I0401 16:15:08.247797 14951 upgrade_proto.cpp:1082] Attempting to upgrade input file specified using deprecated 'solver_type' field (enum)': /mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210401-133302-f5e6/solver.prototxt
I0401 16:15:08.247999 14951 upgrade_proto.cpp:1089] Successfully upgraded file specified using deprecated 'solver_type' field (enum) to 'type' field (string).
W0401 16:15:08.248006 14951 upgrade_proto.cpp:1091] Note that future Caffe releases will only support 'type' field (string) for a solver's type.
I0401 16:15:08.248081 14951 caffe.cpp:218] Using GPUs 3
I0401 16:15:08.266332 14951 caffe.cpp:223] GPU 3: GeForce GTX TITAN X
I0401 16:15:08.500301 14951 solver.cpp:44] Initializing solver from parameters:
test_iter: 26
test_interval: 114
base_lr: 0.001
display: 14
max_iter: 11400
lr_policy: "fixed"
momentum: 0.9
weight_decay: 1.0000001e-05
snapshot: 114
snapshot_prefix: "snapshot"
solver_mode: GPU
device_id: 3
net: "train_val.prototxt"
train_state {
level: 0
stage: ""
}
type: "SGD"
I0401 16:15:08.501219 14951 solver.cpp:87] Creating training net from net file: train_val.prototxt
I0401 16:15:08.501849 14951 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer val-data
I0401 16:15:08.501863 14951 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy
I0401 16:15:08.501991 14951 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-AIN-3/digits/jobs/20210401-115617-b134/mean.binaryproto"
}
data_param {
source: "/mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210401-115617-b134/train_db"
batch_size: 128
backend: LMDB
}
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 96
kernel_size: 11
stride: 4
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "conv1"
top: "conv1"
}
layer {
name: "norm1"
type: "LRN"
bottom: "conv1"
top: "norm1"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool1"
type: "Pooling"
bottom: "norm1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 2
kernel_size: 5
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu2"
type: "ReLU"
bottom: "conv2"
top: "conv2"
}
layer {
name: "norm2"
type: "LRN"
bottom: "conv2"
top: "norm2"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool2"
type: "Pooling"
bottom: "norm2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv3"
type: "Convolution"
bottom: "pool2"
top: "conv3"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu3"
type: "ReLU"
bottom: "conv3"
top: "conv3"
}
layer {
name: "conv4"
type: "Convolution"
bottom: "conv3"
top: "conv4"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu4"
type: "ReLU"
bottom: "conv4"
top: "conv4"
}
layer {
name: "conv5"
type: "Convolution"
bottom: "conv4"
top: "conv5"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu5"
type: "ReLU"
bottom: "conv5"
top: "conv5"
}
layer {
name: "pool5"
type: "Pooling"
bottom: "conv5"
top: "pool5"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "fc6"
type: "InnerProduct"
bottom: "pool5"
top: "fc6"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 4096
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu6"
type: "ReLU"
bottom: "fc6"
top: "fc6"
}
layer {
name: "drop6"
type: "Dropout"
bottom: "fc6"
top: "fc6"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc7"
type: "InnerProduct"
bottom: "fc6"
top: "fc7"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 4096
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu7"
type: "ReLU"
bottom: "fc7"
top: "fc7"
}
layer {
name: "drop7"
type: "Dropout"
bottom: "fc7"
top: "fc7"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc8"
type: "InnerProduct"
bottom: "fc7"
top: "fc8"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 196
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "loss"
type: "SoftmaxWithLoss"
bottom: "fc8"
bottom: "label"
top: "loss"
}
I0401 16:15:08.502074 14951 layer_factory.hpp:77] Creating layer train-data
I0401 16:15:08.517542 14951 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210401-115617-b134/train_db
I0401 16:15:08.532027 14951 net.cpp:84] Creating Layer train-data
I0401 16:15:08.532052 14951 net.cpp:380] train-data -> data
I0401 16:15:08.532073 14951 net.cpp:380] train-data -> label
I0401 16:15:08.532085 14951 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210401-115617-b134/mean.binaryproto
I0401 16:15:08.588140 14951 data_layer.cpp:45] output data size: 128,3,227,227
I0401 16:15:08.725008 14951 net.cpp:122] Setting up train-data
I0401 16:15:08.725035 14951 net.cpp:129] Top shape: 128 3 227 227 (19787136)
I0401 16:15:08.725041 14951 net.cpp:129] Top shape: 128 (128)
I0401 16:15:08.725044 14951 net.cpp:137] Memory required for data: 79149056
I0401 16:15:08.725055 14951 layer_factory.hpp:77] Creating layer conv1
I0401 16:15:08.725078 14951 net.cpp:84] Creating Layer conv1
I0401 16:15:08.725085 14951 net.cpp:406] conv1 <- data
I0401 16:15:08.725098 14951 net.cpp:380] conv1 -> conv1
I0401 16:15:09.384101 14951 net.cpp:122] Setting up conv1
I0401 16:15:09.384127 14951 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0401 16:15:09.384131 14951 net.cpp:137] Memory required for data: 227833856
I0401 16:15:09.384156 14951 layer_factory.hpp:77] Creating layer relu1
I0401 16:15:09.384169 14951 net.cpp:84] Creating Layer relu1
I0401 16:15:09.384173 14951 net.cpp:406] relu1 <- conv1
I0401 16:15:09.384181 14951 net.cpp:367] relu1 -> conv1 (in-place)
I0401 16:15:09.384593 14951 net.cpp:122] Setting up relu1
I0401 16:15:09.384603 14951 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0401 16:15:09.384606 14951 net.cpp:137] Memory required for data: 376518656
I0401 16:15:09.384610 14951 layer_factory.hpp:77] Creating layer norm1
I0401 16:15:09.384620 14951 net.cpp:84] Creating Layer norm1
I0401 16:15:09.384624 14951 net.cpp:406] norm1 <- conv1
I0401 16:15:09.384661 14951 net.cpp:380] norm1 -> norm1
I0401 16:15:09.385291 14951 net.cpp:122] Setting up norm1
I0401 16:15:09.385304 14951 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0401 16:15:09.385308 14951 net.cpp:137] Memory required for data: 525203456
I0401 16:15:09.385313 14951 layer_factory.hpp:77] Creating layer pool1
I0401 16:15:09.385322 14951 net.cpp:84] Creating Layer pool1
I0401 16:15:09.385326 14951 net.cpp:406] pool1 <- norm1
I0401 16:15:09.385332 14951 net.cpp:380] pool1 -> pool1
I0401 16:15:09.385378 14951 net.cpp:122] Setting up pool1
I0401 16:15:09.385385 14951 net.cpp:129] Top shape: 128 96 27 27 (8957952)
I0401 16:15:09.385390 14951 net.cpp:137] Memory required for data: 561035264
I0401 16:15:09.385392 14951 layer_factory.hpp:77] Creating layer conv2
I0401 16:15:09.385433 14951 net.cpp:84] Creating Layer conv2
I0401 16:15:09.385438 14951 net.cpp:406] conv2 <- pool1
I0401 16:15:09.385445 14951 net.cpp:380] conv2 -> conv2
I0401 16:15:09.394045 14951 net.cpp:122] Setting up conv2
I0401 16:15:09.394073 14951 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0401 16:15:09.394078 14951 net.cpp:137] Memory required for data: 656586752
I0401 16:15:09.394093 14951 layer_factory.hpp:77] Creating layer relu2
I0401 16:15:09.394104 14951 net.cpp:84] Creating Layer relu2
I0401 16:15:09.394109 14951 net.cpp:406] relu2 <- conv2
I0401 16:15:09.394116 14951 net.cpp:367] relu2 -> conv2 (in-place)
I0401 16:15:09.394675 14951 net.cpp:122] Setting up relu2
I0401 16:15:09.394687 14951 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0401 16:15:09.394691 14951 net.cpp:137] Memory required for data: 752138240
I0401 16:15:09.394695 14951 layer_factory.hpp:77] Creating layer norm2
I0401 16:15:09.394704 14951 net.cpp:84] Creating Layer norm2
I0401 16:15:09.394708 14951 net.cpp:406] norm2 <- conv2
I0401 16:15:09.394714 14951 net.cpp:380] norm2 -> norm2
I0401 16:15:09.395105 14951 net.cpp:122] Setting up norm2
I0401 16:15:09.395115 14951 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0401 16:15:09.395119 14951 net.cpp:137] Memory required for data: 847689728
I0401 16:15:09.395123 14951 layer_factory.hpp:77] Creating layer pool2
I0401 16:15:09.395133 14951 net.cpp:84] Creating Layer pool2
I0401 16:15:09.395136 14951 net.cpp:406] pool2 <- norm2
I0401 16:15:09.395143 14951 net.cpp:380] pool2 -> pool2
I0401 16:15:09.395177 14951 net.cpp:122] Setting up pool2
I0401 16:15:09.395184 14951 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0401 16:15:09.395187 14951 net.cpp:137] Memory required for data: 869840896
I0401 16:15:09.395190 14951 layer_factory.hpp:77] Creating layer conv3
I0401 16:15:09.395203 14951 net.cpp:84] Creating Layer conv3
I0401 16:15:09.395206 14951 net.cpp:406] conv3 <- pool2
I0401 16:15:09.395212 14951 net.cpp:380] conv3 -> conv3
I0401 16:15:09.415153 14951 net.cpp:122] Setting up conv3
I0401 16:15:09.415174 14951 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0401 16:15:09.415177 14951 net.cpp:137] Memory required for data: 903067648
I0401 16:15:09.415194 14951 layer_factory.hpp:77] Creating layer relu3
I0401 16:15:09.415205 14951 net.cpp:84] Creating Layer relu3
I0401 16:15:09.415208 14951 net.cpp:406] relu3 <- conv3
I0401 16:15:09.415215 14951 net.cpp:367] relu3 -> conv3 (in-place)
I0401 16:15:09.415697 14951 net.cpp:122] Setting up relu3
I0401 16:15:09.415706 14951 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0401 16:15:09.415709 14951 net.cpp:137] Memory required for data: 936294400
I0401 16:15:09.415711 14951 layer_factory.hpp:77] Creating layer conv4
I0401 16:15:09.415719 14951 net.cpp:84] Creating Layer conv4
I0401 16:15:09.415721 14951 net.cpp:406] conv4 <- conv3
I0401 16:15:09.415729 14951 net.cpp:380] conv4 -> conv4
I0401 16:15:09.425261 14951 net.cpp:122] Setting up conv4
I0401 16:15:09.425282 14951 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0401 16:15:09.425283 14951 net.cpp:137] Memory required for data: 969521152
I0401 16:15:09.425293 14951 layer_factory.hpp:77] Creating layer relu4
I0401 16:15:09.425302 14951 net.cpp:84] Creating Layer relu4
I0401 16:15:09.425325 14951 net.cpp:406] relu4 <- conv4
I0401 16:15:09.425333 14951 net.cpp:367] relu4 -> conv4 (in-place)
I0401 16:15:09.425649 14951 net.cpp:122] Setting up relu4
I0401 16:15:09.425658 14951 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0401 16:15:09.425660 14951 net.cpp:137] Memory required for data: 1002747904
I0401 16:15:09.425662 14951 layer_factory.hpp:77] Creating layer conv5
I0401 16:15:09.425673 14951 net.cpp:84] Creating Layer conv5
I0401 16:15:09.425675 14951 net.cpp:406] conv5 <- conv4
I0401 16:15:09.425679 14951 net.cpp:380] conv5 -> conv5
I0401 16:15:09.433162 14951 net.cpp:122] Setting up conv5
I0401 16:15:09.433182 14951 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0401 16:15:09.433185 14951 net.cpp:137] Memory required for data: 1024899072
I0401 16:15:09.433199 14951 layer_factory.hpp:77] Creating layer relu5
I0401 16:15:09.433209 14951 net.cpp:84] Creating Layer relu5
I0401 16:15:09.433212 14951 net.cpp:406] relu5 <- conv5
I0401 16:15:09.433220 14951 net.cpp:367] relu5 -> conv5 (in-place)
I0401 16:15:09.433715 14951 net.cpp:122] Setting up relu5
I0401 16:15:09.433725 14951 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0401 16:15:09.433727 14951 net.cpp:137] Memory required for data: 1047050240
I0401 16:15:09.433730 14951 layer_factory.hpp:77] Creating layer pool5
I0401 16:15:09.433737 14951 net.cpp:84] Creating Layer pool5
I0401 16:15:09.433738 14951 net.cpp:406] pool5 <- conv5
I0401 16:15:09.433745 14951 net.cpp:380] pool5 -> pool5
I0401 16:15:09.433780 14951 net.cpp:122] Setting up pool5
I0401 16:15:09.433785 14951 net.cpp:129] Top shape: 128 256 6 6 (1179648)
I0401 16:15:09.433787 14951 net.cpp:137] Memory required for data: 1051768832
I0401 16:15:09.433789 14951 layer_factory.hpp:77] Creating layer fc6
I0401 16:15:09.433799 14951 net.cpp:84] Creating Layer fc6
I0401 16:15:09.433801 14951 net.cpp:406] fc6 <- pool5
I0401 16:15:09.433805 14951 net.cpp:380] fc6 -> fc6
I0401 16:15:09.878798 14951 net.cpp:122] Setting up fc6
I0401 16:15:09.878819 14951 net.cpp:129] Top shape: 128 4096 (524288)
I0401 16:15:09.878823 14951 net.cpp:137] Memory required for data: 1053865984
I0401 16:15:09.878856 14951 layer_factory.hpp:77] Creating layer relu6
I0401 16:15:09.878866 14951 net.cpp:84] Creating Layer relu6
I0401 16:15:09.878870 14951 net.cpp:406] relu6 <- fc6
I0401 16:15:09.878898 14951 net.cpp:367] relu6 -> fc6 (in-place)
I0401 16:15:09.879698 14951 net.cpp:122] Setting up relu6
I0401 16:15:09.879706 14951 net.cpp:129] Top shape: 128 4096 (524288)
I0401 16:15:09.879709 14951 net.cpp:137] Memory required for data: 1055963136
I0401 16:15:09.879711 14951 layer_factory.hpp:77] Creating layer drop6
I0401 16:15:09.879717 14951 net.cpp:84] Creating Layer drop6
I0401 16:15:09.879720 14951 net.cpp:406] drop6 <- fc6
I0401 16:15:09.879725 14951 net.cpp:367] drop6 -> fc6 (in-place)
I0401 16:15:09.879751 14951 net.cpp:122] Setting up drop6
I0401 16:15:09.879756 14951 net.cpp:129] Top shape: 128 4096 (524288)
I0401 16:15:09.879758 14951 net.cpp:137] Memory required for data: 1058060288
I0401 16:15:09.879760 14951 layer_factory.hpp:77] Creating layer fc7
I0401 16:15:09.879768 14951 net.cpp:84] Creating Layer fc7
I0401 16:15:09.879770 14951 net.cpp:406] fc7 <- fc6
I0401 16:15:09.879776 14951 net.cpp:380] fc7 -> fc7
I0401 16:15:10.054896 14951 net.cpp:122] Setting up fc7
I0401 16:15:10.054919 14951 net.cpp:129] Top shape: 128 4096 (524288)
I0401 16:15:10.054924 14951 net.cpp:137] Memory required for data: 1060157440
I0401 16:15:10.054936 14951 layer_factory.hpp:77] Creating layer relu7
I0401 16:15:10.054947 14951 net.cpp:84] Creating Layer relu7
I0401 16:15:10.054951 14951 net.cpp:406] relu7 <- fc7
I0401 16:15:10.054957 14951 net.cpp:367] relu7 -> fc7 (in-place)
I0401 16:15:10.055416 14951 net.cpp:122] Setting up relu7
I0401 16:15:10.055426 14951 net.cpp:129] Top shape: 128 4096 (524288)
I0401 16:15:10.055429 14951 net.cpp:137] Memory required for data: 1062254592
I0401 16:15:10.055431 14951 layer_factory.hpp:77] Creating layer drop7
I0401 16:15:10.055438 14951 net.cpp:84] Creating Layer drop7
I0401 16:15:10.055471 14951 net.cpp:406] drop7 <- fc7
I0401 16:15:10.055478 14951 net.cpp:367] drop7 -> fc7 (in-place)
I0401 16:15:10.055508 14951 net.cpp:122] Setting up drop7
I0401 16:15:10.055514 14951 net.cpp:129] Top shape: 128 4096 (524288)
I0401 16:15:10.055516 14951 net.cpp:137] Memory required for data: 1064351744
I0401 16:15:10.055518 14951 layer_factory.hpp:77] Creating layer fc8
I0401 16:15:10.055526 14951 net.cpp:84] Creating Layer fc8
I0401 16:15:10.055527 14951 net.cpp:406] fc8 <- fc7
I0401 16:15:10.055531 14951 net.cpp:380] fc8 -> fc8
I0401 16:15:10.063215 14951 net.cpp:122] Setting up fc8
I0401 16:15:10.063236 14951 net.cpp:129] Top shape: 128 196 (25088)
I0401 16:15:10.063239 14951 net.cpp:137] Memory required for data: 1064452096
I0401 16:15:10.063247 14951 layer_factory.hpp:77] Creating layer loss
I0401 16:15:10.063256 14951 net.cpp:84] Creating Layer loss
I0401 16:15:10.063259 14951 net.cpp:406] loss <- fc8
I0401 16:15:10.063263 14951 net.cpp:406] loss <- label
I0401 16:15:10.063271 14951 net.cpp:380] loss -> loss
I0401 16:15:10.063280 14951 layer_factory.hpp:77] Creating layer loss
I0401 16:15:10.064069 14951 net.cpp:122] Setting up loss
I0401 16:15:10.064080 14951 net.cpp:129] Top shape: (1)
I0401 16:15:10.064085 14951 net.cpp:132]     with loss weight 1
I0401 16:15:10.064102 14951 net.cpp:137] Memory required for data: 1064452100
I0401 16:15:10.064105 14951 net.cpp:198] loss needs backward computation.
I0401 16:15:10.064111 14951 net.cpp:198] fc8 needs backward computation.
I0401 16:15:10.064116 14951 net.cpp:198] drop7 needs backward computation.
I0401 16:15:10.064118 14951 net.cpp:198] relu7 needs backward computation.
I0401 16:15:10.064121 14951 net.cpp:198] fc7 needs backward computation.
I0401 16:15:10.064126 14951 net.cpp:198] drop6 needs backward computation.
I0401 16:15:10.064129 14951 net.cpp:198] relu6 needs backward computation.
I0401 16:15:10.064132 14951 net.cpp:198] fc6 needs backward computation.
I0401 16:15:10.064136 14951 net.cpp:198] pool5 needs backward computation.
I0401 16:15:10.064139 14951 net.cpp:198] relu5 needs backward computation.
I0401 16:15:10.064142 14951 net.cpp:198] conv5 needs backward computation.
I0401 16:15:10.064144 14951 net.cpp:198] relu4 needs backward computation.
I0401 16:15:10.064146 14951 net.cpp:198] conv4 needs backward computation.
I0401 16:15:10.064149 14951 net.cpp:198] relu3 needs backward computation.
I0401 16:15:10.064152 14951 net.cpp:198] conv3 needs backward computation.
I0401 16:15:10.064154 14951 net.cpp:198] pool2 needs backward computation.
I0401 16:15:10.064157 14951 net.cpp:198] norm2 needs backward computation.
I0401 16:15:10.064158 14951 net.cpp:198] relu2 needs backward computation.
I0401 16:15:10.064162 14951 net.cpp:198] conv2 needs backward computation.
I0401 16:15:10.064163 14951 net.cpp:198] pool1 needs backward computation.
I0401 16:15:10.064167 14951 net.cpp:198] norm1 needs backward computation.
I0401 16:15:10.064168 14951 net.cpp:198] relu1 needs backward computation.
I0401 16:15:10.064172 14951 net.cpp:198] conv1 needs backward computation.
I0401 16:15:10.064175 14951 net.cpp:200] train-data does not need backward computation.
I0401 16:15:10.064177 14951 net.cpp:242] This network produces output loss
I0401 16:15:10.064190 14951 net.cpp:255] Network initialization done.
I0401 16:15:10.065518 14951 solver.cpp:172] Creating test net (#0) specified by net file: train_val.prototxt
I0401 16:15:10.065549 14951 net.cpp:294] The NetState phase (1) differed from the phase (0) specified by a rule in layer train-data
I0401 16:15:10.065678 14951 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-AIN-3/digits/jobs/20210401-115617-b134/mean.binaryproto"
}
data_param {
source: "/mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210401-115617-b134/val_db"
batch_size: 32
backend: LMDB
}
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 96
kernel_size: 11
stride: 4
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "conv1"
top: "conv1"
}
layer {
name: "norm1"
type: "LRN"
bottom: "conv1"
top: "norm1"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool1"
type: "Pooling"
bottom: "norm1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 2
kernel_size: 5
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu2"
type: "ReLU"
bottom: "conv2"
top: "conv2"
}
layer {
name: "norm2"
type: "LRN"
bottom: "conv2"
top: "norm2"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool2"
type: "Pooling"
bottom: "norm2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv3"
type: "Convolution"
bottom: "pool2"
top: "conv3"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu3"
type: "ReLU"
bottom: "conv3"
top: "conv3"
}
layer {
name: "conv4"
type: "Convolution"
bottom: "conv3"
top: "conv4"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu4"
type: "ReLU"
bottom: "conv4"
top: "conv4"
}
layer {
name: "conv5"
type: "Convolution"
bottom: "conv4"
top: "conv5"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu5"
type: "ReLU"
bottom: "conv5"
top: "conv5"
}
layer {
name: "pool5"
type: "Pooling"
bottom: "conv5"
top: "pool5"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "fc6"
type: "InnerProduct"
bottom: "pool5"
top: "fc6"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 4096
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu6"
type: "ReLU"
bottom: "fc6"
top: "fc6"
}
layer {
name: "drop6"
type: "Dropout"
bottom: "fc6"
top: "fc6"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc7"
type: "InnerProduct"
bottom: "fc6"
top: "fc7"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 4096
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu7"
type: "ReLU"
bottom: "fc7"
top: "fc7"
}
layer {
name: "drop7"
type: "Dropout"
bottom: "fc7"
top: "fc7"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc8"
type: "InnerProduct"
bottom: "fc7"
top: "fc8"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 196
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "accuracy"
type: "Accuracy"
bottom: "fc8"
bottom: "label"
top: "accuracy"
include {
phase: TEST
}
}
layer {
name: "loss"
type: "SoftmaxWithLoss"
bottom: "fc8"
bottom: "label"
top: "loss"
}
I0401 16:15:10.065779 14951 layer_factory.hpp:77] Creating layer val-data
I0401 16:15:10.078147 14951 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210401-115617-b134/val_db
I0401 16:15:10.092274 14951 net.cpp:84] Creating Layer val-data
I0401 16:15:10.092305 14951 net.cpp:380] val-data -> data
I0401 16:15:10.092319 14951 net.cpp:380] val-data -> label
I0401 16:15:10.092326 14951 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210401-115617-b134/mean.binaryproto
I0401 16:15:10.099985 14951 data_layer.cpp:45] output data size: 32,3,227,227
I0401 16:15:10.135915 14951 net.cpp:122] Setting up val-data
I0401 16:15:10.135933 14951 net.cpp:129] Top shape: 32 3 227 227 (4946784)
I0401 16:15:10.135936 14951 net.cpp:129] Top shape: 32 (32)
I0401 16:15:10.135938 14951 net.cpp:137] Memory required for data: 19787264
I0401 16:15:10.135944 14951 layer_factory.hpp:77] Creating layer label_val-data_1_split
I0401 16:15:10.135954 14951 net.cpp:84] Creating Layer label_val-data_1_split
I0401 16:15:10.135958 14951 net.cpp:406] label_val-data_1_split <- label
I0401 16:15:10.135963 14951 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_0
I0401 16:15:10.135972 14951 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_1
I0401 16:15:10.136063 14951 net.cpp:122] Setting up label_val-data_1_split
I0401 16:15:10.136070 14951 net.cpp:129] Top shape: 32 (32)
I0401 16:15:10.136072 14951 net.cpp:129] Top shape: 32 (32)
I0401 16:15:10.136073 14951 net.cpp:137] Memory required for data: 19787520
I0401 16:15:10.136076 14951 layer_factory.hpp:77] Creating layer conv1
I0401 16:15:10.136087 14951 net.cpp:84] Creating Layer conv1
I0401 16:15:10.136090 14951 net.cpp:406] conv1 <- data
I0401 16:15:10.136093 14951 net.cpp:380] conv1 -> conv1
I0401 16:15:10.139055 14951 net.cpp:122] Setting up conv1
I0401 16:15:10.139071 14951 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0401 16:15:10.139073 14951 net.cpp:137] Memory required for data: 56958720
I0401 16:15:10.139088 14951 layer_factory.hpp:77] Creating layer relu1
I0401 16:15:10.139099 14951 net.cpp:84] Creating Layer relu1
I0401 16:15:10.139104 14951 net.cpp:406] relu1 <- conv1
I0401 16:15:10.139111 14951 net.cpp:367] relu1 -> conv1 (in-place)
I0401 16:15:10.139427 14951 net.cpp:122] Setting up relu1
I0401 16:15:10.139438 14951 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0401 16:15:10.139442 14951 net.cpp:137] Memory required for data: 94129920
I0401 16:15:10.139446 14951 layer_factory.hpp:77] Creating layer norm1
I0401 16:15:10.139456 14951 net.cpp:84] Creating Layer norm1
I0401 16:15:10.139459 14951 net.cpp:406] norm1 <- conv1
I0401 16:15:10.139463 14951 net.cpp:380] norm1 -> norm1
I0401 16:15:10.139961 14951 net.cpp:122] Setting up norm1
I0401 16:15:10.139971 14951 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0401 16:15:10.139973 14951 net.cpp:137] Memory required for data: 131301120
I0401 16:15:10.139976 14951 layer_factory.hpp:77] Creating layer pool1
I0401 16:15:10.139981 14951 net.cpp:84] Creating Layer pool1
I0401 16:15:10.139983 14951 net.cpp:406] pool1 <- norm1
I0401 16:15:10.139987 14951 net.cpp:380] pool1 -> pool1
I0401 16:15:10.140012 14951 net.cpp:122] Setting up pool1
I0401 16:15:10.140017 14951 net.cpp:129] Top shape: 32 96 27 27 (2239488)
I0401 16:15:10.140019 14951 net.cpp:137] Memory required for data: 140259072
I0401 16:15:10.140022 14951 layer_factory.hpp:77] Creating layer conv2
I0401 16:15:10.140028 14951 net.cpp:84] Creating Layer conv2
I0401 16:15:10.140031 14951 net.cpp:406] conv2 <- pool1
I0401 16:15:10.140053 14951 net.cpp:380] conv2 -> conv2
I0401 16:15:10.146864 14951 net.cpp:122] Setting up conv2
I0401 16:15:10.146883 14951 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0401 16:15:10.146886 14951 net.cpp:137] Memory required for data: 164146944
I0401 16:15:10.146898 14951 layer_factory.hpp:77] Creating layer relu2
I0401 16:15:10.146906 14951 net.cpp:84] Creating Layer relu2
I0401 16:15:10.146909 14951 net.cpp:406] relu2 <- conv2
I0401 16:15:10.146915 14951 net.cpp:367] relu2 -> conv2 (in-place)
I0401 16:15:10.147446 14951 net.cpp:122] Setting up relu2
I0401 16:15:10.147456 14951 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0401 16:15:10.147460 14951 net.cpp:137] Memory required for data: 188034816
I0401 16:15:10.147464 14951 layer_factory.hpp:77] Creating layer norm2
I0401 16:15:10.147475 14951 net.cpp:84] Creating Layer norm2
I0401 16:15:10.147480 14951 net.cpp:406] norm2 <- conv2
I0401 16:15:10.147487 14951 net.cpp:380] norm2 -> norm2
I0401 16:15:10.148105 14951 net.cpp:122] Setting up norm2
I0401 16:15:10.148116 14951 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0401 16:15:10.148120 14951 net.cpp:137] Memory required for data: 211922688
I0401 16:15:10.148124 14951 layer_factory.hpp:77] Creating layer pool2
I0401 16:15:10.148133 14951 net.cpp:84] Creating Layer pool2
I0401 16:15:10.148136 14951 net.cpp:406] pool2 <- norm2
I0401 16:15:10.148144 14951 net.cpp:380] pool2 -> pool2
I0401 16:15:10.148185 14951 net.cpp:122] Setting up pool2
I0401 16:15:10.148192 14951 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0401 16:15:10.148195 14951 net.cpp:137] Memory required for data: 217460480
I0401 16:15:10.148197 14951 layer_factory.hpp:77] Creating layer conv3
I0401 16:15:10.148209 14951 net.cpp:84] Creating Layer conv3
I0401 16:15:10.148212 14951 net.cpp:406] conv3 <- pool2
I0401 16:15:10.148219 14951 net.cpp:380] conv3 -> conv3
I0401 16:15:10.160068 14951 net.cpp:122] Setting up conv3
I0401 16:15:10.160085 14951 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0401 16:15:10.160089 14951 net.cpp:137] Memory required for data: 225767168
I0401 16:15:10.160099 14951 layer_factory.hpp:77] Creating layer relu3
I0401 16:15:10.160109 14951 net.cpp:84] Creating Layer relu3
I0401 16:15:10.160113 14951 net.cpp:406] relu3 <- conv3
I0401 16:15:10.160118 14951 net.cpp:367] relu3 -> conv3 (in-place)
I0401 16:15:10.160699 14951 net.cpp:122] Setting up relu3
I0401 16:15:10.160708 14951 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0401 16:15:10.160710 14951 net.cpp:137] Memory required for data: 234073856
I0401 16:15:10.160713 14951 layer_factory.hpp:77] Creating layer conv4
I0401 16:15:10.160723 14951 net.cpp:84] Creating Layer conv4
I0401 16:15:10.160727 14951 net.cpp:406] conv4 <- conv3
I0401 16:15:10.160732 14951 net.cpp:380] conv4 -> conv4
I0401 16:15:10.172874 14951 net.cpp:122] Setting up conv4
I0401 16:15:10.172899 14951 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0401 16:15:10.172902 14951 net.cpp:137] Memory required for data: 242380544
I0401 16:15:10.172911 14951 layer_factory.hpp:77] Creating layer relu4
I0401 16:15:10.172919 14951 net.cpp:84] Creating Layer relu4
I0401 16:15:10.172922 14951 net.cpp:406] relu4 <- conv4
I0401 16:15:10.172927 14951 net.cpp:367] relu4 -> conv4 (in-place)
I0401 16:15:10.173256 14951 net.cpp:122] Setting up relu4
I0401 16:15:10.173265 14951 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0401 16:15:10.173267 14951 net.cpp:137] Memory required for data: 250687232
I0401 16:15:10.173269 14951 layer_factory.hpp:77] Creating layer conv5
I0401 16:15:10.173280 14951 net.cpp:84] Creating Layer conv5
I0401 16:15:10.173281 14951 net.cpp:406] conv5 <- conv4
I0401 16:15:10.173286 14951 net.cpp:380] conv5 -> conv5
I0401 16:15:10.197947 14951 net.cpp:122] Setting up conv5
I0401 16:15:10.197969 14951 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0401 16:15:10.197973 14951 net.cpp:137] Memory required for data: 256225024
I0401 16:15:10.197986 14951 layer_factory.hpp:77] Creating layer relu5
I0401 16:15:10.197997 14951 net.cpp:84] Creating Layer relu5
I0401 16:15:10.198004 14951 net.cpp:406] relu5 <- conv5
I0401 16:15:10.198031 14951 net.cpp:367] relu5 -> conv5 (in-place)
I0401 16:15:10.198791 14951 net.cpp:122] Setting up relu5
I0401 16:15:10.198801 14951 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0401 16:15:10.198805 14951 net.cpp:137] Memory required for data: 261762816
I0401 16:15:10.198808 14951 layer_factory.hpp:77] Creating layer pool5
I0401 16:15:10.198818 14951 net.cpp:84] Creating Layer pool5
I0401 16:15:10.198820 14951 net.cpp:406] pool5 <- conv5
I0401 16:15:10.198825 14951 net.cpp:380] pool5 -> pool5
I0401 16:15:10.198870 14951 net.cpp:122] Setting up pool5
I0401 16:15:10.198876 14951 net.cpp:129] Top shape: 32 256 6 6 (294912)
I0401 16:15:10.198879 14951 net.cpp:137] Memory required for data: 262942464
I0401 16:15:10.198882 14951 layer_factory.hpp:77] Creating layer fc6
I0401 16:15:10.198890 14951 net.cpp:84] Creating Layer fc6
I0401 16:15:10.198894 14951 net.cpp:406] fc6 <- pool5
I0401 16:15:10.198899 14951 net.cpp:380] fc6 -> fc6
I0401 16:15:10.593936 14951 net.cpp:122] Setting up fc6
I0401 16:15:10.593959 14951 net.cpp:129] Top shape: 32 4096 (131072)
I0401 16:15:10.593961 14951 net.cpp:137] Memory required for data: 263466752
I0401 16:15:10.593971 14951 layer_factory.hpp:77] Creating layer relu6
I0401 16:15:10.593979 14951 net.cpp:84] Creating Layer relu6
I0401 16:15:10.593983 14951 net.cpp:406] relu6 <- fc6
I0401 16:15:10.593991 14951 net.cpp:367] relu6 -> fc6 (in-place)
I0401 16:15:10.594811 14951 net.cpp:122] Setting up relu6
I0401 16:15:10.594820 14951 net.cpp:129] Top shape: 32 4096 (131072)
I0401 16:15:10.594822 14951 net.cpp:137] Memory required for data: 263991040
I0401 16:15:10.594825 14951 layer_factory.hpp:77] Creating layer drop6
I0401 16:15:10.594831 14951 net.cpp:84] Creating Layer drop6
I0401 16:15:10.594833 14951 net.cpp:406] drop6 <- fc6
I0401 16:15:10.594838 14951 net.cpp:367] drop6 -> fc6 (in-place)
I0401 16:15:10.594863 14951 net.cpp:122] Setting up drop6
I0401 16:15:10.594868 14951 net.cpp:129] Top shape: 32 4096 (131072)
I0401 16:15:10.594869 14951 net.cpp:137] Memory required for data: 264515328
I0401 16:15:10.594872 14951 layer_factory.hpp:77] Creating layer fc7
I0401 16:15:10.594878 14951 net.cpp:84] Creating Layer fc7
I0401 16:15:10.594880 14951 net.cpp:406] fc7 <- fc6
I0401 16:15:10.594885 14951 net.cpp:380] fc7 -> fc7
I0401 16:15:10.790318 14951 net.cpp:122] Setting up fc7
I0401 16:15:10.790338 14951 net.cpp:129] Top shape: 32 4096 (131072)
I0401 16:15:10.790341 14951 net.cpp:137] Memory required for data: 265039616
I0401 16:15:10.790349 14951 layer_factory.hpp:77] Creating layer relu7
I0401 16:15:10.790359 14951 net.cpp:84] Creating Layer relu7
I0401 16:15:10.790361 14951 net.cpp:406] relu7 <- fc7
I0401 16:15:10.790367 14951 net.cpp:367] relu7 -> fc7 (in-place)
I0401 16:15:10.790797 14951 net.cpp:122] Setting up relu7
I0401 16:15:10.790805 14951 net.cpp:129] Top shape: 32 4096 (131072)
I0401 16:15:10.790807 14951 net.cpp:137] Memory required for data: 265563904
I0401 16:15:10.790809 14951 layer_factory.hpp:77] Creating layer drop7
I0401 16:15:10.790817 14951 net.cpp:84] Creating Layer drop7
I0401 16:15:10.790819 14951 net.cpp:406] drop7 <- fc7
I0401 16:15:10.790822 14951 net.cpp:367] drop7 -> fc7 (in-place)
I0401 16:15:10.790845 14951 net.cpp:122] Setting up drop7
I0401 16:15:10.790849 14951 net.cpp:129] Top shape: 32 4096 (131072)
I0401 16:15:10.790851 14951 net.cpp:137] Memory required for data: 266088192
I0401 16:15:10.790853 14951 layer_factory.hpp:77] Creating layer fc8
I0401 16:15:10.790858 14951 net.cpp:84] Creating Layer fc8
I0401 16:15:10.790860 14951 net.cpp:406] fc8 <- fc7
I0401 16:15:10.790865 14951 net.cpp:380] fc8 -> fc8
I0401 16:15:10.798202 14951 net.cpp:122] Setting up fc8
I0401 16:15:10.798225 14951 net.cpp:129] Top shape: 32 196 (6272)
I0401 16:15:10.798228 14951 net.cpp:137] Memory required for data: 266113280
I0401 16:15:10.798236 14951 layer_factory.hpp:77] Creating layer fc8_fc8_0_split
I0401 16:15:10.798243 14951 net.cpp:84] Creating Layer fc8_fc8_0_split
I0401 16:15:10.798247 14951 net.cpp:406] fc8_fc8_0_split <- fc8
I0401 16:15:10.798274 14951 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_0
I0401 16:15:10.798285 14951 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_1
I0401 16:15:10.798319 14951 net.cpp:122] Setting up fc8_fc8_0_split
I0401 16:15:10.798324 14951 net.cpp:129] Top shape: 32 196 (6272)
I0401 16:15:10.798326 14951 net.cpp:129] Top shape: 32 196 (6272)
I0401 16:15:10.798328 14951 net.cpp:137] Memory required for data: 266163456
I0401 16:15:10.798331 14951 layer_factory.hpp:77] Creating layer accuracy
I0401 16:15:10.798337 14951 net.cpp:84] Creating Layer accuracy
I0401 16:15:10.798339 14951 net.cpp:406] accuracy <- fc8_fc8_0_split_0
I0401 16:15:10.798343 14951 net.cpp:406] accuracy <- label_val-data_1_split_0
I0401 16:15:10.798346 14951 net.cpp:380] accuracy -> accuracy
I0401 16:15:10.798353 14951 net.cpp:122] Setting up accuracy
I0401 16:15:10.798357 14951 net.cpp:129] Top shape: (1)
I0401 16:15:10.798357 14951 net.cpp:137] Memory required for data: 266163460
I0401 16:15:10.798359 14951 layer_factory.hpp:77] Creating layer loss
I0401 16:15:10.798364 14951 net.cpp:84] Creating Layer loss
I0401 16:15:10.798367 14951 net.cpp:406] loss <- fc8_fc8_0_split_1
I0401 16:15:10.798368 14951 net.cpp:406] loss <- label_val-data_1_split_1
I0401 16:15:10.798372 14951 net.cpp:380] loss -> loss
I0401 16:15:10.798377 14951 layer_factory.hpp:77] Creating layer loss
I0401 16:15:10.799125 14951 net.cpp:122] Setting up loss
I0401 16:15:10.799134 14951 net.cpp:129] Top shape: (1)
I0401 16:15:10.799136 14951 net.cpp:132]     with loss weight 1
I0401 16:15:10.799146 14951 net.cpp:137] Memory required for data: 266163464
I0401 16:15:10.799149 14951 net.cpp:198] loss needs backward computation.
I0401 16:15:10.799152 14951 net.cpp:200] accuracy does not need backward computation.
I0401 16:15:10.799155 14951 net.cpp:198] fc8_fc8_0_split needs backward computation.
I0401 16:15:10.799157 14951 net.cpp:198] fc8 needs backward computation.
I0401 16:15:10.799160 14951 net.cpp:198] drop7 needs backward computation.
I0401 16:15:10.799162 14951 net.cpp:198] relu7 needs backward computation.
I0401 16:15:10.799165 14951 net.cpp:198] fc7 needs backward computation.
I0401 16:15:10.799166 14951 net.cpp:198] drop6 needs backward computation.
I0401 16:15:10.799168 14951 net.cpp:198] relu6 needs backward computation.
I0401 16:15:10.799170 14951 net.cpp:198] fc6 needs backward computation.
I0401 16:15:10.799173 14951 net.cpp:198] pool5 needs backward computation.
I0401 16:15:10.799175 14951 net.cpp:198] relu5 needs backward computation.
I0401 16:15:10.799177 14951 net.cpp:198] conv5 needs backward computation.
I0401 16:15:10.799180 14951 net.cpp:198] relu4 needs backward computation.
I0401 16:15:10.799182 14951 net.cpp:198] conv4 needs backward computation.
I0401 16:15:10.799185 14951 net.cpp:198] relu3 needs backward computation.
I0401 16:15:10.799186 14951 net.cpp:198] conv3 needs backward computation.
I0401 16:15:10.799190 14951 net.cpp:198] pool2 needs backward computation.
I0401 16:15:10.799191 14951 net.cpp:198] norm2 needs backward computation.
I0401 16:15:10.799194 14951 net.cpp:198] relu2 needs backward computation.
I0401 16:15:10.799196 14951 net.cpp:198] conv2 needs backward computation.
I0401 16:15:10.799199 14951 net.cpp:198] pool1 needs backward computation.
I0401 16:15:10.799202 14951 net.cpp:198] norm1 needs backward computation.
I0401 16:15:10.799204 14951 net.cpp:198] relu1 needs backward computation.
I0401 16:15:10.799206 14951 net.cpp:198] conv1 needs backward computation.
I0401 16:15:10.799208 14951 net.cpp:200] label_val-data_1_split does not need backward computation.
I0401 16:15:10.799211 14951 net.cpp:200] val-data does not need backward computation.
I0401 16:15:10.799213 14951 net.cpp:242] This network produces output accuracy
I0401 16:15:10.799216 14951 net.cpp:242] This network produces output loss
I0401 16:15:10.799233 14951 net.cpp:255] Network initialization done.
I0401 16:15:10.799306 14951 solver.cpp:56] Solver scaffolding done.
I0401 16:15:10.799721 14951 caffe.cpp:248] Starting Optimization
I0401 16:15:10.799729 14951 solver.cpp:272] Solving
I0401 16:15:10.799742 14951 solver.cpp:273] Learning Rate Policy: fixed
I0401 16:15:10.801425 14951 solver.cpp:330] Iteration 0, Testing net (#0)
I0401 16:15:10.801435 14951 net.cpp:676] Ignoring source layer train-data
I0401 16:15:10.910024 14951 blocking_queue.cpp:49] Waiting for data
I0401 16:15:12.991761 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:15:13.061777 14951 solver.cpp:397]     Test net output #0: accuracy = 0.00240385
I0401 16:15:13.061817 14951 solver.cpp:397]     Test net output #1: loss = 5.28192 (* 1 = 5.28192 loss)
I0401 16:15:13.191715 14951 solver.cpp:218] Iteration 0 (-3.54135e-21 iter/s, 2.39189s/14 iters), loss = 5.28285
I0401 16:15:13.193256 14951 solver.cpp:237]     Train net output #0: loss = 5.28285 (* 1 = 5.28285 loss)
I0401 16:15:13.193271 14951 sgd_solver.cpp:105] Iteration 0, lr = 0.001
I0401 16:15:18.515925 14951 solver.cpp:218] Iteration 14 (2.6303 iter/s, 5.32259s/14 iters), loss = 5.29341
I0401 16:15:18.515976 14951 solver.cpp:237]     Train net output #0: loss = 5.29341 (* 1 = 5.29341 loss)
I0401 16:15:18.515985 14951 sgd_solver.cpp:105] Iteration 14, lr = 0.001
I0401 16:15:24.853255 14951 solver.cpp:218] Iteration 28 (2.20918 iter/s, 6.3372s/14 iters), loss = 5.28101
I0401 16:15:24.853299 14951 solver.cpp:237]     Train net output #0: loss = 5.28101 (* 1 = 5.28101 loss)
I0401 16:15:24.853304 14951 sgd_solver.cpp:105] Iteration 28, lr = 0.001
I0401 16:15:31.362491 14951 solver.cpp:218] Iteration 42 (2.15083 iter/s, 6.50911s/14 iters), loss = 5.28117
I0401 16:15:31.362529 14951 solver.cpp:237]     Train net output #0: loss = 5.28117 (* 1 = 5.28117 loss)
I0401 16:15:31.362534 14951 sgd_solver.cpp:105] Iteration 42, lr = 0.001
I0401 16:15:37.831231 14951 solver.cpp:218] Iteration 56 (2.1643 iter/s, 6.46861s/14 iters), loss = 5.27777
I0401 16:15:37.837419 14951 solver.cpp:237]     Train net output #0: loss = 5.27777 (* 1 = 5.27777 loss)
I0401 16:15:37.837438 14951 sgd_solver.cpp:105] Iteration 56, lr = 0.001
I0401 16:15:44.504299 14951 solver.cpp:218] Iteration 70 (2.09995 iter/s, 6.66681s/14 iters), loss = 5.29311
I0401 16:15:44.504396 14951 solver.cpp:237]     Train net output #0: loss = 5.29311 (* 1 = 5.29311 loss)
I0401 16:15:44.504405 14951 sgd_solver.cpp:105] Iteration 70, lr = 0.001
I0401 16:15:50.912488 14951 solver.cpp:218] Iteration 84 (2.18477 iter/s, 6.40801s/14 iters), loss = 5.28479
I0401 16:15:50.912528 14951 solver.cpp:237]     Train net output #0: loss = 5.28479 (* 1 = 5.28479 loss)
I0401 16:15:50.912533 14951 sgd_solver.cpp:105] Iteration 84, lr = 0.001
I0401 16:16:05.953933 14951 solver.cpp:218] Iteration 98 (0.930775 iter/s, 15.0412s/14 iters), loss = 5.28691
I0401 16:16:05.953975 14951 solver.cpp:237]     Train net output #0: loss = 5.28691 (* 1 = 5.28691 loss)
I0401 16:16:05.953981 14951 sgd_solver.cpp:105] Iteration 98, lr = 0.001
I0401 16:16:12.736579 14951 solver.cpp:218] Iteration 112 (2.06413 iter/s, 6.78252s/14 iters), loss = 5.28845
I0401 16:16:12.736624 14951 solver.cpp:237]     Train net output #0: loss = 5.28845 (* 1 = 5.28845 loss)
I0401 16:16:12.736631 14951 sgd_solver.cpp:105] Iteration 112, lr = 0.001
I0401 16:16:13.030683 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:16:13.146536 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_114.caffemodel
I0401 16:16:16.353977 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_114.solverstate
I0401 16:16:18.676074 14951 solver.cpp:330] Iteration 114, Testing net (#0)
I0401 16:16:18.676096 14951 net.cpp:676] Ignoring source layer train-data
I0401 16:16:20.764983 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:16:20.917495 14951 solver.cpp:397]     Test net output #0: accuracy = 0.0108173
I0401 16:16:20.917524 14951 solver.cpp:397]     Test net output #1: loss = 5.27951 (* 1 = 5.27951 loss)
I0401 16:16:25.417851 14951 solver.cpp:218] Iteration 126 (1.10401 iter/s, 12.6811s/14 iters), loss = 5.28269
I0401 16:16:25.417892 14951 solver.cpp:237]     Train net output #0: loss = 5.28269 (* 1 = 5.28269 loss)
I0401 16:16:25.417897 14951 sgd_solver.cpp:105] Iteration 126, lr = 0.001
I0401 16:16:31.630420 14951 solver.cpp:218] Iteration 140 (2.25354 iter/s, 6.21245s/14 iters), loss = 5.29069
I0401 16:16:31.630481 14951 solver.cpp:237]     Train net output #0: loss = 5.29069 (* 1 = 5.29069 loss)
I0401 16:16:31.630491 14951 sgd_solver.cpp:105] Iteration 140, lr = 0.001
I0401 16:16:37.737193 14951 solver.cpp:218] Iteration 154 (2.29258 iter/s, 6.10665s/14 iters), loss = 5.27765
I0401 16:16:37.737233 14951 solver.cpp:237]     Train net output #0: loss = 5.27765 (* 1 = 5.27765 loss)
I0401 16:16:37.737239 14951 sgd_solver.cpp:105] Iteration 154, lr = 0.001
I0401 16:16:44.011591 14951 solver.cpp:218] Iteration 168 (2.23133 iter/s, 6.27427s/14 iters), loss = 5.2926
I0401 16:16:44.011631 14951 solver.cpp:237]     Train net output #0: loss = 5.2926 (* 1 = 5.2926 loss)
I0401 16:16:44.011637 14951 sgd_solver.cpp:105] Iteration 168, lr = 0.001
I0401 16:16:50.384732 14951 solver.cpp:218] Iteration 182 (2.19676 iter/s, 6.37302s/14 iters), loss = 5.25873
I0401 16:16:50.384868 14951 solver.cpp:237]     Train net output #0: loss = 5.25873 (* 1 = 5.25873 loss)
I0401 16:16:50.384877 14951 sgd_solver.cpp:105] Iteration 182, lr = 0.001
I0401 16:16:56.696569 14951 solver.cpp:218] Iteration 196 (2.21813 iter/s, 6.31162s/14 iters), loss = 5.25954
I0401 16:16:56.696620 14951 solver.cpp:237]     Train net output #0: loss = 5.25954 (* 1 = 5.25954 loss)
I0401 16:16:56.696628 14951 sgd_solver.cpp:105] Iteration 196, lr = 0.001
I0401 16:17:02.958115 14951 solver.cpp:218] Iteration 210 (2.23592 iter/s, 6.26142s/14 iters), loss = 5.29218
I0401 16:17:02.964336 14951 solver.cpp:237]     Train net output #0: loss = 5.29218 (* 1 = 5.29218 loss)
I0401 16:17:02.964355 14951 sgd_solver.cpp:105] Iteration 210, lr = 0.001
I0401 16:17:08.775583 14951 solver.cpp:218] Iteration 224 (2.40915 iter/s, 5.81119s/14 iters), loss = 5.28193
I0401 16:17:08.775624 14951 solver.cpp:237]     Train net output #0: loss = 5.28193 (* 1 = 5.28193 loss)
I0401 16:17:08.775630 14951 sgd_solver.cpp:105] Iteration 224, lr = 0.001
I0401 16:17:09.844727 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:17:10.095651 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_228.caffemodel
I0401 16:17:13.112825 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_228.solverstate
I0401 16:17:15.406862 14951 solver.cpp:330] Iteration 228, Testing net (#0)
I0401 16:17:15.406879 14951 net.cpp:676] Ignoring source layer train-data
I0401 16:17:17.359690 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:17:17.595587 14951 solver.cpp:397]     Test net output #0: accuracy = 0.0108173
I0401 16:17:17.595614 14951 solver.cpp:397]     Test net output #1: loss = 5.27808 (* 1 = 5.27808 loss)
I0401 16:17:21.345105 14951 solver.cpp:218] Iteration 238 (1.11382 iter/s, 12.5693s/14 iters), loss = 5.27795
I0401 16:17:21.345213 14951 solver.cpp:237]     Train net output #0: loss = 5.27795 (* 1 = 5.27795 loss)
I0401 16:17:21.345221 14951 sgd_solver.cpp:105] Iteration 238, lr = 0.001
I0401 16:17:27.352049 14951 solver.cpp:218] Iteration 252 (2.33071 iter/s, 6.00676s/14 iters), loss = 5.27535
I0401 16:17:27.352092 14951 solver.cpp:237]     Train net output #0: loss = 5.27535 (* 1 = 5.27535 loss)
I0401 16:17:27.352097 14951 sgd_solver.cpp:105] Iteration 252, lr = 0.001
I0401 16:17:33.528399 14951 solver.cpp:218] Iteration 266 (2.26676 iter/s, 6.17623s/14 iters), loss = 5.28274
I0401 16:17:33.528441 14951 solver.cpp:237]     Train net output #0: loss = 5.28274 (* 1 = 5.28274 loss)
I0401 16:17:33.528447 14951 sgd_solver.cpp:105] Iteration 266, lr = 0.001
I0401 16:17:39.702359 14951 solver.cpp:218] Iteration 280 (2.26763 iter/s, 6.17384s/14 iters), loss = 5.2847
I0401 16:17:39.702399 14951 solver.cpp:237]     Train net output #0: loss = 5.2847 (* 1 = 5.2847 loss)
I0401 16:17:39.702404 14951 sgd_solver.cpp:105] Iteration 280, lr = 0.001
I0401 16:17:46.020206 14951 solver.cpp:218] Iteration 294 (2.21599 iter/s, 6.31773s/14 iters), loss = 5.29129
I0401 16:17:46.020246 14951 solver.cpp:237]     Train net output #0: loss = 5.29129 (* 1 = 5.29129 loss)
I0401 16:17:46.020251 14951 sgd_solver.cpp:105] Iteration 294, lr = 0.001
I0401 16:17:51.941282 14951 solver.cpp:218] Iteration 308 (2.36448 iter/s, 5.92096s/14 iters), loss = 5.26342
I0401 16:17:51.941407 14951 solver.cpp:237]     Train net output #0: loss = 5.26342 (* 1 = 5.26342 loss)
I0401 16:17:51.941416 14951 sgd_solver.cpp:105] Iteration 308, lr = 0.001
I0401 16:17:57.910071 14951 solver.cpp:218] Iteration 322 (2.34561 iter/s, 5.96859s/14 iters), loss = 5.25534
I0401 16:17:57.910117 14951 solver.cpp:237]     Train net output #0: loss = 5.25534 (* 1 = 5.25534 loss)
I0401 16:17:57.910123 14951 sgd_solver.cpp:105] Iteration 322, lr = 0.001
I0401 16:18:03.953626 14951 solver.cpp:218] Iteration 336 (2.31657 iter/s, 6.04343s/14 iters), loss = 5.27956
I0401 16:18:03.953668 14951 solver.cpp:237]     Train net output #0: loss = 5.27956 (* 1 = 5.27956 loss)
I0401 16:18:03.953675 14951 sgd_solver.cpp:105] Iteration 336, lr = 0.001
I0401 16:18:05.774287 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:18:06.010540 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_342.caffemodel
I0401 16:18:09.027801 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_342.solverstate
I0401 16:18:11.436394 14951 solver.cpp:330] Iteration 342, Testing net (#0)
I0401 16:18:11.436416 14951 net.cpp:676] Ignoring source layer train-data
I0401 16:18:13.444774 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:18:13.727444 14951 solver.cpp:397]     Test net output #0: accuracy = 0.0120192
I0401 16:18:13.727475 14951 solver.cpp:397]     Test net output #1: loss = 5.27722 (* 1 = 5.27722 loss)
I0401 16:18:16.533028 14951 solver.cpp:218] Iteration 350 (1.11295 iter/s, 12.5792s/14 iters), loss = 5.28573
I0401 16:18:16.533075 14951 solver.cpp:237]     Train net output #0: loss = 5.28573 (* 1 = 5.28573 loss)
I0401 16:18:16.533083 14951 sgd_solver.cpp:105] Iteration 350, lr = 0.001
I0401 16:18:22.535065 14951 solver.cpp:218] Iteration 364 (2.33259 iter/s, 6.00191s/14 iters), loss = 5.28453
I0401 16:18:22.535198 14951 solver.cpp:237]     Train net output #0: loss = 5.28453 (* 1 = 5.28453 loss)
I0401 16:18:22.535207 14951 sgd_solver.cpp:105] Iteration 364, lr = 0.001
I0401 16:18:28.598022 14951 solver.cpp:218] Iteration 378 (2.30919 iter/s, 6.06274s/14 iters), loss = 5.27108
I0401 16:18:28.598067 14951 solver.cpp:237]     Train net output #0: loss = 5.27108 (* 1 = 5.27108 loss)
I0401 16:18:28.598073 14951 sgd_solver.cpp:105] Iteration 378, lr = 0.001
I0401 16:18:34.854323 14951 solver.cpp:218] Iteration 392 (2.23779 iter/s, 6.25618s/14 iters), loss = 5.2809
I0401 16:18:34.854362 14951 solver.cpp:237]     Train net output #0: loss = 5.2809 (* 1 = 5.2809 loss)
I0401 16:18:34.854368 14951 sgd_solver.cpp:105] Iteration 392, lr = 0.001
I0401 16:18:40.916815 14951 solver.cpp:218] Iteration 406 (2.30932 iter/s, 6.06238s/14 iters), loss = 5.27107
I0401 16:18:40.916851 14951 solver.cpp:237]     Train net output #0: loss = 5.27107 (* 1 = 5.27107 loss)
I0401 16:18:40.916858 14951 sgd_solver.cpp:105] Iteration 406, lr = 0.001
I0401 16:18:47.290505 14951 solver.cpp:218] Iteration 420 (2.19657 iter/s, 6.37357s/14 iters), loss = 5.26044
I0401 16:18:47.290555 14951 solver.cpp:237]     Train net output #0: loss = 5.26044 (* 1 = 5.26044 loss)
I0401 16:18:47.290565 14951 sgd_solver.cpp:105] Iteration 420, lr = 0.001
I0401 16:18:53.557344 14951 solver.cpp:218] Iteration 434 (2.23403 iter/s, 6.2667s/14 iters), loss = 5.2816
I0401 16:18:53.557473 14951 solver.cpp:237]     Train net output #0: loss = 5.2816 (* 1 = 5.2816 loss)
I0401 16:18:53.557482 14951 sgd_solver.cpp:105] Iteration 434, lr = 0.001
I0401 16:18:59.711493 14951 solver.cpp:218] Iteration 448 (2.27497 iter/s, 6.15394s/14 iters), loss = 5.26284
I0401 16:18:59.711552 14951 solver.cpp:237]     Train net output #0: loss = 5.26284 (* 1 = 5.26284 loss)
I0401 16:18:59.711562 14951 sgd_solver.cpp:105] Iteration 448, lr = 0.001
I0401 16:19:02.399072 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:19:02.717850 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_456.caffemodel
I0401 16:19:05.776237 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_456.solverstate
I0401 16:19:08.081964 14951 solver.cpp:330] Iteration 456, Testing net (#0)
I0401 16:19:08.081987 14951 net.cpp:676] Ignoring source layer train-data
I0401 16:19:10.027354 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:19:10.360527 14951 solver.cpp:397]     Test net output #0: accuracy = 0.0108173
I0401 16:19:10.360566 14951 solver.cpp:397]     Test net output #1: loss = 5.27635 (* 1 = 5.27635 loss)
I0401 16:19:12.235813 14951 solver.cpp:218] Iteration 462 (1.11784 iter/s, 12.5241s/14 iters), loss = 5.26863
I0401 16:19:12.235858 14951 solver.cpp:237]     Train net output #0: loss = 5.26863 (* 1 = 5.26863 loss)
I0401 16:19:12.235864 14951 sgd_solver.cpp:105] Iteration 462, lr = 0.001
I0401 16:19:18.427937 14951 solver.cpp:218] Iteration 476 (2.26098 iter/s, 6.19199s/14 iters), loss = 5.25605
I0401 16:19:18.427995 14951 solver.cpp:237]     Train net output #0: loss = 5.25605 (* 1 = 5.25605 loss)
I0401 16:19:18.428004 14951 sgd_solver.cpp:105] Iteration 476, lr = 0.001
I0401 16:19:24.504477 14951 solver.cpp:218] Iteration 490 (2.30399 iter/s, 6.0764s/14 iters), loss = 5.27112
I0401 16:19:24.504626 14951 solver.cpp:237]     Train net output #0: loss = 5.27112 (* 1 = 5.27112 loss)
I0401 16:19:24.504635 14951 sgd_solver.cpp:105] Iteration 490, lr = 0.001
I0401 16:19:30.753115 14951 solver.cpp:218] Iteration 504 (2.24057 iter/s, 6.24841s/14 iters), loss = 5.25644
I0401 16:19:30.753163 14951 solver.cpp:237]     Train net output #0: loss = 5.25644 (* 1 = 5.25644 loss)
I0401 16:19:30.753171 14951 sgd_solver.cpp:105] Iteration 504, lr = 0.001
I0401 16:19:36.920475 14951 solver.cpp:218] Iteration 518 (2.27006 iter/s, 6.16724s/14 iters), loss = 5.25886
I0401 16:19:36.920516 14951 solver.cpp:237]     Train net output #0: loss = 5.25886 (* 1 = 5.25886 loss)
I0401 16:19:36.920521 14951 sgd_solver.cpp:105] Iteration 518, lr = 0.001
I0401 16:19:42.829988 14951 solver.cpp:218] Iteration 532 (2.36911 iter/s, 5.90939s/14 iters), loss = 5.26718
I0401 16:19:42.830047 14951 solver.cpp:237]     Train net output #0: loss = 5.26718 (* 1 = 5.26718 loss)
I0401 16:19:42.830056 14951 sgd_solver.cpp:105] Iteration 532, lr = 0.001
I0401 16:19:48.999318 14951 solver.cpp:218] Iteration 546 (2.26934 iter/s, 6.16919s/14 iters), loss = 5.25664
I0401 16:19:48.999377 14951 solver.cpp:237]     Train net output #0: loss = 5.25664 (* 1 = 5.25664 loss)
I0401 16:19:48.999385 14951 sgd_solver.cpp:105] Iteration 546, lr = 0.001
I0401 16:19:55.198033 14951 solver.cpp:218] Iteration 560 (2.25858 iter/s, 6.19857s/14 iters), loss = 5.27155
I0401 16:19:55.198158 14951 solver.cpp:237]     Train net output #0: loss = 5.27155 (* 1 = 5.27155 loss)
I0401 16:19:55.198168 14951 sgd_solver.cpp:105] Iteration 560, lr = 0.001
I0401 16:19:58.566200 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:19:59.025085 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_570.caffemodel
I0401 16:20:02.078727 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_570.solverstate
I0401 16:20:04.418594 14951 solver.cpp:330] Iteration 570, Testing net (#0)
I0401 16:20:04.418622 14951 net.cpp:676] Ignoring source layer train-data
I0401 16:20:06.178557 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:20:06.622932 14951 solver.cpp:397]     Test net output #0: accuracy = 0.0108173
I0401 16:20:06.622968 14951 solver.cpp:397]     Test net output #1: loss = 5.26984 (* 1 = 5.26984 loss)
I0401 16:20:07.806458 14951 solver.cpp:218] Iteration 574 (1.11039 iter/s, 12.6082s/14 iters), loss = 5.25921
I0401 16:20:07.806502 14951 solver.cpp:237]     Train net output #0: loss = 5.25921 (* 1 = 5.25921 loss)
I0401 16:20:07.806509 14951 sgd_solver.cpp:105] Iteration 574, lr = 0.001
I0401 16:20:14.003857 14951 solver.cpp:218] Iteration 588 (2.25906 iter/s, 6.19728s/14 iters), loss = 5.2575
I0401 16:20:14.003896 14951 solver.cpp:237]     Train net output #0: loss = 5.2575 (* 1 = 5.2575 loss)
I0401 16:20:14.003901 14951 sgd_solver.cpp:105] Iteration 588, lr = 0.001
I0401 16:20:20.113950 14951 solver.cpp:218] Iteration 602 (2.29133 iter/s, 6.10998s/14 iters), loss = 5.26486
I0401 16:20:20.113988 14951 solver.cpp:237]     Train net output #0: loss = 5.26486 (* 1 = 5.26486 loss)
I0401 16:20:20.113993 14951 sgd_solver.cpp:105] Iteration 602, lr = 0.001
I0401 16:20:26.220412 14951 solver.cpp:218] Iteration 616 (2.2927 iter/s, 6.10634s/14 iters), loss = 5.26022
I0401 16:20:26.220542 14951 solver.cpp:237]     Train net output #0: loss = 5.26022 (* 1 = 5.26022 loss)
I0401 16:20:26.220551 14951 sgd_solver.cpp:105] Iteration 616, lr = 0.001
I0401 16:20:32.542044 14951 solver.cpp:218] Iteration 630 (2.21469 iter/s, 6.32142s/14 iters), loss = 5.2901
I0401 16:20:32.542085 14951 solver.cpp:237]     Train net output #0: loss = 5.2901 (* 1 = 5.2901 loss)
I0401 16:20:32.542091 14951 sgd_solver.cpp:105] Iteration 630, lr = 0.001
I0401 16:20:38.960942 14951 solver.cpp:218] Iteration 644 (2.1811 iter/s, 6.41877s/14 iters), loss = 5.26657
I0401 16:20:38.960984 14951 solver.cpp:237]     Train net output #0: loss = 5.26657 (* 1 = 5.26657 loss)
I0401 16:20:38.960990 14951 sgd_solver.cpp:105] Iteration 644, lr = 0.001
I0401 16:20:45.370121 14951 solver.cpp:218] Iteration 658 (2.18441 iter/s, 6.40906s/14 iters), loss = 5.24563
I0401 16:20:45.370157 14951 solver.cpp:237]     Train net output #0: loss = 5.24563 (* 1 = 5.24563 loss)
I0401 16:20:45.370162 14951 sgd_solver.cpp:105] Iteration 658, lr = 0.001
I0401 16:20:51.299659 14951 solver.cpp:218] Iteration 672 (2.3611 iter/s, 5.92943s/14 iters), loss = 5.24802
I0401 16:20:51.299692 14951 solver.cpp:237]     Train net output #0: loss = 5.24802 (* 1 = 5.24802 loss)
I0401 16:20:51.299697 14951 sgd_solver.cpp:105] Iteration 672, lr = 0.001
I0401 16:20:55.442062 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:20:56.012809 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_684.caffemodel
I0401 16:20:59.079876 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_684.solverstate
I0401 16:21:01.385973 14951 solver.cpp:330] Iteration 684, Testing net (#0)
I0401 16:21:01.385998 14951 net.cpp:676] Ignoring source layer train-data
I0401 16:21:03.193279 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:21:03.663826 14951 solver.cpp:397]     Test net output #0: accuracy = 0.0144231
I0401 16:21:03.663861 14951 solver.cpp:397]     Test net output #1: loss = 5.24707 (* 1 = 5.24707 loss)
I0401 16:21:04.077462 14951 solver.cpp:218] Iteration 686 (1.09567 iter/s, 12.7776s/14 iters), loss = 5.25682
I0401 16:21:04.077504 14951 solver.cpp:237]     Train net output #0: loss = 5.25682 (* 1 = 5.25682 loss)
I0401 16:21:04.077512 14951 sgd_solver.cpp:105] Iteration 686, lr = 0.001
I0401 16:21:09.604575 14951 solver.cpp:218] Iteration 700 (2.53302 iter/s, 5.52699s/14 iters), loss = 5.21504
I0401 16:21:09.604636 14951 solver.cpp:237]     Train net output #0: loss = 5.21504 (* 1 = 5.21504 loss)
I0401 16:21:09.604645 14951 sgd_solver.cpp:105] Iteration 700, lr = 0.001
I0401 16:21:15.751886 14951 solver.cpp:218] Iteration 714 (2.27747 iter/s, 6.14718s/14 iters), loss = 5.24394
I0401 16:21:15.751927 14951 solver.cpp:237]     Train net output #0: loss = 5.24394 (* 1 = 5.24394 loss)
I0401 16:21:15.751933 14951 sgd_solver.cpp:105] Iteration 714, lr = 0.001
I0401 16:21:21.724546 14951 solver.cpp:218] Iteration 728 (2.34406 iter/s, 5.97254s/14 iters), loss = 5.21664
I0401 16:21:21.724603 14951 solver.cpp:237]     Train net output #0: loss = 5.21664 (* 1 = 5.21664 loss)
I0401 16:21:21.724612 14951 sgd_solver.cpp:105] Iteration 728, lr = 0.001
I0401 16:21:27.799857 14951 solver.cpp:218] Iteration 742 (2.30446 iter/s, 6.07518s/14 iters), loss = 5.18919
I0401 16:21:27.799897 14951 solver.cpp:237]     Train net output #0: loss = 5.18919 (* 1 = 5.18919 loss)
I0401 16:21:27.799903 14951 sgd_solver.cpp:105] Iteration 742, lr = 0.001
I0401 16:21:34.030431 14951 solver.cpp:218] Iteration 756 (2.24703 iter/s, 6.23046s/14 iters), loss = 5.1829
I0401 16:21:34.030591 14951 solver.cpp:237]     Train net output #0: loss = 5.1829 (* 1 = 5.1829 loss)
I0401 16:21:34.030599 14951 sgd_solver.cpp:105] Iteration 756, lr = 0.001
I0401 16:21:40.272182 14951 solver.cpp:218] Iteration 770 (2.24305 iter/s, 6.24152s/14 iters), loss = 5.20179
I0401 16:21:40.272222 14951 solver.cpp:237]     Train net output #0: loss = 5.20179 (* 1 = 5.20179 loss)
I0401 16:21:40.272228 14951 sgd_solver.cpp:105] Iteration 770, lr = 0.001
I0401 16:21:46.340032 14951 solver.cpp:218] Iteration 784 (2.30729 iter/s, 6.06772s/14 iters), loss = 5.12046
I0401 16:21:46.340095 14951 solver.cpp:237]     Train net output #0: loss = 5.12046 (* 1 = 5.12046 loss)
I0401 16:21:46.340104 14951 sgd_solver.cpp:105] Iteration 784, lr = 0.001
I0401 16:21:51.345700 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:21:52.031333 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_798.caffemodel
I0401 16:21:55.001123 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_798.solverstate
I0401 16:21:57.313998 14951 solver.cpp:330] Iteration 798, Testing net (#0)
I0401 16:21:57.314023 14951 net.cpp:676] Ignoring source layer train-data
I0401 16:21:59.136018 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:21:59.644469 14951 solver.cpp:397]     Test net output #0: accuracy = 0.0180288
I0401 16:21:59.644501 14951 solver.cpp:397]     Test net output #1: loss = 5.14501 (* 1 = 5.14501 loss)
I0401 16:21:59.782718 14951 solver.cpp:218] Iteration 798 (1.04148 iter/s, 13.4425s/14 iters), loss = 5.20688
I0401 16:21:59.784282 14951 solver.cpp:237]     Train net output #0: loss = 5.20688 (* 1 = 5.20688 loss)
I0401 16:21:59.784296 14951 sgd_solver.cpp:105] Iteration 798, lr = 0.001
I0401 16:22:04.969803 14951 solver.cpp:218] Iteration 812 (2.69986 iter/s, 5.18545s/14 iters), loss = 5.22497
I0401 16:22:04.969928 14951 solver.cpp:237]     Train net output #0: loss = 5.22497 (* 1 = 5.22497 loss)
I0401 16:22:04.969936 14951 sgd_solver.cpp:105] Iteration 812, lr = 0.001
I0401 16:22:11.156293 14951 solver.cpp:218] Iteration 826 (2.26307 iter/s, 6.18629s/14 iters), loss = 5.16382
I0401 16:22:11.156354 14951 solver.cpp:237]     Train net output #0: loss = 5.16382 (* 1 = 5.16382 loss)
I0401 16:22:11.156363 14951 sgd_solver.cpp:105] Iteration 826, lr = 0.001
I0401 16:22:17.241717 14951 solver.cpp:218] Iteration 840 (2.30063 iter/s, 6.08528s/14 iters), loss = 5.14197
I0401 16:22:17.241771 14951 solver.cpp:237]     Train net output #0: loss = 5.14197 (* 1 = 5.14197 loss)
I0401 16:22:17.241780 14951 sgd_solver.cpp:105] Iteration 840, lr = 0.001
I0401 16:22:17.628746 14951 blocking_queue.cpp:49] Waiting for data
I0401 16:22:23.471048 14951 solver.cpp:218] Iteration 854 (2.24752 iter/s, 6.2291s/14 iters), loss = 5.18691
I0401 16:22:23.471089 14951 solver.cpp:237]     Train net output #0: loss = 5.18691 (* 1 = 5.18691 loss)
I0401 16:22:23.471096 14951 sgd_solver.cpp:105] Iteration 854, lr = 0.001
I0401 16:22:29.607872 14951 solver.cpp:218] Iteration 868 (2.28136 iter/s, 6.1367s/14 iters), loss = 5.17982
I0401 16:22:29.607939 14951 solver.cpp:237]     Train net output #0: loss = 5.17982 (* 1 = 5.17982 loss)
I0401 16:22:29.607949 14951 sgd_solver.cpp:105] Iteration 868, lr = 0.001
I0401 16:22:35.966223 14951 solver.cpp:218] Iteration 882 (2.20188 iter/s, 6.3582s/14 iters), loss = 5.11338
I0401 16:22:35.966315 14951 solver.cpp:237]     Train net output #0: loss = 5.11338 (* 1 = 5.11338 loss)
I0401 16:22:35.966320 14951 sgd_solver.cpp:105] Iteration 882, lr = 0.001
I0401 16:22:42.141672 14951 solver.cpp:218] Iteration 896 (2.2671 iter/s, 6.17528s/14 iters), loss = 5.18031
I0401 16:22:42.141736 14951 solver.cpp:237]     Train net output #0: loss = 5.18031 (* 1 = 5.18031 loss)
I0401 16:22:42.141744 14951 sgd_solver.cpp:105] Iteration 896, lr = 0.001
I0401 16:22:48.071969 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:22:48.393190 14951 solver.cpp:218] Iteration 910 (2.23951 iter/s, 6.25137s/14 iters), loss = 5.18546
I0401 16:22:48.393249 14951 solver.cpp:237]     Train net output #0: loss = 5.18546 (* 1 = 5.18546 loss)
I0401 16:22:48.393258 14951 sgd_solver.cpp:105] Iteration 910, lr = 0.001
I0401 16:22:48.737255 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_912.caffemodel
I0401 16:22:51.749123 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_912.solverstate
I0401 16:22:54.047973 14951 solver.cpp:330] Iteration 912, Testing net (#0)
I0401 16:22:54.047993 14951 net.cpp:676] Ignoring source layer train-data
I0401 16:22:55.669040 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:22:56.304405 14951 solver.cpp:397]     Test net output #0: accuracy = 0.0204327
I0401 16:22:56.304440 14951 solver.cpp:397]     Test net output #1: loss = 5.12556 (* 1 = 5.12556 loss)
I0401 16:23:00.953701 14951 solver.cpp:218] Iteration 924 (1.11462 iter/s, 12.5603s/14 iters), loss = 5.05853
I0401 16:23:00.953744 14951 solver.cpp:237]     Train net output #0: loss = 5.05853 (* 1 = 5.05853 loss)
I0401 16:23:00.953750 14951 sgd_solver.cpp:105] Iteration 924, lr = 0.001
I0401 16:23:07.044796 14951 solver.cpp:218] Iteration 938 (2.29848 iter/s, 6.09098s/14 iters), loss = 5.12084
I0401 16:23:07.044943 14951 solver.cpp:237]     Train net output #0: loss = 5.12084 (* 1 = 5.12084 loss)
I0401 16:23:07.044950 14951 sgd_solver.cpp:105] Iteration 938, lr = 0.001
I0401 16:23:13.291923 14951 solver.cpp:218] Iteration 952 (2.24162 iter/s, 6.24547s/14 iters), loss = 5.16755
I0401 16:23:13.291972 14951 solver.cpp:237]     Train net output #0: loss = 5.16755 (* 1 = 5.16755 loss)
I0401 16:23:13.291980 14951 sgd_solver.cpp:105] Iteration 952, lr = 0.001
I0401 16:23:19.489686 14951 solver.cpp:218] Iteration 966 (2.25892 iter/s, 6.19765s/14 iters), loss = 5.19116
I0401 16:23:19.489722 14951 solver.cpp:237]     Train net output #0: loss = 5.19116 (* 1 = 5.19116 loss)
I0401 16:23:19.489727 14951 sgd_solver.cpp:105] Iteration 966, lr = 0.001
I0401 16:23:25.629745 14951 solver.cpp:218] Iteration 980 (2.28015 iter/s, 6.13994s/14 iters), loss = 5.15994
I0401 16:23:25.629801 14951 solver.cpp:237]     Train net output #0: loss = 5.15994 (* 1 = 5.15994 loss)
I0401 16:23:25.629808 14951 sgd_solver.cpp:105] Iteration 980, lr = 0.001
I0401 16:23:31.802204 14951 solver.cpp:218] Iteration 994 (2.26819 iter/s, 6.17233s/14 iters), loss = 5.11545
I0401 16:23:31.802242 14951 solver.cpp:237]     Train net output #0: loss = 5.11545 (* 1 = 5.11545 loss)
I0401 16:23:31.802246 14951 sgd_solver.cpp:105] Iteration 994, lr = 0.001
I0401 16:23:37.911455 14951 solver.cpp:218] Iteration 1008 (2.29165 iter/s, 6.10913s/14 iters), loss = 5.21878
I0401 16:23:37.911564 14951 solver.cpp:237]     Train net output #0: loss = 5.21878 (* 1 = 5.21878 loss)
I0401 16:23:37.911574 14951 sgd_solver.cpp:105] Iteration 1008, lr = 0.001
I0401 16:23:44.111943 14951 solver.cpp:218] Iteration 1022 (2.25795 iter/s, 6.2003s/14 iters), loss = 5.16437
I0401 16:23:44.111984 14951 solver.cpp:237]     Train net output #0: loss = 5.16437 (* 1 = 5.16437 loss)
I0401 16:23:44.111989 14951 sgd_solver.cpp:105] Iteration 1022, lr = 0.001
I0401 16:23:44.529320 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:23:45.265774 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1026.caffemodel
I0401 16:23:48.261416 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1026.solverstate
I0401 16:23:50.549496 14951 solver.cpp:330] Iteration 1026, Testing net (#0)
I0401 16:23:50.549515 14951 net.cpp:676] Ignoring source layer train-data
I0401 16:23:51.972337 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:23:52.676306 14951 solver.cpp:397]     Test net output #0: accuracy = 0.0204327
I0401 16:23:52.676340 14951 solver.cpp:397]     Test net output #1: loss = 5.0991 (* 1 = 5.0991 loss)
I0401 16:23:56.316135 14951 solver.cpp:218] Iteration 1036 (1.14716 iter/s, 12.204s/14 iters), loss = 5.11082
I0401 16:23:56.316179 14951 solver.cpp:237]     Train net output #0: loss = 5.11082 (* 1 = 5.11082 loss)
I0401 16:23:56.316185 14951 sgd_solver.cpp:105] Iteration 1036, lr = 0.001
I0401 16:24:02.638620 14951 solver.cpp:218] Iteration 1050 (2.21436 iter/s, 6.32236s/14 iters), loss = 5.12133
I0401 16:24:02.638667 14951 solver.cpp:237]     Train net output #0: loss = 5.12133 (* 1 = 5.12133 loss)
I0401 16:24:02.638674 14951 sgd_solver.cpp:105] Iteration 1050, lr = 0.001
I0401 16:24:08.991334 14951 solver.cpp:218] Iteration 1064 (2.20383 iter/s, 6.35259s/14 iters), loss = 5.16382
I0401 16:24:08.991454 14951 solver.cpp:237]     Train net output #0: loss = 5.16382 (* 1 = 5.16382 loss)
I0401 16:24:08.991461 14951 sgd_solver.cpp:105] Iteration 1064, lr = 0.001
I0401 16:24:15.170446 14951 solver.cpp:218] Iteration 1078 (2.26577 iter/s, 6.17891s/14 iters), loss = 5.1509
I0401 16:24:15.170501 14951 solver.cpp:237]     Train net output #0: loss = 5.1509 (* 1 = 5.1509 loss)
I0401 16:24:15.170511 14951 sgd_solver.cpp:105] Iteration 1078, lr = 0.001
I0401 16:24:21.328040 14951 solver.cpp:218] Iteration 1092 (2.27366 iter/s, 6.15746s/14 iters), loss = 5.14096
I0401 16:24:21.328088 14951 solver.cpp:237]     Train net output #0: loss = 5.14096 (* 1 = 5.14096 loss)
I0401 16:24:21.328097 14951 sgd_solver.cpp:105] Iteration 1092, lr = 0.001
I0401 16:24:27.447831 14951 solver.cpp:218] Iteration 1106 (2.28771 iter/s, 6.11966s/14 iters), loss = 5.08768
I0401 16:24:27.447885 14951 solver.cpp:237]     Train net output #0: loss = 5.08768 (* 1 = 5.08768 loss)
I0401 16:24:27.447892 14951 sgd_solver.cpp:105] Iteration 1106, lr = 0.001
I0401 16:24:33.640866 14951 solver.cpp:218] Iteration 1120 (2.26065 iter/s, 6.19291s/14 iters), loss = 5.00658
I0401 16:24:33.640913 14951 solver.cpp:237]     Train net output #0: loss = 5.00658 (* 1 = 5.00658 loss)
I0401 16:24:33.640918 14951 sgd_solver.cpp:105] Iteration 1120, lr = 0.001
I0401 16:24:39.663005 14951 solver.cpp:218] Iteration 1134 (2.3248 iter/s, 6.02201s/14 iters), loss = 5.15785
I0401 16:24:39.663110 14951 solver.cpp:237]     Train net output #0: loss = 5.15785 (* 1 = 5.15785 loss)
I0401 16:24:39.663116 14951 sgd_solver.cpp:105] Iteration 1134, lr = 0.001
I0401 16:24:40.841173 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:24:41.681986 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1140.caffemodel
I0401 16:24:44.777673 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1140.solverstate
I0401 16:24:47.147891 14951 solver.cpp:330] Iteration 1140, Testing net (#0)
I0401 16:24:47.147914 14951 net.cpp:676] Ignoring source layer train-data
I0401 16:24:48.589236 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:24:49.339509 14951 solver.cpp:397]     Test net output #0: accuracy = 0.0204327
I0401 16:24:49.339543 14951 solver.cpp:397]     Test net output #1: loss = 5.06672 (* 1 = 5.06672 loss)
I0401 16:24:52.187160 14951 solver.cpp:218] Iteration 1148 (1.11786 iter/s, 12.5239s/14 iters), loss = 5.05464
I0401 16:24:52.187197 14951 solver.cpp:237]     Train net output #0: loss = 5.05464 (* 1 = 5.05464 loss)
I0401 16:24:52.187202 14951 sgd_solver.cpp:105] Iteration 1148, lr = 0.001
I0401 16:24:58.344944 14951 solver.cpp:218] Iteration 1162 (2.27359 iter/s, 6.15766s/14 iters), loss = 5.08455
I0401 16:24:58.345001 14951 solver.cpp:237]     Train net output #0: loss = 5.08455 (* 1 = 5.08455 loss)
I0401 16:24:58.345010 14951 sgd_solver.cpp:105] Iteration 1162, lr = 0.001
I0401 16:25:04.323365 14951 solver.cpp:218] Iteration 1176 (2.34181 iter/s, 5.97829s/14 iters), loss = 5.14155
I0401 16:25:04.323413 14951 solver.cpp:237]     Train net output #0: loss = 5.14155 (* 1 = 5.14155 loss)
I0401 16:25:04.323421 14951 sgd_solver.cpp:105] Iteration 1176, lr = 0.001
I0401 16:25:10.517668 14951 solver.cpp:218] Iteration 1190 (2.26019 iter/s, 6.19418s/14 iters), loss = 5.05042
I0401 16:25:10.517787 14951 solver.cpp:237]     Train net output #0: loss = 5.05042 (* 1 = 5.05042 loss)
I0401 16:25:10.517793 14951 sgd_solver.cpp:105] Iteration 1190, lr = 0.001
I0401 16:25:16.512163 14951 solver.cpp:218] Iteration 1204 (2.33555 iter/s, 5.9943s/14 iters), loss = 5.04086
I0401 16:25:16.512202 14951 solver.cpp:237]     Train net output #0: loss = 5.04086 (* 1 = 5.04086 loss)
I0401 16:25:16.512207 14951 sgd_solver.cpp:105] Iteration 1204, lr = 0.001
I0401 16:25:22.682868 14951 solver.cpp:218] Iteration 1218 (2.26883 iter/s, 6.17058s/14 iters), loss = 4.98064
I0401 16:25:22.682927 14951 solver.cpp:237]     Train net output #0: loss = 4.98064 (* 1 = 4.98064 loss)
I0401 16:25:22.682935 14951 sgd_solver.cpp:105] Iteration 1218, lr = 0.001
I0401 16:25:28.570070 14951 solver.cpp:218] Iteration 1232 (2.37809 iter/s, 5.88707s/14 iters), loss = 5.16798
I0401 16:25:28.570113 14951 solver.cpp:237]     Train net output #0: loss = 5.16798 (* 1 = 5.16798 loss)
I0401 16:25:28.570119 14951 sgd_solver.cpp:105] Iteration 1232, lr = 0.001
I0401 16:25:34.924680 14951 solver.cpp:218] Iteration 1246 (2.20317 iter/s, 6.35448s/14 iters), loss = 5.12765
I0401 16:25:34.924736 14951 solver.cpp:237]     Train net output #0: loss = 5.12765 (* 1 = 5.12765 loss)
I0401 16:25:34.924743 14951 sgd_solver.cpp:105] Iteration 1246, lr = 0.001
I0401 16:25:36.934592 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:25:38.115175 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1254.caffemodel
I0401 16:25:42.042882 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1254.solverstate
I0401 16:25:45.791443 14951 solver.cpp:330] Iteration 1254, Testing net (#0)
I0401 16:25:45.791462 14951 net.cpp:676] Ignoring source layer train-data
I0401 16:25:47.222380 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:25:48.045958 14951 solver.cpp:397]     Test net output #0: accuracy = 0.0192308
I0401 16:25:48.045992 14951 solver.cpp:397]     Test net output #1: loss = 5.04451 (* 1 = 5.04451 loss)
I0401 16:25:49.872085 14951 solver.cpp:218] Iteration 1260 (0.936631 iter/s, 14.9472s/14 iters), loss = 5.10988
I0401 16:25:49.872133 14951 solver.cpp:237]     Train net output #0: loss = 5.10988 (* 1 = 5.10988 loss)
I0401 16:25:49.872143 14951 sgd_solver.cpp:105] Iteration 1260, lr = 0.001
I0401 16:25:56.203539 14951 solver.cpp:218] Iteration 1274 (2.21123 iter/s, 6.33132s/14 iters), loss = 4.994
I0401 16:25:56.203589 14951 solver.cpp:237]     Train net output #0: loss = 4.994 (* 1 = 4.994 loss)
I0401 16:25:56.203598 14951 sgd_solver.cpp:105] Iteration 1274, lr = 0.001
I0401 16:26:02.502347 14951 solver.cpp:218] Iteration 1288 (2.22269 iter/s, 6.29868s/14 iters), loss = 5.11577
I0401 16:26:02.502409 14951 solver.cpp:237]     Train net output #0: loss = 5.11577 (* 1 = 5.11577 loss)
I0401 16:26:02.502420 14951 sgd_solver.cpp:105] Iteration 1288, lr = 0.001
I0401 16:26:08.828420 14951 solver.cpp:218] Iteration 1302 (2.21311 iter/s, 6.32593s/14 iters), loss = 5.10053
I0401 16:26:08.828470 14951 solver.cpp:237]     Train net output #0: loss = 5.10053 (* 1 = 5.10053 loss)
I0401 16:26:08.828480 14951 sgd_solver.cpp:105] Iteration 1302, lr = 0.001
I0401 16:26:15.439280 14951 solver.cpp:218] Iteration 1316 (2.11777 iter/s, 6.61072s/14 iters), loss = 5.04069
I0401 16:26:15.439360 14951 solver.cpp:237]     Train net output #0: loss = 5.04069 (* 1 = 5.04069 loss)
I0401 16:26:15.439368 14951 sgd_solver.cpp:105] Iteration 1316, lr = 0.001
I0401 16:26:21.989089 14951 solver.cpp:218] Iteration 1330 (2.13752 iter/s, 6.54964s/14 iters), loss = 4.93042
I0401 16:26:21.989147 14951 solver.cpp:237]     Train net output #0: loss = 4.93042 (* 1 = 4.93042 loss)
I0401 16:26:21.989156 14951 sgd_solver.cpp:105] Iteration 1330, lr = 0.001
I0401 16:26:28.601954 14951 solver.cpp:218] Iteration 1344 (2.11713 iter/s, 6.61273s/14 iters), loss = 5.05517
I0401 16:26:28.601992 14951 solver.cpp:237]     Train net output #0: loss = 5.05517 (* 1 = 5.05517 loss)
I0401 16:26:28.601997 14951 sgd_solver.cpp:105] Iteration 1344, lr = 0.001
I0401 16:26:34.894449 14951 solver.cpp:218] Iteration 1358 (2.22491 iter/s, 6.29238s/14 iters), loss = 4.96865
I0401 16:26:34.894485 14951 solver.cpp:237]     Train net output #0: loss = 4.96865 (* 1 = 4.96865 loss)
I0401 16:26:34.894491 14951 sgd_solver.cpp:105] Iteration 1358, lr = 0.001
I0401 16:26:37.784376 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:26:38.797483 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1368.caffemodel
I0401 16:26:43.989710 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1368.solverstate
I0401 16:26:48.127362 14951 solver.cpp:330] Iteration 1368, Testing net (#0)
I0401 16:26:48.127476 14951 net.cpp:676] Ignoring source layer train-data
I0401 16:26:49.606490 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:26:50.460239 14951 solver.cpp:397]     Test net output #0: accuracy = 0.0192308
I0401 16:26:50.460276 14951 solver.cpp:397]     Test net output #1: loss = 5.02834 (* 1 = 5.02834 loss)
I0401 16:26:51.589905 14951 solver.cpp:218] Iteration 1372 (0.838563 iter/s, 16.6952s/14 iters), loss = 5.05672
I0401 16:26:51.589958 14951 solver.cpp:237]     Train net output #0: loss = 5.05672 (* 1 = 5.05672 loss)
I0401 16:26:51.589967 14951 sgd_solver.cpp:105] Iteration 1372, lr = 0.001
I0401 16:26:57.808243 14951 solver.cpp:218] Iteration 1386 (2.25145 iter/s, 6.2182s/14 iters), loss = 5.15889
I0401 16:26:57.808295 14951 solver.cpp:237]     Train net output #0: loss = 5.15889 (* 1 = 5.15889 loss)
I0401 16:26:57.808303 14951 sgd_solver.cpp:105] Iteration 1386, lr = 0.001
I0401 16:27:03.984597 14951 solver.cpp:218] Iteration 1400 (2.26676 iter/s, 6.17622s/14 iters), loss = 5.00265
I0401 16:27:03.984650 14951 solver.cpp:237]     Train net output #0: loss = 5.00265 (* 1 = 5.00265 loss)
I0401 16:27:03.984658 14951 sgd_solver.cpp:105] Iteration 1400, lr = 0.001
I0401 16:27:10.339759 14951 solver.cpp:218] Iteration 1414 (2.20298 iter/s, 6.35503s/14 iters), loss = 4.99368
I0401 16:27:10.339808 14951 solver.cpp:237]     Train net output #0: loss = 4.99368 (* 1 = 4.99368 loss)
I0401 16:27:10.339815 14951 sgd_solver.cpp:105] Iteration 1414, lr = 0.001
I0401 16:27:16.557109 14951 solver.cpp:218] Iteration 1428 (2.25181 iter/s, 6.21722s/14 iters), loss = 4.96588
I0401 16:27:16.557150 14951 solver.cpp:237]     Train net output #0: loss = 4.96588 (* 1 = 4.96588 loss)
I0401 16:27:16.557157 14951 sgd_solver.cpp:105] Iteration 1428, lr = 0.001
I0401 16:27:22.768061 14951 solver.cpp:218] Iteration 1442 (2.25413 iter/s, 6.21083s/14 iters), loss = 5.12714
I0401 16:27:22.768193 14951 solver.cpp:237]     Train net output #0: loss = 5.12714 (* 1 = 5.12714 loss)
I0401 16:27:22.768203 14951 sgd_solver.cpp:105] Iteration 1442, lr = 0.001
I0401 16:27:29.047987 14951 solver.cpp:218] Iteration 1456 (2.2294 iter/s, 6.27971s/14 iters), loss = 5.04061
I0401 16:27:29.048048 14951 solver.cpp:237]     Train net output #0: loss = 5.04061 (* 1 = 5.04061 loss)
I0401 16:27:29.048056 14951 sgd_solver.cpp:105] Iteration 1456, lr = 0.001
I0401 16:27:35.296671 14951 solver.cpp:218] Iteration 1470 (2.24052 iter/s, 6.24855s/14 iters), loss = 5.00332
I0401 16:27:35.296716 14951 solver.cpp:237]     Train net output #0: loss = 5.00332 (* 1 = 5.00332 loss)
I0401 16:27:35.296722 14951 sgd_solver.cpp:105] Iteration 1470, lr = 0.001
I0401 16:27:38.852214 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:27:39.918102 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1482.caffemodel
I0401 16:27:43.280928 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1482.solverstate
I0401 16:27:45.570574 14951 solver.cpp:330] Iteration 1482, Testing net (#0)
I0401 16:27:45.570598 14951 net.cpp:676] Ignoring source layer train-data
I0401 16:27:46.895844 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:27:47.838376 14951 solver.cpp:397]     Test net output #0: accuracy = 0.0288462
I0401 16:27:47.838410 14951 solver.cpp:397]     Test net output #1: loss = 5.0112 (* 1 = 5.0112 loss)
I0401 16:27:48.247267 14951 solver.cpp:218] Iteration 1484 (1.08105 iter/s, 12.9504s/14 iters), loss = 5.0814
I0401 16:27:48.248823 14951 solver.cpp:237]     Train net output #0: loss = 5.0814 (* 1 = 5.0814 loss)
I0401 16:27:48.248837 14951 sgd_solver.cpp:105] Iteration 1484, lr = 0.001
I0401 16:27:53.982648 14951 solver.cpp:218] Iteration 1498 (2.44168 iter/s, 5.73376s/14 iters), loss = 5.02324
I0401 16:27:53.982817 14951 solver.cpp:237]     Train net output #0: loss = 5.02324 (* 1 = 5.02324 loss)
I0401 16:27:53.982827 14951 sgd_solver.cpp:105] Iteration 1498, lr = 0.001
I0401 16:28:00.277734 14951 solver.cpp:218] Iteration 1512 (2.22404 iter/s, 6.29485s/14 iters), loss = 5.09039
I0401 16:28:00.277773 14951 solver.cpp:237]     Train net output #0: loss = 5.09039 (* 1 = 5.09039 loss)
I0401 16:28:00.277781 14951 sgd_solver.cpp:105] Iteration 1512, lr = 0.001
I0401 16:28:06.459853 14951 solver.cpp:218] Iteration 1526 (2.26464 iter/s, 6.182s/14 iters), loss = 5.08598
I0401 16:28:06.459909 14951 solver.cpp:237]     Train net output #0: loss = 5.08598 (* 1 = 5.08598 loss)
I0401 16:28:06.459918 14951 sgd_solver.cpp:105] Iteration 1526, lr = 0.001
I0401 16:28:12.700788 14951 solver.cpp:218] Iteration 1540 (2.2433 iter/s, 6.2408s/14 iters), loss = 5.09242
I0401 16:28:12.700839 14951 solver.cpp:237]     Train net output #0: loss = 5.09242 (* 1 = 5.09242 loss)
I0401 16:28:12.700845 14951 sgd_solver.cpp:105] Iteration 1540, lr = 0.001
I0401 16:28:18.887403 14951 solver.cpp:218] Iteration 1554 (2.263 iter/s, 6.18649s/14 iters), loss = 5.00959
I0401 16:28:18.887442 14951 solver.cpp:237]     Train net output #0: loss = 5.00959 (* 1 = 5.00959 loss)
I0401 16:28:18.887447 14951 sgd_solver.cpp:105] Iteration 1554, lr = 0.001
I0401 16:28:24.837512 14951 solver.cpp:218] Iteration 1568 (2.35295 iter/s, 5.94999s/14 iters), loss = 5.08524
I0401 16:28:24.837627 14951 solver.cpp:237]     Train net output #0: loss = 5.08524 (* 1 = 5.08524 loss)
I0401 16:28:24.837635 14951 sgd_solver.cpp:105] Iteration 1568, lr = 0.001
I0401 16:28:31.025612 14951 solver.cpp:218] Iteration 1582 (2.26248 iter/s, 6.1879s/14 iters), loss = 5.08145
I0401 16:28:31.025669 14951 solver.cpp:237]     Train net output #0: loss = 5.08145 (* 1 = 5.08145 loss)
I0401 16:28:31.025679 14951 sgd_solver.cpp:105] Iteration 1582, lr = 0.001
I0401 16:28:35.412744 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:28:36.524545 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1596.caffemodel
I0401 16:28:39.548759 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1596.solverstate
I0401 16:28:42.784543 14951 solver.cpp:330] Iteration 1596, Testing net (#0)
I0401 16:28:42.784560 14951 net.cpp:676] Ignoring source layer train-data
I0401 16:28:43.959357 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:28:44.966756 14951 solver.cpp:397]     Test net output #0: accuracy = 0.0276442
I0401 16:28:44.966796 14951 solver.cpp:397]     Test net output #1: loss = 4.99381 (* 1 = 4.99381 loss)
I0401 16:28:45.106593 14951 solver.cpp:218] Iteration 1596 (0.994263 iter/s, 14.0808s/14 iters), loss = 5.11541
I0401 16:28:45.108165 14951 solver.cpp:237]     Train net output #0: loss = 5.11541 (* 1 = 5.11541 loss)
I0401 16:28:45.108175 14951 sgd_solver.cpp:105] Iteration 1596, lr = 0.001
I0401 16:28:50.099792 14951 solver.cpp:218] Iteration 1610 (2.80473 iter/s, 4.99157s/14 iters), loss = 5.0212
I0401 16:28:50.099834 14951 solver.cpp:237]     Train net output #0: loss = 5.0212 (* 1 = 5.0212 loss)
I0401 16:28:50.099840 14951 sgd_solver.cpp:105] Iteration 1610, lr = 0.001
I0401 16:28:56.213832 14951 solver.cpp:218] Iteration 1624 (2.28986 iter/s, 6.11392s/14 iters), loss = 5.01454
I0401 16:28:56.213964 14951 solver.cpp:237]     Train net output #0: loss = 5.01454 (* 1 = 5.01454 loss)
I0401 16:28:56.213973 14951 sgd_solver.cpp:105] Iteration 1624, lr = 0.001
I0401 16:29:02.236266 14951 solver.cpp:218] Iteration 1638 (2.32472 iter/s, 6.02223s/14 iters), loss = 5.03252
I0401 16:29:02.236306 14951 solver.cpp:237]     Train net output #0: loss = 5.03252 (* 1 = 5.03252 loss)
I0401 16:29:02.236311 14951 sgd_solver.cpp:105] Iteration 1638, lr = 0.001
I0401 16:29:08.330585 14951 solver.cpp:218] Iteration 1652 (2.29727 iter/s, 6.0942s/14 iters), loss = 4.94848
I0401 16:29:08.330636 14951 solver.cpp:237]     Train net output #0: loss = 4.94848 (* 1 = 4.94848 loss)
I0401 16:29:08.330644 14951 sgd_solver.cpp:105] Iteration 1652, lr = 0.001
I0401 16:29:14.467660 14951 solver.cpp:218] Iteration 1666 (2.28127 iter/s, 6.13694s/14 iters), loss = 4.91367
I0401 16:29:14.467720 14951 solver.cpp:237]     Train net output #0: loss = 4.91367 (* 1 = 4.91367 loss)
I0401 16:29:14.467728 14951 sgd_solver.cpp:105] Iteration 1666, lr = 0.001
I0401 16:29:20.554474 14951 solver.cpp:218] Iteration 1680 (2.30011 iter/s, 6.08668s/14 iters), loss = 5.02925
I0401 16:29:20.554530 14951 solver.cpp:237]     Train net output #0: loss = 5.02925 (* 1 = 5.02925 loss)
I0401 16:29:20.554539 14951 sgd_solver.cpp:105] Iteration 1680, lr = 0.001
I0401 16:29:26.812423 14951 solver.cpp:218] Iteration 1694 (2.2372 iter/s, 6.25782s/14 iters), loss = 4.94824
I0401 16:29:26.812536 14951 solver.cpp:237]     Train net output #0: loss = 4.94824 (* 1 = 4.94824 loss)
I0401 16:29:26.812544 14951 sgd_solver.cpp:105] Iteration 1694, lr = 0.001
I0401 16:29:29.715767 14951 blocking_queue.cpp:49] Waiting for data
I0401 16:29:31.866648 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:29:32.878926 14951 solver.cpp:218] Iteration 1708 (2.30783 iter/s, 6.06631s/14 iters), loss = 4.98677
I0401 16:29:32.878971 14951 solver.cpp:237]     Train net output #0: loss = 4.98677 (* 1 = 4.98677 loss)
I0401 16:29:32.878978 14951 sgd_solver.cpp:105] Iteration 1708, lr = 0.001
I0401 16:29:33.273433 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1710.caffemodel
I0401 16:29:36.327572 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1710.solverstate
I0401 16:29:40.112378 14951 solver.cpp:330] Iteration 1710, Testing net (#0)
I0401 16:29:40.112401 14951 net.cpp:676] Ignoring source layer train-data
I0401 16:29:41.201941 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:29:42.284063 14951 solver.cpp:397]     Test net output #0: accuracy = 0.0348558
I0401 16:29:42.284102 14951 solver.cpp:397]     Test net output #1: loss = 4.98709 (* 1 = 4.98709 loss)
I0401 16:29:46.585731 14951 solver.cpp:218] Iteration 1722 (1.02141 iter/s, 13.7066s/14 iters), loss = 4.91946
I0401 16:29:46.585780 14951 solver.cpp:237]     Train net output #0: loss = 4.91946 (* 1 = 4.91946 loss)
I0401 16:29:46.585788 14951 sgd_solver.cpp:105] Iteration 1722, lr = 0.001
I0401 16:29:52.793144 14951 solver.cpp:218] Iteration 1736 (2.25542 iter/s, 6.20728s/14 iters), loss = 4.88793
I0401 16:29:52.793187 14951 solver.cpp:237]     Train net output #0: loss = 4.88793 (* 1 = 4.88793 loss)
I0401 16:29:52.793193 14951 sgd_solver.cpp:105] Iteration 1736, lr = 0.001
I0401 16:29:58.838826 14951 solver.cpp:218] Iteration 1750 (2.31575 iter/s, 6.04556s/14 iters), loss = 4.85718
I0401 16:29:58.838951 14951 solver.cpp:237]     Train net output #0: loss = 4.85718 (* 1 = 4.85718 loss)
I0401 16:29:58.838960 14951 sgd_solver.cpp:105] Iteration 1750, lr = 0.001
I0401 16:30:04.966044 14951 solver.cpp:218] Iteration 1764 (2.28496 iter/s, 6.12702s/14 iters), loss = 4.91313
I0401 16:30:04.966085 14951 solver.cpp:237]     Train net output #0: loss = 4.91313 (* 1 = 4.91313 loss)
I0401 16:30:04.966090 14951 sgd_solver.cpp:105] Iteration 1764, lr = 0.001
I0401 16:30:11.074525 14951 solver.cpp:218] Iteration 1778 (2.29194 iter/s, 6.10836s/14 iters), loss = 4.93186
I0401 16:30:11.074577 14951 solver.cpp:237]     Train net output #0: loss = 4.93186 (* 1 = 4.93186 loss)
I0401 16:30:11.074584 14951 sgd_solver.cpp:105] Iteration 1778, lr = 0.001
I0401 16:30:17.122728 14951 solver.cpp:218] Iteration 1792 (2.31479 iter/s, 6.04807s/14 iters), loss = 4.8563
I0401 16:30:17.122786 14951 solver.cpp:237]     Train net output #0: loss = 4.8563 (* 1 = 4.8563 loss)
I0401 16:30:17.122795 14951 sgd_solver.cpp:105] Iteration 1792, lr = 0.001
I0401 16:30:23.119686 14951 solver.cpp:218] Iteration 1806 (2.33457 iter/s, 5.99683s/14 iters), loss = 5.10695
I0401 16:30:23.119742 14951 solver.cpp:237]     Train net output #0: loss = 5.10695 (* 1 = 5.10695 loss)
I0401 16:30:23.119751 14951 sgd_solver.cpp:105] Iteration 1806, lr = 0.001
I0401 16:30:29.280236 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:30:29.513653 14951 solver.cpp:218] Iteration 1820 (2.18961 iter/s, 6.39383s/14 iters), loss = 4.99202
I0401 16:30:29.513696 14951 solver.cpp:237]     Train net output #0: loss = 4.99202 (* 1 = 4.99202 loss)
I0401 16:30:29.513702 14951 sgd_solver.cpp:105] Iteration 1820, lr = 0.001
I0401 16:30:30.791926 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1824.caffemodel
I0401 16:30:35.818547 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1824.solverstate
I0401 16:30:40.027076 14951 solver.cpp:330] Iteration 1824, Testing net (#0)
I0401 16:30:40.027094 14951 net.cpp:676] Ignoring source layer train-data
I0401 16:30:41.033097 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:30:42.274607 14951 solver.cpp:397]     Test net output #0: accuracy = 0.0288462
I0401 16:30:42.274643 14951 solver.cpp:397]     Test net output #1: loss = 4.98569 (* 1 = 4.98569 loss)
I0401 16:30:45.951498 14951 solver.cpp:218] Iteration 1834 (0.851705 iter/s, 16.4376s/14 iters), loss = 5.07633
I0401 16:30:45.951555 14951 solver.cpp:237]     Train net output #0: loss = 5.07633 (* 1 = 5.07633 loss)
I0401 16:30:45.951562 14951 sgd_solver.cpp:105] Iteration 1834, lr = 0.001
I0401 16:30:52.028556 14951 solver.cpp:218] Iteration 1848 (2.3038 iter/s, 6.07692s/14 iters), loss = 4.97492
I0401 16:30:52.028620 14951 solver.cpp:237]     Train net output #0: loss = 4.97492 (* 1 = 4.97492 loss)
I0401 16:30:52.028630 14951 sgd_solver.cpp:105] Iteration 1848, lr = 0.001
I0401 16:30:58.131685 14951 solver.cpp:218] Iteration 1862 (2.29396 iter/s, 6.10298s/14 iters), loss = 4.98646
I0401 16:30:58.131742 14951 solver.cpp:237]     Train net output #0: loss = 4.98646 (* 1 = 4.98646 loss)
I0401 16:30:58.131749 14951 sgd_solver.cpp:105] Iteration 1862, lr = 0.001
I0401 16:31:04.158949 14951 solver.cpp:218] Iteration 1876 (2.32283 iter/s, 6.02714s/14 iters), loss = 4.93928
I0401 16:31:04.159034 14951 solver.cpp:237]     Train net output #0: loss = 4.93928 (* 1 = 4.93928 loss)
I0401 16:31:04.159040 14951 sgd_solver.cpp:105] Iteration 1876, lr = 0.001
I0401 16:31:10.383591 14951 solver.cpp:218] Iteration 1890 (2.24919 iter/s, 6.22447s/14 iters), loss = 5.07121
I0401 16:31:10.383651 14951 solver.cpp:237]     Train net output #0: loss = 5.07121 (* 1 = 5.07121 loss)
I0401 16:31:10.383659 14951 sgd_solver.cpp:105] Iteration 1890, lr = 0.001
I0401 16:31:16.634044 14951 solver.cpp:218] Iteration 1904 (2.23989 iter/s, 6.25032s/14 iters), loss = 4.9275
I0401 16:31:16.634104 14951 solver.cpp:237]     Train net output #0: loss = 4.9275 (* 1 = 4.9275 loss)
I0401 16:31:16.634111 14951 sgd_solver.cpp:105] Iteration 1904, lr = 0.001
I0401 16:31:22.744311 14951 solver.cpp:218] Iteration 1918 (2.29128 iter/s, 6.11013s/14 iters), loss = 5.03388
I0401 16:31:22.744354 14951 solver.cpp:237]     Train net output #0: loss = 5.03388 (* 1 = 5.03388 loss)
I0401 16:31:22.744359 14951 sgd_solver.cpp:105] Iteration 1918, lr = 0.001
I0401 16:31:28.697479 14951 solver.cpp:218] Iteration 1932 (2.35174 iter/s, 5.95304s/14 iters), loss = 4.92515
I0401 16:31:28.697540 14951 solver.cpp:237]     Train net output #0: loss = 4.92515 (* 1 = 4.92515 loss)
I0401 16:31:28.697548 14951 sgd_solver.cpp:105] Iteration 1932, lr = 0.001
I0401 16:31:29.315637 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:31:30.978112 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1938.caffemodel
I0401 16:31:35.696957 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1938.solverstate
I0401 16:31:37.997843 14951 solver.cpp:330] Iteration 1938, Testing net (#0)
I0401 16:31:37.997862 14951 net.cpp:676] Ignoring source layer train-data
I0401 16:31:39.012017 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:31:40.196727 14951 solver.cpp:397]     Test net output #0: accuracy = 0.0336538
I0401 16:31:40.196786 14951 solver.cpp:397]     Test net output #1: loss = 4.95202 (* 1 = 4.95202 loss)
I0401 16:31:42.965714 14951 solver.cpp:218] Iteration 1946 (0.981216 iter/s, 14.268s/14 iters), loss = 4.87453
I0401 16:31:42.965764 14951 solver.cpp:237]     Train net output #0: loss = 4.87453 (* 1 = 4.87453 loss)
I0401 16:31:42.965772 14951 sgd_solver.cpp:105] Iteration 1946, lr = 0.001
I0401 16:31:48.849017 14951 solver.cpp:218] Iteration 1960 (2.37967 iter/s, 5.88318s/14 iters), loss = 4.9959
I0401 16:31:48.849078 14951 solver.cpp:237]     Train net output #0: loss = 4.9959 (* 1 = 4.9959 loss)
I0401 16:31:48.849087 14951 sgd_solver.cpp:105] Iteration 1960, lr = 0.001
I0401 16:31:55.113515 14951 solver.cpp:218] Iteration 1974 (2.23487 iter/s, 6.26436s/14 iters), loss = 5.01851
I0401 16:31:55.113569 14951 solver.cpp:237]     Train net output #0: loss = 5.01851 (* 1 = 5.01851 loss)
I0401 16:31:55.113579 14951 sgd_solver.cpp:105] Iteration 1974, lr = 0.001
I0401 16:32:01.286255 14951 solver.cpp:218] Iteration 1988 (2.26809 iter/s, 6.17261s/14 iters), loss = 4.88534
I0401 16:32:01.286299 14951 solver.cpp:237]     Train net output #0: loss = 4.88534 (* 1 = 4.88534 loss)
I0401 16:32:01.286305 14951 sgd_solver.cpp:105] Iteration 1988, lr = 0.001
I0401 16:32:07.455082 14951 solver.cpp:218] Iteration 2002 (2.26952 iter/s, 6.1687s/14 iters), loss = 4.9429
I0401 16:32:07.455193 14951 solver.cpp:237]     Train net output #0: loss = 4.9429 (* 1 = 4.9429 loss)
I0401 16:32:07.455202 14951 sgd_solver.cpp:105] Iteration 2002, lr = 0.001
I0401 16:32:13.753937 14951 solver.cpp:218] Iteration 2016 (2.22269 iter/s, 6.29867s/14 iters), loss = 4.92314
I0401 16:32:13.753978 14951 solver.cpp:237]     Train net output #0: loss = 4.92314 (* 1 = 4.92314 loss)
I0401 16:32:13.753983 14951 sgd_solver.cpp:105] Iteration 2016, lr = 0.001
I0401 16:32:19.966104 14951 solver.cpp:218] Iteration 2030 (2.25369 iter/s, 6.21205s/14 iters), loss = 4.76951
I0401 16:32:19.966146 14951 solver.cpp:237]     Train net output #0: loss = 4.76951 (* 1 = 4.76951 loss)
I0401 16:32:19.966152 14951 sgd_solver.cpp:105] Iteration 2030, lr = 0.001
I0401 16:32:26.000239 14951 solver.cpp:218] Iteration 2044 (2.32018 iter/s, 6.03401s/14 iters), loss = 5.02762
I0401 16:32:26.000279 14951 solver.cpp:237]     Train net output #0: loss = 5.02762 (* 1 = 5.02762 loss)
I0401 16:32:26.000285 14951 sgd_solver.cpp:105] Iteration 2044, lr = 0.001
I0401 16:32:27.398726 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:32:28.906276 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2052.caffemodel
I0401 16:32:32.027391 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2052.solverstate
I0401 16:32:34.319067 14951 solver.cpp:330] Iteration 2052, Testing net (#0)
I0401 16:32:34.319087 14951 net.cpp:676] Ignoring source layer train-data
I0401 16:32:35.231125 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:32:36.489313 14951 solver.cpp:397]     Test net output #0: accuracy = 0.0336538
I0401 16:32:36.489348 14951 solver.cpp:397]     Test net output #1: loss = 4.92446 (* 1 = 4.92446 loss)
I0401 16:32:38.456547 14951 solver.cpp:218] Iteration 2058 (1.12395 iter/s, 12.4561s/14 iters), loss = 4.7769
I0401 16:32:38.456681 14951 solver.cpp:237]     Train net output #0: loss = 4.7769 (* 1 = 4.7769 loss)
I0401 16:32:38.456689 14951 sgd_solver.cpp:105] Iteration 2058, lr = 0.001
I0401 16:32:44.618530 14951 solver.cpp:218] Iteration 2072 (2.27207 iter/s, 6.16178s/14 iters), loss = 4.92421
I0401 16:32:44.618573 14951 solver.cpp:237]     Train net output #0: loss = 4.92421 (* 1 = 4.92421 loss)
I0401 16:32:44.618579 14951 sgd_solver.cpp:105] Iteration 2072, lr = 0.001
I0401 16:32:50.747992 14951 solver.cpp:218] Iteration 2086 (2.2841 iter/s, 6.12933s/14 iters), loss = 4.92353
I0401 16:32:50.748044 14951 solver.cpp:237]     Train net output #0: loss = 4.92353 (* 1 = 4.92353 loss)
I0401 16:32:50.748054 14951 sgd_solver.cpp:105] Iteration 2086, lr = 0.001
I0401 16:32:56.931830 14951 solver.cpp:218] Iteration 2100 (2.26401 iter/s, 6.18371s/14 iters), loss = 4.85901
I0401 16:32:56.931886 14951 solver.cpp:237]     Train net output #0: loss = 4.85901 (* 1 = 4.85901 loss)
I0401 16:32:56.931895 14951 sgd_solver.cpp:105] Iteration 2100, lr = 0.001
I0401 16:33:03.225239 14951 solver.cpp:218] Iteration 2114 (2.2246 iter/s, 6.29328s/14 iters), loss = 4.77782
I0401 16:33:03.225294 14951 solver.cpp:237]     Train net output #0: loss = 4.77782 (* 1 = 4.77782 loss)
I0401 16:33:03.225302 14951 sgd_solver.cpp:105] Iteration 2114, lr = 0.001
I0401 16:33:09.474807 14951 solver.cpp:218] Iteration 2128 (2.2402 iter/s, 6.24944s/14 iters), loss = 4.73985
I0401 16:33:09.474934 14951 solver.cpp:237]     Train net output #0: loss = 4.73985 (* 1 = 4.73985 loss)
I0401 16:33:09.474941 14951 sgd_solver.cpp:105] Iteration 2128, lr = 0.001
I0401 16:33:15.722977 14951 solver.cpp:218] Iteration 2142 (2.24073 iter/s, 6.24797s/14 iters), loss = 4.91238
I0401 16:33:15.723032 14951 solver.cpp:237]     Train net output #0: loss = 4.91238 (* 1 = 4.91238 loss)
I0401 16:33:15.723042 14951 sgd_solver.cpp:105] Iteration 2142, lr = 0.001
I0401 16:33:21.826050 14951 solver.cpp:218] Iteration 2156 (2.29398 iter/s, 6.10294s/14 iters), loss = 5.04042
I0401 16:33:21.826105 14951 solver.cpp:237]     Train net output #0: loss = 5.04042 (* 1 = 5.04042 loss)
I0401 16:33:21.826113 14951 sgd_solver.cpp:105] Iteration 2156, lr = 0.001
I0401 16:33:24.095329 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:33:25.725886 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2166.caffemodel
I0401 16:33:28.841109 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2166.solverstate
I0401 16:33:31.159554 14951 solver.cpp:330] Iteration 2166, Testing net (#0)
I0401 16:33:31.159575 14951 net.cpp:676] Ignoring source layer train-data
I0401 16:33:31.973101 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:33:33.298305 14951 solver.cpp:397]     Test net output #0: accuracy = 0.0432692
I0401 16:33:33.298341 14951 solver.cpp:397]     Test net output #1: loss = 4.89585 (* 1 = 4.89585 loss)
I0401 16:33:34.297819 14951 solver.cpp:218] Iteration 2170 (1.12255 iter/s, 12.4716s/14 iters), loss = 4.886
I0401 16:33:34.297869 14951 solver.cpp:237]     Train net output #0: loss = 4.886 (* 1 = 4.886 loss)
I0401 16:33:34.297878 14951 sgd_solver.cpp:105] Iteration 2170, lr = 0.001
I0401 16:33:40.343225 14951 solver.cpp:218] Iteration 2184 (2.31586 iter/s, 6.04528s/14 iters), loss = 4.78609
I0401 16:33:40.343322 14951 solver.cpp:237]     Train net output #0: loss = 4.78609 (* 1 = 4.78609 loss)
I0401 16:33:40.343331 14951 sgd_solver.cpp:105] Iteration 2184, lr = 0.001
I0401 16:33:46.459260 14951 solver.cpp:218] Iteration 2198 (2.28913 iter/s, 6.11586s/14 iters), loss = 4.8653
I0401 16:33:46.459313 14951 solver.cpp:237]     Train net output #0: loss = 4.8653 (* 1 = 4.8653 loss)
I0401 16:33:46.459321 14951 sgd_solver.cpp:105] Iteration 2198, lr = 0.001
I0401 16:33:52.756567 14951 solver.cpp:218] Iteration 2212 (2.22322 iter/s, 6.29718s/14 iters), loss = 4.92277
I0401 16:33:52.756608 14951 solver.cpp:237]     Train net output #0: loss = 4.92277 (* 1 = 4.92277 loss)
I0401 16:33:52.756613 14951 sgd_solver.cpp:105] Iteration 2212, lr = 0.001
I0401 16:33:58.986167 14951 solver.cpp:218] Iteration 2226 (2.24738 iter/s, 6.22948s/14 iters), loss = 4.83389
I0401 16:33:58.986208 14951 solver.cpp:237]     Train net output #0: loss = 4.83389 (* 1 = 4.83389 loss)
I0401 16:33:58.986214 14951 sgd_solver.cpp:105] Iteration 2226, lr = 0.001
I0401 16:34:05.229207 14951 solver.cpp:218] Iteration 2240 (2.24254 iter/s, 6.24291s/14 iters), loss = 4.92115
I0401 16:34:05.229259 14951 solver.cpp:237]     Train net output #0: loss = 4.92115 (* 1 = 4.92115 loss)
I0401 16:34:05.229267 14951 sgd_solver.cpp:105] Iteration 2240, lr = 0.001
I0401 16:34:11.616679 14951 solver.cpp:218] Iteration 2254 (2.19184 iter/s, 6.38734s/14 iters), loss = 4.8158
I0401 16:34:11.616842 14951 solver.cpp:237]     Train net output #0: loss = 4.8158 (* 1 = 4.8158 loss)
I0401 16:34:11.616850 14951 sgd_solver.cpp:105] Iteration 2254, lr = 0.001
I0401 16:34:17.853092 14951 solver.cpp:218] Iteration 2268 (2.24497 iter/s, 6.23618s/14 iters), loss = 4.84699
I0401 16:34:17.853134 14951 solver.cpp:237]     Train net output #0: loss = 4.84699 (* 1 = 4.84699 loss)
I0401 16:34:17.853139 14951 sgd_solver.cpp:105] Iteration 2268, lr = 0.001
I0401 16:34:20.716521 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:34:22.557833 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2280.caffemodel
I0401 16:34:25.580184 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2280.solverstate
I0401 16:34:27.886693 14951 solver.cpp:330] Iteration 2280, Testing net (#0)
I0401 16:34:27.886713 14951 net.cpp:676] Ignoring source layer train-data
I0401 16:34:28.618517 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:34:30.123515 14951 solver.cpp:397]     Test net output #0: accuracy = 0.046875
I0401 16:34:30.123556 14951 solver.cpp:397]     Test net output #1: loss = 4.86925 (* 1 = 4.86925 loss)
I0401 16:34:30.538678 14951 solver.cpp:218] Iteration 2282 (1.10363 iter/s, 12.6854s/14 iters), loss = 4.95218
I0401 16:34:30.538739 14951 solver.cpp:237]     Train net output #0: loss = 4.95218 (* 1 = 4.95218 loss)
I0401 16:34:30.538749 14951 sgd_solver.cpp:105] Iteration 2282, lr = 0.001
I0401 16:34:36.180902 14951 solver.cpp:218] Iteration 2296 (2.48135 iter/s, 5.64208s/14 iters), loss = 4.8406
I0401 16:34:36.180944 14951 solver.cpp:237]     Train net output #0: loss = 4.8406 (* 1 = 4.8406 loss)
I0401 16:34:36.180953 14951 sgd_solver.cpp:105] Iteration 2296, lr = 0.001
I0401 16:34:42.200202 14951 solver.cpp:218] Iteration 2310 (2.3259 iter/s, 6.01918s/14 iters), loss = 4.77464
I0401 16:34:42.200292 14951 solver.cpp:237]     Train net output #0: loss = 4.77464 (* 1 = 4.77464 loss)
I0401 16:34:42.200299 14951 sgd_solver.cpp:105] Iteration 2310, lr = 0.001
I0401 16:34:48.231763 14951 solver.cpp:218] Iteration 2324 (2.32119 iter/s, 6.03139s/14 iters), loss = 4.78713
I0401 16:34:48.231808 14951 solver.cpp:237]     Train net output #0: loss = 4.78713 (* 1 = 4.78713 loss)
I0401 16:34:48.231817 14951 sgd_solver.cpp:105] Iteration 2324, lr = 0.001
I0401 16:34:54.349572 14951 solver.cpp:218] Iteration 2338 (2.28845 iter/s, 6.11768s/14 iters), loss = 4.87019
I0401 16:34:54.349617 14951 solver.cpp:237]     Train net output #0: loss = 4.87019 (* 1 = 4.87019 loss)
I0401 16:34:54.349622 14951 sgd_solver.cpp:105] Iteration 2338, lr = 0.001
I0401 16:35:00.547663 14951 solver.cpp:218] Iteration 2352 (2.25881 iter/s, 6.19796s/14 iters), loss = 4.89987
I0401 16:35:00.547709 14951 solver.cpp:237]     Train net output #0: loss = 4.89987 (* 1 = 4.89987 loss)
I0401 16:35:00.547716 14951 sgd_solver.cpp:105] Iteration 2352, lr = 0.001
I0401 16:35:06.744557 14951 solver.cpp:218] Iteration 2366 (2.25924 iter/s, 6.19677s/14 iters), loss = 4.87145
I0401 16:35:06.744598 14951 solver.cpp:237]     Train net output #0: loss = 4.87145 (* 1 = 4.87145 loss)
I0401 16:35:06.744604 14951 sgd_solver.cpp:105] Iteration 2366, lr = 0.001
I0401 16:35:13.080315 14951 solver.cpp:218] Iteration 2380 (2.20972 iter/s, 6.33563s/14 iters), loss = 4.75057
I0401 16:35:13.080423 14951 solver.cpp:237]     Train net output #0: loss = 4.75057 (* 1 = 4.75057 loss)
I0401 16:35:13.080432 14951 sgd_solver.cpp:105] Iteration 2380, lr = 0.001
I0401 16:35:16.741597 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:35:18.605928 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2394.caffemodel
I0401 16:35:22.588023 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2394.solverstate
I0401 16:35:26.362118 14951 solver.cpp:330] Iteration 2394, Testing net (#0)
I0401 16:35:26.362136 14951 net.cpp:676] Ignoring source layer train-data
I0401 16:35:27.117584 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:35:28.546698 14951 solver.cpp:397]     Test net output #0: accuracy = 0.0480769
I0401 16:35:28.546736 14951 solver.cpp:397]     Test net output #1: loss = 4.85427 (* 1 = 4.85427 loss)
I0401 16:35:28.688465 14951 solver.cpp:218] Iteration 2394 (0.896983 iter/s, 15.6079s/14 iters), loss = 4.85346
I0401 16:35:28.690026 14951 solver.cpp:237]     Train net output #0: loss = 4.85346 (* 1 = 4.85346 loss)
I0401 16:35:28.690040 14951 sgd_solver.cpp:105] Iteration 2394, lr = 0.001
I0401 16:35:33.835235 14951 solver.cpp:218] Iteration 2408 (2.72101 iter/s, 5.14515s/14 iters), loss = 4.72206
I0401 16:35:33.835289 14951 solver.cpp:237]     Train net output #0: loss = 4.72206 (* 1 = 4.72206 loss)
I0401 16:35:33.835297 14951 sgd_solver.cpp:105] Iteration 2408, lr = 0.001
I0401 16:35:39.981474 14951 solver.cpp:218] Iteration 2422 (2.27786 iter/s, 6.14611s/14 iters), loss = 4.95251
I0401 16:35:39.981531 14951 solver.cpp:237]     Train net output #0: loss = 4.95251 (* 1 = 4.95251 loss)
I0401 16:35:39.981539 14951 sgd_solver.cpp:105] Iteration 2422, lr = 0.001
I0401 16:35:46.094532 14951 solver.cpp:218] Iteration 2436 (2.29023 iter/s, 6.11292s/14 iters), loss = 4.6502
I0401 16:35:46.094681 14951 solver.cpp:237]     Train net output #0: loss = 4.6502 (* 1 = 4.6502 loss)
I0401 16:35:46.094689 14951 sgd_solver.cpp:105] Iteration 2436, lr = 0.001
I0401 16:35:52.210783 14951 solver.cpp:218] Iteration 2450 (2.28907 iter/s, 6.11602s/14 iters), loss = 4.87791
I0401 16:35:52.210845 14951 solver.cpp:237]     Train net output #0: loss = 4.87791 (* 1 = 4.87791 loss)
I0401 16:35:52.210853 14951 sgd_solver.cpp:105] Iteration 2450, lr = 0.001
I0401 16:35:58.308663 14951 solver.cpp:218] Iteration 2464 (2.29593 iter/s, 6.09774s/14 iters), loss = 4.73971
I0401 16:35:58.308727 14951 solver.cpp:237]     Train net output #0: loss = 4.73971 (* 1 = 4.73971 loss)
I0401 16:35:58.308737 14951 sgd_solver.cpp:105] Iteration 2464, lr = 0.001
I0401 16:36:04.471633 14951 solver.cpp:218] Iteration 2478 (2.27168 iter/s, 6.16283s/14 iters), loss = 4.78718
I0401 16:36:04.471678 14951 solver.cpp:237]     Train net output #0: loss = 4.78718 (* 1 = 4.78718 loss)
I0401 16:36:04.471685 14951 sgd_solver.cpp:105] Iteration 2478, lr = 0.001
I0401 16:36:10.676731 14951 solver.cpp:218] Iteration 2492 (2.25626 iter/s, 6.20497s/14 iters), loss = 4.91527
I0401 16:36:10.676784 14951 solver.cpp:237]     Train net output #0: loss = 4.91527 (* 1 = 4.91527 loss)
I0401 16:36:10.676793 14951 sgd_solver.cpp:105] Iteration 2492, lr = 0.001
I0401 16:36:15.494673 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:36:17.061895 14951 solver.cpp:218] Iteration 2506 (2.19263 iter/s, 6.38503s/14 iters), loss = 4.8668
I0401 16:36:17.062005 14951 solver.cpp:237]     Train net output #0: loss = 4.8668 (* 1 = 4.8668 loss)
I0401 16:36:17.062014 14951 sgd_solver.cpp:105] Iteration 2506, lr = 0.001
I0401 16:36:17.408862 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2508.caffemodel
I0401 16:36:21.288352 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2508.solverstate
I0401 16:36:23.664714 14951 solver.cpp:330] Iteration 2508, Testing net (#0)
I0401 16:36:23.664731 14951 net.cpp:676] Ignoring source layer train-data
I0401 16:36:24.276391 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:36:25.905103 14951 solver.cpp:397]     Test net output #0: accuracy = 0.0528846
I0401 16:36:25.905139 14951 solver.cpp:397]     Test net output #1: loss = 4.82168 (* 1 = 4.82168 loss)
I0401 16:36:30.559968 14951 solver.cpp:218] Iteration 2520 (1.03721 iter/s, 13.4978s/14 iters), loss = 4.87175
I0401 16:36:30.560010 14951 solver.cpp:237]     Train net output #0: loss = 4.87175 (* 1 = 4.87175 loss)
I0401 16:36:30.560017 14951 sgd_solver.cpp:105] Iteration 2520, lr = 0.001
I0401 16:36:36.554185 14951 solver.cpp:218] Iteration 2534 (2.33563 iter/s, 5.9941s/14 iters), loss = 4.79882
I0401 16:36:36.554224 14951 solver.cpp:237]     Train net output #0: loss = 4.79882 (* 1 = 4.79882 loss)
I0401 16:36:36.554229 14951 sgd_solver.cpp:105] Iteration 2534, lr = 0.001
I0401 16:36:39.343356 14951 blocking_queue.cpp:49] Waiting for data
I0401 16:36:42.472848 14951 solver.cpp:218] Iteration 2548 (2.36545 iter/s, 5.91854s/14 iters), loss = 4.80328
I0401 16:36:42.472916 14951 solver.cpp:237]     Train net output #0: loss = 4.80328 (* 1 = 4.80328 loss)
I0401 16:36:42.472925 14951 sgd_solver.cpp:105] Iteration 2548, lr = 0.001
I0401 16:36:48.565047 14951 solver.cpp:218] Iteration 2562 (2.29807 iter/s, 6.09206s/14 iters), loss = 4.84704
I0401 16:36:48.565176 14951 solver.cpp:237]     Train net output #0: loss = 4.84704 (* 1 = 4.84704 loss)
I0401 16:36:48.565182 14951 sgd_solver.cpp:105] Iteration 2562, lr = 0.001
I0401 16:36:54.788384 14951 solver.cpp:218] Iteration 2576 (2.24967 iter/s, 6.22313s/14 iters), loss = 4.7282
I0401 16:36:54.788436 14951 solver.cpp:237]     Train net output #0: loss = 4.7282 (* 1 = 4.7282 loss)
I0401 16:36:54.788445 14951 sgd_solver.cpp:105] Iteration 2576, lr = 0.001
I0401 16:37:01.134567 14951 solver.cpp:218] Iteration 2590 (2.2061 iter/s, 6.34604s/14 iters), loss = 4.76678
I0401 16:37:01.134640 14951 solver.cpp:237]     Train net output #0: loss = 4.76678 (* 1 = 4.76678 loss)
I0401 16:37:01.134650 14951 sgd_solver.cpp:105] Iteration 2590, lr = 0.001
I0401 16:37:07.445616 14951 solver.cpp:218] Iteration 2604 (2.21838 iter/s, 6.3109s/14 iters), loss = 4.79203
I0401 16:37:07.445654 14951 solver.cpp:237]     Train net output #0: loss = 4.79203 (* 1 = 4.79203 loss)
I0401 16:37:07.445660 14951 sgd_solver.cpp:105] Iteration 2604, lr = 0.001
I0401 16:37:12.692835 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:37:13.409752 14951 solver.cpp:218] Iteration 2618 (2.34741 iter/s, 5.96402s/14 iters), loss = 4.76206
I0401 16:37:13.409796 14951 solver.cpp:237]     Train net output #0: loss = 4.76206 (* 1 = 4.76206 loss)
I0401 16:37:13.409802 14951 sgd_solver.cpp:105] Iteration 2618, lr = 0.001
I0401 16:37:14.749858 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2622.caffemodel
I0401 16:37:17.663930 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2622.solverstate
I0401 16:37:19.976613 14951 solver.cpp:330] Iteration 2622, Testing net (#0)
I0401 16:37:19.976704 14951 net.cpp:676] Ignoring source layer train-data
I0401 16:37:20.584028 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:37:22.336127 14951 solver.cpp:397]     Test net output #0: accuracy = 0.0588942
I0401 16:37:22.336156 14951 solver.cpp:397]     Test net output #1: loss = 4.77816 (* 1 = 4.77816 loss)
I0401 16:37:25.980170 14951 solver.cpp:218] Iteration 2632 (1.11374 iter/s, 12.5702s/14 iters), loss = 4.68194
I0401 16:37:25.980228 14951 solver.cpp:237]     Train net output #0: loss = 4.68194 (* 1 = 4.68194 loss)
I0401 16:37:25.980237 14951 sgd_solver.cpp:105] Iteration 2632, lr = 0.001
I0401 16:37:32.061218 14951 solver.cpp:218] Iteration 2646 (2.30229 iter/s, 6.08091s/14 iters), loss = 4.67078
I0401 16:37:32.061272 14951 solver.cpp:237]     Train net output #0: loss = 4.67078 (* 1 = 4.67078 loss)
I0401 16:37:32.061280 14951 sgd_solver.cpp:105] Iteration 2646, lr = 0.001
I0401 16:37:38.292007 14951 solver.cpp:218] Iteration 2660 (2.24695 iter/s, 6.23066s/14 iters), loss = 4.63479
I0401 16:37:38.292064 14951 solver.cpp:237]     Train net output #0: loss = 4.63479 (* 1 = 4.63479 loss)
I0401 16:37:38.292073 14951 sgd_solver.cpp:105] Iteration 2660, lr = 0.001
I0401 16:37:44.432400 14951 solver.cpp:218] Iteration 2674 (2.28003 iter/s, 6.14026s/14 iters), loss = 4.57855
I0401 16:37:44.432446 14951 solver.cpp:237]     Train net output #0: loss = 4.57855 (* 1 = 4.57855 loss)
I0401 16:37:44.432454 14951 sgd_solver.cpp:105] Iteration 2674, lr = 0.001
I0401 16:37:50.507275 14951 solver.cpp:218] Iteration 2688 (2.30462 iter/s, 6.07475s/14 iters), loss = 4.62813
I0401 16:37:50.507421 14951 solver.cpp:237]     Train net output #0: loss = 4.62813 (* 1 = 4.62813 loss)
I0401 16:37:50.507431 14951 sgd_solver.cpp:105] Iteration 2688, lr = 0.001
I0401 16:37:56.585249 14951 solver.cpp:218] Iteration 2702 (2.30348 iter/s, 6.07776s/14 iters), loss = 4.63945
I0401 16:37:56.585286 14951 solver.cpp:237]     Train net output #0: loss = 4.63945 (* 1 = 4.63945 loss)
I0401 16:37:56.585292 14951 sgd_solver.cpp:105] Iteration 2702, lr = 0.001
I0401 16:38:03.131805 14951 solver.cpp:218] Iteration 2716 (2.13857 iter/s, 6.54644s/14 iters), loss = 4.87431
I0401 16:38:03.131850 14951 solver.cpp:237]     Train net output #0: loss = 4.87431 (* 1 = 4.87431 loss)
I0401 16:38:03.131855 14951 sgd_solver.cpp:105] Iteration 2716, lr = 0.001
I0401 16:38:08.984335 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:38:09.011019 14951 solver.cpp:218] Iteration 2730 (2.38132 iter/s, 5.8791s/14 iters), loss = 4.71757
I0401 16:38:09.011060 14951 solver.cpp:237]     Train net output #0: loss = 4.71757 (* 1 = 4.71757 loss)
I0401 16:38:09.011065 14951 sgd_solver.cpp:105] Iteration 2730, lr = 0.001
I0401 16:38:11.036517 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2736.caffemodel
I0401 16:38:14.945900 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2736.solverstate
I0401 16:38:18.793119 14951 solver.cpp:330] Iteration 2736, Testing net (#0)
I0401 16:38:18.793140 14951 net.cpp:676] Ignoring source layer train-data
I0401 16:38:19.417320 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:38:21.028870 14951 solver.cpp:397]     Test net output #0: accuracy = 0.0625
I0401 16:38:21.028975 14951 solver.cpp:397]     Test net output #1: loss = 4.74014 (* 1 = 4.74014 loss)
I0401 16:38:23.762701 14951 solver.cpp:218] Iteration 2744 (0.949058 iter/s, 14.7515s/14 iters), loss = 4.68214
I0401 16:38:23.762743 14951 solver.cpp:237]     Train net output #0: loss = 4.68214 (* 1 = 4.68214 loss)
I0401 16:38:23.762749 14951 sgd_solver.cpp:105] Iteration 2744, lr = 0.001
I0401 16:38:29.896378 14951 solver.cpp:218] Iteration 2758 (2.28253 iter/s, 6.13356s/14 iters), loss = 4.56798
I0401 16:38:29.896414 14951 solver.cpp:237]     Train net output #0: loss = 4.56798 (* 1 = 4.56798 loss)
I0401 16:38:29.896420 14951 sgd_solver.cpp:105] Iteration 2758, lr = 0.001
I0401 16:38:35.992413 14951 solver.cpp:218] Iteration 2772 (2.29662 iter/s, 6.09592s/14 iters), loss = 4.58183
I0401 16:38:35.992458 14951 solver.cpp:237]     Train net output #0: loss = 4.58183 (* 1 = 4.58183 loss)
I0401 16:38:35.992466 14951 sgd_solver.cpp:105] Iteration 2772, lr = 0.001
I0401 16:38:42.157501 14951 solver.cpp:218] Iteration 2786 (2.2709 iter/s, 6.16496s/14 iters), loss = 4.57632
I0401 16:38:42.157559 14951 solver.cpp:237]     Train net output #0: loss = 4.57632 (* 1 = 4.57632 loss)
I0401 16:38:42.157568 14951 sgd_solver.cpp:105] Iteration 2786, lr = 0.001
I0401 16:38:47.929962 14951 solver.cpp:218] Iteration 2800 (2.42536 iter/s, 5.77234s/14 iters), loss = 4.77065
I0401 16:38:47.930002 14951 solver.cpp:237]     Train net output #0: loss = 4.77065 (* 1 = 4.77065 loss)
I0401 16:38:47.930007 14951 sgd_solver.cpp:105] Iteration 2800, lr = 0.001
I0401 16:38:54.143746 14951 solver.cpp:218] Iteration 2814 (2.2531 iter/s, 6.21366s/14 iters), loss = 4.60923
I0401 16:38:54.143851 14951 solver.cpp:237]     Train net output #0: loss = 4.60923 (* 1 = 4.60923 loss)
I0401 16:38:54.143858 14951 sgd_solver.cpp:105] Iteration 2814, lr = 0.001
I0401 16:39:00.296613 14951 solver.cpp:218] Iteration 2828 (2.27543 iter/s, 6.15269s/14 iters), loss = 4.78025
I0401 16:39:00.296674 14951 solver.cpp:237]     Train net output #0: loss = 4.78025 (* 1 = 4.78025 loss)
I0401 16:39:00.296684 14951 sgd_solver.cpp:105] Iteration 2828, lr = 0.001
I0401 16:39:06.325510 14951 solver.cpp:218] Iteration 2842 (2.3222 iter/s, 6.02876s/14 iters), loss = 4.63904
I0401 16:39:06.325551 14951 solver.cpp:237]     Train net output #0: loss = 4.63904 (* 1 = 4.63904 loss)
I0401 16:39:06.325557 14951 sgd_solver.cpp:105] Iteration 2842, lr = 0.001
I0401 16:39:06.901722 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:39:09.094641 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2850.caffemodel
I0401 16:39:13.677546 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2850.solverstate
I0401 16:39:15.984428 14951 solver.cpp:330] Iteration 2850, Testing net (#0)
I0401 16:39:15.984445 14951 net.cpp:676] Ignoring source layer train-data
I0401 16:39:16.414458 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:39:18.173616 14951 solver.cpp:397]     Test net output #0: accuracy = 0.0637019
I0401 16:39:18.173648 14951 solver.cpp:397]     Test net output #1: loss = 4.70729 (* 1 = 4.70729 loss)
I0401 16:39:20.166857 14951 solver.cpp:218] Iteration 2856 (1.01148 iter/s, 13.8411s/14 iters), loss = 4.68161
I0401 16:39:20.166908 14951 solver.cpp:237]     Train net output #0: loss = 4.68161 (* 1 = 4.68161 loss)
I0401 16:39:20.166915 14951 sgd_solver.cpp:105] Iteration 2856, lr = 0.001
I0401 16:39:26.133049 14951 solver.cpp:218] Iteration 2870 (2.34661 iter/s, 5.96606s/14 iters), loss = 4.63985
I0401 16:39:26.133208 14951 solver.cpp:237]     Train net output #0: loss = 4.63985 (* 1 = 4.63985 loss)
I0401 16:39:26.133217 14951 sgd_solver.cpp:105] Iteration 2870, lr = 0.001
I0401 16:39:32.263077 14951 solver.cpp:218] Iteration 2884 (2.28393 iter/s, 6.12978s/14 iters), loss = 4.65569
I0401 16:39:32.263130 14951 solver.cpp:237]     Train net output #0: loss = 4.65569 (* 1 = 4.65569 loss)
I0401 16:39:32.263137 14951 sgd_solver.cpp:105] Iteration 2884, lr = 0.001
I0401 16:39:38.394006 14951 solver.cpp:218] Iteration 2898 (2.28355 iter/s, 6.13079s/14 iters), loss = 4.50392
I0401 16:39:38.394058 14951 solver.cpp:237]     Train net output #0: loss = 4.50392 (* 1 = 4.50392 loss)
I0401 16:39:38.394065 14951 sgd_solver.cpp:105] Iteration 2898, lr = 0.001
I0401 16:39:44.679488 14951 solver.cpp:218] Iteration 2912 (2.2274 iter/s, 6.28535s/14 iters), loss = 4.71895
I0401 16:39:44.685678 14951 solver.cpp:237]     Train net output #0: loss = 4.71895 (* 1 = 4.71895 loss)
I0401 16:39:44.685688 14951 sgd_solver.cpp:105] Iteration 2912, lr = 0.001
I0401 16:39:50.821664 14951 solver.cpp:218] Iteration 2926 (2.28165 iter/s, 6.13592s/14 iters), loss = 4.72642
I0401 16:39:50.821710 14951 solver.cpp:237]     Train net output #0: loss = 4.72642 (* 1 = 4.72642 loss)
I0401 16:39:50.821717 14951 sgd_solver.cpp:105] Iteration 2926, lr = 0.001
I0401 16:39:56.883312 14951 solver.cpp:218] Iteration 2940 (2.30965 iter/s, 6.06152s/14 iters), loss = 4.58987
I0401 16:39:56.883435 14951 solver.cpp:237]     Train net output #0: loss = 4.58987 (* 1 = 4.58987 loss)
I0401 16:39:56.883443 14951 sgd_solver.cpp:105] Iteration 2940, lr = 0.001
I0401 16:40:03.150519 14951 solver.cpp:218] Iteration 2954 (2.23392 iter/s, 6.26701s/14 iters), loss = 4.68372
I0401 16:40:03.150563 14951 solver.cpp:237]     Train net output #0: loss = 4.68372 (* 1 = 4.68372 loss)
I0401 16:40:03.150568 14951 sgd_solver.cpp:105] Iteration 2954, lr = 0.001
I0401 16:40:04.678217 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:40:07.034664 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2964.caffemodel
I0401 16:40:10.097172 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2964.solverstate
I0401 16:40:12.396329 14951 solver.cpp:330] Iteration 2964, Testing net (#0)
I0401 16:40:12.396351 14951 net.cpp:676] Ignoring source layer train-data
I0401 16:40:12.731381 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:40:14.576367 14951 solver.cpp:397]     Test net output #0: accuracy = 0.0697115
I0401 16:40:14.576404 14951 solver.cpp:397]     Test net output #1: loss = 4.64708 (* 1 = 4.64708 loss)
I0401 16:40:15.735567 14951 solver.cpp:218] Iteration 2968 (1.11245 iter/s, 12.5849s/14 iters), loss = 4.54734
I0401 16:40:15.735610 14951 solver.cpp:237]     Train net output #0: loss = 4.54734 (* 1 = 4.54734 loss)
I0401 16:40:15.735615 14951 sgd_solver.cpp:105] Iteration 2968, lr = 0.001
I0401 16:40:21.911039 14951 solver.cpp:218] Iteration 2982 (2.26708 iter/s, 6.17535s/14 iters), loss = 4.39218
I0401 16:40:21.911080 14951 solver.cpp:237]     Train net output #0: loss = 4.39218 (* 1 = 4.39218 loss)
I0401 16:40:21.911087 14951 sgd_solver.cpp:105] Iteration 2982, lr = 0.001
I0401 16:40:28.223383 14951 solver.cpp:218] Iteration 2996 (2.21792 iter/s, 6.31222s/14 iters), loss = 4.38342
I0401 16:40:28.223501 14951 solver.cpp:237]     Train net output #0: loss = 4.38342 (* 1 = 4.38342 loss)
I0401 16:40:28.223508 14951 sgd_solver.cpp:105] Iteration 2996, lr = 0.001
I0401 16:40:34.373083 14951 solver.cpp:218] Iteration 3010 (2.27661 iter/s, 6.1495s/14 iters), loss = 4.75803
I0401 16:40:34.373136 14951 solver.cpp:237]     Train net output #0: loss = 4.75803 (* 1 = 4.75803 loss)
I0401 16:40:34.373144 14951 sgd_solver.cpp:105] Iteration 3010, lr = 0.001
I0401 16:40:40.363616 14951 solver.cpp:218] Iteration 3024 (2.33707 iter/s, 5.9904s/14 iters), loss = 4.36969
I0401 16:40:40.363654 14951 solver.cpp:237]     Train net output #0: loss = 4.36969 (* 1 = 4.36969 loss)
I0401 16:40:40.363660 14951 sgd_solver.cpp:105] Iteration 3024, lr = 0.001
I0401 16:40:46.599085 14951 solver.cpp:218] Iteration 3038 (2.24526 iter/s, 6.23535s/14 iters), loss = 4.4865
I0401 16:40:46.599128 14951 solver.cpp:237]     Train net output #0: loss = 4.4865 (* 1 = 4.4865 loss)
I0401 16:40:46.599133 14951 sgd_solver.cpp:105] Iteration 3038, lr = 0.001
I0401 16:40:52.747853 14951 solver.cpp:218] Iteration 3052 (2.27692 iter/s, 6.14865s/14 iters), loss = 4.64621
I0401 16:40:52.747892 14951 solver.cpp:237]     Train net output #0: loss = 4.64621 (* 1 = 4.64621 loss)
I0401 16:40:52.747898 14951 sgd_solver.cpp:105] Iteration 3052, lr = 0.001
I0401 16:40:58.924372 14951 solver.cpp:218] Iteration 3066 (2.26669 iter/s, 6.1764s/14 iters), loss = 4.54205
I0401 16:40:58.924505 14951 solver.cpp:237]     Train net output #0: loss = 4.54205 (* 1 = 4.54205 loss)
I0401 16:40:58.924515 14951 sgd_solver.cpp:105] Iteration 3066, lr = 0.001
I0401 16:41:01.188503 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:41:03.748697 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3078.caffemodel
I0401 16:41:06.709679 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3078.solverstate
I0401 16:41:09.012812 14951 solver.cpp:330] Iteration 3078, Testing net (#0)
I0401 16:41:09.012830 14951 net.cpp:676] Ignoring source layer train-data
I0401 16:41:09.335264 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:41:11.349807 14951 solver.cpp:397]     Test net output #0: accuracy = 0.0757212
I0401 16:41:11.349855 14951 solver.cpp:397]     Test net output #1: loss = 4.59537 (* 1 = 4.59537 loss)
I0401 16:41:11.760193 14951 solver.cpp:218] Iteration 3080 (1.09072 iter/s, 12.8355s/14 iters), loss = 4.39006
I0401 16:41:11.760251 14951 solver.cpp:237]     Train net output #0: loss = 4.39006 (* 1 = 4.39006 loss)
I0401 16:41:11.760258 14951 sgd_solver.cpp:105] Iteration 3080, lr = 0.001
I0401 16:41:17.680038 14951 solver.cpp:218] Iteration 3094 (2.36498 iter/s, 5.91971s/14 iters), loss = 4.47748
I0401 16:41:17.680078 14951 solver.cpp:237]     Train net output #0: loss = 4.47748 (* 1 = 4.47748 loss)
I0401 16:41:17.680083 14951 sgd_solver.cpp:105] Iteration 3094, lr = 0.001
I0401 16:41:23.907382 14951 solver.cpp:218] Iteration 3108 (2.24819 iter/s, 6.22722s/14 iters), loss = 4.58862
I0401 16:41:23.907423 14951 solver.cpp:237]     Train net output #0: loss = 4.58862 (* 1 = 4.58862 loss)
I0401 16:41:23.907428 14951 sgd_solver.cpp:105] Iteration 3108, lr = 0.001
I0401 16:41:30.044080 14951 solver.cpp:218] Iteration 3122 (2.28198 iter/s, 6.13502s/14 iters), loss = 4.49627
I0401 16:41:30.044198 14951 solver.cpp:237]     Train net output #0: loss = 4.49627 (* 1 = 4.49627 loss)
I0401 16:41:30.044209 14951 sgd_solver.cpp:105] Iteration 3122, lr = 0.001
I0401 16:41:36.275120 14951 solver.cpp:218] Iteration 3136 (2.24689 iter/s, 6.23084s/14 iters), loss = 4.59027
I0401 16:41:36.275163 14951 solver.cpp:237]     Train net output #0: loss = 4.59027 (* 1 = 4.59027 loss)
I0401 16:41:36.275168 14951 sgd_solver.cpp:105] Iteration 3136, lr = 0.001
I0401 16:41:42.322046 14951 solver.cpp:218] Iteration 3150 (2.31527 iter/s, 6.04681s/14 iters), loss = 4.61824
I0401 16:41:42.322079 14951 solver.cpp:237]     Train net output #0: loss = 4.61824 (* 1 = 4.61824 loss)
I0401 16:41:42.322084 14951 sgd_solver.cpp:105] Iteration 3150, lr = 0.001
I0401 16:41:48.463660 14951 solver.cpp:218] Iteration 3164 (2.27957 iter/s, 6.1415s/14 iters), loss = 4.57457
I0401 16:41:48.463713 14951 solver.cpp:237]     Train net output #0: loss = 4.57457 (* 1 = 4.57457 loss)
I0401 16:41:48.463722 14951 sgd_solver.cpp:105] Iteration 3164, lr = 0.001
I0401 16:41:54.478793 14951 solver.cpp:218] Iteration 3178 (2.32751 iter/s, 6.01501s/14 iters), loss = 4.56961
I0401 16:41:54.478834 14951 solver.cpp:237]     Train net output #0: loss = 4.56961 (* 1 = 4.56961 loss)
I0401 16:41:54.478840 14951 sgd_solver.cpp:105] Iteration 3178, lr = 0.001
I0401 16:41:57.517107 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:42:00.052065 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3192.caffemodel
I0401 16:42:02.967559 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3192.solverstate
I0401 16:42:05.279742 14951 solver.cpp:330] Iteration 3192, Testing net (#0)
I0401 16:42:05.279763 14951 net.cpp:676] Ignoring source layer train-data
I0401 16:42:05.497692 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:42:07.503448 14951 solver.cpp:397]     Test net output #0: accuracy = 0.0721154
I0401 16:42:07.503476 14951 solver.cpp:397]     Test net output #1: loss = 4.54439 (* 1 = 4.54439 loss)
I0401 16:42:07.640751 14951 solver.cpp:218] Iteration 3192 (1.06369 iter/s, 13.1618s/14 iters), loss = 4.44358
I0401 16:42:07.640821 14951 solver.cpp:237]     Train net output #0: loss = 4.44358 (* 1 = 4.44358 loss)
I0401 16:42:07.640830 14951 sgd_solver.cpp:105] Iteration 3192, lr = 0.001
I0401 16:42:12.900358 14951 solver.cpp:218] Iteration 3206 (2.66187 iter/s, 5.25946s/14 iters), loss = 4.39137
I0401 16:42:12.900403 14951 solver.cpp:237]     Train net output #0: loss = 4.39137 (* 1 = 4.39137 loss)
I0401 16:42:12.900408 14951 sgd_solver.cpp:105] Iteration 3206, lr = 0.001
I0401 16:42:19.209919 14951 solver.cpp:218] Iteration 3220 (2.2189 iter/s, 6.30944s/14 iters), loss = 4.33294
I0401 16:42:19.209964 14951 solver.cpp:237]     Train net output #0: loss = 4.33294 (* 1 = 4.33294 loss)
I0401 16:42:19.209969 14951 sgd_solver.cpp:105] Iteration 3220, lr = 0.001
I0401 16:42:24.859009 14951 solver.cpp:218] Iteration 3234 (2.47833 iter/s, 5.64897s/14 iters), loss = 4.28743
I0401 16:42:24.859047 14951 solver.cpp:237]     Train net output #0: loss = 4.28743 (* 1 = 4.28743 loss)
I0401 16:42:24.859052 14951 sgd_solver.cpp:105] Iteration 3234, lr = 0.001
I0401 16:42:31.049857 14951 solver.cpp:218] Iteration 3248 (2.26145 iter/s, 6.19073s/14 iters), loss = 4.38133
I0401 16:42:31.049971 14951 solver.cpp:237]     Train net output #0: loss = 4.38133 (* 1 = 4.38133 loss)
I0401 16:42:31.049980 14951 sgd_solver.cpp:105] Iteration 3248, lr = 0.001
I0401 16:42:37.176635 14951 solver.cpp:218] Iteration 3262 (2.28512 iter/s, 6.12658s/14 iters), loss = 4.36007
I0401 16:42:37.182818 14951 solver.cpp:237]     Train net output #0: loss = 4.36007 (* 1 = 4.36007 loss)
I0401 16:42:37.182840 14951 sgd_solver.cpp:105] Iteration 3262, lr = 0.001
I0401 16:42:43.524889 14951 solver.cpp:218] Iteration 3276 (2.2075 iter/s, 6.34201s/14 iters), loss = 4.46395
I0401 16:42:43.524928 14951 solver.cpp:237]     Train net output #0: loss = 4.46395 (* 1 = 4.46395 loss)
I0401 16:42:43.524933 14951 sgd_solver.cpp:105] Iteration 3276, lr = 0.001
I0401 16:42:49.660226 14951 solver.cpp:218] Iteration 3290 (2.28191 iter/s, 6.13521s/14 iters), loss = 4.36449
I0401 16:42:49.660284 14951 solver.cpp:237]     Train net output #0: loss = 4.36449 (* 1 = 4.36449 loss)
I0401 16:42:49.660293 14951 sgd_solver.cpp:105] Iteration 3290, lr = 0.001
I0401 16:42:53.758625 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:42:55.828585 14951 solver.cpp:218] Iteration 3304 (2.2697 iter/s, 6.16822s/14 iters), loss = 4.13605
I0401 16:42:55.828629 14951 solver.cpp:237]     Train net output #0: loss = 4.13605 (* 1 = 4.13605 loss)
I0401 16:42:55.828634 14951 sgd_solver.cpp:105] Iteration 3304, lr = 0.001
I0401 16:42:56.107158 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3306.caffemodel
I0401 16:42:59.144038 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3306.solverstate
I0401 16:43:01.460600 14951 solver.cpp:330] Iteration 3306, Testing net (#0)
I0401 16:43:01.460736 14951 net.cpp:676] Ignoring source layer train-data
I0401 16:43:01.623201 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:43:03.611326 14951 solver.cpp:397]     Test net output #0: accuracy = 0.0829327
I0401 16:43:03.611359 14951 solver.cpp:397]     Test net output #1: loss = 4.48946 (* 1 = 4.48946 loss)
I0401 16:43:08.148296 14951 solver.cpp:218] Iteration 3318 (1.13641 iter/s, 12.3195s/14 iters), loss = 4.40499
I0401 16:43:08.148352 14951 solver.cpp:237]     Train net output #0: loss = 4.40499 (* 1 = 4.40499 loss)
I0401 16:43:08.148360 14951 sgd_solver.cpp:105] Iteration 3318, lr = 0.001
I0401 16:43:14.191586 14951 solver.cpp:218] Iteration 3332 (2.31667 iter/s, 6.04315s/14 iters), loss = 4.58745
I0401 16:43:14.191635 14951 solver.cpp:237]     Train net output #0: loss = 4.58745 (* 1 = 4.58745 loss)
I0401 16:43:14.191643 14951 sgd_solver.cpp:105] Iteration 3332, lr = 0.001
I0401 16:43:20.372139 14951 solver.cpp:218] Iteration 3346 (2.26521 iter/s, 6.18043s/14 iters), loss = 4.28823
I0401 16:43:20.372174 14951 solver.cpp:237]     Train net output #0: loss = 4.28823 (* 1 = 4.28823 loss)
I0401 16:43:20.372180 14951 sgd_solver.cpp:105] Iteration 3346, lr = 0.001
I0401 16:43:26.471151 14951 solver.cpp:218] Iteration 3360 (2.2955 iter/s, 6.09889s/14 iters), loss = 4.44663
I0401 16:43:26.471200 14951 solver.cpp:237]     Train net output #0: loss = 4.44663 (* 1 = 4.44663 loss)
I0401 16:43:26.471206 14951 sgd_solver.cpp:105] Iteration 3360, lr = 0.001
I0401 16:43:32.962639 14951 solver.cpp:218] Iteration 3374 (2.15672 iter/s, 6.49135s/14 iters), loss = 4.43544
I0401 16:43:32.968814 14951 solver.cpp:237]     Train net output #0: loss = 4.43544 (* 1 = 4.43544 loss)
I0401 16:43:32.968832 14951 sgd_solver.cpp:105] Iteration 3374, lr = 0.001
I0401 16:43:39.984359 14951 solver.cpp:218] Iteration 3388 (1.99559 iter/s, 7.01547s/14 iters), loss = 4.55151
I0401 16:43:39.984411 14951 solver.cpp:237]     Train net output #0: loss = 4.55151 (* 1 = 4.55151 loss)
I0401 16:43:39.984421 14951 sgd_solver.cpp:105] Iteration 3388, lr = 0.001
I0401 16:43:45.931545 14951 blocking_queue.cpp:49] Waiting for data
I0401 16:43:46.540076 14951 solver.cpp:218] Iteration 3402 (2.13559 iter/s, 6.55558s/14 iters), loss = 4.30742
I0401 16:43:46.540129 14951 solver.cpp:237]     Train net output #0: loss = 4.30742 (* 1 = 4.30742 loss)
I0401 16:43:46.540138 14951 sgd_solver.cpp:105] Iteration 3402, lr = 0.001
I0401 16:43:51.167876 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:43:52.690311 14951 solver.cpp:218] Iteration 3416 (2.27638 iter/s, 6.1501s/14 iters), loss = 4.34494
I0401 16:43:52.690361 14951 solver.cpp:237]     Train net output #0: loss = 4.34494 (* 1 = 4.34494 loss)
I0401 16:43:52.690367 14951 sgd_solver.cpp:105] Iteration 3416, lr = 0.001
I0401 16:43:54.105892 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3420.caffemodel
I0401 16:43:57.335392 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3420.solverstate
I0401 16:43:59.672859 14951 solver.cpp:330] Iteration 3420, Testing net (#0)
I0401 16:43:59.672878 14951 net.cpp:676] Ignoring source layer train-data
I0401 16:43:59.751169 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:44:01.924113 14951 solver.cpp:397]     Test net output #0: accuracy = 0.078125
I0401 16:44:01.924150 14951 solver.cpp:397]     Test net output #1: loss = 4.45005 (* 1 = 4.45005 loss)
I0401 16:44:02.604306 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:44:05.494606 14951 solver.cpp:218] Iteration 3430 (1.0934 iter/s, 12.8041s/14 iters), loss = 4.54592
I0401 16:44:05.494760 14951 solver.cpp:237]     Train net output #0: loss = 4.54592 (* 1 = 4.54592 loss)
I0401 16:44:05.494769 14951 sgd_solver.cpp:105] Iteration 3430, lr = 0.001
I0401 16:44:11.964445 14951 solver.cpp:218] Iteration 3444 (2.16397 iter/s, 6.4696s/14 iters), loss = 4.29707
I0401 16:44:11.964502 14951 solver.cpp:237]     Train net output #0: loss = 4.29707 (* 1 = 4.29707 loss)
I0401 16:44:11.964510 14951 sgd_solver.cpp:105] Iteration 3444, lr = 0.001
I0401 16:44:18.070859 14951 solver.cpp:218] Iteration 3458 (2.29272 iter/s, 6.10628s/14 iters), loss = 4.12205
I0401 16:44:18.070916 14951 solver.cpp:237]     Train net output #0: loss = 4.12205 (* 1 = 4.12205 loss)
I0401 16:44:18.070924 14951 sgd_solver.cpp:105] Iteration 3458, lr = 0.001
I0401 16:44:24.246758 14951 solver.cpp:218] Iteration 3472 (2.26693 iter/s, 6.17576s/14 iters), loss = 4.3277
I0401 16:44:24.246807 14951 solver.cpp:237]     Train net output #0: loss = 4.3277 (* 1 = 4.3277 loss)
I0401 16:44:24.246816 14951 sgd_solver.cpp:105] Iteration 3472, lr = 0.001
I0401 16:44:30.562662 14951 solver.cpp:218] Iteration 3486 (2.21667 iter/s, 6.31577s/14 iters), loss = 4.09171
I0401 16:44:30.562708 14951 solver.cpp:237]     Train net output #0: loss = 4.09171 (* 1 = 4.09171 loss)
I0401 16:44:30.562716 14951 sgd_solver.cpp:105] Iteration 3486, lr = 0.001
I0401 16:44:36.811076 14951 solver.cpp:218] Iteration 3500 (2.24061 iter/s, 6.24829s/14 iters), loss = 4.18329
I0401 16:44:36.811179 14951 solver.cpp:237]     Train net output #0: loss = 4.18329 (* 1 = 4.18329 loss)
I0401 16:44:36.811187 14951 sgd_solver.cpp:105] Iteration 3500, lr = 0.001
I0401 16:44:43.408951 14951 solver.cpp:218] Iteration 3514 (2.12196 iter/s, 6.59769s/14 iters), loss = 4.36827
I0401 16:44:43.409004 14951 solver.cpp:237]     Train net output #0: loss = 4.36827 (* 1 = 4.36827 loss)
I0401 16:44:43.409013 14951 sgd_solver.cpp:105] Iteration 3514, lr = 0.001
I0401 16:44:49.310935 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:44:50.052562 14951 solver.cpp:218] Iteration 3528 (2.10733 iter/s, 6.64348s/14 iters), loss = 4.48558
I0401 16:44:50.052613 14951 solver.cpp:237]     Train net output #0: loss = 4.48558 (* 1 = 4.48558 loss)
I0401 16:44:50.052620 14951 sgd_solver.cpp:105] Iteration 3528, lr = 0.001
I0401 16:44:52.129997 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3534.caffemodel
I0401 16:44:55.256994 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3534.solverstate
I0401 16:44:57.563781 14951 solver.cpp:330] Iteration 3534, Testing net (#0)
I0401 16:44:57.563802 14951 net.cpp:676] Ignoring source layer train-data
I0401 16:44:59.891225 14951 solver.cpp:397]     Test net output #0: accuracy = 0.0769231
I0401 16:44:59.891259 14951 solver.cpp:397]     Test net output #1: loss = 4.44175 (* 1 = 4.44175 loss)
I0401 16:45:00.438930 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:45:02.959918 14951 solver.cpp:218] Iteration 3542 (1.08467 iter/s, 12.9072s/14 iters), loss = 4.16709
I0401 16:45:02.959960 14951 solver.cpp:237]     Train net output #0: loss = 4.16709 (* 1 = 4.16709 loss)
I0401 16:45:02.959966 14951 sgd_solver.cpp:105] Iteration 3542, lr = 0.001
I0401 16:45:08.989209 14951 solver.cpp:218] Iteration 3556 (2.32205 iter/s, 6.02917s/14 iters), loss = 4.22153
I0401 16:45:08.989362 14951 solver.cpp:237]     Train net output #0: loss = 4.22153 (* 1 = 4.22153 loss)
I0401 16:45:08.989375 14951 sgd_solver.cpp:105] Iteration 3556, lr = 0.001
I0401 16:45:15.419436 14951 solver.cpp:218] Iteration 3570 (2.17729 iter/s, 6.43s/14 iters), loss = 4.12707
I0401 16:45:15.419481 14951 solver.cpp:237]     Train net output #0: loss = 4.12707 (* 1 = 4.12707 loss)
I0401 16:45:15.419490 14951 sgd_solver.cpp:105] Iteration 3570, lr = 0.001
I0401 16:45:21.965168 14951 solver.cpp:218] Iteration 3584 (2.13884 iter/s, 6.5456s/14 iters), loss = 4.10042
I0401 16:45:21.965217 14951 solver.cpp:237]     Train net output #0: loss = 4.10042 (* 1 = 4.10042 loss)
I0401 16:45:21.965226 14951 sgd_solver.cpp:105] Iteration 3584, lr = 0.001
I0401 16:45:28.552954 14951 solver.cpp:218] Iteration 3598 (2.12519 iter/s, 6.58765s/14 iters), loss = 4.11251
I0401 16:45:28.552997 14951 solver.cpp:237]     Train net output #0: loss = 4.11251 (* 1 = 4.11251 loss)
I0401 16:45:28.553004 14951 sgd_solver.cpp:105] Iteration 3598, lr = 0.001
I0401 16:45:35.112129 14951 solver.cpp:218] Iteration 3612 (2.13446 iter/s, 6.55905s/14 iters), loss = 4.0934
I0401 16:45:35.112174 14951 solver.cpp:237]     Train net output #0: loss = 4.0934 (* 1 = 4.0934 loss)
I0401 16:45:35.112179 14951 sgd_solver.cpp:105] Iteration 3612, lr = 0.001
I0401 16:45:41.764856 14951 solver.cpp:218] Iteration 3626 (2.10444 iter/s, 6.65259s/14 iters), loss = 4.3647
I0401 16:45:41.771039 14951 solver.cpp:237]     Train net output #0: loss = 4.3647 (* 1 = 4.3647 loss)
I0401 16:45:41.771060 14951 sgd_solver.cpp:105] Iteration 3626, lr = 0.001
I0401 16:45:48.262166 14951 solver.cpp:218] Iteration 3640 (2.15681 iter/s, 6.49106s/14 iters), loss = 4.27802
I0401 16:45:48.262223 14951 solver.cpp:237]     Train net output #0: loss = 4.27802 (* 1 = 4.27802 loss)
I0401 16:45:48.262230 14951 sgd_solver.cpp:105] Iteration 3640, lr = 0.001
I0401 16:45:48.413491 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:45:51.197912 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3648.caffemodel
I0401 16:45:54.249272 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3648.solverstate
I0401 16:45:56.626744 14951 solver.cpp:330] Iteration 3648, Testing net (#0)
I0401 16:45:56.626761 14951 net.cpp:676] Ignoring source layer train-data
I0401 16:45:58.973150 14951 solver.cpp:397]     Test net output #0: accuracy = 0.103365
I0401 16:45:58.973187 14951 solver.cpp:397]     Test net output #1: loss = 4.31251 (* 1 = 4.31251 loss)
I0401 16:45:59.429924 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:46:00.880605 14951 solver.cpp:218] Iteration 3654 (1.10951 iter/s, 12.6182s/14 iters), loss = 4.18438
I0401 16:46:00.880666 14951 solver.cpp:237]     Train net output #0: loss = 4.18438 (* 1 = 4.18438 loss)
I0401 16:46:00.880676 14951 sgd_solver.cpp:105] Iteration 3654, lr = 0.001
I0401 16:46:07.154894 14951 solver.cpp:218] Iteration 3668 (2.23138 iter/s, 6.27415s/14 iters), loss = 3.97108
I0401 16:46:07.154933 14951 solver.cpp:237]     Train net output #0: loss = 3.97108 (* 1 = 3.97108 loss)
I0401 16:46:07.154938 14951 sgd_solver.cpp:105] Iteration 3668, lr = 0.001
I0401 16:46:13.517060 14951 solver.cpp:218] Iteration 3682 (2.20055 iter/s, 6.36204s/14 iters), loss = 4.11937
I0401 16:46:13.517143 14951 solver.cpp:237]     Train net output #0: loss = 4.11937 (* 1 = 4.11937 loss)
I0401 16:46:13.517150 14951 sgd_solver.cpp:105] Iteration 3682, lr = 0.001
I0401 16:46:20.184334 14951 solver.cpp:218] Iteration 3696 (2.09986 iter/s, 6.6671s/14 iters), loss = 4.24449
I0401 16:46:20.184391 14951 solver.cpp:237]     Train net output #0: loss = 4.24449 (* 1 = 4.24449 loss)
I0401 16:46:20.184398 14951 sgd_solver.cpp:105] Iteration 3696, lr = 0.001
I0401 16:46:26.452524 14951 solver.cpp:218] Iteration 3710 (2.23355 iter/s, 6.26804s/14 iters), loss = 4.30315
I0401 16:46:26.452591 14951 solver.cpp:237]     Train net output #0: loss = 4.30315 (* 1 = 4.30315 loss)
I0401 16:46:26.452601 14951 sgd_solver.cpp:105] Iteration 3710, lr = 0.001
I0401 16:46:32.762058 14951 solver.cpp:218] Iteration 3724 (2.21893 iter/s, 6.30936s/14 iters), loss = 4.02471
I0401 16:46:32.762111 14951 solver.cpp:237]     Train net output #0: loss = 4.02471 (* 1 = 4.02471 loss)
I0401 16:46:32.762120 14951 sgd_solver.cpp:105] Iteration 3724, lr = 0.001
I0401 16:46:39.161046 14951 solver.cpp:218] Iteration 3738 (2.18789 iter/s, 6.39885s/14 iters), loss = 4.36332
I0401 16:46:39.161100 14951 solver.cpp:237]     Train net output #0: loss = 4.36332 (* 1 = 4.36332 loss)
I0401 16:46:39.161108 14951 sgd_solver.cpp:105] Iteration 3738, lr = 0.001
I0401 16:46:45.572772 14951 solver.cpp:218] Iteration 3752 (2.18355 iter/s, 6.41159s/14 iters), loss = 4.25486
I0401 16:46:45.572916 14951 solver.cpp:237]     Train net output #0: loss = 4.25486 (* 1 = 4.25486 loss)
I0401 16:46:45.572922 14951 sgd_solver.cpp:105] Iteration 3752, lr = 0.001
I0401 16:46:46.691150 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:46:49.680294 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3762.caffemodel
I0401 16:46:52.849296 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3762.solverstate
I0401 16:46:55.242022 14951 solver.cpp:330] Iteration 3762, Testing net (#0)
I0401 16:46:55.242044 14951 net.cpp:676] Ignoring source layer train-data
I0401 16:46:57.512959 14951 solver.cpp:397]     Test net output #0: accuracy = 0.126202
I0401 16:46:57.512992 14951 solver.cpp:397]     Test net output #1: loss = 4.26325 (* 1 = 4.26325 loss)
I0401 16:46:57.659708 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:46:58.685751 14951 solver.cpp:218] Iteration 3766 (1.06767 iter/s, 13.1127s/14 iters), loss = 4.03273
I0401 16:46:58.685808 14951 solver.cpp:237]     Train net output #0: loss = 4.03273 (* 1 = 4.03273 loss)
I0401 16:46:58.685817 14951 sgd_solver.cpp:105] Iteration 3766, lr = 0.001
I0401 16:47:05.140112 14951 solver.cpp:218] Iteration 3780 (2.16912 iter/s, 6.45422s/14 iters), loss = 3.97096
I0401 16:47:05.140164 14951 solver.cpp:237]     Train net output #0: loss = 3.97096 (* 1 = 3.97096 loss)
I0401 16:47:05.140172 14951 sgd_solver.cpp:105] Iteration 3780, lr = 0.001
I0401 16:47:11.191831 14951 solver.cpp:218] Iteration 3794 (2.31344 iter/s, 6.05159s/14 iters), loss = 4.30077
I0401 16:47:11.191877 14951 solver.cpp:237]     Train net output #0: loss = 4.30077 (* 1 = 4.30077 loss)
I0401 16:47:11.191884 14951 sgd_solver.cpp:105] Iteration 3794, lr = 0.001
I0401 16:47:17.649914 14951 solver.cpp:218] Iteration 3808 (2.16787 iter/s, 6.45794s/14 iters), loss = 3.93376
I0401 16:47:17.650046 14951 solver.cpp:237]     Train net output #0: loss = 3.93376 (* 1 = 3.93376 loss)
I0401 16:47:17.650058 14951 sgd_solver.cpp:105] Iteration 3808, lr = 0.001
I0401 16:47:24.169584 14951 solver.cpp:218] Iteration 3822 (2.14742 iter/s, 6.51946s/14 iters), loss = 4.26185
I0401 16:47:24.169639 14951 solver.cpp:237]     Train net output #0: loss = 4.26185 (* 1 = 4.26185 loss)
I0401 16:47:24.169648 14951 sgd_solver.cpp:105] Iteration 3822, lr = 0.001
I0401 16:47:30.473707 14951 solver.cpp:218] Iteration 3836 (2.22082 iter/s, 6.30398s/14 iters), loss = 3.96871
I0401 16:47:30.473765 14951 solver.cpp:237]     Train net output #0: loss = 3.96871 (* 1 = 3.96871 loss)
I0401 16:47:30.473773 14951 sgd_solver.cpp:105] Iteration 3836, lr = 0.001
I0401 16:47:36.838945 14951 solver.cpp:218] Iteration 3850 (2.1995 iter/s, 6.36509s/14 iters), loss = 4.02289
I0401 16:47:36.838999 14951 solver.cpp:237]     Train net output #0: loss = 4.02289 (* 1 = 4.02289 loss)
I0401 16:47:36.839006 14951 sgd_solver.cpp:105] Iteration 3850, lr = 0.001
I0401 16:47:43.405098 14951 solver.cpp:218] Iteration 3864 (2.13219 iter/s, 6.56602s/14 iters), loss = 4.00967
I0401 16:47:43.405143 14951 solver.cpp:237]     Train net output #0: loss = 4.00967 (* 1 = 4.00967 loss)
I0401 16:47:43.405148 14951 sgd_solver.cpp:105] Iteration 3864, lr = 0.001
I0401 16:47:45.237399 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:47:48.408479 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3876.caffemodel
I0401 16:47:51.510200 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3876.solverstate
I0401 16:47:55.053591 14951 solver.cpp:330] Iteration 3876, Testing net (#0)
I0401 16:47:55.053608 14951 net.cpp:676] Ignoring source layer train-data
I0401 16:47:57.229334 14951 solver.cpp:397]     Test net output #0: accuracy = 0.114183
I0401 16:47:57.229374 14951 solver.cpp:397]     Test net output #1: loss = 4.23248 (* 1 = 4.23248 loss)
I0401 16:47:57.350054 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:47:57.641829 14951 solver.cpp:218] Iteration 3878 (0.983386 iter/s, 14.2365s/14 iters), loss = 4.00054
I0401 16:47:57.656931 14951 solver.cpp:237]     Train net output #0: loss = 4.00054 (* 1 = 4.00054 loss)
I0401 16:47:57.656952 14951 sgd_solver.cpp:105] Iteration 3878, lr = 0.001
I0401 16:48:03.715976 14951 solver.cpp:218] Iteration 3892 (2.31062 iter/s, 6.05899s/14 iters), loss = 3.93664
I0401 16:48:03.716013 14951 solver.cpp:237]     Train net output #0: loss = 3.93664 (* 1 = 3.93664 loss)
I0401 16:48:03.716018 14951 sgd_solver.cpp:105] Iteration 3892, lr = 0.001
I0401 16:48:09.703929 14951 solver.cpp:218] Iteration 3906 (2.33807 iter/s, 5.98784s/14 iters), loss = 3.91388
I0401 16:48:09.703970 14951 solver.cpp:237]     Train net output #0: loss = 3.91388 (* 1 = 3.91388 loss)
I0401 16:48:09.703976 14951 sgd_solver.cpp:105] Iteration 3906, lr = 0.001
I0401 16:48:15.764165 14951 solver.cpp:218] Iteration 3920 (2.31019 iter/s, 6.06012s/14 iters), loss = 4.0346
I0401 16:48:15.764207 14951 solver.cpp:237]     Train net output #0: loss = 4.0346 (* 1 = 4.0346 loss)
I0401 16:48:15.764214 14951 sgd_solver.cpp:105] Iteration 3920, lr = 0.001
I0401 16:48:21.773350 14951 solver.cpp:218] Iteration 3934 (2.32982 iter/s, 6.00906s/14 iters), loss = 3.91806
I0401 16:48:21.773463 14951 solver.cpp:237]     Train net output #0: loss = 3.91806 (* 1 = 3.91806 loss)
I0401 16:48:21.773473 14951 sgd_solver.cpp:105] Iteration 3934, lr = 0.001
I0401 16:48:28.005139 14951 solver.cpp:218] Iteration 3948 (2.24661 iter/s, 6.2316s/14 iters), loss = 4.0112
I0401 16:48:28.005178 14951 solver.cpp:237]     Train net output #0: loss = 4.0112 (* 1 = 4.0112 loss)
I0401 16:48:28.005185 14951 sgd_solver.cpp:105] Iteration 3948, lr = 0.001
I0401 16:48:34.112387 14951 solver.cpp:218] Iteration 3962 (2.2924 iter/s, 6.10712s/14 iters), loss = 3.90444
I0401 16:48:34.112447 14951 solver.cpp:237]     Train net output #0: loss = 3.90444 (* 1 = 3.90444 loss)
I0401 16:48:34.112455 14951 sgd_solver.cpp:105] Iteration 3962, lr = 0.001
I0401 16:48:40.454377 14951 solver.cpp:218] Iteration 3976 (2.20756 iter/s, 6.34186s/14 iters), loss = 3.70228
I0401 16:48:40.454422 14951 solver.cpp:237]     Train net output #0: loss = 3.70228 (* 1 = 3.70228 loss)
I0401 16:48:40.454429 14951 sgd_solver.cpp:105] Iteration 3976, lr = 0.001
I0401 16:48:42.928082 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:48:46.115097 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3990.caffemodel
I0401 16:48:50.562988 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3990.solverstate
I0401 16:48:54.218849 14951 solver.cpp:330] Iteration 3990, Testing net (#0)
I0401 16:48:54.218930 14951 net.cpp:676] Ignoring source layer train-data
I0401 16:48:56.325600 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:48:56.387137 14951 solver.cpp:397]     Test net output #0: accuracy = 0.112981
I0401 16:48:56.387172 14951 solver.cpp:397]     Test net output #1: loss = 4.24297 (* 1 = 4.24297 loss)
I0401 16:48:56.516238 14951 solver.cpp:218] Iteration 3990 (0.871642 iter/s, 16.0616s/14 iters), loss = 3.8728
I0401 16:48:56.516292 14951 solver.cpp:237]     Train net output #0: loss = 3.8728 (* 1 = 3.8728 loss)
I0401 16:48:56.516300 14951 sgd_solver.cpp:105] Iteration 3990, lr = 0.001
I0401 16:49:01.656478 14951 solver.cpp:218] Iteration 4004 (2.72367 iter/s, 5.14012s/14 iters), loss = 3.98592
I0401 16:49:01.656534 14951 solver.cpp:237]     Train net output #0: loss = 3.98592 (* 1 = 3.98592 loss)
I0401 16:49:01.656543 14951 sgd_solver.cpp:105] Iteration 4004, lr = 0.001
I0401 16:49:07.960769 14951 solver.cpp:218] Iteration 4018 (2.22076 iter/s, 6.30415s/14 iters), loss = 3.98856
I0401 16:49:07.960834 14951 solver.cpp:237]     Train net output #0: loss = 3.98856 (* 1 = 3.98856 loss)
I0401 16:49:07.960842 14951 sgd_solver.cpp:105] Iteration 4018, lr = 0.001
I0401 16:49:14.139559 14951 solver.cpp:218] Iteration 4032 (2.26587 iter/s, 6.17864s/14 iters), loss = 3.92229
I0401 16:49:14.139617 14951 solver.cpp:237]     Train net output #0: loss = 3.92229 (* 1 = 3.92229 loss)
I0401 16:49:14.139627 14951 sgd_solver.cpp:105] Iteration 4032, lr = 0.001
I0401 16:49:20.196398 14951 solver.cpp:218] Iteration 4046 (2.31149 iter/s, 6.0567s/14 iters), loss = 3.89536
I0401 16:49:20.196456 14951 solver.cpp:237]     Train net output #0: loss = 3.89536 (* 1 = 3.89536 loss)
I0401 16:49:20.196465 14951 sgd_solver.cpp:105] Iteration 4046, lr = 0.001
I0401 16:49:26.412493 14951 solver.cpp:218] Iteration 4060 (2.25227 iter/s, 6.21595s/14 iters), loss = 3.97799
I0401 16:49:26.412649 14951 solver.cpp:237]     Train net output #0: loss = 3.97799 (* 1 = 3.97799 loss)
I0401 16:49:26.412659 14951 sgd_solver.cpp:105] Iteration 4060, lr = 0.001
I0401 16:49:32.628476 14951 solver.cpp:218] Iteration 4074 (2.25234 iter/s, 6.21575s/14 iters), loss = 3.98035
I0401 16:49:32.628527 14951 solver.cpp:237]     Train net output #0: loss = 3.98035 (* 1 = 3.98035 loss)
I0401 16:49:32.628535 14951 sgd_solver.cpp:105] Iteration 4074, lr = 0.001
I0401 16:49:38.806005 14951 solver.cpp:218] Iteration 4088 (2.26633 iter/s, 6.17739s/14 iters), loss = 3.83925
I0401 16:49:38.806056 14951 solver.cpp:237]     Train net output #0: loss = 3.83925 (* 1 = 3.83925 loss)
I0401 16:49:38.806064 14951 sgd_solver.cpp:105] Iteration 4088, lr = 0.001
I0401 16:49:42.047248 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:49:44.897635 14951 solver.cpp:218] Iteration 4102 (2.29829 iter/s, 6.0915s/14 iters), loss = 3.86461
I0401 16:49:44.897696 14951 solver.cpp:237]     Train net output #0: loss = 3.86461 (* 1 = 3.86461 loss)
I0401 16:49:44.897706 14951 sgd_solver.cpp:105] Iteration 4102, lr = 0.001
I0401 16:49:45.237478 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4104.caffemodel
I0401 16:49:48.372023 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4104.solverstate
I0401 16:49:50.662304 14951 solver.cpp:330] Iteration 4104, Testing net (#0)
I0401 16:49:50.662324 14951 net.cpp:676] Ignoring source layer train-data
I0401 16:49:52.755141 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:49:52.886446 14951 solver.cpp:397]     Test net output #0: accuracy = 0.116587
I0401 16:49:52.886480 14951 solver.cpp:397]     Test net output #1: loss = 4.11713 (* 1 = 4.11713 loss)
I0401 16:49:57.254042 14951 solver.cpp:218] Iteration 4116 (1.13303 iter/s, 12.3562s/14 iters), loss = 3.88621
I0401 16:49:57.254146 14951 solver.cpp:237]     Train net output #0: loss = 3.88621 (* 1 = 3.88621 loss)
I0401 16:49:57.254153 14951 sgd_solver.cpp:105] Iteration 4116, lr = 0.001
I0401 16:50:03.727522 14951 solver.cpp:218] Iteration 4130 (2.16273 iter/s, 6.4733s/14 iters), loss = 3.69861
I0401 16:50:03.727567 14951 solver.cpp:237]     Train net output #0: loss = 3.69861 (* 1 = 3.69861 loss)
I0401 16:50:03.727577 14951 sgd_solver.cpp:105] Iteration 4130, lr = 0.001
I0401 16:50:09.760170 14951 solver.cpp:218] Iteration 4144 (2.32075 iter/s, 6.03253s/14 iters), loss = 3.81781
I0401 16:50:09.760215 14951 solver.cpp:237]     Train net output #0: loss = 3.81781 (* 1 = 3.81781 loss)
I0401 16:50:09.760219 14951 sgd_solver.cpp:105] Iteration 4144, lr = 0.001
I0401 16:50:15.951481 14951 solver.cpp:218] Iteration 4158 (2.26128 iter/s, 6.19119s/14 iters), loss = 3.7752
I0401 16:50:15.951524 14951 solver.cpp:237]     Train net output #0: loss = 3.7752 (* 1 = 3.7752 loss)
I0401 16:50:15.951529 14951 sgd_solver.cpp:105] Iteration 4158, lr = 0.001
I0401 16:50:22.205183 14951 solver.cpp:218] Iteration 4172 (2.23872 iter/s, 6.25357s/14 iters), loss = 3.72772
I0401 16:50:22.205242 14951 solver.cpp:237]     Train net output #0: loss = 3.72772 (* 1 = 3.72772 loss)
I0401 16:50:22.205251 14951 sgd_solver.cpp:105] Iteration 4172, lr = 0.001
I0401 16:50:28.243907 14951 solver.cpp:218] Iteration 4186 (2.31842 iter/s, 6.03859s/14 iters), loss = 3.8211
I0401 16:50:28.244035 14951 solver.cpp:237]     Train net output #0: loss = 3.8211 (* 1 = 3.8211 loss)
I0401 16:50:28.244043 14951 sgd_solver.cpp:105] Iteration 4186, lr = 0.001
I0401 16:50:34.318101 14951 solver.cpp:218] Iteration 4200 (2.30491 iter/s, 6.07399s/14 iters), loss = 3.58129
I0401 16:50:34.318142 14951 solver.cpp:237]     Train net output #0: loss = 3.58129 (* 1 = 3.58129 loss)
I0401 16:50:34.318148 14951 sgd_solver.cpp:105] Iteration 4200, lr = 0.001
I0401 16:50:38.231101 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:50:40.238854 14951 solver.cpp:218] Iteration 4214 (2.36461 iter/s, 5.92063s/14 iters), loss = 3.50588
I0401 16:50:40.238903 14951 solver.cpp:237]     Train net output #0: loss = 3.50588 (* 1 = 3.50588 loss)
I0401 16:50:40.238912 14951 sgd_solver.cpp:105] Iteration 4214, lr = 0.001
I0401 16:50:41.547303 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4218.caffemodel
I0401 16:50:44.469717 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4218.solverstate
I0401 16:50:47.944367 14951 solver.cpp:330] Iteration 4218, Testing net (#0)
I0401 16:50:47.944386 14951 net.cpp:676] Ignoring source layer train-data
I0401 16:50:49.948210 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:50:50.162410 14951 solver.cpp:397]     Test net output #0: accuracy = 0.137019
I0401 16:50:50.162434 14951 solver.cpp:397]     Test net output #1: loss = 4.08852 (* 1 = 4.08852 loss)
I0401 16:50:53.885975 14951 solver.cpp:218] Iteration 4228 (1.02587 iter/s, 13.6469s/14 iters), loss = 3.62529
I0401 16:50:53.886034 14951 solver.cpp:237]     Train net output #0: loss = 3.62529 (* 1 = 3.62529 loss)
I0401 16:50:53.886041 14951 sgd_solver.cpp:105] Iteration 4228, lr = 0.001
I0401 16:51:00.032660 14951 solver.cpp:218] Iteration 4242 (2.2777 iter/s, 6.14655s/14 iters), loss = 3.94189
I0401 16:51:00.032791 14951 solver.cpp:237]     Train net output #0: loss = 3.94189 (* 1 = 3.94189 loss)
I0401 16:51:00.032799 14951 sgd_solver.cpp:105] Iteration 4242, lr = 0.001
I0401 16:51:00.033023 14951 blocking_queue.cpp:49] Waiting for data
I0401 16:51:06.091843 14951 solver.cpp:218] Iteration 4256 (2.31062 iter/s, 6.05897s/14 iters), loss = 3.77408
I0401 16:51:06.091905 14951 solver.cpp:237]     Train net output #0: loss = 3.77408 (* 1 = 3.77408 loss)
I0401 16:51:06.091914 14951 sgd_solver.cpp:105] Iteration 4256, lr = 0.001
I0401 16:51:12.336040 14951 solver.cpp:218] Iteration 4270 (2.24213 iter/s, 6.24406s/14 iters), loss = 3.80932
I0401 16:51:12.336079 14951 solver.cpp:237]     Train net output #0: loss = 3.80932 (* 1 = 3.80932 loss)
I0401 16:51:12.336086 14951 sgd_solver.cpp:105] Iteration 4270, lr = 0.001
I0401 16:51:18.464179 14951 solver.cpp:218] Iteration 4284 (2.28459 iter/s, 6.12802s/14 iters), loss = 3.63333
I0401 16:51:18.464221 14951 solver.cpp:237]     Train net output #0: loss = 3.63333 (* 1 = 3.63333 loss)
I0401 16:51:18.464227 14951 sgd_solver.cpp:105] Iteration 4284, lr = 0.001
I0401 16:51:24.505008 14951 solver.cpp:218] Iteration 4298 (2.31761 iter/s, 6.04071s/14 iters), loss = 3.58551
I0401 16:51:24.505053 14951 solver.cpp:237]     Train net output #0: loss = 3.58551 (* 1 = 3.58551 loss)
I0401 16:51:24.505059 14951 sgd_solver.cpp:105] Iteration 4298, lr = 0.001
I0401 16:51:30.526158 14951 solver.cpp:218] Iteration 4312 (2.32519 iter/s, 6.02102s/14 iters), loss = 3.64075
I0401 16:51:30.532346 14951 solver.cpp:237]     Train net output #0: loss = 3.64075 (* 1 = 3.64075 loss)
I0401 16:51:30.532369 14951 sgd_solver.cpp:105] Iteration 4312, lr = 0.001
I0401 16:51:35.314841 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:51:36.577734 14951 solver.cpp:218] Iteration 4326 (2.31583 iter/s, 6.04534s/14 iters), loss = 3.52469
I0401 16:51:36.577773 14951 solver.cpp:237]     Train net output #0: loss = 3.52469 (* 1 = 3.52469 loss)
I0401 16:51:36.577780 14951 sgd_solver.cpp:105] Iteration 4326, lr = 0.001
I0401 16:51:38.612371 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4332.caffemodel
I0401 16:51:41.790069 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4332.solverstate
I0401 16:51:45.740717 14951 solver.cpp:330] Iteration 4332, Testing net (#0)
I0401 16:51:45.740741 14951 net.cpp:676] Ignoring source layer train-data
I0401 16:51:47.697928 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:51:47.944249 14951 solver.cpp:397]     Test net output #0: accuracy = 0.143029
I0401 16:51:47.944278 14951 solver.cpp:397]     Test net output #1: loss = 4.0091 (* 1 = 4.0091 loss)
I0401 16:51:50.777892 14951 solver.cpp:218] Iteration 4340 (0.985919 iter/s, 14.2s/14 iters), loss = 3.79235
I0401 16:51:50.777946 14951 solver.cpp:237]     Train net output #0: loss = 3.79235 (* 1 = 3.79235 loss)
I0401 16:51:50.777954 14951 sgd_solver.cpp:105] Iteration 4340, lr = 0.001
I0401 16:51:57.083397 14951 solver.cpp:218] Iteration 4354 (2.22033 iter/s, 6.30537s/14 iters), loss = 3.46767
I0401 16:51:57.083451 14951 solver.cpp:237]     Train net output #0: loss = 3.46767 (* 1 = 3.46767 loss)
I0401 16:51:57.083458 14951 sgd_solver.cpp:105] Iteration 4354, lr = 0.001
I0401 16:52:03.270530 14951 solver.cpp:218] Iteration 4368 (2.26281 iter/s, 6.187s/14 iters), loss = 3.5402
I0401 16:52:03.270709 14951 solver.cpp:237]     Train net output #0: loss = 3.5402 (* 1 = 3.5402 loss)
I0401 16:52:03.270717 14951 sgd_solver.cpp:105] Iteration 4368, lr = 0.001
I0401 16:52:09.401778 14951 solver.cpp:218] Iteration 4382 (2.28348 iter/s, 6.13099s/14 iters), loss = 3.82642
I0401 16:52:09.401831 14951 solver.cpp:237]     Train net output #0: loss = 3.82642 (* 1 = 3.82642 loss)
I0401 16:52:09.401840 14951 sgd_solver.cpp:105] Iteration 4382, lr = 0.001
I0401 16:52:15.732957 14951 solver.cpp:218] Iteration 4396 (2.21133 iter/s, 6.33104s/14 iters), loss = 3.66546
I0401 16:52:15.733001 14951 solver.cpp:237]     Train net output #0: loss = 3.66546 (* 1 = 3.66546 loss)
I0401 16:52:15.733007 14951 sgd_solver.cpp:105] Iteration 4396, lr = 0.001
I0401 16:52:21.885318 14951 solver.cpp:218] Iteration 4410 (2.27559 iter/s, 6.15224s/14 iters), loss = 3.4106
I0401 16:52:21.885357 14951 solver.cpp:237]     Train net output #0: loss = 3.4106 (* 1 = 3.4106 loss)
I0401 16:52:21.885363 14951 sgd_solver.cpp:105] Iteration 4410, lr = 0.001
I0401 16:52:27.965543 14951 solver.cpp:218] Iteration 4424 (2.30259 iter/s, 6.0801s/14 iters), loss = 3.82471
I0401 16:52:27.965582 14951 solver.cpp:237]     Train net output #0: loss = 3.82471 (* 1 = 3.82471 loss)
I0401 16:52:27.965588 14951 sgd_solver.cpp:105] Iteration 4424, lr = 0.001
I0401 16:52:33.545071 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:52:33.959823 14951 solver.cpp:218] Iteration 4438 (2.33561 iter/s, 5.99416s/14 iters), loss = 3.51872
I0401 16:52:33.959869 14951 solver.cpp:237]     Train net output #0: loss = 3.51872 (* 1 = 3.51872 loss)
I0401 16:52:33.959877 14951 sgd_solver.cpp:105] Iteration 4438, lr = 0.001
I0401 16:52:37.054877 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4446.caffemodel
I0401 16:52:42.062881 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4446.solverstate
I0401 16:52:47.903885 14951 solver.cpp:330] Iteration 4446, Testing net (#0)
I0401 16:52:47.903906 14951 net.cpp:676] Ignoring source layer train-data
I0401 16:52:49.856032 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:52:50.172242 14951 solver.cpp:397]     Test net output #0: accuracy = 0.141827
I0401 16:52:50.172278 14951 solver.cpp:397]     Test net output #1: loss = 3.9879 (* 1 = 3.9879 loss)
I0401 16:52:52.037971 14951 solver.cpp:218] Iteration 4452 (0.774426 iter/s, 18.0779s/14 iters), loss = 3.53934
I0401 16:52:52.038015 14951 solver.cpp:237]     Train net output #0: loss = 3.53934 (* 1 = 3.53934 loss)
I0401 16:52:52.038020 14951 sgd_solver.cpp:105] Iteration 4452, lr = 0.001
I0401 16:52:58.117249 14951 solver.cpp:218] Iteration 4466 (2.30295 iter/s, 6.07915s/14 iters), loss = 3.38428
I0401 16:52:58.123417 14951 solver.cpp:237]     Train net output #0: loss = 3.38428 (* 1 = 3.38428 loss)
I0401 16:52:58.123435 14951 sgd_solver.cpp:105] Iteration 4466, lr = 0.001
I0401 16:53:04.097740 14951 solver.cpp:218] Iteration 4480 (2.34338 iter/s, 5.97427s/14 iters), loss = 3.58215
I0401 16:53:04.097854 14951 solver.cpp:237]     Train net output #0: loss = 3.58215 (* 1 = 3.58215 loss)
I0401 16:53:04.097860 14951 sgd_solver.cpp:105] Iteration 4480, lr = 0.001
I0401 16:53:10.289481 14951 solver.cpp:218] Iteration 4494 (2.26115 iter/s, 6.19155s/14 iters), loss = 3.57043
I0401 16:53:10.289526 14951 solver.cpp:237]     Train net output #0: loss = 3.57043 (* 1 = 3.57043 loss)
I0401 16:53:10.289535 14951 sgd_solver.cpp:105] Iteration 4494, lr = 0.001
I0401 16:53:16.387626 14951 solver.cpp:218] Iteration 4508 (2.29583 iter/s, 6.09802s/14 iters), loss = 3.54191
I0401 16:53:16.387672 14951 solver.cpp:237]     Train net output #0: loss = 3.54191 (* 1 = 3.54191 loss)
I0401 16:53:16.387678 14951 sgd_solver.cpp:105] Iteration 4508, lr = 0.001
I0401 16:53:22.539268 14951 solver.cpp:218] Iteration 4522 (2.27586 iter/s, 6.15151s/14 iters), loss = 3.56229
I0401 16:53:22.539307 14951 solver.cpp:237]     Train net output #0: loss = 3.56229 (* 1 = 3.56229 loss)
I0401 16:53:22.539312 14951 sgd_solver.cpp:105] Iteration 4522, lr = 0.001
I0401 16:53:28.740770 14951 solver.cpp:218] Iteration 4536 (2.25756 iter/s, 6.20138s/14 iters), loss = 3.58313
I0401 16:53:28.740825 14951 solver.cpp:237]     Train net output #0: loss = 3.58313 (* 1 = 3.58313 loss)
I0401 16:53:28.740834 14951 sgd_solver.cpp:105] Iteration 4536, lr = 0.001
I0401 16:53:34.942019 14951 solver.cpp:218] Iteration 4550 (2.25766 iter/s, 6.20111s/14 iters), loss = 3.4965
I0401 16:53:34.942131 14951 solver.cpp:237]     Train net output #0: loss = 3.4965 (* 1 = 3.4965 loss)
I0401 16:53:34.942138 14951 sgd_solver.cpp:105] Iteration 4550, lr = 0.001
I0401 16:53:35.227293 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:53:38.831878 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4560.caffemodel
I0401 16:53:45.368011 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4560.solverstate
I0401 16:53:48.949826 14951 solver.cpp:330] Iteration 4560, Testing net (#0)
I0401 16:53:48.949846 14951 net.cpp:676] Ignoring source layer train-data
I0401 16:53:50.869747 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:53:51.258335 14951 solver.cpp:397]     Test net output #0: accuracy = 0.127404
I0401 16:53:51.258364 14951 solver.cpp:397]     Test net output #1: loss = 4.02466 (* 1 = 4.02466 loss)
I0401 16:53:52.327497 14951 solver.cpp:218] Iteration 4564 (0.805284 iter/s, 17.3852s/14 iters), loss = 3.51165
I0401 16:53:52.327544 14951 solver.cpp:237]     Train net output #0: loss = 3.51165 (* 1 = 3.51165 loss)
I0401 16:53:52.327550 14951 sgd_solver.cpp:105] Iteration 4564, lr = 0.001
I0401 16:53:58.413501 14951 solver.cpp:218] Iteration 4578 (2.30041 iter/s, 6.08588s/14 iters), loss = 3.43783
I0401 16:53:58.413540 14951 solver.cpp:237]     Train net output #0: loss = 3.43783 (* 1 = 3.43783 loss)
I0401 16:53:58.413545 14951 sgd_solver.cpp:105] Iteration 4578, lr = 0.001
I0401 16:54:04.428740 14951 solver.cpp:218] Iteration 4592 (2.32747 iter/s, 6.01512s/14 iters), loss = 3.67667
I0401 16:54:04.428795 14951 solver.cpp:237]     Train net output #0: loss = 3.67667 (* 1 = 3.67667 loss)
I0401 16:54:04.428804 14951 sgd_solver.cpp:105] Iteration 4592, lr = 0.001
I0401 16:54:10.608319 14951 solver.cpp:218] Iteration 4606 (2.26558 iter/s, 6.17944s/14 iters), loss = 3.49942
I0401 16:54:10.608422 14951 solver.cpp:237]     Train net output #0: loss = 3.49942 (* 1 = 3.49942 loss)
I0401 16:54:10.608431 14951 sgd_solver.cpp:105] Iteration 4606, lr = 0.001
I0401 16:54:17.128608 14951 solver.cpp:218] Iteration 4620 (2.1472 iter/s, 6.52011s/14 iters), loss = 3.52082
I0401 16:54:17.128655 14951 solver.cpp:237]     Train net output #0: loss = 3.52082 (* 1 = 3.52082 loss)
I0401 16:54:17.128662 14951 sgd_solver.cpp:105] Iteration 4620, lr = 0.001
I0401 16:54:23.504014 14951 solver.cpp:218] Iteration 4634 (2.19599 iter/s, 6.37527s/14 iters), loss = 3.30554
I0401 16:54:23.504065 14951 solver.cpp:237]     Train net output #0: loss = 3.30554 (* 1 = 3.30554 loss)
I0401 16:54:23.504073 14951 sgd_solver.cpp:105] Iteration 4634, lr = 0.001
I0401 16:54:30.051769 14951 solver.cpp:218] Iteration 4648 (2.13818 iter/s, 6.54762s/14 iters), loss = 3.28357
I0401 16:54:30.051827 14951 solver.cpp:237]     Train net output #0: loss = 3.28357 (* 1 = 3.28357 loss)
I0401 16:54:30.051836 14951 sgd_solver.cpp:105] Iteration 4648, lr = 0.001
I0401 16:54:36.555471 14951 solver.cpp:218] Iteration 4662 (2.15267 iter/s, 6.50356s/14 iters), loss = 3.06019
I0401 16:54:36.555524 14951 solver.cpp:237]     Train net output #0: loss = 3.06019 (* 1 = 3.06019 loss)
I0401 16:54:36.555533 14951 sgd_solver.cpp:105] Iteration 4662, lr = 0.001
I0401 16:54:37.690228 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:54:41.432044 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4674.caffemodel
I0401 16:54:44.532352 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4674.solverstate
I0401 16:54:46.841578 14951 solver.cpp:330] Iteration 4674, Testing net (#0)
I0401 16:54:46.841598 14951 net.cpp:676] Ignoring source layer train-data
I0401 16:54:48.512538 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:54:49.071224 14951 solver.cpp:397]     Test net output #0: accuracy = 0.144231
I0401 16:54:49.071254 14951 solver.cpp:397]     Test net output #1: loss = 3.94322 (* 1 = 3.94322 loss)
I0401 16:54:49.480332 14951 solver.cpp:218] Iteration 4676 (1.0832 iter/s, 12.9247s/14 iters), loss = 3.5704
I0401 16:54:49.480391 14951 solver.cpp:237]     Train net output #0: loss = 3.5704 (* 1 = 3.5704 loss)
I0401 16:54:49.480398 14951 sgd_solver.cpp:105] Iteration 4676, lr = 0.001
I0401 16:54:55.866573 14951 solver.cpp:218] Iteration 4690 (2.19226 iter/s, 6.3861s/14 iters), loss = 3.28754
I0401 16:54:55.866613 14951 solver.cpp:237]     Train net output #0: loss = 3.28754 (* 1 = 3.28754 loss)
I0401 16:54:55.866619 14951 sgd_solver.cpp:105] Iteration 4690, lr = 0.001
I0401 16:55:02.088809 14951 solver.cpp:218] Iteration 4704 (2.25004 iter/s, 6.22211s/14 iters), loss = 3.59423
I0401 16:55:02.088860 14951 solver.cpp:237]     Train net output #0: loss = 3.59423 (* 1 = 3.59423 loss)
I0401 16:55:02.088867 14951 sgd_solver.cpp:105] Iteration 4704, lr = 0.001
I0401 16:55:08.610235 14951 solver.cpp:218] Iteration 4718 (2.14681 iter/s, 6.52129s/14 iters), loss = 3.07052
I0401 16:55:08.610280 14951 solver.cpp:237]     Train net output #0: loss = 3.07052 (* 1 = 3.07052 loss)
I0401 16:55:08.610287 14951 sgd_solver.cpp:105] Iteration 4718, lr = 0.001
I0401 16:55:15.218422 14951 solver.cpp:218] Iteration 4732 (2.11862 iter/s, 6.60807s/14 iters), loss = 3.37045
I0401 16:55:15.218530 14951 solver.cpp:237]     Train net output #0: loss = 3.37045 (* 1 = 3.37045 loss)
I0401 16:55:15.218538 14951 sgd_solver.cpp:105] Iteration 4732, lr = 0.001
I0401 16:55:21.734896 14951 solver.cpp:218] Iteration 4746 (2.14846 iter/s, 6.51629s/14 iters), loss = 3.2924
I0401 16:55:21.734936 14951 solver.cpp:237]     Train net output #0: loss = 3.2924 (* 1 = 3.2924 loss)
I0401 16:55:21.734942 14951 sgd_solver.cpp:105] Iteration 4746, lr = 0.001
I0401 16:55:28.232061 14951 solver.cpp:218] Iteration 4760 (2.15483 iter/s, 6.49704s/14 iters), loss = 3.29746
I0401 16:55:28.232120 14951 solver.cpp:237]     Train net output #0: loss = 3.29746 (* 1 = 3.29746 loss)
I0401 16:55:28.232129 14951 sgd_solver.cpp:105] Iteration 4760, lr = 0.001
I0401 16:55:34.756310 14951 solver.cpp:218] Iteration 4774 (2.14589 iter/s, 6.5241s/14 iters), loss = 3.43848
I0401 16:55:34.756367 14951 solver.cpp:237]     Train net output #0: loss = 3.43848 (* 1 = 3.43848 loss)
I0401 16:55:34.756376 14951 sgd_solver.cpp:105] Iteration 4774, lr = 0.001
I0401 16:55:36.623078 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:55:40.484969 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4788.caffemodel
I0401 16:55:43.606532 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4788.solverstate
I0401 16:55:45.919148 14951 solver.cpp:330] Iteration 4788, Testing net (#0)
I0401 16:55:45.919262 14951 net.cpp:676] Ignoring source layer train-data
I0401 16:55:47.621500 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:55:48.154434 14951 solver.cpp:397]     Test net output #0: accuracy = 0.16226
I0401 16:55:48.154464 14951 solver.cpp:397]     Test net output #1: loss = 3.90623 (* 1 = 3.90623 loss)
I0401 16:55:48.297256 14951 solver.cpp:218] Iteration 4788 (1.03392 iter/s, 13.5407s/14 iters), loss = 3.5765
I0401 16:55:48.297313 14951 solver.cpp:237]     Train net output #0: loss = 3.5765 (* 1 = 3.5765 loss)
I0401 16:55:48.297322 14951 sgd_solver.cpp:105] Iteration 4788, lr = 0.001
I0401 16:55:53.702105 14951 solver.cpp:218] Iteration 4802 (2.59033 iter/s, 5.40472s/14 iters), loss = 3.38788
I0401 16:55:53.702162 14951 solver.cpp:237]     Train net output #0: loss = 3.38788 (* 1 = 3.38788 loss)
I0401 16:55:53.702172 14951 sgd_solver.cpp:105] Iteration 4802, lr = 0.001
I0401 16:56:00.086645 14951 solver.cpp:218] Iteration 4816 (2.19284 iter/s, 6.3844s/14 iters), loss = 3.395
I0401 16:56:00.086683 14951 solver.cpp:237]     Train net output #0: loss = 3.395 (* 1 = 3.395 loss)
I0401 16:56:00.086688 14951 sgd_solver.cpp:105] Iteration 4816, lr = 0.001
I0401 16:56:06.603642 14951 solver.cpp:218] Iteration 4830 (2.14827 iter/s, 6.51688s/14 iters), loss = 3.28225
I0401 16:56:06.603694 14951 solver.cpp:237]     Train net output #0: loss = 3.28225 (* 1 = 3.28225 loss)
I0401 16:56:06.603703 14951 sgd_solver.cpp:105] Iteration 4830, lr = 0.001
I0401 16:56:12.908748 14951 solver.cpp:218] Iteration 4844 (2.22047 iter/s, 6.30498s/14 iters), loss = 3.28389
I0401 16:56:12.908788 14951 solver.cpp:237]     Train net output #0: loss = 3.28389 (* 1 = 3.28389 loss)
I0401 16:56:12.908794 14951 sgd_solver.cpp:105] Iteration 4844, lr = 0.001
I0401 16:56:19.322353 14951 solver.cpp:218] Iteration 4858 (2.1829 iter/s, 6.41348s/14 iters), loss = 3.41129
I0401 16:56:19.322450 14951 solver.cpp:237]     Train net output #0: loss = 3.41129 (* 1 = 3.41129 loss)
I0401 16:56:19.322458 14951 sgd_solver.cpp:105] Iteration 4858, lr = 0.001
I0401 16:56:25.447860 14951 solver.cpp:218] Iteration 4872 (2.28559 iter/s, 6.12533s/14 iters), loss = 3.4156
I0401 16:56:25.447917 14951 solver.cpp:237]     Train net output #0: loss = 3.4156 (* 1 = 3.4156 loss)
I0401 16:56:25.447926 14951 sgd_solver.cpp:105] Iteration 4872, lr = 0.001
I0401 16:56:31.105959 14951 solver.cpp:218] Iteration 4886 (2.47439 iter/s, 5.65797s/14 iters), loss = 3.05898
I0401 16:56:31.106015 14951 solver.cpp:237]     Train net output #0: loss = 3.05898 (* 1 = 3.05898 loss)
I0401 16:56:31.106024 14951 sgd_solver.cpp:105] Iteration 4886, lr = 0.001
I0401 16:56:33.870843 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:56:37.201637 14951 solver.cpp:218] Iteration 4900 (2.29676 iter/s, 6.09554s/14 iters), loss = 3.27415
I0401 16:56:37.201679 14951 solver.cpp:237]     Train net output #0: loss = 3.27415 (* 1 = 3.27415 loss)
I0401 16:56:37.201684 14951 sgd_solver.cpp:105] Iteration 4900, lr = 0.001
I0401 16:56:37.561904 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4902.caffemodel
I0401 16:56:40.614948 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4902.solverstate
I0401 16:56:42.923281 14951 solver.cpp:330] Iteration 4902, Testing net (#0)
I0401 16:56:42.923305 14951 net.cpp:676] Ignoring source layer train-data
I0401 16:56:44.452827 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:56:45.033893 14951 solver.cpp:397]     Test net output #0: accuracy = 0.175481
I0401 16:56:45.033927 14951 solver.cpp:397]     Test net output #1: loss = 3.89462 (* 1 = 3.89462 loss)
I0401 16:56:49.699613 14951 solver.cpp:218] Iteration 4914 (1.1202 iter/s, 12.4978s/14 iters), loss = 3.21932
I0401 16:56:49.699790 14951 solver.cpp:237]     Train net output #0: loss = 3.21932 (* 1 = 3.21932 loss)
I0401 16:56:49.699800 14951 sgd_solver.cpp:105] Iteration 4914, lr = 0.001
I0401 16:56:55.884783 14951 solver.cpp:218] Iteration 4928 (2.26357 iter/s, 6.18492s/14 iters), loss = 3.31376
I0401 16:56:55.884833 14951 solver.cpp:237]     Train net output #0: loss = 3.31376 (* 1 = 3.31376 loss)
I0401 16:56:55.884840 14951 sgd_solver.cpp:105] Iteration 4928, lr = 0.001
I0401 16:57:02.185608 14951 solver.cpp:218] Iteration 4942 (2.22198 iter/s, 6.30069s/14 iters), loss = 3.41528
I0401 16:57:02.185665 14951 solver.cpp:237]     Train net output #0: loss = 3.41528 (* 1 = 3.41528 loss)
I0401 16:57:02.185673 14951 sgd_solver.cpp:105] Iteration 4942, lr = 0.001
I0401 16:57:08.434765 14951 solver.cpp:218] Iteration 4956 (2.24035 iter/s, 6.24902s/14 iters), loss = 3.57506
I0401 16:57:08.434810 14951 solver.cpp:237]     Train net output #0: loss = 3.57506 (* 1 = 3.57506 loss)
I0401 16:57:08.434816 14951 sgd_solver.cpp:105] Iteration 4956, lr = 0.001
I0401 16:57:14.558238 14951 solver.cpp:218] Iteration 4970 (2.28633 iter/s, 6.12335s/14 iters), loss = 3.38362
I0401 16:57:14.558281 14951 solver.cpp:237]     Train net output #0: loss = 3.38362 (* 1 = 3.38362 loss)
I0401 16:57:14.558287 14951 sgd_solver.cpp:105] Iteration 4970, lr = 0.001
I0401 16:57:20.707267 14951 solver.cpp:218] Iteration 4984 (2.27683 iter/s, 6.1489s/14 iters), loss = 3.23362
I0401 16:57:20.707368 14951 solver.cpp:237]     Train net output #0: loss = 3.23362 (* 1 = 3.23362 loss)
I0401 16:57:20.707376 14951 sgd_solver.cpp:105] Iteration 4984, lr = 0.001
I0401 16:57:27.119077 14951 solver.cpp:218] Iteration 4998 (2.18353 iter/s, 6.41163s/14 iters), loss = 3.16368
I0401 16:57:27.119117 14951 solver.cpp:237]     Train net output #0: loss = 3.16368 (* 1 = 3.16368 loss)
I0401 16:57:27.119122 14951 sgd_solver.cpp:105] Iteration 4998, lr = 0.001
I0401 16:57:30.541290 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:57:33.215634 14951 solver.cpp:218] Iteration 5012 (2.29642 iter/s, 6.09644s/14 iters), loss = 3.21356
I0401 16:57:33.215685 14951 solver.cpp:237]     Train net output #0: loss = 3.21356 (* 1 = 3.21356 loss)
I0401 16:57:33.215692 14951 sgd_solver.cpp:105] Iteration 5012, lr = 0.001
I0401 16:57:34.475081 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5016.caffemodel
I0401 16:57:39.522365 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5016.solverstate
I0401 16:57:43.861424 14951 solver.cpp:330] Iteration 5016, Testing net (#0)
I0401 16:57:43.861443 14951 net.cpp:676] Ignoring source layer train-data
I0401 16:57:45.380568 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:57:46.070941 14951 solver.cpp:397]     Test net output #0: accuracy = 0.179087
I0401 16:57:46.070976 14951 solver.cpp:397]     Test net output #1: loss = 3.79611 (* 1 = 3.79611 loss)
I0401 16:57:49.938807 14951 solver.cpp:218] Iteration 5026 (0.837174 iter/s, 16.7229s/14 iters), loss = 3.54932
I0401 16:57:49.938854 14951 solver.cpp:237]     Train net output #0: loss = 3.54932 (* 1 = 3.54932 loss)
I0401 16:57:49.938863 14951 sgd_solver.cpp:105] Iteration 5026, lr = 0.001
I0401 16:57:55.990924 14951 solver.cpp:218] Iteration 5040 (2.31329 iter/s, 6.05199s/14 iters), loss = 3.12883
I0401 16:57:55.991041 14951 solver.cpp:237]     Train net output #0: loss = 3.12883 (* 1 = 3.12883 loss)
I0401 16:57:55.991050 14951 sgd_solver.cpp:105] Iteration 5040, lr = 0.001
I0401 16:58:02.127022 14951 solver.cpp:218] Iteration 5054 (2.28165 iter/s, 6.13591s/14 iters), loss = 2.98229
I0401 16:58:02.127059 14951 solver.cpp:237]     Train net output #0: loss = 2.98229 (* 1 = 2.98229 loss)
I0401 16:58:02.127065 14951 sgd_solver.cpp:105] Iteration 5054, lr = 0.001
I0401 16:58:08.288466 14951 solver.cpp:218] Iteration 5068 (2.27224 iter/s, 6.16133s/14 iters), loss = 3.04952
I0401 16:58:08.288501 14951 solver.cpp:237]     Train net output #0: loss = 3.04952 (* 1 = 3.04952 loss)
I0401 16:58:08.288508 14951 sgd_solver.cpp:105] Iteration 5068, lr = 0.001
I0401 16:58:14.689975 14951 solver.cpp:218] Iteration 5082 (2.18703 iter/s, 6.40138s/14 iters), loss = 2.91913
I0401 16:58:14.690026 14951 solver.cpp:237]     Train net output #0: loss = 2.91913 (* 1 = 2.91913 loss)
I0401 16:58:14.690034 14951 sgd_solver.cpp:105] Iteration 5082, lr = 0.001
I0401 16:58:20.884554 14951 solver.cpp:218] Iteration 5096 (2.26009 iter/s, 6.19445s/14 iters), loss = 3.22015
I0401 16:58:20.884603 14951 solver.cpp:237]     Train net output #0: loss = 3.22015 (* 1 = 3.22015 loss)
I0401 16:58:20.884611 14951 sgd_solver.cpp:105] Iteration 5096, lr = 0.001
I0401 16:58:23.496630 14951 blocking_queue.cpp:49] Waiting for data
I0401 16:58:27.100550 14951 solver.cpp:218] Iteration 5110 (2.2523 iter/s, 6.21587s/14 iters), loss = 2.93103
I0401 16:58:27.100693 14951 solver.cpp:237]     Train net output #0: loss = 2.93103 (* 1 = 2.93103 loss)
I0401 16:58:27.100703 14951 sgd_solver.cpp:105] Iteration 5110, lr = 0.001
I0401 16:58:31.373839 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:58:33.296008 14951 solver.cpp:218] Iteration 5124 (2.2598 iter/s, 6.19524s/14 iters), loss = 3.03953
I0401 16:58:33.296059 14951 solver.cpp:237]     Train net output #0: loss = 3.03953 (* 1 = 3.03953 loss)
I0401 16:58:33.296068 14951 sgd_solver.cpp:105] Iteration 5124, lr = 0.001
I0401 16:58:35.480944 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5130.caffemodel
I0401 16:58:38.469094 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5130.solverstate
I0401 16:58:40.819772 14951 solver.cpp:330] Iteration 5130, Testing net (#0)
I0401 16:58:40.819795 14951 net.cpp:676] Ignoring source layer train-data
I0401 16:58:42.466750 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:58:43.232261 14951 solver.cpp:397]     Test net output #0: accuracy = 0.18149
I0401 16:58:43.232288 14951 solver.cpp:397]     Test net output #1: loss = 3.75686 (* 1 = 3.75686 loss)
I0401 16:58:46.139889 14951 solver.cpp:218] Iteration 5138 (1.09003 iter/s, 12.8437s/14 iters), loss = 3.02757
I0401 16:58:46.139948 14951 solver.cpp:237]     Train net output #0: loss = 3.02757 (* 1 = 3.02757 loss)
I0401 16:58:46.139958 14951 sgd_solver.cpp:105] Iteration 5138, lr = 0.001
I0401 16:58:52.309403 14951 solver.cpp:218] Iteration 5152 (2.26927 iter/s, 6.16937s/14 iters), loss = 3.16137
I0401 16:58:52.309448 14951 solver.cpp:237]     Train net output #0: loss = 3.16137 (* 1 = 3.16137 loss)
I0401 16:58:52.309454 14951 sgd_solver.cpp:105] Iteration 5152, lr = 0.001
I0401 16:58:58.438537 14951 solver.cpp:218] Iteration 5166 (2.28422 iter/s, 6.12901s/14 iters), loss = 3.25015
I0401 16:58:58.438647 14951 solver.cpp:237]     Train net output #0: loss = 3.25015 (* 1 = 3.25015 loss)
I0401 16:58:58.438654 14951 sgd_solver.cpp:105] Iteration 5166, lr = 0.001
I0401 16:59:04.457334 14951 solver.cpp:218] Iteration 5180 (2.32612 iter/s, 6.01861s/14 iters), loss = 2.99026
I0401 16:59:04.457393 14951 solver.cpp:237]     Train net output #0: loss = 2.99026 (* 1 = 2.99026 loss)
I0401 16:59:04.457402 14951 sgd_solver.cpp:105] Iteration 5180, lr = 0.001
I0401 16:59:10.884691 14951 solver.cpp:218] Iteration 5194 (2.17824 iter/s, 6.42722s/14 iters), loss = 3.66654
I0401 16:59:10.884755 14951 solver.cpp:237]     Train net output #0: loss = 3.66654 (* 1 = 3.66654 loss)
I0401 16:59:10.884764 14951 sgd_solver.cpp:105] Iteration 5194, lr = 0.001
I0401 16:59:17.003811 14951 solver.cpp:218] Iteration 5208 (2.28796 iter/s, 6.11898s/14 iters), loss = 3.16457
I0401 16:59:17.003865 14951 solver.cpp:237]     Train net output #0: loss = 3.16457 (* 1 = 3.16457 loss)
I0401 16:59:17.003875 14951 sgd_solver.cpp:105] Iteration 5208, lr = 0.001
I0401 16:59:23.156147 14951 solver.cpp:218] Iteration 5222 (2.27561 iter/s, 6.1522s/14 iters), loss = 3.12268
I0401 16:59:23.156193 14951 solver.cpp:237]     Train net output #0: loss = 3.12268 (* 1 = 3.12268 loss)
I0401 16:59:23.156199 14951 sgd_solver.cpp:105] Iteration 5222, lr = 0.001
I0401 16:59:28.152279 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:59:29.187211 14951 solver.cpp:218] Iteration 5236 (2.32136 iter/s, 6.03094s/14 iters), loss = 3.26536
I0401 16:59:29.187319 14951 solver.cpp:237]     Train net output #0: loss = 3.26536 (* 1 = 3.26536 loss)
I0401 16:59:29.187325 14951 sgd_solver.cpp:105] Iteration 5236, lr = 0.001
I0401 16:59:32.137372 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5244.caffemodel
I0401 16:59:35.116849 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5244.solverstate
I0401 16:59:37.456763 14951 solver.cpp:330] Iteration 5244, Testing net (#0)
I0401 16:59:37.456781 14951 net.cpp:676] Ignoring source layer train-data
I0401 16:59:38.872618 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:59:39.672240 14951 solver.cpp:397]     Test net output #0: accuracy = 0.188702
I0401 16:59:39.672274 14951 solver.cpp:397]     Test net output #1: loss = 3.74764 (* 1 = 3.74764 loss)
I0401 16:59:41.537953 14951 solver.cpp:218] Iteration 5250 (1.13356 iter/s, 12.3505s/14 iters), loss = 2.96827
I0401 16:59:41.538012 14951 solver.cpp:237]     Train net output #0: loss = 2.96827 (* 1 = 2.96827 loss)
I0401 16:59:41.538019 14951 sgd_solver.cpp:105] Iteration 5250, lr = 0.001
I0401 16:59:47.462224 14951 solver.cpp:218] Iteration 5264 (2.36321 iter/s, 5.92414s/14 iters), loss = 2.92826
I0401 16:59:47.462266 14951 solver.cpp:237]     Train net output #0: loss = 2.92826 (* 1 = 2.92826 loss)
I0401 16:59:47.462271 14951 sgd_solver.cpp:105] Iteration 5264, lr = 0.001
I0401 16:59:53.631934 14951 solver.cpp:218] Iteration 5278 (2.2692 iter/s, 6.16958s/14 iters), loss = 2.86673
I0401 16:59:53.631986 14951 solver.cpp:237]     Train net output #0: loss = 2.86673 (* 1 = 2.86673 loss)
I0401 16:59:53.631994 14951 sgd_solver.cpp:105] Iteration 5278, lr = 0.001
I0401 16:59:59.828279 14951 solver.cpp:218] Iteration 5292 (2.25944 iter/s, 6.19622s/14 iters), loss = 2.76763
I0401 16:59:59.828372 14951 solver.cpp:237]     Train net output #0: loss = 2.76763 (* 1 = 2.76763 loss)
I0401 16:59:59.828377 14951 sgd_solver.cpp:105] Iteration 5292, lr = 0.001
I0401 17:00:06.109982 14951 solver.cpp:218] Iteration 5306 (2.22876 iter/s, 6.28153s/14 iters), loss = 3.19934
I0401 17:00:06.110038 14951 solver.cpp:237]     Train net output #0: loss = 3.19934 (* 1 = 3.19934 loss)
I0401 17:00:06.110045 14951 sgd_solver.cpp:105] Iteration 5306, lr = 0.001
I0401 17:00:12.344507 14951 solver.cpp:218] Iteration 5320 (2.24561 iter/s, 6.2344s/14 iters), loss = 2.63235
I0401 17:00:12.344542 14951 solver.cpp:237]     Train net output #0: loss = 2.63235 (* 1 = 2.63235 loss)
I0401 17:00:12.344548 14951 sgd_solver.cpp:105] Iteration 5320, lr = 0.001
I0401 17:00:18.560640 14951 solver.cpp:218] Iteration 5334 (2.25225 iter/s, 6.21601s/14 iters), loss = 3.24348
I0401 17:00:18.560683 14951 solver.cpp:237]     Train net output #0: loss = 3.24348 (* 1 = 3.24348 loss)
I0401 17:00:18.560688 14951 sgd_solver.cpp:105] Iteration 5334, lr = 0.001
I0401 17:00:24.393083 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:00:24.691694 14951 solver.cpp:218] Iteration 5348 (2.28351 iter/s, 6.13092s/14 iters), loss = 2.92565
I0401 17:00:24.691742 14951 solver.cpp:237]     Train net output #0: loss = 2.92565 (* 1 = 2.92565 loss)
I0401 17:00:24.691751 14951 sgd_solver.cpp:105] Iteration 5348, lr = 0.001
I0401 17:00:28.572680 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5358.caffemodel
I0401 17:00:31.577440 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5358.solverstate
I0401 17:00:33.906280 14951 solver.cpp:330] Iteration 5358, Testing net (#0)
I0401 17:00:33.906301 14951 net.cpp:676] Ignoring source layer train-data
I0401 17:00:35.222996 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:00:36.085470 14951 solver.cpp:397]     Test net output #0: accuracy = 0.200721
I0401 17:00:36.085505 14951 solver.cpp:397]     Test net output #1: loss = 3.7522 (* 1 = 3.7522 loss)
I0401 17:00:37.121711 14951 solver.cpp:218] Iteration 5362 (1.12632 iter/s, 12.4298s/14 iters), loss = 2.93249
I0401 17:00:37.121764 14951 solver.cpp:237]     Train net output #0: loss = 2.93249 (* 1 = 2.93249 loss)
I0401 17:00:37.121773 14951 sgd_solver.cpp:105] Iteration 5362, lr = 0.001
I0401 17:00:43.388489 14951 solver.cpp:218] Iteration 5376 (2.23405 iter/s, 6.26665s/14 iters), loss = 2.94621
I0401 17:00:43.388527 14951 solver.cpp:237]     Train net output #0: loss = 2.94621 (* 1 = 2.94621 loss)
I0401 17:00:43.388532 14951 sgd_solver.cpp:105] Iteration 5376, lr = 0.001
I0401 17:00:49.446465 14951 solver.cpp:218] Iteration 5390 (2.31105 iter/s, 6.05786s/14 iters), loss = 3.11221
I0401 17:00:49.446509 14951 solver.cpp:237]     Train net output #0: loss = 3.11221 (* 1 = 3.11221 loss)
I0401 17:00:49.446516 14951 sgd_solver.cpp:105] Iteration 5390, lr = 0.001
I0401 17:00:55.604599 14951 solver.cpp:218] Iteration 5404 (2.27346 iter/s, 6.15801s/14 iters), loss = 3.00555
I0401 17:00:55.604645 14951 solver.cpp:237]     Train net output #0: loss = 3.00555 (* 1 = 3.00555 loss)
I0401 17:00:55.604650 14951 sgd_solver.cpp:105] Iteration 5404, lr = 0.001
I0401 17:01:01.799628 14951 solver.cpp:218] Iteration 5418 (2.25992 iter/s, 6.1949s/14 iters), loss = 3.1354
I0401 17:01:01.799772 14951 solver.cpp:237]     Train net output #0: loss = 3.1354 (* 1 = 3.1354 loss)
I0401 17:01:01.799782 14951 sgd_solver.cpp:105] Iteration 5418, lr = 0.001
I0401 17:01:08.423544 14951 solver.cpp:218] Iteration 5432 (2.11362 iter/s, 6.62369s/14 iters), loss = 3.40992
I0401 17:01:08.423585 14951 solver.cpp:237]     Train net output #0: loss = 3.40992 (* 1 = 3.40992 loss)
I0401 17:01:08.423590 14951 sgd_solver.cpp:105] Iteration 5432, lr = 0.001
I0401 17:01:14.646700 14951 solver.cpp:218] Iteration 5446 (2.24971 iter/s, 6.22303s/14 iters), loss = 3.0546
I0401 17:01:14.646739 14951 solver.cpp:237]     Train net output #0: loss = 3.0546 (* 1 = 3.0546 loss)
I0401 17:01:14.646744 14951 sgd_solver.cpp:105] Iteration 5446, lr = 0.001
I0401 17:01:20.772738 14951 solver.cpp:218] Iteration 5460 (2.28537 iter/s, 6.12592s/14 iters), loss = 2.85903
I0401 17:01:20.772789 14951 solver.cpp:237]     Train net output #0: loss = 2.85903 (* 1 = 2.85903 loss)
I0401 17:01:20.772797 14951 sgd_solver.cpp:105] Iteration 5460, lr = 0.001
I0401 17:01:21.316927 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:01:25.505563 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5472.caffemodel
I0401 17:01:28.561905 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5472.solverstate
I0401 17:01:30.854005 14951 solver.cpp:330] Iteration 5472, Testing net (#0)
I0401 17:01:30.854025 14951 net.cpp:676] Ignoring source layer train-data
I0401 17:01:32.113291 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:01:33.049753 14951 solver.cpp:397]     Test net output #0: accuracy = 0.183894
I0401 17:01:33.049790 14951 solver.cpp:397]     Test net output #1: loss = 3.73879 (* 1 = 3.73879 loss)
I0401 17:01:33.472653 14951 solver.cpp:218] Iteration 5474 (1.10239 iter/s, 12.6997s/14 iters), loss = 2.89342
I0401 17:01:33.474273 14951 solver.cpp:237]     Train net output #0: loss = 2.89342 (* 1 = 2.89342 loss)
I0401 17:01:33.474285 14951 sgd_solver.cpp:105] Iteration 5474, lr = 0.001
I0401 17:01:39.343055 14951 solver.cpp:218] Iteration 5488 (2.38553 iter/s, 5.86871s/14 iters), loss = 2.84648
I0401 17:01:39.343093 14951 solver.cpp:237]     Train net output #0: loss = 2.84648 (* 1 = 2.84648 loss)
I0401 17:01:39.343099 14951 sgd_solver.cpp:105] Iteration 5488, lr = 0.001
I0401 17:01:45.651875 14951 solver.cpp:218] Iteration 5502 (2.21916 iter/s, 6.3087s/14 iters), loss = 2.90847
I0401 17:01:45.651926 14951 solver.cpp:237]     Train net output #0: loss = 2.90847 (* 1 = 2.90847 loss)
I0401 17:01:45.651933 14951 sgd_solver.cpp:105] Iteration 5502, lr = 0.001
I0401 17:01:52.071655 14951 solver.cpp:218] Iteration 5516 (2.18081 iter/s, 6.41964s/14 iters), loss = 2.91059
I0401 17:01:52.071710 14951 solver.cpp:237]     Train net output #0: loss = 2.91059 (* 1 = 2.91059 loss)
I0401 17:01:52.071717 14951 sgd_solver.cpp:105] Iteration 5516, lr = 0.001
I0401 17:01:58.582058 14951 solver.cpp:218] Iteration 5530 (2.15045 iter/s, 6.51027s/14 iters), loss = 3.28863
I0401 17:01:58.582099 14951 solver.cpp:237]     Train net output #0: loss = 3.28863 (* 1 = 3.28863 loss)
I0401 17:01:58.582105 14951 sgd_solver.cpp:105] Iteration 5530, lr = 0.001
I0401 17:02:05.038960 14951 solver.cpp:218] Iteration 5544 (2.16827 iter/s, 6.45677s/14 iters), loss = 3.27076
I0401 17:02:05.039073 14951 solver.cpp:237]     Train net output #0: loss = 3.27076 (* 1 = 3.27076 loss)
I0401 17:02:05.039080 14951 sgd_solver.cpp:105] Iteration 5544, lr = 0.001
I0401 17:02:11.674204 14951 solver.cpp:218] Iteration 5558 (2.11001 iter/s, 6.63505s/14 iters), loss = 2.94723
I0401 17:02:11.674247 14951 solver.cpp:237]     Train net output #0: loss = 2.94723 (* 1 = 2.94723 loss)
I0401 17:02:11.674252 14951 sgd_solver.cpp:105] Iteration 5558, lr = 0.001
I0401 17:02:18.061760 14951 solver.cpp:218] Iteration 5572 (2.19181 iter/s, 6.38743s/14 iters), loss = 2.62908
I0401 17:02:18.061815 14951 solver.cpp:237]     Train net output #0: loss = 2.62908 (* 1 = 2.62908 loss)
I0401 17:02:18.061822 14951 sgd_solver.cpp:105] Iteration 5572, lr = 0.001
I0401 17:02:19.533047 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:02:24.097606 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5586.caffemodel
I0401 17:02:27.159199 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5586.solverstate
I0401 17:02:29.464987 14951 solver.cpp:330] Iteration 5586, Testing net (#0)
I0401 17:02:29.465004 14951 net.cpp:676] Ignoring source layer train-data
I0401 17:02:30.985496 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:02:32.024026 14951 solver.cpp:397]     Test net output #0: accuracy = 0.170673
I0401 17:02:32.024065 14951 solver.cpp:397]     Test net output #1: loss = 3.74895 (* 1 = 3.74895 loss)
I0401 17:02:32.164067 14951 solver.cpp:218] Iteration 5586 (0.99276 iter/s, 14.1021s/14 iters), loss = 2.80237
I0401 17:02:32.165628 14951 solver.cpp:237]     Train net output #0: loss = 2.80237 (* 1 = 2.80237 loss)
I0401 17:02:32.165642 14951 sgd_solver.cpp:105] Iteration 5586, lr = 0.001
I0401 17:02:37.683127 14951 solver.cpp:218] Iteration 5600 (2.53741 iter/s, 5.51744s/14 iters), loss = 2.90569
I0401 17:02:37.683219 14951 solver.cpp:237]     Train net output #0: loss = 2.90569 (* 1 = 2.90569 loss)
I0401 17:02:37.683225 14951 sgd_solver.cpp:105] Iteration 5600, lr = 0.001
I0401 17:02:44.178988 14951 solver.cpp:218] Iteration 5614 (2.15528 iter/s, 6.49568s/14 iters), loss = 2.82664
I0401 17:02:44.179035 14951 solver.cpp:237]     Train net output #0: loss = 2.82664 (* 1 = 2.82664 loss)
I0401 17:02:44.179044 14951 sgd_solver.cpp:105] Iteration 5614, lr = 0.001
I0401 17:02:50.601215 14951 solver.cpp:218] Iteration 5628 (2.17997 iter/s, 6.4221s/14 iters), loss = 2.66597
I0401 17:02:50.601263 14951 solver.cpp:237]     Train net output #0: loss = 2.66597 (* 1 = 2.66597 loss)
I0401 17:02:50.601274 14951 sgd_solver.cpp:105] Iteration 5628, lr = 0.001
I0401 17:02:56.959998 14951 solver.cpp:218] Iteration 5642 (2.20172 iter/s, 6.35866s/14 iters), loss = 2.97949
I0401 17:02:56.960040 14951 solver.cpp:237]     Train net output #0: loss = 2.97949 (* 1 = 2.97949 loss)
I0401 17:02:56.960047 14951 sgd_solver.cpp:105] Iteration 5642, lr = 0.001
I0401 17:03:03.343924 14951 solver.cpp:218] Iteration 5656 (2.19305 iter/s, 6.3838s/14 iters), loss = 2.56053
I0401 17:03:03.343961 14951 solver.cpp:237]     Train net output #0: loss = 2.56053 (* 1 = 2.56053 loss)
I0401 17:03:03.343966 14951 sgd_solver.cpp:105] Iteration 5656, lr = 0.001
I0401 17:03:09.715879 14951 solver.cpp:218] Iteration 5670 (2.19717 iter/s, 6.37183s/14 iters), loss = 2.81128
I0401 17:03:09.716008 14951 solver.cpp:237]     Train net output #0: loss = 2.81128 (* 1 = 2.81128 loss)
I0401 17:03:09.716015 14951 sgd_solver.cpp:105] Iteration 5670, lr = 0.001
I0401 17:03:16.215351 14951 solver.cpp:218] Iteration 5684 (2.15409 iter/s, 6.49926s/14 iters), loss = 2.80498
I0401 17:03:16.215397 14951 solver.cpp:237]     Train net output #0: loss = 2.80498 (* 1 = 2.80498 loss)
I0401 17:03:16.215404 14951 sgd_solver.cpp:105] Iteration 5684, lr = 0.001
I0401 17:03:18.540761 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:03:22.615767 14951 solver.cpp:218] Iteration 5698 (2.1874 iter/s, 6.40029s/14 iters), loss = 2.73546
I0401 17:03:22.615810 14951 solver.cpp:237]     Train net output #0: loss = 2.73546 (* 1 = 2.73546 loss)
I0401 17:03:22.615815 14951 sgd_solver.cpp:105] Iteration 5698, lr = 0.001
I0401 17:03:23.028065 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5700.caffemodel
I0401 17:03:26.262288 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5700.solverstate
I0401 17:03:28.666510 14951 solver.cpp:330] Iteration 5700, Testing net (#0)
I0401 17:03:28.666534 14951 net.cpp:676] Ignoring source layer train-data
I0401 17:03:30.007256 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:03:31.246225 14951 solver.cpp:397]     Test net output #0: accuracy = 0.192308
I0401 17:03:31.246259 14951 solver.cpp:397]     Test net output #1: loss = 3.66348 (* 1 = 3.66348 loss)
I0401 17:03:35.961673 14951 solver.cpp:218] Iteration 5712 (1.04903 iter/s, 13.3457s/14 iters), loss = 2.71216
I0401 17:03:35.961726 14951 solver.cpp:237]     Train net output #0: loss = 2.71216 (* 1 = 2.71216 loss)
I0401 17:03:35.961735 14951 sgd_solver.cpp:105] Iteration 5712, lr = 0.001
I0401 17:03:42.599121 14951 solver.cpp:218] Iteration 5726 (2.10929 iter/s, 6.63731s/14 iters), loss = 2.72849
I0401 17:03:42.599225 14951 solver.cpp:237]     Train net output #0: loss = 2.72849 (* 1 = 2.72849 loss)
I0401 17:03:42.599233 14951 sgd_solver.cpp:105] Iteration 5726, lr = 0.001
I0401 17:03:48.952674 14951 solver.cpp:218] Iteration 5740 (2.20355 iter/s, 6.35337s/14 iters), loss = 2.68211
I0401 17:03:48.952716 14951 solver.cpp:237]     Train net output #0: loss = 2.68211 (* 1 = 2.68211 loss)
I0401 17:03:48.952721 14951 sgd_solver.cpp:105] Iteration 5740, lr = 0.001
I0401 17:03:55.759317 14951 solver.cpp:218] Iteration 5754 (2.05686 iter/s, 6.80651s/14 iters), loss = 2.66466
I0401 17:03:55.759377 14951 solver.cpp:237]     Train net output #0: loss = 2.66466 (* 1 = 2.66466 loss)
I0401 17:03:55.759384 14951 sgd_solver.cpp:105] Iteration 5754, lr = 0.001
I0401 17:04:02.429179 14951 solver.cpp:218] Iteration 5768 (2.09904 iter/s, 6.66972s/14 iters), loss = 2.63159
I0401 17:04:02.429215 14951 solver.cpp:237]     Train net output #0: loss = 2.63159 (* 1 = 2.63159 loss)
I0401 17:04:02.429220 14951 sgd_solver.cpp:105] Iteration 5768, lr = 0.001
I0401 17:04:08.958415 14951 solver.cpp:218] Iteration 5782 (2.14424 iter/s, 6.52911s/14 iters), loss = 2.83698
I0401 17:04:08.958469 14951 solver.cpp:237]     Train net output #0: loss = 2.83698 (* 1 = 2.83698 loss)
I0401 17:04:08.958477 14951 sgd_solver.cpp:105] Iteration 5782, lr = 0.001
I0401 17:04:15.528549 14951 solver.cpp:218] Iteration 5796 (2.1309 iter/s, 6.57s/14 iters), loss = 2.31879
I0401 17:04:15.528668 14951 solver.cpp:237]     Train net output #0: loss = 2.31879 (* 1 = 2.31879 loss)
I0401 17:04:15.528677 14951 sgd_solver.cpp:105] Iteration 5796, lr = 0.001
I0401 17:04:18.693536 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:04:22.243887 14951 solver.cpp:218] Iteration 5810 (2.08484 iter/s, 6.71513s/14 iters), loss = 2.43816
I0401 17:04:22.243943 14951 solver.cpp:237]     Train net output #0: loss = 2.43816 (* 1 = 2.43816 loss)
I0401 17:04:22.243952 14951 sgd_solver.cpp:105] Iteration 5810, lr = 0.001
I0401 17:04:23.679553 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5814.caffemodel
I0401 17:04:26.874186 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5814.solverstate
I0401 17:04:29.310993 14951 solver.cpp:330] Iteration 5814, Testing net (#0)
I0401 17:04:29.311017 14951 net.cpp:676] Ignoring source layer train-data
I0401 17:04:30.546296 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:04:31.811781 14951 solver.cpp:397]     Test net output #0: accuracy = 0.19351
I0401 17:04:31.811813 14951 solver.cpp:397]     Test net output #1: loss = 3.74567 (* 1 = 3.74567 loss)
I0401 17:04:35.935804 14951 solver.cpp:218] Iteration 5824 (1.02252 iter/s, 13.6917s/14 iters), loss = 2.49503
I0401 17:04:35.935855 14951 solver.cpp:237]     Train net output #0: loss = 2.49503 (* 1 = 2.49503 loss)
I0401 17:04:35.935866 14951 sgd_solver.cpp:105] Iteration 5824, lr = 0.001
I0401 17:04:42.412271 14951 solver.cpp:218] Iteration 5838 (2.16172 iter/s, 6.47634s/14 iters), loss = 2.36915
I0401 17:04:42.412310 14951 solver.cpp:237]     Train net output #0: loss = 2.36915 (* 1 = 2.36915 loss)
I0401 17:04:42.412317 14951 sgd_solver.cpp:105] Iteration 5838, lr = 0.001
I0401 17:04:48.985975 14951 solver.cpp:218] Iteration 5852 (2.12974 iter/s, 6.57358s/14 iters), loss = 2.4646
I0401 17:04:48.986107 14951 solver.cpp:237]     Train net output #0: loss = 2.4646 (* 1 = 2.4646 loss)
I0401 17:04:48.986114 14951 sgd_solver.cpp:105] Iteration 5852, lr = 0.001
I0401 17:04:55.309124 14951 solver.cpp:218] Iteration 5866 (2.21416 iter/s, 6.32294s/14 iters), loss = 2.59847
I0401 17:04:55.309165 14951 solver.cpp:237]     Train net output #0: loss = 2.59847 (* 1 = 2.59847 loss)
I0401 17:04:55.309171 14951 sgd_solver.cpp:105] Iteration 5866, lr = 0.001
I0401 17:05:01.680573 14951 solver.cpp:218] Iteration 5880 (2.19735 iter/s, 6.37132s/14 iters), loss = 2.63082
I0401 17:05:01.680617 14951 solver.cpp:237]     Train net output #0: loss = 2.63082 (* 1 = 2.63082 loss)
I0401 17:05:01.680624 14951 sgd_solver.cpp:105] Iteration 5880, lr = 0.001
I0401 17:05:07.812506 14951 solver.cpp:218] Iteration 5894 (2.28318 iter/s, 6.13181s/14 iters), loss = 2.66129
I0401 17:05:07.812575 14951 solver.cpp:237]     Train net output #0: loss = 2.66129 (* 1 = 2.66129 loss)
I0401 17:05:07.812587 14951 sgd_solver.cpp:105] Iteration 5894, lr = 0.001
I0401 17:05:14.004204 14951 solver.cpp:218] Iteration 5908 (2.26114 iter/s, 6.19155s/14 iters), loss = 2.38905
I0401 17:05:14.004249 14951 solver.cpp:237]     Train net output #0: loss = 2.38905 (* 1 = 2.38905 loss)
I0401 17:05:14.004256 14951 sgd_solver.cpp:105] Iteration 5908, lr = 0.001
I0401 17:05:17.739143 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:05:19.994948 14951 solver.cpp:218] Iteration 5922 (2.33699 iter/s, 5.99062s/14 iters), loss = 2.3631
I0401 17:05:19.995064 14951 solver.cpp:237]     Train net output #0: loss = 2.3631 (* 1 = 2.3631 loss)
I0401 17:05:19.995072 14951 sgd_solver.cpp:105] Iteration 5922, lr = 0.001
I0401 17:05:22.075147 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5928.caffemodel
I0401 17:05:25.184821 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5928.solverstate
I0401 17:05:27.508560 14951 solver.cpp:330] Iteration 5928, Testing net (#0)
I0401 17:05:27.508579 14951 net.cpp:676] Ignoring source layer train-data
I0401 17:05:28.508625 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:05:29.681159 14951 solver.cpp:397]     Test net output #0: accuracy = 0.207933
I0401 17:05:29.681200 14951 solver.cpp:397]     Test net output #1: loss = 3.73191 (* 1 = 3.73191 loss)
I0401 17:05:32.457877 14951 solver.cpp:218] Iteration 5936 (1.12336 iter/s, 12.4627s/14 iters), loss = 2.65343
I0401 17:05:32.457937 14951 solver.cpp:237]     Train net output #0: loss = 2.65343 (* 1 = 2.65343 loss)
I0401 17:05:32.457947 14951 sgd_solver.cpp:105] Iteration 5936, lr = 0.001
I0401 17:05:34.982852 14951 blocking_queue.cpp:49] Waiting for data
I0401 17:05:38.486858 14951 solver.cpp:218] Iteration 5950 (2.32217 iter/s, 6.02884s/14 iters), loss = 2.24051
I0401 17:05:38.486917 14951 solver.cpp:237]     Train net output #0: loss = 2.24051 (* 1 = 2.24051 loss)
I0401 17:05:38.486924 14951 sgd_solver.cpp:105] Iteration 5950, lr = 0.001
I0401 17:05:44.693924 14951 solver.cpp:218] Iteration 5964 (2.25555 iter/s, 6.20693s/14 iters), loss = 2.37202
I0401 17:05:44.693969 14951 solver.cpp:237]     Train net output #0: loss = 2.37202 (* 1 = 2.37202 loss)
I0401 17:05:44.693974 14951 sgd_solver.cpp:105] Iteration 5964, lr = 0.001
I0401 17:05:50.976907 14951 solver.cpp:218] Iteration 5978 (2.22829 iter/s, 6.28285s/14 iters), loss = 2.38173
I0401 17:05:50.977022 14951 solver.cpp:237]     Train net output #0: loss = 2.38173 (* 1 = 2.38173 loss)
I0401 17:05:50.977028 14951 sgd_solver.cpp:105] Iteration 5978, lr = 0.001
I0401 17:05:57.099905 14951 solver.cpp:218] Iteration 5992 (2.28653 iter/s, 6.12281s/14 iters), loss = 2.48499
I0401 17:05:57.099946 14951 solver.cpp:237]     Train net output #0: loss = 2.48499 (* 1 = 2.48499 loss)
I0401 17:05:57.099952 14951 sgd_solver.cpp:105] Iteration 5992, lr = 0.001
I0401 17:06:03.574986 14951 solver.cpp:218] Iteration 6006 (2.16218 iter/s, 6.47496s/14 iters), loss = 2.47756
I0401 17:06:03.575047 14951 solver.cpp:237]     Train net output #0: loss = 2.47756 (* 1 = 2.47756 loss)
I0401 17:06:03.575055 14951 sgd_solver.cpp:105] Iteration 6006, lr = 0.001
I0401 17:06:09.683449 14951 solver.cpp:218] Iteration 6020 (2.29195 iter/s, 6.10832s/14 iters), loss = 2.52677
I0401 17:06:09.683502 14951 solver.cpp:237]     Train net output #0: loss = 2.52677 (* 1 = 2.52677 loss)
I0401 17:06:09.683511 14951 sgd_solver.cpp:105] Iteration 6020, lr = 0.001
I0401 17:06:14.336915 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:06:16.061105 14951 solver.cpp:218] Iteration 6034 (2.19521 iter/s, 6.37753s/14 iters), loss = 2.45251
I0401 17:06:16.061151 14951 solver.cpp:237]     Train net output #0: loss = 2.45251 (* 1 = 2.45251 loss)
I0401 17:06:16.061158 14951 sgd_solver.cpp:105] Iteration 6034, lr = 0.001
I0401 17:06:18.982764 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6042.caffemodel
I0401 17:06:22.041601 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6042.solverstate
I0401 17:06:24.402170 14951 solver.cpp:330] Iteration 6042, Testing net (#0)
I0401 17:06:24.402194 14951 net.cpp:676] Ignoring source layer train-data
I0401 17:06:25.344563 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:06:26.587628 14951 solver.cpp:397]     Test net output #0: accuracy = 0.213942
I0401 17:06:26.587666 14951 solver.cpp:397]     Test net output #1: loss = 3.61503 (* 1 = 3.61503 loss)
I0401 17:06:28.455057 14951 solver.cpp:218] Iteration 6048 (1.1296 iter/s, 12.3938s/14 iters), loss = 2.60227
I0401 17:06:28.455114 14951 solver.cpp:237]     Train net output #0: loss = 2.60227 (* 1 = 2.60227 loss)
I0401 17:06:28.455123 14951 sgd_solver.cpp:105] Iteration 6048, lr = 0.001
I0401 17:06:34.647358 14951 solver.cpp:218] Iteration 6062 (2.26092 iter/s, 6.19216s/14 iters), loss = 2.19063
I0401 17:06:34.647403 14951 solver.cpp:237]     Train net output #0: loss = 2.19063 (* 1 = 2.19063 loss)
I0401 17:06:34.647408 14951 sgd_solver.cpp:105] Iteration 6062, lr = 0.001
I0401 17:06:40.793928 14951 solver.cpp:218] Iteration 6076 (2.27774 iter/s, 6.14645s/14 iters), loss = 2.22157
I0401 17:06:40.793970 14951 solver.cpp:237]     Train net output #0: loss = 2.22157 (* 1 = 2.22157 loss)
I0401 17:06:40.793977 14951 sgd_solver.cpp:105] Iteration 6076, lr = 0.001
I0401 17:06:47.205147 14951 solver.cpp:218] Iteration 6090 (2.18372 iter/s, 6.41109s/14 iters), loss = 2.24268
I0401 17:06:47.205200 14951 solver.cpp:237]     Train net output #0: loss = 2.24268 (* 1 = 2.24268 loss)
I0401 17:06:47.205209 14951 sgd_solver.cpp:105] Iteration 6090, lr = 0.001
I0401 17:06:53.113144 14951 solver.cpp:218] Iteration 6104 (2.36972 iter/s, 5.90787s/14 iters), loss = 2.2331
I0401 17:06:53.113255 14951 solver.cpp:237]     Train net output #0: loss = 2.2331 (* 1 = 2.2331 loss)
I0401 17:06:53.113261 14951 sgd_solver.cpp:105] Iteration 6104, lr = 0.001
I0401 17:06:59.281857 14951 solver.cpp:218] Iteration 6118 (2.26959 iter/s, 6.16852s/14 iters), loss = 2.88435
I0401 17:06:59.281898 14951 solver.cpp:237]     Train net output #0: loss = 2.88435 (* 1 = 2.88435 loss)
I0401 17:06:59.281903 14951 sgd_solver.cpp:105] Iteration 6118, lr = 0.001
I0401 17:07:05.361768 14951 solver.cpp:218] Iteration 6132 (2.30271 iter/s, 6.0798s/14 iters), loss = 2.56973
I0401 17:07:05.361806 14951 solver.cpp:237]     Train net output #0: loss = 2.56973 (* 1 = 2.56973 loss)
I0401 17:07:05.361812 14951 sgd_solver.cpp:105] Iteration 6132, lr = 0.001
I0401 17:07:10.737537 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:07:11.601833 14951 solver.cpp:218] Iteration 6146 (2.24361 iter/s, 6.23994s/14 iters), loss = 2.37471
I0401 17:07:11.601892 14951 solver.cpp:237]     Train net output #0: loss = 2.37471 (* 1 = 2.37471 loss)
I0401 17:07:11.601900 14951 sgd_solver.cpp:105] Iteration 6146, lr = 0.001
I0401 17:07:15.698452 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6156.caffemodel
I0401 17:07:18.836496 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6156.solverstate
I0401 17:07:21.196534 14951 solver.cpp:330] Iteration 6156, Testing net (#0)
I0401 17:07:21.196558 14951 net.cpp:676] Ignoring source layer train-data
I0401 17:07:22.028650 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:07:23.313750 14951 solver.cpp:397]     Test net output #0: accuracy = 0.223558
I0401 17:07:23.313891 14951 solver.cpp:397]     Test net output #1: loss = 3.54291 (* 1 = 3.54291 loss)
I0401 17:07:24.378649 14951 solver.cpp:218] Iteration 6160 (1.09575 iter/s, 12.7766s/14 iters), loss = 2.35885
I0401 17:07:24.378717 14951 solver.cpp:237]     Train net output #0: loss = 2.35885 (* 1 = 2.35885 loss)
I0401 17:07:24.378726 14951 sgd_solver.cpp:105] Iteration 6160, lr = 0.001
I0401 17:07:30.526108 14951 solver.cpp:218] Iteration 6174 (2.27742 iter/s, 6.14731s/14 iters), loss = 2.15775
I0401 17:07:30.526158 14951 solver.cpp:237]     Train net output #0: loss = 2.15775 (* 1 = 2.15775 loss)
I0401 17:07:30.526166 14951 sgd_solver.cpp:105] Iteration 6174, lr = 0.001
I0401 17:07:36.607092 14951 solver.cpp:218] Iteration 6188 (2.30231 iter/s, 6.08086s/14 iters), loss = 1.72272
I0401 17:07:36.607137 14951 solver.cpp:237]     Train net output #0: loss = 1.72272 (* 1 = 1.72272 loss)
I0401 17:07:36.607144 14951 sgd_solver.cpp:105] Iteration 6188, lr = 0.001
I0401 17:07:42.772429 14951 solver.cpp:218] Iteration 6202 (2.27081 iter/s, 6.16521s/14 iters), loss = 2.08956
I0401 17:07:42.772473 14951 solver.cpp:237]     Train net output #0: loss = 2.08956 (* 1 = 2.08956 loss)
I0401 17:07:42.772478 14951 sgd_solver.cpp:105] Iteration 6202, lr = 0.001
I0401 17:07:48.821166 14951 solver.cpp:218] Iteration 6216 (2.31458 iter/s, 6.04861s/14 iters), loss = 2.21939
I0401 17:07:48.821208 14951 solver.cpp:237]     Train net output #0: loss = 2.21939 (* 1 = 2.21939 loss)
I0401 17:07:48.821213 14951 sgd_solver.cpp:105] Iteration 6216, lr = 0.001
I0401 17:07:54.928223 14951 solver.cpp:218] Iteration 6230 (2.29248 iter/s, 6.10694s/14 iters), loss = 2.20234
I0401 17:07:54.928321 14951 solver.cpp:237]     Train net output #0: loss = 2.20234 (* 1 = 2.20234 loss)
I0401 17:07:54.928328 14951 sgd_solver.cpp:105] Iteration 6230, lr = 0.001
I0401 17:08:00.823926 14951 solver.cpp:218] Iteration 6244 (2.37468 iter/s, 5.89553s/14 iters), loss = 2.44069
I0401 17:08:00.823980 14951 solver.cpp:237]     Train net output #0: loss = 2.44069 (* 1 = 2.44069 loss)
I0401 17:08:00.823988 14951 sgd_solver.cpp:105] Iteration 6244, lr = 0.001
I0401 17:08:06.893100 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:08:07.082211 14951 solver.cpp:218] Iteration 6258 (2.23708 iter/s, 6.25815s/14 iters), loss = 2.22246
I0401 17:08:07.082254 14951 solver.cpp:237]     Train net output #0: loss = 2.22246 (* 1 = 2.22246 loss)
I0401 17:08:07.082260 14951 sgd_solver.cpp:105] Iteration 6258, lr = 0.001
I0401 17:08:11.593720 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6270.caffemodel
I0401 17:08:14.657801 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6270.solverstate
I0401 17:08:17.398849 14951 solver.cpp:330] Iteration 6270, Testing net (#0)
I0401 17:08:17.398867 14951 net.cpp:676] Ignoring source layer train-data
I0401 17:08:18.161114 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:08:19.567060 14951 solver.cpp:397]     Test net output #0: accuracy = 0.204327
I0401 17:08:19.567097 14951 solver.cpp:397]     Test net output #1: loss = 3.73504 (* 1 = 3.73504 loss)
I0401 17:08:19.978796 14951 solver.cpp:218] Iteration 6272 (1.08557 iter/s, 12.8964s/14 iters), loss = 1.77977
I0401 17:08:19.980511 14951 solver.cpp:237]     Train net output #0: loss = 1.77977 (* 1 = 1.77977 loss)
I0401 17:08:19.980525 14951 sgd_solver.cpp:105] Iteration 6272, lr = 0.001
I0401 17:08:25.904999 14951 solver.cpp:218] Iteration 6286 (2.3631 iter/s, 5.92442s/14 iters), loss = 2.12115
I0401 17:08:25.905160 14951 solver.cpp:237]     Train net output #0: loss = 2.12115 (* 1 = 2.12115 loss)
I0401 17:08:25.905170 14951 sgd_solver.cpp:105] Iteration 6286, lr = 0.001
I0401 17:08:32.023720 14951 solver.cpp:218] Iteration 6300 (2.28815 iter/s, 6.11849s/14 iters), loss = 2.20212
I0401 17:08:32.023762 14951 solver.cpp:237]     Train net output #0: loss = 2.20212 (* 1 = 2.20212 loss)
I0401 17:08:32.023767 14951 sgd_solver.cpp:105] Iteration 6300, lr = 0.001
I0401 17:08:38.335914 14951 solver.cpp:218] Iteration 6314 (2.21798 iter/s, 6.31206s/14 iters), loss = 2.11822
I0401 17:08:38.335983 14951 solver.cpp:237]     Train net output #0: loss = 2.11822 (* 1 = 2.11822 loss)
I0401 17:08:38.335991 14951 sgd_solver.cpp:105] Iteration 6314, lr = 0.001
I0401 17:08:44.548888 14951 solver.cpp:218] Iteration 6328 (2.2534 iter/s, 6.21283s/14 iters), loss = 2.44202
I0401 17:08:44.548950 14951 solver.cpp:237]     Train net output #0: loss = 2.44202 (* 1 = 2.44202 loss)
I0401 17:08:44.548959 14951 sgd_solver.cpp:105] Iteration 6328, lr = 0.001
I0401 17:08:50.597949 14951 solver.cpp:218] Iteration 6342 (2.31446 iter/s, 6.04893s/14 iters), loss = 2.32574
I0401 17:08:50.598008 14951 solver.cpp:237]     Train net output #0: loss = 2.32574 (* 1 = 2.32574 loss)
I0401 17:08:50.598017 14951 sgd_solver.cpp:105] Iteration 6342, lr = 0.001
I0401 17:08:56.729162 14951 solver.cpp:218] Iteration 6356 (2.28345 iter/s, 6.13108s/14 iters), loss = 2.25611
I0401 17:08:56.729295 14951 solver.cpp:237]     Train net output #0: loss = 2.25611 (* 1 = 2.25611 loss)
I0401 17:08:56.729303 14951 sgd_solver.cpp:105] Iteration 6356, lr = 0.001
I0401 17:09:02.993705 14951 solver.cpp:218] Iteration 6370 (2.23488 iter/s, 6.26433s/14 iters), loss = 2.01816
I0401 17:09:02.993760 14951 solver.cpp:237]     Train net output #0: loss = 2.01816 (* 1 = 2.01816 loss)
I0401 17:09:02.993768 14951 sgd_solver.cpp:105] Iteration 6370, lr = 0.001
I0401 17:09:03.636616 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:09:08.765095 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6384.caffemodel
I0401 17:09:13.356871 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6384.solverstate
I0401 17:09:16.183178 14951 solver.cpp:330] Iteration 6384, Testing net (#0)
I0401 17:09:16.183199 14951 net.cpp:676] Ignoring source layer train-data
I0401 17:09:16.892309 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:09:18.378710 14951 solver.cpp:397]     Test net output #0: accuracy = 0.203125
I0401 17:09:18.378747 14951 solver.cpp:397]     Test net output #1: loss = 3.80194 (* 1 = 3.80194 loss)
I0401 17:09:18.521832 14951 solver.cpp:218] Iteration 6384 (0.901603 iter/s, 15.5279s/14 iters), loss = 2.12838
I0401 17:09:18.521888 14951 solver.cpp:237]     Train net output #0: loss = 2.12838 (* 1 = 2.12838 loss)
I0401 17:09:18.521895 14951 sgd_solver.cpp:105] Iteration 6384, lr = 0.001
I0401 17:09:23.693743 14951 solver.cpp:218] Iteration 6398 (2.707 iter/s, 5.17179s/14 iters), loss = 2.10561
I0401 17:09:23.693784 14951 solver.cpp:237]     Train net output #0: loss = 2.10561 (* 1 = 2.10561 loss)
I0401 17:09:23.693789 14951 sgd_solver.cpp:105] Iteration 6398, lr = 0.001
I0401 17:09:29.964970 14951 solver.cpp:218] Iteration 6412 (2.23246 iter/s, 6.2711s/14 iters), loss = 2.12958
I0401 17:09:29.965102 14951 solver.cpp:237]     Train net output #0: loss = 2.12958 (* 1 = 2.12958 loss)
I0401 17:09:29.965111 14951 sgd_solver.cpp:105] Iteration 6412, lr = 0.001
I0401 17:09:36.140465 14951 solver.cpp:218] Iteration 6426 (2.2671 iter/s, 6.17528s/14 iters), loss = 2.25152
I0401 17:09:36.140512 14951 solver.cpp:237]     Train net output #0: loss = 2.25152 (* 1 = 2.25152 loss)
I0401 17:09:36.140518 14951 sgd_solver.cpp:105] Iteration 6426, lr = 0.001
I0401 17:09:42.312943 14951 solver.cpp:218] Iteration 6440 (2.26818 iter/s, 6.17235s/14 iters), loss = 2.41232
I0401 17:09:42.312989 14951 solver.cpp:237]     Train net output #0: loss = 2.41232 (* 1 = 2.41232 loss)
I0401 17:09:42.312995 14951 sgd_solver.cpp:105] Iteration 6440, lr = 0.001
I0401 17:09:48.419297 14951 solver.cpp:218] Iteration 6454 (2.29274 iter/s, 6.10623s/14 iters), loss = 2.24243
I0401 17:09:48.419342 14951 solver.cpp:237]     Train net output #0: loss = 2.24243 (* 1 = 2.24243 loss)
I0401 17:09:48.419348 14951 sgd_solver.cpp:105] Iteration 6454, lr = 0.001
I0401 17:09:54.636179 14951 solver.cpp:218] Iteration 6468 (2.25198 iter/s, 6.21675s/14 iters), loss = 2.61207
I0401 17:09:54.636240 14951 solver.cpp:237]     Train net output #0: loss = 2.61207 (* 1 = 2.61207 loss)
I0401 17:09:54.636247 14951 sgd_solver.cpp:105] Iteration 6468, lr = 0.001
I0401 17:10:00.731335 14951 solver.cpp:218] Iteration 6482 (2.29696 iter/s, 6.09502s/14 iters), loss = 2.07422
I0401 17:10:00.731434 14951 solver.cpp:237]     Train net output #0: loss = 2.07422 (* 1 = 2.07422 loss)
I0401 17:10:00.731441 14951 sgd_solver.cpp:105] Iteration 6482, lr = 0.001
I0401 17:10:02.375193 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:10:06.965736 14951 solver.cpp:218] Iteration 6496 (2.24567 iter/s, 6.23422s/14 iters), loss = 1.90825
I0401 17:10:06.965775 14951 solver.cpp:237]     Train net output #0: loss = 1.90825 (* 1 = 1.90825 loss)
I0401 17:10:06.965781 14951 sgd_solver.cpp:105] Iteration 6496, lr = 0.001
I0401 17:10:07.312738 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6498.caffemodel
I0401 17:10:13.967070 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6498.solverstate
I0401 17:10:16.860692 14951 solver.cpp:330] Iteration 6498, Testing net (#0)
I0401 17:10:16.860713 14951 net.cpp:676] Ignoring source layer train-data
I0401 17:10:17.482506 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:10:19.076508 14951 solver.cpp:397]     Test net output #0: accuracy = 0.194712
I0401 17:10:19.076541 14951 solver.cpp:397]     Test net output #1: loss = 3.9959 (* 1 = 3.9959 loss)
I0401 17:10:23.575971 14951 solver.cpp:218] Iteration 6510 (0.842865 iter/s, 16.61s/14 iters), loss = 2.04324
I0401 17:10:23.576037 14951 solver.cpp:237]     Train net output #0: loss = 2.04324 (* 1 = 2.04324 loss)
I0401 17:10:23.576047 14951 sgd_solver.cpp:105] Iteration 6510, lr = 0.001
I0401 17:10:29.498534 14951 solver.cpp:218] Iteration 6524 (2.3639 iter/s, 5.92241s/14 iters), loss = 1.99683
I0401 17:10:29.498589 14951 solver.cpp:237]     Train net output #0: loss = 1.99683 (* 1 = 1.99683 loss)
I0401 17:10:29.498597 14951 sgd_solver.cpp:105] Iteration 6524, lr = 0.001
I0401 17:10:35.542208 14951 solver.cpp:218] Iteration 6538 (2.31652 iter/s, 6.04354s/14 iters), loss = 1.98217
I0401 17:10:35.542309 14951 solver.cpp:237]     Train net output #0: loss = 1.98217 (* 1 = 1.98217 loss)
I0401 17:10:35.542316 14951 sgd_solver.cpp:105] Iteration 6538, lr = 0.001
I0401 17:10:41.643085 14951 solver.cpp:218] Iteration 6552 (2.29482 iter/s, 6.10069s/14 iters), loss = 2.14484
I0401 17:10:41.643137 14951 solver.cpp:237]     Train net output #0: loss = 2.14484 (* 1 = 2.14484 loss)
I0401 17:10:41.643146 14951 sgd_solver.cpp:105] Iteration 6552, lr = 0.001
I0401 17:10:47.662883 14951 solver.cpp:218] Iteration 6566 (2.32571 iter/s, 6.01967s/14 iters), loss = 1.93489
I0401 17:10:47.662925 14951 solver.cpp:237]     Train net output #0: loss = 1.93489 (* 1 = 1.93489 loss)
I0401 17:10:47.662930 14951 sgd_solver.cpp:105] Iteration 6566, lr = 0.001
I0401 17:10:53.511173 14951 solver.cpp:218] Iteration 6580 (2.39391 iter/s, 5.84817s/14 iters), loss = 2.16208
I0401 17:10:53.511214 14951 solver.cpp:237]     Train net output #0: loss = 2.16208 (* 1 = 2.16208 loss)
I0401 17:10:53.511219 14951 sgd_solver.cpp:105] Iteration 6580, lr = 0.001
I0401 17:10:59.550669 14951 solver.cpp:218] Iteration 6594 (2.31812 iter/s, 6.03938s/14 iters), loss = 1.98053
I0401 17:10:59.550715 14951 solver.cpp:237]     Train net output #0: loss = 1.98053 (* 1 = 1.98053 loss)
I0401 17:10:59.550721 14951 sgd_solver.cpp:105] Iteration 6594, lr = 0.001
I0401 17:11:01.727680 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:11:05.536973 14951 solver.cpp:218] Iteration 6608 (2.33872 iter/s, 5.98617s/14 iters), loss = 1.72449
I0401 17:11:05.537042 14951 solver.cpp:237]     Train net output #0: loss = 1.72449 (* 1 = 1.72449 loss)
I0401 17:11:05.537055 14951 sgd_solver.cpp:105] Iteration 6608, lr = 0.001
I0401 17:11:06.820166 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6612.caffemodel
I0401 17:11:11.402601 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6612.solverstate
I0401 17:11:13.717125 14951 solver.cpp:330] Iteration 6612, Testing net (#0)
I0401 17:11:13.717149 14951 net.cpp:676] Ignoring source layer train-data
I0401 17:11:14.270648 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:11:15.805932 14951 solver.cpp:397]     Test net output #0: accuracy = 0.215144
I0401 17:11:15.805967 14951 solver.cpp:397]     Test net output #1: loss = 3.79444 (* 1 = 3.79444 loss)
I0401 17:11:19.459908 14951 solver.cpp:218] Iteration 6622 (1.00555 iter/s, 13.9227s/14 iters), loss = 2.21714
I0401 17:11:19.459965 14951 solver.cpp:237]     Train net output #0: loss = 2.21714 (* 1 = 2.21714 loss)
I0401 17:11:19.459975 14951 sgd_solver.cpp:105] Iteration 6622, lr = 0.001
I0401 17:11:25.546336 14951 solver.cpp:218] Iteration 6636 (2.30025 iter/s, 6.08629s/14 iters), loss = 2.10501
I0401 17:11:25.546384 14951 solver.cpp:237]     Train net output #0: loss = 2.10501 (* 1 = 2.10501 loss)
I0401 17:11:25.546392 14951 sgd_solver.cpp:105] Iteration 6636, lr = 0.001
I0401 17:11:31.950472 14951 solver.cpp:218] Iteration 6650 (2.18613 iter/s, 6.40401s/14 iters), loss = 1.79924
I0401 17:11:31.950511 14951 solver.cpp:237]     Train net output #0: loss = 1.79924 (* 1 = 1.79924 loss)
I0401 17:11:31.950517 14951 sgd_solver.cpp:105] Iteration 6650, lr = 0.001
I0401 17:11:37.932597 14951 solver.cpp:218] Iteration 6664 (2.34035 iter/s, 5.98201s/14 iters), loss = 2.13771
I0401 17:11:37.934154 14951 solver.cpp:237]     Train net output #0: loss = 2.13771 (* 1 = 2.13771 loss)
I0401 17:11:37.934163 14951 sgd_solver.cpp:105] Iteration 6664, lr = 0.001
I0401 17:11:44.157752 14951 solver.cpp:218] Iteration 6678 (2.24953 iter/s, 6.22352s/14 iters), loss = 2.04659
I0401 17:11:44.157795 14951 solver.cpp:237]     Train net output #0: loss = 2.04659 (* 1 = 2.04659 loss)
I0401 17:11:44.157801 14951 sgd_solver.cpp:105] Iteration 6678, lr = 0.001
I0401 17:11:50.306401 14951 solver.cpp:218] Iteration 6692 (2.27697 iter/s, 6.14852s/14 iters), loss = 1.94588
I0401 17:11:50.306455 14951 solver.cpp:237]     Train net output #0: loss = 1.94588 (* 1 = 1.94588 loss)
I0401 17:11:50.306464 14951 sgd_solver.cpp:105] Iteration 6692, lr = 0.001
I0401 17:11:56.272495 14951 solver.cpp:218] Iteration 6706 (2.34664 iter/s, 5.96597s/14 iters), loss = 1.64887
I0401 17:11:56.272532 14951 solver.cpp:237]     Train net output #0: loss = 1.64887 (* 1 = 1.64887 loss)
I0401 17:11:56.272538 14951 sgd_solver.cpp:105] Iteration 6706, lr = 0.001
I0401 17:11:59.295610 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:12:02.450873 14951 solver.cpp:218] Iteration 6720 (2.26601 iter/s, 6.17826s/14 iters), loss = 2.03999
I0401 17:12:02.450917 14951 solver.cpp:237]     Train net output #0: loss = 2.03999 (* 1 = 2.03999 loss)
I0401 17:12:02.450922 14951 sgd_solver.cpp:105] Iteration 6720, lr = 0.001
I0401 17:12:04.379810 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6726.caffemodel
I0401 17:12:08.758992 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6726.solverstate
I0401 17:12:12.410627 14951 solver.cpp:330] Iteration 6726, Testing net (#0)
I0401 17:12:12.410650 14951 net.cpp:676] Ignoring source layer train-data
I0401 17:12:12.908322 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:12:14.582537 14951 solver.cpp:397]     Test net output #0: accuracy = 0.219952
I0401 17:12:14.582568 14951 solver.cpp:397]     Test net output #1: loss = 3.79404 (* 1 = 3.79404 loss)
I0401 17:12:17.287889 14951 solver.cpp:218] Iteration 6734 (0.9436 iter/s, 14.8368s/14 iters), loss = 2.10743
I0401 17:12:17.287947 14951 solver.cpp:237]     Train net output #0: loss = 2.10743 (* 1 = 2.10743 loss)
I0401 17:12:17.287956 14951 sgd_solver.cpp:105] Iteration 6734, lr = 0.001
I0401 17:12:23.160957 14951 solver.cpp:218] Iteration 6748 (2.38382 iter/s, 5.87293s/14 iters), loss = 2.1695
I0401 17:12:23.161073 14951 solver.cpp:237]     Train net output #0: loss = 2.1695 (* 1 = 2.1695 loss)
I0401 17:12:23.161085 14951 sgd_solver.cpp:105] Iteration 6748, lr = 0.001
I0401 17:12:29.297585 14951 solver.cpp:218] Iteration 6762 (2.28143 iter/s, 6.1365s/14 iters), loss = 1.89465
I0401 17:12:29.297629 14951 solver.cpp:237]     Train net output #0: loss = 1.89465 (* 1 = 1.89465 loss)
I0401 17:12:29.297636 14951 sgd_solver.cpp:105] Iteration 6762, lr = 0.001
I0401 17:12:35.233934 14951 solver.cpp:218] Iteration 6776 (2.3584 iter/s, 5.93623s/14 iters), loss = 2.19971
I0401 17:12:35.233989 14951 solver.cpp:237]     Train net output #0: loss = 2.19971 (* 1 = 2.19971 loss)
I0401 17:12:35.233996 14951 sgd_solver.cpp:105] Iteration 6776, lr = 0.001
I0401 17:12:41.377182 14951 solver.cpp:218] Iteration 6790 (2.27898 iter/s, 6.14311s/14 iters), loss = 2.186
I0401 17:12:41.377265 14951 solver.cpp:237]     Train net output #0: loss = 2.186 (* 1 = 2.186 loss)
I0401 17:12:41.377272 14951 sgd_solver.cpp:105] Iteration 6790, lr = 0.001
I0401 17:12:46.869097 14951 blocking_queue.cpp:49] Waiting for data
I0401 17:12:47.846892 14951 solver.cpp:218] Iteration 6804 (2.16398 iter/s, 6.46955s/14 iters), loss = 1.97894
I0401 17:12:47.846930 14951 solver.cpp:237]     Train net output #0: loss = 1.97894 (* 1 = 1.97894 loss)
I0401 17:12:47.846935 14951 sgd_solver.cpp:105] Iteration 6804, lr = 0.001
I0401 17:12:53.860095 14951 solver.cpp:218] Iteration 6818 (2.32826 iter/s, 6.01308s/14 iters), loss = 1.66425
I0401 17:12:53.860142 14951 solver.cpp:237]     Train net output #0: loss = 1.66425 (* 1 = 1.66425 loss)
I0401 17:12:53.860148 14951 sgd_solver.cpp:105] Iteration 6818, lr = 0.001
I0401 17:12:57.668964 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:13:00.064765 14951 solver.cpp:218] Iteration 6832 (2.25641 iter/s, 6.20455s/14 iters), loss = 1.74085
I0401 17:13:00.064805 14951 solver.cpp:237]     Train net output #0: loss = 1.74085 (* 1 = 1.74085 loss)
I0401 17:13:00.064810 14951 sgd_solver.cpp:105] Iteration 6832, lr = 0.001
I0401 17:13:03.007289 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6840.caffemodel
I0401 17:13:07.902858 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6840.solverstate
I0401 17:13:12.094776 14951 solver.cpp:330] Iteration 6840, Testing net (#0)
I0401 17:13:12.094852 14951 net.cpp:676] Ignoring source layer train-data
I0401 17:13:12.535365 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:13:14.292464 14951 solver.cpp:397]     Test net output #0: accuracy = 0.229567
I0401 17:13:14.292493 14951 solver.cpp:397]     Test net output #1: loss = 3.62779 (* 1 = 3.62779 loss)
I0401 17:13:16.202425 14951 solver.cpp:218] Iteration 6846 (0.867548 iter/s, 16.1374s/14 iters), loss = 1.87987
I0401 17:13:16.202471 14951 solver.cpp:237]     Train net output #0: loss = 1.87987 (* 1 = 1.87987 loss)
I0401 17:13:16.202476 14951 sgd_solver.cpp:105] Iteration 6846, lr = 0.001
I0401 17:13:22.221808 14951 solver.cpp:218] Iteration 6860 (2.32587 iter/s, 6.01926s/14 iters), loss = 2.2328
I0401 17:13:22.221864 14951 solver.cpp:237]     Train net output #0: loss = 2.2328 (* 1 = 2.2328 loss)
I0401 17:13:22.221873 14951 sgd_solver.cpp:105] Iteration 6860, lr = 0.001
I0401 17:13:28.274194 14951 solver.cpp:218] Iteration 6874 (2.31319 iter/s, 6.05225s/14 iters), loss = 1.79682
I0401 17:13:28.274236 14951 solver.cpp:237]     Train net output #0: loss = 1.79682 (* 1 = 1.79682 loss)
I0401 17:13:28.274242 14951 sgd_solver.cpp:105] Iteration 6874, lr = 0.001
I0401 17:13:34.379623 14951 solver.cpp:218] Iteration 6888 (2.29309 iter/s, 6.1053s/14 iters), loss = 2.40373
I0401 17:13:34.379667 14951 solver.cpp:237]     Train net output #0: loss = 2.40373 (* 1 = 2.40373 loss)
I0401 17:13:34.379673 14951 sgd_solver.cpp:105] Iteration 6888, lr = 0.001
I0401 17:13:40.336897 14951 solver.cpp:218] Iteration 6902 (2.35012 iter/s, 5.95714s/14 iters), loss = 1.74927
I0401 17:13:40.336948 14951 solver.cpp:237]     Train net output #0: loss = 1.74927 (* 1 = 1.74927 loss)
I0401 17:13:40.336957 14951 sgd_solver.cpp:105] Iteration 6902, lr = 0.001
I0401 17:13:46.419795 14951 solver.cpp:218] Iteration 6916 (2.30158 iter/s, 6.08277s/14 iters), loss = 2.1177
I0401 17:13:46.419924 14951 solver.cpp:237]     Train net output #0: loss = 2.1177 (* 1 = 2.1177 loss)
I0401 17:13:46.419930 14951 sgd_solver.cpp:105] Iteration 6916, lr = 0.001
I0401 17:13:52.503865 14951 solver.cpp:218] Iteration 6930 (2.30117 iter/s, 6.08386s/14 iters), loss = 1.91719
I0401 17:13:52.503921 14951 solver.cpp:237]     Train net output #0: loss = 1.91719 (* 1 = 1.91719 loss)
I0401 17:13:52.503929 14951 sgd_solver.cpp:105] Iteration 6930, lr = 0.001
I0401 17:13:57.043720 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:13:58.466636 14951 solver.cpp:218] Iteration 6944 (2.34795 iter/s, 5.96264s/14 iters), loss = 1.65028
I0401 17:13:58.466689 14951 solver.cpp:237]     Train net output #0: loss = 1.65028 (* 1 = 1.65028 loss)
I0401 17:13:58.466697 14951 sgd_solver.cpp:105] Iteration 6944, lr = 0.001
I0401 17:14:02.037679 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6954.caffemodel
I0401 17:14:05.031725 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6954.solverstate
I0401 17:14:09.308528 14951 solver.cpp:330] Iteration 6954, Testing net (#0)
I0401 17:14:09.308549 14951 net.cpp:676] Ignoring source layer train-data
I0401 17:14:09.716236 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:14:11.472916 14951 solver.cpp:397]     Test net output #0: accuracy = 0.219952
I0401 17:14:11.472959 14951 solver.cpp:397]     Test net output #1: loss = 3.72172 (* 1 = 3.72172 loss)
I0401 17:14:12.624256 14951 solver.cpp:218] Iteration 6958 (0.988882 iter/s, 14.1574s/14 iters), loss = 1.6169
I0401 17:14:12.624315 14951 solver.cpp:237]     Train net output #0: loss = 1.6169 (* 1 = 1.6169 loss)
I0401 17:14:12.624323 14951 sgd_solver.cpp:105] Iteration 6958, lr = 0.001
I0401 17:14:18.538767 14951 solver.cpp:218] Iteration 6972 (2.36711 iter/s, 5.91437s/14 iters), loss = 1.73805
I0401 17:14:18.538861 14951 solver.cpp:237]     Train net output #0: loss = 1.73805 (* 1 = 1.73805 loss)
I0401 17:14:18.538868 14951 sgd_solver.cpp:105] Iteration 6972, lr = 0.001
I0401 17:14:24.527904 14951 solver.cpp:218] Iteration 6986 (2.33763 iter/s, 5.98897s/14 iters), loss = 1.46822
I0401 17:14:24.527948 14951 solver.cpp:237]     Train net output #0: loss = 1.46822 (* 1 = 1.46822 loss)
I0401 17:14:24.527953 14951 sgd_solver.cpp:105] Iteration 6986, lr = 0.001
I0401 17:14:30.726029 14951 solver.cpp:218] Iteration 7000 (2.25879 iter/s, 6.198s/14 iters), loss = 1.80411
I0401 17:14:30.726079 14951 solver.cpp:237]     Train net output #0: loss = 1.80411 (* 1 = 1.80411 loss)
I0401 17:14:30.726087 14951 sgd_solver.cpp:105] Iteration 7000, lr = 0.001
I0401 17:14:36.880347 14951 solver.cpp:218] Iteration 7014 (2.27487 iter/s, 6.15419s/14 iters), loss = 1.97924
I0401 17:14:36.880398 14951 solver.cpp:237]     Train net output #0: loss = 1.97924 (* 1 = 1.97924 loss)
I0401 17:14:36.880406 14951 sgd_solver.cpp:105] Iteration 7014, lr = 0.001
I0401 17:14:43.044288 14951 solver.cpp:218] Iteration 7028 (2.27132 iter/s, 6.16382s/14 iters), loss = 1.69264
I0401 17:14:43.044338 14951 solver.cpp:237]     Train net output #0: loss = 1.69264 (* 1 = 1.69264 loss)
I0401 17:14:43.044346 14951 sgd_solver.cpp:105] Iteration 7028, lr = 0.001
I0401 17:14:49.103668 14951 solver.cpp:218] Iteration 7042 (2.31052 iter/s, 6.05925s/14 iters), loss = 1.67014
I0401 17:14:49.103824 14951 solver.cpp:237]     Train net output #0: loss = 1.67014 (* 1 = 1.67014 loss)
I0401 17:14:49.103833 14951 sgd_solver.cpp:105] Iteration 7042, lr = 0.001
I0401 17:14:54.489826 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:14:55.236055 14951 solver.cpp:218] Iteration 7056 (2.28305 iter/s, 6.13215s/14 iters), loss = 1.41437
I0401 17:14:55.236106 14951 solver.cpp:237]     Train net output #0: loss = 1.41437 (* 1 = 1.41437 loss)
I0401 17:14:55.236114 14951 sgd_solver.cpp:105] Iteration 7056, lr = 0.001
I0401 17:14:59.922966 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7068.caffemodel
I0401 17:15:02.950850 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7068.solverstate
I0401 17:15:05.912319 14951 solver.cpp:330] Iteration 7068, Testing net (#0)
I0401 17:15:05.912343 14951 net.cpp:676] Ignoring source layer train-data
I0401 17:15:06.210167 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:15:07.999636 14951 solver.cpp:397]     Test net output #0: accuracy = 0.221154
I0401 17:15:07.999670 14951 solver.cpp:397]     Test net output #1: loss = 3.76902 (* 1 = 3.76902 loss)
I0401 17:15:08.406889 14951 solver.cpp:218] Iteration 7070 (1.06297 iter/s, 13.1706s/14 iters), loss = 1.67997
I0401 17:15:08.406942 14951 solver.cpp:237]     Train net output #0: loss = 1.67997 (* 1 = 1.67997 loss)
I0401 17:15:08.406950 14951 sgd_solver.cpp:105] Iteration 7070, lr = 0.001
I0401 17:15:14.045025 14951 solver.cpp:218] Iteration 7084 (2.48315 iter/s, 5.63801s/14 iters), loss = 1.92417
I0401 17:15:14.045063 14951 solver.cpp:237]     Train net output #0: loss = 1.92417 (* 1 = 1.92417 loss)
I0401 17:15:14.045068 14951 sgd_solver.cpp:105] Iteration 7084, lr = 0.001
I0401 17:15:20.032330 14951 solver.cpp:218] Iteration 7098 (2.33833 iter/s, 5.98719s/14 iters), loss = 1.79712
I0401 17:15:20.032438 14951 solver.cpp:237]     Train net output #0: loss = 1.79712 (* 1 = 1.79712 loss)
I0401 17:15:20.032446 14951 sgd_solver.cpp:105] Iteration 7098, lr = 0.001
I0401 17:15:26.151453 14951 solver.cpp:218] Iteration 7112 (2.28798 iter/s, 6.11894s/14 iters), loss = 1.81
I0401 17:15:26.151508 14951 solver.cpp:237]     Train net output #0: loss = 1.81 (* 1 = 1.81 loss)
I0401 17:15:26.151518 14951 sgd_solver.cpp:105] Iteration 7112, lr = 0.001
I0401 17:15:32.152972 14951 solver.cpp:218] Iteration 7126 (2.33279 iter/s, 6.00139s/14 iters), loss = 1.75079
I0401 17:15:32.153012 14951 solver.cpp:237]     Train net output #0: loss = 1.75079 (* 1 = 1.75079 loss)
I0401 17:15:32.153017 14951 sgd_solver.cpp:105] Iteration 7126, lr = 0.001
I0401 17:15:38.375386 14951 solver.cpp:218] Iteration 7140 (2.24997 iter/s, 6.22229s/14 iters), loss = 1.48714
I0401 17:15:38.375429 14951 solver.cpp:237]     Train net output #0: loss = 1.48714 (* 1 = 1.48714 loss)
I0401 17:15:38.375435 14951 sgd_solver.cpp:105] Iteration 7140, lr = 0.001
I0401 17:15:44.244431 14951 solver.cpp:218] Iteration 7154 (2.38545 iter/s, 5.86893s/14 iters), loss = 1.55103
I0401 17:15:44.244472 14951 solver.cpp:237]     Train net output #0: loss = 1.55103 (* 1 = 1.55103 loss)
I0401 17:15:44.244477 14951 sgd_solver.cpp:105] Iteration 7154, lr = 0.001
I0401 17:15:50.531158 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:15:50.554852 14951 solver.cpp:218] Iteration 7168 (2.2186 iter/s, 6.3103s/14 iters), loss = 1.58152
I0401 17:15:50.554904 14951 solver.cpp:237]     Train net output #0: loss = 1.58152 (* 1 = 1.58152 loss)
I0401 17:15:50.554913 14951 sgd_solver.cpp:105] Iteration 7168, lr = 0.001
I0401 17:15:56.056782 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7182.caffemodel
I0401 17:15:59.019106 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7182.solverstate
I0401 17:16:01.322815 14951 solver.cpp:330] Iteration 7182, Testing net (#0)
I0401 17:16:01.322839 14951 net.cpp:676] Ignoring source layer train-data
I0401 17:16:01.615284 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:16:03.495491 14951 solver.cpp:397]     Test net output #0: accuracy = 0.219952
I0401 17:16:03.495532 14951 solver.cpp:397]     Test net output #1: loss = 3.79729 (* 1 = 3.79729 loss)
I0401 17:16:03.637627 14951 solver.cpp:218] Iteration 7182 (1.07013 iter/s, 13.0826s/14 iters), loss = 1.40506
I0401 17:16:03.637691 14951 solver.cpp:237]     Train net output #0: loss = 1.40506 (* 1 = 1.40506 loss)
I0401 17:16:03.637701 14951 sgd_solver.cpp:105] Iteration 7182, lr = 0.001
I0401 17:16:08.820039 14951 solver.cpp:218] Iteration 7196 (2.70152 iter/s, 5.18228s/14 iters), loss = 1.96187
I0401 17:16:08.820080 14951 solver.cpp:237]     Train net output #0: loss = 1.96187 (* 1 = 1.96187 loss)
I0401 17:16:08.820086 14951 sgd_solver.cpp:105] Iteration 7196, lr = 0.001
I0401 17:16:14.615556 14951 solver.cpp:218] Iteration 7210 (2.41571 iter/s, 5.7954s/14 iters), loss = 1.8213
I0401 17:16:14.615595 14951 solver.cpp:237]     Train net output #0: loss = 1.8213 (* 1 = 1.8213 loss)
I0401 17:16:14.615600 14951 sgd_solver.cpp:105] Iteration 7210, lr = 0.001
I0401 17:16:20.422247 14951 solver.cpp:218] Iteration 7224 (2.41106 iter/s, 5.80657s/14 iters), loss = 2.13811
I0401 17:16:20.422298 14951 solver.cpp:237]     Train net output #0: loss = 2.13811 (* 1 = 2.13811 loss)
I0401 17:16:20.422307 14951 sgd_solver.cpp:105] Iteration 7224, lr = 0.001
I0401 17:16:26.641072 14951 solver.cpp:218] Iteration 7238 (2.25128 iter/s, 6.21869s/14 iters), loss = 1.51897
I0401 17:16:26.641191 14951 solver.cpp:237]     Train net output #0: loss = 1.51897 (* 1 = 1.51897 loss)
I0401 17:16:26.641199 14951 sgd_solver.cpp:105] Iteration 7238, lr = 0.001
I0401 17:16:32.813194 14951 solver.cpp:218] Iteration 7252 (2.26834 iter/s, 6.17192s/14 iters), loss = 1.50985
I0401 17:16:32.813239 14951 solver.cpp:237]     Train net output #0: loss = 1.50985 (* 1 = 1.50985 loss)
I0401 17:16:32.813246 14951 sgd_solver.cpp:105] Iteration 7252, lr = 0.001
I0401 17:16:39.109385 14951 solver.cpp:218] Iteration 7266 (2.22361 iter/s, 6.29606s/14 iters), loss = 1.52465
I0401 17:16:39.109432 14951 solver.cpp:237]     Train net output #0: loss = 1.52465 (* 1 = 1.52465 loss)
I0401 17:16:39.109438 14951 sgd_solver.cpp:105] Iteration 7266, lr = 0.001
I0401 17:16:45.211349 14951 solver.cpp:218] Iteration 7280 (2.29439 iter/s, 6.10184s/14 iters), loss = 1.50262
I0401 17:16:45.211392 14951 solver.cpp:237]     Train net output #0: loss = 1.50262 (* 1 = 1.50262 loss)
I0401 17:16:45.211398 14951 sgd_solver.cpp:105] Iteration 7280, lr = 0.001
I0401 17:16:45.958272 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:16:51.391316 14951 solver.cpp:218] Iteration 7294 (2.26543 iter/s, 6.17985s/14 iters), loss = 1.12862
I0401 17:16:51.391352 14951 solver.cpp:237]     Train net output #0: loss = 1.12862 (* 1 = 1.12862 loss)
I0401 17:16:51.391357 14951 sgd_solver.cpp:105] Iteration 7294, lr = 0.001
I0401 17:16:51.742168 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7296.caffemodel
I0401 17:16:54.777561 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7296.solverstate
I0401 17:16:59.021459 14951 solver.cpp:330] Iteration 7296, Testing net (#0)
I0401 17:16:59.021574 14951 net.cpp:676] Ignoring source layer train-data
I0401 17:16:59.181926 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:17:01.213668 14951 solver.cpp:397]     Test net output #0: accuracy = 0.223558
I0401 17:17:01.213703 14951 solver.cpp:397]     Test net output #1: loss = 3.71691 (* 1 = 3.71691 loss)
I0401 17:17:05.670419 14951 solver.cpp:218] Iteration 7308 (0.980467 iter/s, 14.2789s/14 iters), loss = 1.60916
I0401 17:17:05.670478 14951 solver.cpp:237]     Train net output #0: loss = 1.60916 (* 1 = 1.60916 loss)
I0401 17:17:05.670487 14951 sgd_solver.cpp:105] Iteration 7308, lr = 0.001
I0401 17:17:11.826411 14951 solver.cpp:218] Iteration 7322 (2.27426 iter/s, 6.15586s/14 iters), loss = 2.10561
I0401 17:17:11.826453 14951 solver.cpp:237]     Train net output #0: loss = 2.10561 (* 1 = 2.10561 loss)
I0401 17:17:11.826459 14951 sgd_solver.cpp:105] Iteration 7322, lr = 0.001
I0401 17:17:17.876641 14951 solver.cpp:218] Iteration 7336 (2.31401 iter/s, 6.0501s/14 iters), loss = 1.69796
I0401 17:17:17.876688 14951 solver.cpp:237]     Train net output #0: loss = 1.69796 (* 1 = 1.69796 loss)
I0401 17:17:17.876693 14951 sgd_solver.cpp:105] Iteration 7336, lr = 0.001
I0401 17:17:24.144323 14951 solver.cpp:218] Iteration 7350 (2.23373 iter/s, 6.26755s/14 iters), loss = 1.54525
I0401 17:17:24.144371 14951 solver.cpp:237]     Train net output #0: loss = 1.54525 (* 1 = 1.54525 loss)
I0401 17:17:24.144377 14951 sgd_solver.cpp:105] Iteration 7350, lr = 0.001
I0401 17:17:30.459231 14951 solver.cpp:218] Iteration 7364 (2.21702 iter/s, 6.31478s/14 iters), loss = 1.53784
I0401 17:17:30.459363 14951 solver.cpp:237]     Train net output #0: loss = 1.53784 (* 1 = 1.53784 loss)
I0401 17:17:30.459372 14951 sgd_solver.cpp:105] Iteration 7364, lr = 0.001
I0401 17:17:36.516480 14951 solver.cpp:218] Iteration 7378 (2.31136 iter/s, 6.05704s/14 iters), loss = 1.40784
I0401 17:17:36.516542 14951 solver.cpp:237]     Train net output #0: loss = 1.40784 (* 1 = 1.40784 loss)
I0401 17:17:36.516551 14951 sgd_solver.cpp:105] Iteration 7378, lr = 0.001
I0401 17:17:42.832239 14951 solver.cpp:218] Iteration 7392 (2.21673 iter/s, 6.31562s/14 iters), loss = 1.6016
I0401 17:17:42.832295 14951 solver.cpp:237]     Train net output #0: loss = 1.6016 (* 1 = 1.6016 loss)
I0401 17:17:42.832305 14951 sgd_solver.cpp:105] Iteration 7392, lr = 0.001
I0401 17:17:44.343621 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:17:48.850349 14951 solver.cpp:218] Iteration 7406 (2.32636 iter/s, 6.01797s/14 iters), loss = 1.44587
I0401 17:17:48.850404 14951 solver.cpp:237]     Train net output #0: loss = 1.44587 (* 1 = 1.44587 loss)
I0401 17:17:48.850414 14951 sgd_solver.cpp:105] Iteration 7406, lr = 0.001
I0401 17:17:50.135437 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7410.caffemodel
I0401 17:17:54.910039 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7410.solverstate
I0401 17:17:57.727135 14951 solver.cpp:330] Iteration 7410, Testing net (#0)
I0401 17:17:57.727155 14951 net.cpp:676] Ignoring source layer train-data
I0401 17:17:57.826730 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:17:59.891353 14951 solver.cpp:397]     Test net output #0: accuracy = 0.221154
I0401 17:17:59.891391 14951 solver.cpp:397]     Test net output #1: loss = 3.72303 (* 1 = 3.72303 loss)
I0401 17:18:03.641135 14951 solver.cpp:218] Iteration 7420 (0.946549 iter/s, 14.7906s/14 iters), loss = 1.29582
I0401 17:18:03.641254 14951 solver.cpp:237]     Train net output #0: loss = 1.29582 (* 1 = 1.29582 loss)
I0401 17:18:03.641263 14951 sgd_solver.cpp:105] Iteration 7420, lr = 0.001
I0401 17:18:09.835450 14951 solver.cpp:218] Iteration 7434 (2.26021 iter/s, 6.19411s/14 iters), loss = 1.6629
I0401 17:18:09.835515 14951 solver.cpp:237]     Train net output #0: loss = 1.6629 (* 1 = 1.6629 loss)
I0401 17:18:09.835525 14951 sgd_solver.cpp:105] Iteration 7434, lr = 0.001
I0401 17:18:16.131781 14951 solver.cpp:218] Iteration 7448 (2.22357 iter/s, 6.29619s/14 iters), loss = 1.48857
I0401 17:18:16.131824 14951 solver.cpp:237]     Train net output #0: loss = 1.48857 (* 1 = 1.48857 loss)
I0401 17:18:16.131830 14951 sgd_solver.cpp:105] Iteration 7448, lr = 0.001
I0401 17:18:22.294819 14951 solver.cpp:218] Iteration 7462 (2.27165 iter/s, 6.16291s/14 iters), loss = 1.72674
I0401 17:18:22.294865 14951 solver.cpp:237]     Train net output #0: loss = 1.72674 (* 1 = 1.72674 loss)
I0401 17:18:22.294872 14951 sgd_solver.cpp:105] Iteration 7462, lr = 0.001
I0401 17:18:28.521565 14951 solver.cpp:218] Iteration 7476 (2.24841 iter/s, 6.22661s/14 iters), loss = 1.26897
I0401 17:18:28.521620 14951 solver.cpp:237]     Train net output #0: loss = 1.26897 (* 1 = 1.26897 loss)
I0401 17:18:28.521627 14951 sgd_solver.cpp:105] Iteration 7476, lr = 0.001
I0401 17:18:34.855517 14951 solver.cpp:218] Iteration 7490 (2.21036 iter/s, 6.33381s/14 iters), loss = 1.54825
I0401 17:18:34.855685 14951 solver.cpp:237]     Train net output #0: loss = 1.54825 (* 1 = 1.54825 loss)
I0401 17:18:34.855695 14951 sgd_solver.cpp:105] Iteration 7490, lr = 0.001
I0401 17:18:40.825237 14951 solver.cpp:218] Iteration 7504 (2.34526 iter/s, 5.96948s/14 iters), loss = 1.31876
I0401 17:18:40.825274 14951 solver.cpp:237]     Train net output #0: loss = 1.31876 (* 1 = 1.31876 loss)
I0401 17:18:40.825279 14951 sgd_solver.cpp:105] Iteration 7504, lr = 0.001
I0401 17:18:43.189657 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:18:47.098829 14951 solver.cpp:218] Iteration 7518 (2.23162 iter/s, 6.27347s/14 iters), loss = 1.29692
I0401 17:18:47.098866 14951 solver.cpp:237]     Train net output #0: loss = 1.29692 (* 1 = 1.29692 loss)
I0401 17:18:47.098872 14951 sgd_solver.cpp:105] Iteration 7518, lr = 0.001
I0401 17:18:49.178835 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7524.caffemodel
I0401 17:18:53.072494 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7524.solverstate
I0401 17:18:55.530042 14951 solver.cpp:330] Iteration 7524, Testing net (#0)
I0401 17:18:55.530062 14951 net.cpp:676] Ignoring source layer train-data
I0401 17:18:55.565194 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:18:57.914248 14951 solver.cpp:397]     Test net output #0: accuracy = 0.230769
I0401 17:18:57.914283 14951 solver.cpp:397]     Test net output #1: loss = 3.77699 (* 1 = 3.77699 loss)
I0401 17:18:58.363566 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:19:00.451907 14951 solver.cpp:218] Iteration 7532 (1.04846 iter/s, 13.3529s/14 iters), loss = 1.91732
I0401 17:19:00.451951 14951 solver.cpp:237]     Train net output #0: loss = 1.91732 (* 1 = 1.91732 loss)
I0401 17:19:00.451958 14951 sgd_solver.cpp:105] Iteration 7532, lr = 0.001
I0401 17:19:06.609037 14951 solver.cpp:218] Iteration 7546 (2.27383 iter/s, 6.15701s/14 iters), loss = 1.78464
I0401 17:19:06.609123 14951 solver.cpp:237]     Train net output #0: loss = 1.78464 (* 1 = 1.78464 loss)
I0401 17:19:06.609133 14951 sgd_solver.cpp:105] Iteration 7546, lr = 0.001
I0401 17:19:12.735743 14951 solver.cpp:218] Iteration 7560 (2.28514 iter/s, 6.12655s/14 iters), loss = 1.54428
I0401 17:19:12.735783 14951 solver.cpp:237]     Train net output #0: loss = 1.54428 (* 1 = 1.54428 loss)
I0401 17:19:12.735788 14951 sgd_solver.cpp:105] Iteration 7560, lr = 0.001
I0401 17:19:19.108538 14951 solver.cpp:218] Iteration 7574 (2.19688 iter/s, 6.37267s/14 iters), loss = 1.58166
I0401 17:19:19.108582 14951 solver.cpp:237]     Train net output #0: loss = 1.58166 (* 1 = 1.58166 loss)
I0401 17:19:19.108587 14951 sgd_solver.cpp:105] Iteration 7574, lr = 0.001
I0401 17:19:25.304317 14951 solver.cpp:218] Iteration 7588 (2.25965 iter/s, 6.19566s/14 iters), loss = 1.50843
I0401 17:19:25.304368 14951 solver.cpp:237]     Train net output #0: loss = 1.50843 (* 1 = 1.50843 loss)
I0401 17:19:25.304374 14951 sgd_solver.cpp:105] Iteration 7588, lr = 0.001
I0401 17:19:31.536195 14951 solver.cpp:218] Iteration 7602 (2.24656 iter/s, 6.23175s/14 iters), loss = 1.23437
I0401 17:19:31.536238 14951 solver.cpp:237]     Train net output #0: loss = 1.23437 (* 1 = 1.23437 loss)
I0401 17:19:31.536244 14951 sgd_solver.cpp:105] Iteration 7602, lr = 0.001
I0401 17:19:37.670393 14951 solver.cpp:218] Iteration 7616 (2.28233 iter/s, 6.13408s/14 iters), loss = 1.30598
I0401 17:19:37.670521 14951 solver.cpp:237]     Train net output #0: loss = 1.30598 (* 1 = 1.30598 loss)
I0401 17:19:37.670529 14951 sgd_solver.cpp:105] Iteration 7616, lr = 0.001
I0401 17:19:40.884068 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:19:43.766995 14951 solver.cpp:218] Iteration 7630 (2.29644 iter/s, 6.0964s/14 iters), loss = 1.28291
I0401 17:19:43.767046 14951 solver.cpp:237]     Train net output #0: loss = 1.28291 (* 1 = 1.28291 loss)
I0401 17:19:43.767056 14951 sgd_solver.cpp:105] Iteration 7630, lr = 0.001
I0401 17:19:46.798029 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7638.caffemodel
I0401 17:19:49.803483 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7638.solverstate
I0401 17:19:52.822789 14951 solver.cpp:330] Iteration 7638, Testing net (#0)
I0401 17:19:52.822809 14951 net.cpp:676] Ignoring source layer train-data
I0401 17:19:55.122516 14951 solver.cpp:397]     Test net output #0: accuracy = 0.22476
I0401 17:19:55.122550 14951 solver.cpp:397]     Test net output #1: loss = 3.78165 (* 1 = 3.78165 loss)
I0401 17:19:55.617199 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:19:56.133404 14951 blocking_queue.cpp:49] Waiting for data
I0401 17:19:57.058528 14951 solver.cpp:218] Iteration 7644 (1.05332 iter/s, 13.2913s/14 iters), loss = 1.73477
I0401 17:19:57.058586 14951 solver.cpp:237]     Train net output #0: loss = 1.73477 (* 1 = 1.73477 loss)
I0401 17:19:57.058594 14951 sgd_solver.cpp:105] Iteration 7644, lr = 0.001
I0401 17:20:03.089848 14951 solver.cpp:218] Iteration 7658 (2.32127 iter/s, 6.03119s/14 iters), loss = 1.79622
I0401 17:20:03.089886 14951 solver.cpp:237]     Train net output #0: loss = 1.79622 (* 1 = 1.79622 loss)
I0401 17:20:03.089891 14951 sgd_solver.cpp:105] Iteration 7658, lr = 0.001
I0401 17:20:09.267220 14951 solver.cpp:218] Iteration 7672 (2.26638 iter/s, 6.17725s/14 iters), loss = 1.57891
I0401 17:20:09.267328 14951 solver.cpp:237]     Train net output #0: loss = 1.57891 (* 1 = 1.57891 loss)
I0401 17:20:09.267335 14951 sgd_solver.cpp:105] Iteration 7672, lr = 0.001
I0401 17:20:15.404314 14951 solver.cpp:218] Iteration 7686 (2.28128 iter/s, 6.1369s/14 iters), loss = 1.69801
I0401 17:20:15.404359 14951 solver.cpp:237]     Train net output #0: loss = 1.69801 (* 1 = 1.69801 loss)
I0401 17:20:15.404366 14951 sgd_solver.cpp:105] Iteration 7686, lr = 0.001
I0401 17:20:21.715550 14951 solver.cpp:218] Iteration 7700 (2.21831 iter/s, 6.31111s/14 iters), loss = 1.25732
I0401 17:20:21.715601 14951 solver.cpp:237]     Train net output #0: loss = 1.25732 (* 1 = 1.25732 loss)
I0401 17:20:21.715608 14951 sgd_solver.cpp:105] Iteration 7700, lr = 0.001
I0401 17:20:27.874795 14951 solver.cpp:218] Iteration 7714 (2.27305 iter/s, 6.15911s/14 iters), loss = 1.03873
I0401 17:20:27.874841 14951 solver.cpp:237]     Train net output #0: loss = 1.03873 (* 1 = 1.03873 loss)
I0401 17:20:27.874847 14951 sgd_solver.cpp:105] Iteration 7714, lr = 0.001
I0401 17:20:34.229089 14951 solver.cpp:218] Iteration 7728 (2.20328 iter/s, 6.35417s/14 iters), loss = 1.05589
I0401 17:20:34.229133 14951 solver.cpp:237]     Train net output #0: loss = 1.05589 (* 1 = 1.05589 loss)
I0401 17:20:34.229138 14951 sgd_solver.cpp:105] Iteration 7728, lr = 0.001
I0401 17:20:38.228235 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:20:40.369982 14951 solver.cpp:218] Iteration 7742 (2.27984 iter/s, 6.14077s/14 iters), loss = 1.20218
I0401 17:20:40.370082 14951 solver.cpp:237]     Train net output #0: loss = 1.20218 (* 1 = 1.20218 loss)
I0401 17:20:40.370088 14951 sgd_solver.cpp:105] Iteration 7742, lr = 0.001
I0401 17:20:44.262805 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7752.caffemodel
I0401 17:20:47.283314 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7752.solverstate
I0401 17:20:49.595829 14951 solver.cpp:330] Iteration 7752, Testing net (#0)
I0401 17:20:49.595849 14951 net.cpp:676] Ignoring source layer train-data
I0401 17:20:51.724776 14951 solver.cpp:397]     Test net output #0: accuracy = 0.209135
I0401 17:20:51.724802 14951 solver.cpp:397]     Test net output #1: loss = 3.92554 (* 1 = 3.92554 loss)
I0401 17:20:51.841245 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:20:52.693987 14951 solver.cpp:218] Iteration 7756 (1.13602 iter/s, 12.3238s/14 iters), loss = 1.47783
I0401 17:20:52.694041 14951 solver.cpp:237]     Train net output #0: loss = 1.47783 (* 1 = 1.47783 loss)
I0401 17:20:52.694049 14951 sgd_solver.cpp:105] Iteration 7756, lr = 0.001
I0401 17:20:58.563066 14951 solver.cpp:218] Iteration 7770 (2.38543 iter/s, 5.86895s/14 iters), loss = 2.00969
I0401 17:20:58.563105 14951 solver.cpp:237]     Train net output #0: loss = 2.00969 (* 1 = 2.00969 loss)
I0401 17:20:58.563112 14951 sgd_solver.cpp:105] Iteration 7770, lr = 0.001
I0401 17:21:04.642010 14951 solver.cpp:218] Iteration 7784 (2.30307 iter/s, 6.07883s/14 iters), loss = 1.4399
I0401 17:21:04.642046 14951 solver.cpp:237]     Train net output #0: loss = 1.4399 (* 1 = 1.4399 loss)
I0401 17:21:04.642051 14951 sgd_solver.cpp:105] Iteration 7784, lr = 0.001
I0401 17:21:10.708494 14951 solver.cpp:218] Iteration 7798 (2.30781 iter/s, 6.06637s/14 iters), loss = 1.15441
I0401 17:21:10.708655 14951 solver.cpp:237]     Train net output #0: loss = 1.15441 (* 1 = 1.15441 loss)
I0401 17:21:10.708664 14951 sgd_solver.cpp:105] Iteration 7798, lr = 0.001
I0401 17:21:16.687250 14951 solver.cpp:218] Iteration 7812 (2.34172 iter/s, 5.97852s/14 iters), loss = 1.31431
I0401 17:21:16.687296 14951 solver.cpp:237]     Train net output #0: loss = 1.31431 (* 1 = 1.31431 loss)
I0401 17:21:16.687301 14951 sgd_solver.cpp:105] Iteration 7812, lr = 0.001
I0401 17:21:22.539052 14951 solver.cpp:218] Iteration 7826 (2.39248 iter/s, 5.85168s/14 iters), loss = 1.11537
I0401 17:21:22.539099 14951 solver.cpp:237]     Train net output #0: loss = 1.11537 (* 1 = 1.11537 loss)
I0401 17:21:22.539105 14951 sgd_solver.cpp:105] Iteration 7826, lr = 0.001
I0401 17:21:28.830497 14951 solver.cpp:218] Iteration 7840 (2.22529 iter/s, 6.29132s/14 iters), loss = 1.18289
I0401 17:21:28.830559 14951 solver.cpp:237]     Train net output #0: loss = 1.18289 (* 1 = 1.18289 loss)
I0401 17:21:28.830566 14951 sgd_solver.cpp:105] Iteration 7840, lr = 0.001
I0401 17:21:33.525694 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:21:34.920101 14951 solver.cpp:218] Iteration 7854 (2.29905 iter/s, 6.08947s/14 iters), loss = 1.34679
I0401 17:21:34.920154 14951 solver.cpp:237]     Train net output #0: loss = 1.34679 (* 1 = 1.34679 loss)
I0401 17:21:34.920164 14951 sgd_solver.cpp:105] Iteration 7854, lr = 0.001
I0401 17:21:39.692250 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7866.caffemodel
I0401 17:21:42.701995 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7866.solverstate
I0401 17:21:45.033160 14951 solver.cpp:330] Iteration 7866, Testing net (#0)
I0401 17:21:45.033185 14951 net.cpp:676] Ignoring source layer train-data
I0401 17:21:47.147064 14951 solver.cpp:397]     Test net output #0: accuracy = 0.239183
I0401 17:21:47.147100 14951 solver.cpp:397]     Test net output #1: loss = 3.679 (* 1 = 3.679 loss)
I0401 17:21:47.225104 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:21:47.547914 14951 solver.cpp:218] Iteration 7868 (1.10868 iter/s, 12.6276s/14 iters), loss = 1.55286
I0401 17:21:47.547960 14951 solver.cpp:237]     Train net output #0: loss = 1.55286 (* 1 = 1.55286 loss)
I0401 17:21:47.547966 14951 sgd_solver.cpp:105] Iteration 7868, lr = 0.001
I0401 17:21:53.261958 14951 solver.cpp:218] Iteration 7882 (2.45015 iter/s, 5.71392s/14 iters), loss = 1.55381
I0401 17:21:53.262013 14951 solver.cpp:237]     Train net output #0: loss = 1.55381 (* 1 = 1.55381 loss)
I0401 17:21:53.262022 14951 sgd_solver.cpp:105] Iteration 7882, lr = 0.001
I0401 17:21:59.321121 14951 solver.cpp:218] Iteration 7896 (2.3106 iter/s, 6.05903s/14 iters), loss = 1.2564
I0401 17:21:59.321174 14951 solver.cpp:237]     Train net output #0: loss = 1.2564 (* 1 = 1.2564 loss)
I0401 17:21:59.321184 14951 sgd_solver.cpp:105] Iteration 7896, lr = 0.001
I0401 17:22:05.607926 14951 solver.cpp:218] Iteration 7910 (2.22693 iter/s, 6.28667s/14 iters), loss = 1.253
I0401 17:22:05.607976 14951 solver.cpp:237]     Train net output #0: loss = 1.253 (* 1 = 1.253 loss)
I0401 17:22:05.607985 14951 sgd_solver.cpp:105] Iteration 7910, lr = 0.001
I0401 17:22:11.814422 14951 solver.cpp:218] Iteration 7924 (2.25575 iter/s, 6.20637s/14 iters), loss = 1.43668
I0401 17:22:11.814474 14951 solver.cpp:237]     Train net output #0: loss = 1.43668 (* 1 = 1.43668 loss)
I0401 17:22:11.814484 14951 sgd_solver.cpp:105] Iteration 7924, lr = 0.001
I0401 17:22:17.819284 14951 solver.cpp:218] Iteration 7938 (2.33149 iter/s, 6.00473s/14 iters), loss = 1.16531
I0401 17:22:17.819427 14951 solver.cpp:237]     Train net output #0: loss = 1.16531 (* 1 = 1.16531 loss)
I0401 17:22:17.819437 14951 sgd_solver.cpp:105] Iteration 7938, lr = 0.001
I0401 17:22:23.888264 14951 solver.cpp:218] Iteration 7952 (2.3069 iter/s, 6.06876s/14 iters), loss = 0.928069
I0401 17:22:23.888306 14951 solver.cpp:237]     Train net output #0: loss = 0.928069 (* 1 = 0.928069 loss)
I0401 17:22:23.888311 14951 sgd_solver.cpp:105] Iteration 7952, lr = 0.001
I0401 17:22:29.438683 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:22:30.010419 14951 solver.cpp:218] Iteration 7966 (2.28682 iter/s, 6.12204s/14 iters), loss = 1.03957
I0401 17:22:30.010468 14951 solver.cpp:237]     Train net output #0: loss = 1.03957 (* 1 = 1.03957 loss)
I0401 17:22:30.010474 14951 sgd_solver.cpp:105] Iteration 7966, lr = 0.001
I0401 17:22:35.295734 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7980.caffemodel
I0401 17:22:38.328900 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7980.solverstate
I0401 17:22:40.626961 14951 solver.cpp:330] Iteration 7980, Testing net (#0)
I0401 17:22:40.626978 14951 net.cpp:676] Ignoring source layer train-data
I0401 17:22:42.698134 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:22:42.750207 14951 solver.cpp:397]     Test net output #0: accuracy = 0.230769
I0401 17:22:42.750236 14951 solver.cpp:397]     Test net output #1: loss = 3.72961 (* 1 = 3.72961 loss)
I0401 17:22:42.886538 14951 solver.cpp:218] Iteration 7980 (1.0873 iter/s, 12.8759s/14 iters), loss = 0.892821
I0401 17:22:42.886605 14951 solver.cpp:237]     Train net output #0: loss = 0.892821 (* 1 = 0.892821 loss)
I0401 17:22:42.886615 14951 sgd_solver.cpp:105] Iteration 7980, lr = 0.001
I0401 17:22:47.973037 14951 solver.cpp:218] Iteration 7994 (2.75246 iter/s, 5.08636s/14 iters), loss = 1.3459
I0401 17:22:47.973151 14951 solver.cpp:237]     Train net output #0: loss = 1.3459 (* 1 = 1.3459 loss)
I0401 17:22:47.973160 14951 sgd_solver.cpp:105] Iteration 7994, lr = 0.001
I0401 17:22:53.898423 14951 solver.cpp:218] Iteration 8008 (2.36279 iter/s, 5.9252s/14 iters), loss = 1.18197
I0401 17:22:53.898474 14951 solver.cpp:237]     Train net output #0: loss = 1.18197 (* 1 = 1.18197 loss)
I0401 17:22:53.898481 14951 sgd_solver.cpp:105] Iteration 8008, lr = 0.001
I0401 17:22:59.997391 14951 solver.cpp:218] Iteration 8022 (2.29552 iter/s, 6.09884s/14 iters), loss = 1.19035
I0401 17:22:59.997428 14951 solver.cpp:237]     Train net output #0: loss = 1.19035 (* 1 = 1.19035 loss)
I0401 17:22:59.997434 14951 sgd_solver.cpp:105] Iteration 8022, lr = 0.001
I0401 17:23:06.237442 14951 solver.cpp:218] Iteration 8036 (2.24361 iter/s, 6.23993s/14 iters), loss = 1.24974
I0401 17:23:06.237488 14951 solver.cpp:237]     Train net output #0: loss = 1.24974 (* 1 = 1.24974 loss)
I0401 17:23:06.237493 14951 sgd_solver.cpp:105] Iteration 8036, lr = 0.001
I0401 17:23:12.345111 14951 solver.cpp:218] Iteration 8050 (2.29225 iter/s, 6.10755s/14 iters), loss = 1.26881
I0401 17:23:12.345153 14951 solver.cpp:237]     Train net output #0: loss = 1.26881 (* 1 = 1.26881 loss)
I0401 17:23:12.345158 14951 sgd_solver.cpp:105] Iteration 8050, lr = 0.001
I0401 17:23:18.507722 14951 solver.cpp:218] Iteration 8064 (2.27181 iter/s, 6.16249s/14 iters), loss = 1.16326
I0401 17:23:18.507884 14951 solver.cpp:237]     Train net output #0: loss = 1.16326 (* 1 = 1.16326 loss)
I0401 17:23:18.507894 14951 sgd_solver.cpp:105] Iteration 8064, lr = 0.001
I0401 17:23:24.686707 14951 solver.cpp:218] Iteration 8078 (2.26583 iter/s, 6.17875s/14 iters), loss = 1.10678
I0401 17:23:24.686755 14951 solver.cpp:237]     Train net output #0: loss = 1.10678 (* 1 = 1.10678 loss)
I0401 17:23:24.686764 14951 sgd_solver.cpp:105] Iteration 8078, lr = 0.001
I0401 17:23:24.853796 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:23:30.465865 14951 solver.cpp:218] Iteration 8092 (2.42255 iter/s, 5.77904s/14 iters), loss = 1.09747
I0401 17:23:30.465909 14951 solver.cpp:237]     Train net output #0: loss = 1.09747 (* 1 = 1.09747 loss)
I0401 17:23:30.465914 14951 sgd_solver.cpp:105] Iteration 8092, lr = 0.001
I0401 17:23:30.822005 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8094.caffemodel
I0401 17:23:33.823019 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8094.solverstate
I0401 17:23:36.137084 14951 solver.cpp:330] Iteration 8094, Testing net (#0)
I0401 17:23:36.137107 14951 net.cpp:676] Ignoring source layer train-data
I0401 17:23:38.108837 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:23:38.231572 14951 solver.cpp:397]     Test net output #0: accuracy = 0.237981
I0401 17:23:38.231606 14951 solver.cpp:397]     Test net output #1: loss = 3.76693 (* 1 = 3.76693 loss)
I0401 17:23:42.548378 14951 solver.cpp:218] Iteration 8106 (1.15872 iter/s, 12.0823s/14 iters), loss = 0.961703
I0401 17:23:42.548420 14951 solver.cpp:237]     Train net output #0: loss = 0.961703 (* 1 = 0.961703 loss)
I0401 17:23:42.548426 14951 sgd_solver.cpp:105] Iteration 8106, lr = 0.001
I0401 17:23:48.289016 14951 solver.cpp:218] Iteration 8120 (2.4388 iter/s, 5.74052s/14 iters), loss = 1.26914
I0401 17:23:48.289054 14951 solver.cpp:237]     Train net output #0: loss = 1.26914 (* 1 = 1.26914 loss)
I0401 17:23:48.289059 14951 sgd_solver.cpp:105] Iteration 8120, lr = 0.001
I0401 17:23:54.450774 14951 solver.cpp:218] Iteration 8134 (2.27212 iter/s, 6.16164s/14 iters), loss = 1.23385
I0401 17:23:54.450894 14951 solver.cpp:237]     Train net output #0: loss = 1.23385 (* 1 = 1.23385 loss)
I0401 17:23:54.450903 14951 sgd_solver.cpp:105] Iteration 8134, lr = 0.001
I0401 17:24:00.333392 14951 solver.cpp:218] Iteration 8148 (2.37997 iter/s, 5.88242s/14 iters), loss = 1.04721
I0401 17:24:00.333437 14951 solver.cpp:237]     Train net output #0: loss = 1.04721 (* 1 = 1.04721 loss)
I0401 17:24:00.333444 14951 sgd_solver.cpp:105] Iteration 8148, lr = 0.001
I0401 17:24:06.377915 14951 solver.cpp:218] Iteration 8162 (2.31619 iter/s, 6.0444s/14 iters), loss = 1.09961
I0401 17:24:06.377969 14951 solver.cpp:237]     Train net output #0: loss = 1.09961 (* 1 = 1.09961 loss)
I0401 17:24:06.377977 14951 sgd_solver.cpp:105] Iteration 8162, lr = 0.001
I0401 17:24:12.465507 14951 solver.cpp:218] Iteration 8176 (2.29981 iter/s, 6.08747s/14 iters), loss = 1.05651
I0401 17:24:12.465553 14951 solver.cpp:237]     Train net output #0: loss = 1.05651 (* 1 = 1.05651 loss)
I0401 17:24:12.465559 14951 sgd_solver.cpp:105] Iteration 8176, lr = 0.001
I0401 17:24:18.602605 14951 solver.cpp:218] Iteration 8190 (2.28126 iter/s, 6.13697s/14 iters), loss = 1.08896
I0401 17:24:18.602663 14951 solver.cpp:237]     Train net output #0: loss = 1.08896 (* 1 = 1.08896 loss)
I0401 17:24:18.602672 14951 sgd_solver.cpp:105] Iteration 8190, lr = 0.001
I0401 17:24:19.589020 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:24:24.675364 14951 solver.cpp:218] Iteration 8204 (2.30543 iter/s, 6.07263s/14 iters), loss = 1.22749
I0401 17:24:24.675530 14951 solver.cpp:237]     Train net output #0: loss = 1.22749 (* 1 = 1.22749 loss)
I0401 17:24:24.675539 14951 sgd_solver.cpp:105] Iteration 8204, lr = 0.001
I0401 17:24:25.905362 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8208.caffemodel
I0401 17:24:28.921830 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8208.solverstate
I0401 17:24:31.230029 14951 solver.cpp:330] Iteration 8208, Testing net (#0)
I0401 17:24:31.230048 14951 net.cpp:676] Ignoring source layer train-data
I0401 17:24:33.204126 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:24:33.377884 14951 solver.cpp:397]     Test net output #0: accuracy = 0.21274
I0401 17:24:33.377918 14951 solver.cpp:397]     Test net output #1: loss = 3.85723 (* 1 = 3.85723 loss)
I0401 17:24:37.004189 14951 solver.cpp:218] Iteration 8218 (1.13558 iter/s, 12.3285s/14 iters), loss = 1.23635
I0401 17:24:37.004238 14951 solver.cpp:237]     Train net output #0: loss = 1.23635 (* 1 = 1.23635 loss)
I0401 17:24:37.004245 14951 sgd_solver.cpp:105] Iteration 8218, lr = 0.001
I0401 17:24:43.131623 14951 solver.cpp:218] Iteration 8232 (2.28485 iter/s, 6.12731s/14 iters), loss = 1.21727
I0401 17:24:43.131664 14951 solver.cpp:237]     Train net output #0: loss = 1.21727 (* 1 = 1.21727 loss)
I0401 17:24:43.131670 14951 sgd_solver.cpp:105] Iteration 8232, lr = 0.001
I0401 17:24:49.265125 14951 solver.cpp:218] Iteration 8246 (2.28259 iter/s, 6.13338s/14 iters), loss = 1.04463
I0401 17:24:49.265164 14951 solver.cpp:237]     Train net output #0: loss = 1.04463 (* 1 = 1.04463 loss)
I0401 17:24:49.265169 14951 sgd_solver.cpp:105] Iteration 8246, lr = 0.001
I0401 17:24:55.415663 14951 solver.cpp:218] Iteration 8260 (2.27627 iter/s, 6.15042s/14 iters), loss = 1.12628
I0401 17:24:55.415763 14951 solver.cpp:237]     Train net output #0: loss = 1.12628 (* 1 = 1.12628 loss)
I0401 17:24:55.415771 14951 sgd_solver.cpp:105] Iteration 8260, lr = 0.001
I0401 17:25:01.810374 14951 solver.cpp:218] Iteration 8274 (2.18937 iter/s, 6.39453s/14 iters), loss = 1.10945
I0401 17:25:01.810421 14951 solver.cpp:237]     Train net output #0: loss = 1.10945 (* 1 = 1.10945 loss)
I0401 17:25:01.810427 14951 sgd_solver.cpp:105] Iteration 8274, lr = 0.001
I0401 17:25:07.997177 14951 solver.cpp:218] Iteration 8288 (2.26293 iter/s, 6.18668s/14 iters), loss = 0.978617
I0401 17:25:07.997217 14951 solver.cpp:237]     Train net output #0: loss = 0.978617 (* 1 = 0.978617 loss)
I0401 17:25:07.997222 14951 sgd_solver.cpp:105] Iteration 8288, lr = 0.001
I0401 17:25:14.181149 14951 solver.cpp:218] Iteration 8302 (2.26396 iter/s, 6.18385s/14 iters), loss = 0.879781
I0401 17:25:14.181203 14951 solver.cpp:237]     Train net output #0: loss = 0.879781 (* 1 = 0.879781 loss)
I0401 17:25:14.181211 14951 sgd_solver.cpp:105] Iteration 8302, lr = 0.001
I0401 17:25:16.033241 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:25:20.332708 14951 solver.cpp:218] Iteration 8316 (2.27589 iter/s, 6.15143s/14 iters), loss = 1.37013
I0401 17:25:20.332744 14951 solver.cpp:237]     Train net output #0: loss = 1.37013 (* 1 = 1.37013 loss)
I0401 17:25:20.332749 14951 sgd_solver.cpp:105] Iteration 8316, lr = 0.001
I0401 17:25:22.522969 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8322.caffemodel
I0401 17:25:25.588280 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8322.solverstate
I0401 17:25:27.906826 14951 solver.cpp:330] Iteration 8322, Testing net (#0)
I0401 17:25:27.906844 14951 net.cpp:676] Ignoring source layer train-data
I0401 17:25:29.809090 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:25:30.051514 14951 solver.cpp:397]     Test net output #0: accuracy = 0.219952
I0401 17:25:30.051543 14951 solver.cpp:397]     Test net output #1: loss = 3.90574 (* 1 = 3.90574 loss)
I0401 17:25:32.924947 14951 solver.cpp:218] Iteration 8330 (1.11181 iter/s, 12.5921s/14 iters), loss = 1.06289
I0401 17:25:32.924981 14951 solver.cpp:237]     Train net output #0: loss = 1.06289 (* 1 = 1.06289 loss)
I0401 17:25:32.924986 14951 sgd_solver.cpp:105] Iteration 8330, lr = 0.001
I0401 17:25:38.904367 14951 solver.cpp:218] Iteration 8344 (2.34141 iter/s, 5.97931s/14 iters), loss = 1.27861
I0401 17:25:38.904405 14951 solver.cpp:237]     Train net output #0: loss = 1.27861 (* 1 = 1.27861 loss)
I0401 17:25:38.904410 14951 sgd_solver.cpp:105] Iteration 8344, lr = 0.001
I0401 17:25:44.954568 14951 solver.cpp:218] Iteration 8358 (2.31402 iter/s, 6.05008s/14 iters), loss = 1.33234
I0401 17:25:44.954612 14951 solver.cpp:237]     Train net output #0: loss = 1.33234 (* 1 = 1.33234 loss)
I0401 17:25:44.954617 14951 sgd_solver.cpp:105] Iteration 8358, lr = 0.001
I0401 17:25:51.044521 14951 solver.cpp:218] Iteration 8372 (2.29891 iter/s, 6.08983s/14 iters), loss = 0.925255
I0401 17:25:51.044560 14951 solver.cpp:237]     Train net output #0: loss = 0.925255 (* 1 = 0.925255 loss)
I0401 17:25:51.044565 14951 sgd_solver.cpp:105] Iteration 8372, lr = 0.001
I0401 17:25:57.085114 14951 solver.cpp:218] Iteration 8386 (2.3177 iter/s, 6.04048s/14 iters), loss = 0.882412
I0401 17:25:57.085228 14951 solver.cpp:237]     Train net output #0: loss = 0.882412 (* 1 = 0.882412 loss)
I0401 17:25:57.085235 14951 sgd_solver.cpp:105] Iteration 8386, lr = 0.001
I0401 17:26:03.211287 14951 solver.cpp:218] Iteration 8400 (2.28535 iter/s, 6.12598s/14 iters), loss = 0.890665
I0401 17:26:03.211344 14951 solver.cpp:237]     Train net output #0: loss = 0.890665 (* 1 = 0.890665 loss)
I0401 17:26:03.211354 14951 sgd_solver.cpp:105] Iteration 8400, lr = 0.001
I0401 17:26:09.256625 14951 solver.cpp:218] Iteration 8414 (2.31589 iter/s, 6.0452s/14 iters), loss = 0.803235
I0401 17:26:09.256676 14951 solver.cpp:237]     Train net output #0: loss = 0.803235 (* 1 = 0.803235 loss)
I0401 17:26:09.256682 14951 sgd_solver.cpp:105] Iteration 8414, lr = 0.001
I0401 17:26:11.763790 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:26:15.281476 14951 solver.cpp:218] Iteration 8428 (2.32376 iter/s, 6.02472s/14 iters), loss = 0.85243
I0401 17:26:15.281520 14951 solver.cpp:237]     Train net output #0: loss = 0.85243 (* 1 = 0.85243 loss)
I0401 17:26:15.281527 14951 sgd_solver.cpp:105] Iteration 8428, lr = 0.001
I0401 17:26:18.299367 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8436.caffemodel
I0401 17:26:21.284962 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8436.solverstate
I0401 17:26:23.621702 14951 solver.cpp:330] Iteration 8436, Testing net (#0)
I0401 17:26:23.621726 14951 net.cpp:676] Ignoring source layer train-data
I0401 17:26:25.409587 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:26:25.723721 14951 solver.cpp:397]     Test net output #0: accuracy = 0.231971
I0401 17:26:25.723748 14951 solver.cpp:397]     Test net output #1: loss = 3.77653 (* 1 = 3.77653 loss)
I0401 17:26:27.638708 14951 solver.cpp:218] Iteration 8442 (1.13296 iter/s, 12.357s/14 iters), loss = 0.767109
I0401 17:26:27.638798 14951 solver.cpp:237]     Train net output #0: loss = 0.767109 (* 1 = 0.767109 loss)
I0401 17:26:27.638804 14951 sgd_solver.cpp:105] Iteration 8442, lr = 0.001
I0401 17:26:33.617276 14951 solver.cpp:218] Iteration 8456 (2.34176 iter/s, 5.9784s/14 iters), loss = 1.11686
I0401 17:26:33.617344 14951 solver.cpp:237]     Train net output #0: loss = 1.11686 (* 1 = 1.11686 loss)
I0401 17:26:33.617353 14951 sgd_solver.cpp:105] Iteration 8456, lr = 0.001
I0401 17:26:39.625443 14951 solver.cpp:218] Iteration 8470 (2.33022 iter/s, 6.00803s/14 iters), loss = 1.16064
I0401 17:26:39.625494 14951 solver.cpp:237]     Train net output #0: loss = 1.16064 (* 1 = 1.16064 loss)
I0401 17:26:39.625501 14951 sgd_solver.cpp:105] Iteration 8470, lr = 0.001
I0401 17:26:45.777025 14951 solver.cpp:218] Iteration 8484 (2.27589 iter/s, 6.15145s/14 iters), loss = 0.9633
I0401 17:26:45.777068 14951 solver.cpp:237]     Train net output #0: loss = 0.9633 (* 1 = 0.9633 loss)
I0401 17:26:45.777074 14951 sgd_solver.cpp:105] Iteration 8484, lr = 0.001
I0401 17:26:51.962992 14951 solver.cpp:218] Iteration 8498 (2.26323 iter/s, 6.18584s/14 iters), loss = 1.06448
I0401 17:26:51.963028 14951 solver.cpp:237]     Train net output #0: loss = 1.06448 (* 1 = 1.06448 loss)
I0401 17:26:51.963034 14951 sgd_solver.cpp:105] Iteration 8498, lr = 0.001
I0401 17:26:53.635174 14951 blocking_queue.cpp:49] Waiting for data
I0401 17:26:58.091830 14951 solver.cpp:218] Iteration 8512 (2.28433 iter/s, 6.12872s/14 iters), loss = 0.948843
I0401 17:26:58.091993 14951 solver.cpp:237]     Train net output #0: loss = 0.948843 (* 1 = 0.948843 loss)
I0401 17:26:58.092003 14951 sgd_solver.cpp:105] Iteration 8512, lr = 0.001
I0401 17:27:04.411371 14951 solver.cpp:218] Iteration 8526 (2.21543 iter/s, 6.31931s/14 iters), loss = 0.7422
I0401 17:27:04.411408 14951 solver.cpp:237]     Train net output #0: loss = 0.7422 (* 1 = 0.7422 loss)
I0401 17:27:04.411413 14951 sgd_solver.cpp:105] Iteration 8526, lr = 0.001
I0401 17:27:07.743677 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:27:10.604247 14951 solver.cpp:218] Iteration 8540 (2.26071 iter/s, 6.19276s/14 iters), loss = 0.901663
I0401 17:27:10.604290 14951 solver.cpp:237]     Train net output #0: loss = 0.901663 (* 1 = 0.901663 loss)
I0401 17:27:10.604296 14951 sgd_solver.cpp:105] Iteration 8540, lr = 0.001
I0401 17:27:14.531867 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8550.caffemodel
I0401 17:27:17.523878 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8550.solverstate
I0401 17:27:21.510643 14951 solver.cpp:330] Iteration 8550, Testing net (#0)
I0401 17:27:21.510663 14951 net.cpp:676] Ignoring source layer train-data
I0401 17:27:23.231263 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:27:23.606242 14951 solver.cpp:397]     Test net output #0: accuracy = 0.22476
I0401 17:27:23.606274 14951 solver.cpp:397]     Test net output #1: loss = 3.84349 (* 1 = 3.84349 loss)
I0401 17:27:24.628093 14951 solver.cpp:218] Iteration 8554 (0.998314 iter/s, 14.0236s/14 iters), loss = 1.08933
I0401 17:27:24.628134 14951 solver.cpp:237]     Train net output #0: loss = 1.08933 (* 1 = 1.08933 loss)
I0401 17:27:24.628139 14951 sgd_solver.cpp:105] Iteration 8554, lr = 0.001
I0401 17:27:30.444169 14951 solver.cpp:218] Iteration 8568 (2.40717 iter/s, 5.81596s/14 iters), loss = 1.14522
I0401 17:27:30.444288 14951 solver.cpp:237]     Train net output #0: loss = 1.14522 (* 1 = 1.14522 loss)
I0401 17:27:30.444298 14951 sgd_solver.cpp:105] Iteration 8568, lr = 0.001
I0401 17:27:36.509794 14951 solver.cpp:218] Iteration 8582 (2.30816 iter/s, 6.06543s/14 iters), loss = 0.995684
I0401 17:27:36.509852 14951 solver.cpp:237]     Train net output #0: loss = 0.995684 (* 1 = 0.995684 loss)
I0401 17:27:36.509861 14951 sgd_solver.cpp:105] Iteration 8582, lr = 0.001
I0401 17:27:42.729696 14951 solver.cpp:218] Iteration 8596 (2.25089 iter/s, 6.21977s/14 iters), loss = 1.09204
I0401 17:27:42.729748 14951 solver.cpp:237]     Train net output #0: loss = 1.09204 (* 1 = 1.09204 loss)
I0401 17:27:42.729756 14951 sgd_solver.cpp:105] Iteration 8596, lr = 0.001
I0401 17:27:48.639865 14951 solver.cpp:218] Iteration 8610 (2.36885 iter/s, 5.91005s/14 iters), loss = 0.874843
I0401 17:27:48.639902 14951 solver.cpp:237]     Train net output #0: loss = 0.874843 (* 1 = 0.874843 loss)
I0401 17:27:48.639909 14951 sgd_solver.cpp:105] Iteration 8610, lr = 0.001
I0401 17:27:54.929566 14951 solver.cpp:218] Iteration 8624 (2.2259 iter/s, 6.28959s/14 iters), loss = 0.820028
I0401 17:27:54.929603 14951 solver.cpp:237]     Train net output #0: loss = 0.820028 (* 1 = 0.820028 loss)
I0401 17:27:54.929608 14951 sgd_solver.cpp:105] Iteration 8624, lr = 0.001
I0401 17:28:01.042922 14951 solver.cpp:218] Iteration 8638 (2.29011 iter/s, 6.11324s/14 iters), loss = 0.778713
I0401 17:28:01.043061 14951 solver.cpp:237]     Train net output #0: loss = 0.778713 (* 1 = 0.778713 loss)
I0401 17:28:01.043068 14951 sgd_solver.cpp:105] Iteration 8638, lr = 0.001
I0401 17:28:05.102001 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:28:07.107398 14951 solver.cpp:218] Iteration 8652 (2.30861 iter/s, 6.06426s/14 iters), loss = 0.812937
I0401 17:28:07.107457 14951 solver.cpp:237]     Train net output #0: loss = 0.812937 (* 1 = 0.812937 loss)
I0401 17:28:07.107465 14951 sgd_solver.cpp:105] Iteration 8652, lr = 0.001
I0401 17:28:11.979748 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8664.caffemodel
I0401 17:28:16.330582 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8664.solverstate
I0401 17:28:19.142859 14951 solver.cpp:330] Iteration 8664, Testing net (#0)
I0401 17:28:19.142882 14951 net.cpp:676] Ignoring source layer train-data
I0401 17:28:20.924547 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:28:21.361268 14951 solver.cpp:397]     Test net output #0: accuracy = 0.233173
I0401 17:28:21.361295 14951 solver.cpp:397]     Test net output #1: loss = 3.81892 (* 1 = 3.81892 loss)
I0401 17:28:21.772666 14951 solver.cpp:218] Iteration 8666 (0.954651 iter/s, 14.6651s/14 iters), loss = 1.21195
I0401 17:28:21.772711 14951 solver.cpp:237]     Train net output #0: loss = 1.21195 (* 1 = 1.21195 loss)
I0401 17:28:21.772717 14951 sgd_solver.cpp:105] Iteration 8666, lr = 0.001
I0401 17:28:27.428305 14951 solver.cpp:218] Iteration 8680 (2.47546 iter/s, 5.65552s/14 iters), loss = 0.824957
I0401 17:28:27.428344 14951 solver.cpp:237]     Train net output #0: loss = 0.824957 (* 1 = 0.824957 loss)
I0401 17:28:27.428349 14951 sgd_solver.cpp:105] Iteration 8680, lr = 0.001
I0401 17:28:33.501658 14951 solver.cpp:218] Iteration 8694 (2.30519 iter/s, 6.07324s/14 iters), loss = 0.992338
I0401 17:28:33.501801 14951 solver.cpp:237]     Train net output #0: loss = 0.992338 (* 1 = 0.992338 loss)
I0401 17:28:33.501809 14951 sgd_solver.cpp:105] Iteration 8694, lr = 0.001
I0401 17:28:39.449482 14951 solver.cpp:218] Iteration 8708 (2.3539 iter/s, 5.94759s/14 iters), loss = 0.931984
I0401 17:28:39.449580 14951 solver.cpp:237]     Train net output #0: loss = 0.931984 (* 1 = 0.931984 loss)
I0401 17:28:39.449590 14951 sgd_solver.cpp:105] Iteration 8708, lr = 0.001
I0401 17:28:45.343401 14951 solver.cpp:218] Iteration 8722 (2.37539 iter/s, 5.89377s/14 iters), loss = 0.887997
I0401 17:28:45.343452 14951 solver.cpp:237]     Train net output #0: loss = 0.887997 (* 1 = 0.887997 loss)
I0401 17:28:45.343461 14951 sgd_solver.cpp:105] Iteration 8722, lr = 0.001
I0401 17:28:51.369400 14951 solver.cpp:218] Iteration 8736 (2.32331 iter/s, 6.02587s/14 iters), loss = 0.565053
I0401 17:28:51.369455 14951 solver.cpp:237]     Train net output #0: loss = 0.565053 (* 1 = 0.565053 loss)
I0401 17:28:51.369465 14951 sgd_solver.cpp:105] Iteration 8736, lr = 0.001
I0401 17:28:57.317937 14951 solver.cpp:218] Iteration 8750 (2.35357 iter/s, 5.9484s/14 iters), loss = 0.653143
I0401 17:28:57.317983 14951 solver.cpp:237]     Train net output #0: loss = 0.653143 (* 1 = 0.653143 loss)
I0401 17:28:57.317989 14951 sgd_solver.cpp:105] Iteration 8750, lr = 0.001
I0401 17:29:02.104558 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:29:03.271706 14951 solver.cpp:218] Iteration 8764 (2.3515 iter/s, 5.95364s/14 iters), loss = 0.808546
I0401 17:29:03.271755 14951 solver.cpp:237]     Train net output #0: loss = 0.808546 (* 1 = 0.808546 loss)
I0401 17:29:03.271760 14951 sgd_solver.cpp:105] Iteration 8764, lr = 0.001
I0401 17:29:08.682354 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8778.caffemodel
I0401 17:29:11.709120 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8778.solverstate
I0401 17:29:14.007375 14951 solver.cpp:330] Iteration 8778, Testing net (#0)
I0401 17:29:14.007395 14951 net.cpp:676] Ignoring source layer train-data
I0401 17:29:15.601544 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:29:16.096547 14951 solver.cpp:397]     Test net output #0: accuracy = 0.25
I0401 17:29:16.096583 14951 solver.cpp:397]     Test net output #1: loss = 3.67972 (* 1 = 3.67972 loss)
I0401 17:29:16.232733 14951 solver.cpp:218] Iteration 8778 (1.08018 iter/s, 12.9608s/14 iters), loss = 0.922965
I0401 17:29:16.232782 14951 solver.cpp:237]     Train net output #0: loss = 0.922965 (* 1 = 0.922965 loss)
I0401 17:29:16.232791 14951 sgd_solver.cpp:105] Iteration 8778, lr = 0.001
I0401 17:29:21.524226 14951 solver.cpp:218] Iteration 8792 (2.64582 iter/s, 5.29137s/14 iters), loss = 1.14677
I0401 17:29:21.524291 14951 solver.cpp:237]     Train net output #0: loss = 1.14677 (* 1 = 1.14677 loss)
I0401 17:29:21.524298 14951 sgd_solver.cpp:105] Iteration 8792, lr = 0.001
I0401 17:29:27.441462 14951 solver.cpp:218] Iteration 8806 (2.36602 iter/s, 5.9171s/14 iters), loss = 0.815305
I0401 17:29:27.441504 14951 solver.cpp:237]     Train net output #0: loss = 0.815305 (* 1 = 0.815305 loss)
I0401 17:29:27.441510 14951 sgd_solver.cpp:105] Iteration 8806, lr = 0.001
I0401 17:29:33.566462 14951 solver.cpp:218] Iteration 8820 (2.28576 iter/s, 6.12487s/14 iters), loss = 0.915069
I0401 17:29:33.566520 14951 solver.cpp:237]     Train net output #0: loss = 0.915069 (* 1 = 0.915069 loss)
I0401 17:29:33.566529 14951 sgd_solver.cpp:105] Iteration 8820, lr = 0.001
I0401 17:29:39.665123 14951 solver.cpp:218] Iteration 8834 (2.29564 iter/s, 6.09852s/14 iters), loss = 0.810651
I0401 17:29:39.665248 14951 solver.cpp:237]     Train net output #0: loss = 0.810651 (* 1 = 0.810651 loss)
I0401 17:29:39.665256 14951 sgd_solver.cpp:105] Iteration 8834, lr = 0.001
I0401 17:29:45.732476 14951 solver.cpp:218] Iteration 8848 (2.30751 iter/s, 6.06715s/14 iters), loss = 0.74824
I0401 17:29:45.732515 14951 solver.cpp:237]     Train net output #0: loss = 0.74824 (* 1 = 0.74824 loss)
I0401 17:29:45.732522 14951 sgd_solver.cpp:105] Iteration 8848, lr = 0.001
I0401 17:29:51.712303 14951 solver.cpp:218] Iteration 8862 (2.34125 iter/s, 5.97971s/14 iters), loss = 0.997366
I0401 17:29:51.712355 14951 solver.cpp:237]     Train net output #0: loss = 0.997366 (* 1 = 0.997366 loss)
I0401 17:29:51.712363 14951 sgd_solver.cpp:105] Iteration 8862, lr = 0.001
I0401 17:29:57.669271 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:29:58.071741 14951 solver.cpp:218] Iteration 8876 (2.2015 iter/s, 6.3593s/14 iters), loss = 0.752227
I0401 17:29:58.071794 14951 solver.cpp:237]     Train net output #0: loss = 0.752227 (* 1 = 0.752227 loss)
I0401 17:29:58.071802 14951 sgd_solver.cpp:105] Iteration 8876, lr = 0.001
I0401 17:30:04.070886 14951 solver.cpp:218] Iteration 8890 (2.33372 iter/s, 5.99902s/14 iters), loss = 0.956861
I0401 17:30:04.070941 14951 solver.cpp:237]     Train net output #0: loss = 0.956861 (* 1 = 0.956861 loss)
I0401 17:30:04.070950 14951 sgd_solver.cpp:105] Iteration 8890, lr = 0.001
I0401 17:30:04.441385 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8892.caffemodel
I0401 17:30:07.454541 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8892.solverstate
I0401 17:30:09.762696 14951 solver.cpp:330] Iteration 8892, Testing net (#0)
I0401 17:30:09.762796 14951 net.cpp:676] Ignoring source layer train-data
I0401 17:30:11.308418 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:30:11.868326 14951 solver.cpp:397]     Test net output #0: accuracy = 0.252404
I0401 17:30:11.868366 14951 solver.cpp:397]     Test net output #1: loss = 3.6945 (* 1 = 3.6945 loss)
I0401 17:30:16.264436 14951 solver.cpp:218] Iteration 8904 (1.14817 iter/s, 12.1934s/14 iters), loss = 1.26336
I0401 17:30:16.264492 14951 solver.cpp:237]     Train net output #0: loss = 1.26336 (* 1 = 1.26336 loss)
I0401 17:30:16.264499 14951 sgd_solver.cpp:105] Iteration 8904, lr = 0.001
I0401 17:30:22.287410 14951 solver.cpp:218] Iteration 8918 (2.32448 iter/s, 6.02284s/14 iters), loss = 0.964206
I0401 17:30:22.287458 14951 solver.cpp:237]     Train net output #0: loss = 0.964206 (* 1 = 0.964206 loss)
I0401 17:30:22.287465 14951 sgd_solver.cpp:105] Iteration 8918, lr = 0.001
I0401 17:30:28.233070 14951 solver.cpp:218] Iteration 8932 (2.35471 iter/s, 5.94554s/14 iters), loss = 0.933862
I0401 17:30:28.233110 14951 solver.cpp:237]     Train net output #0: loss = 0.933862 (* 1 = 0.933862 loss)
I0401 17:30:28.233116 14951 sgd_solver.cpp:105] Iteration 8932, lr = 0.001
I0401 17:30:34.177472 14951 solver.cpp:218] Iteration 8946 (2.35521 iter/s, 5.94428s/14 iters), loss = 0.7396
I0401 17:30:34.177526 14951 solver.cpp:237]     Train net output #0: loss = 0.7396 (* 1 = 0.7396 loss)
I0401 17:30:34.177534 14951 sgd_solver.cpp:105] Iteration 8946, lr = 0.001
I0401 17:30:39.998770 14951 solver.cpp:218] Iteration 8960 (2.40501 iter/s, 5.82117s/14 iters), loss = 0.692055
I0401 17:30:39.998905 14951 solver.cpp:237]     Train net output #0: loss = 0.692055 (* 1 = 0.692055 loss)
I0401 17:30:39.998914 14951 sgd_solver.cpp:105] Iteration 8960, lr = 0.001
I0401 17:30:46.090564 14951 solver.cpp:218] Iteration 8974 (2.29825 iter/s, 6.09158s/14 iters), loss = 0.857366
I0401 17:30:46.090605 14951 solver.cpp:237]     Train net output #0: loss = 0.857366 (* 1 = 0.857366 loss)
I0401 17:30:46.090610 14951 sgd_solver.cpp:105] Iteration 8974, lr = 0.001
I0401 17:30:52.274668 14951 solver.cpp:218] Iteration 8988 (2.26391 iter/s, 6.18399s/14 iters), loss = 0.62841
I0401 17:30:52.274708 14951 solver.cpp:237]     Train net output #0: loss = 0.62841 (* 1 = 0.62841 loss)
I0401 17:30:52.274713 14951 sgd_solver.cpp:105] Iteration 8988, lr = 0.001
I0401 17:30:52.644285 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:30:58.234699 14951 solver.cpp:218] Iteration 9002 (2.34903 iter/s, 5.95991s/14 iters), loss = 1.03616
I0401 17:30:58.234745 14951 solver.cpp:237]     Train net output #0: loss = 1.03616 (* 1 = 1.03616 loss)
I0401 17:30:58.234750 14951 sgd_solver.cpp:105] Iteration 9002, lr = 0.001
I0401 17:30:59.485422 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9006.caffemodel
I0401 17:31:02.476812 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9006.solverstate
I0401 17:31:04.771812 14951 solver.cpp:330] Iteration 9006, Testing net (#0)
I0401 17:31:04.771833 14951 net.cpp:676] Ignoring source layer train-data
I0401 17:31:06.235191 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:31:06.846184 14951 solver.cpp:397]     Test net output #0: accuracy = 0.262019
I0401 17:31:06.846221 14951 solver.cpp:397]     Test net output #1: loss = 3.70374 (* 1 = 3.70374 loss)
I0401 17:31:10.576920 14951 solver.cpp:218] Iteration 9016 (1.13433 iter/s, 12.342s/14 iters), loss = 0.808745
I0401 17:31:10.577021 14951 solver.cpp:237]     Train net output #0: loss = 0.808745 (* 1 = 0.808745 loss)
I0401 17:31:10.577028 14951 sgd_solver.cpp:105] Iteration 9016, lr = 0.001
I0401 17:31:16.495782 14951 solver.cpp:218] Iteration 9030 (2.36539 iter/s, 5.91868s/14 iters), loss = 0.807632
I0401 17:31:16.495831 14951 solver.cpp:237]     Train net output #0: loss = 0.807632 (* 1 = 0.807632 loss)
I0401 17:31:16.495836 14951 sgd_solver.cpp:105] Iteration 9030, lr = 0.001
I0401 17:31:22.391686 14951 solver.cpp:218] Iteration 9044 (2.37458 iter/s, 5.89578s/14 iters), loss = 0.809959
I0401 17:31:22.391731 14951 solver.cpp:237]     Train net output #0: loss = 0.809959 (* 1 = 0.809959 loss)
I0401 17:31:22.391736 14951 sgd_solver.cpp:105] Iteration 9044, lr = 0.001
I0401 17:31:28.501330 14951 solver.cpp:218] Iteration 9058 (2.29151 iter/s, 6.10952s/14 iters), loss = 0.843608
I0401 17:31:28.501381 14951 solver.cpp:237]     Train net output #0: loss = 0.843608 (* 1 = 0.843608 loss)
I0401 17:31:28.501387 14951 sgd_solver.cpp:105] Iteration 9058, lr = 0.001
I0401 17:31:34.582535 14951 solver.cpp:218] Iteration 9072 (2.30222 iter/s, 6.08108s/14 iters), loss = 0.752783
I0401 17:31:34.582585 14951 solver.cpp:237]     Train net output #0: loss = 0.752783 (* 1 = 0.752783 loss)
I0401 17:31:34.582593 14951 sgd_solver.cpp:105] Iteration 9072, lr = 0.001
I0401 17:31:40.615486 14951 solver.cpp:218] Iteration 9086 (2.32064 iter/s, 6.03283s/14 iters), loss = 0.505055
I0401 17:31:40.615576 14951 solver.cpp:237]     Train net output #0: loss = 0.505055 (* 1 = 0.505055 loss)
I0401 17:31:40.615582 14951 sgd_solver.cpp:105] Iteration 9086, lr = 0.001
I0401 17:31:46.578301 14951 solver.cpp:218] Iteration 9100 (2.34795 iter/s, 5.96265s/14 iters), loss = 0.769991
I0401 17:31:46.578343 14951 solver.cpp:237]     Train net output #0: loss = 0.769991 (* 1 = 0.769991 loss)
I0401 17:31:46.578349 14951 sgd_solver.cpp:105] Iteration 9100, lr = 0.001
I0401 17:31:47.667511 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:31:52.570387 14951 solver.cpp:218] Iteration 9114 (2.33646 iter/s, 5.99197s/14 iters), loss = 0.796718
I0401 17:31:52.570433 14951 solver.cpp:237]     Train net output #0: loss = 0.796718 (* 1 = 0.796718 loss)
I0401 17:31:52.570439 14951 sgd_solver.cpp:105] Iteration 9114, lr = 0.001
I0401 17:31:54.827905 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9120.caffemodel
I0401 17:31:57.809670 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9120.solverstate
I0401 17:32:00.100782 14951 solver.cpp:330] Iteration 9120, Testing net (#0)
I0401 17:32:00.100800 14951 net.cpp:676] Ignoring source layer train-data
I0401 17:32:01.480650 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:32:02.161248 14951 solver.cpp:397]     Test net output #0: accuracy = 0.260817
I0401 17:32:02.161284 14951 solver.cpp:397]     Test net output #1: loss = 3.7726 (* 1 = 3.7726 loss)
I0401 17:32:04.976370 14951 solver.cpp:218] Iteration 9128 (1.1285 iter/s, 12.4058s/14 iters), loss = 0.698328
I0401 17:32:04.976420 14951 solver.cpp:237]     Train net output #0: loss = 0.698328 (* 1 = 0.698328 loss)
I0401 17:32:04.976426 14951 sgd_solver.cpp:105] Iteration 9128, lr = 0.001
I0401 17:32:10.827718 14951 solver.cpp:218] Iteration 9142 (2.39266 iter/s, 5.85122s/14 iters), loss = 0.796405
I0401 17:32:10.827880 14951 solver.cpp:237]     Train net output #0: loss = 0.796405 (* 1 = 0.796405 loss)
I0401 17:32:10.827888 14951 sgd_solver.cpp:105] Iteration 9142, lr = 0.001
I0401 17:32:16.819384 14951 solver.cpp:218] Iteration 9156 (2.33667 iter/s, 5.99143s/14 iters), loss = 0.692355
I0401 17:32:16.819430 14951 solver.cpp:237]     Train net output #0: loss = 0.692355 (* 1 = 0.692355 loss)
I0401 17:32:16.819437 14951 sgd_solver.cpp:105] Iteration 9156, lr = 0.001
I0401 17:32:22.928961 14951 solver.cpp:218] Iteration 9170 (2.29153 iter/s, 6.10945s/14 iters), loss = 0.607574
I0401 17:32:22.929001 14951 solver.cpp:237]     Train net output #0: loss = 0.607574 (* 1 = 0.607574 loss)
I0401 17:32:22.929008 14951 sgd_solver.cpp:105] Iteration 9170, lr = 0.001
I0401 17:32:28.774452 14951 solver.cpp:218] Iteration 9184 (2.39506 iter/s, 5.84538s/14 iters), loss = 0.683131
I0401 17:32:28.774492 14951 solver.cpp:237]     Train net output #0: loss = 0.683131 (* 1 = 0.683131 loss)
I0401 17:32:28.774497 14951 sgd_solver.cpp:105] Iteration 9184, lr = 0.001
I0401 17:32:34.878329 14951 solver.cpp:218] Iteration 9198 (2.29367 iter/s, 6.10376s/14 iters), loss = 0.607329
I0401 17:32:34.878373 14951 solver.cpp:237]     Train net output #0: loss = 0.607329 (* 1 = 0.607329 loss)
I0401 17:32:34.878378 14951 sgd_solver.cpp:105] Iteration 9198, lr = 0.001
I0401 17:32:43.382553 14951 solver.cpp:218] Iteration 9212 (1.64627 iter/s, 8.50408s/14 iters), loss = 0.844829
I0401 17:32:43.382685 14951 solver.cpp:237]     Train net output #0: loss = 0.844829 (* 1 = 0.844829 loss)
I0401 17:32:43.382694 14951 sgd_solver.cpp:105] Iteration 9212, lr = 0.001
I0401 17:32:46.024143 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:32:52.074872 14951 solver.cpp:218] Iteration 9226 (1.61077 iter/s, 8.69152s/14 iters), loss = 0.82731
I0401 17:32:52.074928 14951 solver.cpp:237]     Train net output #0: loss = 0.82731 (* 1 = 0.82731 loss)
I0401 17:32:52.074935 14951 sgd_solver.cpp:105] Iteration 9226, lr = 0.001
I0401 17:32:55.671557 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9234.caffemodel
I0401 17:32:59.317939 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9234.solverstate
I0401 17:33:02.130836 14951 solver.cpp:330] Iteration 9234, Testing net (#0)
I0401 17:33:02.130859 14951 net.cpp:676] Ignoring source layer train-data
I0401 17:33:04.093595 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:33:05.191045 14951 solver.cpp:397]     Test net output #0: accuracy = 0.271635
I0401 17:33:05.191085 14951 solver.cpp:397]     Test net output #1: loss = 3.74314 (* 1 = 3.74314 loss)
I0401 17:33:07.776939 14951 solver.cpp:218] Iteration 9240 (0.892445 iter/s, 15.6872s/14 iters), loss = 0.702516
I0401 17:33:07.776999 14951 solver.cpp:237]     Train net output #0: loss = 0.702516 (* 1 = 0.702516 loss)
I0401 17:33:07.777009 14951 sgd_solver.cpp:105] Iteration 9240, lr = 0.001
I0401 17:33:15.054343 14951 solver.cpp:218] Iteration 9254 (1.9238 iter/s, 7.27725s/14 iters), loss = 0.640813
I0401 17:33:15.054687 14951 solver.cpp:237]     Train net output #0: loss = 0.640813 (* 1 = 0.640813 loss)
I0401 17:33:15.054697 14951 sgd_solver.cpp:105] Iteration 9254, lr = 0.001
I0401 17:33:22.667042 14951 solver.cpp:218] Iteration 9268 (1.83914 iter/s, 7.61226s/14 iters), loss = 0.724985
I0401 17:33:22.667107 14951 solver.cpp:237]     Train net output #0: loss = 0.724985 (* 1 = 0.724985 loss)
I0401 17:33:22.667115 14951 sgd_solver.cpp:105] Iteration 9268, lr = 0.001
I0401 17:33:30.250434 14951 solver.cpp:218] Iteration 9282 (1.84618 iter/s, 7.58323s/14 iters), loss = 0.822268
I0401 17:33:30.250494 14951 solver.cpp:237]     Train net output #0: loss = 0.822268 (* 1 = 0.822268 loss)
I0401 17:33:30.250501 14951 sgd_solver.cpp:105] Iteration 9282, lr = 0.001
I0401 17:33:37.729887 14951 solver.cpp:218] Iteration 9296 (1.87183 iter/s, 7.4793s/14 iters), loss = 0.464505
I0401 17:33:37.729945 14951 solver.cpp:237]     Train net output #0: loss = 0.464505 (* 1 = 0.464505 loss)
I0401 17:33:37.729954 14951 sgd_solver.cpp:105] Iteration 9296, lr = 0.001
I0401 17:33:45.124951 14951 solver.cpp:218] Iteration 9310 (1.89394 iter/s, 7.39201s/14 iters), loss = 0.644356
I0401 17:33:45.125066 14951 solver.cpp:237]     Train net output #0: loss = 0.644356 (* 1 = 0.644356 loss)
I0401 17:33:45.125074 14951 sgd_solver.cpp:105] Iteration 9310, lr = 0.001
I0401 17:33:52.892894 14951 solver.cpp:218] Iteration 9324 (1.80233 iter/s, 7.76773s/14 iters), loss = 0.422471
I0401 17:33:52.892948 14951 solver.cpp:237]     Train net output #0: loss = 0.422471 (* 1 = 0.422471 loss)
I0401 17:33:52.892957 14951 sgd_solver.cpp:105] Iteration 9324, lr = 0.001
I0401 17:33:56.114028 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:34:00.302080 14951 solver.cpp:218] Iteration 9338 (1.88958 iter/s, 7.40904s/14 iters), loss = 0.646537
I0401 17:34:00.302141 14951 solver.cpp:237]     Train net output #0: loss = 0.646537 (* 1 = 0.646537 loss)
I0401 17:34:00.302150 14951 sgd_solver.cpp:105] Iteration 9338, lr = 0.001
I0401 17:34:05.044982 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9348.caffemodel
I0401 17:34:12.587622 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9348.solverstate
I0401 17:34:15.953778 14951 solver.cpp:330] Iteration 9348, Testing net (#0)
I0401 17:34:15.953866 14951 net.cpp:676] Ignoring source layer train-data
I0401 17:34:17.630362 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:34:17.747102 14951 blocking_queue.cpp:49] Waiting for data
I0401 17:34:18.594534 14951 solver.cpp:397]     Test net output #0: accuracy = 0.283654
I0401 17:34:18.594570 14951 solver.cpp:397]     Test net output #1: loss = 3.69762 (* 1 = 3.69762 loss)
I0401 17:34:19.599438 14951 solver.cpp:218] Iteration 9352 (0.725498 iter/s, 19.2971s/14 iters), loss = 0.784988
I0401 17:34:19.599509 14951 solver.cpp:237]     Train net output #0: loss = 0.784988 (* 1 = 0.784988 loss)
I0401 17:34:19.599519 14951 sgd_solver.cpp:105] Iteration 9352, lr = 0.001
I0401 17:34:25.587312 14951 solver.cpp:218] Iteration 9366 (2.33812 iter/s, 5.98771s/14 iters), loss = 0.60874
I0401 17:34:25.587404 14951 solver.cpp:237]     Train net output #0: loss = 0.60874 (* 1 = 0.60874 loss)
I0401 17:34:25.587411 14951 sgd_solver.cpp:105] Iteration 9366, lr = 0.001
I0401 17:34:31.781702 14951 solver.cpp:218] Iteration 9380 (2.26016 iter/s, 6.19424s/14 iters), loss = 0.718539
I0401 17:34:31.781751 14951 solver.cpp:237]     Train net output #0: loss = 0.718539 (* 1 = 0.718539 loss)
I0401 17:34:31.781759 14951 sgd_solver.cpp:105] Iteration 9380, lr = 0.001
I0401 17:34:37.672644 14951 solver.cpp:218] Iteration 9394 (2.37658 iter/s, 5.89081s/14 iters), loss = 0.680241
I0401 17:34:37.672701 14951 solver.cpp:237]     Train net output #0: loss = 0.680241 (* 1 = 0.680241 loss)
I0401 17:34:37.672710 14951 sgd_solver.cpp:105] Iteration 9394, lr = 0.001
I0401 17:34:43.835536 14951 solver.cpp:218] Iteration 9408 (2.27171 iter/s, 6.16276s/14 iters), loss = 0.506001
I0401 17:34:43.835579 14951 solver.cpp:237]     Train net output #0: loss = 0.506001 (* 1 = 0.506001 loss)
I0401 17:34:43.835585 14951 sgd_solver.cpp:105] Iteration 9408, lr = 0.001
I0401 17:34:50.049623 14951 solver.cpp:218] Iteration 9422 (2.25299 iter/s, 6.21396s/14 iters), loss = 0.484954
I0401 17:34:50.049772 14951 solver.cpp:237]     Train net output #0: loss = 0.484954 (* 1 = 0.484954 loss)
I0401 17:34:50.049780 14951 sgd_solver.cpp:105] Iteration 9422, lr = 0.001
I0401 17:34:56.106289 14951 solver.cpp:218] Iteration 9436 (2.31158 iter/s, 6.05645s/14 iters), loss = 0.757257
I0401 17:34:56.106330 14951 solver.cpp:237]     Train net output #0: loss = 0.757257 (* 1 = 0.757257 loss)
I0401 17:34:56.106336 14951 sgd_solver.cpp:105] Iteration 9436, lr = 0.001
I0401 17:34:59.763278 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:35:02.373824 14951 solver.cpp:218] Iteration 9450 (2.23377 iter/s, 6.26742s/14 iters), loss = 0.851865
I0401 17:35:02.373864 14951 solver.cpp:237]     Train net output #0: loss = 0.851865 (* 1 = 0.851865 loss)
I0401 17:35:02.373870 14951 sgd_solver.cpp:105] Iteration 9450, lr = 0.001
I0401 17:35:06.992898 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9462.caffemodel
I0401 17:35:13.820163 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9462.solverstate
I0401 17:35:18.036084 14951 solver.cpp:330] Iteration 9462, Testing net (#0)
I0401 17:35:18.036108 14951 net.cpp:676] Ignoring source layer train-data
I0401 17:35:19.326642 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:35:20.203390 14951 solver.cpp:397]     Test net output #0: accuracy = 0.263221
I0401 17:35:20.203490 14951 solver.cpp:397]     Test net output #1: loss = 3.75178 (* 1 = 3.75178 loss)
I0401 17:35:20.619820 14951 solver.cpp:218] Iteration 9464 (0.767301 iter/s, 18.2458s/14 iters), loss = 0.66683
I0401 17:35:20.619863 14951 solver.cpp:237]     Train net output #0: loss = 0.66683 (* 1 = 0.66683 loss)
I0401 17:35:20.619868 14951 sgd_solver.cpp:105] Iteration 9464, lr = 0.001
I0401 17:35:26.635617 14951 solver.cpp:218] Iteration 9478 (2.32725 iter/s, 6.01567s/14 iters), loss = 0.566618
I0401 17:35:26.635660 14951 solver.cpp:237]     Train net output #0: loss = 0.566618 (* 1 = 0.566618 loss)
I0401 17:35:26.635666 14951 sgd_solver.cpp:105] Iteration 9478, lr = 0.001
I0401 17:35:32.618422 14951 solver.cpp:218] Iteration 9492 (2.34009 iter/s, 5.98268s/14 iters), loss = 0.948029
I0401 17:35:32.618463 14951 solver.cpp:237]     Train net output #0: loss = 0.948029 (* 1 = 0.948029 loss)
I0401 17:35:32.618468 14951 sgd_solver.cpp:105] Iteration 9492, lr = 0.001
I0401 17:35:38.608002 14951 solver.cpp:218] Iteration 9506 (2.33744 iter/s, 5.98946s/14 iters), loss = 0.696195
I0401 17:35:38.608042 14951 solver.cpp:237]     Train net output #0: loss = 0.696195 (* 1 = 0.696195 loss)
I0401 17:35:38.608047 14951 sgd_solver.cpp:105] Iteration 9506, lr = 0.001
I0401 17:35:44.794930 14951 solver.cpp:218] Iteration 9520 (2.26288 iter/s, 6.18681s/14 iters), loss = 0.546314
I0401 17:35:44.794991 14951 solver.cpp:237]     Train net output #0: loss = 0.546314 (* 1 = 0.546314 loss)
I0401 17:35:44.794999 14951 sgd_solver.cpp:105] Iteration 9520, lr = 0.001
I0401 17:35:51.042006 14951 solver.cpp:218] Iteration 9534 (2.2411 iter/s, 6.24694s/14 iters), loss = 0.479835
I0401 17:35:51.042155 14951 solver.cpp:237]     Train net output #0: loss = 0.479835 (* 1 = 0.479835 loss)
I0401 17:35:51.042163 14951 sgd_solver.cpp:105] Iteration 9534, lr = 0.001
I0401 17:35:57.180644 14951 solver.cpp:218] Iteration 9548 (2.28072 iter/s, 6.13841s/14 iters), loss = 0.988132
I0401 17:35:57.180706 14951 solver.cpp:237]     Train net output #0: loss = 0.988132 (* 1 = 0.988132 loss)
I0401 17:35:57.180716 14951 sgd_solver.cpp:105] Iteration 9548, lr = 0.001
I0401 17:36:01.325348 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:36:03.228924 14951 solver.cpp:218] Iteration 9562 (2.31476 iter/s, 6.04815s/14 iters), loss = 0.888342
I0401 17:36:03.228962 14951 solver.cpp:237]     Train net output #0: loss = 0.888342 (* 1 = 0.888342 loss)
I0401 17:36:03.228967 14951 sgd_solver.cpp:105] Iteration 9562, lr = 0.001
I0401 17:36:08.780617 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9576.caffemodel
I0401 17:36:14.797026 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9576.solverstate
I0401 17:36:18.076747 14951 solver.cpp:330] Iteration 9576, Testing net (#0)
I0401 17:36:18.076766 14951 net.cpp:676] Ignoring source layer train-data
I0401 17:36:19.276836 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:36:20.214452 14951 solver.cpp:397]     Test net output #0: accuracy = 0.277644
I0401 17:36:20.214500 14951 solver.cpp:397]     Test net output #1: loss = 3.7123 (* 1 = 3.7123 loss)
I0401 17:36:20.356462 14951 solver.cpp:218] Iteration 9576 (0.817408 iter/s, 17.1273s/14 iters), loss = 0.859677
I0401 17:36:20.356511 14951 solver.cpp:237]     Train net output #0: loss = 0.859677 (* 1 = 0.859677 loss)
I0401 17:36:20.356518 14951 sgd_solver.cpp:105] Iteration 9576, lr = 0.001
I0401 17:36:25.473286 14951 solver.cpp:218] Iteration 9590 (2.73614 iter/s, 5.1167s/14 iters), loss = 0.592157
I0401 17:36:25.473417 14951 solver.cpp:237]     Train net output #0: loss = 0.592157 (* 1 = 0.592157 loss)
I0401 17:36:25.473425 14951 sgd_solver.cpp:105] Iteration 9590, lr = 0.001
I0401 17:36:31.269982 14951 solver.cpp:218] Iteration 9604 (2.41525 iter/s, 5.7965s/14 iters), loss = 0.537312
I0401 17:36:31.270022 14951 solver.cpp:237]     Train net output #0: loss = 0.537312 (* 1 = 0.537312 loss)
I0401 17:36:31.270027 14951 sgd_solver.cpp:105] Iteration 9604, lr = 0.001
I0401 17:36:37.568441 14951 solver.cpp:218] Iteration 9618 (2.22281 iter/s, 6.29834s/14 iters), loss = 0.784623
I0401 17:36:37.568478 14951 solver.cpp:237]     Train net output #0: loss = 0.784623 (* 1 = 0.784623 loss)
I0401 17:36:37.568485 14951 sgd_solver.cpp:105] Iteration 9618, lr = 0.001
I0401 17:36:43.628906 14951 solver.cpp:218] Iteration 9632 (2.3101 iter/s, 6.06035s/14 iters), loss = 0.461
I0401 17:36:43.628952 14951 solver.cpp:237]     Train net output #0: loss = 0.461 (* 1 = 0.461 loss)
I0401 17:36:43.628957 14951 sgd_solver.cpp:105] Iteration 9632, lr = 0.001
I0401 17:36:49.568960 14951 solver.cpp:218] Iteration 9646 (2.35693 iter/s, 5.93993s/14 iters), loss = 0.667771
I0401 17:36:49.569007 14951 solver.cpp:237]     Train net output #0: loss = 0.667771 (* 1 = 0.667771 loss)
I0401 17:36:49.569013 14951 sgd_solver.cpp:105] Iteration 9646, lr = 0.001
I0401 17:36:55.594058 14951 solver.cpp:218] Iteration 9660 (2.32366 iter/s, 6.02498s/14 iters), loss = 0.592167
I0401 17:36:55.594162 14951 solver.cpp:237]     Train net output #0: loss = 0.592167 (* 1 = 0.592167 loss)
I0401 17:36:55.594169 14951 sgd_solver.cpp:105] Iteration 9660, lr = 0.001
I0401 17:37:00.581316 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:37:01.665345 14951 solver.cpp:218] Iteration 9674 (2.306 iter/s, 6.07111s/14 iters), loss = 0.756351
I0401 17:37:01.665385 14951 solver.cpp:237]     Train net output #0: loss = 0.756351 (* 1 = 0.756351 loss)
I0401 17:37:01.665391 14951 sgd_solver.cpp:105] Iteration 9674, lr = 0.001
I0401 17:37:07.642274 14951 solver.cpp:218] Iteration 9688 (2.34239 iter/s, 5.97681s/14 iters), loss = 0.402026
I0401 17:37:07.642324 14951 solver.cpp:237]     Train net output #0: loss = 0.402026 (* 1 = 0.402026 loss)
I0401 17:37:07.642333 14951 sgd_solver.cpp:105] Iteration 9688, lr = 0.001
I0401 17:37:08.009491 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9690.caffemodel
I0401 17:37:12.985847 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9690.solverstate
I0401 17:37:17.318706 14951 solver.cpp:330] Iteration 9690, Testing net (#0)
I0401 17:37:17.318725 14951 net.cpp:676] Ignoring source layer train-data
I0401 17:37:18.485993 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:37:19.550521 14951 solver.cpp:397]     Test net output #0: accuracy = 0.27524
I0401 17:37:19.550559 14951 solver.cpp:397]     Test net output #1: loss = 3.88489 (* 1 = 3.88489 loss)
I0401 17:37:24.042826 14951 solver.cpp:218] Iteration 9702 (0.853642 iter/s, 16.4003s/14 iters), loss = 0.638509
I0401 17:37:24.042873 14951 solver.cpp:237]     Train net output #0: loss = 0.638509 (* 1 = 0.638509 loss)
I0401 17:37:24.042879 14951 sgd_solver.cpp:105] Iteration 9702, lr = 0.001
I0401 17:37:30.187979 14951 solver.cpp:218] Iteration 9716 (2.27827 iter/s, 6.14502s/14 iters), loss = 0.532083
I0401 17:37:30.188136 14951 solver.cpp:237]     Train net output #0: loss = 0.532083 (* 1 = 0.532083 loss)
I0401 17:37:30.188143 14951 sgd_solver.cpp:105] Iteration 9716, lr = 0.001
I0401 17:37:36.434721 14951 solver.cpp:218] Iteration 9730 (2.24125 iter/s, 6.24651s/14 iters), loss = 0.584547
I0401 17:37:36.434760 14951 solver.cpp:237]     Train net output #0: loss = 0.584547 (* 1 = 0.584547 loss)
I0401 17:37:36.434767 14951 sgd_solver.cpp:105] Iteration 9730, lr = 0.001
I0401 17:37:42.515929 14951 solver.cpp:218] Iteration 9744 (2.30222 iter/s, 6.08109s/14 iters), loss = 0.532597
I0401 17:37:42.515972 14951 solver.cpp:237]     Train net output #0: loss = 0.532597 (* 1 = 0.532597 loss)
I0401 17:37:42.515978 14951 sgd_solver.cpp:105] Iteration 9744, lr = 0.001
I0401 17:37:48.656599 14951 solver.cpp:218] Iteration 9758 (2.27993 iter/s, 6.14054s/14 iters), loss = 0.602813
I0401 17:37:48.656653 14951 solver.cpp:237]     Train net output #0: loss = 0.602813 (* 1 = 0.602813 loss)
I0401 17:37:48.656661 14951 sgd_solver.cpp:105] Iteration 9758, lr = 0.001
I0401 17:37:54.832971 14951 solver.cpp:218] Iteration 9772 (2.26675 iter/s, 6.17624s/14 iters), loss = 0.42475
I0401 17:37:54.833016 14951 solver.cpp:237]     Train net output #0: loss = 0.42475 (* 1 = 0.42475 loss)
I0401 17:37:54.833021 14951 sgd_solver.cpp:105] Iteration 9772, lr = 0.001
I0401 17:38:00.634346 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:38:00.915037 14951 solver.cpp:218] Iteration 9786 (2.3019 iter/s, 6.08194s/14 iters), loss = 0.425596
I0401 17:38:00.915082 14951 solver.cpp:237]     Train net output #0: loss = 0.425596 (* 1 = 0.425596 loss)
I0401 17:38:00.915089 14951 sgd_solver.cpp:105] Iteration 9786, lr = 0.001
I0401 17:38:07.042455 14951 solver.cpp:218] Iteration 9800 (2.28486 iter/s, 6.1273s/14 iters), loss = 0.595627
I0401 17:38:07.042497 14951 solver.cpp:237]     Train net output #0: loss = 0.595627 (* 1 = 0.595627 loss)
I0401 17:38:07.042503 14951 sgd_solver.cpp:105] Iteration 9800, lr = 0.001
I0401 17:38:08.242266 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9804.caffemodel
I0401 17:38:13.286789 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9804.solverstate
I0401 17:38:17.704447 14951 solver.cpp:330] Iteration 9804, Testing net (#0)
I0401 17:38:17.704470 14951 net.cpp:676] Ignoring source layer train-data
I0401 17:38:18.811537 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:38:19.902861 14951 solver.cpp:397]     Test net output #0: accuracy = 0.292067
I0401 17:38:19.902896 14951 solver.cpp:397]     Test net output #1: loss = 3.83808 (* 1 = 3.83808 loss)
I0401 17:38:23.620878 14951 solver.cpp:218] Iteration 9814 (0.844482 iter/s, 16.5782s/14 iters), loss = 0.538045
I0401 17:38:23.620932 14951 solver.cpp:237]     Train net output #0: loss = 0.538045 (* 1 = 0.538045 loss)
I0401 17:38:23.620940 14951 sgd_solver.cpp:105] Iteration 9814, lr = 0.001
I0401 17:38:29.508471 14951 solver.cpp:218] Iteration 9828 (2.37794 iter/s, 5.88746s/14 iters), loss = 0.532692
I0401 17:38:29.508534 14951 solver.cpp:237]     Train net output #0: loss = 0.532692 (* 1 = 0.532692 loss)
I0401 17:38:29.508543 14951 sgd_solver.cpp:105] Iteration 9828, lr = 0.001
I0401 17:38:35.528529 14951 solver.cpp:218] Iteration 9842 (2.32561 iter/s, 6.01992s/14 iters), loss = 0.49854
I0401 17:38:35.528676 14951 solver.cpp:237]     Train net output #0: loss = 0.49854 (* 1 = 0.49854 loss)
I0401 17:38:35.528683 14951 sgd_solver.cpp:105] Iteration 9842, lr = 0.001
I0401 17:38:41.608412 14951 solver.cpp:218] Iteration 9856 (2.30276 iter/s, 6.07966s/14 iters), loss = 0.602346
I0401 17:38:41.608471 14951 solver.cpp:237]     Train net output #0: loss = 0.602346 (* 1 = 0.602346 loss)
I0401 17:38:41.608479 14951 sgd_solver.cpp:105] Iteration 9856, lr = 0.001
I0401 17:38:47.778903 14951 solver.cpp:218] Iteration 9870 (2.26891 iter/s, 6.17036s/14 iters), loss = 0.698994
I0401 17:38:47.778944 14951 solver.cpp:237]     Train net output #0: loss = 0.698994 (* 1 = 0.698994 loss)
I0401 17:38:47.778950 14951 sgd_solver.cpp:105] Iteration 9870, lr = 0.001
I0401 17:38:54.055537 14951 solver.cpp:218] Iteration 9884 (2.23054 iter/s, 6.27651s/14 iters), loss = 0.586744
I0401 17:38:54.055583 14951 solver.cpp:237]     Train net output #0: loss = 0.586744 (* 1 = 0.586744 loss)
I0401 17:38:54.055588 14951 sgd_solver.cpp:105] Iteration 9884, lr = 0.001
I0401 17:39:00.123564 14951 solver.cpp:218] Iteration 9898 (2.30722 iter/s, 6.0679s/14 iters), loss = 0.393999
I0401 17:39:00.123607 14951 solver.cpp:237]     Train net output #0: loss = 0.393999 (* 1 = 0.393999 loss)
I0401 17:39:00.123612 14951 sgd_solver.cpp:105] Iteration 9898, lr = 0.001
I0401 17:39:00.579092 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:39:06.203568 14951 solver.cpp:218] Iteration 9912 (2.30267 iter/s, 6.07989s/14 iters), loss = 0.539684
I0401 17:39:06.203653 14951 solver.cpp:237]     Train net output #0: loss = 0.539684 (* 1 = 0.539684 loss)
I0401 17:39:06.203660 14951 sgd_solver.cpp:105] Iteration 9912, lr = 0.001
I0401 17:39:08.274905 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9918.caffemodel
I0401 17:39:12.232493 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9918.solverstate
I0401 17:39:14.548171 14951 solver.cpp:330] Iteration 9918, Testing net (#0)
I0401 17:39:14.548193 14951 net.cpp:676] Ignoring source layer train-data
I0401 17:39:15.513383 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:39:16.655655 14951 solver.cpp:397]     Test net output #0: accuracy = 0.283654
I0401 17:39:16.655691 14951 solver.cpp:397]     Test net output #1: loss = 3.79182 (* 1 = 3.79182 loss)
I0401 17:39:19.435102 14951 solver.cpp:218] Iteration 9926 (1.0581 iter/s, 13.2313s/14 iters), loss = 0.567523
I0401 17:39:19.435148 14951 solver.cpp:237]     Train net output #0: loss = 0.567523 (* 1 = 0.567523 loss)
I0401 17:39:19.435154 14951 sgd_solver.cpp:105] Iteration 9926, lr = 0.001
I0401 17:39:25.383046 14951 solver.cpp:218] Iteration 9940 (2.3538 iter/s, 5.94782s/14 iters), loss = 0.552783
I0401 17:39:25.383095 14951 solver.cpp:237]     Train net output #0: loss = 0.552783 (* 1 = 0.552783 loss)
I0401 17:39:25.383101 14951 sgd_solver.cpp:105] Iteration 9940, lr = 0.001
I0401 17:39:31.491251 14951 solver.cpp:218] Iteration 9954 (2.29205 iter/s, 6.10808s/14 iters), loss = 0.672355
I0401 17:39:31.491312 14951 solver.cpp:237]     Train net output #0: loss = 0.672355 (* 1 = 0.672355 loss)
I0401 17:39:31.491322 14951 sgd_solver.cpp:105] Iteration 9954, lr = 0.001
I0401 17:39:37.605665 14951 solver.cpp:218] Iteration 9968 (2.28972 iter/s, 6.11427s/14 iters), loss = 0.453119
I0401 17:39:37.606209 14951 solver.cpp:237]     Train net output #0: loss = 0.453119 (* 1 = 0.453119 loss)
I0401 17:39:37.606218 14951 sgd_solver.cpp:105] Iteration 9968, lr = 0.001
I0401 17:39:43.894464 14951 solver.cpp:218] Iteration 9982 (2.2264 iter/s, 6.28818s/14 iters), loss = 0.484231
I0401 17:39:43.894503 14951 solver.cpp:237]     Train net output #0: loss = 0.484231 (* 1 = 0.484231 loss)
I0401 17:39:43.894510 14951 sgd_solver.cpp:105] Iteration 9982, lr = 0.001
I0401 17:39:50.013152 14951 solver.cpp:218] Iteration 9996 (2.28812 iter/s, 6.11857s/14 iters), loss = 0.553712
I0401 17:39:50.013193 14951 solver.cpp:237]     Train net output #0: loss = 0.553712 (* 1 = 0.553712 loss)
I0401 17:39:50.013200 14951 sgd_solver.cpp:105] Iteration 9996, lr = 0.001
I0401 17:39:56.157758 14951 solver.cpp:218] Iteration 10010 (2.27847 iter/s, 6.14447s/14 iters), loss = 0.60896
I0401 17:39:56.157814 14951 solver.cpp:237]     Train net output #0: loss = 0.60896 (* 1 = 0.60896 loss)
I0401 17:39:56.157822 14951 sgd_solver.cpp:105] Iteration 10010, lr = 0.001
I0401 17:39:57.348999 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:40:02.109257 14951 solver.cpp:218] Iteration 10024 (2.3524 iter/s, 5.95136s/14 iters), loss = 0.629229
I0401 17:40:02.109326 14951 solver.cpp:237]     Train net output #0: loss = 0.629229 (* 1 = 0.629229 loss)
I0401 17:40:02.109336 14951 sgd_solver.cpp:105] Iteration 10024, lr = 0.001
I0401 17:40:05.081418 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10032.caffemodel
I0401 17:40:08.154703 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10032.solverstate
I0401 17:40:10.440989 14951 solver.cpp:330] Iteration 10032, Testing net (#0)
I0401 17:40:10.441007 14951 net.cpp:676] Ignoring source layer train-data
I0401 17:40:11.365913 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:40:12.594099 14951 solver.cpp:397]     Test net output #0: accuracy = 0.288462
I0401 17:40:12.594125 14951 solver.cpp:397]     Test net output #1: loss = 3.7385 (* 1 = 3.7385 loss)
I0401 17:40:14.517290 14951 solver.cpp:218] Iteration 10038 (1.12832 iter/s, 12.4078s/14 iters), loss = 0.579947
I0401 17:40:14.517328 14951 solver.cpp:237]     Train net output #0: loss = 0.579947 (* 1 = 0.579947 loss)
I0401 17:40:14.517333 14951 sgd_solver.cpp:105] Iteration 10038, lr = 0.001
I0401 17:40:20.315443 14951 solver.cpp:218] Iteration 10052 (2.41461 iter/s, 5.79803s/14 iters), loss = 0.319824
I0401 17:40:20.315497 14951 solver.cpp:237]     Train net output #0: loss = 0.319824 (* 1 = 0.319824 loss)
I0401 17:40:20.315507 14951 sgd_solver.cpp:105] Iteration 10052, lr = 0.001
I0401 17:40:26.661602 14951 solver.cpp:218] Iteration 10066 (2.20611 iter/s, 6.34602s/14 iters), loss = 0.512118
I0401 17:40:26.661648 14951 solver.cpp:237]     Train net output #0: loss = 0.512118 (* 1 = 0.512118 loss)
I0401 17:40:26.661653 14951 sgd_solver.cpp:105] Iteration 10066, lr = 0.001
I0401 17:40:32.571269 14951 solver.cpp:218] Iteration 10080 (2.36905 iter/s, 5.90955s/14 iters), loss = 0.414106
I0401 17:40:32.571306 14951 solver.cpp:237]     Train net output #0: loss = 0.414106 (* 1 = 0.414106 loss)
I0401 17:40:32.571312 14951 sgd_solver.cpp:105] Iteration 10080, lr = 0.001
I0401 17:40:38.401024 14951 solver.cpp:218] Iteration 10094 (2.40152 iter/s, 5.82964s/14 iters), loss = 0.407156
I0401 17:40:38.401119 14951 solver.cpp:237]     Train net output #0: loss = 0.407156 (* 1 = 0.407156 loss)
I0401 17:40:38.401125 14951 sgd_solver.cpp:105] Iteration 10094, lr = 0.001
I0401 17:40:44.506891 14951 solver.cpp:218] Iteration 10108 (2.29294 iter/s, 6.1057s/14 iters), loss = 0.472752
I0401 17:40:44.506928 14951 solver.cpp:237]     Train net output #0: loss = 0.472752 (* 1 = 0.472752 loss)
I0401 17:40:44.506933 14951 sgd_solver.cpp:105] Iteration 10108, lr = 0.001
I0401 17:40:50.603941 14951 solver.cpp:218] Iteration 10122 (2.29624 iter/s, 6.09693s/14 iters), loss = 0.533306
I0401 17:40:50.603998 14951 solver.cpp:237]     Train net output #0: loss = 0.533306 (* 1 = 0.533306 loss)
I0401 17:40:50.604007 14951 sgd_solver.cpp:105] Iteration 10122, lr = 0.001
I0401 17:40:52.549702 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:40:56.797730 14951 solver.cpp:218] Iteration 10136 (2.26038 iter/s, 6.19366s/14 iters), loss = 0.6084
I0401 17:40:56.797766 14951 solver.cpp:237]     Train net output #0: loss = 0.6084 (* 1 = 0.6084 loss)
I0401 17:40:56.797772 14951 sgd_solver.cpp:105] Iteration 10136, lr = 0.001
I0401 17:41:00.589078 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10146.caffemodel
I0401 17:41:03.572664 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10146.solverstate
I0401 17:41:06.847831 14951 solver.cpp:330] Iteration 10146, Testing net (#0)
I0401 17:41:06.847851 14951 net.cpp:676] Ignoring source layer train-data
I0401 17:41:07.749277 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:41:09.066310 14951 solver.cpp:397]     Test net output #0: accuracy = 0.305288
I0401 17:41:09.066416 14951 solver.cpp:397]     Test net output #1: loss = 3.73096 (* 1 = 3.73096 loss)
I0401 17:41:10.087746 14951 solver.cpp:218] Iteration 10150 (1.05344 iter/s, 13.2898s/14 iters), loss = 0.523573
I0401 17:41:10.087803 14951 solver.cpp:237]     Train net output #0: loss = 0.523573 (* 1 = 0.523573 loss)
I0401 17:41:10.087811 14951 sgd_solver.cpp:105] Iteration 10150, lr = 0.001
I0401 17:41:16.075911 14951 solver.cpp:218] Iteration 10164 (2.338 iter/s, 5.98804s/14 iters), loss = 0.447718
I0401 17:41:16.075956 14951 solver.cpp:237]     Train net output #0: loss = 0.447718 (* 1 = 0.447718 loss)
I0401 17:41:16.075963 14951 sgd_solver.cpp:105] Iteration 10164, lr = 0.001
I0401 17:41:22.122689 14951 solver.cpp:218] Iteration 10178 (2.31533 iter/s, 6.04665s/14 iters), loss = 0.404673
I0401 17:41:22.122735 14951 solver.cpp:237]     Train net output #0: loss = 0.404673 (* 1 = 0.404673 loss)
I0401 17:41:22.122740 14951 sgd_solver.cpp:105] Iteration 10178, lr = 0.001
I0401 17:41:28.212095 14951 solver.cpp:218] Iteration 10192 (2.29912 iter/s, 6.08928s/14 iters), loss = 0.48638
I0401 17:41:28.212139 14951 solver.cpp:237]     Train net output #0: loss = 0.48638 (* 1 = 0.48638 loss)
I0401 17:41:28.212146 14951 sgd_solver.cpp:105] Iteration 10192, lr = 0.001
I0401 17:41:32.414362 14951 blocking_queue.cpp:49] Waiting for data
I0401 17:41:34.220376 14951 solver.cpp:218] Iteration 10206 (2.33016 iter/s, 6.00816s/14 iters), loss = 0.566883
I0401 17:41:34.220417 14951 solver.cpp:237]     Train net output #0: loss = 0.566883 (* 1 = 0.566883 loss)
I0401 17:41:34.220422 14951 sgd_solver.cpp:105] Iteration 10206, lr = 0.001
I0401 17:41:40.304119 14951 solver.cpp:218] Iteration 10220 (2.30126 iter/s, 6.08362s/14 iters), loss = 0.466659
I0401 17:41:40.304237 14951 solver.cpp:237]     Train net output #0: loss = 0.466659 (* 1 = 0.466659 loss)
I0401 17:41:40.304246 14951 sgd_solver.cpp:105] Iteration 10220, lr = 0.001
I0401 17:41:46.291574 14951 solver.cpp:218] Iteration 10234 (2.3383 iter/s, 5.98726s/14 iters), loss = 0.300179
I0401 17:41:46.291622 14951 solver.cpp:237]     Train net output #0: loss = 0.300179 (* 1 = 0.300179 loss)
I0401 17:41:46.291628 14951 sgd_solver.cpp:105] Iteration 10234, lr = 0.001
I0401 17:41:49.192375 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:41:52.425035 14951 solver.cpp:218] Iteration 10248 (2.28261 iter/s, 6.13333s/14 iters), loss = 0.43929
I0401 17:41:52.425091 14951 solver.cpp:237]     Train net output #0: loss = 0.43929 (* 1 = 0.43929 loss)
I0401 17:41:52.425099 14951 sgd_solver.cpp:105] Iteration 10248, lr = 0.001
I0401 17:41:57.073781 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10260.caffemodel
I0401 17:42:01.570251 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10260.solverstate
I0401 17:42:05.223389 14951 solver.cpp:330] Iteration 10260, Testing net (#0)
I0401 17:42:05.223408 14951 net.cpp:676] Ignoring source layer train-data
I0401 17:42:06.030093 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:42:07.364583 14951 solver.cpp:397]     Test net output #0: accuracy = 0.270433
I0401 17:42:07.364616 14951 solver.cpp:397]     Test net output #1: loss = 3.91953 (* 1 = 3.91953 loss)
I0401 17:42:07.758754 14951 solver.cpp:218] Iteration 10262 (0.913034 iter/s, 15.3335s/14 iters), loss = 0.52145
I0401 17:42:07.758793 14951 solver.cpp:237]     Train net output #0: loss = 0.52145 (* 1 = 0.52145 loss)
I0401 17:42:07.758798 14951 sgd_solver.cpp:105] Iteration 10262, lr = 0.001
I0401 17:42:13.520684 14951 solver.cpp:218] Iteration 10276 (2.42979 iter/s, 5.76181s/14 iters), loss = 0.679659
I0401 17:42:13.520843 14951 solver.cpp:237]     Train net output #0: loss = 0.679659 (* 1 = 0.679659 loss)
I0401 17:42:13.520853 14951 sgd_solver.cpp:105] Iteration 10276, lr = 0.001
I0401 17:42:19.491889 14951 solver.cpp:218] Iteration 10290 (2.34468 iter/s, 5.97097s/14 iters), loss = 0.481316
I0401 17:42:19.491928 14951 solver.cpp:237]     Train net output #0: loss = 0.481316 (* 1 = 0.481316 loss)
I0401 17:42:19.491935 14951 sgd_solver.cpp:105] Iteration 10290, lr = 0.001
I0401 17:42:25.581012 14951 solver.cpp:218] Iteration 10304 (2.29923 iter/s, 6.089s/14 iters), loss = 0.433895
I0401 17:42:25.581077 14951 solver.cpp:237]     Train net output #0: loss = 0.433895 (* 1 = 0.433895 loss)
I0401 17:42:25.581087 14951 sgd_solver.cpp:105] Iteration 10304, lr = 0.001
I0401 17:42:31.657801 14951 solver.cpp:218] Iteration 10318 (2.3039 iter/s, 6.07665s/14 iters), loss = 0.379005
I0401 17:42:31.657850 14951 solver.cpp:237]     Train net output #0: loss = 0.379005 (* 1 = 0.379005 loss)
I0401 17:42:31.657858 14951 sgd_solver.cpp:105] Iteration 10318, lr = 0.001
I0401 17:42:37.726845 14951 solver.cpp:218] Iteration 10332 (2.30684 iter/s, 6.06892s/14 iters), loss = 0.457006
I0401 17:42:37.726894 14951 solver.cpp:237]     Train net output #0: loss = 0.457006 (* 1 = 0.457006 loss)
I0401 17:42:37.726902 14951 sgd_solver.cpp:105] Iteration 10332, lr = 0.001
I0401 17:42:43.859652 14951 solver.cpp:218] Iteration 10346 (2.28285 iter/s, 6.13267s/14 iters), loss = 0.44386
I0401 17:42:43.859776 14951 solver.cpp:237]     Train net output #0: loss = 0.44386 (* 1 = 0.44386 loss)
I0401 17:42:43.859783 14951 sgd_solver.cpp:105] Iteration 10346, lr = 0.001
I0401 17:42:47.491525 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:42:49.959934 14951 solver.cpp:218] Iteration 10360 (2.29505 iter/s, 6.10008s/14 iters), loss = 0.449581
I0401 17:42:49.959990 14951 solver.cpp:237]     Train net output #0: loss = 0.449581 (* 1 = 0.449581 loss)
I0401 17:42:49.959998 14951 sgd_solver.cpp:105] Iteration 10360, lr = 0.001
I0401 17:42:55.613461 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10374.caffemodel
I0401 17:42:58.622342 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10374.solverstate
I0401 17:43:01.875730 14951 solver.cpp:330] Iteration 10374, Testing net (#0)
I0401 17:43:01.875748 14951 net.cpp:676] Ignoring source layer train-data
I0401 17:43:02.632604 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:43:04.037739 14951 solver.cpp:397]     Test net output #0: accuracy = 0.27524
I0401 17:43:04.037770 14951 solver.cpp:397]     Test net output #1: loss = 3.7392 (* 1 = 3.7392 loss)
I0401 17:43:04.182687 14951 solver.cpp:218] Iteration 10374 (0.984353 iter/s, 14.2225s/14 iters), loss = 0.56455
I0401 17:43:04.184263 14951 solver.cpp:237]     Train net output #0: loss = 0.56455 (* 1 = 0.56455 loss)
I0401 17:43:04.184276 14951 sgd_solver.cpp:105] Iteration 10374, lr = 0.001
I0401 17:43:09.078850 14951 solver.cpp:218] Iteration 10388 (2.86033 iter/s, 4.89453s/14 iters), loss = 0.44519
I0401 17:43:09.078886 14951 solver.cpp:237]     Train net output #0: loss = 0.44519 (* 1 = 0.44519 loss)
I0401 17:43:09.078892 14951 sgd_solver.cpp:105] Iteration 10388, lr = 0.001
I0401 17:43:15.114609 14951 solver.cpp:218] Iteration 10402 (2.31956 iter/s, 6.03564s/14 iters), loss = 0.387658
I0401 17:43:15.114746 14951 solver.cpp:237]     Train net output #0: loss = 0.387658 (* 1 = 0.387658 loss)
I0401 17:43:15.114761 14951 sgd_solver.cpp:105] Iteration 10402, lr = 0.001
I0401 17:43:21.169646 14951 solver.cpp:218] Iteration 10416 (2.31221 iter/s, 6.05482s/14 iters), loss = 0.371732
I0401 17:43:21.169703 14951 solver.cpp:237]     Train net output #0: loss = 0.371732 (* 1 = 0.371732 loss)
I0401 17:43:21.169711 14951 sgd_solver.cpp:105] Iteration 10416, lr = 0.001
I0401 17:43:27.354208 14951 solver.cpp:218] Iteration 10430 (2.26385 iter/s, 6.18415s/14 iters), loss = 0.425538
I0401 17:43:27.354261 14951 solver.cpp:237]     Train net output #0: loss = 0.425538 (* 1 = 0.425538 loss)
I0401 17:43:27.354269 14951 sgd_solver.cpp:105] Iteration 10430, lr = 0.001
I0401 17:43:33.519183 14951 solver.cpp:218] Iteration 10444 (2.27094 iter/s, 6.16485s/14 iters), loss = 0.427248
I0401 17:43:33.519228 14951 solver.cpp:237]     Train net output #0: loss = 0.427248 (* 1 = 0.427248 loss)
I0401 17:43:33.519234 14951 sgd_solver.cpp:105] Iteration 10444, lr = 0.001
I0401 17:43:39.544905 14951 solver.cpp:218] Iteration 10458 (2.32342 iter/s, 6.02559s/14 iters), loss = 0.334399
I0401 17:43:39.544950 14951 solver.cpp:237]     Train net output #0: loss = 0.334399 (* 1 = 0.334399 loss)
I0401 17:43:39.544955 14951 sgd_solver.cpp:105] Iteration 10458, lr = 0.001
I0401 17:43:44.126576 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:43:45.768692 14951 solver.cpp:218] Iteration 10472 (2.24948 iter/s, 6.22366s/14 iters), loss = 0.448586
I0401 17:43:45.768880 14951 solver.cpp:237]     Train net output #0: loss = 0.448586 (* 1 = 0.448586 loss)
I0401 17:43:45.768898 14951 sgd_solver.cpp:105] Iteration 10472, lr = 0.001
I0401 17:43:51.847795 14951 solver.cpp:218] Iteration 10486 (2.30307 iter/s, 6.07884s/14 iters), loss = 0.555798
I0401 17:43:51.847832 14951 solver.cpp:237]     Train net output #0: loss = 0.555798 (* 1 = 0.555798 loss)
I0401 17:43:51.847837 14951 sgd_solver.cpp:105] Iteration 10486, lr = 0.001
I0401 17:43:52.230044 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10488.caffemodel
I0401 17:43:57.527302 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10488.solverstate
I0401 17:43:59.967232 14951 solver.cpp:330] Iteration 10488, Testing net (#0)
I0401 17:43:59.967249 14951 net.cpp:676] Ignoring source layer train-data
I0401 17:44:00.586483 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:44:02.034548 14951 solver.cpp:397]     Test net output #0: accuracy = 0.274038
I0401 17:44:02.034585 14951 solver.cpp:397]     Test net output #1: loss = 3.90381 (* 1 = 3.90381 loss)
I0401 17:44:06.393697 14951 solver.cpp:218] Iteration 10500 (0.962484 iter/s, 14.5457s/14 iters), loss = 0.498599
I0401 17:44:06.393754 14951 solver.cpp:237]     Train net output #0: loss = 0.498599 (* 1 = 0.498599 loss)
I0401 17:44:06.393762 14951 sgd_solver.cpp:105] Iteration 10500, lr = 0.001
I0401 17:44:11.894837 14951 solver.cpp:218] Iteration 10514 (2.54499 iter/s, 5.50101s/14 iters), loss = 0.515704
I0401 17:44:11.894896 14951 solver.cpp:237]     Train net output #0: loss = 0.515704 (* 1 = 0.515704 loss)
I0401 17:44:11.894906 14951 sgd_solver.cpp:105] Iteration 10514, lr = 0.001
I0401 17:44:18.049697 14951 solver.cpp:218] Iteration 10528 (2.27468 iter/s, 6.15472s/14 iters), loss = 0.428796
I0401 17:44:18.049818 14951 solver.cpp:237]     Train net output #0: loss = 0.428796 (* 1 = 0.428796 loss)
I0401 17:44:18.049829 14951 sgd_solver.cpp:105] Iteration 10528, lr = 0.001
I0401 17:44:24.118196 14951 solver.cpp:218] Iteration 10542 (2.30707 iter/s, 6.0683s/14 iters), loss = 0.351233
I0401 17:44:24.118240 14951 solver.cpp:237]     Train net output #0: loss = 0.351233 (* 1 = 0.351233 loss)
I0401 17:44:24.118247 14951 sgd_solver.cpp:105] Iteration 10542, lr = 0.001
I0401 17:44:30.179915 14951 solver.cpp:218] Iteration 10556 (2.30963 iter/s, 6.06159s/14 iters), loss = 0.354988
I0401 17:44:30.179967 14951 solver.cpp:237]     Train net output #0: loss = 0.354988 (* 1 = 0.354988 loss)
I0401 17:44:30.179975 14951 sgd_solver.cpp:105] Iteration 10556, lr = 0.001
I0401 17:44:36.122381 14951 solver.cpp:218] Iteration 10570 (2.35597 iter/s, 5.94234s/14 iters), loss = 0.574031
I0401 17:44:36.122426 14951 solver.cpp:237]     Train net output #0: loss = 0.574031 (* 1 = 0.574031 loss)
I0401 17:44:36.122433 14951 sgd_solver.cpp:105] Iteration 10570, lr = 0.001
I0401 17:44:41.475626 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:44:42.384155 14951 solver.cpp:218] Iteration 10584 (2.23583 iter/s, 6.26165s/14 iters), loss = 0.612125
I0401 17:44:42.384196 14951 solver.cpp:237]     Train net output #0: loss = 0.612125 (* 1 = 0.612125 loss)
I0401 17:44:42.384202 14951 sgd_solver.cpp:105] Iteration 10584, lr = 0.001
I0401 17:44:48.486192 14951 solver.cpp:218] Iteration 10598 (2.29436 iter/s, 6.10192s/14 iters), loss = 0.512558
I0401 17:44:48.486335 14951 solver.cpp:237]     Train net output #0: loss = 0.512558 (* 1 = 0.512558 loss)
I0401 17:44:48.486344 14951 sgd_solver.cpp:105] Iteration 10598, lr = 0.001
I0401 17:44:49.585105 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10602.caffemodel
I0401 17:44:52.739990 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10602.solverstate
I0401 17:44:56.267887 14951 solver.cpp:330] Iteration 10602, Testing net (#0)
I0401 17:44:56.267906 14951 net.cpp:676] Ignoring source layer train-data
I0401 17:44:56.849898 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:44:58.391708 14951 solver.cpp:397]     Test net output #0: accuracy = 0.28726
I0401 17:44:58.391744 14951 solver.cpp:397]     Test net output #1: loss = 3.83354 (* 1 = 3.83354 loss)
I0401 17:45:01.936636 14951 solver.cpp:218] Iteration 10612 (1.04088 iter/s, 13.4502s/14 iters), loss = 0.398024
I0401 17:45:01.936676 14951 solver.cpp:237]     Train net output #0: loss = 0.398024 (* 1 = 0.398024 loss)
I0401 17:45:01.936682 14951 sgd_solver.cpp:105] Iteration 10612, lr = 0.001
I0401 17:45:07.972215 14951 solver.cpp:218] Iteration 10626 (2.31963 iter/s, 6.03545s/14 iters), loss = 0.405815
I0401 17:45:07.972267 14951 solver.cpp:237]     Train net output #0: loss = 0.405815 (* 1 = 0.405815 loss)
I0401 17:45:07.972275 14951 sgd_solver.cpp:105] Iteration 10626, lr = 0.001
I0401 17:45:14.113696 14951 solver.cpp:218] Iteration 10640 (2.27963 iter/s, 6.14135s/14 iters), loss = 0.256763
I0401 17:45:14.113749 14951 solver.cpp:237]     Train net output #0: loss = 0.256763 (* 1 = 0.256763 loss)
I0401 17:45:14.113757 14951 sgd_solver.cpp:105] Iteration 10640, lr = 0.001
I0401 17:45:20.313832 14951 solver.cpp:218] Iteration 10654 (2.25806 iter/s, 6.20001s/14 iters), loss = 0.318776
I0401 17:45:20.313931 14951 solver.cpp:237]     Train net output #0: loss = 0.318776 (* 1 = 0.318776 loss)
I0401 17:45:20.313938 14951 sgd_solver.cpp:105] Iteration 10654, lr = 0.001
I0401 17:45:26.427397 14951 solver.cpp:218] Iteration 10668 (2.29006 iter/s, 6.11338s/14 iters), loss = 0.373235
I0401 17:45:26.427444 14951 solver.cpp:237]     Train net output #0: loss = 0.373235 (* 1 = 0.373235 loss)
I0401 17:45:26.427450 14951 sgd_solver.cpp:105] Iteration 10668, lr = 0.001
I0401 17:45:32.447428 14951 solver.cpp:218] Iteration 10682 (2.32562 iter/s, 6.01991s/14 iters), loss = 0.438258
I0401 17:45:32.447474 14951 solver.cpp:237]     Train net output #0: loss = 0.438258 (* 1 = 0.438258 loss)
I0401 17:45:32.447480 14951 sgd_solver.cpp:105] Iteration 10682, lr = 0.001
I0401 17:45:38.482628 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:45:38.649144 14951 solver.cpp:218] Iteration 10696 (2.25749 iter/s, 6.20159s/14 iters), loss = 0.563815
I0401 17:45:38.649194 14951 solver.cpp:237]     Train net output #0: loss = 0.563815 (* 1 = 0.563815 loss)
I0401 17:45:38.649202 14951 sgd_solver.cpp:105] Iteration 10696, lr = 0.001
I0401 17:45:44.834278 14951 solver.cpp:218] Iteration 10710 (2.26354 iter/s, 6.185s/14 iters), loss = 0.442841
I0401 17:45:44.834342 14951 solver.cpp:237]     Train net output #0: loss = 0.442841 (* 1 = 0.442841 loss)
I0401 17:45:44.834352 14951 sgd_solver.cpp:105] Iteration 10710, lr = 0.001
I0401 17:45:46.867096 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10716.caffemodel
I0401 17:45:51.453575 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10716.solverstate
I0401 17:45:54.385488 14951 solver.cpp:330] Iteration 10716, Testing net (#0)
I0401 17:45:54.385514 14951 net.cpp:676] Ignoring source layer train-data
I0401 17:45:54.937372 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:45:56.559576 14951 solver.cpp:397]     Test net output #0: accuracy = 0.282452
I0401 17:45:56.559612 14951 solver.cpp:397]     Test net output #1: loss = 4.01646 (* 1 = 4.01646 loss)
I0401 17:45:59.287606 14951 solver.cpp:218] Iteration 10724 (0.96865 iter/s, 14.4531s/14 iters), loss = 0.287435
I0401 17:45:59.287655 14951 solver.cpp:237]     Train net output #0: loss = 0.287435 (* 1 = 0.287435 loss)
I0401 17:45:59.287663 14951 sgd_solver.cpp:105] Iteration 10724, lr = 0.001
I0401 17:46:05.266456 14951 solver.cpp:218] Iteration 10738 (2.34164 iter/s, 5.97872s/14 iters), loss = 0.363259
I0401 17:46:05.266512 14951 solver.cpp:237]     Train net output #0: loss = 0.363259 (* 1 = 0.363259 loss)
I0401 17:46:05.266520 14951 sgd_solver.cpp:105] Iteration 10738, lr = 0.001
I0401 17:46:11.221849 14951 solver.cpp:218] Iteration 10752 (2.35086 iter/s, 5.95526s/14 iters), loss = 0.455353
I0401 17:46:11.221889 14951 solver.cpp:237]     Train net output #0: loss = 0.455353 (* 1 = 0.455353 loss)
I0401 17:46:11.221895 14951 sgd_solver.cpp:105] Iteration 10752, lr = 0.001
I0401 17:46:17.410120 14951 solver.cpp:218] Iteration 10766 (2.26239 iter/s, 6.18814s/14 iters), loss = 0.411391
I0401 17:46:17.410181 14951 solver.cpp:237]     Train net output #0: loss = 0.411391 (* 1 = 0.411391 loss)
I0401 17:46:17.410192 14951 sgd_solver.cpp:105] Iteration 10766, lr = 0.001
I0401 17:46:23.383306 14951 solver.cpp:218] Iteration 10780 (2.34386 iter/s, 5.97304s/14 iters), loss = 0.3188
I0401 17:46:23.383441 14951 solver.cpp:237]     Train net output #0: loss = 0.3188 (* 1 = 0.3188 loss)
I0401 17:46:23.383450 14951 sgd_solver.cpp:105] Iteration 10780, lr = 0.001
I0401 17:46:29.511868 14951 solver.cpp:218] Iteration 10794 (2.28446 iter/s, 6.12835s/14 iters), loss = 0.363806
I0401 17:46:29.511919 14951 solver.cpp:237]     Train net output #0: loss = 0.363806 (* 1 = 0.363806 loss)
I0401 17:46:29.511927 14951 sgd_solver.cpp:105] Iteration 10794, lr = 0.001
I0401 17:46:35.571645 14951 solver.cpp:218] Iteration 10808 (2.31037 iter/s, 6.05965s/14 iters), loss = 0.365578
I0401 17:46:35.571689 14951 solver.cpp:237]     Train net output #0: loss = 0.365578 (* 1 = 0.365578 loss)
I0401 17:46:35.571696 14951 sgd_solver.cpp:105] Iteration 10808, lr = 0.001
I0401 17:46:36.223968 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:46:41.700016 14951 solver.cpp:218] Iteration 10822 (2.2845 iter/s, 6.12824s/14 iters), loss = 0.659675
I0401 17:46:41.700078 14951 solver.cpp:237]     Train net output #0: loss = 0.659675 (* 1 = 0.659675 loss)
I0401 17:46:41.700088 14951 sgd_solver.cpp:105] Iteration 10822, lr = 0.001
I0401 17:46:44.725395 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10830.caffemodel
I0401 17:46:47.682040 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10830.solverstate
I0401 17:46:49.987040 14951 solver.cpp:330] Iteration 10830, Testing net (#0)
I0401 17:46:49.987061 14951 net.cpp:676] Ignoring source layer train-data
I0401 17:46:50.420728 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:46:52.116492 14951 solver.cpp:397]     Test net output #0: accuracy = 0.294471
I0401 17:46:52.116531 14951 solver.cpp:397]     Test net output #1: loss = 3.94058 (* 1 = 3.94058 loss)
I0401 17:46:53.908742 14951 solver.cpp:218] Iteration 10836 (1.14674 iter/s, 12.2085s/14 iters), loss = 0.401634
I0401 17:46:53.908835 14951 solver.cpp:237]     Train net output #0: loss = 0.401634 (* 1 = 0.401634 loss)
I0401 17:46:53.908841 14951 sgd_solver.cpp:105] Iteration 10836, lr = 0.001
I0401 17:46:59.926478 14951 solver.cpp:218] Iteration 10850 (2.32652 iter/s, 6.01756s/14 iters), loss = 0.506911
I0401 17:46:59.926533 14951 solver.cpp:237]     Train net output #0: loss = 0.506911 (* 1 = 0.506911 loss)
I0401 17:46:59.926543 14951 sgd_solver.cpp:105] Iteration 10850, lr = 0.001
I0401 17:47:06.132658 14951 solver.cpp:218] Iteration 10864 (2.25587 iter/s, 6.20604s/14 iters), loss = 0.298374
I0401 17:47:06.132715 14951 solver.cpp:237]     Train net output #0: loss = 0.298374 (* 1 = 0.298374 loss)
I0401 17:47:06.132725 14951 sgd_solver.cpp:105] Iteration 10864, lr = 0.001
I0401 17:47:12.260499 14951 solver.cpp:218] Iteration 10878 (2.2847 iter/s, 6.12771s/14 iters), loss = 0.294448
I0401 17:47:12.260537 14951 solver.cpp:237]     Train net output #0: loss = 0.294448 (* 1 = 0.294448 loss)
I0401 17:47:12.260543 14951 sgd_solver.cpp:105] Iteration 10878, lr = 0.001
I0401 17:47:18.434872 14951 solver.cpp:218] Iteration 10892 (2.26748 iter/s, 6.17425s/14 iters), loss = 0.355172
I0401 17:47:18.434921 14951 solver.cpp:237]     Train net output #0: loss = 0.355172 (* 1 = 0.355172 loss)
I0401 17:47:18.434927 14951 sgd_solver.cpp:105] Iteration 10892, lr = 0.001
I0401 17:47:24.565541 14951 solver.cpp:218] Iteration 10906 (2.28365 iter/s, 6.13054s/14 iters), loss = 0.258336
I0401 17:47:24.565706 14951 solver.cpp:237]     Train net output #0: loss = 0.258336 (* 1 = 0.258336 loss)
I0401 17:47:24.565714 14951 sgd_solver.cpp:105] Iteration 10906, lr = 0.001
I0401 17:47:30.521260 14951 solver.cpp:218] Iteration 10920 (2.35078 iter/s, 5.95548s/14 iters), loss = 0.371239
I0401 17:47:30.521311 14951 solver.cpp:237]     Train net output #0: loss = 0.371239 (* 1 = 0.371239 loss)
I0401 17:47:30.521320 14951 sgd_solver.cpp:105] Iteration 10920, lr = 0.001
I0401 17:47:31.982868 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:47:36.651855 14951 solver.cpp:218] Iteration 10934 (2.28368 iter/s, 6.13047s/14 iters), loss = 0.492172
I0401 17:47:36.651897 14951 solver.cpp:237]     Train net output #0: loss = 0.492172 (* 1 = 0.492172 loss)
I0401 17:47:36.651903 14951 sgd_solver.cpp:105] Iteration 10934, lr = 0.001
I0401 17:47:40.474287 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10944.caffemodel
I0401 17:47:43.379588 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10944.solverstate
I0401 17:47:45.699229 14951 solver.cpp:330] Iteration 10944, Testing net (#0)
I0401 17:47:45.699255 14951 net.cpp:676] Ignoring source layer train-data
I0401 17:47:46.109051 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:47:47.911842 14951 solver.cpp:397]     Test net output #0: accuracy = 0.301683
I0401 17:47:47.911890 14951 solver.cpp:397]     Test net output #1: loss = 3.89078 (* 1 = 3.89078 loss)
I0401 17:47:49.010432 14951 solver.cpp:218] Iteration 10948 (1.13283 iter/s, 12.3584s/14 iters), loss = 0.382865
I0401 17:47:49.010479 14951 solver.cpp:237]     Train net output #0: loss = 0.382865 (* 1 = 0.382865 loss)
I0401 17:47:49.010486 14951 sgd_solver.cpp:105] Iteration 10948, lr = 0.001
I0401 17:47:54.991214 14951 solver.cpp:218] Iteration 10962 (2.34088 iter/s, 5.98066s/14 iters), loss = 0.430964
I0401 17:47:54.991309 14951 solver.cpp:237]     Train net output #0: loss = 0.430964 (* 1 = 0.430964 loss)
I0401 17:47:54.991315 14951 sgd_solver.cpp:105] Iteration 10962, lr = 0.001
I0401 17:48:01.056854 14951 solver.cpp:218] Iteration 10976 (2.30815 iter/s, 6.06547s/14 iters), loss = 0.330964
I0401 17:48:01.056913 14951 solver.cpp:237]     Train net output #0: loss = 0.330964 (* 1 = 0.330964 loss)
I0401 17:48:01.056922 14951 sgd_solver.cpp:105] Iteration 10976, lr = 0.001
I0401 17:48:07.211483 14951 solver.cpp:218] Iteration 10990 (2.27476 iter/s, 6.15449s/14 iters), loss = 0.507453
I0401 17:48:07.211529 14951 solver.cpp:237]     Train net output #0: loss = 0.507453 (* 1 = 0.507453 loss)
I0401 17:48:07.211534 14951 sgd_solver.cpp:105] Iteration 10990, lr = 0.001
I0401 17:48:13.143240 14951 solver.cpp:218] Iteration 11004 (2.36023 iter/s, 5.93163s/14 iters), loss = 0.39447
I0401 17:48:13.143301 14951 solver.cpp:237]     Train net output #0: loss = 0.39447 (* 1 = 0.39447 loss)
I0401 17:48:13.143309 14951 sgd_solver.cpp:105] Iteration 11004, lr = 0.001
I0401 17:48:19.361225 14951 solver.cpp:218] Iteration 11018 (2.25158 iter/s, 6.21785s/14 iters), loss = 0.346175
I0401 17:48:19.361274 14951 solver.cpp:237]     Train net output #0: loss = 0.346175 (* 1 = 0.346175 loss)
I0401 17:48:19.361279 14951 sgd_solver.cpp:105] Iteration 11018, lr = 0.001
I0401 17:48:25.314132 14951 solver.cpp:218] Iteration 11032 (2.35184 iter/s, 5.95278s/14 iters), loss = 0.330853
I0401 17:48:25.314246 14951 solver.cpp:237]     Train net output #0: loss = 0.330853 (* 1 = 0.330853 loss)
I0401 17:48:25.314254 14951 sgd_solver.cpp:105] Iteration 11032, lr = 0.001
I0401 17:48:27.741847 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:48:31.551860 14951 solver.cpp:218] Iteration 11046 (2.24448 iter/s, 6.23753s/14 iters), loss = 0.620956
I0401 17:48:31.551906 14951 solver.cpp:237]     Train net output #0: loss = 0.620956 (* 1 = 0.620956 loss)
I0401 17:48:31.551913 14951 sgd_solver.cpp:105] Iteration 11046, lr = 0.001
I0401 17:48:36.270627 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_11058.caffemodel
I0401 17:48:39.232319 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_11058.solverstate
I0401 17:48:41.547899 14951 solver.cpp:330] Iteration 11058, Testing net (#0)
I0401 17:48:41.547920 14951 net.cpp:676] Ignoring source layer train-data
I0401 17:48:41.937557 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:48:42.114039 14951 blocking_queue.cpp:49] Waiting for data
I0401 17:48:43.763268 14951 solver.cpp:397]     Test net output #0: accuracy = 0.305288
I0401 17:48:43.763305 14951 solver.cpp:397]     Test net output #1: loss = 3.91563 (* 1 = 3.91563 loss)
I0401 17:48:44.171273 14951 solver.cpp:218] Iteration 11060 (1.10942 iter/s, 12.6192s/14 iters), loss = 0.277962
I0401 17:48:44.171339 14951 solver.cpp:237]     Train net output #0: loss = 0.277962 (* 1 = 0.277962 loss)
I0401 17:48:44.171347 14951 sgd_solver.cpp:105] Iteration 11060, lr = 0.001
I0401 17:48:49.691035 14951 solver.cpp:218] Iteration 11074 (2.5364 iter/s, 5.51963s/14 iters), loss = 0.323949
I0401 17:48:49.691077 14951 solver.cpp:237]     Train net output #0: loss = 0.323949 (* 1 = 0.323949 loss)
I0401 17:48:49.691083 14951 sgd_solver.cpp:105] Iteration 11074, lr = 0.001
I0401 17:48:55.718742 14951 solver.cpp:218] Iteration 11088 (2.32266 iter/s, 6.02758s/14 iters), loss = 0.377695
I0401 17:48:55.718863 14951 solver.cpp:237]     Train net output #0: loss = 0.377695 (* 1 = 0.377695 loss)
I0401 17:48:55.718869 14951 sgd_solver.cpp:105] Iteration 11088, lr = 0.001
I0401 17:49:01.690075 14951 solver.cpp:218] Iteration 11102 (2.34461 iter/s, 5.97114s/14 iters), loss = 0.377752
I0401 17:49:01.690117 14951 solver.cpp:237]     Train net output #0: loss = 0.377752 (* 1 = 0.377752 loss)
I0401 17:49:01.690124 14951 sgd_solver.cpp:105] Iteration 11102, lr = 0.001
I0401 17:49:07.979929 14951 solver.cpp:218] Iteration 11116 (2.22585 iter/s, 6.28973s/14 iters), loss = 0.282995
I0401 17:49:07.979974 14951 solver.cpp:237]     Train net output #0: loss = 0.282995 (* 1 = 0.282995 loss)
I0401 17:49:07.979979 14951 sgd_solver.cpp:105] Iteration 11116, lr = 0.001
I0401 17:49:14.038327 14951 solver.cpp:218] Iteration 11130 (2.31089 iter/s, 6.05828s/14 iters), loss = 0.406004
I0401 17:49:14.038372 14951 solver.cpp:237]     Train net output #0: loss = 0.406004 (* 1 = 0.406004 loss)
I0401 17:49:14.038378 14951 sgd_solver.cpp:105] Iteration 11130, lr = 0.001
I0401 17:49:20.178146 14951 solver.cpp:218] Iteration 11144 (2.28024 iter/s, 6.13969s/14 iters), loss = 0.309175
I0401 17:49:20.178190 14951 solver.cpp:237]     Train net output #0: loss = 0.309175 (* 1 = 0.309175 loss)
I0401 17:49:20.178196 14951 sgd_solver.cpp:105] Iteration 11144, lr = 0.001
I0401 17:49:23.278661 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:49:26.302968 14951 solver.cpp:218] Iteration 11158 (2.28583 iter/s, 6.1247s/14 iters), loss = 0.327253
I0401 17:49:26.303088 14951 solver.cpp:237]     Train net output #0: loss = 0.327253 (* 1 = 0.327253 loss)
I0401 17:49:26.303098 14951 sgd_solver.cpp:105] Iteration 11158, lr = 0.001
I0401 17:49:31.764950 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_11172.caffemodel
I0401 17:49:34.736966 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_11172.solverstate
I0401 17:49:37.044826 14951 solver.cpp:330] Iteration 11172, Testing net (#0)
I0401 17:49:37.044848 14951 net.cpp:676] Ignoring source layer train-data
I0401 17:49:37.289753 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:49:39.176573 14951 solver.cpp:397]     Test net output #0: accuracy = 0.305288
I0401 17:49:39.176605 14951 solver.cpp:397]     Test net output #1: loss = 3.95128 (* 1 = 3.95128 loss)
I0401 17:49:39.318915 14951 solver.cpp:218] Iteration 11172 (1.07563 iter/s, 13.0157s/14 iters), loss = 0.612682
I0401 17:49:39.320519 14951 solver.cpp:237]     Train net output #0: loss = 0.612682 (* 1 = 0.612682 loss)
I0401 17:49:39.320531 14951 sgd_solver.cpp:105] Iteration 11172, lr = 0.001
I0401 17:49:44.507073 14951 solver.cpp:218] Iteration 11186 (2.69932 iter/s, 5.18649s/14 iters), loss = 0.519633
I0401 17:49:44.507138 14951 solver.cpp:237]     Train net output #0: loss = 0.519633 (* 1 = 0.519633 loss)
I0401 17:49:44.507145 14951 sgd_solver.cpp:105] Iteration 11186, lr = 0.001
I0401 17:49:50.339540 14951 solver.cpp:218] Iteration 11200 (2.40041 iter/s, 5.83233s/14 iters), loss = 0.350459
I0401 17:49:50.339591 14951 solver.cpp:237]     Train net output #0: loss = 0.350459 (* 1 = 0.350459 loss)
I0401 17:49:50.339599 14951 sgd_solver.cpp:105] Iteration 11200, lr = 0.001
I0401 17:49:56.326138 14951 solver.cpp:218] Iteration 11214 (2.33861 iter/s, 5.98647s/14 iters), loss = 0.278408
I0401 17:49:56.326305 14951 solver.cpp:237]     Train net output #0: loss = 0.278408 (* 1 = 0.278408 loss)
I0401 17:49:56.326314 14951 sgd_solver.cpp:105] Iteration 11214, lr = 0.001
I0401 17:50:02.561556 14951 solver.cpp:218] Iteration 11228 (2.24533 iter/s, 6.23517s/14 iters), loss = 0.363937
I0401 17:50:02.561615 14951 solver.cpp:237]     Train net output #0: loss = 0.363937 (* 1 = 0.363937 loss)
I0401 17:50:02.561625 14951 sgd_solver.cpp:105] Iteration 11228, lr = 0.001
I0401 17:50:08.691735 14951 solver.cpp:218] Iteration 11242 (2.28383 iter/s, 6.13004s/14 iters), loss = 0.328141
I0401 17:50:08.691792 14951 solver.cpp:237]     Train net output #0: loss = 0.328141 (* 1 = 0.328141 loss)
I0401 17:50:08.691802 14951 sgd_solver.cpp:105] Iteration 11242, lr = 0.001
I0401 17:50:14.812444 14951 solver.cpp:218] Iteration 11256 (2.28737 iter/s, 6.12057s/14 iters), loss = 0.308661
I0401 17:50:14.812487 14951 solver.cpp:237]     Train net output #0: loss = 0.308661 (* 1 = 0.308661 loss)
I0401 17:50:14.812494 14951 sgd_solver.cpp:105] Iteration 11256, lr = 0.001
I0401 17:50:18.379855 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:50:20.739956 14951 solver.cpp:218] Iteration 11270 (2.36192 iter/s, 5.92739s/14 iters), loss = 0.41273
I0401 17:50:20.739998 14951 solver.cpp:237]     Train net output #0: loss = 0.41273 (* 1 = 0.41273 loss)
I0401 17:50:20.740005 14951 sgd_solver.cpp:105] Iteration 11270, lr = 0.001
I0401 17:50:26.802750 14951 solver.cpp:218] Iteration 11284 (2.30923 iter/s, 6.06264s/14 iters), loss = 0.441601
I0401 17:50:26.802872 14951 solver.cpp:237]     Train net output #0: loss = 0.441601 (* 1 = 0.441601 loss)
I0401 17:50:26.802881 14951 sgd_solver.cpp:105] Iteration 11284, lr = 0.001
I0401 17:50:27.150683 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_11286.caffemodel
I0401 17:50:30.197394 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_11286.solverstate
I0401 17:50:34.363865 14951 solver.cpp:330] Iteration 11286, Testing net (#0)
I0401 17:50:34.363890 14951 net.cpp:676] Ignoring source layer train-data
I0401 17:50:34.564842 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:50:36.542636 14951 solver.cpp:397]     Test net output #0: accuracy = 0.27524
I0401 17:50:36.542665 14951 solver.cpp:397]     Test net output #1: loss = 4.05522 (* 1 = 4.05522 loss)
I0401 17:50:41.087445 14951 solver.cpp:218] Iteration 11298 (0.980089 iter/s, 14.2844s/14 iters), loss = 0.442259
I0401 17:50:41.087496 14951 solver.cpp:237]     Train net output #0: loss = 0.442259 (* 1 = 0.442259 loss)
I0401 17:50:41.087502 14951 sgd_solver.cpp:105] Iteration 11298, lr = 0.001
I0401 17:50:47.033121 14951 solver.cpp:218] Iteration 11312 (2.3547 iter/s, 5.94555s/14 iters), loss = 0.203246
I0401 17:50:47.033169 14951 solver.cpp:237]     Train net output #0: loss = 0.203246 (* 1 = 0.203246 loss)
I0401 17:50:47.033179 14951 sgd_solver.cpp:105] Iteration 11312, lr = 0.001
I0401 17:50:53.001762 14951 solver.cpp:218] Iteration 11326 (2.34564 iter/s, 5.96851s/14 iters), loss = 0.457062
I0401 17:50:53.001801 14951 solver.cpp:237]     Train net output #0: loss = 0.457062 (* 1 = 0.457062 loss)
I0401 17:50:53.001807 14951 sgd_solver.cpp:105] Iteration 11326, lr = 0.001
I0401 17:50:59.078516 14951 solver.cpp:218] Iteration 11340 (2.30391 iter/s, 6.07663s/14 iters), loss = 0.280725
I0401 17:50:59.078657 14951 solver.cpp:237]     Train net output #0: loss = 0.280725 (* 1 = 0.280725 loss)
I0401 17:50:59.078666 14951 sgd_solver.cpp:105] Iteration 11340, lr = 0.001
I0401 17:51:05.262204 14951 solver.cpp:218] Iteration 11354 (2.2641 iter/s, 6.18347s/14 iters), loss = 0.19759
I0401 17:51:05.262250 14951 solver.cpp:237]     Train net output #0: loss = 0.19759 (* 1 = 0.19759 loss)
I0401 17:51:05.262257 14951 sgd_solver.cpp:105] Iteration 11354, lr = 0.001
I0401 17:51:11.381249 14951 solver.cpp:218] Iteration 11368 (2.28799 iter/s, 6.11892s/14 iters), loss = 0.387338
I0401 17:51:11.381290 14951 solver.cpp:237]     Train net output #0: loss = 0.387338 (* 1 = 0.387338 loss)
I0401 17:51:11.381296 14951 sgd_solver.cpp:105] Iteration 11368, lr = 0.001
I0401 17:51:15.834697 14982 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:51:17.370252 14951 solver.cpp:218] Iteration 11382 (2.33766 iter/s, 5.98888s/14 iters), loss = 0.544269
I0401 17:51:17.370311 14951 solver.cpp:237]     Train net output #0: loss = 0.544269 (* 1 = 0.544269 loss)
I0401 17:51:17.370321 14951 sgd_solver.cpp:105] Iteration 11382, lr = 0.001
I0401 17:51:23.374382 14951 solver.cpp:218] Iteration 11396 (2.33178 iter/s, 6.00399s/14 iters), loss = 0.266954
I0401 17:51:23.374424 14951 solver.cpp:237]     Train net output #0: loss = 0.266954 (* 1 = 0.266954 loss)
I0401 17:51:23.374429 14951 sgd_solver.cpp:105] Iteration 11396, lr = 0.001
I0401 17:51:24.611948 14951 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_11400.caffemodel
I0401 17:51:27.814960 14951 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_11400.solverstate
I0401 17:51:30.142040 14951 solver.cpp:330] Iteration 11400, Testing net (#0)
I0401 17:51:30.142143 14951 net.cpp:676] Ignoring source layer train-data
I0401 17:51:30.262874 15062 data_layer.cpp:73] Restarting data prefetching from start.
I0401 17:51:32.269413 14951 solver.cpp:397]     Test net output #0: accuracy = 0.302885
I0401 17:51:32.269451 14951 solver.cpp:397]     Test net output #1: loss = 4.16967 (* 1 = 4.16967 loss)
I0401 17:51:32.269459 14951 solver.cpp:315] Optimization Done.
I0401 17:51:32.269462 14951 caffe.cpp:259] Optimization Done.