:43:02.301044 27257 solver.cpp:218] Iteration 7848 (2.40314 iter/s, 4.99346s/12 iters), loss = 4.88196
I0408 16:43:02.301095 27257 solver.cpp:237]     Train net output #0: loss = 4.88196 (* 1 = 4.88196 loss)
I0408 16:43:02.301106 27257 sgd_solver.cpp:105] Iteration 7848, lr = 1.20706e-14
I0408 16:43:04.331459 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7854.caffemodel
I0408 16:43:09.768787 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7854.solverstate
I0408 16:43:12.989892 27257 solver.cpp:330] Iteration 7854, Testing net (#0)
I0408 16:43:12.989919 27257 net.cpp:676] Ignoring source layer train-data
I0408 16:43:14.369626 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:43:17.447993 27257 solver.cpp:397]     Test net output #0: accuracy = 0.0275735
I0408 16:43:17.448041 27257 solver.cpp:397]     Test net output #1: loss = 4.96923 (* 1 = 4.96923 loss)
I0408 16:43:19.463943 27257 solver.cpp:218] Iteration 7860 (0.69921 iter/s, 17.1622s/12 iters), loss = 4.95641
I0408 16:43:19.463990 27257 solver.cpp:237]     Train net output #0: loss = 4.95641 (* 1 = 4.95641 loss)
I0408 16:43:19.464001 27257 sgd_solver.cpp:105] Iteration 7860, lr = 1.15746e-14
I0408 16:43:24.743923 27257 solver.cpp:218] Iteration 7872 (2.27284 iter/s, 5.27974s/12 iters), loss = 5.01503
I0408 16:43:24.743968 27257 solver.cpp:237]     Train net output #0: loss = 5.01503 (* 1 = 5.01503 loss)
I0408 16:43:24.743978 27257 sgd_solver.cpp:105] Iteration 7872, lr = 1.1099e-14
I0408 16:43:29.811122 27257 solver.cpp:218] Iteration 7884 (2.36828 iter/s, 5.06696s/12 iters), loss = 5.06147
I0408 16:43:29.811264 27257 solver.cpp:237]     Train net output #0: loss = 5.06147 (* 1 = 5.06147 loss)
I0408 16:43:29.811277 27257 sgd_solver.cpp:105] Iteration 7884, lr = 1.06429e-14
I0408 16:43:31.936383 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:43:34.793776 27257 solver.cpp:218] Iteration 7896 (2.40851 iter/s, 4.98233s/12 iters), loss = 4.94179
I0408 16:43:34.793820 27257 solver.cpp:237]     Train net output #0: loss = 4.94179 (* 1 = 4.94179 loss)
I0408 16:43:34.793831 27257 sgd_solver.cpp:105] Iteration 7896, lr = 1.02055e-14
I0408 16:43:39.876564 27257 solver.cpp:218] Iteration 7908 (2.36102 iter/s, 5.08255s/12 iters), loss = 4.87412
I0408 16:43:39.876616 27257 solver.cpp:237]     Train net output #0: loss = 4.87412 (* 1 = 4.87412 loss)
I0408 16:43:39.876627 27257 sgd_solver.cpp:105] Iteration 7908, lr = 9.78613e-15
I0408 16:43:44.854913 27257 solver.cpp:218] Iteration 7920 (2.41055 iter/s, 4.97811s/12 iters), loss = 4.82878
I0408 16:43:44.854961 27257 solver.cpp:237]     Train net output #0: loss = 4.82878 (* 1 = 4.82878 loss)
I0408 16:43:44.854974 27257 sgd_solver.cpp:105] Iteration 7920, lr = 9.38399e-15
I0408 16:43:49.907428 27257 solver.cpp:218] Iteration 7932 (2.37517 iter/s, 5.05228s/12 iters), loss = 4.91342
I0408 16:43:49.907474 27257 solver.cpp:237]     Train net output #0: loss = 4.91342 (* 1 = 4.91342 loss)
I0408 16:43:49.907485 27257 sgd_solver.cpp:105] Iteration 7932, lr = 8.99837e-15
I0408 16:43:54.887897 27257 solver.cpp:218] Iteration 7944 (2.40953 iter/s, 4.98023s/12 iters), loss = 5.01358
I0408 16:43:54.887945 27257 solver.cpp:237]     Train net output #0: loss = 5.01358 (* 1 = 5.01358 loss)
I0408 16:43:54.887957 27257 sgd_solver.cpp:105] Iteration 7944, lr = 8.6286e-15
I0408 16:43:59.409410 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7956.caffemodel
I0408 16:44:02.910137 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7956.solverstate
I0408 16:44:05.225018 27257 solver.cpp:330] Iteration 7956, Testing net (#0)
I0408 16:44:05.225044 27257 net.cpp:676] Ignoring source layer train-data
I0408 16:44:06.562397 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:44:09.677405 27257 solver.cpp:397]     Test net output #0: accuracy = 0.0281863
I0408 16:44:09.677453 27257 solver.cpp:397]     Test net output #1: loss = 4.96871 (* 1 = 4.96871 loss)
I0408 16:44:09.767899 27257 solver.cpp:218] Iteration 7956 (0.806483 iter/s, 14.8794s/12 iters), loss = 4.9365
I0408 16:44:09.767951 27257 solver.cpp:237]     Train net output #0: loss = 4.9365 (* 1 = 4.9365 loss)
I0408 16:44:09.767962 27257 sgd_solver.cpp:105] Iteration 7956, lr = 8.27402e-15
I0408 16:44:14.031862 27257 solver.cpp:218] Iteration 7968 (2.81443 iter/s, 4.26375s/12 iters), loss = 4.9277
I0408 16:44:14.031906 27257 solver.cpp:237]     Train net output #0: loss = 4.9277 (* 1 = 4.9277 loss)
I0408 16:44:14.031919 27257 sgd_solver.cpp:105] Iteration 7968, lr = 7.93401e-15
I0408 16:44:19.133150 27257 solver.cpp:218] Iteration 7980 (2.35246 iter/s, 5.10105s/12 iters), loss = 5.03631
I0408 16:44:19.133198 27257 solver.cpp:237]     Train net output #0: loss = 5.03631 (* 1 = 5.03631 loss)
I0408 16:44:19.133208 27257 sgd_solver.cpp:105] Iteration 7980, lr = 7.60798e-15
I0408 16:44:23.479766 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:44:24.205127 27257 solver.cpp:218] Iteration 7992 (2.36605 iter/s, 5.07174s/12 iters), loss = 4.98336
I0408 16:44:24.205160 27257 solver.cpp:237]     Train net output #0: loss = 4.98336 (* 1 = 4.98336 loss)
I0408 16:44:24.205168 27257 sgd_solver.cpp:105] Iteration 7992, lr = 7.29534e-15
I0408 16:44:29.295022 27257 solver.cpp:218] Iteration 8004 (2.35772 iter/s, 5.08967s/12 iters), loss = 4.89175
I0408 16:44:29.295068 27257 solver.cpp:237]     Train net output #0: loss = 4.89175 (* 1 = 4.89175 loss)
I0408 16:44:29.295078 27257 sgd_solver.cpp:105] Iteration 8004, lr = 6.99555e-15
I0408 16:44:34.359115 27257 solver.cpp:218] Iteration 8016 (2.36973 iter/s, 5.06386s/12 iters), loss = 4.92155
I0408 16:44:34.359220 27257 solver.cpp:237]     Train net output #0: loss = 4.92155 (* 1 = 4.92155 loss)
I0408 16:44:34.359230 27257 sgd_solver.cpp:105] Iteration 8016, lr = 6.70808e-15
I0408 16:44:39.334158 27257 solver.cpp:218] Iteration 8028 (2.41218 iter/s, 4.97475s/12 iters), loss = 4.88909
I0408 16:44:39.334215 27257 solver.cpp:237]     Train net output #0: loss = 4.88909 (* 1 = 4.88909 loss)
I0408 16:44:39.334230 27257 sgd_solver.cpp:105] Iteration 8028, lr = 6.43242e-15
I0408 16:44:44.359455 27257 solver.cpp:218] Iteration 8040 (2.38804 iter/s, 5.02505s/12 iters), loss = 4.90196
I0408 16:44:44.359504 27257 solver.cpp:237]     Train net output #0: loss = 4.90196 (* 1 = 4.90196 loss)
I0408 16:44:44.359516 27257 sgd_solver.cpp:105] Iteration 8040, lr = 6.16809e-15
I0408 16:44:49.357117 27257 solver.cpp:218] Iteration 8052 (2.40124 iter/s, 4.99743s/12 iters), loss = 4.95705
I0408 16:44:49.357156 27257 solver.cpp:237]     Train net output #0: loss = 4.95705 (* 1 = 4.95705 loss)
I0408 16:44:49.357165 27257 sgd_solver.cpp:105] Iteration 8052, lr = 5.91463e-15
I0408 16:44:51.388566 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8058.caffemodel
I0408 16:44:56.361740 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8058.solverstate
I0408 16:44:59.642376 27257 solver.cpp:330] Iteration 8058, Testing net (#0)
I0408 16:44:59.642402 27257 net.cpp:676] Ignoring source layer train-data
I0408 16:45:00.949481 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:45:04.109658 27257 solver.cpp:397]     Test net output #0: accuracy = 0.0275735
I0408 16:45:04.109705 27257 solver.cpp:397]     Test net output #1: loss = 4.96829 (* 1 = 4.96829 loss)
I0408 16:45:06.027470 27257 solver.cpp:218] Iteration 8064 (0.719869 iter/s, 16.6697s/12 iters), loss = 5.08316
I0408 16:45:06.027580 27257 solver.cpp:237]     Train net output #0: loss = 5.08316 (* 1 = 5.08316 loss)
I0408 16:45:06.027593 27257 sgd_solver.cpp:105] Iteration 8064, lr = 5.67157e-15
I0408 16:45:11.072703 27257 solver.cpp:218] Iteration 8076 (2.37862 iter/s, 5.04494s/12 iters), loss = 4.89531
I0408 16:45:11.072751 27257 solver.cpp:237]     Train net output #0: loss = 4.89531 (* 1 = 4.89531 loss)
I0408 16:45:11.072762 27257 sgd_solver.cpp:105] Iteration 8076, lr = 5.43851e-15
I0408 16:45:16.159174 27257 solver.cpp:218] Iteration 8088 (2.35931 iter/s, 5.08623s/12 iters), loss = 4.93539
I0408 16:45:16.159219 27257 solver.cpp:237]     Train net output #0: loss = 4.93539 (* 1 = 4.93539 loss)
I0408 16:45:16.159230 27257 sgd_solver.cpp:105] Iteration 8088, lr = 5.21502e-15
I0408 16:45:17.561915 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:45:21.113539 27257 solver.cpp:218] Iteration 8100 (2.42222 iter/s, 4.95414s/12 iters), loss = 4.92818
I0408 16:45:21.113584 27257 solver.cpp:237]     Train net output #0: loss = 4.92818 (* 1 = 4.92818 loss)
I0408 16:45:21.113595 27257 sgd_solver.cpp:105] Iteration 8100, lr = 5.00072e-15
I0408 16:45:26.018857 27257 solver.cpp:218] Iteration 8112 (2.44644 iter/s, 4.90509s/12 iters), loss = 5.07262
I0408 16:45:26.018908 27257 solver.cpp:237]     Train net output #0: loss = 5.07262 (* 1 = 5.07262 loss)
I0408 16:45:26.018919 27257 sgd_solver.cpp:105] Iteration 8112, lr = 4.79523e-15
I0408 16:45:30.916565 27257 solver.cpp:218] Iteration 8124 (2.45024 iter/s, 4.89747s/12 iters), loss = 5.01349
I0408 16:45:30.916612 27257 solver.cpp:237]     Train net output #0: loss = 5.01349 (* 1 = 5.01349 loss)
I0408 16:45:30.916625 27257 sgd_solver.cpp:105] Iteration 8124, lr = 4.59817e-15
I0408 16:45:35.852208 27257 solver.cpp:218] Iteration 8136 (2.43141 iter/s, 4.93541s/12 iters), loss = 4.85618
I0408 16:45:35.852257 27257 solver.cpp:237]     Train net output #0: loss = 4.85618 (* 1 = 4.85618 loss)
I0408 16:45:35.852268 27257 sgd_solver.cpp:105] Iteration 8136, lr = 4.40922e-15
I0408 16:45:40.864315 27257 solver.cpp:218] Iteration 8148 (2.39432 iter/s, 5.01187s/12 iters), loss = 4.99219
I0408 16:45:40.866691 27257 solver.cpp:237]     Train net output #0: loss = 4.99219 (* 1 = 4.99219 loss)
I0408 16:45:40.866704 27257 sgd_solver.cpp:105] Iteration 8148, lr = 4.22803e-15
I0408 16:45:45.425923 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8160.caffemodel
I0408 16:45:50.345716 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8160.solverstate
I0408 16:45:53.842514 27257 solver.cpp:330] Iteration 8160, Testing net (#0)
I0408 16:45:53.842538 27257 net.cpp:676] Ignoring source layer train-data
I0408 16:45:55.106703 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:45:58.296146 27257 solver.cpp:397]     Test net output #0: accuracy = 0.0281863
I0408 16:45:58.296193 27257 solver.cpp:397]     Test net output #1: loss = 4.96835 (* 1 = 4.96835 loss)
I0408 16:45:58.386828 27257 solver.cpp:218] Iteration 8160 (0.684951 iter/s, 17.5195s/12 iters), loss = 4.97999
I0408 16:45:58.386901 27257 solver.cpp:237]     Train net output #0: loss = 4.97999 (* 1 = 4.97999 loss)
I0408 16:45:58.386917 27257 sgd_solver.cpp:105] Iteration 8160, lr = 4.05429e-15
I0408 16:46:02.887260 27257 solver.cpp:218] Iteration 8172 (2.66655 iter/s, 4.50019s/12 iters), loss = 4.87806
I0408 16:46:02.887296 27257 solver.cpp:237]     Train net output #0: loss = 4.87806 (* 1 = 4.87806 loss)
I0408 16:46:02.887305 27257 sgd_solver.cpp:105] Iteration 8172, lr = 3.88768e-15
I0408 16:46:07.845329 27257 solver.cpp:218] Iteration 8184 (2.42041 iter/s, 4.95784s/12 iters), loss = 5.02097
I0408 16:46:07.845376 27257 solver.cpp:237]     Train net output #0: loss = 5.02097 (* 1 = 5.02097 loss)
I0408 16:46:07.845387 27257 sgd_solver.cpp:105] Iteration 8184, lr = 3.72792e-15
I0408 16:46:11.382015 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:46:12.805987 27257 solver.cpp:218] Iteration 8196 (2.41915 iter/s, 4.96042s/12 iters), loss = 4.88635
I0408 16:46:12.806037 27257 solver.cpp:237]     Train net output #0: loss = 4.88635 (* 1 = 4.88635 loss)
I0408 16:46:12.806051 27257 sgd_solver.cpp:105] Iteration 8196, lr = 3.57473e-15
I0408 16:46:17.817106 27257 solver.cpp:218] Iteration 8208 (2.39479 iter/s, 5.01088s/12 iters), loss = 4.88565
I0408 16:46:17.817152 27257 solver.cpp:237]     Train net output #0: loss = 4.88565 (* 1 = 4.88565 loss)
I0408 16:46:17.817162 27257 sgd_solver.cpp:105] Iteration 8208, lr = 3.42783e-15
I0408 16:46:22.982729 27257 solver.cpp:218] Iteration 8220 (2.32316 iter/s, 5.16538s/12 iters), loss = 4.96231
I0408 16:46:22.982776 27257 solver.cpp:237]     Train net output #0: loss = 4.96231 (* 1 = 4.96231 loss)
I0408 16:46:22.982789 27257 sgd_solver.cpp:105] Iteration 8220, lr = 3.28697e-15
I0408 16:46:28.027213 27257 solver.cpp:218] Iteration 8232 (2.37895 iter/s, 5.04425s/12 iters), loss = 4.98207
I0408 16:46:28.027259 27257 solver.cpp:237]     Train net output #0: loss = 4.98207 (* 1 = 4.98207 loss)
I0408 16:46:28.027271 27257 sgd_solver.cpp:105] Iteration 8232, lr = 3.1519e-15
I0408 16:46:33.066422 27257 solver.cpp:218] Iteration 8244 (2.38144 iter/s, 5.03898s/12 iters), loss = 4.87915
I0408 16:46:33.066458 27257 solver.cpp:237]     Train net output #0: loss = 4.87915 (* 1 = 4.87915 loss)
I0408 16:46:33.066468 27257 sgd_solver.cpp:105] Iteration 8244, lr = 3.02238e-15
I0408 16:46:38.037933 27257 solver.cpp:218] Iteration 8256 (2.41387 iter/s, 4.97128s/12 iters), loss = 4.91504
I0408 16:46:38.038002 27257 solver.cpp:237]     Train net output #0: loss = 4.91504 (* 1 = 4.91504 loss)
I0408 16:46:38.038017 27257 sgd_solver.cpp:105] Iteration 8256, lr = 2.89818e-15
I0408 16:46:40.081751 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8262.caffemodel
I0408 16:46:45.592403 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8262.solverstate
I0408 16:46:48.054666 27257 solver.cpp:330] Iteration 8262, Testing net (#0)
I0408 16:46:48.054690 27257 net.cpp:676] Ignoring source layer train-data
I0408 16:46:49.299636 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:46:52.547947 27257 solver.cpp:397]     Test net output #0: accuracy = 0.0281863
I0408 16:46:52.547996 27257 solver.cpp:397]     Test net output #1: loss = 4.97009 (* 1 = 4.97009 loss)
I0408 16:46:54.525161 27257 solver.cpp:218] Iteration 8268 (0.727865 iter/s, 16.4866s/12 iters), loss = 5.0712
I0408 16:46:54.525202 27257 solver.cpp:237]     Train net output #0: loss = 5.0712 (* 1 = 5.0712 loss)
I0408 16:46:54.525211 27257 sgd_solver.cpp:105] Iteration 8268, lr = 2.77908e-15
I0408 16:46:59.601068 27257 solver.cpp:218] Iteration 8280 (2.36422 iter/s, 5.07567s/12 iters), loss = 4.8865
I0408 16:46:59.601114 27257 solver.cpp:237]     Train net output #0: loss = 4.8865 (* 1 = 4.8865 loss)
I0408 16:46:59.601126 27257 sgd_solver.cpp:105] Iteration 8280, lr = 2.66488e-15
I0408 16:47:04.611296 27257 solver.cpp:218] Iteration 8292 (2.39521 iter/s, 5.00999s/12 iters), loss = 4.88427
I0408 16:47:04.611342 27257 solver.cpp:237]     Train net output #0: loss = 4.88427 (* 1 = 4.88427 loss)
I0408 16:47:04.611353 27257 sgd_solver.cpp:105] Iteration 8292, lr = 2.55537e-15
I0408 16:47:05.308981 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:47:09.675776 27257 solver.cpp:218] Iteration 8304 (2.36955 iter/s, 5.06425s/12 iters), loss = 4.95213
I0408 16:47:09.675810 27257 solver.cpp:237]     Train net output #0: loss = 4.95213 (* 1 = 4.95213 loss)
I0408 16:47:09.675819 27257 sgd_solver.cpp:105] Iteration 8304, lr = 2.45036e-15
I0408 16:47:12.516958 27257 blocking_queue.cpp:49] Waiting for data
I0408 16:47:14.640311 27257 solver.cpp:218] Iteration 8316 (2.41725 iter/s, 4.96431s/12 iters), loss = 5.02951
I0408 16:47:14.640354 27257 solver.cpp:237]     Train net output #0: loss = 5.02951 (* 1 = 5.02951 loss)
I0408 16:47:14.640367 27257 sgd_solver.cpp:105] Iteration 8316, lr = 2.34967e-15
I0408 16:47:19.615124 27257 solver.cpp:218] Iteration 8328 (2.41226 iter/s, 4.97458s/12 iters), loss = 4.86738
I0408 16:47:19.615283 27257 solver.cpp:237]     Train net output #0: loss = 4.86738 (* 1 = 4.86738 loss)
I0408 16:47:19.615296 27257 sgd_solver.cpp:105] Iteration 8328, lr = 2.25311e-15
I0408 16:47:24.619510 27257 solver.cpp:218] Iteration 8340 (2.39806 iter/s, 5.00404s/12 iters), loss = 4.83174
I0408 16:47:24.619556 27257 solver.cpp:237]     Train net output #0: loss = 4.83174 (* 1 = 4.83174 loss)
I0408 16:47:24.619568 27257 sgd_solver.cpp:105] Iteration 8340, lr = 2.16053e-15
I0408 16:47:29.662845 27257 solver.cpp:218] Iteration 8352 (2.37949 iter/s, 5.0431s/12 iters), loss = 4.94168
I0408 16:47:29.662892 27257 solver.cpp:237]     Train net output #0: loss = 4.94168 (* 1 = 4.94168 loss)
I0408 16:47:29.662904 27257 sgd_solver.cpp:105] Iteration 8352, lr = 2.07174e-15
I0408 16:47:34.193425 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8364.caffemodel
I0408 16:47:39.460583 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8364.solverstate
I0408 16:47:42.926060 27257 solver.cpp:330] Iteration 8364, Testing net (#0)
I0408 16:47:42.926085 27257 net.cpp:676] Ignoring source layer train-data
I0408 16:47:44.115988 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:47:47.388058 27257 solver.cpp:397]     Test net output #0: accuracy = 0.0275735
I0408 16:47:47.388104 27257 solver.cpp:397]     Test net output #1: loss = 4.97039 (* 1 = 4.97039 loss)
I0408 16:47:47.476577 27257 solver.cpp:218] Iteration 8364 (0.673664 iter/s, 17.813s/12 iters), loss = 4.9534
I0408 16:47:47.476635 27257 solver.cpp:237]     Train net output #0: loss = 4.9534 (* 1 = 4.9534 loss)
I0408 16:47:47.476650 27257 sgd_solver.cpp:105] Iteration 8364, lr = 1.98661e-15
I0408 16:47:51.850718 27257 solver.cpp:218] Iteration 8376 (2.74354 iter/s, 4.37392s/12 iters), loss = 4.86727
I0408 16:47:51.850802 27257 solver.cpp:237]     Train net output #0: loss = 4.86727 (* 1 = 4.86727 loss)
I0408 16:47:51.850811 27257 sgd_solver.cpp:105] Iteration 8376, lr = 1.90497e-15
I0408 16:47:56.837947 27257 solver.cpp:218] Iteration 8388 (2.40628 iter/s, 4.98695s/12 iters), loss = 4.92535
I0408 16:47:56.838009 27257 solver.cpp:237]     Train net output #0: loss = 4.92535 (* 1 = 4.92535 loss)
I0408 16:47:56.838021 27257 sgd_solver.cpp:105] Iteration 8388, lr = 1.82669e-15
I0408 16:47:59.658128 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:48:01.873754 27257 solver.cpp:218] Iteration 8400 (2.38305 iter/s, 5.03556s/12 iters), loss = 4.93698
I0408 16:48:01.873792 27257 solver.cpp:237]     Train net output #0: loss = 4.93698 (* 1 = 4.93698 loss)
I0408 16:48:01.873800 27257 sgd_solver.cpp:105] Iteration 8400, lr = 1.75163e-15
I0408 16:48:06.900887 27257 solver.cpp:218] Iteration 8412 (2.38716 iter/s, 5.0269s/12 iters), loss = 4.89303
I0408 16:48:06.900943 27257 solver.cpp:237]     Train net output #0: loss = 4.89303 (* 1 = 4.89303 loss)
I0408 16:48:06.900957 27257 sgd_solver.cpp:105] Iteration 8412, lr = 1.67965e-15
I0408 16:48:11.881779 27257 solver.cpp:218] Iteration 8424 (2.40933 iter/s, 4.98065s/12 iters), loss = 4.82865
I0408 16:48:11.881834 27257 solver.cpp:237]     Train net output #0: loss = 4.82865 (* 1 = 4.82865 loss)
I0408 16:48:11.881845 27257 sgd_solver.cpp:105] Iteration 8424, lr = 1.61062e-15
I0408 16:48:16.911650 27257 solver.cpp:218] Iteration 8436 (2.38586 iter/s, 5.02963s/12 iters), loss = 4.89841
I0408 16:48:16.911700 27257 solver.cpp:237]     Train net output #0: loss = 4.89841 (* 1 = 4.89841 loss)
I0408 16:48:16.911710 27257 sgd_solver.cpp:105] Iteration 8436, lr = 1.54444e-15
I0408 16:48:21.887681 27257 solver.cpp:218] Iteration 8448 (2.41167 iter/s, 4.9758s/12 iters), loss = 4.99484
I0408 16:48:21.887809 27257 solver.cpp:237]     Train net output #0: loss = 4.99484 (* 1 = 4.99484 loss)
I0408 16:48:21.887818 27257 sgd_solver.cpp:105] Iteration 8448, lr = 1.48097e-15
I0408 16:48:26.919664 27257 solver.cpp:218] Iteration 8460 (2.38489 iter/s, 5.03167s/12 iters), loss = 4.91863
I0408 16:48:26.919701 27257 solver.cpp:237]     Train net output #0: loss = 4.91863 (* 1 = 4.91863 loss)
I0408 16:48:26.919709 27257 sgd_solver.cpp:105] Iteration 8460, lr = 1.42011e-15
I0408 16:48:28.974799 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8466.caffemodel
I0408 16:48:34.672603 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8466.solverstate
I0408 16:48:38.775039 27257 solver.cpp:330] Iteration 8466, Testing net (#0)
I0408 16:48:38.775074 27257 net.cpp:676] Ignoring source layer train-data
I0408 16:48:40.005359 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:48:43.310956 27257 solver.cpp:397]     Test net output #0: accuracy = 0.0281863
I0408 16:48:43.311003 27257 solver.cpp:397]     Test net output #1: loss = 4.96918 (* 1 = 4.96918 loss)
I0408 16:48:45.284113 27257 solver.cpp:218] Iteration 8472 (0.653462 iter/s, 18.3637s/12 iters), loss = 5.05408
I0408 16:48:45.284150 27257 solver.cpp:237]     Train net output #0: loss = 5.05408 (* 1 = 5.05408 loss)
I0408 16:48:45.284157 27257 sgd_solver.cpp:105] Iteration 8472, lr = 1.36176e-15
I0408 16:48:50.246843 27257 solver.cpp:218] Iteration 8484 (2.41814 iter/s, 4.9625s/12 iters), loss = 5.0435
I0408 16:48:50.246891 27257 solver.cpp:237]     Train net output #0: loss = 5.0435 (* 1 = 5.0435 loss)
I0408 16:48:50.246903 27257 sgd_solver.cpp:105] Iteration 8484, lr = 1.3058e-15
I0408 16:48:55.212218 27257 solver.cpp:218] Iteration 8496 (2.41685 iter/s, 4.96514s/12 iters), loss = 4.96949
I0408 16:48:55.212344 27257 solver.cpp:237]     Train net output #0: loss = 4.96949 (* 1 = 4.96949 loss)
I0408 16:48:55.212359 27257 sgd_solver.cpp:105] Iteration 8496, lr = 1.25214e-15
I0408 16:48:55.239899 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:49:00.258781 27257 solver.cpp:218] Iteration 8508 (2.378 iter/s, 5.04625s/12 iters), loss = 4.93025
I0408 16:49:00.258829 27257 solver.cpp:237]     Train net output #0: loss = 4.93025 (* 1 = 4.93025 loss)
I0408 16:49:00.258841 27257 sgd_solver.cpp:105] Iteration 8508, lr = 1.20068e-15
I0408 16:49:05.177446 27257 solver.cpp:218] Iteration 8520 (2.4398 iter/s, 4.91843s/12 iters), loss = 4.87594
I0408 16:49:05.177495 27257 solver.cpp:237]     Train net output #0: loss = 4.87594 (* 1 = 4.87594 loss)
I0408 16:49:05.177506 27257 sgd_solver.cpp:105] Iteration 8520, lr = 1.15134e-15
I0408 16:49:10.126511 27257 solver.cpp:218] Iteration 8532 (2.42481 iter/s, 4.94883s/12 iters), loss = 4.87659
I0408 16:49:10.126545 27257 solver.cpp:237]     Train net output #0: loss = 4.87659 (* 1 = 4.87659 loss)
I0408 16:49:10.126554 27257 sgd_solver.cpp:105] Iteration 8532, lr = 1.10403e-15
I0408 16:49:15.051244 27257 solver.cpp:218] Iteration 8544 (2.43679 iter/s, 4.92451s/12 iters), loss = 5.03985
I0408 16:49:15.051281 27257 solver.cpp:237]     Train net output #0: loss = 5.03985 (* 1 = 5.03985 loss)
I0408 16:49:15.051290 27257 sgd_solver.cpp:105] Iteration 8544, lr = 1.05866e-15
I0408 16:49:19.987931 27257 solver.cpp:218] Iteration 8556 (2.43089 iter/s, 4.93646s/12 iters), loss = 4.89364
I0408 16:49:19.987979 27257 solver.cpp:237]     Train net output #0: loss = 4.89364 (* 1 = 4.89364 loss)
I0408 16:49:19.987991 27257 sgd_solver.cpp:105] Iteration 8556, lr = 1.01516e-15
I0408 16:49:24.537173 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8568.caffemodel
I0408 16:49:28.059880 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8568.solverstate
I0408 16:49:31.727730 27257 solver.cpp:330] Iteration 8568, Testing net (#0)
I0408 16:49:31.727764 27257 net.cpp:676] Ignoring source layer train-data
I0408 16:49:32.841053 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:49:36.195742 27257 solver.cpp:397]     Test net output #0: accuracy = 0.0281863
I0408 16:49:36.195777 27257 solver.cpp:397]     Test net output #1: loss = 4.97031 (* 1 = 4.97031 loss)
I0408 16:49:36.283543 27257 solver.cpp:218] Iteration 8568 (0.736424 iter/s, 16.295s/12 iters), loss = 4.94088
I0408 16:49:36.283597 27257 solver.cpp:237]     Train net output #0: loss = 4.94088 (* 1 = 4.94088 loss)
I0408 16:49:36.283608 27257 sgd_solver.cpp:105] Iteration 8568, lr = 9.73442e-16
I0408 16:49:40.727299 27257 solver.cpp:218] Iteration 8580 (2.70056 iter/s, 4.44353s/12 iters), loss = 4.95543
I0408 16:49:40.727340 27257 solver.cpp:237]     Train net output #0: loss = 4.95543 (* 1 = 4.95543 loss)
I0408 16:49:40.727350 27257 sgd_solver.cpp:105] Iteration 8580, lr = 9.3344e-16
I0408 16:49:45.754603 27257 solver.cpp:218] Iteration 8592 (2.38708 iter/s, 5.02707s/12 iters), loss = 5.08539
I0408 16:49:45.754649 27257 solver.cpp:237]     Train net output #0: loss = 5.08539 (* 1 = 5.08539 loss)
I0408 16:49:45.754660 27257 sgd_solver.cpp:105] Iteration 8592, lr = 8.95082e-16
I0408 16:49:48.125407 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:49:51.199865 27257 solver.cpp:218] Iteration 8604 (2.20385 iter/s, 5.44501s/12 iters), loss = 4.87716
I0408 16:49:51.199913 27257 solver.cpp:237]     Train net output #0: loss = 4.87716 (* 1 = 4.87716 loss)
I0408 16:49:51.199923 27257 sgd_solver.cpp:105] Iteration 8604, lr = 8.583e-16
I0408 16:49:56.212198 27257 solver.cpp:218] Iteration 8616 (2.39421 iter/s, 5.01209s/12 iters), loss = 4.96136
I0408 16:49:56.212239 27257 solver.cpp:237]     Train net output #0: loss = 4.96136 (* 1 = 4.96136 loss)
I0408 16:49:56.212250 27257 sgd_solver.cpp:105] Iteration 8616, lr = 8.2303e-16
I0408 16:50:01.273629 27257 solver.cpp:218] Iteration 8628 (2.37098 iter/s, 5.0612s/12 iters), loss = 4.87082
I0408 16:50:01.273747 27257 solver.cpp:237]     Train net output #0: loss = 4.87082 (* 1 = 4.87082 loss)
I0408 16:50:01.273761 27257 sgd_solver.cpp:105] Iteration 8628, lr = 7.89209e-16
I0408 16:50:06.140630 27257 solver.cpp:218] Iteration 8640 (2.46574 iter/s, 4.8667s/12 iters), loss = 4.97291
I0408 16:50:06.140671 27257 solver.cpp:237]     Train net output #0: loss = 4.97291 (* 1 = 4.97291 loss)
I0408 16:50:06.140681 27257 sgd_solver.cpp:105] Iteration 8640, lr = 7.56778e-16
I0408 16:50:11.099876 27257 solver.cpp:218] Iteration 8652 (2.41984 iter/s, 4.95902s/12 iters), loss = 4.92563
I0408 16:50:11.099920 27257 solver.cpp:237]     Train net output #0: loss = 4.92563 (* 1 = 4.92563 loss)
I0408 16:50:11.099929 27257 sgd_solver.cpp:105] Iteration 8652, lr = 7.25679e-16
I0408 16:50:16.116533 27257 solver.cpp:218] Iteration 8664 (2.39214 iter/s, 5.01642s/12 iters), loss = 4.88299
I0408 16:50:16.116581 27257 solver.cpp:237]     Train net output #0: loss = 4.88299 (* 1 = 4.88299 loss)
I0408 16:50:16.116593 27257 sgd_solver.cpp:105] Iteration 8664, lr = 6.95858e-16
I0408 16:50:18.254211 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8670.caffemodel
I0408 16:50:21.254076 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8670.solverstate
I0408 16:50:26.077761 27257 solver.cpp:330] Iteration 8670, Testing net (#0)
I0408 16:50:26.077790 27257 net.cpp:676] Ignoring source layer train-data
I0408 16:50:27.135711 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:50:30.532002 27257 solver.cpp:397]     Test net output #0: accuracy = 0.0275735
I0408 16:50:30.532052 27257 solver.cpp:397]     Test net output #1: loss = 4.97058 (* 1 = 4.97058 loss)
I0408 16:50:32.515413 27257 solver.cpp:218] Iteration 8676 (0.731786 iter/s, 16.3982s/12 iters), loss = 4.88578
I0408 16:50:32.515533 27257 solver.cpp:237]     Train net output #0: loss = 4.88578 (* 1 = 4.88578 loss)
I0408 16:50:32.515547 27257 sgd_solver.cpp:105] Iteration 8676, lr = 6.67263e-16
I0408 16:50:37.686806 27257 solver.cpp:218] Iteration 8688 (2.3206 iter/s, 5.17108s/12 iters), loss = 4.94305
I0408 16:50:37.686842 27257 solver.cpp:237]     Train net output #0: loss = 4.94305 (* 1 = 4.94305 loss)
I0408 16:50:37.686851 27257 sgd_solver.cpp:105] Iteration 8688, lr = 6.39843e-16
I0408 16:50:42.012267 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:50:42.690654 27257 solver.cpp:218] Iteration 8700 (2.39826 iter/s, 5.00362s/12 iters), loss = 5.05251
I0408 16:50:42.690701 27257 solver.cpp:237]     Train net output #0: loss = 5.05251 (* 1 = 5.05251 loss)
I0408 16:50:42.690712 27257 sgd_solver.cpp:105] Iteration 8700, lr = 6.1355e-16
I0408 16:50:47.730782 27257 solver.cpp:218] Iteration 8712 (2.38101 iter/s, 5.03989s/12 iters), loss = 4.92444
I0408 16:50:47.730830 27257 solver.cpp:237]     Train net output #0: loss = 4.92444 (* 1 = 4.92444 loss)
I0408 16:50:47.730844 27257 sgd_solver.cpp:105] Iteration 8712, lr = 5.88337e-16
I0408 16:50:52.764508 27257 solver.cpp:218] Iteration 8724 (2.38403 iter/s, 5.03349s/12 iters), loss = 4.87189
I0408 16:50:52.764552 27257 solver.cpp:237]     Train net output #0: loss = 4.87189 (* 1 = 4.87189 loss)
I0408 16:50:52.764564 27257 sgd_solver.cpp:105] Iteration 8724, lr = 5.6416e-16
I0408 16:50:57.694423 27257 solver.cpp:218] Iteration 8736 (2.43423 iter/s, 4.92969s/12 iters), loss = 4.93732
I0408 16:50:57.694459 27257 solver.cpp:237]     Train net output #0: loss = 4.93732 (* 1 = 4.93732 loss)
I0408 16:50:57.694469 27257 sgd_solver.cpp:105] Iteration 8736, lr = 5.40977e-16
I0408 16:51:02.687597 27257 solver.cpp:218] Iteration 8748 (2.40339 iter/s, 4.99295s/12 iters), loss = 4.8835
I0408 16:51:02.687709 27257 solver.cpp:237]     Train net output #0: loss = 4.8835 (* 1 = 4.8835 loss)
I0408 16:51:02.687723 27257 sgd_solver.cpp:105] Iteration 8748, lr = 5.18747e-16
I0408 16:51:07.646559 27257 solver.cpp:218] Iteration 8760 (2.42001 iter/s, 4.95866s/12 iters), loss = 4.95853
I0408 16:51:07.646603 27257 solver.cpp:237]     Train net output #0: loss = 4.95853 (* 1 = 4.95853 loss)
I0408 16:51:07.646615 27257 sgd_solver.cpp:105] Iteration 8760, lr = 4.9743e-16
I0408 16:51:12.181298 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8772.caffemodel
I0408 16:51:15.128721 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8772.solverstate
I0408 16:51:18.312341 27257 solver.cpp:330] Iteration 8772, Testing net (#0)
I0408 16:51:18.312374 27257 net.cpp:676] Ignoring source layer train-data
I0408 16:51:19.343052 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:51:22.807862 27257 solver.cpp:397]     Test net output #0: accuracy = 0.0275735
I0408 16:51:22.807910 27257 solver.cpp:397]     Test net output #1: loss = 4.97007 (* 1 = 4.97007 loss)
I0408 16:51:22.898440 27257 solver.cpp:218] Iteration 8772 (0.786819 iter/s, 15.2513s/12 iters), loss = 5.0223
I0408 16:51:22.898488 27257 solver.cpp:237]     Train net output #0: loss = 5.0223 (* 1 = 5.0223 loss)
I0408 16:51:22.898500 27257 sgd_solver.cpp:105] Iteration 8772, lr = 4.76989e-16
I0408 16:51:27.258625 27257 solver.cpp:218] Iteration 8784 (2.75231 iter/s, 4.35997s/12 iters), loss = 4.91904
I0408 16:51:27.258659 27257 solver.cpp:237]     Train net output #0: loss = 4.91904 (* 1 = 4.91904 loss)
I0408 16:51:27.258667 27257 sgd_solver.cpp:105] Iteration 8784, lr = 4.57388e-16
I0408 16:51:32.318334 27257 solver.cpp:218] Iteration 8796 (2.37179 iter/s, 5.05948s/12 iters), loss = 4.93947
I0408 16:51:32.318383 27257 solver.cpp:237]     Train net output #0: loss = 4.93947 (* 1 = 4.93947 loss)
I0408 16:51:32.318395 27257 sgd_solver.cpp:105] Iteration 8796, lr = 4.38592e-16
I0408 16:51:33.753159 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:51:37.265760 27257 solver.cpp:218] Iteration 8808 (2.42562 iter/s, 4.94718s/12 iters), loss = 5.01237
I0408 16:51:37.265810 27257 solver.cpp:237]     Train net output #0: loss = 5.01237 (* 1 = 5.01237 loss)
I0408 16:51:37.265822 27257 sgd_solver.cpp:105] Iteration 8808, lr = 4.20569e-16
I0408 16:51:42.288640 27257 solver.cpp:218] Iteration 8820 (2.38918 iter/s, 5.02264s/12 iters), loss = 5.05665
I0408 16:51:42.288684 27257 solver.cpp:237]     Train net output #0: loss = 5.05665 (* 1 = 5.05665 loss)
I0408 16:51:42.288695 27257 sgd_solver.cpp:105] Iteration 8820, lr = 4.03286e-16
I0408 16:51:47.294589 27257 solver.cpp:218] Iteration 8832 (2.39726 iter/s, 5.00571s/12 iters), loss = 5.00464
I0408 16:51:47.294638 27257 solver.cpp:237]     Train net output #0: loss = 5.00464 (* 1 = 5.00464 loss)
I0408 16:51:47.294651 27257 sgd_solver.cpp:105] Iteration 8832, lr = 3.86714e-16
I0408 16:51:52.303102 27257 solver.cpp:218] Iteration 8844 (2.39604 iter/s, 5.00827s/12 iters), loss = 4.89113
I0408 16:51:52.303149 27257 solver.cpp:237]     Train net output #0: loss = 4.89113 (* 1 = 4.89113 loss)
I0408 16:51:52.303161 27257 sgd_solver.cpp:105] Iteration 8844, lr = 3.70823e-16
I0408 16:51:57.314574 27257 solver.cpp:218] Iteration 8856 (2.39462 iter/s, 5.01124s/12 iters), loss = 4.96428
I0408 16:51:57.314611 27257 solver.cpp:237]     Train net output #0: loss = 4.96428 (* 1 = 4.96428 loss)
I0408 16:51:57.314620 27257 sgd_solver.cpp:105] Iteration 8856, lr = 3.55584e-16
I0408 16:52:02.300974 27257 solver.cpp:218] Iteration 8868 (2.40666 iter/s, 4.98617s/12 iters), loss = 4.98163
I0408 16:52:02.301012 27257 solver.cpp:237]     Train net output #0: loss = 4.98163 (* 1 = 4.98163 loss)
I0408 16:52:02.301020 27257 sgd_solver.cpp:105] Iteration 8868, lr = 3.40972e-16
I0408 16:52:04.348589 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8874.caffemodel
I0408 16:52:08.345896 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8874.solverstate
I0408 16:52:11.505039 27257 solver.cpp:330] Iteration 8874, Testing net (#0)
I0408 16:52:11.505065 27257 net.cpp:676] Ignoring source layer train-data
I0408 16:52:12.485146 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:52:15.996100 27257 solver.cpp:397]     Test net output #0: accuracy = 0.0275735
I0408 16:52:15.996146 27257 solver.cpp:397]     Test net output #1: loss = 4.96829 (* 1 = 4.96829 loss)
I0408 16:52:17.879959 27257 solver.cpp:218] Iteration 8880 (0.770299 iter/s, 15.5784s/12 iters), loss = 4.8515
I0408 16:52:17.880018 27257 solver.cpp:237]     Train net output #0: loss = 4.8515 (* 1 = 4.8515 loss)
I0408 16:52:17.880033 27257 sgd_solver.cpp:105] Iteration 8880, lr = 3.2696e-16
I0408 16:52:22.867731 27257 solver.cpp:218] Iteration 8892 (2.406 iter/s, 4.98753s/12 iters), loss = 5.02265
I0408 16:52:22.867769 27257 solver.cpp:237]     Train net output #0: loss = 5.02265 (* 1 = 5.02265 loss)
I0408 16:52:22.867779 27257 sgd_solver.cpp:105] Iteration 8892, lr = 3.13524e-16
I0408 16:52:26.466938 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:52:27.892431 27257 solver.cpp:218] Iteration 8904 (2.38831 iter/s, 5.02447s/12 iters), loss = 4.88554
I0408 16:52:27.892477 27257 solver.cpp:237]     Train net output #0: loss = 4.88554 (* 1 = 4.88554 loss)
I0408 16:52:27.892489 27257 sgd_solver.cpp:105] Iteration 8904, lr = 3.00641e-16
I0408 16:52:32.957832 27257 solver.cpp:218] Iteration 8916 (2.36912 iter/s, 5.06516s/12 iters), loss = 4.91547
I0408 16:52:32.957877 27257 solver.cpp:237]     Train net output #0: loss = 4.91547 (* 1 = 4.91547 loss)
I0408 16:52:32.957890 27257 sgd_solver.cpp:105] Iteration 8916, lr = 2.88286e-16
I0408 16:52:37.979483 27257 solver.cpp:218] Iteration 8928 (2.38977 iter/s, 5.02141s/12 iters), loss = 4.89089
I0408 16:52:37.982656 27257 solver.cpp:237]     Train net output #0: loss = 4.89089 (* 1 = 4.89089 loss)
I0408 16:52:37.982671 27257 sgd_solver.cpp:105] Iteration 8928, lr = 2.7644e-16
I0408 16:52:42.781359 27257 solver.cpp:218] Iteration 8940 (2.50077 iter/s, 4.79852s/12 iters), loss = 4.99906
I0408 16:52:42.781406 27257 solver.cpp:237]     Train net output #0: loss = 4.99906 (* 1 = 4.99906 loss)
I0408 16:52:42.781419 27257 sgd_solver.cpp:105] Iteration 8940, lr = 2.6508e-16
I0408 16:52:47.843983 27257 solver.cpp:218] Iteration 8952 (2.37042 iter/s, 5.06238s/12 iters), loss = 4.884
I0408 16:52:47.844029 27257 solver.cpp:237]     Train net output #0: loss = 4.884 (* 1 = 4.884 loss)
I0408 16:52:47.844043 27257 sgd_solver.cpp:105] Iteration 8952, lr = 2.54187e-16
I0408 16:52:52.905599 27257 solver.cpp:218] Iteration 8964 (2.3709 iter/s, 5.06137s/12 iters), loss = 4.92719
I0408 16:52:52.905652 27257 solver.cpp:237]     Train net output #0: loss = 4.92719 (* 1 = 4.92719 loss)
I0408 16:52:52.905664 27257 sgd_solver.cpp:105] Iteration 8964, lr = 2.43742e-16
I0408 16:52:57.473670 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8976.caffemodel
I0408 16:53:00.500334 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8976.solverstate
I0408 16:53:02.826571 27257 solver.cpp:330] Iteration 8976, Testing net (#0)
I0408 16:53:02.826598 27257 net.cpp:676] Ignoring source layer train-data
I0408 16:53:03.800164 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:53:07.368206 27257 solver.cpp:397]     Test net output #0: accuracy = 0.0275735
I0408 16:53:07.368252 27257 solver.cpp:397]     Test net output #1: loss = 4.97073 (* 1 = 4.97073 loss)
I0408 16:53:07.458752 27257 solver.cpp:218] Iteration 8976 (0.824597 iter/s, 14.5526s/12 iters), loss = 5.08758
I0408 16:53:07.458796 27257 solver.cpp:237]     Train net output #0: loss = 5.08758 (* 1 = 5.08758 loss)
I0408 16:53:07.458807 27257 sgd_solver.cpp:105] Iteration 8976, lr = 2.33725e-16
I0408 16:53:11.714334 27257 solver.cpp:218] Iteration 8988 (2.81996 iter/s, 4.25538s/12 iters), loss = 4.83157
I0408 16:53:11.714421 27257 solver.cpp:237]     Train net output #0: loss = 4.83157 (* 1 = 4.83157 loss)
I0408 16:53:11.714430 27257 sgd_solver.cpp:105] Iteration 8988, lr = 2.24121e-16
I0408 16:53:15.036991 27257 blocking_queue.cpp:49] Waiting for data
I0408 16:53:16.771417 27257 solver.cpp:218] Iteration 9000 (2.37304 iter/s, 5.0568s/12 iters), loss = 4.91761
I0408 16:53:16.771471 27257 solver.cpp:237]     Train net output #0: loss = 4.91761 (* 1 = 4.91761 loss)
I0408 16:53:16.771486 27257 sgd_solver.cpp:105] Iteration 9000, lr = 2.14911e-16
I0408 16:53:17.458746 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:53:21.791581 27257 solver.cpp:218] Iteration 9012 (2.39048 iter/s, 5.01992s/12 iters), loss = 4.92293
I0408 16:53:21.791626 27257 solver.cpp:237]     Train net output #0: loss = 4.92293 (* 1 = 4.92293 loss)
I0408 16:53:21.791637 27257 sgd_solver.cpp:105] Iteration 9012, lr = 2.0608e-16
I0408 16:53:26.882236 27257 solver.cpp:218] Iteration 9024 (2.35737 iter/s, 5.09042s/12 iters), loss = 4.99745
I0408 16:53:26.882282 27257 solver.cpp:237]     Train net output #0: loss = 4.99745 (* 1 = 4.99745 loss)
I0408 16:53:26.882294 27257 sgd_solver.cpp:105] Iteration 9024, lr = 1.97611e-16
I0408 16:53:31.936604 27257 solver.cpp:218] Iteration 9036 (2.3743 iter/s, 5.05413s/12 iters), loss = 4.92609
I0408 16:53:31.936661 27257 solver.cpp:237]     Train net output #0: loss = 4.92609 (* 1 = 4.92609 loss)
I0408 16:53:31.936674 27257 sgd_solver.cpp:105] Iteration 9036, lr = 1.89491e-16
I0408 16:53:36.944114 27257 solver.cpp:218] Iteration 9048 (2.39652 iter/s, 5.00726s/12 iters), loss = 4.90258
I0408 16:53:36.944164 27257 solver.cpp:237]     Train net output #0: loss = 4.90258 (* 1 = 4.90258 loss)
I0408 16:53:36.944175 27257 sgd_solver.cpp:105] Iteration 9048, lr = 1.81704e-16
I0408 16:53:41.950914 27257 solver.cpp:218] Iteration 9060 (2.39685 iter/s, 5.00657s/12 iters), loss = 4.9235
I0408 16:53:41.951036 27257 solver.cpp:237]     Train net output #0: loss = 4.9235 (* 1 = 4.9235 loss)
I0408 16:53:41.951046 27257 sgd_solver.cpp:105] Iteration 9060, lr = 1.74237e-16
I0408 16:53:46.936291 27257 solver.cpp:218] Iteration 9072 (2.40719 iter/s, 4.98507s/12 iters), loss = 4.95133
I0408 16:53:46.936336 27257 solver.cpp:237]     Train net output #0: loss = 4.95133 (* 1 = 4.95133 loss)
I0408 16:53:46.936347 27257 sgd_solver.cpp:105] Iteration 9072, lr = 1.67077e-16
I0408 16:53:49.002468 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9078.caffemodel
I0408 16:53:51.735451 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9078.solverstate
I0408 16:53:54.055943 27257 solver.cpp:330] Iteration 9078, Testing net (#0)
I0408 16:53:54.055971 27257 net.cpp:676] Ignoring source layer train-data
I0408 16:53:55.039032 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:53:58.608222 27257 solver.cpp:397]     Test net output #0: accuracy = 0.0275735
I0408 16:53:58.608270 27257 solver.cpp:397]     Test net output #1: loss = 4.97079 (* 1 = 4.97079 loss)
I0408 16:54:00.587857 27257 solver.cpp:218] Iteration 9084 (0.879054 iter/s, 13.651s/12 iters), loss = 4.90815
I0408 16:54:00.587901 27257 solver.cpp:237]     Train net output #0: loss = 4.90815 (* 1 = 4.90815 loss)
I0408 16:54:00.587913 27257 sgd_solver.cpp:105] Iteration 9084, lr = 1.60211e-16
I0408 16:54:05.741596 27257 solver.cpp:218] Iteration 9096 (2.32852 iter/s, 5.1535s/12 iters), loss = 4.8769
I0408 16:54:05.741645 27257 solver.cpp:237]     Train net output #0: loss = 4.8769 (* 1 = 4.8769 loss)
I0408 16:54:05.741657 27257 sgd_solver.cpp:105] Iteration 9096, lr = 1.53628e-16
I0408 16:54:08.686025 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:54:10.786751 27257 solver.cpp:218] Iteration 9108 (2.37863 iter/s, 5.04492s/12 iters), loss = 4.9704
I0408 16:54:10.786792 27257 solver.cpp:237]     Train net output #0: loss = 4.9704 (* 1 = 4.9704 loss)
I0408 16:54:10.786803 27257 sgd_solver.cpp:105] Iteration 9108, lr = 1.47315e-16
I0408 16:54:15.821466 27257 solver.cpp:218] Iteration 9120 (2.38356 iter/s, 5.03449s/12 iters), loss = 4.96375
I0408 16:54:15.821574 27257 solver.cpp:237]     Train net output #0: loss = 4.96375 (* 1 = 4.96375 loss)
I0408 16:54:15.821586 27257 sgd_solver.cpp:105] Iteration 9120, lr = 1.41261e-16
I0408 16:54:20.840355 27257 solver.cpp:218] Iteration 9132 (2.39111 iter/s, 5.01859s/12 iters), loss = 4.90049
I0408 16:54:20.840400 27257 solver.cpp:237]     Train net output #0: loss = 4.90049 (* 1 = 4.90049 loss)
I0408 16:54:20.840410 27257 sgd_solver.cpp:105] Iteration 9132, lr = 1.35456e-16
I0408 16:54:25.857488 27257 solver.cpp:218] Iteration 9144 (2.39191 iter/s, 5.0169s/12 iters), loss = 4.9582
I0408 16:54:25.857534 27257 solver.cpp:237]     Train net output #0: loss = 4.9582 (* 1 = 4.9582 loss)
I0408 16:54:25.857547 27257 sgd_solver.cpp:105] Iteration 9144, lr = 1.2989e-16
I0408 16:54:30.863430 27257 solver.cpp:218] Iteration 9156 (2.39727 iter/s, 5.0057s/12 iters), loss = 4.88867
I0408 16:54:30.863479 27257 solver.cpp:237]     Train net output #0: loss = 4.88867 (* 1 = 4.88867 loss)
I0408 16:54:30.863490 27257 sgd_solver.cpp:105] Iteration 9156, lr = 1.24552e-16
I0408 16:54:35.908851 27257 solver.cpp:218] Iteration 9168 (2.3785 iter/s, 5.04519s/12 iters), loss = 4.95698
I0408 16:54:35.908896 27257 solver.cpp:237]     Train net output #0: loss = 4.95698 (* 1 = 4.95698 loss)
I0408 16:54:35.908907 27257 sgd_solver.cpp:105] Iteration 9168, lr = 1.19434e-16
I0408 16:54:40.489579 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9180.caffemodel
I0408 16:54:43.510995 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9180.solverstate
I0408 16:54:46.085944 27257 solver.cpp:330] Iteration 9180, Testing net (#0)
I0408 16:54:46.086086 27257 net.cpp:676] Ignoring source layer train-data
I0408 16:54:46.966292 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:54:50.557615 27257 solver.cpp:397]     Test net output #0: accuracy = 0.0275735
I0408 16:54:50.557660 27257 solver.cpp:397]     Test net output #1: loss = 4.96765 (* 1 = 4.96765 loss)
I0408 16:54:50.648350 27257 solver.cpp:218] Iteration 9180 (0.814171 iter/s, 14.7389s/12 iters), loss = 5.10362
I0408 16:54:50.648403 27257 solver.cpp:237]     Train net output #0: loss = 5.10362 (* 1 = 5.10362 loss)
I0408 16:54:50.648416 27257 sgd_solver.cpp:105] Iteration 9180, lr = 1.14526e-16
I0408 16:54:54.880264 27257 solver.cpp:218] Iteration 9192 (2.83574 iter/s, 4.2317s/12 iters), loss = 5.01551
I0408 16:54:54.880311 27257 solver.cpp:237]     Train net output #0: loss = 5.01551 (* 1 = 5.01551 loss)
I0408 16:54:54.880322 27257 sgd_solver.cpp:105] Iteration 9192, lr = 1.0982e-16
I0408 16:54:59.885306 27257 solver.cpp:218] Iteration 9204 (2.39769 iter/s, 5.00482s/12 iters), loss = 4.99926
I0408 16:54:59.885339 27257 solver.cpp:237]     Train net output #0: loss = 4.99926 (* 1 = 4.99926 loss)
I0408 16:54:59.885346 27257 sgd_solver.cpp:105] Iteration 9204, lr = 1.05307e-16
I0408 16:54:59.952867 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:55:04.733455 27257 solver.cpp:218] Iteration 9216 (2.47528 iter/s, 4.84793s/12 iters), loss = 4.89909
I0408 16:55:04.733506 27257 solver.cpp:237]     Train net output #0: loss = 4.89909 (* 1 = 4.89909 loss)
I0408 16:55:04.733518 27257 sgd_solver.cpp:105] Iteration 9216, lr = 1.00979e-16
I0408 16:55:09.712553 27257 solver.cpp:218] Iteration 9228 (2.41019 iter/s, 4.97886s/12 iters), loss = 4.8562
I0408 16:55:09.712590 27257 solver.cpp:237]     Train net output #0: loss = 4.8562 (* 1 = 4.8562 loss)
I0408 16:55:09.712599 27257 sgd_solver.cpp:105] Iteration 9228, lr = 9.68298e-17
I0408 16:55:14.738298 27257 solver.cpp:218] Iteration 9240 (2.38781 iter/s, 5.02552s/12 iters), loss = 4.85519
I0408 16:55:14.738344 27257 solver.cpp:237]     Train net output #0: loss = 4.85519 (* 1 = 4.85519 loss)
I0408 16:55:14.738356 27257 sgd_solver.cpp:105] Iteration 9240, lr = 9.28508e-17
I0408 16:55:19.776895 27257 solver.cpp:218] Iteration 9252 (2.38173 iter/s, 5.03836s/12 iters), loss = 5.05268
I0408 16:55:19.778337 27257 solver.cpp:237]     Train net output #0: loss = 5.05268 (* 1 = 5.05268 loss)
I0408 16:55:19.778348 27257 sgd_solver.cpp:105] Iteration 9252, lr = 8.90352e-17
I0408 16:55:24.808158 27257 solver.cpp:218] Iteration 9264 (2.38586 iter/s, 5.02963s/12 iters), loss = 4.92397
I0408 16:55:24.808202 27257 solver.cpp:237]     Train net output #0: loss = 4.92397 (* 1 = 4.92397 loss)
I0408 16:55:24.808212 27257 sgd_solver.cpp:105] Iteration 9264, lr = 8.53765e-17
I0408 16:55:29.836755 27257 solver.cpp:218] Iteration 9276 (2.38646 iter/s, 5.02836s/12 iters), loss = 4.96453
I0408 16:55:29.836802 27257 solver.cpp:237]     Train net output #0: loss = 4.96453 (* 1 = 4.96453 loss)
I0408 16:55:29.836817 27257 sgd_solver.cpp:105] Iteration 9276, lr = 8.18681e-17
I0408 16:55:31.871248 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9282.caffemodel
I0408 16:55:34.870560 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9282.solverstate
I0408 16:55:37.197145 27257 solver.cpp:330] Iteration 9282, Testing net (#0)
I0408 16:55:37.197172 27257 net.cpp:676] Ignoring source layer train-data
I0408 16:55:38.020462 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:55:41.656275 27257 solver.cpp:397]     Test net output #0: accuracy = 0.0275735
I0408 16:55:41.656322 27257 solver.cpp:397]     Test net output #1: loss = 4.97199 (* 1 = 4.97199 loss)
I0408 16:55:43.455962 27257 solver.cpp:218] Iteration 9288 (0.881143 iter/s, 13.6187s/12 iters), loss = 4.90005
I0408 16:55:43.456010 27257 solver.cpp:237]     Train net output #0: loss = 4.90005 (* 1 = 4.90005 loss)
I0408 16:55:43.456022 27257 sgd_solver.cpp:105] Iteration 9288, lr = 7.85038e-17
I0408 16:55:48.408154 27257 solver.cpp:218] Iteration 9300 (2.42328 iter/s, 4.95196s/12 iters), loss = 5.06722
I0408 16:55:48.408205 27257 solver.cpp:237]     Train net output #0: loss = 5.06722 (* 1 = 5.06722 loss)
I0408 16:55:48.408216 27257 sgd_solver.cpp:105] Iteration 9300, lr = 7.52778e-17
I0408 16:55:50.582283 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:55:53.347087 27257 solver.cpp:218] Iteration 9312 (2.42979 iter/s, 4.93869s/12 iters), loss = 4.91331
I0408 16:55:53.347136 27257 solver.cpp:237]     Train net output #0: loss = 4.91331 (* 1 = 4.91331 loss)
I0408 16:55:53.347148 27257 sgd_solver.cpp:105] Iteration 9312, lr = 7.21844e-17
I0408 16:55:58.284231 27257 solver.cpp:218] Iteration 9324 (2.43067 iter/s, 4.93691s/12 iters), loss = 4.95267
I0408 16:55:58.284283 27257 solver.cpp:237]     Train net output #0: loss = 4.95267 (* 1 = 4.95267 loss)
I0408 16:55:58.284294 27257 sgd_solver.cpp:105] Iteration 9324, lr = 6.92181e-17
I0408 16:56:03.212690 27257 solver.cpp:218] Iteration 9336 (2.43495 iter/s, 4.92823s/12 iters), loss = 4.86582
I0408 16:56:03.212740 27257 solver.cpp:237]     Train net output #0: loss = 4.86582 (* 1 = 4.86582 loss)
I0408 16:56:03.212752 27257 sgd_solver.cpp:105] Iteration 9336, lr = 6.63737e-17
I0408 16:56:08.128841 27257 solver.cpp:218] Iteration 9348 (2.44105 iter/s, 4.91592s/12 iters), loss = 5.0275
I0408 16:56:08.128888 27257 solver.cpp:237]     Train net output #0: loss = 5.0275 (* 1 = 5.0275 loss)
I0408 16:56:08.128898 27257 sgd_solver.cpp:105] Iteration 9348, lr = 6.36462e-17
I0408 16:56:13.059922 27257 solver.cpp:218] Iteration 9360 (2.43366 iter/s, 4.93085s/12 iters), loss = 4.9916
I0408 16:56:13.059963 27257 solver.cpp:237]     Train net output #0: loss = 4.9916 (* 1 = 4.9916 loss)
I0408 16:56:13.059974 27257 sgd_solver.cpp:105] Iteration 9360, lr = 6.10308e-17
I0408 16:56:17.989967 27257 solver.cpp:218] Iteration 9372 (2.43417 iter/s, 4.92981s/12 iters), loss = 4.86379
I0408 16:56:17.990010 27257 solver.cpp:237]     Train net output #0: loss = 4.86379 (* 1 = 4.86379 loss)
I0408 16:56:17.990021 27257 sgd_solver.cpp:105] Iteration 9372, lr = 5.85228e-17
I0408 16:56:22.464357 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9384.caffemodel
I0408 16:56:25.526669 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9384.solverstate
I0408 16:56:27.859230 27257 solver.cpp:330] Iteration 9384, Testing net (#0)
I0408 16:56:27.859256 27257 net.cpp:676] Ignoring source layer train-data
I0408 16:56:28.650107 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:56:32.326705 27257 solver.cpp:397]     Test net output #0: accuracy = 0.0281863
I0408 16:56:32.326756 27257 solver.cpp:397]     Test net output #1: loss = 4.96876 (* 1 = 4.96876 loss)
I0408 16:56:32.417320 27257 solver.cpp:218] Iteration 9384 (0.831786 iter/s, 14.4268s/12 iters), loss = 4.87339
I0408 16:56:32.417369 27257 solver.cpp:237]     Train net output #0: loss = 4.87339 (* 1 = 4.87339 loss)
I0408 16:56:32.417380 27257 sgd_solver.cpp:105] Iteration 9384, lr = 5.61179e-17
I0408 16:56:36.663417 27257 solver.cpp:218] Iteration 9396 (2.82626 iter/s, 4.24589s/12 iters), loss = 4.97394
I0408 16:56:36.663450 27257 solver.cpp:237]     Train net output #0: loss = 4.97394 (* 1 = 4.97394 loss)
I0408 16:56:36.663458 27257 sgd_solver.cpp:105] Iteration 9396, lr = 5.38119e-17
I0408 16:56:41.039985 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:56:41.697046 27257 solver.cpp:218] Iteration 9408 (2.38407 iter/s, 5.0334s/12 iters), loss = 5.03552
I0408 16:56:41.697091 27257 solver.cpp:237]     Train net output #0: loss = 5.03552 (* 1 = 5.03552 loss)
I0408 16:56:41.697103 27257 sgd_solver.cpp:105] Iteration 9408, lr = 5.16005e-17
I0408 16:56:46.784843 27257 solver.cpp:218] Iteration 9420 (2.35869 iter/s, 5.08756s/12 iters), loss = 4.95236
I0408 16:56:46.784888 27257 solver.cpp:237]     Train net output #0: loss = 4.95236 (* 1 = 4.95236 loss)
I0408 16:56:46.784899 27257 sgd_solver.cpp:105] Iteration 9420, lr = 4.94801e-17
I0408 16:56:51.810719 27257 solver.cpp:218] Iteration 9432 (2.38776 iter/s, 5.02564s/12 iters), loss = 4.95154
I0408 16:56:51.810768 27257 solver.cpp:237]     Train net output #0: loss = 4.95154 (* 1 = 4.95154 loss)
I0408 16:56:51.810781 27257 sgd_solver.cpp:105] Iteration 9432, lr = 4.74468e-17
I0408 16:56:56.762668 27257 solver.cpp:218] Iteration 9444 (2.4234 iter/s, 4.95171s/12 iters), loss = 4.87305
I0408 16:56:56.762806 27257 solver.cpp:237]     Train net output #0: loss = 4.87305 (* 1 = 4.87305 loss)
I0408 16:56:56.762820 27257 sgd_solver.cpp:105] Iteration 9444, lr = 4.54971e-17
I0408 16:57:01.799436 27257 solver.cpp:218] Iteration 9456 (2.38263 iter/s, 5.03645s/12 iters), loss = 4.85311
I0408 16:57:01.799479 27257 solver.cpp:237]     Train net output #0: loss = 4.85311 (* 1 = 4.85311 loss)
I0408 16:57:01.799491 27257 sgd_solver.cpp:105] Iteration 9456, lr = 4.36274e-17
I0408 16:57:06.820339 27257 solver.cpp:218] Iteration 9468 (2.39012 iter/s, 5.02067s/12 iters), loss = 4.95224
I0408 16:57:06.820382 27257 solver.cpp:237]     Train net output #0: loss = 4.95224 (* 1 = 4.95224 loss)
I0408 16:57:06.820394 27257 sgd_solver.cpp:105] Iteration 9468, lr = 4.18346e-17
I0408 16:57:11.852902 27257 solver.cpp:218] Iteration 9480 (2.38458 iter/s, 5.03233s/12 iters), loss = 4.98021
I0408 16:57:11.852948 27257 solver.cpp:237]     Train net output #0: loss = 4.98021 (* 1 = 4.98021 loss)
I0408 16:57:11.852962 27257 sgd_solver.cpp:105] Iteration 9480, lr = 4.01155e-17
I0408 16:57:13.907238 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9486.caffemodel
I0408 16:57:16.930310 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9486.solverstate
I0408 16:57:19.317243 27257 solver.cpp:330] Iteration 9486, Testing net (#0)
I0408 16:57:19.317268 27257 net.cpp:676] Ignoring source layer train-data
I0408 16:57:20.035324 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:57:23.775843 27257 solver.cpp:397]     Test net output #0: accuracy = 0.0275735
I0408 16:57:23.775892 27257 solver.cpp:397]     Test net output #1: loss = 4.96784 (* 1 = 4.96784 loss)
I0408 16:57:25.682441 27257 solver.cpp:218] Iteration 9492 (0.867742 iter/s, 13.829s/12 iters), loss = 4.89696
I0408 16:57:25.682489 27257 solver.cpp:237]     Train net output #0: loss = 4.89696 (* 1 = 4.89696 loss)
I0408 16:57:25.682502 27257 sgd_solver.cpp:105] Iteration 9492, lr = 3.8467e-17
I0408 16:57:30.694013 27257 solver.cpp:218] Iteration 9504 (2.39457 iter/s, 5.01134s/12 iters), loss = 4.99673
I0408 16:57:30.694133 27257 solver.cpp:237]     Train net output #0: loss = 4.99673 (* 1 = 4.99673 loss)
I0408 16:57:30.694146 27257 sgd_solver.cpp:105] Iteration 9504, lr = 3.68863e-17
I0408 16:57:32.097360 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:57:35.545784 27257 solver.cpp:218] Iteration 9516 (2.47348 iter/s, 4.85147s/12 iters), loss = 4.96317
I0408 16:57:35.545830 27257 solver.cpp:237]     Train net output #0: loss = 4.96317 (* 1 = 4.96317 loss)
I0408 16:57:35.545841 27257 sgd_solver.cpp:105] Iteration 9516, lr = 3.53705e-17
I0408 16:57:40.554276 27257 solver.cpp:218] Iteration 9528 (2.39604 iter/s, 5.00826s/12 iters), loss = 5.08359
I0408 16:57:40.554322 27257 solver.cpp:237]     Train net output #0: loss = 5.08359 (* 1 = 5.08359 loss)
I0408 16:57:40.554334 27257 sgd_solver.cpp:105] Iteration 9528, lr = 3.3917e-17
I0408 16:57:45.566370 27257 solver.cpp:218] Iteration 9540 (2.39432 iter/s, 5.01186s/12 iters), loss = 5.00395
I0408 16:57:45.566413 27257 solver.cpp:237]     Train net output #0: loss = 5.00395 (* 1 = 5.00395 loss)
I0408 16:57:45.566424 27257 sgd_solver.cpp:105] Iteration 9540, lr = 3.25233e-17
I0408 16:57:50.602679 27257 solver.cpp:218] Iteration 9552 (2.38281 iter/s, 5.03608s/12 iters), loss = 4.85486
I0408 16:57:50.602726 27257 solver.cpp:237]     Train net output #0: loss = 4.85486 (* 1 = 4.85486 loss)
I0408 16:57:50.602738 27257 sgd_solver.cpp:105] Iteration 9552, lr = 3.11868e-17
I0408 16:57:55.588151 27257 solver.cpp:218] Iteration 9564 (2.40711 iter/s, 4.98524s/12 iters), loss = 4.97236
I0408 16:57:55.588196 27257 solver.cpp:237]     Train net output #0: loss = 4.97236 (* 1 = 4.97236 loss)
I0408 16:57:55.588208 27257 sgd_solver.cpp:105] Iteration 9564, lr = 2.99052e-17
I0408 16:58:00.616583 27257 solver.cpp:218] Iteration 9576 (2.38654 iter/s, 5.0282s/12 iters), loss = 4.9449
I0408 16:58:00.616628 27257 solver.cpp:237]     Train net output #0: loss = 4.9449 (* 1 = 4.9449 loss)
I0408 16:58:00.616641 27257 sgd_solver.cpp:105] Iteration 9576, lr = 2.86763e-17
I0408 16:58:05.205132 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9588.caffemodel
I0408 16:58:08.435760 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9588.solverstate
I0408 16:58:10.793327 27257 solver.cpp:330] Iteration 9588, Testing net (#0)
I0408 16:58:10.793352 27257 net.cpp:676] Ignoring source layer train-data
I0408 16:58:11.505981 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:58:15.343433 27257 solver.cpp:397]     Test net output #0: accuracy = 0.0275735
I0408 16:58:15.343480 27257 solver.cpp:397]     Test net output #1: loss = 4.96739 (* 1 = 4.96739 loss)
I0408 16:58:15.434087 27257 solver.cpp:218] Iteration 9588 (0.809885 iter/s, 14.8169s/12 iters), loss = 4.88476
I0408 16:58:15.434134 27257 solver.cpp:237]     Train net output #0: loss = 4.88476 (* 1 = 4.88476 loss)
I0408 16:58:15.434146 27257 sgd_solver.cpp:105] Iteration 9588, lr = 2.74979e-17
I0408 16:58:19.622303 27257 solver.cpp:218] Iteration 9600 (2.86532 iter/s, 4.18801s/12 iters), loss = 5.00082
I0408 16:58:19.622341 27257 solver.cpp:237]     Train net output #0: loss = 5.00082 (* 1 = 5.00082 loss)
I0408 16:58:19.622350 27257 sgd_solver.cpp:105] Iteration 9600, lr = 2.63679e-17
I0408 16:58:23.236552 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:58:24.634214 27257 solver.cpp:218] Iteration 9612 (2.39441 iter/s, 5.01168s/12 iters), loss = 4.89383
I0408 16:58:24.634274 27257 solver.cpp:237]     Train net output #0: loss = 4.89383 (* 1 = 4.89383 loss)
I0408 16:58:24.634285 27257 sgd_solver.cpp:105] Iteration 9612, lr = 2.52844e-17
I0408 16:58:29.606055 27257 solver.cpp:218] Iteration 9624 (2.41371 iter/s, 4.9716s/12 iters), loss = 4.88057
I0408 16:58:29.606101 27257 solver.cpp:237]     Train net output #0: loss = 4.88057 (* 1 = 4.88057 loss)
I0408 16:58:29.606112 27257 sgd_solver.cpp:105] Iteration 9624, lr = 2.42454e-17
I0408 16:58:34.610785 27257 solver.cpp:218] Iteration 9636 (2.39784 iter/s, 5.0045s/12 iters), loss = 5.00086
I0408 16:58:34.610822 27257 solver.cpp:237]     Train net output #0: loss = 5.00086 (* 1 = 5.00086 loss)
I0408 16:58:34.610831 27257 sgd_solver.cpp:105] Iteration 9636, lr = 2.3249e-17
I0408 16:58:39.679328 27257 solver.cpp:218] Iteration 9648 (2.36765 iter/s, 5.06832s/12 iters), loss = 4.92564
I0408 16:58:39.679438 27257 solver.cpp:237]     Train net output #0: loss = 4.92564 (* 1 = 4.92564 loss)
I0408 16:58:39.679451 27257 sgd_solver.cpp:105] Iteration 9648, lr = 2.22936e-17
I0408 16:58:44.717816 27257 solver.cpp:218] Iteration 9660 (2.38181 iter/s, 5.03819s/12 iters), loss = 4.95438
I0408 16:58:44.717864 27257 solver.cpp:237]     Train net output #0: loss = 4.95438 (* 1 = 4.95438 loss)
I0408 16:58:44.717876 27257 sgd_solver.cpp:105] Iteration 9660, lr = 2.13775e-17
I0408 16:58:49.757052 27257 solver.cpp:218] Iteration 9672 (2.38143 iter/s, 5.039s/12 iters), loss = 4.91802
I0408 16:58:49.757099 27257 solver.cpp:237]     Train net output #0: loss = 4.91802 (* 1 = 4.91802 loss)
I0408 16:58:49.757112 27257 sgd_solver.cpp:105] Iteration 9672, lr = 2.04991e-17
I0408 16:58:54.770079 27257 solver.cpp:218] Iteration 9684 (2.39387 iter/s, 5.01279s/12 iters), loss = 5.09916
I0408 16:58:54.770119 27257 solver.cpp:237]     Train net output #0: loss = 5.09916 (* 1 = 5.09916 loss)
I0408 16:58:54.770128 27257 sgd_solver.cpp:105] Iteration 9684, lr = 1.96567e-17
I0408 16:58:56.829165 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9690.caffemodel
I0408 16:58:59.861642 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9690.solverstate
I0408 16:59:02.188563 27257 solver.cpp:330] Iteration 9690, Testing net (#0)
I0408 16:59:02.188592 27257 net.cpp:676] Ignoring source layer train-data
I0408 16:59:02.899673 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:59:05.734088 27257 blocking_queue.cpp:49] Waiting for data
I0408 16:59:06.719758 27257 solver.cpp:397]     Test net output #0: accuracy = 0.0275735
I0408 16:59:06.719805 27257 solver.cpp:397]     Test net output #1: loss = 4.97172 (* 1 = 4.97172 loss)
I0408 16:59:08.708014 27257 solver.cpp:218] Iteration 9696 (0.860993 iter/s, 13.9374s/12 iters), loss = 4.81695
I0408 16:59:08.708056 27257 solver.cpp:237]     Train net output #0: loss = 4.81695 (* 1 = 4.81695 loss)
I0408 16:59:08.708067 27257 sgd_solver.cpp:105] Iteration 9696, lr = 1.88489e-17
I0408 16:59:14.114050 27257 solver.cpp:218] Iteration 9708 (2.21984 iter/s, 5.40578s/12 iters), loss = 4.94525
I0408 16:59:14.114208 27257 solver.cpp:237]     Train net output #0: loss = 4.94525 (* 1 = 4.94525 loss)
I0408 16:59:14.114219 27257 sgd_solver.cpp:105] Iteration 9708, lr = 1.80744e-17
I0408 16:59:14.859679 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:59:19.060254 27257 solver.cpp:218] Iteration 9720 (2.42627 iter/s, 4.94586s/12 iters), loss = 4.89453
I0408 16:59:19.060302 27257 solver.cpp:237]     Train net output #0: loss = 4.89453 (* 1 = 4.89453 loss)
I0408 16:59:19.060313 27257 sgd_solver.cpp:105] Iteration 9720, lr = 1.73316e-17
I0408 16:59:24.086163 27257 solver.cpp:218] Iteration 9732 (2.38774 iter/s, 5.02567s/12 iters), loss = 5.00696
I0408 16:59:24.086215 27257 solver.cpp:237]     Train net output #0: loss = 5.00696 (* 1 = 5.00696 loss)
I0408 16:59:24.086225 27257 sgd_solver.cpp:105] Iteration 9732, lr = 1.66194e-17
I0408 16:59:29.031802 27257 solver.cpp:218] Iteration 9744 (2.4265 iter/s, 4.9454s/12 iters), loss = 4.86965
I0408 16:59:29.031848 27257 solver.cpp:237]     Train net output #0: loss = 4.86965 (* 1 = 4.86965 loss)
I0408 16:59:29.031862 27257 sgd_solver.cpp:105] Iteration 9744, lr = 1.59365e-17
I0408 16:59:34.037899 27257 solver.cpp:218] Iteration 9756 (2.39719 iter/s, 5.00587s/12 iters), loss = 4.83383
I0408 16:59:34.037946 27257 solver.cpp:237]     Train net output #0: loss = 4.83383 (* 1 = 4.83383 loss)
I0408 16:59:34.037968 27257 sgd_solver.cpp:105] Iteration 9756, lr = 1.52816e-17
I0408 16:59:39.058562 27257 solver.cpp:218] Iteration 9768 (2.39023 iter/s, 5.02043s/12 iters), loss = 4.98705
I0408 16:59:39.058598 27257 solver.cpp:237]     Train net output #0: loss = 4.98705 (* 1 = 4.98705 loss)
I0408 16:59:39.058609 27257 sgd_solver.cpp:105] Iteration 9768, lr = 1.46536e-17
I0408 16:59:44.129918 27257 solver.cpp:218] Iteration 9780 (2.36634 iter/s, 5.07113s/12 iters), loss = 5.02061
I0408 16:59:44.130023 27257 solver.cpp:237]     Train net output #0: loss = 5.02061 (* 1 = 5.02061 loss)
I0408 16:59:44.130033 27257 sgd_solver.cpp:105] Iteration 9780, lr = 1.40514e-17
I0408 16:59:48.773165 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9792.caffemodel
I0408 16:59:51.758775 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9792.solverstate
I0408 16:59:54.084501 27257 solver.cpp:330] Iteration 9792, Testing net (#0)
I0408 16:59:54.084527 27257 net.cpp:676] Ignoring source layer train-data
I0408 16:59:54.692447 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:59:58.539728 27257 solver.cpp:397]     Test net output #0: accuracy = 0.0281863
I0408 16:59:58.539773 27257 solver.cpp:397]     Test net output #1: loss = 4.9671 (* 1 = 4.9671 loss)
I0408 16:59:58.627740 27257 solver.cpp:218] Iteration 9792 (0.827747 iter/s, 14.4972s/12 iters), loss = 4.95557
I0408 16:59:58.627791 27257 solver.cpp:237]     Train net output #0: loss = 4.95557 (* 1 = 4.95557 loss)
I0408 16:59:58.627804 27257 sgd_solver.cpp:105] Iteration 9792, lr = 1.3474e-17
I0408 17:00:02.908641 27257 solver.cpp:218] Iteration 9804 (2.80329 iter/s, 4.28069s/12 iters), loss = 5.00536
I0408 17:00:02.908689 27257 solver.cpp:237]     Train net output #0: loss = 5.00536 (* 1 = 5.00536 loss)
I0408 17:00:02.908701 27257 sgd_solver.cpp:105] Iteration 9804, lr = 1.29203e-17
I0408 17:00:05.901372 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 17:00:07.960510 27257 solver.cpp:218] Iteration 9816 (2.37547 iter/s, 5.05163s/12 iters), loss = 4.99884
I0408 17:00:07.960552 27257 solver.cpp:237]     Train net output #0: loss = 4.99884 (* 1 = 4.99884 loss)
I0408 17:00:07.960564 27257 sgd_solver.cpp:105] Iteration 9816, lr = 1.23894e-17
I0408 17:00:12.969341 27257 solver.cpp:218] Iteration 9828 (2.39588 iter/s, 5.0086s/12 iters), loss = 4.99087
I0408 17:00:12.969389 27257 solver.cpp:237]     Train net output #0: loss = 4.99087 (* 1 = 4.99087 loss)
I0408 17:00:12.969401 27257 sgd_solver.cpp:105] Iteration 9828, lr = 1.18803e-17
I0408 17:00:17.985435 27257 solver.cpp:218] Iteration 9840 (2.39241 iter/s, 5.01586s/12 iters), loss = 4.90961
I0408 17:00:17.985599 27257 solver.cpp:237]     Train net output #0: loss = 4.90961 (* 1 = 4.90961 loss)
I0408 17:00:17.985613 27257 sgd_solver.cpp:105] Iteration 9840, lr = 1.13921e-17
I0408 17:00:23.027413 27257 solver.cpp:218] Iteration 9852 (2.38018 iter/s, 5.04163s/12 iters), loss = 4.96484
I0408 17:00:23.027457 27257 solver.cpp:237]     Train net output #0: loss = 4.96484 (* 1 = 4.96484 loss)
I0408 17:00:23.027468 27257 sgd_solver.cpp:105] Iteration 9852, lr = 1.09239e-17
I0408 17:00:28.061162 27257 solver.cpp:218] Iteration 9864 (2.38402 iter/s, 5.03351s/12 iters), loss = 4.93859
I0408 17:00:28.061205 27257 solver.cpp:237]     Train net output #0: loss = 4.93859 (* 1 = 4.93859 loss)
I0408 17:00:28.061218 27257 sgd_solver.cpp:105] Iteration 9864, lr = 1.0475e-17
I0408 17:00:33.092061 27257 solver.cpp:218] Iteration 9876 (2.38537 iter/s, 5.03066s/12 iters), loss = 4.98865
I0408 17:00:33.092104 27257 solver.cpp:237]     Train net output #0: loss = 4.98865 (* 1 = 4.98865 loss)
I0408 17:00:33.092113 27257 sgd_solver.cpp:105] Iteration 9876, lr = 1.00446e-17
I0408 17:00:38.153527 27257 solver.cpp:218] Iteration 9888 (2.37097 iter/s, 5.06123s/12 iters), loss = 5.12442
I0408 17:00:38.153563 27257 solver.cpp:237]     Train net output #0: loss = 5.12442 (* 1 = 5.12442 loss)
I0408 17:00:38.153571 27257 sgd_solver.cpp:105] Iteration 9888, lr = 9.63181e-18
I0408 17:00:40.191577 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9894.caffemodel
I0408 17:00:43.923696 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9894.solverstate
I0408 17:00:46.250473 27257 solver.cpp:330] Iteration 9894, Testing net (#0)
I0408 17:00:46.250495 27257 net.cpp:676] Ignoring source layer train-data
I0408 17:00:46.828006 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 17:00:50.727538 27257 solver.cpp:397]     Test net output #0: accuracy = 0.0275735
I0408 17:00:50.727670 27257 solver.cpp:397]     Test net output #1: loss = 4.96783 (* 1 = 4.96783 loss)
I0408 17:00:52.733191 27257 solver.cpp:218] Iteration 9900 (0.823096 iter/s, 14.5791s/12 iters), loss = 5.05885
I0408 17:00:52.733238 27257 solver.cpp:237]     Train net output #0: loss = 5.05885 (* 1 = 5.05885 loss)
I0408 17:00:52.733250 27257 sgd_solver.cpp:105] Iteration 9900, lr = 9.23601e-18
I0408 17:00:57.725358 27257 solver.cpp:218] Iteration 9912 (2.40388 iter/s, 4.99193s/12 iters), loss = 5.00932
I0408 17:00:57.725406 27257 solver.cpp:237]     Train net output #0: loss = 5.00932 (* 1 = 5.00932 loss)
I0408 17:00:57.725419 27257 sgd_solver.cpp:105] Iteration 9912, lr = 8.85647e-18
I0408 17:00:57.837658 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 17:01:02.701193 27257 solver.cpp:218] Iteration 9924 (2.41177 iter/s, 4.9756s/12 iters), loss = 4.94913
I0408 17:01:02.701251 27257 solver.cpp:237]     Train net output #0: loss = 4.94913 (* 1 = 4.94913 loss)
I0408 17:01:02.701267 27257 sgd_solver.cpp:105] Iteration 9924, lr = 8.49253e-18
I0408 17:01:07.729154 27257 solver.cpp:218] Iteration 9936 (2.38677 iter/s, 5.02772s/12 iters), loss = 4.89294
I0408 17:01:07.729190 27257 solver.cpp:237]     Train net output #0: loss = 4.89294 (* 1 = 4.89294 loss)
I0408 17:01:07.729198 27257 sgd_solver.cpp:105] Iteration 9936, lr = 8.14354e-18
I0408 17:01:12.757331 27257 solver.cpp:218] Iteration 9948 (2.38666 iter/s, 5.02795s/12 iters), loss = 4.83584
I0408 17:01:12.757375 27257 solver.cpp:237]     Train net output #0: loss = 4.83584 (* 1 = 4.83584 loss)
I0408 17:01:12.757386 27257 sgd_solver.cpp:105] Iteration 9948, lr = 7.8089e-18
I0408 17:01:17.713876 27257 solver.cpp:218] Iteration 9960 (2.42116 iter/s, 4.95631s/12 iters), loss = 5.01724
I0408 17:01:17.713925 27257 solver.cpp:237]     Train net output #0: loss = 5.01724 (* 1 = 5.01724 loss)
I0408 17:01:17.713937 27257 sgd_solver.cpp:105] Iteration 9960, lr = 7.48801e-18
I0408 17:01:22.692941 27257 solver.cpp:218] Iteration 9972 (2.41021 iter/s, 4.97883s/12 iters), loss = 4.88564
I0408 17:01:22.693101 27257 solver.cpp:237]     Train net output #0: loss = 4.88564 (* 1 = 4.88564 loss)
I0408 17:01:22.693115 27257 sgd_solver.cpp:105] Iteration 9972, lr = 7.1803e-18
I0408 17:01:27.682616 27257 solver.cpp:218] Iteration 9984 (2.40513 iter/s, 4.98933s/12 iters), loss = 4.96186
I0408 17:01:27.682667 27257 solver.cpp:237]     Train net output #0: loss = 4.96186 (* 1 = 4.96186 loss)
I0408 17:01:27.682679 27257 sgd_solver.cpp:105] Iteration 9984, lr = 6.88524e-18
I0408 17:01:32.267132 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9996.caffemodel
I0408 17:01:35.276619 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9996.solverstate
I0408 17:01:37.603838 27257 solver.cpp:330] Iteration 9996, Testing net (#0)
I0408 17:01:37.603864 27257 net.cpp:676] Ignoring source layer train-data
I0408 17:01:38.123574 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 17:01:42.214462 27257 solver.cpp:397]     Test net output #0: accuracy = 0.0275735
I0408 17:01:42.214509 27257 solver.cpp:397]     Test net output #1: loss = 4.9681 (* 1 = 4.9681 loss)
I0408 17:01:42.305145 27257 solver.cpp:218] Iteration 9996 (0.820684 iter/s, 14.6219s/12 iters), loss = 4.88585
I0408 17:01:42.305197 27257 solver.cpp:237]     Train net output #0: loss = 4.88585 (* 1 = 4.88585 loss)
I0408 17:01:42.305208 27257 sgd_solver.cpp:105] Iteration 9996, lr = 6.6023e-18
I0408 17:01:46.536041 27257 solver.cpp:218] Iteration 10008 (2.83642 iter/s, 4.23068s/12 iters), loss = 5.09032
I0408 17:01:46.536088 27257 solver.cpp:237]     Train net output #0: loss = 5.09032 (* 1 = 5.09032 loss)
I0408 17:01:46.536100 27257 sgd_solver.cpp:105] Iteration 10008, lr = 6.33099e-18
I0408 17:01:48.774322 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 17:01:51.478708 27257 solver.cpp:218] Iteration 10020 (2.42795 iter/s, 4.94243s/12 iters), loss = 4.925
I0408 17:01:51.478754 27257 solver.cpp:237]     Train net output #0: loss = 4.925 (* 1 = 4.925 loss)
I0408 17:01:51.478765 27257 sgd_solver.cpp:105] Iteration 10020, lr = 6.07083e-18
I0408 17:01:56.496419 27257 solver.cpp:218] Iteration 10032 (2.39165 iter/s, 5.01746s/12 iters), loss = 4.92808
I0408 17:01:56.496538 27257 solver.cpp:237]     Train net output #0: loss = 4.92808 (* 1 = 4.92808 loss)
I0408 17:01:56.496552 27257 sgd_solver.cpp:105] Iteration 10032, lr = 5.82136e-18
I0408 17:02:01.525684 27257 solver.cpp:218] Iteration 10044 (2.38618 iter/s, 5.02896s/12 iters), loss = 4.93462
I0408 17:02:01.525729 27257 solver.cpp:237]     Train net output #0: loss = 4.93462 (* 1 = 4.93462 loss)
I0408 17:02:01.525740 27257 sgd_solver.cpp:105] Iteration 10044, lr = 5.58214e-18
I0408 17:02:06.540024 27257 solver.cpp:218] Iteration 10056 (2.39325 iter/s, 5.0141s/12 iters), loss = 5.01056
I0408 17:02:06.540071 27257 solver.cpp:237]     Train net output #0: loss = 5.01056 (* 1 = 5.01056 loss)
I0408 17:02:06.540084 27257 sgd_solver.cpp:105] Iteration 10056, lr = 5.35275e-18
I0408 17:02:11.549288 27257 solver.cpp:218] Iteration 10068 (2.39568 iter/s, 5.00902s/12 iters), loss = 4.97694
I0408 17:02:11.549337 27257 solver.cpp:237]     Train net output #0: loss = 4.97694 (* 1 = 4.97694 loss)
I0408 17:02:11.549348 27257 sgd_solver.cpp:105] Iteration 10068, lr = 5.13279e-18
I0408 17:02:16.546064 27257 solver.cpp:218] Iteration 10080 (2.40166 iter/s, 4.99654s/12 iters), loss = 4.79155
I0408 17:02:16.546108 27257 solver.cpp:237]     Train net output #0: loss = 4.79155 (* 1 = 4.79155 loss)
I0408 17:02:16.546119 27257 sgd_solver.cpp:105] Iteration 10080, lr = 4.92186e-18
I0408 17:02:21.697404 27257 solver.cpp:218] Iteration 10092 (2.3296 iter/s, 5.1511s/12 iters), loss = 4.84905
I0408 17:02:21.697453 27257 solver.cpp:237]     Train net output #0: loss = 4.84905 (* 1 = 4.84905 loss)
I0408 17:02:21.697465 27257 sgd_solver.cpp:105] Iteration 10092, lr = 4.71961e-18
I0408 17:02:23.723022 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10098.caffemodel
I0408 17:02:26.743753 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10098.solverstate
I0408 17:02:29.064265 27257 solver.cpp:330] Iteration 10098, Testing net (#0)
I0408 17:02:29.064291 27257 net.cpp:676] Ignoring source layer train-data
I0408 17:02:29.540457 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 17:02:33.660813 27257 solver.cpp:397]     Test net output #0: accuracy = 0.0275735
I0408 17:02:33.660858 27257 solver.cpp:397]     Test net output #1: loss = 4.97095 (* 1 = 4.97095 loss)
I0408 17:02:35.603477 27257 solver.cpp:218] Iteration 10104 (0.862967 iter/s, 13.9055s/12 iters), loss = 5.03946
I0408 17:02:35.603528 27257 solver.cpp:237]     Train net output #0: loss = 5.03946 (* 1 = 5.03946 loss)
I0408 17:02:35.603540 27257 sgd_solver.cpp:105] Iteration 10104, lr = 4.52566e-18
I0408 17:02:40.074640 27261 data_layer.cpp:73] Restarting data prefetching from start.
I0408 17:02:40.705761 27257 solver.cpp:218] Iteration 10116 (2.352 iter/s, 5.10204s/12 iters), loss = 4.96016
I0408 17:02:40.705806 27257 solver.cpp:237]     Train net output #0: loss = 4.96016 (* 1 = 4.96016 loss)
I0408 17:02:40.705818 27257 sgd_solver.cpp:105] Iteration 10116, lr = 4.33969e-18
I0408 17:02:45.754565 27257 solver.cpp:218] Iteration 10128 (2.37691 iter/s, 5.04857s/12 iters), loss = 4.95262
I0408 17:02:45.754597 27257 solver.cpp:237]     Train net output #0: loss = 4.95262 (* 1 = 4.95262 loss)
I0408 17:02:45.754606 27257 sgd_solver.cpp:105] Iteration 10128, lr = 4.16136e-18
I0408 17:02:50.702013 27257 solver.cpp:218] Iteration 10140 (2.4256 iter/s, 4.94723s/12 iters), loss = 4.93702
I0408 17:02:50.702059 27257 solver.cpp:237]     Train net output #0: loss = 4.93702 (* 1 = 4.93702 loss)
I0408 17:02:50.702070 27257 sgd_solver.cpp:105] Iteration 10140, lr = 3.99035e-18
I0408 17:02:55.719803 27257 solver.cpp:218] Iteration 10152 (2.3916 iter/s, 5.01755s/12 iters), loss = 4.90359
I0408 17:02:55.719847 27257 solver.cpp:237]     Train net output #0: loss = 4.90359 (* 1 = 4.90359 loss)
I0408 17:02:55.719858 27257 sgd_solver.cpp:105] Iteration 10152, lr = 3.82638e-18
I0408 17:03:00.788651 27257 solver.cpp:218] Iteration 10164 (2.36751 iter/s, 5.06861s/12 iters), loss = 4.958
I0408 17:03:00.788769 27257 solver.cpp:237]     Train net output #0: loss = 4.958 (* 1 = 4.958 loss)
I0408 17:03:00.788780 27257 sgd_solver.cpp:105] Iteration 10164, lr = 3.66914e-18
I0408 17:03:05.853255 27257 solver.cpp:218] Iteration 10176 (2.36953 iter/s, 5.0643s/12 iters), loss = 5.00236
I0408 17:03:05.853302 27257 solver.cpp:237]     Train net output #0: loss = 5.00236 (* 1 = 5.00236 loss)
I0408 17:03:05.853314 27257 sgd_solver.cpp:105] Iteration 10176, lr = 3.51836e-18
I0408 17:03:10.880393 27257 solver.cpp:218] Iteration 10188 (2.38716 iter/s, 5.0269s/12 iters), loss = 5.01783
I0408 17:03:10.880443 27257 solver.cpp:237]     Train net output #0: loss = 5.01783 (* 1 = 5.01783 loss)
I0408 17:03:10.880455 27257 sgd_solver.cpp:105] Iteration 10188, lr = 3.37378e-18
I0408 17:03:15.479367 27257 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10200.caffemodel
I0408 17:03:18.451491 27257 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10200.solverstate
I0408 17:03:20.834847 27257 solver.cpp:310] Iteration 10200, loss = 4.81335
I0408 17:03:20.834877 27257 solver.cpp:330] Iteration 10200, Testing net (#0)
I0408 17:03:20.834883 27257 net.cpp:676] Ignoring source layer train-data
I0408 17:03:21.251238 27262 data_layer.cpp:73] Restarting data prefetching from start.
I0408 17:03:25.268174 27257 solver.cpp:397]     Test net output #0: accuracy = 0.0275735
I0408 17:03:25.268221 27257 solver.cpp:397]     Test net output #1: loss = 4.97038 (* 1 = 4.97038 loss)
I0408 17:03:25.268232 27257 solver.cpp:315] Optimization Done.
I0408 17:03:25.268240 27257 caffe.cpp:259] Optimization Done.