DIGITS-CNN/cars/lr-investigations/step-down/1e-2/33_0.25/caffe_output.log

I0407 09:40:51.550113 17183 upgrade_proto.cpp:1082] Attempting to upgrade input file specified using deprecated 'solver_type' field (enum)': /mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210407-094049-37d4/solver.prototxt
I0407 09:40:51.550261 17183 upgrade_proto.cpp:1089] Successfully upgraded file specified using deprecated 'solver_type' field (enum) to 'type' field (string).
W0407 09:40:51.550264 17183 upgrade_proto.cpp:1091] Note that future Caffe releases will only support 'type' field (string) for a solver's type.
I0407 09:40:51.550316 17183 caffe.cpp:218] Using GPUs 3
I0407 09:40:51.602154 17183 caffe.cpp:223] GPU 3: GeForce GTX TITAN X
I0407 09:40:51.817802 17183 solver.cpp:44] Initializing solver from parameters:
test_iter: 51
test_interval: 102
base_lr: 0.01
display: 12
max_iter: 10200
lr_policy: "step"
gamma: 0.25
momentum: 0.9
weight_decay: 0.0001
stepsize: 3366
snapshot: 102
snapshot_prefix: "snapshot"
solver_mode: GPU
device_id: 3
net: "train_val.prototxt"
train_state {
level: 0
stage: ""
}
type: "SGD"
I0407 09:40:51.818681 17183 solver.cpp:87] Creating training net from net file: train_val.prototxt
I0407 09:40:51.819336 17183 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer val-data
I0407 09:40:51.819350 17183 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy
I0407 09:40:51.819473 17183 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/20210401-115716-aaf7/mean.binaryproto"
}
data_param {
source: "/mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210401-115716-aaf7/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"
}
I0407 09:40:51.819552 17183 layer_factory.hpp:77] Creating layer train-data
I0407 09:40:51.822512 17183 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210401-115716-aaf7/train_db
I0407 09:40:51.822808 17183 net.cpp:84] Creating Layer train-data
I0407 09:40:51.822826 17183 net.cpp:380] train-data -> data
I0407 09:40:51.822854 17183 net.cpp:380] train-data -> label
I0407 09:40:51.822870 17183 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210401-115716-aaf7/mean.binaryproto
I0407 09:40:51.828150 17183 data_layer.cpp:45] output data size: 128,3,227,227
I0407 09:40:51.950438 17183 net.cpp:122] Setting up train-data
I0407 09:40:51.950459 17183 net.cpp:129] Top shape: 128 3 227 227 (19787136)
I0407 09:40:51.950464 17183 net.cpp:129] Top shape: 128 (128)
I0407 09:40:51.950465 17183 net.cpp:137] Memory required for data: 79149056
I0407 09:40:51.950472 17183 layer_factory.hpp:77] Creating layer conv1
I0407 09:40:51.950490 17183 net.cpp:84] Creating Layer conv1
I0407 09:40:51.950495 17183 net.cpp:406] conv1 <- data
I0407 09:40:51.950505 17183 net.cpp:380] conv1 -> conv1
I0407 09:40:52.410439 17183 net.cpp:122] Setting up conv1
I0407 09:40:52.410459 17183 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0407 09:40:52.410461 17183 net.cpp:137] Memory required for data: 227833856
I0407 09:40:52.410480 17183 layer_factory.hpp:77] Creating layer relu1
I0407 09:40:52.410490 17183 net.cpp:84] Creating Layer relu1
I0407 09:40:52.410492 17183 net.cpp:406] relu1 <- conv1
I0407 09:40:52.410497 17183 net.cpp:367] relu1 -> conv1 (in-place)
I0407 09:40:52.410756 17183 net.cpp:122] Setting up relu1
I0407 09:40:52.410763 17183 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0407 09:40:52.410765 17183 net.cpp:137] Memory required for data: 376518656
I0407 09:40:52.410768 17183 layer_factory.hpp:77] Creating layer norm1
I0407 09:40:52.410775 17183 net.cpp:84] Creating Layer norm1
I0407 09:40:52.410801 17183 net.cpp:406] norm1 <- conv1
I0407 09:40:52.410806 17183 net.cpp:380] norm1 -> norm1
I0407 09:40:52.412657 17183 net.cpp:122] Setting up norm1
I0407 09:40:52.412665 17183 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0407 09:40:52.412668 17183 net.cpp:137] Memory required for data: 525203456
I0407 09:40:52.412670 17183 layer_factory.hpp:77] Creating layer pool1
I0407 09:40:52.412678 17183 net.cpp:84] Creating Layer pool1
I0407 09:40:52.412679 17183 net.cpp:406] pool1 <- norm1
I0407 09:40:52.412684 17183 net.cpp:380] pool1 -> pool1
I0407 09:40:52.412719 17183 net.cpp:122] Setting up pool1
I0407 09:40:52.412727 17183 net.cpp:129] Top shape: 128 96 27 27 (8957952)
I0407 09:40:52.412730 17183 net.cpp:137] Memory required for data: 561035264
I0407 09:40:52.412734 17183 layer_factory.hpp:77] Creating layer conv2
I0407 09:40:52.412745 17183 net.cpp:84] Creating Layer conv2
I0407 09:40:52.412750 17183 net.cpp:406] conv2 <- pool1
I0407 09:40:52.412755 17183 net.cpp:380] conv2 -> conv2
I0407 09:40:52.420058 17183 net.cpp:122] Setting up conv2
I0407 09:40:52.420078 17183 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0407 09:40:52.420080 17183 net.cpp:137] Memory required for data: 656586752
I0407 09:40:52.420090 17183 layer_factory.hpp:77] Creating layer relu2
I0407 09:40:52.420100 17183 net.cpp:84] Creating Layer relu2
I0407 09:40:52.420104 17183 net.cpp:406] relu2 <- conv2
I0407 09:40:52.420109 17183 net.cpp:367] relu2 -> conv2 (in-place)
I0407 09:40:52.420578 17183 net.cpp:122] Setting up relu2
I0407 09:40:52.420586 17183 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0407 09:40:52.420588 17183 net.cpp:137] Memory required for data: 752138240
I0407 09:40:52.420591 17183 layer_factory.hpp:77] Creating layer norm2
I0407 09:40:52.420598 17183 net.cpp:84] Creating Layer norm2
I0407 09:40:52.420600 17183 net.cpp:406] norm2 <- conv2
I0407 09:40:52.420605 17183 net.cpp:380] norm2 -> norm2
I0407 09:40:52.420943 17183 net.cpp:122] Setting up norm2
I0407 09:40:52.420950 17183 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0407 09:40:52.420953 17183 net.cpp:137] Memory required for data: 847689728
I0407 09:40:52.420954 17183 layer_factory.hpp:77] Creating layer pool2
I0407 09:40:52.420962 17183 net.cpp:84] Creating Layer pool2
I0407 09:40:52.420965 17183 net.cpp:406] pool2 <- norm2
I0407 09:40:52.420969 17183 net.cpp:380] pool2 -> pool2
I0407 09:40:52.421000 17183 net.cpp:122] Setting up pool2
I0407 09:40:52.421003 17183 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0407 09:40:52.421005 17183 net.cpp:137] Memory required for data: 869840896
I0407 09:40:52.421007 17183 layer_factory.hpp:77] Creating layer conv3
I0407 09:40:52.421016 17183 net.cpp:84] Creating Layer conv3
I0407 09:40:52.421018 17183 net.cpp:406] conv3 <- pool2
I0407 09:40:52.421023 17183 net.cpp:380] conv3 -> conv3
I0407 09:40:52.434401 17183 net.cpp:122] Setting up conv3
I0407 09:40:52.434424 17183 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0407 09:40:52.434428 17183 net.cpp:137] Memory required for data: 903067648
I0407 09:40:52.434444 17183 layer_factory.hpp:77] Creating layer relu3
I0407 09:40:52.434454 17183 net.cpp:84] Creating Layer relu3
I0407 09:40:52.434459 17183 net.cpp:406] relu3 <- conv3
I0407 09:40:52.434468 17183 net.cpp:367] relu3 -> conv3 (in-place)
I0407 09:40:52.435096 17183 net.cpp:122] Setting up relu3
I0407 09:40:52.435106 17183 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0407 09:40:52.435109 17183 net.cpp:137] Memory required for data: 936294400
I0407 09:40:52.435113 17183 layer_factory.hpp:77] Creating layer conv4
I0407 09:40:52.435127 17183 net.cpp:84] Creating Layer conv4
I0407 09:40:52.435130 17183 net.cpp:406] conv4 <- conv3
I0407 09:40:52.435138 17183 net.cpp:380] conv4 -> conv4
I0407 09:40:52.449146 17183 net.cpp:122] Setting up conv4
I0407 09:40:52.449172 17183 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0407 09:40:52.449177 17183 net.cpp:137] Memory required for data: 969521152
I0407 09:40:52.449187 17183 layer_factory.hpp:77] Creating layer relu4
I0407 09:40:52.449198 17183 net.cpp:84] Creating Layer relu4
I0407 09:40:52.449230 17183 net.cpp:406] relu4 <- conv4
I0407 09:40:52.449240 17183 net.cpp:367] relu4 -> conv4 (in-place)
I0407 09:40:52.449688 17183 net.cpp:122] Setting up relu4
I0407 09:40:52.449698 17183 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0407 09:40:52.449703 17183 net.cpp:137] Memory required for data: 1002747904
I0407 09:40:52.449707 17183 layer_factory.hpp:77] Creating layer conv5
I0407 09:40:52.449719 17183 net.cpp:84] Creating Layer conv5
I0407 09:40:52.449723 17183 net.cpp:406] conv5 <- conv4
I0407 09:40:52.449730 17183 net.cpp:380] conv5 -> conv5
I0407 09:40:52.460681 17183 net.cpp:122] Setting up conv5
I0407 09:40:52.460707 17183 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0407 09:40:52.460711 17183 net.cpp:137] Memory required for data: 1024899072
I0407 09:40:52.460726 17183 layer_factory.hpp:77] Creating layer relu5
I0407 09:40:52.460737 17183 net.cpp:84] Creating Layer relu5
I0407 09:40:52.460742 17183 net.cpp:406] relu5 <- conv5
I0407 09:40:52.460750 17183 net.cpp:367] relu5 -> conv5 (in-place)
I0407 09:40:52.461380 17183 net.cpp:122] Setting up relu5
I0407 09:40:52.461393 17183 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0407 09:40:52.461396 17183 net.cpp:137] Memory required for data: 1047050240
I0407 09:40:52.461400 17183 layer_factory.hpp:77] Creating layer pool5
I0407 09:40:52.461408 17183 net.cpp:84] Creating Layer pool5
I0407 09:40:52.461412 17183 net.cpp:406] pool5 <- conv5
I0407 09:40:52.461419 17183 net.cpp:380] pool5 -> pool5
I0407 09:40:52.461462 17183 net.cpp:122] Setting up pool5
I0407 09:40:52.461468 17183 net.cpp:129] Top shape: 128 256 6 6 (1179648)
I0407 09:40:52.461472 17183 net.cpp:137] Memory required for data: 1051768832
I0407 09:40:52.461475 17183 layer_factory.hpp:77] Creating layer fc6
I0407 09:40:52.461488 17183 net.cpp:84] Creating Layer fc6
I0407 09:40:52.461491 17183 net.cpp:406] fc6 <- pool5
I0407 09:40:52.461498 17183 net.cpp:380] fc6 -> fc6
I0407 09:40:52.830471 17183 net.cpp:122] Setting up fc6
I0407 09:40:52.830494 17183 net.cpp:129] Top shape: 128 4096 (524288)
I0407 09:40:52.830497 17183 net.cpp:137] Memory required for data: 1053865984
I0407 09:40:52.830505 17183 layer_factory.hpp:77] Creating layer relu6
I0407 09:40:52.830515 17183 net.cpp:84] Creating Layer relu6
I0407 09:40:52.830518 17183 net.cpp:406] relu6 <- fc6
I0407 09:40:52.830523 17183 net.cpp:367] relu6 -> fc6 (in-place)
I0407 09:40:52.831136 17183 net.cpp:122] Setting up relu6
I0407 09:40:52.831147 17183 net.cpp:129] Top shape: 128 4096 (524288)
I0407 09:40:52.831149 17183 net.cpp:137] Memory required for data: 1055963136
I0407 09:40:52.831152 17183 layer_factory.hpp:77] Creating layer drop6
I0407 09:40:52.831158 17183 net.cpp:84] Creating Layer drop6
I0407 09:40:52.831161 17183 net.cpp:406] drop6 <- fc6
I0407 09:40:52.831166 17183 net.cpp:367] drop6 -> fc6 (in-place)
I0407 09:40:52.831192 17183 net.cpp:122] Setting up drop6
I0407 09:40:52.831195 17183 net.cpp:129] Top shape: 128 4096 (524288)
I0407 09:40:52.831197 17183 net.cpp:137] Memory required for data: 1058060288
I0407 09:40:52.831199 17183 layer_factory.hpp:77] Creating layer fc7
I0407 09:40:52.831207 17183 net.cpp:84] Creating Layer fc7
I0407 09:40:52.831208 17183 net.cpp:406] fc7 <- fc6
I0407 09:40:52.831212 17183 net.cpp:380] fc7 -> fc7
I0407 09:40:52.980631 17183 net.cpp:122] Setting up fc7
I0407 09:40:52.980650 17183 net.cpp:129] Top shape: 128 4096 (524288)
I0407 09:40:52.980652 17183 net.cpp:137] Memory required for data: 1060157440
I0407 09:40:52.980661 17183 layer_factory.hpp:77] Creating layer relu7
I0407 09:40:52.980669 17183 net.cpp:84] Creating Layer relu7
I0407 09:40:52.980671 17183 net.cpp:406] relu7 <- fc7
I0407 09:40:52.980677 17183 net.cpp:367] relu7 -> fc7 (in-place)
I0407 09:40:52.981060 17183 net.cpp:122] Setting up relu7
I0407 09:40:52.981068 17183 net.cpp:129] Top shape: 128 4096 (524288)
I0407 09:40:52.981070 17183 net.cpp:137] Memory required for data: 1062254592
I0407 09:40:52.981073 17183 layer_factory.hpp:77] Creating layer drop7
I0407 09:40:52.981079 17183 net.cpp:84] Creating Layer drop7
I0407 09:40:52.981101 17183 net.cpp:406] drop7 <- fc7
I0407 09:40:52.981106 17183 net.cpp:367] drop7 -> fc7 (in-place)
I0407 09:40:52.981127 17183 net.cpp:122] Setting up drop7
I0407 09:40:52.981132 17183 net.cpp:129] Top shape: 128 4096 (524288)
I0407 09:40:52.981133 17183 net.cpp:137] Memory required for data: 1064351744
I0407 09:40:52.981135 17183 layer_factory.hpp:77] Creating layer fc8
I0407 09:40:52.981142 17183 net.cpp:84] Creating Layer fc8
I0407 09:40:52.981143 17183 net.cpp:406] fc8 <- fc7
I0407 09:40:52.981148 17183 net.cpp:380] fc8 -> fc8
I0407 09:40:52.988312 17183 net.cpp:122] Setting up fc8
I0407 09:40:52.988333 17183 net.cpp:129] Top shape: 128 196 (25088)
I0407 09:40:52.988337 17183 net.cpp:137] Memory required for data: 1064452096
I0407 09:40:52.988344 17183 layer_factory.hpp:77] Creating layer loss
I0407 09:40:52.988351 17183 net.cpp:84] Creating Layer loss
I0407 09:40:52.988354 17183 net.cpp:406] loss <- fc8
I0407 09:40:52.988359 17183 net.cpp:406] loss <- label
I0407 09:40:52.988364 17183 net.cpp:380] loss -> loss
I0407 09:40:52.988373 17183 layer_factory.hpp:77] Creating layer loss
I0407 09:40:52.989091 17183 net.cpp:122] Setting up loss
I0407 09:40:52.989099 17183 net.cpp:129] Top shape: (1)
I0407 09:40:52.989101 17183 net.cpp:132]     with loss weight 1
I0407 09:40:52.989117 17183 net.cpp:137] Memory required for data: 1064452100
I0407 09:40:52.989120 17183 net.cpp:198] loss needs backward computation.
I0407 09:40:52.989125 17183 net.cpp:198] fc8 needs backward computation.
I0407 09:40:52.989128 17183 net.cpp:198] drop7 needs backward computation.
I0407 09:40:52.989130 17183 net.cpp:198] relu7 needs backward computation.
I0407 09:40:52.989132 17183 net.cpp:198] fc7 needs backward computation.
I0407 09:40:52.989135 17183 net.cpp:198] drop6 needs backward computation.
I0407 09:40:52.989137 17183 net.cpp:198] relu6 needs backward computation.
I0407 09:40:52.989140 17183 net.cpp:198] fc6 needs backward computation.
I0407 09:40:52.989142 17183 net.cpp:198] pool5 needs backward computation.
I0407 09:40:52.989145 17183 net.cpp:198] relu5 needs backward computation.
I0407 09:40:52.989147 17183 net.cpp:198] conv5 needs backward computation.
I0407 09:40:52.989149 17183 net.cpp:198] relu4 needs backward computation.
I0407 09:40:52.989151 17183 net.cpp:198] conv4 needs backward computation.
I0407 09:40:52.989154 17183 net.cpp:198] relu3 needs backward computation.
I0407 09:40:52.989156 17183 net.cpp:198] conv3 needs backward computation.
I0407 09:40:52.989158 17183 net.cpp:198] pool2 needs backward computation.
I0407 09:40:52.989161 17183 net.cpp:198] norm2 needs backward computation.
I0407 09:40:52.989163 17183 net.cpp:198] relu2 needs backward computation.
I0407 09:40:52.989166 17183 net.cpp:198] conv2 needs backward computation.
I0407 09:40:52.989168 17183 net.cpp:198] pool1 needs backward computation.
I0407 09:40:52.989171 17183 net.cpp:198] norm1 needs backward computation.
I0407 09:40:52.989173 17183 net.cpp:198] relu1 needs backward computation.
I0407 09:40:52.989176 17183 net.cpp:198] conv1 needs backward computation.
I0407 09:40:52.989178 17183 net.cpp:200] train-data does not need backward computation.
I0407 09:40:52.989181 17183 net.cpp:242] This network produces output loss
I0407 09:40:52.989192 17183 net.cpp:255] Network initialization done.
I0407 09:40:52.989683 17183 solver.cpp:172] Creating test net (#0) specified by net file: train_val.prototxt
I0407 09:40:52.989710 17183 net.cpp:294] The NetState phase (1) differed from the phase (0) specified by a rule in layer train-data
I0407 09:40:52.989840 17183 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/20210401-115716-aaf7/mean.binaryproto"
}
data_param {
source: "/mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210401-115716-aaf7/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"
}
I0407 09:40:52.989938 17183 layer_factory.hpp:77] Creating layer val-data
I0407 09:40:52.998039 17183 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210401-115716-aaf7/val_db
I0407 09:40:52.998294 17183 net.cpp:84] Creating Layer val-data
I0407 09:40:52.998309 17183 net.cpp:380] val-data -> data
I0407 09:40:52.998320 17183 net.cpp:380] val-data -> label
I0407 09:40:52.998327 17183 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210401-115716-aaf7/mean.binaryproto
I0407 09:40:53.002041 17183 data_layer.cpp:45] output data size: 32,3,227,227
I0407 09:40:53.040927 17183 net.cpp:122] Setting up val-data
I0407 09:40:53.040952 17183 net.cpp:129] Top shape: 32 3 227 227 (4946784)
I0407 09:40:53.040958 17183 net.cpp:129] Top shape: 32 (32)
I0407 09:40:53.040961 17183 net.cpp:137] Memory required for data: 19787264
I0407 09:40:53.040967 17183 layer_factory.hpp:77] Creating layer label_val-data_1_split
I0407 09:40:53.040980 17183 net.cpp:84] Creating Layer label_val-data_1_split
I0407 09:40:53.040984 17183 net.cpp:406] label_val-data_1_split <- label
I0407 09:40:53.040992 17183 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_0
I0407 09:40:53.041003 17183 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_1
I0407 09:40:53.041097 17183 net.cpp:122] Setting up label_val-data_1_split
I0407 09:40:53.041107 17183 net.cpp:129] Top shape: 32 (32)
I0407 09:40:53.041111 17183 net.cpp:129] Top shape: 32 (32)
I0407 09:40:53.041115 17183 net.cpp:137] Memory required for data: 19787520
I0407 09:40:53.041118 17183 layer_factory.hpp:77] Creating layer conv1
I0407 09:40:53.041132 17183 net.cpp:84] Creating Layer conv1
I0407 09:40:53.041136 17183 net.cpp:406] conv1 <- data
I0407 09:40:53.041143 17183 net.cpp:380] conv1 -> conv1
I0407 09:40:53.044275 17183 net.cpp:122] Setting up conv1
I0407 09:40:53.044296 17183 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0407 09:40:53.044299 17183 net.cpp:137] Memory required for data: 56958720
I0407 09:40:53.044312 17183 layer_factory.hpp:77] Creating layer relu1
I0407 09:40:53.044322 17183 net.cpp:84] Creating Layer relu1
I0407 09:40:53.044325 17183 net.cpp:406] relu1 <- conv1
I0407 09:40:53.044333 17183 net.cpp:367] relu1 -> conv1 (in-place)
I0407 09:40:53.044724 17183 net.cpp:122] Setting up relu1
I0407 09:40:53.044734 17183 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0407 09:40:53.044737 17183 net.cpp:137] Memory required for data: 94129920
I0407 09:40:53.044741 17183 layer_factory.hpp:77] Creating layer norm1
I0407 09:40:53.044751 17183 net.cpp:84] Creating Layer norm1
I0407 09:40:53.044755 17183 net.cpp:406] norm1 <- conv1
I0407 09:40:53.044761 17183 net.cpp:380] norm1 -> norm1
I0407 09:40:53.045359 17183 net.cpp:122] Setting up norm1
I0407 09:40:53.045372 17183 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0407 09:40:53.045377 17183 net.cpp:137] Memory required for data: 131301120
I0407 09:40:53.045380 17183 layer_factory.hpp:77] Creating layer pool1
I0407 09:40:53.045387 17183 net.cpp:84] Creating Layer pool1
I0407 09:40:53.045392 17183 net.cpp:406] pool1 <- norm1
I0407 09:40:53.045397 17183 net.cpp:380] pool1 -> pool1
I0407 09:40:53.045433 17183 net.cpp:122] Setting up pool1
I0407 09:40:53.045440 17183 net.cpp:129] Top shape: 32 96 27 27 (2239488)
I0407 09:40:53.045444 17183 net.cpp:137] Memory required for data: 140259072
I0407 09:40:53.045446 17183 layer_factory.hpp:77] Creating layer conv2
I0407 09:40:53.045457 17183 net.cpp:84] Creating Layer conv2
I0407 09:40:53.045461 17183 net.cpp:406] conv2 <- pool1
I0407 09:40:53.045492 17183 net.cpp:380] conv2 -> conv2
I0407 09:40:53.054517 17183 net.cpp:122] Setting up conv2
I0407 09:40:53.054541 17183 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0407 09:40:53.054546 17183 net.cpp:137] Memory required for data: 164146944
I0407 09:40:53.054560 17183 layer_factory.hpp:77] Creating layer relu2
I0407 09:40:53.054571 17183 net.cpp:84] Creating Layer relu2
I0407 09:40:53.054575 17183 net.cpp:406] relu2 <- conv2
I0407 09:40:53.054587 17183 net.cpp:367] relu2 -> conv2 (in-place)
I0407 09:40:53.055248 17183 net.cpp:122] Setting up relu2
I0407 09:40:53.055258 17183 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0407 09:40:53.055263 17183 net.cpp:137] Memory required for data: 188034816
I0407 09:40:53.055266 17183 layer_factory.hpp:77] Creating layer norm2
I0407 09:40:53.055279 17183 net.cpp:84] Creating Layer norm2
I0407 09:40:53.055284 17183 net.cpp:406] norm2 <- conv2
I0407 09:40:53.055289 17183 net.cpp:380] norm2 -> norm2
I0407 09:40:53.055980 17183 net.cpp:122] Setting up norm2
I0407 09:40:53.055992 17183 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0407 09:40:53.055996 17183 net.cpp:137] Memory required for data: 211922688
I0407 09:40:53.056000 17183 layer_factory.hpp:77] Creating layer pool2
I0407 09:40:53.056010 17183 net.cpp:84] Creating Layer pool2
I0407 09:40:53.056015 17183 net.cpp:406] pool2 <- norm2
I0407 09:40:53.056022 17183 net.cpp:380] pool2 -> pool2
I0407 09:40:53.056061 17183 net.cpp:122] Setting up pool2
I0407 09:40:53.056066 17183 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0407 09:40:53.056069 17183 net.cpp:137] Memory required for data: 217460480
I0407 09:40:53.056073 17183 layer_factory.hpp:77] Creating layer conv3
I0407 09:40:53.056085 17183 net.cpp:84] Creating Layer conv3
I0407 09:40:53.056089 17183 net.cpp:406] conv3 <- pool2
I0407 09:40:53.056097 17183 net.cpp:380] conv3 -> conv3
I0407 09:40:53.071759 17183 net.cpp:122] Setting up conv3
I0407 09:40:53.071787 17183 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0407 09:40:53.071791 17183 net.cpp:137] Memory required for data: 225767168
I0407 09:40:53.071810 17183 layer_factory.hpp:77] Creating layer relu3
I0407 09:40:53.071820 17183 net.cpp:84] Creating Layer relu3
I0407 09:40:53.071825 17183 net.cpp:406] relu3 <- conv3
I0407 09:40:53.071832 17183 net.cpp:367] relu3 -> conv3 (in-place)
I0407 09:40:53.072486 17183 net.cpp:122] Setting up relu3
I0407 09:40:53.072497 17183 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0407 09:40:53.072500 17183 net.cpp:137] Memory required for data: 234073856
I0407 09:40:53.072504 17183 layer_factory.hpp:77] Creating layer conv4
I0407 09:40:53.072520 17183 net.cpp:84] Creating Layer conv4
I0407 09:40:53.072525 17183 net.cpp:406] conv4 <- conv3
I0407 09:40:53.072532 17183 net.cpp:380] conv4 -> conv4
I0407 09:40:53.086604 17183 net.cpp:122] Setting up conv4
I0407 09:40:53.086629 17183 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0407 09:40:53.086633 17183 net.cpp:137] Memory required for data: 242380544
I0407 09:40:53.086644 17183 layer_factory.hpp:77] Creating layer relu4
I0407 09:40:53.086658 17183 net.cpp:84] Creating Layer relu4
I0407 09:40:53.086663 17183 net.cpp:406] relu4 <- conv4
I0407 09:40:53.086671 17183 net.cpp:367] relu4 -> conv4 (in-place)
I0407 09:40:53.087139 17183 net.cpp:122] Setting up relu4
I0407 09:40:53.087148 17183 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0407 09:40:53.087152 17183 net.cpp:137] Memory required for data: 250687232
I0407 09:40:53.087155 17183 layer_factory.hpp:77] Creating layer conv5
I0407 09:40:53.087169 17183 net.cpp:84] Creating Layer conv5
I0407 09:40:53.087175 17183 net.cpp:406] conv5 <- conv4
I0407 09:40:53.087182 17183 net.cpp:380] conv5 -> conv5
I0407 09:40:53.099390 17183 net.cpp:122] Setting up conv5
I0407 09:40:53.099412 17183 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0407 09:40:53.099416 17183 net.cpp:137] Memory required for data: 256225024
I0407 09:40:53.099434 17183 layer_factory.hpp:77] Creating layer relu5
I0407 09:40:53.099445 17183 net.cpp:84] Creating Layer relu5
I0407 09:40:53.099473 17183 net.cpp:406] relu5 <- conv5
I0407 09:40:53.099483 17183 net.cpp:367] relu5 -> conv5 (in-place)
I0407 09:40:53.100147 17183 net.cpp:122] Setting up relu5
I0407 09:40:53.100159 17183 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0407 09:40:53.100163 17183 net.cpp:137] Memory required for data: 261762816
I0407 09:40:53.100167 17183 layer_factory.hpp:77] Creating layer pool5
I0407 09:40:53.100179 17183 net.cpp:84] Creating Layer pool5
I0407 09:40:53.100184 17183 net.cpp:406] pool5 <- conv5
I0407 09:40:53.100193 17183 net.cpp:380] pool5 -> pool5
I0407 09:40:53.100239 17183 net.cpp:122] Setting up pool5
I0407 09:40:53.100246 17183 net.cpp:129] Top shape: 32 256 6 6 (294912)
I0407 09:40:53.100250 17183 net.cpp:137] Memory required for data: 262942464
I0407 09:40:53.100253 17183 layer_factory.hpp:77] Creating layer fc6
I0407 09:40:53.100261 17183 net.cpp:84] Creating Layer fc6
I0407 09:40:53.100265 17183 net.cpp:406] fc6 <- pool5
I0407 09:40:53.100275 17183 net.cpp:380] fc6 -> fc6
I0407 09:40:53.479776 17183 net.cpp:122] Setting up fc6
I0407 09:40:53.479799 17183 net.cpp:129] Top shape: 32 4096 (131072)
I0407 09:40:53.479800 17183 net.cpp:137] Memory required for data: 263466752
I0407 09:40:53.479809 17183 layer_factory.hpp:77] Creating layer relu6
I0407 09:40:53.479817 17183 net.cpp:84] Creating Layer relu6
I0407 09:40:53.479820 17183 net.cpp:406] relu6 <- fc6
I0407 09:40:53.479827 17183 net.cpp:367] relu6 -> fc6 (in-place)
I0407 09:40:53.480592 17183 net.cpp:122] Setting up relu6
I0407 09:40:53.480600 17183 net.cpp:129] Top shape: 32 4096 (131072)
I0407 09:40:53.480602 17183 net.cpp:137] Memory required for data: 263991040
I0407 09:40:53.480605 17183 layer_factory.hpp:77] Creating layer drop6
I0407 09:40:53.480610 17183 net.cpp:84] Creating Layer drop6
I0407 09:40:53.480613 17183 net.cpp:406] drop6 <- fc6
I0407 09:40:53.480618 17183 net.cpp:367] drop6 -> fc6 (in-place)
I0407 09:40:53.480639 17183 net.cpp:122] Setting up drop6
I0407 09:40:53.480643 17183 net.cpp:129] Top shape: 32 4096 (131072)
I0407 09:40:53.480645 17183 net.cpp:137] Memory required for data: 264515328
I0407 09:40:53.480648 17183 layer_factory.hpp:77] Creating layer fc7
I0407 09:40:53.480654 17183 net.cpp:84] Creating Layer fc7
I0407 09:40:53.480656 17183 net.cpp:406] fc7 <- fc6
I0407 09:40:53.480661 17183 net.cpp:380] fc7 -> fc7
I0407 09:40:53.628304 17183 net.cpp:122] Setting up fc7
I0407 09:40:53.628324 17183 net.cpp:129] Top shape: 32 4096 (131072)
I0407 09:40:53.628325 17183 net.cpp:137] Memory required for data: 265039616
I0407 09:40:53.628334 17183 layer_factory.hpp:77] Creating layer relu7
I0407 09:40:53.628340 17183 net.cpp:84] Creating Layer relu7
I0407 09:40:53.628343 17183 net.cpp:406] relu7 <- fc7
I0407 09:40:53.628348 17183 net.cpp:367] relu7 -> fc7 (in-place)
I0407 09:40:53.628734 17183 net.cpp:122] Setting up relu7
I0407 09:40:53.628741 17183 net.cpp:129] Top shape: 32 4096 (131072)
I0407 09:40:53.628743 17183 net.cpp:137] Memory required for data: 265563904
I0407 09:40:53.628746 17183 layer_factory.hpp:77] Creating layer drop7
I0407 09:40:53.628751 17183 net.cpp:84] Creating Layer drop7
I0407 09:40:53.628755 17183 net.cpp:406] drop7 <- fc7
I0407 09:40:53.628758 17183 net.cpp:367] drop7 -> fc7 (in-place)
I0407 09:40:53.628779 17183 net.cpp:122] Setting up drop7
I0407 09:40:53.628783 17183 net.cpp:129] Top shape: 32 4096 (131072)
I0407 09:40:53.628785 17183 net.cpp:137] Memory required for data: 266088192
I0407 09:40:53.628787 17183 layer_factory.hpp:77] Creating layer fc8
I0407 09:40:53.628795 17183 net.cpp:84] Creating Layer fc8
I0407 09:40:53.628798 17183 net.cpp:406] fc8 <- fc7
I0407 09:40:53.628801 17183 net.cpp:380] fc8 -> fc8
I0407 09:40:53.636118 17183 net.cpp:122] Setting up fc8
I0407 09:40:53.636137 17183 net.cpp:129] Top shape: 32 196 (6272)
I0407 09:40:53.636139 17183 net.cpp:137] Memory required for data: 266113280
I0407 09:40:53.636147 17183 layer_factory.hpp:77] Creating layer fc8_fc8_0_split
I0407 09:40:53.636154 17183 net.cpp:84] Creating Layer fc8_fc8_0_split
I0407 09:40:53.636157 17183 net.cpp:406] fc8_fc8_0_split <- fc8
I0407 09:40:53.636181 17183 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_0
I0407 09:40:53.636190 17183 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_1
I0407 09:40:53.636224 17183 net.cpp:122] Setting up fc8_fc8_0_split
I0407 09:40:53.636227 17183 net.cpp:129] Top shape: 32 196 (6272)
I0407 09:40:53.636229 17183 net.cpp:129] Top shape: 32 196 (6272)
I0407 09:40:53.636231 17183 net.cpp:137] Memory required for data: 266163456
I0407 09:40:53.636234 17183 layer_factory.hpp:77] Creating layer accuracy
I0407 09:40:53.636240 17183 net.cpp:84] Creating Layer accuracy
I0407 09:40:53.636242 17183 net.cpp:406] accuracy <- fc8_fc8_0_split_0
I0407 09:40:53.636245 17183 net.cpp:406] accuracy <- label_val-data_1_split_0
I0407 09:40:53.636250 17183 net.cpp:380] accuracy -> accuracy
I0407 09:40:53.636255 17183 net.cpp:122] Setting up accuracy
I0407 09:40:53.636258 17183 net.cpp:129] Top shape: (1)
I0407 09:40:53.636260 17183 net.cpp:137] Memory required for data: 266163460
I0407 09:40:53.636261 17183 layer_factory.hpp:77] Creating layer loss
I0407 09:40:53.636267 17183 net.cpp:84] Creating Layer loss
I0407 09:40:53.636269 17183 net.cpp:406] loss <- fc8_fc8_0_split_1
I0407 09:40:53.636271 17183 net.cpp:406] loss <- label_val-data_1_split_1
I0407 09:40:53.636276 17183 net.cpp:380] loss -> loss
I0407 09:40:53.636281 17183 layer_factory.hpp:77] Creating layer loss
I0407 09:40:53.636956 17183 net.cpp:122] Setting up loss
I0407 09:40:53.636965 17183 net.cpp:129] Top shape: (1)
I0407 09:40:53.636966 17183 net.cpp:132]     with loss weight 1
I0407 09:40:53.636976 17183 net.cpp:137] Memory required for data: 266163464
I0407 09:40:53.636978 17183 net.cpp:198] loss needs backward computation.
I0407 09:40:53.636982 17183 net.cpp:200] accuracy does not need backward computation.
I0407 09:40:53.636986 17183 net.cpp:198] fc8_fc8_0_split needs backward computation.
I0407 09:40:53.636987 17183 net.cpp:198] fc8 needs backward computation.
I0407 09:40:53.636989 17183 net.cpp:198] drop7 needs backward computation.
I0407 09:40:53.636991 17183 net.cpp:198] relu7 needs backward computation.
I0407 09:40:53.636993 17183 net.cpp:198] fc7 needs backward computation.
I0407 09:40:53.636996 17183 net.cpp:198] drop6 needs backward computation.
I0407 09:40:53.636997 17183 net.cpp:198] relu6 needs backward computation.
I0407 09:40:53.636999 17183 net.cpp:198] fc6 needs backward computation.
I0407 09:40:53.637002 17183 net.cpp:198] pool5 needs backward computation.
I0407 09:40:53.637006 17183 net.cpp:198] relu5 needs backward computation.
I0407 09:40:53.637007 17183 net.cpp:198] conv5 needs backward computation.
I0407 09:40:53.637009 17183 net.cpp:198] relu4 needs backward computation.
I0407 09:40:53.637012 17183 net.cpp:198] conv4 needs backward computation.
I0407 09:40:53.637014 17183 net.cpp:198] relu3 needs backward computation.
I0407 09:40:53.637017 17183 net.cpp:198] conv3 needs backward computation.
I0407 09:40:53.637018 17183 net.cpp:198] pool2 needs backward computation.
I0407 09:40:53.637022 17183 net.cpp:198] norm2 needs backward computation.
I0407 09:40:53.637023 17183 net.cpp:198] relu2 needs backward computation.
I0407 09:40:53.637025 17183 net.cpp:198] conv2 needs backward computation.
I0407 09:40:53.637027 17183 net.cpp:198] pool1 needs backward computation.
I0407 09:40:53.637032 17183 net.cpp:198] norm1 needs backward computation.
I0407 09:40:53.637033 17183 net.cpp:198] relu1 needs backward computation.
I0407 09:40:53.637035 17183 net.cpp:198] conv1 needs backward computation.
I0407 09:40:53.637037 17183 net.cpp:200] label_val-data_1_split does not need backward computation.
I0407 09:40:53.637040 17183 net.cpp:200] val-data does not need backward computation.
I0407 09:40:53.637042 17183 net.cpp:242] This network produces output accuracy
I0407 09:40:53.637044 17183 net.cpp:242] This network produces output loss
I0407 09:40:53.637059 17183 net.cpp:255] Network initialization done.
I0407 09:40:53.637126 17183 solver.cpp:56] Solver scaffolding done.
I0407 09:40:53.637513 17183 caffe.cpp:248] Starting Optimization
I0407 09:40:53.637521 17183 solver.cpp:272] Solving
I0407 09:40:53.637533 17183 solver.cpp:273] Learning Rate Policy: step
I0407 09:40:53.639103 17183 solver.cpp:330] Iteration 0, Testing net (#0)
I0407 09:40:53.639112 17183 net.cpp:676] Ignoring source layer train-data
I0407 09:40:53.741817 17183 blocking_queue.cpp:49] Waiting for data
I0407 09:40:57.921883 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:40:57.969542 17183 solver.cpp:397]     Test net output #0: accuracy = 0.00183824
I0407 09:40:57.969573 17183 solver.cpp:397]     Test net output #1: loss = 5.28051 (* 1 = 5.28051 loss)
I0407 09:40:58.117000 17183 solver.cpp:218] Iteration 0 (-1.70797e+23 iter/s, 4.47934s/12 iters), loss = 5.29034
I0407 09:40:58.118562 17183 solver.cpp:237]     Train net output #0: loss = 5.29034 (* 1 = 5.29034 loss)
I0407 09:40:58.118590 17183 sgd_solver.cpp:105] Iteration 0, lr = 0.01
I0407 09:41:02.039294 17183 solver.cpp:218] Iteration 12 (3.06072 iter/s, 3.92065s/12 iters), loss = 5.29101
I0407 09:41:02.039347 17183 solver.cpp:237]     Train net output #0: loss = 5.29101 (* 1 = 5.29101 loss)
I0407 09:41:02.039357 17183 sgd_solver.cpp:105] Iteration 12, lr = 0.01
I0407 09:41:07.163018 17183 solver.cpp:218] Iteration 24 (2.34211 iter/s, 5.12357s/12 iters), loss = 5.2893
I0407 09:41:07.163064 17183 solver.cpp:237]     Train net output #0: loss = 5.2893 (* 1 = 5.2893 loss)
I0407 09:41:07.163071 17183 sgd_solver.cpp:105] Iteration 24, lr = 0.01
I0407 09:41:12.418853 17183 solver.cpp:218] Iteration 36 (2.28324 iter/s, 5.2557s/12 iters), loss = 5.27906
I0407 09:41:12.418891 17183 solver.cpp:237]     Train net output #0: loss = 5.27906 (* 1 = 5.27906 loss)
I0407 09:41:12.418900 17183 sgd_solver.cpp:105] Iteration 36, lr = 0.01
I0407 09:41:17.654757 17183 solver.cpp:218] Iteration 48 (2.29192 iter/s, 5.23579s/12 iters), loss = 5.29165
I0407 09:41:17.654798 17183 solver.cpp:237]     Train net output #0: loss = 5.29165 (* 1 = 5.29165 loss)
I0407 09:41:17.654806 17183 sgd_solver.cpp:105] Iteration 48, lr = 0.01
I0407 09:41:22.932030 17183 solver.cpp:218] Iteration 60 (2.27396 iter/s, 5.27714s/12 iters), loss = 5.2749
I0407 09:41:22.933955 17183 solver.cpp:237]     Train net output #0: loss = 5.2749 (* 1 = 5.2749 loss)
I0407 09:41:22.933964 17183 sgd_solver.cpp:105] Iteration 60, lr = 0.01
I0407 09:41:27.930671 17183 solver.cpp:218] Iteration 72 (2.40162 iter/s, 4.99663s/12 iters), loss = 5.31652
I0407 09:41:27.930714 17183 solver.cpp:237]     Train net output #0: loss = 5.31652 (* 1 = 5.31652 loss)
I0407 09:41:27.930722 17183 sgd_solver.cpp:105] Iteration 72, lr = 0.01
I0407 09:41:32.885418 17183 solver.cpp:218] Iteration 84 (2.42198 iter/s, 4.95462s/12 iters), loss = 5.3062
I0407 09:41:32.885458 17183 solver.cpp:237]     Train net output #0: loss = 5.3062 (* 1 = 5.3062 loss)
I0407 09:41:32.885465 17183 sgd_solver.cpp:105] Iteration 84, lr = 0.01
I0407 09:41:38.002714 17183 solver.cpp:218] Iteration 96 (2.34505 iter/s, 5.11716s/12 iters), loss = 5.28013
I0407 09:41:38.002768 17183 solver.cpp:237]     Train net output #0: loss = 5.28013 (* 1 = 5.28013 loss)
I0407 09:41:38.002777 17183 sgd_solver.cpp:105] Iteration 96, lr = 0.01
I0407 09:41:39.776338 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:41:40.077988 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_102.caffemodel
I0407 09:41:43.168503 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_102.solverstate
I0407 09:41:45.464030 17183 solver.cpp:330] Iteration 102, Testing net (#0)
I0407 09:41:45.464049 17183 net.cpp:676] Ignoring source layer train-data
I0407 09:41:49.816871 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:41:49.895164 17183 solver.cpp:397]     Test net output #0: accuracy = 0.00612745
I0407 09:41:49.895200 17183 solver.cpp:397]     Test net output #1: loss = 5.29209 (* 1 = 5.29209 loss)
I0407 09:41:51.884996 17183 solver.cpp:218] Iteration 108 (0.864429 iter/s, 13.882s/12 iters), loss = 5.27759
I0407 09:41:51.885035 17183 solver.cpp:237]     Train net output #0: loss = 5.27759 (* 1 = 5.27759 loss)
I0407 09:41:51.885042 17183 sgd_solver.cpp:105] Iteration 108, lr = 0.01
I0407 09:41:57.035794 17183 solver.cpp:218] Iteration 120 (2.3298 iter/s, 5.15066s/12 iters), loss = 5.25749
I0407 09:41:57.035985 17183 solver.cpp:237]     Train net output #0: loss = 5.25749 (* 1 = 5.25749 loss)
I0407 09:41:57.035995 17183 sgd_solver.cpp:105] Iteration 120, lr = 0.01
I0407 09:42:02.191797 17183 solver.cpp:218] Iteration 132 (2.32751 iter/s, 5.15572s/12 iters), loss = 5.26898
I0407 09:42:02.191843 17183 solver.cpp:237]     Train net output #0: loss = 5.26898 (* 1 = 5.26898 loss)
I0407 09:42:02.191851 17183 sgd_solver.cpp:105] Iteration 132, lr = 0.01
I0407 09:42:07.433768 17183 solver.cpp:218] Iteration 144 (2.28928 iter/s, 5.24183s/12 iters), loss = 5.23283
I0407 09:42:07.433806 17183 solver.cpp:237]     Train net output #0: loss = 5.23283 (* 1 = 5.23283 loss)
I0407 09:42:07.433813 17183 sgd_solver.cpp:105] Iteration 144, lr = 0.01
I0407 09:42:12.842806 17183 solver.cpp:218] Iteration 156 (2.21857 iter/s, 5.4089s/12 iters), loss = 5.23414
I0407 09:42:12.842849 17183 solver.cpp:237]     Train net output #0: loss = 5.23414 (* 1 = 5.23414 loss)
I0407 09:42:12.842856 17183 sgd_solver.cpp:105] Iteration 156, lr = 0.01
I0407 09:42:17.864326 17183 solver.cpp:218] Iteration 168 (2.38978 iter/s, 5.02138s/12 iters), loss = 5.22246
I0407 09:42:17.864373 17183 solver.cpp:237]     Train net output #0: loss = 5.22246 (* 1 = 5.22246 loss)
I0407 09:42:17.864382 17183 sgd_solver.cpp:105] Iteration 168, lr = 0.01
I0407 09:42:23.089356 17183 solver.cpp:218] Iteration 180 (2.2967 iter/s, 5.22488s/12 iters), loss = 5.24721
I0407 09:42:23.089412 17183 solver.cpp:237]     Train net output #0: loss = 5.24721 (* 1 = 5.24721 loss)
I0407 09:42:23.089422 17183 sgd_solver.cpp:105] Iteration 180, lr = 0.01
I0407 09:42:28.234350 17183 solver.cpp:218] Iteration 192 (2.33243 iter/s, 5.14484s/12 iters), loss = 5.06714
I0407 09:42:28.234495 17183 solver.cpp:237]     Train net output #0: loss = 5.06714 (* 1 = 5.06714 loss)
I0407 09:42:28.234506 17183 sgd_solver.cpp:105] Iteration 192, lr = 0.01
I0407 09:42:32.296821 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:42:32.997891 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_204.caffemodel
I0407 09:42:36.165145 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_204.solverstate
I0407 09:42:38.463747 17183 solver.cpp:330] Iteration 204, Testing net (#0)
I0407 09:42:38.463768 17183 net.cpp:676] Ignoring source layer train-data
I0407 09:42:42.792953 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:42:42.916755 17183 solver.cpp:397]     Test net output #0: accuracy = 0.0104167
I0407 09:42:42.916790 17183 solver.cpp:397]     Test net output #1: loss = 5.18289 (* 1 = 5.18289 loss)
I0407 09:42:43.053799 17183 solver.cpp:218] Iteration 204 (0.809767 iter/s, 14.8191s/12 iters), loss = 5.16634
I0407 09:42:43.053854 17183 solver.cpp:237]     Train net output #0: loss = 5.16634 (* 1 = 5.16634 loss)
I0407 09:42:43.053864 17183 sgd_solver.cpp:105] Iteration 204, lr = 0.01
I0407 09:42:47.336104 17183 solver.cpp:218] Iteration 216 (2.80232 iter/s, 4.28217s/12 iters), loss = 5.23323
I0407 09:42:47.336148 17183 solver.cpp:237]     Train net output #0: loss = 5.23323 (* 1 = 5.23323 loss)
I0407 09:42:47.336155 17183 sgd_solver.cpp:105] Iteration 216, lr = 0.01
I0407 09:42:52.242489 17183 solver.cpp:218] Iteration 228 (2.44586 iter/s, 4.90625s/12 iters), loss = 5.21328
I0407 09:42:52.242537 17183 solver.cpp:237]     Train net output #0: loss = 5.21328 (* 1 = 5.21328 loss)
I0407 09:42:52.242543 17183 sgd_solver.cpp:105] Iteration 228, lr = 0.01
I0407 09:42:57.406203 17183 solver.cpp:218] Iteration 240 (2.32397 iter/s, 5.16357s/12 iters), loss = 5.15556
I0407 09:42:57.406255 17183 solver.cpp:237]     Train net output #0: loss = 5.15556 (* 1 = 5.15556 loss)
I0407 09:42:57.406265 17183 sgd_solver.cpp:105] Iteration 240, lr = 0.01
I0407 09:43:02.420445 17183 solver.cpp:218] Iteration 252 (2.39325 iter/s, 5.0141s/12 iters), loss = 5.22382
I0407 09:43:02.420601 17183 solver.cpp:237]     Train net output #0: loss = 5.22382 (* 1 = 5.22382 loss)
I0407 09:43:02.420611 17183 sgd_solver.cpp:105] Iteration 252, lr = 0.01
I0407 09:43:07.474092 17183 solver.cpp:218] Iteration 264 (2.37463 iter/s, 5.05341s/12 iters), loss = 5.1227
I0407 09:43:07.474128 17183 solver.cpp:237]     Train net output #0: loss = 5.1227 (* 1 = 5.1227 loss)
I0407 09:43:07.474135 17183 sgd_solver.cpp:105] Iteration 264, lr = 0.01
I0407 09:43:12.536469 17183 solver.cpp:218] Iteration 276 (2.37048 iter/s, 5.06226s/12 iters), loss = 5.1105
I0407 09:43:12.536509 17183 solver.cpp:237]     Train net output #0: loss = 5.1105 (* 1 = 5.1105 loss)
I0407 09:43:12.536516 17183 sgd_solver.cpp:105] Iteration 276, lr = 0.01
I0407 09:43:17.734089 17183 solver.cpp:218] Iteration 288 (2.3088 iter/s, 5.1975s/12 iters), loss = 5.14251
I0407 09:43:17.734131 17183 solver.cpp:237]     Train net output #0: loss = 5.14251 (* 1 = 5.14251 loss)
I0407 09:43:17.734138 17183 sgd_solver.cpp:105] Iteration 288, lr = 0.01
I0407 09:43:22.862918 17183 solver.cpp:218] Iteration 300 (2.33977 iter/s, 5.1287s/12 iters), loss = 5.23258
I0407 09:43:22.862962 17183 solver.cpp:237]     Train net output #0: loss = 5.23258 (* 1 = 5.23258 loss)
I0407 09:43:22.862970 17183 sgd_solver.cpp:105] Iteration 300, lr = 0.01
I0407 09:43:23.881915 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:43:24.975782 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_306.caffemodel
I0407 09:43:28.042697 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_306.solverstate
I0407 09:43:30.346369 17183 solver.cpp:330] Iteration 306, Testing net (#0)
I0407 09:43:30.346388 17183 net.cpp:676] Ignoring source layer train-data
I0407 09:43:34.454638 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:43:34.611027 17183 solver.cpp:397]     Test net output #0: accuracy = 0.0104167
I0407 09:43:34.611060 17183 solver.cpp:397]     Test net output #1: loss = 5.15683 (* 1 = 5.15683 loss)
I0407 09:43:36.517375 17183 solver.cpp:218] Iteration 312 (0.878849 iter/s, 13.6542s/12 iters), loss = 5.12517
I0407 09:43:36.517421 17183 solver.cpp:237]     Train net output #0: loss = 5.12517 (* 1 = 5.12517 loss)
I0407 09:43:36.517427 17183 sgd_solver.cpp:105] Iteration 312, lr = 0.01
I0407 09:43:41.658918 17183 solver.cpp:218] Iteration 324 (2.33398 iter/s, 5.14142s/12 iters), loss = 5.21573
I0407 09:43:41.658962 17183 solver.cpp:237]     Train net output #0: loss = 5.21573 (* 1 = 5.21573 loss)
I0407 09:43:41.658969 17183 sgd_solver.cpp:105] Iteration 324, lr = 0.01
I0407 09:43:46.650516 17183 solver.cpp:218] Iteration 336 (2.4041 iter/s, 4.99148s/12 iters), loss = 5.13969
I0407 09:43:46.650553 17183 solver.cpp:237]     Train net output #0: loss = 5.13969 (* 1 = 5.13969 loss)
I0407 09:43:46.650561 17183 sgd_solver.cpp:105] Iteration 336, lr = 0.01
I0407 09:43:51.529753 17183 solver.cpp:218] Iteration 348 (2.45946 iter/s, 4.87913s/12 iters), loss = 5.12106
I0407 09:43:51.529796 17183 solver.cpp:237]     Train net output #0: loss = 5.12106 (* 1 = 5.12106 loss)
I0407 09:43:51.529803 17183 sgd_solver.cpp:105] Iteration 348, lr = 0.01
I0407 09:43:56.575244 17183 solver.cpp:218] Iteration 360 (2.37842 iter/s, 5.04537s/12 iters), loss = 5.16823
I0407 09:43:56.575301 17183 solver.cpp:237]     Train net output #0: loss = 5.16823 (* 1 = 5.16823 loss)
I0407 09:43:56.575311 17183 sgd_solver.cpp:105] Iteration 360, lr = 0.01
I0407 09:44:01.734870 17183 solver.cpp:218] Iteration 372 (2.32581 iter/s, 5.15949s/12 iters), loss = 5.13872
I0407 09:44:01.734936 17183 solver.cpp:237]     Train net output #0: loss = 5.13872 (* 1 = 5.13872 loss)
I0407 09:44:01.734946 17183 sgd_solver.cpp:105] Iteration 372, lr = 0.01
I0407 09:44:07.137075 17183 solver.cpp:218] Iteration 384 (2.22137 iter/s, 5.40206s/12 iters), loss = 5.22298
I0407 09:44:07.137188 17183 solver.cpp:237]     Train net output #0: loss = 5.22298 (* 1 = 5.22298 loss)
I0407 09:44:07.137197 17183 sgd_solver.cpp:105] Iteration 384, lr = 0.01
I0407 09:44:12.549729 17183 solver.cpp:218] Iteration 396 (2.2171 iter/s, 5.41246s/12 iters), loss = 5.10874
I0407 09:44:12.549772 17183 solver.cpp:237]     Train net output #0: loss = 5.10874 (* 1 = 5.10874 loss)
I0407 09:44:12.549778 17183 sgd_solver.cpp:105] Iteration 396, lr = 0.01
I0407 09:44:15.599061 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:44:17.077685 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_408.caffemodel
I0407 09:44:20.184353 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_408.solverstate
I0407 09:44:22.513068 17183 solver.cpp:330] Iteration 408, Testing net (#0)
I0407 09:44:22.513087 17183 net.cpp:676] Ignoring source layer train-data
I0407 09:44:26.790239 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:44:26.994776 17183 solver.cpp:397]     Test net output #0: accuracy = 0.0153186
I0407 09:44:26.994808 17183 solver.cpp:397]     Test net output #1: loss = 5.11342 (* 1 = 5.11342 loss)
I0407 09:44:27.128578 17183 solver.cpp:218] Iteration 408 (0.823123 iter/s, 14.5786s/12 iters), loss = 5.12656
I0407 09:44:27.128635 17183 solver.cpp:237]     Train net output #0: loss = 5.12656 (* 1 = 5.12656 loss)
I0407 09:44:27.128646 17183 sgd_solver.cpp:105] Iteration 408, lr = 0.01
I0407 09:44:31.182662 17183 solver.cpp:218] Iteration 420 (2.96006 iter/s, 4.05397s/12 iters), loss = 5.07724
I0407 09:44:31.182704 17183 solver.cpp:237]     Train net output #0: loss = 5.07724 (* 1 = 5.07724 loss)
I0407 09:44:31.182713 17183 sgd_solver.cpp:105] Iteration 420, lr = 0.01
I0407 09:44:36.227298 17183 solver.cpp:218] Iteration 432 (2.37882 iter/s, 5.04452s/12 iters), loss = 5.0367
I0407 09:44:36.227358 17183 solver.cpp:237]     Train net output #0: loss = 5.0367 (* 1 = 5.0367 loss)
I0407 09:44:36.227370 17183 sgd_solver.cpp:105] Iteration 432, lr = 0.01
I0407 09:44:41.330307 17183 solver.cpp:218] Iteration 444 (2.35162 iter/s, 5.10287s/12 iters), loss = 5.09827
I0407 09:44:41.330462 17183 solver.cpp:237]     Train net output #0: loss = 5.09827 (* 1 = 5.09827 loss)
I0407 09:44:41.330472 17183 sgd_solver.cpp:105] Iteration 444, lr = 0.01
I0407 09:44:46.499207 17183 solver.cpp:218] Iteration 456 (2.32168 iter/s, 5.16867s/12 iters), loss = 5.15358
I0407 09:44:46.499269 17183 solver.cpp:237]     Train net output #0: loss = 5.15358 (* 1 = 5.15358 loss)
I0407 09:44:46.499281 17183 sgd_solver.cpp:105] Iteration 456, lr = 0.01
I0407 09:44:51.587787 17183 solver.cpp:218] Iteration 468 (2.35828 iter/s, 5.08845s/12 iters), loss = 5.05654
I0407 09:44:51.587836 17183 solver.cpp:237]     Train net output #0: loss = 5.05654 (* 1 = 5.05654 loss)
I0407 09:44:51.587842 17183 sgd_solver.cpp:105] Iteration 468, lr = 0.01
I0407 09:44:56.848628 17183 solver.cpp:218] Iteration 480 (2.28106 iter/s, 5.26072s/12 iters), loss = 4.99937
I0407 09:44:56.848675 17183 solver.cpp:237]     Train net output #0: loss = 4.99937 (* 1 = 4.99937 loss)
I0407 09:44:56.848683 17183 sgd_solver.cpp:105] Iteration 480, lr = 0.01
I0407 09:45:01.871268 17183 solver.cpp:218] Iteration 492 (2.38924 iter/s, 5.02253s/12 iters), loss = 5.11694
I0407 09:45:01.871309 17183 solver.cpp:237]     Train net output #0: loss = 5.11694 (* 1 = 5.11694 loss)
I0407 09:45:01.871315 17183 sgd_solver.cpp:105] Iteration 492, lr = 0.01
I0407 09:45:06.979441 17183 solver.cpp:218] Iteration 504 (2.34923 iter/s, 5.10806s/12 iters), loss = 5.08163
I0407 09:45:06.979494 17183 solver.cpp:237]     Train net output #0: loss = 5.08163 (* 1 = 5.08163 loss)
I0407 09:45:06.979504 17183 sgd_solver.cpp:105] Iteration 504, lr = 0.01
I0407 09:45:07.213981 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:45:09.122180 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_510.caffemodel
I0407 09:45:12.146181 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_510.solverstate
I0407 09:45:14.474560 17183 solver.cpp:330] Iteration 510, Testing net (#0)
I0407 09:45:14.474578 17183 net.cpp:676] Ignoring source layer train-data
I0407 09:45:18.517946 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:45:18.754789 17183 solver.cpp:397]     Test net output #0: accuracy = 0.0196078
I0407 09:45:18.754822 17183 solver.cpp:397]     Test net output #1: loss = 5.0829 (* 1 = 5.0829 loss)
I0407 09:45:20.604251 17183 solver.cpp:218] Iteration 516 (0.88076 iter/s, 13.6246s/12 iters), loss = 5.07928
I0407 09:45:20.604306 17183 solver.cpp:237]     Train net output #0: loss = 5.07928 (* 1 = 5.07928 loss)
I0407 09:45:20.604315 17183 sgd_solver.cpp:105] Iteration 516, lr = 0.01
I0407 09:45:25.686888 17183 solver.cpp:218] Iteration 528 (2.36103 iter/s, 5.08253s/12 iters), loss = 5.11948
I0407 09:45:25.686919 17183 solver.cpp:237]     Train net output #0: loss = 5.11948 (* 1 = 5.11948 loss)
I0407 09:45:25.686925 17183 sgd_solver.cpp:105] Iteration 528, lr = 0.01
I0407 09:45:30.821233 17183 solver.cpp:218] Iteration 540 (2.33725 iter/s, 5.13424s/12 iters), loss = 5.00821
I0407 09:45:30.821275 17183 solver.cpp:237]     Train net output #0: loss = 5.00821 (* 1 = 5.00821 loss)
I0407 09:45:30.821282 17183 sgd_solver.cpp:105] Iteration 540, lr = 0.01
I0407 09:45:36.041779 17183 solver.cpp:218] Iteration 552 (2.29866 iter/s, 5.22043s/12 iters), loss = 5.14879
I0407 09:45:36.041834 17183 solver.cpp:237]     Train net output #0: loss = 5.14879 (* 1 = 5.14879 loss)
I0407 09:45:36.041846 17183 sgd_solver.cpp:105] Iteration 552, lr = 0.01
I0407 09:45:41.277374 17183 solver.cpp:218] Iteration 564 (2.29206 iter/s, 5.23547s/12 iters), loss = 5.05796
I0407 09:45:41.277417 17183 solver.cpp:237]     Train net output #0: loss = 5.05796 (* 1 = 5.05796 loss)
I0407 09:45:41.277424 17183 sgd_solver.cpp:105] Iteration 564, lr = 0.01
I0407 09:45:46.303522 17183 solver.cpp:218] Iteration 576 (2.38757 iter/s, 5.02604s/12 iters), loss = 4.94882
I0407 09:45:46.303633 17183 solver.cpp:237]     Train net output #0: loss = 4.94882 (* 1 = 4.94882 loss)
I0407 09:45:46.303642 17183 sgd_solver.cpp:105] Iteration 576, lr = 0.01
I0407 09:45:51.251864 17183 solver.cpp:218] Iteration 588 (2.42514 iter/s, 4.94816s/12 iters), loss = 5.0636
I0407 09:45:51.251924 17183 solver.cpp:237]     Train net output #0: loss = 5.0636 (* 1 = 5.0636 loss)
I0407 09:45:51.251935 17183 sgd_solver.cpp:105] Iteration 588, lr = 0.01
I0407 09:45:56.445529 17183 solver.cpp:218] Iteration 600 (2.31057 iter/s, 5.19353s/12 iters), loss = 5.04418
I0407 09:45:56.445595 17183 solver.cpp:237]     Train net output #0: loss = 5.04418 (* 1 = 5.04418 loss)
I0407 09:45:56.445605 17183 sgd_solver.cpp:105] Iteration 600, lr = 0.01
I0407 09:45:58.840363 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:46:01.152096 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_612.caffemodel
I0407 09:46:04.163636 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_612.solverstate
I0407 09:46:06.468438 17183 solver.cpp:330] Iteration 612, Testing net (#0)
I0407 09:46:06.468458 17183 net.cpp:676] Ignoring source layer train-data
I0407 09:46:10.469460 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:46:10.754643 17183 solver.cpp:397]     Test net output #0: accuracy = 0.0214461
I0407 09:46:10.754670 17183 solver.cpp:397]     Test net output #1: loss = 5.01698 (* 1 = 5.01698 loss)
I0407 09:46:10.896261 17183 solver.cpp:218] Iteration 612 (0.830421 iter/s, 14.4505s/12 iters), loss = 4.98794
I0407 09:46:10.897833 17183 solver.cpp:237]     Train net output #0: loss = 4.98794 (* 1 = 4.98794 loss)
I0407 09:46:10.897845 17183 sgd_solver.cpp:105] Iteration 612, lr = 0.01
I0407 09:46:15.175691 17183 solver.cpp:218] Iteration 624 (2.80518 iter/s, 4.27781s/12 iters), loss = 4.94075
I0407 09:46:15.175732 17183 solver.cpp:237]     Train net output #0: loss = 4.94075 (* 1 = 4.94075 loss)
I0407 09:46:15.175740 17183 sgd_solver.cpp:105] Iteration 624, lr = 0.01
I0407 09:46:20.221472 17183 solver.cpp:218] Iteration 636 (2.37828 iter/s, 5.04567s/12 iters), loss = 4.97043
I0407 09:46:20.221591 17183 solver.cpp:237]     Train net output #0: loss = 4.97043 (* 1 = 4.97043 loss)
I0407 09:46:20.221599 17183 sgd_solver.cpp:105] Iteration 636, lr = 0.01
I0407 09:46:25.220638 17183 solver.cpp:218] Iteration 648 (2.40049 iter/s, 4.99898s/12 iters), loss = 4.93795
I0407 09:46:25.220682 17183 solver.cpp:237]     Train net output #0: loss = 4.93795 (* 1 = 4.93795 loss)
I0407 09:46:25.220690 17183 sgd_solver.cpp:105] Iteration 648, lr = 0.01
I0407 09:46:30.325280 17183 solver.cpp:218] Iteration 660 (2.35085 iter/s, 5.10453s/12 iters), loss = 4.9114
I0407 09:46:30.325322 17183 solver.cpp:237]     Train net output #0: loss = 4.9114 (* 1 = 4.9114 loss)
I0407 09:46:30.325330 17183 sgd_solver.cpp:105] Iteration 660, lr = 0.01
I0407 09:46:35.635972 17183 solver.cpp:218] Iteration 672 (2.25964 iter/s, 5.31058s/12 iters), loss = 5.00311
I0407 09:46:35.636024 17183 solver.cpp:237]     Train net output #0: loss = 5.00311 (* 1 = 5.00311 loss)
I0407 09:46:35.636034 17183 sgd_solver.cpp:105] Iteration 672, lr = 0.01
I0407 09:46:40.890777 17183 solver.cpp:218] Iteration 684 (2.28368 iter/s, 5.25469s/12 iters), loss = 4.97703
I0407 09:46:40.890820 17183 solver.cpp:237]     Train net output #0: loss = 4.97703 (* 1 = 4.97703 loss)
I0407 09:46:40.890827 17183 sgd_solver.cpp:105] Iteration 684, lr = 0.01
I0407 09:46:41.677608 17183 blocking_queue.cpp:49] Waiting for data
I0407 09:46:46.043313 17183 solver.cpp:218] Iteration 696 (2.329 iter/s, 5.15242s/12 iters), loss = 4.86391
I0407 09:46:46.043359 17183 solver.cpp:237]     Train net output #0: loss = 4.86391 (* 1 = 4.86391 loss)
I0407 09:46:46.043368 17183 sgd_solver.cpp:105] Iteration 696, lr = 0.01
I0407 09:46:50.535465 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:46:50.937342 17183 solver.cpp:218] Iteration 708 (2.45202 iter/s, 4.89392s/12 iters), loss = 4.98269
I0407 09:46:50.937381 17183 solver.cpp:237]     Train net output #0: loss = 4.98269 (* 1 = 4.98269 loss)
I0407 09:46:50.937387 17183 sgd_solver.cpp:105] Iteration 708, lr = 0.01
I0407 09:46:52.956530 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_714.caffemodel
I0407 09:46:56.089017 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_714.solverstate
I0407 09:46:58.396445 17183 solver.cpp:330] Iteration 714, Testing net (#0)
I0407 09:46:58.396464 17183 net.cpp:676] Ignoring source layer train-data
I0407 09:47:02.421568 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:47:02.745668 17183 solver.cpp:397]     Test net output #0: accuracy = 0.0416667
I0407 09:47:02.745703 17183 solver.cpp:397]     Test net output #1: loss = 4.91351 (* 1 = 4.91351 loss)
I0407 09:47:04.581557 17183 solver.cpp:218] Iteration 720 (0.879506 iter/s, 13.644s/12 iters), loss = 4.93016
I0407 09:47:04.581614 17183 solver.cpp:237]     Train net output #0: loss = 4.93016 (* 1 = 4.93016 loss)
I0407 09:47:04.581624 17183 sgd_solver.cpp:105] Iteration 720, lr = 0.01
I0407 09:47:09.457695 17183 solver.cpp:218] Iteration 732 (2.46103 iter/s, 4.87602s/12 iters), loss = 4.83353
I0407 09:47:09.457737 17183 solver.cpp:237]     Train net output #0: loss = 4.83353 (* 1 = 4.83353 loss)
I0407 09:47:09.457746 17183 sgd_solver.cpp:105] Iteration 732, lr = 0.01
I0407 09:47:14.688465 17183 solver.cpp:218] Iteration 744 (2.29417 iter/s, 5.23066s/12 iters), loss = 4.84066
I0407 09:47:14.688508 17183 solver.cpp:237]     Train net output #0: loss = 4.84066 (* 1 = 4.84066 loss)
I0407 09:47:14.688514 17183 sgd_solver.cpp:105] Iteration 744, lr = 0.01
I0407 09:47:19.802726 17183 solver.cpp:218] Iteration 756 (2.34643 iter/s, 5.11415s/12 iters), loss = 4.7123
I0407 09:47:19.802783 17183 solver.cpp:237]     Train net output #0: loss = 4.7123 (* 1 = 4.7123 loss)
I0407 09:47:19.802793 17183 sgd_solver.cpp:105] Iteration 756, lr = 0.01
I0407 09:47:25.045045 17183 solver.cpp:218] Iteration 768 (2.28912 iter/s, 5.2422s/12 iters), loss = 4.82834
I0407 09:47:25.045197 17183 solver.cpp:237]     Train net output #0: loss = 4.82834 (* 1 = 4.82834 loss)
I0407 09:47:25.045207 17183 sgd_solver.cpp:105] Iteration 768, lr = 0.01
I0407 09:47:30.364351 17183 solver.cpp:218] Iteration 780 (2.25602 iter/s, 5.31909s/12 iters), loss = 4.7501
I0407 09:47:30.364385 17183 solver.cpp:237]     Train net output #0: loss = 4.7501 (* 1 = 4.7501 loss)
I0407 09:47:30.364392 17183 sgd_solver.cpp:105] Iteration 780, lr = 0.01
I0407 09:47:35.616895 17183 solver.cpp:218] Iteration 792 (2.28466 iter/s, 5.25243s/12 iters), loss = 4.96578
I0407 09:47:35.616950 17183 solver.cpp:237]     Train net output #0: loss = 4.96578 (* 1 = 4.96578 loss)
I0407 09:47:35.616961 17183 sgd_solver.cpp:105] Iteration 792, lr = 0.01
I0407 09:47:40.779835 17183 solver.cpp:218] Iteration 804 (2.32432 iter/s, 5.16281s/12 iters), loss = 4.76117
I0407 09:47:40.779898 17183 solver.cpp:237]     Train net output #0: loss = 4.76117 (* 1 = 4.76117 loss)
I0407 09:47:40.779911 17183 sgd_solver.cpp:105] Iteration 804, lr = 0.01
I0407 09:47:42.624337 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:47:45.505318 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_816.caffemodel
I0407 09:47:50.624723 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_816.solverstate
I0407 09:47:52.925487 17183 solver.cpp:330] Iteration 816, Testing net (#0)
I0407 09:47:52.925506 17183 net.cpp:676] Ignoring source layer train-data
I0407 09:47:56.915741 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:47:57.285372 17183 solver.cpp:397]     Test net output #0: accuracy = 0.0496324
I0407 09:47:57.285405 17183 solver.cpp:397]     Test net output #1: loss = 4.85616 (* 1 = 4.85616 loss)
I0407 09:47:57.429162 17183 solver.cpp:218] Iteration 816 (0.72076 iter/s, 16.6491s/12 iters), loss = 4.86812
I0407 09:47:57.429221 17183 solver.cpp:237]     Train net output #0: loss = 4.86812 (* 1 = 4.86812 loss)
I0407 09:47:57.429231 17183 sgd_solver.cpp:105] Iteration 816, lr = 0.01
I0407 09:48:01.650287 17183 solver.cpp:218] Iteration 828 (2.84292 iter/s, 4.22101s/12 iters), loss = 4.7782
I0407 09:48:01.650339 17183 solver.cpp:237]     Train net output #0: loss = 4.7782 (* 1 = 4.7782 loss)
I0407 09:48:01.650349 17183 sgd_solver.cpp:105] Iteration 828, lr = 0.01
I0407 09:48:06.647303 17183 solver.cpp:218] Iteration 840 (2.40149 iter/s, 4.9969s/12 iters), loss = 4.6755
I0407 09:48:06.647347 17183 solver.cpp:237]     Train net output #0: loss = 4.6755 (* 1 = 4.6755 loss)
I0407 09:48:06.647353 17183 sgd_solver.cpp:105] Iteration 840, lr = 0.01
I0407 09:48:11.794504 17183 solver.cpp:218] Iteration 852 (2.33141 iter/s, 5.14709s/12 iters), loss = 4.64862
I0407 09:48:11.794551 17183 solver.cpp:237]     Train net output #0: loss = 4.64862 (* 1 = 4.64862 loss)
I0407 09:48:11.794557 17183 sgd_solver.cpp:105] Iteration 852, lr = 0.01
I0407 09:48:17.056268 17183 solver.cpp:218] Iteration 864 (2.28066 iter/s, 5.26164s/12 iters), loss = 4.78376
I0407 09:48:17.056329 17183 solver.cpp:237]     Train net output #0: loss = 4.78376 (* 1 = 4.78376 loss)
I0407 09:48:17.056339 17183 sgd_solver.cpp:105] Iteration 864, lr = 0.01
I0407 09:48:22.145890 17183 solver.cpp:218] Iteration 876 (2.3578 iter/s, 5.0895s/12 iters), loss = 4.66092
I0407 09:48:22.145933 17183 solver.cpp:237]     Train net output #0: loss = 4.66092 (* 1 = 4.66092 loss)
I0407 09:48:22.145941 17183 sgd_solver.cpp:105] Iteration 876, lr = 0.01
I0407 09:48:27.135820 17183 solver.cpp:218] Iteration 888 (2.4049 iter/s, 4.98982s/12 iters), loss = 4.74521
I0407 09:48:27.135908 17183 solver.cpp:237]     Train net output #0: loss = 4.74521 (* 1 = 4.74521 loss)
I0407 09:48:27.135916 17183 sgd_solver.cpp:105] Iteration 888, lr = 0.01
I0407 09:48:32.086153 17183 solver.cpp:218] Iteration 900 (2.42415 iter/s, 4.95018s/12 iters), loss = 4.59849
I0407 09:48:32.086197 17183 solver.cpp:237]     Train net output #0: loss = 4.59849 (* 1 = 4.59849 loss)
I0407 09:48:32.086205 17183 sgd_solver.cpp:105] Iteration 900, lr = 0.01
I0407 09:48:35.979648 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:48:37.185350 17183 solver.cpp:218] Iteration 912 (2.35336 iter/s, 5.09908s/12 iters), loss = 4.69207
I0407 09:48:37.185403 17183 solver.cpp:237]     Train net output #0: loss = 4.69207 (* 1 = 4.69207 loss)
I0407 09:48:37.185412 17183 sgd_solver.cpp:105] Iteration 912, lr = 0.01
I0407 09:48:39.223618 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_918.caffemodel
I0407 09:48:44.095527 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_918.solverstate
I0407 09:48:47.950829 17183 solver.cpp:330] Iteration 918, Testing net (#0)
I0407 09:48:47.950850 17183 net.cpp:676] Ignoring source layer train-data
I0407 09:48:51.874034 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:48:52.383752 17183 solver.cpp:397]     Test net output #0: accuracy = 0.0569853
I0407 09:48:52.383790 17183 solver.cpp:397]     Test net output #1: loss = 4.63344 (* 1 = 4.63344 loss)
I0407 09:48:54.170779 17183 solver.cpp:218] Iteration 924 (0.706498 iter/s, 16.9852s/12 iters), loss = 4.62143
I0407 09:48:54.170819 17183 solver.cpp:237]     Train net output #0: loss = 4.62143 (* 1 = 4.62143 loss)
I0407 09:48:54.170826 17183 sgd_solver.cpp:105] Iteration 924, lr = 0.01
I0407 09:48:59.074852 17183 solver.cpp:218] Iteration 936 (2.447 iter/s, 4.90397s/12 iters), loss = 4.69614
I0407 09:48:59.074992 17183 solver.cpp:237]     Train net output #0: loss = 4.69614 (* 1 = 4.69614 loss)
I0407 09:48:59.075001 17183 sgd_solver.cpp:105] Iteration 936, lr = 0.01
I0407 09:49:03.862555 17183 solver.cpp:218] Iteration 948 (2.50653 iter/s, 4.7875s/12 iters), loss = 4.64414
I0407 09:49:03.862603 17183 solver.cpp:237]     Train net output #0: loss = 4.64414 (* 1 = 4.64414 loss)
I0407 09:49:03.862612 17183 sgd_solver.cpp:105] Iteration 948, lr = 0.01
I0407 09:49:09.058517 17183 solver.cpp:218] Iteration 960 (2.30954 iter/s, 5.19584s/12 iters), loss = 4.6928
I0407 09:49:09.058564 17183 solver.cpp:237]     Train net output #0: loss = 4.6928 (* 1 = 4.6928 loss)
I0407 09:49:09.058571 17183 sgd_solver.cpp:105] Iteration 960, lr = 0.01
I0407 09:49:14.332981 17183 solver.cpp:218] Iteration 972 (2.27516 iter/s, 5.27435s/12 iters), loss = 4.45633
I0407 09:49:14.333043 17183 solver.cpp:237]     Train net output #0: loss = 4.45633 (* 1 = 4.45633 loss)
I0407 09:49:14.333055 17183 sgd_solver.cpp:105] Iteration 972, lr = 0.01
I0407 09:49:19.547904 17183 solver.cpp:218] Iteration 984 (2.30114 iter/s, 5.2148s/12 iters), loss = 4.48336
I0407 09:49:19.547945 17183 solver.cpp:237]     Train net output #0: loss = 4.48336 (* 1 = 4.48336 loss)
I0407 09:49:19.547952 17183 sgd_solver.cpp:105] Iteration 984, lr = 0.01
I0407 09:49:24.661311 17183 solver.cpp:218] Iteration 996 (2.34682 iter/s, 5.1133s/12 iters), loss = 4.40351
I0407 09:49:24.661376 17183 solver.cpp:237]     Train net output #0: loss = 4.40351 (* 1 = 4.40351 loss)
I0407 09:49:24.661387 17183 sgd_solver.cpp:105] Iteration 996, lr = 0.01
I0407 09:49:29.780774 17183 solver.cpp:218] Iteration 1008 (2.34406 iter/s, 5.11933s/12 iters), loss = 4.55959
I0407 09:49:29.780906 17183 solver.cpp:237]     Train net output #0: loss = 4.55959 (* 1 = 4.55959 loss)
I0407 09:49:29.780915 17183 sgd_solver.cpp:105] Iteration 1008, lr = 0.01
I0407 09:49:30.831336 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:49:34.464773 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1020.caffemodel
I0407 09:49:39.105170 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1020.solverstate
I0407 09:49:43.078788 17183 solver.cpp:330] Iteration 1020, Testing net (#0)
I0407 09:49:43.078809 17183 net.cpp:676] Ignoring source layer train-data
I0407 09:49:46.945564 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:49:47.369377 17183 solver.cpp:397]     Test net output #0: accuracy = 0.0655637
I0407 09:49:47.369412 17183 solver.cpp:397]     Test net output #1: loss = 4.49743 (* 1 = 4.49743 loss)
I0407 09:49:47.511412 17183 solver.cpp:218] Iteration 1020 (0.676807 iter/s, 17.7303s/12 iters), loss = 4.46157
I0407 09:49:47.511461 17183 solver.cpp:237]     Train net output #0: loss = 4.46157 (* 1 = 4.46157 loss)
I0407 09:49:47.511469 17183 sgd_solver.cpp:105] Iteration 1020, lr = 0.01
I0407 09:49:51.862474 17183 solver.cpp:218] Iteration 1032 (2.75802 iter/s, 4.35095s/12 iters), loss = 4.8251
I0407 09:49:51.862526 17183 solver.cpp:237]     Train net output #0: loss = 4.8251 (* 1 = 4.8251 loss)
I0407 09:49:51.862535 17183 sgd_solver.cpp:105] Iteration 1032, lr = 0.01
I0407 09:49:56.974475 17183 solver.cpp:218] Iteration 1044 (2.34747 iter/s, 5.11188s/12 iters), loss = 4.47834
I0407 09:49:56.974527 17183 solver.cpp:237]     Train net output #0: loss = 4.47834 (* 1 = 4.47834 loss)
I0407 09:49:56.974539 17183 sgd_solver.cpp:105] Iteration 1044, lr = 0.01
I0407 09:50:02.007140 17183 solver.cpp:218] Iteration 1056 (2.38448 iter/s, 5.03254s/12 iters), loss = 4.47833
I0407 09:50:02.007272 17183 solver.cpp:237]     Train net output #0: loss = 4.47833 (* 1 = 4.47833 loss)
I0407 09:50:02.007279 17183 sgd_solver.cpp:105] Iteration 1056, lr = 0.01
I0407 09:50:07.239732 17183 solver.cpp:218] Iteration 1068 (2.29341 iter/s, 5.23239s/12 iters), loss = 4.40389
I0407 09:50:07.239796 17183 solver.cpp:237]     Train net output #0: loss = 4.40389 (* 1 = 4.40389 loss)
I0407 09:50:07.239809 17183 sgd_solver.cpp:105] Iteration 1068, lr = 0.01
I0407 09:50:12.121832 17183 solver.cpp:218] Iteration 1080 (2.45802 iter/s, 4.88197s/12 iters), loss = 4.14075
I0407 09:50:12.121906 17183 solver.cpp:237]     Train net output #0: loss = 4.14075 (* 1 = 4.14075 loss)
I0407 09:50:12.121919 17183 sgd_solver.cpp:105] Iteration 1080, lr = 0.01
I0407 09:50:17.466854 17183 solver.cpp:218] Iteration 1092 (2.24514 iter/s, 5.34488s/12 iters), loss = 4.44049
I0407 09:50:17.466912 17183 solver.cpp:237]     Train net output #0: loss = 4.44049 (* 1 = 4.44049 loss)
I0407 09:50:17.466922 17183 sgd_solver.cpp:105] Iteration 1092, lr = 0.01
I0407 09:50:22.697124 17183 solver.cpp:218] Iteration 1104 (2.29439 iter/s, 5.23014s/12 iters), loss = 4.27978
I0407 09:50:22.697183 17183 solver.cpp:237]     Train net output #0: loss = 4.27978 (* 1 = 4.27978 loss)
I0407 09:50:22.697194 17183 sgd_solver.cpp:105] Iteration 1104, lr = 0.01
I0407 09:50:25.974720 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:50:27.947036 17183 solver.cpp:218] Iteration 1116 (2.28581 iter/s, 5.24978s/12 iters), loss = 4.3328
I0407 09:50:27.947089 17183 solver.cpp:237]     Train net output #0: loss = 4.3328 (* 1 = 4.3328 loss)
I0407 09:50:27.947098 17183 sgd_solver.cpp:105] Iteration 1116, lr = 0.01
I0407 09:50:30.068538 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1122.caffemodel
I0407 09:50:33.529832 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1122.solverstate
I0407 09:50:37.288450 17183 solver.cpp:330] Iteration 1122, Testing net (#0)
I0407 09:50:37.288468 17183 net.cpp:676] Ignoring source layer train-data
I0407 09:50:41.103741 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:50:41.573868 17183 solver.cpp:397]     Test net output #0: accuracy = 0.0772059
I0407 09:50:41.573904 17183 solver.cpp:397]     Test net output #1: loss = 4.35837 (* 1 = 4.35837 loss)
I0407 09:50:43.371472 17183 solver.cpp:218] Iteration 1128 (0.777997 iter/s, 15.4242s/12 iters), loss = 4.24875
I0407 09:50:43.371516 17183 solver.cpp:237]     Train net output #0: loss = 4.24875 (* 1 = 4.24875 loss)
I0407 09:50:43.371523 17183 sgd_solver.cpp:105] Iteration 1128, lr = 0.01
I0407 09:50:48.551532 17183 solver.cpp:218] Iteration 1140 (2.31663 iter/s, 5.17995s/12 iters), loss = 4.35367
I0407 09:50:48.551590 17183 solver.cpp:237]     Train net output #0: loss = 4.35367 (* 1 = 4.35367 loss)
I0407 09:50:48.551604 17183 sgd_solver.cpp:105] Iteration 1140, lr = 0.01
I0407 09:50:53.607841 17183 solver.cpp:218] Iteration 1152 (2.37333 iter/s, 5.05619s/12 iters), loss = 4.48762
I0407 09:50:53.607883 17183 solver.cpp:237]     Train net output #0: loss = 4.48762 (* 1 = 4.48762 loss)
I0407 09:50:53.607890 17183 sgd_solver.cpp:105] Iteration 1152, lr = 0.01
I0407 09:50:58.670126 17183 solver.cpp:218] Iteration 1164 (2.37052 iter/s, 5.06217s/12 iters), loss = 4.53733
I0407 09:50:58.670176 17183 solver.cpp:237]     Train net output #0: loss = 4.53733 (* 1 = 4.53733 loss)
I0407 09:50:58.670186 17183 sgd_solver.cpp:105] Iteration 1164, lr = 0.01
I0407 09:51:03.956380 17183 solver.cpp:218] Iteration 1176 (2.27009 iter/s, 5.28613s/12 iters), loss = 4.25441
I0407 09:51:03.956516 17183 solver.cpp:237]     Train net output #0: loss = 4.25441 (* 1 = 4.25441 loss)
I0407 09:51:03.956527 17183 sgd_solver.cpp:105] Iteration 1176, lr = 0.01
I0407 09:51:09.214826 17183 solver.cpp:218] Iteration 1188 (2.28213 iter/s, 5.25824s/12 iters), loss = 4.14306
I0407 09:51:09.214872 17183 solver.cpp:237]     Train net output #0: loss = 4.14306 (* 1 = 4.14306 loss)
I0407 09:51:09.214880 17183 sgd_solver.cpp:105] Iteration 1188, lr = 0.01
I0407 09:51:14.345281 17183 solver.cpp:218] Iteration 1200 (2.33903 iter/s, 5.13034s/12 iters), loss = 3.90948
I0407 09:51:14.345328 17183 solver.cpp:237]     Train net output #0: loss = 3.90948 (* 1 = 3.90948 loss)
I0407 09:51:14.345336 17183 sgd_solver.cpp:105] Iteration 1200, lr = 0.01
I0407 09:51:19.660706 17183 solver.cpp:218] Iteration 1212 (2.25763 iter/s, 5.31531s/12 iters), loss = 4.15126
I0407 09:51:19.660764 17183 solver.cpp:237]     Train net output #0: loss = 4.15126 (* 1 = 4.15126 loss)
I0407 09:51:19.660774 17183 sgd_solver.cpp:105] Iteration 1212, lr = 0.01
I0407 09:51:19.918256 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:51:24.400861 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1224.caffemodel
I0407 09:51:27.436376 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1224.solverstate
I0407 09:51:31.259100 17183 solver.cpp:330] Iteration 1224, Testing net (#0)
I0407 09:51:31.259124 17183 net.cpp:676] Ignoring source layer train-data
I0407 09:51:35.136149 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:51:35.636590 17183 solver.cpp:397]     Test net output #0: accuracy = 0.0833333
I0407 09:51:35.636626 17183 solver.cpp:397]     Test net output #1: loss = 4.23949 (* 1 = 4.23949 loss)
I0407 09:51:35.777688 17183 solver.cpp:218] Iteration 1224 (0.744567 iter/s, 16.1168s/12 iters), loss = 4.21311
I0407 09:51:35.777751 17183 solver.cpp:237]     Train net output #0: loss = 4.21311 (* 1 = 4.21311 loss)
I0407 09:51:35.777760 17183 sgd_solver.cpp:105] Iteration 1224, lr = 0.01
I0407 09:51:39.976814 17183 solver.cpp:218] Iteration 1236 (2.85782 iter/s, 4.199s/12 iters), loss = 4.13797
I0407 09:51:39.976857 17183 solver.cpp:237]     Train net output #0: loss = 4.13797 (* 1 = 4.13797 loss)
I0407 09:51:39.976864 17183 sgd_solver.cpp:105] Iteration 1236, lr = 0.01
I0407 09:51:44.917941 17183 solver.cpp:218] Iteration 1248 (2.42865 iter/s, 4.94101s/12 iters), loss = 4.11259
I0407 09:51:44.917999 17183 solver.cpp:237]     Train net output #0: loss = 4.11259 (* 1 = 4.11259 loss)
I0407 09:51:44.918009 17183 sgd_solver.cpp:105] Iteration 1248, lr = 0.01
I0407 09:51:50.209956 17183 solver.cpp:218] Iteration 1260 (2.26762 iter/s, 5.29189s/12 iters), loss = 4.22741
I0407 09:51:50.209998 17183 solver.cpp:237]     Train net output #0: loss = 4.22741 (* 1 = 4.22741 loss)
I0407 09:51:50.210005 17183 sgd_solver.cpp:105] Iteration 1260, lr = 0.01
I0407 09:51:55.559880 17183 solver.cpp:218] Iteration 1272 (2.24307 iter/s, 5.34981s/12 iters), loss = 4.2107
I0407 09:51:55.559927 17183 solver.cpp:237]     Train net output #0: loss = 4.2107 (* 1 = 4.2107 loss)
I0407 09:51:55.559937 17183 sgd_solver.cpp:105] Iteration 1272, lr = 0.01
I0407 09:52:00.714527 17183 solver.cpp:218] Iteration 1284 (2.32805 iter/s, 5.15453s/12 iters), loss = 4.04798
I0407 09:52:00.714586 17183 solver.cpp:237]     Train net output #0: loss = 4.04798 (* 1 = 4.04798 loss)
I0407 09:52:00.714596 17183 sgd_solver.cpp:105] Iteration 1284, lr = 0.01
I0407 09:52:05.736647 17183 solver.cpp:218] Iteration 1296 (2.38949 iter/s, 5.022s/12 iters), loss = 4.34512
I0407 09:52:05.736773 17183 solver.cpp:237]     Train net output #0: loss = 4.34512 (* 1 = 4.34512 loss)
I0407 09:52:05.736779 17183 sgd_solver.cpp:105] Iteration 1296, lr = 0.01
I0407 09:52:11.021034 17183 solver.cpp:218] Iteration 1308 (2.27092 iter/s, 5.28419s/12 iters), loss = 4.29548
I0407 09:52:11.021078 17183 solver.cpp:237]     Train net output #0: loss = 4.29548 (* 1 = 4.29548 loss)
I0407 09:52:11.021086 17183 sgd_solver.cpp:105] Iteration 1308, lr = 0.01
I0407 09:52:13.429229 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:52:16.030648 17183 solver.cpp:218] Iteration 1320 (2.39545 iter/s, 5.0095s/12 iters), loss = 4.26152
I0407 09:52:16.030691 17183 solver.cpp:237]     Train net output #0: loss = 4.26152 (* 1 = 4.26152 loss)
I0407 09:52:16.030699 17183 sgd_solver.cpp:105] Iteration 1320, lr = 0.01
I0407 09:52:18.168612 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1326.caffemodel
I0407 09:52:21.187659 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1326.solverstate
I0407 09:52:25.206555 17183 solver.cpp:330] Iteration 1326, Testing net (#0)
I0407 09:52:25.206575 17183 net.cpp:676] Ignoring source layer train-data
I0407 09:52:28.943747 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:52:29.497139 17183 solver.cpp:397]     Test net output #0: accuracy = 0.10049
I0407 09:52:29.497176 17183 solver.cpp:397]     Test net output #1: loss = 4.08941 (* 1 = 4.08941 loss)
I0407 09:52:31.247265 17183 solver.cpp:218] Iteration 1332 (0.788623 iter/s, 15.2164s/12 iters), loss = 3.93242
I0407 09:52:31.247310 17183 solver.cpp:237]     Train net output #0: loss = 3.93242 (* 1 = 3.93242 loss)
I0407 09:52:31.247318 17183 sgd_solver.cpp:105] Iteration 1332, lr = 0.01
I0407 09:52:36.272330 17183 solver.cpp:218] Iteration 1344 (2.38808 iter/s, 5.02495s/12 iters), loss = 4.09424
I0407 09:52:36.272451 17183 solver.cpp:237]     Train net output #0: loss = 4.09424 (* 1 = 4.09424 loss)
I0407 09:52:36.272461 17183 sgd_solver.cpp:105] Iteration 1344, lr = 0.01
I0407 09:52:41.341920 17183 solver.cpp:218] Iteration 1356 (2.36714 iter/s, 5.0694s/12 iters), loss = 3.791
I0407 09:52:41.341979 17183 solver.cpp:237]     Train net output #0: loss = 3.791 (* 1 = 3.791 loss)
I0407 09:52:41.341989 17183 sgd_solver.cpp:105] Iteration 1356, lr = 0.01
I0407 09:52:46.521721 17183 solver.cpp:218] Iteration 1368 (2.31675 iter/s, 5.17967s/12 iters), loss = 3.83632
I0407 09:52:46.521777 17183 solver.cpp:237]     Train net output #0: loss = 3.83632 (* 1 = 3.83632 loss)
I0407 09:52:46.521786 17183 sgd_solver.cpp:105] Iteration 1368, lr = 0.01
I0407 09:52:47.822561 17183 blocking_queue.cpp:49] Waiting for data
I0407 09:52:51.550354 17183 solver.cpp:218] Iteration 1380 (2.38639 iter/s, 5.02851s/12 iters), loss = 3.75876
I0407 09:52:51.550395 17183 solver.cpp:237]     Train net output #0: loss = 3.75876 (* 1 = 3.75876 loss)
I0407 09:52:51.550401 17183 sgd_solver.cpp:105] Iteration 1380, lr = 0.01
I0407 09:52:56.544658 17183 solver.cpp:218] Iteration 1392 (2.40279 iter/s, 4.9942s/12 iters), loss = 4.00892
I0407 09:52:56.544696 17183 solver.cpp:237]     Train net output #0: loss = 4.00892 (* 1 = 4.00892 loss)
I0407 09:52:56.544703 17183 sgd_solver.cpp:105] Iteration 1392, lr = 0.01
I0407 09:53:01.774585 17183 solver.cpp:218] Iteration 1404 (2.29454 iter/s, 5.22982s/12 iters), loss = 3.87323
I0407 09:53:01.774631 17183 solver.cpp:237]     Train net output #0: loss = 3.87323 (* 1 = 3.87323 loss)
I0407 09:53:01.774637 17183 sgd_solver.cpp:105] Iteration 1404, lr = 0.01
I0407 09:53:06.231284 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:53:06.606586 17183 solver.cpp:218] Iteration 1416 (2.48357 iter/s, 4.83175s/12 iters), loss = 3.97928
I0407 09:53:06.606709 17183 solver.cpp:237]     Train net output #0: loss = 3.97928 (* 1 = 3.97928 loss)
I0407 09:53:06.606721 17183 sgd_solver.cpp:105] Iteration 1416, lr = 0.01
I0407 09:53:11.225461 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1428.caffemodel
I0407 09:53:14.291221 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1428.solverstate
I0407 09:53:16.600060 17183 solver.cpp:330] Iteration 1428, Testing net (#0)
I0407 09:53:16.600086 17183 net.cpp:676] Ignoring source layer train-data
I0407 09:53:20.392937 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:53:20.974970 17183 solver.cpp:397]     Test net output #0: accuracy = 0.114583
I0407 09:53:20.974998 17183 solver.cpp:397]     Test net output #1: loss = 3.97463 (* 1 = 3.97463 loss)
I0407 09:53:21.116662 17183 solver.cpp:218] Iteration 1428 (0.827028 iter/s, 14.5098s/12 iters), loss = 3.99685
I0407 09:53:21.116735 17183 solver.cpp:237]     Train net output #0: loss = 3.99685 (* 1 = 3.99685 loss)
I0407 09:53:21.116745 17183 sgd_solver.cpp:105] Iteration 1428, lr = 0.01
I0407 09:53:25.145871 17183 solver.cpp:218] Iteration 1440 (2.97834 iter/s, 4.02909s/12 iters), loss = 3.76189
I0407 09:53:25.145915 17183 solver.cpp:237]     Train net output #0: loss = 3.76189 (* 1 = 3.76189 loss)
I0407 09:53:25.145923 17183 sgd_solver.cpp:105] Iteration 1440, lr = 0.01
I0407 09:53:30.159947 17183 solver.cpp:218] Iteration 1452 (2.39332 iter/s, 5.01396s/12 iters), loss = 3.76785
I0407 09:53:30.159993 17183 solver.cpp:237]     Train net output #0: loss = 3.76785 (* 1 = 3.76785 loss)
I0407 09:53:30.160001 17183 sgd_solver.cpp:105] Iteration 1452, lr = 0.01
I0407 09:53:35.157681 17183 solver.cpp:218] Iteration 1464 (2.40114 iter/s, 4.99763s/12 iters), loss = 3.55741
I0407 09:53:35.157717 17183 solver.cpp:237]     Train net output #0: loss = 3.55741 (* 1 = 3.55741 loss)
I0407 09:53:35.157724 17183 sgd_solver.cpp:105] Iteration 1464, lr = 0.01
I0407 09:53:40.451547 17183 solver.cpp:218] Iteration 1476 (2.26682 iter/s, 5.29376s/12 iters), loss = 3.75603
I0407 09:53:40.451691 17183 solver.cpp:237]     Train net output #0: loss = 3.75603 (* 1 = 3.75603 loss)
I0407 09:53:40.451700 17183 sgd_solver.cpp:105] Iteration 1476, lr = 0.01
I0407 09:53:45.392560 17183 solver.cpp:218] Iteration 1488 (2.42875 iter/s, 4.94081s/12 iters), loss = 3.98901
I0407 09:53:45.392599 17183 solver.cpp:237]     Train net output #0: loss = 3.98901 (* 1 = 3.98901 loss)
I0407 09:53:45.392606 17183 sgd_solver.cpp:105] Iteration 1488, lr = 0.01
I0407 09:53:50.705410 17183 solver.cpp:218] Iteration 1500 (2.25872 iter/s, 5.31275s/12 iters), loss = 3.88407
I0407 09:53:50.705452 17183 solver.cpp:237]     Train net output #0: loss = 3.88407 (* 1 = 3.88407 loss)
I0407 09:53:50.705461 17183 sgd_solver.cpp:105] Iteration 1500, lr = 0.01
I0407 09:53:55.811244 17183 solver.cpp:218] Iteration 1512 (2.3503 iter/s, 5.10572s/12 iters), loss = 3.73061
I0407 09:53:55.811287 17183 solver.cpp:237]     Train net output #0: loss = 3.73061 (* 1 = 3.73061 loss)
I0407 09:53:55.811296 17183 sgd_solver.cpp:105] Iteration 1512, lr = 0.01
I0407 09:53:57.731810 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:54:01.130892 17183 solver.cpp:218] Iteration 1524 (2.25584 iter/s, 5.31953s/12 iters), loss = 3.65038
I0407 09:54:01.130935 17183 solver.cpp:237]     Train net output #0: loss = 3.65038 (* 1 = 3.65038 loss)
I0407 09:54:01.130942 17183 sgd_solver.cpp:105] Iteration 1524, lr = 0.01
I0407 09:54:03.229619 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1530.caffemodel
I0407 09:54:06.288148 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1530.solverstate
I0407 09:54:08.766551 17183 solver.cpp:330] Iteration 1530, Testing net (#0)
I0407 09:54:08.766569 17183 net.cpp:676] Ignoring source layer train-data
I0407 09:54:12.411720 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:54:13.043355 17183 solver.cpp:397]     Test net output #0: accuracy = 0.137255
I0407 09:54:13.043395 17183 solver.cpp:397]     Test net output #1: loss = 3.8735 (* 1 = 3.8735 loss)
I0407 09:54:14.933131 17183 solver.cpp:218] Iteration 1536 (0.869436 iter/s, 13.802s/12 iters), loss = 3.26798
I0407 09:54:14.933174 17183 solver.cpp:237]     Train net output #0: loss = 3.26798 (* 1 = 3.26798 loss)
I0407 09:54:14.933182 17183 sgd_solver.cpp:105] Iteration 1536, lr = 0.01
I0407 09:54:20.165257 17183 solver.cpp:218] Iteration 1548 (2.29357 iter/s, 5.23202s/12 iters), loss = 3.74141
I0407 09:54:20.165307 17183 solver.cpp:237]     Train net output #0: loss = 3.74141 (* 1 = 3.74141 loss)
I0407 09:54:20.165316 17183 sgd_solver.cpp:105] Iteration 1548, lr = 0.01
I0407 09:54:25.202978 17183 solver.cpp:218] Iteration 1560 (2.38209 iter/s, 5.0376s/12 iters), loss = 3.54658
I0407 09:54:25.203032 17183 solver.cpp:237]     Train net output #0: loss = 3.54658 (* 1 = 3.54658 loss)
I0407 09:54:25.203042 17183 sgd_solver.cpp:105] Iteration 1560, lr = 0.01
I0407 09:54:30.357834 17183 solver.cpp:218] Iteration 1572 (2.32796 iter/s, 5.15473s/12 iters), loss = 3.47997
I0407 09:54:30.357875 17183 solver.cpp:237]     Train net output #0: loss = 3.47997 (* 1 = 3.47997 loss)
I0407 09:54:30.357882 17183 sgd_solver.cpp:105] Iteration 1572, lr = 0.01
I0407 09:54:35.639465 17183 solver.cpp:218] Iteration 1584 (2.27207 iter/s, 5.28152s/12 iters), loss = 3.78554
I0407 09:54:35.639518 17183 solver.cpp:237]     Train net output #0: loss = 3.78554 (* 1 = 3.78554 loss)
I0407 09:54:35.639528 17183 sgd_solver.cpp:105] Iteration 1584, lr = 0.01
I0407 09:54:40.891793 17183 solver.cpp:218] Iteration 1596 (2.28475 iter/s, 5.25221s/12 iters), loss = 3.53825
I0407 09:54:40.891840 17183 solver.cpp:237]     Train net output #0: loss = 3.53825 (* 1 = 3.53825 loss)
I0407 09:54:40.891849 17183 sgd_solver.cpp:105] Iteration 1596, lr = 0.01
I0407 09:54:46.299116 17183 solver.cpp:218] Iteration 1608 (2.21926 iter/s, 5.40721s/12 iters), loss = 3.55311
I0407 09:54:46.299226 17183 solver.cpp:237]     Train net output #0: loss = 3.55311 (* 1 = 3.55311 loss)
I0407 09:54:46.299233 17183 sgd_solver.cpp:105] Iteration 1608, lr = 0.01
I0407 09:54:50.377770 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:54:51.561973 17183 solver.cpp:218] Iteration 1620 (2.28021 iter/s, 5.26268s/12 iters), loss = 3.53434
I0407 09:54:51.562017 17183 solver.cpp:237]     Train net output #0: loss = 3.53434 (* 1 = 3.53434 loss)
I0407 09:54:51.562024 17183 sgd_solver.cpp:105] Iteration 1620, lr = 0.01
I0407 09:54:56.232908 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1632.caffemodel
I0407 09:54:59.252789 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1632.solverstate
I0407 09:55:01.552546 17183 solver.cpp:330] Iteration 1632, Testing net (#0)
I0407 09:55:01.552564 17183 net.cpp:676] Ignoring source layer train-data
I0407 09:55:05.265812 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:55:05.931038 17183 solver.cpp:397]     Test net output #0: accuracy = 0.158701
I0407 09:55:05.931074 17183 solver.cpp:397]     Test net output #1: loss = 3.72675 (* 1 = 3.72675 loss)
I0407 09:55:06.071094 17183 solver.cpp:218] Iteration 1632 (0.827077 iter/s, 14.5089s/12 iters), loss = 3.39378
I0407 09:55:06.072669 17183 solver.cpp:237]     Train net output #0: loss = 3.39378 (* 1 = 3.39378 loss)
I0407 09:55:06.072682 17183 sgd_solver.cpp:105] Iteration 1632, lr = 0.01
I0407 09:55:10.218612 17183 solver.cpp:218] Iteration 1644 (2.89443 iter/s, 4.1459s/12 iters), loss = 3.71475
I0407 09:55:10.218652 17183 solver.cpp:237]     Train net output #0: loss = 3.71475 (* 1 = 3.71475 loss)
I0407 09:55:10.218660 17183 sgd_solver.cpp:105] Iteration 1644, lr = 0.01
I0407 09:55:15.292634 17183 solver.cpp:218] Iteration 1656 (2.36504 iter/s, 5.07392s/12 iters), loss = 3.524
I0407 09:55:15.292675 17183 solver.cpp:237]     Train net output #0: loss = 3.524 (* 1 = 3.524 loss)
I0407 09:55:15.292682 17183 sgd_solver.cpp:105] Iteration 1656, lr = 0.01
I0407 09:55:20.168550 17183 solver.cpp:218] Iteration 1668 (2.46113 iter/s, 4.87581s/12 iters), loss = 3.4417
I0407 09:55:20.168695 17183 solver.cpp:237]     Train net output #0: loss = 3.4417 (* 1 = 3.4417 loss)
I0407 09:55:20.168704 17183 sgd_solver.cpp:105] Iteration 1668, lr = 0.01
I0407 09:55:25.295473 17183 solver.cpp:218] Iteration 1680 (2.34068 iter/s, 5.12671s/12 iters), loss = 3.18948
I0407 09:55:25.295521 17183 solver.cpp:237]     Train net output #0: loss = 3.18948 (* 1 = 3.18948 loss)
I0407 09:55:25.295527 17183 sgd_solver.cpp:105] Iteration 1680, lr = 0.01
I0407 09:55:30.632254 17183 solver.cpp:218] Iteration 1692 (2.24859 iter/s, 5.33667s/12 iters), loss = 3.15276
I0407 09:55:30.632299 17183 solver.cpp:237]     Train net output #0: loss = 3.15276 (* 1 = 3.15276 loss)
I0407 09:55:30.632306 17183 sgd_solver.cpp:105] Iteration 1692, lr = 0.01
I0407 09:55:35.964936 17183 solver.cpp:218] Iteration 1704 (2.25032 iter/s, 5.33256s/12 iters), loss = 3.01205
I0407 09:55:35.965000 17183 solver.cpp:237]     Train net output #0: loss = 3.01205 (* 1 = 3.01205 loss)
I0407 09:55:35.965011 17183 sgd_solver.cpp:105] Iteration 1704, lr = 0.01
I0407 09:55:41.003408 17183 solver.cpp:218] Iteration 1716 (2.38174 iter/s, 5.03834s/12 iters), loss = 3.43825
I0407 09:55:41.003468 17183 solver.cpp:237]     Train net output #0: loss = 3.43825 (* 1 = 3.43825 loss)
I0407 09:55:41.003479 17183 sgd_solver.cpp:105] Iteration 1716, lr = 0.01
I0407 09:55:42.078418 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:55:46.266727 17183 solver.cpp:218] Iteration 1728 (2.27998 iter/s, 5.2632s/12 iters), loss = 3.28556
I0407 09:55:46.266770 17183 solver.cpp:237]     Train net output #0: loss = 3.28556 (* 1 = 3.28556 loss)
I0407 09:55:46.266779 17183 sgd_solver.cpp:105] Iteration 1728, lr = 0.01
I0407 09:55:48.334223 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1734.caffemodel
I0407 09:55:51.330019 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1734.solverstate
I0407 09:55:53.631388 17183 solver.cpp:330] Iteration 1734, Testing net (#0)
I0407 09:55:53.631403 17183 net.cpp:676] Ignoring source layer train-data
I0407 09:55:57.195892 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:55:57.888947 17183 solver.cpp:397]     Test net output #0: accuracy = 0.175245
I0407 09:55:57.888983 17183 solver.cpp:397]     Test net output #1: loss = 3.55883 (* 1 = 3.55883 loss)
I0407 09:55:59.702648 17183 solver.cpp:218] Iteration 1740 (0.893141 iter/s, 13.4357s/12 iters), loss = 3.58067
I0407 09:55:59.702697 17183 solver.cpp:237]     Train net output #0: loss = 3.58067 (* 1 = 3.58067 loss)
I0407 09:55:59.702704 17183 sgd_solver.cpp:105] Iteration 1740, lr = 0.01
I0407 09:56:04.868996 17183 solver.cpp:218] Iteration 1752 (2.32278 iter/s, 5.16623s/12 iters), loss = 3.33776
I0407 09:56:04.869036 17183 solver.cpp:237]     Train net output #0: loss = 3.33776 (* 1 = 3.33776 loss)
I0407 09:56:04.869045 17183 sgd_solver.cpp:105] Iteration 1752, lr = 0.01
I0407 09:56:09.986668 17183 solver.cpp:218] Iteration 1764 (2.34487 iter/s, 5.11756s/12 iters), loss = 3.13355
I0407 09:56:09.986713 17183 solver.cpp:237]     Train net output #0: loss = 3.13355 (* 1 = 3.13355 loss)
I0407 09:56:09.986721 17183 sgd_solver.cpp:105] Iteration 1764, lr = 0.01
I0407 09:56:15.144060 17183 solver.cpp:218] Iteration 1776 (2.32681 iter/s, 5.15728s/12 iters), loss = 2.97817
I0407 09:56:15.144101 17183 solver.cpp:237]     Train net output #0: loss = 2.97817 (* 1 = 2.97817 loss)
I0407 09:56:15.144107 17183 sgd_solver.cpp:105] Iteration 1776, lr = 0.01
I0407 09:56:20.198084 17183 solver.cpp:218] Iteration 1788 (2.37439 iter/s, 5.05392s/12 iters), loss = 2.92333
I0407 09:56:20.198127 17183 solver.cpp:237]     Train net output #0: loss = 2.92333 (* 1 = 2.92333 loss)
I0407 09:56:20.198134 17183 sgd_solver.cpp:105] Iteration 1788, lr = 0.01
I0407 09:56:25.226393 17183 solver.cpp:218] Iteration 1800 (2.38654 iter/s, 5.0282s/12 iters), loss = 3.33015
I0407 09:56:25.226500 17183 solver.cpp:237]     Train net output #0: loss = 3.33015 (* 1 = 3.33015 loss)
I0407 09:56:25.226509 17183 sgd_solver.cpp:105] Iteration 1800, lr = 0.01
I0407 09:56:30.392530 17183 solver.cpp:218] Iteration 1812 (2.3229 iter/s, 5.16596s/12 iters), loss = 3.0642
I0407 09:56:30.392575 17183 solver.cpp:237]     Train net output #0: loss = 3.0642 (* 1 = 3.0642 loss)
I0407 09:56:30.392583 17183 sgd_solver.cpp:105] Iteration 1812, lr = 0.01
I0407 09:56:33.760773 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:56:35.562568 17183 solver.cpp:218] Iteration 1824 (2.32112 iter/s, 5.16993s/12 iters), loss = 3.60327
I0407 09:56:35.562610 17183 solver.cpp:237]     Train net output #0: loss = 3.60327 (* 1 = 3.60327 loss)
I0407 09:56:35.562618 17183 sgd_solver.cpp:105] Iteration 1824, lr = 0.01
I0407 09:56:40.128608 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1836.caffemodel
I0407 09:56:43.069499 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1836.solverstate
I0407 09:56:46.427471 17183 solver.cpp:330] Iteration 1836, Testing net (#0)
I0407 09:56:46.427490 17183 net.cpp:676] Ignoring source layer train-data
I0407 09:56:49.938205 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:56:50.676868 17183 solver.cpp:397]     Test net output #0: accuracy = 0.20527
I0407 09:56:50.676904 17183 solver.cpp:397]     Test net output #1: loss = 3.45656 (* 1 = 3.45656 loss)
I0407 09:56:50.813747 17183 solver.cpp:218] Iteration 1836 (0.786835 iter/s, 15.251s/12 iters), loss = 2.84192
I0407 09:56:50.813791 17183 solver.cpp:237]     Train net output #0: loss = 2.84192 (* 1 = 2.84192 loss)
I0407 09:56:50.813796 17183 sgd_solver.cpp:105] Iteration 1836, lr = 0.01
I0407 09:56:55.158865 17183 solver.cpp:218] Iteration 1848 (2.76178 iter/s, 4.34502s/12 iters), loss = 3.33767
I0407 09:56:55.158908 17183 solver.cpp:237]     Train net output #0: loss = 3.33767 (* 1 = 3.33767 loss)
I0407 09:56:55.158915 17183 sgd_solver.cpp:105] Iteration 1848, lr = 0.01
I0407 09:57:00.194886 17183 solver.cpp:218] Iteration 1860 (2.38289 iter/s, 5.03591s/12 iters), loss = 3.20238
I0407 09:57:00.195070 17183 solver.cpp:237]     Train net output #0: loss = 3.20238 (* 1 = 3.20238 loss)
I0407 09:57:00.195086 17183 sgd_solver.cpp:105] Iteration 1860, lr = 0.01
I0407 09:57:05.331207 17183 solver.cpp:218] Iteration 1872 (2.33641 iter/s, 5.13608s/12 iters), loss = 3.2007
I0407 09:57:05.331245 17183 solver.cpp:237]     Train net output #0: loss = 3.2007 (* 1 = 3.2007 loss)
I0407 09:57:05.331252 17183 sgd_solver.cpp:105] Iteration 1872, lr = 0.01
I0407 09:57:10.490664 17183 solver.cpp:218] Iteration 1884 (2.32587 iter/s, 5.15935s/12 iters), loss = 2.97282
I0407 09:57:10.490712 17183 solver.cpp:237]     Train net output #0: loss = 2.97282 (* 1 = 2.97282 loss)
I0407 09:57:10.490722 17183 sgd_solver.cpp:105] Iteration 1884, lr = 0.01
I0407 09:57:15.580597 17183 solver.cpp:218] Iteration 1896 (2.35765 iter/s, 5.08982s/12 iters), loss = 3.08313
I0407 09:57:15.580638 17183 solver.cpp:237]     Train net output #0: loss = 3.08313 (* 1 = 3.08313 loss)
I0407 09:57:15.580646 17183 sgd_solver.cpp:105] Iteration 1896, lr = 0.01
I0407 09:57:20.853224 17183 solver.cpp:218] Iteration 1908 (2.27595 iter/s, 5.27252s/12 iters), loss = 3.1944
I0407 09:57:20.853267 17183 solver.cpp:237]     Train net output #0: loss = 3.1944 (* 1 = 3.1944 loss)
I0407 09:57:20.853274 17183 sgd_solver.cpp:105] Iteration 1908, lr = 0.01
I0407 09:57:25.953251 17183 solver.cpp:218] Iteration 1920 (2.35298 iter/s, 5.09992s/12 iters), loss = 2.95372
I0407 09:57:25.953295 17183 solver.cpp:237]     Train net output #0: loss = 2.95372 (* 1 = 2.95372 loss)
I0407 09:57:25.953301 17183 sgd_solver.cpp:105] Iteration 1920, lr = 0.01
I0407 09:57:26.240049 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:57:31.193421 17183 solver.cpp:218] Iteration 1932 (2.29005 iter/s, 5.24006s/12 iters), loss = 3.0928
I0407 09:57:31.193604 17183 solver.cpp:237]     Train net output #0: loss = 3.0928 (* 1 = 3.0928 loss)
I0407 09:57:31.193614 17183 sgd_solver.cpp:105] Iteration 1932, lr = 0.01
I0407 09:57:33.249708 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1938.caffemodel
I0407 09:57:37.394665 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1938.solverstate
I0407 09:57:40.041360 17183 solver.cpp:330] Iteration 1938, Testing net (#0)
I0407 09:57:40.041380 17183 net.cpp:676] Ignoring source layer train-data
I0407 09:57:43.720651 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:57:44.498277 17183 solver.cpp:397]     Test net output #0: accuracy = 0.200368
I0407 09:57:44.498335 17183 solver.cpp:397]     Test net output #1: loss = 3.46356 (* 1 = 3.46356 loss)
I0407 09:57:46.396625 17183 solver.cpp:218] Iteration 1944 (0.789325 iter/s, 15.2029s/12 iters), loss = 3.27975
I0407 09:57:46.396662 17183 solver.cpp:237]     Train net output #0: loss = 3.27975 (* 1 = 3.27975 loss)
I0407 09:57:46.396668 17183 sgd_solver.cpp:105] Iteration 1944, lr = 0.01
I0407 09:57:51.585678 17183 solver.cpp:218] Iteration 1956 (2.31261 iter/s, 5.18895s/12 iters), loss = 3.16364
I0407 09:57:51.585724 17183 solver.cpp:237]     Train net output #0: loss = 3.16364 (* 1 = 3.16364 loss)
I0407 09:57:51.585731 17183 sgd_solver.cpp:105] Iteration 1956, lr = 0.01
I0407 09:57:56.666510 17183 solver.cpp:218] Iteration 1968 (2.36187 iter/s, 5.08072s/12 iters), loss = 3.07119
I0407 09:57:56.666568 17183 solver.cpp:237]     Train net output #0: loss = 3.07119 (* 1 = 3.07119 loss)
I0407 09:57:56.666579 17183 sgd_solver.cpp:105] Iteration 1968, lr = 0.01
I0407 09:58:01.807052 17183 solver.cpp:218] Iteration 1980 (2.33444 iter/s, 5.14042s/12 iters), loss = 3.02507
I0407 09:58:01.807224 17183 solver.cpp:237]     Train net output #0: loss = 3.02507 (* 1 = 3.02507 loss)
I0407 09:58:01.807236 17183 sgd_solver.cpp:105] Iteration 1980, lr = 0.01
I0407 09:58:07.021049 17183 solver.cpp:218] Iteration 1992 (2.30161 iter/s, 5.21375s/12 iters), loss = 2.92048
I0407 09:58:07.021109 17183 solver.cpp:237]     Train net output #0: loss = 2.92048 (* 1 = 2.92048 loss)
I0407 09:58:07.021121 17183 sgd_solver.cpp:105] Iteration 1992, lr = 0.01
I0407 09:58:12.310776 17183 solver.cpp:218] Iteration 2004 (2.2686 iter/s, 5.2896s/12 iters), loss = 3.02942
I0407 09:58:12.310822 17183 solver.cpp:237]     Train net output #0: loss = 3.02942 (* 1 = 3.02942 loss)
I0407 09:58:12.310830 17183 sgd_solver.cpp:105] Iteration 2004, lr = 0.01
I0407 09:58:17.623934 17183 solver.cpp:218] Iteration 2016 (2.25859 iter/s, 5.31304s/12 iters), loss = 3.09015
I0407 09:58:17.623975 17183 solver.cpp:237]     Train net output #0: loss = 3.09015 (* 1 = 3.09015 loss)
I0407 09:58:17.623982 17183 sgd_solver.cpp:105] Iteration 2016, lr = 0.01
I0407 09:58:20.369274 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:58:22.979001 17183 solver.cpp:218] Iteration 2028 (2.24091 iter/s, 5.35496s/12 iters), loss = 3.31056
I0407 09:58:22.979045 17183 solver.cpp:237]     Train net output #0: loss = 3.31056 (* 1 = 3.31056 loss)
I0407 09:58:22.979053 17183 sgd_solver.cpp:105] Iteration 2028, lr = 0.01
I0407 09:58:27.650743 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2040.caffemodel
I0407 09:58:30.682168 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2040.solverstate
I0407 09:58:32.997978 17183 solver.cpp:330] Iteration 2040, Testing net (#0)
I0407 09:58:32.998056 17183 net.cpp:676] Ignoring source layer train-data
I0407 09:58:36.587478 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:58:37.416524 17183 solver.cpp:397]     Test net output #0: accuracy = 0.201593
I0407 09:58:37.416558 17183 solver.cpp:397]     Test net output #1: loss = 3.41041 (* 1 = 3.41041 loss)
I0407 09:58:37.552012 17183 solver.cpp:218] Iteration 2040 (0.823451 iter/s, 14.5728s/12 iters), loss = 3.01822
I0407 09:58:37.552075 17183 solver.cpp:237]     Train net output #0: loss = 3.01822 (* 1 = 3.01822 loss)
I0407 09:58:37.552084 17183 sgd_solver.cpp:105] Iteration 2040, lr = 0.01
I0407 09:58:41.651499 17183 solver.cpp:218] Iteration 2052 (2.92728 iter/s, 4.09936s/12 iters), loss = 2.91112
I0407 09:58:41.651548 17183 solver.cpp:237]     Train net output #0: loss = 2.91112 (* 1 = 2.91112 loss)
I0407 09:58:41.651558 17183 sgd_solver.cpp:105] Iteration 2052, lr = 0.01
I0407 09:58:43.317924 17183 blocking_queue.cpp:49] Waiting for data
I0407 09:58:46.836632 17183 solver.cpp:218] Iteration 2064 (2.31436 iter/s, 5.18502s/12 iters), loss = 2.60481
I0407 09:58:46.836673 17183 solver.cpp:237]     Train net output #0: loss = 2.60481 (* 1 = 2.60481 loss)
I0407 09:58:46.836679 17183 sgd_solver.cpp:105] Iteration 2064, lr = 0.01
I0407 09:58:52.119794 17183 solver.cpp:218] Iteration 2076 (2.27142 iter/s, 5.28305s/12 iters), loss = 2.81362
I0407 09:58:52.119841 17183 solver.cpp:237]     Train net output #0: loss = 2.81362 (* 1 = 2.81362 loss)
I0407 09:58:52.119850 17183 sgd_solver.cpp:105] Iteration 2076, lr = 0.01
I0407 09:58:57.242998 17183 solver.cpp:218] Iteration 2088 (2.34234 iter/s, 5.12309s/12 iters), loss = 2.84939
I0407 09:58:57.243037 17183 solver.cpp:237]     Train net output #0: loss = 2.84939 (* 1 = 2.84939 loss)
I0407 09:58:57.243043 17183 sgd_solver.cpp:105] Iteration 2088, lr = 0.01
I0407 09:59:02.368389 17183 solver.cpp:218] Iteration 2100 (2.34133 iter/s, 5.12529s/12 iters), loss = 2.73641
I0407 09:59:02.368432 17183 solver.cpp:237]     Train net output #0: loss = 2.73641 (* 1 = 2.73641 loss)
I0407 09:59:02.368440 17183 sgd_solver.cpp:105] Iteration 2100, lr = 0.01
I0407 09:59:07.615687 17183 solver.cpp:218] Iteration 2112 (2.28694 iter/s, 5.24719s/12 iters), loss = 2.8175
I0407 09:59:07.615825 17183 solver.cpp:237]     Train net output #0: loss = 2.8175 (* 1 = 2.8175 loss)
I0407 09:59:07.615833 17183 sgd_solver.cpp:105] Iteration 2112, lr = 0.01
I0407 09:59:12.544168 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:59:12.896149 17183 solver.cpp:218] Iteration 2124 (2.27262 iter/s, 5.28026s/12 iters), loss = 3.19814
I0407 09:59:12.896196 17183 solver.cpp:237]     Train net output #0: loss = 3.19814 (* 1 = 3.19814 loss)
I0407 09:59:12.896203 17183 sgd_solver.cpp:105] Iteration 2124, lr = 0.01
I0407 09:59:18.099382 17183 solver.cpp:218] Iteration 2136 (2.30631 iter/s, 5.20312s/12 iters), loss = 3.29561
I0407 09:59:18.099429 17183 solver.cpp:237]     Train net output #0: loss = 3.29561 (* 1 = 3.29561 loss)
I0407 09:59:18.099439 17183 sgd_solver.cpp:105] Iteration 2136, lr = 0.01
I0407 09:59:20.173444 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2142.caffemodel
I0407 09:59:23.197353 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2142.solverstate
I0407 09:59:25.499326 17183 solver.cpp:330] Iteration 2142, Testing net (#0)
I0407 09:59:25.499351 17183 net.cpp:676] Ignoring source layer train-data
I0407 09:59:28.909832 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:59:29.799922 17183 solver.cpp:397]     Test net output #0: accuracy = 0.215686
I0407 09:59:29.799959 17183 solver.cpp:397]     Test net output #1: loss = 3.35335 (* 1 = 3.35335 loss)
I0407 09:59:31.684741 17183 solver.cpp:218] Iteration 2148 (0.883316 iter/s, 13.5852s/12 iters), loss = 2.91122
I0407 09:59:31.684789 17183 solver.cpp:237]     Train net output #0: loss = 2.91122 (* 1 = 2.91122 loss)
I0407 09:59:31.684798 17183 sgd_solver.cpp:105] Iteration 2148, lr = 0.01
I0407 09:59:36.867785 17183 solver.cpp:218] Iteration 2160 (2.31529 iter/s, 5.18293s/12 iters), loss = 2.55644
I0407 09:59:36.867831 17183 solver.cpp:237]     Train net output #0: loss = 2.55644 (* 1 = 2.55644 loss)
I0407 09:59:36.867841 17183 sgd_solver.cpp:105] Iteration 2160, lr = 0.01
I0407 09:59:41.796795 17183 solver.cpp:218] Iteration 2172 (2.43462 iter/s, 4.9289s/12 iters), loss = 2.73659
I0407 09:59:41.796916 17183 solver.cpp:237]     Train net output #0: loss = 2.73659 (* 1 = 2.73659 loss)
I0407 09:59:41.796924 17183 sgd_solver.cpp:105] Iteration 2172, lr = 0.01
I0407 09:59:46.928295 17183 solver.cpp:218] Iteration 2184 (2.33858 iter/s, 5.13131s/12 iters), loss = 2.91078
I0407 09:59:46.928339 17183 solver.cpp:237]     Train net output #0: loss = 2.91078 (* 1 = 2.91078 loss)
I0407 09:59:46.928346 17183 sgd_solver.cpp:105] Iteration 2184, lr = 0.01
I0407 09:59:52.008430 17183 solver.cpp:218] Iteration 2196 (2.36219 iter/s, 5.08002s/12 iters), loss = 2.85662
I0407 09:59:52.008491 17183 solver.cpp:237]     Train net output #0: loss = 2.85662 (* 1 = 2.85662 loss)
I0407 09:59:52.008502 17183 sgd_solver.cpp:105] Iteration 2196, lr = 0.01
I0407 09:59:57.147320 17183 solver.cpp:218] Iteration 2208 (2.33519 iter/s, 5.13876s/12 iters), loss = 2.55525
I0407 09:59:57.147373 17183 solver.cpp:237]     Train net output #0: loss = 2.55525 (* 1 = 2.55525 loss)
I0407 09:59:57.147383 17183 sgd_solver.cpp:105] Iteration 2208, lr = 0.01
I0407 10:00:02.226804 17183 solver.cpp:218] Iteration 2220 (2.3625 iter/s, 5.07937s/12 iters), loss = 2.59775
I0407 10:00:02.226845 17183 solver.cpp:237]     Train net output #0: loss = 2.59775 (* 1 = 2.59775 loss)
I0407 10:00:02.226851 17183 sgd_solver.cpp:105] Iteration 2220, lr = 0.01
I0407 10:00:04.058775 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:00:07.352958 17183 solver.cpp:218] Iteration 2232 (2.34098 iter/s, 5.12605s/12 iters), loss = 2.64993
I0407 10:00:07.352999 17183 solver.cpp:237]     Train net output #0: loss = 2.64993 (* 1 = 2.64993 loss)
I0407 10:00:07.353005 17183 sgd_solver.cpp:105] Iteration 2232, lr = 0.01
I0407 10:00:12.087119 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2244.caffemodel
I0407 10:00:16.336810 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2244.solverstate
I0407 10:00:18.808895 17183 solver.cpp:330] Iteration 2244, Testing net (#0)
I0407 10:00:18.808920 17183 net.cpp:676] Ignoring source layer train-data
I0407 10:00:22.282609 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:00:23.179617 17183 solver.cpp:397]     Test net output #0: accuracy = 0.243873
I0407 10:00:23.179656 17183 solver.cpp:397]     Test net output #1: loss = 3.20866 (* 1 = 3.20866 loss)
I0407 10:00:23.320924 17183 solver.cpp:218] Iteration 2244 (0.751514 iter/s, 15.9678s/12 iters), loss = 2.52339
I0407 10:00:23.320973 17183 solver.cpp:237]     Train net output #0: loss = 2.52339 (* 1 = 2.52339 loss)
I0407 10:00:23.320982 17183 sgd_solver.cpp:105] Iteration 2244, lr = 0.01
I0407 10:00:27.554651 17183 solver.cpp:218] Iteration 2256 (2.83445 iter/s, 4.23362s/12 iters), loss = 2.6586
I0407 10:00:27.554690 17183 solver.cpp:237]     Train net output #0: loss = 2.6586 (* 1 = 2.6586 loss)
I0407 10:00:27.554698 17183 sgd_solver.cpp:105] Iteration 2256, lr = 0.01
I0407 10:00:32.538568 17183 solver.cpp:218] Iteration 2268 (2.40779 iter/s, 4.98382s/12 iters), loss = 2.58638
I0407 10:00:32.538601 17183 solver.cpp:237]     Train net output #0: loss = 2.58638 (* 1 = 2.58638 loss)
I0407 10:00:32.538609 17183 sgd_solver.cpp:105] Iteration 2268, lr = 0.01
I0407 10:00:37.654844 17183 solver.cpp:218] Iteration 2280 (2.3455 iter/s, 5.11617s/12 iters), loss = 2.52302
I0407 10:00:37.654886 17183 solver.cpp:237]     Train net output #0: loss = 2.52302 (* 1 = 2.52302 loss)
I0407 10:00:37.654893 17183 sgd_solver.cpp:105] Iteration 2280, lr = 0.01
I0407 10:00:42.885804 17183 solver.cpp:218] Iteration 2292 (2.29408 iter/s, 5.23085s/12 iters), loss = 3.07674
I0407 10:00:42.885963 17183 solver.cpp:237]     Train net output #0: loss = 3.07674 (* 1 = 3.07674 loss)
I0407 10:00:42.885974 17183 sgd_solver.cpp:105] Iteration 2292, lr = 0.01
I0407 10:00:48.047407 17183 solver.cpp:218] Iteration 2304 (2.32496 iter/s, 5.16138s/12 iters), loss = 2.48843
I0407 10:00:48.047463 17183 solver.cpp:237]     Train net output #0: loss = 2.48843 (* 1 = 2.48843 loss)
I0407 10:00:48.047473 17183 sgd_solver.cpp:105] Iteration 2304, lr = 0.01
I0407 10:00:53.274519 17183 solver.cpp:218] Iteration 2316 (2.29578 iter/s, 5.22699s/12 iters), loss = 2.6718
I0407 10:00:53.274564 17183 solver.cpp:237]     Train net output #0: loss = 2.6718 (* 1 = 2.6718 loss)
I0407 10:00:53.274570 17183 sgd_solver.cpp:105] Iteration 2316, lr = 0.01
I0407 10:00:57.388087 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:00:58.461983 17183 solver.cpp:218] Iteration 2328 (2.31332 iter/s, 5.18735s/12 iters), loss = 2.49642
I0407 10:00:58.462028 17183 solver.cpp:237]     Train net output #0: loss = 2.49642 (* 1 = 2.49642 loss)
I0407 10:00:58.462035 17183 sgd_solver.cpp:105] Iteration 2328, lr = 0.01
I0407 10:01:03.642524 17183 solver.cpp:218] Iteration 2340 (2.31641 iter/s, 5.18043s/12 iters), loss = 2.5101
I0407 10:01:03.642576 17183 solver.cpp:237]     Train net output #0: loss = 2.5101 (* 1 = 2.5101 loss)
I0407 10:01:03.642586 17183 sgd_solver.cpp:105] Iteration 2340, lr = 0.01
I0407 10:01:05.742681 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2346.caffemodel
I0407 10:01:10.265969 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2346.solverstate
I0407 10:01:12.582108 17183 solver.cpp:330] Iteration 2346, Testing net (#0)
I0407 10:01:12.582131 17183 net.cpp:676] Ignoring source layer train-data
I0407 10:01:16.067025 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:01:17.047690 17183 solver.cpp:397]     Test net output #0: accuracy = 0.215686
I0407 10:01:17.047729 17183 solver.cpp:397]     Test net output #1: loss = 3.40289 (* 1 = 3.40289 loss)
I0407 10:01:19.044692 17183 solver.cpp:218] Iteration 2352 (0.779122 iter/s, 15.402s/12 iters), loss = 2.60058
I0407 10:01:19.044736 17183 solver.cpp:237]     Train net output #0: loss = 2.60058 (* 1 = 2.60058 loss)
I0407 10:01:19.044742 17183 sgd_solver.cpp:105] Iteration 2352, lr = 0.01
I0407 10:01:24.096052 17183 solver.cpp:218] Iteration 2364 (2.37565 iter/s, 5.05125s/12 iters), loss = 2.52184
I0407 10:01:24.096099 17183 solver.cpp:237]     Train net output #0: loss = 2.52184 (* 1 = 2.52184 loss)
I0407 10:01:24.096109 17183 sgd_solver.cpp:105] Iteration 2364, lr = 0.01
I0407 10:01:29.195179 17183 solver.cpp:218] Iteration 2376 (2.3534 iter/s, 5.09902s/12 iters), loss = 2.30901
I0407 10:01:29.195216 17183 solver.cpp:237]     Train net output #0: loss = 2.30901 (* 1 = 2.30901 loss)
I0407 10:01:29.195222 17183 sgd_solver.cpp:105] Iteration 2376, lr = 0.01
I0407 10:01:34.265159 17183 solver.cpp:218] Iteration 2388 (2.36692 iter/s, 5.06987s/12 iters), loss = 2.39091
I0407 10:01:34.265204 17183 solver.cpp:237]     Train net output #0: loss = 2.39091 (* 1 = 2.39091 loss)
I0407 10:01:34.265211 17183 sgd_solver.cpp:105] Iteration 2388, lr = 0.01
I0407 10:01:39.410161 17183 solver.cpp:218] Iteration 2400 (2.33241 iter/s, 5.14489s/12 iters), loss = 2.32995
I0407 10:01:39.410207 17183 solver.cpp:237]     Train net output #0: loss = 2.32995 (* 1 = 2.32995 loss)
I0407 10:01:39.410218 17183 sgd_solver.cpp:105] Iteration 2400, lr = 0.01
I0407 10:01:44.655221 17183 solver.cpp:218] Iteration 2412 (2.28792 iter/s, 5.24494s/12 iters), loss = 2.43187
I0407 10:01:44.655277 17183 solver.cpp:237]     Train net output #0: loss = 2.43187 (* 1 = 2.43187 loss)
I0407 10:01:44.655287 17183 sgd_solver.cpp:105] Iteration 2412, lr = 0.01
I0407 10:01:49.772440 17183 solver.cpp:218] Iteration 2424 (2.34508 iter/s, 5.1171s/12 iters), loss = 2.56607
I0407 10:01:49.772547 17183 solver.cpp:237]     Train net output #0: loss = 2.56607 (* 1 = 2.56607 loss)
I0407 10:01:49.772557 17183 sgd_solver.cpp:105] Iteration 2424, lr = 0.01
I0407 10:01:50.891171 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:01:55.019294 17183 solver.cpp:218] Iteration 2436 (2.28716 iter/s, 5.24668s/12 iters), loss = 2.29006
I0407 10:01:55.019340 17183 solver.cpp:237]     Train net output #0: loss = 2.29006 (* 1 = 2.29006 loss)
I0407 10:01:55.019348 17183 sgd_solver.cpp:105] Iteration 2436, lr = 0.01
I0407 10:01:59.616739 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2448.caffemodel
I0407 10:02:02.702136 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2448.solverstate
I0407 10:02:05.008981 17183 solver.cpp:330] Iteration 2448, Testing net (#0)
I0407 10:02:05.009001 17183 net.cpp:676] Ignoring source layer train-data
I0407 10:02:08.314381 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:02:09.275681 17183 solver.cpp:397]     Test net output #0: accuracy = 0.26348
I0407 10:02:09.275722 17183 solver.cpp:397]     Test net output #1: loss = 3.07803 (* 1 = 3.07803 loss)
I0407 10:02:09.417346 17183 solver.cpp:218] Iteration 2448 (0.833458 iter/s, 14.3979s/12 iters), loss = 2.53475
I0407 10:02:09.417385 17183 solver.cpp:237]     Train net output #0: loss = 2.53475 (* 1 = 2.53475 loss)
I0407 10:02:09.417392 17183 sgd_solver.cpp:105] Iteration 2448, lr = 0.01
I0407 10:02:13.723677 17183 solver.cpp:218] Iteration 2460 (2.78666 iter/s, 4.30623s/12 iters), loss = 2.54172
I0407 10:02:13.723737 17183 solver.cpp:237]     Train net output #0: loss = 2.54172 (* 1 = 2.54172 loss)
I0407 10:02:13.723747 17183 sgd_solver.cpp:105] Iteration 2460, lr = 0.01
I0407 10:02:18.874725 17183 solver.cpp:218] Iteration 2472 (2.32968 iter/s, 5.15092s/12 iters), loss = 2.63666
I0407 10:02:18.874788 17183 solver.cpp:237]     Train net output #0: loss = 2.63666 (* 1 = 2.63666 loss)
I0407 10:02:18.874799 17183 sgd_solver.cpp:105] Iteration 2472, lr = 0.01
I0407 10:02:23.882112 17183 solver.cpp:218] Iteration 2484 (2.39652 iter/s, 5.00726s/12 iters), loss = 1.98343
I0407 10:02:23.882282 17183 solver.cpp:237]     Train net output #0: loss = 1.98343 (* 1 = 1.98343 loss)
I0407 10:02:23.882292 17183 sgd_solver.cpp:105] Iteration 2484, lr = 0.01
I0407 10:02:29.165390 17183 solver.cpp:218] Iteration 2496 (2.27142 iter/s, 5.28304s/12 iters), loss = 2.28107
I0407 10:02:29.165436 17183 solver.cpp:237]     Train net output #0: loss = 2.28107 (* 1 = 2.28107 loss)
I0407 10:02:29.165442 17183 sgd_solver.cpp:105] Iteration 2496, lr = 0.01
I0407 10:02:34.378875 17183 solver.cpp:218] Iteration 2508 (2.30177 iter/s, 5.21337s/12 iters), loss = 2.30153
I0407 10:02:34.378927 17183 solver.cpp:237]     Train net output #0: loss = 2.30153 (* 1 = 2.30153 loss)
I0407 10:02:34.378937 17183 sgd_solver.cpp:105] Iteration 2508, lr = 0.01
I0407 10:02:39.520553 17183 solver.cpp:218] Iteration 2520 (2.33392 iter/s, 5.14156s/12 iters), loss = 2.04957
I0407 10:02:39.520598 17183 solver.cpp:237]     Train net output #0: loss = 2.04957 (* 1 = 2.04957 loss)
I0407 10:02:39.520606 17183 sgd_solver.cpp:105] Iteration 2520, lr = 0.01
I0407 10:02:42.624280 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:02:44.478077 17183 solver.cpp:218] Iteration 2532 (2.42062 iter/s, 4.95741s/12 iters), loss = 2.26588
I0407 10:02:44.478116 17183 solver.cpp:237]     Train net output #0: loss = 2.26588 (* 1 = 2.26588 loss)
I0407 10:02:44.478122 17183 sgd_solver.cpp:105] Iteration 2532, lr = 0.01
I0407 10:02:49.847723 17183 solver.cpp:218] Iteration 2544 (2.23483 iter/s, 5.36954s/12 iters), loss = 2.34032
I0407 10:02:49.847764 17183 solver.cpp:237]     Train net output #0: loss = 2.34032 (* 1 = 2.34032 loss)
I0407 10:02:49.847770 17183 sgd_solver.cpp:105] Iteration 2544, lr = 0.01
I0407 10:02:52.057236 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2550.caffemodel
I0407 10:02:55.105159 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2550.solverstate
I0407 10:02:57.402341 17183 solver.cpp:330] Iteration 2550, Testing net (#0)
I0407 10:02:57.402362 17183 net.cpp:676] Ignoring source layer train-data
I0407 10:03:00.796530 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:03:01.802660 17183 solver.cpp:397]     Test net output #0: accuracy = 0.279412
I0407 10:03:01.802698 17183 solver.cpp:397]     Test net output #1: loss = 3.05345 (* 1 = 3.05345 loss)
I0407 10:03:03.855355 17183 solver.cpp:218] Iteration 2556 (0.856688 iter/s, 14.0074s/12 iters), loss = 2.46324
I0407 10:03:03.855415 17183 solver.cpp:237]     Train net output #0: loss = 2.46324 (* 1 = 2.46324 loss)
I0407 10:03:03.855425 17183 sgd_solver.cpp:105] Iteration 2556, lr = 0.01
I0407 10:03:09.002228 17183 solver.cpp:218] Iteration 2568 (2.33157 iter/s, 5.14675s/12 iters), loss = 2.12094
I0407 10:03:09.002270 17183 solver.cpp:237]     Train net output #0: loss = 2.12094 (* 1 = 2.12094 loss)
I0407 10:03:09.002279 17183 sgd_solver.cpp:105] Iteration 2568, lr = 0.01
I0407 10:03:14.210784 17183 solver.cpp:218] Iteration 2580 (2.30395 iter/s, 5.20844s/12 iters), loss = 2.30328
I0407 10:03:14.210844 17183 solver.cpp:237]     Train net output #0: loss = 2.30328 (* 1 = 2.30328 loss)
I0407 10:03:14.210853 17183 sgd_solver.cpp:105] Iteration 2580, lr = 0.01
I0407 10:03:19.401515 17183 solver.cpp:218] Iteration 2592 (2.31187 iter/s, 5.19061s/12 iters), loss = 2.26808
I0407 10:03:19.401556 17183 solver.cpp:237]     Train net output #0: loss = 2.26808 (* 1 = 2.26808 loss)
I0407 10:03:19.401563 17183 sgd_solver.cpp:105] Iteration 2592, lr = 0.01
I0407 10:03:24.532021 17183 solver.cpp:218] Iteration 2604 (2.339 iter/s, 5.1304s/12 iters), loss = 2.38723
I0407 10:03:24.532068 17183 solver.cpp:237]     Train net output #0: loss = 2.38723 (* 1 = 2.38723 loss)
I0407 10:03:24.532074 17183 sgd_solver.cpp:105] Iteration 2604, lr = 0.01
I0407 10:03:29.790395 17183 solver.cpp:218] Iteration 2616 (2.28212 iter/s, 5.25826s/12 iters), loss = 2.13255
I0407 10:03:29.790534 17183 solver.cpp:237]     Train net output #0: loss = 2.13255 (* 1 = 2.13255 loss)
I0407 10:03:29.790542 17183 sgd_solver.cpp:105] Iteration 2616, lr = 0.01
I0407 10:03:34.814038 17183 solver.cpp:218] Iteration 2628 (2.3888 iter/s, 5.02344s/12 iters), loss = 1.99
I0407 10:03:34.814083 17183 solver.cpp:237]     Train net output #0: loss = 1.99 (* 1 = 1.99 loss)
I0407 10:03:34.814090 17183 sgd_solver.cpp:105] Iteration 2628, lr = 0.01
I0407 10:03:35.265050 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:03:40.113629 17183 solver.cpp:218] Iteration 2640 (2.26437 iter/s, 5.29948s/12 iters), loss = 2.19922
I0407 10:03:40.113674 17183 solver.cpp:237]     Train net output #0: loss = 2.19922 (* 1 = 2.19922 loss)
I0407 10:03:40.113682 17183 sgd_solver.cpp:105] Iteration 2640, lr = 0.01
I0407 10:03:44.768877 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2652.caffemodel
I0407 10:03:47.792428 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2652.solverstate
I0407 10:03:50.116482 17183 solver.cpp:330] Iteration 2652, Testing net (#0)
I0407 10:03:50.116501 17183 net.cpp:676] Ignoring source layer train-data
I0407 10:03:53.339778 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:03:54.394706 17183 solver.cpp:397]     Test net output #0: accuracy = 0.289828
I0407 10:03:54.394734 17183 solver.cpp:397]     Test net output #1: loss = 3.09958 (* 1 = 3.09958 loss)
I0407 10:03:54.531035 17183 solver.cpp:218] Iteration 2652 (0.832339 iter/s, 14.4172s/12 iters), loss = 2.4559
I0407 10:03:54.531085 17183 solver.cpp:237]     Train net output #0: loss = 2.4559 (* 1 = 2.4559 loss)
I0407 10:03:54.531092 17183 sgd_solver.cpp:105] Iteration 2652, lr = 0.01
I0407 10:03:58.585762 17183 solver.cpp:218] Iteration 2664 (2.95958 iter/s, 4.05462s/12 iters), loss = 2.22556
I0407 10:03:58.585803 17183 solver.cpp:237]     Train net output #0: loss = 2.22556 (* 1 = 2.22556 loss)
I0407 10:03:58.585809 17183 sgd_solver.cpp:105] Iteration 2664, lr = 0.01
I0407 10:04:03.685214 17183 solver.cpp:218] Iteration 2676 (2.35325 iter/s, 5.09934s/12 iters), loss = 2.0099
I0407 10:04:03.685322 17183 solver.cpp:237]     Train net output #0: loss = 2.0099 (* 1 = 2.0099 loss)
I0407 10:04:03.685333 17183 sgd_solver.cpp:105] Iteration 2676, lr = 0.01
I0407 10:04:08.971036 17183 solver.cpp:218] Iteration 2688 (2.2703 iter/s, 5.28565s/12 iters), loss = 1.93899
I0407 10:04:08.971081 17183 solver.cpp:237]     Train net output #0: loss = 1.93899 (* 1 = 1.93899 loss)
I0407 10:04:08.971088 17183 sgd_solver.cpp:105] Iteration 2688, lr = 0.01
I0407 10:04:14.038110 17183 solver.cpp:218] Iteration 2700 (2.36828 iter/s, 5.06696s/12 iters), loss = 2.19075
I0407 10:04:14.038157 17183 solver.cpp:237]     Train net output #0: loss = 2.19075 (* 1 = 2.19075 loss)
I0407 10:04:14.038165 17183 sgd_solver.cpp:105] Iteration 2700, lr = 0.01
I0407 10:04:19.169168 17183 solver.cpp:218] Iteration 2712 (2.33875 iter/s, 5.13094s/12 iters), loss = 2.24219
I0407 10:04:19.169224 17183 solver.cpp:237]     Train net output #0: loss = 2.24219 (* 1 = 2.24219 loss)
I0407 10:04:19.169232 17183 sgd_solver.cpp:105] Iteration 2712, lr = 0.01
I0407 10:04:24.371438 17183 solver.cpp:218] Iteration 2724 (2.30674 iter/s, 5.20214s/12 iters), loss = 2.57129
I0407 10:04:24.371495 17183 solver.cpp:237]     Train net output #0: loss = 2.57129 (* 1 = 2.57129 loss)
I0407 10:04:24.371505 17183 sgd_solver.cpp:105] Iteration 2724, lr = 0.01
I0407 10:04:27.116446 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:04:29.644691 17183 solver.cpp:218] Iteration 2736 (2.27569 iter/s, 5.27313s/12 iters), loss = 2.15618
I0407 10:04:29.644735 17183 solver.cpp:237]     Train net output #0: loss = 2.15618 (* 1 = 2.15618 loss)
I0407 10:04:29.644742 17183 sgd_solver.cpp:105] Iteration 2736, lr = 0.01
I0407 10:04:34.809602 17183 solver.cpp:218] Iteration 2748 (2.32342 iter/s, 5.1648s/12 iters), loss = 2.14553
I0407 10:04:34.809734 17183 solver.cpp:237]     Train net output #0: loss = 2.14553 (* 1 = 2.14553 loss)
I0407 10:04:34.809746 17183 sgd_solver.cpp:105] Iteration 2748, lr = 0.01
I0407 10:04:36.904255 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2754.caffemodel
I0407 10:04:39.931028 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2754.solverstate
I0407 10:04:42.233776 17183 solver.cpp:330] Iteration 2754, Testing net (#0)
I0407 10:04:42.233798 17183 net.cpp:676] Ignoring source layer train-data
I0407 10:04:45.324985 17183 blocking_queue.cpp:49] Waiting for data
I0407 10:04:45.555754 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:04:46.650954 17183 solver.cpp:397]     Test net output #0: accuracy = 0.291667
I0407 10:04:46.650988 17183 solver.cpp:397]     Test net output #1: loss = 3.1267 (* 1 = 3.1267 loss)
I0407 10:04:48.428969 17183 solver.cpp:218] Iteration 2760 (0.881116 iter/s, 13.6191s/12 iters), loss = 2.13846
I0407 10:04:48.429014 17183 solver.cpp:237]     Train net output #0: loss = 2.13846 (* 1 = 2.13846 loss)
I0407 10:04:48.429021 17183 sgd_solver.cpp:105] Iteration 2760, lr = 0.01
I0407 10:04:53.587978 17183 solver.cpp:218] Iteration 2772 (2.32608 iter/s, 5.15889s/12 iters), loss = 2.00872
I0407 10:04:53.588019 17183 solver.cpp:237]     Train net output #0: loss = 2.00872 (* 1 = 2.00872 loss)
I0407 10:04:53.588027 17183 sgd_solver.cpp:105] Iteration 2772, lr = 0.01
I0407 10:04:58.750988 17183 solver.cpp:218] Iteration 2784 (2.32428 iter/s, 5.1629s/12 iters), loss = 2.04535
I0407 10:04:58.751030 17183 solver.cpp:237]     Train net output #0: loss = 2.04535 (* 1 = 2.04535 loss)
I0407 10:04:58.751037 17183 sgd_solver.cpp:105] Iteration 2784, lr = 0.01
I0407 10:05:03.862161 17183 solver.cpp:218] Iteration 2796 (2.34785 iter/s, 5.11106s/12 iters), loss = 2.20868
I0407 10:05:03.862206 17183 solver.cpp:237]     Train net output #0: loss = 2.20868 (* 1 = 2.20868 loss)
I0407 10:05:03.862213 17183 sgd_solver.cpp:105] Iteration 2796, lr = 0.01
I0407 10:05:09.014668 17183 solver.cpp:218] Iteration 2808 (2.32902 iter/s, 5.15239s/12 iters), loss = 1.87988
I0407 10:05:09.014812 17183 solver.cpp:237]     Train net output #0: loss = 1.87988 (* 1 = 1.87988 loss)
I0407 10:05:09.014824 17183 sgd_solver.cpp:105] Iteration 2808, lr = 0.01
I0407 10:05:14.178679 17183 solver.cpp:218] Iteration 2820 (2.32387 iter/s, 5.1638s/12 iters), loss = 1.96404
I0407 10:05:14.178719 17183 solver.cpp:237]     Train net output #0: loss = 1.96404 (* 1 = 1.96404 loss)
I0407 10:05:14.178726 17183 sgd_solver.cpp:105] Iteration 2820, lr = 0.01
I0407 10:05:19.109717 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:05:19.441059 17183 solver.cpp:218] Iteration 2832 (2.28039 iter/s, 5.26227s/12 iters), loss = 1.96936
I0407 10:05:19.441102 17183 solver.cpp:237]     Train net output #0: loss = 1.96936 (* 1 = 1.96936 loss)
I0407 10:05:19.441109 17183 sgd_solver.cpp:105] Iteration 2832, lr = 0.01
I0407 10:05:24.325770 17183 solver.cpp:218] Iteration 2844 (2.4567 iter/s, 4.8846s/12 iters), loss = 2.13049
I0407 10:05:24.325831 17183 solver.cpp:237]     Train net output #0: loss = 2.13049 (* 1 = 2.13049 loss)
I0407 10:05:24.325842 17183 sgd_solver.cpp:105] Iteration 2844, lr = 0.01
I0407 10:05:28.948444 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2856.caffemodel
I0407 10:05:31.984757 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2856.solverstate
I0407 10:05:34.304783 17183 solver.cpp:330] Iteration 2856, Testing net (#0)
I0407 10:05:34.304805 17183 net.cpp:676] Ignoring source layer train-data
I0407 10:05:37.540134 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:05:38.657763 17183 solver.cpp:397]     Test net output #0: accuracy = 0.302696
I0407 10:05:38.657802 17183 solver.cpp:397]     Test net output #1: loss = 3.03169 (* 1 = 3.03169 loss)
I0407 10:05:38.800361 17183 solver.cpp:218] Iteration 2856 (0.829051 iter/s, 14.4744s/12 iters), loss = 2.07213
I0407 10:05:38.803387 17183 solver.cpp:237]     Train net output #0: loss = 2.07213 (* 1 = 2.07213 loss)
I0407 10:05:38.803403 17183 sgd_solver.cpp:105] Iteration 2856, lr = 0.01
I0407 10:05:42.865212 17183 solver.cpp:218] Iteration 2868 (2.95437 iter/s, 4.06178s/12 iters), loss = 2.21084
I0407 10:05:42.865339 17183 solver.cpp:237]     Train net output #0: loss = 2.21084 (* 1 = 2.21084 loss)
I0407 10:05:42.865346 17183 sgd_solver.cpp:105] Iteration 2868, lr = 0.01
I0407 10:05:48.030014 17183 solver.cpp:218] Iteration 2880 (2.32351 iter/s, 5.16461s/12 iters), loss = 1.57981
I0407 10:05:48.030062 17183 solver.cpp:237]     Train net output #0: loss = 1.57981 (* 1 = 1.57981 loss)
I0407 10:05:48.030071 17183 sgd_solver.cpp:105] Iteration 2880, lr = 0.01
I0407 10:05:53.234555 17183 solver.cpp:218] Iteration 2892 (2.30573 iter/s, 5.20443s/12 iters), loss = 2.20648
I0407 10:05:53.234601 17183 solver.cpp:237]     Train net output #0: loss = 2.20648 (* 1 = 2.20648 loss)
I0407 10:05:53.234607 17183 sgd_solver.cpp:105] Iteration 2892, lr = 0.01
I0407 10:05:58.345345 17183 solver.cpp:218] Iteration 2904 (2.34803 iter/s, 5.11067s/12 iters), loss = 2.05133
I0407 10:05:58.345388 17183 solver.cpp:237]     Train net output #0: loss = 2.05133 (* 1 = 2.05133 loss)
I0407 10:05:58.345396 17183 sgd_solver.cpp:105] Iteration 2904, lr = 0.01
I0407 10:06:03.441679 17183 solver.cpp:218] Iteration 2916 (2.35468 iter/s, 5.09622s/12 iters), loss = 1.72584
I0407 10:06:03.441727 17183 solver.cpp:237]     Train net output #0: loss = 1.72584 (* 1 = 1.72584 loss)
I0407 10:06:03.441738 17183 sgd_solver.cpp:105] Iteration 2916, lr = 0.01
I0407 10:06:08.374037 17183 solver.cpp:218] Iteration 2928 (2.43297 iter/s, 4.93225s/12 iters), loss = 2.18127
I0407 10:06:08.374083 17183 solver.cpp:237]     Train net output #0: loss = 2.18127 (* 1 = 2.18127 loss)
I0407 10:06:08.374090 17183 sgd_solver.cpp:105] Iteration 2928, lr = 0.01
I0407 10:06:10.267897 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:06:13.590720 17183 solver.cpp:218] Iteration 2940 (2.30036 iter/s, 5.21657s/12 iters), loss = 1.79257
I0407 10:06:13.590847 17183 solver.cpp:237]     Train net output #0: loss = 1.79257 (* 1 = 1.79257 loss)
I0407 10:06:13.590855 17183 sgd_solver.cpp:105] Iteration 2940, lr = 0.01
I0407 10:06:18.595013 17183 solver.cpp:218] Iteration 2952 (2.39803 iter/s, 5.0041s/12 iters), loss = 2.11507
I0407 10:06:18.595067 17183 solver.cpp:237]     Train net output #0: loss = 2.11507 (* 1 = 2.11507 loss)
I0407 10:06:18.595075 17183 sgd_solver.cpp:105] Iteration 2952, lr = 0.01
I0407 10:06:20.829280 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2958.caffemodel
I0407 10:06:23.813624 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2958.solverstate
I0407 10:06:26.119606 17183 solver.cpp:330] Iteration 2958, Testing net (#0)
I0407 10:06:26.119627 17183 net.cpp:676] Ignoring source layer train-data
I0407 10:06:29.249169 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:06:30.437016 17183 solver.cpp:397]     Test net output #0: accuracy = 0.305147
I0407 10:06:30.437047 17183 solver.cpp:397]     Test net output #1: loss = 3.06394 (* 1 = 3.06394 loss)
I0407 10:06:32.245649 17183 solver.cpp:218] Iteration 2964 (0.879093 iter/s, 13.6504s/12 iters), loss = 2.32909
I0407 10:06:32.245697 17183 solver.cpp:237]     Train net output #0: loss = 2.32909 (* 1 = 2.32909 loss)
I0407 10:06:32.245704 17183 sgd_solver.cpp:105] Iteration 2964, lr = 0.01
I0407 10:06:37.528815 17183 solver.cpp:218] Iteration 2976 (2.27142 iter/s, 5.28305s/12 iters), loss = 1.57811
I0407 10:06:37.528873 17183 solver.cpp:237]     Train net output #0: loss = 1.57811 (* 1 = 1.57811 loss)
I0407 10:06:37.528889 17183 sgd_solver.cpp:105] Iteration 2976, lr = 0.01
I0407 10:06:42.682343 17183 solver.cpp:218] Iteration 2988 (2.32856 iter/s, 5.1534s/12 iters), loss = 1.96127
I0407 10:06:42.682397 17183 solver.cpp:237]     Train net output #0: loss = 1.96127 (* 1 = 1.96127 loss)
I0407 10:06:42.682407 17183 sgd_solver.cpp:105] Iteration 2988, lr = 0.01
I0407 10:06:47.820394 17183 solver.cpp:218] Iteration 3000 (2.33557 iter/s, 5.13793s/12 iters), loss = 1.84551
I0407 10:06:47.820559 17183 solver.cpp:237]     Train net output #0: loss = 1.84551 (* 1 = 1.84551 loss)
I0407 10:06:47.820570 17183 sgd_solver.cpp:105] Iteration 3000, lr = 0.01
I0407 10:06:52.868841 17183 solver.cpp:218] Iteration 3012 (2.37708 iter/s, 5.04822s/12 iters), loss = 2.05433
I0407 10:06:52.868889 17183 solver.cpp:237]     Train net output #0: loss = 2.05433 (* 1 = 2.05433 loss)
I0407 10:06:52.868897 17183 sgd_solver.cpp:105] Iteration 3012, lr = 0.01
I0407 10:06:57.991451 17183 solver.cpp:218] Iteration 3024 (2.34261 iter/s, 5.1225s/12 iters), loss = 1.87281
I0407 10:06:57.991492 17183 solver.cpp:237]     Train net output #0: loss = 1.87281 (* 1 = 1.87281 loss)
I0407 10:06:57.991500 17183 sgd_solver.cpp:105] Iteration 3024, lr = 0.01
I0407 10:07:02.131184 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:07:03.238617 17183 solver.cpp:218] Iteration 3036 (2.287 iter/s, 5.24706s/12 iters), loss = 1.94262
I0407 10:07:03.238659 17183 solver.cpp:237]     Train net output #0: loss = 1.94262 (* 1 = 1.94262 loss)
I0407 10:07:03.238667 17183 sgd_solver.cpp:105] Iteration 3036, lr = 0.01
I0407 10:07:08.223712 17183 solver.cpp:218] Iteration 3048 (2.40723 iter/s, 4.98498s/12 iters), loss = 1.62104
I0407 10:07:08.223757 17183 solver.cpp:237]     Train net output #0: loss = 1.62104 (* 1 = 1.62104 loss)
I0407 10:07:08.223763 17183 sgd_solver.cpp:105] Iteration 3048, lr = 0.01
I0407 10:07:12.814374 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3060.caffemodel
I0407 10:07:15.887858 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3060.solverstate
I0407 10:07:18.202167 17183 solver.cpp:330] Iteration 3060, Testing net (#0)
I0407 10:07:18.202239 17183 net.cpp:676] Ignoring source layer train-data
I0407 10:07:21.294560 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:07:22.493077 17183 solver.cpp:397]     Test net output #0: accuracy = 0.310662
I0407 10:07:22.493113 17183 solver.cpp:397]     Test net output #1: loss = 3.04783 (* 1 = 3.04783 loss)
I0407 10:07:22.629998 17183 solver.cpp:218] Iteration 3060 (0.832981 iter/s, 14.4061s/12 iters), loss = 1.89414
I0407 10:07:22.630043 17183 solver.cpp:237]     Train net output #0: loss = 1.89414 (* 1 = 1.89414 loss)
I0407 10:07:22.630050 17183 sgd_solver.cpp:105] Iteration 3060, lr = 0.01
I0407 10:07:26.884171 17183 solver.cpp:218] Iteration 3072 (2.82083 iter/s, 4.25407s/12 iters), loss = 1.82064
I0407 10:07:26.884219 17183 solver.cpp:237]     Train net output #0: loss = 1.82064 (* 1 = 1.82064 loss)
I0407 10:07:26.884227 17183 sgd_solver.cpp:105] Iteration 3072, lr = 0.01
I0407 10:07:32.050166 17183 solver.cpp:218] Iteration 3084 (2.32294 iter/s, 5.16587s/12 iters), loss = 1.80069
I0407 10:07:32.050211 17183 solver.cpp:237]     Train net output #0: loss = 1.80069 (* 1 = 1.80069 loss)
I0407 10:07:32.050218 17183 sgd_solver.cpp:105] Iteration 3084, lr = 0.01
I0407 10:07:37.224099 17183 solver.cpp:218] Iteration 3096 (2.31937 iter/s, 5.17382s/12 iters), loss = 1.83894
I0407 10:07:37.224144 17183 solver.cpp:237]     Train net output #0: loss = 1.83894 (* 1 = 1.83894 loss)
I0407 10:07:37.224151 17183 sgd_solver.cpp:105] Iteration 3096, lr = 0.01
I0407 10:07:42.411152 17183 solver.cpp:218] Iteration 3108 (2.3135 iter/s, 5.18694s/12 iters), loss = 1.71658
I0407 10:07:42.411206 17183 solver.cpp:237]     Train net output #0: loss = 1.71658 (* 1 = 1.71658 loss)
I0407 10:07:42.411216 17183 sgd_solver.cpp:105] Iteration 3108, lr = 0.01
I0407 10:07:47.424216 17183 solver.cpp:218] Iteration 3120 (2.3938 iter/s, 5.01294s/12 iters), loss = 1.82585
I0407 10:07:47.424261 17183 solver.cpp:237]     Train net output #0: loss = 1.82585 (* 1 = 1.82585 loss)
I0407 10:07:47.424268 17183 sgd_solver.cpp:105] Iteration 3120, lr = 0.01
I0407 10:07:52.660461 17183 solver.cpp:218] Iteration 3132 (2.29177 iter/s, 5.23613s/12 iters), loss = 1.82511
I0407 10:07:52.660616 17183 solver.cpp:237]     Train net output #0: loss = 1.82511 (* 1 = 1.82511 loss)
I0407 10:07:52.660627 17183 sgd_solver.cpp:105] Iteration 3132, lr = 0.01
I0407 10:07:53.781437 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:07:57.899796 17183 solver.cpp:218] Iteration 3144 (2.29046 iter/s, 5.23911s/12 iters), loss = 1.8693
I0407 10:07:57.899838 17183 solver.cpp:237]     Train net output #0: loss = 1.8693 (* 1 = 1.8693 loss)
I0407 10:07:57.899845 17183 sgd_solver.cpp:105] Iteration 3144, lr = 0.01
I0407 10:08:03.112880 17183 solver.cpp:218] Iteration 3156 (2.30195 iter/s, 5.21297s/12 iters), loss = 1.76339
I0407 10:08:03.112939 17183 solver.cpp:237]     Train net output #0: loss = 1.76339 (* 1 = 1.76339 loss)
I0407 10:08:03.112949 17183 sgd_solver.cpp:105] Iteration 3156, lr = 0.01
I0407 10:08:05.199622 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3162.caffemodel
I0407 10:08:08.227169 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3162.solverstate
I0407 10:08:10.545219 17183 solver.cpp:330] Iteration 3162, Testing net (#0)
I0407 10:08:10.545240 17183 net.cpp:676] Ignoring source layer train-data
I0407 10:08:13.624083 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:08:14.867118 17183 solver.cpp:397]     Test net output #0: accuracy = 0.320466
I0407 10:08:14.867156 17183 solver.cpp:397]     Test net output #1: loss = 2.80917 (* 1 = 2.80917 loss)
I0407 10:08:16.674572 17183 solver.cpp:218] Iteration 3168 (0.884859 iter/s, 13.5615s/12 iters), loss = 1.87324
I0407 10:08:16.674614 17183 solver.cpp:237]     Train net output #0: loss = 1.87324 (* 1 = 1.87324 loss)
I0407 10:08:16.674621 17183 sgd_solver.cpp:105] Iteration 3168, lr = 0.01
I0407 10:08:21.771212 17183 solver.cpp:218] Iteration 3180 (2.35454 iter/s, 5.09653s/12 iters), loss = 1.92472
I0407 10:08:21.771255 17183 solver.cpp:237]     Train net output #0: loss = 1.92472 (* 1 = 1.92472 loss)
I0407 10:08:21.771261 17183 sgd_solver.cpp:105] Iteration 3180, lr = 0.01
I0407 10:08:26.966935 17183 solver.cpp:218] Iteration 3192 (2.30964 iter/s, 5.19561s/12 iters), loss = 2.0196
I0407 10:08:26.967027 17183 solver.cpp:237]     Train net output #0: loss = 2.0196 (* 1 = 2.0196 loss)
I0407 10:08:26.967036 17183 sgd_solver.cpp:105] Iteration 3192, lr = 0.01
I0407 10:08:32.128837 17183 solver.cpp:218] Iteration 3204 (2.3248 iter/s, 5.16174s/12 iters), loss = 1.65546
I0407 10:08:32.128899 17183 solver.cpp:237]     Train net output #0: loss = 1.65546 (* 1 = 1.65546 loss)
I0407 10:08:32.128912 17183 sgd_solver.cpp:105] Iteration 3204, lr = 0.01
I0407 10:08:37.373157 17183 solver.cpp:218] Iteration 3216 (2.28824 iter/s, 5.2442s/12 iters), loss = 1.85165
I0407 10:08:37.373209 17183 solver.cpp:237]     Train net output #0: loss = 1.85165 (* 1 = 1.85165 loss)
I0407 10:08:37.373219 17183 sgd_solver.cpp:105] Iteration 3216, lr = 0.01
I0407 10:08:42.428428 17183 solver.cpp:218] Iteration 3228 (2.37381 iter/s, 5.05516s/12 iters), loss = 1.71253
I0407 10:08:42.428468 17183 solver.cpp:237]     Train net output #0: loss = 1.71253 (* 1 = 1.71253 loss)
I0407 10:08:42.428475 17183 sgd_solver.cpp:105] Iteration 3228, lr = 0.01
I0407 10:08:45.585083 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:08:47.393229 17183 solver.cpp:218] Iteration 3240 (2.41707 iter/s, 4.96469s/12 iters), loss = 1.69964
I0407 10:08:47.393268 17183 solver.cpp:237]     Train net output #0: loss = 1.69964 (* 1 = 1.69964 loss)
I0407 10:08:47.393275 17183 sgd_solver.cpp:105] Iteration 3240, lr = 0.01
I0407 10:08:52.703002 17183 solver.cpp:218] Iteration 3252 (2.26003 iter/s, 5.30966s/12 iters), loss = 1.71023
I0407 10:08:52.703061 17183 solver.cpp:237]     Train net output #0: loss = 1.71023 (* 1 = 1.71023 loss)
I0407 10:08:52.703071 17183 sgd_solver.cpp:105] Iteration 3252, lr = 0.01
I0407 10:08:57.376361 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3264.caffemodel
I0407 10:09:00.315748 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3264.solverstate
I0407 10:09:03.696338 17183 solver.cpp:330] Iteration 3264, Testing net (#0)
I0407 10:09:03.696364 17183 net.cpp:676] Ignoring source layer train-data
I0407 10:09:06.822008 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:09:08.143457 17183 solver.cpp:397]     Test net output #0: accuracy = 0.327819
I0407 10:09:08.143488 17183 solver.cpp:397]     Test net output #1: loss = 2.9066 (* 1 = 2.9066 loss)
I0407 10:09:08.285216 17183 solver.cpp:218] Iteration 3264 (0.77012 iter/s, 15.582s/12 iters), loss = 1.71003
I0407 10:09:08.285264 17183 solver.cpp:237]     Train net output #0: loss = 1.71003 (* 1 = 1.71003 loss)
I0407 10:09:08.285271 17183 sgd_solver.cpp:105] Iteration 3264, lr = 0.01
I0407 10:09:12.521534 17183 solver.cpp:218] Iteration 3276 (2.83272 iter/s, 4.23621s/12 iters), loss = 1.51897
I0407 10:09:12.521589 17183 solver.cpp:237]     Train net output #0: loss = 1.51897 (* 1 = 1.51897 loss)
I0407 10:09:12.521597 17183 sgd_solver.cpp:105] Iteration 3276, lr = 0.01
I0407 10:09:17.590492 17183 solver.cpp:218] Iteration 3288 (2.3674 iter/s, 5.06884s/12 iters), loss = 1.57043
I0407 10:09:17.590544 17183 solver.cpp:237]     Train net output #0: loss = 1.57043 (* 1 = 1.57043 loss)
I0407 10:09:17.590553 17183 sgd_solver.cpp:105] Iteration 3288, lr = 0.01
I0407 10:09:22.772508 17183 solver.cpp:218] Iteration 3300 (2.31576 iter/s, 5.18189s/12 iters), loss = 1.72871
I0407 10:09:22.772563 17183 solver.cpp:237]     Train net output #0: loss = 1.72871 (* 1 = 1.72871 loss)
I0407 10:09:22.772573 17183 sgd_solver.cpp:105] Iteration 3300, lr = 0.01
I0407 10:09:28.001161 17183 solver.cpp:218] Iteration 3312 (2.2951 iter/s, 5.22853s/12 iters), loss = 1.92742
I0407 10:09:28.001267 17183 solver.cpp:237]     Train net output #0: loss = 1.92742 (* 1 = 1.92742 loss)
I0407 10:09:28.001276 17183 sgd_solver.cpp:105] Iteration 3312, lr = 0.01
I0407 10:09:32.987623 17183 solver.cpp:218] Iteration 3324 (2.4066 iter/s, 4.98629s/12 iters), loss = 1.78495
I0407 10:09:32.987675 17183 solver.cpp:237]     Train net output #0: loss = 1.78495 (* 1 = 1.78495 loss)
I0407 10:09:32.987684 17183 sgd_solver.cpp:105] Iteration 3324, lr = 0.01
I0407 10:09:38.164166 17183 solver.cpp:218] Iteration 3336 (2.3182 iter/s, 5.17642s/12 iters), loss = 1.58932
I0407 10:09:38.164204 17183 solver.cpp:237]     Train net output #0: loss = 1.58932 (* 1 = 1.58932 loss)
I0407 10:09:38.164211 17183 sgd_solver.cpp:105] Iteration 3336, lr = 0.01
I0407 10:09:38.662662 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:09:43.411368 17183 solver.cpp:218] Iteration 3348 (2.28698 iter/s, 5.24709s/12 iters), loss = 1.6632
I0407 10:09:43.411412 17183 solver.cpp:237]     Train net output #0: loss = 1.6632 (* 1 = 1.6632 loss)
I0407 10:09:43.411419 17183 sgd_solver.cpp:105] Iteration 3348, lr = 0.01
I0407 10:09:48.609447 17183 solver.cpp:218] Iteration 3360 (2.3086 iter/s, 5.19796s/12 iters), loss = 1.77603
I0407 10:09:48.609504 17183 solver.cpp:237]     Train net output #0: loss = 1.77603 (* 1 = 1.77603 loss)
I0407 10:09:48.609514 17183 sgd_solver.cpp:105] Iteration 3360, lr = 0.01
I0407 10:09:50.692361 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3366.caffemodel
I0407 10:09:54.754874 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3366.solverstate
I0407 10:09:57.786700 17183 solver.cpp:330] Iteration 3366, Testing net (#0)
I0407 10:09:57.786720 17183 net.cpp:676] Ignoring source layer train-data
I0407 10:10:00.789830 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:10:02.180903 17183 solver.cpp:397]     Test net output #0: accuracy = 0.313726
I0407 10:10:02.180932 17183 solver.cpp:397]     Test net output #1: loss = 3.09423 (* 1 = 3.09423 loss)
I0407 10:10:03.968730 17183 solver.cpp:218] Iteration 3372 (0.781298 iter/s, 15.3591s/12 iters), loss = 2.67122
I0407 10:10:03.968773 17183 solver.cpp:237]     Train net output #0: loss = 2.67122 (* 1 = 2.67122 loss)
I0407 10:10:03.968781 17183 sgd_solver.cpp:105] Iteration 3372, lr = 0.0025
I0407 10:10:09.017493 17183 solver.cpp:218] Iteration 3384 (2.37687 iter/s, 5.04866s/12 iters), loss = 1.59515
I0407 10:10:09.017531 17183 solver.cpp:237]     Train net output #0: loss = 1.59515 (* 1 = 1.59515 loss)
I0407 10:10:09.017539 17183 sgd_solver.cpp:105] Iteration 3384, lr = 0.0025
I0407 10:10:14.169629 17183 solver.cpp:218] Iteration 3396 (2.32918 iter/s, 5.15203s/12 iters), loss = 1.75154
I0407 10:10:14.169674 17183 solver.cpp:237]     Train net output #0: loss = 1.75154 (* 1 = 1.75154 loss)
I0407 10:10:14.169682 17183 sgd_solver.cpp:105] Iteration 3396, lr = 0.0025
I0407 10:10:19.067953 17183 solver.cpp:218] Iteration 3408 (2.44988 iter/s, 4.89821s/12 iters), loss = 1.28764
I0407 10:10:19.067997 17183 solver.cpp:237]     Train net output #0: loss = 1.28764 (* 1 = 1.28764 loss)
I0407 10:10:19.068004 17183 sgd_solver.cpp:105] Iteration 3408, lr = 0.0025
I0407 10:10:24.350530 17183 solver.cpp:218] Iteration 3420 (2.27167 iter/s, 5.28246s/12 iters), loss = 1.1938
I0407 10:10:24.350569 17183 solver.cpp:237]     Train net output #0: loss = 1.1938 (* 1 = 1.1938 loss)
I0407 10:10:24.350576 17183 sgd_solver.cpp:105] Iteration 3420, lr = 0.0025
I0407 10:10:29.456220 17183 solver.cpp:218] Iteration 3432 (2.35037 iter/s, 5.10558s/12 iters), loss = 1.21263
I0407 10:10:29.456264 17183 solver.cpp:237]     Train net output #0: loss = 1.21263 (* 1 = 1.21263 loss)
I0407 10:10:29.456271 17183 sgd_solver.cpp:105] Iteration 3432, lr = 0.0025
I0407 10:10:32.252969 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:10:34.788952 17183 solver.cpp:218] Iteration 3444 (2.2503 iter/s, 5.33262s/12 iters), loss = 1.15806
I0407 10:10:34.788993 17183 solver.cpp:237]     Train net output #0: loss = 1.15806 (* 1 = 1.15806 loss)
I0407 10:10:34.789000 17183 sgd_solver.cpp:105] Iteration 3444, lr = 0.0025
I0407 10:10:39.776813 17183 solver.cpp:218] Iteration 3456 (2.40589 iter/s, 4.98775s/12 iters), loss = 1.12229
I0407 10:10:39.776855 17183 solver.cpp:237]     Train net output #0: loss = 1.12229 (* 1 = 1.12229 loss)
I0407 10:10:39.776862 17183 sgd_solver.cpp:105] Iteration 3456, lr = 0.0025
I0407 10:10:44.288223 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3468.caffemodel
I0407 10:10:47.838124 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3468.solverstate
I0407 10:10:51.295894 17183 solver.cpp:330] Iteration 3468, Testing net (#0)
I0407 10:10:51.295914 17183 net.cpp:676] Ignoring source layer train-data
I0407 10:10:51.705919 17183 blocking_queue.cpp:49] Waiting for data
I0407 10:10:54.252835 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:10:55.608388 17183 solver.cpp:397]     Test net output #0: accuracy = 0.393995
I0407 10:10:55.608428 17183 solver.cpp:397]     Test net output #1: loss = 2.61836 (* 1 = 2.61836 loss)
I0407 10:10:55.744931 17183 solver.cpp:218] Iteration 3468 (0.751508 iter/s, 15.9679s/12 iters), loss = 1.28307
I0407 10:10:55.744990 17183 solver.cpp:237]     Train net output #0: loss = 1.28307 (* 1 = 1.28307 loss)
I0407 10:10:55.744999 17183 sgd_solver.cpp:105] Iteration 3468, lr = 0.0025
I0407 10:11:00.228626 17183 solver.cpp:218] Iteration 3480 (2.67644 iter/s, 4.48357s/12 iters), loss = 1.45224
I0407 10:11:00.228668 17183 solver.cpp:237]     Train net output #0: loss = 1.45224 (* 1 = 1.45224 loss)
I0407 10:11:00.228675 17183 sgd_solver.cpp:105] Iteration 3480, lr = 0.0025
I0407 10:11:05.389381 17183 solver.cpp:218] Iteration 3492 (2.32529 iter/s, 5.16064s/12 iters), loss = 0.943942
I0407 10:11:05.389534 17183 solver.cpp:237]     Train net output #0: loss = 0.943942 (* 1 = 0.943942 loss)
I0407 10:11:05.389544 17183 sgd_solver.cpp:105] Iteration 3492, lr = 0.0025
I0407 10:11:10.460686 17183 solver.cpp:218] Iteration 3504 (2.36636 iter/s, 5.07109s/12 iters), loss = 1.02462
I0407 10:11:10.460738 17183 solver.cpp:237]     Train net output #0: loss = 1.02462 (* 1 = 1.02462 loss)
I0407 10:11:10.460748 17183 sgd_solver.cpp:105] Iteration 3504, lr = 0.0025
I0407 10:11:15.519251 17183 solver.cpp:218] Iteration 3516 (2.37227 iter/s, 5.05844s/12 iters), loss = 0.984118
I0407 10:11:15.519294 17183 solver.cpp:237]     Train net output #0: loss = 0.984118 (* 1 = 0.984118 loss)
I0407 10:11:15.519302 17183 sgd_solver.cpp:105] Iteration 3516, lr = 0.0025
I0407 10:11:20.604281 17183 solver.cpp:218] Iteration 3528 (2.35992 iter/s, 5.08491s/12 iters), loss = 0.977207
I0407 10:11:20.604326 17183 solver.cpp:237]     Train net output #0: loss = 0.977207 (* 1 = 0.977207 loss)
I0407 10:11:20.604333 17183 sgd_solver.cpp:105] Iteration 3528, lr = 0.0025
I0407 10:11:25.587393 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:11:25.877672 17183 solver.cpp:218] Iteration 3540 (2.27562 iter/s, 5.27328s/12 iters), loss = 0.740473
I0407 10:11:25.877714 17183 solver.cpp:237]     Train net output #0: loss = 0.740473 (* 1 = 0.740473 loss)
I0407 10:11:25.877722 17183 sgd_solver.cpp:105] Iteration 3540, lr = 0.0025
I0407 10:11:31.158681 17183 solver.cpp:218] Iteration 3552 (2.27234 iter/s, 5.2809s/12 iters), loss = 0.881177
I0407 10:11:31.158723 17183 solver.cpp:237]     Train net output #0: loss = 0.881177 (* 1 = 0.881177 loss)
I0407 10:11:31.158730 17183 sgd_solver.cpp:105] Iteration 3552, lr = 0.0025
I0407 10:11:36.208832 17183 solver.cpp:218] Iteration 3564 (2.37622 iter/s, 5.05004s/12 iters), loss = 0.836472
I0407 10:11:36.208957 17183 solver.cpp:237]     Train net output #0: loss = 0.836472 (* 1 = 0.836472 loss)
I0407 10:11:36.208967 17183 sgd_solver.cpp:105] Iteration 3564, lr = 0.0025
I0407 10:11:38.237344 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3570.caffemodel
I0407 10:11:41.239814 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3570.solverstate
I0407 10:11:43.537638 17183 solver.cpp:330] Iteration 3570, Testing net (#0)
I0407 10:11:43.537658 17183 net.cpp:676] Ignoring source layer train-data
I0407 10:11:46.551434 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:11:48.049095 17183 solver.cpp:397]     Test net output #0: accuracy = 0.405637
I0407 10:11:48.049140 17183 solver.cpp:397]     Test net output #1: loss = 2.62035 (* 1 = 2.62035 loss)
I0407 10:11:49.909406 17183 solver.cpp:218] Iteration 3576 (0.875893 iter/s, 13.7003s/12 iters), loss = 1.04673
I0407 10:11:49.909448 17183 solver.cpp:237]     Train net output #0: loss = 1.04673 (* 1 = 1.04673 loss)
I0407 10:11:49.909456 17183 sgd_solver.cpp:105] Iteration 3576, lr = 0.0025
I0407 10:11:54.936431 17183 solver.cpp:218] Iteration 3588 (2.38715 iter/s, 5.02692s/12 iters), loss = 1.00992
I0407 10:11:54.936470 17183 solver.cpp:237]     Train net output #0: loss = 1.00992 (* 1 = 1.00992 loss)
I0407 10:11:54.936477 17183 sgd_solver.cpp:105] Iteration 3588, lr = 0.0025
I0407 10:12:00.149516 17183 solver.cpp:218] Iteration 3600 (2.30195 iter/s, 5.21297s/12 iters), loss = 0.838173
I0407 10:12:00.149581 17183 solver.cpp:237]     Train net output #0: loss = 0.838173 (* 1 = 0.838173 loss)
I0407 10:12:00.149592 17183 sgd_solver.cpp:105] Iteration 3600, lr = 0.0025
I0407 10:12:05.312902 17183 solver.cpp:218] Iteration 3612 (2.32412 iter/s, 5.16325s/12 iters), loss = 0.628365
I0407 10:12:05.312945 17183 solver.cpp:237]     Train net output #0: loss = 0.628365 (* 1 = 0.628365 loss)
I0407 10:12:05.312952 17183 sgd_solver.cpp:105] Iteration 3612, lr = 0.0025
I0407 10:12:10.425918 17183 solver.cpp:218] Iteration 3624 (2.347 iter/s, 5.11291s/12 iters), loss = 0.782762
I0407 10:12:10.426059 17183 solver.cpp:237]     Train net output #0: loss = 0.782762 (* 1 = 0.782762 loss)
I0407 10:12:10.426067 17183 sgd_solver.cpp:105] Iteration 3624, lr = 0.0025
I0407 10:12:15.464936 17183 solver.cpp:218] Iteration 3636 (2.38151 iter/s, 5.03881s/12 iters), loss = 0.782921
I0407 10:12:15.464982 17183 solver.cpp:237]     Train net output #0: loss = 0.782921 (* 1 = 0.782921 loss)
I0407 10:12:15.464989 17183 sgd_solver.cpp:105] Iteration 3636, lr = 0.0025
I0407 10:12:17.284198 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:12:20.525005 17183 solver.cpp:218] Iteration 3648 (2.37156 iter/s, 5.05996s/12 iters), loss = 0.594459
I0407 10:12:20.525066 17183 solver.cpp:237]     Train net output #0: loss = 0.594459 (* 1 = 0.594459 loss)
I0407 10:12:20.525076 17183 sgd_solver.cpp:105] Iteration 3648, lr = 0.0025
I0407 10:12:25.691985 17183 solver.cpp:218] Iteration 3660 (2.3225 iter/s, 5.16686s/12 iters), loss = 0.746011
I0407 10:12:25.692030 17183 solver.cpp:237]     Train net output #0: loss = 0.746011 (* 1 = 0.746011 loss)
I0407 10:12:25.692039 17183 sgd_solver.cpp:105] Iteration 3660, lr = 0.0025
I0407 10:12:30.416465 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3672.caffemodel
I0407 10:12:33.445029 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3672.solverstate
I0407 10:12:37.377777 17183 solver.cpp:330] Iteration 3672, Testing net (#0)
I0407 10:12:37.377801 17183 net.cpp:676] Ignoring source layer train-data
I0407 10:12:40.203860 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:12:41.643488 17183 solver.cpp:397]     Test net output #0: accuracy = 0.421569
I0407 10:12:41.643596 17183 solver.cpp:397]     Test net output #1: loss = 2.60361 (* 1 = 2.60361 loss)
I0407 10:12:41.781692 17183 solver.cpp:218] Iteration 3672 (0.745829 iter/s, 16.0895s/12 iters), loss = 0.631886
I0407 10:12:41.781751 17183 solver.cpp:237]     Train net output #0: loss = 0.631886 (* 1 = 0.631886 loss)
I0407 10:12:41.781764 17183 sgd_solver.cpp:105] Iteration 3672, lr = 0.0025
I0407 10:12:46.068361 17183 solver.cpp:218] Iteration 3684 (2.79946 iter/s, 4.28654s/12 iters), loss = 0.826152
I0407 10:12:46.068423 17183 solver.cpp:237]     Train net output #0: loss = 0.826152 (* 1 = 0.826152 loss)
I0407 10:12:46.068434 17183 sgd_solver.cpp:105] Iteration 3684, lr = 0.0025
I0407 10:12:50.997898 17183 solver.cpp:218] Iteration 3696 (2.43437 iter/s, 4.92941s/12 iters), loss = 0.640615
I0407 10:12:50.997953 17183 solver.cpp:237]     Train net output #0: loss = 0.640615 (* 1 = 0.640615 loss)
I0407 10:12:50.997964 17183 sgd_solver.cpp:105] Iteration 3696, lr = 0.0025
I0407 10:12:56.044075 17183 solver.cpp:218] Iteration 3708 (2.3781 iter/s, 5.04605s/12 iters), loss = 0.462328
I0407 10:12:56.044117 17183 solver.cpp:237]     Train net output #0: loss = 0.462328 (* 1 = 0.462328 loss)
I0407 10:12:56.044126 17183 sgd_solver.cpp:105] Iteration 3708, lr = 0.0025
I0407 10:13:01.191921 17183 solver.cpp:218] Iteration 3720 (2.33112 iter/s, 5.14773s/12 iters), loss = 0.831446
I0407 10:13:01.191983 17183 solver.cpp:237]     Train net output #0: loss = 0.831446 (* 1 = 0.831446 loss)
I0407 10:13:01.191992 17183 sgd_solver.cpp:105] Iteration 3720, lr = 0.0025
I0407 10:13:06.467835 17183 solver.cpp:218] Iteration 3732 (2.27454 iter/s, 5.27578s/12 iters), loss = 0.804265
I0407 10:13:06.467888 17183 solver.cpp:237]     Train net output #0: loss = 0.804265 (* 1 = 0.804265 loss)
I0407 10:13:06.467897 17183 sgd_solver.cpp:105] Iteration 3732, lr = 0.0025
I0407 10:13:10.581534 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:13:11.657810 17183 solver.cpp:218] Iteration 3744 (2.3122 iter/s, 5.18986s/12 iters), loss = 0.556356
I0407 10:13:11.657917 17183 solver.cpp:237]     Train net output #0: loss = 0.556356 (* 1 = 0.556356 loss)
I0407 10:13:11.657925 17183 sgd_solver.cpp:105] Iteration 3744, lr = 0.0025
I0407 10:13:16.797057 17183 solver.cpp:218] Iteration 3756 (2.33505 iter/s, 5.13907s/12 iters), loss = 0.881564
I0407 10:13:16.797103 17183 solver.cpp:237]     Train net output #0: loss = 0.881564 (* 1 = 0.881564 loss)
I0407 10:13:16.797111 17183 sgd_solver.cpp:105] Iteration 3756, lr = 0.0025
I0407 10:13:22.019157 17183 solver.cpp:218] Iteration 3768 (2.29798 iter/s, 5.22198s/12 iters), loss = 0.709457
I0407 10:13:22.019208 17183 solver.cpp:237]     Train net output #0: loss = 0.709457 (* 1 = 0.709457 loss)
I0407 10:13:22.019217 17183 sgd_solver.cpp:105] Iteration 3768, lr = 0.0025
I0407 10:13:24.001451 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3774.caffemodel
I0407 10:13:28.173316 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3774.solverstate
I0407 10:13:30.846745 17183 solver.cpp:330] Iteration 3774, Testing net (#0)
I0407 10:13:30.846769 17183 net.cpp:676] Ignoring source layer train-data
I0407 10:13:33.748576 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:13:35.255475 17183 solver.cpp:397]     Test net output #0: accuracy = 0.443015
I0407 10:13:35.255506 17183 solver.cpp:397]     Test net output #1: loss = 2.51588 (* 1 = 2.51588 loss)
I0407 10:13:37.192574 17183 solver.cpp:218] Iteration 3780 (0.790868 iter/s, 15.1732s/12 iters), loss = 0.573203
I0407 10:13:37.192627 17183 solver.cpp:237]     Train net output #0: loss = 0.573203 (* 1 = 0.573203 loss)
I0407 10:13:37.192636 17183 sgd_solver.cpp:105] Iteration 3780, lr = 0.0025
I0407 10:13:42.366313 17183 solver.cpp:218] Iteration 3792 (2.31946 iter/s, 5.17362s/12 iters), loss = 0.505776
I0407 10:13:42.366797 17183 solver.cpp:237]     Train net output #0: loss = 0.505776 (* 1 = 0.505776 loss)
I0407 10:13:42.366808 17183 sgd_solver.cpp:105] Iteration 3792, lr = 0.0025
I0407 10:13:47.491988 17183 solver.cpp:218] Iteration 3804 (2.34141 iter/s, 5.12512s/12 iters), loss = 0.567716
I0407 10:13:47.492035 17183 solver.cpp:237]     Train net output #0: loss = 0.567716 (* 1 = 0.567716 loss)
I0407 10:13:47.492043 17183 sgd_solver.cpp:105] Iteration 3804, lr = 0.0025
I0407 10:13:52.586012 17183 solver.cpp:218] Iteration 3816 (2.35576 iter/s, 5.0939s/12 iters), loss = 0.829055
I0407 10:13:52.586076 17183 solver.cpp:237]     Train net output #0: loss = 0.829055 (* 1 = 0.829055 loss)
I0407 10:13:52.586086 17183 sgd_solver.cpp:105] Iteration 3816, lr = 0.0025
I0407 10:13:57.667217 17183 solver.cpp:218] Iteration 3828 (2.36171 iter/s, 5.08107s/12 iters), loss = 0.891977
I0407 10:13:57.667280 17183 solver.cpp:237]     Train net output #0: loss = 0.891977 (* 1 = 0.891977 loss)
I0407 10:13:57.667291 17183 sgd_solver.cpp:105] Iteration 3828, lr = 0.0025
I0407 10:14:02.876487 17183 solver.cpp:218] Iteration 3840 (2.30364 iter/s, 5.20914s/12 iters), loss = 0.659003
I0407 10:14:02.876528 17183 solver.cpp:237]     Train net output #0: loss = 0.659003 (* 1 = 0.659003 loss)
I0407 10:14:02.876536 17183 sgd_solver.cpp:105] Iteration 3840, lr = 0.0025
I0407 10:14:04.042191 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:14:08.192857 17183 solver.cpp:218] Iteration 3852 (2.25723 iter/s, 5.31626s/12 iters), loss = 0.524741
I0407 10:14:08.192914 17183 solver.cpp:237]     Train net output #0: loss = 0.524741 (* 1 = 0.524741 loss)
I0407 10:14:08.192924 17183 sgd_solver.cpp:105] Iteration 3852, lr = 0.0025
I0407 10:14:13.454485 17183 solver.cpp:218] Iteration 3864 (2.28072 iter/s, 5.2615s/12 iters), loss = 0.794476
I0407 10:14:13.454649 17183 solver.cpp:237]     Train net output #0: loss = 0.794476 (* 1 = 0.794476 loss)
I0407 10:14:13.454660 17183 sgd_solver.cpp:105] Iteration 3864, lr = 0.0025
I0407 10:14:18.170958 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3876.caffemodel
I0407 10:14:21.195945 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3876.solverstate
I0407 10:14:23.505106 17183 solver.cpp:330] Iteration 3876, Testing net (#0)
I0407 10:14:23.505127 17183 net.cpp:676] Ignoring source layer train-data
I0407 10:14:26.276165 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:14:27.790833 17183 solver.cpp:397]     Test net output #0: accuracy = 0.439338
I0407 10:14:27.790868 17183 solver.cpp:397]     Test net output #1: loss = 2.59356 (* 1 = 2.59356 loss)
I0407 10:14:27.932307 17183 solver.cpp:218] Iteration 3876 (0.828872 iter/s, 14.4775s/12 iters), loss = 0.693466
I0407 10:14:27.932358 17183 solver.cpp:237]     Train net output #0: loss = 0.693466 (* 1 = 0.693466 loss)
I0407 10:14:27.932368 17183 sgd_solver.cpp:105] Iteration 3876, lr = 0.0025
I0407 10:14:32.125921 17183 solver.cpp:218] Iteration 3888 (2.86157 iter/s, 4.1935s/12 iters), loss = 0.869559
I0407 10:14:32.125964 17183 solver.cpp:237]     Train net output #0: loss = 0.869559 (* 1 = 0.869559 loss)
I0407 10:14:32.125972 17183 sgd_solver.cpp:105] Iteration 3888, lr = 0.0025
I0407 10:14:37.035310 17183 solver.cpp:218] Iteration 3900 (2.44435 iter/s, 4.90927s/12 iters), loss = 0.604007
I0407 10:14:37.035364 17183 solver.cpp:237]     Train net output #0: loss = 0.604007 (* 1 = 0.604007 loss)
I0407 10:14:37.035374 17183 sgd_solver.cpp:105] Iteration 3900, lr = 0.0025
I0407 10:14:42.242249 17183 solver.cpp:218] Iteration 3912 (2.30467 iter/s, 5.20682s/12 iters), loss = 0.451595
I0407 10:14:42.242298 17183 solver.cpp:237]     Train net output #0: loss = 0.451595 (* 1 = 0.451595 loss)
I0407 10:14:42.242305 17183 sgd_solver.cpp:105] Iteration 3912, lr = 0.0025
I0407 10:14:47.252440 17183 solver.cpp:218] Iteration 3924 (2.39517 iter/s, 5.01008s/12 iters), loss = 0.738555
I0407 10:14:47.252578 17183 solver.cpp:237]     Train net output #0: loss = 0.738555 (* 1 = 0.738555 loss)
I0407 10:14:47.252585 17183 sgd_solver.cpp:105] Iteration 3924, lr = 0.0025
I0407 10:14:52.365129 17183 solver.cpp:218] Iteration 3936 (2.34719 iter/s, 5.11249s/12 iters), loss = 0.424005
I0407 10:14:52.365173 17183 solver.cpp:237]     Train net output #0: loss = 0.424005 (* 1 = 0.424005 loss)
I0407 10:14:52.365180 17183 sgd_solver.cpp:105] Iteration 3936, lr = 0.0025
I0407 10:14:55.690486 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:14:57.399653 17183 solver.cpp:218] Iteration 3948 (2.3836 iter/s, 5.03441s/12 iters), loss = 0.548268
I0407 10:14:57.399713 17183 solver.cpp:237]     Train net output #0: loss = 0.548268 (* 1 = 0.548268 loss)
I0407 10:14:57.399722 17183 sgd_solver.cpp:105] Iteration 3948, lr = 0.0025
I0407 10:15:02.334431 17183 solver.cpp:218] Iteration 3960 (2.43178 iter/s, 4.93465s/12 iters), loss = 0.501915
I0407 10:15:02.334479 17183 solver.cpp:237]     Train net output #0: loss = 0.501915 (* 1 = 0.501915 loss)
I0407 10:15:02.334486 17183 sgd_solver.cpp:105] Iteration 3960, lr = 0.0025
I0407 10:15:07.558970 17183 solver.cpp:218] Iteration 3972 (2.29691 iter/s, 5.22442s/12 iters), loss = 0.579525
I0407 10:15:07.559031 17183 solver.cpp:237]     Train net output #0: loss = 0.579525 (* 1 = 0.579525 loss)
I0407 10:15:07.559041 17183 sgd_solver.cpp:105] Iteration 3972, lr = 0.0025
I0407 10:15:09.657516 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3978.caffemodel
I0407 10:15:12.687501 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3978.solverstate
I0407 10:15:15.028405 17183 solver.cpp:330] Iteration 3978, Testing net (#0)
I0407 10:15:15.028424 17183 net.cpp:676] Ignoring source layer train-data
I0407 10:15:17.856566 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:15:19.410315 17183 solver.cpp:397]     Test net output #0: accuracy = 0.438113
I0407 10:15:19.410342 17183 solver.cpp:397]     Test net output #1: loss = 2.6544 (* 1 = 2.6544 loss)
I0407 10:15:21.299437 17183 solver.cpp:218] Iteration 3984 (0.873347 iter/s, 13.7403s/12 iters), loss = 0.673105
I0407 10:15:21.299489 17183 solver.cpp:237]     Train net output #0: loss = 0.673105 (* 1 = 0.673105 loss)
I0407 10:15:21.299499 17183 sgd_solver.cpp:105] Iteration 3984, lr = 0.0025
I0407 10:15:26.203053 17183 solver.cpp:218] Iteration 3996 (2.44723 iter/s, 4.9035s/12 iters), loss = 0.642366
I0407 10:15:26.203094 17183 solver.cpp:237]     Train net output #0: loss = 0.642366 (* 1 = 0.642366 loss)
I0407 10:15:26.203101 17183 sgd_solver.cpp:105] Iteration 3996, lr = 0.0025
I0407 10:15:31.383720 17183 solver.cpp:218] Iteration 4008 (2.31635 iter/s, 5.18056s/12 iters), loss = 0.386158
I0407 10:15:31.383770 17183 solver.cpp:237]     Train net output #0: loss = 0.386158 (* 1 = 0.386158 loss)
I0407 10:15:31.383780 17183 sgd_solver.cpp:105] Iteration 4008, lr = 0.0025
I0407 10:15:36.616613 17183 solver.cpp:218] Iteration 4020 (2.29324 iter/s, 5.23277s/12 iters), loss = 0.736973
I0407 10:15:36.616675 17183 solver.cpp:237]     Train net output #0: loss = 0.736973 (* 1 = 0.736973 loss)
I0407 10:15:36.616686 17183 sgd_solver.cpp:105] Iteration 4020, lr = 0.0025
I0407 10:15:41.823276 17183 solver.cpp:218] Iteration 4032 (2.3048 iter/s, 5.20653s/12 iters), loss = 0.656751
I0407 10:15:41.823323 17183 solver.cpp:237]     Train net output #0: loss = 0.656751 (* 1 = 0.656751 loss)
I0407 10:15:41.823331 17183 sgd_solver.cpp:105] Iteration 4032, lr = 0.0025
I0407 10:15:47.041893 17183 solver.cpp:218] Iteration 4044 (2.29951 iter/s, 5.2185s/12 iters), loss = 0.684348
I0407 10:15:47.041939 17183 solver.cpp:237]     Train net output #0: loss = 0.684348 (* 1 = 0.684348 loss)
I0407 10:15:47.041946 17183 sgd_solver.cpp:105] Iteration 4044, lr = 0.0025
I0407 10:15:47.566455 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:15:52.271622 17183 solver.cpp:218] Iteration 4056 (2.29463 iter/s, 5.22961s/12 iters), loss = 0.4777
I0407 10:15:52.271781 17183 solver.cpp:237]     Train net output #0: loss = 0.4777 (* 1 = 0.4777 loss)
I0407 10:15:52.271792 17183 sgd_solver.cpp:105] Iteration 4056, lr = 0.0025
I0407 10:15:57.330313 17183 solver.cpp:218] Iteration 4068 (2.37226 iter/s, 5.05846s/12 iters), loss = 0.362794
I0407 10:15:57.330366 17183 solver.cpp:237]     Train net output #0: loss = 0.362794 (* 1 = 0.362794 loss)
I0407 10:15:57.330376 17183 sgd_solver.cpp:105] Iteration 4068, lr = 0.0025
I0407 10:16:02.072518 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4080.caffemodel
I0407 10:16:05.103482 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4080.solverstate
I0407 10:16:07.431254 17183 solver.cpp:330] Iteration 4080, Testing net (#0)
I0407 10:16:07.431273 17183 net.cpp:676] Ignoring source layer train-data
I0407 10:16:10.195096 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:16:11.775631 17183 solver.cpp:397]     Test net output #0: accuracy = 0.447917
I0407 10:16:11.775665 17183 solver.cpp:397]     Test net output #1: loss = 2.56608 (* 1 = 2.56608 loss)
I0407 10:16:11.911178 17183 solver.cpp:218] Iteration 4080 (0.823009 iter/s, 14.5806s/12 iters), loss = 0.420921
I0407 10:16:11.911240 17183 solver.cpp:237]     Train net output #0: loss = 0.420921 (* 1 = 0.420921 loss)
I0407 10:16:11.911249 17183 sgd_solver.cpp:105] Iteration 4080, lr = 0.0025
I0407 10:16:16.094555 17183 solver.cpp:218] Iteration 4092 (2.86858 iter/s, 4.18326s/12 iters), loss = 0.658057
I0407 10:16:16.094610 17183 solver.cpp:237]     Train net output #0: loss = 0.658057 (* 1 = 0.658057 loss)
I0407 10:16:16.094620 17183 sgd_solver.cpp:105] Iteration 4092, lr = 0.0025
I0407 10:16:21.255543 17183 solver.cpp:218] Iteration 4104 (2.32519 iter/s, 5.16086s/12 iters), loss = 0.356784
I0407 10:16:21.255596 17183 solver.cpp:237]     Train net output #0: loss = 0.356784 (* 1 = 0.356784 loss)
I0407 10:16:21.255606 17183 sgd_solver.cpp:105] Iteration 4104, lr = 0.0025
I0407 10:16:26.234791 17183 solver.cpp:218] Iteration 4116 (2.41006 iter/s, 4.97913s/12 iters), loss = 0.506653
I0407 10:16:26.234933 17183 solver.cpp:237]     Train net output #0: loss = 0.506653 (* 1 = 0.506653 loss)
I0407 10:16:26.234943 17183 sgd_solver.cpp:105] Iteration 4116, lr = 0.0025
I0407 10:16:31.456159 17183 solver.cpp:218] Iteration 4128 (2.29834 iter/s, 5.22116s/12 iters), loss = 0.540867
I0407 10:16:31.456199 17183 solver.cpp:237]     Train net output #0: loss = 0.540867 (* 1 = 0.540867 loss)
I0407 10:16:31.456207 17183 sgd_solver.cpp:105] Iteration 4128, lr = 0.0025
I0407 10:16:36.462790 17183 solver.cpp:218] Iteration 4140 (2.39687 iter/s, 5.00652s/12 iters), loss = 0.536663
I0407 10:16:36.462831 17183 solver.cpp:237]     Train net output #0: loss = 0.536663 (* 1 = 0.536663 loss)
I0407 10:16:36.462837 17183 sgd_solver.cpp:105] Iteration 4140, lr = 0.0025
I0407 10:16:39.268455 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:16:41.668239 17183 solver.cpp:218] Iteration 4152 (2.30533 iter/s, 5.20534s/12 iters), loss = 0.41883
I0407 10:16:41.668284 17183 solver.cpp:237]     Train net output #0: loss = 0.41883 (* 1 = 0.41883 loss)
I0407 10:16:41.668292 17183 sgd_solver.cpp:105] Iteration 4152, lr = 0.0025
I0407 10:16:43.322203 17183 blocking_queue.cpp:49] Waiting for data
I0407 10:16:46.845906 17183 solver.cpp:218] Iteration 4164 (2.3177 iter/s, 5.17755s/12 iters), loss = 0.419307
I0407 10:16:46.845953 17183 solver.cpp:237]     Train net output #0: loss = 0.419307 (* 1 = 0.419307 loss)
I0407 10:16:46.845964 17183 sgd_solver.cpp:105] Iteration 4164, lr = 0.0025
I0407 10:16:51.924021 17183 solver.cpp:218] Iteration 4176 (2.36314 iter/s, 5.078s/12 iters), loss = 0.741033
I0407 10:16:51.924067 17183 solver.cpp:237]     Train net output #0: loss = 0.741033 (* 1 = 0.741033 loss)
I0407 10:16:51.924073 17183 sgd_solver.cpp:105] Iteration 4176, lr = 0.0025
I0407 10:16:54.011833 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4182.caffemodel
I0407 10:16:57.031061 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4182.solverstate
I0407 10:16:59.349893 17183 solver.cpp:330] Iteration 4182, Testing net (#0)
I0407 10:16:59.349917 17183 net.cpp:676] Ignoring source layer train-data
I0407 10:17:02.012493 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:17:03.632977 17183 solver.cpp:397]     Test net output #0: accuracy = 0.448529
I0407 10:17:03.633020 17183 solver.cpp:397]     Test net output #1: loss = 2.64189 (* 1 = 2.64189 loss)
I0407 10:17:05.369194 17183 solver.cpp:218] Iteration 4188 (0.892527 iter/s, 13.445s/12 iters), loss = 0.622479
I0407 10:17:05.369253 17183 solver.cpp:237]     Train net output #0: loss = 0.622479 (* 1 = 0.622479 loss)
I0407 10:17:05.369263 17183 sgd_solver.cpp:105] Iteration 4188, lr = 0.0025
I0407 10:17:10.492476 17183 solver.cpp:218] Iteration 4200 (2.34231 iter/s, 5.12315s/12 iters), loss = 0.56985
I0407 10:17:10.492519 17183 solver.cpp:237]     Train net output #0: loss = 0.56985 (* 1 = 0.56985 loss)
I0407 10:17:10.492527 17183 sgd_solver.cpp:105] Iteration 4200, lr = 0.0025
I0407 10:17:15.591650 17183 solver.cpp:218] Iteration 4212 (2.35337 iter/s, 5.09906s/12 iters), loss = 0.443526
I0407 10:17:15.591697 17183 solver.cpp:237]     Train net output #0: loss = 0.443526 (* 1 = 0.443526 loss)
I0407 10:17:15.591704 17183 sgd_solver.cpp:105] Iteration 4212, lr = 0.0025
I0407 10:17:20.776404 17183 solver.cpp:218] Iteration 4224 (2.31453 iter/s, 5.18464s/12 iters), loss = 0.627288
I0407 10:17:20.776460 17183 solver.cpp:237]     Train net output #0: loss = 0.627288 (* 1 = 0.627288 loss)
I0407 10:17:20.776470 17183 sgd_solver.cpp:105] Iteration 4224, lr = 0.0025
I0407 10:17:26.140846 17183 solver.cpp:218] Iteration 4236 (2.23701 iter/s, 5.36431s/12 iters), loss = 0.586836
I0407 10:17:26.140894 17183 solver.cpp:237]     Train net output #0: loss = 0.586836 (* 1 = 0.586836 loss)
I0407 10:17:26.140902 17183 sgd_solver.cpp:105] Iteration 4236, lr = 0.0025
I0407 10:17:31.036772 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:17:31.314411 17183 solver.cpp:218] Iteration 4248 (2.31954 iter/s, 5.17345s/12 iters), loss = 0.362686
I0407 10:17:31.314467 17183 solver.cpp:237]     Train net output #0: loss = 0.362686 (* 1 = 0.362686 loss)
I0407 10:17:31.314477 17183 sgd_solver.cpp:105] Iteration 4248, lr = 0.0025
I0407 10:17:36.654412 17183 solver.cpp:218] Iteration 4260 (2.24724 iter/s, 5.33988s/12 iters), loss = 0.456123
I0407 10:17:36.654458 17183 solver.cpp:237]     Train net output #0: loss = 0.456123 (* 1 = 0.456123 loss)
I0407 10:17:36.654467 17183 sgd_solver.cpp:105] Iteration 4260, lr = 0.0025
I0407 10:17:41.811009 17183 solver.cpp:218] Iteration 4272 (2.32716 iter/s, 5.15649s/12 iters), loss = 0.326916
I0407 10:17:41.811044 17183 solver.cpp:237]     Train net output #0: loss = 0.326916 (* 1 = 0.326916 loss)
I0407 10:17:41.811050 17183 sgd_solver.cpp:105] Iteration 4272, lr = 0.0025
I0407 10:17:46.494594 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4284.caffemodel
I0407 10:17:49.553737 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4284.solverstate
I0407 10:17:51.854821 17183 solver.cpp:330] Iteration 4284, Testing net (#0)
I0407 10:17:51.854841 17183 net.cpp:676] Ignoring source layer train-data
I0407 10:17:54.505087 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:17:56.154449 17183 solver.cpp:397]     Test net output #0: accuracy = 0.443015
I0407 10:17:56.154489 17183 solver.cpp:397]     Test net output #1: loss = 2.66028 (* 1 = 2.66028 loss)
I0407 10:17:56.290364 17183 solver.cpp:218] Iteration 4284 (0.828778 iter/s, 14.4792s/12 iters), loss = 0.532969
I0407 10:17:56.290436 17183 solver.cpp:237]     Train net output #0: loss = 0.532969 (* 1 = 0.532969 loss)
I0407 10:17:56.290444 17183 sgd_solver.cpp:105] Iteration 4284, lr = 0.0025
I0407 10:18:00.486631 17183 solver.cpp:218] Iteration 4296 (2.85977 iter/s, 4.19613s/12 iters), loss = 0.50994
I0407 10:18:00.486682 17183 solver.cpp:237]     Train net output #0: loss = 0.50994 (* 1 = 0.50994 loss)
I0407 10:18:00.486693 17183 sgd_solver.cpp:105] Iteration 4296, lr = 0.0025
I0407 10:18:05.706326 17183 solver.cpp:218] Iteration 4308 (2.29904 iter/s, 5.21958s/12 iters), loss = 0.254356
I0407 10:18:05.706451 17183 solver.cpp:237]     Train net output #0: loss = 0.254356 (* 1 = 0.254356 loss)
I0407 10:18:05.706459 17183 sgd_solver.cpp:105] Iteration 4308, lr = 0.0025
I0407 10:18:10.762583 17183 solver.cpp:218] Iteration 4320 (2.37339 iter/s, 5.05606s/12 iters), loss = 0.539742
I0407 10:18:10.762626 17183 solver.cpp:237]     Train net output #0: loss = 0.539742 (* 1 = 0.539742 loss)
I0407 10:18:10.762634 17183 sgd_solver.cpp:105] Iteration 4320, lr = 0.0025
I0407 10:18:15.930840 17183 solver.cpp:218] Iteration 4332 (2.32192 iter/s, 5.16814s/12 iters), loss = 0.393544
I0407 10:18:15.930900 17183 solver.cpp:237]     Train net output #0: loss = 0.393544 (* 1 = 0.393544 loss)
I0407 10:18:15.930912 17183 sgd_solver.cpp:105] Iteration 4332, lr = 0.0025
I0407 10:18:21.143196 17183 solver.cpp:218] Iteration 4344 (2.30228 iter/s, 5.21222s/12 iters), loss = 0.397841
I0407 10:18:21.143258 17183 solver.cpp:237]     Train net output #0: loss = 0.397841 (* 1 = 0.397841 loss)
I0407 10:18:21.143268 17183 sgd_solver.cpp:105] Iteration 4344, lr = 0.0025
I0407 10:18:23.116185 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:18:26.397249 17183 solver.cpp:218] Iteration 4356 (2.28401 iter/s, 5.25392s/12 iters), loss = 0.389841
I0407 10:18:26.397306 17183 solver.cpp:237]     Train net output #0: loss = 0.389841 (* 1 = 0.389841 loss)
I0407 10:18:26.397317 17183 sgd_solver.cpp:105] Iteration 4356, lr = 0.0025
I0407 10:18:31.578590 17183 solver.cpp:218] Iteration 4368 (2.31606 iter/s, 5.18122s/12 iters), loss = 0.483776
I0407 10:18:31.578639 17183 solver.cpp:237]     Train net output #0: loss = 0.483776 (* 1 = 0.483776 loss)
I0407 10:18:31.578646 17183 sgd_solver.cpp:105] Iteration 4368, lr = 0.0025
I0407 10:18:36.802268 17183 solver.cpp:218] Iteration 4380 (2.29729 iter/s, 5.22356s/12 iters), loss = 0.460386
I0407 10:18:36.802393 17183 solver.cpp:237]     Train net output #0: loss = 0.460386 (* 1 = 0.460386 loss)
I0407 10:18:36.802403 17183 sgd_solver.cpp:105] Iteration 4380, lr = 0.0025
I0407 10:18:38.908830 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4386.caffemodel
I0407 10:18:41.895390 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4386.solverstate
I0407 10:18:44.214500 17183 solver.cpp:330] Iteration 4386, Testing net (#0)
I0407 10:18:44.214520 17183 net.cpp:676] Ignoring source layer train-data
I0407 10:18:46.860122 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:18:48.587927 17183 solver.cpp:397]     Test net output #0: accuracy = 0.436887
I0407 10:18:48.587956 17183 solver.cpp:397]     Test net output #1: loss = 2.68207 (* 1 = 2.68207 loss)
I0407 10:18:50.336827 17183 solver.cpp:218] Iteration 4392 (0.886637 iter/s, 13.5343s/12 iters), loss = 0.303958
I0407 10:18:50.336880 17183 solver.cpp:237]     Train net output #0: loss = 0.303958 (* 1 = 0.303958 loss)
I0407 10:18:50.336897 17183 sgd_solver.cpp:105] Iteration 4392, lr = 0.0025
I0407 10:18:55.431546 17183 solver.cpp:218] Iteration 4404 (2.35544 iter/s, 5.0946s/12 iters), loss = 0.414626
I0407 10:18:55.431608 17183 solver.cpp:237]     Train net output #0: loss = 0.414626 (* 1 = 0.414626 loss)
I0407 10:18:55.431622 17183 sgd_solver.cpp:105] Iteration 4404, lr = 0.0025
I0407 10:19:00.410998 17183 solver.cpp:218] Iteration 4416 (2.40996 iter/s, 4.97933s/12 iters), loss = 0.497003
I0407 10:19:00.411042 17183 solver.cpp:237]     Train net output #0: loss = 0.497003 (* 1 = 0.497003 loss)
I0407 10:19:00.411051 17183 sgd_solver.cpp:105] Iteration 4416, lr = 0.0025
I0407 10:19:05.628082 17183 solver.cpp:218] Iteration 4428 (2.30019 iter/s, 5.21697s/12 iters), loss = 0.518696
I0407 10:19:05.628141 17183 solver.cpp:237]     Train net output #0: loss = 0.518696 (* 1 = 0.518696 loss)
I0407 10:19:05.628151 17183 sgd_solver.cpp:105] Iteration 4428, lr = 0.0025
I0407 10:19:10.618098 17183 solver.cpp:218] Iteration 4440 (2.40486 iter/s, 4.98989s/12 iters), loss = 0.472697
I0407 10:19:10.618319 17183 solver.cpp:237]     Train net output #0: loss = 0.472697 (* 1 = 0.472697 loss)
I0407 10:19:10.618330 17183 sgd_solver.cpp:105] Iteration 4440, lr = 0.0025
I0407 10:19:14.755008 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:19:15.804265 17183 solver.cpp:218] Iteration 4452 (2.31398 iter/s, 5.18588s/12 iters), loss = 0.382334
I0407 10:19:15.804323 17183 solver.cpp:237]     Train net output #0: loss = 0.382334 (* 1 = 0.382334 loss)
I0407 10:19:15.804332 17183 sgd_solver.cpp:105] Iteration 4452, lr = 0.0025
I0407 10:19:21.010036 17183 solver.cpp:218] Iteration 4464 (2.30519 iter/s, 5.20564s/12 iters), loss = 0.293454
I0407 10:19:21.010094 17183 solver.cpp:237]     Train net output #0: loss = 0.293454 (* 1 = 0.293454 loss)
I0407 10:19:21.010105 17183 sgd_solver.cpp:105] Iteration 4464, lr = 0.0025
I0407 10:19:26.042012 17183 solver.cpp:218] Iteration 4476 (2.38481 iter/s, 5.03185s/12 iters), loss = 0.331533
I0407 10:19:26.042069 17183 solver.cpp:237]     Train net output #0: loss = 0.331533 (* 1 = 0.331533 loss)
I0407 10:19:26.042078 17183 sgd_solver.cpp:105] Iteration 4476, lr = 0.0025
I0407 10:19:30.740952 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4488.caffemodel
I0407 10:19:33.749655 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4488.solverstate
I0407 10:19:36.063824 17183 solver.cpp:330] Iteration 4488, Testing net (#0)
I0407 10:19:36.063843 17183 net.cpp:676] Ignoring source layer train-data
I0407 10:19:38.663717 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:19:40.470198 17183 solver.cpp:397]     Test net output #0: accuracy = 0.45098
I0407 10:19:40.470233 17183 solver.cpp:397]     Test net output #1: loss = 2.69544 (* 1 = 2.69544 loss)
I0407 10:19:40.611760 17183 solver.cpp:218] Iteration 4488 (0.823637 iter/s, 14.5695s/12 iters), loss = 0.354681
I0407 10:19:40.611812 17183 solver.cpp:237]     Train net output #0: loss = 0.354681 (* 1 = 0.354681 loss)
I0407 10:19:40.611820 17183 sgd_solver.cpp:105] Iteration 4488, lr = 0.0025
I0407 10:19:44.927356 17183 solver.cpp:218] Iteration 4500 (2.78068 iter/s, 4.31549s/12 iters), loss = 0.313856
I0407 10:19:44.927455 17183 solver.cpp:237]     Train net output #0: loss = 0.313856 (* 1 = 0.313856 loss)
I0407 10:19:44.927464 17183 sgd_solver.cpp:105] Iteration 4500, lr = 0.0025
I0407 10:19:49.942579 17183 solver.cpp:218] Iteration 4512 (2.39279 iter/s, 5.01506s/12 iters), loss = 0.526052
I0407 10:19:49.942625 17183 solver.cpp:237]     Train net output #0: loss = 0.526052 (* 1 = 0.526052 loss)
I0407 10:19:49.942632 17183 sgd_solver.cpp:105] Iteration 4512, lr = 0.0025
I0407 10:19:54.889880 17183 solver.cpp:218] Iteration 4524 (2.42563 iter/s, 4.94717s/12 iters), loss = 0.405811
I0407 10:19:54.889936 17183 solver.cpp:237]     Train net output #0: loss = 0.405811 (* 1 = 0.405811 loss)
I0407 10:19:54.889947 17183 sgd_solver.cpp:105] Iteration 4524, lr = 0.0025
I0407 10:20:00.048046 17183 solver.cpp:218] Iteration 4536 (2.32646 iter/s, 5.15805s/12 iters), loss = 0.481367
I0407 10:20:00.048086 17183 solver.cpp:237]     Train net output #0: loss = 0.481367 (* 1 = 0.481367 loss)
I0407 10:20:00.048094 17183 sgd_solver.cpp:105] Iteration 4536, lr = 0.0025
I0407 10:20:05.053306 17183 solver.cpp:218] Iteration 4548 (2.39753 iter/s, 5.00516s/12 iters), loss = 0.394925
I0407 10:20:05.053344 17183 solver.cpp:237]     Train net output #0: loss = 0.394925 (* 1 = 0.394925 loss)
I0407 10:20:05.053354 17183 sgd_solver.cpp:105] Iteration 4548, lr = 0.0025
I0407 10:20:06.363859 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:20:10.251817 17183 solver.cpp:218] Iteration 4560 (2.3084 iter/s, 5.1984s/12 iters), loss = 0.190777
I0407 10:20:10.251861 17183 solver.cpp:237]     Train net output #0: loss = 0.190777 (* 1 = 0.190777 loss)
I0407 10:20:10.251868 17183 sgd_solver.cpp:105] Iteration 4560, lr = 0.0025
I0407 10:20:15.454391 17183 solver.cpp:218] Iteration 4572 (2.3066 iter/s, 5.20246s/12 iters), loss = 0.297531
I0407 10:20:15.454510 17183 solver.cpp:237]     Train net output #0: loss = 0.297531 (* 1 = 0.297531 loss)
I0407 10:20:15.454519 17183 sgd_solver.cpp:105] Iteration 4572, lr = 0.0025
I0407 10:20:20.541483 17183 solver.cpp:218] Iteration 4584 (2.359 iter/s, 5.08691s/12 iters), loss = 0.403601
I0407 10:20:20.541527 17183 solver.cpp:237]     Train net output #0: loss = 0.403601 (* 1 = 0.403601 loss)
I0407 10:20:20.541534 17183 sgd_solver.cpp:105] Iteration 4584, lr = 0.0025
I0407 10:20:22.565346 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4590.caffemodel
I0407 10:20:25.551050 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4590.solverstate
I0407 10:20:29.299542 17183 solver.cpp:330] Iteration 4590, Testing net (#0)
I0407 10:20:29.299566 17183 net.cpp:676] Ignoring source layer train-data
I0407 10:20:31.794690 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:20:33.767879 17183 solver.cpp:397]     Test net output #0: accuracy = 0.448529
I0407 10:20:33.767916 17183 solver.cpp:397]     Test net output #1: loss = 2.65861 (* 1 = 2.65861 loss)
I0407 10:20:35.773480 17183 solver.cpp:218] Iteration 4596 (0.787826 iter/s, 15.2318s/12 iters), loss = 0.462016
I0407 10:20:35.773520 17183 solver.cpp:237]     Train net output #0: loss = 0.462016 (* 1 = 0.462016 loss)
I0407 10:20:35.773526 17183 sgd_solver.cpp:105] Iteration 4596, lr = 0.0025
I0407 10:20:41.007143 17183 solver.cpp:218] Iteration 4608 (2.2929 iter/s, 5.23355s/12 iters), loss = 0.457469
I0407 10:20:41.007186 17183 solver.cpp:237]     Train net output #0: loss = 0.457469 (* 1 = 0.457469 loss)
I0407 10:20:41.007194 17183 sgd_solver.cpp:105] Iteration 4608, lr = 0.0025
I0407 10:20:46.260360 17183 solver.cpp:218] Iteration 4620 (2.28437 iter/s, 5.2531s/12 iters), loss = 0.392305
I0407 10:20:46.260495 17183 solver.cpp:237]     Train net output #0: loss = 0.392305 (* 1 = 0.392305 loss)
I0407 10:20:46.260506 17183 sgd_solver.cpp:105] Iteration 4620, lr = 0.0025
I0407 10:20:51.382228 17183 solver.cpp:218] Iteration 4632 (2.34299 iter/s, 5.12167s/12 iters), loss = 0.42014
I0407 10:20:51.382272 17183 solver.cpp:237]     Train net output #0: loss = 0.42014 (* 1 = 0.42014 loss)
I0407 10:20:51.382279 17183 sgd_solver.cpp:105] Iteration 4632, lr = 0.0025
I0407 10:20:56.576215 17183 solver.cpp:218] Iteration 4644 (2.31041 iter/s, 5.19387s/12 iters), loss = 0.303295
I0407 10:20:56.576268 17183 solver.cpp:237]     Train net output #0: loss = 0.303295 (* 1 = 0.303295 loss)
I0407 10:20:56.576278 17183 sgd_solver.cpp:105] Iteration 4644, lr = 0.0025
I0407 10:20:59.895913 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:21:01.627733 17183 solver.cpp:218] Iteration 4656 (2.37558 iter/s, 5.05139s/12 iters), loss = 0.332903
I0407 10:21:01.627799 17183 solver.cpp:237]     Train net output #0: loss = 0.332903 (* 1 = 0.332903 loss)
I0407 10:21:01.627810 17183 sgd_solver.cpp:105] Iteration 4656, lr = 0.0025
I0407 10:21:06.852490 17183 solver.cpp:218] Iteration 4668 (2.29682 iter/s, 5.22462s/12 iters), loss = 0.383156
I0407 10:21:06.852538 17183 solver.cpp:237]     Train net output #0: loss = 0.383156 (* 1 = 0.383156 loss)
I0407 10:21:06.852546 17183 sgd_solver.cpp:105] Iteration 4668, lr = 0.0025
I0407 10:21:11.952150 17183 solver.cpp:218] Iteration 4680 (2.35315 iter/s, 5.09954s/12 iters), loss = 0.251044
I0407 10:21:11.952198 17183 solver.cpp:237]     Train net output #0: loss = 0.251044 (* 1 = 0.251044 loss)
I0407 10:21:11.952205 17183 sgd_solver.cpp:105] Iteration 4680, lr = 0.0025
I0407 10:21:16.504642 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4692.caffemodel
I0407 10:21:19.896311 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4692.solverstate
I0407 10:21:22.207664 17183 solver.cpp:330] Iteration 4692, Testing net (#0)
I0407 10:21:22.207684 17183 net.cpp:676] Ignoring source layer train-data
I0407 10:21:24.685101 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:21:26.507333 17183 solver.cpp:397]     Test net output #0: accuracy = 0.454657
I0407 10:21:26.507367 17183 solver.cpp:397]     Test net output #1: loss = 2.74233 (* 1 = 2.74233 loss)
I0407 10:21:26.648669 17183 solver.cpp:218] Iteration 4692 (0.816532 iter/s, 14.6963s/12 iters), loss = 0.320323
I0407 10:21:26.648711 17183 solver.cpp:237]     Train net output #0: loss = 0.320323 (* 1 = 0.320323 loss)
I0407 10:21:26.648718 17183 sgd_solver.cpp:105] Iteration 4692, lr = 0.0025
I0407 10:21:30.761613 17183 solver.cpp:218] Iteration 4704 (2.91769 iter/s, 4.11285s/12 iters), loss = 0.249088
I0407 10:21:30.761654 17183 solver.cpp:237]     Train net output #0: loss = 0.249088 (* 1 = 0.249088 loss)
I0407 10:21:30.761662 17183 sgd_solver.cpp:105] Iteration 4704, lr = 0.0025
I0407 10:21:35.745687 17183 solver.cpp:218] Iteration 4716 (2.40772 iter/s, 4.98397s/12 iters), loss = 0.182893
I0407 10:21:35.745724 17183 solver.cpp:237]     Train net output #0: loss = 0.182893 (* 1 = 0.182893 loss)
I0407 10:21:35.745730 17183 sgd_solver.cpp:105] Iteration 4716, lr = 0.0025
I0407 10:21:40.777284 17183 solver.cpp:218] Iteration 4728 (2.38498 iter/s, 5.03149s/12 iters), loss = 0.46416
I0407 10:21:40.777326 17183 solver.cpp:237]     Train net output #0: loss = 0.46416 (* 1 = 0.46416 loss)
I0407 10:21:40.777334 17183 sgd_solver.cpp:105] Iteration 4728, lr = 0.0025
I0407 10:21:45.931288 17183 solver.cpp:218] Iteration 4740 (2.32834 iter/s, 5.15389s/12 iters), loss = 0.449705
I0407 10:21:45.931345 17183 solver.cpp:237]     Train net output #0: loss = 0.449705 (* 1 = 0.449705 loss)
I0407 10:21:45.931356 17183 sgd_solver.cpp:105] Iteration 4740, lr = 0.0025
I0407 10:21:51.058018 17183 solver.cpp:218] Iteration 4752 (2.34073 iter/s, 5.12661s/12 iters), loss = 0.466394
I0407 10:21:51.058125 17183 solver.cpp:237]     Train net output #0: loss = 0.466394 (* 1 = 0.466394 loss)
I0407 10:21:51.058133 17183 sgd_solver.cpp:105] Iteration 4752, lr = 0.0025
I0407 10:21:51.605834 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:21:56.282999 17183 solver.cpp:218] Iteration 4764 (2.29674 iter/s, 5.22481s/12 iters), loss = 0.357509
I0407 10:21:56.283046 17183 solver.cpp:237]     Train net output #0: loss = 0.357509 (* 1 = 0.357509 loss)
I0407 10:21:56.283052 17183 sgd_solver.cpp:105] Iteration 4764, lr = 0.0025
I0407 10:22:01.459841 17183 solver.cpp:218] Iteration 4776 (2.31807 iter/s, 5.17673s/12 iters), loss = 0.298622
I0407 10:22:01.459885 17183 solver.cpp:237]     Train net output #0: loss = 0.298622 (* 1 = 0.298622 loss)
I0407 10:22:01.459893 17183 sgd_solver.cpp:105] Iteration 4776, lr = 0.0025
I0407 10:22:06.677798 17183 solver.cpp:218] Iteration 4788 (2.2998 iter/s, 5.21784s/12 iters), loss = 0.256058
I0407 10:22:06.677863 17183 solver.cpp:237]     Train net output #0: loss = 0.256058 (* 1 = 0.256058 loss)
I0407 10:22:06.677875 17183 sgd_solver.cpp:105] Iteration 4788, lr = 0.0025
I0407 10:22:08.772044 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4794.caffemodel
I0407 10:22:11.810783 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4794.solverstate
I0407 10:22:14.138603 17183 solver.cpp:330] Iteration 4794, Testing net (#0)
I0407 10:22:14.138630 17183 net.cpp:676] Ignoring source layer train-data
I0407 10:22:16.617596 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:22:18.487118 17183 solver.cpp:397]     Test net output #0: accuracy = 0.458946
I0407 10:22:18.487152 17183 solver.cpp:397]     Test net output #1: loss = 2.64791 (* 1 = 2.64791 loss)
I0407 10:22:20.331171 17183 solver.cpp:218] Iteration 4800 (0.878918 iter/s, 13.6532s/12 iters), loss = 0.352332
I0407 10:22:20.331218 17183 solver.cpp:237]     Train net output #0: loss = 0.352332 (* 1 = 0.352332 loss)
I0407 10:22:20.331224 17183 sgd_solver.cpp:105] Iteration 4800, lr = 0.0025
I0407 10:22:25.360589 17183 solver.cpp:218] Iteration 4812 (2.38602 iter/s, 5.0293s/12 iters), loss = 0.261992
I0407 10:22:25.360759 17183 solver.cpp:237]     Train net output #0: loss = 0.261992 (* 1 = 0.261992 loss)
I0407 10:22:25.360769 17183 sgd_solver.cpp:105] Iteration 4812, lr = 0.0025
I0407 10:22:30.534862 17183 solver.cpp:218] Iteration 4824 (2.31927 iter/s, 5.17403s/12 iters), loss = 0.23625
I0407 10:22:30.534914 17183 solver.cpp:237]     Train net output #0: loss = 0.23625 (* 1 = 0.23625 loss)
I0407 10:22:30.534924 17183 sgd_solver.cpp:105] Iteration 4824, lr = 0.0025
I0407 10:22:35.784459 17183 solver.cpp:218] Iteration 4836 (2.28594 iter/s, 5.24948s/12 iters), loss = 0.316175
I0407 10:22:35.784504 17183 solver.cpp:237]     Train net output #0: loss = 0.316175 (* 1 = 0.316175 loss)
I0407 10:22:35.784512 17183 sgd_solver.cpp:105] Iteration 4836, lr = 0.0025
I0407 10:22:37.970134 17183 blocking_queue.cpp:49] Waiting for data
I0407 10:22:41.081032 17183 solver.cpp:218] Iteration 4848 (2.26567 iter/s, 5.29645s/12 iters), loss = 0.342482
I0407 10:22:41.081079 17183 solver.cpp:237]     Train net output #0: loss = 0.342482 (* 1 = 0.342482 loss)
I0407 10:22:41.081086 17183 sgd_solver.cpp:105] Iteration 4848, lr = 0.0025
I0407 10:22:43.931039 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:22:46.299804 17183 solver.cpp:218] Iteration 4860 (2.29944 iter/s, 5.21865s/12 iters), loss = 0.409695
I0407 10:22:46.299857 17183 solver.cpp:237]     Train net output #0: loss = 0.409695 (* 1 = 0.409695 loss)
I0407 10:22:46.299868 17183 sgd_solver.cpp:105] Iteration 4860, lr = 0.0025
I0407 10:22:51.686316 17183 solver.cpp:218] Iteration 4872 (2.22784 iter/s, 5.38639s/12 iters), loss = 0.371034
I0407 10:22:51.686362 17183 solver.cpp:237]     Train net output #0: loss = 0.371034 (* 1 = 0.371034 loss)
I0407 10:22:51.686369 17183 sgd_solver.cpp:105] Iteration 4872, lr = 0.0025
I0407 10:22:56.842797 17183 solver.cpp:218] Iteration 4884 (2.32722 iter/s, 5.15637s/12 iters), loss = 0.35912
I0407 10:22:56.842890 17183 solver.cpp:237]     Train net output #0: loss = 0.35912 (* 1 = 0.35912 loss)
I0407 10:22:56.842897 17183 sgd_solver.cpp:105] Iteration 4884, lr = 0.0025
I0407 10:23:01.589329 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4896.caffemodel
I0407 10:23:04.625654 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4896.solverstate
I0407 10:23:07.971539 17183 solver.cpp:330] Iteration 4896, Testing net (#0)
I0407 10:23:07.971558 17183 net.cpp:676] Ignoring source layer train-data
I0407 10:23:10.449517 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:23:12.381713 17183 solver.cpp:397]     Test net output #0: accuracy = 0.445466
I0407 10:23:12.381750 17183 solver.cpp:397]     Test net output #1: loss = 2.75499 (* 1 = 2.75499 loss)
I0407 10:23:12.523211 17183 solver.cpp:218] Iteration 4896 (0.765299 iter/s, 15.6801s/12 iters), loss = 0.342514
I0407 10:23:12.523263 17183 solver.cpp:237]     Train net output #0: loss = 0.342514 (* 1 = 0.342514 loss)
I0407 10:23:12.523273 17183 sgd_solver.cpp:105] Iteration 4896, lr = 0.0025
I0407 10:23:16.805629 17183 solver.cpp:218] Iteration 4908 (2.80223 iter/s, 4.28231s/12 iters), loss = 0.391856
I0407 10:23:16.805687 17183 solver.cpp:237]     Train net output #0: loss = 0.391856 (* 1 = 0.391856 loss)
I0407 10:23:16.805697 17183 sgd_solver.cpp:105] Iteration 4908, lr = 0.0025
I0407 10:23:21.981463 17183 solver.cpp:218] Iteration 4920 (2.31852 iter/s, 5.17571s/12 iters), loss = 0.290087
I0407 10:23:21.981528 17183 solver.cpp:237]     Train net output #0: loss = 0.290088 (* 1 = 0.290088 loss)
I0407 10:23:21.981539 17183 sgd_solver.cpp:105] Iteration 4920, lr = 0.0025
I0407 10:23:27.071528 17183 solver.cpp:218] Iteration 4932 (2.35759 iter/s, 5.08994s/12 iters), loss = 0.353459
I0407 10:23:27.071650 17183 solver.cpp:237]     Train net output #0: loss = 0.353459 (* 1 = 0.353459 loss)
I0407 10:23:27.071657 17183 sgd_solver.cpp:105] Iteration 4932, lr = 0.0025
I0407 10:23:32.205955 17183 solver.cpp:218] Iteration 4944 (2.33725 iter/s, 5.13424s/12 iters), loss = 0.241096
I0407 10:23:32.205998 17183 solver.cpp:237]     Train net output #0: loss = 0.241096 (* 1 = 0.241096 loss)
I0407 10:23:32.206005 17183 sgd_solver.cpp:105] Iteration 4944, lr = 0.0025
I0407 10:23:37.142261 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:23:37.384610 17183 solver.cpp:218] Iteration 4956 (2.31854 iter/s, 5.17567s/12 iters), loss = 0.305865
I0407 10:23:37.384655 17183 solver.cpp:237]     Train net output #0: loss = 0.305866 (* 1 = 0.305866 loss)
I0407 10:23:37.384665 17183 sgd_solver.cpp:105] Iteration 4956, lr = 0.0025
I0407 10:23:42.375368 17183 solver.cpp:218] Iteration 4968 (2.4045 iter/s, 4.99065s/12 iters), loss = 0.209439
I0407 10:23:42.375423 17183 solver.cpp:237]     Train net output #0: loss = 0.209439 (* 1 = 0.209439 loss)
I0407 10:23:42.375432 17183 sgd_solver.cpp:105] Iteration 4968, lr = 0.0025
I0407 10:23:47.377924 17183 solver.cpp:218] Iteration 4980 (2.39882 iter/s, 5.00245s/12 iters), loss = 0.342865
I0407 10:23:47.377979 17183 solver.cpp:237]     Train net output #0: loss = 0.342865 (* 1 = 0.342865 loss)
I0407 10:23:47.377988 17183 sgd_solver.cpp:105] Iteration 4980, lr = 0.0025
I0407 10:23:52.447587 17183 solver.cpp:218] Iteration 4992 (2.36708 iter/s, 5.06954s/12 iters), loss = 0.305662
I0407 10:23:52.447634 17183 solver.cpp:237]     Train net output #0: loss = 0.305663 (* 1 = 0.305663 loss)
I0407 10:23:52.447644 17183 sgd_solver.cpp:105] Iteration 4992, lr = 0.0025
I0407 10:23:54.560818 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4998.caffemodel
I0407 10:23:58.553134 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4998.solverstate
I0407 10:24:01.579598 17183 solver.cpp:330] Iteration 4998, Testing net (#0)
I0407 10:24:01.579620 17183 net.cpp:676] Ignoring source layer train-data
I0407 10:24:03.930256 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:24:05.917088 17183 solver.cpp:397]     Test net output #0: accuracy = 0.449142
I0407 10:24:05.917119 17183 solver.cpp:397]     Test net output #1: loss = 2.84797 (* 1 = 2.84797 loss)
I0407 10:24:07.897035 17183 solver.cpp:218] Iteration 5004 (0.776738 iter/s, 15.4492s/12 iters), loss = 0.39069
I0407 10:24:07.897085 17183 solver.cpp:237]     Train net output #0: loss = 0.39069 (* 1 = 0.39069 loss)
I0407 10:24:07.897094 17183 sgd_solver.cpp:105] Iteration 5004, lr = 0.0025
I0407 10:24:12.728781 17183 solver.cpp:218] Iteration 5016 (2.48363 iter/s, 4.83163s/12 iters), loss = 0.293237
I0407 10:24:12.728823 17183 solver.cpp:237]     Train net output #0: loss = 0.293237 (* 1 = 0.293237 loss)
I0407 10:24:12.728830 17183 sgd_solver.cpp:105] Iteration 5016, lr = 0.0025
I0407 10:24:17.803263 17183 solver.cpp:218] Iteration 5028 (2.36482 iter/s, 5.07438s/12 iters), loss = 0.32887
I0407 10:24:17.803300 17183 solver.cpp:237]     Train net output #0: loss = 0.32887 (* 1 = 0.32887 loss)
I0407 10:24:17.803308 17183 sgd_solver.cpp:105] Iteration 5028, lr = 0.0025
I0407 10:24:23.009848 17183 solver.cpp:218] Iteration 5040 (2.30482 iter/s, 5.20647s/12 iters), loss = 0.204171
I0407 10:24:23.009889 17183 solver.cpp:237]     Train net output #0: loss = 0.204171 (* 1 = 0.204171 loss)
I0407 10:24:23.009896 17183 sgd_solver.cpp:105] Iteration 5040, lr = 0.0025
I0407 10:24:28.227174 17183 solver.cpp:218] Iteration 5052 (2.30008 iter/s, 5.21721s/12 iters), loss = 0.368754
I0407 10:24:28.227229 17183 solver.cpp:237]     Train net output #0: loss = 0.368755 (* 1 = 0.368755 loss)
I0407 10:24:28.227239 17183 sgd_solver.cpp:105] Iteration 5052, lr = 0.0025
I0407 10:24:30.158519 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:24:33.313671 17183 solver.cpp:218] Iteration 5064 (2.35924 iter/s, 5.08637s/12 iters), loss = 0.210764
I0407 10:24:33.313711 17183 solver.cpp:237]     Train net output #0: loss = 0.210764 (* 1 = 0.210764 loss)
I0407 10:24:33.313719 17183 sgd_solver.cpp:105] Iteration 5064, lr = 0.0025
I0407 10:24:38.369974 17183 solver.cpp:218] Iteration 5076 (2.37333 iter/s, 5.0562s/12 iters), loss = 0.158833
I0407 10:24:38.370012 17183 solver.cpp:237]     Train net output #0: loss = 0.158834 (* 1 = 0.158834 loss)
I0407 10:24:38.370020 17183 sgd_solver.cpp:105] Iteration 5076, lr = 0.0025
I0407 10:24:43.613279 17183 solver.cpp:218] Iteration 5088 (2.28868 iter/s, 5.24319s/12 iters), loss = 0.38199
I0407 10:24:43.613322 17183 solver.cpp:237]     Train net output #0: loss = 0.38199 (* 1 = 0.38199 loss)
I0407 10:24:43.613328 17183 sgd_solver.cpp:105] Iteration 5088, lr = 0.0025
I0407 10:24:48.216670 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5100.caffemodel
I0407 10:24:51.317682 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5100.solverstate
I0407 10:24:53.634045 17183 solver.cpp:330] Iteration 5100, Testing net (#0)
I0407 10:24:53.634064 17183 net.cpp:676] Ignoring source layer train-data
I0407 10:24:55.955725 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:24:57.928580 17183 solver.cpp:397]     Test net output #0: accuracy = 0.464461
I0407 10:24:57.928612 17183 solver.cpp:397]     Test net output #1: loss = 2.76872 (* 1 = 2.76872 loss)
I0407 10:24:58.070153 17183 solver.cpp:218] Iteration 5100 (0.830066 iter/s, 14.4567s/12 iters), loss = 0.141954
I0407 10:24:58.070204 17183 solver.cpp:237]     Train net output #0: loss = 0.141954 (* 1 = 0.141954 loss)
I0407 10:24:58.070211 17183 sgd_solver.cpp:105] Iteration 5100, lr = 0.0025
I0407 10:25:02.152760 17183 solver.cpp:218] Iteration 5112 (2.93938 iter/s, 4.08249s/12 iters), loss = 0.289309
I0407 10:25:02.152899 17183 solver.cpp:237]     Train net output #0: loss = 0.28931 (* 1 = 0.28931 loss)
I0407 10:25:02.152910 17183 sgd_solver.cpp:105] Iteration 5112, lr = 0.0025
I0407 10:25:07.206542 17183 solver.cpp:218] Iteration 5124 (2.37455 iter/s, 5.05359s/12 iters), loss = 0.412876
I0407 10:25:07.206583 17183 solver.cpp:237]     Train net output #0: loss = 0.412876 (* 1 = 0.412876 loss)
I0407 10:25:07.206590 17183 sgd_solver.cpp:105] Iteration 5124, lr = 0.0025
I0407 10:25:12.405898 17183 solver.cpp:218] Iteration 5136 (2.30803 iter/s, 5.19924s/12 iters), loss = 0.195952
I0407 10:25:12.405942 17183 solver.cpp:237]     Train net output #0: loss = 0.195952 (* 1 = 0.195952 loss)
I0407 10:25:12.405951 17183 sgd_solver.cpp:105] Iteration 5136, lr = 0.0025
I0407 10:25:17.592084 17183 solver.cpp:218] Iteration 5148 (2.31389 iter/s, 5.18608s/12 iters), loss = 0.335027
I0407 10:25:17.592121 17183 solver.cpp:237]     Train net output #0: loss = 0.335027 (* 1 = 0.335027 loss)
I0407 10:25:17.592128 17183 sgd_solver.cpp:105] Iteration 5148, lr = 0.0025
I0407 10:25:21.666591 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:25:22.714613 17183 solver.cpp:218] Iteration 5160 (2.34264 iter/s, 5.12242s/12 iters), loss = 0.399367
I0407 10:25:22.714658 17183 solver.cpp:237]     Train net output #0: loss = 0.399367 (* 1 = 0.399367 loss)
I0407 10:25:22.714665 17183 sgd_solver.cpp:105] Iteration 5160, lr = 0.0025
I0407 10:25:27.894282 17183 solver.cpp:218] Iteration 5172 (2.3168 iter/s, 5.17955s/12 iters), loss = 0.267967
I0407 10:25:27.894336 17183 solver.cpp:237]     Train net output #0: loss = 0.267967 (* 1 = 0.267967 loss)
I0407 10:25:27.894346 17183 sgd_solver.cpp:105] Iteration 5172, lr = 0.0025
I0407 10:25:32.904697 17183 solver.cpp:218] Iteration 5184 (2.39507 iter/s, 5.0103s/12 iters), loss = 0.160912
I0407 10:25:32.904824 17183 solver.cpp:237]     Train net output #0: loss = 0.160912 (* 1 = 0.160912 loss)
I0407 10:25:32.904832 17183 sgd_solver.cpp:105] Iteration 5184, lr = 0.0025
I0407 10:25:38.053015 17183 solver.cpp:218] Iteration 5196 (2.33095 iter/s, 5.14812s/12 iters), loss = 0.386388
I0407 10:25:38.053066 17183 solver.cpp:237]     Train net output #0: loss = 0.386388 (* 1 = 0.386388 loss)
I0407 10:25:38.053073 17183 sgd_solver.cpp:105] Iteration 5196, lr = 0.0025
I0407 10:25:40.215587 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5202.caffemodel
I0407 10:25:43.151369 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5202.solverstate
I0407 10:25:46.517268 17183 solver.cpp:330] Iteration 5202, Testing net (#0)
I0407 10:25:46.517287 17183 net.cpp:676] Ignoring source layer train-data
I0407 10:25:48.769186 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:25:50.810488 17183 solver.cpp:397]     Test net output #0: accuracy = 0.460784
I0407 10:25:50.810523 17183 solver.cpp:397]     Test net output #1: loss = 2.76178 (* 1 = 2.76178 loss)
I0407 10:25:52.539764 17183 solver.cpp:218] Iteration 5208 (0.828355 iter/s, 14.4865s/12 iters), loss = 0.210667
I0407 10:25:52.539825 17183 solver.cpp:237]     Train net output #0: loss = 0.210668 (* 1 = 0.210668 loss)
I0407 10:25:52.539835 17183 sgd_solver.cpp:105] Iteration 5208, lr = 0.0025
I0407 10:25:57.484964 17183 solver.cpp:218] Iteration 5220 (2.42666 iter/s, 4.94507s/12 iters), loss = 0.209492
I0407 10:25:57.485009 17183 solver.cpp:237]     Train net output #0: loss = 0.209492 (* 1 = 0.209492 loss)
I0407 10:25:57.485018 17183 sgd_solver.cpp:105] Iteration 5220, lr = 0.0025
I0407 10:26:02.671610 17183 solver.cpp:218] Iteration 5232 (2.31368 iter/s, 5.18653s/12 iters), loss = 0.357862
I0407 10:26:02.671651 17183 solver.cpp:237]     Train net output #0: loss = 0.357862 (* 1 = 0.357862 loss)
I0407 10:26:02.671658 17183 sgd_solver.cpp:105] Iteration 5232, lr = 0.0025
I0407 10:26:07.930617 17183 solver.cpp:218] Iteration 5244 (2.28185 iter/s, 5.25889s/12 iters), loss = 0.286545
I0407 10:26:07.930739 17183 solver.cpp:237]     Train net output #0: loss = 0.286545 (* 1 = 0.286545 loss)
I0407 10:26:07.930749 17183 sgd_solver.cpp:105] Iteration 5244, lr = 0.0025
I0407 10:26:13.107225 17183 solver.cpp:218] Iteration 5256 (2.3182 iter/s, 5.17642s/12 iters), loss = 0.174735
I0407 10:26:13.107271 17183 solver.cpp:237]     Train net output #0: loss = 0.174736 (* 1 = 0.174736 loss)
I0407 10:26:13.107278 17183 sgd_solver.cpp:105] Iteration 5256, lr = 0.0025
I0407 10:26:14.474427 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:26:18.278066 17183 solver.cpp:218] Iteration 5268 (2.32076 iter/s, 5.17073s/12 iters), loss = 0.280127
I0407 10:26:18.278110 17183 solver.cpp:237]     Train net output #0: loss = 0.280128 (* 1 = 0.280128 loss)
I0407 10:26:18.278117 17183 sgd_solver.cpp:105] Iteration 5268, lr = 0.0025
I0407 10:26:23.501999 17183 solver.cpp:218] Iteration 5280 (2.29717 iter/s, 5.22382s/12 iters), loss = 0.21558
I0407 10:26:23.502044 17183 solver.cpp:237]     Train net output #0: loss = 0.21558 (* 1 = 0.21558 loss)
I0407 10:26:23.502051 17183 sgd_solver.cpp:105] Iteration 5280, lr = 0.0025
I0407 10:26:28.655233 17183 solver.cpp:218] Iteration 5292 (2.32869 iter/s, 5.15312s/12 iters), loss = 0.273289
I0407 10:26:28.655287 17183 solver.cpp:237]     Train net output #0: loss = 0.273289 (* 1 = 0.273289 loss)
I0407 10:26:28.655295 17183 sgd_solver.cpp:105] Iteration 5292, lr = 0.0025
I0407 10:26:33.277899 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5304.caffemodel
I0407 10:26:37.922930 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5304.solverstate
I0407 10:26:40.325150 17183 solver.cpp:330] Iteration 5304, Testing net (#0)
I0407 10:26:40.325249 17183 net.cpp:676] Ignoring source layer train-data
I0407 10:26:42.572016 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:26:44.743993 17183 solver.cpp:397]     Test net output #0: accuracy = 0.463848
I0407 10:26:44.744029 17183 solver.cpp:397]     Test net output #1: loss = 2.67957 (* 1 = 2.67957 loss)
I0407 10:26:44.880497 17183 solver.cpp:218] Iteration 5304 (0.739598 iter/s, 16.225s/12 iters), loss = 0.411012
I0407 10:26:44.880553 17183 solver.cpp:237]     Train net output #0: loss = 0.411012 (* 1 = 0.411012 loss)
I0407 10:26:44.880561 17183 sgd_solver.cpp:105] Iteration 5304, lr = 0.0025
I0407 10:26:49.243592 17183 solver.cpp:218] Iteration 5316 (2.75041 iter/s, 4.36298s/12 iters), loss = 0.368727
I0407 10:26:49.243636 17183 solver.cpp:237]     Train net output #0: loss = 0.368728 (* 1 = 0.368728 loss)
I0407 10:26:49.243644 17183 sgd_solver.cpp:105] Iteration 5316, lr = 0.0025
I0407 10:26:54.410871 17183 solver.cpp:218] Iteration 5328 (2.32236 iter/s, 5.16716s/12 iters), loss = 0.253173
I0407 10:26:54.410912 17183 solver.cpp:237]     Train net output #0: loss = 0.253173 (* 1 = 0.253173 loss)
I0407 10:26:54.410919 17183 sgd_solver.cpp:105] Iteration 5328, lr = 0.0025
I0407 10:26:59.501228 17183 solver.cpp:218] Iteration 5340 (2.35745 iter/s, 5.09025s/12 iters), loss = 0.206135
I0407 10:26:59.501277 17183 solver.cpp:237]     Train net output #0: loss = 0.206135 (* 1 = 0.206135 loss)
I0407 10:26:59.501286 17183 sgd_solver.cpp:105] Iteration 5340, lr = 0.0025
I0407 10:27:04.718174 17183 solver.cpp:218] Iteration 5352 (2.30025 iter/s, 5.21683s/12 iters), loss = 0.430822
I0407 10:27:04.718216 17183 solver.cpp:237]     Train net output #0: loss = 0.430822 (* 1 = 0.430822 loss)
I0407 10:27:04.718223 17183 sgd_solver.cpp:105] Iteration 5352, lr = 0.0025
I0407 10:27:08.152915 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:27:09.810063 17183 solver.cpp:218] Iteration 5364 (2.35674 iter/s, 5.09177s/12 iters), loss = 0.234376
I0407 10:27:09.810117 17183 solver.cpp:237]     Train net output #0: loss = 0.234376 (* 1 = 0.234376 loss)
I0407 10:27:09.810127 17183 sgd_solver.cpp:105] Iteration 5364, lr = 0.0025
I0407 10:27:15.055370 17183 solver.cpp:218] Iteration 5376 (2.28781 iter/s, 5.24519s/12 iters), loss = 0.320699
I0407 10:27:15.055460 17183 solver.cpp:237]     Train net output #0: loss = 0.320699 (* 1 = 0.320699 loss)
I0407 10:27:15.055469 17183 sgd_solver.cpp:105] Iteration 5376, lr = 0.0025
I0407 10:27:20.205781 17183 solver.cpp:218] Iteration 5388 (2.32998 iter/s, 5.15025s/12 iters), loss = 0.410816
I0407 10:27:20.205821 17183 solver.cpp:237]     Train net output #0: loss = 0.410816 (* 1 = 0.410816 loss)
I0407 10:27:20.205827 17183 sgd_solver.cpp:105] Iteration 5388, lr = 0.0025
I0407 10:27:25.535892 17183 solver.cpp:218] Iteration 5400 (2.25141 iter/s, 5.33s/12 iters), loss = 0.223305
I0407 10:27:25.535950 17183 solver.cpp:237]     Train net output #0: loss = 0.223305 (* 1 = 0.223305 loss)
I0407 10:27:25.535959 17183 sgd_solver.cpp:105] Iteration 5400, lr = 0.0025
I0407 10:27:27.520188 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5406.caffemodel
I0407 10:27:30.528316 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5406.solverstate
I0407 10:27:32.824752 17183 solver.cpp:330] Iteration 5406, Testing net (#0)
I0407 10:27:32.824771 17183 net.cpp:676] Ignoring source layer train-data
I0407 10:27:35.021176 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:27:37.139189 17183 solver.cpp:397]     Test net output #0: accuracy = 0.463848
I0407 10:27:37.139223 17183 solver.cpp:397]     Test net output #1: loss = 2.75521 (* 1 = 2.75521 loss)
I0407 10:27:38.981998 17183 solver.cpp:218] Iteration 5412 (0.892466 iter/s, 13.4459s/12 iters), loss = 0.217541
I0407 10:27:38.982055 17183 solver.cpp:237]     Train net output #0: loss = 0.217541 (* 1 = 0.217541 loss)
I0407 10:27:38.982064 17183 sgd_solver.cpp:105] Iteration 5412, lr = 0.0025
I0407 10:27:44.092602 17183 solver.cpp:218] Iteration 5424 (2.34812 iter/s, 5.11048s/12 iters), loss = 0.230395
I0407 10:27:44.092659 17183 solver.cpp:237]     Train net output #0: loss = 0.230395 (* 1 = 0.230395 loss)
I0407 10:27:44.092669 17183 sgd_solver.cpp:105] Iteration 5424, lr = 0.0025
I0407 10:27:49.245440 17183 solver.cpp:218] Iteration 5436 (2.32887 iter/s, 5.15271s/12 iters), loss = 0.199966
I0407 10:27:49.245611 17183 solver.cpp:237]     Train net output #0: loss = 0.199966 (* 1 = 0.199966 loss)
I0407 10:27:49.245626 17183 sgd_solver.cpp:105] Iteration 5436, lr = 0.0025
I0407 10:27:54.303028 17183 solver.cpp:218] Iteration 5448 (2.37278 iter/s, 5.05736s/12 iters), loss = 0.21152
I0407 10:27:54.303076 17183 solver.cpp:237]     Train net output #0: loss = 0.21152 (* 1 = 0.21152 loss)
I0407 10:27:54.303082 17183 sgd_solver.cpp:105] Iteration 5448, lr = 0.0025
I0407 10:27:59.534829 17183 solver.cpp:218] Iteration 5460 (2.29372 iter/s, 5.23168s/12 iters), loss = 0.189702
I0407 10:27:59.534873 17183 solver.cpp:237]     Train net output #0: loss = 0.189702 (* 1 = 0.189702 loss)
I0407 10:27:59.534880 17183 sgd_solver.cpp:105] Iteration 5460, lr = 0.0025
I0407 10:28:00.039397 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:28:04.836308 17183 solver.cpp:218] Iteration 5472 (2.26357 iter/s, 5.30136s/12 iters), loss = 0.207885
I0407 10:28:04.836356 17183 solver.cpp:237]     Train net output #0: loss = 0.207885 (* 1 = 0.207885 loss)
I0407 10:28:04.836364 17183 sgd_solver.cpp:105] Iteration 5472, lr = 0.0025
I0407 10:28:10.064255 17183 solver.cpp:218] Iteration 5484 (2.29541 iter/s, 5.22783s/12 iters), loss = 0.289476
I0407 10:28:10.064301 17183 solver.cpp:237]     Train net output #0: loss = 0.289476 (* 1 = 0.289476 loss)
I0407 10:28:10.064307 17183 sgd_solver.cpp:105] Iteration 5484, lr = 0.0025
I0407 10:28:15.129221 17183 solver.cpp:218] Iteration 5496 (2.36927 iter/s, 5.06485s/12 iters), loss = 0.439828
I0407 10:28:15.129268 17183 solver.cpp:237]     Train net output #0: loss = 0.439829 (* 1 = 0.439829 loss)
I0407 10:28:15.129276 17183 sgd_solver.cpp:105] Iteration 5496, lr = 0.0025
I0407 10:28:19.834794 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5508.caffemodel
I0407 10:28:22.864028 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5508.solverstate
I0407 10:28:25.166968 17183 solver.cpp:330] Iteration 5508, Testing net (#0)
I0407 10:28:25.166990 17183 net.cpp:676] Ignoring source layer train-data
I0407 10:28:27.438499 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:28:29.576911 17183 solver.cpp:397]     Test net output #0: accuracy = 0.444853
I0407 10:28:29.576944 17183 solver.cpp:397]     Test net output #1: loss = 2.82574 (* 1 = 2.82574 loss)
I0407 10:28:29.717650 17183 solver.cpp:218] Iteration 5508 (0.822581 iter/s, 14.5882s/12 iters), loss = 0.287273
I0407 10:28:29.717712 17183 solver.cpp:237]     Train net output #0: loss = 0.287273 (* 1 = 0.287273 loss)
I0407 10:28:29.717721 17183 sgd_solver.cpp:105] Iteration 5508, lr = 0.0025
I0407 10:28:33.898599 17183 solver.cpp:218] Iteration 5520 (2.87024 iter/s, 4.18083s/12 iters), loss = 0.371602
I0407 10:28:33.898640 17183 solver.cpp:237]     Train net output #0: loss = 0.371602 (* 1 = 0.371602 loss)
I0407 10:28:33.898648 17183 sgd_solver.cpp:105] Iteration 5520, lr = 0.0025
I0407 10:28:36.326890 17183 blocking_queue.cpp:49] Waiting for data
I0407 10:28:38.834280 17183 solver.cpp:218] Iteration 5532 (2.43133 iter/s, 4.93557s/12 iters), loss = 0.22128
I0407 10:28:38.834321 17183 solver.cpp:237]     Train net output #0: loss = 0.22128 (* 1 = 0.22128 loss)
I0407 10:28:38.834328 17183 sgd_solver.cpp:105] Iteration 5532, lr = 0.0025
I0407 10:28:43.942912 17183 solver.cpp:218] Iteration 5544 (2.34902 iter/s, 5.10852s/12 iters), loss = 0.210653
I0407 10:28:43.942958 17183 solver.cpp:237]     Train net output #0: loss = 0.210653 (* 1 = 0.210653 loss)
I0407 10:28:43.942965 17183 sgd_solver.cpp:105] Iteration 5544, lr = 0.0025
I0407 10:28:48.901530 17183 solver.cpp:218] Iteration 5556 (2.42009 iter/s, 4.9585s/12 iters), loss = 0.268755
I0407 10:28:48.901582 17183 solver.cpp:237]     Train net output #0: loss = 0.268755 (* 1 = 0.268755 loss)
I0407 10:28:48.901592 17183 sgd_solver.cpp:105] Iteration 5556, lr = 0.0025
I0407 10:28:51.724838 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:28:54.089596 17183 solver.cpp:218] Iteration 5568 (2.31305 iter/s, 5.18795s/12 iters), loss = 0.272839
I0407 10:28:54.089630 17183 solver.cpp:237]     Train net output #0: loss = 0.272839 (* 1 = 0.272839 loss)
I0407 10:28:54.089638 17183 sgd_solver.cpp:105] Iteration 5568, lr = 0.0025
I0407 10:28:59.400878 17183 solver.cpp:218] Iteration 5580 (2.25939 iter/s, 5.31117s/12 iters), loss = 0.399178
I0407 10:28:59.400949 17183 solver.cpp:237]     Train net output #0: loss = 0.399178 (* 1 = 0.399178 loss)
I0407 10:28:59.400959 17183 sgd_solver.cpp:105] Iteration 5580, lr = 0.0025
I0407 10:29:04.588850 17183 solver.cpp:218] Iteration 5592 (2.3131 iter/s, 5.18783s/12 iters), loss = 0.20863
I0407 10:29:04.588905 17183 solver.cpp:237]     Train net output #0: loss = 0.208631 (* 1 = 0.208631 loss)
I0407 10:29:04.588914 17183 sgd_solver.cpp:105] Iteration 5592, lr = 0.0025
I0407 10:29:09.846290 17183 solver.cpp:218] Iteration 5604 (2.28253 iter/s, 5.25731s/12 iters), loss = 0.171755
I0407 10:29:09.846345 17183 solver.cpp:237]     Train net output #0: loss = 0.171755 (* 1 = 0.171755 loss)
I0407 10:29:09.846355 17183 sgd_solver.cpp:105] Iteration 5604, lr = 0.0025
I0407 10:29:11.928277 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5610.caffemodel
I0407 10:29:15.361121 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5610.solverstate
I0407 10:29:18.385010 17183 solver.cpp:330] Iteration 5610, Testing net (#0)
I0407 10:29:18.385030 17183 net.cpp:676] Ignoring source layer train-data
I0407 10:29:20.680027 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:29:22.931376 17183 solver.cpp:397]     Test net output #0: accuracy = 0.459559
I0407 10:29:22.931486 17183 solver.cpp:397]     Test net output #1: loss = 2.83792 (* 1 = 2.83792 loss)
I0407 10:29:24.781383 17183 solver.cpp:218] Iteration 5616 (0.803488 iter/s, 14.9349s/12 iters), loss = 0.248601
I0407 10:29:24.781431 17183 solver.cpp:237]     Train net output #0: loss = 0.248601 (* 1 = 0.248601 loss)
I0407 10:29:24.781438 17183 sgd_solver.cpp:105] Iteration 5616, lr = 0.0025
I0407 10:29:30.002136 17183 solver.cpp:218] Iteration 5628 (2.29857 iter/s, 5.22063s/12 iters), loss = 0.167395
I0407 10:29:30.002182 17183 solver.cpp:237]     Train net output #0: loss = 0.167395 (* 1 = 0.167395 loss)
I0407 10:29:30.002189 17183 sgd_solver.cpp:105] Iteration 5628, lr = 0.0025
I0407 10:29:35.050954 17183 solver.cpp:218] Iteration 5640 (2.37685 iter/s, 5.0487s/12 iters), loss = 0.165034
I0407 10:29:35.050999 17183 solver.cpp:237]     Train net output #0: loss = 0.165034 (* 1 = 0.165034 loss)
I0407 10:29:35.051007 17183 sgd_solver.cpp:105] Iteration 5640, lr = 0.0025
I0407 10:29:40.107182 17183 solver.cpp:218] Iteration 5652 (2.37337 iter/s, 5.05611s/12 iters), loss = 0.205237
I0407 10:29:40.107239 17183 solver.cpp:237]     Train net output #0: loss = 0.205237 (* 1 = 0.205237 loss)
I0407 10:29:40.107249 17183 sgd_solver.cpp:105] Iteration 5652, lr = 0.0025
I0407 10:29:45.134636 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:29:45.342800 17183 solver.cpp:218] Iteration 5664 (2.29205 iter/s, 5.23549s/12 iters), loss = 0.198269
I0407 10:29:45.342852 17183 solver.cpp:237]     Train net output #0: loss = 0.198269 (* 1 = 0.198269 loss)
I0407 10:29:45.342864 17183 sgd_solver.cpp:105] Iteration 5664, lr = 0.0025
I0407 10:29:50.620031 17183 solver.cpp:218] Iteration 5676 (2.27397 iter/s, 5.27711s/12 iters), loss = 0.214025
I0407 10:29:50.620074 17183 solver.cpp:237]     Train net output #0: loss = 0.214025 (* 1 = 0.214025 loss)
I0407 10:29:50.620080 17183 sgd_solver.cpp:105] Iteration 5676, lr = 0.0025
I0407 10:29:55.922969 17183 solver.cpp:218] Iteration 5688 (2.26295 iter/s, 5.30282s/12 iters), loss = 0.156899
I0407 10:29:55.923130 17183 solver.cpp:237]     Train net output #0: loss = 0.156899 (* 1 = 0.156899 loss)
I0407 10:29:55.923141 17183 sgd_solver.cpp:105] Iteration 5688, lr = 0.0025
I0407 10:30:01.295130 17183 solver.cpp:218] Iteration 5700 (2.23383 iter/s, 5.37193s/12 iters), loss = 0.300712
I0407 10:30:01.295186 17183 solver.cpp:237]     Train net output #0: loss = 0.300712 (* 1 = 0.300712 loss)
I0407 10:30:01.295195 17183 sgd_solver.cpp:105] Iteration 5700, lr = 0.0025
I0407 10:30:05.873708 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5712.caffemodel
I0407 10:30:08.875749 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5712.solverstate
I0407 10:30:11.180730 17183 solver.cpp:330] Iteration 5712, Testing net (#0)
I0407 10:30:11.180749 17183 net.cpp:676] Ignoring source layer train-data
I0407 10:30:13.337939 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:30:15.541538 17183 solver.cpp:397]     Test net output #0: accuracy = 0.456495
I0407 10:30:15.541565 17183 solver.cpp:397]     Test net output #1: loss = 2.81862 (* 1 = 2.81862 loss)
I0407 10:30:15.670332 17183 solver.cpp:218] Iteration 5712 (0.834783 iter/s, 14.375s/12 iters), loss = 0.147507
I0407 10:30:15.670398 17183 solver.cpp:237]     Train net output #0: loss = 0.147507 (* 1 = 0.147507 loss)
I0407 10:30:15.670406 17183 sgd_solver.cpp:105] Iteration 5712, lr = 0.0025
I0407 10:30:19.721019 17183 solver.cpp:218] Iteration 5724 (2.96255 iter/s, 4.05056s/12 iters), loss = 0.375671
I0407 10:30:19.721063 17183 solver.cpp:237]     Train net output #0: loss = 0.375672 (* 1 = 0.375672 loss)
I0407 10:30:19.721071 17183 sgd_solver.cpp:105] Iteration 5724, lr = 0.0025
I0407 10:30:24.629151 17183 solver.cpp:218] Iteration 5736 (2.44498 iter/s, 4.90802s/12 iters), loss = 0.22059
I0407 10:30:24.629194 17183 solver.cpp:237]     Train net output #0: loss = 0.220591 (* 1 = 0.220591 loss)
I0407 10:30:24.629201 17183 sgd_solver.cpp:105] Iteration 5736, lr = 0.0025
I0407 10:30:29.729966 17183 solver.cpp:218] Iteration 5748 (2.35262 iter/s, 5.1007s/12 iters), loss = 0.392258
I0407 10:30:29.730054 17183 solver.cpp:237]     Train net output #0: loss = 0.392258 (* 1 = 0.392258 loss)
I0407 10:30:29.730062 17183 sgd_solver.cpp:105] Iteration 5748, lr = 0.0025
I0407 10:30:35.006841 17183 solver.cpp:218] Iteration 5760 (2.27414 iter/s, 5.27672s/12 iters), loss = 0.223901
I0407 10:30:35.006892 17183 solver.cpp:237]     Train net output #0: loss = 0.223901 (* 1 = 0.223901 loss)
I0407 10:30:35.006902 17183 sgd_solver.cpp:105] Iteration 5760, lr = 0.0025
I0407 10:30:37.070922 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:30:40.317464 17183 solver.cpp:218] Iteration 5772 (2.25967 iter/s, 5.3105s/12 iters), loss = 0.304775
I0407 10:30:40.317508 17183 solver.cpp:237]     Train net output #0: loss = 0.304775 (* 1 = 0.304775 loss)
I0407 10:30:40.317517 17183 sgd_solver.cpp:105] Iteration 5772, lr = 0.0025
I0407 10:30:45.366400 17183 solver.cpp:218] Iteration 5784 (2.37679 iter/s, 5.04882s/12 iters), loss = 0.174954
I0407 10:30:45.366456 17183 solver.cpp:237]     Train net output #0: loss = 0.174954 (* 1 = 0.174954 loss)
I0407 10:30:45.366466 17183 sgd_solver.cpp:105] Iteration 5784, lr = 0.0025
I0407 10:30:50.550452 17183 solver.cpp:218] Iteration 5796 (2.31485 iter/s, 5.18393s/12 iters), loss = 0.1661
I0407 10:30:50.550494 17183 solver.cpp:237]     Train net output #0: loss = 0.166101 (* 1 = 0.166101 loss)
I0407 10:30:50.550500 17183 sgd_solver.cpp:105] Iteration 5796, lr = 0.0025
I0407 10:30:55.792109 17183 solver.cpp:218] Iteration 5808 (2.2894 iter/s, 5.24155s/12 iters), loss = 0.261145
I0407 10:30:55.792147 17183 solver.cpp:237]     Train net output #0: loss = 0.261146 (* 1 = 0.261146 loss)
I0407 10:30:55.792155 17183 sgd_solver.cpp:105] Iteration 5808, lr = 0.0025
I0407 10:30:57.829808 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5814.caffemodel
I0407 10:31:00.854471 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5814.solverstate
I0407 10:31:03.193992 17183 solver.cpp:330] Iteration 5814, Testing net (#0)
I0407 10:31:03.194012 17183 net.cpp:676] Ignoring source layer train-data
I0407 10:31:05.341480 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:31:07.593492 17183 solver.cpp:397]     Test net output #0: accuracy = 0.463235
I0407 10:31:07.593536 17183 solver.cpp:397]     Test net output #1: loss = 2.80418 (* 1 = 2.80418 loss)
I0407 10:31:09.476294 17183 solver.cpp:218] Iteration 5820 (0.876937 iter/s, 13.684s/12 iters), loss = 0.295368
I0407 10:31:09.476338 17183 solver.cpp:237]     Train net output #0: loss = 0.295369 (* 1 = 0.295369 loss)
I0407 10:31:09.476346 17183 sgd_solver.cpp:105] Iteration 5820, lr = 0.0025
I0407 10:31:14.568866 17183 solver.cpp:218] Iteration 5832 (2.35643 iter/s, 5.09246s/12 iters), loss = 0.237225
I0407 10:31:14.568917 17183 solver.cpp:237]     Train net output #0: loss = 0.237225 (* 1 = 0.237225 loss)
I0407 10:31:14.568925 17183 sgd_solver.cpp:105] Iteration 5832, lr = 0.0025
I0407 10:31:19.683396 17183 solver.cpp:218] Iteration 5844 (2.34631 iter/s, 5.11441s/12 iters), loss = 0.234138
I0407 10:31:19.683442 17183 solver.cpp:237]     Train net output #0: loss = 0.234138 (* 1 = 0.234138 loss)
I0407 10:31:19.683451 17183 sgd_solver.cpp:105] Iteration 5844, lr = 0.0025
I0407 10:31:25.108944 17183 solver.cpp:218] Iteration 5856 (2.21181 iter/s, 5.42543s/12 iters), loss = 0.211855
I0407 10:31:25.108991 17183 solver.cpp:237]     Train net output #0: loss = 0.211855 (* 1 = 0.211855 loss)
I0407 10:31:25.108999 17183 sgd_solver.cpp:105] Iteration 5856, lr = 0.0025
I0407 10:31:29.378409 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:31:30.267983 17183 solver.cpp:218] Iteration 5868 (2.32607 iter/s, 5.15893s/12 iters), loss = 0.189083
I0407 10:31:30.268023 17183 solver.cpp:237]     Train net output #0: loss = 0.189083 (* 1 = 0.189083 loss)
I0407 10:31:30.268030 17183 sgd_solver.cpp:105] Iteration 5868, lr = 0.0025
I0407 10:31:35.447813 17183 solver.cpp:218] Iteration 5880 (2.31673 iter/s, 5.17972s/12 iters), loss = 0.140537
I0407 10:31:35.447901 17183 solver.cpp:237]     Train net output #0: loss = 0.140537 (* 1 = 0.140537 loss)
I0407 10:31:35.447909 17183 sgd_solver.cpp:105] Iteration 5880, lr = 0.0025
I0407 10:31:40.654471 17183 solver.cpp:218] Iteration 5892 (2.30481 iter/s, 5.2065s/12 iters), loss = 0.216597
I0407 10:31:40.654517 17183 solver.cpp:237]     Train net output #0: loss = 0.216597 (* 1 = 0.216597 loss)
I0407 10:31:40.654525 17183 sgd_solver.cpp:105] Iteration 5892, lr = 0.0025
I0407 10:31:45.683352 17183 solver.cpp:218] Iteration 5904 (2.38627 iter/s, 5.02877s/12 iters), loss = 0.0963077
I0407 10:31:45.683396 17183 solver.cpp:237]     Train net output #0: loss = 0.0963079 (* 1 = 0.0963079 loss)
I0407 10:31:45.683403 17183 sgd_solver.cpp:105] Iteration 5904, lr = 0.0025
I0407 10:31:50.239240 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5916.caffemodel
I0407 10:31:53.263057 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5916.solverstate
I0407 10:31:55.596891 17183 solver.cpp:330] Iteration 5916, Testing net (#0)
I0407 10:31:55.596912 17183 net.cpp:676] Ignoring source layer train-data
I0407 10:31:57.650635 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:31:59.930358 17183 solver.cpp:397]     Test net output #0: accuracy = 0.472426
I0407 10:31:59.930397 17183 solver.cpp:397]     Test net output #1: loss = 2.80387 (* 1 = 2.80387 loss)
I0407 10:32:00.064553 17183 solver.cpp:218] Iteration 5916 (0.834435 iter/s, 14.381s/12 iters), loss = 0.32122
I0407 10:32:00.064600 17183 solver.cpp:237]     Train net output #0: loss = 0.32122 (* 1 = 0.32122 loss)
I0407 10:32:00.064608 17183 sgd_solver.cpp:105] Iteration 5916, lr = 0.0025
I0407 10:32:04.356837 17183 solver.cpp:218] Iteration 5928 (2.79578 iter/s, 4.29218s/12 iters), loss = 0.240482
I0407 10:32:04.356879 17183 solver.cpp:237]     Train net output #0: loss = 0.240482 (* 1 = 0.240482 loss)
I0407 10:32:04.356892 17183 sgd_solver.cpp:105] Iteration 5928, lr = 0.0025
I0407 10:32:09.522089 17183 solver.cpp:218] Iteration 5940 (2.32327 iter/s, 5.16514s/12 iters), loss = 0.225969
I0407 10:32:09.522234 17183 solver.cpp:237]     Train net output #0: loss = 0.22597 (* 1 = 0.22597 loss)
I0407 10:32:09.522245 17183 sgd_solver.cpp:105] Iteration 5940, lr = 0.0025
I0407 10:32:14.623790 17183 solver.cpp:218] Iteration 5952 (2.35225 iter/s, 5.10149s/12 iters), loss = 0.201364
I0407 10:32:14.623827 17183 solver.cpp:237]     Train net output #0: loss = 0.201364 (* 1 = 0.201364 loss)
I0407 10:32:14.623836 17183 sgd_solver.cpp:105] Iteration 5952, lr = 0.0025
I0407 10:32:19.803325 17183 solver.cpp:218] Iteration 5964 (2.31686 iter/s, 5.17943s/12 iters), loss = 0.126091
I0407 10:32:19.803371 17183 solver.cpp:237]     Train net output #0: loss = 0.126092 (* 1 = 0.126092 loss)
I0407 10:32:19.803378 17183 sgd_solver.cpp:105] Iteration 5964, lr = 0.0025
I0407 10:32:21.206034 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:32:25.046576 17183 solver.cpp:218] Iteration 5976 (2.28871 iter/s, 5.24313s/12 iters), loss = 0.133589
I0407 10:32:25.046633 17183 solver.cpp:237]     Train net output #0: loss = 0.133589 (* 1 = 0.133589 loss)
I0407 10:32:25.046643 17183 sgd_solver.cpp:105] Iteration 5976, lr = 0.0025
I0407 10:32:30.078467 17183 solver.cpp:218] Iteration 5988 (2.38485 iter/s, 5.03177s/12 iters), loss = 0.110964
I0407 10:32:30.078509 17183 solver.cpp:237]     Train net output #0: loss = 0.110964 (* 1 = 0.110964 loss)
I0407 10:32:30.078516 17183 sgd_solver.cpp:105] Iteration 5988, lr = 0.0025
I0407 10:32:35.235800 17183 solver.cpp:218] Iteration 6000 (2.32683 iter/s, 5.15722s/12 iters), loss = 0.0931256
I0407 10:32:35.235842 17183 solver.cpp:237]     Train net output #0: loss = 0.0931257 (* 1 = 0.0931257 loss)
I0407 10:32:35.235850 17183 sgd_solver.cpp:105] Iteration 6000, lr = 0.0025
I0407 10:32:40.462591 17183 solver.cpp:218] Iteration 6012 (2.29591 iter/s, 5.22668s/12 iters), loss = 0.233485
I0407 10:32:40.462678 17183 solver.cpp:237]     Train net output #0: loss = 0.233485 (* 1 = 0.233485 loss)
I0407 10:32:40.462687 17183 sgd_solver.cpp:105] Iteration 6012, lr = 0.0025
I0407 10:32:42.554951 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6018.caffemodel
I0407 10:32:45.631475 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6018.solverstate
I0407 10:32:47.945921 17183 solver.cpp:330] Iteration 6018, Testing net (#0)
I0407 10:32:47.945945 17183 net.cpp:676] Ignoring source layer train-data
I0407 10:32:49.915088 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:32:52.240090 17183 solver.cpp:397]     Test net output #0: accuracy = 0.471814
I0407 10:32:52.240128 17183 solver.cpp:397]     Test net output #1: loss = 2.77414 (* 1 = 2.77414 loss)
I0407 10:32:54.064587 17183 solver.cpp:218] Iteration 6024 (0.882239 iter/s, 13.6018s/12 iters), loss = 0.176333
I0407 10:32:54.064637 17183 solver.cpp:237]     Train net output #0: loss = 0.176333 (* 1 = 0.176333 loss)
I0407 10:32:54.064646 17183 sgd_solver.cpp:105] Iteration 6024, lr = 0.0025
I0407 10:32:59.153517 17183 solver.cpp:218] Iteration 6036 (2.35811 iter/s, 5.08881s/12 iters), loss = 0.137353
I0407 10:32:59.153565 17183 solver.cpp:237]     Train net output #0: loss = 0.137353 (* 1 = 0.137353 loss)
I0407 10:32:59.153575 17183 sgd_solver.cpp:105] Iteration 6036, lr = 0.0025
I0407 10:33:04.293184 17183 solver.cpp:218] Iteration 6048 (2.33484 iter/s, 5.13955s/12 iters), loss = 0.183304
I0407 10:33:04.293234 17183 solver.cpp:237]     Train net output #0: loss = 0.183304 (* 1 = 0.183304 loss)
I0407 10:33:04.293243 17183 sgd_solver.cpp:105] Iteration 6048, lr = 0.0025
I0407 10:33:09.471050 17183 solver.cpp:218] Iteration 6060 (2.31761 iter/s, 5.17775s/12 iters), loss = 0.254612
I0407 10:33:09.471096 17183 solver.cpp:237]     Train net output #0: loss = 0.254612 (* 1 = 0.254612 loss)
I0407 10:33:09.471102 17183 sgd_solver.cpp:105] Iteration 6060, lr = 0.0025
I0407 10:33:13.068292 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:33:14.546576 17183 solver.cpp:218] Iteration 6072 (2.36434 iter/s, 5.07541s/12 iters), loss = 0.16019
I0407 10:33:14.546620 17183 solver.cpp:237]     Train net output #0: loss = 0.16019 (* 1 = 0.16019 loss)
I0407 10:33:14.546628 17183 sgd_solver.cpp:105] Iteration 6072, lr = 0.0025
I0407 10:33:19.676923 17183 solver.cpp:218] Iteration 6084 (2.33907 iter/s, 5.13024s/12 iters), loss = 0.150795
I0407 10:33:19.676965 17183 solver.cpp:237]     Train net output #0: loss = 0.150795 (* 1 = 0.150795 loss)
I0407 10:33:19.676973 17183 sgd_solver.cpp:105] Iteration 6084, lr = 0.0025
I0407 10:33:24.703339 17183 solver.cpp:218] Iteration 6096 (2.38744 iter/s, 5.0263s/12 iters), loss = 0.153682
I0407 10:33:24.703390 17183 solver.cpp:237]     Train net output #0: loss = 0.153683 (* 1 = 0.153683 loss)
I0407 10:33:24.703399 17183 sgd_solver.cpp:105] Iteration 6096, lr = 0.0025
I0407 10:33:29.533957 17183 solver.cpp:218] Iteration 6108 (2.48421 iter/s, 4.8305s/12 iters), loss = 0.209118
I0407 10:33:29.534011 17183 solver.cpp:237]     Train net output #0: loss = 0.209118 (* 1 = 0.209118 loss)
I0407 10:33:29.534020 17183 sgd_solver.cpp:105] Iteration 6108, lr = 0.0025
I0407 10:33:34.296844 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6120.caffemodel
I0407 10:33:37.398461 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6120.solverstate
I0407 10:33:39.754546 17183 solver.cpp:330] Iteration 6120, Testing net (#0)
I0407 10:33:39.754567 17183 net.cpp:676] Ignoring source layer train-data
I0407 10:33:41.674561 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:33:44.028537 17183 solver.cpp:397]     Test net output #0: accuracy = 0.461397
I0407 10:33:44.028666 17183 solver.cpp:397]     Test net output #1: loss = 2.87519 (* 1 = 2.87519 loss)
I0407 10:33:44.170173 17183 solver.cpp:218] Iteration 6120 (0.819896 iter/s, 14.636s/12 iters), loss = 0.313397
I0407 10:33:44.170230 17183 solver.cpp:237]     Train net output #0: loss = 0.313398 (* 1 = 0.313398 loss)
I0407 10:33:44.170239 17183 sgd_solver.cpp:105] Iteration 6120, lr = 0.0025
I0407 10:33:48.349038 17183 solver.cpp:218] Iteration 6132 (2.87167 iter/s, 4.17875s/12 iters), loss = 0.172789
I0407 10:33:48.349092 17183 solver.cpp:237]     Train net output #0: loss = 0.172789 (* 1 = 0.172789 loss)
I0407 10:33:48.349102 17183 sgd_solver.cpp:105] Iteration 6132, lr = 0.0025
I0407 10:33:53.571574 17183 solver.cpp:218] Iteration 6144 (2.29779 iter/s, 5.22241s/12 iters), loss = 0.174373
I0407 10:33:53.571617 17183 solver.cpp:237]     Train net output #0: loss = 0.174373 (* 1 = 0.174373 loss)
I0407 10:33:53.571624 17183 sgd_solver.cpp:105] Iteration 6144, lr = 0.0025
I0407 10:33:58.716513 17183 solver.cpp:218] Iteration 6156 (2.33244 iter/s, 5.14483s/12 iters), loss = 0.15049
I0407 10:33:58.716555 17183 solver.cpp:237]     Train net output #0: loss = 0.15049 (* 1 = 0.15049 loss)
I0407 10:33:58.716562 17183 sgd_solver.cpp:105] Iteration 6156, lr = 0.0025
I0407 10:34:03.809051 17183 solver.cpp:218] Iteration 6168 (2.35644 iter/s, 5.09243s/12 iters), loss = 0.192061
I0407 10:34:03.809100 17183 solver.cpp:237]     Train net output #0: loss = 0.192062 (* 1 = 0.192062 loss)
I0407 10:34:03.809108 17183 sgd_solver.cpp:105] Iteration 6168, lr = 0.0025
I0407 10:34:04.437690 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:34:09.129240 17183 solver.cpp:218] Iteration 6180 (2.25561 iter/s, 5.32007s/12 iters), loss = 0.327129
I0407 10:34:09.129282 17183 solver.cpp:237]     Train net output #0: loss = 0.32713 (* 1 = 0.32713 loss)
I0407 10:34:09.129289 17183 sgd_solver.cpp:105] Iteration 6180, lr = 0.0025
I0407 10:34:14.247476 17183 solver.cpp:218] Iteration 6192 (2.34461 iter/s, 5.11812s/12 iters), loss = 0.210869
I0407 10:34:14.247619 17183 solver.cpp:237]     Train net output #0: loss = 0.21087 (* 1 = 0.21087 loss)
I0407 10:34:14.247628 17183 sgd_solver.cpp:105] Iteration 6192, lr = 0.0025
I0407 10:34:19.387768 17183 solver.cpp:218] Iteration 6204 (2.33459 iter/s, 5.14008s/12 iters), loss = 0.138616
I0407 10:34:19.387814 17183 solver.cpp:237]     Train net output #0: loss = 0.138616 (* 1 = 0.138616 loss)
I0407 10:34:19.387820 17183 sgd_solver.cpp:105] Iteration 6204, lr = 0.0025
I0407 10:34:24.609688 17183 solver.cpp:218] Iteration 6216 (2.29805 iter/s, 5.22181s/12 iters), loss = 0.13685
I0407 10:34:24.609732 17183 solver.cpp:237]     Train net output #0: loss = 0.13685 (* 1 = 0.13685 loss)
I0407 10:34:24.609740 17183 sgd_solver.cpp:105] Iteration 6216, lr = 0.0025
I0407 10:34:26.684417 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6222.caffemodel
I0407 10:34:29.676774 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6222.solverstate
I0407 10:34:31.985508 17183 solver.cpp:330] Iteration 6222, Testing net (#0)
I0407 10:34:31.985528 17183 net.cpp:676] Ignoring source layer train-data
I0407 10:34:33.859192 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:34:35.112025 17183 blocking_queue.cpp:49] Waiting for data
I0407 10:34:36.352979 17183 solver.cpp:397]     Test net output #0: accuracy = 0.468137
I0407 10:34:36.353016 17183 solver.cpp:397]     Test net output #1: loss = 2.82827 (* 1 = 2.82827 loss)
I0407 10:34:38.255311 17183 solver.cpp:218] Iteration 6228 (0.879415 iter/s, 13.6454s/12 iters), loss = 0.224754
I0407 10:34:38.255348 17183 solver.cpp:237]     Train net output #0: loss = 0.224755 (* 1 = 0.224755 loss)
I0407 10:34:38.255354 17183 sgd_solver.cpp:105] Iteration 6228, lr = 0.0025
I0407 10:34:43.193006 17183 solver.cpp:218] Iteration 6240 (2.43034 iter/s, 4.93759s/12 iters), loss = 0.252742
I0407 10:34:43.193061 17183 solver.cpp:237]     Train net output #0: loss = 0.252743 (* 1 = 0.252743 loss)
I0407 10:34:43.193073 17183 sgd_solver.cpp:105] Iteration 6240, lr = 0.0025
I0407 10:34:48.228013 17183 solver.cpp:218] Iteration 6252 (2.38337 iter/s, 5.03489s/12 iters), loss = 0.33237
I0407 10:34:48.228148 17183 solver.cpp:237]     Train net output #0: loss = 0.33237 (* 1 = 0.33237 loss)
I0407 10:34:48.228157 17183 sgd_solver.cpp:105] Iteration 6252, lr = 0.0025
I0407 10:34:53.178261 17183 solver.cpp:218] Iteration 6264 (2.42422 iter/s, 4.95005s/12 iters), loss = 0.182624
I0407 10:34:53.178303 17183 solver.cpp:237]     Train net output #0: loss = 0.182624 (* 1 = 0.182624 loss)
I0407 10:34:53.178310 17183 sgd_solver.cpp:105] Iteration 6264, lr = 0.0025
I0407 10:34:56.039064 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:34:58.452306 17183 solver.cpp:218] Iteration 6276 (2.27534 iter/s, 5.27393s/12 iters), loss = 0.204793
I0407 10:34:58.452353 17183 solver.cpp:237]     Train net output #0: loss = 0.204793 (* 1 = 0.204793 loss)
I0407 10:34:58.452360 17183 sgd_solver.cpp:105] Iteration 6276, lr = 0.0025
I0407 10:35:03.474650 17183 solver.cpp:218] Iteration 6288 (2.38938 iter/s, 5.02223s/12 iters), loss = 0.21792
I0407 10:35:03.474701 17183 solver.cpp:237]     Train net output #0: loss = 0.21792 (* 1 = 0.21792 loss)
I0407 10:35:03.474709 17183 sgd_solver.cpp:105] Iteration 6288, lr = 0.0025
I0407 10:35:08.706540 17183 solver.cpp:218] Iteration 6300 (2.29368 iter/s, 5.23177s/12 iters), loss = 0.198343
I0407 10:35:08.706589 17183 solver.cpp:237]     Train net output #0: loss = 0.198343 (* 1 = 0.198343 loss)
I0407 10:35:08.706598 17183 sgd_solver.cpp:105] Iteration 6300, lr = 0.0025
I0407 10:35:13.934671 17183 solver.cpp:218] Iteration 6312 (2.29533 iter/s, 5.22801s/12 iters), loss = 0.235605
I0407 10:35:13.934715 17183 solver.cpp:237]     Train net output #0: loss = 0.235605 (* 1 = 0.235605 loss)
I0407 10:35:13.934722 17183 sgd_solver.cpp:105] Iteration 6312, lr = 0.0025
I0407 10:35:18.570780 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6324.caffemodel
I0407 10:35:21.627866 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6324.solverstate
I0407 10:35:23.950585 17183 solver.cpp:330] Iteration 6324, Testing net (#0)
I0407 10:35:23.950605 17183 net.cpp:676] Ignoring source layer train-data
I0407 10:35:25.894979 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:35:28.408087 17183 solver.cpp:397]     Test net output #0: accuracy = 0.465686
I0407 10:35:28.408118 17183 solver.cpp:397]     Test net output #1: loss = 2.79449 (* 1 = 2.79449 loss)
I0407 10:35:28.549597 17183 solver.cpp:218] Iteration 6324 (0.82109 iter/s, 14.6147s/12 iters), loss = 0.080764
I0407 10:35:28.549641 17183 solver.cpp:237]     Train net output #0: loss = 0.0807641 (* 1 = 0.0807641 loss)
I0407 10:35:28.549649 17183 sgd_solver.cpp:105] Iteration 6324, lr = 0.0025
I0407 10:35:32.710855 17183 solver.cpp:218] Iteration 6336 (2.88382 iter/s, 4.16115s/12 iters), loss = 0.162865
I0407 10:35:32.710901 17183 solver.cpp:237]     Train net output #0: loss = 0.162865 (* 1 = 0.162865 loss)
I0407 10:35:32.710908 17183 sgd_solver.cpp:105] Iteration 6336, lr = 0.0025
I0407 10:35:37.807552 17183 solver.cpp:218] Iteration 6348 (2.35452 iter/s, 5.09658s/12 iters), loss = 0.140145
I0407 10:35:37.807615 17183 solver.cpp:237]     Train net output #0: loss = 0.140145 (* 1 = 0.140145 loss)
I0407 10:35:37.807627 17183 sgd_solver.cpp:105] Iteration 6348, lr = 0.0025
I0407 10:35:42.895730 17183 solver.cpp:218] Iteration 6360 (2.35846 iter/s, 5.08806s/12 iters), loss = 0.135163
I0407 10:35:42.895766 17183 solver.cpp:237]     Train net output #0: loss = 0.135163 (* 1 = 0.135163 loss)
I0407 10:35:42.895774 17183 sgd_solver.cpp:105] Iteration 6360, lr = 0.0025
I0407 10:35:47.793190 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:35:47.976692 17183 solver.cpp:218] Iteration 6372 (2.36181 iter/s, 5.08086s/12 iters), loss = 0.165443
I0407 10:35:47.976740 17183 solver.cpp:237]     Train net output #0: loss = 0.165443 (* 1 = 0.165443 loss)
I0407 10:35:47.976747 17183 sgd_solver.cpp:105] Iteration 6372, lr = 0.0025
I0407 10:35:53.227947 17183 solver.cpp:218] Iteration 6384 (2.28522 iter/s, 5.25114s/12 iters), loss = 0.188304
I0407 10:35:53.228050 17183 solver.cpp:237]     Train net output #0: loss = 0.188304 (* 1 = 0.188304 loss)
I0407 10:35:53.228060 17183 sgd_solver.cpp:105] Iteration 6384, lr = 0.0025
I0407 10:35:58.468961 17183 solver.cpp:218] Iteration 6396 (2.28971 iter/s, 5.24084s/12 iters), loss = 0.158067
I0407 10:35:58.469009 17183 solver.cpp:237]     Train net output #0: loss = 0.158067 (* 1 = 0.158067 loss)
I0407 10:35:58.469017 17183 sgd_solver.cpp:105] Iteration 6396, lr = 0.0025
I0407 10:36:03.597553 17183 solver.cpp:218] Iteration 6408 (2.33988 iter/s, 5.12848s/12 iters), loss = 0.194425
I0407 10:36:03.597599 17183 solver.cpp:237]     Train net output #0: loss = 0.194425 (* 1 = 0.194425 loss)
I0407 10:36:03.597606 17183 sgd_solver.cpp:105] Iteration 6408, lr = 0.0025
I0407 10:36:08.681640 17183 solver.cpp:218] Iteration 6420 (2.36036 iter/s, 5.08397s/12 iters), loss = 0.191943
I0407 10:36:08.681697 17183 solver.cpp:237]     Train net output #0: loss = 0.191943 (* 1 = 0.191943 loss)
I0407 10:36:08.681706 17183 sgd_solver.cpp:105] Iteration 6420, lr = 0.0025
I0407 10:36:10.821529 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6426.caffemodel
I0407 10:36:13.863489 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6426.solverstate
I0407 10:36:17.269295 17183 solver.cpp:330] Iteration 6426, Testing net (#0)
I0407 10:36:17.269317 17183 net.cpp:676] Ignoring source layer train-data
I0407 10:36:19.098260 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:36:21.579015 17183 solver.cpp:397]     Test net output #0: accuracy = 0.473039
I0407 10:36:21.579052 17183 solver.cpp:397]     Test net output #1: loss = 2.7519 (* 1 = 2.7519 loss)
I0407 10:36:23.385107 17183 solver.cpp:218] Iteration 6432 (0.816146 iter/s, 14.7032s/12 iters), loss = 0.154411
I0407 10:36:23.385274 17183 solver.cpp:237]     Train net output #0: loss = 0.154411 (* 1 = 0.154411 loss)
I0407 10:36:23.385288 17183 sgd_solver.cpp:105] Iteration 6432, lr = 0.0025
I0407 10:36:28.610769 17183 solver.cpp:218] Iteration 6444 (2.29648 iter/s, 5.22538s/12 iters), loss = 0.217768
I0407 10:36:28.610814 17183 solver.cpp:237]     Train net output #0: loss = 0.217768 (* 1 = 0.217768 loss)
I0407 10:36:28.610821 17183 sgd_solver.cpp:105] Iteration 6444, lr = 0.0025
I0407 10:36:33.663105 17183 solver.cpp:218] Iteration 6456 (2.37519 iter/s, 5.05222s/12 iters), loss = 0.108717
I0407 10:36:33.663157 17183 solver.cpp:237]     Train net output #0: loss = 0.108718 (* 1 = 0.108718 loss)
I0407 10:36:33.663166 17183 sgd_solver.cpp:105] Iteration 6456, lr = 0.0025
I0407 10:36:38.834540 17183 solver.cpp:218] Iteration 6468 (2.32049 iter/s, 5.17131s/12 iters), loss = 0.172585
I0407 10:36:38.834594 17183 solver.cpp:237]     Train net output #0: loss = 0.172585 (* 1 = 0.172585 loss)
I0407 10:36:38.834604 17183 sgd_solver.cpp:105] Iteration 6468, lr = 0.0025
I0407 10:36:40.879837 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:36:44.073299 17183 solver.cpp:218] Iteration 6480 (2.29067 iter/s, 5.23864s/12 iters), loss = 0.141487
I0407 10:36:44.073355 17183 solver.cpp:237]     Train net output #0: loss = 0.141487 (* 1 = 0.141487 loss)
I0407 10:36:44.073365 17183 sgd_solver.cpp:105] Iteration 6480, lr = 0.0025
I0407 10:36:49.272018 17183 solver.cpp:218] Iteration 6492 (2.30832 iter/s, 5.1986s/12 iters), loss = 0.0926582
I0407 10:36:49.272064 17183 solver.cpp:237]     Train net output #0: loss = 0.0926583 (* 1 = 0.0926583 loss)
I0407 10:36:49.272071 17183 sgd_solver.cpp:105] Iteration 6492, lr = 0.0025
I0407 10:36:54.444054 17183 solver.cpp:218] Iteration 6504 (2.32022 iter/s, 5.17192s/12 iters), loss = 0.092945
I0407 10:36:54.444149 17183 solver.cpp:237]     Train net output #0: loss = 0.0929452 (* 1 = 0.0929452 loss)
I0407 10:36:54.444156 17183 sgd_solver.cpp:105] Iteration 6504, lr = 0.0025
I0407 10:36:59.650108 17183 solver.cpp:218] Iteration 6516 (2.30508 iter/s, 5.20589s/12 iters), loss = 0.149896
I0407 10:36:59.650168 17183 solver.cpp:237]     Train net output #0: loss = 0.149896 (* 1 = 0.149896 loss)
I0407 10:36:59.650179 17183 sgd_solver.cpp:105] Iteration 6516, lr = 0.0025
I0407 10:37:04.377311 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6528.caffemodel
I0407 10:37:08.373148 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6528.solverstate
I0407 10:37:11.456727 17183 solver.cpp:330] Iteration 6528, Testing net (#0)
I0407 10:37:11.456748 17183 net.cpp:676] Ignoring source layer train-data
I0407 10:37:13.264516 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:37:15.838470 17183 solver.cpp:397]     Test net output #0: accuracy = 0.46201
I0407 10:37:15.838502 17183 solver.cpp:397]     Test net output #1: loss = 2.94178 (* 1 = 2.94178 loss)
I0407 10:37:15.979064 17183 solver.cpp:218] Iteration 6528 (0.734902 iter/s, 16.3287s/12 iters), loss = 0.120463
I0407 10:37:15.979118 17183 solver.cpp:237]     Train net output #0: loss = 0.120463 (* 1 = 0.120463 loss)
I0407 10:37:15.979130 17183 sgd_solver.cpp:105] Iteration 6528, lr = 0.0025
I0407 10:37:20.136093 17183 solver.cpp:218] Iteration 6540 (2.88675 iter/s, 4.15692s/12 iters), loss = 0.159697
I0407 10:37:20.136144 17183 solver.cpp:237]     Train net output #0: loss = 0.159698 (* 1 = 0.159698 loss)
I0407 10:37:20.136153 17183 sgd_solver.cpp:105] Iteration 6540, lr = 0.0025
I0407 10:37:25.166375 17183 solver.cpp:218] Iteration 6552 (2.38561 iter/s, 5.03017s/12 iters), loss = 0.227867
I0407 10:37:25.166494 17183 solver.cpp:237]     Train net output #0: loss = 0.227867 (* 1 = 0.227867 loss)
I0407 10:37:25.166502 17183 sgd_solver.cpp:105] Iteration 6552, lr = 0.0025
I0407 10:37:30.153337 17183 solver.cpp:218] Iteration 6564 (2.40636 iter/s, 4.98678s/12 iters), loss = 0.130073
I0407 10:37:30.153376 17183 solver.cpp:237]     Train net output #0: loss = 0.130074 (* 1 = 0.130074 loss)
I0407 10:37:30.153383 17183 sgd_solver.cpp:105] Iteration 6564, lr = 0.0025
I0407 10:37:34.409091 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:37:35.228256 17183 solver.cpp:218] Iteration 6576 (2.36462 iter/s, 5.07481s/12 iters), loss = 0.282708
I0407 10:37:35.228299 17183 solver.cpp:237]     Train net output #0: loss = 0.282708 (* 1 = 0.282708 loss)
I0407 10:37:35.228307 17183 sgd_solver.cpp:105] Iteration 6576, lr = 0.0025
I0407 10:37:40.416213 17183 solver.cpp:218] Iteration 6588 (2.3131 iter/s, 5.18784s/12 iters), loss = 0.219129
I0407 10:37:40.416260 17183 solver.cpp:237]     Train net output #0: loss = 0.21913 (* 1 = 0.21913 loss)
I0407 10:37:40.416267 17183 sgd_solver.cpp:105] Iteration 6588, lr = 0.0025
I0407 10:37:45.558792 17183 solver.cpp:218] Iteration 6600 (2.33351 iter/s, 5.14246s/12 iters), loss = 0.15337
I0407 10:37:45.558837 17183 solver.cpp:237]     Train net output #0: loss = 0.15337 (* 1 = 0.15337 loss)
I0407 10:37:45.558845 17183 sgd_solver.cpp:105] Iteration 6600, lr = 0.0025
I0407 10:37:50.791785 17183 solver.cpp:218] Iteration 6612 (2.29319 iter/s, 5.23288s/12 iters), loss = 0.145266
I0407 10:37:50.791837 17183 solver.cpp:237]     Train net output #0: loss = 0.145266 (* 1 = 0.145266 loss)
I0407 10:37:50.791846 17183 sgd_solver.cpp:105] Iteration 6612, lr = 0.0025
I0407 10:37:55.777801 17183 solver.cpp:218] Iteration 6624 (2.40679 iter/s, 4.9859s/12 iters), loss = 0.13015
I0407 10:37:55.777930 17183 solver.cpp:237]     Train net output #0: loss = 0.13015 (* 1 = 0.13015 loss)
I0407 10:37:55.777940 17183 sgd_solver.cpp:105] Iteration 6624, lr = 0.0025
I0407 10:37:57.829141 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6630.caffemodel
I0407 10:38:01.520119 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6630.solverstate
I0407 10:38:03.822474 17183 solver.cpp:330] Iteration 6630, Testing net (#0)
I0407 10:38:03.822494 17183 net.cpp:676] Ignoring source layer train-data
I0407 10:38:05.727015 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:38:08.356781 17183 solver.cpp:397]     Test net output #0: accuracy = 0.452819
I0407 10:38:08.356818 17183 solver.cpp:397]     Test net output #1: loss = 2.90699 (* 1 = 2.90699 loss)
I0407 10:38:10.295200 17183 solver.cpp:218] Iteration 6636 (0.82661 iter/s, 14.5171s/12 iters), loss = 0.133132
I0407 10:38:10.295244 17183 solver.cpp:237]     Train net output #0: loss = 0.133132 (* 1 = 0.133132 loss)
I0407 10:38:10.295251 17183 sgd_solver.cpp:105] Iteration 6636, lr = 0.0025
I0407 10:38:15.161711 17183 solver.cpp:218] Iteration 6648 (2.46589 iter/s, 4.8664s/12 iters), loss = 0.119367
I0407 10:38:15.161752 17183 solver.cpp:237]     Train net output #0: loss = 0.119367 (* 1 = 0.119367 loss)
I0407 10:38:15.161761 17183 sgd_solver.cpp:105] Iteration 6648, lr = 0.0025
I0407 10:38:20.374091 17183 solver.cpp:218] Iteration 6660 (2.30226 iter/s, 5.21227s/12 iters), loss = 0.0970739
I0407 10:38:20.374155 17183 solver.cpp:237]     Train net output #0: loss = 0.0970741 (* 1 = 0.0970741 loss)
I0407 10:38:20.374166 17183 sgd_solver.cpp:105] Iteration 6660, lr = 0.0025
I0407 10:38:25.597259 17183 solver.cpp:218] Iteration 6672 (2.29751 iter/s, 5.22304s/12 iters), loss = 0.304232
I0407 10:38:25.597311 17183 solver.cpp:237]     Train net output #0: loss = 0.304232 (* 1 = 0.304232 loss)
I0407 10:38:25.597321 17183 sgd_solver.cpp:105] Iteration 6672, lr = 0.0025
I0407 10:38:26.966840 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:38:30.797565 17183 solver.cpp:218] Iteration 6684 (2.30761 iter/s, 5.20018s/12 iters), loss = 0.0845576
I0407 10:38:30.797616 17183 solver.cpp:237]     Train net output #0: loss = 0.0845578 (* 1 = 0.0845578 loss)
I0407 10:38:30.797624 17183 sgd_solver.cpp:105] Iteration 6684, lr = 0.0025
I0407 10:38:36.010218 17183 solver.cpp:218] Iteration 6696 (2.30215 iter/s, 5.21253s/12 iters), loss = 0.146792
I0407 10:38:36.010275 17183 solver.cpp:237]     Train net output #0: loss = 0.146792 (* 1 = 0.146792 loss)
I0407 10:38:36.010285 17183 sgd_solver.cpp:105] Iteration 6696, lr = 0.0025
I0407 10:38:41.269165 17183 solver.cpp:218] Iteration 6708 (2.28188 iter/s, 5.25882s/12 iters), loss = 0.297679
I0407 10:38:41.269212 17183 solver.cpp:237]     Train net output #0: loss = 0.29768 (* 1 = 0.29768 loss)
I0407 10:38:41.269218 17183 sgd_solver.cpp:105] Iteration 6708, lr = 0.0025
I0407 10:38:46.478662 17183 solver.cpp:218] Iteration 6720 (2.30354 iter/s, 5.20938s/12 iters), loss = 0.157019
I0407 10:38:46.478725 17183 solver.cpp:237]     Train net output #0: loss = 0.15702 (* 1 = 0.15702 loss)
I0407 10:38:46.478736 17183 sgd_solver.cpp:105] Iteration 6720, lr = 0.0025
I0407 10:38:51.162761 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6732.caffemodel
I0407 10:38:55.803769 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6732.solverstate
I0407 10:38:59.125169 17183 solver.cpp:330] Iteration 6732, Testing net (#0)
I0407 10:38:59.125270 17183 net.cpp:676] Ignoring source layer train-data
I0407 10:39:00.880607 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:39:03.620345 17183 solver.cpp:397]     Test net output #0: accuracy = 0.468137
I0407 10:39:03.620398 17183 solver.cpp:397]     Test net output #1: loss = 2.82261 (* 1 = 2.82261 loss)
I0407 10:39:03.756143 17183 solver.cpp:218] Iteration 6732 (0.694556 iter/s, 17.2772s/12 iters), loss = 0.236948
I0407 10:39:03.756192 17183 solver.cpp:237]     Train net output #0: loss = 0.236948 (* 1 = 0.236948 loss)
I0407 10:39:03.756201 17183 sgd_solver.cpp:105] Iteration 6732, lr = 0.000625
I0407 10:39:07.980870 17183 solver.cpp:218] Iteration 6744 (2.84049 iter/s, 4.22462s/12 iters), loss = 0.137971
I0407 10:39:07.980926 17183 solver.cpp:237]     Train net output #0: loss = 0.137971 (* 1 = 0.137971 loss)
I0407 10:39:07.980937 17183 sgd_solver.cpp:105] Iteration 6744, lr = 0.000625
I0407 10:39:13.012703 17183 solver.cpp:218] Iteration 6756 (2.38488 iter/s, 5.0317s/12 iters), loss = 0.265228
I0407 10:39:13.012750 17183 solver.cpp:237]     Train net output #0: loss = 0.265229 (* 1 = 0.265229 loss)
I0407 10:39:13.012760 17183 sgd_solver.cpp:105] Iteration 6756, lr = 0.000625
I0407 10:39:18.110090 17183 solver.cpp:218] Iteration 6768 (2.3542 iter/s, 5.09727s/12 iters), loss = 0.104921
I0407 10:39:18.110143 17183 solver.cpp:237]     Train net output #0: loss = 0.104921 (* 1 = 0.104921 loss)
I0407 10:39:18.110152 17183 sgd_solver.cpp:105] Iteration 6768, lr = 0.000625
I0407 10:39:21.754649 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:39:23.378418 17183 solver.cpp:218] Iteration 6780 (2.27781 iter/s, 5.26821s/12 iters), loss = 0.163493
I0407 10:39:23.378465 17183 solver.cpp:237]     Train net output #0: loss = 0.163493 (* 1 = 0.163493 loss)
I0407 10:39:23.378473 17183 sgd_solver.cpp:105] Iteration 6780, lr = 0.000625
I0407 10:39:28.496083 17183 solver.cpp:218] Iteration 6792 (2.34487 iter/s, 5.11755s/12 iters), loss = 0.110128
I0407 10:39:28.496125 17183 solver.cpp:237]     Train net output #0: loss = 0.110128 (* 1 = 0.110128 loss)
I0407 10:39:28.496135 17183 sgd_solver.cpp:105] Iteration 6792, lr = 0.000625
I0407 10:39:33.663318 17183 solver.cpp:218] Iteration 6804 (2.32237 iter/s, 5.16713s/12 iters), loss = 0.151358
I0407 10:39:33.663424 17183 solver.cpp:237]     Train net output #0: loss = 0.151359 (* 1 = 0.151359 loss)
I0407 10:39:33.663431 17183 sgd_solver.cpp:105] Iteration 6804, lr = 0.000625
I0407 10:39:38.758268 17183 solver.cpp:218] Iteration 6816 (2.35535 iter/s, 5.09478s/12 iters), loss = 0.073526
I0407 10:39:38.758306 17183 solver.cpp:237]     Train net output #0: loss = 0.0735262 (* 1 = 0.0735262 loss)
I0407 10:39:38.758313 17183 sgd_solver.cpp:105] Iteration 6816, lr = 0.000625
I0407 10:39:43.921228 17183 solver.cpp:218] Iteration 6828 (2.3243 iter/s, 5.16286s/12 iters), loss = 0.209088
I0407 10:39:43.921267 17183 solver.cpp:237]     Train net output #0: loss = 0.209088 (* 1 = 0.209088 loss)
I0407 10:39:43.921275 17183 sgd_solver.cpp:105] Iteration 6828, lr = 0.000625
I0407 10:39:46.167722 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6834.caffemodel
I0407 10:39:49.298911 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6834.solverstate
I0407 10:39:51.773653 17183 solver.cpp:330] Iteration 6834, Testing net (#0)
I0407 10:39:51.773674 17183 net.cpp:676] Ignoring source layer train-data
I0407 10:39:53.410012 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:39:56.034823 17183 solver.cpp:397]     Test net output #0: accuracy = 0.479167
I0407 10:39:56.034857 17183 solver.cpp:397]     Test net output #1: loss = 2.78734 (* 1 = 2.78734 loss)
I0407 10:39:57.923017 17183 solver.cpp:218] Iteration 6840 (0.857045 iter/s, 14.0016s/12 iters), loss = 0.138393
I0407 10:39:57.923059 17183 solver.cpp:237]     Train net output #0: loss = 0.138394 (* 1 = 0.138394 loss)
I0407 10:39:57.923065 17183 sgd_solver.cpp:105] Iteration 6840, lr = 0.000625
I0407 10:40:02.818085 17183 solver.cpp:218] Iteration 6852 (2.4515 iter/s, 4.89497s/12 iters), loss = 0.153274
I0407 10:40:02.818118 17183 solver.cpp:237]     Train net output #0: loss = 0.153274 (* 1 = 0.153274 loss)
I0407 10:40:02.818125 17183 sgd_solver.cpp:105] Iteration 6852, lr = 0.000625
I0407 10:40:08.071842 17183 solver.cpp:218] Iteration 6864 (2.28412 iter/s, 5.25365s/12 iters), loss = 0.115637
I0407 10:40:08.072026 17183 solver.cpp:237]     Train net output #0: loss = 0.115637 (* 1 = 0.115637 loss)
I0407 10:40:08.072037 17183 sgd_solver.cpp:105] Iteration 6864, lr = 0.000625
I0407 10:40:13.320591 17183 solver.cpp:218] Iteration 6876 (2.28637 iter/s, 5.2485s/12 iters), loss = 0.13847
I0407 10:40:13.320644 17183 solver.cpp:237]     Train net output #0: loss = 0.13847 (* 1 = 0.13847 loss)
I0407 10:40:13.320653 17183 sgd_solver.cpp:105] Iteration 6876, lr = 0.000625
I0407 10:40:13.948331 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:40:18.380220 17183 solver.cpp:218] Iteration 6888 (2.37177 iter/s, 5.05952s/12 iters), loss = 0.155745
I0407 10:40:18.380259 17183 solver.cpp:237]     Train net output #0: loss = 0.155745 (* 1 = 0.155745 loss)
I0407 10:40:18.380266 17183 sgd_solver.cpp:105] Iteration 6888, lr = 0.000625
I0407 10:40:23.469751 17183 solver.cpp:218] Iteration 6900 (2.35783 iter/s, 5.08942s/12 iters), loss = 0.0846866
I0407 10:40:23.469799 17183 solver.cpp:237]     Train net output #0: loss = 0.0846868 (* 1 = 0.0846868 loss)
I0407 10:40:23.469806 17183 sgd_solver.cpp:105] Iteration 6900, lr = 0.000625
I0407 10:40:28.736104 17183 solver.cpp:218] Iteration 6912 (2.27867 iter/s, 5.26623s/12 iters), loss = 0.0791787
I0407 10:40:28.736167 17183 solver.cpp:237]     Train net output #0: loss = 0.0791789 (* 1 = 0.0791789 loss)
I0407 10:40:28.736177 17183 sgd_solver.cpp:105] Iteration 6912, lr = 0.000625
I0407 10:40:33.970269 17183 solver.cpp:218] Iteration 6924 (2.29269 iter/s, 5.23403s/12 iters), loss = 0.110175
I0407 10:40:33.970326 17183 solver.cpp:237]     Train net output #0: loss = 0.110175 (* 1 = 0.110175 loss)
I0407 10:40:33.970335 17183 sgd_solver.cpp:105] Iteration 6924, lr = 0.000625
I0407 10:40:38.651475 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6936.caffemodel
I0407 10:40:41.680464 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6936.solverstate
I0407 10:40:43.987392 17183 solver.cpp:330] Iteration 6936, Testing net (#0)
I0407 10:40:43.987418 17183 net.cpp:676] Ignoring source layer train-data
I0407 10:40:44.665272 17183 blocking_queue.cpp:49] Waiting for data
I0407 10:40:45.825034 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:40:48.572225 17183 solver.cpp:397]     Test net output #0: accuracy = 0.477328
I0407 10:40:48.572260 17183 solver.cpp:397]     Test net output #1: loss = 2.77448 (* 1 = 2.77448 loss)
I0407 10:40:48.713582 17183 solver.cpp:218] Iteration 6936 (0.81394 iter/s, 14.7431s/12 iters), loss = 0.0433232
I0407 10:40:48.713649 17183 solver.cpp:237]     Train net output #0: loss = 0.0433234 (* 1 = 0.0433234 loss)
I0407 10:40:48.713656 17183 sgd_solver.cpp:105] Iteration 6936, lr = 0.000625
I0407 10:40:52.881536 17183 solver.cpp:218] Iteration 6948 (2.8792 iter/s, 4.16783s/12 iters), loss = 0.0740364
I0407 10:40:52.881583 17183 solver.cpp:237]     Train net output #0: loss = 0.0740365 (* 1 = 0.0740365 loss)
I0407 10:40:52.881592 17183 sgd_solver.cpp:105] Iteration 6948, lr = 0.000625
I0407 10:40:58.122710 17183 solver.cpp:218] Iteration 6960 (2.28962 iter/s, 5.24105s/12 iters), loss = 0.300182
I0407 10:40:58.122768 17183 solver.cpp:237]     Train net output #0: loss = 0.300182 (* 1 = 0.300182 loss)
I0407 10:40:58.122778 17183 sgd_solver.cpp:105] Iteration 6960, lr = 0.000625
I0407 10:41:03.264199 17183 solver.cpp:218] Iteration 6972 (2.33401 iter/s, 5.14136s/12 iters), loss = 0.0564694
I0407 10:41:03.264242 17183 solver.cpp:237]     Train net output #0: loss = 0.0564696 (* 1 = 0.0564696 loss)
I0407 10:41:03.264250 17183 sgd_solver.cpp:105] Iteration 6972, lr = 0.000625
I0407 10:41:06.104723 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:41:08.492282 17183 solver.cpp:218] Iteration 6984 (2.29535 iter/s, 5.22797s/12 iters), loss = 0.164253
I0407 10:41:08.492344 17183 solver.cpp:237]     Train net output #0: loss = 0.164253 (* 1 = 0.164253 loss)
I0407 10:41:08.492354 17183 sgd_solver.cpp:105] Iteration 6984, lr = 0.000625
I0407 10:41:13.617175 17183 solver.cpp:218] Iteration 6996 (2.34157 iter/s, 5.12477s/12 iters), loss = 0.0821292
I0407 10:41:13.617347 17183 solver.cpp:237]     Train net output #0: loss = 0.0821293 (* 1 = 0.0821293 loss)
I0407 10:41:13.617357 17183 sgd_solver.cpp:105] Iteration 6996, lr = 0.000625
I0407 10:41:18.775774 17183 solver.cpp:218] Iteration 7008 (2.32632 iter/s, 5.15836s/12 iters), loss = 0.122798
I0407 10:41:18.775815 17183 solver.cpp:237]     Train net output #0: loss = 0.122799 (* 1 = 0.122799 loss)
I0407 10:41:18.775823 17183 sgd_solver.cpp:105] Iteration 7008, lr = 0.000625
I0407 10:41:23.916632 17183 solver.cpp:218] Iteration 7020 (2.33429 iter/s, 5.14074s/12 iters), loss = 0.159962
I0407 10:41:23.916694 17183 solver.cpp:237]     Train net output #0: loss = 0.159963 (* 1 = 0.159963 loss)
I0407 10:41:23.916705 17183 sgd_solver.cpp:105] Iteration 7020, lr = 0.000625
I0407 10:41:29.036160 17183 solver.cpp:218] Iteration 7032 (2.34403 iter/s, 5.1194s/12 iters), loss = 0.217651
I0407 10:41:29.036208 17183 solver.cpp:237]     Train net output #0: loss = 0.217651 (* 1 = 0.217651 loss)
I0407 10:41:29.036216 17183 sgd_solver.cpp:105] Iteration 7032, lr = 0.000625
I0407 10:41:31.097173 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7038.caffemodel
I0407 10:41:34.087843 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7038.solverstate
I0407 10:41:36.383074 17183 solver.cpp:330] Iteration 7038, Testing net (#0)
I0407 10:41:36.383093 17183 net.cpp:676] Ignoring source layer train-data
I0407 10:41:38.006250 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:41:40.728029 17183 solver.cpp:397]     Test net output #0: accuracy = 0.487132
I0407 10:41:40.728062 17183 solver.cpp:397]     Test net output #1: loss = 2.78693 (* 1 = 2.78693 loss)
I0407 10:41:42.580466 17183 solver.cpp:218] Iteration 7044 (0.885994 iter/s, 13.5441s/12 iters), loss = 0.0420517
I0407 10:41:42.580515 17183 solver.cpp:237]     Train net output #0: loss = 0.0420519 (* 1 = 0.0420519 loss)
I0407 10:41:42.580523 17183 sgd_solver.cpp:105] Iteration 7044, lr = 0.000625
I0407 10:41:47.675645 17183 solver.cpp:218] Iteration 7056 (2.35522 iter/s, 5.09506s/12 iters), loss = 0.0993195
I0407 10:41:47.675758 17183 solver.cpp:237]     Train net output #0: loss = 0.0993197 (* 1 = 0.0993197 loss)
I0407 10:41:47.675768 17183 sgd_solver.cpp:105] Iteration 7056, lr = 0.000625
I0407 10:41:52.608660 17183 solver.cpp:218] Iteration 7068 (2.43268 iter/s, 4.93284s/12 iters), loss = 0.066225
I0407 10:41:52.608721 17183 solver.cpp:237]     Train net output #0: loss = 0.0662252 (* 1 = 0.0662252 loss)
I0407 10:41:52.608731 17183 sgd_solver.cpp:105] Iteration 7068, lr = 0.000625
I0407 10:41:57.627357 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:41:57.784543 17183 solver.cpp:218] Iteration 7080 (2.3185 iter/s, 5.17576s/12 iters), loss = 0.0590799
I0407 10:41:57.784593 17183 solver.cpp:237]     Train net output #0: loss = 0.0590801 (* 1 = 0.0590801 loss)
I0407 10:41:57.784603 17183 sgd_solver.cpp:105] Iteration 7080, lr = 0.000625
I0407 10:42:02.853157 17183 solver.cpp:218] Iteration 7092 (2.36757 iter/s, 5.0685s/12 iters), loss = 0.0820355
I0407 10:42:02.853209 17183 solver.cpp:237]     Train net output #0: loss = 0.0820357 (* 1 = 0.0820357 loss)
I0407 10:42:02.853217 17183 sgd_solver.cpp:105] Iteration 7092, lr = 0.000625
I0407 10:42:07.978914 17183 solver.cpp:218] Iteration 7104 (2.34117 iter/s, 5.12563s/12 iters), loss = 0.076736
I0407 10:42:07.978960 17183 solver.cpp:237]     Train net output #0: loss = 0.0767361 (* 1 = 0.0767361 loss)
I0407 10:42:07.978968 17183 sgd_solver.cpp:105] Iteration 7104, lr = 0.000625
I0407 10:42:13.117415 17183 solver.cpp:218] Iteration 7116 (2.33536 iter/s, 5.13838s/12 iters), loss = 0.0953959
I0407 10:42:13.117458 17183 solver.cpp:237]     Train net output #0: loss = 0.0953961 (* 1 = 0.0953961 loss)
I0407 10:42:13.117465 17183 sgd_solver.cpp:105] Iteration 7116, lr = 0.000625
I0407 10:42:18.107007 17183 solver.cpp:218] Iteration 7128 (2.40506 iter/s, 4.98948s/12 iters), loss = 0.0896063
I0407 10:42:18.107129 17183 solver.cpp:237]     Train net output #0: loss = 0.0896065 (* 1 = 0.0896065 loss)
I0407 10:42:18.107137 17183 sgd_solver.cpp:105] Iteration 7128, lr = 0.000625
I0407 10:42:22.784034 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7140.caffemodel
I0407 10:42:25.886086 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7140.solverstate
I0407 10:42:28.365314 17183 solver.cpp:330] Iteration 7140, Testing net (#0)
I0407 10:42:28.365332 17183 net.cpp:676] Ignoring source layer train-data
I0407 10:42:29.944402 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:42:32.719724 17183 solver.cpp:397]     Test net output #0: accuracy = 0.496936
I0407 10:42:32.719754 17183 solver.cpp:397]     Test net output #1: loss = 2.77837 (* 1 = 2.77837 loss)
I0407 10:42:32.855803 17183 solver.cpp:218] Iteration 7140 (0.813642 iter/s, 14.7485s/12 iters), loss = 0.129288
I0407 10:42:32.855861 17183 solver.cpp:237]     Train net output #0: loss = 0.129288 (* 1 = 0.129288 loss)
I0407 10:42:32.855871 17183 sgd_solver.cpp:105] Iteration 7140, lr = 0.000625
I0407 10:42:37.060470 17183 solver.cpp:218] Iteration 7152 (2.85405 iter/s, 4.20455s/12 iters), loss = 0.163211
I0407 10:42:37.060515 17183 solver.cpp:237]     Train net output #0: loss = 0.163211 (* 1 = 0.163211 loss)
I0407 10:42:37.060523 17183 sgd_solver.cpp:105] Iteration 7152, lr = 0.000625
I0407 10:42:42.069026 17183 solver.cpp:218] Iteration 7164 (2.39595 iter/s, 5.00845s/12 iters), loss = 0.130572
I0407 10:42:42.069069 17183 solver.cpp:237]     Train net output #0: loss = 0.130572 (* 1 = 0.130572 loss)
I0407 10:42:42.069077 17183 sgd_solver.cpp:105] Iteration 7164, lr = 0.000625
I0407 10:42:46.977059 17183 solver.cpp:218] Iteration 7176 (2.44502 iter/s, 4.90793s/12 iters), loss = 0.04814
I0407 10:42:46.977100 17183 solver.cpp:237]     Train net output #0: loss = 0.0481401 (* 1 = 0.0481401 loss)
I0407 10:42:46.977108 17183 sgd_solver.cpp:105] Iteration 7176, lr = 0.000625
I0407 10:42:49.239660 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:42:52.151451 17183 solver.cpp:218] Iteration 7188 (2.31916 iter/s, 5.17428s/12 iters), loss = 0.1268
I0407 10:42:52.151499 17183 solver.cpp:237]     Train net output #0: loss = 0.1268 (* 1 = 0.1268 loss)
I0407 10:42:52.151506 17183 sgd_solver.cpp:105] Iteration 7188, lr = 0.000625
I0407 10:42:57.238150 17183 solver.cpp:218] Iteration 7200 (2.35915 iter/s, 5.08658s/12 iters), loss = 0.0626261
I0407 10:42:57.238200 17183 solver.cpp:237]     Train net output #0: loss = 0.0626263 (* 1 = 0.0626263 loss)
I0407 10:42:57.238209 17183 sgd_solver.cpp:105] Iteration 7200, lr = 0.000625
I0407 10:43:02.726229 17183 solver.cpp:218] Iteration 7212 (2.1866 iter/s, 5.48796s/12 iters), loss = 0.0613317
I0407 10:43:02.726269 17183 solver.cpp:237]     Train net output #0: loss = 0.0613319 (* 1 = 0.0613319 loss)
I0407 10:43:02.726276 17183 sgd_solver.cpp:105] Iteration 7212, lr = 0.000625
I0407 10:43:07.884410 17183 solver.cpp:218] Iteration 7224 (2.32645 iter/s, 5.15807s/12 iters), loss = 0.0683475
I0407 10:43:07.884464 17183 solver.cpp:237]     Train net output #0: loss = 0.0683476 (* 1 = 0.0683476 loss)
I0407 10:43:07.884474 17183 sgd_solver.cpp:105] Iteration 7224, lr = 0.000625
I0407 10:43:12.978675 17183 solver.cpp:218] Iteration 7236 (2.35565 iter/s, 5.09414s/12 iters), loss = 0.130896
I0407 10:43:12.978731 17183 solver.cpp:237]     Train net output #0: loss = 0.130896 (* 1 = 0.130896 loss)
I0407 10:43:12.978740 17183 sgd_solver.cpp:105] Iteration 7236, lr = 0.000625
I0407 10:43:15.081908 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7242.caffemodel
I0407 10:43:18.085486 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7242.solverstate
I0407 10:43:20.385556 17183 solver.cpp:330] Iteration 7242, Testing net (#0)
I0407 10:43:20.385648 17183 net.cpp:676] Ignoring source layer train-data
I0407 10:43:21.877491 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:43:24.701167 17183 solver.cpp:397]     Test net output #0: accuracy = 0.496324
I0407 10:43:24.701201 17183 solver.cpp:397]     Test net output #1: loss = 2.79173 (* 1 = 2.79173 loss)
I0407 10:43:26.551350 17183 solver.cpp:218] Iteration 7248 (0.884143 iter/s, 13.5725s/12 iters), loss = 0.175584
I0407 10:43:26.551388 17183 solver.cpp:237]     Train net output #0: loss = 0.175584 (* 1 = 0.175584 loss)
I0407 10:43:26.551396 17183 sgd_solver.cpp:105] Iteration 7248, lr = 0.000625
I0407 10:43:31.719614 17183 solver.cpp:218] Iteration 7260 (2.32191 iter/s, 5.16815s/12 iters), loss = 0.14419
I0407 10:43:31.719657 17183 solver.cpp:237]     Train net output #0: loss = 0.14419 (* 1 = 0.14419 loss)
I0407 10:43:31.719663 17183 sgd_solver.cpp:105] Iteration 7260, lr = 0.000625
I0407 10:43:36.817317 17183 solver.cpp:218] Iteration 7272 (2.35405 iter/s, 5.09759s/12 iters), loss = 0.111242
I0407 10:43:36.817363 17183 solver.cpp:237]     Train net output #0: loss = 0.111242 (* 1 = 0.111242 loss)
I0407 10:43:36.817370 17183 sgd_solver.cpp:105] Iteration 7272, lr = 0.000625
I0407 10:43:41.264302 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:43:42.097543 17183 solver.cpp:218] Iteration 7284 (2.27268 iter/s, 5.28011s/12 iters), loss = 0.0842072
I0407 10:43:42.097602 17183 solver.cpp:237]     Train net output #0: loss = 0.0842074 (* 1 = 0.0842074 loss)
I0407 10:43:42.097615 17183 sgd_solver.cpp:105] Iteration 7284, lr = 0.000625
I0407 10:43:47.410876 17183 solver.cpp:218] Iteration 7296 (2.25852 iter/s, 5.31321s/12 iters), loss = 0.0510699
I0407 10:43:47.410920 17183 solver.cpp:237]     Train net output #0: loss = 0.0510701 (* 1 = 0.0510701 loss)
I0407 10:43:47.410928 17183 sgd_solver.cpp:105] Iteration 7296, lr = 0.000625
I0407 10:43:52.604214 17183 solver.cpp:218] Iteration 7308 (2.3107 iter/s, 5.19323s/12 iters), loss = 0.0211966
I0407 10:43:52.604311 17183 solver.cpp:237]     Train net output #0: loss = 0.0211968 (* 1 = 0.0211968 loss)
I0407 10:43:52.604321 17183 sgd_solver.cpp:105] Iteration 7308, lr = 0.000625
I0407 10:43:57.678745 17183 solver.cpp:218] Iteration 7320 (2.36483 iter/s, 5.07437s/12 iters), loss = 0.0572782
I0407 10:43:57.678792 17183 solver.cpp:237]     Train net output #0: loss = 0.0572784 (* 1 = 0.0572784 loss)
I0407 10:43:57.678802 17183 sgd_solver.cpp:105] Iteration 7320, lr = 0.000625
I0407 10:44:02.861146 17183 solver.cpp:218] Iteration 7332 (2.31558 iter/s, 5.18228s/12 iters), loss = 0.0197616
I0407 10:44:02.861196 17183 solver.cpp:237]     Train net output #0: loss = 0.0197618 (* 1 = 0.0197618 loss)
I0407 10:44:02.861203 17183 sgd_solver.cpp:105] Iteration 7332, lr = 0.000625
I0407 10:44:07.605645 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7344.caffemodel
I0407 10:44:10.644377 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7344.solverstate
I0407 10:44:12.938130 17183 solver.cpp:330] Iteration 7344, Testing net (#0)
I0407 10:44:12.938151 17183 net.cpp:676] Ignoring source layer train-data
I0407 10:44:14.450620 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:44:17.343645 17183 solver.cpp:397]     Test net output #0: accuracy = 0.497549
I0407 10:44:17.343674 17183 solver.cpp:397]     Test net output #1: loss = 2.77067 (* 1 = 2.77067 loss)
I0407 10:44:17.485185 17183 solver.cpp:218] Iteration 7344 (0.820579 iter/s, 14.6238s/12 iters), loss = 0.0885054
I0407 10:44:17.485231 17183 solver.cpp:237]     Train net output #0: loss = 0.0885056 (* 1 = 0.0885056 loss)
I0407 10:44:17.485239 17183 sgd_solver.cpp:105] Iteration 7344, lr = 0.000625
I0407 10:44:21.746076 17183 solver.cpp:218] Iteration 7356 (2.81638 iter/s, 4.26078s/12 iters), loss = 0.0931674
I0407 10:44:21.746119 17183 solver.cpp:237]     Train net output #0: loss = 0.0931676 (* 1 = 0.0931676 loss)
I0407 10:44:21.746126 17183 sgd_solver.cpp:105] Iteration 7356, lr = 0.000625
I0407 10:44:26.997606 17183 solver.cpp:218] Iteration 7368 (2.2851 iter/s, 5.25142s/12 iters), loss = 0.0840888
I0407 10:44:26.997761 17183 solver.cpp:237]     Train net output #0: loss = 0.084089 (* 1 = 0.084089 loss)
I0407 10:44:26.997774 17183 sgd_solver.cpp:105] Iteration 7368, lr = 0.000625
I0407 10:44:31.834458 17183 solver.cpp:218] Iteration 7380 (2.48106 iter/s, 4.83664s/12 iters), loss = 0.0929687
I0407 10:44:31.834503 17183 solver.cpp:237]     Train net output #0: loss = 0.0929689 (* 1 = 0.0929689 loss)
I0407 10:44:31.834511 17183 sgd_solver.cpp:105] Iteration 7380, lr = 0.000625
I0407 10:44:33.225000 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:44:37.053614 17183 solver.cpp:218] Iteration 7392 (2.29927 iter/s, 5.21904s/12 iters), loss = 0.0651
I0407 10:44:37.053660 17183 solver.cpp:237]     Train net output #0: loss = 0.0651002 (* 1 = 0.0651002 loss)
I0407 10:44:37.053668 17183 sgd_solver.cpp:105] Iteration 7392, lr = 0.000625
I0407 10:44:41.868613 17183 solver.cpp:218] Iteration 7404 (2.49227 iter/s, 4.81489s/12 iters), loss = 0.110215
I0407 10:44:41.868661 17183 solver.cpp:237]     Train net output #0: loss = 0.110215 (* 1 = 0.110215 loss)
I0407 10:44:41.868669 17183 sgd_solver.cpp:105] Iteration 7404, lr = 0.000625
I0407 10:44:46.828691 17183 solver.cpp:218] Iteration 7416 (2.41938 iter/s, 4.95996s/12 iters), loss = 0.150626
I0407 10:44:46.828753 17183 solver.cpp:237]     Train net output #0: loss = 0.150626 (* 1 = 0.150626 loss)
I0407 10:44:46.828764 17183 sgd_solver.cpp:105] Iteration 7416, lr = 0.000625
I0407 10:44:51.998296 17183 solver.cpp:218] Iteration 7428 (2.32132 iter/s, 5.16947s/12 iters), loss = 0.0998622
I0407 10:44:51.998358 17183 solver.cpp:237]     Train net output #0: loss = 0.0998624 (* 1 = 0.0998624 loss)
I0407 10:44:51.998369 17183 sgd_solver.cpp:105] Iteration 7428, lr = 0.000625
I0407 10:44:57.225405 17183 solver.cpp:218] Iteration 7440 (2.29578 iter/s, 5.22698s/12 iters), loss = 0.0474961
I0407 10:44:57.225518 17183 solver.cpp:237]     Train net output #0: loss = 0.0474963 (* 1 = 0.0474963 loss)
I0407 10:44:57.225528 17183 sgd_solver.cpp:105] Iteration 7440, lr = 0.000625
I0407 10:44:59.264817 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7446.caffemodel
I0407 10:45:02.293642 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7446.solverstate
I0407 10:45:04.659176 17183 solver.cpp:330] Iteration 7446, Testing net (#0)
I0407 10:45:04.659195 17183 net.cpp:676] Ignoring source layer train-data
I0407 10:45:06.067338 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:45:08.944420 17183 solver.cpp:397]     Test net output #0: accuracy = 0.490809
I0407 10:45:08.944447 17183 solver.cpp:397]     Test net output #1: loss = 2.81781 (* 1 = 2.81781 loss)
I0407 10:45:10.766327 17183 solver.cpp:218] Iteration 7452 (0.88622 iter/s, 13.5407s/12 iters), loss = 0.163638
I0407 10:45:10.766373 17183 solver.cpp:237]     Train net output #0: loss = 0.163638 (* 1 = 0.163638 loss)
I0407 10:45:10.766381 17183 sgd_solver.cpp:105] Iteration 7452, lr = 0.000625
I0407 10:45:15.918179 17183 solver.cpp:218] Iteration 7464 (2.32931 iter/s, 5.15174s/12 iters), loss = 0.066337
I0407 10:45:15.918239 17183 solver.cpp:237]     Train net output #0: loss = 0.0663372 (* 1 = 0.0663372 loss)
I0407 10:45:15.918251 17183 sgd_solver.cpp:105] Iteration 7464, lr = 0.000625
I0407 10:45:21.059988 17183 solver.cpp:218] Iteration 7476 (2.33387 iter/s, 5.14168s/12 iters), loss = 0.105771
I0407 10:45:21.060053 17183 solver.cpp:237]     Train net output #0: loss = 0.105771 (* 1 = 0.105771 loss)
I0407 10:45:21.060063 17183 sgd_solver.cpp:105] Iteration 7476, lr = 0.000625
I0407 10:45:24.617605 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:45:26.214040 17183 solver.cpp:218] Iteration 7488 (2.32833 iter/s, 5.15392s/12 iters), loss = 0.115985
I0407 10:45:26.214087 17183 solver.cpp:237]     Train net output #0: loss = 0.115985 (* 1 = 0.115985 loss)
I0407 10:45:26.214093 17183 sgd_solver.cpp:105] Iteration 7488, lr = 0.000625
I0407 10:45:31.303032 17183 solver.cpp:218] Iteration 7500 (2.35809 iter/s, 5.08887s/12 iters), loss = 0.125554
I0407 10:45:31.303175 17183 solver.cpp:237]     Train net output #0: loss = 0.125554 (* 1 = 0.125554 loss)
I0407 10:45:31.303184 17183 sgd_solver.cpp:105] Iteration 7500, lr = 0.000625
I0407 10:45:36.416129 17183 solver.cpp:218] Iteration 7512 (2.34701 iter/s, 5.11289s/12 iters), loss = 0.131361
I0407 10:45:36.416182 17183 solver.cpp:237]     Train net output #0: loss = 0.131361 (* 1 = 0.131361 loss)
I0407 10:45:36.416189 17183 sgd_solver.cpp:105] Iteration 7512, lr = 0.000625
I0407 10:45:41.665640 17183 solver.cpp:218] Iteration 7524 (2.28598 iter/s, 5.24939s/12 iters), loss = 0.0537122
I0407 10:45:41.665678 17183 solver.cpp:237]     Train net output #0: loss = 0.0537124 (* 1 = 0.0537124 loss)
I0407 10:45:41.665684 17183 sgd_solver.cpp:105] Iteration 7524, lr = 0.000625
I0407 10:45:46.795408 17183 solver.cpp:218] Iteration 7536 (2.33934 iter/s, 5.12966s/12 iters), loss = 0.141322
I0407 10:45:46.795447 17183 solver.cpp:237]     Train net output #0: loss = 0.141322 (* 1 = 0.141322 loss)
I0407 10:45:46.795455 17183 sgd_solver.cpp:105] Iteration 7536, lr = 0.000625
I0407 10:45:51.473227 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7548.caffemodel
I0407 10:45:54.484745 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7548.solverstate
I0407 10:45:56.804306 17183 solver.cpp:330] Iteration 7548, Testing net (#0)
I0407 10:45:56.804327 17183 net.cpp:676] Ignoring source layer train-data
I0407 10:45:58.188892 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:46:01.084265 17183 solver.cpp:397]     Test net output #0: accuracy = 0.495711
I0407 10:46:01.084300 17183 solver.cpp:397]     Test net output #1: loss = 2.78143 (* 1 = 2.78143 loss)
I0407 10:46:01.226002 17183 solver.cpp:218] Iteration 7548 (0.831578 iter/s, 14.4304s/12 iters), loss = 0.138212
I0407 10:46:01.226071 17183 solver.cpp:237]     Train net output #0: loss = 0.138212 (* 1 = 0.138212 loss)
I0407 10:46:01.226079 17183 sgd_solver.cpp:105] Iteration 7548, lr = 0.000625
I0407 10:46:05.568764 17183 solver.cpp:218] Iteration 7560 (2.7633 iter/s, 4.34263s/12 iters), loss = 0.132511
I0407 10:46:05.568912 17183 solver.cpp:237]     Train net output #0: loss = 0.132512 (* 1 = 0.132512 loss)
I0407 10:46:05.568920 17183 sgd_solver.cpp:105] Iteration 7560, lr = 0.000625
I0407 10:46:10.673276 17183 solver.cpp:218] Iteration 7572 (2.35096 iter/s, 5.10429s/12 iters), loss = 0.105429
I0407 10:46:10.673319 17183 solver.cpp:237]     Train net output #0: loss = 0.10543 (* 1 = 0.10543 loss)
I0407 10:46:10.673326 17183 sgd_solver.cpp:105] Iteration 7572, lr = 0.000625
I0407 10:46:15.775395 17183 solver.cpp:218] Iteration 7584 (2.35202 iter/s, 5.102s/12 iters), loss = 0.0990291
I0407 10:46:15.775460 17183 solver.cpp:237]     Train net output #0: loss = 0.0990293 (* 1 = 0.0990293 loss)
I0407 10:46:15.775471 17183 sgd_solver.cpp:105] Iteration 7584, lr = 0.000625
I0407 10:46:16.437811 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:46:20.920928 17183 solver.cpp:218] Iteration 7596 (2.33218 iter/s, 5.1454s/12 iters), loss = 0.104306
I0407 10:46:20.920984 17183 solver.cpp:237]     Train net output #0: loss = 0.104307 (* 1 = 0.104307 loss)
I0407 10:46:20.920994 17183 sgd_solver.cpp:105] Iteration 7596, lr = 0.000625
I0407 10:46:25.919775 17183 solver.cpp:218] Iteration 7608 (2.40061 iter/s, 4.99872s/12 iters), loss = 0.139916
I0407 10:46:25.919824 17183 solver.cpp:237]     Train net output #0: loss = 0.139916 (* 1 = 0.139916 loss)
I0407 10:46:25.919833 17183 sgd_solver.cpp:105] Iteration 7608, lr = 0.000625
I0407 10:46:31.103971 17183 solver.cpp:218] Iteration 7620 (2.31478 iter/s, 5.18408s/12 iters), loss = 0.0698311
I0407 10:46:31.104013 17183 solver.cpp:237]     Train net output #0: loss = 0.0698313 (* 1 = 0.0698313 loss)
I0407 10:46:31.104020 17183 sgd_solver.cpp:105] Iteration 7620, lr = 0.000625
I0407 10:46:33.703755 17183 blocking_queue.cpp:49] Waiting for data
I0407 10:46:36.218199 17183 solver.cpp:218] Iteration 7632 (2.34645 iter/s, 5.11411s/12 iters), loss = 0.151984
I0407 10:46:36.218343 17183 solver.cpp:237]     Train net output #0: loss = 0.151985 (* 1 = 0.151985 loss)
I0407 10:46:36.218353 17183 sgd_solver.cpp:105] Iteration 7632, lr = 0.000625
I0407 10:46:41.251776 17183 solver.cpp:218] Iteration 7644 (2.38409 iter/s, 5.03337s/12 iters), loss = 0.0956514
I0407 10:46:41.251834 17183 solver.cpp:237]     Train net output #0: loss = 0.0956516 (* 1 = 0.0956516 loss)
I0407 10:46:41.251845 17183 sgd_solver.cpp:105] Iteration 7644, lr = 0.000625
I0407 10:46:43.205859 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7650.caffemodel
I0407 10:46:46.216697 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7650.solverstate
I0407 10:46:48.514979 17183 solver.cpp:330] Iteration 7650, Testing net (#0)
I0407 10:46:48.514998 17183 net.cpp:676] Ignoring source layer train-data
I0407 10:46:49.888808 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:46:52.826272 17183 solver.cpp:397]     Test net output #0: accuracy = 0.499387
I0407 10:46:52.826308 17183 solver.cpp:397]     Test net output #1: loss = 2.7858 (* 1 = 2.7858 loss)
I0407 10:46:54.711141 17183 solver.cpp:218] Iteration 7656 (0.891587 iter/s, 13.4592s/12 iters), loss = 0.133368
I0407 10:46:54.711202 17183 solver.cpp:237]     Train net output #0: loss = 0.133368 (* 1 = 0.133368 loss)
I0407 10:46:54.711213 17183 sgd_solver.cpp:105] Iteration 7656, lr = 0.000625
I0407 10:46:59.751241 17183 solver.cpp:218] Iteration 7668 (2.38096 iter/s, 5.03997s/12 iters), loss = 0.0627986
I0407 10:46:59.751289 17183 solver.cpp:237]     Train net output #0: loss = 0.0627988 (* 1 = 0.0627988 loss)
I0407 10:46:59.751296 17183 sgd_solver.cpp:105] Iteration 7668, lr = 0.000625
I0407 10:47:04.947093 17183 solver.cpp:218] Iteration 7680 (2.30959 iter/s, 5.19573s/12 iters), loss = 0.141565
I0407 10:47:04.947150 17183 solver.cpp:237]     Train net output #0: loss = 0.141565 (* 1 = 0.141565 loss)
I0407 10:47:04.947160 17183 sgd_solver.cpp:105] Iteration 7680, lr = 0.000625
I0407 10:47:07.882141 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:47:10.203778 17183 solver.cpp:218] Iteration 7692 (2.28286 iter/s, 5.25656s/12 iters), loss = 0.0844639
I0407 10:47:10.203832 17183 solver.cpp:237]     Train net output #0: loss = 0.0844641 (* 1 = 0.0844641 loss)
I0407 10:47:10.203843 17183 sgd_solver.cpp:105] Iteration 7692, lr = 0.000625
I0407 10:47:15.322875 17183 solver.cpp:218] Iteration 7704 (2.34422 iter/s, 5.11898s/12 iters), loss = 0.0639084
I0407 10:47:15.322917 17183 solver.cpp:237]     Train net output #0: loss = 0.0639086 (* 1 = 0.0639086 loss)
I0407 10:47:15.322924 17183 sgd_solver.cpp:105] Iteration 7704, lr = 0.000625
I0407 10:47:20.379634 17183 solver.cpp:218] Iteration 7716 (2.37311 iter/s, 5.05665s/12 iters), loss = 0.167115
I0407 10:47:20.379679 17183 solver.cpp:237]     Train net output #0: loss = 0.167115 (* 1 = 0.167115 loss)
I0407 10:47:20.379686 17183 sgd_solver.cpp:105] Iteration 7716, lr = 0.000625
I0407 10:47:25.536959 17183 solver.cpp:218] Iteration 7728 (2.32684 iter/s, 5.15721s/12 iters), loss = 0.109285
I0407 10:47:25.537014 17183 solver.cpp:237]     Train net output #0: loss = 0.109286 (* 1 = 0.109286 loss)
I0407 10:47:25.537024 17183 sgd_solver.cpp:105] Iteration 7728, lr = 0.000625
I0407 10:47:30.655807 17183 solver.cpp:218] Iteration 7740 (2.34433 iter/s, 5.11872s/12 iters), loss = 0.130328
I0407 10:47:30.655858 17183 solver.cpp:237]     Train net output #0: loss = 0.130328 (* 1 = 0.130328 loss)
I0407 10:47:30.655866 17183 sgd_solver.cpp:105] Iteration 7740, lr = 0.000625
I0407 10:47:35.291069 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7752.caffemodel
I0407 10:47:38.307904 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7752.solverstate
I0407 10:47:40.607266 17183 solver.cpp:330] Iteration 7752, Testing net (#0)
I0407 10:47:40.607285 17183 net.cpp:676] Ignoring source layer train-data
I0407 10:47:41.970609 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:47:44.991654 17183 solver.cpp:397]     Test net output #0: accuracy = 0.499387
I0407 10:47:44.991695 17183 solver.cpp:397]     Test net output #1: loss = 2.78529 (* 1 = 2.78529 loss)
I0407 10:47:45.131342 17183 solver.cpp:218] Iteration 7752 (0.828997 iter/s, 14.4753s/12 iters), loss = 0.0665317
I0407 10:47:45.131387 17183 solver.cpp:237]     Train net output #0: loss = 0.066532 (* 1 = 0.066532 loss)
I0407 10:47:45.131395 17183 sgd_solver.cpp:105] Iteration 7752, lr = 0.000625
I0407 10:47:49.412628 17183 solver.cpp:218] Iteration 7764 (2.80297 iter/s, 4.28118s/12 iters), loss = 0.0841167
I0407 10:47:49.412681 17183 solver.cpp:237]     Train net output #0: loss = 0.084117 (* 1 = 0.084117 loss)
I0407 10:47:49.412691 17183 sgd_solver.cpp:105] Iteration 7764, lr = 0.000625
I0407 10:47:54.515532 17183 solver.cpp:218] Iteration 7776 (2.35166 iter/s, 5.10279s/12 iters), loss = 0.057899
I0407 10:47:54.515573 17183 solver.cpp:237]     Train net output #0: loss = 0.0578992 (* 1 = 0.0578992 loss)
I0407 10:47:54.515580 17183 sgd_solver.cpp:105] Iteration 7776, lr = 0.000625
I0407 10:47:59.468458 17183 solver.cpp:218] Iteration 7788 (2.42286 iter/s, 4.95282s/12 iters), loss = 0.173774
I0407 10:47:59.468504 17183 solver.cpp:237]     Train net output #0: loss = 0.173774 (* 1 = 0.173774 loss)
I0407 10:47:59.468513 17183 sgd_solver.cpp:105] Iteration 7788, lr = 0.000625
I0407 10:47:59.476480 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:48:04.590095 17183 solver.cpp:218] Iteration 7800 (2.34305 iter/s, 5.12152s/12 iters), loss = 0.068956
I0407 10:48:04.590143 17183 solver.cpp:237]     Train net output #0: loss = 0.0689562 (* 1 = 0.0689562 loss)
I0407 10:48:04.590152 17183 sgd_solver.cpp:105] Iteration 7800, lr = 0.000625
I0407 10:48:09.721776 17183 solver.cpp:218] Iteration 7812 (2.33847 iter/s, 5.13156s/12 iters), loss = 0.0193418
I0407 10:48:09.721913 17183 solver.cpp:237]     Train net output #0: loss = 0.019342 (* 1 = 0.019342 loss)
I0407 10:48:09.721925 17183 sgd_solver.cpp:105] Iteration 7812, lr = 0.000625
I0407 10:48:14.879925 17183 solver.cpp:218] Iteration 7824 (2.32651 iter/s, 5.15794s/12 iters), loss = 0.0868451
I0407 10:48:14.879981 17183 solver.cpp:237]     Train net output #0: loss = 0.0868453 (* 1 = 0.0868453 loss)
I0407 10:48:14.879992 17183 sgd_solver.cpp:105] Iteration 7824, lr = 0.000625
I0407 10:48:20.084789 17183 solver.cpp:218] Iteration 7836 (2.30559 iter/s, 5.20474s/12 iters), loss = 0.0455478
I0407 10:48:20.084847 17183 solver.cpp:237]     Train net output #0: loss = 0.045548 (* 1 = 0.045548 loss)
I0407 10:48:20.084856 17183 sgd_solver.cpp:105] Iteration 7836, lr = 0.000625
I0407 10:48:25.429148 17183 solver.cpp:218] Iteration 7848 (2.24541 iter/s, 5.34423s/12 iters), loss = 0.0600933
I0407 10:48:25.429208 17183 solver.cpp:237]     Train net output #0: loss = 0.0600935 (* 1 = 0.0600935 loss)
I0407 10:48:25.429216 17183 sgd_solver.cpp:105] Iteration 7848, lr = 0.000625
I0407 10:48:27.427043 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7854.caffemodel
I0407 10:48:30.511484 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7854.solverstate
I0407 10:48:34.086158 17183 solver.cpp:330] Iteration 7854, Testing net (#0)
I0407 10:48:34.086180 17183 net.cpp:676] Ignoring source layer train-data
I0407 10:48:35.375555 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:48:38.388116 17183 solver.cpp:397]     Test net output #0: accuracy = 0.499387
I0407 10:48:38.388154 17183 solver.cpp:397]     Test net output #1: loss = 2.81593 (* 1 = 2.81593 loss)
I0407 10:48:40.287773 17183 solver.cpp:218] Iteration 7860 (0.807624 iter/s, 14.8584s/12 iters), loss = 0.078988
I0407 10:48:40.287979 17183 solver.cpp:237]     Train net output #0: loss = 0.0789882 (* 1 = 0.0789882 loss)
I0407 10:48:40.287990 17183 sgd_solver.cpp:105] Iteration 7860, lr = 0.000625
I0407 10:48:45.140177 17183 solver.cpp:218] Iteration 7872 (2.47314 iter/s, 4.85214s/12 iters), loss = 0.0448492
I0407 10:48:45.140233 17183 solver.cpp:237]     Train net output #0: loss = 0.0448494 (* 1 = 0.0448494 loss)
I0407 10:48:45.140241 17183 sgd_solver.cpp:105] Iteration 7872, lr = 0.000625
I0407 10:48:50.121717 17183 solver.cpp:218] Iteration 7884 (2.40895 iter/s, 4.98142s/12 iters), loss = 0.104341
I0407 10:48:50.121762 17183 solver.cpp:237]     Train net output #0: loss = 0.104342 (* 1 = 0.104342 loss)
I0407 10:48:50.121769 17183 sgd_solver.cpp:105] Iteration 7884, lr = 0.000625
I0407 10:48:52.357937 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:48:55.498816 17183 solver.cpp:218] Iteration 7896 (2.23173 iter/s, 5.37699s/12 iters), loss = 0.0966493
I0407 10:48:55.498855 17183 solver.cpp:237]     Train net output #0: loss = 0.0966495 (* 1 = 0.0966495 loss)
I0407 10:48:55.498863 17183 sgd_solver.cpp:105] Iteration 7896, lr = 0.000625
I0407 10:49:00.888164 17183 solver.cpp:218] Iteration 7908 (2.22666 iter/s, 5.38924s/12 iters), loss = 0.174524
I0407 10:49:00.888217 17183 solver.cpp:237]     Train net output #0: loss = 0.174524 (* 1 = 0.174524 loss)
I0407 10:49:00.888228 17183 sgd_solver.cpp:105] Iteration 7908, lr = 0.000625
I0407 10:49:06.021306 17183 solver.cpp:218] Iteration 7920 (2.3378 iter/s, 5.13302s/12 iters), loss = 0.0797732
I0407 10:49:06.021350 17183 solver.cpp:237]     Train net output #0: loss = 0.0797734 (* 1 = 0.0797734 loss)
I0407 10:49:06.021359 17183 sgd_solver.cpp:105] Iteration 7920, lr = 0.000625
I0407 10:49:11.430550 17183 solver.cpp:218] Iteration 7932 (2.21847 iter/s, 5.40913s/12 iters), loss = 0.0859019
I0407 10:49:11.430646 17183 solver.cpp:237]     Train net output #0: loss = 0.0859021 (* 1 = 0.0859021 loss)
I0407 10:49:11.430655 17183 sgd_solver.cpp:105] Iteration 7932, lr = 0.000625
I0407 10:49:16.786032 17183 solver.cpp:218] Iteration 7944 (2.24076 iter/s, 5.35531s/12 iters), loss = 0.0487056
I0407 10:49:16.786077 17183 solver.cpp:237]     Train net output #0: loss = 0.0487058 (* 1 = 0.0487058 loss)
I0407 10:49:16.786084 17183 sgd_solver.cpp:105] Iteration 7944, lr = 0.000625
I0407 10:49:21.452817 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7956.caffemodel
I0407 10:49:24.503623 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7956.solverstate
I0407 10:49:26.817879 17183 solver.cpp:330] Iteration 7956, Testing net (#0)
I0407 10:49:26.817898 17183 net.cpp:676] Ignoring source layer train-data
I0407 10:49:28.062170 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:49:31.198323 17183 solver.cpp:397]     Test net output #0: accuracy = 0.495098
I0407 10:49:31.198352 17183 solver.cpp:397]     Test net output #1: loss = 2.82778 (* 1 = 2.82778 loss)
I0407 10:49:31.339125 17183 solver.cpp:218] Iteration 7956 (0.824579 iter/s, 14.5529s/12 iters), loss = 0.127453
I0407 10:49:31.340698 17183 solver.cpp:237]     Train net output #0: loss = 0.127453 (* 1 = 0.127453 loss)
I0407 10:49:31.340709 17183 sgd_solver.cpp:105] Iteration 7956, lr = 0.000625
I0407 10:49:35.823721 17183 solver.cpp:218] Iteration 7968 (2.6768 iter/s, 4.48297s/12 iters), loss = 0.120595
I0407 10:49:35.823776 17183 solver.cpp:237]     Train net output #0: loss = 0.120595 (* 1 = 0.120595 loss)
I0407 10:49:35.823786 17183 sgd_solver.cpp:105] Iteration 7968, lr = 0.000625
I0407 10:49:40.780792 17183 solver.cpp:218] Iteration 7980 (2.42084 iter/s, 4.95695s/12 iters), loss = 0.0692724
I0407 10:49:40.780854 17183 solver.cpp:237]     Train net output #0: loss = 0.0692726 (* 1 = 0.0692726 loss)
I0407 10:49:40.780865 17183 sgd_solver.cpp:105] Iteration 7980, lr = 0.000625
I0407 10:49:45.198104 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:49:45.981840 17183 solver.cpp:218] Iteration 7992 (2.30728 iter/s, 5.20092s/12 iters), loss = 0.129522
I0407 10:49:45.981881 17183 solver.cpp:237]     Train net output #0: loss = 0.129523 (* 1 = 0.129523 loss)
I0407 10:49:45.981889 17183 sgd_solver.cpp:105] Iteration 7992, lr = 0.000625
I0407 10:49:51.093328 17183 solver.cpp:218] Iteration 8004 (2.3477 iter/s, 5.11138s/12 iters), loss = 0.0792974
I0407 10:49:51.093376 17183 solver.cpp:237]     Train net output #0: loss = 0.0792976 (* 1 = 0.0792976 loss)
I0407 10:49:51.093384 17183 sgd_solver.cpp:105] Iteration 8004, lr = 0.000625
I0407 10:49:56.325394 17183 solver.cpp:218] Iteration 8016 (2.2936 iter/s, 5.23194s/12 iters), loss = 0.0774986
I0407 10:49:56.325457 17183 solver.cpp:237]     Train net output #0: loss = 0.0774988 (* 1 = 0.0774988 loss)
I0407 10:49:56.325467 17183 sgd_solver.cpp:105] Iteration 8016, lr = 0.000625
I0407 10:50:01.581503 17183 solver.cpp:218] Iteration 8028 (2.28311 iter/s, 5.25598s/12 iters), loss = 0.0295496
I0407 10:50:01.581549 17183 solver.cpp:237]     Train net output #0: loss = 0.0295498 (* 1 = 0.0295498 loss)
I0407 10:50:01.581558 17183 sgd_solver.cpp:105] Iteration 8028, lr = 0.000625
I0407 10:50:06.611490 17183 solver.cpp:218] Iteration 8040 (2.38574 iter/s, 5.02988s/12 iters), loss = 0.0258309
I0407 10:50:06.611534 17183 solver.cpp:237]     Train net output #0: loss = 0.0258311 (* 1 = 0.0258311 loss)
I0407 10:50:06.611541 17183 sgd_solver.cpp:105] Iteration 8040, lr = 0.000625
I0407 10:50:11.823264 17183 solver.cpp:218] Iteration 8052 (2.30253 iter/s, 5.21166s/12 iters), loss = 0.0460411
I0407 10:50:11.823312 17183 solver.cpp:237]     Train net output #0: loss = 0.0460413 (* 1 = 0.0460413 loss)
I0407 10:50:11.823320 17183 sgd_solver.cpp:105] Iteration 8052, lr = 0.000625
I0407 10:50:13.977751 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8058.caffemodel
I0407 10:50:16.990370 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8058.solverstate
I0407 10:50:20.401538 17183 solver.cpp:330] Iteration 8058, Testing net (#0)
I0407 10:50:20.401561 17183 net.cpp:676] Ignoring source layer train-data
I0407 10:50:21.626996 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:50:24.758708 17183 solver.cpp:397]     Test net output #0: accuracy = 0.497549
I0407 10:50:24.758745 17183 solver.cpp:397]     Test net output #1: loss = 2.84237 (* 1 = 2.84237 loss)
I0407 10:50:26.748024 17183 solver.cpp:218] Iteration 8064 (0.804044 iter/s, 14.9245s/12 iters), loss = 0.0622957
I0407 10:50:26.748066 17183 solver.cpp:237]     Train net output #0: loss = 0.0622959 (* 1 = 0.0622959 loss)
I0407 10:50:26.748075 17183 sgd_solver.cpp:105] Iteration 8064, lr = 0.000625
I0407 10:50:31.826516 17183 solver.cpp:218] Iteration 8076 (2.36296 iter/s, 5.07838s/12 iters), loss = 0.0563571
I0407 10:50:31.826565 17183 solver.cpp:237]     Train net output #0: loss = 0.0563574 (* 1 = 0.0563574 loss)
I0407 10:50:31.826575 17183 sgd_solver.cpp:105] Iteration 8076, lr = 0.000625
I0407 10:50:36.995244 17183 solver.cpp:218] Iteration 8088 (2.32171 iter/s, 5.16861s/12 iters), loss = 0.0744766
I0407 10:50:36.995292 17183 solver.cpp:237]     Train net output #0: loss = 0.0744768 (* 1 = 0.0744768 loss)
I0407 10:50:36.995301 17183 sgd_solver.cpp:105] Iteration 8088, lr = 0.000625
I0407 10:50:38.439569 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:50:42.146201 17183 solver.cpp:218] Iteration 8100 (2.32972 iter/s, 5.15084s/12 iters), loss = 0.0400681
I0407 10:50:42.146248 17183 solver.cpp:237]     Train net output #0: loss = 0.0400683 (* 1 = 0.0400683 loss)
I0407 10:50:42.146256 17183 sgd_solver.cpp:105] Iteration 8100, lr = 0.000625
I0407 10:50:47.340406 17183 solver.cpp:218] Iteration 8112 (2.31032 iter/s, 5.19409s/12 iters), loss = 0.0472931
I0407 10:50:47.340544 17183 solver.cpp:237]     Train net output #0: loss = 0.0472933 (* 1 = 0.0472933 loss)
I0407 10:50:47.340553 17183 sgd_solver.cpp:105] Iteration 8112, lr = 0.000625
I0407 10:50:52.622375 17183 solver.cpp:218] Iteration 8124 (2.27197 iter/s, 5.28176s/12 iters), loss = 0.148101
I0407 10:50:52.622417 17183 solver.cpp:237]     Train net output #0: loss = 0.148102 (* 1 = 0.148102 loss)
I0407 10:50:52.622426 17183 sgd_solver.cpp:105] Iteration 8124, lr = 0.000625
I0407 10:50:57.906605 17183 solver.cpp:218] Iteration 8136 (2.27096 iter/s, 5.28412s/12 iters), loss = 0.0640443
I0407 10:50:57.906666 17183 solver.cpp:237]     Train net output #0: loss = 0.0640445 (* 1 = 0.0640445 loss)
I0407 10:50:57.906677 17183 sgd_solver.cpp:105] Iteration 8136, lr = 0.000625
I0407 10:51:03.107659 17183 solver.cpp:218] Iteration 8148 (2.30728 iter/s, 5.20093s/12 iters), loss = 0.050891
I0407 10:51:03.107702 17183 solver.cpp:237]     Train net output #0: loss = 0.0508913 (* 1 = 0.0508913 loss)
I0407 10:51:03.107710 17183 sgd_solver.cpp:105] Iteration 8148, lr = 0.000625
I0407 10:51:07.874775 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8160.caffemodel
I0407 10:51:12.605633 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8160.solverstate
I0407 10:51:15.957407 17183 solver.cpp:330] Iteration 8160, Testing net (#0)
I0407 10:51:15.957429 17183 net.cpp:676] Ignoring source layer train-data
I0407 10:51:17.103458 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:51:20.307984 17183 solver.cpp:397]     Test net output #0: accuracy = 0.498774
I0407 10:51:20.308212 17183 solver.cpp:397]     Test net output #1: loss = 2.82267 (* 1 = 2.82267 loss)
I0407 10:51:20.446274 17183 solver.cpp:218] Iteration 8160 (0.692106 iter/s, 17.3384s/12 iters), loss = 0.0956313
I0407 10:51:20.446318 17183 solver.cpp:237]     Train net output #0: loss = 0.0956316 (* 1 = 0.0956316 loss)
I0407 10:51:20.446326 17183 sgd_solver.cpp:105] Iteration 8160, lr = 0.000625
I0407 10:51:24.826339 17183 solver.cpp:218] Iteration 8172 (2.73975 iter/s, 4.37996s/12 iters), loss = 0.117277
I0407 10:51:24.826401 17183 solver.cpp:237]     Train net output #0: loss = 0.117277 (* 1 = 0.117277 loss)
I0407 10:51:24.826411 17183 sgd_solver.cpp:105] Iteration 8172, lr = 0.000625
I0407 10:51:29.973881 17183 solver.cpp:218] Iteration 8184 (2.33127 iter/s, 5.14741s/12 iters), loss = 0.113563
I0407 10:51:29.973943 17183 solver.cpp:237]     Train net output #0: loss = 0.113563 (* 1 = 0.113563 loss)
I0407 10:51:29.973954 17183 sgd_solver.cpp:105] Iteration 8184, lr = 0.000625
I0407 10:51:33.605048 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:51:35.136714 17183 solver.cpp:218] Iteration 8196 (2.32436 iter/s, 5.1627s/12 iters), loss = 0.149667
I0407 10:51:35.136756 17183 solver.cpp:237]     Train net output #0: loss = 0.149667 (* 1 = 0.149667 loss)
I0407 10:51:35.136765 17183 sgd_solver.cpp:105] Iteration 8196, lr = 0.000625
I0407 10:51:40.204393 17183 solver.cpp:218] Iteration 8208 (2.368 iter/s, 5.06757s/12 iters), loss = 0.0947396
I0407 10:51:40.204434 17183 solver.cpp:237]     Train net output #0: loss = 0.0947399 (* 1 = 0.0947399 loss)
I0407 10:51:40.204442 17183 sgd_solver.cpp:105] Iteration 8208, lr = 0.000625
I0407 10:51:45.444911 17183 solver.cpp:218] Iteration 8220 (2.28991 iter/s, 5.24038s/12 iters), loss = 0.0526855
I0407 10:51:45.444957 17183 solver.cpp:237]     Train net output #0: loss = 0.0526857 (* 1 = 0.0526857 loss)
I0407 10:51:45.444963 17183 sgd_solver.cpp:105] Iteration 8220, lr = 0.000625
I0407 10:51:50.816505 17183 solver.cpp:218] Iteration 8232 (2.23402 iter/s, 5.37148s/12 iters), loss = 0.0731491
I0407 10:51:50.816644 17183 solver.cpp:237]     Train net output #0: loss = 0.0731494 (* 1 = 0.0731494 loss)
I0407 10:51:50.816653 17183 sgd_solver.cpp:105] Iteration 8232, lr = 0.000625
I0407 10:51:56.018822 17183 solver.cpp:218] Iteration 8244 (2.30676 iter/s, 5.20211s/12 iters), loss = 0.0383172
I0407 10:51:56.018877 17183 solver.cpp:237]     Train net output #0: loss = 0.0383175 (* 1 = 0.0383175 loss)
I0407 10:51:56.018887 17183 sgd_solver.cpp:105] Iteration 8244, lr = 0.000625
I0407 10:52:00.987519 17183 solver.cpp:218] Iteration 8256 (2.41518 iter/s, 4.96858s/12 iters), loss = 0.0581677
I0407 10:52:00.987565 17183 solver.cpp:237]     Train net output #0: loss = 0.0581679 (* 1 = 0.0581679 loss)
I0407 10:52:00.987573 17183 sgd_solver.cpp:105] Iteration 8256, lr = 0.000625
I0407 10:52:03.046933 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8262.caffemodel
I0407 10:52:07.100895 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8262.solverstate
I0407 10:52:10.145215 17183 solver.cpp:330] Iteration 8262, Testing net (#0)
I0407 10:52:10.145236 17183 net.cpp:676] Ignoring source layer train-data
I0407 10:52:11.238119 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:52:14.447471 17183 solver.cpp:397]     Test net output #0: accuracy = 0.496936
I0407 10:52:14.447510 17183 solver.cpp:397]     Test net output #1: loss = 2.8008 (* 1 = 2.8008 loss)
I0407 10:52:16.222399 17183 solver.cpp:218] Iteration 8268 (0.787678 iter/s, 15.2347s/12 iters), loss = 0.0581801
I0407 10:52:16.222457 17183 solver.cpp:237]     Train net output #0: loss = 0.0581804 (* 1 = 0.0581804 loss)
I0407 10:52:16.222467 17183 sgd_solver.cpp:105] Iteration 8268, lr = 0.000625
I0407 10:52:21.420068 17183 solver.cpp:218] Iteration 8280 (2.30878 iter/s, 5.19754s/12 iters), loss = 0.0568836
I0407 10:52:21.420161 17183 solver.cpp:237]     Train net output #0: loss = 0.0568839 (* 1 = 0.0568839 loss)
I0407 10:52:21.420171 17183 sgd_solver.cpp:105] Iteration 8280, lr = 0.000625
I0407 10:52:26.687366 17183 solver.cpp:218] Iteration 8292 (2.27828 iter/s, 5.26714s/12 iters), loss = 0.0713823
I0407 10:52:26.687408 17183 solver.cpp:237]     Train net output #0: loss = 0.0713825 (* 1 = 0.0713825 loss)
I0407 10:52:26.687415 17183 sgd_solver.cpp:105] Iteration 8292, lr = 0.000625
I0407 10:52:27.290712 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:52:31.662210 17183 solver.cpp:218] Iteration 8304 (2.41219 iter/s, 4.97474s/12 iters), loss = 0.122087
I0407 10:52:31.662250 17183 solver.cpp:237]     Train net output #0: loss = 0.122087 (* 1 = 0.122087 loss)
I0407 10:52:31.662257 17183 sgd_solver.cpp:105] Iteration 8304, lr = 0.000625
I0407 10:52:34.559530 17183 blocking_queue.cpp:49] Waiting for data
I0407 10:52:36.845928 17183 solver.cpp:218] Iteration 8316 (2.31499 iter/s, 5.18361s/12 iters), loss = 0.121684
I0407 10:52:36.845983 17183 solver.cpp:237]     Train net output #0: loss = 0.121684 (* 1 = 0.121684 loss)
I0407 10:52:36.845993 17183 sgd_solver.cpp:105] Iteration 8316, lr = 0.000625
I0407 10:52:42.135859 17183 solver.cpp:218] Iteration 8328 (2.26851 iter/s, 5.28981s/12 iters), loss = 0.133001
I0407 10:52:42.135910 17183 solver.cpp:237]     Train net output #0: loss = 0.133001 (* 1 = 0.133001 loss)
I0407 10:52:42.135918 17183 sgd_solver.cpp:105] Iteration 8328, lr = 0.000625
I0407 10:52:47.208473 17183 solver.cpp:218] Iteration 8340 (2.3657 iter/s, 5.0725s/12 iters), loss = 0.0221935
I0407 10:52:47.208513 17183 solver.cpp:237]     Train net output #0: loss = 0.0221938 (* 1 = 0.0221938 loss)
I0407 10:52:47.208519 17183 sgd_solver.cpp:105] Iteration 8340, lr = 0.000625
I0407 10:52:52.299271 17183 solver.cpp:218] Iteration 8352 (2.35725 iter/s, 5.09069s/12 iters), loss = 0.123597
I0407 10:52:52.299425 17183 solver.cpp:237]     Train net output #0: loss = 0.123598 (* 1 = 0.123598 loss)
I0407 10:52:52.299436 17183 sgd_solver.cpp:105] Iteration 8352, lr = 0.000625
I0407 10:52:56.922571 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8364.caffemodel
I0407 10:53:00.748839 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8364.solverstate
I0407 10:53:03.065932 17183 solver.cpp:330] Iteration 8364, Testing net (#0)
I0407 10:53:03.065949 17183 net.cpp:676] Ignoring source layer train-data
I0407 10:53:04.218346 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:53:07.447512 17183 solver.cpp:397]     Test net output #0: accuracy = 0.491422
I0407 10:53:07.447551 17183 solver.cpp:397]     Test net output #1: loss = 2.81816 (* 1 = 2.81816 loss)
I0407 10:53:07.589078 17183 solver.cpp:218] Iteration 8364 (0.784853 iter/s, 15.2895s/12 iters), loss = 0.0855221
I0407 10:53:07.590648 17183 solver.cpp:237]     Train net output #0: loss = 0.0855224 (* 1 = 0.0855224 loss)
I0407 10:53:07.590660 17183 sgd_solver.cpp:105] Iteration 8364, lr = 0.000625
I0407 10:53:11.805603 17183 solver.cpp:218] Iteration 8376 (2.84704 iter/s, 4.21491s/12 iters), loss = 0.221027
I0407 10:53:11.805649 17183 solver.cpp:237]     Train net output #0: loss = 0.221028 (* 1 = 0.221028 loss)
I0407 10:53:11.805656 17183 sgd_solver.cpp:105] Iteration 8376, lr = 0.000625
I0407 10:53:16.873870 17183 solver.cpp:218] Iteration 8388 (2.36773 iter/s, 5.06815s/12 iters), loss = 0.101348
I0407 10:53:16.873914 17183 solver.cpp:237]     Train net output #0: loss = 0.101348 (* 1 = 0.101348 loss)
I0407 10:53:16.873922 17183 sgd_solver.cpp:105] Iteration 8388, lr = 0.000625
I0407 10:53:19.682777 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:53:22.020473 17183 solver.cpp:218] Iteration 8400 (2.33169 iter/s, 5.14649s/12 iters), loss = 0.0967995
I0407 10:53:22.020519 17183 solver.cpp:237]     Train net output #0: loss = 0.0967998 (* 1 = 0.0967998 loss)
I0407 10:53:22.020526 17183 sgd_solver.cpp:105] Iteration 8400, lr = 0.000625
I0407 10:53:27.061833 17183 solver.cpp:218] Iteration 8412 (2.38037 iter/s, 5.04124s/12 iters), loss = 0.164131
I0407 10:53:27.061933 17183 solver.cpp:237]     Train net output #0: loss = 0.164131 (* 1 = 0.164131 loss)
I0407 10:53:27.061941 17183 sgd_solver.cpp:105] Iteration 8412, lr = 0.000625
I0407 10:53:32.262173 17183 solver.cpp:218] Iteration 8424 (2.30762 iter/s, 5.20017s/12 iters), loss = 0.104581
I0407 10:53:32.262225 17183 solver.cpp:237]     Train net output #0: loss = 0.104581 (* 1 = 0.104581 loss)
I0407 10:53:32.262235 17183 sgd_solver.cpp:105] Iteration 8424, lr = 0.000625
I0407 10:53:37.488566 17183 solver.cpp:218] Iteration 8436 (2.29609 iter/s, 5.22627s/12 iters), loss = 0.0887682
I0407 10:53:37.488622 17183 solver.cpp:237]     Train net output #0: loss = 0.0887685 (* 1 = 0.0887685 loss)
I0407 10:53:37.488632 17183 sgd_solver.cpp:105] Iteration 8436, lr = 0.000625
I0407 10:53:42.586324 17183 solver.cpp:218] Iteration 8448 (2.35404 iter/s, 5.09763s/12 iters), loss = 0.0333019
I0407 10:53:42.586385 17183 solver.cpp:237]     Train net output #0: loss = 0.0333022 (* 1 = 0.0333022 loss)
I0407 10:53:42.586396 17183 sgd_solver.cpp:105] Iteration 8448, lr = 0.000625
I0407 10:53:47.740259 17183 solver.cpp:218] Iteration 8460 (2.32838 iter/s, 5.15381s/12 iters), loss = 0.086842
I0407 10:53:47.740305 17183 solver.cpp:237]     Train net output #0: loss = 0.0868423 (* 1 = 0.0868423 loss)
I0407 10:53:47.740314 17183 sgd_solver.cpp:105] Iteration 8460, lr = 0.000625
I0407 10:53:49.992254 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8466.caffemodel
I0407 10:53:52.945000 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8466.solverstate
I0407 10:53:55.287552 17183 solver.cpp:330] Iteration 8466, Testing net (#0)
I0407 10:53:55.287572 17183 net.cpp:676] Ignoring source layer train-data
I0407 10:53:56.407876 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:53:59.684511 17183 solver.cpp:397]     Test net output #0: accuracy = 0.496936
I0407 10:53:59.684650 17183 solver.cpp:397]     Test net output #1: loss = 2.82864 (* 1 = 2.82864 loss)
I0407 10:54:01.585420 17183 solver.cpp:218] Iteration 8472 (0.866741 iter/s, 13.845s/12 iters), loss = 0.0716025
I0407 10:54:01.585461 17183 solver.cpp:237]     Train net output #0: loss = 0.0716028 (* 1 = 0.0716028 loss)
I0407 10:54:01.585469 17183 sgd_solver.cpp:105] Iteration 8472, lr = 0.000625
I0407 10:54:06.616379 17183 solver.cpp:218] Iteration 8484 (2.38528 iter/s, 5.03085s/12 iters), loss = 0.0846204
I0407 10:54:06.616425 17183 solver.cpp:237]     Train net output #0: loss = 0.0846207 (* 1 = 0.0846207 loss)
I0407 10:54:06.616432 17183 sgd_solver.cpp:105] Iteration 8484, lr = 0.000625
I0407 10:54:11.835515 17183 solver.cpp:218] Iteration 8496 (2.29928 iter/s, 5.21902s/12 iters), loss = 0.0762481
I0407 10:54:11.835561 17183 solver.cpp:237]     Train net output #0: loss = 0.0762484 (* 1 = 0.0762484 loss)
I0407 10:54:11.835568 17183 sgd_solver.cpp:105] Iteration 8496, lr = 0.000625
I0407 10:54:11.871704 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:54:16.998478 17183 solver.cpp:218] Iteration 8508 (2.3243 iter/s, 5.16285s/12 iters), loss = 0.272719
I0407 10:54:16.998522 17183 solver.cpp:237]     Train net output #0: loss = 0.272719 (* 1 = 0.272719 loss)
I0407 10:54:16.998529 17183 sgd_solver.cpp:105] Iteration 8508, lr = 0.000625
I0407 10:54:22.209151 17183 solver.cpp:218] Iteration 8520 (2.30302 iter/s, 5.21056s/12 iters), loss = 0.0406908
I0407 10:54:22.209199 17183 solver.cpp:237]     Train net output #0: loss = 0.0406911 (* 1 = 0.0406911 loss)
I0407 10:54:22.209211 17183 sgd_solver.cpp:105] Iteration 8520, lr = 0.000625
I0407 10:54:27.392156 17183 solver.cpp:218] Iteration 8532 (2.31531 iter/s, 5.18288s/12 iters), loss = 0.0479939
I0407 10:54:27.392207 17183 solver.cpp:237]     Train net output #0: loss = 0.0479942 (* 1 = 0.0479942 loss)
I0407 10:54:27.392216 17183 sgd_solver.cpp:105] Iteration 8532, lr = 0.000625
I0407 10:54:32.574036 17183 solver.cpp:218] Iteration 8544 (2.31582 iter/s, 5.18176s/12 iters), loss = 0.0673806
I0407 10:54:32.574159 17183 solver.cpp:237]     Train net output #0: loss = 0.0673809 (* 1 = 0.0673809 loss)
I0407 10:54:32.574170 17183 sgd_solver.cpp:105] Iteration 8544, lr = 0.000625
I0407 10:54:37.842453 17183 solver.cpp:218] Iteration 8556 (2.27781 iter/s, 5.26822s/12 iters), loss = 0.0798057
I0407 10:54:37.842515 17183 solver.cpp:237]     Train net output #0: loss = 0.079806 (* 1 = 0.079806 loss)
I0407 10:54:37.842525 17183 sgd_solver.cpp:105] Iteration 8556, lr = 0.000625
I0407 10:54:42.541260 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8568.caffemodel
I0407 10:54:45.540438 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8568.solverstate
I0407 10:54:47.836719 17183 solver.cpp:330] Iteration 8568, Testing net (#0)
I0407 10:54:47.836740 17183 net.cpp:676] Ignoring source layer train-data
I0407 10:54:48.833452 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:54:52.118090 17183 solver.cpp:397]     Test net output #0: accuracy = 0.501838
I0407 10:54:52.118132 17183 solver.cpp:397]     Test net output #1: loss = 2.79541 (* 1 = 2.79541 loss)
I0407 10:54:52.255467 17183 solver.cpp:218] Iteration 8568 (0.832594 iter/s, 14.4128s/12 iters), loss = 0.0899906
I0407 10:54:52.255511 17183 solver.cpp:237]     Train net output #0: loss = 0.0899909 (* 1 = 0.0899909 loss)
I0407 10:54:52.255518 17183 sgd_solver.cpp:105] Iteration 8568, lr = 0.000625
I0407 10:54:56.547456 17183 solver.cpp:218] Iteration 8580 (2.79598 iter/s, 4.29188s/12 iters), loss = 0.0689557
I0407 10:54:56.547514 17183 solver.cpp:237]     Train net output #0: loss = 0.068956 (* 1 = 0.068956 loss)
I0407 10:54:56.547525 17183 sgd_solver.cpp:105] Iteration 8580, lr = 0.000625
I0407 10:55:01.651691 17183 solver.cpp:218] Iteration 8592 (2.35105 iter/s, 5.10411s/12 iters), loss = 0.11646
I0407 10:55:01.651747 17183 solver.cpp:237]     Train net output #0: loss = 0.116461 (* 1 = 0.116461 loss)
I0407 10:55:01.651758 17183 sgd_solver.cpp:105] Iteration 8592, lr = 0.000625
I0407 10:55:03.846390 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:55:06.827105 17183 solver.cpp:218] Iteration 8604 (2.31871 iter/s, 5.17529s/12 iters), loss = 0.0802505
I0407 10:55:06.827157 17183 solver.cpp:237]     Train net output #0: loss = 0.0802509 (* 1 = 0.0802509 loss)
I0407 10:55:06.827168 17183 sgd_solver.cpp:105] Iteration 8604, lr = 0.000625
I0407 10:55:12.053822 17183 solver.cpp:218] Iteration 8616 (2.29595 iter/s, 5.2266s/12 iters), loss = 0.0748164
I0407 10:55:12.053875 17183 solver.cpp:237]     Train net output #0: loss = 0.0748167 (* 1 = 0.0748167 loss)
I0407 10:55:12.053885 17183 sgd_solver.cpp:105] Iteration 8616, lr = 0.000625
I0407 10:55:17.379108 17183 solver.cpp:218] Iteration 8628 (2.25345 iter/s, 5.32516s/12 iters), loss = 0.0539216
I0407 10:55:17.379151 17183 solver.cpp:237]     Train net output #0: loss = 0.0539219 (* 1 = 0.0539219 loss)
I0407 10:55:17.379158 17183 sgd_solver.cpp:105] Iteration 8628, lr = 0.000625
I0407 10:55:22.464814 17183 solver.cpp:218] Iteration 8640 (2.35961 iter/s, 5.0856s/12 iters), loss = 0.0516931
I0407 10:55:22.464862 17183 solver.cpp:237]     Train net output #0: loss = 0.0516934 (* 1 = 0.0516934 loss)
I0407 10:55:22.464871 17183 sgd_solver.cpp:105] Iteration 8640, lr = 0.000625
I0407 10:55:27.585688 17183 solver.cpp:218] Iteration 8652 (2.3434 iter/s, 5.12076s/12 iters), loss = 0.115303
I0407 10:55:27.585726 17183 solver.cpp:237]     Train net output #0: loss = 0.115303 (* 1 = 0.115303 loss)
I0407 10:55:27.585732 17183 sgd_solver.cpp:105] Iteration 8652, lr = 0.000625
I0407 10:55:32.787847 17183 solver.cpp:218] Iteration 8664 (2.30678 iter/s, 5.20205s/12 iters), loss = 0.0426769
I0407 10:55:32.787890 17183 solver.cpp:237]     Train net output #0: loss = 0.0426772 (* 1 = 0.0426772 loss)
I0407 10:55:32.787897 17183 sgd_solver.cpp:105] Iteration 8664, lr = 0.000625
I0407 10:55:34.909842 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8670.caffemodel
I0407 10:55:37.914333 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8670.solverstate
I0407 10:55:40.250905 17183 solver.cpp:330] Iteration 8670, Testing net (#0)
I0407 10:55:40.250933 17183 net.cpp:676] Ignoring source layer train-data
I0407 10:55:41.212442 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:55:44.613659 17183 solver.cpp:397]     Test net output #0: accuracy = 0.492034
I0407 10:55:44.613696 17183 solver.cpp:397]     Test net output #1: loss = 2.84157 (* 1 = 2.84157 loss)
I0407 10:55:46.489437 17183 solver.cpp:218] Iteration 8676 (0.875823 iter/s, 13.7014s/12 iters), loss = 0.0562081
I0407 10:55:46.489478 17183 solver.cpp:237]     Train net output #0: loss = 0.0562084 (* 1 = 0.0562084 loss)
I0407 10:55:46.489485 17183 sgd_solver.cpp:105] Iteration 8676, lr = 0.000625
I0407 10:55:51.606194 17183 solver.cpp:218] Iteration 8688 (2.34528 iter/s, 5.11665s/12 iters), loss = 0.059537
I0407 10:55:51.606241 17183 solver.cpp:237]     Train net output #0: loss = 0.0595373 (* 1 = 0.0595373 loss)
I0407 10:55:51.606249 17183 sgd_solver.cpp:105] Iteration 8688, lr = 0.000625
I0407 10:55:55.971415 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:55:56.703048 17183 solver.cpp:218] Iteration 8700 (2.35445 iter/s, 5.09673s/12 iters), loss = 0.115664
I0407 10:55:56.703114 17183 solver.cpp:237]     Train net output #0: loss = 0.115665 (* 1 = 0.115665 loss)
I0407 10:55:56.703125 17183 sgd_solver.cpp:105] Iteration 8700, lr = 0.000625
I0407 10:56:01.929777 17183 solver.cpp:218] Iteration 8712 (2.29595 iter/s, 5.2266s/12 iters), loss = 0.0628223
I0407 10:56:01.929826 17183 solver.cpp:237]     Train net output #0: loss = 0.0628226 (* 1 = 0.0628226 loss)
I0407 10:56:01.929836 17183 sgd_solver.cpp:105] Iteration 8712, lr = 0.000625
I0407 10:56:07.209653 17183 solver.cpp:218] Iteration 8724 (2.27283 iter/s, 5.27976s/12 iters), loss = 0.0883033
I0407 10:56:07.209811 17183 solver.cpp:237]     Train net output #0: loss = 0.0883036 (* 1 = 0.0883036 loss)
I0407 10:56:07.209822 17183 sgd_solver.cpp:105] Iteration 8724, lr = 0.000625
I0407 10:56:12.428586 17183 solver.cpp:218] Iteration 8736 (2.29942 iter/s, 5.21871s/12 iters), loss = 0.103778
I0407 10:56:12.428632 17183 solver.cpp:237]     Train net output #0: loss = 0.103778 (* 1 = 0.103778 loss)
I0407 10:56:12.428642 17183 sgd_solver.cpp:105] Iteration 8736, lr = 0.000625
I0407 10:56:17.809468 17183 solver.cpp:218] Iteration 8748 (2.23017 iter/s, 5.38076s/12 iters), loss = 0.0907561
I0407 10:56:17.809511 17183 solver.cpp:237]     Train net output #0: loss = 0.0907564 (* 1 = 0.0907564 loss)
I0407 10:56:17.809518 17183 sgd_solver.cpp:105] Iteration 8748, lr = 0.000625
I0407 10:56:23.102252 17183 solver.cpp:218] Iteration 8760 (2.26729 iter/s, 5.29267s/12 iters), loss = 0.126445
I0407 10:56:23.102289 17183 solver.cpp:237]     Train net output #0: loss = 0.126445 (* 1 = 0.126445 loss)
I0407 10:56:23.102296 17183 sgd_solver.cpp:105] Iteration 8760, lr = 0.000625
I0407 10:56:27.709012 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8772.caffemodel
I0407 10:56:31.531308 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8772.solverstate
I0407 10:56:34.155480 17183 solver.cpp:330] Iteration 8772, Testing net (#0)
I0407 10:56:34.155504 17183 net.cpp:676] Ignoring source layer train-data
I0407 10:56:35.147351 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:56:38.671772 17183 solver.cpp:397]     Test net output #0: accuracy = 0.49326
I0407 10:56:38.671877 17183 solver.cpp:397]     Test net output #1: loss = 2.84669 (* 1 = 2.84669 loss)
I0407 10:56:38.807230 17183 solver.cpp:218] Iteration 8772 (0.764099 iter/s, 15.7048s/12 iters), loss = 0.0766443
I0407 10:56:38.807296 17183 solver.cpp:237]     Train net output #0: loss = 0.0766446 (* 1 = 0.0766446 loss)
I0407 10:56:38.807305 17183 sgd_solver.cpp:105] Iteration 8772, lr = 0.000625
I0407 10:56:43.136296 17183 solver.cpp:218] Iteration 8784 (2.77204 iter/s, 4.32895s/12 iters), loss = 0.0911338
I0407 10:56:43.136338 17183 solver.cpp:237]     Train net output #0: loss = 0.0911341 (* 1 = 0.0911341 loss)
I0407 10:56:43.136345 17183 sgd_solver.cpp:105] Iteration 8784, lr = 0.000625
I0407 10:56:48.126014 17183 solver.cpp:218] Iteration 8796 (2.405 iter/s, 4.98961s/12 iters), loss = 0.108596
I0407 10:56:48.126072 17183 solver.cpp:237]     Train net output #0: loss = 0.108596 (* 1 = 0.108596 loss)
I0407 10:56:48.126082 17183 sgd_solver.cpp:105] Iteration 8796, lr = 0.000625
I0407 10:56:49.551486 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:56:53.270771 17183 solver.cpp:218] Iteration 8808 (2.33253 iter/s, 5.14463s/12 iters), loss = 0.0430252
I0407 10:56:53.270826 17183 solver.cpp:237]     Train net output #0: loss = 0.0430255 (* 1 = 0.0430255 loss)
I0407 10:56:53.270836 17183 sgd_solver.cpp:105] Iteration 8808, lr = 0.000625
I0407 10:56:58.451680 17183 solver.cpp:218] Iteration 8820 (2.31625 iter/s, 5.18079s/12 iters), loss = 0.0830306
I0407 10:56:58.451722 17183 solver.cpp:237]     Train net output #0: loss = 0.0830309 (* 1 = 0.0830309 loss)
I0407 10:56:58.451730 17183 sgd_solver.cpp:105] Iteration 8820, lr = 0.000625
I0407 10:57:03.571573 17183 solver.cpp:218] Iteration 8832 (2.34385 iter/s, 5.11978s/12 iters), loss = 0.10723
I0407 10:57:03.571617 17183 solver.cpp:237]     Train net output #0: loss = 0.107231 (* 1 = 0.107231 loss)
I0407 10:57:03.571625 17183 sgd_solver.cpp:105] Iteration 8832, lr = 0.000625
I0407 10:57:08.783054 17183 solver.cpp:218] Iteration 8844 (2.30266 iter/s, 5.21137s/12 iters), loss = 0.0618417
I0407 10:57:08.783185 17183 solver.cpp:237]     Train net output #0: loss = 0.061842 (* 1 = 0.061842 loss)
I0407 10:57:08.783193 17183 sgd_solver.cpp:105] Iteration 8844, lr = 0.000625
I0407 10:57:13.892057 17183 solver.cpp:218] Iteration 8856 (2.34888 iter/s, 5.10881s/12 iters), loss = 0.0426024
I0407 10:57:13.892100 17183 solver.cpp:237]     Train net output #0: loss = 0.0426027 (* 1 = 0.0426027 loss)
I0407 10:57:13.892107 17183 sgd_solver.cpp:105] Iteration 8856, lr = 0.000625
I0407 10:57:19.135627 17183 solver.cpp:218] Iteration 8868 (2.28857 iter/s, 5.24346s/12 iters), loss = 0.115248
I0407 10:57:19.135672 17183 solver.cpp:237]     Train net output #0: loss = 0.115248 (* 1 = 0.115248 loss)
I0407 10:57:19.135680 17183 sgd_solver.cpp:105] Iteration 8868, lr = 0.000625
I0407 10:57:21.197113 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8874.caffemodel
I0407 10:57:24.214674 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8874.solverstate
I0407 10:57:26.516913 17183 solver.cpp:330] Iteration 8874, Testing net (#0)
I0407 10:57:26.516932 17183 net.cpp:676] Ignoring source layer train-data
I0407 10:57:27.435693 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:57:30.894924 17183 solver.cpp:397]     Test net output #0: accuracy = 0.496936
I0407 10:57:30.894964 17183 solver.cpp:397]     Test net output #1: loss = 2.84499 (* 1 = 2.84499 loss)
I0407 10:57:32.728359 17183 solver.cpp:218] Iteration 8880 (0.882838 iter/s, 13.5925s/12 iters), loss = 0.0606245
I0407 10:57:32.728415 17183 solver.cpp:237]     Train net output #0: loss = 0.0606248 (* 1 = 0.0606248 loss)
I0407 10:57:32.728425 17183 sgd_solver.cpp:105] Iteration 8880, lr = 0.000625
I0407 10:57:37.862569 17183 solver.cpp:218] Iteration 8892 (2.33732 iter/s, 5.13409s/12 iters), loss = 0.0726081
I0407 10:57:37.862615 17183 solver.cpp:237]     Train net output #0: loss = 0.0726084 (* 1 = 0.0726084 loss)
I0407 10:57:37.862623 17183 sgd_solver.cpp:105] Iteration 8892, lr = 0.000625
I0407 10:57:41.501886 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:57:42.948820 17183 solver.cpp:218] Iteration 8904 (2.35935 iter/s, 5.08614s/12 iters), loss = 0.0477114
I0407 10:57:42.948863 17183 solver.cpp:237]     Train net output #0: loss = 0.0477117 (* 1 = 0.0477117 loss)
I0407 10:57:42.948870 17183 sgd_solver.cpp:105] Iteration 8904, lr = 0.000625
I0407 10:57:47.981956 17183 solver.cpp:218] Iteration 8916 (2.38425 iter/s, 5.03302s/12 iters), loss = 0.0786811
I0407 10:57:47.982007 17183 solver.cpp:237]     Train net output #0: loss = 0.0786814 (* 1 = 0.0786814 loss)
I0407 10:57:47.982017 17183 sgd_solver.cpp:105] Iteration 8916, lr = 0.000625
I0407 10:57:53.224943 17183 solver.cpp:218] Iteration 8928 (2.28882 iter/s, 5.24287s/12 iters), loss = 0.0316718
I0407 10:57:53.224987 17183 solver.cpp:237]     Train net output #0: loss = 0.0316721 (* 1 = 0.0316721 loss)
I0407 10:57:53.224993 17183 sgd_solver.cpp:105] Iteration 8928, lr = 0.000625
I0407 10:57:58.405278 17183 solver.cpp:218] Iteration 8940 (2.3165 iter/s, 5.18022s/12 iters), loss = 0.0681814
I0407 10:57:58.405330 17183 solver.cpp:237]     Train net output #0: loss = 0.0681816 (* 1 = 0.0681816 loss)
I0407 10:57:58.405339 17183 sgd_solver.cpp:105] Iteration 8940, lr = 0.000625
I0407 10:58:03.544909 17183 solver.cpp:218] Iteration 8952 (2.33486 iter/s, 5.1395s/12 iters), loss = 0.0360047
I0407 10:58:03.544953 17183 solver.cpp:237]     Train net output #0: loss = 0.036005 (* 1 = 0.036005 loss)
I0407 10:58:03.544961 17183 sgd_solver.cpp:105] Iteration 8952, lr = 0.000625
I0407 10:58:08.531267 17183 solver.cpp:218] Iteration 8964 (2.40662 iter/s, 4.98625s/12 iters), loss = 0.141451
I0407 10:58:08.531317 17183 solver.cpp:237]     Train net output #0: loss = 0.141451 (* 1 = 0.141451 loss)
I0407 10:58:08.531327 17183 sgd_solver.cpp:105] Iteration 8964, lr = 0.000625
I0407 10:58:12.998548 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8976.caffemodel
I0407 10:58:16.066444 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8976.solverstate
I0407 10:58:18.423681 17183 solver.cpp:330] Iteration 8976, Testing net (#0)
I0407 10:58:18.423703 17183 net.cpp:676] Ignoring source layer train-data
I0407 10:58:19.258040 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:58:22.743201 17183 solver.cpp:397]     Test net output #0: accuracy = 0.497549
I0407 10:58:22.743238 17183 solver.cpp:397]     Test net output #1: loss = 2.83794 (* 1 = 2.83794 loss)
I0407 10:58:22.884630 17183 solver.cpp:218] Iteration 8976 (0.836053 iter/s, 14.3532s/12 iters), loss = 0.0461502
I0407 10:58:22.884697 17183 solver.cpp:237]     Train net output #0: loss = 0.0461504 (* 1 = 0.0461504 loss)
I0407 10:58:22.884706 17183 sgd_solver.cpp:105] Iteration 8976, lr = 0.000625
I0407 10:58:27.154700 17183 solver.cpp:218] Iteration 8988 (2.81034 iter/s, 4.26994s/12 iters), loss = 0.0458021
I0407 10:58:27.154747 17183 solver.cpp:237]     Train net output #0: loss = 0.0458024 (* 1 = 0.0458024 loss)
I0407 10:58:27.154753 17183 sgd_solver.cpp:105] Iteration 8988, lr = 0.000625
I0407 10:58:30.456094 17183 blocking_queue.cpp:49] Waiting for data
I0407 10:58:32.284741 17183 solver.cpp:218] Iteration 9000 (2.33922 iter/s, 5.12993s/12 iters), loss = 0.19601
I0407 10:58:32.284786 17183 solver.cpp:237]     Train net output #0: loss = 0.19601 (* 1 = 0.19601 loss)
I0407 10:58:32.284795 17183 sgd_solver.cpp:105] Iteration 9000, lr = 0.000625
I0407 10:58:33.124868 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:58:37.696902 17183 solver.cpp:218] Iteration 9012 (2.21728 iter/s, 5.41204s/12 iters), loss = 0.0205743
I0407 10:58:37.696944 17183 solver.cpp:237]     Train net output #0: loss = 0.0205745 (* 1 = 0.0205745 loss)
I0407 10:58:37.696950 17183 sgd_solver.cpp:105] Iteration 9012, lr = 0.000625
I0407 10:58:42.939730 17183 solver.cpp:218] Iteration 9024 (2.28889 iter/s, 5.24272s/12 iters), loss = 0.111482
I0407 10:58:42.939792 17183 solver.cpp:237]     Train net output #0: loss = 0.111482 (* 1 = 0.111482 loss)
I0407 10:58:42.939802 17183 sgd_solver.cpp:105] Iteration 9024, lr = 0.000625
I0407 10:58:48.194247 17183 solver.cpp:218] Iteration 9036 (2.28381 iter/s, 5.25438s/12 iters), loss = 0.0408824
I0407 10:58:48.194411 17183 solver.cpp:237]     Train net output #0: loss = 0.0408826 (* 1 = 0.0408826 loss)
I0407 10:58:48.194422 17183 sgd_solver.cpp:105] Iteration 9036, lr = 0.000625
I0407 10:58:53.407296 17183 solver.cpp:218] Iteration 9048 (2.30202 iter/s, 5.21282s/12 iters), loss = 0.0492892
I0407 10:58:53.407339 17183 solver.cpp:237]     Train net output #0: loss = 0.0492895 (* 1 = 0.0492895 loss)
I0407 10:58:53.407346 17183 sgd_solver.cpp:105] Iteration 9048, lr = 0.000625
I0407 10:58:58.658058 17183 solver.cpp:218] Iteration 9060 (2.28543 iter/s, 5.25066s/12 iters), loss = 0.0474904
I0407 10:58:58.658094 17183 solver.cpp:237]     Train net output #0: loss = 0.0474906 (* 1 = 0.0474906 loss)
I0407 10:58:58.658102 17183 sgd_solver.cpp:105] Iteration 9060, lr = 0.000625
I0407 10:59:03.911315 17183 solver.cpp:218] Iteration 9072 (2.28434 iter/s, 5.25315s/12 iters), loss = 0.0687165
I0407 10:59:03.911365 17183 solver.cpp:237]     Train net output #0: loss = 0.0687168 (* 1 = 0.0687168 loss)
I0407 10:59:03.911372 17183 sgd_solver.cpp:105] Iteration 9072, lr = 0.000625
I0407 10:59:05.837116 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9078.caffemodel
I0407 10:59:08.860743 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9078.solverstate
I0407 10:59:11.201076 17183 solver.cpp:330] Iteration 9078, Testing net (#0)
I0407 10:59:11.201100 17183 net.cpp:676] Ignoring source layer train-data
I0407 10:59:12.027361 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:59:15.559492 17183 solver.cpp:397]     Test net output #0: accuracy = 0.5
I0407 10:59:15.559530 17183 solver.cpp:397]     Test net output #1: loss = 2.8534 (* 1 = 2.8534 loss)
I0407 10:59:17.404902 17183 solver.cpp:218] Iteration 9084 (0.889325 iter/s, 13.4934s/12 iters), loss = 0.0725912
I0407 10:59:17.404958 17183 solver.cpp:237]     Train net output #0: loss = 0.0725915 (* 1 = 0.0725915 loss)
I0407 10:59:17.404968 17183 sgd_solver.cpp:105] Iteration 9084, lr = 0.000625
I0407 10:59:22.635094 17183 solver.cpp:218] Iteration 9096 (2.29443 iter/s, 5.23007s/12 iters), loss = 0.0357006
I0407 10:59:22.635548 17183 solver.cpp:237]     Train net output #0: loss = 0.0357008 (* 1 = 0.0357008 loss)
I0407 10:59:22.635560 17183 sgd_solver.cpp:105] Iteration 9096, lr = 0.000625
I0407 10:59:25.535604 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 10:59:27.700920 17183 solver.cpp:218] Iteration 9108 (2.36906 iter/s, 5.06531s/12 iters), loss = 0.0576347
I0407 10:59:27.700971 17183 solver.cpp:237]     Train net output #0: loss = 0.0576349 (* 1 = 0.0576349 loss)
I0407 10:59:27.700981 17183 sgd_solver.cpp:105] Iteration 9108, lr = 0.000625
I0407 10:59:32.913882 17183 solver.cpp:218] Iteration 9120 (2.30201 iter/s, 5.21284s/12 iters), loss = 0.115841
I0407 10:59:32.913928 17183 solver.cpp:237]     Train net output #0: loss = 0.115842 (* 1 = 0.115842 loss)
I0407 10:59:32.913934 17183 sgd_solver.cpp:105] Iteration 9120, lr = 0.000625
I0407 10:59:38.099220 17183 solver.cpp:218] Iteration 9132 (2.31427 iter/s, 5.18523s/12 iters), loss = 0.119416
I0407 10:59:38.099267 17183 solver.cpp:237]     Train net output #0: loss = 0.119416 (* 1 = 0.119416 loss)
I0407 10:59:38.099273 17183 sgd_solver.cpp:105] Iteration 9132, lr = 0.000625
I0407 10:59:43.304898 17183 solver.cpp:218] Iteration 9144 (2.30523 iter/s, 5.20556s/12 iters), loss = 0.0767432
I0407 10:59:43.304935 17183 solver.cpp:237]     Train net output #0: loss = 0.0767434 (* 1 = 0.0767434 loss)
I0407 10:59:43.304942 17183 sgd_solver.cpp:105] Iteration 9144, lr = 0.000625
I0407 10:59:48.555496 17183 solver.cpp:218] Iteration 9156 (2.2855 iter/s, 5.25049s/12 iters), loss = 0.0512038
I0407 10:59:48.555541 17183 solver.cpp:237]     Train net output #0: loss = 0.051204 (* 1 = 0.051204 loss)
I0407 10:59:48.555548 17183 sgd_solver.cpp:105] Iteration 9156, lr = 0.000625
I0407 10:59:53.761422 17183 solver.cpp:218] Iteration 9168 (2.30512 iter/s, 5.20581s/12 iters), loss = 0.00547451
I0407 10:59:53.761536 17183 solver.cpp:237]     Train net output #0: loss = 0.00547473 (* 1 = 0.00547473 loss)
I0407 10:59:53.761546 17183 sgd_solver.cpp:105] Iteration 9168, lr = 0.000625
I0407 10:59:58.431537 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9180.caffemodel
I0407 11:00:01.456522 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9180.solverstate
I0407 11:00:03.819615 17183 solver.cpp:330] Iteration 9180, Testing net (#0)
I0407 11:00:03.819635 17183 net.cpp:676] Ignoring source layer train-data
I0407 11:00:04.605696 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 11:00:08.246425 17183 solver.cpp:397]     Test net output #0: accuracy = 0.498774
I0407 11:00:08.246460 17183 solver.cpp:397]     Test net output #1: loss = 2.85911 (* 1 = 2.85911 loss)
I0407 11:00:08.382411 17183 solver.cpp:218] Iteration 9180 (0.820753 iter/s, 14.6207s/12 iters), loss = 0.10471
I0407 11:00:08.382469 17183 solver.cpp:237]     Train net output #0: loss = 0.10471 (* 1 = 0.10471 loss)
I0407 11:00:08.382478 17183 sgd_solver.cpp:105] Iteration 9180, lr = 0.000625
I0407 11:00:12.401229 17183 solver.cpp:218] Iteration 9192 (2.98604 iter/s, 4.0187s/12 iters), loss = 0.0806283
I0407 11:00:12.401286 17183 solver.cpp:237]     Train net output #0: loss = 0.0806285 (* 1 = 0.0806285 loss)
I0407 11:00:12.401302 17183 sgd_solver.cpp:105] Iteration 9192, lr = 0.000625
I0407 11:00:17.557528 17183 solver.cpp:218] Iteration 9204 (2.32731 iter/s, 5.15617s/12 iters), loss = 0.0733044
I0407 11:00:17.557571 17183 solver.cpp:237]     Train net output #0: loss = 0.0733047 (* 1 = 0.0733047 loss)
I0407 11:00:17.557579 17183 sgd_solver.cpp:105] Iteration 9204, lr = 0.000625
I0407 11:00:17.619436 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 11:00:22.624244 17183 solver.cpp:218] Iteration 9216 (2.36845 iter/s, 5.06661s/12 iters), loss = 0.059496
I0407 11:00:22.624287 17183 solver.cpp:237]     Train net output #0: loss = 0.0594962 (* 1 = 0.0594962 loss)
I0407 11:00:22.624294 17183 sgd_solver.cpp:105] Iteration 9216, lr = 0.000625
I0407 11:00:27.689179 17183 solver.cpp:218] Iteration 9228 (2.36928 iter/s, 5.06483s/12 iters), loss = 0.0558586
I0407 11:00:27.689297 17183 solver.cpp:237]     Train net output #0: loss = 0.0558589 (* 1 = 0.0558589 loss)
I0407 11:00:27.689306 17183 sgd_solver.cpp:105] Iteration 9228, lr = 0.000625
I0407 11:00:32.754711 17183 solver.cpp:218] Iteration 9240 (2.36904 iter/s, 5.06534s/12 iters), loss = 0.0475089
I0407 11:00:32.754760 17183 solver.cpp:237]     Train net output #0: loss = 0.0475092 (* 1 = 0.0475092 loss)
I0407 11:00:32.754766 17183 sgd_solver.cpp:105] Iteration 9240, lr = 0.000625
I0407 11:00:38.084257 17183 solver.cpp:218] Iteration 9252 (2.25165 iter/s, 5.32943s/12 iters), loss = 0.0625424
I0407 11:00:38.084306 17183 solver.cpp:237]     Train net output #0: loss = 0.0625426 (* 1 = 0.0625426 loss)
I0407 11:00:38.084314 17183 sgd_solver.cpp:105] Iteration 9252, lr = 0.000625
I0407 11:00:43.373674 17183 solver.cpp:218] Iteration 9264 (2.26873 iter/s, 5.2893s/12 iters), loss = 0.0910423
I0407 11:00:43.373725 17183 solver.cpp:237]     Train net output #0: loss = 0.0910425 (* 1 = 0.0910425 loss)
I0407 11:00:43.373736 17183 sgd_solver.cpp:105] Iteration 9264, lr = 0.000625
I0407 11:00:48.580983 17183 solver.cpp:218] Iteration 9276 (2.30451 iter/s, 5.20719s/12 iters), loss = 0.0292371
I0407 11:00:48.581048 17183 solver.cpp:237]     Train net output #0: loss = 0.0292373 (* 1 = 0.0292373 loss)
I0407 11:00:48.581059 17183 sgd_solver.cpp:105] Iteration 9276, lr = 0.000625
I0407 11:00:50.742193 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9282.caffemodel
I0407 11:00:53.737398 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9282.solverstate
I0407 11:00:56.076421 17183 solver.cpp:330] Iteration 9282, Testing net (#0)
I0407 11:00:56.076445 17183 net.cpp:676] Ignoring source layer train-data
I0407 11:00:56.826378 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 11:01:00.390094 17183 solver.cpp:397]     Test net output #0: accuracy = 0.49326
I0407 11:01:00.390193 17183 solver.cpp:397]     Test net output #1: loss = 2.87215 (* 1 = 2.87215 loss)
I0407 11:01:02.272573 17183 solver.cpp:218] Iteration 9288 (0.876464 iter/s, 13.6914s/12 iters), loss = 0.12189
I0407 11:01:02.272615 17183 solver.cpp:237]     Train net output #0: loss = 0.121891 (* 1 = 0.121891 loss)
I0407 11:01:02.272622 17183 sgd_solver.cpp:105] Iteration 9288, lr = 0.000625
I0407 11:01:07.438139 17183 solver.cpp:218] Iteration 9300 (2.32312 iter/s, 5.16546s/12 iters), loss = 0.0630254
I0407 11:01:07.438182 17183 solver.cpp:237]     Train net output #0: loss = 0.0630257 (* 1 = 0.0630257 loss)
I0407 11:01:07.438189 17183 sgd_solver.cpp:105] Iteration 9300, lr = 0.000625
I0407 11:01:09.777866 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 11:01:12.514816 17183 solver.cpp:218] Iteration 9312 (2.36381 iter/s, 5.07656s/12 iters), loss = 0.108852
I0407 11:01:12.514886 17183 solver.cpp:237]     Train net output #0: loss = 0.108852 (* 1 = 0.108852 loss)
I0407 11:01:12.514897 17183 sgd_solver.cpp:105] Iteration 9312, lr = 0.000625
I0407 11:01:17.832684 17183 solver.cpp:218] Iteration 9324 (2.2566 iter/s, 5.31773s/12 iters), loss = 0.0541554
I0407 11:01:17.832741 17183 solver.cpp:237]     Train net output #0: loss = 0.0541556 (* 1 = 0.0541556 loss)
I0407 11:01:17.832751 17183 sgd_solver.cpp:105] Iteration 9324, lr = 0.000625
I0407 11:01:23.070257 17183 solver.cpp:218] Iteration 9336 (2.29119 iter/s, 5.23745s/12 iters), loss = 0.104558
I0407 11:01:23.070302 17183 solver.cpp:237]     Train net output #0: loss = 0.104558 (* 1 = 0.104558 loss)
I0407 11:01:23.070310 17183 sgd_solver.cpp:105] Iteration 9336, lr = 0.000625
I0407 11:01:28.181887 17183 solver.cpp:218] Iteration 9348 (2.34764 iter/s, 5.11151s/12 iters), loss = 0.0462349
I0407 11:01:28.181932 17183 solver.cpp:237]     Train net output #0: loss = 0.0462351 (* 1 = 0.0462351 loss)
I0407 11:01:28.181939 17183 sgd_solver.cpp:105] Iteration 9348, lr = 0.000625
I0407 11:01:33.255532 17183 solver.cpp:218] Iteration 9360 (2.36522 iter/s, 5.07353s/12 iters), loss = 0.103655
I0407 11:01:33.255702 17183 solver.cpp:237]     Train net output #0: loss = 0.103655 (* 1 = 0.103655 loss)
I0407 11:01:33.255713 17183 sgd_solver.cpp:105] Iteration 9360, lr = 0.000625
I0407 11:01:38.422103 17183 solver.cpp:218] Iteration 9372 (2.32273 iter/s, 5.16634s/12 iters), loss = 0.0291684
I0407 11:01:38.422148 17183 solver.cpp:237]     Train net output #0: loss = 0.0291686 (* 1 = 0.0291686 loss)
I0407 11:01:38.422155 17183 sgd_solver.cpp:105] Iteration 9372, lr = 0.000625
I0407 11:01:42.837277 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9384.caffemodel
I0407 11:01:45.830507 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9384.solverstate
I0407 11:01:48.284402 17183 solver.cpp:330] Iteration 9384, Testing net (#0)
I0407 11:01:48.284422 17183 net.cpp:676] Ignoring source layer train-data
I0407 11:01:49.018276 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 11:01:52.682646 17183 solver.cpp:397]     Test net output #0: accuracy = 0.495098
I0407 11:01:52.682674 17183 solver.cpp:397]     Test net output #1: loss = 2.85031 (* 1 = 2.85031 loss)
I0407 11:01:52.824106 17183 solver.cpp:218] Iteration 9384 (0.833229 iter/s, 14.4018s/12 iters), loss = 0.0454598
I0407 11:01:52.824151 17183 solver.cpp:237]     Train net output #0: loss = 0.04546 (* 1 = 0.04546 loss)
I0407 11:01:52.824157 17183 sgd_solver.cpp:105] Iteration 9384, lr = 0.000625
I0407 11:01:57.031847 17183 solver.cpp:218] Iteration 9396 (2.85196 iter/s, 4.20764s/12 iters), loss = 0.066486
I0407 11:01:57.031909 17183 solver.cpp:237]     Train net output #0: loss = 0.0664863 (* 1 = 0.0664863 loss)
I0407 11:01:57.031920 17183 sgd_solver.cpp:105] Iteration 9396, lr = 0.000625
I0407 11:02:01.501863 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 11:02:02.228363 17183 solver.cpp:218] Iteration 9408 (2.3093 iter/s, 5.19638s/12 iters), loss = 0.0616383
I0407 11:02:02.228408 17183 solver.cpp:237]     Train net output #0: loss = 0.0616385 (* 1 = 0.0616385 loss)
I0407 11:02:02.228415 17183 sgd_solver.cpp:105] Iteration 9408, lr = 0.000625
I0407 11:02:07.318958 17183 solver.cpp:218] Iteration 9420 (2.35734 iter/s, 5.09048s/12 iters), loss = 0.0540313
I0407 11:02:07.319065 17183 solver.cpp:237]     Train net output #0: loss = 0.0540315 (* 1 = 0.0540315 loss)
I0407 11:02:07.319075 17183 sgd_solver.cpp:105] Iteration 9420, lr = 0.000625
I0407 11:02:12.543519 17183 solver.cpp:218] Iteration 9432 (2.29692 iter/s, 5.22439s/12 iters), loss = 0.0922911
I0407 11:02:12.543563 17183 solver.cpp:237]     Train net output #0: loss = 0.0922913 (* 1 = 0.0922913 loss)
I0407 11:02:12.543571 17183 sgd_solver.cpp:105] Iteration 9432, lr = 0.000625
I0407 11:02:17.560480 17183 solver.cpp:218] Iteration 9444 (2.39194 iter/s, 5.01685s/12 iters), loss = 0.0282788
I0407 11:02:17.560521 17183 solver.cpp:237]     Train net output #0: loss = 0.028279 (* 1 = 0.028279 loss)
I0407 11:02:17.560528 17183 sgd_solver.cpp:105] Iteration 9444, lr = 0.000625
I0407 11:02:22.646811 17183 solver.cpp:218] Iteration 9456 (2.35932 iter/s, 5.08622s/12 iters), loss = 0.0620817
I0407 11:02:22.646852 17183 solver.cpp:237]     Train net output #0: loss = 0.0620819 (* 1 = 0.0620819 loss)
I0407 11:02:22.646859 17183 sgd_solver.cpp:105] Iteration 9456, lr = 0.000625
I0407 11:02:27.561288 17183 solver.cpp:218] Iteration 9468 (2.44182 iter/s, 4.91437s/12 iters), loss = 0.023654
I0407 11:02:27.561333 17183 solver.cpp:237]     Train net output #0: loss = 0.0236542 (* 1 = 0.0236542 loss)
I0407 11:02:27.561342 17183 sgd_solver.cpp:105] Iteration 9468, lr = 0.000625
I0407 11:02:32.610605 17183 solver.cpp:218] Iteration 9480 (2.37661 iter/s, 5.0492s/12 iters), loss = 0.0823722
I0407 11:02:32.610671 17183 solver.cpp:237]     Train net output #0: loss = 0.0823724 (* 1 = 0.0823724 loss)
I0407 11:02:32.610683 17183 sgd_solver.cpp:105] Iteration 9480, lr = 0.000625
I0407 11:02:34.611799 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9486.caffemodel
I0407 11:02:37.615571 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9486.solverstate
I0407 11:02:41.560250 17183 solver.cpp:330] Iteration 9486, Testing net (#0)
I0407 11:02:41.560268 17183 net.cpp:676] Ignoring source layer train-data
I0407 11:02:42.242463 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 11:02:45.971122 17183 solver.cpp:397]     Test net output #0: accuracy = 0.496936
I0407 11:02:45.971159 17183 solver.cpp:397]     Test net output #1: loss = 2.88611 (* 1 = 2.88611 loss)
I0407 11:02:47.904897 17183 solver.cpp:218] Iteration 9492 (0.784618 iter/s, 15.2941s/12 iters), loss = 0.0729228
I0407 11:02:47.904947 17183 solver.cpp:237]     Train net output #0: loss = 0.072923 (* 1 = 0.072923 loss)
I0407 11:02:47.904953 17183 sgd_solver.cpp:105] Iteration 9492, lr = 0.000625
I0407 11:02:53.085057 17183 solver.cpp:218] Iteration 9504 (2.31659 iter/s, 5.18004s/12 iters), loss = 0.0417487
I0407 11:02:53.085117 17183 solver.cpp:237]     Train net output #0: loss = 0.0417489 (* 1 = 0.0417489 loss)
I0407 11:02:53.085129 17183 sgd_solver.cpp:105] Iteration 9504, lr = 0.000625
I0407 11:02:54.585783 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 11:02:58.280331 17183 solver.cpp:218] Iteration 9516 (2.30985 iter/s, 5.19515s/12 iters), loss = 0.0723165
I0407 11:02:58.280371 17183 solver.cpp:237]     Train net output #0: loss = 0.0723167 (* 1 = 0.0723167 loss)
I0407 11:02:58.280378 17183 sgd_solver.cpp:105] Iteration 9516, lr = 0.000625
I0407 11:03:03.408612 17183 solver.cpp:218] Iteration 9528 (2.34002 iter/s, 5.12817s/12 iters), loss = 0.0293174
I0407 11:03:03.408656 17183 solver.cpp:237]     Train net output #0: loss = 0.0293176 (* 1 = 0.0293176 loss)
I0407 11:03:03.408663 17183 sgd_solver.cpp:105] Iteration 9528, lr = 0.000625
I0407 11:03:08.642390 17183 solver.cpp:218] Iteration 9540 (2.29285 iter/s, 5.23367s/12 iters), loss = 0.061959
I0407 11:03:08.642482 17183 solver.cpp:237]     Train net output #0: loss = 0.0619592 (* 1 = 0.0619592 loss)
I0407 11:03:08.642488 17183 sgd_solver.cpp:105] Iteration 9540, lr = 0.000625
I0407 11:03:13.915906 17183 solver.cpp:218] Iteration 9552 (2.27559 iter/s, 5.27336s/12 iters), loss = 0.0737501
I0407 11:03:13.915949 17183 solver.cpp:237]     Train net output #0: loss = 0.0737503 (* 1 = 0.0737503 loss)
I0407 11:03:13.915956 17183 sgd_solver.cpp:105] Iteration 9552, lr = 0.000625
I0407 11:03:19.111284 17183 solver.cpp:218] Iteration 9564 (2.3098 iter/s, 5.19527s/12 iters), loss = 0.0154118
I0407 11:03:19.111333 17183 solver.cpp:237]     Train net output #0: loss = 0.015412 (* 1 = 0.015412 loss)
I0407 11:03:19.111341 17183 sgd_solver.cpp:105] Iteration 9564, lr = 0.000625
I0407 11:03:24.338812 17183 solver.cpp:218] Iteration 9576 (2.29559 iter/s, 5.22741s/12 iters), loss = 0.130841
I0407 11:03:24.338860 17183 solver.cpp:237]     Train net output #0: loss = 0.130841 (* 1 = 0.130841 loss)
I0407 11:03:24.338868 17183 sgd_solver.cpp:105] Iteration 9576, lr = 0.000625
I0407 11:03:29.047703 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9588.caffemodel
I0407 11:03:33.554659 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9588.solverstate
I0407 11:03:35.866094 17183 solver.cpp:330] Iteration 9588, Testing net (#0)
I0407 11:03:35.866113 17183 net.cpp:676] Ignoring source layer train-data
I0407 11:03:36.568804 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 11:03:40.497380 17183 solver.cpp:397]     Test net output #0: accuracy = 0.49326
I0407 11:03:40.497479 17183 solver.cpp:397]     Test net output #1: loss = 2.89987 (* 1 = 2.89987 loss)
I0407 11:03:40.635424 17183 solver.cpp:218] Iteration 9588 (0.736359 iter/s, 16.2964s/12 iters), loss = 0.0597941
I0407 11:03:40.636984 17183 solver.cpp:237]     Train net output #0: loss = 0.0597943 (* 1 = 0.0597943 loss)
I0407 11:03:40.637001 17183 sgd_solver.cpp:105] Iteration 9588, lr = 0.000625
I0407 11:03:44.631580 17183 solver.cpp:218] Iteration 9600 (3.0041 iter/s, 3.99455s/12 iters), loss = 0.0527467
I0407 11:03:44.631629 17183 solver.cpp:237]     Train net output #0: loss = 0.0527469 (* 1 = 0.0527469 loss)
I0407 11:03:44.631695 17183 sgd_solver.cpp:105] Iteration 9600, lr = 0.000625
I0407 11:03:48.333767 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 11:03:49.859426 17183 solver.cpp:218] Iteration 9612 (2.29545 iter/s, 5.22773s/12 iters), loss = 0.0481031
I0407 11:03:49.859465 17183 solver.cpp:237]     Train net output #0: loss = 0.0481034 (* 1 = 0.0481034 loss)
I0407 11:03:49.859472 17183 sgd_solver.cpp:105] Iteration 9612, lr = 0.000625
I0407 11:03:55.044307 17183 solver.cpp:218] Iteration 9624 (2.31447 iter/s, 5.18477s/12 iters), loss = 0.119394
I0407 11:03:55.044374 17183 solver.cpp:237]     Train net output #0: loss = 0.119394 (* 1 = 0.119394 loss)
I0407 11:03:55.044385 17183 sgd_solver.cpp:105] Iteration 9624, lr = 0.000625
I0407 11:04:00.235947 17183 solver.cpp:218] Iteration 9636 (2.31147 iter/s, 5.19151s/12 iters), loss = 0.0480237
I0407 11:04:00.236006 17183 solver.cpp:237]     Train net output #0: loss = 0.048024 (* 1 = 0.048024 loss)
I0407 11:04:00.236016 17183 sgd_solver.cpp:105] Iteration 9636, lr = 0.000625
I0407 11:04:05.391024 17183 solver.cpp:218] Iteration 9648 (2.32786 iter/s, 5.15495s/12 iters), loss = 0.0522662
I0407 11:04:05.391067 17183 solver.cpp:237]     Train net output #0: loss = 0.0522665 (* 1 = 0.0522665 loss)
I0407 11:04:05.391074 17183 sgd_solver.cpp:105] Iteration 9648, lr = 0.000625
I0407 11:04:10.469396 17183 solver.cpp:218] Iteration 9660 (2.36301 iter/s, 5.07826s/12 iters), loss = 0.0645649
I0407 11:04:10.469437 17183 solver.cpp:237]     Train net output #0: loss = 0.0645651 (* 1 = 0.0645651 loss)
I0407 11:04:10.469444 17183 sgd_solver.cpp:105] Iteration 9660, lr = 0.000625
I0407 11:04:15.581437 17183 solver.cpp:218] Iteration 9672 (2.34745 iter/s, 5.11193s/12 iters), loss = 0.109858
I0407 11:04:15.581595 17183 solver.cpp:237]     Train net output #0: loss = 0.109858 (* 1 = 0.109858 loss)
I0407 11:04:15.581606 17183 sgd_solver.cpp:105] Iteration 9672, lr = 0.000625
I0407 11:04:20.891834 17183 solver.cpp:218] Iteration 9684 (2.25981 iter/s, 5.31017s/12 iters), loss = 0.0842325
I0407 11:04:20.891877 17183 solver.cpp:237]     Train net output #0: loss = 0.0842328 (* 1 = 0.0842328 loss)
I0407 11:04:20.891885 17183 sgd_solver.cpp:105] Iteration 9684, lr = 0.000625
I0407 11:04:23.012789 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9690.caffemodel
I0407 11:04:26.484498 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9690.solverstate
I0407 11:04:29.925619 17183 solver.cpp:330] Iteration 9690, Testing net (#0)
I0407 11:04:29.925642 17183 net.cpp:676] Ignoring source layer train-data
I0407 11:04:30.471827 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 11:04:33.297014 17183 blocking_queue.cpp:49] Waiting for data
I0407 11:04:34.344172 17183 solver.cpp:397]     Test net output #0: accuracy = 0.492034
I0407 11:04:34.344200 17183 solver.cpp:397]     Test net output #1: loss = 2.90298 (* 1 = 2.90298 loss)
I0407 11:04:36.195674 17183 solver.cpp:218] Iteration 9696 (0.784128 iter/s, 15.3036s/12 iters), loss = 0.0796609
I0407 11:04:36.195732 17183 solver.cpp:237]     Train net output #0: loss = 0.0796612 (* 1 = 0.0796612 loss)
I0407 11:04:36.195744 17183 sgd_solver.cpp:105] Iteration 9696, lr = 0.000625
I0407 11:04:41.254607 17183 solver.cpp:218] Iteration 9708 (2.3721 iter/s, 5.05881s/12 iters), loss = 0.0531675
I0407 11:04:41.254654 17183 solver.cpp:237]     Train net output #0: loss = 0.0531678 (* 1 = 0.0531678 loss)
I0407 11:04:41.254662 17183 sgd_solver.cpp:105] Iteration 9708, lr = 0.000625
I0407 11:04:41.981658 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 11:04:46.306460 17183 solver.cpp:218] Iteration 9720 (2.37542 iter/s, 5.05174s/12 iters), loss = 0.0993365
I0407 11:04:46.306625 17183 solver.cpp:237]     Train net output #0: loss = 0.0993367 (* 1 = 0.0993367 loss)
I0407 11:04:46.306632 17183 sgd_solver.cpp:105] Iteration 9720, lr = 0.000625
I0407 11:04:51.421028 17183 solver.cpp:218] Iteration 9732 (2.34635 iter/s, 5.11434s/12 iters), loss = 0.0822986
I0407 11:04:51.421075 17183 solver.cpp:237]     Train net output #0: loss = 0.0822989 (* 1 = 0.0822989 loss)
I0407 11:04:51.421083 17183 sgd_solver.cpp:105] Iteration 9732, lr = 0.000625
I0407 11:04:56.506784 17183 solver.cpp:218] Iteration 9744 (2.35959 iter/s, 5.08564s/12 iters), loss = 0.0203321
I0407 11:04:56.506837 17183 solver.cpp:237]     Train net output #0: loss = 0.0203324 (* 1 = 0.0203324 loss)
I0407 11:04:56.506846 17183 sgd_solver.cpp:105] Iteration 9744, lr = 0.000625
I0407 11:05:01.503914 17183 solver.cpp:218] Iteration 9756 (2.40144 iter/s, 4.99701s/12 iters), loss = 0.0516047
I0407 11:05:01.503963 17183 solver.cpp:237]     Train net output #0: loss = 0.051605 (* 1 = 0.051605 loss)
I0407 11:05:01.503973 17183 sgd_solver.cpp:105] Iteration 9756, lr = 0.000625
I0407 11:05:06.694772 17183 solver.cpp:218] Iteration 9768 (2.31181 iter/s, 5.19074s/12 iters), loss = 0.0359456
I0407 11:05:06.694815 17183 solver.cpp:237]     Train net output #0: loss = 0.0359458 (* 1 = 0.0359458 loss)
I0407 11:05:06.694823 17183 sgd_solver.cpp:105] Iteration 9768, lr = 0.000625
I0407 11:05:11.796515 17183 solver.cpp:218] Iteration 9780 (2.35219 iter/s, 5.10163s/12 iters), loss = 0.059686
I0407 11:05:11.796574 17183 solver.cpp:237]     Train net output #0: loss = 0.0596862 (* 1 = 0.0596862 loss)
I0407 11:05:11.796586 17183 sgd_solver.cpp:105] Iteration 9780, lr = 0.000625
I0407 11:05:16.594588 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9792.caffemodel
I0407 11:05:21.054201 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9792.solverstate
I0407 11:05:24.190517 17183 solver.cpp:330] Iteration 9792, Testing net (#0)
I0407 11:05:24.190536 17183 net.cpp:676] Ignoring source layer train-data
I0407 11:05:24.720036 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 11:05:28.575722 17183 solver.cpp:397]     Test net output #0: accuracy = 0.498774
I0407 11:05:28.575753 17183 solver.cpp:397]     Test net output #1: loss = 2.87703 (* 1 = 2.87703 loss)
I0407 11:05:28.712024 17183 solver.cpp:218] Iteration 9792 (0.709418 iter/s, 16.9153s/12 iters), loss = 0.0208924
I0407 11:05:28.713618 17183 solver.cpp:237]     Train net output #0: loss = 0.0208927 (* 1 = 0.0208927 loss)
I0407 11:05:28.713631 17183 sgd_solver.cpp:105] Iteration 9792, lr = 0.000625
I0407 11:05:32.986035 17183 solver.cpp:218] Iteration 9804 (2.80875 iter/s, 4.27237s/12 iters), loss = 0.0276653
I0407 11:05:32.986088 17183 solver.cpp:237]     Train net output #0: loss = 0.0276655 (* 1 = 0.0276655 loss)
I0407 11:05:32.986097 17183 sgd_solver.cpp:105] Iteration 9804, lr = 0.000625
I0407 11:05:36.007798 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 11:05:38.032269 17183 solver.cpp:218] Iteration 9816 (2.37807 iter/s, 5.04612s/12 iters), loss = 0.0787867
I0407 11:05:38.032312 17183 solver.cpp:237]     Train net output #0: loss = 0.078787 (* 1 = 0.078787 loss)
I0407 11:05:38.032320 17183 sgd_solver.cpp:105] Iteration 9816, lr = 0.000625
I0407 11:05:43.175941 17183 solver.cpp:218] Iteration 9828 (2.33302 iter/s, 5.14356s/12 iters), loss = 0.0661278
I0407 11:05:43.175984 17183 solver.cpp:237]     Train net output #0: loss = 0.066128 (* 1 = 0.066128 loss)
I0407 11:05:43.175992 17183 sgd_solver.cpp:105] Iteration 9828, lr = 0.000625
I0407 11:05:48.295409 17183 solver.cpp:218] Iteration 9840 (2.34404 iter/s, 5.11936s/12 iters), loss = 0.0690923
I0407 11:05:48.295512 17183 solver.cpp:237]     Train net output #0: loss = 0.0690925 (* 1 = 0.0690925 loss)
I0407 11:05:48.295522 17183 sgd_solver.cpp:105] Iteration 9840, lr = 0.000625
I0407 11:05:53.504964 17183 solver.cpp:218] Iteration 9852 (2.30353 iter/s, 5.20939s/12 iters), loss = 0.0219736
I0407 11:05:53.505008 17183 solver.cpp:237]     Train net output #0: loss = 0.0219738 (* 1 = 0.0219738 loss)
I0407 11:05:53.505014 17183 sgd_solver.cpp:105] Iteration 9852, lr = 0.000625
I0407 11:05:58.578145 17183 solver.cpp:218] Iteration 9864 (2.36543 iter/s, 5.07306s/12 iters), loss = 0.0170607
I0407 11:05:58.578194 17183 solver.cpp:237]     Train net output #0: loss = 0.0170609 (* 1 = 0.0170609 loss)
I0407 11:05:58.578202 17183 sgd_solver.cpp:105] Iteration 9864, lr = 0.000625
I0407 11:06:03.759099 17183 solver.cpp:218] Iteration 9876 (2.31623 iter/s, 5.18084s/12 iters), loss = 0.0869968
I0407 11:06:03.759140 17183 solver.cpp:237]     Train net output #0: loss = 0.086997 (* 1 = 0.086997 loss)
I0407 11:06:03.759148 17183 sgd_solver.cpp:105] Iteration 9876, lr = 0.000625
I0407 11:06:08.912544 17183 solver.cpp:218] Iteration 9888 (2.32859 iter/s, 5.15334s/12 iters), loss = 0.0670038
I0407 11:06:08.912587 17183 solver.cpp:237]     Train net output #0: loss = 0.067004 (* 1 = 0.067004 loss)
I0407 11:06:08.912595 17183 sgd_solver.cpp:105] Iteration 9888, lr = 0.000625
I0407 11:06:11.008460 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9894.caffemodel
I0407 11:06:13.934430 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9894.solverstate
I0407 11:06:16.250850 17183 solver.cpp:330] Iteration 9894, Testing net (#0)
I0407 11:06:16.250872 17183 net.cpp:676] Ignoring source layer train-data
I0407 11:06:16.741780 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 11:06:20.551988 17183 solver.cpp:397]     Test net output #0: accuracy = 0.501225
I0407 11:06:20.552134 17183 solver.cpp:397]     Test net output #1: loss = 2.87823 (* 1 = 2.87823 loss)
I0407 11:06:22.452913 17183 solver.cpp:218] Iteration 9900 (0.886252 iter/s, 13.5402s/12 iters), loss = 0.0786477
I0407 11:06:22.452960 17183 solver.cpp:237]     Train net output #0: loss = 0.0786479 (* 1 = 0.0786479 loss)
I0407 11:06:22.452968 17183 sgd_solver.cpp:105] Iteration 9900, lr = 0.000625
I0407 11:06:27.498230 17183 solver.cpp:218] Iteration 9912 (2.3785 iter/s, 5.0452s/12 iters), loss = 0.0378857
I0407 11:06:27.498272 17183 solver.cpp:237]     Train net output #0: loss = 0.0378859 (* 1 = 0.0378859 loss)
I0407 11:06:27.498281 17183 sgd_solver.cpp:105] Iteration 9912, lr = 0.000625
I0407 11:06:27.586537 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 11:06:32.593569 17183 solver.cpp:218] Iteration 9924 (2.35515 iter/s, 5.09522s/12 iters), loss = 0.0669824
I0407 11:06:32.593614 17183 solver.cpp:237]     Train net output #0: loss = 0.0669826 (* 1 = 0.0669826 loss)
I0407 11:06:32.593621 17183 sgd_solver.cpp:105] Iteration 9924, lr = 0.000625
I0407 11:06:37.657033 17183 solver.cpp:218] Iteration 9936 (2.36997 iter/s, 5.06335s/12 iters), loss = 0.0288544
I0407 11:06:37.657078 17183 solver.cpp:237]     Train net output #0: loss = 0.0288546 (* 1 = 0.0288546 loss)
I0407 11:06:37.657086 17183 sgd_solver.cpp:105] Iteration 9936, lr = 0.000625
I0407 11:06:42.777243 17183 solver.cpp:218] Iteration 9948 (2.34371 iter/s, 5.1201s/12 iters), loss = 0.0531572
I0407 11:06:42.777288 17183 solver.cpp:237]     Train net output #0: loss = 0.0531574 (* 1 = 0.0531574 loss)
I0407 11:06:42.777295 17183 sgd_solver.cpp:105] Iteration 9948, lr = 0.000625
I0407 11:06:48.038772 17183 solver.cpp:218] Iteration 9960 (2.28076 iter/s, 5.26141s/12 iters), loss = 0.0595446
I0407 11:06:48.038821 17183 solver.cpp:237]     Train net output #0: loss = 0.0595448 (* 1 = 0.0595448 loss)
I0407 11:06:48.038830 17183 sgd_solver.cpp:105] Iteration 9960, lr = 0.000625
I0407 11:06:53.261581 17183 solver.cpp:218] Iteration 9972 (2.29766 iter/s, 5.22269s/12 iters), loss = 0.0498927
I0407 11:06:53.261693 17183 solver.cpp:237]     Train net output #0: loss = 0.0498929 (* 1 = 0.0498929 loss)
I0407 11:06:53.261703 17183 sgd_solver.cpp:105] Iteration 9972, lr = 0.000625
I0407 11:06:58.357590 17183 solver.cpp:218] Iteration 9984 (2.35487 iter/s, 5.09583s/12 iters), loss = 0.0119424
I0407 11:06:58.357650 17183 solver.cpp:237]     Train net output #0: loss = 0.0119426 (* 1 = 0.0119426 loss)
I0407 11:06:58.357661 17183 sgd_solver.cpp:105] Iteration 9984, lr = 0.000625
I0407 11:07:03.011516 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9996.caffemodel
I0407 11:07:06.021677 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9996.solverstate
I0407 11:07:08.318918 17183 solver.cpp:330] Iteration 9996, Testing net (#0)
I0407 11:07:08.318939 17183 net.cpp:676] Ignoring source layer train-data
I0407 11:07:08.769259 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 11:07:12.654264 17183 solver.cpp:397]     Test net output #0: accuracy = 0.507966
I0407 11:07:12.654299 17183 solver.cpp:397]     Test net output #1: loss = 2.8816 (* 1 = 2.8816 loss)
I0407 11:07:12.787097 17183 solver.cpp:218] Iteration 9996 (0.831642 iter/s, 14.4293s/12 iters), loss = 0.025883
I0407 11:07:12.787150 17183 solver.cpp:237]     Train net output #0: loss = 0.0258832 (* 1 = 0.0258832 loss)
I0407 11:07:12.787160 17183 sgd_solver.cpp:105] Iteration 9996, lr = 0.000625
I0407 11:07:17.119249 17183 solver.cpp:218] Iteration 10008 (2.77006 iter/s, 4.33204s/12 iters), loss = 0.0553499
I0407 11:07:17.119307 17183 solver.cpp:237]     Train net output #0: loss = 0.0553501 (* 1 = 0.0553501 loss)
I0407 11:07:17.119318 17183 sgd_solver.cpp:105] Iteration 10008, lr = 0.000625
I0407 11:07:19.399224 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 11:07:22.341140 17183 solver.cpp:218] Iteration 10020 (2.29807 iter/s, 5.22176s/12 iters), loss = 0.077295
I0407 11:07:22.341183 17183 solver.cpp:237]     Train net output #0: loss = 0.0772952 (* 1 = 0.0772952 loss)
I0407 11:07:22.341190 17183 sgd_solver.cpp:105] Iteration 10020, lr = 0.000625
I0407 11:07:27.473882 17183 solver.cpp:218] Iteration 10032 (2.33798 iter/s, 5.13264s/12 iters), loss = 0.0744407
I0407 11:07:27.473982 17183 solver.cpp:237]     Train net output #0: loss = 0.0744409 (* 1 = 0.0744409 loss)
I0407 11:07:27.473989 17183 sgd_solver.cpp:105] Iteration 10032, lr = 0.000625
I0407 11:07:32.762785 17183 solver.cpp:218] Iteration 10044 (2.26897 iter/s, 5.28873s/12 iters), loss = 0.080975
I0407 11:07:32.762835 17183 solver.cpp:237]     Train net output #0: loss = 0.0809752 (* 1 = 0.0809752 loss)
I0407 11:07:32.762841 17183 sgd_solver.cpp:105] Iteration 10044, lr = 0.000625
I0407 11:07:38.005766 17183 solver.cpp:218] Iteration 10056 (2.28883 iter/s, 5.24286s/12 iters), loss = 0.0901572
I0407 11:07:38.005815 17183 solver.cpp:237]     Train net output #0: loss = 0.0901574 (* 1 = 0.0901574 loss)
I0407 11:07:38.005826 17183 sgd_solver.cpp:105] Iteration 10056, lr = 0.000625
I0407 11:07:43.086123 17183 solver.cpp:218] Iteration 10068 (2.36209 iter/s, 5.08024s/12 iters), loss = 0.033751
I0407 11:07:43.086169 17183 solver.cpp:237]     Train net output #0: loss = 0.0337512 (* 1 = 0.0337512 loss)
I0407 11:07:43.086179 17183 sgd_solver.cpp:105] Iteration 10068, lr = 0.000625
I0407 11:07:48.564287 17183 solver.cpp:218] Iteration 10080 (2.19056 iter/s, 5.47805s/12 iters), loss = 0.1037
I0407 11:07:48.564329 17183 solver.cpp:237]     Train net output #0: loss = 0.1037 (* 1 = 0.1037 loss)
I0407 11:07:48.564337 17183 sgd_solver.cpp:105] Iteration 10080, lr = 0.000625
I0407 11:07:53.799026 17183 solver.cpp:218] Iteration 10092 (2.29243 iter/s, 5.23463s/12 iters), loss = 0.0371198
I0407 11:07:53.799067 17183 solver.cpp:237]     Train net output #0: loss = 0.03712 (* 1 = 0.03712 loss)
I0407 11:07:53.799074 17183 sgd_solver.cpp:105] Iteration 10092, lr = 0.000625
I0407 11:07:55.877570 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10098.caffemodel
I0407 11:07:58.921484 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10098.solverstate
I0407 11:08:01.225492 17183 solver.cpp:330] Iteration 10098, Testing net (#0)
I0407 11:08:01.225512 17183 net.cpp:676] Ignoring source layer train-data
I0407 11:08:01.635864 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 11:08:05.548941 17183 solver.cpp:397]     Test net output #0: accuracy = 0.503064
I0407 11:08:05.548975 17183 solver.cpp:397]     Test net output #1: loss = 2.90819 (* 1 = 2.90819 loss)
I0407 11:08:07.362542 17183 solver.cpp:218] Iteration 10104 (0.884739 iter/s, 13.5633s/12 iters), loss = 0.0458407
I0407 11:08:07.362592 17183 solver.cpp:237]     Train net output #0: loss = 0.0458409 (* 1 = 0.0458409 loss)
I0407 11:08:07.362601 17183 sgd_solver.cpp:105] Iteration 10104, lr = 0.00015625
I0407 11:08:11.910910 17205 data_layer.cpp:73] Restarting data prefetching from start.
I0407 11:08:12.588498 17183 solver.cpp:218] Iteration 10116 (2.29628 iter/s, 5.22584s/12 iters), loss = 0.0651947
I0407 11:08:12.588542 17183 solver.cpp:237]     Train net output #0: loss = 0.0651949 (* 1 = 0.0651949 loss)
I0407 11:08:12.588549 17183 sgd_solver.cpp:105] Iteration 10116, lr = 0.00015625
I0407 11:08:17.565800 17183 solver.cpp:218] Iteration 10128 (2.411 iter/s, 4.97719s/12 iters), loss = 0.0732039
I0407 11:08:17.565845 17183 solver.cpp:237]     Train net output #0: loss = 0.0732041 (* 1 = 0.0732041 loss)
I0407 11:08:17.565853 17183 sgd_solver.cpp:105] Iteration 10128, lr = 0.00015625
I0407 11:08:22.620164 17183 solver.cpp:218] Iteration 10140 (2.37424 iter/s, 5.05425s/12 iters), loss = 0.0765288
I0407 11:08:22.620205 17183 solver.cpp:237]     Train net output #0: loss = 0.076529 (* 1 = 0.076529 loss)
I0407 11:08:22.620214 17183 sgd_solver.cpp:105] Iteration 10140, lr = 0.00015625
I0407 11:08:27.744654 17183 solver.cpp:218] Iteration 10152 (2.34175 iter/s, 5.12438s/12 iters), loss = 0.0311094
I0407 11:08:27.744716 17183 solver.cpp:237]     Train net output #0: loss = 0.0311096 (* 1 = 0.0311096 loss)
I0407 11:08:27.744727 17183 sgd_solver.cpp:105] Iteration 10152, lr = 0.00015625
I0407 11:08:33.012399 17183 solver.cpp:218] Iteration 10164 (2.27807 iter/s, 5.26761s/12 iters), loss = 0.0208912
I0407 11:08:33.012542 17183 solver.cpp:237]     Train net output #0: loss = 0.0208914 (* 1 = 0.0208914 loss)
I0407 11:08:33.012552 17183 sgd_solver.cpp:105] Iteration 10164, lr = 0.00015625
I0407 11:08:38.234930 17183 solver.cpp:218] Iteration 10176 (2.29783 iter/s, 5.22232s/12 iters), loss = 0.0108636
I0407 11:08:38.234974 17183 solver.cpp:237]     Train net output #0: loss = 0.0108638 (* 1 = 0.0108638 loss)
I0407 11:08:38.234982 17183 sgd_solver.cpp:105] Iteration 10176, lr = 0.00015625
I0407 11:08:43.406455 17183 solver.cpp:218] Iteration 10188 (2.32045 iter/s, 5.17141s/12 iters), loss = 0.0615019
I0407 11:08:43.406502 17183 solver.cpp:237]     Train net output #0: loss = 0.0615021 (* 1 = 0.0615021 loss)
I0407 11:08:43.406509 17183 sgd_solver.cpp:105] Iteration 10188, lr = 0.00015625
I0407 11:08:48.017257 17183 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10200.caffemodel
I0407 11:08:51.018172 17183 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10200.solverstate
I0407 11:08:53.381999 17183 solver.cpp:310] Iteration 10200, loss = 0.0693943
I0407 11:08:53.382025 17183 solver.cpp:330] Iteration 10200, Testing net (#0)
I0407 11:08:53.382028 17183 net.cpp:676] Ignoring source layer train-data
I0407 11:08:53.748608 17236 data_layer.cpp:73] Restarting data prefetching from start.
I0407 11:08:57.608974 17183 solver.cpp:397]     Test net output #0: accuracy = 0.498774
I0407 11:08:57.609014 17183 solver.cpp:397]     Test net output #1: loss = 2.89834 (* 1 = 2.89834 loss)
I0407 11:08:57.609019 17183 solver.cpp:315] Optimization Done.
I0407 11:08:57.609023 17183 caffe.cpp:259] Optimization Done.