DIGITS-CNN/cars/architecture-investigations/fc/1-layer/2048/caffe_output.log

I0409 21:27:10.353022 26212 upgrade_proto.cpp:1082] Attempting to upgrade input file specified using deprecated 'solver_type' field (enum)': /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210409-205145-4d0e/solver.prototxt
I0409 21:27:10.353245 26212 upgrade_proto.cpp:1089] Successfully upgraded file specified using deprecated 'solver_type' field (enum) to 'type' field (string).
W0409 21:27:10.353253 26212 upgrade_proto.cpp:1091] Note that future Caffe releases will only support 'type' field (string) for a solver's type.
I0409 21:27:10.353345 26212 caffe.cpp:218] Using GPUs 0
I0409 21:27:10.374718 26212 caffe.cpp:223] GPU 0: GeForce GTX 1080 Ti
I0409 21:27:10.650171 26212 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 21:27:10.650967 26212 solver.cpp:87] Creating training net from net file: train_val.prototxt
I0409 21:27:10.651518 26212 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer val-data
I0409 21:27:10.651533 26212 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy
I0409 21:27:10.651664 26212 net.cpp:51] Initializing net from parameters:
state {
phase: TRAIN
level: 0
stage: ""
}
layer {
name: "train-data"
type: "Data"
top: "data"
top: "label"
include {
phase: TRAIN
}
transform_param {
mirror: true
crop_size: 227
mean_file: "/mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto"
}
data_param {
source: "/mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/train_db"
batch_size: 128
backend: LMDB
}
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 96
kernel_size: 11
stride: 4
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "conv1"
top: "conv1"
}
layer {
name: "norm1"
type: "LRN"
bottom: "conv1"
top: "norm1"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool1"
type: "Pooling"
bottom: "norm1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 2
kernel_size: 5
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu2"
type: "ReLU"
bottom: "conv2"
top: "conv2"
}
layer {
name: "norm2"
type: "LRN"
bottom: "conv2"
top: "norm2"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool2"
type: "Pooling"
bottom: "norm2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv3"
type: "Convolution"
bottom: "pool2"
top: "conv3"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu3"
type: "ReLU"
bottom: "conv3"
top: "conv3"
}
layer {
name: "conv4"
type: "Convolution"
bottom: "conv3"
top: "conv4"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu4"
type: "ReLU"
bottom: "conv4"
top: "conv4"
}
layer {
name: "conv5"
type: "Convolution"
bottom: "conv4"
top: "conv5"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu5"
type: "ReLU"
bottom: "conv5"
top: "conv5"
}
layer {
name: "pool5"
type: "Pooling"
bottom: "conv5"
top: "pool5"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "fc6"
type: "InnerProduct"
bottom: "pool5"
top: "fc6"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 2048
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu6"
type: "ReLU"
bottom: "fc6"
top: "fc6"
}
layer {
name: "drop6"
type: "Dropout"
bottom: "fc6"
top: "fc6"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc8"
type: "InnerProduct"
bottom: "fc6"
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 21:27:10.651747 26212 layer_factory.hpp:77] Creating layer train-data
I0409 21:27:10.653405 26212 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/train_db
I0409 21:27:10.653612 26212 net.cpp:84] Creating Layer train-data
I0409 21:27:10.653622 26212 net.cpp:380] train-data -> data
I0409 21:27:10.653642 26212 net.cpp:380] train-data -> label
I0409 21:27:10.653654 26212 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto
I0409 21:27:10.658577 26212 data_layer.cpp:45] output data size: 128,3,227,227
I0409 21:27:10.786224 26212 net.cpp:122] Setting up train-data
I0409 21:27:10.786248 26212 net.cpp:129] Top shape: 128 3 227 227 (19787136)
I0409 21:27:10.786254 26212 net.cpp:129] Top shape: 128 (128)
I0409 21:27:10.786257 26212 net.cpp:137] Memory required for data: 79149056
I0409 21:27:10.786268 26212 layer_factory.hpp:77] Creating layer conv1
I0409 21:27:10.786290 26212 net.cpp:84] Creating Layer conv1
I0409 21:27:10.786295 26212 net.cpp:406] conv1 <- data
I0409 21:27:10.786307 26212 net.cpp:380] conv1 -> conv1
I0409 21:27:11.467214 26212 net.cpp:122] Setting up conv1
I0409 21:27:11.467239 26212 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0409 21:27:11.467245 26212 net.cpp:137] Memory required for data: 227833856
I0409 21:27:11.467267 26212 layer_factory.hpp:77] Creating layer relu1
I0409 21:27:11.467278 26212 net.cpp:84] Creating Layer relu1
I0409 21:27:11.467283 26212 net.cpp:406] relu1 <- conv1
I0409 21:27:11.467288 26212 net.cpp:367] relu1 -> conv1 (in-place)
I0409 21:27:11.467609 26212 net.cpp:122] Setting up relu1
I0409 21:27:11.467622 26212 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0409 21:27:11.467626 26212 net.cpp:137] Memory required for data: 376518656
I0409 21:27:11.467631 26212 layer_factory.hpp:77] Creating layer norm1
I0409 21:27:11.467641 26212 net.cpp:84] Creating Layer norm1
I0409 21:27:11.467645 26212 net.cpp:406] norm1 <- conv1
I0409 21:27:11.467651 26212 net.cpp:380] norm1 -> norm1
I0409 21:27:11.468145 26212 net.cpp:122] Setting up norm1
I0409 21:27:11.468155 26212 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0409 21:27:11.468159 26212 net.cpp:137] Memory required for data: 525203456
I0409 21:27:11.468163 26212 layer_factory.hpp:77] Creating layer pool1
I0409 21:27:11.468173 26212 net.cpp:84] Creating Layer pool1
I0409 21:27:11.468176 26212 net.cpp:406] pool1 <- norm1
I0409 21:27:11.468181 26212 net.cpp:380] pool1 -> pool1
I0409 21:27:11.468245 26212 net.cpp:122] Setting up pool1
I0409 21:27:11.468251 26212 net.cpp:129] Top shape: 128 96 27 27 (8957952)
I0409 21:27:11.468255 26212 net.cpp:137] Memory required for data: 561035264
I0409 21:27:11.468259 26212 layer_factory.hpp:77] Creating layer conv2
I0409 21:27:11.468271 26212 net.cpp:84] Creating Layer conv2
I0409 21:27:11.468274 26212 net.cpp:406] conv2 <- pool1
I0409 21:27:11.468281 26212 net.cpp:380] conv2 -> conv2
I0409 21:27:11.475793 26212 net.cpp:122] Setting up conv2
I0409 21:27:11.475811 26212 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0409 21:27:11.475814 26212 net.cpp:137] Memory required for data: 656586752
I0409 21:27:11.475826 26212 layer_factory.hpp:77] Creating layer relu2
I0409 21:27:11.475833 26212 net.cpp:84] Creating Layer relu2
I0409 21:27:11.475837 26212 net.cpp:406] relu2 <- conv2
I0409 21:27:11.475843 26212 net.cpp:367] relu2 -> conv2 (in-place)
I0409 21:27:11.476351 26212 net.cpp:122] Setting up relu2
I0409 21:27:11.476362 26212 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0409 21:27:11.476366 26212 net.cpp:137] Memory required for data: 752138240
I0409 21:27:11.476372 26212 layer_factory.hpp:77] Creating layer norm2
I0409 21:27:11.476382 26212 net.cpp:84] Creating Layer norm2
I0409 21:27:11.476385 26212 net.cpp:406] norm2 <- conv2
I0409 21:27:11.476393 26212 net.cpp:380] norm2 -> norm2
I0409 21:27:11.476724 26212 net.cpp:122] Setting up norm2
I0409 21:27:11.476734 26212 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0409 21:27:11.476738 26212 net.cpp:137] Memory required for data: 847689728
I0409 21:27:11.476745 26212 layer_factory.hpp:77] Creating layer pool2
I0409 21:27:11.476755 26212 net.cpp:84] Creating Layer pool2
I0409 21:27:11.476760 26212 net.cpp:406] pool2 <- norm2
I0409 21:27:11.476765 26212 net.cpp:380] pool2 -> pool2
I0409 21:27:11.476796 26212 net.cpp:122] Setting up pool2
I0409 21:27:11.476801 26212 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0409 21:27:11.476805 26212 net.cpp:137] Memory required for data: 869840896
I0409 21:27:11.476809 26212 layer_factory.hpp:77] Creating layer conv3
I0409 21:27:11.476819 26212 net.cpp:84] Creating Layer conv3
I0409 21:27:11.476821 26212 net.cpp:406] conv3 <- pool2
I0409 21:27:11.476826 26212 net.cpp:380] conv3 -> conv3
I0409 21:27:11.487946 26212 net.cpp:122] Setting up conv3
I0409 21:27:11.487962 26212 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0409 21:27:11.487967 26212 net.cpp:137] Memory required for data: 903067648
I0409 21:27:11.487977 26212 layer_factory.hpp:77] Creating layer relu3
I0409 21:27:11.487984 26212 net.cpp:84] Creating Layer relu3
I0409 21:27:11.487988 26212 net.cpp:406] relu3 <- conv3
I0409 21:27:11.487994 26212 net.cpp:367] relu3 -> conv3 (in-place)
I0409 21:27:11.490367 26212 net.cpp:122] Setting up relu3
I0409 21:27:11.490377 26212 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0409 21:27:11.490382 26212 net.cpp:137] Memory required for data: 936294400
I0409 21:27:11.490387 26212 layer_factory.hpp:77] Creating layer conv4
I0409 21:27:11.490399 26212 net.cpp:84] Creating Layer conv4
I0409 21:27:11.490406 26212 net.cpp:406] conv4 <- conv3
I0409 21:27:11.490417 26212 net.cpp:380] conv4 -> conv4
I0409 21:27:11.508656 26212 net.cpp:122] Setting up conv4
I0409 21:27:11.508672 26212 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0409 21:27:11.508675 26212 net.cpp:137] Memory required for data: 969521152
I0409 21:27:11.508684 26212 layer_factory.hpp:77] Creating layer relu4
I0409 21:27:11.508692 26212 net.cpp:84] Creating Layer relu4
I0409 21:27:11.508697 26212 net.cpp:406] relu4 <- conv4
I0409 21:27:11.508702 26212 net.cpp:367] relu4 -> conv4 (in-place)
I0409 21:27:11.509075 26212 net.cpp:122] Setting up relu4
I0409 21:27:11.509084 26212 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0409 21:27:11.509088 26212 net.cpp:137] Memory required for data: 1002747904
I0409 21:27:11.509091 26212 layer_factory.hpp:77] Creating layer conv5
I0409 21:27:11.509104 26212 net.cpp:84] Creating Layer conv5
I0409 21:27:11.509107 26212 net.cpp:406] conv5 <- conv4
I0409 21:27:11.509131 26212 net.cpp:380] conv5 -> conv5
I0409 21:27:11.518518 26212 net.cpp:122] Setting up conv5
I0409 21:27:11.518537 26212 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0409 21:27:11.518540 26212 net.cpp:137] Memory required for data: 1024899072
I0409 21:27:11.518555 26212 layer_factory.hpp:77] Creating layer relu5
I0409 21:27:11.518566 26212 net.cpp:84] Creating Layer relu5
I0409 21:27:11.518571 26212 net.cpp:406] relu5 <- conv5
I0409 21:27:11.518579 26212 net.cpp:367] relu5 -> conv5 (in-place)
I0409 21:27:11.519124 26212 net.cpp:122] Setting up relu5
I0409 21:27:11.519134 26212 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0409 21:27:11.519137 26212 net.cpp:137] Memory required for data: 1047050240
I0409 21:27:11.519141 26212 layer_factory.hpp:77] Creating layer pool5
I0409 21:27:11.519150 26212 net.cpp:84] Creating Layer pool5
I0409 21:27:11.519153 26212 net.cpp:406] pool5 <- conv5
I0409 21:27:11.519160 26212 net.cpp:380] pool5 -> pool5
I0409 21:27:11.519203 26212 net.cpp:122] Setting up pool5
I0409 21:27:11.519210 26212 net.cpp:129] Top shape: 128 256 6 6 (1179648)
I0409 21:27:11.519213 26212 net.cpp:137] Memory required for data: 1051768832
I0409 21:27:11.519217 26212 layer_factory.hpp:77] Creating layer fc6
I0409 21:27:11.519227 26212 net.cpp:84] Creating Layer fc6
I0409 21:27:11.519230 26212 net.cpp:406] fc6 <- pool5
I0409 21:27:11.519237 26212 net.cpp:380] fc6 -> fc6
I0409 21:27:11.715147 26212 net.cpp:122] Setting up fc6
I0409 21:27:11.715169 26212 net.cpp:129] Top shape: 128 2048 (262144)
I0409 21:27:11.715173 26212 net.cpp:137] Memory required for data: 1052817408
I0409 21:27:11.715184 26212 layer_factory.hpp:77] Creating layer relu6
I0409 21:27:11.715193 26212 net.cpp:84] Creating Layer relu6
I0409 21:27:11.715198 26212 net.cpp:406] relu6 <- fc6
I0409 21:27:11.715204 26212 net.cpp:367] relu6 -> fc6 (in-place)
I0409 21:27:11.718268 26212 net.cpp:122] Setting up relu6
I0409 21:27:11.718278 26212 net.cpp:129] Top shape: 128 2048 (262144)
I0409 21:27:11.718282 26212 net.cpp:137] Memory required for data: 1053865984
I0409 21:27:11.718286 26212 layer_factory.hpp:77] Creating layer drop6
I0409 21:27:11.718293 26212 net.cpp:84] Creating Layer drop6
I0409 21:27:11.718297 26212 net.cpp:406] drop6 <- fc6
I0409 21:27:11.718304 26212 net.cpp:367] drop6 -> fc6 (in-place)
I0409 21:27:11.718333 26212 net.cpp:122] Setting up drop6
I0409 21:27:11.718338 26212 net.cpp:129] Top shape: 128 2048 (262144)
I0409 21:27:11.718341 26212 net.cpp:137] Memory required for data: 1054914560
I0409 21:27:11.718345 26212 layer_factory.hpp:77] Creating layer fc8
I0409 21:27:11.718353 26212 net.cpp:84] Creating Layer fc8
I0409 21:27:11.718358 26212 net.cpp:406] fc8 <- fc6
I0409 21:27:11.718362 26212 net.cpp:380] fc8 -> fc8
I0409 21:27:11.722781 26212 net.cpp:122] Setting up fc8
I0409 21:27:11.722791 26212 net.cpp:129] Top shape: 128 196 (25088)
I0409 21:27:11.722795 26212 net.cpp:137] Memory required for data: 1055014912
I0409 21:27:11.722801 26212 layer_factory.hpp:77] Creating layer loss
I0409 21:27:11.722808 26212 net.cpp:84] Creating Layer loss
I0409 21:27:11.722812 26212 net.cpp:406] loss <- fc8
I0409 21:27:11.722817 26212 net.cpp:406] loss <- label
I0409 21:27:11.722824 26212 net.cpp:380] loss -> loss
I0409 21:27:11.722834 26212 layer_factory.hpp:77] Creating layer loss
I0409 21:27:11.723449 26212 net.cpp:122] Setting up loss
I0409 21:27:11.723459 26212 net.cpp:129] Top shape: (1)
I0409 21:27:11.723462 26212 net.cpp:132]     with loss weight 1
I0409 21:27:11.723481 26212 net.cpp:137] Memory required for data: 1055014916
I0409 21:27:11.723485 26212 net.cpp:198] loss needs backward computation.
I0409 21:27:11.723492 26212 net.cpp:198] fc8 needs backward computation.
I0409 21:27:11.723496 26212 net.cpp:198] drop6 needs backward computation.
I0409 21:27:11.723500 26212 net.cpp:198] relu6 needs backward computation.
I0409 21:27:11.723503 26212 net.cpp:198] fc6 needs backward computation.
I0409 21:27:11.723506 26212 net.cpp:198] pool5 needs backward computation.
I0409 21:27:11.723510 26212 net.cpp:198] relu5 needs backward computation.
I0409 21:27:11.723531 26212 net.cpp:198] conv5 needs backward computation.
I0409 21:27:11.723536 26212 net.cpp:198] relu4 needs backward computation.
I0409 21:27:11.723538 26212 net.cpp:198] conv4 needs backward computation.
I0409 21:27:11.723542 26212 net.cpp:198] relu3 needs backward computation.
I0409 21:27:11.723546 26212 net.cpp:198] conv3 needs backward computation.
I0409 21:27:11.723549 26212 net.cpp:198] pool2 needs backward computation.
I0409 21:27:11.723553 26212 net.cpp:198] norm2 needs backward computation.
I0409 21:27:11.723557 26212 net.cpp:198] relu2 needs backward computation.
I0409 21:27:11.723562 26212 net.cpp:198] conv2 needs backward computation.
I0409 21:27:11.723565 26212 net.cpp:198] pool1 needs backward computation.
I0409 21:27:11.723569 26212 net.cpp:198] norm1 needs backward computation.
I0409 21:27:11.723573 26212 net.cpp:198] relu1 needs backward computation.
I0409 21:27:11.723577 26212 net.cpp:198] conv1 needs backward computation.
I0409 21:27:11.723582 26212 net.cpp:200] train-data does not need backward computation.
I0409 21:27:11.723587 26212 net.cpp:242] This network produces output loss
I0409 21:27:11.723601 26212 net.cpp:255] Network initialization done.
I0409 21:27:11.764161 26212 solver.cpp:172] Creating test net (#0) specified by net file: train_val.prototxt
I0409 21:27:11.764245 26212 net.cpp:294] The NetState phase (1) differed from the phase (0) specified by a rule in layer train-data
I0409 21:27:11.764637 26212 net.cpp:51] Initializing net from parameters:
state {
phase: TEST
}
layer {
name: "val-data"
type: "Data"
top: "data"
top: "label"
include {
phase: TEST
}
transform_param {
crop_size: 227
mean_file: "/mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto"
}
data_param {
source: "/mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/val_db"
batch_size: 32
backend: LMDB
}
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 96
kernel_size: 11
stride: 4
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "conv1"
top: "conv1"
}
layer {
name: "norm1"
type: "LRN"
bottom: "conv1"
top: "norm1"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool1"
type: "Pooling"
bottom: "norm1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 2
kernel_size: 5
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu2"
type: "ReLU"
bottom: "conv2"
top: "conv2"
}
layer {
name: "norm2"
type: "LRN"
bottom: "conv2"
top: "norm2"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool2"
type: "Pooling"
bottom: "norm2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv3"
type: "Convolution"
bottom: "pool2"
top: "conv3"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu3"
type: "ReLU"
bottom: "conv3"
top: "conv3"
}
layer {
name: "conv4"
type: "Convolution"
bottom: "conv3"
top: "conv4"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu4"
type: "ReLU"
bottom: "conv4"
top: "conv4"
}
layer {
name: "conv5"
type: "Convolution"
bottom: "conv4"
top: "conv5"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu5"
type: "ReLU"
bottom: "conv5"
top: "conv5"
}
layer {
name: "pool5"
type: "Pooling"
bottom: "conv5"
top: "pool5"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "fc6"
type: "InnerProduct"
bottom: "pool5"
top: "fc6"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 2048
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu6"
type: "ReLU"
bottom: "fc6"
top: "fc6"
}
layer {
name: "drop6"
type: "Dropout"
bottom: "fc6"
top: "fc6"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc8"
type: "InnerProduct"
bottom: "fc6"
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 21:27:11.764869 26212 layer_factory.hpp:77] Creating layer val-data
I0409 21:27:12.016528 26212 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/val_db
I0409 21:27:12.017174 26212 net.cpp:84] Creating Layer val-data
I0409 21:27:12.017206 26212 net.cpp:380] val-data -> data
I0409 21:27:12.017230 26212 net.cpp:380] val-data -> label
I0409 21:27:12.017247 26212 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto
I0409 21:27:12.051597 26212 data_layer.cpp:45] output data size: 32,3,227,227
I0409 21:27:12.095098 26212 net.cpp:122] Setting up val-data
I0409 21:27:12.095120 26212 net.cpp:129] Top shape: 32 3 227 227 (4946784)
I0409 21:27:12.095126 26212 net.cpp:129] Top shape: 32 (32)
I0409 21:27:12.095129 26212 net.cpp:137] Memory required for data: 19787264
I0409 21:27:12.095136 26212 layer_factory.hpp:77] Creating layer label_val-data_1_split
I0409 21:27:12.095149 26212 net.cpp:84] Creating Layer label_val-data_1_split
I0409 21:27:12.095155 26212 net.cpp:406] label_val-data_1_split <- label
I0409 21:27:12.095161 26212 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_0
I0409 21:27:12.095173 26212 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_1
I0409 21:27:12.095218 26212 net.cpp:122] Setting up label_val-data_1_split
I0409 21:27:12.095224 26212 net.cpp:129] Top shape: 32 (32)
I0409 21:27:12.095230 26212 net.cpp:129] Top shape: 32 (32)
I0409 21:27:12.095233 26212 net.cpp:137] Memory required for data: 19787520
I0409 21:27:12.095237 26212 layer_factory.hpp:77] Creating layer conv1
I0409 21:27:12.095250 26212 net.cpp:84] Creating Layer conv1
I0409 21:27:12.095254 26212 net.cpp:406] conv1 <- data
I0409 21:27:12.095261 26212 net.cpp:380] conv1 -> conv1
I0409 21:27:12.099725 26212 net.cpp:122] Setting up conv1
I0409 21:27:12.099742 26212 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0409 21:27:12.099748 26212 net.cpp:137] Memory required for data: 56958720
I0409 21:27:12.099762 26212 layer_factory.hpp:77] Creating layer relu1
I0409 21:27:12.099772 26212 net.cpp:84] Creating Layer relu1
I0409 21:27:12.099803 26212 net.cpp:406] relu1 <- conv1
I0409 21:27:12.099812 26212 net.cpp:367] relu1 -> conv1 (in-place)
I0409 21:27:12.100543 26212 net.cpp:122] Setting up relu1
I0409 21:27:12.100558 26212 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0409 21:27:12.100562 26212 net.cpp:137] Memory required for data: 94129920
I0409 21:27:12.100569 26212 layer_factory.hpp:77] Creating layer norm1
I0409 21:27:12.100581 26212 net.cpp:84] Creating Layer norm1
I0409 21:27:12.100587 26212 net.cpp:406] norm1 <- conv1
I0409 21:27:12.100595 26212 net.cpp:380] norm1 -> norm1
I0409 21:27:12.101099 26212 net.cpp:122] Setting up norm1
I0409 21:27:12.101111 26212 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0409 21:27:12.101116 26212 net.cpp:137] Memory required for data: 131301120
I0409 21:27:12.101121 26212 layer_factory.hpp:77] Creating layer pool1
I0409 21:27:12.101131 26212 net.cpp:84] Creating Layer pool1
I0409 21:27:12.101136 26212 net.cpp:406] pool1 <- norm1
I0409 21:27:12.101145 26212 net.cpp:380] pool1 -> pool1
I0409 21:27:12.101188 26212 net.cpp:122] Setting up pool1
I0409 21:27:12.101197 26212 net.cpp:129] Top shape: 32 96 27 27 (2239488)
I0409 21:27:12.101202 26212 net.cpp:137] Memory required for data: 140259072
I0409 21:27:12.101207 26212 layer_factory.hpp:77] Creating layer conv2
I0409 21:27:12.101218 26212 net.cpp:84] Creating Layer conv2
I0409 21:27:12.101223 26212 net.cpp:406] conv2 <- pool1
I0409 21:27:12.101231 26212 net.cpp:380] conv2 -> conv2
I0409 21:27:12.114305 26212 net.cpp:122] Setting up conv2
I0409 21:27:12.114331 26212 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0409 21:27:12.114337 26212 net.cpp:137] Memory required for data: 164146944
I0409 21:27:12.114354 26212 layer_factory.hpp:77] Creating layer relu2
I0409 21:27:12.114367 26212 net.cpp:84] Creating Layer relu2
I0409 21:27:12.114373 26212 net.cpp:406] relu2 <- conv2
I0409 21:27:12.114387 26212 net.cpp:367] relu2 -> conv2 (in-place)
I0409 21:27:12.116688 26212 net.cpp:122] Setting up relu2
I0409 21:27:12.116705 26212 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0409 21:27:12.116710 26212 net.cpp:137] Memory required for data: 188034816
I0409 21:27:12.116716 26212 layer_factory.hpp:77] Creating layer norm2
I0409 21:27:12.116732 26212 net.cpp:84] Creating Layer norm2
I0409 21:27:12.116739 26212 net.cpp:406] norm2 <- conv2
I0409 21:27:12.116748 26212 net.cpp:380] norm2 -> norm2
I0409 21:27:12.117571 26212 net.cpp:122] Setting up norm2
I0409 21:27:12.117588 26212 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0409 21:27:12.117594 26212 net.cpp:137] Memory required for data: 211922688
I0409 21:27:12.117599 26212 layer_factory.hpp:77] Creating layer pool2
I0409 21:27:12.117610 26212 net.cpp:84] Creating Layer pool2
I0409 21:27:12.117616 26212 net.cpp:406] pool2 <- norm2
I0409 21:27:12.117624 26212 net.cpp:380] pool2 -> pool2
I0409 21:27:12.117674 26212 net.cpp:122] Setting up pool2
I0409 21:27:12.117682 26212 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0409 21:27:12.117686 26212 net.cpp:137] Memory required for data: 217460480
I0409 21:27:12.117692 26212 layer_factory.hpp:77] Creating layer conv3
I0409 21:27:12.117707 26212 net.cpp:84] Creating Layer conv3
I0409 21:27:12.117712 26212 net.cpp:406] conv3 <- pool2
I0409 21:27:12.117722 26212 net.cpp:380] conv3 -> conv3
I0409 21:27:12.138408 26212 net.cpp:122] Setting up conv3
I0409 21:27:12.138432 26212 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0409 21:27:12.138437 26212 net.cpp:137] Memory required for data: 225767168
I0409 21:27:12.138453 26212 layer_factory.hpp:77] Creating layer relu3
I0409 21:27:12.138464 26212 net.cpp:84] Creating Layer relu3
I0409 21:27:12.138470 26212 net.cpp:406] relu3 <- conv3
I0409 21:27:12.138478 26212 net.cpp:367] relu3 -> conv3 (in-place)
I0409 21:27:12.140584 26212 net.cpp:122] Setting up relu3
I0409 21:27:12.140600 26212 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0409 21:27:12.140605 26212 net.cpp:137] Memory required for data: 234073856
I0409 21:27:12.140610 26212 layer_factory.hpp:77] Creating layer conv4
I0409 21:27:12.140648 26212 net.cpp:84] Creating Layer conv4
I0409 21:27:12.140655 26212 net.cpp:406] conv4 <- conv3
I0409 21:27:12.140663 26212 net.cpp:380] conv4 -> conv4
I0409 21:27:12.157246 26212 net.cpp:122] Setting up conv4
I0409 21:27:12.157272 26212 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0409 21:27:12.157279 26212 net.cpp:137] Memory required for data: 242380544
I0409 21:27:12.157290 26212 layer_factory.hpp:77] Creating layer relu4
I0409 21:27:12.157301 26212 net.cpp:84] Creating Layer relu4
I0409 21:27:12.157306 26212 net.cpp:406] relu4 <- conv4
I0409 21:27:12.157315 26212 net.cpp:367] relu4 -> conv4 (in-place)
I0409 21:27:12.168238 26212 net.cpp:122] Setting up relu4
I0409 21:27:12.168257 26212 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0409 21:27:12.168262 26212 net.cpp:137] Memory required for data: 250687232
I0409 21:27:12.168267 26212 layer_factory.hpp:77] Creating layer conv5
I0409 21:27:12.168283 26212 net.cpp:84] Creating Layer conv5
I0409 21:27:12.168289 26212 net.cpp:406] conv5 <- conv4
I0409 21:27:12.168298 26212 net.cpp:380] conv5 -> conv5
I0409 21:27:12.183733 26212 net.cpp:122] Setting up conv5
I0409 21:27:12.183753 26212 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0409 21:27:12.183756 26212 net.cpp:137] Memory required for data: 256225024
I0409 21:27:12.183771 26212 layer_factory.hpp:77] Creating layer relu5
I0409 21:27:12.183784 26212 net.cpp:84] Creating Layer relu5
I0409 21:27:12.183789 26212 net.cpp:406] relu5 <- conv5
I0409 21:27:12.183796 26212 net.cpp:367] relu5 -> conv5 (in-place)
I0409 21:27:12.184439 26212 net.cpp:122] Setting up relu5
I0409 21:27:12.184451 26212 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0409 21:27:12.184456 26212 net.cpp:137] Memory required for data: 261762816
I0409 21:27:12.184460 26212 layer_factory.hpp:77] Creating layer pool5
I0409 21:27:12.184473 26212 net.cpp:84] Creating Layer pool5
I0409 21:27:12.184478 26212 net.cpp:406] pool5 <- conv5
I0409 21:27:12.184484 26212 net.cpp:380] pool5 -> pool5
I0409 21:27:12.184533 26212 net.cpp:122] Setting up pool5
I0409 21:27:12.184540 26212 net.cpp:129] Top shape: 32 256 6 6 (294912)
I0409 21:27:12.184543 26212 net.cpp:137] Memory required for data: 262942464
I0409 21:27:12.184547 26212 layer_factory.hpp:77] Creating layer fc6
I0409 21:27:12.184557 26212 net.cpp:84] Creating Layer fc6
I0409 21:27:12.184561 26212 net.cpp:406] fc6 <- pool5
I0409 21:27:12.184569 26212 net.cpp:380] fc6 -> fc6
I0409 21:27:12.386214 26212 net.cpp:122] Setting up fc6
I0409 21:27:12.386235 26212 net.cpp:129] Top shape: 32 2048 (65536)
I0409 21:27:12.386238 26212 net.cpp:137] Memory required for data: 263204608
I0409 21:27:12.386248 26212 layer_factory.hpp:77] Creating layer relu6
I0409 21:27:12.386260 26212 net.cpp:84] Creating Layer relu6
I0409 21:27:12.386265 26212 net.cpp:406] relu6 <- fc6
I0409 21:27:12.386270 26212 net.cpp:367] relu6 -> fc6 (in-place)
I0409 21:27:12.386961 26212 net.cpp:122] Setting up relu6
I0409 21:27:12.386970 26212 net.cpp:129] Top shape: 32 2048 (65536)
I0409 21:27:12.386974 26212 net.cpp:137] Memory required for data: 263466752
I0409 21:27:12.386978 26212 layer_factory.hpp:77] Creating layer drop6
I0409 21:27:12.386986 26212 net.cpp:84] Creating Layer drop6
I0409 21:27:12.386991 26212 net.cpp:406] drop6 <- fc6
I0409 21:27:12.386997 26212 net.cpp:367] drop6 -> fc6 (in-place)
I0409 21:27:12.387023 26212 net.cpp:122] Setting up drop6
I0409 21:27:12.387028 26212 net.cpp:129] Top shape: 32 2048 (65536)
I0409 21:27:12.387032 26212 net.cpp:137] Memory required for data: 263728896
I0409 21:27:12.387034 26212 layer_factory.hpp:77] Creating layer fc8
I0409 21:27:12.387043 26212 net.cpp:84] Creating Layer fc8
I0409 21:27:12.387048 26212 net.cpp:406] fc8 <- fc6
I0409 21:27:12.387053 26212 net.cpp:380] fc8 -> fc8
I0409 21:27:12.391535 26212 net.cpp:122] Setting up fc8
I0409 21:27:12.391546 26212 net.cpp:129] Top shape: 32 196 (6272)
I0409 21:27:12.391549 26212 net.cpp:137] Memory required for data: 263753984
I0409 21:27:12.391557 26212 layer_factory.hpp:77] Creating layer fc8_fc8_0_split
I0409 21:27:12.391564 26212 net.cpp:84] Creating Layer fc8_fc8_0_split
I0409 21:27:12.391588 26212 net.cpp:406] fc8_fc8_0_split <- fc8
I0409 21:27:12.391595 26212 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_0
I0409 21:27:12.391606 26212 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_1
I0409 21:27:12.391644 26212 net.cpp:122] Setting up fc8_fc8_0_split
I0409 21:27:12.391651 26212 net.cpp:129] Top shape: 32 196 (6272)
I0409 21:27:12.391654 26212 net.cpp:129] Top shape: 32 196 (6272)
I0409 21:27:12.391659 26212 net.cpp:137] Memory required for data: 263804160
I0409 21:27:12.391661 26212 layer_factory.hpp:77] Creating layer accuracy
I0409 21:27:12.391669 26212 net.cpp:84] Creating Layer accuracy
I0409 21:27:12.391671 26212 net.cpp:406] accuracy <- fc8_fc8_0_split_0
I0409 21:27:12.391676 26212 net.cpp:406] accuracy <- label_val-data_1_split_0
I0409 21:27:12.391681 26212 net.cpp:380] accuracy -> accuracy
I0409 21:27:12.391688 26212 net.cpp:122] Setting up accuracy
I0409 21:27:12.391692 26212 net.cpp:129] Top shape: (1)
I0409 21:27:12.391696 26212 net.cpp:137] Memory required for data: 263804164
I0409 21:27:12.391700 26212 layer_factory.hpp:77] Creating layer loss
I0409 21:27:12.391705 26212 net.cpp:84] Creating Layer loss
I0409 21:27:12.391708 26212 net.cpp:406] loss <- fc8_fc8_0_split_1
I0409 21:27:12.391712 26212 net.cpp:406] loss <- label_val-data_1_split_1
I0409 21:27:12.391718 26212 net.cpp:380] loss -> loss
I0409 21:27:12.391726 26212 layer_factory.hpp:77] Creating layer loss
I0409 21:27:12.392608 26212 net.cpp:122] Setting up loss
I0409 21:27:12.392618 26212 net.cpp:129] Top shape: (1)
I0409 21:27:12.392621 26212 net.cpp:132]     with loss weight 1
I0409 21:27:12.392632 26212 net.cpp:137] Memory required for data: 263804168
I0409 21:27:12.392637 26212 net.cpp:198] loss needs backward computation.
I0409 21:27:12.392642 26212 net.cpp:200] accuracy does not need backward computation.
I0409 21:27:12.392647 26212 net.cpp:198] fc8_fc8_0_split needs backward computation.
I0409 21:27:12.392649 26212 net.cpp:198] fc8 needs backward computation.
I0409 21:27:12.392653 26212 net.cpp:198] drop6 needs backward computation.
I0409 21:27:12.392657 26212 net.cpp:198] relu6 needs backward computation.
I0409 21:27:12.392660 26212 net.cpp:198] fc6 needs backward computation.
I0409 21:27:12.392663 26212 net.cpp:198] pool5 needs backward computation.
I0409 21:27:12.392668 26212 net.cpp:198] relu5 needs backward computation.
I0409 21:27:12.392671 26212 net.cpp:198] conv5 needs backward computation.
I0409 21:27:12.392675 26212 net.cpp:198] relu4 needs backward computation.
I0409 21:27:12.392678 26212 net.cpp:198] conv4 needs backward computation.
I0409 21:27:12.392683 26212 net.cpp:198] relu3 needs backward computation.
I0409 21:27:12.392685 26212 net.cpp:198] conv3 needs backward computation.
I0409 21:27:12.392689 26212 net.cpp:198] pool2 needs backward computation.
I0409 21:27:12.392693 26212 net.cpp:198] norm2 needs backward computation.
I0409 21:27:12.392696 26212 net.cpp:198] relu2 needs backward computation.
I0409 21:27:12.392700 26212 net.cpp:198] conv2 needs backward computation.
I0409 21:27:12.392704 26212 net.cpp:198] pool1 needs backward computation.
I0409 21:27:12.392707 26212 net.cpp:198] norm1 needs backward computation.
I0409 21:27:12.392711 26212 net.cpp:198] relu1 needs backward computation.
I0409 21:27:12.392714 26212 net.cpp:198] conv1 needs backward computation.
I0409 21:27:12.392719 26212 net.cpp:200] label_val-data_1_split does not need backward computation.
I0409 21:27:12.392724 26212 net.cpp:200] val-data does not need backward computation.
I0409 21:27:12.392726 26212 net.cpp:242] This network produces output accuracy
I0409 21:27:12.392730 26212 net.cpp:242] This network produces output loss
I0409 21:27:12.392746 26212 net.cpp:255] Network initialization done.
I0409 21:27:12.392812 26212 solver.cpp:56] Solver scaffolding done.
I0409 21:27:12.393215 26212 caffe.cpp:248] Starting Optimization
I0409 21:27:12.393224 26212 solver.cpp:272] Solving
I0409 21:27:12.393227 26212 solver.cpp:273] Learning Rate Policy: exp
I0409 21:27:12.395452 26212 solver.cpp:330] Iteration 0, Testing net (#0)
I0409 21:27:12.395476 26212 net.cpp:676] Ignoring source layer train-data
I0409 21:27:12.448077 26212 blocking_queue.cpp:49] Waiting for data
I0409 21:27:16.993917 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:27:17.038866 26212 solver.cpp:397]     Test net output #0: accuracy = 0.00306373
I0409 21:27:17.038910 26212 solver.cpp:397]     Test net output #1: loss = 5.28106 (* 1 = 5.28106 loss)
I0409 21:27:17.127070 26212 solver.cpp:218] Iteration 0 (8.98577e+36 iter/s, 4.73369s/12 iters), loss = 5.28199
I0409 21:27:17.127123 26212 solver.cpp:237]     Train net output #0: loss = 5.28199 (* 1 = 5.28199 loss)
I0409 21:27:17.127151 26212 sgd_solver.cpp:105] Iteration 0, lr = 0.01
I0409 21:27:20.989408 26212 solver.cpp:218] Iteration 12 (3.10706 iter/s, 3.86217s/12 iters), loss = 5.28043
I0409 21:27:20.989465 26212 solver.cpp:237]     Train net output #0: loss = 5.28043 (* 1 = 5.28043 loss)
I0409 21:27:20.989477 26212 sgd_solver.cpp:105] Iteration 12, lr = 0.00997626
I0409 21:27:25.983983 26212 solver.cpp:218] Iteration 24 (2.40269 iter/s, 4.99439s/12 iters), loss = 5.28696
I0409 21:27:25.984028 26212 solver.cpp:237]     Train net output #0: loss = 5.28696 (* 1 = 5.28696 loss)
I0409 21:27:25.984036 26212 sgd_solver.cpp:105] Iteration 24, lr = 0.00995257
I0409 21:27:30.799474 26212 solver.cpp:218] Iteration 36 (2.49205 iter/s, 4.81532s/12 iters), loss = 5.2892
I0409 21:27:30.799526 26212 solver.cpp:237]     Train net output #0: loss = 5.2892 (* 1 = 5.2892 loss)
I0409 21:27:30.799537 26212 sgd_solver.cpp:105] Iteration 36, lr = 0.00992894
I0409 21:27:35.751219 26212 solver.cpp:218] Iteration 48 (2.42347 iter/s, 4.95157s/12 iters), loss = 5.29514
I0409 21:27:35.751260 26212 solver.cpp:237]     Train net output #0: loss = 5.29514 (* 1 = 5.29514 loss)
I0409 21:27:35.751268 26212 sgd_solver.cpp:105] Iteration 48, lr = 0.00990537
I0409 21:27:40.992801 26212 solver.cpp:218] Iteration 60 (2.28946 iter/s, 5.24141s/12 iters), loss = 5.28608
I0409 21:27:40.992978 26212 solver.cpp:237]     Train net output #0: loss = 5.28608 (* 1 = 5.28608 loss)
I0409 21:27:40.992992 26212 sgd_solver.cpp:105] Iteration 60, lr = 0.00988185
I0409 21:27:45.975383 26212 solver.cpp:218] Iteration 72 (2.40854 iter/s, 4.98228s/12 iters), loss = 5.29024
I0409 21:27:45.975436 26212 solver.cpp:237]     Train net output #0: loss = 5.29024 (* 1 = 5.29024 loss)
I0409 21:27:45.975450 26212 sgd_solver.cpp:105] Iteration 72, lr = 0.00985839
I0409 21:27:51.085595 26212 solver.cpp:218] Iteration 84 (2.34832 iter/s, 5.11004s/12 iters), loss = 5.2868
I0409 21:27:51.085636 26212 solver.cpp:237]     Train net output #0: loss = 5.2868 (* 1 = 5.2868 loss)
I0409 21:27:51.085645 26212 sgd_solver.cpp:105] Iteration 84, lr = 0.00983498
I0409 21:27:56.148658 26212 solver.cpp:218] Iteration 96 (2.37019 iter/s, 5.06289s/12 iters), loss = 5.29062
I0409 21:27:56.148713 26212 solver.cpp:237]     Train net output #0: loss = 5.29062 (* 1 = 5.29062 loss)
I0409 21:27:56.148723 26212 sgd_solver.cpp:105] Iteration 96, lr = 0.00981163
I0409 21:27:57.849164 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:27:58.161402 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_102.caffemodel
I0409 21:28:00.364192 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_102.solverstate
I0409 21:28:02.171010 26212 solver.cpp:330] Iteration 102, Testing net (#0)
I0409 21:28:02.171038 26212 net.cpp:676] Ignoring source layer train-data
I0409 21:28:06.607657 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:28:06.684370 26212 solver.cpp:397]     Test net output #0: accuracy = 0.00612745
I0409 21:28:06.684417 26212 solver.cpp:397]     Test net output #1: loss = 5.26429 (* 1 = 5.26429 loss)
I0409 21:28:08.517701 26212 solver.cpp:218] Iteration 108 (0.970214 iter/s, 12.3684s/12 iters), loss = 5.23915
I0409 21:28:08.517767 26212 solver.cpp:237]     Train net output #0: loss = 5.23915 (* 1 = 5.23915 loss)
I0409 21:28:08.517778 26212 sgd_solver.cpp:105] Iteration 108, lr = 0.00978834
I0409 21:28:13.550650 26212 solver.cpp:218] Iteration 120 (2.38437 iter/s, 5.03277s/12 iters), loss = 5.20867
I0409 21:28:13.550765 26212 solver.cpp:237]     Train net output #0: loss = 5.20867 (* 1 = 5.20867 loss)
I0409 21:28:13.550776 26212 sgd_solver.cpp:105] Iteration 120, lr = 0.0097651
I0409 21:28:18.598001 26212 solver.cpp:218] Iteration 132 (2.37761 iter/s, 5.04709s/12 iters), loss = 5.19453
I0409 21:28:18.598045 26212 solver.cpp:237]     Train net output #0: loss = 5.19453 (* 1 = 5.19453 loss)
I0409 21:28:18.598054 26212 sgd_solver.cpp:105] Iteration 132, lr = 0.00974192
I0409 21:28:23.549329 26212 solver.cpp:218] Iteration 144 (2.42368 iter/s, 4.95116s/12 iters), loss = 5.19501
I0409 21:28:23.549371 26212 solver.cpp:237]     Train net output #0: loss = 5.19501 (* 1 = 5.19501 loss)
I0409 21:28:23.549379 26212 sgd_solver.cpp:105] Iteration 144, lr = 0.00971879
I0409 21:28:28.982185 26212 solver.cpp:218] Iteration 156 (2.20886 iter/s, 5.43267s/12 iters), loss = 5.1966
I0409 21:28:28.982249 26212 solver.cpp:237]     Train net output #0: loss = 5.1966 (* 1 = 5.1966 loss)
I0409 21:28:28.982261 26212 sgd_solver.cpp:105] Iteration 156, lr = 0.00969571
I0409 21:28:33.817914 26212 solver.cpp:218] Iteration 168 (2.48163 iter/s, 4.83553s/12 iters), loss = 5.15233
I0409 21:28:33.818009 26212 solver.cpp:237]     Train net output #0: loss = 5.15233 (* 1 = 5.15233 loss)
I0409 21:28:33.818022 26212 sgd_solver.cpp:105] Iteration 168, lr = 0.00967269
I0409 21:28:38.734650 26212 solver.cpp:218] Iteration 180 (2.44075 iter/s, 4.91651s/12 iters), loss = 5.10694
I0409 21:28:38.734710 26212 solver.cpp:237]     Train net output #0: loss = 5.10694 (* 1 = 5.10694 loss)
I0409 21:28:38.734722 26212 sgd_solver.cpp:105] Iteration 180, lr = 0.00964973
I0409 21:28:44.684674 26212 solver.cpp:218] Iteration 192 (2.01687 iter/s, 5.94982s/12 iters), loss = 5.20377
I0409 21:28:44.710072 26212 solver.cpp:237]     Train net output #0: loss = 5.20377 (* 1 = 5.20377 loss)
I0409 21:28:44.710086 26212 sgd_solver.cpp:105] Iteration 192, lr = 0.00962682
I0409 21:28:49.230159 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:28:50.122251 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_204.caffemodel
I0409 21:28:51.262176 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_204.solverstate
I0409 21:28:52.126974 26212 solver.cpp:330] Iteration 204, Testing net (#0)
I0409 21:28:52.126998 26212 net.cpp:676] Ignoring source layer train-data
I0409 21:28:56.525000 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:28:56.647271 26212 solver.cpp:397]     Test net output #0: accuracy = 0.00980392
I0409 21:28:56.647306 26212 solver.cpp:397]     Test net output #1: loss = 5.16936 (* 1 = 5.16936 loss)
I0409 21:28:56.731236 26212 solver.cpp:218] Iteration 204 (0.998263 iter/s, 12.0209s/12 iters), loss = 5.08856
I0409 21:28:56.731295 26212 solver.cpp:237]     Train net output #0: loss = 5.08856 (* 1 = 5.08856 loss)
I0409 21:28:56.731305 26212 sgd_solver.cpp:105] Iteration 204, lr = 0.00960396
I0409 21:29:01.187283 26212 solver.cpp:218] Iteration 216 (2.69308 iter/s, 4.45587s/12 iters), loss = 5.13367
I0409 21:29:01.187332 26212 solver.cpp:237]     Train net output #0: loss = 5.13367 (* 1 = 5.13367 loss)
I0409 21:29:01.187342 26212 sgd_solver.cpp:105] Iteration 216, lr = 0.00958116
I0409 21:29:06.415378 26212 solver.cpp:218] Iteration 228 (2.29537 iter/s, 5.22791s/12 iters), loss = 5.20116
I0409 21:29:06.415442 26212 solver.cpp:237]     Train net output #0: loss = 5.20116 (* 1 = 5.20116 loss)
I0409 21:29:06.415453 26212 sgd_solver.cpp:105] Iteration 228, lr = 0.00955841
I0409 21:29:12.318975 26212 solver.cpp:218] Iteration 240 (2.03273 iter/s, 5.90338s/12 iters), loss = 5.16907
I0409 21:29:12.319028 26212 solver.cpp:237]     Train net output #0: loss = 5.16907 (* 1 = 5.16907 loss)
I0409 21:29:12.319039 26212 sgd_solver.cpp:105] Iteration 240, lr = 0.00953572
I0409 21:29:17.594421 26212 solver.cpp:218] Iteration 252 (2.27477 iter/s, 5.27526s/12 iters), loss = 5.06367
I0409 21:29:17.594589 26212 solver.cpp:237]     Train net output #0: loss = 5.06367 (* 1 = 5.06367 loss)
I0409 21:29:17.594604 26212 sgd_solver.cpp:105] Iteration 252, lr = 0.00951308
I0409 21:29:22.613765 26212 solver.cpp:218] Iteration 264 (2.39089 iter/s, 5.01906s/12 iters), loss = 5.18205
I0409 21:29:22.613806 26212 solver.cpp:237]     Train net output #0: loss = 5.18205 (* 1 = 5.18205 loss)
I0409 21:29:22.613816 26212 sgd_solver.cpp:105] Iteration 264, lr = 0.00949049
I0409 21:29:27.603812 26212 solver.cpp:218] Iteration 276 (2.40487 iter/s, 4.98988s/12 iters), loss = 5.11848
I0409 21:29:27.603859 26212 solver.cpp:237]     Train net output #0: loss = 5.11848 (* 1 = 5.11848 loss)
I0409 21:29:27.603868 26212 sgd_solver.cpp:105] Iteration 276, lr = 0.00946796
I0409 21:29:32.997272 26212 solver.cpp:218] Iteration 288 (2.225 iter/s, 5.39327s/12 iters), loss = 4.96565
I0409 21:29:32.997336 26212 solver.cpp:237]     Train net output #0: loss = 4.96565 (* 1 = 4.96565 loss)
I0409 21:29:32.997347 26212 sgd_solver.cpp:105] Iteration 288, lr = 0.00944548
I0409 21:29:39.043251 26212 solver.cpp:218] Iteration 300 (1.98486 iter/s, 6.04576s/12 iters), loss = 5.1159
I0409 21:29:39.043308 26212 solver.cpp:237]     Train net output #0: loss = 5.1159 (* 1 = 5.1159 loss)
I0409 21:29:39.043319 26212 sgd_solver.cpp:105] Iteration 300, lr = 0.00942305
I0409 21:29:40.030473 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:29:41.387926 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_306.caffemodel
I0409 21:29:43.809895 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_306.solverstate
I0409 21:29:45.126104 26212 solver.cpp:330] Iteration 306, Testing net (#0)
I0409 21:29:45.126124 26212 net.cpp:676] Ignoring source layer train-data
I0409 21:29:50.580682 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:29:50.806869 26212 solver.cpp:397]     Test net output #0: accuracy = 0.0208333
I0409 21:29:50.806910 26212 solver.cpp:397]     Test net output #1: loss = 5.0645 (* 1 = 5.0645 loss)
I0409 21:29:52.974444 26212 solver.cpp:218] Iteration 312 (0.861401 iter/s, 13.9308s/12 iters), loss = 4.97656
I0409 21:29:52.974503 26212 solver.cpp:237]     Train net output #0: loss = 4.97656 (* 1 = 4.97656 loss)
I0409 21:29:52.974512 26212 sgd_solver.cpp:105] Iteration 312, lr = 0.00940068
I0409 21:29:58.790462 26212 solver.cpp:218] Iteration 324 (2.06334 iter/s, 5.81581s/12 iters), loss = 5.07614
I0409 21:29:58.790525 26212 solver.cpp:237]     Train net output #0: loss = 5.07614 (* 1 = 5.07614 loss)
I0409 21:29:58.790536 26212 sgd_solver.cpp:105] Iteration 324, lr = 0.00937836
I0409 21:30:03.702713 26212 solver.cpp:218] Iteration 336 (2.44297 iter/s, 4.91206s/12 iters), loss = 5.02409
I0409 21:30:03.702778 26212 solver.cpp:237]     Train net output #0: loss = 5.02409 (* 1 = 5.02409 loss)
I0409 21:30:03.702790 26212 sgd_solver.cpp:105] Iteration 336, lr = 0.0093561
I0409 21:30:09.272266 26212 solver.cpp:218] Iteration 348 (2.15465 iter/s, 5.56935s/12 iters), loss = 5.03029
I0409 21:30:09.272325 26212 solver.cpp:237]     Train net output #0: loss = 5.03029 (* 1 = 5.03029 loss)
I0409 21:30:09.272336 26212 sgd_solver.cpp:105] Iteration 348, lr = 0.00933388
I0409 21:30:14.203006 26212 solver.cpp:218] Iteration 360 (2.43381 iter/s, 4.93055s/12 iters), loss = 5.03199
I0409 21:30:14.203071 26212 solver.cpp:237]     Train net output #0: loss = 5.03199 (* 1 = 5.03199 loss)
I0409 21:30:14.203083 26212 sgd_solver.cpp:105] Iteration 360, lr = 0.00931172
I0409 21:30:19.769886 26212 solver.cpp:218] Iteration 372 (2.15569 iter/s, 5.56666s/12 iters), loss = 4.98074
I0409 21:30:19.769951 26212 solver.cpp:237]     Train net output #0: loss = 4.98074 (* 1 = 4.98074 loss)
I0409 21:30:19.769994 26212 sgd_solver.cpp:105] Iteration 372, lr = 0.00928961
I0409 21:30:25.604372 26212 solver.cpp:218] Iteration 384 (2.05681 iter/s, 5.83427s/12 iters), loss = 5.05121
I0409 21:30:25.604494 26212 solver.cpp:237]     Train net output #0: loss = 5.05121 (* 1 = 5.05121 loss)
I0409 21:30:25.604506 26212 sgd_solver.cpp:105] Iteration 384, lr = 0.00926756
I0409 21:30:30.539572 26212 solver.cpp:218] Iteration 396 (2.43164 iter/s, 4.93495s/12 iters), loss = 5.0702
I0409 21:30:30.539628 26212 solver.cpp:237]     Train net output #0: loss = 5.0702 (* 1 = 5.0702 loss)
I0409 21:30:30.539638 26212 sgd_solver.cpp:105] Iteration 396, lr = 0.00924556
I0409 21:30:33.698709 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:30:35.072392 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_408.caffemodel
I0409 21:30:37.730219 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_408.solverstate
I0409 21:30:39.556203 26212 solver.cpp:330] Iteration 408, Testing net (#0)
I0409 21:30:39.556227 26212 net.cpp:676] Ignoring source layer train-data
I0409 21:30:44.367681 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:30:44.571748 26212 solver.cpp:397]     Test net output #0: accuracy = 0.0232843
I0409 21:30:44.571781 26212 solver.cpp:397]     Test net output #1: loss = 5.03116 (* 1 = 5.03116 loss)
I0409 21:30:44.655552 26212 solver.cpp:218] Iteration 408 (0.850124 iter/s, 14.1156s/12 iters), loss = 5.08635
I0409 21:30:44.655597 26212 solver.cpp:237]     Train net output #0: loss = 5.08635 (* 1 = 5.08635 loss)
I0409 21:30:44.655606 26212 sgd_solver.cpp:105] Iteration 408, lr = 0.00922361
I0409 21:30:49.451177 26212 solver.cpp:218] Iteration 420 (2.50237 iter/s, 4.79545s/12 iters), loss = 5.03858
I0409 21:30:49.451233 26212 solver.cpp:237]     Train net output #0: loss = 5.03858 (* 1 = 5.03858 loss)
I0409 21:30:49.451246 26212 sgd_solver.cpp:105] Iteration 420, lr = 0.00920171
I0409 21:30:54.750938 26212 solver.cpp:218] Iteration 432 (2.26434 iter/s, 5.29956s/12 iters), loss = 4.96589
I0409 21:30:54.750994 26212 solver.cpp:237]     Train net output #0: loss = 4.96589 (* 1 = 4.96589 loss)
I0409 21:30:54.751004 26212 sgd_solver.cpp:105] Iteration 432, lr = 0.00917986
I0409 21:31:00.942865 26212 solver.cpp:218] Iteration 444 (1.93808 iter/s, 6.19171s/12 iters), loss = 4.90305
I0409 21:31:00.943004 26212 solver.cpp:237]     Train net output #0: loss = 4.90305 (* 1 = 4.90305 loss)
I0409 21:31:00.943017 26212 sgd_solver.cpp:105] Iteration 444, lr = 0.00915807
I0409 21:31:05.801894 26212 solver.cpp:218] Iteration 456 (2.46977 iter/s, 4.85876s/12 iters), loss = 4.89926
I0409 21:31:05.801951 26212 solver.cpp:237]     Train net output #0: loss = 4.89926 (* 1 = 4.89926 loss)
I0409 21:31:05.801991 26212 sgd_solver.cpp:105] Iteration 456, lr = 0.00913632
I0409 21:31:11.169996 26212 solver.cpp:218] Iteration 468 (2.23552 iter/s, 5.36787s/12 iters), loss = 4.92521
I0409 21:31:11.170042 26212 solver.cpp:237]     Train net output #0: loss = 4.92521 (* 1 = 4.92521 loss)
I0409 21:31:11.170049 26212 sgd_solver.cpp:105] Iteration 468, lr = 0.00911463
I0409 21:31:16.892985 26212 solver.cpp:218] Iteration 480 (2.09688 iter/s, 5.72279s/12 iters), loss = 4.85751
I0409 21:31:16.893043 26212 solver.cpp:237]     Train net output #0: loss = 4.85751 (* 1 = 4.85751 loss)
I0409 21:31:16.893054 26212 sgd_solver.cpp:105] Iteration 480, lr = 0.00909299
I0409 21:31:23.108265 26212 solver.cpp:218] Iteration 492 (1.93079 iter/s, 6.21506s/12 iters), loss = 4.9256
I0409 21:31:23.108328 26212 solver.cpp:237]     Train net output #0: loss = 4.9256 (* 1 = 4.9256 loss)
I0409 21:31:23.108340 26212 sgd_solver.cpp:105] Iteration 492, lr = 0.0090714
I0409 21:31:28.475455 26212 solver.cpp:218] Iteration 504 (2.23589 iter/s, 5.367s/12 iters), loss = 5.02007
I0409 21:31:28.475486 26212 solver.cpp:237]     Train net output #0: loss = 5.02007 (* 1 = 5.02007 loss)
I0409 21:31:28.475494 26212 sgd_solver.cpp:105] Iteration 504, lr = 0.00904986
I0409 21:31:28.731734 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:31:30.733530 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_510.caffemodel
I0409 21:31:32.840525 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_510.solverstate
I0409 21:31:33.751473 26212 solver.cpp:330] Iteration 510, Testing net (#0)
I0409 21:31:33.751498 26212 net.cpp:676] Ignoring source layer train-data
I0409 21:31:38.676018 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:31:38.926390 26212 solver.cpp:397]     Test net output #0: accuracy = 0.0392157
I0409 21:31:38.926430 26212 solver.cpp:397]     Test net output #1: loss = 4.93293 (* 1 = 4.93293 loss)
I0409 21:31:40.721006 26212 solver.cpp:218] Iteration 516 (0.979975 iter/s, 12.2452s/12 iters), loss = 4.87732
I0409 21:31:40.721065 26212 solver.cpp:237]     Train net output #0: loss = 4.87732 (* 1 = 4.87732 loss)
I0409 21:31:40.721076 26212 sgd_solver.cpp:105] Iteration 516, lr = 0.00902838
I0409 21:31:46.444535 26212 solver.cpp:218] Iteration 528 (2.09669 iter/s, 5.72332s/12 iters), loss = 4.92258
I0409 21:31:46.444592 26212 solver.cpp:237]     Train net output #0: loss = 4.92258 (* 1 = 4.92258 loss)
I0409 21:31:46.444602 26212 sgd_solver.cpp:105] Iteration 528, lr = 0.00900694
I0409 21:31:52.040834 26212 solver.cpp:218] Iteration 540 (2.14435 iter/s, 5.59609s/12 iters), loss = 4.73164
I0409 21:31:52.046865 26212 solver.cpp:237]     Train net output #0: loss = 4.73164 (* 1 = 4.73164 loss)
I0409 21:31:52.046878 26212 sgd_solver.cpp:105] Iteration 540, lr = 0.00898556
I0409 21:31:57.495656 26212 solver.cpp:218] Iteration 552 (2.20238 iter/s, 5.44865s/12 iters), loss = 5.03377
I0409 21:31:57.495723 26212 solver.cpp:237]     Train net output #0: loss = 5.03377 (* 1 = 5.03377 loss)
I0409 21:31:57.495738 26212 sgd_solver.cpp:105] Iteration 552, lr = 0.00896423
I0409 21:32:03.299789 26212 solver.cpp:218] Iteration 564 (2.06757 iter/s, 5.80392s/12 iters), loss = 4.87754
I0409 21:32:03.299914 26212 solver.cpp:237]     Train net output #0: loss = 4.87754 (* 1 = 4.87754 loss)
I0409 21:32:03.299929 26212 sgd_solver.cpp:105] Iteration 564, lr = 0.00894294
I0409 21:32:08.500196 26212 solver.cpp:218] Iteration 576 (2.30763 iter/s, 5.20015s/12 iters), loss = 4.87333
I0409 21:32:08.500259 26212 solver.cpp:237]     Train net output #0: loss = 4.87333 (* 1 = 4.87333 loss)
I0409 21:32:08.500272 26212 sgd_solver.cpp:105] Iteration 576, lr = 0.00892171
I0409 21:32:14.370476 26212 solver.cpp:218] Iteration 588 (2.04427 iter/s, 5.87006s/12 iters), loss = 4.72744
I0409 21:32:14.370535 26212 solver.cpp:237]     Train net output #0: loss = 4.72744 (* 1 = 4.72744 loss)
I0409 21:32:14.370546 26212 sgd_solver.cpp:105] Iteration 588, lr = 0.00890053
I0409 21:32:19.888098 26212 solver.cpp:218] Iteration 600 (2.17493 iter/s, 5.51742s/12 iters), loss = 4.82535
I0409 21:32:19.888144 26212 solver.cpp:237]     Train net output #0: loss = 4.82535 (* 1 = 4.82535 loss)
I0409 21:32:19.888151 26212 sgd_solver.cpp:105] Iteration 600, lr = 0.0088794
I0409 21:32:22.409520 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:32:24.574338 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_612.caffemodel
I0409 21:32:25.749612 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_612.solverstate
I0409 21:32:26.628639 26212 solver.cpp:330] Iteration 612, Testing net (#0)
I0409 21:32:26.628672 26212 net.cpp:676] Ignoring source layer train-data
I0409 21:32:31.420035 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:32:31.715523 26212 solver.cpp:397]     Test net output #0: accuracy = 0.0379902
I0409 21:32:31.715562 26212 solver.cpp:397]     Test net output #1: loss = 4.86818 (* 1 = 4.86818 loss)
I0409 21:32:31.801131 26212 solver.cpp:218] Iteration 612 (1.00733 iter/s, 11.9127s/12 iters), loss = 4.72335
I0409 21:32:31.801184 26212 solver.cpp:237]     Train net output #0: loss = 4.72335 (* 1 = 4.72335 loss)
I0409 21:32:31.801198 26212 sgd_solver.cpp:105] Iteration 612, lr = 0.00885831
I0409 21:32:36.341361 26212 solver.cpp:218] Iteration 624 (2.64365 iter/s, 4.53919s/12 iters), loss = 4.66959
I0409 21:32:36.341516 26212 solver.cpp:237]     Train net output #0: loss = 4.66959 (* 1 = 4.66959 loss)
I0409 21:32:36.341527 26212 sgd_solver.cpp:105] Iteration 624, lr = 0.00883728
I0409 21:32:41.760103 26212 solver.cpp:218] Iteration 636 (2.21466 iter/s, 5.41844s/12 iters), loss = 4.68253
I0409 21:32:41.760160 26212 solver.cpp:237]     Train net output #0: loss = 4.68253 (* 1 = 4.68253 loss)
I0409 21:32:41.760170 26212 sgd_solver.cpp:105] Iteration 636, lr = 0.0088163
I0409 21:32:46.779724 26212 solver.cpp:218] Iteration 648 (2.39071 iter/s, 5.01943s/12 iters), loss = 4.92691
I0409 21:32:46.779783 26212 solver.cpp:237]     Train net output #0: loss = 4.92691 (* 1 = 4.92691 loss)
I0409 21:32:46.779794 26212 sgd_solver.cpp:105] Iteration 648, lr = 0.00879537
I0409 21:32:51.664592 26212 solver.cpp:218] Iteration 660 (2.45666 iter/s, 4.88468s/12 iters), loss = 4.90638
I0409 21:32:51.664645 26212 solver.cpp:237]     Train net output #0: loss = 4.90638 (* 1 = 4.90638 loss)
I0409 21:32:51.664657 26212 sgd_solver.cpp:105] Iteration 660, lr = 0.00877449
I0409 21:32:56.564687 26212 solver.cpp:218] Iteration 672 (2.44902 iter/s, 4.89991s/12 iters), loss = 4.62752
I0409 21:32:56.564739 26212 solver.cpp:237]     Train net output #0: loss = 4.62752 (* 1 = 4.62752 loss)
I0409 21:32:56.564750 26212 sgd_solver.cpp:105] Iteration 672, lr = 0.00875366
I0409 21:33:01.667054 26212 solver.cpp:218] Iteration 684 (2.35194 iter/s, 5.10218s/12 iters), loss = 4.7867
I0409 21:33:01.667106 26212 solver.cpp:237]     Train net output #0: loss = 4.7867 (* 1 = 4.7867 loss)
I0409 21:33:01.667115 26212 sgd_solver.cpp:105] Iteration 684, lr = 0.00873287
I0409 21:33:02.051638 26212 blocking_queue.cpp:49] Waiting for data
I0409 21:33:06.821194 26212 solver.cpp:218] Iteration 696 (2.32832 iter/s, 5.15394s/12 iters), loss = 4.55949
I0409 21:33:06.821338 26212 solver.cpp:237]     Train net output #0: loss = 4.55949 (* 1 = 4.55949 loss)
I0409 21:33:06.821353 26212 sgd_solver.cpp:105] Iteration 696, lr = 0.00871214
I0409 21:33:11.377610 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:33:11.753849 26212 solver.cpp:218] Iteration 708 (2.4329 iter/s, 4.93238s/12 iters), loss = 4.80404
I0409 21:33:11.753911 26212 solver.cpp:237]     Train net output #0: loss = 4.80404 (* 1 = 4.80404 loss)
I0409 21:33:11.753923 26212 sgd_solver.cpp:105] Iteration 708, lr = 0.00869145
I0409 21:33:13.743686 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_714.caffemodel
I0409 21:33:18.849402 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_714.solverstate
I0409 21:33:21.022512 26212 solver.cpp:330] Iteration 714, Testing net (#0)
I0409 21:33:21.022541 26212 net.cpp:676] Ignoring source layer train-data
I0409 21:33:25.236179 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:33:25.555510 26212 solver.cpp:397]     Test net output #0: accuracy = 0.0496324
I0409 21:33:25.555546 26212 solver.cpp:397]     Test net output #1: loss = 4.8211 (* 1 = 4.8211 loss)
I0409 21:33:27.397625 26212 solver.cpp:218] Iteration 720 (0.7671 iter/s, 15.6433s/12 iters), loss = 4.85561
I0409 21:33:27.397684 26212 solver.cpp:237]     Train net output #0: loss = 4.85561 (* 1 = 4.85561 loss)
I0409 21:33:27.397696 26212 sgd_solver.cpp:105] Iteration 720, lr = 0.00867082
I0409 21:33:32.756112 26212 solver.cpp:218] Iteration 732 (2.23952 iter/s, 5.35829s/12 iters), loss = 4.79113
I0409 21:33:32.756158 26212 solver.cpp:237]     Train net output #0: loss = 4.79113 (* 1 = 4.79113 loss)
I0409 21:33:32.756168 26212 sgd_solver.cpp:105] Iteration 732, lr = 0.00865023
I0409 21:33:37.986877 26212 solver.cpp:218] Iteration 744 (2.29421 iter/s, 5.23056s/12 iters), loss = 4.7636
I0409 21:33:37.987005 26212 solver.cpp:237]     Train net output #0: loss = 4.7636 (* 1 = 4.7636 loss)
I0409 21:33:37.987017 26212 sgd_solver.cpp:105] Iteration 744, lr = 0.0086297
I0409 21:33:42.897686 26212 solver.cpp:218] Iteration 756 (2.44372 iter/s, 4.91054s/12 iters), loss = 4.76748
I0409 21:33:42.897747 26212 solver.cpp:237]     Train net output #0: loss = 4.76748 (* 1 = 4.76748 loss)
I0409 21:33:42.897758 26212 sgd_solver.cpp:105] Iteration 756, lr = 0.00860921
I0409 21:33:47.765183 26212 solver.cpp:218] Iteration 768 (2.46543 iter/s, 4.8673s/12 iters), loss = 4.75643
I0409 21:33:47.765237 26212 solver.cpp:237]     Train net output #0: loss = 4.75643 (* 1 = 4.75643 loss)
I0409 21:33:47.765247 26212 sgd_solver.cpp:105] Iteration 768, lr = 0.00858877
I0409 21:33:53.477996 26212 solver.cpp:218] Iteration 780 (2.10062 iter/s, 5.71259s/12 iters), loss = 4.74176
I0409 21:33:53.478044 26212 solver.cpp:237]     Train net output #0: loss = 4.74176 (* 1 = 4.74176 loss)
I0409 21:33:53.478052 26212 sgd_solver.cpp:105] Iteration 780, lr = 0.00856838
I0409 21:33:58.439492 26212 solver.cpp:218] Iteration 792 (2.41872 iter/s, 4.96131s/12 iters), loss = 4.58734
I0409 21:33:58.439555 26212 solver.cpp:237]     Train net output #0: loss = 4.58734 (* 1 = 4.58734 loss)
I0409 21:33:58.439568 26212 sgd_solver.cpp:105] Iteration 792, lr = 0.00854803
I0409 21:34:03.357020 26212 solver.cpp:218] Iteration 804 (2.44035 iter/s, 4.91733s/12 iters), loss = 4.57304
I0409 21:34:03.357081 26212 solver.cpp:237]     Train net output #0: loss = 4.57304 (* 1 = 4.57304 loss)
I0409 21:34:03.357092 26212 sgd_solver.cpp:105] Iteration 804, lr = 0.00852774
I0409 21:34:05.438580 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:34:08.814405 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_816.caffemodel
I0409 21:34:12.112543 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_816.solverstate
I0409 21:34:15.024753 26212 solver.cpp:330] Iteration 816, Testing net (#0)
I0409 21:34:15.024772 26212 net.cpp:676] Ignoring source layer train-data
I0409 21:34:19.050316 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:34:19.415891 26212 solver.cpp:397]     Test net output #0: accuracy = 0.0514706
I0409 21:34:19.415936 26212 solver.cpp:397]     Test net output #1: loss = 4.76569 (* 1 = 4.76569 loss)
I0409 21:34:19.498903 26212 solver.cpp:218] Iteration 816 (0.743429 iter/s, 16.1414s/12 iters), loss = 4.49096
I0409 21:34:19.498952 26212 solver.cpp:237]     Train net output #0: loss = 4.49096 (* 1 = 4.49096 loss)
I0409 21:34:19.498962 26212 sgd_solver.cpp:105] Iteration 816, lr = 0.00850749
I0409 21:34:23.911481 26212 solver.cpp:218] Iteration 828 (2.71961 iter/s, 4.4124s/12 iters), loss = 4.83506
I0409 21:34:23.911548 26212 solver.cpp:237]     Train net output #0: loss = 4.83506 (* 1 = 4.83506 loss)
I0409 21:34:23.911561 26212 sgd_solver.cpp:105] Iteration 828, lr = 0.00848729
I0409 21:34:28.956604 26212 solver.cpp:218] Iteration 840 (2.37863 iter/s, 5.04492s/12 iters), loss = 4.36367
I0409 21:34:28.956658 26212 solver.cpp:237]     Train net output #0: loss = 4.36367 (* 1 = 4.36367 loss)
I0409 21:34:28.956668 26212 sgd_solver.cpp:105] Iteration 840, lr = 0.00846714
I0409 21:34:34.111718 26212 solver.cpp:218] Iteration 852 (2.32787 iter/s, 5.15492s/12 iters), loss = 4.43327
I0409 21:34:34.111778 26212 solver.cpp:237]     Train net output #0: loss = 4.43327 (* 1 = 4.43327 loss)
I0409 21:34:34.111790 26212 sgd_solver.cpp:105] Iteration 852, lr = 0.00844704
I0409 21:34:39.131599 26212 solver.cpp:218] Iteration 864 (2.39059 iter/s, 5.01968s/12 iters), loss = 4.42005
I0409 21:34:39.131711 26212 solver.cpp:237]     Train net output #0: loss = 4.42005 (* 1 = 4.42005 loss)
I0409 21:34:39.131721 26212 sgd_solver.cpp:105] Iteration 864, lr = 0.00842698
I0409 21:34:44.104082 26212 solver.cpp:218] Iteration 876 (2.4134 iter/s, 4.97224s/12 iters), loss = 4.45399
I0409 21:34:44.104142 26212 solver.cpp:237]     Train net output #0: loss = 4.45399 (* 1 = 4.45399 loss)
I0409 21:34:44.104156 26212 sgd_solver.cpp:105] Iteration 876, lr = 0.00840698
I0409 21:34:49.553678 26212 solver.cpp:218] Iteration 888 (2.20208 iter/s, 5.44939s/12 iters), loss = 4.45261
I0409 21:34:49.553732 26212 solver.cpp:237]     Train net output #0: loss = 4.45261 (* 1 = 4.45261 loss)
I0409 21:34:49.553743 26212 sgd_solver.cpp:105] Iteration 888, lr = 0.00838702
I0409 21:34:54.753996 26212 solver.cpp:218] Iteration 900 (2.30765 iter/s, 5.2001s/12 iters), loss = 4.5822
I0409 21:34:54.754043 26212 solver.cpp:237]     Train net output #0: loss = 4.5822 (* 1 = 4.5822 loss)
I0409 21:34:54.754051 26212 sgd_solver.cpp:105] Iteration 900, lr = 0.0083671
I0409 21:34:58.785624 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:35:00.176127 26212 solver.cpp:218] Iteration 912 (2.21323 iter/s, 5.42193s/12 iters), loss = 4.39589
I0409 21:35:00.176182 26212 solver.cpp:237]     Train net output #0: loss = 4.39589 (* 1 = 4.39589 loss)
I0409 21:35:00.176193 26212 sgd_solver.cpp:105] Iteration 912, lr = 0.00834724
I0409 21:35:02.217121 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_918.caffemodel
I0409 21:35:03.482630 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_918.solverstate
I0409 21:35:04.344166 26212 solver.cpp:330] Iteration 918, Testing net (#0)
I0409 21:35:04.344189 26212 net.cpp:676] Ignoring source layer train-data
I0409 21:35:08.427918 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:35:08.848268 26212 solver.cpp:397]     Test net output #0: accuracy = 0.0631127
I0409 21:35:08.848340 26212 solver.cpp:397]     Test net output #1: loss = 4.84949 (* 1 = 4.84949 loss)
I0409 21:35:10.640839 26212 solver.cpp:218] Iteration 924 (1.14675 iter/s, 10.4644s/12 iters), loss = 4.44667
I0409 21:35:10.640967 26212 solver.cpp:237]     Train net output #0: loss = 4.44667 (* 1 = 4.44667 loss)
I0409 21:35:10.640980 26212 sgd_solver.cpp:105] Iteration 924, lr = 0.00832742
I0409 21:35:16.096351 26212 solver.cpp:218] Iteration 936 (2.19972 iter/s, 5.45524s/12 iters), loss = 4.47218
I0409 21:35:16.096410 26212 solver.cpp:237]     Train net output #0: loss = 4.47218 (* 1 = 4.47218 loss)
I0409 21:35:16.096421 26212 sgd_solver.cpp:105] Iteration 936, lr = 0.00830765
I0409 21:35:20.965071 26212 solver.cpp:218] Iteration 948 (2.46481 iter/s, 4.86853s/12 iters), loss = 4.48928
I0409 21:35:20.965138 26212 solver.cpp:237]     Train net output #0: loss = 4.48928 (* 1 = 4.48928 loss)
I0409 21:35:20.965150 26212 sgd_solver.cpp:105] Iteration 948, lr = 0.00828793
I0409 21:35:26.567862 26212 solver.cpp:218] Iteration 960 (2.14187 iter/s, 5.60257s/12 iters), loss = 4.28063
I0409 21:35:26.567911 26212 solver.cpp:237]     Train net output #0: loss = 4.28063 (* 1 = 4.28063 loss)
I0409 21:35:26.567920 26212 sgd_solver.cpp:105] Iteration 960, lr = 0.00826825
I0409 21:35:31.697301 26212 solver.cpp:218] Iteration 972 (2.33953 iter/s, 5.12924s/12 iters), loss = 4.33949
I0409 21:35:31.697361 26212 solver.cpp:237]     Train net output #0: loss = 4.33949 (* 1 = 4.33949 loss)
I0409 21:35:31.697372 26212 sgd_solver.cpp:105] Iteration 972, lr = 0.00824862
I0409 21:35:36.879387 26212 solver.cpp:218] Iteration 984 (2.31576 iter/s, 5.18189s/12 iters), loss = 4.24957
I0409 21:35:36.879432 26212 solver.cpp:237]     Train net output #0: loss = 4.24957 (* 1 = 4.24957 loss)
I0409 21:35:36.879442 26212 sgd_solver.cpp:105] Iteration 984, lr = 0.00822903
I0409 21:35:41.878023 26212 solver.cpp:218] Iteration 996 (2.40074 iter/s, 4.99845s/12 iters), loss = 4.19929
I0409 21:35:41.878141 26212 solver.cpp:237]     Train net output #0: loss = 4.19929 (* 1 = 4.19929 loss)
I0409 21:35:41.878152 26212 sgd_solver.cpp:105] Iteration 996, lr = 0.0082095
I0409 21:35:47.041009 26212 solver.cpp:218] Iteration 1008 (2.32435 iter/s, 5.16274s/12 iters), loss = 4.30683
I0409 21:35:47.041052 26212 solver.cpp:237]     Train net output #0: loss = 4.30683 (* 1 = 4.30683 loss)
I0409 21:35:47.041061 26212 sgd_solver.cpp:105] Iteration 1008, lr = 0.00819001
I0409 21:35:48.027458 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:35:51.496042 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1020.caffemodel
I0409 21:35:53.635640 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1020.solverstate
I0409 21:35:55.424019 26212 solver.cpp:330] Iteration 1020, Testing net (#0)
I0409 21:35:55.424041 26212 net.cpp:676] Ignoring source layer train-data
I0409 21:35:59.766443 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:36:00.306684 26212 solver.cpp:397]     Test net output #0: accuracy = 0.0643382
I0409 21:36:00.306735 26212 solver.cpp:397]     Test net output #1: loss = 4.81927 (* 1 = 4.81927 loss)
I0409 21:36:00.391817 26212 solver.cpp:218] Iteration 1020 (0.898848 iter/s, 13.3504s/12 iters), loss = 4.26777
I0409 21:36:00.391880 26212 solver.cpp:237]     Train net output #0: loss = 4.26777 (* 1 = 4.26777 loss)
I0409 21:36:00.391894 26212 sgd_solver.cpp:105] Iteration 1020, lr = 0.00817056
I0409 21:36:05.338071 26212 solver.cpp:218] Iteration 1032 (2.42618 iter/s, 4.94606s/12 iters), loss = 4.36022
I0409 21:36:05.338130 26212 solver.cpp:237]     Train net output #0: loss = 4.36022 (* 1 = 4.36022 loss)
I0409 21:36:05.338141 26212 sgd_solver.cpp:105] Iteration 1032, lr = 0.00815116
I0409 21:36:10.339903 26212 solver.cpp:218] Iteration 1044 (2.39922 iter/s, 5.00163s/12 iters), loss = 4.2289
I0409 21:36:10.339954 26212 solver.cpp:237]     Train net output #0: loss = 4.2289 (* 1 = 4.2289 loss)
I0409 21:36:10.339964 26212 sgd_solver.cpp:105] Iteration 1044, lr = 0.00813181
I0409 21:36:15.539542 26212 solver.cpp:218] Iteration 1056 (2.30794 iter/s, 5.19945s/12 iters), loss = 4.38729
I0409 21:36:15.539724 26212 solver.cpp:237]     Train net output #0: loss = 4.38729 (* 1 = 4.38729 loss)
I0409 21:36:15.539736 26212 sgd_solver.cpp:105] Iteration 1056, lr = 0.0081125
I0409 21:36:20.526019 26212 solver.cpp:218] Iteration 1068 (2.40666 iter/s, 4.98617s/12 iters), loss = 4.02351
I0409 21:36:20.526057 26212 solver.cpp:237]     Train net output #0: loss = 4.02351 (* 1 = 4.02351 loss)
I0409 21:36:20.526063 26212 sgd_solver.cpp:105] Iteration 1068, lr = 0.00809324
I0409 21:36:26.036358 26212 solver.cpp:218] Iteration 1080 (2.1778 iter/s, 5.51015s/12 iters), loss = 4.04046
I0409 21:36:26.036418 26212 solver.cpp:237]     Train net output #0: loss = 4.04046 (* 1 = 4.04046 loss)
I0409 21:36:26.036432 26212 sgd_solver.cpp:105] Iteration 1080, lr = 0.00807403
I0409 21:36:31.208240 26212 solver.cpp:218] Iteration 1092 (2.32033 iter/s, 5.17168s/12 iters), loss = 4.24412
I0409 21:36:31.208295 26212 solver.cpp:237]     Train net output #0: loss = 4.24412 (* 1 = 4.24412 loss)
I0409 21:36:31.208305 26212 sgd_solver.cpp:105] Iteration 1092, lr = 0.00805486
I0409 21:36:36.126768 26212 solver.cpp:218] Iteration 1104 (2.43985 iter/s, 4.91834s/12 iters), loss = 4.32899
I0409 21:36:36.126825 26212 solver.cpp:237]     Train net output #0: loss = 4.32899 (* 1 = 4.32899 loss)
I0409 21:36:36.126837 26212 sgd_solver.cpp:105] Iteration 1104, lr = 0.00803573
I0409 21:36:39.247232 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:36:41.089550 26212 solver.cpp:218] Iteration 1116 (2.4181 iter/s, 4.96258s/12 iters), loss = 4.17009
I0409 21:36:41.089615 26212 solver.cpp:237]     Train net output #0: loss = 4.17009 (* 1 = 4.17009 loss)
I0409 21:36:41.089627 26212 sgd_solver.cpp:105] Iteration 1116, lr = 0.00801666
I0409 21:36:43.138954 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1122.caffemodel
I0409 21:36:45.980532 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1122.solverstate
I0409 21:36:50.050660 26212 solver.cpp:330] Iteration 1122, Testing net (#0)
I0409 21:36:50.050691 26212 net.cpp:676] Ignoring source layer train-data
I0409 21:36:54.305814 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:36:54.778970 26212 solver.cpp:397]     Test net output #0: accuracy = 0.0759804
I0409 21:36:54.779000 26212 solver.cpp:397]     Test net output #1: loss = 4.58278 (* 1 = 4.58278 loss)
I0409 21:36:56.614506 26212 solver.cpp:218] Iteration 1128 (0.772972 iter/s, 15.5245s/12 iters), loss = 4.25865
I0409 21:36:56.614563 26212 solver.cpp:237]     Train net output #0: loss = 4.25865 (* 1 = 4.25865 loss)
I0409 21:36:56.614575 26212 sgd_solver.cpp:105] Iteration 1128, lr = 0.00799762
I0409 21:37:01.577731 26212 solver.cpp:218] Iteration 1140 (2.41787 iter/s, 4.96304s/12 iters), loss = 4.09639
I0409 21:37:01.577775 26212 solver.cpp:237]     Train net output #0: loss = 4.09639 (* 1 = 4.09639 loss)
I0409 21:37:01.577786 26212 sgd_solver.cpp:105] Iteration 1140, lr = 0.00797863
I0409 21:37:06.423377 26212 solver.cpp:218] Iteration 1152 (2.47654 iter/s, 4.84547s/12 iters), loss = 3.82361
I0409 21:37:06.423420 26212 solver.cpp:237]     Train net output #0: loss = 3.82361 (* 1 = 3.82361 loss)
I0409 21:37:06.423432 26212 sgd_solver.cpp:105] Iteration 1152, lr = 0.00795969
I0409 21:37:11.383039 26212 solver.cpp:218] Iteration 1164 (2.41961 iter/s, 4.95948s/12 iters), loss = 4.11898
I0409 21:37:11.383098 26212 solver.cpp:237]     Train net output #0: loss = 4.11898 (* 1 = 4.11898 loss)
I0409 21:37:11.383111 26212 sgd_solver.cpp:105] Iteration 1164, lr = 0.00794079
I0409 21:37:16.326267 26212 solver.cpp:218] Iteration 1176 (2.42766 iter/s, 4.94303s/12 iters), loss = 3.9777
I0409 21:37:16.326437 26212 solver.cpp:237]     Train net output #0: loss = 3.9777 (* 1 = 3.9777 loss)
I0409 21:37:16.326452 26212 sgd_solver.cpp:105] Iteration 1176, lr = 0.00792194
I0409 21:37:21.323647 26212 solver.cpp:218] Iteration 1188 (2.4014 iter/s, 4.99708s/12 iters), loss = 4.15514
I0409 21:37:21.323702 26212 solver.cpp:237]     Train net output #0: loss = 4.15514 (* 1 = 4.15514 loss)
I0409 21:37:21.323716 26212 sgd_solver.cpp:105] Iteration 1188, lr = 0.00790313
I0409 21:37:26.313252 26212 solver.cpp:218] Iteration 1200 (2.40509 iter/s, 4.98942s/12 iters), loss = 4.00147
I0409 21:37:26.313305 26212 solver.cpp:237]     Train net output #0: loss = 4.00147 (* 1 = 4.00147 loss)
I0409 21:37:26.313316 26212 sgd_solver.cpp:105] Iteration 1200, lr = 0.00788437
I0409 21:37:31.257676 26212 solver.cpp:218] Iteration 1212 (2.42707 iter/s, 4.94423s/12 iters), loss = 3.99537
I0409 21:37:31.257733 26212 solver.cpp:237]     Train net output #0: loss = 3.99537 (* 1 = 3.99537 loss)
I0409 21:37:31.257746 26212 sgd_solver.cpp:105] Iteration 1212, lr = 0.00786565
I0409 21:37:31.549392 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:37:35.756541 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1224.caffemodel
I0409 21:37:37.161541 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1224.solverstate
I0409 21:37:38.600926 26212 solver.cpp:330] Iteration 1224, Testing net (#0)
I0409 21:37:38.600945 26212 net.cpp:676] Ignoring source layer train-data
I0409 21:37:42.727064 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:37:43.236367 26212 solver.cpp:397]     Test net output #0: accuracy = 0.0876225
I0409 21:37:43.236405 26212 solver.cpp:397]     Test net output #1: loss = 4.36031 (* 1 = 4.36031 loss)
I0409 21:37:43.320281 26212 solver.cpp:218] Iteration 1224 (0.994839 iter/s, 12.0622s/12 iters), loss = 3.89386
I0409 21:37:43.320324 26212 solver.cpp:237]     Train net output #0: loss = 3.89386 (* 1 = 3.89386 loss)
I0409 21:37:43.320334 26212 sgd_solver.cpp:105] Iteration 1224, lr = 0.00784697
I0409 21:37:47.615370 26212 solver.cpp:218] Iteration 1236 (2.79399 iter/s, 4.29493s/12 iters), loss = 4.31022
I0409 21:37:47.615478 26212 solver.cpp:237]     Train net output #0: loss = 4.31022 (* 1 = 4.31022 loss)
I0409 21:37:47.615487 26212 sgd_solver.cpp:105] Iteration 1236, lr = 0.00782834
I0409 21:37:52.539974 26212 solver.cpp:218] Iteration 1248 (2.43686 iter/s, 4.92437s/12 iters), loss = 3.95978
I0409 21:37:52.540020 26212 solver.cpp:237]     Train net output #0: loss = 3.95978 (* 1 = 3.95978 loss)
I0409 21:37:52.540030 26212 sgd_solver.cpp:105] Iteration 1248, lr = 0.00780976
I0409 21:37:57.686501 26212 solver.cpp:218] Iteration 1260 (2.33175 iter/s, 5.14634s/12 iters), loss = 4.00826
I0409 21:37:57.686550 26212 solver.cpp:237]     Train net output #0: loss = 4.00826 (* 1 = 4.00826 loss)
I0409 21:37:57.686561 26212 sgd_solver.cpp:105] Iteration 1260, lr = 0.00779122
I0409 21:38:02.591778 26212 solver.cpp:218] Iteration 1272 (2.44644 iter/s, 4.90508s/12 iters), loss = 4.08048
I0409 21:38:02.591840 26212 solver.cpp:237]     Train net output #0: loss = 4.08048 (* 1 = 4.08048 loss)
I0409 21:38:02.591852 26212 sgd_solver.cpp:105] Iteration 1272, lr = 0.00777272
I0409 21:38:07.642048 26212 solver.cpp:218] Iteration 1284 (2.3762 iter/s, 5.05007s/12 iters), loss = 4.38194
I0409 21:38:07.642091 26212 solver.cpp:237]     Train net output #0: loss = 4.38194 (* 1 = 4.38194 loss)
I0409 21:38:07.642100 26212 sgd_solver.cpp:105] Iteration 1284, lr = 0.00775426
I0409 21:38:12.901624 26212 solver.cpp:218] Iteration 1296 (2.28164 iter/s, 5.25938s/12 iters), loss = 4.03464
I0409 21:38:12.901681 26212 solver.cpp:237]     Train net output #0: loss = 4.03464 (* 1 = 4.03464 loss)
I0409 21:38:12.901695 26212 sgd_solver.cpp:105] Iteration 1296, lr = 0.00773585
I0409 21:38:18.161154 26212 solver.cpp:218] Iteration 1308 (2.28166 iter/s, 5.25934s/12 iters), loss = 3.97245
I0409 21:38:18.161264 26212 solver.cpp:237]     Train net output #0: loss = 3.97245 (* 1 = 3.97245 loss)
I0409 21:38:18.161276 26212 sgd_solver.cpp:105] Iteration 1308, lr = 0.00771749
I0409 21:38:20.794670 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:38:23.275107 26212 solver.cpp:218] Iteration 1320 (2.34663 iter/s, 5.11371s/12 iters), loss = 3.55951
I0409 21:38:23.275152 26212 solver.cpp:237]     Train net output #0: loss = 3.55951 (* 1 = 3.55951 loss)
I0409 21:38:23.275163 26212 sgd_solver.cpp:105] Iteration 1320, lr = 0.00769916
I0409 21:38:25.294400 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1326.caffemodel
I0409 21:38:26.633453 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1326.solverstate
I0409 21:38:29.300155 26212 solver.cpp:330] Iteration 1326, Testing net (#0)
I0409 21:38:29.300175 26212 net.cpp:676] Ignoring source layer train-data
I0409 21:38:33.315331 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:38:33.870623 26212 solver.cpp:397]     Test net output #0: accuracy = 0.0876225
I0409 21:38:33.870671 26212 solver.cpp:397]     Test net output #1: loss = 4.44041 (* 1 = 4.44041 loss)
I0409 21:38:35.631090 26212 solver.cpp:218] Iteration 1332 (0.971218 iter/s, 12.3556s/12 iters), loss = 3.85108
I0409 21:38:35.631147 26212 solver.cpp:237]     Train net output #0: loss = 3.85108 (* 1 = 3.85108 loss)
I0409 21:38:35.631160 26212 sgd_solver.cpp:105] Iteration 1332, lr = 0.00768088
I0409 21:38:40.674821 26212 solver.cpp:218] Iteration 1344 (2.37928 iter/s, 5.04353s/12 iters), loss = 3.9428
I0409 21:38:40.674875 26212 solver.cpp:237]     Train net output #0: loss = 3.9428 (* 1 = 3.9428 loss)
I0409 21:38:40.674886 26212 sgd_solver.cpp:105] Iteration 1344, lr = 0.00766265
I0409 21:38:45.784497 26212 solver.cpp:218] Iteration 1356 (2.34857 iter/s, 5.10948s/12 iters), loss = 3.66229
I0409 21:38:45.784548 26212 solver.cpp:237]     Train net output #0: loss = 3.66229 (* 1 = 3.66229 loss)
I0409 21:38:45.784561 26212 sgd_solver.cpp:105] Iteration 1356, lr = 0.00764446
I0409 21:38:50.768299 26212 solver.cpp:218] Iteration 1368 (2.40789 iter/s, 4.98361s/12 iters), loss = 3.74705
I0409 21:38:50.768419 26212 solver.cpp:237]     Train net output #0: loss = 3.74705 (* 1 = 3.74705 loss)
I0409 21:38:50.768430 26212 sgd_solver.cpp:105] Iteration 1368, lr = 0.00762631
I0409 21:38:51.549763 26212 blocking_queue.cpp:49] Waiting for data
I0409 21:38:55.606106 26212 solver.cpp:218] Iteration 1380 (2.48059 iter/s, 4.83756s/12 iters), loss = 3.45879
I0409 21:38:55.606155 26212 solver.cpp:237]     Train net output #0: loss = 3.45879 (* 1 = 3.45879 loss)
I0409 21:38:55.606168 26212 sgd_solver.cpp:105] Iteration 1380, lr = 0.0076082
I0409 21:39:00.529620 26212 solver.cpp:218] Iteration 1392 (2.43738 iter/s, 4.92333s/12 iters), loss = 3.77652
I0409 21:39:00.529670 26212 solver.cpp:237]     Train net output #0: loss = 3.77652 (* 1 = 3.77652 loss)
I0409 21:39:00.529678 26212 sgd_solver.cpp:105] Iteration 1392, lr = 0.00759014
I0409 21:39:05.497893 26212 solver.cpp:218] Iteration 1404 (2.41542 iter/s, 4.96809s/12 iters), loss = 3.92707
I0409 21:39:05.497941 26212 solver.cpp:237]     Train net output #0: loss = 3.92707 (* 1 = 3.92707 loss)
I0409 21:39:05.497948 26212 sgd_solver.cpp:105] Iteration 1404, lr = 0.00757212
I0409 21:39:10.060559 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:39:10.403506 26212 solver.cpp:218] Iteration 1416 (2.44627 iter/s, 4.90543s/12 iters), loss = 3.4248
I0409 21:39:10.403563 26212 solver.cpp:237]     Train net output #0: loss = 3.4248 (* 1 = 3.4248 loss)
I0409 21:39:10.403575 26212 sgd_solver.cpp:105] Iteration 1416, lr = 0.00755414
I0409 21:39:14.848039 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1428.caffemodel
I0409 21:39:17.046412 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1428.solverstate
I0409 21:39:18.861567 26212 solver.cpp:330] Iteration 1428, Testing net (#0)
I0409 21:39:18.861594 26212 net.cpp:676] Ignoring source layer train-data
I0409 21:39:22.669929 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:39:23.259163 26212 solver.cpp:397]     Test net output #0: accuracy = 0.0974265
I0409 21:39:23.259209 26212 solver.cpp:397]     Test net output #1: loss = 4.34493 (* 1 = 4.34493 loss)
I0409 21:39:23.343134 26212 solver.cpp:218] Iteration 1428 (0.927411 iter/s, 12.9392s/12 iters), loss = 3.79256
I0409 21:39:23.343190 26212 solver.cpp:237]     Train net output #0: loss = 3.79256 (* 1 = 3.79256 loss)
I0409 21:39:23.343201 26212 sgd_solver.cpp:105] Iteration 1428, lr = 0.0075362
I0409 21:39:27.624868 26212 solver.cpp:218] Iteration 1440 (2.80271 iter/s, 4.28156s/12 iters), loss = 3.60299
I0409 21:39:27.624908 26212 solver.cpp:237]     Train net output #0: loss = 3.60299 (* 1 = 3.60299 loss)
I0409 21:39:27.624917 26212 sgd_solver.cpp:105] Iteration 1440, lr = 0.00751831
I0409 21:39:32.533110 26212 solver.cpp:218] Iteration 1452 (2.44496 iter/s, 4.90806s/12 iters), loss = 3.93554
I0409 21:39:32.533169 26212 solver.cpp:237]     Train net output #0: loss = 3.93554 (* 1 = 3.93554 loss)
I0409 21:39:32.533183 26212 sgd_solver.cpp:105] Iteration 1452, lr = 0.00750046
I0409 21:39:37.394006 26212 solver.cpp:218] Iteration 1464 (2.46878 iter/s, 4.86071s/12 iters), loss = 3.79815
I0409 21:39:37.394044 26212 solver.cpp:237]     Train net output #0: loss = 3.79815 (* 1 = 3.79815 loss)
I0409 21:39:37.394052 26212 sgd_solver.cpp:105] Iteration 1464, lr = 0.00748265
I0409 21:39:42.311153 26212 solver.cpp:218] Iteration 1476 (2.44052 iter/s, 4.91698s/12 iters), loss = 3.54488
I0409 21:39:42.311197 26212 solver.cpp:237]     Train net output #0: loss = 3.54488 (* 1 = 3.54488 loss)
I0409 21:39:42.311208 26212 sgd_solver.cpp:105] Iteration 1476, lr = 0.00746489
I0409 21:39:47.237893 26212 solver.cpp:218] Iteration 1488 (2.43578 iter/s, 4.92656s/12 iters), loss = 3.5723
I0409 21:39:47.237948 26212 solver.cpp:237]     Train net output #0: loss = 3.5723 (* 1 = 3.5723 loss)
I0409 21:39:47.237975 26212 sgd_solver.cpp:105] Iteration 1488, lr = 0.00744716
I0409 21:39:52.134052 26212 solver.cpp:218] Iteration 1500 (2.451 iter/s, 4.89597s/12 iters), loss = 3.20206
I0409 21:39:52.134100 26212 solver.cpp:237]     Train net output #0: loss = 3.20206 (* 1 = 3.20206 loss)
I0409 21:39:52.134110 26212 sgd_solver.cpp:105] Iteration 1500, lr = 0.00742948
I0409 21:39:57.104132 26212 solver.cpp:218] Iteration 1512 (2.41454 iter/s, 4.9699s/12 iters), loss = 3.53522
I0409 21:39:57.104239 26212 solver.cpp:237]     Train net output #0: loss = 3.53522 (* 1 = 3.53522 loss)
I0409 21:39:57.104257 26212 sgd_solver.cpp:105] Iteration 1512, lr = 0.00741184
I0409 21:39:58.851480 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:40:02.090867 26212 solver.cpp:218] Iteration 1524 (2.4065 iter/s, 4.98649s/12 iters), loss = 3.6913
I0409 21:40:02.090922 26212 solver.cpp:237]     Train net output #0: loss = 3.6913 (* 1 = 3.6913 loss)
I0409 21:40:02.090934 26212 sgd_solver.cpp:105] Iteration 1524, lr = 0.00739425
I0409 21:40:04.084960 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1530.caffemodel
I0409 21:40:05.281455 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1530.solverstate
I0409 21:40:06.154017 26212 solver.cpp:330] Iteration 1530, Testing net (#0)
I0409 21:40:06.154042 26212 net.cpp:676] Ignoring source layer train-data
I0409 21:40:10.166644 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:40:10.801647 26212 solver.cpp:397]     Test net output #0: accuracy = 0.109069
I0409 21:40:10.801698 26212 solver.cpp:397]     Test net output #1: loss = 4.30129 (* 1 = 4.30129 loss)
I0409 21:40:12.677202 26212 solver.cpp:218] Iteration 1536 (1.13357 iter/s, 10.586s/12 iters), loss = 3.59255
I0409 21:40:12.677256 26212 solver.cpp:237]     Train net output #0: loss = 3.59255 (* 1 = 3.59255 loss)
I0409 21:40:12.677269 26212 sgd_solver.cpp:105] Iteration 1536, lr = 0.00737669
I0409 21:40:17.545930 26212 solver.cpp:218] Iteration 1548 (2.4648 iter/s, 4.86854s/12 iters), loss = 3.40408
I0409 21:40:17.545984 26212 solver.cpp:237]     Train net output #0: loss = 3.40408 (* 1 = 3.40408 loss)
I0409 21:40:17.545992 26212 sgd_solver.cpp:105] Iteration 1548, lr = 0.00735918
I0409 21:40:22.474681 26212 solver.cpp:218] Iteration 1560 (2.43479 iter/s, 4.92856s/12 iters), loss = 3.32914
I0409 21:40:22.474732 26212 solver.cpp:237]     Train net output #0: loss = 3.32914 (* 1 = 3.32914 loss)
I0409 21:40:22.474743 26212 sgd_solver.cpp:105] Iteration 1560, lr = 0.00734171
I0409 21:40:27.413653 26212 solver.cpp:218] Iteration 1572 (2.42975 iter/s, 4.93879s/12 iters), loss = 3.62857
I0409 21:40:27.413812 26212 solver.cpp:237]     Train net output #0: loss = 3.62857 (* 1 = 3.62857 loss)
I0409 21:40:27.413826 26212 sgd_solver.cpp:105] Iteration 1572, lr = 0.00732427
I0409 21:40:32.289247 26212 solver.cpp:218] Iteration 1584 (2.46138 iter/s, 4.87531s/12 iters), loss = 3.38936
I0409 21:40:32.289281 26212 solver.cpp:237]     Train net output #0: loss = 3.38936 (* 1 = 3.38936 loss)
I0409 21:40:32.289289 26212 sgd_solver.cpp:105] Iteration 1584, lr = 0.00730688
I0409 21:40:37.235746 26212 solver.cpp:218] Iteration 1596 (2.42604 iter/s, 4.94633s/12 iters), loss = 3.69908
I0409 21:40:37.235790 26212 solver.cpp:237]     Train net output #0: loss = 3.69908 (* 1 = 3.69908 loss)
I0409 21:40:37.235800 26212 sgd_solver.cpp:105] Iteration 1596, lr = 0.00728954
I0409 21:40:42.106590 26212 solver.cpp:218] Iteration 1608 (2.46373 iter/s, 4.87067s/12 iters), loss = 3.52287
I0409 21:40:42.106635 26212 solver.cpp:237]     Train net output #0: loss = 3.52287 (* 1 = 3.52287 loss)
I0409 21:40:42.106647 26212 sgd_solver.cpp:105] Iteration 1608, lr = 0.00727223
I0409 21:40:45.995398 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:40:47.062826 26212 solver.cpp:218] Iteration 1620 (2.42128 iter/s, 4.95606s/12 iters), loss = 3.25033
I0409 21:40:47.062867 26212 solver.cpp:237]     Train net output #0: loss = 3.25033 (* 1 = 3.25033 loss)
I0409 21:40:47.062876 26212 sgd_solver.cpp:105] Iteration 1620, lr = 0.00725496
I0409 21:40:51.498597 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1632.caffemodel
I0409 21:40:53.394177 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1632.solverstate
I0409 21:40:54.267490 26212 solver.cpp:330] Iteration 1632, Testing net (#0)
I0409 21:40:54.267515 26212 net.cpp:676] Ignoring source layer train-data
I0409 21:40:58.047219 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:40:58.719462 26212 solver.cpp:397]     Test net output #0: accuracy = 0.11826
I0409 21:40:58.719506 26212 solver.cpp:397]     Test net output #1: loss = 4.20198 (* 1 = 4.20198 loss)
I0409 21:40:58.803377 26212 solver.cpp:218] Iteration 1632 (1.02213 iter/s, 11.7402s/12 iters), loss = 3.35558
I0409 21:40:58.803433 26212 solver.cpp:237]     Train net output #0: loss = 3.35558 (* 1 = 3.35558 loss)
I0409 21:40:58.803444 26212 sgd_solver.cpp:105] Iteration 1632, lr = 0.00723774
I0409 21:41:02.974195 26212 solver.cpp:218] Iteration 1644 (2.87725 iter/s, 4.17064s/12 iters), loss = 3.64195
I0409 21:41:02.974246 26212 solver.cpp:237]     Train net output #0: loss = 3.64195 (* 1 = 3.64195 loss)
I0409 21:41:02.974256 26212 sgd_solver.cpp:105] Iteration 1644, lr = 0.00722056
I0409 21:41:08.136546 26212 solver.cpp:218] Iteration 1656 (2.32461 iter/s, 5.16216s/12 iters), loss = 3.25215
I0409 21:41:08.136598 26212 solver.cpp:237]     Train net output #0: loss = 3.25215 (* 1 = 3.25215 loss)
I0409 21:41:08.136610 26212 sgd_solver.cpp:105] Iteration 1656, lr = 0.00720341
I0409 21:41:13.046674 26212 solver.cpp:218] Iteration 1668 (2.44402 iter/s, 4.90994s/12 iters), loss = 3.01745
I0409 21:41:13.046726 26212 solver.cpp:237]     Train net output #0: loss = 3.01745 (* 1 = 3.01745 loss)
I0409 21:41:13.046738 26212 sgd_solver.cpp:105] Iteration 1668, lr = 0.00718631
I0409 21:41:17.917850 26212 solver.cpp:218] Iteration 1680 (2.46357 iter/s, 4.87099s/12 iters), loss = 3.23442
I0409 21:41:17.917912 26212 solver.cpp:237]     Train net output #0: loss = 3.23442 (* 1 = 3.23442 loss)
I0409 21:41:17.917923 26212 sgd_solver.cpp:105] Iteration 1680, lr = 0.00716925
I0409 21:41:22.820689 26212 solver.cpp:218] Iteration 1692 (2.44766 iter/s, 4.90265s/12 iters), loss = 3.15281
I0409 21:41:22.820742 26212 solver.cpp:237]     Train net output #0: loss = 3.15281 (* 1 = 3.15281 loss)
I0409 21:41:22.820755 26212 sgd_solver.cpp:105] Iteration 1692, lr = 0.00715223
I0409 21:41:27.756752 26212 solver.cpp:218] Iteration 1704 (2.43118 iter/s, 4.93588s/12 iters), loss = 3.00839
I0409 21:41:27.756790 26212 solver.cpp:237]     Train net output #0: loss = 3.00839 (* 1 = 3.00839 loss)
I0409 21:41:27.756799 26212 sgd_solver.cpp:105] Iteration 1704, lr = 0.00713525
I0409 21:41:33.153993 26212 solver.cpp:218] Iteration 1716 (2.22344 iter/s, 5.39704s/12 iters), loss = 3.28981
I0409 21:41:33.154106 26212 solver.cpp:237]     Train net output #0: loss = 3.28981 (* 1 = 3.28981 loss)
I0409 21:41:33.154117 26212 sgd_solver.cpp:105] Iteration 1716, lr = 0.00711831
I0409 21:41:34.296068 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:41:38.394556 26212 solver.cpp:218] Iteration 1728 (2.28994 iter/s, 5.24031s/12 iters), loss = 3.29875
I0409 21:41:38.394600 26212 solver.cpp:237]     Train net output #0: loss = 3.29875 (* 1 = 3.29875 loss)
I0409 21:41:38.394608 26212 sgd_solver.cpp:105] Iteration 1728, lr = 0.00710141
I0409 21:41:40.379477 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1734.caffemodel
I0409 21:41:42.536340 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1734.solverstate
I0409 21:41:44.963644 26212 solver.cpp:330] Iteration 1734, Testing net (#0)
I0409 21:41:44.963673 26212 net.cpp:676] Ignoring source layer train-data
I0409 21:41:48.718871 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:41:49.422878 26212 solver.cpp:397]     Test net output #0: accuracy = 0.120711
I0409 21:41:49.422919 26212 solver.cpp:397]     Test net output #1: loss = 4.09184 (* 1 = 4.09184 loss)
I0409 21:41:51.409191 26212 solver.cpp:218] Iteration 1740 (0.922066 iter/s, 13.0142s/12 iters), loss = 3.39696
I0409 21:41:51.409255 26212 solver.cpp:237]     Train net output #0: loss = 3.39696 (* 1 = 3.39696 loss)
I0409 21:41:51.409269 26212 sgd_solver.cpp:105] Iteration 1740, lr = 0.00708455
I0409 21:41:56.387797 26212 solver.cpp:218] Iteration 1752 (2.41041 iter/s, 4.97841s/12 iters), loss = 3.25572
I0409 21:41:56.387856 26212 solver.cpp:237]     Train net output #0: loss = 3.25572 (* 1 = 3.25572 loss)
I0409 21:41:56.387867 26212 sgd_solver.cpp:105] Iteration 1752, lr = 0.00706773
I0409 21:42:01.275169 26212 solver.cpp:218] Iteration 1764 (2.45541 iter/s, 4.88718s/12 iters), loss = 2.96634
I0409 21:42:01.275229 26212 solver.cpp:237]     Train net output #0: loss = 2.96634 (* 1 = 2.96634 loss)
I0409 21:42:01.275241 26212 sgd_solver.cpp:105] Iteration 1764, lr = 0.00705094
I0409 21:42:06.187297 26212 solver.cpp:218] Iteration 1776 (2.44303 iter/s, 4.91193s/12 iters), loss = 3.33405
I0409 21:42:06.187440 26212 solver.cpp:237]     Train net output #0: loss = 3.33405 (* 1 = 3.33405 loss)
I0409 21:42:06.187453 26212 sgd_solver.cpp:105] Iteration 1776, lr = 0.0070342
I0409 21:42:11.256372 26212 solver.cpp:218] Iteration 1788 (2.36742 iter/s, 5.0688s/12 iters), loss = 3.13185
I0409 21:42:11.256418 26212 solver.cpp:237]     Train net output #0: loss = 3.13185 (* 1 = 3.13185 loss)
I0409 21:42:11.256428 26212 sgd_solver.cpp:105] Iteration 1788, lr = 0.0070175
I0409 21:42:16.270092 26212 solver.cpp:218] Iteration 1800 (2.39352 iter/s, 5.01354s/12 iters), loss = 3.1711
I0409 21:42:16.270133 26212 solver.cpp:237]     Train net output #0: loss = 3.1711 (* 1 = 3.1711 loss)
I0409 21:42:16.270143 26212 sgd_solver.cpp:105] Iteration 1800, lr = 0.00700084
I0409 21:42:21.280630 26212 solver.cpp:218] Iteration 1812 (2.39504 iter/s, 5.01037s/12 iters), loss = 3.03314
I0409 21:42:21.280668 26212 solver.cpp:237]     Train net output #0: loss = 3.03314 (* 1 = 3.03314 loss)
I0409 21:42:21.280676 26212 sgd_solver.cpp:105] Iteration 1812, lr = 0.00698422
I0409 21:42:24.445467 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:42:26.259776 26212 solver.cpp:218] Iteration 1824 (2.41014 iter/s, 4.97897s/12 iters), loss = 3.10861
I0409 21:42:26.259820 26212 solver.cpp:237]     Train net output #0: loss = 3.10861 (* 1 = 3.10861 loss)
I0409 21:42:26.259829 26212 sgd_solver.cpp:105] Iteration 1824, lr = 0.00696764
I0409 21:42:30.848207 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1836.caffemodel
I0409 21:42:32.044905 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1836.solverstate
I0409 21:42:32.931979 26212 solver.cpp:330] Iteration 1836, Testing net (#0)
I0409 21:42:32.932003 26212 net.cpp:676] Ignoring source layer train-data
I0409 21:42:36.796720 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:42:37.552090 26212 solver.cpp:397]     Test net output #0: accuracy = 0.13848
I0409 21:42:37.552160 26212 solver.cpp:397]     Test net output #1: loss = 4.12282 (* 1 = 4.12282 loss)
I0409 21:42:37.636485 26212 solver.cpp:218] Iteration 1836 (1.05482 iter/s, 11.3764s/12 iters), loss = 3.06162
I0409 21:42:37.636543 26212 solver.cpp:237]     Train net output #0: loss = 3.06162 (* 1 = 3.06162 loss)
I0409 21:42:37.636554 26212 sgd_solver.cpp:105] Iteration 1836, lr = 0.0069511
I0409 21:42:41.970299 26212 solver.cpp:218] Iteration 1848 (2.76904 iter/s, 4.33363s/12 iters), loss = 3.33411
I0409 21:42:41.970348 26212 solver.cpp:237]     Train net output #0: loss = 3.33411 (* 1 = 3.33411 loss)
I0409 21:42:41.970357 26212 sgd_solver.cpp:105] Iteration 1848, lr = 0.00693459
I0409 21:42:46.872892 26212 solver.cpp:218] Iteration 1860 (2.44777 iter/s, 4.90242s/12 iters), loss = 3.04137
I0409 21:42:46.872926 26212 solver.cpp:237]     Train net output #0: loss = 3.04137 (* 1 = 3.04137 loss)
I0409 21:42:46.872934 26212 sgd_solver.cpp:105] Iteration 1860, lr = 0.00691813
I0409 21:42:51.769181 26212 solver.cpp:218] Iteration 1872 (2.45092 iter/s, 4.89612s/12 iters), loss = 3.32072
I0409 21:42:51.769219 26212 solver.cpp:237]     Train net output #0: loss = 3.32072 (* 1 = 3.32072 loss)
I0409 21:42:51.769227 26212 sgd_solver.cpp:105] Iteration 1872, lr = 0.0069017
I0409 21:42:56.663223 26212 solver.cpp:218] Iteration 1884 (2.45205 iter/s, 4.89387s/12 iters), loss = 3.15133
I0409 21:42:56.663259 26212 solver.cpp:237]     Train net output #0: loss = 3.15133 (* 1 = 3.15133 loss)
I0409 21:42:56.663267 26212 sgd_solver.cpp:105] Iteration 1884, lr = 0.00688532
I0409 21:43:01.596081 26212 solver.cpp:218] Iteration 1896 (2.43275 iter/s, 4.93268s/12 iters), loss = 3.19952
I0409 21:43:01.596127 26212 solver.cpp:237]     Train net output #0: loss = 3.19952 (* 1 = 3.19952 loss)
I0409 21:43:01.596138 26212 sgd_solver.cpp:105] Iteration 1896, lr = 0.00686897
I0409 21:43:06.539194 26212 solver.cpp:218] Iteration 1908 (2.42771 iter/s, 4.94293s/12 iters), loss = 3.12505
I0409 21:43:06.539232 26212 solver.cpp:237]     Train net output #0: loss = 3.12505 (* 1 = 3.12505 loss)
I0409 21:43:06.539239 26212 sgd_solver.cpp:105] Iteration 1908, lr = 0.00685266
I0409 21:43:11.748904 26212 solver.cpp:218] Iteration 1920 (2.30347 iter/s, 5.20953s/12 iters), loss = 2.5837
I0409 21:43:11.749028 26212 solver.cpp:237]     Train net output #0: loss = 2.5837 (* 1 = 2.5837 loss)
I0409 21:43:11.749038 26212 sgd_solver.cpp:105] Iteration 1920, lr = 0.00683639
I0409 21:43:12.061374 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:43:16.798576 26212 solver.cpp:218] Iteration 1932 (2.37652 iter/s, 5.0494s/12 iters), loss = 2.98121
I0409 21:43:16.798638 26212 solver.cpp:237]     Train net output #0: loss = 2.98121 (* 1 = 2.98121 loss)
I0409 21:43:16.798653 26212 sgd_solver.cpp:105] Iteration 1932, lr = 0.00682016
I0409 21:43:18.955124 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1938.caffemodel
I0409 21:43:20.120028 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1938.solverstate
I0409 21:43:20.980777 26212 solver.cpp:330] Iteration 1938, Testing net (#0)
I0409 21:43:20.980798 26212 net.cpp:676] Ignoring source layer train-data
I0409 21:43:24.738588 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:43:25.557832 26212 solver.cpp:397]     Test net output #0: accuracy = 0.135417
I0409 21:43:25.557871 26212 solver.cpp:397]     Test net output #1: loss = 4.37344 (* 1 = 4.37344 loss)
I0409 21:43:27.414129 26212 solver.cpp:218] Iteration 1944 (1.13045 iter/s, 10.6152s/12 iters), loss = 3.42982
I0409 21:43:27.414180 26212 solver.cpp:237]     Train net output #0: loss = 3.42982 (* 1 = 3.42982 loss)
I0409 21:43:27.414187 26212 sgd_solver.cpp:105] Iteration 1944, lr = 0.00680397
I0409 21:43:32.337779 26212 solver.cpp:218] Iteration 1956 (2.43731 iter/s, 4.92346s/12 iters), loss = 3.02815
I0409 21:43:32.337823 26212 solver.cpp:237]     Train net output #0: loss = 3.02815 (* 1 = 3.02815 loss)
I0409 21:43:32.337832 26212 sgd_solver.cpp:105] Iteration 1956, lr = 0.00678782
I0409 21:43:37.238696 26212 solver.cpp:218] Iteration 1968 (2.44861 iter/s, 4.90074s/12 iters), loss = 2.81927
I0409 21:43:37.238746 26212 solver.cpp:237]     Train net output #0: loss = 2.81927 (* 1 = 2.81927 loss)
I0409 21:43:37.238757 26212 sgd_solver.cpp:105] Iteration 1968, lr = 0.0067717
I0409 21:43:42.139271 26212 solver.cpp:218] Iteration 1980 (2.44878 iter/s, 4.90039s/12 iters), loss = 2.95158
I0409 21:43:42.139379 26212 solver.cpp:237]     Train net output #0: loss = 2.95158 (* 1 = 2.95158 loss)
I0409 21:43:42.139391 26212 sgd_solver.cpp:105] Iteration 1980, lr = 0.00675562
I0409 21:43:47.221154 26212 solver.cpp:218] Iteration 1992 (2.36144 iter/s, 5.08164s/12 iters), loss = 3.08181
I0409 21:43:47.221201 26212 solver.cpp:237]     Train net output #0: loss = 3.08181 (* 1 = 3.08181 loss)
I0409 21:43:47.221213 26212 sgd_solver.cpp:105] Iteration 1992, lr = 0.00673958
I0409 21:43:52.377987 26212 solver.cpp:218] Iteration 2004 (2.3271 iter/s, 5.15663s/12 iters), loss = 2.93158
I0409 21:43:52.378038 26212 solver.cpp:237]     Train net output #0: loss = 2.93158 (* 1 = 2.93158 loss)
I0409 21:43:52.378048 26212 sgd_solver.cpp:105] Iteration 2004, lr = 0.00672358
I0409 21:43:57.646559 26212 solver.cpp:218] Iteration 2016 (2.27774 iter/s, 5.26838s/12 iters), loss = 2.6328
I0409 21:43:57.646595 26212 solver.cpp:237]     Train net output #0: loss = 2.6328 (* 1 = 2.6328 loss)
I0409 21:43:57.646603 26212 sgd_solver.cpp:105] Iteration 2016, lr = 0.00670762
I0409 21:44:00.221495 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:44:02.653992 26212 solver.cpp:218] Iteration 2028 (2.39652 iter/s, 5.00726s/12 iters), loss = 2.61424
I0409 21:44:02.654039 26212 solver.cpp:237]     Train net output #0: loss = 2.61424 (* 1 = 2.61424 loss)
I0409 21:44:02.654049 26212 sgd_solver.cpp:105] Iteration 2028, lr = 0.00669169
I0409 21:44:07.114223 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2040.caffemodel
I0409 21:44:09.954111 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2040.solverstate
I0409 21:44:12.674326 26212 solver.cpp:330] Iteration 2040, Testing net (#0)
I0409 21:44:12.674439 26212 net.cpp:676] Ignoring source layer train-data
I0409 21:44:16.394944 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:44:17.337034 26212 solver.cpp:397]     Test net output #0: accuracy = 0.139093
I0409 21:44:17.337082 26212 solver.cpp:397]     Test net output #1: loss = 4.12101 (* 1 = 4.12101 loss)
I0409 21:44:17.420917 26212 solver.cpp:218] Iteration 2040 (0.81265 iter/s, 14.7665s/12 iters), loss = 3.20861
I0409 21:44:17.420967 26212 solver.cpp:237]     Train net output #0: loss = 3.20861 (* 1 = 3.20861 loss)
I0409 21:44:17.420979 26212 sgd_solver.cpp:105] Iteration 2040, lr = 0.00667581
I0409 21:44:21.639376 26212 solver.cpp:218] Iteration 2052 (2.84475 iter/s, 4.21829s/12 iters), loss = 3.06416
I0409 21:44:21.639425 26212 solver.cpp:237]     Train net output #0: loss = 3.06416 (* 1 = 3.06416 loss)
I0409 21:44:21.639436 26212 sgd_solver.cpp:105] Iteration 2052, lr = 0.00665996
I0409 21:44:22.819006 26212 blocking_queue.cpp:49] Waiting for data
I0409 21:44:26.628057 26212 solver.cpp:218] Iteration 2064 (2.40554 iter/s, 4.98849s/12 iters), loss = 2.83139
I0409 21:44:26.628104 26212 solver.cpp:237]     Train net output #0: loss = 2.83139 (* 1 = 2.83139 loss)
I0409 21:44:26.628113 26212 sgd_solver.cpp:105] Iteration 2064, lr = 0.00664414
I0409 21:44:31.544495 26212 solver.cpp:218] Iteration 2076 (2.44088 iter/s, 4.91626s/12 iters), loss = 3.0014
I0409 21:44:31.544534 26212 solver.cpp:237]     Train net output #0: loss = 3.0014 (* 1 = 3.0014 loss)
I0409 21:44:31.544543 26212 sgd_solver.cpp:105] Iteration 2076, lr = 0.00662837
I0409 21:44:36.537775 26212 solver.cpp:218] Iteration 2088 (2.40332 iter/s, 4.9931s/12 iters), loss = 3.12097
I0409 21:44:36.537828 26212 solver.cpp:237]     Train net output #0: loss = 3.12097 (* 1 = 3.12097 loss)
I0409 21:44:36.537840 26212 sgd_solver.cpp:105] Iteration 2088, lr = 0.00661263
I0409 21:44:41.548700 26212 solver.cpp:218] Iteration 2100 (2.39486 iter/s, 5.01074s/12 iters), loss = 2.75089
I0409 21:44:41.548750 26212 solver.cpp:237]     Train net output #0: loss = 2.75089 (* 1 = 2.75089 loss)
I0409 21:44:41.548763 26212 sgd_solver.cpp:105] Iteration 2100, lr = 0.00659693
I0409 21:44:46.529665 26212 solver.cpp:218] Iteration 2112 (2.40926 iter/s, 4.98078s/12 iters), loss = 2.64912
I0409 21:44:46.529780 26212 solver.cpp:237]     Train net output #0: loss = 2.64912 (* 1 = 2.64912 loss)
I0409 21:44:46.529793 26212 sgd_solver.cpp:105] Iteration 2112, lr = 0.00658127
I0409 21:44:51.209128 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:44:51.528407 26212 solver.cpp:218] Iteration 2124 (2.40072 iter/s, 4.9985s/12 iters), loss = 2.53353
I0409 21:44:51.528452 26212 solver.cpp:237]     Train net output #0: loss = 2.53353 (* 1 = 2.53353 loss)
I0409 21:44:51.528463 26212 sgd_solver.cpp:105] Iteration 2124, lr = 0.00656564
I0409 21:44:56.519552 26212 solver.cpp:218] Iteration 2136 (2.40435 iter/s, 4.99096s/12 iters), loss = 3.2626
I0409 21:44:56.519605 26212 solver.cpp:237]     Train net output #0: loss = 3.2626 (* 1 = 3.2626 loss)
I0409 21:44:56.519618 26212 sgd_solver.cpp:105] Iteration 2136, lr = 0.00655006
I0409 21:44:58.628214 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2142.caffemodel
I0409 21:44:59.855140 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2142.solverstate
I0409 21:45:00.839674 26212 solver.cpp:330] Iteration 2142, Testing net (#0)
I0409 21:45:00.839702 26212 net.cpp:676] Ignoring source layer train-data
I0409 21:45:04.528537 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:45:05.486924 26212 solver.cpp:397]     Test net output #0: accuracy = 0.145221
I0409 21:45:05.486958 26212 solver.cpp:397]     Test net output #1: loss = 4.04576 (* 1 = 4.04576 loss)
I0409 21:45:07.273751 26212 solver.cpp:218] Iteration 2148 (1.11588 iter/s, 10.7539s/12 iters), loss = 2.55152
I0409 21:45:07.273804 26212 solver.cpp:237]     Train net output #0: loss = 2.55152 (* 1 = 2.55152 loss)
I0409 21:45:07.273816 26212 sgd_solver.cpp:105] Iteration 2148, lr = 0.00653451
I0409 21:45:12.188167 26212 solver.cpp:218] Iteration 2160 (2.44189 iter/s, 4.91423s/12 iters), loss = 3.02554
I0409 21:45:12.188207 26212 solver.cpp:237]     Train net output #0: loss = 3.02554 (* 1 = 3.02554 loss)
I0409 21:45:12.188215 26212 sgd_solver.cpp:105] Iteration 2160, lr = 0.00651899
I0409 21:45:17.326797 26212 solver.cpp:218] Iteration 2172 (2.33534 iter/s, 5.13845s/12 iters), loss = 2.74738
I0409 21:45:17.326952 26212 solver.cpp:237]     Train net output #0: loss = 2.74738 (* 1 = 2.74738 loss)
I0409 21:45:17.326967 26212 sgd_solver.cpp:105] Iteration 2172, lr = 0.00650351
I0409 21:45:22.479045 26212 solver.cpp:218] Iteration 2184 (2.32921 iter/s, 5.15196s/12 iters), loss = 2.79423
I0409 21:45:22.479099 26212 solver.cpp:237]     Train net output #0: loss = 2.79423 (* 1 = 2.79423 loss)
I0409 21:45:22.479112 26212 sgd_solver.cpp:105] Iteration 2184, lr = 0.00648807
I0409 21:45:27.541947 26212 solver.cpp:218] Iteration 2196 (2.37027 iter/s, 5.06271s/12 iters), loss = 2.43933
I0409 21:45:27.542032 26212 solver.cpp:237]     Train net output #0: loss = 2.43933 (* 1 = 2.43933 loss)
I0409 21:45:27.542043 26212 sgd_solver.cpp:105] Iteration 2196, lr = 0.00647267
I0409 21:45:32.601315 26212 solver.cpp:218] Iteration 2208 (2.37194 iter/s, 5.05915s/12 iters), loss = 2.38439
I0409 21:45:32.601370 26212 solver.cpp:237]     Train net output #0: loss = 2.38439 (* 1 = 2.38439 loss)
I0409 21:45:32.601382 26212 sgd_solver.cpp:105] Iteration 2208, lr = 0.0064573
I0409 21:45:37.689110 26212 solver.cpp:218] Iteration 2220 (2.35868 iter/s, 5.0876s/12 iters), loss = 2.36096
I0409 21:45:37.689164 26212 solver.cpp:237]     Train net output #0: loss = 2.36096 (* 1 = 2.36096 loss)
I0409 21:45:37.689175 26212 sgd_solver.cpp:105] Iteration 2220, lr = 0.00644197
I0409 21:45:39.456593 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:45:42.585155 26212 solver.cpp:218] Iteration 2232 (2.45105 iter/s, 4.89585s/12 iters), loss = 2.68232
I0409 21:45:42.585211 26212 solver.cpp:237]     Train net output #0: loss = 2.68232 (* 1 = 2.68232 loss)
I0409 21:45:42.585223 26212 sgd_solver.cpp:105] Iteration 2232, lr = 0.00642668
I0409 21:45:47.052340 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2244.caffemodel
I0409 21:45:49.794023 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2244.solverstate
I0409 21:45:51.597441 26212 solver.cpp:330] Iteration 2244, Testing net (#0)
I0409 21:45:51.597473 26212 net.cpp:676] Ignoring source layer train-data
I0409 21:45:55.136909 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:45:56.040796 26212 solver.cpp:397]     Test net output #0: accuracy = 0.168505
I0409 21:45:56.040843 26212 solver.cpp:397]     Test net output #1: loss = 4.08704 (* 1 = 4.08704 loss)
I0409 21:45:56.124717 26212 solver.cpp:218] Iteration 2244 (0.886318 iter/s, 13.5392s/12 iters), loss = 2.81016
I0409 21:45:56.124771 26212 solver.cpp:237]     Train net output #0: loss = 2.81016 (* 1 = 2.81016 loss)
I0409 21:45:56.124783 26212 sgd_solver.cpp:105] Iteration 2244, lr = 0.00641142
I0409 21:46:00.326333 26212 solver.cpp:218] Iteration 2256 (2.85616 iter/s, 4.20144s/12 iters), loss = 2.59502
I0409 21:46:00.326380 26212 solver.cpp:237]     Train net output #0: loss = 2.59502 (* 1 = 2.59502 loss)
I0409 21:46:00.326388 26212 sgd_solver.cpp:105] Iteration 2256, lr = 0.0063962
I0409 21:46:05.286481 26212 solver.cpp:218] Iteration 2268 (2.41937 iter/s, 4.95997s/12 iters), loss = 2.59459
I0409 21:46:05.286523 26212 solver.cpp:237]     Train net output #0: loss = 2.59459 (* 1 = 2.59459 loss)
I0409 21:46:05.286532 26212 sgd_solver.cpp:105] Iteration 2268, lr = 0.00638101
I0409 21:46:10.351665 26212 solver.cpp:218] Iteration 2280 (2.3692 iter/s, 5.065s/12 iters), loss = 2.76875
I0409 21:46:10.351714 26212 solver.cpp:237]     Train net output #0: loss = 2.76875 (* 1 = 2.76875 loss)
I0409 21:46:10.351725 26212 sgd_solver.cpp:105] Iteration 2280, lr = 0.00636586
I0409 21:46:15.353370 26212 solver.cpp:218] Iteration 2292 (2.39927 iter/s, 5.00152s/12 iters), loss = 2.54414
I0409 21:46:15.353410 26212 solver.cpp:237]     Train net output #0: loss = 2.54414 (* 1 = 2.54414 loss)
I0409 21:46:15.353418 26212 sgd_solver.cpp:105] Iteration 2292, lr = 0.00635075
I0409 21:46:20.296947 26212 solver.cpp:218] Iteration 2304 (2.42748 iter/s, 4.9434s/12 iters), loss = 2.41976
I0409 21:46:20.297081 26212 solver.cpp:237]     Train net output #0: loss = 2.41976 (* 1 = 2.41976 loss)
I0409 21:46:20.297093 26212 sgd_solver.cpp:105] Iteration 2304, lr = 0.00633567
I0409 21:46:25.219563 26212 solver.cpp:218] Iteration 2316 (2.43786 iter/s, 4.92234s/12 iters), loss = 2.43022
I0409 21:46:25.219619 26212 solver.cpp:237]     Train net output #0: loss = 2.43022 (* 1 = 2.43022 loss)
I0409 21:46:25.219630 26212 sgd_solver.cpp:105] Iteration 2316, lr = 0.00632063
I0409 21:46:29.099576 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:46:30.130321 26212 solver.cpp:218] Iteration 2328 (2.44372 iter/s, 4.91056s/12 iters), loss = 2.28277
I0409 21:46:30.130367 26212 solver.cpp:237]     Train net output #0: loss = 2.28277 (* 1 = 2.28277 loss)
I0409 21:46:30.130378 26212 sgd_solver.cpp:105] Iteration 2328, lr = 0.00630562
I0409 21:46:35.071492 26212 solver.cpp:218] Iteration 2340 (2.42866 iter/s, 4.94099s/12 iters), loss = 2.5939
I0409 21:46:35.071537 26212 solver.cpp:237]     Train net output #0: loss = 2.5939 (* 1 = 2.5939 loss)
I0409 21:46:35.071544 26212 sgd_solver.cpp:105] Iteration 2340, lr = 0.00629065
I0409 21:46:37.114729 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2346.caffemodel
I0409 21:46:40.319824 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2346.solverstate
I0409 21:46:44.057154 26212 solver.cpp:330] Iteration 2346, Testing net (#0)
I0409 21:46:44.057179 26212 net.cpp:676] Ignoring source layer train-data
I0409 21:46:47.599870 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:46:48.542076 26212 solver.cpp:397]     Test net output #0: accuracy = 0.157475
I0409 21:46:48.542115 26212 solver.cpp:397]     Test net output #1: loss = 4.09191 (* 1 = 4.09191 loss)
I0409 21:46:50.389542 26212 solver.cpp:218] Iteration 2352 (0.783411 iter/s, 15.3176s/12 iters), loss = 2.54441
I0409 21:46:50.389637 26212 solver.cpp:237]     Train net output #0: loss = 2.54441 (* 1 = 2.54441 loss)
I0409 21:46:50.389647 26212 sgd_solver.cpp:105] Iteration 2352, lr = 0.00627571
I0409 21:46:55.314438 26212 solver.cpp:218] Iteration 2364 (2.43671 iter/s, 4.92466s/12 iters), loss = 2.5893
I0409 21:46:55.314491 26212 solver.cpp:237]     Train net output #0: loss = 2.5893 (* 1 = 2.5893 loss)
I0409 21:46:55.314503 26212 sgd_solver.cpp:105] Iteration 2364, lr = 0.00626081
I0409 21:47:00.193111 26212 solver.cpp:218] Iteration 2376 (2.45978 iter/s, 4.87848s/12 iters), loss = 2.57728
I0409 21:47:00.193164 26212 solver.cpp:237]     Train net output #0: loss = 2.57728 (* 1 = 2.57728 loss)
I0409 21:47:00.193177 26212 sgd_solver.cpp:105] Iteration 2376, lr = 0.00624595
I0409 21:47:05.160717 26212 solver.cpp:218] Iteration 2388 (2.41575 iter/s, 4.96741s/12 iters), loss = 2.49589
I0409 21:47:05.160779 26212 solver.cpp:237]     Train net output #0: loss = 2.49589 (* 1 = 2.49589 loss)
I0409 21:47:05.160794 26212 sgd_solver.cpp:105] Iteration 2388, lr = 0.00623112
I0409 21:47:10.071352 26212 solver.cpp:218] Iteration 2400 (2.44377 iter/s, 4.91044s/12 iters), loss = 2.50776
I0409 21:47:10.071393 26212 solver.cpp:237]     Train net output #0: loss = 2.50776 (* 1 = 2.50776 loss)
I0409 21:47:10.071404 26212 sgd_solver.cpp:105] Iteration 2400, lr = 0.00621633
I0409 21:47:14.978235 26212 solver.cpp:218] Iteration 2412 (2.44563 iter/s, 4.90671s/12 iters), loss = 2.20055
I0409 21:47:14.978279 26212 solver.cpp:237]     Train net output #0: loss = 2.20055 (* 1 = 2.20055 loss)
I0409 21:47:14.978289 26212 sgd_solver.cpp:105] Iteration 2412, lr = 0.00620157
I0409 21:47:19.950585 26212 solver.cpp:218] Iteration 2424 (2.41343 iter/s, 4.97217s/12 iters), loss = 2.07583
I0409 21:47:19.950623 26212 solver.cpp:237]     Train net output #0: loss = 2.07583 (* 1 = 2.07583 loss)
I0409 21:47:19.950630 26212 sgd_solver.cpp:105] Iteration 2424, lr = 0.00618684
I0409 21:47:21.026402 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:47:25.097646 26212 solver.cpp:218] Iteration 2436 (2.33151 iter/s, 5.14688s/12 iters), loss = 2.21439
I0409 21:47:25.097697 26212 solver.cpp:237]     Train net output #0: loss = 2.21439 (* 1 = 2.21439 loss)
I0409 21:47:25.097708 26212 sgd_solver.cpp:105] Iteration 2436, lr = 0.00617215
I0409 21:47:29.641999 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2448.caffemodel
I0409 21:47:32.278453 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2448.solverstate
I0409 21:47:34.752774 26212 solver.cpp:330] Iteration 2448, Testing net (#0)
I0409 21:47:34.752807 26212 net.cpp:676] Ignoring source layer train-data
I0409 21:47:38.164527 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:47:39.140969 26212 solver.cpp:397]     Test net output #0: accuracy = 0.183211
I0409 21:47:39.141008 26212 solver.cpp:397]     Test net output #1: loss = 4.06655 (* 1 = 4.06655 loss)
I0409 21:47:39.224854 26212 solver.cpp:218] Iteration 2448 (0.84945 iter/s, 14.1268s/12 iters), loss = 2.22091
I0409 21:47:39.224928 26212 solver.cpp:237]     Train net output #0: loss = 2.22091 (* 1 = 2.22091 loss)
I0409 21:47:39.224946 26212 sgd_solver.cpp:105] Iteration 2448, lr = 0.0061575
I0409 21:47:43.440047 26212 solver.cpp:218] Iteration 2460 (2.84697 iter/s, 4.215s/12 iters), loss = 2.10677
I0409 21:47:43.440100 26212 solver.cpp:237]     Train net output #0: loss = 2.10677 (* 1 = 2.10677 loss)
I0409 21:47:43.440114 26212 sgd_solver.cpp:105] Iteration 2460, lr = 0.00614288
I0409 21:47:48.573912 26212 solver.cpp:218] Iteration 2472 (2.33751 iter/s, 5.13367s/12 iters), loss = 2.35763
I0409 21:47:48.573972 26212 solver.cpp:237]     Train net output #0: loss = 2.35763 (* 1 = 2.35763 loss)
I0409 21:47:48.573982 26212 sgd_solver.cpp:105] Iteration 2472, lr = 0.0061283
I0409 21:47:53.637684 26212 solver.cpp:218] Iteration 2484 (2.36986 iter/s, 5.06359s/12 iters), loss = 2.6783
I0409 21:47:53.637759 26212 solver.cpp:237]     Train net output #0: loss = 2.6783 (* 1 = 2.6783 loss)
I0409 21:47:53.637769 26212 sgd_solver.cpp:105] Iteration 2484, lr = 0.00611375
I0409 21:47:58.570695 26212 solver.cpp:218] Iteration 2496 (2.43269 iter/s, 4.9328s/12 iters), loss = 2.48514
I0409 21:47:58.570741 26212 solver.cpp:237]     Train net output #0: loss = 2.48514 (* 1 = 2.48514 loss)
I0409 21:47:58.570751 26212 sgd_solver.cpp:105] Iteration 2496, lr = 0.00609923
I0409 21:48:03.503993 26212 solver.cpp:218] Iteration 2508 (2.43254 iter/s, 4.93311s/12 iters), loss = 1.81481
I0409 21:48:03.504040 26212 solver.cpp:237]     Train net output #0: loss = 1.81481 (* 1 = 1.81481 loss)
I0409 21:48:03.504050 26212 sgd_solver.cpp:105] Iteration 2508, lr = 0.00608475
I0409 21:48:08.524763 26212 solver.cpp:218] Iteration 2520 (2.39016 iter/s, 5.02058s/12 iters), loss = 2.2134
I0409 21:48:08.524814 26212 solver.cpp:237]     Train net output #0: loss = 2.2134 (* 1 = 2.2134 loss)
I0409 21:48:08.524825 26212 sgd_solver.cpp:105] Iteration 2520, lr = 0.0060703
I0409 21:48:11.773599 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:48:13.519587 26212 solver.cpp:218] Iteration 2532 (2.40258 iter/s, 4.99464s/12 iters), loss = 2.40623
I0409 21:48:13.519631 26212 solver.cpp:237]     Train net output #0: loss = 2.40623 (* 1 = 2.40623 loss)
I0409 21:48:13.519644 26212 sgd_solver.cpp:105] Iteration 2532, lr = 0.00605589
I0409 21:48:18.406364 26212 solver.cpp:218] Iteration 2544 (2.4557 iter/s, 4.88659s/12 iters), loss = 2.58032
I0409 21:48:18.406421 26212 solver.cpp:237]     Train net output #0: loss = 2.58032 (* 1 = 2.58032 loss)
I0409 21:48:18.406433 26212 sgd_solver.cpp:105] Iteration 2544, lr = 0.00604151
I0409 21:48:20.442950 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2550.caffemodel
I0409 21:48:21.682472 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2550.solverstate
I0409 21:48:22.560510 26212 solver.cpp:330] Iteration 2550, Testing net (#0)
I0409 21:48:22.560534 26212 net.cpp:676] Ignoring source layer train-data
I0409 21:48:25.992569 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:48:27.144312 26212 solver.cpp:397]     Test net output #0: accuracy = 0.173407
I0409 21:48:27.144363 26212 solver.cpp:397]     Test net output #1: loss = 4.05699 (* 1 = 4.05699 loss)
I0409 21:48:29.070452 26212 solver.cpp:218] Iteration 2556 (1.12531 iter/s, 10.6638s/12 iters), loss = 2.48498
I0409 21:48:29.070514 26212 solver.cpp:237]     Train net output #0: loss = 2.48498 (* 1 = 2.48498 loss)
I0409 21:48:29.070528 26212 sgd_solver.cpp:105] Iteration 2556, lr = 0.00602717
I0409 21:48:33.964859 26212 solver.cpp:218] Iteration 2568 (2.45187 iter/s, 4.89422s/12 iters), loss = 2.29897
I0409 21:48:33.964906 26212 solver.cpp:237]     Train net output #0: loss = 2.29897 (* 1 = 2.29897 loss)
I0409 21:48:33.964917 26212 sgd_solver.cpp:105] Iteration 2568, lr = 0.00601286
I0409 21:48:38.925747 26212 solver.cpp:218] Iteration 2580 (2.41901 iter/s, 4.9607s/12 iters), loss = 2.50797
I0409 21:48:38.925804 26212 solver.cpp:237]     Train net output #0: loss = 2.50797 (* 1 = 2.50797 loss)
I0409 21:48:38.925817 26212 sgd_solver.cpp:105] Iteration 2580, lr = 0.00599858
I0409 21:48:43.907635 26212 solver.cpp:218] Iteration 2592 (2.40882 iter/s, 4.9817s/12 iters), loss = 2.25737
I0409 21:48:43.907689 26212 solver.cpp:237]     Train net output #0: loss = 2.25737 (* 1 = 2.25737 loss)
I0409 21:48:43.907701 26212 sgd_solver.cpp:105] Iteration 2592, lr = 0.00598434
I0409 21:48:48.996976 26212 solver.cpp:218] Iteration 2604 (2.35796 iter/s, 5.08915s/12 iters), loss = 2.38504
I0409 21:48:48.997032 26212 solver.cpp:237]     Train net output #0: loss = 2.38504 (* 1 = 2.38504 loss)
I0409 21:48:48.997045 26212 sgd_solver.cpp:105] Iteration 2604, lr = 0.00597013
I0409 21:48:54.019886 26212 solver.cpp:218] Iteration 2616 (2.38914 iter/s, 5.02272s/12 iters), loss = 2.12231
I0409 21:48:54.019932 26212 solver.cpp:237]     Train net output #0: loss = 2.12231 (* 1 = 2.12231 loss)
I0409 21:48:54.019942 26212 sgd_solver.cpp:105] Iteration 2616, lr = 0.00595596
I0409 21:48:59.000720 26212 solver.cpp:218] Iteration 2628 (2.40933 iter/s, 4.98065s/12 iters), loss = 1.82563
I0409 21:48:59.000828 26212 solver.cpp:237]     Train net output #0: loss = 1.82563 (* 1 = 1.82563 loss)
I0409 21:48:59.000841 26212 sgd_solver.cpp:105] Iteration 2628, lr = 0.00594182
I0409 21:48:59.431192 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:49:03.929308 26212 solver.cpp:218] Iteration 2640 (2.43489 iter/s, 4.92835s/12 iters), loss = 2.31644
I0409 21:49:03.929355 26212 solver.cpp:237]     Train net output #0: loss = 2.31644 (* 1 = 2.31644 loss)
I0409 21:49:03.929366 26212 sgd_solver.cpp:105] Iteration 2640, lr = 0.00592771
I0409 21:49:08.393347 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2652.caffemodel
I0409 21:49:09.632071 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2652.solverstate
I0409 21:49:10.515218 26212 solver.cpp:330] Iteration 2652, Testing net (#0)
I0409 21:49:10.515247 26212 net.cpp:676] Ignoring source layer train-data
I0409 21:49:13.915545 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:49:14.969615 26212 solver.cpp:397]     Test net output #0: accuracy = 0.177696
I0409 21:49:14.969656 26212 solver.cpp:397]     Test net output #1: loss = 4.12609 (* 1 = 4.12609 loss)
I0409 21:49:15.053370 26212 solver.cpp:218] Iteration 2652 (1.07877 iter/s, 11.1237s/12 iters), loss = 2.30863
I0409 21:49:15.053417 26212 solver.cpp:237]     Train net output #0: loss = 2.30863 (* 1 = 2.30863 loss)
I0409 21:49:15.053427 26212 sgd_solver.cpp:105] Iteration 2652, lr = 0.00591364
I0409 21:49:19.261308 26212 solver.cpp:218] Iteration 2664 (2.85187 iter/s, 4.20777s/12 iters), loss = 2.22034
I0409 21:49:19.261358 26212 solver.cpp:237]     Train net output #0: loss = 2.22034 (* 1 = 2.22034 loss)
I0409 21:49:19.261368 26212 sgd_solver.cpp:105] Iteration 2664, lr = 0.0058996
I0409 21:49:24.177536 26212 solver.cpp:218] Iteration 2676 (2.44099 iter/s, 4.91604s/12 iters), loss = 2.12651
I0409 21:49:24.177590 26212 solver.cpp:237]     Train net output #0: loss = 2.12651 (* 1 = 2.12651 loss)
I0409 21:49:24.177603 26212 sgd_solver.cpp:105] Iteration 2676, lr = 0.00588559
I0409 21:49:29.112623 26212 solver.cpp:218] Iteration 2688 (2.43166 iter/s, 4.9349s/12 iters), loss = 2.39576
I0409 21:49:29.114269 26212 solver.cpp:237]     Train net output #0: loss = 2.39576 (* 1 = 2.39576 loss)
I0409 21:49:29.114279 26212 sgd_solver.cpp:105] Iteration 2688, lr = 0.00587162
I0409 21:49:34.000967 26212 solver.cpp:218] Iteration 2700 (2.45571 iter/s, 4.88656s/12 iters), loss = 2.28968
I0409 21:49:34.001014 26212 solver.cpp:237]     Train net output #0: loss = 2.28968 (* 1 = 2.28968 loss)
I0409 21:49:34.001024 26212 sgd_solver.cpp:105] Iteration 2700, lr = 0.00585768
I0409 21:49:38.943192 26212 solver.cpp:218] Iteration 2712 (2.42815 iter/s, 4.94204s/12 iters), loss = 1.99971
I0409 21:49:38.943244 26212 solver.cpp:237]     Train net output #0: loss = 1.99971 (* 1 = 1.99971 loss)
I0409 21:49:38.943256 26212 sgd_solver.cpp:105] Iteration 2712, lr = 0.00584377
I0409 21:49:43.846277 26212 solver.cpp:218] Iteration 2724 (2.44753 iter/s, 4.90289s/12 iters), loss = 1.90837
I0409 21:49:43.846334 26212 solver.cpp:237]     Train net output #0: loss = 1.90837 (* 1 = 1.90837 loss)
I0409 21:49:43.846346 26212 sgd_solver.cpp:105] Iteration 2724, lr = 0.0058299
I0409 21:49:46.414037 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:49:48.791472 26212 solver.cpp:218] Iteration 2736 (2.42669 iter/s, 4.94501s/12 iters), loss = 2.18039
I0409 21:49:48.791512 26212 solver.cpp:237]     Train net output #0: loss = 2.18039 (* 1 = 2.18039 loss)
I0409 21:49:48.791520 26212 sgd_solver.cpp:105] Iteration 2736, lr = 0.00581605
I0409 21:49:53.726402 26212 solver.cpp:218] Iteration 2748 (2.43173 iter/s, 4.93475s/12 iters), loss = 2.37218
I0409 21:49:53.726459 26212 solver.cpp:237]     Train net output #0: loss = 2.37218 (* 1 = 2.37218 loss)
I0409 21:49:53.726471 26212 sgd_solver.cpp:105] Iteration 2748, lr = 0.00580225
I0409 21:49:55.700606 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2754.caffemodel
I0409 21:50:04.701388 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2754.solverstate
I0409 21:50:08.223609 26212 solver.cpp:330] Iteration 2754, Testing net (#0)
I0409 21:50:08.223636 26212 net.cpp:676] Ignoring source layer train-data
I0409 21:50:11.285084 26212 blocking_queue.cpp:49] Waiting for data
I0409 21:50:11.611495 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:50:12.710388 26212 solver.cpp:397]     Test net output #0: accuracy = 0.183824
I0409 21:50:12.710433 26212 solver.cpp:397]     Test net output #1: loss = 4.09913 (* 1 = 4.09913 loss)
I0409 21:50:14.609859 26212 solver.cpp:218] Iteration 2760 (0.574633 iter/s, 20.8829s/12 iters), loss = 2.00658
I0409 21:50:14.615936 26212 solver.cpp:237]     Train net output #0: loss = 2.00658 (* 1 = 2.00658 loss)
I0409 21:50:14.615948 26212 sgd_solver.cpp:105] Iteration 2760, lr = 0.00578847
I0409 21:50:19.481465 26212 solver.cpp:218] Iteration 2772 (2.4664 iter/s, 4.8654s/12 iters), loss = 2.13271
I0409 21:50:19.481511 26212 solver.cpp:237]     Train net output #0: loss = 2.13271 (* 1 = 2.13271 loss)
I0409 21:50:19.481520 26212 sgd_solver.cpp:105] Iteration 2772, lr = 0.00577473
I0409 21:50:24.373783 26212 solver.cpp:218] Iteration 2784 (2.45292 iter/s, 4.89213s/12 iters), loss = 1.8854
I0409 21:50:24.373834 26212 solver.cpp:237]     Train net output #0: loss = 1.8854 (* 1 = 1.8854 loss)
I0409 21:50:24.373844 26212 sgd_solver.cpp:105] Iteration 2784, lr = 0.00576102
I0409 21:50:29.282732 26212 solver.cpp:218] Iteration 2796 (2.44461 iter/s, 4.90876s/12 iters), loss = 1.9425
I0409 21:50:29.282783 26212 solver.cpp:237]     Train net output #0: loss = 1.9425 (* 1 = 1.9425 loss)
I0409 21:50:29.282794 26212 sgd_solver.cpp:105] Iteration 2796, lr = 0.00574734
I0409 21:50:34.203436 26212 solver.cpp:218] Iteration 2808 (2.43877 iter/s, 4.92052s/12 iters), loss = 1.75084
I0409 21:50:34.203485 26212 solver.cpp:237]     Train net output #0: loss = 1.75084 (* 1 = 1.75084 loss)
I0409 21:50:34.203496 26212 sgd_solver.cpp:105] Iteration 2808, lr = 0.00573369
I0409 21:50:39.148124 26212 solver.cpp:218] Iteration 2820 (2.42694 iter/s, 4.9445s/12 iters), loss = 1.71746
I0409 21:50:39.148265 26212 solver.cpp:237]     Train net output #0: loss = 1.71746 (* 1 = 1.71746 loss)
I0409 21:50:39.148277 26212 sgd_solver.cpp:105] Iteration 2820, lr = 0.00572008
I0409 21:50:43.769454 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:50:44.051375 26212 solver.cpp:218] Iteration 2832 (2.44749 iter/s, 4.90298s/12 iters), loss = 1.86919
I0409 21:50:44.051434 26212 solver.cpp:237]     Train net output #0: loss = 1.86919 (* 1 = 1.86919 loss)
I0409 21:50:44.051446 26212 sgd_solver.cpp:105] Iteration 2832, lr = 0.0057065
I0409 21:50:48.976971 26212 solver.cpp:218] Iteration 2844 (2.43635 iter/s, 4.9254s/12 iters), loss = 1.84512
I0409 21:50:48.977030 26212 solver.cpp:237]     Train net output #0: loss = 1.84512 (* 1 = 1.84512 loss)
I0409 21:50:48.977041 26212 sgd_solver.cpp:105] Iteration 2844, lr = 0.00569295
I0409 21:50:53.452440 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2856.caffemodel
I0409 21:50:55.736390 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2856.solverstate
I0409 21:51:00.804965 26212 solver.cpp:330] Iteration 2856, Testing net (#0)
I0409 21:51:00.804993 26212 net.cpp:676] Ignoring source layer train-data
I0409 21:51:04.107661 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:51:05.248642 26212 solver.cpp:397]     Test net output #0: accuracy = 0.185049
I0409 21:51:05.248689 26212 solver.cpp:397]     Test net output #1: loss = 4.29802 (* 1 = 4.29802 loss)
I0409 21:51:05.332742 26212 solver.cpp:218] Iteration 2856 (0.733707 iter/s, 16.3553s/12 iters), loss = 2.10229
I0409 21:51:05.332783 26212 solver.cpp:237]     Train net output #0: loss = 2.10229 (* 1 = 2.10229 loss)
I0409 21:51:05.332795 26212 sgd_solver.cpp:105] Iteration 2856, lr = 0.00567944
I0409 21:51:09.659817 26212 solver.cpp:218] Iteration 2868 (2.77334 iter/s, 4.32691s/12 iters), loss = 2.1476
I0409 21:51:09.659891 26212 solver.cpp:237]     Train net output #0: loss = 2.1476 (* 1 = 2.1476 loss)
I0409 21:51:09.659904 26212 sgd_solver.cpp:105] Iteration 2868, lr = 0.00566595
I0409 21:51:14.570977 26212 solver.cpp:218] Iteration 2880 (2.44352 iter/s, 4.91094s/12 iters), loss = 2.42132
I0409 21:51:14.571033 26212 solver.cpp:237]     Train net output #0: loss = 2.42132 (* 1 = 2.42132 loss)
I0409 21:51:14.571044 26212 sgd_solver.cpp:105] Iteration 2880, lr = 0.0056525
I0409 21:51:19.453666 26212 solver.cpp:218] Iteration 2892 (2.45776 iter/s, 4.8825s/12 iters), loss = 2.13879
I0409 21:51:19.453713 26212 solver.cpp:237]     Train net output #0: loss = 2.13879 (* 1 = 2.13879 loss)
I0409 21:51:19.453725 26212 sgd_solver.cpp:105] Iteration 2892, lr = 0.00563908
I0409 21:51:24.418367 26212 solver.cpp:218] Iteration 2904 (2.41715 iter/s, 4.96452s/12 iters), loss = 1.49851
I0409 21:51:24.418421 26212 solver.cpp:237]     Train net output #0: loss = 1.49851 (* 1 = 1.49851 loss)
I0409 21:51:24.418432 26212 sgd_solver.cpp:105] Iteration 2904, lr = 0.00562569
I0409 21:51:29.291359 26212 solver.cpp:218] Iteration 2916 (2.46266 iter/s, 4.87279s/12 iters), loss = 1.49218
I0409 21:51:29.291419 26212 solver.cpp:237]     Train net output #0: loss = 1.49218 (* 1 = 1.49218 loss)
I0409 21:51:29.291431 26212 sgd_solver.cpp:105] Iteration 2916, lr = 0.00561233
I0409 21:51:34.208149 26212 solver.cpp:218] Iteration 2928 (2.44071 iter/s, 4.9166s/12 iters), loss = 1.50965
I0409 21:51:34.208194 26212 solver.cpp:237]     Train net output #0: loss = 1.50965 (* 1 = 1.50965 loss)
I0409 21:51:34.208202 26212 sgd_solver.cpp:105] Iteration 2928, lr = 0.00559901
I0409 21:51:36.030341 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:51:39.182772 26212 solver.cpp:218] Iteration 2940 (2.41233 iter/s, 4.97445s/12 iters), loss = 1.97094
I0409 21:51:39.182811 26212 solver.cpp:237]     Train net output #0: loss = 1.97094 (* 1 = 1.97094 loss)
I0409 21:51:39.182821 26212 sgd_solver.cpp:105] Iteration 2940, lr = 0.00558572
I0409 21:51:44.171247 26212 solver.cpp:218] Iteration 2952 (2.40563 iter/s, 4.9883s/12 iters), loss = 1.57882
I0409 21:51:44.171366 26212 solver.cpp:237]     Train net output #0: loss = 1.57882 (* 1 = 1.57882 loss)
I0409 21:51:44.171376 26212 sgd_solver.cpp:105] Iteration 2952, lr = 0.00557245
I0409 21:51:46.184502 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2958.caffemodel
I0409 21:51:47.447278 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2958.solverstate
I0409 21:51:48.325701 26212 solver.cpp:330] Iteration 2958, Testing net (#0)
I0409 21:51:48.325731 26212 net.cpp:676] Ignoring source layer train-data
I0409 21:51:51.627131 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:51:52.807401 26212 solver.cpp:397]     Test net output #0: accuracy = 0.185049
I0409 21:51:52.807443 26212 solver.cpp:397]     Test net output #1: loss = 4.23523 (* 1 = 4.23523 loss)
I0409 21:51:54.595124 26212 solver.cpp:218] Iteration 2964 (1.15125 iter/s, 10.4235s/12 iters), loss = 1.96493
I0409 21:51:54.595189 26212 solver.cpp:237]     Train net output #0: loss = 1.96493 (* 1 = 1.96493 loss)
I0409 21:51:54.595201 26212 sgd_solver.cpp:105] Iteration 2964, lr = 0.00555922
I0409 21:51:59.606068 26212 solver.cpp:218] Iteration 2976 (2.39486 iter/s, 5.01074s/12 iters), loss = 1.73719
I0409 21:51:59.606137 26212 solver.cpp:237]     Train net output #0: loss = 1.73719 (* 1 = 1.73719 loss)
I0409 21:51:59.606148 26212 sgd_solver.cpp:105] Iteration 2976, lr = 0.00554603
I0409 21:52:04.596735 26212 solver.cpp:218] Iteration 2988 (2.40459 iter/s, 4.99046s/12 iters), loss = 1.89485
I0409 21:52:04.596786 26212 solver.cpp:237]     Train net output #0: loss = 1.89485 (* 1 = 1.89485 loss)
I0409 21:52:04.596797 26212 sgd_solver.cpp:105] Iteration 2988, lr = 0.00553286
I0409 21:52:09.526854 26212 solver.cpp:218] Iteration 3000 (2.43411 iter/s, 4.92993s/12 iters), loss = 1.60704
I0409 21:52:09.526906 26212 solver.cpp:237]     Train net output #0: loss = 1.60704 (* 1 = 1.60704 loss)
I0409 21:52:09.526917 26212 sgd_solver.cpp:105] Iteration 3000, lr = 0.00551972
I0409 21:52:14.557797 26212 solver.cpp:218] Iteration 3012 (2.38532 iter/s, 5.03076s/12 iters), loss = 1.65001
I0409 21:52:14.557886 26212 solver.cpp:237]     Train net output #0: loss = 1.65001 (* 1 = 1.65001 loss)
I0409 21:52:14.557894 26212 sgd_solver.cpp:105] Iteration 3012, lr = 0.00550662
I0409 21:52:19.519791 26212 solver.cpp:218] Iteration 3024 (2.41849 iter/s, 4.96176s/12 iters), loss = 1.45413
I0409 21:52:19.519848 26212 solver.cpp:237]     Train net output #0: loss = 1.45413 (* 1 = 1.45413 loss)
I0409 21:52:19.519860 26212 sgd_solver.cpp:105] Iteration 3024, lr = 0.00549354
I0409 21:52:23.500262 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:52:24.535032 26212 solver.cpp:218] Iteration 3036 (2.3928 iter/s, 5.01505s/12 iters), loss = 1.43565
I0409 21:52:24.535079 26212 solver.cpp:237]     Train net output #0: loss = 1.43565 (* 1 = 1.43565 loss)
I0409 21:52:24.535089 26212 sgd_solver.cpp:105] Iteration 3036, lr = 0.0054805
I0409 21:52:29.591806 26212 solver.cpp:218] Iteration 3048 (2.37314 iter/s, 5.05659s/12 iters), loss = 1.88173
I0409 21:52:29.591857 26212 solver.cpp:237]     Train net output #0: loss = 1.88173 (* 1 = 1.88173 loss)
I0409 21:52:29.591868 26212 sgd_solver.cpp:105] Iteration 3048, lr = 0.00546749
I0409 21:52:33.987103 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3060.caffemodel
I0409 21:52:36.148772 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3060.solverstate
I0409 21:52:37.688513 26212 solver.cpp:330] Iteration 3060, Testing net (#0)
I0409 21:52:37.688535 26212 net.cpp:676] Ignoring source layer train-data
I0409 21:52:40.925367 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:52:42.137522 26212 solver.cpp:397]     Test net output #0: accuracy = 0.193015
I0409 21:52:42.137562 26212 solver.cpp:397]     Test net output #1: loss = 4.13872 (* 1 = 4.13872 loss)
I0409 21:52:42.221365 26212 solver.cpp:218] Iteration 3060 (0.95018 iter/s, 12.6292s/12 iters), loss = 1.58276
I0409 21:52:42.221413 26212 solver.cpp:237]     Train net output #0: loss = 1.58276 (* 1 = 1.58276 loss)
I0409 21:52:42.221424 26212 sgd_solver.cpp:105] Iteration 3060, lr = 0.00545451
I0409 21:52:46.499155 26212 solver.cpp:218] Iteration 3072 (2.8053 iter/s, 4.27762s/12 iters), loss = 1.62413
I0409 21:52:46.499275 26212 solver.cpp:237]     Train net output #0: loss = 1.62413 (* 1 = 1.62413 loss)
I0409 21:52:46.499285 26212 sgd_solver.cpp:105] Iteration 3072, lr = 0.00544156
I0409 21:52:51.602278 26212 solver.cpp:218] Iteration 3084 (2.35162 iter/s, 5.10287s/12 iters), loss = 1.95319
I0409 21:52:51.602320 26212 solver.cpp:237]     Train net output #0: loss = 1.95319 (* 1 = 1.95319 loss)
I0409 21:52:51.602329 26212 sgd_solver.cpp:105] Iteration 3084, lr = 0.00542864
I0409 21:52:56.624238 26212 solver.cpp:218] Iteration 3096 (2.38959 iter/s, 5.02178s/12 iters), loss = 1.74123
I0409 21:52:56.624285 26212 solver.cpp:237]     Train net output #0: loss = 1.74123 (* 1 = 1.74123 loss)
I0409 21:52:56.624294 26212 sgd_solver.cpp:105] Iteration 3096, lr = 0.00541575
I0409 21:53:01.694005 26212 solver.cpp:218] Iteration 3108 (2.36706 iter/s, 5.06958s/12 iters), loss = 1.61069
I0409 21:53:01.694044 26212 solver.cpp:237]     Train net output #0: loss = 1.61069 (* 1 = 1.61069 loss)
I0409 21:53:01.694054 26212 sgd_solver.cpp:105] Iteration 3108, lr = 0.00540289
I0409 21:53:06.579907 26212 solver.cpp:218] Iteration 3120 (2.45613 iter/s, 4.88573s/12 iters), loss = 1.45698
I0409 21:53:06.579954 26212 solver.cpp:237]     Train net output #0: loss = 1.45698 (* 1 = 1.45698 loss)
I0409 21:53:06.579964 26212 sgd_solver.cpp:105] Iteration 3120, lr = 0.00539006
I0409 21:53:11.534257 26212 solver.cpp:218] Iteration 3132 (2.4222 iter/s, 4.95416s/12 iters), loss = 1.32114
I0409 21:53:11.534308 26212 solver.cpp:237]     Train net output #0: loss = 1.32114 (* 1 = 1.32114 loss)
I0409 21:53:11.534320 26212 sgd_solver.cpp:105] Iteration 3132, lr = 0.00537727
I0409 21:53:12.602818 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:53:16.428326 26212 solver.cpp:218] Iteration 3144 (2.45204 iter/s, 4.89388s/12 iters), loss = 1.81495
I0409 21:53:16.428381 26212 solver.cpp:237]     Train net output #0: loss = 1.81495 (* 1 = 1.81495 loss)
I0409 21:53:16.428395 26212 sgd_solver.cpp:105] Iteration 3144, lr = 0.0053645
I0409 21:53:21.357084 26212 solver.cpp:218] Iteration 3156 (2.43478 iter/s, 4.92857s/12 iters), loss = 1.897
I0409 21:53:21.357188 26212 solver.cpp:237]     Train net output #0: loss = 1.897 (* 1 = 1.897 loss)
I0409 21:53:21.357197 26212 sgd_solver.cpp:105] Iteration 3156, lr = 0.00535176
I0409 21:53:23.357820 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3162.caffemodel
I0409 21:53:24.502938 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3162.solverstate
I0409 21:53:26.253520 26212 solver.cpp:330] Iteration 3162, Testing net (#0)
I0409 21:53:26.253554 26212 net.cpp:676] Ignoring source layer train-data
I0409 21:53:29.411410 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:53:30.677835 26212 solver.cpp:397]     Test net output #0: accuracy = 0.197917
I0409 21:53:30.677881 26212 solver.cpp:397]     Test net output #1: loss = 4.17745 (* 1 = 4.17745 loss)
I0409 21:53:32.470399 26212 solver.cpp:218] Iteration 3168 (1.07982 iter/s, 11.1129s/12 iters), loss = 1.58225
I0409 21:53:32.470453 26212 solver.cpp:237]     Train net output #0: loss = 1.58225 (* 1 = 1.58225 loss)
I0409 21:53:32.470463 26212 sgd_solver.cpp:105] Iteration 3168, lr = 0.00533906
I0409 21:53:37.289449 26212 solver.cpp:218] Iteration 3180 (2.49021 iter/s, 4.81886s/12 iters), loss = 1.62624
I0409 21:53:37.289505 26212 solver.cpp:237]     Train net output #0: loss = 1.62624 (* 1 = 1.62624 loss)
I0409 21:53:37.289517 26212 sgd_solver.cpp:105] Iteration 3180, lr = 0.00532638
I0409 21:53:42.197538 26212 solver.cpp:218] Iteration 3192 (2.44504 iter/s, 4.9079s/12 iters), loss = 1.49408
I0409 21:53:42.197598 26212 solver.cpp:237]     Train net output #0: loss = 1.49408 (* 1 = 1.49408 loss)
I0409 21:53:42.197609 26212 sgd_solver.cpp:105] Iteration 3192, lr = 0.00531374
I0409 21:53:47.088610 26212 solver.cpp:218] Iteration 3204 (2.45355 iter/s, 4.89087s/12 iters), loss = 1.58899
I0409 21:53:47.088668 26212 solver.cpp:237]     Train net output #0: loss = 1.58899 (* 1 = 1.58899 loss)
I0409 21:53:47.088680 26212 sgd_solver.cpp:105] Iteration 3204, lr = 0.00530112
I0409 21:53:52.006165 26212 solver.cpp:218] Iteration 3216 (2.44033 iter/s, 4.91736s/12 iters), loss = 1.83094
I0409 21:53:52.006292 26212 solver.cpp:237]     Train net output #0: loss = 1.83094 (* 1 = 1.83094 loss)
I0409 21:53:52.006302 26212 sgd_solver.cpp:105] Iteration 3216, lr = 0.00528853
I0409 21:53:56.968451 26212 solver.cpp:218] Iteration 3228 (2.41837 iter/s, 4.96202s/12 iters), loss = 1.67658
I0409 21:53:56.968508 26212 solver.cpp:237]     Train net output #0: loss = 1.67658 (* 1 = 1.67658 loss)
I0409 21:53:56.968518 26212 sgd_solver.cpp:105] Iteration 3228, lr = 0.00527598
I0409 21:54:00.167908 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:54:01.884475 26212 solver.cpp:218] Iteration 3240 (2.44109 iter/s, 4.91583s/12 iters), loss = 1.98512
I0409 21:54:01.884528 26212 solver.cpp:237]     Train net output #0: loss = 1.98512 (* 1 = 1.98512 loss)
I0409 21:54:01.884542 26212 sgd_solver.cpp:105] Iteration 3240, lr = 0.00526345
I0409 21:54:06.763299 26212 solver.cpp:218] Iteration 3252 (2.45971 iter/s, 4.87863s/12 iters), loss = 1.57737
I0409 21:54:06.763352 26212 solver.cpp:237]     Train net output #0: loss = 1.57737 (* 1 = 1.57737 loss)
I0409 21:54:06.763363 26212 sgd_solver.cpp:105] Iteration 3252, lr = 0.00525095
I0409 21:54:11.284711 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3264.caffemodel
I0409 21:54:12.554452 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3264.solverstate
I0409 21:54:14.337278 26212 solver.cpp:330] Iteration 3264, Testing net (#0)
I0409 21:54:14.337301 26212 net.cpp:676] Ignoring source layer train-data
I0409 21:54:17.442873 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:54:18.740236 26212 solver.cpp:397]     Test net output #0: accuracy = 0.201593
I0409 21:54:18.740278 26212 solver.cpp:397]     Test net output #1: loss = 4.33312 (* 1 = 4.33312 loss)
I0409 21:54:18.824002 26212 solver.cpp:218] Iteration 3264 (0.994997 iter/s, 12.0603s/12 iters), loss = 1.32798
I0409 21:54:18.824057 26212 solver.cpp:237]     Train net output #0: loss = 1.32798 (* 1 = 1.32798 loss)
I0409 21:54:18.824070 26212 sgd_solver.cpp:105] Iteration 3264, lr = 0.00523849
I0409 21:54:23.141806 26212 solver.cpp:218] Iteration 3276 (2.7793 iter/s, 4.31763s/12 iters), loss = 1.35962
I0409 21:54:23.141902 26212 solver.cpp:237]     Train net output #0: loss = 1.35962 (* 1 = 1.35962 loss)
I0409 21:54:23.141911 26212 sgd_solver.cpp:105] Iteration 3276, lr = 0.00522605
I0409 21:54:28.075196 26212 solver.cpp:218] Iteration 3288 (2.43252 iter/s, 4.93316s/12 iters), loss = 1.74245
I0409 21:54:28.075240 26212 solver.cpp:237]     Train net output #0: loss = 1.74245 (* 1 = 1.74245 loss)
I0409 21:54:28.075249 26212 sgd_solver.cpp:105] Iteration 3288, lr = 0.00521364
I0409 21:54:33.022190 26212 solver.cpp:218] Iteration 3300 (2.42581 iter/s, 4.94681s/12 iters), loss = 1.33162
I0409 21:54:33.022230 26212 solver.cpp:237]     Train net output #0: loss = 1.33162 (* 1 = 1.33162 loss)
I0409 21:54:33.022239 26212 sgd_solver.cpp:105] Iteration 3300, lr = 0.00520126
I0409 21:54:37.951411 26212 solver.cpp:218] Iteration 3312 (2.43455 iter/s, 4.92904s/12 iters), loss = 1.56099
I0409 21:54:37.951454 26212 solver.cpp:237]     Train net output #0: loss = 1.56099 (* 1 = 1.56099 loss)
I0409 21:54:37.951463 26212 sgd_solver.cpp:105] Iteration 3312, lr = 0.00518892
I0409 21:54:42.895918 26212 solver.cpp:218] Iteration 3324 (2.42702 iter/s, 4.94433s/12 iters), loss = 1.72285
I0409 21:54:42.895957 26212 solver.cpp:237]     Train net output #0: loss = 1.72285 (* 1 = 1.72285 loss)
I0409 21:54:42.895967 26212 sgd_solver.cpp:105] Iteration 3324, lr = 0.0051766
I0409 21:54:47.764678 26212 solver.cpp:218] Iteration 3336 (2.46478 iter/s, 4.86859s/12 iters), loss = 1.27429
I0409 21:54:47.764715 26212 solver.cpp:237]     Train net output #0: loss = 1.27429 (* 1 = 1.27429 loss)
I0409 21:54:47.764724 26212 sgd_solver.cpp:105] Iteration 3336, lr = 0.00516431
I0409 21:54:48.214046 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:54:52.704407 26212 solver.cpp:218] Iteration 3348 (2.42936 iter/s, 4.93956s/12 iters), loss = 1.1238
I0409 21:54:52.704452 26212 solver.cpp:237]     Train net output #0: loss = 1.1238 (* 1 = 1.1238 loss)
I0409 21:54:52.704460 26212 sgd_solver.cpp:105] Iteration 3348, lr = 0.00515204
I0409 21:54:57.796854 26212 solver.cpp:218] Iteration 3360 (2.35651 iter/s, 5.09227s/12 iters), loss = 1.65042
I0409 21:54:57.796972 26212 solver.cpp:237]     Train net output #0: loss = 1.65042 (* 1 = 1.65042 loss)
I0409 21:54:57.796981 26212 sgd_solver.cpp:105] Iteration 3360, lr = 0.00513981
I0409 21:54:59.825726 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3366.caffemodel
I0409 21:55:01.089357 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3366.solverstate
I0409 21:55:02.738880 26212 solver.cpp:330] Iteration 3366, Testing net (#0)
I0409 21:55:02.738903 26212 net.cpp:676] Ignoring source layer train-data
I0409 21:55:06.052971 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:55:07.399672 26212 solver.cpp:397]     Test net output #0: accuracy = 0.196078
I0409 21:55:07.399708 26212 solver.cpp:397]     Test net output #1: loss = 4.23658 (* 1 = 4.23658 loss)
I0409 21:55:09.349203 26212 solver.cpp:218] Iteration 3372 (1.03879 iter/s, 11.552s/12 iters), loss = 1.55695
I0409 21:55:09.349257 26212 solver.cpp:237]     Train net output #0: loss = 1.55695 (* 1 = 1.55695 loss)
I0409 21:55:09.349268 26212 sgd_solver.cpp:105] Iteration 3372, lr = 0.00512761
I0409 21:55:14.236971 26212 solver.cpp:218] Iteration 3384 (2.4552 iter/s, 4.88759s/12 iters), loss = 1.82917
I0409 21:55:14.237025 26212 solver.cpp:237]     Train net output #0: loss = 1.82917 (* 1 = 1.82917 loss)
I0409 21:55:14.237036 26212 sgd_solver.cpp:105] Iteration 3384, lr = 0.00511544
I0409 21:55:19.141767 26212 solver.cpp:218] Iteration 3396 (2.44668 iter/s, 4.90461s/12 iters), loss = 1.38705
I0409 21:55:19.141815 26212 solver.cpp:237]     Train net output #0: loss = 1.38705 (* 1 = 1.38705 loss)
I0409 21:55:19.141826 26212 sgd_solver.cpp:105] Iteration 3396, lr = 0.00510329
I0409 21:55:24.226639 26212 solver.cpp:218] Iteration 3408 (2.36003 iter/s, 5.08469s/12 iters), loss = 1.66249
I0409 21:55:24.226698 26212 solver.cpp:237]     Train net output #0: loss = 1.66249 (* 1 = 1.66249 loss)
I0409 21:55:24.226709 26212 sgd_solver.cpp:105] Iteration 3408, lr = 0.00509117
I0409 21:55:29.091725 26212 solver.cpp:218] Iteration 3420 (2.46665 iter/s, 4.8649s/12 iters), loss = 1.46646
I0409 21:55:29.091858 26212 solver.cpp:237]     Train net output #0: loss = 1.46646 (* 1 = 1.46646 loss)
I0409 21:55:29.091871 26212 sgd_solver.cpp:105] Iteration 3420, lr = 0.00507909
I0409 21:55:34.050251 26212 solver.cpp:218] Iteration 3432 (2.4202 iter/s, 4.95827s/12 iters), loss = 1.68958
I0409 21:55:34.050294 26212 solver.cpp:237]     Train net output #0: loss = 1.68958 (* 1 = 1.68958 loss)
I0409 21:55:34.050302 26212 sgd_solver.cpp:105] Iteration 3432, lr = 0.00506703
I0409 21:55:36.600342 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:55:38.970096 26212 solver.cpp:218] Iteration 3444 (2.43919 iter/s, 4.91967s/12 iters), loss = 1.3557
I0409 21:55:38.970152 26212 solver.cpp:237]     Train net output #0: loss = 1.3557 (* 1 = 1.3557 loss)
I0409 21:55:38.970162 26212 sgd_solver.cpp:105] Iteration 3444, lr = 0.005055
I0409 21:55:43.924501 26212 solver.cpp:218] Iteration 3456 (2.42218 iter/s, 4.95422s/12 iters), loss = 1.32351
I0409 21:55:43.924551 26212 solver.cpp:237]     Train net output #0: loss = 1.32351 (* 1 = 1.32351 loss)
I0409 21:55:43.924562 26212 sgd_solver.cpp:105] Iteration 3456, lr = 0.005043
I0409 21:55:48.325868 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3468.caffemodel
I0409 21:55:50.247099 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3468.solverstate
I0409 21:55:51.125444 26212 solver.cpp:330] Iteration 3468, Testing net (#0)
I0409 21:55:51.125473 26212 net.cpp:676] Ignoring source layer train-data
I0409 21:55:51.452340 26212 blocking_queue.cpp:49] Waiting for data
I0409 21:55:54.152366 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:55:55.528687 26212 solver.cpp:397]     Test net output #0: accuracy = 0.17402
I0409 21:55:55.528723 26212 solver.cpp:397]     Test net output #1: loss = 4.66663 (* 1 = 4.66663 loss)
I0409 21:55:55.612331 26212 solver.cpp:218] Iteration 3468 (1.02674 iter/s, 11.6875s/12 iters), loss = 1.39795
I0409 21:55:55.612372 26212 solver.cpp:237]     Train net output #0: loss = 1.39795 (* 1 = 1.39795 loss)
I0409 21:55:55.612381 26212 sgd_solver.cpp:105] Iteration 3468, lr = 0.00503102
I0409 21:55:59.854543 26212 solver.cpp:218] Iteration 3480 (2.82881 iter/s, 4.24206s/12 iters), loss = 1.52742
I0409 21:55:59.854684 26212 solver.cpp:237]     Train net output #0: loss = 1.52742 (* 1 = 1.52742 loss)
I0409 21:55:59.854701 26212 sgd_solver.cpp:105] Iteration 3480, lr = 0.00501908
I0409 21:56:04.800439 26212 solver.cpp:218] Iteration 3492 (2.42639 iter/s, 4.94563s/12 iters), loss = 1.20811
I0409 21:56:04.800494 26212 solver.cpp:237]     Train net output #0: loss = 1.20811 (* 1 = 1.20811 loss)
I0409 21:56:04.800506 26212 sgd_solver.cpp:105] Iteration 3492, lr = 0.00500716
I0409 21:56:09.717972 26212 solver.cpp:218] Iteration 3504 (2.44034 iter/s, 4.91734s/12 iters), loss = 1.25849
I0409 21:56:09.718011 26212 solver.cpp:237]     Train net output #0: loss = 1.25849 (* 1 = 1.25849 loss)
I0409 21:56:09.718020 26212 sgd_solver.cpp:105] Iteration 3504, lr = 0.00499527
I0409 21:56:14.624716 26212 solver.cpp:218] Iteration 3516 (2.44569 iter/s, 4.90658s/12 iters), loss = 1.13026
I0409 21:56:14.624749 26212 solver.cpp:237]     Train net output #0: loss = 1.13026 (* 1 = 1.13026 loss)
I0409 21:56:14.624758 26212 sgd_solver.cpp:105] Iteration 3516, lr = 0.00498341
I0409 21:56:19.529994 26212 solver.cpp:218] Iteration 3528 (2.44644 iter/s, 4.90509s/12 iters), loss = 1.54966
I0409 21:56:19.530050 26212 solver.cpp:237]     Train net output #0: loss = 1.54966 (* 1 = 1.54966 loss)
I0409 21:56:19.530062 26212 sgd_solver.cpp:105] Iteration 3528, lr = 0.00497158
I0409 21:56:24.370163 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:56:24.626361 26212 solver.cpp:218] Iteration 3540 (2.3547 iter/s, 5.09618s/12 iters), loss = 1.34104
I0409 21:56:24.626406 26212 solver.cpp:237]     Train net output #0: loss = 1.34104 (* 1 = 1.34104 loss)
I0409 21:56:24.626417 26212 sgd_solver.cpp:105] Iteration 3540, lr = 0.00495978
I0409 21:56:29.561152 26212 solver.cpp:218] Iteration 3552 (2.4318 iter/s, 4.93462s/12 iters), loss = 1.11496
I0409 21:56:29.561197 26212 solver.cpp:237]     Train net output #0: loss = 1.11496 (* 1 = 1.11496 loss)
I0409 21:56:29.561208 26212 sgd_solver.cpp:105] Iteration 3552, lr = 0.004948
I0409 21:56:34.544121 26212 solver.cpp:218] Iteration 3564 (2.40829 iter/s, 4.9828s/12 iters), loss = 1.35712
I0409 21:56:34.586066 26212 solver.cpp:237]     Train net output #0: loss = 1.35712 (* 1 = 1.35712 loss)
I0409 21:56:34.586079 26212 sgd_solver.cpp:105] Iteration 3564, lr = 0.00493626
I0409 21:56:36.595626 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3570.caffemodel
I0409 21:56:37.781105 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3570.solverstate
I0409 21:56:38.655241 26212 solver.cpp:330] Iteration 3570, Testing net (#0)
I0409 21:56:38.655267 26212 net.cpp:676] Ignoring source layer train-data
I0409 21:56:41.658134 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:56:43.070873 26212 solver.cpp:397]     Test net output #0: accuracy = 0.204044
I0409 21:56:43.070927 26212 solver.cpp:397]     Test net output #1: loss = 4.33482 (* 1 = 4.33482 loss)
I0409 21:56:44.860749 26212 solver.cpp:218] Iteration 3576 (1.16795 iter/s, 10.2744s/12 iters), loss = 1.39811
I0409 21:56:44.860790 26212 solver.cpp:237]     Train net output #0: loss = 1.39811 (* 1 = 1.39811 loss)
I0409 21:56:44.860798 26212 sgd_solver.cpp:105] Iteration 3576, lr = 0.00492454
I0409 21:56:49.804201 26212 solver.cpp:218] Iteration 3588 (2.42754 iter/s, 4.94327s/12 iters), loss = 1.50558
I0409 21:56:49.804257 26212 solver.cpp:237]     Train net output #0: loss = 1.50558 (* 1 = 1.50558 loss)
I0409 21:56:49.804270 26212 sgd_solver.cpp:105] Iteration 3588, lr = 0.00491284
I0409 21:56:54.719614 26212 solver.cpp:218] Iteration 3600 (2.44139 iter/s, 4.91524s/12 iters), loss = 1.14325
I0409 21:56:54.719643 26212 solver.cpp:237]     Train net output #0: loss = 1.14325 (* 1 = 1.14325 loss)
I0409 21:56:54.719651 26212 sgd_solver.cpp:105] Iteration 3600, lr = 0.00490118
I0409 21:56:59.653226 26212 solver.cpp:218] Iteration 3612 (2.43237 iter/s, 4.93345s/12 iters), loss = 1.27019
I0409 21:56:59.653272 26212 solver.cpp:237]     Train net output #0: loss = 1.27019 (* 1 = 1.27019 loss)
I0409 21:56:59.653281 26212 sgd_solver.cpp:105] Iteration 3612, lr = 0.00488954
I0409 21:57:04.528195 26212 solver.cpp:218] Iteration 3624 (2.46164 iter/s, 4.8748s/12 iters), loss = 1.09854
I0409 21:57:04.528239 26212 solver.cpp:237]     Train net output #0: loss = 1.09854 (* 1 = 1.09854 loss)
I0409 21:57:04.528247 26212 sgd_solver.cpp:105] Iteration 3624, lr = 0.00487793
I0409 21:57:09.438455 26212 solver.cpp:218] Iteration 3636 (2.44395 iter/s, 4.91009s/12 iters), loss = 1.23945
I0409 21:57:09.438568 26212 solver.cpp:237]     Train net output #0: loss = 1.23945 (* 1 = 1.23945 loss)
I0409 21:57:09.438578 26212 sgd_solver.cpp:105] Iteration 3636, lr = 0.00486635
I0409 21:57:11.291397 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:57:14.386904 26212 solver.cpp:218] Iteration 3648 (2.42512 iter/s, 4.94821s/12 iters), loss = 1.19783
I0409 21:57:14.386943 26212 solver.cpp:237]     Train net output #0: loss = 1.19783 (* 1 = 1.19783 loss)
I0409 21:57:14.386950 26212 sgd_solver.cpp:105] Iteration 3648, lr = 0.0048548
I0409 21:57:19.333478 26212 solver.cpp:218] Iteration 3660 (2.426 iter/s, 4.94641s/12 iters), loss = 1.16711
I0409 21:57:19.333525 26212 solver.cpp:237]     Train net output #0: loss = 1.16711 (* 1 = 1.16711 loss)
I0409 21:57:19.333536 26212 sgd_solver.cpp:105] Iteration 3660, lr = 0.00484327
I0409 21:57:23.764341 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3672.caffemodel
I0409 21:57:25.874246 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3672.solverstate
I0409 21:57:26.880836 26212 solver.cpp:330] Iteration 3672, Testing net (#0)
I0409 21:57:26.880861 26212 net.cpp:676] Ignoring source layer train-data
I0409 21:57:29.885215 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:57:31.379837 26212 solver.cpp:397]     Test net output #0: accuracy = 0.219363
I0409 21:57:31.379874 26212 solver.cpp:397]     Test net output #1: loss = 4.57437 (* 1 = 4.57437 loss)
I0409 21:57:31.463666 26212 solver.cpp:218] Iteration 3672 (0.989296 iter/s, 12.1298s/12 iters), loss = 1.00906
I0409 21:57:31.463712 26212 solver.cpp:237]     Train net output #0: loss = 1.00906 (* 1 = 1.00906 loss)
I0409 21:57:31.463721 26212 sgd_solver.cpp:105] Iteration 3672, lr = 0.00483177
I0409 21:57:35.700042 26212 solver.cpp:218] Iteration 3684 (2.83272 iter/s, 4.23621s/12 iters), loss = 1.35999
I0409 21:57:35.700089 26212 solver.cpp:237]     Train net output #0: loss = 1.35999 (* 1 = 1.35999 loss)
I0409 21:57:35.700099 26212 sgd_solver.cpp:105] Iteration 3684, lr = 0.0048203
I0409 21:57:40.627301 26212 solver.cpp:218] Iteration 3696 (2.43552 iter/s, 4.92708s/12 iters), loss = 1.23381
I0409 21:57:40.627420 26212 solver.cpp:237]     Train net output #0: loss = 1.23381 (* 1 = 1.23381 loss)
I0409 21:57:40.627430 26212 sgd_solver.cpp:105] Iteration 3696, lr = 0.00480886
I0409 21:57:45.532299 26212 solver.cpp:218] Iteration 3708 (2.44661 iter/s, 4.90475s/12 iters), loss = 1.26977
I0409 21:57:45.532348 26212 solver.cpp:237]     Train net output #0: loss = 1.26977 (* 1 = 1.26977 loss)
I0409 21:57:45.532357 26212 sgd_solver.cpp:105] Iteration 3708, lr = 0.00479744
I0409 21:57:50.472036 26212 solver.cpp:218] Iteration 3720 (2.42937 iter/s, 4.93955s/12 iters), loss = 0.973702
I0409 21:57:50.472092 26212 solver.cpp:237]     Train net output #0: loss = 0.973702 (* 1 = 0.973702 loss)
I0409 21:57:50.472105 26212 sgd_solver.cpp:105] Iteration 3720, lr = 0.00478605
I0409 21:57:55.361248 26212 solver.cpp:218] Iteration 3732 (2.45448 iter/s, 4.88902s/12 iters), loss = 1.21679
I0409 21:57:55.361294 26212 solver.cpp:237]     Train net output #0: loss = 1.21679 (* 1 = 1.21679 loss)
I0409 21:57:55.361302 26212 sgd_solver.cpp:105] Iteration 3732, lr = 0.00477469
I0409 21:57:59.346441 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:58:00.296697 26212 solver.cpp:218] Iteration 3744 (2.43147 iter/s, 4.93528s/12 iters), loss = 1.1174
I0409 21:58:00.296741 26212 solver.cpp:237]     Train net output #0: loss = 1.1174 (* 1 = 1.1174 loss)
I0409 21:58:00.296749 26212 sgd_solver.cpp:105] Iteration 3744, lr = 0.00476335
I0409 21:58:05.235859 26212 solver.cpp:218] Iteration 3756 (2.42965 iter/s, 4.93899s/12 iters), loss = 1.26264
I0409 21:58:05.235905 26212 solver.cpp:237]     Train net output #0: loss = 1.26264 (* 1 = 1.26264 loss)
I0409 21:58:05.235916 26212 sgd_solver.cpp:105] Iteration 3756, lr = 0.00475204
I0409 21:58:10.139776 26212 solver.cpp:218] Iteration 3768 (2.44711 iter/s, 4.90375s/12 iters), loss = 1.12986
I0409 21:58:10.139812 26212 solver.cpp:237]     Train net output #0: loss = 1.12986 (* 1 = 1.12986 loss)
I0409 21:58:10.139820 26212 sgd_solver.cpp:105] Iteration 3768, lr = 0.00474076
I0409 21:58:12.137338 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3774.caffemodel
I0409 21:58:16.706295 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3774.solverstate
I0409 21:58:22.435499 26212 solver.cpp:330] Iteration 3774, Testing net (#0)
I0409 21:58:22.435528 26212 net.cpp:676] Ignoring source layer train-data
I0409 21:58:25.379791 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:58:26.873178 26212 solver.cpp:397]     Test net output #0: accuracy = 0.239583
I0409 21:58:26.873230 26212 solver.cpp:397]     Test net output #1: loss = 4.40733 (* 1 = 4.40733 loss)
I0409 21:58:28.765663 26212 solver.cpp:218] Iteration 3780 (0.644281 iter/s, 18.6254s/12 iters), loss = 1.19619
I0409 21:58:28.765715 26212 solver.cpp:237]     Train net output #0: loss = 1.19619 (* 1 = 1.19619 loss)
I0409 21:58:28.765727 26212 sgd_solver.cpp:105] Iteration 3780, lr = 0.00472951
I0409 21:58:33.663568 26212 solver.cpp:218] Iteration 3792 (2.45012 iter/s, 4.89773s/12 iters), loss = 1.1253
I0409 21:58:33.663619 26212 solver.cpp:237]     Train net output #0: loss = 1.1253 (* 1 = 1.1253 loss)
I0409 21:58:33.663630 26212 sgd_solver.cpp:105] Iteration 3792, lr = 0.00471828
I0409 21:58:38.583519 26212 solver.cpp:218] Iteration 3804 (2.43914 iter/s, 4.91977s/12 iters), loss = 1.02617
I0409 21:58:38.583566 26212 solver.cpp:237]     Train net output #0: loss = 1.02617 (* 1 = 1.02617 loss)
I0409 21:58:38.583577 26212 sgd_solver.cpp:105] Iteration 3804, lr = 0.00470707
I0409 21:58:43.558756 26212 solver.cpp:218] Iteration 3816 (2.41203 iter/s, 4.97506s/12 iters), loss = 0.978196
I0409 21:58:43.558856 26212 solver.cpp:237]     Train net output #0: loss = 0.978196 (* 1 = 0.978196 loss)
I0409 21:58:43.558867 26212 sgd_solver.cpp:105] Iteration 3816, lr = 0.0046959
I0409 21:58:48.629118 26212 solver.cpp:218] Iteration 3828 (2.3668 iter/s, 5.07013s/12 iters), loss = 1.3623
I0409 21:58:48.629168 26212 solver.cpp:237]     Train net output #0: loss = 1.3623 (* 1 = 1.3623 loss)
I0409 21:58:48.629179 26212 sgd_solver.cpp:105] Iteration 3828, lr = 0.00468475
I0409 21:58:53.759450 26212 solver.cpp:218] Iteration 3840 (2.33911 iter/s, 5.13015s/12 iters), loss = 0.883686
I0409 21:58:53.759491 26212 solver.cpp:237]     Train net output #0: loss = 0.883686 (* 1 = 0.883686 loss)
I0409 21:58:53.759501 26212 sgd_solver.cpp:105] Iteration 3840, lr = 0.00467363
I0409 21:58:54.898444 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:58:58.677865 26212 solver.cpp:218] Iteration 3852 (2.43989 iter/s, 4.91825s/12 iters), loss = 0.870832
I0409 21:58:58.677908 26212 solver.cpp:237]     Train net output #0: loss = 0.870832 (* 1 = 0.870832 loss)
I0409 21:58:58.677919 26212 sgd_solver.cpp:105] Iteration 3852, lr = 0.00466253
I0409 21:59:03.550371 26212 solver.cpp:218] Iteration 3864 (2.46288 iter/s, 4.87234s/12 iters), loss = 1.10439
I0409 21:59:03.550411 26212 solver.cpp:237]     Train net output #0: loss = 1.10439 (* 1 = 1.10439 loss)
I0409 21:59:03.550420 26212 sgd_solver.cpp:105] Iteration 3864, lr = 0.00465146
I0409 21:59:07.995070 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3876.caffemodel
I0409 21:59:09.479092 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3876.solverstate
I0409 21:59:11.195948 26212 solver.cpp:330] Iteration 3876, Testing net (#0)
I0409 21:59:11.195971 26212 net.cpp:676] Ignoring source layer train-data
I0409 21:59:14.073698 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:59:15.613739 26212 solver.cpp:397]     Test net output #0: accuracy = 0.224265
I0409 21:59:15.613771 26212 solver.cpp:397]     Test net output #1: loss = 4.50598 (* 1 = 4.50598 loss)
I0409 21:59:15.697422 26212 solver.cpp:218] Iteration 3876 (0.987921 iter/s, 12.1467s/12 iters), loss = 1.09279
I0409 21:59:15.697465 26212 solver.cpp:237]     Train net output #0: loss = 1.09279 (* 1 = 1.09279 loss)
I0409 21:59:15.697475 26212 sgd_solver.cpp:105] Iteration 3876, lr = 0.00464042
I0409 21:59:19.976526 26212 solver.cpp:218] Iteration 3888 (2.80443 iter/s, 4.27894s/12 iters), loss = 0.957843
I0409 21:59:19.976567 26212 solver.cpp:237]     Train net output #0: loss = 0.957843 (* 1 = 0.957843 loss)
I0409 21:59:19.976575 26212 sgd_solver.cpp:105] Iteration 3888, lr = 0.0046294
I0409 21:59:24.884748 26212 solver.cpp:218] Iteration 3900 (2.44496 iter/s, 4.90805s/12 iters), loss = 1.01818
I0409 21:59:24.884783 26212 solver.cpp:237]     Train net output #0: loss = 1.01818 (* 1 = 1.01818 loss)
I0409 21:59:24.884791 26212 sgd_solver.cpp:105] Iteration 3900, lr = 0.00461841
I0409 21:59:29.797766 26212 solver.cpp:218] Iteration 3912 (2.44257 iter/s, 4.91285s/12 iters), loss = 1.12299
I0409 21:59:29.797811 26212 solver.cpp:237]     Train net output #0: loss = 1.12299 (* 1 = 1.12299 loss)
I0409 21:59:29.797822 26212 sgd_solver.cpp:105] Iteration 3912, lr = 0.00460744
I0409 21:59:34.726531 26212 solver.cpp:218] Iteration 3924 (2.43477 iter/s, 4.92859s/12 iters), loss = 0.92033
I0409 21:59:34.726591 26212 solver.cpp:237]     Train net output #0: loss = 0.92033 (* 1 = 0.92033 loss)
I0409 21:59:34.726603 26212 sgd_solver.cpp:105] Iteration 3924, lr = 0.0045965
I0409 21:59:39.627625 26212 solver.cpp:218] Iteration 3936 (2.44853 iter/s, 4.9009s/12 iters), loss = 1.23918
I0409 21:59:39.627681 26212 solver.cpp:237]     Train net output #0: loss = 1.23918 (* 1 = 1.23918 loss)
I0409 21:59:39.627692 26212 sgd_solver.cpp:105] Iteration 3936, lr = 0.00458559
I0409 21:59:42.911795 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:59:44.510378 26212 solver.cpp:218] Iteration 3948 (2.45772 iter/s, 4.88257s/12 iters), loss = 1.00411
I0409 21:59:44.510462 26212 solver.cpp:237]     Train net output #0: loss = 1.00411 (* 1 = 1.00411 loss)
I0409 21:59:44.510473 26212 sgd_solver.cpp:105] Iteration 3948, lr = 0.0045747
I0409 21:59:49.409801 26212 solver.cpp:218] Iteration 3960 (2.44937 iter/s, 4.89921s/12 iters), loss = 1.04631
I0409 21:59:49.409857 26212 solver.cpp:237]     Train net output #0: loss = 1.04631 (* 1 = 1.04631 loss)
I0409 21:59:49.409868 26212 sgd_solver.cpp:105] Iteration 3960, lr = 0.00456384
I0409 21:59:54.302433 26212 solver.cpp:218] Iteration 3972 (2.45276 iter/s, 4.89244s/12 iters), loss = 0.828668
I0409 21:59:54.302493 26212 solver.cpp:237]     Train net output #0: loss = 0.828668 (* 1 = 0.828668 loss)
I0409 21:59:54.302506 26212 sgd_solver.cpp:105] Iteration 3972, lr = 0.00455301
I0409 21:59:56.303423 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3978.caffemodel
I0409 21:59:57.546689 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3978.solverstate
I0409 21:59:59.165704 26212 solver.cpp:330] Iteration 3978, Testing net (#0)
I0409 21:59:59.165735 26212 net.cpp:676] Ignoring source layer train-data
I0409 22:00:01.909145 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:00:03.483729 26212 solver.cpp:397]     Test net output #0: accuracy = 0.227941
I0409 22:00:03.483778 26212 solver.cpp:397]     Test net output #1: loss = 4.6844 (* 1 = 4.6844 loss)
I0409 22:00:05.474843 26212 solver.cpp:218] Iteration 3984 (1.07411 iter/s, 11.1721s/12 iters), loss = 1.27857
I0409 22:00:05.474896 26212 solver.cpp:237]     Train net output #0: loss = 1.27857 (* 1 = 1.27857 loss)
I0409 22:00:05.474910 26212 sgd_solver.cpp:105] Iteration 3984, lr = 0.0045422
I0409 22:00:10.846755 26212 solver.cpp:218] Iteration 3996 (2.23392 iter/s, 5.37171s/12 iters), loss = 1.28355
I0409 22:00:10.846808 26212 solver.cpp:237]     Train net output #0: loss = 1.28355 (* 1 = 1.28355 loss)
I0409 22:00:10.846822 26212 sgd_solver.cpp:105] Iteration 3996, lr = 0.00453141
I0409 22:00:15.860513 26212 solver.cpp:218] Iteration 4008 (2.3935 iter/s, 5.01357s/12 iters), loss = 1.02288
I0409 22:00:15.860662 26212 solver.cpp:237]     Train net output #0: loss = 1.02288 (* 1 = 1.02288 loss)
I0409 22:00:15.860674 26212 sgd_solver.cpp:105] Iteration 4008, lr = 0.00452066
I0409 22:00:20.814966 26212 solver.cpp:218] Iteration 4020 (2.4222 iter/s, 4.95418s/12 iters), loss = 0.943212
I0409 22:00:20.815021 26212 solver.cpp:237]     Train net output #0: loss = 0.943212 (* 1 = 0.943212 loss)
I0409 22:00:20.815033 26212 sgd_solver.cpp:105] Iteration 4020, lr = 0.00450992
I0409 22:00:25.702869 26212 solver.cpp:218] Iteration 4032 (2.45513 iter/s, 4.88772s/12 iters), loss = 0.78673
I0409 22:00:25.702914 26212 solver.cpp:237]     Train net output #0: loss = 0.78673 (* 1 = 0.78673 loss)
I0409 22:00:25.702922 26212 sgd_solver.cpp:105] Iteration 4032, lr = 0.00449921
I0409 22:00:30.648237 26212 solver.cpp:218] Iteration 4044 (2.4266 iter/s, 4.94519s/12 iters), loss = 1.09887
I0409 22:00:30.648294 26212 solver.cpp:237]     Train net output #0: loss = 1.09887 (* 1 = 1.09887 loss)
I0409 22:00:30.648308 26212 sgd_solver.cpp:105] Iteration 4044, lr = 0.00448853
I0409 22:00:31.153561 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:00:35.571974 26212 solver.cpp:218] Iteration 4056 (2.43726 iter/s, 4.92356s/12 iters), loss = 0.853674
I0409 22:00:35.572016 26212 solver.cpp:237]     Train net output #0: loss = 0.853674 (* 1 = 0.853674 loss)
I0409 22:00:35.572026 26212 sgd_solver.cpp:105] Iteration 4056, lr = 0.00447788
I0409 22:00:40.485476 26212 solver.cpp:218] Iteration 4068 (2.44234 iter/s, 4.91333s/12 iters), loss = 0.92641
I0409 22:00:40.485528 26212 solver.cpp:237]     Train net output #0: loss = 0.92641 (* 1 = 0.92641 loss)
I0409 22:00:40.485541 26212 sgd_solver.cpp:105] Iteration 4068, lr = 0.00446724
I0409 22:00:44.918097 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4080.caffemodel
I0409 22:00:46.662305 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4080.solverstate
I0409 22:00:50.866461 26212 solver.cpp:330] Iteration 4080, Testing net (#0)
I0409 22:00:50.866487 26212 net.cpp:676] Ignoring source layer train-data
I0409 22:00:53.950240 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:00:55.598974 26212 solver.cpp:397]     Test net output #0: accuracy = 0.234681
I0409 22:00:55.599009 26212 solver.cpp:397]     Test net output #1: loss = 4.43083 (* 1 = 4.43083 loss)
I0409 22:00:55.682592 26212 solver.cpp:218] Iteration 4080 (0.789645 iter/s, 15.1967s/12 iters), loss = 0.910637
I0409 22:00:55.682638 26212 solver.cpp:237]     Train net output #0: loss = 0.910637 (* 1 = 0.910637 loss)
I0409 22:00:55.682647 26212 sgd_solver.cpp:105] Iteration 4080, lr = 0.00445664
I0409 22:01:00.030092 26212 solver.cpp:218] Iteration 4092 (2.76031 iter/s, 4.34734s/12 iters), loss = 1.00588
I0409 22:01:00.030134 26212 solver.cpp:237]     Train net output #0: loss = 1.00588 (* 1 = 1.00588 loss)
I0409 22:01:00.030145 26212 sgd_solver.cpp:105] Iteration 4092, lr = 0.00444606
I0409 22:01:05.059917 26212 solver.cpp:218] Iteration 4104 (2.38585 iter/s, 5.02965s/12 iters), loss = 0.833181
I0409 22:01:05.059969 26212 solver.cpp:237]     Train net output #0: loss = 0.833181 (* 1 = 0.833181 loss)
I0409 22:01:05.059980 26212 sgd_solver.cpp:105] Iteration 4104, lr = 0.0044355
I0409 22:01:09.959331 26212 solver.cpp:218] Iteration 4116 (2.44936 iter/s, 4.89923s/12 iters), loss = 0.726383
I0409 22:01:09.959381 26212 solver.cpp:237]     Train net output #0: loss = 0.726383 (* 1 = 0.726383 loss)
I0409 22:01:09.959393 26212 sgd_solver.cpp:105] Iteration 4116, lr = 0.00442497
I0409 22:01:14.864703 26212 solver.cpp:218] Iteration 4128 (2.44639 iter/s, 4.90519s/12 iters), loss = 0.906758
I0409 22:01:14.864759 26212 solver.cpp:237]     Train net output #0: loss = 0.906758 (* 1 = 0.906758 loss)
I0409 22:01:14.864773 26212 sgd_solver.cpp:105] Iteration 4128, lr = 0.00441447
I0409 22:01:19.793193 26212 solver.cpp:218] Iteration 4140 (2.43491 iter/s, 4.9283s/12 iters), loss = 0.615004
I0409 22:01:19.793345 26212 solver.cpp:237]     Train net output #0: loss = 0.615004 (* 1 = 0.615004 loss)
I0409 22:01:19.793359 26212 sgd_solver.cpp:105] Iteration 4140, lr = 0.00440398
I0409 22:01:22.398367 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:01:24.752997 26212 solver.cpp:218] Iteration 4152 (2.41958 iter/s, 4.95953s/12 iters), loss = 0.849372
I0409 22:01:24.753037 26212 solver.cpp:237]     Train net output #0: loss = 0.849372 (* 1 = 0.849372 loss)
I0409 22:01:24.753046 26212 sgd_solver.cpp:105] Iteration 4152, lr = 0.00439353
I0409 22:01:25.932932 26212 blocking_queue.cpp:49] Waiting for data
I0409 22:01:29.785679 26212 solver.cpp:218] Iteration 4164 (2.3845 iter/s, 5.0325s/12 iters), loss = 0.983014
I0409 22:01:29.785737 26212 solver.cpp:237]     Train net output #0: loss = 0.983014 (* 1 = 0.983014 loss)
I0409 22:01:29.785749 26212 sgd_solver.cpp:105] Iteration 4164, lr = 0.0043831
I0409 22:01:34.746898 26212 solver.cpp:218] Iteration 4176 (2.41885 iter/s, 4.96103s/12 iters), loss = 1.07935
I0409 22:01:34.746943 26212 solver.cpp:237]     Train net output #0: loss = 1.07935 (* 1 = 1.07935 loss)
I0409 22:01:34.746953 26212 sgd_solver.cpp:105] Iteration 4176, lr = 0.00437269
I0409 22:01:36.786921 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4182.caffemodel
I0409 22:01:37.998972 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4182.solverstate
I0409 22:01:38.864059 26212 solver.cpp:330] Iteration 4182, Testing net (#0)
I0409 22:01:38.864078 26212 net.cpp:676] Ignoring source layer train-data
I0409 22:01:41.573415 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:01:43.226193 26212 solver.cpp:397]     Test net output #0: accuracy = 0.238358
I0409 22:01:43.226222 26212 solver.cpp:397]     Test net output #1: loss = 4.43004 (* 1 = 4.43004 loss)
I0409 22:01:45.136950 26212 solver.cpp:218] Iteration 4188 (1.15499 iter/s, 10.3897s/12 iters), loss = 0.914842
I0409 22:01:45.137013 26212 solver.cpp:237]     Train net output #0: loss = 0.914842 (* 1 = 0.914842 loss)
I0409 22:01:45.137025 26212 sgd_solver.cpp:105] Iteration 4188, lr = 0.00436231
I0409 22:01:50.109400 26212 solver.cpp:218] Iteration 4200 (2.41339 iter/s, 4.97226s/12 iters), loss = 0.69351
I0409 22:01:50.110117 26212 solver.cpp:237]     Train net output #0: loss = 0.69351 (* 1 = 0.69351 loss)
I0409 22:01:50.110132 26212 sgd_solver.cpp:105] Iteration 4200, lr = 0.00435195
I0409 22:01:55.259419 26212 solver.cpp:218] Iteration 4212 (2.33047 iter/s, 5.14917s/12 iters), loss = 0.696593
I0409 22:01:55.259467 26212 solver.cpp:237]     Train net output #0: loss = 0.696593 (* 1 = 0.696593 loss)
I0409 22:01:55.259477 26212 sgd_solver.cpp:105] Iteration 4212, lr = 0.00434162
I0409 22:02:00.236891 26212 solver.cpp:218] Iteration 4224 (2.41095 iter/s, 4.97729s/12 iters), loss = 0.710181
I0409 22:02:00.236941 26212 solver.cpp:237]     Train net output #0: loss = 0.710181 (* 1 = 0.710181 loss)
I0409 22:02:00.236953 26212 sgd_solver.cpp:105] Iteration 4224, lr = 0.00433131
I0409 22:02:05.415325 26212 solver.cpp:218] Iteration 4236 (2.31739 iter/s, 5.17825s/12 iters), loss = 0.80757
I0409 22:02:05.415371 26212 solver.cpp:237]     Train net output #0: loss = 0.80757 (* 1 = 0.80757 loss)
I0409 22:02:05.415380 26212 sgd_solver.cpp:105] Iteration 4236, lr = 0.00432103
I0409 22:02:10.257548 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:02:10.488927 26212 solver.cpp:218] Iteration 4248 (2.36527 iter/s, 5.07342s/12 iters), loss = 0.578928
I0409 22:02:10.488986 26212 solver.cpp:237]     Train net output #0: loss = 0.578928 (* 1 = 0.578928 loss)
I0409 22:02:10.488998 26212 sgd_solver.cpp:105] Iteration 4248, lr = 0.00431077
I0409 22:02:15.523865 26212 solver.cpp:218] Iteration 4260 (2.38343 iter/s, 5.03475s/12 iters), loss = 1.04346
I0409 22:02:15.523905 26212 solver.cpp:237]     Train net output #0: loss = 1.04346 (* 1 = 1.04346 loss)
I0409 22:02:15.523914 26212 sgd_solver.cpp:105] Iteration 4260, lr = 0.00430053
I0409 22:02:20.585420 26212 solver.cpp:218] Iteration 4272 (2.37089 iter/s, 5.06138s/12 iters), loss = 0.787332
I0409 22:02:20.585515 26212 solver.cpp:237]     Train net output #0: loss = 0.787332 (* 1 = 0.787332 loss)
I0409 22:02:20.585525 26212 sgd_solver.cpp:105] Iteration 4272, lr = 0.00429032
I0409 22:02:25.047905 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4284.caffemodel
I0409 22:02:26.824247 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4284.solverstate
I0409 22:02:28.622697 26212 solver.cpp:330] Iteration 4284, Testing net (#0)
I0409 22:02:28.622725 26212 net.cpp:676] Ignoring source layer train-data
I0409 22:02:31.412140 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:02:33.150005 26212 solver.cpp:397]     Test net output #0: accuracy = 0.259191
I0409 22:02:33.150053 26212 solver.cpp:397]     Test net output #1: loss = 4.37944 (* 1 = 4.37944 loss)
I0409 22:02:33.233676 26212 solver.cpp:218] Iteration 4284 (0.948778 iter/s, 12.6478s/12 iters), loss = 0.558084
I0409 22:02:33.233747 26212 solver.cpp:237]     Train net output #0: loss = 0.558084 (* 1 = 0.558084 loss)
I0409 22:02:33.233762 26212 sgd_solver.cpp:105] Iteration 4284, lr = 0.00428014
I0409 22:02:37.694787 26212 solver.cpp:218] Iteration 4296 (2.69003 iter/s, 4.46092s/12 iters), loss = 0.720647
I0409 22:02:37.694842 26212 solver.cpp:237]     Train net output #0: loss = 0.720647 (* 1 = 0.720647 loss)
I0409 22:02:37.694854 26212 sgd_solver.cpp:105] Iteration 4296, lr = 0.00426998
I0409 22:02:42.740852 26212 solver.cpp:218] Iteration 4308 (2.37818 iter/s, 5.04588s/12 iters), loss = 0.674942
I0409 22:02:42.740908 26212 solver.cpp:237]     Train net output #0: loss = 0.674942 (* 1 = 0.674942 loss)
I0409 22:02:42.740921 26212 sgd_solver.cpp:105] Iteration 4308, lr = 0.00425984
I0409 22:02:47.709368 26212 solver.cpp:218] Iteration 4320 (2.4153 iter/s, 4.96833s/12 iters), loss = 0.990967
I0409 22:02:47.709417 26212 solver.cpp:237]     Train net output #0: loss = 0.990967 (* 1 = 0.990967 loss)
I0409 22:02:47.709426 26212 sgd_solver.cpp:105] Iteration 4320, lr = 0.00424972
I0409 22:02:52.746832 26212 solver.cpp:218] Iteration 4332 (2.38224 iter/s, 5.03728s/12 iters), loss = 0.652241
I0409 22:02:52.746991 26212 solver.cpp:237]     Train net output #0: loss = 0.652241 (* 1 = 0.652241 loss)
I0409 22:02:52.747005 26212 sgd_solver.cpp:105] Iteration 4332, lr = 0.00423964
I0409 22:02:57.673686 26212 solver.cpp:218] Iteration 4344 (2.43577 iter/s, 4.92657s/12 iters), loss = 1.01847
I0409 22:02:57.673734 26212 solver.cpp:237]     Train net output #0: loss = 1.01847 (* 1 = 1.01847 loss)
I0409 22:02:57.673744 26212 sgd_solver.cpp:105] Iteration 4344, lr = 0.00422957
I0409 22:02:59.550089 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:03:02.649924 26212 solver.cpp:218] Iteration 4356 (2.41155 iter/s, 4.97605s/12 iters), loss = 0.698877
I0409 22:03:02.649987 26212 solver.cpp:237]     Train net output #0: loss = 0.698877 (* 1 = 0.698877 loss)
I0409 22:03:02.650000 26212 sgd_solver.cpp:105] Iteration 4356, lr = 0.00421953
I0409 22:03:07.678951 26212 solver.cpp:218] Iteration 4368 (2.38624 iter/s, 5.02883s/12 iters), loss = 0.778806
I0409 22:03:07.679000 26212 solver.cpp:237]     Train net output #0: loss = 0.778806 (* 1 = 0.778806 loss)
I0409 22:03:07.679013 26212 sgd_solver.cpp:105] Iteration 4368, lr = 0.00420951
I0409 22:03:12.668138 26212 solver.cpp:218] Iteration 4380 (2.40529 iter/s, 4.98901s/12 iters), loss = 0.566137
I0409 22:03:12.668190 26212 solver.cpp:237]     Train net output #0: loss = 0.566137 (* 1 = 0.566137 loss)
I0409 22:03:12.668201 26212 sgd_solver.cpp:105] Iteration 4380, lr = 0.00419952
I0409 22:03:14.653355 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4386.caffemodel
I0409 22:03:18.516546 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4386.solverstate
I0409 22:03:21.651410 26212 solver.cpp:330] Iteration 4386, Testing net (#0)
I0409 22:03:21.651439 26212 net.cpp:676] Ignoring source layer train-data
I0409 22:03:24.421149 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:03:26.156373 26212 solver.cpp:397]     Test net output #0: accuracy = 0.248775
I0409 22:03:26.156420 26212 solver.cpp:397]     Test net output #1: loss = 4.54305 (* 1 = 4.54305 loss)
I0409 22:03:28.142904 26212 solver.cpp:218] Iteration 4392 (0.775477 iter/s, 15.4743s/12 iters), loss = 0.929962
I0409 22:03:28.142943 26212 solver.cpp:237]     Train net output #0: loss = 0.929962 (* 1 = 0.929962 loss)
I0409 22:03:28.142952 26212 sgd_solver.cpp:105] Iteration 4392, lr = 0.00418954
I0409 22:03:33.063052 26212 solver.cpp:218] Iteration 4404 (2.43904 iter/s, 4.91997s/12 iters), loss = 0.761133
I0409 22:03:33.063107 26212 solver.cpp:237]     Train net output #0: loss = 0.761133 (* 1 = 0.761133 loss)
I0409 22:03:33.063118 26212 sgd_solver.cpp:105] Iteration 4404, lr = 0.0041796
I0409 22:03:38.006537 26212 solver.cpp:218] Iteration 4416 (2.42753 iter/s, 4.9433s/12 iters), loss = 0.854372
I0409 22:03:38.006587 26212 solver.cpp:237]     Train net output #0: loss = 0.854372 (* 1 = 0.854372 loss)
I0409 22:03:38.006597 26212 sgd_solver.cpp:105] Iteration 4416, lr = 0.00416967
I0409 22:03:43.017642 26212 solver.cpp:218] Iteration 4428 (2.39477 iter/s, 5.01092s/12 iters), loss = 0.736586
I0409 22:03:43.017700 26212 solver.cpp:237]     Train net output #0: loss = 0.736586 (* 1 = 0.736586 loss)
I0409 22:03:43.017712 26212 sgd_solver.cpp:105] Iteration 4428, lr = 0.00415977
I0409 22:03:48.020023 26212 solver.cpp:218] Iteration 4440 (2.39895 iter/s, 5.00219s/12 iters), loss = 0.952751
I0409 22:03:48.020078 26212 solver.cpp:237]     Train net output #0: loss = 0.952751 (* 1 = 0.952751 loss)
I0409 22:03:48.020092 26212 sgd_solver.cpp:105] Iteration 4440, lr = 0.0041499
I0409 22:03:51.994222 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:03:52.949339 26212 solver.cpp:218] Iteration 4452 (2.43451 iter/s, 4.92913s/12 iters), loss = 0.573975
I0409 22:03:52.949399 26212 solver.cpp:237]     Train net output #0: loss = 0.573975 (* 1 = 0.573975 loss)
I0409 22:03:52.949412 26212 sgd_solver.cpp:105] Iteration 4452, lr = 0.00414005
I0409 22:03:57.918732 26212 solver.cpp:218] Iteration 4464 (2.41488 iter/s, 4.9692s/12 iters), loss = 0.719034
I0409 22:03:57.918869 26212 solver.cpp:237]     Train net output #0: loss = 0.719034 (* 1 = 0.719034 loss)
I0409 22:03:57.918882 26212 sgd_solver.cpp:105] Iteration 4464, lr = 0.00413022
I0409 22:04:02.952972 26212 solver.cpp:218] Iteration 4476 (2.3838 iter/s, 5.03397s/12 iters), loss = 0.797012
I0409 22:04:02.953029 26212 solver.cpp:237]     Train net output #0: loss = 0.797012 (* 1 = 0.797012 loss)
I0409 22:04:02.953042 26212 sgd_solver.cpp:105] Iteration 4476, lr = 0.00412041
I0409 22:04:07.450621 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4488.caffemodel
I0409 22:04:08.630594 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4488.solverstate
I0409 22:04:09.497002 26212 solver.cpp:330] Iteration 4488, Testing net (#0)
I0409 22:04:09.497028 26212 net.cpp:676] Ignoring source layer train-data
I0409 22:04:12.301949 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:04:14.068305 26212 solver.cpp:397]     Test net output #0: accuracy = 0.25674
I0409 22:04:14.068341 26212 solver.cpp:397]     Test net output #1: loss = 4.53109 (* 1 = 4.53109 loss)
I0409 22:04:14.151898 26212 solver.cpp:218] Iteration 4488 (1.07156 iter/s, 11.1986s/12 iters), loss = 0.603469
I0409 22:04:14.151948 26212 solver.cpp:237]     Train net output #0: loss = 0.603469 (* 1 = 0.603469 loss)
I0409 22:04:14.151958 26212 sgd_solver.cpp:105] Iteration 4488, lr = 0.00411063
I0409 22:04:18.390002 26212 solver.cpp:218] Iteration 4500 (2.83158 iter/s, 4.23791s/12 iters), loss = 0.435947
I0409 22:04:18.390053 26212 solver.cpp:237]     Train net output #0: loss = 0.435947 (* 1 = 0.435947 loss)
I0409 22:04:18.390064 26212 sgd_solver.cpp:105] Iteration 4500, lr = 0.00410087
I0409 22:04:23.304374 26212 solver.cpp:218] Iteration 4512 (2.44191 iter/s, 4.91419s/12 iters), loss = 0.708339
I0409 22:04:23.304435 26212 solver.cpp:237]     Train net output #0: loss = 0.708339 (* 1 = 0.708339 loss)
I0409 22:04:23.304448 26212 sgd_solver.cpp:105] Iteration 4512, lr = 0.00409113
I0409 22:04:28.183014 26212 solver.cpp:218] Iteration 4524 (2.4598 iter/s, 4.87845s/12 iters), loss = 0.647493
I0409 22:04:28.183130 26212 solver.cpp:237]     Train net output #0: loss = 0.647493 (* 1 = 0.647493 loss)
I0409 22:04:28.183142 26212 sgd_solver.cpp:105] Iteration 4524, lr = 0.00408142
I0409 22:04:33.133561 26212 solver.cpp:218] Iteration 4536 (2.4241 iter/s, 4.9503s/12 iters), loss = 0.747275
I0409 22:04:33.133615 26212 solver.cpp:237]     Train net output #0: loss = 0.747275 (* 1 = 0.747275 loss)
I0409 22:04:33.133628 26212 sgd_solver.cpp:105] Iteration 4536, lr = 0.00407173
I0409 22:04:38.079591 26212 solver.cpp:218] Iteration 4548 (2.42628 iter/s, 4.94584s/12 iters), loss = 0.706436
I0409 22:04:38.079656 26212 solver.cpp:237]     Train net output #0: loss = 0.706436 (* 1 = 0.706436 loss)
I0409 22:04:38.079672 26212 sgd_solver.cpp:105] Iteration 4548, lr = 0.00406206
I0409 22:04:39.314282 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:04:43.039006 26212 solver.cpp:218] Iteration 4560 (2.41973 iter/s, 4.95922s/12 iters), loss = 1.09648
I0409 22:04:43.039057 26212 solver.cpp:237]     Train net output #0: loss = 1.09648 (* 1 = 1.09648 loss)
I0409 22:04:43.039069 26212 sgd_solver.cpp:105] Iteration 4560, lr = 0.00405242
I0409 22:04:48.034121 26212 solver.cpp:218] Iteration 4572 (2.40244 iter/s, 4.99493s/12 iters), loss = 0.888087
I0409 22:04:48.034171 26212 solver.cpp:237]     Train net output #0: loss = 0.888087 (* 1 = 0.888087 loss)
I0409 22:04:48.034183 26212 sgd_solver.cpp:105] Iteration 4572, lr = 0.0040428
I0409 22:04:53.080260 26212 solver.cpp:218] Iteration 4584 (2.37814 iter/s, 5.04595s/12 iters), loss = 0.893013
I0409 22:04:53.080318 26212 solver.cpp:237]     Train net output #0: loss = 0.893013 (* 1 = 0.893013 loss)
I0409 22:04:53.080330 26212 sgd_solver.cpp:105] Iteration 4584, lr = 0.0040332
I0409 22:04:55.116281 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4590.caffemodel
I0409 22:04:56.364122 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4590.solverstate
I0409 22:04:57.245033 26212 solver.cpp:330] Iteration 4590, Testing net (#0)
I0409 22:04:57.245062 26212 net.cpp:676] Ignoring source layer train-data
I0409 22:04:59.877009 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:05:01.745581 26212 solver.cpp:397]     Test net output #0: accuracy = 0.259804
I0409 22:05:01.745626 26212 solver.cpp:397]     Test net output #1: loss = 4.51067 (* 1 = 4.51067 loss)
I0409 22:05:03.653461 26212 solver.cpp:218] Iteration 4596 (1.13498 iter/s, 10.5729s/12 iters), loss = 0.672685
I0409 22:05:03.653515 26212 solver.cpp:237]     Train net output #0: loss = 0.672685 (* 1 = 0.672685 loss)
I0409 22:05:03.653527 26212 sgd_solver.cpp:105] Iteration 4596, lr = 0.00402362
I0409 22:05:08.754982 26212 solver.cpp:218] Iteration 4608 (2.35233 iter/s, 5.10133s/12 iters), loss = 0.658235
I0409 22:05:08.755039 26212 solver.cpp:237]     Train net output #0: loss = 0.658235 (* 1 = 0.658235 loss)
I0409 22:05:08.755051 26212 sgd_solver.cpp:105] Iteration 4608, lr = 0.00401407
I0409 22:05:13.697223 26212 solver.cpp:218] Iteration 4620 (2.42814 iter/s, 4.94205s/12 iters), loss = 0.544832
I0409 22:05:13.697268 26212 solver.cpp:237]     Train net output #0: loss = 0.544832 (* 1 = 0.544832 loss)
I0409 22:05:13.697278 26212 sgd_solver.cpp:105] Iteration 4620, lr = 0.00400454
I0409 22:05:18.607955 26212 solver.cpp:218] Iteration 4632 (2.44371 iter/s, 4.91056s/12 iters), loss = 0.669677
I0409 22:05:18.607993 26212 solver.cpp:237]     Train net output #0: loss = 0.669677 (* 1 = 0.669677 loss)
I0409 22:05:18.608001 26212 sgd_solver.cpp:105] Iteration 4632, lr = 0.00399503
I0409 22:05:23.546298 26212 solver.cpp:218] Iteration 4644 (2.43005 iter/s, 4.93817s/12 iters), loss = 0.743489
I0409 22:05:23.546344 26212 solver.cpp:237]     Train net output #0: loss = 0.743489 (* 1 = 0.743489 loss)
I0409 22:05:23.546352 26212 sgd_solver.cpp:105] Iteration 4644, lr = 0.00398555
I0409 22:05:26.889367 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:05:28.444285 26212 solver.cpp:218] Iteration 4656 (2.45007 iter/s, 4.89781s/12 iters), loss = 0.701341
I0409 22:05:28.444329 26212 solver.cpp:237]     Train net output #0: loss = 0.701341 (* 1 = 0.701341 loss)
I0409 22:05:28.444337 26212 sgd_solver.cpp:105] Iteration 4656, lr = 0.00397608
I0409 22:05:33.261062 26212 solver.cpp:218] Iteration 4668 (2.49138 iter/s, 4.8166s/12 iters), loss = 0.755057
I0409 22:05:33.261142 26212 solver.cpp:237]     Train net output #0: loss = 0.755057 (* 1 = 0.755057 loss)
I0409 22:05:33.261153 26212 sgd_solver.cpp:105] Iteration 4668, lr = 0.00396664
I0409 22:05:38.239960 26212 solver.cpp:218] Iteration 4680 (2.41027 iter/s, 4.97869s/12 iters), loss = 0.688403
I0409 22:05:38.240012 26212 solver.cpp:237]     Train net output #0: loss = 0.688403 (* 1 = 0.688403 loss)
I0409 22:05:38.240025 26212 sgd_solver.cpp:105] Iteration 4680, lr = 0.00395723
I0409 22:05:42.727939 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4692.caffemodel
I0409 22:05:45.036964 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4692.solverstate
I0409 22:05:46.336637 26212 solver.cpp:330] Iteration 4692, Testing net (#0)
I0409 22:05:46.336663 26212 net.cpp:676] Ignoring source layer train-data
I0409 22:05:49.283370 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:05:51.149703 26212 solver.cpp:397]     Test net output #0: accuracy = 0.254902
I0409 22:05:51.149762 26212 solver.cpp:397]     Test net output #1: loss = 4.70089 (* 1 = 4.70089 loss)
I0409 22:05:51.233819 26212 solver.cpp:218] Iteration 4692 (0.92354 iter/s, 12.9935s/12 iters), loss = 0.696809
I0409 22:05:51.233865 26212 solver.cpp:237]     Train net output #0: loss = 0.696809 (* 1 = 0.696809 loss)
I0409 22:05:51.233876 26212 sgd_solver.cpp:105] Iteration 4692, lr = 0.00394783
I0409 22:05:55.608036 26212 solver.cpp:218] Iteration 4704 (2.74345 iter/s, 4.37405s/12 iters), loss = 0.80525
I0409 22:05:55.608081 26212 solver.cpp:237]     Train net output #0: loss = 0.80525 (* 1 = 0.80525 loss)
I0409 22:05:55.608089 26212 sgd_solver.cpp:105] Iteration 4704, lr = 0.00393846
I0409 22:06:00.503211 26212 solver.cpp:218] Iteration 4716 (2.45148 iter/s, 4.895s/12 iters), loss = 0.649059
I0409 22:06:00.503268 26212 solver.cpp:237]     Train net output #0: loss = 0.649059 (* 1 = 0.649059 loss)
I0409 22:06:00.503280 26212 sgd_solver.cpp:105] Iteration 4716, lr = 0.00392911
I0409 22:06:05.445983 26212 solver.cpp:218] Iteration 4728 (2.42789 iter/s, 4.94257s/12 iters), loss = 0.758662
I0409 22:06:05.446100 26212 solver.cpp:237]     Train net output #0: loss = 0.758662 (* 1 = 0.758662 loss)
I0409 22:06:05.446110 26212 sgd_solver.cpp:105] Iteration 4728, lr = 0.00391978
I0409 22:06:10.408102 26212 solver.cpp:218] Iteration 4740 (2.41845 iter/s, 4.96186s/12 iters), loss = 0.62999
I0409 22:06:10.408154 26212 solver.cpp:237]     Train net output #0: loss = 0.62999 (* 1 = 0.62999 loss)
I0409 22:06:10.408164 26212 sgd_solver.cpp:105] Iteration 4740, lr = 0.00391047
I0409 22:06:15.705583 26212 solver.cpp:218] Iteration 4752 (2.26531 iter/s, 5.29728s/12 iters), loss = 0.703512
I0409 22:06:15.705639 26212 solver.cpp:237]     Train net output #0: loss = 0.703512 (* 1 = 0.703512 loss)
I0409 22:06:15.705652 26212 sgd_solver.cpp:105] Iteration 4752, lr = 0.00390119
I0409 22:06:16.220185 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:06:20.803349 26212 solver.cpp:218] Iteration 4764 (2.35406 iter/s, 5.09757s/12 iters), loss = 0.809304
I0409 22:06:20.803406 26212 solver.cpp:237]     Train net output #0: loss = 0.809304 (* 1 = 0.809304 loss)
I0409 22:06:20.803417 26212 sgd_solver.cpp:105] Iteration 4764, lr = 0.00389193
I0409 22:06:25.788426 26212 solver.cpp:218] Iteration 4776 (2.40727 iter/s, 4.98489s/12 iters), loss = 0.92713
I0409 22:06:25.788468 26212 solver.cpp:237]     Train net output #0: loss = 0.92713 (* 1 = 0.92713 loss)
I0409 22:06:25.788476 26212 sgd_solver.cpp:105] Iteration 4776, lr = 0.00388269
I0409 22:06:30.901206 26212 solver.cpp:218] Iteration 4788 (2.34714 iter/s, 5.1126s/12 iters), loss = 0.475866
I0409 22:06:30.901257 26212 solver.cpp:237]     Train net output #0: loss = 0.475866 (* 1 = 0.475866 loss)
I0409 22:06:30.901266 26212 sgd_solver.cpp:105] Iteration 4788, lr = 0.00387347
I0409 22:06:32.859797 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4794.caffemodel
I0409 22:06:34.091531 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4794.solverstate
I0409 22:06:35.067198 26212 solver.cpp:330] Iteration 4794, Testing net (#0)
I0409 22:06:35.067217 26212 net.cpp:676] Ignoring source layer train-data
I0409 22:06:37.506080 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:06:39.399583 26212 solver.cpp:397]     Test net output #0: accuracy = 0.254289
I0409 22:06:39.399639 26212 solver.cpp:397]     Test net output #1: loss = 4.5331 (* 1 = 4.5331 loss)
I0409 22:06:41.176833 26212 solver.cpp:218] Iteration 4800 (1.16785 iter/s, 10.2753s/12 iters), loss = 0.899121
I0409 22:06:41.176882 26212 solver.cpp:237]     Train net output #0: loss = 0.899121 (* 1 = 0.899121 loss)
I0409 22:06:41.176890 26212 sgd_solver.cpp:105] Iteration 4800, lr = 0.00386427
I0409 22:06:46.257378 26212 solver.cpp:218] Iteration 4812 (2.36204 iter/s, 5.08036s/12 iters), loss = 0.794091
I0409 22:06:46.257433 26212 solver.cpp:237]     Train net output #0: loss = 0.794091 (* 1 = 0.794091 loss)
I0409 22:06:46.257444 26212 sgd_solver.cpp:105] Iteration 4812, lr = 0.0038551
I0409 22:06:51.220104 26212 solver.cpp:218] Iteration 4824 (2.41812 iter/s, 4.96254s/12 iters), loss = 0.682188
I0409 22:06:51.220161 26212 solver.cpp:237]     Train net output #0: loss = 0.682188 (* 1 = 0.682188 loss)
I0409 22:06:51.220172 26212 sgd_solver.cpp:105] Iteration 4824, lr = 0.00384594
I0409 22:06:56.209313 26212 solver.cpp:218] Iteration 4836 (2.40528 iter/s, 4.98902s/12 iters), loss = 0.562611
I0409 22:06:56.209370 26212 solver.cpp:237]     Train net output #0: loss = 0.562611 (* 1 = 0.562611 loss)
I0409 22:06:56.209383 26212 sgd_solver.cpp:105] Iteration 4836, lr = 0.00383681
I0409 22:06:57.791378 26212 blocking_queue.cpp:49] Waiting for data
I0409 22:07:01.178862 26212 solver.cpp:218] Iteration 4848 (2.4148 iter/s, 4.96936s/12 iters), loss = 0.681937
I0409 22:07:01.178908 26212 solver.cpp:237]     Train net output #0: loss = 0.681937 (* 1 = 0.681937 loss)
I0409 22:07:01.178920 26212 sgd_solver.cpp:105] Iteration 4848, lr = 0.0038277
I0409 22:07:03.811414 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:07:06.102468 26212 solver.cpp:218] Iteration 4860 (2.43733 iter/s, 4.92343s/12 iters), loss = 0.666825
I0409 22:07:06.102514 26212 solver.cpp:237]     Train net output #0: loss = 0.666825 (* 1 = 0.666825 loss)
I0409 22:07:06.102524 26212 sgd_solver.cpp:105] Iteration 4860, lr = 0.00381862
I0409 22:07:11.021016 26212 solver.cpp:218] Iteration 4872 (2.43983 iter/s, 4.91837s/12 iters), loss = 0.584885
I0409 22:07:11.021157 26212 solver.cpp:237]     Train net output #0: loss = 0.584885 (* 1 = 0.584885 loss)
I0409 22:07:11.021170 26212 sgd_solver.cpp:105] Iteration 4872, lr = 0.00380955
I0409 22:07:15.978524 26212 solver.cpp:218] Iteration 4884 (2.4207 iter/s, 4.95724s/12 iters), loss = 0.755541
I0409 22:07:15.978569 26212 solver.cpp:237]     Train net output #0: loss = 0.755541 (* 1 = 0.755541 loss)
I0409 22:07:15.978579 26212 sgd_solver.cpp:105] Iteration 4884, lr = 0.0038005
I0409 22:07:20.520921 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4896.caffemodel
I0409 22:07:21.711443 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4896.solverstate
I0409 22:07:22.589624 26212 solver.cpp:330] Iteration 4896, Testing net (#0)
I0409 22:07:22.589654 26212 net.cpp:676] Ignoring source layer train-data
I0409 22:07:25.268246 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:07:27.232986 26212 solver.cpp:397]     Test net output #0: accuracy = 0.275735
I0409 22:07:27.233032 26212 solver.cpp:397]     Test net output #1: loss = 4.5539 (* 1 = 4.5539 loss)
I0409 22:07:27.316669 26212 solver.cpp:218] Iteration 4896 (1.0584 iter/s, 11.3378s/12 iters), loss = 0.876741
I0409 22:07:27.316718 26212 solver.cpp:237]     Train net output #0: loss = 0.876741 (* 1 = 0.876741 loss)
I0409 22:07:27.316730 26212 sgd_solver.cpp:105] Iteration 4896, lr = 0.00379148
I0409 22:07:31.532935 26212 solver.cpp:218] Iteration 4908 (2.84623 iter/s, 4.2161s/12 iters), loss = 0.544758
I0409 22:07:31.532991 26212 solver.cpp:237]     Train net output #0: loss = 0.544758 (* 1 = 0.544758 loss)
I0409 22:07:31.533004 26212 sgd_solver.cpp:105] Iteration 4908, lr = 0.00378248
I0409 22:07:36.537992 26212 solver.cpp:218] Iteration 4920 (2.39767 iter/s, 5.00487s/12 iters), loss = 0.702047
I0409 22:07:36.538043 26212 solver.cpp:237]     Train net output #0: loss = 0.702047 (* 1 = 0.702047 loss)
I0409 22:07:36.538055 26212 sgd_solver.cpp:105] Iteration 4920, lr = 0.0037735
I0409 22:07:41.529820 26212 solver.cpp:218] Iteration 4932 (2.40402 iter/s, 4.99164s/12 iters), loss = 0.607853
I0409 22:07:41.529940 26212 solver.cpp:237]     Train net output #0: loss = 0.607853 (* 1 = 0.607853 loss)
I0409 22:07:41.529970 26212 sgd_solver.cpp:105] Iteration 4932, lr = 0.00376454
I0409 22:07:46.530797 26212 solver.cpp:218] Iteration 4944 (2.39965 iter/s, 5.00072s/12 iters), loss = 0.563478
I0409 22:07:46.530859 26212 solver.cpp:237]     Train net output #0: loss = 0.563478 (* 1 = 0.563478 loss)
I0409 22:07:46.530872 26212 sgd_solver.cpp:105] Iteration 4944, lr = 0.0037556
I0409 22:07:51.348466 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:07:51.548147 26212 solver.cpp:218] Iteration 4956 (2.39179 iter/s, 5.01716s/12 iters), loss = 0.522344
I0409 22:07:51.548197 26212 solver.cpp:237]     Train net output #0: loss = 0.522344 (* 1 = 0.522344 loss)
I0409 22:07:51.548207 26212 sgd_solver.cpp:105] Iteration 4956, lr = 0.00374669
I0409 22:07:56.545404 26212 solver.cpp:218] Iteration 4968 (2.4014 iter/s, 4.99708s/12 iters), loss = 0.529362
I0409 22:07:56.545441 26212 solver.cpp:237]     Train net output #0: loss = 0.529362 (* 1 = 0.529362 loss)
I0409 22:07:56.545449 26212 sgd_solver.cpp:105] Iteration 4968, lr = 0.00373779
I0409 22:08:01.462559 26212 solver.cpp:218] Iteration 4980 (2.44052 iter/s, 4.91699s/12 iters), loss = 0.591989
I0409 22:08:01.462607 26212 solver.cpp:237]     Train net output #0: loss = 0.591989 (* 1 = 0.591989 loss)
I0409 22:08:01.462620 26212 sgd_solver.cpp:105] Iteration 4980, lr = 0.00372892
I0409 22:08:06.272164 26212 solver.cpp:218] Iteration 4992 (2.4951 iter/s, 4.80943s/12 iters), loss = 0.746548
I0409 22:08:06.272219 26212 solver.cpp:237]     Train net output #0: loss = 0.746548 (* 1 = 0.746548 loss)
I0409 22:08:06.272231 26212 sgd_solver.cpp:105] Iteration 4992, lr = 0.00372006
I0409 22:08:08.289264 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4998.caffemodel
I0409 22:08:12.851728 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4998.solverstate
I0409 22:08:13.779561 26212 solver.cpp:330] Iteration 4998, Testing net (#0)
I0409 22:08:13.779587 26212 net.cpp:676] Ignoring source layer train-data
I0409 22:08:16.265532 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:08:18.228663 26212 solver.cpp:397]     Test net output #0: accuracy = 0.258578
I0409 22:08:18.228694 26212 solver.cpp:397]     Test net output #1: loss = 4.60015 (* 1 = 4.60015 loss)
I0409 22:08:20.102929 26212 solver.cpp:218] Iteration 5004 (0.867656 iter/s, 13.8304s/12 iters), loss = 0.584861
I0409 22:08:20.102972 26212 solver.cpp:237]     Train net output #0: loss = 0.584861 (* 1 = 0.584861 loss)
I0409 22:08:20.102979 26212 sgd_solver.cpp:105] Iteration 5004, lr = 0.00371123
I0409 22:08:24.929247 26212 solver.cpp:218] Iteration 5016 (2.48646 iter/s, 4.82615s/12 iters), loss = 0.769373
I0409 22:08:24.929297 26212 solver.cpp:237]     Train net output #0: loss = 0.769373 (* 1 = 0.769373 loss)
I0409 22:08:24.929307 26212 sgd_solver.cpp:105] Iteration 5016, lr = 0.00370242
I0409 22:08:29.855535 26212 solver.cpp:218] Iteration 5028 (2.436 iter/s, 4.92611s/12 iters), loss = 0.442628
I0409 22:08:29.855578 26212 solver.cpp:237]     Train net output #0: loss = 0.442628 (* 1 = 0.442628 loss)
I0409 22:08:29.855587 26212 sgd_solver.cpp:105] Iteration 5028, lr = 0.00369363
I0409 22:08:34.716588 26212 solver.cpp:218] Iteration 5040 (2.46869 iter/s, 4.86088s/12 iters), loss = 0.58157
I0409 22:08:34.716626 26212 solver.cpp:237]     Train net output #0: loss = 0.58157 (* 1 = 0.58157 loss)
I0409 22:08:34.716636 26212 sgd_solver.cpp:105] Iteration 5040, lr = 0.00368486
I0409 22:08:39.682329 26212 solver.cpp:218] Iteration 5052 (2.41664 iter/s, 4.96556s/12 iters), loss = 0.494756
I0409 22:08:39.682374 26212 solver.cpp:237]     Train net output #0: loss = 0.494756 (* 1 = 0.494756 loss)
I0409 22:08:39.682384 26212 sgd_solver.cpp:105] Iteration 5052, lr = 0.00367611
I0409 22:08:41.599141 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:08:44.624716 26212 solver.cpp:218] Iteration 5064 (2.42806 iter/s, 4.94221s/12 iters), loss = 0.680142
I0409 22:08:44.624815 26212 solver.cpp:237]     Train net output #0: loss = 0.680142 (* 1 = 0.680142 loss)
I0409 22:08:44.624825 26212 sgd_solver.cpp:105] Iteration 5064, lr = 0.00366738
I0409 22:08:49.531960 26212 solver.cpp:218] Iteration 5076 (2.44548 iter/s, 4.90701s/12 iters), loss = 0.615727
I0409 22:08:49.532008 26212 solver.cpp:237]     Train net output #0: loss = 0.615727 (* 1 = 0.615727 loss)
I0409 22:08:49.532019 26212 sgd_solver.cpp:105] Iteration 5076, lr = 0.00365868
I0409 22:08:54.422374 26212 solver.cpp:218] Iteration 5088 (2.45387 iter/s, 4.89024s/12 iters), loss = 0.450902
I0409 22:08:54.422420 26212 solver.cpp:237]     Train net output #0: loss = 0.450902 (* 1 = 0.450902 loss)
I0409 22:08:54.422432 26212 sgd_solver.cpp:105] Iteration 5088, lr = 0.00364999
I0409 22:08:58.905100 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5100.caffemodel
I0409 22:09:00.876173 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5100.solverstate
I0409 22:09:02.046730 26212 solver.cpp:330] Iteration 5100, Testing net (#0)
I0409 22:09:02.046756 26212 net.cpp:676] Ignoring source layer train-data
I0409 22:09:04.376731 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:09:06.389914 26212 solver.cpp:397]     Test net output #0: accuracy = 0.254902
I0409 22:09:06.389979 26212 solver.cpp:397]     Test net output #1: loss = 4.66426 (* 1 = 4.66426 loss)
I0409 22:09:06.473951 26212 solver.cpp:218] Iteration 5100 (0.995749 iter/s, 12.0512s/12 iters), loss = 0.380256
I0409 22:09:06.474050 26212 solver.cpp:237]     Train net output #0: loss = 0.380256 (* 1 = 0.380256 loss)
I0409 22:09:06.474067 26212 sgd_solver.cpp:105] Iteration 5100, lr = 0.00364132
I0409 22:09:10.644780 26212 solver.cpp:218] Iteration 5112 (2.87727 iter/s, 4.17062s/12 iters), loss = 0.496192
I0409 22:09:10.644832 26212 solver.cpp:237]     Train net output #0: loss = 0.496192 (* 1 = 0.496192 loss)
I0409 22:09:10.644845 26212 sgd_solver.cpp:105] Iteration 5112, lr = 0.00363268
I0409 22:09:15.584738 26212 solver.cpp:218] Iteration 5124 (2.42926 iter/s, 4.93978s/12 iters), loss = 0.822161
I0409 22:09:15.584839 26212 solver.cpp:237]     Train net output #0: loss = 0.822161 (* 1 = 0.822161 loss)
I0409 22:09:15.584849 26212 sgd_solver.cpp:105] Iteration 5124, lr = 0.00362405
I0409 22:09:20.597491 26212 solver.cpp:218] Iteration 5136 (2.39401 iter/s, 5.01251s/12 iters), loss = 0.531926
I0409 22:09:20.597546 26212 solver.cpp:237]     Train net output #0: loss = 0.531926 (* 1 = 0.531926 loss)
I0409 22:09:20.597558 26212 sgd_solver.cpp:105] Iteration 5136, lr = 0.00361545
I0409 22:09:25.583616 26212 solver.cpp:218] Iteration 5148 (2.40677 iter/s, 4.98594s/12 iters), loss = 0.63597
I0409 22:09:25.583667 26212 solver.cpp:237]     Train net output #0: loss = 0.63597 (* 1 = 0.63597 loss)
I0409 22:09:25.583678 26212 sgd_solver.cpp:105] Iteration 5148, lr = 0.00360687
I0409 22:09:29.575937 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:09:30.548508 26212 solver.cpp:218] Iteration 5160 (2.41706 iter/s, 4.96471s/12 iters), loss = 0.838415
I0409 22:09:30.548557 26212 solver.cpp:237]     Train net output #0: loss = 0.838415 (* 1 = 0.838415 loss)
I0409 22:09:30.548569 26212 sgd_solver.cpp:105] Iteration 5160, lr = 0.0035983
I0409 22:09:35.457198 26212 solver.cpp:218] Iteration 5172 (2.44473 iter/s, 4.90851s/12 iters), loss = 0.45502
I0409 22:09:35.457245 26212 solver.cpp:237]     Train net output #0: loss = 0.45502 (* 1 = 0.45502 loss)
I0409 22:09:35.457254 26212 sgd_solver.cpp:105] Iteration 5172, lr = 0.00358976
I0409 22:09:40.489156 26212 solver.cpp:218] Iteration 5184 (2.38484 iter/s, 5.03177s/12 iters), loss = 0.522159
I0409 22:09:40.489209 26212 solver.cpp:237]     Train net output #0: loss = 0.522159 (* 1 = 0.522159 loss)
I0409 22:09:40.489223 26212 sgd_solver.cpp:105] Iteration 5184, lr = 0.00358124
I0409 22:09:45.459981 26212 solver.cpp:218] Iteration 5196 (2.41418 iter/s, 4.97064s/12 iters), loss = 0.686708
I0409 22:09:45.460027 26212 solver.cpp:237]     Train net output #0: loss = 0.686708 (* 1 = 0.686708 loss)
I0409 22:09:45.460037 26212 sgd_solver.cpp:105] Iteration 5196, lr = 0.00357273
I0409 22:09:47.510179 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5202.caffemodel
I0409 22:09:48.722182 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5202.solverstate
I0409 22:09:49.596184 26212 solver.cpp:330] Iteration 5202, Testing net (#0)
I0409 22:09:49.596210 26212 net.cpp:676] Ignoring source layer train-data
I0409 22:09:52.037210 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:09:54.130795 26212 solver.cpp:397]     Test net output #0: accuracy = 0.270221
I0409 22:09:54.130834 26212 solver.cpp:397]     Test net output #1: loss = 4.56199 (* 1 = 4.56199 loss)
I0409 22:09:56.138361 26212 solver.cpp:218] Iteration 5208 (1.1238 iter/s, 10.6781s/12 iters), loss = 0.541501
I0409 22:09:56.138408 26212 solver.cpp:237]     Train net output #0: loss = 0.541501 (* 1 = 0.541501 loss)
I0409 22:09:56.138418 26212 sgd_solver.cpp:105] Iteration 5208, lr = 0.00356425
I0409 22:10:01.030997 26212 solver.cpp:218] Iteration 5220 (2.45275 iter/s, 4.89246s/12 iters), loss = 0.380019
I0409 22:10:01.031044 26212 solver.cpp:237]     Train net output #0: loss = 0.380019 (* 1 = 0.380019 loss)
I0409 22:10:01.031054 26212 sgd_solver.cpp:105] Iteration 5220, lr = 0.00355579
I0409 22:10:05.913408 26212 solver.cpp:218] Iteration 5232 (2.45789 iter/s, 4.88223s/12 iters), loss = 0.373126
I0409 22:10:05.913455 26212 solver.cpp:237]     Train net output #0: loss = 0.373126 (* 1 = 0.373126 loss)
I0409 22:10:05.913465 26212 sgd_solver.cpp:105] Iteration 5232, lr = 0.00354735
I0409 22:10:10.825403 26212 solver.cpp:218] Iteration 5244 (2.44309 iter/s, 4.91182s/12 iters), loss = 0.451242
I0409 22:10:10.825450 26212 solver.cpp:237]     Train net output #0: loss = 0.451242 (* 1 = 0.451242 loss)
I0409 22:10:10.825459 26212 sgd_solver.cpp:105] Iteration 5244, lr = 0.00353892
I0409 22:10:15.947201 26212 solver.cpp:218] Iteration 5256 (2.34301 iter/s, 5.12161s/12 iters), loss = 0.560017
I0409 22:10:15.947252 26212 solver.cpp:237]     Train net output #0: loss = 0.560017 (* 1 = 0.560017 loss)
I0409 22:10:15.947264 26212 sgd_solver.cpp:105] Iteration 5256, lr = 0.00353052
I0409 22:10:17.195711 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:10:20.864127 26212 solver.cpp:218] Iteration 5268 (2.44064 iter/s, 4.91675s/12 iters), loss = 0.637897
I0409 22:10:20.864226 26212 solver.cpp:237]     Train net output #0: loss = 0.637897 (* 1 = 0.637897 loss)
I0409 22:10:20.864236 26212 sgd_solver.cpp:105] Iteration 5268, lr = 0.00352214
I0409 22:10:25.815524 26212 solver.cpp:218] Iteration 5280 (2.42367 iter/s, 4.95117s/12 iters), loss = 0.66787
I0409 22:10:25.815573 26212 solver.cpp:237]     Train net output #0: loss = 0.66787 (* 1 = 0.66787 loss)
I0409 22:10:25.815583 26212 sgd_solver.cpp:105] Iteration 5280, lr = 0.00351378
I0409 22:10:31.034507 26212 solver.cpp:218] Iteration 5292 (2.29938 iter/s, 5.21879s/12 iters), loss = 0.37052
I0409 22:10:31.034564 26212 solver.cpp:237]     Train net output #0: loss = 0.37052 (* 1 = 0.37052 loss)
I0409 22:10:31.034576 26212 sgd_solver.cpp:105] Iteration 5292, lr = 0.00350544
I0409 22:10:35.512027 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5304.caffemodel
I0409 22:10:39.203536 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5304.solverstate
I0409 22:10:41.694818 26212 solver.cpp:330] Iteration 5304, Testing net (#0)
I0409 22:10:41.694846 26212 net.cpp:676] Ignoring source layer train-data
I0409 22:10:44.054165 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:10:46.254487 26212 solver.cpp:397]     Test net output #0: accuracy = 0.293505
I0409 22:10:46.254515 26212 solver.cpp:397]     Test net output #1: loss = 4.45313 (* 1 = 4.45313 loss)
I0409 22:10:46.338243 26212 solver.cpp:218] Iteration 5304 (0.784144 iter/s, 15.3033s/12 iters), loss = 0.655817
I0409 22:10:46.338285 26212 solver.cpp:237]     Train net output #0: loss = 0.655817 (* 1 = 0.655817 loss)
I0409 22:10:46.338294 26212 sgd_solver.cpp:105] Iteration 5304, lr = 0.00349711
I0409 22:10:50.834566 26212 solver.cpp:218] Iteration 5316 (2.66895 iter/s, 4.49616s/12 iters), loss = 0.410387
I0409 22:10:50.834611 26212 solver.cpp:237]     Train net output #0: loss = 0.410387 (* 1 = 0.410387 loss)
I0409 22:10:50.834622 26212 sgd_solver.cpp:105] Iteration 5316, lr = 0.00348881
I0409 22:10:55.896129 26212 solver.cpp:218] Iteration 5328 (2.3709 iter/s, 5.06138s/12 iters), loss = 0.507712
I0409 22:10:55.896200 26212 solver.cpp:237]     Train net output #0: loss = 0.507712 (* 1 = 0.507712 loss)
I0409 22:10:55.896209 26212 sgd_solver.cpp:105] Iteration 5328, lr = 0.00348053
I0409 22:11:00.995085 26212 solver.cpp:218] Iteration 5340 (2.35352 iter/s, 5.09875s/12 iters), loss = 0.398671
I0409 22:11:00.995138 26212 solver.cpp:237]     Train net output #0: loss = 0.398671 (* 1 = 0.398671 loss)
I0409 22:11:00.995149 26212 sgd_solver.cpp:105] Iteration 5340, lr = 0.00347226
I0409 22:11:06.154242 26212 solver.cpp:218] Iteration 5352 (2.32605 iter/s, 5.15896s/12 iters), loss = 0.399007
I0409 22:11:06.154295 26212 solver.cpp:237]     Train net output #0: loss = 0.399007 (* 1 = 0.399007 loss)
I0409 22:11:06.154306 26212 sgd_solver.cpp:105] Iteration 5352, lr = 0.00346402
I0409 22:11:09.526564 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:11:11.044101 26212 solver.cpp:218] Iteration 5364 (2.45415 iter/s, 4.88967s/12 iters), loss = 0.646314
I0409 22:11:11.044158 26212 solver.cpp:237]     Train net output #0: loss = 0.646314 (* 1 = 0.646314 loss)
I0409 22:11:11.044170 26212 sgd_solver.cpp:105] Iteration 5364, lr = 0.0034558
I0409 22:11:15.943285 26212 solver.cpp:218] Iteration 5376 (2.44948 iter/s, 4.899s/12 iters), loss = 0.445443
I0409 22:11:15.943334 26212 solver.cpp:237]     Train net output #0: loss = 0.445443 (* 1 = 0.445443 loss)
I0409 22:11:15.943346 26212 sgd_solver.cpp:105] Iteration 5376, lr = 0.00344759
I0409 22:11:20.840593 26212 solver.cpp:218] Iteration 5388 (2.45042 iter/s, 4.89713s/12 iters), loss = 0.582704
I0409 22:11:20.840646 26212 solver.cpp:237]     Train net output #0: loss = 0.582704 (* 1 = 0.582704 loss)
I0409 22:11:20.840657 26212 sgd_solver.cpp:105] Iteration 5388, lr = 0.00343941
I0409 22:11:25.740849 26212 solver.cpp:218] Iteration 5400 (2.44894 iter/s, 4.90007s/12 iters), loss = 0.409756
I0409 22:11:25.740896 26212 solver.cpp:237]     Train net output #0: loss = 0.409756 (* 1 = 0.409756 loss)
I0409 22:11:25.740906 26212 sgd_solver.cpp:105] Iteration 5400, lr = 0.00343124
I0409 22:11:27.833775 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5406.caffemodel
I0409 22:11:30.014125 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5406.solverstate
I0409 22:11:31.296066 26212 solver.cpp:330] Iteration 5406, Testing net (#0)
I0409 22:11:31.296097 26212 net.cpp:676] Ignoring source layer train-data
I0409 22:11:33.580601 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:11:35.701793 26212 solver.cpp:397]     Test net output #0: accuracy = 0.283088
I0409 22:11:35.701828 26212 solver.cpp:397]     Test net output #1: loss = 4.5738 (* 1 = 4.5738 loss)
I0409 22:11:37.516471 26212 solver.cpp:218] Iteration 5412 (1.01908 iter/s, 11.7753s/12 iters), loss = 0.499897
I0409 22:11:37.516520 26212 solver.cpp:237]     Train net output #0: loss = 0.499897 (* 1 = 0.499897 loss)
I0409 22:11:37.516531 26212 sgd_solver.cpp:105] Iteration 5412, lr = 0.00342309
I0409 22:11:42.372113 26212 solver.cpp:218] Iteration 5424 (2.47144 iter/s, 4.85546s/12 iters), loss = 0.473806
I0409 22:11:42.372165 26212 solver.cpp:237]     Train net output #0: loss = 0.473806 (* 1 = 0.473806 loss)
I0409 22:11:42.372177 26212 sgd_solver.cpp:105] Iteration 5424, lr = 0.00341497
I0409 22:11:47.289722 26212 solver.cpp:218] Iteration 5436 (2.44031 iter/s, 4.91742s/12 iters), loss = 0.441471
I0409 22:11:47.289783 26212 solver.cpp:237]     Train net output #0: loss = 0.441471 (* 1 = 0.441471 loss)
I0409 22:11:47.289794 26212 sgd_solver.cpp:105] Iteration 5436, lr = 0.00340686
I0409 22:11:52.188889 26212 solver.cpp:218] Iteration 5448 (2.44949 iter/s, 4.89898s/12 iters), loss = 0.169179
I0409 22:11:52.188930 26212 solver.cpp:237]     Train net output #0: loss = 0.169179 (* 1 = 0.169179 loss)
I0409 22:11:52.188938 26212 sgd_solver.cpp:105] Iteration 5448, lr = 0.00339877
I0409 22:11:57.106876 26212 solver.cpp:218] Iteration 5460 (2.44011 iter/s, 4.91781s/12 iters), loss = 0.583199
I0409 22:11:57.106945 26212 solver.cpp:237]     Train net output #0: loss = 0.583199 (* 1 = 0.583199 loss)
I0409 22:11:57.106962 26212 sgd_solver.cpp:105] Iteration 5460, lr = 0.0033907
I0409 22:11:57.666062 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:12:02.051623 26212 solver.cpp:218] Iteration 5472 (2.42691 iter/s, 4.94455s/12 iters), loss = 0.302082
I0409 22:12:02.051749 26212 solver.cpp:237]     Train net output #0: loss = 0.302082 (* 1 = 0.302082 loss)
I0409 22:12:02.051762 26212 sgd_solver.cpp:105] Iteration 5472, lr = 0.00338265
I0409 22:12:06.960968 26212 solver.cpp:218] Iteration 5484 (2.44444 iter/s, 4.90909s/12 iters), loss = 0.353245
I0409 22:12:06.961014 26212 solver.cpp:237]     Train net output #0: loss = 0.353245 (* 1 = 0.353245 loss)
I0409 22:12:06.961024 26212 sgd_solver.cpp:105] Iteration 5484, lr = 0.00337462
I0409 22:12:11.892026 26212 solver.cpp:218] Iteration 5496 (2.43364 iter/s, 4.93088s/12 iters), loss = 0.428139
I0409 22:12:11.892079 26212 solver.cpp:237]     Train net output #0: loss = 0.428139 (* 1 = 0.428139 loss)
I0409 22:12:11.892091 26212 sgd_solver.cpp:105] Iteration 5496, lr = 0.00336661
I0409 22:12:16.349339 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5508.caffemodel
I0409 22:12:17.531579 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5508.solverstate
I0409 22:12:18.411826 26212 solver.cpp:330] Iteration 5508, Testing net (#0)
I0409 22:12:18.411857 26212 net.cpp:676] Ignoring source layer train-data
I0409 22:12:20.703912 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:12:22.903545 26212 solver.cpp:397]     Test net output #0: accuracy = 0.275735
I0409 22:12:22.903587 26212 solver.cpp:397]     Test net output #1: loss = 4.58669 (* 1 = 4.58669 loss)
I0409 22:12:22.987287 26212 solver.cpp:218] Iteration 5508 (1.08158 iter/s, 11.0949s/12 iters), loss = 0.52101
I0409 22:12:22.987337 26212 solver.cpp:237]     Train net output #0: loss = 0.52101 (* 1 = 0.52101 loss)
I0409 22:12:22.987347 26212 sgd_solver.cpp:105] Iteration 5508, lr = 0.00335861
I0409 22:12:27.221153 26212 solver.cpp:218] Iteration 5520 (2.8344 iter/s, 4.2337s/12 iters), loss = 0.350691
I0409 22:12:27.221202 26212 solver.cpp:237]     Train net output #0: loss = 0.350691 (* 1 = 0.350691 loss)
I0409 22:12:27.221213 26212 sgd_solver.cpp:105] Iteration 5520, lr = 0.00335064
I0409 22:12:29.224481 26212 blocking_queue.cpp:49] Waiting for data
I0409 22:12:32.159579 26212 solver.cpp:218] Iteration 5532 (2.43001 iter/s, 4.93825s/12 iters), loss = 0.461501
I0409 22:12:32.159684 26212 solver.cpp:237]     Train net output #0: loss = 0.461501 (* 1 = 0.461501 loss)
I0409 22:12:32.159693 26212 sgd_solver.cpp:105] Iteration 5532, lr = 0.00334268
I0409 22:12:37.046016 26212 solver.cpp:218] Iteration 5544 (2.4559 iter/s, 4.8862s/12 iters), loss = 0.332446
I0409 22:12:37.046070 26212 solver.cpp:237]     Train net output #0: loss = 0.332446 (* 1 = 0.332446 loss)
I0409 22:12:37.046082 26212 sgd_solver.cpp:105] Iteration 5544, lr = 0.00333475
I0409 22:12:41.939664 26212 solver.cpp:218] Iteration 5556 (2.45225 iter/s, 4.89347s/12 iters), loss = 0.292927
I0409 22:12:41.939710 26212 solver.cpp:237]     Train net output #0: loss = 0.292927 (* 1 = 0.292927 loss)
I0409 22:12:41.939719 26212 sgd_solver.cpp:105] Iteration 5556, lr = 0.00332683
I0409 22:12:44.620493 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:12:46.874413 26212 solver.cpp:218] Iteration 5568 (2.43182 iter/s, 4.93457s/12 iters), loss = 0.713288
I0409 22:12:46.874451 26212 solver.cpp:237]     Train net output #0: loss = 0.713288 (* 1 = 0.713288 loss)
I0409 22:12:46.874460 26212 sgd_solver.cpp:105] Iteration 5568, lr = 0.00331893
I0409 22:12:52.068004 26212 solver.cpp:218] Iteration 5580 (2.31062 iter/s, 5.19341s/12 iters), loss = 0.484369
I0409 22:12:52.068049 26212 solver.cpp:237]     Train net output #0: loss = 0.484369 (* 1 = 0.484369 loss)
I0409 22:12:52.068059 26212 sgd_solver.cpp:105] Iteration 5580, lr = 0.00331105
I0409 22:12:56.997002 26212 solver.cpp:218] Iteration 5592 (2.43466 iter/s, 4.92882s/12 iters), loss = 0.484102
I0409 22:12:56.997061 26212 solver.cpp:237]     Train net output #0: loss = 0.484102 (* 1 = 0.484102 loss)
I0409 22:12:56.997074 26212 sgd_solver.cpp:105] Iteration 5592, lr = 0.00330319
I0409 22:13:01.984526 26212 solver.cpp:218] Iteration 5604 (2.40609 iter/s, 4.98734s/12 iters), loss = 0.512598
I0409 22:13:01.984577 26212 solver.cpp:237]     Train net output #0: loss = 0.512598 (* 1 = 0.512598 loss)
I0409 22:13:01.984588 26212 sgd_solver.cpp:105] Iteration 5604, lr = 0.00329535
I0409 22:13:03.982121 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5610.caffemodel
I0409 22:13:07.750339 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5610.solverstate
I0409 22:13:09.515033 26212 solver.cpp:330] Iteration 5610, Testing net (#0)
I0409 22:13:09.515061 26212 net.cpp:676] Ignoring source layer train-data
I0409 22:13:11.810729 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:13:14.054987 26212 solver.cpp:397]     Test net output #0: accuracy = 0.287377
I0409 22:13:14.055037 26212 solver.cpp:397]     Test net output #1: loss = 4.54518 (* 1 = 4.54518 loss)
I0409 22:13:16.002138 26212 solver.cpp:218] Iteration 5616 (0.85609 iter/s, 14.0172s/12 iters), loss = 0.355831
I0409 22:13:16.002188 26212 solver.cpp:237]     Train net output #0: loss = 0.355831 (* 1 = 0.355831 loss)
I0409 22:13:16.002199 26212 sgd_solver.cpp:105] Iteration 5616, lr = 0.00328752
I0409 22:13:21.056998 26212 solver.cpp:218] Iteration 5628 (2.37404 iter/s, 5.05467s/12 iters), loss = 0.277619
I0409 22:13:21.057049 26212 solver.cpp:237]     Train net output #0: loss = 0.277619 (* 1 = 0.277619 loss)
I0409 22:13:21.057060 26212 sgd_solver.cpp:105] Iteration 5628, lr = 0.00327972
I0409 22:13:26.037744 26212 solver.cpp:218] Iteration 5640 (2.40937 iter/s, 4.98056s/12 iters), loss = 0.299316
I0409 22:13:26.037806 26212 solver.cpp:237]     Train net output #0: loss = 0.299316 (* 1 = 0.299316 loss)
I0409 22:13:26.037817 26212 sgd_solver.cpp:105] Iteration 5640, lr = 0.00327193
I0409 22:13:30.942297 26212 solver.cpp:218] Iteration 5652 (2.4468 iter/s, 4.90437s/12 iters), loss = 0.37395
I0409 22:13:30.942373 26212 solver.cpp:237]     Train net output #0: loss = 0.37395 (* 1 = 0.37395 loss)
I0409 22:13:30.942385 26212 sgd_solver.cpp:105] Iteration 5652, lr = 0.00326416
I0409 22:13:35.662479 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:13:35.829993 26212 solver.cpp:218] Iteration 5664 (2.45524 iter/s, 4.88751s/12 iters), loss = 0.358466
I0409 22:13:35.830060 26212 solver.cpp:237]     Train net output #0: loss = 0.358466 (* 1 = 0.358466 loss)
I0409 22:13:35.830073 26212 sgd_solver.cpp:105] Iteration 5664, lr = 0.00325641
I0409 22:13:40.736402 26212 solver.cpp:218] Iteration 5676 (2.44588 iter/s, 4.90621s/12 iters), loss = 0.397368
I0409 22:13:40.736439 26212 solver.cpp:237]     Train net output #0: loss = 0.397368 (* 1 = 0.397368 loss)
I0409 22:13:40.736447 26212 sgd_solver.cpp:105] Iteration 5676, lr = 0.00324868
I0409 22:13:45.658474 26212 solver.cpp:218] Iteration 5688 (2.43809 iter/s, 4.9219s/12 iters), loss = 0.35463
I0409 22:13:45.658524 26212 solver.cpp:237]     Train net output #0: loss = 0.35463 (* 1 = 0.35463 loss)
I0409 22:13:45.658532 26212 sgd_solver.cpp:105] Iteration 5688, lr = 0.00324097
I0409 22:13:50.645674 26212 solver.cpp:218] Iteration 5700 (2.40625 iter/s, 4.98702s/12 iters), loss = 0.44354
I0409 22:13:50.645716 26212 solver.cpp:237]     Train net output #0: loss = 0.44354 (* 1 = 0.44354 loss)
I0409 22:13:50.645725 26212 sgd_solver.cpp:105] Iteration 5700, lr = 0.00323328
I0409 22:13:55.230682 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5712.caffemodel
I0409 22:13:56.479458 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5712.solverstate
I0409 22:13:57.351480 26212 solver.cpp:330] Iteration 5712, Testing net (#0)
I0409 22:13:57.351501 26212 net.cpp:676] Ignoring source layer train-data
I0409 22:13:59.570550 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:14:01.935359 26212 solver.cpp:397]     Test net output #0: accuracy = 0.299632
I0409 22:14:01.935387 26212 solver.cpp:397]     Test net output #1: loss = 4.46831 (* 1 = 4.46831 loss)
I0409 22:14:02.019044 26212 solver.cpp:218] Iteration 5712 (1.05513 iter/s, 11.373s/12 iters), loss = 0.469197
I0409 22:14:02.019089 26212 solver.cpp:237]     Train net output #0: loss = 0.469197 (* 1 = 0.469197 loss)
I0409 22:14:02.019100 26212 sgd_solver.cpp:105] Iteration 5712, lr = 0.0032256
I0409 22:14:06.341598 26212 solver.cpp:218] Iteration 5724 (2.77624 iter/s, 4.32239s/12 iters), loss = 0.316873
I0409 22:14:06.341738 26212 solver.cpp:237]     Train net output #0: loss = 0.316873 (* 1 = 0.316873 loss)
I0409 22:14:06.341750 26212 sgd_solver.cpp:105] Iteration 5724, lr = 0.00321794
I0409 22:14:11.298044 26212 solver.cpp:218] Iteration 5736 (2.42122 iter/s, 4.95618s/12 iters), loss = 0.236192
I0409 22:14:11.298095 26212 solver.cpp:237]     Train net output #0: loss = 0.236192 (* 1 = 0.236192 loss)
I0409 22:14:11.298107 26212 sgd_solver.cpp:105] Iteration 5736, lr = 0.0032103
I0409 22:14:16.301930 26212 solver.cpp:218] Iteration 5748 (2.39823 iter/s, 5.0037s/12 iters), loss = 0.362014
I0409 22:14:16.302007 26212 solver.cpp:237]     Train net output #0: loss = 0.362014 (* 1 = 0.362014 loss)
I0409 22:14:16.302021 26212 sgd_solver.cpp:105] Iteration 5748, lr = 0.00320268
I0409 22:14:21.337682 26212 solver.cpp:218] Iteration 5760 (2.38306 iter/s, 5.03554s/12 iters), loss = 0.517797
I0409 22:14:21.337739 26212 solver.cpp:237]     Train net output #0: loss = 0.517797 (* 1 = 0.517797 loss)
I0409 22:14:21.337754 26212 sgd_solver.cpp:105] Iteration 5760, lr = 0.00319508
I0409 22:14:23.275910 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:14:26.406019 26212 solver.cpp:218] Iteration 5772 (2.36773 iter/s, 5.06815s/12 iters), loss = 0.480603
I0409 22:14:26.406067 26212 solver.cpp:237]     Train net output #0: loss = 0.480603 (* 1 = 0.480603 loss)
I0409 22:14:26.406080 26212 sgd_solver.cpp:105] Iteration 5772, lr = 0.00318749
I0409 22:14:31.339354 26212 solver.cpp:218] Iteration 5784 (2.43252 iter/s, 4.93315s/12 iters), loss = 0.284174
I0409 22:14:31.339407 26212 solver.cpp:237]     Train net output #0: loss = 0.284174 (* 1 = 0.284174 loss)
I0409 22:14:31.339419 26212 sgd_solver.cpp:105] Iteration 5784, lr = 0.00317992
I0409 22:14:36.262796 26212 solver.cpp:218] Iteration 5796 (2.43741 iter/s, 4.92325s/12 iters), loss = 0.460316
I0409 22:14:36.262850 26212 solver.cpp:237]     Train net output #0: loss = 0.460316 (* 1 = 0.460316 loss)
I0409 22:14:36.262863 26212 sgd_solver.cpp:105] Iteration 5796, lr = 0.00317237
I0409 22:14:41.190006 26212 solver.cpp:218] Iteration 5808 (2.43555 iter/s, 4.92702s/12 iters), loss = 0.523575
I0409 22:14:41.191318 26212 solver.cpp:237]     Train net output #0: loss = 0.523575 (* 1 = 0.523575 loss)
I0409 22:14:41.191330 26212 sgd_solver.cpp:105] Iteration 5808, lr = 0.00316484
I0409 22:14:43.168795 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5814.caffemodel
I0409 22:14:44.528439 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5814.solverstate
I0409 22:14:45.628671 26212 solver.cpp:330] Iteration 5814, Testing net (#0)
I0409 22:14:45.628705 26212 net.cpp:676] Ignoring source layer train-data
I0409 22:14:47.760533 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:14:50.046155 26212 solver.cpp:397]     Test net output #0: accuracy = 0.292892
I0409 22:14:50.046201 26212 solver.cpp:397]     Test net output #1: loss = 4.70998 (* 1 = 4.70998 loss)
I0409 22:14:51.800403 26212 solver.cpp:218] Iteration 5820 (1.13113 iter/s, 10.6088s/12 iters), loss = 0.305595
I0409 22:14:51.800457 26212 solver.cpp:237]     Train net output #0: loss = 0.305595 (* 1 = 0.305595 loss)
I0409 22:14:51.800469 26212 sgd_solver.cpp:105] Iteration 5820, lr = 0.00315733
I0409 22:14:56.724726 26212 solver.cpp:218] Iteration 5832 (2.43698 iter/s, 4.92414s/12 iters), loss = 0.429003
I0409 22:14:56.724782 26212 solver.cpp:237]     Train net output #0: loss = 0.429003 (* 1 = 0.429003 loss)
I0409 22:14:56.724794 26212 sgd_solver.cpp:105] Iteration 5832, lr = 0.00314983
I0409 22:15:01.861184 26212 solver.cpp:218] Iteration 5844 (2.33633 iter/s, 5.13627s/12 iters), loss = 0.476967
I0409 22:15:01.861227 26212 solver.cpp:237]     Train net output #0: loss = 0.476967 (* 1 = 0.476967 loss)
I0409 22:15:01.861238 26212 sgd_solver.cpp:105] Iteration 5844, lr = 0.00314235
I0409 22:15:06.819501 26212 solver.cpp:218] Iteration 5856 (2.42026 iter/s, 4.95814s/12 iters), loss = 0.495528
I0409 22:15:06.819545 26212 solver.cpp:237]     Train net output #0: loss = 0.495528 (* 1 = 0.495528 loss)
I0409 22:15:06.819553 26212 sgd_solver.cpp:105] Iteration 5856, lr = 0.00313489
I0409 22:15:11.012905 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:15:11.817849 26212 solver.cpp:218] Iteration 5868 (2.40088 iter/s, 4.99817s/12 iters), loss = 0.456849
I0409 22:15:11.817984 26212 solver.cpp:237]     Train net output #0: loss = 0.456849 (* 1 = 0.456849 loss)
I0409 22:15:11.817993 26212 sgd_solver.cpp:105] Iteration 5868, lr = 0.00312745
I0409 22:15:16.833132 26212 solver.cpp:218] Iteration 5880 (2.39281 iter/s, 5.01503s/12 iters), loss = 0.175407
I0409 22:15:16.833184 26212 solver.cpp:237]     Train net output #0: loss = 0.175407 (* 1 = 0.175407 loss)
I0409 22:15:16.833194 26212 sgd_solver.cpp:105] Iteration 5880, lr = 0.00312002
I0409 22:15:21.742416 26212 solver.cpp:218] Iteration 5892 (2.44444 iter/s, 4.9091s/12 iters), loss = 0.688136
I0409 22:15:21.742467 26212 solver.cpp:237]     Train net output #0: loss = 0.688136 (* 1 = 0.688136 loss)
I0409 22:15:21.742478 26212 sgd_solver.cpp:105] Iteration 5892, lr = 0.00311262
I0409 22:15:26.605540 26212 solver.cpp:218] Iteration 5904 (2.46764 iter/s, 4.86294s/12 iters), loss = 0.616192
I0409 22:15:26.605592 26212 solver.cpp:237]     Train net output #0: loss = 0.616192 (* 1 = 0.616192 loss)
I0409 22:15:26.605604 26212 sgd_solver.cpp:105] Iteration 5904, lr = 0.00310523
I0409 22:15:31.032168 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5916.caffemodel
I0409 22:15:33.693042 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5916.solverstate
I0409 22:15:35.484429 26212 solver.cpp:330] Iteration 5916, Testing net (#0)
I0409 22:15:35.484454 26212 net.cpp:676] Ignoring source layer train-data
I0409 22:15:37.612902 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:15:39.932368 26212 solver.cpp:397]     Test net output #0: accuracy = 0.303922
I0409 22:15:39.932410 26212 solver.cpp:397]     Test net output #1: loss = 4.59453 (* 1 = 4.59453 loss)
I0409 22:15:40.015729 26212 solver.cpp:218] Iteration 5916 (0.894868 iter/s, 13.4098s/12 iters), loss = 0.225011
I0409 22:15:40.015763 26212 solver.cpp:237]     Train net output #0: loss = 0.225011 (* 1 = 0.225011 loss)
I0409 22:15:40.015774 26212 sgd_solver.cpp:105] Iteration 5916, lr = 0.00309785
I0409 22:15:44.430220 26212 solver.cpp:218] Iteration 5928 (2.71842 iter/s, 4.41433s/12 iters), loss = 0.413245
I0409 22:15:44.430330 26212 solver.cpp:237]     Train net output #0: loss = 0.413245 (* 1 = 0.413245 loss)
I0409 22:15:44.430341 26212 sgd_solver.cpp:105] Iteration 5928, lr = 0.0030905
I0409 22:15:49.351917 26212 solver.cpp:218] Iteration 5940 (2.4383 iter/s, 4.92145s/12 iters), loss = 0.221539
I0409 22:15:49.351966 26212 solver.cpp:237]     Train net output #0: loss = 0.221539 (* 1 = 0.221539 loss)
I0409 22:15:49.351975 26212 sgd_solver.cpp:105] Iteration 5940, lr = 0.00308316
I0409 22:15:54.477363 26212 solver.cpp:218] Iteration 5952 (2.34135 iter/s, 5.12526s/12 iters), loss = 0.340941
I0409 22:15:54.477413 26212 solver.cpp:237]     Train net output #0: loss = 0.340941 (* 1 = 0.340941 loss)
I0409 22:15:54.477425 26212 sgd_solver.cpp:105] Iteration 5952, lr = 0.00307584
I0409 22:15:59.535089 26212 solver.cpp:218] Iteration 5964 (2.37269 iter/s, 5.05754s/12 iters), loss = 0.403398
I0409 22:15:59.535130 26212 solver.cpp:237]     Train net output #0: loss = 0.403398 (* 1 = 0.403398 loss)
I0409 22:15:59.535137 26212 sgd_solver.cpp:105] Iteration 5964, lr = 0.00306854
I0409 22:16:00.876667 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:16:04.490834 26212 solver.cpp:218] Iteration 5976 (2.42152 iter/s, 4.95556s/12 iters), loss = 0.381655
I0409 22:16:04.490906 26212 solver.cpp:237]     Train net output #0: loss = 0.381655 (* 1 = 0.381655 loss)
I0409 22:16:04.490918 26212 sgd_solver.cpp:105] Iteration 5976, lr = 0.00306125
I0409 22:16:09.440372 26212 solver.cpp:218] Iteration 5988 (2.42457 iter/s, 4.94933s/12 iters), loss = 0.176678
I0409 22:16:09.440423 26212 solver.cpp:237]     Train net output #0: loss = 0.176678 (* 1 = 0.176678 loss)
I0409 22:16:09.440433 26212 sgd_solver.cpp:105] Iteration 5988, lr = 0.00305398
I0409 22:16:14.587981 26212 solver.cpp:218] Iteration 6000 (2.33127 iter/s, 5.14742s/12 iters), loss = 0.364291
I0409 22:16:14.588119 26212 solver.cpp:237]     Train net output #0: loss = 0.364291 (* 1 = 0.364291 loss)
I0409 22:16:14.588129 26212 sgd_solver.cpp:105] Iteration 6000, lr = 0.00304673
I0409 22:16:19.443388 26212 solver.cpp:218] Iteration 6012 (2.47161 iter/s, 4.85513s/12 iters), loss = 0.38501
I0409 22:16:19.443449 26212 solver.cpp:237]     Train net output #0: loss = 0.38501 (* 1 = 0.38501 loss)
I0409 22:16:19.443460 26212 sgd_solver.cpp:105] Iteration 6012, lr = 0.0030395
I0409 22:16:21.516014 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6018.caffemodel
I0409 22:16:23.572707 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6018.solverstate
I0409 22:16:25.415057 26212 solver.cpp:330] Iteration 6018, Testing net (#0)
I0409 22:16:25.415081 26212 net.cpp:676] Ignoring source layer train-data
I0409 22:16:27.618811 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:16:30.073544 26212 solver.cpp:397]     Test net output #0: accuracy = 0.300245
I0409 22:16:30.073571 26212 solver.cpp:397]     Test net output #1: loss = 4.40529 (* 1 = 4.40529 loss)
I0409 22:16:31.795786 26212 solver.cpp:218] Iteration 6024 (0.971501 iter/s, 12.352s/12 iters), loss = 0.365615
I0409 22:16:31.795845 26212 solver.cpp:237]     Train net output #0: loss = 0.365615 (* 1 = 0.365615 loss)
I0409 22:16:31.795857 26212 sgd_solver.cpp:105] Iteration 6024, lr = 0.00303228
I0409 22:16:36.648953 26212 solver.cpp:218] Iteration 6036 (2.47271 iter/s, 4.85298s/12 iters), loss = 0.411009
I0409 22:16:36.649000 26212 solver.cpp:237]     Train net output #0: loss = 0.411009 (* 1 = 0.411009 loss)
I0409 22:16:36.649010 26212 sgd_solver.cpp:105] Iteration 6036, lr = 0.00302508
I0409 22:16:41.612754 26212 solver.cpp:218] Iteration 6048 (2.41759 iter/s, 4.96361s/12 iters), loss = 0.447455
I0409 22:16:41.612815 26212 solver.cpp:237]     Train net output #0: loss = 0.447455 (* 1 = 0.447455 loss)
I0409 22:16:41.612828 26212 sgd_solver.cpp:105] Iteration 6048, lr = 0.0030179
I0409 22:16:46.523862 26212 solver.cpp:218] Iteration 6060 (2.44354 iter/s, 4.91092s/12 iters), loss = 0.39623
I0409 22:16:46.523950 26212 solver.cpp:237]     Train net output #0: loss = 0.39623 (* 1 = 0.39623 loss)
I0409 22:16:46.523962 26212 sgd_solver.cpp:105] Iteration 6060, lr = 0.00301074
I0409 22:16:49.919773 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:16:51.571401 26212 solver.cpp:218] Iteration 6072 (2.3775 iter/s, 5.04731s/12 iters), loss = 0.293262
I0409 22:16:51.571460 26212 solver.cpp:237]     Train net output #0: loss = 0.293262 (* 1 = 0.293262 loss)
I0409 22:16:51.571473 26212 sgd_solver.cpp:105] Iteration 6072, lr = 0.00300359
I0409 22:16:56.752671 26212 solver.cpp:218] Iteration 6084 (2.31612 iter/s, 5.18107s/12 iters), loss = 0.275532
I0409 22:16:56.752728 26212 solver.cpp:237]     Train net output #0: loss = 0.275532 (* 1 = 0.275532 loss)
I0409 22:16:56.752739 26212 sgd_solver.cpp:105] Iteration 6084, lr = 0.00299646
I0409 22:17:01.752696 26212 solver.cpp:218] Iteration 6096 (2.40008 iter/s, 4.99983s/12 iters), loss = 0.268004
I0409 22:17:01.752744 26212 solver.cpp:237]     Train net output #0: loss = 0.268004 (* 1 = 0.268004 loss)
I0409 22:17:01.752753 26212 sgd_solver.cpp:105] Iteration 6096, lr = 0.00298934
I0409 22:17:06.729372 26212 solver.cpp:218] Iteration 6108 (2.41134 iter/s, 4.97649s/12 iters), loss = 0.305786
I0409 22:17:06.729418 26212 solver.cpp:237]     Train net output #0: loss = 0.305786 (* 1 = 0.305786 loss)
I0409 22:17:06.729426 26212 sgd_solver.cpp:105] Iteration 6108, lr = 0.00298225
I0409 22:17:11.244525 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6120.caffemodel
I0409 22:17:12.418601 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6120.solverstate
I0409 22:17:13.291821 26212 solver.cpp:330] Iteration 6120, Testing net (#0)
I0409 22:17:13.291846 26212 net.cpp:676] Ignoring source layer train-data
I0409 22:17:15.501722 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:17:18.062378 26212 solver.cpp:397]     Test net output #0: accuracy = 0.302696
I0409 22:17:18.062505 26212 solver.cpp:397]     Test net output #1: loss = 4.57973 (* 1 = 4.57973 loss)
I0409 22:17:18.146340 26212 solver.cpp:218] Iteration 6120 (1.0511 iter/s, 11.4166s/12 iters), loss = 0.261805
I0409 22:17:18.146395 26212 solver.cpp:237]     Train net output #0: loss = 0.261805 (* 1 = 0.261805 loss)
I0409 22:17:18.146406 26212 sgd_solver.cpp:105] Iteration 6120, lr = 0.00297517
I0409 22:17:22.572476 26212 solver.cpp:218] Iteration 6132 (2.71128 iter/s, 4.42596s/12 iters), loss = 0.278882
I0409 22:17:22.572522 26212 solver.cpp:237]     Train net output #0: loss = 0.278882 (* 1 = 0.278882 loss)
I0409 22:17:22.572531 26212 sgd_solver.cpp:105] Iteration 6132, lr = 0.0029681
I0409 22:17:27.518129 26212 solver.cpp:218] Iteration 6144 (2.42646 iter/s, 4.94547s/12 iters), loss = 0.325256
I0409 22:17:27.518179 26212 solver.cpp:237]     Train net output #0: loss = 0.325256 (* 1 = 0.325256 loss)
I0409 22:17:27.518189 26212 sgd_solver.cpp:105] Iteration 6144, lr = 0.00296105
I0409 22:17:32.519556 26212 solver.cpp:218] Iteration 6156 (2.3994 iter/s, 5.00124s/12 iters), loss = 0.311781
I0409 22:17:32.519601 26212 solver.cpp:237]     Train net output #0: loss = 0.311781 (* 1 = 0.311781 loss)
I0409 22:17:32.519613 26212 sgd_solver.cpp:105] Iteration 6156, lr = 0.00295402
I0409 22:17:37.477161 26212 solver.cpp:218] Iteration 6168 (2.42061 iter/s, 4.95743s/12 iters), loss = 0.320676
I0409 22:17:37.477210 26212 solver.cpp:237]     Train net output #0: loss = 0.320676 (* 1 = 0.320676 loss)
I0409 22:17:37.477221 26212 sgd_solver.cpp:105] Iteration 6168, lr = 0.00294701
I0409 22:17:38.064713 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:17:42.444532 26212 solver.cpp:218] Iteration 6180 (2.41585 iter/s, 4.96719s/12 iters), loss = 0.31442
I0409 22:17:42.444586 26212 solver.cpp:237]     Train net output #0: loss = 0.31442 (* 1 = 0.31442 loss)
I0409 22:17:42.444598 26212 sgd_solver.cpp:105] Iteration 6180, lr = 0.00294001
I0409 22:17:47.324350 26212 solver.cpp:218] Iteration 6192 (2.4592 iter/s, 4.87963s/12 iters), loss = 0.474753
I0409 22:17:47.324406 26212 solver.cpp:237]     Train net output #0: loss = 0.474753 (* 1 = 0.474753 loss)
I0409 22:17:47.324417 26212 sgd_solver.cpp:105] Iteration 6192, lr = 0.00293303
I0409 22:17:52.275621 26212 solver.cpp:218] Iteration 6204 (2.42371 iter/s, 4.95108s/12 iters), loss = 0.306345
I0409 22:17:52.275707 26212 solver.cpp:237]     Train net output #0: loss = 0.306345 (* 1 = 0.306345 loss)
I0409 22:17:52.275720 26212 sgd_solver.cpp:105] Iteration 6204, lr = 0.00292607
I0409 22:17:57.205119 26212 solver.cpp:218] Iteration 6216 (2.43443 iter/s, 4.92928s/12 iters), loss = 0.436504
I0409 22:17:57.205176 26212 solver.cpp:237]     Train net output #0: loss = 0.436504 (* 1 = 0.436504 loss)
I0409 22:17:57.205188 26212 sgd_solver.cpp:105] Iteration 6216, lr = 0.00291912
I0409 22:17:59.221261 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6222.caffemodel
I0409 22:18:01.781067 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6222.solverstate
I0409 22:18:02.655542 26212 solver.cpp:330] Iteration 6222, Testing net (#0)
I0409 22:18:02.655570 26212 net.cpp:676] Ignoring source layer train-data
I0409 22:18:04.697067 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:18:05.927865 26212 blocking_queue.cpp:49] Waiting for data
I0409 22:18:07.504617 26212 solver.cpp:397]     Test net output #0: accuracy = 0.308211
I0409 22:18:07.504668 26212 solver.cpp:397]     Test net output #1: loss = 4.45287 (* 1 = 4.45287 loss)
I0409 22:18:09.352015 26212 solver.cpp:218] Iteration 6228 (0.987936 iter/s, 12.1465s/12 iters), loss = 0.27874
I0409 22:18:09.352061 26212 solver.cpp:237]     Train net output #0: loss = 0.27874 (* 1 = 0.27874 loss)
I0409 22:18:09.352070 26212 sgd_solver.cpp:105] Iteration 6228, lr = 0.00291219
I0409 22:18:14.384506 26212 solver.cpp:218] Iteration 6240 (2.38459 iter/s, 5.0323s/12 iters), loss = 0.366699
I0409 22:18:14.384569 26212 solver.cpp:237]     Train net output #0: loss = 0.366699 (* 1 = 0.366699 loss)
I0409 22:18:14.384582 26212 sgd_solver.cpp:105] Iteration 6240, lr = 0.00290528
I0409 22:18:19.358111 26212 solver.cpp:218] Iteration 6252 (2.41283 iter/s, 4.97341s/12 iters), loss = 0.272196
I0409 22:18:19.358173 26212 solver.cpp:237]     Train net output #0: loss = 0.272196 (* 1 = 0.272196 loss)
I0409 22:18:19.358186 26212 sgd_solver.cpp:105] Iteration 6252, lr = 0.00289838
I0409 22:18:24.380561 26212 solver.cpp:218] Iteration 6264 (2.38937 iter/s, 5.02225s/12 iters), loss = 0.258441
I0409 22:18:24.380873 26212 solver.cpp:237]     Train net output #0: loss = 0.258441 (* 1 = 0.258441 loss)
I0409 22:18:24.380885 26212 sgd_solver.cpp:105] Iteration 6264, lr = 0.0028915
I0409 22:18:27.061731 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:18:29.305579 26212 solver.cpp:218] Iteration 6276 (2.43675 iter/s, 4.92458s/12 iters), loss = 0.471811
I0409 22:18:29.305621 26212 solver.cpp:237]     Train net output #0: loss = 0.471811 (* 1 = 0.471811 loss)
I0409 22:18:29.305631 26212 sgd_solver.cpp:105] Iteration 6276, lr = 0.00288463
I0409 22:18:34.623618 26212 solver.cpp:218] Iteration 6288 (2.25655 iter/s, 5.31785s/12 iters), loss = 0.350517
I0409 22:18:34.623670 26212 solver.cpp:237]     Train net output #0: loss = 0.350517 (* 1 = 0.350517 loss)
I0409 22:18:34.623682 26212 sgd_solver.cpp:105] Iteration 6288, lr = 0.00287779
I0409 22:18:39.906431 26212 solver.cpp:218] Iteration 6300 (2.2716 iter/s, 5.28261s/12 iters), loss = 0.197135
I0409 22:18:39.906498 26212 solver.cpp:237]     Train net output #0: loss = 0.197135 (* 1 = 0.197135 loss)
I0409 22:18:39.906512 26212 sgd_solver.cpp:105] Iteration 6300, lr = 0.00287095
I0409 22:18:44.883051 26212 solver.cpp:218] Iteration 6312 (2.41137 iter/s, 4.97642s/12 iters), loss = 0.659115
I0409 22:18:44.883109 26212 solver.cpp:237]     Train net output #0: loss = 0.659115 (* 1 = 0.659115 loss)
I0409 22:18:44.883122 26212 sgd_solver.cpp:105] Iteration 6312, lr = 0.00286414
I0409 22:18:49.333881 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6324.caffemodel
I0409 22:18:51.498939 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6324.solverstate
I0409 22:18:53.289409 26212 solver.cpp:330] Iteration 6324, Testing net (#0)
I0409 22:18:53.289439 26212 net.cpp:676] Ignoring source layer train-data
I0409 22:18:55.260583 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:18:57.826740 26212 solver.cpp:397]     Test net output #0: accuracy = 0.3125
I0409 22:18:57.826783 26212 solver.cpp:397]     Test net output #1: loss = 4.55187 (* 1 = 4.55187 loss)
I0409 22:18:57.910737 26212 solver.cpp:218] Iteration 6324 (0.921142 iter/s, 13.0273s/12 iters), loss = 0.290128
I0409 22:18:57.910786 26212 solver.cpp:237]     Train net output #0: loss = 0.290128 (* 1 = 0.290128 loss)
I0409 22:18:57.910795 26212 sgd_solver.cpp:105] Iteration 6324, lr = 0.00285734
I0409 22:19:02.037819 26212 solver.cpp:218] Iteration 6336 (2.90774 iter/s, 4.12691s/12 iters), loss = 0.205594
I0409 22:19:02.037879 26212 solver.cpp:237]     Train net output #0: loss = 0.205594 (* 1 = 0.205594 loss)
I0409 22:19:02.037891 26212 sgd_solver.cpp:105] Iteration 6336, lr = 0.00285055
I0409 22:19:07.070895 26212 solver.cpp:218] Iteration 6348 (2.38432 iter/s, 5.03288s/12 iters), loss = 0.217463
I0409 22:19:07.070947 26212 solver.cpp:237]     Train net output #0: loss = 0.217463 (* 1 = 0.217463 loss)
I0409 22:19:07.070960 26212 sgd_solver.cpp:105] Iteration 6348, lr = 0.00284379
I0409 22:19:11.957003 26212 solver.cpp:218] Iteration 6360 (2.45603 iter/s, 4.88592s/12 iters), loss = 0.258577
I0409 22:19:11.957052 26212 solver.cpp:237]     Train net output #0: loss = 0.258577 (* 1 = 0.258577 loss)
I0409 22:19:11.957063 26212 sgd_solver.cpp:105] Iteration 6360, lr = 0.00283703
I0409 22:19:16.731498 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:19:16.864604 26212 solver.cpp:218] Iteration 6372 (2.44528 iter/s, 4.90742s/12 iters), loss = 0.270924
I0409 22:19:16.864655 26212 solver.cpp:237]     Train net output #0: loss = 0.270924 (* 1 = 0.270924 loss)
I0409 22:19:16.864667 26212 sgd_solver.cpp:105] Iteration 6372, lr = 0.0028303
I0409 22:19:21.761258 26212 solver.cpp:218] Iteration 6384 (2.45074 iter/s, 4.89648s/12 iters), loss = 0.293246
I0409 22:19:21.761296 26212 solver.cpp:237]     Train net output #0: loss = 0.293246 (* 1 = 0.293246 loss)
I0409 22:19:21.761305 26212 sgd_solver.cpp:105] Iteration 6384, lr = 0.00282358
I0409 22:19:26.957295 26212 solver.cpp:218] Iteration 6396 (2.30953 iter/s, 5.19586s/12 iters), loss = 0.294605
I0409 22:19:26.957387 26212 solver.cpp:237]     Train net output #0: loss = 0.294605 (* 1 = 0.294605 loss)
I0409 22:19:26.957396 26212 sgd_solver.cpp:105] Iteration 6396, lr = 0.00281687
I0409 22:19:32.035446 26212 solver.cpp:218] Iteration 6408 (2.36317 iter/s, 5.07792s/12 iters), loss = 0.211143
I0409 22:19:32.035506 26212 solver.cpp:237]     Train net output #0: loss = 0.211143 (* 1 = 0.211143 loss)
I0409 22:19:32.035516 26212 sgd_solver.cpp:105] Iteration 6408, lr = 0.00281019
I0409 22:19:36.935039 26212 solver.cpp:218] Iteration 6420 (2.44928 iter/s, 4.8994s/12 iters), loss = 0.507501
I0409 22:19:36.935089 26212 solver.cpp:237]     Train net output #0: loss = 0.507501 (* 1 = 0.507501 loss)
I0409 22:19:36.935101 26212 sgd_solver.cpp:105] Iteration 6420, lr = 0.00280351
I0409 22:19:38.914538 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6426.caffemodel
I0409 22:19:40.203130 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6426.solverstate
I0409 22:19:41.268813 26212 solver.cpp:330] Iteration 6426, Testing net (#0)
I0409 22:19:41.268844 26212 net.cpp:676] Ignoring source layer train-data
I0409 22:19:43.274473 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:19:45.827351 26212 solver.cpp:397]     Test net output #0: accuracy = 0.3125
I0409 22:19:45.827386 26212 solver.cpp:397]     Test net output #1: loss = 4.58027 (* 1 = 4.58027 loss)
I0409 22:19:47.801736 26212 solver.cpp:218] Iteration 6432 (1.10433 iter/s, 10.8664s/12 iters), loss = 0.296544
I0409 22:19:47.801798 26212 solver.cpp:237]     Train net output #0: loss = 0.296544 (* 1 = 0.296544 loss)
I0409 22:19:47.801810 26212 sgd_solver.cpp:105] Iteration 6432, lr = 0.00279686
I0409 22:19:52.866068 26212 solver.cpp:218] Iteration 6444 (2.36961 iter/s, 5.06413s/12 iters), loss = 0.244343
I0409 22:19:52.866122 26212 solver.cpp:237]     Train net output #0: loss = 0.244343 (* 1 = 0.244343 loss)
I0409 22:19:52.866133 26212 sgd_solver.cpp:105] Iteration 6444, lr = 0.00279022
I0409 22:19:57.990120 26212 solver.cpp:218] Iteration 6456 (2.34199 iter/s, 5.12386s/12 iters), loss = 0.273141
I0409 22:19:57.990229 26212 solver.cpp:237]     Train net output #0: loss = 0.273141 (* 1 = 0.273141 loss)
I0409 22:19:57.990239 26212 sgd_solver.cpp:105] Iteration 6456, lr = 0.00278359
I0409 22:20:02.903865 26212 solver.cpp:218] Iteration 6468 (2.44225 iter/s, 4.91351s/12 iters), loss = 0.167195
I0409 22:20:02.903923 26212 solver.cpp:237]     Train net output #0: loss = 0.167195 (* 1 = 0.167195 loss)
I0409 22:20:02.903934 26212 sgd_solver.cpp:105] Iteration 6468, lr = 0.00277698
I0409 22:20:04.900175 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:20:07.851068 26212 solver.cpp:218] Iteration 6480 (2.42571 iter/s, 4.94701s/12 iters), loss = 0.260538
I0409 22:20:07.851125 26212 solver.cpp:237]     Train net output #0: loss = 0.260538 (* 1 = 0.260538 loss)
I0409 22:20:07.851137 26212 sgd_solver.cpp:105] Iteration 6480, lr = 0.00277039
I0409 22:20:12.780699 26212 solver.cpp:218] Iteration 6492 (2.43435 iter/s, 4.92945s/12 iters), loss = 0.153085
I0409 22:20:12.780740 26212 solver.cpp:237]     Train net output #0: loss = 0.153085 (* 1 = 0.153085 loss)
I0409 22:20:12.780750 26212 sgd_solver.cpp:105] Iteration 6492, lr = 0.00276381
I0409 22:20:17.825237 26212 solver.cpp:218] Iteration 6504 (2.37889 iter/s, 5.04436s/12 iters), loss = 0.171498
I0409 22:20:17.825284 26212 solver.cpp:237]     Train net output #0: loss = 0.171498 (* 1 = 0.171498 loss)
I0409 22:20:17.825295 26212 sgd_solver.cpp:105] Iteration 6504, lr = 0.00275725
I0409 22:20:22.765691 26212 solver.cpp:218] Iteration 6516 (2.42901 iter/s, 4.94028s/12 iters), loss = 0.241881
I0409 22:20:22.765743 26212 solver.cpp:237]     Train net output #0: loss = 0.241881 (* 1 = 0.241881 loss)
I0409 22:20:22.765754 26212 sgd_solver.cpp:105] Iteration 6516, lr = 0.00275071
I0409 22:20:27.258102 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6528.caffemodel
I0409 22:20:28.995684 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6528.solverstate
I0409 22:20:29.873129 26212 solver.cpp:330] Iteration 6528, Testing net (#0)
I0409 22:20:29.873158 26212 net.cpp:676] Ignoring source layer train-data
I0409 22:20:31.695586 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:20:34.338486 26212 solver.cpp:397]     Test net output #0: accuracy = 0.320466
I0409 22:20:34.338543 26212 solver.cpp:397]     Test net output #1: loss = 4.56279 (* 1 = 4.56279 loss)
I0409 22:20:34.422334 26212 solver.cpp:218] Iteration 6528 (1.02949 iter/s, 11.6563s/12 iters), loss = 0.36502
I0409 22:20:34.422384 26212 solver.cpp:237]     Train net output #0: loss = 0.36502 (* 1 = 0.36502 loss)
I0409 22:20:34.422395 26212 sgd_solver.cpp:105] Iteration 6528, lr = 0.00274418
I0409 22:20:38.825186 26212 solver.cpp:218] Iteration 6540 (2.72561 iter/s, 4.40268s/12 iters), loss = 0.318452
I0409 22:20:38.825235 26212 solver.cpp:237]     Train net output #0: loss = 0.318452 (* 1 = 0.318452 loss)
I0409 22:20:38.825244 26212 sgd_solver.cpp:105] Iteration 6540, lr = 0.00273766
I0409 22:20:43.781028 26212 solver.cpp:218] Iteration 6552 (2.42147 iter/s, 4.95566s/12 iters), loss = 0.243403
I0409 22:20:43.781070 26212 solver.cpp:237]     Train net output #0: loss = 0.243403 (* 1 = 0.243403 loss)
I0409 22:20:43.781080 26212 sgd_solver.cpp:105] Iteration 6552, lr = 0.00273116
I0409 22:20:49.050213 26212 solver.cpp:218] Iteration 6564 (2.27747 iter/s, 5.26899s/12 iters), loss = 0.18383
I0409 22:20:49.050274 26212 solver.cpp:237]     Train net output #0: loss = 0.18383 (* 1 = 0.18383 loss)
I0409 22:20:49.050287 26212 sgd_solver.cpp:105] Iteration 6564, lr = 0.00272468
I0409 22:20:53.154551 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:20:53.929774 26212 solver.cpp:218] Iteration 6576 (2.45933 iter/s, 4.87937s/12 iters), loss = 0.264345
I0409 22:20:53.929827 26212 solver.cpp:237]     Train net output #0: loss = 0.264345 (* 1 = 0.264345 loss)
I0409 22:20:53.929839 26212 sgd_solver.cpp:105] Iteration 6576, lr = 0.00271821
I0409 22:20:58.882735 26212 solver.cpp:218] Iteration 6588 (2.42288 iter/s, 4.95278s/12 iters), loss = 0.194418
I0409 22:20:58.882788 26212 solver.cpp:237]     Train net output #0: loss = 0.194418 (* 1 = 0.194418 loss)
I0409 22:20:58.882800 26212 sgd_solver.cpp:105] Iteration 6588, lr = 0.00271175
I0409 22:21:03.744809 26212 solver.cpp:218] Iteration 6600 (2.46818 iter/s, 4.86189s/12 iters), loss = 0.461835
I0409 22:21:03.744933 26212 solver.cpp:237]     Train net output #0: loss = 0.461835 (* 1 = 0.461835 loss)
I0409 22:21:03.744946 26212 sgd_solver.cpp:105] Iteration 6600, lr = 0.00270532
I0409 22:21:08.685075 26212 solver.cpp:218] Iteration 6612 (2.42915 iter/s, 4.94001s/12 iters), loss = 0.173626
I0409 22:21:08.685132 26212 solver.cpp:237]     Train net output #0: loss = 0.173626 (* 1 = 0.173626 loss)
I0409 22:21:08.685143 26212 sgd_solver.cpp:105] Iteration 6612, lr = 0.00269889
I0409 22:21:13.587285 26212 solver.cpp:218] Iteration 6624 (2.44797 iter/s, 4.90202s/12 iters), loss = 0.280779
I0409 22:21:13.587339 26212 solver.cpp:237]     Train net output #0: loss = 0.280779 (* 1 = 0.280779 loss)
I0409 22:21:13.587352 26212 sgd_solver.cpp:105] Iteration 6624, lr = 0.00269248
I0409 22:21:15.587226 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6630.caffemodel
I0409 22:21:16.820055 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6630.solverstate
I0409 22:21:18.887856 26212 solver.cpp:330] Iteration 6630, Testing net (#0)
I0409 22:21:18.887874 26212 net.cpp:676] Ignoring source layer train-data
I0409 22:21:20.616739 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:21:23.207234 26212 solver.cpp:397]     Test net output #0: accuracy = 0.322304
I0409 22:21:23.207268 26212 solver.cpp:397]     Test net output #1: loss = 4.52421 (* 1 = 4.52421 loss)
I0409 22:21:25.061295 26212 solver.cpp:218] Iteration 6636 (1.04587 iter/s, 11.4737s/12 iters), loss = 0.207946
I0409 22:21:25.061336 26212 solver.cpp:237]     Train net output #0: loss = 0.207946 (* 1 = 0.207946 loss)
I0409 22:21:25.061343 26212 sgd_solver.cpp:105] Iteration 6636, lr = 0.00268609
I0409 22:21:29.985643 26212 solver.cpp:218] Iteration 6648 (2.43696 iter/s, 4.92418s/12 iters), loss = 0.289304
I0409 22:21:29.985688 26212 solver.cpp:237]     Train net output #0: loss = 0.289304 (* 1 = 0.289304 loss)
I0409 22:21:29.985699 26212 sgd_solver.cpp:105] Iteration 6648, lr = 0.00267971
I0409 22:21:34.886665 26212 solver.cpp:218] Iteration 6660 (2.44856 iter/s, 4.90085s/12 iters), loss = 0.27051
I0409 22:21:34.886803 26212 solver.cpp:237]     Train net output #0: loss = 0.27051 (* 1 = 0.27051 loss)
I0409 22:21:34.886812 26212 sgd_solver.cpp:105] Iteration 6660, lr = 0.00267335
I0409 22:21:39.847481 26212 solver.cpp:218] Iteration 6672 (2.41909 iter/s, 4.96055s/12 iters), loss = 0.213947
I0409 22:21:39.847522 26212 solver.cpp:237]     Train net output #0: loss = 0.213947 (* 1 = 0.213947 loss)
I0409 22:21:39.847530 26212 sgd_solver.cpp:105] Iteration 6672, lr = 0.00266701
I0409 22:21:41.197841 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:21:44.919770 26212 solver.cpp:218] Iteration 6684 (2.36588 iter/s, 5.07211s/12 iters), loss = 0.333015
I0409 22:21:44.919821 26212 solver.cpp:237]     Train net output #0: loss = 0.333015 (* 1 = 0.333015 loss)
I0409 22:21:44.919833 26212 sgd_solver.cpp:105] Iteration 6684, lr = 0.00266067
I0409 22:21:49.833532 26212 solver.cpp:218] Iteration 6696 (2.44221 iter/s, 4.91358s/12 iters), loss = 0.304935
I0409 22:21:49.833576 26212 solver.cpp:237]     Train net output #0: loss = 0.304935 (* 1 = 0.304935 loss)
I0409 22:21:49.833585 26212 sgd_solver.cpp:105] Iteration 6696, lr = 0.00265436
I0409 22:21:54.721266 26212 solver.cpp:218] Iteration 6708 (2.45521 iter/s, 4.88756s/12 iters), loss = 0.286667
I0409 22:21:54.721312 26212 solver.cpp:237]     Train net output #0: loss = 0.286667 (* 1 = 0.286667 loss)
I0409 22:21:54.721320 26212 sgd_solver.cpp:105] Iteration 6708, lr = 0.00264805
I0409 22:21:59.646947 26212 solver.cpp:218] Iteration 6720 (2.4363 iter/s, 4.9255s/12 iters), loss = 0.319498
I0409 22:21:59.647004 26212 solver.cpp:237]     Train net output #0: loss = 0.319498 (* 1 = 0.319498 loss)
I0409 22:21:59.647017 26212 sgd_solver.cpp:105] Iteration 6720, lr = 0.00264177
I0409 22:22:04.180444 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6732.caffemodel
I0409 22:22:05.405342 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6732.solverstate
I0409 22:22:06.285820 26212 solver.cpp:330] Iteration 6732, Testing net (#0)
I0409 22:22:06.285848 26212 net.cpp:676] Ignoring source layer train-data
I0409 22:22:08.008021 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:22:10.694095 26212 solver.cpp:397]     Test net output #0: accuracy = 0.329044
I0409 22:22:10.694146 26212 solver.cpp:397]     Test net output #1: loss = 4.6 (* 1 = 4.6 loss)
I0409 22:22:10.778626 26212 solver.cpp:218] Iteration 6732 (1.07804 iter/s, 11.1313s/12 iters), loss = 0.171107
I0409 22:22:10.778673 26212 solver.cpp:237]     Train net output #0: loss = 0.171107 (* 1 = 0.171107 loss)
I0409 22:22:10.778685 26212 sgd_solver.cpp:105] Iteration 6732, lr = 0.0026355
I0409 22:22:15.254896 26212 solver.cpp:218] Iteration 6744 (2.68091 iter/s, 4.47609s/12 iters), loss = 0.324832
I0409 22:22:15.254957 26212 solver.cpp:237]     Train net output #0: loss = 0.324832 (* 1 = 0.324832 loss)
I0409 22:22:15.254969 26212 sgd_solver.cpp:105] Iteration 6744, lr = 0.00262924
I0409 22:22:20.197404 26212 solver.cpp:218] Iteration 6756 (2.42801 iter/s, 4.94232s/12 iters), loss = 0.24192
I0409 22:22:20.197444 26212 solver.cpp:237]     Train net output #0: loss = 0.24192 (* 1 = 0.24192 loss)
I0409 22:22:20.197453 26212 sgd_solver.cpp:105] Iteration 6756, lr = 0.002623
I0409 22:22:25.091303 26212 solver.cpp:218] Iteration 6768 (2.45212 iter/s, 4.89373s/12 iters), loss = 0.266686
I0409 22:22:25.091351 26212 solver.cpp:237]     Train net output #0: loss = 0.266686 (* 1 = 0.266686 loss)
I0409 22:22:25.091363 26212 sgd_solver.cpp:105] Iteration 6768, lr = 0.00261677
I0409 22:22:28.510538 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:22:29.987818 26212 solver.cpp:218] Iteration 6780 (2.45081 iter/s, 4.89633s/12 iters), loss = 0.341062
I0409 22:22:29.987866 26212 solver.cpp:237]     Train net output #0: loss = 0.341062 (* 1 = 0.341062 loss)
I0409 22:22:29.987875 26212 sgd_solver.cpp:105] Iteration 6780, lr = 0.00261056
I0409 22:22:34.950942 26212 solver.cpp:218] Iteration 6792 (2.41792 iter/s, 4.96294s/12 iters), loss = 0.267335
I0409 22:22:34.950992 26212 solver.cpp:237]     Train net output #0: loss = 0.267335 (* 1 = 0.267335 loss)
I0409 22:22:34.951004 26212 sgd_solver.cpp:105] Iteration 6792, lr = 0.00260436
I0409 22:22:39.983338 26212 solver.cpp:218] Iteration 6804 (2.38464 iter/s, 5.03221s/12 iters), loss = 0.257873
I0409 22:22:39.983480 26212 solver.cpp:237]     Train net output #0: loss = 0.257873 (* 1 = 0.257873 loss)
I0409 22:22:39.983495 26212 sgd_solver.cpp:105] Iteration 6804, lr = 0.00259817
I0409 22:22:44.982969 26212 solver.cpp:218] Iteration 6816 (2.40031 iter/s, 4.99936s/12 iters), loss = 0.292406
I0409 22:22:44.983026 26212 solver.cpp:237]     Train net output #0: loss = 0.292406 (* 1 = 0.292406 loss)
I0409 22:22:44.983037 26212 sgd_solver.cpp:105] Iteration 6816, lr = 0.00259201
I0409 22:22:50.002363 26212 solver.cpp:218] Iteration 6828 (2.39082 iter/s, 5.01921s/12 iters), loss = 0.271217
I0409 22:22:50.002413 26212 solver.cpp:237]     Train net output #0: loss = 0.271217 (* 1 = 0.271217 loss)
I0409 22:22:50.002424 26212 sgd_solver.cpp:105] Iteration 6828, lr = 0.00258585
I0409 22:22:52.236069 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6834.caffemodel
I0409 22:22:53.470247 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6834.solverstate
I0409 22:22:54.357008 26212 solver.cpp:330] Iteration 6834, Testing net (#0)
I0409 22:22:54.357033 26212 net.cpp:676] Ignoring source layer train-data
I0409 22:22:56.122099 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:22:58.792105 26212 solver.cpp:397]     Test net output #0: accuracy = 0.316789
I0409 22:22:58.792143 26212 solver.cpp:397]     Test net output #1: loss = 4.52969 (* 1 = 4.52969 loss)
I0409 22:23:00.745182 26212 solver.cpp:218] Iteration 6840 (1.11706 iter/s, 10.7425s/12 iters), loss = 0.418126
I0409 22:23:00.745241 26212 solver.cpp:237]     Train net output #0: loss = 0.418126 (* 1 = 0.418126 loss)
I0409 22:23:00.745254 26212 sgd_solver.cpp:105] Iteration 6840, lr = 0.00257971
I0409 22:23:05.917420 26212 solver.cpp:218] Iteration 6852 (2.32017 iter/s, 5.17203s/12 iters), loss = 0.403964
I0409 22:23:05.917479 26212 solver.cpp:237]     Train net output #0: loss = 0.403964 (* 1 = 0.403964 loss)
I0409 22:23:05.917493 26212 sgd_solver.cpp:105] Iteration 6852, lr = 0.00257359
I0409 22:23:10.914458 26212 solver.cpp:218] Iteration 6864 (2.40152 iter/s, 4.99684s/12 iters), loss = 0.163022
I0409 22:23:10.914609 26212 solver.cpp:237]     Train net output #0: loss = 0.163022 (* 1 = 0.163022 loss)
I0409 22:23:10.914620 26212 sgd_solver.cpp:105] Iteration 6864, lr = 0.00256748
I0409 22:23:15.894160 26212 solver.cpp:218] Iteration 6876 (2.40992 iter/s, 4.97941s/12 iters), loss = 0.35447
I0409 22:23:15.894222 26212 solver.cpp:237]     Train net output #0: loss = 0.35447 (* 1 = 0.35447 loss)
I0409 22:23:15.894232 26212 sgd_solver.cpp:105] Iteration 6876, lr = 0.00256138
I0409 22:23:16.493435 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:23:20.764267 26212 solver.cpp:218] Iteration 6888 (2.46411 iter/s, 4.86992s/12 iters), loss = 0.175694
I0409 22:23:20.764309 26212 solver.cpp:237]     Train net output #0: loss = 0.175694 (* 1 = 0.175694 loss)
I0409 22:23:20.764318 26212 sgd_solver.cpp:105] Iteration 6888, lr = 0.0025553
I0409 22:23:25.688863 26212 solver.cpp:218] Iteration 6900 (2.43683 iter/s, 4.92442s/12 iters), loss = 0.157695
I0409 22:23:25.688907 26212 solver.cpp:237]     Train net output #0: loss = 0.157695 (* 1 = 0.157695 loss)
I0409 22:23:25.688916 26212 sgd_solver.cpp:105] Iteration 6900, lr = 0.00254923
I0409 22:23:30.622503 26212 solver.cpp:218] Iteration 6912 (2.43237 iter/s, 4.93346s/12 iters), loss = 0.372178
I0409 22:23:30.622555 26212 solver.cpp:237]     Train net output #0: loss = 0.372178 (* 1 = 0.372178 loss)
I0409 22:23:30.622568 26212 sgd_solver.cpp:105] Iteration 6912, lr = 0.00254318
I0409 22:23:35.532184 26212 solver.cpp:218] Iteration 6924 (2.44424 iter/s, 4.90949s/12 iters), loss = 0.276685
I0409 22:23:35.532236 26212 solver.cpp:237]     Train net output #0: loss = 0.276685 (* 1 = 0.276685 loss)
I0409 22:23:35.532248 26212 sgd_solver.cpp:105] Iteration 6924, lr = 0.00253714
I0409 22:23:39.987785 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6936.caffemodel
I0409 22:23:42.057701 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6936.solverstate
I0409 22:23:48.401242 26212 solver.cpp:330] Iteration 6936, Testing net (#0)
I0409 22:23:48.401271 26212 net.cpp:676] Ignoring source layer train-data
I0409 22:23:48.956377 26212 blocking_queue.cpp:49] Waiting for data
I0409 22:23:50.121001 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:23:52.834873 26212 solver.cpp:397]     Test net output #0: accuracy = 0.332108
I0409 22:23:52.834909 26212 solver.cpp:397]     Test net output #1: loss = 4.56966 (* 1 = 4.56966 loss)
I0409 22:23:52.918570 26212 solver.cpp:218] Iteration 6936 (0.690215 iter/s, 17.3859s/12 iters), loss = 0.232556
I0409 22:23:52.918628 26212 solver.cpp:237]     Train net output #0: loss = 0.232556 (* 1 = 0.232556 loss)
I0409 22:23:52.918640 26212 sgd_solver.cpp:105] Iteration 6936, lr = 0.00253112
I0409 22:23:57.297142 26212 solver.cpp:218] Iteration 6948 (2.74073 iter/s, 4.3784s/12 iters), loss = 0.205603
I0409 22:23:57.297195 26212 solver.cpp:237]     Train net output #0: loss = 0.205603 (* 1 = 0.205603 loss)
I0409 22:23:57.297206 26212 sgd_solver.cpp:105] Iteration 6948, lr = 0.00252511
I0409 22:24:02.391484 26212 solver.cpp:218] Iteration 6960 (2.35564 iter/s, 5.09415s/12 iters), loss = 0.192945
I0409 22:24:02.391531 26212 solver.cpp:237]     Train net output #0: loss = 0.192945 (* 1 = 0.192945 loss)
I0409 22:24:02.391543 26212 sgd_solver.cpp:105] Iteration 6960, lr = 0.00251911
I0409 22:24:07.275852 26212 solver.cpp:218] Iteration 6972 (2.45691 iter/s, 4.88419s/12 iters), loss = 0.175621
I0409 22:24:07.275897 26212 solver.cpp:237]     Train net output #0: loss = 0.175621 (* 1 = 0.175621 loss)
I0409 22:24:07.275907 26212 sgd_solver.cpp:105] Iteration 6972, lr = 0.00251313
I0409 22:24:09.986829 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:24:12.210916 26212 solver.cpp:218] Iteration 6984 (2.43167 iter/s, 4.93488s/12 iters), loss = 0.44749
I0409 22:24:12.211872 26212 solver.cpp:237]     Train net output #0: loss = 0.44749 (* 1 = 0.44749 loss)
I0409 22:24:12.211885 26212 sgd_solver.cpp:105] Iteration 6984, lr = 0.00250717
I0409 22:24:17.252421 26212 solver.cpp:218] Iteration 6996 (2.38076 iter/s, 5.04041s/12 iters), loss = 0.205323
I0409 22:24:17.252478 26212 solver.cpp:237]     Train net output #0: loss = 0.205323 (* 1 = 0.205323 loss)
I0409 22:24:17.252492 26212 sgd_solver.cpp:105] Iteration 6996, lr = 0.00250121
I0409 22:24:22.189450 26212 solver.cpp:218] Iteration 7008 (2.43071 iter/s, 4.93684s/12 iters), loss = 0.172403
I0409 22:24:22.189500 26212 solver.cpp:237]     Train net output #0: loss = 0.172403 (* 1 = 0.172403 loss)
I0409 22:24:22.189512 26212 sgd_solver.cpp:105] Iteration 7008, lr = 0.00249528
I0409 22:24:27.105867 26212 solver.cpp:218] Iteration 7020 (2.44089 iter/s, 4.91624s/12 iters), loss = 0.279628
I0409 22:24:27.105911 26212 solver.cpp:237]     Train net output #0: loss = 0.279628 (* 1 = 0.279628 loss)
I0409 22:24:27.105918 26212 sgd_solver.cpp:105] Iteration 7020, lr = 0.00248935
I0409 22:24:32.073521 26212 solver.cpp:218] Iteration 7032 (2.41571 iter/s, 4.96748s/12 iters), loss = 0.265059
I0409 22:24:32.073561 26212 solver.cpp:237]     Train net output #0: loss = 0.265059 (* 1 = 0.265059 loss)
I0409 22:24:32.073570 26212 sgd_solver.cpp:105] Iteration 7032, lr = 0.00248344
I0409 22:24:34.085095 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7038.caffemodel
I0409 22:24:35.279589 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7038.solverstate
I0409 22:24:36.741308 26212 solver.cpp:330] Iteration 7038, Testing net (#0)
I0409 22:24:36.741338 26212 net.cpp:676] Ignoring source layer train-data
I0409 22:24:38.441545 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:24:41.187721 26212 solver.cpp:397]     Test net output #0: accuracy = 0.324142
I0409 22:24:41.187762 26212 solver.cpp:397]     Test net output #1: loss = 4.60014 (* 1 = 4.60014 loss)
I0409 22:24:42.927310 26212 solver.cpp:218] Iteration 7044 (1.10564 iter/s, 10.8535s/12 iters), loss = 0.19867
I0409 22:24:42.927480 26212 solver.cpp:237]     Train net output #0: loss = 0.19867 (* 1 = 0.19867 loss)
I0409 22:24:42.927496 26212 sgd_solver.cpp:105] Iteration 7044, lr = 0.00247755
I0409 22:24:47.885896 26212 solver.cpp:218] Iteration 7056 (2.42019 iter/s, 4.95829s/12 iters), loss = 0.284557
I0409 22:24:47.885946 26212 solver.cpp:237]     Train net output #0: loss = 0.284557 (* 1 = 0.284557 loss)
I0409 22:24:47.885970 26212 sgd_solver.cpp:105] Iteration 7056, lr = 0.00247166
I0409 22:24:52.761011 26212 solver.cpp:218] Iteration 7068 (2.46157 iter/s, 4.87493s/12 iters), loss = 0.267253
I0409 22:24:52.761065 26212 solver.cpp:237]     Train net output #0: loss = 0.267253 (* 1 = 0.267253 loss)
I0409 22:24:52.761077 26212 sgd_solver.cpp:105] Iteration 7068, lr = 0.0024658
I0409 22:24:57.737541 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:24:57.849488 26212 solver.cpp:218] Iteration 7080 (2.35836 iter/s, 5.08828s/12 iters), loss = 0.247106
I0409 22:24:57.849542 26212 solver.cpp:237]     Train net output #0: loss = 0.247106 (* 1 = 0.247106 loss)
I0409 22:24:57.849552 26212 sgd_solver.cpp:105] Iteration 7080, lr = 0.00245994
I0409 22:25:02.809362 26212 solver.cpp:218] Iteration 7092 (2.41951 iter/s, 4.95969s/12 iters), loss = 0.211792
I0409 22:25:02.809404 26212 solver.cpp:237]     Train net output #0: loss = 0.211792 (* 1 = 0.211792 loss)
I0409 22:25:02.809415 26212 sgd_solver.cpp:105] Iteration 7092, lr = 0.0024541
I0409 22:25:07.718061 26212 solver.cpp:218] Iteration 7104 (2.44473 iter/s, 4.90852s/12 iters), loss = 0.23442
I0409 22:25:07.718128 26212 solver.cpp:237]     Train net output #0: loss = 0.23442 (* 1 = 0.23442 loss)
I0409 22:25:07.718143 26212 sgd_solver.cpp:105] Iteration 7104, lr = 0.00244827
I0409 22:25:12.594764 26212 solver.cpp:218] Iteration 7116 (2.46078 iter/s, 4.8765s/12 iters), loss = 0.227834
I0409 22:25:12.594823 26212 solver.cpp:237]     Train net output #0: loss = 0.227834 (* 1 = 0.227834 loss)
I0409 22:25:12.594835 26212 sgd_solver.cpp:105] Iteration 7116, lr = 0.00244246
I0409 22:25:17.518165 26212 solver.cpp:218] Iteration 7128 (2.43743 iter/s, 4.92321s/12 iters), loss = 0.398088
I0409 22:25:17.518276 26212 solver.cpp:237]     Train net output #0: loss = 0.398088 (* 1 = 0.398088 loss)
I0409 22:25:17.518286 26212 sgd_solver.cpp:105] Iteration 7128, lr = 0.00243666
I0409 22:25:21.995429 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7140.caffemodel
I0409 22:25:23.888094 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7140.solverstate
I0409 22:25:24.765170 26212 solver.cpp:330] Iteration 7140, Testing net (#0)
I0409 22:25:24.765193 26212 net.cpp:676] Ignoring source layer train-data
I0409 22:25:26.393139 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:25:29.233124 26212 solver.cpp:397]     Test net output #0: accuracy = 0.321691
I0409 22:25:29.233163 26212 solver.cpp:397]     Test net output #1: loss = 4.73121 (* 1 = 4.73121 loss)
I0409 22:25:29.316839 26212 solver.cpp:218] Iteration 7140 (1.0171 iter/s, 11.7983s/12 iters), loss = 0.191828
I0409 22:25:29.316888 26212 solver.cpp:237]     Train net output #0: loss = 0.191828 (* 1 = 0.191828 loss)
I0409 22:25:29.316900 26212 sgd_solver.cpp:105] Iteration 7140, lr = 0.00243088
I0409 22:25:33.665264 26212 solver.cpp:218] Iteration 7152 (2.75973 iter/s, 4.34826s/12 iters), loss = 0.224985
I0409 22:25:33.665310 26212 solver.cpp:237]     Train net output #0: loss = 0.224985 (* 1 = 0.224985 loss)
I0409 22:25:33.665320 26212 sgd_solver.cpp:105] Iteration 7152, lr = 0.00242511
I0409 22:25:38.668985 26212 solver.cpp:218] Iteration 7164 (2.3983 iter/s, 5.00354s/12 iters), loss = 0.10092
I0409 22:25:38.669031 26212 solver.cpp:237]     Train net output #0: loss = 0.10092 (* 1 = 0.10092 loss)
I0409 22:25:38.669044 26212 sgd_solver.cpp:105] Iteration 7164, lr = 0.00241935
I0409 22:25:43.677975 26212 solver.cpp:218] Iteration 7176 (2.39579 iter/s, 5.00879s/12 iters), loss = 0.242348
I0409 22:25:43.678028 26212 solver.cpp:237]     Train net output #0: loss = 0.242348 (* 1 = 0.242348 loss)
I0409 22:25:43.678040 26212 sgd_solver.cpp:105] Iteration 7176, lr = 0.0024136
I0409 22:25:45.800734 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:25:48.646139 26212 solver.cpp:218] Iteration 7188 (2.41547 iter/s, 4.96798s/12 iters), loss = 0.338012
I0409 22:25:48.646235 26212 solver.cpp:237]     Train net output #0: loss = 0.338012 (* 1 = 0.338012 loss)
I0409 22:25:48.646245 26212 sgd_solver.cpp:105] Iteration 7188, lr = 0.00240787
I0409 22:25:53.644282 26212 solver.cpp:218] Iteration 7200 (2.401 iter/s, 4.99791s/12 iters), loss = 0.145385
I0409 22:25:53.644332 26212 solver.cpp:237]     Train net output #0: loss = 0.145385 (* 1 = 0.145385 loss)
I0409 22:25:53.644342 26212 sgd_solver.cpp:105] Iteration 7200, lr = 0.00240216
I0409 22:25:58.673501 26212 solver.cpp:218] Iteration 7212 (2.38614 iter/s, 5.02904s/12 iters), loss = 0.115871
I0409 22:25:58.673542 26212 solver.cpp:237]     Train net output #0: loss = 0.115871 (* 1 = 0.115871 loss)
I0409 22:25:58.673552 26212 sgd_solver.cpp:105] Iteration 7212, lr = 0.00239645
I0409 22:26:03.682530 26212 solver.cpp:218] Iteration 7224 (2.39576 iter/s, 5.00884s/12 iters), loss = 0.255835
I0409 22:26:03.682590 26212 solver.cpp:237]     Train net output #0: loss = 0.255835 (* 1 = 0.255835 loss)
I0409 22:26:03.682600 26212 sgd_solver.cpp:105] Iteration 7224, lr = 0.00239076
I0409 22:26:08.673079 26212 solver.cpp:218] Iteration 7236 (2.40464 iter/s, 4.99036s/12 iters), loss = 0.263978
I0409 22:26:08.673128 26212 solver.cpp:237]     Train net output #0: loss = 0.263978 (* 1 = 0.263978 loss)
I0409 22:26:08.673141 26212 sgd_solver.cpp:105] Iteration 7236, lr = 0.00238509
I0409 22:26:10.682185 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7242.caffemodel
I0409 22:26:12.853010 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7242.solverstate
I0409 22:26:13.860661 26212 solver.cpp:330] Iteration 7242, Testing net (#0)
I0409 22:26:13.860689 26212 net.cpp:676] Ignoring source layer train-data
I0409 22:26:15.413506 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:26:18.271905 26212 solver.cpp:397]     Test net output #0: accuracy = 0.322917
I0409 22:26:18.271955 26212 solver.cpp:397]     Test net output #1: loss = 4.64453 (* 1 = 4.64453 loss)
I0409 22:26:20.287554 26212 solver.cpp:218] Iteration 7248 (1.03322 iter/s, 11.6141s/12 iters), loss = 0.119196
I0409 22:26:20.287652 26212 solver.cpp:237]     Train net output #0: loss = 0.119196 (* 1 = 0.119196 loss)
I0409 22:26:20.287663 26212 sgd_solver.cpp:105] Iteration 7248, lr = 0.00237942
I0409 22:26:25.350782 26212 solver.cpp:218] Iteration 7260 (2.37014 iter/s, 5.06299s/12 iters), loss = 0.123095
I0409 22:26:25.350832 26212 solver.cpp:237]     Train net output #0: loss = 0.123095 (* 1 = 0.123095 loss)
I0409 22:26:25.350844 26212 sgd_solver.cpp:105] Iteration 7260, lr = 0.00237378
I0409 22:26:30.228984 26212 solver.cpp:218] Iteration 7272 (2.46001 iter/s, 4.87802s/12 iters), loss = 0.237763
I0409 22:26:30.229036 26212 solver.cpp:237]     Train net output #0: loss = 0.237763 (* 1 = 0.237763 loss)
I0409 22:26:30.229045 26212 sgd_solver.cpp:105] Iteration 7272, lr = 0.00236814
I0409 22:26:34.370502 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:26:35.112252 26212 solver.cpp:218] Iteration 7284 (2.45746 iter/s, 4.88308s/12 iters), loss = 0.319923
I0409 22:26:35.112301 26212 solver.cpp:237]     Train net output #0: loss = 0.319923 (* 1 = 0.319923 loss)
I0409 22:26:35.112313 26212 sgd_solver.cpp:105] Iteration 7284, lr = 0.00236252
I0409 22:26:40.108852 26212 solver.cpp:218] Iteration 7296 (2.40172 iter/s, 4.99641s/12 iters), loss = 0.181383
I0409 22:26:40.108904 26212 solver.cpp:237]     Train net output #0: loss = 0.181383 (* 1 = 0.181383 loss)
I0409 22:26:40.108919 26212 sgd_solver.cpp:105] Iteration 7296, lr = 0.00235691
I0409 22:26:45.104187 26212 solver.cpp:218] Iteration 7308 (2.40233 iter/s, 4.99515s/12 iters), loss = 0.259926
I0409 22:26:45.104230 26212 solver.cpp:237]     Train net output #0: loss = 0.259926 (* 1 = 0.259926 loss)
I0409 22:26:45.104239 26212 sgd_solver.cpp:105] Iteration 7308, lr = 0.00235131
I0409 22:26:50.021018 26212 solver.cpp:218] Iteration 7320 (2.44069 iter/s, 4.91665s/12 iters), loss = 0.140664
I0409 22:26:50.021073 26212 solver.cpp:237]     Train net output #0: loss = 0.140664 (* 1 = 0.140664 loss)
I0409 22:26:50.021085 26212 sgd_solver.cpp:105] Iteration 7320, lr = 0.00234573
I0409 22:26:54.974680 26212 solver.cpp:218] Iteration 7332 (2.42254 iter/s, 4.95347s/12 iters), loss = 0.193536
I0409 22:26:54.974798 26212 solver.cpp:237]     Train net output #0: loss = 0.193536 (* 1 = 0.193536 loss)
I0409 22:26:54.974809 26212 sgd_solver.cpp:105] Iteration 7332, lr = 0.00234016
I0409 22:26:59.410828 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7344.caffemodel
I0409 22:27:00.641319 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7344.solverstate
I0409 22:27:01.870354 26212 solver.cpp:330] Iteration 7344, Testing net (#0)
I0409 22:27:01.870379 26212 net.cpp:676] Ignoring source layer train-data
I0409 22:27:03.467190 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:27:06.387943 26212 solver.cpp:397]     Test net output #0: accuracy = 0.324755
I0409 22:27:06.387986 26212 solver.cpp:397]     Test net output #1: loss = 4.55016 (* 1 = 4.55016 loss)
I0409 22:27:06.471747 26212 solver.cpp:218] Iteration 7344 (1.04378 iter/s, 11.4967s/12 iters), loss = 0.350118
I0409 22:27:06.471801 26212 solver.cpp:237]     Train net output #0: loss = 0.350118 (* 1 = 0.350118 loss)
I0409 22:27:06.471810 26212 sgd_solver.cpp:105] Iteration 7344, lr = 0.0023346
I0409 22:27:10.879755 26212 solver.cpp:218] Iteration 7356 (2.72243 iter/s, 4.40783s/12 iters), loss = 0.122277
I0409 22:27:10.879817 26212 solver.cpp:237]     Train net output #0: loss = 0.122277 (* 1 = 0.122277 loss)
I0409 22:27:10.879827 26212 sgd_solver.cpp:105] Iteration 7356, lr = 0.00232906
I0409 22:27:15.851562 26212 solver.cpp:218] Iteration 7368 (2.4137 iter/s, 4.97161s/12 iters), loss = 0.315226
I0409 22:27:15.851606 26212 solver.cpp:237]     Train net output #0: loss = 0.315226 (* 1 = 0.315226 loss)
I0409 22:27:15.851615 26212 sgd_solver.cpp:105] Iteration 7368, lr = 0.00232353
I0409 22:27:20.817909 26212 solver.cpp:218] Iteration 7380 (2.41635 iter/s, 4.96617s/12 iters), loss = 0.26657
I0409 22:27:20.817966 26212 solver.cpp:237]     Train net output #0: loss = 0.26657 (* 1 = 0.26657 loss)
I0409 22:27:20.817977 26212 sgd_solver.cpp:105] Iteration 7380, lr = 0.00231802
I0409 22:27:22.171370 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:27:25.858749 26212 solver.cpp:218] Iteration 7392 (2.38064 iter/s, 5.04066s/12 iters), loss = 0.24449
I0409 22:27:25.858844 26212 solver.cpp:237]     Train net output #0: loss = 0.24449 (* 1 = 0.24449 loss)
I0409 22:27:25.858853 26212 sgd_solver.cpp:105] Iteration 7392, lr = 0.00231251
I0409 22:27:30.839728 26212 solver.cpp:218] Iteration 7404 (2.40928 iter/s, 4.98075s/12 iters), loss = 0.167429
I0409 22:27:30.839773 26212 solver.cpp:237]     Train net output #0: loss = 0.167429 (* 1 = 0.167429 loss)
I0409 22:27:30.839781 26212 sgd_solver.cpp:105] Iteration 7404, lr = 0.00230702
I0409 22:27:35.772997 26212 solver.cpp:218] Iteration 7416 (2.43255 iter/s, 4.93309s/12 iters), loss = 0.0949896
I0409 22:27:35.773043 26212 solver.cpp:237]     Train net output #0: loss = 0.0949896 (* 1 = 0.0949896 loss)
I0409 22:27:35.773053 26212 sgd_solver.cpp:105] Iteration 7416, lr = 0.00230154
I0409 22:27:40.677740 26212 solver.cpp:218] Iteration 7428 (2.4467 iter/s, 4.90456s/12 iters), loss = 0.171571
I0409 22:27:40.677785 26212 solver.cpp:237]     Train net output #0: loss = 0.171571 (* 1 = 0.171571 loss)
I0409 22:27:40.677795 26212 sgd_solver.cpp:105] Iteration 7428, lr = 0.00229608
I0409 22:27:45.579542 26212 solver.cpp:218] Iteration 7440 (2.44817 iter/s, 4.90162s/12 iters), loss = 0.33712
I0409 22:27:45.579589 26212 solver.cpp:237]     Train net output #0: loss = 0.33712 (* 1 = 0.33712 loss)
I0409 22:27:45.579598 26212 sgd_solver.cpp:105] Iteration 7440, lr = 0.00229063
I0409 22:27:47.584017 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7446.caffemodel
I0409 22:27:48.812001 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7446.solverstate
I0409 22:27:49.704432 26212 solver.cpp:330] Iteration 7446, Testing net (#0)
I0409 22:27:49.704463 26212 net.cpp:676] Ignoring source layer train-data
I0409 22:27:51.224339 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:27:54.140180 26212 solver.cpp:397]     Test net output #0: accuracy = 0.33027
I0409 22:27:54.140218 26212 solver.cpp:397]     Test net output #1: loss = 4.65987 (* 1 = 4.65987 loss)
I0409 22:27:55.993255 26212 solver.cpp:218] Iteration 7452 (1.15236 iter/s, 10.4134s/12 iters), loss = 0.125825
I0409 22:27:55.993362 26212 solver.cpp:237]     Train net output #0: loss = 0.125825 (* 1 = 0.125825 loss)
I0409 22:27:55.993373 26212 sgd_solver.cpp:105] Iteration 7452, lr = 0.00228519
I0409 22:28:00.930989 26212 solver.cpp:218] Iteration 7464 (2.43039 iter/s, 4.93749s/12 iters), loss = 0.0806937
I0409 22:28:00.931049 26212 solver.cpp:237]     Train net output #0: loss = 0.0806937 (* 1 = 0.0806937 loss)
I0409 22:28:00.931061 26212 sgd_solver.cpp:105] Iteration 7464, lr = 0.00227976
I0409 22:28:05.866989 26212 solver.cpp:218] Iteration 7476 (2.43121 iter/s, 4.93581s/12 iters), loss = 0.132329
I0409 22:28:05.867041 26212 solver.cpp:237]     Train net output #0: loss = 0.132329 (* 1 = 0.132329 loss)
I0409 22:28:05.867051 26212 sgd_solver.cpp:105] Iteration 7476, lr = 0.00227435
I0409 22:28:09.303165 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:28:10.763792 26212 solver.cpp:218] Iteration 7488 (2.45068 iter/s, 4.89659s/12 iters), loss = 0.18156
I0409 22:28:10.763841 26212 solver.cpp:237]     Train net output #0: loss = 0.18156 (* 1 = 0.18156 loss)
I0409 22:28:10.763854 26212 sgd_solver.cpp:105] Iteration 7488, lr = 0.00226895
I0409 22:28:15.688246 26212 solver.cpp:218] Iteration 7500 (2.43691 iter/s, 4.92427s/12 iters), loss = 0.289033
I0409 22:28:15.688298 26212 solver.cpp:237]     Train net output #0: loss = 0.289033 (* 1 = 0.289033 loss)
I0409 22:28:15.688310 26212 sgd_solver.cpp:105] Iteration 7500, lr = 0.00226357
I0409 22:28:20.622138 26212 solver.cpp:218] Iteration 7512 (2.43225 iter/s, 4.9337s/12 iters), loss = 0.180001
I0409 22:28:20.622177 26212 solver.cpp:237]     Train net output #0: loss = 0.180001 (* 1 = 0.180001 loss)
I0409 22:28:20.622186 26212 sgd_solver.cpp:105] Iteration 7512, lr = 0.00225819
I0409 22:28:25.485253 26212 solver.cpp:218] Iteration 7524 (2.46764 iter/s, 4.86294s/12 iters), loss = 0.132044
I0409 22:28:25.485306 26212 solver.cpp:237]     Train net output #0: loss = 0.132044 (* 1 = 0.132044 loss)
I0409 22:28:25.485319 26212 sgd_solver.cpp:105] Iteration 7524, lr = 0.00225283
I0409 22:28:30.450258 26212 solver.cpp:218] Iteration 7536 (2.417 iter/s, 4.96482s/12 iters), loss = 0.154727
I0409 22:28:30.450374 26212 solver.cpp:237]     Train net output #0: loss = 0.154727 (* 1 = 0.154727 loss)
I0409 22:28:30.450388 26212 sgd_solver.cpp:105] Iteration 7536, lr = 0.00224748
I0409 22:28:34.869307 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7548.caffemodel
I0409 22:28:36.090369 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7548.solverstate
I0409 22:28:36.971556 26212 solver.cpp:330] Iteration 7548, Testing net (#0)
I0409 22:28:36.971586 26212 net.cpp:676] Ignoring source layer train-data
I0409 22:28:38.501969 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:28:41.518656 26212 solver.cpp:397]     Test net output #0: accuracy = 0.333946
I0409 22:28:41.518694 26212 solver.cpp:397]     Test net output #1: loss = 4.69686 (* 1 = 4.69686 loss)
I0409 22:28:41.602454 26212 solver.cpp:218] Iteration 7548 (1.07606 iter/s, 11.1518s/12 iters), loss = 0.174331
I0409 22:28:41.602506 26212 solver.cpp:237]     Train net output #0: loss = 0.174331 (* 1 = 0.174331 loss)
I0409 22:28:41.602517 26212 sgd_solver.cpp:105] Iteration 7548, lr = 0.00224215
I0409 22:28:45.843606 26212 solver.cpp:218] Iteration 7560 (2.82953 iter/s, 4.24098s/12 iters), loss = 0.227614
I0409 22:28:45.843667 26212 solver.cpp:237]     Train net output #0: loss = 0.227614 (* 1 = 0.227614 loss)
I0409 22:28:45.843678 26212 sgd_solver.cpp:105] Iteration 7560, lr = 0.00223682
I0409 22:28:50.888386 26212 solver.cpp:218] Iteration 7572 (2.37879 iter/s, 5.04458s/12 iters), loss = 0.129095
I0409 22:28:50.888435 26212 solver.cpp:237]     Train net output #0: loss = 0.129095 (* 1 = 0.129095 loss)
I0409 22:28:50.888446 26212 sgd_solver.cpp:105] Iteration 7572, lr = 0.00223151
I0409 22:28:55.954582 26212 solver.cpp:218] Iteration 7584 (2.36873 iter/s, 5.06601s/12 iters), loss = 0.153651
I0409 22:28:55.954633 26212 solver.cpp:237]     Train net output #0: loss = 0.153651 (* 1 = 0.153651 loss)
I0409 22:28:55.954643 26212 sgd_solver.cpp:105] Iteration 7584, lr = 0.00222621
I0409 22:28:56.665102 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:29:01.163447 26212 solver.cpp:218] Iteration 7596 (2.30385 iter/s, 5.20868s/12 iters), loss = 0.274018
I0409 22:29:01.163535 26212 solver.cpp:237]     Train net output #0: loss = 0.274018 (* 1 = 0.274018 loss)
I0409 22:29:01.163548 26212 sgd_solver.cpp:105] Iteration 7596, lr = 0.00222093
I0409 22:29:06.032369 26212 solver.cpp:218] Iteration 7608 (2.46472 iter/s, 4.8687s/12 iters), loss = 0.157368
I0409 22:29:06.032433 26212 solver.cpp:237]     Train net output #0: loss = 0.157368 (* 1 = 0.157368 loss)
I0409 22:29:06.032444 26212 sgd_solver.cpp:105] Iteration 7608, lr = 0.00221565
I0409 22:29:11.011183 26212 solver.cpp:218] Iteration 7620 (2.4103 iter/s, 4.97863s/12 iters), loss = 0.134246
I0409 22:29:11.011227 26212 solver.cpp:237]     Train net output #0: loss = 0.134247 (* 1 = 0.134247 loss)
I0409 22:29:11.011237 26212 sgd_solver.cpp:105] Iteration 7620, lr = 0.00221039
I0409 22:29:12.973315 26212 blocking_queue.cpp:49] Waiting for data
I0409 22:29:15.870960 26212 solver.cpp:218] Iteration 7632 (2.46934 iter/s, 4.8596s/12 iters), loss = 0.129903
I0409 22:29:15.871013 26212 solver.cpp:237]     Train net output #0: loss = 0.129903 (* 1 = 0.129903 loss)
I0409 22:29:15.871026 26212 sgd_solver.cpp:105] Iteration 7632, lr = 0.00220515
I0409 22:29:20.854034 26212 solver.cpp:218] Iteration 7644 (2.40824 iter/s, 4.98289s/12 iters), loss = 0.179299
I0409 22:29:20.854084 26212 solver.cpp:237]     Train net output #0: loss = 0.179299 (* 1 = 0.179299 loss)
I0409 22:29:20.854094 26212 sgd_solver.cpp:105] Iteration 7644, lr = 0.00219991
I0409 22:29:22.854183 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7650.caffemodel
I0409 22:29:25.614266 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7650.solverstate
I0409 22:29:29.438112 26212 solver.cpp:330] Iteration 7650, Testing net (#0)
I0409 22:29:29.438139 26212 net.cpp:676] Ignoring source layer train-data
I0409 22:29:30.876749 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:29:33.865139 26212 solver.cpp:397]     Test net output #0: accuracy = 0.323529
I0409 22:29:33.865244 26212 solver.cpp:397]     Test net output #1: loss = 4.7224 (* 1 = 4.7224 loss)
I0409 22:29:35.698005 26212 solver.cpp:218] Iteration 7656 (0.808431 iter/s, 14.8436s/12 iters), loss = 0.148187
I0409 22:29:35.698051 26212 solver.cpp:237]     Train net output #0: loss = 0.148187 (* 1 = 0.148187 loss)
I0409 22:29:35.698062 26212 sgd_solver.cpp:105] Iteration 7656, lr = 0.00219469
I0409 22:29:40.688109 26212 solver.cpp:218] Iteration 7668 (2.40484 iter/s, 4.98993s/12 iters), loss = 0.130978
I0409 22:29:40.688158 26212 solver.cpp:237]     Train net output #0: loss = 0.130978 (* 1 = 0.130978 loss)
I0409 22:29:40.688170 26212 sgd_solver.cpp:105] Iteration 7668, lr = 0.00218948
I0409 22:29:45.575946 26212 solver.cpp:218] Iteration 7680 (2.45516 iter/s, 4.88766s/12 iters), loss = 0.117559
I0409 22:29:45.576000 26212 solver.cpp:237]     Train net output #0: loss = 0.117559 (* 1 = 0.117559 loss)
I0409 22:29:45.576011 26212 sgd_solver.cpp:105] Iteration 7680, lr = 0.00218428
I0409 22:29:48.295121 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:29:50.493217 26212 solver.cpp:218] Iteration 7692 (2.44047 iter/s, 4.91709s/12 iters), loss = 0.229414
I0409 22:29:50.493258 26212 solver.cpp:237]     Train net output #0: loss = 0.229414 (* 1 = 0.229414 loss)
I0409 22:29:50.493269 26212 sgd_solver.cpp:105] Iteration 7692, lr = 0.00217909
I0409 22:29:55.614454 26212 solver.cpp:218] Iteration 7704 (2.34326 iter/s, 5.12106s/12 iters), loss = 0.265324
I0409 22:29:55.614501 26212 solver.cpp:237]     Train net output #0: loss = 0.265324 (* 1 = 0.265324 loss)
I0409 22:29:55.614511 26212 sgd_solver.cpp:105] Iteration 7704, lr = 0.00217392
I0409 22:30:00.631327 26212 solver.cpp:218] Iteration 7716 (2.39201 iter/s, 5.0167s/12 iters), loss = 0.257962
I0409 22:30:00.631379 26212 solver.cpp:237]     Train net output #0: loss = 0.257962 (* 1 = 0.257962 loss)
I0409 22:30:00.631390 26212 sgd_solver.cpp:105] Iteration 7716, lr = 0.00216876
I0409 22:30:05.600994 26212 solver.cpp:218] Iteration 7728 (2.41474 iter/s, 4.96949s/12 iters), loss = 0.167283
I0409 22:30:05.601094 26212 solver.cpp:237]     Train net output #0: loss = 0.167283 (* 1 = 0.167283 loss)
I0409 22:30:05.601104 26212 sgd_solver.cpp:105] Iteration 7728, lr = 0.00216361
I0409 22:30:10.544291 26212 solver.cpp:218] Iteration 7740 (2.42764 iter/s, 4.94307s/12 iters), loss = 0.187889
I0409 22:30:10.544340 26212 solver.cpp:237]     Train net output #0: loss = 0.187889 (* 1 = 0.187889 loss)
I0409 22:30:10.544349 26212 sgd_solver.cpp:105] Iteration 7740, lr = 0.00215847
I0409 22:30:15.031944 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7752.caffemodel
I0409 22:30:16.419521 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7752.solverstate
I0409 22:30:17.977807 26212 solver.cpp:330] Iteration 7752, Testing net (#0)
I0409 22:30:17.977830 26212 net.cpp:676] Ignoring source layer train-data
I0409 22:30:19.358754 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:30:22.471357 26212 solver.cpp:397]     Test net output #0: accuracy = 0.34375
I0409 22:30:22.471405 26212 solver.cpp:397]     Test net output #1: loss = 4.60617 (* 1 = 4.60617 loss)
I0409 22:30:22.555256 26212 solver.cpp:218] Iteration 7752 (0.999116 iter/s, 12.0106s/12 iters), loss = 0.231047
I0409 22:30:22.555311 26212 solver.cpp:237]     Train net output #0: loss = 0.231047 (* 1 = 0.231047 loss)
I0409 22:30:22.555322 26212 sgd_solver.cpp:105] Iteration 7752, lr = 0.00215335
I0409 22:30:26.872082 26212 solver.cpp:218] Iteration 7764 (2.77993 iter/s, 4.31665s/12 iters), loss = 0.202197
I0409 22:30:26.872126 26212 solver.cpp:237]     Train net output #0: loss = 0.202197 (* 1 = 0.202197 loss)
I0409 22:30:26.872135 26212 sgd_solver.cpp:105] Iteration 7764, lr = 0.00214823
I0409 22:30:31.931811 26212 solver.cpp:218] Iteration 7776 (2.37175 iter/s, 5.05955s/12 iters), loss = 0.288105
I0409 22:30:31.931851 26212 solver.cpp:237]     Train net output #0: loss = 0.288105 (* 1 = 0.288105 loss)
I0409 22:30:31.931860 26212 sgd_solver.cpp:105] Iteration 7776, lr = 0.00214313
I0409 22:30:36.883038 26212 solver.cpp:218] Iteration 7788 (2.42373 iter/s, 4.95105s/12 iters), loss = 0.10027
I0409 22:30:36.883209 26212 solver.cpp:237]     Train net output #0: loss = 0.10027 (* 1 = 0.10027 loss)
I0409 22:30:36.883224 26212 sgd_solver.cpp:105] Iteration 7788, lr = 0.00213805
I0409 22:30:36.891628 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:30:41.909232 26212 solver.cpp:218] Iteration 7800 (2.38764 iter/s, 5.02589s/12 iters), loss = 0.169958
I0409 22:30:41.909286 26212 solver.cpp:237]     Train net output #0: loss = 0.169958 (* 1 = 0.169958 loss)
I0409 22:30:41.909297 26212 sgd_solver.cpp:105] Iteration 7800, lr = 0.00213297
I0409 22:30:46.774464 26212 solver.cpp:218] Iteration 7812 (2.46658 iter/s, 4.86505s/12 iters), loss = 0.106015
I0409 22:30:46.774528 26212 solver.cpp:237]     Train net output #0: loss = 0.106015 (* 1 = 0.106015 loss)
I0409 22:30:46.774539 26212 sgd_solver.cpp:105] Iteration 7812, lr = 0.00212791
I0409 22:30:51.674192 26212 solver.cpp:218] Iteration 7824 (2.44921 iter/s, 4.89954s/12 iters), loss = 0.18552
I0409 22:30:51.674242 26212 solver.cpp:237]     Train net output #0: loss = 0.18552 (* 1 = 0.18552 loss)
I0409 22:30:51.674252 26212 sgd_solver.cpp:105] Iteration 7824, lr = 0.00212285
I0409 22:30:56.563947 26212 solver.cpp:218] Iteration 7836 (2.4542 iter/s, 4.88958s/12 iters), loss = 0.136803
I0409 22:30:56.563994 26212 solver.cpp:237]     Train net output #0: loss = 0.136803 (* 1 = 0.136803 loss)
I0409 22:30:56.564007 26212 sgd_solver.cpp:105] Iteration 7836, lr = 0.00211781
I0409 22:31:01.463546 26212 solver.cpp:218] Iteration 7848 (2.44927 iter/s, 4.89942s/12 iters), loss = 0.150246
I0409 22:31:01.463598 26212 solver.cpp:237]     Train net output #0: loss = 0.150246 (* 1 = 0.150246 loss)
I0409 22:31:01.463610 26212 sgd_solver.cpp:105] Iteration 7848, lr = 0.00211279
I0409 22:31:03.445737 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7854.caffemodel
I0409 22:31:09.238194 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7854.solverstate
I0409 22:31:13.980307 26212 solver.cpp:330] Iteration 7854, Testing net (#0)
I0409 22:31:13.980337 26212 net.cpp:676] Ignoring source layer train-data
I0409 22:31:15.353386 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:31:18.446053 26212 solver.cpp:397]     Test net output #0: accuracy = 0.346814
I0409 22:31:18.446089 26212 solver.cpp:397]     Test net output #1: loss = 4.52889 (* 1 = 4.52889 loss)
I0409 22:31:20.214406 26212 solver.cpp:218] Iteration 7860 (0.639988 iter/s, 18.7503s/12 iters), loss = 0.0744798
I0409 22:31:20.214453 26212 solver.cpp:237]     Train net output #0: loss = 0.0744798 (* 1 = 0.0744798 loss)
I0409 22:31:20.214463 26212 sgd_solver.cpp:105] Iteration 7860, lr = 0.00210777
I0409 22:31:25.114135 26212 solver.cpp:218] Iteration 7872 (2.44921 iter/s, 4.89955s/12 iters), loss = 0.0691407
I0409 22:31:25.114181 26212 solver.cpp:237]     Train net output #0: loss = 0.0691407 (* 1 = 0.0691407 loss)
I0409 22:31:25.114189 26212 sgd_solver.cpp:105] Iteration 7872, lr = 0.00210277
I0409 22:31:30.109705 26212 solver.cpp:218] Iteration 7884 (2.40221 iter/s, 4.99539s/12 iters), loss = 0.145002
I0409 22:31:30.109746 26212 solver.cpp:237]     Train net output #0: loss = 0.145002 (* 1 = 0.145002 loss)
I0409 22:31:30.109756 26212 sgd_solver.cpp:105] Iteration 7884, lr = 0.00209777
I0409 22:31:32.183645 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:31:34.939110 26212 solver.cpp:218] Iteration 7896 (2.48487 iter/s, 4.82923s/12 iters), loss = 0.254597
I0409 22:31:34.939153 26212 solver.cpp:237]     Train net output #0: loss = 0.254597 (* 1 = 0.254597 loss)
I0409 22:31:34.939162 26212 sgd_solver.cpp:105] Iteration 7896, lr = 0.00209279
I0409 22:31:39.907871 26212 solver.cpp:218] Iteration 7908 (2.41517 iter/s, 4.96859s/12 iters), loss = 0.0991175
I0409 22:31:39.907996 26212 solver.cpp:237]     Train net output #0: loss = 0.0991175 (* 1 = 0.0991175 loss)
I0409 22:31:39.908008 26212 sgd_solver.cpp:105] Iteration 7908, lr = 0.00208782
I0409 22:31:44.860127 26212 solver.cpp:218] Iteration 7920 (2.42326 iter/s, 4.952s/12 iters), loss = 0.147715
I0409 22:31:44.860178 26212 solver.cpp:237]     Train net output #0: loss = 0.147715 (* 1 = 0.147715 loss)
I0409 22:31:44.860188 26212 sgd_solver.cpp:105] Iteration 7920, lr = 0.00208287
I0409 22:31:49.715842 26212 solver.cpp:218] Iteration 7932 (2.4714 iter/s, 4.85554s/12 iters), loss = 0.191364
I0409 22:31:49.715881 26212 solver.cpp:237]     Train net output #0: loss = 0.191364 (* 1 = 0.191364 loss)
I0409 22:31:49.715890 26212 sgd_solver.cpp:105] Iteration 7932, lr = 0.00207792
I0409 22:31:54.645033 26212 solver.cpp:218] Iteration 7944 (2.43456 iter/s, 4.92902s/12 iters), loss = 0.150943
I0409 22:31:54.645077 26212 solver.cpp:237]     Train net output #0: loss = 0.150943 (* 1 = 0.150943 loss)
I0409 22:31:54.645087 26212 sgd_solver.cpp:105] Iteration 7944, lr = 0.00207299
I0409 22:31:59.129372 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7956.caffemodel
I0409 22:32:00.350695 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7956.solverstate
I0409 22:32:01.217764 26212 solver.cpp:330] Iteration 7956, Testing net (#0)
I0409 22:32:01.217783 26212 net.cpp:676] Ignoring source layer train-data
I0409 22:32:02.526144 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:32:05.732156 26212 solver.cpp:397]     Test net output #0: accuracy = 0.339461
I0409 22:32:05.732192 26212 solver.cpp:397]     Test net output #1: loss = 4.67081 (* 1 = 4.67081 loss)
I0409 22:32:05.815693 26212 solver.cpp:218] Iteration 7956 (1.07427 iter/s, 11.1703s/12 iters), loss = 0.138468
I0409 22:32:05.815733 26212 solver.cpp:237]     Train net output #0: loss = 0.138468 (* 1 = 0.138468 loss)
I0409 22:32:05.815742 26212 sgd_solver.cpp:105] Iteration 7956, lr = 0.00206807
I0409 22:32:09.947326 26212 solver.cpp:218] Iteration 7968 (2.90454 iter/s, 4.13147s/12 iters), loss = 0.169603
I0409 22:32:09.947453 26212 solver.cpp:237]     Train net output #0: loss = 0.169603 (* 1 = 0.169603 loss)
I0409 22:32:09.947466 26212 sgd_solver.cpp:105] Iteration 7968, lr = 0.00206316
I0409 22:32:15.118458 26212 solver.cpp:218] Iteration 7980 (2.32069 iter/s, 5.17087s/12 iters), loss = 0.0772719
I0409 22:32:15.118505 26212 solver.cpp:237]     Train net output #0: loss = 0.0772719 (* 1 = 0.0772719 loss)
I0409 22:32:15.118515 26212 sgd_solver.cpp:105] Iteration 7980, lr = 0.00205826
I0409 22:32:19.301436 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:32:19.992738 26212 solver.cpp:218] Iteration 7992 (2.46199 iter/s, 4.8741s/12 iters), loss = 0.280464
I0409 22:32:19.992794 26212 solver.cpp:237]     Train net output #0: loss = 0.280464 (* 1 = 0.280464 loss)
I0409 22:32:19.992805 26212 sgd_solver.cpp:105] Iteration 7992, lr = 0.00205337
I0409 22:32:24.939688 26212 solver.cpp:218] Iteration 8004 (2.42583 iter/s, 4.94676s/12 iters), loss = 0.12946
I0409 22:32:24.939749 26212 solver.cpp:237]     Train net output #0: loss = 0.12946 (* 1 = 0.12946 loss)
I0409 22:32:24.939759 26212 sgd_solver.cpp:105] Iteration 8004, lr = 0.0020485
I0409 22:32:29.829838 26212 solver.cpp:218] Iteration 8016 (2.45401 iter/s, 4.88996s/12 iters), loss = 0.129044
I0409 22:32:29.829890 26212 solver.cpp:237]     Train net output #0: loss = 0.129044 (* 1 = 0.129044 loss)
I0409 22:32:29.829902 26212 sgd_solver.cpp:105] Iteration 8016, lr = 0.00204363
I0409 22:32:34.734700 26212 solver.cpp:218] Iteration 8028 (2.44664 iter/s, 4.90468s/12 iters), loss = 0.139135
I0409 22:32:34.734750 26212 solver.cpp:237]     Train net output #0: loss = 0.139135 (* 1 = 0.139135 loss)
I0409 22:32:34.734760 26212 sgd_solver.cpp:105] Iteration 8028, lr = 0.00203878
I0409 22:32:39.666936 26212 solver.cpp:218] Iteration 8040 (2.43306 iter/s, 4.93205s/12 iters), loss = 0.0897768
I0409 22:32:39.666994 26212 solver.cpp:237]     Train net output #0: loss = 0.0897768 (* 1 = 0.0897768 loss)
I0409 22:32:39.667007 26212 sgd_solver.cpp:105] Iteration 8040, lr = 0.00203394
I0409 22:32:45.003417 26212 solver.cpp:218] Iteration 8052 (2.24875 iter/s, 5.33629s/12 iters), loss = 0.187411
I0409 22:32:45.003540 26212 solver.cpp:237]     Train net output #0: loss = 0.187411 (* 1 = 0.187411 loss)
I0409 22:32:45.003551 26212 sgd_solver.cpp:105] Iteration 8052, lr = 0.00202911
I0409 22:32:47.210086 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8058.caffemodel
I0409 22:32:49.178694 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8058.solverstate
I0409 22:32:50.043454 26212 solver.cpp:330] Iteration 8058, Testing net (#0)
I0409 22:32:50.043473 26212 net.cpp:676] Ignoring source layer train-data
I0409 22:32:51.361255 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:32:54.513267 26212 solver.cpp:397]     Test net output #0: accuracy = 0.340686
I0409 22:32:54.513314 26212 solver.cpp:397]     Test net output #1: loss = 4.66972 (* 1 = 4.66972 loss)
I0409 22:32:56.492007 26212 solver.cpp:218] Iteration 8064 (1.04455 iter/s, 11.4882s/12 iters), loss = 0.0554346
I0409 22:32:56.492065 26212 solver.cpp:237]     Train net output #0: loss = 0.0554347 (* 1 = 0.0554347 loss)
I0409 22:32:56.492079 26212 sgd_solver.cpp:105] Iteration 8064, lr = 0.00202429
I0409 22:33:01.549891 26212 solver.cpp:218] Iteration 8076 (2.37262 iter/s, 5.05769s/12 iters), loss = 0.0766045
I0409 22:33:01.549937 26212 solver.cpp:237]     Train net output #0: loss = 0.0766045 (* 1 = 0.0766045 loss)
I0409 22:33:01.549947 26212 sgd_solver.cpp:105] Iteration 8076, lr = 0.00201949
I0409 22:33:06.515445 26212 solver.cpp:218] Iteration 8088 (2.41673 iter/s, 4.96538s/12 iters), loss = 0.12389
I0409 22:33:06.515486 26212 solver.cpp:237]     Train net output #0: loss = 0.12389 (* 1 = 0.12389 loss)
I0409 22:33:06.515494 26212 sgd_solver.cpp:105] Iteration 8088, lr = 0.00201469
I0409 22:33:07.925657 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:33:11.442013 26212 solver.cpp:218] Iteration 8100 (2.43586 iter/s, 4.92639s/12 iters), loss = 0.132174
I0409 22:33:11.442059 26212 solver.cpp:237]     Train net output #0: loss = 0.132174 (* 1 = 0.132174 loss)
I0409 22:33:11.442068 26212 sgd_solver.cpp:105] Iteration 8100, lr = 0.00200991
I0409 22:33:16.330417 26212 solver.cpp:218] Iteration 8112 (2.45488 iter/s, 4.88823s/12 iters), loss = 0.111715
I0409 22:33:16.330543 26212 solver.cpp:237]     Train net output #0: loss = 0.111715 (* 1 = 0.111715 loss)
I0409 22:33:16.330554 26212 sgd_solver.cpp:105] Iteration 8112, lr = 0.00200514
I0409 22:33:21.285120 26212 solver.cpp:218] Iteration 8124 (2.42207 iter/s, 4.95445s/12 iters), loss = 0.206127
I0409 22:33:21.285168 26212 solver.cpp:237]     Train net output #0: loss = 0.206127 (* 1 = 0.206127 loss)
I0409 22:33:21.285181 26212 sgd_solver.cpp:105] Iteration 8124, lr = 0.00200038
I0409 22:33:26.153851 26212 solver.cpp:218] Iteration 8136 (2.4648 iter/s, 4.86855s/12 iters), loss = 0.202434
I0409 22:33:26.153898 26212 solver.cpp:237]     Train net output #0: loss = 0.202434 (* 1 = 0.202434 loss)
I0409 22:33:26.153906 26212 sgd_solver.cpp:105] Iteration 8136, lr = 0.00199563
I0409 22:33:31.071437 26212 solver.cpp:218] Iteration 8148 (2.44031 iter/s, 4.91741s/12 iters), loss = 0.1511
I0409 22:33:31.071487 26212 solver.cpp:237]     Train net output #0: loss = 0.151101 (* 1 = 0.151101 loss)
I0409 22:33:31.071498 26212 sgd_solver.cpp:105] Iteration 8148, lr = 0.00199089
I0409 22:33:35.535672 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8160.caffemodel
I0409 22:33:45.955250 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8160.solverstate
I0409 22:33:53.831799 26212 solver.cpp:330] Iteration 8160, Testing net (#0)
I0409 22:33:53.831909 26212 net.cpp:676] Ignoring source layer train-data
I0409 22:33:55.187793 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:33:58.444630 26212 solver.cpp:397]     Test net output #0: accuracy = 0.331495
I0409 22:33:58.444684 26212 solver.cpp:397]     Test net output #1: loss = 4.72582 (* 1 = 4.72582 loss)
I0409 22:33:58.528698 26212 solver.cpp:218] Iteration 8160 (0.437054 iter/s, 27.4565s/12 iters), loss = 0.155812
I0409 22:33:58.528754 26212 solver.cpp:237]     Train net output #0: loss = 0.155812 (* 1 = 0.155812 loss)
I0409 22:33:58.528765 26212 sgd_solver.cpp:105] Iteration 8160, lr = 0.00198616
I0409 22:34:02.616173 26212 solver.cpp:218] Iteration 8172 (2.93592 iter/s, 4.08731s/12 iters), loss = 0.17667
I0409 22:34:02.616223 26212 solver.cpp:237]     Train net output #0: loss = 0.17667 (* 1 = 0.17667 loss)
I0409 22:34:02.616235 26212 sgd_solver.cpp:105] Iteration 8172, lr = 0.00198145
I0409 22:34:07.550348 26212 solver.cpp:218] Iteration 8184 (2.43211 iter/s, 4.93399s/12 iters), loss = 0.130523
I0409 22:34:07.550393 26212 solver.cpp:237]     Train net output #0: loss = 0.130523 (* 1 = 0.130523 loss)
I0409 22:34:07.550405 26212 sgd_solver.cpp:105] Iteration 8184, lr = 0.00197674
I0409 22:34:11.054648 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:34:12.463975 26212 solver.cpp:218] Iteration 8196 (2.44228 iter/s, 4.91344s/12 iters), loss = 0.317314
I0409 22:34:12.464032 26212 solver.cpp:237]     Train net output #0: loss = 0.317314 (* 1 = 0.317314 loss)
I0409 22:34:12.464043 26212 sgd_solver.cpp:105] Iteration 8196, lr = 0.00197205
I0409 22:34:17.391862 26212 solver.cpp:218] Iteration 8208 (2.43521 iter/s, 4.92771s/12 iters), loss = 0.0916969
I0409 22:34:17.391903 26212 solver.cpp:237]     Train net output #0: loss = 0.0916969 (* 1 = 0.0916969 loss)
I0409 22:34:17.391912 26212 sgd_solver.cpp:105] Iteration 8208, lr = 0.00196737
I0409 22:34:22.270751 26212 solver.cpp:218] Iteration 8220 (2.45966 iter/s, 4.87872s/12 iters), loss = 0.13917
I0409 22:34:22.270797 26212 solver.cpp:237]     Train net output #0: loss = 0.13917 (* 1 = 0.13917 loss)
I0409 22:34:22.270807 26212 sgd_solver.cpp:105] Iteration 8220, lr = 0.0019627
I0409 22:34:27.199836 26212 solver.cpp:218] Iteration 8232 (2.43462 iter/s, 4.9289s/12 iters), loss = 0.097716
I0409 22:34:27.199946 26212 solver.cpp:237]     Train net output #0: loss = 0.097716 (* 1 = 0.097716 loss)
I0409 22:34:27.199959 26212 sgd_solver.cpp:105] Iteration 8232, lr = 0.00195804
I0409 22:34:32.110992 26212 solver.cpp:218] Iteration 8244 (2.44354 iter/s, 4.91092s/12 iters), loss = 0.144133
I0409 22:34:32.111040 26212 solver.cpp:237]     Train net output #0: loss = 0.144133 (* 1 = 0.144133 loss)
I0409 22:34:32.111052 26212 sgd_solver.cpp:105] Iteration 8244, lr = 0.00195339
I0409 22:34:36.997110 26212 solver.cpp:218] Iteration 8256 (2.45603 iter/s, 4.88594s/12 iters), loss = 0.152395
I0409 22:34:36.997156 26212 solver.cpp:237]     Train net output #0: loss = 0.152395 (* 1 = 0.152395 loss)
I0409 22:34:36.997165 26212 sgd_solver.cpp:105] Iteration 8256, lr = 0.00194875
I0409 22:34:38.999827 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8262.caffemodel
I0409 22:34:41.766861 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8262.solverstate
I0409 22:34:43.560231 26212 solver.cpp:330] Iteration 8262, Testing net (#0)
I0409 22:34:43.560259 26212 net.cpp:676] Ignoring source layer train-data
I0409 22:34:44.752014 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:34:47.991006 26212 solver.cpp:397]     Test net output #0: accuracy = 0.332108
I0409 22:34:47.991055 26212 solver.cpp:397]     Test net output #1: loss = 4.64476 (* 1 = 4.64476 loss)
I0409 22:34:49.765997 26212 solver.cpp:218] Iteration 8268 (0.939811 iter/s, 12.7685s/12 iters), loss = 0.12121
I0409 22:34:49.766049 26212 solver.cpp:237]     Train net output #0: loss = 0.12121 (* 1 = 0.12121 loss)
I0409 22:34:49.766060 26212 sgd_solver.cpp:105] Iteration 8268, lr = 0.00194412
I0409 22:34:54.711216 26212 solver.cpp:218] Iteration 8280 (2.42668 iter/s, 4.94503s/12 iters), loss = 0.183639
I0409 22:34:54.711272 26212 solver.cpp:237]     Train net output #0: loss = 0.183639 (* 1 = 0.183639 loss)
I0409 22:34:54.711282 26212 sgd_solver.cpp:105] Iteration 8280, lr = 0.00193951
I0409 22:34:59.718735 26212 solver.cpp:218] Iteration 8292 (2.39649 iter/s, 5.00733s/12 iters), loss = 0.0974761
I0409 22:34:59.718896 26212 solver.cpp:237]     Train net output #0: loss = 0.0974762 (* 1 = 0.0974762 loss)
I0409 22:34:59.718909 26212 sgd_solver.cpp:105] Iteration 8292, lr = 0.0019349
I0409 22:35:00.403995 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:35:04.686408 26212 solver.cpp:218] Iteration 8304 (2.41576 iter/s, 4.96738s/12 iters), loss = 0.155555
I0409 22:35:04.686460 26212 solver.cpp:237]     Train net output #0: loss = 0.155555 (* 1 = 0.155555 loss)
I0409 22:35:04.686472 26212 sgd_solver.cpp:105] Iteration 8304, lr = 0.00193031
I0409 22:35:07.097743 26212 blocking_queue.cpp:49] Waiting for data
I0409 22:35:09.614667 26212 solver.cpp:218] Iteration 8316 (2.43503 iter/s, 4.92808s/12 iters), loss = 0.173446
I0409 22:35:09.614717 26212 solver.cpp:237]     Train net output #0: loss = 0.173446 (* 1 = 0.173446 loss)
I0409 22:35:09.614727 26212 sgd_solver.cpp:105] Iteration 8316, lr = 0.00192573
I0409 22:35:14.574748 26212 solver.cpp:218] Iteration 8328 (2.4194 iter/s, 4.9599s/12 iters), loss = 0.348969
I0409 22:35:14.574788 26212 solver.cpp:237]     Train net output #0: loss = 0.348969 (* 1 = 0.348969 loss)
I0409 22:35:14.574797 26212 sgd_solver.cpp:105] Iteration 8328, lr = 0.00192115
I0409 22:35:19.579102 26212 solver.cpp:218] Iteration 8340 (2.398 iter/s, 5.00418s/12 iters), loss = 0.129485
I0409 22:35:19.579146 26212 solver.cpp:237]     Train net output #0: loss = 0.129485 (* 1 = 0.129485 loss)
I0409 22:35:19.579155 26212 sgd_solver.cpp:105] Iteration 8340, lr = 0.00191659
I0409 22:35:24.720124 26212 solver.cpp:218] Iteration 8352 (2.33425 iter/s, 5.14084s/12 iters), loss = 0.127178
I0409 22:35:24.720172 26212 solver.cpp:237]     Train net output #0: loss = 0.127178 (* 1 = 0.127178 loss)
I0409 22:35:24.720181 26212 sgd_solver.cpp:105] Iteration 8352, lr = 0.00191204
I0409 22:35:29.319362 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8364.caffemodel
I0409 22:35:33.674922 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8364.solverstate
I0409 22:35:37.864959 26212 solver.cpp:330] Iteration 8364, Testing net (#0)
I0409 22:35:37.864990 26212 net.cpp:676] Ignoring source layer train-data
I0409 22:35:39.044518 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:35:42.330058 26212 solver.cpp:397]     Test net output #0: accuracy = 0.333333
I0409 22:35:42.330106 26212 solver.cpp:397]     Test net output #1: loss = 4.606 (* 1 = 4.606 loss)
I0409 22:35:42.413805 26212 solver.cpp:218] Iteration 8364 (0.678227 iter/s, 17.6932s/12 iters), loss = 0.177618
I0409 22:35:42.413858 26212 solver.cpp:237]     Train net output #0: loss = 0.177618 (* 1 = 0.177618 loss)
I0409 22:35:42.413870 26212 sgd_solver.cpp:105] Iteration 8364, lr = 0.0019075
I0409 22:35:46.656499 26212 solver.cpp:218] Iteration 8376 (2.82851 iter/s, 4.24252s/12 iters), loss = 0.286815
I0409 22:35:46.656546 26212 solver.cpp:237]     Train net output #0: loss = 0.286815 (* 1 = 0.286815 loss)
I0409 22:35:46.656555 26212 sgd_solver.cpp:105] Iteration 8376, lr = 0.00190297
I0409 22:35:51.567414 26212 solver.cpp:218] Iteration 8388 (2.44362 iter/s, 4.91074s/12 iters), loss = 0.115757
I0409 22:35:51.567451 26212 solver.cpp:237]     Train net output #0: loss = 0.115757 (* 1 = 0.115757 loss)
I0409 22:35:51.567459 26212 sgd_solver.cpp:105] Iteration 8388, lr = 0.00189846
I0409 22:35:54.425581 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:35:56.623950 26212 solver.cpp:218] Iteration 8400 (2.37325 iter/s, 5.05636s/12 iters), loss = 0.206004
I0409 22:35:56.623999 26212 solver.cpp:237]     Train net output #0: loss = 0.206004 (* 1 = 0.206004 loss)
I0409 22:35:56.624011 26212 sgd_solver.cpp:105] Iteration 8400, lr = 0.00189395
I0409 22:36:01.592407 26212 solver.cpp:218] Iteration 8412 (2.41532 iter/s, 4.96828s/12 iters), loss = 0.179339
I0409 22:36:01.592450 26212 solver.cpp:237]     Train net output #0: loss = 0.179339 (* 1 = 0.179339 loss)
I0409 22:36:01.592459 26212 sgd_solver.cpp:105] Iteration 8412, lr = 0.00188945
I0409 22:36:06.487140 26212 solver.cpp:218] Iteration 8424 (2.45171 iter/s, 4.89455s/12 iters), loss = 0.222852
I0409 22:36:06.487277 26212 solver.cpp:237]     Train net output #0: loss = 0.222852 (* 1 = 0.222852 loss)
I0409 22:36:06.487287 26212 sgd_solver.cpp:105] Iteration 8424, lr = 0.00188497
I0409 22:36:11.613219 26212 solver.cpp:218] Iteration 8436 (2.34109 iter/s, 5.12581s/12 iters), loss = 0.0822012
I0409 22:36:11.613261 26212 solver.cpp:237]     Train net output #0: loss = 0.0822013 (* 1 = 0.0822013 loss)
I0409 22:36:11.613271 26212 sgd_solver.cpp:105] Iteration 8436, lr = 0.00188049
I0409 22:36:16.522488 26212 solver.cpp:218] Iteration 8448 (2.44444 iter/s, 4.9091s/12 iters), loss = 0.107559
I0409 22:36:16.522534 26212 solver.cpp:237]     Train net output #0: loss = 0.107559 (* 1 = 0.107559 loss)
I0409 22:36:16.522545 26212 sgd_solver.cpp:105] Iteration 8448, lr = 0.00187603
I0409 22:36:21.479643 26212 solver.cpp:218] Iteration 8460 (2.42083 iter/s, 4.95698s/12 iters), loss = 0.178179
I0409 22:36:21.479678 26212 solver.cpp:237]     Train net output #0: loss = 0.178179 (* 1 = 0.178179 loss)
I0409 22:36:21.479686 26212 sgd_solver.cpp:105] Iteration 8460, lr = 0.00187157
I0409 22:36:23.461367 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8466.caffemodel
I0409 22:36:30.216418 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8466.solverstate
I0409 22:36:37.817922 26212 solver.cpp:330] Iteration 8466, Testing net (#0)
I0409 22:36:37.818037 26212 net.cpp:676] Ignoring source layer train-data
I0409 22:36:38.960770 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:36:42.275776 26212 solver.cpp:397]     Test net output #0: accuracy = 0.336397
I0409 22:36:42.275822 26212 solver.cpp:397]     Test net output #1: loss = 4.56164 (* 1 = 4.56164 loss)
I0409 22:36:44.155339 26212 solver.cpp:218] Iteration 8472 (0.529215 iter/s, 22.6751s/12 iters), loss = 0.144271
I0409 22:36:44.155395 26212 solver.cpp:237]     Train net output #0: loss = 0.144271 (* 1 = 0.144271 loss)
I0409 22:36:44.155408 26212 sgd_solver.cpp:105] Iteration 8472, lr = 0.00186713
I0409 22:36:49.117311 26212 solver.cpp:218] Iteration 8484 (2.41849 iter/s, 4.96178s/12 iters), loss = 0.0625695
I0409 22:36:49.117359 26212 solver.cpp:237]     Train net output #0: loss = 0.0625696 (* 1 = 0.0625696 loss)
I0409 22:36:49.117369 26212 sgd_solver.cpp:105] Iteration 8484, lr = 0.0018627
I0409 22:36:54.038147 26212 solver.cpp:218] Iteration 8496 (2.4387 iter/s, 4.92065s/12 iters), loss = 0.107452
I0409 22:36:54.038203 26212 solver.cpp:237]     Train net output #0: loss = 0.107452 (* 1 = 0.107452 loss)
I0409 22:36:54.038218 26212 sgd_solver.cpp:105] Iteration 8496, lr = 0.00185827
I0409 22:36:54.085886 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:36:58.900636 26212 solver.cpp:218] Iteration 8508 (2.46797 iter/s, 4.8623s/12 iters), loss = 0.101723
I0409 22:36:58.900686 26212 solver.cpp:237]     Train net output #0: loss = 0.101723 (* 1 = 0.101723 loss)
I0409 22:36:58.900696 26212 sgd_solver.cpp:105] Iteration 8508, lr = 0.00185386
I0409 22:37:03.905364 26212 solver.cpp:218] Iteration 8520 (2.39782 iter/s, 5.00455s/12 iters), loss = 0.222643
I0409 22:37:03.905400 26212 solver.cpp:237]     Train net output #0: loss = 0.222643 (* 1 = 0.222643 loss)
I0409 22:37:03.905407 26212 sgd_solver.cpp:105] Iteration 8520, lr = 0.00184946
I0409 22:37:08.789129 26212 solver.cpp:218] Iteration 8532 (2.45721 iter/s, 4.88359s/12 iters), loss = 0.188962
I0409 22:37:08.789283 26212 solver.cpp:237]     Train net output #0: loss = 0.188962 (* 1 = 0.188962 loss)
I0409 22:37:08.789296 26212 sgd_solver.cpp:105] Iteration 8532, lr = 0.00184507
I0409 22:37:13.723588 26212 solver.cpp:218] Iteration 8544 (2.43202 iter/s, 4.93418s/12 iters), loss = 0.143123
I0409 22:37:13.723640 26212 solver.cpp:237]     Train net output #0: loss = 0.143123 (* 1 = 0.143123 loss)
I0409 22:37:13.723654 26212 sgd_solver.cpp:105] Iteration 8544, lr = 0.00184069
I0409 22:37:18.660377 26212 solver.cpp:218] Iteration 8556 (2.43082 iter/s, 4.9366s/12 iters), loss = 0.227708
I0409 22:37:18.660432 26212 solver.cpp:237]     Train net output #0: loss = 0.227708 (* 1 = 0.227708 loss)
I0409 22:37:18.660444 26212 sgd_solver.cpp:105] Iteration 8556, lr = 0.00183632
I0409 22:37:23.123461 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8568.caffemodel
I0409 22:37:25.978209 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8568.solverstate
I0409 22:37:28.257660 26212 solver.cpp:330] Iteration 8568, Testing net (#0)
I0409 22:37:28.257684 26212 net.cpp:676] Ignoring source layer train-data
I0409 22:37:29.362423 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:37:32.702853 26212 solver.cpp:397]     Test net output #0: accuracy = 0.326593
I0409 22:37:32.702900 26212 solver.cpp:397]     Test net output #1: loss = 4.5516 (* 1 = 4.5516 loss)
I0409 22:37:32.786571 26212 solver.cpp:218] Iteration 8568 (0.84951 iter/s, 14.1258s/12 iters), loss = 0.0867213
I0409 22:37:32.786614 26212 solver.cpp:237]     Train net output #0: loss = 0.0867214 (* 1 = 0.0867214 loss)
I0409 22:37:32.786623 26212 sgd_solver.cpp:105] Iteration 8568, lr = 0.00183196
I0409 22:37:37.024705 26212 solver.cpp:218] Iteration 8580 (2.83154 iter/s, 4.23798s/12 iters), loss = 0.180105
I0409 22:37:37.024750 26212 solver.cpp:237]     Train net output #0: loss = 0.180105 (* 1 = 0.180105 loss)
I0409 22:37:37.024761 26212 sgd_solver.cpp:105] Iteration 8580, lr = 0.00182761
I0409 22:37:41.951470 26212 solver.cpp:218] Iteration 8592 (2.43576 iter/s, 4.92658s/12 iters), loss = 0.178808
I0409 22:37:41.951579 26212 solver.cpp:237]     Train net output #0: loss = 0.178808 (* 1 = 0.178808 loss)
I0409 22:37:41.951592 26212 sgd_solver.cpp:105] Iteration 8592, lr = 0.00182327
I0409 22:37:44.108276 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:37:47.051529 26212 solver.cpp:218] Iteration 8604 (2.35303 iter/s, 5.09981s/12 iters), loss = 0.150904
I0409 22:37:47.051584 26212 solver.cpp:237]     Train net output #0: loss = 0.150904 (* 1 = 0.150904 loss)
I0409 22:37:47.051595 26212 sgd_solver.cpp:105] Iteration 8604, lr = 0.00181894
I0409 22:37:51.989677 26212 solver.cpp:218] Iteration 8616 (2.43015 iter/s, 4.93796s/12 iters), loss = 0.346274
I0409 22:37:51.989732 26212 solver.cpp:237]     Train net output #0: loss = 0.346274 (* 1 = 0.346274 loss)
I0409 22:37:51.989744 26212 sgd_solver.cpp:105] Iteration 8616, lr = 0.00181462
I0409 22:37:56.907263 26212 solver.cpp:218] Iteration 8628 (2.44031 iter/s, 4.9174s/12 iters), loss = 0.0651573
I0409 22:37:56.907316 26212 solver.cpp:237]     Train net output #0: loss = 0.0651574 (* 1 = 0.0651574 loss)
I0409 22:37:56.907326 26212 sgd_solver.cpp:105] Iteration 8628, lr = 0.00181031
I0409 22:38:01.831321 26212 solver.cpp:218] Iteration 8640 (2.43711 iter/s, 4.92387s/12 iters), loss = 0.156927
I0409 22:38:01.831372 26212 solver.cpp:237]     Train net output #0: loss = 0.156927 (* 1 = 0.156927 loss)
I0409 22:38:01.831382 26212 sgd_solver.cpp:105] Iteration 8640, lr = 0.00180602
I0409 22:38:06.974176 26212 solver.cpp:218] Iteration 8652 (2.33342 iter/s, 5.14267s/12 iters), loss = 0.0452631
I0409 22:38:06.974225 26212 solver.cpp:237]     Train net output #0: loss = 0.0452632 (* 1 = 0.0452632 loss)
I0409 22:38:06.974234 26212 sgd_solver.cpp:105] Iteration 8652, lr = 0.00180173
I0409 22:38:12.004808 26212 solver.cpp:218] Iteration 8664 (2.38547 iter/s, 5.03045s/12 iters), loss = 0.119677
I0409 22:38:12.004925 26212 solver.cpp:237]     Train net output #0: loss = 0.119677 (* 1 = 0.119677 loss)
I0409 22:38:12.004933 26212 sgd_solver.cpp:105] Iteration 8664, lr = 0.00179745
I0409 22:38:14.054401 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8670.caffemodel
I0409 22:38:15.247642 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8670.solverstate
I0409 22:38:16.116750 26212 solver.cpp:330] Iteration 8670, Testing net (#0)
I0409 22:38:16.116773 26212 net.cpp:676] Ignoring source layer train-data
I0409 22:38:17.278522 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:38:20.704669 26212 solver.cpp:397]     Test net output #0: accuracy = 0.336397
I0409 22:38:20.704713 26212 solver.cpp:397]     Test net output #1: loss = 4.56706 (* 1 = 4.56706 loss)
I0409 22:38:22.576328 26212 solver.cpp:218] Iteration 8676 (1.13517 iter/s, 10.5711s/12 iters), loss = 0.110448
I0409 22:38:22.576376 26212 solver.cpp:237]     Train net output #0: loss = 0.110448 (* 1 = 0.110448 loss)
I0409 22:38:22.576386 26212 sgd_solver.cpp:105] Iteration 8676, lr = 0.00179318
I0409 22:38:27.559603 26212 solver.cpp:218] Iteration 8688 (2.40814 iter/s, 4.98309s/12 iters), loss = 0.0384772
I0409 22:38:27.559654 26212 solver.cpp:237]     Train net output #0: loss = 0.0384773 (* 1 = 0.0384773 loss)
I0409 22:38:27.559666 26212 sgd_solver.cpp:105] Iteration 8688, lr = 0.00178893
I0409 22:38:31.884088 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:38:32.562490 26212 solver.cpp:218] Iteration 8700 (2.3987 iter/s, 5.0027s/12 iters), loss = 0.279553
I0409 22:38:32.562537 26212 solver.cpp:237]     Train net output #0: loss = 0.279553 (* 1 = 0.279553 loss)
I0409 22:38:32.562548 26212 sgd_solver.cpp:105] Iteration 8700, lr = 0.00178468
I0409 22:38:37.518333 26212 solver.cpp:218] Iteration 8712 (2.42147 iter/s, 4.95567s/12 iters), loss = 0.0914812
I0409 22:38:37.518378 26212 solver.cpp:237]     Train net output #0: loss = 0.0914813 (* 1 = 0.0914813 loss)
I0409 22:38:37.518386 26212 sgd_solver.cpp:105] Iteration 8712, lr = 0.00178044
I0409 22:38:42.457434 26212 solver.cpp:218] Iteration 8724 (2.42968 iter/s, 4.93892s/12 iters), loss = 0.125234
I0409 22:38:42.457553 26212 solver.cpp:237]     Train net output #0: loss = 0.125234 (* 1 = 0.125234 loss)
I0409 22:38:42.457567 26212 sgd_solver.cpp:105] Iteration 8724, lr = 0.00177621
I0409 22:38:47.326084 26212 solver.cpp:218] Iteration 8736 (2.46487 iter/s, 4.8684s/12 iters), loss = 0.162254
I0409 22:38:47.326135 26212 solver.cpp:237]     Train net output #0: loss = 0.162254 (* 1 = 0.162254 loss)
I0409 22:38:47.326148 26212 sgd_solver.cpp:105] Iteration 8736, lr = 0.001772
I0409 22:38:52.328498 26212 solver.cpp:218] Iteration 8748 (2.39893 iter/s, 5.00223s/12 iters), loss = 0.215031
I0409 22:38:52.328547 26212 solver.cpp:237]     Train net output #0: loss = 0.215031 (* 1 = 0.215031 loss)
I0409 22:38:52.328558 26212 sgd_solver.cpp:105] Iteration 8748, lr = 0.00176779
I0409 22:38:57.341609 26212 solver.cpp:218] Iteration 8760 (2.39381 iter/s, 5.01293s/12 iters), loss = 0.0754132
I0409 22:38:57.341653 26212 solver.cpp:237]     Train net output #0: loss = 0.0754133 (* 1 = 0.0754133 loss)
I0409 22:38:57.341666 26212 sgd_solver.cpp:105] Iteration 8760, lr = 0.00176359
I0409 22:39:01.791463 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8772.caffemodel
I0409 22:39:02.942719 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8772.solverstate
I0409 22:39:04.139106 26212 solver.cpp:330] Iteration 8772, Testing net (#0)
I0409 22:39:04.139134 26212 net.cpp:676] Ignoring source layer train-data
I0409 22:39:05.047643 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:39:08.584075 26212 solver.cpp:397]     Test net output #0: accuracy = 0.341299
I0409 22:39:08.584123 26212 solver.cpp:397]     Test net output #1: loss = 4.63287 (* 1 = 4.63287 loss)
I0409 22:39:08.668011 26212 solver.cpp:218] Iteration 8772 (1.0595 iter/s, 11.3261s/12 iters), loss = 0.124398
I0409 22:39:08.668063 26212 solver.cpp:237]     Train net output #0: loss = 0.124399 (* 1 = 0.124399 loss)
I0409 22:39:08.668074 26212 sgd_solver.cpp:105] Iteration 8772, lr = 0.00175941
I0409 22:39:12.936020 26212 solver.cpp:218] Iteration 8784 (2.81173 iter/s, 4.26784s/12 iters), loss = 0.122423
I0409 22:39:12.936169 26212 solver.cpp:237]     Train net output #0: loss = 0.122423 (* 1 = 0.122423 loss)
I0409 22:39:12.936184 26212 sgd_solver.cpp:105] Iteration 8784, lr = 0.00175523
I0409 22:39:17.982172 26212 solver.cpp:218] Iteration 8796 (2.37818 iter/s, 5.04587s/12 iters), loss = 0.148618
I0409 22:39:17.982228 26212 solver.cpp:237]     Train net output #0: loss = 0.148618 (* 1 = 0.148618 loss)
I0409 22:39:17.982240 26212 sgd_solver.cpp:105] Iteration 8796, lr = 0.00175106
I0409 22:39:19.431970 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:39:22.960021 26212 solver.cpp:218] Iteration 8808 (2.41077 iter/s, 4.97766s/12 iters), loss = 0.253156
I0409 22:39:22.960072 26212 solver.cpp:237]     Train net output #0: loss = 0.253156 (* 1 = 0.253156 loss)
I0409 22:39:22.960084 26212 sgd_solver.cpp:105] Iteration 8808, lr = 0.0017469
I0409 22:39:27.885740 26212 solver.cpp:218] Iteration 8820 (2.43629 iter/s, 4.92553s/12 iters), loss = 0.0967663
I0409 22:39:27.885792 26212 solver.cpp:237]     Train net output #0: loss = 0.0967664 (* 1 = 0.0967664 loss)
I0409 22:39:27.885802 26212 sgd_solver.cpp:105] Iteration 8820, lr = 0.00174276
I0409 22:39:32.763861 26212 solver.cpp:218] Iteration 8832 (2.46006 iter/s, 4.87794s/12 iters), loss = 0.020213
I0409 22:39:32.763911 26212 solver.cpp:237]     Train net output #0: loss = 0.0202131 (* 1 = 0.0202131 loss)
I0409 22:39:32.763921 26212 sgd_solver.cpp:105] Iteration 8832, lr = 0.00173862
I0409 22:39:37.658886 26212 solver.cpp:218] Iteration 8844 (2.45156 iter/s, 4.89484s/12 iters), loss = 0.208747
I0409 22:39:37.658943 26212 solver.cpp:237]     Train net output #0: loss = 0.208747 (* 1 = 0.208747 loss)
I0409 22:39:37.658955 26212 sgd_solver.cpp:105] Iteration 8844, lr = 0.00173449
I0409 22:39:42.590401 26212 solver.cpp:218] Iteration 8856 (2.43342 iter/s, 4.93133s/12 iters), loss = 0.102527
I0409 22:39:42.590451 26212 solver.cpp:237]     Train net output #0: loss = 0.102527 (* 1 = 0.102527 loss)
I0409 22:39:42.590461 26212 sgd_solver.cpp:105] Iteration 8856, lr = 0.00173037
I0409 22:39:47.515007 26212 solver.cpp:218] Iteration 8868 (2.43683 iter/s, 4.92442s/12 iters), loss = 0.0749054
I0409 22:39:47.515116 26212 solver.cpp:237]     Train net output #0: loss = 0.0749054 (* 1 = 0.0749054 loss)
I0409 22:39:47.515128 26212 sgd_solver.cpp:105] Iteration 8868, lr = 0.00172626
I0409 22:39:49.513772 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8874.caffemodel
I0409 22:39:50.884433 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8874.solverstate
I0409 22:39:51.761193 26212 solver.cpp:330] Iteration 8874, Testing net (#0)
I0409 22:39:51.761219 26212 net.cpp:676] Ignoring source layer train-data
I0409 22:39:52.739738 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:39:56.307806 26212 solver.cpp:397]     Test net output #0: accuracy = 0.343137
I0409 22:39:56.307855 26212 solver.cpp:397]     Test net output #1: loss = 4.58827 (* 1 = 4.58827 loss)
I0409 22:39:58.274169 26212 solver.cpp:218] Iteration 8880 (1.11537 iter/s, 10.7588s/12 iters), loss = 0.155962
I0409 22:39:58.274219 26212 solver.cpp:237]     Train net output #0: loss = 0.155962 (* 1 = 0.155962 loss)
I0409 22:39:58.274226 26212 sgd_solver.cpp:105] Iteration 8880, lr = 0.00172217
I0409 22:40:03.293519 26212 solver.cpp:218] Iteration 8892 (2.39083 iter/s, 5.01917s/12 iters), loss = 0.100151
I0409 22:40:03.293617 26212 solver.cpp:237]     Train net output #0: loss = 0.100151 (* 1 = 0.100151 loss)
I0409 22:40:03.293627 26212 sgd_solver.cpp:105] Iteration 8892, lr = 0.00171808
I0409 22:40:06.795336 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:40:08.203088 26212 solver.cpp:218] Iteration 8904 (2.44432 iter/s, 4.90934s/12 iters), loss = 0.123714
I0409 22:40:08.203131 26212 solver.cpp:237]     Train net output #0: loss = 0.123714 (* 1 = 0.123714 loss)
I0409 22:40:08.203140 26212 sgd_solver.cpp:105] Iteration 8904, lr = 0.001714
I0409 22:40:13.222110 26212 solver.cpp:218] Iteration 8916 (2.39099 iter/s, 5.01885s/12 iters), loss = 0.0602998
I0409 22:40:13.222158 26212 solver.cpp:237]     Train net output #0: loss = 0.0602998 (* 1 = 0.0602998 loss)
I0409 22:40:13.222170 26212 sgd_solver.cpp:105] Iteration 8916, lr = 0.00170993
I0409 22:40:18.216938 26212 solver.cpp:218] Iteration 8928 (2.40257 iter/s, 4.99465s/12 iters), loss = 0.111358
I0409 22:40:18.217038 26212 solver.cpp:237]     Train net output #0: loss = 0.111358 (* 1 = 0.111358 loss)
I0409 22:40:18.217051 26212 sgd_solver.cpp:105] Iteration 8928, lr = 0.00170587
I0409 22:40:23.179234 26212 solver.cpp:218] Iteration 8940 (2.41835 iter/s, 4.96207s/12 iters), loss = 0.112661
I0409 22:40:23.179291 26212 solver.cpp:237]     Train net output #0: loss = 0.112661 (* 1 = 0.112661 loss)
I0409 22:40:23.179302 26212 sgd_solver.cpp:105] Iteration 8940, lr = 0.00170182
I0409 22:40:28.509829 26212 solver.cpp:218] Iteration 8952 (2.25124 iter/s, 5.3304s/12 iters), loss = 0.0587462
I0409 22:40:28.509881 26212 solver.cpp:237]     Train net output #0: loss = 0.0587462 (* 1 = 0.0587462 loss)
I0409 22:40:28.509892 26212 sgd_solver.cpp:105] Iteration 8952, lr = 0.00169778
I0409 22:40:33.520553 26212 solver.cpp:218] Iteration 8964 (2.39495 iter/s, 5.01054s/12 iters), loss = 0.162182
I0409 22:40:33.520599 26212 solver.cpp:237]     Train net output #0: loss = 0.162182 (* 1 = 0.162182 loss)
I0409 22:40:33.520609 26212 sgd_solver.cpp:105] Iteration 8964, lr = 0.00169375
I0409 22:40:38.018038 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8976.caffemodel
I0409 22:40:43.116717 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8976.solverstate
I0409 22:40:47.578085 26212 solver.cpp:330] Iteration 8976, Testing net (#0)
I0409 22:40:47.578114 26212 net.cpp:676] Ignoring source layer train-data
I0409 22:40:48.537446 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:40:52.031507 26212 solver.cpp:397]     Test net output #0: accuracy = 0.352328
I0409 22:40:52.031558 26212 solver.cpp:397]     Test net output #1: loss = 4.61622 (* 1 = 4.61622 loss)
I0409 22:40:52.115212 26212 solver.cpp:218] Iteration 8976 (0.645364 iter/s, 18.5942s/12 iters), loss = 0.0838579
I0409 22:40:52.115264 26212 solver.cpp:237]     Train net output #0: loss = 0.083858 (* 1 = 0.083858 loss)
I0409 22:40:52.115276 26212 sgd_solver.cpp:105] Iteration 8976, lr = 0.00168973
I0409 22:40:56.510519 26212 solver.cpp:218] Iteration 8988 (2.73029 iter/s, 4.39513s/12 iters), loss = 0.085899
I0409 22:40:56.510571 26212 solver.cpp:237]     Train net output #0: loss = 0.0858991 (* 1 = 0.0858991 loss)
I0409 22:40:56.510581 26212 sgd_solver.cpp:105] Iteration 8988, lr = 0.00168571
I0409 22:40:59.379206 26212 blocking_queue.cpp:49] Waiting for data
I0409 22:41:01.682574 26212 solver.cpp:218] Iteration 9000 (2.32024 iter/s, 5.17187s/12 iters), loss = 0.0566122
I0409 22:41:01.682608 26212 solver.cpp:237]     Train net output #0: loss = 0.0566122 (* 1 = 0.0566122 loss)
I0409 22:41:01.682618 26212 sgd_solver.cpp:105] Iteration 9000, lr = 0.00168171
I0409 22:41:02.441802 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:41:06.694278 26212 solver.cpp:218] Iteration 9012 (2.39448 iter/s, 5.01153s/12 iters), loss = 0.171234
I0409 22:41:06.694326 26212 solver.cpp:237]     Train net output #0: loss = 0.171234 (* 1 = 0.171234 loss)
I0409 22:41:06.694336 26212 sgd_solver.cpp:105] Iteration 9012, lr = 0.00167772
I0409 22:41:11.650805 26212 solver.cpp:218] Iteration 9024 (2.42114 iter/s, 4.95633s/12 iters), loss = 0.0441779
I0409 22:41:11.650862 26212 solver.cpp:237]     Train net output #0: loss = 0.044178 (* 1 = 0.044178 loss)
I0409 22:41:11.650874 26212 sgd_solver.cpp:105] Iteration 9024, lr = 0.00167374
I0409 22:41:16.876534 26212 solver.cpp:218] Iteration 9036 (2.29642 iter/s, 5.22554s/12 iters), loss = 0.120393
I0409 22:41:16.876583 26212 solver.cpp:237]     Train net output #0: loss = 0.120393 (* 1 = 0.120393 loss)
I0409 22:41:16.876593 26212 sgd_solver.cpp:105] Iteration 9036, lr = 0.00166976
I0409 22:41:21.815081 26212 solver.cpp:218] Iteration 9048 (2.42996 iter/s, 4.93836s/12 iters), loss = 0.0994819
I0409 22:41:21.815233 26212 solver.cpp:237]     Train net output #0: loss = 0.099482 (* 1 = 0.099482 loss)
I0409 22:41:21.815248 26212 sgd_solver.cpp:105] Iteration 9048, lr = 0.0016658
I0409 22:41:26.818676 26212 solver.cpp:218] Iteration 9060 (2.39841 iter/s, 5.00331s/12 iters), loss = 0.243607
I0409 22:41:26.818724 26212 solver.cpp:237]     Train net output #0: loss = 0.243607 (* 1 = 0.243607 loss)
I0409 22:41:26.818735 26212 sgd_solver.cpp:105] Iteration 9060, lr = 0.00166184
I0409 22:41:31.823408 26212 solver.cpp:218] Iteration 9072 (2.39782 iter/s, 5.00455s/12 iters), loss = 0.126849
I0409 22:41:31.823455 26212 solver.cpp:237]     Train net output #0: loss = 0.12685 (* 1 = 0.12685 loss)
I0409 22:41:31.823467 26212 sgd_solver.cpp:105] Iteration 9072, lr = 0.0016579
I0409 22:41:33.876961 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9078.caffemodel
I0409 22:41:35.157531 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9078.solverstate
I0409 22:41:36.676195 26212 solver.cpp:330] Iteration 9078, Testing net (#0)
I0409 22:41:36.676220 26212 net.cpp:676] Ignoring source layer train-data
I0409 22:41:37.585351 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:41:41.331100 26212 solver.cpp:397]     Test net output #0: accuracy = 0.340074
I0409 22:41:41.331149 26212 solver.cpp:397]     Test net output #1: loss = 4.63693 (* 1 = 4.63693 loss)
I0409 22:41:43.245560 26212 solver.cpp:218] Iteration 9084 (1.05062 iter/s, 11.4218s/12 iters), loss = 0.145347
I0409 22:41:43.245600 26212 solver.cpp:237]     Train net output #0: loss = 0.145347 (* 1 = 0.145347 loss)
I0409 22:41:43.245609 26212 sgd_solver.cpp:105] Iteration 9084, lr = 0.00165396
I0409 22:41:48.160331 26212 solver.cpp:218] Iteration 9096 (2.44171 iter/s, 4.9146s/12 iters), loss = 0.0738767
I0409 22:41:48.160372 26212 solver.cpp:237]     Train net output #0: loss = 0.0738767 (* 1 = 0.0738767 loss)
I0409 22:41:48.160382 26212 sgd_solver.cpp:105] Iteration 9096, lr = 0.00165003
I0409 22:41:51.025168 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:41:53.054950 26212 solver.cpp:218] Iteration 9108 (2.45176 iter/s, 4.89444s/12 iters), loss = 0.0925117
I0409 22:41:53.055032 26212 solver.cpp:237]     Train net output #0: loss = 0.0925117 (* 1 = 0.0925117 loss)
I0409 22:41:53.055044 26212 sgd_solver.cpp:105] Iteration 9108, lr = 0.00164612
I0409 22:41:58.014016 26212 solver.cpp:218] Iteration 9120 (2.41991 iter/s, 4.95886s/12 iters), loss = 0.115717
I0409 22:41:58.014056 26212 solver.cpp:237]     Train net output #0: loss = 0.115717 (* 1 = 0.115717 loss)
I0409 22:41:58.014065 26212 sgd_solver.cpp:105] Iteration 9120, lr = 0.00164221
I0409 22:42:02.891459 26212 solver.cpp:218] Iteration 9132 (2.46039 iter/s, 4.87727s/12 iters), loss = 0.0800612
I0409 22:42:02.891505 26212 solver.cpp:237]     Train net output #0: loss = 0.0800612 (* 1 = 0.0800612 loss)
I0409 22:42:02.891516 26212 sgd_solver.cpp:105] Iteration 9132, lr = 0.00163831
I0409 22:42:07.759620 26212 solver.cpp:218] Iteration 9144 (2.46509 iter/s, 4.86798s/12 iters), loss = 0.144848
I0409 22:42:07.759668 26212 solver.cpp:237]     Train net output #0: loss = 0.144848 (* 1 = 0.144848 loss)
I0409 22:42:07.759680 26212 sgd_solver.cpp:105] Iteration 9144, lr = 0.00163442
I0409 22:42:12.705330 26212 solver.cpp:218] Iteration 9156 (2.42644 iter/s, 4.94553s/12 iters), loss = 0.116353
I0409 22:42:12.705384 26212 solver.cpp:237]     Train net output #0: loss = 0.116353 (* 1 = 0.116353 loss)
I0409 22:42:12.705395 26212 sgd_solver.cpp:105] Iteration 9156, lr = 0.00163054
I0409 22:42:17.682781 26212 solver.cpp:218] Iteration 9168 (2.41096 iter/s, 4.97726s/12 iters), loss = 0.0965873
I0409 22:42:17.682827 26212 solver.cpp:237]     Train net output #0: loss = 0.0965874 (* 1 = 0.0965874 loss)
I0409 22:42:17.682835 26212 sgd_solver.cpp:105] Iteration 9168, lr = 0.00162667
I0409 22:42:22.274276 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9180.caffemodel
I0409 22:42:23.462585 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9180.solverstate
I0409 22:42:24.344966 26212 solver.cpp:330] Iteration 9180, Testing net (#0)
I0409 22:42:24.344993 26212 net.cpp:676] Ignoring source layer train-data
I0409 22:42:25.217806 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:42:28.807993 26212 solver.cpp:397]     Test net output #0: accuracy = 0.344976
I0409 22:42:28.808039 26212 solver.cpp:397]     Test net output #1: loss = 4.756 (* 1 = 4.756 loss)
I0409 22:42:28.891883 26212 solver.cpp:218] Iteration 9180 (1.07059 iter/s, 11.2088s/12 iters), loss = 0.128176
I0409 22:42:28.891937 26212 solver.cpp:237]     Train net output #0: loss = 0.128176 (* 1 = 0.128176 loss)
I0409 22:42:28.891948 26212 sgd_solver.cpp:105] Iteration 9180, lr = 0.00162281
I0409 22:42:33.129200 26212 solver.cpp:218] Iteration 9192 (2.83209 iter/s, 4.23715s/12 iters), loss = 0.167715
I0409 22:42:33.129243 26212 solver.cpp:237]     Train net output #0: loss = 0.167715 (* 1 = 0.167715 loss)
I0409 22:42:33.129251 26212 sgd_solver.cpp:105] Iteration 9192, lr = 0.00161895
I0409 22:42:38.191020 26212 solver.cpp:218] Iteration 9204 (2.37077 iter/s, 5.06164s/12 iters), loss = 0.0878915
I0409 22:42:38.191061 26212 solver.cpp:237]     Train net output #0: loss = 0.0878916 (* 1 = 0.0878916 loss)
I0409 22:42:38.191069 26212 sgd_solver.cpp:105] Iteration 9204, lr = 0.00161511
I0409 22:42:38.258693 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:42:43.159428 26212 solver.cpp:218] Iteration 9216 (2.41534 iter/s, 4.96824s/12 iters), loss = 0.0884897
I0409 22:42:43.159471 26212 solver.cpp:237]     Train net output #0: loss = 0.0884897 (* 1 = 0.0884897 loss)
I0409 22:42:43.159482 26212 sgd_solver.cpp:105] Iteration 9216, lr = 0.00161128
I0409 22:42:48.113998 26212 solver.cpp:218] Iteration 9228 (2.42209 iter/s, 4.95439s/12 iters), loss = 0.101971
I0409 22:42:48.114042 26212 solver.cpp:237]     Train net output #0: loss = 0.101971 (* 1 = 0.101971 loss)
I0409 22:42:48.114049 26212 sgd_solver.cpp:105] Iteration 9228, lr = 0.00160745
I0409 22:42:53.023793 26212 solver.cpp:218] Iteration 9240 (2.44418 iter/s, 4.90962s/12 iters), loss = 0.0575161
I0409 22:42:53.023849 26212 solver.cpp:237]     Train net output #0: loss = 0.0575161 (* 1 = 0.0575161 loss)
I0409 22:42:53.023861 26212 sgd_solver.cpp:105] Iteration 9240, lr = 0.00160363
I0409 22:42:57.976028 26212 solver.cpp:218] Iteration 9252 (2.42324 iter/s, 4.95204s/12 iters), loss = 0.0692765
I0409 22:42:57.976137 26212 solver.cpp:237]     Train net output #0: loss = 0.0692765 (* 1 = 0.0692765 loss)
I0409 22:42:57.976148 26212 sgd_solver.cpp:105] Iteration 9252, lr = 0.00159983
I0409 22:43:02.961855 26212 solver.cpp:218] Iteration 9264 (2.40694 iter/s, 4.98559s/12 iters), loss = 0.125306
I0409 22:43:02.961905 26212 solver.cpp:237]     Train net output #0: loss = 0.125306 (* 1 = 0.125306 loss)
I0409 22:43:02.961918 26212 sgd_solver.cpp:105] Iteration 9264, lr = 0.00159603
I0409 22:43:07.912415 26212 solver.cpp:218] Iteration 9276 (2.42406 iter/s, 4.95037s/12 iters), loss = 0.161242
I0409 22:43:07.912462 26212 solver.cpp:237]     Train net output #0: loss = 0.161242 (* 1 = 0.161242 loss)
I0409 22:43:07.912472 26212 sgd_solver.cpp:105] Iteration 9276, lr = 0.00159224
I0409 22:43:10.101132 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9282.caffemodel
I0409 22:43:13.107921 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9282.solverstate
I0409 22:43:15.662510 26212 solver.cpp:330] Iteration 9282, Testing net (#0)
I0409 22:43:15.662535 26212 net.cpp:676] Ignoring source layer train-data
I0409 22:43:16.463465 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:43:20.416425 26212 solver.cpp:397]     Test net output #0: accuracy = 0.346814
I0409 22:43:20.416476 26212 solver.cpp:397]     Test net output #1: loss = 4.77812 (* 1 = 4.77812 loss)
I0409 22:43:22.604265 26212 solver.cpp:218] Iteration 9288 (0.816803 iter/s, 14.6914s/12 iters), loss = 0.0855955
I0409 22:43:22.604321 26212 solver.cpp:237]     Train net output #0: loss = 0.0855956 (* 1 = 0.0855956 loss)
I0409 22:43:22.604331 26212 sgd_solver.cpp:105] Iteration 9288, lr = 0.00158846
I0409 22:43:27.440441 26212 solver.cpp:218] Iteration 9300 (2.48139 iter/s, 4.83599s/12 iters), loss = 0.0546625
I0409 22:43:27.440491 26212 solver.cpp:237]     Train net output #0: loss = 0.0546626 (* 1 = 0.0546626 loss)
I0409 22:43:27.440502 26212 sgd_solver.cpp:105] Iteration 9300, lr = 0.00158469
I0409 22:43:29.600668 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:43:32.378549 26212 solver.cpp:218] Iteration 9312 (2.43017 iter/s, 4.93793s/12 iters), loss = 0.134778
I0409 22:43:32.378594 26212 solver.cpp:237]     Train net output #0: loss = 0.134778 (* 1 = 0.134778 loss)
I0409 22:43:32.378605 26212 sgd_solver.cpp:105] Iteration 9312, lr = 0.00158092
I0409 22:43:37.305091 26212 solver.cpp:218] Iteration 9324 (2.43587 iter/s, 4.92636s/12 iters), loss = 0.158155
I0409 22:43:37.305145 26212 solver.cpp:237]     Train net output #0: loss = 0.158155 (* 1 = 0.158155 loss)
I0409 22:43:37.305155 26212 sgd_solver.cpp:105] Iteration 9324, lr = 0.00157717
I0409 22:43:42.198158 26212 solver.cpp:218] Iteration 9336 (2.45254 iter/s, 4.89288s/12 iters), loss = 0.07538
I0409 22:43:42.198211 26212 solver.cpp:237]     Train net output #0: loss = 0.07538 (* 1 = 0.07538 loss)
I0409 22:43:42.198223 26212 sgd_solver.cpp:105] Iteration 9336, lr = 0.00157343
I0409 22:43:47.126590 26212 solver.cpp:218] Iteration 9348 (2.43494 iter/s, 4.92824s/12 iters), loss = 0.0856389
I0409 22:43:47.126642 26212 solver.cpp:237]     Train net output #0: loss = 0.085639 (* 1 = 0.085639 loss)
I0409 22:43:47.126654 26212 sgd_solver.cpp:105] Iteration 9348, lr = 0.00156969
I0409 22:43:52.047202 26212 solver.cpp:218] Iteration 9360 (2.43881 iter/s, 4.92043s/12 iters), loss = 0.108789
I0409 22:43:52.047257 26212 solver.cpp:237]     Train net output #0: loss = 0.108789 (* 1 = 0.108789 loss)
I0409 22:43:52.047268 26212 sgd_solver.cpp:105] Iteration 9360, lr = 0.00156596
I0409 22:43:57.172475 26212 solver.cpp:218] Iteration 9372 (2.34143 iter/s, 5.12508s/12 iters), loss = 0.110247
I0409 22:43:57.172518 26212 solver.cpp:237]     Train net output #0: loss = 0.110247 (* 1 = 0.110247 loss)
I0409 22:43:57.172528 26212 sgd_solver.cpp:105] Iteration 9372, lr = 0.00156225
I0409 22:44:01.635704 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9384.caffemodel
I0409 22:44:03.357340 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9384.solverstate
I0409 22:44:05.133538 26212 solver.cpp:330] Iteration 9384, Testing net (#0)
I0409 22:44:05.133559 26212 net.cpp:676] Ignoring source layer train-data
I0409 22:44:05.915907 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:44:09.702569 26212 solver.cpp:397]     Test net output #0: accuracy = 0.346201
I0409 22:44:09.702622 26212 solver.cpp:397]     Test net output #1: loss = 4.64437 (* 1 = 4.64437 loss)
I0409 22:44:09.786419 26212 solver.cpp:218] Iteration 9384 (0.951355 iter/s, 12.6136s/12 iters), loss = 0.0540299
I0409 22:44:09.786468 26212 solver.cpp:237]     Train net output #0: loss = 0.05403 (* 1 = 0.05403 loss)
I0409 22:44:09.786479 26212 sgd_solver.cpp:105] Iteration 9384, lr = 0.00155854
I0409 22:44:13.881273 26212 solver.cpp:218] Iteration 9396 (2.93063 iter/s, 4.09468s/12 iters), loss = 0.0714277
I0409 22:44:13.881330 26212 solver.cpp:237]     Train net output #0: loss = 0.0714277 (* 1 = 0.0714277 loss)
I0409 22:44:13.881345 26212 sgd_solver.cpp:105] Iteration 9396, lr = 0.00155484
I0409 22:44:18.420727 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:44:19.068171 26212 solver.cpp:218] Iteration 9408 (2.31361 iter/s, 5.1867s/12 iters), loss = 0.0489557
I0409 22:44:19.068222 26212 solver.cpp:237]     Train net output #0: loss = 0.0489557 (* 1 = 0.0489557 loss)
I0409 22:44:19.068233 26212 sgd_solver.cpp:105] Iteration 9408, lr = 0.00155114
I0409 22:44:23.983973 26212 solver.cpp:218] Iteration 9420 (2.4412 iter/s, 4.91562s/12 iters), loss = 0.117327
I0409 22:44:23.984020 26212 solver.cpp:237]     Train net output #0: loss = 0.117327 (* 1 = 0.117327 loss)
I0409 22:44:23.984032 26212 sgd_solver.cpp:105] Iteration 9420, lr = 0.00154746
I0409 22:44:28.891937 26212 solver.cpp:218] Iteration 9432 (2.4451 iter/s, 4.90778s/12 iters), loss = 0.0363557
I0409 22:44:28.891989 26212 solver.cpp:237]     Train net output #0: loss = 0.0363557 (* 1 = 0.0363557 loss)
I0409 22:44:28.892001 26212 sgd_solver.cpp:105] Iteration 9432, lr = 0.00154379
I0409 22:44:33.820508 26212 solver.cpp:218] Iteration 9444 (2.43487 iter/s, 4.92838s/12 iters), loss = 0.116273
I0409 22:44:33.820624 26212 solver.cpp:237]     Train net output #0: loss = 0.116274 (* 1 = 0.116274 loss)
I0409 22:44:33.820636 26212 sgd_solver.cpp:105] Iteration 9444, lr = 0.00154012
I0409 22:44:38.746285 26212 solver.cpp:218] Iteration 9456 (2.43629 iter/s, 4.92553s/12 iters), loss = 0.0879398
I0409 22:44:38.746330 26212 solver.cpp:237]     Train net output #0: loss = 0.0879398 (* 1 = 0.0879398 loss)
I0409 22:44:38.746340 26212 sgd_solver.cpp:105] Iteration 9456, lr = 0.00153647
I0409 22:44:43.789258 26212 solver.cpp:218] Iteration 9468 (2.37964 iter/s, 5.04278s/12 iters), loss = 0.0601639
I0409 22:44:43.789319 26212 solver.cpp:237]     Train net output #0: loss = 0.0601639 (* 1 = 0.0601639 loss)
I0409 22:44:43.789331 26212 sgd_solver.cpp:105] Iteration 9468, lr = 0.00153282
I0409 22:44:48.774549 26212 solver.cpp:218] Iteration 9480 (2.40717 iter/s, 4.9851s/12 iters), loss = 0.11602
I0409 22:44:48.774593 26212 solver.cpp:237]     Train net output #0: loss = 0.11602 (* 1 = 0.11602 loss)
I0409 22:44:48.774603 26212 sgd_solver.cpp:105] Iteration 9480, lr = 0.00152918
I0409 22:44:50.772388 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9486.caffemodel
I0409 22:44:53.111703 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9486.solverstate
I0409 22:44:53.990301 26212 solver.cpp:330] Iteration 9486, Testing net (#0)
I0409 22:44:53.990329 26212 net.cpp:676] Ignoring source layer train-data
I0409 22:44:54.742539 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:44:58.572746 26212 solver.cpp:397]     Test net output #0: accuracy = 0.349877
I0409 22:44:58.572786 26212 solver.cpp:397]     Test net output #1: loss = 4.6357 (* 1 = 4.6357 loss)
I0409 22:45:00.485736 26212 solver.cpp:218] Iteration 9492 (1.02469 iter/s, 11.7108s/12 iters), loss = 0.154141
I0409 22:45:00.485787 26212 solver.cpp:237]     Train net output #0: loss = 0.154141 (* 1 = 0.154141 loss)
I0409 22:45:00.485797 26212 sgd_solver.cpp:105] Iteration 9492, lr = 0.00152555
I0409 22:45:05.333776 26212 solver.cpp:218] Iteration 9504 (2.47532 iter/s, 4.84786s/12 iters), loss = 0.0857641
I0409 22:45:05.333860 26212 solver.cpp:237]     Train net output #0: loss = 0.0857642 (* 1 = 0.0857642 loss)
I0409 22:45:05.333873 26212 sgd_solver.cpp:105] Iteration 9504, lr = 0.00152193
I0409 22:45:06.793825 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:45:10.281174 26212 solver.cpp:218] Iteration 9516 (2.42562 iter/s, 4.94719s/12 iters), loss = 0.167086
I0409 22:45:10.281217 26212 solver.cpp:237]     Train net output #0: loss = 0.167086 (* 1 = 0.167086 loss)
I0409 22:45:10.281227 26212 sgd_solver.cpp:105] Iteration 9516, lr = 0.00151831
I0409 22:45:15.237483 26212 solver.cpp:218] Iteration 9528 (2.42124 iter/s, 4.95614s/12 iters), loss = 0.0468863
I0409 22:45:15.237521 26212 solver.cpp:237]     Train net output #0: loss = 0.0468864 (* 1 = 0.0468864 loss)
I0409 22:45:15.237529 26212 sgd_solver.cpp:105] Iteration 9528, lr = 0.00151471
I0409 22:45:20.187494 26212 solver.cpp:218] Iteration 9540 (2.42433 iter/s, 4.94983s/12 iters), loss = 0.062985
I0409 22:45:20.187552 26212 solver.cpp:237]     Train net output #0: loss = 0.0629851 (* 1 = 0.0629851 loss)
I0409 22:45:20.187566 26212 sgd_solver.cpp:105] Iteration 9540, lr = 0.00151111
I0409 22:45:25.185541 26212 solver.cpp:218] Iteration 9552 (2.40103 iter/s, 4.99786s/12 iters), loss = 0.062302
I0409 22:45:25.185583 26212 solver.cpp:237]     Train net output #0: loss = 0.0623021 (* 1 = 0.0623021 loss)
I0409 22:45:25.185591 26212 sgd_solver.cpp:105] Iteration 9552, lr = 0.00150752
I0409 22:45:30.198248 26212 solver.cpp:218] Iteration 9564 (2.394 iter/s, 5.01253s/12 iters), loss = 0.0421453
I0409 22:45:30.198297 26212 solver.cpp:237]     Train net output #0: loss = 0.0421454 (* 1 = 0.0421454 loss)
I0409 22:45:30.198307 26212 sgd_solver.cpp:105] Iteration 9564, lr = 0.00150395
I0409 22:45:35.106534 26212 solver.cpp:218] Iteration 9576 (2.44494 iter/s, 4.9081s/12 iters), loss = 0.120664
I0409 22:45:35.106576 26212 solver.cpp:237]     Train net output #0: loss = 0.120664 (* 1 = 0.120664 loss)
I0409 22:45:35.106586 26212 sgd_solver.cpp:105] Iteration 9576, lr = 0.00150037
I0409 22:45:39.547325 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9588.caffemodel
I0409 22:45:46.649102 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9588.solverstate
I0409 22:45:48.884299 26212 solver.cpp:330] Iteration 9588, Testing net (#0)
I0409 22:45:48.884330 26212 net.cpp:676] Ignoring source layer train-data
I0409 22:45:49.574793 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:45:53.323396 26212 solver.cpp:397]     Test net output #0: accuracy = 0.351103
I0409 22:45:53.323444 26212 solver.cpp:397]     Test net output #1: loss = 4.72907 (* 1 = 4.72907 loss)
I0409 22:45:53.407660 26212 solver.cpp:218] Iteration 9588 (0.655715 iter/s, 18.3006s/12 iters), loss = 0.109105
I0409 22:45:53.407707 26212 solver.cpp:237]     Train net output #0: loss = 0.109105 (* 1 = 0.109105 loss)
I0409 22:45:53.407718 26212 sgd_solver.cpp:105] Iteration 9588, lr = 0.00149681
I0409 22:45:57.747014 26212 solver.cpp:218] Iteration 9600 (2.7655 iter/s, 4.33919s/12 iters), loss = 0.0586801
I0409 22:45:57.747068 26212 solver.cpp:237]     Train net output #0: loss = 0.0586802 (* 1 = 0.0586802 loss)
I0409 22:45:57.747081 26212 sgd_solver.cpp:105] Iteration 9600, lr = 0.00149326
I0409 22:46:01.489845 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:46:02.864388 26212 solver.cpp:218] Iteration 9612 (2.34504 iter/s, 5.11719s/12 iters), loss = 0.114788
I0409 22:46:02.864439 26212 solver.cpp:237]     Train net output #0: loss = 0.114788 (* 1 = 0.114788 loss)
I0409 22:46:02.864450 26212 sgd_solver.cpp:105] Iteration 9612, lr = 0.00148971
I0409 22:46:07.848650 26212 solver.cpp:218] Iteration 9624 (2.40767 iter/s, 4.98408s/12 iters), loss = 0.0344825
I0409 22:46:07.848701 26212 solver.cpp:237]     Train net output #0: loss = 0.0344826 (* 1 = 0.0344826 loss)
I0409 22:46:07.848713 26212 sgd_solver.cpp:105] Iteration 9624, lr = 0.00148618
I0409 22:46:12.751163 26212 solver.cpp:218] Iteration 9636 (2.44781 iter/s, 4.90233s/12 iters), loss = 0.0936473
I0409 22:46:12.751262 26212 solver.cpp:237]     Train net output #0: loss = 0.0936474 (* 1 = 0.0936474 loss)
I0409 22:46:12.751274 26212 sgd_solver.cpp:105] Iteration 9636, lr = 0.00148265
I0409 22:46:17.668301 26212 solver.cpp:218] Iteration 9648 (2.44056 iter/s, 4.91691s/12 iters), loss = 0.0939582
I0409 22:46:17.668349 26212 solver.cpp:237]     Train net output #0: loss = 0.0939583 (* 1 = 0.0939583 loss)
I0409 22:46:17.668360 26212 sgd_solver.cpp:105] Iteration 9648, lr = 0.00147913
I0409 22:46:22.652777 26212 solver.cpp:218] Iteration 9660 (2.40756 iter/s, 4.98429s/12 iters), loss = 0.161629
I0409 22:46:22.652846 26212 solver.cpp:237]     Train net output #0: loss = 0.16163 (* 1 = 0.16163 loss)
I0409 22:46:22.652864 26212 sgd_solver.cpp:105] Iteration 9660, lr = 0.00147562
I0409 22:46:27.636445 26212 solver.cpp:218] Iteration 9672 (2.40796 iter/s, 4.98347s/12 iters), loss = 0.0600221
I0409 22:46:27.636487 26212 solver.cpp:237]     Train net output #0: loss = 0.0600222 (* 1 = 0.0600222 loss)
I0409 22:46:27.636495 26212 sgd_solver.cpp:105] Iteration 9672, lr = 0.00147211
I0409 22:46:32.602015 26212 solver.cpp:218] Iteration 9684 (2.41673 iter/s, 4.96539s/12 iters), loss = 0.0765866
I0409 22:46:32.602066 26212 solver.cpp:237]     Train net output #0: loss = 0.0765866 (* 1 = 0.0765866 loss)
I0409 22:46:32.602077 26212 sgd_solver.cpp:105] Iteration 9684, lr = 0.00146862
I0409 22:46:34.670738 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9690.caffemodel
I0409 22:46:35.912439 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9690.solverstate
I0409 22:46:36.781011 26212 solver.cpp:330] Iteration 9690, Testing net (#0)
I0409 22:46:36.781030 26212 net.cpp:676] Ignoring source layer train-data
I0409 22:46:37.413352 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:46:40.237360 26212 blocking_queue.cpp:49] Waiting for data
I0409 22:46:41.312408 26212 solver.cpp:397]     Test net output #0: accuracy = 0.351716
I0409 22:46:41.312463 26212 solver.cpp:397]     Test net output #1: loss = 4.70905 (* 1 = 4.70905 loss)
I0409 22:46:43.118635 26212 solver.cpp:218] Iteration 9696 (1.14109 iter/s, 10.5163s/12 iters), loss = 0.126726
I0409 22:46:43.118785 26212 solver.cpp:237]     Train net output #0: loss = 0.126727 (* 1 = 0.126727 loss)
I0409 22:46:43.118800 26212 sgd_solver.cpp:105] Iteration 9696, lr = 0.00146513
I0409 22:46:48.004747 26212 solver.cpp:218] Iteration 9708 (2.45608 iter/s, 4.88584s/12 iters), loss = 0.0557281
I0409 22:46:48.004792 26212 solver.cpp:237]     Train net output #0: loss = 0.0557282 (* 1 = 0.0557282 loss)
I0409 22:46:48.004801 26212 sgd_solver.cpp:105] Iteration 9708, lr = 0.00146165
I0409 22:46:48.737491 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:46:52.902750 26212 solver.cpp:218] Iteration 9720 (2.45007 iter/s, 4.89782s/12 iters), loss = 0.0981355
I0409 22:46:52.902801 26212 solver.cpp:237]     Train net output #0: loss = 0.0981356 (* 1 = 0.0981356 loss)
I0409 22:46:52.902812 26212 sgd_solver.cpp:105] Iteration 9720, lr = 0.00145818
I0409 22:46:57.838431 26212 solver.cpp:218] Iteration 9732 (2.43136 iter/s, 4.9355s/12 iters), loss = 0.0491651
I0409 22:46:57.838475 26212 solver.cpp:237]     Train net output #0: loss = 0.0491652 (* 1 = 0.0491652 loss)
I0409 22:46:57.838483 26212 sgd_solver.cpp:105] Iteration 9732, lr = 0.00145472
I0409 22:47:02.751346 26212 solver.cpp:218] Iteration 9744 (2.44263 iter/s, 4.91274s/12 iters), loss = 0.0740233
I0409 22:47:02.751389 26212 solver.cpp:237]     Train net output #0: loss = 0.0740234 (* 1 = 0.0740234 loss)
I0409 22:47:02.751399 26212 sgd_solver.cpp:105] Iteration 9744, lr = 0.00145127
I0409 22:47:07.820446 26212 solver.cpp:218] Iteration 9756 (2.36737 iter/s, 5.06891s/12 iters), loss = 0.142906
I0409 22:47:07.820500 26212 solver.cpp:237]     Train net output #0: loss = 0.142906 (* 1 = 0.142906 loss)
I0409 22:47:07.820513 26212 sgd_solver.cpp:105] Iteration 9756, lr = 0.00144782
I0409 22:47:12.745805 26212 solver.cpp:218] Iteration 9768 (2.43646 iter/s, 4.92517s/12 iters), loss = 0.0543392
I0409 22:47:12.745851 26212 solver.cpp:237]     Train net output #0: loss = 0.0543393 (* 1 = 0.0543393 loss)
I0409 22:47:12.745862 26212 sgd_solver.cpp:105] Iteration 9768, lr = 0.00144438
I0409 22:47:17.653744 26212 solver.cpp:218] Iteration 9780 (2.44511 iter/s, 4.90776s/12 iters), loss = 0.0731684
I0409 22:47:17.653885 26212 solver.cpp:237]     Train net output #0: loss = 0.0731684 (* 1 = 0.0731684 loss)
I0409 22:47:17.653898 26212 sgd_solver.cpp:105] Iteration 9780, lr = 0.00144095
I0409 22:47:22.143218 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9792.caffemodel
I0409 22:47:27.798089 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9792.solverstate
I0409 22:47:29.632023 26212 solver.cpp:330] Iteration 9792, Testing net (#0)
I0409 22:47:29.632053 26212 net.cpp:676] Ignoring source layer train-data
I0409 22:47:30.240471 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:47:34.073750 26212 solver.cpp:397]     Test net output #0: accuracy = 0.35723
I0409 22:47:34.073799 26212 solver.cpp:397]     Test net output #1: loss = 4.76535 (* 1 = 4.76535 loss)
I0409 22:47:34.157492 26212 solver.cpp:218] Iteration 9792 (0.727132 iter/s, 16.5032s/12 iters), loss = 0.029818
I0409 22:47:34.157548 26212 solver.cpp:237]     Train net output #0: loss = 0.0298181 (* 1 = 0.0298181 loss)
I0409 22:47:34.157559 26212 sgd_solver.cpp:105] Iteration 9792, lr = 0.00143753
I0409 22:47:38.353947 26212 solver.cpp:218] Iteration 9804 (2.85967 iter/s, 4.19629s/12 iters), loss = 0.159372
I0409 22:47:38.354005 26212 solver.cpp:237]     Train net output #0: loss = 0.159372 (* 1 = 0.159372 loss)
I0409 22:47:38.354017 26212 sgd_solver.cpp:105] Iteration 9804, lr = 0.00143412
I0409 22:47:41.427824 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:47:43.454767 26212 solver.cpp:218] Iteration 9816 (2.35265 iter/s, 5.10063s/12 iters), loss = 0.091673
I0409 22:47:43.454811 26212 solver.cpp:237]     Train net output #0: loss = 0.0916731 (* 1 = 0.0916731 loss)
I0409 22:47:43.454821 26212 sgd_solver.cpp:105] Iteration 9816, lr = 0.00143072
I0409 22:47:48.413581 26212 solver.cpp:218] Iteration 9828 (2.42002 iter/s, 4.95864s/12 iters), loss = 0.11622
I0409 22:47:48.413699 26212 solver.cpp:237]     Train net output #0: loss = 0.11622 (* 1 = 0.11622 loss)
I0409 22:47:48.413713 26212 sgd_solver.cpp:105] Iteration 9828, lr = 0.00142732
I0409 22:47:53.740842 26212 solver.cpp:218] Iteration 9840 (2.25267 iter/s, 5.32701s/12 iters), loss = 0.0348081
I0409 22:47:53.740886 26212 solver.cpp:237]     Train net output #0: loss = 0.0348082 (* 1 = 0.0348082 loss)
I0409 22:47:53.740895 26212 sgd_solver.cpp:105] Iteration 9840, lr = 0.00142393
I0409 22:47:58.658476 26212 solver.cpp:218] Iteration 9852 (2.44029 iter/s, 4.91745s/12 iters), loss = 0.0860014
I0409 22:47:58.658535 26212 solver.cpp:237]     Train net output #0: loss = 0.0860014 (* 1 = 0.0860014 loss)
I0409 22:47:58.658547 26212 sgd_solver.cpp:105] Iteration 9852, lr = 0.00142055
I0409 22:48:03.685768 26212 solver.cpp:218] Iteration 9864 (2.38706 iter/s, 5.0271s/12 iters), loss = 0.0510495
I0409 22:48:03.685825 26212 solver.cpp:237]     Train net output #0: loss = 0.0510495 (* 1 = 0.0510495 loss)
I0409 22:48:03.685837 26212 sgd_solver.cpp:105] Iteration 9864, lr = 0.00141718
I0409 22:48:08.666901 26212 solver.cpp:218] Iteration 9876 (2.40918 iter/s, 4.98095s/12 iters), loss = 0.0874415
I0409 22:48:08.666942 26212 solver.cpp:237]     Train net output #0: loss = 0.0874415 (* 1 = 0.0874415 loss)
I0409 22:48:08.666950 26212 sgd_solver.cpp:105] Iteration 9876, lr = 0.00141381
I0409 22:48:13.608419 26212 solver.cpp:218] Iteration 9888 (2.42849 iter/s, 4.94134s/12 iters), loss = 0.124632
I0409 22:48:13.608479 26212 solver.cpp:237]     Train net output #0: loss = 0.124632 (* 1 = 0.124632 loss)
I0409 22:48:13.608492 26212 sgd_solver.cpp:105] Iteration 9888, lr = 0.00141045
I0409 22:48:15.579021 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9894.caffemodel
I0409 22:48:19.808284 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9894.solverstate
I0409 22:48:21.910261 26212 solver.cpp:330] Iteration 9894, Testing net (#0)
I0409 22:48:21.910286 26212 net.cpp:676] Ignoring source layer train-data
I0409 22:48:22.445320 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:48:26.381407 26212 solver.cpp:397]     Test net output #0: accuracy = 0.353554
I0409 22:48:26.381454 26212 solver.cpp:397]     Test net output #1: loss = 4.84504 (* 1 = 4.84504 loss)
I0409 22:48:28.291350 26212 solver.cpp:218] Iteration 9900 (0.817299 iter/s, 14.6825s/12 iters), loss = 0.0685987
I0409 22:48:28.291393 26212 solver.cpp:237]     Train net output #0: loss = 0.0685987 (* 1 = 0.0685987 loss)
I0409 22:48:28.291402 26212 sgd_solver.cpp:105] Iteration 9900, lr = 0.00140711
I0409 22:48:33.257045 26212 solver.cpp:218] Iteration 9912 (2.41667 iter/s, 4.96551s/12 iters), loss = 0.0606093
I0409 22:48:33.257095 26212 solver.cpp:237]     Train net output #0: loss = 0.0606094 (* 1 = 0.0606094 loss)
I0409 22:48:33.257107 26212 sgd_solver.cpp:105] Iteration 9912, lr = 0.00140377
I0409 22:48:33.355629 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:48:38.172204 26212 solver.cpp:218] Iteration 9924 (2.44152 iter/s, 4.91498s/12 iters), loss = 0.0807601
I0409 22:48:38.172255 26212 solver.cpp:237]     Train net output #0: loss = 0.0807602 (* 1 = 0.0807602 loss)
I0409 22:48:38.172267 26212 sgd_solver.cpp:105] Iteration 9924, lr = 0.00140043
I0409 22:48:43.050325 26212 solver.cpp:218] Iteration 9936 (2.46006 iter/s, 4.87794s/12 iters), loss = 0.0600362
I0409 22:48:43.050379 26212 solver.cpp:237]     Train net output #0: loss = 0.0600362 (* 1 = 0.0600362 loss)
I0409 22:48:43.050390 26212 sgd_solver.cpp:105] Iteration 9936, lr = 0.00139711
I0409 22:48:47.960705 26212 solver.cpp:218] Iteration 9948 (2.4439 iter/s, 4.91019s/12 iters), loss = 0.0266919
I0409 22:48:47.960765 26212 solver.cpp:237]     Train net output #0: loss = 0.026692 (* 1 = 0.026692 loss)
I0409 22:48:47.960778 26212 sgd_solver.cpp:105] Iteration 9948, lr = 0.00139379
I0409 22:48:52.845293 26212 solver.cpp:218] Iteration 9960 (2.4568 iter/s, 4.8844s/12 iters), loss = 0.10035
I0409 22:48:52.845407 26212 solver.cpp:237]     Train net output #0: loss = 0.10035 (* 1 = 0.10035 loss)
I0409 22:48:52.845418 26212 sgd_solver.cpp:105] Iteration 9960, lr = 0.00139048
I0409 22:48:57.757222 26212 solver.cpp:218] Iteration 9972 (2.44316 iter/s, 4.91168s/12 iters), loss = 0.0366195
I0409 22:48:57.757282 26212 solver.cpp:237]     Train net output #0: loss = 0.0366195 (* 1 = 0.0366195 loss)
I0409 22:48:57.757293 26212 sgd_solver.cpp:105] Iteration 9972, lr = 0.00138718
I0409 22:49:02.757129 26212 solver.cpp:218] Iteration 9984 (2.40014 iter/s, 4.99971s/12 iters), loss = 0.109402
I0409 22:49:02.757189 26212 solver.cpp:237]     Train net output #0: loss = 0.109403 (* 1 = 0.109403 loss)
I0409 22:49:02.757200 26212 sgd_solver.cpp:105] Iteration 9984, lr = 0.00138389
I0409 22:49:07.250823 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9996.caffemodel
I0409 22:49:10.771119 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9996.solverstate
I0409 22:49:12.210006 26212 solver.cpp:330] Iteration 9996, Testing net (#0)
I0409 22:49:12.210034 26212 net.cpp:676] Ignoring source layer train-data
I0409 22:49:12.707247 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:49:16.919775 26212 solver.cpp:397]     Test net output #0: accuracy = 0.346814
I0409 22:49:16.919823 26212 solver.cpp:397]     Test net output #1: loss = 4.72342 (* 1 = 4.72342 loss)
I0409 22:49:17.003624 26212 solver.cpp:218] Iteration 9996 (0.842337 iter/s, 14.2461s/12 iters), loss = 0.0647587
I0409 22:49:17.003671 26212 solver.cpp:237]     Train net output #0: loss = 0.0647588 (* 1 = 0.0647588 loss)
I0409 22:49:17.003684 26212 sgd_solver.cpp:105] Iteration 9996, lr = 0.0013806
I0409 22:49:21.409598 26212 solver.cpp:218] Iteration 10008 (2.72368 iter/s, 4.40581s/12 iters), loss = 0.0555512
I0409 22:49:21.409646 26212 solver.cpp:237]     Train net output #0: loss = 0.0555513 (* 1 = 0.0555513 loss)
I0409 22:49:21.409654 26212 sgd_solver.cpp:105] Iteration 10008, lr = 0.00137732
I0409 22:49:23.714412 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:49:26.526181 26212 solver.cpp:218] Iteration 10020 (2.3454 iter/s, 5.1164s/12 iters), loss = 0.0342413
I0409 22:49:26.526222 26212 solver.cpp:237]     Train net output #0: loss = 0.0342414 (* 1 = 0.0342414 loss)
I0409 22:49:26.526232 26212 sgd_solver.cpp:105] Iteration 10020, lr = 0.00137405
I0409 22:49:31.614656 26212 solver.cpp:218] Iteration 10032 (2.35836 iter/s, 5.08829s/12 iters), loss = 0.101784
I0409 22:49:31.614713 26212 solver.cpp:237]     Train net output #0: loss = 0.101784 (* 1 = 0.101784 loss)
I0409 22:49:31.614727 26212 sgd_solver.cpp:105] Iteration 10032, lr = 0.00137079
I0409 22:49:36.620050 26212 solver.cpp:218] Iteration 10044 (2.39751 iter/s, 5.0052s/12 iters), loss = 0.0770627
I0409 22:49:36.620103 26212 solver.cpp:237]     Train net output #0: loss = 0.0770627 (* 1 = 0.0770627 loss)
I0409 22:49:36.620113 26212 sgd_solver.cpp:105] Iteration 10044, lr = 0.00136754
I0409 22:49:41.622308 26212 solver.cpp:218] Iteration 10056 (2.399 iter/s, 5.00207s/12 iters), loss = 0.0648907
I0409 22:49:41.622352 26212 solver.cpp:237]     Train net output #0: loss = 0.0648907 (* 1 = 0.0648907 loss)
I0409 22:49:41.622361 26212 sgd_solver.cpp:105] Iteration 10056, lr = 0.00136429
I0409 22:49:46.630822 26212 solver.cpp:218] Iteration 10068 (2.39601 iter/s, 5.00833s/12 iters), loss = 0.0133974
I0409 22:49:46.630872 26212 solver.cpp:237]     Train net output #0: loss = 0.0133974 (* 1 = 0.0133974 loss)
I0409 22:49:46.630885 26212 sgd_solver.cpp:105] Iteration 10068, lr = 0.00136105
I0409 22:49:51.518422 26212 solver.cpp:218] Iteration 10080 (2.45528 iter/s, 4.88742s/12 iters), loss = 0.0962947
I0409 22:49:51.518479 26212 solver.cpp:237]     Train net output #0: loss = 0.0962947 (* 1 = 0.0962947 loss)
I0409 22:49:51.518491 26212 sgd_solver.cpp:105] Iteration 10080, lr = 0.00135782
I0409 22:49:56.394043 26212 solver.cpp:218] Iteration 10092 (2.46132 iter/s, 4.87543s/12 iters), loss = 0.0414961
I0409 22:49:56.394170 26212 solver.cpp:237]     Train net output #0: loss = 0.0414962 (* 1 = 0.0414962 loss)
I0409 22:49:56.394183 26212 sgd_solver.cpp:105] Iteration 10092, lr = 0.0013546
I0409 22:49:58.366453 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10098.caffemodel
I0409 22:50:01.278537 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10098.solverstate
I0409 22:50:08.046830 26212 solver.cpp:330] Iteration 10098, Testing net (#0)
I0409 22:50:08.046861 26212 net.cpp:676] Ignoring source layer train-data
I0409 22:50:08.529423 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:50:12.493360 26212 solver.cpp:397]     Test net output #0: accuracy = 0.356618
I0409 22:50:12.493407 26212 solver.cpp:397]     Test net output #1: loss = 4.73502 (* 1 = 4.73502 loss)
I0409 22:50:14.470778 26212 solver.cpp:218] Iteration 10104 (0.663858 iter/s, 18.0762s/12 iters), loss = 0.0260807
I0409 22:50:14.470827 26212 solver.cpp:237]     Train net output #0: loss = 0.0260807 (* 1 = 0.0260807 loss)
I0409 22:50:14.470839 26212 sgd_solver.cpp:105] Iteration 10104, lr = 0.00135138
I0409 22:50:18.846957 26216 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:50:19.478655 26212 solver.cpp:218] Iteration 10116 (2.39631 iter/s, 5.00769s/12 iters), loss = 0.0291563
I0409 22:50:19.478706 26212 solver.cpp:237]     Train net output #0: loss = 0.0291563 (* 1 = 0.0291563 loss)
I0409 22:50:19.478718 26212 sgd_solver.cpp:105] Iteration 10116, lr = 0.00134817
I0409 22:50:24.433029 26212 solver.cpp:218] Iteration 10128 (2.42219 iter/s, 4.95419s/12 iters), loss = 0.0623541
I0409 22:50:24.433082 26212 solver.cpp:237]     Train net output #0: loss = 0.0623541 (* 1 = 0.0623541 loss)
I0409 22:50:24.433094 26212 sgd_solver.cpp:105] Iteration 10128, lr = 0.00134497
I0409 22:50:29.600848 26212 solver.cpp:218] Iteration 10140 (2.32215 iter/s, 5.16763s/12 iters), loss = 0.0845767
I0409 22:50:29.600916 26212 solver.cpp:237]     Train net output #0: loss = 0.0845768 (* 1 = 0.0845768 loss)
I0409 22:50:29.600925 26212 sgd_solver.cpp:105] Iteration 10140, lr = 0.00134178
I0409 22:50:34.668792 26212 solver.cpp:218] Iteration 10152 (2.36792 iter/s, 5.06773s/12 iters), loss = 0.0292964
I0409 22:50:34.668853 26212 solver.cpp:237]     Train net output #0: loss = 0.0292965 (* 1 = 0.0292965 loss)
I0409 22:50:34.668864 26212 sgd_solver.cpp:105] Iteration 10152, lr = 0.00133859
I0409 22:50:39.706400 26212 solver.cpp:218] Iteration 10164 (2.38217 iter/s, 5.03741s/12 iters), loss = 0.131535
I0409 22:50:39.706445 26212 solver.cpp:237]     Train net output #0: loss = 0.131535 (* 1 = 0.131535 loss)
I0409 22:50:39.706454 26212 sgd_solver.cpp:105] Iteration 10164, lr = 0.00133541
I0409 22:50:44.715399 26212 solver.cpp:218] Iteration 10176 (2.39577 iter/s, 5.00882s/12 iters), loss = 0.0351587
I0409 22:50:44.715441 26212 solver.cpp:237]     Train net output #0: loss = 0.0351587 (* 1 = 0.0351587 loss)
I0409 22:50:44.715451 26212 sgd_solver.cpp:105] Iteration 10176, lr = 0.00133224
I0409 22:50:49.624992 26212 solver.cpp:218] Iteration 10188 (2.44428 iter/s, 4.90942s/12 iters), loss = 0.0529175
I0409 22:50:49.625030 26212 solver.cpp:237]     Train net output #0: loss = 0.0529175 (* 1 = 0.0529175 loss)
I0409 22:50:49.625039 26212 sgd_solver.cpp:105] Iteration 10188, lr = 0.00132908
I0409 22:50:54.110814 26212 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10200.caffemodel
I0409 22:50:55.289240 26212 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10200.solverstate
I0409 22:50:56.270336 26212 solver.cpp:310] Iteration 10200, loss = 0.153424
I0409 22:50:56.270371 26212 solver.cpp:330] Iteration 10200, Testing net (#0)
I0409 22:50:56.270380 26212 net.cpp:676] Ignoring source layer train-data
I0409 22:50:56.698228 26227 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:51:00.714498 26212 solver.cpp:397]     Test net output #0: accuracy = 0.35723
I0409 22:51:00.714613 26212 solver.cpp:397]     Test net output #1: loss = 4.65752 (* 1 = 4.65752 loss)
I0409 22:51:00.714624 26212 solver.cpp:315] Optimization Done.
I0409 22:51:00.714630 26212 caffe.cpp:259] Optimization Done.