DIGITS-CNN/cars/architecture-investigations/fc/3-layers/4096/caffe_output.log

I0409 19:55:34.022364 14789 upgrade_proto.cpp:1082] Attempting to upgrade input file specified using deprecated 'solver_type' field (enum)': /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210409-195532-1b40/solver.prototxt
I0409 19:55:34.022600 14789 upgrade_proto.cpp:1089] Successfully upgraded file specified using deprecated 'solver_type' field (enum) to 'type' field (string).
W0409 19:55:34.022609 14789 upgrade_proto.cpp:1091] Note that future Caffe releases will only support 'type' field (string) for a solver's type.
I0409 19:55:34.022713 14789 caffe.cpp:218] Using GPUs 0
I0409 19:55:34.051936 14789 caffe.cpp:223] GPU 0: GeForce GTX 1080 Ti
I0409 19:55:34.341562 14789 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: 0
net: "train_val.prototxt"
train_state {
level: 0
stage: ""
}
type: "SGD"
I0409 19:55:34.342264 14789 solver.cpp:87] Creating training net from net file: train_val.prototxt
I0409 19:55:34.342895 14789 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer val-data
I0409 19:55:34.342912 14789 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy
I0409 19:55:34.343075 14789 net.cpp:51] Initializing net from parameters:
state {
phase: TRAIN
level: 0
stage: ""
}
layer {
name: "train-data"
type: "Data"
top: "data"
top: "label"
include {
phase: TRAIN
}
transform_param {
mirror: true
crop_size: 227
mean_file: "/mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto"
}
data_param {
source: "/mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/train_db"
batch_size: 128
backend: LMDB
}
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 96
kernel_size: 11
stride: 4
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "conv1"
top: "conv1"
}
layer {
name: "norm1"
type: "LRN"
bottom: "conv1"
top: "norm1"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool1"
type: "Pooling"
bottom: "norm1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 2
kernel_size: 5
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu2"
type: "ReLU"
bottom: "conv2"
top: "conv2"
}
layer {
name: "norm2"
type: "LRN"
bottom: "conv2"
top: "norm2"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool2"
type: "Pooling"
bottom: "norm2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv3"
type: "Convolution"
bottom: "pool2"
top: "conv3"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu3"
type: "ReLU"
bottom: "conv3"
top: "conv3"
}
layer {
name: "conv4"
type: "Convolution"
bottom: "conv3"
top: "conv4"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu4"
type: "ReLU"
bottom: "conv4"
top: "conv4"
}
layer {
name: "conv5"
type: "Convolution"
bottom: "conv4"
top: "conv5"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu5"
type: "ReLU"
bottom: "conv5"
top: "conv5"
}
layer {
name: "pool5"
type: "Pooling"
bottom: "conv5"
top: "pool5"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "fc6"
type: "InnerProduct"
bottom: "pool5"
top: "fc6"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 4096
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu6"
type: "ReLU"
bottom: "fc6"
top: "fc6"
}
layer {
name: "drop6"
type: "Dropout"
bottom: "fc6"
top: "fc6"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc7"
type: "InnerProduct"
bottom: "fc6"
top: "fc7"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 4096
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu7"
type: "ReLU"
bottom: "fc7"
top: "fc7"
}
layer {
name: "drop7"
type: "Dropout"
bottom: "fc7"
top: "fc7"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "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: 4096
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 19:55:34.343175 14789 layer_factory.hpp:77] Creating layer train-data
I0409 19:55:34.402631 14789 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/train_db
I0409 19:55:34.402873 14789 net.cpp:84] Creating Layer train-data
I0409 19:55:34.402904 14789 net.cpp:380] train-data -> data
I0409 19:55:34.402945 14789 net.cpp:380] train-data -> label
I0409 19:55:34.402968 14789 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto
I0409 19:55:34.413990 14789 data_layer.cpp:45] output data size: 128,3,227,227
I0409 19:55:34.604995 14789 net.cpp:122] Setting up train-data
I0409 19:55:34.605020 14789 net.cpp:129] Top shape: 128 3 227 227 (19787136)
I0409 19:55:34.605026 14789 net.cpp:129] Top shape: 128 (128)
I0409 19:55:34.605031 14789 net.cpp:137] Memory required for data: 79149056
I0409 19:55:34.605041 14789 layer_factory.hpp:77] Creating layer conv1
I0409 19:55:34.605067 14789 net.cpp:84] Creating Layer conv1
I0409 19:55:34.605074 14789 net.cpp:406] conv1 <- data
I0409 19:55:34.605088 14789 net.cpp:380] conv1 -> conv1
I0409 19:55:35.195547 14789 net.cpp:122] Setting up conv1
I0409 19:55:35.195574 14789 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0409 19:55:35.195578 14789 net.cpp:137] Memory required for data: 227833856
I0409 19:55:35.195601 14789 layer_factory.hpp:77] Creating layer relu1
I0409 19:55:35.195636 14789 net.cpp:84] Creating Layer relu1
I0409 19:55:35.195641 14789 net.cpp:406] relu1 <- conv1
I0409 19:55:35.195647 14789 net.cpp:367] relu1 -> conv1 (in-place)
I0409 19:55:35.195973 14789 net.cpp:122] Setting up relu1
I0409 19:55:35.195983 14789 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0409 19:55:35.195987 14789 net.cpp:137] Memory required for data: 376518656
I0409 19:55:35.195991 14789 layer_factory.hpp:77] Creating layer norm1
I0409 19:55:35.196002 14789 net.cpp:84] Creating Layer norm1
I0409 19:55:35.196007 14789 net.cpp:406] norm1 <- conv1
I0409 19:55:35.196012 14789 net.cpp:380] norm1 -> norm1
I0409 19:55:35.196507 14789 net.cpp:122] Setting up norm1
I0409 19:55:35.196518 14789 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0409 19:55:35.196522 14789 net.cpp:137] Memory required for data: 525203456
I0409 19:55:35.196527 14789 layer_factory.hpp:77] Creating layer pool1
I0409 19:55:35.196537 14789 net.cpp:84] Creating Layer pool1
I0409 19:55:35.196540 14789 net.cpp:406] pool1 <- norm1
I0409 19:55:35.196547 14789 net.cpp:380] pool1 -> pool1
I0409 19:55:35.196588 14789 net.cpp:122] Setting up pool1
I0409 19:55:35.196594 14789 net.cpp:129] Top shape: 128 96 27 27 (8957952)
I0409 19:55:35.196599 14789 net.cpp:137] Memory required for data: 561035264
I0409 19:55:35.196602 14789 layer_factory.hpp:77] Creating layer conv2
I0409 19:55:35.196614 14789 net.cpp:84] Creating Layer conv2
I0409 19:55:35.196617 14789 net.cpp:406] conv2 <- pool1
I0409 19:55:35.196624 14789 net.cpp:380] conv2 -> conv2
I0409 19:55:35.204030 14789 net.cpp:122] Setting up conv2
I0409 19:55:35.204044 14789 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0409 19:55:35.204048 14789 net.cpp:137] Memory required for data: 656586752
I0409 19:55:35.204058 14789 layer_factory.hpp:77] Creating layer relu2
I0409 19:55:35.204066 14789 net.cpp:84] Creating Layer relu2
I0409 19:55:35.204071 14789 net.cpp:406] relu2 <- conv2
I0409 19:55:35.204075 14789 net.cpp:367] relu2 -> conv2 (in-place)
I0409 19:55:35.204551 14789 net.cpp:122] Setting up relu2
I0409 19:55:35.204562 14789 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0409 19:55:35.204566 14789 net.cpp:137] Memory required for data: 752138240
I0409 19:55:35.204571 14789 layer_factory.hpp:77] Creating layer norm2
I0409 19:55:35.204578 14789 net.cpp:84] Creating Layer norm2
I0409 19:55:35.204582 14789 net.cpp:406] norm2 <- conv2
I0409 19:55:35.204588 14789 net.cpp:380] norm2 -> norm2
I0409 19:55:35.204908 14789 net.cpp:122] Setting up norm2
I0409 19:55:35.204917 14789 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0409 19:55:35.204921 14789 net.cpp:137] Memory required for data: 847689728
I0409 19:55:35.204926 14789 layer_factory.hpp:77] Creating layer pool2
I0409 19:55:35.204933 14789 net.cpp:84] Creating Layer pool2
I0409 19:55:35.204937 14789 net.cpp:406] pool2 <- norm2
I0409 19:55:35.204943 14789 net.cpp:380] pool2 -> pool2
I0409 19:55:35.204973 14789 net.cpp:122] Setting up pool2
I0409 19:55:35.204979 14789 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0409 19:55:35.204983 14789 net.cpp:137] Memory required for data: 869840896
I0409 19:55:35.204986 14789 layer_factory.hpp:77] Creating layer conv3
I0409 19:55:35.204995 14789 net.cpp:84] Creating Layer conv3
I0409 19:55:35.204999 14789 net.cpp:406] conv3 <- pool2
I0409 19:55:35.205004 14789 net.cpp:380] conv3 -> conv3
I0409 19:55:35.215792 14789 net.cpp:122] Setting up conv3
I0409 19:55:35.215804 14789 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0409 19:55:35.215808 14789 net.cpp:137] Memory required for data: 903067648
I0409 19:55:35.215818 14789 layer_factory.hpp:77] Creating layer relu3
I0409 19:55:35.215826 14789 net.cpp:84] Creating Layer relu3
I0409 19:55:35.215829 14789 net.cpp:406] relu3 <- conv3
I0409 19:55:35.215835 14789 net.cpp:367] relu3 -> conv3 (in-place)
I0409 19:55:35.216308 14789 net.cpp:122] Setting up relu3
I0409 19:55:35.216320 14789 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0409 19:55:35.216323 14789 net.cpp:137] Memory required for data: 936294400
I0409 19:55:35.216328 14789 layer_factory.hpp:77] Creating layer conv4
I0409 19:55:35.216356 14789 net.cpp:84] Creating Layer conv4
I0409 19:55:35.216361 14789 net.cpp:406] conv4 <- conv3
I0409 19:55:35.216367 14789 net.cpp:380] conv4 -> conv4
I0409 19:55:35.227380 14789 net.cpp:122] Setting up conv4
I0409 19:55:35.227392 14789 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0409 19:55:35.227396 14789 net.cpp:137] Memory required for data: 969521152
I0409 19:55:35.227404 14789 layer_factory.hpp:77] Creating layer relu4
I0409 19:55:35.227411 14789 net.cpp:84] Creating Layer relu4
I0409 19:55:35.227416 14789 net.cpp:406] relu4 <- conv4
I0409 19:55:35.227421 14789 net.cpp:367] relu4 -> conv4 (in-place)
I0409 19:55:35.227728 14789 net.cpp:122] Setting up relu4
I0409 19:55:35.227737 14789 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0409 19:55:35.227741 14789 net.cpp:137] Memory required for data: 1002747904
I0409 19:55:35.227746 14789 layer_factory.hpp:77] Creating layer conv5
I0409 19:55:35.227756 14789 net.cpp:84] Creating Layer conv5
I0409 19:55:35.227759 14789 net.cpp:406] conv5 <- conv4
I0409 19:55:35.227766 14789 net.cpp:380] conv5 -> conv5
I0409 19:55:35.236719 14789 net.cpp:122] Setting up conv5
I0409 19:55:35.236732 14789 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0409 19:55:35.236737 14789 net.cpp:137] Memory required for data: 1024899072
I0409 19:55:35.236748 14789 layer_factory.hpp:77] Creating layer relu5
I0409 19:55:35.236755 14789 net.cpp:84] Creating Layer relu5
I0409 19:55:35.236759 14789 net.cpp:406] relu5 <- conv5
I0409 19:55:35.236765 14789 net.cpp:367] relu5 -> conv5 (in-place)
I0409 19:55:35.237311 14789 net.cpp:122] Setting up relu5
I0409 19:55:35.237323 14789 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0409 19:55:35.237326 14789 net.cpp:137] Memory required for data: 1047050240
I0409 19:55:35.237330 14789 layer_factory.hpp:77] Creating layer pool5
I0409 19:55:35.237337 14789 net.cpp:84] Creating Layer pool5
I0409 19:55:35.237342 14789 net.cpp:406] pool5 <- conv5
I0409 19:55:35.237350 14789 net.cpp:380] pool5 -> pool5
I0409 19:55:35.237391 14789 net.cpp:122] Setting up pool5
I0409 19:55:35.237397 14789 net.cpp:129] Top shape: 128 256 6 6 (1179648)
I0409 19:55:35.237401 14789 net.cpp:137] Memory required for data: 1051768832
I0409 19:55:35.237404 14789 layer_factory.hpp:77] Creating layer fc6
I0409 19:55:35.237417 14789 net.cpp:84] Creating Layer fc6
I0409 19:55:35.237421 14789 net.cpp:406] fc6 <- pool5
I0409 19:55:35.237427 14789 net.cpp:380] fc6 -> fc6
I0409 19:55:35.605899 14789 net.cpp:122] Setting up fc6
I0409 19:55:35.605921 14789 net.cpp:129] Top shape: 128 4096 (524288)
I0409 19:55:35.605926 14789 net.cpp:137] Memory required for data: 1053865984
I0409 19:55:35.605934 14789 layer_factory.hpp:77] Creating layer relu6
I0409 19:55:35.605944 14789 net.cpp:84] Creating Layer relu6
I0409 19:55:35.605949 14789 net.cpp:406] relu6 <- fc6
I0409 19:55:35.605962 14789 net.cpp:367] relu6 -> fc6 (in-place)
I0409 19:55:35.606606 14789 net.cpp:122] Setting up relu6
I0409 19:55:35.606617 14789 net.cpp:129] Top shape: 128 4096 (524288)
I0409 19:55:35.606621 14789 net.cpp:137] Memory required for data: 1055963136
I0409 19:55:35.606624 14789 layer_factory.hpp:77] Creating layer drop6
I0409 19:55:35.606631 14789 net.cpp:84] Creating Layer drop6
I0409 19:55:35.606634 14789 net.cpp:406] drop6 <- fc6
I0409 19:55:35.606639 14789 net.cpp:367] drop6 -> fc6 (in-place)
I0409 19:55:35.606667 14789 net.cpp:122] Setting up drop6
I0409 19:55:35.606673 14789 net.cpp:129] Top shape: 128 4096 (524288)
I0409 19:55:35.606676 14789 net.cpp:137] Memory required for data: 1058060288
I0409 19:55:35.606679 14789 layer_factory.hpp:77] Creating layer fc7
I0409 19:55:35.606688 14789 net.cpp:84] Creating Layer fc7
I0409 19:55:35.606691 14789 net.cpp:406] fc7 <- fc6
I0409 19:55:35.606696 14789 net.cpp:380] fc7 -> fc7
I0409 19:55:35.763430 14789 net.cpp:122] Setting up fc7
I0409 19:55:35.763450 14789 net.cpp:129] Top shape: 128 4096 (524288)
I0409 19:55:35.763454 14789 net.cpp:137] Memory required for data: 1060157440
I0409 19:55:35.763463 14789 layer_factory.hpp:77] Creating layer relu7
I0409 19:55:35.763490 14789 net.cpp:84] Creating Layer relu7
I0409 19:55:35.763494 14789 net.cpp:406] relu7 <- fc7
I0409 19:55:35.763501 14789 net.cpp:367] relu7 -> fc7 (in-place)
I0409 19:55:35.764115 14789 net.cpp:122] Setting up relu7
I0409 19:55:35.764124 14789 net.cpp:129] Top shape: 128 4096 (524288)
I0409 19:55:35.764128 14789 net.cpp:137] Memory required for data: 1062254592
I0409 19:55:35.764132 14789 layer_factory.hpp:77] Creating layer drop7
I0409 19:55:35.764138 14789 net.cpp:84] Creating Layer drop7
I0409 19:55:35.764142 14789 net.cpp:406] drop7 <- fc7
I0409 19:55:35.764148 14789 net.cpp:367] drop7 -> fc7 (in-place)
I0409 19:55:35.764171 14789 net.cpp:122] Setting up drop7
I0409 19:55:35.764178 14789 net.cpp:129] Top shape: 128 4096 (524288)
I0409 19:55:35.764180 14789 net.cpp:137] Memory required for data: 1064351744
I0409 19:55:35.764183 14789 layer_factory.hpp:77] Creating layer fc7.5
I0409 19:55:35.764191 14789 net.cpp:84] Creating Layer fc7.5
I0409 19:55:35.764194 14789 net.cpp:406] fc7.5 <- fc7
I0409 19:55:35.764200 14789 net.cpp:380] fc7.5 -> fc7.5
I0409 19:55:35.920796 14789 net.cpp:122] Setting up fc7.5
I0409 19:55:35.920815 14789 net.cpp:129] Top shape: 128 4096 (524288)
I0409 19:55:35.920820 14789 net.cpp:137] Memory required for data: 1066448896
I0409 19:55:35.920830 14789 layer_factory.hpp:77] Creating layer relu7.5
I0409 19:55:35.920838 14789 net.cpp:84] Creating Layer relu7.5
I0409 19:55:35.920843 14789 net.cpp:406] relu7.5 <- fc7.5
I0409 19:55:35.920850 14789 net.cpp:367] relu7.5 -> fc7.5 (in-place)
I0409 19:55:35.921483 14789 net.cpp:122] Setting up relu7.5
I0409 19:55:35.921491 14789 net.cpp:129] Top shape: 128 4096 (524288)
I0409 19:55:35.921495 14789 net.cpp:137] Memory required for data: 1068546048
I0409 19:55:35.921499 14789 layer_factory.hpp:77] Creating layer drop7.5
I0409 19:55:35.921505 14789 net.cpp:84] Creating Layer drop7.5
I0409 19:55:35.921509 14789 net.cpp:406] drop7.5 <- fc7.5
I0409 19:55:35.921515 14789 net.cpp:367] drop7.5 -> fc7.5 (in-place)
I0409 19:55:35.921538 14789 net.cpp:122] Setting up drop7.5
I0409 19:55:35.921545 14789 net.cpp:129] Top shape: 128 4096 (524288)
I0409 19:55:35.921548 14789 net.cpp:137] Memory required for data: 1070643200
I0409 19:55:35.921552 14789 layer_factory.hpp:77] Creating layer fc8
I0409 19:55:35.921558 14789 net.cpp:84] Creating Layer fc8
I0409 19:55:35.921562 14789 net.cpp:406] fc8 <- fc7.5
I0409 19:55:35.921568 14789 net.cpp:380] fc8 -> fc8
I0409 19:55:35.929358 14789 net.cpp:122] Setting up fc8
I0409 19:55:35.929368 14789 net.cpp:129] Top shape: 128 196 (25088)
I0409 19:55:35.929371 14789 net.cpp:137] Memory required for data: 1070743552
I0409 19:55:35.929383 14789 layer_factory.hpp:77] Creating layer loss
I0409 19:55:35.929390 14789 net.cpp:84] Creating Layer loss
I0409 19:55:35.929394 14789 net.cpp:406] loss <- fc8
I0409 19:55:35.929399 14789 net.cpp:406] loss <- label
I0409 19:55:35.929407 14789 net.cpp:380] loss -> loss
I0409 19:55:35.929417 14789 layer_factory.hpp:77] Creating layer loss
I0409 19:55:35.930022 14789 net.cpp:122] Setting up loss
I0409 19:55:35.930032 14789 net.cpp:129] Top shape: (1)
I0409 19:55:35.930034 14789 net.cpp:132]     with loss weight 1
I0409 19:55:35.930050 14789 net.cpp:137] Memory required for data: 1070743556
I0409 19:55:35.930054 14789 net.cpp:198] loss needs backward computation.
I0409 19:55:35.930061 14789 net.cpp:198] fc8 needs backward computation.
I0409 19:55:35.930065 14789 net.cpp:198] drop7.5 needs backward computation.
I0409 19:55:35.930068 14789 net.cpp:198] relu7.5 needs backward computation.
I0409 19:55:35.930071 14789 net.cpp:198] fc7.5 needs backward computation.
I0409 19:55:35.930075 14789 net.cpp:198] drop7 needs backward computation.
I0409 19:55:35.930078 14789 net.cpp:198] relu7 needs backward computation.
I0409 19:55:35.930083 14789 net.cpp:198] fc7 needs backward computation.
I0409 19:55:35.930085 14789 net.cpp:198] drop6 needs backward computation.
I0409 19:55:35.930089 14789 net.cpp:198] relu6 needs backward computation.
I0409 19:55:35.930092 14789 net.cpp:198] fc6 needs backward computation.
I0409 19:55:35.930112 14789 net.cpp:198] pool5 needs backward computation.
I0409 19:55:35.930116 14789 net.cpp:198] relu5 needs backward computation.
I0409 19:55:35.930119 14789 net.cpp:198] conv5 needs backward computation.
I0409 19:55:35.930124 14789 net.cpp:198] relu4 needs backward computation.
I0409 19:55:35.930126 14789 net.cpp:198] conv4 needs backward computation.
I0409 19:55:35.930130 14789 net.cpp:198] relu3 needs backward computation.
I0409 19:55:35.930135 14789 net.cpp:198] conv3 needs backward computation.
I0409 19:55:35.930138 14789 net.cpp:198] pool2 needs backward computation.
I0409 19:55:35.930141 14789 net.cpp:198] norm2 needs backward computation.
I0409 19:55:35.930145 14789 net.cpp:198] relu2 needs backward computation.
I0409 19:55:35.930148 14789 net.cpp:198] conv2 needs backward computation.
I0409 19:55:35.930152 14789 net.cpp:198] pool1 needs backward computation.
I0409 19:55:35.930156 14789 net.cpp:198] norm1 needs backward computation.
I0409 19:55:35.930160 14789 net.cpp:198] relu1 needs backward computation.
I0409 19:55:35.930163 14789 net.cpp:198] conv1 needs backward computation.
I0409 19:55:35.930167 14789 net.cpp:200] train-data does not need backward computation.
I0409 19:55:35.930171 14789 net.cpp:242] This network produces output loss
I0409 19:55:35.930186 14789 net.cpp:255] Network initialization done.
I0409 19:55:35.930718 14789 solver.cpp:172] Creating test net (#0) specified by net file: train_val.prototxt
I0409 19:55:35.930750 14789 net.cpp:294] The NetState phase (1) differed from the phase (0) specified by a rule in layer train-data
I0409 19:55:35.930900 14789 net.cpp:51] Initializing net from parameters:
state {
phase: TEST
}
layer {
name: "val-data"
type: "Data"
top: "data"
top: "label"
include {
phase: TEST
}
transform_param {
crop_size: 227
mean_file: "/mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto"
}
data_param {
source: "/mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/val_db"
batch_size: 32
backend: LMDB
}
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 96
kernel_size: 11
stride: 4
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "conv1"
top: "conv1"
}
layer {
name: "norm1"
type: "LRN"
bottom: "conv1"
top: "norm1"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool1"
type: "Pooling"
bottom: "norm1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 2
kernel_size: 5
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu2"
type: "ReLU"
bottom: "conv2"
top: "conv2"
}
layer {
name: "norm2"
type: "LRN"
bottom: "conv2"
top: "norm2"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool2"
type: "Pooling"
bottom: "norm2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv3"
type: "Convolution"
bottom: "pool2"
top: "conv3"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu3"
type: "ReLU"
bottom: "conv3"
top: "conv3"
}
layer {
name: "conv4"
type: "Convolution"
bottom: "conv3"
top: "conv4"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu4"
type: "ReLU"
bottom: "conv4"
top: "conv4"
}
layer {
name: "conv5"
type: "Convolution"
bottom: "conv4"
top: "conv5"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu5"
type: "ReLU"
bottom: "conv5"
top: "conv5"
}
layer {
name: "pool5"
type: "Pooling"
bottom: "conv5"
top: "pool5"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "fc6"
type: "InnerProduct"
bottom: "pool5"
top: "fc6"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 4096
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu6"
type: "ReLU"
bottom: "fc6"
top: "fc6"
}
layer {
name: "drop6"
type: "Dropout"
bottom: "fc6"
top: "fc6"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc7"
type: "InnerProduct"
bottom: "fc6"
top: "fc7"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 4096
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu7"
type: "ReLU"
bottom: "fc7"
top: "fc7"
}
layer {
name: "drop7"
type: "Dropout"
bottom: "fc7"
top: "fc7"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "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: 4096
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 19:55:35.930996 14789 layer_factory.hpp:77] Creating layer val-data
I0409 19:55:35.944375 14789 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/val_db
I0409 19:55:35.944551 14789 net.cpp:84] Creating Layer val-data
I0409 19:55:35.944561 14789 net.cpp:380] val-data -> data
I0409 19:55:35.944569 14789 net.cpp:380] val-data -> label
I0409 19:55:35.944576 14789 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto
I0409 19:55:35.948438 14789 data_layer.cpp:45] output data size: 32,3,227,227
I0409 19:55:35.978394 14789 net.cpp:122] Setting up val-data
I0409 19:55:35.978413 14789 net.cpp:129] Top shape: 32 3 227 227 (4946784)
I0409 19:55:35.978418 14789 net.cpp:129] Top shape: 32 (32)
I0409 19:55:35.978421 14789 net.cpp:137] Memory required for data: 19787264
I0409 19:55:35.978444 14789 layer_factory.hpp:77] Creating layer label_val-data_1_split
I0409 19:55:35.978456 14789 net.cpp:84] Creating Layer label_val-data_1_split
I0409 19:55:35.978461 14789 net.cpp:406] label_val-data_1_split <- label
I0409 19:55:35.978468 14789 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_0
I0409 19:55:35.978477 14789 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_1
I0409 19:55:35.978569 14789 net.cpp:122] Setting up label_val-data_1_split
I0409 19:55:35.978575 14789 net.cpp:129] Top shape: 32 (32)
I0409 19:55:35.978579 14789 net.cpp:129] Top shape: 32 (32)
I0409 19:55:35.978582 14789 net.cpp:137] Memory required for data: 19787520
I0409 19:55:35.978585 14789 layer_factory.hpp:77] Creating layer conv1
I0409 19:55:35.978597 14789 net.cpp:84] Creating Layer conv1
I0409 19:55:35.978601 14789 net.cpp:406] conv1 <- data
I0409 19:55:35.978606 14789 net.cpp:380] conv1 -> conv1
I0409 19:55:35.980556 14789 net.cpp:122] Setting up conv1
I0409 19:55:35.980567 14789 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0409 19:55:35.980571 14789 net.cpp:137] Memory required for data: 56958720
I0409 19:55:35.980581 14789 layer_factory.hpp:77] Creating layer relu1
I0409 19:55:35.980587 14789 net.cpp:84] Creating Layer relu1
I0409 19:55:35.980592 14789 net.cpp:406] relu1 <- conv1
I0409 19:55:35.980597 14789 net.cpp:367] relu1 -> conv1 (in-place)
I0409 19:55:35.981037 14789 net.cpp:122] Setting up relu1
I0409 19:55:35.981047 14789 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0409 19:55:35.981050 14789 net.cpp:137] Memory required for data: 94129920
I0409 19:55:35.981055 14789 layer_factory.hpp:77] Creating layer norm1
I0409 19:55:35.981062 14789 net.cpp:84] Creating Layer norm1
I0409 19:55:35.981066 14789 net.cpp:406] norm1 <- conv1
I0409 19:55:35.981072 14789 net.cpp:380] norm1 -> norm1
I0409 19:55:35.982574 14789 net.cpp:122] Setting up norm1
I0409 19:55:35.982584 14789 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0409 19:55:35.982589 14789 net.cpp:137] Memory required for data: 131301120
I0409 19:55:35.982592 14789 layer_factory.hpp:77] Creating layer pool1
I0409 19:55:35.982599 14789 net.cpp:84] Creating Layer pool1
I0409 19:55:35.982604 14789 net.cpp:406] pool1 <- norm1
I0409 19:55:35.982609 14789 net.cpp:380] pool1 -> pool1
I0409 19:55:35.982637 14789 net.cpp:122] Setting up pool1
I0409 19:55:35.982643 14789 net.cpp:129] Top shape: 32 96 27 27 (2239488)
I0409 19:55:35.982646 14789 net.cpp:137] Memory required for data: 140259072
I0409 19:55:35.982650 14789 layer_factory.hpp:77] Creating layer conv2
I0409 19:55:35.982657 14789 net.cpp:84] Creating Layer conv2
I0409 19:55:35.982661 14789 net.cpp:406] conv2 <- pool1
I0409 19:55:35.982666 14789 net.cpp:380] conv2 -> conv2
I0409 19:55:35.989471 14789 net.cpp:122] Setting up conv2
I0409 19:55:35.989483 14789 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0409 19:55:35.989487 14789 net.cpp:137] Memory required for data: 164146944
I0409 19:55:35.989497 14789 layer_factory.hpp:77] Creating layer relu2
I0409 19:55:35.989504 14789 net.cpp:84] Creating Layer relu2
I0409 19:55:35.989508 14789 net.cpp:406] relu2 <- conv2
I0409 19:55:35.989513 14789 net.cpp:367] relu2 -> conv2 (in-place)
I0409 19:55:35.990025 14789 net.cpp:122] Setting up relu2
I0409 19:55:35.990034 14789 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0409 19:55:35.990038 14789 net.cpp:137] Memory required for data: 188034816
I0409 19:55:35.990042 14789 layer_factory.hpp:77] Creating layer norm2
I0409 19:55:35.990051 14789 net.cpp:84] Creating Layer norm2
I0409 19:55:35.990056 14789 net.cpp:406] norm2 <- conv2
I0409 19:55:35.990061 14789 net.cpp:380] norm2 -> norm2
I0409 19:55:35.990422 14789 net.cpp:122] Setting up norm2
I0409 19:55:35.990430 14789 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0409 19:55:35.990434 14789 net.cpp:137] Memory required for data: 211922688
I0409 19:55:35.990437 14789 layer_factory.hpp:77] Creating layer pool2
I0409 19:55:35.990444 14789 net.cpp:84] Creating Layer pool2
I0409 19:55:35.990448 14789 net.cpp:406] pool2 <- norm2
I0409 19:55:35.990466 14789 net.cpp:380] pool2 -> pool2
I0409 19:55:35.990505 14789 net.cpp:122] Setting up pool2
I0409 19:55:35.990510 14789 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0409 19:55:35.990514 14789 net.cpp:137] Memory required for data: 217460480
I0409 19:55:35.990517 14789 layer_factory.hpp:77] Creating layer conv3
I0409 19:55:35.990526 14789 net.cpp:84] Creating Layer conv3
I0409 19:55:35.990530 14789 net.cpp:406] conv3 <- pool2
I0409 19:55:35.990536 14789 net.cpp:380] conv3 -> conv3
I0409 19:55:36.001590 14789 net.cpp:122] Setting up conv3
I0409 19:55:36.001603 14789 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0409 19:55:36.001607 14789 net.cpp:137] Memory required for data: 225767168
I0409 19:55:36.001619 14789 layer_factory.hpp:77] Creating layer relu3
I0409 19:55:36.001626 14789 net.cpp:84] Creating Layer relu3
I0409 19:55:36.001631 14789 net.cpp:406] relu3 <- conv3
I0409 19:55:36.001636 14789 net.cpp:367] relu3 -> conv3 (in-place)
I0409 19:55:36.001999 14789 net.cpp:122] Setting up relu3
I0409 19:55:36.002008 14789 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0409 19:55:36.002012 14789 net.cpp:137] Memory required for data: 234073856
I0409 19:55:36.002015 14789 layer_factory.hpp:77] Creating layer conv4
I0409 19:55:36.002025 14789 net.cpp:84] Creating Layer conv4
I0409 19:55:36.002029 14789 net.cpp:406] conv4 <- conv3
I0409 19:55:36.002035 14789 net.cpp:380] conv4 -> conv4
I0409 19:55:36.011394 14789 net.cpp:122] Setting up conv4
I0409 19:55:36.011406 14789 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0409 19:55:36.011410 14789 net.cpp:137] Memory required for data: 242380544
I0409 19:55:36.011416 14789 layer_factory.hpp:77] Creating layer relu4
I0409 19:55:36.011422 14789 net.cpp:84] Creating Layer relu4
I0409 19:55:36.011426 14789 net.cpp:406] relu4 <- conv4
I0409 19:55:36.011432 14789 net.cpp:367] relu4 -> conv4 (in-place)
I0409 19:55:36.011924 14789 net.cpp:122] Setting up relu4
I0409 19:55:36.011932 14789 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0409 19:55:36.011936 14789 net.cpp:137] Memory required for data: 250687232
I0409 19:55:36.011940 14789 layer_factory.hpp:77] Creating layer conv5
I0409 19:55:36.011950 14789 net.cpp:84] Creating Layer conv5
I0409 19:55:36.011953 14789 net.cpp:406] conv5 <- conv4
I0409 19:55:36.011960 14789 net.cpp:380] conv5 -> conv5
I0409 19:55:36.020454 14789 net.cpp:122] Setting up conv5
I0409 19:55:36.020467 14789 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0409 19:55:36.020470 14789 net.cpp:137] Memory required for data: 256225024
I0409 19:55:36.020483 14789 layer_factory.hpp:77] Creating layer relu5
I0409 19:55:36.020488 14789 net.cpp:84] Creating Layer relu5
I0409 19:55:36.020493 14789 net.cpp:406] relu5 <- conv5
I0409 19:55:36.020499 14789 net.cpp:367] relu5 -> conv5 (in-place)
I0409 19:55:36.021189 14789 net.cpp:122] Setting up relu5
I0409 19:55:36.021199 14789 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0409 19:55:36.021203 14789 net.cpp:137] Memory required for data: 261762816
I0409 19:55:36.021206 14789 layer_factory.hpp:77] Creating layer pool5
I0409 19:55:36.021216 14789 net.cpp:84] Creating Layer pool5
I0409 19:55:36.021220 14789 net.cpp:406] pool5 <- conv5
I0409 19:55:36.021226 14789 net.cpp:380] pool5 -> pool5
I0409 19:55:36.021265 14789 net.cpp:122] Setting up pool5
I0409 19:55:36.021270 14789 net.cpp:129] Top shape: 32 256 6 6 (294912)
I0409 19:55:36.021275 14789 net.cpp:137] Memory required for data: 262942464
I0409 19:55:36.021277 14789 layer_factory.hpp:77] Creating layer fc6
I0409 19:55:36.021286 14789 net.cpp:84] Creating Layer fc6
I0409 19:55:36.021289 14789 net.cpp:406] fc6 <- pool5
I0409 19:55:36.021294 14789 net.cpp:380] fc6 -> fc6
I0409 19:55:36.373950 14789 net.cpp:122] Setting up fc6
I0409 19:55:36.373980 14789 net.cpp:129] Top shape: 32 4096 (131072)
I0409 19:55:36.373984 14789 net.cpp:137] Memory required for data: 263466752
I0409 19:55:36.373994 14789 layer_factory.hpp:77] Creating layer relu6
I0409 19:55:36.374003 14789 net.cpp:84] Creating Layer relu6
I0409 19:55:36.374008 14789 net.cpp:406] relu6 <- fc6
I0409 19:55:36.374035 14789 net.cpp:367] relu6 -> fc6 (in-place)
I0409 19:55:36.374460 14789 net.cpp:122] Setting up relu6
I0409 19:55:36.374469 14789 net.cpp:129] Top shape: 32 4096 (131072)
I0409 19:55:36.374473 14789 net.cpp:137] Memory required for data: 263991040
I0409 19:55:36.374477 14789 layer_factory.hpp:77] Creating layer drop6
I0409 19:55:36.374483 14789 net.cpp:84] Creating Layer drop6
I0409 19:55:36.374487 14789 net.cpp:406] drop6 <- fc6
I0409 19:55:36.374493 14789 net.cpp:367] drop6 -> fc6 (in-place)
I0409 19:55:36.374517 14789 net.cpp:122] Setting up drop6
I0409 19:55:36.374522 14789 net.cpp:129] Top shape: 32 4096 (131072)
I0409 19:55:36.374526 14789 net.cpp:137] Memory required for data: 264515328
I0409 19:55:36.374529 14789 layer_factory.hpp:77] Creating layer fc7
I0409 19:55:36.374537 14789 net.cpp:84] Creating Layer fc7
I0409 19:55:36.374541 14789 net.cpp:406] fc7 <- fc6
I0409 19:55:36.374547 14789 net.cpp:380] fc7 -> fc7
I0409 19:55:36.531072 14789 net.cpp:122] Setting up fc7
I0409 19:55:36.531095 14789 net.cpp:129] Top shape: 32 4096 (131072)
I0409 19:55:36.531098 14789 net.cpp:137] Memory required for data: 265039616
I0409 19:55:36.531107 14789 layer_factory.hpp:77] Creating layer relu7
I0409 19:55:36.531116 14789 net.cpp:84] Creating Layer relu7
I0409 19:55:36.531121 14789 net.cpp:406] relu7 <- fc7
I0409 19:55:36.531127 14789 net.cpp:367] relu7 -> fc7 (in-place)
I0409 19:55:36.531798 14789 net.cpp:122] Setting up relu7
I0409 19:55:36.531808 14789 net.cpp:129] Top shape: 32 4096 (131072)
I0409 19:55:36.531811 14789 net.cpp:137] Memory required for data: 265563904
I0409 19:55:36.531816 14789 layer_factory.hpp:77] Creating layer drop7
I0409 19:55:36.531822 14789 net.cpp:84] Creating Layer drop7
I0409 19:55:36.531826 14789 net.cpp:406] drop7 <- fc7
I0409 19:55:36.531832 14789 net.cpp:367] drop7 -> fc7 (in-place)
I0409 19:55:36.531855 14789 net.cpp:122] Setting up drop7
I0409 19:55:36.531862 14789 net.cpp:129] Top shape: 32 4096 (131072)
I0409 19:55:36.531865 14789 net.cpp:137] Memory required for data: 266088192
I0409 19:55:36.531868 14789 layer_factory.hpp:77] Creating layer fc7.5
I0409 19:55:36.531875 14789 net.cpp:84] Creating Layer fc7.5
I0409 19:55:36.531879 14789 net.cpp:406] fc7.5 <- fc7
I0409 19:55:36.531885 14789 net.cpp:380] fc7.5 -> fc7.5
I0409 19:55:36.688879 14789 net.cpp:122] Setting up fc7.5
I0409 19:55:36.688903 14789 net.cpp:129] Top shape: 32 4096 (131072)
I0409 19:55:36.688907 14789 net.cpp:137] Memory required for data: 266612480
I0409 19:55:36.688916 14789 layer_factory.hpp:77] Creating layer relu7.5
I0409 19:55:36.688925 14789 net.cpp:84] Creating Layer relu7.5
I0409 19:55:36.688930 14789 net.cpp:406] relu7.5 <- fc7.5
I0409 19:55:36.688937 14789 net.cpp:367] relu7.5 -> fc7.5 (in-place)
I0409 19:55:36.689599 14789 net.cpp:122] Setting up relu7.5
I0409 19:55:36.689608 14789 net.cpp:129] Top shape: 32 4096 (131072)
I0409 19:55:36.689611 14789 net.cpp:137] Memory required for data: 267136768
I0409 19:55:36.689615 14789 layer_factory.hpp:77] Creating layer drop7.5
I0409 19:55:36.689623 14789 net.cpp:84] Creating Layer drop7.5
I0409 19:55:36.689627 14789 net.cpp:406] drop7.5 <- fc7.5
I0409 19:55:36.689632 14789 net.cpp:367] drop7.5 -> fc7.5 (in-place)
I0409 19:55:36.689657 14789 net.cpp:122] Setting up drop7.5
I0409 19:55:36.689662 14789 net.cpp:129] Top shape: 32 4096 (131072)
I0409 19:55:36.689666 14789 net.cpp:137] Memory required for data: 267661056
I0409 19:55:36.689669 14789 layer_factory.hpp:77] Creating layer fc8
I0409 19:55:36.689677 14789 net.cpp:84] Creating Layer fc8
I0409 19:55:36.689679 14789 net.cpp:406] fc8 <- fc7.5
I0409 19:55:36.689685 14789 net.cpp:380] fc8 -> fc8
I0409 19:55:36.697326 14789 net.cpp:122] Setting up fc8
I0409 19:55:36.697335 14789 net.cpp:129] Top shape: 32 196 (6272)
I0409 19:55:36.697340 14789 net.cpp:137] Memory required for data: 267686144
I0409 19:55:36.697350 14789 layer_factory.hpp:77] Creating layer fc8_fc8_0_split
I0409 19:55:36.697357 14789 net.cpp:84] Creating Layer fc8_fc8_0_split
I0409 19:55:36.697361 14789 net.cpp:406] fc8_fc8_0_split <- fc8
I0409 19:55:36.697384 14789 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_0
I0409 19:55:36.697391 14789 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_1
I0409 19:55:36.697423 14789 net.cpp:122] Setting up fc8_fc8_0_split
I0409 19:55:36.697429 14789 net.cpp:129] Top shape: 32 196 (6272)
I0409 19:55:36.697432 14789 net.cpp:129] Top shape: 32 196 (6272)
I0409 19:55:36.697435 14789 net.cpp:137] Memory required for data: 267736320
I0409 19:55:36.697439 14789 layer_factory.hpp:77] Creating layer accuracy
I0409 19:55:36.697445 14789 net.cpp:84] Creating Layer accuracy
I0409 19:55:36.697449 14789 net.cpp:406] accuracy <- fc8_fc8_0_split_0
I0409 19:55:36.697453 14789 net.cpp:406] accuracy <- label_val-data_1_split_0
I0409 19:55:36.697459 14789 net.cpp:380] accuracy -> accuracy
I0409 19:55:36.697468 14789 net.cpp:122] Setting up accuracy
I0409 19:55:36.697471 14789 net.cpp:129] Top shape: (1)
I0409 19:55:36.697474 14789 net.cpp:137] Memory required for data: 267736324
I0409 19:55:36.697477 14789 layer_factory.hpp:77] Creating layer loss
I0409 19:55:36.697484 14789 net.cpp:84] Creating Layer loss
I0409 19:55:36.697487 14789 net.cpp:406] loss <- fc8_fc8_0_split_1
I0409 19:55:36.697491 14789 net.cpp:406] loss <- label_val-data_1_split_1
I0409 19:55:36.697495 14789 net.cpp:380] loss -> loss
I0409 19:55:36.697502 14789 layer_factory.hpp:77] Creating layer loss
I0409 19:55:36.699191 14789 net.cpp:122] Setting up loss
I0409 19:55:36.699201 14789 net.cpp:129] Top shape: (1)
I0409 19:55:36.699204 14789 net.cpp:132]     with loss weight 1
I0409 19:55:36.699216 14789 net.cpp:137] Memory required for data: 267736328
I0409 19:55:36.699220 14789 net.cpp:198] loss needs backward computation.
I0409 19:55:36.699225 14789 net.cpp:200] accuracy does not need backward computation.
I0409 19:55:36.699229 14789 net.cpp:198] fc8_fc8_0_split needs backward computation.
I0409 19:55:36.699232 14789 net.cpp:198] fc8 needs backward computation.
I0409 19:55:36.699236 14789 net.cpp:198] drop7.5 needs backward computation.
I0409 19:55:36.699239 14789 net.cpp:198] relu7.5 needs backward computation.
I0409 19:55:36.699242 14789 net.cpp:198] fc7.5 needs backward computation.
I0409 19:55:36.699246 14789 net.cpp:198] drop7 needs backward computation.
I0409 19:55:36.699249 14789 net.cpp:198] relu7 needs backward computation.
I0409 19:55:36.699254 14789 net.cpp:198] fc7 needs backward computation.
I0409 19:55:36.699257 14789 net.cpp:198] drop6 needs backward computation.
I0409 19:55:36.699260 14789 net.cpp:198] relu6 needs backward computation.
I0409 19:55:36.699263 14789 net.cpp:198] fc6 needs backward computation.
I0409 19:55:36.699267 14789 net.cpp:198] pool5 needs backward computation.
I0409 19:55:36.699272 14789 net.cpp:198] relu5 needs backward computation.
I0409 19:55:36.699276 14789 net.cpp:198] conv5 needs backward computation.
I0409 19:55:36.699280 14789 net.cpp:198] relu4 needs backward computation.
I0409 19:55:36.699283 14789 net.cpp:198] conv4 needs backward computation.
I0409 19:55:36.699286 14789 net.cpp:198] relu3 needs backward computation.
I0409 19:55:36.699290 14789 net.cpp:198] conv3 needs backward computation.
I0409 19:55:36.699293 14789 net.cpp:198] pool2 needs backward computation.
I0409 19:55:36.699296 14789 net.cpp:198] norm2 needs backward computation.
I0409 19:55:36.699301 14789 net.cpp:198] relu2 needs backward computation.
I0409 19:55:36.699304 14789 net.cpp:198] conv2 needs backward computation.
I0409 19:55:36.699307 14789 net.cpp:198] pool1 needs backward computation.
I0409 19:55:36.699311 14789 net.cpp:198] norm1 needs backward computation.
I0409 19:55:36.699314 14789 net.cpp:198] relu1 needs backward computation.
I0409 19:55:36.699317 14789 net.cpp:198] conv1 needs backward computation.
I0409 19:55:36.699321 14789 net.cpp:200] label_val-data_1_split does not need backward computation.
I0409 19:55:36.699326 14789 net.cpp:200] val-data does not need backward computation.
I0409 19:55:36.699329 14789 net.cpp:242] This network produces output accuracy
I0409 19:55:36.699333 14789 net.cpp:242] This network produces output loss
I0409 19:55:36.699360 14789 net.cpp:255] Network initialization done.
I0409 19:55:36.699431 14789 solver.cpp:56] Solver scaffolding done.
I0409 19:55:36.699898 14789 caffe.cpp:248] Starting Optimization
I0409 19:55:36.699906 14789 solver.cpp:272] Solving
I0409 19:55:36.699909 14789 solver.cpp:273] Learning Rate Policy: exp
I0409 19:55:36.701473 14789 solver.cpp:330] Iteration 0, Testing net (#0)
I0409 19:55:36.701483 14789 net.cpp:676] Ignoring source layer train-data
I0409 19:55:36.793540 14789 blocking_queue.cpp:49] Waiting for data
I0409 19:55:41.097575 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 19:55:41.142181 14789 solver.cpp:397]     Test net output #0: accuracy = 0.00490196
I0409 19:55:41.142204 14789 solver.cpp:397]     Test net output #1: loss = 5.28164 (* 1 = 5.28164 loss)
I0409 19:55:41.237164 14789 solver.cpp:218] Iteration 0 (-1.6209e-40 iter/s, 4.53708s/12 iters), loss = 5.28928
I0409 19:55:41.238677 14789 solver.cpp:237]     Train net output #0: loss = 5.28928 (* 1 = 5.28928 loss)
I0409 19:55:41.238696 14789 sgd_solver.cpp:105] Iteration 0, lr = 0.01
I0409 19:55:45.167677 14789 solver.cpp:218] Iteration 12 (3.05433 iter/s, 3.92885s/12 iters), loss = 5.28115
I0409 19:55:45.167724 14789 solver.cpp:237]     Train net output #0: loss = 5.28115 (* 1 = 5.28115 loss)
I0409 19:55:45.167737 14789 sgd_solver.cpp:105] Iteration 12, lr = 0.00997626
I0409 19:55:49.974691 14789 solver.cpp:218] Iteration 24 (2.49647 iter/s, 4.80679s/12 iters), loss = 5.28663
I0409 19:55:49.974736 14789 solver.cpp:237]     Train net output #0: loss = 5.28663 (* 1 = 5.28663 loss)
I0409 19:55:49.974748 14789 sgd_solver.cpp:105] Iteration 24, lr = 0.00995257
I0409 19:55:54.740339 14789 solver.cpp:218] Iteration 36 (2.51813 iter/s, 4.76544s/12 iters), loss = 5.28543
I0409 19:55:54.740386 14789 solver.cpp:237]     Train net output #0: loss = 5.28543 (* 1 = 5.28543 loss)
I0409 19:55:54.740399 14789 sgd_solver.cpp:105] Iteration 36, lr = 0.00992894
I0409 19:55:59.311345 14789 solver.cpp:218] Iteration 48 (2.62536 iter/s, 4.57079s/12 iters), loss = 5.32393
I0409 19:55:59.311390 14789 solver.cpp:237]     Train net output #0: loss = 5.32393 (* 1 = 5.32393 loss)
I0409 19:55:59.311401 14789 sgd_solver.cpp:105] Iteration 48, lr = 0.00990537
I0409 19:56:03.935276 14789 solver.cpp:218] Iteration 60 (2.59531 iter/s, 4.62372s/12 iters), loss = 5.30139
I0409 19:56:03.935321 14789 solver.cpp:237]     Train net output #0: loss = 5.30139 (* 1 = 5.30139 loss)
I0409 19:56:03.935333 14789 sgd_solver.cpp:105] Iteration 60, lr = 0.00988185
I0409 19:56:08.703403 14789 solver.cpp:218] Iteration 72 (2.51683 iter/s, 4.76791s/12 iters), loss = 5.3062
I0409 19:56:08.703498 14789 solver.cpp:237]     Train net output #0: loss = 5.3062 (* 1 = 5.3062 loss)
I0409 19:56:08.703510 14789 sgd_solver.cpp:105] Iteration 72, lr = 0.00985839
I0409 19:56:13.464148 14789 solver.cpp:218] Iteration 84 (2.52076 iter/s, 4.76048s/12 iters), loss = 5.31253
I0409 19:56:13.464190 14789 solver.cpp:237]     Train net output #0: loss = 5.31253 (* 1 = 5.31253 loss)
I0409 19:56:13.464202 14789 sgd_solver.cpp:105] Iteration 84, lr = 0.00983498
I0409 19:56:18.281612 14789 solver.cpp:218] Iteration 96 (2.49105 iter/s, 4.81725s/12 iters), loss = 5.30792
I0409 19:56:18.281659 14789 solver.cpp:237]     Train net output #0: loss = 5.30792 (* 1 = 5.30792 loss)
I0409 19:56:18.281672 14789 sgd_solver.cpp:105] Iteration 96, lr = 0.00981163
I0409 19:56:19.915613 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 19:56:20.225608 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_102.caffemodel
I0409 19:56:24.137832 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_102.solverstate
I0409 19:56:28.851475 14789 solver.cpp:330] Iteration 102, Testing net (#0)
I0409 19:56:28.851508 14789 net.cpp:676] Ignoring source layer train-data
I0409 19:56:33.229704 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 19:56:33.306675 14789 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0409 19:56:33.306721 14789 solver.cpp:397]     Test net output #1: loss = 5.29055 (* 1 = 5.29055 loss)
I0409 19:56:35.077025 14789 solver.cpp:218] Iteration 108 (0.714508 iter/s, 16.7948s/12 iters), loss = 5.30816
I0409 19:56:35.077065 14789 solver.cpp:237]     Train net output #0: loss = 5.30816 (* 1 = 5.30816 loss)
I0409 19:56:35.077075 14789 sgd_solver.cpp:105] Iteration 108, lr = 0.00978834
I0409 19:56:39.827971 14789 solver.cpp:218] Iteration 120 (2.52593 iter/s, 4.75073s/12 iters), loss = 5.28348
I0409 19:56:39.828142 14789 solver.cpp:237]     Train net output #0: loss = 5.28348 (* 1 = 5.28348 loss)
I0409 19:56:39.828158 14789 sgd_solver.cpp:105] Iteration 120, lr = 0.0097651
I0409 19:56:44.540987 14789 solver.cpp:218] Iteration 132 (2.54632 iter/s, 4.71268s/12 iters), loss = 5.2493
I0409 19:56:44.541035 14789 solver.cpp:237]     Train net output #0: loss = 5.2493 (* 1 = 5.2493 loss)
I0409 19:56:44.541046 14789 sgd_solver.cpp:105] Iteration 132, lr = 0.00974192
I0409 19:56:49.366976 14789 solver.cpp:218] Iteration 144 (2.48665 iter/s, 4.82577s/12 iters), loss = 5.32666
I0409 19:56:49.367024 14789 solver.cpp:237]     Train net output #0: loss = 5.32666 (* 1 = 5.32666 loss)
I0409 19:56:49.367036 14789 sgd_solver.cpp:105] Iteration 144, lr = 0.00971879
I0409 19:56:54.168574 14789 solver.cpp:218] Iteration 156 (2.49929 iter/s, 4.80137s/12 iters), loss = 5.25593
I0409 19:56:54.168622 14789 solver.cpp:237]     Train net output #0: loss = 5.25593 (* 1 = 5.25593 loss)
I0409 19:56:54.168632 14789 sgd_solver.cpp:105] Iteration 156, lr = 0.00969571
I0409 19:56:58.784734 14789 solver.cpp:218] Iteration 168 (2.59968 iter/s, 4.61594s/12 iters), loss = 5.26973
I0409 19:56:58.784786 14789 solver.cpp:237]     Train net output #0: loss = 5.26973 (* 1 = 5.26973 loss)
I0409 19:56:58.784799 14789 sgd_solver.cpp:105] Iteration 168, lr = 0.00967269
I0409 19:57:03.696733 14789 solver.cpp:218] Iteration 180 (2.44311 iter/s, 4.91176s/12 iters), loss = 5.27379
I0409 19:57:03.696789 14789 solver.cpp:237]     Train net output #0: loss = 5.27379 (* 1 = 5.27379 loss)
I0409 19:57:03.696805 14789 sgd_solver.cpp:105] Iteration 180, lr = 0.00964973
I0409 19:57:08.499123 14789 solver.cpp:218] Iteration 192 (2.49888 iter/s, 4.80216s/12 iters), loss = 5.28689
I0409 19:57:08.499169 14789 solver.cpp:237]     Train net output #0: loss = 5.28689 (* 1 = 5.28689 loss)
I0409 19:57:08.499181 14789 sgd_solver.cpp:105] Iteration 192, lr = 0.00962682
I0409 19:57:12.141564 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 19:57:12.872905 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_204.caffemodel
I0409 19:57:16.563421 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_204.solverstate
I0409 19:57:20.164602 14789 solver.cpp:330] Iteration 204, Testing net (#0)
I0409 19:57:20.164625 14789 net.cpp:676] Ignoring source layer train-data
I0409 19:57:24.462656 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 19:57:24.585719 14789 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0409 19:57:24.585769 14789 solver.cpp:397]     Test net output #1: loss = 5.28846 (* 1 = 5.28846 loss)
I0409 19:57:24.677681 14789 solver.cpp:218] Iteration 204 (0.741751 iter/s, 16.1779s/12 iters), loss = 5.28179
I0409 19:57:24.677732 14789 solver.cpp:237]     Train net output #0: loss = 5.28179 (* 1 = 5.28179 loss)
I0409 19:57:24.677743 14789 sgd_solver.cpp:105] Iteration 204, lr = 0.00960396
I0409 19:57:28.844952 14789 solver.cpp:218] Iteration 216 (2.87972 iter/s, 4.16707s/12 iters), loss = 5.27603
I0409 19:57:28.844991 14789 solver.cpp:237]     Train net output #0: loss = 5.27603 (* 1 = 5.27603 loss)
I0409 19:57:28.845000 14789 sgd_solver.cpp:105] Iteration 216, lr = 0.00958116
I0409 19:57:33.708155 14789 solver.cpp:218] Iteration 228 (2.46762 iter/s, 4.86298s/12 iters), loss = 5.27414
I0409 19:57:33.708201 14789 solver.cpp:237]     Train net output #0: loss = 5.27414 (* 1 = 5.27414 loss)
I0409 19:57:33.708211 14789 sgd_solver.cpp:105] Iteration 228, lr = 0.00955841
I0409 19:57:38.409952 14789 solver.cpp:218] Iteration 240 (2.55234 iter/s, 4.70157s/12 iters), loss = 5.30501
I0409 19:57:38.410018 14789 solver.cpp:237]     Train net output #0: loss = 5.30501 (* 1 = 5.30501 loss)
I0409 19:57:38.410032 14789 sgd_solver.cpp:105] Iteration 240, lr = 0.00953572
I0409 19:57:43.356726 14789 solver.cpp:218] Iteration 252 (2.42594 iter/s, 4.94653s/12 iters), loss = 5.28595
I0409 19:57:43.356868 14789 solver.cpp:237]     Train net output #0: loss = 5.28595 (* 1 = 5.28595 loss)
I0409 19:57:43.356880 14789 sgd_solver.cpp:105] Iteration 252, lr = 0.00951308
I0409 19:57:48.247190 14789 solver.cpp:218] Iteration 264 (2.45391 iter/s, 4.89015s/12 iters), loss = 5.28233
I0409 19:57:48.247238 14789 solver.cpp:237]     Train net output #0: loss = 5.28233 (* 1 = 5.28233 loss)
I0409 19:57:48.247251 14789 sgd_solver.cpp:105] Iteration 264, lr = 0.00949049
I0409 19:57:53.412107 14789 solver.cpp:218] Iteration 276 (2.32347 iter/s, 5.16468s/12 iters), loss = 5.29687
I0409 19:57:53.412150 14789 solver.cpp:237]     Train net output #0: loss = 5.29687 (* 1 = 5.29687 loss)
I0409 19:57:53.412161 14789 sgd_solver.cpp:105] Iteration 276, lr = 0.00946796
I0409 19:57:58.287814 14789 solver.cpp:218] Iteration 288 (2.46129 iter/s, 4.87548s/12 iters), loss = 5.28163
I0409 19:57:58.287866 14789 solver.cpp:237]     Train net output #0: loss = 5.28163 (* 1 = 5.28163 loss)
I0409 19:57:58.287879 14789 sgd_solver.cpp:105] Iteration 288, lr = 0.00944548
I0409 19:58:02.932855 14789 solver.cpp:218] Iteration 300 (2.58352 iter/s, 4.64482s/12 iters), loss = 5.2911
I0409 19:58:02.932904 14789 solver.cpp:237]     Train net output #0: loss = 5.2911 (* 1 = 5.2911 loss)
I0409 19:58:02.932916 14789 sgd_solver.cpp:105] Iteration 300, lr = 0.00942305
I0409 19:58:03.918116 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 19:58:04.967306 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_306.caffemodel
I0409 19:58:08.783735 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_306.solverstate
I0409 19:58:11.778841 14789 solver.cpp:330] Iteration 306, Testing net (#0)
I0409 19:58:11.778862 14789 net.cpp:676] Ignoring source layer train-data
I0409 19:58:15.964641 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 19:58:16.122367 14789 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0409 19:58:16.122408 14789 solver.cpp:397]     Test net output #1: loss = 5.28385 (* 1 = 5.28385 loss)
I0409 19:58:17.966544 14789 solver.cpp:218] Iteration 312 (0.798237 iter/s, 15.0331s/12 iters), loss = 5.28309
I0409 19:58:17.966583 14789 solver.cpp:237]     Train net output #0: loss = 5.28309 (* 1 = 5.28309 loss)
I0409 19:58:17.966593 14789 sgd_solver.cpp:105] Iteration 312, lr = 0.00940068
I0409 19:58:23.232802 14789 solver.cpp:218] Iteration 324 (2.27876 iter/s, 5.26602s/12 iters), loss = 5.25487
I0409 19:58:23.232851 14789 solver.cpp:237]     Train net output #0: loss = 5.25487 (* 1 = 5.25487 loss)
I0409 19:58:23.232859 14789 sgd_solver.cpp:105] Iteration 324, lr = 0.00937836
I0409 19:58:28.027026 14789 solver.cpp:218] Iteration 336 (2.50313 iter/s, 4.794s/12 iters), loss = 5.25138
I0409 19:58:28.027072 14789 solver.cpp:237]     Train net output #0: loss = 5.25138 (* 1 = 5.25138 loss)
I0409 19:58:28.027081 14789 sgd_solver.cpp:105] Iteration 336, lr = 0.0093561
I0409 19:58:32.875989 14789 solver.cpp:218] Iteration 348 (2.47487 iter/s, 4.84874s/12 iters), loss = 5.25421
I0409 19:58:32.876032 14789 solver.cpp:237]     Train net output #0: loss = 5.25421 (* 1 = 5.25421 loss)
I0409 19:58:32.876041 14789 sgd_solver.cpp:105] Iteration 348, lr = 0.00933388
I0409 19:58:37.789981 14789 solver.cpp:218] Iteration 360 (2.44212 iter/s, 4.91376s/12 iters), loss = 5.28783
I0409 19:58:37.790026 14789 solver.cpp:237]     Train net output #0: loss = 5.28783 (* 1 = 5.28783 loss)
I0409 19:58:37.790035 14789 sgd_solver.cpp:105] Iteration 360, lr = 0.00931172
I0409 19:58:42.584656 14789 solver.cpp:218] Iteration 372 (2.5029 iter/s, 4.79445s/12 iters), loss = 5.22736
I0409 19:58:42.584726 14789 solver.cpp:237]     Train net output #0: loss = 5.22736 (* 1 = 5.22736 loss)
I0409 19:58:42.584744 14789 sgd_solver.cpp:105] Iteration 372, lr = 0.00928961
I0409 19:58:47.209846 14789 solver.cpp:218] Iteration 384 (2.59462 iter/s, 4.62496s/12 iters), loss = 5.21306
I0409 19:58:47.210047 14789 solver.cpp:237]     Train net output #0: loss = 5.21306 (* 1 = 5.21306 loss)
I0409 19:58:47.210062 14789 sgd_solver.cpp:105] Iteration 384, lr = 0.00926756
I0409 19:58:52.159576 14789 solver.cpp:218] Iteration 396 (2.42456 iter/s, 4.94935s/12 iters), loss = 5.12821
I0409 19:58:52.159629 14789 solver.cpp:237]     Train net output #0: loss = 5.12821 (* 1 = 5.12821 loss)
I0409 19:58:52.159641 14789 sgd_solver.cpp:105] Iteration 396, lr = 0.00924556
I0409 19:58:55.126591 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 19:58:56.510015 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_408.caffemodel
I0409 19:59:00.226704 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_408.solverstate
I0409 19:59:03.222864 14789 solver.cpp:330] Iteration 408, Testing net (#0)
I0409 19:59:03.222892 14789 net.cpp:676] Ignoring source layer train-data
I0409 19:59:07.447938 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 19:59:07.657325 14789 solver.cpp:397]     Test net output #0: accuracy = 0.00796569
I0409 19:59:07.657372 14789 solver.cpp:397]     Test net output #1: loss = 5.18651 (* 1 = 5.18651 loss)
I0409 19:59:07.749099 14789 solver.cpp:218] Iteration 408 (0.769776 iter/s, 15.5889s/12 iters), loss = 5.24528
I0409 19:59:07.749151 14789 solver.cpp:237]     Train net output #0: loss = 5.24528 (* 1 = 5.24528 loss)
I0409 19:59:07.749162 14789 sgd_solver.cpp:105] Iteration 408, lr = 0.00922361
I0409 19:59:12.003737 14789 solver.cpp:218] Iteration 420 (2.82059 iter/s, 4.25443s/12 iters), loss = 5.24977
I0409 19:59:12.003798 14789 solver.cpp:237]     Train net output #0: loss = 5.24977 (* 1 = 5.24977 loss)
I0409 19:59:12.003813 14789 sgd_solver.cpp:105] Iteration 420, lr = 0.00920171
I0409 19:59:16.661473 14789 solver.cpp:218] Iteration 432 (2.57649 iter/s, 4.65751s/12 iters), loss = 5.21881
I0409 19:59:16.661525 14789 solver.cpp:237]     Train net output #0: loss = 5.21881 (* 1 = 5.21881 loss)
I0409 19:59:16.661536 14789 sgd_solver.cpp:105] Iteration 432, lr = 0.00917986
I0409 19:59:21.831514 14789 solver.cpp:218] Iteration 444 (2.32117 iter/s, 5.16981s/12 iters), loss = 5.18064
I0409 19:59:21.831614 14789 solver.cpp:237]     Train net output #0: loss = 5.18064 (* 1 = 5.18064 loss)
I0409 19:59:21.831626 14789 sgd_solver.cpp:105] Iteration 444, lr = 0.00915807
I0409 19:59:26.853046 14789 solver.cpp:218] Iteration 456 (2.38984 iter/s, 5.02125s/12 iters), loss = 5.22397
I0409 19:59:26.853103 14789 solver.cpp:237]     Train net output #0: loss = 5.22397 (* 1 = 5.22397 loss)
I0409 19:59:26.853116 14789 sgd_solver.cpp:105] Iteration 456, lr = 0.00913632
I0409 19:59:31.685690 14789 solver.cpp:218] Iteration 468 (2.48323 iter/s, 4.83242s/12 iters), loss = 5.1984
I0409 19:59:31.685742 14789 solver.cpp:237]     Train net output #0: loss = 5.1984 (* 1 = 5.1984 loss)
I0409 19:59:31.685755 14789 sgd_solver.cpp:105] Iteration 468, lr = 0.00911463
I0409 19:59:36.555563 14789 solver.cpp:218] Iteration 480 (2.46424 iter/s, 4.86965s/12 iters), loss = 5.13178
I0409 19:59:36.555606 14789 solver.cpp:237]     Train net output #0: loss = 5.13178 (* 1 = 5.13178 loss)
I0409 19:59:36.555615 14789 sgd_solver.cpp:105] Iteration 480, lr = 0.00909299
I0409 19:59:41.451692 14789 solver.cpp:218] Iteration 492 (2.45102 iter/s, 4.89591s/12 iters), loss = 5.18153
I0409 19:59:41.451732 14789 solver.cpp:237]     Train net output #0: loss = 5.18153 (* 1 = 5.18153 loss)
I0409 19:59:41.451741 14789 sgd_solver.cpp:105] Iteration 492, lr = 0.0090714
I0409 19:59:46.303136 14789 solver.cpp:218] Iteration 504 (2.4736 iter/s, 4.85123s/12 iters), loss = 5.17579
I0409 19:59:46.303187 14789 solver.cpp:237]     Train net output #0: loss = 5.17579 (* 1 = 5.17579 loss)
I0409 19:59:46.303198 14789 sgd_solver.cpp:105] Iteration 504, lr = 0.00904986
I0409 19:59:46.565364 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 19:59:48.308789 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_510.caffemodel
I0409 19:59:52.097627 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_510.solverstate
I0409 19:59:56.898538 14789 solver.cpp:330] Iteration 510, Testing net (#0)
I0409 19:59:56.898564 14789 net.cpp:676] Ignoring source layer train-data
I0409 20:00:01.152590 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:00:01.396853 14789 solver.cpp:397]     Test net output #0: accuracy = 0.00857843
I0409 20:00:01.396903 14789 solver.cpp:397]     Test net output #1: loss = 5.15955 (* 1 = 5.15955 loss)
I0409 20:00:03.409459 14789 solver.cpp:218] Iteration 516 (0.701521 iter/s, 17.1057s/12 iters), loss = 5.13891
I0409 20:00:03.409513 14789 solver.cpp:237]     Train net output #0: loss = 5.13891 (* 1 = 5.13891 loss)
I0409 20:00:03.409524 14789 sgd_solver.cpp:105] Iteration 516, lr = 0.00902838
I0409 20:00:08.293540 14789 solver.cpp:218] Iteration 528 (2.45708 iter/s, 4.88385s/12 iters), loss = 5.19952
I0409 20:00:08.293596 14789 solver.cpp:237]     Train net output #0: loss = 5.19952 (* 1 = 5.19952 loss)
I0409 20:00:08.293607 14789 sgd_solver.cpp:105] Iteration 528, lr = 0.00900694
I0409 20:00:13.009192 14789 solver.cpp:218] Iteration 540 (2.54483 iter/s, 4.71544s/12 iters), loss = 5.16508
I0409 20:00:13.009227 14789 solver.cpp:237]     Train net output #0: loss = 5.16508 (* 1 = 5.16508 loss)
I0409 20:00:13.009235 14789 sgd_solver.cpp:105] Iteration 540, lr = 0.00898556
I0409 20:00:17.700163 14789 solver.cpp:218] Iteration 552 (2.55822 iter/s, 4.69077s/12 iters), loss = 5.12141
I0409 20:00:17.700215 14789 solver.cpp:237]     Train net output #0: loss = 5.12141 (* 1 = 5.12141 loss)
I0409 20:00:17.700227 14789 sgd_solver.cpp:105] Iteration 552, lr = 0.00896423
I0409 20:00:22.524637 14789 solver.cpp:218] Iteration 564 (2.48743 iter/s, 4.82425s/12 iters), loss = 5.14369
I0409 20:00:22.525522 14789 solver.cpp:237]     Train net output #0: loss = 5.14369 (* 1 = 5.14369 loss)
I0409 20:00:22.525537 14789 sgd_solver.cpp:105] Iteration 564, lr = 0.00894294
I0409 20:00:27.168270 14789 solver.cpp:218] Iteration 576 (2.58477 iter/s, 4.64259s/12 iters), loss = 5.12798
I0409 20:00:27.168324 14789 solver.cpp:237]     Train net output #0: loss = 5.12798 (* 1 = 5.12798 loss)
I0409 20:00:27.168336 14789 sgd_solver.cpp:105] Iteration 576, lr = 0.00892171
I0409 20:00:31.520931 14789 solver.cpp:218] Iteration 588 (2.75706 iter/s, 4.35246s/12 iters), loss = 5.10665
I0409 20:00:31.520977 14789 solver.cpp:237]     Train net output #0: loss = 5.10665 (* 1 = 5.10665 loss)
I0409 20:00:31.520987 14789 sgd_solver.cpp:105] Iteration 588, lr = 0.00890053
I0409 20:00:36.314347 14789 solver.cpp:218] Iteration 600 (2.50354 iter/s, 4.79321s/12 iters), loss = 5.17978
I0409 20:00:36.314401 14789 solver.cpp:237]     Train net output #0: loss = 5.17978 (* 1 = 5.17978 loss)
I0409 20:00:36.314415 14789 sgd_solver.cpp:105] Iteration 600, lr = 0.0088794
I0409 20:00:38.628389 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:00:40.566731 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_612.caffemodel
I0409 20:00:44.326558 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_612.solverstate
I0409 20:00:47.381067 14789 solver.cpp:330] Iteration 612, Testing net (#0)
I0409 20:00:47.381093 14789 net.cpp:676] Ignoring source layer train-data
I0409 20:00:51.510049 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:00:51.797063 14789 solver.cpp:397]     Test net output #0: accuracy = 0.00980392
I0409 20:00:51.797101 14789 solver.cpp:397]     Test net output #1: loss = 5.14436 (* 1 = 5.14436 loss)
I0409 20:00:51.888754 14789 solver.cpp:218] Iteration 612 (0.770522 iter/s, 15.5739s/12 iters), loss = 5.1868
I0409 20:00:51.888813 14789 solver.cpp:237]     Train net output #0: loss = 5.1868 (* 1 = 5.1868 loss)
I0409 20:00:51.888824 14789 sgd_solver.cpp:105] Iteration 612, lr = 0.00885831
I0409 20:00:56.000382 14789 solver.cpp:218] Iteration 624 (2.9187 iter/s, 4.11142s/12 iters), loss = 5.17252
I0409 20:00:56.000536 14789 solver.cpp:237]     Train net output #0: loss = 5.17252 (* 1 = 5.17252 loss)
I0409 20:00:56.000548 14789 sgd_solver.cpp:105] Iteration 624, lr = 0.00883728
I0409 20:01:00.807880 14789 solver.cpp:218] Iteration 636 (2.49626 iter/s, 4.80718s/12 iters), loss = 5.05374
I0409 20:01:00.807940 14789 solver.cpp:237]     Train net output #0: loss = 5.05374 (* 1 = 5.05374 loss)
I0409 20:01:00.807952 14789 sgd_solver.cpp:105] Iteration 636, lr = 0.0088163
I0409 20:01:05.681478 14789 solver.cpp:218] Iteration 648 (2.46236 iter/s, 4.87337s/12 iters), loss = 5.18891
I0409 20:01:05.681532 14789 solver.cpp:237]     Train net output #0: loss = 5.18891 (* 1 = 5.18891 loss)
I0409 20:01:05.681545 14789 sgd_solver.cpp:105] Iteration 648, lr = 0.00879537
I0409 20:01:10.710543 14789 solver.cpp:218] Iteration 660 (2.38623 iter/s, 5.02885s/12 iters), loss = 5.14915
I0409 20:01:10.710582 14789 solver.cpp:237]     Train net output #0: loss = 5.14915 (* 1 = 5.14915 loss)
I0409 20:01:10.710592 14789 sgd_solver.cpp:105] Iteration 660, lr = 0.00877449
I0409 20:01:15.554389 14789 solver.cpp:218] Iteration 672 (2.47748 iter/s, 4.84364s/12 iters), loss = 5.09904
I0409 20:01:15.554441 14789 solver.cpp:237]     Train net output #0: loss = 5.09904 (* 1 = 5.09904 loss)
I0409 20:01:15.554455 14789 sgd_solver.cpp:105] Iteration 672, lr = 0.00875366
I0409 20:01:20.259886 14789 solver.cpp:218] Iteration 684 (2.55033 iter/s, 4.70528s/12 iters), loss = 4.96337
I0409 20:01:20.259930 14789 solver.cpp:237]     Train net output #0: loss = 4.96337 (* 1 = 4.96337 loss)
I0409 20:01:20.259939 14789 sgd_solver.cpp:105] Iteration 684, lr = 0.00873287
I0409 20:01:21.090492 14789 blocking_queue.cpp:49] Waiting for data
I0409 20:01:25.053601 14789 solver.cpp:218] Iteration 696 (2.50338 iter/s, 4.79351s/12 iters), loss = 5.11423
I0409 20:01:25.053642 14789 solver.cpp:237]     Train net output #0: loss = 5.11423 (* 1 = 5.11423 loss)
I0409 20:01:25.053650 14789 sgd_solver.cpp:105] Iteration 696, lr = 0.00871214
I0409 20:01:29.417935 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:01:29.802702 14789 solver.cpp:218] Iteration 708 (2.5269 iter/s, 4.7489s/12 iters), loss = 5.15993
I0409 20:01:29.802757 14789 solver.cpp:237]     Train net output #0: loss = 5.15993 (* 1 = 5.15993 loss)
I0409 20:01:29.802769 14789 sgd_solver.cpp:105] Iteration 708, lr = 0.00869145
I0409 20:01:31.887549 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_714.caffemodel
I0409 20:01:35.565752 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_714.solverstate
I0409 20:01:38.625100 14789 solver.cpp:330] Iteration 714, Testing net (#0)
I0409 20:01:38.625120 14789 net.cpp:676] Ignoring source layer train-data
I0409 20:01:42.800741 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:01:43.122172 14789 solver.cpp:397]     Test net output #0: accuracy = 0.00919118
I0409 20:01:43.122205 14789 solver.cpp:397]     Test net output #1: loss = 5.10709 (* 1 = 5.10709 loss)
I0409 20:01:44.792400 14789 solver.cpp:218] Iteration 720 (0.800577 iter/s, 14.9892s/12 iters), loss = 5.17366
I0409 20:01:44.792450 14789 solver.cpp:237]     Train net output #0: loss = 5.17366 (* 1 = 5.17366 loss)
I0409 20:01:44.792461 14789 sgd_solver.cpp:105] Iteration 720, lr = 0.00867082
I0409 20:01:49.597739 14789 solver.cpp:218] Iteration 732 (2.49734 iter/s, 4.80512s/12 iters), loss = 5.04272
I0409 20:01:49.597800 14789 solver.cpp:237]     Train net output #0: loss = 5.04272 (* 1 = 5.04272 loss)
I0409 20:01:49.597813 14789 sgd_solver.cpp:105] Iteration 732, lr = 0.00865023
I0409 20:01:54.520433 14789 solver.cpp:218] Iteration 744 (2.4378 iter/s, 4.92247s/12 iters), loss = 5.04924
I0409 20:01:54.520471 14789 solver.cpp:237]     Train net output #0: loss = 5.04924 (* 1 = 5.04924 loss)
I0409 20:01:54.520479 14789 sgd_solver.cpp:105] Iteration 744, lr = 0.0086297
I0409 20:01:59.243439 14789 solver.cpp:218] Iteration 756 (2.54086 iter/s, 4.72281s/12 iters), loss = 5.11919
I0409 20:01:59.243485 14789 solver.cpp:237]     Train net output #0: loss = 5.11919 (* 1 = 5.11919 loss)
I0409 20:01:59.243494 14789 sgd_solver.cpp:105] Iteration 756, lr = 0.00860921
I0409 20:02:04.137253 14789 solver.cpp:218] Iteration 768 (2.45218 iter/s, 4.89361s/12 iters), loss = 5.14076
I0409 20:02:04.137401 14789 solver.cpp:237]     Train net output #0: loss = 5.14076 (* 1 = 5.14076 loss)
I0409 20:02:04.137415 14789 sgd_solver.cpp:105] Iteration 768, lr = 0.00858877
I0409 20:02:08.966804 14789 solver.cpp:218] Iteration 780 (2.48486 iter/s, 4.82925s/12 iters), loss = 5.15946
I0409 20:02:08.966846 14789 solver.cpp:237]     Train net output #0: loss = 5.15946 (* 1 = 5.15946 loss)
I0409 20:02:08.966856 14789 sgd_solver.cpp:105] Iteration 780, lr = 0.00856838
I0409 20:02:13.715469 14789 solver.cpp:218] Iteration 792 (2.52713 iter/s, 4.74846s/12 iters), loss = 5.02431
I0409 20:02:13.715528 14789 solver.cpp:237]     Train net output #0: loss = 5.02431 (* 1 = 5.02431 loss)
I0409 20:02:13.715539 14789 sgd_solver.cpp:105] Iteration 792, lr = 0.00854803
I0409 20:02:18.513423 14789 solver.cpp:218] Iteration 804 (2.50118 iter/s, 4.79774s/12 iters), loss = 5.13854
I0409 20:02:18.513470 14789 solver.cpp:237]     Train net output #0: loss = 5.13854 (* 1 = 5.13854 loss)
I0409 20:02:18.513481 14789 sgd_solver.cpp:105] Iteration 804, lr = 0.00852774
I0409 20:02:20.227411 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:02:22.965526 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_816.caffemodel
I0409 20:02:26.746049 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_816.solverstate
I0409 20:02:29.775615 14789 solver.cpp:330] Iteration 816, Testing net (#0)
I0409 20:02:29.775637 14789 net.cpp:676] Ignoring source layer train-data
I0409 20:02:33.958477 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:02:34.316123 14789 solver.cpp:397]     Test net output #0: accuracy = 0.0104167
I0409 20:02:34.316251 14789 solver.cpp:397]     Test net output #1: loss = 5.06008 (* 1 = 5.06008 loss)
I0409 20:02:34.407280 14789 solver.cpp:218] Iteration 816 (0.755034 iter/s, 15.8933s/12 iters), loss = 5.10165
I0409 20:02:34.407331 14789 solver.cpp:237]     Train net output #0: loss = 5.10165 (* 1 = 5.10165 loss)
I0409 20:02:34.407342 14789 sgd_solver.cpp:105] Iteration 816, lr = 0.00850749
I0409 20:02:38.530426 14789 solver.cpp:218] Iteration 828 (2.91053 iter/s, 4.12296s/12 iters), loss = 5.15649
I0409 20:02:38.530475 14789 solver.cpp:237]     Train net output #0: loss = 5.15649 (* 1 = 5.15649 loss)
I0409 20:02:38.530486 14789 sgd_solver.cpp:105] Iteration 828, lr = 0.00848729
I0409 20:02:43.143504 14789 solver.cpp:218] Iteration 840 (2.60141 iter/s, 4.61288s/12 iters), loss = 5.05261
I0409 20:02:43.143550 14789 solver.cpp:237]     Train net output #0: loss = 5.05261 (* 1 = 5.05261 loss)
I0409 20:02:43.143563 14789 sgd_solver.cpp:105] Iteration 840, lr = 0.00846714
I0409 20:02:48.122977 14789 solver.cpp:218] Iteration 852 (2.40999 iter/s, 4.97926s/12 iters), loss = 4.98161
I0409 20:02:48.123025 14789 solver.cpp:237]     Train net output #0: loss = 4.98161 (* 1 = 4.98161 loss)
I0409 20:02:48.123036 14789 sgd_solver.cpp:105] Iteration 852, lr = 0.00844704
I0409 20:02:52.910713 14789 solver.cpp:218] Iteration 864 (2.50651 iter/s, 4.78753s/12 iters), loss = 5.04108
I0409 20:02:52.910759 14789 solver.cpp:237]     Train net output #0: loss = 5.04108 (* 1 = 5.04108 loss)
I0409 20:02:52.910768 14789 sgd_solver.cpp:105] Iteration 864, lr = 0.00842698
I0409 20:02:57.468576 14789 solver.cpp:218] Iteration 876 (2.63292 iter/s, 4.55767s/12 iters), loss = 5.02228
I0409 20:02:57.468621 14789 solver.cpp:237]     Train net output #0: loss = 5.02228 (* 1 = 5.02228 loss)
I0409 20:02:57.468631 14789 sgd_solver.cpp:105] Iteration 876, lr = 0.00840698
I0409 20:03:02.374002 14789 solver.cpp:218] Iteration 888 (2.44639 iter/s, 4.90519s/12 iters), loss = 4.94157
I0409 20:03:02.374056 14789 solver.cpp:237]     Train net output #0: loss = 4.94157 (* 1 = 4.94157 loss)
I0409 20:03:02.374068 14789 sgd_solver.cpp:105] Iteration 888, lr = 0.00838702
I0409 20:03:07.130779 14789 solver.cpp:218] Iteration 900 (2.52282 iter/s, 4.75657s/12 iters), loss = 5.10293
I0409 20:03:07.130872 14789 solver.cpp:237]     Train net output #0: loss = 5.10293 (* 1 = 5.10293 loss)
I0409 20:03:07.130882 14789 sgd_solver.cpp:105] Iteration 900, lr = 0.0083671
I0409 20:03:10.965318 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:03:12.101125 14789 solver.cpp:218] Iteration 912 (2.41444 iter/s, 4.97009s/12 iters), loss = 4.92789
I0409 20:03:12.101179 14789 solver.cpp:237]     Train net output #0: loss = 4.92789 (* 1 = 4.92789 loss)
I0409 20:03:12.101191 14789 sgd_solver.cpp:105] Iteration 912, lr = 0.00834724
I0409 20:03:14.072801 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_918.caffemodel
I0409 20:03:20.795182 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_918.solverstate
I0409 20:03:25.311889 14789 solver.cpp:330] Iteration 918, Testing net (#0)
I0409 20:03:25.311913 14789 net.cpp:676] Ignoring source layer train-data
I0409 20:03:29.632086 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:03:30.032925 14789 solver.cpp:397]     Test net output #0: accuracy = 0.0159314
I0409 20:03:30.032977 14789 solver.cpp:397]     Test net output #1: loss = 5.01894 (* 1 = 5.01894 loss)
I0409 20:03:31.971243 14789 solver.cpp:218] Iteration 924 (0.603942 iter/s, 19.8695s/12 iters), loss = 5.03466
I0409 20:03:31.971299 14789 solver.cpp:237]     Train net output #0: loss = 5.03466 (* 1 = 5.03466 loss)
I0409 20:03:31.971312 14789 sgd_solver.cpp:105] Iteration 924, lr = 0.00832742
I0409 20:03:36.884289 14789 solver.cpp:218] Iteration 936 (2.44258 iter/s, 4.91284s/12 iters), loss = 5.07808
I0409 20:03:36.884335 14789 solver.cpp:237]     Train net output #0: loss = 5.07808 (* 1 = 5.07808 loss)
I0409 20:03:36.884346 14789 sgd_solver.cpp:105] Iteration 936, lr = 0.00830765
I0409 20:03:41.759593 14789 solver.cpp:218] Iteration 948 (2.46149 iter/s, 4.8751s/12 iters), loss = 4.98162
I0409 20:03:41.759711 14789 solver.cpp:237]     Train net output #0: loss = 4.98162 (* 1 = 4.98162 loss)
I0409 20:03:41.759723 14789 sgd_solver.cpp:105] Iteration 948, lr = 0.00828793
I0409 20:03:46.495343 14789 solver.cpp:218] Iteration 960 (2.53406 iter/s, 4.73548s/12 iters), loss = 4.92277
I0409 20:03:46.495394 14789 solver.cpp:237]     Train net output #0: loss = 4.92277 (* 1 = 4.92277 loss)
I0409 20:03:46.495405 14789 sgd_solver.cpp:105] Iteration 960, lr = 0.00826825
I0409 20:03:51.775336 14789 solver.cpp:218] Iteration 972 (2.27282 iter/s, 5.27977s/12 iters), loss = 4.99607
I0409 20:03:51.775389 14789 solver.cpp:237]     Train net output #0: loss = 4.99607 (* 1 = 4.99607 loss)
I0409 20:03:51.775400 14789 sgd_solver.cpp:105] Iteration 972, lr = 0.00824862
I0409 20:03:56.397259 14789 solver.cpp:218] Iteration 984 (2.59644 iter/s, 4.62172s/12 iters), loss = 5.02254
I0409 20:03:56.397311 14789 solver.cpp:237]     Train net output #0: loss = 5.02254 (* 1 = 5.02254 loss)
I0409 20:03:56.397323 14789 sgd_solver.cpp:105] Iteration 984, lr = 0.00822903
I0409 20:04:01.299572 14789 solver.cpp:218] Iteration 996 (2.44793 iter/s, 4.9021s/12 iters), loss = 4.92552
I0409 20:04:01.299623 14789 solver.cpp:237]     Train net output #0: loss = 4.92552 (* 1 = 4.92552 loss)
I0409 20:04:01.299633 14789 sgd_solver.cpp:105] Iteration 996, lr = 0.0082095
I0409 20:04:06.175793 14789 solver.cpp:218] Iteration 1008 (2.46103 iter/s, 4.87601s/12 iters), loss = 5.04211
I0409 20:04:06.175848 14789 solver.cpp:237]     Train net output #0: loss = 5.04211 (* 1 = 5.04211 loss)
I0409 20:04:06.175859 14789 sgd_solver.cpp:105] Iteration 1008, lr = 0.00819001
I0409 20:04:07.101125 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:04:10.607455 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1020.caffemodel
I0409 20:04:15.379314 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1020.solverstate
I0409 20:04:23.984361 14789 solver.cpp:330] Iteration 1020, Testing net (#0)
I0409 20:04:23.984386 14789 net.cpp:676] Ignoring source layer train-data
I0409 20:04:28.034348 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:04:28.467207 14789 solver.cpp:397]     Test net output #0: accuracy = 0.0214461
I0409 20:04:28.467257 14789 solver.cpp:397]     Test net output #1: loss = 4.96303 (* 1 = 4.96303 loss)
I0409 20:04:28.559459 14789 solver.cpp:218] Iteration 1020 (0.536122 iter/s, 22.383s/12 iters), loss = 4.90311
I0409 20:04:28.559502 14789 solver.cpp:237]     Train net output #0: loss = 4.90311 (* 1 = 4.90311 loss)
I0409 20:04:28.559514 14789 sgd_solver.cpp:105] Iteration 1020, lr = 0.00817056
I0409 20:04:32.622478 14789 solver.cpp:218] Iteration 1032 (2.9536 iter/s, 4.06284s/12 iters), loss = 4.95504
I0409 20:04:32.622529 14789 solver.cpp:237]     Train net output #0: loss = 4.95504 (* 1 = 4.95504 loss)
I0409 20:04:32.622539 14789 sgd_solver.cpp:105] Iteration 1032, lr = 0.00815116
I0409 20:04:37.416779 14789 solver.cpp:218] Iteration 1044 (2.50308 iter/s, 4.7941s/12 iters), loss = 4.94988
I0409 20:04:37.416831 14789 solver.cpp:237]     Train net output #0: loss = 4.94988 (* 1 = 4.94988 loss)
I0409 20:04:37.416842 14789 sgd_solver.cpp:105] Iteration 1044, lr = 0.00813181
I0409 20:04:42.261971 14789 solver.cpp:218] Iteration 1056 (2.47679 iter/s, 4.84498s/12 iters), loss = 4.91216
I0409 20:04:42.262013 14789 solver.cpp:237]     Train net output #0: loss = 4.91216 (* 1 = 4.91216 loss)
I0409 20:04:42.262023 14789 sgd_solver.cpp:105] Iteration 1056, lr = 0.0081125
I0409 20:04:47.007234 14789 solver.cpp:218] Iteration 1068 (2.52894 iter/s, 4.74507s/12 iters), loss = 4.97071
I0409 20:04:47.007354 14789 solver.cpp:237]     Train net output #0: loss = 4.97071 (* 1 = 4.97071 loss)
I0409 20:04:47.007369 14789 sgd_solver.cpp:105] Iteration 1068, lr = 0.00809324
I0409 20:04:51.597425 14789 solver.cpp:218] Iteration 1080 (2.61442 iter/s, 4.58993s/12 iters), loss = 4.92708
I0409 20:04:51.597481 14789 solver.cpp:237]     Train net output #0: loss = 4.92708 (* 1 = 4.92708 loss)
I0409 20:04:51.597496 14789 sgd_solver.cpp:105] Iteration 1080, lr = 0.00807403
I0409 20:04:56.338770 14789 solver.cpp:218] Iteration 1092 (2.53103 iter/s, 4.74114s/12 iters), loss = 4.96612
I0409 20:04:56.338814 14789 solver.cpp:237]     Train net output #0: loss = 4.96612 (* 1 = 4.96612 loss)
I0409 20:04:56.338825 14789 sgd_solver.cpp:105] Iteration 1092, lr = 0.00805486
I0409 20:05:01.291693 14789 solver.cpp:218] Iteration 1104 (2.42291 iter/s, 4.95272s/12 iters), loss = 4.91303
I0409 20:05:01.291750 14789 solver.cpp:237]     Train net output #0: loss = 4.91303 (* 1 = 4.91303 loss)
I0409 20:05:01.291764 14789 sgd_solver.cpp:105] Iteration 1104, lr = 0.00803573
I0409 20:05:04.201624 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:05:05.979518 14789 solver.cpp:218] Iteration 1116 (2.55993 iter/s, 4.68762s/12 iters), loss = 4.90959
I0409 20:05:05.979573 14789 solver.cpp:237]     Train net output #0: loss = 4.90959 (* 1 = 4.90959 loss)
I0409 20:05:05.979584 14789 sgd_solver.cpp:105] Iteration 1116, lr = 0.00801666
I0409 20:05:07.959467 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1122.caffemodel
I0409 20:05:15.359248 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1122.solverstate
I0409 20:05:25.349361 14789 solver.cpp:330] Iteration 1122, Testing net (#0)
I0409 20:05:25.349436 14789 net.cpp:676] Ignoring source layer train-data
I0409 20:05:29.365721 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:05:29.844825 14789 solver.cpp:397]     Test net output #0: accuracy = 0.0177696
I0409 20:05:29.844871 14789 solver.cpp:397]     Test net output #1: loss = 4.93914 (* 1 = 4.93914 loss)
I0409 20:05:31.606787 14789 solver.cpp:218] Iteration 1128 (0.468266 iter/s, 25.6265s/12 iters), loss = 5.04317
I0409 20:05:31.606853 14789 solver.cpp:237]     Train net output #0: loss = 5.04317 (* 1 = 5.04317 loss)
I0409 20:05:31.606869 14789 sgd_solver.cpp:105] Iteration 1128, lr = 0.00799762
I0409 20:05:36.336958 14789 solver.cpp:218] Iteration 1140 (2.53702 iter/s, 4.72996s/12 iters), loss = 4.87247
I0409 20:05:36.337004 14789 solver.cpp:237]     Train net output #0: loss = 4.87247 (* 1 = 4.87247 loss)
I0409 20:05:36.337013 14789 sgd_solver.cpp:105] Iteration 1140, lr = 0.00797863
I0409 20:05:41.156723 14789 solver.cpp:218] Iteration 1152 (2.48985 iter/s, 4.81957s/12 iters), loss = 4.83326
I0409 20:05:41.156767 14789 solver.cpp:237]     Train net output #0: loss = 4.83326 (* 1 = 4.83326 loss)
I0409 20:05:41.156777 14789 sgd_solver.cpp:105] Iteration 1152, lr = 0.00795969
I0409 20:05:46.069888 14789 solver.cpp:218] Iteration 1164 (2.44252 iter/s, 4.91297s/12 iters), loss = 4.83979
I0409 20:05:46.069929 14789 solver.cpp:237]     Train net output #0: loss = 4.83979 (* 1 = 4.83979 loss)
I0409 20:05:46.069938 14789 sgd_solver.cpp:105] Iteration 1164, lr = 0.00794079
I0409 20:05:50.856271 14789 solver.cpp:218] Iteration 1176 (2.50721 iter/s, 4.78619s/12 iters), loss = 4.80483
I0409 20:05:50.856315 14789 solver.cpp:237]     Train net output #0: loss = 4.80483 (* 1 = 4.80483 loss)
I0409 20:05:50.856324 14789 sgd_solver.cpp:105] Iteration 1176, lr = 0.00792194
I0409 20:05:55.534060 14789 solver.cpp:218] Iteration 1188 (2.56542 iter/s, 4.67759s/12 iters), loss = 4.88015
I0409 20:05:55.534200 14789 solver.cpp:237]     Train net output #0: loss = 4.88015 (* 1 = 4.88015 loss)
I0409 20:05:55.534214 14789 sgd_solver.cpp:105] Iteration 1188, lr = 0.00790313
I0409 20:06:00.150458 14789 solver.cpp:218] Iteration 1200 (2.59959 iter/s, 4.61611s/12 iters), loss = 4.90501
I0409 20:06:00.150509 14789 solver.cpp:237]     Train net output #0: loss = 4.90501 (* 1 = 4.90501 loss)
I0409 20:06:00.150521 14789 sgd_solver.cpp:105] Iteration 1200, lr = 0.00788437
I0409 20:06:04.805686 14789 solver.cpp:218] Iteration 1212 (2.57785 iter/s, 4.65504s/12 iters), loss = 4.94461
I0409 20:06:04.805727 14789 solver.cpp:237]     Train net output #0: loss = 4.94461 (* 1 = 4.94461 loss)
I0409 20:06:04.805737 14789 sgd_solver.cpp:105] Iteration 1212, lr = 0.00786565
I0409 20:06:05.083395 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:06:09.110777 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1224.caffemodel
I0409 20:06:12.930162 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1224.solverstate
I0409 20:06:19.666878 14789 solver.cpp:330] Iteration 1224, Testing net (#0)
I0409 20:06:19.666903 14789 net.cpp:676] Ignoring source layer train-data
I0409 20:06:23.651986 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:06:24.176431 14789 solver.cpp:397]     Test net output #0: accuracy = 0.0324755
I0409 20:06:24.176470 14789 solver.cpp:397]     Test net output #1: loss = 4.81682 (* 1 = 4.81682 loss)
I0409 20:06:24.268275 14789 solver.cpp:218] Iteration 1224 (0.616587 iter/s, 19.462s/12 iters), loss = 4.8003
I0409 20:06:24.268322 14789 solver.cpp:237]     Train net output #0: loss = 4.8003 (* 1 = 4.8003 loss)
I0409 20:06:24.268330 14789 sgd_solver.cpp:105] Iteration 1224, lr = 0.00784697
I0409 20:06:28.029529 14789 solver.cpp:218] Iteration 1236 (3.19057 iter/s, 3.76109s/12 iters), loss = 4.8684
I0409 20:06:28.038028 14789 solver.cpp:237]     Train net output #0: loss = 4.8684 (* 1 = 4.8684 loss)
I0409 20:06:28.038040 14789 sgd_solver.cpp:105] Iteration 1236, lr = 0.00782834
I0409 20:06:32.738696 14789 solver.cpp:218] Iteration 1248 (2.5529 iter/s, 4.70053s/12 iters), loss = 4.75287
I0409 20:06:32.738739 14789 solver.cpp:237]     Train net output #0: loss = 4.75287 (* 1 = 4.75287 loss)
I0409 20:06:32.738747 14789 sgd_solver.cpp:105] Iteration 1248, lr = 0.00780976
I0409 20:06:37.372144 14789 solver.cpp:218] Iteration 1260 (2.58997 iter/s, 4.63326s/12 iters), loss = 4.69614
I0409 20:06:37.372200 14789 solver.cpp:237]     Train net output #0: loss = 4.69614 (* 1 = 4.69614 loss)
I0409 20:06:37.372211 14789 sgd_solver.cpp:105] Iteration 1260, lr = 0.00779122
I0409 20:06:42.629731 14789 solver.cpp:218] Iteration 1272 (2.28251 iter/s, 5.25738s/12 iters), loss = 4.73285
I0409 20:06:42.629773 14789 solver.cpp:237]     Train net output #0: loss = 4.73285 (* 1 = 4.73285 loss)
I0409 20:06:42.629782 14789 sgd_solver.cpp:105] Iteration 1272, lr = 0.00777272
I0409 20:06:47.424871 14789 solver.cpp:218] Iteration 1284 (2.50263 iter/s, 4.79495s/12 iters), loss = 4.81803
I0409 20:06:47.424917 14789 solver.cpp:237]     Train net output #0: loss = 4.81803 (* 1 = 4.81803 loss)
I0409 20:06:47.424927 14789 sgd_solver.cpp:105] Iteration 1284, lr = 0.00775426
I0409 20:06:52.259323 14789 solver.cpp:218] Iteration 1296 (2.48229 iter/s, 4.83425s/12 iters), loss = 4.65166
I0409 20:06:52.259375 14789 solver.cpp:237]     Train net output #0: loss = 4.65166 (* 1 = 4.65166 loss)
I0409 20:06:52.259387 14789 sgd_solver.cpp:105] Iteration 1296, lr = 0.00773585
I0409 20:06:57.194717 14789 solver.cpp:218] Iteration 1308 (2.43152 iter/s, 4.93519s/12 iters), loss = 4.66518
I0409 20:06:57.194775 14789 solver.cpp:237]     Train net output #0: loss = 4.66518 (* 1 = 4.66518 loss)
I0409 20:06:57.194788 14789 sgd_solver.cpp:105] Iteration 1308, lr = 0.00771749
I0409 20:06:59.551965 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:07:02.067917 14789 solver.cpp:218] Iteration 1320 (2.46255 iter/s, 4.87299s/12 iters), loss = 4.69271
I0409 20:07:02.067962 14789 solver.cpp:237]     Train net output #0: loss = 4.69271 (* 1 = 4.69271 loss)
I0409 20:07:02.067971 14789 sgd_solver.cpp:105] Iteration 1320, lr = 0.00769916
I0409 20:07:03.975777 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1326.caffemodel
I0409 20:07:09.377733 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1326.solverstate
I0409 20:07:12.375154 14789 solver.cpp:330] Iteration 1326, Testing net (#0)
I0409 20:07:12.375178 14789 net.cpp:676] Ignoring source layer train-data
I0409 20:07:16.318092 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:07:16.875121 14789 solver.cpp:397]     Test net output #0: accuracy = 0.0318627
I0409 20:07:16.875174 14789 solver.cpp:397]     Test net output #1: loss = 4.7692 (* 1 = 4.7692 loss)
I0409 20:07:18.748450 14789 solver.cpp:218] Iteration 1332 (0.719424 iter/s, 16.68s/12 iters), loss = 4.66655
I0409 20:07:18.748495 14789 solver.cpp:237]     Train net output #0: loss = 4.66655 (* 1 = 4.66655 loss)
I0409 20:07:18.748505 14789 sgd_solver.cpp:105] Iteration 1332, lr = 0.00768088
I0409 20:07:23.782449 14789 solver.cpp:218] Iteration 1344 (2.38389 iter/s, 5.03379s/12 iters), loss = 4.5756
I0409 20:07:23.782498 14789 solver.cpp:237]     Train net output #0: loss = 4.5756 (* 1 = 4.5756 loss)
I0409 20:07:23.782507 14789 sgd_solver.cpp:105] Iteration 1344, lr = 0.00766265
I0409 20:07:28.956001 14789 solver.cpp:218] Iteration 1356 (2.31958 iter/s, 5.17335s/12 iters), loss = 4.74605
I0409 20:07:28.956044 14789 solver.cpp:237]     Train net output #0: loss = 4.74605 (* 1 = 4.74605 loss)
I0409 20:07:28.956053 14789 sgd_solver.cpp:105] Iteration 1356, lr = 0.00764446
I0409 20:07:33.942765 14789 solver.cpp:218] Iteration 1368 (2.40647 iter/s, 4.98657s/12 iters), loss = 4.70434
I0409 20:07:33.942857 14789 solver.cpp:237]     Train net output #0: loss = 4.70434 (* 1 = 4.70434 loss)
I0409 20:07:33.942865 14789 sgd_solver.cpp:105] Iteration 1368, lr = 0.00762631
I0409 20:07:35.189150 14789 blocking_queue.cpp:49] Waiting for data
I0409 20:07:39.122869 14789 solver.cpp:218] Iteration 1380 (2.31667 iter/s, 5.17985s/12 iters), loss = 4.43765
I0409 20:07:39.122921 14789 solver.cpp:237]     Train net output #0: loss = 4.43765 (* 1 = 4.43765 loss)
I0409 20:07:39.122933 14789 sgd_solver.cpp:105] Iteration 1380, lr = 0.0076082
I0409 20:07:44.321031 14789 solver.cpp:218] Iteration 1392 (2.3086 iter/s, 5.19795s/12 iters), loss = 4.58509
I0409 20:07:44.321074 14789 solver.cpp:237]     Train net output #0: loss = 4.58509 (* 1 = 4.58509 loss)
I0409 20:07:44.321084 14789 sgd_solver.cpp:105] Iteration 1392, lr = 0.00759014
I0409 20:07:49.206635 14789 solver.cpp:218] Iteration 1404 (2.45629 iter/s, 4.88541s/12 iters), loss = 4.57345
I0409 20:07:49.206684 14789 solver.cpp:237]     Train net output #0: loss = 4.57345 (* 1 = 4.57345 loss)
I0409 20:07:49.206696 14789 sgd_solver.cpp:105] Iteration 1404, lr = 0.00757212
I0409 20:07:53.727007 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:07:54.076375 14789 solver.cpp:218] Iteration 1416 (2.4643 iter/s, 4.86954s/12 iters), loss = 4.45352
I0409 20:07:54.076421 14789 solver.cpp:237]     Train net output #0: loss = 4.45352 (* 1 = 4.45352 loss)
I0409 20:07:54.076432 14789 sgd_solver.cpp:105] Iteration 1416, lr = 0.00755414
I0409 20:07:58.247843 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1428.caffemodel
I0409 20:08:06.219892 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1428.solverstate
I0409 20:08:10.124166 14789 solver.cpp:330] Iteration 1428, Testing net (#0)
I0409 20:08:10.124192 14789 net.cpp:676] Ignoring source layer train-data
I0409 20:08:13.999583 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:08:14.643968 14789 solver.cpp:397]     Test net output #0: accuracy = 0.0453431
I0409 20:08:14.644007 14789 solver.cpp:397]     Test net output #1: loss = 4.59595 (* 1 = 4.59595 loss)
I0409 20:08:14.735692 14789 solver.cpp:218] Iteration 1428 (0.58087 iter/s, 20.6587s/12 iters), loss = 4.60379
I0409 20:08:14.735735 14789 solver.cpp:237]     Train net output #0: loss = 4.60379 (* 1 = 4.60379 loss)
I0409 20:08:14.735745 14789 sgd_solver.cpp:105] Iteration 1428, lr = 0.0075362
I0409 20:08:18.865672 14789 solver.cpp:218] Iteration 1440 (2.90571 iter/s, 4.1298s/12 iters), loss = 4.50164
I0409 20:08:18.865725 14789 solver.cpp:237]     Train net output #0: loss = 4.50164 (* 1 = 4.50164 loss)
I0409 20:08:18.865736 14789 sgd_solver.cpp:105] Iteration 1440, lr = 0.00751831
I0409 20:08:23.697865 14789 solver.cpp:218] Iteration 1452 (2.48345 iter/s, 4.83199s/12 iters), loss = 4.76848
I0409 20:08:23.697921 14789 solver.cpp:237]     Train net output #0: loss = 4.76848 (* 1 = 4.76848 loss)
I0409 20:08:23.697932 14789 sgd_solver.cpp:105] Iteration 1452, lr = 0.00750046
I0409 20:08:28.531494 14789 solver.cpp:218] Iteration 1464 (2.48271 iter/s, 4.83343s/12 iters), loss = 4.64218
I0409 20:08:28.531548 14789 solver.cpp:237]     Train net output #0: loss = 4.64218 (* 1 = 4.64218 loss)
I0409 20:08:28.531560 14789 sgd_solver.cpp:105] Iteration 1464, lr = 0.00748265
I0409 20:08:33.445889 14789 solver.cpp:218] Iteration 1476 (2.44191 iter/s, 4.91419s/12 iters), loss = 4.552
I0409 20:08:33.445932 14789 solver.cpp:237]     Train net output #0: loss = 4.552 (* 1 = 4.552 loss)
I0409 20:08:33.445941 14789 sgd_solver.cpp:105] Iteration 1476, lr = 0.00746489
I0409 20:08:38.278650 14789 solver.cpp:218] Iteration 1488 (2.48315 iter/s, 4.83257s/12 iters), loss = 4.67329
I0409 20:08:38.278762 14789 solver.cpp:237]     Train net output #0: loss = 4.67329 (* 1 = 4.67329 loss)
I0409 20:08:38.278775 14789 sgd_solver.cpp:105] Iteration 1488, lr = 0.00744716
I0409 20:08:43.415144 14789 solver.cpp:218] Iteration 1500 (2.33635 iter/s, 5.13623s/12 iters), loss = 4.31789
I0409 20:08:43.415194 14789 solver.cpp:237]     Train net output #0: loss = 4.31789 (* 1 = 4.31789 loss)
I0409 20:08:43.415206 14789 sgd_solver.cpp:105] Iteration 1500, lr = 0.00742948
I0409 20:08:48.257491 14789 solver.cpp:218] Iteration 1512 (2.47824 iter/s, 4.84215s/12 iters), loss = 4.55697
I0409 20:08:48.257529 14789 solver.cpp:237]     Train net output #0: loss = 4.55697 (* 1 = 4.55697 loss)
I0409 20:08:48.257537 14789 sgd_solver.cpp:105] Iteration 1512, lr = 0.00741184
I0409 20:08:49.959559 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:08:52.968508 14789 solver.cpp:218] Iteration 1524 (2.54732 iter/s, 4.71083s/12 iters), loss = 4.56119
I0409 20:08:52.968564 14789 solver.cpp:237]     Train net output #0: loss = 4.56119 (* 1 = 4.56119 loss)
I0409 20:08:52.968575 14789 sgd_solver.cpp:105] Iteration 1524, lr = 0.00739425
I0409 20:08:55.018199 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1530.caffemodel
I0409 20:08:59.446586 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1530.solverstate
I0409 20:09:02.516465 14789 solver.cpp:330] Iteration 1530, Testing net (#0)
I0409 20:09:02.516490 14789 net.cpp:676] Ignoring source layer train-data
I0409 20:09:06.293476 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:09:06.939823 14789 solver.cpp:397]     Test net output #0: accuracy = 0.0373775
I0409 20:09:06.939853 14789 solver.cpp:397]     Test net output #1: loss = 4.58292 (* 1 = 4.58292 loss)
I0409 20:09:08.942579 14789 solver.cpp:218] Iteration 1536 (0.751242 iter/s, 15.9736s/12 iters), loss = 4.69982
I0409 20:09:08.942744 14789 solver.cpp:237]     Train net output #0: loss = 4.69982 (* 1 = 4.69982 loss)
I0409 20:09:08.942759 14789 sgd_solver.cpp:105] Iteration 1536, lr = 0.00737669
I0409 20:09:13.770880 14789 solver.cpp:218] Iteration 1548 (2.4855 iter/s, 4.828s/12 iters), loss = 4.24886
I0409 20:09:13.770929 14789 solver.cpp:237]     Train net output #0: loss = 4.24886 (* 1 = 4.24886 loss)
I0409 20:09:13.770941 14789 sgd_solver.cpp:105] Iteration 1548, lr = 0.00735918
I0409 20:09:18.419199 14789 solver.cpp:218] Iteration 1560 (2.58168 iter/s, 4.64813s/12 iters), loss = 4.5439
I0409 20:09:18.419241 14789 solver.cpp:237]     Train net output #0: loss = 4.5439 (* 1 = 4.5439 loss)
I0409 20:09:18.419250 14789 sgd_solver.cpp:105] Iteration 1560, lr = 0.00734171
I0409 20:09:23.183897 14789 solver.cpp:218] Iteration 1572 (2.51862 iter/s, 4.76451s/12 iters), loss = 4.36683
I0409 20:09:23.183949 14789 solver.cpp:237]     Train net output #0: loss = 4.36683 (* 1 = 4.36683 loss)
I0409 20:09:23.183962 14789 sgd_solver.cpp:105] Iteration 1572, lr = 0.00732427
I0409 20:09:27.773783 14789 solver.cpp:218] Iteration 1584 (2.61455 iter/s, 4.5897s/12 iters), loss = 4.36342
I0409 20:09:27.773823 14789 solver.cpp:237]     Train net output #0: loss = 4.36342 (* 1 = 4.36342 loss)
I0409 20:09:27.773829 14789 sgd_solver.cpp:105] Iteration 1584, lr = 0.00730688
I0409 20:09:32.517149 14789 solver.cpp:218] Iteration 1596 (2.52995 iter/s, 4.74317s/12 iters), loss = 4.42592
I0409 20:09:32.517203 14789 solver.cpp:237]     Train net output #0: loss = 4.42592 (* 1 = 4.42592 loss)
I0409 20:09:32.517215 14789 sgd_solver.cpp:105] Iteration 1596, lr = 0.00728954
I0409 20:09:37.241219 14789 solver.cpp:218] Iteration 1608 (2.54029 iter/s, 4.72387s/12 iters), loss = 4.34301
I0409 20:09:37.241261 14789 solver.cpp:237]     Train net output #0: loss = 4.34301 (* 1 = 4.34301 loss)
I0409 20:09:37.241272 14789 sgd_solver.cpp:105] Iteration 1608, lr = 0.00727223
I0409 20:09:41.060369 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:09:42.257151 14789 solver.cpp:218] Iteration 1620 (2.39247 iter/s, 5.01573s/12 iters), loss = 4.20479
I0409 20:09:42.257207 14789 solver.cpp:237]     Train net output #0: loss = 4.20479 (* 1 = 4.20479 loss)
I0409 20:09:42.257218 14789 sgd_solver.cpp:105] Iteration 1620, lr = 0.00725496
I0409 20:09:47.021049 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1632.caffemodel
I0409 20:09:50.775770 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1632.solverstate
I0409 20:09:53.786499 14789 solver.cpp:330] Iteration 1632, Testing net (#0)
I0409 20:09:53.786525 14789 net.cpp:676] Ignoring source layer train-data
I0409 20:09:57.847800 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:09:58.546506 14789 solver.cpp:397]     Test net output #0: accuracy = 0.0612745
I0409 20:09:58.546552 14789 solver.cpp:397]     Test net output #1: loss = 4.38448 (* 1 = 4.38448 loss)
I0409 20:09:58.638217 14789 solver.cpp:218] Iteration 1632 (0.732576 iter/s, 16.3805s/12 iters), loss = 4.34812
I0409 20:09:58.638267 14789 solver.cpp:237]     Train net output #0: loss = 4.34812 (* 1 = 4.34812 loss)
I0409 20:09:58.638278 14789 sgd_solver.cpp:105] Iteration 1632, lr = 0.00723774
I0409 20:10:02.496920 14789 solver.cpp:218] Iteration 1644 (3.10999 iter/s, 3.85853s/12 iters), loss = 4.31157
I0409 20:10:02.496974 14789 solver.cpp:237]     Train net output #0: loss = 4.31157 (* 1 = 4.31157 loss)
I0409 20:10:02.496986 14789 sgd_solver.cpp:105] Iteration 1644, lr = 0.00722056
I0409 20:10:07.427935 14789 solver.cpp:218] Iteration 1656 (2.43368 iter/s, 4.93081s/12 iters), loss = 4.30122
I0409 20:10:07.427989 14789 solver.cpp:237]     Train net output #0: loss = 4.30122 (* 1 = 4.30122 loss)
I0409 20:10:07.428000 14789 sgd_solver.cpp:105] Iteration 1656, lr = 0.00720341
I0409 20:10:12.305665 14789 solver.cpp:218] Iteration 1668 (2.46026 iter/s, 4.87753s/12 iters), loss = 4.13632
I0409 20:10:12.305773 14789 solver.cpp:237]     Train net output #0: loss = 4.13632 (* 1 = 4.13632 loss)
I0409 20:10:12.305785 14789 sgd_solver.cpp:105] Iteration 1668, lr = 0.00718631
I0409 20:10:17.331660 14789 solver.cpp:218] Iteration 1680 (2.38771 iter/s, 5.02573s/12 iters), loss = 4.29654
I0409 20:10:17.331715 14789 solver.cpp:237]     Train net output #0: loss = 4.29654 (* 1 = 4.29654 loss)
I0409 20:10:17.331728 14789 sgd_solver.cpp:105] Iteration 1680, lr = 0.00716925
I0409 20:10:22.034107 14789 solver.cpp:218] Iteration 1692 (2.55197 iter/s, 4.70225s/12 iters), loss = 4.25194
I0409 20:10:22.034164 14789 solver.cpp:237]     Train net output #0: loss = 4.25194 (* 1 = 4.25194 loss)
I0409 20:10:22.034175 14789 sgd_solver.cpp:105] Iteration 1692, lr = 0.00715223
I0409 20:10:26.811410 14789 solver.cpp:218] Iteration 1704 (2.51198 iter/s, 4.7771s/12 iters), loss = 4.07109
I0409 20:10:26.811463 14789 solver.cpp:237]     Train net output #0: loss = 4.07109 (* 1 = 4.07109 loss)
I0409 20:10:26.811475 14789 sgd_solver.cpp:105] Iteration 1704, lr = 0.00713525
I0409 20:10:31.779556 14789 solver.cpp:218] Iteration 1716 (2.41549 iter/s, 4.96794s/12 iters), loss = 4.28142
I0409 20:10:31.779595 14789 solver.cpp:237]     Train net output #0: loss = 4.28142 (* 1 = 4.28142 loss)
I0409 20:10:31.779605 14789 sgd_solver.cpp:105] Iteration 1716, lr = 0.00711831
I0409 20:10:32.824736 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:10:36.737275 14789 solver.cpp:218] Iteration 1728 (2.42056 iter/s, 4.95752s/12 iters), loss = 4.16238
I0409 20:10:36.737329 14789 solver.cpp:237]     Train net output #0: loss = 4.16238 (* 1 = 4.16238 loss)
I0409 20:10:36.737339 14789 sgd_solver.cpp:105] Iteration 1728, lr = 0.00710141
I0409 20:10:38.793574 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1734.caffemodel
I0409 20:10:45.050355 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1734.solverstate
I0409 20:10:48.186777 14789 solver.cpp:330] Iteration 1734, Testing net (#0)
I0409 20:10:48.186805 14789 net.cpp:676] Ignoring source layer train-data
I0409 20:10:51.949128 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:10:52.655050 14789 solver.cpp:397]     Test net output #0: accuracy = 0.0729167
I0409 20:10:52.655097 14789 solver.cpp:397]     Test net output #1: loss = 4.24887 (* 1 = 4.24887 loss)
I0409 20:10:54.624766 14789 solver.cpp:218] Iteration 1740 (0.670881 iter/s, 17.8869s/12 iters), loss = 4.26923
I0409 20:10:54.624811 14789 solver.cpp:237]     Train net output #0: loss = 4.26923 (* 1 = 4.26923 loss)
I0409 20:10:54.624820 14789 sgd_solver.cpp:105] Iteration 1740, lr = 0.00708455
I0409 20:10:59.593006 14789 solver.cpp:218] Iteration 1752 (2.41544 iter/s, 4.96804s/12 iters), loss = 4.32719
I0409 20:10:59.593055 14789 solver.cpp:237]     Train net output #0: loss = 4.32719 (* 1 = 4.32719 loss)
I0409 20:10:59.593068 14789 sgd_solver.cpp:105] Iteration 1752, lr = 0.00706773
I0409 20:11:04.257445 14789 solver.cpp:218] Iteration 1764 (2.57276 iter/s, 4.66424s/12 iters), loss = 4.17638
I0409 20:11:04.257495 14789 solver.cpp:237]     Train net output #0: loss = 4.17638 (* 1 = 4.17638 loss)
I0409 20:11:04.257508 14789 sgd_solver.cpp:105] Iteration 1764, lr = 0.00705094
I0409 20:11:09.113020 14789 solver.cpp:218] Iteration 1776 (2.47149 iter/s, 4.85538s/12 iters), loss = 4.30539
I0409 20:11:09.113065 14789 solver.cpp:237]     Train net output #0: loss = 4.30539 (* 1 = 4.30539 loss)
I0409 20:11:09.113075 14789 sgd_solver.cpp:105] Iteration 1776, lr = 0.0070342
I0409 20:11:14.086434 14789 solver.cpp:218] Iteration 1788 (2.41293 iter/s, 4.97322s/12 iters), loss = 4.30769
I0409 20:11:14.086481 14789 solver.cpp:237]     Train net output #0: loss = 4.30769 (* 1 = 4.30769 loss)
I0409 20:11:14.086489 14789 sgd_solver.cpp:105] Iteration 1788, lr = 0.0070175
I0409 20:11:18.771447 14789 solver.cpp:218] Iteration 1800 (2.56147 iter/s, 4.68482s/12 iters), loss = 4.07957
I0409 20:11:18.771574 14789 solver.cpp:237]     Train net output #0: loss = 4.07957 (* 1 = 4.07957 loss)
I0409 20:11:18.771584 14789 sgd_solver.cpp:105] Iteration 1800, lr = 0.00700084
I0409 20:11:23.885023 14789 solver.cpp:218] Iteration 1812 (2.34682 iter/s, 5.1133s/12 iters), loss = 4.10921
I0409 20:11:23.885066 14789 solver.cpp:237]     Train net output #0: loss = 4.10921 (* 1 = 4.10921 loss)
I0409 20:11:23.885076 14789 sgd_solver.cpp:105] Iteration 1812, lr = 0.00698422
I0409 20:11:26.944672 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:11:28.779294 14789 solver.cpp:218] Iteration 1824 (2.45194 iter/s, 4.89408s/12 iters), loss = 4.12953
I0409 20:11:28.779342 14789 solver.cpp:237]     Train net output #0: loss = 4.12953 (* 1 = 4.12953 loss)
I0409 20:11:28.779352 14789 sgd_solver.cpp:105] Iteration 1824, lr = 0.00696764
I0409 20:11:33.587965 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1836.caffemodel
I0409 20:11:37.336925 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1836.solverstate
I0409 20:11:40.332182 14789 solver.cpp:330] Iteration 1836, Testing net (#0)
I0409 20:11:40.332208 14789 net.cpp:676] Ignoring source layer train-data
I0409 20:11:43.918642 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:11:44.668722 14789 solver.cpp:397]     Test net output #0: accuracy = 0.0931373
I0409 20:11:44.668752 14789 solver.cpp:397]     Test net output #1: loss = 4.14314 (* 1 = 4.14314 loss)
I0409 20:11:44.760556 14789 solver.cpp:218] Iteration 1836 (0.750903 iter/s, 15.9808s/12 iters), loss = 4.12055
I0409 20:11:44.760601 14789 solver.cpp:237]     Train net output #0: loss = 4.12055 (* 1 = 4.12055 loss)
I0409 20:11:44.760610 14789 sgd_solver.cpp:105] Iteration 1836, lr = 0.0069511
I0409 20:11:48.926180 14789 solver.cpp:218] Iteration 1848 (2.88085 iter/s, 4.16544s/12 iters), loss = 4.27627
I0409 20:11:48.926265 14789 solver.cpp:237]     Train net output #0: loss = 4.27627 (* 1 = 4.27627 loss)
I0409 20:11:48.926277 14789 sgd_solver.cpp:105] Iteration 1848, lr = 0.00693459
I0409 20:11:53.420186 14789 solver.cpp:218] Iteration 1860 (2.67036 iter/s, 4.49377s/12 iters), loss = 4.01355
I0409 20:11:53.420239 14789 solver.cpp:237]     Train net output #0: loss = 4.01355 (* 1 = 4.01355 loss)
I0409 20:11:53.420253 14789 sgd_solver.cpp:105] Iteration 1860, lr = 0.00691813
I0409 20:11:58.831535 14789 solver.cpp:218] Iteration 1872 (2.21765 iter/s, 5.41114s/12 iters), loss = 4.09613
I0409 20:11:58.831579 14789 solver.cpp:237]     Train net output #0: loss = 4.09613 (* 1 = 4.09613 loss)
I0409 20:11:58.831591 14789 sgd_solver.cpp:105] Iteration 1872, lr = 0.0069017
I0409 20:12:03.765609 14789 solver.cpp:218] Iteration 1884 (2.43216 iter/s, 4.93388s/12 iters), loss = 4.24405
I0409 20:12:03.765663 14789 solver.cpp:237]     Train net output #0: loss = 4.24405 (* 1 = 4.24405 loss)
I0409 20:12:03.765674 14789 sgd_solver.cpp:105] Iteration 1884, lr = 0.00688532
I0409 20:12:08.471944 14789 solver.cpp:218] Iteration 1896 (2.54986 iter/s, 4.70614s/12 iters), loss = 4.10548
I0409 20:12:08.471994 14789 solver.cpp:237]     Train net output #0: loss = 4.10548 (* 1 = 4.10548 loss)
I0409 20:12:08.472005 14789 sgd_solver.cpp:105] Iteration 1896, lr = 0.00686897
I0409 20:12:13.366808 14789 solver.cpp:218] Iteration 1908 (2.45165 iter/s, 4.89466s/12 iters), loss = 4.04402
I0409 20:12:13.366870 14789 solver.cpp:237]     Train net output #0: loss = 4.04402 (* 1 = 4.04402 loss)
I0409 20:12:13.366883 14789 sgd_solver.cpp:105] Iteration 1908, lr = 0.00685266
I0409 20:12:18.491914 14789 solver.cpp:218] Iteration 1920 (2.34151 iter/s, 5.12489s/12 iters), loss = 4.16567
I0409 20:12:18.491966 14789 solver.cpp:237]     Train net output #0: loss = 4.16567 (* 1 = 4.16567 loss)
I0409 20:12:18.491976 14789 sgd_solver.cpp:105] Iteration 1920, lr = 0.00683639
I0409 20:12:18.746721 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:12:23.118708 14789 solver.cpp:218] Iteration 1932 (2.5937 iter/s, 4.6266s/12 iters), loss = 4.05518
I0409 20:12:23.118821 14789 solver.cpp:237]     Train net output #0: loss = 4.05518 (* 1 = 4.05518 loss)
I0409 20:12:23.118831 14789 sgd_solver.cpp:105] Iteration 1932, lr = 0.00682016
I0409 20:12:25.033540 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1938.caffemodel
I0409 20:12:28.873879 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1938.solverstate
I0409 20:12:31.856957 14789 solver.cpp:330] Iteration 1938, Testing net (#0)
I0409 20:12:31.856978 14789 net.cpp:676] Ignoring source layer train-data
I0409 20:12:35.555586 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:12:36.344023 14789 solver.cpp:397]     Test net output #0: accuracy = 0.0906863
I0409 20:12:36.344069 14789 solver.cpp:397]     Test net output #1: loss = 3.98316 (* 1 = 3.98316 loss)
I0409 20:12:38.169574 14789 solver.cpp:218] Iteration 1944 (0.797325 iter/s, 15.0503s/12 iters), loss = 3.89004
I0409 20:12:38.169631 14789 solver.cpp:237]     Train net output #0: loss = 3.89004 (* 1 = 3.89004 loss)
I0409 20:12:38.169642 14789 sgd_solver.cpp:105] Iteration 1944, lr = 0.00680397
I0409 20:12:43.098882 14789 solver.cpp:218] Iteration 1956 (2.43452 iter/s, 4.9291s/12 iters), loss = 3.95338
I0409 20:12:43.098945 14789 solver.cpp:237]     Train net output #0: loss = 3.95338 (* 1 = 3.95338 loss)
I0409 20:12:43.098959 14789 sgd_solver.cpp:105] Iteration 1956, lr = 0.00678782
I0409 20:12:48.210664 14789 solver.cpp:218] Iteration 1968 (2.34762 iter/s, 5.11156s/12 iters), loss = 4.13035
I0409 20:12:48.210721 14789 solver.cpp:237]     Train net output #0: loss = 4.13035 (* 1 = 4.13035 loss)
I0409 20:12:48.210732 14789 sgd_solver.cpp:105] Iteration 1968, lr = 0.0067717
I0409 20:12:53.326400 14789 solver.cpp:218] Iteration 1980 (2.3458 iter/s, 5.11553s/12 iters), loss = 3.96497
I0409 20:12:53.326474 14789 solver.cpp:237]     Train net output #0: loss = 3.96497 (* 1 = 3.96497 loss)
I0409 20:12:53.326483 14789 sgd_solver.cpp:105] Iteration 1980, lr = 0.00675562
I0409 20:12:58.259393 14789 solver.cpp:218] Iteration 1992 (2.43271 iter/s, 4.93277s/12 iters), loss = 4.05501
I0409 20:12:58.259438 14789 solver.cpp:237]     Train net output #0: loss = 4.05501 (* 1 = 4.05501 loss)
I0409 20:12:58.259450 14789 sgd_solver.cpp:105] Iteration 1992, lr = 0.00673958
I0409 20:13:03.088413 14789 solver.cpp:218] Iteration 2004 (2.48508 iter/s, 4.82882s/12 iters), loss = 3.8321
I0409 20:13:03.088471 14789 solver.cpp:237]     Train net output #0: loss = 3.8321 (* 1 = 3.8321 loss)
I0409 20:13:03.088487 14789 sgd_solver.cpp:105] Iteration 2004, lr = 0.00672358
I0409 20:13:07.905272 14789 solver.cpp:218] Iteration 2016 (2.49135 iter/s, 4.81666s/12 iters), loss = 3.82623
I0409 20:13:07.905321 14789 solver.cpp:237]     Train net output #0: loss = 3.82623 (* 1 = 3.82623 loss)
I0409 20:13:07.905333 14789 sgd_solver.cpp:105] Iteration 2016, lr = 0.00670762
I0409 20:13:10.271610 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:13:12.709897 14789 solver.cpp:218] Iteration 2028 (2.4977 iter/s, 4.80443s/12 iters), loss = 3.66179
I0409 20:13:12.709944 14789 solver.cpp:237]     Train net output #0: loss = 3.66179 (* 1 = 3.66179 loss)
I0409 20:13:12.709952 14789 sgd_solver.cpp:105] Iteration 2028, lr = 0.00669169
I0409 20:13:17.358685 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2040.caffemodel
I0409 20:13:23.336146 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2040.solverstate
I0409 20:13:29.499194 14789 solver.cpp:330] Iteration 2040, Testing net (#0)
I0409 20:13:29.499220 14789 net.cpp:676] Ignoring source layer train-data
I0409 20:13:33.106387 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:13:34.038952 14789 solver.cpp:397]     Test net output #0: accuracy = 0.0980392
I0409 20:13:34.039000 14789 solver.cpp:397]     Test net output #1: loss = 3.92608 (* 1 = 3.92608 loss)
I0409 20:13:34.130589 14789 solver.cpp:218] Iteration 2040 (0.560223 iter/s, 21.42s/12 iters), loss = 3.99477
I0409 20:13:34.130640 14789 solver.cpp:237]     Train net output #0: loss = 3.99477 (* 1 = 3.99477 loss)
I0409 20:13:34.130651 14789 sgd_solver.cpp:105] Iteration 2040, lr = 0.00667581
I0409 20:13:38.159693 14789 solver.cpp:218] Iteration 2052 (2.97846 iter/s, 4.02892s/12 iters), loss = 3.79964
I0409 20:13:38.159744 14789 solver.cpp:237]     Train net output #0: loss = 3.79964 (* 1 = 3.79964 loss)
I0409 20:13:38.159756 14789 sgd_solver.cpp:105] Iteration 2052, lr = 0.00665996
I0409 20:13:39.711884 14789 blocking_queue.cpp:49] Waiting for data
I0409 20:13:43.193943 14789 solver.cpp:218] Iteration 2064 (2.38377 iter/s, 5.03405s/12 iters), loss = 3.82812
I0409 20:13:43.194008 14789 solver.cpp:237]     Train net output #0: loss = 3.82812 (* 1 = 3.82812 loss)
I0409 20:13:43.194021 14789 sgd_solver.cpp:105] Iteration 2064, lr = 0.00664414
I0409 20:13:48.096256 14789 solver.cpp:218] Iteration 2076 (2.44793 iter/s, 4.9021s/12 iters), loss = 3.76339
I0409 20:13:48.096305 14789 solver.cpp:237]     Train net output #0: loss = 3.76339 (* 1 = 3.76339 loss)
I0409 20:13:48.096318 14789 sgd_solver.cpp:105] Iteration 2076, lr = 0.00662837
I0409 20:13:53.286478 14789 solver.cpp:218] Iteration 2088 (2.31214 iter/s, 5.19001s/12 iters), loss = 3.60447
I0409 20:13:53.286536 14789 solver.cpp:237]     Train net output #0: loss = 3.60447 (* 1 = 3.60447 loss)
I0409 20:13:53.286550 14789 sgd_solver.cpp:105] Iteration 2088, lr = 0.00661263
I0409 20:13:58.015753 14789 solver.cpp:218] Iteration 2100 (2.53749 iter/s, 4.72907s/12 iters), loss = 3.8239
I0409 20:13:58.015857 14789 solver.cpp:237]     Train net output #0: loss = 3.8239 (* 1 = 3.8239 loss)
I0409 20:13:58.015872 14789 sgd_solver.cpp:105] Iteration 2100, lr = 0.00659693
I0409 20:14:02.614737 14789 solver.cpp:218] Iteration 2112 (2.60941 iter/s, 4.59874s/12 iters), loss = 3.84345
I0409 20:14:02.614787 14789 solver.cpp:237]     Train net output #0: loss = 3.84345 (* 1 = 3.84345 loss)
I0409 20:14:02.614799 14789 sgd_solver.cpp:105] Iteration 2112, lr = 0.00658127
I0409 20:14:07.348541 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:14:07.669651 14789 solver.cpp:218] Iteration 2124 (2.37402 iter/s, 5.05471s/12 iters), loss = 3.49996
I0409 20:14:07.669700 14789 solver.cpp:237]     Train net output #0: loss = 3.49996 (* 1 = 3.49996 loss)
I0409 20:14:07.669711 14789 sgd_solver.cpp:105] Iteration 2124, lr = 0.00656564
I0409 20:14:12.484464 14789 solver.cpp:218] Iteration 2136 (2.49241 iter/s, 4.81462s/12 iters), loss = 3.77327
I0409 20:14:12.484513 14789 solver.cpp:237]     Train net output #0: loss = 3.77327 (* 1 = 3.77327 loss)
I0409 20:14:12.484525 14789 sgd_solver.cpp:105] Iteration 2136, lr = 0.00655006
I0409 20:14:14.574443 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2142.caffemodel
I0409 20:14:21.687670 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2142.solverstate
I0409 20:14:24.815491 14789 solver.cpp:330] Iteration 2142, Testing net (#0)
I0409 20:14:24.815518 14789 net.cpp:676] Ignoring source layer train-data
I0409 20:14:28.425491 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:14:29.293665 14789 solver.cpp:397]     Test net output #0: accuracy = 0.11826
I0409 20:14:29.293715 14789 solver.cpp:397]     Test net output #1: loss = 3.79597 (* 1 = 3.79597 loss)
I0409 20:14:31.074213 14789 solver.cpp:218] Iteration 2148 (0.645537 iter/s, 18.5892s/12 iters), loss = 3.97446
I0409 20:14:31.074276 14789 solver.cpp:237]     Train net output #0: loss = 3.97446 (* 1 = 3.97446 loss)
I0409 20:14:31.074288 14789 sgd_solver.cpp:105] Iteration 2148, lr = 0.00653451
I0409 20:14:35.855857 14789 solver.cpp:218] Iteration 2160 (2.50971 iter/s, 4.78144s/12 iters), loss = 3.88465
I0409 20:14:35.855906 14789 solver.cpp:237]     Train net output #0: loss = 3.88465 (* 1 = 3.88465 loss)
I0409 20:14:35.855918 14789 sgd_solver.cpp:105] Iteration 2160, lr = 0.00651899
I0409 20:14:40.790319 14789 solver.cpp:218] Iteration 2172 (2.43198 iter/s, 4.93425s/12 iters), loss = 3.77115
I0409 20:14:40.790376 14789 solver.cpp:237]     Train net output #0: loss = 3.77115 (* 1 = 3.77115 loss)
I0409 20:14:40.790388 14789 sgd_solver.cpp:105] Iteration 2172, lr = 0.00650351
I0409 20:14:45.705530 14789 solver.cpp:218] Iteration 2184 (2.4415 iter/s, 4.91501s/12 iters), loss = 3.77231
I0409 20:14:45.705585 14789 solver.cpp:237]     Train net output #0: loss = 3.77231 (* 1 = 3.77231 loss)
I0409 20:14:45.705596 14789 sgd_solver.cpp:105] Iteration 2184, lr = 0.00648807
I0409 20:14:50.641533 14789 solver.cpp:218] Iteration 2196 (2.43122 iter/s, 4.9358s/12 iters), loss = 3.56362
I0409 20:14:50.641578 14789 solver.cpp:237]     Train net output #0: loss = 3.56362 (* 1 = 3.56362 loss)
I0409 20:14:50.641587 14789 sgd_solver.cpp:105] Iteration 2196, lr = 0.00647267
I0409 20:14:55.620285 14789 solver.cpp:218] Iteration 2208 (2.41034 iter/s, 4.97855s/12 iters), loss = 3.55551
I0409 20:14:55.620332 14789 solver.cpp:237]     Train net output #0: loss = 3.55551 (* 1 = 3.55551 loss)
I0409 20:14:55.620342 14789 sgd_solver.cpp:105] Iteration 2208, lr = 0.0064573
I0409 20:15:00.552665 14789 solver.cpp:218] Iteration 2220 (2.433 iter/s, 4.93219s/12 iters), loss = 3.83878
I0409 20:15:00.556044 14789 solver.cpp:237]     Train net output #0: loss = 3.83878 (* 1 = 3.83878 loss)
I0409 20:15:00.556054 14789 sgd_solver.cpp:105] Iteration 2220, lr = 0.00644197
I0409 20:15:02.272194 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:15:05.445895 14789 solver.cpp:218] Iteration 2232 (2.45414 iter/s, 4.8897s/12 iters), loss = 3.56565
I0409 20:15:05.445946 14789 solver.cpp:237]     Train net output #0: loss = 3.56565 (* 1 = 3.56565 loss)
I0409 20:15:05.445978 14789 sgd_solver.cpp:105] Iteration 2232, lr = 0.00642668
I0409 20:15:09.837491 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2244.caffemodel
I0409 20:15:15.892506 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2244.solverstate
I0409 20:15:19.175169 14789 solver.cpp:330] Iteration 2244, Testing net (#0)
I0409 20:15:19.175194 14789 net.cpp:676] Ignoring source layer train-data
I0409 20:15:22.766464 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:15:23.678290 14789 solver.cpp:397]     Test net output #0: accuracy = 0.136642
I0409 20:15:23.678334 14789 solver.cpp:397]     Test net output #1: loss = 3.65965 (* 1 = 3.65965 loss)
I0409 20:15:23.770056 14789 solver.cpp:218] Iteration 2244 (0.654894 iter/s, 18.3236s/12 iters), loss = 3.67686
I0409 20:15:23.770109 14789 solver.cpp:237]     Train net output #0: loss = 3.67686 (* 1 = 3.67686 loss)
I0409 20:15:23.770121 14789 sgd_solver.cpp:105] Iteration 2244, lr = 0.00641142
I0409 20:15:27.617164 14789 solver.cpp:218] Iteration 2256 (3.11936 iter/s, 3.84694s/12 iters), loss = 3.49799
I0409 20:15:27.617211 14789 solver.cpp:237]     Train net output #0: loss = 3.49799 (* 1 = 3.49799 loss)
I0409 20:15:27.617223 14789 sgd_solver.cpp:105] Iteration 2256, lr = 0.0063962
I0409 20:15:32.441584 14789 solver.cpp:218] Iteration 2268 (2.48745 iter/s, 4.82422s/12 iters), loss = 3.61995
I0409 20:15:32.441684 14789 solver.cpp:237]     Train net output #0: loss = 3.61995 (* 1 = 3.61995 loss)
I0409 20:15:32.441695 14789 sgd_solver.cpp:105] Iteration 2268, lr = 0.00638101
I0409 20:15:37.285311 14789 solver.cpp:218] Iteration 2280 (2.47756 iter/s, 4.84348s/12 iters), loss = 3.51412
I0409 20:15:37.285365 14789 solver.cpp:237]     Train net output #0: loss = 3.51412 (* 1 = 3.51412 loss)
I0409 20:15:37.285377 14789 sgd_solver.cpp:105] Iteration 2280, lr = 0.00636586
I0409 20:15:42.209663 14789 solver.cpp:218] Iteration 2292 (2.43697 iter/s, 4.92414s/12 iters), loss = 3.52176
I0409 20:15:42.209722 14789 solver.cpp:237]     Train net output #0: loss = 3.52176 (* 1 = 3.52176 loss)
I0409 20:15:42.209738 14789 sgd_solver.cpp:105] Iteration 2292, lr = 0.00635075
I0409 20:15:47.282989 14789 solver.cpp:218] Iteration 2304 (2.36541 iter/s, 5.07312s/12 iters), loss = 3.68637
I0409 20:15:47.283027 14789 solver.cpp:237]     Train net output #0: loss = 3.68637 (* 1 = 3.68637 loss)
I0409 20:15:47.283036 14789 sgd_solver.cpp:105] Iteration 2304, lr = 0.00633567
I0409 20:15:52.324662 14789 solver.cpp:218] Iteration 2316 (2.38025 iter/s, 5.04148s/12 iters), loss = 3.55113
I0409 20:15:52.324714 14789 solver.cpp:237]     Train net output #0: loss = 3.55113 (* 1 = 3.55113 loss)
I0409 20:15:52.324725 14789 sgd_solver.cpp:105] Iteration 2316, lr = 0.00632063
I0409 20:15:56.214498 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:15:57.246309 14789 solver.cpp:218] Iteration 2328 (2.43831 iter/s, 4.92143s/12 iters), loss = 3.52796
I0409 20:15:57.246361 14789 solver.cpp:237]     Train net output #0: loss = 3.52796 (* 1 = 3.52796 loss)
I0409 20:15:57.246372 14789 sgd_solver.cpp:105] Iteration 2328, lr = 0.00630562
I0409 20:16:01.974032 14789 solver.cpp:218] Iteration 2340 (2.53833 iter/s, 4.72752s/12 iters), loss = 3.39677
I0409 20:16:01.974084 14789 solver.cpp:237]     Train net output #0: loss = 3.39677 (* 1 = 3.39677 loss)
I0409 20:16:01.974095 14789 sgd_solver.cpp:105] Iteration 2340, lr = 0.00629065
I0409 20:16:04.042333 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2346.caffemodel
I0409 20:16:09.196231 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2346.solverstate
I0409 20:16:12.172062 14789 solver.cpp:330] Iteration 2346, Testing net (#0)
I0409 20:16:12.172086 14789 net.cpp:676] Ignoring source layer train-data
I0409 20:16:15.622632 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:16:16.567248 14789 solver.cpp:397]     Test net output #0: accuracy = 0.133578
I0409 20:16:16.567286 14789 solver.cpp:397]     Test net output #1: loss = 3.83205 (* 1 = 3.83205 loss)
I0409 20:16:18.592918 14789 solver.cpp:218] Iteration 2352 (0.722093 iter/s, 16.6184s/12 iters), loss = 3.64895
I0409 20:16:18.592969 14789 solver.cpp:237]     Train net output #0: loss = 3.64895 (* 1 = 3.64895 loss)
I0409 20:16:18.592979 14789 sgd_solver.cpp:105] Iteration 2352, lr = 0.00627571
I0409 20:16:23.925235 14789 solver.cpp:218] Iteration 2364 (2.25052 iter/s, 5.3321s/12 iters), loss = 3.22304
I0409 20:16:23.925297 14789 solver.cpp:237]     Train net output #0: loss = 3.22304 (* 1 = 3.22304 loss)
I0409 20:16:23.925313 14789 sgd_solver.cpp:105] Iteration 2364, lr = 0.00626081
I0409 20:16:28.502058 14789 solver.cpp:218] Iteration 2376 (2.62202 iter/s, 4.57662s/12 iters), loss = 3.36252
I0409 20:16:28.502115 14789 solver.cpp:237]     Train net output #0: loss = 3.36252 (* 1 = 3.36252 loss)
I0409 20:16:28.502127 14789 sgd_solver.cpp:105] Iteration 2376, lr = 0.00624595
I0409 20:16:33.379282 14789 solver.cpp:218] Iteration 2388 (2.46052 iter/s, 4.87702s/12 iters), loss = 3.30728
I0409 20:16:33.379338 14789 solver.cpp:237]     Train net output #0: loss = 3.30728 (* 1 = 3.30728 loss)
I0409 20:16:33.379351 14789 sgd_solver.cpp:105] Iteration 2388, lr = 0.00623112
I0409 20:16:38.467203 14789 solver.cpp:218] Iteration 2400 (2.35862 iter/s, 5.08771s/12 iters), loss = 3.39333
I0409 20:16:38.468039 14789 solver.cpp:237]     Train net output #0: loss = 3.39333 (* 1 = 3.39333 loss)
I0409 20:16:38.468052 14789 sgd_solver.cpp:105] Iteration 2400, lr = 0.00621633
I0409 20:16:43.715485 14789 solver.cpp:218] Iteration 2412 (2.2869 iter/s, 5.24729s/12 iters), loss = 3.25873
I0409 20:16:43.715536 14789 solver.cpp:237]     Train net output #0: loss = 3.25873 (* 1 = 3.25873 loss)
I0409 20:16:43.715548 14789 sgd_solver.cpp:105] Iteration 2412, lr = 0.00620157
I0409 20:16:48.787395 14789 solver.cpp:218] Iteration 2424 (2.36607 iter/s, 5.07171s/12 iters), loss = 3.35719
I0409 20:16:48.787441 14789 solver.cpp:237]     Train net output #0: loss = 3.35719 (* 1 = 3.35719 loss)
I0409 20:16:48.787451 14789 sgd_solver.cpp:105] Iteration 2424, lr = 0.00618684
I0409 20:16:49.892884 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:16:54.020007 14789 solver.cpp:218] Iteration 2436 (2.2934 iter/s, 5.2324s/12 iters), loss = 3.31622
I0409 20:16:54.020066 14789 solver.cpp:237]     Train net output #0: loss = 3.31622 (* 1 = 3.31622 loss)
I0409 20:16:54.020077 14789 sgd_solver.cpp:105] Iteration 2436, lr = 0.00617215
I0409 20:16:58.687147 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2448.caffemodel
I0409 20:17:02.656469 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2448.solverstate
I0409 20:17:07.815999 14789 solver.cpp:330] Iteration 2448, Testing net (#0)
I0409 20:17:07.816023 14789 net.cpp:676] Ignoring source layer train-data
I0409 20:17:11.354094 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:17:12.476207 14789 solver.cpp:397]     Test net output #0: accuracy = 0.150735
I0409 20:17:12.476236 14789 solver.cpp:397]     Test net output #1: loss = 3.5802 (* 1 = 3.5802 loss)
I0409 20:17:12.561950 14789 solver.cpp:218] Iteration 2448 (0.647202 iter/s, 18.5414s/12 iters), loss = 3.2615
I0409 20:17:12.567993 14789 solver.cpp:237]     Train net output #0: loss = 3.2615 (* 1 = 3.2615 loss)
I0409 20:17:12.568004 14789 sgd_solver.cpp:105] Iteration 2448, lr = 0.0061575
I0409 20:17:16.616371 14789 solver.cpp:218] Iteration 2460 (2.96424 iter/s, 4.04825s/12 iters), loss = 3.24037
I0409 20:17:16.616417 14789 solver.cpp:237]     Train net output #0: loss = 3.24037 (* 1 = 3.24037 loss)
I0409 20:17:16.616426 14789 sgd_solver.cpp:105] Iteration 2460, lr = 0.00614288
I0409 20:17:21.416463 14789 solver.cpp:218] Iteration 2472 (2.50005 iter/s, 4.7999s/12 iters), loss = 3.22518
I0409 20:17:21.416518 14789 solver.cpp:237]     Train net output #0: loss = 3.22518 (* 1 = 3.22518 loss)
I0409 20:17:21.416532 14789 sgd_solver.cpp:105] Iteration 2472, lr = 0.0061283
I0409 20:17:26.129293 14789 solver.cpp:218] Iteration 2484 (2.54635 iter/s, 4.71263s/12 iters), loss = 3.25249
I0409 20:17:26.129343 14789 solver.cpp:237]     Train net output #0: loss = 3.25249 (* 1 = 3.25249 loss)
I0409 20:17:26.129354 14789 sgd_solver.cpp:105] Iteration 2484, lr = 0.00611375
I0409 20:17:31.006131 14789 solver.cpp:218] Iteration 2496 (2.46071 iter/s, 4.87664s/12 iters), loss = 3.19343
I0409 20:17:31.006187 14789 solver.cpp:237]     Train net output #0: loss = 3.19343 (* 1 = 3.19343 loss)
I0409 20:17:31.006201 14789 sgd_solver.cpp:105] Iteration 2496, lr = 0.00609923
I0409 20:17:35.843437 14789 solver.cpp:218] Iteration 2508 (2.48082 iter/s, 4.8371s/12 iters), loss = 3.36366
I0409 20:17:35.843487 14789 solver.cpp:237]     Train net output #0: loss = 3.36366 (* 1 = 3.36366 loss)
I0409 20:17:35.843497 14789 sgd_solver.cpp:105] Iteration 2508, lr = 0.00608475
I0409 20:17:41.112187 14789 solver.cpp:218] Iteration 2520 (2.27767 iter/s, 5.26854s/12 iters), loss = 3.0953
I0409 20:17:41.112246 14789 solver.cpp:237]     Train net output #0: loss = 3.0953 (* 1 = 3.0953 loss)
I0409 20:17:41.112257 14789 sgd_solver.cpp:105] Iteration 2520, lr = 0.0060703
I0409 20:17:44.179116 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:17:45.955561 14789 solver.cpp:218] Iteration 2532 (2.47772 iter/s, 4.84316s/12 iters), loss = 3.41774
I0409 20:17:45.955601 14789 solver.cpp:237]     Train net output #0: loss = 3.41774 (* 1 = 3.41774 loss)
I0409 20:17:45.955610 14789 sgd_solver.cpp:105] Iteration 2532, lr = 0.00605589
I0409 20:17:50.936578 14789 solver.cpp:218] Iteration 2544 (2.40924 iter/s, 4.98082s/12 iters), loss = 3.05555
I0409 20:17:50.936630 14789 solver.cpp:237]     Train net output #0: loss = 3.05555 (* 1 = 3.05555 loss)
I0409 20:17:50.936640 14789 sgd_solver.cpp:105] Iteration 2544, lr = 0.00604151
I0409 20:17:52.890990 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2550.caffemodel
I0409 20:17:56.712680 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2550.solverstate
I0409 20:17:59.691674 14789 solver.cpp:330] Iteration 2550, Testing net (#0)
I0409 20:17:59.691699 14789 net.cpp:676] Ignoring source layer train-data
I0409 20:18:03.312502 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:18:04.336434 14789 solver.cpp:397]     Test net output #0: accuracy = 0.172794
I0409 20:18:04.336480 14789 solver.cpp:397]     Test net output #1: loss = 3.40626 (* 1 = 3.40626 loss)
I0409 20:18:06.269702 14789 solver.cpp:218] Iteration 2556 (0.782645 iter/s, 15.3326s/12 iters), loss = 3.5581
I0409 20:18:06.269755 14789 solver.cpp:237]     Train net output #0: loss = 3.5581 (* 1 = 3.5581 loss)
I0409 20:18:06.269767 14789 sgd_solver.cpp:105] Iteration 2556, lr = 0.00602717
I0409 20:18:11.437263 14789 solver.cpp:218] Iteration 2568 (2.32227 iter/s, 5.16735s/12 iters), loss = 3.02531
I0409 20:18:11.437319 14789 solver.cpp:237]     Train net output #0: loss = 3.02531 (* 1 = 3.02531 loss)
I0409 20:18:11.437331 14789 sgd_solver.cpp:105] Iteration 2568, lr = 0.00601286
I0409 20:18:16.511564 14789 solver.cpp:218] Iteration 2580 (2.36495 iter/s, 5.07409s/12 iters), loss = 3.29211
I0409 20:18:16.511718 14789 solver.cpp:237]     Train net output #0: loss = 3.29211 (* 1 = 3.29211 loss)
I0409 20:18:16.511731 14789 sgd_solver.cpp:105] Iteration 2580, lr = 0.00599858
I0409 20:18:21.354607 14789 solver.cpp:218] Iteration 2592 (2.47794 iter/s, 4.84274s/12 iters), loss = 3.28395
I0409 20:18:21.354666 14789 solver.cpp:237]     Train net output #0: loss = 3.28395 (* 1 = 3.28395 loss)
I0409 20:18:21.354681 14789 sgd_solver.cpp:105] Iteration 2592, lr = 0.00598434
I0409 20:18:26.540722 14789 solver.cpp:218] Iteration 2604 (2.31397 iter/s, 5.1859s/12 iters), loss = 3.25717
I0409 20:18:26.540769 14789 solver.cpp:237]     Train net output #0: loss = 3.25717 (* 1 = 3.25717 loss)
I0409 20:18:26.540781 14789 sgd_solver.cpp:105] Iteration 2604, lr = 0.00597013
I0409 20:18:31.387887 14789 solver.cpp:218] Iteration 2616 (2.47578 iter/s, 4.84697s/12 iters), loss = 3.37244
I0409 20:18:31.387938 14789 solver.cpp:237]     Train net output #0: loss = 3.37244 (* 1 = 3.37244 loss)
I0409 20:18:31.387950 14789 sgd_solver.cpp:105] Iteration 2616, lr = 0.00595596
I0409 20:18:36.309185 14789 solver.cpp:218] Iteration 2628 (2.43848 iter/s, 4.92109s/12 iters), loss = 2.89975
I0409 20:18:36.309227 14789 solver.cpp:237]     Train net output #0: loss = 2.89975 (* 1 = 2.89975 loss)
I0409 20:18:36.309237 14789 sgd_solver.cpp:105] Iteration 2628, lr = 0.00594182
I0409 20:18:36.642081 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:18:41.044337 14789 solver.cpp:218] Iteration 2640 (2.53434 iter/s, 4.73496s/12 iters), loss = 3.17275
I0409 20:18:41.044384 14789 solver.cpp:237]     Train net output #0: loss = 3.17275 (* 1 = 3.17275 loss)
I0409 20:18:41.044397 14789 sgd_solver.cpp:105] Iteration 2640, lr = 0.00592771
I0409 20:18:45.596612 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2652.caffemodel
I0409 20:18:49.443290 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2652.solverstate
I0409 20:18:52.441772 14789 solver.cpp:330] Iteration 2652, Testing net (#0)
I0409 20:18:52.441800 14789 net.cpp:676] Ignoring source layer train-data
I0409 20:18:55.906147 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:18:56.964498 14789 solver.cpp:397]     Test net output #0: accuracy = 0.201593
I0409 20:18:56.964540 14789 solver.cpp:397]     Test net output #1: loss = 3.28098 (* 1 = 3.28098 loss)
I0409 20:18:57.056214 14789 solver.cpp:218] Iteration 2652 (0.749467 iter/s, 16.0114s/12 iters), loss = 2.93835
I0409 20:18:57.056262 14789 solver.cpp:237]     Train net output #0: loss = 2.93835 (* 1 = 2.93835 loss)
I0409 20:18:57.056273 14789 sgd_solver.cpp:105] Iteration 2652, lr = 0.00591364
I0409 20:19:01.349718 14789 solver.cpp:218] Iteration 2664 (2.79504 iter/s, 4.29332s/12 iters), loss = 3.08236
I0409 20:19:01.349766 14789 solver.cpp:237]     Train net output #0: loss = 3.08236 (* 1 = 3.08236 loss)
I0409 20:19:01.349778 14789 sgd_solver.cpp:105] Iteration 2664, lr = 0.0058996
I0409 20:19:06.198403 14789 solver.cpp:218] Iteration 2676 (2.475 iter/s, 4.84849s/12 iters), loss = 2.98611
I0409 20:19:06.198451 14789 solver.cpp:237]     Train net output #0: loss = 2.98611 (* 1 = 2.98611 loss)
I0409 20:19:06.198462 14789 sgd_solver.cpp:105] Iteration 2676, lr = 0.00588559
I0409 20:19:11.062017 14789 solver.cpp:218] Iteration 2688 (2.4674 iter/s, 4.86342s/12 iters), loss = 2.94729
I0409 20:19:11.062060 14789 solver.cpp:237]     Train net output #0: loss = 2.94729 (* 1 = 2.94729 loss)
I0409 20:19:11.062068 14789 sgd_solver.cpp:105] Iteration 2688, lr = 0.00587162
I0409 20:19:15.925163 14789 solver.cpp:218] Iteration 2700 (2.46764 iter/s, 4.86295s/12 iters), loss = 2.98825
I0409 20:19:15.925204 14789 solver.cpp:237]     Train net output #0: loss = 2.98825 (* 1 = 2.98825 loss)
I0409 20:19:15.925213 14789 sgd_solver.cpp:105] Iteration 2700, lr = 0.00585768
I0409 20:19:20.729024 14789 solver.cpp:218] Iteration 2712 (2.49809 iter/s, 4.80367s/12 iters), loss = 2.8215
I0409 20:19:20.729144 14789 solver.cpp:237]     Train net output #0: loss = 2.8215 (* 1 = 2.8215 loss)
I0409 20:19:20.729158 14789 sgd_solver.cpp:105] Iteration 2712, lr = 0.00584377
I0409 20:19:25.728288 14789 solver.cpp:218] Iteration 2724 (2.40048 iter/s, 4.99899s/12 iters), loss = 3.18696
I0409 20:19:25.728333 14789 solver.cpp:237]     Train net output #0: loss = 3.18696 (* 1 = 3.18696 loss)
I0409 20:19:25.728343 14789 sgd_solver.cpp:105] Iteration 2724, lr = 0.0058299
I0409 20:19:28.173295 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:19:30.515698 14789 solver.cpp:218] Iteration 2736 (2.50668 iter/s, 4.78721s/12 iters), loss = 3.05481
I0409 20:19:30.515755 14789 solver.cpp:237]     Train net output #0: loss = 3.05481 (* 1 = 3.05481 loss)
I0409 20:19:30.515769 14789 sgd_solver.cpp:105] Iteration 2736, lr = 0.00581605
I0409 20:19:35.359338 14789 solver.cpp:218] Iteration 2748 (2.47758 iter/s, 4.84343s/12 iters), loss = 2.83326
I0409 20:19:35.359397 14789 solver.cpp:237]     Train net output #0: loss = 2.83326 (* 1 = 2.83326 loss)
I0409 20:19:35.359408 14789 sgd_solver.cpp:105] Iteration 2748, lr = 0.00580225
I0409 20:19:37.388048 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2754.caffemodel
I0409 20:19:43.994766 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2754.solverstate
I0409 20:19:46.974967 14789 solver.cpp:330] Iteration 2754, Testing net (#0)
I0409 20:19:46.974988 14789 net.cpp:676] Ignoring source layer train-data
I0409 20:19:50.102166 14789 blocking_queue.cpp:49] Waiting for data
I0409 20:19:50.338551 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:19:51.452421 14789 solver.cpp:397]     Test net output #0: accuracy = 0.21201
I0409 20:19:51.452528 14789 solver.cpp:397]     Test net output #1: loss = 3.18838 (* 1 = 3.18838 loss)
I0409 20:19:53.329269 14789 solver.cpp:218] Iteration 2760 (0.667803 iter/s, 17.9694s/12 iters), loss = 3.03245
I0409 20:19:53.329309 14789 solver.cpp:237]     Train net output #0: loss = 3.03245 (* 1 = 3.03245 loss)
I0409 20:19:53.329319 14789 sgd_solver.cpp:105] Iteration 2760, lr = 0.00578847
I0409 20:19:58.443078 14789 solver.cpp:218] Iteration 2772 (2.34668 iter/s, 5.11361s/12 iters), loss = 2.80552
I0409 20:19:58.443141 14789 solver.cpp:237]     Train net output #0: loss = 2.80552 (* 1 = 2.80552 loss)
I0409 20:19:58.443157 14789 sgd_solver.cpp:105] Iteration 2772, lr = 0.00577473
I0409 20:20:03.386597 14789 solver.cpp:218] Iteration 2784 (2.42752 iter/s, 4.94331s/12 iters), loss = 2.93877
I0409 20:20:03.386646 14789 solver.cpp:237]     Train net output #0: loss = 2.93877 (* 1 = 2.93877 loss)
I0409 20:20:03.386657 14789 sgd_solver.cpp:105] Iteration 2784, lr = 0.00576102
I0409 20:20:08.207623 14789 solver.cpp:218] Iteration 2796 (2.4892 iter/s, 4.82083s/12 iters), loss = 2.87118
I0409 20:20:08.207676 14789 solver.cpp:237]     Train net output #0: loss = 2.87118 (* 1 = 2.87118 loss)
I0409 20:20:08.207690 14789 sgd_solver.cpp:105] Iteration 2796, lr = 0.00574734
I0409 20:20:13.021785 14789 solver.cpp:218] Iteration 2808 (2.49275 iter/s, 4.81396s/12 iters), loss = 2.85666
I0409 20:20:13.021840 14789 solver.cpp:237]     Train net output #0: loss = 2.85666 (* 1 = 2.85666 loss)
I0409 20:20:13.021855 14789 sgd_solver.cpp:105] Iteration 2808, lr = 0.00573369
I0409 20:20:18.087234 14789 solver.cpp:218] Iteration 2820 (2.36909 iter/s, 5.06524s/12 iters), loss = 2.85477
I0409 20:20:18.087289 14789 solver.cpp:237]     Train net output #0: loss = 2.85477 (* 1 = 2.85477 loss)
I0409 20:20:18.087301 14789 sgd_solver.cpp:105] Iteration 2820, lr = 0.00572008
I0409 20:20:22.620168 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:20:22.914662 14789 solver.cpp:218] Iteration 2832 (2.4859 iter/s, 4.82722s/12 iters), loss = 2.85317
I0409 20:20:22.914716 14789 solver.cpp:237]     Train net output #0: loss = 2.85317 (* 1 = 2.85317 loss)
I0409 20:20:22.914727 14789 sgd_solver.cpp:105] Iteration 2832, lr = 0.0057065
I0409 20:20:27.940131 14789 solver.cpp:218] Iteration 2844 (2.38794 iter/s, 5.02526s/12 iters), loss = 2.78133
I0409 20:20:27.940191 14789 solver.cpp:237]     Train net output #0: loss = 2.78133 (* 1 = 2.78133 loss)
I0409 20:20:27.940202 14789 sgd_solver.cpp:105] Iteration 2844, lr = 0.00569295
I0409 20:20:32.683377 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2856.caffemodel
I0409 20:20:40.289618 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2856.solverstate
I0409 20:20:43.706941 14789 solver.cpp:330] Iteration 2856, Testing net (#0)
I0409 20:20:43.706965 14789 net.cpp:676] Ignoring source layer train-data
I0409 20:20:47.166039 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:20:48.305510 14789 solver.cpp:397]     Test net output #0: accuracy = 0.237745
I0409 20:20:48.305557 14789 solver.cpp:397]     Test net output #1: loss = 3.08394 (* 1 = 3.08394 loss)
I0409 20:20:48.397192 14789 solver.cpp:218] Iteration 2856 (0.586613 iter/s, 20.4564s/12 iters), loss = 2.86158
I0409 20:20:48.397246 14789 solver.cpp:237]     Train net output #0: loss = 2.86158 (* 1 = 2.86158 loss)
I0409 20:20:48.397258 14789 sgd_solver.cpp:105] Iteration 2856, lr = 0.00567944
I0409 20:20:52.775135 14789 solver.cpp:218] Iteration 2868 (2.74113 iter/s, 4.37775s/12 iters), loss = 2.69727
I0409 20:20:52.779600 14789 solver.cpp:237]     Train net output #0: loss = 2.69727 (* 1 = 2.69727 loss)
I0409 20:20:52.779614 14789 sgd_solver.cpp:105] Iteration 2868, lr = 0.00566595
I0409 20:20:57.834148 14789 solver.cpp:218] Iteration 2880 (2.37417 iter/s, 5.0544s/12 iters), loss = 2.6381
I0409 20:20:57.834213 14789 solver.cpp:237]     Train net output #0: loss = 2.6381 (* 1 = 2.6381 loss)
I0409 20:20:57.834225 14789 sgd_solver.cpp:105] Iteration 2880, lr = 0.0056525
I0409 20:21:02.935518 14789 solver.cpp:218] Iteration 2892 (2.35241 iter/s, 5.10115s/12 iters), loss = 2.90524
I0409 20:21:02.935573 14789 solver.cpp:237]     Train net output #0: loss = 2.90524 (* 1 = 2.90524 loss)
I0409 20:21:02.935586 14789 sgd_solver.cpp:105] Iteration 2892, lr = 0.00563908
I0409 20:21:07.848798 14789 solver.cpp:218] Iteration 2904 (2.44246 iter/s, 4.91307s/12 iters), loss = 2.80565
I0409 20:21:07.848848 14789 solver.cpp:237]     Train net output #0: loss = 2.80565 (* 1 = 2.80565 loss)
I0409 20:21:07.848862 14789 sgd_solver.cpp:105] Iteration 2904, lr = 0.00562569
I0409 20:21:12.925086 14789 solver.cpp:218] Iteration 2916 (2.36403 iter/s, 5.07608s/12 iters), loss = 2.71477
I0409 20:21:12.925143 14789 solver.cpp:237]     Train net output #0: loss = 2.71477 (* 1 = 2.71477 loss)
I0409 20:21:12.925155 14789 sgd_solver.cpp:105] Iteration 2916, lr = 0.00561233
I0409 20:21:18.290535 14789 solver.cpp:218] Iteration 2928 (2.23662 iter/s, 5.36523s/12 iters), loss = 2.79192
I0409 20:21:18.290586 14789 solver.cpp:237]     Train net output #0: loss = 2.79192 (* 1 = 2.79192 loss)
I0409 20:21:18.290598 14789 sgd_solver.cpp:105] Iteration 2928, lr = 0.00559901
I0409 20:21:20.216868 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:21:23.286775 14789 solver.cpp:218] Iteration 2940 (2.4019 iter/s, 4.99604s/12 iters), loss = 2.59358
I0409 20:21:23.286931 14789 solver.cpp:237]     Train net output #0: loss = 2.59358 (* 1 = 2.59358 loss)
I0409 20:21:23.286944 14789 sgd_solver.cpp:105] Iteration 2940, lr = 0.00558572
I0409 20:21:28.154322 14789 solver.cpp:218] Iteration 2952 (2.46546 iter/s, 4.86724s/12 iters), loss = 2.77984
I0409 20:21:28.154378 14789 solver.cpp:237]     Train net output #0: loss = 2.77984 (* 1 = 2.77984 loss)
I0409 20:21:28.154389 14789 sgd_solver.cpp:105] Iteration 2952, lr = 0.00557245
I0409 20:21:30.219148 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2958.caffemodel
I0409 20:21:37.864724 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2958.solverstate
I0409 20:21:40.852880 14789 solver.cpp:330] Iteration 2958, Testing net (#0)
I0409 20:21:40.852905 14789 net.cpp:676] Ignoring source layer train-data
I0409 20:21:44.135795 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:21:45.320385 14789 solver.cpp:397]     Test net output #0: accuracy = 0.224877
I0409 20:21:45.320420 14789 solver.cpp:397]     Test net output #1: loss = 3.04018 (* 1 = 3.04018 loss)
I0409 20:21:47.375600 14789 solver.cpp:218] Iteration 2964 (0.624328 iter/s, 19.2207s/12 iters), loss = 2.71356
I0409 20:21:47.375655 14789 solver.cpp:237]     Train net output #0: loss = 2.71356 (* 1 = 2.71356 loss)
I0409 20:21:47.375666 14789 sgd_solver.cpp:105] Iteration 2964, lr = 0.00555922
I0409 20:21:52.638715 14789 solver.cpp:218] Iteration 2976 (2.28011 iter/s, 5.2629s/12 iters), loss = 2.8767
I0409 20:21:52.638769 14789 solver.cpp:237]     Train net output #0: loss = 2.8767 (* 1 = 2.8767 loss)
I0409 20:21:52.638782 14789 sgd_solver.cpp:105] Iteration 2976, lr = 0.00554603
I0409 20:21:57.978430 14789 solver.cpp:218] Iteration 2988 (2.2474 iter/s, 5.3395s/12 iters), loss = 2.53156
I0409 20:21:57.978525 14789 solver.cpp:237]     Train net output #0: loss = 2.53156 (* 1 = 2.53156 loss)
I0409 20:21:57.978536 14789 sgd_solver.cpp:105] Iteration 2988, lr = 0.00553286
I0409 20:22:02.849608 14789 solver.cpp:218] Iteration 3000 (2.46359 iter/s, 4.87093s/12 iters), loss = 2.69034
I0409 20:22:02.849658 14789 solver.cpp:237]     Train net output #0: loss = 2.69034 (* 1 = 2.69034 loss)
I0409 20:22:02.849671 14789 sgd_solver.cpp:105] Iteration 3000, lr = 0.00551972
I0409 20:22:07.914698 14789 solver.cpp:218] Iteration 3012 (2.36926 iter/s, 5.06488s/12 iters), loss = 2.82424
I0409 20:22:07.914750 14789 solver.cpp:237]     Train net output #0: loss = 2.82424 (* 1 = 2.82424 loss)
I0409 20:22:07.914762 14789 sgd_solver.cpp:105] Iteration 3012, lr = 0.00550662
I0409 20:22:13.009500 14789 solver.cpp:218] Iteration 3024 (2.35544 iter/s, 5.09459s/12 iters), loss = 2.51547
I0409 20:22:13.009548 14789 solver.cpp:237]     Train net output #0: loss = 2.51547 (* 1 = 2.51547 loss)
I0409 20:22:13.009557 14789 sgd_solver.cpp:105] Iteration 3024, lr = 0.00549354
I0409 20:22:17.042241 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:22:18.076596 14789 solver.cpp:218] Iteration 3036 (2.36832 iter/s, 5.06689s/12 iters), loss = 2.54188
I0409 20:22:18.076650 14789 solver.cpp:237]     Train net output #0: loss = 2.54188 (* 1 = 2.54188 loss)
I0409 20:22:18.076663 14789 sgd_solver.cpp:105] Iteration 3036, lr = 0.0054805
I0409 20:22:23.289273 14789 solver.cpp:218] Iteration 3048 (2.30218 iter/s, 5.21246s/12 iters), loss = 2.55247
I0409 20:22:23.289331 14789 solver.cpp:237]     Train net output #0: loss = 2.55247 (* 1 = 2.55247 loss)
I0409 20:22:23.289343 14789 sgd_solver.cpp:105] Iteration 3048, lr = 0.00546749
I0409 20:22:27.786790 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3060.caffemodel
I0409 20:22:32.904433 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3060.solverstate
I0409 20:22:38.186897 14789 solver.cpp:330] Iteration 3060, Testing net (#0)
I0409 20:22:38.186921 14789 net.cpp:676] Ignoring source layer train-data
I0409 20:22:41.417850 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:22:42.641407 14789 solver.cpp:397]     Test net output #0: accuracy = 0.25674
I0409 20:22:42.641455 14789 solver.cpp:397]     Test net output #1: loss = 2.9565 (* 1 = 2.9565 loss)
I0409 20:22:42.733156 14789 solver.cpp:218] Iteration 3060 (0.61718 iter/s, 19.4433s/12 iters), loss = 2.7754
I0409 20:22:42.733204 14789 solver.cpp:237]     Train net output #0: loss = 2.7754 (* 1 = 2.7754 loss)
I0409 20:22:42.733215 14789 sgd_solver.cpp:105] Iteration 3060, lr = 0.00545451
I0409 20:22:46.732337 14789 solver.cpp:218] Iteration 3072 (3.00074 iter/s, 3.99901s/12 iters), loss = 2.51405
I0409 20:22:46.732375 14789 solver.cpp:237]     Train net output #0: loss = 2.51405 (* 1 = 2.51405 loss)
I0409 20:22:46.732385 14789 sgd_solver.cpp:105] Iteration 3072, lr = 0.00544156
I0409 20:22:51.498091 14789 solver.cpp:218] Iteration 3084 (2.51806 iter/s, 4.76556s/12 iters), loss = 2.61677
I0409 20:22:51.498147 14789 solver.cpp:237]     Train net output #0: loss = 2.61677 (* 1 = 2.61677 loss)
I0409 20:22:51.498160 14789 sgd_solver.cpp:105] Iteration 3084, lr = 0.00542864
I0409 20:22:56.214728 14789 solver.cpp:218] Iteration 3096 (2.5443 iter/s, 4.71643s/12 iters), loss = 2.61533
I0409 20:22:56.214785 14789 solver.cpp:237]     Train net output #0: loss = 2.61533 (* 1 = 2.61533 loss)
I0409 20:22:56.214797 14789 sgd_solver.cpp:105] Iteration 3096, lr = 0.00541575
I0409 20:23:01.325080 14789 solver.cpp:218] Iteration 3108 (2.34827 iter/s, 5.11014s/12 iters), loss = 2.43637
I0409 20:23:01.325124 14789 solver.cpp:237]     Train net output #0: loss = 2.43637 (* 1 = 2.43637 loss)
I0409 20:23:01.325134 14789 sgd_solver.cpp:105] Iteration 3108, lr = 0.00540289
I0409 20:23:06.383297 14789 solver.cpp:218] Iteration 3120 (2.37247 iter/s, 5.05802s/12 iters), loss = 2.54311
I0409 20:23:06.383409 14789 solver.cpp:237]     Train net output #0: loss = 2.54311 (* 1 = 2.54311 loss)
I0409 20:23:06.383419 14789 sgd_solver.cpp:105] Iteration 3120, lr = 0.00539006
I0409 20:23:11.244494 14789 solver.cpp:218] Iteration 3132 (2.46866 iter/s, 4.86094s/12 iters), loss = 2.48017
I0409 20:23:11.244544 14789 solver.cpp:237]     Train net output #0: loss = 2.48017 (* 1 = 2.48017 loss)
I0409 20:23:11.244555 14789 sgd_solver.cpp:105] Iteration 3132, lr = 0.00537727
I0409 20:23:12.257540 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:23:16.252569 14789 solver.cpp:218] Iteration 3144 (2.39623 iter/s, 5.00787s/12 iters), loss = 2.32429
I0409 20:23:16.252619 14789 solver.cpp:237]     Train net output #0: loss = 2.32429 (* 1 = 2.32429 loss)
I0409 20:23:16.252629 14789 sgd_solver.cpp:105] Iteration 3144, lr = 0.0053645
I0409 20:23:21.222383 14789 solver.cpp:218] Iteration 3156 (2.41467 iter/s, 4.96961s/12 iters), loss = 2.45319
I0409 20:23:21.222425 14789 solver.cpp:237]     Train net output #0: loss = 2.45319 (* 1 = 2.45319 loss)
I0409 20:23:21.222436 14789 sgd_solver.cpp:105] Iteration 3156, lr = 0.00535176
I0409 20:23:23.302531 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3162.caffemodel
I0409 20:23:27.071979 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3162.solverstate
I0409 20:23:33.547435 14789 solver.cpp:330] Iteration 3162, Testing net (#0)
I0409 20:23:33.547459 14789 net.cpp:676] Ignoring source layer train-data
I0409 20:23:36.752341 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:23:38.039412 14789 solver.cpp:397]     Test net output #0: accuracy = 0.295956
I0409 20:23:38.039443 14789 solver.cpp:397]     Test net output #1: loss = 2.81898 (* 1 = 2.81898 loss)
I0409 20:23:39.681406 14789 solver.cpp:218] Iteration 3168 (0.650109 iter/s, 18.4584s/12 iters), loss = 2.36021
I0409 20:23:39.681459 14789 solver.cpp:237]     Train net output #0: loss = 2.36021 (* 1 = 2.36021 loss)
I0409 20:23:39.681471 14789 sgd_solver.cpp:105] Iteration 3168, lr = 0.00533906
I0409 20:23:44.426643 14789 solver.cpp:218] Iteration 3180 (2.52896 iter/s, 4.74504s/12 iters), loss = 2.62431
I0409 20:23:44.426690 14789 solver.cpp:237]     Train net output #0: loss = 2.62431 (* 1 = 2.62431 loss)
I0409 20:23:44.426699 14789 sgd_solver.cpp:105] Iteration 3180, lr = 0.00532638
I0409 20:23:49.389860 14789 solver.cpp:218] Iteration 3192 (2.41788 iter/s, 4.96302s/12 iters), loss = 2.33426
I0409 20:23:49.389905 14789 solver.cpp:237]     Train net output #0: loss = 2.33426 (* 1 = 2.33426 loss)
I0409 20:23:49.389914 14789 sgd_solver.cpp:105] Iteration 3192, lr = 0.00531374
I0409 20:23:54.220940 14789 solver.cpp:218] Iteration 3204 (2.48401 iter/s, 4.83089s/12 iters), loss = 2.64137
I0409 20:23:54.220980 14789 solver.cpp:237]     Train net output #0: loss = 2.64137 (* 1 = 2.64137 loss)
I0409 20:23:54.220989 14789 sgd_solver.cpp:105] Iteration 3204, lr = 0.00530112
I0409 20:23:59.066238 14789 solver.cpp:218] Iteration 3216 (2.47673 iter/s, 4.84511s/12 iters), loss = 2.40679
I0409 20:23:59.066289 14789 solver.cpp:237]     Train net output #0: loss = 2.40679 (* 1 = 2.40679 loss)
I0409 20:23:59.066299 14789 sgd_solver.cpp:105] Iteration 3216, lr = 0.00528853
I0409 20:24:04.047281 14789 solver.cpp:218] Iteration 3228 (2.40923 iter/s, 4.98084s/12 iters), loss = 2.1085
I0409 20:24:04.047310 14789 solver.cpp:237]     Train net output #0: loss = 2.1085 (* 1 = 2.1085 loss)
I0409 20:24:04.047317 14789 sgd_solver.cpp:105] Iteration 3228, lr = 0.00527598
I0409 20:24:07.178402 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:24:08.954453 14789 solver.cpp:218] Iteration 3240 (2.44549 iter/s, 4.90699s/12 iters), loss = 2.33445
I0409 20:24:08.954504 14789 solver.cpp:237]     Train net output #0: loss = 2.33445 (* 1 = 2.33445 loss)
I0409 20:24:08.954516 14789 sgd_solver.cpp:105] Iteration 3240, lr = 0.00526345
I0409 20:24:13.950902 14789 solver.cpp:218] Iteration 3252 (2.4018 iter/s, 4.99625s/12 iters), loss = 2.21243
I0409 20:24:13.950955 14789 solver.cpp:237]     Train net output #0: loss = 2.21243 (* 1 = 2.21243 loss)
I0409 20:24:13.950968 14789 sgd_solver.cpp:105] Iteration 3252, lr = 0.00525095
I0409 20:24:18.375932 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3264.caffemodel
I0409 20:24:22.731307 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3264.solverstate
I0409 20:24:26.272828 14789 solver.cpp:330] Iteration 3264, Testing net (#0)
I0409 20:24:26.272853 14789 net.cpp:676] Ignoring source layer train-data
I0409 20:24:29.550129 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:24:30.853551 14789 solver.cpp:397]     Test net output #0: accuracy = 0.281863
I0409 20:24:30.853598 14789 solver.cpp:397]     Test net output #1: loss = 2.89606 (* 1 = 2.89606 loss)
I0409 20:24:30.945271 14789 solver.cpp:218] Iteration 3264 (0.706139 iter/s, 16.9938s/12 iters), loss = 2.67399
I0409 20:24:30.945323 14789 solver.cpp:237]     Train net output #0: loss = 2.67399 (* 1 = 2.67399 loss)
I0409 20:24:30.945334 14789 sgd_solver.cpp:105] Iteration 3264, lr = 0.00523849
I0409 20:24:34.926292 14789 solver.cpp:218] Iteration 3276 (3.01444 iter/s, 3.98084s/12 iters), loss = 2.32423
I0409 20:24:34.926353 14789 solver.cpp:237]     Train net output #0: loss = 2.32423 (* 1 = 2.32423 loss)
I0409 20:24:34.926369 14789 sgd_solver.cpp:105] Iteration 3276, lr = 0.00522605
I0409 20:24:39.678570 14789 solver.cpp:218] Iteration 3288 (2.52521 iter/s, 4.75208s/12 iters), loss = 2.39674
I0409 20:24:39.678639 14789 solver.cpp:237]     Train net output #0: loss = 2.39674 (* 1 = 2.39674 loss)
I0409 20:24:39.678651 14789 sgd_solver.cpp:105] Iteration 3288, lr = 0.00521364
I0409 20:24:44.618314 14789 solver.cpp:218] Iteration 3300 (2.42939 iter/s, 4.93952s/12 iters), loss = 2.33159
I0409 20:24:44.618367 14789 solver.cpp:237]     Train net output #0: loss = 2.33159 (* 1 = 2.33159 loss)
I0409 20:24:44.618379 14789 sgd_solver.cpp:105] Iteration 3300, lr = 0.00520126
I0409 20:24:49.430807 14789 solver.cpp:218] Iteration 3312 (2.49362 iter/s, 4.81229s/12 iters), loss = 2.43107
I0409 20:24:49.430860 14789 solver.cpp:237]     Train net output #0: loss = 2.43107 (* 1 = 2.43107 loss)
I0409 20:24:49.430871 14789 sgd_solver.cpp:105] Iteration 3312, lr = 0.00518892
I0409 20:24:54.154001 14789 solver.cpp:218] Iteration 3324 (2.54076 iter/s, 4.723s/12 iters), loss = 2.2597
I0409 20:24:54.154044 14789 solver.cpp:237]     Train net output #0: loss = 2.2597 (* 1 = 2.2597 loss)
I0409 20:24:54.154053 14789 sgd_solver.cpp:105] Iteration 3324, lr = 0.0051766
I0409 20:24:58.824352 14789 solver.cpp:218] Iteration 3336 (2.5695 iter/s, 4.67016s/12 iters), loss = 2.25675
I0409 20:24:58.824398 14789 solver.cpp:237]     Train net output #0: loss = 2.25675 (* 1 = 2.25675 loss)
I0409 20:24:58.824407 14789 sgd_solver.cpp:105] Iteration 3336, lr = 0.00516431
I0409 20:24:59.267632 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:25:03.525533 14789 solver.cpp:218] Iteration 3348 (2.55265 iter/s, 4.70099s/12 iters), loss = 2.20357
I0409 20:25:03.525594 14789 solver.cpp:237]     Train net output #0: loss = 2.20357 (* 1 = 2.20357 loss)
I0409 20:25:03.525610 14789 sgd_solver.cpp:105] Iteration 3348, lr = 0.00515204
I0409 20:25:08.501343 14789 solver.cpp:218] Iteration 3360 (2.41177 iter/s, 4.9756s/12 iters), loss = 2.03536
I0409 20:25:08.501399 14789 solver.cpp:237]     Train net output #0: loss = 2.03536 (* 1 = 2.03536 loss)
I0409 20:25:08.501410 14789 sgd_solver.cpp:105] Iteration 3360, lr = 0.00513981
I0409 20:25:10.521917 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3366.caffemodel
I0409 20:25:14.378888 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3366.solverstate
I0409 20:25:19.339838 14789 solver.cpp:330] Iteration 3366, Testing net (#0)
I0409 20:25:19.339864 14789 net.cpp:676] Ignoring source layer train-data
I0409 20:25:22.419368 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:25:23.769307 14789 solver.cpp:397]     Test net output #0: accuracy = 0.325368
I0409 20:25:23.769343 14789 solver.cpp:397]     Test net output #1: loss = 2.72271 (* 1 = 2.72271 loss)
I0409 20:25:25.607998 14789 solver.cpp:218] Iteration 3372 (0.701504 iter/s, 17.1061s/12 iters), loss = 2.19758
I0409 20:25:25.608062 14789 solver.cpp:237]     Train net output #0: loss = 2.19758 (* 1 = 2.19758 loss)
I0409 20:25:25.608073 14789 sgd_solver.cpp:105] Iteration 3372, lr = 0.00512761
I0409 20:25:30.509547 14789 solver.cpp:218] Iteration 3384 (2.44831 iter/s, 4.90134s/12 iters), loss = 2.36517
I0409 20:25:30.509598 14789 solver.cpp:237]     Train net output #0: loss = 2.36517 (* 1 = 2.36517 loss)
I0409 20:25:30.509610 14789 sgd_solver.cpp:105] Iteration 3384, lr = 0.00511544
I0409 20:25:35.173468 14789 solver.cpp:218] Iteration 3396 (2.57305 iter/s, 4.66372s/12 iters), loss = 2.33
I0409 20:25:35.173521 14789 solver.cpp:237]     Train net output #0: loss = 2.33 (* 1 = 2.33 loss)
I0409 20:25:35.173532 14789 sgd_solver.cpp:105] Iteration 3396, lr = 0.00510329
I0409 20:25:40.336867 14789 solver.cpp:218] Iteration 3408 (2.32414 iter/s, 5.16319s/12 iters), loss = 2.25563
I0409 20:25:40.336899 14789 solver.cpp:237]     Train net output #0: loss = 2.25563 (* 1 = 2.25563 loss)
I0409 20:25:40.336908 14789 sgd_solver.cpp:105] Iteration 3408, lr = 0.00509117
I0409 20:25:45.371775 14789 solver.cpp:218] Iteration 3420 (2.38345 iter/s, 5.03472s/12 iters), loss = 2.16773
I0409 20:25:45.371850 14789 solver.cpp:237]     Train net output #0: loss = 2.16773 (* 1 = 2.16773 loss)
I0409 20:25:45.371860 14789 sgd_solver.cpp:105] Iteration 3420, lr = 0.00507909
I0409 20:25:50.157002 14789 solver.cpp:218] Iteration 3432 (2.50784 iter/s, 4.785s/12 iters), loss = 2.23536
I0409 20:25:50.157058 14789 solver.cpp:237]     Train net output #0: loss = 2.23536 (* 1 = 2.23536 loss)
I0409 20:25:50.157070 14789 sgd_solver.cpp:105] Iteration 3432, lr = 0.00506703
I0409 20:25:52.653726 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:25:55.018401 14789 solver.cpp:218] Iteration 3444 (2.46853 iter/s, 4.86119s/12 iters), loss = 1.84092
I0409 20:25:55.018450 14789 solver.cpp:237]     Train net output #0: loss = 1.84092 (* 1 = 1.84092 loss)
I0409 20:25:55.018460 14789 sgd_solver.cpp:105] Iteration 3444, lr = 0.005055
I0409 20:25:59.947366 14789 solver.cpp:218] Iteration 3456 (2.43469 iter/s, 4.92877s/12 iters), loss = 2.34926
I0409 20:25:59.947407 14789 solver.cpp:237]     Train net output #0: loss = 2.34926 (* 1 = 2.34926 loss)
I0409 20:25:59.947415 14789 sgd_solver.cpp:105] Iteration 3456, lr = 0.005043
I0409 20:26:04.310065 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3468.caffemodel
I0409 20:26:10.535274 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3468.solverstate
I0409 20:26:13.543179 14789 solver.cpp:330] Iteration 3468, Testing net (#0)
I0409 20:26:13.543205 14789 net.cpp:676] Ignoring source layer train-data
I0409 20:26:13.987581 14789 blocking_queue.cpp:49] Waiting for data
I0409 20:26:16.715497 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:26:18.132977 14789 solver.cpp:397]     Test net output #0: accuracy = 0.336397
I0409 20:26:18.133018 14789 solver.cpp:397]     Test net output #1: loss = 2.67536 (* 1 = 2.67536 loss)
I0409 20:26:18.224572 14789 solver.cpp:218] Iteration 3468 (0.656576 iter/s, 18.2766s/12 iters), loss = 2.1329
I0409 20:26:18.224615 14789 solver.cpp:237]     Train net output #0: loss = 2.1329 (* 1 = 2.1329 loss)
I0409 20:26:18.224623 14789 sgd_solver.cpp:105] Iteration 3468, lr = 0.00503102
I0409 20:26:22.883173 14789 solver.cpp:218] Iteration 3480 (2.57599 iter/s, 4.65841s/12 iters), loss = 2.2463
I0409 20:26:22.883219 14789 solver.cpp:237]     Train net output #0: loss = 2.2463 (* 1 = 2.2463 loss)
I0409 20:26:22.883227 14789 sgd_solver.cpp:105] Iteration 3480, lr = 0.00501908
I0409 20:26:28.086190 14789 solver.cpp:218] Iteration 3492 (2.30644 iter/s, 5.20281s/12 iters), loss = 2.18623
I0409 20:26:28.086231 14789 solver.cpp:237]     Train net output #0: loss = 2.18623 (* 1 = 2.18623 loss)
I0409 20:26:28.086241 14789 sgd_solver.cpp:105] Iteration 3492, lr = 0.00500716
I0409 20:26:33.315368 14789 solver.cpp:218] Iteration 3504 (2.2949 iter/s, 5.22898s/12 iters), loss = 2.32948
I0409 20:26:33.315409 14789 solver.cpp:237]     Train net output #0: loss = 2.32948 (* 1 = 2.32948 loss)
I0409 20:26:33.315419 14789 sgd_solver.cpp:105] Iteration 3504, lr = 0.00499527
I0409 20:26:38.378543 14789 solver.cpp:218] Iteration 3516 (2.37015 iter/s, 5.06297s/12 iters), loss = 1.95226
I0409 20:26:38.378589 14789 solver.cpp:237]     Train net output #0: loss = 1.95226 (* 1 = 1.95226 loss)
I0409 20:26:38.378597 14789 sgd_solver.cpp:105] Iteration 3516, lr = 0.00498341
I0409 20:26:43.412111 14789 solver.cpp:218] Iteration 3528 (2.38409 iter/s, 5.03337s/12 iters), loss = 2.0545
I0409 20:26:43.412164 14789 solver.cpp:237]     Train net output #0: loss = 2.0545 (* 1 = 2.0545 loss)
I0409 20:26:43.412176 14789 sgd_solver.cpp:105] Iteration 3528, lr = 0.00497158
I0409 20:26:48.476490 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:26:48.754088 14789 solver.cpp:218] Iteration 3540 (2.24645 iter/s, 5.34176s/12 iters), loss = 2.18894
I0409 20:26:48.754151 14789 solver.cpp:237]     Train net output #0: loss = 2.18894 (* 1 = 2.18894 loss)
I0409 20:26:48.754166 14789 sgd_solver.cpp:105] Iteration 3540, lr = 0.00495978
I0409 20:26:53.608549 14789 solver.cpp:218] Iteration 3552 (2.47206 iter/s, 4.85425s/12 iters), loss = 1.97005
I0409 20:26:53.608600 14789 solver.cpp:237]     Train net output #0: loss = 1.97005 (* 1 = 1.97005 loss)
I0409 20:26:53.608611 14789 sgd_solver.cpp:105] Iteration 3552, lr = 0.004948
I0409 20:26:58.706063 14789 solver.cpp:218] Iteration 3564 (2.35419 iter/s, 5.0973s/12 iters), loss = 2.07537
I0409 20:26:58.706115 14789 solver.cpp:237]     Train net output #0: loss = 2.07537 (* 1 = 2.07537 loss)
I0409 20:26:58.706126 14789 sgd_solver.cpp:105] Iteration 3564, lr = 0.00493626
I0409 20:27:00.818212 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3570.caffemodel
I0409 20:27:04.534724 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3570.solverstate
I0409 20:27:08.469496 14789 solver.cpp:330] Iteration 3570, Testing net (#0)
I0409 20:27:08.469521 14789 net.cpp:676] Ignoring source layer train-data
I0409 20:27:11.586241 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:27:13.001659 14789 solver.cpp:397]     Test net output #0: accuracy = 0.329657
I0409 20:27:13.001684 14789 solver.cpp:397]     Test net output #1: loss = 2.66623 (* 1 = 2.66623 loss)
I0409 20:27:14.845616 14789 solver.cpp:218] Iteration 3576 (0.743539 iter/s, 16.139s/12 iters), loss = 1.91979
I0409 20:27:14.845677 14789 solver.cpp:237]     Train net output #0: loss = 1.91979 (* 1 = 1.91979 loss)
I0409 20:27:14.845690 14789 sgd_solver.cpp:105] Iteration 3576, lr = 0.00492454
I0409 20:27:19.895184 14789 solver.cpp:218] Iteration 3588 (2.37654 iter/s, 5.04935s/12 iters), loss = 2.09602
I0409 20:27:19.895330 14789 solver.cpp:237]     Train net output #0: loss = 2.09602 (* 1 = 2.09602 loss)
I0409 20:27:19.895341 14789 sgd_solver.cpp:105] Iteration 3588, lr = 0.00491284
I0409 20:27:24.512869 14789 solver.cpp:218] Iteration 3600 (2.59887 iter/s, 4.6174s/12 iters), loss = 1.85217
I0409 20:27:24.512918 14789 solver.cpp:237]     Train net output #0: loss = 1.85217 (* 1 = 1.85217 loss)
I0409 20:27:24.512930 14789 sgd_solver.cpp:105] Iteration 3600, lr = 0.00490118
I0409 20:27:29.614313 14789 solver.cpp:218] Iteration 3612 (2.35237 iter/s, 5.10124s/12 iters), loss = 2.03669
I0409 20:27:29.614365 14789 solver.cpp:237]     Train net output #0: loss = 2.03669 (* 1 = 2.03669 loss)
I0409 20:27:29.614377 14789 sgd_solver.cpp:105] Iteration 3612, lr = 0.00488954
I0409 20:27:34.574548 14789 solver.cpp:218] Iteration 3624 (2.41934 iter/s, 4.96003s/12 iters), loss = 1.88842
I0409 20:27:34.574605 14789 solver.cpp:237]     Train net output #0: loss = 1.88842 (* 1 = 1.88842 loss)
I0409 20:27:34.574621 14789 sgd_solver.cpp:105] Iteration 3624, lr = 0.00487793
I0409 20:27:39.656095 14789 solver.cpp:218] Iteration 3636 (2.36158 iter/s, 5.08133s/12 iters), loss = 1.8922
I0409 20:27:39.656147 14789 solver.cpp:237]     Train net output #0: loss = 1.8922 (* 1 = 1.8922 loss)
I0409 20:27:39.656159 14789 sgd_solver.cpp:105] Iteration 3636, lr = 0.00486635
I0409 20:27:41.723609 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:27:45.212057 14789 solver.cpp:218] Iteration 3648 (2.15993 iter/s, 5.55574s/12 iters), loss = 1.81378
I0409 20:27:45.212105 14789 solver.cpp:237]     Train net output #0: loss = 1.81378 (* 1 = 1.81378 loss)
I0409 20:27:45.212117 14789 sgd_solver.cpp:105] Iteration 3648, lr = 0.0048548
I0409 20:27:50.279625 14789 solver.cpp:218] Iteration 3660 (2.36809 iter/s, 5.06737s/12 iters), loss = 1.88154
I0409 20:27:50.279697 14789 solver.cpp:237]     Train net output #0: loss = 1.88154 (* 1 = 1.88154 loss)
I0409 20:27:50.279707 14789 sgd_solver.cpp:105] Iteration 3660, lr = 0.00484327
I0409 20:27:54.481148 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3672.caffemodel
I0409 20:27:58.620991 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3672.solverstate
I0409 20:28:03.364599 14789 solver.cpp:330] Iteration 3672, Testing net (#0)
I0409 20:28:03.364624 14789 net.cpp:676] Ignoring source layer train-data
I0409 20:28:06.370280 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:28:07.833056 14789 solver.cpp:397]     Test net output #0: accuracy = 0.348652
I0409 20:28:07.833096 14789 solver.cpp:397]     Test net output #1: loss = 2.61859 (* 1 = 2.61859 loss)
I0409 20:28:07.923681 14789 solver.cpp:218] Iteration 3672 (0.680138 iter/s, 17.6435s/12 iters), loss = 1.6645
I0409 20:28:07.923729 14789 solver.cpp:237]     Train net output #0: loss = 1.6645 (* 1 = 1.6645 loss)
I0409 20:28:07.923738 14789 sgd_solver.cpp:105] Iteration 3672, lr = 0.00483177
I0409 20:28:12.401093 14789 solver.cpp:218] Iteration 3684 (2.68023 iter/s, 4.47722s/12 iters), loss = 2.07473
I0409 20:28:12.401147 14789 solver.cpp:237]     Train net output #0: loss = 2.07473 (* 1 = 2.07473 loss)
I0409 20:28:12.401160 14789 sgd_solver.cpp:105] Iteration 3684, lr = 0.0048203
I0409 20:28:17.426599 14789 solver.cpp:218] Iteration 3696 (2.38792 iter/s, 5.0253s/12 iters), loss = 1.49774
I0409 20:28:17.426656 14789 solver.cpp:237]     Train net output #0: loss = 1.49774 (* 1 = 1.49774 loss)
I0409 20:28:17.426667 14789 sgd_solver.cpp:105] Iteration 3696, lr = 0.00480886
I0409 20:28:22.163616 14789 solver.cpp:218] Iteration 3708 (2.53335 iter/s, 4.73682s/12 iters), loss = 1.8828
I0409 20:28:22.163743 14789 solver.cpp:237]     Train net output #0: loss = 1.8828 (* 1 = 1.8828 loss)
I0409 20:28:22.163754 14789 sgd_solver.cpp:105] Iteration 3708, lr = 0.00479744
I0409 20:28:27.219406 14789 solver.cpp:218] Iteration 3720 (2.37365 iter/s, 5.05551s/12 iters), loss = 2.17711
I0409 20:28:27.219465 14789 solver.cpp:237]     Train net output #0: loss = 2.17711 (* 1 = 2.17711 loss)
I0409 20:28:27.219478 14789 sgd_solver.cpp:105] Iteration 3720, lr = 0.00478605
I0409 20:28:32.183933 14789 solver.cpp:218] Iteration 3732 (2.41725 iter/s, 4.96431s/12 iters), loss = 1.73083
I0409 20:28:32.183992 14789 solver.cpp:237]     Train net output #0: loss = 1.73083 (* 1 = 1.73083 loss)
I0409 20:28:32.184006 14789 sgd_solver.cpp:105] Iteration 3732, lr = 0.00477469
I0409 20:28:36.597723 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:28:37.768014 14789 solver.cpp:218] Iteration 3744 (2.14905 iter/s, 5.58385s/12 iters), loss = 1.77419
I0409 20:28:37.768061 14789 solver.cpp:237]     Train net output #0: loss = 1.77419 (* 1 = 1.77419 loss)
I0409 20:28:37.768070 14789 sgd_solver.cpp:105] Iteration 3744, lr = 0.00476335
I0409 20:28:43.177532 14789 solver.cpp:218] Iteration 3756 (2.2184 iter/s, 5.4093s/12 iters), loss = 1.99174
I0409 20:28:43.177585 14789 solver.cpp:237]     Train net output #0: loss = 1.99174 (* 1 = 1.99174 loss)
I0409 20:28:43.177597 14789 sgd_solver.cpp:105] Iteration 3756, lr = 0.00475204
I0409 20:28:48.236049 14789 solver.cpp:218] Iteration 3768 (2.37233 iter/s, 5.05831s/12 iters), loss = 1.93419
I0409 20:28:48.236099 14789 solver.cpp:237]     Train net output #0: loss = 1.93419 (* 1 = 1.93419 loss)
I0409 20:28:48.236107 14789 sgd_solver.cpp:105] Iteration 3768, lr = 0.00474076
I0409 20:28:50.300848 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3774.caffemodel
I0409 20:28:54.143393 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3774.solverstate
I0409 20:28:57.156982 14789 solver.cpp:330] Iteration 3774, Testing net (#0)
I0409 20:28:57.157008 14789 net.cpp:676] Ignoring source layer train-data
I0409 20:29:00.102807 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:29:01.602231 14789 solver.cpp:397]     Test net output #0: accuracy = 0.34375
I0409 20:29:01.602277 14789 solver.cpp:397]     Test net output #1: loss = 2.66419 (* 1 = 2.66419 loss)
I0409 20:29:03.275409 14789 solver.cpp:218] Iteration 3780 (0.797932 iter/s, 15.0389s/12 iters), loss = 1.67701
I0409 20:29:03.275467 14789 solver.cpp:237]     Train net output #0: loss = 1.67701 (* 1 = 1.67701 loss)
I0409 20:29:03.275480 14789 sgd_solver.cpp:105] Iteration 3780, lr = 0.00472951
I0409 20:29:08.086186 14789 solver.cpp:218] Iteration 3792 (2.49451 iter/s, 4.81057s/12 iters), loss = 1.67269
I0409 20:29:08.086231 14789 solver.cpp:237]     Train net output #0: loss = 1.67269 (* 1 = 1.67269 loss)
I0409 20:29:08.086241 14789 sgd_solver.cpp:105] Iteration 3792, lr = 0.00471828
I0409 20:29:12.966992 14789 solver.cpp:218] Iteration 3804 (2.45871 iter/s, 4.88061s/12 iters), loss = 1.6645
I0409 20:29:12.967036 14789 solver.cpp:237]     Train net output #0: loss = 1.6645 (* 1 = 1.6645 loss)
I0409 20:29:12.967046 14789 sgd_solver.cpp:105] Iteration 3804, lr = 0.00470707
I0409 20:29:17.963362 14789 solver.cpp:218] Iteration 3816 (2.40184 iter/s, 4.99617s/12 iters), loss = 1.82035
I0409 20:29:17.963408 14789 solver.cpp:237]     Train net output #0: loss = 1.82035 (* 1 = 1.82035 loss)
I0409 20:29:17.963418 14789 sgd_solver.cpp:105] Iteration 3816, lr = 0.0046959
I0409 20:29:22.883610 14789 solver.cpp:218] Iteration 3828 (2.439 iter/s, 4.92004s/12 iters), loss = 1.49004
I0409 20:29:22.883659 14789 solver.cpp:237]     Train net output #0: loss = 1.49004 (* 1 = 1.49004 loss)
I0409 20:29:22.883671 14789 sgd_solver.cpp:105] Iteration 3828, lr = 0.00468475
I0409 20:29:27.747139 14789 solver.cpp:218] Iteration 3840 (2.46745 iter/s, 4.86333s/12 iters), loss = 1.8008
I0409 20:29:27.747272 14789 solver.cpp:237]     Train net output #0: loss = 1.8008 (* 1 = 1.8008 loss)
I0409 20:29:27.747282 14789 sgd_solver.cpp:105] Iteration 3840, lr = 0.00467363
I0409 20:29:28.855598 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:29:33.056133 14789 solver.cpp:218] Iteration 3852 (2.26044 iter/s, 5.3087s/12 iters), loss = 1.89341
I0409 20:29:33.056183 14789 solver.cpp:237]     Train net output #0: loss = 1.89341 (* 1 = 1.89341 loss)
I0409 20:29:33.056195 14789 sgd_solver.cpp:105] Iteration 3852, lr = 0.00466253
I0409 20:29:38.513572 14789 solver.cpp:218] Iteration 3864 (2.19892 iter/s, 5.45722s/12 iters), loss = 1.78736
I0409 20:29:38.513628 14789 solver.cpp:237]     Train net output #0: loss = 1.78736 (* 1 = 1.78736 loss)
I0409 20:29:38.513643 14789 sgd_solver.cpp:105] Iteration 3864, lr = 0.00465146
I0409 20:29:43.099583 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3876.caffemodel
I0409 20:29:46.886296 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3876.solverstate
I0409 20:29:49.876487 14789 solver.cpp:330] Iteration 3876, Testing net (#0)
I0409 20:29:49.876514 14789 net.cpp:676] Ignoring source layer train-data
I0409 20:29:52.706899 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:29:54.269631 14789 solver.cpp:397]     Test net output #0: accuracy = 0.372549
I0409 20:29:54.269696 14789 solver.cpp:397]     Test net output #1: loss = 2.47145 (* 1 = 2.47145 loss)
I0409 20:29:54.361444 14789 solver.cpp:218] Iteration 3876 (0.757224 iter/s, 15.8474s/12 iters), loss = 1.74105
I0409 20:29:54.361497 14789 solver.cpp:237]     Train net output #0: loss = 1.74105 (* 1 = 1.74105 loss)
I0409 20:29:54.361508 14789 sgd_solver.cpp:105] Iteration 3876, lr = 0.00464042
I0409 20:29:58.651724 14789 solver.cpp:218] Iteration 3888 (2.79714 iter/s, 4.29009s/12 iters), loss = 1.73636
I0409 20:29:58.651798 14789 solver.cpp:237]     Train net output #0: loss = 1.73636 (* 1 = 1.73636 loss)
I0409 20:29:58.651809 14789 sgd_solver.cpp:105] Iteration 3888, lr = 0.0046294
I0409 20:30:03.772923 14789 solver.cpp:218] Iteration 3900 (2.3433 iter/s, 5.12097s/12 iters), loss = 1.81856
I0409 20:30:03.772961 14789 solver.cpp:237]     Train net output #0: loss = 1.81856 (* 1 = 1.81856 loss)
I0409 20:30:03.772970 14789 sgd_solver.cpp:105] Iteration 3900, lr = 0.00461841
I0409 20:30:08.636027 14789 solver.cpp:218] Iteration 3912 (2.46766 iter/s, 4.86291s/12 iters), loss = 1.73176
I0409 20:30:08.636080 14789 solver.cpp:237]     Train net output #0: loss = 1.73176 (* 1 = 1.73176 loss)
I0409 20:30:08.636092 14789 sgd_solver.cpp:105] Iteration 3912, lr = 0.00460744
I0409 20:30:13.633589 14789 solver.cpp:218] Iteration 3924 (2.40127 iter/s, 4.99736s/12 iters), loss = 1.78623
I0409 20:30:13.633635 14789 solver.cpp:237]     Train net output #0: loss = 1.78623 (* 1 = 1.78623 loss)
I0409 20:30:13.633646 14789 sgd_solver.cpp:105] Iteration 3924, lr = 0.0045965
I0409 20:30:18.584965 14789 solver.cpp:218] Iteration 3936 (2.42367 iter/s, 4.95117s/12 iters), loss = 1.54018
I0409 20:30:18.585027 14789 solver.cpp:237]     Train net output #0: loss = 1.54018 (* 1 = 1.54018 loss)
I0409 20:30:18.585042 14789 sgd_solver.cpp:105] Iteration 3936, lr = 0.00458559
I0409 20:30:21.996227 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:30:23.575201 14789 solver.cpp:218] Iteration 3948 (2.4048 iter/s, 4.99002s/12 iters), loss = 1.44494
I0409 20:30:23.575258 14789 solver.cpp:237]     Train net output #0: loss = 1.44494 (* 1 = 1.44494 loss)
I0409 20:30:23.575270 14789 sgd_solver.cpp:105] Iteration 3948, lr = 0.0045747
I0409 20:30:28.485749 14789 solver.cpp:218] Iteration 3960 (2.44382 iter/s, 4.91034s/12 iters), loss = 1.74789
I0409 20:30:28.485803 14789 solver.cpp:237]     Train net output #0: loss = 1.74789 (* 1 = 1.74789 loss)
I0409 20:30:28.485816 14789 sgd_solver.cpp:105] Iteration 3960, lr = 0.00456384
I0409 20:30:33.508513 14789 solver.cpp:218] Iteration 3972 (2.38922 iter/s, 5.02255s/12 iters), loss = 1.82882
I0409 20:30:33.508630 14789 solver.cpp:237]     Train net output #0: loss = 1.82882 (* 1 = 1.82882 loss)
I0409 20:30:33.508643 14789 sgd_solver.cpp:105] Iteration 3972, lr = 0.00455301
I0409 20:30:35.577905 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3978.caffemodel
I0409 20:30:39.368237 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3978.solverstate
I0409 20:30:42.883260 14789 solver.cpp:330] Iteration 3978, Testing net (#0)
I0409 20:30:42.883285 14789 net.cpp:676] Ignoring source layer train-data
I0409 20:30:45.794965 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:30:47.408367 14789 solver.cpp:397]     Test net output #0: accuracy = 0.401961
I0409 20:30:47.408412 14789 solver.cpp:397]     Test net output #1: loss = 2.36586 (* 1 = 2.36586 loss)
I0409 20:30:49.380316 14789 solver.cpp:218] Iteration 3984 (0.756085 iter/s, 15.8712s/12 iters), loss = 1.82739
I0409 20:30:49.380367 14789 solver.cpp:237]     Train net output #0: loss = 1.82739 (* 1 = 1.82739 loss)
I0409 20:30:49.380380 14789 sgd_solver.cpp:105] Iteration 3984, lr = 0.0045422
I0409 20:30:54.239070 14789 solver.cpp:218] Iteration 3996 (2.46987 iter/s, 4.85855s/12 iters), loss = 1.68455
I0409 20:30:54.239123 14789 solver.cpp:237]     Train net output #0: loss = 1.68455 (* 1 = 1.68455 loss)
I0409 20:30:54.239135 14789 sgd_solver.cpp:105] Iteration 3996, lr = 0.00453141
I0409 20:30:59.149632 14789 solver.cpp:218] Iteration 4008 (2.44381 iter/s, 4.91036s/12 iters), loss = 1.76286
I0409 20:30:59.149673 14789 solver.cpp:237]     Train net output #0: loss = 1.76286 (* 1 = 1.76286 loss)
I0409 20:30:59.149683 14789 sgd_solver.cpp:105] Iteration 4008, lr = 0.00452066
I0409 20:31:03.944548 14789 solver.cpp:218] Iteration 4020 (2.50275 iter/s, 4.79473s/12 iters), loss = 1.62884
I0409 20:31:03.944623 14789 solver.cpp:237]     Train net output #0: loss = 1.62884 (* 1 = 1.62884 loss)
I0409 20:31:03.944635 14789 sgd_solver.cpp:105] Iteration 4020, lr = 0.00450992
I0409 20:31:08.930959 14789 solver.cpp:218] Iteration 4032 (2.40665 iter/s, 4.98618s/12 iters), loss = 1.68784
I0409 20:31:08.931011 14789 solver.cpp:237]     Train net output #0: loss = 1.68784 (* 1 = 1.68784 loss)
I0409 20:31:08.931025 14789 sgd_solver.cpp:105] Iteration 4032, lr = 0.00449921
I0409 20:31:13.981145 14789 solver.cpp:218] Iteration 4044 (2.37625 iter/s, 5.04998s/12 iters), loss = 1.46478
I0409 20:31:13.981190 14789 solver.cpp:237]     Train net output #0: loss = 1.46478 (* 1 = 1.46478 loss)
I0409 20:31:13.981199 14789 sgd_solver.cpp:105] Iteration 4044, lr = 0.00448853
I0409 20:31:14.385057 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:31:18.668421 14789 solver.cpp:218] Iteration 4056 (2.56023 iter/s, 4.68709s/12 iters), loss = 1.53552
I0409 20:31:18.668469 14789 solver.cpp:237]     Train net output #0: loss = 1.53552 (* 1 = 1.53552 loss)
I0409 20:31:18.668478 14789 sgd_solver.cpp:105] Iteration 4056, lr = 0.00447788
I0409 20:31:23.522051 14789 solver.cpp:218] Iteration 4068 (2.47248 iter/s, 4.85343s/12 iters), loss = 1.66419
I0409 20:31:23.522094 14789 solver.cpp:237]     Train net output #0: loss = 1.66419 (* 1 = 1.66419 loss)
I0409 20:31:23.522104 14789 sgd_solver.cpp:105] Iteration 4068, lr = 0.00446724
I0409 20:31:28.082901 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4080.caffemodel
I0409 20:31:31.930907 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4080.solverstate
I0409 20:31:34.938995 14789 solver.cpp:330] Iteration 4080, Testing net (#0)
I0409 20:31:34.939085 14789 net.cpp:676] Ignoring source layer train-data
I0409 20:31:37.764708 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:31:39.382014 14789 solver.cpp:397]     Test net output #0: accuracy = 0.392157
I0409 20:31:39.382066 14789 solver.cpp:397]     Test net output #1: loss = 2.38521 (* 1 = 2.38521 loss)
I0409 20:31:39.474241 14789 solver.cpp:218] Iteration 4080 (0.752272 iter/s, 15.9517s/12 iters), loss = 1.63861
I0409 20:31:39.474295 14789 solver.cpp:237]     Train net output #0: loss = 1.63861 (* 1 = 1.63861 loss)
I0409 20:31:39.474306 14789 sgd_solver.cpp:105] Iteration 4080, lr = 0.00445664
I0409 20:31:43.833815 14789 solver.cpp:218] Iteration 4092 (2.75268 iter/s, 4.35938s/12 iters), loss = 1.63566
I0409 20:31:43.833868 14789 solver.cpp:237]     Train net output #0: loss = 1.63566 (* 1 = 1.63566 loss)
I0409 20:31:43.833879 14789 sgd_solver.cpp:105] Iteration 4092, lr = 0.00444606
I0409 20:31:48.703668 14789 solver.cpp:218] Iteration 4104 (2.46424 iter/s, 4.86965s/12 iters), loss = 1.5907
I0409 20:31:48.703713 14789 solver.cpp:237]     Train net output #0: loss = 1.5907 (* 1 = 1.5907 loss)
I0409 20:31:48.703723 14789 sgd_solver.cpp:105] Iteration 4104, lr = 0.0044355
I0409 20:31:53.784081 14789 solver.cpp:218] Iteration 4116 (2.36211 iter/s, 5.08021s/12 iters), loss = 1.57647
I0409 20:31:53.784126 14789 solver.cpp:237]     Train net output #0: loss = 1.57647 (* 1 = 1.57647 loss)
I0409 20:31:53.784134 14789 sgd_solver.cpp:105] Iteration 4116, lr = 0.00442497
I0409 20:31:58.709831 14789 solver.cpp:218] Iteration 4128 (2.43627 iter/s, 4.92555s/12 iters), loss = 1.69526
I0409 20:31:58.709873 14789 solver.cpp:237]     Train net output #0: loss = 1.69526 (* 1 = 1.69526 loss)
I0409 20:31:58.709882 14789 sgd_solver.cpp:105] Iteration 4128, lr = 0.00441447
I0409 20:32:03.833925 14789 solver.cpp:218] Iteration 4140 (2.34197 iter/s, 5.12389s/12 iters), loss = 1.61176
I0409 20:32:03.833995 14789 solver.cpp:237]     Train net output #0: loss = 1.61176 (* 1 = 1.61176 loss)
I0409 20:32:03.834009 14789 sgd_solver.cpp:105] Iteration 4140, lr = 0.00440398
I0409 20:32:06.352243 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:32:08.754176 14789 solver.cpp:218] Iteration 4152 (2.43901 iter/s, 4.92003s/12 iters), loss = 1.23558
I0409 20:32:08.754223 14789 solver.cpp:237]     Train net output #0: loss = 1.23558 (* 1 = 1.23558 loss)
I0409 20:32:08.754233 14789 sgd_solver.cpp:105] Iteration 4152, lr = 0.00439353
I0409 20:32:10.280707 14789 blocking_queue.cpp:49] Waiting for data
I0409 20:32:13.734391 14789 solver.cpp:218] Iteration 4164 (2.40964 iter/s, 4.98001s/12 iters), loss = 1.62929
I0409 20:32:13.734447 14789 solver.cpp:237]     Train net output #0: loss = 1.62929 (* 1 = 1.62929 loss)
I0409 20:32:13.734457 14789 sgd_solver.cpp:105] Iteration 4164, lr = 0.0043831
I0409 20:32:18.681578 14789 solver.cpp:218] Iteration 4176 (2.42572 iter/s, 4.94699s/12 iters), loss = 1.66178
I0409 20:32:18.681628 14789 solver.cpp:237]     Train net output #0: loss = 1.66178 (* 1 = 1.66178 loss)
I0409 20:32:18.681638 14789 sgd_solver.cpp:105] Iteration 4176, lr = 0.00437269
I0409 20:32:20.593554 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4182.caffemodel
I0409 20:32:25.019224 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4182.solverstate
I0409 20:32:28.031344 14789 solver.cpp:330] Iteration 4182, Testing net (#0)
I0409 20:32:28.031370 14789 net.cpp:676] Ignoring source layer train-data
I0409 20:32:30.881525 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:32:32.593056 14789 solver.cpp:397]     Test net output #0: accuracy = 0.444853
I0409 20:32:32.593089 14789 solver.cpp:397]     Test net output #1: loss = 2.18071 (* 1 = 2.18071 loss)
I0409 20:32:34.555150 14789 solver.cpp:218] Iteration 4188 (0.755998 iter/s, 15.8731s/12 iters), loss = 1.3588
I0409 20:32:34.555205 14789 solver.cpp:237]     Train net output #0: loss = 1.3588 (* 1 = 1.3588 loss)
I0409 20:32:34.555217 14789 sgd_solver.cpp:105] Iteration 4188, lr = 0.00436231
I0409 20:32:39.277240 14789 solver.cpp:218] Iteration 4200 (2.54136 iter/s, 4.72189s/12 iters), loss = 1.33902
I0409 20:32:39.277356 14789 solver.cpp:237]     Train net output #0: loss = 1.33902 (* 1 = 1.33902 loss)
I0409 20:32:39.277370 14789 sgd_solver.cpp:105] Iteration 4200, lr = 0.00435195
I0409 20:32:44.210158 14789 solver.cpp:218] Iteration 4212 (2.43277 iter/s, 4.93266s/12 iters), loss = 1.45155
I0409 20:32:44.210204 14789 solver.cpp:237]     Train net output #0: loss = 1.45155 (* 1 = 1.45155 loss)
I0409 20:32:44.210214 14789 sgd_solver.cpp:105] Iteration 4212, lr = 0.00434162
I0409 20:32:49.117512 14789 solver.cpp:218] Iteration 4224 (2.44541 iter/s, 4.90715s/12 iters), loss = 1.73402
I0409 20:32:49.117565 14789 solver.cpp:237]     Train net output #0: loss = 1.73402 (* 1 = 1.73402 loss)
I0409 20:32:49.117578 14789 sgd_solver.cpp:105] Iteration 4224, lr = 0.00433131
I0409 20:32:54.084812 14789 solver.cpp:218] Iteration 4236 (2.4159 iter/s, 4.9671s/12 iters), loss = 1.59149
I0409 20:32:54.084854 14789 solver.cpp:237]     Train net output #0: loss = 1.59149 (* 1 = 1.59149 loss)
I0409 20:32:54.084863 14789 sgd_solver.cpp:105] Iteration 4236, lr = 0.00432103
I0409 20:32:58.724524 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:32:58.960610 14789 solver.cpp:218] Iteration 4248 (2.46123 iter/s, 4.8756s/12 iters), loss = 1.31656
I0409 20:32:58.960659 14789 solver.cpp:237]     Train net output #0: loss = 1.31656 (* 1 = 1.31656 loss)
I0409 20:32:58.960671 14789 sgd_solver.cpp:105] Iteration 4248, lr = 0.00431077
I0409 20:33:03.920711 14789 solver.cpp:218] Iteration 4260 (2.4194 iter/s, 4.9599s/12 iters), loss = 1.42966
I0409 20:33:03.920764 14789 solver.cpp:237]     Train net output #0: loss = 1.42966 (* 1 = 1.42966 loss)
I0409 20:33:03.920776 14789 sgd_solver.cpp:105] Iteration 4260, lr = 0.00430053
I0409 20:33:08.953366 14789 solver.cpp:218] Iteration 4272 (2.38452 iter/s, 5.03245s/12 iters), loss = 1.3147
I0409 20:33:08.953413 14789 solver.cpp:237]     Train net output #0: loss = 1.3147 (* 1 = 1.3147 loss)
I0409 20:33:08.953423 14789 sgd_solver.cpp:105] Iteration 4272, lr = 0.00429032
I0409 20:33:13.444550 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4284.caffemodel
I0409 20:33:17.936266 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4284.solverstate
I0409 20:33:23.403939 14789 solver.cpp:330] Iteration 4284, Testing net (#0)
I0409 20:33:23.403964 14789 net.cpp:676] Ignoring source layer train-data
I0409 20:33:26.313484 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:33:28.180088 14789 solver.cpp:397]     Test net output #0: accuracy = 0.441789
I0409 20:33:28.180138 14789 solver.cpp:397]     Test net output #1: loss = 2.19062 (* 1 = 2.19062 loss)
I0409 20:33:28.271823 14789 solver.cpp:218] Iteration 4284 (0.621187 iter/s, 19.3179s/12 iters), loss = 1.36308
I0409 20:33:28.271875 14789 solver.cpp:237]     Train net output #0: loss = 1.36308 (* 1 = 1.36308 loss)
I0409 20:33:28.271888 14789 sgd_solver.cpp:105] Iteration 4284, lr = 0.00428014
I0409 20:33:32.557407 14789 solver.cpp:218] Iteration 4296 (2.80021 iter/s, 4.2854s/12 iters), loss = 1.33275
I0409 20:33:32.557456 14789 solver.cpp:237]     Train net output #0: loss = 1.33275 (* 1 = 1.33275 loss)
I0409 20:33:32.557464 14789 sgd_solver.cpp:105] Iteration 4296, lr = 0.00426998
I0409 20:33:37.505668 14789 solver.cpp:218] Iteration 4308 (2.42519 iter/s, 4.94806s/12 iters), loss = 1.44152
I0409 20:33:37.505726 14789 solver.cpp:237]     Train net output #0: loss = 1.44152 (* 1 = 1.44152 loss)
I0409 20:33:37.505739 14789 sgd_solver.cpp:105] Iteration 4308, lr = 0.00425984
I0409 20:33:42.581173 14789 solver.cpp:218] Iteration 4320 (2.3644 iter/s, 5.07529s/12 iters), loss = 1.67908
I0409 20:33:42.581224 14789 solver.cpp:237]     Train net output #0: loss = 1.67908 (* 1 = 1.67908 loss)
I0409 20:33:42.581235 14789 sgd_solver.cpp:105] Iteration 4320, lr = 0.00424972
I0409 20:33:48.079443 14789 solver.cpp:218] Iteration 4332 (2.18259 iter/s, 5.49805s/12 iters), loss = 1.49389
I0409 20:33:48.079564 14789 solver.cpp:237]     Train net output #0: loss = 1.49389 (* 1 = 1.49389 loss)
I0409 20:33:48.079578 14789 sgd_solver.cpp:105] Iteration 4332, lr = 0.00423964
I0409 20:33:53.035365 14789 solver.cpp:218] Iteration 4344 (2.42148 iter/s, 4.95565s/12 iters), loss = 1.28344
I0409 20:33:53.035419 14789 solver.cpp:237]     Train net output #0: loss = 1.28344 (* 1 = 1.28344 loss)
I0409 20:33:53.035432 14789 sgd_solver.cpp:105] Iteration 4344, lr = 0.00422957
I0409 20:33:54.826565 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:33:57.869271 14789 solver.cpp:218] Iteration 4356 (2.48257 iter/s, 4.8337s/12 iters), loss = 1.4502
I0409 20:33:57.869323 14789 solver.cpp:237]     Train net output #0: loss = 1.4502 (* 1 = 1.4502 loss)
I0409 20:33:57.869334 14789 sgd_solver.cpp:105] Iteration 4356, lr = 0.00421953
I0409 20:34:02.768980 14789 solver.cpp:218] Iteration 4368 (2.44923 iter/s, 4.89951s/12 iters), loss = 1.31078
I0409 20:34:02.769021 14789 solver.cpp:237]     Train net output #0: loss = 1.31078 (* 1 = 1.31078 loss)
I0409 20:34:02.769030 14789 sgd_solver.cpp:105] Iteration 4368, lr = 0.00420951
I0409 20:34:07.731160 14789 solver.cpp:218] Iteration 4380 (2.41839 iter/s, 4.96199s/12 iters), loss = 1.09739
I0409 20:34:07.731201 14789 solver.cpp:237]     Train net output #0: loss = 1.09739 (* 1 = 1.09739 loss)
I0409 20:34:07.731211 14789 sgd_solver.cpp:105] Iteration 4380, lr = 0.00419952
I0409 20:34:09.726815 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4386.caffemodel
I0409 20:34:14.534534 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4386.solverstate
I0409 20:34:19.128660 14789 solver.cpp:330] Iteration 4386, Testing net (#0)
I0409 20:34:19.128711 14789 net.cpp:676] Ignoring source layer train-data
I0409 20:34:21.967221 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:34:23.755621 14789 solver.cpp:397]     Test net output #0: accuracy = 0.439951
I0409 20:34:23.755669 14789 solver.cpp:397]     Test net output #1: loss = 2.27128 (* 1 = 2.27128 loss)
I0409 20:34:25.564623 14789 solver.cpp:218] Iteration 4392 (0.672913 iter/s, 17.8329s/12 iters), loss = 1.53924
I0409 20:34:25.564666 14789 solver.cpp:237]     Train net output #0: loss = 1.53924 (* 1 = 1.53924 loss)
I0409 20:34:25.564675 14789 sgd_solver.cpp:105] Iteration 4392, lr = 0.00418954
I0409 20:34:30.479694 14789 solver.cpp:218] Iteration 4404 (2.44157 iter/s, 4.91487s/12 iters), loss = 1.0133
I0409 20:34:30.479751 14789 solver.cpp:237]     Train net output #0: loss = 1.0133 (* 1 = 1.0133 loss)
I0409 20:34:30.479763 14789 sgd_solver.cpp:105] Iteration 4404, lr = 0.0041796
I0409 20:34:35.303001 14789 solver.cpp:218] Iteration 4416 (2.48802 iter/s, 4.82311s/12 iters), loss = 1.2089
I0409 20:34:35.303043 14789 solver.cpp:237]     Train net output #0: loss = 1.2089 (* 1 = 1.2089 loss)
I0409 20:34:35.303056 14789 sgd_solver.cpp:105] Iteration 4416, lr = 0.00416967
I0409 20:34:40.252543 14789 solver.cpp:218] Iteration 4428 (2.42457 iter/s, 4.94934s/12 iters), loss = 1.19199
I0409 20:34:40.252593 14789 solver.cpp:237]     Train net output #0: loss = 1.19199 (* 1 = 1.19199 loss)
I0409 20:34:40.252604 14789 sgd_solver.cpp:105] Iteration 4428, lr = 0.00415977
I0409 20:34:44.970391 14789 solver.cpp:218] Iteration 4440 (2.54364 iter/s, 4.71765s/12 iters), loss = 1.18525
I0409 20:34:44.970448 14789 solver.cpp:237]     Train net output #0: loss = 1.18525 (* 1 = 1.18525 loss)
I0409 20:34:44.970459 14789 sgd_solver.cpp:105] Iteration 4440, lr = 0.0041499
I0409 20:34:48.809554 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:34:49.745337 14789 solver.cpp:218] Iteration 4452 (2.51322 iter/s, 4.77474s/12 iters), loss = 1.25079
I0409 20:34:49.747895 14789 solver.cpp:237]     Train net output #0: loss = 1.25079 (* 1 = 1.25079 loss)
I0409 20:34:49.747905 14789 sgd_solver.cpp:105] Iteration 4452, lr = 0.00414005
I0409 20:34:54.821012 14789 solver.cpp:218] Iteration 4464 (2.36548 iter/s, 5.07296s/12 iters), loss = 1.37446
I0409 20:34:54.821055 14789 solver.cpp:237]     Train net output #0: loss = 1.37446 (* 1 = 1.37446 loss)
I0409 20:34:54.821063 14789 sgd_solver.cpp:105] Iteration 4464, lr = 0.00413022
I0409 20:35:00.265013 14789 solver.cpp:218] Iteration 4476 (2.20435 iter/s, 5.44379s/12 iters), loss = 1.17153
I0409 20:35:00.265069 14789 solver.cpp:237]     Train net output #0: loss = 1.17153 (* 1 = 1.17153 loss)
I0409 20:35:00.265081 14789 sgd_solver.cpp:105] Iteration 4476, lr = 0.00412041
I0409 20:35:04.943457 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4488.caffemodel
I0409 20:35:08.720491 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4488.solverstate
I0409 20:35:11.730708 14789 solver.cpp:330] Iteration 4488, Testing net (#0)
I0409 20:35:11.730736 14789 net.cpp:676] Ignoring source layer train-data
I0409 20:35:14.319938 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:35:16.143697 14789 solver.cpp:397]     Test net output #0: accuracy = 0.427696
I0409 20:35:16.143743 14789 solver.cpp:397]     Test net output #1: loss = 2.32391 (* 1 = 2.32391 loss)
I0409 20:35:16.235741 14789 solver.cpp:218] Iteration 4488 (0.751399 iter/s, 15.9702s/12 iters), loss = 1.21643
I0409 20:35:16.235791 14789 solver.cpp:237]     Train net output #0: loss = 1.21643 (* 1 = 1.21643 loss)
I0409 20:35:16.235801 14789 sgd_solver.cpp:105] Iteration 4488, lr = 0.00411063
I0409 20:35:20.812358 14789 solver.cpp:218] Iteration 4500 (2.62213 iter/s, 4.57643s/12 iters), loss = 1.18384
I0409 20:35:20.812464 14789 solver.cpp:237]     Train net output #0: loss = 1.18384 (* 1 = 1.18384 loss)
I0409 20:35:20.812477 14789 sgd_solver.cpp:105] Iteration 4500, lr = 0.00410087
I0409 20:35:25.887243 14789 solver.cpp:218] Iteration 4512 (2.36471 iter/s, 5.07462s/12 iters), loss = 1.31847
I0409 20:35:25.887295 14789 solver.cpp:237]     Train net output #0: loss = 1.31847 (* 1 = 1.31847 loss)
I0409 20:35:25.887306 14789 sgd_solver.cpp:105] Iteration 4512, lr = 0.00409113
I0409 20:35:30.736732 14789 solver.cpp:218] Iteration 4524 (2.47459 iter/s, 4.84928s/12 iters), loss = 1.2041
I0409 20:35:30.736786 14789 solver.cpp:237]     Train net output #0: loss = 1.2041 (* 1 = 1.2041 loss)
I0409 20:35:30.736799 14789 sgd_solver.cpp:105] Iteration 4524, lr = 0.00408142
I0409 20:35:35.766618 14789 solver.cpp:218] Iteration 4536 (2.38584 iter/s, 5.02968s/12 iters), loss = 1.19876
I0409 20:35:35.766665 14789 solver.cpp:237]     Train net output #0: loss = 1.19876 (* 1 = 1.19876 loss)
I0409 20:35:35.766676 14789 sgd_solver.cpp:105] Iteration 4536, lr = 0.00407173
I0409 20:35:40.839839 14789 solver.cpp:218] Iteration 4548 (2.36546 iter/s, 5.07301s/12 iters), loss = 1.10792
I0409 20:35:40.839892 14789 solver.cpp:237]     Train net output #0: loss = 1.10792 (* 1 = 1.10792 loss)
I0409 20:35:40.839906 14789 sgd_solver.cpp:105] Iteration 4548, lr = 0.00406206
I0409 20:35:42.126281 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:35:45.868872 14789 solver.cpp:218] Iteration 4560 (2.38624 iter/s, 5.02882s/12 iters), loss = 0.96952
I0409 20:35:45.868927 14789 solver.cpp:237]     Train net output #0: loss = 0.96952 (* 1 = 0.96952 loss)
I0409 20:35:45.868937 14789 sgd_solver.cpp:105] Iteration 4560, lr = 0.00405242
I0409 20:35:50.876756 14789 solver.cpp:218] Iteration 4572 (2.39632 iter/s, 5.00768s/12 iters), loss = 1.22626
I0409 20:35:50.876852 14789 solver.cpp:237]     Train net output #0: loss = 1.22626 (* 1 = 1.22626 loss)
I0409 20:35:50.876863 14789 sgd_solver.cpp:105] Iteration 4572, lr = 0.0040428
I0409 20:35:55.685781 14789 solver.cpp:218] Iteration 4584 (2.49543 iter/s, 4.80878s/12 iters), loss = 1.16322
I0409 20:35:55.685834 14789 solver.cpp:237]     Train net output #0: loss = 1.16322 (* 1 = 1.16322 loss)
I0409 20:35:55.685848 14789 sgd_solver.cpp:105] Iteration 4584, lr = 0.0040332
I0409 20:35:57.577265 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4590.caffemodel
I0409 20:36:03.745896 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4590.solverstate
I0409 20:36:06.757282 14789 solver.cpp:330] Iteration 4590, Testing net (#0)
I0409 20:36:06.757308 14789 net.cpp:676] Ignoring source layer train-data
I0409 20:36:09.491111 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:36:11.311338 14789 solver.cpp:397]     Test net output #0: accuracy = 0.44424
I0409 20:36:11.311385 14789 solver.cpp:397]     Test net output #1: loss = 2.32599 (* 1 = 2.32599 loss)
I0409 20:36:13.178287 14789 solver.cpp:218] Iteration 4596 (0.68603 iter/s, 17.492s/12 iters), loss = 1.26446
I0409 20:36:13.178340 14789 solver.cpp:237]     Train net output #0: loss = 1.26446 (* 1 = 1.26446 loss)
I0409 20:36:13.178354 14789 sgd_solver.cpp:105] Iteration 4596, lr = 0.00402362
I0409 20:36:18.104214 14789 solver.cpp:218] Iteration 4608 (2.43619 iter/s, 4.92572s/12 iters), loss = 1.12811
I0409 20:36:18.104274 14789 solver.cpp:237]     Train net output #0: loss = 1.12811 (* 1 = 1.12811 loss)
I0409 20:36:18.104290 14789 sgd_solver.cpp:105] Iteration 4608, lr = 0.00401407
I0409 20:36:22.953222 14789 solver.cpp:218] Iteration 4620 (2.47484 iter/s, 4.8488s/12 iters), loss = 1.0747
I0409 20:36:22.953364 14789 solver.cpp:237]     Train net output #0: loss = 1.0747 (* 1 = 1.0747 loss)
I0409 20:36:22.953377 14789 sgd_solver.cpp:105] Iteration 4620, lr = 0.00400454
I0409 20:36:27.905862 14789 solver.cpp:218] Iteration 4632 (2.42309 iter/s, 4.95235s/12 iters), loss = 1.2421
I0409 20:36:27.905920 14789 solver.cpp:237]     Train net output #0: loss = 1.2421 (* 1 = 1.2421 loss)
I0409 20:36:27.905931 14789 sgd_solver.cpp:105] Iteration 4632, lr = 0.00399503
I0409 20:36:33.074506 14789 solver.cpp:218] Iteration 4644 (2.32179 iter/s, 5.16843s/12 iters), loss = 1.13472
I0409 20:36:33.074553 14789 solver.cpp:237]     Train net output #0: loss = 1.13472 (* 1 = 1.13472 loss)
I0409 20:36:33.074563 14789 sgd_solver.cpp:105] Iteration 4644, lr = 0.00398555
I0409 20:36:36.311278 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:36:37.962123 14789 solver.cpp:218] Iteration 4656 (2.45529 iter/s, 4.88741s/12 iters), loss = 1.00822
I0409 20:36:37.962170 14789 solver.cpp:237]     Train net output #0: loss = 1.00822 (* 1 = 1.00822 loss)
I0409 20:36:37.962182 14789 sgd_solver.cpp:105] Iteration 4656, lr = 0.00397608
I0409 20:36:43.046124 14789 solver.cpp:218] Iteration 4668 (2.36044 iter/s, 5.0838s/12 iters), loss = 1.06232
I0409 20:36:43.046162 14789 solver.cpp:237]     Train net output #0: loss = 1.06232 (* 1 = 1.06232 loss)
I0409 20:36:43.046171 14789 sgd_solver.cpp:105] Iteration 4668, lr = 0.00396664
I0409 20:36:47.957335 14789 solver.cpp:218] Iteration 4680 (2.44349 iter/s, 4.91102s/12 iters), loss = 1.13327
I0409 20:36:47.957389 14789 solver.cpp:237]     Train net output #0: loss = 1.13327 (* 1 = 1.13327 loss)
I0409 20:36:47.957401 14789 sgd_solver.cpp:105] Iteration 4680, lr = 0.00395723
I0409 20:36:52.238564 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4692.caffemodel
I0409 20:36:58.847188 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4692.solverstate
I0409 20:37:03.315064 14789 solver.cpp:330] Iteration 4692, Testing net (#0)
I0409 20:37:03.315090 14789 net.cpp:676] Ignoring source layer train-data
I0409 20:37:05.841892 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:37:07.700472 14789 solver.cpp:397]     Test net output #0: accuracy = 0.46201
I0409 20:37:07.700522 14789 solver.cpp:397]     Test net output #1: loss = 2.27789 (* 1 = 2.27789 loss)
I0409 20:37:07.792429 14789 solver.cpp:218] Iteration 4692 (0.605007 iter/s, 19.8345s/12 iters), loss = 1.04031
I0409 20:37:07.792486 14789 solver.cpp:237]     Train net output #0: loss = 1.04031 (* 1 = 1.04031 loss)
I0409 20:37:07.792498 14789 sgd_solver.cpp:105] Iteration 4692, lr = 0.00394783
I0409 20:37:11.897294 14789 solver.cpp:218] Iteration 4704 (2.92349 iter/s, 4.10468s/12 iters), loss = 1.0127
I0409 20:37:11.897351 14789 solver.cpp:237]     Train net output #0: loss = 1.0127 (* 1 = 1.0127 loss)
I0409 20:37:11.897362 14789 sgd_solver.cpp:105] Iteration 4704, lr = 0.00393846
I0409 20:37:16.759897 14789 solver.cpp:218] Iteration 4716 (2.46792 iter/s, 4.8624s/12 iters), loss = 1.16796
I0409 20:37:16.759954 14789 solver.cpp:237]     Train net output #0: loss = 1.16796 (* 1 = 1.16796 loss)
I0409 20:37:16.759968 14789 sgd_solver.cpp:105] Iteration 4716, lr = 0.00392911
I0409 20:37:21.729130 14789 solver.cpp:218] Iteration 4728 (2.41496 iter/s, 4.96902s/12 iters), loss = 1.1019
I0409 20:37:21.729178 14789 solver.cpp:237]     Train net output #0: loss = 1.1019 (* 1 = 1.1019 loss)
I0409 20:37:21.729187 14789 sgd_solver.cpp:105] Iteration 4728, lr = 0.00391978
I0409 20:37:26.911339 14789 solver.cpp:218] Iteration 4740 (2.31571 iter/s, 5.182s/12 iters), loss = 0.979001
I0409 20:37:26.911387 14789 solver.cpp:237]     Train net output #0: loss = 0.979001 (* 1 = 0.979001 loss)
I0409 20:37:26.911397 14789 sgd_solver.cpp:105] Iteration 4740, lr = 0.00391047
I0409 20:37:31.808003 14789 solver.cpp:218] Iteration 4752 (2.45075 iter/s, 4.89646s/12 iters), loss = 0.874812
I0409 20:37:31.808133 14789 solver.cpp:237]     Train net output #0: loss = 0.874812 (* 1 = 0.874812 loss)
I0409 20:37:31.808143 14789 sgd_solver.cpp:105] Iteration 4752, lr = 0.00390119
I0409 20:37:32.297598 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:37:36.763089 14789 solver.cpp:218] Iteration 4764 (2.42189 iter/s, 4.9548s/12 iters), loss = 0.940647
I0409 20:37:36.763151 14789 solver.cpp:237]     Train net output #0: loss = 0.940647 (* 1 = 0.940647 loss)
I0409 20:37:36.763164 14789 sgd_solver.cpp:105] Iteration 4764, lr = 0.00389193
I0409 20:37:41.621449 14789 solver.cpp:218] Iteration 4776 (2.47008 iter/s, 4.85815s/12 iters), loss = 1.01332
I0409 20:37:41.621501 14789 solver.cpp:237]     Train net output #0: loss = 1.01332 (* 1 = 1.01332 loss)
I0409 20:37:41.621515 14789 sgd_solver.cpp:105] Iteration 4776, lr = 0.00388269
I0409 20:37:46.349602 14789 solver.cpp:218] Iteration 4788 (2.5381 iter/s, 4.72795s/12 iters), loss = 1.08394
I0409 20:37:46.349658 14789 solver.cpp:237]     Train net output #0: loss = 1.08394 (* 1 = 1.08394 loss)
I0409 20:37:46.349670 14789 sgd_solver.cpp:105] Iteration 4788, lr = 0.00387347
I0409 20:37:48.332396 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4794.caffemodel
I0409 20:37:54.160821 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4794.solverstate
I0409 20:37:57.746824 14789 solver.cpp:330] Iteration 4794, Testing net (#0)
I0409 20:37:57.746850 14789 net.cpp:676] Ignoring source layer train-data
I0409 20:38:00.265084 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:38:02.168094 14789 solver.cpp:397]     Test net output #0: accuracy = 0.456495
I0409 20:38:02.168212 14789 solver.cpp:397]     Test net output #1: loss = 2.28258 (* 1 = 2.28258 loss)
I0409 20:38:04.052997 14789 solver.cpp:218] Iteration 4800 (0.677858 iter/s, 17.7028s/12 iters), loss = 1.12643
I0409 20:38:04.053038 14789 solver.cpp:237]     Train net output #0: loss = 1.12643 (* 1 = 1.12643 loss)
I0409 20:38:04.053047 14789 sgd_solver.cpp:105] Iteration 4800, lr = 0.00386427
I0409 20:38:08.865284 14789 solver.cpp:218] Iteration 4812 (2.49372 iter/s, 4.8121s/12 iters), loss = 0.977898
I0409 20:38:08.865334 14789 solver.cpp:237]     Train net output #0: loss = 0.977898 (* 1 = 0.977898 loss)
I0409 20:38:08.865346 14789 sgd_solver.cpp:105] Iteration 4812, lr = 0.0038551
I0409 20:38:13.743891 14789 solver.cpp:218] Iteration 4824 (2.45982 iter/s, 4.87841s/12 iters), loss = 1.03801
I0409 20:38:13.743947 14789 solver.cpp:237]     Train net output #0: loss = 1.03801 (* 1 = 1.03801 loss)
I0409 20:38:13.743960 14789 sgd_solver.cpp:105] Iteration 4824, lr = 0.00384594
I0409 20:38:18.596385 14789 solver.cpp:218] Iteration 4836 (2.47306 iter/s, 4.85229s/12 iters), loss = 1.19346
I0409 20:38:18.596433 14789 solver.cpp:237]     Train net output #0: loss = 1.19346 (* 1 = 1.19346 loss)
I0409 20:38:18.596444 14789 sgd_solver.cpp:105] Iteration 4836, lr = 0.00383681
I0409 20:38:20.588285 14789 blocking_queue.cpp:49] Waiting for data
I0409 20:38:23.619573 14789 solver.cpp:218] Iteration 4848 (2.38902 iter/s, 5.02298s/12 iters), loss = 0.999521
I0409 20:38:23.619630 14789 solver.cpp:237]     Train net output #0: loss = 0.999521 (* 1 = 0.999521 loss)
I0409 20:38:23.619642 14789 sgd_solver.cpp:105] Iteration 4848, lr = 0.0038277
I0409 20:38:26.233650 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:38:28.425016 14789 solver.cpp:218] Iteration 4860 (2.49727 iter/s, 4.80524s/12 iters), loss = 0.930553
I0409 20:38:28.425069 14789 solver.cpp:237]     Train net output #0: loss = 0.930553 (* 1 = 0.930553 loss)
I0409 20:38:28.425081 14789 sgd_solver.cpp:105] Iteration 4860, lr = 0.00381862
I0409 20:38:33.712488 14789 solver.cpp:218] Iteration 4872 (2.26961 iter/s, 5.28726s/12 iters), loss = 0.811084
I0409 20:38:33.712602 14789 solver.cpp:237]     Train net output #0: loss = 0.811084 (* 1 = 0.811084 loss)
I0409 20:38:33.712615 14789 sgd_solver.cpp:105] Iteration 4872, lr = 0.00380955
I0409 20:38:38.500917 14789 solver.cpp:218] Iteration 4884 (2.50618 iter/s, 4.78817s/12 iters), loss = 1.03351
I0409 20:38:38.500967 14789 solver.cpp:237]     Train net output #0: loss = 1.03351 (* 1 = 1.03351 loss)
I0409 20:38:38.500977 14789 sgd_solver.cpp:105] Iteration 4884, lr = 0.0038005
I0409 20:38:42.820042 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4896.caffemodel
I0409 20:38:49.903780 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4896.solverstate
I0409 20:38:55.102277 14789 solver.cpp:330] Iteration 4896, Testing net (#0)
I0409 20:38:55.102304 14789 net.cpp:676] Ignoring source layer train-data
I0409 20:38:57.672175 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:38:59.707702 14789 solver.cpp:397]     Test net output #0: accuracy = 0.461397
I0409 20:38:59.707751 14789 solver.cpp:397]     Test net output #1: loss = 2.2331 (* 1 = 2.2331 loss)
I0409 20:38:59.799552 14789 solver.cpp:218] Iteration 4896 (0.563434 iter/s, 21.298s/12 iters), loss = 1.06961
I0409 20:38:59.799603 14789 solver.cpp:237]     Train net output #0: loss = 1.06961 (* 1 = 1.06961 loss)
I0409 20:38:59.799614 14789 sgd_solver.cpp:105] Iteration 4896, lr = 0.00379148
I0409 20:39:03.991616 14789 solver.cpp:218] Iteration 4908 (2.86267 iter/s, 4.19189s/12 iters), loss = 1.09292
I0409 20:39:03.991701 14789 solver.cpp:237]     Train net output #0: loss = 1.09292 (* 1 = 1.09292 loss)
I0409 20:39:03.991709 14789 sgd_solver.cpp:105] Iteration 4908, lr = 0.00378248
I0409 20:39:09.248581 14789 solver.cpp:218] Iteration 4920 (2.28279 iter/s, 5.25672s/12 iters), loss = 1.05221
I0409 20:39:09.248632 14789 solver.cpp:237]     Train net output #0: loss = 1.05221 (* 1 = 1.05221 loss)
I0409 20:39:09.248644 14789 sgd_solver.cpp:105] Iteration 4920, lr = 0.0037735
I0409 20:39:14.226521 14789 solver.cpp:218] Iteration 4932 (2.41074 iter/s, 4.97773s/12 iters), loss = 0.959007
I0409 20:39:14.226570 14789 solver.cpp:237]     Train net output #0: loss = 0.959007 (* 1 = 0.959007 loss)
I0409 20:39:14.226580 14789 sgd_solver.cpp:105] Iteration 4932, lr = 0.00376454
I0409 20:39:18.950953 14789 solver.cpp:218] Iteration 4944 (2.54009 iter/s, 4.72424s/12 iters), loss = 1.05384
I0409 20:39:18.951001 14789 solver.cpp:237]     Train net output #0: loss = 1.05384 (* 1 = 1.05384 loss)
I0409 20:39:18.951014 14789 sgd_solver.cpp:105] Iteration 4944, lr = 0.0037556
I0409 20:39:23.707017 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:39:23.903666 14789 solver.cpp:218] Iteration 4956 (2.42301 iter/s, 4.95251s/12 iters), loss = 0.851368
I0409 20:39:23.903723 14789 solver.cpp:237]     Train net output #0: loss = 0.851368 (* 1 = 0.851368 loss)
I0409 20:39:23.903735 14789 sgd_solver.cpp:105] Iteration 4956, lr = 0.00374669
I0409 20:39:29.286115 14789 solver.cpp:218] Iteration 4968 (2.22956 iter/s, 5.38223s/12 iters), loss = 0.930916
I0409 20:39:29.286168 14789 solver.cpp:237]     Train net output #0: loss = 0.930916 (* 1 = 0.930916 loss)
I0409 20:39:29.286180 14789 sgd_solver.cpp:105] Iteration 4968, lr = 0.00373779
I0409 20:39:34.777009 14789 solver.cpp:218] Iteration 4980 (2.18552 iter/s, 5.49068s/12 iters), loss = 0.732286
I0409 20:39:34.777135 14789 solver.cpp:237]     Train net output #0: loss = 0.732286 (* 1 = 0.732286 loss)
I0409 20:39:34.777146 14789 sgd_solver.cpp:105] Iteration 4980, lr = 0.00372892
I0409 20:39:39.819052 14789 solver.cpp:218] Iteration 4992 (2.38012 iter/s, 5.04176s/12 iters), loss = 0.904215
I0409 20:39:39.819108 14789 solver.cpp:237]     Train net output #0: loss = 0.904215 (* 1 = 0.904215 loss)
I0409 20:39:39.819123 14789 sgd_solver.cpp:105] Iteration 4992, lr = 0.00372006
I0409 20:39:41.907410 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4998.caffemodel
I0409 20:39:48.401702 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4998.solverstate
I0409 20:39:52.831126 14789 solver.cpp:330] Iteration 4998, Testing net (#0)
I0409 20:39:52.831148 14789 net.cpp:676] Ignoring source layer train-data
I0409 20:39:55.657289 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:39:57.786927 14789 solver.cpp:397]     Test net output #0: accuracy = 0.45098
I0409 20:39:57.786967 14789 solver.cpp:397]     Test net output #1: loss = 2.32919 (* 1 = 2.32919 loss)
I0409 20:39:59.509151 14789 solver.cpp:218] Iteration 5004 (0.609462 iter/s, 19.6895s/12 iters), loss = 1.05848
I0409 20:39:59.509197 14789 solver.cpp:237]     Train net output #0: loss = 1.05848 (* 1 = 1.05848 loss)
I0409 20:39:59.509207 14789 sgd_solver.cpp:105] Iteration 5004, lr = 0.00371123
I0409 20:40:04.424623 14789 solver.cpp:218] Iteration 5016 (2.44137 iter/s, 4.91527s/12 iters), loss = 1.00564
I0409 20:40:04.424669 14789 solver.cpp:237]     Train net output #0: loss = 1.00564 (* 1 = 1.00564 loss)
I0409 20:40:04.424680 14789 sgd_solver.cpp:105] Iteration 5016, lr = 0.00370242
I0409 20:40:09.589713 14789 solver.cpp:218] Iteration 5028 (2.32338 iter/s, 5.16488s/12 iters), loss = 0.992401
I0409 20:40:09.589793 14789 solver.cpp:237]     Train net output #0: loss = 0.992401 (* 1 = 0.992401 loss)
I0409 20:40:09.589805 14789 sgd_solver.cpp:105] Iteration 5028, lr = 0.00369363
I0409 20:40:14.593995 14789 solver.cpp:218] Iteration 5040 (2.39806 iter/s, 5.00405s/12 iters), loss = 0.943709
I0409 20:40:14.594039 14789 solver.cpp:237]     Train net output #0: loss = 0.943709 (* 1 = 0.943709 loss)
I0409 20:40:14.594049 14789 sgd_solver.cpp:105] Iteration 5040, lr = 0.00368486
I0409 20:40:19.329880 14789 solver.cpp:218] Iteration 5052 (2.53395 iter/s, 4.73569s/12 iters), loss = 0.809974
I0409 20:40:19.329931 14789 solver.cpp:237]     Train net output #0: loss = 0.809974 (* 1 = 0.809974 loss)
I0409 20:40:19.329941 14789 sgd_solver.cpp:105] Iteration 5052, lr = 0.00367611
I0409 20:40:21.229512 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:40:24.256405 14789 solver.cpp:218] Iteration 5064 (2.4359 iter/s, 4.92632s/12 iters), loss = 0.9767
I0409 20:40:24.256450 14789 solver.cpp:237]     Train net output #0: loss = 0.9767 (* 1 = 0.9767 loss)
I0409 20:40:24.256459 14789 sgd_solver.cpp:105] Iteration 5064, lr = 0.00366738
I0409 20:40:29.065107 14789 solver.cpp:218] Iteration 5076 (2.49558 iter/s, 4.8085s/12 iters), loss = 1.07546
I0409 20:40:29.065161 14789 solver.cpp:237]     Train net output #0: loss = 1.07546 (* 1 = 1.07546 loss)
I0409 20:40:29.065173 14789 sgd_solver.cpp:105] Iteration 5076, lr = 0.00365868
I0409 20:40:34.258332 14789 solver.cpp:218] Iteration 5088 (2.3108 iter/s, 5.19301s/12 iters), loss = 0.688864
I0409 20:40:34.258391 14789 solver.cpp:237]     Train net output #0: loss = 0.688864 (* 1 = 0.688864 loss)
I0409 20:40:34.258404 14789 sgd_solver.cpp:105] Iteration 5088, lr = 0.00364999
I0409 20:40:38.406106 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5100.caffemodel
I0409 20:40:47.045073 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5100.solverstate
I0409 20:40:51.128130 14789 solver.cpp:330] Iteration 5100, Testing net (#0)
I0409 20:40:51.128155 14789 net.cpp:676] Ignoring source layer train-data
I0409 20:40:53.586750 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:40:55.746161 14789 solver.cpp:397]     Test net output #0: accuracy = 0.458946
I0409 20:40:55.746209 14789 solver.cpp:397]     Test net output #1: loss = 2.29144 (* 1 = 2.29144 loss)
I0409 20:40:55.837721 14789 solver.cpp:218] Iteration 5100 (0.556104 iter/s, 21.5787s/12 iters), loss = 0.989769
I0409 20:40:55.837769 14789 solver.cpp:237]     Train net output #0: loss = 0.989769 (* 1 = 0.989769 loss)
I0409 20:40:55.837779 14789 sgd_solver.cpp:105] Iteration 5100, lr = 0.00364132
I0409 20:41:00.338896 14789 solver.cpp:218] Iteration 5112 (2.66608 iter/s, 4.50099s/12 iters), loss = 0.640519
I0409 20:41:00.338944 14789 solver.cpp:237]     Train net output #0: loss = 0.640519 (* 1 = 0.640519 loss)
I0409 20:41:00.338953 14789 sgd_solver.cpp:105] Iteration 5112, lr = 0.00363268
I0409 20:41:05.616941 14789 solver.cpp:218] Iteration 5124 (2.27366 iter/s, 5.27783s/12 iters), loss = 0.8018
I0409 20:41:05.616986 14789 solver.cpp:237]     Train net output #0: loss = 0.8018 (* 1 = 0.8018 loss)
I0409 20:41:05.616995 14789 sgd_solver.cpp:105] Iteration 5124, lr = 0.00362405
I0409 20:41:10.593442 14789 solver.cpp:218] Iteration 5136 (2.41143 iter/s, 4.9763s/12 iters), loss = 1.04486
I0409 20:41:10.593490 14789 solver.cpp:237]     Train net output #0: loss = 1.04486 (* 1 = 1.04486 loss)
I0409 20:41:10.593503 14789 sgd_solver.cpp:105] Iteration 5136, lr = 0.00361545
I0409 20:41:15.402490 14789 solver.cpp:218] Iteration 5148 (2.4954 iter/s, 4.80885s/12 iters), loss = 0.826427
I0409 20:41:15.402551 14789 solver.cpp:237]     Train net output #0: loss = 0.826427 (* 1 = 0.826427 loss)
I0409 20:41:15.402566 14789 sgd_solver.cpp:105] Iteration 5148, lr = 0.00360687
I0409 20:41:19.392017 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:41:20.370844 14789 solver.cpp:218] Iteration 5160 (2.41539 iter/s, 4.96814s/12 iters), loss = 0.69561
I0409 20:41:20.370896 14789 solver.cpp:237]     Train net output #0: loss = 0.69561 (* 1 = 0.69561 loss)
I0409 20:41:20.370908 14789 sgd_solver.cpp:105] Iteration 5160, lr = 0.0035983
I0409 20:41:25.254586 14789 solver.cpp:218] Iteration 5172 (2.45723 iter/s, 4.88354s/12 iters), loss = 0.85433
I0409 20:41:25.254634 14789 solver.cpp:237]     Train net output #0: loss = 0.85433 (* 1 = 0.85433 loss)
I0409 20:41:25.254647 14789 sgd_solver.cpp:105] Iteration 5172, lr = 0.00358976
I0409 20:41:30.169544 14789 solver.cpp:218] Iteration 5184 (2.44163 iter/s, 4.91476s/12 iters), loss = 0.903227
I0409 20:41:30.169593 14789 solver.cpp:237]     Train net output #0: loss = 0.903227 (* 1 = 0.903227 loss)
I0409 20:41:30.169603 14789 sgd_solver.cpp:105] Iteration 5184, lr = 0.00358124
I0409 20:41:34.879551 14789 solver.cpp:218] Iteration 5196 (2.54787 iter/s, 4.70981s/12 iters), loss = 0.759306
I0409 20:41:34.879608 14789 solver.cpp:237]     Train net output #0: loss = 0.759306 (* 1 = 0.759306 loss)
I0409 20:41:34.879621 14789 sgd_solver.cpp:105] Iteration 5196, lr = 0.00357273
I0409 20:41:36.687888 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5202.caffemodel
I0409 20:41:40.515348 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5202.solverstate
I0409 20:41:45.982756 14789 solver.cpp:330] Iteration 5202, Testing net (#0)
I0409 20:41:45.982779 14789 net.cpp:676] Ignoring source layer train-data
I0409 20:41:48.696245 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:41:50.886138 14789 solver.cpp:397]     Test net output #0: accuracy = 0.476716
I0409 20:41:50.886277 14789 solver.cpp:397]     Test net output #1: loss = 2.18689 (* 1 = 2.18689 loss)
I0409 20:41:52.872771 14789 solver.cpp:218] Iteration 5208 (0.666939 iter/s, 17.9926s/12 iters), loss = 0.756979
I0409 20:41:52.872819 14789 solver.cpp:237]     Train net output #0: loss = 0.756979 (* 1 = 0.756979 loss)
I0409 20:41:52.872829 14789 sgd_solver.cpp:105] Iteration 5208, lr = 0.00356425
I0409 20:41:58.404991 14789 solver.cpp:218] Iteration 5220 (2.1692 iter/s, 5.532s/12 iters), loss = 0.834255
I0409 20:41:58.405041 14789 solver.cpp:237]     Train net output #0: loss = 0.834255 (* 1 = 0.834255 loss)
I0409 20:41:58.405053 14789 sgd_solver.cpp:105] Iteration 5220, lr = 0.00355579
I0409 20:42:03.403177 14789 solver.cpp:218] Iteration 5232 (2.40097 iter/s, 4.99798s/12 iters), loss = 0.875381
I0409 20:42:03.403234 14789 solver.cpp:237]     Train net output #0: loss = 0.875381 (* 1 = 0.875381 loss)
I0409 20:42:03.403245 14789 sgd_solver.cpp:105] Iteration 5232, lr = 0.00354735
I0409 20:42:08.036231 14789 solver.cpp:218] Iteration 5244 (2.5902 iter/s, 4.63285s/12 iters), loss = 0.809731
I0409 20:42:08.036276 14789 solver.cpp:237]     Train net output #0: loss = 0.809731 (* 1 = 0.809731 loss)
I0409 20:42:08.036288 14789 sgd_solver.cpp:105] Iteration 5244, lr = 0.00353892
I0409 20:42:13.012029 14789 solver.cpp:218] Iteration 5256 (2.41177 iter/s, 4.9756s/12 iters), loss = 0.886301
I0409 20:42:13.012076 14789 solver.cpp:237]     Train net output #0: loss = 0.886301 (* 1 = 0.886301 loss)
I0409 20:42:13.012087 14789 sgd_solver.cpp:105] Iteration 5256, lr = 0.00353052
I0409 20:42:14.169675 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:42:17.812496 14789 solver.cpp:218] Iteration 5268 (2.49986 iter/s, 4.80027s/12 iters), loss = 0.676092
I0409 20:42:17.812554 14789 solver.cpp:237]     Train net output #0: loss = 0.676092 (* 1 = 0.676092 loss)
I0409 20:42:17.812568 14789 sgd_solver.cpp:105] Iteration 5268, lr = 0.00352214
I0409 20:42:22.531385 14789 solver.cpp:218] Iteration 5280 (2.54308 iter/s, 4.71869s/12 iters), loss = 0.856976
I0409 20:42:22.531498 14789 solver.cpp:237]     Train net output #0: loss = 0.856976 (* 1 = 0.856976 loss)
I0409 20:42:22.531512 14789 sgd_solver.cpp:105] Iteration 5280, lr = 0.00351378
I0409 20:42:27.293990 14789 solver.cpp:218] Iteration 5292 (2.51976 iter/s, 4.76235s/12 iters), loss = 0.689426
I0409 20:42:27.294041 14789 solver.cpp:237]     Train net output #0: loss = 0.689426 (* 1 = 0.689426 loss)
I0409 20:42:27.294052 14789 sgd_solver.cpp:105] Iteration 5292, lr = 0.00350544
I0409 20:42:31.520560 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5304.caffemodel
I0409 20:42:37.105252 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5304.solverstate
I0409 20:42:44.602829 14789 solver.cpp:330] Iteration 5304, Testing net (#0)
I0409 20:42:44.602851 14789 net.cpp:676] Ignoring source layer train-data
I0409 20:42:46.971999 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:42:49.074203 14789 solver.cpp:397]     Test net output #0: accuracy = 0.494485
I0409 20:42:49.074252 14789 solver.cpp:397]     Test net output #1: loss = 2.1914 (* 1 = 2.1914 loss)
I0409 20:42:49.165869 14789 solver.cpp:218] Iteration 5304 (0.548667 iter/s, 21.8712s/12 iters), loss = 0.730765
I0409 20:42:49.165923 14789 solver.cpp:237]     Train net output #0: loss = 0.730765 (* 1 = 0.730765 loss)
I0409 20:42:49.165935 14789 sgd_solver.cpp:105] Iteration 5304, lr = 0.00349711
I0409 20:42:53.724745 14789 solver.cpp:218] Iteration 5316 (2.63234 iter/s, 4.55867s/12 iters), loss = 0.722594
I0409 20:42:53.724830 14789 solver.cpp:237]     Train net output #0: loss = 0.722594 (* 1 = 0.722594 loss)
I0409 20:42:53.724843 14789 sgd_solver.cpp:105] Iteration 5316, lr = 0.00348881
I0409 20:42:58.643163 14789 solver.cpp:218] Iteration 5328 (2.43992 iter/s, 4.91819s/12 iters), loss = 0.79928
I0409 20:42:58.643211 14789 solver.cpp:237]     Train net output #0: loss = 0.79928 (* 1 = 0.79928 loss)
I0409 20:42:58.643224 14789 sgd_solver.cpp:105] Iteration 5328, lr = 0.00348053
I0409 20:43:03.587080 14789 solver.cpp:218] Iteration 5340 (2.42732 iter/s, 4.94372s/12 iters), loss = 0.634095
I0409 20:43:03.587131 14789 solver.cpp:237]     Train net output #0: loss = 0.634095 (* 1 = 0.634095 loss)
I0409 20:43:03.587143 14789 sgd_solver.cpp:105] Iteration 5340, lr = 0.00347226
I0409 20:43:08.491519 14789 solver.cpp:218] Iteration 5352 (2.44686 iter/s, 4.90424s/12 iters), loss = 0.72329
I0409 20:43:08.491569 14789 solver.cpp:237]     Train net output #0: loss = 0.72329 (* 1 = 0.72329 loss)
I0409 20:43:08.491580 14789 sgd_solver.cpp:105] Iteration 5352, lr = 0.00346402
I0409 20:43:11.875057 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:43:13.487990 14789 solver.cpp:218] Iteration 5364 (2.40179 iter/s, 4.99627s/12 iters), loss = 0.71007
I0409 20:43:13.488037 14789 solver.cpp:237]     Train net output #0: loss = 0.71007 (* 1 = 0.71007 loss)
I0409 20:43:13.488047 14789 sgd_solver.cpp:105] Iteration 5364, lr = 0.0034558
I0409 20:43:18.510926 14789 solver.cpp:218] Iteration 5376 (2.38914 iter/s, 5.02273s/12 iters), loss = 0.730094
I0409 20:43:18.510979 14789 solver.cpp:237]     Train net output #0: loss = 0.730094 (* 1 = 0.730094 loss)
I0409 20:43:18.510991 14789 sgd_solver.cpp:105] Iteration 5376, lr = 0.00344759
I0409 20:43:23.411429 14789 solver.cpp:218] Iteration 5388 (2.44883 iter/s, 4.9003s/12 iters), loss = 0.947358
I0409 20:43:23.411480 14789 solver.cpp:237]     Train net output #0: loss = 0.947358 (* 1 = 0.947358 loss)
I0409 20:43:23.411490 14789 sgd_solver.cpp:105] Iteration 5388, lr = 0.00343941
I0409 20:43:28.311581 14789 solver.cpp:218] Iteration 5400 (2.449 iter/s, 4.89995s/12 iters), loss = 0.77668
I0409 20:43:28.311753 14789 solver.cpp:237]     Train net output #0: loss = 0.77668 (* 1 = 0.77668 loss)
I0409 20:43:28.311766 14789 sgd_solver.cpp:105] Iteration 5400, lr = 0.00343124
I0409 20:43:30.437292 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5406.caffemodel
I0409 20:43:34.218914 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5406.solverstate
I0409 20:43:37.516829 14789 solver.cpp:330] Iteration 5406, Testing net (#0)
I0409 20:43:37.516855 14789 net.cpp:676] Ignoring source layer train-data
I0409 20:43:39.894016 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:43:42.083086 14789 solver.cpp:397]     Test net output #0: accuracy = 0.500613
I0409 20:43:42.083134 14789 solver.cpp:397]     Test net output #1: loss = 2.19303 (* 1 = 2.19303 loss)
I0409 20:43:43.947168 14789 solver.cpp:218] Iteration 5412 (0.76751 iter/s, 15.635s/12 iters), loss = 0.7738
I0409 20:43:43.947223 14789 solver.cpp:237]     Train net output #0: loss = 0.7738 (* 1 = 0.7738 loss)
I0409 20:43:43.947234 14789 sgd_solver.cpp:105] Iteration 5412, lr = 0.00342309
I0409 20:43:49.448876 14789 solver.cpp:218] Iteration 5424 (2.18123 iter/s, 5.50148s/12 iters), loss = 0.721541
I0409 20:43:49.448923 14789 solver.cpp:237]     Train net output #0: loss = 0.721541 (* 1 = 0.721541 loss)
I0409 20:43:49.448932 14789 sgd_solver.cpp:105] Iteration 5424, lr = 0.00341497
I0409 20:43:54.489475 14789 solver.cpp:218] Iteration 5436 (2.38077 iter/s, 5.04039s/12 iters), loss = 0.799485
I0409 20:43:54.489531 14789 solver.cpp:237]     Train net output #0: loss = 0.799485 (* 1 = 0.799485 loss)
I0409 20:43:54.489543 14789 sgd_solver.cpp:105] Iteration 5436, lr = 0.00340686
I0409 20:43:59.341739 14789 solver.cpp:218] Iteration 5448 (2.47318 iter/s, 4.85206s/12 iters), loss = 0.742908
I0409 20:43:59.341840 14789 solver.cpp:237]     Train net output #0: loss = 0.742908 (* 1 = 0.742908 loss)
I0409 20:43:59.341850 14789 sgd_solver.cpp:105] Iteration 5448, lr = 0.00339877
I0409 20:44:04.183926 14789 solver.cpp:218] Iteration 5460 (2.47835 iter/s, 4.84193s/12 iters), loss = 0.936023
I0409 20:44:04.183981 14789 solver.cpp:237]     Train net output #0: loss = 0.936023 (* 1 = 0.936023 loss)
I0409 20:44:04.183995 14789 sgd_solver.cpp:105] Iteration 5460, lr = 0.0033907
I0409 20:44:04.643949 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:44:09.158843 14789 solver.cpp:218] Iteration 5472 (2.4122 iter/s, 4.9747s/12 iters), loss = 0.671331
I0409 20:44:09.158893 14789 solver.cpp:237]     Train net output #0: loss = 0.671331 (* 1 = 0.671331 loss)
I0409 20:44:09.158902 14789 sgd_solver.cpp:105] Iteration 5472, lr = 0.00338265
I0409 20:44:14.069301 14789 solver.cpp:218] Iteration 5484 (2.44386 iter/s, 4.91026s/12 iters), loss = 0.895099
I0409 20:44:14.069342 14789 solver.cpp:237]     Train net output #0: loss = 0.895099 (* 1 = 0.895099 loss)
I0409 20:44:14.069351 14789 sgd_solver.cpp:105] Iteration 5484, lr = 0.00337462
I0409 20:44:19.044742 14789 solver.cpp:218] Iteration 5496 (2.41194 iter/s, 4.97524s/12 iters), loss = 0.6836
I0409 20:44:19.044791 14789 solver.cpp:237]     Train net output #0: loss = 0.6836 (* 1 = 0.6836 loss)
I0409 20:44:19.044800 14789 sgd_solver.cpp:105] Iteration 5496, lr = 0.00336661
I0409 20:44:23.538280 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5508.caffemodel
I0409 20:44:28.305445 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5508.solverstate
I0409 20:44:31.814690 14789 solver.cpp:330] Iteration 5508, Testing net (#0)
I0409 20:44:31.814807 14789 net.cpp:676] Ignoring source layer train-data
I0409 20:44:34.032613 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:44:36.239450 14789 solver.cpp:397]     Test net output #0: accuracy = 0.479779
I0409 20:44:36.239492 14789 solver.cpp:397]     Test net output #1: loss = 2.27054 (* 1 = 2.27054 loss)
I0409 20:44:36.331514 14789 solver.cpp:218] Iteration 5508 (0.694194 iter/s, 17.2862s/12 iters), loss = 0.743697
I0409 20:44:36.331576 14789 solver.cpp:237]     Train net output #0: loss = 0.743697 (* 1 = 0.743697 loss)
I0409 20:44:36.331589 14789 sgd_solver.cpp:105] Iteration 5508, lr = 0.00335861
I0409 20:44:40.401046 14789 solver.cpp:218] Iteration 5520 (2.94888 iter/s, 4.06934s/12 iters), loss = 0.751683
I0409 20:44:40.401113 14789 solver.cpp:237]     Train net output #0: loss = 0.751683 (* 1 = 0.751683 loss)
I0409 20:44:40.401130 14789 sgd_solver.cpp:105] Iteration 5520, lr = 0.00335064
I0409 20:44:42.576624 14789 blocking_queue.cpp:49] Waiting for data
I0409 20:44:45.107919 14789 solver.cpp:218] Iteration 5532 (2.54958 iter/s, 4.70666s/12 iters), loss = 0.658938
I0409 20:44:45.107976 14789 solver.cpp:237]     Train net output #0: loss = 0.658938 (* 1 = 0.658938 loss)
I0409 20:44:45.107988 14789 sgd_solver.cpp:105] Iteration 5532, lr = 0.00334268
I0409 20:44:49.836567 14789 solver.cpp:218] Iteration 5544 (2.53783 iter/s, 4.72844s/12 iters), loss = 0.672443
I0409 20:44:49.836625 14789 solver.cpp:237]     Train net output #0: loss = 0.672443 (* 1 = 0.672443 loss)
I0409 20:44:49.836637 14789 sgd_solver.cpp:105] Iteration 5544, lr = 0.00333475
I0409 20:44:54.771409 14789 solver.cpp:218] Iteration 5556 (2.43179 iter/s, 4.93464s/12 iters), loss = 0.447037
I0409 20:44:54.771450 14789 solver.cpp:237]     Train net output #0: loss = 0.447037 (* 1 = 0.447037 loss)
I0409 20:44:54.771459 14789 sgd_solver.cpp:105] Iteration 5556, lr = 0.00332683
I0409 20:44:57.374037 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:44:59.679616 14789 solver.cpp:218] Iteration 5568 (2.44498 iter/s, 4.90802s/12 iters), loss = 0.516011
I0409 20:44:59.679662 14789 solver.cpp:237]     Train net output #0: loss = 0.516011 (* 1 = 0.516011 loss)
I0409 20:44:59.679673 14789 sgd_solver.cpp:105] Iteration 5568, lr = 0.00331893
I0409 20:45:04.617916 14789 solver.cpp:218] Iteration 5580 (2.43009 iter/s, 4.9381s/12 iters), loss = 0.740056
I0409 20:45:04.618074 14789 solver.cpp:237]     Train net output #0: loss = 0.740056 (* 1 = 0.740056 loss)
I0409 20:45:04.618089 14789 sgd_solver.cpp:105] Iteration 5580, lr = 0.00331105
I0409 20:45:09.375716 14789 solver.cpp:218] Iteration 5592 (2.52233 iter/s, 4.75751s/12 iters), loss = 0.647125
I0409 20:45:09.375753 14789 solver.cpp:237]     Train net output #0: loss = 0.647125 (* 1 = 0.647125 loss)
I0409 20:45:09.375761 14789 sgd_solver.cpp:105] Iteration 5592, lr = 0.00330319
I0409 20:45:14.167697 14789 solver.cpp:218] Iteration 5604 (2.50428 iter/s, 4.79179s/12 iters), loss = 0.599558
I0409 20:45:14.167750 14789 solver.cpp:237]     Train net output #0: loss = 0.599558 (* 1 = 0.599558 loss)
I0409 20:45:14.167762 14789 sgd_solver.cpp:105] Iteration 5604, lr = 0.00329535
I0409 20:45:16.114986 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5610.caffemodel
I0409 20:45:22.124289 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5610.solverstate
I0409 20:45:25.128491 14789 solver.cpp:330] Iteration 5610, Testing net (#0)
I0409 20:45:25.128520 14789 net.cpp:676] Ignoring source layer train-data
I0409 20:45:27.408273 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:45:29.633837 14789 solver.cpp:397]     Test net output #0: accuracy = 0.483456
I0409 20:45:29.633885 14789 solver.cpp:397]     Test net output #1: loss = 2.29675 (* 1 = 2.29675 loss)
I0409 20:45:31.651571 14789 solver.cpp:218] Iteration 5616 (0.686369 iter/s, 17.4833s/12 iters), loss = 0.588155
I0409 20:45:31.651625 14789 solver.cpp:237]     Train net output #0: loss = 0.588155 (* 1 = 0.588155 loss)
I0409 20:45:31.651638 14789 sgd_solver.cpp:105] Iteration 5616, lr = 0.00328752
I0409 20:45:36.546844 14789 solver.cpp:218] Iteration 5628 (2.45145 iter/s, 4.89506s/12 iters), loss = 0.68437
I0409 20:45:36.546989 14789 solver.cpp:237]     Train net output #0: loss = 0.68437 (* 1 = 0.68437 loss)
I0409 20:45:36.547003 14789 sgd_solver.cpp:105] Iteration 5628, lr = 0.00327972
I0409 20:45:41.548051 14789 solver.cpp:218] Iteration 5640 (2.39956 iter/s, 5.00092s/12 iters), loss = 0.600025
I0409 20:45:41.548103 14789 solver.cpp:237]     Train net output #0: loss = 0.600025 (* 1 = 0.600025 loss)
I0409 20:45:41.548116 14789 sgd_solver.cpp:105] Iteration 5640, lr = 0.00327193
I0409 20:45:46.596626 14789 solver.cpp:218] Iteration 5652 (2.37701 iter/s, 5.04837s/12 iters), loss = 0.699285
I0409 20:45:46.596668 14789 solver.cpp:237]     Train net output #0: loss = 0.699285 (* 1 = 0.699285 loss)
I0409 20:45:46.596678 14789 sgd_solver.cpp:105] Iteration 5652, lr = 0.00326416
I0409 20:45:51.041018 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:45:51.216325 14789 solver.cpp:218] Iteration 5664 (2.59768 iter/s, 4.61951s/12 iters), loss = 0.498176
I0409 20:45:51.216372 14789 solver.cpp:237]     Train net output #0: loss = 0.498176 (* 1 = 0.498176 loss)
I0409 20:45:51.216382 14789 sgd_solver.cpp:105] Iteration 5664, lr = 0.00325641
I0409 20:45:55.914538 14789 solver.cpp:218] Iteration 5676 (2.55427 iter/s, 4.69802s/12 iters), loss = 0.635391
I0409 20:45:55.914584 14789 solver.cpp:237]     Train net output #0: loss = 0.635391 (* 1 = 0.635391 loss)
I0409 20:45:55.914593 14789 sgd_solver.cpp:105] Iteration 5676, lr = 0.00324868
I0409 20:46:00.695785 14789 solver.cpp:218] Iteration 5688 (2.50991 iter/s, 4.78105s/12 iters), loss = 0.556555
I0409 20:46:00.695827 14789 solver.cpp:237]     Train net output #0: loss = 0.556555 (* 1 = 0.556555 loss)
I0409 20:46:00.695837 14789 sgd_solver.cpp:105] Iteration 5688, lr = 0.00324097
I0409 20:46:05.541189 14789 solver.cpp:218] Iteration 5700 (2.47667 iter/s, 4.84522s/12 iters), loss = 0.534571
I0409 20:46:05.541239 14789 solver.cpp:237]     Train net output #0: loss = 0.534571 (* 1 = 0.534571 loss)
I0409 20:46:05.541250 14789 sgd_solver.cpp:105] Iteration 5700, lr = 0.00323328
I0409 20:46:09.815129 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5712.caffemodel
I0409 20:46:13.661689 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5712.solverstate
I0409 20:46:16.663108 14789 solver.cpp:330] Iteration 5712, Testing net (#0)
I0409 20:46:16.663133 14789 net.cpp:676] Ignoring source layer train-data
I0409 20:46:18.828644 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:46:21.077890 14789 solver.cpp:397]     Test net output #0: accuracy = 0.490809
I0409 20:46:21.077934 14789 solver.cpp:397]     Test net output #1: loss = 2.31706 (* 1 = 2.31706 loss)
I0409 20:46:21.169652 14789 solver.cpp:218] Iteration 5712 (0.767854 iter/s, 15.628s/12 iters), loss = 0.682681
I0409 20:46:21.169701 14789 solver.cpp:237]     Train net output #0: loss = 0.682681 (* 1 = 0.682681 loss)
I0409 20:46:21.169711 14789 sgd_solver.cpp:105] Iteration 5712, lr = 0.0032256
I0409 20:46:25.669390 14789 solver.cpp:218] Iteration 5724 (2.66693 iter/s, 4.49955s/12 iters), loss = 0.632205
I0409 20:46:25.669433 14789 solver.cpp:237]     Train net output #0: loss = 0.632205 (* 1 = 0.632205 loss)
I0409 20:46:25.669442 14789 sgd_solver.cpp:105] Iteration 5724, lr = 0.00321794
I0409 20:46:30.526779 14789 solver.cpp:218] Iteration 5736 (2.47054 iter/s, 4.85723s/12 iters), loss = 0.664025
I0409 20:46:30.526831 14789 solver.cpp:237]     Train net output #0: loss = 0.664025 (* 1 = 0.664025 loss)
I0409 20:46:30.526842 14789 sgd_solver.cpp:105] Iteration 5736, lr = 0.0032103
I0409 20:46:35.115134 14789 solver.cpp:218] Iteration 5748 (2.61537 iter/s, 4.58826s/12 iters), loss = 0.664377
I0409 20:46:35.115180 14789 solver.cpp:237]     Train net output #0: loss = 0.664377 (* 1 = 0.664377 loss)
I0409 20:46:35.115188 14789 sgd_solver.cpp:105] Iteration 5748, lr = 0.00320268
I0409 20:46:39.882918 14789 solver.cpp:218] Iteration 5760 (2.51694 iter/s, 4.76769s/12 iters), loss = 0.495167
I0409 20:46:39.883093 14789 solver.cpp:237]     Train net output #0: loss = 0.495167 (* 1 = 0.495167 loss)
I0409 20:46:39.883107 14789 sgd_solver.cpp:105] Iteration 5760, lr = 0.00319508
I0409 20:46:41.772140 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:46:44.622057 14789 solver.cpp:218] Iteration 5772 (2.53222 iter/s, 4.73892s/12 iters), loss = 0.458252
I0409 20:46:44.622108 14789 solver.cpp:237]     Train net output #0: loss = 0.458252 (* 1 = 0.458252 loss)
I0409 20:46:44.622120 14789 sgd_solver.cpp:105] Iteration 5772, lr = 0.00318749
I0409 20:46:49.497916 14789 solver.cpp:218] Iteration 5784 (2.46115 iter/s, 4.87576s/12 iters), loss = 0.616402
I0409 20:46:49.497979 14789 solver.cpp:237]     Train net output #0: loss = 0.616402 (* 1 = 0.616402 loss)
I0409 20:46:49.497992 14789 sgd_solver.cpp:105] Iteration 5784, lr = 0.00317992
I0409 20:46:55.026684 14789 solver.cpp:218] Iteration 5796 (2.17051 iter/s, 5.52864s/12 iters), loss = 0.706553
I0409 20:46:55.026729 14789 solver.cpp:237]     Train net output #0: loss = 0.706553 (* 1 = 0.706553 loss)
I0409 20:46:55.026738 14789 sgd_solver.cpp:105] Iteration 5796, lr = 0.00317237
I0409 20:46:59.920967 14789 solver.cpp:218] Iteration 5808 (2.45189 iter/s, 4.89418s/12 iters), loss = 0.653872
I0409 20:46:59.921020 14789 solver.cpp:237]     Train net output #0: loss = 0.653872 (* 1 = 0.653872 loss)
I0409 20:46:59.921031 14789 sgd_solver.cpp:105] Iteration 5808, lr = 0.00316484
I0409 20:47:01.952541 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5814.caffemodel
I0409 20:47:05.674471 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5814.solverstate
I0409 20:47:08.643294 14789 solver.cpp:330] Iteration 5814, Testing net (#0)
I0409 20:47:08.643319 14789 net.cpp:676] Ignoring source layer train-data
I0409 20:47:10.976613 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:47:13.505331 14789 solver.cpp:397]     Test net output #0: accuracy = 0.503676
I0409 20:47:13.505378 14789 solver.cpp:397]     Test net output #1: loss = 2.20597 (* 1 = 2.20597 loss)
I0409 20:47:15.438740 14789 solver.cpp:218] Iteration 5820 (0.773317 iter/s, 15.5176s/12 iters), loss = 0.643049
I0409 20:47:15.438800 14789 solver.cpp:237]     Train net output #0: loss = 0.643049 (* 1 = 0.643049 loss)
I0409 20:47:15.438813 14789 sgd_solver.cpp:105] Iteration 5820, lr = 0.00315733
I0409 20:47:20.701560 14789 solver.cpp:218] Iteration 5832 (2.2802 iter/s, 5.2627s/12 iters), loss = 0.566951
I0409 20:47:20.701607 14789 solver.cpp:237]     Train net output #0: loss = 0.566951 (* 1 = 0.566951 loss)
I0409 20:47:20.701617 14789 sgd_solver.cpp:105] Iteration 5832, lr = 0.00314983
I0409 20:47:25.759778 14789 solver.cpp:218] Iteration 5844 (2.37243 iter/s, 5.05811s/12 iters), loss = 0.540238
I0409 20:47:25.759829 14789 solver.cpp:237]     Train net output #0: loss = 0.540238 (* 1 = 0.540238 loss)
I0409 20:47:25.759840 14789 sgd_solver.cpp:105] Iteration 5844, lr = 0.00314235
I0409 20:47:30.793553 14789 solver.cpp:218] Iteration 5856 (2.38395 iter/s, 5.03366s/12 iters), loss = 0.464811
I0409 20:47:30.793606 14789 solver.cpp:237]     Train net output #0: loss = 0.464811 (* 1 = 0.464811 loss)
I0409 20:47:30.793617 14789 sgd_solver.cpp:105] Iteration 5856, lr = 0.00313489
I0409 20:47:34.951607 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:47:35.774700 14789 solver.cpp:218] Iteration 5868 (2.40914 iter/s, 4.98104s/12 iters), loss = 0.51185
I0409 20:47:35.774744 14789 solver.cpp:237]     Train net output #0: loss = 0.51185 (* 1 = 0.51185 loss)
I0409 20:47:35.774752 14789 sgd_solver.cpp:105] Iteration 5868, lr = 0.00312745
I0409 20:47:40.974548 14789 solver.cpp:218] Iteration 5880 (2.30781 iter/s, 5.19974s/12 iters), loss = 0.449227
I0409 20:47:40.974602 14789 solver.cpp:237]     Train net output #0: loss = 0.449227 (* 1 = 0.449227 loss)
I0409 20:47:40.974614 14789 sgd_solver.cpp:105] Iteration 5880, lr = 0.00312002
I0409 20:47:45.614152 14789 solver.cpp:218] Iteration 5892 (2.58649 iter/s, 4.63949s/12 iters), loss = 0.729742
I0409 20:47:45.614305 14789 solver.cpp:237]     Train net output #0: loss = 0.729742 (* 1 = 0.729742 loss)
I0409 20:47:45.614318 14789 sgd_solver.cpp:105] Iteration 5892, lr = 0.00311262
I0409 20:47:50.478919 14789 solver.cpp:218] Iteration 5904 (2.46682 iter/s, 4.86456s/12 iters), loss = 0.611395
I0409 20:47:50.478973 14789 solver.cpp:237]     Train net output #0: loss = 0.611395 (* 1 = 0.611395 loss)
I0409 20:47:50.478987 14789 sgd_solver.cpp:105] Iteration 5904, lr = 0.00310523
I0409 20:47:55.020478 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5916.caffemodel
I0409 20:47:59.206421 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5916.solverstate
I0409 20:48:03.755412 14789 solver.cpp:330] Iteration 5916, Testing net (#0)
I0409 20:48:03.755439 14789 net.cpp:676] Ignoring source layer train-data
I0409 20:48:05.948657 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:48:08.278607 14789 solver.cpp:397]     Test net output #0: accuracy = 0.503064
I0409 20:48:08.278661 14789 solver.cpp:397]     Test net output #1: loss = 2.31062 (* 1 = 2.31062 loss)
I0409 20:48:08.370995 14789 solver.cpp:218] Iteration 5916 (0.670697 iter/s, 17.8918s/12 iters), loss = 0.491653
I0409 20:48:08.371048 14789 solver.cpp:237]     Train net output #0: loss = 0.491653 (* 1 = 0.491653 loss)
I0409 20:48:08.371059 14789 sgd_solver.cpp:105] Iteration 5916, lr = 0.00309785
I0409 20:48:12.371776 14789 solver.cpp:218] Iteration 5928 (2.99949 iter/s, 4.00068s/12 iters), loss = 0.560996
I0409 20:48:12.371821 14789 solver.cpp:237]     Train net output #0: loss = 0.560996 (* 1 = 0.560996 loss)
I0409 20:48:12.371830 14789 sgd_solver.cpp:105] Iteration 5928, lr = 0.0030905
I0409 20:48:17.151626 14789 solver.cpp:218] Iteration 5940 (2.5106 iter/s, 4.77974s/12 iters), loss = 0.734226
I0409 20:48:17.156666 14789 solver.cpp:237]     Train net output #0: loss = 0.734226 (* 1 = 0.734226 loss)
I0409 20:48:17.156677 14789 sgd_solver.cpp:105] Iteration 5940, lr = 0.00308316
I0409 20:48:22.038192 14789 solver.cpp:218] Iteration 5952 (2.45828 iter/s, 4.88146s/12 iters), loss = 0.620046
I0409 20:48:22.038245 14789 solver.cpp:237]     Train net output #0: loss = 0.620046 (* 1 = 0.620046 loss)
I0409 20:48:22.038257 14789 sgd_solver.cpp:105] Iteration 5952, lr = 0.00307584
I0409 20:48:26.619850 14789 solver.cpp:218] Iteration 5964 (2.61921 iter/s, 4.58154s/12 iters), loss = 0.459039
I0409 20:48:26.619902 14789 solver.cpp:237]     Train net output #0: loss = 0.459039 (* 1 = 0.459039 loss)
I0409 20:48:26.619915 14789 sgd_solver.cpp:105] Iteration 5964, lr = 0.00306854
I0409 20:48:27.963922 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:48:31.658186 14789 solver.cpp:218] Iteration 5976 (2.3818 iter/s, 5.03822s/12 iters), loss = 0.641459
I0409 20:48:31.658238 14789 solver.cpp:237]     Train net output #0: loss = 0.641459 (* 1 = 0.641459 loss)
I0409 20:48:31.658252 14789 sgd_solver.cpp:105] Iteration 5976, lr = 0.00306125
I0409 20:48:36.379297 14789 solver.cpp:218] Iteration 5988 (2.54184 iter/s, 4.72099s/12 iters), loss = 0.626253
I0409 20:48:36.379352 14789 solver.cpp:237]     Train net output #0: loss = 0.626253 (* 1 = 0.626253 loss)
I0409 20:48:36.379364 14789 sgd_solver.cpp:105] Iteration 5988, lr = 0.00305398
I0409 20:48:41.238612 14789 solver.cpp:218] Iteration 6000 (2.46954 iter/s, 4.8592s/12 iters), loss = 0.405574
I0409 20:48:41.238667 14789 solver.cpp:237]     Train net output #0: loss = 0.405574 (* 1 = 0.405574 loss)
I0409 20:48:41.238678 14789 sgd_solver.cpp:105] Iteration 6000, lr = 0.00304673
I0409 20:48:46.123358 14789 solver.cpp:218] Iteration 6012 (2.45669 iter/s, 4.88462s/12 iters), loss = 0.444424
I0409 20:48:46.123411 14789 solver.cpp:237]     Train net output #0: loss = 0.444424 (* 1 = 0.444424 loss)
I0409 20:48:46.123423 14789 sgd_solver.cpp:105] Iteration 6012, lr = 0.0030395
I0409 20:48:48.118945 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6018.caffemodel
I0409 20:48:51.895576 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6018.solverstate
I0409 20:48:56.208737 14789 solver.cpp:330] Iteration 6018, Testing net (#0)
I0409 20:48:56.208762 14789 net.cpp:676] Ignoring source layer train-data
I0409 20:48:58.318780 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:49:00.724215 14789 solver.cpp:397]     Test net output #0: accuracy = 0.506127
I0409 20:49:00.724265 14789 solver.cpp:397]     Test net output #1: loss = 2.28284 (* 1 = 2.28284 loss)
I0409 20:49:02.868629 14789 solver.cpp:218] Iteration 6024 (0.716631 iter/s, 16.745s/12 iters), loss = 0.488124
I0409 20:49:02.868685 14789 solver.cpp:237]     Train net output #0: loss = 0.488124 (* 1 = 0.488124 loss)
I0409 20:49:02.868695 14789 sgd_solver.cpp:105] Iteration 6024, lr = 0.00303228
I0409 20:49:07.949836 14789 solver.cpp:218] Iteration 6036 (2.3617 iter/s, 5.08108s/12 iters), loss = 0.500255
I0409 20:49:07.949887 14789 solver.cpp:237]     Train net output #0: loss = 0.500255 (* 1 = 0.500255 loss)
I0409 20:49:07.949900 14789 sgd_solver.cpp:105] Iteration 6036, lr = 0.00302508
I0409 20:49:13.124056 14789 solver.cpp:218] Iteration 6048 (2.31925 iter/s, 5.17409s/12 iters), loss = 0.564491
I0409 20:49:13.124105 14789 solver.cpp:237]     Train net output #0: loss = 0.564491 (* 1 = 0.564491 loss)
I0409 20:49:13.124115 14789 sgd_solver.cpp:105] Iteration 6048, lr = 0.0030179
I0409 20:49:18.008488 14789 solver.cpp:218] Iteration 6060 (2.45685 iter/s, 4.88431s/12 iters), loss = 0.610434
I0409 20:49:18.008533 14789 solver.cpp:237]     Train net output #0: loss = 0.610434 (* 1 = 0.610434 loss)
I0409 20:49:18.008541 14789 sgd_solver.cpp:105] Iteration 6060, lr = 0.00301074
I0409 20:49:21.415186 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:49:22.957298 14789 solver.cpp:218] Iteration 6072 (2.42489 iter/s, 4.94869s/12 iters), loss = 0.429755
I0409 20:49:22.957353 14789 solver.cpp:237]     Train net output #0: loss = 0.429755 (* 1 = 0.429755 loss)
I0409 20:49:22.957366 14789 sgd_solver.cpp:105] Iteration 6072, lr = 0.00300359
I0409 20:49:28.065811 14789 solver.cpp:218] Iteration 6084 (2.34908 iter/s, 5.10838s/12 iters), loss = 0.598197
I0409 20:49:28.065860 14789 solver.cpp:237]     Train net output #0: loss = 0.598197 (* 1 = 0.598197 loss)
I0409 20:49:28.065869 14789 sgd_solver.cpp:105] Iteration 6084, lr = 0.00299646
I0409 20:49:32.660526 14789 solver.cpp:218] Iteration 6096 (2.61176 iter/s, 4.5946s/12 iters), loss = 0.599594
I0409 20:49:32.660576 14789 solver.cpp:237]     Train net output #0: loss = 0.599594 (* 1 = 0.599594 loss)
I0409 20:49:32.660588 14789 sgd_solver.cpp:105] Iteration 6096, lr = 0.00298934
I0409 20:49:37.189323 14789 solver.cpp:218] Iteration 6108 (2.64978 iter/s, 4.52868s/12 iters), loss = 0.573957
I0409 20:49:37.189374 14789 solver.cpp:237]     Train net output #0: loss = 0.573957 (* 1 = 0.573957 loss)
I0409 20:49:37.189385 14789 sgd_solver.cpp:105] Iteration 6108, lr = 0.00298225
I0409 20:49:41.634227 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6120.caffemodel
I0409 20:49:50.012612 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6120.solverstate
I0409 20:49:53.286655 14789 solver.cpp:330] Iteration 6120, Testing net (#0)
I0409 20:49:53.286747 14789 net.cpp:676] Ignoring source layer train-data
I0409 20:49:55.349308 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:49:57.759754 14789 solver.cpp:397]     Test net output #0: accuracy = 0.506127
I0409 20:49:57.759799 14789 solver.cpp:397]     Test net output #1: loss = 2.30347 (* 1 = 2.30347 loss)
I0409 20:49:57.851903 14789 solver.cpp:218] Iteration 6120 (0.580769 iter/s, 20.6622s/12 iters), loss = 0.481069
I0409 20:49:57.851956 14789 solver.cpp:237]     Train net output #0: loss = 0.481069 (* 1 = 0.481069 loss)
I0409 20:49:57.851969 14789 sgd_solver.cpp:105] Iteration 6120, lr = 0.00297517
I0409 20:50:02.060104 14789 solver.cpp:218] Iteration 6132 (2.85166 iter/s, 4.20808s/12 iters), loss = 0.536307
I0409 20:50:02.060148 14789 solver.cpp:237]     Train net output #0: loss = 0.536307 (* 1 = 0.536307 loss)
I0409 20:50:02.060159 14789 sgd_solver.cpp:105] Iteration 6132, lr = 0.0029681
I0409 20:50:07.034126 14789 solver.cpp:218] Iteration 6144 (2.4126 iter/s, 4.97389s/12 iters), loss = 0.527997
I0409 20:50:07.034195 14789 solver.cpp:237]     Train net output #0: loss = 0.527997 (* 1 = 0.527997 loss)
I0409 20:50:07.034207 14789 sgd_solver.cpp:105] Iteration 6144, lr = 0.00296105
I0409 20:50:12.002755 14789 solver.cpp:218] Iteration 6156 (2.41522 iter/s, 4.96849s/12 iters), loss = 0.365332
I0409 20:50:12.002810 14789 solver.cpp:237]     Train net output #0: loss = 0.365332 (* 1 = 0.365332 loss)
I0409 20:50:12.002823 14789 sgd_solver.cpp:105] Iteration 6156, lr = 0.00295402
I0409 20:50:17.163015 14789 solver.cpp:218] Iteration 6168 (2.32553 iter/s, 5.16012s/12 iters), loss = 0.44509
I0409 20:50:17.163071 14789 solver.cpp:237]     Train net output #0: loss = 0.44509 (* 1 = 0.44509 loss)
I0409 20:50:17.163086 14789 sgd_solver.cpp:105] Iteration 6168, lr = 0.00294701
I0409 20:50:17.783645 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:50:22.134654 14789 solver.cpp:218] Iteration 6180 (2.41376 iter/s, 4.9715s/12 iters), loss = 0.45306
I0409 20:50:22.134703 14789 solver.cpp:237]     Train net output #0: loss = 0.45306 (* 1 = 0.45306 loss)
I0409 20:50:22.134716 14789 sgd_solver.cpp:105] Iteration 6180, lr = 0.00294001
I0409 20:50:26.891152 14789 solver.cpp:218] Iteration 6192 (2.52293 iter/s, 4.75637s/12 iters), loss = 0.411716
I0409 20:50:26.891260 14789 solver.cpp:237]     Train net output #0: loss = 0.411716 (* 1 = 0.411716 loss)
I0409 20:50:26.891271 14789 sgd_solver.cpp:105] Iteration 6192, lr = 0.00293303
I0409 20:50:31.748822 14789 solver.cpp:218] Iteration 6204 (2.47042 iter/s, 4.85748s/12 iters), loss = 0.526897
I0409 20:50:31.748881 14789 solver.cpp:237]     Train net output #0: loss = 0.526897 (* 1 = 0.526897 loss)
I0409 20:50:31.748894 14789 sgd_solver.cpp:105] Iteration 6204, lr = 0.00292607
I0409 20:50:36.722152 14789 solver.cpp:218] Iteration 6216 (2.41294 iter/s, 4.97319s/12 iters), loss = 0.348076
I0409 20:50:36.722200 14789 solver.cpp:237]     Train net output #0: loss = 0.348076 (* 1 = 0.348076 loss)
I0409 20:50:36.722211 14789 sgd_solver.cpp:105] Iteration 6216, lr = 0.00291912
I0409 20:50:38.747149 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6222.caffemodel
I0409 20:50:42.593849 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6222.solverstate
I0409 20:50:47.442181 14789 solver.cpp:330] Iteration 6222, Testing net (#0)
I0409 20:50:47.442210 14789 net.cpp:676] Ignoring source layer train-data
I0409 20:50:49.461827 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:50:50.739177 14789 blocking_queue.cpp:49] Waiting for data
I0409 20:50:51.940053 14789 solver.cpp:397]     Test net output #0: accuracy = 0.495098
I0409 20:50:51.940104 14789 solver.cpp:397]     Test net output #1: loss = 2.36579 (* 1 = 2.36579 loss)
I0409 20:50:53.892405 14789 solver.cpp:218] Iteration 6228 (0.698896 iter/s, 17.1699s/12 iters), loss = 0.474821
I0409 20:50:53.892463 14789 solver.cpp:237]     Train net output #0: loss = 0.474821 (* 1 = 0.474821 loss)
I0409 20:50:53.892475 14789 sgd_solver.cpp:105] Iteration 6228, lr = 0.00291219
I0409 20:50:58.730767 14789 solver.cpp:218] Iteration 6240 (2.48025 iter/s, 4.83822s/12 iters), loss = 0.59183
I0409 20:50:58.730921 14789 solver.cpp:237]     Train net output #0: loss = 0.59183 (* 1 = 0.59183 loss)
I0409 20:50:58.730934 14789 sgd_solver.cpp:105] Iteration 6240, lr = 0.00290528
I0409 20:51:03.488325 14789 solver.cpp:218] Iteration 6252 (2.52243 iter/s, 4.75733s/12 iters), loss = 0.399153
I0409 20:51:03.488371 14789 solver.cpp:237]     Train net output #0: loss = 0.399153 (* 1 = 0.399153 loss)
I0409 20:51:03.488381 14789 sgd_solver.cpp:105] Iteration 6252, lr = 0.00289838
I0409 20:51:08.542263 14789 solver.cpp:218] Iteration 6264 (2.37445 iter/s, 5.05381s/12 iters), loss = 0.543059
I0409 20:51:08.542315 14789 solver.cpp:237]     Train net output #0: loss = 0.543059 (* 1 = 0.543059 loss)
I0409 20:51:08.542326 14789 sgd_solver.cpp:105] Iteration 6264, lr = 0.0028915
I0409 20:51:11.190469 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:51:13.355742 14789 solver.cpp:218] Iteration 6276 (2.49307 iter/s, 4.81335s/12 iters), loss = 0.470577
I0409 20:51:13.355793 14789 solver.cpp:237]     Train net output #0: loss = 0.470577 (* 1 = 0.470577 loss)
I0409 20:51:13.355804 14789 sgd_solver.cpp:105] Iteration 6276, lr = 0.00288463
I0409 20:51:18.041574 14789 solver.cpp:218] Iteration 6288 (2.56099 iter/s, 4.68569s/12 iters), loss = 0.589849
I0409 20:51:18.041625 14789 solver.cpp:237]     Train net output #0: loss = 0.589849 (* 1 = 0.589849 loss)
I0409 20:51:18.041635 14789 sgd_solver.cpp:105] Iteration 6288, lr = 0.00287779
I0409 20:51:22.658824 14789 solver.cpp:218] Iteration 6300 (2.59903 iter/s, 4.61711s/12 iters), loss = 0.479753
I0409 20:51:22.658880 14789 solver.cpp:237]     Train net output #0: loss = 0.479753 (* 1 = 0.479753 loss)
I0409 20:51:22.658893 14789 sgd_solver.cpp:105] Iteration 6300, lr = 0.00287095
I0409 20:51:27.345368 14789 solver.cpp:218] Iteration 6312 (2.5606 iter/s, 4.6864s/12 iters), loss = 0.622866
I0409 20:51:27.345423 14789 solver.cpp:237]     Train net output #0: loss = 0.622866 (* 1 = 0.622866 loss)
I0409 20:51:27.345435 14789 sgd_solver.cpp:105] Iteration 6312, lr = 0.00286414
I0409 20:51:31.595818 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6324.caffemodel
I0409 20:51:36.145218 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6324.solverstate
I0409 20:51:39.152520 14789 solver.cpp:330] Iteration 6324, Testing net (#0)
I0409 20:51:39.152544 14789 net.cpp:676] Ignoring source layer train-data
I0409 20:51:41.036839 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:51:43.571717 14789 solver.cpp:397]     Test net output #0: accuracy = 0.515931
I0409 20:51:43.571775 14789 solver.cpp:397]     Test net output #1: loss = 2.26837 (* 1 = 2.26837 loss)
I0409 20:51:43.663427 14789 solver.cpp:218] Iteration 6324 (0.735396 iter/s, 16.3177s/12 iters), loss = 0.417641
I0409 20:51:43.663482 14789 solver.cpp:237]     Train net output #0: loss = 0.417641 (* 1 = 0.417641 loss)
I0409 20:51:43.663493 14789 sgd_solver.cpp:105] Iteration 6324, lr = 0.00285734
I0409 20:51:47.443480 14789 solver.cpp:218] Iteration 6336 (3.17467 iter/s, 3.77993s/12 iters), loss = 0.479421
I0409 20:51:47.443526 14789 solver.cpp:237]     Train net output #0: loss = 0.479421 (* 1 = 0.479421 loss)
I0409 20:51:47.443536 14789 sgd_solver.cpp:105] Iteration 6336, lr = 0.00285055
I0409 20:51:52.360846 14789 solver.cpp:218] Iteration 6348 (2.4404 iter/s, 4.91723s/12 iters), loss = 0.458754
I0409 20:51:52.360895 14789 solver.cpp:237]     Train net output #0: loss = 0.458754 (* 1 = 0.458754 loss)
I0409 20:51:52.360905 14789 sgd_solver.cpp:105] Iteration 6348, lr = 0.00284379
I0409 20:51:57.253785 14789 solver.cpp:218] Iteration 6360 (2.45258 iter/s, 4.89281s/12 iters), loss = 0.460876
I0409 20:51:57.253826 14789 solver.cpp:237]     Train net output #0: loss = 0.460876 (* 1 = 0.460876 loss)
I0409 20:51:57.253835 14789 sgd_solver.cpp:105] Iteration 6360, lr = 0.00283703
I0409 20:52:02.033910 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:52:02.181319 14789 solver.cpp:218] Iteration 6372 (2.43536 iter/s, 4.9274s/12 iters), loss = 0.391049
I0409 20:52:02.181378 14789 solver.cpp:237]     Train net output #0: loss = 0.391049 (* 1 = 0.391049 loss)
I0409 20:52:02.181391 14789 sgd_solver.cpp:105] Iteration 6372, lr = 0.0028303
I0409 20:52:07.161778 14789 solver.cpp:218] Iteration 6384 (2.40949 iter/s, 4.98031s/12 iters), loss = 0.43483
I0409 20:52:07.161828 14789 solver.cpp:237]     Train net output #0: loss = 0.43483 (* 1 = 0.43483 loss)
I0409 20:52:07.161837 14789 sgd_solver.cpp:105] Iteration 6384, lr = 0.00282358
I0409 20:52:11.978495 14789 solver.cpp:218] Iteration 6396 (2.4914 iter/s, 4.81658s/12 iters), loss = 0.372765
I0409 20:52:11.978551 14789 solver.cpp:237]     Train net output #0: loss = 0.372765 (* 1 = 0.372765 loss)
I0409 20:52:11.978564 14789 sgd_solver.cpp:105] Iteration 6396, lr = 0.00281687
I0409 20:52:17.399830 14789 solver.cpp:218] Iteration 6408 (2.21354 iter/s, 5.42118s/12 iters), loss = 0.410211
I0409 20:52:17.399883 14789 solver.cpp:237]     Train net output #0: loss = 0.410211 (* 1 = 0.410211 loss)
I0409 20:52:17.399897 14789 sgd_solver.cpp:105] Iteration 6408, lr = 0.00281019
I0409 20:52:22.435679 14789 solver.cpp:218] Iteration 6420 (2.38298 iter/s, 5.0357s/12 iters), loss = 0.398574
I0409 20:52:22.435731 14789 solver.cpp:237]     Train net output #0: loss = 0.398574 (* 1 = 0.398574 loss)
I0409 20:52:22.435745 14789 sgd_solver.cpp:105] Iteration 6420, lr = 0.00280351
I0409 20:52:24.518882 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6426.caffemodel
I0409 20:52:29.936851 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6426.solverstate
I0409 20:52:34.770587 14789 solver.cpp:330] Iteration 6426, Testing net (#0)
I0409 20:52:34.770653 14789 net.cpp:676] Ignoring source layer train-data
I0409 20:52:36.831943 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:52:39.364964 14789 solver.cpp:397]     Test net output #0: accuracy = 0.488971
I0409 20:52:39.365016 14789 solver.cpp:397]     Test net output #1: loss = 2.33566 (* 1 = 2.33566 loss)
I0409 20:52:41.381404 14789 solver.cpp:218] Iteration 6432 (0.633401 iter/s, 18.9453s/12 iters), loss = 0.330768
I0409 20:52:41.381474 14789 solver.cpp:237]     Train net output #0: loss = 0.330768 (* 1 = 0.330768 loss)
I0409 20:52:41.381489 14789 sgd_solver.cpp:105] Iteration 6432, lr = 0.00279686
I0409 20:52:46.118292 14789 solver.cpp:218] Iteration 6444 (2.53339 iter/s, 4.73673s/12 iters), loss = 0.291802
I0409 20:52:46.118346 14789 solver.cpp:237]     Train net output #0: loss = 0.291802 (* 1 = 0.291802 loss)
I0409 20:52:46.118358 14789 sgd_solver.cpp:105] Iteration 6444, lr = 0.00279022
I0409 20:52:50.899433 14789 solver.cpp:218] Iteration 6456 (2.50994 iter/s, 4.78099s/12 iters), loss = 0.549952
I0409 20:52:50.899487 14789 solver.cpp:237]     Train net output #0: loss = 0.549952 (* 1 = 0.549952 loss)
I0409 20:52:50.899497 14789 sgd_solver.cpp:105] Iteration 6456, lr = 0.00278359
I0409 20:52:56.332316 14789 solver.cpp:218] Iteration 6468 (2.20884 iter/s, 5.43273s/12 iters), loss = 0.47836
I0409 20:52:56.332370 14789 solver.cpp:237]     Train net output #0: loss = 0.47836 (* 1 = 0.47836 loss)
I0409 20:52:56.332381 14789 sgd_solver.cpp:105] Iteration 6468, lr = 0.00277698
I0409 20:52:58.559084 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:53:01.452602 14789 solver.cpp:218] Iteration 6480 (2.34369 iter/s, 5.12013s/12 iters), loss = 0.300012
I0409 20:53:01.452651 14789 solver.cpp:237]     Train net output #0: loss = 0.300012 (* 1 = 0.300012 loss)
I0409 20:53:01.452663 14789 sgd_solver.cpp:105] Iteration 6480, lr = 0.00277039
I0409 20:53:06.206485 14789 solver.cpp:218] Iteration 6492 (2.52433 iter/s, 4.75374s/12 iters), loss = 0.333784
I0409 20:53:06.206590 14789 solver.cpp:237]     Train net output #0: loss = 0.333784 (* 1 = 0.333784 loss)
I0409 20:53:06.206604 14789 sgd_solver.cpp:105] Iteration 6492, lr = 0.00276381
I0409 20:53:11.077533 14789 solver.cpp:218] Iteration 6504 (2.46364 iter/s, 4.87085s/12 iters), loss = 0.465732
I0409 20:53:11.077594 14789 solver.cpp:237]     Train net output #0: loss = 0.465732 (* 1 = 0.465732 loss)
I0409 20:53:11.077605 14789 sgd_solver.cpp:105] Iteration 6504, lr = 0.00275725
I0409 20:53:16.064515 14789 solver.cpp:218] Iteration 6516 (2.40634 iter/s, 4.98682s/12 iters), loss = 0.284322
I0409 20:53:16.064563 14789 solver.cpp:237]     Train net output #0: loss = 0.284322 (* 1 = 0.284322 loss)
I0409 20:53:16.064574 14789 sgd_solver.cpp:105] Iteration 6516, lr = 0.00275071
I0409 20:53:20.658959 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6528.caffemodel
I0409 20:53:26.956435 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6528.solverstate
I0409 20:53:29.931663 14789 solver.cpp:330] Iteration 6528, Testing net (#0)
I0409 20:53:29.931684 14789 net.cpp:676] Ignoring source layer train-data
I0409 20:53:31.837932 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:53:34.406828 14789 solver.cpp:397]     Test net output #0: accuracy = 0.527574
I0409 20:53:34.406867 14789 solver.cpp:397]     Test net output #1: loss = 2.15237 (* 1 = 2.15237 loss)
I0409 20:53:34.498771 14789 solver.cpp:218] Iteration 6528 (0.650976 iter/s, 18.4339s/12 iters), loss = 0.379043
I0409 20:53:34.498831 14789 solver.cpp:237]     Train net output #0: loss = 0.379043 (* 1 = 0.379043 loss)
I0409 20:53:34.498845 14789 sgd_solver.cpp:105] Iteration 6528, lr = 0.00274418
I0409 20:53:38.714560 14789 solver.cpp:218] Iteration 6540 (2.84654 iter/s, 4.21564s/12 iters), loss = 0.523217
I0409 20:53:38.714696 14789 solver.cpp:237]     Train net output #0: loss = 0.523217 (* 1 = 0.523217 loss)
I0409 20:53:38.714710 14789 sgd_solver.cpp:105] Iteration 6540, lr = 0.00273766
I0409 20:53:43.610188 14789 solver.cpp:218] Iteration 6552 (2.45128 iter/s, 4.8954s/12 iters), loss = 0.28252
I0409 20:53:43.610231 14789 solver.cpp:237]     Train net output #0: loss = 0.28252 (* 1 = 0.28252 loss)
I0409 20:53:43.610241 14789 sgd_solver.cpp:105] Iteration 6552, lr = 0.00273116
I0409 20:53:48.577981 14789 solver.cpp:218] Iteration 6564 (2.41563 iter/s, 4.96765s/12 iters), loss = 0.484282
I0409 20:53:48.578023 14789 solver.cpp:237]     Train net output #0: loss = 0.484282 (* 1 = 0.484282 loss)
I0409 20:53:48.578032 14789 sgd_solver.cpp:105] Iteration 6564, lr = 0.00272468
I0409 20:53:52.767745 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:53:53.500955 14789 solver.cpp:218] Iteration 6576 (2.43762 iter/s, 4.92283s/12 iters), loss = 0.403635
I0409 20:53:53.501008 14789 solver.cpp:237]     Train net output #0: loss = 0.403635 (* 1 = 0.403635 loss)
I0409 20:53:53.501020 14789 sgd_solver.cpp:105] Iteration 6576, lr = 0.00271821
I0409 20:53:58.410184 14789 solver.cpp:218] Iteration 6588 (2.44445 iter/s, 4.90908s/12 iters), loss = 0.316017
I0409 20:53:58.410240 14789 solver.cpp:237]     Train net output #0: loss = 0.316017 (* 1 = 0.316017 loss)
I0409 20:53:58.410254 14789 sgd_solver.cpp:105] Iteration 6588, lr = 0.00271175
I0409 20:54:03.395351 14789 solver.cpp:218] Iteration 6600 (2.40722 iter/s, 4.98501s/12 iters), loss = 0.407187
I0409 20:54:03.395408 14789 solver.cpp:237]     Train net output #0: loss = 0.407187 (* 1 = 0.407187 loss)
I0409 20:54:03.395421 14789 sgd_solver.cpp:105] Iteration 6600, lr = 0.00270532
I0409 20:54:08.219128 14789 solver.cpp:218] Iteration 6612 (2.48776 iter/s, 4.82362s/12 iters), loss = 0.486799
I0409 20:54:08.219183 14789 solver.cpp:237]     Train net output #0: loss = 0.486799 (* 1 = 0.486799 loss)
I0409 20:54:08.219197 14789 sgd_solver.cpp:105] Iteration 6612, lr = 0.00269889
I0409 20:54:12.847740 14789 solver.cpp:218] Iteration 6624 (2.59265 iter/s, 4.62846s/12 iters), loss = 0.341388
I0409 20:54:12.847898 14789 solver.cpp:237]     Train net output #0: loss = 0.341388 (* 1 = 0.341388 loss)
I0409 20:54:12.847914 14789 sgd_solver.cpp:105] Iteration 6624, lr = 0.00269248
I0409 20:54:14.655709 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6630.caffemodel
I0409 20:54:20.229480 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6630.solverstate
I0409 20:54:28.235343 14789 solver.cpp:330] Iteration 6630, Testing net (#0)
I0409 20:54:28.235368 14789 net.cpp:676] Ignoring source layer train-data
I0409 20:54:30.038604 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:54:32.651031 14789 solver.cpp:397]     Test net output #0: accuracy = 0.535539
I0409 20:54:32.651083 14789 solver.cpp:397]     Test net output #1: loss = 2.14298 (* 1 = 2.14298 loss)
I0409 20:54:34.517227 14789 solver.cpp:218] Iteration 6636 (0.553788 iter/s, 21.6689s/12 iters), loss = 0.423407
I0409 20:54:34.517287 14789 solver.cpp:237]     Train net output #0: loss = 0.423407 (* 1 = 0.423407 loss)
I0409 20:54:34.517300 14789 sgd_solver.cpp:105] Iteration 6636, lr = 0.00268609
I0409 20:54:39.932464 14789 solver.cpp:218] Iteration 6648 (2.21604 iter/s, 5.41507s/12 iters), loss = 0.377797
I0409 20:54:39.932520 14789 solver.cpp:237]     Train net output #0: loss = 0.377797 (* 1 = 0.377797 loss)
I0409 20:54:39.932533 14789 sgd_solver.cpp:105] Iteration 6648, lr = 0.00267971
I0409 20:54:44.955757 14789 solver.cpp:218] Iteration 6660 (2.38895 iter/s, 5.02313s/12 iters), loss = 0.303842
I0409 20:54:44.955889 14789 solver.cpp:237]     Train net output #0: loss = 0.303842 (* 1 = 0.303842 loss)
I0409 20:54:44.955900 14789 sgd_solver.cpp:105] Iteration 6660, lr = 0.00267335
I0409 20:54:49.975970 14789 solver.cpp:218] Iteration 6672 (2.39045 iter/s, 5.01997s/12 iters), loss = 0.398769
I0409 20:54:49.976017 14789 solver.cpp:237]     Train net output #0: loss = 0.398769 (* 1 = 0.398769 loss)
I0409 20:54:49.976027 14789 sgd_solver.cpp:105] Iteration 6672, lr = 0.00266701
I0409 20:54:51.304713 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:54:54.885747 14789 solver.cpp:218] Iteration 6684 (2.44418 iter/s, 4.90962s/12 iters), loss = 0.322733
I0409 20:54:54.885795 14789 solver.cpp:237]     Train net output #0: loss = 0.322733 (* 1 = 0.322733 loss)
I0409 20:54:54.885807 14789 sgd_solver.cpp:105] Iteration 6684, lr = 0.00266067
I0409 20:54:59.804281 14789 solver.cpp:218] Iteration 6696 (2.43983 iter/s, 4.91838s/12 iters), loss = 0.276529
I0409 20:54:59.804333 14789 solver.cpp:237]     Train net output #0: loss = 0.276529 (* 1 = 0.276529 loss)
I0409 20:54:59.804345 14789 sgd_solver.cpp:105] Iteration 6696, lr = 0.00265436
I0409 20:55:04.667136 14789 solver.cpp:218] Iteration 6708 (2.46777 iter/s, 4.8627s/12 iters), loss = 0.300157
I0409 20:55:04.667201 14789 solver.cpp:237]     Train net output #0: loss = 0.300157 (* 1 = 0.300157 loss)
I0409 20:55:04.667219 14789 sgd_solver.cpp:105] Iteration 6708, lr = 0.00264805
I0409 20:55:09.646142 14789 solver.cpp:218] Iteration 6720 (2.4102 iter/s, 4.97884s/12 iters), loss = 0.245555
I0409 20:55:09.646199 14789 solver.cpp:237]     Train net output #0: loss = 0.245555 (* 1 = 0.245555 loss)
I0409 20:55:09.646211 14789 sgd_solver.cpp:105] Iteration 6720, lr = 0.00264177
I0409 20:55:13.976361 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6732.caffemodel
I0409 20:55:18.338770 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6732.solverstate
I0409 20:55:21.393724 14789 solver.cpp:330] Iteration 6732, Testing net (#0)
I0409 20:55:21.393754 14789 net.cpp:676] Ignoring source layer train-data
I0409 20:55:23.228456 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:55:25.884708 14789 solver.cpp:397]     Test net output #0: accuracy = 0.530637
I0409 20:55:25.884750 14789 solver.cpp:397]     Test net output #1: loss = 2.25264 (* 1 = 2.25264 loss)
I0409 20:55:25.976742 14789 solver.cpp:218] Iteration 6732 (0.734834 iter/s, 16.3302s/12 iters), loss = 0.41997
I0409 20:55:25.976802 14789 solver.cpp:237]     Train net output #0: loss = 0.41997 (* 1 = 0.41997 loss)
I0409 20:55:25.976815 14789 sgd_solver.cpp:105] Iteration 6732, lr = 0.0026355
I0409 20:55:30.047497 14789 solver.cpp:218] Iteration 6744 (2.94796 iter/s, 4.07061s/12 iters), loss = 0.438553
I0409 20:55:30.047542 14789 solver.cpp:237]     Train net output #0: loss = 0.438553 (* 1 = 0.438553 loss)
I0409 20:55:30.047551 14789 sgd_solver.cpp:105] Iteration 6744, lr = 0.00262924
I0409 20:55:34.886564 14789 solver.cpp:218] Iteration 6756 (2.47989 iter/s, 4.83892s/12 iters), loss = 0.48209
I0409 20:55:34.886610 14789 solver.cpp:237]     Train net output #0: loss = 0.48209 (* 1 = 0.48209 loss)
I0409 20:55:34.886618 14789 sgd_solver.cpp:105] Iteration 6756, lr = 0.002623
I0409 20:55:39.934113 14789 solver.cpp:218] Iteration 6768 (2.37746 iter/s, 5.0474s/12 iters), loss = 0.283802
I0409 20:55:39.934149 14789 solver.cpp:237]     Train net output #0: loss = 0.283802 (* 1 = 0.283802 loss)
I0409 20:55:39.934157 14789 sgd_solver.cpp:105] Iteration 6768, lr = 0.00261677
I0409 20:55:43.051623 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:55:44.490947 14789 solver.cpp:218] Iteration 6780 (2.63349 iter/s, 4.55669s/12 iters), loss = 0.316067
I0409 20:55:44.490994 14789 solver.cpp:237]     Train net output #0: loss = 0.316067 (* 1 = 0.316067 loss)
I0409 20:55:44.491004 14789 sgd_solver.cpp:105] Iteration 6780, lr = 0.00261056
I0409 20:55:49.145893 14789 solver.cpp:218] Iteration 6792 (2.57799 iter/s, 4.6548s/12 iters), loss = 0.272076
I0409 20:55:49.146028 14789 solver.cpp:237]     Train net output #0: loss = 0.272076 (* 1 = 0.272076 loss)
I0409 20:55:49.146040 14789 sgd_solver.cpp:105] Iteration 6792, lr = 0.00260436
I0409 20:55:53.873641 14789 solver.cpp:218] Iteration 6804 (2.53833 iter/s, 4.72751s/12 iters), loss = 0.37389
I0409 20:55:53.873698 14789 solver.cpp:237]     Train net output #0: loss = 0.37389 (* 1 = 0.37389 loss)
I0409 20:55:53.873710 14789 sgd_solver.cpp:105] Iteration 6804, lr = 0.00259817
I0409 20:55:58.564767 14789 solver.cpp:218] Iteration 6816 (2.55811 iter/s, 4.69096s/12 iters), loss = 0.307549
I0409 20:55:58.564848 14789 solver.cpp:237]     Train net output #0: loss = 0.307549 (* 1 = 0.307549 loss)
I0409 20:55:58.564869 14789 sgd_solver.cpp:105] Iteration 6816, lr = 0.00259201
I0409 20:56:03.631006 14789 solver.cpp:218] Iteration 6828 (2.36871 iter/s, 5.06605s/12 iters), loss = 0.281777
I0409 20:56:03.631059 14789 solver.cpp:237]     Train net output #0: loss = 0.281777 (* 1 = 0.281777 loss)
I0409 20:56:03.631072 14789 sgd_solver.cpp:105] Iteration 6828, lr = 0.00258585
I0409 20:56:05.547863 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6834.caffemodel
I0409 20:56:09.373883 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6834.solverstate
I0409 20:56:12.381078 14789 solver.cpp:330] Iteration 6834, Testing net (#0)
I0409 20:56:12.381103 14789 net.cpp:676] Ignoring source layer train-data
I0409 20:56:14.108634 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:56:16.795058 14789 solver.cpp:397]     Test net output #0: accuracy = 0.521446
I0409 20:56:16.795111 14789 solver.cpp:397]     Test net output #1: loss = 2.28528 (* 1 = 2.28528 loss)
I0409 20:56:18.564714 14789 solver.cpp:218] Iteration 6840 (0.803571 iter/s, 14.9333s/12 iters), loss = 0.31844
I0409 20:56:18.564764 14789 solver.cpp:237]     Train net output #0: loss = 0.31844 (* 1 = 0.31844 loss)
I0409 20:56:18.564771 14789 sgd_solver.cpp:105] Iteration 6840, lr = 0.00257971
I0409 20:56:23.809674 14789 solver.cpp:218] Iteration 6852 (2.28798 iter/s, 5.24479s/12 iters), loss = 0.360129
I0409 20:56:23.809808 14789 solver.cpp:237]     Train net output #0: loss = 0.360129 (* 1 = 0.360129 loss)
I0409 20:56:23.809819 14789 sgd_solver.cpp:105] Iteration 6852, lr = 0.00257359
I0409 20:56:28.853629 14789 solver.cpp:218] Iteration 6864 (2.3792 iter/s, 5.04371s/12 iters), loss = 0.251744
I0409 20:56:28.853680 14789 solver.cpp:237]     Train net output #0: loss = 0.251744 (* 1 = 0.251744 loss)
I0409 20:56:28.853693 14789 sgd_solver.cpp:105] Iteration 6864, lr = 0.00256748
I0409 20:56:33.782869 14789 solver.cpp:218] Iteration 6876 (2.43453 iter/s, 4.92908s/12 iters), loss = 0.281811
I0409 20:56:33.782917 14789 solver.cpp:237]     Train net output #0: loss = 0.281811 (* 1 = 0.281811 loss)
I0409 20:56:33.782927 14789 sgd_solver.cpp:105] Iteration 6876, lr = 0.00256138
I0409 20:56:34.365777 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:56:38.551784 14789 solver.cpp:218] Iteration 6888 (2.51638 iter/s, 4.76875s/12 iters), loss = 0.243946
I0409 20:56:38.551836 14789 solver.cpp:237]     Train net output #0: loss = 0.243946 (* 1 = 0.243946 loss)
I0409 20:56:38.551846 14789 sgd_solver.cpp:105] Iteration 6888, lr = 0.0025553
I0409 20:56:43.446815 14789 solver.cpp:218] Iteration 6900 (2.45155 iter/s, 4.89487s/12 iters), loss = 0.32877
I0409 20:56:43.446858 14789 solver.cpp:237]     Train net output #0: loss = 0.32877 (* 1 = 0.32877 loss)
I0409 20:56:43.446867 14789 sgd_solver.cpp:105] Iteration 6900, lr = 0.00254923
I0409 20:56:48.508960 14789 solver.cpp:218] Iteration 6912 (2.37061 iter/s, 5.06198s/12 iters), loss = 0.327504
I0409 20:56:48.509022 14789 solver.cpp:237]     Train net output #0: loss = 0.327504 (* 1 = 0.327504 loss)
I0409 20:56:48.509033 14789 sgd_solver.cpp:105] Iteration 6912, lr = 0.00254318
I0409 20:56:53.418434 14789 solver.cpp:218] Iteration 6924 (2.44434 iter/s, 4.9093s/12 iters), loss = 0.313332
I0409 20:56:53.418486 14789 solver.cpp:237]     Train net output #0: loss = 0.313332 (* 1 = 0.313332 loss)
I0409 20:56:53.418498 14789 sgd_solver.cpp:105] Iteration 6924, lr = 0.00253714
I0409 20:56:58.125403 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6936.caffemodel
I0409 20:57:01.982393 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6936.solverstate
I0409 20:57:05.713932 14789 solver.cpp:330] Iteration 6936, Testing net (#0)
I0409 20:57:05.713968 14789 net.cpp:676] Ignoring source layer train-data
I0409 20:57:06.379427 14789 blocking_queue.cpp:49] Waiting for data
I0409 20:57:07.479610 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:57:10.284214 14789 solver.cpp:397]     Test net output #0: accuracy = 0.533701
I0409 20:57:10.284263 14789 solver.cpp:397]     Test net output #1: loss = 2.34657 (* 1 = 2.34657 loss)
I0409 20:57:10.376085 14789 solver.cpp:218] Iteration 6936 (0.707662 iter/s, 16.9573s/12 iters), loss = 0.27954
I0409 20:57:10.376135 14789 solver.cpp:237]     Train net output #0: loss = 0.27954 (* 1 = 0.27954 loss)
I0409 20:57:10.376147 14789 sgd_solver.cpp:105] Iteration 6936, lr = 0.00253112
I0409 20:57:14.244547 14789 solver.cpp:218] Iteration 6948 (3.10212 iter/s, 3.86832s/12 iters), loss = 0.286946
I0409 20:57:14.244609 14789 solver.cpp:237]     Train net output #0: loss = 0.286946 (* 1 = 0.286946 loss)
I0409 20:57:14.244626 14789 sgd_solver.cpp:105] Iteration 6948, lr = 0.00252511
I0409 20:57:18.710403 14789 solver.cpp:218] Iteration 6960 (2.68715 iter/s, 4.4657s/12 iters), loss = 0.273847
I0409 20:57:18.710445 14789 solver.cpp:237]     Train net output #0: loss = 0.273847 (* 1 = 0.273847 loss)
I0409 20:57:18.710454 14789 sgd_solver.cpp:105] Iteration 6960, lr = 0.00251911
I0409 20:57:23.313344 14789 solver.cpp:218] Iteration 6972 (2.60711 iter/s, 4.60279s/12 iters), loss = 0.261483
I0409 20:57:23.313392 14789 solver.cpp:237]     Train net output #0: loss = 0.261483 (* 1 = 0.261483 loss)
I0409 20:57:23.313401 14789 sgd_solver.cpp:105] Iteration 6972, lr = 0.00251313
I0409 20:57:26.276788 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:57:28.583055 14789 solver.cpp:218] Iteration 6984 (2.27724 iter/s, 5.26954s/12 iters), loss = 0.295822
I0409 20:57:28.583201 14789 solver.cpp:237]     Train net output #0: loss = 0.295822 (* 1 = 0.295822 loss)
I0409 20:57:28.583214 14789 sgd_solver.cpp:105] Iteration 6984, lr = 0.00250717
I0409 20:57:33.451207 14789 solver.cpp:218] Iteration 6996 (2.46513 iter/s, 4.8679s/12 iters), loss = 0.408706
I0409 20:57:33.451256 14789 solver.cpp:237]     Train net output #0: loss = 0.408706 (* 1 = 0.408706 loss)
I0409 20:57:33.451265 14789 sgd_solver.cpp:105] Iteration 6996, lr = 0.00250121
I0409 20:57:38.495626 14789 solver.cpp:218] Iteration 7008 (2.37895 iter/s, 5.04425s/12 iters), loss = 0.19968
I0409 20:57:38.495678 14789 solver.cpp:237]     Train net output #0: loss = 0.19968 (* 1 = 0.19968 loss)
I0409 20:57:38.495692 14789 sgd_solver.cpp:105] Iteration 7008, lr = 0.00249528
I0409 20:57:43.741998 14789 solver.cpp:218] Iteration 7020 (2.28737 iter/s, 5.2462s/12 iters), loss = 0.242756
I0409 20:57:43.742043 14789 solver.cpp:237]     Train net output #0: loss = 0.242756 (* 1 = 0.242756 loss)
I0409 20:57:43.742053 14789 sgd_solver.cpp:105] Iteration 7020, lr = 0.00248935
I0409 20:57:48.756880 14789 solver.cpp:218] Iteration 7032 (2.39296 iter/s, 5.01471s/12 iters), loss = 0.294449
I0409 20:57:48.756938 14789 solver.cpp:237]     Train net output #0: loss = 0.294449 (* 1 = 0.294449 loss)
I0409 20:57:48.756951 14789 sgd_solver.cpp:105] Iteration 7032, lr = 0.00248344
I0409 20:57:50.836403 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7038.caffemodel
I0409 20:57:57.670504 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7038.solverstate
I0409 20:58:00.679248 14789 solver.cpp:330] Iteration 7038, Testing net (#0)
I0409 20:58:00.679329 14789 net.cpp:676] Ignoring source layer train-data
I0409 20:58:02.391500 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:58:05.250506 14789 solver.cpp:397]     Test net output #0: accuracy = 0.531863
I0409 20:58:05.250531 14789 solver.cpp:397]     Test net output #1: loss = 2.24857 (* 1 = 2.24857 loss)
I0409 20:58:07.286697 14789 solver.cpp:218] Iteration 7044 (0.647621 iter/s, 18.5294s/12 iters), loss = 0.207034
I0409 20:58:07.286753 14789 solver.cpp:237]     Train net output #0: loss = 0.207034 (* 1 = 0.207034 loss)
I0409 20:58:07.286765 14789 sgd_solver.cpp:105] Iteration 7044, lr = 0.00247755
I0409 20:58:12.596362 14789 solver.cpp:218] Iteration 7056 (2.2601 iter/s, 5.30949s/12 iters), loss = 0.362618
I0409 20:58:12.596406 14789 solver.cpp:237]     Train net output #0: loss = 0.362618 (* 1 = 0.362618 loss)
I0409 20:58:12.596415 14789 sgd_solver.cpp:105] Iteration 7056, lr = 0.00247166
I0409 20:58:17.802896 14789 solver.cpp:218] Iteration 7068 (2.30487 iter/s, 5.20638s/12 iters), loss = 0.243684
I0409 20:58:17.802930 14789 solver.cpp:237]     Train net output #0: loss = 0.243684 (* 1 = 0.243684 loss)
I0409 20:58:17.802938 14789 sgd_solver.cpp:105] Iteration 7068, lr = 0.0024658
I0409 20:58:22.962797 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:58:23.080699 14789 solver.cpp:218] Iteration 7080 (2.27374 iter/s, 5.27764s/12 iters), loss = 0.167453
I0409 20:58:23.080756 14789 solver.cpp:237]     Train net output #0: loss = 0.167453 (* 1 = 0.167453 loss)
I0409 20:58:23.080768 14789 sgd_solver.cpp:105] Iteration 7080, lr = 0.00245994
I0409 20:58:28.071596 14789 solver.cpp:218] Iteration 7092 (2.40446 iter/s, 4.99073s/12 iters), loss = 0.262479
I0409 20:58:28.071636 14789 solver.cpp:237]     Train net output #0: loss = 0.262479 (* 1 = 0.262479 loss)
I0409 20:58:28.071645 14789 sgd_solver.cpp:105] Iteration 7092, lr = 0.0024541
I0409 20:58:32.977800 14789 solver.cpp:218] Iteration 7104 (2.44596 iter/s, 4.90604s/12 iters), loss = 0.205531
I0409 20:58:32.977900 14789 solver.cpp:237]     Train net output #0: loss = 0.205531 (* 1 = 0.205531 loss)
I0409 20:58:32.977910 14789 sgd_solver.cpp:105] Iteration 7104, lr = 0.00244827
I0409 20:58:37.913017 14789 solver.cpp:218] Iteration 7116 (2.43161 iter/s, 4.935s/12 iters), loss = 0.216333
I0409 20:58:37.913069 14789 solver.cpp:237]     Train net output #0: loss = 0.216333 (* 1 = 0.216333 loss)
I0409 20:58:37.913079 14789 sgd_solver.cpp:105] Iteration 7116, lr = 0.00244246
I0409 20:58:42.766753 14789 solver.cpp:218] Iteration 7128 (2.47241 iter/s, 4.85357s/12 iters), loss = 0.368079
I0409 20:58:42.766801 14789 solver.cpp:237]     Train net output #0: loss = 0.368079 (* 1 = 0.368079 loss)
I0409 20:58:42.766810 14789 sgd_solver.cpp:105] Iteration 7128, lr = 0.00243666
I0409 20:58:47.248886 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7140.caffemodel
I0409 20:58:56.747467 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7140.solverstate
I0409 20:59:03.460585 14789 solver.cpp:330] Iteration 7140, Testing net (#0)
I0409 20:59:03.460701 14789 net.cpp:676] Ignoring source layer train-data
I0409 20:59:05.077291 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:59:07.889258 14789 solver.cpp:397]     Test net output #0: accuracy = 0.545343
I0409 20:59:07.889307 14789 solver.cpp:397]     Test net output #1: loss = 2.30987 (* 1 = 2.30987 loss)
I0409 20:59:07.981737 14789 solver.cpp:218] Iteration 7140 (0.475919 iter/s, 25.2144s/12 iters), loss = 0.303646
I0409 20:59:07.981812 14789 solver.cpp:237]     Train net output #0: loss = 0.303646 (* 1 = 0.303646 loss)
I0409 20:59:07.981827 14789 sgd_solver.cpp:105] Iteration 7140, lr = 0.00243088
I0409 20:59:12.303316 14789 solver.cpp:218] Iteration 7152 (2.77688 iter/s, 4.3214s/12 iters), loss = 0.14031
I0409 20:59:12.303366 14789 solver.cpp:237]     Train net output #0: loss = 0.14031 (* 1 = 0.14031 loss)
I0409 20:59:12.303377 14789 sgd_solver.cpp:105] Iteration 7152, lr = 0.00242511
I0409 20:59:17.138551 14789 solver.cpp:218] Iteration 7164 (2.48187 iter/s, 4.83507s/12 iters), loss = 0.437178
I0409 20:59:17.138605 14789 solver.cpp:237]     Train net output #0: loss = 0.437178 (* 1 = 0.437178 loss)
I0409 20:59:17.138617 14789 sgd_solver.cpp:105] Iteration 7164, lr = 0.00241935
I0409 20:59:22.106282 14789 solver.cpp:218] Iteration 7176 (2.41567 iter/s, 4.96756s/12 iters), loss = 0.192726
I0409 20:59:22.106331 14789 solver.cpp:237]     Train net output #0: loss = 0.192726 (* 1 = 0.192726 loss)
I0409 20:59:22.106343 14789 sgd_solver.cpp:105] Iteration 7176, lr = 0.0024136
I0409 20:59:24.247128 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:59:27.135430 14789 solver.cpp:218] Iteration 7188 (2.38617 iter/s, 5.02898s/12 iters), loss = 0.390184
I0409 20:59:27.135493 14789 solver.cpp:237]     Train net output #0: loss = 0.390184 (* 1 = 0.390184 loss)
I0409 20:59:27.135509 14789 sgd_solver.cpp:105] Iteration 7188, lr = 0.00240787
I0409 20:59:32.090479 14789 solver.cpp:218] Iteration 7200 (2.42186 iter/s, 4.95487s/12 iters), loss = 0.328292
I0409 20:59:32.090533 14789 solver.cpp:237]     Train net output #0: loss = 0.328292 (* 1 = 0.328292 loss)
I0409 20:59:32.090546 14789 sgd_solver.cpp:105] Iteration 7200, lr = 0.00240216
I0409 20:59:37.221802 14789 solver.cpp:218] Iteration 7212 (2.33866 iter/s, 5.13115s/12 iters), loss = 0.340679
I0409 20:59:37.221922 14789 solver.cpp:237]     Train net output #0: loss = 0.340679 (* 1 = 0.340679 loss)
I0409 20:59:37.221931 14789 sgd_solver.cpp:105] Iteration 7212, lr = 0.00239645
I0409 20:59:42.276829 14789 solver.cpp:218] Iteration 7224 (2.37399 iter/s, 5.05478s/12 iters), loss = 0.245002
I0409 20:59:42.276880 14789 solver.cpp:237]     Train net output #0: loss = 0.245002 (* 1 = 0.245002 loss)
I0409 20:59:42.276892 14789 sgd_solver.cpp:105] Iteration 7224, lr = 0.00239076
I0409 20:59:47.224292 14789 solver.cpp:218] Iteration 7236 (2.42557 iter/s, 4.94729s/12 iters), loss = 0.15022
I0409 20:59:47.224341 14789 solver.cpp:237]     Train net output #0: loss = 0.15022 (* 1 = 0.15022 loss)
I0409 20:59:47.224352 14789 sgd_solver.cpp:105] Iteration 7236, lr = 0.00238509
I0409 20:59:49.419652 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7242.caffemodel
I0409 20:59:55.362294 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7242.solverstate
I0409 20:59:58.374326 14789 solver.cpp:330] Iteration 7242, Testing net (#0)
I0409 20:59:58.374352 14789 net.cpp:676] Ignoring source layer train-data
I0409 21:00:00.000046 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:00:02.842849 14789 solver.cpp:397]     Test net output #0: accuracy = 0.549632
I0409 21:00:02.842881 14789 solver.cpp:397]     Test net output #1: loss = 2.25722 (* 1 = 2.25722 loss)
I0409 21:00:04.557894 14789 solver.cpp:218] Iteration 7248 (0.692314 iter/s, 17.3332s/12 iters), loss = 0.232138
I0409 21:00:04.557934 14789 solver.cpp:237]     Train net output #0: loss = 0.232138 (* 1 = 0.232138 loss)
I0409 21:00:04.557942 14789 sgd_solver.cpp:105] Iteration 7248, lr = 0.00237942
I0409 21:00:09.527539 14789 solver.cpp:218] Iteration 7260 (2.41474 iter/s, 4.96948s/12 iters), loss = 0.267156
I0409 21:00:09.527688 14789 solver.cpp:237]     Train net output #0: loss = 0.267156 (* 1 = 0.267156 loss)
I0409 21:00:09.527699 14789 sgd_solver.cpp:105] Iteration 7260, lr = 0.00237378
I0409 21:00:14.303639 14789 solver.cpp:218] Iteration 7272 (2.51265 iter/s, 4.77584s/12 iters), loss = 0.310978
I0409 21:00:14.303683 14789 solver.cpp:237]     Train net output #0: loss = 0.310978 (* 1 = 0.310978 loss)
I0409 21:00:14.303692 14789 sgd_solver.cpp:105] Iteration 7272, lr = 0.00236814
I0409 21:00:18.451750 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:00:19.152264 14789 solver.cpp:218] Iteration 7284 (2.47501 iter/s, 4.84846s/12 iters), loss = 0.284657
I0409 21:00:19.152321 14789 solver.cpp:237]     Train net output #0: loss = 0.284657 (* 1 = 0.284657 loss)
I0409 21:00:19.152333 14789 sgd_solver.cpp:105] Iteration 7284, lr = 0.00236252
I0409 21:00:23.949939 14789 solver.cpp:218] Iteration 7296 (2.5013 iter/s, 4.7975s/12 iters), loss = 0.216584
I0409 21:00:23.949995 14789 solver.cpp:237]     Train net output #0: loss = 0.216584 (* 1 = 0.216584 loss)
I0409 21:00:23.950006 14789 sgd_solver.cpp:105] Iteration 7296, lr = 0.00235691
I0409 21:00:28.702447 14789 solver.cpp:218] Iteration 7308 (2.52508 iter/s, 4.75233s/12 iters), loss = 0.248665
I0409 21:00:28.702500 14789 solver.cpp:237]     Train net output #0: loss = 0.248665 (* 1 = 0.248665 loss)
I0409 21:00:28.702514 14789 sgd_solver.cpp:105] Iteration 7308, lr = 0.00235131
I0409 21:00:33.659009 14789 solver.cpp:218] Iteration 7320 (2.42111 iter/s, 4.9564s/12 iters), loss = 0.282789
I0409 21:00:33.659045 14789 solver.cpp:237]     Train net output #0: loss = 0.282789 (* 1 = 0.282789 loss)
I0409 21:00:33.659054 14789 sgd_solver.cpp:105] Iteration 7320, lr = 0.00234573
I0409 21:00:38.439261 14789 solver.cpp:218] Iteration 7332 (2.51041 iter/s, 4.7801s/12 iters), loss = 0.166912
I0409 21:00:38.439306 14789 solver.cpp:237]     Train net output #0: loss = 0.166912 (* 1 = 0.166912 loss)
I0409 21:00:38.439314 14789 sgd_solver.cpp:105] Iteration 7332, lr = 0.00234016
I0409 21:00:43.566495 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7344.caffemodel
I0409 21:00:49.077718 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7344.solverstate
I0409 21:00:52.086498 14789 solver.cpp:330] Iteration 7344, Testing net (#0)
I0409 21:00:52.086525 14789 net.cpp:676] Ignoring source layer train-data
I0409 21:00:53.690194 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:00:56.567296 14789 solver.cpp:397]     Test net output #0: accuracy = 0.552083
I0409 21:00:56.567332 14789 solver.cpp:397]     Test net output #1: loss = 2.22483 (* 1 = 2.22483 loss)
I0409 21:00:56.659086 14789 solver.cpp:218] Iteration 7344 (0.65864 iter/s, 18.2194s/12 iters), loss = 0.137219
I0409 21:00:56.659157 14789 solver.cpp:237]     Train net output #0: loss = 0.137219 (* 1 = 0.137219 loss)
I0409 21:00:56.659171 14789 sgd_solver.cpp:105] Iteration 7344, lr = 0.0023346
I0409 21:01:01.160663 14789 solver.cpp:218] Iteration 7356 (2.66584 iter/s, 4.5014s/12 iters), loss = 0.0878384
I0409 21:01:01.160720 14789 solver.cpp:237]     Train net output #0: loss = 0.0878384 (* 1 = 0.0878384 loss)
I0409 21:01:01.160733 14789 sgd_solver.cpp:105] Iteration 7356, lr = 0.00232906
I0409 21:01:05.972558 14789 solver.cpp:218] Iteration 7368 (2.49391 iter/s, 4.81172s/12 iters), loss = 0.16049
I0409 21:01:05.972600 14789 solver.cpp:237]     Train net output #0: loss = 0.16049 (* 1 = 0.16049 loss)
I0409 21:01:05.972609 14789 sgd_solver.cpp:105] Iteration 7368, lr = 0.00232353
I0409 21:01:10.835110 14789 solver.cpp:218] Iteration 7380 (2.46792 iter/s, 4.86239s/12 iters), loss = 0.248276
I0409 21:01:10.835157 14789 solver.cpp:237]     Train net output #0: loss = 0.248276 (* 1 = 0.248276 loss)
I0409 21:01:10.835168 14789 sgd_solver.cpp:105] Iteration 7380, lr = 0.00231802
I0409 21:01:12.196820 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:01:15.621770 14789 solver.cpp:218] Iteration 7392 (2.50705 iter/s, 4.7865s/12 iters), loss = 0.232995
I0409 21:01:15.621899 14789 solver.cpp:237]     Train net output #0: loss = 0.232995 (* 1 = 0.232995 loss)
I0409 21:01:15.621910 14789 sgd_solver.cpp:105] Iteration 7392, lr = 0.00231251
I0409 21:01:20.440004 14789 solver.cpp:218] Iteration 7404 (2.49067 iter/s, 4.81799s/12 iters), loss = 0.164871
I0409 21:01:20.440054 14789 solver.cpp:237]     Train net output #0: loss = 0.164871 (* 1 = 0.164871 loss)
I0409 21:01:20.440065 14789 sgd_solver.cpp:105] Iteration 7404, lr = 0.00230702
I0409 21:01:25.706121 14789 solver.cpp:218] Iteration 7416 (2.2788 iter/s, 5.26593s/12 iters), loss = 0.228752
I0409 21:01:25.706171 14789 solver.cpp:237]     Train net output #0: loss = 0.228752 (* 1 = 0.228752 loss)
I0409 21:01:25.706183 14789 sgd_solver.cpp:105] Iteration 7416, lr = 0.00230154
I0409 21:01:30.666785 14789 solver.cpp:218] Iteration 7428 (2.41911 iter/s, 4.96049s/12 iters), loss = 0.194285
I0409 21:01:30.666826 14789 solver.cpp:237]     Train net output #0: loss = 0.194285 (* 1 = 0.194285 loss)
I0409 21:01:30.666836 14789 sgd_solver.cpp:105] Iteration 7428, lr = 0.00229608
I0409 21:01:35.400166 14789 solver.cpp:218] Iteration 7440 (2.53527 iter/s, 4.73322s/12 iters), loss = 0.151762
I0409 21:01:35.400218 14789 solver.cpp:237]     Train net output #0: loss = 0.151762 (* 1 = 0.151762 loss)
I0409 21:01:35.400228 14789 sgd_solver.cpp:105] Iteration 7440, lr = 0.00229063
I0409 21:01:37.506433 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7446.caffemodel
I0409 21:01:43.783006 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7446.solverstate
I0409 21:01:46.813732 14789 solver.cpp:330] Iteration 7446, Testing net (#0)
I0409 21:01:46.813798 14789 net.cpp:676] Ignoring source layer train-data
I0409 21:01:48.286062 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:01:51.490855 14789 solver.cpp:397]     Test net output #0: accuracy = 0.549632
I0409 21:01:51.490895 14789 solver.cpp:397]     Test net output #1: loss = 2.23803 (* 1 = 2.23803 loss)
I0409 21:01:53.280267 14789 solver.cpp:218] Iteration 7452 (0.671155 iter/s, 17.8796s/12 iters), loss = 0.233449
I0409 21:01:53.280333 14789 solver.cpp:237]     Train net output #0: loss = 0.233449 (* 1 = 0.233449 loss)
I0409 21:01:53.280345 14789 sgd_solver.cpp:105] Iteration 7452, lr = 0.00228519
I0409 21:01:58.440004 14789 solver.cpp:218] Iteration 7464 (2.32579 iter/s, 5.15954s/12 iters), loss = 0.159465
I0409 21:01:58.440052 14789 solver.cpp:237]     Train net output #0: loss = 0.159465 (* 1 = 0.159465 loss)
I0409 21:01:58.440059 14789 sgd_solver.cpp:105] Iteration 7464, lr = 0.00227976
I0409 21:02:03.155748 14789 solver.cpp:218] Iteration 7476 (2.54476 iter/s, 4.71558s/12 iters), loss = 0.293593
I0409 21:02:03.155799 14789 solver.cpp:237]     Train net output #0: loss = 0.293593 (* 1 = 0.293593 loss)
I0409 21:02:03.155812 14789 sgd_solver.cpp:105] Iteration 7476, lr = 0.00227435
I0409 21:02:06.612915 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:02:08.055650 14789 solver.cpp:218] Iteration 7488 (2.44912 iter/s, 4.89973s/12 iters), loss = 0.132993
I0409 21:02:08.055708 14789 solver.cpp:237]     Train net output #0: loss = 0.132993 (* 1 = 0.132993 loss)
I0409 21:02:08.055721 14789 sgd_solver.cpp:105] Iteration 7488, lr = 0.00226895
I0409 21:02:12.940754 14789 solver.cpp:218] Iteration 7500 (2.45654 iter/s, 4.88493s/12 iters), loss = 0.154691
I0409 21:02:12.940795 14789 solver.cpp:237]     Train net output #0: loss = 0.154691 (* 1 = 0.154691 loss)
I0409 21:02:12.940804 14789 sgd_solver.cpp:105] Iteration 7500, lr = 0.00226357
I0409 21:02:17.898631 14789 solver.cpp:218] Iteration 7512 (2.42047 iter/s, 4.95771s/12 iters), loss = 0.150931
I0409 21:02:17.898785 14789 solver.cpp:237]     Train net output #0: loss = 0.150931 (* 1 = 0.150931 loss)
I0409 21:02:17.898797 14789 sgd_solver.cpp:105] Iteration 7512, lr = 0.00225819
I0409 21:02:22.743785 14789 solver.cpp:218] Iteration 7524 (2.47684 iter/s, 4.84488s/12 iters), loss = 0.135739
I0409 21:02:22.743847 14789 solver.cpp:237]     Train net output #0: loss = 0.135739 (* 1 = 0.135739 loss)
I0409 21:02:22.743860 14789 sgd_solver.cpp:105] Iteration 7524, lr = 0.00225283
I0409 21:02:27.667531 14789 solver.cpp:218] Iteration 7536 (2.43726 iter/s, 4.92356s/12 iters), loss = 0.11763
I0409 21:02:27.667591 14789 solver.cpp:237]     Train net output #0: loss = 0.11763 (* 1 = 0.11763 loss)
I0409 21:02:27.667604 14789 sgd_solver.cpp:105] Iteration 7536, lr = 0.00224748
I0409 21:02:32.215863 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7548.caffemodel
I0409 21:02:36.443534 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7548.solverstate
I0409 21:02:43.657145 14789 solver.cpp:330] Iteration 7548, Testing net (#0)
I0409 21:02:43.657172 14789 net.cpp:676] Ignoring source layer train-data
I0409 21:02:45.403854 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:02:48.362151 14789 solver.cpp:397]     Test net output #0: accuracy = 0.558211
I0409 21:02:48.362267 14789 solver.cpp:397]     Test net output #1: loss = 2.27763 (* 1 = 2.27763 loss)
I0409 21:02:48.454167 14789 solver.cpp:218] Iteration 7548 (0.577309 iter/s, 20.7861s/12 iters), loss = 0.1713
I0409 21:02:48.454218 14789 solver.cpp:237]     Train net output #0: loss = 0.1713 (* 1 = 0.1713 loss)
I0409 21:02:48.454236 14789 sgd_solver.cpp:105] Iteration 7548, lr = 0.00224215
I0409 21:02:52.523036 14789 solver.cpp:218] Iteration 7560 (2.94934 iter/s, 4.06871s/12 iters), loss = 0.129343
I0409 21:02:52.523088 14789 solver.cpp:237]     Train net output #0: loss = 0.129343 (* 1 = 0.129343 loss)
I0409 21:02:52.523099 14789 sgd_solver.cpp:105] Iteration 7560, lr = 0.00223682
I0409 21:02:57.614841 14789 solver.cpp:218] Iteration 7572 (2.35681 iter/s, 5.09163s/12 iters), loss = 0.186408
I0409 21:02:57.614886 14789 solver.cpp:237]     Train net output #0: loss = 0.186408 (* 1 = 0.186408 loss)
I0409 21:02:57.614897 14789 sgd_solver.cpp:105] Iteration 7572, lr = 0.00223151
I0409 21:03:02.490751 14789 solver.cpp:218] Iteration 7584 (2.46117 iter/s, 4.87574s/12 iters), loss = 0.187001
I0409 21:03:02.490811 14789 solver.cpp:237]     Train net output #0: loss = 0.187001 (* 1 = 0.187001 loss)
I0409 21:03:02.490823 14789 sgd_solver.cpp:105] Iteration 7584, lr = 0.00222621
I0409 21:03:03.163239 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:03:07.383505 14789 solver.cpp:218] Iteration 7596 (2.4527 iter/s, 4.89257s/12 iters), loss = 0.237717
I0409 21:03:07.383556 14789 solver.cpp:237]     Train net output #0: loss = 0.237717 (* 1 = 0.237717 loss)
I0409 21:03:07.383566 14789 sgd_solver.cpp:105] Iteration 7596, lr = 0.00222093
I0409 21:03:12.321067 14789 solver.cpp:218] Iteration 7608 (2.43044 iter/s, 4.93738s/12 iters), loss = 0.22471
I0409 21:03:12.321121 14789 solver.cpp:237]     Train net output #0: loss = 0.22471 (* 1 = 0.22471 loss)
I0409 21:03:12.321135 14789 sgd_solver.cpp:105] Iteration 7608, lr = 0.00221565
I0409 21:03:17.252945 14789 solver.cpp:218] Iteration 7620 (2.43324 iter/s, 4.93169s/12 iters), loss = 0.157676
I0409 21:03:17.253001 14789 solver.cpp:237]     Train net output #0: loss = 0.157676 (* 1 = 0.157676 loss)
I0409 21:03:17.253013 14789 sgd_solver.cpp:105] Iteration 7620, lr = 0.00221039
I0409 21:03:19.952569 14789 blocking_queue.cpp:49] Waiting for data
I0409 21:03:22.738885 14789 solver.cpp:218] Iteration 7632 (2.18749 iter/s, 5.48574s/12 iters), loss = 0.375562
I0409 21:03:22.738952 14789 solver.cpp:237]     Train net output #0: loss = 0.375562 (* 1 = 0.375562 loss)
I0409 21:03:22.738968 14789 sgd_solver.cpp:105] Iteration 7632, lr = 0.00220515
I0409 21:03:27.677356 14789 solver.cpp:218] Iteration 7644 (2.43 iter/s, 4.93828s/12 iters), loss = 0.219893
I0409 21:03:27.677409 14789 solver.cpp:237]     Train net output #0: loss = 0.219893 (* 1 = 0.219893 loss)
I0409 21:03:27.677421 14789 sgd_solver.cpp:105] Iteration 7644, lr = 0.00219991
I0409 21:03:29.769595 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7650.caffemodel
I0409 21:03:35.737190 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7650.solverstate
I0409 21:03:40.717761 14789 solver.cpp:330] Iteration 7650, Testing net (#0)
I0409 21:03:40.717787 14789 net.cpp:676] Ignoring source layer train-data
I0409 21:03:42.184336 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:03:45.205324 14789 solver.cpp:397]     Test net output #0: accuracy = 0.541054
I0409 21:03:45.205374 14789 solver.cpp:397]     Test net output #1: loss = 2.43067 (* 1 = 2.43067 loss)
I0409 21:03:47.130260 14789 solver.cpp:218] Iteration 7656 (0.616891 iter/s, 19.4524s/12 iters), loss = 0.284577
I0409 21:03:47.130313 14789 solver.cpp:237]     Train net output #0: loss = 0.284577 (* 1 = 0.284577 loss)
I0409 21:03:47.130322 14789 sgd_solver.cpp:105] Iteration 7656, lr = 0.00219469
I0409 21:03:52.006546 14789 solver.cpp:218] Iteration 7668 (2.46098 iter/s, 4.8761s/12 iters), loss = 0.123877
I0409 21:03:52.008975 14789 solver.cpp:237]     Train net output #0: loss = 0.123877 (* 1 = 0.123877 loss)
I0409 21:03:52.008986 14789 sgd_solver.cpp:105] Iteration 7668, lr = 0.00218948
I0409 21:03:57.140090 14789 solver.cpp:218] Iteration 7680 (2.33873 iter/s, 5.13098s/12 iters), loss = 0.249907
I0409 21:03:57.140146 14789 solver.cpp:237]     Train net output #0: loss = 0.249907 (* 1 = 0.249907 loss)
I0409 21:03:57.140156 14789 sgd_solver.cpp:105] Iteration 7680, lr = 0.00218428
I0409 21:03:59.950829 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:04:02.129038 14789 solver.cpp:218] Iteration 7692 (2.4054 iter/s, 4.98877s/12 iters), loss = 0.247374
I0409 21:04:02.129083 14789 solver.cpp:237]     Train net output #0: loss = 0.247374 (* 1 = 0.247374 loss)
I0409 21:04:02.129093 14789 sgd_solver.cpp:105] Iteration 7692, lr = 0.00217909
I0409 21:04:07.226411 14789 solver.cpp:218] Iteration 7704 (2.35423 iter/s, 5.0972s/12 iters), loss = 0.168633
I0409 21:04:07.226454 14789 solver.cpp:237]     Train net output #0: loss = 0.168633 (* 1 = 0.168633 loss)
I0409 21:04:07.226464 14789 sgd_solver.cpp:105] Iteration 7704, lr = 0.00217392
I0409 21:04:12.182955 14789 solver.cpp:218] Iteration 7716 (2.42113 iter/s, 4.95637s/12 iters), loss = 0.271088
I0409 21:04:12.183001 14789 solver.cpp:237]     Train net output #0: loss = 0.271088 (* 1 = 0.271088 loss)
I0409 21:04:12.183010 14789 sgd_solver.cpp:105] Iteration 7716, lr = 0.00216876
I0409 21:04:17.081607 14789 solver.cpp:218] Iteration 7728 (2.44974 iter/s, 4.89848s/12 iters), loss = 0.161144
I0409 21:04:17.081651 14789 solver.cpp:237]     Train net output #0: loss = 0.161144 (* 1 = 0.161144 loss)
I0409 21:04:17.081660 14789 sgd_solver.cpp:105] Iteration 7728, lr = 0.00216361
I0409 21:04:21.973974 14789 solver.cpp:218] Iteration 7740 (2.45289 iter/s, 4.89218s/12 iters), loss = 0.162223
I0409 21:04:21.974020 14789 solver.cpp:237]     Train net output #0: loss = 0.162223 (* 1 = 0.162223 loss)
I0409 21:04:21.974030 14789 sgd_solver.cpp:105] Iteration 7740, lr = 0.00215847
I0409 21:04:26.597012 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7752.caffemodel
I0409 21:04:30.749706 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7752.solverstate
I0409 21:04:38.038733 14789 solver.cpp:330] Iteration 7752, Testing net (#0)
I0409 21:04:38.038755 14789 net.cpp:676] Ignoring source layer train-data
I0409 21:04:39.452036 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:04:42.544649 14789 solver.cpp:397]     Test net output #0: accuracy = 0.567402
I0409 21:04:42.544680 14789 solver.cpp:397]     Test net output #1: loss = 2.25873 (* 1 = 2.25873 loss)
I0409 21:04:42.636060 14789 solver.cpp:218] Iteration 7752 (0.580789 iter/s, 20.6616s/12 iters), loss = 0.293629
I0409 21:04:42.636106 14789 solver.cpp:237]     Train net output #0: loss = 0.293629 (* 1 = 0.293629 loss)
I0409 21:04:42.636116 14789 sgd_solver.cpp:105] Iteration 7752, lr = 0.00215335
I0409 21:04:47.112437 14789 solver.cpp:218] Iteration 7764 (2.68084 iter/s, 4.47621s/12 iters), loss = 0.25652
I0409 21:04:47.112486 14789 solver.cpp:237]     Train net output #0: loss = 0.25652 (* 1 = 0.25652 loss)
I0409 21:04:47.112498 14789 sgd_solver.cpp:105] Iteration 7764, lr = 0.00214823
I0409 21:04:52.291072 14789 solver.cpp:218] Iteration 7776 (2.3173 iter/s, 5.17845s/12 iters), loss = 0.201707
I0409 21:04:52.291122 14789 solver.cpp:237]     Train net output #0: loss = 0.201707 (* 1 = 0.201707 loss)
I0409 21:04:52.291133 14789 sgd_solver.cpp:105] Iteration 7776, lr = 0.00214313
I0409 21:04:57.242455 14789 solver.cpp:218] Iteration 7788 (2.42365 iter/s, 4.9512s/12 iters), loss = 0.186256
I0409 21:04:57.242594 14789 solver.cpp:237]     Train net output #0: loss = 0.186256 (* 1 = 0.186256 loss)
I0409 21:04:57.242605 14789 sgd_solver.cpp:105] Iteration 7788, lr = 0.00213805
I0409 21:04:57.252725 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:05:02.180518 14789 solver.cpp:218] Iteration 7800 (2.43024 iter/s, 4.93779s/12 iters), loss = 0.270547
I0409 21:05:02.180573 14789 solver.cpp:237]     Train net output #0: loss = 0.270547 (* 1 = 0.270547 loss)
I0409 21:05:02.180585 14789 sgd_solver.cpp:105] Iteration 7800, lr = 0.00213297
I0409 21:05:07.097846 14789 solver.cpp:218] Iteration 7812 (2.44044 iter/s, 4.91714s/12 iters), loss = 0.165432
I0409 21:05:07.097904 14789 solver.cpp:237]     Train net output #0: loss = 0.165432 (* 1 = 0.165432 loss)
I0409 21:05:07.097919 14789 sgd_solver.cpp:105] Iteration 7812, lr = 0.00212791
I0409 21:05:11.981765 14789 solver.cpp:218] Iteration 7824 (2.45713 iter/s, 4.88374s/12 iters), loss = 0.296965
I0409 21:05:11.981804 14789 solver.cpp:237]     Train net output #0: loss = 0.296965 (* 1 = 0.296965 loss)
I0409 21:05:11.981813 14789 sgd_solver.cpp:105] Iteration 7824, lr = 0.00212285
I0409 21:05:16.794076 14789 solver.cpp:218] Iteration 7836 (2.4937 iter/s, 4.81214s/12 iters), loss = 0.218541
I0409 21:05:16.794137 14789 solver.cpp:237]     Train net output #0: loss = 0.218541 (* 1 = 0.218541 loss)
I0409 21:05:16.794149 14789 sgd_solver.cpp:105] Iteration 7836, lr = 0.00211781
I0409 21:05:21.743650 14789 solver.cpp:218] Iteration 7848 (2.42454 iter/s, 4.94938s/12 iters), loss = 0.139323
I0409 21:05:21.743703 14789 solver.cpp:237]     Train net output #0: loss = 0.139323 (* 1 = 0.139323 loss)
I0409 21:05:21.743714 14789 sgd_solver.cpp:105] Iteration 7848, lr = 0.00211279
I0409 21:05:23.652140 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7854.caffemodel
I0409 21:05:27.397559 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7854.solverstate
I0409 21:05:32.295290 14789 solver.cpp:330] Iteration 7854, Testing net (#0)
I0409 21:05:32.295315 14789 net.cpp:676] Ignoring source layer train-data
I0409 21:05:33.686476 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:05:36.800283 14789 solver.cpp:397]     Test net output #0: accuracy = 0.568015
I0409 21:05:36.800335 14789 solver.cpp:397]     Test net output #1: loss = 2.28631 (* 1 = 2.28631 loss)
I0409 21:05:38.602527 14789 solver.cpp:218] Iteration 7860 (0.711811 iter/s, 16.8584s/12 iters), loss = 0.161377
I0409 21:05:38.602583 14789 solver.cpp:237]     Train net output #0: loss = 0.161377 (* 1 = 0.161377 loss)
I0409 21:05:38.602594 14789 sgd_solver.cpp:105] Iteration 7860, lr = 0.00210777
I0409 21:05:43.431332 14789 solver.cpp:218] Iteration 7872 (2.48518 iter/s, 4.82862s/12 iters), loss = 0.173878
I0409 21:05:43.431380 14789 solver.cpp:237]     Train net output #0: loss = 0.173878 (* 1 = 0.173878 loss)
I0409 21:05:43.431389 14789 sgd_solver.cpp:105] Iteration 7872, lr = 0.00210277
I0409 21:05:48.494012 14789 solver.cpp:218] Iteration 7884 (2.37037 iter/s, 5.0625s/12 iters), loss = 0.179846
I0409 21:05:48.494060 14789 solver.cpp:237]     Train net output #0: loss = 0.179846 (* 1 = 0.179846 loss)
I0409 21:05:48.494068 14789 sgd_solver.cpp:105] Iteration 7884, lr = 0.00209777
I0409 21:05:50.446995 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:05:53.172912 14789 solver.cpp:218] Iteration 7896 (2.5648 iter/s, 4.67873s/12 iters), loss = 0.171176
I0409 21:05:53.172971 14789 solver.cpp:237]     Train net output #0: loss = 0.171176 (* 1 = 0.171176 loss)
I0409 21:05:53.172982 14789 sgd_solver.cpp:105] Iteration 7896, lr = 0.00209279
I0409 21:05:57.831712 14789 solver.cpp:218] Iteration 7908 (2.57587 iter/s, 4.65862s/12 iters), loss = 0.232322
I0409 21:05:57.831838 14789 solver.cpp:237]     Train net output #0: loss = 0.232322 (* 1 = 0.232322 loss)
I0409 21:05:57.831848 14789 sgd_solver.cpp:105] Iteration 7908, lr = 0.00208782
I0409 21:06:02.627321 14789 solver.cpp:218] Iteration 7920 (2.50242 iter/s, 4.79535s/12 iters), loss = 0.155894
I0409 21:06:02.627375 14789 solver.cpp:237]     Train net output #0: loss = 0.155894 (* 1 = 0.155894 loss)
I0409 21:06:02.627389 14789 sgd_solver.cpp:105] Iteration 7920, lr = 0.00208287
I0409 21:06:07.603863 14789 solver.cpp:218] Iteration 7932 (2.4114 iter/s, 4.97636s/12 iters), loss = 0.201934
I0409 21:06:07.603914 14789 solver.cpp:237]     Train net output #0: loss = 0.201934 (* 1 = 0.201934 loss)
I0409 21:06:07.603924 14789 sgd_solver.cpp:105] Iteration 7932, lr = 0.00207792
I0409 21:06:12.823158 14789 solver.cpp:218] Iteration 7944 (2.29924 iter/s, 5.21911s/12 iters), loss = 0.270128
I0409 21:06:12.823204 14789 solver.cpp:237]     Train net output #0: loss = 0.270128 (* 1 = 0.270128 loss)
I0409 21:06:12.823213 14789 sgd_solver.cpp:105] Iteration 7944, lr = 0.00207299
I0409 21:06:17.256927 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7956.caffemodel
I0409 21:06:21.106829 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7956.solverstate
I0409 21:06:24.104715 14789 solver.cpp:330] Iteration 7956, Testing net (#0)
I0409 21:06:24.104738 14789 net.cpp:676] Ignoring source layer train-data
I0409 21:06:25.565188 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:06:28.755187 14789 solver.cpp:397]     Test net output #0: accuracy = 0.558211
I0409 21:06:28.755287 14789 solver.cpp:397]     Test net output #1: loss = 2.23056 (* 1 = 2.23056 loss)
I0409 21:06:28.847050 14789 solver.cpp:218] Iteration 7956 (0.748902 iter/s, 16.0235s/12 iters), loss = 0.211416
I0409 21:06:28.847102 14789 solver.cpp:237]     Train net output #0: loss = 0.211416 (* 1 = 0.211416 loss)
I0409 21:06:28.847115 14789 sgd_solver.cpp:105] Iteration 7956, lr = 0.00206807
I0409 21:06:33.053369 14789 solver.cpp:218] Iteration 7968 (2.85296 iter/s, 4.20615s/12 iters), loss = 0.107148
I0409 21:06:33.053414 14789 solver.cpp:237]     Train net output #0: loss = 0.107148 (* 1 = 0.107148 loss)
I0409 21:06:33.053424 14789 sgd_solver.cpp:105] Iteration 7968, lr = 0.00206316
I0409 21:06:38.039912 14789 solver.cpp:218] Iteration 7980 (2.40656 iter/s, 4.98636s/12 iters), loss = 0.125398
I0409 21:06:38.039963 14789 solver.cpp:237]     Train net output #0: loss = 0.125398 (* 1 = 0.125398 loss)
I0409 21:06:38.039976 14789 sgd_solver.cpp:105] Iteration 7980, lr = 0.00205826
I0409 21:06:42.229387 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:06:43.033586 14789 solver.cpp:218] Iteration 7992 (2.40313 iter/s, 4.99349s/12 iters), loss = 0.175986
I0409 21:06:43.033629 14789 solver.cpp:237]     Train net output #0: loss = 0.175986 (* 1 = 0.175986 loss)
I0409 21:06:43.033639 14789 sgd_solver.cpp:105] Iteration 7992, lr = 0.00205337
I0409 21:06:48.257236 14789 solver.cpp:218] Iteration 8004 (2.29732 iter/s, 5.22347s/12 iters), loss = 0.136075
I0409 21:06:48.257284 14789 solver.cpp:237]     Train net output #0: loss = 0.136075 (* 1 = 0.136075 loss)
I0409 21:06:48.257295 14789 sgd_solver.cpp:105] Iteration 8004, lr = 0.0020485
I0409 21:06:53.635150 14789 solver.cpp:218] Iteration 8016 (2.23143 iter/s, 5.37772s/12 iters), loss = 0.139182
I0409 21:06:53.635206 14789 solver.cpp:237]     Train net output #0: loss = 0.139182 (* 1 = 0.139182 loss)
I0409 21:06:53.635218 14789 sgd_solver.cpp:105] Iteration 8016, lr = 0.00204363
I0409 21:06:58.822923 14789 solver.cpp:218] Iteration 8028 (2.31322 iter/s, 5.18758s/12 iters), loss = 0.158098
I0409 21:06:58.823046 14789 solver.cpp:237]     Train net output #0: loss = 0.158098 (* 1 = 0.158098 loss)
I0409 21:06:58.823060 14789 sgd_solver.cpp:105] Iteration 8028, lr = 0.00203878
I0409 21:07:03.612205 14789 solver.cpp:218] Iteration 8040 (2.50572 iter/s, 4.78903s/12 iters), loss = 0.18275
I0409 21:07:03.612258 14789 solver.cpp:237]     Train net output #0: loss = 0.18275 (* 1 = 0.18275 loss)
I0409 21:07:03.612272 14789 sgd_solver.cpp:105] Iteration 8040, lr = 0.00203394
I0409 21:07:08.625885 14789 solver.cpp:218] Iteration 8052 (2.39354 iter/s, 5.0135s/12 iters), loss = 0.099245
I0409 21:07:08.625938 14789 solver.cpp:237]     Train net output #0: loss = 0.099245 (* 1 = 0.099245 loss)
I0409 21:07:08.625950 14789 sgd_solver.cpp:105] Iteration 8052, lr = 0.00202911
I0409 21:07:10.592840 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8058.caffemodel
I0409 21:07:14.457793 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8058.solverstate
I0409 21:07:17.436889 14789 solver.cpp:330] Iteration 8058, Testing net (#0)
I0409 21:07:17.436913 14789 net.cpp:676] Ignoring source layer train-data
I0409 21:07:18.728579 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:07:22.202179 14789 solver.cpp:397]     Test net output #0: accuracy = 0.536765
I0409 21:07:22.202219 14789 solver.cpp:397]     Test net output #1: loss = 2.3215 (* 1 = 2.3215 loss)
I0409 21:07:24.224171 14789 solver.cpp:218] Iteration 8064 (0.769337 iter/s, 15.5979s/12 iters), loss = 0.168672
I0409 21:07:24.224222 14789 solver.cpp:237]     Train net output #0: loss = 0.168672 (* 1 = 0.168672 loss)
I0409 21:07:24.224233 14789 sgd_solver.cpp:105] Iteration 8064, lr = 0.00202429
I0409 21:07:29.190433 14789 solver.cpp:218] Iteration 8076 (2.41639 iter/s, 4.96608s/12 iters), loss = 0.254527
I0409 21:07:29.190539 14789 solver.cpp:237]     Train net output #0: loss = 0.254527 (* 1 = 0.254527 loss)
I0409 21:07:29.190551 14789 sgd_solver.cpp:105] Iteration 8076, lr = 0.00201949
I0409 21:07:34.001996 14789 solver.cpp:218] Iteration 8088 (2.49412 iter/s, 4.81131s/12 iters), loss = 0.220242
I0409 21:07:34.002053 14789 solver.cpp:237]     Train net output #0: loss = 0.220242 (* 1 = 0.220242 loss)
I0409 21:07:34.002065 14789 sgd_solver.cpp:105] Iteration 8088, lr = 0.00201469
I0409 21:07:35.357787 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:07:38.793610 14789 solver.cpp:218] Iteration 8100 (2.50447 iter/s, 4.79143s/12 iters), loss = 0.144548
I0409 21:07:38.793668 14789 solver.cpp:237]     Train net output #0: loss = 0.144548 (* 1 = 0.144548 loss)
I0409 21:07:38.793679 14789 sgd_solver.cpp:105] Iteration 8100, lr = 0.00200991
I0409 21:07:43.841686 14789 solver.cpp:218] Iteration 8112 (2.37723 iter/s, 5.04788s/12 iters), loss = 0.314469
I0409 21:07:43.841742 14789 solver.cpp:237]     Train net output #0: loss = 0.314469 (* 1 = 0.314469 loss)
I0409 21:07:43.841755 14789 sgd_solver.cpp:105] Iteration 8112, lr = 0.00200514
I0409 21:07:48.599808 14789 solver.cpp:218] Iteration 8124 (2.5221 iter/s, 4.75794s/12 iters), loss = 0.158398
I0409 21:07:48.599856 14789 solver.cpp:237]     Train net output #0: loss = 0.158398 (* 1 = 0.158398 loss)
I0409 21:07:48.599865 14789 sgd_solver.cpp:105] Iteration 8124, lr = 0.00200038
I0409 21:07:53.561209 14789 solver.cpp:218] Iteration 8136 (2.41876 iter/s, 4.96122s/12 iters), loss = 0.138158
I0409 21:07:53.561265 14789 solver.cpp:237]     Train net output #0: loss = 0.138158 (* 1 = 0.138158 loss)
I0409 21:07:53.561277 14789 sgd_solver.cpp:105] Iteration 8136, lr = 0.00199563
I0409 21:07:58.471887 14789 solver.cpp:218] Iteration 8148 (2.44375 iter/s, 4.91049s/12 iters), loss = 0.068393
I0409 21:07:58.471940 14789 solver.cpp:237]     Train net output #0: loss = 0.068393 (* 1 = 0.068393 loss)
I0409 21:07:58.471949 14789 sgd_solver.cpp:105] Iteration 8148, lr = 0.00199089
I0409 21:08:02.895488 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8160.caffemodel
I0409 21:08:07.108387 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8160.solverstate
I0409 21:08:11.431488 14789 solver.cpp:330] Iteration 8160, Testing net (#0)
I0409 21:08:11.431514 14789 net.cpp:676] Ignoring source layer train-data
I0409 21:08:12.884285 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:08:16.397828 14789 solver.cpp:397]     Test net output #0: accuracy = 0.550858
I0409 21:08:16.397873 14789 solver.cpp:397]     Test net output #1: loss = 2.25036 (* 1 = 2.25036 loss)
I0409 21:08:16.489720 14789 solver.cpp:218] Iteration 8160 (0.666025 iter/s, 18.0173s/12 iters), loss = 0.0682073
I0409 21:08:16.489768 14789 solver.cpp:237]     Train net output #0: loss = 0.0682073 (* 1 = 0.0682073 loss)
I0409 21:08:16.489779 14789 sgd_solver.cpp:105] Iteration 8160, lr = 0.00198616
I0409 21:08:20.737136 14789 solver.cpp:218] Iteration 8172 (2.82535 iter/s, 4.24726s/12 iters), loss = 0.143727
I0409 21:08:20.737183 14789 solver.cpp:237]     Train net output #0: loss = 0.143727 (* 1 = 0.143727 loss)
I0409 21:08:20.737192 14789 sgd_solver.cpp:105] Iteration 8172, lr = 0.00198145
I0409 21:08:25.648860 14789 solver.cpp:218] Iteration 8184 (2.44322 iter/s, 4.91155s/12 iters), loss = 0.169799
I0409 21:08:25.648903 14789 solver.cpp:237]     Train net output #0: loss = 0.169799 (* 1 = 0.169799 loss)
I0409 21:08:25.648913 14789 sgd_solver.cpp:105] Iteration 8184, lr = 0.00197674
I0409 21:08:29.024536 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:08:30.451450 14789 solver.cpp:218] Iteration 8196 (2.49874 iter/s, 4.80242s/12 iters), loss = 0.155691
I0409 21:08:30.451503 14789 solver.cpp:237]     Train net output #0: loss = 0.155691 (* 1 = 0.155691 loss)
I0409 21:08:30.451511 14789 sgd_solver.cpp:105] Iteration 8196, lr = 0.00197205
I0409 21:08:35.382705 14789 solver.cpp:218] Iteration 8208 (2.43355 iter/s, 4.93107s/12 iters), loss = 0.0782599
I0409 21:08:35.382815 14789 solver.cpp:237]     Train net output #0: loss = 0.0782599 (* 1 = 0.0782599 loss)
I0409 21:08:35.382830 14789 sgd_solver.cpp:105] Iteration 8208, lr = 0.00196737
I0409 21:08:40.237182 14789 solver.cpp:218] Iteration 8220 (2.47207 iter/s, 4.85424s/12 iters), loss = 0.226434
I0409 21:08:40.237229 14789 solver.cpp:237]     Train net output #0: loss = 0.226434 (* 1 = 0.226434 loss)
I0409 21:08:40.237241 14789 sgd_solver.cpp:105] Iteration 8220, lr = 0.0019627
I0409 21:08:45.529199 14789 solver.cpp:218] Iteration 8232 (2.26765 iter/s, 5.29183s/12 iters), loss = 0.153357
I0409 21:08:45.529247 14789 solver.cpp:237]     Train net output #0: loss = 0.153357 (* 1 = 0.153357 loss)
I0409 21:08:45.529258 14789 sgd_solver.cpp:105] Iteration 8232, lr = 0.00195804
I0409 21:08:50.357836 14789 solver.cpp:218] Iteration 8244 (2.48527 iter/s, 4.82846s/12 iters), loss = 0.166025
I0409 21:08:50.357887 14789 solver.cpp:237]     Train net output #0: loss = 0.166025 (* 1 = 0.166025 loss)
I0409 21:08:50.357897 14789 sgd_solver.cpp:105] Iteration 8244, lr = 0.00195339
I0409 21:08:55.167744 14789 solver.cpp:218] Iteration 8256 (2.49494 iter/s, 4.80973s/12 iters), loss = 0.1433
I0409 21:08:55.167793 14789 solver.cpp:237]     Train net output #0: loss = 0.1433 (* 1 = 0.1433 loss)
I0409 21:08:55.167804 14789 sgd_solver.cpp:105] Iteration 8256, lr = 0.00194875
I0409 21:08:57.119233 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8262.caffemodel
I0409 21:09:03.211270 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8262.solverstate
I0409 21:09:07.092144 14789 solver.cpp:330] Iteration 8262, Testing net (#0)
I0409 21:09:07.092263 14789 net.cpp:676] Ignoring source layer train-data
I0409 21:09:08.216552 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:09:11.538336 14789 solver.cpp:397]     Test net output #0: accuracy = 0.571078
I0409 21:09:11.538378 14789 solver.cpp:397]     Test net output #1: loss = 2.21883 (* 1 = 2.21883 loss)
I0409 21:09:13.187201 14789 solver.cpp:218] Iteration 8268 (0.665965 iter/s, 18.019s/12 iters), loss = 0.131672
I0409 21:09:13.187247 14789 solver.cpp:237]     Train net output #0: loss = 0.131672 (* 1 = 0.131672 loss)
I0409 21:09:13.187256 14789 sgd_solver.cpp:105] Iteration 8268, lr = 0.00194412
I0409 21:09:17.975244 14789 solver.cpp:218] Iteration 8280 (2.50634 iter/s, 4.78786s/12 iters), loss = 0.103999
I0409 21:09:17.975301 14789 solver.cpp:237]     Train net output #0: loss = 0.103999 (* 1 = 0.103999 loss)
I0409 21:09:17.975313 14789 sgd_solver.cpp:105] Iteration 8280, lr = 0.00193951
I0409 21:09:23.060433 14789 solver.cpp:218] Iteration 8292 (2.35988 iter/s, 5.085s/12 iters), loss = 0.138153
I0409 21:09:23.060482 14789 solver.cpp:237]     Train net output #0: loss = 0.138153 (* 1 = 0.138153 loss)
I0409 21:09:23.060493 14789 sgd_solver.cpp:105] Iteration 8292, lr = 0.0019349
I0409 21:09:23.648321 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:09:27.739454 14789 solver.cpp:218] Iteration 8304 (2.56474 iter/s, 4.67884s/12 iters), loss = 0.121828
I0409 21:09:27.739508 14789 solver.cpp:237]     Train net output #0: loss = 0.121828 (* 1 = 0.121828 loss)
I0409 21:09:27.739521 14789 sgd_solver.cpp:105] Iteration 8304, lr = 0.00193031
I0409 21:09:30.725849 14789 blocking_queue.cpp:49] Waiting for data
I0409 21:09:32.862272 14789 solver.cpp:218] Iteration 8316 (2.34255 iter/s, 5.12262s/12 iters), loss = 0.194448
I0409 21:09:32.862332 14789 solver.cpp:237]     Train net output #0: loss = 0.194448 (* 1 = 0.194448 loss)
I0409 21:09:32.862346 14789 sgd_solver.cpp:105] Iteration 8316, lr = 0.00192573
I0409 21:09:37.836511 14789 solver.cpp:218] Iteration 8328 (2.41252 iter/s, 4.97404s/12 iters), loss = 0.064737
I0409 21:09:37.836649 14789 solver.cpp:237]     Train net output #0: loss = 0.064737 (* 1 = 0.064737 loss)
I0409 21:09:37.836663 14789 sgd_solver.cpp:105] Iteration 8328, lr = 0.00192115
I0409 21:09:42.764273 14789 solver.cpp:218] Iteration 8340 (2.43531 iter/s, 4.9275s/12 iters), loss = 0.126515
I0409 21:09:42.764318 14789 solver.cpp:237]     Train net output #0: loss = 0.126515 (* 1 = 0.126515 loss)
I0409 21:09:42.764326 14789 sgd_solver.cpp:105] Iteration 8340, lr = 0.00191659
I0409 21:09:48.169574 14789 solver.cpp:218] Iteration 8352 (2.22012 iter/s, 5.40511s/12 iters), loss = 0.167808
I0409 21:09:48.169621 14789 solver.cpp:237]     Train net output #0: loss = 0.167808 (* 1 = 0.167808 loss)
I0409 21:09:48.169632 14789 sgd_solver.cpp:105] Iteration 8352, lr = 0.00191204
I0409 21:09:52.839793 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8364.caffemodel
I0409 21:09:57.291545 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8364.solverstate
I0409 21:10:00.274407 14789 solver.cpp:330] Iteration 8364, Testing net (#0)
I0409 21:10:00.274430 14789 net.cpp:676] Ignoring source layer train-data
I0409 21:10:01.495874 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:10:04.972692 14789 solver.cpp:397]     Test net output #0: accuracy = 0.566176
I0409 21:10:04.972744 14789 solver.cpp:397]     Test net output #1: loss = 2.26965 (* 1 = 2.26965 loss)
I0409 21:10:05.064903 14789 solver.cpp:218] Iteration 8364 (0.710275 iter/s, 16.8949s/12 iters), loss = 0.172759
I0409 21:10:05.064958 14789 solver.cpp:237]     Train net output #0: loss = 0.172759 (* 1 = 0.172759 loss)
I0409 21:10:05.064970 14789 sgd_solver.cpp:105] Iteration 8364, lr = 0.0019075
I0409 21:10:09.390437 14789 solver.cpp:218] Iteration 8376 (2.77433 iter/s, 4.32536s/12 iters), loss = 0.0680164
I0409 21:10:09.390614 14789 solver.cpp:237]     Train net output #0: loss = 0.0680164 (* 1 = 0.0680164 loss)
I0409 21:10:09.390630 14789 sgd_solver.cpp:105] Iteration 8376, lr = 0.00190297
I0409 21:10:14.907258 14789 solver.cpp:218] Iteration 8388 (2.17529 iter/s, 5.5165s/12 iters), loss = 0.173841
I0409 21:10:14.907306 14789 solver.cpp:237]     Train net output #0: loss = 0.173841 (* 1 = 0.173841 loss)
I0409 21:10:14.907315 14789 sgd_solver.cpp:105] Iteration 8388, lr = 0.00189846
I0409 21:10:17.781915 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:10:20.040882 14789 solver.cpp:218] Iteration 8400 (2.33762 iter/s, 5.13343s/12 iters), loss = 0.179574
I0409 21:10:20.040948 14789 solver.cpp:237]     Train net output #0: loss = 0.179574 (* 1 = 0.179574 loss)
I0409 21:10:20.040966 14789 sgd_solver.cpp:105] Iteration 8400, lr = 0.00189395
I0409 21:10:24.893297 14789 solver.cpp:218] Iteration 8412 (2.4731 iter/s, 4.85222s/12 iters), loss = 0.10208
I0409 21:10:24.893354 14789 solver.cpp:237]     Train net output #0: loss = 0.10208 (* 1 = 0.10208 loss)
I0409 21:10:24.893368 14789 sgd_solver.cpp:105] Iteration 8412, lr = 0.00188945
I0409 21:10:29.633550 14789 solver.cpp:218] Iteration 8424 (2.53161 iter/s, 4.74007s/12 iters), loss = 0.0671573
I0409 21:10:29.633605 14789 solver.cpp:237]     Train net output #0: loss = 0.0671573 (* 1 = 0.0671573 loss)
I0409 21:10:29.633616 14789 sgd_solver.cpp:105] Iteration 8424, lr = 0.00188497
I0409 21:10:34.560149 14789 solver.cpp:218] Iteration 8436 (2.43585 iter/s, 4.92641s/12 iters), loss = 0.117697
I0409 21:10:34.560214 14789 solver.cpp:237]     Train net output #0: loss = 0.117697 (* 1 = 0.117697 loss)
I0409 21:10:34.560230 14789 sgd_solver.cpp:105] Iteration 8436, lr = 0.00188049
I0409 21:10:39.538686 14789 solver.cpp:218] Iteration 8448 (2.41044 iter/s, 4.97834s/12 iters), loss = 0.0959334
I0409 21:10:39.539297 14789 solver.cpp:237]     Train net output #0: loss = 0.0959334 (* 1 = 0.0959334 loss)
I0409 21:10:39.539309 14789 sgd_solver.cpp:105] Iteration 8448, lr = 0.00187603
I0409 21:10:44.343340 14789 solver.cpp:218] Iteration 8460 (2.49796 iter/s, 4.80391s/12 iters), loss = 0.156654
I0409 21:10:44.343402 14789 solver.cpp:237]     Train net output #0: loss = 0.156654 (* 1 = 0.156654 loss)
I0409 21:10:44.343418 14789 sgd_solver.cpp:105] Iteration 8460, lr = 0.00187157
I0409 21:10:46.296623 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8466.caffemodel
I0409 21:10:50.092922 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8466.solverstate
I0409 21:10:53.059412 14789 solver.cpp:330] Iteration 8466, Testing net (#0)
I0409 21:10:53.059433 14789 net.cpp:676] Ignoring source layer train-data
I0409 21:10:54.202306 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:10:57.550961 14789 solver.cpp:397]     Test net output #0: accuracy = 0.566176
I0409 21:10:57.550992 14789 solver.cpp:397]     Test net output #1: loss = 2.23435 (* 1 = 2.23435 loss)
I0409 21:10:59.510917 14789 solver.cpp:218] Iteration 8472 (0.791184 iter/s, 15.1671s/12 iters), loss = 0.150264
I0409 21:10:59.510967 14789 solver.cpp:237]     Train net output #0: loss = 0.150264 (* 1 = 0.150264 loss)
I0409 21:10:59.510975 14789 sgd_solver.cpp:105] Iteration 8472, lr = 0.00186713
I0409 21:11:04.419864 14789 solver.cpp:218] Iteration 8484 (2.44461 iter/s, 4.90877s/12 iters), loss = 0.260313
I0409 21:11:04.419915 14789 solver.cpp:237]     Train net output #0: loss = 0.260313 (* 1 = 0.260313 loss)
I0409 21:11:04.419927 14789 sgd_solver.cpp:105] Iteration 8484, lr = 0.0018627
I0409 21:11:09.349893 14789 solver.cpp:218] Iteration 8496 (2.43415 iter/s, 4.92985s/12 iters), loss = 0.154858
I0409 21:11:09.349943 14789 solver.cpp:237]     Train net output #0: loss = 0.154858 (* 1 = 0.154858 loss)
I0409 21:11:09.349973 14789 sgd_solver.cpp:105] Iteration 8496, lr = 0.00185827
I0409 21:11:09.398754 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:11:14.669307 14789 solver.cpp:218] Iteration 8508 (2.25597 iter/s, 5.31922s/12 iters), loss = 0.0822637
I0409 21:11:14.669436 14789 solver.cpp:237]     Train net output #0: loss = 0.0822637 (* 1 = 0.0822637 loss)
I0409 21:11:14.669447 14789 sgd_solver.cpp:105] Iteration 8508, lr = 0.00185386
I0409 21:11:19.793994 14789 solver.cpp:218] Iteration 8520 (2.34173 iter/s, 5.12442s/12 iters), loss = 0.12523
I0409 21:11:19.794049 14789 solver.cpp:237]     Train net output #0: loss = 0.12523 (* 1 = 0.12523 loss)
I0409 21:11:19.794061 14789 sgd_solver.cpp:105] Iteration 8520, lr = 0.00184946
I0409 21:11:24.633555 14789 solver.cpp:218] Iteration 8532 (2.47966 iter/s, 4.83937s/12 iters), loss = 0.054611
I0409 21:11:24.633605 14789 solver.cpp:237]     Train net output #0: loss = 0.054611 (* 1 = 0.054611 loss)
I0409 21:11:24.633616 14789 sgd_solver.cpp:105] Iteration 8532, lr = 0.00184507
I0409 21:11:29.489537 14789 solver.cpp:218] Iteration 8544 (2.47127 iter/s, 4.8558s/12 iters), loss = 0.120826
I0409 21:11:29.489584 14789 solver.cpp:237]     Train net output #0: loss = 0.120826 (* 1 = 0.120826 loss)
I0409 21:11:29.489594 14789 sgd_solver.cpp:105] Iteration 8544, lr = 0.00184069
I0409 21:11:34.253537 14789 solver.cpp:218] Iteration 8556 (2.51899 iter/s, 4.76382s/12 iters), loss = 0.116358
I0409 21:11:34.253598 14789 solver.cpp:237]     Train net output #0: loss = 0.116358 (* 1 = 0.116358 loss)
I0409 21:11:34.253613 14789 sgd_solver.cpp:105] Iteration 8556, lr = 0.00183632
I0409 21:11:38.743906 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8568.caffemodel
I0409 21:11:42.533787 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8568.solverstate
I0409 21:11:45.604579 14789 solver.cpp:330] Iteration 8568, Testing net (#0)
I0409 21:11:45.604635 14789 net.cpp:676] Ignoring source layer train-data
I0409 21:11:46.731058 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:11:50.175559 14789 solver.cpp:397]     Test net output #0: accuracy = 0.573529
I0409 21:11:50.175608 14789 solver.cpp:397]     Test net output #1: loss = 2.31883 (* 1 = 2.31883 loss)
I0409 21:11:50.267371 14789 solver.cpp:218] Iteration 8568 (0.749373 iter/s, 16.0134s/12 iters), loss = 0.0777587
I0409 21:11:50.267426 14789 solver.cpp:237]     Train net output #0: loss = 0.0777588 (* 1 = 0.0777588 loss)
I0409 21:11:50.267437 14789 sgd_solver.cpp:105] Iteration 8568, lr = 0.00183196
I0409 21:11:54.349828 14789 solver.cpp:218] Iteration 8580 (2.93953 iter/s, 4.08229s/12 iters), loss = 0.174625
I0409 21:11:54.349875 14789 solver.cpp:237]     Train net output #0: loss = 0.174625 (* 1 = 0.174625 loss)
I0409 21:11:54.349886 14789 sgd_solver.cpp:105] Iteration 8580, lr = 0.00182761
I0409 21:11:59.330967 14789 solver.cpp:218] Iteration 8592 (2.40917 iter/s, 4.98096s/12 iters), loss = 0.0868388
I0409 21:11:59.331009 14789 solver.cpp:237]     Train net output #0: loss = 0.0868389 (* 1 = 0.0868389 loss)
I0409 21:11:59.331019 14789 sgd_solver.cpp:105] Iteration 8592, lr = 0.00182327
I0409 21:12:01.567884 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:12:04.518312 14789 solver.cpp:218] Iteration 8604 (2.3134 iter/s, 5.18716s/12 iters), loss = 0.123529
I0409 21:12:04.518363 14789 solver.cpp:237]     Train net output #0: loss = 0.123529 (* 1 = 0.123529 loss)
I0409 21:12:04.518374 14789 sgd_solver.cpp:105] Iteration 8604, lr = 0.00181894
I0409 21:12:09.628562 14789 solver.cpp:218] Iteration 8616 (2.34831 iter/s, 5.11006s/12 iters), loss = 0.0524486
I0409 21:12:09.628616 14789 solver.cpp:237]     Train net output #0: loss = 0.0524486 (* 1 = 0.0524486 loss)
I0409 21:12:09.628628 14789 sgd_solver.cpp:105] Iteration 8616, lr = 0.00181462
I0409 21:12:14.384255 14789 solver.cpp:218] Iteration 8628 (2.52339 iter/s, 4.75551s/12 iters), loss = 0.0779247
I0409 21:12:14.384306 14789 solver.cpp:237]     Train net output #0: loss = 0.0779248 (* 1 = 0.0779248 loss)
I0409 21:12:14.384317 14789 sgd_solver.cpp:105] Iteration 8628, lr = 0.00181031
I0409 21:12:19.184250 14789 solver.cpp:218] Iteration 8640 (2.5001 iter/s, 4.79981s/12 iters), loss = 0.0961382
I0409 21:12:19.184371 14789 solver.cpp:237]     Train net output #0: loss = 0.0961382 (* 1 = 0.0961382 loss)
I0409 21:12:19.184386 14789 sgd_solver.cpp:105] Iteration 8640, lr = 0.00180602
I0409 21:12:24.286141 14789 solver.cpp:218] Iteration 8652 (2.35219 iter/s, 5.10164s/12 iters), loss = 0.0560353
I0409 21:12:24.286190 14789 solver.cpp:237]     Train net output #0: loss = 0.0560354 (* 1 = 0.0560354 loss)
I0409 21:12:24.286198 14789 sgd_solver.cpp:105] Iteration 8652, lr = 0.00180173
I0409 21:12:29.145584 14789 solver.cpp:218] Iteration 8664 (2.46951 iter/s, 4.85926s/12 iters), loss = 0.131895
I0409 21:12:29.145637 14789 solver.cpp:237]     Train net output #0: loss = 0.131895 (* 1 = 0.131895 loss)
I0409 21:12:29.145649 14789 sgd_solver.cpp:105] Iteration 8664, lr = 0.00179745
I0409 21:12:31.237610 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8670.caffemodel
I0409 21:12:36.950767 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8670.solverstate
I0409 21:12:39.924793 14789 solver.cpp:330] Iteration 8670, Testing net (#0)
I0409 21:12:39.924818 14789 net.cpp:676] Ignoring source layer train-data
I0409 21:12:41.003191 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:12:44.399776 14789 solver.cpp:397]     Test net output #0: accuracy = 0.5625
I0409 21:12:44.399819 14789 solver.cpp:397]     Test net output #1: loss = 2.37342 (* 1 = 2.37342 loss)
I0409 21:12:46.097308 14789 solver.cpp:218] Iteration 8676 (0.707913 iter/s, 16.9512s/12 iters), loss = 0.107312
I0409 21:12:46.097365 14789 solver.cpp:237]     Train net output #0: loss = 0.107312 (* 1 = 0.107312 loss)
I0409 21:12:46.097378 14789 sgd_solver.cpp:105] Iteration 8676, lr = 0.00179318
I0409 21:12:51.012310 14789 solver.cpp:218] Iteration 8688 (2.4416 iter/s, 4.91481s/12 iters), loss = 0.128057
I0409 21:12:51.012388 14789 solver.cpp:237]     Train net output #0: loss = 0.128057 (* 1 = 0.128057 loss)
I0409 21:12:51.012400 14789 sgd_solver.cpp:105] Iteration 8688, lr = 0.00178893
I0409 21:12:55.322930 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:12:56.094182 14789 solver.cpp:218] Iteration 8700 (2.36144 iter/s, 5.08165s/12 iters), loss = 0.096281
I0409 21:12:56.094241 14789 solver.cpp:237]     Train net output #0: loss = 0.096281 (* 1 = 0.096281 loss)
I0409 21:12:56.094254 14789 sgd_solver.cpp:105] Iteration 8700, lr = 0.00178468
I0409 21:13:01.154850 14789 solver.cpp:218] Iteration 8712 (2.37132 iter/s, 5.06047s/12 iters), loss = 0.208367
I0409 21:13:01.154894 14789 solver.cpp:237]     Train net output #0: loss = 0.208367 (* 1 = 0.208367 loss)
I0409 21:13:01.154903 14789 sgd_solver.cpp:105] Iteration 8712, lr = 0.00178044
I0409 21:13:06.397739 14789 solver.cpp:218] Iteration 8724 (2.2889 iter/s, 5.2427s/12 iters), loss = 0.219011
I0409 21:13:06.397789 14789 solver.cpp:237]     Train net output #0: loss = 0.219011 (* 1 = 0.219011 loss)
I0409 21:13:06.397799 14789 sgd_solver.cpp:105] Iteration 8724, lr = 0.00177621
I0409 21:13:11.377568 14789 solver.cpp:218] Iteration 8736 (2.40982 iter/s, 4.97963s/12 iters), loss = 0.207008
I0409 21:13:11.377633 14789 solver.cpp:237]     Train net output #0: loss = 0.207008 (* 1 = 0.207008 loss)
I0409 21:13:11.377645 14789 sgd_solver.cpp:105] Iteration 8736, lr = 0.001772
I0409 21:13:16.262780 14789 solver.cpp:218] Iteration 8748 (2.45649 iter/s, 4.88502s/12 iters), loss = 0.0786102
I0409 21:13:16.262833 14789 solver.cpp:237]     Train net output #0: loss = 0.0786102 (* 1 = 0.0786102 loss)
I0409 21:13:16.262843 14789 sgd_solver.cpp:105] Iteration 8748, lr = 0.00176779
I0409 21:13:21.573132 14789 solver.cpp:218] Iteration 8760 (2.25982 iter/s, 5.31015s/12 iters), loss = 0.101279
I0409 21:13:21.573233 14789 solver.cpp:237]     Train net output #0: loss = 0.101279 (* 1 = 0.101279 loss)
I0409 21:13:21.573246 14789 sgd_solver.cpp:105] Iteration 8760, lr = 0.00176359
I0409 21:13:26.529501 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8772.caffemodel
I0409 21:13:30.395859 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8772.solverstate
I0409 21:13:33.530630 14789 solver.cpp:330] Iteration 8772, Testing net (#0)
I0409 21:13:33.530658 14789 net.cpp:676] Ignoring source layer train-data
I0409 21:13:34.550380 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:13:38.060122 14789 solver.cpp:397]     Test net output #0: accuracy = 0.554534
I0409 21:13:38.060154 14789 solver.cpp:397]     Test net output #1: loss = 2.41751 (* 1 = 2.41751 loss)
I0409 21:13:38.151980 14789 solver.cpp:218] Iteration 8772 (0.723836 iter/s, 16.5783s/12 iters), loss = 0.0969053
I0409 21:13:38.152021 14789 solver.cpp:237]     Train net output #0: loss = 0.0969053 (* 1 = 0.0969053 loss)
I0409 21:13:38.152030 14789 sgd_solver.cpp:105] Iteration 8772, lr = 0.00175941
I0409 21:13:42.496646 14789 solver.cpp:218] Iteration 8784 (2.76211 iter/s, 4.3445s/12 iters), loss = 0.103478
I0409 21:13:42.496701 14789 solver.cpp:237]     Train net output #0: loss = 0.103478 (* 1 = 0.103478 loss)
I0409 21:13:42.496716 14789 sgd_solver.cpp:105] Iteration 8784, lr = 0.00175523
I0409 21:13:47.421042 14789 solver.cpp:218] Iteration 8796 (2.43694 iter/s, 4.92421s/12 iters), loss = 0.0457454
I0409 21:13:47.421097 14789 solver.cpp:237]     Train net output #0: loss = 0.0457454 (* 1 = 0.0457454 loss)
I0409 21:13:47.421109 14789 sgd_solver.cpp:105] Iteration 8796, lr = 0.00175106
I0409 21:13:48.711599 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:13:52.281095 14789 solver.cpp:218] Iteration 8808 (2.4692 iter/s, 4.85987s/12 iters), loss = 0.125824
I0409 21:13:52.281213 14789 solver.cpp:237]     Train net output #0: loss = 0.125824 (* 1 = 0.125824 loss)
I0409 21:13:52.281225 14789 sgd_solver.cpp:105] Iteration 8808, lr = 0.0017469
I0409 21:13:57.337316 14789 solver.cpp:218] Iteration 8820 (2.37343 iter/s, 5.05597s/12 iters), loss = 0.1171
I0409 21:13:57.337366 14789 solver.cpp:237]     Train net output #0: loss = 0.1171 (* 1 = 0.1171 loss)
I0409 21:13:57.337378 14789 sgd_solver.cpp:105] Iteration 8820, lr = 0.00174276
I0409 21:14:02.280179 14789 solver.cpp:218] Iteration 8832 (2.42783 iter/s, 4.94268s/12 iters), loss = 0.0802035
I0409 21:14:02.280239 14789 solver.cpp:237]     Train net output #0: loss = 0.0802035 (* 1 = 0.0802035 loss)
I0409 21:14:02.280254 14789 sgd_solver.cpp:105] Iteration 8832, lr = 0.00173862
I0409 21:14:07.216032 14789 solver.cpp:218] Iteration 8844 (2.43129 iter/s, 4.93566s/12 iters), loss = 0.171759
I0409 21:14:07.216081 14789 solver.cpp:237]     Train net output #0: loss = 0.171759 (* 1 = 0.171759 loss)
I0409 21:14:07.216090 14789 sgd_solver.cpp:105] Iteration 8844, lr = 0.00173449
I0409 21:14:12.178488 14789 solver.cpp:218] Iteration 8856 (2.41825 iter/s, 4.96227s/12 iters), loss = 0.0576777
I0409 21:14:12.178536 14789 solver.cpp:237]     Train net output #0: loss = 0.0576777 (* 1 = 0.0576777 loss)
I0409 21:14:12.178545 14789 sgd_solver.cpp:105] Iteration 8856, lr = 0.00173037
I0409 21:14:17.213678 14789 solver.cpp:218] Iteration 8868 (2.38332 iter/s, 5.035s/12 iters), loss = 0.0593743
I0409 21:14:17.213728 14789 solver.cpp:237]     Train net output #0: loss = 0.0593743 (* 1 = 0.0593743 loss)
I0409 21:14:17.213737 14789 sgd_solver.cpp:105] Iteration 8868, lr = 0.00172626
I0409 21:14:19.291188 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8874.caffemodel
I0409 21:14:25.044867 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8874.solverstate
I0409 21:14:28.022714 14789 solver.cpp:330] Iteration 8874, Testing net (#0)
I0409 21:14:28.022742 14789 net.cpp:676] Ignoring source layer train-data
I0409 21:14:29.031616 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:14:32.582932 14789 solver.cpp:397]     Test net output #0: accuracy = 0.558824
I0409 21:14:32.582967 14789 solver.cpp:397]     Test net output #1: loss = 2.41025 (* 1 = 2.41025 loss)
I0409 21:14:34.415417 14789 solver.cpp:218] Iteration 8880 (0.697623 iter/s, 17.2013s/12 iters), loss = 0.19678
I0409 21:14:34.415464 14789 solver.cpp:237]     Train net output #0: loss = 0.19678 (* 1 = 0.19678 loss)
I0409 21:14:34.415477 14789 sgd_solver.cpp:105] Iteration 8880, lr = 0.00172217
I0409 21:14:38.999541 14789 solver.cpp:218] Iteration 8892 (2.61783 iter/s, 4.58395s/12 iters), loss = 0.184013
I0409 21:14:38.999585 14789 solver.cpp:237]     Train net output #0: loss = 0.184013 (* 1 = 0.184013 loss)
I0409 21:14:38.999596 14789 sgd_solver.cpp:105] Iteration 8892, lr = 0.00171808
I0409 21:14:42.436007 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:14:43.789232 14789 solver.cpp:218] Iteration 8904 (2.50547 iter/s, 4.78952s/12 iters), loss = 0.11701
I0409 21:14:43.789281 14789 solver.cpp:237]     Train net output #0: loss = 0.11701 (* 1 = 0.11701 loss)
I0409 21:14:43.789294 14789 sgd_solver.cpp:105] Iteration 8904, lr = 0.001714
I0409 21:14:48.381223 14789 solver.cpp:218] Iteration 8916 (2.61335 iter/s, 4.59182s/12 iters), loss = 0.0900235
I0409 21:14:48.381276 14789 solver.cpp:237]     Train net output #0: loss = 0.0900235 (* 1 = 0.0900235 loss)
I0409 21:14:48.381287 14789 sgd_solver.cpp:105] Iteration 8916, lr = 0.00170993
I0409 21:14:53.158505 14789 solver.cpp:218] Iteration 8928 (2.51199 iter/s, 4.7771s/12 iters), loss = 0.135595
I0409 21:14:53.158561 14789 solver.cpp:237]     Train net output #0: loss = 0.135595 (* 1 = 0.135595 loss)
I0409 21:14:53.158573 14789 sgd_solver.cpp:105] Iteration 8928, lr = 0.00170587
I0409 21:14:57.832545 14789 solver.cpp:218] Iteration 8940 (2.56747 iter/s, 4.67386s/12 iters), loss = 0.102192
I0409 21:14:57.832662 14789 solver.cpp:237]     Train net output #0: loss = 0.102192 (* 1 = 0.102192 loss)
I0409 21:14:57.832676 14789 sgd_solver.cpp:105] Iteration 8940, lr = 0.00170182
I0409 21:15:02.690536 14789 solver.cpp:218] Iteration 8952 (2.47028 iter/s, 4.85774s/12 iters), loss = 0.119692
I0409 21:15:02.690593 14789 solver.cpp:237]     Train net output #0: loss = 0.119692 (* 1 = 0.119692 loss)
I0409 21:15:02.690606 14789 sgd_solver.cpp:105] Iteration 8952, lr = 0.00169778
I0409 21:15:07.259209 14789 solver.cpp:218] Iteration 8964 (2.62669 iter/s, 4.5685s/12 iters), loss = 0.177689
I0409 21:15:07.259248 14789 solver.cpp:237]     Train net output #0: loss = 0.177689 (* 1 = 0.177689 loss)
I0409 21:15:07.259255 14789 sgd_solver.cpp:105] Iteration 8964, lr = 0.00169375
I0409 21:15:11.771059 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8976.caffemodel
I0409 21:15:16.543431 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8976.solverstate
I0409 21:15:19.985008 14789 solver.cpp:330] Iteration 8976, Testing net (#0)
I0409 21:15:19.985035 14789 net.cpp:676] Ignoring source layer train-data
I0409 21:15:20.872524 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:15:24.387603 14789 solver.cpp:397]     Test net output #0: accuracy = 0.560662
I0409 21:15:24.387655 14789 solver.cpp:397]     Test net output #1: loss = 2.45641 (* 1 = 2.45641 loss)
I0409 21:15:24.479866 14789 solver.cpp:218] Iteration 8976 (0.696857 iter/s, 17.2202s/12 iters), loss = 0.175727
I0409 21:15:24.479921 14789 solver.cpp:237]     Train net output #0: loss = 0.175727 (* 1 = 0.175727 loss)
I0409 21:15:24.479933 14789 sgd_solver.cpp:105] Iteration 8976, lr = 0.00168973
I0409 21:15:28.582707 14789 solver.cpp:218] Iteration 8988 (2.92492 iter/s, 4.10267s/12 iters), loss = 0.0529274
I0409 21:15:28.582803 14789 solver.cpp:237]     Train net output #0: loss = 0.0529274 (* 1 = 0.0529274 loss)
I0409 21:15:28.582814 14789 sgd_solver.cpp:105] Iteration 8988, lr = 0.00168571
I0409 21:15:31.755046 14789 blocking_queue.cpp:49] Waiting for data
I0409 21:15:33.494076 14789 solver.cpp:218] Iteration 9000 (2.44342 iter/s, 4.91114s/12 iters), loss = 0.159266
I0409 21:15:33.494122 14789 solver.cpp:237]     Train net output #0: loss = 0.159266 (* 1 = 0.159266 loss)
I0409 21:15:33.494133 14789 sgd_solver.cpp:105] Iteration 9000, lr = 0.00168171
I0409 21:15:34.187654 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:15:38.324497 14789 solver.cpp:218] Iteration 9012 (2.48435 iter/s, 4.83024s/12 iters), loss = 0.16494
I0409 21:15:38.324559 14789 solver.cpp:237]     Train net output #0: loss = 0.16494 (* 1 = 0.16494 loss)
I0409 21:15:38.324573 14789 sgd_solver.cpp:105] Iteration 9012, lr = 0.00167772
I0409 21:15:43.363682 14789 solver.cpp:218] Iteration 9024 (2.38143 iter/s, 5.03899s/12 iters), loss = 0.115123
I0409 21:15:43.363744 14789 solver.cpp:237]     Train net output #0: loss = 0.115123 (* 1 = 0.115123 loss)
I0409 21:15:43.363756 14789 sgd_solver.cpp:105] Iteration 9024, lr = 0.00167374
I0409 21:15:48.331094 14789 solver.cpp:218] Iteration 9036 (2.41584 iter/s, 4.96722s/12 iters), loss = 0.117811
I0409 21:15:48.331161 14789 solver.cpp:237]     Train net output #0: loss = 0.117811 (* 1 = 0.117811 loss)
I0409 21:15:48.331172 14789 sgd_solver.cpp:105] Iteration 9036, lr = 0.00166976
I0409 21:15:53.282618 14789 solver.cpp:218] Iteration 9048 (2.4236 iter/s, 4.95132s/12 iters), loss = 0.125288
I0409 21:15:53.282681 14789 solver.cpp:237]     Train net output #0: loss = 0.125288 (* 1 = 0.125288 loss)
I0409 21:15:53.282696 14789 sgd_solver.cpp:105] Iteration 9048, lr = 0.0016658
I0409 21:15:58.291317 14789 solver.cpp:218] Iteration 9060 (2.39593 iter/s, 5.0085s/12 iters), loss = 0.0600042
I0409 21:15:58.291363 14789 solver.cpp:237]     Train net output #0: loss = 0.0600042 (* 1 = 0.0600042 loss)
I0409 21:15:58.291373 14789 sgd_solver.cpp:105] Iteration 9060, lr = 0.00166184
I0409 21:16:03.680531 14789 solver.cpp:218] Iteration 9072 (2.22675 iter/s, 5.38902s/12 iters), loss = 0.150993
I0409 21:16:03.680697 14789 solver.cpp:237]     Train net output #0: loss = 0.150993 (* 1 = 0.150993 loss)
I0409 21:16:03.680711 14789 sgd_solver.cpp:105] Iteration 9072, lr = 0.0016579
I0409 21:16:05.706198 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9078.caffemodel
I0409 21:16:13.150492 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9078.solverstate
I0409 21:16:16.165446 14789 solver.cpp:330] Iteration 9078, Testing net (#0)
I0409 21:16:16.165473 14789 net.cpp:676] Ignoring source layer train-data
I0409 21:16:17.071766 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:16:20.772259 14789 solver.cpp:397]     Test net output #0: accuracy = 0.553309
I0409 21:16:20.772296 14789 solver.cpp:397]     Test net output #1: loss = 2.45749 (* 1 = 2.45749 loss)
I0409 21:16:22.641455 14789 solver.cpp:218] Iteration 9084 (0.632902 iter/s, 18.9603s/12 iters), loss = 0.0994117
I0409 21:16:22.641516 14789 solver.cpp:237]     Train net output #0: loss = 0.0994117 (* 1 = 0.0994117 loss)
I0409 21:16:22.641528 14789 sgd_solver.cpp:105] Iteration 9084, lr = 0.00165396
I0409 21:16:27.657768 14789 solver.cpp:218] Iteration 9096 (2.39229 iter/s, 5.01612s/12 iters), loss = 0.0669136
I0409 21:16:27.657814 14789 solver.cpp:237]     Train net output #0: loss = 0.0669136 (* 1 = 0.0669136 loss)
I0409 21:16:27.657824 14789 sgd_solver.cpp:105] Iteration 9096, lr = 0.00165003
I0409 21:16:30.432039 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:16:32.518944 14789 solver.cpp:218] Iteration 9108 (2.46863 iter/s, 4.861s/12 iters), loss = 0.210562
I0409 21:16:32.518998 14789 solver.cpp:237]     Train net output #0: loss = 0.210562 (* 1 = 0.210562 loss)
I0409 21:16:32.519011 14789 sgd_solver.cpp:105] Iteration 9108, lr = 0.00164612
I0409 21:16:37.518328 14789 solver.cpp:218] Iteration 9120 (2.40039 iter/s, 4.99919s/12 iters), loss = 0.0999546
I0409 21:16:37.518443 14789 solver.cpp:237]     Train net output #0: loss = 0.0999546 (* 1 = 0.0999546 loss)
I0409 21:16:37.518455 14789 sgd_solver.cpp:105] Iteration 9120, lr = 0.00164221
I0409 21:16:42.500818 14789 solver.cpp:218] Iteration 9132 (2.40855 iter/s, 4.98224s/12 iters), loss = 0.0896747
I0409 21:16:42.500869 14789 solver.cpp:237]     Train net output #0: loss = 0.0896747 (* 1 = 0.0896747 loss)
I0409 21:16:42.500880 14789 sgd_solver.cpp:105] Iteration 9132, lr = 0.00163831
I0409 21:16:47.528527 14789 solver.cpp:218] Iteration 9144 (2.38686 iter/s, 5.02752s/12 iters), loss = 0.100503
I0409 21:16:47.528579 14789 solver.cpp:237]     Train net output #0: loss = 0.100503 (* 1 = 0.100503 loss)
I0409 21:16:47.528592 14789 sgd_solver.cpp:105] Iteration 9144, lr = 0.00163442
I0409 21:16:52.507052 14789 solver.cpp:218] Iteration 9156 (2.41044 iter/s, 4.97834s/12 iters), loss = 0.0568849
I0409 21:16:52.507107 14789 solver.cpp:237]     Train net output #0: loss = 0.0568849 (* 1 = 0.0568849 loss)
I0409 21:16:52.507119 14789 sgd_solver.cpp:105] Iteration 9156, lr = 0.00163054
I0409 21:16:57.565762 14789 solver.cpp:218] Iteration 9168 (2.37223 iter/s, 5.05852s/12 iters), loss = 0.0792464
I0409 21:16:57.565802 14789 solver.cpp:237]     Train net output #0: loss = 0.0792464 (* 1 = 0.0792464 loss)
I0409 21:16:57.565812 14789 sgd_solver.cpp:105] Iteration 9168, lr = 0.00162667
I0409 21:17:01.935758 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9180.caffemodel
I0409 21:17:07.449327 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9180.solverstate
I0409 21:17:11.932286 14789 solver.cpp:330] Iteration 9180, Testing net (#0)
I0409 21:17:11.932410 14789 net.cpp:676] Ignoring source layer train-data
I0409 21:17:12.813091 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:17:16.418068 14789 solver.cpp:397]     Test net output #0: accuracy = 0.564951
I0409 21:17:16.418112 14789 solver.cpp:397]     Test net output #1: loss = 2.5018 (* 1 = 2.5018 loss)
I0409 21:17:16.509737 14789 solver.cpp:218] Iteration 9180 (0.633464 iter/s, 18.9435s/12 iters), loss = 0.10498
I0409 21:17:16.509780 14789 solver.cpp:237]     Train net output #0: loss = 0.10498 (* 1 = 0.10498 loss)
I0409 21:17:16.509789 14789 sgd_solver.cpp:105] Iteration 9180, lr = 0.00162281
I0409 21:17:20.995316 14789 solver.cpp:218] Iteration 9192 (2.67534 iter/s, 4.48541s/12 iters), loss = 0.0866026
I0409 21:17:20.995357 14789 solver.cpp:237]     Train net output #0: loss = 0.0866026 (* 1 = 0.0866026 loss)
I0409 21:17:20.995368 14789 sgd_solver.cpp:105] Iteration 9192, lr = 0.00161895
I0409 21:17:26.007597 14789 solver.cpp:218] Iteration 9204 (2.39421 iter/s, 5.0121s/12 iters), loss = 0.104513
I0409 21:17:26.007643 14789 solver.cpp:237]     Train net output #0: loss = 0.104513 (* 1 = 0.104513 loss)
I0409 21:17:26.007652 14789 sgd_solver.cpp:105] Iteration 9204, lr = 0.00161511
I0409 21:17:26.085860 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:17:30.969220 14789 solver.cpp:218] Iteration 9216 (2.41865 iter/s, 4.96144s/12 iters), loss = 0.079792
I0409 21:17:30.969267 14789 solver.cpp:237]     Train net output #0: loss = 0.079792 (* 1 = 0.079792 loss)
I0409 21:17:30.969277 14789 sgd_solver.cpp:105] Iteration 9216, lr = 0.00161128
I0409 21:17:36.144796 14789 solver.cpp:218] Iteration 9228 (2.31867 iter/s, 5.17538s/12 iters), loss = 0.118847
I0409 21:17:36.144851 14789 solver.cpp:237]     Train net output #0: loss = 0.118847 (* 1 = 0.118847 loss)
I0409 21:17:36.144863 14789 sgd_solver.cpp:105] Iteration 9228, lr = 0.00160745
I0409 21:17:41.554150 14789 solver.cpp:218] Iteration 9240 (2.21846 iter/s, 5.40915s/12 iters), loss = 0.101898
I0409 21:17:41.554199 14789 solver.cpp:237]     Train net output #0: loss = 0.101898 (* 1 = 0.101898 loss)
I0409 21:17:41.554214 14789 sgd_solver.cpp:105] Iteration 9240, lr = 0.00160363
I0409 21:17:47.078572 14789 solver.cpp:218] Iteration 9252 (2.17225 iter/s, 5.52422s/12 iters), loss = 0.0471345
I0409 21:17:47.078698 14789 solver.cpp:237]     Train net output #0: loss = 0.0471345 (* 1 = 0.0471345 loss)
I0409 21:17:47.078709 14789 sgd_solver.cpp:105] Iteration 9252, lr = 0.00159983
I0409 21:17:52.513121 14789 solver.cpp:218] Iteration 9264 (2.20821 iter/s, 5.43427s/12 iters), loss = 0.0679224
I0409 21:17:52.513177 14789 solver.cpp:237]     Train net output #0: loss = 0.0679224 (* 1 = 0.0679224 loss)
I0409 21:17:52.513188 14789 sgd_solver.cpp:105] Iteration 9264, lr = 0.00159603
I0409 21:17:58.042445 14789 solver.cpp:218] Iteration 9276 (2.17033 iter/s, 5.52912s/12 iters), loss = 0.059432
I0409 21:17:58.042487 14789 solver.cpp:237]     Train net output #0: loss = 0.059432 (* 1 = 0.059432 loss)
I0409 21:17:58.042495 14789 sgd_solver.cpp:105] Iteration 9276, lr = 0.00159224
I0409 21:18:00.275667 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9282.caffemodel
I0409 21:18:04.298487 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9282.solverstate
I0409 21:18:15.989748 14789 solver.cpp:330] Iteration 9282, Testing net (#0)
I0409 21:18:15.989771 14789 net.cpp:676] Ignoring source layer train-data
I0409 21:18:16.878304 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:18:20.666644 14789 solver.cpp:397]     Test net output #0: accuracy = 0.571691
I0409 21:18:20.666817 14789 solver.cpp:397]     Test net output #1: loss = 2.39814 (* 1 = 2.39814 loss)
I0409 21:18:22.702631 14789 solver.cpp:218] Iteration 9288 (0.486628 iter/s, 24.6595s/12 iters), loss = 0.177542
I0409 21:18:22.702680 14789 solver.cpp:237]     Train net output #0: loss = 0.177542 (* 1 = 0.177542 loss)
I0409 21:18:22.702692 14789 sgd_solver.cpp:105] Iteration 9288, lr = 0.00158846
I0409 21:18:27.828356 14789 solver.cpp:218] Iteration 9300 (2.34122 iter/s, 5.12553s/12 iters), loss = 0.136927
I0409 21:18:27.828413 14789 solver.cpp:237]     Train net output #0: loss = 0.136927 (* 1 = 0.136927 loss)
I0409 21:18:27.828426 14789 sgd_solver.cpp:105] Iteration 9300, lr = 0.00158469
I0409 21:18:30.228735 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:18:33.223667 14789 solver.cpp:218] Iteration 9312 (2.22424 iter/s, 5.39511s/12 iters), loss = 0.0604243
I0409 21:18:33.223716 14789 solver.cpp:237]     Train net output #0: loss = 0.0604243 (* 1 = 0.0604243 loss)
I0409 21:18:33.223726 14789 sgd_solver.cpp:105] Iteration 9312, lr = 0.00158092
I0409 21:18:38.722376 14789 solver.cpp:218] Iteration 9324 (2.18241 iter/s, 5.49851s/12 iters), loss = 0.135775
I0409 21:18:38.722429 14789 solver.cpp:237]     Train net output #0: loss = 0.135775 (* 1 = 0.135775 loss)
I0409 21:18:38.722442 14789 sgd_solver.cpp:105] Iteration 9324, lr = 0.00157717
I0409 21:18:43.628283 14789 solver.cpp:218] Iteration 9336 (2.44612 iter/s, 4.90572s/12 iters), loss = 0.0412117
I0409 21:18:43.628329 14789 solver.cpp:237]     Train net output #0: loss = 0.0412117 (* 1 = 0.0412117 loss)
I0409 21:18:43.628342 14789 sgd_solver.cpp:105] Iteration 9336, lr = 0.00157343
I0409 21:18:48.499446 14789 solver.cpp:218] Iteration 9348 (2.46357 iter/s, 4.87098s/12 iters), loss = 0.0433047
I0409 21:18:48.499507 14789 solver.cpp:237]     Train net output #0: loss = 0.0433047 (* 1 = 0.0433047 loss)
I0409 21:18:48.499523 14789 sgd_solver.cpp:105] Iteration 9348, lr = 0.00156969
I0409 21:18:53.415774 14789 solver.cpp:218] Iteration 9360 (2.44094 iter/s, 4.91614s/12 iters), loss = 0.0569197
I0409 21:18:53.415892 14789 solver.cpp:237]     Train net output #0: loss = 0.0569197 (* 1 = 0.0569197 loss)
I0409 21:18:53.415904 14789 sgd_solver.cpp:105] Iteration 9360, lr = 0.00156596
I0409 21:18:58.156388 14789 solver.cpp:218] Iteration 9372 (2.53145 iter/s, 4.74036s/12 iters), loss = 0.0328899
I0409 21:18:58.156448 14789 solver.cpp:237]     Train net output #0: loss = 0.0328899 (* 1 = 0.0328899 loss)
I0409 21:18:58.156464 14789 sgd_solver.cpp:105] Iteration 9372, lr = 0.00156225
I0409 21:19:02.518326 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9384.caffemodel
I0409 21:19:12.276425 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9384.solverstate
I0409 21:19:25.190627 14789 solver.cpp:330] Iteration 9384, Testing net (#0)
I0409 21:19:25.190678 14789 net.cpp:676] Ignoring source layer train-data
I0409 21:19:25.944597 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:19:29.620777 14789 solver.cpp:397]     Test net output #0: accuracy = 0.57598
I0409 21:19:29.620822 14789 solver.cpp:397]     Test net output #1: loss = 2.2985 (* 1 = 2.2985 loss)
I0409 21:19:29.712436 14789 solver.cpp:218] Iteration 9384 (0.380286 iter/s, 31.5552s/12 iters), loss = 0.0815551
I0409 21:19:29.712499 14789 solver.cpp:237]     Train net output #0: loss = 0.0815551 (* 1 = 0.0815551 loss)
I0409 21:19:29.712513 14789 sgd_solver.cpp:105] Iteration 9384, lr = 0.00155854
I0409 21:19:34.123235 14789 solver.cpp:218] Iteration 9396 (2.72071 iter/s, 4.41061s/12 iters), loss = 0.114621
I0409 21:19:34.123288 14789 solver.cpp:237]     Train net output #0: loss = 0.114621 (* 1 = 0.114621 loss)
I0409 21:19:34.123301 14789 sgd_solver.cpp:105] Iteration 9396, lr = 0.00155484
I0409 21:19:38.272084 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:19:38.934862 14789 solver.cpp:218] Iteration 9408 (2.49406 iter/s, 4.81144s/12 iters), loss = 0.0925001
I0409 21:19:38.934916 14789 solver.cpp:237]     Train net output #0: loss = 0.0925001 (* 1 = 0.0925001 loss)
I0409 21:19:38.934927 14789 sgd_solver.cpp:105] Iteration 9408, lr = 0.00155114
I0409 21:19:43.780652 14789 solver.cpp:218] Iteration 9420 (2.47647 iter/s, 4.8456s/12 iters), loss = 0.123961
I0409 21:19:43.780747 14789 solver.cpp:237]     Train net output #0: loss = 0.123961 (* 1 = 0.123961 loss)
I0409 21:19:43.780798 14789 sgd_solver.cpp:105] Iteration 9420, lr = 0.00154746
I0409 21:19:48.582051 14789 solver.cpp:218] Iteration 9432 (2.49937 iter/s, 4.80121s/12 iters), loss = 0.0889846
I0409 21:19:48.582111 14789 solver.cpp:237]     Train net output #0: loss = 0.0889846 (* 1 = 0.0889846 loss)
I0409 21:19:48.582121 14789 sgd_solver.cpp:105] Iteration 9432, lr = 0.00154379
I0409 21:19:53.474483 14789 solver.cpp:218] Iteration 9444 (2.45287 iter/s, 4.89223s/12 iters), loss = 0.0589256
I0409 21:19:53.474539 14789 solver.cpp:237]     Train net output #0: loss = 0.0589256 (* 1 = 0.0589256 loss)
I0409 21:19:53.474550 14789 sgd_solver.cpp:105] Iteration 9444, lr = 0.00154012
I0409 21:19:58.254128 14789 solver.cpp:218] Iteration 9456 (2.51075 iter/s, 4.77945s/12 iters), loss = 0.108215
I0409 21:19:58.254252 14789 solver.cpp:237]     Train net output #0: loss = 0.108215 (* 1 = 0.108215 loss)
I0409 21:19:58.254262 14789 sgd_solver.cpp:105] Iteration 9456, lr = 0.00153647
I0409 21:20:03.062636 14789 solver.cpp:218] Iteration 9468 (2.49571 iter/s, 4.80825s/12 iters), loss = 0.0725811
I0409 21:20:03.062693 14789 solver.cpp:237]     Train net output #0: loss = 0.0725811 (* 1 = 0.0725811 loss)
I0409 21:20:03.062707 14789 sgd_solver.cpp:105] Iteration 9468, lr = 0.00153282
I0409 21:20:07.899103 14789 solver.cpp:218] Iteration 9480 (2.48125 iter/s, 4.83628s/12 iters), loss = 0.0333479
I0409 21:20:07.899154 14789 solver.cpp:237]     Train net output #0: loss = 0.0333479 (* 1 = 0.0333479 loss)
I0409 21:20:07.899166 14789 sgd_solver.cpp:105] Iteration 9480, lr = 0.00152918
I0409 21:20:09.974304 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9486.caffemodel
I0409 21:20:15.201944 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9486.solverstate
I0409 21:20:19.257462 14789 solver.cpp:330] Iteration 9486, Testing net (#0)
I0409 21:20:19.257486 14789 net.cpp:676] Ignoring source layer train-data
I0409 21:20:19.979943 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:20:23.734697 14789 solver.cpp:397]     Test net output #0: accuracy = 0.575368
I0409 21:20:23.734745 14789 solver.cpp:397]     Test net output #1: loss = 2.33935 (* 1 = 2.33935 loss)
I0409 21:20:25.327317 14789 solver.cpp:218] Iteration 9492 (0.688558 iter/s, 17.4277s/12 iters), loss = 0.115204
I0409 21:20:25.327375 14789 solver.cpp:237]     Train net output #0: loss = 0.115204 (* 1 = 0.115204 loss)
I0409 21:20:25.327390 14789 sgd_solver.cpp:105] Iteration 9492, lr = 0.00152555
I0409 21:20:30.462412 14789 solver.cpp:218] Iteration 9504 (2.33695 iter/s, 5.1349s/12 iters), loss = 0.0411671
I0409 21:20:30.462504 14789 solver.cpp:237]     Train net output #0: loss = 0.0411671 (* 1 = 0.0411671 loss)
I0409 21:20:30.462515 14789 sgd_solver.cpp:105] Iteration 9504, lr = 0.00152193
I0409 21:20:32.064985 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:20:35.944873 14789 solver.cpp:218] Iteration 9516 (2.1889 iter/s, 5.48221s/12 iters), loss = 0.0862485
I0409 21:20:35.944921 14789 solver.cpp:237]     Train net output #0: loss = 0.0862485 (* 1 = 0.0862485 loss)
I0409 21:20:35.944929 14789 sgd_solver.cpp:105] Iteration 9516, lr = 0.00151831
I0409 21:20:41.454095 14789 solver.cpp:218] Iteration 9528 (2.17824 iter/s, 5.50904s/12 iters), loss = 0.165505
I0409 21:20:41.454154 14789 solver.cpp:237]     Train net output #0: loss = 0.165505 (* 1 = 0.165505 loss)
I0409 21:20:41.454164 14789 sgd_solver.cpp:105] Iteration 9528, lr = 0.00151471
I0409 21:20:46.542739 14789 solver.cpp:218] Iteration 9540 (2.35827 iter/s, 5.08848s/12 iters), loss = 0.0257107
I0409 21:20:46.542783 14789 solver.cpp:237]     Train net output #0: loss = 0.0257107 (* 1 = 0.0257107 loss)
I0409 21:20:46.542793 14789 sgd_solver.cpp:105] Iteration 9540, lr = 0.00151111
I0409 21:20:51.497103 14789 solver.cpp:218] Iteration 9552 (2.42219 iter/s, 4.9542s/12 iters), loss = 0.107377
I0409 21:20:51.497164 14789 solver.cpp:237]     Train net output #0: loss = 0.107377 (* 1 = 0.107377 loss)
I0409 21:20:51.497176 14789 sgd_solver.cpp:105] Iteration 9552, lr = 0.00150752
I0409 21:20:56.499519 14789 solver.cpp:218] Iteration 9564 (2.39893 iter/s, 5.00224s/12 iters), loss = 0.0745979
I0409 21:20:56.499577 14789 solver.cpp:237]     Train net output #0: loss = 0.0745979 (* 1 = 0.0745979 loss)
I0409 21:20:56.499588 14789 sgd_solver.cpp:105] Iteration 9564, lr = 0.00150395
I0409 21:21:01.330322 14789 solver.cpp:218] Iteration 9576 (2.48415 iter/s, 4.83063s/12 iters), loss = 0.0488731
I0409 21:21:01.330478 14789 solver.cpp:237]     Train net output #0: loss = 0.0488731 (* 1 = 0.0488731 loss)
I0409 21:21:01.330494 14789 sgd_solver.cpp:105] Iteration 9576, lr = 0.00150037
I0409 21:21:05.733223 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9588.caffemodel
I0409 21:21:11.769747 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9588.solverstate
I0409 21:21:15.836453 14789 solver.cpp:330] Iteration 9588, Testing net (#0)
I0409 21:21:15.836480 14789 net.cpp:676] Ignoring source layer train-data
I0409 21:21:16.490046 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:21:20.248242 14789 solver.cpp:397]     Test net output #0: accuracy = 0.566176
I0409 21:21:20.248291 14789 solver.cpp:397]     Test net output #1: loss = 2.39679 (* 1 = 2.39679 loss)
I0409 21:21:20.340291 14789 solver.cpp:218] Iteration 9588 (0.631266 iter/s, 19.0094s/12 iters), loss = 0.0944505
I0409 21:21:20.340353 14789 solver.cpp:237]     Train net output #0: loss = 0.0944505 (* 1 = 0.0944505 loss)
I0409 21:21:20.340366 14789 sgd_solver.cpp:105] Iteration 9588, lr = 0.00149681
I0409 21:21:24.420091 14789 solver.cpp:218] Iteration 9600 (2.94144 iter/s, 4.07964s/12 iters), loss = 0.194677
I0409 21:21:24.420159 14789 solver.cpp:237]     Train net output #0: loss = 0.194677 (* 1 = 0.194677 loss)
I0409 21:21:24.420172 14789 sgd_solver.cpp:105] Iteration 9600, lr = 0.00149326
I0409 21:21:28.000469 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:21:29.487270 14789 solver.cpp:218] Iteration 9612 (2.36827 iter/s, 5.067s/12 iters), loss = 0.0304825
I0409 21:21:29.487313 14789 solver.cpp:237]     Train net output #0: loss = 0.0304825 (* 1 = 0.0304825 loss)
I0409 21:21:29.487323 14789 sgd_solver.cpp:105] Iteration 9612, lr = 0.00148971
I0409 21:21:34.423125 14789 solver.cpp:218] Iteration 9624 (2.43127 iter/s, 4.9357s/12 iters), loss = 0.0891707
I0409 21:21:34.423219 14789 solver.cpp:237]     Train net output #0: loss = 0.0891707 (* 1 = 0.0891707 loss)
I0409 21:21:34.423228 14789 sgd_solver.cpp:105] Iteration 9624, lr = 0.00148618
I0409 21:21:39.156744 14789 solver.cpp:218] Iteration 9636 (2.53517 iter/s, 4.73342s/12 iters), loss = 0.0990379
I0409 21:21:39.156803 14789 solver.cpp:237]     Train net output #0: loss = 0.0990379 (* 1 = 0.0990379 loss)
I0409 21:21:39.156821 14789 sgd_solver.cpp:105] Iteration 9636, lr = 0.00148265
I0409 21:21:44.126551 14789 solver.cpp:218] Iteration 9648 (2.41467 iter/s, 4.96963s/12 iters), loss = 0.114065
I0409 21:21:44.126606 14789 solver.cpp:237]     Train net output #0: loss = 0.114065 (* 1 = 0.114065 loss)
I0409 21:21:44.126621 14789 sgd_solver.cpp:105] Iteration 9648, lr = 0.00147913
I0409 21:21:49.262286 14789 solver.cpp:218] Iteration 9660 (2.33665 iter/s, 5.13556s/12 iters), loss = 0.0365449
I0409 21:21:49.262331 14789 solver.cpp:237]     Train net output #0: loss = 0.0365449 (* 1 = 0.0365449 loss)
I0409 21:21:49.262342 14789 sgd_solver.cpp:105] Iteration 9660, lr = 0.00147562
I0409 21:21:54.137042 14789 solver.cpp:218] Iteration 9672 (2.46174 iter/s, 4.87459s/12 iters), loss = 0.0925782
I0409 21:21:54.137097 14789 solver.cpp:237]     Train net output #0: loss = 0.0925782 (* 1 = 0.0925782 loss)
I0409 21:21:54.137109 14789 sgd_solver.cpp:105] Iteration 9672, lr = 0.00147211
I0409 21:21:59.361830 14789 solver.cpp:218] Iteration 9684 (2.29682 iter/s, 5.22462s/12 iters), loss = 0.101854
I0409 21:21:59.361879 14789 solver.cpp:237]     Train net output #0: loss = 0.101854 (* 1 = 0.101854 loss)
I0409 21:21:59.361891 14789 sgd_solver.cpp:105] Iteration 9684, lr = 0.00146862
I0409 21:22:01.365357 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9690.caffemodel
I0409 21:22:13.242106 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9690.solverstate
I0409 21:22:17.955024 14789 solver.cpp:330] Iteration 9690, Testing net (#0)
I0409 21:22:17.955049 14789 net.cpp:676] Ignoring source layer train-data
I0409 21:22:18.556115 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:22:21.387938 14789 blocking_queue.cpp:49] Waiting for data
I0409 21:22:22.406239 14789 solver.cpp:397]     Test net output #0: accuracy = 0.568627
I0409 21:22:22.406287 14789 solver.cpp:397]     Test net output #1: loss = 2.36142 (* 1 = 2.36142 loss)
I0409 21:22:24.337529 14789 solver.cpp:218] Iteration 9696 (0.480478 iter/s, 24.9751s/12 iters), loss = 0.132954
I0409 21:22:24.337571 14789 solver.cpp:237]     Train net output #0: loss = 0.132954 (* 1 = 0.132954 loss)
I0409 21:22:24.337581 14789 sgd_solver.cpp:105] Iteration 9696, lr = 0.00146513
I0409 21:22:29.305430 14789 solver.cpp:218] Iteration 9708 (2.41559 iter/s, 4.96774s/12 iters), loss = 0.0967051
I0409 21:22:29.305474 14789 solver.cpp:237]     Train net output #0: loss = 0.0967051 (* 1 = 0.0967051 loss)
I0409 21:22:29.305485 14789 sgd_solver.cpp:105] Iteration 9708, lr = 0.00146165
I0409 21:22:29.998347 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:22:34.420984 14789 solver.cpp:218] Iteration 9720 (2.34586 iter/s, 5.11539s/12 iters), loss = 0.0396755
I0409 21:22:34.421051 14789 solver.cpp:237]     Train net output #0: loss = 0.0396755 (* 1 = 0.0396755 loss)
I0409 21:22:34.421066 14789 sgd_solver.cpp:105] Iteration 9720, lr = 0.00145818
I0409 21:22:39.296130 14789 solver.cpp:218] Iteration 9732 (2.46156 iter/s, 4.87497s/12 iters), loss = 0.103466
I0409 21:22:39.296190 14789 solver.cpp:237]     Train net output #0: loss = 0.103466 (* 1 = 0.103466 loss)
I0409 21:22:39.296203 14789 sgd_solver.cpp:105] Iteration 9732, lr = 0.00145472
I0409 21:22:44.192005 14789 solver.cpp:218] Iteration 9744 (2.45113 iter/s, 4.8957s/12 iters), loss = 0.107564
I0409 21:22:44.192098 14789 solver.cpp:237]     Train net output #0: loss = 0.107564 (* 1 = 0.107564 loss)
I0409 21:22:44.192111 14789 sgd_solver.cpp:105] Iteration 9744, lr = 0.00145127
I0409 21:22:49.202277 14789 solver.cpp:218] Iteration 9756 (2.39518 iter/s, 5.01006s/12 iters), loss = 0.180466
I0409 21:22:49.202330 14789 solver.cpp:237]     Train net output #0: loss = 0.180466 (* 1 = 0.180466 loss)
I0409 21:22:49.202343 14789 sgd_solver.cpp:105] Iteration 9756, lr = 0.00144782
I0409 21:22:54.024809 14789 solver.cpp:218] Iteration 9768 (2.48841 iter/s, 4.82237s/12 iters), loss = 0.0799739
I0409 21:22:54.024866 14789 solver.cpp:237]     Train net output #0: loss = 0.0799739 (* 1 = 0.0799739 loss)
I0409 21:22:54.024879 14789 sgd_solver.cpp:105] Iteration 9768, lr = 0.00144438
I0409 21:22:59.077661 14789 solver.cpp:218] Iteration 9780 (2.37498 iter/s, 5.05267s/12 iters), loss = 0.0829566
I0409 21:22:59.077728 14789 solver.cpp:237]     Train net output #0: loss = 0.0829566 (* 1 = 0.0829566 loss)
I0409 21:22:59.077742 14789 sgd_solver.cpp:105] Iteration 9780, lr = 0.00144095
I0409 21:23:03.626521 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9792.caffemodel
I0409 21:23:14.598408 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9792.solverstate
I0409 21:23:21.523671 14789 solver.cpp:330] Iteration 9792, Testing net (#0)
I0409 21:23:21.523696 14789 net.cpp:676] Ignoring source layer train-data
I0409 21:23:22.049763 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:23:25.913501 14789 solver.cpp:397]     Test net output #0: accuracy = 0.569853
I0409 21:23:25.913548 14789 solver.cpp:397]     Test net output #1: loss = 2.32463 (* 1 = 2.32463 loss)
I0409 21:23:26.005025 14789 solver.cpp:218] Iteration 9792 (0.445654 iter/s, 26.9267s/12 iters), loss = 0.0216745
I0409 21:23:26.005074 14789 solver.cpp:237]     Train net output #0: loss = 0.0216745 (* 1 = 0.0216745 loss)
I0409 21:23:26.005084 14789 sgd_solver.cpp:105] Iteration 9792, lr = 0.00143753
I0409 21:23:30.552976 14789 solver.cpp:218] Iteration 9804 (2.63864 iter/s, 4.54779s/12 iters), loss = 0.125873
I0409 21:23:30.553030 14789 solver.cpp:237]     Train net output #0: loss = 0.125873 (* 1 = 0.125873 loss)
I0409 21:23:30.553042 14789 sgd_solver.cpp:105] Iteration 9804, lr = 0.00143412
I0409 21:23:33.704458 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:23:35.709175 14789 solver.cpp:218] Iteration 9816 (2.32737 iter/s, 5.15603s/12 iters), loss = 0.0399244
I0409 21:23:35.709228 14789 solver.cpp:237]     Train net output #0: loss = 0.0399244 (* 1 = 0.0399244 loss)
I0409 21:23:35.709239 14789 sgd_solver.cpp:105] Iteration 9816, lr = 0.00143072
I0409 21:23:40.535527 14789 solver.cpp:218] Iteration 9828 (2.48644 iter/s, 4.82618s/12 iters), loss = 0.105217
I0409 21:23:40.535574 14789 solver.cpp:237]     Train net output #0: loss = 0.105217 (* 1 = 0.105217 loss)
I0409 21:23:40.535584 14789 sgd_solver.cpp:105] Iteration 9828, lr = 0.00142732
I0409 21:23:45.524791 14789 solver.cpp:218] Iteration 9840 (2.40524 iter/s, 4.9891s/12 iters), loss = 0.12576
I0409 21:23:45.524900 14789 solver.cpp:237]     Train net output #0: loss = 0.12576 (* 1 = 0.12576 loss)
I0409 21:23:45.524911 14789 sgd_solver.cpp:105] Iteration 9840, lr = 0.00142393
I0409 21:23:50.727775 14789 solver.cpp:218] Iteration 9852 (2.30647 iter/s, 5.20275s/12 iters), loss = 0.0398833
I0409 21:23:50.727824 14789 solver.cpp:237]     Train net output #0: loss = 0.0398833 (* 1 = 0.0398833 loss)
I0409 21:23:50.727833 14789 sgd_solver.cpp:105] Iteration 9852, lr = 0.00142055
I0409 21:23:55.718641 14789 solver.cpp:218] Iteration 9864 (2.40448 iter/s, 4.99069s/12 iters), loss = 0.0792621
I0409 21:23:55.718691 14789 solver.cpp:237]     Train net output #0: loss = 0.0792621 (* 1 = 0.0792621 loss)
I0409 21:23:55.718700 14789 sgd_solver.cpp:105] Iteration 9864, lr = 0.00141718
I0409 21:24:00.567725 14789 solver.cpp:218] Iteration 9876 (2.47478 iter/s, 4.84892s/12 iters), loss = 0.0438574
I0409 21:24:00.567772 14789 solver.cpp:237]     Train net output #0: loss = 0.0438574 (* 1 = 0.0438574 loss)
I0409 21:24:00.567781 14789 sgd_solver.cpp:105] Iteration 9876, lr = 0.00141381
I0409 21:24:05.644347 14789 solver.cpp:218] Iteration 9888 (2.36386 iter/s, 5.07645s/12 iters), loss = 0.0440977
I0409 21:24:05.644395 14789 solver.cpp:237]     Train net output #0: loss = 0.0440977 (* 1 = 0.0440977 loss)
I0409 21:24:05.644407 14789 sgd_solver.cpp:105] Iteration 9888, lr = 0.00141045
I0409 21:24:07.691654 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9894.caffemodel
I0409 21:24:11.429538 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9894.solverstate
I0409 21:24:15.732370 14789 solver.cpp:330] Iteration 9894, Testing net (#0)
I0409 21:24:15.732482 14789 net.cpp:676] Ignoring source layer train-data
I0409 21:24:16.301745 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:24:20.328303 14789 solver.cpp:397]     Test net output #0: accuracy = 0.581495
I0409 21:24:20.328339 14789 solver.cpp:397]     Test net output #1: loss = 2.39495 (* 1 = 2.39495 loss)
I0409 21:24:22.154467 14789 solver.cpp:218] Iteration 9900 (0.726845 iter/s, 16.5097s/12 iters), loss = 0.0920536
I0409 21:24:22.154508 14789 solver.cpp:237]     Train net output #0: loss = 0.0920536 (* 1 = 0.0920536 loss)
I0409 21:24:22.154516 14789 sgd_solver.cpp:105] Iteration 9900, lr = 0.00140711
I0409 21:24:26.900307 14789 solver.cpp:218] Iteration 9912 (2.52861 iter/s, 4.74569s/12 iters), loss = 0.0593542
I0409 21:24:26.900352 14789 solver.cpp:237]     Train net output #0: loss = 0.0593542 (* 1 = 0.0593542 loss)
I0409 21:24:26.900362 14789 sgd_solver.cpp:105] Iteration 9912, lr = 0.00140377
I0409 21:24:26.951778 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:24:31.765493 14789 solver.cpp:218] Iteration 9924 (2.46659 iter/s, 4.86501s/12 iters), loss = 0.103608
I0409 21:24:31.765548 14789 solver.cpp:237]     Train net output #0: loss = 0.103608 (* 1 = 0.103608 loss)
I0409 21:24:31.765559 14789 sgd_solver.cpp:105] Iteration 9924, lr = 0.00140043
I0409 21:24:36.701247 14789 solver.cpp:218] Iteration 9936 (2.43132 iter/s, 4.93558s/12 iters), loss = 0.0454369
I0409 21:24:36.701303 14789 solver.cpp:237]     Train net output #0: loss = 0.0454369 (* 1 = 0.0454369 loss)
I0409 21:24:36.701313 14789 sgd_solver.cpp:105] Iteration 9936, lr = 0.00139711
I0409 21:24:41.921661 14789 solver.cpp:218] Iteration 9948 (2.29875 iter/s, 5.22024s/12 iters), loss = 0.0545026
I0409 21:24:41.921703 14789 solver.cpp:237]     Train net output #0: loss = 0.0545026 (* 1 = 0.0545026 loss)
I0409 21:24:41.921711 14789 sgd_solver.cpp:105] Iteration 9948, lr = 0.00139379
I0409 21:24:47.011518 14789 solver.cpp:218] Iteration 9960 (2.35771 iter/s, 5.08969s/12 iters), loss = 0.0564882
I0409 21:24:47.011610 14789 solver.cpp:237]     Train net output #0: loss = 0.0564882 (* 1 = 0.0564882 loss)
I0409 21:24:47.011618 14789 sgd_solver.cpp:105] Iteration 9960, lr = 0.00139048
I0409 21:24:51.876915 14789 solver.cpp:218] Iteration 9972 (2.46651 iter/s, 4.86518s/12 iters), loss = 0.0655479
I0409 21:24:51.876973 14789 solver.cpp:237]     Train net output #0: loss = 0.0655479 (* 1 = 0.0655479 loss)
I0409 21:24:51.876986 14789 sgd_solver.cpp:105] Iteration 9972, lr = 0.00138718
I0409 21:24:56.846839 14789 solver.cpp:218] Iteration 9984 (2.41461 iter/s, 4.96974s/12 iters), loss = 0.0611029
I0409 21:24:56.846884 14789 solver.cpp:237]     Train net output #0: loss = 0.0611028 (* 1 = 0.0611028 loss)
I0409 21:24:56.846892 14789 sgd_solver.cpp:105] Iteration 9984, lr = 0.00138389
I0409 21:25:01.750393 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9996.caffemodel
I0409 21:25:05.542738 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9996.solverstate
I0409 21:25:08.504707 14789 solver.cpp:330] Iteration 9996, Testing net (#0)
I0409 21:25:08.504727 14789 net.cpp:676] Ignoring source layer train-data
I0409 21:25:09.015786 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:25:12.996588 14789 solver.cpp:397]     Test net output #0: accuracy = 0.560049
I0409 21:25:12.996634 14789 solver.cpp:397]     Test net output #1: loss = 2.42951 (* 1 = 2.42951 loss)
I0409 21:25:13.088379 14789 solver.cpp:218] Iteration 9996 (0.738865 iter/s, 16.2411s/12 iters), loss = 0.103189
I0409 21:25:13.088443 14789 solver.cpp:237]     Train net output #0: loss = 0.103189 (* 1 = 0.103189 loss)
I0409 21:25:13.088455 14789 sgd_solver.cpp:105] Iteration 9996, lr = 0.0013806
I0409 21:25:17.318080 14789 solver.cpp:218] Iteration 10008 (2.83719 iter/s, 4.22953s/12 iters), loss = 0.0231776
I0409 21:25:17.318200 14789 solver.cpp:237]     Train net output #0: loss = 0.0231776 (* 1 = 0.0231776 loss)
I0409 21:25:17.318210 14789 sgd_solver.cpp:105] Iteration 10008, lr = 0.00137732
I0409 21:25:19.442217 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:25:22.058611 14789 solver.cpp:218] Iteration 10020 (2.53149 iter/s, 4.7403s/12 iters), loss = 0.0873786
I0409 21:25:22.058658 14789 solver.cpp:237]     Train net output #0: loss = 0.0873786 (* 1 = 0.0873786 loss)
I0409 21:25:22.058670 14789 sgd_solver.cpp:105] Iteration 10020, lr = 0.00137405
I0409 21:25:27.056650 14789 solver.cpp:218] Iteration 10032 (2.40103 iter/s, 4.99786s/12 iters), loss = 0.0305851
I0409 21:25:27.056715 14789 solver.cpp:237]     Train net output #0: loss = 0.0305851 (* 1 = 0.0305851 loss)
I0409 21:25:27.056730 14789 sgd_solver.cpp:105] Iteration 10032, lr = 0.00137079
I0409 21:25:32.013749 14789 solver.cpp:218] Iteration 10044 (2.42086 iter/s, 4.95691s/12 iters), loss = 0.0295618
I0409 21:25:32.013804 14789 solver.cpp:237]     Train net output #0: loss = 0.0295618 (* 1 = 0.0295618 loss)
I0409 21:25:32.013818 14789 sgd_solver.cpp:105] Iteration 10044, lr = 0.00136754
I0409 21:25:37.355114 14789 solver.cpp:218] Iteration 10056 (2.2467 iter/s, 5.34118s/12 iters), loss = 0.0533106
I0409 21:25:37.355170 14789 solver.cpp:237]     Train net output #0: loss = 0.0533106 (* 1 = 0.0533106 loss)
I0409 21:25:37.355181 14789 sgd_solver.cpp:105] Iteration 10056, lr = 0.00136429
I0409 21:25:42.265262 14789 solver.cpp:218] Iteration 10068 (2.44401 iter/s, 4.90997s/12 iters), loss = 0.0743793
I0409 21:25:42.265308 14789 solver.cpp:237]     Train net output #0: loss = 0.0743793 (* 1 = 0.0743793 loss)
I0409 21:25:42.265319 14789 sgd_solver.cpp:105] Iteration 10068, lr = 0.00136105
I0409 21:25:47.104558 14789 solver.cpp:218] Iteration 10080 (2.47979 iter/s, 4.83913s/12 iters), loss = 0.059594
I0409 21:25:47.104609 14789 solver.cpp:237]     Train net output #0: loss = 0.059594 (* 1 = 0.059594 loss)
I0409 21:25:47.104619 14789 sgd_solver.cpp:105] Iteration 10080, lr = 0.00135782
I0409 21:25:52.085927 14789 solver.cpp:218] Iteration 10092 (2.40906 iter/s, 4.9812s/12 iters), loss = 0.0445644
I0409 21:25:52.086100 14789 solver.cpp:237]     Train net output #0: loss = 0.0445644 (* 1 = 0.0445644 loss)
I0409 21:25:52.086115 14789 sgd_solver.cpp:105] Iteration 10092, lr = 0.0013546
I0409 21:25:54.090039 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10098.caffemodel
I0409 21:26:00.063144 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10098.solverstate
I0409 21:26:03.070531 14789 solver.cpp:330] Iteration 10098, Testing net (#0)
I0409 21:26:03.070562 14789 net.cpp:676] Ignoring source layer train-data
I0409 21:26:03.496222 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:26:07.448699 14789 solver.cpp:397]     Test net output #0: accuracy = 0.58027
I0409 21:26:07.448750 14789 solver.cpp:397]     Test net output #1: loss = 2.41691 (* 1 = 2.41691 loss)
I0409 21:26:09.480711 14789 solver.cpp:218] Iteration 10104 (0.689884 iter/s, 17.3942s/12 iters), loss = 0.0444278
I0409 21:26:09.480758 14789 solver.cpp:237]     Train net output #0: loss = 0.0444278 (* 1 = 0.0444278 loss)
I0409 21:26:09.480768 14789 sgd_solver.cpp:105] Iteration 10104, lr = 0.00135138
I0409 21:26:13.772989 14800 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:26:14.343775 14789 solver.cpp:218] Iteration 10116 (2.46767 iter/s, 4.8629s/12 iters), loss = 0.0417337
I0409 21:26:14.343827 14789 solver.cpp:237]     Train net output #0: loss = 0.0417337 (* 1 = 0.0417337 loss)
I0409 21:26:14.343837 14789 sgd_solver.cpp:105] Iteration 10116, lr = 0.00134817
I0409 21:26:19.528479 14789 solver.cpp:218] Iteration 10128 (2.31458 iter/s, 5.18453s/12 iters), loss = 0.145479
I0409 21:26:19.528520 14789 solver.cpp:237]     Train net output #0: loss = 0.145479 (* 1 = 0.145479 loss)
I0409 21:26:19.528529 14789 sgd_solver.cpp:105] Iteration 10128, lr = 0.00134497
I0409 21:26:24.846246 14789 solver.cpp:218] Iteration 10140 (2.25666 iter/s, 5.3176s/12 iters), loss = 0.0838602
I0409 21:26:24.846387 14789 solver.cpp:237]     Train net output #0: loss = 0.0838602 (* 1 = 0.0838602 loss)
I0409 21:26:24.846398 14789 sgd_solver.cpp:105] Iteration 10140, lr = 0.00134178
I0409 21:26:29.829680 14789 solver.cpp:218] Iteration 10152 (2.40811 iter/s, 4.98317s/12 iters), loss = 0.0689777
I0409 21:26:29.829726 14789 solver.cpp:237]     Train net output #0: loss = 0.0689777 (* 1 = 0.0689777 loss)
I0409 21:26:29.829737 14789 sgd_solver.cpp:105] Iteration 10152, lr = 0.00133859
I0409 21:26:34.795835 14789 solver.cpp:218] Iteration 10164 (2.41644 iter/s, 4.96598s/12 iters), loss = 0.0330069
I0409 21:26:34.795877 14789 solver.cpp:237]     Train net output #0: loss = 0.0330069 (* 1 = 0.0330069 loss)
I0409 21:26:34.795886 14789 sgd_solver.cpp:105] Iteration 10164, lr = 0.00133541
I0409 21:26:39.635154 14789 solver.cpp:218] Iteration 10176 (2.47977 iter/s, 4.83916s/12 iters), loss = 0.0491489
I0409 21:26:39.635207 14789 solver.cpp:237]     Train net output #0: loss = 0.0491489 (* 1 = 0.0491489 loss)
I0409 21:26:39.635218 14789 sgd_solver.cpp:105] Iteration 10176, lr = 0.00133224
I0409 21:26:44.443771 14789 solver.cpp:218] Iteration 10188 (2.49561 iter/s, 4.80844s/12 iters), loss = 0.0152659
I0409 21:26:44.443830 14789 solver.cpp:237]     Train net output #0: loss = 0.0152659 (* 1 = 0.0152659 loss)
I0409 21:26:44.443841 14789 sgd_solver.cpp:105] Iteration 10188, lr = 0.00132908
I0409 21:26:48.982056 14789 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10200.caffemodel
I0409 21:26:58.951804 14789 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10200.solverstate
I0409 21:27:04.810017 14789 solver.cpp:310] Iteration 10200, loss = 0.0943946
I0409 21:27:04.810047 14789 solver.cpp:330] Iteration 10200, Testing net (#0)
I0409 21:27:04.810055 14789 net.cpp:676] Ignoring source layer train-data
I0409 21:27:05.203008 14801 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:27:09.272575 14789 solver.cpp:397]     Test net output #0: accuracy = 0.571691
I0409 21:27:09.272608 14789 solver.cpp:397]     Test net output #1: loss = 2.38578 (* 1 = 2.38578 loss)
I0409 21:27:09.272614 14789 solver.cpp:315] Optimization Done.
I0409 21:27:09.272617 14789 caffe.cpp:259] Optimization Done.