DIGITS-CNN/cars/architecture-investigations/fc/4-layers/4096/caffe_output.log

I0410 00:38:40.617875 18059 upgrade_proto.cpp:1082] Attempting to upgrade input file specified using deprecated 'solver_type' field (enum)': /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210409-220814-599f/solver.prototxt
I0410 00:38:40.618121 18059 upgrade_proto.cpp:1089] Successfully upgraded file specified using deprecated 'solver_type' field (enum) to 'type' field (string).
W0410 00:38:40.618130 18059 upgrade_proto.cpp:1091] Note that future Caffe releases will only support 'type' field (string) for a solver's type.
I0410 00:38:40.618218 18059 caffe.cpp:218] Using GPUs 2
I0410 00:38:40.643354 18059 caffe.cpp:223] GPU 2: GeForce GTX 1080 Ti
I0410 00:38:40.955613 18059 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: 2
net: "train_val.prototxt"
train_state {
level: 0
stage: ""
}
type: "SGD"
I0410 00:38:40.956374 18059 solver.cpp:87] Creating training net from net file: train_val.prototxt
I0410 00:38:40.956935 18059 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer val-data
I0410 00:38:40.956950 18059 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy
I0410 00:38:40.957098 18059 net.cpp:51] Initializing net from parameters:
state {
phase: TRAIN
level: 0
stage: ""
}
layer {
name: "train-data"
type: "Data"
top: "data"
top: "label"
include {
phase: TRAIN
}
transform_param {
mirror: true
crop_size: 227
mean_file: "/mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto"
}
data_param {
source: "/mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/train_db"
batch_size: 128
backend: LMDB
}
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 96
kernel_size: 11
stride: 4
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "conv1"
top: "conv1"
}
layer {
name: "norm1"
type: "LRN"
bottom: "conv1"
top: "norm1"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool1"
type: "Pooling"
bottom: "norm1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 2
kernel_size: 5
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu2"
type: "ReLU"
bottom: "conv2"
top: "conv2"
}
layer {
name: "norm2"
type: "LRN"
bottom: "conv2"
top: "norm2"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool2"
type: "Pooling"
bottom: "norm2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv3"
type: "Convolution"
bottom: "pool2"
top: "conv3"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu3"
type: "ReLU"
bottom: "conv3"
top: "conv3"
}
layer {
name: "conv4"
type: "Convolution"
bottom: "conv3"
top: "conv4"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu4"
type: "ReLU"
bottom: "conv4"
top: "conv4"
}
layer {
name: "conv5"
type: "Convolution"
bottom: "conv4"
top: "conv5"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu5"
type: "ReLU"
bottom: "conv5"
top: "conv5"
}
layer {
name: "pool5"
type: "Pooling"
bottom: "conv5"
top: "pool5"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "fc6"
type: "InnerProduct"
bottom: "pool5"
top: "fc6"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 4096
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu6"
type: "ReLU"
bottom: "fc6"
top: "fc6"
}
layer {
name: "drop6"
type: "Dropout"
bottom: "fc6"
top: "fc6"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc7"
type: "InnerProduct"
bottom: "fc6"
top: "fc7"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 4096
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu7"
type: "ReLU"
bottom: "fc7"
top: "fc7"
}
layer {
name: "drop7"
type: "Dropout"
bottom: "fc7"
top: "fc7"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc7.5"
type: "InnerProduct"
bottom: "fc7"
top: "fc7.5"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 4096
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu7.5"
type: "ReLU"
bottom: "fc7.5"
top: "fc7.5"
}
layer {
name: "drop7.5"
type: "Dropout"
bottom: "fc7.5"
top: "fc7.5"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc7.6"
type: "InnerProduct"
bottom: "fc7.5"
top: "fc7.6"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 4096
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu7.6"
type: "ReLU"
bottom: "fc7.6"
top: "fc7.6"
}
layer {
name: "drop7.6"
type: "Dropout"
bottom: "fc7.6"
top: "fc7.6"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc8"
type: "InnerProduct"
bottom: "fc7.6"
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"
}
I0410 00:38:40.957187 18059 layer_factory.hpp:77] Creating layer train-data
I0410 00:38:40.958853 18059 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/train_db
I0410 00:38:40.959057 18059 net.cpp:84] Creating Layer train-data
I0410 00:38:40.959067 18059 net.cpp:380] train-data -> data
I0410 00:38:40.959085 18059 net.cpp:380] train-data -> label
I0410 00:38:40.959095 18059 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto
I0410 00:38:40.963582 18059 data_layer.cpp:45] output data size: 128,3,227,227
I0410 00:38:41.084195 18059 net.cpp:122] Setting up train-data
I0410 00:38:41.084216 18059 net.cpp:129] Top shape: 128 3 227 227 (19787136)
I0410 00:38:41.084223 18059 net.cpp:129] Top shape: 128 (128)
I0410 00:38:41.084225 18059 net.cpp:137] Memory required for data: 79149056
I0410 00:38:41.084254 18059 layer_factory.hpp:77] Creating layer conv1
I0410 00:38:41.084275 18059 net.cpp:84] Creating Layer conv1
I0410 00:38:41.084280 18059 net.cpp:406] conv1 <- data
I0410 00:38:41.084291 18059 net.cpp:380] conv1 -> conv1
I0410 00:38:41.619902 18059 net.cpp:122] Setting up conv1
I0410 00:38:41.619922 18059 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0410 00:38:41.619926 18059 net.cpp:137] Memory required for data: 227833856
I0410 00:38:41.619946 18059 layer_factory.hpp:77] Creating layer relu1
I0410 00:38:41.619956 18059 net.cpp:84] Creating Layer relu1
I0410 00:38:41.619959 18059 net.cpp:406] relu1 <- conv1
I0410 00:38:41.619966 18059 net.cpp:367] relu1 -> conv1 (in-place)
I0410 00:38:41.620251 18059 net.cpp:122] Setting up relu1
I0410 00:38:41.620260 18059 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0410 00:38:41.620263 18059 net.cpp:137] Memory required for data: 376518656
I0410 00:38:41.620267 18059 layer_factory.hpp:77] Creating layer norm1
I0410 00:38:41.620276 18059 net.cpp:84] Creating Layer norm1
I0410 00:38:41.620280 18059 net.cpp:406] norm1 <- conv1
I0410 00:38:41.620285 18059 net.cpp:380] norm1 -> norm1
I0410 00:38:41.620714 18059 net.cpp:122] Setting up norm1
I0410 00:38:41.620724 18059 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0410 00:38:41.620728 18059 net.cpp:137] Memory required for data: 525203456
I0410 00:38:41.620733 18059 layer_factory.hpp:77] Creating layer pool1
I0410 00:38:41.620739 18059 net.cpp:84] Creating Layer pool1
I0410 00:38:41.620743 18059 net.cpp:406] pool1 <- norm1
I0410 00:38:41.620748 18059 net.cpp:380] pool1 -> pool1
I0410 00:38:41.620784 18059 net.cpp:122] Setting up pool1
I0410 00:38:41.620790 18059 net.cpp:129] Top shape: 128 96 27 27 (8957952)
I0410 00:38:41.620793 18059 net.cpp:137] Memory required for data: 561035264
I0410 00:38:41.620796 18059 layer_factory.hpp:77] Creating layer conv2
I0410 00:38:41.620805 18059 net.cpp:84] Creating Layer conv2
I0410 00:38:41.620810 18059 net.cpp:406] conv2 <- pool1
I0410 00:38:41.620815 18059 net.cpp:380] conv2 -> conv2
I0410 00:38:41.627739 18059 net.cpp:122] Setting up conv2
I0410 00:38:41.627754 18059 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0410 00:38:41.627758 18059 net.cpp:137] Memory required for data: 656586752
I0410 00:38:41.627768 18059 layer_factory.hpp:77] Creating layer relu2
I0410 00:38:41.627776 18059 net.cpp:84] Creating Layer relu2
I0410 00:38:41.627780 18059 net.cpp:406] relu2 <- conv2
I0410 00:38:41.627787 18059 net.cpp:367] relu2 -> conv2 (in-place)
I0410 00:38:41.630023 18059 net.cpp:122] Setting up relu2
I0410 00:38:41.630033 18059 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0410 00:38:41.630035 18059 net.cpp:137] Memory required for data: 752138240
I0410 00:38:41.630039 18059 layer_factory.hpp:77] Creating layer norm2
I0410 00:38:41.630048 18059 net.cpp:84] Creating Layer norm2
I0410 00:38:41.630051 18059 net.cpp:406] norm2 <- conv2
I0410 00:38:41.630059 18059 net.cpp:380] norm2 -> norm2
I0410 00:38:41.630414 18059 net.cpp:122] Setting up norm2
I0410 00:38:41.630421 18059 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0410 00:38:41.630424 18059 net.cpp:137] Memory required for data: 847689728
I0410 00:38:41.630429 18059 layer_factory.hpp:77] Creating layer pool2
I0410 00:38:41.630437 18059 net.cpp:84] Creating Layer pool2
I0410 00:38:41.630440 18059 net.cpp:406] pool2 <- norm2
I0410 00:38:41.630445 18059 net.cpp:380] pool2 -> pool2
I0410 00:38:41.630475 18059 net.cpp:122] Setting up pool2
I0410 00:38:41.630481 18059 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0410 00:38:41.630483 18059 net.cpp:137] Memory required for data: 869840896
I0410 00:38:41.630486 18059 layer_factory.hpp:77] Creating layer conv3
I0410 00:38:41.630496 18059 net.cpp:84] Creating Layer conv3
I0410 00:38:41.630501 18059 net.cpp:406] conv3 <- pool2
I0410 00:38:41.630506 18059 net.cpp:380] conv3 -> conv3
I0410 00:38:41.641641 18059 net.cpp:122] Setting up conv3
I0410 00:38:41.641659 18059 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0410 00:38:41.641662 18059 net.cpp:137] Memory required for data: 903067648
I0410 00:38:41.641692 18059 layer_factory.hpp:77] Creating layer relu3
I0410 00:38:41.641700 18059 net.cpp:84] Creating Layer relu3
I0410 00:38:41.641705 18059 net.cpp:406] relu3 <- conv3
I0410 00:38:41.641712 18059 net.cpp:367] relu3 -> conv3 (in-place)
I0410 00:38:41.642241 18059 net.cpp:122] Setting up relu3
I0410 00:38:41.642251 18059 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0410 00:38:41.642256 18059 net.cpp:137] Memory required for data: 936294400
I0410 00:38:41.642259 18059 layer_factory.hpp:77] Creating layer conv4
I0410 00:38:41.642271 18059 net.cpp:84] Creating Layer conv4
I0410 00:38:41.642274 18059 net.cpp:406] conv4 <- conv3
I0410 00:38:41.642282 18059 net.cpp:380] conv4 -> conv4
I0410 00:38:41.654093 18059 net.cpp:122] Setting up conv4
I0410 00:38:41.654109 18059 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0410 00:38:41.654114 18059 net.cpp:137] Memory required for data: 969521152
I0410 00:38:41.654122 18059 layer_factory.hpp:77] Creating layer relu4
I0410 00:38:41.654130 18059 net.cpp:84] Creating Layer relu4
I0410 00:38:41.654134 18059 net.cpp:406] relu4 <- conv4
I0410 00:38:41.654141 18059 net.cpp:367] relu4 -> conv4 (in-place)
I0410 00:38:41.654480 18059 net.cpp:122] Setting up relu4
I0410 00:38:41.654489 18059 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0410 00:38:41.654491 18059 net.cpp:137] Memory required for data: 1002747904
I0410 00:38:41.654495 18059 layer_factory.hpp:77] Creating layer conv5
I0410 00:38:41.654506 18059 net.cpp:84] Creating Layer conv5
I0410 00:38:41.654510 18059 net.cpp:406] conv5 <- conv4
I0410 00:38:41.654516 18059 net.cpp:380] conv5 -> conv5
I0410 00:38:41.664628 18059 net.cpp:122] Setting up conv5
I0410 00:38:41.664645 18059 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0410 00:38:41.664649 18059 net.cpp:137] Memory required for data: 1024899072
I0410 00:38:41.664660 18059 layer_factory.hpp:77] Creating layer relu5
I0410 00:38:41.664669 18059 net.cpp:84] Creating Layer relu5
I0410 00:38:41.664672 18059 net.cpp:406] relu5 <- conv5
I0410 00:38:41.664680 18059 net.cpp:367] relu5 -> conv5 (in-place)
I0410 00:38:41.665159 18059 net.cpp:122] Setting up relu5
I0410 00:38:41.665169 18059 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0410 00:38:41.665172 18059 net.cpp:137] Memory required for data: 1047050240
I0410 00:38:41.665176 18059 layer_factory.hpp:77] Creating layer pool5
I0410 00:38:41.665185 18059 net.cpp:84] Creating Layer pool5
I0410 00:38:41.665189 18059 net.cpp:406] pool5 <- conv5
I0410 00:38:41.665194 18059 net.cpp:380] pool5 -> pool5
I0410 00:38:41.665232 18059 net.cpp:122] Setting up pool5
I0410 00:38:41.665238 18059 net.cpp:129] Top shape: 128 256 6 6 (1179648)
I0410 00:38:41.665241 18059 net.cpp:137] Memory required for data: 1051768832
I0410 00:38:41.665246 18059 layer_factory.hpp:77] Creating layer fc6
I0410 00:38:41.665256 18059 net.cpp:84] Creating Layer fc6
I0410 00:38:41.665259 18059 net.cpp:406] fc6 <- pool5
I0410 00:38:41.665264 18059 net.cpp:380] fc6 -> fc6
I0410 00:38:42.021314 18059 net.cpp:122] Setting up fc6
I0410 00:38:42.021334 18059 net.cpp:129] Top shape: 128 4096 (524288)
I0410 00:38:42.021339 18059 net.cpp:137] Memory required for data: 1053865984
I0410 00:38:42.021348 18059 layer_factory.hpp:77] Creating layer relu6
I0410 00:38:42.021358 18059 net.cpp:84] Creating Layer relu6
I0410 00:38:42.021361 18059 net.cpp:406] relu6 <- fc6
I0410 00:38:42.021368 18059 net.cpp:367] relu6 -> fc6 (in-place)
I0410 00:38:42.022020 18059 net.cpp:122] Setting up relu6
I0410 00:38:42.022029 18059 net.cpp:129] Top shape: 128 4096 (524288)
I0410 00:38:42.022033 18059 net.cpp:137] Memory required for data: 1055963136
I0410 00:38:42.022037 18059 layer_factory.hpp:77] Creating layer drop6
I0410 00:38:42.022044 18059 net.cpp:84] Creating Layer drop6
I0410 00:38:42.022047 18059 net.cpp:406] drop6 <- fc6
I0410 00:38:42.022053 18059 net.cpp:367] drop6 -> fc6 (in-place)
I0410 00:38:42.022081 18059 net.cpp:122] Setting up drop6
I0410 00:38:42.022087 18059 net.cpp:129] Top shape: 128 4096 (524288)
I0410 00:38:42.022109 18059 net.cpp:137] Memory required for data: 1058060288
I0410 00:38:42.022112 18059 layer_factory.hpp:77] Creating layer fc7
I0410 00:38:42.022121 18059 net.cpp:84] Creating Layer fc7
I0410 00:38:42.022125 18059 net.cpp:406] fc7 <- fc6
I0410 00:38:42.022131 18059 net.cpp:380] fc7 -> fc7
I0410 00:38:42.179236 18059 net.cpp:122] Setting up fc7
I0410 00:38:42.179257 18059 net.cpp:129] Top shape: 128 4096 (524288)
I0410 00:38:42.179261 18059 net.cpp:137] Memory required for data: 1060157440
I0410 00:38:42.179271 18059 layer_factory.hpp:77] Creating layer relu7
I0410 00:38:42.179281 18059 net.cpp:84] Creating Layer relu7
I0410 00:38:42.179286 18059 net.cpp:406] relu7 <- fc7
I0410 00:38:42.179294 18059 net.cpp:367] relu7 -> fc7 (in-place)
I0410 00:38:42.179941 18059 net.cpp:122] Setting up relu7
I0410 00:38:42.179953 18059 net.cpp:129] Top shape: 128 4096 (524288)
I0410 00:38:42.179957 18059 net.cpp:137] Memory required for data: 1062254592
I0410 00:38:42.179963 18059 layer_factory.hpp:77] Creating layer drop7
I0410 00:38:42.179971 18059 net.cpp:84] Creating Layer drop7
I0410 00:38:42.179975 18059 net.cpp:406] drop7 <- fc7
I0410 00:38:42.179981 18059 net.cpp:367] drop7 -> fc7 (in-place)
I0410 00:38:42.180009 18059 net.cpp:122] Setting up drop7
I0410 00:38:42.180014 18059 net.cpp:129] Top shape: 128 4096 (524288)
I0410 00:38:42.180018 18059 net.cpp:137] Memory required for data: 1064351744
I0410 00:38:42.180022 18059 layer_factory.hpp:77] Creating layer fc7.5
I0410 00:38:42.180029 18059 net.cpp:84] Creating Layer fc7.5
I0410 00:38:42.180033 18059 net.cpp:406] fc7.5 <- fc7
I0410 00:38:42.180038 18059 net.cpp:380] fc7.5 -> fc7.5
I0410 00:38:42.337098 18059 net.cpp:122] Setting up fc7.5
I0410 00:38:42.337117 18059 net.cpp:129] Top shape: 128 4096 (524288)
I0410 00:38:42.337121 18059 net.cpp:137] Memory required for data: 1066448896
I0410 00:38:42.337131 18059 layer_factory.hpp:77] Creating layer relu7.5
I0410 00:38:42.337141 18059 net.cpp:84] Creating Layer relu7.5
I0410 00:38:42.337144 18059 net.cpp:406] relu7.5 <- fc7.5
I0410 00:38:42.337152 18059 net.cpp:367] relu7.5 -> fc7.5 (in-place)
I0410 00:38:42.337805 18059 net.cpp:122] Setting up relu7.5
I0410 00:38:42.337813 18059 net.cpp:129] Top shape: 128 4096 (524288)
I0410 00:38:42.337817 18059 net.cpp:137] Memory required for data: 1068546048
I0410 00:38:42.337821 18059 layer_factory.hpp:77] Creating layer drop7.5
I0410 00:38:42.337828 18059 net.cpp:84] Creating Layer drop7.5
I0410 00:38:42.337832 18059 net.cpp:406] drop7.5 <- fc7.5
I0410 00:38:42.337837 18059 net.cpp:367] drop7.5 -> fc7.5 (in-place)
I0410 00:38:42.337862 18059 net.cpp:122] Setting up drop7.5
I0410 00:38:42.337867 18059 net.cpp:129] Top shape: 128 4096 (524288)
I0410 00:38:42.337869 18059 net.cpp:137] Memory required for data: 1070643200
I0410 00:38:42.337873 18059 layer_factory.hpp:77] Creating layer fc7.6
I0410 00:38:42.337879 18059 net.cpp:84] Creating Layer fc7.6
I0410 00:38:42.337883 18059 net.cpp:406] fc7.6 <- fc7.5
I0410 00:38:42.337890 18059 net.cpp:380] fc7.6 -> fc7.6
I0410 00:38:42.494966 18059 net.cpp:122] Setting up fc7.6
I0410 00:38:42.494987 18059 net.cpp:129] Top shape: 128 4096 (524288)
I0410 00:38:42.494990 18059 net.cpp:137] Memory required for data: 1072740352
I0410 00:38:42.495003 18059 layer_factory.hpp:77] Creating layer relu7.6
I0410 00:38:42.495012 18059 net.cpp:84] Creating Layer relu7.6
I0410 00:38:42.495016 18059 net.cpp:406] relu7.6 <- fc7.6
I0410 00:38:42.495024 18059 net.cpp:367] relu7.6 -> fc7.6 (in-place)
I0410 00:38:42.496397 18059 net.cpp:122] Setting up relu7.6
I0410 00:38:42.496407 18059 net.cpp:129] Top shape: 128 4096 (524288)
I0410 00:38:42.496409 18059 net.cpp:137] Memory required for data: 1074837504
I0410 00:38:42.496413 18059 layer_factory.hpp:77] Creating layer drop7.6
I0410 00:38:42.496420 18059 net.cpp:84] Creating Layer drop7.6
I0410 00:38:42.496423 18059 net.cpp:406] drop7.6 <- fc7.6
I0410 00:38:42.496430 18059 net.cpp:367] drop7.6 -> fc7.6 (in-place)
I0410 00:38:42.496454 18059 net.cpp:122] Setting up drop7.6
I0410 00:38:42.496459 18059 net.cpp:129] Top shape: 128 4096 (524288)
I0410 00:38:42.496481 18059 net.cpp:137] Memory required for data: 1076934656
I0410 00:38:42.496484 18059 layer_factory.hpp:77] Creating layer fc8
I0410 00:38:42.496491 18059 net.cpp:84] Creating Layer fc8
I0410 00:38:42.496495 18059 net.cpp:406] fc8 <- fc7.6
I0410 00:38:42.496501 18059 net.cpp:380] fc8 -> fc8
I0410 00:38:42.504160 18059 net.cpp:122] Setting up fc8
I0410 00:38:42.504173 18059 net.cpp:129] Top shape: 128 196 (25088)
I0410 00:38:42.504175 18059 net.cpp:137] Memory required for data: 1077035008
I0410 00:38:42.504182 18059 layer_factory.hpp:77] Creating layer loss
I0410 00:38:42.504189 18059 net.cpp:84] Creating Layer loss
I0410 00:38:42.504194 18059 net.cpp:406] loss <- fc8
I0410 00:38:42.504197 18059 net.cpp:406] loss <- label
I0410 00:38:42.504204 18059 net.cpp:380] loss -> loss
I0410 00:38:42.504212 18059 layer_factory.hpp:77] Creating layer loss
I0410 00:38:42.504840 18059 net.cpp:122] Setting up loss
I0410 00:38:42.504850 18059 net.cpp:129] Top shape: (1)
I0410 00:38:42.504853 18059 net.cpp:132]     with loss weight 1
I0410 00:38:42.504870 18059 net.cpp:137] Memory required for data: 1077035012
I0410 00:38:42.504874 18059 net.cpp:198] loss needs backward computation.
I0410 00:38:42.504882 18059 net.cpp:198] fc8 needs backward computation.
I0410 00:38:42.504885 18059 net.cpp:198] drop7.6 needs backward computation.
I0410 00:38:42.504889 18059 net.cpp:198] relu7.6 needs backward computation.
I0410 00:38:42.504892 18059 net.cpp:198] fc7.6 needs backward computation.
I0410 00:38:42.504895 18059 net.cpp:198] drop7.5 needs backward computation.
I0410 00:38:42.504899 18059 net.cpp:198] relu7.5 needs backward computation.
I0410 00:38:42.504902 18059 net.cpp:198] fc7.5 needs backward computation.
I0410 00:38:42.504906 18059 net.cpp:198] drop7 needs backward computation.
I0410 00:38:42.504909 18059 net.cpp:198] relu7 needs backward computation.
I0410 00:38:42.504914 18059 net.cpp:198] fc7 needs backward computation.
I0410 00:38:42.504916 18059 net.cpp:198] drop6 needs backward computation.
I0410 00:38:42.504920 18059 net.cpp:198] relu6 needs backward computation.
I0410 00:38:42.504923 18059 net.cpp:198] fc6 needs backward computation.
I0410 00:38:42.504927 18059 net.cpp:198] pool5 needs backward computation.
I0410 00:38:42.504930 18059 net.cpp:198] relu5 needs backward computation.
I0410 00:38:42.504935 18059 net.cpp:198] conv5 needs backward computation.
I0410 00:38:42.504937 18059 net.cpp:198] relu4 needs backward computation.
I0410 00:38:42.504941 18059 net.cpp:198] conv4 needs backward computation.
I0410 00:38:42.504945 18059 net.cpp:198] relu3 needs backward computation.
I0410 00:38:42.504948 18059 net.cpp:198] conv3 needs backward computation.
I0410 00:38:42.504951 18059 net.cpp:198] pool2 needs backward computation.
I0410 00:38:42.504956 18059 net.cpp:198] norm2 needs backward computation.
I0410 00:38:42.504959 18059 net.cpp:198] relu2 needs backward computation.
I0410 00:38:42.504962 18059 net.cpp:198] conv2 needs backward computation.
I0410 00:38:42.504966 18059 net.cpp:198] pool1 needs backward computation.
I0410 00:38:42.504971 18059 net.cpp:198] norm1 needs backward computation.
I0410 00:38:42.504973 18059 net.cpp:198] relu1 needs backward computation.
I0410 00:38:42.504977 18059 net.cpp:198] conv1 needs backward computation.
I0410 00:38:42.504981 18059 net.cpp:200] train-data does not need backward computation.
I0410 00:38:42.504984 18059 net.cpp:242] This network produces output loss
I0410 00:38:42.505000 18059 net.cpp:255] Network initialization done.
I0410 00:38:42.506880 18059 solver.cpp:172] Creating test net (#0) specified by net file: train_val.prototxt
I0410 00:38:42.506912 18059 net.cpp:294] The NetState phase (1) differed from the phase (0) specified by a rule in layer train-data
I0410 00:38:42.507072 18059 net.cpp:51] Initializing net from parameters:
state {
phase: TEST
}
layer {
name: "val-data"
type: "Data"
top: "data"
top: "label"
include {
phase: TEST
}
transform_param {
crop_size: 227
mean_file: "/mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto"
}
data_param {
source: "/mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/val_db"
batch_size: 32
backend: LMDB
}
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 96
kernel_size: 11
stride: 4
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "conv1"
top: "conv1"
}
layer {
name: "norm1"
type: "LRN"
bottom: "conv1"
top: "norm1"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool1"
type: "Pooling"
bottom: "norm1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 2
kernel_size: 5
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu2"
type: "ReLU"
bottom: "conv2"
top: "conv2"
}
layer {
name: "norm2"
type: "LRN"
bottom: "conv2"
top: "norm2"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool2"
type: "Pooling"
bottom: "norm2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv3"
type: "Convolution"
bottom: "pool2"
top: "conv3"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu3"
type: "ReLU"
bottom: "conv3"
top: "conv3"
}
layer {
name: "conv4"
type: "Convolution"
bottom: "conv3"
top: "conv4"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu4"
type: "ReLU"
bottom: "conv4"
top: "conv4"
}
layer {
name: "conv5"
type: "Convolution"
bottom: "conv4"
top: "conv5"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu5"
type: "ReLU"
bottom: "conv5"
top: "conv5"
}
layer {
name: "pool5"
type: "Pooling"
bottom: "conv5"
top: "pool5"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "fc6"
type: "InnerProduct"
bottom: "pool5"
top: "fc6"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 4096
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu6"
type: "ReLU"
bottom: "fc6"
top: "fc6"
}
layer {
name: "drop6"
type: "Dropout"
bottom: "fc6"
top: "fc6"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc7"
type: "InnerProduct"
bottom: "fc6"
top: "fc7"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 4096
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu7"
type: "ReLU"
bottom: "fc7"
top: "fc7"
}
layer {
name: "drop7"
type: "Dropout"
bottom: "fc7"
top: "fc7"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc7.5"
type: "InnerProduct"
bottom: "fc7"
top: "fc7.5"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 4096
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu7.5"
type: "ReLU"
bottom: "fc7.5"
top: "fc7.5"
}
layer {
name: "drop7.5"
type: "Dropout"
bottom: "fc7.5"
top: "fc7.5"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc7.6"
type: "InnerProduct"
bottom: "fc7.5"
top: "fc7.6"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 4096
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu7.6"
type: "ReLU"
bottom: "fc7.6"
top: "fc7.6"
}
layer {
name: "drop7.6"
type: "Dropout"
bottom: "fc7.6"
top: "fc7.6"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc8"
type: "InnerProduct"
bottom: "fc7.6"
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"
}
I0410 00:38:42.507184 18059 layer_factory.hpp:77] Creating layer val-data
I0410 00:38:42.508975 18059 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/val_db
I0410 00:38:42.509179 18059 net.cpp:84] Creating Layer val-data
I0410 00:38:42.509189 18059 net.cpp:380] val-data -> data
I0410 00:38:42.509198 18059 net.cpp:380] val-data -> label
I0410 00:38:42.509204 18059 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto
I0410 00:38:42.513054 18059 data_layer.cpp:45] output data size: 32,3,227,227
I0410 00:38:42.544879 18059 net.cpp:122] Setting up val-data
I0410 00:38:42.544899 18059 net.cpp:129] Top shape: 32 3 227 227 (4946784)
I0410 00:38:42.544904 18059 net.cpp:129] Top shape: 32 (32)
I0410 00:38:42.544909 18059 net.cpp:137] Memory required for data: 19787264
I0410 00:38:42.544915 18059 layer_factory.hpp:77] Creating layer label_val-data_1_split
I0410 00:38:42.544927 18059 net.cpp:84] Creating Layer label_val-data_1_split
I0410 00:38:42.544931 18059 net.cpp:406] label_val-data_1_split <- label
I0410 00:38:42.544939 18059 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_0
I0410 00:38:42.544950 18059 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_1
I0410 00:38:42.545001 18059 net.cpp:122] Setting up label_val-data_1_split
I0410 00:38:42.545006 18059 net.cpp:129] Top shape: 32 (32)
I0410 00:38:42.545011 18059 net.cpp:129] Top shape: 32 (32)
I0410 00:38:42.545013 18059 net.cpp:137] Memory required for data: 19787520
I0410 00:38:42.545017 18059 layer_factory.hpp:77] Creating layer conv1
I0410 00:38:42.545029 18059 net.cpp:84] Creating Layer conv1
I0410 00:38:42.545033 18059 net.cpp:406] conv1 <- data
I0410 00:38:42.545039 18059 net.cpp:380] conv1 -> conv1
I0410 00:38:42.546798 18059 net.cpp:122] Setting up conv1
I0410 00:38:42.546809 18059 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0410 00:38:42.546814 18059 net.cpp:137] Memory required for data: 56958720
I0410 00:38:42.546823 18059 layer_factory.hpp:77] Creating layer relu1
I0410 00:38:42.546830 18059 net.cpp:84] Creating Layer relu1
I0410 00:38:42.546835 18059 net.cpp:406] relu1 <- conv1
I0410 00:38:42.546859 18059 net.cpp:367] relu1 -> conv1 (in-place)
I0410 00:38:42.548261 18059 net.cpp:122] Setting up relu1
I0410 00:38:42.548271 18059 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0410 00:38:42.548275 18059 net.cpp:137] Memory required for data: 94129920
I0410 00:38:42.548280 18059 layer_factory.hpp:77] Creating layer norm1
I0410 00:38:42.548288 18059 net.cpp:84] Creating Layer norm1
I0410 00:38:42.548292 18059 net.cpp:406] norm1 <- conv1
I0410 00:38:42.548297 18059 net.cpp:380] norm1 -> norm1
I0410 00:38:42.548744 18059 net.cpp:122] Setting up norm1
I0410 00:38:42.548754 18059 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0410 00:38:42.548758 18059 net.cpp:137] Memory required for data: 131301120
I0410 00:38:42.548763 18059 layer_factory.hpp:77] Creating layer pool1
I0410 00:38:42.548769 18059 net.cpp:84] Creating Layer pool1
I0410 00:38:42.548774 18059 net.cpp:406] pool1 <- norm1
I0410 00:38:42.548779 18059 net.cpp:380] pool1 -> pool1
I0410 00:38:42.548807 18059 net.cpp:122] Setting up pool1
I0410 00:38:42.548812 18059 net.cpp:129] Top shape: 32 96 27 27 (2239488)
I0410 00:38:42.548815 18059 net.cpp:137] Memory required for data: 140259072
I0410 00:38:42.548820 18059 layer_factory.hpp:77] Creating layer conv2
I0410 00:38:42.548828 18059 net.cpp:84] Creating Layer conv2
I0410 00:38:42.548831 18059 net.cpp:406] conv2 <- pool1
I0410 00:38:42.548836 18059 net.cpp:380] conv2 -> conv2
I0410 00:38:42.555155 18059 net.cpp:122] Setting up conv2
I0410 00:38:42.555167 18059 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0410 00:38:42.555171 18059 net.cpp:137] Memory required for data: 164146944
I0410 00:38:42.555181 18059 layer_factory.hpp:77] Creating layer relu2
I0410 00:38:42.555189 18059 net.cpp:84] Creating Layer relu2
I0410 00:38:42.555193 18059 net.cpp:406] relu2 <- conv2
I0410 00:38:42.555199 18059 net.cpp:367] relu2 -> conv2 (in-place)
I0410 00:38:42.555546 18059 net.cpp:122] Setting up relu2
I0410 00:38:42.555553 18059 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0410 00:38:42.555558 18059 net.cpp:137] Memory required for data: 188034816
I0410 00:38:42.555562 18059 layer_factory.hpp:77] Creating layer norm2
I0410 00:38:42.555570 18059 net.cpp:84] Creating Layer norm2
I0410 00:38:42.555574 18059 net.cpp:406] norm2 <- conv2
I0410 00:38:42.555580 18059 net.cpp:380] norm2 -> norm2
I0410 00:38:42.556221 18059 net.cpp:122] Setting up norm2
I0410 00:38:42.556231 18059 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0410 00:38:42.556234 18059 net.cpp:137] Memory required for data: 211922688
I0410 00:38:42.556238 18059 layer_factory.hpp:77] Creating layer pool2
I0410 00:38:42.556246 18059 net.cpp:84] Creating Layer pool2
I0410 00:38:42.556250 18059 net.cpp:406] pool2 <- norm2
I0410 00:38:42.556255 18059 net.cpp:380] pool2 -> pool2
I0410 00:38:42.556285 18059 net.cpp:122] Setting up pool2
I0410 00:38:42.556290 18059 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0410 00:38:42.556293 18059 net.cpp:137] Memory required for data: 217460480
I0410 00:38:42.556298 18059 layer_factory.hpp:77] Creating layer conv3
I0410 00:38:42.556308 18059 net.cpp:84] Creating Layer conv3
I0410 00:38:42.556313 18059 net.cpp:406] conv3 <- pool2
I0410 00:38:42.556318 18059 net.cpp:380] conv3 -> conv3
I0410 00:38:42.567440 18059 net.cpp:122] Setting up conv3
I0410 00:38:42.567456 18059 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0410 00:38:42.567461 18059 net.cpp:137] Memory required for data: 225767168
I0410 00:38:42.567473 18059 layer_factory.hpp:77] Creating layer relu3
I0410 00:38:42.567481 18059 net.cpp:84] Creating Layer relu3
I0410 00:38:42.567487 18059 net.cpp:406] relu3 <- conv3
I0410 00:38:42.567492 18059 net.cpp:367] relu3 -> conv3 (in-place)
I0410 00:38:42.568193 18059 net.cpp:122] Setting up relu3
I0410 00:38:42.568204 18059 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0410 00:38:42.568207 18059 net.cpp:137] Memory required for data: 234073856
I0410 00:38:42.568212 18059 layer_factory.hpp:77] Creating layer conv4
I0410 00:38:42.568223 18059 net.cpp:84] Creating Layer conv4
I0410 00:38:42.568228 18059 net.cpp:406] conv4 <- conv3
I0410 00:38:42.568253 18059 net.cpp:380] conv4 -> conv4
I0410 00:38:42.582379 18059 net.cpp:122] Setting up conv4
I0410 00:38:42.582398 18059 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0410 00:38:42.582402 18059 net.cpp:137] Memory required for data: 242380544
I0410 00:38:42.582412 18059 layer_factory.hpp:77] Creating layer relu4
I0410 00:38:42.582419 18059 net.cpp:84] Creating Layer relu4
I0410 00:38:42.582424 18059 net.cpp:406] relu4 <- conv4
I0410 00:38:42.582429 18059 net.cpp:367] relu4 -> conv4 (in-place)
I0410 00:38:42.583966 18059 net.cpp:122] Setting up relu4
I0410 00:38:42.583976 18059 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0410 00:38:42.583979 18059 net.cpp:137] Memory required for data: 250687232
I0410 00:38:42.583984 18059 layer_factory.hpp:77] Creating layer conv5
I0410 00:38:42.583998 18059 net.cpp:84] Creating Layer conv5
I0410 00:38:42.584002 18059 net.cpp:406] conv5 <- conv4
I0410 00:38:42.584010 18059 net.cpp:380] conv5 -> conv5
I0410 00:38:42.591679 18059 net.cpp:122] Setting up conv5
I0410 00:38:42.591693 18059 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0410 00:38:42.591696 18059 net.cpp:137] Memory required for data: 256225024
I0410 00:38:42.591709 18059 layer_factory.hpp:77] Creating layer relu5
I0410 00:38:42.591717 18059 net.cpp:84] Creating Layer relu5
I0410 00:38:42.591720 18059 net.cpp:406] relu5 <- conv5
I0410 00:38:42.591727 18059 net.cpp:367] relu5 -> conv5 (in-place)
I0410 00:38:42.592077 18059 net.cpp:122] Setting up relu5
I0410 00:38:42.592087 18059 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0410 00:38:42.592090 18059 net.cpp:137] Memory required for data: 261762816
I0410 00:38:42.592094 18059 layer_factory.hpp:77] Creating layer pool5
I0410 00:38:42.592104 18059 net.cpp:84] Creating Layer pool5
I0410 00:38:42.592108 18059 net.cpp:406] pool5 <- conv5
I0410 00:38:42.592113 18059 net.cpp:380] pool5 -> pool5
I0410 00:38:42.592150 18059 net.cpp:122] Setting up pool5
I0410 00:38:42.592156 18059 net.cpp:129] Top shape: 32 256 6 6 (294912)
I0410 00:38:42.592159 18059 net.cpp:137] Memory required for data: 262942464
I0410 00:38:42.592162 18059 layer_factory.hpp:77] Creating layer fc6
I0410 00:38:42.592170 18059 net.cpp:84] Creating Layer fc6
I0410 00:38:42.592175 18059 net.cpp:406] fc6 <- pool5
I0410 00:38:42.592180 18059 net.cpp:380] fc6 -> fc6
I0410 00:38:42.945850 18059 net.cpp:122] Setting up fc6
I0410 00:38:42.945871 18059 net.cpp:129] Top shape: 32 4096 (131072)
I0410 00:38:42.945875 18059 net.cpp:137] Memory required for data: 263466752
I0410 00:38:42.945884 18059 layer_factory.hpp:77] Creating layer relu6
I0410 00:38:42.945894 18059 net.cpp:84] Creating Layer relu6
I0410 00:38:42.945899 18059 net.cpp:406] relu6 <- fc6
I0410 00:38:42.945905 18059 net.cpp:367] relu6 -> fc6 (in-place)
I0410 00:38:42.949446 18059 net.cpp:122] Setting up relu6
I0410 00:38:42.949457 18059 net.cpp:129] Top shape: 32 4096 (131072)
I0410 00:38:42.949460 18059 net.cpp:137] Memory required for data: 263991040
I0410 00:38:42.949465 18059 layer_factory.hpp:77] Creating layer drop6
I0410 00:38:42.949471 18059 net.cpp:84] Creating Layer drop6
I0410 00:38:42.949476 18059 net.cpp:406] drop6 <- fc6
I0410 00:38:42.949481 18059 net.cpp:367] drop6 -> fc6 (in-place)
I0410 00:38:42.949506 18059 net.cpp:122] Setting up drop6
I0410 00:38:42.949512 18059 net.cpp:129] Top shape: 32 4096 (131072)
I0410 00:38:42.949515 18059 net.cpp:137] Memory required for data: 264515328
I0410 00:38:42.949518 18059 layer_factory.hpp:77] Creating layer fc7
I0410 00:38:42.949527 18059 net.cpp:84] Creating Layer fc7
I0410 00:38:42.949529 18059 net.cpp:406] fc7 <- fc6
I0410 00:38:42.949535 18059 net.cpp:380] fc7 -> fc7
I0410 00:38:43.106271 18059 net.cpp:122] Setting up fc7
I0410 00:38:43.106292 18059 net.cpp:129] Top shape: 32 4096 (131072)
I0410 00:38:43.106297 18059 net.cpp:137] Memory required for data: 265039616
I0410 00:38:43.106305 18059 layer_factory.hpp:77] Creating layer relu7
I0410 00:38:43.106314 18059 net.cpp:84] Creating Layer relu7
I0410 00:38:43.106318 18059 net.cpp:406] relu7 <- fc7
I0410 00:38:43.106343 18059 net.cpp:367] relu7 -> fc7 (in-place)
I0410 00:38:43.106988 18059 net.cpp:122] Setting up relu7
I0410 00:38:43.106998 18059 net.cpp:129] Top shape: 32 4096 (131072)
I0410 00:38:43.107002 18059 net.cpp:137] Memory required for data: 265563904
I0410 00:38:43.107007 18059 layer_factory.hpp:77] Creating layer drop7
I0410 00:38:43.107012 18059 net.cpp:84] Creating Layer drop7
I0410 00:38:43.107017 18059 net.cpp:406] drop7 <- fc7
I0410 00:38:43.107023 18059 net.cpp:367] drop7 -> fc7 (in-place)
I0410 00:38:43.107048 18059 net.cpp:122] Setting up drop7
I0410 00:38:43.107053 18059 net.cpp:129] Top shape: 32 4096 (131072)
I0410 00:38:43.107055 18059 net.cpp:137] Memory required for data: 266088192
I0410 00:38:43.107059 18059 layer_factory.hpp:77] Creating layer fc7.5
I0410 00:38:43.107066 18059 net.cpp:84] Creating Layer fc7.5
I0410 00:38:43.107070 18059 net.cpp:406] fc7.5 <- fc7
I0410 00:38:43.107075 18059 net.cpp:380] fc7.5 -> fc7.5
I0410 00:38:43.264123 18059 net.cpp:122] Setting up fc7.5
I0410 00:38:43.264145 18059 net.cpp:129] Top shape: 32 4096 (131072)
I0410 00:38:43.264149 18059 net.cpp:137] Memory required for data: 266612480
I0410 00:38:43.264158 18059 layer_factory.hpp:77] Creating layer relu7.5
I0410 00:38:43.264168 18059 net.cpp:84] Creating Layer relu7.5
I0410 00:38:43.264171 18059 net.cpp:406] relu7.5 <- fc7.5
I0410 00:38:43.264178 18059 net.cpp:367] relu7.5 -> fc7.5 (in-place)
I0410 00:38:43.265874 18059 net.cpp:122] Setting up relu7.5
I0410 00:38:43.265884 18059 net.cpp:129] Top shape: 32 4096 (131072)
I0410 00:38:43.265887 18059 net.cpp:137] Memory required for data: 267136768
I0410 00:38:43.265892 18059 layer_factory.hpp:77] Creating layer drop7.5
I0410 00:38:43.265899 18059 net.cpp:84] Creating Layer drop7.5
I0410 00:38:43.265903 18059 net.cpp:406] drop7.5 <- fc7.5
I0410 00:38:43.265908 18059 net.cpp:367] drop7.5 -> fc7.5 (in-place)
I0410 00:38:43.265933 18059 net.cpp:122] Setting up drop7.5
I0410 00:38:43.265938 18059 net.cpp:129] Top shape: 32 4096 (131072)
I0410 00:38:43.265941 18059 net.cpp:137] Memory required for data: 267661056
I0410 00:38:43.265944 18059 layer_factory.hpp:77] Creating layer fc7.6
I0410 00:38:43.265967 18059 net.cpp:84] Creating Layer fc7.6
I0410 00:38:43.265971 18059 net.cpp:406] fc7.6 <- fc7.5
I0410 00:38:43.265976 18059 net.cpp:380] fc7.6 -> fc7.6
I0410 00:38:43.422699 18059 net.cpp:122] Setting up fc7.6
I0410 00:38:43.422720 18059 net.cpp:129] Top shape: 32 4096 (131072)
I0410 00:38:43.422724 18059 net.cpp:137] Memory required for data: 268185344
I0410 00:38:43.422739 18059 layer_factory.hpp:77] Creating layer relu7.6
I0410 00:38:43.422747 18059 net.cpp:84] Creating Layer relu7.6
I0410 00:38:43.422752 18059 net.cpp:406] relu7.6 <- fc7.6
I0410 00:38:43.422758 18059 net.cpp:367] relu7.6 -> fc7.6 (in-place)
I0410 00:38:43.423166 18059 net.cpp:122] Setting up relu7.6
I0410 00:38:43.423173 18059 net.cpp:129] Top shape: 32 4096 (131072)
I0410 00:38:43.423177 18059 net.cpp:137] Memory required for data: 268709632
I0410 00:38:43.423180 18059 layer_factory.hpp:77] Creating layer drop7.6
I0410 00:38:43.423187 18059 net.cpp:84] Creating Layer drop7.6
I0410 00:38:43.423192 18059 net.cpp:406] drop7.6 <- fc7.6
I0410 00:38:43.423197 18059 net.cpp:367] drop7.6 -> fc7.6 (in-place)
I0410 00:38:43.423220 18059 net.cpp:122] Setting up drop7.6
I0410 00:38:43.423225 18059 net.cpp:129] Top shape: 32 4096 (131072)
I0410 00:38:43.423228 18059 net.cpp:137] Memory required for data: 269233920
I0410 00:38:43.423233 18059 layer_factory.hpp:77] Creating layer fc8
I0410 00:38:43.423241 18059 net.cpp:84] Creating Layer fc8
I0410 00:38:43.423244 18059 net.cpp:406] fc8 <- fc7.6
I0410 00:38:43.423250 18059 net.cpp:380] fc8 -> fc8
I0410 00:38:43.430936 18059 net.cpp:122] Setting up fc8
I0410 00:38:43.430945 18059 net.cpp:129] Top shape: 32 196 (6272)
I0410 00:38:43.430949 18059 net.cpp:137] Memory required for data: 269259008
I0410 00:38:43.430956 18059 layer_factory.hpp:77] Creating layer fc8_fc8_0_split
I0410 00:38:43.430963 18059 net.cpp:84] Creating Layer fc8_fc8_0_split
I0410 00:38:43.430966 18059 net.cpp:406] fc8_fc8_0_split <- fc8
I0410 00:38:43.430991 18059 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_0
I0410 00:38:43.430999 18059 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_1
I0410 00:38:43.431031 18059 net.cpp:122] Setting up fc8_fc8_0_split
I0410 00:38:43.431036 18059 net.cpp:129] Top shape: 32 196 (6272)
I0410 00:38:43.431041 18059 net.cpp:129] Top shape: 32 196 (6272)
I0410 00:38:43.431043 18059 net.cpp:137] Memory required for data: 269309184
I0410 00:38:43.431047 18059 layer_factory.hpp:77] Creating layer accuracy
I0410 00:38:43.431054 18059 net.cpp:84] Creating Layer accuracy
I0410 00:38:43.431058 18059 net.cpp:406] accuracy <- fc8_fc8_0_split_0
I0410 00:38:43.431062 18059 net.cpp:406] accuracy <- label_val-data_1_split_0
I0410 00:38:43.431067 18059 net.cpp:380] accuracy -> accuracy
I0410 00:38:43.431075 18059 net.cpp:122] Setting up accuracy
I0410 00:38:43.431079 18059 net.cpp:129] Top shape: (1)
I0410 00:38:43.431082 18059 net.cpp:137] Memory required for data: 269309188
I0410 00:38:43.431087 18059 layer_factory.hpp:77] Creating layer loss
I0410 00:38:43.431099 18059 net.cpp:84] Creating Layer loss
I0410 00:38:43.431102 18059 net.cpp:406] loss <- fc8_fc8_0_split_1
I0410 00:38:43.431107 18059 net.cpp:406] loss <- label_val-data_1_split_1
I0410 00:38:43.431111 18059 net.cpp:380] loss -> loss
I0410 00:38:43.431118 18059 layer_factory.hpp:77] Creating layer loss
I0410 00:38:43.431716 18059 net.cpp:122] Setting up loss
I0410 00:38:43.431725 18059 net.cpp:129] Top shape: (1)
I0410 00:38:43.431727 18059 net.cpp:132]     with loss weight 1
I0410 00:38:43.431737 18059 net.cpp:137] Memory required for data: 269309192
I0410 00:38:43.431741 18059 net.cpp:198] loss needs backward computation.
I0410 00:38:43.431747 18059 net.cpp:200] accuracy does not need backward computation.
I0410 00:38:43.431754 18059 net.cpp:198] fc8_fc8_0_split needs backward computation.
I0410 00:38:43.431758 18059 net.cpp:198] fc8 needs backward computation.
I0410 00:38:43.431762 18059 net.cpp:198] drop7.6 needs backward computation.
I0410 00:38:43.431766 18059 net.cpp:198] relu7.6 needs backward computation.
I0410 00:38:43.431769 18059 net.cpp:198] fc7.6 needs backward computation.
I0410 00:38:43.431772 18059 net.cpp:198] drop7.5 needs backward computation.
I0410 00:38:43.431777 18059 net.cpp:198] relu7.5 needs backward computation.
I0410 00:38:43.431780 18059 net.cpp:198] fc7.5 needs backward computation.
I0410 00:38:43.431783 18059 net.cpp:198] drop7 needs backward computation.
I0410 00:38:43.431787 18059 net.cpp:198] relu7 needs backward computation.
I0410 00:38:43.431790 18059 net.cpp:198] fc7 needs backward computation.
I0410 00:38:43.431794 18059 net.cpp:198] drop6 needs backward computation.
I0410 00:38:43.431798 18059 net.cpp:198] relu6 needs backward computation.
I0410 00:38:43.431802 18059 net.cpp:198] fc6 needs backward computation.
I0410 00:38:43.431804 18059 net.cpp:198] pool5 needs backward computation.
I0410 00:38:43.431808 18059 net.cpp:198] relu5 needs backward computation.
I0410 00:38:43.431813 18059 net.cpp:198] conv5 needs backward computation.
I0410 00:38:43.431816 18059 net.cpp:198] relu4 needs backward computation.
I0410 00:38:43.431819 18059 net.cpp:198] conv4 needs backward computation.
I0410 00:38:43.431823 18059 net.cpp:198] relu3 needs backward computation.
I0410 00:38:43.431826 18059 net.cpp:198] conv3 needs backward computation.
I0410 00:38:43.431830 18059 net.cpp:198] pool2 needs backward computation.
I0410 00:38:43.431834 18059 net.cpp:198] norm2 needs backward computation.
I0410 00:38:43.431838 18059 net.cpp:198] relu2 needs backward computation.
I0410 00:38:43.431841 18059 net.cpp:198] conv2 needs backward computation.
I0410 00:38:43.431844 18059 net.cpp:198] pool1 needs backward computation.
I0410 00:38:43.431849 18059 net.cpp:198] norm1 needs backward computation.
I0410 00:38:43.431851 18059 net.cpp:198] relu1 needs backward computation.
I0410 00:38:43.431855 18059 net.cpp:198] conv1 needs backward computation.
I0410 00:38:43.431859 18059 net.cpp:200] label_val-data_1_split does not need backward computation.
I0410 00:38:43.431871 18059 net.cpp:200] val-data does not need backward computation.
I0410 00:38:43.431875 18059 net.cpp:242] This network produces output accuracy
I0410 00:38:43.431879 18059 net.cpp:242] This network produces output loss
I0410 00:38:43.431898 18059 net.cpp:255] Network initialization done.
I0410 00:38:43.431977 18059 solver.cpp:56] Solver scaffolding done.
I0410 00:38:43.432515 18059 caffe.cpp:248] Starting Optimization
I0410 00:38:43.432524 18059 solver.cpp:272] Solving
I0410 00:38:43.432528 18059 solver.cpp:273] Learning Rate Policy: exp
I0410 00:38:43.434279 18059 solver.cpp:330] Iteration 0, Testing net (#0)
I0410 00:38:43.434289 18059 net.cpp:676] Ignoring source layer train-data
I0410 00:38:43.542757 18059 blocking_queue.cpp:49] Waiting for data
I0410 00:38:47.880010 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:38:47.925151 18059 solver.cpp:397]     Test net output #0: accuracy = 0.00428922
I0410 00:38:47.925180 18059 solver.cpp:397]     Test net output #1: loss = 5.27981 (* 1 = 5.27981 loss)
I0410 00:38:48.020196 18059 solver.cpp:218] Iteration 0 (-7.80733e-40 iter/s, 4.58747s/12 iters), loss = 5.28938
I0410 00:38:48.021708 18059 solver.cpp:237]     Train net output #0: loss = 5.28938 (* 1 = 5.28938 loss)
I0410 00:38:48.021734 18059 sgd_solver.cpp:105] Iteration 0, lr = 0.01
I0410 00:38:52.049644 18059 solver.cpp:218] Iteration 12 (2.97931 iter/s, 4.02778s/12 iters), loss = 5.2837
I0410 00:38:52.049685 18059 solver.cpp:237]     Train net output #0: loss = 5.2837 (* 1 = 5.2837 loss)
I0410 00:38:52.049692 18059 sgd_solver.cpp:105] Iteration 12, lr = 0.00997626
I0410 00:38:57.141191 18059 solver.cpp:218] Iteration 24 (2.35696 iter/s, 5.09131s/12 iters), loss = 5.27517
I0410 00:38:57.141242 18059 solver.cpp:237]     Train net output #0: loss = 5.27517 (* 1 = 5.27517 loss)
I0410 00:38:57.141252 18059 sgd_solver.cpp:105] Iteration 24, lr = 0.00995257
I0410 00:39:01.996469 18059 solver.cpp:218] Iteration 36 (2.47166 iter/s, 4.85505s/12 iters), loss = 5.28315
I0410 00:39:01.996510 18059 solver.cpp:237]     Train net output #0: loss = 5.28315 (* 1 = 5.28315 loss)
I0410 00:39:01.996520 18059 sgd_solver.cpp:105] Iteration 36, lr = 0.00992894
I0410 00:39:06.764797 18059 solver.cpp:218] Iteration 48 (2.51672 iter/s, 4.7681s/12 iters), loss = 5.30048
I0410 00:39:06.764853 18059 solver.cpp:237]     Train net output #0: loss = 5.30048 (* 1 = 5.30048 loss)
I0410 00:39:06.764865 18059 sgd_solver.cpp:105] Iteration 48, lr = 0.00990537
I0410 00:39:11.688233 18059 solver.cpp:218] Iteration 60 (2.43744 iter/s, 4.9232s/12 iters), loss = 5.30395
I0410 00:39:11.688378 18059 solver.cpp:237]     Train net output #0: loss = 5.30395 (* 1 = 5.30395 loss)
I0410 00:39:11.688390 18059 sgd_solver.cpp:105] Iteration 60, lr = 0.00988185
I0410 00:39:16.434103 18059 solver.cpp:218] Iteration 72 (2.52869 iter/s, 4.74555s/12 iters), loss = 5.30006
I0410 00:39:16.434167 18059 solver.cpp:237]     Train net output #0: loss = 5.30006 (* 1 = 5.30006 loss)
I0410 00:39:16.434185 18059 sgd_solver.cpp:105] Iteration 72, lr = 0.00985839
I0410 00:39:21.235028 18059 solver.cpp:218] Iteration 84 (2.49964 iter/s, 4.80068s/12 iters), loss = 5.31048
I0410 00:39:21.235077 18059 solver.cpp:237]     Train net output #0: loss = 5.31048 (* 1 = 5.31048 loss)
I0410 00:39:21.235090 18059 sgd_solver.cpp:105] Iteration 84, lr = 0.00983498
I0410 00:39:26.018934 18059 solver.cpp:218] Iteration 96 (2.50853 iter/s, 4.78368s/12 iters), loss = 5.31912
I0410 00:39:26.018976 18059 solver.cpp:237]     Train net output #0: loss = 5.31912 (* 1 = 5.31912 loss)
I0410 00:39:26.018986 18059 sgd_solver.cpp:105] Iteration 96, lr = 0.00981163
I0410 00:39:27.626227 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:39:27.947477 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_102.caffemodel
I0410 00:39:34.676877 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_102.solverstate
I0410 00:39:40.189254 18059 solver.cpp:330] Iteration 102, Testing net (#0)
I0410 00:39:40.189281 18059 net.cpp:676] Ignoring source layer train-data
I0410 00:39:44.731765 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:39:44.809338 18059 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 00:39:44.809386 18059 solver.cpp:397]     Test net output #1: loss = 5.29057 (* 1 = 5.29057 loss)
I0410 00:39:46.461560 18059 solver.cpp:218] Iteration 108 (0.587031 iter/s, 20.4419s/12 iters), loss = 5.32004
I0410 00:39:46.461627 18059 solver.cpp:237]     Train net output #0: loss = 5.32004 (* 1 = 5.32004 loss)
I0410 00:39:46.461644 18059 sgd_solver.cpp:105] Iteration 108, lr = 0.00978834
I0410 00:39:50.994786 18059 solver.cpp:218] Iteration 120 (2.64726 iter/s, 4.53299s/12 iters), loss = 5.29215
I0410 00:39:50.994838 18059 solver.cpp:237]     Train net output #0: loss = 5.29215 (* 1 = 5.29215 loss)
I0410 00:39:50.994851 18059 sgd_solver.cpp:105] Iteration 120, lr = 0.0097651
I0410 00:39:55.773627 18059 solver.cpp:218] Iteration 132 (2.51119 iter/s, 4.77861s/12 iters), loss = 5.254
I0410 00:39:55.773674 18059 solver.cpp:237]     Train net output #0: loss = 5.254 (* 1 = 5.254 loss)
I0410 00:39:55.773686 18059 sgd_solver.cpp:105] Iteration 132, lr = 0.00974192
I0410 00:40:00.325260 18059 solver.cpp:218] Iteration 144 (2.63654 iter/s, 4.55142s/12 iters), loss = 5.31278
I0410 00:40:00.325309 18059 solver.cpp:237]     Train net output #0: loss = 5.31278 (* 1 = 5.31278 loss)
I0410 00:40:00.325320 18059 sgd_solver.cpp:105] Iteration 144, lr = 0.00971879
I0410 00:40:05.149154 18059 solver.cpp:218] Iteration 156 (2.48774 iter/s, 4.82366s/12 iters), loss = 5.27104
I0410 00:40:05.149209 18059 solver.cpp:237]     Train net output #0: loss = 5.27104 (* 1 = 5.27104 loss)
I0410 00:40:05.149222 18059 sgd_solver.cpp:105] Iteration 156, lr = 0.00969571
I0410 00:40:09.871086 18059 solver.cpp:218] Iteration 168 (2.54146 iter/s, 4.7217s/12 iters), loss = 5.26837
I0410 00:40:09.871140 18059 solver.cpp:237]     Train net output #0: loss = 5.26837 (* 1 = 5.26837 loss)
I0410 00:40:09.871153 18059 sgd_solver.cpp:105] Iteration 168, lr = 0.00967269
I0410 00:40:14.558470 18059 solver.cpp:218] Iteration 180 (2.56019 iter/s, 4.68715s/12 iters), loss = 5.27171
I0410 00:40:14.558518 18059 solver.cpp:237]     Train net output #0: loss = 5.27171 (* 1 = 5.27171 loss)
I0410 00:40:14.558527 18059 sgd_solver.cpp:105] Iteration 180, lr = 0.00964973
I0410 00:40:19.366132 18059 solver.cpp:218] Iteration 192 (2.49614 iter/s, 4.80743s/12 iters), loss = 5.28985
I0410 00:40:19.366232 18059 solver.cpp:237]     Train net output #0: loss = 5.28985 (* 1 = 5.28985 loss)
I0410 00:40:19.366245 18059 sgd_solver.cpp:105] Iteration 192, lr = 0.00962682
I0410 00:40:22.747807 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:40:23.379828 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_204.caffemodel
I0410 00:40:28.200361 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_204.solverstate
I0410 00:40:32.998968 18059 solver.cpp:330] Iteration 204, Testing net (#0)
I0410 00:40:32.998993 18059 net.cpp:676] Ignoring source layer train-data
I0410 00:40:37.359846 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:40:37.484016 18059 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 00:40:37.484078 18059 solver.cpp:397]     Test net output #1: loss = 5.28925 (* 1 = 5.28925 loss)
I0410 00:40:37.576603 18059 solver.cpp:218] Iteration 204 (0.658989 iter/s, 18.2097s/12 iters), loss = 5.27225
I0410 00:40:37.576656 18059 solver.cpp:237]     Train net output #0: loss = 5.27225 (* 1 = 5.27225 loss)
I0410 00:40:37.576668 18059 sgd_solver.cpp:105] Iteration 204, lr = 0.00960396
I0410 00:40:41.903841 18059 solver.cpp:218] Iteration 216 (2.77327 iter/s, 4.32702s/12 iters), loss = 5.28275
I0410 00:40:41.903882 18059 solver.cpp:237]     Train net output #0: loss = 5.28275 (* 1 = 5.28275 loss)
I0410 00:40:41.903889 18059 sgd_solver.cpp:105] Iteration 216, lr = 0.00958116
I0410 00:40:46.857769 18059 solver.cpp:218] Iteration 228 (2.42243 iter/s, 4.9537s/12 iters), loss = 5.2786
I0410 00:40:46.857817 18059 solver.cpp:237]     Train net output #0: loss = 5.2786 (* 1 = 5.2786 loss)
I0410 00:40:46.857827 18059 sgd_solver.cpp:105] Iteration 228, lr = 0.00955841
I0410 00:40:51.666108 18059 solver.cpp:218] Iteration 240 (2.49578 iter/s, 4.80811s/12 iters), loss = 5.29907
I0410 00:40:51.666283 18059 solver.cpp:237]     Train net output #0: loss = 5.29907 (* 1 = 5.29907 loss)
I0410 00:40:51.666296 18059 sgd_solver.cpp:105] Iteration 240, lr = 0.00953572
I0410 00:40:56.388149 18059 solver.cpp:218] Iteration 252 (2.54146 iter/s, 4.72169s/12 iters), loss = 5.26302
I0410 00:40:56.388197 18059 solver.cpp:237]     Train net output #0: loss = 5.26302 (* 1 = 5.26302 loss)
I0410 00:40:56.388209 18059 sgd_solver.cpp:105] Iteration 252, lr = 0.00951308
I0410 00:41:01.438370 18059 solver.cpp:218] Iteration 264 (2.37625 iter/s, 5.04997s/12 iters), loss = 5.27778
I0410 00:41:01.438424 18059 solver.cpp:237]     Train net output #0: loss = 5.27778 (* 1 = 5.27778 loss)
I0410 00:41:01.438436 18059 sgd_solver.cpp:105] Iteration 264, lr = 0.00949049
I0410 00:41:06.781800 18059 solver.cpp:218] Iteration 276 (2.24586 iter/s, 5.34318s/12 iters), loss = 5.30034
I0410 00:41:06.781847 18059 solver.cpp:237]     Train net output #0: loss = 5.30034 (* 1 = 5.30034 loss)
I0410 00:41:06.781858 18059 sgd_solver.cpp:105] Iteration 276, lr = 0.00946796
I0410 00:41:12.144114 18059 solver.cpp:218] Iteration 288 (2.23794 iter/s, 5.36207s/12 iters), loss = 5.29032
I0410 00:41:12.144165 18059 solver.cpp:237]     Train net output #0: loss = 5.29032 (* 1 = 5.29032 loss)
I0410 00:41:12.144177 18059 sgd_solver.cpp:105] Iteration 288, lr = 0.00944548
I0410 00:41:17.525485 18059 solver.cpp:218] Iteration 300 (2.23002 iter/s, 5.38112s/12 iters), loss = 5.29706
I0410 00:41:17.525530 18059 solver.cpp:237]     Train net output #0: loss = 5.29706 (* 1 = 5.29706 loss)
I0410 00:41:17.525539 18059 sgd_solver.cpp:105] Iteration 300, lr = 0.00942305
I0410 00:41:18.567159 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:41:19.653383 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_306.caffemodel
I0410 00:41:24.249497 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_306.solverstate
I0410 00:41:30.122972 18059 solver.cpp:330] Iteration 306, Testing net (#0)
I0410 00:41:30.122993 18059 net.cpp:676] Ignoring source layer train-data
I0410 00:41:34.419270 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:41:34.577162 18059 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 00:41:34.577203 18059 solver.cpp:397]     Test net output #1: loss = 5.28775 (* 1 = 5.28775 loss)
I0410 00:41:36.360720 18059 solver.cpp:218] Iteration 312 (0.637128 iter/s, 18.8345s/12 iters), loss = 5.27998
I0410 00:41:36.360762 18059 solver.cpp:237]     Train net output #0: loss = 5.27998 (* 1 = 5.27998 loss)
I0410 00:41:36.360770 18059 sgd_solver.cpp:105] Iteration 312, lr = 0.00940068
I0410 00:41:41.382892 18059 solver.cpp:218] Iteration 324 (2.38952 iter/s, 5.02194s/12 iters), loss = 5.25093
I0410 00:41:41.382947 18059 solver.cpp:237]     Train net output #0: loss = 5.25093 (* 1 = 5.25093 loss)
I0410 00:41:41.382959 18059 sgd_solver.cpp:105] Iteration 324, lr = 0.00937836
I0410 00:41:46.165791 18059 solver.cpp:218] Iteration 336 (2.50906 iter/s, 4.78266s/12 iters), loss = 5.27168
I0410 00:41:46.165843 18059 solver.cpp:237]     Train net output #0: loss = 5.27168 (* 1 = 5.27168 loss)
I0410 00:41:46.165855 18059 sgd_solver.cpp:105] Iteration 336, lr = 0.0093561
I0410 00:41:51.033550 18059 solver.cpp:218] Iteration 348 (2.46532 iter/s, 4.86752s/12 iters), loss = 5.26886
I0410 00:41:51.033602 18059 solver.cpp:237]     Train net output #0: loss = 5.26886 (* 1 = 5.26886 loss)
I0410 00:41:51.033613 18059 sgd_solver.cpp:105] Iteration 348, lr = 0.00933388
I0410 00:41:55.749632 18059 solver.cpp:218] Iteration 360 (2.54461 iter/s, 4.71586s/12 iters), loss = 5.29723
I0410 00:41:55.749758 18059 solver.cpp:237]     Train net output #0: loss = 5.29723 (* 1 = 5.29723 loss)
I0410 00:41:55.749768 18059 sgd_solver.cpp:105] Iteration 360, lr = 0.00931172
I0410 00:42:00.475124 18059 solver.cpp:218] Iteration 372 (2.53958 iter/s, 4.72519s/12 iters), loss = 5.29179
I0410 00:42:00.475184 18059 solver.cpp:237]     Train net output #0: loss = 5.29179 (* 1 = 5.29179 loss)
I0410 00:42:00.475200 18059 sgd_solver.cpp:105] Iteration 372, lr = 0.00928961
I0410 00:42:05.604425 18059 solver.cpp:218] Iteration 384 (2.33961 iter/s, 5.12905s/12 iters), loss = 5.28905
I0410 00:42:05.604476 18059 solver.cpp:237]     Train net output #0: loss = 5.28905 (* 1 = 5.28905 loss)
I0410 00:42:05.604490 18059 sgd_solver.cpp:105] Iteration 384, lr = 0.00926756
I0410 00:42:10.436851 18059 solver.cpp:218] Iteration 396 (2.48334 iter/s, 4.83219s/12 iters), loss = 5.28307
I0410 00:42:10.436902 18059 solver.cpp:237]     Train net output #0: loss = 5.28307 (* 1 = 5.28307 loss)
I0410 00:42:10.436914 18059 sgd_solver.cpp:105] Iteration 396, lr = 0.00924556
I0410 00:42:13.422428 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:42:14.780201 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_408.caffemodel
I0410 00:42:22.201004 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_408.solverstate
I0410 00:42:26.590270 18059 solver.cpp:330] Iteration 408, Testing net (#0)
I0410 00:42:26.590348 18059 net.cpp:676] Ignoring source layer train-data
I0410 00:42:30.954254 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:42:31.163503 18059 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 00:42:31.163552 18059 solver.cpp:397]     Test net output #1: loss = 5.28818 (* 1 = 5.28818 loss)
I0410 00:42:31.257575 18059 solver.cpp:218] Iteration 408 (0.576371 iter/s, 20.8199s/12 iters), loss = 5.29155
I0410 00:42:31.257622 18059 solver.cpp:237]     Train net output #0: loss = 5.29155 (* 1 = 5.29155 loss)
I0410 00:42:31.257633 18059 sgd_solver.cpp:105] Iteration 408, lr = 0.00922361
I0410 00:42:35.424340 18059 solver.cpp:218] Iteration 420 (2.88007 iter/s, 4.16656s/12 iters), loss = 5.28008
I0410 00:42:35.424388 18059 solver.cpp:237]     Train net output #0: loss = 5.28008 (* 1 = 5.28008 loss)
I0410 00:42:35.424398 18059 sgd_solver.cpp:105] Iteration 420, lr = 0.00920171
I0410 00:42:40.175726 18059 solver.cpp:218] Iteration 432 (2.5257 iter/s, 4.75116s/12 iters), loss = 5.27452
I0410 00:42:40.175776 18059 solver.cpp:237]     Train net output #0: loss = 5.27452 (* 1 = 5.27452 loss)
I0410 00:42:40.175787 18059 sgd_solver.cpp:105] Iteration 432, lr = 0.00917986
I0410 00:42:44.911679 18059 solver.cpp:218] Iteration 444 (2.53393 iter/s, 4.73572s/12 iters), loss = 5.29746
I0410 00:42:44.911731 18059 solver.cpp:237]     Train net output #0: loss = 5.29746 (* 1 = 5.29746 loss)
I0410 00:42:44.911741 18059 sgd_solver.cpp:105] Iteration 444, lr = 0.00915807
I0410 00:42:49.697121 18059 solver.cpp:218] Iteration 456 (2.50773 iter/s, 4.78521s/12 iters), loss = 5.28807
I0410 00:42:49.697178 18059 solver.cpp:237]     Train net output #0: loss = 5.28807 (* 1 = 5.28807 loss)
I0410 00:42:49.697190 18059 sgd_solver.cpp:105] Iteration 456, lr = 0.00913632
I0410 00:42:54.157225 18059 solver.cpp:218] Iteration 468 (2.69066 iter/s, 4.45988s/12 iters), loss = 5.28272
I0410 00:42:54.157279 18059 solver.cpp:237]     Train net output #0: loss = 5.28272 (* 1 = 5.28272 loss)
I0410 00:42:54.157289 18059 sgd_solver.cpp:105] Iteration 468, lr = 0.00911463
I0410 00:42:58.647097 18059 solver.cpp:218] Iteration 480 (2.67282 iter/s, 4.48965s/12 iters), loss = 5.27831
I0410 00:42:58.647188 18059 solver.cpp:237]     Train net output #0: loss = 5.27831 (* 1 = 5.27831 loss)
I0410 00:42:58.647199 18059 sgd_solver.cpp:105] Iteration 480, lr = 0.00909299
I0410 00:43:03.822501 18059 solver.cpp:218] Iteration 492 (2.31879 iter/s, 5.17512s/12 iters), loss = 5.30241
I0410 00:43:03.822547 18059 solver.cpp:237]     Train net output #0: loss = 5.30241 (* 1 = 5.30241 loss)
I0410 00:43:03.822556 18059 sgd_solver.cpp:105] Iteration 492, lr = 0.0090714
I0410 00:43:08.814532 18059 solver.cpp:218] Iteration 504 (2.40394 iter/s, 4.9918s/12 iters), loss = 5.27902
I0410 00:43:08.814574 18059 solver.cpp:237]     Train net output #0: loss = 5.27902 (* 1 = 5.27902 loss)
I0410 00:43:08.814581 18059 sgd_solver.cpp:105] Iteration 504, lr = 0.00904986
I0410 00:43:09.012266 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:43:10.738620 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_510.caffemodel
I0410 00:43:23.184347 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_510.solverstate
I0410 00:43:31.635821 18059 solver.cpp:330] Iteration 510, Testing net (#0)
I0410 00:43:31.635897 18059 net.cpp:676] Ignoring source layer train-data
I0410 00:43:36.126041 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:43:36.365054 18059 solver.cpp:397]     Test net output #0: accuracy = 0.00612745
I0410 00:43:36.365098 18059 solver.cpp:397]     Test net output #1: loss = 5.2865 (* 1 = 5.2865 loss)
I0410 00:43:38.317996 18059 solver.cpp:218] Iteration 516 (0.406747 iter/s, 29.5024s/12 iters), loss = 5.28511
I0410 00:43:38.318055 18059 solver.cpp:237]     Train net output #0: loss = 5.28511 (* 1 = 5.28511 loss)
I0410 00:43:38.318068 18059 sgd_solver.cpp:105] Iteration 516, lr = 0.00902838
I0410 00:43:43.111186 18059 solver.cpp:218] Iteration 528 (2.50368 iter/s, 4.79295s/12 iters), loss = 5.2816
I0410 00:43:43.111241 18059 solver.cpp:237]     Train net output #0: loss = 5.2816 (* 1 = 5.2816 loss)
I0410 00:43:43.111253 18059 sgd_solver.cpp:105] Iteration 528, lr = 0.00900694
I0410 00:43:47.826002 18059 solver.cpp:218] Iteration 540 (2.54531 iter/s, 4.71456s/12 iters), loss = 5.28116
I0410 00:43:47.826053 18059 solver.cpp:237]     Train net output #0: loss = 5.28116 (* 1 = 5.28116 loss)
I0410 00:43:47.826066 18059 sgd_solver.cpp:105] Iteration 540, lr = 0.00898556
I0410 00:43:52.573503 18059 solver.cpp:218] Iteration 552 (2.52777 iter/s, 4.74727s/12 iters), loss = 5.26996
I0410 00:43:52.573559 18059 solver.cpp:237]     Train net output #0: loss = 5.26996 (* 1 = 5.26996 loss)
I0410 00:43:52.573575 18059 sgd_solver.cpp:105] Iteration 552, lr = 0.00896423
I0410 00:43:57.498116 18059 solver.cpp:218] Iteration 564 (2.43686 iter/s, 4.92437s/12 iters), loss = 5.2546
I0410 00:43:57.498163 18059 solver.cpp:237]     Train net output #0: loss = 5.2546 (* 1 = 5.2546 loss)
I0410 00:43:57.498175 18059 sgd_solver.cpp:105] Iteration 564, lr = 0.00894294
I0410 00:44:02.192267 18059 solver.cpp:218] Iteration 576 (2.55649 iter/s, 4.69393s/12 iters), loss = 5.28252
I0410 00:44:02.192353 18059 solver.cpp:237]     Train net output #0: loss = 5.28252 (* 1 = 5.28252 loss)
I0410 00:44:02.192363 18059 sgd_solver.cpp:105] Iteration 576, lr = 0.00892171
I0410 00:44:06.730180 18059 solver.cpp:218] Iteration 588 (2.64454 iter/s, 4.53764s/12 iters), loss = 5.27884
I0410 00:44:06.730229 18059 solver.cpp:237]     Train net output #0: loss = 5.27884 (* 1 = 5.27884 loss)
I0410 00:44:06.730242 18059 sgd_solver.cpp:105] Iteration 588, lr = 0.00890053
I0410 00:44:11.429366 18059 solver.cpp:218] Iteration 600 (2.55376 iter/s, 4.69896s/12 iters), loss = 5.27356
I0410 00:44:11.429422 18059 solver.cpp:237]     Train net output #0: loss = 5.27356 (* 1 = 5.27356 loss)
I0410 00:44:11.429435 18059 sgd_solver.cpp:105] Iteration 600, lr = 0.0088794
I0410 00:44:13.523886 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:44:15.578485 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_612.caffemodel
I0410 00:44:25.015872 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_612.solverstate
I0410 00:44:33.054688 18059 solver.cpp:330] Iteration 612, Testing net (#0)
I0410 00:44:33.054775 18059 net.cpp:676] Ignoring source layer train-data
I0410 00:44:37.260623 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:44:37.548966 18059 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 00:44:37.549002 18059 solver.cpp:397]     Test net output #1: loss = 5.28624 (* 1 = 5.28624 loss)
I0410 00:44:37.642738 18059 solver.cpp:218] Iteration 612 (0.457799 iter/s, 26.2124s/12 iters), loss = 5.28258
I0410 00:44:37.642794 18059 solver.cpp:237]     Train net output #0: loss = 5.28258 (* 1 = 5.28258 loss)
I0410 00:44:37.642805 18059 sgd_solver.cpp:105] Iteration 612, lr = 0.00885831
I0410 00:44:41.612546 18059 solver.cpp:218] Iteration 624 (3.02298 iter/s, 3.9696s/12 iters), loss = 5.29072
I0410 00:44:41.612602 18059 solver.cpp:237]     Train net output #0: loss = 5.29072 (* 1 = 5.29072 loss)
I0410 00:44:41.612615 18059 sgd_solver.cpp:105] Iteration 624, lr = 0.00883728
I0410 00:44:45.909740 18059 solver.cpp:218] Iteration 636 (2.79266 iter/s, 4.29698s/12 iters), loss = 5.29027
I0410 00:44:45.909790 18059 solver.cpp:237]     Train net output #0: loss = 5.29027 (* 1 = 5.29027 loss)
I0410 00:44:45.909801 18059 sgd_solver.cpp:105] Iteration 636, lr = 0.0088163
I0410 00:44:50.209208 18059 solver.cpp:218] Iteration 648 (2.79118 iter/s, 4.29925s/12 iters), loss = 5.27814
I0410 00:44:50.209252 18059 solver.cpp:237]     Train net output #0: loss = 5.27814 (* 1 = 5.27814 loss)
I0410 00:44:50.209259 18059 sgd_solver.cpp:105] Iteration 648, lr = 0.00879537
I0410 00:44:54.800992 18059 solver.cpp:218] Iteration 660 (2.61349 iter/s, 4.59156s/12 iters), loss = 5.27565
I0410 00:44:54.801041 18059 solver.cpp:237]     Train net output #0: loss = 5.27565 (* 1 = 5.27565 loss)
I0410 00:44:54.801050 18059 sgd_solver.cpp:105] Iteration 660, lr = 0.00877449
I0410 00:44:59.548969 18059 solver.cpp:218] Iteration 672 (2.52751 iter/s, 4.74775s/12 iters), loss = 5.28391
I0410 00:44:59.549010 18059 solver.cpp:237]     Train net output #0: loss = 5.28391 (* 1 = 5.28391 loss)
I0410 00:44:59.549017 18059 sgd_solver.cpp:105] Iteration 672, lr = 0.00875366
I0410 00:45:04.385622 18059 solver.cpp:218] Iteration 684 (2.48117 iter/s, 4.83643s/12 iters), loss = 5.27919
I0410 00:45:04.385707 18059 solver.cpp:237]     Train net output #0: loss = 5.27919 (* 1 = 5.27919 loss)
I0410 00:45:04.385717 18059 sgd_solver.cpp:105] Iteration 684, lr = 0.00873287
I0410 00:45:05.110186 18059 blocking_queue.cpp:49] Waiting for data
I0410 00:45:09.246031 18059 solver.cpp:218] Iteration 696 (2.46906 iter/s, 4.86014s/12 iters), loss = 5.27239
I0410 00:45:09.246074 18059 solver.cpp:237]     Train net output #0: loss = 5.27239 (* 1 = 5.27239 loss)
I0410 00:45:09.246083 18059 sgd_solver.cpp:105] Iteration 696, lr = 0.00871214
I0410 00:45:13.692473 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:45:14.116925 18059 solver.cpp:218] Iteration 708 (2.46373 iter/s, 4.87066s/12 iters), loss = 5.25695
I0410 00:45:14.116977 18059 solver.cpp:237]     Train net output #0: loss = 5.25695 (* 1 = 5.25695 loss)
I0410 00:45:14.116988 18059 sgd_solver.cpp:105] Iteration 708, lr = 0.00869145
I0410 00:45:16.140983 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_714.caffemodel
I0410 00:45:21.791208 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_714.solverstate
I0410 00:45:25.682129 18059 solver.cpp:330] Iteration 714, Testing net (#0)
I0410 00:45:25.682155 18059 net.cpp:676] Ignoring source layer train-data
I0410 00:45:29.716116 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:45:30.036296 18059 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 00:45:30.036345 18059 solver.cpp:397]     Test net output #1: loss = 5.28536 (* 1 = 5.28536 loss)
I0410 00:45:31.619671 18059 solver.cpp:218] Iteration 720 (0.685633 iter/s, 17.5021s/12 iters), loss = 5.27043
I0410 00:45:31.619719 18059 solver.cpp:237]     Train net output #0: loss = 5.27043 (* 1 = 5.27043 loss)
I0410 00:45:31.619727 18059 sgd_solver.cpp:105] Iteration 720, lr = 0.00867082
I0410 00:45:36.287215 18059 solver.cpp:218] Iteration 732 (2.57107 iter/s, 4.66732s/12 iters), loss = 5.28428
I0410 00:45:36.287351 18059 solver.cpp:237]     Train net output #0: loss = 5.28428 (* 1 = 5.28428 loss)
I0410 00:45:36.287361 18059 sgd_solver.cpp:105] Iteration 732, lr = 0.00865023
I0410 00:45:41.403964 18059 solver.cpp:218] Iteration 744 (2.34539 iter/s, 5.11642s/12 iters), loss = 5.2788
I0410 00:45:41.404012 18059 solver.cpp:237]     Train net output #0: loss = 5.2788 (* 1 = 5.2788 loss)
I0410 00:45:41.404022 18059 sgd_solver.cpp:105] Iteration 744, lr = 0.0086297
I0410 00:45:46.529489 18059 solver.cpp:218] Iteration 756 (2.34133 iter/s, 5.12528s/12 iters), loss = 5.28146
I0410 00:45:46.529536 18059 solver.cpp:237]     Train net output #0: loss = 5.28146 (* 1 = 5.28146 loss)
I0410 00:45:46.529544 18059 sgd_solver.cpp:105] Iteration 756, lr = 0.00860921
I0410 00:45:51.342226 18059 solver.cpp:218] Iteration 768 (2.4935 iter/s, 4.8125s/12 iters), loss = 5.28388
I0410 00:45:51.342279 18059 solver.cpp:237]     Train net output #0: loss = 5.28388 (* 1 = 5.28388 loss)
I0410 00:45:51.342291 18059 sgd_solver.cpp:105] Iteration 768, lr = 0.00858877
I0410 00:45:56.029279 18059 solver.cpp:218] Iteration 780 (2.56037 iter/s, 4.68682s/12 iters), loss = 5.27232
I0410 00:45:56.029325 18059 solver.cpp:237]     Train net output #0: loss = 5.27232 (* 1 = 5.27232 loss)
I0410 00:45:56.029335 18059 sgd_solver.cpp:105] Iteration 780, lr = 0.00856838
I0410 00:46:00.822335 18059 solver.cpp:218] Iteration 792 (2.50374 iter/s, 4.79283s/12 iters), loss = 5.27658
I0410 00:46:00.822387 18059 solver.cpp:237]     Train net output #0: loss = 5.27658 (* 1 = 5.27658 loss)
I0410 00:46:00.822399 18059 sgd_solver.cpp:105] Iteration 792, lr = 0.00854803
I0410 00:46:05.812906 18059 solver.cpp:218] Iteration 804 (2.40465 iter/s, 4.99033s/12 iters), loss = 5.28775
I0410 00:46:05.812958 18059 solver.cpp:237]     Train net output #0: loss = 5.28775 (* 1 = 5.28775 loss)
I0410 00:46:05.812970 18059 sgd_solver.cpp:105] Iteration 804, lr = 0.00852774
I0410 00:46:07.492255 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:46:10.257177 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_816.caffemodel
I0410 00:46:14.876462 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_816.solverstate
I0410 00:46:18.537233 18059 solver.cpp:330] Iteration 816, Testing net (#0)
I0410 00:46:18.537258 18059 net.cpp:676] Ignoring source layer train-data
I0410 00:46:22.660204 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:46:23.016855 18059 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 00:46:23.016897 18059 solver.cpp:397]     Test net output #1: loss = 5.27738 (* 1 = 5.27738 loss)
I0410 00:46:23.110241 18059 solver.cpp:218] Iteration 816 (0.693775 iter/s, 17.2967s/12 iters), loss = 5.28199
I0410 00:46:23.110288 18059 solver.cpp:237]     Train net output #0: loss = 5.28199 (* 1 = 5.28199 loss)
I0410 00:46:23.110298 18059 sgd_solver.cpp:105] Iteration 816, lr = 0.00850749
I0410 00:46:27.529554 18059 solver.cpp:218] Iteration 828 (2.71548 iter/s, 4.4191s/12 iters), loss = 5.2827
I0410 00:46:27.529597 18059 solver.cpp:237]     Train net output #0: loss = 5.2827 (* 1 = 5.2827 loss)
I0410 00:46:27.529605 18059 sgd_solver.cpp:105] Iteration 828, lr = 0.00848729
I0410 00:46:32.210470 18059 solver.cpp:218] Iteration 840 (2.56372 iter/s, 4.6807s/12 iters), loss = 5.22119
I0410 00:46:32.210522 18059 solver.cpp:237]     Train net output #0: loss = 5.22119 (* 1 = 5.22119 loss)
I0410 00:46:32.210534 18059 sgd_solver.cpp:105] Iteration 840, lr = 0.00846714
I0410 00:46:37.020524 18059 solver.cpp:218] Iteration 852 (2.49489 iter/s, 4.80983s/12 iters), loss = 5.2755
I0410 00:46:37.020570 18059 solver.cpp:237]     Train net output #0: loss = 5.2755 (* 1 = 5.2755 loss)
I0410 00:46:37.020579 18059 sgd_solver.cpp:105] Iteration 852, lr = 0.00844704
I0410 00:46:41.803864 18059 solver.cpp:218] Iteration 864 (2.50882 iter/s, 4.78312s/12 iters), loss = 5.23191
I0410 00:46:41.803992 18059 solver.cpp:237]     Train net output #0: loss = 5.23191 (* 1 = 5.23191 loss)
I0410 00:46:41.804003 18059 sgd_solver.cpp:105] Iteration 864, lr = 0.00842698
I0410 00:46:46.538241 18059 solver.cpp:218] Iteration 876 (2.53481 iter/s, 4.73407s/12 iters), loss = 5.18163
I0410 00:46:46.538301 18059 solver.cpp:237]     Train net output #0: loss = 5.18163 (* 1 = 5.18163 loss)
I0410 00:46:46.538317 18059 sgd_solver.cpp:105] Iteration 876, lr = 0.00840698
I0410 00:46:51.264668 18059 solver.cpp:218] Iteration 888 (2.53904 iter/s, 4.7262s/12 iters), loss = 5.12855
I0410 00:46:51.264712 18059 solver.cpp:237]     Train net output #0: loss = 5.12855 (* 1 = 5.12855 loss)
I0410 00:46:51.264722 18059 sgd_solver.cpp:105] Iteration 888, lr = 0.00838702
I0410 00:46:56.031498 18059 solver.cpp:218] Iteration 900 (2.51752 iter/s, 4.7666s/12 iters), loss = 5.24561
I0410 00:46:56.031548 18059 solver.cpp:237]     Train net output #0: loss = 5.24561 (* 1 = 5.24561 loss)
I0410 00:46:56.031559 18059 sgd_solver.cpp:105] Iteration 900, lr = 0.0083671
I0410 00:46:59.522151 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:47:00.583897 18059 solver.cpp:218] Iteration 912 (2.6361 iter/s, 4.55218s/12 iters), loss = 5.11799
I0410 00:47:00.583947 18059 solver.cpp:237]     Train net output #0: loss = 5.11799 (* 1 = 5.11799 loss)
I0410 00:47:00.583959 18059 sgd_solver.cpp:105] Iteration 912, lr = 0.00834724
I0410 00:47:02.506332 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_918.caffemodel
I0410 00:47:09.858865 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_918.solverstate
I0410 00:47:15.016377 18059 solver.cpp:330] Iteration 918, Testing net (#0)
I0410 00:47:15.016455 18059 net.cpp:676] Ignoring source layer train-data
I0410 00:47:19.010541 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:47:19.413878 18059 solver.cpp:397]     Test net output #0: accuracy = 0.0110294
I0410 00:47:19.413914 18059 solver.cpp:397]     Test net output #1: loss = 5.18175 (* 1 = 5.18175 loss)
I0410 00:47:21.100229 18059 solver.cpp:218] Iteration 924 (0.584922 iter/s, 20.5156s/12 iters), loss = 5.21015
I0410 00:47:21.100275 18059 solver.cpp:237]     Train net output #0: loss = 5.21015 (* 1 = 5.21015 loss)
I0410 00:47:21.100284 18059 sgd_solver.cpp:105] Iteration 924, lr = 0.00832742
I0410 00:47:25.630342 18059 solver.cpp:218] Iteration 936 (2.64907 iter/s, 4.52989s/12 iters), loss = 5.20855
I0410 00:47:25.630393 18059 solver.cpp:237]     Train net output #0: loss = 5.20855 (* 1 = 5.20855 loss)
I0410 00:47:25.630404 18059 sgd_solver.cpp:105] Iteration 936, lr = 0.00830765
I0410 00:47:30.273516 18059 solver.cpp:218] Iteration 948 (2.58456 iter/s, 4.64295s/12 iters), loss = 5.19048
I0410 00:47:30.273566 18059 solver.cpp:237]     Train net output #0: loss = 5.19048 (* 1 = 5.19048 loss)
I0410 00:47:30.273576 18059 sgd_solver.cpp:105] Iteration 948, lr = 0.00828793
I0410 00:47:35.386373 18059 solver.cpp:218] Iteration 960 (2.34714 iter/s, 5.11261s/12 iters), loss = 5.09435
I0410 00:47:35.386442 18059 solver.cpp:237]     Train net output #0: loss = 5.09435 (* 1 = 5.09435 loss)
I0410 00:47:35.386458 18059 sgd_solver.cpp:105] Iteration 960, lr = 0.00826825
I0410 00:47:40.606048 18059 solver.cpp:218] Iteration 972 (2.29911 iter/s, 5.21941s/12 iters), loss = 5.2005
I0410 00:47:40.606112 18059 solver.cpp:237]     Train net output #0: loss = 5.2005 (* 1 = 5.2005 loss)
I0410 00:47:40.606124 18059 sgd_solver.cpp:105] Iteration 972, lr = 0.00824862
I0410 00:47:45.412529 18059 solver.cpp:218] Iteration 984 (2.49675 iter/s, 4.80624s/12 iters), loss = 5.19618
I0410 00:47:45.412590 18059 solver.cpp:237]     Train net output #0: loss = 5.19618 (* 1 = 5.19618 loss)
I0410 00:47:45.412600 18059 sgd_solver.cpp:105] Iteration 984, lr = 0.00822903
I0410 00:47:50.268688 18059 solver.cpp:218] Iteration 996 (2.47121 iter/s, 4.85591s/12 iters), loss = 5.08699
I0410 00:47:50.268738 18059 solver.cpp:237]     Train net output #0: loss = 5.08699 (* 1 = 5.08699 loss)
I0410 00:47:50.268748 18059 sgd_solver.cpp:105] Iteration 996, lr = 0.0082095
I0410 00:47:55.242878 18059 solver.cpp:218] Iteration 1008 (2.41257 iter/s, 4.97396s/12 iters), loss = 5.19734
I0410 00:47:55.242923 18059 solver.cpp:237]     Train net output #0: loss = 5.19734 (* 1 = 5.19734 loss)
I0410 00:47:55.242933 18059 sgd_solver.cpp:105] Iteration 1008, lr = 0.00819001
I0410 00:47:56.573202 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:48:00.960121 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1020.caffemodel
I0410 00:48:09.014674 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1020.solverstate
I0410 00:48:16.097508 18059 solver.cpp:330] Iteration 1020, Testing net (#0)
I0410 00:48:16.097591 18059 net.cpp:676] Ignoring source layer train-data
I0410 00:48:20.122807 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:48:20.554982 18059 solver.cpp:397]     Test net output #0: accuracy = 0.00796569
I0410 00:48:20.555027 18059 solver.cpp:397]     Test net output #1: loss = 5.16364 (* 1 = 5.16364 loss)
I0410 00:48:20.648918 18059 solver.cpp:218] Iteration 1020 (0.472346 iter/s, 25.4051s/12 iters), loss = 5.13991
I0410 00:48:20.648968 18059 solver.cpp:237]     Train net output #0: loss = 5.13991 (* 1 = 5.13991 loss)
I0410 00:48:20.648978 18059 sgd_solver.cpp:105] Iteration 1020, lr = 0.00817056
I0410 00:48:24.726814 18059 solver.cpp:218] Iteration 1032 (2.94284 iter/s, 4.07769s/12 iters), loss = 5.17525
I0410 00:48:24.726855 18059 solver.cpp:237]     Train net output #0: loss = 5.17525 (* 1 = 5.17525 loss)
I0410 00:48:24.726866 18059 sgd_solver.cpp:105] Iteration 1032, lr = 0.00815116
I0410 00:48:29.708837 18059 solver.cpp:218] Iteration 1044 (2.40877 iter/s, 4.98179s/12 iters), loss = 5.20468
I0410 00:48:29.708892 18059 solver.cpp:237]     Train net output #0: loss = 5.20468 (* 1 = 5.20468 loss)
I0410 00:48:29.708904 18059 sgd_solver.cpp:105] Iteration 1044, lr = 0.00813181
I0410 00:48:34.324237 18059 solver.cpp:218] Iteration 1056 (2.60012 iter/s, 4.61517s/12 iters), loss = 5.15913
I0410 00:48:34.324291 18059 solver.cpp:237]     Train net output #0: loss = 5.15913 (* 1 = 5.15913 loss)
I0410 00:48:34.324304 18059 sgd_solver.cpp:105] Iteration 1056, lr = 0.0081125
I0410 00:48:38.818943 18059 solver.cpp:218] Iteration 1068 (2.66994 iter/s, 4.49448s/12 iters), loss = 5.21968
I0410 00:48:38.818982 18059 solver.cpp:237]     Train net output #0: loss = 5.21968 (* 1 = 5.21968 loss)
I0410 00:48:38.818991 18059 sgd_solver.cpp:105] Iteration 1068, lr = 0.00809324
I0410 00:48:44.166507 18059 solver.cpp:218] Iteration 1080 (2.24411 iter/s, 5.34732s/12 iters), loss = 5.13764
I0410 00:48:44.166558 18059 solver.cpp:237]     Train net output #0: loss = 5.13764 (* 1 = 5.13764 loss)
I0410 00:48:44.166569 18059 sgd_solver.cpp:105] Iteration 1080, lr = 0.00807403
I0410 00:48:49.536262 18059 solver.cpp:218] Iteration 1092 (2.23484 iter/s, 5.3695s/12 iters), loss = 5.13912
I0410 00:48:49.536375 18059 solver.cpp:237]     Train net output #0: loss = 5.13912 (* 1 = 5.13912 loss)
I0410 00:48:49.536386 18059 sgd_solver.cpp:105] Iteration 1092, lr = 0.00805486
I0410 00:48:54.316761 18059 solver.cpp:218] Iteration 1104 (2.51035 iter/s, 4.78021s/12 iters), loss = 5.10704
I0410 00:48:54.316813 18059 solver.cpp:237]     Train net output #0: loss = 5.10704 (* 1 = 5.10704 loss)
I0410 00:48:54.316824 18059 sgd_solver.cpp:105] Iteration 1104, lr = 0.00803573
I0410 00:48:57.044193 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:48:58.884529 18059 solver.cpp:218] Iteration 1116 (2.62723 iter/s, 4.56754s/12 iters), loss = 5.17589
I0410 00:48:58.884582 18059 solver.cpp:237]     Train net output #0: loss = 5.17589 (* 1 = 5.17589 loss)
I0410 00:48:58.884596 18059 sgd_solver.cpp:105] Iteration 1116, lr = 0.00801666
I0410 00:49:00.804356 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1122.caffemodel
I0410 00:49:05.487954 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1122.solverstate
I0410 00:49:12.585338 18059 solver.cpp:330] Iteration 1122, Testing net (#0)
I0410 00:49:12.585364 18059 net.cpp:676] Ignoring source layer train-data
I0410 00:49:16.480341 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:49:16.985754 18059 solver.cpp:397]     Test net output #0: accuracy = 0.00796569
I0410 00:49:16.985796 18059 solver.cpp:397]     Test net output #1: loss = 5.15694 (* 1 = 5.15694 loss)
I0410 00:49:18.930958 18059 solver.cpp:218] Iteration 1128 (0.598633 iter/s, 20.0457s/12 iters), loss = 5.22032
I0410 00:49:18.931011 18059 solver.cpp:237]     Train net output #0: loss = 5.22032 (* 1 = 5.22032 loss)
I0410 00:49:18.931025 18059 sgd_solver.cpp:105] Iteration 1128, lr = 0.00799762
I0410 00:49:23.697034 18059 solver.cpp:218] Iteration 1140 (2.51792 iter/s, 4.76584s/12 iters), loss = 5.16867
I0410 00:49:23.698354 18059 solver.cpp:237]     Train net output #0: loss = 5.16867 (* 1 = 5.16867 loss)
I0410 00:49:23.698367 18059 sgd_solver.cpp:105] Iteration 1140, lr = 0.00797863
I0410 00:49:28.621812 18059 solver.cpp:218] Iteration 1152 (2.4374 iter/s, 4.92328s/12 iters), loss = 5.10673
I0410 00:49:28.621872 18059 solver.cpp:237]     Train net output #0: loss = 5.10673 (* 1 = 5.10673 loss)
I0410 00:49:28.621886 18059 sgd_solver.cpp:105] Iteration 1152, lr = 0.00795969
I0410 00:49:33.937227 18059 solver.cpp:218] Iteration 1164 (2.2577 iter/s, 5.31515s/12 iters), loss = 5.16919
I0410 00:49:33.937283 18059 solver.cpp:237]     Train net output #0: loss = 5.16919 (* 1 = 5.16919 loss)
I0410 00:49:33.937295 18059 sgd_solver.cpp:105] Iteration 1164, lr = 0.00794079
I0410 00:49:38.611996 18059 solver.cpp:218] Iteration 1176 (2.5671 iter/s, 4.67454s/12 iters), loss = 5.16512
I0410 00:49:38.612048 18059 solver.cpp:237]     Train net output #0: loss = 5.16512 (* 1 = 5.16512 loss)
I0410 00:49:38.612061 18059 sgd_solver.cpp:105] Iteration 1176, lr = 0.00792194
I0410 00:49:43.308679 18059 solver.cpp:218] Iteration 1188 (2.55512 iter/s, 4.69646s/12 iters), loss = 5.12947
I0410 00:49:43.308727 18059 solver.cpp:237]     Train net output #0: loss = 5.12947 (* 1 = 5.12947 loss)
I0410 00:49:43.308738 18059 sgd_solver.cpp:105] Iteration 1188, lr = 0.00790313
I0410 00:49:48.209918 18059 solver.cpp:218] Iteration 1200 (2.44848 iter/s, 4.90101s/12 iters), loss = 5.17374
I0410 00:49:48.209988 18059 solver.cpp:237]     Train net output #0: loss = 5.17374 (* 1 = 5.17374 loss)
I0410 00:49:48.210002 18059 sgd_solver.cpp:105] Iteration 1200, lr = 0.00788437
I0410 00:49:53.092378 18059 solver.cpp:218] Iteration 1212 (2.4579 iter/s, 4.88221s/12 iters), loss = 5.18405
I0410 00:49:53.092432 18059 solver.cpp:237]     Train net output #0: loss = 5.18405 (* 1 = 5.18405 loss)
I0410 00:49:53.092443 18059 sgd_solver.cpp:105] Iteration 1212, lr = 0.00786565
I0410 00:49:53.387491 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:49:57.239881 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1224.caffemodel
I0410 00:50:01.869478 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1224.solverstate
I0410 00:50:05.542902 18059 solver.cpp:330] Iteration 1224, Testing net (#0)
I0410 00:50:05.542930 18059 net.cpp:676] Ignoring source layer train-data
I0410 00:50:09.481530 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:50:09.996090 18059 solver.cpp:397]     Test net output #0: accuracy = 0.00857843
I0410 00:50:09.996138 18059 solver.cpp:397]     Test net output #1: loss = 5.14613 (* 1 = 5.14613 loss)
I0410 00:50:10.089646 18059 solver.cpp:218] Iteration 1224 (0.706023 iter/s, 16.9966s/12 iters), loss = 5.08621
I0410 00:50:10.089704 18059 solver.cpp:237]     Train net output #0: loss = 5.08621 (* 1 = 5.08621 loss)
I0410 00:50:10.089716 18059 sgd_solver.cpp:105] Iteration 1224, lr = 0.00784697
I0410 00:50:13.971500 18059 solver.cpp:218] Iteration 1236 (3.09147 iter/s, 3.88165s/12 iters), loss = 5.20889
I0410 00:50:13.971554 18059 solver.cpp:237]     Train net output #0: loss = 5.20889 (* 1 = 5.20889 loss)
I0410 00:50:13.971565 18059 sgd_solver.cpp:105] Iteration 1236, lr = 0.00782834
I0410 00:50:18.591069 18059 solver.cpp:218] Iteration 1248 (2.59777 iter/s, 4.61934s/12 iters), loss = 5.08766
I0410 00:50:18.591120 18059 solver.cpp:237]     Train net output #0: loss = 5.08766 (* 1 = 5.08766 loss)
I0410 00:50:18.591130 18059 sgd_solver.cpp:105] Iteration 1248, lr = 0.00780976
I0410 00:50:23.191658 18059 solver.cpp:218] Iteration 1260 (2.60849 iter/s, 4.60037s/12 iters), loss = 5.10942
I0410 00:50:23.191699 18059 solver.cpp:237]     Train net output #0: loss = 5.10942 (* 1 = 5.10942 loss)
I0410 00:50:23.191709 18059 sgd_solver.cpp:105] Iteration 1260, lr = 0.00779122
I0410 00:50:27.905194 18059 solver.cpp:218] Iteration 1272 (2.54598 iter/s, 4.71332s/12 iters), loss = 5.11313
I0410 00:50:27.905313 18059 solver.cpp:237]     Train net output #0: loss = 5.11313 (* 1 = 5.11313 loss)
I0410 00:50:27.905324 18059 sgd_solver.cpp:105] Iteration 1272, lr = 0.00777272
I0410 00:50:32.860206 18059 solver.cpp:218] Iteration 1284 (2.42194 iter/s, 4.95471s/12 iters), loss = 5.12771
I0410 00:50:32.860256 18059 solver.cpp:237]     Train net output #0: loss = 5.12771 (* 1 = 5.12771 loss)
I0410 00:50:32.860267 18059 sgd_solver.cpp:105] Iteration 1284, lr = 0.00775426
I0410 00:50:37.657407 18059 solver.cpp:218] Iteration 1296 (2.50158 iter/s, 4.79696s/12 iters), loss = 5.11024
I0410 00:50:37.657464 18059 solver.cpp:237]     Train net output #0: loss = 5.11024 (* 1 = 5.11024 loss)
I0410 00:50:37.657477 18059 sgd_solver.cpp:105] Iteration 1296, lr = 0.00773585
I0410 00:50:43.195632 18059 solver.cpp:218] Iteration 1308 (2.16686 iter/s, 5.53796s/12 iters), loss = 5.13992
I0410 00:50:43.195673 18059 solver.cpp:237]     Train net output #0: loss = 5.13992 (* 1 = 5.13992 loss)
I0410 00:50:43.195681 18059 sgd_solver.cpp:105] Iteration 1308, lr = 0.00771749
I0410 00:50:45.658236 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:50:48.056440 18059 solver.cpp:218] Iteration 1320 (2.46884 iter/s, 4.86058s/12 iters), loss = 5.1481
I0410 00:50:48.056484 18059 solver.cpp:237]     Train net output #0: loss = 5.1481 (* 1 = 5.1481 loss)
I0410 00:50:48.056493 18059 sgd_solver.cpp:105] Iteration 1320, lr = 0.00769916
I0410 00:50:50.138449 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1326.caffemodel
I0410 00:50:54.764458 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1326.solverstate
I0410 00:50:58.449455 18059 solver.cpp:330] Iteration 1326, Testing net (#0)
I0410 00:50:58.449542 18059 net.cpp:676] Ignoring source layer train-data
I0410 00:51:02.543270 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:51:03.148092 18059 solver.cpp:397]     Test net output #0: accuracy = 0.00796569
I0410 00:51:03.148141 18059 solver.cpp:397]     Test net output #1: loss = 5.14657 (* 1 = 5.14657 loss)
I0410 00:51:05.060539 18059 solver.cpp:218] Iteration 1332 (0.705739 iter/s, 17.0035s/12 iters), loss = 5.14058
I0410 00:51:05.060585 18059 solver.cpp:237]     Train net output #0: loss = 5.14058 (* 1 = 5.14058 loss)
I0410 00:51:05.060593 18059 sgd_solver.cpp:105] Iteration 1332, lr = 0.00768088
I0410 00:51:10.256222 18059 solver.cpp:218] Iteration 1344 (2.30971 iter/s, 5.19545s/12 iters), loss = 5.07223
I0410 00:51:10.256264 18059 solver.cpp:237]     Train net output #0: loss = 5.07223 (* 1 = 5.07223 loss)
I0410 00:51:10.256273 18059 sgd_solver.cpp:105] Iteration 1344, lr = 0.00766265
I0410 00:51:15.635397 18059 solver.cpp:218] Iteration 1356 (2.23093 iter/s, 5.37893s/12 iters), loss = 5.09521
I0410 00:51:15.635447 18059 solver.cpp:237]     Train net output #0: loss = 5.09521 (* 1 = 5.09521 loss)
I0410 00:51:15.635457 18059 sgd_solver.cpp:105] Iteration 1356, lr = 0.00764446
I0410 00:51:20.428642 18059 solver.cpp:218] Iteration 1368 (2.50364 iter/s, 4.79302s/12 iters), loss = 5.17012
I0410 00:51:20.428689 18059 solver.cpp:237]     Train net output #0: loss = 5.17012 (* 1 = 5.17012 loss)
I0410 00:51:20.428699 18059 sgd_solver.cpp:105] Iteration 1368, lr = 0.00762631
I0410 00:51:21.504186 18059 blocking_queue.cpp:49] Waiting for data
I0410 00:51:25.087471 18059 solver.cpp:218] Iteration 1380 (2.57588 iter/s, 4.65861s/12 iters), loss = 5.1154
I0410 00:51:25.087517 18059 solver.cpp:237]     Train net output #0: loss = 5.1154 (* 1 = 5.1154 loss)
I0410 00:51:25.087527 18059 sgd_solver.cpp:105] Iteration 1380, lr = 0.0076082
I0410 00:51:30.364945 18059 solver.cpp:218] Iteration 1392 (2.27392 iter/s, 5.27724s/12 iters), loss = 4.9534
I0410 00:51:30.365084 18059 solver.cpp:237]     Train net output #0: loss = 4.9534 (* 1 = 4.9534 loss)
I0410 00:51:30.365097 18059 sgd_solver.cpp:105] Iteration 1392, lr = 0.00759014
I0410 00:51:35.030169 18059 solver.cpp:218] Iteration 1404 (2.5724 iter/s, 4.66491s/12 iters), loss = 5.14797
I0410 00:51:35.030225 18059 solver.cpp:237]     Train net output #0: loss = 5.14797 (* 1 = 5.14797 loss)
I0410 00:51:35.030237 18059 sgd_solver.cpp:105] Iteration 1404, lr = 0.00757212
I0410 00:51:39.753803 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:51:40.140163 18059 solver.cpp:218] Iteration 1416 (2.34845 iter/s, 5.10975s/12 iters), loss = 5.13004
I0410 00:51:40.140206 18059 solver.cpp:237]     Train net output #0: loss = 5.13004 (* 1 = 5.13004 loss)
I0410 00:51:40.140216 18059 sgd_solver.cpp:105] Iteration 1416, lr = 0.00755414
I0410 00:51:44.537827 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1428.caffemodel
I0410 00:51:52.560570 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1428.solverstate
I0410 00:51:57.967167 18059 solver.cpp:330] Iteration 1428, Testing net (#0)
I0410 00:51:57.967193 18059 net.cpp:676] Ignoring source layer train-data
I0410 00:52:01.837867 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:52:02.430099 18059 solver.cpp:397]     Test net output #0: accuracy = 0.00919118
I0410 00:52:02.430146 18059 solver.cpp:397]     Test net output #1: loss = 5.13239 (* 1 = 5.13239 loss)
I0410 00:52:02.523882 18059 solver.cpp:218] Iteration 1428 (0.536124 iter/s, 22.3829s/12 iters), loss = 5.19277
I0410 00:52:02.523934 18059 solver.cpp:237]     Train net output #0: loss = 5.19277 (* 1 = 5.19277 loss)
I0410 00:52:02.523943 18059 sgd_solver.cpp:105] Iteration 1428, lr = 0.0075362
I0410 00:52:06.408731 18059 solver.cpp:218] Iteration 1440 (3.08908 iter/s, 3.88465s/12 iters), loss = 5.1024
I0410 00:52:06.408780 18059 solver.cpp:237]     Train net output #0: loss = 5.1024 (* 1 = 5.1024 loss)
I0410 00:52:06.408790 18059 sgd_solver.cpp:105] Iteration 1440, lr = 0.00751831
I0410 00:52:11.131194 18059 solver.cpp:218] Iteration 1452 (2.54117 iter/s, 4.72224s/12 iters), loss = 5.11742
I0410 00:52:11.131232 18059 solver.cpp:237]     Train net output #0: loss = 5.11742 (* 1 = 5.11742 loss)
I0410 00:52:11.131242 18059 sgd_solver.cpp:105] Iteration 1452, lr = 0.00750046
I0410 00:52:15.970319 18059 solver.cpp:218] Iteration 1464 (2.4799 iter/s, 4.8389s/12 iters), loss = 5.12507
I0410 00:52:15.970372 18059 solver.cpp:237]     Train net output #0: loss = 5.12507 (* 1 = 5.12507 loss)
I0410 00:52:15.970383 18059 sgd_solver.cpp:105] Iteration 1464, lr = 0.00748265
I0410 00:52:20.688894 18059 solver.cpp:218] Iteration 1476 (2.54327 iter/s, 4.71834s/12 iters), loss = 5.10197
I0410 00:52:20.688947 18059 solver.cpp:237]     Train net output #0: loss = 5.10197 (* 1 = 5.10197 loss)
I0410 00:52:20.688959 18059 sgd_solver.cpp:105] Iteration 1476, lr = 0.00746489
I0410 00:52:25.627585 18059 solver.cpp:218] Iteration 1488 (2.42991 iter/s, 4.93845s/12 iters), loss = 5.11843
I0410 00:52:25.627641 18059 solver.cpp:237]     Train net output #0: loss = 5.11843 (* 1 = 5.11843 loss)
I0410 00:52:25.627652 18059 sgd_solver.cpp:105] Iteration 1488, lr = 0.00744716
I0410 00:52:30.371523 18059 solver.cpp:218] Iteration 1500 (2.52967 iter/s, 4.7437s/12 iters), loss = 5.09064
I0410 00:52:30.371572 18059 solver.cpp:237]     Train net output #0: loss = 5.09064 (* 1 = 5.09064 loss)
I0410 00:52:30.371582 18059 sgd_solver.cpp:105] Iteration 1500, lr = 0.00742948
I0410 00:52:35.054883 18059 solver.cpp:218] Iteration 1512 (2.56239 iter/s, 4.68314s/12 iters), loss = 5.12438
I0410 00:52:35.055009 18059 solver.cpp:237]     Train net output #0: loss = 5.12438 (* 1 = 5.12438 loss)
I0410 00:52:35.055019 18059 sgd_solver.cpp:105] Iteration 1512, lr = 0.00741184
I0410 00:52:36.730114 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:52:39.730334 18059 solver.cpp:218] Iteration 1524 (2.56676 iter/s, 4.67515s/12 iters), loss = 5.13615
I0410 00:52:39.730386 18059 solver.cpp:237]     Train net output #0: loss = 5.13615 (* 1 = 5.13615 loss)
I0410 00:52:39.730399 18059 sgd_solver.cpp:105] Iteration 1524, lr = 0.00739425
I0410 00:52:41.741221 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1530.caffemodel
I0410 00:52:54.325027 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1530.solverstate
I0410 00:53:00.850652 18059 solver.cpp:330] Iteration 1530, Testing net (#0)
I0410 00:53:00.850677 18059 net.cpp:676] Ignoring source layer train-data
I0410 00:53:04.718302 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:53:05.354717 18059 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0410 00:53:05.355487 18059 solver.cpp:397]     Test net output #1: loss = 5.13402 (* 1 = 5.13402 loss)
I0410 00:53:07.254066 18059 solver.cpp:218] Iteration 1536 (0.436003 iter/s, 27.5227s/12 iters), loss = 5.13698
I0410 00:53:07.254113 18059 solver.cpp:237]     Train net output #0: loss = 5.13698 (* 1 = 5.13698 loss)
I0410 00:53:07.254122 18059 sgd_solver.cpp:105] Iteration 1536, lr = 0.00737669
I0410 00:53:12.100587 18059 solver.cpp:218] Iteration 1548 (2.47612 iter/s, 4.84629s/12 iters), loss = 5.09324
I0410 00:53:12.100633 18059 solver.cpp:237]     Train net output #0: loss = 5.09324 (* 1 = 5.09324 loss)
I0410 00:53:12.100643 18059 sgd_solver.cpp:105] Iteration 1548, lr = 0.00735918
I0410 00:53:16.798930 18059 solver.cpp:218] Iteration 1560 (2.55421 iter/s, 4.69812s/12 iters), loss = 5.09607
I0410 00:53:16.798979 18059 solver.cpp:237]     Train net output #0: loss = 5.09607 (* 1 = 5.09607 loss)
I0410 00:53:16.798990 18059 sgd_solver.cpp:105] Iteration 1560, lr = 0.00734171
I0410 00:53:21.385394 18059 solver.cpp:218] Iteration 1572 (2.61652 iter/s, 4.58624s/12 iters), loss = 5.08732
I0410 00:53:21.385435 18059 solver.cpp:237]     Train net output #0: loss = 5.08732 (* 1 = 5.08732 loss)
I0410 00:53:21.385443 18059 sgd_solver.cpp:105] Iteration 1572, lr = 0.00732427
I0410 00:53:25.951871 18059 solver.cpp:218] Iteration 1584 (2.62797 iter/s, 4.56626s/12 iters), loss = 5.17445
I0410 00:53:25.951918 18059 solver.cpp:237]     Train net output #0: loss = 5.17445 (* 1 = 5.17445 loss)
I0410 00:53:25.951928 18059 sgd_solver.cpp:105] Iteration 1584, lr = 0.00730688
I0410 00:53:30.743959 18059 solver.cpp:218] Iteration 1596 (2.50425 iter/s, 4.79186s/12 iters), loss = 5.03149
I0410 00:53:30.744007 18059 solver.cpp:237]     Train net output #0: loss = 5.03149 (* 1 = 5.03149 loss)
I0410 00:53:30.744019 18059 sgd_solver.cpp:105] Iteration 1596, lr = 0.00728954
I0410 00:53:35.393537 18059 solver.cpp:218] Iteration 1608 (2.581 iter/s, 4.64935s/12 iters), loss = 5.08494
I0410 00:53:35.393651 18059 solver.cpp:237]     Train net output #0: loss = 5.08494 (* 1 = 5.08494 loss)
I0410 00:53:35.393664 18059 sgd_solver.cpp:105] Iteration 1608, lr = 0.00727223
I0410 00:53:39.118877 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:53:40.234622 18059 solver.cpp:218] Iteration 1620 (2.47894 iter/s, 4.84079s/12 iters), loss = 5.04377
I0410 00:53:40.234688 18059 solver.cpp:237]     Train net output #0: loss = 5.04377 (* 1 = 5.04377 loss)
I0410 00:53:40.234699 18059 sgd_solver.cpp:105] Iteration 1620, lr = 0.00725496
I0410 00:53:45.108781 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1632.caffemodel
I0410 00:53:49.709343 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1632.solverstate
I0410 00:53:57.345921 18059 solver.cpp:330] Iteration 1632, Testing net (#0)
I0410 00:53:57.345947 18059 net.cpp:676] Ignoring source layer train-data
I0410 00:54:01.383909 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:54:02.055253 18059 solver.cpp:397]     Test net output #0: accuracy = 0.0122549
I0410 00:54:02.055299 18059 solver.cpp:397]     Test net output #1: loss = 5.09559 (* 1 = 5.09559 loss)
I0410 00:54:02.149734 18059 solver.cpp:218] Iteration 1632 (0.547588 iter/s, 21.9143s/12 iters), loss = 5.11841
I0410 00:54:02.149787 18059 solver.cpp:237]     Train net output #0: loss = 5.11841 (* 1 = 5.11841 loss)
I0410 00:54:02.149798 18059 sgd_solver.cpp:105] Iteration 1632, lr = 0.00723774
I0410 00:54:06.054386 18059 solver.cpp:218] Iteration 1644 (3.07342 iter/s, 3.90445s/12 iters), loss = 5.13269
I0410 00:54:06.054504 18059 solver.cpp:237]     Train net output #0: loss = 5.13269 (* 1 = 5.13269 loss)
I0410 00:54:06.054517 18059 sgd_solver.cpp:105] Iteration 1644, lr = 0.00722056
I0410 00:54:10.649360 18059 solver.cpp:218] Iteration 1656 (2.61171 iter/s, 4.59469s/12 iters), loss = 5.15512
I0410 00:54:10.649396 18059 solver.cpp:237]     Train net output #0: loss = 5.15512 (* 1 = 5.15512 loss)
I0410 00:54:10.649405 18059 sgd_solver.cpp:105] Iteration 1656, lr = 0.00720341
I0410 00:54:15.410251 18059 solver.cpp:218] Iteration 1668 (2.52065 iter/s, 4.76068s/12 iters), loss = 5.04245
I0410 00:54:15.410292 18059 solver.cpp:237]     Train net output #0: loss = 5.04245 (* 1 = 5.04245 loss)
I0410 00:54:15.410300 18059 sgd_solver.cpp:105] Iteration 1668, lr = 0.00718631
I0410 00:54:20.269542 18059 solver.cpp:218] Iteration 1680 (2.46961 iter/s, 4.85907s/12 iters), loss = 5.10526
I0410 00:54:20.269588 18059 solver.cpp:237]     Train net output #0: loss = 5.10526 (* 1 = 5.10526 loss)
I0410 00:54:20.269598 18059 sgd_solver.cpp:105] Iteration 1680, lr = 0.00716925
I0410 00:54:25.115257 18059 solver.cpp:218] Iteration 1692 (2.47653 iter/s, 4.84548s/12 iters), loss = 5.12156
I0410 00:54:25.115306 18059 solver.cpp:237]     Train net output #0: loss = 5.12156 (* 1 = 5.12156 loss)
I0410 00:54:25.115316 18059 sgd_solver.cpp:105] Iteration 1692, lr = 0.00715223
I0410 00:54:29.803561 18059 solver.cpp:218] Iteration 1704 (2.55968 iter/s, 4.68808s/12 iters), loss = 4.95562
I0410 00:54:29.803606 18059 solver.cpp:237]     Train net output #0: loss = 4.95562 (* 1 = 4.95562 loss)
I0410 00:54:29.803615 18059 sgd_solver.cpp:105] Iteration 1704, lr = 0.00713525
I0410 00:54:34.850332 18059 solver.cpp:218] Iteration 1716 (2.37787 iter/s, 5.04653s/12 iters), loss = 5.11185
I0410 00:54:34.850387 18059 solver.cpp:237]     Train net output #0: loss = 5.11185 (* 1 = 5.11185 loss)
I0410 00:54:34.850399 18059 sgd_solver.cpp:105] Iteration 1716, lr = 0.00711831
I0410 00:54:35.801483 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:54:39.449255 18059 solver.cpp:218] Iteration 1728 (2.60944 iter/s, 4.5987s/12 iters), loss = 5.06435
I0410 00:54:39.449365 18059 solver.cpp:237]     Train net output #0: loss = 5.06435 (* 1 = 5.06435 loss)
I0410 00:54:39.449378 18059 sgd_solver.cpp:105] Iteration 1728, lr = 0.00710141
I0410 00:54:41.406121 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1734.caffemodel
I0410 00:54:47.479331 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1734.solverstate
I0410 00:54:51.091143 18059 solver.cpp:330] Iteration 1734, Testing net (#0)
I0410 00:54:51.091163 18059 net.cpp:676] Ignoring source layer train-data
I0410 00:54:54.912462 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:54:55.618923 18059 solver.cpp:397]     Test net output #0: accuracy = 0.00980392
I0410 00:54:55.618971 18059 solver.cpp:397]     Test net output #1: loss = 5.0895 (* 1 = 5.0895 loss)
I0410 00:54:57.237515 18059 solver.cpp:218] Iteration 1740 (0.67463 iter/s, 17.7875s/12 iters), loss = 5.07113
I0410 00:54:57.237560 18059 solver.cpp:237]     Train net output #0: loss = 5.07113 (* 1 = 5.07113 loss)
I0410 00:54:57.237569 18059 sgd_solver.cpp:105] Iteration 1740, lr = 0.00708455
I0410 00:55:02.063160 18059 solver.cpp:218] Iteration 1752 (2.48683 iter/s, 4.82542s/12 iters), loss = 5.06131
I0410 00:55:02.063206 18059 solver.cpp:237]     Train net output #0: loss = 5.06131 (* 1 = 5.06131 loss)
I0410 00:55:02.063216 18059 sgd_solver.cpp:105] Iteration 1752, lr = 0.00706773
I0410 00:55:07.149214 18059 solver.cpp:218] Iteration 1764 (2.3595 iter/s, 5.08581s/12 iters), loss = 5.08191
I0410 00:55:07.149261 18059 solver.cpp:237]     Train net output #0: loss = 5.08191 (* 1 = 5.08191 loss)
I0410 00:55:07.149271 18059 sgd_solver.cpp:105] Iteration 1764, lr = 0.00705094
I0410 00:55:12.195785 18059 solver.cpp:218] Iteration 1776 (2.37796 iter/s, 5.04633s/12 iters), loss = 5.1018
I0410 00:55:12.195930 18059 solver.cpp:237]     Train net output #0: loss = 5.1018 (* 1 = 5.1018 loss)
I0410 00:55:12.195941 18059 sgd_solver.cpp:105] Iteration 1776, lr = 0.0070342
I0410 00:55:16.710405 18059 solver.cpp:218] Iteration 1788 (2.65821 iter/s, 4.51431s/12 iters), loss = 5.09947
I0410 00:55:16.710453 18059 solver.cpp:237]     Train net output #0: loss = 5.09947 (* 1 = 5.09947 loss)
I0410 00:55:16.710462 18059 sgd_solver.cpp:105] Iteration 1788, lr = 0.0070175
I0410 00:55:21.514400 18059 solver.cpp:218] Iteration 1800 (2.49804 iter/s, 4.80376s/12 iters), loss = 5.01605
I0410 00:55:21.514446 18059 solver.cpp:237]     Train net output #0: loss = 5.01605 (* 1 = 5.01605 loss)
I0410 00:55:21.514454 18059 sgd_solver.cpp:105] Iteration 1800, lr = 0.00700084
I0410 00:55:26.425554 18059 solver.cpp:218] Iteration 1812 (2.44353 iter/s, 4.91093s/12 iters), loss = 5.00221
I0410 00:55:26.425607 18059 solver.cpp:237]     Train net output #0: loss = 5.00221 (* 1 = 5.00221 loss)
I0410 00:55:26.425619 18059 sgd_solver.cpp:105] Iteration 1812, lr = 0.00698422
I0410 00:55:29.662658 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:55:31.471311 18059 solver.cpp:218] Iteration 1824 (2.37835 iter/s, 5.04552s/12 iters), loss = 5.05401
I0410 00:55:31.471364 18059 solver.cpp:237]     Train net output #0: loss = 5.05401 (* 1 = 5.05401 loss)
I0410 00:55:31.471375 18059 sgd_solver.cpp:105] Iteration 1824, lr = 0.00696764
I0410 00:55:35.755662 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1836.caffemodel
I0410 00:55:43.234251 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1836.solverstate
I0410 00:55:47.327848 18059 solver.cpp:330] Iteration 1836, Testing net (#0)
I0410 00:55:47.327872 18059 net.cpp:676] Ignoring source layer train-data
I0410 00:55:51.048897 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:55:51.801148 18059 solver.cpp:397]     Test net output #0: accuracy = 0.0134804
I0410 00:55:51.801196 18059 solver.cpp:397]     Test net output #1: loss = 5.04671 (* 1 = 5.04671 loss)
I0410 00:55:51.895016 18059 solver.cpp:218] Iteration 1836 (0.587575 iter/s, 20.4229s/12 iters), loss = 5.08968
I0410 00:55:51.895068 18059 solver.cpp:237]     Train net output #0: loss = 5.08968 (* 1 = 5.08968 loss)
I0410 00:55:51.895079 18059 sgd_solver.cpp:105] Iteration 1836, lr = 0.0069511
I0410 00:55:55.594262 18059 solver.cpp:218] Iteration 1848 (3.24408 iter/s, 3.69905s/12 iters), loss = 5.09698
I0410 00:55:55.594324 18059 solver.cpp:237]     Train net output #0: loss = 5.09698 (* 1 = 5.09698 loss)
I0410 00:55:55.594339 18059 sgd_solver.cpp:105] Iteration 1848, lr = 0.00693459
I0410 00:56:00.069098 18059 solver.cpp:218] Iteration 1860 (2.6818 iter/s, 4.47461s/12 iters), loss = 4.98078
I0410 00:56:00.069152 18059 solver.cpp:237]     Train net output #0: loss = 4.98078 (* 1 = 4.98078 loss)
I0410 00:56:00.069164 18059 sgd_solver.cpp:105] Iteration 1860, lr = 0.00691813
I0410 00:56:04.921445 18059 solver.cpp:218] Iteration 1872 (2.47315 iter/s, 4.85212s/12 iters), loss = 5.07271
I0410 00:56:04.921487 18059 solver.cpp:237]     Train net output #0: loss = 5.07271 (* 1 = 5.07271 loss)
I0410 00:56:04.921496 18059 sgd_solver.cpp:105] Iteration 1872, lr = 0.0069017
I0410 00:56:09.811570 18059 solver.cpp:218] Iteration 1884 (2.45404 iter/s, 4.8899s/12 iters), loss = 5.06182
I0410 00:56:09.811619 18059 solver.cpp:237]     Train net output #0: loss = 5.06182 (* 1 = 5.06182 loss)
I0410 00:56:09.811630 18059 sgd_solver.cpp:105] Iteration 1884, lr = 0.00688532
I0410 00:56:14.120071 18059 solver.cpp:218] Iteration 1896 (2.78533 iter/s, 4.30829s/12 iters), loss = 4.97503
I0410 00:56:14.120193 18059 solver.cpp:237]     Train net output #0: loss = 4.97503 (* 1 = 4.97503 loss)
I0410 00:56:14.120203 18059 sgd_solver.cpp:105] Iteration 1896, lr = 0.00686897
I0410 00:56:19.022888 18059 solver.cpp:218] Iteration 1908 (2.44773 iter/s, 4.90251s/12 iters), loss = 5.06791
I0410 00:56:19.022941 18059 solver.cpp:237]     Train net output #0: loss = 5.06791 (* 1 = 5.06791 loss)
I0410 00:56:19.022953 18059 sgd_solver.cpp:105] Iteration 1908, lr = 0.00685266
I0410 00:56:23.840458 18059 solver.cpp:218] Iteration 1920 (2.491 iter/s, 4.81734s/12 iters), loss = 5.04937
I0410 00:56:23.840507 18059 solver.cpp:237]     Train net output #0: loss = 5.04937 (* 1 = 5.04937 loss)
I0410 00:56:23.840518 18059 sgd_solver.cpp:105] Iteration 1920, lr = 0.00683639
I0410 00:56:24.155850 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:56:28.614666 18059 solver.cpp:218] Iteration 1932 (2.51363 iter/s, 4.77398s/12 iters), loss = 4.96684
I0410 00:56:28.614711 18059 solver.cpp:237]     Train net output #0: loss = 4.96684 (* 1 = 4.96684 loss)
I0410 00:56:28.614722 18059 sgd_solver.cpp:105] Iteration 1932, lr = 0.00682016
I0410 00:56:30.470954 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1938.caffemodel
I0410 00:56:35.075423 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1938.solverstate
I0410 00:56:40.596549 18059 solver.cpp:330] Iteration 1938, Testing net (#0)
I0410 00:56:40.596572 18059 net.cpp:676] Ignoring source layer train-data
I0410 00:56:44.329468 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:56:45.132654 18059 solver.cpp:397]     Test net output #0: accuracy = 0.0165441
I0410 00:56:45.132704 18059 solver.cpp:397]     Test net output #1: loss = 5.01369 (* 1 = 5.01369 loss)
I0410 00:56:46.943495 18059 solver.cpp:218] Iteration 1944 (0.654731 iter/s, 18.3281s/12 iters), loss = 5.0886
I0410 00:56:46.943543 18059 solver.cpp:237]     Train net output #0: loss = 5.0886 (* 1 = 5.0886 loss)
I0410 00:56:46.943557 18059 sgd_solver.cpp:105] Iteration 1944, lr = 0.00680397
I0410 00:56:51.838974 18059 solver.cpp:218] Iteration 1956 (2.45136 iter/s, 4.89525s/12 iters), loss = 4.98868
I0410 00:56:51.839031 18059 solver.cpp:237]     Train net output #0: loss = 4.98868 (* 1 = 4.98868 loss)
I0410 00:56:51.839043 18059 sgd_solver.cpp:105] Iteration 1956, lr = 0.00678782
I0410 00:56:56.441059 18059 solver.cpp:218] Iteration 1968 (2.60764 iter/s, 4.60186s/12 iters), loss = 4.97197
I0410 00:56:56.441102 18059 solver.cpp:237]     Train net output #0: loss = 4.97197 (* 1 = 4.97197 loss)
I0410 00:56:56.441112 18059 sgd_solver.cpp:105] Iteration 1968, lr = 0.0067717
I0410 00:57:01.523102 18059 solver.cpp:218] Iteration 1980 (2.36136 iter/s, 5.08181s/12 iters), loss = 4.96121
I0410 00:57:01.523140 18059 solver.cpp:237]     Train net output #0: loss = 4.96121 (* 1 = 4.96121 loss)
I0410 00:57:01.523149 18059 sgd_solver.cpp:105] Iteration 1980, lr = 0.00675562
I0410 00:57:06.476856 18059 solver.cpp:218] Iteration 1992 (2.42251 iter/s, 4.95353s/12 iters), loss = 5.02869
I0410 00:57:06.476895 18059 solver.cpp:237]     Train net output #0: loss = 5.02869 (* 1 = 5.02869 loss)
I0410 00:57:06.476903 18059 sgd_solver.cpp:105] Iteration 1992, lr = 0.00673958
I0410 00:57:11.300515 18059 solver.cpp:218] Iteration 2004 (2.48785 iter/s, 4.82343s/12 iters), loss = 4.90664
I0410 00:57:11.300560 18059 solver.cpp:237]     Train net output #0: loss = 4.90664 (* 1 = 4.90664 loss)
I0410 00:57:11.300570 18059 sgd_solver.cpp:105] Iteration 2004, lr = 0.00672358
I0410 00:57:16.367355 18059 solver.cpp:218] Iteration 2016 (2.36845 iter/s, 5.06661s/12 iters), loss = 4.97291
I0410 00:57:16.367445 18059 solver.cpp:237]     Train net output #0: loss = 4.97291 (* 1 = 4.97291 loss)
I0410 00:57:16.367455 18059 sgd_solver.cpp:105] Iteration 2016, lr = 0.00670762
I0410 00:57:18.796464 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:57:21.176453 18059 solver.cpp:218] Iteration 2028 (2.49541 iter/s, 4.80883s/12 iters), loss = 4.93137
I0410 00:57:21.176491 18059 solver.cpp:237]     Train net output #0: loss = 4.93137 (* 1 = 4.93137 loss)
I0410 00:57:21.176499 18059 sgd_solver.cpp:105] Iteration 2028, lr = 0.00669169
I0410 00:57:25.528442 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2040.caffemodel
I0410 00:57:30.126976 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2040.solverstate
I0410 00:57:33.991787 18059 solver.cpp:330] Iteration 2040, Testing net (#0)
I0410 00:57:33.991811 18059 net.cpp:676] Ignoring source layer train-data
I0410 00:57:37.524907 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:57:38.356004 18059 solver.cpp:397]     Test net output #0: accuracy = 0.0171569
I0410 00:57:38.356053 18059 solver.cpp:397]     Test net output #1: loss = 4.97369 (* 1 = 4.97369 loss)
I0410 00:57:38.449584 18059 solver.cpp:218] Iteration 2040 (0.694747 iter/s, 17.2725s/12 iters), loss = 4.98741
I0410 00:57:38.449632 18059 solver.cpp:237]     Train net output #0: loss = 4.98741 (* 1 = 4.98741 loss)
I0410 00:57:38.449643 18059 sgd_solver.cpp:105] Iteration 2040, lr = 0.00667581
I0410 00:57:42.259713 18059 solver.cpp:218] Iteration 2052 (3.14966 iter/s, 3.80993s/12 iters), loss = 4.84259
I0410 00:57:42.259764 18059 solver.cpp:237]     Train net output #0: loss = 4.84259 (* 1 = 4.84259 loss)
I0410 00:57:42.259775 18059 sgd_solver.cpp:105] Iteration 2052, lr = 0.00665996
I0410 00:57:43.791620 18059 blocking_queue.cpp:49] Waiting for data
I0410 00:57:46.995091 18059 solver.cpp:218] Iteration 2064 (2.53424 iter/s, 4.73515s/12 iters), loss = 4.96314
I0410 00:57:46.995184 18059 solver.cpp:237]     Train net output #0: loss = 4.96314 (* 1 = 4.96314 loss)
I0410 00:57:46.995193 18059 sgd_solver.cpp:105] Iteration 2064, lr = 0.00664414
I0410 00:57:51.726430 18059 solver.cpp:218] Iteration 2076 (2.53643 iter/s, 4.73107s/12 iters), loss = 4.97898
I0410 00:57:51.726471 18059 solver.cpp:237]     Train net output #0: loss = 4.97898 (* 1 = 4.97898 loss)
I0410 00:57:51.726480 18059 sgd_solver.cpp:105] Iteration 2076, lr = 0.00662837
I0410 00:57:56.433894 18059 solver.cpp:218] Iteration 2088 (2.54926 iter/s, 4.70725s/12 iters), loss = 4.90728
I0410 00:57:56.433948 18059 solver.cpp:237]     Train net output #0: loss = 4.90728 (* 1 = 4.90728 loss)
I0410 00:57:56.433975 18059 sgd_solver.cpp:105] Iteration 2088, lr = 0.00661263
I0410 00:58:01.266474 18059 solver.cpp:218] Iteration 2100 (2.48327 iter/s, 4.83234s/12 iters), loss = 4.87384
I0410 00:58:01.266527 18059 solver.cpp:237]     Train net output #0: loss = 4.87384 (* 1 = 4.87384 loss)
I0410 00:58:01.266539 18059 sgd_solver.cpp:105] Iteration 2100, lr = 0.00659693
I0410 00:58:06.209326 18059 solver.cpp:218] Iteration 2112 (2.42787 iter/s, 4.94261s/12 iters), loss = 4.96875
I0410 00:58:06.209378 18059 solver.cpp:237]     Train net output #0: loss = 4.96875 (* 1 = 4.96875 loss)
I0410 00:58:06.209389 18059 sgd_solver.cpp:105] Iteration 2112, lr = 0.00658127
I0410 00:58:11.228344 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:58:11.580008 18059 solver.cpp:218] Iteration 2124 (2.23446 iter/s, 5.37043s/12 iters), loss = 4.91093
I0410 00:58:11.580056 18059 solver.cpp:237]     Train net output #0: loss = 4.91093 (* 1 = 4.91093 loss)
I0410 00:58:11.580068 18059 sgd_solver.cpp:105] Iteration 2124, lr = 0.00656564
I0410 00:58:16.635514 18059 solver.cpp:218] Iteration 2136 (2.37376 iter/s, 5.05527s/12 iters), loss = 4.97592
I0410 00:58:16.635560 18059 solver.cpp:237]     Train net output #0: loss = 4.97592 (* 1 = 4.97592 loss)
I0410 00:58:16.635571 18059 sgd_solver.cpp:105] Iteration 2136, lr = 0.00655006
I0410 00:58:19.142720 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2142.caffemodel
I0410 00:58:25.292572 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2142.solverstate
I0410 00:58:28.909503 18059 solver.cpp:330] Iteration 2142, Testing net (#0)
I0410 00:58:28.909526 18059 net.cpp:676] Ignoring source layer train-data
I0410 00:58:32.516106 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:58:33.380605 18059 solver.cpp:397]     Test net output #0: accuracy = 0.0208333
I0410 00:58:33.380654 18059 solver.cpp:397]     Test net output #1: loss = 4.92345 (* 1 = 4.92345 loss)
I0410 00:58:35.301771 18059 solver.cpp:218] Iteration 2148 (0.642896 iter/s, 18.6656s/12 iters), loss = 4.79278
I0410 00:58:35.301820 18059 solver.cpp:237]     Train net output #0: loss = 4.79278 (* 1 = 4.79278 loss)
I0410 00:58:35.301831 18059 sgd_solver.cpp:105] Iteration 2148, lr = 0.00653451
I0410 00:58:40.376956 18059 solver.cpp:218] Iteration 2160 (2.36456 iter/s, 5.07494s/12 iters), loss = 5.01157
I0410 00:58:40.377007 18059 solver.cpp:237]     Train net output #0: loss = 5.01157 (* 1 = 5.01157 loss)
I0410 00:58:40.377018 18059 sgd_solver.cpp:105] Iteration 2160, lr = 0.00651899
I0410 00:58:45.426774 18059 solver.cpp:218] Iteration 2172 (2.37644 iter/s, 5.04957s/12 iters), loss = 5.05801
I0410 00:58:45.426826 18059 solver.cpp:237]     Train net output #0: loss = 5.05801 (* 1 = 5.05801 loss)
I0410 00:58:45.426838 18059 sgd_solver.cpp:105] Iteration 2172, lr = 0.00650351
I0410 00:58:50.861093 18059 solver.cpp:218] Iteration 2184 (2.20829 iter/s, 5.43407s/12 iters), loss = 4.90141
I0410 00:58:50.861227 18059 solver.cpp:237]     Train net output #0: loss = 4.90141 (* 1 = 4.90141 loss)
I0410 00:58:50.861238 18059 sgd_solver.cpp:105] Iteration 2184, lr = 0.00648807
I0410 00:58:56.063060 18059 solver.cpp:218] Iteration 2196 (2.30697 iter/s, 5.20164s/12 iters), loss = 4.8529
I0410 00:58:56.063110 18059 solver.cpp:237]     Train net output #0: loss = 4.8529 (* 1 = 4.8529 loss)
I0410 00:58:56.063122 18059 sgd_solver.cpp:105] Iteration 2196, lr = 0.00647267
I0410 00:59:00.949401 18059 solver.cpp:218] Iteration 2208 (2.45594 iter/s, 4.8861s/12 iters), loss = 4.87414
I0410 00:59:00.949450 18059 solver.cpp:237]     Train net output #0: loss = 4.87414 (* 1 = 4.87414 loss)
I0410 00:59:00.949465 18059 sgd_solver.cpp:105] Iteration 2208, lr = 0.0064573
I0410 00:59:05.897891 18059 solver.cpp:218] Iteration 2220 (2.4251 iter/s, 4.94825s/12 iters), loss = 4.93718
I0410 00:59:05.897936 18059 solver.cpp:237]     Train net output #0: loss = 4.93718 (* 1 = 4.93718 loss)
I0410 00:59:05.897944 18059 sgd_solver.cpp:105] Iteration 2220, lr = 0.00644197
I0410 00:59:07.809749 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:59:10.884795 18059 solver.cpp:218] Iteration 2232 (2.40642 iter/s, 4.98667s/12 iters), loss = 4.92016
I0410 00:59:10.884840 18059 solver.cpp:237]     Train net output #0: loss = 4.92016 (* 1 = 4.92016 loss)
I0410 00:59:10.884850 18059 sgd_solver.cpp:105] Iteration 2232, lr = 0.00642668
I0410 00:59:15.363658 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2244.caffemodel
I0410 00:59:25.255463 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2244.solverstate
I0410 00:59:31.349860 18059 solver.cpp:330] Iteration 2244, Testing net (#0)
I0410 00:59:31.349886 18059 net.cpp:676] Ignoring source layer train-data
I0410 00:59:35.057372 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:59:35.994616 18059 solver.cpp:397]     Test net output #0: accuracy = 0.0196078
I0410 00:59:35.994665 18059 solver.cpp:397]     Test net output #1: loss = 4.90215 (* 1 = 4.90215 loss)
I0410 00:59:36.085176 18059 solver.cpp:218] Iteration 2244 (0.476201 iter/s, 25.1995s/12 iters), loss = 4.93046
I0410 00:59:36.085222 18059 solver.cpp:237]     Train net output #0: loss = 4.93046 (* 1 = 4.93046 loss)
I0410 00:59:36.085230 18059 sgd_solver.cpp:105] Iteration 2244, lr = 0.00641142
I0410 00:59:40.435365 18059 solver.cpp:218] Iteration 2256 (2.75864 iter/s, 4.34998s/12 iters), loss = 4.77602
I0410 00:59:40.435410 18059 solver.cpp:237]     Train net output #0: loss = 4.77602 (* 1 = 4.77602 loss)
I0410 00:59:40.435420 18059 sgd_solver.cpp:105] Iteration 2256, lr = 0.0063962
I0410 00:59:45.297495 18059 solver.cpp:218] Iteration 2268 (2.46817 iter/s, 4.8619s/12 iters), loss = 4.8627
I0410 00:59:45.297545 18059 solver.cpp:237]     Train net output #0: loss = 4.8627 (* 1 = 4.8627 loss)
I0410 00:59:45.297556 18059 sgd_solver.cpp:105] Iteration 2268, lr = 0.00638101
I0410 00:59:50.275419 18059 solver.cpp:218] Iteration 2280 (2.41076 iter/s, 4.97769s/12 iters), loss = 4.77417
I0410 00:59:50.275466 18059 solver.cpp:237]     Train net output #0: loss = 4.77417 (* 1 = 4.77417 loss)
I0410 00:59:50.275478 18059 sgd_solver.cpp:105] Iteration 2280, lr = 0.00636586
I0410 00:59:54.856667 18059 solver.cpp:218] Iteration 2292 (2.6195 iter/s, 4.58103s/12 iters), loss = 4.83768
I0410 00:59:54.856712 18059 solver.cpp:237]     Train net output #0: loss = 4.83768 (* 1 = 4.83768 loss)
I0410 00:59:54.856722 18059 sgd_solver.cpp:105] Iteration 2292, lr = 0.00635075
I0410 00:59:59.478369 18059 solver.cpp:218] Iteration 2304 (2.59658 iter/s, 4.62147s/12 iters), loss = 4.83451
I0410 00:59:59.479422 18059 solver.cpp:237]     Train net output #0: loss = 4.83451 (* 1 = 4.83451 loss)
I0410 00:59:59.479434 18059 sgd_solver.cpp:105] Iteration 2304, lr = 0.00633567
I0410 01:00:04.454701 18059 solver.cpp:218] Iteration 2316 (2.41201 iter/s, 4.9751s/12 iters), loss = 4.76899
I0410 01:00:04.454741 18059 solver.cpp:237]     Train net output #0: loss = 4.76899 (* 1 = 4.76899 loss)
I0410 01:00:04.454748 18059 sgd_solver.cpp:105] Iteration 2316, lr = 0.00632063
I0410 01:00:08.442056 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:00:09.404512 18059 solver.cpp:218] Iteration 2328 (2.42445 iter/s, 4.94958s/12 iters), loss = 4.81942
I0410 01:00:09.404567 18059 solver.cpp:237]     Train net output #0: loss = 4.81942 (* 1 = 4.81942 loss)
I0410 01:00:09.404580 18059 sgd_solver.cpp:105] Iteration 2328, lr = 0.00630562
I0410 01:00:14.242265 18059 solver.cpp:218] Iteration 2340 (2.48061 iter/s, 4.83752s/12 iters), loss = 4.91799
I0410 01:00:14.242309 18059 solver.cpp:237]     Train net output #0: loss = 4.91799 (* 1 = 4.91799 loss)
I0410 01:00:14.242321 18059 sgd_solver.cpp:105] Iteration 2340, lr = 0.00629065
I0410 01:00:16.256165 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2346.caffemodel
I0410 01:00:30.252712 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2346.solverstate
I0410 01:00:37.318905 18059 solver.cpp:330] Iteration 2346, Testing net (#0)
I0410 01:00:37.318928 18059 net.cpp:676] Ignoring source layer train-data
I0410 01:00:40.835917 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:00:41.795428 18059 solver.cpp:397]     Test net output #0: accuracy = 0.0238971
I0410 01:00:41.795477 18059 solver.cpp:397]     Test net output #1: loss = 4.82247 (* 1 = 4.82247 loss)
I0410 01:00:43.645108 18059 solver.cpp:218] Iteration 2352 (0.408139 iter/s, 29.4018s/12 iters), loss = 4.7923
I0410 01:00:43.645154 18059 solver.cpp:237]     Train net output #0: loss = 4.7923 (* 1 = 4.7923 loss)
I0410 01:00:43.645164 18059 sgd_solver.cpp:105] Iteration 2352, lr = 0.00627571
I0410 01:00:48.452818 18059 solver.cpp:218] Iteration 2364 (2.49611 iter/s, 4.80748s/12 iters), loss = 4.64774
I0410 01:00:48.452867 18059 solver.cpp:237]     Train net output #0: loss = 4.64774 (* 1 = 4.64774 loss)
I0410 01:00:48.452879 18059 sgd_solver.cpp:105] Iteration 2364, lr = 0.00626081
I0410 01:00:53.184415 18059 solver.cpp:218] Iteration 2376 (2.53627 iter/s, 4.73136s/12 iters), loss = 4.58863
I0410 01:00:53.184481 18059 solver.cpp:237]     Train net output #0: loss = 4.58863 (* 1 = 4.58863 loss)
I0410 01:00:53.184496 18059 sgd_solver.cpp:105] Iteration 2376, lr = 0.00624595
I0410 01:00:57.867166 18059 solver.cpp:218] Iteration 2388 (2.56273 iter/s, 4.68251s/12 iters), loss = 4.78887
I0410 01:00:57.867225 18059 solver.cpp:237]     Train net output #0: loss = 4.78887 (* 1 = 4.78887 loss)
I0410 01:00:57.867238 18059 sgd_solver.cpp:105] Iteration 2388, lr = 0.00623112
I0410 01:01:02.896232 18059 solver.cpp:218] Iteration 2400 (2.38625 iter/s, 5.02882s/12 iters), loss = 4.71295
I0410 01:01:02.896368 18059 solver.cpp:237]     Train net output #0: loss = 4.71295 (* 1 = 4.71295 loss)
I0410 01:01:02.896381 18059 sgd_solver.cpp:105] Iteration 2400, lr = 0.00621633
I0410 01:01:07.572136 18059 solver.cpp:218] Iteration 2412 (2.56652 iter/s, 4.67559s/12 iters), loss = 4.55288
I0410 01:01:07.572188 18059 solver.cpp:237]     Train net output #0: loss = 4.55288 (* 1 = 4.55288 loss)
I0410 01:01:07.572201 18059 sgd_solver.cpp:105] Iteration 2412, lr = 0.00620157
I0410 01:01:12.346040 18059 solver.cpp:218] Iteration 2424 (2.51379 iter/s, 4.77368s/12 iters), loss = 4.86332
I0410 01:01:12.346077 18059 solver.cpp:237]     Train net output #0: loss = 4.86332 (* 1 = 4.86332 loss)
I0410 01:01:12.346086 18059 sgd_solver.cpp:105] Iteration 2424, lr = 0.00618684
I0410 01:01:13.259822 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:01:16.732208 18059 solver.cpp:218] Iteration 2436 (2.736 iter/s, 4.38596s/12 iters), loss = 4.66212
I0410 01:01:16.732254 18059 solver.cpp:237]     Train net output #0: loss = 4.66212 (* 1 = 4.66212 loss)
I0410 01:01:16.732264 18059 sgd_solver.cpp:105] Iteration 2436, lr = 0.00617215
I0410 01:01:20.674463 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2448.caffemodel
I0410 01:01:29.468937 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2448.solverstate
I0410 01:01:36.241837 18059 solver.cpp:330] Iteration 2448, Testing net (#0)
I0410 01:01:36.241887 18059 net.cpp:676] Ignoring source layer train-data
I0410 01:01:39.899613 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:01:40.878018 18059 solver.cpp:397]     Test net output #0: accuracy = 0.0386029
I0410 01:01:40.878069 18059 solver.cpp:397]     Test net output #1: loss = 4.70522 (* 1 = 4.70522 loss)
I0410 01:01:40.972026 18059 solver.cpp:218] Iteration 2448 (0.495072 iter/s, 24.2389s/12 iters), loss = 4.55878
I0410 01:01:40.972087 18059 solver.cpp:237]     Train net output #0: loss = 4.55878 (* 1 = 4.55878 loss)
I0410 01:01:40.972101 18059 sgd_solver.cpp:105] Iteration 2448, lr = 0.0061575
I0410 01:01:45.301530 18059 solver.cpp:218] Iteration 2460 (2.77182 iter/s, 4.32928s/12 iters), loss = 4.67051
I0410 01:01:45.301585 18059 solver.cpp:237]     Train net output #0: loss = 4.67051 (* 1 = 4.67051 loss)
I0410 01:01:45.301599 18059 sgd_solver.cpp:105] Iteration 2460, lr = 0.00614288
I0410 01:01:50.172379 18059 solver.cpp:218] Iteration 2472 (2.46376 iter/s, 4.87061s/12 iters), loss = 4.67389
I0410 01:01:50.172430 18059 solver.cpp:237]     Train net output #0: loss = 4.67389 (* 1 = 4.67389 loss)
I0410 01:01:50.172442 18059 sgd_solver.cpp:105] Iteration 2472, lr = 0.0061283
I0410 01:01:55.101065 18059 solver.cpp:218] Iteration 2484 (2.43484 iter/s, 4.92845s/12 iters), loss = 4.7482
I0410 01:01:55.101109 18059 solver.cpp:237]     Train net output #0: loss = 4.7482 (* 1 = 4.7482 loss)
I0410 01:01:55.101116 18059 sgd_solver.cpp:105] Iteration 2484, lr = 0.00611375
I0410 01:02:00.011590 18059 solver.cpp:218] Iteration 2496 (2.44384 iter/s, 4.9103s/12 iters), loss = 4.80327
I0410 01:02:00.011634 18059 solver.cpp:237]     Train net output #0: loss = 4.80327 (* 1 = 4.80327 loss)
I0410 01:02:00.011643 18059 sgd_solver.cpp:105] Iteration 2496, lr = 0.00609923
I0410 01:02:04.888265 18059 solver.cpp:218] Iteration 2508 (2.46081 iter/s, 4.87644s/12 iters), loss = 4.6898
I0410 01:02:04.888320 18059 solver.cpp:237]     Train net output #0: loss = 4.6898 (* 1 = 4.6898 loss)
I0410 01:02:04.888334 18059 sgd_solver.cpp:105] Iteration 2508, lr = 0.00608475
I0410 01:02:09.691928 18059 solver.cpp:218] Iteration 2520 (2.49821 iter/s, 4.80343s/12 iters), loss = 4.75193
I0410 01:02:09.692006 18059 solver.cpp:237]     Train net output #0: loss = 4.75193 (* 1 = 4.75193 loss)
I0410 01:02:09.692018 18059 sgd_solver.cpp:105] Iteration 2520, lr = 0.0060703
I0410 01:02:12.793835 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:02:14.545576 18059 solver.cpp:218] Iteration 2532 (2.4725 iter/s, 4.85339s/12 iters), loss = 4.68843
I0410 01:02:14.545617 18059 solver.cpp:237]     Train net output #0: loss = 4.68843 (* 1 = 4.68843 loss)
I0410 01:02:14.545626 18059 sgd_solver.cpp:105] Iteration 2532, lr = 0.00605589
I0410 01:02:19.574596 18059 solver.cpp:218] Iteration 2544 (2.38626 iter/s, 5.02879s/12 iters), loss = 4.66031
I0410 01:02:19.574636 18059 solver.cpp:237]     Train net output #0: loss = 4.66031 (* 1 = 4.66031 loss)
I0410 01:02:19.574645 18059 sgd_solver.cpp:105] Iteration 2544, lr = 0.00604151
I0410 01:02:21.556157 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2550.caffemodel
I0410 01:02:26.214586 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2550.solverstate
I0410 01:02:32.369685 18059 solver.cpp:330] Iteration 2550, Testing net (#0)
I0410 01:02:32.369705 18059 net.cpp:676] Ignoring source layer train-data
I0410 01:02:35.862167 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:02:36.898285 18059 solver.cpp:397]     Test net output #0: accuracy = 0.0379902
I0410 01:02:36.898334 18059 solver.cpp:397]     Test net output #1: loss = 4.60635 (* 1 = 4.60635 loss)
I0410 01:02:38.757915 18059 solver.cpp:218] Iteration 2556 (0.625567 iter/s, 19.1826s/12 iters), loss = 4.70472
I0410 01:02:38.757987 18059 solver.cpp:237]     Train net output #0: loss = 4.70472 (* 1 = 4.70472 loss)
I0410 01:02:38.757998 18059 sgd_solver.cpp:105] Iteration 2556, lr = 0.00602717
I0410 01:02:43.405474 18059 solver.cpp:218] Iteration 2568 (2.58214 iter/s, 4.6473s/12 iters), loss = 4.62757
I0410 01:02:43.405611 18059 solver.cpp:237]     Train net output #0: loss = 4.62757 (* 1 = 4.62757 loss)
I0410 01:02:43.405623 18059 sgd_solver.cpp:105] Iteration 2568, lr = 0.00601286
I0410 01:02:48.080461 18059 solver.cpp:218] Iteration 2580 (2.56702 iter/s, 4.67467s/12 iters), loss = 4.63097
I0410 01:02:48.080514 18059 solver.cpp:237]     Train net output #0: loss = 4.63097 (* 1 = 4.63097 loss)
I0410 01:02:48.080526 18059 sgd_solver.cpp:105] Iteration 2580, lr = 0.00599858
I0410 01:02:52.471879 18059 solver.cpp:218] Iteration 2592 (2.73274 iter/s, 4.39119s/12 iters), loss = 4.60069
I0410 01:02:52.471930 18059 solver.cpp:237]     Train net output #0: loss = 4.60069 (* 1 = 4.60069 loss)
I0410 01:02:52.471940 18059 sgd_solver.cpp:105] Iteration 2592, lr = 0.00598434
I0410 01:02:57.237625 18059 solver.cpp:218] Iteration 2604 (2.51809 iter/s, 4.76552s/12 iters), loss = 4.63618
I0410 01:02:57.237671 18059 solver.cpp:237]     Train net output #0: loss = 4.63618 (* 1 = 4.63618 loss)
I0410 01:02:57.237682 18059 sgd_solver.cpp:105] Iteration 2604, lr = 0.00597013
I0410 01:03:01.999774 18059 solver.cpp:218] Iteration 2616 (2.51999 iter/s, 4.76192s/12 iters), loss = 4.64088
I0410 01:03:01.999825 18059 solver.cpp:237]     Train net output #0: loss = 4.64088 (* 1 = 4.64088 loss)
I0410 01:03:01.999836 18059 sgd_solver.cpp:105] Iteration 2616, lr = 0.00595596
I0410 01:03:06.707839 18059 solver.cpp:218] Iteration 2628 (2.54894 iter/s, 4.70784s/12 iters), loss = 4.62473
I0410 01:03:06.707890 18059 solver.cpp:237]     Train net output #0: loss = 4.62473 (* 1 = 4.62473 loss)
I0410 01:03:06.707901 18059 sgd_solver.cpp:105] Iteration 2628, lr = 0.00594182
I0410 01:03:07.103487 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:03:11.482345 18059 solver.cpp:218] Iteration 2640 (2.51347 iter/s, 4.77428s/12 iters), loss = 4.53868
I0410 01:03:11.482394 18059 solver.cpp:237]     Train net output #0: loss = 4.53868 (* 1 = 4.53868 loss)
I0410 01:03:11.482407 18059 sgd_solver.cpp:105] Iteration 2640, lr = 0.00592771
I0410 01:03:15.648057 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2652.caffemodel
I0410 01:03:20.453476 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2652.solverstate
I0410 01:03:24.127359 18059 solver.cpp:330] Iteration 2652, Testing net (#0)
I0410 01:03:24.127385 18059 net.cpp:676] Ignoring source layer train-data
I0410 01:03:27.529944 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:03:28.645468 18059 solver.cpp:397]     Test net output #0: accuracy = 0.0330882
I0410 01:03:28.645507 18059 solver.cpp:397]     Test net output #1: loss = 4.55712 (* 1 = 4.55712 loss)
I0410 01:03:28.739002 18059 solver.cpp:218] Iteration 2652 (0.69541 iter/s, 17.256s/12 iters), loss = 4.4798
I0410 01:03:28.739048 18059 solver.cpp:237]     Train net output #0: loss = 4.4798 (* 1 = 4.4798 loss)
I0410 01:03:28.739059 18059 sgd_solver.cpp:105] Iteration 2652, lr = 0.00591364
I0410 01:03:32.651410 18059 solver.cpp:218] Iteration 2664 (3.06732 iter/s, 3.91221s/12 iters), loss = 4.38888
I0410 01:03:32.651464 18059 solver.cpp:237]     Train net output #0: loss = 4.38888 (* 1 = 4.38888 loss)
I0410 01:03:32.651476 18059 sgd_solver.cpp:105] Iteration 2664, lr = 0.0058996
I0410 01:03:37.615825 18059 solver.cpp:218] Iteration 2676 (2.41732 iter/s, 4.96417s/12 iters), loss = 4.4475
I0410 01:03:37.615871 18059 solver.cpp:237]     Train net output #0: loss = 4.4475 (* 1 = 4.4475 loss)
I0410 01:03:37.615881 18059 sgd_solver.cpp:105] Iteration 2676, lr = 0.00588559
I0410 01:03:42.403637 18059 solver.cpp:218] Iteration 2688 (2.50648 iter/s, 4.78759s/12 iters), loss = 4.40724
I0410 01:03:42.403685 18059 solver.cpp:237]     Train net output #0: loss = 4.40724 (* 1 = 4.40724 loss)
I0410 01:03:42.403698 18059 sgd_solver.cpp:105] Iteration 2688, lr = 0.00587162
I0410 01:03:47.076478 18059 solver.cpp:218] Iteration 2700 (2.56815 iter/s, 4.67262s/12 iters), loss = 4.48269
I0410 01:03:47.076601 18059 solver.cpp:237]     Train net output #0: loss = 4.48269 (* 1 = 4.48269 loss)
I0410 01:03:47.076613 18059 sgd_solver.cpp:105] Iteration 2700, lr = 0.00585768
I0410 01:03:51.960928 18059 solver.cpp:218] Iteration 2712 (2.45693 iter/s, 4.88415s/12 iters), loss = 4.56396
I0410 01:03:51.960965 18059 solver.cpp:237]     Train net output #0: loss = 4.56396 (* 1 = 4.56396 loss)
I0410 01:03:51.960974 18059 sgd_solver.cpp:105] Iteration 2712, lr = 0.00584377
I0410 01:03:56.628363 18059 solver.cpp:218] Iteration 2724 (2.57112 iter/s, 4.66722s/12 iters), loss = 4.57099
I0410 01:03:56.628412 18059 solver.cpp:237]     Train net output #0: loss = 4.57099 (* 1 = 4.57099 loss)
I0410 01:03:56.628422 18059 sgd_solver.cpp:105] Iteration 2724, lr = 0.0058299
I0410 01:03:59.194684 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:04:01.524087 18059 solver.cpp:218] Iteration 2736 (2.45123 iter/s, 4.8955s/12 iters), loss = 4.36705
I0410 01:04:01.524132 18059 solver.cpp:237]     Train net output #0: loss = 4.36705 (* 1 = 4.36705 loss)
I0410 01:04:01.524142 18059 sgd_solver.cpp:105] Iteration 2736, lr = 0.00581605
I0410 01:04:06.222643 18059 solver.cpp:218] Iteration 2748 (2.5541 iter/s, 4.69833s/12 iters), loss = 4.43993
I0410 01:04:06.222697 18059 solver.cpp:237]     Train net output #0: loss = 4.43993 (* 1 = 4.43993 loss)
I0410 01:04:06.222707 18059 sgd_solver.cpp:105] Iteration 2748, lr = 0.00580225
I0410 01:04:08.187742 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2754.caffemodel
I0410 01:04:14.566783 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2754.solverstate
I0410 01:04:18.230346 18059 solver.cpp:330] Iteration 2754, Testing net (#0)
I0410 01:04:18.230396 18059 net.cpp:676] Ignoring source layer train-data
I0410 01:04:21.420222 18059 blocking_queue.cpp:49] Waiting for data
I0410 01:04:21.661942 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:04:22.785851 18059 solver.cpp:397]     Test net output #0: accuracy = 0.0404412
I0410 01:04:22.785895 18059 solver.cpp:397]     Test net output #1: loss = 4.53601 (* 1 = 4.53601 loss)
I0410 01:04:24.464324 18059 solver.cpp:218] Iteration 2760 (0.657859 iter/s, 18.241s/12 iters), loss = 4.36324
I0410 01:04:24.464370 18059 solver.cpp:237]     Train net output #0: loss = 4.36324 (* 1 = 4.36324 loss)
I0410 01:04:24.464378 18059 sgd_solver.cpp:105] Iteration 2760, lr = 0.00578847
I0410 01:04:29.301174 18059 solver.cpp:218] Iteration 2772 (2.48107 iter/s, 4.83662s/12 iters), loss = 4.60269
I0410 01:04:29.301223 18059 solver.cpp:237]     Train net output #0: loss = 4.60269 (* 1 = 4.60269 loss)
I0410 01:04:29.301232 18059 sgd_solver.cpp:105] Iteration 2772, lr = 0.00577473
I0410 01:04:34.278178 18059 solver.cpp:218] Iteration 2784 (2.4112 iter/s, 4.97677s/12 iters), loss = 4.52611
I0410 01:04:34.278234 18059 solver.cpp:237]     Train net output #0: loss = 4.52611 (* 1 = 4.52611 loss)
I0410 01:04:34.278246 18059 sgd_solver.cpp:105] Iteration 2784, lr = 0.00576102
I0410 01:04:39.494478 18059 solver.cpp:218] Iteration 2796 (2.30059 iter/s, 5.21605s/12 iters), loss = 4.48106
I0410 01:04:39.494526 18059 solver.cpp:237]     Train net output #0: loss = 4.48106 (* 1 = 4.48106 loss)
I0410 01:04:39.494537 18059 sgd_solver.cpp:105] Iteration 2796, lr = 0.00574734
I0410 01:04:44.132366 18059 solver.cpp:218] Iteration 2808 (2.5875 iter/s, 4.63767s/12 iters), loss = 4.37784
I0410 01:04:44.132405 18059 solver.cpp:237]     Train net output #0: loss = 4.37784 (* 1 = 4.37784 loss)
I0410 01:04:44.132416 18059 sgd_solver.cpp:105] Iteration 2808, lr = 0.00573369
I0410 01:04:48.809478 18059 solver.cpp:218] Iteration 2820 (2.5658 iter/s, 4.6769s/12 iters), loss = 4.46399
I0410 01:04:48.809574 18059 solver.cpp:237]     Train net output #0: loss = 4.46399 (* 1 = 4.46399 loss)
I0410 01:04:48.809585 18059 sgd_solver.cpp:105] Iteration 2820, lr = 0.00572008
I0410 01:04:53.480757 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:04:53.774240 18059 solver.cpp:218] Iteration 2832 (2.41717 iter/s, 4.96447s/12 iters), loss = 4.21794
I0410 01:04:53.774292 18059 solver.cpp:237]     Train net output #0: loss = 4.21794 (* 1 = 4.21794 loss)
I0410 01:04:53.774304 18059 sgd_solver.cpp:105] Iteration 2832, lr = 0.0057065
I0410 01:04:58.592401 18059 solver.cpp:218] Iteration 2844 (2.4907 iter/s, 4.81793s/12 iters), loss = 4.40647
I0410 01:04:58.592449 18059 solver.cpp:237]     Train net output #0: loss = 4.40647 (* 1 = 4.40647 loss)
I0410 01:04:58.592460 18059 sgd_solver.cpp:105] Iteration 2844, lr = 0.00569295
I0410 01:05:02.870134 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2856.caffemodel
I0410 01:05:10.995424 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2856.solverstate
I0410 01:05:14.634143 18059 solver.cpp:330] Iteration 2856, Testing net (#0)
I0410 01:05:14.634164 18059 net.cpp:676] Ignoring source layer train-data
I0410 01:05:18.112826 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:05:19.300390 18059 solver.cpp:397]     Test net output #0: accuracy = 0.0367647
I0410 01:05:19.300482 18059 solver.cpp:397]     Test net output #1: loss = 4.52195 (* 1 = 4.52195 loss)
I0410 01:05:19.394444 18059 solver.cpp:218] Iteration 2856 (0.576888 iter/s, 20.8013s/12 iters), loss = 4.33933
I0410 01:05:19.394520 18059 solver.cpp:237]     Train net output #0: loss = 4.33933 (* 1 = 4.33933 loss)
I0410 01:05:19.394537 18059 sgd_solver.cpp:105] Iteration 2856, lr = 0.00567944
I0410 01:05:23.280352 18059 solver.cpp:218] Iteration 2868 (3.08826 iter/s, 3.88569s/12 iters), loss = 4.61917
I0410 01:05:23.280400 18059 solver.cpp:237]     Train net output #0: loss = 4.61917 (* 1 = 4.61917 loss)
I0410 01:05:23.280411 18059 sgd_solver.cpp:105] Iteration 2868, lr = 0.00566595
I0410 01:05:27.745175 18059 solver.cpp:218] Iteration 2880 (2.68781 iter/s, 4.4646s/12 iters), loss = 4.36079
I0410 01:05:27.745227 18059 solver.cpp:237]     Train net output #0: loss = 4.36079 (* 1 = 4.36079 loss)
I0410 01:05:27.745237 18059 sgd_solver.cpp:105] Iteration 2880, lr = 0.0056525
I0410 01:05:32.203516 18059 solver.cpp:218] Iteration 2892 (2.69172 iter/s, 4.45812s/12 iters), loss = 4.33326
I0410 01:05:32.203572 18059 solver.cpp:237]     Train net output #0: loss = 4.33326 (* 1 = 4.33326 loss)
I0410 01:05:32.203584 18059 sgd_solver.cpp:105] Iteration 2892, lr = 0.00563908
I0410 01:05:36.574304 18059 solver.cpp:218] Iteration 2904 (2.74564 iter/s, 4.37057s/12 iters), loss = 4.37671
I0410 01:05:36.574348 18059 solver.cpp:237]     Train net output #0: loss = 4.37671 (* 1 = 4.37671 loss)
I0410 01:05:36.574359 18059 sgd_solver.cpp:105] Iteration 2904, lr = 0.00562569
I0410 01:05:41.377998 18059 solver.cpp:218] Iteration 2916 (2.49821 iter/s, 4.80344s/12 iters), loss = 4.20874
I0410 01:05:41.378053 18059 solver.cpp:237]     Train net output #0: loss = 4.20874 (* 1 = 4.20874 loss)
I0410 01:05:41.378064 18059 sgd_solver.cpp:105] Iteration 2916, lr = 0.00561233
I0410 01:05:46.022239 18059 solver.cpp:218] Iteration 2928 (2.58397 iter/s, 4.64401s/12 iters), loss = 4.5332
I0410 01:05:46.022292 18059 solver.cpp:237]     Train net output #0: loss = 4.5332 (* 1 = 4.5332 loss)
I0410 01:05:46.022303 18059 sgd_solver.cpp:105] Iteration 2928, lr = 0.00559901
I0410 01:05:47.699577 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:05:50.817739 18059 solver.cpp:218] Iteration 2940 (2.50247 iter/s, 4.79526s/12 iters), loss = 4.33811
I0410 01:05:50.817860 18059 solver.cpp:237]     Train net output #0: loss = 4.33811 (* 1 = 4.33811 loss)
I0410 01:05:50.817874 18059 sgd_solver.cpp:105] Iteration 2940, lr = 0.00558572
I0410 01:05:55.504251 18059 solver.cpp:218] Iteration 2952 (2.5607 iter/s, 4.68622s/12 iters), loss = 4.44375
I0410 01:05:55.504292 18059 solver.cpp:237]     Train net output #0: loss = 4.44375 (* 1 = 4.44375 loss)
I0410 01:05:55.504302 18059 sgd_solver.cpp:105] Iteration 2952, lr = 0.00557245
I0410 01:05:57.345607 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2958.caffemodel
I0410 01:06:07.439977 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2958.solverstate
I0410 01:06:11.113126 18059 solver.cpp:330] Iteration 2958, Testing net (#0)
I0410 01:06:11.113150 18059 net.cpp:676] Ignoring source layer train-data
I0410 01:06:14.391072 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:06:15.812543 18059 solver.cpp:397]     Test net output #0: accuracy = 0.0447304
I0410 01:06:15.812572 18059 solver.cpp:397]     Test net output #1: loss = 4.43381 (* 1 = 4.43381 loss)
I0410 01:06:17.552145 18059 solver.cpp:218] Iteration 2964 (0.54429 iter/s, 22.0471s/12 iters), loss = 4.11012
I0410 01:06:17.552191 18059 solver.cpp:237]     Train net output #0: loss = 4.11012 (* 1 = 4.11012 loss)
I0410 01:06:17.552199 18059 sgd_solver.cpp:105] Iteration 2964, lr = 0.00555922
I0410 01:06:22.787273 18059 solver.cpp:218] Iteration 2976 (2.29232 iter/s, 5.23488s/12 iters), loss = 4.46952
I0410 01:06:22.789834 18059 solver.cpp:237]     Train net output #0: loss = 4.46952 (* 1 = 4.46952 loss)
I0410 01:06:22.789846 18059 sgd_solver.cpp:105] Iteration 2976, lr = 0.00554603
I0410 01:06:27.621630 18059 solver.cpp:218] Iteration 2988 (2.48364 iter/s, 4.83162s/12 iters), loss = 4.23625
I0410 01:06:27.621668 18059 solver.cpp:237]     Train net output #0: loss = 4.23625 (* 1 = 4.23625 loss)
I0410 01:06:27.621676 18059 sgd_solver.cpp:105] Iteration 2988, lr = 0.00553286
I0410 01:06:32.273429 18059 solver.cpp:218] Iteration 3000 (2.57977 iter/s, 4.65159s/12 iters), loss = 4.41091
I0410 01:06:32.273475 18059 solver.cpp:237]     Train net output #0: loss = 4.41091 (* 1 = 4.41091 loss)
I0410 01:06:32.273486 18059 sgd_solver.cpp:105] Iteration 3000, lr = 0.00551972
I0410 01:06:37.065445 18059 solver.cpp:218] Iteration 3012 (2.50428 iter/s, 4.79179s/12 iters), loss = 4.3278
I0410 01:06:37.065487 18059 solver.cpp:237]     Train net output #0: loss = 4.3278 (* 1 = 4.3278 loss)
I0410 01:06:37.065495 18059 sgd_solver.cpp:105] Iteration 3012, lr = 0.00550662
I0410 01:06:42.162791 18059 solver.cpp:218] Iteration 3024 (2.35427 iter/s, 5.09711s/12 iters), loss = 4.30659
I0410 01:06:42.162835 18059 solver.cpp:237]     Train net output #0: loss = 4.30659 (* 1 = 4.30659 loss)
I0410 01:06:42.162844 18059 sgd_solver.cpp:105] Iteration 3024, lr = 0.00549354
I0410 01:06:45.895009 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:06:46.807807 18059 solver.cpp:218] Iteration 3036 (2.58354 iter/s, 4.6448s/12 iters), loss = 4.16038
I0410 01:06:46.807854 18059 solver.cpp:237]     Train net output #0: loss = 4.16038 (* 1 = 4.16038 loss)
I0410 01:06:46.807863 18059 sgd_solver.cpp:105] Iteration 3036, lr = 0.0054805
I0410 01:06:51.845288 18059 solver.cpp:218] Iteration 3048 (2.38226 iter/s, 5.03724s/12 iters), loss = 4.31551
I0410 01:06:51.845341 18059 solver.cpp:237]     Train net output #0: loss = 4.31551 (* 1 = 4.31551 loss)
I0410 01:06:51.845353 18059 sgd_solver.cpp:105] Iteration 3048, lr = 0.00546749
I0410 01:06:56.231712 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3060.caffemodel
I0410 01:07:06.425909 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3060.solverstate
I0410 01:07:10.110988 18059 solver.cpp:330] Iteration 3060, Testing net (#0)
I0410 01:07:10.111014 18059 net.cpp:676] Ignoring source layer train-data
I0410 01:07:13.410089 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:07:14.662696 18059 solver.cpp:397]     Test net output #0: accuracy = 0.0539216
I0410 01:07:14.662730 18059 solver.cpp:397]     Test net output #1: loss = 4.27018 (* 1 = 4.27018 loss)
I0410 01:07:14.757447 18059 solver.cpp:218] Iteration 3060 (0.523759 iter/s, 22.9113s/12 iters), loss = 4.17647
I0410 01:07:14.757489 18059 solver.cpp:237]     Train net output #0: loss = 4.17647 (* 1 = 4.17647 loss)
I0410 01:07:14.757498 18059 sgd_solver.cpp:105] Iteration 3060, lr = 0.00545451
I0410 01:07:18.569923 18059 solver.cpp:218] Iteration 3072 (3.14772 iter/s, 3.81228s/12 iters), loss = 4.17591
I0410 01:07:18.569994 18059 solver.cpp:237]     Train net output #0: loss = 4.17591 (* 1 = 4.17591 loss)
I0410 01:07:18.570006 18059 sgd_solver.cpp:105] Iteration 3072, lr = 0.00544156
I0410 01:07:23.693166 18059 solver.cpp:218] Iteration 3084 (2.34239 iter/s, 5.12298s/12 iters), loss = 4.24917
I0410 01:07:23.693212 18059 solver.cpp:237]     Train net output #0: loss = 4.24917 (* 1 = 4.24917 loss)
I0410 01:07:23.693223 18059 sgd_solver.cpp:105] Iteration 3084, lr = 0.00542864
I0410 01:07:28.472584 18059 solver.cpp:218] Iteration 3096 (2.51088 iter/s, 4.77919s/12 iters), loss = 4.2284
I0410 01:07:28.472693 18059 solver.cpp:237]     Train net output #0: loss = 4.2284 (* 1 = 4.2284 loss)
I0410 01:07:28.472703 18059 sgd_solver.cpp:105] Iteration 3096, lr = 0.00541575
I0410 01:07:33.215633 18059 solver.cpp:218] Iteration 3108 (2.53017 iter/s, 4.74276s/12 iters), loss = 4.23673
I0410 01:07:33.215685 18059 solver.cpp:237]     Train net output #0: loss = 4.23673 (* 1 = 4.23673 loss)
I0410 01:07:33.215696 18059 sgd_solver.cpp:105] Iteration 3108, lr = 0.00540289
I0410 01:07:38.408176 18059 solver.cpp:218] Iteration 3120 (2.31112 iter/s, 5.1923s/12 iters), loss = 4.19133
I0410 01:07:38.408231 18059 solver.cpp:237]     Train net output #0: loss = 4.19133 (* 1 = 4.19133 loss)
I0410 01:07:38.408242 18059 sgd_solver.cpp:105] Iteration 3120, lr = 0.00539006
I0410 01:07:43.529127 18059 solver.cpp:218] Iteration 3132 (2.34343 iter/s, 5.12071s/12 iters), loss = 4.30884
I0410 01:07:43.529173 18059 solver.cpp:237]     Train net output #0: loss = 4.30884 (* 1 = 4.30884 loss)
I0410 01:07:43.529184 18059 sgd_solver.cpp:105] Iteration 3132, lr = 0.00537727
I0410 01:07:44.622934 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:07:48.503806 18059 solver.cpp:218] Iteration 3144 (2.41233 iter/s, 4.97444s/12 iters), loss = 4.00255
I0410 01:07:48.503861 18059 solver.cpp:237]     Train net output #0: loss = 4.00255 (* 1 = 4.00255 loss)
I0410 01:07:48.503873 18059 sgd_solver.cpp:105] Iteration 3144, lr = 0.0053645
I0410 01:07:53.256474 18059 solver.cpp:218] Iteration 3156 (2.52502 iter/s, 4.75243s/12 iters), loss = 4.08277
I0410 01:07:53.256525 18059 solver.cpp:237]     Train net output #0: loss = 4.08277 (* 1 = 4.08277 loss)
I0410 01:07:53.256536 18059 sgd_solver.cpp:105] Iteration 3156, lr = 0.00535176
I0410 01:07:55.186375 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3162.caffemodel
I0410 01:08:07.461972 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3162.solverstate
I0410 01:08:11.264160 18059 solver.cpp:330] Iteration 3162, Testing net (#0)
I0410 01:08:11.264183 18059 net.cpp:676] Ignoring source layer train-data
I0410 01:08:14.670858 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:08:15.970821 18059 solver.cpp:397]     Test net output #0: accuracy = 0.0588235
I0410 01:08:15.970863 18059 solver.cpp:397]     Test net output #1: loss = 4.23939 (* 1 = 4.23939 loss)
I0410 01:08:17.560814 18059 solver.cpp:218] Iteration 3168 (0.493757 iter/s, 24.3034s/12 iters), loss = 4.21458
I0410 01:08:17.560863 18059 solver.cpp:237]     Train net output #0: loss = 4.21458 (* 1 = 4.21458 loss)
I0410 01:08:17.560873 18059 sgd_solver.cpp:105] Iteration 3168, lr = 0.00533906
I0410 01:08:21.954102 18059 solver.cpp:218] Iteration 3180 (2.73157 iter/s, 4.39307s/12 iters), loss = 4.13635
I0410 01:08:21.954150 18059 solver.cpp:237]     Train net output #0: loss = 4.13635 (* 1 = 4.13635 loss)
I0410 01:08:21.954161 18059 sgd_solver.cpp:105] Iteration 3180, lr = 0.00532638
I0410 01:08:26.544775 18059 solver.cpp:218] Iteration 3192 (2.61412 iter/s, 4.59045s/12 iters), loss = 4.13274
I0410 01:08:26.544822 18059 solver.cpp:237]     Train net output #0: loss = 4.13274 (* 1 = 4.13274 loss)
I0410 01:08:26.544836 18059 sgd_solver.cpp:105] Iteration 3192, lr = 0.00531374
I0410 01:08:31.159334 18059 solver.cpp:218] Iteration 3204 (2.60059 iter/s, 4.61434s/12 iters), loss = 4.0684
I0410 01:08:31.159384 18059 solver.cpp:237]     Train net output #0: loss = 4.0684 (* 1 = 4.0684 loss)
I0410 01:08:31.159392 18059 sgd_solver.cpp:105] Iteration 3204, lr = 0.00530112
I0410 01:08:35.774230 18059 solver.cpp:218] Iteration 3216 (2.6004 iter/s, 4.61467s/12 iters), loss = 4.20238
I0410 01:08:35.774271 18059 solver.cpp:237]     Train net output #0: loss = 4.20238 (* 1 = 4.20238 loss)
I0410 01:08:35.774281 18059 sgd_solver.cpp:105] Iteration 3216, lr = 0.00528853
I0410 01:08:40.308414 18059 solver.cpp:218] Iteration 3228 (2.64669 iter/s, 4.53397s/12 iters), loss = 4.28495
I0410 01:08:40.308511 18059 solver.cpp:237]     Train net output #0: loss = 4.28495 (* 1 = 4.28495 loss)
I0410 01:08:40.308519 18059 sgd_solver.cpp:105] Iteration 3228, lr = 0.00527598
I0410 01:08:43.299736 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:08:44.910183 18059 solver.cpp:218] Iteration 3240 (2.60785 iter/s, 4.60149s/12 iters), loss = 4.27813
I0410 01:08:44.910240 18059 solver.cpp:237]     Train net output #0: loss = 4.27813 (* 1 = 4.27813 loss)
I0410 01:08:44.910251 18059 sgd_solver.cpp:105] Iteration 3240, lr = 0.00526345
I0410 01:08:49.520207 18059 solver.cpp:218] Iteration 3252 (2.60315 iter/s, 4.60979s/12 iters), loss = 4.12354
I0410 01:08:49.520253 18059 solver.cpp:237]     Train net output #0: loss = 4.12354 (* 1 = 4.12354 loss)
I0410 01:08:49.520262 18059 sgd_solver.cpp:105] Iteration 3252, lr = 0.00525095
I0410 01:08:53.785313 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3264.caffemodel
I0410 01:09:08.204553 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3264.solverstate
I0410 01:09:17.777475 18059 solver.cpp:330] Iteration 3264, Testing net (#0)
I0410 01:09:17.777521 18059 net.cpp:676] Ignoring source layer train-data
I0410 01:09:20.929008 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:09:22.230904 18059 solver.cpp:397]     Test net output #0: accuracy = 0.0661765
I0410 01:09:22.230939 18059 solver.cpp:397]     Test net output #1: loss = 4.13038 (* 1 = 4.13038 loss)
I0410 01:09:22.324491 18059 solver.cpp:218] Iteration 3264 (0.365819 iter/s, 32.8031s/12 iters), loss = 4.27377
I0410 01:09:22.324546 18059 solver.cpp:237]     Train net output #0: loss = 4.27377 (* 1 = 4.27377 loss)
I0410 01:09:22.324558 18059 sgd_solver.cpp:105] Iteration 3264, lr = 0.00523849
I0410 01:09:26.183722 18059 solver.cpp:218] Iteration 3276 (3.10959 iter/s, 3.85903s/12 iters), loss = 4.14351
I0410 01:09:26.183763 18059 solver.cpp:237]     Train net output #0: loss = 4.14351 (* 1 = 4.14351 loss)
I0410 01:09:26.183771 18059 sgd_solver.cpp:105] Iteration 3276, lr = 0.00522605
I0410 01:09:31.003937 18059 solver.cpp:218] Iteration 3288 (2.48963 iter/s, 4.81999s/12 iters), loss = 4.03163
I0410 01:09:31.003993 18059 solver.cpp:237]     Train net output #0: loss = 4.03163 (* 1 = 4.03163 loss)
I0410 01:09:31.004005 18059 sgd_solver.cpp:105] Iteration 3288, lr = 0.00521364
I0410 01:09:35.853085 18059 solver.cpp:218] Iteration 3300 (2.47478 iter/s, 4.84891s/12 iters), loss = 4.13255
I0410 01:09:35.853132 18059 solver.cpp:237]     Train net output #0: loss = 4.13255 (* 1 = 4.13255 loss)
I0410 01:09:35.853145 18059 sgd_solver.cpp:105] Iteration 3300, lr = 0.00520126
I0410 01:09:40.650821 18059 solver.cpp:218] Iteration 3312 (2.5013 iter/s, 4.79751s/12 iters), loss = 4.0134
I0410 01:09:40.650871 18059 solver.cpp:237]     Train net output #0: loss = 4.0134 (* 1 = 4.0134 loss)
I0410 01:09:40.650882 18059 sgd_solver.cpp:105] Iteration 3312, lr = 0.00518892
I0410 01:09:45.554229 18059 solver.cpp:218] Iteration 3324 (2.44739 iter/s, 4.90318s/12 iters), loss = 4.04841
I0410 01:09:45.554278 18059 solver.cpp:237]     Train net output #0: loss = 4.04841 (* 1 = 4.04841 loss)
I0410 01:09:45.554289 18059 sgd_solver.cpp:105] Iteration 3324, lr = 0.0051766
I0410 01:09:50.807010 18059 solver.cpp:218] Iteration 3336 (2.28461 iter/s, 5.25254s/12 iters), loss = 4.11397
I0410 01:09:50.807126 18059 solver.cpp:237]     Train net output #0: loss = 4.11397 (* 1 = 4.11397 loss)
I0410 01:09:50.807137 18059 sgd_solver.cpp:105] Iteration 3336, lr = 0.00516431
I0410 01:09:51.231564 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:09:55.592159 18059 solver.cpp:218] Iteration 3348 (2.50791 iter/s, 4.78485s/12 iters), loss = 3.88755
I0410 01:09:55.592207 18059 solver.cpp:237]     Train net output #0: loss = 3.88755 (* 1 = 3.88755 loss)
I0410 01:09:55.592218 18059 sgd_solver.cpp:105] Iteration 3348, lr = 0.00515204
I0410 01:10:00.253000 18059 solver.cpp:218] Iteration 3360 (2.57477 iter/s, 4.66061s/12 iters), loss = 4.10545
I0410 01:10:00.253057 18059 solver.cpp:237]     Train net output #0: loss = 4.10545 (* 1 = 4.10545 loss)
I0410 01:10:00.253070 18059 sgd_solver.cpp:105] Iteration 3360, lr = 0.00513981
I0410 01:10:02.134829 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3366.caffemodel
I0410 01:10:14.278925 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3366.solverstate
I0410 01:10:24.672869 18059 solver.cpp:330] Iteration 3366, Testing net (#0)
I0410 01:10:24.672942 18059 net.cpp:676] Ignoring source layer train-data
I0410 01:10:27.853231 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:10:29.194165 18059 solver.cpp:397]     Test net output #0: accuracy = 0.0778186
I0410 01:10:29.194195 18059 solver.cpp:397]     Test net output #1: loss = 4.096 (* 1 = 4.096 loss)
I0410 01:10:30.770956 18059 solver.cpp:218] Iteration 3372 (0.393226 iter/s, 30.5168s/12 iters), loss = 3.90049
I0410 01:10:30.771015 18059 solver.cpp:237]     Train net output #0: loss = 3.90049 (* 1 = 3.90049 loss)
I0410 01:10:30.771028 18059 sgd_solver.cpp:105] Iteration 3372, lr = 0.00512761
I0410 01:10:35.444885 18059 solver.cpp:218] Iteration 3384 (2.56756 iter/s, 4.67369s/12 iters), loss = 3.96299
I0410 01:10:35.444932 18059 solver.cpp:237]     Train net output #0: loss = 3.96299 (* 1 = 3.96299 loss)
I0410 01:10:35.444943 18059 sgd_solver.cpp:105] Iteration 3384, lr = 0.00511544
I0410 01:10:40.276185 18059 solver.cpp:218] Iteration 3396 (2.48392 iter/s, 4.83107s/12 iters), loss = 3.88307
I0410 01:10:40.276239 18059 solver.cpp:237]     Train net output #0: loss = 3.88307 (* 1 = 3.88307 loss)
I0410 01:10:40.276250 18059 sgd_solver.cpp:105] Iteration 3396, lr = 0.00510329
I0410 01:10:44.921321 18059 solver.cpp:218] Iteration 3408 (2.58347 iter/s, 4.64491s/12 iters), loss = 4.13018
I0410 01:10:44.921375 18059 solver.cpp:237]     Train net output #0: loss = 4.13018 (* 1 = 4.13018 loss)
I0410 01:10:44.921386 18059 sgd_solver.cpp:105] Iteration 3408, lr = 0.00509117
I0410 01:10:49.671357 18059 solver.cpp:218] Iteration 3420 (2.52642 iter/s, 4.7498s/12 iters), loss = 3.94576
I0410 01:10:49.671422 18059 solver.cpp:237]     Train net output #0: loss = 3.94576 (* 1 = 3.94576 loss)
I0410 01:10:49.671437 18059 sgd_solver.cpp:105] Iteration 3420, lr = 0.00507909
I0410 01:10:54.421463 18059 solver.cpp:218] Iteration 3432 (2.52639 iter/s, 4.74986s/12 iters), loss = 4.08044
I0410 01:10:54.421514 18059 solver.cpp:237]     Train net output #0: loss = 4.08044 (* 1 = 4.08044 loss)
I0410 01:10:54.421526 18059 sgd_solver.cpp:105] Iteration 3432, lr = 0.00506703
I0410 01:10:56.981492 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:10:59.319639 18059 solver.cpp:218] Iteration 3444 (2.45001 iter/s, 4.89794s/12 iters), loss = 3.82502
I0410 01:10:59.319685 18059 solver.cpp:237]     Train net output #0: loss = 3.82502 (* 1 = 3.82502 loss)
I0410 01:10:59.319695 18059 sgd_solver.cpp:105] Iteration 3444, lr = 0.005055
I0410 01:11:04.017176 18059 solver.cpp:218] Iteration 3456 (2.55465 iter/s, 4.69731s/12 iters), loss = 3.87795
I0410 01:11:04.017227 18059 solver.cpp:237]     Train net output #0: loss = 3.87795 (* 1 = 3.87795 loss)
I0410 01:11:04.017241 18059 sgd_solver.cpp:105] Iteration 3456, lr = 0.005043
I0410 01:11:08.376171 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3468.caffemodel
I0410 01:11:20.337035 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3468.solverstate
I0410 01:11:34.554488 18059 solver.cpp:330] Iteration 3468, Testing net (#0)
I0410 01:11:34.554591 18059 net.cpp:676] Ignoring source layer train-data
I0410 01:11:34.985657 18059 blocking_queue.cpp:49] Waiting for data
I0410 01:11:37.823205 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:11:39.310153 18059 solver.cpp:397]     Test net output #0: accuracy = 0.0851716
I0410 01:11:39.310196 18059 solver.cpp:397]     Test net output #1: loss = 3.95841 (* 1 = 3.95841 loss)
I0410 01:11:39.401244 18059 solver.cpp:218] Iteration 3468 (0.339148 iter/s, 35.3828s/12 iters), loss = 3.79247
I0410 01:11:39.401294 18059 solver.cpp:237]     Train net output #0: loss = 3.79247 (* 1 = 3.79247 loss)
I0410 01:11:39.401305 18059 sgd_solver.cpp:105] Iteration 3468, lr = 0.00503102
I0410 01:11:43.503574 18059 solver.cpp:218] Iteration 3480 (2.92531 iter/s, 4.10213s/12 iters), loss = 3.79629
I0410 01:11:43.503612 18059 solver.cpp:237]     Train net output #0: loss = 3.79629 (* 1 = 3.79629 loss)
I0410 01:11:43.503621 18059 sgd_solver.cpp:105] Iteration 3480, lr = 0.00501908
I0410 01:11:48.412418 18059 solver.cpp:218] Iteration 3492 (2.44468 iter/s, 4.90862s/12 iters), loss = 4.06929
I0410 01:11:48.412470 18059 solver.cpp:237]     Train net output #0: loss = 4.06929 (* 1 = 4.06929 loss)
I0410 01:11:48.412482 18059 sgd_solver.cpp:105] Iteration 3492, lr = 0.00500716
I0410 01:11:53.466202 18059 solver.cpp:218] Iteration 3504 (2.37457 iter/s, 5.05354s/12 iters), loss = 3.78897
I0410 01:11:53.466260 18059 solver.cpp:237]     Train net output #0: loss = 3.78897 (* 1 = 3.78897 loss)
I0410 01:11:53.466271 18059 sgd_solver.cpp:105] Iteration 3504, lr = 0.00499527
I0410 01:11:58.078651 18059 solver.cpp:218] Iteration 3516 (2.60178 iter/s, 4.61222s/12 iters), loss = 3.85368
I0410 01:11:58.078691 18059 solver.cpp:237]     Train net output #0: loss = 3.85368 (* 1 = 3.85368 loss)
I0410 01:11:58.078699 18059 sgd_solver.cpp:105] Iteration 3516, lr = 0.00498341
I0410 01:12:02.868285 18059 solver.cpp:218] Iteration 3528 (2.50553 iter/s, 4.78941s/12 iters), loss = 3.7453
I0410 01:12:02.868326 18059 solver.cpp:237]     Train net output #0: loss = 3.7453 (* 1 = 3.7453 loss)
I0410 01:12:02.868335 18059 sgd_solver.cpp:105] Iteration 3528, lr = 0.00497158
I0410 01:12:07.358974 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:12:07.618326 18059 solver.cpp:218] Iteration 3540 (2.52641 iter/s, 4.74982s/12 iters), loss = 3.86719
I0410 01:12:07.618378 18059 solver.cpp:237]     Train net output #0: loss = 3.86719 (* 1 = 3.86719 loss)
I0410 01:12:07.618391 18059 sgd_solver.cpp:105] Iteration 3540, lr = 0.00495978
I0410 01:12:12.581570 18059 solver.cpp:218] Iteration 3552 (2.41789 iter/s, 4.96301s/12 iters), loss = 3.78442
I0410 01:12:12.581611 18059 solver.cpp:237]     Train net output #0: loss = 3.78442 (* 1 = 3.78442 loss)
I0410 01:12:12.581621 18059 sgd_solver.cpp:105] Iteration 3552, lr = 0.004948
I0410 01:12:17.488214 18059 solver.cpp:218] Iteration 3564 (2.44578 iter/s, 4.90641s/12 iters), loss = 3.87875
I0410 01:12:17.488270 18059 solver.cpp:237]     Train net output #0: loss = 3.87875 (* 1 = 3.87875 loss)
I0410 01:12:17.488281 18059 sgd_solver.cpp:105] Iteration 3564, lr = 0.00493626
I0410 01:12:19.690621 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3570.caffemodel
I0410 01:12:28.952666 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3570.solverstate
I0410 01:12:37.061240 18059 solver.cpp:330] Iteration 3570, Testing net (#0)
I0410 01:12:37.061265 18059 net.cpp:676] Ignoring source layer train-data
I0410 01:12:40.117086 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:12:41.666471 18059 solver.cpp:397]     Test net output #0: accuracy = 0.103554
I0410 01:12:41.666510 18059 solver.cpp:397]     Test net output #1: loss = 3.87145 (* 1 = 3.87145 loss)
I0410 01:12:43.630210 18059 solver.cpp:218] Iteration 3576 (0.459049 iter/s, 26.141s/12 iters), loss = 4.22565
I0410 01:12:43.630260 18059 solver.cpp:237]     Train net output #0: loss = 4.22565 (* 1 = 4.22565 loss)
I0410 01:12:43.630271 18059 sgd_solver.cpp:105] Iteration 3576, lr = 0.00492454
I0410 01:12:48.616233 18059 solver.cpp:218] Iteration 3588 (2.40685 iter/s, 4.98578s/12 iters), loss = 3.83469
I0410 01:12:48.616294 18059 solver.cpp:237]     Train net output #0: loss = 3.83469 (* 1 = 3.83469 loss)
I0410 01:12:48.616312 18059 sgd_solver.cpp:105] Iteration 3588, lr = 0.00491284
I0410 01:12:53.227332 18059 solver.cpp:218] Iteration 3600 (2.60255 iter/s, 4.61087s/12 iters), loss = 3.9219
I0410 01:12:53.227378 18059 solver.cpp:237]     Train net output #0: loss = 3.9219 (* 1 = 3.9219 loss)
I0410 01:12:53.227388 18059 sgd_solver.cpp:105] Iteration 3600, lr = 0.00490118
I0410 01:12:57.964100 18059 solver.cpp:218] Iteration 3612 (2.53349 iter/s, 4.73654s/12 iters), loss = 3.96886
I0410 01:12:57.964144 18059 solver.cpp:237]     Train net output #0: loss = 3.96886 (* 1 = 3.96886 loss)
I0410 01:12:57.964154 18059 sgd_solver.cpp:105] Iteration 3612, lr = 0.00488954
I0410 01:13:02.943840 18059 solver.cpp:218] Iteration 3624 (2.40988 iter/s, 4.97951s/12 iters), loss = 3.64704
I0410 01:13:02.943884 18059 solver.cpp:237]     Train net output #0: loss = 3.64704 (* 1 = 3.64704 loss)
I0410 01:13:02.943893 18059 sgd_solver.cpp:105] Iteration 3624, lr = 0.00487793
I0410 01:13:07.762351 18059 solver.cpp:218] Iteration 3636 (2.49051 iter/s, 4.81829s/12 iters), loss = 4.04125
I0410 01:13:07.762389 18059 solver.cpp:237]     Train net output #0: loss = 4.04125 (* 1 = 4.04125 loss)
I0410 01:13:07.762398 18059 sgd_solver.cpp:105] Iteration 3636, lr = 0.00486635
I0410 01:13:09.490468 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:13:12.465762 18059 solver.cpp:218] Iteration 3648 (2.55146 iter/s, 4.70319s/12 iters), loss = 3.7685
I0410 01:13:12.465853 18059 solver.cpp:237]     Train net output #0: loss = 3.7685 (* 1 = 3.7685 loss)
I0410 01:13:12.465867 18059 sgd_solver.cpp:105] Iteration 3648, lr = 0.0048548
I0410 01:13:17.320051 18059 solver.cpp:218] Iteration 3660 (2.47218 iter/s, 4.85402s/12 iters), loss = 3.67849
I0410 01:13:17.320094 18059 solver.cpp:237]     Train net output #0: loss = 3.67849 (* 1 = 3.67849 loss)
I0410 01:13:17.320104 18059 sgd_solver.cpp:105] Iteration 3660, lr = 0.00484327
I0410 01:13:21.581805 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3672.caffemodel
I0410 01:13:28.353130 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3672.solverstate
I0410 01:13:33.494022 18059 solver.cpp:330] Iteration 3672, Testing net (#0)
I0410 01:13:33.494050 18059 net.cpp:676] Ignoring source layer train-data
I0410 01:13:36.578195 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:13:38.041188 18059 solver.cpp:397]     Test net output #0: accuracy = 0.096201
I0410 01:13:38.041218 18059 solver.cpp:397]     Test net output #1: loss = 3.85917 (* 1 = 3.85917 loss)
I0410 01:13:38.134744 18059 solver.cpp:218] Iteration 3672 (0.576537 iter/s, 20.8139s/12 iters), loss = 3.50235
I0410 01:13:38.134789 18059 solver.cpp:237]     Train net output #0: loss = 3.50235 (* 1 = 3.50235 loss)
I0410 01:13:38.134799 18059 sgd_solver.cpp:105] Iteration 3672, lr = 0.00483177
I0410 01:13:42.264101 18059 solver.cpp:218] Iteration 3684 (2.90617 iter/s, 4.12915s/12 iters), loss = 3.72633
I0410 01:13:42.264153 18059 solver.cpp:237]     Train net output #0: loss = 3.72633 (* 1 = 3.72633 loss)
I0410 01:13:42.264164 18059 sgd_solver.cpp:105] Iteration 3684, lr = 0.0048203
I0410 01:13:46.864428 18059 solver.cpp:218] Iteration 3696 (2.60864 iter/s, 4.6001s/12 iters), loss = 3.74149
I0410 01:13:46.864553 18059 solver.cpp:237]     Train net output #0: loss = 3.74149 (* 1 = 3.74149 loss)
I0410 01:13:46.864564 18059 sgd_solver.cpp:105] Iteration 3696, lr = 0.00480886
I0410 01:13:51.548489 18059 solver.cpp:218] Iteration 3708 (2.56204 iter/s, 4.68376s/12 iters), loss = 3.62765
I0410 01:13:51.548527 18059 solver.cpp:237]     Train net output #0: loss = 3.62765 (* 1 = 3.62765 loss)
I0410 01:13:51.548535 18059 sgd_solver.cpp:105] Iteration 3708, lr = 0.00479744
I0410 01:13:56.264884 18059 solver.cpp:218] Iteration 3720 (2.54443 iter/s, 4.71618s/12 iters), loss = 3.8047
I0410 01:13:56.264930 18059 solver.cpp:237]     Train net output #0: loss = 3.8047 (* 1 = 3.8047 loss)
I0410 01:13:56.264940 18059 sgd_solver.cpp:105] Iteration 3720, lr = 0.00478605
I0410 01:14:00.936937 18059 solver.cpp:218] Iteration 3732 (2.56859 iter/s, 4.67183s/12 iters), loss = 3.72726
I0410 01:14:00.936985 18059 solver.cpp:237]     Train net output #0: loss = 3.72726 (* 1 = 3.72726 loss)
I0410 01:14:00.936995 18059 sgd_solver.cpp:105] Iteration 3732, lr = 0.00477469
I0410 01:14:04.489564 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:14:05.386608 18059 solver.cpp:218] Iteration 3744 (2.69696 iter/s, 4.44945s/12 iters), loss = 3.7351
I0410 01:14:05.386664 18059 solver.cpp:237]     Train net output #0: loss = 3.7351 (* 1 = 3.7351 loss)
I0410 01:14:05.386677 18059 sgd_solver.cpp:105] Iteration 3744, lr = 0.00476335
I0410 01:14:09.942839 18059 solver.cpp:218] Iteration 3756 (2.63389 iter/s, 4.556s/12 iters), loss = 3.69497
I0410 01:14:09.942894 18059 solver.cpp:237]     Train net output #0: loss = 3.69497 (* 1 = 3.69497 loss)
I0410 01:14:09.942904 18059 sgd_solver.cpp:105] Iteration 3756, lr = 0.00475204
I0410 01:14:14.481048 18059 solver.cpp:218] Iteration 3768 (2.64435 iter/s, 4.53798s/12 iters), loss = 3.46165
I0410 01:14:14.481099 18059 solver.cpp:237]     Train net output #0: loss = 3.46165 (* 1 = 3.46165 loss)
I0410 01:14:14.481110 18059 sgd_solver.cpp:105] Iteration 3768, lr = 0.00474076
I0410 01:14:16.311795 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3774.caffemodel
I0410 01:14:22.777806 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3774.solverstate
I0410 01:14:28.918853 18059 solver.cpp:330] Iteration 3774, Testing net (#0)
I0410 01:14:28.918879 18059 net.cpp:676] Ignoring source layer train-data
I0410 01:14:31.971060 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:14:33.495069 18059 solver.cpp:397]     Test net output #0: accuracy = 0.0974265
I0410 01:14:33.495112 18059 solver.cpp:397]     Test net output #1: loss = 3.78119 (* 1 = 3.78119 loss)
I0410 01:14:35.359412 18059 solver.cpp:218] Iteration 3780 (0.574779 iter/s, 20.8776s/12 iters), loss = 3.61455
I0410 01:14:35.359452 18059 solver.cpp:237]     Train net output #0: loss = 3.61455 (* 1 = 3.61455 loss)
I0410 01:14:35.359460 18059 sgd_solver.cpp:105] Iteration 3780, lr = 0.00472951
I0410 01:14:40.281625 18059 solver.cpp:218] Iteration 3792 (2.43804 iter/s, 4.92199s/12 iters), loss = 3.69344
I0410 01:14:40.281663 18059 solver.cpp:237]     Train net output #0: loss = 3.69344 (* 1 = 3.69344 loss)
I0410 01:14:40.281672 18059 sgd_solver.cpp:105] Iteration 3792, lr = 0.00471828
I0410 01:14:45.023856 18059 solver.cpp:218] Iteration 3804 (2.53057 iter/s, 4.74201s/12 iters), loss = 3.53371
I0410 01:14:45.023901 18059 solver.cpp:237]     Train net output #0: loss = 3.53371 (* 1 = 3.53371 loss)
I0410 01:14:45.023911 18059 sgd_solver.cpp:105] Iteration 3804, lr = 0.00470707
I0410 01:14:49.882429 18059 solver.cpp:218] Iteration 3816 (2.46998 iter/s, 4.85834s/12 iters), loss = 3.6217
I0410 01:14:49.882475 18059 solver.cpp:237]     Train net output #0: loss = 3.6217 (* 1 = 3.6217 loss)
I0410 01:14:49.882486 18059 sgd_solver.cpp:105] Iteration 3816, lr = 0.0046959
I0410 01:14:55.192386 18059 solver.cpp:218] Iteration 3828 (2.26001 iter/s, 5.30971s/12 iters), loss = 3.36382
I0410 01:14:55.192509 18059 solver.cpp:237]     Train net output #0: loss = 3.36382 (* 1 = 3.36382 loss)
I0410 01:14:55.192518 18059 sgd_solver.cpp:105] Iteration 3828, lr = 0.00468475
I0410 01:14:59.812530 18059 solver.cpp:218] Iteration 3840 (2.59749 iter/s, 4.61985s/12 iters), loss = 3.80476
I0410 01:14:59.812585 18059 solver.cpp:237]     Train net output #0: loss = 3.80476 (* 1 = 3.80476 loss)
I0410 01:14:59.812597 18059 sgd_solver.cpp:105] Iteration 3840, lr = 0.00467363
I0410 01:15:00.821022 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:15:04.478979 18059 solver.cpp:218] Iteration 3852 (2.57168 iter/s, 4.66622s/12 iters), loss = 3.76762
I0410 01:15:04.479030 18059 solver.cpp:237]     Train net output #0: loss = 3.76762 (* 1 = 3.76762 loss)
I0410 01:15:04.479041 18059 sgd_solver.cpp:105] Iteration 3852, lr = 0.00466253
I0410 01:15:09.422071 18059 solver.cpp:218] Iteration 3864 (2.42775 iter/s, 4.94285s/12 iters), loss = 3.69406
I0410 01:15:09.422128 18059 solver.cpp:237]     Train net output #0: loss = 3.69406 (* 1 = 3.69406 loss)
I0410 01:15:09.422139 18059 sgd_solver.cpp:105] Iteration 3864, lr = 0.00465146
I0410 01:15:13.828281 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3876.caffemodel
I0410 01:15:21.101824 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3876.solverstate
I0410 01:15:25.038667 18059 solver.cpp:330] Iteration 3876, Testing net (#0)
I0410 01:15:25.038693 18059 net.cpp:676] Ignoring source layer train-data
I0410 01:15:27.927839 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:15:29.607000 18059 solver.cpp:397]     Test net output #0: accuracy = 0.102941
I0410 01:15:29.607029 18059 solver.cpp:397]     Test net output #1: loss = 3.69187 (* 1 = 3.69187 loss)
I0410 01:15:29.700115 18059 solver.cpp:218] Iteration 3876 (0.591795 iter/s, 20.2773s/12 iters), loss = 3.6158
I0410 01:15:29.700153 18059 solver.cpp:237]     Train net output #0: loss = 3.6158 (* 1 = 3.6158 loss)
I0410 01:15:29.700161 18059 sgd_solver.cpp:105] Iteration 3876, lr = 0.00464042
I0410 01:15:34.054273 18059 solver.cpp:218] Iteration 3888 (2.75612 iter/s, 4.35395s/12 iters), loss = 3.55478
I0410 01:15:34.054327 18059 solver.cpp:237]     Train net output #0: loss = 3.55478 (* 1 = 3.55478 loss)
I0410 01:15:34.054337 18059 sgd_solver.cpp:105] Iteration 3888, lr = 0.0046294
I0410 01:15:38.832896 18059 solver.cpp:218] Iteration 3900 (2.51131 iter/s, 4.77839s/12 iters), loss = 3.56052
I0410 01:15:38.832945 18059 solver.cpp:237]     Train net output #0: loss = 3.56052 (* 1 = 3.56052 loss)
I0410 01:15:38.832954 18059 sgd_solver.cpp:105] Iteration 3900, lr = 0.00461841
I0410 01:15:43.644707 18059 solver.cpp:218] Iteration 3912 (2.49398 iter/s, 4.81158s/12 iters), loss = 3.69847
I0410 01:15:43.644760 18059 solver.cpp:237]     Train net output #0: loss = 3.69847 (* 1 = 3.69847 loss)
I0410 01:15:43.644773 18059 sgd_solver.cpp:105] Iteration 3912, lr = 0.00460744
I0410 01:15:48.352031 18059 solver.cpp:218] Iteration 3924 (2.54934 iter/s, 4.70709s/12 iters), loss = 3.55977
I0410 01:15:48.352083 18059 solver.cpp:237]     Train net output #0: loss = 3.55977 (* 1 = 3.55977 loss)
I0410 01:15:48.352097 18059 sgd_solver.cpp:105] Iteration 3924, lr = 0.0045965
I0410 01:15:53.180064 18059 solver.cpp:218] Iteration 3936 (2.48561 iter/s, 4.8278s/12 iters), loss = 3.48971
I0410 01:15:53.180120 18059 solver.cpp:237]     Train net output #0: loss = 3.48971 (* 1 = 3.48971 loss)
I0410 01:15:53.180132 18059 sgd_solver.cpp:105] Iteration 3936, lr = 0.00458559
I0410 01:15:56.331799 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:15:57.986275 18059 solver.cpp:218] Iteration 3948 (2.49689 iter/s, 4.80597s/12 iters), loss = 3.59548
I0410 01:15:57.986439 18059 solver.cpp:237]     Train net output #0: loss = 3.59548 (* 1 = 3.59548 loss)
I0410 01:15:57.986454 18059 sgd_solver.cpp:105] Iteration 3948, lr = 0.0045747
I0410 01:16:02.831830 18059 solver.cpp:218] Iteration 3960 (2.47667 iter/s, 4.84521s/12 iters), loss = 3.53624
I0410 01:16:02.831882 18059 solver.cpp:237]     Train net output #0: loss = 3.53624 (* 1 = 3.53624 loss)
I0410 01:16:02.831893 18059 sgd_solver.cpp:105] Iteration 3960, lr = 0.00456384
I0410 01:16:07.695430 18059 solver.cpp:218] Iteration 3972 (2.46743 iter/s, 4.86337s/12 iters), loss = 3.60799
I0410 01:16:07.695472 18059 solver.cpp:237]     Train net output #0: loss = 3.60799 (* 1 = 3.60799 loss)
I0410 01:16:07.695482 18059 sgd_solver.cpp:105] Iteration 3972, lr = 0.00455301
I0410 01:16:09.704579 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3978.caffemodel
I0410 01:16:16.851742 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3978.solverstate
I0410 01:16:21.817359 18059 solver.cpp:330] Iteration 3978, Testing net (#0)
I0410 01:16:21.817385 18059 net.cpp:676] Ignoring source layer train-data
I0410 01:16:24.772452 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:16:26.366179 18059 solver.cpp:397]     Test net output #0: accuracy = 0.114583
I0410 01:16:26.366228 18059 solver.cpp:397]     Test net output #1: loss = 3.65727 (* 1 = 3.65727 loss)
I0410 01:16:27.916043 18059 solver.cpp:218] Iteration 3984 (0.593476 iter/s, 20.2199s/12 iters), loss = 3.52641
I0410 01:16:27.916103 18059 solver.cpp:237]     Train net output #0: loss = 3.52641 (* 1 = 3.52641 loss)
I0410 01:16:27.916115 18059 sgd_solver.cpp:105] Iteration 3984, lr = 0.0045422
I0410 01:16:32.684887 18059 solver.cpp:218] Iteration 3996 (2.51646 iter/s, 4.76861s/12 iters), loss = 3.50416
I0410 01:16:32.684960 18059 solver.cpp:237]     Train net output #0: loss = 3.50416 (* 1 = 3.50416 loss)
I0410 01:16:32.684971 18059 sgd_solver.cpp:105] Iteration 3996, lr = 0.00453141
I0410 01:16:37.303602 18059 solver.cpp:218] Iteration 4008 (2.59827 iter/s, 4.61847s/12 iters), loss = 3.59806
I0410 01:16:37.303653 18059 solver.cpp:237]     Train net output #0: loss = 3.59806 (* 1 = 3.59806 loss)
I0410 01:16:37.303664 18059 sgd_solver.cpp:105] Iteration 4008, lr = 0.00452066
I0410 01:16:41.888301 18059 solver.cpp:218] Iteration 4020 (2.61753 iter/s, 4.58448s/12 iters), loss = 3.5305
I0410 01:16:41.888339 18059 solver.cpp:237]     Train net output #0: loss = 3.5305 (* 1 = 3.5305 loss)
I0410 01:16:41.888348 18059 sgd_solver.cpp:105] Iteration 4020, lr = 0.00450992
I0410 01:16:46.633587 18059 solver.cpp:218] Iteration 4032 (2.52894 iter/s, 4.74507s/12 iters), loss = 3.45028
I0410 01:16:46.633641 18059 solver.cpp:237]     Train net output #0: loss = 3.45028 (* 1 = 3.45028 loss)
I0410 01:16:46.633652 18059 sgd_solver.cpp:105] Iteration 4032, lr = 0.00449921
I0410 01:16:51.099099 18059 solver.cpp:218] Iteration 4044 (2.68739 iter/s, 4.46529s/12 iters), loss = 3.40861
I0410 01:16:51.099138 18059 solver.cpp:237]     Train net output #0: loss = 3.40861 (* 1 = 3.40861 loss)
I0410 01:16:51.099146 18059 sgd_solver.cpp:105] Iteration 4044, lr = 0.00448853
I0410 01:16:51.620131 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:16:56.126698 18059 solver.cpp:218] Iteration 4056 (2.38693 iter/s, 5.02737s/12 iters), loss = 3.40482
I0410 01:16:56.126742 18059 solver.cpp:237]     Train net output #0: loss = 3.40482 (* 1 = 3.40482 loss)
I0410 01:16:56.126754 18059 sgd_solver.cpp:105] Iteration 4056, lr = 0.00447788
I0410 01:17:00.936892 18059 solver.cpp:218] Iteration 4068 (2.49482 iter/s, 4.80997s/12 iters), loss = 3.24697
I0410 01:17:00.936945 18059 solver.cpp:237]     Train net output #0: loss = 3.24697 (* 1 = 3.24697 loss)
I0410 01:17:00.936957 18059 sgd_solver.cpp:105] Iteration 4068, lr = 0.00446724
I0410 01:17:05.749135 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4080.caffemodel
I0410 01:17:10.289042 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4080.solverstate
I0410 01:17:14.821911 18059 solver.cpp:330] Iteration 4080, Testing net (#0)
I0410 01:17:14.821936 18059 net.cpp:676] Ignoring source layer train-data
I0410 01:17:17.700449 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:17:19.360188 18059 solver.cpp:397]     Test net output #0: accuracy = 0.128676
I0410 01:17:19.360244 18059 solver.cpp:397]     Test net output #1: loss = 3.60129 (* 1 = 3.60129 loss)
I0410 01:17:19.453831 18059 solver.cpp:218] Iteration 4080 (0.64808 iter/s, 18.5162s/12 iters), loss = 3.34294
I0410 01:17:19.453889 18059 solver.cpp:237]     Train net output #0: loss = 3.34294 (* 1 = 3.34294 loss)
I0410 01:17:19.453902 18059 sgd_solver.cpp:105] Iteration 4080, lr = 0.00445664
I0410 01:17:23.429661 18059 solver.cpp:218] Iteration 4092 (3.0184 iter/s, 3.97562s/12 iters), loss = 3.37909
I0410 01:17:23.429713 18059 solver.cpp:237]     Train net output #0: loss = 3.37909 (* 1 = 3.37909 loss)
I0410 01:17:23.429725 18059 sgd_solver.cpp:105] Iteration 4092, lr = 0.00444606
I0410 01:17:28.255861 18059 solver.cpp:218] Iteration 4104 (2.48655 iter/s, 4.82597s/12 iters), loss = 3.50402
I0410 01:17:28.255913 18059 solver.cpp:237]     Train net output #0: loss = 3.50402 (* 1 = 3.50402 loss)
I0410 01:17:28.255925 18059 sgd_solver.cpp:105] Iteration 4104, lr = 0.0044355
I0410 01:17:32.876432 18059 solver.cpp:218] Iteration 4116 (2.59721 iter/s, 4.62034s/12 iters), loss = 3.34654
I0410 01:17:32.876484 18059 solver.cpp:237]     Train net output #0: loss = 3.34654 (* 1 = 3.34654 loss)
I0410 01:17:32.876497 18059 sgd_solver.cpp:105] Iteration 4116, lr = 0.00442497
I0410 01:17:37.666317 18059 solver.cpp:218] Iteration 4128 (2.5054 iter/s, 4.78965s/12 iters), loss = 3.43989
I0410 01:17:37.666426 18059 solver.cpp:237]     Train net output #0: loss = 3.43989 (* 1 = 3.43989 loss)
I0410 01:17:37.666440 18059 sgd_solver.cpp:105] Iteration 4128, lr = 0.00441447
I0410 01:17:42.687079 18059 solver.cpp:218] Iteration 4140 (2.39022 iter/s, 5.02047s/12 iters), loss = 3.61688
I0410 01:17:42.687130 18059 solver.cpp:237]     Train net output #0: loss = 3.61688 (* 1 = 3.61688 loss)
I0410 01:17:42.687142 18059 sgd_solver.cpp:105] Iteration 4140, lr = 0.00440398
I0410 01:17:45.266206 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:17:47.561815 18059 solver.cpp:218] Iteration 4152 (2.46179 iter/s, 4.8745s/12 iters), loss = 3.31347
I0410 01:17:47.561856 18059 solver.cpp:237]     Train net output #0: loss = 3.31347 (* 1 = 3.31347 loss)
I0410 01:17:47.561864 18059 sgd_solver.cpp:105] Iteration 4152, lr = 0.00439353
I0410 01:17:49.160290 18059 blocking_queue.cpp:49] Waiting for data
I0410 01:17:52.734710 18059 solver.cpp:218] Iteration 4164 (2.31989 iter/s, 5.17266s/12 iters), loss = 3.21659
I0410 01:17:52.734760 18059 solver.cpp:237]     Train net output #0: loss = 3.21659 (* 1 = 3.21659 loss)
I0410 01:17:52.734771 18059 sgd_solver.cpp:105] Iteration 4164, lr = 0.0043831
I0410 01:17:57.675671 18059 solver.cpp:218] Iteration 4176 (2.4288 iter/s, 4.94072s/12 iters), loss = 3.08742
I0410 01:17:57.675725 18059 solver.cpp:237]     Train net output #0: loss = 3.08742 (* 1 = 3.08742 loss)
I0410 01:17:57.675738 18059 sgd_solver.cpp:105] Iteration 4176, lr = 0.00437269
I0410 01:17:59.869283 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4182.caffemodel
I0410 01:18:05.961796 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4182.solverstate
I0410 01:18:09.595314 18059 solver.cpp:330] Iteration 4182, Testing net (#0)
I0410 01:18:09.595386 18059 net.cpp:676] Ignoring source layer train-data
I0410 01:18:12.435575 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:18:14.118901 18059 solver.cpp:397]     Test net output #0: accuracy = 0.143382
I0410 01:18:14.118950 18059 solver.cpp:397]     Test net output #1: loss = 3.47111 (* 1 = 3.47111 loss)
I0410 01:18:15.657519 18059 solver.cpp:218] Iteration 4188 (0.667365 iter/s, 17.9812s/12 iters), loss = 3.2859
I0410 01:18:15.657570 18059 solver.cpp:237]     Train net output #0: loss = 3.2859 (* 1 = 3.2859 loss)
I0410 01:18:15.657582 18059 sgd_solver.cpp:105] Iteration 4188, lr = 0.00436231
I0410 01:18:20.461884 18059 solver.cpp:218] Iteration 4200 (2.49785 iter/s, 4.80413s/12 iters), loss = 3.41339
I0410 01:18:20.461932 18059 solver.cpp:237]     Train net output #0: loss = 3.41339 (* 1 = 3.41339 loss)
I0410 01:18:20.461943 18059 sgd_solver.cpp:105] Iteration 4200, lr = 0.00435195
I0410 01:18:24.888491 18059 solver.cpp:218] Iteration 4212 (2.71101 iter/s, 4.42639s/12 iters), loss = 3.12016
I0410 01:18:24.888540 18059 solver.cpp:237]     Train net output #0: loss = 3.12016 (* 1 = 3.12016 loss)
I0410 01:18:24.888551 18059 sgd_solver.cpp:105] Iteration 4212, lr = 0.00434162
I0410 01:18:29.633098 18059 solver.cpp:218] Iteration 4224 (2.52931 iter/s, 4.74438s/12 iters), loss = 3.24075
I0410 01:18:29.633144 18059 solver.cpp:237]     Train net output #0: loss = 3.24075 (* 1 = 3.24075 loss)
I0410 01:18:29.633152 18059 sgd_solver.cpp:105] Iteration 4224, lr = 0.00433131
I0410 01:18:34.451206 18059 solver.cpp:218] Iteration 4236 (2.49072 iter/s, 4.81788s/12 iters), loss = 3.31709
I0410 01:18:34.451252 18059 solver.cpp:237]     Train net output #0: loss = 3.31709 (* 1 = 3.31709 loss)
I0410 01:18:34.451262 18059 sgd_solver.cpp:105] Iteration 4236, lr = 0.00432103
I0410 01:18:38.864818 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:18:39.097195 18059 solver.cpp:218] Iteration 4248 (2.583 iter/s, 4.64577s/12 iters), loss = 3.30871
I0410 01:18:39.097237 18059 solver.cpp:237]     Train net output #0: loss = 3.30871 (* 1 = 3.30871 loss)
I0410 01:18:39.097246 18059 sgd_solver.cpp:105] Iteration 4248, lr = 0.00431077
I0410 01:18:44.118846 18059 solver.cpp:218] Iteration 4260 (2.38976 iter/s, 5.02142s/12 iters), loss = 3.357
I0410 01:18:44.118927 18059 solver.cpp:237]     Train net output #0: loss = 3.357 (* 1 = 3.357 loss)
I0410 01:18:44.118940 18059 sgd_solver.cpp:105] Iteration 4260, lr = 0.00430053
I0410 01:18:48.760481 18059 solver.cpp:218] Iteration 4272 (2.58544 iter/s, 4.64138s/12 iters), loss = 2.90621
I0410 01:18:48.760531 18059 solver.cpp:237]     Train net output #0: loss = 2.90621 (* 1 = 2.90621 loss)
I0410 01:18:48.760545 18059 sgd_solver.cpp:105] Iteration 4272, lr = 0.00429032
I0410 01:18:53.152644 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4284.caffemodel
I0410 01:18:57.803475 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4284.solverstate
I0410 01:19:01.452744 18059 solver.cpp:330] Iteration 4284, Testing net (#0)
I0410 01:19:01.452767 18059 net.cpp:676] Ignoring source layer train-data
I0410 01:19:04.234946 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:19:05.929749 18059 solver.cpp:397]     Test net output #0: accuracy = 0.142157
I0410 01:19:05.929782 18059 solver.cpp:397]     Test net output #1: loss = 3.45184 (* 1 = 3.45184 loss)
I0410 01:19:06.023108 18059 solver.cpp:218] Iteration 4284 (0.69517 iter/s, 17.262s/12 iters), loss = 3.35894
I0410 01:19:06.023147 18059 solver.cpp:237]     Train net output #0: loss = 3.35894 (* 1 = 3.35894 loss)
I0410 01:19:06.023154 18059 sgd_solver.cpp:105] Iteration 4284, lr = 0.00428014
I0410 01:19:10.049597 18059 solver.cpp:218] Iteration 4296 (2.98041 iter/s, 4.02629s/12 iters), loss = 3.27234
I0410 01:19:10.049645 18059 solver.cpp:237]     Train net output #0: loss = 3.27234 (* 1 = 3.27234 loss)
I0410 01:19:10.049656 18059 sgd_solver.cpp:105] Iteration 4296, lr = 0.00426998
I0410 01:19:14.619832 18059 solver.cpp:218] Iteration 4308 (2.62581 iter/s, 4.57001s/12 iters), loss = 3.17846
I0410 01:19:14.619979 18059 solver.cpp:237]     Train net output #0: loss = 3.17846 (* 1 = 3.17846 loss)
I0410 01:19:14.619992 18059 sgd_solver.cpp:105] Iteration 4308, lr = 0.00425984
I0410 01:19:19.153723 18059 solver.cpp:218] Iteration 4320 (2.64692 iter/s, 4.53357s/12 iters), loss = 3.42407
I0410 01:19:19.153776 18059 solver.cpp:237]     Train net output #0: loss = 3.42407 (* 1 = 3.42407 loss)
I0410 01:19:19.153787 18059 sgd_solver.cpp:105] Iteration 4320, lr = 0.00424972
I0410 01:19:23.540766 18059 solver.cpp:218] Iteration 4332 (2.73547 iter/s, 4.38682s/12 iters), loss = 3.43973
I0410 01:19:23.540819 18059 solver.cpp:237]     Train net output #0: loss = 3.43973 (* 1 = 3.43973 loss)
I0410 01:19:23.540832 18059 sgd_solver.cpp:105] Iteration 4332, lr = 0.00423964
I0410 01:19:28.341552 18059 solver.cpp:218] Iteration 4344 (2.49971 iter/s, 4.80056s/12 iters), loss = 3.30232
I0410 01:19:28.341594 18059 solver.cpp:237]     Train net output #0: loss = 3.30232 (* 1 = 3.30232 loss)
I0410 01:19:28.341604 18059 sgd_solver.cpp:105] Iteration 4344, lr = 0.00422957
I0410 01:19:30.083956 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:19:33.260027 18059 solver.cpp:218] Iteration 4356 (2.43989 iter/s, 4.91825s/12 iters), loss = 3.126
I0410 01:19:33.260079 18059 solver.cpp:237]     Train net output #0: loss = 3.126 (* 1 = 3.126 loss)
I0410 01:19:33.260092 18059 sgd_solver.cpp:105] Iteration 4356, lr = 0.00421953
I0410 01:19:37.821830 18059 solver.cpp:218] Iteration 4368 (2.63067 iter/s, 4.56157s/12 iters), loss = 3.2066
I0410 01:19:37.821952 18059 solver.cpp:237]     Train net output #0: loss = 3.2066 (* 1 = 3.2066 loss)
I0410 01:19:37.821979 18059 sgd_solver.cpp:105] Iteration 4368, lr = 0.00420951
I0410 01:19:42.248122 18059 solver.cpp:218] Iteration 4380 (2.71125 iter/s, 4.42601s/12 iters), loss = 3.08161
I0410 01:19:42.248160 18059 solver.cpp:237]     Train net output #0: loss = 3.08161 (* 1 = 3.08161 loss)
I0410 01:19:42.248168 18059 sgd_solver.cpp:105] Iteration 4380, lr = 0.00419952
I0410 01:19:43.986913 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4386.caffemodel
I0410 01:19:52.084911 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4386.solverstate
I0410 01:19:55.856266 18059 solver.cpp:330] Iteration 4386, Testing net (#0)
I0410 01:19:55.856292 18059 net.cpp:676] Ignoring source layer train-data
I0410 01:19:58.519977 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:20:00.280869 18059 solver.cpp:397]     Test net output #0: accuracy = 0.150735
I0410 01:20:00.280903 18059 solver.cpp:397]     Test net output #1: loss = 3.46139 (* 1 = 3.46139 loss)
I0410 01:20:02.210633 18059 solver.cpp:218] Iteration 4392 (0.601149 iter/s, 19.9618s/12 iters), loss = 3.35616
I0410 01:20:02.210688 18059 solver.cpp:237]     Train net output #0: loss = 3.35616 (* 1 = 3.35616 loss)
I0410 01:20:02.210700 18059 sgd_solver.cpp:105] Iteration 4392, lr = 0.00418954
I0410 01:20:06.897001 18059 solver.cpp:218] Iteration 4404 (2.56074 iter/s, 4.68614s/12 iters), loss = 3.06362
I0410 01:20:06.897053 18059 solver.cpp:237]     Train net output #0: loss = 3.06362 (* 1 = 3.06362 loss)
I0410 01:20:06.897068 18059 sgd_solver.cpp:105] Iteration 4404, lr = 0.0041796
I0410 01:20:11.720396 18059 solver.cpp:218] Iteration 4416 (2.48799 iter/s, 4.82316s/12 iters), loss = 3.26566
I0410 01:20:11.720439 18059 solver.cpp:237]     Train net output #0: loss = 3.26566 (* 1 = 3.26566 loss)
I0410 01:20:11.720448 18059 sgd_solver.cpp:105] Iteration 4416, lr = 0.00416967
I0410 01:20:16.605247 18059 solver.cpp:218] Iteration 4428 (2.45669 iter/s, 4.88462s/12 iters), loss = 3.21213
I0410 01:20:16.605293 18059 solver.cpp:237]     Train net output #0: loss = 3.21213 (* 1 = 3.21213 loss)
I0410 01:20:16.605302 18059 sgd_solver.cpp:105] Iteration 4428, lr = 0.00415977
I0410 01:20:21.360898 18059 solver.cpp:218] Iteration 4440 (2.52343 iter/s, 4.75543s/12 iters), loss = 3.05881
I0410 01:20:21.360944 18059 solver.cpp:237]     Train net output #0: loss = 3.05881 (* 1 = 3.05881 loss)
I0410 01:20:21.360955 18059 sgd_solver.cpp:105] Iteration 4440, lr = 0.0041499
I0410 01:20:25.414510 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:20:26.368268 18059 solver.cpp:218] Iteration 4452 (2.39658 iter/s, 5.00714s/12 iters), loss = 3.116
I0410 01:20:26.368297 18059 solver.cpp:237]     Train net output #0: loss = 3.116 (* 1 = 3.116 loss)
I0410 01:20:26.368305 18059 sgd_solver.cpp:105] Iteration 4452, lr = 0.00414005
I0410 01:20:31.026993 18059 solver.cpp:218] Iteration 4464 (2.57593 iter/s, 4.65852s/12 iters), loss = 3.27196
I0410 01:20:31.027035 18059 solver.cpp:237]     Train net output #0: loss = 3.27196 (* 1 = 3.27196 loss)
I0410 01:20:31.027047 18059 sgd_solver.cpp:105] Iteration 4464, lr = 0.00413022
I0410 01:20:35.898855 18059 solver.cpp:218] Iteration 4476 (2.46324 iter/s, 4.87163s/12 iters), loss = 3.00343
I0410 01:20:35.898911 18059 solver.cpp:237]     Train net output #0: loss = 3.00343 (* 1 = 3.00343 loss)
I0410 01:20:35.898922 18059 sgd_solver.cpp:105] Iteration 4476, lr = 0.00412041
I0410 01:20:40.088647 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4488.caffemodel
I0410 01:20:46.835909 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4488.solverstate
I0410 01:20:54.042542 18059 solver.cpp:330] Iteration 4488, Testing net (#0)
I0410 01:20:54.042570 18059 net.cpp:676] Ignoring source layer train-data
I0410 01:20:56.772102 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:20:58.759471 18059 solver.cpp:397]     Test net output #0: accuracy = 0.163603
I0410 01:20:58.759516 18059 solver.cpp:397]     Test net output #1: loss = 3.39305 (* 1 = 3.39305 loss)
I0410 01:20:58.853011 18059 solver.cpp:218] Iteration 4488 (0.522801 iter/s, 22.9533s/12 iters), loss = 2.84353
I0410 01:20:58.853058 18059 solver.cpp:237]     Train net output #0: loss = 2.84353 (* 1 = 2.84353 loss)
I0410 01:20:58.853068 18059 sgd_solver.cpp:105] Iteration 4488, lr = 0.00411063
I0410 01:21:02.994802 18059 solver.cpp:218] Iteration 4500 (2.89744 iter/s, 4.14159s/12 iters), loss = 2.98812
I0410 01:21:02.994843 18059 solver.cpp:237]     Train net output #0: loss = 2.98812 (* 1 = 2.98812 loss)
I0410 01:21:02.994853 18059 sgd_solver.cpp:105] Iteration 4500, lr = 0.00410087
I0410 01:21:07.689114 18059 solver.cpp:218] Iteration 4512 (2.55641 iter/s, 4.69409s/12 iters), loss = 3.0271
I0410 01:21:07.689163 18059 solver.cpp:237]     Train net output #0: loss = 3.0271 (* 1 = 3.0271 loss)
I0410 01:21:07.689174 18059 sgd_solver.cpp:105] Iteration 4512, lr = 0.00409113
I0410 01:21:12.280419 18059 solver.cpp:218] Iteration 4524 (2.61376 iter/s, 4.59108s/12 iters), loss = 3.06555
I0410 01:21:12.280475 18059 solver.cpp:237]     Train net output #0: loss = 3.06555 (* 1 = 3.06555 loss)
I0410 01:21:12.280488 18059 sgd_solver.cpp:105] Iteration 4524, lr = 0.00408142
I0410 01:21:16.867837 18059 solver.cpp:218] Iteration 4536 (2.61598 iter/s, 4.58718s/12 iters), loss = 2.8778
I0410 01:21:16.867894 18059 solver.cpp:237]     Train net output #0: loss = 2.8778 (* 1 = 2.8778 loss)
I0410 01:21:16.867906 18059 sgd_solver.cpp:105] Iteration 4536, lr = 0.00407173
I0410 01:21:21.737854 18059 solver.cpp:218] Iteration 4548 (2.46418 iter/s, 4.86978s/12 iters), loss = 3.24653
I0410 01:21:21.737898 18059 solver.cpp:237]     Train net output #0: loss = 3.24653 (* 1 = 3.24653 loss)
I0410 01:21:21.737907 18059 sgd_solver.cpp:105] Iteration 4548, lr = 0.00406206
I0410 01:21:22.836762 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:21:26.539816 18059 solver.cpp:218] Iteration 4560 (2.4991 iter/s, 4.80174s/12 iters), loss = 2.83792
I0410 01:21:26.539860 18059 solver.cpp:237]     Train net output #0: loss = 2.83792 (* 1 = 2.83792 loss)
I0410 01:21:26.539870 18059 sgd_solver.cpp:105] Iteration 4560, lr = 0.00405242
I0410 01:21:31.139655 18059 solver.cpp:218] Iteration 4572 (2.60891 iter/s, 4.59962s/12 iters), loss = 2.94523
I0410 01:21:31.139829 18059 solver.cpp:237]     Train net output #0: loss = 2.94523 (* 1 = 2.94523 loss)
I0410 01:21:31.139847 18059 sgd_solver.cpp:105] Iteration 4572, lr = 0.0040428
I0410 01:21:35.928061 18059 solver.cpp:218] Iteration 4584 (2.50623 iter/s, 4.78806s/12 iters), loss = 3.08512
I0410 01:21:35.928112 18059 solver.cpp:237]     Train net output #0: loss = 3.08512 (* 1 = 3.08512 loss)
I0410 01:21:35.928122 18059 sgd_solver.cpp:105] Iteration 4584, lr = 0.0040332
I0410 01:21:37.826575 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4590.caffemodel
I0410 01:21:42.456950 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4590.solverstate
I0410 01:21:48.021312 18059 solver.cpp:330] Iteration 4590, Testing net (#0)
I0410 01:21:48.021333 18059 net.cpp:676] Ignoring source layer train-data
I0410 01:21:50.662634 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:21:52.487380 18059 solver.cpp:397]     Test net output #0: accuracy = 0.170343
I0410 01:21:52.487417 18059 solver.cpp:397]     Test net output #1: loss = 3.36882 (* 1 = 3.36882 loss)
I0410 01:21:54.125560 18059 solver.cpp:218] Iteration 4596 (0.659457 iter/s, 18.1968s/12 iters), loss = 2.9979
I0410 01:21:54.125617 18059 solver.cpp:237]     Train net output #0: loss = 2.9979 (* 1 = 2.9979 loss)
I0410 01:21:54.125630 18059 sgd_solver.cpp:105] Iteration 4596, lr = 0.00402362
I0410 01:21:59.033020 18059 solver.cpp:218] Iteration 4608 (2.44538 iter/s, 4.90722s/12 iters), loss = 3.11983
I0410 01:21:59.033068 18059 solver.cpp:237]     Train net output #0: loss = 3.11983 (* 1 = 3.11983 loss)
I0410 01:21:59.033079 18059 sgd_solver.cpp:105] Iteration 4608, lr = 0.00401407
I0410 01:22:03.678396 18059 solver.cpp:218] Iteration 4620 (2.58334 iter/s, 4.64515s/12 iters), loss = 3.08261
I0410 01:22:03.678505 18059 solver.cpp:237]     Train net output #0: loss = 3.08261 (* 1 = 3.08261 loss)
I0410 01:22:03.678515 18059 sgd_solver.cpp:105] Iteration 4620, lr = 0.00400454
I0410 01:22:08.511658 18059 solver.cpp:218] Iteration 4632 (2.48295 iter/s, 4.83297s/12 iters), loss = 3.03753
I0410 01:22:08.511713 18059 solver.cpp:237]     Train net output #0: loss = 3.03753 (* 1 = 3.03753 loss)
I0410 01:22:08.511723 18059 sgd_solver.cpp:105] Iteration 4632, lr = 0.00399503
I0410 01:22:13.165159 18059 solver.cpp:218] Iteration 4644 (2.57883 iter/s, 4.65328s/12 iters), loss = 2.92869
I0410 01:22:13.165203 18059 solver.cpp:237]     Train net output #0: loss = 2.92869 (* 1 = 2.92869 loss)
I0410 01:22:13.165213 18059 sgd_solver.cpp:105] Iteration 4644, lr = 0.00398555
I0410 01:22:16.310369 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:22:17.910192 18059 solver.cpp:218] Iteration 4656 (2.52908 iter/s, 4.74481s/12 iters), loss = 3.05116
I0410 01:22:17.910246 18059 solver.cpp:237]     Train net output #0: loss = 3.05116 (* 1 = 3.05116 loss)
I0410 01:22:17.910259 18059 sgd_solver.cpp:105] Iteration 4656, lr = 0.00397608
I0410 01:22:22.662132 18059 solver.cpp:218] Iteration 4668 (2.52541 iter/s, 4.75171s/12 iters), loss = 2.84371
I0410 01:22:22.662181 18059 solver.cpp:237]     Train net output #0: loss = 2.84371 (* 1 = 2.84371 loss)
I0410 01:22:22.662192 18059 sgd_solver.cpp:105] Iteration 4668, lr = 0.00396664
I0410 01:22:27.437358 18059 solver.cpp:218] Iteration 4680 (2.51309 iter/s, 4.775s/12 iters), loss = 3.05161
I0410 01:22:27.437410 18059 solver.cpp:237]     Train net output #0: loss = 3.05161 (* 1 = 3.05161 loss)
I0410 01:22:27.437422 18059 sgd_solver.cpp:105] Iteration 4680, lr = 0.00395723
I0410 01:22:31.728238 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4692.caffemodel
I0410 01:22:36.359783 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4692.solverstate
I0410 01:22:40.030943 18059 solver.cpp:330] Iteration 4692, Testing net (#0)
I0410 01:22:40.030969 18059 net.cpp:676] Ignoring source layer train-data
I0410 01:22:42.624641 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:22:44.531797 18059 solver.cpp:397]     Test net output #0: accuracy = 0.189338
I0410 01:22:44.531839 18059 solver.cpp:397]     Test net output #1: loss = 3.17761 (* 1 = 3.17761 loss)
I0410 01:22:44.625344 18059 solver.cpp:218] Iteration 4692 (0.698189 iter/s, 17.1873s/12 iters), loss = 2.92142
I0410 01:22:44.625391 18059 solver.cpp:237]     Train net output #0: loss = 2.92142 (* 1 = 2.92142 loss)
I0410 01:22:44.625401 18059 sgd_solver.cpp:105] Iteration 4692, lr = 0.00394783
I0410 01:22:48.747736 18059 solver.cpp:218] Iteration 4704 (2.91108 iter/s, 4.12219s/12 iters), loss = 2.81517
I0410 01:22:48.747787 18059 solver.cpp:237]     Train net output #0: loss = 2.81517 (* 1 = 2.81517 loss)
I0410 01:22:48.747799 18059 sgd_solver.cpp:105] Iteration 4704, lr = 0.00393846
I0410 01:22:53.414214 18059 solver.cpp:218] Iteration 4716 (2.57166 iter/s, 4.66625s/12 iters), loss = 2.88287
I0410 01:22:53.414263 18059 solver.cpp:237]     Train net output #0: loss = 2.88287 (* 1 = 2.88287 loss)
I0410 01:22:53.414273 18059 sgd_solver.cpp:105] Iteration 4716, lr = 0.00392911
I0410 01:22:58.200201 18059 solver.cpp:218] Iteration 4728 (2.50744 iter/s, 4.78576s/12 iters), loss = 2.88555
I0410 01:22:58.200237 18059 solver.cpp:237]     Train net output #0: loss = 2.88555 (* 1 = 2.88555 loss)
I0410 01:22:58.200246 18059 sgd_solver.cpp:105] Iteration 4728, lr = 0.00391978
I0410 01:23:03.044214 18059 solver.cpp:218] Iteration 4740 (2.4774 iter/s, 4.84379s/12 iters), loss = 2.8638
I0410 01:23:03.044265 18059 solver.cpp:237]     Train net output #0: loss = 2.8638 (* 1 = 2.8638 loss)
I0410 01:23:03.044276 18059 sgd_solver.cpp:105] Iteration 4740, lr = 0.00391047
I0410 01:23:07.778772 18059 solver.cpp:218] Iteration 4752 (2.53468 iter/s, 4.73433s/12 iters), loss = 2.91346
I0410 01:23:07.778906 18059 solver.cpp:237]     Train net output #0: loss = 2.91346 (* 1 = 2.91346 loss)
I0410 01:23:07.778918 18059 sgd_solver.cpp:105] Iteration 4752, lr = 0.00390119
I0410 01:23:08.312341 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:23:12.605258 18059 solver.cpp:218] Iteration 4764 (2.48644 iter/s, 4.82617s/12 iters), loss = 2.88372
I0410 01:23:12.605314 18059 solver.cpp:237]     Train net output #0: loss = 2.88372 (* 1 = 2.88372 loss)
I0410 01:23:12.605327 18059 sgd_solver.cpp:105] Iteration 4764, lr = 0.00389193
I0410 01:23:17.460397 18059 solver.cpp:218] Iteration 4776 (2.47173 iter/s, 4.8549s/12 iters), loss = 2.69818
I0410 01:23:17.460443 18059 solver.cpp:237]     Train net output #0: loss = 2.69818 (* 1 = 2.69818 loss)
I0410 01:23:17.460453 18059 sgd_solver.cpp:105] Iteration 4776, lr = 0.00388269
I0410 01:23:22.952420 18059 solver.cpp:218] Iteration 4788 (2.18509 iter/s, 5.49177s/12 iters), loss = 2.72372
I0410 01:23:22.952469 18059 solver.cpp:237]     Train net output #0: loss = 2.72372 (* 1 = 2.72372 loss)
I0410 01:23:22.952481 18059 sgd_solver.cpp:105] Iteration 4788, lr = 0.00387347
I0410 01:23:25.009927 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4794.caffemodel
I0410 01:23:29.661383 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4794.solverstate
I0410 01:23:33.321996 18059 solver.cpp:330] Iteration 4794, Testing net (#0)
I0410 01:23:33.322021 18059 net.cpp:676] Ignoring source layer train-data
I0410 01:23:35.839694 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:23:37.790211 18059 solver.cpp:397]     Test net output #0: accuracy = 0.19424
I0410 01:23:37.790273 18059 solver.cpp:397]     Test net output #1: loss = 3.19466 (* 1 = 3.19466 loss)
I0410 01:23:39.650331 18059 solver.cpp:218] Iteration 4800 (0.71868 iter/s, 16.6973s/12 iters), loss = 2.62514
I0410 01:23:39.650378 18059 solver.cpp:237]     Train net output #0: loss = 2.62514 (* 1 = 2.62514 loss)
I0410 01:23:39.650386 18059 sgd_solver.cpp:105] Iteration 4800, lr = 0.00386427
I0410 01:23:44.324302 18059 solver.cpp:218] Iteration 4812 (2.56753 iter/s, 4.67375s/12 iters), loss = 2.89663
I0410 01:23:44.324363 18059 solver.cpp:237]     Train net output #0: loss = 2.89663 (* 1 = 2.89663 loss)
I0410 01:23:44.324379 18059 sgd_solver.cpp:105] Iteration 4812, lr = 0.0038551
I0410 01:23:49.261354 18059 solver.cpp:218] Iteration 4824 (2.43072 iter/s, 4.93681s/12 iters), loss = 2.84081
I0410 01:23:49.261409 18059 solver.cpp:237]     Train net output #0: loss = 2.84081 (* 1 = 2.84081 loss)
I0410 01:23:49.261422 18059 sgd_solver.cpp:105] Iteration 4824, lr = 0.00384594
I0410 01:23:54.070226 18059 solver.cpp:218] Iteration 4836 (2.49551 iter/s, 4.80864s/12 iters), loss = 2.63984
I0410 01:23:54.070274 18059 solver.cpp:237]     Train net output #0: loss = 2.63984 (* 1 = 2.63984 loss)
I0410 01:23:54.070287 18059 sgd_solver.cpp:105] Iteration 4836, lr = 0.00383681
I0410 01:23:56.129038 18059 blocking_queue.cpp:49] Waiting for data
I0410 01:23:59.085466 18059 solver.cpp:218] Iteration 4848 (2.39282 iter/s, 5.015s/12 iters), loss = 3.15821
I0410 01:23:59.085518 18059 solver.cpp:237]     Train net output #0: loss = 3.15821 (* 1 = 3.15821 loss)
I0410 01:23:59.085530 18059 sgd_solver.cpp:105] Iteration 4848, lr = 0.0038277
I0410 01:24:01.990471 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:24:04.215730 18059 solver.cpp:218] Iteration 4860 (2.33917 iter/s, 5.13002s/12 iters), loss = 2.75481
I0410 01:24:04.215781 18059 solver.cpp:237]     Train net output #0: loss = 2.75481 (* 1 = 2.75481 loss)
I0410 01:24:04.215792 18059 sgd_solver.cpp:105] Iteration 4860, lr = 0.00381862
I0410 01:24:08.807966 18059 solver.cpp:218] Iteration 4872 (2.61324 iter/s, 4.59201s/12 iters), loss = 2.99803
I0410 01:24:08.808144 18059 solver.cpp:237]     Train net output #0: loss = 2.99803 (* 1 = 2.99803 loss)
I0410 01:24:08.808158 18059 sgd_solver.cpp:105] Iteration 4872, lr = 0.00380955
I0410 01:24:13.603354 18059 solver.cpp:218] Iteration 4884 (2.50259 iter/s, 4.79503s/12 iters), loss = 2.87746
I0410 01:24:13.603399 18059 solver.cpp:237]     Train net output #0: loss = 2.87746 (* 1 = 2.87746 loss)
I0410 01:24:13.603407 18059 sgd_solver.cpp:105] Iteration 4884, lr = 0.0038005
I0410 01:24:17.810572 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4896.caffemodel
I0410 01:24:24.445948 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4896.solverstate
I0410 01:24:28.132711 18059 solver.cpp:330] Iteration 4896, Testing net (#0)
I0410 01:24:28.132738 18059 net.cpp:676] Ignoring source layer train-data
I0410 01:24:30.621683 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:24:32.558923 18059 solver.cpp:397]     Test net output #0: accuracy = 0.196078
I0410 01:24:32.558970 18059 solver.cpp:397]     Test net output #1: loss = 3.21629 (* 1 = 3.21629 loss)
I0410 01:24:32.654440 18059 solver.cpp:218] Iteration 4896 (0.629909 iter/s, 19.0504s/12 iters), loss = 2.78908
I0410 01:24:32.654491 18059 solver.cpp:237]     Train net output #0: loss = 2.78908 (* 1 = 2.78908 loss)
I0410 01:24:32.654501 18059 sgd_solver.cpp:105] Iteration 4896, lr = 0.00379148
I0410 01:24:36.651921 18059 solver.cpp:218] Iteration 4908 (3.00205 iter/s, 3.99727s/12 iters), loss = 2.72388
I0410 01:24:36.651970 18059 solver.cpp:237]     Train net output #0: loss = 2.72388 (* 1 = 2.72388 loss)
I0410 01:24:36.651981 18059 sgd_solver.cpp:105] Iteration 4908, lr = 0.00378248
I0410 01:24:41.347048 18059 solver.cpp:218] Iteration 4920 (2.55597 iter/s, 4.6949s/12 iters), loss = 2.65978
I0410 01:24:41.347138 18059 solver.cpp:237]     Train net output #0: loss = 2.65978 (* 1 = 2.65978 loss)
I0410 01:24:41.347151 18059 sgd_solver.cpp:105] Iteration 4920, lr = 0.0037735
I0410 01:24:46.108656 18059 solver.cpp:218] Iteration 4932 (2.5203 iter/s, 4.76134s/12 iters), loss = 3.05197
I0410 01:24:46.108708 18059 solver.cpp:237]     Train net output #0: loss = 3.05197 (* 1 = 3.05197 loss)
I0410 01:24:46.108719 18059 sgd_solver.cpp:105] Iteration 4932, lr = 0.00376454
I0410 01:24:51.327633 18059 solver.cpp:218] Iteration 4944 (2.29941 iter/s, 5.21873s/12 iters), loss = 2.8545
I0410 01:24:51.327685 18059 solver.cpp:237]     Train net output #0: loss = 2.8545 (* 1 = 2.8545 loss)
I0410 01:24:51.327697 18059 sgd_solver.cpp:105] Iteration 4944, lr = 0.0037556
I0410 01:24:55.682432 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:24:55.889612 18059 solver.cpp:218] Iteration 4956 (2.63057 iter/s, 4.56176s/12 iters), loss = 2.85854
I0410 01:24:55.889655 18059 solver.cpp:237]     Train net output #0: loss = 2.85854 (* 1 = 2.85854 loss)
I0410 01:24:55.889664 18059 sgd_solver.cpp:105] Iteration 4956, lr = 0.00374669
I0410 01:25:00.497987 18059 solver.cpp:218] Iteration 4968 (2.60409 iter/s, 4.60814s/12 iters), loss = 2.70084
I0410 01:25:00.498041 18059 solver.cpp:237]     Train net output #0: loss = 2.70084 (* 1 = 2.70084 loss)
I0410 01:25:00.498054 18059 sgd_solver.cpp:105] Iteration 4968, lr = 0.00373779
I0410 01:25:05.118717 18059 solver.cpp:218] Iteration 4980 (2.59712 iter/s, 4.62051s/12 iters), loss = 2.40519
I0410 01:25:05.118757 18059 solver.cpp:237]     Train net output #0: loss = 2.40519 (* 1 = 2.40519 loss)
I0410 01:25:05.118765 18059 sgd_solver.cpp:105] Iteration 4980, lr = 0.00372892
I0410 01:25:09.978770 18059 solver.cpp:218] Iteration 4992 (2.46922 iter/s, 4.85983s/12 iters), loss = 2.82387
I0410 01:25:09.978811 18059 solver.cpp:237]     Train net output #0: loss = 2.82387 (* 1 = 2.82387 loss)
I0410 01:25:09.978818 18059 sgd_solver.cpp:105] Iteration 4992, lr = 0.00372006
I0410 01:25:11.873479 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4998.caffemodel
I0410 01:25:21.208525 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4998.solverstate
I0410 01:25:28.864076 18059 solver.cpp:330] Iteration 4998, Testing net (#0)
I0410 01:25:28.864104 18059 net.cpp:676] Ignoring source layer train-data
I0410 01:25:31.312500 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:25:33.285761 18059 solver.cpp:397]     Test net output #0: accuracy = 0.207108
I0410 01:25:33.285804 18059 solver.cpp:397]     Test net output #1: loss = 3.10772 (* 1 = 3.10772 loss)
I0410 01:25:34.885273 18059 solver.cpp:218] Iteration 5004 (0.48182 iter/s, 24.9056s/12 iters), loss = 2.50038
I0410 01:25:34.885332 18059 solver.cpp:237]     Train net output #0: loss = 2.50038 (* 1 = 2.50038 loss)
I0410 01:25:34.885346 18059 sgd_solver.cpp:105] Iteration 5004, lr = 0.00371123
I0410 01:25:39.560037 18059 solver.cpp:218] Iteration 5016 (2.5671 iter/s, 4.67453s/12 iters), loss = 2.63017
I0410 01:25:39.560081 18059 solver.cpp:237]     Train net output #0: loss = 2.63017 (* 1 = 2.63017 loss)
I0410 01:25:39.560089 18059 sgd_solver.cpp:105] Iteration 5016, lr = 0.00370242
I0410 01:25:44.074903 18059 solver.cpp:218] Iteration 5028 (2.65801 iter/s, 4.51465s/12 iters), loss = 2.83279
I0410 01:25:44.074990 18059 solver.cpp:237]     Train net output #0: loss = 2.83279 (* 1 = 2.83279 loss)
I0410 01:25:44.075001 18059 sgd_solver.cpp:105] Iteration 5028, lr = 0.00369363
I0410 01:25:48.729586 18059 solver.cpp:218] Iteration 5040 (2.5782 iter/s, 4.65442s/12 iters), loss = 2.77611
I0410 01:25:48.729632 18059 solver.cpp:237]     Train net output #0: loss = 2.77611 (* 1 = 2.77611 loss)
I0410 01:25:48.729642 18059 sgd_solver.cpp:105] Iteration 5040, lr = 0.00368486
I0410 01:25:53.480906 18059 solver.cpp:218] Iteration 5052 (2.52574 iter/s, 4.75109s/12 iters), loss = 2.63577
I0410 01:25:53.480957 18059 solver.cpp:237]     Train net output #0: loss = 2.63577 (* 1 = 2.63577 loss)
I0410 01:25:53.480968 18059 sgd_solver.cpp:105] Iteration 5052, lr = 0.00367611
I0410 01:25:55.256978 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:25:58.013393 18059 solver.cpp:218] Iteration 5064 (2.64768 iter/s, 4.53226s/12 iters), loss = 2.72034
I0410 01:25:58.013442 18059 solver.cpp:237]     Train net output #0: loss = 2.72034 (* 1 = 2.72034 loss)
I0410 01:25:58.013454 18059 sgd_solver.cpp:105] Iteration 5064, lr = 0.00366738
I0410 01:26:02.621212 18059 solver.cpp:218] Iteration 5076 (2.6044 iter/s, 4.60759s/12 iters), loss = 2.65356
I0410 01:26:02.621264 18059 solver.cpp:237]     Train net output #0: loss = 2.65356 (* 1 = 2.65356 loss)
I0410 01:26:02.621276 18059 sgd_solver.cpp:105] Iteration 5076, lr = 0.00365868
I0410 01:26:07.271872 18059 solver.cpp:218] Iteration 5088 (2.58041 iter/s, 4.65043s/12 iters), loss = 2.54894
I0410 01:26:07.271925 18059 solver.cpp:237]     Train net output #0: loss = 2.54894 (* 1 = 2.54894 loss)
I0410 01:26:07.271939 18059 sgd_solver.cpp:105] Iteration 5088, lr = 0.00364999
I0410 01:26:11.580296 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5100.caffemodel
I0410 01:26:25.490357 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5100.solverstate
I0410 01:26:29.151541 18059 solver.cpp:330] Iteration 5100, Testing net (#0)
I0410 01:26:29.151563 18059 net.cpp:676] Ignoring source layer train-data
I0410 01:26:31.637656 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:26:33.714365 18059 solver.cpp:397]     Test net output #0: accuracy = 0.226716
I0410 01:26:33.714403 18059 solver.cpp:397]     Test net output #1: loss = 3.05625 (* 1 = 3.05625 loss)
I0410 01:26:33.807771 18059 solver.cpp:218] Iteration 5100 (0.452234 iter/s, 26.5349s/12 iters), loss = 2.83502
I0410 01:26:33.807804 18059 solver.cpp:237]     Train net output #0: loss = 2.83502 (* 1 = 2.83502 loss)
I0410 01:26:33.807814 18059 sgd_solver.cpp:105] Iteration 5100, lr = 0.00364132
I0410 01:26:37.749575 18059 solver.cpp:218] Iteration 5112 (3.04444 iter/s, 3.94161s/12 iters), loss = 2.48989
I0410 01:26:37.749620 18059 solver.cpp:237]     Train net output #0: loss = 2.48989 (* 1 = 2.48989 loss)
I0410 01:26:37.749630 18059 sgd_solver.cpp:105] Iteration 5112, lr = 0.00363268
I0410 01:26:42.300151 18059 solver.cpp:218] Iteration 5124 (2.63716 iter/s, 4.55035s/12 iters), loss = 2.52912
I0410 01:26:42.300221 18059 solver.cpp:237]     Train net output #0: loss = 2.52912 (* 1 = 2.52912 loss)
I0410 01:26:42.300238 18059 sgd_solver.cpp:105] Iteration 5124, lr = 0.00362405
I0410 01:26:46.942731 18059 solver.cpp:218] Iteration 5136 (2.58491 iter/s, 4.64234s/12 iters), loss = 2.66904
I0410 01:26:46.942783 18059 solver.cpp:237]     Train net output #0: loss = 2.66904 (* 1 = 2.66904 loss)
I0410 01:26:46.942795 18059 sgd_solver.cpp:105] Iteration 5136, lr = 0.00361545
I0410 01:26:51.606801 18059 solver.cpp:218] Iteration 5148 (2.57299 iter/s, 4.66384s/12 iters), loss = 2.61774
I0410 01:26:51.606859 18059 solver.cpp:237]     Train net output #0: loss = 2.61774 (* 1 = 2.61774 loss)
I0410 01:26:51.606870 18059 sgd_solver.cpp:105] Iteration 5148, lr = 0.00360687
I0410 01:26:55.250391 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:26:56.096640 18059 solver.cpp:218] Iteration 5160 (2.67284 iter/s, 4.48961s/12 iters), loss = 2.48446
I0410 01:26:56.096750 18059 solver.cpp:237]     Train net output #0: loss = 2.48446 (* 1 = 2.48446 loss)
I0410 01:26:56.096765 18059 sgd_solver.cpp:105] Iteration 5160, lr = 0.0035983
I0410 01:27:00.940237 18059 solver.cpp:218] Iteration 5172 (2.47764 iter/s, 4.84331s/12 iters), loss = 2.7541
I0410 01:27:00.940275 18059 solver.cpp:237]     Train net output #0: loss = 2.7541 (* 1 = 2.7541 loss)
I0410 01:27:00.940284 18059 sgd_solver.cpp:105] Iteration 5172, lr = 0.00358976
I0410 01:27:05.712086 18059 solver.cpp:218] Iteration 5184 (2.51487 iter/s, 4.77162s/12 iters), loss = 2.81382
I0410 01:27:05.712144 18059 solver.cpp:237]     Train net output #0: loss = 2.81382 (* 1 = 2.81382 loss)
I0410 01:27:05.712157 18059 sgd_solver.cpp:105] Iteration 5184, lr = 0.00358124
I0410 01:27:10.238565 18059 solver.cpp:218] Iteration 5196 (2.6512 iter/s, 4.52625s/12 iters), loss = 2.6014
I0410 01:27:10.238610 18059 solver.cpp:237]     Train net output #0: loss = 2.6014 (* 1 = 2.6014 loss)
I0410 01:27:10.238617 18059 sgd_solver.cpp:105] Iteration 5196, lr = 0.00357273
I0410 01:27:12.133159 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5202.caffemodel
I0410 01:27:22.547782 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5202.solverstate
I0410 01:27:27.157750 18059 solver.cpp:330] Iteration 5202, Testing net (#0)
I0410 01:27:27.157805 18059 net.cpp:676] Ignoring source layer train-data
I0410 01:27:29.579838 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:27:31.633805 18059 solver.cpp:397]     Test net output #0: accuracy = 0.224265
I0410 01:27:31.633841 18059 solver.cpp:397]     Test net output #1: loss = 3.06796 (* 1 = 3.06796 loss)
I0410 01:27:33.535681 18059 solver.cpp:218] Iteration 5208 (0.515104 iter/s, 23.2962s/12 iters), loss = 2.53444
I0410 01:27:33.535732 18059 solver.cpp:237]     Train net output #0: loss = 2.53444 (* 1 = 2.53444 loss)
I0410 01:27:33.535742 18059 sgd_solver.cpp:105] Iteration 5208, lr = 0.00356425
I0410 01:27:38.245569 18059 solver.cpp:218] Iteration 5220 (2.54796 iter/s, 4.70966s/12 iters), loss = 2.60309
I0410 01:27:38.245611 18059 solver.cpp:237]     Train net output #0: loss = 2.60309 (* 1 = 2.60309 loss)
I0410 01:27:38.245620 18059 sgd_solver.cpp:105] Iteration 5220, lr = 0.00355579
I0410 01:27:42.885679 18059 solver.cpp:218] Iteration 5232 (2.58627 iter/s, 4.63989s/12 iters), loss = 2.54754
I0410 01:27:42.885735 18059 solver.cpp:237]     Train net output #0: loss = 2.54754 (* 1 = 2.54754 loss)
I0410 01:27:42.885749 18059 sgd_solver.cpp:105] Iteration 5232, lr = 0.00354735
I0410 01:27:47.533118 18059 solver.cpp:218] Iteration 5244 (2.58219 iter/s, 4.64721s/12 iters), loss = 2.50194
I0410 01:27:47.533166 18059 solver.cpp:237]     Train net output #0: loss = 2.50194 (* 1 = 2.50194 loss)
I0410 01:27:47.533176 18059 sgd_solver.cpp:105] Iteration 5244, lr = 0.00353892
I0410 01:27:52.194316 18059 solver.cpp:218] Iteration 5256 (2.57457 iter/s, 4.66097s/12 iters), loss = 2.33618
I0410 01:27:52.194371 18059 solver.cpp:237]     Train net output #0: loss = 2.33618 (* 1 = 2.33618 loss)
I0410 01:27:52.194384 18059 sgd_solver.cpp:105] Iteration 5256, lr = 0.00353052
I0410 01:27:53.353747 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:27:57.107033 18059 solver.cpp:218] Iteration 5268 (2.44276 iter/s, 4.91248s/12 iters), loss = 2.49063
I0410 01:27:57.107086 18059 solver.cpp:237]     Train net output #0: loss = 2.49063 (* 1 = 2.49063 loss)
I0410 01:27:57.107100 18059 sgd_solver.cpp:105] Iteration 5268, lr = 0.00352214
I0410 01:28:01.625946 18059 solver.cpp:218] Iteration 5280 (2.65564 iter/s, 4.51869s/12 iters), loss = 2.57916
I0410 01:28:01.626087 18059 solver.cpp:237]     Train net output #0: loss = 2.57916 (* 1 = 2.57916 loss)
I0410 01:28:01.626101 18059 sgd_solver.cpp:105] Iteration 5280, lr = 0.00351378
I0410 01:28:06.379643 18059 solver.cpp:218] Iteration 5292 (2.52452 iter/s, 4.75338s/12 iters), loss = 2.50364
I0410 01:28:06.379695 18059 solver.cpp:237]     Train net output #0: loss = 2.50364 (* 1 = 2.50364 loss)
I0410 01:28:06.379706 18059 sgd_solver.cpp:105] Iteration 5292, lr = 0.00350544
I0410 01:28:11.245605 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5304.caffemodel
I0410 01:28:18.554744 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5304.solverstate
I0410 01:28:24.273053 18059 solver.cpp:330] Iteration 5304, Testing net (#0)
I0410 01:28:24.273073 18059 net.cpp:676] Ignoring source layer train-data
I0410 01:28:26.641260 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:28:28.741554 18059 solver.cpp:397]     Test net output #0: accuracy = 0.226103
I0410 01:28:28.741587 18059 solver.cpp:397]     Test net output #1: loss = 3.03432 (* 1 = 3.03432 loss)
I0410 01:28:28.835078 18059 solver.cpp:218] Iteration 5304 (0.534412 iter/s, 22.4546s/12 iters), loss = 2.51925
I0410 01:28:28.835124 18059 solver.cpp:237]     Train net output #0: loss = 2.51925 (* 1 = 2.51925 loss)
I0410 01:28:28.835134 18059 sgd_solver.cpp:105] Iteration 5304, lr = 0.00349711
I0410 01:28:32.756569 18059 solver.cpp:218] Iteration 5316 (3.06021 iter/s, 3.9213s/12 iters), loss = 2.64199
I0410 01:28:32.756670 18059 solver.cpp:237]     Train net output #0: loss = 2.64199 (* 1 = 2.64199 loss)
I0410 01:28:32.756682 18059 sgd_solver.cpp:105] Iteration 5316, lr = 0.00348881
I0410 01:28:37.458775 18059 solver.cpp:218] Iteration 5328 (2.55214 iter/s, 4.70193s/12 iters), loss = 2.5792
I0410 01:28:37.458827 18059 solver.cpp:237]     Train net output #0: loss = 2.5792 (* 1 = 2.5792 loss)
I0410 01:28:37.458838 18059 sgd_solver.cpp:105] Iteration 5328, lr = 0.00348053
I0410 01:28:42.078431 18059 solver.cpp:218] Iteration 5340 (2.59772 iter/s, 4.61943s/12 iters), loss = 2.62761
I0410 01:28:42.078485 18059 solver.cpp:237]     Train net output #0: loss = 2.62761 (* 1 = 2.62761 loss)
I0410 01:28:42.078497 18059 sgd_solver.cpp:105] Iteration 5340, lr = 0.00347226
I0410 01:28:46.908566 18059 solver.cpp:218] Iteration 5352 (2.48453 iter/s, 4.8299s/12 iters), loss = 2.44665
I0410 01:28:46.908620 18059 solver.cpp:237]     Train net output #0: loss = 2.44665 (* 1 = 2.44665 loss)
I0410 01:28:46.908632 18059 sgd_solver.cpp:105] Iteration 5352, lr = 0.00346402
I0410 01:28:50.157864 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:28:51.629827 18059 solver.cpp:218] Iteration 5364 (2.54182 iter/s, 4.72103s/12 iters), loss = 2.48103
I0410 01:28:51.629875 18059 solver.cpp:237]     Train net output #0: loss = 2.48103 (* 1 = 2.48103 loss)
I0410 01:28:51.629886 18059 sgd_solver.cpp:105] Iteration 5364, lr = 0.0034558
I0410 01:28:56.312014 18059 solver.cpp:218] Iteration 5376 (2.56303 iter/s, 4.68196s/12 iters), loss = 2.41396
I0410 01:28:56.312072 18059 solver.cpp:237]     Train net output #0: loss = 2.41396 (* 1 = 2.41396 loss)
I0410 01:28:56.312088 18059 sgd_solver.cpp:105] Iteration 5376, lr = 0.00344759
I0410 01:29:00.923761 18059 solver.cpp:218] Iteration 5388 (2.60218 iter/s, 4.61152s/12 iters), loss = 2.48609
I0410 01:29:00.923808 18059 solver.cpp:237]     Train net output #0: loss = 2.48609 (* 1 = 2.48609 loss)
I0410 01:29:00.923818 18059 sgd_solver.cpp:105] Iteration 5388, lr = 0.00343941
I0410 01:29:05.607980 18059 solver.cpp:218] Iteration 5400 (2.56192 iter/s, 4.68399s/12 iters), loss = 2.71708
I0410 01:29:05.608105 18059 solver.cpp:237]     Train net output #0: loss = 2.71708 (* 1 = 2.71708 loss)
I0410 01:29:05.608115 18059 sgd_solver.cpp:105] Iteration 5400, lr = 0.00343124
I0410 01:29:07.538324 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5406.caffemodel
I0410 01:29:15.815623 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5406.solverstate
I0410 01:29:19.732651 18059 solver.cpp:330] Iteration 5406, Testing net (#0)
I0410 01:29:19.732677 18059 net.cpp:676] Ignoring source layer train-data
I0410 01:29:22.039094 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:29:24.223398 18059 solver.cpp:397]     Test net output #0: accuracy = 0.227328
I0410 01:29:24.223438 18059 solver.cpp:397]     Test net output #1: loss = 3.00748 (* 1 = 3.00748 loss)
I0410 01:29:25.905570 18059 solver.cpp:218] Iteration 5412 (0.591228 iter/s, 20.2967s/12 iters), loss = 2.5272
I0410 01:29:25.905628 18059 solver.cpp:237]     Train net output #0: loss = 2.5272 (* 1 = 2.5272 loss)
I0410 01:29:25.905640 18059 sgd_solver.cpp:105] Iteration 5412, lr = 0.00342309
I0410 01:29:30.485342 18059 solver.cpp:218] Iteration 5424 (2.62035 iter/s, 4.57954s/12 iters), loss = 2.51001
I0410 01:29:30.485389 18059 solver.cpp:237]     Train net output #0: loss = 2.51001 (* 1 = 2.51001 loss)
I0410 01:29:30.485400 18059 sgd_solver.cpp:105] Iteration 5424, lr = 0.00341497
I0410 01:29:35.007558 18059 solver.cpp:218] Iteration 5436 (2.65369 iter/s, 4.522s/12 iters), loss = 2.5094
I0410 01:29:35.007601 18059 solver.cpp:237]     Train net output #0: loss = 2.5094 (* 1 = 2.5094 loss)
I0410 01:29:35.007609 18059 sgd_solver.cpp:105] Iteration 5436, lr = 0.00340686
I0410 01:29:39.741130 18059 solver.cpp:218] Iteration 5448 (2.53521 iter/s, 4.73334s/12 iters), loss = 2.35887
I0410 01:29:39.741273 18059 solver.cpp:237]     Train net output #0: loss = 2.35887 (* 1 = 2.35887 loss)
I0410 01:29:39.741290 18059 sgd_solver.cpp:105] Iteration 5448, lr = 0.00339877
I0410 01:29:44.545226 18059 solver.cpp:218] Iteration 5460 (2.49803 iter/s, 4.80378s/12 iters), loss = 2.29797
I0410 01:29:44.545274 18059 solver.cpp:237]     Train net output #0: loss = 2.29797 (* 1 = 2.29797 loss)
I0410 01:29:44.545284 18059 sgd_solver.cpp:105] Iteration 5460, lr = 0.0033907
I0410 01:29:45.110329 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:29:49.527318 18059 solver.cpp:218] Iteration 5472 (2.40874 iter/s, 4.98186s/12 iters), loss = 2.22613
I0410 01:29:49.527359 18059 solver.cpp:237]     Train net output #0: loss = 2.22613 (* 1 = 2.22613 loss)
I0410 01:29:49.527369 18059 sgd_solver.cpp:105] Iteration 5472, lr = 0.00338265
I0410 01:29:54.188203 18059 solver.cpp:218] Iteration 5484 (2.57474 iter/s, 4.66067s/12 iters), loss = 2.26241
I0410 01:29:54.188253 18059 solver.cpp:237]     Train net output #0: loss = 2.26241 (* 1 = 2.26241 loss)
I0410 01:29:54.188266 18059 sgd_solver.cpp:105] Iteration 5484, lr = 0.00337462
I0410 01:29:58.887194 18059 solver.cpp:218] Iteration 5496 (2.55386 iter/s, 4.69876s/12 iters), loss = 2.48767
I0410 01:29:58.887254 18059 solver.cpp:237]     Train net output #0: loss = 2.48767 (* 1 = 2.48767 loss)
I0410 01:29:58.887270 18059 sgd_solver.cpp:105] Iteration 5496, lr = 0.00336661
I0410 01:30:03.126935 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5508.caffemodel
I0410 01:30:09.427564 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5508.solverstate
I0410 01:30:17.219755 18059 solver.cpp:330] Iteration 5508, Testing net (#0)
I0410 01:30:17.219849 18059 net.cpp:676] Ignoring source layer train-data
I0410 01:30:19.462992 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:30:21.790843 18059 solver.cpp:397]     Test net output #0: accuracy = 0.230392
I0410 01:30:21.790880 18059 solver.cpp:397]     Test net output #1: loss = 3.01556 (* 1 = 3.01556 loss)
I0410 01:30:21.884281 18059 solver.cpp:218] Iteration 5508 (0.521825 iter/s, 22.9962s/12 iters), loss = 2.39315
I0410 01:30:21.884337 18059 solver.cpp:237]     Train net output #0: loss = 2.39315 (* 1 = 2.39315 loss)
I0410 01:30:21.884348 18059 sgd_solver.cpp:105] Iteration 5508, lr = 0.00335861
I0410 01:30:25.731086 18059 solver.cpp:218] Iteration 5520 (3.11964 iter/s, 3.8466s/12 iters), loss = 2.39256
I0410 01:30:25.731137 18059 solver.cpp:237]     Train net output #0: loss = 2.39256 (* 1 = 2.39256 loss)
I0410 01:30:25.731148 18059 sgd_solver.cpp:105] Iteration 5520, lr = 0.00335064
I0410 01:30:27.997540 18059 blocking_queue.cpp:49] Waiting for data
I0410 01:30:30.426640 18059 solver.cpp:218] Iteration 5532 (2.55573 iter/s, 4.69532s/12 iters), loss = 2.39788
I0410 01:30:30.426692 18059 solver.cpp:237]     Train net output #0: loss = 2.39788 (* 1 = 2.39788 loss)
I0410 01:30:30.426702 18059 sgd_solver.cpp:105] Iteration 5532, lr = 0.00334268
I0410 01:30:35.074568 18059 solver.cpp:218] Iteration 5544 (2.58192 iter/s, 4.6477s/12 iters), loss = 2.33943
I0410 01:30:35.074622 18059 solver.cpp:237]     Train net output #0: loss = 2.33943 (* 1 = 2.33943 loss)
I0410 01:30:35.074635 18059 sgd_solver.cpp:105] Iteration 5544, lr = 0.00333475
I0410 01:30:39.821132 18059 solver.cpp:218] Iteration 5556 (2.52827 iter/s, 4.74633s/12 iters), loss = 2.48521
I0410 01:30:39.821182 18059 solver.cpp:237]     Train net output #0: loss = 2.48521 (* 1 = 2.48521 loss)
I0410 01:30:39.821194 18059 sgd_solver.cpp:105] Iteration 5556, lr = 0.00332683
I0410 01:30:42.430604 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:30:44.662658 18059 solver.cpp:218] Iteration 5568 (2.47868 iter/s, 4.8413s/12 iters), loss = 2.13463
I0410 01:30:44.662701 18059 solver.cpp:237]     Train net output #0: loss = 2.13463 (* 1 = 2.13463 loss)
I0410 01:30:44.662710 18059 sgd_solver.cpp:105] Iteration 5568, lr = 0.00331893
I0410 01:30:49.398087 18059 solver.cpp:218] Iteration 5580 (2.53421 iter/s, 4.73521s/12 iters), loss = 2.42997
I0410 01:30:49.398166 18059 solver.cpp:237]     Train net output #0: loss = 2.42997 (* 1 = 2.42997 loss)
I0410 01:30:49.398178 18059 sgd_solver.cpp:105] Iteration 5580, lr = 0.00331105
I0410 01:30:54.109455 18059 solver.cpp:218] Iteration 5592 (2.54717 iter/s, 4.71111s/12 iters), loss = 2.33165
I0410 01:30:54.109508 18059 solver.cpp:237]     Train net output #0: loss = 2.33165 (* 1 = 2.33165 loss)
I0410 01:30:54.109519 18059 sgd_solver.cpp:105] Iteration 5592, lr = 0.00330319
I0410 01:30:59.004575 18059 solver.cpp:218] Iteration 5604 (2.45154 iter/s, 4.89488s/12 iters), loss = 2.28224
I0410 01:30:59.004619 18059 solver.cpp:237]     Train net output #0: loss = 2.28224 (* 1 = 2.28224 loss)
I0410 01:30:59.004629 18059 sgd_solver.cpp:105] Iteration 5604, lr = 0.00329535
I0410 01:31:01.091598 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5610.caffemodel
I0410 01:31:11.986459 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5610.solverstate
I0410 01:31:23.092669 18059 solver.cpp:330] Iteration 5610, Testing net (#0)
I0410 01:31:23.092780 18059 net.cpp:676] Ignoring source layer train-data
I0410 01:31:25.351366 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:31:27.570222 18059 solver.cpp:397]     Test net output #0: accuracy = 0.268995
I0410 01:31:27.570268 18059 solver.cpp:397]     Test net output #1: loss = 2.83426 (* 1 = 2.83426 loss)
I0410 01:31:29.486017 18059 solver.cpp:218] Iteration 5616 (0.393697 iter/s, 30.4803s/12 iters), loss = 2.26816
I0410 01:31:29.486075 18059 solver.cpp:237]     Train net output #0: loss = 2.26816 (* 1 = 2.26816 loss)
I0410 01:31:29.486088 18059 sgd_solver.cpp:105] Iteration 5616, lr = 0.00328752
I0410 01:31:34.353262 18059 solver.cpp:218] Iteration 5628 (2.46558 iter/s, 4.867s/12 iters), loss = 2.42712
I0410 01:31:34.353317 18059 solver.cpp:237]     Train net output #0: loss = 2.42712 (* 1 = 2.42712 loss)
I0410 01:31:34.353328 18059 sgd_solver.cpp:105] Iteration 5628, lr = 0.00327972
I0410 01:31:39.119108 18059 solver.cpp:218] Iteration 5640 (2.51804 iter/s, 4.76561s/12 iters), loss = 2.41779
I0410 01:31:39.119163 18059 solver.cpp:237]     Train net output #0: loss = 2.41779 (* 1 = 2.41779 loss)
I0410 01:31:39.119177 18059 sgd_solver.cpp:105] Iteration 5640, lr = 0.00327193
I0410 01:31:43.730245 18059 solver.cpp:218] Iteration 5652 (2.60252 iter/s, 4.61091s/12 iters), loss = 2.39447
I0410 01:31:43.730293 18059 solver.cpp:237]     Train net output #0: loss = 2.39447 (* 1 = 2.39447 loss)
I0410 01:31:43.730304 18059 sgd_solver.cpp:105] Iteration 5652, lr = 0.00326416
I0410 01:31:48.148110 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:31:48.319267 18059 solver.cpp:218] Iteration 5664 (2.61507 iter/s, 4.5888s/12 iters), loss = 2.28235
I0410 01:31:48.319321 18059 solver.cpp:237]     Train net output #0: loss = 2.28235 (* 1 = 2.28235 loss)
I0410 01:31:48.319332 18059 sgd_solver.cpp:105] Iteration 5664, lr = 0.00325641
I0410 01:31:53.014789 18059 solver.cpp:218] Iteration 5676 (2.55575 iter/s, 4.69529s/12 iters), loss = 2.10356
I0410 01:31:53.014843 18059 solver.cpp:237]     Train net output #0: loss = 2.10356 (* 1 = 2.10356 loss)
I0410 01:31:53.014855 18059 sgd_solver.cpp:105] Iteration 5676, lr = 0.00324868
I0410 01:31:58.037806 18059 solver.cpp:218] Iteration 5688 (2.38912 iter/s, 5.02277s/12 iters), loss = 1.82617
I0410 01:31:58.037897 18059 solver.cpp:237]     Train net output #0: loss = 1.82617 (* 1 = 1.82617 loss)
I0410 01:31:58.037910 18059 sgd_solver.cpp:105] Iteration 5688, lr = 0.00324097
I0410 01:32:02.814839 18059 solver.cpp:218] Iteration 5700 (2.51216 iter/s, 4.77677s/12 iters), loss = 2.33507
I0410 01:32:02.814889 18059 solver.cpp:237]     Train net output #0: loss = 2.33507 (* 1 = 2.33507 loss)
I0410 01:32:02.814900 18059 sgd_solver.cpp:105] Iteration 5700, lr = 0.00323328
I0410 01:32:06.945796 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5712.caffemodel
I0410 01:32:11.526171 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5712.solverstate
I0410 01:32:15.566051 18059 solver.cpp:330] Iteration 5712, Testing net (#0)
I0410 01:32:15.566073 18059 net.cpp:676] Ignoring source layer train-data
I0410 01:32:17.811154 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:32:20.072196 18059 solver.cpp:397]     Test net output #0: accuracy = 0.250613
I0410 01:32:20.072237 18059 solver.cpp:397]     Test net output #1: loss = 2.82181 (* 1 = 2.82181 loss)
I0410 01:32:20.165832 18059 solver.cpp:218] Iteration 5712 (0.69163 iter/s, 17.3503s/12 iters), loss = 2.04281
I0410 01:32:20.165894 18059 solver.cpp:237]     Train net output #0: loss = 2.04281 (* 1 = 2.04281 loss)
I0410 01:32:20.165907 18059 sgd_solver.cpp:105] Iteration 5712, lr = 0.0032256
I0410 01:32:24.007117 18059 solver.cpp:218] Iteration 5724 (3.12413 iter/s, 3.84107s/12 iters), loss = 2.17639
I0410 01:32:24.007166 18059 solver.cpp:237]     Train net output #0: loss = 2.17639 (* 1 = 2.17639 loss)
I0410 01:32:24.007179 18059 sgd_solver.cpp:105] Iteration 5724, lr = 0.00321794
I0410 01:32:28.936982 18059 solver.cpp:218] Iteration 5736 (2.43426 iter/s, 4.92963s/12 iters), loss = 2.2964
I0410 01:32:28.937104 18059 solver.cpp:237]     Train net output #0: loss = 2.2964 (* 1 = 2.2964 loss)
I0410 01:32:28.937114 18059 sgd_solver.cpp:105] Iteration 5736, lr = 0.0032103
I0410 01:32:33.818480 18059 solver.cpp:218] Iteration 5748 (2.45842 iter/s, 4.88119s/12 iters), loss = 2.41521
I0410 01:32:33.818537 18059 solver.cpp:237]     Train net output #0: loss = 2.41521 (* 1 = 2.41521 loss)
I0410 01:32:33.818549 18059 sgd_solver.cpp:105] Iteration 5748, lr = 0.00320268
I0410 01:32:38.364998 18059 solver.cpp:218] Iteration 5760 (2.63951 iter/s, 4.54629s/12 iters), loss = 2.21397
I0410 01:32:38.365039 18059 solver.cpp:237]     Train net output #0: loss = 2.21397 (* 1 = 2.21397 loss)
I0410 01:32:38.365051 18059 sgd_solver.cpp:105] Iteration 5760, lr = 0.00319508
I0410 01:32:40.065503 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:32:42.865351 18059 solver.cpp:218] Iteration 5772 (2.66659 iter/s, 4.50014s/12 iters), loss = 2.14489
I0410 01:32:42.865396 18059 solver.cpp:237]     Train net output #0: loss = 2.14489 (* 1 = 2.14489 loss)
I0410 01:32:42.865406 18059 sgd_solver.cpp:105] Iteration 5772, lr = 0.00318749
I0410 01:32:47.645706 18059 solver.cpp:218] Iteration 5784 (2.51039 iter/s, 4.78013s/12 iters), loss = 2.12382
I0410 01:32:47.645752 18059 solver.cpp:237]     Train net output #0: loss = 2.12382 (* 1 = 2.12382 loss)
I0410 01:32:47.645763 18059 sgd_solver.cpp:105] Iteration 5784, lr = 0.00317992
I0410 01:32:52.411793 18059 solver.cpp:218] Iteration 5796 (2.51791 iter/s, 4.76586s/12 iters), loss = 2.01478
I0410 01:32:52.411850 18059 solver.cpp:237]     Train net output #0: loss = 2.01478 (* 1 = 2.01478 loss)
I0410 01:32:52.411865 18059 sgd_solver.cpp:105] Iteration 5796, lr = 0.00317237
I0410 01:32:57.234354 18059 solver.cpp:218] Iteration 5808 (2.48843 iter/s, 4.82232s/12 iters), loss = 2.13908
I0410 01:32:57.234416 18059 solver.cpp:237]     Train net output #0: loss = 2.13908 (* 1 = 2.13908 loss)
I0410 01:32:57.234429 18059 sgd_solver.cpp:105] Iteration 5808, lr = 0.00316484
I0410 01:32:59.161458 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5814.caffemodel
I0410 01:33:06.287165 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5814.solverstate
I0410 01:33:09.929183 18059 solver.cpp:330] Iteration 5814, Testing net (#0)
I0410 01:33:09.929203 18059 net.cpp:676] Ignoring source layer train-data
I0410 01:33:12.071270 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:33:14.371676 18059 solver.cpp:397]     Test net output #0: accuracy = 0.272059
I0410 01:33:14.371703 18059 solver.cpp:397]     Test net output #1: loss = 2.85768 (* 1 = 2.85768 loss)
I0410 01:33:15.975358 18059 solver.cpp:218] Iteration 5820 (0.640332 iter/s, 18.7403s/12 iters), loss = 2.17132
I0410 01:33:15.975410 18059 solver.cpp:237]     Train net output #0: loss = 2.17132 (* 1 = 2.17132 loss)
I0410 01:33:15.975422 18059 sgd_solver.cpp:105] Iteration 5820, lr = 0.00315733
I0410 01:33:20.571972 18059 solver.cpp:218] Iteration 5832 (2.61074 iter/s, 4.59639s/12 iters), loss = 2.08257
I0410 01:33:20.572013 18059 solver.cpp:237]     Train net output #0: loss = 2.08257 (* 1 = 2.08257 loss)
I0410 01:33:20.572026 18059 sgd_solver.cpp:105] Iteration 5832, lr = 0.00314983
I0410 01:33:25.161916 18059 solver.cpp:218] Iteration 5844 (2.61453 iter/s, 4.58973s/12 iters), loss = 2.39702
I0410 01:33:25.161983 18059 solver.cpp:237]     Train net output #0: loss = 2.39702 (* 1 = 2.39702 loss)
I0410 01:33:25.161996 18059 sgd_solver.cpp:105] Iteration 5844, lr = 0.00314235
I0410 01:33:29.718971 18059 solver.cpp:218] Iteration 5856 (2.63342 iter/s, 4.55682s/12 iters), loss = 2.17505
I0410 01:33:29.719077 18059 solver.cpp:237]     Train net output #0: loss = 2.17505 (* 1 = 2.17505 loss)
I0410 01:33:29.719089 18059 sgd_solver.cpp:105] Iteration 5856, lr = 0.00313489
I0410 01:33:33.497426 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:33:34.275933 18059 solver.cpp:218] Iteration 5868 (2.6335 iter/s, 4.55668s/12 iters), loss = 1.86246
I0410 01:33:34.275986 18059 solver.cpp:237]     Train net output #0: loss = 1.86246 (* 1 = 1.86246 loss)
I0410 01:33:34.275997 18059 sgd_solver.cpp:105] Iteration 5868, lr = 0.00312745
I0410 01:33:38.997689 18059 solver.cpp:218] Iteration 5880 (2.54155 iter/s, 4.72152s/12 iters), loss = 2.10715
I0410 01:33:38.997737 18059 solver.cpp:237]     Train net output #0: loss = 2.10715 (* 1 = 2.10715 loss)
I0410 01:33:38.997747 18059 sgd_solver.cpp:105] Iteration 5880, lr = 0.00312002
I0410 01:33:43.830858 18059 solver.cpp:218] Iteration 5892 (2.48296 iter/s, 4.83294s/12 iters), loss = 2.11229
I0410 01:33:43.830907 18059 solver.cpp:237]     Train net output #0: loss = 2.11229 (* 1 = 2.11229 loss)
I0410 01:33:43.830920 18059 sgd_solver.cpp:105] Iteration 5892, lr = 0.00311262
I0410 01:33:48.569133 18059 solver.cpp:218] Iteration 5904 (2.53269 iter/s, 4.73805s/12 iters), loss = 1.85869
I0410 01:33:48.569185 18059 solver.cpp:237]     Train net output #0: loss = 1.85869 (* 1 = 1.85869 loss)
I0410 01:33:48.569196 18059 sgd_solver.cpp:105] Iteration 5904, lr = 0.00310523
I0410 01:33:52.797526 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5916.caffemodel
I0410 01:34:08.407575 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5916.solverstate
I0410 01:34:25.467744 18059 solver.cpp:330] Iteration 5916, Testing net (#0)
I0410 01:34:25.467768 18059 net.cpp:676] Ignoring source layer train-data
I0410 01:34:27.603317 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:34:29.931095 18059 solver.cpp:397]     Test net output #0: accuracy = 0.280637
I0410 01:34:29.931145 18059 solver.cpp:397]     Test net output #1: loss = 2.74812 (* 1 = 2.74812 loss)
I0410 01:34:30.024540 18059 solver.cpp:218] Iteration 5916 (0.289478 iter/s, 41.4539s/12 iters), loss = 2.09007
I0410 01:34:30.024583 18059 solver.cpp:237]     Train net output #0: loss = 2.09007 (* 1 = 2.09007 loss)
I0410 01:34:30.024595 18059 sgd_solver.cpp:105] Iteration 5916, lr = 0.00309785
I0410 01:34:34.050920 18059 solver.cpp:218] Iteration 5928 (2.98049 iter/s, 4.02618s/12 iters), loss = 2.08264
I0410 01:34:34.050963 18059 solver.cpp:237]     Train net output #0: loss = 2.08264 (* 1 = 2.08264 loss)
I0410 01:34:34.050974 18059 sgd_solver.cpp:105] Iteration 5928, lr = 0.0030905
I0410 01:34:39.071270 18059 solver.cpp:218] Iteration 5940 (2.39038 iter/s, 5.02012s/12 iters), loss = 2.14476
I0410 01:34:39.071339 18059 solver.cpp:237]     Train net output #0: loss = 2.14476 (* 1 = 2.14476 loss)
I0410 01:34:39.071348 18059 sgd_solver.cpp:105] Iteration 5940, lr = 0.00308316
I0410 01:34:43.929751 18059 solver.cpp:218] Iteration 5952 (2.47004 iter/s, 4.85822s/12 iters), loss = 2.06013
I0410 01:34:43.929796 18059 solver.cpp:237]     Train net output #0: loss = 2.06013 (* 1 = 2.06013 loss)
I0410 01:34:43.929805 18059 sgd_solver.cpp:105] Iteration 5952, lr = 0.00307584
I0410 01:34:48.881207 18059 solver.cpp:218] Iteration 5964 (2.42365 iter/s, 4.95122s/12 iters), loss = 1.89491
I0410 01:34:48.881263 18059 solver.cpp:237]     Train net output #0: loss = 1.89491 (* 1 = 1.89491 loss)
I0410 01:34:48.881274 18059 sgd_solver.cpp:105] Iteration 5964, lr = 0.00306854
I0410 01:34:50.252148 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:34:53.683256 18059 solver.cpp:218] Iteration 5976 (2.49906 iter/s, 4.80181s/12 iters), loss = 1.73971
I0410 01:34:53.683310 18059 solver.cpp:237]     Train net output #0: loss = 1.73971 (* 1 = 1.73971 loss)
I0410 01:34:53.683322 18059 sgd_solver.cpp:105] Iteration 5976, lr = 0.00306125
I0410 01:34:58.338912 18059 solver.cpp:218] Iteration 5988 (2.57764 iter/s, 4.65542s/12 iters), loss = 2.08937
I0410 01:34:58.338963 18059 solver.cpp:237]     Train net output #0: loss = 2.08937 (* 1 = 2.08937 loss)
I0410 01:34:58.338973 18059 sgd_solver.cpp:105] Iteration 5988, lr = 0.00305398
I0410 01:35:02.967610 18059 solver.cpp:218] Iteration 6000 (2.59265 iter/s, 4.62847s/12 iters), loss = 2.18782
I0410 01:35:02.967665 18059 solver.cpp:237]     Train net output #0: loss = 2.18782 (* 1 = 2.18782 loss)
I0410 01:35:02.967677 18059 sgd_solver.cpp:105] Iteration 6000, lr = 0.00304673
I0410 01:35:07.688879 18059 solver.cpp:218] Iteration 6012 (2.54181 iter/s, 4.72104s/12 iters), loss = 2.01183
I0410 01:35:07.688922 18059 solver.cpp:237]     Train net output #0: loss = 2.01183 (* 1 = 2.01183 loss)
I0410 01:35:07.688931 18059 sgd_solver.cpp:105] Iteration 6012, lr = 0.0030395
I0410 01:35:09.853868 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6018.caffemodel
I0410 01:35:15.650439 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6018.solverstate
I0410 01:35:29.667448 18059 solver.cpp:330] Iteration 6018, Testing net (#0)
I0410 01:35:29.667474 18059 net.cpp:676] Ignoring source layer train-data
I0410 01:35:31.765259 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:35:34.136057 18059 solver.cpp:397]     Test net output #0: accuracy = 0.28125
I0410 01:35:34.136103 18059 solver.cpp:397]     Test net output #1: loss = 2.80871 (* 1 = 2.80871 loss)
I0410 01:35:36.017653 18059 solver.cpp:218] Iteration 6024 (0.423613 iter/s, 28.3277s/12 iters), loss = 1.98583
I0410 01:35:36.017707 18059 solver.cpp:237]     Train net output #0: loss = 1.98583 (* 1 = 1.98583 loss)
I0410 01:35:36.017719 18059 sgd_solver.cpp:105] Iteration 6024, lr = 0.00303228
I0410 01:35:40.394229 18059 solver.cpp:218] Iteration 6036 (2.74201 iter/s, 4.37636s/12 iters), loss = 2.16027
I0410 01:35:40.394322 18059 solver.cpp:237]     Train net output #0: loss = 2.16027 (* 1 = 2.16027 loss)
I0410 01:35:40.394335 18059 sgd_solver.cpp:105] Iteration 6036, lr = 0.00302508
I0410 01:35:45.027746 18059 solver.cpp:218] Iteration 6048 (2.58998 iter/s, 4.63325s/12 iters), loss = 2.18087
I0410 01:35:45.027807 18059 solver.cpp:237]     Train net output #0: loss = 2.18087 (* 1 = 2.18087 loss)
I0410 01:35:45.027820 18059 sgd_solver.cpp:105] Iteration 6048, lr = 0.0030179
I0410 01:35:49.946911 18059 solver.cpp:218] Iteration 6060 (2.43956 iter/s, 4.91892s/12 iters), loss = 2.01468
I0410 01:35:49.946961 18059 solver.cpp:237]     Train net output #0: loss = 2.01468 (* 1 = 2.01468 loss)
I0410 01:35:49.946974 18059 sgd_solver.cpp:105] Iteration 6060, lr = 0.00301074
I0410 01:35:53.256749 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:35:54.722060 18059 solver.cpp:218] Iteration 6072 (2.51313 iter/s, 4.77492s/12 iters), loss = 2.17193
I0410 01:35:54.722107 18059 solver.cpp:237]     Train net output #0: loss = 2.17193 (* 1 = 2.17193 loss)
I0410 01:35:54.722118 18059 sgd_solver.cpp:105] Iteration 6072, lr = 0.00300359
I0410 01:35:59.419059 18059 solver.cpp:218] Iteration 6084 (2.55495 iter/s, 4.69677s/12 iters), loss = 1.98056
I0410 01:35:59.419106 18059 solver.cpp:237]     Train net output #0: loss = 1.98056 (* 1 = 1.98056 loss)
I0410 01:35:59.419116 18059 sgd_solver.cpp:105] Iteration 6084, lr = 0.00299646
I0410 01:36:04.099534 18059 solver.cpp:218] Iteration 6096 (2.56397 iter/s, 4.68025s/12 iters), loss = 1.75999
I0410 01:36:04.099593 18059 solver.cpp:237]     Train net output #0: loss = 1.75999 (* 1 = 1.75999 loss)
I0410 01:36:04.099608 18059 sgd_solver.cpp:105] Iteration 6096, lr = 0.00298934
I0410 01:36:09.295823 18059 solver.cpp:218] Iteration 6108 (2.30945 iter/s, 5.19604s/12 iters), loss = 2.04325
I0410 01:36:09.295871 18059 solver.cpp:237]     Train net output #0: loss = 2.04325 (* 1 = 2.04325 loss)
I0410 01:36:09.295881 18059 sgd_solver.cpp:105] Iteration 6108, lr = 0.00298225
I0410 01:36:14.020925 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6120.caffemodel
I0410 01:36:20.288957 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6120.solverstate
I0410 01:36:28.057206 18059 solver.cpp:330] Iteration 6120, Testing net (#0)
I0410 01:36:28.057230 18059 net.cpp:676] Ignoring source layer train-data
I0410 01:36:30.171016 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:36:32.583353 18059 solver.cpp:397]     Test net output #0: accuracy = 0.275735
I0410 01:36:32.583400 18059 solver.cpp:397]     Test net output #1: loss = 2.78425 (* 1 = 2.78425 loss)
I0410 01:36:32.677168 18059 solver.cpp:218] Iteration 6120 (0.513249 iter/s, 23.3805s/12 iters), loss = 2.10482
I0410 01:36:32.677217 18059 solver.cpp:237]     Train net output #0: loss = 2.10482 (* 1 = 2.10482 loss)
I0410 01:36:32.677228 18059 sgd_solver.cpp:105] Iteration 6120, lr = 0.00297517
I0410 01:36:36.726173 18059 solver.cpp:218] Iteration 6132 (2.96384 iter/s, 4.0488s/12 iters), loss = 2.04742
I0410 01:36:36.726230 18059 solver.cpp:237]     Train net output #0: loss = 2.04742 (* 1 = 2.04742 loss)
I0410 01:36:36.726243 18059 sgd_solver.cpp:105] Iteration 6132, lr = 0.0029681
I0410 01:36:41.555188 18059 solver.cpp:218] Iteration 6144 (2.4851 iter/s, 4.82877s/12 iters), loss = 2.34216
I0410 01:36:41.555240 18059 solver.cpp:237]     Train net output #0: loss = 2.34216 (* 1 = 2.34216 loss)
I0410 01:36:41.555253 18059 sgd_solver.cpp:105] Iteration 6144, lr = 0.00296105
I0410 01:36:46.280673 18059 solver.cpp:218] Iteration 6156 (2.53955 iter/s, 4.72525s/12 iters), loss = 1.8191
I0410 01:36:46.280789 18059 solver.cpp:237]     Train net output #0: loss = 1.8191 (* 1 = 1.8191 loss)
I0410 01:36:46.280802 18059 sgd_solver.cpp:105] Iteration 6156, lr = 0.00295402
I0410 01:36:51.029528 18059 solver.cpp:218] Iteration 6168 (2.52708 iter/s, 4.74857s/12 iters), loss = 1.90703
I0410 01:36:51.029564 18059 solver.cpp:237]     Train net output #0: loss = 1.90703 (* 1 = 1.90703 loss)
I0410 01:36:51.029572 18059 sgd_solver.cpp:105] Iteration 6168, lr = 0.00294701
I0410 01:36:51.644135 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:36:55.994805 18059 solver.cpp:218] Iteration 6180 (2.41689 iter/s, 4.96505s/12 iters), loss = 1.86838
I0410 01:36:55.994858 18059 solver.cpp:237]     Train net output #0: loss = 1.86838 (* 1 = 1.86838 loss)
I0410 01:36:55.994869 18059 sgd_solver.cpp:105] Iteration 6180, lr = 0.00294001
I0410 01:37:00.824045 18059 solver.cpp:218] Iteration 6192 (2.48498 iter/s, 4.82901s/12 iters), loss = 2.38604
I0410 01:37:00.824088 18059 solver.cpp:237]     Train net output #0: loss = 2.38604 (* 1 = 2.38604 loss)
I0410 01:37:00.824096 18059 sgd_solver.cpp:105] Iteration 6192, lr = 0.00293303
I0410 01:37:05.735682 18059 solver.cpp:218] Iteration 6204 (2.44329 iter/s, 4.91141s/12 iters), loss = 1.92752
I0410 01:37:05.735739 18059 solver.cpp:237]     Train net output #0: loss = 1.92752 (* 1 = 1.92752 loss)
I0410 01:37:05.735751 18059 sgd_solver.cpp:105] Iteration 6204, lr = 0.00292607
I0410 01:37:10.544975 18059 solver.cpp:218] Iteration 6216 (2.49529 iter/s, 4.80905s/12 iters), loss = 1.96813
I0410 01:37:10.545030 18059 solver.cpp:237]     Train net output #0: loss = 1.96813 (* 1 = 1.96813 loss)
I0410 01:37:10.545042 18059 sgd_solver.cpp:105] Iteration 6216, lr = 0.00291912
I0410 01:37:12.387763 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6222.caffemodel
I0410 01:37:20.105247 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6222.solverstate
I0410 01:37:27.031829 18059 solver.cpp:330] Iteration 6222, Testing net (#0)
I0410 01:37:27.031850 18059 net.cpp:676] Ignoring source layer train-data
I0410 01:37:29.022485 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:37:30.460770 18059 blocking_queue.cpp:49] Waiting for data
I0410 01:37:31.634177 18059 solver.cpp:397]     Test net output #0: accuracy = 0.284926
I0410 01:37:31.634204 18059 solver.cpp:397]     Test net output #1: loss = 2.88117 (* 1 = 2.88117 loss)
I0410 01:37:33.283805 18059 solver.cpp:218] Iteration 6228 (0.527752 iter/s, 22.738s/12 iters), loss = 1.94463
I0410 01:37:33.283865 18059 solver.cpp:237]     Train net output #0: loss = 1.94463 (* 1 = 1.94463 loss)
I0410 01:37:33.283879 18059 sgd_solver.cpp:105] Iteration 6228, lr = 0.00291219
I0410 01:37:37.929618 18059 solver.cpp:218] Iteration 6240 (2.5831 iter/s, 4.64558s/12 iters), loss = 2.08718
I0410 01:37:37.929658 18059 solver.cpp:237]     Train net output #0: loss = 2.08718 (* 1 = 2.08718 loss)
I0410 01:37:37.929667 18059 sgd_solver.cpp:105] Iteration 6240, lr = 0.00290528
I0410 01:37:42.756809 18059 solver.cpp:218] Iteration 6252 (2.48603 iter/s, 4.82697s/12 iters), loss = 1.82809
I0410 01:37:42.756847 18059 solver.cpp:237]     Train net output #0: loss = 1.82809 (* 1 = 1.82809 loss)
I0410 01:37:42.756856 18059 sgd_solver.cpp:105] Iteration 6252, lr = 0.00289838
I0410 01:37:47.692828 18059 solver.cpp:218] Iteration 6264 (2.43122 iter/s, 4.93579s/12 iters), loss = 2.00673
I0410 01:37:47.692878 18059 solver.cpp:237]     Train net output #0: loss = 2.00673 (* 1 = 2.00673 loss)
I0410 01:37:47.692888 18059 sgd_solver.cpp:105] Iteration 6264, lr = 0.0028915
I0410 01:37:50.293107 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:37:52.456446 18059 solver.cpp:218] Iteration 6276 (2.51921 iter/s, 4.76339s/12 iters), loss = 1.92462
I0410 01:37:52.456496 18059 solver.cpp:237]     Train net output #0: loss = 1.92462 (* 1 = 1.92462 loss)
I0410 01:37:52.456506 18059 sgd_solver.cpp:105] Iteration 6276, lr = 0.00288463
I0410 01:37:57.137053 18059 solver.cpp:218] Iteration 6288 (2.56389 iter/s, 4.68038s/12 iters), loss = 1.95652
I0410 01:37:57.137105 18059 solver.cpp:237]     Train net output #0: loss = 1.95652 (* 1 = 1.95652 loss)
I0410 01:37:57.137117 18059 sgd_solver.cpp:105] Iteration 6288, lr = 0.00287779
I0410 01:38:01.794477 18059 solver.cpp:218] Iteration 6300 (2.57665 iter/s, 4.65721s/12 iters), loss = 1.86782
I0410 01:38:01.794512 18059 solver.cpp:237]     Train net output #0: loss = 1.86782 (* 1 = 1.86782 loss)
I0410 01:38:01.794521 18059 sgd_solver.cpp:105] Iteration 6300, lr = 0.00287095
I0410 01:38:06.613828 18059 solver.cpp:218] Iteration 6312 (2.49008 iter/s, 4.81913s/12 iters), loss = 1.7513
I0410 01:38:06.613883 18059 solver.cpp:237]     Train net output #0: loss = 1.7513 (* 1 = 1.7513 loss)
I0410 01:38:06.613894 18059 sgd_solver.cpp:105] Iteration 6312, lr = 0.00286414
I0410 01:38:10.838006 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6324.caffemodel
I0410 01:38:19.717890 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6324.solverstate
I0410 01:38:34.296842 18059 solver.cpp:330] Iteration 6324, Testing net (#0)
I0410 01:38:34.296896 18059 net.cpp:676] Ignoring source layer train-data
I0410 01:38:36.254252 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:38:38.751776 18059 solver.cpp:397]     Test net output #0: accuracy = 0.310049
I0410 01:38:38.751806 18059 solver.cpp:397]     Test net output #1: loss = 2.58423 (* 1 = 2.58423 loss)
I0410 01:38:38.845312 18059 solver.cpp:218] Iteration 6324 (0.37232 iter/s, 32.2303s/12 iters), loss = 1.76485
I0410 01:38:38.845351 18059 solver.cpp:237]     Train net output #0: loss = 1.76485 (* 1 = 1.76485 loss)
I0410 01:38:38.845360 18059 sgd_solver.cpp:105] Iteration 6324, lr = 0.00285734
I0410 01:38:42.745201 18059 solver.cpp:218] Iteration 6336 (3.07716 iter/s, 3.8997s/12 iters), loss = 1.85141
I0410 01:38:42.745250 18059 solver.cpp:237]     Train net output #0: loss = 1.85141 (* 1 = 1.85141 loss)
I0410 01:38:42.745260 18059 sgd_solver.cpp:105] Iteration 6336, lr = 0.00285055
I0410 01:38:47.414165 18059 solver.cpp:218] Iteration 6348 (2.57029 iter/s, 4.66873s/12 iters), loss = 1.86758
I0410 01:38:47.414222 18059 solver.cpp:237]     Train net output #0: loss = 1.86758 (* 1 = 1.86758 loss)
I0410 01:38:47.414235 18059 sgd_solver.cpp:105] Iteration 6348, lr = 0.00284379
I0410 01:38:51.994822 18059 solver.cpp:218] Iteration 6360 (2.61984 iter/s, 4.58043s/12 iters), loss = 2.00832
I0410 01:38:51.994871 18059 solver.cpp:237]     Train net output #0: loss = 2.00832 (* 1 = 2.00832 loss)
I0410 01:38:51.994884 18059 sgd_solver.cpp:105] Iteration 6360, lr = 0.00283703
I0410 01:38:56.711387 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:38:56.890061 18059 solver.cpp:218] Iteration 6372 (2.45148 iter/s, 4.89501s/12 iters), loss = 1.90416
I0410 01:38:56.890101 18059 solver.cpp:237]     Train net output #0: loss = 1.90416 (* 1 = 1.90416 loss)
I0410 01:38:56.890110 18059 sgd_solver.cpp:105] Iteration 6372, lr = 0.0028303
I0410 01:39:02.200963 18059 solver.cpp:218] Iteration 6384 (2.25961 iter/s, 5.31066s/12 iters), loss = 1.50617
I0410 01:39:02.201009 18059 solver.cpp:237]     Train net output #0: loss = 1.50617 (* 1 = 1.50617 loss)
I0410 01:39:02.201020 18059 sgd_solver.cpp:105] Iteration 6384, lr = 0.00282358
I0410 01:39:06.861752 18059 solver.cpp:218] Iteration 6396 (2.5748 iter/s, 4.66056s/12 iters), loss = 1.91403
I0410 01:39:06.861918 18059 solver.cpp:237]     Train net output #0: loss = 1.91403 (* 1 = 1.91403 loss)
I0410 01:39:06.861933 18059 sgd_solver.cpp:105] Iteration 6396, lr = 0.00281687
I0410 01:39:11.566090 18059 solver.cpp:218] Iteration 6408 (2.55102 iter/s, 4.704s/12 iters), loss = 1.6488
I0410 01:39:11.566143 18059 solver.cpp:237]     Train net output #0: loss = 1.6488 (* 1 = 1.6488 loss)
I0410 01:39:11.566154 18059 sgd_solver.cpp:105] Iteration 6408, lr = 0.00281019
I0410 01:39:16.210867 18059 solver.cpp:218] Iteration 6420 (2.58367 iter/s, 4.64455s/12 iters), loss = 1.71031
I0410 01:39:16.210918 18059 solver.cpp:237]     Train net output #0: loss = 1.71031 (* 1 = 1.71031 loss)
I0410 01:39:16.210930 18059 sgd_solver.cpp:105] Iteration 6420, lr = 0.00280351
I0410 01:39:18.215553 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6426.caffemodel
I0410 01:39:25.037160 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6426.solverstate
I0410 01:39:31.024518 18059 solver.cpp:330] Iteration 6426, Testing net (#0)
I0410 01:39:31.024546 18059 net.cpp:676] Ignoring source layer train-data
I0410 01:39:32.963018 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:39:35.590402 18059 solver.cpp:397]     Test net output #0: accuracy = 0.326593
I0410 01:39:35.590430 18059 solver.cpp:397]     Test net output #1: loss = 2.67261 (* 1 = 2.67261 loss)
I0410 01:39:37.508213 18059 solver.cpp:218] Iteration 6432 (0.563472 iter/s, 21.2965s/12 iters), loss = 1.78305
I0410 01:39:37.508293 18059 solver.cpp:237]     Train net output #0: loss = 1.78305 (* 1 = 1.78305 loss)
I0410 01:39:37.508306 18059 sgd_solver.cpp:105] Iteration 6432, lr = 0.00279686
I0410 01:39:42.041633 18059 solver.cpp:218] Iteration 6444 (2.64716 iter/s, 4.53316s/12 iters), loss = 1.71967
I0410 01:39:42.041682 18059 solver.cpp:237]     Train net output #0: loss = 1.71967 (* 1 = 1.71967 loss)
I0410 01:39:42.041693 18059 sgd_solver.cpp:105] Iteration 6444, lr = 0.00279022
I0410 01:39:46.697557 18059 solver.cpp:218] Iteration 6456 (2.57748 iter/s, 4.6557s/12 iters), loss = 2.03797
I0410 01:39:46.697608 18059 solver.cpp:237]     Train net output #0: loss = 2.03797 (* 1 = 2.03797 loss)
I0410 01:39:46.697620 18059 sgd_solver.cpp:105] Iteration 6456, lr = 0.00278359
I0410 01:39:51.376881 18059 solver.cpp:218] Iteration 6468 (2.5646 iter/s, 4.6791s/12 iters), loss = 1.63558
I0410 01:39:51.376933 18059 solver.cpp:237]     Train net output #0: loss = 1.63558 (* 1 = 1.63558 loss)
I0410 01:39:51.376945 18059 sgd_solver.cpp:105] Iteration 6468, lr = 0.00277698
I0410 01:39:53.245110 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:39:55.998591 18059 solver.cpp:218] Iteration 6480 (2.59657 iter/s, 4.62148s/12 iters), loss = 1.92728
I0410 01:39:55.998642 18059 solver.cpp:237]     Train net output #0: loss = 1.92728 (* 1 = 1.92728 loss)
I0410 01:39:55.998653 18059 sgd_solver.cpp:105] Iteration 6480, lr = 0.00277039
I0410 01:40:00.720638 18059 solver.cpp:218] Iteration 6492 (2.54139 iter/s, 4.72182s/12 iters), loss = 1.68357
I0410 01:40:00.720695 18059 solver.cpp:237]     Train net output #0: loss = 1.68357 (* 1 = 1.68357 loss)
I0410 01:40:00.720711 18059 sgd_solver.cpp:105] Iteration 6492, lr = 0.00276381
I0410 01:40:05.495298 18059 solver.cpp:218] Iteration 6504 (2.51339 iter/s, 4.77443s/12 iters), loss = 1.81882
I0410 01:40:05.495342 18059 solver.cpp:237]     Train net output #0: loss = 1.81882 (* 1 = 1.81882 loss)
I0410 01:40:05.495352 18059 sgd_solver.cpp:105] Iteration 6504, lr = 0.00275725
I0410 01:40:10.195077 18059 solver.cpp:218] Iteration 6516 (2.55343 iter/s, 4.69956s/12 iters), loss = 1.91722
I0410 01:40:10.195170 18059 solver.cpp:237]     Train net output #0: loss = 1.91722 (* 1 = 1.91722 loss)
I0410 01:40:10.195179 18059 sgd_solver.cpp:105] Iteration 6516, lr = 0.00275071
I0410 01:40:14.509994 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6528.caffemodel
I0410 01:40:21.894708 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6528.solverstate
I0410 01:40:27.246636 18059 solver.cpp:330] Iteration 6528, Testing net (#0)
I0410 01:40:27.246665 18059 net.cpp:676] Ignoring source layer train-data
I0410 01:40:29.241365 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:40:31.952697 18059 solver.cpp:397]     Test net output #0: accuracy = 0.308824
I0410 01:40:31.952745 18059 solver.cpp:397]     Test net output #1: loss = 2.6345 (* 1 = 2.6345 loss)
I0410 01:40:32.046898 18059 solver.cpp:218] Iteration 6528 (0.549175 iter/s, 21.851s/12 iters), loss = 1.5344
I0410 01:40:32.048419 18059 solver.cpp:237]     Train net output #0: loss = 1.5344 (* 1 = 1.5344 loss)
I0410 01:40:32.048434 18059 sgd_solver.cpp:105] Iteration 6528, lr = 0.00274418
I0410 01:40:35.767889 18059 solver.cpp:218] Iteration 6540 (3.22639 iter/s, 3.71933s/12 iters), loss = 1.7219
I0410 01:40:35.767930 18059 solver.cpp:237]     Train net output #0: loss = 1.7219 (* 1 = 1.7219 loss)
I0410 01:40:35.767938 18059 sgd_solver.cpp:105] Iteration 6540, lr = 0.00273766
I0410 01:40:40.474378 18059 solver.cpp:218] Iteration 6552 (2.54979 iter/s, 4.70627s/12 iters), loss = 1.77413
I0410 01:40:40.474507 18059 solver.cpp:237]     Train net output #0: loss = 1.77413 (* 1 = 1.77413 loss)
I0410 01:40:40.474524 18059 sgd_solver.cpp:105] Iteration 6552, lr = 0.00273116
I0410 01:40:45.247234 18059 solver.cpp:218] Iteration 6564 (2.51438 iter/s, 4.77255s/12 iters), loss = 1.42307
I0410 01:40:45.247280 18059 solver.cpp:237]     Train net output #0: loss = 1.42307 (* 1 = 1.42307 loss)
I0410 01:40:45.247292 18059 sgd_solver.cpp:105] Iteration 6564, lr = 0.00272468
I0410 01:40:49.327399 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:40:50.090966 18059 solver.cpp:218] Iteration 6576 (2.47755 iter/s, 4.8435s/12 iters), loss = 1.52161
I0410 01:40:50.091022 18059 solver.cpp:237]     Train net output #0: loss = 1.52161 (* 1 = 1.52161 loss)
I0410 01:40:50.091032 18059 sgd_solver.cpp:105] Iteration 6576, lr = 0.00271821
I0410 01:40:54.775542 18059 solver.cpp:218] Iteration 6588 (2.56173 iter/s, 4.68434s/12 iters), loss = 1.66896
I0410 01:40:54.775602 18059 solver.cpp:237]     Train net output #0: loss = 1.66896 (* 1 = 1.66896 loss)
I0410 01:40:54.775615 18059 sgd_solver.cpp:105] Iteration 6588, lr = 0.00271175
I0410 01:40:59.368821 18059 solver.cpp:218] Iteration 6600 (2.61264 iter/s, 4.59305s/12 iters), loss = 2.00611
I0410 01:40:59.368865 18059 solver.cpp:237]     Train net output #0: loss = 2.00611 (* 1 = 2.00611 loss)
I0410 01:40:59.368873 18059 sgd_solver.cpp:105] Iteration 6600, lr = 0.00270532
I0410 01:41:04.028223 18059 solver.cpp:218] Iteration 6612 (2.57556 iter/s, 4.65918s/12 iters), loss = 1.39879
I0410 01:41:04.028270 18059 solver.cpp:237]     Train net output #0: loss = 1.39879 (* 1 = 1.39879 loss)
I0410 01:41:04.028280 18059 sgd_solver.cpp:105] Iteration 6612, lr = 0.00269889
I0410 01:41:08.765080 18059 solver.cpp:218] Iteration 6624 (2.53344 iter/s, 4.73664s/12 iters), loss = 1.72551
I0410 01:41:08.765123 18059 solver.cpp:237]     Train net output #0: loss = 1.72551 (* 1 = 1.72551 loss)
I0410 01:41:08.765134 18059 sgd_solver.cpp:105] Iteration 6624, lr = 0.00269248
I0410 01:41:10.702443 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6630.caffemodel
I0410 01:41:16.835211 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6630.solverstate
I0410 01:41:30.757930 18059 solver.cpp:330] Iteration 6630, Testing net (#0)
I0410 01:41:30.757970 18059 net.cpp:676] Ignoring source layer train-data
I0410 01:41:32.629930 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:41:35.377166 18059 solver.cpp:397]     Test net output #0: accuracy = 0.321078
I0410 01:41:35.377210 18059 solver.cpp:397]     Test net output #1: loss = 2.65756 (* 1 = 2.65756 loss)
I0410 01:41:37.077515 18059 solver.cpp:218] Iteration 6636 (0.423858 iter/s, 28.3114s/12 iters), loss = 1.62317
I0410 01:41:37.077560 18059 solver.cpp:237]     Train net output #0: loss = 1.62317 (* 1 = 1.62317 loss)
I0410 01:41:37.077570 18059 sgd_solver.cpp:105] Iteration 6636, lr = 0.00268609
I0410 01:41:41.651883 18059 solver.cpp:218] Iteration 6648 (2.62344 iter/s, 4.57415s/12 iters), loss = 1.52786
I0410 01:41:41.651979 18059 solver.cpp:237]     Train net output #0: loss = 1.52786 (* 1 = 1.52786 loss)
I0410 01:41:41.651988 18059 sgd_solver.cpp:105] Iteration 6648, lr = 0.00267971
I0410 01:41:46.402384 18059 solver.cpp:218] Iteration 6660 (2.5262 iter/s, 4.75023s/12 iters), loss = 1.56726
I0410 01:41:46.402437 18059 solver.cpp:237]     Train net output #0: loss = 1.56726 (* 1 = 1.56726 loss)
I0410 01:41:46.402449 18059 sgd_solver.cpp:105] Iteration 6660, lr = 0.00267335
I0410 01:41:51.283835 18059 solver.cpp:218] Iteration 6672 (2.45841 iter/s, 4.88121s/12 iters), loss = 1.67385
I0410 01:41:51.283891 18059 solver.cpp:237]     Train net output #0: loss = 1.67385 (* 1 = 1.67385 loss)
I0410 01:41:51.283905 18059 sgd_solver.cpp:105] Iteration 6672, lr = 0.00266701
I0410 01:41:52.588232 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:41:56.154942 18059 solver.cpp:218] Iteration 6684 (2.46363 iter/s, 4.87086s/12 iters), loss = 1.66788
I0410 01:41:56.154990 18059 solver.cpp:237]     Train net output #0: loss = 1.66788 (* 1 = 1.66788 loss)
I0410 01:41:56.155001 18059 sgd_solver.cpp:105] Iteration 6684, lr = 0.00266067
I0410 01:42:01.245049 18059 solver.cpp:218] Iteration 6696 (2.35763 iter/s, 5.08987s/12 iters), loss = 1.67981
I0410 01:42:01.245100 18059 solver.cpp:237]     Train net output #0: loss = 1.67981 (* 1 = 1.67981 loss)
I0410 01:42:01.245110 18059 sgd_solver.cpp:105] Iteration 6696, lr = 0.00265436
I0410 01:42:06.462517 18059 solver.cpp:218] Iteration 6708 (2.30008 iter/s, 5.21722s/12 iters), loss = 1.71142
I0410 01:42:06.462561 18059 solver.cpp:237]     Train net output #0: loss = 1.71142 (* 1 = 1.71142 loss)
I0410 01:42:06.462571 18059 sgd_solver.cpp:105] Iteration 6708, lr = 0.00264805
I0410 01:42:11.147485 18059 solver.cpp:218] Iteration 6720 (2.56151 iter/s, 4.68474s/12 iters), loss = 1.5512
I0410 01:42:11.147531 18059 solver.cpp:237]     Train net output #0: loss = 1.5512 (* 1 = 1.5512 loss)
I0410 01:42:11.147540 18059 sgd_solver.cpp:105] Iteration 6720, lr = 0.00264177
I0410 01:42:15.518824 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6732.caffemodel
I0410 01:42:20.283496 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6732.solverstate
I0410 01:42:27.043455 18059 solver.cpp:330] Iteration 6732, Testing net (#0)
I0410 01:42:27.043481 18059 net.cpp:676] Ignoring source layer train-data
I0410 01:42:28.856159 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:42:31.628669 18059 solver.cpp:397]     Test net output #0: accuracy = 0.319853
I0410 01:42:31.628712 18059 solver.cpp:397]     Test net output #1: loss = 2.74396 (* 1 = 2.74396 loss)
I0410 01:42:31.722008 18059 solver.cpp:218] Iteration 6732 (0.583268 iter/s, 20.5737s/12 iters), loss = 1.68812
I0410 01:42:31.722067 18059 solver.cpp:237]     Train net output #0: loss = 1.68812 (* 1 = 1.68812 loss)
I0410 01:42:31.722079 18059 sgd_solver.cpp:105] Iteration 6732, lr = 0.0026355
I0410 01:42:35.497143 18059 solver.cpp:218] Iteration 6744 (3.17887 iter/s, 3.77493s/12 iters), loss = 1.60116
I0410 01:42:35.497195 18059 solver.cpp:237]     Train net output #0: loss = 1.60116 (* 1 = 1.60116 loss)
I0410 01:42:35.497206 18059 sgd_solver.cpp:105] Iteration 6744, lr = 0.00262924
I0410 01:42:40.280463 18059 solver.cpp:218] Iteration 6756 (2.50884 iter/s, 4.78309s/12 iters), loss = 1.65754
I0410 01:42:40.280504 18059 solver.cpp:237]     Train net output #0: loss = 1.65754 (* 1 = 1.65754 loss)
I0410 01:42:40.280514 18059 sgd_solver.cpp:105] Iteration 6756, lr = 0.002623
I0410 01:42:45.217083 18059 solver.cpp:218] Iteration 6768 (2.43093 iter/s, 4.93639s/12 iters), loss = 1.58907
I0410 01:42:45.217128 18059 solver.cpp:237]     Train net output #0: loss = 1.58907 (* 1 = 1.58907 loss)
I0410 01:42:45.217139 18059 sgd_solver.cpp:105] Iteration 6768, lr = 0.00261677
I0410 01:42:48.456809 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:42:49.880100 18059 solver.cpp:218] Iteration 6780 (2.57356 iter/s, 4.6628s/12 iters), loss = 1.98443
I0410 01:42:49.880146 18059 solver.cpp:237]     Train net output #0: loss = 1.98443 (* 1 = 1.98443 loss)
I0410 01:42:49.880156 18059 sgd_solver.cpp:105] Iteration 6780, lr = 0.00261056
I0410 01:42:54.572435 18059 solver.cpp:218] Iteration 6792 (2.55748 iter/s, 4.69212s/12 iters), loss = 1.75231
I0410 01:42:54.572470 18059 solver.cpp:237]     Train net output #0: loss = 1.75231 (* 1 = 1.75231 loss)
I0410 01:42:54.572479 18059 sgd_solver.cpp:105] Iteration 6792, lr = 0.00260436
I0410 01:42:59.166633 18059 solver.cpp:218] Iteration 6804 (2.61211 iter/s, 4.59399s/12 iters), loss = 1.53997
I0410 01:42:59.166677 18059 solver.cpp:237]     Train net output #0: loss = 1.53997 (* 1 = 1.53997 loss)
I0410 01:42:59.166687 18059 sgd_solver.cpp:105] Iteration 6804, lr = 0.00259817
I0410 01:43:04.184388 18059 solver.cpp:218] Iteration 6816 (2.39162 iter/s, 5.01752s/12 iters), loss = 1.72336
I0410 01:43:04.184434 18059 solver.cpp:237]     Train net output #0: loss = 1.72336 (* 1 = 1.72336 loss)
I0410 01:43:04.184445 18059 sgd_solver.cpp:105] Iteration 6816, lr = 0.00259201
I0410 01:43:09.208266 18059 solver.cpp:218] Iteration 6828 (2.3887 iter/s, 5.02364s/12 iters), loss = 1.379
I0410 01:43:09.208309 18059 solver.cpp:237]     Train net output #0: loss = 1.379 (* 1 = 1.379 loss)
I0410 01:43:09.208319 18059 sgd_solver.cpp:105] Iteration 6828, lr = 0.00258585
I0410 01:43:11.156788 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6834.caffemodel
I0410 01:43:17.426970 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6834.solverstate
I0410 01:43:21.096925 18059 solver.cpp:330] Iteration 6834, Testing net (#0)
I0410 01:43:21.096974 18059 net.cpp:676] Ignoring source layer train-data
I0410 01:43:22.934492 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:43:25.766223 18059 solver.cpp:397]     Test net output #0: accuracy = 0.342524
I0410 01:43:25.766258 18059 solver.cpp:397]     Test net output #1: loss = 2.68783 (* 1 = 2.68783 loss)
I0410 01:43:27.529367 18059 solver.cpp:218] Iteration 6840 (0.655007 iter/s, 18.3204s/12 iters), loss = 1.51292
I0410 01:43:27.529408 18059 solver.cpp:237]     Train net output #0: loss = 1.51292 (* 1 = 1.51292 loss)
I0410 01:43:27.529417 18059 sgd_solver.cpp:105] Iteration 6840, lr = 0.00257971
I0410 01:43:32.393484 18059 solver.cpp:218] Iteration 6852 (2.46716 iter/s, 4.86389s/12 iters), loss = 1.73875
I0410 01:43:32.393535 18059 solver.cpp:237]     Train net output #0: loss = 1.73875 (* 1 = 1.73875 loss)
I0410 01:43:32.393545 18059 sgd_solver.cpp:105] Iteration 6852, lr = 0.00257359
I0410 01:43:37.124545 18059 solver.cpp:218] Iteration 6864 (2.53655 iter/s, 4.73083s/12 iters), loss = 1.35471
I0410 01:43:37.124595 18059 solver.cpp:237]     Train net output #0: loss = 1.35471 (* 1 = 1.35471 loss)
I0410 01:43:37.124609 18059 sgd_solver.cpp:105] Iteration 6864, lr = 0.00256748
I0410 01:43:41.829758 18059 solver.cpp:218] Iteration 6876 (2.55048 iter/s, 4.70499s/12 iters), loss = 1.86968
I0410 01:43:41.829797 18059 solver.cpp:237]     Train net output #0: loss = 1.86968 (* 1 = 1.86968 loss)
I0410 01:43:41.829805 18059 sgd_solver.cpp:105] Iteration 6876, lr = 0.00256138
I0410 01:43:42.411554 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:43:46.552502 18059 solver.cpp:218] Iteration 6888 (2.54102 iter/s, 4.72252s/12 iters), loss = 1.65766
I0410 01:43:46.552558 18059 solver.cpp:237]     Train net output #0: loss = 1.65766 (* 1 = 1.65766 loss)
I0410 01:43:46.552568 18059 sgd_solver.cpp:105] Iteration 6888, lr = 0.0025553
I0410 01:43:51.384235 18059 solver.cpp:218] Iteration 6900 (2.4837 iter/s, 4.8315s/12 iters), loss = 1.29311
I0410 01:43:51.384372 18059 solver.cpp:237]     Train net output #0: loss = 1.29311 (* 1 = 1.29311 loss)
I0410 01:43:51.384384 18059 sgd_solver.cpp:105] Iteration 6900, lr = 0.00254923
I0410 01:43:56.235771 18059 solver.cpp:218] Iteration 6912 (2.47361 iter/s, 4.85122s/12 iters), loss = 1.38262
I0410 01:43:56.235826 18059 solver.cpp:237]     Train net output #0: loss = 1.38262 (* 1 = 1.38262 loss)
I0410 01:43:56.235836 18059 sgd_solver.cpp:105] Iteration 6912, lr = 0.00254318
I0410 01:44:01.032407 18059 solver.cpp:218] Iteration 6924 (2.50187 iter/s, 4.7964s/12 iters), loss = 1.5314
I0410 01:44:01.032455 18059 solver.cpp:237]     Train net output #0: loss = 1.5314 (* 1 = 1.5314 loss)
I0410 01:44:01.032465 18059 sgd_solver.cpp:105] Iteration 6924, lr = 0.00253714
I0410 01:44:05.484357 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6936.caffemodel
I0410 01:44:16.527827 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6936.solverstate
I0410 01:44:38.546716 18059 solver.cpp:330] Iteration 6936, Testing net (#0)
I0410 01:44:38.546763 18059 net.cpp:676] Ignoring source layer train-data
I0410 01:44:39.215924 18059 blocking_queue.cpp:49] Waiting for data
I0410 01:44:40.300155 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:44:43.075090 18059 solver.cpp:397]     Test net output #0: accuracy = 0.359069
I0410 01:44:43.075125 18059 solver.cpp:397]     Test net output #1: loss = 2.59291 (* 1 = 2.59291 loss)
I0410 01:44:43.168583 18059 solver.cpp:218] Iteration 6936 (0.284801 iter/s, 42.1346s/12 iters), loss = 1.27414
I0410 01:44:43.168635 18059 solver.cpp:237]     Train net output #0: loss = 1.27414 (* 1 = 1.27414 loss)
I0410 01:44:43.168648 18059 sgd_solver.cpp:105] Iteration 6936, lr = 0.00253112
I0410 01:44:47.680809 18059 solver.cpp:218] Iteration 6948 (2.65958 iter/s, 4.51199s/12 iters), loss = 1.434
I0410 01:44:47.680869 18059 solver.cpp:237]     Train net output #0: loss = 1.434 (* 1 = 1.434 loss)
I0410 01:44:47.680882 18059 sgd_solver.cpp:105] Iteration 6948, lr = 0.00252511
I0410 01:44:52.670596 18059 solver.cpp:218] Iteration 6960 (2.40503 iter/s, 4.98954s/12 iters), loss = 1.347
I0410 01:44:52.670637 18059 solver.cpp:237]     Train net output #0: loss = 1.347 (* 1 = 1.347 loss)
I0410 01:44:52.670646 18059 sgd_solver.cpp:105] Iteration 6960, lr = 0.00251911
I0410 01:44:57.304492 18059 solver.cpp:218] Iteration 6972 (2.58974 iter/s, 4.63368s/12 iters), loss = 1.57704
I0410 01:44:57.304543 18059 solver.cpp:237]     Train net output #0: loss = 1.57704 (* 1 = 1.57704 loss)
I0410 01:44:57.304551 18059 sgd_solver.cpp:105] Iteration 6972, lr = 0.00251313
I0410 01:44:59.823320 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:45:02.003461 18059 solver.cpp:218] Iteration 6984 (2.55388 iter/s, 4.69874s/12 iters), loss = 1.26725
I0410 01:45:02.003506 18059 solver.cpp:237]     Train net output #0: loss = 1.26725 (* 1 = 1.26725 loss)
I0410 01:45:02.003517 18059 sgd_solver.cpp:105] Iteration 6984, lr = 0.00250717
I0410 01:45:06.815765 18059 solver.cpp:218] Iteration 6996 (2.49373 iter/s, 4.81208s/12 iters), loss = 1.57765
I0410 01:45:06.815817 18059 solver.cpp:237]     Train net output #0: loss = 1.57765 (* 1 = 1.57765 loss)
I0410 01:45:06.815830 18059 sgd_solver.cpp:105] Iteration 6996, lr = 0.00250121
I0410 01:45:11.718536 18059 solver.cpp:218] Iteration 7008 (2.44771 iter/s, 4.90254s/12 iters), loss = 1.35654
I0410 01:45:11.719267 18059 solver.cpp:237]     Train net output #0: loss = 1.35654 (* 1 = 1.35654 loss)
I0410 01:45:11.719277 18059 sgd_solver.cpp:105] Iteration 7008, lr = 0.00249528
I0410 01:45:16.545475 18059 solver.cpp:218] Iteration 7020 (2.48652 iter/s, 4.82603s/12 iters), loss = 1.41935
I0410 01:45:16.545531 18059 solver.cpp:237]     Train net output #0: loss = 1.41935 (* 1 = 1.41935 loss)
I0410 01:45:16.545542 18059 sgd_solver.cpp:105] Iteration 7020, lr = 0.00248935
I0410 01:45:21.452180 18059 solver.cpp:218] Iteration 7032 (2.44575 iter/s, 4.90646s/12 iters), loss = 1.42619
I0410 01:45:21.452236 18059 solver.cpp:237]     Train net output #0: loss = 1.42619 (* 1 = 1.42619 loss)
I0410 01:45:21.452250 18059 sgd_solver.cpp:105] Iteration 7032, lr = 0.00248344
I0410 01:45:23.437860 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7038.caffemodel
I0410 01:45:28.234458 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7038.solverstate
I0410 01:45:48.286425 18059 solver.cpp:330] Iteration 7038, Testing net (#0)
I0410 01:45:48.286479 18059 net.cpp:676] Ignoring source layer train-data
I0410 01:45:49.990532 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:45:52.751014 18059 solver.cpp:397]     Test net output #0: accuracy = 0.36152
I0410 01:45:52.751063 18059 solver.cpp:397]     Test net output #1: loss = 2.6024 (* 1 = 2.6024 loss)
I0410 01:45:54.439904 18059 solver.cpp:218] Iteration 7044 (0.363785 iter/s, 32.9865s/12 iters), loss = 1.37175
I0410 01:45:54.439955 18059 solver.cpp:237]     Train net output #0: loss = 1.37175 (* 1 = 1.37175 loss)
I0410 01:45:54.439966 18059 sgd_solver.cpp:105] Iteration 7044, lr = 0.00247755
I0410 01:45:58.803061 18059 solver.cpp:218] Iteration 7056 (2.75044 iter/s, 4.36294s/12 iters), loss = 1.40267
I0410 01:45:58.803112 18059 solver.cpp:237]     Train net output #0: loss = 1.40267 (* 1 = 1.40267 loss)
I0410 01:45:58.803125 18059 sgd_solver.cpp:105] Iteration 7056, lr = 0.00247166
I0410 01:46:03.290127 18059 solver.cpp:218] Iteration 7068 (2.67449 iter/s, 4.48684s/12 iters), loss = 1.62301
I0410 01:46:03.290189 18059 solver.cpp:237]     Train net output #0: loss = 1.62301 (* 1 = 1.62301 loss)
I0410 01:46:03.290206 18059 sgd_solver.cpp:105] Iteration 7068, lr = 0.0024658
I0410 01:46:07.849459 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:46:07.964581 18059 solver.cpp:218] Iteration 7080 (2.56727 iter/s, 4.67422s/12 iters), loss = 1.4896
I0410 01:46:07.964630 18059 solver.cpp:237]     Train net output #0: loss = 1.4896 (* 1 = 1.4896 loss)
I0410 01:46:07.964643 18059 sgd_solver.cpp:105] Iteration 7080, lr = 0.00245994
I0410 01:46:12.493587 18059 solver.cpp:218] Iteration 7092 (2.64972 iter/s, 4.52878s/12 iters), loss = 1.22142
I0410 01:46:12.493635 18059 solver.cpp:237]     Train net output #0: loss = 1.22142 (* 1 = 1.22142 loss)
I0410 01:46:12.493646 18059 sgd_solver.cpp:105] Iteration 7092, lr = 0.0024541
I0410 01:46:17.144274 18059 solver.cpp:218] Iteration 7104 (2.58039 iter/s, 4.65046s/12 iters), loss = 1.12719
I0410 01:46:17.144327 18059 solver.cpp:237]     Train net output #0: loss = 1.12719 (* 1 = 1.12719 loss)
I0410 01:46:17.144338 18059 sgd_solver.cpp:105] Iteration 7104, lr = 0.00244827
I0410 01:46:21.662853 18059 solver.cpp:218] Iteration 7116 (2.65583 iter/s, 4.51835s/12 iters), loss = 1.32525
I0410 01:46:21.662955 18059 solver.cpp:237]     Train net output #0: loss = 1.32525 (* 1 = 1.32525 loss)
I0410 01:46:21.662966 18059 sgd_solver.cpp:105] Iteration 7116, lr = 0.00244246
I0410 01:46:26.508210 18059 solver.cpp:218] Iteration 7128 (2.47674 iter/s, 4.84507s/12 iters), loss = 1.50474
I0410 01:46:26.508260 18059 solver.cpp:237]     Train net output #0: loss = 1.50474 (* 1 = 1.50474 loss)
I0410 01:46:26.508270 18059 sgd_solver.cpp:105] Iteration 7128, lr = 0.00243666
I0410 01:46:31.293511 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7140.caffemodel
I0410 01:46:44.919157 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7140.solverstate
I0410 01:46:49.842083 18059 solver.cpp:330] Iteration 7140, Testing net (#0)
I0410 01:46:49.842106 18059 net.cpp:676] Ignoring source layer train-data
I0410 01:46:51.583364 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:46:54.477097 18059 solver.cpp:397]     Test net output #0: accuracy = 0.365196
I0410 01:46:54.477280 18059 solver.cpp:397]     Test net output #1: loss = 2.63412 (* 1 = 2.63412 loss)
I0410 01:46:54.571180 18059 solver.cpp:218] Iteration 7140 (0.427626 iter/s, 28.0619s/12 iters), loss = 1.44068
I0410 01:46:54.571230 18059 solver.cpp:237]     Train net output #0: loss = 1.44068 (* 1 = 1.44068 loss)
I0410 01:46:54.571240 18059 sgd_solver.cpp:105] Iteration 7140, lr = 0.00243088
I0410 01:46:58.815367 18059 solver.cpp:218] Iteration 7152 (2.82754 iter/s, 4.24397s/12 iters), loss = 1.47451
I0410 01:46:58.815438 18059 solver.cpp:237]     Train net output #0: loss = 1.47451 (* 1 = 1.47451 loss)
I0410 01:46:58.815457 18059 sgd_solver.cpp:105] Iteration 7152, lr = 0.00242511
I0410 01:47:03.894147 18059 solver.cpp:218] Iteration 7164 (2.36289 iter/s, 5.07852s/12 iters), loss = 1.46804
I0410 01:47:03.894202 18059 solver.cpp:237]     Train net output #0: loss = 1.46804 (* 1 = 1.46804 loss)
I0410 01:47:03.894214 18059 sgd_solver.cpp:105] Iteration 7164, lr = 0.00241935
I0410 01:47:08.579819 18059 solver.cpp:218] Iteration 7176 (2.56112 iter/s, 4.68544s/12 iters), loss = 1.47185
I0410 01:47:08.579867 18059 solver.cpp:237]     Train net output #0: loss = 1.47185 (* 1 = 1.47185 loss)
I0410 01:47:08.579880 18059 sgd_solver.cpp:105] Iteration 7176, lr = 0.0024136
I0410 01:47:10.539459 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:47:13.355885 18059 solver.cpp:218] Iteration 7188 (2.51265 iter/s, 4.77584s/12 iters), loss = 1.53948
I0410 01:47:13.355938 18059 solver.cpp:237]     Train net output #0: loss = 1.53948 (* 1 = 1.53948 loss)
I0410 01:47:13.355949 18059 sgd_solver.cpp:105] Iteration 7188, lr = 0.00240787
I0410 01:47:18.082439 18059 solver.cpp:218] Iteration 7200 (2.53897 iter/s, 4.72633s/12 iters), loss = 1.20915
I0410 01:47:18.082475 18059 solver.cpp:237]     Train net output #0: loss = 1.20915 (* 1 = 1.20915 loss)
I0410 01:47:18.082484 18059 sgd_solver.cpp:105] Iteration 7200, lr = 0.00240216
I0410 01:47:22.819178 18059 solver.cpp:218] Iteration 7212 (2.53351 iter/s, 4.73652s/12 iters), loss = 1.16255
I0410 01:47:22.819232 18059 solver.cpp:237]     Train net output #0: loss = 1.16255 (* 1 = 1.16255 loss)
I0410 01:47:22.819245 18059 sgd_solver.cpp:105] Iteration 7212, lr = 0.00239645
I0410 01:47:27.710580 18059 solver.cpp:218] Iteration 7224 (2.4534 iter/s, 4.89117s/12 iters), loss = 1.41942
I0410 01:47:27.710641 18059 solver.cpp:237]     Train net output #0: loss = 1.41942 (* 1 = 1.41942 loss)
I0410 01:47:27.710650 18059 sgd_solver.cpp:105] Iteration 7224, lr = 0.00239076
I0410 01:47:32.471280 18059 solver.cpp:218] Iteration 7236 (2.52077 iter/s, 4.76046s/12 iters), loss = 1.06682
I0410 01:47:32.471328 18059 solver.cpp:237]     Train net output #0: loss = 1.06682 (* 1 = 1.06682 loss)
I0410 01:47:32.471338 18059 sgd_solver.cpp:105] Iteration 7236, lr = 0.00238509
I0410 01:47:34.440094 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7242.caffemodel
I0410 01:47:44.233999 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7242.solverstate
I0410 01:47:51.005517 18059 solver.cpp:330] Iteration 7242, Testing net (#0)
I0410 01:47:51.005538 18059 net.cpp:676] Ignoring source layer train-data
I0410 01:47:52.575875 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:47:55.449036 18059 solver.cpp:397]     Test net output #0: accuracy = 0.354167
I0410 01:47:55.449087 18059 solver.cpp:397]     Test net output #1: loss = 2.65549 (* 1 = 2.65549 loss)
I0410 01:47:57.138021 18059 solver.cpp:218] Iteration 7248 (0.486503 iter/s, 24.6658s/12 iters), loss = 1.34158
I0410 01:47:57.138073 18059 solver.cpp:237]     Train net output #0: loss = 1.34158 (* 1 = 1.34158 loss)
I0410 01:47:57.138085 18059 sgd_solver.cpp:105] Iteration 7248, lr = 0.00237942
I0410 01:48:01.633203 18059 solver.cpp:218] Iteration 7260 (2.66966 iter/s, 4.49496s/12 iters), loss = 1.19536
I0410 01:48:01.633324 18059 solver.cpp:237]     Train net output #0: loss = 1.19536 (* 1 = 1.19536 loss)
I0410 01:48:01.633337 18059 sgd_solver.cpp:105] Iteration 7260, lr = 0.00237378
I0410 01:48:06.632793 18059 solver.cpp:218] Iteration 7272 (2.40034 iter/s, 4.99929s/12 iters), loss = 1.29454
I0410 01:48:06.632835 18059 solver.cpp:237]     Train net output #0: loss = 1.29454 (* 1 = 1.29454 loss)
I0410 01:48:06.632843 18059 sgd_solver.cpp:105] Iteration 7272, lr = 0.00236814
I0410 01:48:10.754715 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:48:11.539255 18059 solver.cpp:218] Iteration 7284 (2.44587 iter/s, 4.90624s/12 iters), loss = 1.36149
I0410 01:48:11.539299 18059 solver.cpp:237]     Train net output #0: loss = 1.36149 (* 1 = 1.36149 loss)
I0410 01:48:11.539306 18059 sgd_solver.cpp:105] Iteration 7284, lr = 0.00236252
I0410 01:48:16.069367 18059 solver.cpp:218] Iteration 7296 (2.64907 iter/s, 4.5299s/12 iters), loss = 1.18726
I0410 01:48:16.069430 18059 solver.cpp:237]     Train net output #0: loss = 1.18726 (* 1 = 1.18726 loss)
I0410 01:48:16.069447 18059 sgd_solver.cpp:105] Iteration 7296, lr = 0.00235691
I0410 01:48:20.999933 18059 solver.cpp:218] Iteration 7308 (2.43392 iter/s, 4.93032s/12 iters), loss = 1.40373
I0410 01:48:20.999984 18059 solver.cpp:237]     Train net output #0: loss = 1.40373 (* 1 = 1.40373 loss)
I0410 01:48:20.999995 18059 sgd_solver.cpp:105] Iteration 7308, lr = 0.00235131
I0410 01:48:25.553071 18059 solver.cpp:218] Iteration 7320 (2.63567 iter/s, 4.55292s/12 iters), loss = 1.18469
I0410 01:48:25.553114 18059 solver.cpp:237]     Train net output #0: loss = 1.18469 (* 1 = 1.18469 loss)
I0410 01:48:25.553124 18059 sgd_solver.cpp:105] Iteration 7320, lr = 0.00234573
I0410 01:48:30.382380 18059 solver.cpp:218] Iteration 7332 (2.48494 iter/s, 4.82909s/12 iters), loss = 1.54931
I0410 01:48:30.382431 18059 solver.cpp:237]     Train net output #0: loss = 1.54931 (* 1 = 1.54931 loss)
I0410 01:48:30.382444 18059 sgd_solver.cpp:105] Iteration 7332, lr = 0.00234016
I0410 01:48:34.815608 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7344.caffemodel
I0410 01:48:51.703205 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7344.solverstate
I0410 01:49:02.088413 18059 solver.cpp:330] Iteration 7344, Testing net (#0)
I0410 01:49:02.088438 18059 net.cpp:676] Ignoring source layer train-data
I0410 01:49:03.666676 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:49:06.556479 18059 solver.cpp:397]     Test net output #0: accuracy = 0.372549
I0410 01:49:06.556557 18059 solver.cpp:397]     Test net output #1: loss = 2.65442 (* 1 = 2.65442 loss)
I0410 01:49:06.648237 18059 solver.cpp:218] Iteration 7344 (0.330902 iter/s, 36.2645s/12 iters), loss = 1.1972
I0410 01:49:06.648293 18059 solver.cpp:237]     Train net output #0: loss = 1.1972 (* 1 = 1.1972 loss)
I0410 01:49:06.648304 18059 sgd_solver.cpp:105] Iteration 7344, lr = 0.0023346
I0410 01:49:10.755333 18059 solver.cpp:218] Iteration 7356 (2.92192 iter/s, 4.10689s/12 iters), loss = 1.41134
I0410 01:49:10.755371 18059 solver.cpp:237]     Train net output #0: loss = 1.41134 (* 1 = 1.41134 loss)
I0410 01:49:10.755380 18059 sgd_solver.cpp:105] Iteration 7356, lr = 0.00232906
I0410 01:49:15.365152 18059 solver.cpp:218] Iteration 7368 (2.60326 iter/s, 4.6096s/12 iters), loss = 1.20799
I0410 01:49:15.365193 18059 solver.cpp:237]     Train net output #0: loss = 1.20799 (* 1 = 1.20799 loss)
I0410 01:49:15.365202 18059 sgd_solver.cpp:105] Iteration 7368, lr = 0.00232353
I0410 01:49:20.038862 18059 solver.cpp:218] Iteration 7380 (2.56768 iter/s, 4.67349s/12 iters), loss = 1.32298
I0410 01:49:20.038918 18059 solver.cpp:237]     Train net output #0: loss = 1.32298 (* 1 = 1.32298 loss)
I0410 01:49:20.038929 18059 sgd_solver.cpp:105] Iteration 7380, lr = 0.00231802
I0410 01:49:21.294732 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:49:25.084206 18059 solver.cpp:218] Iteration 7392 (2.37855 iter/s, 5.0451s/12 iters), loss = 1.39223
I0410 01:49:25.084259 18059 solver.cpp:237]     Train net output #0: loss = 1.39223 (* 1 = 1.39223 loss)
I0410 01:49:25.084270 18059 sgd_solver.cpp:105] Iteration 7392, lr = 0.00231251
I0410 01:49:29.931478 18059 solver.cpp:218] Iteration 7404 (2.47574 iter/s, 4.84703s/12 iters), loss = 1.2305
I0410 01:49:29.931525 18059 solver.cpp:237]     Train net output #0: loss = 1.2305 (* 1 = 1.2305 loss)
I0410 01:49:29.931536 18059 sgd_solver.cpp:105] Iteration 7404, lr = 0.00230702
I0410 01:49:35.141626 18059 solver.cpp:218] Iteration 7416 (2.3033 iter/s, 5.20991s/12 iters), loss = 1.05935
I0410 01:49:35.141669 18059 solver.cpp:237]     Train net output #0: loss = 1.05935 (* 1 = 1.05935 loss)
I0410 01:49:35.141678 18059 sgd_solver.cpp:105] Iteration 7416, lr = 0.00230154
I0410 01:49:40.422063 18059 solver.cpp:218] Iteration 7428 (2.27264 iter/s, 5.2802s/12 iters), loss = 1.07182
I0410 01:49:40.422190 18059 solver.cpp:237]     Train net output #0: loss = 1.07182 (* 1 = 1.07182 loss)
I0410 01:49:40.422201 18059 sgd_solver.cpp:105] Iteration 7428, lr = 0.00229608
I0410 01:49:44.925195 18059 solver.cpp:218] Iteration 7440 (2.66499 iter/s, 4.50283s/12 iters), loss = 1.16394
I0410 01:49:44.925251 18059 solver.cpp:237]     Train net output #0: loss = 1.16394 (* 1 = 1.16394 loss)
I0410 01:49:44.925262 18059 sgd_solver.cpp:105] Iteration 7440, lr = 0.00229063
I0410 01:49:46.854084 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7446.caffemodel
I0410 01:49:56.257606 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7446.solverstate
I0410 01:50:07.524441 18059 solver.cpp:330] Iteration 7446, Testing net (#0)
I0410 01:50:07.524471 18059 net.cpp:676] Ignoring source layer train-data
I0410 01:50:09.067847 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:50:11.984091 18059 solver.cpp:397]     Test net output #0: accuracy = 0.382353
I0410 01:50:11.984175 18059 solver.cpp:397]     Test net output #1: loss = 2.65906 (* 1 = 2.65906 loss)
I0410 01:50:13.721916 18059 solver.cpp:218] Iteration 7452 (0.41673 iter/s, 28.7957s/12 iters), loss = 1.11204
I0410 01:50:13.721990 18059 solver.cpp:237]     Train net output #0: loss = 1.11204 (* 1 = 1.11204 loss)
I0410 01:50:13.722004 18059 sgd_solver.cpp:105] Iteration 7452, lr = 0.00228519
I0410 01:50:18.303462 18059 solver.cpp:218] Iteration 7464 (2.61934 iter/s, 4.5813s/12 iters), loss = 1.08067
I0410 01:50:18.303508 18059 solver.cpp:237]     Train net output #0: loss = 1.08067 (* 1 = 1.08067 loss)
I0410 01:50:18.303519 18059 sgd_solver.cpp:105] Iteration 7464, lr = 0.00227976
I0410 01:50:22.936506 18059 solver.cpp:218] Iteration 7476 (2.59022 iter/s, 4.63281s/12 iters), loss = 1.30063
I0410 01:50:22.936563 18059 solver.cpp:237]     Train net output #0: loss = 1.30063 (* 1 = 1.30063 loss)
I0410 01:50:22.936574 18059 sgd_solver.cpp:105] Iteration 7476, lr = 0.00227435
I0410 01:50:26.187381 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:50:27.628955 18059 solver.cpp:218] Iteration 7488 (2.55743 iter/s, 4.69221s/12 iters), loss = 1.44126
I0410 01:50:27.629006 18059 solver.cpp:237]     Train net output #0: loss = 1.44126 (* 1 = 1.44126 loss)
I0410 01:50:27.629019 18059 sgd_solver.cpp:105] Iteration 7488, lr = 0.00226895
I0410 01:50:32.629629 18059 solver.cpp:218] Iteration 7500 (2.39979 iter/s, 5.00043s/12 iters), loss = 0.996348
I0410 01:50:32.629688 18059 solver.cpp:237]     Train net output #0: loss = 0.996348 (* 1 = 0.996348 loss)
I0410 01:50:32.629701 18059 sgd_solver.cpp:105] Iteration 7500, lr = 0.00226357
I0410 01:50:37.662355 18059 solver.cpp:218] Iteration 7512 (2.38451 iter/s, 5.03248s/12 iters), loss = 1.21295
I0410 01:50:37.662405 18059 solver.cpp:237]     Train net output #0: loss = 1.21295 (* 1 = 1.21295 loss)
I0410 01:50:37.662417 18059 sgd_solver.cpp:105] Iteration 7512, lr = 0.00225819
I0410 01:50:42.402388 18059 solver.cpp:218] Iteration 7524 (2.53175 iter/s, 4.73981s/12 iters), loss = 1.17597
I0410 01:50:42.402534 18059 solver.cpp:237]     Train net output #0: loss = 1.17597 (* 1 = 1.17597 loss)
I0410 01:50:42.402546 18059 sgd_solver.cpp:105] Iteration 7524, lr = 0.00225283
I0410 01:50:47.087975 18059 solver.cpp:218] Iteration 7536 (2.56122 iter/s, 4.68527s/12 iters), loss = 1.06781
I0410 01:50:47.088021 18059 solver.cpp:237]     Train net output #0: loss = 1.06781 (* 1 = 1.06781 loss)
I0410 01:50:47.088032 18059 sgd_solver.cpp:105] Iteration 7536, lr = 0.00224748
I0410 01:50:51.235152 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7548.caffemodel
I0410 01:50:57.951119 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7548.solverstate
I0410 01:51:04.316265 18059 solver.cpp:330] Iteration 7548, Testing net (#0)
I0410 01:51:04.316285 18059 net.cpp:676] Ignoring source layer train-data
I0410 01:51:05.872712 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:51:08.830375 18059 solver.cpp:397]     Test net output #0: accuracy = 0.397672
I0410 01:51:08.830404 18059 solver.cpp:397]     Test net output #1: loss = 2.59965 (* 1 = 2.59965 loss)
I0410 01:51:08.924378 18059 solver.cpp:218] Iteration 7548 (0.549561 iter/s, 21.8356s/12 iters), loss = 0.993326
I0410 01:51:08.924429 18059 solver.cpp:237]     Train net output #0: loss = 0.993326 (* 1 = 0.993326 loss)
I0410 01:51:08.924440 18059 sgd_solver.cpp:105] Iteration 7548, lr = 0.00224215
I0410 01:51:13.426553 18059 solver.cpp:218] Iteration 7560 (2.66551 iter/s, 4.50196s/12 iters), loss = 1.21982
I0410 01:51:13.426654 18059 solver.cpp:237]     Train net output #0: loss = 1.21982 (* 1 = 1.21982 loss)
I0410 01:51:13.426664 18059 sgd_solver.cpp:105] Iteration 7560, lr = 0.00223682
I0410 01:51:18.111855 18059 solver.cpp:218] Iteration 7572 (2.56135 iter/s, 4.68502s/12 iters), loss = 1.01471
I0410 01:51:18.111901 18059 solver.cpp:237]     Train net output #0: loss = 1.01471 (* 1 = 1.01471 loss)
I0410 01:51:18.111910 18059 sgd_solver.cpp:105] Iteration 7572, lr = 0.00223151
I0410 01:51:22.747896 18059 solver.cpp:218] Iteration 7584 (2.58854 iter/s, 4.63582s/12 iters), loss = 1.28123
I0410 01:51:22.747952 18059 solver.cpp:237]     Train net output #0: loss = 1.28123 (* 1 = 1.28123 loss)
I0410 01:51:22.747965 18059 sgd_solver.cpp:105] Iteration 7584, lr = 0.00222621
I0410 01:51:23.419759 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:51:27.444856 18059 solver.cpp:218] Iteration 7596 (2.55497 iter/s, 4.69673s/12 iters), loss = 1.3199
I0410 01:51:27.444900 18059 solver.cpp:237]     Train net output #0: loss = 1.3199 (* 1 = 1.3199 loss)
I0410 01:51:27.444911 18059 sgd_solver.cpp:105] Iteration 7596, lr = 0.00222093
I0410 01:51:32.190306 18059 solver.cpp:218] Iteration 7608 (2.52885 iter/s, 4.74523s/12 iters), loss = 1.13998
I0410 01:51:32.190341 18059 solver.cpp:237]     Train net output #0: loss = 1.13998 (* 1 = 1.13998 loss)
I0410 01:51:32.190349 18059 sgd_solver.cpp:105] Iteration 7608, lr = 0.00221565
I0410 01:51:37.153113 18059 solver.cpp:218] Iteration 7620 (2.4181 iter/s, 4.96258s/12 iters), loss = 1.06693
I0410 01:51:37.153156 18059 solver.cpp:237]     Train net output #0: loss = 1.06693 (* 1 = 1.06693 loss)
I0410 01:51:37.153167 18059 sgd_solver.cpp:105] Iteration 7620, lr = 0.00221039
I0410 01:51:39.375378 18059 blocking_queue.cpp:49] Waiting for data
I0410 01:51:41.728686 18059 solver.cpp:218] Iteration 7632 (2.62275 iter/s, 4.57535s/12 iters), loss = 1.46341
I0410 01:51:41.728741 18059 solver.cpp:237]     Train net output #0: loss = 1.46341 (* 1 = 1.46341 loss)
I0410 01:51:41.728752 18059 sgd_solver.cpp:105] Iteration 7632, lr = 0.00220515
I0410 01:51:46.380491 18059 solver.cpp:218] Iteration 7644 (2.57977 iter/s, 4.65158s/12 iters), loss = 1.19456
I0410 01:51:46.380628 18059 solver.cpp:237]     Train net output #0: loss = 1.19456 (* 1 = 1.19456 loss)
I0410 01:51:46.380640 18059 sgd_solver.cpp:105] Iteration 7644, lr = 0.00219991
I0410 01:51:48.228173 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7650.caffemodel
I0410 01:52:02.467574 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7650.solverstate
I0410 01:52:06.429731 18059 solver.cpp:330] Iteration 7650, Testing net (#0)
I0410 01:52:06.429757 18059 net.cpp:676] Ignoring source layer train-data
I0410 01:52:07.868237 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:52:11.028990 18059 solver.cpp:397]     Test net output #0: accuracy = 0.377451
I0410 01:52:11.029037 18059 solver.cpp:397]     Test net output #1: loss = 2.64223 (* 1 = 2.64223 loss)
I0410 01:52:12.731520 18059 solver.cpp:218] Iteration 7656 (0.455408 iter/s, 26.35s/12 iters), loss = 1.15562
I0410 01:52:12.731570 18059 solver.cpp:237]     Train net output #0: loss = 1.15562 (* 1 = 1.15562 loss)
I0410 01:52:12.731580 18059 sgd_solver.cpp:105] Iteration 7656, lr = 0.00219469
I0410 01:52:17.309219 18059 solver.cpp:218] Iteration 7668 (2.62153 iter/s, 4.57748s/12 iters), loss = 0.986301
I0410 01:52:17.309300 18059 solver.cpp:237]     Train net output #0: loss = 0.986301 (* 1 = 0.986301 loss)
I0410 01:52:17.309314 18059 sgd_solver.cpp:105] Iteration 7668, lr = 0.00218948
I0410 01:52:21.778139 18059 solver.cpp:218] Iteration 7680 (2.68537 iter/s, 4.46866s/12 iters), loss = 1.06038
I0410 01:52:21.778193 18059 solver.cpp:237]     Train net output #0: loss = 1.06038 (* 1 = 1.06038 loss)
I0410 01:52:21.778205 18059 sgd_solver.cpp:105] Iteration 7680, lr = 0.00218428
I0410 01:52:24.604460 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:52:26.695760 18059 solver.cpp:218] Iteration 7692 (2.44033 iter/s, 4.91738s/12 iters), loss = 1.24069
I0410 01:52:26.695812 18059 solver.cpp:237]     Train net output #0: loss = 1.24069 (* 1 = 1.24069 loss)
I0410 01:52:26.695822 18059 sgd_solver.cpp:105] Iteration 7692, lr = 0.00217909
I0410 01:52:31.587599 18059 solver.cpp:218] Iteration 7704 (2.45318 iter/s, 4.8916s/12 iters), loss = 0.95321
I0410 01:52:31.587651 18059 solver.cpp:237]     Train net output #0: loss = 0.95321 (* 1 = 0.95321 loss)
I0410 01:52:31.587661 18059 sgd_solver.cpp:105] Iteration 7704, lr = 0.00217392
I0410 01:52:36.230053 18059 solver.cpp:218] Iteration 7716 (2.58497 iter/s, 4.64223s/12 iters), loss = 1.04933
I0410 01:52:36.230101 18059 solver.cpp:237]     Train net output #0: loss = 1.04933 (* 1 = 1.04933 loss)
I0410 01:52:36.230111 18059 sgd_solver.cpp:105] Iteration 7716, lr = 0.00216876
I0410 01:52:40.765159 18059 solver.cpp:218] Iteration 7728 (2.64615 iter/s, 4.53488s/12 iters), loss = 1.19307
I0410 01:52:40.765208 18059 solver.cpp:237]     Train net output #0: loss = 1.19307 (* 1 = 1.19307 loss)
I0410 01:52:40.765219 18059 sgd_solver.cpp:105] Iteration 7728, lr = 0.00216361
I0410 01:52:45.791218 18059 solver.cpp:218] Iteration 7740 (2.38767 iter/s, 5.02582s/12 iters), loss = 1.23699
I0410 01:52:45.791263 18059 solver.cpp:237]     Train net output #0: loss = 1.23699 (* 1 = 1.23699 loss)
I0410 01:52:45.791275 18059 sgd_solver.cpp:105] Iteration 7740, lr = 0.00215847
I0410 01:52:50.026747 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7752.caffemodel
I0410 01:53:01.042574 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7752.solverstate
I0410 01:53:10.149922 18059 solver.cpp:330] Iteration 7752, Testing net (#0)
I0410 01:53:10.149948 18059 net.cpp:676] Ignoring source layer train-data
I0410 01:53:11.580363 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:53:14.647914 18059 solver.cpp:397]     Test net output #0: accuracy = 0.384191
I0410 01:53:14.647951 18059 solver.cpp:397]     Test net output #1: loss = 2.71116 (* 1 = 2.71116 loss)
I0410 01:53:14.741365 18059 solver.cpp:218] Iteration 7752 (0.414521 iter/s, 28.9491s/12 iters), loss = 1.05458
I0410 01:53:14.741411 18059 solver.cpp:237]     Train net output #0: loss = 1.05458 (* 1 = 1.05458 loss)
I0410 01:53:14.741420 18059 sgd_solver.cpp:105] Iteration 7752, lr = 0.00215335
I0410 01:53:18.600414 18059 solver.cpp:218] Iteration 7764 (3.10973 iter/s, 3.85885s/12 iters), loss = 1.09844
I0410 01:53:18.600466 18059 solver.cpp:237]     Train net output #0: loss = 1.09844 (* 1 = 1.09844 loss)
I0410 01:53:18.600479 18059 sgd_solver.cpp:105] Iteration 7764, lr = 0.00214823
I0410 01:53:23.243520 18059 solver.cpp:218] Iteration 7776 (2.5846 iter/s, 4.64288s/12 iters), loss = 1.298
I0410 01:53:23.243667 18059 solver.cpp:237]     Train net output #0: loss = 1.298 (* 1 = 1.298 loss)
I0410 01:53:23.243682 18059 sgd_solver.cpp:105] Iteration 7776, lr = 0.00214313
I0410 01:53:27.928846 18059 solver.cpp:218] Iteration 7788 (2.56136 iter/s, 4.68501s/12 iters), loss = 1.23206
I0410 01:53:27.928895 18059 solver.cpp:237]     Train net output #0: loss = 1.23206 (* 1 = 1.23206 loss)
I0410 01:53:27.928907 18059 sgd_solver.cpp:105] Iteration 7788, lr = 0.00213805
I0410 01:53:27.939711 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:53:32.626225 18059 solver.cpp:218] Iteration 7800 (2.55474 iter/s, 4.69716s/12 iters), loss = 1.22757
I0410 01:53:32.626272 18059 solver.cpp:237]     Train net output #0: loss = 1.22757 (* 1 = 1.22757 loss)
I0410 01:53:32.626284 18059 sgd_solver.cpp:105] Iteration 7800, lr = 0.00213297
I0410 01:53:37.378734 18059 solver.cpp:218] Iteration 7812 (2.5251 iter/s, 4.75229s/12 iters), loss = 1.00025
I0410 01:53:37.378780 18059 solver.cpp:237]     Train net output #0: loss = 1.00025 (* 1 = 1.00025 loss)
I0410 01:53:37.378791 18059 sgd_solver.cpp:105] Iteration 7812, lr = 0.00212791
I0410 01:53:42.065992 18059 solver.cpp:218] Iteration 7824 (2.56025 iter/s, 4.68704s/12 iters), loss = 0.903547
I0410 01:53:42.066028 18059 solver.cpp:237]     Train net output #0: loss = 0.903547 (* 1 = 0.903547 loss)
I0410 01:53:42.066036 18059 sgd_solver.cpp:105] Iteration 7824, lr = 0.00212285
I0410 01:53:47.184165 18059 solver.cpp:218] Iteration 7836 (2.34469 iter/s, 5.11794s/12 iters), loss = 1.35223
I0410 01:53:47.184216 18059 solver.cpp:237]     Train net output #0: loss = 1.35223 (* 1 = 1.35223 loss)
I0410 01:53:47.184227 18059 sgd_solver.cpp:105] Iteration 7836, lr = 0.00211781
I0410 01:53:51.707842 18059 solver.cpp:218] Iteration 7848 (2.65284 iter/s, 4.52345s/12 iters), loss = 1.04691
I0410 01:53:51.707897 18059 solver.cpp:237]     Train net output #0: loss = 1.04691 (* 1 = 1.04691 loss)
I0410 01:53:51.707911 18059 sgd_solver.cpp:105] Iteration 7848, lr = 0.00211279
I0410 01:53:53.446853 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7854.caffemodel
I0410 01:53:58.146636 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7854.solverstate
I0410 01:54:03.881183 18059 solver.cpp:330] Iteration 7854, Testing net (#0)
I0410 01:54:03.881204 18059 net.cpp:676] Ignoring source layer train-data
I0410 01:54:05.191188 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:54:08.277344 18059 solver.cpp:397]     Test net output #0: accuracy = 0.373162
I0410 01:54:08.277395 18059 solver.cpp:397]     Test net output #1: loss = 2.80225 (* 1 = 2.80225 loss)
I0410 01:54:09.901592 18059 solver.cpp:218] Iteration 7860 (0.659592 iter/s, 18.1931s/12 iters), loss = 0.980075
I0410 01:54:09.901643 18059 solver.cpp:237]     Train net output #0: loss = 0.980075 (* 1 = 0.980075 loss)
I0410 01:54:09.901654 18059 sgd_solver.cpp:105] Iteration 7860, lr = 0.00210777
I0410 01:54:14.710865 18059 solver.cpp:218] Iteration 7872 (2.4953 iter/s, 4.80904s/12 iters), loss = 1.04737
I0410 01:54:14.710909 18059 solver.cpp:237]     Train net output #0: loss = 1.04737 (* 1 = 1.04737 loss)
I0410 01:54:14.710919 18059 sgd_solver.cpp:105] Iteration 7872, lr = 0.00210277
I0410 01:54:20.185376 18059 solver.cpp:218] Iteration 7884 (2.19208 iter/s, 5.47426s/12 iters), loss = 0.896883
I0410 01:54:20.185426 18059 solver.cpp:237]     Train net output #0: loss = 0.896883 (* 1 = 0.896883 loss)
I0410 01:54:20.185437 18059 sgd_solver.cpp:105] Iteration 7884, lr = 0.00209777
I0410 01:54:22.308724 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:54:25.089026 18059 solver.cpp:218] Iteration 7896 (2.44727 iter/s, 4.90342s/12 iters), loss = 1.17593
I0410 01:54:25.089206 18059 solver.cpp:237]     Train net output #0: loss = 1.17593 (* 1 = 1.17593 loss)
I0410 01:54:25.089224 18059 sgd_solver.cpp:105] Iteration 7896, lr = 0.00209279
I0410 01:54:30.224542 18059 solver.cpp:218] Iteration 7908 (2.33684 iter/s, 5.13515s/12 iters), loss = 0.862396
I0410 01:54:30.224592 18059 solver.cpp:237]     Train net output #0: loss = 0.862396 (* 1 = 0.862396 loss)
I0410 01:54:30.224602 18059 sgd_solver.cpp:105] Iteration 7908, lr = 0.00208782
I0410 01:54:35.110946 18059 solver.cpp:218] Iteration 7920 (2.45591 iter/s, 4.88617s/12 iters), loss = 0.883446
I0410 01:54:35.110998 18059 solver.cpp:237]     Train net output #0: loss = 0.883446 (* 1 = 0.883446 loss)
I0410 01:54:35.111011 18059 sgd_solver.cpp:105] Iteration 7920, lr = 0.00208287
I0410 01:54:40.308576 18059 solver.cpp:218] Iteration 7932 (2.30885 iter/s, 5.19739s/12 iters), loss = 1.22053
I0410 01:54:40.308624 18059 solver.cpp:237]     Train net output #0: loss = 1.22053 (* 1 = 1.22053 loss)
I0410 01:54:40.308635 18059 sgd_solver.cpp:105] Iteration 7932, lr = 0.00207792
I0410 01:54:44.901540 18059 solver.cpp:218] Iteration 7944 (2.61282 iter/s, 4.59275s/12 iters), loss = 0.819958
I0410 01:54:44.901579 18059 solver.cpp:237]     Train net output #0: loss = 0.819958 (* 1 = 0.819958 loss)
I0410 01:54:44.901587 18059 sgd_solver.cpp:105] Iteration 7944, lr = 0.00207299
I0410 01:54:49.289270 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7956.caffemodel
I0410 01:54:59.467609 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7956.solverstate
I0410 01:55:11.487174 18059 solver.cpp:330] Iteration 7956, Testing net (#0)
I0410 01:55:11.487198 18059 net.cpp:676] Ignoring source layer train-data
I0410 01:55:12.851073 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:55:15.991153 18059 solver.cpp:397]     Test net output #0: accuracy = 0.378676
I0410 01:55:15.991185 18059 solver.cpp:397]     Test net output #1: loss = 2.89932 (* 1 = 2.89932 loss)
I0410 01:55:16.085011 18059 solver.cpp:218] Iteration 7956 (0.384833 iter/s, 31.1823s/12 iters), loss = 1.11642
I0410 01:55:16.085054 18059 solver.cpp:237]     Train net output #0: loss = 1.11642 (* 1 = 1.11642 loss)
I0410 01:55:16.085063 18059 sgd_solver.cpp:105] Iteration 7956, lr = 0.00206807
I0410 01:55:20.420228 18059 solver.cpp:218] Iteration 7968 (2.76816 iter/s, 4.335s/12 iters), loss = 1.01399
I0410 01:55:20.420280 18059 solver.cpp:237]     Train net output #0: loss = 1.01399 (* 1 = 1.01399 loss)
I0410 01:55:20.420292 18059 sgd_solver.cpp:105] Iteration 7968, lr = 0.00206316
I0410 01:55:25.010720 18059 solver.cpp:218] Iteration 7980 (2.61423 iter/s, 4.59027s/12 iters), loss = 1.05971
I0410 01:55:25.010768 18059 solver.cpp:237]     Train net output #0: loss = 1.05971 (* 1 = 1.05971 loss)
I0410 01:55:25.010779 18059 sgd_solver.cpp:105] Iteration 7980, lr = 0.00205826
I0410 01:55:29.132143 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:55:29.792842 18059 solver.cpp:218] Iteration 7992 (2.50947 iter/s, 4.78189s/12 iters), loss = 1.16355
I0410 01:55:29.792943 18059 solver.cpp:237]     Train net output #0: loss = 1.16355 (* 1 = 1.16355 loss)
I0410 01:55:29.792954 18059 sgd_solver.cpp:105] Iteration 7992, lr = 0.00205337
I0410 01:55:34.247944 18059 solver.cpp:218] Iteration 8004 (2.6937 iter/s, 4.45483s/12 iters), loss = 1.12764
I0410 01:55:34.247983 18059 solver.cpp:237]     Train net output #0: loss = 1.12764 (* 1 = 1.12764 loss)
I0410 01:55:34.247992 18059 sgd_solver.cpp:105] Iteration 8004, lr = 0.0020485
I0410 01:55:38.902595 18059 solver.cpp:218] Iteration 8016 (2.57819 iter/s, 4.65444s/12 iters), loss = 1.06783
I0410 01:55:38.902642 18059 solver.cpp:237]     Train net output #0: loss = 1.06783 (* 1 = 1.06783 loss)
I0410 01:55:38.902652 18059 sgd_solver.cpp:105] Iteration 8016, lr = 0.00204363
I0410 01:55:43.491928 18059 solver.cpp:218] Iteration 8028 (2.61488 iter/s, 4.58912s/12 iters), loss = 0.794392
I0410 01:55:43.491972 18059 solver.cpp:237]     Train net output #0: loss = 0.794392 (* 1 = 0.794392 loss)
I0410 01:55:43.491982 18059 sgd_solver.cpp:105] Iteration 8028, lr = 0.00203878
I0410 01:55:47.814373 18059 solver.cpp:218] Iteration 8040 (2.77634 iter/s, 4.32223s/12 iters), loss = 1.13181
I0410 01:55:47.814426 18059 solver.cpp:237]     Train net output #0: loss = 1.13181 (* 1 = 1.13181 loss)
I0410 01:55:47.814437 18059 sgd_solver.cpp:105] Iteration 8040, lr = 0.00203394
I0410 01:55:52.215973 18059 solver.cpp:218] Iteration 8052 (2.72641 iter/s, 4.40138s/12 iters), loss = 0.911475
I0410 01:55:52.216012 18059 solver.cpp:237]     Train net output #0: loss = 0.911475 (* 1 = 0.911475 loss)
I0410 01:55:52.216019 18059 sgd_solver.cpp:105] Iteration 8052, lr = 0.00202911
I0410 01:55:54.266016 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8058.caffemodel
I0410 01:56:02.472875 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8058.solverstate
I0410 01:56:21.113212 18059 solver.cpp:330] Iteration 8058, Testing net (#0)
I0410 01:56:21.113241 18059 net.cpp:676] Ignoring source layer train-data
I0410 01:56:22.346511 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:56:25.511271 18059 solver.cpp:397]     Test net output #0: accuracy = 0.38848
I0410 01:56:25.511304 18059 solver.cpp:397]     Test net output #1: loss = 2.80171 (* 1 = 2.80171 loss)
I0410 01:56:27.369092 18059 solver.cpp:218] Iteration 8064 (0.341376 iter/s, 35.1518s/12 iters), loss = 1.02732
I0410 01:56:27.369139 18059 solver.cpp:237]     Train net output #0: loss = 1.02732 (* 1 = 1.02732 loss)
I0410 01:56:27.369153 18059 sgd_solver.cpp:105] Iteration 8064, lr = 0.00202429
I0410 01:56:32.239537 18059 solver.cpp:218] Iteration 8076 (2.46396 iter/s, 4.87021s/12 iters), loss = 0.813994
I0410 01:56:32.239593 18059 solver.cpp:237]     Train net output #0: loss = 0.813994 (* 1 = 0.813994 loss)
I0410 01:56:32.239604 18059 sgd_solver.cpp:105] Iteration 8076, lr = 0.00201949
I0410 01:56:37.018368 18059 solver.cpp:218] Iteration 8088 (2.5112 iter/s, 4.7786s/12 iters), loss = 0.960895
I0410 01:56:37.018447 18059 solver.cpp:237]     Train net output #0: loss = 0.960895 (* 1 = 0.960895 loss)
I0410 01:56:37.018460 18059 sgd_solver.cpp:105] Iteration 8088, lr = 0.00201469
I0410 01:56:38.268087 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:56:41.691346 18059 solver.cpp:218] Iteration 8100 (2.56809 iter/s, 4.67273s/12 iters), loss = 1.04202
I0410 01:56:41.691390 18059 solver.cpp:237]     Train net output #0: loss = 1.04202 (* 1 = 1.04202 loss)
I0410 01:56:41.691398 18059 sgd_solver.cpp:105] Iteration 8100, lr = 0.00200991
I0410 01:56:46.346222 18059 solver.cpp:218] Iteration 8112 (2.57806 iter/s, 4.65466s/12 iters), loss = 0.86814
I0410 01:56:46.346282 18059 solver.cpp:237]     Train net output #0: loss = 0.86814 (* 1 = 0.86814 loss)
I0410 01:56:46.346298 18059 sgd_solver.cpp:105] Iteration 8112, lr = 0.00200514
I0410 01:56:51.036135 18059 solver.cpp:218] Iteration 8124 (2.55881 iter/s, 4.68968s/12 iters), loss = 1.06339
I0410 01:56:51.036185 18059 solver.cpp:237]     Train net output #0: loss = 1.06339 (* 1 = 1.06339 loss)
I0410 01:56:51.036197 18059 sgd_solver.cpp:105] Iteration 8124, lr = 0.00200038
I0410 01:56:55.814604 18059 solver.cpp:218] Iteration 8136 (2.51138 iter/s, 4.77824s/12 iters), loss = 0.901929
I0410 01:56:55.814654 18059 solver.cpp:237]     Train net output #0: loss = 0.901929 (* 1 = 0.901929 loss)
I0410 01:56:55.814668 18059 sgd_solver.cpp:105] Iteration 8136, lr = 0.00199563
I0410 01:57:00.584442 18059 solver.cpp:218] Iteration 8148 (2.51593 iter/s, 4.7696s/12 iters), loss = 1.0027
I0410 01:57:00.584496 18059 solver.cpp:237]     Train net output #0: loss = 1.0027 (* 1 = 1.0027 loss)
I0410 01:57:00.584508 18059 sgd_solver.cpp:105] Iteration 8148, lr = 0.00199089
I0410 01:57:05.045357 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8160.caffemodel
I0410 01:57:09.718684 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8160.solverstate
I0410 01:57:16.364995 18059 solver.cpp:330] Iteration 8160, Testing net (#0)
I0410 01:57:16.365021 18059 net.cpp:676] Ignoring source layer train-data
I0410 01:57:17.504406 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:57:20.711316 18059 solver.cpp:397]     Test net output #0: accuracy = 0.397672
I0410 01:57:20.711344 18059 solver.cpp:397]     Test net output #1: loss = 2.81371 (* 1 = 2.81371 loss)
I0410 01:57:20.804638 18059 solver.cpp:218] Iteration 8160 (0.593488 iter/s, 20.2194s/12 iters), loss = 1.0719
I0410 01:57:20.804673 18059 solver.cpp:237]     Train net output #0: loss = 1.0719 (* 1 = 1.0719 loss)
I0410 01:57:20.804682 18059 sgd_solver.cpp:105] Iteration 8160, lr = 0.00198616
I0410 01:57:24.853291 18059 solver.cpp:218] Iteration 8172 (2.96409 iter/s, 4.04846s/12 iters), loss = 0.949846
I0410 01:57:24.853332 18059 solver.cpp:237]     Train net output #0: loss = 0.949846 (* 1 = 0.949846 loss)
I0410 01:57:24.853341 18059 sgd_solver.cpp:105] Iteration 8172, lr = 0.00198145
I0410 01:57:29.523227 18059 solver.cpp:218] Iteration 8184 (2.56975 iter/s, 4.66972s/12 iters), loss = 0.983093
I0410 01:57:29.523274 18059 solver.cpp:237]     Train net output #0: loss = 0.983093 (* 1 = 0.983093 loss)
I0410 01:57:29.523283 18059 sgd_solver.cpp:105] Iteration 8184, lr = 0.00197674
I0410 01:57:32.760825 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:57:34.085848 18059 solver.cpp:218] Iteration 8196 (2.63019 iter/s, 4.5624s/12 iters), loss = 1.0074
I0410 01:57:34.085889 18059 solver.cpp:237]     Train net output #0: loss = 1.0074 (* 1 = 1.0074 loss)
I0410 01:57:34.085898 18059 sgd_solver.cpp:105] Iteration 8196, lr = 0.00197205
I0410 01:57:38.607297 18059 solver.cpp:218] Iteration 8208 (2.65414 iter/s, 4.52123s/12 iters), loss = 1.01248
I0410 01:57:38.607342 18059 solver.cpp:237]     Train net output #0: loss = 1.01248 (* 1 = 1.01248 loss)
I0410 01:57:38.607353 18059 sgd_solver.cpp:105] Iteration 8208, lr = 0.00196737
I0410 01:57:43.395211 18059 solver.cpp:218] Iteration 8220 (2.50643 iter/s, 4.78769s/12 iters), loss = 0.905815
I0410 01:57:43.395279 18059 solver.cpp:237]     Train net output #0: loss = 0.905815 (* 1 = 0.905815 loss)
I0410 01:57:43.395290 18059 sgd_solver.cpp:105] Iteration 8220, lr = 0.0019627
I0410 01:57:47.985020 18059 solver.cpp:218] Iteration 8232 (2.61463 iter/s, 4.58957s/12 iters), loss = 1.0272
I0410 01:57:47.985074 18059 solver.cpp:237]     Train net output #0: loss = 1.0272 (* 1 = 1.0272 loss)
I0410 01:57:47.985086 18059 sgd_solver.cpp:105] Iteration 8232, lr = 0.00195804
I0410 01:57:52.875624 18059 solver.cpp:218] Iteration 8244 (2.4538 iter/s, 4.89037s/12 iters), loss = 0.924009
I0410 01:57:52.875667 18059 solver.cpp:237]     Train net output #0: loss = 0.924009 (* 1 = 0.924009 loss)
I0410 01:57:52.875675 18059 sgd_solver.cpp:105] Iteration 8244, lr = 0.00195339
I0410 01:57:57.458408 18059 solver.cpp:218] Iteration 8256 (2.61862 iter/s, 4.58257s/12 iters), loss = 0.961643
I0410 01:57:57.458460 18059 solver.cpp:237]     Train net output #0: loss = 0.961643 (* 1 = 0.961643 loss)
I0410 01:57:57.458472 18059 sgd_solver.cpp:105] Iteration 8256, lr = 0.00194875
I0410 01:57:59.375402 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8262.caffemodel
I0410 01:58:07.490907 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8262.solverstate
I0410 01:58:11.168184 18059 solver.cpp:330] Iteration 8262, Testing net (#0)
I0410 01:58:11.168212 18059 net.cpp:676] Ignoring source layer train-data
I0410 01:58:12.373198 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:58:15.692382 18059 solver.cpp:397]     Test net output #0: accuracy = 0.397059
I0410 01:58:15.692564 18059 solver.cpp:397]     Test net output #1: loss = 2.82975 (* 1 = 2.82975 loss)
I0410 01:58:17.277935 18059 solver.cpp:218] Iteration 8268 (0.605487 iter/s, 19.8188s/12 iters), loss = 0.867264
I0410 01:58:17.277997 18059 solver.cpp:237]     Train net output #0: loss = 0.867264 (* 1 = 0.867264 loss)
I0410 01:58:17.278008 18059 sgd_solver.cpp:105] Iteration 8268, lr = 0.00194412
I0410 01:58:22.085774 18059 solver.cpp:218] Iteration 8280 (2.49605 iter/s, 4.8076s/12 iters), loss = 0.70362
I0410 01:58:22.085819 18059 solver.cpp:237]     Train net output #0: loss = 0.70362 (* 1 = 0.70362 loss)
I0410 01:58:22.085827 18059 sgd_solver.cpp:105] Iteration 8280, lr = 0.00193951
I0410 01:58:27.114992 18059 solver.cpp:218] Iteration 8292 (2.38617 iter/s, 5.02899s/12 iters), loss = 0.761984
I0410 01:58:27.115031 18059 solver.cpp:237]     Train net output #0: loss = 0.761984 (* 1 = 0.761984 loss)
I0410 01:58:27.115037 18059 sgd_solver.cpp:105] Iteration 8292, lr = 0.0019349
I0410 01:58:27.694779 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:58:31.576639 18059 solver.cpp:218] Iteration 8304 (2.68971 iter/s, 4.46144s/12 iters), loss = 0.931404
I0410 01:58:31.576683 18059 solver.cpp:237]     Train net output #0: loss = 0.931404 (* 1 = 0.931404 loss)
I0410 01:58:31.576692 18059 sgd_solver.cpp:105] Iteration 8304, lr = 0.00193031
I0410 01:58:34.237130 18059 blocking_queue.cpp:49] Waiting for data
I0410 01:58:36.325700 18059 solver.cpp:218] Iteration 8316 (2.52694 iter/s, 4.74884s/12 iters), loss = 0.914506
I0410 01:58:36.325747 18059 solver.cpp:237]     Train net output #0: loss = 0.914506 (* 1 = 0.914506 loss)
I0410 01:58:36.325757 18059 sgd_solver.cpp:105] Iteration 8316, lr = 0.00192573
I0410 01:58:41.390609 18059 solver.cpp:218] Iteration 8328 (2.36935 iter/s, 5.06467s/12 iters), loss = 1.0513
I0410 01:58:41.390655 18059 solver.cpp:237]     Train net output #0: loss = 1.0513 (* 1 = 1.0513 loss)
I0410 01:58:41.390663 18059 sgd_solver.cpp:105] Iteration 8328, lr = 0.00192115
I0410 01:58:46.303757 18059 solver.cpp:218] Iteration 8340 (2.44254 iter/s, 4.91292s/12 iters), loss = 1.03155
I0410 01:58:46.304255 18059 solver.cpp:237]     Train net output #0: loss = 1.03155 (* 1 = 1.03155 loss)
I0410 01:58:46.304266 18059 sgd_solver.cpp:105] Iteration 8340, lr = 0.00191659
I0410 01:58:51.048542 18059 solver.cpp:218] Iteration 8352 (2.52945 iter/s, 4.74411s/12 iters), loss = 0.875651
I0410 01:58:51.048596 18059 solver.cpp:237]     Train net output #0: loss = 0.875651 (* 1 = 0.875651 loss)
I0410 01:58:51.048609 18059 sgd_solver.cpp:105] Iteration 8352, lr = 0.00191204
I0410 01:58:55.664345 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8364.caffemodel
I0410 01:59:04.211596 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8364.solverstate
I0410 01:59:12.474822 18059 solver.cpp:330] Iteration 8364, Testing net (#0)
I0410 01:59:12.474843 18059 net.cpp:676] Ignoring source layer train-data
I0410 01:59:13.664068 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:59:17.040127 18059 solver.cpp:397]     Test net output #0: accuracy = 0.41973
I0410 01:59:17.042176 18059 solver.cpp:397]     Test net output #1: loss = 2.71095 (* 1 = 2.71095 loss)
I0410 01:59:17.135696 18059 solver.cpp:218] Iteration 8364 (0.460014 iter/s, 26.0862s/12 iters), loss = 1.0073
I0410 01:59:17.137217 18059 solver.cpp:237]     Train net output #0: loss = 1.0073 (* 1 = 1.0073 loss)
I0410 01:59:17.137231 18059 sgd_solver.cpp:105] Iteration 8364, lr = 0.0019075
I0410 01:59:20.875264 18059 solver.cpp:218] Iteration 8376 (3.21035 iter/s, 3.73791s/12 iters), loss = 0.692563
I0410 01:59:20.875317 18059 solver.cpp:237]     Train net output #0: loss = 0.692563 (* 1 = 0.692563 loss)
I0410 01:59:20.875329 18059 sgd_solver.cpp:105] Iteration 8376, lr = 0.00190297
I0410 01:59:25.464359 18059 solver.cpp:218] Iteration 8388 (2.61502 iter/s, 4.58887s/12 iters), loss = 0.803539
I0410 01:59:25.464407 18059 solver.cpp:237]     Train net output #0: loss = 0.803539 (* 1 = 0.803539 loss)
I0410 01:59:25.464418 18059 sgd_solver.cpp:105] Iteration 8388, lr = 0.00189846
I0410 01:59:28.212153 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:59:30.233316 18059 solver.cpp:218] Iteration 8400 (2.51639 iter/s, 4.76873s/12 iters), loss = 0.833535
I0410 01:59:30.233359 18059 solver.cpp:237]     Train net output #0: loss = 0.833535 (* 1 = 0.833535 loss)
I0410 01:59:30.233371 18059 sgd_solver.cpp:105] Iteration 8400, lr = 0.00189395
I0410 01:59:35.098503 18059 solver.cpp:218] Iteration 8412 (2.46662 iter/s, 4.86496s/12 iters), loss = 0.8751
I0410 01:59:35.098552 18059 solver.cpp:237]     Train net output #0: loss = 0.8751 (* 1 = 0.8751 loss)
I0410 01:59:35.098563 18059 sgd_solver.cpp:105] Iteration 8412, lr = 0.00188945
I0410 01:59:39.677886 18059 solver.cpp:218] Iteration 8424 (2.62057 iter/s, 4.57916s/12 iters), loss = 0.822652
I0410 01:59:39.677932 18059 solver.cpp:237]     Train net output #0: loss = 0.822652 (* 1 = 0.822652 loss)
I0410 01:59:39.677940 18059 sgd_solver.cpp:105] Iteration 8424, lr = 0.00188497
I0410 01:59:44.469758 18059 solver.cpp:218] Iteration 8436 (2.50436 iter/s, 4.79165s/12 iters), loss = 0.838925
I0410 01:59:44.469807 18059 solver.cpp:237]     Train net output #0: loss = 0.838925 (* 1 = 0.838925 loss)
I0410 01:59:44.469820 18059 sgd_solver.cpp:105] Iteration 8436, lr = 0.00188049
I0410 01:59:49.332643 18059 solver.cpp:218] Iteration 8448 (2.46779 iter/s, 4.86265s/12 iters), loss = 0.825537
I0410 01:59:49.332803 18059 solver.cpp:237]     Train net output #0: loss = 0.825537 (* 1 = 0.825537 loss)
I0410 01:59:49.332816 18059 sgd_solver.cpp:105] Iteration 8448, lr = 0.00187603
I0410 01:59:54.160676 18059 solver.cpp:218] Iteration 8460 (2.48566 iter/s, 4.8277s/12 iters), loss = 0.947772
I0410 01:59:54.160729 18059 solver.cpp:237]     Train net output #0: loss = 0.947772 (* 1 = 0.947772 loss)
I0410 01:59:54.160742 18059 sgd_solver.cpp:105] Iteration 8460, lr = 0.00187157
I0410 01:59:56.093045 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8466.caffemodel
I0410 02:00:02.499650 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8466.solverstate
I0410 02:00:06.127332 18059 solver.cpp:330] Iteration 8466, Testing net (#0)
I0410 02:00:06.127353 18059 net.cpp:676] Ignoring source layer train-data
I0410 02:00:07.189621 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:00:10.591769 18059 solver.cpp:397]     Test net output #0: accuracy = 0.397059
I0410 02:00:10.591812 18059 solver.cpp:397]     Test net output #1: loss = 2.82377 (* 1 = 2.82377 loss)
I0410 02:00:12.570211 18059 solver.cpp:218] Iteration 8472 (0.651861 iter/s, 18.4088s/12 iters), loss = 0.839206
I0410 02:00:12.570261 18059 solver.cpp:237]     Train net output #0: loss = 0.839206 (* 1 = 0.839206 loss)
I0410 02:00:12.570271 18059 sgd_solver.cpp:105] Iteration 8472, lr = 0.00186713
I0410 02:00:17.606889 18059 solver.cpp:218] Iteration 8484 (2.38264 iter/s, 5.03644s/12 iters), loss = 0.972234
I0410 02:00:17.606935 18059 solver.cpp:237]     Train net output #0: loss = 0.972234 (* 1 = 0.972234 loss)
I0410 02:00:17.606946 18059 sgd_solver.cpp:105] Iteration 8484, lr = 0.0018627
I0410 02:00:22.206398 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:00:22.219645 18059 solver.cpp:218] Iteration 8496 (2.60161 iter/s, 4.61253s/12 iters), loss = 0.920694
I0410 02:00:22.219702 18059 solver.cpp:237]     Train net output #0: loss = 0.920694 (* 1 = 0.920694 loss)
I0410 02:00:22.219714 18059 sgd_solver.cpp:105] Iteration 8496, lr = 0.00185827
I0410 02:00:27.249223 18059 solver.cpp:218] Iteration 8508 (2.386 iter/s, 5.02933s/12 iters), loss = 0.931669
I0410 02:00:27.249269 18059 solver.cpp:237]     Train net output #0: loss = 0.931669 (* 1 = 0.931669 loss)
I0410 02:00:27.249281 18059 sgd_solver.cpp:105] Iteration 8508, lr = 0.00185386
I0410 02:00:32.231261 18059 solver.cpp:218] Iteration 8520 (2.40877 iter/s, 4.9818s/12 iters), loss = 0.791059
I0410 02:00:32.231319 18059 solver.cpp:237]     Train net output #0: loss = 0.791059 (* 1 = 0.791059 loss)
I0410 02:00:32.231331 18059 sgd_solver.cpp:105] Iteration 8520, lr = 0.00184946
I0410 02:00:37.602085 18059 solver.cpp:218] Iteration 8532 (2.2344 iter/s, 5.37057s/12 iters), loss = 0.929356
I0410 02:00:37.602133 18059 solver.cpp:237]     Train net output #0: loss = 0.929356 (* 1 = 0.929356 loss)
I0410 02:00:37.602144 18059 sgd_solver.cpp:105] Iteration 8532, lr = 0.00184507
I0410 02:00:42.358448 18059 solver.cpp:218] Iteration 8544 (2.52306 iter/s, 4.75613s/12 iters), loss = 0.849669
I0410 02:00:42.358510 18059 solver.cpp:237]     Train net output #0: loss = 0.849669 (* 1 = 0.849669 loss)
I0410 02:00:42.358523 18059 sgd_solver.cpp:105] Iteration 8544, lr = 0.00184069
I0410 02:00:47.449409 18059 solver.cpp:218] Iteration 8556 (2.35724 iter/s, 5.0907s/12 iters), loss = 1.01877
I0410 02:00:47.449471 18059 solver.cpp:237]     Train net output #0: loss = 1.01877 (* 1 = 1.01877 loss)
I0410 02:00:47.449483 18059 sgd_solver.cpp:105] Iteration 8556, lr = 0.00183632
I0410 02:00:51.574879 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8568.caffemodel
I0410 02:00:59.629140 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8568.solverstate
I0410 02:01:07.249130 18059 solver.cpp:330] Iteration 8568, Testing net (#0)
I0410 02:01:07.249153 18059 net.cpp:676] Ignoring source layer train-data
I0410 02:01:08.333174 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:01:11.690569 18059 solver.cpp:397]     Test net output #0: accuracy = 0.420343
I0410 02:01:11.690609 18059 solver.cpp:397]     Test net output #1: loss = 2.69226 (* 1 = 2.69226 loss)
I0410 02:01:11.784374 18059 solver.cpp:218] Iteration 8568 (0.493136 iter/s, 24.334s/12 iters), loss = 0.862448
I0410 02:01:11.784430 18059 solver.cpp:237]     Train net output #0: loss = 0.862448 (* 1 = 0.862448 loss)
I0410 02:01:11.784443 18059 sgd_solver.cpp:105] Iteration 8568, lr = 0.00183196
I0410 02:01:15.767699 18059 solver.cpp:218] Iteration 8580 (3.01271 iter/s, 3.98312s/12 iters), loss = 0.964832
I0410 02:01:15.767740 18059 solver.cpp:237]     Train net output #0: loss = 0.964832 (* 1 = 0.964832 loss)
I0410 02:01:15.767747 18059 sgd_solver.cpp:105] Iteration 8580, lr = 0.00182761
I0410 02:01:20.421274 18059 solver.cpp:218] Iteration 8592 (2.57878 iter/s, 4.65336s/12 iters), loss = 0.828572
I0410 02:01:20.421321 18059 solver.cpp:237]     Train net output #0: loss = 0.828572 (* 1 = 0.828572 loss)
I0410 02:01:20.421334 18059 sgd_solver.cpp:105] Iteration 8592, lr = 0.00182327
I0410 02:01:22.294668 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:01:24.953406 18059 solver.cpp:218] Iteration 8604 (2.64789 iter/s, 4.53192s/12 iters), loss = 0.837176
I0410 02:01:24.953446 18059 solver.cpp:237]     Train net output #0: loss = 0.837176 (* 1 = 0.837176 loss)
I0410 02:01:24.953455 18059 sgd_solver.cpp:105] Iteration 8604, lr = 0.00181894
I0410 02:01:29.526369 18059 solver.cpp:218] Iteration 8616 (2.62424 iter/s, 4.57274s/12 iters), loss = 0.593889
I0410 02:01:29.526417 18059 solver.cpp:237]     Train net output #0: loss = 0.593889 (* 1 = 0.593889 loss)
I0410 02:01:29.526427 18059 sgd_solver.cpp:105] Iteration 8616, lr = 0.00181462
I0410 02:01:34.291534 18059 solver.cpp:218] Iteration 8628 (2.5184 iter/s, 4.76494s/12 iters), loss = 0.741635
I0410 02:01:34.291647 18059 solver.cpp:237]     Train net output #0: loss = 0.741635 (* 1 = 0.741635 loss)
I0410 02:01:34.291659 18059 sgd_solver.cpp:105] Iteration 8628, lr = 0.00181031
I0410 02:01:39.028636 18059 solver.cpp:218] Iteration 8640 (2.53335 iter/s, 4.73681s/12 iters), loss = 0.962987
I0410 02:01:39.028688 18059 solver.cpp:237]     Train net output #0: loss = 0.962987 (* 1 = 0.962987 loss)
I0410 02:01:39.028699 18059 sgd_solver.cpp:105] Iteration 8640, lr = 0.00180602
I0410 02:01:43.488489 18059 solver.cpp:218] Iteration 8652 (2.6908 iter/s, 4.45964s/12 iters), loss = 0.650479
I0410 02:01:43.488533 18059 solver.cpp:237]     Train net output #0: loss = 0.650479 (* 1 = 0.650479 loss)
I0410 02:01:43.488541 18059 sgd_solver.cpp:105] Iteration 8652, lr = 0.00180173
I0410 02:01:48.097518 18059 solver.cpp:218] Iteration 8664 (2.60371 iter/s, 4.60881s/12 iters), loss = 0.927027
I0410 02:01:48.097580 18059 solver.cpp:237]     Train net output #0: loss = 0.927027 (* 1 = 0.927027 loss)
I0410 02:01:48.097595 18059 sgd_solver.cpp:105] Iteration 8664, lr = 0.00179745
I0410 02:01:49.973122 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8670.caffemodel
I0410 02:01:55.677814 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8670.solverstate
I0410 02:02:02.581580 18059 solver.cpp:330] Iteration 8670, Testing net (#0)
I0410 02:02:02.581609 18059 net.cpp:676] Ignoring source layer train-data
I0410 02:02:03.661849 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:02:07.051606 18059 solver.cpp:397]     Test net output #0: accuracy = 0.434436
I0410 02:02:07.051795 18059 solver.cpp:397]     Test net output #1: loss = 2.7267 (* 1 = 2.7267 loss)
I0410 02:02:08.665084 18059 solver.cpp:218] Iteration 8676 (0.583465 iter/s, 20.5668s/12 iters), loss = 0.683119
I0410 02:02:08.665135 18059 solver.cpp:237]     Train net output #0: loss = 0.683119 (* 1 = 0.683119 loss)
I0410 02:02:08.665148 18059 sgd_solver.cpp:105] Iteration 8676, lr = 0.00179318
I0410 02:02:13.183648 18059 solver.cpp:218] Iteration 8688 (2.65584 iter/s, 4.51834s/12 iters), loss = 0.625383
I0410 02:02:13.183692 18059 solver.cpp:237]     Train net output #0: loss = 0.625383 (* 1 = 0.625383 loss)
I0410 02:02:13.183702 18059 sgd_solver.cpp:105] Iteration 8688, lr = 0.00178893
I0410 02:02:16.979044 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:02:17.625730 18059 solver.cpp:218] Iteration 8700 (2.70157 iter/s, 4.44187s/12 iters), loss = 0.833019
I0410 02:02:17.625774 18059 solver.cpp:237]     Train net output #0: loss = 0.833019 (* 1 = 0.833019 loss)
I0410 02:02:17.625783 18059 sgd_solver.cpp:105] Iteration 8700, lr = 0.00178468
I0410 02:02:22.384608 18059 solver.cpp:218] Iteration 8712 (2.52172 iter/s, 4.75865s/12 iters), loss = 0.93442
I0410 02:02:22.384656 18059 solver.cpp:237]     Train net output #0: loss = 0.93442 (* 1 = 0.93442 loss)
I0410 02:02:22.384668 18059 sgd_solver.cpp:105] Iteration 8712, lr = 0.00178044
I0410 02:02:27.226263 18059 solver.cpp:218] Iteration 8724 (2.47861 iter/s, 4.84142s/12 iters), loss = 0.769082
I0410 02:02:27.226308 18059 solver.cpp:237]     Train net output #0: loss = 0.769082 (* 1 = 0.769082 loss)
I0410 02:02:27.226317 18059 sgd_solver.cpp:105] Iteration 8724, lr = 0.00177621
I0410 02:02:32.022198 18059 solver.cpp:218] Iteration 8736 (2.50224 iter/s, 4.79571s/12 iters), loss = 0.809694
I0410 02:02:32.022249 18059 solver.cpp:237]     Train net output #0: loss = 0.809694 (* 1 = 0.809694 loss)
I0410 02:02:32.022261 18059 sgd_solver.cpp:105] Iteration 8736, lr = 0.001772
I0410 02:02:36.774814 18059 solver.cpp:218] Iteration 8748 (2.52505 iter/s, 4.75239s/12 iters), loss = 0.832934
I0410 02:02:36.774859 18059 solver.cpp:237]     Train net output #0: loss = 0.832934 (* 1 = 0.832934 loss)
I0410 02:02:36.774868 18059 sgd_solver.cpp:105] Iteration 8748, lr = 0.00176779
I0410 02:02:41.431962 18059 solver.cpp:218] Iteration 8760 (2.57681 iter/s, 4.65692s/12 iters), loss = 0.745793
I0410 02:02:41.432072 18059 solver.cpp:237]     Train net output #0: loss = 0.745793 (* 1 = 0.745793 loss)
I0410 02:02:41.432086 18059 sgd_solver.cpp:105] Iteration 8760, lr = 0.00176359
I0410 02:02:45.570982 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8772.caffemodel
I0410 02:02:51.225996 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8772.solverstate
I0410 02:02:54.903978 18059 solver.cpp:330] Iteration 8772, Testing net (#0)
I0410 02:02:54.904004 18059 net.cpp:676] Ignoring source layer train-data
I0410 02:02:55.880580 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:02:59.486002 18059 solver.cpp:397]     Test net output #0: accuracy = 0.41973
I0410 02:02:59.486050 18059 solver.cpp:397]     Test net output #1: loss = 2.77466 (* 1 = 2.77466 loss)
I0410 02:02:59.579658 18059 solver.cpp:218] Iteration 8772 (0.661268 iter/s, 18.1469s/12 iters), loss = 0.857009
I0410 02:02:59.579715 18059 solver.cpp:237]     Train net output #0: loss = 0.857009 (* 1 = 0.857009 loss)
I0410 02:02:59.579726 18059 sgd_solver.cpp:105] Iteration 8772, lr = 0.00175941
I0410 02:03:03.852360 18059 solver.cpp:218] Iteration 8784 (2.80867 iter/s, 4.27248s/12 iters), loss = 0.778479
I0410 02:03:03.852414 18059 solver.cpp:237]     Train net output #0: loss = 0.778479 (* 1 = 0.778479 loss)
I0410 02:03:03.852427 18059 sgd_solver.cpp:105] Iteration 8784, lr = 0.00175523
I0410 02:03:09.112289 18059 solver.cpp:218] Iteration 8796 (2.28151 iter/s, 5.25968s/12 iters), loss = 0.604078
I0410 02:03:09.112346 18059 solver.cpp:237]     Train net output #0: loss = 0.604078 (* 1 = 0.604078 loss)
I0410 02:03:09.112360 18059 sgd_solver.cpp:105] Iteration 8796, lr = 0.00175106
I0410 02:03:10.459798 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:03:13.970196 18059 solver.cpp:218] Iteration 8808 (2.47032 iter/s, 4.85767s/12 iters), loss = 0.660988
I0410 02:03:13.970352 18059 solver.cpp:237]     Train net output #0: loss = 0.660988 (* 1 = 0.660988 loss)
I0410 02:03:13.970366 18059 sgd_solver.cpp:105] Iteration 8808, lr = 0.0017469
I0410 02:03:18.717659 18059 solver.cpp:218] Iteration 8820 (2.52784 iter/s, 4.74714s/12 iters), loss = 0.68856
I0410 02:03:18.717707 18059 solver.cpp:237]     Train net output #0: loss = 0.68856 (* 1 = 0.68856 loss)
I0410 02:03:18.717720 18059 sgd_solver.cpp:105] Iteration 8820, lr = 0.00174276
I0410 02:03:23.524935 18059 solver.cpp:218] Iteration 8832 (2.49634 iter/s, 4.80704s/12 iters), loss = 0.578177
I0410 02:03:23.524993 18059 solver.cpp:237]     Train net output #0: loss = 0.578177 (* 1 = 0.578177 loss)
I0410 02:03:23.525005 18059 sgd_solver.cpp:105] Iteration 8832, lr = 0.00173862
I0410 02:03:28.516840 18059 solver.cpp:218] Iteration 8844 (2.40401 iter/s, 4.99166s/12 iters), loss = 0.838102
I0410 02:03:28.516891 18059 solver.cpp:237]     Train net output #0: loss = 0.838102 (* 1 = 0.838102 loss)
I0410 02:03:28.516902 18059 sgd_solver.cpp:105] Iteration 8844, lr = 0.00173449
I0410 02:03:33.181351 18059 solver.cpp:218] Iteration 8856 (2.57274 iter/s, 4.66429s/12 iters), loss = 0.860948
I0410 02:03:33.181407 18059 solver.cpp:237]     Train net output #0: loss = 0.860948 (* 1 = 0.860948 loss)
I0410 02:03:33.181421 18059 sgd_solver.cpp:105] Iteration 8856, lr = 0.00173037
I0410 02:03:38.379035 18059 solver.cpp:218] Iteration 8868 (2.30883 iter/s, 5.19744s/12 iters), loss = 0.718375
I0410 02:03:38.379094 18059 solver.cpp:237]     Train net output #0: loss = 0.718375 (* 1 = 0.718375 loss)
I0410 02:03:38.379106 18059 sgd_solver.cpp:105] Iteration 8868, lr = 0.00172626
I0410 02:03:40.580484 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8874.caffemodel
I0410 02:03:49.240741 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8874.solverstate
I0410 02:03:55.340739 18059 solver.cpp:330] Iteration 8874, Testing net (#0)
I0410 02:03:55.340766 18059 net.cpp:676] Ignoring source layer train-data
I0410 02:03:56.291620 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:03:59.884435 18059 solver.cpp:397]     Test net output #0: accuracy = 0.434436
I0410 02:03:59.884465 18059 solver.cpp:397]     Test net output #1: loss = 2.65001 (* 1 = 2.65001 loss)
I0410 02:04:01.515995 18059 solver.cpp:218] Iteration 8880 (0.51867 iter/s, 23.1361s/12 iters), loss = 0.643754
I0410 02:04:01.516041 18059 solver.cpp:237]     Train net output #0: loss = 0.643754 (* 1 = 0.643754 loss)
I0410 02:04:01.516052 18059 sgd_solver.cpp:105] Iteration 8880, lr = 0.00172217
I0410 02:04:06.229183 18059 solver.cpp:218] Iteration 8892 (2.54617 iter/s, 4.71296s/12 iters), loss = 0.838405
I0410 02:04:06.229233 18059 solver.cpp:237]     Train net output #0: loss = 0.838405 (* 1 = 0.838405 loss)
I0410 02:04:06.229243 18059 sgd_solver.cpp:105] Iteration 8892, lr = 0.00171808
I0410 02:04:09.617149 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:04:11.069689 18059 solver.cpp:218] Iteration 8904 (2.4792 iter/s, 4.84028s/12 iters), loss = 0.638282
I0410 02:04:11.069737 18059 solver.cpp:237]     Train net output #0: loss = 0.638282 (* 1 = 0.638282 loss)
I0410 02:04:11.069747 18059 sgd_solver.cpp:105] Iteration 8904, lr = 0.001714
I0410 02:04:16.352923 18059 solver.cpp:218] Iteration 8916 (2.27144 iter/s, 5.28299s/12 iters), loss = 0.567201
I0410 02:04:16.352974 18059 solver.cpp:237]     Train net output #0: loss = 0.567201 (* 1 = 0.567201 loss)
I0410 02:04:16.352986 18059 sgd_solver.cpp:105] Iteration 8916, lr = 0.00170993
I0410 02:04:21.163223 18059 solver.cpp:218] Iteration 8928 (2.49477 iter/s, 4.81007s/12 iters), loss = 0.73361
I0410 02:04:21.163571 18059 solver.cpp:237]     Train net output #0: loss = 0.73361 (* 1 = 0.73361 loss)
I0410 02:04:21.163583 18059 sgd_solver.cpp:105] Iteration 8928, lr = 0.00170587
I0410 02:04:26.503239 18059 solver.cpp:218] Iteration 8940 (2.24763 iter/s, 5.33895s/12 iters), loss = 0.741071
I0410 02:04:26.503336 18059 solver.cpp:237]     Train net output #0: loss = 0.741071 (* 1 = 0.741071 loss)
I0410 02:04:26.503346 18059 sgd_solver.cpp:105] Iteration 8940, lr = 0.00170182
I0410 02:04:31.391796 18059 solver.cpp:218] Iteration 8952 (2.45484 iter/s, 4.8883s/12 iters), loss = 0.787851
I0410 02:04:31.391845 18059 solver.cpp:237]     Train net output #0: loss = 0.787851 (* 1 = 0.787851 loss)
I0410 02:04:31.391855 18059 sgd_solver.cpp:105] Iteration 8952, lr = 0.00169778
I0410 02:04:36.137353 18059 solver.cpp:218] Iteration 8964 (2.5288 iter/s, 4.74533s/12 iters), loss = 0.607487
I0410 02:04:36.137399 18059 solver.cpp:237]     Train net output #0: loss = 0.607487 (* 1 = 0.607487 loss)
I0410 02:04:36.137408 18059 sgd_solver.cpp:105] Iteration 8964, lr = 0.00169375
I0410 02:04:40.457571 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8976.caffemodel
I0410 02:04:49.543722 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8976.solverstate
I0410 02:05:02.143415 18059 solver.cpp:330] Iteration 8976, Testing net (#0)
I0410 02:05:02.143486 18059 net.cpp:676] Ignoring source layer train-data
I0410 02:05:03.103067 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:05:06.608690 18059 solver.cpp:397]     Test net output #0: accuracy = 0.435662
I0410 02:05:06.608741 18059 solver.cpp:397]     Test net output #1: loss = 2.7559 (* 1 = 2.7559 loss)
I0410 02:05:06.702639 18059 solver.cpp:218] Iteration 8976 (0.392617 iter/s, 30.5642s/12 iters), loss = 0.911105
I0410 02:05:06.702692 18059 solver.cpp:237]     Train net output #0: loss = 0.911105 (* 1 = 0.911105 loss)
I0410 02:05:06.702702 18059 sgd_solver.cpp:105] Iteration 8976, lr = 0.00168973
I0410 02:05:11.011893 18059 solver.cpp:218] Iteration 8988 (2.78485 iter/s, 4.30903s/12 iters), loss = 0.790744
I0410 02:05:11.011950 18059 solver.cpp:237]     Train net output #0: loss = 0.790744 (* 1 = 0.790744 loss)
I0410 02:05:11.011960 18059 sgd_solver.cpp:105] Iteration 8988, lr = 0.00168571
I0410 02:05:14.405623 18059 blocking_queue.cpp:49] Waiting for data
I0410 02:05:16.134106 18059 solver.cpp:218] Iteration 9000 (2.34285 iter/s, 5.12197s/12 iters), loss = 0.706181
I0410 02:05:16.134158 18059 solver.cpp:237]     Train net output #0: loss = 0.706181 (* 1 = 0.706181 loss)
I0410 02:05:16.134171 18059 sgd_solver.cpp:105] Iteration 9000, lr = 0.00168171
I0410 02:05:16.872774 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:05:21.013236 18059 solver.cpp:218] Iteration 9012 (2.45957 iter/s, 4.87889s/12 iters), loss = 0.609565
I0410 02:05:21.013281 18059 solver.cpp:237]     Train net output #0: loss = 0.609565 (* 1 = 0.609565 loss)
I0410 02:05:21.013290 18059 sgd_solver.cpp:105] Iteration 9012, lr = 0.00167772
I0410 02:05:25.821512 18059 solver.cpp:218] Iteration 9024 (2.49582 iter/s, 4.80804s/12 iters), loss = 0.692877
I0410 02:05:25.821559 18059 solver.cpp:237]     Train net output #0: loss = 0.692877 (* 1 = 0.692877 loss)
I0410 02:05:25.821568 18059 sgd_solver.cpp:105] Iteration 9024, lr = 0.00167374
I0410 02:05:30.532805 18059 solver.cpp:218] Iteration 9036 (2.54719 iter/s, 4.71107s/12 iters), loss = 0.653247
I0410 02:05:30.532853 18059 solver.cpp:237]     Train net output #0: loss = 0.653247 (* 1 = 0.653247 loss)
I0410 02:05:30.532864 18059 sgd_solver.cpp:105] Iteration 9036, lr = 0.00166976
I0410 02:05:35.407673 18059 solver.cpp:218] Iteration 9048 (2.46172 iter/s, 4.87463s/12 iters), loss = 0.713556
I0410 02:05:35.407827 18059 solver.cpp:237]     Train net output #0: loss = 0.713556 (* 1 = 0.713556 loss)
I0410 02:05:35.407840 18059 sgd_solver.cpp:105] Iteration 9048, lr = 0.0016658
I0410 02:05:40.234794 18059 solver.cpp:218] Iteration 9060 (2.48613 iter/s, 4.82679s/12 iters), loss = 0.74487
I0410 02:05:40.234846 18059 solver.cpp:237]     Train net output #0: loss = 0.74487 (* 1 = 0.74487 loss)
I0410 02:05:40.234858 18059 sgd_solver.cpp:105] Iteration 9060, lr = 0.00166184
I0410 02:05:45.059617 18059 solver.cpp:218] Iteration 9072 (2.48726 iter/s, 4.82459s/12 iters), loss = 0.665378
I0410 02:05:45.059661 18059 solver.cpp:237]     Train net output #0: loss = 0.665378 (* 1 = 0.665378 loss)
I0410 02:05:45.059670 18059 sgd_solver.cpp:105] Iteration 9072, lr = 0.0016579
I0410 02:05:47.153394 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9078.caffemodel
I0410 02:05:53.887444 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9078.solverstate
I0410 02:06:07.248975 18059 solver.cpp:330] Iteration 9078, Testing net (#0)
I0410 02:06:07.249029 18059 net.cpp:676] Ignoring source layer train-data
I0410 02:06:08.160303 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:06:11.716321 18059 solver.cpp:397]     Test net output #0: accuracy = 0.439951
I0410 02:06:11.716370 18059 solver.cpp:397]     Test net output #1: loss = 2.77848 (* 1 = 2.77848 loss)
I0410 02:06:13.436969 18059 solver.cpp:218] Iteration 9084 (0.422888 iter/s, 28.3763s/12 iters), loss = 0.751731
I0410 02:06:13.437026 18059 solver.cpp:237]     Train net output #0: loss = 0.751731 (* 1 = 0.751731 loss)
I0410 02:06:13.437037 18059 sgd_solver.cpp:105] Iteration 9084, lr = 0.00165396
I0410 02:06:17.883169 18059 solver.cpp:218] Iteration 9096 (2.69907 iter/s, 4.44598s/12 iters), loss = 0.733052
I0410 02:06:17.883217 18059 solver.cpp:237]     Train net output #0: loss = 0.733052 (* 1 = 0.733052 loss)
I0410 02:06:17.883229 18059 sgd_solver.cpp:105] Iteration 9096, lr = 0.00165003
I0410 02:06:20.426666 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:06:22.309296 18059 solver.cpp:218] Iteration 9108 (2.71131 iter/s, 4.42591s/12 iters), loss = 0.683427
I0410 02:06:22.309351 18059 solver.cpp:237]     Train net output #0: loss = 0.683427 (* 1 = 0.683427 loss)
I0410 02:06:22.309363 18059 sgd_solver.cpp:105] Iteration 9108, lr = 0.00164612
I0410 02:06:26.859591 18059 solver.cpp:218] Iteration 9120 (2.63733 iter/s, 4.55007s/12 iters), loss = 0.769758
I0410 02:06:26.859648 18059 solver.cpp:237]     Train net output #0: loss = 0.769758 (* 1 = 0.769758 loss)
I0410 02:06:26.859660 18059 sgd_solver.cpp:105] Iteration 9120, lr = 0.00164221
I0410 02:06:31.393571 18059 solver.cpp:218] Iteration 9132 (2.64682 iter/s, 4.53375s/12 iters), loss = 0.729607
I0410 02:06:31.393627 18059 solver.cpp:237]     Train net output #0: loss = 0.729607 (* 1 = 0.729607 loss)
I0410 02:06:31.393640 18059 sgd_solver.cpp:105] Iteration 9132, lr = 0.00163831
I0410 02:06:35.841027 18059 solver.cpp:218] Iteration 9144 (2.69831 iter/s, 4.44723s/12 iters), loss = 0.539826
I0410 02:06:35.841078 18059 solver.cpp:237]     Train net output #0: loss = 0.539826 (* 1 = 0.539826 loss)
I0410 02:06:35.841089 18059 sgd_solver.cpp:105] Iteration 9144, lr = 0.00163442
I0410 02:06:40.288341 18059 solver.cpp:218] Iteration 9156 (2.69839 iter/s, 4.44709s/12 iters), loss = 0.744341
I0410 02:06:40.288458 18059 solver.cpp:237]     Train net output #0: loss = 0.744341 (* 1 = 0.744341 loss)
I0410 02:06:40.288470 18059 sgd_solver.cpp:105] Iteration 9156, lr = 0.00163054
I0410 02:06:44.761302 18059 solver.cpp:218] Iteration 9168 (2.68296 iter/s, 4.47268s/12 iters), loss = 0.736696
I0410 02:06:44.761355 18059 solver.cpp:237]     Train net output #0: loss = 0.736696 (* 1 = 0.736696 loss)
I0410 02:06:44.761368 18059 sgd_solver.cpp:105] Iteration 9168, lr = 0.00162667
I0410 02:06:48.840299 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9180.caffemodel
I0410 02:06:56.654628 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9180.solverstate
I0410 02:07:02.949900 18059 solver.cpp:330] Iteration 9180, Testing net (#0)
I0410 02:07:02.949928 18059 net.cpp:676] Ignoring source layer train-data
I0410 02:07:03.777477 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:07:07.443229 18059 solver.cpp:397]     Test net output #0: accuracy = 0.447917
I0410 02:07:07.443270 18059 solver.cpp:397]     Test net output #1: loss = 2.66337 (* 1 = 2.66337 loss)
I0410 02:07:07.537510 18059 solver.cpp:218] Iteration 9180 (0.526885 iter/s, 22.7753s/12 iters), loss = 0.746835
I0410 02:07:07.539036 18059 solver.cpp:237]     Train net output #0: loss = 0.746835 (* 1 = 0.746835 loss)
I0410 02:07:07.539059 18059 sgd_solver.cpp:105] Iteration 9180, lr = 0.00162281
I0410 02:07:11.468329 18059 solver.cpp:218] Iteration 9192 (3.05409 iter/s, 3.92916s/12 iters), loss = 0.582044
I0410 02:07:11.468479 18059 solver.cpp:237]     Train net output #0: loss = 0.582044 (* 1 = 0.582044 loss)
I0410 02:07:11.468493 18059 sgd_solver.cpp:105] Iteration 9192, lr = 0.00161895
I0410 02:07:16.143334 18059 solver.cpp:218] Iteration 9204 (2.56702 iter/s, 4.67468s/12 iters), loss = 0.566664
I0410 02:07:16.143388 18059 solver.cpp:237]     Train net output #0: loss = 0.566664 (* 1 = 0.566664 loss)
I0410 02:07:16.143399 18059 sgd_solver.cpp:105] Iteration 9204, lr = 0.00161511
I0410 02:07:16.229796 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:07:20.762686 18059 solver.cpp:218] Iteration 9216 (2.5979 iter/s, 4.61912s/12 iters), loss = 0.778791
I0410 02:07:20.762745 18059 solver.cpp:237]     Train net output #0: loss = 0.778791 (* 1 = 0.778791 loss)
I0410 02:07:20.762759 18059 sgd_solver.cpp:105] Iteration 9216, lr = 0.00161128
I0410 02:07:25.656450 18059 solver.cpp:218] Iteration 9228 (2.45222 iter/s, 4.89352s/12 iters), loss = 0.569468
I0410 02:07:25.656502 18059 solver.cpp:237]     Train net output #0: loss = 0.569468 (* 1 = 0.569468 loss)
I0410 02:07:25.656513 18059 sgd_solver.cpp:105] Iteration 9228, lr = 0.00160745
I0410 02:07:30.385293 18059 solver.cpp:218] Iteration 9240 (2.53774 iter/s, 4.72862s/12 iters), loss = 0.711718
I0410 02:07:30.385341 18059 solver.cpp:237]     Train net output #0: loss = 0.711718 (* 1 = 0.711718 loss)
I0410 02:07:30.385352 18059 sgd_solver.cpp:105] Iteration 9240, lr = 0.00160363
I0410 02:07:35.250675 18059 solver.cpp:218] Iteration 9252 (2.46652 iter/s, 4.86515s/12 iters), loss = 0.78144
I0410 02:07:35.250733 18059 solver.cpp:237]     Train net output #0: loss = 0.78144 (* 1 = 0.78144 loss)
I0410 02:07:35.250746 18059 sgd_solver.cpp:105] Iteration 9252, lr = 0.00159983
I0410 02:07:40.097252 18059 solver.cpp:218] Iteration 9264 (2.4761 iter/s, 4.84634s/12 iters), loss = 0.661366
I0410 02:07:40.097302 18059 solver.cpp:237]     Train net output #0: loss = 0.661366 (* 1 = 0.661366 loss)
I0410 02:07:40.097312 18059 sgd_solver.cpp:105] Iteration 9264, lr = 0.00159603
I0410 02:07:45.010078 18059 solver.cpp:218] Iteration 9276 (2.4427 iter/s, 4.91259s/12 iters), loss = 0.631604
I0410 02:07:45.010161 18059 solver.cpp:237]     Train net output #0: loss = 0.631604 (* 1 = 0.631604 loss)
I0410 02:07:45.010174 18059 sgd_solver.cpp:105] Iteration 9276, lr = 0.00159224
I0410 02:07:46.900146 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9282.caffemodel
I0410 02:07:54.233448 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9282.solverstate
I0410 02:07:57.876852 18059 solver.cpp:330] Iteration 9282, Testing net (#0)
I0410 02:07:57.876874 18059 net.cpp:676] Ignoring source layer train-data
I0410 02:07:58.756079 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:08:02.401690 18059 solver.cpp:397]     Test net output #0: accuracy = 0.441176
I0410 02:08:02.401724 18059 solver.cpp:397]     Test net output #1: loss = 2.75486 (* 1 = 2.75486 loss)
I0410 02:08:04.340020 18059 solver.cpp:218] Iteration 9288 (0.620823 iter/s, 19.3292s/12 iters), loss = 0.694022
I0410 02:08:04.340075 18059 solver.cpp:237]     Train net output #0: loss = 0.694022 (* 1 = 0.694022 loss)
I0410 02:08:04.340086 18059 sgd_solver.cpp:105] Iteration 9288, lr = 0.00158846
I0410 02:08:09.048427 18059 solver.cpp:218] Iteration 9300 (2.54876 iter/s, 4.70817s/12 iters), loss = 0.713315
I0410 02:08:09.048478 18059 solver.cpp:237]     Train net output #0: loss = 0.713315 (* 1 = 0.713315 loss)
I0410 02:08:09.048491 18059 sgd_solver.cpp:105] Iteration 9300, lr = 0.00158469
I0410 02:08:11.392737 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:08:14.229591 18059 solver.cpp:218] Iteration 9312 (2.31619 iter/s, 5.18092s/12 iters), loss = 0.534105
I0410 02:08:14.229645 18059 solver.cpp:237]     Train net output #0: loss = 0.534105 (* 1 = 0.534105 loss)
I0410 02:08:14.229656 18059 sgd_solver.cpp:105] Iteration 9312, lr = 0.00158092
I0410 02:08:19.030885 18059 solver.cpp:218] Iteration 9324 (2.49945 iter/s, 4.80106s/12 iters), loss = 0.460319
I0410 02:08:19.031047 18059 solver.cpp:237]     Train net output #0: loss = 0.460319 (* 1 = 0.460319 loss)
I0410 02:08:19.031060 18059 sgd_solver.cpp:105] Iteration 9324, lr = 0.00157717
I0410 02:08:23.896139 18059 solver.cpp:218] Iteration 9336 (2.46664 iter/s, 4.86491s/12 iters), loss = 0.625974
I0410 02:08:23.896198 18059 solver.cpp:237]     Train net output #0: loss = 0.625974 (* 1 = 0.625974 loss)
I0410 02:08:23.896210 18059 sgd_solver.cpp:105] Iteration 9336, lr = 0.00157343
I0410 02:08:28.823552 18059 solver.cpp:218] Iteration 9348 (2.43548 iter/s, 4.92717s/12 iters), loss = 0.707165
I0410 02:08:28.823604 18059 solver.cpp:237]     Train net output #0: loss = 0.707165 (* 1 = 0.707165 loss)
I0410 02:08:28.823616 18059 sgd_solver.cpp:105] Iteration 9348, lr = 0.00156969
I0410 02:08:33.667325 18059 solver.cpp:218] Iteration 9360 (2.47753 iter/s, 4.84354s/12 iters), loss = 0.510655
I0410 02:08:33.667373 18059 solver.cpp:237]     Train net output #0: loss = 0.510655 (* 1 = 0.510655 loss)
I0410 02:08:33.667384 18059 sgd_solver.cpp:105] Iteration 9360, lr = 0.00156596
I0410 02:08:38.559523 18059 solver.cpp:218] Iteration 9372 (2.453 iter/s, 4.89197s/12 iters), loss = 0.640047
I0410 02:08:38.559582 18059 solver.cpp:237]     Train net output #0: loss = 0.640047 (* 1 = 0.640047 loss)
I0410 02:08:38.559598 18059 sgd_solver.cpp:105] Iteration 9372, lr = 0.00156225
I0410 02:08:42.828686 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9384.caffemodel
I0410 02:08:55.135298 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9384.solverstate
I0410 02:09:00.289896 18059 solver.cpp:330] Iteration 9384, Testing net (#0)
I0410 02:09:00.289921 18059 net.cpp:676] Ignoring source layer train-data
I0410 02:09:01.073036 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:09:04.871477 18059 solver.cpp:397]     Test net output #0: accuracy = 0.436887
I0410 02:09:04.871529 18059 solver.cpp:397]     Test net output #1: loss = 2.86069 (* 1 = 2.86069 loss)
I0410 02:09:04.964972 18059 solver.cpp:218] Iteration 9384 (0.454469 iter/s, 26.4045s/12 iters), loss = 0.566545
I0410 02:09:04.965019 18059 solver.cpp:237]     Train net output #0: loss = 0.566545 (* 1 = 0.566545 loss)
I0410 02:09:04.965031 18059 sgd_solver.cpp:105] Iteration 9384, lr = 0.00155854
I0410 02:09:09.328994 18059 solver.cpp:218] Iteration 9396 (2.74989 iter/s, 4.36381s/12 iters), loss = 0.661738
I0410 02:09:09.329046 18059 solver.cpp:237]     Train net output #0: loss = 0.661738 (* 1 = 0.661738 loss)
I0410 02:09:09.329058 18059 sgd_solver.cpp:105] Iteration 9396, lr = 0.00155484
I0410 02:09:13.322031 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:09:13.993925 18059 solver.cpp:218] Iteration 9408 (2.57251 iter/s, 4.6647s/12 iters), loss = 0.719728
I0410 02:09:13.994009 18059 solver.cpp:237]     Train net output #0: loss = 0.719728 (* 1 = 0.719728 loss)
I0410 02:09:13.994022 18059 sgd_solver.cpp:105] Iteration 9408, lr = 0.00155114
I0410 02:09:18.522871 18059 solver.cpp:218] Iteration 9420 (2.64977 iter/s, 4.52869s/12 iters), loss = 0.537039
I0410 02:09:18.522913 18059 solver.cpp:237]     Train net output #0: loss = 0.537039 (* 1 = 0.537039 loss)
I0410 02:09:18.522922 18059 sgd_solver.cpp:105] Iteration 9420, lr = 0.00154746
I0410 02:09:23.368515 18059 solver.cpp:218] Iteration 9432 (2.47657 iter/s, 4.84542s/12 iters), loss = 0.735068
I0410 02:09:23.368567 18059 solver.cpp:237]     Train net output #0: loss = 0.735068 (* 1 = 0.735068 loss)
I0410 02:09:23.368577 18059 sgd_solver.cpp:105] Iteration 9432, lr = 0.00154379
I0410 02:09:28.247228 18059 solver.cpp:218] Iteration 9444 (2.45979 iter/s, 4.87847s/12 iters), loss = 0.464756
I0410 02:09:28.247776 18059 solver.cpp:237]     Train net output #0: loss = 0.464756 (* 1 = 0.464756 loss)
I0410 02:09:28.247788 18059 sgd_solver.cpp:105] Iteration 9444, lr = 0.00154012
I0410 02:09:33.234369 18059 solver.cpp:218] Iteration 9456 (2.40654 iter/s, 4.98641s/12 iters), loss = 0.811723
I0410 02:09:33.234411 18059 solver.cpp:237]     Train net output #0: loss = 0.811723 (* 1 = 0.811723 loss)
I0410 02:09:33.234419 18059 sgd_solver.cpp:105] Iteration 9456, lr = 0.00153647
I0410 02:09:37.794883 18059 solver.cpp:218] Iteration 9468 (2.63141 iter/s, 4.5603s/12 iters), loss = 0.466976
I0410 02:09:37.794932 18059 solver.cpp:237]     Train net output #0: loss = 0.466976 (* 1 = 0.466976 loss)
I0410 02:09:37.794941 18059 sgd_solver.cpp:105] Iteration 9468, lr = 0.00153282
I0410 02:09:42.928220 18059 solver.cpp:218] Iteration 9480 (2.33777 iter/s, 5.1331s/12 iters), loss = 0.653714
I0410 02:09:42.928318 18059 solver.cpp:237]     Train net output #0: loss = 0.653714 (* 1 = 0.653714 loss)
I0410 02:09:42.928328 18059 sgd_solver.cpp:105] Iteration 9480, lr = 0.00152918
I0410 02:09:45.203052 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9486.caffemodel
I0410 02:09:52.204644 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9486.solverstate
I0410 02:09:55.957312 18059 solver.cpp:330] Iteration 9486, Testing net (#0)
I0410 02:09:55.957337 18059 net.cpp:676] Ignoring source layer train-data
I0410 02:09:56.681257 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:10:00.420181 18059 solver.cpp:397]     Test net output #0: accuracy = 0.458946
I0410 02:10:00.420341 18059 solver.cpp:397]     Test net output #1: loss = 2.727 (* 1 = 2.727 loss)
I0410 02:10:02.240259 18059 solver.cpp:218] Iteration 9492 (0.621399 iter/s, 19.3113s/12 iters), loss = 0.496031
I0410 02:10:02.240309 18059 solver.cpp:237]     Train net output #0: loss = 0.496031 (* 1 = 0.496031 loss)
I0410 02:10:02.240319 18059 sgd_solver.cpp:105] Iteration 9492, lr = 0.00152555
I0410 02:10:07.028283 18059 solver.cpp:218] Iteration 9504 (2.50637 iter/s, 4.78779s/12 iters), loss = 0.580359
I0410 02:10:07.028329 18059 solver.cpp:237]     Train net output #0: loss = 0.580359 (* 1 = 0.580359 loss)
I0410 02:10:07.028338 18059 sgd_solver.cpp:105] Iteration 9504, lr = 0.00152193
I0410 02:10:08.493258 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:10:11.776727 18059 solver.cpp:218] Iteration 9516 (2.52726 iter/s, 4.74822s/12 iters), loss = 0.681694
I0410 02:10:11.776779 18059 solver.cpp:237]     Train net output #0: loss = 0.681694 (* 1 = 0.681694 loss)
I0410 02:10:11.776790 18059 sgd_solver.cpp:105] Iteration 9516, lr = 0.00151831
I0410 02:10:16.462795 18059 solver.cpp:218] Iteration 9528 (2.56091 iter/s, 4.68584s/12 iters), loss = 0.646593
I0410 02:10:16.462862 18059 solver.cpp:237]     Train net output #0: loss = 0.646593 (* 1 = 0.646593 loss)
I0410 02:10:16.462879 18059 sgd_solver.cpp:105] Iteration 9528, lr = 0.00151471
I0410 02:10:21.072279 18059 solver.cpp:218] Iteration 9540 (2.60346 iter/s, 4.60925s/12 iters), loss = 0.725858
I0410 02:10:21.072331 18059 solver.cpp:237]     Train net output #0: loss = 0.725858 (* 1 = 0.725858 loss)
I0410 02:10:21.072343 18059 sgd_solver.cpp:105] Iteration 9540, lr = 0.00151111
I0410 02:10:25.873159 18059 solver.cpp:218] Iteration 9552 (2.49966 iter/s, 4.80064s/12 iters), loss = 0.492021
I0410 02:10:25.873210 18059 solver.cpp:237]     Train net output #0: loss = 0.492021 (* 1 = 0.492021 loss)
I0410 02:10:25.873221 18059 sgd_solver.cpp:105] Iteration 9552, lr = 0.00150752
I0410 02:10:30.484459 18059 solver.cpp:218] Iteration 9564 (2.60243 iter/s, 4.61108s/12 iters), loss = 0.582934
I0410 02:10:30.484563 18059 solver.cpp:237]     Train net output #0: loss = 0.582934 (* 1 = 0.582934 loss)
I0410 02:10:30.484573 18059 sgd_solver.cpp:105] Iteration 9564, lr = 0.00150395
I0410 02:10:35.178261 18059 solver.cpp:218] Iteration 9576 (2.55671 iter/s, 4.69352s/12 iters), loss = 0.649541
I0410 02:10:35.178306 18059 solver.cpp:237]     Train net output #0: loss = 0.649541 (* 1 = 0.649541 loss)
I0410 02:10:35.178318 18059 sgd_solver.cpp:105] Iteration 9576, lr = 0.00150037
I0410 02:10:39.647559 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9588.caffemodel
I0410 02:10:46.416802 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9588.solverstate
I0410 02:10:53.145025 18059 solver.cpp:330] Iteration 9588, Testing net (#0)
I0410 02:10:53.145054 18059 net.cpp:676] Ignoring source layer train-data
I0410 02:10:53.843932 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:10:57.650008 18059 solver.cpp:397]     Test net output #0: accuracy = 0.443015
I0410 02:10:57.650048 18059 solver.cpp:397]     Test net output #1: loss = 2.79593 (* 1 = 2.79593 loss)
I0410 02:10:57.743613 18059 solver.cpp:218] Iteration 9588 (0.531809 iter/s, 22.5645s/12 iters), loss = 0.566527
I0410 02:10:57.743656 18059 solver.cpp:237]     Train net output #0: loss = 0.566527 (* 1 = 0.566527 loss)
I0410 02:10:57.743665 18059 sgd_solver.cpp:105] Iteration 9588, lr = 0.00149681
I0410 02:11:02.049991 18059 solver.cpp:218] Iteration 9600 (2.7867 iter/s, 4.30616s/12 iters), loss = 0.685317
I0410 02:11:02.050122 18059 solver.cpp:237]     Train net output #0: loss = 0.685317 (* 1 = 0.685317 loss)
I0410 02:11:02.050137 18059 sgd_solver.cpp:105] Iteration 9600, lr = 0.00149326
I0410 02:11:05.550923 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:11:06.964929 18059 solver.cpp:218] Iteration 9612 (2.44169 iter/s, 4.91463s/12 iters), loss = 0.65615
I0410 02:11:06.964974 18059 solver.cpp:237]     Train net output #0: loss = 0.65615 (* 1 = 0.65615 loss)
I0410 02:11:06.964983 18059 sgd_solver.cpp:105] Iteration 9612, lr = 0.00148971
I0410 02:11:11.992925 18059 solver.cpp:218] Iteration 9624 (2.38675 iter/s, 5.02775s/12 iters), loss = 0.523796
I0410 02:11:11.992985 18059 solver.cpp:237]     Train net output #0: loss = 0.523796 (* 1 = 0.523796 loss)
I0410 02:11:11.992997 18059 sgd_solver.cpp:105] Iteration 9624, lr = 0.00148618
I0410 02:11:16.812454 18059 solver.cpp:218] Iteration 9636 (2.48999 iter/s, 4.81929s/12 iters), loss = 0.479668
I0410 02:11:16.812500 18059 solver.cpp:237]     Train net output #0: loss = 0.479668 (* 1 = 0.479668 loss)
I0410 02:11:16.812508 18059 sgd_solver.cpp:105] Iteration 9636, lr = 0.00148265
I0410 02:11:21.555896 18059 solver.cpp:218] Iteration 9648 (2.52993 iter/s, 4.74321s/12 iters), loss = 0.650267
I0410 02:11:21.555951 18059 solver.cpp:237]     Train net output #0: loss = 0.650267 (* 1 = 0.650267 loss)
I0410 02:11:21.555963 18059 sgd_solver.cpp:105] Iteration 9648, lr = 0.00147913
I0410 02:11:26.607352 18059 solver.cpp:218] Iteration 9660 (2.37567 iter/s, 5.05121s/12 iters), loss = 0.450031
I0410 02:11:26.607405 18059 solver.cpp:237]     Train net output #0: loss = 0.450031 (* 1 = 0.450031 loss)
I0410 02:11:26.607417 18059 sgd_solver.cpp:105] Iteration 9660, lr = 0.00147562
I0410 02:11:31.361726 18059 solver.cpp:218] Iteration 9672 (2.52412 iter/s, 4.75414s/12 iters), loss = 0.404308
I0410 02:11:31.361778 18059 solver.cpp:237]     Train net output #0: loss = 0.404308 (* 1 = 0.404308 loss)
I0410 02:11:31.361791 18059 sgd_solver.cpp:105] Iteration 9672, lr = 0.00147211
I0410 02:11:36.142441 18059 solver.cpp:218] Iteration 9684 (2.51021 iter/s, 4.78048s/12 iters), loss = 0.437594
I0410 02:11:36.142563 18059 solver.cpp:237]     Train net output #0: loss = 0.437594 (* 1 = 0.437594 loss)
I0410 02:11:36.142576 18059 sgd_solver.cpp:105] Iteration 9684, lr = 0.00146862
I0410 02:11:38.107033 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9690.caffemodel
I0410 02:11:51.510113 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9690.solverstate
I0410 02:11:55.269244 18059 solver.cpp:330] Iteration 9690, Testing net (#0)
I0410 02:11:55.269266 18059 net.cpp:676] Ignoring source layer train-data
I0410 02:11:55.891192 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:11:58.762181 18059 blocking_queue.cpp:49] Waiting for data
I0410 02:11:59.764241 18059 solver.cpp:397]     Test net output #0: accuracy = 0.449755
I0410 02:11:59.764292 18059 solver.cpp:397]     Test net output #1: loss = 2.9336 (* 1 = 2.9336 loss)
I0410 02:12:01.435432 18059 solver.cpp:218] Iteration 9696 (0.474459 iter/s, 25.292s/12 iters), loss = 0.429848
I0410 02:12:01.435480 18059 solver.cpp:237]     Train net output #0: loss = 0.429848 (* 1 = 0.429848 loss)
I0410 02:12:01.435489 18059 sgd_solver.cpp:105] Iteration 9696, lr = 0.00146513
I0410 02:12:06.064829 18059 solver.cpp:218] Iteration 9708 (2.59226 iter/s, 4.62917s/12 iters), loss = 0.564353
I0410 02:12:06.064875 18059 solver.cpp:237]     Train net output #0: loss = 0.564353 (* 1 = 0.564353 loss)
I0410 02:12:06.064884 18059 sgd_solver.cpp:105] Iteration 9708, lr = 0.00146165
I0410 02:12:06.749341 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:12:10.739591 18059 solver.cpp:218] Iteration 9720 (2.5671 iter/s, 4.67454s/12 iters), loss = 0.591935
I0410 02:12:10.739632 18059 solver.cpp:237]     Train net output #0: loss = 0.591935 (* 1 = 0.591935 loss)
I0410 02:12:10.739641 18059 sgd_solver.cpp:105] Iteration 9720, lr = 0.00145818
I0410 02:12:15.776173 18059 solver.cpp:218] Iteration 9732 (2.38268 iter/s, 5.03635s/12 iters), loss = 0.586391
I0410 02:12:15.776216 18059 solver.cpp:237]     Train net output #0: loss = 0.586391 (* 1 = 0.586391 loss)
I0410 02:12:15.776226 18059 sgd_solver.cpp:105] Iteration 9732, lr = 0.00145472
I0410 02:12:20.522351 18059 solver.cpp:218] Iteration 9744 (2.52847 iter/s, 4.74596s/12 iters), loss = 0.677302
I0410 02:12:20.522393 18059 solver.cpp:237]     Train net output #0: loss = 0.677302 (* 1 = 0.677302 loss)
I0410 02:12:20.522403 18059 sgd_solver.cpp:105] Iteration 9744, lr = 0.00145127
I0410 02:12:25.085412 18059 solver.cpp:218] Iteration 9756 (2.62994 iter/s, 4.56285s/12 iters), loss = 0.622177
I0410 02:12:25.085456 18059 solver.cpp:237]     Train net output #0: loss = 0.622177 (* 1 = 0.622177 loss)
I0410 02:12:25.085467 18059 sgd_solver.cpp:105] Iteration 9756, lr = 0.00144782
I0410 02:12:29.825116 18059 solver.cpp:218] Iteration 9768 (2.53192 iter/s, 4.73948s/12 iters), loss = 0.607404
I0410 02:12:29.825167 18059 solver.cpp:237]     Train net output #0: loss = 0.607404 (* 1 = 0.607404 loss)
I0410 02:12:29.825181 18059 sgd_solver.cpp:105] Iteration 9768, lr = 0.00144438
I0410 02:12:34.776829 18059 solver.cpp:218] Iteration 9780 (2.42352 iter/s, 4.95148s/12 iters), loss = 0.51353
I0410 02:12:34.776880 18059 solver.cpp:237]     Train net output #0: loss = 0.51353 (* 1 = 0.51353 loss)
I0410 02:12:34.776891 18059 sgd_solver.cpp:105] Iteration 9780, lr = 0.00144095
I0410 02:12:39.145505 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9792.caffemodel
I0410 02:12:51.745167 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9792.solverstate
I0410 02:13:00.310593 18059 solver.cpp:330] Iteration 9792, Testing net (#0)
I0410 02:13:00.310621 18059 net.cpp:676] Ignoring source layer train-data
I0410 02:13:00.881649 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:13:04.719970 18059 solver.cpp:397]     Test net output #0: accuracy = 0.452819
I0410 02:13:04.720005 18059 solver.cpp:397]     Test net output #1: loss = 2.81264 (* 1 = 2.81264 loss)
I0410 02:13:04.813614 18059 solver.cpp:218] Iteration 9792 (0.399525 iter/s, 30.0357s/12 iters), loss = 0.451519
I0410 02:13:04.813669 18059 solver.cpp:237]     Train net output #0: loss = 0.451519 (* 1 = 0.451519 loss)
I0410 02:13:04.813681 18059 sgd_solver.cpp:105] Iteration 9792, lr = 0.00143753
I0410 02:13:08.826385 18059 solver.cpp:218] Iteration 9804 (2.99061 iter/s, 4.01256s/12 iters), loss = 0.575496
I0410 02:13:08.826428 18059 solver.cpp:237]     Train net output #0: loss = 0.575496 (* 1 = 0.575496 loss)
I0410 02:13:08.826438 18059 sgd_solver.cpp:105] Iteration 9804, lr = 0.00143412
I0410 02:13:11.523077 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:13:13.432423 18059 solver.cpp:218] Iteration 9816 (2.6054 iter/s, 4.60582s/12 iters), loss = 0.487673
I0410 02:13:13.432468 18059 solver.cpp:237]     Train net output #0: loss = 0.487673 (* 1 = 0.487673 loss)
I0410 02:13:13.432479 18059 sgd_solver.cpp:105] Iteration 9816, lr = 0.00143072
I0410 02:13:18.287699 18059 solver.cpp:218] Iteration 9828 (2.47165 iter/s, 4.85505s/12 iters), loss = 0.646175
I0410 02:13:18.287735 18059 solver.cpp:237]     Train net output #0: loss = 0.646175 (* 1 = 0.646175 loss)
I0410 02:13:18.287744 18059 sgd_solver.cpp:105] Iteration 9828, lr = 0.00142732
I0410 02:13:22.781811 18059 solver.cpp:218] Iteration 9840 (2.67029 iter/s, 4.4939s/12 iters), loss = 0.523789
I0410 02:13:22.781862 18059 solver.cpp:237]     Train net output #0: loss = 0.523789 (* 1 = 0.523789 loss)
I0410 02:13:22.781873 18059 sgd_solver.cpp:105] Iteration 9840, lr = 0.00142393
I0410 02:13:27.661412 18059 solver.cpp:218] Iteration 9852 (2.45933 iter/s, 4.87937s/12 iters), loss = 0.397487
I0410 02:13:27.661460 18059 solver.cpp:237]     Train net output #0: loss = 0.397487 (* 1 = 0.397487 loss)
I0410 02:13:27.661470 18059 sgd_solver.cpp:105] Iteration 9852, lr = 0.00142055
I0410 02:13:32.476032 18059 solver.cpp:218] Iteration 9864 (2.49253 iter/s, 4.81439s/12 iters), loss = 0.518874
I0410 02:13:32.476090 18059 solver.cpp:237]     Train net output #0: loss = 0.518874 (* 1 = 0.518874 loss)
I0410 02:13:32.476105 18059 sgd_solver.cpp:105] Iteration 9864, lr = 0.00141718
I0410 02:13:37.392596 18059 solver.cpp:218] Iteration 9876 (2.44085 iter/s, 4.91632s/12 iters), loss = 0.603038
I0410 02:13:37.392649 18059 solver.cpp:237]     Train net output #0: loss = 0.603038 (* 1 = 0.603038 loss)
I0410 02:13:37.392660 18059 sgd_solver.cpp:105] Iteration 9876, lr = 0.00141381
I0410 02:13:42.008911 18059 solver.cpp:218] Iteration 9888 (2.5996 iter/s, 4.61609s/12 iters), loss = 0.457373
I0410 02:13:42.009006 18059 solver.cpp:237]     Train net output #0: loss = 0.457373 (* 1 = 0.457373 loss)
I0410 02:13:42.009017 18059 sgd_solver.cpp:105] Iteration 9888, lr = 0.00141045
I0410 02:13:44.102712 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9894.caffemodel
I0410 02:13:53.290266 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9894.solverstate
I0410 02:13:56.967154 18059 solver.cpp:330] Iteration 9894, Testing net (#0)
I0410 02:13:56.967178 18059 net.cpp:676] Ignoring source layer train-data
I0410 02:13:57.524441 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:14:01.512039 18059 solver.cpp:397]     Test net output #0: accuracy = 0.439338
I0410 02:14:01.512089 18059 solver.cpp:397]     Test net output #1: loss = 2.96707 (* 1 = 2.96707 loss)
I0410 02:14:03.247066 18059 solver.cpp:218] Iteration 9900 (0.565043 iter/s, 21.2373s/12 iters), loss = 0.601617
I0410 02:14:03.247123 18059 solver.cpp:237]     Train net output #0: loss = 0.601617 (* 1 = 0.601617 loss)
I0410 02:14:03.247133 18059 sgd_solver.cpp:105] Iteration 9900, lr = 0.00140711
I0410 02:14:08.407449 18059 solver.cpp:218] Iteration 9912 (2.32552 iter/s, 5.16013s/12 iters), loss = 0.481419
I0410 02:14:08.407505 18059 solver.cpp:237]     Train net output #0: loss = 0.481419 (* 1 = 0.481419 loss)
I0410 02:14:08.407516 18059 sgd_solver.cpp:105] Iteration 9912, lr = 0.00140377
I0410 02:14:08.459439 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:14:13.168427 18059 solver.cpp:218] Iteration 9924 (2.52061 iter/s, 4.76075s/12 iters), loss = 0.458612
I0410 02:14:13.168584 18059 solver.cpp:237]     Train net output #0: loss = 0.458612 (* 1 = 0.458612 loss)
I0410 02:14:13.168597 18059 sgd_solver.cpp:105] Iteration 9924, lr = 0.00140043
I0410 02:14:17.756538 18059 solver.cpp:218] Iteration 9936 (2.61564 iter/s, 4.58778s/12 iters), loss = 0.481199
I0410 02:14:17.756590 18059 solver.cpp:237]     Train net output #0: loss = 0.481199 (* 1 = 0.481199 loss)
I0410 02:14:17.756603 18059 sgd_solver.cpp:105] Iteration 9936, lr = 0.00139711
I0410 02:14:22.320513 18059 solver.cpp:218] Iteration 9948 (2.62942 iter/s, 4.56375s/12 iters), loss = 0.591496
I0410 02:14:22.320571 18059 solver.cpp:237]     Train net output #0: loss = 0.591496 (* 1 = 0.591496 loss)
I0410 02:14:22.320586 18059 sgd_solver.cpp:105] Iteration 9948, lr = 0.00139379
I0410 02:14:26.887010 18059 solver.cpp:218] Iteration 9960 (2.62797 iter/s, 4.56626s/12 iters), loss = 0.502744
I0410 02:14:26.887055 18059 solver.cpp:237]     Train net output #0: loss = 0.502744 (* 1 = 0.502744 loss)
I0410 02:14:26.887064 18059 sgd_solver.cpp:105] Iteration 9960, lr = 0.00139048
I0410 02:14:31.679577 18059 solver.cpp:218] Iteration 9972 (2.504 iter/s, 4.79233s/12 iters), loss = 0.624552
I0410 02:14:31.679631 18059 solver.cpp:237]     Train net output #0: loss = 0.624552 (* 1 = 0.624552 loss)
I0410 02:14:31.679641 18059 sgd_solver.cpp:105] Iteration 9972, lr = 0.00138718
I0410 02:14:36.211762 18059 solver.cpp:218] Iteration 9984 (2.64786 iter/s, 4.53196s/12 iters), loss = 0.486058
I0410 02:14:36.211802 18059 solver.cpp:237]     Train net output #0: loss = 0.486058 (* 1 = 0.486058 loss)
I0410 02:14:36.211809 18059 sgd_solver.cpp:105] Iteration 9984, lr = 0.00138389
I0410 02:14:40.626863 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9996.caffemodel
I0410 02:14:47.262135 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9996.solverstate
I0410 02:14:53.755669 18059 solver.cpp:330] Iteration 9996, Testing net (#0)
I0410 02:14:53.755692 18059 net.cpp:676] Ignoring source layer train-data
I0410 02:14:54.274773 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:14:58.328073 18059 solver.cpp:397]     Test net output #0: accuracy = 0.450368
I0410 02:14:58.328119 18059 solver.cpp:397]     Test net output #1: loss = 2.84265 (* 1 = 2.84265 loss)
I0410 02:14:58.420362 18059 solver.cpp:218] Iteration 9996 (0.540351 iter/s, 22.2078s/12 iters), loss = 0.441289
I0410 02:14:58.420416 18059 solver.cpp:237]     Train net output #0: loss = 0.441289 (* 1 = 0.441289 loss)
I0410 02:14:58.420428 18059 sgd_solver.cpp:105] Iteration 9996, lr = 0.0013806
I0410 02:15:02.338721 18059 solver.cpp:218] Iteration 10008 (3.06267 iter/s, 3.91815s/12 iters), loss = 0.607945
I0410 02:15:02.338762 18059 solver.cpp:237]     Train net output #0: loss = 0.607945 (* 1 = 0.607945 loss)
I0410 02:15:02.338771 18059 sgd_solver.cpp:105] Iteration 10008, lr = 0.00137732
I0410 02:15:04.467154 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:15:06.952598 18059 solver.cpp:218] Iteration 10020 (2.60097 iter/s, 4.61366s/12 iters), loss = 0.460957
I0410 02:15:06.952653 18059 solver.cpp:237]     Train net output #0: loss = 0.460957 (* 1 = 0.460957 loss)
I0410 02:15:06.952666 18059 sgd_solver.cpp:105] Iteration 10020, lr = 0.00137405
I0410 02:15:11.710332 18059 solver.cpp:218] Iteration 10032 (2.52234 iter/s, 4.75749s/12 iters), loss = 0.385058
I0410 02:15:11.710398 18059 solver.cpp:237]     Train net output #0: loss = 0.385058 (* 1 = 0.385058 loss)
I0410 02:15:11.710414 18059 sgd_solver.cpp:105] Iteration 10032, lr = 0.00137079
I0410 02:15:17.709944 18059 solver.cpp:218] Iteration 10044 (2.00022 iter/s, 5.99933s/12 iters), loss = 0.338589
I0410 02:15:17.710112 18059 solver.cpp:237]     Train net output #0: loss = 0.338589 (* 1 = 0.338589 loss)
I0410 02:15:17.710126 18059 sgd_solver.cpp:105] Iteration 10044, lr = 0.00136754
I0410 02:15:22.759495 18059 solver.cpp:218] Iteration 10056 (2.37662 iter/s, 5.0492s/12 iters), loss = 0.343156
I0410 02:15:22.759548 18059 solver.cpp:237]     Train net output #0: loss = 0.343156 (* 1 = 0.343156 loss)
I0410 02:15:22.759559 18059 sgd_solver.cpp:105] Iteration 10056, lr = 0.00136429
I0410 02:15:27.466392 18059 solver.cpp:218] Iteration 10068 (2.54958 iter/s, 4.70667s/12 iters), loss = 0.418899
I0410 02:15:27.466434 18059 solver.cpp:237]     Train net output #0: loss = 0.418899 (* 1 = 0.418899 loss)
I0410 02:15:27.466442 18059 sgd_solver.cpp:105] Iteration 10068, lr = 0.00136105
I0410 02:15:32.266084 18059 solver.cpp:218] Iteration 10080 (2.50028 iter/s, 4.79947s/12 iters), loss = 0.447421
I0410 02:15:32.266132 18059 solver.cpp:237]     Train net output #0: loss = 0.447421 (* 1 = 0.447421 loss)
I0410 02:15:32.266144 18059 sgd_solver.cpp:105] Iteration 10080, lr = 0.00135782
I0410 02:15:37.209705 18059 solver.cpp:218] Iteration 10092 (2.42749 iter/s, 4.94338s/12 iters), loss = 0.462576
I0410 02:15:37.209764 18059 solver.cpp:237]     Train net output #0: loss = 0.462576 (* 1 = 0.462576 loss)
I0410 02:15:37.209777 18059 sgd_solver.cpp:105] Iteration 10092, lr = 0.0013546
I0410 02:15:39.079095 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10098.caffemodel
I0410 02:15:50.654222 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10098.solverstate
I0410 02:15:57.639569 18059 solver.cpp:330] Iteration 10098, Testing net (#0)
I0410 02:15:57.639595 18059 net.cpp:676] Ignoring source layer train-data
I0410 02:15:58.143671 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:16:02.127887 18059 solver.cpp:397]     Test net output #0: accuracy = 0.446078
I0410 02:16:02.127935 18059 solver.cpp:397]     Test net output #1: loss = 2.91369 (* 1 = 2.91369 loss)
I0410 02:16:04.003408 18059 solver.cpp:218] Iteration 10104 (0.447883 iter/s, 26.7927s/12 iters), loss = 0.462333
I0410 02:16:04.003466 18059 solver.cpp:237]     Train net output #0: loss = 0.462333 (* 1 = 0.462333 loss)
I0410 02:16:04.003479 18059 sgd_solver.cpp:105] Iteration 10104, lr = 0.00135138
I0410 02:16:08.041508 18063 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:16:08.645980 18059 solver.cpp:218] Iteration 10116 (2.58491 iter/s, 4.64232s/12 iters), loss = 0.332476
I0410 02:16:08.646029 18059 solver.cpp:237]     Train net output #0: loss = 0.332476 (* 1 = 0.332476 loss)
I0410 02:16:08.646040 18059 sgd_solver.cpp:105] Iteration 10116, lr = 0.00134817
I0410 02:16:13.713119 18059 solver.cpp:218] Iteration 10128 (2.36831 iter/s, 5.0669s/12 iters), loss = 0.492604
I0410 02:16:13.713160 18059 solver.cpp:237]     Train net output #0: loss = 0.492604 (* 1 = 0.492604 loss)
I0410 02:16:13.713169 18059 sgd_solver.cpp:105] Iteration 10128, lr = 0.00134497
I0410 02:16:18.350436 18059 solver.cpp:218] Iteration 10140 (2.58782 iter/s, 4.6371s/12 iters), loss = 0.612126
I0410 02:16:18.350482 18059 solver.cpp:237]     Train net output #0: loss = 0.612126 (* 1 = 0.612126 loss)
I0410 02:16:18.350492 18059 sgd_solver.cpp:105] Iteration 10140, lr = 0.00134178
I0410 02:16:23.269871 18059 solver.cpp:218] Iteration 10152 (2.43942 iter/s, 4.9192s/12 iters), loss = 0.517958
I0410 02:16:23.269986 18059 solver.cpp:237]     Train net output #0: loss = 0.517958 (* 1 = 0.517958 loss)
I0410 02:16:23.269997 18059 sgd_solver.cpp:105] Iteration 10152, lr = 0.00133859
I0410 02:16:28.197862 18059 solver.cpp:218] Iteration 10164 (2.43522 iter/s, 4.92769s/12 iters), loss = 0.479012
I0410 02:16:28.197916 18059 solver.cpp:237]     Train net output #0: loss = 0.479012 (* 1 = 0.479012 loss)
I0410 02:16:28.197929 18059 sgd_solver.cpp:105] Iteration 10164, lr = 0.00133541
I0410 02:16:32.992254 18059 solver.cpp:218] Iteration 10176 (2.50305 iter/s, 4.79415s/12 iters), loss = 0.428601
I0410 02:16:32.992311 18059 solver.cpp:237]     Train net output #0: loss = 0.428601 (* 1 = 0.428601 loss)
I0410 02:16:32.992322 18059 sgd_solver.cpp:105] Iteration 10176, lr = 0.00133224
I0410 02:16:37.925436 18059 solver.cpp:218] Iteration 10188 (2.43263 iter/s, 4.93294s/12 iters), loss = 0.396243
I0410 02:16:37.925487 18059 solver.cpp:237]     Train net output #0: loss = 0.396243 (* 1 = 0.396243 loss)
I0410 02:16:37.925498 18059 sgd_solver.cpp:105] Iteration 10188, lr = 0.00132908
I0410 02:16:42.199240 18059 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10200.caffemodel
I0410 02:16:47.942646 18059 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10200.solverstate
I0410 02:16:54.989090 18059 solver.cpp:310] Iteration 10200, loss = 0.428939
I0410 02:16:54.989212 18059 solver.cpp:330] Iteration 10200, Testing net (#0)
I0410 02:16:54.989220 18059 net.cpp:676] Ignoring source layer train-data
I0410 02:16:55.351312 18064 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:16:59.335506 18059 solver.cpp:397]     Test net output #0: accuracy = 0.463235
I0410 02:16:59.335551 18059 solver.cpp:397]     Test net output #1: loss = 2.81398 (* 1 = 2.81398 loss)
I0410 02:16:59.335559 18059 solver.cpp:315] Optimization Done.
I0410 02:16:59.335566 18059 caffe.cpp:259] Optimization Done.