DIGITS-CNN/cars/architecture-investigations/fc/3-layers/2048/caffe_output.log

I0409 19:56:53.419628 14973 upgrade_proto.cpp:1082] Attempting to upgrade input file specified using deprecated 'solver_type' field (enum)': /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210409-195651-879c/solver.prototxt
I0409 19:56:53.419838 14973 upgrade_proto.cpp:1089] Successfully upgraded file specified using deprecated 'solver_type' field (enum) to 'type' field (string).
W0409 19:56:53.419847 14973 upgrade_proto.cpp:1091] Note that future Caffe releases will only support 'type' field (string) for a solver's type.
I0409 19:56:53.419936 14973 caffe.cpp:218] Using GPUs 1
I0409 19:56:53.445366 14973 caffe.cpp:223] GPU 1: GeForce GTX 1080 Ti
I0409 19:56:53.728822 14973 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: 0.99980193
momentum: 0.9
weight_decay: 0.0001
snapshot: 102
snapshot_prefix: "snapshot"
solver_mode: GPU
device_id: 1
net: "train_val.prototxt"
train_state {
level: 0
stage: ""
}
type: "SGD"
I0409 19:56:53.729578 14973 solver.cpp:87] Creating training net from net file: train_val.prototxt
I0409 19:56:53.730175 14973 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer val-data
I0409 19:56:53.730191 14973 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy
I0409 19:56:53.730343 14973 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: 2048
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: 2048
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: "fc7.5"
type: "InnerProduct"
bottom: "fc7"
top: "fc7.5"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 2048
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu7.5"
type: "ReLU"
bottom: "fc7.5"
top: "fc7.5"
}
layer {
name: "drop7.5"
type: "Dropout"
bottom: "fc7.5"
top: "fc7.5"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc8"
type: "InnerProduct"
bottom: "fc7.5"
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"
}
I0409 19:56:53.730439 14973 layer_factory.hpp:77] Creating layer train-data
I0409 19:56:53.732549 14973 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/train_db
I0409 19:56:53.732756 14973 net.cpp:84] Creating Layer train-data
I0409 19:56:53.732767 14973 net.cpp:380] train-data -> data
I0409 19:56:53.732787 14973 net.cpp:380] train-data -> label
I0409 19:56:53.732798 14973 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto
I0409 19:56:53.737419 14973 data_layer.cpp:45] output data size: 128,3,227,227
I0409 19:56:53.861343 14973 net.cpp:122] Setting up train-data
I0409 19:56:53.861366 14973 net.cpp:129] Top shape: 128 3 227 227 (19787136)
I0409 19:56:53.861371 14973 net.cpp:129] Top shape: 128 (128)
I0409 19:56:53.861374 14973 net.cpp:137] Memory required for data: 79149056
I0409 19:56:53.861384 14973 layer_factory.hpp:77] Creating layer conv1
I0409 19:56:53.861405 14973 net.cpp:84] Creating Layer conv1
I0409 19:56:53.861413 14973 net.cpp:406] conv1 <- data
I0409 19:56:53.861424 14973 net.cpp:380] conv1 -> conv1
I0409 19:56:54.407604 14973 net.cpp:122] Setting up conv1
I0409 19:56:54.407625 14973 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0409 19:56:54.407629 14973 net.cpp:137] Memory required for data: 227833856
I0409 19:56:54.407649 14973 layer_factory.hpp:77] Creating layer relu1
I0409 19:56:54.407678 14973 net.cpp:84] Creating Layer relu1
I0409 19:56:54.407683 14973 net.cpp:406] relu1 <- conv1
I0409 19:56:54.407689 14973 net.cpp:367] relu1 -> conv1 (in-place)
I0409 19:56:54.407975 14973 net.cpp:122] Setting up relu1
I0409 19:56:54.407984 14973 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0409 19:56:54.407987 14973 net.cpp:137] Memory required for data: 376518656
I0409 19:56:54.407991 14973 layer_factory.hpp:77] Creating layer norm1
I0409 19:56:54.408000 14973 net.cpp:84] Creating Layer norm1
I0409 19:56:54.408004 14973 net.cpp:406] norm1 <- conv1
I0409 19:56:54.408008 14973 net.cpp:380] norm1 -> norm1
I0409 19:56:54.408445 14973 net.cpp:122] Setting up norm1
I0409 19:56:54.408454 14973 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0409 19:56:54.408458 14973 net.cpp:137] Memory required for data: 525203456
I0409 19:56:54.408463 14973 layer_factory.hpp:77] Creating layer pool1
I0409 19:56:54.408470 14973 net.cpp:84] Creating Layer pool1
I0409 19:56:54.408473 14973 net.cpp:406] pool1 <- norm1
I0409 19:56:54.408478 14973 net.cpp:380] pool1 -> pool1
I0409 19:56:54.408514 14973 net.cpp:122] Setting up pool1
I0409 19:56:54.408520 14973 net.cpp:129] Top shape: 128 96 27 27 (8957952)
I0409 19:56:54.408524 14973 net.cpp:137] Memory required for data: 561035264
I0409 19:56:54.408526 14973 layer_factory.hpp:77] Creating layer conv2
I0409 19:56:54.408536 14973 net.cpp:84] Creating Layer conv2
I0409 19:56:54.408540 14973 net.cpp:406] conv2 <- pool1
I0409 19:56:54.408545 14973 net.cpp:380] conv2 -> conv2
I0409 19:56:54.415040 14973 net.cpp:122] Setting up conv2
I0409 19:56:54.415052 14973 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0409 19:56:54.415056 14973 net.cpp:137] Memory required for data: 656586752
I0409 19:56:54.415066 14973 layer_factory.hpp:77] Creating layer relu2
I0409 19:56:54.415072 14973 net.cpp:84] Creating Layer relu2
I0409 19:56:54.415076 14973 net.cpp:406] relu2 <- conv2
I0409 19:56:54.415081 14973 net.cpp:367] relu2 -> conv2 (in-place)
I0409 19:56:54.415498 14973 net.cpp:122] Setting up relu2
I0409 19:56:54.415508 14973 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0409 19:56:54.415511 14973 net.cpp:137] Memory required for data: 752138240
I0409 19:56:54.415515 14973 layer_factory.hpp:77] Creating layer norm2
I0409 19:56:54.415522 14973 net.cpp:84] Creating Layer norm2
I0409 19:56:54.415526 14973 net.cpp:406] norm2 <- conv2
I0409 19:56:54.415531 14973 net.cpp:380] norm2 -> norm2
I0409 19:56:54.415817 14973 net.cpp:122] Setting up norm2
I0409 19:56:54.415825 14973 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0409 19:56:54.415828 14973 net.cpp:137] Memory required for data: 847689728
I0409 19:56:54.415832 14973 layer_factory.hpp:77] Creating layer pool2
I0409 19:56:54.415839 14973 net.cpp:84] Creating Layer pool2
I0409 19:56:54.415843 14973 net.cpp:406] pool2 <- norm2
I0409 19:56:54.415848 14973 net.cpp:380] pool2 -> pool2
I0409 19:56:54.415874 14973 net.cpp:122] Setting up pool2
I0409 19:56:54.415879 14973 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0409 19:56:54.415881 14973 net.cpp:137] Memory required for data: 869840896
I0409 19:56:54.415885 14973 layer_factory.hpp:77] Creating layer conv3
I0409 19:56:54.415894 14973 net.cpp:84] Creating Layer conv3
I0409 19:56:54.415897 14973 net.cpp:406] conv3 <- pool2
I0409 19:56:54.415902 14973 net.cpp:380] conv3 -> conv3
I0409 19:56:54.425623 14973 net.cpp:122] Setting up conv3
I0409 19:56:54.425634 14973 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0409 19:56:54.425638 14973 net.cpp:137] Memory required for data: 903067648
I0409 19:56:54.425647 14973 layer_factory.hpp:77] Creating layer relu3
I0409 19:56:54.425653 14973 net.cpp:84] Creating Layer relu3
I0409 19:56:54.425657 14973 net.cpp:406] relu3 <- conv3
I0409 19:56:54.425662 14973 net.cpp:367] relu3 -> conv3 (in-place)
I0409 19:56:54.426086 14973 net.cpp:122] Setting up relu3
I0409 19:56:54.426095 14973 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0409 19:56:54.426100 14973 net.cpp:137] Memory required for data: 936294400
I0409 19:56:54.426103 14973 layer_factory.hpp:77] Creating layer conv4
I0409 19:56:54.426129 14973 net.cpp:84] Creating Layer conv4
I0409 19:56:54.426133 14973 net.cpp:406] conv4 <- conv3
I0409 19:56:54.426138 14973 net.cpp:380] conv4 -> conv4
I0409 19:56:54.436084 14973 net.cpp:122] Setting up conv4
I0409 19:56:54.436097 14973 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0409 19:56:54.436101 14973 net.cpp:137] Memory required for data: 969521152
I0409 19:56:54.436108 14973 layer_factory.hpp:77] Creating layer relu4
I0409 19:56:54.436115 14973 net.cpp:84] Creating Layer relu4
I0409 19:56:54.436120 14973 net.cpp:406] relu4 <- conv4
I0409 19:56:54.436125 14973 net.cpp:367] relu4 -> conv4 (in-place)
I0409 19:56:54.436457 14973 net.cpp:122] Setting up relu4
I0409 19:56:54.436465 14973 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0409 19:56:54.436468 14973 net.cpp:137] Memory required for data: 1002747904
I0409 19:56:54.436472 14973 layer_factory.hpp:77] Creating layer conv5
I0409 19:56:54.436482 14973 net.cpp:84] Creating Layer conv5
I0409 19:56:54.436486 14973 net.cpp:406] conv5 <- conv4
I0409 19:56:54.436492 14973 net.cpp:380] conv5 -> conv5
I0409 19:56:54.445986 14973 net.cpp:122] Setting up conv5
I0409 19:56:54.445998 14973 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0409 19:56:54.446003 14973 net.cpp:137] Memory required for data: 1024899072
I0409 19:56:54.446015 14973 layer_factory.hpp:77] Creating layer relu5
I0409 19:56:54.446022 14973 net.cpp:84] Creating Layer relu5
I0409 19:56:54.446025 14973 net.cpp:406] relu5 <- conv5
I0409 19:56:54.446033 14973 net.cpp:367] relu5 -> conv5 (in-place)
I0409 19:56:54.446525 14973 net.cpp:122] Setting up relu5
I0409 19:56:54.446535 14973 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0409 19:56:54.446539 14973 net.cpp:137] Memory required for data: 1047050240
I0409 19:56:54.446542 14973 layer_factory.hpp:77] Creating layer pool5
I0409 19:56:54.446550 14973 net.cpp:84] Creating Layer pool5
I0409 19:56:54.446554 14973 net.cpp:406] pool5 <- conv5
I0409 19:56:54.446561 14973 net.cpp:380] pool5 -> pool5
I0409 19:56:54.446599 14973 net.cpp:122] Setting up pool5
I0409 19:56:54.446604 14973 net.cpp:129] Top shape: 128 256 6 6 (1179648)
I0409 19:56:54.446609 14973 net.cpp:137] Memory required for data: 1051768832
I0409 19:56:54.446612 14973 layer_factory.hpp:77] Creating layer fc6
I0409 19:56:54.446622 14973 net.cpp:84] Creating Layer fc6
I0409 19:56:54.446626 14973 net.cpp:406] fc6 <- pool5
I0409 19:56:54.446632 14973 net.cpp:380] fc6 -> fc6
I0409 19:56:54.628520 14973 net.cpp:122] Setting up fc6
I0409 19:56:54.628540 14973 net.cpp:129] Top shape: 128 2048 (262144)
I0409 19:56:54.628543 14973 net.cpp:137] Memory required for data: 1052817408
I0409 19:56:54.628553 14973 layer_factory.hpp:77] Creating layer relu6
I0409 19:56:54.628563 14973 net.cpp:84] Creating Layer relu6
I0409 19:56:54.628567 14973 net.cpp:406] relu6 <- fc6
I0409 19:56:54.628574 14973 net.cpp:367] relu6 -> fc6 (in-place)
I0409 19:56:54.629189 14973 net.cpp:122] Setting up relu6
I0409 19:56:54.629199 14973 net.cpp:129] Top shape: 128 2048 (262144)
I0409 19:56:54.629202 14973 net.cpp:137] Memory required for data: 1053865984
I0409 19:56:54.629206 14973 layer_factory.hpp:77] Creating layer drop6
I0409 19:56:54.629213 14973 net.cpp:84] Creating Layer drop6
I0409 19:56:54.629216 14973 net.cpp:406] drop6 <- fc6
I0409 19:56:54.629222 14973 net.cpp:367] drop6 -> fc6 (in-place)
I0409 19:56:54.629248 14973 net.cpp:122] Setting up drop6
I0409 19:56:54.629254 14973 net.cpp:129] Top shape: 128 2048 (262144)
I0409 19:56:54.629257 14973 net.cpp:137] Memory required for data: 1054914560
I0409 19:56:54.629261 14973 layer_factory.hpp:77] Creating layer fc7
I0409 19:56:54.629269 14973 net.cpp:84] Creating Layer fc7
I0409 19:56:54.629272 14973 net.cpp:406] fc7 <- fc6
I0409 19:56:54.629278 14973 net.cpp:380] fc7 -> fc7
I0409 19:56:54.668452 14973 net.cpp:122] Setting up fc7
I0409 19:56:54.668471 14973 net.cpp:129] Top shape: 128 2048 (262144)
I0409 19:56:54.668475 14973 net.cpp:137] Memory required for data: 1055963136
I0409 19:56:54.668484 14973 layer_factory.hpp:77] Creating layer relu7
I0409 19:56:54.668512 14973 net.cpp:84] Creating Layer relu7
I0409 19:56:54.668516 14973 net.cpp:406] relu7 <- fc7
I0409 19:56:54.668524 14973 net.cpp:367] relu7 -> fc7 (in-place)
I0409 19:56:54.669131 14973 net.cpp:122] Setting up relu7
I0409 19:56:54.669142 14973 net.cpp:129] Top shape: 128 2048 (262144)
I0409 19:56:54.669144 14973 net.cpp:137] Memory required for data: 1057011712
I0409 19:56:54.669148 14973 layer_factory.hpp:77] Creating layer drop7
I0409 19:56:54.669155 14973 net.cpp:84] Creating Layer drop7
I0409 19:56:54.669159 14973 net.cpp:406] drop7 <- fc7
I0409 19:56:54.669164 14973 net.cpp:367] drop7 -> fc7 (in-place)
I0409 19:56:54.669188 14973 net.cpp:122] Setting up drop7
I0409 19:56:54.669193 14973 net.cpp:129] Top shape: 128 2048 (262144)
I0409 19:56:54.669196 14973 net.cpp:137] Memory required for data: 1058060288
I0409 19:56:54.669200 14973 layer_factory.hpp:77] Creating layer fc7.5
I0409 19:56:54.669206 14973 net.cpp:84] Creating Layer fc7.5
I0409 19:56:54.669209 14973 net.cpp:406] fc7.5 <- fc7
I0409 19:56:54.669216 14973 net.cpp:380] fc7.5 -> fc7.5
I0409 19:56:54.708346 14973 net.cpp:122] Setting up fc7.5
I0409 19:56:54.708366 14973 net.cpp:129] Top shape: 128 2048 (262144)
I0409 19:56:54.708370 14973 net.cpp:137] Memory required for data: 1059108864
I0409 19:56:54.708380 14973 layer_factory.hpp:77] Creating layer relu7.5
I0409 19:56:54.708389 14973 net.cpp:84] Creating Layer relu7.5
I0409 19:56:54.708395 14973 net.cpp:406] relu7.5 <- fc7.5
I0409 19:56:54.708400 14973 net.cpp:367] relu7.5 -> fc7.5 (in-place)
I0409 19:56:54.709041 14973 net.cpp:122] Setting up relu7.5
I0409 19:56:54.709049 14973 net.cpp:129] Top shape: 128 2048 (262144)
I0409 19:56:54.709053 14973 net.cpp:137] Memory required for data: 1060157440
I0409 19:56:54.709057 14973 layer_factory.hpp:77] Creating layer drop7.5
I0409 19:56:54.709064 14973 net.cpp:84] Creating Layer drop7.5
I0409 19:56:54.709069 14973 net.cpp:406] drop7.5 <- fc7.5
I0409 19:56:54.709074 14973 net.cpp:367] drop7.5 -> fc7.5 (in-place)
I0409 19:56:54.709097 14973 net.cpp:122] Setting up drop7.5
I0409 19:56:54.709102 14973 net.cpp:129] Top shape: 128 2048 (262144)
I0409 19:56:54.709105 14973 net.cpp:137] Memory required for data: 1061206016
I0409 19:56:54.709110 14973 layer_factory.hpp:77] Creating layer fc8
I0409 19:56:54.709116 14973 net.cpp:84] Creating Layer fc8
I0409 19:56:54.709120 14973 net.cpp:406] fc8 <- fc7.5
I0409 19:56:54.709125 14973 net.cpp:380] fc8 -> fc8
I0409 19:56:54.713171 14973 net.cpp:122] Setting up fc8
I0409 19:56:54.713181 14973 net.cpp:129] Top shape: 128 196 (25088)
I0409 19:56:54.713184 14973 net.cpp:137] Memory required for data: 1061306368
I0409 19:56:54.713193 14973 layer_factory.hpp:77] Creating layer loss
I0409 19:56:54.713201 14973 net.cpp:84] Creating Layer loss
I0409 19:56:54.713204 14973 net.cpp:406] loss <- fc8
I0409 19:56:54.713209 14973 net.cpp:406] loss <- label
I0409 19:56:54.713215 14973 net.cpp:380] loss -> loss
I0409 19:56:54.713227 14973 layer_factory.hpp:77] Creating layer loss
I0409 19:56:54.713812 14973 net.cpp:122] Setting up loss
I0409 19:56:54.713821 14973 net.cpp:129] Top shape: (1)
I0409 19:56:54.713824 14973 net.cpp:132]     with loss weight 1
I0409 19:56:54.713841 14973 net.cpp:137] Memory required for data: 1061306372
I0409 19:56:54.713845 14973 net.cpp:198] loss needs backward computation.
I0409 19:56:54.713852 14973 net.cpp:198] fc8 needs backward computation.
I0409 19:56:54.713856 14973 net.cpp:198] drop7.5 needs backward computation.
I0409 19:56:54.713860 14973 net.cpp:198] relu7.5 needs backward computation.
I0409 19:56:54.713862 14973 net.cpp:198] fc7.5 needs backward computation.
I0409 19:56:54.713866 14973 net.cpp:198] drop7 needs backward computation.
I0409 19:56:54.713869 14973 net.cpp:198] relu7 needs backward computation.
I0409 19:56:54.713872 14973 net.cpp:198] fc7 needs backward computation.
I0409 19:56:54.713876 14973 net.cpp:198] drop6 needs backward computation.
I0409 19:56:54.713879 14973 net.cpp:198] relu6 needs backward computation.
I0409 19:56:54.713882 14973 net.cpp:198] fc6 needs backward computation.
I0409 19:56:54.713903 14973 net.cpp:198] pool5 needs backward computation.
I0409 19:56:54.713907 14973 net.cpp:198] relu5 needs backward computation.
I0409 19:56:54.713910 14973 net.cpp:198] conv5 needs backward computation.
I0409 19:56:54.713914 14973 net.cpp:198] relu4 needs backward computation.
I0409 19:56:54.713917 14973 net.cpp:198] conv4 needs backward computation.
I0409 19:56:54.713922 14973 net.cpp:198] relu3 needs backward computation.
I0409 19:56:54.713924 14973 net.cpp:198] conv3 needs backward computation.
I0409 19:56:54.713929 14973 net.cpp:198] pool2 needs backward computation.
I0409 19:56:54.713933 14973 net.cpp:198] norm2 needs backward computation.
I0409 19:56:54.713937 14973 net.cpp:198] relu2 needs backward computation.
I0409 19:56:54.713940 14973 net.cpp:198] conv2 needs backward computation.
I0409 19:56:54.713943 14973 net.cpp:198] pool1 needs backward computation.
I0409 19:56:54.713948 14973 net.cpp:198] norm1 needs backward computation.
I0409 19:56:54.713950 14973 net.cpp:198] relu1 needs backward computation.
I0409 19:56:54.713965 14973 net.cpp:198] conv1 needs backward computation.
I0409 19:56:54.713970 14973 net.cpp:200] train-data does not need backward computation.
I0409 19:56:54.713973 14973 net.cpp:242] This network produces output loss
I0409 19:56:54.713989 14973 net.cpp:255] Network initialization done.
I0409 19:56:54.714499 14973 solver.cpp:172] Creating test net (#0) specified by net file: train_val.prototxt
I0409 19:56:54.714534 14973 net.cpp:294] The NetState phase (1) differed from the phase (0) specified by a rule in layer train-data
I0409 19:56:54.714709 14973 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: 2048
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: 2048
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: "fc7.5"
type: "InnerProduct"
bottom: "fc7"
top: "fc7.5"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 2048
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu7.5"
type: "ReLU"
bottom: "fc7.5"
top: "fc7.5"
}
layer {
name: "drop7.5"
type: "Dropout"
bottom: "fc7.5"
top: "fc7.5"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc8"
type: "InnerProduct"
bottom: "fc7.5"
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"
}
I0409 19:56:54.714804 14973 layer_factory.hpp:77] Creating layer val-data
I0409 19:56:54.716434 14973 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/val_db
I0409 19:56:54.716639 14973 net.cpp:84] Creating Layer val-data
I0409 19:56:54.716648 14973 net.cpp:380] val-data -> data
I0409 19:56:54.716656 14973 net.cpp:380] val-data -> label
I0409 19:56:54.716663 14973 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto
I0409 19:56:54.720525 14973 data_layer.cpp:45] output data size: 32,3,227,227
I0409 19:56:54.750816 14973 net.cpp:122] Setting up val-data
I0409 19:56:54.750838 14973 net.cpp:129] Top shape: 32 3 227 227 (4946784)
I0409 19:56:54.750841 14973 net.cpp:129] Top shape: 32 (32)
I0409 19:56:54.750845 14973 net.cpp:137] Memory required for data: 19787264
I0409 19:56:54.750869 14973 layer_factory.hpp:77] Creating layer label_val-data_1_split
I0409 19:56:54.750880 14973 net.cpp:84] Creating Layer label_val-data_1_split
I0409 19:56:54.750885 14973 net.cpp:406] label_val-data_1_split <- label
I0409 19:56:54.750891 14973 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_0
I0409 19:56:54.750900 14973 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_1
I0409 19:56:54.751008 14973 net.cpp:122] Setting up label_val-data_1_split
I0409 19:56:54.751014 14973 net.cpp:129] Top shape: 32 (32)
I0409 19:56:54.751017 14973 net.cpp:129] Top shape: 32 (32)
I0409 19:56:54.751020 14973 net.cpp:137] Memory required for data: 19787520
I0409 19:56:54.751024 14973 layer_factory.hpp:77] Creating layer conv1
I0409 19:56:54.751035 14973 net.cpp:84] Creating Layer conv1
I0409 19:56:54.751039 14973 net.cpp:406] conv1 <- data
I0409 19:56:54.751044 14973 net.cpp:380] conv1 -> conv1
I0409 19:56:54.753171 14973 net.cpp:122] Setting up conv1
I0409 19:56:54.753181 14973 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0409 19:56:54.753185 14973 net.cpp:137] Memory required for data: 56958720
I0409 19:56:54.753196 14973 layer_factory.hpp:77] Creating layer relu1
I0409 19:56:54.753202 14973 net.cpp:84] Creating Layer relu1
I0409 19:56:54.753206 14973 net.cpp:406] relu1 <- conv1
I0409 19:56:54.753211 14973 net.cpp:367] relu1 -> conv1 (in-place)
I0409 19:56:54.753654 14973 net.cpp:122] Setting up relu1
I0409 19:56:54.753662 14973 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0409 19:56:54.753665 14973 net.cpp:137] Memory required for data: 94129920
I0409 19:56:54.753669 14973 layer_factory.hpp:77] Creating layer norm1
I0409 19:56:54.753677 14973 net.cpp:84] Creating Layer norm1
I0409 19:56:54.753681 14973 net.cpp:406] norm1 <- conv1
I0409 19:56:54.753686 14973 net.cpp:380] norm1 -> norm1
I0409 19:56:54.755100 14973 net.cpp:122] Setting up norm1
I0409 19:56:54.755110 14973 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0409 19:56:54.755113 14973 net.cpp:137] Memory required for data: 131301120
I0409 19:56:54.755117 14973 layer_factory.hpp:77] Creating layer pool1
I0409 19:56:54.755125 14973 net.cpp:84] Creating Layer pool1
I0409 19:56:54.755127 14973 net.cpp:406] pool1 <- norm1
I0409 19:56:54.755133 14973 net.cpp:380] pool1 -> pool1
I0409 19:56:54.755162 14973 net.cpp:122] Setting up pool1
I0409 19:56:54.755167 14973 net.cpp:129] Top shape: 32 96 27 27 (2239488)
I0409 19:56:54.755170 14973 net.cpp:137] Memory required for data: 140259072
I0409 19:56:54.755173 14973 layer_factory.hpp:77] Creating layer conv2
I0409 19:56:54.755182 14973 net.cpp:84] Creating Layer conv2
I0409 19:56:54.755184 14973 net.cpp:406] conv2 <- pool1
I0409 19:56:54.755189 14973 net.cpp:380] conv2 -> conv2
I0409 19:56:54.762917 14973 net.cpp:122] Setting up conv2
I0409 19:56:54.762930 14973 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0409 19:56:54.762934 14973 net.cpp:137] Memory required for data: 164146944
I0409 19:56:54.762944 14973 layer_factory.hpp:77] Creating layer relu2
I0409 19:56:54.762953 14973 net.cpp:84] Creating Layer relu2
I0409 19:56:54.762956 14973 net.cpp:406] relu2 <- conv2
I0409 19:56:54.762961 14973 net.cpp:367] relu2 -> conv2 (in-place)
I0409 19:56:54.763459 14973 net.cpp:122] Setting up relu2
I0409 19:56:54.763468 14973 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0409 19:56:54.763473 14973 net.cpp:137] Memory required for data: 188034816
I0409 19:56:54.763475 14973 layer_factory.hpp:77] Creating layer norm2
I0409 19:56:54.763486 14973 net.cpp:84] Creating Layer norm2
I0409 19:56:54.763489 14973 net.cpp:406] norm2 <- conv2
I0409 19:56:54.763495 14973 net.cpp:380] norm2 -> norm2
I0409 19:56:54.763859 14973 net.cpp:122] Setting up norm2
I0409 19:56:54.763867 14973 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0409 19:56:54.763870 14973 net.cpp:137] Memory required for data: 211922688
I0409 19:56:54.763875 14973 layer_factory.hpp:77] Creating layer pool2
I0409 19:56:54.763882 14973 net.cpp:84] Creating Layer pool2
I0409 19:56:54.763885 14973 net.cpp:406] pool2 <- norm2
I0409 19:56:54.763906 14973 net.cpp:380] pool2 -> pool2
I0409 19:56:54.763938 14973 net.cpp:122] Setting up pool2
I0409 19:56:54.763944 14973 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0409 19:56:54.763947 14973 net.cpp:137] Memory required for data: 217460480
I0409 19:56:54.763950 14973 layer_factory.hpp:77] Creating layer conv3
I0409 19:56:54.763959 14973 net.cpp:84] Creating Layer conv3
I0409 19:56:54.763963 14973 net.cpp:406] conv3 <- pool2
I0409 19:56:54.763969 14973 net.cpp:380] conv3 -> conv3
I0409 19:56:54.774989 14973 net.cpp:122] Setting up conv3
I0409 19:56:54.775007 14973 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0409 19:56:54.775009 14973 net.cpp:137] Memory required for data: 225767168
I0409 19:56:54.775020 14973 layer_factory.hpp:77] Creating layer relu3
I0409 19:56:54.775027 14973 net.cpp:84] Creating Layer relu3
I0409 19:56:54.775032 14973 net.cpp:406] relu3 <- conv3
I0409 19:56:54.775038 14973 net.cpp:367] relu3 -> conv3 (in-place)
I0409 19:56:54.775385 14973 net.cpp:122] Setting up relu3
I0409 19:56:54.775393 14973 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0409 19:56:54.775396 14973 net.cpp:137] Memory required for data: 234073856
I0409 19:56:54.775400 14973 layer_factory.hpp:77] Creating layer conv4
I0409 19:56:54.775410 14973 net.cpp:84] Creating Layer conv4
I0409 19:56:54.775414 14973 net.cpp:406] conv4 <- conv3
I0409 19:56:54.775421 14973 net.cpp:380] conv4 -> conv4
I0409 19:56:54.784818 14973 net.cpp:122] Setting up conv4
I0409 19:56:54.784830 14973 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0409 19:56:54.784834 14973 net.cpp:137] Memory required for data: 242380544
I0409 19:56:54.784842 14973 layer_factory.hpp:77] Creating layer relu4
I0409 19:56:54.784850 14973 net.cpp:84] Creating Layer relu4
I0409 19:56:54.784853 14973 net.cpp:406] relu4 <- conv4
I0409 19:56:54.784859 14973 net.cpp:367] relu4 -> conv4 (in-place)
I0409 19:56:54.785346 14973 net.cpp:122] Setting up relu4
I0409 19:56:54.785356 14973 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0409 19:56:54.785358 14973 net.cpp:137] Memory required for data: 250687232
I0409 19:56:54.785362 14973 layer_factory.hpp:77] Creating layer conv5
I0409 19:56:54.785372 14973 net.cpp:84] Creating Layer conv5
I0409 19:56:54.785377 14973 net.cpp:406] conv5 <- conv4
I0409 19:56:54.785383 14973 net.cpp:380] conv5 -> conv5
I0409 19:56:54.793856 14973 net.cpp:122] Setting up conv5
I0409 19:56:54.793872 14973 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0409 19:56:54.793876 14973 net.cpp:137] Memory required for data: 256225024
I0409 19:56:54.793887 14973 layer_factory.hpp:77] Creating layer relu5
I0409 19:56:54.793895 14973 net.cpp:84] Creating Layer relu5
I0409 19:56:54.793898 14973 net.cpp:406] relu5 <- conv5
I0409 19:56:54.793905 14973 net.cpp:367] relu5 -> conv5 (in-place)
I0409 19:56:54.794603 14973 net.cpp:122] Setting up relu5
I0409 19:56:54.794613 14973 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0409 19:56:54.794616 14973 net.cpp:137] Memory required for data: 261762816
I0409 19:56:54.794620 14973 layer_factory.hpp:77] Creating layer pool5
I0409 19:56:54.794631 14973 net.cpp:84] Creating Layer pool5
I0409 19:56:54.794641 14973 net.cpp:406] pool5 <- conv5
I0409 19:56:54.794646 14973 net.cpp:380] pool5 -> pool5
I0409 19:56:54.794685 14973 net.cpp:122] Setting up pool5
I0409 19:56:54.794690 14973 net.cpp:129] Top shape: 32 256 6 6 (294912)
I0409 19:56:54.794694 14973 net.cpp:137] Memory required for data: 262942464
I0409 19:56:54.794697 14973 layer_factory.hpp:77] Creating layer fc6
I0409 19:56:54.794705 14973 net.cpp:84] Creating Layer fc6
I0409 19:56:54.794709 14973 net.cpp:406] fc6 <- pool5
I0409 19:56:54.794714 14973 net.cpp:380] fc6 -> fc6
I0409 19:56:54.971413 14973 net.cpp:122] Setting up fc6
I0409 19:56:54.971432 14973 net.cpp:129] Top shape: 32 2048 (65536)
I0409 19:56:54.971436 14973 net.cpp:137] Memory required for data: 263204608
I0409 19:56:54.971444 14973 layer_factory.hpp:77] Creating layer relu6
I0409 19:56:54.971454 14973 net.cpp:84] Creating Layer relu6
I0409 19:56:54.971459 14973 net.cpp:406] relu6 <- fc6
I0409 19:56:54.971483 14973 net.cpp:367] relu6 -> fc6 (in-place)
I0409 19:56:54.971920 14973 net.cpp:122] Setting up relu6
I0409 19:56:54.971928 14973 net.cpp:129] Top shape: 32 2048 (65536)
I0409 19:56:54.971932 14973 net.cpp:137] Memory required for data: 263466752
I0409 19:56:54.971935 14973 layer_factory.hpp:77] Creating layer drop6
I0409 19:56:54.971942 14973 net.cpp:84] Creating Layer drop6
I0409 19:56:54.971946 14973 net.cpp:406] drop6 <- fc6
I0409 19:56:54.971951 14973 net.cpp:367] drop6 -> fc6 (in-place)
I0409 19:56:54.971977 14973 net.cpp:122] Setting up drop6
I0409 19:56:54.971983 14973 net.cpp:129] Top shape: 32 2048 (65536)
I0409 19:56:54.971987 14973 net.cpp:137] Memory required for data: 263728896
I0409 19:56:54.971989 14973 layer_factory.hpp:77] Creating layer fc7
I0409 19:56:54.971997 14973 net.cpp:84] Creating Layer fc7
I0409 19:56:54.971999 14973 net.cpp:406] fc7 <- fc6
I0409 19:56:54.972007 14973 net.cpp:380] fc7 -> fc7
I0409 19:56:55.011413 14973 net.cpp:122] Setting up fc7
I0409 19:56:55.011432 14973 net.cpp:129] Top shape: 32 2048 (65536)
I0409 19:56:55.011435 14973 net.cpp:137] Memory required for data: 263991040
I0409 19:56:55.011445 14973 layer_factory.hpp:77] Creating layer relu7
I0409 19:56:55.011454 14973 net.cpp:84] Creating Layer relu7
I0409 19:56:55.011458 14973 net.cpp:406] relu7 <- fc7
I0409 19:56:55.011466 14973 net.cpp:367] relu7 -> fc7 (in-place)
I0409 19:56:55.012089 14973 net.cpp:122] Setting up relu7
I0409 19:56:55.012097 14973 net.cpp:129] Top shape: 32 2048 (65536)
I0409 19:56:55.012100 14973 net.cpp:137] Memory required for data: 264253184
I0409 19:56:55.012104 14973 layer_factory.hpp:77] Creating layer drop7
I0409 19:56:55.012110 14973 net.cpp:84] Creating Layer drop7
I0409 19:56:55.012113 14973 net.cpp:406] drop7 <- fc7
I0409 19:56:55.012120 14973 net.cpp:367] drop7 -> fc7 (in-place)
I0409 19:56:55.012143 14973 net.cpp:122] Setting up drop7
I0409 19:56:55.012148 14973 net.cpp:129] Top shape: 32 2048 (65536)
I0409 19:56:55.012151 14973 net.cpp:137] Memory required for data: 264515328
I0409 19:56:55.012154 14973 layer_factory.hpp:77] Creating layer fc7.5
I0409 19:56:55.012162 14973 net.cpp:84] Creating Layer fc7.5
I0409 19:56:55.012166 14973 net.cpp:406] fc7.5 <- fc7
I0409 19:56:55.012171 14973 net.cpp:380] fc7.5 -> fc7.5
I0409 19:56:55.051532 14973 net.cpp:122] Setting up fc7.5
I0409 19:56:55.051551 14973 net.cpp:129] Top shape: 32 2048 (65536)
I0409 19:56:55.051554 14973 net.cpp:137] Memory required for data: 264777472
I0409 19:56:55.051563 14973 layer_factory.hpp:77] Creating layer relu7.5
I0409 19:56:55.051573 14973 net.cpp:84] Creating Layer relu7.5
I0409 19:56:55.051578 14973 net.cpp:406] relu7.5 <- fc7.5
I0409 19:56:55.051584 14973 net.cpp:367] relu7.5 -> fc7.5 (in-place)
I0409 19:56:55.052227 14973 net.cpp:122] Setting up relu7.5
I0409 19:56:55.052234 14973 net.cpp:129] Top shape: 32 2048 (65536)
I0409 19:56:55.052237 14973 net.cpp:137] Memory required for data: 265039616
I0409 19:56:55.052242 14973 layer_factory.hpp:77] Creating layer drop7.5
I0409 19:56:55.052249 14973 net.cpp:84] Creating Layer drop7.5
I0409 19:56:55.052253 14973 net.cpp:406] drop7.5 <- fc7.5
I0409 19:56:55.052259 14973 net.cpp:367] drop7.5 -> fc7.5 (in-place)
I0409 19:56:55.052284 14973 net.cpp:122] Setting up drop7.5
I0409 19:56:55.052287 14973 net.cpp:129] Top shape: 32 2048 (65536)
I0409 19:56:55.052290 14973 net.cpp:137] Memory required for data: 265301760
I0409 19:56:55.052294 14973 layer_factory.hpp:77] Creating layer fc8
I0409 19:56:55.052302 14973 net.cpp:84] Creating Layer fc8
I0409 19:56:55.052305 14973 net.cpp:406] fc8 <- fc7.5
I0409 19:56:55.052310 14973 net.cpp:380] fc8 -> fc8
I0409 19:56:55.056371 14973 net.cpp:122] Setting up fc8
I0409 19:56:55.056381 14973 net.cpp:129] Top shape: 32 196 (6272)
I0409 19:56:55.056385 14973 net.cpp:137] Memory required for data: 265326848
I0409 19:56:55.056396 14973 layer_factory.hpp:77] Creating layer fc8_fc8_0_split
I0409 19:56:55.056401 14973 net.cpp:84] Creating Layer fc8_fc8_0_split
I0409 19:56:55.056406 14973 net.cpp:406] fc8_fc8_0_split <- fc8
I0409 19:56:55.056427 14973 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_0
I0409 19:56:55.056437 14973 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_1
I0409 19:56:55.056468 14973 net.cpp:122] Setting up fc8_fc8_0_split
I0409 19:56:55.056473 14973 net.cpp:129] Top shape: 32 196 (6272)
I0409 19:56:55.056476 14973 net.cpp:129] Top shape: 32 196 (6272)
I0409 19:56:55.056479 14973 net.cpp:137] Memory required for data: 265377024
I0409 19:56:55.056483 14973 layer_factory.hpp:77] Creating layer accuracy
I0409 19:56:55.056489 14973 net.cpp:84] Creating Layer accuracy
I0409 19:56:55.056493 14973 net.cpp:406] accuracy <- fc8_fc8_0_split_0
I0409 19:56:55.056496 14973 net.cpp:406] accuracy <- label_val-data_1_split_0
I0409 19:56:55.056501 14973 net.cpp:380] accuracy -> accuracy
I0409 19:56:55.056509 14973 net.cpp:122] Setting up accuracy
I0409 19:56:55.056514 14973 net.cpp:129] Top shape: (1)
I0409 19:56:55.056515 14973 net.cpp:137] Memory required for data: 265377028
I0409 19:56:55.056519 14973 layer_factory.hpp:77] Creating layer loss
I0409 19:56:55.056525 14973 net.cpp:84] Creating Layer loss
I0409 19:56:55.056529 14973 net.cpp:406] loss <- fc8_fc8_0_split_1
I0409 19:56:55.056532 14973 net.cpp:406] loss <- label_val-data_1_split_1
I0409 19:56:55.056537 14973 net.cpp:380] loss -> loss
I0409 19:56:55.056545 14973 layer_factory.hpp:77] Creating layer loss
I0409 19:56:55.058184 14973 net.cpp:122] Setting up loss
I0409 19:56:55.058194 14973 net.cpp:129] Top shape: (1)
I0409 19:56:55.058197 14973 net.cpp:132]     with loss weight 1
I0409 19:56:55.058207 14973 net.cpp:137] Memory required for data: 265377032
I0409 19:56:55.058212 14973 net.cpp:198] loss needs backward computation.
I0409 19:56:55.058216 14973 net.cpp:200] accuracy does not need backward computation.
I0409 19:56:55.058220 14973 net.cpp:198] fc8_fc8_0_split needs backward computation.
I0409 19:56:55.058224 14973 net.cpp:198] fc8 needs backward computation.
I0409 19:56:55.058228 14973 net.cpp:198] drop7.5 needs backward computation.
I0409 19:56:55.058230 14973 net.cpp:198] relu7.5 needs backward computation.
I0409 19:56:55.058233 14973 net.cpp:198] fc7.5 needs backward computation.
I0409 19:56:55.058238 14973 net.cpp:198] drop7 needs backward computation.
I0409 19:56:55.058240 14973 net.cpp:198] relu7 needs backward computation.
I0409 19:56:55.058244 14973 net.cpp:198] fc7 needs backward computation.
I0409 19:56:55.058248 14973 net.cpp:198] drop6 needs backward computation.
I0409 19:56:55.058250 14973 net.cpp:198] relu6 needs backward computation.
I0409 19:56:55.058254 14973 net.cpp:198] fc6 needs backward computation.
I0409 19:56:55.058257 14973 net.cpp:198] pool5 needs backward computation.
I0409 19:56:55.058261 14973 net.cpp:198] relu5 needs backward computation.
I0409 19:56:55.058264 14973 net.cpp:198] conv5 needs backward computation.
I0409 19:56:55.058267 14973 net.cpp:198] relu4 needs backward computation.
I0409 19:56:55.058271 14973 net.cpp:198] conv4 needs backward computation.
I0409 19:56:55.058274 14973 net.cpp:198] relu3 needs backward computation.
I0409 19:56:55.058277 14973 net.cpp:198] conv3 needs backward computation.
I0409 19:56:55.058281 14973 net.cpp:198] pool2 needs backward computation.
I0409 19:56:55.058284 14973 net.cpp:198] norm2 needs backward computation.
I0409 19:56:55.058287 14973 net.cpp:198] relu2 needs backward computation.
I0409 19:56:55.058291 14973 net.cpp:198] conv2 needs backward computation.
I0409 19:56:55.058295 14973 net.cpp:198] pool1 needs backward computation.
I0409 19:56:55.058298 14973 net.cpp:198] norm1 needs backward computation.
I0409 19:56:55.058301 14973 net.cpp:198] relu1 needs backward computation.
I0409 19:56:55.058305 14973 net.cpp:198] conv1 needs backward computation.
I0409 19:56:55.058308 14973 net.cpp:200] label_val-data_1_split does not need backward computation.
I0409 19:56:55.058312 14973 net.cpp:200] val-data does not need backward computation.
I0409 19:56:55.058315 14973 net.cpp:242] This network produces output accuracy
I0409 19:56:55.058320 14973 net.cpp:242] This network produces output loss
I0409 19:56:55.058348 14973 net.cpp:255] Network initialization done.
I0409 19:56:55.058452 14973 solver.cpp:56] Solver scaffolding done.
I0409 19:56:55.058940 14973 caffe.cpp:248] Starting Optimization
I0409 19:56:55.058949 14973 solver.cpp:272] Solving
I0409 19:56:55.058952 14973 solver.cpp:273] Learning Rate Policy: exp
I0409 19:56:55.062439 14973 solver.cpp:330] Iteration 0, Testing net (#0)
I0409 19:56:55.062449 14973 net.cpp:676] Ignoring source layer train-data
I0409 19:56:55.114454 14973 blocking_queue.cpp:49] Waiting for data
I0409 19:56:59.497351 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 19:56:59.541640 14973 solver.cpp:397]     Test net output #0: accuracy = 0.00428922
I0409 19:56:59.541678 14973 solver.cpp:397]     Test net output #1: loss = 5.27792 (* 1 = 5.27792 loss)
I0409 19:56:59.631487 14973 solver.cpp:218] Iteration 0 (-9.61941e+12 iter/s, 4.57235s/12 iters), loss = 5.27253
I0409 19:56:59.632997 14973 solver.cpp:237]     Train net output #0: loss = 5.27253 (* 1 = 5.27253 loss)
I0409 19:56:59.633020 14973 sgd_solver.cpp:105] Iteration 0, lr = 0.01
I0409 19:57:03.610705 14973 solver.cpp:218] Iteration 12 (3.01692 iter/s, 3.97756s/12 iters), loss = 5.28886
I0409 19:57:03.610747 14973 solver.cpp:237]     Train net output #0: loss = 5.28886 (* 1 = 5.28886 loss)
I0409 19:57:03.610757 14973 sgd_solver.cpp:105] Iteration 12, lr = 0.00997626
I0409 19:57:08.551846 14973 solver.cpp:218] Iteration 24 (2.42869 iter/s, 4.94093s/12 iters), loss = 5.28111
I0409 19:57:08.551882 14973 solver.cpp:237]     Train net output #0: loss = 5.28111 (* 1 = 5.28111 loss)
I0409 19:57:08.551892 14973 sgd_solver.cpp:105] Iteration 24, lr = 0.00995257
I0409 19:57:13.402068 14973 solver.cpp:218] Iteration 36 (2.47422 iter/s, 4.85001s/12 iters), loss = 5.27293
I0409 19:57:13.402123 14973 solver.cpp:237]     Train net output #0: loss = 5.27293 (* 1 = 5.27293 loss)
I0409 19:57:13.402135 14973 sgd_solver.cpp:105] Iteration 36, lr = 0.00992894
I0409 19:57:18.496943 14973 solver.cpp:218] Iteration 48 (2.35541 iter/s, 5.09465s/12 iters), loss = 5.29169
I0409 19:57:18.496992 14973 solver.cpp:237]     Train net output #0: loss = 5.29169 (* 1 = 5.29169 loss)
I0409 19:57:18.497005 14973 sgd_solver.cpp:105] Iteration 48, lr = 0.00990537
I0409 19:57:23.400864 14973 solver.cpp:218] Iteration 60 (2.44713 iter/s, 4.90371s/12 iters), loss = 5.28648
I0409 19:57:23.400910 14973 solver.cpp:237]     Train net output #0: loss = 5.28648 (* 1 = 5.28648 loss)
I0409 19:57:23.400923 14973 sgd_solver.cpp:105] Iteration 60, lr = 0.00988185
I0409 19:57:28.342644 14973 solver.cpp:218] Iteration 72 (2.42838 iter/s, 4.94156s/12 iters), loss = 5.2917
I0409 19:57:28.342767 14973 solver.cpp:237]     Train net output #0: loss = 5.2917 (* 1 = 5.2917 loss)
I0409 19:57:28.342783 14973 sgd_solver.cpp:105] Iteration 72, lr = 0.00985839
I0409 19:57:33.251125 14973 solver.cpp:218] Iteration 84 (2.44489 iter/s, 4.9082s/12 iters), loss = 5.28326
I0409 19:57:33.251161 14973 solver.cpp:237]     Train net output #0: loss = 5.28326 (* 1 = 5.28326 loss)
I0409 19:57:33.251169 14973 sgd_solver.cpp:105] Iteration 84, lr = 0.00983498
I0409 19:57:38.211352 14973 solver.cpp:218] Iteration 96 (2.41935 iter/s, 4.96002s/12 iters), loss = 5.31002
I0409 19:57:38.211402 14973 solver.cpp:237]     Train net output #0: loss = 5.31002 (* 1 = 5.31002 loss)
I0409 19:57:38.211414 14973 sgd_solver.cpp:105] Iteration 96, lr = 0.00981163
I0409 19:57:39.924098 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 19:57:40.231770 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_102.caffemodel
I0409 19:57:41.809195 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_102.solverstate
I0409 19:57:43.019388 14973 solver.cpp:330] Iteration 102, Testing net (#0)
I0409 19:57:43.019416 14973 net.cpp:676] Ignoring source layer train-data
I0409 19:57:47.385898 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 19:57:47.462251 14973 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0409 19:57:47.462297 14973 solver.cpp:397]     Test net output #1: loss = 5.28377 (* 1 = 5.28377 loss)
I0409 19:57:49.279136 14973 solver.cpp:218] Iteration 108 (1.08427 iter/s, 11.0674s/12 iters), loss = 5.2913
I0409 19:57:49.279187 14973 solver.cpp:237]     Train net output #0: loss = 5.2913 (* 1 = 5.2913 loss)
I0409 19:57:49.279198 14973 sgd_solver.cpp:105] Iteration 108, lr = 0.00978834
I0409 19:57:54.207916 14973 solver.cpp:218] Iteration 120 (2.43479 iter/s, 4.92856s/12 iters), loss = 5.28
I0409 19:57:54.207960 14973 solver.cpp:237]     Train net output #0: loss = 5.28 (* 1 = 5.28 loss)
I0409 19:57:54.207971 14973 sgd_solver.cpp:105] Iteration 120, lr = 0.0097651
I0409 19:57:59.128409 14973 solver.cpp:218] Iteration 132 (2.43889 iter/s, 4.92028s/12 iters), loss = 5.24697
I0409 19:57:59.128566 14973 solver.cpp:237]     Train net output #0: loss = 5.24697 (* 1 = 5.24697 loss)
I0409 19:57:59.128578 14973 sgd_solver.cpp:105] Iteration 132, lr = 0.00974192
I0409 19:58:04.006321 14973 solver.cpp:218] Iteration 144 (2.46023 iter/s, 4.87759s/12 iters), loss = 5.30857
I0409 19:58:04.006366 14973 solver.cpp:237]     Train net output #0: loss = 5.30857 (* 1 = 5.30857 loss)
I0409 19:58:04.006376 14973 sgd_solver.cpp:105] Iteration 144, lr = 0.00971879
I0409 19:58:08.953208 14973 solver.cpp:218] Iteration 156 (2.42587 iter/s, 4.94667s/12 iters), loss = 5.25782
I0409 19:58:08.953251 14973 solver.cpp:237]     Train net output #0: loss = 5.25782 (* 1 = 5.25782 loss)
I0409 19:58:08.953260 14973 sgd_solver.cpp:105] Iteration 156, lr = 0.00969571
I0409 19:58:13.913789 14973 solver.cpp:218] Iteration 168 (2.41918 iter/s, 4.96036s/12 iters), loss = 5.27825
I0409 19:58:13.913830 14973 solver.cpp:237]     Train net output #0: loss = 5.27825 (* 1 = 5.27825 loss)
I0409 19:58:13.913838 14973 sgd_solver.cpp:105] Iteration 168, lr = 0.00967269
I0409 19:58:18.881568 14973 solver.cpp:218] Iteration 180 (2.41567 iter/s, 4.96757s/12 iters), loss = 5.26555
I0409 19:58:18.881606 14973 solver.cpp:237]     Train net output #0: loss = 5.26555 (* 1 = 5.26555 loss)
I0409 19:58:18.881616 14973 sgd_solver.cpp:105] Iteration 180, lr = 0.00964973
I0409 19:58:23.820084 14973 solver.cpp:218] Iteration 192 (2.42999 iter/s, 4.9383s/12 iters), loss = 5.28502
I0409 19:58:23.820133 14973 solver.cpp:237]     Train net output #0: loss = 5.28502 (* 1 = 5.28502 loss)
I0409 19:58:23.820144 14973 sgd_solver.cpp:105] Iteration 192, lr = 0.00962682
I0409 19:58:27.629952 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 19:58:28.305687 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_204.caffemodel
I0409 19:58:30.397531 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_204.solverstate
I0409 19:58:32.674646 14973 solver.cpp:330] Iteration 204, Testing net (#0)
I0409 19:58:32.674672 14973 net.cpp:676] Ignoring source layer train-data
I0409 19:58:36.993821 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 19:58:37.116370 14973 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0409 19:58:37.116418 14973 solver.cpp:397]     Test net output #1: loss = 5.2858 (* 1 = 5.2858 loss)
I0409 19:58:37.202718 14973 solver.cpp:218] Iteration 204 (0.896718 iter/s, 13.3821s/12 iters), loss = 5.26668
I0409 19:58:37.202790 14973 solver.cpp:237]     Train net output #0: loss = 5.26668 (* 1 = 5.26668 loss)
I0409 19:58:37.202805 14973 sgd_solver.cpp:105] Iteration 204, lr = 0.00960396
I0409 19:58:41.486235 14973 solver.cpp:218] Iteration 216 (2.80158 iter/s, 4.2833s/12 iters), loss = 5.27662
I0409 19:58:41.486294 14973 solver.cpp:237]     Train net output #0: loss = 5.27662 (* 1 = 5.27662 loss)
I0409 19:58:41.486307 14973 sgd_solver.cpp:105] Iteration 216, lr = 0.00958116
I0409 19:58:46.368399 14973 solver.cpp:218] Iteration 228 (2.45804 iter/s, 4.88193s/12 iters), loss = 5.26878
I0409 19:58:46.368458 14973 solver.cpp:237]     Train net output #0: loss = 5.26878 (* 1 = 5.26878 loss)
I0409 19:58:46.368472 14973 sgd_solver.cpp:105] Iteration 228, lr = 0.00955841
I0409 19:58:51.225842 14973 solver.cpp:218] Iteration 240 (2.47055 iter/s, 4.85722s/12 iters), loss = 5.29482
I0409 19:58:51.225881 14973 solver.cpp:237]     Train net output #0: loss = 5.29482 (* 1 = 5.29482 loss)
I0409 19:58:51.225889 14973 sgd_solver.cpp:105] Iteration 240, lr = 0.00953572
I0409 19:58:56.138231 14973 solver.cpp:218] Iteration 252 (2.44291 iter/s, 4.91218s/12 iters), loss = 5.27358
I0409 19:58:56.138280 14973 solver.cpp:237]     Train net output #0: loss = 5.27358 (* 1 = 5.27358 loss)
I0409 19:58:56.138293 14973 sgd_solver.cpp:105] Iteration 252, lr = 0.00951308
I0409 19:59:01.026167 14973 solver.cpp:218] Iteration 264 (2.45514 iter/s, 4.88771s/12 iters), loss = 5.27121
I0409 19:59:01.026338 14973 solver.cpp:237]     Train net output #0: loss = 5.27121 (* 1 = 5.27121 loss)
I0409 19:59:01.026358 14973 sgd_solver.cpp:105] Iteration 264, lr = 0.00949049
I0409 19:59:05.957600 14973 solver.cpp:218] Iteration 276 (2.43354 iter/s, 4.9311s/12 iters), loss = 5.30028
I0409 19:59:05.957648 14973 solver.cpp:237]     Train net output #0: loss = 5.30028 (* 1 = 5.30028 loss)
I0409 19:59:05.957659 14973 sgd_solver.cpp:105] Iteration 276, lr = 0.00946796
I0409 19:59:10.891045 14973 solver.cpp:218] Iteration 288 (2.43249 iter/s, 4.93322s/12 iters), loss = 5.28076
I0409 19:59:10.891098 14973 solver.cpp:237]     Train net output #0: loss = 5.28076 (* 1 = 5.28076 loss)
I0409 19:59:10.891109 14973 sgd_solver.cpp:105] Iteration 288, lr = 0.00944548
I0409 19:59:15.808277 14973 solver.cpp:218] Iteration 300 (2.44051 iter/s, 4.91701s/12 iters), loss = 5.28636
I0409 19:59:15.808323 14973 solver.cpp:237]     Train net output #0: loss = 5.28636 (* 1 = 5.28636 loss)
I0409 19:59:15.808334 14973 sgd_solver.cpp:105] Iteration 300, lr = 0.00942305
I0409 19:59:16.797437 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 19:59:17.831272 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_306.caffemodel
I0409 19:59:19.380293 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_306.solverstate
I0409 19:59:20.602465 14973 solver.cpp:330] Iteration 306, Testing net (#0)
I0409 19:59:20.602494 14973 net.cpp:676] Ignoring source layer train-data
I0409 19:59:24.963207 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 19:59:25.121765 14973 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0409 19:59:25.121814 14973 solver.cpp:397]     Test net output #1: loss = 5.28587 (* 1 = 5.28587 loss)
I0409 19:59:26.951578 14973 solver.cpp:218] Iteration 312 (1.07692 iter/s, 11.1429s/12 iters), loss = 5.28307
I0409 19:59:26.951624 14973 solver.cpp:237]     Train net output #0: loss = 5.28307 (* 1 = 5.28307 loss)
I0409 19:59:26.951635 14973 sgd_solver.cpp:105] Iteration 312, lr = 0.00940068
I0409 19:59:31.879070 14973 solver.cpp:218] Iteration 324 (2.43543 iter/s, 4.92727s/12 iters), loss = 5.25056
I0409 19:59:31.879161 14973 solver.cpp:237]     Train net output #0: loss = 5.25056 (* 1 = 5.25056 loss)
I0409 19:59:31.879174 14973 sgd_solver.cpp:105] Iteration 324, lr = 0.00937836
I0409 19:59:36.840584 14973 solver.cpp:218] Iteration 336 (2.41875 iter/s, 4.96125s/12 iters), loss = 5.26147
I0409 19:59:36.840628 14973 solver.cpp:237]     Train net output #0: loss = 5.26147 (* 1 = 5.26147 loss)
I0409 19:59:36.840637 14973 sgd_solver.cpp:105] Iteration 336, lr = 0.0093561
I0409 19:59:41.829353 14973 solver.cpp:218] Iteration 348 (2.40551 iter/s, 4.98855s/12 iters), loss = 5.27478
I0409 19:59:41.829391 14973 solver.cpp:237]     Train net output #0: loss = 5.27478 (* 1 = 5.27478 loss)
I0409 19:59:41.829401 14973 sgd_solver.cpp:105] Iteration 348, lr = 0.00933388
I0409 19:59:46.786610 14973 solver.cpp:218] Iteration 360 (2.4208 iter/s, 4.95705s/12 iters), loss = 5.28919
I0409 19:59:46.786646 14973 solver.cpp:237]     Train net output #0: loss = 5.28919 (* 1 = 5.28919 loss)
I0409 19:59:46.786655 14973 sgd_solver.cpp:105] Iteration 360, lr = 0.00931172
I0409 19:59:51.723973 14973 solver.cpp:218] Iteration 372 (2.43055 iter/s, 4.93715s/12 iters), loss = 5.27441
I0409 19:59:51.724020 14973 solver.cpp:237]     Train net output #0: loss = 5.27441 (* 1 = 5.27441 loss)
I0409 19:59:51.724030 14973 sgd_solver.cpp:105] Iteration 372, lr = 0.00928961
I0409 19:59:56.663478 14973 solver.cpp:218] Iteration 384 (2.4295 iter/s, 4.93928s/12 iters), loss = 5.28379
I0409 19:59:56.663529 14973 solver.cpp:237]     Train net output #0: loss = 5.28379 (* 1 = 5.28379 loss)
I0409 19:59:56.663542 14973 sgd_solver.cpp:105] Iteration 384, lr = 0.00926756
I0409 20:00:01.612993 14973 solver.cpp:218] Iteration 396 (2.42459 iter/s, 4.94929s/12 iters), loss = 5.27597
I0409 20:00:01.613041 14973 solver.cpp:237]     Train net output #0: loss = 5.27597 (* 1 = 5.27597 loss)
I0409 20:00:01.613052 14973 sgd_solver.cpp:105] Iteration 396, lr = 0.00924556
I0409 20:00:04.721246 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:00:06.114338 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_408.caffemodel
I0409 20:00:07.697593 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_408.solverstate
I0409 20:00:08.903945 14973 solver.cpp:330] Iteration 408, Testing net (#0)
I0409 20:00:08.903972 14973 net.cpp:676] Ignoring source layer train-data
I0409 20:00:13.264314 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:00:13.467507 14973 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0409 20:00:13.467556 14973 solver.cpp:397]     Test net output #1: loss = 5.2867 (* 1 = 5.2867 loss)
I0409 20:00:13.554463 14973 solver.cpp:218] Iteration 408 (1.00494 iter/s, 11.941s/12 iters), loss = 5.28015
I0409 20:00:13.554519 14973 solver.cpp:237]     Train net output #0: loss = 5.28015 (* 1 = 5.28015 loss)
I0409 20:00:13.554530 14973 sgd_solver.cpp:105] Iteration 408, lr = 0.00922361
I0409 20:00:17.833242 14973 solver.cpp:218] Iteration 420 (2.80468 iter/s, 4.27857s/12 iters), loss = 5.27781
I0409 20:00:17.833284 14973 solver.cpp:237]     Train net output #0: loss = 5.27781 (* 1 = 5.27781 loss)
I0409 20:00:17.833293 14973 sgd_solver.cpp:105] Iteration 420, lr = 0.00920171
I0409 20:00:22.776582 14973 solver.cpp:218] Iteration 432 (2.42762 iter/s, 4.94312s/12 iters), loss = 5.26672
I0409 20:00:22.776626 14973 solver.cpp:237]     Train net output #0: loss = 5.26672 (* 1 = 5.26672 loss)
I0409 20:00:22.776635 14973 sgd_solver.cpp:105] Iteration 432, lr = 0.00917986
I0409 20:00:27.776326 14973 solver.cpp:218] Iteration 444 (2.40023 iter/s, 4.99952s/12 iters), loss = 5.2931
I0409 20:00:27.776366 14973 solver.cpp:237]     Train net output #0: loss = 5.2931 (* 1 = 5.2931 loss)
I0409 20:00:27.776376 14973 sgd_solver.cpp:105] Iteration 444, lr = 0.00915807
I0409 20:00:32.764036 14973 solver.cpp:218] Iteration 456 (2.40602 iter/s, 4.98749s/12 iters), loss = 5.28783
I0409 20:00:32.764102 14973 solver.cpp:237]     Train net output #0: loss = 5.28783 (* 1 = 5.28783 loss)
I0409 20:00:32.764120 14973 sgd_solver.cpp:105] Iteration 456, lr = 0.00913632
I0409 20:00:37.691562 14973 solver.cpp:218] Iteration 468 (2.43542 iter/s, 4.92729s/12 iters), loss = 5.29183
I0409 20:00:37.691666 14973 solver.cpp:237]     Train net output #0: loss = 5.29183 (* 1 = 5.29183 loss)
I0409 20:00:37.691677 14973 sgd_solver.cpp:105] Iteration 468, lr = 0.00911463
I0409 20:00:42.703860 14973 solver.cpp:218] Iteration 480 (2.39425 iter/s, 5.01201s/12 iters), loss = 5.26579
I0409 20:00:42.703907 14973 solver.cpp:237]     Train net output #0: loss = 5.26579 (* 1 = 5.26579 loss)
I0409 20:00:42.703915 14973 sgd_solver.cpp:105] Iteration 480, lr = 0.00909299
I0409 20:00:47.781985 14973 solver.cpp:218] Iteration 492 (2.36319 iter/s, 5.07787s/12 iters), loss = 5.29667
I0409 20:00:47.782042 14973 solver.cpp:237]     Train net output #0: loss = 5.29667 (* 1 = 5.29667 loss)
I0409 20:00:47.782054 14973 sgd_solver.cpp:105] Iteration 492, lr = 0.0090714
I0409 20:00:52.957239 14973 solver.cpp:218] Iteration 504 (2.31883 iter/s, 5.17502s/12 iters), loss = 5.26956
I0409 20:00:52.957280 14973 solver.cpp:237]     Train net output #0: loss = 5.26956 (* 1 = 5.26956 loss)
I0409 20:00:52.957289 14973 sgd_solver.cpp:105] Iteration 504, lr = 0.00904986
I0409 20:00:53.215728 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:00:54.940971 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_510.caffemodel
I0409 20:00:56.475788 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_510.solverstate
I0409 20:00:57.676679 14973 solver.cpp:330] Iteration 510, Testing net (#0)
I0409 20:00:57.676703 14973 net.cpp:676] Ignoring source layer train-data
I0409 20:01:01.847218 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:01:02.085325 14973 solver.cpp:397]     Test net output #0: accuracy = 0.00612745
I0409 20:01:02.085368 14973 solver.cpp:397]     Test net output #1: loss = 5.28486 (* 1 = 5.28486 loss)
I0409 20:01:04.042073 14973 solver.cpp:218] Iteration 516 (1.0826 iter/s, 11.0844s/12 iters), loss = 5.27861
I0409 20:01:04.042120 14973 solver.cpp:237]     Train net output #0: loss = 5.27861 (* 1 = 5.27861 loss)
I0409 20:01:04.042131 14973 sgd_solver.cpp:105] Iteration 516, lr = 0.00902838
I0409 20:01:09.032759 14973 solver.cpp:218] Iteration 528 (2.40459 iter/s, 4.99045s/12 iters), loss = 5.26836
I0409 20:01:09.032889 14973 solver.cpp:237]     Train net output #0: loss = 5.26836 (* 1 = 5.26836 loss)
I0409 20:01:09.032900 14973 sgd_solver.cpp:105] Iteration 528, lr = 0.00900694
I0409 20:01:13.982901 14973 solver.cpp:218] Iteration 540 (2.42432 iter/s, 4.94984s/12 iters), loss = 5.27357
I0409 20:01:13.982954 14973 solver.cpp:237]     Train net output #0: loss = 5.27357 (* 1 = 5.27357 loss)
I0409 20:01:13.982966 14973 sgd_solver.cpp:105] Iteration 540, lr = 0.00898556
I0409 20:01:18.925354 14973 solver.cpp:218] Iteration 552 (2.42806 iter/s, 4.94222s/12 iters), loss = 5.27145
I0409 20:01:18.925411 14973 solver.cpp:237]     Train net output #0: loss = 5.27145 (* 1 = 5.27145 loss)
I0409 20:01:18.925424 14973 sgd_solver.cpp:105] Iteration 552, lr = 0.00896423
I0409 20:01:23.884348 14973 solver.cpp:218] Iteration 564 (2.41996 iter/s, 4.95876s/12 iters), loss = 5.26029
I0409 20:01:23.884402 14973 solver.cpp:237]     Train net output #0: loss = 5.26029 (* 1 = 5.26029 loss)
I0409 20:01:23.884413 14973 sgd_solver.cpp:105] Iteration 564, lr = 0.00894294
I0409 20:01:28.777251 14973 solver.cpp:218] Iteration 576 (2.45265 iter/s, 4.89267s/12 iters), loss = 5.27424
I0409 20:01:28.777303 14973 solver.cpp:237]     Train net output #0: loss = 5.27424 (* 1 = 5.27424 loss)
I0409 20:01:28.777318 14973 sgd_solver.cpp:105] Iteration 576, lr = 0.00892171
I0409 20:01:33.670388 14973 solver.cpp:218] Iteration 588 (2.45253 iter/s, 4.89291s/12 iters), loss = 5.25765
I0409 20:01:33.670444 14973 solver.cpp:237]     Train net output #0: loss = 5.25765 (* 1 = 5.25765 loss)
I0409 20:01:33.670454 14973 sgd_solver.cpp:105] Iteration 588, lr = 0.00890053
I0409 20:01:38.620959 14973 solver.cpp:218] Iteration 600 (2.42408 iter/s, 4.95033s/12 iters), loss = 5.25455
I0409 20:01:38.621014 14973 solver.cpp:237]     Train net output #0: loss = 5.25455 (* 1 = 5.25455 loss)
I0409 20:01:38.621026 14973 sgd_solver.cpp:105] Iteration 600, lr = 0.0088794
I0409 20:01:40.970142 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:01:43.036005 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_612.caffemodel
I0409 20:01:44.614686 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_612.solverstate
I0409 20:01:45.814258 14973 solver.cpp:330] Iteration 612, Testing net (#0)
I0409 20:01:45.814280 14973 net.cpp:676] Ignoring source layer train-data
I0409 20:01:50.000419 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:01:50.284759 14973 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0409 20:01:50.284806 14973 solver.cpp:397]     Test net output #1: loss = 5.27779 (* 1 = 5.27779 loss)
I0409 20:01:50.371800 14973 solver.cpp:218] Iteration 612 (1.02124 iter/s, 11.7504s/12 iters), loss = 5.27076
I0409 20:01:50.371852 14973 solver.cpp:237]     Train net output #0: loss = 5.27076 (* 1 = 5.27076 loss)
I0409 20:01:50.371863 14973 sgd_solver.cpp:105] Iteration 612, lr = 0.00885831
I0409 20:01:54.602674 14973 solver.cpp:218] Iteration 624 (2.83643 iter/s, 4.23067s/12 iters), loss = 5.28228
I0409 20:01:54.602715 14973 solver.cpp:237]     Train net output #0: loss = 5.28228 (* 1 = 5.28228 loss)
I0409 20:01:54.602723 14973 sgd_solver.cpp:105] Iteration 624, lr = 0.00883728
I0409 20:01:59.533246 14973 solver.cpp:218] Iteration 636 (2.4339 iter/s, 4.93036s/12 iters), loss = 5.26589
I0409 20:01:59.533290 14973 solver.cpp:237]     Train net output #0: loss = 5.26589 (* 1 = 5.26589 loss)
I0409 20:01:59.533299 14973 sgd_solver.cpp:105] Iteration 636, lr = 0.0088163
I0409 20:02:04.455359 14973 solver.cpp:218] Iteration 648 (2.43809 iter/s, 4.92189s/12 iters), loss = 5.27027
I0409 20:02:04.455410 14973 solver.cpp:237]     Train net output #0: loss = 5.27027 (* 1 = 5.27027 loss)
I0409 20:02:04.455420 14973 sgd_solver.cpp:105] Iteration 648, lr = 0.00879537
I0409 20:02:09.378993 14973 solver.cpp:218] Iteration 660 (2.43734 iter/s, 4.92339s/12 iters), loss = 5.22932
I0409 20:02:09.379053 14973 solver.cpp:237]     Train net output #0: loss = 5.22932 (* 1 = 5.22932 loss)
I0409 20:02:09.379065 14973 sgd_solver.cpp:105] Iteration 660, lr = 0.00877449
I0409 20:02:14.393487 14973 solver.cpp:218] Iteration 672 (2.39318 iter/s, 5.01426s/12 iters), loss = 5.21204
I0409 20:02:14.395651 14973 solver.cpp:237]     Train net output #0: loss = 5.21204 (* 1 = 5.21204 loss)
I0409 20:02:14.395664 14973 sgd_solver.cpp:105] Iteration 672, lr = 0.00875366
I0409 20:02:19.300305 14973 solver.cpp:218] Iteration 684 (2.44674 iter/s, 4.90449s/12 iters), loss = 5.09705
I0409 20:02:19.300346 14973 solver.cpp:237]     Train net output #0: loss = 5.09705 (* 1 = 5.09705 loss)
I0409 20:02:19.300354 14973 sgd_solver.cpp:105] Iteration 684, lr = 0.00873287
I0409 20:02:19.660981 14973 blocking_queue.cpp:49] Waiting for data
I0409 20:02:24.242095 14973 solver.cpp:218] Iteration 696 (2.42838 iter/s, 4.94157s/12 iters), loss = 5.15406
I0409 20:02:24.242146 14973 solver.cpp:237]     Train net output #0: loss = 5.15406 (* 1 = 5.15406 loss)
I0409 20:02:24.242154 14973 sgd_solver.cpp:105] Iteration 696, lr = 0.00871214
I0409 20:02:28.802198 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:02:29.174085 14973 solver.cpp:218] Iteration 708 (2.4332 iter/s, 4.93177s/12 iters), loss = 5.22054
I0409 20:02:29.174127 14973 solver.cpp:237]     Train net output #0: loss = 5.22054 (* 1 = 5.22054 loss)
I0409 20:02:29.174136 14973 sgd_solver.cpp:105] Iteration 708, lr = 0.00869145
I0409 20:02:31.214253 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_714.caffemodel
I0409 20:02:34.606223 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_714.solverstate
I0409 20:02:37.976619 14973 solver.cpp:330] Iteration 714, Testing net (#0)
I0409 20:02:37.976642 14973 net.cpp:676] Ignoring source layer train-data
I0409 20:02:42.271840 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:02:42.596597 14973 solver.cpp:397]     Test net output #0: accuracy = 0.00796569
I0409 20:02:42.596632 14973 solver.cpp:397]     Test net output #1: loss = 5.18088 (* 1 = 5.18088 loss)
I0409 20:02:44.561419 14973 solver.cpp:218] Iteration 720 (0.77989 iter/s, 15.3868s/12 iters), loss = 5.20402
I0409 20:02:44.561532 14973 solver.cpp:237]     Train net output #0: loss = 5.20402 (* 1 = 5.20402 loss)
I0409 20:02:44.561544 14973 sgd_solver.cpp:105] Iteration 720, lr = 0.00867082
I0409 20:02:49.560170 14973 solver.cpp:218] Iteration 732 (2.40074 iter/s, 4.99847s/12 iters), loss = 5.1319
I0409 20:02:49.560212 14973 solver.cpp:237]     Train net output #0: loss = 5.1319 (* 1 = 5.1319 loss)
I0409 20:02:49.560221 14973 sgd_solver.cpp:105] Iteration 732, lr = 0.00865023
I0409 20:02:54.464093 14973 solver.cpp:218] Iteration 744 (2.44713 iter/s, 4.90371s/12 iters), loss = 5.16156
I0409 20:02:54.464135 14973 solver.cpp:237]     Train net output #0: loss = 5.16156 (* 1 = 5.16156 loss)
I0409 20:02:54.464145 14973 sgd_solver.cpp:105] Iteration 744, lr = 0.0086297
I0409 20:02:59.391870 14973 solver.cpp:218] Iteration 756 (2.43528 iter/s, 4.92756s/12 iters), loss = 5.16908
I0409 20:02:59.391921 14973 solver.cpp:237]     Train net output #0: loss = 5.16908 (* 1 = 5.16908 loss)
I0409 20:02:59.391932 14973 sgd_solver.cpp:105] Iteration 756, lr = 0.00860921
I0409 20:03:04.295233 14973 solver.cpp:218] Iteration 768 (2.44741 iter/s, 4.90314s/12 iters), loss = 5.18216
I0409 20:03:04.295282 14973 solver.cpp:237]     Train net output #0: loss = 5.18216 (* 1 = 5.18216 loss)
I0409 20:03:04.295293 14973 sgd_solver.cpp:105] Iteration 768, lr = 0.00858877
I0409 20:03:09.237680 14973 solver.cpp:218] Iteration 780 (2.42806 iter/s, 4.94222s/12 iters), loss = 5.19314
I0409 20:03:09.237732 14973 solver.cpp:237]     Train net output #0: loss = 5.19314 (* 1 = 5.19314 loss)
I0409 20:03:09.237744 14973 sgd_solver.cpp:105] Iteration 780, lr = 0.00856838
I0409 20:03:14.141361 14973 solver.cpp:218] Iteration 792 (2.44725 iter/s, 4.90346s/12 iters), loss = 5.11308
I0409 20:03:14.141402 14973 solver.cpp:237]     Train net output #0: loss = 5.11308 (* 1 = 5.11308 loss)
I0409 20:03:14.141410 14973 sgd_solver.cpp:105] Iteration 792, lr = 0.00854803
I0409 20:03:19.090559 14973 solver.cpp:218] Iteration 804 (2.42474 iter/s, 4.94898s/12 iters), loss = 5.15981
I0409 20:03:19.090677 14973 solver.cpp:237]     Train net output #0: loss = 5.15981 (* 1 = 5.15981 loss)
I0409 20:03:19.090689 14973 sgd_solver.cpp:105] Iteration 804, lr = 0.00852774
I0409 20:03:20.838987 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:03:23.592057 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_816.caffemodel
I0409 20:03:27.539288 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_816.solverstate
I0409 20:03:28.859211 14973 solver.cpp:330] Iteration 816, Testing net (#0)
I0409 20:03:28.859231 14973 net.cpp:676] Ignoring source layer train-data
I0409 20:03:32.969063 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:03:33.323140 14973 solver.cpp:397]     Test net output #0: accuracy = 0.00735294
I0409 20:03:33.323169 14973 solver.cpp:397]     Test net output #1: loss = 5.15658 (* 1 = 5.15658 loss)
I0409 20:03:33.407887 14973 solver.cpp:218] Iteration 816 (0.838179 iter/s, 14.3167s/12 iters), loss = 5.18307
I0409 20:03:33.407930 14973 solver.cpp:237]     Train net output #0: loss = 5.18307 (* 1 = 5.18307 loss)
I0409 20:03:33.407938 14973 sgd_solver.cpp:105] Iteration 816, lr = 0.00850749
I0409 20:03:37.648016 14973 solver.cpp:218] Iteration 828 (2.83023 iter/s, 4.23993s/12 iters), loss = 5.20271
I0409 20:03:37.648066 14973 solver.cpp:237]     Train net output #0: loss = 5.20271 (* 1 = 5.20271 loss)
I0409 20:03:37.648077 14973 sgd_solver.cpp:105] Iteration 828, lr = 0.00848729
I0409 20:03:42.537150 14973 solver.cpp:218] Iteration 840 (2.45453 iter/s, 4.88891s/12 iters), loss = 5.13948
I0409 20:03:42.537205 14973 solver.cpp:237]     Train net output #0: loss = 5.13948 (* 1 = 5.13948 loss)
I0409 20:03:42.537220 14973 sgd_solver.cpp:105] Iteration 840, lr = 0.00846714
I0409 20:03:47.493363 14973 solver.cpp:218] Iteration 852 (2.42132 iter/s, 4.95598s/12 iters), loss = 5.12251
I0409 20:03:47.493425 14973 solver.cpp:237]     Train net output #0: loss = 5.12251 (* 1 = 5.12251 loss)
I0409 20:03:47.493436 14973 sgd_solver.cpp:105] Iteration 852, lr = 0.00844704
I0409 20:03:52.419947 14973 solver.cpp:218] Iteration 864 (2.43588 iter/s, 4.92635s/12 iters), loss = 5.11592
I0409 20:03:52.420066 14973 solver.cpp:237]     Train net output #0: loss = 5.11592 (* 1 = 5.11592 loss)
I0409 20:03:52.420078 14973 sgd_solver.cpp:105] Iteration 864, lr = 0.00842698
I0409 20:03:57.384707 14973 solver.cpp:218] Iteration 876 (2.41718 iter/s, 4.96447s/12 iters), loss = 5.15888
I0409 20:03:57.384761 14973 solver.cpp:237]     Train net output #0: loss = 5.15888 (* 1 = 5.15888 loss)
I0409 20:03:57.384774 14973 sgd_solver.cpp:105] Iteration 876, lr = 0.00840698
I0409 20:04:02.405592 14973 solver.cpp:218] Iteration 888 (2.39013 iter/s, 5.02065s/12 iters), loss = 5.05866
I0409 20:04:02.405649 14973 solver.cpp:237]     Train net output #0: loss = 5.05866 (* 1 = 5.05866 loss)
I0409 20:04:02.405663 14973 sgd_solver.cpp:105] Iteration 888, lr = 0.00838702
I0409 20:04:07.358515 14973 solver.cpp:218] Iteration 900 (2.42292 iter/s, 4.95269s/12 iters), loss = 5.21211
I0409 20:04:07.358568 14973 solver.cpp:237]     Train net output #0: loss = 5.21211 (* 1 = 5.21211 loss)
I0409 20:04:07.358582 14973 sgd_solver.cpp:105] Iteration 900, lr = 0.0083671
I0409 20:04:11.354846 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:04:12.552352 14973 solver.cpp:218] Iteration 912 (2.31053 iter/s, 5.19361s/12 iters), loss = 4.98455
I0409 20:04:12.552398 14973 solver.cpp:237]     Train net output #0: loss = 4.98455 (* 1 = 4.98455 loss)
I0409 20:04:12.552408 14973 sgd_solver.cpp:105] Iteration 912, lr = 0.00834724
I0409 20:04:14.613656 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_918.caffemodel
I0409 20:04:19.574404 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_918.solverstate
I0409 20:04:23.369643 14973 solver.cpp:330] Iteration 918, Testing net (#0)
I0409 20:04:23.369758 14973 net.cpp:676] Ignoring source layer train-data
I0409 20:04:27.556126 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:04:27.959810 14973 solver.cpp:397]     Test net output #0: accuracy = 0.00919118
I0409 20:04:27.959858 14973 solver.cpp:397]     Test net output #1: loss = 5.10663 (* 1 = 5.10663 loss)
I0409 20:04:29.850320 14973 solver.cpp:218] Iteration 924 (0.693748 iter/s, 17.2973s/12 iters), loss = 5.14058
I0409 20:04:29.850374 14973 solver.cpp:237]     Train net output #0: loss = 5.14058 (* 1 = 5.14058 loss)
I0409 20:04:29.850386 14973 sgd_solver.cpp:105] Iteration 924, lr = 0.00832742
I0409 20:04:34.780447 14973 solver.cpp:218] Iteration 936 (2.43413 iter/s, 4.9299s/12 iters), loss = 5.19173
I0409 20:04:34.780493 14973 solver.cpp:237]     Train net output #0: loss = 5.19173 (* 1 = 5.19173 loss)
I0409 20:04:34.780504 14973 sgd_solver.cpp:105] Iteration 936, lr = 0.00830765
I0409 20:04:39.651499 14973 solver.cpp:218] Iteration 948 (2.46364 iter/s, 4.87083s/12 iters), loss = 5.10057
I0409 20:04:39.651556 14973 solver.cpp:237]     Train net output #0: loss = 5.10057 (* 1 = 5.10057 loss)
I0409 20:04:39.651567 14973 sgd_solver.cpp:105] Iteration 948, lr = 0.00828793
I0409 20:04:44.648790 14973 solver.cpp:218] Iteration 960 (2.40141 iter/s, 4.99707s/12 iters), loss = 5.09373
I0409 20:04:44.648838 14973 solver.cpp:237]     Train net output #0: loss = 5.09373 (* 1 = 5.09373 loss)
I0409 20:04:44.648845 14973 sgd_solver.cpp:105] Iteration 960, lr = 0.00826825
I0409 20:04:49.562893 14973 solver.cpp:218] Iteration 972 (2.44206 iter/s, 4.91388s/12 iters), loss = 5.18666
I0409 20:04:49.562935 14973 solver.cpp:237]     Train net output #0: loss = 5.18666 (* 1 = 5.18666 loss)
I0409 20:04:49.562945 14973 sgd_solver.cpp:105] Iteration 972, lr = 0.00824862
I0409 20:04:54.503340 14973 solver.cpp:218] Iteration 984 (2.42904 iter/s, 4.94023s/12 iters), loss = 5.11068
I0409 20:04:54.503435 14973 solver.cpp:237]     Train net output #0: loss = 5.11068 (* 1 = 5.11068 loss)
I0409 20:04:54.503446 14973 sgd_solver.cpp:105] Iteration 984, lr = 0.00822903
I0409 20:04:59.414170 14973 solver.cpp:218] Iteration 996 (2.44371 iter/s, 4.91056s/12 iters), loss = 4.98862
I0409 20:04:59.414234 14973 solver.cpp:237]     Train net output #0: loss = 4.98862 (* 1 = 4.98862 loss)
I0409 20:04:59.414247 14973 sgd_solver.cpp:105] Iteration 996, lr = 0.0082095
I0409 20:05:04.493587 14973 solver.cpp:218] Iteration 1008 (2.36258 iter/s, 5.07919s/12 iters), loss = 5.11034
I0409 20:05:04.493640 14973 solver.cpp:237]     Train net output #0: loss = 5.11034 (* 1 = 5.11034 loss)
I0409 20:05:04.493651 14973 sgd_solver.cpp:105] Iteration 1008, lr = 0.00819001
I0409 20:05:05.548516 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:05:09.083909 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1020.caffemodel
I0409 20:05:12.541898 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1020.solverstate
I0409 20:05:15.685099 14973 solver.cpp:330] Iteration 1020, Testing net (#0)
I0409 20:05:15.685123 14973 net.cpp:676] Ignoring source layer train-data
I0409 20:05:19.871438 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:05:20.312831 14973 solver.cpp:397]     Test net output #0: accuracy = 0.00980392
I0409 20:05:20.312881 14973 solver.cpp:397]     Test net output #1: loss = 5.08216 (* 1 = 5.08216 loss)
I0409 20:05:20.399210 14973 solver.cpp:218] Iteration 1020 (0.754477 iter/s, 15.9051s/12 iters), loss = 5.02574
I0409 20:05:20.399250 14973 solver.cpp:237]     Train net output #0: loss = 5.02574 (* 1 = 5.02574 loss)
I0409 20:05:20.399260 14973 sgd_solver.cpp:105] Iteration 1020, lr = 0.00817056
I0409 20:05:24.550204 14973 solver.cpp:218] Iteration 1032 (2.89101 iter/s, 4.1508s/12 iters), loss = 5.08171
I0409 20:05:24.550359 14973 solver.cpp:237]     Train net output #0: loss = 5.08171 (* 1 = 5.08171 loss)
I0409 20:05:24.550374 14973 sgd_solver.cpp:105] Iteration 1032, lr = 0.00815116
I0409 20:05:29.631175 14973 solver.cpp:218] Iteration 1044 (2.3619 iter/s, 5.08065s/12 iters), loss = 5.12979
I0409 20:05:29.631214 14973 solver.cpp:237]     Train net output #0: loss = 5.12979 (* 1 = 5.12979 loss)
I0409 20:05:29.631222 14973 sgd_solver.cpp:105] Iteration 1044, lr = 0.00813181
I0409 20:05:34.584410 14973 solver.cpp:218] Iteration 1056 (2.42277 iter/s, 4.95302s/12 iters), loss = 5.06982
I0409 20:05:34.584465 14973 solver.cpp:237]     Train net output #0: loss = 5.06982 (* 1 = 5.06982 loss)
I0409 20:05:34.584477 14973 sgd_solver.cpp:105] Iteration 1056, lr = 0.0081125
I0409 20:05:39.489516 14973 solver.cpp:218] Iteration 1068 (2.44654 iter/s, 4.90489s/12 iters), loss = 5.08575
I0409 20:05:39.489563 14973 solver.cpp:237]     Train net output #0: loss = 5.08575 (* 1 = 5.08575 loss)
I0409 20:05:39.489573 14973 sgd_solver.cpp:105] Iteration 1068, lr = 0.00809324
I0409 20:05:44.443732 14973 solver.cpp:218] Iteration 1080 (2.42229 iter/s, 4.954s/12 iters), loss = 5.06607
I0409 20:05:44.443775 14973 solver.cpp:237]     Train net output #0: loss = 5.06607 (* 1 = 5.06607 loss)
I0409 20:05:44.443783 14973 sgd_solver.cpp:105] Iteration 1080, lr = 0.00807403
I0409 20:05:49.468474 14973 solver.cpp:218] Iteration 1092 (2.38829 iter/s, 5.02452s/12 iters), loss = 5.06999
I0409 20:05:49.468521 14973 solver.cpp:237]     Train net output #0: loss = 5.06999 (* 1 = 5.06999 loss)
I0409 20:05:49.468530 14973 sgd_solver.cpp:105] Iteration 1092, lr = 0.00805486
I0409 20:05:54.469305 14973 solver.cpp:218] Iteration 1104 (2.39971 iter/s, 5.00061s/12 iters), loss = 4.99353
I0409 20:05:54.469349 14973 solver.cpp:237]     Train net output #0: loss = 4.99353 (* 1 = 4.99353 loss)
I0409 20:05:54.469359 14973 sgd_solver.cpp:105] Iteration 1104, lr = 0.00803573
I0409 20:05:57.770586 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:05:59.685515 14973 solver.cpp:218] Iteration 1116 (2.30062 iter/s, 5.21598s/12 iters), loss = 5.11135
I0409 20:05:59.685556 14973 solver.cpp:237]     Train net output #0: loss = 5.11135 (* 1 = 5.11135 loss)
I0409 20:05:59.685565 14973 sgd_solver.cpp:105] Iteration 1116, lr = 0.00801666
I0409 20:06:01.761696 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1122.caffemodel
I0409 20:06:03.325273 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1122.solverstate
I0409 20:06:04.525360 14973 solver.cpp:330] Iteration 1122, Testing net (#0)
I0409 20:06:04.525389 14973 net.cpp:676] Ignoring source layer train-data
I0409 20:06:08.604518 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:06:09.082069 14973 solver.cpp:397]     Test net output #0: accuracy = 0.0110294
I0409 20:06:09.082118 14973 solver.cpp:397]     Test net output #1: loss = 5.07451 (* 1 = 5.07451 loss)
I0409 20:06:10.825980 14973 solver.cpp:218] Iteration 1128 (1.07719 iter/s, 11.1401s/12 iters), loss = 5.15994
I0409 20:06:10.826025 14973 solver.cpp:237]     Train net output #0: loss = 5.15994 (* 1 = 5.15994 loss)
I0409 20:06:10.826033 14973 sgd_solver.cpp:105] Iteration 1128, lr = 0.00799762
I0409 20:06:15.788836 14973 solver.cpp:218] Iteration 1140 (2.41807 iter/s, 4.96264s/12 iters), loss = 5.09363
I0409 20:06:15.788883 14973 solver.cpp:237]     Train net output #0: loss = 5.09363 (* 1 = 5.09363 loss)
I0409 20:06:15.788892 14973 sgd_solver.cpp:105] Iteration 1140, lr = 0.00797863
I0409 20:06:20.829206 14973 solver.cpp:218] Iteration 1152 (2.38088 iter/s, 5.04015s/12 iters), loss = 5.01999
I0409 20:06:20.829257 14973 solver.cpp:237]     Train net output #0: loss = 5.01999 (* 1 = 5.01999 loss)
I0409 20:06:20.829269 14973 sgd_solver.cpp:105] Iteration 1152, lr = 0.00795969
I0409 20:06:25.770648 14973 solver.cpp:218] Iteration 1164 (2.42855 iter/s, 4.94122s/12 iters), loss = 5.04814
I0409 20:06:25.770697 14973 solver.cpp:237]     Train net output #0: loss = 5.04814 (* 1 = 5.04814 loss)
I0409 20:06:25.770707 14973 sgd_solver.cpp:105] Iteration 1164, lr = 0.00794079
I0409 20:06:30.785907 14973 solver.cpp:218] Iteration 1176 (2.3928 iter/s, 5.01504s/12 iters), loss = 5.06832
I0409 20:06:30.786037 14973 solver.cpp:237]     Train net output #0: loss = 5.06832 (* 1 = 5.06832 loss)
I0409 20:06:30.786049 14973 sgd_solver.cpp:105] Iteration 1176, lr = 0.00792194
I0409 20:06:35.783368 14973 solver.cpp:218] Iteration 1188 (2.40136 iter/s, 4.99716s/12 iters), loss = 5.03436
I0409 20:06:35.783422 14973 solver.cpp:237]     Train net output #0: loss = 5.03436 (* 1 = 5.03436 loss)
I0409 20:06:35.783433 14973 sgd_solver.cpp:105] Iteration 1188, lr = 0.00790313
I0409 20:06:40.790287 14973 solver.cpp:218] Iteration 1200 (2.39679 iter/s, 5.00669s/12 iters), loss = 5.11447
I0409 20:06:40.790333 14973 solver.cpp:237]     Train net output #0: loss = 5.11447 (* 1 = 5.11447 loss)
I0409 20:06:40.790342 14973 sgd_solver.cpp:105] Iteration 1200, lr = 0.00788437
I0409 20:06:45.789974 14973 solver.cpp:218] Iteration 1212 (2.40026 iter/s, 4.99945s/12 iters), loss = 5.06178
I0409 20:06:45.790016 14973 solver.cpp:237]     Train net output #0: loss = 5.06178 (* 1 = 5.06178 loss)
I0409 20:06:45.790024 14973 sgd_solver.cpp:105] Iteration 1212, lr = 0.00786565
I0409 20:06:46.083006 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:06:50.353101 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1224.caffemodel
I0409 20:06:51.892767 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1224.solverstate
I0409 20:06:53.092505 14973 solver.cpp:330] Iteration 1224, Testing net (#0)
I0409 20:06:53.092535 14973 net.cpp:676] Ignoring source layer train-data
I0409 20:06:57.037896 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:06:57.556838 14973 solver.cpp:397]     Test net output #0: accuracy = 0.0122549
I0409 20:06:57.556888 14973 solver.cpp:397]     Test net output #1: loss = 5.02911 (* 1 = 5.02911 loss)
I0409 20:06:57.643553 14973 solver.cpp:218] Iteration 1224 (1.01239 iter/s, 11.8531s/12 iters), loss = 4.98388
I0409 20:06:57.643601 14973 solver.cpp:237]     Train net output #0: loss = 4.98388 (* 1 = 4.98388 loss)
I0409 20:06:57.643615 14973 sgd_solver.cpp:105] Iteration 1224, lr = 0.00784697
I0409 20:07:02.031544 14973 solver.cpp:218] Iteration 1236 (2.73486 iter/s, 4.38779s/12 iters), loss = 5.11721
I0409 20:07:02.031621 14973 solver.cpp:237]     Train net output #0: loss = 5.11721 (* 1 = 5.11721 loss)
I0409 20:07:02.031633 14973 sgd_solver.cpp:105] Iteration 1236, lr = 0.00782834
I0409 20:07:07.159057 14973 solver.cpp:218] Iteration 1248 (2.34043 iter/s, 5.12726s/12 iters), loss = 4.95379
I0409 20:07:07.159102 14973 solver.cpp:237]     Train net output #0: loss = 4.95379 (* 1 = 4.95379 loss)
I0409 20:07:07.159112 14973 sgd_solver.cpp:105] Iteration 1248, lr = 0.00780976
I0409 20:07:12.093909 14973 solver.cpp:218] Iteration 1260 (2.43179 iter/s, 4.93464s/12 iters), loss = 5.03458
I0409 20:07:12.093976 14973 solver.cpp:237]     Train net output #0: loss = 5.03458 (* 1 = 5.03458 loss)
I0409 20:07:12.093989 14973 sgd_solver.cpp:105] Iteration 1260, lr = 0.00779122
I0409 20:07:17.134991 14973 solver.cpp:218] Iteration 1272 (2.38055 iter/s, 5.04085s/12 iters), loss = 5.01001
I0409 20:07:17.135049 14973 solver.cpp:237]     Train net output #0: loss = 5.01001 (* 1 = 5.01001 loss)
I0409 20:07:17.135063 14973 sgd_solver.cpp:105] Iteration 1272, lr = 0.00777272
I0409 20:07:22.282336 14973 solver.cpp:218] Iteration 1284 (2.33141 iter/s, 5.1471s/12 iters), loss = 5.01665
I0409 20:07:22.282399 14973 solver.cpp:237]     Train net output #0: loss = 5.01665 (* 1 = 5.01665 loss)
I0409 20:07:22.282413 14973 sgd_solver.cpp:105] Iteration 1284, lr = 0.00775426
I0409 20:07:27.385802 14973 solver.cpp:218] Iteration 1296 (2.35145 iter/s, 5.10323s/12 iters), loss = 4.90396
I0409 20:07:27.385848 14973 solver.cpp:237]     Train net output #0: loss = 4.90396 (* 1 = 4.90396 loss)
I0409 20:07:27.385859 14973 sgd_solver.cpp:105] Iteration 1296, lr = 0.00773585
I0409 20:07:32.411614 14973 solver.cpp:218] Iteration 1308 (2.38778 iter/s, 5.02559s/12 iters), loss = 4.97485
I0409 20:07:32.411741 14973 solver.cpp:237]     Train net output #0: loss = 4.97485 (* 1 = 4.97485 loss)
I0409 20:07:32.411751 14973 sgd_solver.cpp:105] Iteration 1308, lr = 0.00771749
I0409 20:07:34.941630 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:07:37.428700 14973 solver.cpp:218] Iteration 1320 (2.39197 iter/s, 5.01679s/12 iters), loss = 4.9197
I0409 20:07:37.428746 14973 solver.cpp:237]     Train net output #0: loss = 4.9197 (* 1 = 4.9197 loss)
I0409 20:07:37.428756 14973 sgd_solver.cpp:105] Iteration 1320, lr = 0.00769916
I0409 20:07:39.461949 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1326.caffemodel
I0409 20:07:41.152484 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1326.solverstate
I0409 20:07:42.362825 14973 solver.cpp:330] Iteration 1326, Testing net (#0)
I0409 20:07:42.362851 14973 net.cpp:676] Ignoring source layer train-data
I0409 20:07:46.315810 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:07:46.871716 14973 solver.cpp:397]     Test net output #0: accuracy = 0.0153186
I0409 20:07:46.871759 14973 solver.cpp:397]     Test net output #1: loss = 4.97619 (* 1 = 4.97619 loss)
I0409 20:07:48.712105 14973 solver.cpp:218] Iteration 1332 (1.06355 iter/s, 11.283s/12 iters), loss = 4.96267
I0409 20:07:48.712155 14973 solver.cpp:237]     Train net output #0: loss = 4.96267 (* 1 = 4.96267 loss)
I0409 20:07:48.712167 14973 sgd_solver.cpp:105] Iteration 1332, lr = 0.00768088
I0409 20:07:53.649255 14973 solver.cpp:218] Iteration 1344 (2.43066 iter/s, 4.93693s/12 iters), loss = 4.83908
I0409 20:07:53.649308 14973 solver.cpp:237]     Train net output #0: loss = 4.83908 (* 1 = 4.83908 loss)
I0409 20:07:53.649322 14973 sgd_solver.cpp:105] Iteration 1344, lr = 0.00766265
I0409 20:07:58.605607 14973 solver.cpp:218] Iteration 1356 (2.42125 iter/s, 4.95613s/12 iters), loss = 4.94003
I0409 20:07:58.605651 14973 solver.cpp:237]     Train net output #0: loss = 4.94003 (* 1 = 4.94003 loss)
I0409 20:07:58.605661 14973 sgd_solver.cpp:105] Iteration 1356, lr = 0.00764446
I0409 20:08:03.662843 14973 solver.cpp:218] Iteration 1368 (2.37294 iter/s, 5.05702s/12 iters), loss = 4.93982
I0409 20:08:03.662919 14973 solver.cpp:237]     Train net output #0: loss = 4.93982 (* 1 = 4.93982 loss)
I0409 20:08:03.662930 14973 sgd_solver.cpp:105] Iteration 1368, lr = 0.00762631
I0409 20:08:04.473217 14973 blocking_queue.cpp:49] Waiting for data
I0409 20:08:08.681766 14973 solver.cpp:218] Iteration 1380 (2.39107 iter/s, 5.01868s/12 iters), loss = 4.82034
I0409 20:08:08.681818 14973 solver.cpp:237]     Train net output #0: loss = 4.82034 (* 1 = 4.82034 loss)
I0409 20:08:08.681830 14973 sgd_solver.cpp:105] Iteration 1380, lr = 0.0076082
I0409 20:08:13.742226 14973 solver.cpp:218] Iteration 1392 (2.37143 iter/s, 5.06023s/12 iters), loss = 4.69461
I0409 20:08:13.742282 14973 solver.cpp:237]     Train net output #0: loss = 4.69461 (* 1 = 4.69461 loss)
I0409 20:08:13.742295 14973 sgd_solver.cpp:105] Iteration 1392, lr = 0.00759014
I0409 20:08:18.843675 14973 solver.cpp:218] Iteration 1404 (2.35238 iter/s, 5.10122s/12 iters), loss = 4.92787
I0409 20:08:18.843719 14973 solver.cpp:237]     Train net output #0: loss = 4.92787 (* 1 = 4.92787 loss)
I0409 20:08:18.843729 14973 sgd_solver.cpp:105] Iteration 1404, lr = 0.00757212
I0409 20:08:23.558054 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:08:23.914263 14973 solver.cpp:218] Iteration 1416 (2.36669 iter/s, 5.07037s/12 iters), loss = 4.8621
I0409 20:08:23.914302 14973 solver.cpp:237]     Train net output #0: loss = 4.8621 (* 1 = 4.8621 loss)
I0409 20:08:23.914310 14973 sgd_solver.cpp:105] Iteration 1416, lr = 0.00755414
I0409 20:08:28.577118 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1428.caffemodel
I0409 20:08:30.207978 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1428.solverstate
I0409 20:08:31.408763 14973 solver.cpp:330] Iteration 1428, Testing net (#0)
I0409 20:08:31.408790 14973 net.cpp:676] Ignoring source layer train-data
I0409 20:08:35.297295 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:08:35.901203 14973 solver.cpp:397]     Test net output #0: accuracy = 0.03125
I0409 20:08:35.901252 14973 solver.cpp:397]     Test net output #1: loss = 4.86268 (* 1 = 4.86268 loss)
I0409 20:08:35.988032 14973 solver.cpp:218] Iteration 1428 (0.993926 iter/s, 12.0733s/12 iters), loss = 4.9026
I0409 20:08:35.988090 14973 solver.cpp:237]     Train net output #0: loss = 4.9026 (* 1 = 4.9026 loss)
I0409 20:08:35.988101 14973 sgd_solver.cpp:105] Iteration 1428, lr = 0.0075362
I0409 20:08:40.294198 14973 solver.cpp:218] Iteration 1440 (2.78684 iter/s, 4.30596s/12 iters), loss = 4.74275
I0409 20:08:40.294247 14973 solver.cpp:237]     Train net output #0: loss = 4.74275 (* 1 = 4.74275 loss)
I0409 20:08:40.294258 14973 sgd_solver.cpp:105] Iteration 1440, lr = 0.00751831
I0409 20:08:45.399755 14973 solver.cpp:218] Iteration 1452 (2.35048 iter/s, 5.10533s/12 iters), loss = 4.90547
I0409 20:08:45.399799 14973 solver.cpp:237]     Train net output #0: loss = 4.90547 (* 1 = 4.90547 loss)
I0409 20:08:45.399808 14973 sgd_solver.cpp:105] Iteration 1452, lr = 0.00750046
I0409 20:08:50.393122 14973 solver.cpp:218] Iteration 1464 (2.40329 iter/s, 4.99315s/12 iters), loss = 4.85976
I0409 20:08:50.393173 14973 solver.cpp:237]     Train net output #0: loss = 4.85976 (* 1 = 4.85976 loss)
I0409 20:08:50.393184 14973 sgd_solver.cpp:105] Iteration 1464, lr = 0.00748265
I0409 20:08:55.303818 14973 solver.cpp:218] Iteration 1476 (2.44376 iter/s, 4.91047s/12 iters), loss = 4.86923
I0409 20:08:55.303875 14973 solver.cpp:237]     Train net output #0: loss = 4.86923 (* 1 = 4.86923 loss)
I0409 20:08:55.303889 14973 sgd_solver.cpp:105] Iteration 1476, lr = 0.00746489
I0409 20:09:00.450809 14973 solver.cpp:218] Iteration 1488 (2.33156 iter/s, 5.14676s/12 iters), loss = 4.899
I0409 20:09:00.450855 14973 solver.cpp:237]     Train net output #0: loss = 4.899 (* 1 = 4.899 loss)
I0409 20:09:00.450865 14973 sgd_solver.cpp:105] Iteration 1488, lr = 0.00744716
I0409 20:09:05.363158 14973 solver.cpp:218] Iteration 1500 (2.44293 iter/s, 4.91213s/12 iters), loss = 4.71108
I0409 20:09:05.363257 14973 solver.cpp:237]     Train net output #0: loss = 4.71108 (* 1 = 4.71108 loss)
I0409 20:09:05.363268 14973 sgd_solver.cpp:105] Iteration 1500, lr = 0.00742948
I0409 20:09:10.389737 14973 solver.cpp:218] Iteration 1512 (2.38744 iter/s, 5.02631s/12 iters), loss = 4.87965
I0409 20:09:10.389794 14973 solver.cpp:237]     Train net output #0: loss = 4.87965 (* 1 = 4.87965 loss)
I0409 20:09:10.389806 14973 sgd_solver.cpp:105] Iteration 1512, lr = 0.00741184
I0409 20:09:12.174367 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:09:15.408465 14973 solver.cpp:218] Iteration 1524 (2.39115 iter/s, 5.0185s/12 iters), loss = 4.87119
I0409 20:09:15.408509 14973 solver.cpp:237]     Train net output #0: loss = 4.87119 (* 1 = 4.87119 loss)
I0409 20:09:15.408517 14973 sgd_solver.cpp:105] Iteration 1524, lr = 0.00739425
I0409 20:09:17.411975 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1530.caffemodel
I0409 20:09:19.083644 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1530.solverstate
I0409 20:09:22.314455 14973 solver.cpp:330] Iteration 1530, Testing net (#0)
I0409 20:09:22.314481 14973 net.cpp:676] Ignoring source layer train-data
I0409 20:09:26.261557 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:09:26.911695 14973 solver.cpp:397]     Test net output #0: accuracy = 0.03125
I0409 20:09:26.911721 14973 solver.cpp:397]     Test net output #1: loss = 4.81197 (* 1 = 4.81197 loss)
I0409 20:09:28.797261 14973 solver.cpp:218] Iteration 1536 (0.896304 iter/s, 13.3883s/12 iters), loss = 4.84745
I0409 20:09:28.797317 14973 solver.cpp:237]     Train net output #0: loss = 4.84745 (* 1 = 4.84745 loss)
I0409 20:09:28.797328 14973 sgd_solver.cpp:105] Iteration 1536, lr = 0.00737669
I0409 20:09:33.769428 14973 solver.cpp:218] Iteration 1548 (2.41355 iter/s, 4.97194s/12 iters), loss = 4.59665
I0409 20:09:33.769484 14973 solver.cpp:237]     Train net output #0: loss = 4.59665 (* 1 = 4.59665 loss)
I0409 20:09:33.769496 14973 sgd_solver.cpp:105] Iteration 1548, lr = 0.00735918
I0409 20:09:38.709738 14973 solver.cpp:218] Iteration 1560 (2.42911 iter/s, 4.94008s/12 iters), loss = 4.75286
I0409 20:09:38.709874 14973 solver.cpp:237]     Train net output #0: loss = 4.75286 (* 1 = 4.75286 loss)
I0409 20:09:38.709885 14973 sgd_solver.cpp:105] Iteration 1560, lr = 0.00734171
I0409 20:09:43.782933 14973 solver.cpp:218] Iteration 1572 (2.36552 iter/s, 5.07289s/12 iters), loss = 4.77371
I0409 20:09:43.782976 14973 solver.cpp:237]     Train net output #0: loss = 4.77371 (* 1 = 4.77371 loss)
I0409 20:09:43.782987 14973 sgd_solver.cpp:105] Iteration 1572, lr = 0.00732427
I0409 20:09:48.722201 14973 solver.cpp:218] Iteration 1584 (2.42961 iter/s, 4.93906s/12 iters), loss = 4.80542
I0409 20:09:48.722244 14973 solver.cpp:237]     Train net output #0: loss = 4.80542 (* 1 = 4.80542 loss)
I0409 20:09:48.722254 14973 sgd_solver.cpp:105] Iteration 1584, lr = 0.00730688
I0409 20:09:53.643103 14973 solver.cpp:218] Iteration 1596 (2.43868 iter/s, 4.92069s/12 iters), loss = 4.73114
I0409 20:09:53.643152 14973 solver.cpp:237]     Train net output #0: loss = 4.73114 (* 1 = 4.73114 loss)
I0409 20:09:53.643164 14973 sgd_solver.cpp:105] Iteration 1596, lr = 0.00728954
I0409 20:09:58.571038 14973 solver.cpp:218] Iteration 1608 (2.4352 iter/s, 4.92772s/12 iters), loss = 4.72643
I0409 20:09:58.571070 14973 solver.cpp:237]     Train net output #0: loss = 4.72643 (* 1 = 4.72643 loss)
I0409 20:09:58.571079 14973 sgd_solver.cpp:105] Iteration 1608, lr = 0.00727223
I0409 20:10:02.411614 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:10:03.482656 14973 solver.cpp:218] Iteration 1620 (2.44329 iter/s, 4.91141s/12 iters), loss = 4.50521
I0409 20:10:03.482705 14973 solver.cpp:237]     Train net output #0: loss = 4.50521 (* 1 = 4.50521 loss)
I0409 20:10:03.482717 14973 sgd_solver.cpp:105] Iteration 1620, lr = 0.00725496
I0409 20:10:08.107265 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1632.caffemodel
I0409 20:10:09.703668 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1632.solverstate
I0409 20:10:10.913740 14973 solver.cpp:330] Iteration 1632, Testing net (#0)
I0409 20:10:10.913769 14973 net.cpp:676] Ignoring source layer train-data
I0409 20:10:14.632961 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:10:15.325014 14973 solver.cpp:397]     Test net output #0: accuracy = 0.028799
I0409 20:10:15.325062 14973 solver.cpp:397]     Test net output #1: loss = 4.81455 (* 1 = 4.81455 loss)
I0409 20:10:15.411972 14973 solver.cpp:218] Iteration 1632 (1.00596 iter/s, 11.9289s/12 iters), loss = 4.86465
I0409 20:10:15.412026 14973 solver.cpp:237]     Train net output #0: loss = 4.86465 (* 1 = 4.86465 loss)
I0409 20:10:15.412039 14973 sgd_solver.cpp:105] Iteration 1632, lr = 0.00723774
I0409 20:10:19.674988 14973 solver.cpp:218] Iteration 1644 (2.81504 iter/s, 4.26281s/12 iters), loss = 4.76898
I0409 20:10:19.675033 14973 solver.cpp:237]     Train net output #0: loss = 4.76898 (* 1 = 4.76898 loss)
I0409 20:10:19.675045 14973 sgd_solver.cpp:105] Iteration 1644, lr = 0.00722056
I0409 20:10:24.606801 14973 solver.cpp:218] Iteration 1656 (2.43329 iter/s, 4.93159s/12 iters), loss = 4.60121
I0409 20:10:24.606856 14973 solver.cpp:237]     Train net output #0: loss = 4.60121 (* 1 = 4.60121 loss)
I0409 20:10:24.606868 14973 sgd_solver.cpp:105] Iteration 1656, lr = 0.00720341
I0409 20:10:29.567435 14973 solver.cpp:218] Iteration 1668 (2.41915 iter/s, 4.96041s/12 iters), loss = 4.54084
I0409 20:10:29.567477 14973 solver.cpp:237]     Train net output #0: loss = 4.54084 (* 1 = 4.54084 loss)
I0409 20:10:29.567487 14973 sgd_solver.cpp:105] Iteration 1668, lr = 0.00718631
I0409 20:10:34.520196 14973 solver.cpp:218] Iteration 1680 (2.423 iter/s, 4.95254s/12 iters), loss = 4.61241
I0409 20:10:34.520257 14973 solver.cpp:237]     Train net output #0: loss = 4.61241 (* 1 = 4.61241 loss)
I0409 20:10:34.520272 14973 sgd_solver.cpp:105] Iteration 1680, lr = 0.00716925
I0409 20:10:39.522110 14973 solver.cpp:218] Iteration 1692 (2.39919 iter/s, 5.00168s/12 iters), loss = 4.81874
I0409 20:10:39.522157 14973 solver.cpp:237]     Train net output #0: loss = 4.81874 (* 1 = 4.81874 loss)
I0409 20:10:39.522166 14973 sgd_solver.cpp:105] Iteration 1692, lr = 0.00715223
I0409 20:10:44.419521 14973 solver.cpp:218] Iteration 1704 (2.45038 iter/s, 4.89719s/12 iters), loss = 4.48821
I0409 20:10:44.419670 14973 solver.cpp:237]     Train net output #0: loss = 4.48821 (* 1 = 4.48821 loss)
I0409 20:10:44.419683 14973 sgd_solver.cpp:105] Iteration 1704, lr = 0.00713525
I0409 20:10:49.355336 14973 solver.cpp:218] Iteration 1716 (2.43137 iter/s, 4.9355s/12 iters), loss = 4.6897
I0409 20:10:49.355392 14973 solver.cpp:237]     Train net output #0: loss = 4.6897 (* 1 = 4.6897 loss)
I0409 20:10:49.355404 14973 sgd_solver.cpp:105] Iteration 1716, lr = 0.00711831
I0409 20:10:50.376405 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:10:54.287233 14973 solver.cpp:218] Iteration 1728 (2.43325 iter/s, 4.93167s/12 iters), loss = 4.51135
I0409 20:10:54.287286 14973 solver.cpp:237]     Train net output #0: loss = 4.51135 (* 1 = 4.51135 loss)
I0409 20:10:54.287299 14973 sgd_solver.cpp:105] Iteration 1728, lr = 0.00710141
I0409 20:10:56.284927 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1734.caffemodel
I0409 20:10:58.730693 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1734.solverstate
I0409 20:11:01.480443 14973 solver.cpp:330] Iteration 1734, Testing net (#0)
I0409 20:11:01.480465 14973 net.cpp:676] Ignoring source layer train-data
I0409 20:11:05.387091 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:11:06.091729 14973 solver.cpp:397]     Test net output #0: accuracy = 0.0318627
I0409 20:11:06.091778 14973 solver.cpp:397]     Test net output #1: loss = 4.72568 (* 1 = 4.72568 loss)
I0409 20:11:07.869757 14973 solver.cpp:218] Iteration 1740 (0.88352 iter/s, 13.582s/12 iters), loss = 4.43763
I0409 20:11:07.869803 14973 solver.cpp:237]     Train net output #0: loss = 4.43763 (* 1 = 4.43763 loss)
I0409 20:11:07.869812 14973 sgd_solver.cpp:105] Iteration 1740, lr = 0.00708455
I0409 20:11:12.777714 14973 solver.cpp:218] Iteration 1752 (2.44512 iter/s, 4.90774s/12 iters), loss = 4.56893
I0409 20:11:12.777752 14973 solver.cpp:237]     Train net output #0: loss = 4.56893 (* 1 = 4.56893 loss)
I0409 20:11:12.777760 14973 sgd_solver.cpp:105] Iteration 1752, lr = 0.00706773
I0409 20:11:17.662269 14973 solver.cpp:218] Iteration 1764 (2.45683 iter/s, 4.88434s/12 iters), loss = 4.59446
I0409 20:11:17.662429 14973 solver.cpp:237]     Train net output #0: loss = 4.59446 (* 1 = 4.59446 loss)
I0409 20:11:17.662443 14973 sgd_solver.cpp:105] Iteration 1764, lr = 0.00705094
I0409 20:11:22.635742 14973 solver.cpp:218] Iteration 1776 (2.41296 iter/s, 4.97315s/12 iters), loss = 4.68882
I0409 20:11:22.635798 14973 solver.cpp:237]     Train net output #0: loss = 4.68882 (* 1 = 4.68882 loss)
I0409 20:11:22.635809 14973 sgd_solver.cpp:105] Iteration 1776, lr = 0.0070342
I0409 20:11:27.658035 14973 solver.cpp:218] Iteration 1788 (2.38946 iter/s, 5.02206s/12 iters), loss = 4.76243
I0409 20:11:27.658088 14973 solver.cpp:237]     Train net output #0: loss = 4.76243 (* 1 = 4.76243 loss)
I0409 20:11:27.658100 14973 sgd_solver.cpp:105] Iteration 1788, lr = 0.0070175
I0409 20:11:32.592900 14973 solver.cpp:218] Iteration 1800 (2.43179 iter/s, 4.93464s/12 iters), loss = 4.52207
I0409 20:11:32.592954 14973 solver.cpp:237]     Train net output #0: loss = 4.52207 (* 1 = 4.52207 loss)
I0409 20:11:32.592967 14973 sgd_solver.cpp:105] Iteration 1800, lr = 0.00700084
I0409 20:11:37.572696 14973 solver.cpp:218] Iteration 1812 (2.40985 iter/s, 4.97957s/12 iters), loss = 4.57227
I0409 20:11:37.572755 14973 solver.cpp:237]     Train net output #0: loss = 4.57227 (* 1 = 4.57227 loss)
I0409 20:11:37.572767 14973 sgd_solver.cpp:105] Iteration 1812, lr = 0.00698422
I0409 20:11:40.700161 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:11:42.506846 14973 solver.cpp:218] Iteration 1824 (2.43214 iter/s, 4.93392s/12 iters), loss = 4.44397
I0409 20:11:42.506908 14973 solver.cpp:237]     Train net output #0: loss = 4.44397 (* 1 = 4.44397 loss)
I0409 20:11:42.506919 14973 sgd_solver.cpp:105] Iteration 1824, lr = 0.00696764
I0409 20:11:47.175822 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1836.caffemodel
I0409 20:11:48.767578 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1836.solverstate
I0409 20:11:49.978863 14973 solver.cpp:330] Iteration 1836, Testing net (#0)
I0409 20:11:49.978888 14973 net.cpp:676] Ignoring source layer train-data
I0409 20:11:53.798449 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:11:54.548173 14973 solver.cpp:397]     Test net output #0: accuracy = 0.0373775
I0409 20:11:54.548203 14973 solver.cpp:397]     Test net output #1: loss = 4.55432 (* 1 = 4.55432 loss)
I0409 20:11:54.634795 14973 solver.cpp:218] Iteration 1836 (0.989487 iter/s, 12.1275s/12 iters), loss = 4.68189
I0409 20:11:54.634835 14973 solver.cpp:237]     Train net output #0: loss = 4.68189 (* 1 = 4.68189 loss)
I0409 20:11:54.634845 14973 sgd_solver.cpp:105] Iteration 1836, lr = 0.0069511
I0409 20:11:58.981153 14973 solver.cpp:218] Iteration 1848 (2.76106 iter/s, 4.34616s/12 iters), loss = 4.72849
I0409 20:11:58.981194 14973 solver.cpp:237]     Train net output #0: loss = 4.72849 (* 1 = 4.72849 loss)
I0409 20:11:58.981202 14973 sgd_solver.cpp:105] Iteration 1848, lr = 0.00693459
I0409 20:12:03.991088 14973 solver.cpp:218] Iteration 1860 (2.39535 iter/s, 5.00971s/12 iters), loss = 4.53508
I0409 20:12:03.991144 14973 solver.cpp:237]     Train net output #0: loss = 4.53508 (* 1 = 4.53508 loss)
I0409 20:12:03.991156 14973 sgd_solver.cpp:105] Iteration 1860, lr = 0.00691813
I0409 20:12:09.090274 14973 solver.cpp:218] Iteration 1872 (2.35342 iter/s, 5.09895s/12 iters), loss = 4.50293
I0409 20:12:09.090325 14973 solver.cpp:237]     Train net output #0: loss = 4.50293 (* 1 = 4.50293 loss)
I0409 20:12:09.090337 14973 sgd_solver.cpp:105] Iteration 1872, lr = 0.0069017
I0409 20:12:14.097892 14973 solver.cpp:218] Iteration 1884 (2.39646 iter/s, 5.00739s/12 iters), loss = 4.67461
I0409 20:12:14.097934 14973 solver.cpp:237]     Train net output #0: loss = 4.67461 (* 1 = 4.67461 loss)
I0409 20:12:14.097941 14973 sgd_solver.cpp:105] Iteration 1884, lr = 0.00688532
I0409 20:12:19.097097 14973 solver.cpp:218] Iteration 1896 (2.40049 iter/s, 4.99899s/12 iters), loss = 4.45403
I0409 20:12:19.104326 14973 solver.cpp:237]     Train net output #0: loss = 4.45403 (* 1 = 4.45403 loss)
I0409 20:12:19.104339 14973 sgd_solver.cpp:105] Iteration 1896, lr = 0.00686897
I0409 20:12:24.071823 14973 solver.cpp:218] Iteration 1908 (2.41578 iter/s, 4.96733s/12 iters), loss = 4.71273
I0409 20:12:24.071867 14973 solver.cpp:237]     Train net output #0: loss = 4.71273 (* 1 = 4.71273 loss)
I0409 20:12:24.071877 14973 sgd_solver.cpp:105] Iteration 1908, lr = 0.00685266
I0409 20:12:29.152354 14973 solver.cpp:218] Iteration 1920 (2.36206 iter/s, 5.0803s/12 iters), loss = 4.71834
I0409 20:12:29.152401 14973 solver.cpp:237]     Train net output #0: loss = 4.71834 (* 1 = 4.71834 loss)
I0409 20:12:29.152411 14973 sgd_solver.cpp:105] Iteration 1920, lr = 0.00683639
I0409 20:12:29.459558 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:12:34.080303 14973 solver.cpp:218] Iteration 1932 (2.4352 iter/s, 4.92773s/12 iters), loss = 4.46552
I0409 20:12:34.080358 14973 solver.cpp:237]     Train net output #0: loss = 4.46552 (* 1 = 4.46552 loss)
I0409 20:12:34.080369 14973 sgd_solver.cpp:105] Iteration 1932, lr = 0.00682016
I0409 20:12:36.142005 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1938.caffemodel
I0409 20:12:37.716843 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1938.solverstate
I0409 20:12:38.916951 14973 solver.cpp:330] Iteration 1938, Testing net (#0)
I0409 20:12:38.916975 14973 net.cpp:676] Ignoring source layer train-data
I0409 20:12:42.602955 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:12:43.388381 14973 solver.cpp:397]     Test net output #0: accuracy = 0.0422794
I0409 20:12:43.388428 14973 solver.cpp:397]     Test net output #1: loss = 4.47401 (* 1 = 4.47401 loss)
I0409 20:12:45.183565 14973 solver.cpp:218] Iteration 1944 (1.0808 iter/s, 11.1028s/12 iters), loss = 4.51938
I0409 20:12:45.183624 14973 solver.cpp:237]     Train net output #0: loss = 4.51938 (* 1 = 4.51938 loss)
I0409 20:12:45.183635 14973 sgd_solver.cpp:105] Iteration 1944, lr = 0.00680397
I0409 20:12:50.330442 14973 solver.cpp:218] Iteration 1956 (2.33162 iter/s, 5.14664s/12 iters), loss = 4.49951
I0409 20:12:50.330524 14973 solver.cpp:237]     Train net output #0: loss = 4.49951 (* 1 = 4.49951 loss)
I0409 20:12:50.330535 14973 sgd_solver.cpp:105] Iteration 1956, lr = 0.00678782
I0409 20:12:55.363771 14973 solver.cpp:218] Iteration 1968 (2.38423 iter/s, 5.03308s/12 iters), loss = 4.36021
I0409 20:12:55.363813 14973 solver.cpp:237]     Train net output #0: loss = 4.36021 (* 1 = 4.36021 loss)
I0409 20:12:55.363823 14973 sgd_solver.cpp:105] Iteration 1968, lr = 0.0067717
I0409 20:13:00.334492 14973 solver.cpp:218] Iteration 1980 (2.41424 iter/s, 4.97051s/12 iters), loss = 4.34148
I0409 20:13:00.334538 14973 solver.cpp:237]     Train net output #0: loss = 4.34148 (* 1 = 4.34148 loss)
I0409 20:13:00.334547 14973 sgd_solver.cpp:105] Iteration 1980, lr = 0.00675562
I0409 20:13:05.377034 14973 solver.cpp:218] Iteration 1992 (2.37986 iter/s, 5.04232s/12 iters), loss = 4.42255
I0409 20:13:05.377087 14973 solver.cpp:237]     Train net output #0: loss = 4.42255 (* 1 = 4.42255 loss)
I0409 20:13:05.377100 14973 sgd_solver.cpp:105] Iteration 1992, lr = 0.00673958
I0409 20:13:10.316305 14973 solver.cpp:218] Iteration 2004 (2.42962 iter/s, 4.93905s/12 iters), loss = 4.46571
I0409 20:13:10.316347 14973 solver.cpp:237]     Train net output #0: loss = 4.46571 (* 1 = 4.46571 loss)
I0409 20:13:10.316359 14973 sgd_solver.cpp:105] Iteration 2004, lr = 0.00672358
I0409 20:13:15.347420 14973 solver.cpp:218] Iteration 2016 (2.38526 iter/s, 5.0309s/12 iters), loss = 4.38931
I0409 20:13:15.347470 14973 solver.cpp:237]     Train net output #0: loss = 4.38931 (* 1 = 4.38931 loss)
I0409 20:13:15.347479 14973 sgd_solver.cpp:105] Iteration 2016, lr = 0.00670762
I0409 20:13:17.895591 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:13:20.325892 14973 solver.cpp:218] Iteration 2028 (2.41049 iter/s, 4.97825s/12 iters), loss = 4.21678
I0409 20:13:20.325949 14973 solver.cpp:237]     Train net output #0: loss = 4.21678 (* 1 = 4.21678 loss)
I0409 20:13:20.325976 14973 sgd_solver.cpp:105] Iteration 2028, lr = 0.00669169
I0409 20:13:24.848490 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2040.caffemodel
I0409 20:13:28.057348 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2040.solverstate
I0409 20:13:29.908414 14973 solver.cpp:330] Iteration 2040, Testing net (#0)
I0409 20:13:29.908435 14973 net.cpp:676] Ignoring source layer train-data
I0409 20:13:33.560876 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:13:34.510380 14973 solver.cpp:397]     Test net output #0: accuracy = 0.0582108
I0409 20:13:34.510421 14973 solver.cpp:397]     Test net output #1: loss = 4.34222 (* 1 = 4.34222 loss)
I0409 20:13:34.597317 14973 solver.cpp:218] Iteration 2040 (0.840872 iter/s, 14.2709s/12 iters), loss = 4.27034
I0409 20:13:34.597358 14973 solver.cpp:237]     Train net output #0: loss = 4.27034 (* 1 = 4.27034 loss)
I0409 20:13:34.597366 14973 sgd_solver.cpp:105] Iteration 2040, lr = 0.00667581
I0409 20:13:38.809669 14973 solver.cpp:218] Iteration 2052 (2.8489 iter/s, 4.21216s/12 iters), loss = 4.16359
I0409 20:13:38.809720 14973 solver.cpp:237]     Train net output #0: loss = 4.16359 (* 1 = 4.16359 loss)
I0409 20:13:38.809732 14973 sgd_solver.cpp:105] Iteration 2052, lr = 0.00665996
I0409 20:13:40.014853 14973 blocking_queue.cpp:49] Waiting for data
I0409 20:13:43.771832 14973 solver.cpp:218] Iteration 2064 (2.41841 iter/s, 4.96194s/12 iters), loss = 4.32029
I0409 20:13:43.771888 14973 solver.cpp:237]     Train net output #0: loss = 4.32029 (* 1 = 4.32029 loss)
I0409 20:13:43.771899 14973 sgd_solver.cpp:105] Iteration 2064, lr = 0.00664414
I0409 20:13:48.800213 14973 solver.cpp:218] Iteration 2076 (2.38656 iter/s, 5.02815s/12 iters), loss = 4.56168
I0409 20:13:48.800266 14973 solver.cpp:237]     Train net output #0: loss = 4.56168 (* 1 = 4.56168 loss)
I0409 20:13:48.800277 14973 sgd_solver.cpp:105] Iteration 2076, lr = 0.00662837
I0409 20:13:53.964349 14973 solver.cpp:218] Iteration 2088 (2.32382 iter/s, 5.1639s/12 iters), loss = 4.2373
I0409 20:13:53.964397 14973 solver.cpp:237]     Train net output #0: loss = 4.2373 (* 1 = 4.2373 loss)
I0409 20:13:53.964411 14973 sgd_solver.cpp:105] Iteration 2088, lr = 0.00661263
I0409 20:13:59.020750 14973 solver.cpp:218] Iteration 2100 (2.37333 iter/s, 5.05618s/12 iters), loss = 4.30319
I0409 20:13:59.020825 14973 solver.cpp:237]     Train net output #0: loss = 4.30319 (* 1 = 4.30319 loss)
I0409 20:13:59.020834 14973 sgd_solver.cpp:105] Iteration 2100, lr = 0.00659693
I0409 20:14:03.974095 14973 solver.cpp:218] Iteration 2112 (2.42273 iter/s, 4.9531s/12 iters), loss = 4.2703
I0409 20:14:03.974138 14973 solver.cpp:237]     Train net output #0: loss = 4.2703 (* 1 = 4.2703 loss)
I0409 20:14:03.974149 14973 sgd_solver.cpp:105] Iteration 2112, lr = 0.00658127
I0409 20:14:08.645301 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:14:08.968672 14973 solver.cpp:218] Iteration 2124 (2.40271 iter/s, 4.99436s/12 iters), loss = 4.14459
I0409 20:14:08.968719 14973 solver.cpp:237]     Train net output #0: loss = 4.14459 (* 1 = 4.14459 loss)
I0409 20:14:08.968730 14973 sgd_solver.cpp:105] Iteration 2124, lr = 0.00656564
I0409 20:14:13.936821 14973 solver.cpp:218] Iteration 2136 (2.41549 iter/s, 4.96793s/12 iters), loss = 4.22287
I0409 20:14:13.936867 14973 solver.cpp:237]     Train net output #0: loss = 4.22287 (* 1 = 4.22287 loss)
I0409 20:14:13.936877 14973 sgd_solver.cpp:105] Iteration 2136, lr = 0.00655006
I0409 20:14:15.969663 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2142.caffemodel
I0409 20:14:20.002040 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2142.solverstate
I0409 20:14:21.800189 14973 solver.cpp:330] Iteration 2142, Testing net (#0)
I0409 20:14:21.800216 14973 net.cpp:676] Ignoring source layer train-data
I0409 20:14:25.655869 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:14:26.565950 14973 solver.cpp:397]     Test net output #0: accuracy = 0.0594363
I0409 20:14:26.566012 14973 solver.cpp:397]     Test net output #1: loss = 4.3346 (* 1 = 4.3346 loss)
I0409 20:14:28.492964 14973 solver.cpp:218] Iteration 2148 (0.824424 iter/s, 14.5556s/12 iters), loss = 4.24387
I0409 20:14:28.493013 14973 solver.cpp:237]     Train net output #0: loss = 4.24387 (* 1 = 4.24387 loss)
I0409 20:14:28.493023 14973 sgd_solver.cpp:105] Iteration 2148, lr = 0.00653451
I0409 20:14:33.423372 14973 solver.cpp:218] Iteration 2160 (2.43399 iter/s, 4.93018s/12 iters), loss = 4.46939
I0409 20:14:33.423485 14973 solver.cpp:237]     Train net output #0: loss = 4.46939 (* 1 = 4.46939 loss)
I0409 20:14:33.423498 14973 sgd_solver.cpp:105] Iteration 2160, lr = 0.00651899
I0409 20:14:38.400115 14973 solver.cpp:218] Iteration 2172 (2.41136 iter/s, 4.97645s/12 iters), loss = 4.24771
I0409 20:14:38.400169 14973 solver.cpp:237]     Train net output #0: loss = 4.24771 (* 1 = 4.24771 loss)
I0409 20:14:38.400182 14973 sgd_solver.cpp:105] Iteration 2172, lr = 0.00650351
I0409 20:14:43.416543 14973 solver.cpp:218] Iteration 2184 (2.39225 iter/s, 5.0162s/12 iters), loss = 4.06149
I0409 20:14:43.416596 14973 solver.cpp:237]     Train net output #0: loss = 4.06149 (* 1 = 4.06149 loss)
I0409 20:14:43.416608 14973 sgd_solver.cpp:105] Iteration 2184, lr = 0.00648807
I0409 20:14:48.448390 14973 solver.cpp:218] Iteration 2196 (2.38492 iter/s, 5.03161s/12 iters), loss = 4.2753
I0409 20:14:48.448441 14973 solver.cpp:237]     Train net output #0: loss = 4.2753 (* 1 = 4.2753 loss)
I0409 20:14:48.448451 14973 sgd_solver.cpp:105] Iteration 2196, lr = 0.00647267
I0409 20:14:53.392732 14973 solver.cpp:218] Iteration 2208 (2.42713 iter/s, 4.94412s/12 iters), loss = 4.10116
I0409 20:14:53.392779 14973 solver.cpp:237]     Train net output #0: loss = 4.10116 (* 1 = 4.10116 loss)
I0409 20:14:53.392788 14973 sgd_solver.cpp:105] Iteration 2208, lr = 0.0064573
I0409 20:14:58.437597 14973 solver.cpp:218] Iteration 2220 (2.37876 iter/s, 5.04464s/12 iters), loss = 4.25803
I0409 20:14:58.437641 14973 solver.cpp:237]     Train net output #0: loss = 4.25803 (* 1 = 4.25803 loss)
I0409 20:14:58.437651 14973 sgd_solver.cpp:105] Iteration 2220, lr = 0.00644197
I0409 20:15:00.232625 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:15:03.403851 14973 solver.cpp:218] Iteration 2232 (2.41641 iter/s, 4.96604s/12 iters), loss = 4.23809
I0409 20:15:03.403903 14973 solver.cpp:237]     Train net output #0: loss = 4.23809 (* 1 = 4.23809 loss)
I0409 20:15:03.403914 14973 sgd_solver.cpp:105] Iteration 2232, lr = 0.00642668
I0409 20:15:07.973023 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2244.caffemodel
I0409 20:15:11.201710 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2244.solverstate
I0409 20:15:14.452289 14973 solver.cpp:330] Iteration 2244, Testing net (#0)
I0409 20:15:14.452314 14973 net.cpp:676] Ignoring source layer train-data
I0409 20:15:18.182004 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:15:19.094928 14973 solver.cpp:397]     Test net output #0: accuracy = 0.0661765
I0409 20:15:19.094976 14973 solver.cpp:397]     Test net output #1: loss = 4.2317 (* 1 = 4.2317 loss)
I0409 20:15:19.181669 14973 solver.cpp:218] Iteration 2244 (0.760589 iter/s, 15.7772s/12 iters), loss = 4.30129
I0409 20:15:19.181716 14973 solver.cpp:237]     Train net output #0: loss = 4.30129 (* 1 = 4.30129 loss)
I0409 20:15:19.181727 14973 sgd_solver.cpp:105] Iteration 2244, lr = 0.00641142
I0409 20:15:23.702127 14973 solver.cpp:218] Iteration 2256 (2.65472 iter/s, 4.52025s/12 iters), loss = 3.90611
I0409 20:15:23.702172 14973 solver.cpp:237]     Train net output #0: loss = 3.90611 (* 1 = 3.90611 loss)
I0409 20:15:23.702184 14973 sgd_solver.cpp:105] Iteration 2256, lr = 0.0063962
I0409 20:15:28.781256 14973 solver.cpp:218] Iteration 2268 (2.36271 iter/s, 5.07891s/12 iters), loss = 4.16355
I0409 20:15:28.781291 14973 solver.cpp:237]     Train net output #0: loss = 4.16355 (* 1 = 4.16355 loss)
I0409 20:15:28.781301 14973 sgd_solver.cpp:105] Iteration 2268, lr = 0.00638101
I0409 20:15:33.839251 14973 solver.cpp:218] Iteration 2280 (2.37259 iter/s, 5.05777s/12 iters), loss = 4.03098
I0409 20:15:33.839310 14973 solver.cpp:237]     Train net output #0: loss = 4.03098 (* 1 = 4.03098 loss)
I0409 20:15:33.839323 14973 sgd_solver.cpp:105] Iteration 2280, lr = 0.00636586
I0409 20:15:38.779685 14973 solver.cpp:218] Iteration 2292 (2.42905 iter/s, 4.94021s/12 iters), loss = 4.06645
I0409 20:15:38.779814 14973 solver.cpp:237]     Train net output #0: loss = 4.06645 (* 1 = 4.06645 loss)
I0409 20:15:38.779824 14973 sgd_solver.cpp:105] Iteration 2292, lr = 0.00635075
I0409 20:15:43.742055 14973 solver.cpp:218] Iteration 2304 (2.41835 iter/s, 4.96207s/12 iters), loss = 4.32918
I0409 20:15:43.742105 14973 solver.cpp:237]     Train net output #0: loss = 4.32918 (* 1 = 4.32918 loss)
I0409 20:15:43.742116 14973 sgd_solver.cpp:105] Iteration 2304, lr = 0.00633567
I0409 20:15:48.819597 14973 solver.cpp:218] Iteration 2316 (2.36345 iter/s, 5.07732s/12 iters), loss = 3.9424
I0409 20:15:48.819636 14973 solver.cpp:237]     Train net output #0: loss = 3.9424 (* 1 = 3.9424 loss)
I0409 20:15:48.819644 14973 sgd_solver.cpp:105] Iteration 2316, lr = 0.00632063
I0409 20:15:52.724123 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:15:53.771536 14973 solver.cpp:218] Iteration 2328 (2.4234 iter/s, 4.95173s/12 iters), loss = 4.1323
I0409 20:15:53.771584 14973 solver.cpp:237]     Train net output #0: loss = 4.1323 (* 1 = 4.1323 loss)
I0409 20:15:53.771596 14973 sgd_solver.cpp:105] Iteration 2328, lr = 0.00630562
I0409 20:15:58.823549 14973 solver.cpp:218] Iteration 2340 (2.3754 iter/s, 5.05179s/12 iters), loss = 3.89054
I0409 20:15:58.823606 14973 solver.cpp:237]     Train net output #0: loss = 3.89054 (* 1 = 3.89054 loss)
I0409 20:15:58.823617 14973 sgd_solver.cpp:105] Iteration 2340, lr = 0.00629065
I0409 20:16:00.845887 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2346.caffemodel
I0409 20:16:04.455442 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2346.solverstate
I0409 20:16:06.901710 14973 solver.cpp:330] Iteration 2346, Testing net (#0)
I0409 20:16:06.901738 14973 net.cpp:676] Ignoring source layer train-data
I0409 20:16:10.409687 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:16:11.350992 14973 solver.cpp:397]     Test net output #0: accuracy = 0.0845588
I0409 20:16:11.351039 14973 solver.cpp:397]     Test net output #1: loss = 4.04674 (* 1 = 4.04674 loss)
I0409 20:16:13.326748 14973 solver.cpp:218] Iteration 2352 (0.827434 iter/s, 14.5027s/12 iters), loss = 3.8847
I0409 20:16:13.326797 14973 solver.cpp:237]     Train net output #0: loss = 3.8847 (* 1 = 3.8847 loss)
I0409 20:16:13.326805 14973 sgd_solver.cpp:105] Iteration 2352, lr = 0.00627571
I0409 20:16:18.304646 14973 solver.cpp:218] Iteration 2364 (2.41076 iter/s, 4.97768s/12 iters), loss = 3.96732
I0409 20:16:18.304692 14973 solver.cpp:237]     Train net output #0: loss = 3.96732 (* 1 = 3.96732 loss)
I0409 20:16:18.304700 14973 sgd_solver.cpp:105] Iteration 2364, lr = 0.00626081
I0409 20:16:23.248708 14973 solver.cpp:218] Iteration 2376 (2.42726 iter/s, 4.94384s/12 iters), loss = 3.92027
I0409 20:16:23.248751 14973 solver.cpp:237]     Train net output #0: loss = 3.92027 (* 1 = 3.92027 loss)
I0409 20:16:23.248759 14973 sgd_solver.cpp:105] Iteration 2376, lr = 0.00624595
I0409 20:16:28.307240 14973 solver.cpp:218] Iteration 2388 (2.37233 iter/s, 5.05831s/12 iters), loss = 3.94279
I0409 20:16:28.307293 14973 solver.cpp:237]     Train net output #0: loss = 3.94279 (* 1 = 3.94279 loss)
I0409 20:16:28.307304 14973 sgd_solver.cpp:105] Iteration 2388, lr = 0.00623112
I0409 20:16:33.278061 14973 solver.cpp:218] Iteration 2400 (2.4142 iter/s, 4.9706s/12 iters), loss = 4.04243
I0409 20:16:33.278111 14973 solver.cpp:237]     Train net output #0: loss = 4.04243 (* 1 = 4.04243 loss)
I0409 20:16:33.278123 14973 sgd_solver.cpp:105] Iteration 2400, lr = 0.00621633
I0409 20:16:38.262099 14973 solver.cpp:218] Iteration 2412 (2.4078 iter/s, 4.98381s/12 iters), loss = 3.8025
I0409 20:16:38.262151 14973 solver.cpp:237]     Train net output #0: loss = 3.8025 (* 1 = 3.8025 loss)
I0409 20:16:38.262161 14973 sgd_solver.cpp:105] Iteration 2412, lr = 0.00620157
I0409 20:16:43.561007 14973 solver.cpp:218] Iteration 2424 (2.26472 iter/s, 5.29867s/12 iters), loss = 4.01739
I0409 20:16:43.561151 14973 solver.cpp:237]     Train net output #0: loss = 4.01739 (* 1 = 4.01739 loss)
I0409 20:16:43.561165 14973 sgd_solver.cpp:105] Iteration 2424, lr = 0.00618684
I0409 20:16:44.735554 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:16:49.016331 14973 solver.cpp:218] Iteration 2436 (2.19982 iter/s, 5.455s/12 iters), loss = 3.75699
I0409 20:16:49.016373 14973 solver.cpp:237]     Train net output #0: loss = 3.75699 (* 1 = 3.75699 loss)
I0409 20:16:49.016383 14973 sgd_solver.cpp:105] Iteration 2436, lr = 0.00617215
I0409 20:16:53.701876 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2448.caffemodel
I0409 20:16:55.308944 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2448.solverstate
I0409 20:16:56.527052 14973 solver.cpp:330] Iteration 2448, Testing net (#0)
I0409 20:16:56.527081 14973 net.cpp:676] Ignoring source layer train-data
I0409 20:17:00.352478 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:17:01.386788 14973 solver.cpp:397]     Test net output #0: accuracy = 0.0876225
I0409 20:17:01.386819 14973 solver.cpp:397]     Test net output #1: loss = 4.02767 (* 1 = 4.02767 loss)
I0409 20:17:01.473646 14973 solver.cpp:218] Iteration 2448 (0.963325 iter/s, 12.4569s/12 iters), loss = 3.80738
I0409 20:17:01.473695 14973 solver.cpp:237]     Train net output #0: loss = 3.80738 (* 1 = 3.80738 loss)
I0409 20:17:01.473702 14973 sgd_solver.cpp:105] Iteration 2448, lr = 0.0061575
I0409 20:17:05.621618 14973 solver.cpp:218] Iteration 2460 (2.89312 iter/s, 4.14777s/12 iters), loss = 3.88233
I0409 20:17:05.621662 14973 solver.cpp:237]     Train net output #0: loss = 3.88233 (* 1 = 3.88233 loss)
I0409 20:17:05.621671 14973 sgd_solver.cpp:105] Iteration 2460, lr = 0.00614288
I0409 20:17:10.589607 14973 solver.cpp:218] Iteration 2472 (2.41557 iter/s, 4.96777s/12 iters), loss = 3.78003
I0409 20:17:10.589660 14973 solver.cpp:237]     Train net output #0: loss = 3.78003 (* 1 = 3.78003 loss)
I0409 20:17:10.589670 14973 sgd_solver.cpp:105] Iteration 2472, lr = 0.0061283
I0409 20:17:15.613195 14973 solver.cpp:218] Iteration 2484 (2.38884 iter/s, 5.02336s/12 iters), loss = 3.88797
I0409 20:17:15.613304 14973 solver.cpp:237]     Train net output #0: loss = 3.88797 (* 1 = 3.88797 loss)
I0409 20:17:15.613317 14973 sgd_solver.cpp:105] Iteration 2484, lr = 0.00611375
I0409 20:17:20.588349 14973 solver.cpp:218] Iteration 2496 (2.41212 iter/s, 4.97487s/12 iters), loss = 4.00967
I0409 20:17:20.588402 14973 solver.cpp:237]     Train net output #0: loss = 4.00967 (* 1 = 4.00967 loss)
I0409 20:17:20.588413 14973 sgd_solver.cpp:105] Iteration 2496, lr = 0.00609923
I0409 20:17:25.628203 14973 solver.cpp:218] Iteration 2508 (2.38113 iter/s, 5.03963s/12 iters), loss = 3.93199
I0409 20:17:25.628253 14973 solver.cpp:237]     Train net output #0: loss = 3.93199 (* 1 = 3.93199 loss)
I0409 20:17:25.628265 14973 sgd_solver.cpp:105] Iteration 2508, lr = 0.00608475
I0409 20:17:30.632359 14973 solver.cpp:218] Iteration 2520 (2.39811 iter/s, 5.00394s/12 iters), loss = 3.91417
I0409 20:17:30.632408 14973 solver.cpp:237]     Train net output #0: loss = 3.91417 (* 1 = 3.91417 loss)
I0409 20:17:30.632417 14973 sgd_solver.cpp:105] Iteration 2520, lr = 0.0060703
I0409 20:17:33.853878 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:17:35.635094 14973 solver.cpp:218] Iteration 2532 (2.3988 iter/s, 5.00251s/12 iters), loss = 3.93225
I0409 20:17:35.635151 14973 solver.cpp:237]     Train net output #0: loss = 3.93225 (* 1 = 3.93225 loss)
I0409 20:17:35.635164 14973 sgd_solver.cpp:105] Iteration 2532, lr = 0.00605589
I0409 20:17:41.050415 14973 solver.cpp:218] Iteration 2544 (2.21604 iter/s, 5.41508s/12 iters), loss = 3.94553
I0409 20:17:41.050462 14973 solver.cpp:237]     Train net output #0: loss = 3.94553 (* 1 = 3.94553 loss)
I0409 20:17:41.050472 14973 sgd_solver.cpp:105] Iteration 2544, lr = 0.00604151
I0409 20:17:43.054177 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2550.caffemodel
I0409 20:17:47.295125 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2550.solverstate
I0409 20:17:48.498867 14973 solver.cpp:330] Iteration 2550, Testing net (#0)
I0409 20:17:48.498888 14973 net.cpp:676] Ignoring source layer train-data
I0409 20:17:51.843753 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:17:52.867046 14973 solver.cpp:397]     Test net output #0: accuracy = 0.103554
I0409 20:17:52.867094 14973 solver.cpp:397]     Test net output #1: loss = 3.88803 (* 1 = 3.88803 loss)
I0409 20:17:54.733651 14973 solver.cpp:218] Iteration 2556 (0.877017 iter/s, 13.6827s/12 iters), loss = 4.03272
I0409 20:17:54.733697 14973 solver.cpp:237]     Train net output #0: loss = 4.03272 (* 1 = 4.03272 loss)
I0409 20:17:54.733706 14973 sgd_solver.cpp:105] Iteration 2556, lr = 0.00602717
I0409 20:17:59.701086 14973 solver.cpp:218] Iteration 2568 (2.41584 iter/s, 4.96721s/12 iters), loss = 3.78426
I0409 20:17:59.701125 14973 solver.cpp:237]     Train net output #0: loss = 3.78426 (* 1 = 3.78426 loss)
I0409 20:17:59.701135 14973 sgd_solver.cpp:105] Iteration 2568, lr = 0.00601286
I0409 20:18:04.683559 14973 solver.cpp:218] Iteration 2580 (2.40855 iter/s, 4.98225s/12 iters), loss = 3.93408
I0409 20:18:04.683614 14973 solver.cpp:237]     Train net output #0: loss = 3.93408 (* 1 = 3.93408 loss)
I0409 20:18:04.683624 14973 sgd_solver.cpp:105] Iteration 2580, lr = 0.00599858
I0409 20:18:09.725406 14973 solver.cpp:218] Iteration 2592 (2.38019 iter/s, 5.04162s/12 iters), loss = 4.07745
I0409 20:18:09.725458 14973 solver.cpp:237]     Train net output #0: loss = 4.07745 (* 1 = 4.07745 loss)
I0409 20:18:09.725471 14973 sgd_solver.cpp:105] Iteration 2592, lr = 0.00598434
I0409 20:18:14.770731 14973 solver.cpp:218] Iteration 2604 (2.37855 iter/s, 5.04509s/12 iters), loss = 3.89611
I0409 20:18:14.770785 14973 solver.cpp:237]     Train net output #0: loss = 3.89611 (* 1 = 3.89611 loss)
I0409 20:18:14.770797 14973 sgd_solver.cpp:105] Iteration 2604, lr = 0.00597013
I0409 20:18:19.785044 14973 solver.cpp:218] Iteration 2616 (2.39326 iter/s, 5.01408s/12 iters), loss = 3.85516
I0409 20:18:19.785179 14973 solver.cpp:237]     Train net output #0: loss = 3.85516 (* 1 = 3.85516 loss)
I0409 20:18:19.785193 14973 sgd_solver.cpp:105] Iteration 2616, lr = 0.00595596
I0409 20:18:24.795475 14973 solver.cpp:218] Iteration 2628 (2.39515 iter/s, 5.01012s/12 iters), loss = 3.82003
I0409 20:18:24.795523 14973 solver.cpp:237]     Train net output #0: loss = 3.82003 (* 1 = 3.82003 loss)
I0409 20:18:24.795534 14973 sgd_solver.cpp:105] Iteration 2628, lr = 0.00594182
I0409 20:18:25.227898 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:18:29.795303 14973 solver.cpp:218] Iteration 2640 (2.40019 iter/s, 4.99961s/12 iters), loss = 3.85615
I0409 20:18:29.795361 14973 solver.cpp:237]     Train net output #0: loss = 3.85615 (* 1 = 3.85615 loss)
I0409 20:18:29.795372 14973 sgd_solver.cpp:105] Iteration 2640, lr = 0.00592771
I0409 20:18:34.311592 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2652.caffemodel
I0409 20:18:35.851145 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2652.solverstate
I0409 20:18:37.050559 14973 solver.cpp:330] Iteration 2652, Testing net (#0)
I0409 20:18:37.050587 14973 net.cpp:676] Ignoring source layer train-data
I0409 20:18:40.640111 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:18:41.697821 14973 solver.cpp:397]     Test net output #0: accuracy = 0.110294
I0409 20:18:41.697866 14973 solver.cpp:397]     Test net output #1: loss = 3.7782 (* 1 = 3.7782 loss)
I0409 20:18:41.784667 14973 solver.cpp:218] Iteration 2652 (1.00093 iter/s, 11.9889s/12 iters), loss = 3.59996
I0409 20:18:41.784729 14973 solver.cpp:237]     Train net output #0: loss = 3.59996 (* 1 = 3.59996 loss)
I0409 20:18:41.784744 14973 sgd_solver.cpp:105] Iteration 2652, lr = 0.00591364
I0409 20:18:46.015519 14973 solver.cpp:218] Iteration 2664 (2.83645 iter/s, 4.23064s/12 iters), loss = 3.60219
I0409 20:18:46.015578 14973 solver.cpp:237]     Train net output #0: loss = 3.60219 (* 1 = 3.60219 loss)
I0409 20:18:46.015590 14973 sgd_solver.cpp:105] Iteration 2664, lr = 0.0058996
I0409 20:18:50.923142 14973 solver.cpp:218] Iteration 2676 (2.44529 iter/s, 4.9074s/12 iters), loss = 3.68411
I0409 20:18:50.923255 14973 solver.cpp:237]     Train net output #0: loss = 3.68411 (* 1 = 3.68411 loss)
I0409 20:18:50.923269 14973 sgd_solver.cpp:105] Iteration 2676, lr = 0.00588559
I0409 20:18:55.895505 14973 solver.cpp:218] Iteration 2688 (2.41348 iter/s, 4.97208s/12 iters), loss = 3.75938
I0409 20:18:55.895550 14973 solver.cpp:237]     Train net output #0: loss = 3.75938 (* 1 = 3.75938 loss)
I0409 20:18:55.895560 14973 sgd_solver.cpp:105] Iteration 2688, lr = 0.00587162
I0409 20:19:01.010926 14973 solver.cpp:218] Iteration 2700 (2.34595 iter/s, 5.1152s/12 iters), loss = 3.88679
I0409 20:19:01.010982 14973 solver.cpp:237]     Train net output #0: loss = 3.88679 (* 1 = 3.88679 loss)
I0409 20:19:01.010993 14973 sgd_solver.cpp:105] Iteration 2700, lr = 0.00585768
I0409 20:19:06.017104 14973 solver.cpp:218] Iteration 2712 (2.39715 iter/s, 5.00595s/12 iters), loss = 3.7528
I0409 20:19:06.017149 14973 solver.cpp:237]     Train net output #0: loss = 3.7528 (* 1 = 3.7528 loss)
I0409 20:19:06.017159 14973 sgd_solver.cpp:105] Iteration 2712, lr = 0.00584377
I0409 20:19:10.995466 14973 solver.cpp:218] Iteration 2724 (2.41054 iter/s, 4.97815s/12 iters), loss = 3.82304
I0409 20:19:10.995501 14973 solver.cpp:237]     Train net output #0: loss = 3.82304 (* 1 = 3.82304 loss)
I0409 20:19:10.995509 14973 sgd_solver.cpp:105] Iteration 2724, lr = 0.0058299
I0409 20:19:13.545295 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:19:15.935492 14973 solver.cpp:218] Iteration 2736 (2.42924 iter/s, 4.93982s/12 iters), loss = 3.52015
I0409 20:19:15.935525 14973 solver.cpp:237]     Train net output #0: loss = 3.52015 (* 1 = 3.52015 loss)
I0409 20:19:15.935535 14973 sgd_solver.cpp:105] Iteration 2736, lr = 0.00581605
I0409 20:19:20.889454 14973 solver.cpp:218] Iteration 2748 (2.42241 iter/s, 4.95375s/12 iters), loss = 3.76106
I0409 20:19:20.889503 14973 solver.cpp:237]     Train net output #0: loss = 3.76106 (* 1 = 3.76106 loss)
I0409 20:19:20.889513 14973 sgd_solver.cpp:105] Iteration 2748, lr = 0.00580225
I0409 20:19:23.047978 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2754.caffemodel
I0409 20:19:24.553885 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2754.solverstate
I0409 20:19:25.752715 14973 solver.cpp:330] Iteration 2754, Testing net (#0)
I0409 20:19:25.752740 14973 net.cpp:676] Ignoring source layer train-data
I0409 20:19:28.780223 14973 blocking_queue.cpp:49] Waiting for data
I0409 20:19:29.104569 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:19:30.206867 14973 solver.cpp:397]     Test net output #0: accuracy = 0.125613
I0409 20:19:30.206912 14973 solver.cpp:397]     Test net output #1: loss = 3.75032 (* 1 = 3.75032 loss)
I0409 20:19:32.186856 14973 solver.cpp:218] Iteration 2760 (1.06223 iter/s, 11.297s/12 iters), loss = 3.40005
I0409 20:19:32.186905 14973 solver.cpp:237]     Train net output #0: loss = 3.40005 (* 1 = 3.40005 loss)
I0409 20:19:32.186916 14973 sgd_solver.cpp:105] Iteration 2760, lr = 0.00578847
I0409 20:19:37.201198 14973 solver.cpp:218] Iteration 2772 (2.39324 iter/s, 5.01412s/12 iters), loss = 3.54439
I0409 20:19:37.201246 14973 solver.cpp:237]     Train net output #0: loss = 3.54439 (* 1 = 3.54439 loss)
I0409 20:19:37.201256 14973 sgd_solver.cpp:105] Iteration 2772, lr = 0.00577473
I0409 20:19:42.220046 14973 solver.cpp:218] Iteration 2784 (2.3911 iter/s, 5.01862s/12 iters), loss = 3.66623
I0409 20:19:42.220098 14973 solver.cpp:237]     Train net output #0: loss = 3.66623 (* 1 = 3.66623 loss)
I0409 20:19:42.220108 14973 sgd_solver.cpp:105] Iteration 2784, lr = 0.00576102
I0409 20:19:47.352136 14973 solver.cpp:218] Iteration 2796 (2.33833 iter/s, 5.13186s/12 iters), loss = 3.55347
I0409 20:19:47.352175 14973 solver.cpp:237]     Train net output #0: loss = 3.55347 (* 1 = 3.55347 loss)
I0409 20:19:47.352185 14973 sgd_solver.cpp:105] Iteration 2796, lr = 0.00574734
I0409 20:19:52.592859 14973 solver.cpp:218] Iteration 2808 (2.28986 iter/s, 5.2405s/12 iters), loss = 3.54791
I0409 20:19:52.592901 14973 solver.cpp:237]     Train net output #0: loss = 3.54791 (* 1 = 3.54791 loss)
I0409 20:19:52.592911 14973 sgd_solver.cpp:105] Iteration 2808, lr = 0.00573369
I0409 20:19:57.655139 14973 solver.cpp:218] Iteration 2820 (2.37058 iter/s, 5.06206s/12 iters), loss = 3.51254
I0409 20:19:57.655253 14973 solver.cpp:237]     Train net output #0: loss = 3.51254 (* 1 = 3.51254 loss)
I0409 20:19:57.655263 14973 sgd_solver.cpp:105] Iteration 2820, lr = 0.00572008
I0409 20:20:02.332376 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:20:02.625396 14973 solver.cpp:218] Iteration 2832 (2.4145 iter/s, 4.96997s/12 iters), loss = 3.53724
I0409 20:20:02.625439 14973 solver.cpp:237]     Train net output #0: loss = 3.53724 (* 1 = 3.53724 loss)
I0409 20:20:02.625447 14973 sgd_solver.cpp:105] Iteration 2832, lr = 0.0057065
I0409 20:20:07.692787 14973 solver.cpp:218] Iteration 2844 (2.36819 iter/s, 5.06717s/12 iters), loss = 3.29808
I0409 20:20:07.692832 14973 solver.cpp:237]     Train net output #0: loss = 3.29808 (* 1 = 3.29808 loss)
I0409 20:20:07.692842 14973 sgd_solver.cpp:105] Iteration 2844, lr = 0.00569295
I0409 20:20:12.191691 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2856.caffemodel
I0409 20:20:13.760763 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2856.solverstate
I0409 20:20:14.979554 14973 solver.cpp:330] Iteration 2856, Testing net (#0)
I0409 20:20:14.979580 14973 net.cpp:676] Ignoring source layer train-data
I0409 20:20:18.167419 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:20:19.307143 14973 solver.cpp:397]     Test net output #0: accuracy = 0.113971
I0409 20:20:19.307171 14973 solver.cpp:397]     Test net output #1: loss = 3.76992 (* 1 = 3.76992 loss)
I0409 20:20:19.394022 14973 solver.cpp:218] Iteration 2856 (1.02557 iter/s, 11.7008s/12 iters), loss = 3.50237
I0409 20:20:19.394068 14973 solver.cpp:237]     Train net output #0: loss = 3.50237 (* 1 = 3.50237 loss)
I0409 20:20:19.394076 14973 sgd_solver.cpp:105] Iteration 2856, lr = 0.00567944
I0409 20:20:23.611804 14973 solver.cpp:218] Iteration 2868 (2.84523 iter/s, 4.21759s/12 iters), loss = 3.6412
I0409 20:20:23.611851 14973 solver.cpp:237]     Train net output #0: loss = 3.6412 (* 1 = 3.6412 loss)
I0409 20:20:23.611861 14973 sgd_solver.cpp:105] Iteration 2868, lr = 0.00566595
I0409 20:20:28.598335 14973 solver.cpp:218] Iteration 2880 (2.40659 iter/s, 4.98631s/12 iters), loss = 3.43002
I0409 20:20:28.598404 14973 solver.cpp:237]     Train net output #0: loss = 3.43002 (* 1 = 3.43002 loss)
I0409 20:20:28.598413 14973 sgd_solver.cpp:105] Iteration 2880, lr = 0.0056525
I0409 20:20:33.531914 14973 solver.cpp:218] Iteration 2892 (2.43244 iter/s, 4.93333s/12 iters), loss = 3.40235
I0409 20:20:33.531975 14973 solver.cpp:237]     Train net output #0: loss = 3.40235 (* 1 = 3.40235 loss)
I0409 20:20:33.531986 14973 sgd_solver.cpp:105] Iteration 2892, lr = 0.00563908
I0409 20:20:38.599123 14973 solver.cpp:218] Iteration 2904 (2.36828 iter/s, 5.06698s/12 iters), loss = 3.51432
I0409 20:20:38.599174 14973 solver.cpp:237]     Train net output #0: loss = 3.51432 (* 1 = 3.51432 loss)
I0409 20:20:38.599185 14973 sgd_solver.cpp:105] Iteration 2904, lr = 0.00562569
I0409 20:20:43.511140 14973 solver.cpp:218] Iteration 2916 (2.4431 iter/s, 4.9118s/12 iters), loss = 3.42062
I0409 20:20:43.511188 14973 solver.cpp:237]     Train net output #0: loss = 3.42062 (* 1 = 3.42062 loss)
I0409 20:20:43.511198 14973 sgd_solver.cpp:105] Iteration 2916, lr = 0.00561233
I0409 20:20:48.573805 14973 solver.cpp:218] Iteration 2928 (2.3704 iter/s, 5.06244s/12 iters), loss = 3.58621
I0409 20:20:48.573846 14973 solver.cpp:237]     Train net output #0: loss = 3.58621 (* 1 = 3.58621 loss)
I0409 20:20:48.573858 14973 sgd_solver.cpp:105] Iteration 2928, lr = 0.00559901
I0409 20:20:50.434913 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:20:53.858817 14973 solver.cpp:218] Iteration 2940 (2.27067 iter/s, 5.28479s/12 iters), loss = 3.37454
I0409 20:20:53.858863 14973 solver.cpp:237]     Train net output #0: loss = 3.37454 (* 1 = 3.37454 loss)
I0409 20:20:53.858873 14973 sgd_solver.cpp:105] Iteration 2940, lr = 0.00558572
I0409 20:20:58.917990 14973 solver.cpp:218] Iteration 2952 (2.37204 iter/s, 5.05893s/12 iters), loss = 3.35521
I0409 20:20:58.918133 14973 solver.cpp:237]     Train net output #0: loss = 3.35521 (* 1 = 3.35521 loss)
I0409 20:20:58.918145 14973 sgd_solver.cpp:105] Iteration 2952, lr = 0.00557245
I0409 20:21:00.945725 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2958.caffemodel
I0409 20:21:02.719293 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2958.solverstate
I0409 20:21:03.920938 14973 solver.cpp:330] Iteration 2958, Testing net (#0)
I0409 20:21:03.920964 14973 net.cpp:676] Ignoring source layer train-data
I0409 20:21:07.175549 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:21:08.358114 14973 solver.cpp:397]     Test net output #0: accuracy = 0.141544
I0409 20:21:08.358141 14973 solver.cpp:397]     Test net output #1: loss = 3.59439 (* 1 = 3.59439 loss)
I0409 20:21:10.293846 14973 solver.cpp:218] Iteration 2964 (1.05491 iter/s, 11.3753s/12 iters), loss = 3.29729
I0409 20:21:10.293890 14973 solver.cpp:237]     Train net output #0: loss = 3.29729 (* 1 = 3.29729 loss)
I0409 20:21:10.293900 14973 sgd_solver.cpp:105] Iteration 2964, lr = 0.00555922
I0409 20:21:15.440269 14973 solver.cpp:218] Iteration 2976 (2.33182 iter/s, 5.1462s/12 iters), loss = 3.58381
I0409 20:21:15.440310 14973 solver.cpp:237]     Train net output #0: loss = 3.58381 (* 1 = 3.58381 loss)
I0409 20:21:15.440318 14973 sgd_solver.cpp:105] Iteration 2976, lr = 0.00554603
I0409 20:21:20.395074 14973 solver.cpp:218] Iteration 2988 (2.422 iter/s, 4.95459s/12 iters), loss = 3.1744
I0409 20:21:20.395123 14973 solver.cpp:237]     Train net output #0: loss = 3.1744 (* 1 = 3.1744 loss)
I0409 20:21:20.395135 14973 sgd_solver.cpp:105] Iteration 2988, lr = 0.00553286
I0409 20:21:25.357893 14973 solver.cpp:218] Iteration 3000 (2.41809 iter/s, 4.9626s/12 iters), loss = 3.47225
I0409 20:21:25.357940 14973 solver.cpp:237]     Train net output #0: loss = 3.47225 (* 1 = 3.47225 loss)
I0409 20:21:25.357951 14973 sgd_solver.cpp:105] Iteration 3000, lr = 0.00551972
I0409 20:21:30.437697 14973 solver.cpp:218] Iteration 3012 (2.3624 iter/s, 5.07957s/12 iters), loss = 3.50494
I0409 20:21:30.437810 14973 solver.cpp:237]     Train net output #0: loss = 3.50494 (* 1 = 3.50494 loss)
I0409 20:21:30.437824 14973 sgd_solver.cpp:105] Iteration 3012, lr = 0.00550662
I0409 20:21:35.359751 14973 solver.cpp:218] Iteration 3024 (2.43815 iter/s, 4.92177s/12 iters), loss = 3.16318
I0409 20:21:35.359798 14973 solver.cpp:237]     Train net output #0: loss = 3.16318 (* 1 = 3.16318 loss)
I0409 20:21:35.359809 14973 sgd_solver.cpp:105] Iteration 3024, lr = 0.00549354
I0409 20:21:39.188453 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:21:40.206828 14973 solver.cpp:218] Iteration 3036 (2.47583 iter/s, 4.84686s/12 iters), loss = 3.34885
I0409 20:21:40.206871 14973 solver.cpp:237]     Train net output #0: loss = 3.34885 (* 1 = 3.34885 loss)
I0409 20:21:40.206879 14973 sgd_solver.cpp:105] Iteration 3036, lr = 0.0054805
I0409 20:21:45.164310 14973 solver.cpp:218] Iteration 3048 (2.42069 iter/s, 4.95726s/12 iters), loss = 3.46956
I0409 20:21:45.164366 14973 solver.cpp:237]     Train net output #0: loss = 3.46956 (* 1 = 3.46956 loss)
I0409 20:21:45.164378 14973 sgd_solver.cpp:105] Iteration 3048, lr = 0.00546749
I0409 20:21:49.726223 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3060.caffemodel
I0409 20:21:51.906229 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3060.solverstate
I0409 20:21:54.150760 14973 solver.cpp:330] Iteration 3060, Testing net (#0)
I0409 20:21:54.150782 14973 net.cpp:676] Ignoring source layer train-data
I0409 20:21:57.329789 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:21:58.568017 14973 solver.cpp:397]     Test net output #0: accuracy = 0.14951
I0409 20:21:58.568066 14973 solver.cpp:397]     Test net output #1: loss = 3.55259 (* 1 = 3.55259 loss)
I0409 20:21:58.654964 14973 solver.cpp:218] Iteration 3060 (0.889538 iter/s, 13.4901s/12 iters), loss = 3.37672
I0409 20:21:58.655019 14973 solver.cpp:237]     Train net output #0: loss = 3.37672 (* 1 = 3.37672 loss)
I0409 20:21:58.655031 14973 sgd_solver.cpp:105] Iteration 3060, lr = 0.00545451
I0409 20:22:02.862282 14973 solver.cpp:218] Iteration 3072 (2.85231 iter/s, 4.20712s/12 iters), loss = 3.25473
I0409 20:22:02.862617 14973 solver.cpp:237]     Train net output #0: loss = 3.25473 (* 1 = 3.25473 loss)
I0409 20:22:02.862624 14973 sgd_solver.cpp:105] Iteration 3072, lr = 0.00544156
I0409 20:22:07.885177 14973 solver.cpp:218] Iteration 3084 (2.38931 iter/s, 5.02238s/12 iters), loss = 3.42815
I0409 20:22:07.885231 14973 solver.cpp:237]     Train net output #0: loss = 3.42815 (* 1 = 3.42815 loss)
I0409 20:22:07.885242 14973 sgd_solver.cpp:105] Iteration 3084, lr = 0.00542864
I0409 20:22:12.832720 14973 solver.cpp:218] Iteration 3096 (2.42556 iter/s, 4.94731s/12 iters), loss = 3.32736
I0409 20:22:12.832767 14973 solver.cpp:237]     Train net output #0: loss = 3.32736 (* 1 = 3.32736 loss)
I0409 20:22:12.832778 14973 sgd_solver.cpp:105] Iteration 3096, lr = 0.00541575
I0409 20:22:17.942991 14973 solver.cpp:218] Iteration 3108 (2.34832 iter/s, 5.11004s/12 iters), loss = 3.07028
I0409 20:22:17.943051 14973 solver.cpp:237]     Train net output #0: loss = 3.07028 (* 1 = 3.07028 loss)
I0409 20:22:17.943063 14973 sgd_solver.cpp:105] Iteration 3108, lr = 0.00540289
I0409 20:22:22.980902 14973 solver.cpp:218] Iteration 3120 (2.38205 iter/s, 5.03767s/12 iters), loss = 3.08111
I0409 20:22:22.980952 14973 solver.cpp:237]     Train net output #0: loss = 3.08111 (* 1 = 3.08111 loss)
I0409 20:22:22.980962 14973 sgd_solver.cpp:105] Iteration 3120, lr = 0.00539006
I0409 20:22:28.329574 14973 solver.cpp:218] Iteration 3132 (2.24365 iter/s, 5.34843s/12 iters), loss = 3.39438
I0409 20:22:28.329632 14973 solver.cpp:237]     Train net output #0: loss = 3.39438 (* 1 = 3.39438 loss)
I0409 20:22:28.329644 14973 sgd_solver.cpp:105] Iteration 3132, lr = 0.00537727
I0409 20:22:29.437288 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:22:33.321596 14973 solver.cpp:218] Iteration 3144 (2.40395 iter/s, 4.99179s/12 iters), loss = 3.03939
I0409 20:22:33.321678 14973 solver.cpp:237]     Train net output #0: loss = 3.03939 (* 1 = 3.03939 loss)
I0409 20:22:33.321691 14973 sgd_solver.cpp:105] Iteration 3144, lr = 0.0053645
I0409 20:22:38.512466 14973 solver.cpp:218] Iteration 3156 (2.31187 iter/s, 5.19061s/12 iters), loss = 3.03876
I0409 20:22:38.512516 14973 solver.cpp:237]     Train net output #0: loss = 3.03876 (* 1 = 3.03876 loss)
I0409 20:22:38.512527 14973 sgd_solver.cpp:105] Iteration 3156, lr = 0.00535176
I0409 20:22:40.564498 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3162.caffemodel
I0409 20:22:42.143720 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3162.solverstate
I0409 20:22:43.360208 14973 solver.cpp:330] Iteration 3162, Testing net (#0)
I0409 20:22:43.360236 14973 net.cpp:676] Ignoring source layer train-data
I0409 20:22:46.658773 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:22:48.034447 14973 solver.cpp:397]     Test net output #0: accuracy = 0.183824
I0409 20:22:48.034494 14973 solver.cpp:397]     Test net output #1: loss = 3.2984 (* 1 = 3.2984 loss)
I0409 20:22:49.889464 14973 solver.cpp:218] Iteration 3168 (1.0548 iter/s, 11.3766s/12 iters), loss = 3.07807
I0409 20:22:49.889509 14973 solver.cpp:237]     Train net output #0: loss = 3.07807 (* 1 = 3.07807 loss)
I0409 20:22:49.889520 14973 sgd_solver.cpp:105] Iteration 3168, lr = 0.00533906
I0409 20:22:54.831135 14973 solver.cpp:218] Iteration 3180 (2.42844 iter/s, 4.94145s/12 iters), loss = 3.26325
I0409 20:22:54.831183 14973 solver.cpp:237]     Train net output #0: loss = 3.26325 (* 1 = 3.26325 loss)
I0409 20:22:54.831194 14973 sgd_solver.cpp:105] Iteration 3180, lr = 0.00532638
I0409 20:22:59.727584 14973 solver.cpp:218] Iteration 3192 (2.45087 iter/s, 4.89623s/12 iters), loss = 3.11262
I0409 20:22:59.727633 14973 solver.cpp:237]     Train net output #0: loss = 3.11262 (* 1 = 3.11262 loss)
I0409 20:22:59.727644 14973 sgd_solver.cpp:105] Iteration 3192, lr = 0.00531374
I0409 20:23:04.783886 14973 solver.cpp:218] Iteration 3204 (2.37338 iter/s, 5.05608s/12 iters), loss = 3.31757
I0409 20:23:04.784046 14973 solver.cpp:237]     Train net output #0: loss = 3.31757 (* 1 = 3.31757 loss)
I0409 20:23:04.784058 14973 sgd_solver.cpp:105] Iteration 3204, lr = 0.00530112
I0409 20:23:09.798374 14973 solver.cpp:218] Iteration 3216 (2.39323 iter/s, 5.01415s/12 iters), loss = 3.27673
I0409 20:23:09.798439 14973 solver.cpp:237]     Train net output #0: loss = 3.27673 (* 1 = 3.27673 loss)
I0409 20:23:09.798451 14973 sgd_solver.cpp:105] Iteration 3216, lr = 0.00528853
I0409 20:23:14.932570 14973 solver.cpp:218] Iteration 3228 (2.33737 iter/s, 5.13397s/12 iters), loss = 2.92212
I0409 20:23:14.932611 14973 solver.cpp:237]     Train net output #0: loss = 2.92212 (* 1 = 2.92212 loss)
I0409 20:23:14.932619 14973 sgd_solver.cpp:105] Iteration 3228, lr = 0.00527598
I0409 20:23:18.210688 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:23:20.010995 14973 solver.cpp:218] Iteration 3240 (2.36304 iter/s, 5.07821s/12 iters), loss = 3.34027
I0409 20:23:20.011032 14973 solver.cpp:237]     Train net output #0: loss = 3.34027 (* 1 = 3.34027 loss)
I0409 20:23:20.011044 14973 sgd_solver.cpp:105] Iteration 3240, lr = 0.00526345
I0409 20:23:25.168589 14973 solver.cpp:218] Iteration 3252 (2.32676 iter/s, 5.15738s/12 iters), loss = 3.00141
I0409 20:23:25.168632 14973 solver.cpp:237]     Train net output #0: loss = 3.00141 (* 1 = 3.00141 loss)
I0409 20:23:25.168642 14973 sgd_solver.cpp:105] Iteration 3252, lr = 0.00525095
I0409 20:23:30.003221 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3264.caffemodel
I0409 20:23:36.374601 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3264.solverstate
I0409 20:23:37.898993 14973 solver.cpp:330] Iteration 3264, Testing net (#0)
I0409 20:23:37.899013 14973 net.cpp:676] Ignoring source layer train-data
I0409 20:23:41.205973 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:23:42.508710 14973 solver.cpp:397]     Test net output #0: accuracy = 0.175858
I0409 20:23:42.508749 14973 solver.cpp:397]     Test net output #1: loss = 3.3216 (* 1 = 3.3216 loss)
I0409 20:23:42.595515 14973 solver.cpp:218] Iteration 3264 (0.688614 iter/s, 17.4263s/12 iters), loss = 3.21411
I0409 20:23:42.595562 14973 solver.cpp:237]     Train net output #0: loss = 3.21411 (* 1 = 3.21411 loss)
I0409 20:23:42.595572 14973 sgd_solver.cpp:105] Iteration 3264, lr = 0.00523849
I0409 20:23:46.781206 14973 solver.cpp:218] Iteration 3276 (2.86705 iter/s, 4.18549s/12 iters), loss = 3.09895
I0409 20:23:46.781257 14973 solver.cpp:237]     Train net output #0: loss = 3.09895 (* 1 = 3.09895 loss)
I0409 20:23:46.781268 14973 sgd_solver.cpp:105] Iteration 3276, lr = 0.00522605
I0409 20:23:52.129490 14973 solver.cpp:218] Iteration 3288 (2.24381 iter/s, 5.34804s/12 iters), loss = 3.06427
I0409 20:23:52.129549 14973 solver.cpp:237]     Train net output #0: loss = 3.06427 (* 1 = 3.06427 loss)
I0409 20:23:52.129563 14973 sgd_solver.cpp:105] Iteration 3288, lr = 0.00521364
I0409 20:23:57.326959 14973 solver.cpp:218] Iteration 3300 (2.30892 iter/s, 5.19723s/12 iters), loss = 3.1763
I0409 20:23:57.327008 14973 solver.cpp:237]     Train net output #0: loss = 3.1763 (* 1 = 3.1763 loss)
I0409 20:23:57.327019 14973 sgd_solver.cpp:105] Iteration 3300, lr = 0.00520126
I0409 20:24:02.356601 14973 solver.cpp:218] Iteration 3312 (2.38596 iter/s, 5.02941s/12 iters), loss = 3.19213
I0409 20:24:02.356648 14973 solver.cpp:237]     Train net output #0: loss = 3.19213 (* 1 = 3.19213 loss)
I0409 20:24:02.356658 14973 sgd_solver.cpp:105] Iteration 3312, lr = 0.00518892
I0409 20:24:07.573369 14973 solver.cpp:218] Iteration 3324 (2.30038 iter/s, 5.21653s/12 iters), loss = 2.87251
I0409 20:24:07.573530 14973 solver.cpp:237]     Train net output #0: loss = 2.87251 (* 1 = 2.87251 loss)
I0409 20:24:07.573546 14973 sgd_solver.cpp:105] Iteration 3324, lr = 0.0051766
I0409 20:24:12.593677 14973 solver.cpp:218] Iteration 3336 (2.39045 iter/s, 5.01998s/12 iters), loss = 2.97564
I0409 20:24:12.593721 14973 solver.cpp:237]     Train net output #0: loss = 2.97564 (* 1 = 2.97564 loss)
I0409 20:24:12.593731 14973 sgd_solver.cpp:105] Iteration 3336, lr = 0.00516431
I0409 20:24:13.072580 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:24:17.632536 14973 solver.cpp:218] Iteration 3348 (2.3816 iter/s, 5.03864s/12 iters), loss = 2.92304
I0409 20:24:17.632586 14973 solver.cpp:237]     Train net output #0: loss = 2.92304 (* 1 = 2.92304 loss)
I0409 20:24:17.632597 14973 sgd_solver.cpp:105] Iteration 3348, lr = 0.00515204
I0409 20:24:22.631040 14973 solver.cpp:218] Iteration 3360 (2.40083 iter/s, 4.99827s/12 iters), loss = 2.80966
I0409 20:24:22.631094 14973 solver.cpp:237]     Train net output #0: loss = 2.80966 (* 1 = 2.80966 loss)
I0409 20:24:22.631106 14973 sgd_solver.cpp:105] Iteration 3360, lr = 0.00513981
I0409 20:24:24.665621 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3366.caffemodel
I0409 20:24:26.903697 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3366.solverstate
I0409 20:24:28.110496 14973 solver.cpp:330] Iteration 3366, Testing net (#0)
I0409 20:24:28.110517 14973 net.cpp:676] Ignoring source layer train-data
I0409 20:24:31.103734 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:24:32.440632 14973 solver.cpp:397]     Test net output #0: accuracy = 0.200368
I0409 20:24:32.440682 14973 solver.cpp:397]     Test net output #1: loss = 3.2265 (* 1 = 3.2265 loss)
I0409 20:24:34.315446 14973 solver.cpp:218] Iteration 3372 (1.02705 iter/s, 11.684s/12 iters), loss = 2.89646
I0409 20:24:34.315507 14973 solver.cpp:237]     Train net output #0: loss = 2.89646 (* 1 = 2.89646 loss)
I0409 20:24:34.315521 14973 sgd_solver.cpp:105] Iteration 3372, lr = 0.00512761
I0409 20:24:39.474586 14973 solver.cpp:218] Iteration 3384 (2.32608 iter/s, 5.1589s/12 iters), loss = 3.0405
I0409 20:24:39.474684 14973 solver.cpp:237]     Train net output #0: loss = 3.0405 (* 1 = 3.0405 loss)
I0409 20:24:39.474694 14973 sgd_solver.cpp:105] Iteration 3384, lr = 0.00511544
I0409 20:24:44.593333 14973 solver.cpp:218] Iteration 3396 (2.34445 iter/s, 5.11846s/12 iters), loss = 2.96346
I0409 20:24:44.593376 14973 solver.cpp:237]     Train net output #0: loss = 2.96346 (* 1 = 2.96346 loss)
I0409 20:24:44.593386 14973 sgd_solver.cpp:105] Iteration 3396, lr = 0.00510329
I0409 20:24:49.559070 14973 solver.cpp:218] Iteration 3408 (2.41667 iter/s, 4.96552s/12 iters), loss = 3.00926
I0409 20:24:49.559126 14973 solver.cpp:237]     Train net output #0: loss = 3.00926 (* 1 = 3.00926 loss)
I0409 20:24:49.559139 14973 sgd_solver.cpp:105] Iteration 3408, lr = 0.00509117
I0409 20:24:54.604719 14973 solver.cpp:218] Iteration 3420 (2.3784 iter/s, 5.04542s/12 iters), loss = 2.77281
I0409 20:24:54.604761 14973 solver.cpp:237]     Train net output #0: loss = 2.77281 (* 1 = 2.77281 loss)
I0409 20:24:54.604770 14973 sgd_solver.cpp:105] Iteration 3420, lr = 0.00507909
I0409 20:24:59.650498 14973 solver.cpp:218] Iteration 3432 (2.37833 iter/s, 5.04555s/12 iters), loss = 3.15709
I0409 20:24:59.650557 14973 solver.cpp:237]     Train net output #0: loss = 3.15709 (* 1 = 3.15709 loss)
I0409 20:24:59.650568 14973 sgd_solver.cpp:105] Iteration 3432, lr = 0.00506703
I0409 20:25:02.246320 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:25:04.659587 14973 solver.cpp:218] Iteration 3444 (2.39576 iter/s, 5.00886s/12 iters), loss = 2.54808
I0409 20:25:04.659634 14973 solver.cpp:237]     Train net output #0: loss = 2.54808 (* 1 = 2.54808 loss)
I0409 20:25:04.659646 14973 sgd_solver.cpp:105] Iteration 3444, lr = 0.005055
I0409 20:25:09.702584 14973 solver.cpp:218] Iteration 3456 (2.37964 iter/s, 5.04277s/12 iters), loss = 3.06535
I0409 20:25:09.702713 14973 solver.cpp:237]     Train net output #0: loss = 3.06535 (* 1 = 3.06535 loss)
I0409 20:25:09.702724 14973 sgd_solver.cpp:105] Iteration 3456, lr = 0.005043
I0409 20:25:14.239523 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3468.caffemodel
I0409 20:25:16.669231 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3468.solverstate
I0409 20:25:19.085685 14973 solver.cpp:330] Iteration 3468, Testing net (#0)
I0409 20:25:19.085705 14973 net.cpp:676] Ignoring source layer train-data
I0409 20:25:19.451304 14973 blocking_queue.cpp:49] Waiting for data
I0409 20:25:22.172417 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:25:23.553292 14973 solver.cpp:397]     Test net output #0: accuracy = 0.231618
I0409 20:25:23.553334 14973 solver.cpp:397]     Test net output #1: loss = 3.06823 (* 1 = 3.06823 loss)
I0409 20:25:23.641780 14973 solver.cpp:218] Iteration 3468 (0.860919 iter/s, 13.9386s/12 iters), loss = 2.96034
I0409 20:25:23.641851 14973 solver.cpp:237]     Train net output #0: loss = 2.96034 (* 1 = 2.96034 loss)
I0409 20:25:23.641866 14973 sgd_solver.cpp:105] Iteration 3468, lr = 0.00503102
I0409 20:25:27.800437 14973 solver.cpp:218] Iteration 3480 (2.8857 iter/s, 4.15844s/12 iters), loss = 2.90325
I0409 20:25:27.800498 14973 solver.cpp:237]     Train net output #0: loss = 2.90325 (* 1 = 2.90325 loss)
I0409 20:25:27.800511 14973 sgd_solver.cpp:105] Iteration 3480, lr = 0.00501908
I0409 20:25:33.020917 14973 solver.cpp:218] Iteration 3492 (2.29875 iter/s, 5.22024s/12 iters), loss = 2.97746
I0409 20:25:33.020970 14973 solver.cpp:237]     Train net output #0: loss = 2.97746 (* 1 = 2.97746 loss)
I0409 20:25:33.020983 14973 sgd_solver.cpp:105] Iteration 3492, lr = 0.00500716
I0409 20:25:38.029764 14973 solver.cpp:218] Iteration 3504 (2.39587 iter/s, 5.00862s/12 iters), loss = 2.96487
I0409 20:25:38.029817 14973 solver.cpp:237]     Train net output #0: loss = 2.96487 (* 1 = 2.96487 loss)
I0409 20:25:38.029830 14973 sgd_solver.cpp:105] Iteration 3504, lr = 0.00499527
I0409 20:25:43.023869 14973 solver.cpp:218] Iteration 3516 (2.40294 iter/s, 4.99388s/12 iters), loss = 2.494
I0409 20:25:43.023988 14973 solver.cpp:237]     Train net output #0: loss = 2.494 (* 1 = 2.494 loss)
I0409 20:25:43.024000 14973 sgd_solver.cpp:105] Iteration 3516, lr = 0.00498341
I0409 20:25:48.060894 14973 solver.cpp:218] Iteration 3528 (2.3825 iter/s, 5.03673s/12 iters), loss = 2.66403
I0409 20:25:48.060945 14973 solver.cpp:237]     Train net output #0: loss = 2.66403 (* 1 = 2.66403 loss)
I0409 20:25:48.060956 14973 sgd_solver.cpp:105] Iteration 3528, lr = 0.00497158
I0409 20:25:52.906647 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:25:53.165482 14973 solver.cpp:218] Iteration 3540 (2.35093 iter/s, 5.10435s/12 iters), loss = 2.81836
I0409 20:25:53.165537 14973 solver.cpp:237]     Train net output #0: loss = 2.81836 (* 1 = 2.81836 loss)
I0409 20:25:53.165549 14973 sgd_solver.cpp:105] Iteration 3540, lr = 0.00495978
I0409 20:25:58.113538 14973 solver.cpp:218] Iteration 3552 (2.42531 iter/s, 4.94783s/12 iters), loss = 2.76947
I0409 20:25:58.113590 14973 solver.cpp:237]     Train net output #0: loss = 2.76947 (* 1 = 2.76947 loss)
I0409 20:25:58.113601 14973 sgd_solver.cpp:105] Iteration 3552, lr = 0.004948
I0409 20:26:03.044037 14973 solver.cpp:218] Iteration 3564 (2.43394 iter/s, 4.93027s/12 iters), loss = 2.40315
I0409 20:26:03.044080 14973 solver.cpp:237]     Train net output #0: loss = 2.40315 (* 1 = 2.40315 loss)
I0409 20:26:03.044090 14973 sgd_solver.cpp:105] Iteration 3564, lr = 0.00493626
I0409 20:26:05.057577 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3570.caffemodel
I0409 20:26:08.496304 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3570.solverstate
I0409 20:26:10.480744 14973 solver.cpp:330] Iteration 3570, Testing net (#0)
I0409 20:26:10.480767 14973 net.cpp:676] Ignoring source layer train-data
I0409 20:26:13.590977 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:26:15.032097 14973 solver.cpp:397]     Test net output #0: accuracy = 0.262255
I0409 20:26:15.032142 14973 solver.cpp:397]     Test net output #1: loss = 2.9025 (* 1 = 2.9025 loss)
I0409 20:26:16.933631 14973 solver.cpp:218] Iteration 3576 (0.863988 iter/s, 13.8891s/12 iters), loss = 3.01769
I0409 20:26:16.933681 14973 solver.cpp:237]     Train net output #0: loss = 3.01769 (* 1 = 3.01769 loss)
I0409 20:26:16.933692 14973 sgd_solver.cpp:105] Iteration 3576, lr = 0.00492454
I0409 20:26:21.888150 14973 solver.cpp:218] Iteration 3588 (2.42214 iter/s, 4.95429s/12 iters), loss = 2.62601
I0409 20:26:21.888201 14973 solver.cpp:237]     Train net output #0: loss = 2.62601 (* 1 = 2.62601 loss)
I0409 20:26:21.888212 14973 sgd_solver.cpp:105] Iteration 3588, lr = 0.00491284
I0409 20:26:26.847107 14973 solver.cpp:218] Iteration 3600 (2.41997 iter/s, 4.95873s/12 iters), loss = 2.54831
I0409 20:26:26.847153 14973 solver.cpp:237]     Train net output #0: loss = 2.54831 (* 1 = 2.54831 loss)
I0409 20:26:26.847162 14973 sgd_solver.cpp:105] Iteration 3600, lr = 0.00490118
I0409 20:26:31.929636 14973 solver.cpp:218] Iteration 3612 (2.36113 iter/s, 5.08231s/12 iters), loss = 2.62722
I0409 20:26:31.929679 14973 solver.cpp:237]     Train net output #0: loss = 2.62722 (* 1 = 2.62722 loss)
I0409 20:26:31.929689 14973 sgd_solver.cpp:105] Iteration 3612, lr = 0.00488954
I0409 20:26:36.943223 14973 solver.cpp:218] Iteration 3624 (2.3936 iter/s, 5.01337s/12 iters), loss = 2.83041
I0409 20:26:36.943276 14973 solver.cpp:237]     Train net output #0: loss = 2.83041 (* 1 = 2.83041 loss)
I0409 20:26:36.943289 14973 sgd_solver.cpp:105] Iteration 3624, lr = 0.00487793
I0409 20:26:41.964031 14973 solver.cpp:218] Iteration 3636 (2.39016 iter/s, 5.02058s/12 iters), loss = 2.67005
I0409 20:26:41.964087 14973 solver.cpp:237]     Train net output #0: loss = 2.67005 (* 1 = 2.67005 loss)
I0409 20:26:41.964099 14973 sgd_solver.cpp:105] Iteration 3636, lr = 0.00486635
I0409 20:26:43.857475 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:26:47.079447 14973 solver.cpp:218] Iteration 3648 (2.34596 iter/s, 5.11518s/12 iters), loss = 2.53121
I0409 20:26:47.079490 14973 solver.cpp:237]     Train net output #0: loss = 2.53121 (* 1 = 2.53121 loss)
I0409 20:26:47.079499 14973 sgd_solver.cpp:105] Iteration 3648, lr = 0.0048548
I0409 20:26:52.191956 14973 solver.cpp:218] Iteration 3660 (2.34729 iter/s, 5.11229s/12 iters), loss = 2.48151
I0409 20:26:52.192004 14973 solver.cpp:237]     Train net output #0: loss = 2.48151 (* 1 = 2.48151 loss)
I0409 20:26:52.192015 14973 sgd_solver.cpp:105] Iteration 3660, lr = 0.00484327
I0409 20:26:56.619081 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3672.caffemodel
I0409 20:26:58.203464 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3672.solverstate
I0409 20:26:59.416782 14973 solver.cpp:330] Iteration 3672, Testing net (#0)
I0409 20:26:59.416811 14973 net.cpp:676] Ignoring source layer train-data
I0409 20:27:02.348613 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:27:03.813233 14973 solver.cpp:397]     Test net output #0: accuracy = 0.264706
I0409 20:27:03.813271 14973 solver.cpp:397]     Test net output #1: loss = 2.85791 (* 1 = 2.85791 loss)
I0409 20:27:03.900044 14973 solver.cpp:218] Iteration 3672 (1.02497 iter/s, 11.7076s/12 iters), loss = 2.4511
I0409 20:27:03.900094 14973 solver.cpp:237]     Train net output #0: loss = 2.4511 (* 1 = 2.4511 loss)
I0409 20:27:03.900107 14973 sgd_solver.cpp:105] Iteration 3672, lr = 0.00483177
I0409 20:27:08.126497 14973 solver.cpp:218] Iteration 3684 (2.83939 iter/s, 4.22626s/12 iters), loss = 2.77639
I0409 20:27:08.126541 14973 solver.cpp:237]     Train net output #0: loss = 2.77639 (* 1 = 2.77639 loss)
I0409 20:27:08.126552 14973 sgd_solver.cpp:105] Iteration 3684, lr = 0.0048203
I0409 20:27:13.079535 14973 solver.cpp:218] Iteration 3696 (2.42287 iter/s, 4.95281s/12 iters), loss = 2.48155
I0409 20:27:13.079589 14973 solver.cpp:237]     Train net output #0: loss = 2.48155 (* 1 = 2.48155 loss)
I0409 20:27:13.079600 14973 sgd_solver.cpp:105] Iteration 3696, lr = 0.00480886
I0409 20:27:17.989650 14973 solver.cpp:218] Iteration 3708 (2.44405 iter/s, 4.90989s/12 iters), loss = 2.67158
I0409 20:27:17.989802 14973 solver.cpp:237]     Train net output #0: loss = 2.67158 (* 1 = 2.67158 loss)
I0409 20:27:17.989816 14973 sgd_solver.cpp:105] Iteration 3708, lr = 0.00479744
I0409 20:27:23.070976 14973 solver.cpp:218] Iteration 3720 (2.36174 iter/s, 5.081s/12 iters), loss = 2.5707
I0409 20:27:23.071022 14973 solver.cpp:237]     Train net output #0: loss = 2.5707 (* 1 = 2.5707 loss)
I0409 20:27:23.071033 14973 sgd_solver.cpp:105] Iteration 3720, lr = 0.00478605
I0409 20:27:28.013994 14973 solver.cpp:218] Iteration 3732 (2.42778 iter/s, 4.94279s/12 iters), loss = 2.44478
I0409 20:27:28.014045 14973 solver.cpp:237]     Train net output #0: loss = 2.44478 (* 1 = 2.44478 loss)
I0409 20:27:28.014055 14973 sgd_solver.cpp:105] Iteration 3732, lr = 0.00477469
I0409 20:27:31.992806 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:27:32.944429 14973 solver.cpp:218] Iteration 3744 (2.43397 iter/s, 4.93021s/12 iters), loss = 2.56281
I0409 20:27:32.944484 14973 solver.cpp:237]     Train net output #0: loss = 2.56281 (* 1 = 2.56281 loss)
I0409 20:27:32.944497 14973 sgd_solver.cpp:105] Iteration 3744, lr = 0.00476335
I0409 20:27:37.948542 14973 solver.cpp:218] Iteration 3756 (2.39814 iter/s, 5.00388s/12 iters), loss = 2.58525
I0409 20:27:37.948596 14973 solver.cpp:237]     Train net output #0: loss = 2.58525 (* 1 = 2.58525 loss)
I0409 20:27:37.948606 14973 sgd_solver.cpp:105] Iteration 3756, lr = 0.00475204
I0409 20:27:43.740322 14973 solver.cpp:218] Iteration 3768 (2.07199 iter/s, 5.79153s/12 iters), loss = 2.21009
I0409 20:27:43.740373 14973 solver.cpp:237]     Train net output #0: loss = 2.21009 (* 1 = 2.21009 loss)
I0409 20:27:43.740382 14973 sgd_solver.cpp:105] Iteration 3768, lr = 0.00474076
I0409 20:27:45.783118 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3774.caffemodel
I0409 20:27:48.277844 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3774.solverstate
I0409 20:27:49.639549 14973 solver.cpp:330] Iteration 3774, Testing net (#0)
I0409 20:27:49.639573 14973 net.cpp:676] Ignoring source layer train-data
I0409 20:27:52.627148 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:27:54.180828 14973 solver.cpp:397]     Test net output #0: accuracy = 0.289216
I0409 20:27:54.180858 14973 solver.cpp:397]     Test net output #1: loss = 2.85089 (* 1 = 2.85089 loss)
I0409 20:27:56.113376 14973 solver.cpp:218] Iteration 3780 (0.969886 iter/s, 12.3726s/12 iters), loss = 2.481
I0409 20:27:56.113425 14973 solver.cpp:237]     Train net output #0: loss = 2.481 (* 1 = 2.481 loss)
I0409 20:27:56.113435 14973 sgd_solver.cpp:105] Iteration 3780, lr = 0.00472951
I0409 20:28:01.217059 14973 solver.cpp:218] Iteration 3792 (2.35135 iter/s, 5.10346s/12 iters), loss = 2.43964
I0409 20:28:01.217097 14973 solver.cpp:237]     Train net output #0: loss = 2.43964 (* 1 = 2.43964 loss)
I0409 20:28:01.217106 14973 sgd_solver.cpp:105] Iteration 3792, lr = 0.00471828
I0409 20:28:06.151883 14973 solver.cpp:218] Iteration 3804 (2.43181 iter/s, 4.93461s/12 iters), loss = 2.46718
I0409 20:28:06.151935 14973 solver.cpp:237]     Train net output #0: loss = 2.46718 (* 1 = 2.46718 loss)
I0409 20:28:06.151947 14973 sgd_solver.cpp:105] Iteration 3804, lr = 0.00470707
I0409 20:28:11.067747 14973 solver.cpp:218] Iteration 3816 (2.44119 iter/s, 4.91564s/12 iters), loss = 2.37207
I0409 20:28:11.067790 14973 solver.cpp:237]     Train net output #0: loss = 2.37207 (* 1 = 2.37207 loss)
I0409 20:28:11.067801 14973 sgd_solver.cpp:105] Iteration 3816, lr = 0.0046959
I0409 20:28:16.079866 14973 solver.cpp:218] Iteration 3828 (2.3943 iter/s, 5.01189s/12 iters), loss = 2.28491
I0409 20:28:16.079921 14973 solver.cpp:237]     Train net output #0: loss = 2.28491 (* 1 = 2.28491 loss)
I0409 20:28:16.079932 14973 sgd_solver.cpp:105] Iteration 3828, lr = 0.00468475
I0409 20:28:21.091789 14973 solver.cpp:218] Iteration 3840 (2.3944 iter/s, 5.01169s/12 iters), loss = 2.66475
I0409 20:28:21.091912 14973 solver.cpp:237]     Train net output #0: loss = 2.66475 (* 1 = 2.66475 loss)
I0409 20:28:21.091922 14973 sgd_solver.cpp:105] Iteration 3840, lr = 0.00467363
I0409 20:28:22.213083 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:28:26.037585 14973 solver.cpp:218] Iteration 3852 (2.42645 iter/s, 4.94549s/12 iters), loss = 2.18749
I0409 20:28:26.037633 14973 solver.cpp:237]     Train net output #0: loss = 2.18749 (* 1 = 2.18749 loss)
I0409 20:28:26.037642 14973 sgd_solver.cpp:105] Iteration 3852, lr = 0.00466253
I0409 20:28:31.054658 14973 solver.cpp:218] Iteration 3864 (2.39194 iter/s, 5.01685s/12 iters), loss = 2.47139
I0409 20:28:31.054700 14973 solver.cpp:237]     Train net output #0: loss = 2.47139 (* 1 = 2.47139 loss)
I0409 20:28:31.054709 14973 sgd_solver.cpp:105] Iteration 3864, lr = 0.00465146
I0409 20:28:35.589164 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3876.caffemodel
I0409 20:28:37.158525 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3876.solverstate
I0409 20:28:39.974182 14973 solver.cpp:330] Iteration 3876, Testing net (#0)
I0409 20:28:39.974211 14973 net.cpp:676] Ignoring source layer train-data
I0409 20:28:43.141693 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:28:44.712987 14973 solver.cpp:397]     Test net output #0: accuracy = 0.302696
I0409 20:28:44.713043 14973 solver.cpp:397]     Test net output #1: loss = 2.7588 (* 1 = 2.7588 loss)
I0409 20:28:44.800012 14973 solver.cpp:218] Iteration 3876 (0.873054 iter/s, 13.7448s/12 iters), loss = 2.41733
I0409 20:28:44.800079 14973 solver.cpp:237]     Train net output #0: loss = 2.41733 (* 1 = 2.41733 loss)
I0409 20:28:44.800094 14973 sgd_solver.cpp:105] Iteration 3876, lr = 0.00464042
I0409 20:28:49.370640 14973 solver.cpp:218] Iteration 3888 (2.62559 iter/s, 4.5704s/12 iters), loss = 2.42944
I0409 20:28:49.370690 14973 solver.cpp:237]     Train net output #0: loss = 2.42944 (* 1 = 2.42944 loss)
I0409 20:28:49.370702 14973 sgd_solver.cpp:105] Iteration 3888, lr = 0.0046294
I0409 20:28:54.557430 14973 solver.cpp:218] Iteration 3900 (2.31367 iter/s, 5.18656s/12 iters), loss = 2.38894
I0409 20:28:54.557520 14973 solver.cpp:237]     Train net output #0: loss = 2.38894 (* 1 = 2.38894 loss)
I0409 20:28:54.557530 14973 sgd_solver.cpp:105] Iteration 3900, lr = 0.00461841
I0409 20:28:59.540849 14973 solver.cpp:218] Iteration 3912 (2.40811 iter/s, 4.98315s/12 iters), loss = 2.42808
I0409 20:28:59.540900 14973 solver.cpp:237]     Train net output #0: loss = 2.42808 (* 1 = 2.42808 loss)
I0409 20:28:59.540912 14973 sgd_solver.cpp:105] Iteration 3912, lr = 0.00460744
I0409 20:29:04.635135 14973 solver.cpp:218] Iteration 3924 (2.35569 iter/s, 5.09405s/12 iters), loss = 2.57612
I0409 20:29:04.635190 14973 solver.cpp:237]     Train net output #0: loss = 2.57612 (* 1 = 2.57612 loss)
I0409 20:29:04.635201 14973 sgd_solver.cpp:105] Iteration 3924, lr = 0.0045965
I0409 20:29:09.749712 14973 solver.cpp:218] Iteration 3936 (2.34634 iter/s, 5.11434s/12 iters), loss = 2.27396
I0409 20:29:09.749760 14973 solver.cpp:237]     Train net output #0: loss = 2.27396 (* 1 = 2.27396 loss)
I0409 20:29:09.749769 14973 sgd_solver.cpp:105] Iteration 3936, lr = 0.00458559
I0409 20:29:13.107153 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:29:14.704977 14973 solver.cpp:218] Iteration 3948 (2.42177 iter/s, 4.95504s/12 iters), loss = 2.46699
I0409 20:29:14.705016 14973 solver.cpp:237]     Train net output #0: loss = 2.46699 (* 1 = 2.46699 loss)
I0409 20:29:14.705026 14973 sgd_solver.cpp:105] Iteration 3948, lr = 0.0045747
I0409 20:29:19.717770 14973 solver.cpp:218] Iteration 3960 (2.39398 iter/s, 5.01258s/12 iters), loss = 2.44344
I0409 20:29:19.717818 14973 solver.cpp:237]     Train net output #0: loss = 2.44344 (* 1 = 2.44344 loss)
I0409 20:29:19.717828 14973 sgd_solver.cpp:105] Iteration 3960, lr = 0.00456384
I0409 20:29:24.703321 14973 solver.cpp:218] Iteration 3972 (2.40706 iter/s, 4.98533s/12 iters), loss = 2.41879
I0409 20:29:24.704721 14973 solver.cpp:237]     Train net output #0: loss = 2.41879 (* 1 = 2.41879 loss)
I0409 20:29:24.704731 14973 sgd_solver.cpp:105] Iteration 3972, lr = 0.00455301
I0409 20:29:26.717914 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3978.caffemodel
I0409 20:29:30.339130 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3978.solverstate
I0409 20:29:31.687297 14973 solver.cpp:330] Iteration 3978, Testing net (#0)
I0409 20:29:31.687326 14973 net.cpp:676] Ignoring source layer train-data
I0409 20:29:34.713564 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:29:36.300092 14973 solver.cpp:397]     Test net output #0: accuracy = 0.296569
I0409 20:29:36.300143 14973 solver.cpp:397]     Test net output #1: loss = 2.82461 (* 1 = 2.82461 loss)
I0409 20:29:38.271687 14973 solver.cpp:218] Iteration 3984 (0.884531 iter/s, 13.5665s/12 iters), loss = 2.34163
I0409 20:29:38.271736 14973 solver.cpp:237]     Train net output #0: loss = 2.34163 (* 1 = 2.34163 loss)
I0409 20:29:38.271744 14973 sgd_solver.cpp:105] Iteration 3984, lr = 0.0045422
I0409 20:29:43.283129 14973 solver.cpp:218] Iteration 3996 (2.39463 iter/s, 5.01121s/12 iters), loss = 2.37491
I0409 20:29:43.283176 14973 solver.cpp:237]     Train net output #0: loss = 2.37491 (* 1 = 2.37491 loss)
I0409 20:29:43.283186 14973 sgd_solver.cpp:105] Iteration 3996, lr = 0.00453141
I0409 20:29:48.298130 14973 solver.cpp:218] Iteration 4008 (2.39293 iter/s, 5.01477s/12 iters), loss = 2.5273
I0409 20:29:48.298174 14973 solver.cpp:237]     Train net output #0: loss = 2.5273 (* 1 = 2.5273 loss)
I0409 20:29:48.298184 14973 sgd_solver.cpp:105] Iteration 4008, lr = 0.00452066
I0409 20:29:53.263887 14973 solver.cpp:218] Iteration 4020 (2.41666 iter/s, 4.96553s/12 iters), loss = 2.45156
I0409 20:29:53.263942 14973 solver.cpp:237]     Train net output #0: loss = 2.45156 (* 1 = 2.45156 loss)
I0409 20:29:53.263954 14973 sgd_solver.cpp:105] Iteration 4020, lr = 0.00450992
I0409 20:29:58.324621 14973 solver.cpp:218] Iteration 4032 (2.3713 iter/s, 5.06051s/12 iters), loss = 2.29529
I0409 20:29:58.324721 14973 solver.cpp:237]     Train net output #0: loss = 2.29529 (* 1 = 2.29529 loss)
I0409 20:29:58.324730 14973 sgd_solver.cpp:105] Iteration 4032, lr = 0.00449921
I0409 20:30:03.256209 14973 solver.cpp:218] Iteration 4044 (2.43343 iter/s, 4.93131s/12 iters), loss = 2.25583
I0409 20:30:03.256258 14973 solver.cpp:237]     Train net output #0: loss = 2.25583 (* 1 = 2.25583 loss)
I0409 20:30:03.256269 14973 sgd_solver.cpp:105] Iteration 4044, lr = 0.00448853
I0409 20:30:03.762244 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:30:08.421326 14973 solver.cpp:218] Iteration 4056 (2.32338 iter/s, 5.16488s/12 iters), loss = 2.31464
I0409 20:30:08.421370 14973 solver.cpp:237]     Train net output #0: loss = 2.31464 (* 1 = 2.31464 loss)
I0409 20:30:08.421380 14973 sgd_solver.cpp:105] Iteration 4056, lr = 0.00447788
I0409 20:30:13.478370 14973 solver.cpp:218] Iteration 4068 (2.37303 iter/s, 5.05682s/12 iters), loss = 2.27864
I0409 20:30:13.478415 14973 solver.cpp:237]     Train net output #0: loss = 2.27864 (* 1 = 2.27864 loss)
I0409 20:30:13.478425 14973 sgd_solver.cpp:105] Iteration 4068, lr = 0.00446724
I0409 20:30:17.974997 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4080.caffemodel
I0409 20:30:19.546838 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4080.solverstate
I0409 20:30:20.752647 14973 solver.cpp:330] Iteration 4080, Testing net (#0)
I0409 20:30:20.752676 14973 net.cpp:676] Ignoring source layer train-data
I0409 20:30:23.802754 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:30:25.448101 14973 solver.cpp:397]     Test net output #0: accuracy = 0.315564
I0409 20:30:25.448148 14973 solver.cpp:397]     Test net output #1: loss = 2.6406 (* 1 = 2.6406 loss)
I0409 20:30:25.535264 14973 solver.cpp:218] Iteration 4080 (0.995318 iter/s, 12.0564s/12 iters), loss = 2.30944
I0409 20:30:25.535320 14973 solver.cpp:237]     Train net output #0: loss = 2.30944 (* 1 = 2.30944 loss)
I0409 20:30:25.535331 14973 sgd_solver.cpp:105] Iteration 4080, lr = 0.00445664
I0409 20:30:29.854739 14973 solver.cpp:218] Iteration 4092 (2.77825 iter/s, 4.31926s/12 iters), loss = 2.3747
I0409 20:30:29.854874 14973 solver.cpp:237]     Train net output #0: loss = 2.3747 (* 1 = 2.3747 loss)
I0409 20:30:29.854887 14973 sgd_solver.cpp:105] Iteration 4092, lr = 0.00444606
I0409 20:30:35.247120 14973 solver.cpp:218] Iteration 4104 (2.2255 iter/s, 5.39206s/12 iters), loss = 2.34953
I0409 20:30:35.247180 14973 solver.cpp:237]     Train net output #0: loss = 2.34953 (* 1 = 2.34953 loss)
I0409 20:30:35.247193 14973 sgd_solver.cpp:105] Iteration 4104, lr = 0.0044355
I0409 20:30:40.373102 14973 solver.cpp:218] Iteration 4116 (2.34112 iter/s, 5.12575s/12 iters), loss = 2.28574
I0409 20:30:40.373139 14973 solver.cpp:237]     Train net output #0: loss = 2.28574 (* 1 = 2.28574 loss)
I0409 20:30:40.373147 14973 sgd_solver.cpp:105] Iteration 4116, lr = 0.00442497
I0409 20:30:45.703410 14973 solver.cpp:218] Iteration 4128 (2.25137 iter/s, 5.33008s/12 iters), loss = 1.84178
I0409 20:30:45.703451 14973 solver.cpp:237]     Train net output #0: loss = 1.84178 (* 1 = 1.84178 loss)
I0409 20:30:45.703460 14973 sgd_solver.cpp:105] Iteration 4128, lr = 0.00441447
I0409 20:30:50.866354 14973 solver.cpp:218] Iteration 4140 (2.32436 iter/s, 5.16272s/12 iters), loss = 2.28583
I0409 20:30:50.866408 14973 solver.cpp:237]     Train net output #0: loss = 2.28583 (* 1 = 2.28583 loss)
I0409 20:30:50.866421 14973 sgd_solver.cpp:105] Iteration 4140, lr = 0.00440398
I0409 20:30:53.545753 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:30:55.931767 14973 solver.cpp:218] Iteration 4152 (2.36912 iter/s, 5.06518s/12 iters), loss = 2.07915
I0409 20:30:55.931823 14973 solver.cpp:237]     Train net output #0: loss = 2.07915 (* 1 = 2.07915 loss)
I0409 20:30:55.931835 14973 sgd_solver.cpp:105] Iteration 4152, lr = 0.00439353
I0409 20:30:57.121979 14973 blocking_queue.cpp:49] Waiting for data
I0409 20:31:01.031764 14973 solver.cpp:218] Iteration 4164 (2.35305 iter/s, 5.09976s/12 iters), loss = 2.2569
I0409 20:31:01.031877 14973 solver.cpp:237]     Train net output #0: loss = 2.2569 (* 1 = 2.2569 loss)
I0409 20:31:01.031889 14973 sgd_solver.cpp:105] Iteration 4164, lr = 0.0043831
I0409 20:31:06.107983 14973 solver.cpp:218] Iteration 4176 (2.3641 iter/s, 5.07593s/12 iters), loss = 2.3284
I0409 20:31:06.108029 14973 solver.cpp:237]     Train net output #0: loss = 2.3284 (* 1 = 2.3284 loss)
I0409 20:31:06.108039 14973 sgd_solver.cpp:105] Iteration 4176, lr = 0.00437269
I0409 20:31:08.159718 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4182.caffemodel
I0409 20:31:09.850329 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4182.solverstate
I0409 20:31:12.121742 14973 solver.cpp:330] Iteration 4182, Testing net (#0)
I0409 20:31:12.121769 14973 net.cpp:676] Ignoring source layer train-data
I0409 20:31:15.056810 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:31:16.715294 14973 solver.cpp:397]     Test net output #0: accuracy = 0.323529
I0409 20:31:16.715337 14973 solver.cpp:397]     Test net output #1: loss = 2.59921 (* 1 = 2.59921 loss)
I0409 20:31:18.590469 14973 solver.cpp:218] Iteration 4188 (0.961383 iter/s, 12.482s/12 iters), loss = 2.13889
I0409 20:31:18.590519 14973 solver.cpp:237]     Train net output #0: loss = 2.13889 (* 1 = 2.13889 loss)
I0409 20:31:18.590531 14973 sgd_solver.cpp:105] Iteration 4188, lr = 0.00436231
I0409 20:31:23.613237 14973 solver.cpp:218] Iteration 4200 (2.38923 iter/s, 5.02254s/12 iters), loss = 2.12995
I0409 20:31:23.613293 14973 solver.cpp:237]     Train net output #0: loss = 2.12995 (* 1 = 2.12995 loss)
I0409 20:31:23.613304 14973 sgd_solver.cpp:105] Iteration 4200, lr = 0.00435195
I0409 20:31:28.625553 14973 solver.cpp:218] Iteration 4212 (2.39422 iter/s, 5.01208s/12 iters), loss = 2.11846
I0409 20:31:28.625617 14973 solver.cpp:237]     Train net output #0: loss = 2.11846 (* 1 = 2.11846 loss)
I0409 20:31:28.625629 14973 sgd_solver.cpp:105] Iteration 4212, lr = 0.00434162
I0409 20:31:33.609623 14973 solver.cpp:218] Iteration 4224 (2.40778 iter/s, 4.98384s/12 iters), loss = 2.06737
I0409 20:31:33.609727 14973 solver.cpp:237]     Train net output #0: loss = 2.06737 (* 1 = 2.06737 loss)
I0409 20:31:33.609740 14973 sgd_solver.cpp:105] Iteration 4224, lr = 0.00433131
I0409 20:31:38.588223 14973 solver.cpp:218] Iteration 4236 (2.41045 iter/s, 4.97832s/12 iters), loss = 1.94832
I0409 20:31:38.588263 14973 solver.cpp:237]     Train net output #0: loss = 1.94832 (* 1 = 1.94832 loss)
I0409 20:31:38.588271 14973 sgd_solver.cpp:105] Iteration 4236, lr = 0.00432103
I0409 20:31:43.390002 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:31:43.615211 14973 solver.cpp:218] Iteration 4248 (2.38722 iter/s, 5.02677s/12 iters), loss = 2.16021
I0409 20:31:43.615255 14973 solver.cpp:237]     Train net output #0: loss = 2.16021 (* 1 = 2.16021 loss)
I0409 20:31:43.615264 14973 sgd_solver.cpp:105] Iteration 4248, lr = 0.00431077
I0409 20:31:48.523440 14973 solver.cpp:218] Iteration 4260 (2.44498 iter/s, 4.90801s/12 iters), loss = 1.87588
I0409 20:31:48.523483 14973 solver.cpp:237]     Train net output #0: loss = 1.87588 (* 1 = 1.87588 loss)
I0409 20:31:48.523491 14973 sgd_solver.cpp:105] Iteration 4260, lr = 0.00430053
I0409 20:31:53.627187 14973 solver.cpp:218] Iteration 4272 (2.35132 iter/s, 5.10352s/12 iters), loss = 2.05423
I0409 20:31:53.627229 14973 solver.cpp:237]     Train net output #0: loss = 2.05423 (* 1 = 2.05423 loss)
I0409 20:31:53.627238 14973 sgd_solver.cpp:105] Iteration 4272, lr = 0.00429032
I0409 20:31:58.095664 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4284.caffemodel
I0409 20:32:00.108875 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4284.solverstate
I0409 20:32:01.307929 14973 solver.cpp:330] Iteration 4284, Testing net (#0)
I0409 20:32:01.307951 14973 net.cpp:676] Ignoring source layer train-data
I0409 20:32:04.222676 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:32:06.102520 14973 solver.cpp:397]     Test net output #0: accuracy = 0.33701
I0409 20:32:06.102564 14973 solver.cpp:397]     Test net output #1: loss = 2.60582 (* 1 = 2.60582 loss)
I0409 20:32:06.189553 14973 solver.cpp:218] Iteration 4284 (0.95527 iter/s, 12.5619s/12 iters), loss = 2.16431
I0409 20:32:06.189625 14973 solver.cpp:237]     Train net output #0: loss = 2.16431 (* 1 = 2.16431 loss)
I0409 20:32:06.189640 14973 sgd_solver.cpp:105] Iteration 4284, lr = 0.00428014
I0409 20:32:10.584038 14973 solver.cpp:218] Iteration 4296 (2.73084 iter/s, 4.39426s/12 iters), loss = 2.0102
I0409 20:32:10.584084 14973 solver.cpp:237]     Train net output #0: loss = 2.0102 (* 1 = 2.0102 loss)
I0409 20:32:10.584092 14973 sgd_solver.cpp:105] Iteration 4296, lr = 0.00426998
I0409 20:32:15.848747 14973 solver.cpp:218] Iteration 4308 (2.27943 iter/s, 5.26448s/12 iters), loss = 2.00653
I0409 20:32:15.848793 14973 solver.cpp:237]     Train net output #0: loss = 2.00653 (* 1 = 2.00653 loss)
I0409 20:32:15.848801 14973 sgd_solver.cpp:105] Iteration 4308, lr = 0.00425984
I0409 20:32:21.019800 14973 solver.cpp:218] Iteration 4320 (2.32072 iter/s, 5.17082s/12 iters), loss = 2.04223
I0409 20:32:21.019858 14973 solver.cpp:237]     Train net output #0: loss = 2.04223 (* 1 = 2.04223 loss)
I0409 20:32:21.019871 14973 sgd_solver.cpp:105] Iteration 4320, lr = 0.00424972
I0409 20:32:26.003330 14973 solver.cpp:218] Iteration 4332 (2.40804 iter/s, 4.9833s/12 iters), loss = 2.13564
I0409 20:32:26.003371 14973 solver.cpp:237]     Train net output #0: loss = 2.13564 (* 1 = 2.13564 loss)
I0409 20:32:26.003379 14973 sgd_solver.cpp:105] Iteration 4332, lr = 0.00423964
I0409 20:32:30.926643 14973 solver.cpp:218] Iteration 4344 (2.43749 iter/s, 4.9231s/12 iters), loss = 2.01769
I0409 20:32:30.926690 14973 solver.cpp:237]     Train net output #0: loss = 2.01769 (* 1 = 2.01769 loss)
I0409 20:32:30.926702 14973 sgd_solver.cpp:105] Iteration 4344, lr = 0.00422957
I0409 20:32:32.804360 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:32:35.910817 14973 solver.cpp:218] Iteration 4356 (2.40773 iter/s, 4.98395s/12 iters), loss = 1.99067
I0409 20:32:35.910960 14973 solver.cpp:237]     Train net output #0: loss = 1.99067 (* 1 = 1.99067 loss)
I0409 20:32:35.910971 14973 sgd_solver.cpp:105] Iteration 4356, lr = 0.00421953
I0409 20:32:41.340178 14973 solver.cpp:218] Iteration 4368 (2.21034 iter/s, 5.42904s/12 iters), loss = 2.05562
I0409 20:32:41.340216 14973 solver.cpp:237]     Train net output #0: loss = 2.05562 (* 1 = 2.05562 loss)
I0409 20:32:41.340225 14973 sgd_solver.cpp:105] Iteration 4368, lr = 0.00420951
I0409 20:32:46.332427 14973 solver.cpp:218] Iteration 4380 (2.40383 iter/s, 4.99202s/12 iters), loss = 1.94576
I0409 20:32:46.332485 14973 solver.cpp:237]     Train net output #0: loss = 1.94576 (* 1 = 1.94576 loss)
I0409 20:32:46.332497 14973 sgd_solver.cpp:105] Iteration 4380, lr = 0.00419952
I0409 20:32:48.381922 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4386.caffemodel
I0409 20:32:49.984835 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4386.solverstate
I0409 20:32:51.197454 14973 solver.cpp:330] Iteration 4386, Testing net (#0)
I0409 20:32:51.197481 14973 net.cpp:676] Ignoring source layer train-data
I0409 20:32:53.835467 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:32:55.690178 14973 solver.cpp:397]     Test net output #0: accuracy = 0.362132
I0409 20:32:55.690227 14973 solver.cpp:397]     Test net output #1: loss = 2.50574 (* 1 = 2.50574 loss)
I0409 20:32:57.697631 14973 solver.cpp:218] Iteration 4392 (1.05589 iter/s, 11.3648s/12 iters), loss = 2.18884
I0409 20:32:57.697674 14973 solver.cpp:237]     Train net output #0: loss = 2.18884 (* 1 = 2.18884 loss)
I0409 20:32:57.697685 14973 sgd_solver.cpp:105] Iteration 4392, lr = 0.00418954
I0409 20:33:02.724886 14973 solver.cpp:218] Iteration 4404 (2.38709 iter/s, 5.02704s/12 iters), loss = 2.05819
I0409 20:33:02.724939 14973 solver.cpp:237]     Train net output #0: loss = 2.05819 (* 1 = 2.05819 loss)
I0409 20:33:02.724951 14973 sgd_solver.cpp:105] Iteration 4404, lr = 0.0041796
I0409 20:33:07.751879 14973 solver.cpp:218] Iteration 4416 (2.38722 iter/s, 5.02676s/12 iters), loss = 1.92146
I0409 20:33:07.752005 14973 solver.cpp:237]     Train net output #0: loss = 1.92146 (* 1 = 1.92146 loss)
I0409 20:33:07.752018 14973 sgd_solver.cpp:105] Iteration 4416, lr = 0.00416967
I0409 20:33:12.743664 14973 solver.cpp:218] Iteration 4428 (2.40409 iter/s, 4.99149s/12 iters), loss = 2.07795
I0409 20:33:12.743710 14973 solver.cpp:237]     Train net output #0: loss = 2.07795 (* 1 = 2.07795 loss)
I0409 20:33:12.743719 14973 sgd_solver.cpp:105] Iteration 4428, lr = 0.00415977
I0409 20:33:17.814898 14973 solver.cpp:218] Iteration 4440 (2.36639 iter/s, 5.07101s/12 iters), loss = 1.84439
I0409 20:33:17.814954 14973 solver.cpp:237]     Train net output #0: loss = 1.84439 (* 1 = 1.84439 loss)
I0409 20:33:17.814966 14973 sgd_solver.cpp:105] Iteration 4440, lr = 0.0041499
I0409 20:33:21.797999 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:33:22.747902 14973 solver.cpp:218] Iteration 4452 (2.43271 iter/s, 4.93277s/12 iters), loss = 1.77582
I0409 20:33:22.747959 14973 solver.cpp:237]     Train net output #0: loss = 1.77582 (* 1 = 1.77582 loss)
I0409 20:33:22.747972 14973 sgd_solver.cpp:105] Iteration 4452, lr = 0.00414005
I0409 20:33:27.760710 14973 solver.cpp:218] Iteration 4464 (2.39398 iter/s, 5.01258s/12 iters), loss = 2.02713
I0409 20:33:27.760761 14973 solver.cpp:237]     Train net output #0: loss = 2.02713 (* 1 = 2.02713 loss)
I0409 20:33:27.760773 14973 sgd_solver.cpp:105] Iteration 4464, lr = 0.00413022
I0409 20:33:32.783612 14973 solver.cpp:218] Iteration 4476 (2.38916 iter/s, 5.02268s/12 iters), loss = 1.99092
I0409 20:33:32.783653 14973 solver.cpp:237]     Train net output #0: loss = 1.99092 (* 1 = 1.99092 loss)
I0409 20:33:32.783661 14973 sgd_solver.cpp:105] Iteration 4476, lr = 0.00412041
I0409 20:33:37.292527 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4488.caffemodel
I0409 20:33:40.233135 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4488.solverstate
I0409 20:33:42.195001 14973 solver.cpp:330] Iteration 4488, Testing net (#0)
I0409 20:33:42.195021 14973 net.cpp:676] Ignoring source layer train-data
I0409 20:33:45.030545 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:33:46.804201 14973 solver.cpp:397]     Test net output #0: accuracy = 0.362745
I0409 20:33:46.804250 14973 solver.cpp:397]     Test net output #1: loss = 2.5348 (* 1 = 2.5348 loss)
I0409 20:33:46.890949 14973 solver.cpp:218] Iteration 4488 (0.850652 iter/s, 14.1068s/12 iters), loss = 1.91004
I0409 20:33:46.890996 14973 solver.cpp:237]     Train net output #0: loss = 1.91004 (* 1 = 1.91004 loss)
I0409 20:33:46.891007 14973 sgd_solver.cpp:105] Iteration 4488, lr = 0.00411063
I0409 20:33:51.151952 14973 solver.cpp:218] Iteration 4500 (2.81637 iter/s, 4.2608s/12 iters), loss = 2.00776
I0409 20:33:51.152009 14973 solver.cpp:237]     Train net output #0: loss = 2.00776 (* 1 = 2.00776 loss)
I0409 20:33:51.152022 14973 sgd_solver.cpp:105] Iteration 4500, lr = 0.00410087
I0409 20:33:56.122728 14973 solver.cpp:218] Iteration 4512 (2.41422 iter/s, 4.97054s/12 iters), loss = 1.98473
I0409 20:33:56.122787 14973 solver.cpp:237]     Train net output #0: loss = 1.98473 (* 1 = 1.98473 loss)
I0409 20:33:56.122799 14973 sgd_solver.cpp:105] Iteration 4512, lr = 0.00409113
I0409 20:34:01.156599 14973 solver.cpp:218] Iteration 4524 (2.38396 iter/s, 5.03364s/12 iters), loss = 1.82289
I0409 20:34:01.156651 14973 solver.cpp:237]     Train net output #0: loss = 1.82289 (* 1 = 1.82289 loss)
I0409 20:34:01.156661 14973 sgd_solver.cpp:105] Iteration 4524, lr = 0.00408142
I0409 20:34:06.214839 14973 solver.cpp:218] Iteration 4536 (2.37247 iter/s, 5.05801s/12 iters), loss = 1.90515
I0409 20:34:06.214880 14973 solver.cpp:237]     Train net output #0: loss = 1.90515 (* 1 = 1.90515 loss)
I0409 20:34:06.214888 14973 sgd_solver.cpp:105] Iteration 4536, lr = 0.00407173
I0409 20:34:11.292425 14973 solver.cpp:218] Iteration 4548 (2.36343 iter/s, 5.07736s/12 iters), loss = 2.01614
I0409 20:34:11.292505 14973 solver.cpp:237]     Train net output #0: loss = 2.01614 (* 1 = 2.01614 loss)
I0409 20:34:11.292517 14973 sgd_solver.cpp:105] Iteration 4548, lr = 0.00406206
I0409 20:34:12.589316 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:34:16.330767 14973 solver.cpp:218] Iteration 4560 (2.38186 iter/s, 5.03809s/12 iters), loss = 1.77468
I0409 20:34:16.330816 14973 solver.cpp:237]     Train net output #0: loss = 1.77468 (* 1 = 1.77468 loss)
I0409 20:34:16.330828 14973 sgd_solver.cpp:105] Iteration 4560, lr = 0.00405242
I0409 20:34:21.274144 14973 solver.cpp:218] Iteration 4572 (2.4276 iter/s, 4.94316s/12 iters), loss = 2.03013
I0409 20:34:21.274191 14973 solver.cpp:237]     Train net output #0: loss = 2.03013 (* 1 = 2.03013 loss)
I0409 20:34:21.274202 14973 sgd_solver.cpp:105] Iteration 4572, lr = 0.0040428
I0409 20:34:26.284013 14973 solver.cpp:218] Iteration 4584 (2.39538 iter/s, 5.00964s/12 iters), loss = 1.76366
I0409 20:34:26.284067 14973 solver.cpp:237]     Train net output #0: loss = 1.76366 (* 1 = 1.76366 loss)
I0409 20:34:26.284080 14973 sgd_solver.cpp:105] Iteration 4584, lr = 0.0040332
I0409 20:34:28.336911 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4590.caffemodel
I0409 20:34:29.953464 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4590.solverstate
I0409 20:34:31.519526 14973 solver.cpp:330] Iteration 4590, Testing net (#0)
I0409 20:34:31.519556 14973 net.cpp:676] Ignoring source layer train-data
I0409 20:34:34.204401 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:34:36.163341 14973 solver.cpp:397]     Test net output #0: accuracy = 0.392157
I0409 20:34:36.163375 14973 solver.cpp:397]     Test net output #1: loss = 2.42287 (* 1 = 2.42287 loss)
I0409 20:34:38.062760 14973 solver.cpp:218] Iteration 4596 (1.01882 iter/s, 11.7783s/12 iters), loss = 1.696
I0409 20:34:38.062816 14973 solver.cpp:237]     Train net output #0: loss = 1.696 (* 1 = 1.696 loss)
I0409 20:34:38.062827 14973 sgd_solver.cpp:105] Iteration 4596, lr = 0.00402362
I0409 20:34:43.072136 14973 solver.cpp:218] Iteration 4608 (2.39562 iter/s, 5.00914s/12 iters), loss = 1.75872
I0409 20:34:43.072280 14973 solver.cpp:237]     Train net output #0: loss = 1.75872 (* 1 = 1.75872 loss)
I0409 20:34:43.072291 14973 sgd_solver.cpp:105] Iteration 4608, lr = 0.00401407
I0409 20:34:48.000751 14973 solver.cpp:218] Iteration 4620 (2.43492 iter/s, 4.9283s/12 iters), loss = 1.81523
I0409 20:34:48.000804 14973 solver.cpp:237]     Train net output #0: loss = 1.81523 (* 1 = 1.81523 loss)
I0409 20:34:48.000815 14973 sgd_solver.cpp:105] Iteration 4620, lr = 0.00400454
I0409 20:34:53.025091 14973 solver.cpp:218] Iteration 4632 (2.38848 iter/s, 5.02411s/12 iters), loss = 2.08428
I0409 20:34:53.025143 14973 solver.cpp:237]     Train net output #0: loss = 2.08428 (* 1 = 2.08428 loss)
I0409 20:34:53.025156 14973 sgd_solver.cpp:105] Iteration 4632, lr = 0.00399503
I0409 20:34:58.169929 14973 solver.cpp:218] Iteration 4644 (2.33254 iter/s, 5.1446s/12 iters), loss = 1.63195
I0409 20:34:58.170001 14973 solver.cpp:237]     Train net output #0: loss = 1.63195 (* 1 = 1.63195 loss)
I0409 20:34:58.170014 14973 sgd_solver.cpp:105] Iteration 4644, lr = 0.00398555
I0409 20:35:01.571513 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:35:03.286840 14973 solver.cpp:218] Iteration 4656 (2.34528 iter/s, 5.11666s/12 iters), loss = 1.70625
I0409 20:35:03.286885 14973 solver.cpp:237]     Train net output #0: loss = 1.70625 (* 1 = 1.70625 loss)
I0409 20:35:03.286895 14973 sgd_solver.cpp:105] Iteration 4656, lr = 0.00397608
I0409 20:35:08.582396 14973 solver.cpp:218] Iteration 4668 (2.26615 iter/s, 5.29532s/12 iters), loss = 2.06239
I0409 20:35:08.582449 14973 solver.cpp:237]     Train net output #0: loss = 2.06239 (* 1 = 2.06239 loss)
I0409 20:35:08.582461 14973 sgd_solver.cpp:105] Iteration 4668, lr = 0.00396664
I0409 20:35:13.679304 14973 solver.cpp:218] Iteration 4680 (2.35448 iter/s, 5.09668s/12 iters), loss = 1.95989
I0409 20:35:13.679384 14973 solver.cpp:237]     Train net output #0: loss = 1.95989 (* 1 = 1.95989 loss)
I0409 20:35:13.679396 14973 sgd_solver.cpp:105] Iteration 4680, lr = 0.00395723
I0409 20:35:18.247881 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4692.caffemodel
I0409 20:35:20.592630 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4692.solverstate
I0409 20:35:21.861055 14973 solver.cpp:330] Iteration 4692, Testing net (#0)
I0409 20:35:21.861079 14973 net.cpp:676] Ignoring source layer train-data
I0409 20:35:24.446183 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:35:26.300271 14973 solver.cpp:397]     Test net output #0: accuracy = 0.39277
I0409 20:35:26.300300 14973 solver.cpp:397]     Test net output #1: loss = 2.41059 (* 1 = 2.41059 loss)
I0409 20:35:26.386998 14973 solver.cpp:218] Iteration 4692 (0.944347 iter/s, 12.7072s/12 iters), loss = 1.96627
I0409 20:35:26.387050 14973 solver.cpp:237]     Train net output #0: loss = 1.96627 (* 1 = 1.96627 loss)
I0409 20:35:26.387060 14973 sgd_solver.cpp:105] Iteration 4692, lr = 0.00394783
I0409 20:35:30.761704 14973 solver.cpp:218] Iteration 4704 (2.74317 iter/s, 4.3745s/12 iters), loss = 1.95084
I0409 20:35:30.761749 14973 solver.cpp:237]     Train net output #0: loss = 1.95084 (* 1 = 1.95084 loss)
I0409 20:35:30.761759 14973 sgd_solver.cpp:105] Iteration 4704, lr = 0.00393846
I0409 20:35:35.804545 14973 solver.cpp:218] Iteration 4716 (2.37972 iter/s, 5.04262s/12 iters), loss = 1.73956
I0409 20:35:35.804592 14973 solver.cpp:237]     Train net output #0: loss = 1.73956 (* 1 = 1.73956 loss)
I0409 20:35:35.804602 14973 sgd_solver.cpp:105] Iteration 4716, lr = 0.00392911
I0409 20:35:40.802474 14973 solver.cpp:218] Iteration 4728 (2.4011 iter/s, 4.9977s/12 iters), loss = 1.5665
I0409 20:35:40.802522 14973 solver.cpp:237]     Train net output #0: loss = 1.5665 (* 1 = 1.5665 loss)
I0409 20:35:40.802534 14973 sgd_solver.cpp:105] Iteration 4728, lr = 0.00391978
I0409 20:35:45.833746 14973 solver.cpp:218] Iteration 4740 (2.38519 iter/s, 5.03105s/12 iters), loss = 1.65341
I0409 20:35:45.833892 14973 solver.cpp:237]     Train net output #0: loss = 1.65341 (* 1 = 1.65341 loss)
I0409 20:35:45.833904 14973 sgd_solver.cpp:105] Iteration 4740, lr = 0.00391047
I0409 20:35:50.858208 14973 solver.cpp:218] Iteration 4752 (2.38847 iter/s, 5.02414s/12 iters), loss = 1.51862
I0409 20:35:50.858264 14973 solver.cpp:237]     Train net output #0: loss = 1.51862 (* 1 = 1.51862 loss)
I0409 20:35:50.858278 14973 sgd_solver.cpp:105] Iteration 4752, lr = 0.00390119
I0409 20:35:51.402864 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:35:55.936517 14973 solver.cpp:218] Iteration 4764 (2.3631 iter/s, 5.07808s/12 iters), loss = 1.61065
I0409 20:35:55.936570 14973 solver.cpp:237]     Train net output #0: loss = 1.61065 (* 1 = 1.61065 loss)
I0409 20:35:55.936583 14973 sgd_solver.cpp:105] Iteration 4764, lr = 0.00389193
I0409 20:36:01.268102 14973 solver.cpp:218] Iteration 4776 (2.25084 iter/s, 5.33135s/12 iters), loss = 1.73428
I0409 20:36:01.268148 14973 solver.cpp:237]     Train net output #0: loss = 1.73428 (* 1 = 1.73428 loss)
I0409 20:36:01.268160 14973 sgd_solver.cpp:105] Iteration 4776, lr = 0.00388269
I0409 20:36:06.540709 14973 solver.cpp:218] Iteration 4788 (2.27601 iter/s, 5.27238s/12 iters), loss = 1.73524
I0409 20:36:06.540766 14973 solver.cpp:237]     Train net output #0: loss = 1.73524 (* 1 = 1.73524 loss)
I0409 20:36:06.540781 14973 sgd_solver.cpp:105] Iteration 4788, lr = 0.00387347
I0409 20:36:08.524436 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4794.caffemodel
I0409 20:36:14.478845 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4794.solverstate
I0409 20:36:18.848800 14973 solver.cpp:330] Iteration 4794, Testing net (#0)
I0409 20:36:18.848881 14973 net.cpp:676] Ignoring source layer train-data
I0409 20:36:21.324187 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:36:23.229148 14973 solver.cpp:397]     Test net output #0: accuracy = 0.415441
I0409 20:36:23.229197 14973 solver.cpp:397]     Test net output #1: loss = 2.35043 (* 1 = 2.35043 loss)
I0409 20:36:25.230417 14973 solver.cpp:218] Iteration 4800 (0.642088 iter/s, 18.689s/12 iters), loss = 1.55555
I0409 20:36:25.230465 14973 solver.cpp:237]     Train net output #0: loss = 1.55555 (* 1 = 1.55555 loss)
I0409 20:36:25.230476 14973 sgd_solver.cpp:105] Iteration 4800, lr = 0.00386427
I0409 20:36:30.260759 14973 solver.cpp:218] Iteration 4812 (2.38563 iter/s, 5.03011s/12 iters), loss = 1.70311
I0409 20:36:30.260810 14973 solver.cpp:237]     Train net output #0: loss = 1.70311 (* 1 = 1.70311 loss)
I0409 20:36:30.260821 14973 sgd_solver.cpp:105] Iteration 4812, lr = 0.0038551
I0409 20:36:35.645403 14973 solver.cpp:218] Iteration 4824 (2.22866 iter/s, 5.3844s/12 iters), loss = 1.7281
I0409 20:36:35.645457 14973 solver.cpp:237]     Train net output #0: loss = 1.7281 (* 1 = 1.7281 loss)
I0409 20:36:35.645469 14973 sgd_solver.cpp:105] Iteration 4824, lr = 0.00384594
I0409 20:36:40.707629 14973 solver.cpp:218] Iteration 4836 (2.37061 iter/s, 5.062s/12 iters), loss = 1.43648
I0409 20:36:40.707687 14973 solver.cpp:237]     Train net output #0: loss = 1.43648 (* 1 = 1.43648 loss)
I0409 20:36:40.707702 14973 sgd_solver.cpp:105] Iteration 4836, lr = 0.00383681
I0409 20:36:42.371480 14973 blocking_queue.cpp:49] Waiting for data
I0409 20:36:45.909759 14973 solver.cpp:218] Iteration 4848 (2.30685 iter/s, 5.20189s/12 iters), loss = 1.5009
I0409 20:36:45.909811 14973 solver.cpp:237]     Train net output #0: loss = 1.5009 (* 1 = 1.5009 loss)
I0409 20:36:45.909822 14973 sgd_solver.cpp:105] Iteration 4848, lr = 0.0038277
I0409 20:36:48.842370 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:36:51.362439 14973 solver.cpp:218] Iteration 4860 (2.20085 iter/s, 5.45244s/12 iters), loss = 1.48367
I0409 20:36:51.362537 14973 solver.cpp:237]     Train net output #0: loss = 1.48367 (* 1 = 1.48367 loss)
I0409 20:36:51.362547 14973 sgd_solver.cpp:105] Iteration 4860, lr = 0.00381862
I0409 20:36:56.636480 14973 solver.cpp:218] Iteration 4872 (2.27542 iter/s, 5.27376s/12 iters), loss = 1.66681
I0409 20:36:56.636518 14973 solver.cpp:237]     Train net output #0: loss = 1.66681 (* 1 = 1.66681 loss)
I0409 20:36:56.636528 14973 sgd_solver.cpp:105] Iteration 4872, lr = 0.00380955
I0409 20:37:01.897430 14973 solver.cpp:218] Iteration 4884 (2.28105 iter/s, 5.26073s/12 iters), loss = 1.66995
I0409 20:37:01.897482 14973 solver.cpp:237]     Train net output #0: loss = 1.66995 (* 1 = 1.66995 loss)
I0409 20:37:01.897495 14973 sgd_solver.cpp:105] Iteration 4884, lr = 0.0038005
I0409 20:37:06.510609 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4896.caffemodel
I0409 20:37:08.323197 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4896.solverstate
I0409 20:37:10.351737 14973 solver.cpp:330] Iteration 4896, Testing net (#0)
I0409 20:37:10.351765 14973 net.cpp:676] Ignoring source layer train-data
I0409 20:37:12.891803 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:37:14.859346 14973 solver.cpp:397]     Test net output #0: accuracy = 0.416667
I0409 20:37:14.859375 14973 solver.cpp:397]     Test net output #1: loss = 2.32099 (* 1 = 2.32099 loss)
I0409 20:37:14.945976 14973 solver.cpp:218] Iteration 4896 (0.919679 iter/s, 13.048s/12 iters), loss = 1.5561
I0409 20:37:14.946038 14973 solver.cpp:237]     Train net output #0: loss = 1.5561 (* 1 = 1.5561 loss)
I0409 20:37:14.946050 14973 sgd_solver.cpp:105] Iteration 4896, lr = 0.00379148
I0409 20:37:19.062703 14973 solver.cpp:218] Iteration 4908 (2.91508 iter/s, 4.11652s/12 iters), loss = 1.39713
I0409 20:37:19.062764 14973 solver.cpp:237]     Train net output #0: loss = 1.39713 (* 1 = 1.39713 loss)
I0409 20:37:19.062775 14973 sgd_solver.cpp:105] Iteration 4908, lr = 0.00378248
I0409 20:37:24.201512 14973 solver.cpp:218] Iteration 4920 (2.33528 iter/s, 5.13857s/12 iters), loss = 1.50622
I0409 20:37:24.201632 14973 solver.cpp:237]     Train net output #0: loss = 1.50622 (* 1 = 1.50622 loss)
I0409 20:37:24.201647 14973 sgd_solver.cpp:105] Iteration 4920, lr = 0.0037735
I0409 20:37:29.188851 14973 solver.cpp:218] Iteration 4932 (2.40624 iter/s, 4.98704s/12 iters), loss = 1.72323
I0409 20:37:29.188910 14973 solver.cpp:237]     Train net output #0: loss = 1.72323 (* 1 = 1.72323 loss)
I0409 20:37:29.188921 14973 sgd_solver.cpp:105] Iteration 4932, lr = 0.00376454
I0409 20:37:34.175977 14973 solver.cpp:218] Iteration 4944 (2.40631 iter/s, 4.9869s/12 iters), loss = 1.46992
I0409 20:37:34.176020 14973 solver.cpp:237]     Train net output #0: loss = 1.46992 (* 1 = 1.46992 loss)
I0409 20:37:34.176029 14973 sgd_solver.cpp:105] Iteration 4944, lr = 0.0037556
I0409 20:37:38.987679 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:37:39.189110 14973 solver.cpp:218] Iteration 4956 (2.39382 iter/s, 5.01291s/12 iters), loss = 1.45302
I0409 20:37:39.189159 14973 solver.cpp:237]     Train net output #0: loss = 1.45302 (* 1 = 1.45302 loss)
I0409 20:37:39.189170 14973 sgd_solver.cpp:105] Iteration 4956, lr = 0.00374669
I0409 20:37:44.240221 14973 solver.cpp:218] Iteration 4968 (2.37582 iter/s, 5.05088s/12 iters), loss = 1.612
I0409 20:37:44.240285 14973 solver.cpp:237]     Train net output #0: loss = 1.612 (* 1 = 1.612 loss)
I0409 20:37:44.240298 14973 sgd_solver.cpp:105] Iteration 4968, lr = 0.00373779
I0409 20:37:49.172413 14973 solver.cpp:218] Iteration 4980 (2.43311 iter/s, 4.93195s/12 iters), loss = 1.51101
I0409 20:37:49.172474 14973 solver.cpp:237]     Train net output #0: loss = 1.51101 (* 1 = 1.51101 loss)
I0409 20:37:49.172487 14973 sgd_solver.cpp:105] Iteration 4980, lr = 0.00372892
I0409 20:37:54.089792 14973 solver.cpp:218] Iteration 4992 (2.44044 iter/s, 4.91714s/12 iters), loss = 1.60088
I0409 20:37:54.089857 14973 solver.cpp:237]     Train net output #0: loss = 1.60088 (* 1 = 1.60088 loss)
I0409 20:37:54.089870 14973 sgd_solver.cpp:105] Iteration 4992, lr = 0.00372006
I0409 20:37:56.086743 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4998.caffemodel
I0409 20:37:58.365140 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4998.solverstate
I0409 20:37:59.584837 14973 solver.cpp:330] Iteration 4998, Testing net (#0)
I0409 20:37:59.584865 14973 net.cpp:676] Ignoring source layer train-data
I0409 20:38:02.039137 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:38:04.009759 14973 solver.cpp:397]     Test net output #0: accuracy = 0.427083
I0409 20:38:04.009809 14973 solver.cpp:397]     Test net output #1: loss = 2.28352 (* 1 = 2.28352 loss)
I0409 20:38:05.991433 14973 solver.cpp:218] Iteration 5004 (1.0083 iter/s, 11.9012s/12 iters), loss = 1.55573
I0409 20:38:05.991473 14973 solver.cpp:237]     Train net output #0: loss = 1.55573 (* 1 = 1.55573 loss)
I0409 20:38:05.991482 14973 sgd_solver.cpp:105] Iteration 5004, lr = 0.00371123
I0409 20:38:11.036182 14973 solver.cpp:218] Iteration 5016 (2.37881 iter/s, 5.04453s/12 iters), loss = 1.50365
I0409 20:38:11.036228 14973 solver.cpp:237]     Train net output #0: loss = 1.50365 (* 1 = 1.50365 loss)
I0409 20:38:11.036237 14973 sgd_solver.cpp:105] Iteration 5016, lr = 0.00370242
I0409 20:38:16.018534 14973 solver.cpp:218] Iteration 5028 (2.40861 iter/s, 4.98213s/12 iters), loss = 1.31257
I0409 20:38:16.018582 14973 solver.cpp:237]     Train net output #0: loss = 1.31257 (* 1 = 1.31257 loss)
I0409 20:38:16.018594 14973 sgd_solver.cpp:105] Iteration 5028, lr = 0.00369363
I0409 20:38:21.226692 14973 solver.cpp:218] Iteration 5040 (2.30418 iter/s, 5.20793s/12 iters), loss = 1.69633
I0409 20:38:21.226743 14973 solver.cpp:237]     Train net output #0: loss = 1.69633 (* 1 = 1.69633 loss)
I0409 20:38:21.226758 14973 sgd_solver.cpp:105] Iteration 5040, lr = 0.00368486
I0409 20:38:26.293045 14973 solver.cpp:218] Iteration 5052 (2.36867 iter/s, 5.06613s/12 iters), loss = 1.41729
I0409 20:38:26.293152 14973 solver.cpp:237]     Train net output #0: loss = 1.41729 (* 1 = 1.41729 loss)
I0409 20:38:26.293162 14973 sgd_solver.cpp:105] Iteration 5052, lr = 0.00367611
I0409 20:38:28.154810 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:38:31.239315 14973 solver.cpp:218] Iteration 5064 (2.42621 iter/s, 4.94599s/12 iters), loss = 1.54395
I0409 20:38:31.239367 14973 solver.cpp:237]     Train net output #0: loss = 1.54395 (* 1 = 1.54395 loss)
I0409 20:38:31.239378 14973 sgd_solver.cpp:105] Iteration 5064, lr = 0.00366738
I0409 20:38:36.252621 14973 solver.cpp:218] Iteration 5076 (2.39374 iter/s, 5.01307s/12 iters), loss = 1.78993
I0409 20:38:36.252679 14973 solver.cpp:237]     Train net output #0: loss = 1.78993 (* 1 = 1.78993 loss)
I0409 20:38:36.252691 14973 sgd_solver.cpp:105] Iteration 5076, lr = 0.00365868
I0409 20:38:41.205605 14973 solver.cpp:218] Iteration 5088 (2.4229 iter/s, 4.95275s/12 iters), loss = 1.42039
I0409 20:38:41.205659 14973 solver.cpp:237]     Train net output #0: loss = 1.42039 (* 1 = 1.42039 loss)
I0409 20:38:41.205672 14973 sgd_solver.cpp:105] Iteration 5088, lr = 0.00364999
I0409 20:38:45.713438 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5100.caffemodel
I0409 20:38:49.661319 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5100.solverstate
I0409 20:38:53.143543 14973 solver.cpp:330] Iteration 5100, Testing net (#0)
I0409 20:38:53.143565 14973 net.cpp:676] Ignoring source layer train-data
I0409 20:38:55.585291 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:38:57.617432 14973 solver.cpp:397]     Test net output #0: accuracy = 0.407476
I0409 20:38:57.617564 14973 solver.cpp:397]     Test net output #1: loss = 2.35819 (* 1 = 2.35819 loss)
I0409 20:38:57.704262 14973 solver.cpp:218] Iteration 5100 (0.727358 iter/s, 16.4981s/12 iters), loss = 1.64661
I0409 20:38:57.704304 14973 solver.cpp:237]     Train net output #0: loss = 1.64661 (* 1 = 1.64661 loss)
I0409 20:38:57.704314 14973 sgd_solver.cpp:105] Iteration 5100, lr = 0.00364132
I0409 20:39:02.033344 14973 solver.cpp:218] Iteration 5112 (2.77208 iter/s, 4.32889s/12 iters), loss = 1.43878
I0409 20:39:02.033386 14973 solver.cpp:237]     Train net output #0: loss = 1.43878 (* 1 = 1.43878 loss)
I0409 20:39:02.033396 14973 sgd_solver.cpp:105] Iteration 5112, lr = 0.00363268
I0409 20:39:07.001659 14973 solver.cpp:218] Iteration 5124 (2.41541 iter/s, 4.96809s/12 iters), loss = 1.75023
I0409 20:39:07.001704 14973 solver.cpp:237]     Train net output #0: loss = 1.75023 (* 1 = 1.75023 loss)
I0409 20:39:07.001714 14973 sgd_solver.cpp:105] Iteration 5124, lr = 0.00362405
I0409 20:39:12.282397 14973 solver.cpp:218] Iteration 5136 (2.27251 iter/s, 5.28051s/12 iters), loss = 1.34154
I0409 20:39:12.282449 14973 solver.cpp:237]     Train net output #0: loss = 1.34154 (* 1 = 1.34154 loss)
I0409 20:39:12.282460 14973 sgd_solver.cpp:105] Iteration 5136, lr = 0.00361545
I0409 20:39:17.308300 14973 solver.cpp:218] Iteration 5148 (2.38774 iter/s, 5.02568s/12 iters), loss = 1.26549
I0409 20:39:17.308347 14973 solver.cpp:237]     Train net output #0: loss = 1.26549 (* 1 = 1.26549 loss)
I0409 20:39:17.308358 14973 sgd_solver.cpp:105] Iteration 5148, lr = 0.00360687
I0409 20:39:21.417553 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:39:22.335023 14973 solver.cpp:218] Iteration 5160 (2.38735 iter/s, 5.0265s/12 iters), loss = 1.40736
I0409 20:39:22.335075 14973 solver.cpp:237]     Train net output #0: loss = 1.40736 (* 1 = 1.40736 loss)
I0409 20:39:22.335088 14973 sgd_solver.cpp:105] Iteration 5160, lr = 0.0035983
I0409 20:39:27.414086 14973 solver.cpp:218] Iteration 5172 (2.36275 iter/s, 5.07883s/12 iters), loss = 1.5775
I0409 20:39:27.414145 14973 solver.cpp:237]     Train net output #0: loss = 1.5775 (* 1 = 1.5775 loss)
I0409 20:39:27.414158 14973 sgd_solver.cpp:105] Iteration 5172, lr = 0.00358976
I0409 20:39:32.513653 14973 solver.cpp:218] Iteration 5184 (2.35325 iter/s, 5.09933s/12 iters), loss = 1.51146
I0409 20:39:32.513732 14973 solver.cpp:237]     Train net output #0: loss = 1.51146 (* 1 = 1.51146 loss)
I0409 20:39:32.513743 14973 sgd_solver.cpp:105] Iteration 5184, lr = 0.00358124
I0409 20:39:37.461992 14973 solver.cpp:218] Iteration 5196 (2.42519 iter/s, 4.94807s/12 iters), loss = 1.25117
I0409 20:39:37.462044 14973 solver.cpp:237]     Train net output #0: loss = 1.25117 (* 1 = 1.25117 loss)
I0409 20:39:37.462055 14973 sgd_solver.cpp:105] Iteration 5196, lr = 0.00357273
I0409 20:39:39.500664 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5202.caffemodel
I0409 20:39:41.012847 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5202.solverstate
I0409 20:39:42.700153 14973 solver.cpp:330] Iteration 5202, Testing net (#0)
I0409 20:39:42.700181 14973 net.cpp:676] Ignoring source layer train-data
I0409 20:39:45.250453 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:39:47.301141 14973 solver.cpp:397]     Test net output #0: accuracy = 0.426471
I0409 20:39:47.301192 14973 solver.cpp:397]     Test net output #1: loss = 2.34327 (* 1 = 2.34327 loss)
I0409 20:39:49.309358 14973 solver.cpp:218] Iteration 5208 (1.01292 iter/s, 11.8469s/12 iters), loss = 1.16253
I0409 20:39:49.309417 14973 solver.cpp:237]     Train net output #0: loss = 1.16253 (* 1 = 1.16253 loss)
I0409 20:39:49.309428 14973 sgd_solver.cpp:105] Iteration 5208, lr = 0.00356425
I0409 20:39:54.440631 14973 solver.cpp:218] Iteration 5220 (2.33871 iter/s, 5.13104s/12 iters), loss = 1.25559
I0409 20:39:54.440675 14973 solver.cpp:237]     Train net output #0: loss = 1.25559 (* 1 = 1.25559 loss)
I0409 20:39:54.440683 14973 sgd_solver.cpp:105] Iteration 5220, lr = 0.00355579
I0409 20:39:59.637522 14973 solver.cpp:218] Iteration 5232 (2.30917 iter/s, 5.19666s/12 iters), loss = 1.43569
I0409 20:39:59.637575 14973 solver.cpp:237]     Train net output #0: loss = 1.43569 (* 1 = 1.43569 loss)
I0409 20:39:59.637586 14973 sgd_solver.cpp:105] Iteration 5232, lr = 0.00354735
I0409 20:40:04.710837 14973 solver.cpp:218] Iteration 5244 (2.36543 iter/s, 5.07308s/12 iters), loss = 1.50774
I0409 20:40:04.710956 14973 solver.cpp:237]     Train net output #0: loss = 1.50774 (* 1 = 1.50774 loss)
I0409 20:40:04.710965 14973 sgd_solver.cpp:105] Iteration 5244, lr = 0.00353892
I0409 20:40:09.788362 14973 solver.cpp:218] Iteration 5256 (2.3635 iter/s, 5.07723s/12 iters), loss = 1.23085
I0409 20:40:09.788406 14973 solver.cpp:237]     Train net output #0: loss = 1.23085 (* 1 = 1.23085 loss)
I0409 20:40:09.788416 14973 sgd_solver.cpp:105] Iteration 5256, lr = 0.00353052
I0409 20:40:11.098541 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:40:14.867611 14973 solver.cpp:218] Iteration 5268 (2.36266 iter/s, 5.07902s/12 iters), loss = 1.30382
I0409 20:40:14.867658 14973 solver.cpp:237]     Train net output #0: loss = 1.30382 (* 1 = 1.30382 loss)
I0409 20:40:14.867667 14973 sgd_solver.cpp:105] Iteration 5268, lr = 0.00352214
I0409 20:40:19.946328 14973 solver.cpp:218] Iteration 5280 (2.36291 iter/s, 5.07848s/12 iters), loss = 1.38321
I0409 20:40:19.946380 14973 solver.cpp:237]     Train net output #0: loss = 1.38321 (* 1 = 1.38321 loss)
I0409 20:40:19.946393 14973 sgd_solver.cpp:105] Iteration 5280, lr = 0.00351378
I0409 20:40:25.036149 14973 solver.cpp:218] Iteration 5292 (2.35775 iter/s, 5.08959s/12 iters), loss = 1.27474
I0409 20:40:25.036190 14973 solver.cpp:237]     Train net output #0: loss = 1.27474 (* 1 = 1.27474 loss)
I0409 20:40:25.036198 14973 sgd_solver.cpp:105] Iteration 5292, lr = 0.00350544
I0409 20:40:29.676491 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5304.caffemodel
I0409 20:40:31.500802 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5304.solverstate
I0409 20:40:32.744271 14973 solver.cpp:330] Iteration 5304, Testing net (#0)
I0409 20:40:32.744292 14973 net.cpp:676] Ignoring source layer train-data
I0409 20:40:35.343681 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:40:37.466495 14973 solver.cpp:397]     Test net output #0: accuracy = 0.443015
I0409 20:40:37.466542 14973 solver.cpp:397]     Test net output #1: loss = 2.28201 (* 1 = 2.28201 loss)
I0409 20:40:37.553067 14973 solver.cpp:218] Iteration 5304 (0.958738 iter/s, 12.5165s/12 iters), loss = 1.32232
I0409 20:40:37.553112 14973 solver.cpp:237]     Train net output #0: loss = 1.32232 (* 1 = 1.32232 loss)
I0409 20:40:37.553122 14973 sgd_solver.cpp:105] Iteration 5304, lr = 0.00349711
I0409 20:40:41.859673 14973 solver.cpp:218] Iteration 5316 (2.78655 iter/s, 4.30641s/12 iters), loss = 1.18609
I0409 20:40:41.859724 14973 solver.cpp:237]     Train net output #0: loss = 1.18609 (* 1 = 1.18609 loss)
I0409 20:40:41.859735 14973 sgd_solver.cpp:105] Iteration 5316, lr = 0.00348881
I0409 20:40:46.848744 14973 solver.cpp:218] Iteration 5328 (2.40537 iter/s, 4.98884s/12 iters), loss = 1.36872
I0409 20:40:46.848798 14973 solver.cpp:237]     Train net output #0: loss = 1.36872 (* 1 = 1.36872 loss)
I0409 20:40:46.848810 14973 sgd_solver.cpp:105] Iteration 5328, lr = 0.00348053
I0409 20:40:51.925420 14973 solver.cpp:218] Iteration 5340 (2.36386 iter/s, 5.07644s/12 iters), loss = 1.42014
I0409 20:40:51.925477 14973 solver.cpp:237]     Train net output #0: loss = 1.42014 (* 1 = 1.42014 loss)
I0409 20:40:51.925488 14973 sgd_solver.cpp:105] Iteration 5340, lr = 0.00347226
I0409 20:40:56.982758 14973 solver.cpp:218] Iteration 5352 (2.3729 iter/s, 5.0571s/12 iters), loss = 1.43557
I0409 20:40:56.982812 14973 solver.cpp:237]     Train net output #0: loss = 1.43557 (* 1 = 1.43557 loss)
I0409 20:40:56.982823 14973 sgd_solver.cpp:105] Iteration 5352, lr = 0.00346402
I0409 20:41:00.345443 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:41:01.890796 14973 solver.cpp:218] Iteration 5364 (2.44508 iter/s, 4.90781s/12 iters), loss = 1.39304
I0409 20:41:01.890839 14973 solver.cpp:237]     Train net output #0: loss = 1.39304 (* 1 = 1.39304 loss)
I0409 20:41:01.890848 14973 sgd_solver.cpp:105] Iteration 5364, lr = 0.0034558
I0409 20:41:06.934345 14973 solver.cpp:218] Iteration 5376 (2.37938 iter/s, 5.04332s/12 iters), loss = 1.33508
I0409 20:41:06.934463 14973 solver.cpp:237]     Train net output #0: loss = 1.33508 (* 1 = 1.33508 loss)
I0409 20:41:06.934473 14973 sgd_solver.cpp:105] Iteration 5376, lr = 0.00344759
I0409 20:41:11.947077 14973 solver.cpp:218] Iteration 5388 (2.39404 iter/s, 5.01244s/12 iters), loss = 1.30854
I0409 20:41:11.947125 14973 solver.cpp:237]     Train net output #0: loss = 1.30854 (* 1 = 1.30854 loss)
I0409 20:41:11.947134 14973 sgd_solver.cpp:105] Iteration 5388, lr = 0.00343941
I0409 20:41:16.880847 14973 solver.cpp:218] Iteration 5400 (2.43233 iter/s, 4.93355s/12 iters), loss = 1.42278
I0409 20:41:16.880894 14973 solver.cpp:237]     Train net output #0: loss = 1.42278 (* 1 = 1.42278 loss)
I0409 20:41:16.880903 14973 sgd_solver.cpp:105] Iteration 5400, lr = 0.00343124
I0409 20:41:18.934469 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5406.caffemodel
I0409 20:41:20.504999 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5406.solverstate
I0409 20:41:21.696324 14973 solver.cpp:330] Iteration 5406, Testing net (#0)
I0409 20:41:21.696346 14973 net.cpp:676] Ignoring source layer train-data
I0409 20:41:24.018491 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:41:26.151523 14973 solver.cpp:397]     Test net output #0: accuracy = 0.436887
I0409 20:41:26.151574 14973 solver.cpp:397]     Test net output #1: loss = 2.24654 (* 1 = 2.24654 loss)
I0409 20:41:27.953161 14973 solver.cpp:218] Iteration 5412 (1.08383 iter/s, 11.0719s/12 iters), loss = 1.18212
I0409 20:41:27.953217 14973 solver.cpp:237]     Train net output #0: loss = 1.18212 (* 1 = 1.18212 loss)
I0409 20:41:27.953229 14973 sgd_solver.cpp:105] Iteration 5412, lr = 0.00342309
I0409 20:41:32.943148 14973 solver.cpp:218] Iteration 5424 (2.40493 iter/s, 4.98976s/12 iters), loss = 1.25583
I0409 20:41:32.943192 14973 solver.cpp:237]     Train net output #0: loss = 1.25583 (* 1 = 1.25583 loss)
I0409 20:41:32.943200 14973 sgd_solver.cpp:105] Iteration 5424, lr = 0.00341497
I0409 20:41:37.967353 14973 solver.cpp:218] Iteration 5436 (2.38854 iter/s, 5.02398s/12 iters), loss = 1.16539
I0409 20:41:37.967450 14973 solver.cpp:237]     Train net output #0: loss = 1.16539 (* 1 = 1.16539 loss)
I0409 20:41:37.967461 14973 sgd_solver.cpp:105] Iteration 5436, lr = 0.00340686
I0409 20:41:43.200621 14973 solver.cpp:218] Iteration 5448 (2.29314 iter/s, 5.23299s/12 iters), loss = 1.21081
I0409 20:41:43.200664 14973 solver.cpp:237]     Train net output #0: loss = 1.21081 (* 1 = 1.21081 loss)
I0409 20:41:43.200673 14973 sgd_solver.cpp:105] Iteration 5448, lr = 0.00339877
I0409 20:41:48.292436 14973 solver.cpp:218] Iteration 5460 (2.35683 iter/s, 5.09159s/12 iters), loss = 1.38779
I0409 20:41:48.292496 14973 solver.cpp:237]     Train net output #0: loss = 1.38779 (* 1 = 1.38779 loss)
I0409 20:41:48.292510 14973 sgd_solver.cpp:105] Iteration 5460, lr = 0.0033907
I0409 20:41:48.843441 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:41:53.285158 14973 solver.cpp:218] Iteration 5472 (2.40361 iter/s, 4.99249s/12 iters), loss = 0.955256
I0409 20:41:53.285197 14973 solver.cpp:237]     Train net output #0: loss = 0.955256 (* 1 = 0.955256 loss)
I0409 20:41:53.285205 14973 sgd_solver.cpp:105] Iteration 5472, lr = 0.00338265
I0409 20:41:58.318822 14973 solver.cpp:218] Iteration 5484 (2.38406 iter/s, 5.03344s/12 iters), loss = 1.26996
I0409 20:41:58.318884 14973 solver.cpp:237]     Train net output #0: loss = 1.26996 (* 1 = 1.26996 loss)
I0409 20:41:58.318898 14973 sgd_solver.cpp:105] Iteration 5484, lr = 0.00337462
I0409 20:42:03.375592 14973 solver.cpp:218] Iteration 5496 (2.37317 iter/s, 5.05652s/12 iters), loss = 1.13469
I0409 20:42:03.375648 14973 solver.cpp:237]     Train net output #0: loss = 1.13469 (* 1 = 1.13469 loss)
I0409 20:42:03.375667 14973 sgd_solver.cpp:105] Iteration 5496, lr = 0.00336661
I0409 20:42:07.991008 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5508.caffemodel
I0409 20:42:09.526432 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5508.solverstate
I0409 20:42:10.740049 14973 solver.cpp:330] Iteration 5508, Testing net (#0)
I0409 20:42:10.740073 14973 net.cpp:676] Ignoring source layer train-data
I0409 20:42:13.118002 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:42:15.429872 14973 solver.cpp:397]     Test net output #0: accuracy = 0.455882
I0409 20:42:15.429913 14973 solver.cpp:397]     Test net output #1: loss = 2.23131 (* 1 = 2.23131 loss)
I0409 20:42:15.516532 14973 solver.cpp:218] Iteration 5508 (0.988429 iter/s, 12.1405s/12 iters), loss = 1.46782
I0409 20:42:15.516577 14973 solver.cpp:237]     Train net output #0: loss = 1.46782 (* 1 = 1.46782 loss)
I0409 20:42:15.516585 14973 sgd_solver.cpp:105] Iteration 5508, lr = 0.00335861
I0409 20:42:19.694229 14973 solver.cpp:218] Iteration 5520 (2.87253 iter/s, 4.1775s/12 iters), loss = 1.17175
I0409 20:42:19.694278 14973 solver.cpp:237]     Train net output #0: loss = 1.17175 (* 1 = 1.17175 loss)
I0409 20:42:19.694288 14973 sgd_solver.cpp:105] Iteration 5520, lr = 0.00335064
I0409 20:42:21.781435 14973 blocking_queue.cpp:49] Waiting for data
I0409 20:42:24.837260 14973 solver.cpp:218] Iteration 5532 (2.33336 iter/s, 5.1428s/12 iters), loss = 1.19745
I0409 20:42:24.837306 14973 solver.cpp:237]     Train net output #0: loss = 1.19745 (* 1 = 1.19745 loss)
I0409 20:42:24.837316 14973 sgd_solver.cpp:105] Iteration 5532, lr = 0.00334268
I0409 20:42:29.795413 14973 solver.cpp:218] Iteration 5544 (2.42037 iter/s, 4.95793s/12 iters), loss = 1.1892
I0409 20:42:29.795465 14973 solver.cpp:237]     Train net output #0: loss = 1.1892 (* 1 = 1.1892 loss)
I0409 20:42:29.795478 14973 sgd_solver.cpp:105] Iteration 5544, lr = 0.00333475
I0409 20:42:34.725792 14973 solver.cpp:218] Iteration 5556 (2.434 iter/s, 4.93016s/12 iters), loss = 0.959507
I0409 20:42:34.725834 14973 solver.cpp:237]     Train net output #0: loss = 0.959507 (* 1 = 0.959507 loss)
I0409 20:42:34.725844 14973 sgd_solver.cpp:105] Iteration 5556, lr = 0.00332683
I0409 20:42:37.382110 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:42:39.645977 14973 solver.cpp:218] Iteration 5568 (2.43905 iter/s, 4.91995s/12 iters), loss = 1.01602
I0409 20:42:39.646095 14973 solver.cpp:237]     Train net output #0: loss = 1.01602 (* 1 = 1.01602 loss)
I0409 20:42:39.646108 14973 sgd_solver.cpp:105] Iteration 5568, lr = 0.00331893
I0409 20:42:44.708117 14973 solver.cpp:218] Iteration 5580 (2.37067 iter/s, 5.06185s/12 iters), loss = 1.24915
I0409 20:42:44.708165 14973 solver.cpp:237]     Train net output #0: loss = 1.24915 (* 1 = 1.24915 loss)
I0409 20:42:44.708176 14973 sgd_solver.cpp:105] Iteration 5580, lr = 0.00331105
I0409 20:42:49.856901 14973 solver.cpp:218] Iteration 5592 (2.33075 iter/s, 5.14855s/12 iters), loss = 1.15793
I0409 20:42:49.856945 14973 solver.cpp:237]     Train net output #0: loss = 1.15793 (* 1 = 1.15793 loss)
I0409 20:42:49.856956 14973 sgd_solver.cpp:105] Iteration 5592, lr = 0.00330319
I0409 20:42:54.906067 14973 solver.cpp:218] Iteration 5604 (2.37674 iter/s, 5.04894s/12 iters), loss = 1.12265
I0409 20:42:54.906121 14973 solver.cpp:237]     Train net output #0: loss = 1.12265 (* 1 = 1.12265 loss)
I0409 20:42:54.906132 14973 sgd_solver.cpp:105] Iteration 5604, lr = 0.00329535
I0409 20:42:56.966472 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5610.caffemodel
I0409 20:43:01.513841 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5610.solverstate
I0409 20:43:02.727337 14973 solver.cpp:330] Iteration 5610, Testing net (#0)
I0409 20:43:02.727365 14973 net.cpp:676] Ignoring source layer train-data
I0409 20:43:04.986876 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:43:07.201030 14973 solver.cpp:397]     Test net output #0: accuracy = 0.472426
I0409 20:43:07.201081 14973 solver.cpp:397]     Test net output #1: loss = 2.15339 (* 1 = 2.15339 loss)
I0409 20:43:09.133868 14973 solver.cpp:218] Iteration 5616 (0.84345 iter/s, 14.2273s/12 iters), loss = 1.05471
I0409 20:43:09.133922 14973 solver.cpp:237]     Train net output #0: loss = 1.05471 (* 1 = 1.05471 loss)
I0409 20:43:09.133934 14973 sgd_solver.cpp:105] Iteration 5616, lr = 0.00328752
I0409 20:43:14.241037 14973 solver.cpp:218] Iteration 5628 (2.34975 iter/s, 5.10693s/12 iters), loss = 1.18717
I0409 20:43:14.241192 14973 solver.cpp:237]     Train net output #0: loss = 1.18717 (* 1 = 1.18717 loss)
I0409 20:43:14.241205 14973 sgd_solver.cpp:105] Iteration 5628, lr = 0.00327972
I0409 20:43:19.336092 14973 solver.cpp:218] Iteration 5640 (2.35538 iter/s, 5.09473s/12 iters), loss = 1.18519
I0409 20:43:19.336143 14973 solver.cpp:237]     Train net output #0: loss = 1.18519 (* 1 = 1.18519 loss)
I0409 20:43:19.336154 14973 sgd_solver.cpp:105] Iteration 5640, lr = 0.00327193
I0409 20:43:24.549382 14973 solver.cpp:218] Iteration 5652 (2.30191 iter/s, 5.21306s/12 iters), loss = 1.15186
I0409 20:43:24.549432 14973 solver.cpp:237]     Train net output #0: loss = 1.15186 (* 1 = 1.15186 loss)
I0409 20:43:24.549445 14973 sgd_solver.cpp:105] Iteration 5652, lr = 0.00326416
I0409 20:43:29.442759 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:43:29.611418 14973 solver.cpp:218] Iteration 5664 (2.37069 iter/s, 5.06181s/12 iters), loss = 1.17172
I0409 20:43:29.611470 14973 solver.cpp:237]     Train net output #0: loss = 1.17172 (* 1 = 1.17172 loss)
I0409 20:43:29.611485 14973 sgd_solver.cpp:105] Iteration 5664, lr = 0.00325641
I0409 20:43:34.682209 14973 solver.cpp:218] Iteration 5676 (2.3666 iter/s, 5.07056s/12 iters), loss = 1.12862
I0409 20:43:34.682256 14973 solver.cpp:237]     Train net output #0: loss = 1.12862 (* 1 = 1.12862 loss)
I0409 20:43:34.682265 14973 sgd_solver.cpp:105] Iteration 5676, lr = 0.00324868
I0409 20:43:39.737821 14973 solver.cpp:218] Iteration 5688 (2.37371 iter/s, 5.05538s/12 iters), loss = 0.854473
I0409 20:43:39.737877 14973 solver.cpp:237]     Train net output #0: loss = 0.854473 (* 1 = 0.854473 loss)
I0409 20:43:39.737888 14973 sgd_solver.cpp:105] Iteration 5688, lr = 0.00324097
I0409 20:43:44.844475 14973 solver.cpp:218] Iteration 5700 (2.34998 iter/s, 5.10642s/12 iters), loss = 0.955741
I0409 20:43:44.844592 14973 solver.cpp:237]     Train net output #0: loss = 0.955741 (* 1 = 0.955741 loss)
I0409 20:43:44.844605 14973 sgd_solver.cpp:105] Iteration 5700, lr = 0.00323328
I0409 20:43:49.597513 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5712.caffemodel
I0409 20:43:51.134608 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5712.solverstate
I0409 20:43:53.485868 14973 solver.cpp:330] Iteration 5712, Testing net (#0)
I0409 20:43:53.485896 14973 net.cpp:676] Ignoring source layer train-data
I0409 20:43:55.736896 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:43:58.152465 14973 solver.cpp:397]     Test net output #0: accuracy = 0.468137
I0409 20:43:58.152494 14973 solver.cpp:397]     Test net output #1: loss = 2.16321 (* 1 = 2.16321 loss)
I0409 20:43:58.239152 14973 solver.cpp:218] Iteration 5712 (0.895916 iter/s, 13.3941s/12 iters), loss = 1.23783
I0409 20:43:58.239194 14973 solver.cpp:237]     Train net output #0: loss = 1.23783 (* 1 = 1.23783 loss)
I0409 20:43:58.239203 14973 sgd_solver.cpp:105] Iteration 5712, lr = 0.0032256
I0409 20:44:02.392902 14973 solver.cpp:218] Iteration 5724 (2.88909 iter/s, 4.15356s/12 iters), loss = 1.05672
I0409 20:44:02.392947 14973 solver.cpp:237]     Train net output #0: loss = 1.05672 (* 1 = 1.05672 loss)
I0409 20:44:02.392957 14973 sgd_solver.cpp:105] Iteration 5724, lr = 0.00321794
I0409 20:44:07.365736 14973 solver.cpp:218] Iteration 5736 (2.41322 iter/s, 4.97261s/12 iters), loss = 0.938147
I0409 20:44:07.365787 14973 solver.cpp:237]     Train net output #0: loss = 0.938147 (* 1 = 0.938147 loss)
I0409 20:44:07.365798 14973 sgd_solver.cpp:105] Iteration 5736, lr = 0.0032103
I0409 20:44:12.377529 14973 solver.cpp:218] Iteration 5748 (2.39446 iter/s, 5.01156s/12 iters), loss = 1.26271
I0409 20:44:12.377583 14973 solver.cpp:237]     Train net output #0: loss = 1.26271 (* 1 = 1.26271 loss)
I0409 20:44:12.377594 14973 sgd_solver.cpp:105] Iteration 5748, lr = 0.00320268
I0409 20:44:17.296938 14973 solver.cpp:218] Iteration 5760 (2.43943 iter/s, 4.91919s/12 iters), loss = 1.1793
I0409 20:44:17.297052 14973 solver.cpp:237]     Train net output #0: loss = 1.1793 (* 1 = 1.1793 loss)
I0409 20:44:17.297061 14973 sgd_solver.cpp:105] Iteration 5760, lr = 0.00319508
I0409 20:44:19.215454 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:44:22.183976 14973 solver.cpp:218] Iteration 5772 (2.45562 iter/s, 4.88675s/12 iters), loss = 0.957742
I0409 20:44:22.184024 14973 solver.cpp:237]     Train net output #0: loss = 0.957742 (* 1 = 0.957742 loss)
I0409 20:44:22.184034 14973 sgd_solver.cpp:105] Iteration 5772, lr = 0.00318749
I0409 20:44:27.177803 14973 solver.cpp:218] Iteration 5784 (2.40308 iter/s, 4.9936s/12 iters), loss = 1.07329
I0409 20:44:27.177850 14973 solver.cpp:237]     Train net output #0: loss = 1.07329 (* 1 = 1.07329 loss)
I0409 20:44:27.177861 14973 sgd_solver.cpp:105] Iteration 5784, lr = 0.00317992
I0409 20:44:32.094669 14973 solver.cpp:218] Iteration 5796 (2.44069 iter/s, 4.91664s/12 iters), loss = 0.948173
I0409 20:44:32.094791 14973 solver.cpp:237]     Train net output #0: loss = 0.948173 (* 1 = 0.948173 loss)
I0409 20:44:32.094805 14973 sgd_solver.cpp:105] Iteration 5796, lr = 0.00317237
I0409 20:44:37.249480 14973 solver.cpp:218] Iteration 5808 (2.32806 iter/s, 5.15452s/12 iters), loss = 1.06251
I0409 20:44:37.249526 14973 solver.cpp:237]     Train net output #0: loss = 1.06251 (* 1 = 1.06251 loss)
I0409 20:44:37.249536 14973 sgd_solver.cpp:105] Iteration 5808, lr = 0.00316484
I0409 20:44:39.317447 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5814.caffemodel
I0409 20:44:42.388343 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5814.solverstate
I0409 20:44:43.601245 14973 solver.cpp:330] Iteration 5814, Testing net (#0)
I0409 20:44:43.601270 14973 net.cpp:676] Ignoring source layer train-data
I0409 20:44:45.732892 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:44:48.183881 14973 solver.cpp:397]     Test net output #0: accuracy = 0.479779
I0409 20:44:48.183960 14973 solver.cpp:397]     Test net output #1: loss = 2.13796 (* 1 = 2.13796 loss)
I0409 20:44:50.169174 14973 solver.cpp:218] Iteration 5820 (0.928849 iter/s, 12.9192s/12 iters), loss = 0.960933
I0409 20:44:50.169216 14973 solver.cpp:237]     Train net output #0: loss = 0.960933 (* 1 = 0.960933 loss)
I0409 20:44:50.169226 14973 sgd_solver.cpp:105] Iteration 5820, lr = 0.00315733
I0409 20:44:55.209940 14973 solver.cpp:218] Iteration 5832 (2.38069 iter/s, 5.04055s/12 iters), loss = 1.10346
I0409 20:44:55.210006 14973 solver.cpp:237]     Train net output #0: loss = 1.10346 (* 1 = 1.10346 loss)
I0409 20:44:55.210016 14973 sgd_solver.cpp:105] Iteration 5832, lr = 0.00314983
I0409 20:45:00.173812 14973 solver.cpp:218] Iteration 5844 (2.41758 iter/s, 4.96363s/12 iters), loss = 1.11829
I0409 20:45:00.173856 14973 solver.cpp:237]     Train net output #0: loss = 1.11829 (* 1 = 1.11829 loss)
I0409 20:45:00.173866 14973 sgd_solver.cpp:105] Iteration 5844, lr = 0.00314235
I0409 20:45:05.292749 14973 solver.cpp:218] Iteration 5856 (2.34434 iter/s, 5.11871s/12 iters), loss = 0.897465
I0409 20:45:05.292798 14973 solver.cpp:237]     Train net output #0: loss = 0.897465 (* 1 = 0.897465 loss)
I0409 20:45:05.292806 14973 sgd_solver.cpp:105] Iteration 5856, lr = 0.00313489
I0409 20:45:09.501238 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:45:10.305830 14973 solver.cpp:218] Iteration 5868 (2.39385 iter/s, 5.01285s/12 iters), loss = 1.00127
I0409 20:45:10.305876 14973 solver.cpp:237]     Train net output #0: loss = 1.00127 (* 1 = 1.00127 loss)
I0409 20:45:10.305887 14973 sgd_solver.cpp:105] Iteration 5868, lr = 0.00312745
I0409 20:45:15.279808 14973 solver.cpp:218] Iteration 5880 (2.41266 iter/s, 4.97376s/12 iters), loss = 1.08315
I0409 20:45:15.279848 14973 solver.cpp:237]     Train net output #0: loss = 1.08315 (* 1 = 1.08315 loss)
I0409 20:45:15.279857 14973 sgd_solver.cpp:105] Iteration 5880, lr = 0.00312002
I0409 20:45:20.222715 14973 solver.cpp:218] Iteration 5892 (2.42783 iter/s, 4.94269s/12 iters), loss = 0.978527
I0409 20:45:20.222858 14973 solver.cpp:237]     Train net output #0: loss = 0.978527 (* 1 = 0.978527 loss)
I0409 20:45:20.222872 14973 sgd_solver.cpp:105] Iteration 5892, lr = 0.00311262
I0409 20:45:25.395351 14973 solver.cpp:218] Iteration 5904 (2.32004 iter/s, 5.17232s/12 iters), loss = 0.795836
I0409 20:45:25.395398 14973 solver.cpp:237]     Train net output #0: loss = 0.795836 (* 1 = 0.795836 loss)
I0409 20:45:25.395408 14973 sgd_solver.cpp:105] Iteration 5904, lr = 0.00310523
I0409 20:45:29.954166 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5916.caffemodel
I0409 20:45:33.615159 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5916.solverstate
I0409 20:45:35.990278 14973 solver.cpp:330] Iteration 5916, Testing net (#0)
I0409 20:45:35.990298 14973 net.cpp:676] Ignoring source layer train-data
I0409 20:45:38.071815 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:45:40.405865 14973 solver.cpp:397]     Test net output #0: accuracy = 0.468137
I0409 20:45:40.405908 14973 solver.cpp:397]     Test net output #1: loss = 2.19856 (* 1 = 2.19856 loss)
I0409 20:45:40.493079 14973 solver.cpp:218] Iteration 5916 (0.794851 iter/s, 15.0972s/12 iters), loss = 0.9979
I0409 20:45:40.493130 14973 solver.cpp:237]     Train net output #0: loss = 0.9979 (* 1 = 0.9979 loss)
I0409 20:45:40.493141 14973 sgd_solver.cpp:105] Iteration 5916, lr = 0.00309785
I0409 20:45:44.740077 14973 solver.cpp:218] Iteration 5928 (2.82566 iter/s, 4.24679s/12 iters), loss = 0.927344
I0409 20:45:44.740134 14973 solver.cpp:237]     Train net output #0: loss = 0.927344 (* 1 = 0.927344 loss)
I0409 20:45:44.740147 14973 sgd_solver.cpp:105] Iteration 5928, lr = 0.0030905
I0409 20:45:49.726526 14973 solver.cpp:218] Iteration 5940 (2.40663 iter/s, 4.98622s/12 iters), loss = 1.00723
I0409 20:45:49.726568 14973 solver.cpp:237]     Train net output #0: loss = 1.00723 (* 1 = 1.00723 loss)
I0409 20:45:49.726577 14973 sgd_solver.cpp:105] Iteration 5940, lr = 0.00308316
I0409 20:45:54.804436 14973 solver.cpp:218] Iteration 5952 (2.36328 iter/s, 5.07769s/12 iters), loss = 1.02094
I0409 20:45:54.804754 14973 solver.cpp:237]     Train net output #0: loss = 1.02094 (* 1 = 1.02094 loss)
I0409 20:45:54.804764 14973 sgd_solver.cpp:105] Iteration 5952, lr = 0.00307584
I0409 20:45:59.879848 14973 solver.cpp:218] Iteration 5964 (2.36457 iter/s, 5.07492s/12 iters), loss = 1.21576
I0409 20:45:59.879900 14973 solver.cpp:237]     Train net output #0: loss = 1.21576 (* 1 = 1.21576 loss)
I0409 20:45:59.879912 14973 sgd_solver.cpp:105] Iteration 5964, lr = 0.00306854
I0409 20:46:01.211107 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:46:04.851542 14973 solver.cpp:218] Iteration 5976 (2.41378 iter/s, 4.97147s/12 iters), loss = 0.895423
I0409 20:46:04.851596 14973 solver.cpp:237]     Train net output #0: loss = 0.895423 (* 1 = 0.895423 loss)
I0409 20:46:04.851609 14973 sgd_solver.cpp:105] Iteration 5976, lr = 0.00306125
I0409 20:46:09.764942 14973 solver.cpp:218] Iteration 5988 (2.44241 iter/s, 4.91317s/12 iters), loss = 1.1513
I0409 20:46:09.764988 14973 solver.cpp:237]     Train net output #0: loss = 1.1513 (* 1 = 1.1513 loss)
I0409 20:46:09.764998 14973 sgd_solver.cpp:105] Iteration 5988, lr = 0.00305398
I0409 20:46:14.799949 14973 solver.cpp:218] Iteration 6000 (2.38342 iter/s, 5.03478s/12 iters), loss = 0.86239
I0409 20:46:14.799995 14973 solver.cpp:237]     Train net output #0: loss = 0.86239 (* 1 = 0.86239 loss)
I0409 20:46:14.800004 14973 sgd_solver.cpp:105] Iteration 6000, lr = 0.00304673
I0409 20:46:19.825032 14973 solver.cpp:218] Iteration 6012 (2.38813 iter/s, 5.02486s/12 iters), loss = 0.83863
I0409 20:46:19.825081 14973 solver.cpp:237]     Train net output #0: loss = 0.83863 (* 1 = 0.83863 loss)
I0409 20:46:19.825093 14973 sgd_solver.cpp:105] Iteration 6012, lr = 0.0030395
I0409 20:46:21.873098 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6018.caffemodel
I0409 20:46:23.450031 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6018.solverstate
I0409 20:46:24.655352 14973 solver.cpp:330] Iteration 6018, Testing net (#0)
I0409 20:46:24.655376 14973 net.cpp:676] Ignoring source layer train-data
I0409 20:46:26.910919 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:46:29.306574 14973 solver.cpp:397]     Test net output #0: accuracy = 0.472426
I0409 20:46:29.306607 14973 solver.cpp:397]     Test net output #1: loss = 2.22769 (* 1 = 2.22769 loss)
I0409 20:46:31.260356 14973 solver.cpp:218] Iteration 6024 (1.04941 iter/s, 11.4349s/12 iters), loss = 0.918847
I0409 20:46:31.260401 14973 solver.cpp:237]     Train net output #0: loss = 0.918847 (* 1 = 0.918847 loss)
I0409 20:46:31.260411 14973 sgd_solver.cpp:105] Iteration 6024, lr = 0.00303228
I0409 20:46:36.206880 14973 solver.cpp:218] Iteration 6036 (2.426 iter/s, 4.9464s/12 iters), loss = 0.868312
I0409 20:46:36.206924 14973 solver.cpp:237]     Train net output #0: loss = 0.868312 (* 1 = 0.868312 loss)
I0409 20:46:36.206933 14973 sgd_solver.cpp:105] Iteration 6036, lr = 0.00302508
I0409 20:46:41.234172 14973 solver.cpp:218] Iteration 6048 (2.38703 iter/s, 5.02718s/12 iters), loss = 1.00798
I0409 20:46:41.234217 14973 solver.cpp:237]     Train net output #0: loss = 1.00798 (* 1 = 1.00798 loss)
I0409 20:46:41.234226 14973 sgd_solver.cpp:105] Iteration 6048, lr = 0.0030179
I0409 20:46:46.214628 14973 solver.cpp:218] Iteration 6060 (2.40948 iter/s, 4.98033s/12 iters), loss = 1.15233
I0409 20:46:46.214681 14973 solver.cpp:237]     Train net output #0: loss = 1.15233 (* 1 = 1.15233 loss)
I0409 20:46:46.214691 14973 sgd_solver.cpp:105] Iteration 6060, lr = 0.00301074
I0409 20:46:49.684274 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:46:51.258420 14973 solver.cpp:218] Iteration 6072 (2.37922 iter/s, 5.04367s/12 iters), loss = 0.803831
I0409 20:46:51.258472 14973 solver.cpp:237]     Train net output #0: loss = 0.803831 (* 1 = 0.803831 loss)
I0409 20:46:51.258486 14973 sgd_solver.cpp:105] Iteration 6072, lr = 0.00300359
I0409 20:46:56.330464 14973 solver.cpp:218] Iteration 6084 (2.36597 iter/s, 5.07192s/12 iters), loss = 0.730164
I0409 20:46:56.330515 14973 solver.cpp:237]     Train net output #0: loss = 0.730164 (* 1 = 0.730164 loss)
I0409 20:46:56.330526 14973 sgd_solver.cpp:105] Iteration 6084, lr = 0.00299646
I0409 20:47:01.368755 14973 solver.cpp:218] Iteration 6096 (2.38182 iter/s, 5.03816s/12 iters), loss = 1.21086
I0409 20:47:01.368873 14973 solver.cpp:237]     Train net output #0: loss = 1.21086 (* 1 = 1.21086 loss)
I0409 20:47:01.368885 14973 sgd_solver.cpp:105] Iteration 6096, lr = 0.00298934
I0409 20:47:06.288774 14973 solver.cpp:218] Iteration 6108 (2.43911 iter/s, 4.91983s/12 iters), loss = 1.01988
I0409 20:47:06.288821 14973 solver.cpp:237]     Train net output #0: loss = 1.01988 (* 1 = 1.01988 loss)
I0409 20:47:06.288830 14973 sgd_solver.cpp:105] Iteration 6108, lr = 0.00298225
I0409 20:47:10.755237 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6120.caffemodel
I0409 20:47:12.415989 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6120.solverstate
I0409 20:47:13.618789 14973 solver.cpp:330] Iteration 6120, Testing net (#0)
I0409 20:47:13.618815 14973 net.cpp:676] Ignoring source layer train-data
I0409 20:47:15.674835 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:47:18.132104 14973 solver.cpp:397]     Test net output #0: accuracy = 0.484069
I0409 20:47:18.132153 14973 solver.cpp:397]     Test net output #1: loss = 2.14696 (* 1 = 2.14696 loss)
I0409 20:47:18.218853 14973 solver.cpp:218] Iteration 6120 (1.00588 iter/s, 11.9299s/12 iters), loss = 0.700197
I0409 20:47:18.218911 14973 solver.cpp:237]     Train net output #0: loss = 0.700197 (* 1 = 0.700197 loss)
I0409 20:47:18.218924 14973 sgd_solver.cpp:105] Iteration 6120, lr = 0.00297517
I0409 20:47:22.523603 14973 solver.cpp:218] Iteration 6132 (2.7877 iter/s, 4.30462s/12 iters), loss = 0.82342
I0409 20:47:22.523654 14973 solver.cpp:237]     Train net output #0: loss = 0.82342 (* 1 = 0.82342 loss)
I0409 20:47:22.523666 14973 sgd_solver.cpp:105] Iteration 6132, lr = 0.0029681
I0409 20:47:27.563345 14973 solver.cpp:218] Iteration 6144 (2.38113 iter/s, 5.03961s/12 iters), loss = 1.08418
I0409 20:47:27.563387 14973 solver.cpp:237]     Train net output #0: loss = 1.08418 (* 1 = 1.08418 loss)
I0409 20:47:27.563396 14973 sgd_solver.cpp:105] Iteration 6144, lr = 0.00296105
I0409 20:47:32.569018 14973 solver.cpp:218] Iteration 6156 (2.39734 iter/s, 5.00555s/12 iters), loss = 0.985789
I0409 20:47:32.569167 14973 solver.cpp:237]     Train net output #0: loss = 0.985789 (* 1 = 0.985789 loss)
I0409 20:47:32.569180 14973 sgd_solver.cpp:105] Iteration 6156, lr = 0.00295402
I0409 20:47:37.567950 14973 solver.cpp:218] Iteration 6168 (2.40062 iter/s, 4.99871s/12 iters), loss = 0.92165
I0409 20:47:37.567993 14973 solver.cpp:237]     Train net output #0: loss = 0.92165 (* 1 = 0.92165 loss)
I0409 20:47:37.568002 14973 sgd_solver.cpp:105] Iteration 6168, lr = 0.00294701
I0409 20:47:38.160528 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:47:42.529677 14973 solver.cpp:218] Iteration 6180 (2.41857 iter/s, 4.9616s/12 iters), loss = 0.775502
I0409 20:47:42.529722 14973 solver.cpp:237]     Train net output #0: loss = 0.775502 (* 1 = 0.775502 loss)
I0409 20:47:42.529731 14973 sgd_solver.cpp:105] Iteration 6180, lr = 0.00294001
I0409 20:47:47.537415 14973 solver.cpp:218] Iteration 6192 (2.39635 iter/s, 5.00761s/12 iters), loss = 0.83752
I0409 20:47:47.537472 14973 solver.cpp:237]     Train net output #0: loss = 0.83752 (* 1 = 0.83752 loss)
I0409 20:47:47.537484 14973 sgd_solver.cpp:105] Iteration 6192, lr = 0.00293303
I0409 20:47:52.599905 14973 solver.cpp:218] Iteration 6204 (2.37044 iter/s, 5.06235s/12 iters), loss = 0.808433
I0409 20:47:52.599957 14973 solver.cpp:237]     Train net output #0: loss = 0.808433 (* 1 = 0.808433 loss)
I0409 20:47:52.599968 14973 sgd_solver.cpp:105] Iteration 6204, lr = 0.00292607
I0409 20:47:57.599133 14973 solver.cpp:218] Iteration 6216 (2.40044 iter/s, 4.99909s/12 iters), loss = 1.01899
I0409 20:47:57.599179 14973 solver.cpp:237]     Train net output #0: loss = 1.01899 (* 1 = 1.01899 loss)
I0409 20:47:57.599187 14973 sgd_solver.cpp:105] Iteration 6216, lr = 0.00291912
I0409 20:47:59.640928 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6222.caffemodel
I0409 20:48:02.460332 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6222.solverstate
I0409 20:48:04.265251 14973 solver.cpp:330] Iteration 6222, Testing net (#0)
I0409 20:48:04.265324 14973 net.cpp:676] Ignoring source layer train-data
I0409 20:48:06.415442 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:48:07.595659 14973 blocking_queue.cpp:49] Waiting for data
I0409 20:48:08.862988 14973 solver.cpp:397]     Test net output #0: accuracy = 0.497549
I0409 20:48:08.863030 14973 solver.cpp:397]     Test net output #1: loss = 2.14728 (* 1 = 2.14728 loss)
I0409 20:48:10.843556 14973 solver.cpp:218] Iteration 6228 (0.906059 iter/s, 13.2442s/12 iters), loss = 0.79849
I0409 20:48:10.843607 14973 solver.cpp:237]     Train net output #0: loss = 0.79849 (* 1 = 0.79849 loss)
I0409 20:48:10.843617 14973 sgd_solver.cpp:105] Iteration 6228, lr = 0.00291219
I0409 20:48:15.877163 14973 solver.cpp:218] Iteration 6240 (2.38404 iter/s, 5.03347s/12 iters), loss = 0.771394
I0409 20:48:15.877202 14973 solver.cpp:237]     Train net output #0: loss = 0.771394 (* 1 = 0.771394 loss)
I0409 20:48:15.877209 14973 sgd_solver.cpp:105] Iteration 6240, lr = 0.00290528
I0409 20:48:20.967326 14973 solver.cpp:218] Iteration 6252 (2.35755 iter/s, 5.09003s/12 iters), loss = 0.704886
I0409 20:48:20.967375 14973 solver.cpp:237]     Train net output #0: loss = 0.704886 (* 1 = 0.704886 loss)
I0409 20:48:20.967383 14973 sgd_solver.cpp:105] Iteration 6252, lr = 0.00289838
I0409 20:48:25.989389 14973 solver.cpp:218] Iteration 6264 (2.38952 iter/s, 5.02192s/12 iters), loss = 0.910423
I0409 20:48:25.989429 14973 solver.cpp:237]     Train net output #0: loss = 0.910423 (* 1 = 0.910423 loss)
I0409 20:48:25.989439 14973 sgd_solver.cpp:105] Iteration 6264, lr = 0.0028915
I0409 20:48:28.842504 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:48:31.072554 14973 solver.cpp:218] Iteration 6276 (2.3608 iter/s, 5.08303s/12 iters), loss = 0.818248
I0409 20:48:31.072618 14973 solver.cpp:237]     Train net output #0: loss = 0.818248 (* 1 = 0.818248 loss)
I0409 20:48:31.072629 14973 sgd_solver.cpp:105] Iteration 6276, lr = 0.00288463
I0409 20:48:36.096453 14973 solver.cpp:218] Iteration 6288 (2.38866 iter/s, 5.02375s/12 iters), loss = 1.02297
I0409 20:48:36.096586 14973 solver.cpp:237]     Train net output #0: loss = 1.02297 (* 1 = 1.02297 loss)
I0409 20:48:36.096596 14973 sgd_solver.cpp:105] Iteration 6288, lr = 0.00287779
I0409 20:48:41.114681 14973 solver.cpp:218] Iteration 6300 (2.39139 iter/s, 5.018s/12 iters), loss = 0.981281
I0409 20:48:41.114730 14973 solver.cpp:237]     Train net output #0: loss = 0.981281 (* 1 = 0.981281 loss)
I0409 20:48:41.114740 14973 sgd_solver.cpp:105] Iteration 6300, lr = 0.00287095
I0409 20:48:46.210938 14973 solver.cpp:218] Iteration 6312 (2.35474 iter/s, 5.09611s/12 iters), loss = 0.972965
I0409 20:48:46.210980 14973 solver.cpp:237]     Train net output #0: loss = 0.972965 (* 1 = 0.972965 loss)
I0409 20:48:46.210990 14973 sgd_solver.cpp:105] Iteration 6312, lr = 0.00286414
I0409 20:48:50.986272 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6324.caffemodel
I0409 20:48:52.805294 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6324.solverstate
I0409 20:48:55.172222 14973 solver.cpp:330] Iteration 6324, Testing net (#0)
I0409 20:48:55.172247 14973 net.cpp:676] Ignoring source layer train-data
I0409 20:48:57.235774 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:48:59.806054 14973 solver.cpp:397]     Test net output #0: accuracy = 0.51348
I0409 20:48:59.806110 14973 solver.cpp:397]     Test net output #1: loss = 2.022 (* 1 = 2.022 loss)
I0409 20:48:59.891996 14973 solver.cpp:218] Iteration 6324 (0.877143 iter/s, 13.6808s/12 iters), loss = 0.887364
I0409 20:48:59.892069 14973 solver.cpp:237]     Train net output #0: loss = 0.887364 (* 1 = 0.887364 loss)
I0409 20:48:59.892086 14973 sgd_solver.cpp:105] Iteration 6324, lr = 0.00285734
I0409 20:49:04.233040 14973 solver.cpp:218] Iteration 6336 (2.76441 iter/s, 4.34089s/12 iters), loss = 0.868419
I0409 20:49:04.233084 14973 solver.cpp:237]     Train net output #0: loss = 0.868419 (* 1 = 0.868419 loss)
I0409 20:49:04.233094 14973 sgd_solver.cpp:105] Iteration 6336, lr = 0.00285055
I0409 20:49:09.220840 14973 solver.cpp:218] Iteration 6348 (2.40594 iter/s, 4.98766s/12 iters), loss = 0.750007
I0409 20:49:09.220944 14973 solver.cpp:237]     Train net output #0: loss = 0.750007 (* 1 = 0.750007 loss)
I0409 20:49:09.220955 14973 sgd_solver.cpp:105] Iteration 6348, lr = 0.00284379
I0409 20:49:14.206501 14973 solver.cpp:218] Iteration 6360 (2.407 iter/s, 4.98546s/12 iters), loss = 0.741356
I0409 20:49:14.206553 14973 solver.cpp:237]     Train net output #0: loss = 0.741356 (* 1 = 0.741356 loss)
I0409 20:49:14.206564 14973 sgd_solver.cpp:105] Iteration 6360, lr = 0.00283703
I0409 20:49:19.062260 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:49:19.197873 14973 solver.cpp:218] Iteration 6372 (2.40422 iter/s, 4.99122s/12 iters), loss = 0.685971
I0409 20:49:19.197926 14973 solver.cpp:237]     Train net output #0: loss = 0.685971 (* 1 = 0.685971 loss)
I0409 20:49:19.197937 14973 sgd_solver.cpp:105] Iteration 6372, lr = 0.0028303
I0409 20:49:24.231114 14973 solver.cpp:218] Iteration 6384 (2.38422 iter/s, 5.03309s/12 iters), loss = 0.746327
I0409 20:49:24.231158 14973 solver.cpp:237]     Train net output #0: loss = 0.746327 (* 1 = 0.746327 loss)
I0409 20:49:24.231166 14973 sgd_solver.cpp:105] Iteration 6384, lr = 0.00282358
I0409 20:49:29.233611 14973 solver.cpp:218] Iteration 6396 (2.39887 iter/s, 5.00235s/12 iters), loss = 0.712303
I0409 20:49:29.233657 14973 solver.cpp:237]     Train net output #0: loss = 0.712303 (* 1 = 0.712303 loss)
I0409 20:49:29.233666 14973 sgd_solver.cpp:105] Iteration 6396, lr = 0.00281687
I0409 20:49:34.191426 14973 solver.cpp:218] Iteration 6408 (2.42049 iter/s, 4.95767s/12 iters), loss = 0.982332
I0409 20:49:34.191470 14973 solver.cpp:237]     Train net output #0: loss = 0.982332 (* 1 = 0.982332 loss)
I0409 20:49:34.191480 14973 sgd_solver.cpp:105] Iteration 6408, lr = 0.00281019
I0409 20:49:39.238845 14973 solver.cpp:218] Iteration 6420 (2.37752 iter/s, 5.04727s/12 iters), loss = 0.821711
I0409 20:49:39.238992 14973 solver.cpp:237]     Train net output #0: loss = 0.821711 (* 1 = 0.821711 loss)
I0409 20:49:39.239006 14973 sgd_solver.cpp:105] Iteration 6420, lr = 0.00280351
I0409 20:49:41.284956 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6426.caffemodel
I0409 20:49:43.105329 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6426.solverstate
I0409 20:49:47.291098 14973 solver.cpp:330] Iteration 6426, Testing net (#0)
I0409 20:49:47.291122 14973 net.cpp:676] Ignoring source layer train-data
I0409 20:49:49.227738 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:49:51.755707 14973 solver.cpp:397]     Test net output #0: accuracy = 0.484069
I0409 20:49:51.755764 14973 solver.cpp:397]     Test net output #1: loss = 2.229 (* 1 = 2.229 loss)
I0409 20:49:53.609695 14973 solver.cpp:218] Iteration 6432 (0.835047 iter/s, 14.3704s/12 iters), loss = 0.893705
I0409 20:49:53.609743 14973 solver.cpp:237]     Train net output #0: loss = 0.893705 (* 1 = 0.893705 loss)
I0409 20:49:53.609752 14973 sgd_solver.cpp:105] Iteration 6432, lr = 0.00279686
I0409 20:49:58.712905 14973 solver.cpp:218] Iteration 6444 (2.35153 iter/s, 5.10306s/12 iters), loss = 0.92761
I0409 20:49:58.712957 14973 solver.cpp:237]     Train net output #0: loss = 0.92761 (* 1 = 0.92761 loss)
I0409 20:49:58.712971 14973 sgd_solver.cpp:105] Iteration 6444, lr = 0.00279022
I0409 20:50:03.798048 14973 solver.cpp:218] Iteration 6456 (2.35989 iter/s, 5.08499s/12 iters), loss = 0.934367
I0409 20:50:03.798091 14973 solver.cpp:237]     Train net output #0: loss = 0.934367 (* 1 = 0.934367 loss)
I0409 20:50:03.798099 14973 sgd_solver.cpp:105] Iteration 6456, lr = 0.00278359
I0409 20:50:09.217986 14973 solver.cpp:218] Iteration 6468 (2.21412 iter/s, 5.41977s/12 iters), loss = 0.687641
I0409 20:50:09.218040 14973 solver.cpp:237]     Train net output #0: loss = 0.687641 (* 1 = 0.687641 loss)
I0409 20:50:09.218052 14973 sgd_solver.cpp:105] Iteration 6468, lr = 0.00277698
I0409 20:50:11.420105 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:50:14.667125 14973 solver.cpp:218] Iteration 6480 (2.20225 iter/s, 5.44897s/12 iters), loss = 0.921849
I0409 20:50:14.667172 14973 solver.cpp:237]     Train net output #0: loss = 0.921849 (* 1 = 0.921849 loss)
I0409 20:50:14.667181 14973 sgd_solver.cpp:105] Iteration 6480, lr = 0.00277039
I0409 20:50:19.719316 14973 solver.cpp:218] Iteration 6492 (2.37528 iter/s, 5.05204s/12 iters), loss = 0.966965
I0409 20:50:19.719365 14973 solver.cpp:237]     Train net output #0: loss = 0.966965 (* 1 = 0.966965 loss)
I0409 20:50:19.719377 14973 sgd_solver.cpp:105] Iteration 6492, lr = 0.00276381
I0409 20:50:24.726785 14973 solver.cpp:218] Iteration 6504 (2.3965 iter/s, 5.00731s/12 iters), loss = 0.623767
I0409 20:50:24.726845 14973 solver.cpp:237]     Train net output #0: loss = 0.623767 (* 1 = 0.623767 loss)
I0409 20:50:24.726856 14973 sgd_solver.cpp:105] Iteration 6504, lr = 0.00275725
I0409 20:50:29.623724 14973 solver.cpp:218] Iteration 6516 (2.45059 iter/s, 4.89678s/12 iters), loss = 0.759936
I0409 20:50:29.623771 14973 solver.cpp:237]     Train net output #0: loss = 0.759936 (* 1 = 0.759936 loss)
I0409 20:50:29.623780 14973 sgd_solver.cpp:105] Iteration 6516, lr = 0.00275071
I0409 20:50:34.178323 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6528.caffemodel
I0409 20:50:35.780310 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6528.solverstate
I0409 20:50:36.987397 14973 solver.cpp:330] Iteration 6528, Testing net (#0)
I0409 20:50:36.987422 14973 net.cpp:676] Ignoring source layer train-data
I0409 20:50:38.871855 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:50:41.435282 14973 solver.cpp:397]     Test net output #0: accuracy = 0.501838
I0409 20:50:41.435398 14973 solver.cpp:397]     Test net output #1: loss = 2.13368 (* 1 = 2.13368 loss)
I0409 20:50:41.522330 14973 solver.cpp:218] Iteration 6528 (1.00855 iter/s, 11.8983s/12 iters), loss = 0.556582
I0409 20:50:41.522399 14973 solver.cpp:237]     Train net output #0: loss = 0.556582 (* 1 = 0.556582 loss)
I0409 20:50:41.522416 14973 sgd_solver.cpp:105] Iteration 6528, lr = 0.00274418
I0409 20:50:45.818778 14973 solver.cpp:218] Iteration 6540 (2.79311 iter/s, 4.29629s/12 iters), loss = 0.799398
I0409 20:50:45.818825 14973 solver.cpp:237]     Train net output #0: loss = 0.799398 (* 1 = 0.799398 loss)
I0409 20:50:45.818837 14973 sgd_solver.cpp:105] Iteration 6540, lr = 0.00273766
I0409 20:50:50.825368 14973 solver.cpp:218] Iteration 6552 (2.39692 iter/s, 5.00643s/12 iters), loss = 0.743905
I0409 20:50:50.825415 14973 solver.cpp:237]     Train net output #0: loss = 0.743905 (* 1 = 0.743905 loss)
I0409 20:50:50.825426 14973 sgd_solver.cpp:105] Iteration 6552, lr = 0.00273116
I0409 20:50:55.921263 14973 solver.cpp:218] Iteration 6564 (2.35491 iter/s, 5.09574s/12 iters), loss = 0.651217
I0409 20:50:55.921314 14973 solver.cpp:237]     Train net output #0: loss = 0.651217 (* 1 = 0.651217 loss)
I0409 20:50:55.921324 14973 sgd_solver.cpp:105] Iteration 6564, lr = 0.00272468
I0409 20:51:00.207821 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:51:00.995188 14973 solver.cpp:218] Iteration 6576 (2.36511 iter/s, 5.07377s/12 iters), loss = 1.07065
I0409 20:51:00.995239 14973 solver.cpp:237]     Train net output #0: loss = 1.07065 (* 1 = 1.07065 loss)
I0409 20:51:00.995249 14973 sgd_solver.cpp:105] Iteration 6576, lr = 0.00271821
I0409 20:51:06.097280 14973 solver.cpp:218] Iteration 6588 (2.35205 iter/s, 5.10193s/12 iters), loss = 0.611942
I0409 20:51:06.097326 14973 solver.cpp:237]     Train net output #0: loss = 0.611942 (* 1 = 0.611942 loss)
I0409 20:51:06.097337 14973 sgd_solver.cpp:105] Iteration 6588, lr = 0.00271175
I0409 20:51:11.140202 14973 solver.cpp:218] Iteration 6600 (2.37965 iter/s, 5.04276s/12 iters), loss = 0.857387
I0409 20:51:11.140259 14973 solver.cpp:237]     Train net output #0: loss = 0.857387 (* 1 = 0.857387 loss)
I0409 20:51:11.140270 14973 sgd_solver.cpp:105] Iteration 6600, lr = 0.00270532
I0409 20:51:16.142992 14973 solver.cpp:218] Iteration 6612 (2.39874 iter/s, 5.00262s/12 iters), loss = 0.650487
I0409 20:51:16.143092 14973 solver.cpp:237]     Train net output #0: loss = 0.650487 (* 1 = 0.650487 loss)
I0409 20:51:16.143102 14973 sgd_solver.cpp:105] Iteration 6612, lr = 0.00269889
I0409 20:51:21.254518 14973 solver.cpp:218] Iteration 6624 (2.34773 iter/s, 5.11131s/12 iters), loss = 0.881974
I0409 20:51:21.254566 14973 solver.cpp:237]     Train net output #0: loss = 0.881974 (* 1 = 0.881974 loss)
I0409 20:51:21.254577 14973 sgd_solver.cpp:105] Iteration 6624, lr = 0.00269248
I0409 20:51:23.302309 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6630.caffemodel
I0409 20:51:26.445302 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6630.solverstate
I0409 20:51:28.419946 14973 solver.cpp:330] Iteration 6630, Testing net (#0)
I0409 20:51:28.419972 14973 net.cpp:676] Ignoring source layer train-data
I0409 20:51:30.259853 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:51:32.963857 14973 solver.cpp:397]     Test net output #0: accuracy = 0.495098
I0409 20:51:32.963917 14973 solver.cpp:397]     Test net output #1: loss = 2.19306 (* 1 = 2.19306 loss)
I0409 20:51:34.898051 14973 solver.cpp:218] Iteration 6636 (0.879558 iter/s, 13.6432s/12 iters), loss = 0.656907
I0409 20:51:34.898092 14973 solver.cpp:237]     Train net output #0: loss = 0.656907 (* 1 = 0.656907 loss)
I0409 20:51:34.898099 14973 sgd_solver.cpp:105] Iteration 6636, lr = 0.00268609
I0409 20:51:40.137939 14973 solver.cpp:218] Iteration 6648 (2.2902 iter/s, 5.23973s/12 iters), loss = 0.750546
I0409 20:51:40.138010 14973 solver.cpp:237]     Train net output #0: loss = 0.750546 (* 1 = 0.750546 loss)
I0409 20:51:40.138021 14973 sgd_solver.cpp:105] Iteration 6648, lr = 0.00267971
I0409 20:51:45.184582 14973 solver.cpp:218] Iteration 6660 (2.3779 iter/s, 5.04646s/12 iters), loss = 0.727034
I0409 20:51:45.184634 14973 solver.cpp:237]     Train net output #0: loss = 0.727034 (* 1 = 0.727034 loss)
I0409 20:51:45.184645 14973 sgd_solver.cpp:105] Iteration 6660, lr = 0.00267335
I0409 20:51:50.135066 14973 solver.cpp:218] Iteration 6672 (2.42409 iter/s, 4.95032s/12 iters), loss = 0.649049
I0409 20:51:50.135186 14973 solver.cpp:237]     Train net output #0: loss = 0.649049 (* 1 = 0.649049 loss)
I0409 20:51:50.135196 14973 sgd_solver.cpp:105] Iteration 6672, lr = 0.00266701
I0409 20:51:51.494895 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:51:55.274130 14973 solver.cpp:218] Iteration 6684 (2.33516 iter/s, 5.13883s/12 iters), loss = 0.770838
I0409 20:51:55.274180 14973 solver.cpp:237]     Train net output #0: loss = 0.770838 (* 1 = 0.770838 loss)
I0409 20:51:55.274192 14973 sgd_solver.cpp:105] Iteration 6684, lr = 0.00266067
I0409 20:52:00.219529 14973 solver.cpp:218] Iteration 6696 (2.42658 iter/s, 4.94524s/12 iters), loss = 0.750379
I0409 20:52:00.219578 14973 solver.cpp:237]     Train net output #0: loss = 0.750379 (* 1 = 0.750379 loss)
I0409 20:52:00.219586 14973 sgd_solver.cpp:105] Iteration 6696, lr = 0.00265436
I0409 20:52:05.253140 14973 solver.cpp:218] Iteration 6708 (2.38405 iter/s, 5.03345s/12 iters), loss = 0.650722
I0409 20:52:05.253181 14973 solver.cpp:237]     Train net output #0: loss = 0.650722 (* 1 = 0.650722 loss)
I0409 20:52:05.253190 14973 sgd_solver.cpp:105] Iteration 6708, lr = 0.00264805
I0409 20:52:10.284020 14973 solver.cpp:218] Iteration 6720 (2.38534 iter/s, 5.03072s/12 iters), loss = 0.597869
I0409 20:52:10.284067 14973 solver.cpp:237]     Train net output #0: loss = 0.597869 (* 1 = 0.597869 loss)
I0409 20:52:10.284077 14973 sgd_solver.cpp:105] Iteration 6720, lr = 0.00264177
I0409 20:52:15.027098 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6732.caffemodel
I0409 20:52:16.631384 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6732.solverstate
I0409 20:52:17.834525 14973 solver.cpp:330] Iteration 6732, Testing net (#0)
I0409 20:52:17.834549 14973 net.cpp:676] Ignoring source layer train-data
I0409 20:52:19.609019 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:52:22.255182 14973 solver.cpp:397]     Test net output #0: accuracy = 0.504902
I0409 20:52:22.255277 14973 solver.cpp:397]     Test net output #1: loss = 2.12516 (* 1 = 2.12516 loss)
I0409 20:52:22.341936 14973 solver.cpp:218] Iteration 6732 (0.995222 iter/s, 12.0576s/12 iters), loss = 0.780496
I0409 20:52:22.342010 14973 solver.cpp:237]     Train net output #0: loss = 0.780496 (* 1 = 0.780496 loss)
I0409 20:52:22.342022 14973 sgd_solver.cpp:105] Iteration 6732, lr = 0.0026355
I0409 20:52:26.581918 14973 solver.cpp:218] Iteration 6744 (2.83032 iter/s, 4.23981s/12 iters), loss = 0.664026
I0409 20:52:26.581982 14973 solver.cpp:237]     Train net output #0: loss = 0.664026 (* 1 = 0.664026 loss)
I0409 20:52:26.581995 14973 sgd_solver.cpp:105] Iteration 6744, lr = 0.00262924
I0409 20:52:31.582536 14973 solver.cpp:218] Iteration 6756 (2.39979 iter/s, 5.00044s/12 iters), loss = 0.643112
I0409 20:52:31.582576 14973 solver.cpp:237]     Train net output #0: loss = 0.643112 (* 1 = 0.643112 loss)
I0409 20:52:31.582587 14973 sgd_solver.cpp:105] Iteration 6756, lr = 0.002623
I0409 20:52:36.623628 14973 solver.cpp:218] Iteration 6768 (2.38051 iter/s, 5.04093s/12 iters), loss = 0.665314
I0409 20:52:36.623682 14973 solver.cpp:237]     Train net output #0: loss = 0.665314 (* 1 = 0.665314 loss)
I0409 20:52:36.623694 14973 sgd_solver.cpp:105] Iteration 6768, lr = 0.00261677
I0409 20:52:40.153581 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:52:41.699111 14973 solver.cpp:218] Iteration 6780 (2.36439 iter/s, 5.07531s/12 iters), loss = 0.571738
I0409 20:52:41.699160 14973 solver.cpp:237]     Train net output #0: loss = 0.571738 (* 1 = 0.571738 loss)
I0409 20:52:41.699170 14973 sgd_solver.cpp:105] Iteration 6780, lr = 0.00261056
I0409 20:52:46.803501 14973 solver.cpp:218] Iteration 6792 (2.351 iter/s, 5.10422s/12 iters), loss = 0.802762
I0409 20:52:46.803547 14973 solver.cpp:237]     Train net output #0: loss = 0.802762 (* 1 = 0.802762 loss)
I0409 20:52:46.803558 14973 sgd_solver.cpp:105] Iteration 6792, lr = 0.00260436
I0409 20:52:51.895592 14973 solver.cpp:218] Iteration 6804 (2.35667 iter/s, 5.09192s/12 iters), loss = 0.671716
I0409 20:52:51.895642 14973 solver.cpp:237]     Train net output #0: loss = 0.671716 (* 1 = 0.671716 loss)
I0409 20:52:51.895653 14973 sgd_solver.cpp:105] Iteration 6804, lr = 0.00259817
I0409 20:52:56.963675 14973 solver.cpp:218] Iteration 6816 (2.36784 iter/s, 5.06791s/12 iters), loss = 0.669321
I0409 20:52:56.963806 14973 solver.cpp:237]     Train net output #0: loss = 0.669321 (* 1 = 0.669321 loss)
I0409 20:52:56.963816 14973 sgd_solver.cpp:105] Iteration 6816, lr = 0.00259201
I0409 20:53:02.013537 14973 solver.cpp:218] Iteration 6828 (2.37642 iter/s, 5.04961s/12 iters), loss = 0.527671
I0409 20:53:02.013593 14973 solver.cpp:237]     Train net output #0: loss = 0.527671 (* 1 = 0.527671 loss)
I0409 20:53:02.013605 14973 sgd_solver.cpp:105] Iteration 6828, lr = 0.00258585
I0409 20:53:04.172466 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6834.caffemodel
I0409 20:53:05.763698 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6834.solverstate
I0409 20:53:09.543798 14973 solver.cpp:330] Iteration 6834, Testing net (#0)
I0409 20:53:09.543826 14973 net.cpp:676] Ignoring source layer train-data
I0409 20:53:11.325436 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:53:13.998324 14973 solver.cpp:397]     Test net output #0: accuracy = 0.518382
I0409 20:53:13.998389 14973 solver.cpp:397]     Test net output #1: loss = 2.15945 (* 1 = 2.15945 loss)
I0409 20:53:15.843523 14973 solver.cpp:218] Iteration 6840 (0.867703 iter/s, 13.8296s/12 iters), loss = 0.512604
I0409 20:53:15.843580 14973 solver.cpp:237]     Train net output #0: loss = 0.512604 (* 1 = 0.512604 loss)
I0409 20:53:15.843590 14973 sgd_solver.cpp:105] Iteration 6840, lr = 0.00257971
I0409 20:53:20.840407 14973 solver.cpp:218] Iteration 6852 (2.40158 iter/s, 4.9967s/12 iters), loss = 0.705076
I0409 20:53:20.840466 14973 solver.cpp:237]     Train net output #0: loss = 0.705076 (* 1 = 0.705076 loss)
I0409 20:53:20.840479 14973 sgd_solver.cpp:105] Iteration 6852, lr = 0.00257359
I0409 20:53:25.844127 14973 solver.cpp:218] Iteration 6864 (2.3983 iter/s, 5.00354s/12 iters), loss = 0.662749
I0409 20:53:25.844177 14973 solver.cpp:237]     Train net output #0: loss = 0.662749 (* 1 = 0.662749 loss)
I0409 20:53:25.844188 14973 sgd_solver.cpp:105] Iteration 6864, lr = 0.00256748
I0409 20:53:30.910945 14973 solver.cpp:218] Iteration 6876 (2.36843 iter/s, 5.06664s/12 iters), loss = 0.619522
I0409 20:53:30.911090 14973 solver.cpp:237]     Train net output #0: loss = 0.619522 (* 1 = 0.619522 loss)
I0409 20:53:30.911103 14973 sgd_solver.cpp:105] Iteration 6876, lr = 0.00256138
I0409 20:53:31.546907 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:53:36.004325 14973 solver.cpp:218] Iteration 6888 (2.35613 iter/s, 5.09311s/12 iters), loss = 0.479029
I0409 20:53:36.004380 14973 solver.cpp:237]     Train net output #0: loss = 0.479029 (* 1 = 0.479029 loss)
I0409 20:53:36.004391 14973 sgd_solver.cpp:105] Iteration 6888, lr = 0.0025553
I0409 20:53:40.978780 14973 solver.cpp:218] Iteration 6900 (2.41241 iter/s, 4.97427s/12 iters), loss = 0.548161
I0409 20:53:40.978847 14973 solver.cpp:237]     Train net output #0: loss = 0.548161 (* 1 = 0.548161 loss)
I0409 20:53:40.978859 14973 sgd_solver.cpp:105] Iteration 6900, lr = 0.00254923
I0409 20:53:46.097232 14973 solver.cpp:218] Iteration 6912 (2.34455 iter/s, 5.11826s/12 iters), loss = 0.525235
I0409 20:53:46.097286 14973 solver.cpp:237]     Train net output #0: loss = 0.525235 (* 1 = 0.525235 loss)
I0409 20:53:46.097298 14973 sgd_solver.cpp:105] Iteration 6912, lr = 0.00254318
I0409 20:53:51.171696 14973 solver.cpp:218] Iteration 6924 (2.36487 iter/s, 5.07428s/12 iters), loss = 0.633619
I0409 20:53:51.171747 14973 solver.cpp:237]     Train net output #0: loss = 0.633619 (* 1 = 0.633619 loss)
I0409 20:53:51.171759 14973 sgd_solver.cpp:105] Iteration 6924, lr = 0.00253714
I0409 20:53:55.748366 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6936.caffemodel
I0409 20:53:57.969713 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6936.solverstate
I0409 20:53:59.171123 14973 solver.cpp:330] Iteration 6936, Testing net (#0)
I0409 20:53:59.171144 14973 net.cpp:676] Ignoring source layer train-data
I0409 20:53:59.845541 14973 blocking_queue.cpp:49] Waiting for data
I0409 20:54:01.049403 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:54:03.769178 14973 solver.cpp:397]     Test net output #0: accuracy = 0.502451
I0409 20:54:03.769214 14973 solver.cpp:397]     Test net output #1: loss = 2.27614 (* 1 = 2.27614 loss)
I0409 20:54:03.855818 14973 solver.cpp:218] Iteration 6936 (0.946091 iter/s, 12.6838s/12 iters), loss = 0.681013
I0409 20:54:03.855873 14973 solver.cpp:237]     Train net output #0: loss = 0.681013 (* 1 = 0.681013 loss)
I0409 20:54:03.855885 14973 sgd_solver.cpp:105] Iteration 6936, lr = 0.00253112
I0409 20:54:08.137507 14973 solver.cpp:218] Iteration 6948 (2.80274 iter/s, 4.28153s/12 iters), loss = 0.547602
I0409 20:54:08.137548 14973 solver.cpp:237]     Train net output #0: loss = 0.547602 (* 1 = 0.547602 loss)
I0409 20:54:08.137557 14973 sgd_solver.cpp:105] Iteration 6948, lr = 0.00252511
I0409 20:54:13.208324 14973 solver.cpp:218] Iteration 6960 (2.36657 iter/s, 5.07063s/12 iters), loss = 0.556525
I0409 20:54:13.208381 14973 solver.cpp:237]     Train net output #0: loss = 0.556525 (* 1 = 0.556525 loss)
I0409 20:54:13.208393 14973 sgd_solver.cpp:105] Iteration 6960, lr = 0.00251911
I0409 20:54:18.241689 14973 solver.cpp:218] Iteration 6972 (2.38418 iter/s, 5.03318s/12 iters), loss = 0.63254
I0409 20:54:18.241735 14973 solver.cpp:237]     Train net output #0: loss = 0.63254 (* 1 = 0.63254 loss)
I0409 20:54:18.241744 14973 sgd_solver.cpp:105] Iteration 6972, lr = 0.00251313
I0409 20:54:21.002899 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:54:23.280484 14973 solver.cpp:218] Iteration 6984 (2.3816 iter/s, 5.03862s/12 iters), loss = 0.561846
I0409 20:54:23.280524 14973 solver.cpp:237]     Train net output #0: loss = 0.561846 (* 1 = 0.561846 loss)
I0409 20:54:23.280532 14973 sgd_solver.cpp:105] Iteration 6984, lr = 0.00250717
I0409 20:54:28.306183 14973 solver.cpp:218] Iteration 6996 (2.38781 iter/s, 5.02553s/12 iters), loss = 0.468757
I0409 20:54:28.306234 14973 solver.cpp:237]     Train net output #0: loss = 0.468757 (* 1 = 0.468757 loss)
I0409 20:54:28.306246 14973 sgd_solver.cpp:105] Iteration 6996, lr = 0.00250121
I0409 20:54:33.331485 14973 solver.cpp:218] Iteration 7008 (2.388 iter/s, 5.02512s/12 iters), loss = 0.458794
I0409 20:54:33.331629 14973 solver.cpp:237]     Train net output #0: loss = 0.458794 (* 1 = 0.458794 loss)
I0409 20:54:33.331641 14973 sgd_solver.cpp:105] Iteration 7008, lr = 0.00249528
I0409 20:54:38.347333 14973 solver.cpp:218] Iteration 7020 (2.39255 iter/s, 5.01558s/12 iters), loss = 0.642572
I0409 20:54:38.347378 14973 solver.cpp:237]     Train net output #0: loss = 0.642572 (* 1 = 0.642572 loss)
I0409 20:54:38.347388 14973 sgd_solver.cpp:105] Iteration 7020, lr = 0.00248935
I0409 20:54:43.363024 14973 solver.cpp:218] Iteration 7032 (2.39257 iter/s, 5.01552s/12 iters), loss = 0.471535
I0409 20:54:43.363073 14973 solver.cpp:237]     Train net output #0: loss = 0.471535 (* 1 = 0.471535 loss)
I0409 20:54:43.363085 14973 sgd_solver.cpp:105] Iteration 7032, lr = 0.00248344
I0409 20:54:45.405715 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7038.caffemodel
I0409 20:54:46.987911 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7038.solverstate
I0409 20:54:48.211053 14973 solver.cpp:330] Iteration 7038, Testing net (#0)
I0409 20:54:48.211078 14973 net.cpp:676] Ignoring source layer train-data
I0409 20:54:50.070605 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:54:52.965018 14973 solver.cpp:397]     Test net output #0: accuracy = 0.527574
I0409 20:54:52.965055 14973 solver.cpp:397]     Test net output #1: loss = 2.16528 (* 1 = 2.16528 loss)
I0409 20:54:54.950286 14973 solver.cpp:218] Iteration 7044 (1.03565 iter/s, 11.5869s/12 iters), loss = 0.66828
I0409 20:54:54.950327 14973 solver.cpp:237]     Train net output #0: loss = 0.66828 (* 1 = 0.66828 loss)
I0409 20:54:54.950335 14973 sgd_solver.cpp:105] Iteration 7044, lr = 0.00247755
I0409 20:54:59.966198 14973 solver.cpp:218] Iteration 7056 (2.39247 iter/s, 5.01574s/12 iters), loss = 0.655061
I0409 20:54:59.966249 14973 solver.cpp:237]     Train net output #0: loss = 0.655061 (* 1 = 0.655061 loss)
I0409 20:54:59.966260 14973 sgd_solver.cpp:105] Iteration 7056, lr = 0.00247166
I0409 20:55:04.919440 14973 solver.cpp:218] Iteration 7068 (2.42274 iter/s, 4.95306s/12 iters), loss = 0.753632
I0409 20:55:04.919531 14973 solver.cpp:237]     Train net output #0: loss = 0.753632 (* 1 = 0.753632 loss)
I0409 20:55:04.919544 14973 sgd_solver.cpp:105] Iteration 7068, lr = 0.0024658
I0409 20:55:09.831413 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:55:09.944547 14973 solver.cpp:218] Iteration 7080 (2.38811 iter/s, 5.02489s/12 iters), loss = 0.763357
I0409 20:55:09.944605 14973 solver.cpp:237]     Train net output #0: loss = 0.763357 (* 1 = 0.763357 loss)
I0409 20:55:09.944617 14973 sgd_solver.cpp:105] Iteration 7080, lr = 0.00245994
I0409 20:55:14.957020 14973 solver.cpp:218] Iteration 7092 (2.39412 iter/s, 5.01229s/12 iters), loss = 0.657265
I0409 20:55:14.957068 14973 solver.cpp:237]     Train net output #0: loss = 0.657265 (* 1 = 0.657265 loss)
I0409 20:55:14.957077 14973 sgd_solver.cpp:105] Iteration 7092, lr = 0.0024541
I0409 20:55:19.884958 14973 solver.cpp:218] Iteration 7104 (2.43519 iter/s, 4.92776s/12 iters), loss = 0.54947
I0409 20:55:19.885012 14973 solver.cpp:237]     Train net output #0: loss = 0.54947 (* 1 = 0.54947 loss)
I0409 20:55:19.885025 14973 sgd_solver.cpp:105] Iteration 7104, lr = 0.00244827
I0409 20:55:24.881186 14973 solver.cpp:218] Iteration 7116 (2.4019 iter/s, 4.99604s/12 iters), loss = 0.674957
I0409 20:55:24.881237 14973 solver.cpp:237]     Train net output #0: loss = 0.674957 (* 1 = 0.674957 loss)
I0409 20:55:24.881249 14973 sgd_solver.cpp:105] Iteration 7116, lr = 0.00244246
I0409 20:55:29.887866 14973 solver.cpp:218] Iteration 7128 (2.39688 iter/s, 5.0065s/12 iters), loss = 0.472486
I0409 20:55:29.887907 14973 solver.cpp:237]     Train net output #0: loss = 0.472486 (* 1 = 0.472486 loss)
I0409 20:55:29.887917 14973 sgd_solver.cpp:105] Iteration 7128, lr = 0.00243666
I0409 20:55:34.466272 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7140.caffemodel
I0409 20:55:36.036891 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7140.solverstate
I0409 20:55:37.250129 14973 solver.cpp:330] Iteration 7140, Testing net (#0)
I0409 20:55:37.250149 14973 net.cpp:676] Ignoring source layer train-data
I0409 20:55:38.909076 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:55:41.708755 14973 solver.cpp:397]     Test net output #0: accuracy = 0.526348
I0409 20:55:41.708794 14973 solver.cpp:397]     Test net output #1: loss = 2.25831 (* 1 = 2.25831 loss)
I0409 20:55:41.795773 14973 solver.cpp:218] Iteration 7140 (1.00776 iter/s, 11.9076s/12 iters), loss = 0.630583
I0409 20:55:41.795819 14973 solver.cpp:237]     Train net output #0: loss = 0.630583 (* 1 = 0.630583 loss)
I0409 20:55:41.795827 14973 sgd_solver.cpp:105] Iteration 7140, lr = 0.00243088
I0409 20:55:46.123678 14973 solver.cpp:218] Iteration 7152 (2.77281 iter/s, 4.32774s/12 iters), loss = 0.479828
I0409 20:55:46.123728 14973 solver.cpp:237]     Train net output #0: loss = 0.479828 (* 1 = 0.479828 loss)
I0409 20:55:46.123736 14973 sgd_solver.cpp:105] Iteration 7152, lr = 0.00242511
I0409 20:55:51.170764 14973 solver.cpp:218] Iteration 7164 (2.3777 iter/s, 5.0469s/12 iters), loss = 0.502285
I0409 20:55:51.170815 14973 solver.cpp:237]     Train net output #0: loss = 0.502285 (* 1 = 0.502285 loss)
I0409 20:55:51.170827 14973 sgd_solver.cpp:105] Iteration 7164, lr = 0.00241935
I0409 20:55:56.205615 14973 solver.cpp:218] Iteration 7176 (2.38348 iter/s, 5.03467s/12 iters), loss = 0.526289
I0409 20:55:56.205663 14973 solver.cpp:237]     Train net output #0: loss = 0.526289 (* 1 = 0.526289 loss)
I0409 20:55:56.205675 14973 sgd_solver.cpp:105] Iteration 7176, lr = 0.0024136
I0409 20:55:58.300056 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:56:01.199575 14973 solver.cpp:218] Iteration 7188 (2.40299 iter/s, 4.99378s/12 iters), loss = 0.458631
I0409 20:56:01.199623 14973 solver.cpp:237]     Train net output #0: loss = 0.458631 (* 1 = 0.458631 loss)
I0409 20:56:01.199632 14973 sgd_solver.cpp:105] Iteration 7188, lr = 0.00240787
I0409 20:56:06.134645 14973 solver.cpp:218] Iteration 7200 (2.43167 iter/s, 4.93488s/12 iters), loss = 0.481086
I0409 20:56:06.134969 14973 solver.cpp:237]     Train net output #0: loss = 0.481086 (* 1 = 0.481086 loss)
I0409 20:56:06.134979 14973 sgd_solver.cpp:105] Iteration 7200, lr = 0.00240216
I0409 20:56:11.219918 14973 solver.cpp:218] Iteration 7212 (2.35997 iter/s, 5.08481s/12 iters), loss = 0.465815
I0409 20:56:11.219976 14973 solver.cpp:237]     Train net output #0: loss = 0.465815 (* 1 = 0.465815 loss)
I0409 20:56:11.219987 14973 sgd_solver.cpp:105] Iteration 7212, lr = 0.00239645
I0409 20:56:16.250102 14973 solver.cpp:218] Iteration 7224 (2.38569 iter/s, 5.02999s/12 iters), loss = 0.450585
I0409 20:56:16.250155 14973 solver.cpp:237]     Train net output #0: loss = 0.450585 (* 1 = 0.450585 loss)
I0409 20:56:16.250167 14973 sgd_solver.cpp:105] Iteration 7224, lr = 0.00239076
I0409 20:56:21.263258 14973 solver.cpp:218] Iteration 7236 (2.39379 iter/s, 5.01297s/12 iters), loss = 0.340135
I0409 20:56:21.263306 14973 solver.cpp:237]     Train net output #0: loss = 0.340135 (* 1 = 0.340135 loss)
I0409 20:56:21.263316 14973 sgd_solver.cpp:105] Iteration 7236, lr = 0.00238509
I0409 20:56:23.288589 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7242.caffemodel
I0409 20:56:24.885486 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7242.solverstate
I0409 20:56:26.097142 14973 solver.cpp:330] Iteration 7242, Testing net (#0)
I0409 20:56:26.097172 14973 net.cpp:676] Ignoring source layer train-data
I0409 20:56:27.630131 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:56:30.514149 14973 solver.cpp:397]     Test net output #0: accuracy = 0.515931
I0409 20:56:30.514199 14973 solver.cpp:397]     Test net output #1: loss = 2.29105 (* 1 = 2.29105 loss)
I0409 20:56:32.378250 14973 solver.cpp:218] Iteration 7248 (1.07966 iter/s, 11.1147s/12 iters), loss = 0.561415
I0409 20:56:32.378301 14973 solver.cpp:237]     Train net output #0: loss = 0.561415 (* 1 = 0.561415 loss)
I0409 20:56:32.378314 14973 sgd_solver.cpp:105] Iteration 7248, lr = 0.00237942
I0409 20:56:37.315269 14973 solver.cpp:218] Iteration 7260 (2.43071 iter/s, 4.93684s/12 iters), loss = 0.521178
I0409 20:56:37.315405 14973 solver.cpp:237]     Train net output #0: loss = 0.521178 (* 1 = 0.521178 loss)
I0409 20:56:37.315415 14973 sgd_solver.cpp:105] Iteration 7260, lr = 0.00237378
I0409 20:56:42.358598 14973 solver.cpp:218] Iteration 7272 (2.37951 iter/s, 5.04306s/12 iters), loss = 0.789499
I0409 20:56:42.358649 14973 solver.cpp:237]     Train net output #0: loss = 0.789499 (* 1 = 0.789499 loss)
I0409 20:56:42.358659 14973 sgd_solver.cpp:105] Iteration 7272, lr = 0.00236814
I0409 20:56:46.581254 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:56:47.330454 14973 solver.cpp:218] Iteration 7284 (2.41367 iter/s, 4.97167s/12 iters), loss = 0.646955
I0409 20:56:47.330516 14973 solver.cpp:237]     Train net output #0: loss = 0.646955 (* 1 = 0.646955 loss)
I0409 20:56:47.330528 14973 sgd_solver.cpp:105] Iteration 7284, lr = 0.00236252
I0409 20:56:52.420361 14973 solver.cpp:218] Iteration 7296 (2.3577 iter/s, 5.08971s/12 iters), loss = 0.632836
I0409 20:56:52.420413 14973 solver.cpp:237]     Train net output #0: loss = 0.632836 (* 1 = 0.632836 loss)
I0409 20:56:52.420423 14973 sgd_solver.cpp:105] Iteration 7296, lr = 0.00235691
I0409 20:56:57.442544 14973 solver.cpp:218] Iteration 7308 (2.38949 iter/s, 5.02199s/12 iters), loss = 0.681748
I0409 20:56:57.442600 14973 solver.cpp:237]     Train net output #0: loss = 0.681748 (* 1 = 0.681748 loss)
I0409 20:56:57.442611 14973 sgd_solver.cpp:105] Iteration 7308, lr = 0.00235131
I0409 20:57:02.513850 14973 solver.cpp:218] Iteration 7320 (2.36635 iter/s, 5.07111s/12 iters), loss = 0.521178
I0409 20:57:02.513898 14973 solver.cpp:237]     Train net output #0: loss = 0.521178 (* 1 = 0.521178 loss)
I0409 20:57:02.513907 14973 sgd_solver.cpp:105] Iteration 7320, lr = 0.00234573
I0409 20:57:07.611989 14973 solver.cpp:218] Iteration 7332 (2.35389 iter/s, 5.09795s/12 iters), loss = 0.541101
I0409 20:57:07.612099 14973 solver.cpp:237]     Train net output #0: loss = 0.541101 (* 1 = 0.541101 loss)
I0409 20:57:07.612113 14973 sgd_solver.cpp:105] Iteration 7332, lr = 0.00234016
I0409 20:57:12.150635 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7344.caffemodel
I0409 20:57:13.758764 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7344.solverstate
I0409 20:57:16.246892 14973 solver.cpp:330] Iteration 7344, Testing net (#0)
I0409 20:57:16.246913 14973 net.cpp:676] Ignoring source layer train-data
I0409 20:57:17.814980 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:57:20.694793 14973 solver.cpp:397]     Test net output #0: accuracy = 0.536152
I0409 20:57:20.694840 14973 solver.cpp:397]     Test net output #1: loss = 2.21531 (* 1 = 2.21531 loss)
I0409 20:57:20.781668 14973 solver.cpp:218] Iteration 7344 (0.911215 iter/s, 13.1692s/12 iters), loss = 0.529463
I0409 20:57:20.781721 14973 solver.cpp:237]     Train net output #0: loss = 0.529463 (* 1 = 0.529463 loss)
I0409 20:57:20.781733 14973 sgd_solver.cpp:105] Iteration 7344, lr = 0.0023346
I0409 20:57:25.095890 14973 solver.cpp:218] Iteration 7356 (2.78161 iter/s, 4.31405s/12 iters), loss = 0.495542
I0409 20:57:25.095929 14973 solver.cpp:237]     Train net output #0: loss = 0.495542 (* 1 = 0.495542 loss)
I0409 20:57:25.095937 14973 sgd_solver.cpp:105] Iteration 7356, lr = 0.00232906
I0409 20:57:30.084815 14973 solver.cpp:218] Iteration 7368 (2.40541 iter/s, 4.98875s/12 iters), loss = 0.533771
I0409 20:57:30.084858 14973 solver.cpp:237]     Train net output #0: loss = 0.533771 (* 1 = 0.533771 loss)
I0409 20:57:30.084868 14973 sgd_solver.cpp:105] Iteration 7368, lr = 0.00232353
I0409 20:57:35.165537 14973 solver.cpp:218] Iteration 7380 (2.36195 iter/s, 5.08054s/12 iters), loss = 0.609611
I0409 20:57:35.165583 14973 solver.cpp:237]     Train net output #0: loss = 0.609611 (* 1 = 0.609611 loss)
I0409 20:57:35.165593 14973 sgd_solver.cpp:105] Iteration 7380, lr = 0.00231802
I0409 20:57:36.540657 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:57:40.146682 14973 solver.cpp:218] Iteration 7392 (2.40917 iter/s, 4.98096s/12 iters), loss = 0.378824
I0409 20:57:40.146814 14973 solver.cpp:237]     Train net output #0: loss = 0.378824 (* 1 = 0.378824 loss)
I0409 20:57:40.146824 14973 sgd_solver.cpp:105] Iteration 7392, lr = 0.00231251
I0409 20:57:45.169366 14973 solver.cpp:218] Iteration 7404 (2.38929 iter/s, 5.02241s/12 iters), loss = 0.505941
I0409 20:57:45.169421 14973 solver.cpp:237]     Train net output #0: loss = 0.505941 (* 1 = 0.505941 loss)
I0409 20:57:45.169435 14973 sgd_solver.cpp:105] Iteration 7404, lr = 0.00230702
I0409 20:57:50.156642 14973 solver.cpp:218] Iteration 7416 (2.40621 iter/s, 4.98709s/12 iters), loss = 0.438522
I0409 20:57:50.156687 14973 solver.cpp:237]     Train net output #0: loss = 0.438522 (* 1 = 0.438522 loss)
I0409 20:57:50.156697 14973 sgd_solver.cpp:105] Iteration 7416, lr = 0.00230154
I0409 20:57:55.302088 14973 solver.cpp:218] Iteration 7428 (2.33225 iter/s, 5.14526s/12 iters), loss = 0.492621
I0409 20:57:55.302141 14973 solver.cpp:237]     Train net output #0: loss = 0.492621 (* 1 = 0.492621 loss)
I0409 20:57:55.302155 14973 sgd_solver.cpp:105] Iteration 7428, lr = 0.00229608
I0409 20:58:00.244521 14973 solver.cpp:218] Iteration 7440 (2.42805 iter/s, 4.94225s/12 iters), loss = 0.412823
I0409 20:58:00.244570 14973 solver.cpp:237]     Train net output #0: loss = 0.412823 (* 1 = 0.412823 loss)
I0409 20:58:00.244583 14973 sgd_solver.cpp:105] Iteration 7440, lr = 0.00229063
I0409 20:58:02.279527 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7446.caffemodel
I0409 20:58:03.861532 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7446.solverstate
I0409 20:58:05.086050 14973 solver.cpp:330] Iteration 7446, Testing net (#0)
I0409 20:58:05.086076 14973 net.cpp:676] Ignoring source layer train-data
I0409 20:58:06.550438 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:58:09.468523 14973 solver.cpp:397]     Test net output #0: accuracy = 0.543505
I0409 20:58:09.468570 14973 solver.cpp:397]     Test net output #1: loss = 2.19994 (* 1 = 2.19994 loss)
I0409 20:58:11.337158 14973 solver.cpp:218] Iteration 7452 (1.08183 iter/s, 11.0923s/12 iters), loss = 0.500239
I0409 20:58:11.337275 14973 solver.cpp:237]     Train net output #0: loss = 0.500239 (* 1 = 0.500239 loss)
I0409 20:58:11.337286 14973 sgd_solver.cpp:105] Iteration 7452, lr = 0.00228519
I0409 20:58:16.304857 14973 solver.cpp:218] Iteration 7464 (2.41573 iter/s, 4.96744s/12 iters), loss = 0.445331
I0409 20:58:16.304919 14973 solver.cpp:237]     Train net output #0: loss = 0.445331 (* 1 = 0.445331 loss)
I0409 20:58:16.304930 14973 sgd_solver.cpp:105] Iteration 7464, lr = 0.00227976
I0409 20:58:21.426579 14973 solver.cpp:218] Iteration 7476 (2.34305 iter/s, 5.12152s/12 iters), loss = 0.438461
I0409 20:58:21.426627 14973 solver.cpp:237]     Train net output #0: loss = 0.438461 (* 1 = 0.438461 loss)
I0409 20:58:21.426637 14973 sgd_solver.cpp:105] Iteration 7476, lr = 0.00227435
I0409 20:58:25.003379 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:58:26.466421 14973 solver.cpp:218] Iteration 7488 (2.38112 iter/s, 5.03965s/12 iters), loss = 0.414902
I0409 20:58:26.466472 14973 solver.cpp:237]     Train net output #0: loss = 0.414902 (* 1 = 0.414902 loss)
I0409 20:58:26.466485 14973 sgd_solver.cpp:105] Iteration 7488, lr = 0.00226895
I0409 20:58:31.508194 14973 solver.cpp:218] Iteration 7500 (2.3802 iter/s, 5.04159s/12 iters), loss = 0.563855
I0409 20:58:31.508235 14973 solver.cpp:237]     Train net output #0: loss = 0.563855 (* 1 = 0.563855 loss)
I0409 20:58:31.508246 14973 sgd_solver.cpp:105] Iteration 7500, lr = 0.00226357
I0409 20:58:36.464619 14973 solver.cpp:218] Iteration 7512 (2.42119 iter/s, 4.95625s/12 iters), loss = 0.385317
I0409 20:58:36.464660 14973 solver.cpp:237]     Train net output #0: loss = 0.385317 (* 1 = 0.385317 loss)
I0409 20:58:36.464671 14973 sgd_solver.cpp:105] Iteration 7512, lr = 0.00225819
I0409 20:58:41.547886 14973 solver.cpp:218] Iteration 7524 (2.36077 iter/s, 5.08308s/12 iters), loss = 0.526322
I0409 20:58:41.548039 14973 solver.cpp:237]     Train net output #0: loss = 0.526322 (* 1 = 0.526322 loss)
I0409 20:58:41.548053 14973 sgd_solver.cpp:105] Iteration 7524, lr = 0.00225283
I0409 20:58:46.635741 14973 solver.cpp:218] Iteration 7536 (2.35869 iter/s, 5.08756s/12 iters), loss = 0.324132
I0409 20:58:46.635792 14973 solver.cpp:237]     Train net output #0: loss = 0.324132 (* 1 = 0.324132 loss)
I0409 20:58:46.635803 14973 sgd_solver.cpp:105] Iteration 7536, lr = 0.00224748
I0409 20:58:51.227435 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7548.caffemodel
I0409 20:58:57.266870 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7548.solverstate
I0409 20:59:01.874949 14973 solver.cpp:330] Iteration 7548, Testing net (#0)
I0409 20:59:01.874977 14973 net.cpp:676] Ignoring source layer train-data
I0409 20:59:03.504312 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:59:06.533650 14973 solver.cpp:397]     Test net output #0: accuracy = 0.542279
I0409 20:59:06.533711 14973 solver.cpp:397]     Test net output #1: loss = 2.2681 (* 1 = 2.2681 loss)
I0409 20:59:06.620113 14973 solver.cpp:218] Iteration 7548 (0.600487 iter/s, 19.9838s/12 iters), loss = 0.498963
I0409 20:59:06.620180 14973 solver.cpp:237]     Train net output #0: loss = 0.498963 (* 1 = 0.498963 loss)
I0409 20:59:06.620198 14973 sgd_solver.cpp:105] Iteration 7548, lr = 0.00224215
I0409 20:59:10.870116 14973 solver.cpp:218] Iteration 7560 (2.82365 iter/s, 4.24982s/12 iters), loss = 0.56065
I0409 20:59:10.870157 14973 solver.cpp:237]     Train net output #0: loss = 0.56065 (* 1 = 0.56065 loss)
I0409 20:59:10.870167 14973 sgd_solver.cpp:105] Iteration 7560, lr = 0.00223682
I0409 20:59:15.826869 14973 solver.cpp:218] Iteration 7572 (2.42103 iter/s, 4.95656s/12 iters), loss = 0.330296
I0409 20:59:15.826988 14973 solver.cpp:237]     Train net output #0: loss = 0.330296 (* 1 = 0.330296 loss)
I0409 20:59:15.827000 14973 sgd_solver.cpp:105] Iteration 7572, lr = 0.00223151
I0409 20:59:20.888972 14973 solver.cpp:218] Iteration 7584 (2.37068 iter/s, 5.06184s/12 iters), loss = 0.359135
I0409 20:59:20.889024 14973 solver.cpp:237]     Train net output #0: loss = 0.359135 (* 1 = 0.359135 loss)
I0409 20:59:20.889037 14973 sgd_solver.cpp:105] Iteration 7584, lr = 0.00222621
I0409 20:59:21.557662 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:59:25.843215 14973 solver.cpp:218] Iteration 7596 (2.42226 iter/s, 4.95406s/12 iters), loss = 0.376676
I0409 20:59:25.843252 14973 solver.cpp:237]     Train net output #0: loss = 0.376676 (* 1 = 0.376676 loss)
I0409 20:59:25.843261 14973 sgd_solver.cpp:105] Iteration 7596, lr = 0.00222093
I0409 20:59:30.857147 14973 solver.cpp:218] Iteration 7608 (2.39342 iter/s, 5.01375s/12 iters), loss = 0.517799
I0409 20:59:30.857195 14973 solver.cpp:237]     Train net output #0: loss = 0.517799 (* 1 = 0.517799 loss)
I0409 20:59:30.857205 14973 sgd_solver.cpp:105] Iteration 7608, lr = 0.00221565
I0409 20:59:36.014911 14973 solver.cpp:218] Iteration 7620 (2.32668 iter/s, 5.15756s/12 iters), loss = 0.452048
I0409 20:59:36.014961 14973 solver.cpp:237]     Train net output #0: loss = 0.452048 (* 1 = 0.452048 loss)
I0409 20:59:36.014972 14973 sgd_solver.cpp:105] Iteration 7620, lr = 0.00221039
I0409 20:59:38.126680 14973 blocking_queue.cpp:49] Waiting for data
I0409 20:59:41.363277 14973 solver.cpp:218] Iteration 7632 (2.24376 iter/s, 5.34816s/12 iters), loss = 0.498489
I0409 20:59:41.363327 14973 solver.cpp:237]     Train net output #0: loss = 0.498489 (* 1 = 0.498489 loss)
I0409 20:59:41.363337 14973 sgd_solver.cpp:105] Iteration 7632, lr = 0.00220515
I0409 20:59:46.712432 14973 solver.cpp:218] Iteration 7644 (2.24343 iter/s, 5.34896s/12 iters), loss = 0.505364
I0409 20:59:46.712549 14973 solver.cpp:237]     Train net output #0: loss = 0.505364 (* 1 = 0.505364 loss)
I0409 20:59:46.712558 14973 sgd_solver.cpp:105] Iteration 7644, lr = 0.00219991
I0409 20:59:48.725477 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7650.caffemodel
I0409 20:59:51.283497 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7650.solverstate
I0409 20:59:54.232084 14973 solver.cpp:330] Iteration 7650, Testing net (#0)
I0409 20:59:54.232112 14973 net.cpp:676] Ignoring source layer train-data
I0409 20:59:55.672928 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:59:58.975453 14973 solver.cpp:397]     Test net output #0: accuracy = 0.532475
I0409 20:59:58.975481 14973 solver.cpp:397]     Test net output #1: loss = 2.28566 (* 1 = 2.28566 loss)
I0409 21:00:00.823403 14973 solver.cpp:218] Iteration 7656 (0.850432 iter/s, 14.1105s/12 iters), loss = 0.41305
I0409 21:00:00.823446 14973 solver.cpp:237]     Train net output #0: loss = 0.41305 (* 1 = 0.41305 loss)
I0409 21:00:00.823454 14973 sgd_solver.cpp:105] Iteration 7656, lr = 0.00219469
I0409 21:00:05.899490 14973 solver.cpp:218] Iteration 7668 (2.36412 iter/s, 5.07589s/12 iters), loss = 0.37374
I0409 21:00:05.899545 14973 solver.cpp:237]     Train net output #0: loss = 0.37374 (* 1 = 0.37374 loss)
I0409 21:00:05.899556 14973 sgd_solver.cpp:105] Iteration 7668, lr = 0.00218948
I0409 21:00:10.945493 14973 solver.cpp:218] Iteration 7680 (2.37822 iter/s, 5.0458s/12 iters), loss = 0.365766
I0409 21:00:10.945550 14973 solver.cpp:237]     Train net output #0: loss = 0.365766 (* 1 = 0.365766 loss)
I0409 21:00:10.945562 14973 sgd_solver.cpp:105] Iteration 7680, lr = 0.00218428
I0409 21:00:13.765584 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:00:15.981317 14973 solver.cpp:218] Iteration 7692 (2.38302 iter/s, 5.03562s/12 iters), loss = 0.407353
I0409 21:00:15.981364 14973 solver.cpp:237]     Train net output #0: loss = 0.407353 (* 1 = 0.407353 loss)
I0409 21:00:15.981375 14973 sgd_solver.cpp:105] Iteration 7692, lr = 0.00217909
I0409 21:00:21.007225 14973 solver.cpp:218] Iteration 7704 (2.38772 iter/s, 5.02571s/12 iters), loss = 0.424403
I0409 21:00:21.007356 14973 solver.cpp:237]     Train net output #0: loss = 0.424403 (* 1 = 0.424403 loss)
I0409 21:00:21.007369 14973 sgd_solver.cpp:105] Iteration 7704, lr = 0.00217392
I0409 21:00:26.111632 14973 solver.cpp:218] Iteration 7716 (2.35103 iter/s, 5.10414s/12 iters), loss = 0.509849
I0409 21:00:26.111671 14973 solver.cpp:237]     Train net output #0: loss = 0.509849 (* 1 = 0.509849 loss)
I0409 21:00:26.111680 14973 sgd_solver.cpp:105] Iteration 7716, lr = 0.00216876
I0409 21:00:31.178082 14973 solver.cpp:218] Iteration 7728 (2.36861 iter/s, 5.06626s/12 iters), loss = 0.534541
I0409 21:00:31.178124 14973 solver.cpp:237]     Train net output #0: loss = 0.534541 (* 1 = 0.534541 loss)
I0409 21:00:31.178133 14973 sgd_solver.cpp:105] Iteration 7728, lr = 0.00216361
I0409 21:00:36.207489 14973 solver.cpp:218] Iteration 7740 (2.38606 iter/s, 5.02922s/12 iters), loss = 0.41721
I0409 21:00:36.207533 14973 solver.cpp:237]     Train net output #0: loss = 0.41721 (* 1 = 0.41721 loss)
I0409 21:00:36.207542 14973 sgd_solver.cpp:105] Iteration 7740, lr = 0.00215847
I0409 21:00:41.398314 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7752.caffemodel
I0409 21:00:43.413983 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7752.solverstate
I0409 21:00:46.399109 14973 solver.cpp:330] Iteration 7752, Testing net (#0)
I0409 21:00:46.399139 14973 net.cpp:676] Ignoring source layer train-data
I0409 21:00:47.768404 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:00:50.928931 14973 solver.cpp:397]     Test net output #0: accuracy = 0.542279
I0409 21:00:50.928961 14973 solver.cpp:397]     Test net output #1: loss = 2.26847 (* 1 = 2.26847 loss)
I0409 21:00:51.015396 14973 solver.cpp:218] Iteration 7752 (0.810402 iter/s, 14.8075s/12 iters), loss = 0.351134
I0409 21:00:51.015486 14973 solver.cpp:237]     Train net output #0: loss = 0.351134 (* 1 = 0.351134 loss)
I0409 21:00:51.015496 14973 sgd_solver.cpp:105] Iteration 7752, lr = 0.00215335
I0409 21:00:55.276886 14973 solver.cpp:218] Iteration 7764 (2.81606 iter/s, 4.26127s/12 iters), loss = 0.523327
I0409 21:00:55.276939 14973 solver.cpp:237]     Train net output #0: loss = 0.523327 (* 1 = 0.523327 loss)
I0409 21:00:55.276950 14973 sgd_solver.cpp:105] Iteration 7764, lr = 0.00214823
I0409 21:01:00.247594 14973 solver.cpp:218] Iteration 7776 (2.41424 iter/s, 4.97051s/12 iters), loss = 0.434754
I0409 21:01:00.247635 14973 solver.cpp:237]     Train net output #0: loss = 0.434754 (* 1 = 0.434754 loss)
I0409 21:01:00.247644 14973 sgd_solver.cpp:105] Iteration 7776, lr = 0.00214313
I0409 21:01:05.168823 14973 solver.cpp:218] Iteration 7788 (2.43851 iter/s, 4.92104s/12 iters), loss = 0.415788
I0409 21:01:05.168876 14973 solver.cpp:237]     Train net output #0: loss = 0.415788 (* 1 = 0.415788 loss)
I0409 21:01:05.168889 14973 sgd_solver.cpp:105] Iteration 7788, lr = 0.00213805
I0409 21:01:05.179734 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:01:10.470410 14973 solver.cpp:218] Iteration 7800 (2.26356 iter/s, 5.30138s/12 iters), loss = 0.414806
I0409 21:01:10.470468 14973 solver.cpp:237]     Train net output #0: loss = 0.414806 (* 1 = 0.414806 loss)
I0409 21:01:10.470480 14973 sgd_solver.cpp:105] Iteration 7800, lr = 0.00213297
I0409 21:01:15.475281 14973 solver.cpp:218] Iteration 7812 (2.39776 iter/s, 5.00466s/12 iters), loss = 0.435451
I0409 21:01:15.475337 14973 solver.cpp:237]     Train net output #0: loss = 0.435451 (* 1 = 0.435451 loss)
I0409 21:01:15.475348 14973 sgd_solver.cpp:105] Iteration 7812, lr = 0.00212791
I0409 21:01:20.532516 14973 solver.cpp:218] Iteration 7824 (2.37293 iter/s, 5.05703s/12 iters), loss = 0.410195
I0409 21:01:20.532564 14973 solver.cpp:237]     Train net output #0: loss = 0.410195 (* 1 = 0.410195 loss)
I0409 21:01:20.532574 14973 sgd_solver.cpp:105] Iteration 7824, lr = 0.00212285
I0409 21:01:25.760433 14973 solver.cpp:218] Iteration 7836 (2.29546 iter/s, 5.22772s/12 iters), loss = 0.317727
I0409 21:01:25.760545 14973 solver.cpp:237]     Train net output #0: loss = 0.317727 (* 1 = 0.317727 loss)
I0409 21:01:25.760557 14973 sgd_solver.cpp:105] Iteration 7836, lr = 0.00211781
I0409 21:01:30.850327 14973 solver.cpp:218] Iteration 7848 (2.35773 iter/s, 5.08964s/12 iters), loss = 0.449837
I0409 21:01:30.850380 14973 solver.cpp:237]     Train net output #0: loss = 0.449837 (* 1 = 0.449837 loss)
I0409 21:01:30.850392 14973 sgd_solver.cpp:105] Iteration 7848, lr = 0.00211279
I0409 21:01:32.853300 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7854.caffemodel
I0409 21:01:34.392689 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7854.solverstate
I0409 21:01:35.591979 14973 solver.cpp:330] Iteration 7854, Testing net (#0)
I0409 21:01:35.592006 14973 net.cpp:676] Ignoring source layer train-data
I0409 21:01:36.960204 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:01:40.039844 14973 solver.cpp:397]     Test net output #0: accuracy = 0.545956
I0409 21:01:40.039880 14973 solver.cpp:397]     Test net output #1: loss = 2.36527 (* 1 = 2.36527 loss)
I0409 21:01:41.865880 14973 solver.cpp:218] Iteration 7860 (1.0894 iter/s, 11.0152s/12 iters), loss = 0.387001
I0409 21:01:41.865942 14973 solver.cpp:237]     Train net output #0: loss = 0.387001 (* 1 = 0.387001 loss)
I0409 21:01:41.865986 14973 sgd_solver.cpp:105] Iteration 7860, lr = 0.00210777
I0409 21:01:46.803823 14973 solver.cpp:218] Iteration 7872 (2.43026 iter/s, 4.93774s/12 iters), loss = 0.33417
I0409 21:01:46.803879 14973 solver.cpp:237]     Train net output #0: loss = 0.33417 (* 1 = 0.33417 loss)
I0409 21:01:46.803890 14973 sgd_solver.cpp:105] Iteration 7872, lr = 0.00210277
I0409 21:01:51.769338 14973 solver.cpp:218] Iteration 7884 (2.41677 iter/s, 4.96531s/12 iters), loss = 0.430037
I0409 21:01:51.769402 14973 solver.cpp:237]     Train net output #0: loss = 0.430037 (* 1 = 0.430037 loss)
I0409 21:01:51.769413 14973 sgd_solver.cpp:105] Iteration 7884, lr = 0.00209777
I0409 21:01:53.890437 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:01:56.709353 14973 solver.cpp:218] Iteration 7896 (2.42924 iter/s, 4.93981s/12 iters), loss = 0.343087
I0409 21:01:56.709491 14973 solver.cpp:237]     Train net output #0: loss = 0.343087 (* 1 = 0.343087 loss)
I0409 21:01:56.709502 14973 sgd_solver.cpp:105] Iteration 7896, lr = 0.00209279
I0409 21:02:01.894834 14973 solver.cpp:218] Iteration 7908 (2.31428 iter/s, 5.18519s/12 iters), loss = 0.410909
I0409 21:02:01.894886 14973 solver.cpp:237]     Train net output #0: loss = 0.410909 (* 1 = 0.410909 loss)
I0409 21:02:01.894897 14973 sgd_solver.cpp:105] Iteration 7908, lr = 0.00208782
I0409 21:02:06.970851 14973 solver.cpp:218] Iteration 7920 (2.36415 iter/s, 5.07581s/12 iters), loss = 0.271553
I0409 21:02:06.970907 14973 solver.cpp:237]     Train net output #0: loss = 0.271553 (* 1 = 0.271553 loss)
I0409 21:02:06.970917 14973 sgd_solver.cpp:105] Iteration 7920, lr = 0.00208287
I0409 21:02:11.974740 14973 solver.cpp:218] Iteration 7932 (2.39823 iter/s, 5.00368s/12 iters), loss = 0.431665
I0409 21:02:11.974779 14973 solver.cpp:237]     Train net output #0: loss = 0.431665 (* 1 = 0.431665 loss)
I0409 21:02:11.974787 14973 sgd_solver.cpp:105] Iteration 7932, lr = 0.00207792
I0409 21:02:17.042033 14973 solver.cpp:218] Iteration 7944 (2.36822 iter/s, 5.06711s/12 iters), loss = 0.316831
I0409 21:02:17.042074 14973 solver.cpp:237]     Train net output #0: loss = 0.316831 (* 1 = 0.316831 loss)
I0409 21:02:17.042083 14973 sgd_solver.cpp:105] Iteration 7944, lr = 0.00207299
I0409 21:02:21.622040 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7956.caffemodel
I0409 21:02:23.213820 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7956.solverstate
I0409 21:02:24.432669 14973 solver.cpp:330] Iteration 7956, Testing net (#0)
I0409 21:02:24.432694 14973 net.cpp:676] Ignoring source layer train-data
I0409 21:02:25.845307 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:02:28.968410 14973 solver.cpp:397]     Test net output #0: accuracy = 0.542279
I0409 21:02:28.968538 14973 solver.cpp:397]     Test net output #1: loss = 2.29068 (* 1 = 2.29068 loss)
I0409 21:02:29.052574 14973 solver.cpp:218] Iteration 7956 (0.999155 iter/s, 12.0102s/12 iters), loss = 0.371545
I0409 21:02:29.052652 14973 solver.cpp:237]     Train net output #0: loss = 0.371545 (* 1 = 0.371545 loss)
I0409 21:02:29.052668 14973 sgd_solver.cpp:105] Iteration 7956, lr = 0.00206807
I0409 21:02:33.218031 14973 solver.cpp:218] Iteration 7968 (2.88098 iter/s, 4.16526s/12 iters), loss = 0.529093
I0409 21:02:33.218080 14973 solver.cpp:237]     Train net output #0: loss = 0.529093 (* 1 = 0.529093 loss)
I0409 21:02:33.218089 14973 sgd_solver.cpp:105] Iteration 7968, lr = 0.00206316
I0409 21:02:38.214103 14973 solver.cpp:218] Iteration 7980 (2.40198 iter/s, 4.99588s/12 iters), loss = 0.452088
I0409 21:02:38.214145 14973 solver.cpp:237]     Train net output #0: loss = 0.452088 (* 1 = 0.452088 loss)
I0409 21:02:38.214157 14973 sgd_solver.cpp:105] Iteration 7980, lr = 0.00205826
I0409 21:02:42.505297 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:02:43.220407 14973 solver.cpp:218] Iteration 7992 (2.39707 iter/s, 5.00611s/12 iters), loss = 0.306532
I0409 21:02:43.220454 14973 solver.cpp:237]     Train net output #0: loss = 0.306532 (* 1 = 0.306532 loss)
I0409 21:02:43.220464 14973 sgd_solver.cpp:105] Iteration 7992, lr = 0.00205337
I0409 21:02:48.224246 14973 solver.cpp:218] Iteration 8004 (2.39825 iter/s, 5.00365s/12 iters), loss = 0.321436
I0409 21:02:48.224292 14973 solver.cpp:237]     Train net output #0: loss = 0.321436 (* 1 = 0.321436 loss)
I0409 21:02:48.224301 14973 sgd_solver.cpp:105] Iteration 8004, lr = 0.0020485
I0409 21:02:53.281175 14973 solver.cpp:218] Iteration 8016 (2.37308 iter/s, 5.05673s/12 iters), loss = 0.301437
I0409 21:02:53.281225 14973 solver.cpp:237]     Train net output #0: loss = 0.301437 (* 1 = 0.301437 loss)
I0409 21:02:53.281236 14973 sgd_solver.cpp:105] Iteration 8016, lr = 0.00204363
I0409 21:02:58.249538 14973 solver.cpp:218] Iteration 8028 (2.41538 iter/s, 4.96817s/12 iters), loss = 0.323127
I0409 21:02:58.249590 14973 solver.cpp:237]     Train net output #0: loss = 0.323127 (* 1 = 0.323127 loss)
I0409 21:02:58.249603 14973 sgd_solver.cpp:105] Iteration 8028, lr = 0.00203878
I0409 21:03:03.546687 14973 solver.cpp:218] Iteration 8040 (2.26546 iter/s, 5.29695s/12 iters), loss = 0.369769
I0409 21:03:03.546818 14973 solver.cpp:237]     Train net output #0: loss = 0.369769 (* 1 = 0.369769 loss)
I0409 21:03:03.546828 14973 sgd_solver.cpp:105] Iteration 8040, lr = 0.00203394
I0409 21:03:08.480307 14973 solver.cpp:218] Iteration 8052 (2.43243 iter/s, 4.93334s/12 iters), loss = 0.445308
I0409 21:03:08.480360 14973 solver.cpp:237]     Train net output #0: loss = 0.445308 (* 1 = 0.445308 loss)
I0409 21:03:08.480370 14973 sgd_solver.cpp:105] Iteration 8052, lr = 0.00202911
I0409 21:03:10.526489 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8058.caffemodel
I0409 21:03:12.095036 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8058.solverstate
I0409 21:03:13.305521 14973 solver.cpp:330] Iteration 8058, Testing net (#0)
I0409 21:03:13.305548 14973 net.cpp:676] Ignoring source layer train-data
I0409 21:03:14.655035 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:03:17.872833 14973 solver.cpp:397]     Test net output #0: accuracy = 0.555147
I0409 21:03:17.872872 14973 solver.cpp:397]     Test net output #1: loss = 2.21826 (* 1 = 2.21826 loss)
I0409 21:03:19.692947 14973 solver.cpp:218] Iteration 8064 (1.07026 iter/s, 11.2123s/12 iters), loss = 0.258841
I0409 21:03:19.693001 14973 solver.cpp:237]     Train net output #0: loss = 0.258841 (* 1 = 0.258841 loss)
I0409 21:03:19.693011 14973 sgd_solver.cpp:105] Iteration 8064, lr = 0.00202429
I0409 21:03:24.794524 14973 solver.cpp:218] Iteration 8076 (2.35231 iter/s, 5.10137s/12 iters), loss = 0.25442
I0409 21:03:24.794570 14973 solver.cpp:237]     Train net output #0: loss = 0.25442 (* 1 = 0.25442 loss)
I0409 21:03:24.794579 14973 sgd_solver.cpp:105] Iteration 8076, lr = 0.00201949
I0409 21:03:29.910521 14973 solver.cpp:218] Iteration 8088 (2.34568 iter/s, 5.1158s/12 iters), loss = 0.40274
I0409 21:03:29.910568 14973 solver.cpp:237]     Train net output #0: loss = 0.40274 (* 1 = 0.40274 loss)
I0409 21:03:29.910578 14973 sgd_solver.cpp:105] Iteration 8088, lr = 0.00201469
I0409 21:03:31.356276 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:03:34.946611 14973 solver.cpp:218] Iteration 8100 (2.38289 iter/s, 5.0359s/12 iters), loss = 0.576813
I0409 21:03:34.946710 14973 solver.cpp:237]     Train net output #0: loss = 0.576813 (* 1 = 0.576813 loss)
I0409 21:03:34.946719 14973 sgd_solver.cpp:105] Iteration 8100, lr = 0.00200991
I0409 21:03:39.988361 14973 solver.cpp:218] Iteration 8112 (2.38024 iter/s, 5.0415s/12 iters), loss = 0.313486
I0409 21:03:39.988407 14973 solver.cpp:237]     Train net output #0: loss = 0.313486 (* 1 = 0.313486 loss)
I0409 21:03:39.988416 14973 sgd_solver.cpp:105] Iteration 8112, lr = 0.00200514
I0409 21:03:45.289136 14973 solver.cpp:218] Iteration 8124 (2.26391 iter/s, 5.30056s/12 iters), loss = 0.260175
I0409 21:03:45.289211 14973 solver.cpp:237]     Train net output #0: loss = 0.260175 (* 1 = 0.260175 loss)
I0409 21:03:45.289227 14973 sgd_solver.cpp:105] Iteration 8124, lr = 0.00200038
I0409 21:03:50.229205 14973 solver.cpp:218] Iteration 8136 (2.42922 iter/s, 4.93985s/12 iters), loss = 0.311489
I0409 21:03:50.229247 14973 solver.cpp:237]     Train net output #0: loss = 0.311489 (* 1 = 0.311489 loss)
I0409 21:03:50.229255 14973 sgd_solver.cpp:105] Iteration 8136, lr = 0.00199563
I0409 21:03:55.287747 14973 solver.cpp:218] Iteration 8148 (2.37232 iter/s, 5.05834s/12 iters), loss = 0.282683
I0409 21:03:55.287793 14973 solver.cpp:237]     Train net output #0: loss = 0.282683 (* 1 = 0.282683 loss)
I0409 21:03:55.287802 14973 sgd_solver.cpp:105] Iteration 8148, lr = 0.00199089
I0409 21:03:59.878931 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8160.caffemodel
I0409 21:04:01.400270 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8160.solverstate
I0409 21:04:02.598449 14973 solver.cpp:330] Iteration 8160, Testing net (#0)
I0409 21:04:02.598469 14973 net.cpp:676] Ignoring source layer train-data
I0409 21:04:03.867966 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:04:07.128302 14973 solver.cpp:397]     Test net output #0: accuracy = 0.550858
I0409 21:04:07.128484 14973 solver.cpp:397]     Test net output #1: loss = 2.2863 (* 1 = 2.2863 loss)
I0409 21:04:07.215315 14973 solver.cpp:218] Iteration 8160 (1.00611 iter/s, 11.9272s/12 iters), loss = 0.305827
I0409 21:04:07.215368 14973 solver.cpp:237]     Train net output #0: loss = 0.305827 (* 1 = 0.305827 loss)
I0409 21:04:07.215379 14973 sgd_solver.cpp:105] Iteration 8160, lr = 0.00198616
I0409 21:04:11.490679 14973 solver.cpp:218] Iteration 8172 (2.8069 iter/s, 4.27518s/12 iters), loss = 0.361042
I0409 21:04:11.490734 14973 solver.cpp:237]     Train net output #0: loss = 0.361042 (* 1 = 0.361042 loss)
I0409 21:04:11.490746 14973 sgd_solver.cpp:105] Iteration 8172, lr = 0.00198145
I0409 21:04:16.611682 14973 solver.cpp:218] Iteration 8184 (2.34339 iter/s, 5.1208s/12 iters), loss = 0.434908
I0409 21:04:16.611727 14973 solver.cpp:237]     Train net output #0: loss = 0.434908 (* 1 = 0.434908 loss)
I0409 21:04:16.611737 14973 sgd_solver.cpp:105] Iteration 8184, lr = 0.00197674
I0409 21:04:20.149170 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:04:21.599431 14973 solver.cpp:218] Iteration 8196 (2.40599 iter/s, 4.98755s/12 iters), loss = 0.314244
I0409 21:04:21.599484 14973 solver.cpp:237]     Train net output #0: loss = 0.314244 (* 1 = 0.314244 loss)
I0409 21:04:21.599496 14973 sgd_solver.cpp:105] Iteration 8196, lr = 0.00197205
I0409 21:04:26.660617 14973 solver.cpp:218] Iteration 8208 (2.37108 iter/s, 5.06098s/12 iters), loss = 0.381298
I0409 21:04:26.660666 14973 solver.cpp:237]     Train net output #0: loss = 0.381298 (* 1 = 0.381298 loss)
I0409 21:04:26.660679 14973 sgd_solver.cpp:105] Iteration 8208, lr = 0.00196737
I0409 21:04:31.648202 14973 solver.cpp:218] Iteration 8220 (2.40607 iter/s, 4.98739s/12 iters), loss = 0.461457
I0409 21:04:31.648252 14973 solver.cpp:237]     Train net output #0: loss = 0.461457 (* 1 = 0.461457 loss)
I0409 21:04:31.648263 14973 sgd_solver.cpp:105] Iteration 8220, lr = 0.0019627
I0409 21:04:36.683692 14973 solver.cpp:218] Iteration 8232 (2.38318 iter/s, 5.03529s/12 iters), loss = 0.41797
I0409 21:04:36.683744 14973 solver.cpp:237]     Train net output #0: loss = 0.41797 (* 1 = 0.41797 loss)
I0409 21:04:36.683754 14973 sgd_solver.cpp:105] Iteration 8232, lr = 0.00195804
I0409 21:04:41.633322 14973 solver.cpp:218] Iteration 8244 (2.42452 iter/s, 4.94943s/12 iters), loss = 0.190965
I0409 21:04:41.633426 14973 solver.cpp:237]     Train net output #0: loss = 0.190965 (* 1 = 0.190965 loss)
I0409 21:04:41.633438 14973 sgd_solver.cpp:105] Iteration 8244, lr = 0.00195339
I0409 21:04:46.577167 14973 solver.cpp:218] Iteration 8256 (2.42739 iter/s, 4.94358s/12 iters), loss = 0.419613
I0409 21:04:46.577226 14973 solver.cpp:237]     Train net output #0: loss = 0.419613 (* 1 = 0.419613 loss)
I0409 21:04:46.577239 14973 sgd_solver.cpp:105] Iteration 8256, lr = 0.00194875
I0409 21:04:48.635928 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8262.caffemodel
I0409 21:04:50.860523 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8262.solverstate
I0409 21:04:52.074337 14973 solver.cpp:330] Iteration 8262, Testing net (#0)
I0409 21:04:52.074364 14973 net.cpp:676] Ignoring source layer train-data
I0409 21:04:53.268707 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:04:56.508971 14973 solver.cpp:397]     Test net output #0: accuracy = 0.557598
I0409 21:04:56.509022 14973 solver.cpp:397]     Test net output #1: loss = 2.24026 (* 1 = 2.24026 loss)
I0409 21:04:58.416364 14973 solver.cpp:218] Iteration 8268 (1.01362 iter/s, 11.8388s/12 iters), loss = 0.483302
I0409 21:04:58.416410 14973 solver.cpp:237]     Train net output #0: loss = 0.483302 (* 1 = 0.483302 loss)
I0409 21:04:58.416419 14973 sgd_solver.cpp:105] Iteration 8268, lr = 0.00194412
I0409 21:05:03.499761 14973 solver.cpp:218] Iteration 8280 (2.36072 iter/s, 5.08319s/12 iters), loss = 0.236318
I0409 21:05:03.499805 14973 solver.cpp:237]     Train net output #0: loss = 0.236318 (* 1 = 0.236318 loss)
I0409 21:05:03.499814 14973 sgd_solver.cpp:105] Iteration 8280, lr = 0.00193951
I0409 21:05:08.564476 14973 solver.cpp:218] Iteration 8292 (2.36943 iter/s, 5.06452s/12 iters), loss = 0.424884
I0409 21:05:08.564522 14973 solver.cpp:237]     Train net output #0: loss = 0.424884 (* 1 = 0.424884 loss)
I0409 21:05:08.564532 14973 sgd_solver.cpp:105] Iteration 8292, lr = 0.0019349
I0409 21:05:09.266574 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:05:13.647547 14973 solver.cpp:218] Iteration 8304 (2.36087 iter/s, 5.08287s/12 iters), loss = 0.285336
I0409 21:05:13.647667 14973 solver.cpp:237]     Train net output #0: loss = 0.285336 (* 1 = 0.285336 loss)
I0409 21:05:13.647677 14973 sgd_solver.cpp:105] Iteration 8304, lr = 0.00193031
I0409 21:05:16.183468 14973 blocking_queue.cpp:49] Waiting for data
I0409 21:05:18.692031 14973 solver.cpp:218] Iteration 8316 (2.37897 iter/s, 5.04421s/12 iters), loss = 0.257565
I0409 21:05:18.692088 14973 solver.cpp:237]     Train net output #0: loss = 0.257565 (* 1 = 0.257565 loss)
I0409 21:05:18.692103 14973 sgd_solver.cpp:105] Iteration 8316, lr = 0.00192573
I0409 21:05:23.708573 14973 solver.cpp:218] Iteration 8328 (2.39218 iter/s, 5.01634s/12 iters), loss = 0.25564
I0409 21:05:23.708621 14973 solver.cpp:237]     Train net output #0: loss = 0.25564 (* 1 = 0.25564 loss)
I0409 21:05:23.708634 14973 sgd_solver.cpp:105] Iteration 8328, lr = 0.00192115
I0409 21:05:28.745760 14973 solver.cpp:218] Iteration 8340 (2.38238 iter/s, 5.03699s/12 iters), loss = 0.326747
I0409 21:05:28.745806 14973 solver.cpp:237]     Train net output #0: loss = 0.326747 (* 1 = 0.326747 loss)
I0409 21:05:28.745815 14973 sgd_solver.cpp:105] Iteration 8340, lr = 0.00191659
I0409 21:05:33.827883 14973 solver.cpp:218] Iteration 8352 (2.36131 iter/s, 5.08192s/12 iters), loss = 0.361866
I0409 21:05:33.827934 14973 solver.cpp:237]     Train net output #0: loss = 0.361866 (* 1 = 0.361866 loss)
I0409 21:05:33.827944 14973 sgd_solver.cpp:105] Iteration 8352, lr = 0.00191204
I0409 21:05:38.388106 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8364.caffemodel
I0409 21:05:39.982206 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8364.solverstate
I0409 21:05:41.191509 14973 solver.cpp:330] Iteration 8364, Testing net (#0)
I0409 21:05:41.191534 14973 net.cpp:676] Ignoring source layer train-data
I0409 21:05:42.520871 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:05:45.801892 14973 solver.cpp:397]     Test net output #0: accuracy = 0.568015
I0409 21:05:45.802024 14973 solver.cpp:397]     Test net output #1: loss = 2.15395 (* 1 = 2.15395 loss)
I0409 21:05:45.888721 14973 solver.cpp:218] Iteration 8364 (0.994989 iter/s, 12.0604s/12 iters), loss = 0.280469
I0409 21:05:45.888770 14973 solver.cpp:237]     Train net output #0: loss = 0.280469 (* 1 = 0.280469 loss)
I0409 21:05:45.888780 14973 sgd_solver.cpp:105] Iteration 8364, lr = 0.0019075
I0409 21:05:50.045918 14973 solver.cpp:218] Iteration 8376 (2.88668 iter/s, 4.15702s/12 iters), loss = 0.259453
I0409 21:05:50.045982 14973 solver.cpp:237]     Train net output #0: loss = 0.259453 (* 1 = 0.259453 loss)
I0409 21:05:50.045990 14973 sgd_solver.cpp:105] Iteration 8376, lr = 0.00190297
I0409 21:05:55.024695 14973 solver.cpp:218] Iteration 8388 (2.41033 iter/s, 4.97858s/12 iters), loss = 0.338957
I0409 21:05:55.024742 14973 solver.cpp:237]     Train net output #0: loss = 0.338957 (* 1 = 0.338957 loss)
I0409 21:05:55.024752 14973 sgd_solver.cpp:105] Iteration 8388, lr = 0.00189846
I0409 21:05:57.907125 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:06:00.268430 14973 solver.cpp:218] Iteration 8400 (2.28854 iter/s, 5.24353s/12 iters), loss = 0.470426
I0409 21:06:00.268478 14973 solver.cpp:237]     Train net output #0: loss = 0.470426 (* 1 = 0.470426 loss)
I0409 21:06:00.268486 14973 sgd_solver.cpp:105] Iteration 8400, lr = 0.00189395
I0409 21:06:05.405290 14973 solver.cpp:218] Iteration 8412 (2.33615 iter/s, 5.13665s/12 iters), loss = 0.343653
I0409 21:06:05.405335 14973 solver.cpp:237]     Train net output #0: loss = 0.343653 (* 1 = 0.343653 loss)
I0409 21:06:05.405344 14973 sgd_solver.cpp:105] Iteration 8412, lr = 0.00188945
I0409 21:06:10.471112 14973 solver.cpp:218] Iteration 8424 (2.36891 iter/s, 5.06561s/12 iters), loss = 0.258387
I0409 21:06:10.471172 14973 solver.cpp:237]     Train net output #0: loss = 0.258387 (* 1 = 0.258387 loss)
I0409 21:06:10.471184 14973 sgd_solver.cpp:105] Iteration 8424, lr = 0.00188497
I0409 21:06:15.440747 14973 solver.cpp:218] Iteration 8436 (2.41477 iter/s, 4.96942s/12 iters), loss = 0.258514
I0409 21:06:15.440804 14973 solver.cpp:237]     Train net output #0: loss = 0.258514 (* 1 = 0.258514 loss)
I0409 21:06:15.440817 14973 sgd_solver.cpp:105] Iteration 8436, lr = 0.00188049
I0409 21:06:20.428733 14973 solver.cpp:218] Iteration 8448 (2.40588 iter/s, 4.98778s/12 iters), loss = 0.232158
I0409 21:06:20.428840 14973 solver.cpp:237]     Train net output #0: loss = 0.232158 (* 1 = 0.232158 loss)
I0409 21:06:20.428853 14973 sgd_solver.cpp:105] Iteration 8448, lr = 0.00187603
I0409 21:06:25.431087 14973 solver.cpp:218] Iteration 8460 (2.39899 iter/s, 5.0021s/12 iters), loss = 0.298583
I0409 21:06:25.431128 14973 solver.cpp:237]     Train net output #0: loss = 0.298583 (* 1 = 0.298583 loss)
I0409 21:06:25.431136 14973 sgd_solver.cpp:105] Iteration 8460, lr = 0.00187157
I0409 21:06:27.473129 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8466.caffemodel
I0409 21:06:30.755774 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8466.solverstate
I0409 21:06:32.848682 14973 solver.cpp:330] Iteration 8466, Testing net (#0)
I0409 21:06:32.848701 14973 net.cpp:676] Ignoring source layer train-data
I0409 21:06:34.083062 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:06:37.486248 14973 solver.cpp:397]     Test net output #0: accuracy = 0.567402
I0409 21:06:37.486297 14973 solver.cpp:397]     Test net output #1: loss = 2.27669 (* 1 = 2.27669 loss)
I0409 21:06:39.324910 14973 solver.cpp:218] Iteration 8472 (0.863721 iter/s, 13.8934s/12 iters), loss = 0.420082
I0409 21:06:39.324963 14973 solver.cpp:237]     Train net output #0: loss = 0.420082 (* 1 = 0.420082 loss)
I0409 21:06:39.324976 14973 sgd_solver.cpp:105] Iteration 8472, lr = 0.00186713
I0409 21:06:44.224572 14973 solver.cpp:218] Iteration 8484 (2.44925 iter/s, 4.89946s/12 iters), loss = 0.521991
I0409 21:06:44.224678 14973 solver.cpp:237]     Train net output #0: loss = 0.521991 (* 1 = 0.521991 loss)
I0409 21:06:44.224689 14973 sgd_solver.cpp:105] Iteration 8484, lr = 0.0018627
I0409 21:06:49.239284 14973 solver.cpp:218] Iteration 8496 (2.39307 iter/s, 5.01447s/12 iters), loss = 0.348632
I0409 21:06:49.239332 14973 solver.cpp:237]     Train net output #0: loss = 0.348632 (* 1 = 0.348632 loss)
I0409 21:06:49.239342 14973 sgd_solver.cpp:105] Iteration 8496, lr = 0.00185827
I0409 21:06:49.278139 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:06:54.360563 14973 solver.cpp:218] Iteration 8508 (2.34326 iter/s, 5.12107s/12 iters), loss = 0.320677
I0409 21:06:54.360724 14973 solver.cpp:237]     Train net output #0: loss = 0.320677 (* 1 = 0.320677 loss)
I0409 21:06:54.360738 14973 sgd_solver.cpp:105] Iteration 8508, lr = 0.00185386
I0409 21:06:59.415905 14973 solver.cpp:218] Iteration 8520 (2.37387 iter/s, 5.05503s/12 iters), loss = 0.16939
I0409 21:06:59.415959 14973 solver.cpp:237]     Train net output #0: loss = 0.16939 (* 1 = 0.16939 loss)
I0409 21:06:59.415972 14973 sgd_solver.cpp:105] Iteration 8520, lr = 0.00184946
I0409 21:07:04.456722 14973 solver.cpp:218] Iteration 8532 (2.38066 iter/s, 5.04061s/12 iters), loss = 0.316473
I0409 21:07:04.456773 14973 solver.cpp:237]     Train net output #0: loss = 0.316473 (* 1 = 0.316473 loss)
I0409 21:07:04.456784 14973 sgd_solver.cpp:105] Iteration 8532, lr = 0.00184507
I0409 21:07:09.480095 14973 solver.cpp:218] Iteration 8544 (2.38893 iter/s, 5.02317s/12 iters), loss = 0.412131
I0409 21:07:09.480144 14973 solver.cpp:237]     Train net output #0: loss = 0.412131 (* 1 = 0.412131 loss)
I0409 21:07:09.480155 14973 sgd_solver.cpp:105] Iteration 8544, lr = 0.00184069
I0409 21:07:14.584616 14973 solver.cpp:218] Iteration 8556 (2.35095 iter/s, 5.10432s/12 iters), loss = 0.350041
I0409 21:07:14.584661 14973 solver.cpp:237]     Train net output #0: loss = 0.350041 (* 1 = 0.350041 loss)
I0409 21:07:14.584671 14973 sgd_solver.cpp:105] Iteration 8556, lr = 0.00183632
I0409 21:07:19.198186 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8568.caffemodel
I0409 21:07:20.741510 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8568.solverstate
I0409 21:07:21.955456 14973 solver.cpp:330] Iteration 8568, Testing net (#0)
I0409 21:07:21.955485 14973 net.cpp:676] Ignoring source layer train-data
I0409 21:07:23.337708 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:07:26.824158 14973 solver.cpp:397]     Test net output #0: accuracy = 0.560049
I0409 21:07:26.824234 14973 solver.cpp:397]     Test net output #1: loss = 2.21958 (* 1 = 2.21958 loss)
I0409 21:07:26.910950 14973 solver.cpp:218] Iteration 8568 (0.973558 iter/s, 12.3259s/12 iters), loss = 0.326795
I0409 21:07:26.911010 14973 solver.cpp:237]     Train net output #0: loss = 0.326795 (* 1 = 0.326795 loss)
I0409 21:07:26.911022 14973 sgd_solver.cpp:105] Iteration 8568, lr = 0.00183196
I0409 21:07:31.085479 14973 solver.cpp:218] Iteration 8580 (2.87471 iter/s, 4.17434s/12 iters), loss = 0.279213
I0409 21:07:31.085538 14973 solver.cpp:237]     Train net output #0: loss = 0.279213 (* 1 = 0.279213 loss)
I0409 21:07:31.085551 14973 sgd_solver.cpp:105] Iteration 8580, lr = 0.00182761
I0409 21:07:36.066535 14973 solver.cpp:218] Iteration 8592 (2.40923 iter/s, 4.98084s/12 iters), loss = 0.338707
I0409 21:07:36.066596 14973 solver.cpp:237]     Train net output #0: loss = 0.338707 (* 1 = 0.338707 loss)
I0409 21:07:36.066609 14973 sgd_solver.cpp:105] Iteration 8592, lr = 0.00182327
I0409 21:07:38.219146 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:07:41.019659 14973 solver.cpp:218] Iteration 8604 (2.42282 iter/s, 4.95291s/12 iters), loss = 0.192011
I0409 21:07:41.019713 14973 solver.cpp:237]     Train net output #0: loss = 0.192011 (* 1 = 0.192011 loss)
I0409 21:07:41.019726 14973 sgd_solver.cpp:105] Iteration 8604, lr = 0.00181894
I0409 21:07:45.952301 14973 solver.cpp:218] Iteration 8616 (2.43288 iter/s, 4.93243s/12 iters), loss = 0.331403
I0409 21:07:45.952366 14973 solver.cpp:237]     Train net output #0: loss = 0.331403 (* 1 = 0.331403 loss)
I0409 21:07:45.952380 14973 sgd_solver.cpp:105] Iteration 8616, lr = 0.00181462
I0409 21:07:50.872189 14973 solver.cpp:218] Iteration 8628 (2.43919 iter/s, 4.91967s/12 iters), loss = 0.28992
I0409 21:07:50.872246 14973 solver.cpp:237]     Train net output #0: loss = 0.28992 (* 1 = 0.28992 loss)
I0409 21:07:50.872259 14973 sgd_solver.cpp:105] Iteration 8628, lr = 0.00181031
I0409 21:07:55.769171 14973 solver.cpp:218] Iteration 8640 (2.45059 iter/s, 4.89677s/12 iters), loss = 0.2387
I0409 21:07:55.769227 14973 solver.cpp:237]     Train net output #0: loss = 0.2387 (* 1 = 0.2387 loss)
I0409 21:07:55.769239 14973 sgd_solver.cpp:105] Iteration 8640, lr = 0.00180602
I0409 21:08:00.695983 14973 solver.cpp:218] Iteration 8652 (2.43575 iter/s, 4.92661s/12 iters), loss = 0.367153
I0409 21:08:00.696147 14973 solver.cpp:237]     Train net output #0: loss = 0.367153 (* 1 = 0.367153 loss)
I0409 21:08:00.696159 14973 sgd_solver.cpp:105] Iteration 8652, lr = 0.00180173
I0409 21:08:05.820683 14973 solver.cpp:218] Iteration 8664 (2.34175 iter/s, 5.12437s/12 iters), loss = 0.222483
I0409 21:08:05.820736 14973 solver.cpp:237]     Train net output #0: loss = 0.222483 (* 1 = 0.222483 loss)
I0409 21:08:05.820747 14973 sgd_solver.cpp:105] Iteration 8664, lr = 0.00179745
I0409 21:08:07.833375 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8670.caffemodel
I0409 21:08:10.629164 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8670.solverstate
I0409 21:08:12.147855 14973 solver.cpp:330] Iteration 8670, Testing net (#0)
I0409 21:08:12.147877 14973 net.cpp:676] Ignoring source layer train-data
I0409 21:08:13.265632 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:08:16.655970 14973 solver.cpp:397]     Test net output #0: accuracy = 0.555147
I0409 21:08:16.656008 14973 solver.cpp:397]     Test net output #1: loss = 2.23554 (* 1 = 2.23554 loss)
I0409 21:08:18.453140 14973 solver.cpp:218] Iteration 8676 (0.949966 iter/s, 12.632s/12 iters), loss = 0.319632
I0409 21:08:18.453191 14973 solver.cpp:237]     Train net output #0: loss = 0.319632 (* 1 = 0.319632 loss)
I0409 21:08:18.453203 14973 sgd_solver.cpp:105] Iteration 8676, lr = 0.00179318
I0409 21:08:23.580855 14973 solver.cpp:218] Iteration 8688 (2.34032 iter/s, 5.12751s/12 iters), loss = 0.2619
I0409 21:08:23.580899 14973 solver.cpp:237]     Train net output #0: loss = 0.2619 (* 1 = 0.2619 loss)
I0409 21:08:23.580909 14973 sgd_solver.cpp:105] Iteration 8688, lr = 0.00178893
I0409 21:08:27.889480 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:08:28.560796 14973 solver.cpp:218] Iteration 8700 (2.40976 iter/s, 4.97975s/12 iters), loss = 0.343513
I0409 21:08:28.560835 14973 solver.cpp:237]     Train net output #0: loss = 0.343513 (* 1 = 0.343513 loss)
I0409 21:08:28.560844 14973 sgd_solver.cpp:105] Iteration 8700, lr = 0.00178468
I0409 21:08:33.680311 14973 solver.cpp:218] Iteration 8712 (2.34406 iter/s, 5.11932s/12 iters), loss = 0.282264
I0409 21:08:33.680421 14973 solver.cpp:237]     Train net output #0: loss = 0.282264 (* 1 = 0.282264 loss)
I0409 21:08:33.680434 14973 sgd_solver.cpp:105] Iteration 8712, lr = 0.00178044
I0409 21:08:39.114332 14973 solver.cpp:218] Iteration 8724 (2.20842 iter/s, 5.43374s/12 iters), loss = 0.244052
I0409 21:08:39.114398 14973 solver.cpp:237]     Train net output #0: loss = 0.244052 (* 1 = 0.244052 loss)
I0409 21:08:39.114413 14973 sgd_solver.cpp:105] Iteration 8724, lr = 0.00177621
I0409 21:08:44.313427 14973 solver.cpp:218] Iteration 8736 (2.30819 iter/s, 5.19887s/12 iters), loss = 0.315495
I0409 21:08:44.313472 14973 solver.cpp:237]     Train net output #0: loss = 0.315495 (* 1 = 0.315495 loss)
I0409 21:08:44.313481 14973 sgd_solver.cpp:105] Iteration 8736, lr = 0.001772
I0409 21:08:49.283522 14973 solver.cpp:218] Iteration 8748 (2.41454 iter/s, 4.96989s/12 iters), loss = 0.225028
I0409 21:08:49.283569 14973 solver.cpp:237]     Train net output #0: loss = 0.225028 (* 1 = 0.225028 loss)
I0409 21:08:49.283578 14973 sgd_solver.cpp:105] Iteration 8748, lr = 0.00176779
I0409 21:08:54.268301 14973 solver.cpp:218] Iteration 8760 (2.40743 iter/s, 4.98458s/12 iters), loss = 0.243027
I0409 21:08:54.268343 14973 solver.cpp:237]     Train net output #0: loss = 0.243027 (* 1 = 0.243027 loss)
I0409 21:08:54.268353 14973 sgd_solver.cpp:105] Iteration 8760, lr = 0.00176359
I0409 21:08:59.067109 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8772.caffemodel
I0409 21:09:02.742136 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8772.solverstate
I0409 21:09:05.220818 14973 solver.cpp:330] Iteration 8772, Testing net (#0)
I0409 21:09:05.221011 14973 net.cpp:676] Ignoring source layer train-data
I0409 21:09:06.287422 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:09:09.738577 14973 solver.cpp:397]     Test net output #0: accuracy = 0.563113
I0409 21:09:09.738627 14973 solver.cpp:397]     Test net output #1: loss = 2.26004 (* 1 = 2.26004 loss)
I0409 21:09:09.825688 14973 solver.cpp:218] Iteration 8772 (0.771362 iter/s, 15.5569s/12 iters), loss = 0.249523
I0409 21:09:09.825739 14973 solver.cpp:237]     Train net output #0: loss = 0.249523 (* 1 = 0.249523 loss)
I0409 21:09:09.825750 14973 sgd_solver.cpp:105] Iteration 8772, lr = 0.00175941
I0409 21:09:14.027403 14973 solver.cpp:218] Iteration 8784 (2.8561 iter/s, 4.20153s/12 iters), loss = 0.141775
I0409 21:09:14.027462 14973 solver.cpp:237]     Train net output #0: loss = 0.141775 (* 1 = 0.141775 loss)
I0409 21:09:14.027473 14973 sgd_solver.cpp:105] Iteration 8784, lr = 0.00175523
I0409 21:09:19.092547 14973 solver.cpp:218] Iteration 8796 (2.36924 iter/s, 5.06492s/12 iters), loss = 0.154917
I0409 21:09:19.092602 14973 solver.cpp:237]     Train net output #0: loss = 0.154917 (* 1 = 0.154917 loss)
I0409 21:09:19.092613 14973 sgd_solver.cpp:105] Iteration 8796, lr = 0.00175106
I0409 21:09:20.580667 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:09:24.084596 14973 solver.cpp:218] Iteration 8808 (2.40392 iter/s, 4.99184s/12 iters), loss = 0.294588
I0409 21:09:24.084647 14973 solver.cpp:237]     Train net output #0: loss = 0.294588 (* 1 = 0.294588 loss)
I0409 21:09:24.084658 14973 sgd_solver.cpp:105] Iteration 8808, lr = 0.0017469
I0409 21:09:28.981984 14973 solver.cpp:218] Iteration 8820 (2.45039 iter/s, 4.89719s/12 iters), loss = 0.19501
I0409 21:09:28.982023 14973 solver.cpp:237]     Train net output #0: loss = 0.19501 (* 1 = 0.19501 loss)
I0409 21:09:28.982033 14973 sgd_solver.cpp:105] Iteration 8820, lr = 0.00174276
I0409 21:09:34.153031 14973 solver.cpp:218] Iteration 8832 (2.3207 iter/s, 5.17085s/12 iters), loss = 0.226801
I0409 21:09:34.153079 14973 solver.cpp:237]     Train net output #0: loss = 0.226801 (* 1 = 0.226801 loss)
I0409 21:09:34.153087 14973 sgd_solver.cpp:105] Iteration 8832, lr = 0.00173862
I0409 21:09:39.330297 14973 solver.cpp:218] Iteration 8844 (2.31792 iter/s, 5.17705s/12 iters), loss = 0.2234
I0409 21:09:39.330394 14973 solver.cpp:237]     Train net output #0: loss = 0.2234 (* 1 = 0.2234 loss)
I0409 21:09:39.330404 14973 sgd_solver.cpp:105] Iteration 8844, lr = 0.00173449
I0409 21:09:44.392911 14973 solver.cpp:218] Iteration 8856 (2.37044 iter/s, 5.06236s/12 iters), loss = 0.211565
I0409 21:09:44.392967 14973 solver.cpp:237]     Train net output #0: loss = 0.211565 (* 1 = 0.211565 loss)
I0409 21:09:44.392980 14973 sgd_solver.cpp:105] Iteration 8856, lr = 0.00173037
I0409 21:09:49.404361 14973 solver.cpp:218] Iteration 8868 (2.39462 iter/s, 5.01124s/12 iters), loss = 0.308077
I0409 21:09:49.404415 14973 solver.cpp:237]     Train net output #0: loss = 0.308077 (* 1 = 0.308077 loss)
I0409 21:09:49.404430 14973 sgd_solver.cpp:105] Iteration 8868, lr = 0.00172626
I0409 21:09:51.436632 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8874.caffemodel
I0409 21:09:53.048180 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8874.solverstate
I0409 21:09:54.746402 14973 solver.cpp:330] Iteration 8874, Testing net (#0)
I0409 21:09:54.746434 14973 net.cpp:676] Ignoring source layer train-data
I0409 21:09:55.748186 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:09:59.215360 14973 solver.cpp:397]     Test net output #0: accuracy = 0.550245
I0409 21:09:59.215404 14973 solver.cpp:397]     Test net output #1: loss = 2.31759 (* 1 = 2.31759 loss)
I0409 21:10:01.111560 14973 solver.cpp:218] Iteration 8880 (1.02505 iter/s, 11.7068s/12 iters), loss = 0.236353
I0409 21:10:01.111610 14973 solver.cpp:237]     Train net output #0: loss = 0.236353 (* 1 = 0.236353 loss)
I0409 21:10:01.111620 14973 sgd_solver.cpp:105] Iteration 8880, lr = 0.00172217
I0409 21:10:06.081552 14973 solver.cpp:218] Iteration 8892 (2.4146 iter/s, 4.96978s/12 iters), loss = 0.3175
I0409 21:10:06.081604 14973 solver.cpp:237]     Train net output #0: loss = 0.3175 (* 1 = 0.3175 loss)
I0409 21:10:06.081619 14973 sgd_solver.cpp:105] Iteration 8892, lr = 0.00171808
I0409 21:10:09.619029 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:10:11.051790 14973 solver.cpp:218] Iteration 8904 (2.41447 iter/s, 4.97003s/12 iters), loss = 0.241727
I0409 21:10:11.051837 14973 solver.cpp:237]     Train net output #0: loss = 0.241727 (* 1 = 0.241727 loss)
I0409 21:10:11.051849 14973 sgd_solver.cpp:105] Iteration 8904, lr = 0.001714
I0409 21:10:16.054543 14973 solver.cpp:218] Iteration 8916 (2.39878 iter/s, 5.00255s/12 iters), loss = 0.355763
I0409 21:10:16.054589 14973 solver.cpp:237]     Train net output #0: loss = 0.355763 (* 1 = 0.355763 loss)
I0409 21:10:16.054598 14973 sgd_solver.cpp:105] Iteration 8916, lr = 0.00170993
I0409 21:10:21.021252 14973 solver.cpp:218] Iteration 8928 (2.41619 iter/s, 4.96651s/12 iters), loss = 0.239452
I0409 21:10:21.021298 14973 solver.cpp:237]     Train net output #0: loss = 0.239452 (* 1 = 0.239452 loss)
I0409 21:10:21.021307 14973 sgd_solver.cpp:105] Iteration 8928, lr = 0.00170587
I0409 21:10:25.973004 14973 solver.cpp:218] Iteration 8940 (2.42348 iter/s, 4.95155s/12 iters), loss = 0.338319
I0409 21:10:25.973053 14973 solver.cpp:237]     Train net output #0: loss = 0.338319 (* 1 = 0.338319 loss)
I0409 21:10:25.973062 14973 sgd_solver.cpp:105] Iteration 8940, lr = 0.00170182
I0409 21:10:30.915041 14973 solver.cpp:218] Iteration 8952 (2.42825 iter/s, 4.94183s/12 iters), loss = 0.257296
I0409 21:10:30.915093 14973 solver.cpp:237]     Train net output #0: loss = 0.257296 (* 1 = 0.257296 loss)
I0409 21:10:30.915104 14973 sgd_solver.cpp:105] Iteration 8952, lr = 0.00169778
I0409 21:10:36.118979 14973 solver.cpp:218] Iteration 8964 (2.30604 iter/s, 5.20372s/12 iters), loss = 0.235618
I0409 21:10:36.119032 14973 solver.cpp:237]     Train net output #0: loss = 0.235618 (* 1 = 0.235618 loss)
I0409 21:10:36.119043 14973 sgd_solver.cpp:105] Iteration 8964, lr = 0.00169375
I0409 21:10:40.867471 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8976.caffemodel
I0409 21:10:42.399427 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8976.solverstate
I0409 21:10:43.636293 14973 solver.cpp:330] Iteration 8976, Testing net (#0)
I0409 21:10:43.636322 14973 net.cpp:676] Ignoring source layer train-data
I0409 21:10:44.566836 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:10:48.207767 14973 solver.cpp:397]     Test net output #0: accuracy = 0.575368
I0409 21:10:48.207811 14973 solver.cpp:397]     Test net output #1: loss = 2.23882 (* 1 = 2.23882 loss)
I0409 21:10:48.294538 14973 solver.cpp:218] Iteration 8976 (0.985614 iter/s, 12.1751s/12 iters), loss = 0.293528
I0409 21:10:48.294586 14973 solver.cpp:237]     Train net output #0: loss = 0.293528 (* 1 = 0.293528 loss)
I0409 21:10:48.294597 14973 sgd_solver.cpp:105] Iteration 8976, lr = 0.00168973
I0409 21:10:52.790175 14973 solver.cpp:218] Iteration 8988 (2.66937 iter/s, 4.49545s/12 iters), loss = 0.184708
I0409 21:10:52.790225 14973 solver.cpp:237]     Train net output #0: loss = 0.184708 (* 1 = 0.184708 loss)
I0409 21:10:52.790237 14973 sgd_solver.cpp:105] Iteration 8988, lr = 0.00168571
I0409 21:10:55.678335 14973 blocking_queue.cpp:49] Waiting for data
I0409 21:10:57.823653 14973 solver.cpp:218] Iteration 9000 (2.38414 iter/s, 5.03327s/12 iters), loss = 0.402689
I0409 21:10:57.823702 14973 solver.cpp:237]     Train net output #0: loss = 0.402689 (* 1 = 0.402689 loss)
I0409 21:10:57.823714 14973 sgd_solver.cpp:105] Iteration 9000, lr = 0.00168171
I0409 21:10:58.549998 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:11:02.790251 14973 solver.cpp:218] Iteration 9012 (2.41624 iter/s, 4.96638s/12 iters), loss = 0.222928
I0409 21:11:02.790314 14973 solver.cpp:237]     Train net output #0: loss = 0.222928 (* 1 = 0.222928 loss)
I0409 21:11:02.790328 14973 sgd_solver.cpp:105] Iteration 9012, lr = 0.00167772
I0409 21:11:07.773252 14973 solver.cpp:218] Iteration 9024 (2.40829 iter/s, 4.98278s/12 iters), loss = 0.303979
I0409 21:11:07.773303 14973 solver.cpp:237]     Train net output #0: loss = 0.303979 (* 1 = 0.303979 loss)
I0409 21:11:07.773315 14973 sgd_solver.cpp:105] Iteration 9024, lr = 0.00167374
I0409 21:11:13.121064 14973 solver.cpp:218] Iteration 9036 (2.244 iter/s, 5.3476s/12 iters), loss = 0.329143
I0409 21:11:13.121215 14973 solver.cpp:237]     Train net output #0: loss = 0.329143 (* 1 = 0.329143 loss)
I0409 21:11:13.121227 14973 sgd_solver.cpp:105] Iteration 9036, lr = 0.00166976
I0409 21:11:18.327791 14973 solver.cpp:218] Iteration 9048 (2.30485 iter/s, 5.20641s/12 iters), loss = 0.267823
I0409 21:11:18.327839 14973 solver.cpp:237]     Train net output #0: loss = 0.267823 (* 1 = 0.267823 loss)
I0409 21:11:18.327849 14973 sgd_solver.cpp:105] Iteration 9048, lr = 0.0016658
I0409 21:11:23.332723 14973 solver.cpp:218] Iteration 9060 (2.39774 iter/s, 5.00472s/12 iters), loss = 0.253944
I0409 21:11:23.332772 14973 solver.cpp:237]     Train net output #0: loss = 0.253944 (* 1 = 0.253944 loss)
I0409 21:11:23.332782 14973 sgd_solver.cpp:105] Iteration 9060, lr = 0.00166184
I0409 21:11:28.471379 14973 solver.cpp:218] Iteration 9072 (2.33534 iter/s, 5.13844s/12 iters), loss = 0.325171
I0409 21:11:28.471431 14973 solver.cpp:237]     Train net output #0: loss = 0.325171 (* 1 = 0.325171 loss)
I0409 21:11:28.471443 14973 sgd_solver.cpp:105] Iteration 9072, lr = 0.0016579
I0409 21:11:30.526633 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9078.caffemodel
I0409 21:11:32.165743 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9078.solverstate
I0409 21:11:33.384685 14973 solver.cpp:330] Iteration 9078, Testing net (#0)
I0409 21:11:33.384711 14973 net.cpp:676] Ignoring source layer train-data
I0409 21:11:34.311609 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:11:38.094254 14973 solver.cpp:397]     Test net output #0: accuracy = 0.567402
I0409 21:11:38.094280 14973 solver.cpp:397]     Test net output #1: loss = 2.3236 (* 1 = 2.3236 loss)
I0409 21:11:39.880347 14973 solver.cpp:218] Iteration 9084 (1.05184 iter/s, 11.4085s/12 iters), loss = 0.265747
I0409 21:11:39.880398 14973 solver.cpp:237]     Train net output #0: loss = 0.265747 (* 1 = 0.265747 loss)
I0409 21:11:39.880409 14973 sgd_solver.cpp:105] Iteration 9084, lr = 0.00165396
I0409 21:11:44.983182 14973 solver.cpp:218] Iteration 9096 (2.35173 iter/s, 5.10263s/12 iters), loss = 0.183202
I0409 21:11:44.983285 14973 solver.cpp:237]     Train net output #0: loss = 0.183202 (* 1 = 0.183202 loss)
I0409 21:11:44.983297 14973 sgd_solver.cpp:105] Iteration 9096, lr = 0.00165003
I0409 21:11:47.931931 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:11:50.051126 14973 solver.cpp:218] Iteration 9108 (2.36795 iter/s, 5.06767s/12 iters), loss = 0.348513
I0409 21:11:50.051183 14973 solver.cpp:237]     Train net output #0: loss = 0.348513 (* 1 = 0.348513 loss)
I0409 21:11:50.051195 14973 sgd_solver.cpp:105] Iteration 9108, lr = 0.00164612
I0409 21:11:55.139534 14973 solver.cpp:218] Iteration 9120 (2.3584 iter/s, 5.08819s/12 iters), loss = 0.14758
I0409 21:11:55.139588 14973 solver.cpp:237]     Train net output #0: loss = 0.14758 (* 1 = 0.14758 loss)
I0409 21:11:55.139601 14973 sgd_solver.cpp:105] Iteration 9120, lr = 0.00164221
I0409 21:12:00.178599 14973 solver.cpp:218] Iteration 9132 (2.38149 iter/s, 5.03886s/12 iters), loss = 0.169827
I0409 21:12:00.178637 14973 solver.cpp:237]     Train net output #0: loss = 0.169827 (* 1 = 0.169827 loss)
I0409 21:12:00.178647 14973 sgd_solver.cpp:105] Iteration 9132, lr = 0.00163831
I0409 21:12:05.265125 14973 solver.cpp:218] Iteration 9144 (2.35927 iter/s, 5.08633s/12 iters), loss = 0.169976
I0409 21:12:05.265172 14973 solver.cpp:237]     Train net output #0: loss = 0.169976 (* 1 = 0.169976 loss)
I0409 21:12:05.265180 14973 sgd_solver.cpp:105] Iteration 9144, lr = 0.00163442
I0409 21:12:10.377923 14973 solver.cpp:218] Iteration 9156 (2.34715 iter/s, 5.11259s/12 iters), loss = 0.153356
I0409 21:12:10.377986 14973 solver.cpp:237]     Train net output #0: loss = 0.153356 (* 1 = 0.153356 loss)
I0409 21:12:10.377995 14973 sgd_solver.cpp:105] Iteration 9156, lr = 0.00163054
I0409 21:12:15.381753 14973 solver.cpp:218] Iteration 9168 (2.39827 iter/s, 5.00361s/12 iters), loss = 0.293042
I0409 21:12:15.381903 14973 solver.cpp:237]     Train net output #0: loss = 0.293042 (* 1 = 0.293042 loss)
I0409 21:12:15.381917 14973 sgd_solver.cpp:105] Iteration 9168, lr = 0.00162667
I0409 21:12:20.061867 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9180.caffemodel
I0409 21:12:23.503084 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9180.solverstate
I0409 21:12:25.144718 14973 solver.cpp:330] Iteration 9180, Testing net (#0)
I0409 21:12:25.144743 14973 net.cpp:676] Ignoring source layer train-data
I0409 21:12:26.024144 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:12:29.616014 14973 solver.cpp:397]     Test net output #0: accuracy = 0.567402
I0409 21:12:29.616055 14973 solver.cpp:397]     Test net output #1: loss = 2.31213 (* 1 = 2.31213 loss)
I0409 21:12:29.702556 14973 solver.cpp:218] Iteration 9180 (0.837975 iter/s, 14.3202s/12 iters), loss = 0.290889
I0409 21:12:29.702601 14973 solver.cpp:237]     Train net output #0: loss = 0.290889 (* 1 = 0.290889 loss)
I0409 21:12:29.702610 14973 sgd_solver.cpp:105] Iteration 9180, lr = 0.00162281
I0409 21:12:33.884366 14973 solver.cpp:218] Iteration 9192 (2.86969 iter/s, 4.18163s/12 iters), loss = 0.289549
I0409 21:12:33.884402 14973 solver.cpp:237]     Train net output #0: loss = 0.289549 (* 1 = 0.289549 loss)
I0409 21:12:33.884411 14973 sgd_solver.cpp:105] Iteration 9192, lr = 0.00161895
I0409 21:12:38.839524 14973 solver.cpp:218] Iteration 9204 (2.42182 iter/s, 4.95496s/12 iters), loss = 0.166675
I0409 21:12:38.839583 14973 solver.cpp:237]     Train net output #0: loss = 0.166675 (* 1 = 0.166675 loss)
I0409 21:12:38.839596 14973 sgd_solver.cpp:105] Iteration 9204, lr = 0.00161511
I0409 21:12:38.907114 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:12:43.954144 14973 solver.cpp:218] Iteration 9216 (2.34632 iter/s, 5.1144s/12 iters), loss = 0.188454
I0409 21:12:43.954195 14973 solver.cpp:237]     Train net output #0: loss = 0.188454 (* 1 = 0.188454 loss)
I0409 21:12:43.954205 14973 sgd_solver.cpp:105] Iteration 9216, lr = 0.00161128
I0409 21:12:48.920589 14973 solver.cpp:218] Iteration 9228 (2.41632 iter/s, 4.96623s/12 iters), loss = 0.224474
I0409 21:12:48.920706 14973 solver.cpp:237]     Train net output #0: loss = 0.224474 (* 1 = 0.224474 loss)
I0409 21:12:48.920722 14973 sgd_solver.cpp:105] Iteration 9228, lr = 0.00160745
I0409 21:12:53.949028 14973 solver.cpp:218] Iteration 9240 (2.38656 iter/s, 5.02816s/12 iters), loss = 0.175409
I0409 21:12:53.949093 14973 solver.cpp:237]     Train net output #0: loss = 0.175409 (* 1 = 0.175409 loss)
I0409 21:12:53.949106 14973 sgd_solver.cpp:105] Iteration 9240, lr = 0.00160363
I0409 21:12:58.885982 14973 solver.cpp:218] Iteration 9252 (2.43077 iter/s, 4.93671s/12 iters), loss = 0.291688
I0409 21:12:58.886034 14973 solver.cpp:237]     Train net output #0: loss = 0.291688 (* 1 = 0.291688 loss)
I0409 21:12:58.886045 14973 sgd_solver.cpp:105] Iteration 9252, lr = 0.00159983
I0409 21:13:04.293751 14973 solver.cpp:218] Iteration 9264 (2.21912 iter/s, 5.40755s/12 iters), loss = 0.234022
I0409 21:13:04.293798 14973 solver.cpp:237]     Train net output #0: loss = 0.234022 (* 1 = 0.234022 loss)
I0409 21:13:04.293807 14973 sgd_solver.cpp:105] Iteration 9264, lr = 0.00159603
I0409 21:13:09.296993 14973 solver.cpp:218] Iteration 9276 (2.39855 iter/s, 5.00303s/12 iters), loss = 0.312878
I0409 21:13:09.297042 14973 solver.cpp:237]     Train net output #0: loss = 0.312878 (* 1 = 0.312878 loss)
I0409 21:13:09.297051 14973 sgd_solver.cpp:105] Iteration 9276, lr = 0.00159224
I0409 21:13:11.301335 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9282.caffemodel
I0409 21:13:12.883265 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9282.solverstate
I0409 21:13:14.081812 14973 solver.cpp:330] Iteration 9282, Testing net (#0)
I0409 21:13:14.081833 14973 net.cpp:676] Ignoring source layer train-data
I0409 21:13:14.832533 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:13:18.494647 14973 solver.cpp:397]     Test net output #0: accuracy = 0.579657
I0409 21:13:18.494688 14973 solver.cpp:397]     Test net output #1: loss = 2.36366 (* 1 = 2.36366 loss)
I0409 21:13:20.308219 14973 solver.cpp:218] Iteration 9288 (1.08983 iter/s, 11.0108s/12 iters), loss = 0.118952
I0409 21:13:20.311878 14973 solver.cpp:237]     Train net output #0: loss = 0.118952 (* 1 = 0.118952 loss)
I0409 21:13:20.311892 14973 sgd_solver.cpp:105] Iteration 9288, lr = 0.00158846
I0409 21:13:25.405828 14973 solver.cpp:218] Iteration 9300 (2.35581 iter/s, 5.09379s/12 iters), loss = 0.240383
I0409 21:13:25.405885 14973 solver.cpp:237]     Train net output #0: loss = 0.240383 (* 1 = 0.240383 loss)
I0409 21:13:25.405897 14973 sgd_solver.cpp:105] Iteration 9300, lr = 0.00158469
I0409 21:13:27.818359 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:13:30.856281 14973 solver.cpp:218] Iteration 9312 (2.20175 iter/s, 5.45022s/12 iters), loss = 0.371416
I0409 21:13:30.856334 14973 solver.cpp:237]     Train net output #0: loss = 0.371416 (* 1 = 0.371416 loss)
I0409 21:13:30.856348 14973 sgd_solver.cpp:105] Iteration 9312, lr = 0.00158092
I0409 21:13:35.989578 14973 solver.cpp:218] Iteration 9324 (2.33778 iter/s, 5.13308s/12 iters), loss = 0.12198
I0409 21:13:35.989631 14973 solver.cpp:237]     Train net output #0: loss = 0.12198 (* 1 = 0.12198 loss)
I0409 21:13:35.989642 14973 sgd_solver.cpp:105] Iteration 9324, lr = 0.00157717
I0409 21:13:40.989552 14973 solver.cpp:218] Iteration 9336 (2.40011 iter/s, 4.99977s/12 iters), loss = 0.172887
I0409 21:13:40.989593 14973 solver.cpp:237]     Train net output #0: loss = 0.172887 (* 1 = 0.172887 loss)
I0409 21:13:40.989603 14973 sgd_solver.cpp:105] Iteration 9336, lr = 0.00157343
I0409 21:13:45.925012 14973 solver.cpp:218] Iteration 9348 (2.43148 iter/s, 4.93526s/12 iters), loss = 0.214727
I0409 21:13:45.925071 14973 solver.cpp:237]     Train net output #0: loss = 0.214727 (* 1 = 0.214727 loss)
I0409 21:13:45.925082 14973 sgd_solver.cpp:105] Iteration 9348, lr = 0.00156969
I0409 21:13:50.920441 14973 solver.cpp:218] Iteration 9360 (2.4023 iter/s, 4.99522s/12 iters), loss = 0.219712
I0409 21:13:50.920531 14973 solver.cpp:237]     Train net output #0: loss = 0.219712 (* 1 = 0.219712 loss)
I0409 21:13:50.920540 14973 sgd_solver.cpp:105] Iteration 9360, lr = 0.00156596
I0409 21:13:56.054388 14973 solver.cpp:218] Iteration 9372 (2.3375 iter/s, 5.1337s/12 iters), loss = 0.2195
I0409 21:13:56.054426 14973 solver.cpp:237]     Train net output #0: loss = 0.2195 (* 1 = 0.2195 loss)
I0409 21:13:56.054435 14973 sgd_solver.cpp:105] Iteration 9372, lr = 0.00156225
I0409 21:14:00.654222 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9384.caffemodel
I0409 21:14:03.635068 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9384.solverstate
I0409 21:14:04.847008 14973 solver.cpp:330] Iteration 9384, Testing net (#0)
I0409 21:14:04.847033 14973 net.cpp:676] Ignoring source layer train-data
I0409 21:14:05.565151 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:14:09.233052 14973 solver.cpp:397]     Test net output #0: accuracy = 0.567402
I0409 21:14:09.233088 14973 solver.cpp:397]     Test net output #1: loss = 2.37118 (* 1 = 2.37118 loss)
I0409 21:14:09.319613 14973 solver.cpp:218] Iteration 9384 (0.90465 iter/s, 13.2648s/12 iters), loss = 0.174534
I0409 21:14:09.319658 14973 solver.cpp:237]     Train net output #0: loss = 0.174534 (* 1 = 0.174534 loss)
I0409 21:14:09.319665 14973 sgd_solver.cpp:105] Iteration 9384, lr = 0.00155854
I0409 21:14:13.628595 14973 solver.cpp:218] Iteration 9396 (2.785 iter/s, 4.30879s/12 iters), loss = 0.158879
I0409 21:14:13.628643 14973 solver.cpp:237]     Train net output #0: loss = 0.158879 (* 1 = 0.158879 loss)
I0409 21:14:13.628652 14973 sgd_solver.cpp:105] Iteration 9396, lr = 0.00155484
I0409 21:14:18.001636 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:14:18.666522 14973 solver.cpp:218] Iteration 9408 (2.38203 iter/s, 5.03772s/12 iters), loss = 0.262903
I0409 21:14:18.666568 14973 solver.cpp:237]     Train net output #0: loss = 0.262903 (* 1 = 0.262903 loss)
I0409 21:14:18.666579 14973 sgd_solver.cpp:105] Iteration 9408, lr = 0.00155114
I0409 21:14:23.808480 14973 solver.cpp:218] Iteration 9420 (2.33384 iter/s, 5.14174s/12 iters), loss = 0.193471
I0409 21:14:23.808616 14973 solver.cpp:237]     Train net output #0: loss = 0.193471 (* 1 = 0.193471 loss)
I0409 21:14:23.808629 14973 sgd_solver.cpp:105] Iteration 9420, lr = 0.00154746
I0409 21:14:29.280140 14973 solver.cpp:218] Iteration 9432 (2.19324 iter/s, 5.47136s/12 iters), loss = 0.292696
I0409 21:14:29.280181 14973 solver.cpp:237]     Train net output #0: loss = 0.292696 (* 1 = 0.292696 loss)
I0409 21:14:29.280190 14973 sgd_solver.cpp:105] Iteration 9432, lr = 0.00154379
I0409 21:14:34.391539 14973 solver.cpp:218] Iteration 9444 (2.34779 iter/s, 5.1112s/12 iters), loss = 0.166876
I0409 21:14:34.391583 14973 solver.cpp:237]     Train net output #0: loss = 0.166876 (* 1 = 0.166876 loss)
I0409 21:14:34.391593 14973 sgd_solver.cpp:105] Iteration 9444, lr = 0.00154012
I0409 21:14:39.524327 14973 solver.cpp:218] Iteration 9456 (2.33801 iter/s, 5.13258s/12 iters), loss = 0.276944
I0409 21:14:39.524375 14973 solver.cpp:237]     Train net output #0: loss = 0.276944 (* 1 = 0.276944 loss)
I0409 21:14:39.524384 14973 sgd_solver.cpp:105] Iteration 9456, lr = 0.00153647
I0409 21:14:44.729903 14973 solver.cpp:218] Iteration 9468 (2.30531 iter/s, 5.20536s/12 iters), loss = 0.195698
I0409 21:14:44.729952 14973 solver.cpp:237]     Train net output #0: loss = 0.195698 (* 1 = 0.195698 loss)
I0409 21:14:44.729970 14973 sgd_solver.cpp:105] Iteration 9468, lr = 0.00153282
I0409 21:14:49.854063 14973 solver.cpp:218] Iteration 9480 (2.34194 iter/s, 5.12395s/12 iters), loss = 0.224929
I0409 21:14:49.854106 14973 solver.cpp:237]     Train net output #0: loss = 0.224929 (* 1 = 0.224929 loss)
I0409 21:14:49.854115 14973 sgd_solver.cpp:105] Iteration 9480, lr = 0.00152918
I0409 21:14:51.881467 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9486.caffemodel
I0409 21:14:53.420572 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9486.solverstate
I0409 21:14:54.894981 14973 solver.cpp:330] Iteration 9486, Testing net (#0)
I0409 21:14:54.895051 14973 net.cpp:676] Ignoring source layer train-data
I0409 21:14:55.630355 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:14:59.370631 14973 solver.cpp:397]     Test net output #0: accuracy = 0.578431
I0409 21:14:59.370684 14973 solver.cpp:397]     Test net output #1: loss = 2.26332 (* 1 = 2.26332 loss)
I0409 21:15:01.257617 14973 solver.cpp:218] Iteration 9492 (1.05234 iter/s, 11.4032s/12 iters), loss = 0.214546
I0409 21:15:01.257674 14973 solver.cpp:237]     Train net output #0: loss = 0.214546 (* 1 = 0.214546 loss)
I0409 21:15:01.257686 14973 sgd_solver.cpp:105] Iteration 9492, lr = 0.00152555
I0409 21:15:06.454015 14973 solver.cpp:218] Iteration 9504 (2.30939 iter/s, 5.19618s/12 iters), loss = 0.166911
I0409 21:15:06.454056 14973 solver.cpp:237]     Train net output #0: loss = 0.166911 (* 1 = 0.166911 loss)
I0409 21:15:06.454063 14973 sgd_solver.cpp:105] Iteration 9504, lr = 0.00152193
I0409 21:15:07.937702 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:15:11.419606 14973 solver.cpp:218] Iteration 9516 (2.41673 iter/s, 4.96539s/12 iters), loss = 0.156885
I0409 21:15:11.419659 14973 solver.cpp:237]     Train net output #0: loss = 0.156885 (* 1 = 0.156885 loss)
I0409 21:15:11.419669 14973 sgd_solver.cpp:105] Iteration 9516, lr = 0.00151831
I0409 21:15:16.674763 14973 solver.cpp:218] Iteration 9528 (2.28357 iter/s, 5.25493s/12 iters), loss = 0.175481
I0409 21:15:16.674823 14973 solver.cpp:237]     Train net output #0: loss = 0.175481 (* 1 = 0.175481 loss)
I0409 21:15:16.674835 14973 sgd_solver.cpp:105] Iteration 9528, lr = 0.00151471
I0409 21:15:21.910219 14973 solver.cpp:218] Iteration 9540 (2.29216 iter/s, 5.23523s/12 iters), loss = 0.156356
I0409 21:15:21.910269 14973 solver.cpp:237]     Train net output #0: loss = 0.156356 (* 1 = 0.156356 loss)
I0409 21:15:21.910281 14973 sgd_solver.cpp:105] Iteration 9540, lr = 0.00151111
I0409 21:15:26.954788 14973 solver.cpp:218] Iteration 9552 (2.37889 iter/s, 5.04436s/12 iters), loss = 0.353212
I0409 21:15:26.954934 14973 solver.cpp:237]     Train net output #0: loss = 0.353212 (* 1 = 0.353212 loss)
I0409 21:15:26.954946 14973 sgd_solver.cpp:105] Iteration 9552, lr = 0.00150752
I0409 21:15:31.939220 14973 solver.cpp:218] Iteration 9564 (2.40764 iter/s, 4.98413s/12 iters), loss = 0.148285
I0409 21:15:31.939280 14973 solver.cpp:237]     Train net output #0: loss = 0.148285 (* 1 = 0.148285 loss)
I0409 21:15:31.939293 14973 sgd_solver.cpp:105] Iteration 9564, lr = 0.00150395
I0409 21:15:36.995710 14973 solver.cpp:218] Iteration 9576 (2.37329 iter/s, 5.05627s/12 iters), loss = 0.14407
I0409 21:15:36.995750 14973 solver.cpp:237]     Train net output #0: loss = 0.14407 (* 1 = 0.14407 loss)
I0409 21:15:36.995759 14973 sgd_solver.cpp:105] Iteration 9576, lr = 0.00150037
I0409 21:15:41.537585 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9588.caffemodel
I0409 21:15:43.129503 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9588.solverstate
I0409 21:15:44.417815 14973 solver.cpp:330] Iteration 9588, Testing net (#0)
I0409 21:15:44.417845 14973 net.cpp:676] Ignoring source layer train-data
I0409 21:15:45.047610 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:15:48.820614 14973 solver.cpp:397]     Test net output #0: accuracy = 0.561275
I0409 21:15:48.820654 14973 solver.cpp:397]     Test net output #1: loss = 2.3715 (* 1 = 2.3715 loss)
I0409 21:15:48.907483 14973 solver.cpp:218] Iteration 9588 (1.00744 iter/s, 11.9114s/12 iters), loss = 0.246845
I0409 21:15:48.907533 14973 solver.cpp:237]     Train net output #0: loss = 0.246845 (* 1 = 0.246845 loss)
I0409 21:15:48.907542 14973 sgd_solver.cpp:105] Iteration 9588, lr = 0.00149681
I0409 21:15:53.202873 14973 solver.cpp:218] Iteration 9600 (2.79382 iter/s, 4.2952s/12 iters), loss = 0.210109
I0409 21:15:53.202929 14973 solver.cpp:237]     Train net output #0: loss = 0.210109 (* 1 = 0.210109 loss)
I0409 21:15:53.202939 14973 sgd_solver.cpp:105] Iteration 9600, lr = 0.00149326
I0409 21:15:56.747596 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:15:58.240010 14973 solver.cpp:218] Iteration 9612 (2.38241 iter/s, 5.03692s/12 iters), loss = 0.162309
I0409 21:15:58.240118 14973 solver.cpp:237]     Train net output #0: loss = 0.162309 (* 1 = 0.162309 loss)
I0409 21:15:58.240128 14973 sgd_solver.cpp:105] Iteration 9612, lr = 0.00148971
I0409 21:16:03.408311 14973 solver.cpp:218] Iteration 9624 (2.32197 iter/s, 5.16803s/12 iters), loss = 0.22606
I0409 21:16:03.408365 14973 solver.cpp:237]     Train net output #0: loss = 0.22606 (* 1 = 0.22606 loss)
I0409 21:16:03.408375 14973 sgd_solver.cpp:105] Iteration 9624, lr = 0.00148618
I0409 21:16:08.345479 14973 solver.cpp:218] Iteration 9636 (2.43065 iter/s, 4.93695s/12 iters), loss = 0.188334
I0409 21:16:08.345538 14973 solver.cpp:237]     Train net output #0: loss = 0.188334 (* 1 = 0.188334 loss)
I0409 21:16:08.345551 14973 sgd_solver.cpp:105] Iteration 9636, lr = 0.00148265
I0409 21:16:13.234752 14973 solver.cpp:218] Iteration 9648 (2.45446 iter/s, 4.88905s/12 iters), loss = 0.399386
I0409 21:16:13.234809 14973 solver.cpp:237]     Train net output #0: loss = 0.399386 (* 1 = 0.399386 loss)
I0409 21:16:13.234822 14973 sgd_solver.cpp:105] Iteration 9648, lr = 0.00147913
I0409 21:16:18.181214 14973 solver.cpp:218] Iteration 9660 (2.42608 iter/s, 4.94624s/12 iters), loss = 0.0850261
I0409 21:16:18.181272 14973 solver.cpp:237]     Train net output #0: loss = 0.0850261 (* 1 = 0.0850261 loss)
I0409 21:16:18.181284 14973 sgd_solver.cpp:105] Iteration 9660, lr = 0.00147562
I0409 21:16:23.363852 14973 solver.cpp:218] Iteration 9672 (2.31552 iter/s, 5.18242s/12 iters), loss = 0.237299
I0409 21:16:23.363896 14973 solver.cpp:237]     Train net output #0: loss = 0.237299 (* 1 = 0.237299 loss)
I0409 21:16:23.363905 14973 sgd_solver.cpp:105] Iteration 9672, lr = 0.00147211
I0409 21:16:28.349499 14973 solver.cpp:218] Iteration 9684 (2.40701 iter/s, 4.98545s/12 iters), loss = 0.123628
I0409 21:16:28.349607 14973 solver.cpp:237]     Train net output #0: loss = 0.123628 (* 1 = 0.123628 loss)
I0409 21:16:28.349618 14973 sgd_solver.cpp:105] Iteration 9684, lr = 0.00146862
I0409 21:16:30.394567 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9690.caffemodel
I0409 21:16:31.961438 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9690.solverstate
I0409 21:16:33.164306 14973 solver.cpp:330] Iteration 9690, Testing net (#0)
I0409 21:16:33.164335 14973 net.cpp:676] Ignoring source layer train-data
I0409 21:16:33.805377 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:16:36.581274 14973 blocking_queue.cpp:49] Waiting for data
I0409 21:16:37.727659 14973 solver.cpp:397]     Test net output #0: accuracy = 0.575368
I0409 21:16:37.727686 14973 solver.cpp:397]     Test net output #1: loss = 2.37969 (* 1 = 2.37969 loss)
I0409 21:16:39.511652 14973 solver.cpp:218] Iteration 9696 (1.0751 iter/s, 11.1617s/12 iters), loss = 0.186855
I0409 21:16:39.511699 14973 solver.cpp:237]     Train net output #0: loss = 0.186855 (* 1 = 0.186855 loss)
I0409 21:16:39.511709 14973 sgd_solver.cpp:105] Iteration 9696, lr = 0.00146513
I0409 21:16:44.626941 14973 solver.cpp:218] Iteration 9708 (2.34601 iter/s, 5.11507s/12 iters), loss = 0.215152
I0409 21:16:44.626989 14973 solver.cpp:237]     Train net output #0: loss = 0.215152 (* 1 = 0.215152 loss)
I0409 21:16:44.626999 14973 sgd_solver.cpp:105] Iteration 9708, lr = 0.00146165
I0409 21:16:45.375499 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:16:49.752071 14973 solver.cpp:218] Iteration 9720 (2.3415 iter/s, 5.12491s/12 iters), loss = 0.251889
I0409 21:16:49.752126 14973 solver.cpp:237]     Train net output #0: loss = 0.251889 (* 1 = 0.251889 loss)
I0409 21:16:49.752140 14973 sgd_solver.cpp:105] Iteration 9720, lr = 0.00145818
I0409 21:16:54.810916 14973 solver.cpp:218] Iteration 9732 (2.37218 iter/s, 5.05863s/12 iters), loss = 0.314336
I0409 21:16:54.810973 14973 solver.cpp:237]     Train net output #0: loss = 0.314336 (* 1 = 0.314336 loss)
I0409 21:16:54.810989 14973 sgd_solver.cpp:105] Iteration 9732, lr = 0.00145472
I0409 21:16:59.736971 14973 solver.cpp:218] Iteration 9744 (2.43613 iter/s, 4.92584s/12 iters), loss = 0.206586
I0409 21:16:59.737079 14973 solver.cpp:237]     Train net output #0: loss = 0.206586 (* 1 = 0.206586 loss)
I0409 21:16:59.737092 14973 sgd_solver.cpp:105] Iteration 9744, lr = 0.00145127
I0409 21:17:04.774269 14973 solver.cpp:218] Iteration 9756 (2.38235 iter/s, 5.03703s/12 iters), loss = 0.220593
I0409 21:17:04.774317 14973 solver.cpp:237]     Train net output #0: loss = 0.220593 (* 1 = 0.220593 loss)
I0409 21:17:04.774327 14973 sgd_solver.cpp:105] Iteration 9756, lr = 0.00144782
I0409 21:17:09.758908 14973 solver.cpp:218] Iteration 9768 (2.4075 iter/s, 4.98443s/12 iters), loss = 0.208111
I0409 21:17:09.758960 14973 solver.cpp:237]     Train net output #0: loss = 0.208111 (* 1 = 0.208111 loss)
I0409 21:17:09.758970 14973 sgd_solver.cpp:105] Iteration 9768, lr = 0.00144438
I0409 21:17:14.703197 14973 solver.cpp:218] Iteration 9780 (2.42715 iter/s, 4.94408s/12 iters), loss = 0.290814
I0409 21:17:14.703239 14973 solver.cpp:237]     Train net output #0: loss = 0.290814 (* 1 = 0.290814 loss)
I0409 21:17:14.703248 14973 sgd_solver.cpp:105] Iteration 9780, lr = 0.00144095
I0409 21:17:19.168406 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9792.caffemodel
I0409 21:17:20.777395 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9792.solverstate
I0409 21:17:22.393473 14973 solver.cpp:330] Iteration 9792, Testing net (#0)
I0409 21:17:22.393501 14973 net.cpp:676] Ignoring source layer train-data
I0409 21:17:23.012267 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:17:26.856474 14973 solver.cpp:397]     Test net output #0: accuracy = 0.571078
I0409 21:17:26.856524 14973 solver.cpp:397]     Test net output #1: loss = 2.34807 (* 1 = 2.34807 loss)
I0409 21:17:26.943529 14973 solver.cpp:218] Iteration 9792 (0.980399 iter/s, 12.2399s/12 iters), loss = 0.195115
I0409 21:17:26.943601 14973 solver.cpp:237]     Train net output #0: loss = 0.195115 (* 1 = 0.195115 loss)
I0409 21:17:26.943617 14973 sgd_solver.cpp:105] Iteration 9792, lr = 0.00143753
I0409 21:17:31.196663 14973 solver.cpp:218] Iteration 9804 (2.82159 iter/s, 4.25293s/12 iters), loss = 0.162791
I0409 21:17:31.197700 14973 solver.cpp:237]     Train net output #0: loss = 0.162791 (* 1 = 0.162791 loss)
I0409 21:17:31.197713 14973 sgd_solver.cpp:105] Iteration 9804, lr = 0.00143412
I0409 21:17:34.131811 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:17:36.150625 14973 solver.cpp:218] Iteration 9816 (2.42289 iter/s, 4.95277s/12 iters), loss = 0.137732
I0409 21:17:36.150668 14973 solver.cpp:237]     Train net output #0: loss = 0.137732 (* 1 = 0.137732 loss)
I0409 21:17:36.150678 14973 sgd_solver.cpp:105] Iteration 9816, lr = 0.00143072
I0409 21:17:41.092000 14973 solver.cpp:218] Iteration 9828 (2.42858 iter/s, 4.94117s/12 iters), loss = 0.239886
I0409 21:17:41.092061 14973 solver.cpp:237]     Train net output #0: loss = 0.239886 (* 1 = 0.239886 loss)
I0409 21:17:41.092072 14973 sgd_solver.cpp:105] Iteration 9828, lr = 0.00142732
I0409 21:17:46.024518 14973 solver.cpp:218] Iteration 9840 (2.43294 iter/s, 4.9323s/12 iters), loss = 0.130738
I0409 21:17:46.024564 14973 solver.cpp:237]     Train net output #0: loss = 0.130738 (* 1 = 0.130738 loss)
I0409 21:17:46.024574 14973 sgd_solver.cpp:105] Iteration 9840, lr = 0.00142393
I0409 21:17:51.197757 14973 solver.cpp:218] Iteration 9852 (2.31973 iter/s, 5.17302s/12 iters), loss = 0.144581
I0409 21:17:51.197805 14973 solver.cpp:237]     Train net output #0: loss = 0.144581 (* 1 = 0.144581 loss)
I0409 21:17:51.197814 14973 sgd_solver.cpp:105] Iteration 9852, lr = 0.00142055
I0409 21:17:56.246505 14973 solver.cpp:218] Iteration 9864 (2.37693 iter/s, 5.04854s/12 iters), loss = 0.158725
I0409 21:17:56.246552 14973 solver.cpp:237]     Train net output #0: loss = 0.158725 (* 1 = 0.158725 loss)
I0409 21:17:56.246562 14973 sgd_solver.cpp:105] Iteration 9864, lr = 0.00141718
I0409 21:18:01.249922 14973 solver.cpp:218] Iteration 9876 (2.39846 iter/s, 5.00321s/12 iters), loss = 0.159556
I0409 21:18:01.250028 14973 solver.cpp:237]     Train net output #0: loss = 0.159556 (* 1 = 0.159556 loss)
I0409 21:18:01.250038 14973 sgd_solver.cpp:105] Iteration 9876, lr = 0.00141381
I0409 21:18:06.454738 14973 solver.cpp:218] Iteration 9888 (2.30568 iter/s, 5.20455s/12 iters), loss = 0.181994
I0409 21:18:06.454777 14973 solver.cpp:237]     Train net output #0: loss = 0.181994 (* 1 = 0.181994 loss)
I0409 21:18:06.454787 14973 sgd_solver.cpp:105] Iteration 9888, lr = 0.00141045
I0409 21:18:08.510668 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9894.caffemodel
I0409 21:18:15.013715 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9894.solverstate
I0409 21:18:18.616555 14973 solver.cpp:330] Iteration 9894, Testing net (#0)
I0409 21:18:18.616582 14973 net.cpp:676] Ignoring source layer train-data
I0409 21:18:19.243549 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:18:23.304752 14973 solver.cpp:397]     Test net output #0: accuracy = 0.576593
I0409 21:18:23.304788 14973 solver.cpp:397]     Test net output #1: loss = 2.26313 (* 1 = 2.26313 loss)
I0409 21:18:25.117663 14973 solver.cpp:218] Iteration 9900 (0.643007 iter/s, 18.6623s/12 iters), loss = 0.209702
I0409 21:18:25.117717 14973 solver.cpp:237]     Train net output #0: loss = 0.209702 (* 1 = 0.209702 loss)
I0409 21:18:25.117727 14973 sgd_solver.cpp:105] Iteration 9900, lr = 0.00140711
I0409 21:18:30.600827 14973 solver.cpp:218] Iteration 9912 (2.18861 iter/s, 5.48293s/12 iters), loss = 0.1365
I0409 21:18:30.600883 14973 solver.cpp:237]     Train net output #0: loss = 0.1365 (* 1 = 0.1365 loss)
I0409 21:18:30.600895 14973 sgd_solver.cpp:105] Iteration 9912, lr = 0.00140377
I0409 21:18:30.723515 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:18:35.767571 14973 solver.cpp:218] Iteration 9924 (2.32265 iter/s, 5.16652s/12 iters), loss = 0.191591
I0409 21:18:35.767702 14973 solver.cpp:237]     Train net output #0: loss = 0.191591 (* 1 = 0.191591 loss)
I0409 21:18:35.767711 14973 sgd_solver.cpp:105] Iteration 9924, lr = 0.00140043
I0409 21:18:40.756055 14973 solver.cpp:218] Iteration 9936 (2.40568 iter/s, 4.98819s/12 iters), loss = 0.126748
I0409 21:18:40.756114 14973 solver.cpp:237]     Train net output #0: loss = 0.126748 (* 1 = 0.126748 loss)
I0409 21:18:40.756127 14973 sgd_solver.cpp:105] Iteration 9936, lr = 0.00139711
I0409 21:18:45.800105 14973 solver.cpp:218] Iteration 9948 (2.37914 iter/s, 5.04383s/12 iters), loss = 0.194686
I0409 21:18:45.800155 14973 solver.cpp:237]     Train net output #0: loss = 0.194686 (* 1 = 0.194686 loss)
I0409 21:18:45.800165 14973 sgd_solver.cpp:105] Iteration 9948, lr = 0.00139379
I0409 21:18:50.909664 14973 solver.cpp:218] Iteration 9960 (2.34864 iter/s, 5.10935s/12 iters), loss = 0.190632
I0409 21:18:50.909708 14973 solver.cpp:237]     Train net output #0: loss = 0.190632 (* 1 = 0.190632 loss)
I0409 21:18:50.909718 14973 sgd_solver.cpp:105] Iteration 9960, lr = 0.00139048
I0409 21:18:55.906911 14973 solver.cpp:218] Iteration 9972 (2.40142 iter/s, 4.99704s/12 iters), loss = 0.0794514
I0409 21:18:55.906965 14973 solver.cpp:237]     Train net output #0: loss = 0.0794514 (* 1 = 0.0794514 loss)
I0409 21:18:55.906975 14973 sgd_solver.cpp:105] Iteration 9972, lr = 0.00138718
I0409 21:19:00.887099 14973 solver.cpp:218] Iteration 9984 (2.40965 iter/s, 4.97998s/12 iters), loss = 0.220457
I0409 21:19:00.887132 14973 solver.cpp:237]     Train net output #0: loss = 0.220457 (* 1 = 0.220457 loss)
I0409 21:19:00.887140 14973 sgd_solver.cpp:105] Iteration 9984, lr = 0.00138389
I0409 21:19:05.437219 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9996.caffemodel
I0409 21:19:12.243173 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9996.solverstate
I0409 21:19:16.142457 14973 solver.cpp:330] Iteration 9996, Testing net (#0)
I0409 21:19:16.142489 14973 net.cpp:676] Ignoring source layer train-data
I0409 21:19:16.663563 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:19:20.602027 14973 solver.cpp:397]     Test net output #0: accuracy = 0.577206
I0409 21:19:20.602073 14973 solver.cpp:397]     Test net output #1: loss = 2.36838 (* 1 = 2.36838 loss)
I0409 21:19:20.687391 14973 solver.cpp:218] Iteration 9996 (0.606071 iter/s, 19.7997s/12 iters), loss = 0.210054
I0409 21:19:20.687435 14973 solver.cpp:237]     Train net output #0: loss = 0.210054 (* 1 = 0.210054 loss)
I0409 21:19:20.687445 14973 sgd_solver.cpp:105] Iteration 9996, lr = 0.0013806
I0409 21:19:25.012729 14973 solver.cpp:218] Iteration 10008 (2.77447 iter/s, 4.32516s/12 iters), loss = 0.162366
I0409 21:19:25.012776 14973 solver.cpp:237]     Train net output #0: loss = 0.162366 (* 1 = 0.162366 loss)
I0409 21:19:25.012787 14973 sgd_solver.cpp:105] Iteration 10008, lr = 0.00137732
I0409 21:19:27.257155 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:19:30.021397 14973 solver.cpp:218] Iteration 10020 (2.39595 iter/s, 5.00846s/12 iters), loss = 0.13617
I0409 21:19:30.021451 14973 solver.cpp:237]     Train net output #0: loss = 0.13617 (* 1 = 0.13617 loss)
I0409 21:19:30.021463 14973 sgd_solver.cpp:105] Iteration 10020, lr = 0.00137405
I0409 21:19:35.090260 14973 solver.cpp:218] Iteration 10032 (2.3675 iter/s, 5.06865s/12 iters), loss = 0.183136
I0409 21:19:35.090303 14973 solver.cpp:237]     Train net output #0: loss = 0.183136 (* 1 = 0.183136 loss)
I0409 21:19:35.090313 14973 sgd_solver.cpp:105] Iteration 10032, lr = 0.00137079
I0409 21:19:40.110858 14973 solver.cpp:218] Iteration 10044 (2.39026 iter/s, 5.02038s/12 iters), loss = 0.153457
I0409 21:19:40.110924 14973 solver.cpp:237]     Train net output #0: loss = 0.153457 (* 1 = 0.153457 loss)
I0409 21:19:40.110934 14973 sgd_solver.cpp:105] Iteration 10044, lr = 0.00136754
I0409 21:19:45.030642 14973 solver.cpp:218] Iteration 10056 (2.43924 iter/s, 4.91956s/12 iters), loss = 0.213474
I0409 21:19:45.030807 14973 solver.cpp:237]     Train net output #0: loss = 0.213474 (* 1 = 0.213474 loss)
I0409 21:19:45.030822 14973 sgd_solver.cpp:105] Iteration 10056, lr = 0.00136429
I0409 21:19:50.004530 14973 solver.cpp:218] Iteration 10068 (2.41276 iter/s, 4.97357s/12 iters), loss = 0.0993008
I0409 21:19:50.004581 14973 solver.cpp:237]     Train net output #0: loss = 0.0993009 (* 1 = 0.0993009 loss)
I0409 21:19:50.004590 14973 sgd_solver.cpp:105] Iteration 10068, lr = 0.00136105
I0409 21:19:55.198273 14973 solver.cpp:218] Iteration 10080 (2.31057 iter/s, 5.19352s/12 iters), loss = 0.165423
I0409 21:19:55.198320 14973 solver.cpp:237]     Train net output #0: loss = 0.165423 (* 1 = 0.165423 loss)
I0409 21:19:55.198329 14973 sgd_solver.cpp:105] Iteration 10080, lr = 0.00135782
I0409 21:20:00.152205 14973 solver.cpp:218] Iteration 10092 (2.42242 iter/s, 4.95373s/12 iters), loss = 0.105674
I0409 21:20:00.152248 14973 solver.cpp:237]     Train net output #0: loss = 0.105674 (* 1 = 0.105674 loss)
I0409 21:20:00.152257 14973 sgd_solver.cpp:105] Iteration 10092, lr = 0.0013546
I0409 21:20:02.185003 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10098.caffemodel
I0409 21:20:05.430665 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10098.solverstate
I0409 21:20:08.317332 14973 solver.cpp:330] Iteration 10098, Testing net (#0)
I0409 21:20:08.317358 14973 net.cpp:676] Ignoring source layer train-data
I0409 21:20:08.721644 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:20:12.816856 14973 solver.cpp:397]     Test net output #0: accuracy = 0.572304
I0409 21:20:12.816905 14973 solver.cpp:397]     Test net output #1: loss = 2.37648 (* 1 = 2.37648 loss)
I0409 21:20:14.717856 14973 solver.cpp:218] Iteration 10104 (0.823884 iter/s, 14.5652s/12 iters), loss = 0.0670836
I0409 21:20:14.717911 14973 solver.cpp:237]     Train net output #0: loss = 0.0670836 (* 1 = 0.0670836 loss)
I0409 21:20:14.717923 14973 sgd_solver.cpp:105] Iteration 10104, lr = 0.00135138
I0409 21:20:19.055954 14977 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:20:19.678221 14973 solver.cpp:218] Iteration 10116 (2.41928 iter/s, 4.96015s/12 iters), loss = 0.133631
I0409 21:20:19.678274 14973 solver.cpp:237]     Train net output #0: loss = 0.133631 (* 1 = 0.133631 loss)
I0409 21:20:19.678287 14973 sgd_solver.cpp:105] Iteration 10116, lr = 0.00134817
I0409 21:20:24.714727 14973 solver.cpp:218] Iteration 10128 (2.38271 iter/s, 5.03629s/12 iters), loss = 0.2093
I0409 21:20:24.714774 14973 solver.cpp:237]     Train net output #0: loss = 0.2093 (* 1 = 0.2093 loss)
I0409 21:20:24.714783 14973 sgd_solver.cpp:105] Iteration 10128, lr = 0.00134497
I0409 21:20:29.690042 14973 solver.cpp:218] Iteration 10140 (2.41201 iter/s, 4.97511s/12 iters), loss = 0.167565
I0409 21:20:29.690089 14973 solver.cpp:237]     Train net output #0: loss = 0.167565 (* 1 = 0.167565 loss)
I0409 21:20:29.690099 14973 sgd_solver.cpp:105] Iteration 10140, lr = 0.00134178
I0409 21:20:34.921309 14973 solver.cpp:218] Iteration 10152 (2.29399 iter/s, 5.23105s/12 iters), loss = 0.0540824
I0409 21:20:34.921355 14973 solver.cpp:237]     Train net output #0: loss = 0.0540825 (* 1 = 0.0540825 loss)
I0409 21:20:34.921365 14973 sgd_solver.cpp:105] Iteration 10152, lr = 0.00133859
I0409 21:20:39.947266 14973 solver.cpp:218] Iteration 10164 (2.3877 iter/s, 5.02577s/12 iters), loss = 0.244093
I0409 21:20:39.947307 14973 solver.cpp:237]     Train net output #0: loss = 0.244093 (* 1 = 0.244093 loss)
I0409 21:20:39.947319 14973 sgd_solver.cpp:105] Iteration 10164, lr = 0.00133541
I0409 21:20:44.984776 14973 solver.cpp:218] Iteration 10176 (2.38221 iter/s, 5.03734s/12 iters), loss = 0.199517
I0409 21:20:44.984815 14973 solver.cpp:237]     Train net output #0: loss = 0.199517 (* 1 = 0.199517 loss)
I0409 21:20:44.984824 14973 sgd_solver.cpp:105] Iteration 10176, lr = 0.00133224
I0409 21:20:49.973946 14973 solver.cpp:218] Iteration 10188 (2.4053 iter/s, 4.98899s/12 iters), loss = 0.173105
I0409 21:20:49.974097 14973 solver.cpp:237]     Train net output #0: loss = 0.173105 (* 1 = 0.173105 loss)
I0409 21:20:49.974109 14973 sgd_solver.cpp:105] Iteration 10188, lr = 0.00132908
I0409 21:20:54.516166 14973 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10200.caffemodel
I0409 21:20:56.072039 14973 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10200.solverstate
I0409 21:20:57.315937 14973 solver.cpp:310] Iteration 10200, loss = 0.18823
I0409 21:20:57.315973 14973 solver.cpp:330] Iteration 10200, Testing net (#0)
I0409 21:20:57.315980 14973 net.cpp:676] Ignoring source layer train-data
I0409 21:20:57.713214 14985 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:21:01.822232 14973 solver.cpp:397]     Test net output #0: accuracy = 0.582108
I0409 21:21:01.822278 14973 solver.cpp:397]     Test net output #1: loss = 2.37954 (* 1 = 2.37954 loss)
I0409 21:21:01.822289 14973 solver.cpp:315] Optimization Done.
I0409 21:21:01.822297 14973 caffe.cpp:259] Optimization Done.