DIGITS-CNN/cars/lr-investigations/fixed/5e-2/caffe_output.log

I0406 15:08:12.757879 21485 upgrade_proto.cpp:1082] Attempting to upgrade input file specified using deprecated 'solver_type' field (enum)': /mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210406-124620-e947/solver.prototxt
I0406 15:08:12.758024 21485 upgrade_proto.cpp:1089] Successfully upgraded file specified using deprecated 'solver_type' field (enum) to 'type' field (string).
W0406 15:08:12.758028 21485 upgrade_proto.cpp:1091] Note that future Caffe releases will only support 'type' field (string) for a solver's type.
I0406 15:08:12.758081 21485 caffe.cpp:218] Using GPUs 2
I0406 15:08:12.775249 21485 caffe.cpp:223] GPU 2: GeForce GTX TITAN X
I0406 15:08:12.973495 21485 solver.cpp:44] Initializing solver from parameters:
test_iter: 51
test_interval: 102
base_lr: 0.05
display: 12
max_iter: 20400
lr_policy: "fixed"
momentum: 0.9
weight_decay: 0.0005
snapshot: 102
snapshot_prefix: "snapshot"
solver_mode: GPU
device_id: 2
net: "train_val.prototxt"
train_state {
level: 0
stage: ""
}
type: "SGD"
I0406 15:08:12.974469 21485 solver.cpp:87] Creating training net from net file: train_val.prototxt
I0406 15:08:12.975101 21485 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer val-data
I0406 15:08:12.975114 21485 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy
I0406 15:08:12.975234 21485 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"
}
I0406 15:08:12.975313 21485 layer_factory.hpp:77] Creating layer train-data
I0406 15:08:13.002948 21485 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210401-115716-aaf7/train_db
I0406 15:08:13.003217 21485 net.cpp:84] Creating Layer train-data
I0406 15:08:13.003234 21485 net.cpp:380] train-data -> data
I0406 15:08:13.003257 21485 net.cpp:380] train-data -> label
I0406 15:08:13.003266 21485 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210401-115716-aaf7/mean.binaryproto
I0406 15:08:13.009089 21485 data_layer.cpp:45] output data size: 128,3,227,227
I0406 15:08:13.146806 21485 net.cpp:122] Setting up train-data
I0406 15:08:13.146826 21485 net.cpp:129] Top shape: 128 3 227 227 (19787136)
I0406 15:08:13.146831 21485 net.cpp:129] Top shape: 128 (128)
I0406 15:08:13.146832 21485 net.cpp:137] Memory required for data: 79149056
I0406 15:08:13.146840 21485 layer_factory.hpp:77] Creating layer conv1
I0406 15:08:13.146860 21485 net.cpp:84] Creating Layer conv1
I0406 15:08:13.146864 21485 net.cpp:406] conv1 <- data
I0406 15:08:13.146874 21485 net.cpp:380] conv1 -> conv1
I0406 15:08:13.583338 21485 net.cpp:122] Setting up conv1
I0406 15:08:13.583362 21485 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0406 15:08:13.583366 21485 net.cpp:137] Memory required for data: 227833856
I0406 15:08:13.583390 21485 layer_factory.hpp:77] Creating layer relu1
I0406 15:08:13.583401 21485 net.cpp:84] Creating Layer relu1
I0406 15:08:13.583406 21485 net.cpp:406] relu1 <- conv1
I0406 15:08:13.583412 21485 net.cpp:367] relu1 -> conv1 (in-place)
I0406 15:08:13.583760 21485 net.cpp:122] Setting up relu1
I0406 15:08:13.583771 21485 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0406 15:08:13.583775 21485 net.cpp:137] Memory required for data: 376518656
I0406 15:08:13.583778 21485 layer_factory.hpp:77] Creating layer norm1
I0406 15:08:13.583787 21485 net.cpp:84] Creating Layer norm1
I0406 15:08:13.583791 21485 net.cpp:406] norm1 <- conv1
I0406 15:08:13.583822 21485 net.cpp:380] norm1 -> norm1
I0406 15:08:13.584401 21485 net.cpp:122] Setting up norm1
I0406 15:08:13.584411 21485 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0406 15:08:13.584414 21485 net.cpp:137] Memory required for data: 525203456
I0406 15:08:13.584416 21485 layer_factory.hpp:77] Creating layer pool1
I0406 15:08:13.584424 21485 net.cpp:84] Creating Layer pool1
I0406 15:08:13.584426 21485 net.cpp:406] pool1 <- norm1
I0406 15:08:13.584430 21485 net.cpp:380] pool1 -> pool1
I0406 15:08:13.584461 21485 net.cpp:122] Setting up pool1
I0406 15:08:13.584466 21485 net.cpp:129] Top shape: 128 96 27 27 (8957952)
I0406 15:08:13.584468 21485 net.cpp:137] Memory required for data: 561035264
I0406 15:08:13.584470 21485 layer_factory.hpp:77] Creating layer conv2
I0406 15:08:13.584478 21485 net.cpp:84] Creating Layer conv2
I0406 15:08:13.584481 21485 net.cpp:406] conv2 <- pool1
I0406 15:08:13.584486 21485 net.cpp:380] conv2 -> conv2
I0406 15:08:13.597965 21485 net.cpp:122] Setting up conv2
I0406 15:08:13.597987 21485 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0406 15:08:13.597991 21485 net.cpp:137] Memory required for data: 656586752
I0406 15:08:13.598007 21485 layer_factory.hpp:77] Creating layer relu2
I0406 15:08:13.598021 21485 net.cpp:84] Creating Layer relu2
I0406 15:08:13.598026 21485 net.cpp:406] relu2 <- conv2
I0406 15:08:13.598033 21485 net.cpp:367] relu2 -> conv2 (in-place)
I0406 15:08:13.598706 21485 net.cpp:122] Setting up relu2
I0406 15:08:13.598719 21485 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0406 15:08:13.598723 21485 net.cpp:137] Memory required for data: 752138240
I0406 15:08:13.598728 21485 layer_factory.hpp:77] Creating layer norm2
I0406 15:08:13.598737 21485 net.cpp:84] Creating Layer norm2
I0406 15:08:13.598742 21485 net.cpp:406] norm2 <- conv2
I0406 15:08:13.598748 21485 net.cpp:380] norm2 -> norm2
I0406 15:08:13.599257 21485 net.cpp:122] Setting up norm2
I0406 15:08:13.599267 21485 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0406 15:08:13.599272 21485 net.cpp:137] Memory required for data: 847689728
I0406 15:08:13.599275 21485 layer_factory.hpp:77] Creating layer pool2
I0406 15:08:13.599287 21485 net.cpp:84] Creating Layer pool2
I0406 15:08:13.599290 21485 net.cpp:406] pool2 <- norm2
I0406 15:08:13.599298 21485 net.cpp:380] pool2 -> pool2
I0406 15:08:13.599336 21485 net.cpp:122] Setting up pool2
I0406 15:08:13.599344 21485 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0406 15:08:13.599346 21485 net.cpp:137] Memory required for data: 869840896
I0406 15:08:13.599350 21485 layer_factory.hpp:77] Creating layer conv3
I0406 15:08:13.599364 21485 net.cpp:84] Creating Layer conv3
I0406 15:08:13.599368 21485 net.cpp:406] conv3 <- pool2
I0406 15:08:13.599377 21485 net.cpp:380] conv3 -> conv3
I0406 15:08:13.615664 21485 net.cpp:122] Setting up conv3
I0406 15:08:13.615689 21485 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0406 15:08:13.615693 21485 net.cpp:137] Memory required for data: 903067648
I0406 15:08:13.615711 21485 layer_factory.hpp:77] Creating layer relu3
I0406 15:08:13.615722 21485 net.cpp:84] Creating Layer relu3
I0406 15:08:13.615726 21485 net.cpp:406] relu3 <- conv3
I0406 15:08:13.615736 21485 net.cpp:367] relu3 -> conv3 (in-place)
I0406 15:08:13.616405 21485 net.cpp:122] Setting up relu3
I0406 15:08:13.616416 21485 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0406 15:08:13.616420 21485 net.cpp:137] Memory required for data: 936294400
I0406 15:08:13.616425 21485 layer_factory.hpp:77] Creating layer conv4
I0406 15:08:13.616439 21485 net.cpp:84] Creating Layer conv4
I0406 15:08:13.616443 21485 net.cpp:406] conv4 <- conv3
I0406 15:08:13.616453 21485 net.cpp:380] conv4 -> conv4
I0406 15:08:13.631665 21485 net.cpp:122] Setting up conv4
I0406 15:08:13.631690 21485 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0406 15:08:13.631695 21485 net.cpp:137] Memory required for data: 969521152
I0406 15:08:13.631706 21485 layer_factory.hpp:77] Creating layer relu4
I0406 15:08:13.631717 21485 net.cpp:84] Creating Layer relu4
I0406 15:08:13.631722 21485 net.cpp:406] relu4 <- conv4
I0406 15:08:13.631757 21485 net.cpp:367] relu4 -> conv4 (in-place)
I0406 15:08:13.632243 21485 net.cpp:122] Setting up relu4
I0406 15:08:13.632254 21485 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0406 15:08:13.632259 21485 net.cpp:137] Memory required for data: 1002747904
I0406 15:08:13.632263 21485 layer_factory.hpp:77] Creating layer conv5
I0406 15:08:13.632277 21485 net.cpp:84] Creating Layer conv5
I0406 15:08:13.632282 21485 net.cpp:406] conv5 <- conv4
I0406 15:08:13.632289 21485 net.cpp:380] conv5 -> conv5
I0406 15:08:13.644105 21485 net.cpp:122] Setting up conv5
I0406 15:08:13.644134 21485 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0406 15:08:13.644138 21485 net.cpp:137] Memory required for data: 1024899072
I0406 15:08:13.644156 21485 layer_factory.hpp:77] Creating layer relu5
I0406 15:08:13.644166 21485 net.cpp:84] Creating Layer relu5
I0406 15:08:13.644171 21485 net.cpp:406] relu5 <- conv5
I0406 15:08:13.644181 21485 net.cpp:367] relu5 -> conv5 (in-place)
I0406 15:08:13.644824 21485 net.cpp:122] Setting up relu5
I0406 15:08:13.644838 21485 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0406 15:08:13.644841 21485 net.cpp:137] Memory required for data: 1047050240
I0406 15:08:13.644846 21485 layer_factory.hpp:77] Creating layer pool5
I0406 15:08:13.644853 21485 net.cpp:84] Creating Layer pool5
I0406 15:08:13.644857 21485 net.cpp:406] pool5 <- conv5
I0406 15:08:13.644865 21485 net.cpp:380] pool5 -> pool5
I0406 15:08:13.644923 21485 net.cpp:122] Setting up pool5
I0406 15:08:13.644932 21485 net.cpp:129] Top shape: 128 256 6 6 (1179648)
I0406 15:08:13.644935 21485 net.cpp:137] Memory required for data: 1051768832
I0406 15:08:13.644938 21485 layer_factory.hpp:77] Creating layer fc6
I0406 15:08:13.644953 21485 net.cpp:84] Creating Layer fc6
I0406 15:08:13.644956 21485 net.cpp:406] fc6 <- pool5
I0406 15:08:13.644963 21485 net.cpp:380] fc6 -> fc6
I0406 15:08:14.076429 21485 net.cpp:122] Setting up fc6
I0406 15:08:14.076452 21485 net.cpp:129] Top shape: 128 4096 (524288)
I0406 15:08:14.076453 21485 net.cpp:137] Memory required for data: 1053865984
I0406 15:08:14.076462 21485 layer_factory.hpp:77] Creating layer relu6
I0406 15:08:14.076473 21485 net.cpp:84] Creating Layer relu6
I0406 15:08:14.076476 21485 net.cpp:406] relu6 <- fc6
I0406 15:08:14.076481 21485 net.cpp:367] relu6 -> fc6 (in-place)
I0406 15:08:14.077132 21485 net.cpp:122] Setting up relu6
I0406 15:08:14.077142 21485 net.cpp:129] Top shape: 128 4096 (524288)
I0406 15:08:14.077143 21485 net.cpp:137] Memory required for data: 1055963136
I0406 15:08:14.077147 21485 layer_factory.hpp:77] Creating layer drop6
I0406 15:08:14.077152 21485 net.cpp:84] Creating Layer drop6
I0406 15:08:14.077154 21485 net.cpp:406] drop6 <- fc6
I0406 15:08:14.077158 21485 net.cpp:367] drop6 -> fc6 (in-place)
I0406 15:08:14.077184 21485 net.cpp:122] Setting up drop6
I0406 15:08:14.077188 21485 net.cpp:129] Top shape: 128 4096 (524288)
I0406 15:08:14.077190 21485 net.cpp:137] Memory required for data: 1058060288
I0406 15:08:14.077193 21485 layer_factory.hpp:77] Creating layer fc7
I0406 15:08:14.077199 21485 net.cpp:84] Creating Layer fc7
I0406 15:08:14.077203 21485 net.cpp:406] fc7 <- fc6
I0406 15:08:14.077205 21485 net.cpp:380] fc7 -> fc7
I0406 15:08:14.224916 21485 net.cpp:122] Setting up fc7
I0406 15:08:14.224936 21485 net.cpp:129] Top shape: 128 4096 (524288)
I0406 15:08:14.224939 21485 net.cpp:137] Memory required for data: 1060157440
I0406 15:08:14.224947 21485 layer_factory.hpp:77] Creating layer relu7
I0406 15:08:14.224956 21485 net.cpp:84] Creating Layer relu7
I0406 15:08:14.224958 21485 net.cpp:406] relu7 <- fc7
I0406 15:08:14.224964 21485 net.cpp:367] relu7 -> fc7 (in-place)
I0406 15:08:14.225333 21485 net.cpp:122] Setting up relu7
I0406 15:08:14.225342 21485 net.cpp:129] Top shape: 128 4096 (524288)
I0406 15:08:14.225343 21485 net.cpp:137] Memory required for data: 1062254592
I0406 15:08:14.225345 21485 layer_factory.hpp:77] Creating layer drop7
I0406 15:08:14.225353 21485 net.cpp:84] Creating Layer drop7
I0406 15:08:14.225355 21485 net.cpp:406] drop7 <- fc7
I0406 15:08:14.225380 21485 net.cpp:367] drop7 -> fc7 (in-place)
I0406 15:08:14.225402 21485 net.cpp:122] Setting up drop7
I0406 15:08:14.225406 21485 net.cpp:129] Top shape: 128 4096 (524288)
I0406 15:08:14.225409 21485 net.cpp:137] Memory required for data: 1064351744
I0406 15:08:14.225410 21485 layer_factory.hpp:77] Creating layer fc8
I0406 15:08:14.225416 21485 net.cpp:84] Creating Layer fc8
I0406 15:08:14.225419 21485 net.cpp:406] fc8 <- fc7
I0406 15:08:14.225423 21485 net.cpp:380] fc8 -> fc8
I0406 15:08:14.232615 21485 net.cpp:122] Setting up fc8
I0406 15:08:14.232635 21485 net.cpp:129] Top shape: 128 196 (25088)
I0406 15:08:14.232636 21485 net.cpp:137] Memory required for data: 1064452096
I0406 15:08:14.232643 21485 layer_factory.hpp:77] Creating layer loss
I0406 15:08:14.232650 21485 net.cpp:84] Creating Layer loss
I0406 15:08:14.232652 21485 net.cpp:406] loss <- fc8
I0406 15:08:14.232656 21485 net.cpp:406] loss <- label
I0406 15:08:14.232661 21485 net.cpp:380] loss -> loss
I0406 15:08:14.232671 21485 layer_factory.hpp:77] Creating layer loss
I0406 15:08:14.233363 21485 net.cpp:122] Setting up loss
I0406 15:08:14.233372 21485 net.cpp:129] Top shape: (1)
I0406 15:08:14.233376 21485 net.cpp:132]     with loss weight 1
I0406 15:08:14.233397 21485 net.cpp:137] Memory required for data: 1064452100
I0406 15:08:14.233400 21485 net.cpp:198] loss needs backward computation.
I0406 15:08:14.233407 21485 net.cpp:198] fc8 needs backward computation.
I0406 15:08:14.233408 21485 net.cpp:198] drop7 needs backward computation.
I0406 15:08:14.233410 21485 net.cpp:198] relu7 needs backward computation.
I0406 15:08:14.233412 21485 net.cpp:198] fc7 needs backward computation.
I0406 15:08:14.233414 21485 net.cpp:198] drop6 needs backward computation.
I0406 15:08:14.233417 21485 net.cpp:198] relu6 needs backward computation.
I0406 15:08:14.233419 21485 net.cpp:198] fc6 needs backward computation.
I0406 15:08:14.233422 21485 net.cpp:198] pool5 needs backward computation.
I0406 15:08:14.233424 21485 net.cpp:198] relu5 needs backward computation.
I0406 15:08:14.233426 21485 net.cpp:198] conv5 needs backward computation.
I0406 15:08:14.233428 21485 net.cpp:198] relu4 needs backward computation.
I0406 15:08:14.233430 21485 net.cpp:198] conv4 needs backward computation.
I0406 15:08:14.233433 21485 net.cpp:198] relu3 needs backward computation.
I0406 15:08:14.233434 21485 net.cpp:198] conv3 needs backward computation.
I0406 15:08:14.233438 21485 net.cpp:198] pool2 needs backward computation.
I0406 15:08:14.233440 21485 net.cpp:198] norm2 needs backward computation.
I0406 15:08:14.233443 21485 net.cpp:198] relu2 needs backward computation.
I0406 15:08:14.233444 21485 net.cpp:198] conv2 needs backward computation.
I0406 15:08:14.233448 21485 net.cpp:198] pool1 needs backward computation.
I0406 15:08:14.233449 21485 net.cpp:198] norm1 needs backward computation.
I0406 15:08:14.233451 21485 net.cpp:198] relu1 needs backward computation.
I0406 15:08:14.233453 21485 net.cpp:198] conv1 needs backward computation.
I0406 15:08:14.233456 21485 net.cpp:200] train-data does not need backward computation.
I0406 15:08:14.233458 21485 net.cpp:242] This network produces output loss
I0406 15:08:14.233470 21485 net.cpp:255] Network initialization done.
I0406 15:08:14.234035 21485 solver.cpp:172] Creating test net (#0) specified by net file: train_val.prototxt
I0406 15:08:14.234066 21485 net.cpp:294] The NetState phase (1) differed from the phase (0) specified by a rule in layer train-data
I0406 15:08:14.234198 21485 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"
}
I0406 15:08:14.234293 21485 layer_factory.hpp:77] Creating layer val-data
I0406 15:08:14.236456 21485 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210401-115716-aaf7/val_db
I0406 15:08:14.236693 21485 net.cpp:84] Creating Layer val-data
I0406 15:08:14.236704 21485 net.cpp:380] val-data -> data
I0406 15:08:14.236711 21485 net.cpp:380] val-data -> label
I0406 15:08:14.236717 21485 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210401-115716-aaf7/mean.binaryproto
I0406 15:08:14.240403 21485 data_layer.cpp:45] output data size: 32,3,227,227
I0406 15:08:14.274221 21485 net.cpp:122] Setting up val-data
I0406 15:08:14.274241 21485 net.cpp:129] Top shape: 32 3 227 227 (4946784)
I0406 15:08:14.274245 21485 net.cpp:129] Top shape: 32 (32)
I0406 15:08:14.274247 21485 net.cpp:137] Memory required for data: 19787264
I0406 15:08:14.274251 21485 layer_factory.hpp:77] Creating layer label_val-data_1_split
I0406 15:08:14.274262 21485 net.cpp:84] Creating Layer label_val-data_1_split
I0406 15:08:14.274266 21485 net.cpp:406] label_val-data_1_split <- label
I0406 15:08:14.274271 21485 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_0
I0406 15:08:14.274278 21485 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_1
I0406 15:08:14.274375 21485 net.cpp:122] Setting up label_val-data_1_split
I0406 15:08:14.274381 21485 net.cpp:129] Top shape: 32 (32)
I0406 15:08:14.274384 21485 net.cpp:129] Top shape: 32 (32)
I0406 15:08:14.274385 21485 net.cpp:137] Memory required for data: 19787520
I0406 15:08:14.274389 21485 layer_factory.hpp:77] Creating layer conv1
I0406 15:08:14.274399 21485 net.cpp:84] Creating Layer conv1
I0406 15:08:14.274400 21485 net.cpp:406] conv1 <- data
I0406 15:08:14.274405 21485 net.cpp:380] conv1 -> conv1
I0406 15:08:14.276787 21485 net.cpp:122] Setting up conv1
I0406 15:08:14.276799 21485 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0406 15:08:14.276801 21485 net.cpp:137] Memory required for data: 56958720
I0406 15:08:14.276811 21485 layer_factory.hpp:77] Creating layer relu1
I0406 15:08:14.276818 21485 net.cpp:84] Creating Layer relu1
I0406 15:08:14.276820 21485 net.cpp:406] relu1 <- conv1
I0406 15:08:14.276823 21485 net.cpp:367] relu1 -> conv1 (in-place)
I0406 15:08:14.277096 21485 net.cpp:122] Setting up relu1
I0406 15:08:14.277105 21485 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0406 15:08:14.277107 21485 net.cpp:137] Memory required for data: 94129920
I0406 15:08:14.277110 21485 layer_factory.hpp:77] Creating layer norm1
I0406 15:08:14.277117 21485 net.cpp:84] Creating Layer norm1
I0406 15:08:14.277119 21485 net.cpp:406] norm1 <- conv1
I0406 15:08:14.277123 21485 net.cpp:380] norm1 -> norm1
I0406 15:08:14.277542 21485 net.cpp:122] Setting up norm1
I0406 15:08:14.277550 21485 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0406 15:08:14.277552 21485 net.cpp:137] Memory required for data: 131301120
I0406 15:08:14.277555 21485 layer_factory.hpp:77] Creating layer pool1
I0406 15:08:14.277561 21485 net.cpp:84] Creating Layer pool1
I0406 15:08:14.277563 21485 net.cpp:406] pool1 <- norm1
I0406 15:08:14.277567 21485 net.cpp:380] pool1 -> pool1
I0406 15:08:14.277592 21485 net.cpp:122] Setting up pool1
I0406 15:08:14.277596 21485 net.cpp:129] Top shape: 32 96 27 27 (2239488)
I0406 15:08:14.277598 21485 net.cpp:137] Memory required for data: 140259072
I0406 15:08:14.277601 21485 layer_factory.hpp:77] Creating layer conv2
I0406 15:08:14.277608 21485 net.cpp:84] Creating Layer conv2
I0406 15:08:14.277611 21485 net.cpp:406] conv2 <- pool1
I0406 15:08:14.277634 21485 net.cpp:380] conv2 -> conv2
I0406 15:08:14.283883 21485 net.cpp:122] Setting up conv2
I0406 15:08:14.283901 21485 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0406 15:08:14.283905 21485 net.cpp:137] Memory required for data: 164146944
I0406 15:08:14.283915 21485 layer_factory.hpp:77] Creating layer relu2
I0406 15:08:14.283923 21485 net.cpp:84] Creating Layer relu2
I0406 15:08:14.283926 21485 net.cpp:406] relu2 <- conv2
I0406 15:08:14.283932 21485 net.cpp:367] relu2 -> conv2 (in-place)
I0406 15:08:14.284404 21485 net.cpp:122] Setting up relu2
I0406 15:08:14.284415 21485 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0406 15:08:14.284416 21485 net.cpp:137] Memory required for data: 188034816
I0406 15:08:14.284420 21485 layer_factory.hpp:77] Creating layer norm2
I0406 15:08:14.284430 21485 net.cpp:84] Creating Layer norm2
I0406 15:08:14.284432 21485 net.cpp:406] norm2 <- conv2
I0406 15:08:14.284438 21485 net.cpp:380] norm2 -> norm2
I0406 15:08:14.284952 21485 net.cpp:122] Setting up norm2
I0406 15:08:14.284962 21485 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0406 15:08:14.284965 21485 net.cpp:137] Memory required for data: 211922688
I0406 15:08:14.284967 21485 layer_factory.hpp:77] Creating layer pool2
I0406 15:08:14.284974 21485 net.cpp:84] Creating Layer pool2
I0406 15:08:14.284977 21485 net.cpp:406] pool2 <- norm2
I0406 15:08:14.284982 21485 net.cpp:380] pool2 -> pool2
I0406 15:08:14.285008 21485 net.cpp:122] Setting up pool2
I0406 15:08:14.285012 21485 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0406 15:08:14.285014 21485 net.cpp:137] Memory required for data: 217460480
I0406 15:08:14.285017 21485 layer_factory.hpp:77] Creating layer conv3
I0406 15:08:14.285027 21485 net.cpp:84] Creating Layer conv3
I0406 15:08:14.285028 21485 net.cpp:406] conv3 <- pool2
I0406 15:08:14.285033 21485 net.cpp:380] conv3 -> conv3
I0406 15:08:14.297250 21485 net.cpp:122] Setting up conv3
I0406 15:08:14.297271 21485 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0406 15:08:14.297273 21485 net.cpp:137] Memory required for data: 225767168
I0406 15:08:14.297287 21485 layer_factory.hpp:77] Creating layer relu3
I0406 15:08:14.297297 21485 net.cpp:84] Creating Layer relu3
I0406 15:08:14.297299 21485 net.cpp:406] relu3 <- conv3
I0406 15:08:14.297304 21485 net.cpp:367] relu3 -> conv3 (in-place)
I0406 15:08:14.297775 21485 net.cpp:122] Setting up relu3
I0406 15:08:14.297783 21485 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0406 15:08:14.297785 21485 net.cpp:137] Memory required for data: 234073856
I0406 15:08:14.297788 21485 layer_factory.hpp:77] Creating layer conv4
I0406 15:08:14.297799 21485 net.cpp:84] Creating Layer conv4
I0406 15:08:14.297801 21485 net.cpp:406] conv4 <- conv3
I0406 15:08:14.297807 21485 net.cpp:380] conv4 -> conv4
I0406 15:08:14.306867 21485 net.cpp:122] Setting up conv4
I0406 15:08:14.306885 21485 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0406 15:08:14.306887 21485 net.cpp:137] Memory required for data: 242380544
I0406 15:08:14.306896 21485 layer_factory.hpp:77] Creating layer relu4
I0406 15:08:14.306906 21485 net.cpp:84] Creating Layer relu4
I0406 15:08:14.306910 21485 net.cpp:406] relu4 <- conv4
I0406 15:08:14.306915 21485 net.cpp:367] relu4 -> conv4 (in-place)
I0406 15:08:14.307235 21485 net.cpp:122] Setting up relu4
I0406 15:08:14.307242 21485 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0406 15:08:14.307245 21485 net.cpp:137] Memory required for data: 250687232
I0406 15:08:14.307246 21485 layer_factory.hpp:77] Creating layer conv5
I0406 15:08:14.307258 21485 net.cpp:84] Creating Layer conv5
I0406 15:08:14.307260 21485 net.cpp:406] conv5 <- conv4
I0406 15:08:14.307265 21485 net.cpp:380] conv5 -> conv5
I0406 15:08:14.315160 21485 net.cpp:122] Setting up conv5
I0406 15:08:14.315181 21485 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0406 15:08:14.315182 21485 net.cpp:137] Memory required for data: 256225024
I0406 15:08:14.315196 21485 layer_factory.hpp:77] Creating layer relu5
I0406 15:08:14.315204 21485 net.cpp:84] Creating Layer relu5
I0406 15:08:14.315207 21485 net.cpp:406] relu5 <- conv5
I0406 15:08:14.315232 21485 net.cpp:367] relu5 -> conv5 (in-place)
I0406 15:08:14.315707 21485 net.cpp:122] Setting up relu5
I0406 15:08:14.315718 21485 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0406 15:08:14.315721 21485 net.cpp:137] Memory required for data: 261762816
I0406 15:08:14.315723 21485 layer_factory.hpp:77] Creating layer pool5
I0406 15:08:14.315732 21485 net.cpp:84] Creating Layer pool5
I0406 15:08:14.315735 21485 net.cpp:406] pool5 <- conv5
I0406 15:08:14.315739 21485 net.cpp:380] pool5 -> pool5
I0406 15:08:14.315774 21485 net.cpp:122] Setting up pool5
I0406 15:08:14.315778 21485 net.cpp:129] Top shape: 32 256 6 6 (294912)
I0406 15:08:14.315780 21485 net.cpp:137] Memory required for data: 262942464
I0406 15:08:14.315783 21485 layer_factory.hpp:77] Creating layer fc6
I0406 15:08:14.315788 21485 net.cpp:84] Creating Layer fc6
I0406 15:08:14.315790 21485 net.cpp:406] fc6 <- pool5
I0406 15:08:14.315794 21485 net.cpp:380] fc6 -> fc6
I0406 15:08:14.685638 21485 net.cpp:122] Setting up fc6
I0406 15:08:14.685660 21485 net.cpp:129] Top shape: 32 4096 (131072)
I0406 15:08:14.685662 21485 net.cpp:137] Memory required for data: 263466752
I0406 15:08:14.685672 21485 layer_factory.hpp:77] Creating layer relu6
I0406 15:08:14.685678 21485 net.cpp:84] Creating Layer relu6
I0406 15:08:14.685683 21485 net.cpp:406] relu6 <- fc6
I0406 15:08:14.685688 21485 net.cpp:367] relu6 -> fc6 (in-place)
I0406 15:08:14.686980 21485 net.cpp:122] Setting up relu6
I0406 15:08:14.686996 21485 net.cpp:129] Top shape: 32 4096 (131072)
I0406 15:08:14.687000 21485 net.cpp:137] Memory required for data: 263991040
I0406 15:08:14.687003 21485 layer_factory.hpp:77] Creating layer drop6
I0406 15:08:14.687012 21485 net.cpp:84] Creating Layer drop6
I0406 15:08:14.687016 21485 net.cpp:406] drop6 <- fc6
I0406 15:08:14.687024 21485 net.cpp:367] drop6 -> fc6 (in-place)
I0406 15:08:14.687058 21485 net.cpp:122] Setting up drop6
I0406 15:08:14.687064 21485 net.cpp:129] Top shape: 32 4096 (131072)
I0406 15:08:14.687067 21485 net.cpp:137] Memory required for data: 264515328
I0406 15:08:14.687069 21485 layer_factory.hpp:77] Creating layer fc7
I0406 15:08:14.687079 21485 net.cpp:84] Creating Layer fc7
I0406 15:08:14.687083 21485 net.cpp:406] fc7 <- fc6
I0406 15:08:14.687088 21485 net.cpp:380] fc7 -> fc7
I0406 15:08:14.859316 21485 net.cpp:122] Setting up fc7
I0406 15:08:14.859338 21485 net.cpp:129] Top shape: 32 4096 (131072)
I0406 15:08:14.859340 21485 net.cpp:137] Memory required for data: 265039616
I0406 15:08:14.859349 21485 layer_factory.hpp:77] Creating layer relu7
I0406 15:08:14.859359 21485 net.cpp:84] Creating Layer relu7
I0406 15:08:14.859361 21485 net.cpp:406] relu7 <- fc7
I0406 15:08:14.859367 21485 net.cpp:367] relu7 -> fc7 (in-place)
I0406 15:08:14.859766 21485 net.cpp:122] Setting up relu7
I0406 15:08:14.859773 21485 net.cpp:129] Top shape: 32 4096 (131072)
I0406 15:08:14.859776 21485 net.cpp:137] Memory required for data: 265563904
I0406 15:08:14.859777 21485 layer_factory.hpp:77] Creating layer drop7
I0406 15:08:14.859783 21485 net.cpp:84] Creating Layer drop7
I0406 15:08:14.859786 21485 net.cpp:406] drop7 <- fc7
I0406 15:08:14.859791 21485 net.cpp:367] drop7 -> fc7 (in-place)
I0406 15:08:14.859812 21485 net.cpp:122] Setting up drop7
I0406 15:08:14.859815 21485 net.cpp:129] Top shape: 32 4096 (131072)
I0406 15:08:14.859817 21485 net.cpp:137] Memory required for data: 266088192
I0406 15:08:14.859819 21485 layer_factory.hpp:77] Creating layer fc8
I0406 15:08:14.859827 21485 net.cpp:84] Creating Layer fc8
I0406 15:08:14.859828 21485 net.cpp:406] fc8 <- fc7
I0406 15:08:14.859833 21485 net.cpp:380] fc8 -> fc8
I0406 15:08:14.868227 21485 net.cpp:122] Setting up fc8
I0406 15:08:14.868247 21485 net.cpp:129] Top shape: 32 196 (6272)
I0406 15:08:14.868249 21485 net.cpp:137] Memory required for data: 266113280
I0406 15:08:14.868258 21485 layer_factory.hpp:77] Creating layer fc8_fc8_0_split
I0406 15:08:14.868266 21485 net.cpp:84] Creating Layer fc8_fc8_0_split
I0406 15:08:14.868270 21485 net.cpp:406] fc8_fc8_0_split <- fc8
I0406 15:08:14.868296 21485 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_0
I0406 15:08:14.868305 21485 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_1
I0406 15:08:14.868338 21485 net.cpp:122] Setting up fc8_fc8_0_split
I0406 15:08:14.868342 21485 net.cpp:129] Top shape: 32 196 (6272)
I0406 15:08:14.868345 21485 net.cpp:129] Top shape: 32 196 (6272)
I0406 15:08:14.868346 21485 net.cpp:137] Memory required for data: 266163456
I0406 15:08:14.868348 21485 layer_factory.hpp:77] Creating layer accuracy
I0406 15:08:14.868357 21485 net.cpp:84] Creating Layer accuracy
I0406 15:08:14.868360 21485 net.cpp:406] accuracy <- fc8_fc8_0_split_0
I0406 15:08:14.868363 21485 net.cpp:406] accuracy <- label_val-data_1_split_0
I0406 15:08:14.868366 21485 net.cpp:380] accuracy -> accuracy
I0406 15:08:14.868372 21485 net.cpp:122] Setting up accuracy
I0406 15:08:14.868374 21485 net.cpp:129] Top shape: (1)
I0406 15:08:14.868376 21485 net.cpp:137] Memory required for data: 266163460
I0406 15:08:14.868378 21485 layer_factory.hpp:77] Creating layer loss
I0406 15:08:14.868384 21485 net.cpp:84] Creating Layer loss
I0406 15:08:14.868386 21485 net.cpp:406] loss <- fc8_fc8_0_split_1
I0406 15:08:14.868388 21485 net.cpp:406] loss <- label_val-data_1_split_1
I0406 15:08:14.868392 21485 net.cpp:380] loss -> loss
I0406 15:08:14.868398 21485 layer_factory.hpp:77] Creating layer loss
I0406 15:08:14.869068 21485 net.cpp:122] Setting up loss
I0406 15:08:14.869076 21485 net.cpp:129] Top shape: (1)
I0406 15:08:14.869078 21485 net.cpp:132]     with loss weight 1
I0406 15:08:14.869087 21485 net.cpp:137] Memory required for data: 266163464
I0406 15:08:14.869091 21485 net.cpp:198] loss needs backward computation.
I0406 15:08:14.869093 21485 net.cpp:200] accuracy does not need backward computation.
I0406 15:08:14.869096 21485 net.cpp:198] fc8_fc8_0_split needs backward computation.
I0406 15:08:14.869098 21485 net.cpp:198] fc8 needs backward computation.
I0406 15:08:14.869100 21485 net.cpp:198] drop7 needs backward computation.
I0406 15:08:14.869102 21485 net.cpp:198] relu7 needs backward computation.
I0406 15:08:14.869104 21485 net.cpp:198] fc7 needs backward computation.
I0406 15:08:14.869107 21485 net.cpp:198] drop6 needs backward computation.
I0406 15:08:14.869109 21485 net.cpp:198] relu6 needs backward computation.
I0406 15:08:14.869112 21485 net.cpp:198] fc6 needs backward computation.
I0406 15:08:14.869114 21485 net.cpp:198] pool5 needs backward computation.
I0406 15:08:14.869117 21485 net.cpp:198] relu5 needs backward computation.
I0406 15:08:14.869118 21485 net.cpp:198] conv5 needs backward computation.
I0406 15:08:14.869120 21485 net.cpp:198] relu4 needs backward computation.
I0406 15:08:14.869123 21485 net.cpp:198] conv4 needs backward computation.
I0406 15:08:14.869125 21485 net.cpp:198] relu3 needs backward computation.
I0406 15:08:14.869127 21485 net.cpp:198] conv3 needs backward computation.
I0406 15:08:14.869129 21485 net.cpp:198] pool2 needs backward computation.
I0406 15:08:14.869132 21485 net.cpp:198] norm2 needs backward computation.
I0406 15:08:14.869134 21485 net.cpp:198] relu2 needs backward computation.
I0406 15:08:14.869136 21485 net.cpp:198] conv2 needs backward computation.
I0406 15:08:14.869138 21485 net.cpp:198] pool1 needs backward computation.
I0406 15:08:14.869144 21485 net.cpp:198] norm1 needs backward computation.
I0406 15:08:14.869146 21485 net.cpp:198] relu1 needs backward computation.
I0406 15:08:14.869148 21485 net.cpp:198] conv1 needs backward computation.
I0406 15:08:14.869151 21485 net.cpp:200] label_val-data_1_split does not need backward computation.
I0406 15:08:14.869154 21485 net.cpp:200] val-data does not need backward computation.
I0406 15:08:14.869156 21485 net.cpp:242] This network produces output accuracy
I0406 15:08:14.869158 21485 net.cpp:242] This network produces output loss
I0406 15:08:14.869174 21485 net.cpp:255] Network initialization done.
I0406 15:08:14.869251 21485 solver.cpp:56] Solver scaffolding done.
I0406 15:08:14.869648 21485 caffe.cpp:248] Starting Optimization
I0406 15:08:14.869655 21485 solver.cpp:272] Solving
I0406 15:08:14.869666 21485 solver.cpp:273] Learning Rate Policy: fixed
I0406 15:08:14.871213 21485 solver.cpp:330] Iteration 0, Testing net (#0)
I0406 15:08:14.871222 21485 net.cpp:676] Ignoring source layer train-data
I0406 15:08:14.977043 21485 blocking_queue.cpp:49] Waiting for data
I0406 15:08:19.097761 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:08:19.145442 21485 solver.cpp:397]     Test net output #0: accuracy = 0.00796569
I0406 15:08:19.145473 21485 solver.cpp:397]     Test net output #1: loss = 5.27967 (* 1 = 5.27967 loss)
I0406 15:08:19.278540 21485 solver.cpp:218] Iteration 0 (-3.50817e-35 iter/s, 4.40882s/12 iters), loss = 5.26957
I0406 15:08:19.280133 21485 solver.cpp:237]     Train net output #0: loss = 5.26957 (* 1 = 5.26957 loss)
I0406 15:08:19.280140 21485 sgd_solver.cpp:105] Iteration 0, lr = 0.05
I0406 15:08:23.163614 21485 solver.cpp:218] Iteration 12 (3.09003 iter/s, 3.88346s/12 iters), loss = 5.31115
I0406 15:08:23.163657 21485 solver.cpp:237]     Train net output #0: loss = 5.31115 (* 1 = 5.31115 loss)
I0406 15:08:23.163663 21485 sgd_solver.cpp:105] Iteration 12, lr = 0.05
I0406 15:08:28.317729 21485 solver.cpp:218] Iteration 24 (2.32826 iter/s, 5.15406s/12 iters), loss = 5.27699
I0406 15:08:28.317773 21485 solver.cpp:237]     Train net output #0: loss = 5.27699 (* 1 = 5.27699 loss)
I0406 15:08:28.317778 21485 sgd_solver.cpp:105] Iteration 24, lr = 0.05
I0406 15:08:33.564311 21485 solver.cpp:218] Iteration 36 (2.28723 iter/s, 5.24652s/12 iters), loss = 5.27722
I0406 15:08:33.564358 21485 solver.cpp:237]     Train net output #0: loss = 5.27722 (* 1 = 5.27722 loss)
I0406 15:08:33.564364 21485 sgd_solver.cpp:105] Iteration 36, lr = 0.05
I0406 15:08:38.707691 21485 solver.cpp:218] Iteration 48 (2.33312 iter/s, 5.14332s/12 iters), loss = 5.30493
I0406 15:08:38.707749 21485 solver.cpp:237]     Train net output #0: loss = 5.30493 (* 1 = 5.30493 loss)
I0406 15:08:38.707758 21485 sgd_solver.cpp:105] Iteration 48, lr = 0.05
I0406 15:08:43.960297 21485 solver.cpp:218] Iteration 60 (2.28461 iter/s, 5.25254s/12 iters), loss = 5.27736
I0406 15:08:43.960433 21485 solver.cpp:237]     Train net output #0: loss = 5.27736 (* 1 = 5.27736 loss)
I0406 15:08:43.960440 21485 sgd_solver.cpp:105] Iteration 60, lr = 0.05
I0406 15:08:48.992343 21485 solver.cpp:218] Iteration 72 (2.38478 iter/s, 5.0319s/12 iters), loss = 5.31523
I0406 15:08:48.992383 21485 solver.cpp:237]     Train net output #0: loss = 5.31523 (* 1 = 5.31523 loss)
I0406 15:08:48.992388 21485 sgd_solver.cpp:105] Iteration 72, lr = 0.05
I0406 15:08:54.001533 21485 solver.cpp:218] Iteration 84 (2.39562 iter/s, 5.00914s/12 iters), loss = 5.2985
I0406 15:08:54.001574 21485 solver.cpp:237]     Train net output #0: loss = 5.2985 (* 1 = 5.2985 loss)
I0406 15:08:54.001580 21485 sgd_solver.cpp:105] Iteration 84, lr = 0.05
I0406 15:08:59.196974 21485 solver.cpp:218] Iteration 96 (2.30974 iter/s, 5.19538s/12 iters), loss = 5.27311
I0406 15:08:59.197012 21485 solver.cpp:237]     Train net output #0: loss = 5.27311 (* 1 = 5.27311 loss)
I0406 15:08:59.197017 21485 sgd_solver.cpp:105] Iteration 96, lr = 0.05
I0406 15:09:00.942948 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:09:01.244379 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_102.caffemodel
I0406 15:09:04.364462 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_102.solverstate
I0406 15:09:06.648600 21485 solver.cpp:330] Iteration 102, Testing net (#0)
I0406 15:09:06.648622 21485 net.cpp:676] Ignoring source layer train-data
I0406 15:09:10.924160 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:09:11.002494 21485 solver.cpp:397]     Test net output #0: accuracy = 0.00612745
I0406 15:09:11.002524 21485 solver.cpp:397]     Test net output #1: loss = 5.28927 (* 1 = 5.28927 loss)
I0406 15:09:12.972643 21485 solver.cpp:218] Iteration 108 (0.871104 iter/s, 13.7756s/12 iters), loss = 5.28118
I0406 15:09:12.972692 21485 solver.cpp:237]     Train net output #0: loss = 5.28118 (* 1 = 5.28118 loss)
I0406 15:09:12.972697 21485 sgd_solver.cpp:105] Iteration 108, lr = 0.05
I0406 15:09:18.156615 21485 solver.cpp:218] Iteration 120 (2.31485 iter/s, 5.18391s/12 iters), loss = 5.2651
I0406 15:09:18.156731 21485 solver.cpp:237]     Train net output #0: loss = 5.2651 (* 1 = 5.2651 loss)
I0406 15:09:18.156738 21485 sgd_solver.cpp:105] Iteration 120, lr = 0.05
I0406 15:09:23.454475 21485 solver.cpp:218] Iteration 132 (2.26512 iter/s, 5.29773s/12 iters), loss = 5.24859
I0406 15:09:23.454521 21485 solver.cpp:237]     Train net output #0: loss = 5.24859 (* 1 = 5.24859 loss)
I0406 15:09:23.454527 21485 sgd_solver.cpp:105] Iteration 132, lr = 0.05
I0406 15:09:28.587599 21485 solver.cpp:218] Iteration 144 (2.33779 iter/s, 5.13306s/12 iters), loss = 5.21516
I0406 15:09:28.587642 21485 solver.cpp:237]     Train net output #0: loss = 5.21516 (* 1 = 5.21516 loss)
I0406 15:09:28.587648 21485 sgd_solver.cpp:105] Iteration 144, lr = 0.05
I0406 15:09:33.494776 21485 solver.cpp:218] Iteration 156 (2.44542 iter/s, 4.90712s/12 iters), loss = 5.26189
I0406 15:09:33.494814 21485 solver.cpp:237]     Train net output #0: loss = 5.26189 (* 1 = 5.26189 loss)
I0406 15:09:33.494820 21485 sgd_solver.cpp:105] Iteration 156, lr = 0.05
I0406 15:09:38.433480 21485 solver.cpp:218] Iteration 168 (2.42981 iter/s, 4.93865s/12 iters), loss = 5.27496
I0406 15:09:38.433521 21485 solver.cpp:237]     Train net output #0: loss = 5.27496 (* 1 = 5.27496 loss)
I0406 15:09:38.433526 21485 sgd_solver.cpp:105] Iteration 168, lr = 0.05
I0406 15:09:43.739598 21485 solver.cpp:218] Iteration 180 (2.26157 iter/s, 5.30606s/12 iters), loss = 5.2703
I0406 15:09:43.739646 21485 solver.cpp:237]     Train net output #0: loss = 5.2703 (* 1 = 5.2703 loss)
I0406 15:09:43.739655 21485 sgd_solver.cpp:105] Iteration 180, lr = 0.05
I0406 15:09:48.785398 21485 solver.cpp:218] Iteration 192 (2.37824 iter/s, 5.04574s/12 iters), loss = 5.0748
I0406 15:09:48.785480 21485 solver.cpp:237]     Train net output #0: loss = 5.0748 (* 1 = 5.0748 loss)
I0406 15:09:48.785485 21485 sgd_solver.cpp:105] Iteration 192, lr = 0.05
I0406 15:09:52.833981 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:09:53.529623 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_204.caffemodel
I0406 15:09:56.564513 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_204.solverstate
I0406 15:09:59.639734 21485 solver.cpp:330] Iteration 204, Testing net (#0)
I0406 15:09:59.639753 21485 net.cpp:676] Ignoring source layer train-data
I0406 15:10:03.882398 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:10:04.005807 21485 solver.cpp:397]     Test net output #0: accuracy = 0.00796569
I0406 15:10:04.005854 21485 solver.cpp:397]     Test net output #1: loss = 5.19874 (* 1 = 5.19874 loss)
I0406 15:10:04.140909 21485 solver.cpp:218] Iteration 204 (0.781482 iter/s, 15.3554s/12 iters), loss = 5.19217
I0406 15:10:04.140949 21485 solver.cpp:237]     Train net output #0: loss = 5.19217 (* 1 = 5.19217 loss)
I0406 15:10:04.140955 21485 sgd_solver.cpp:105] Iteration 204, lr = 0.05
I0406 15:10:08.506407 21485 solver.cpp:218] Iteration 216 (2.74886 iter/s, 4.36545s/12 iters), loss = 5.22153
I0406 15:10:08.506446 21485 solver.cpp:237]     Train net output #0: loss = 5.22153 (* 1 = 5.22153 loss)
I0406 15:10:08.506453 21485 sgd_solver.cpp:105] Iteration 216, lr = 0.05
I0406 15:10:13.560828 21485 solver.cpp:218] Iteration 228 (2.37418 iter/s, 5.05437s/12 iters), loss = 5.22915
I0406 15:10:13.560874 21485 solver.cpp:237]     Train net output #0: loss = 5.22915 (* 1 = 5.22915 loss)
I0406 15:10:13.560880 21485 sgd_solver.cpp:105] Iteration 228, lr = 0.05
I0406 15:10:18.659107 21485 solver.cpp:218] Iteration 240 (2.35376 iter/s, 5.09822s/12 iters), loss = 5.1698
I0406 15:10:18.659147 21485 solver.cpp:237]     Train net output #0: loss = 5.1698 (* 1 = 5.1698 loss)
I0406 15:10:18.659152 21485 sgd_solver.cpp:105] Iteration 240, lr = 0.05
I0406 15:10:23.747915 21485 solver.cpp:218] Iteration 252 (2.35814 iter/s, 5.08875s/12 iters), loss = 5.20913
I0406 15:10:23.748078 21485 solver.cpp:237]     Train net output #0: loss = 5.20913 (* 1 = 5.20913 loss)
I0406 15:10:23.748086 21485 sgd_solver.cpp:105] Iteration 252, lr = 0.05
I0406 15:10:28.839241 21485 solver.cpp:218] Iteration 264 (2.35703 iter/s, 5.09115s/12 iters), loss = 5.12985
I0406 15:10:28.839287 21485 solver.cpp:237]     Train net output #0: loss = 5.12985 (* 1 = 5.12985 loss)
I0406 15:10:28.839294 21485 sgd_solver.cpp:105] Iteration 264, lr = 0.05
I0406 15:10:34.135617 21485 solver.cpp:218] Iteration 276 (2.26573 iter/s, 5.29632s/12 iters), loss = 5.11146
I0406 15:10:34.135666 21485 solver.cpp:237]     Train net output #0: loss = 5.11146 (* 1 = 5.11146 loss)
I0406 15:10:34.135671 21485 sgd_solver.cpp:105] Iteration 276, lr = 0.05
I0406 15:10:39.562108 21485 solver.cpp:218] Iteration 288 (2.2114 iter/s, 5.42643s/12 iters), loss = 5.13077
I0406 15:10:39.562148 21485 solver.cpp:237]     Train net output #0: loss = 5.13077 (* 1 = 5.13077 loss)
I0406 15:10:39.562153 21485 sgd_solver.cpp:105] Iteration 288, lr = 0.05
I0406 15:10:44.641142 21485 solver.cpp:218] Iteration 300 (2.36268 iter/s, 5.07898s/12 iters), loss = 5.279
I0406 15:10:44.641181 21485 solver.cpp:237]     Train net output #0: loss = 5.279 (* 1 = 5.279 loss)
I0406 15:10:44.641186 21485 sgd_solver.cpp:105] Iteration 300, lr = 0.05
I0406 15:10:45.702667 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:10:46.812204 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_306.caffemodel
I0406 15:10:49.839176 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_306.solverstate
I0406 15:10:52.143044 21485 solver.cpp:330] Iteration 306, Testing net (#0)
I0406 15:10:52.143065 21485 net.cpp:676] Ignoring source layer train-data
I0406 15:10:56.279366 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:10:56.436651 21485 solver.cpp:397]     Test net output #0: accuracy = 0.00919118
I0406 15:10:56.436686 21485 solver.cpp:397]     Test net output #1: loss = 5.17455 (* 1 = 5.17455 loss)
I0406 15:10:58.290931 21485 solver.cpp:218] Iteration 312 (0.879138 iter/s, 13.6497s/12 iters), loss = 5.14209
I0406 15:10:58.290987 21485 solver.cpp:237]     Train net output #0: loss = 5.14209 (* 1 = 5.14209 loss)
I0406 15:10:58.290994 21485 sgd_solver.cpp:105] Iteration 312, lr = 0.05
I0406 15:11:03.416211 21485 solver.cpp:218] Iteration 324 (2.34137 iter/s, 5.12521s/12 iters), loss = 5.25079
I0406 15:11:03.416265 21485 solver.cpp:237]     Train net output #0: loss = 5.25079 (* 1 = 5.25079 loss)
I0406 15:11:03.416273 21485 sgd_solver.cpp:105] Iteration 324, lr = 0.05
I0406 15:11:08.632855 21485 solver.cpp:218] Iteration 336 (2.30036 iter/s, 5.21658s/12 iters), loss = 5.18157
I0406 15:11:08.632905 21485 solver.cpp:237]     Train net output #0: loss = 5.18157 (* 1 = 5.18157 loss)
I0406 15:11:08.632910 21485 sgd_solver.cpp:105] Iteration 336, lr = 0.05
I0406 15:11:13.931895 21485 solver.cpp:218] Iteration 348 (2.26459 iter/s, 5.29898s/12 iters), loss = 5.14881
I0406 15:11:13.931936 21485 solver.cpp:237]     Train net output #0: loss = 5.14881 (* 1 = 5.14881 loss)
I0406 15:11:13.931941 21485 sgd_solver.cpp:105] Iteration 348, lr = 0.05
I0406 15:11:19.205327 21485 solver.cpp:218] Iteration 360 (2.27558 iter/s, 5.27338s/12 iters), loss = 5.18477
I0406 15:11:19.205370 21485 solver.cpp:237]     Train net output #0: loss = 5.18477 (* 1 = 5.18477 loss)
I0406 15:11:19.205376 21485 sgd_solver.cpp:105] Iteration 360, lr = 0.05
I0406 15:11:24.453723 21485 solver.cpp:218] Iteration 372 (2.28644 iter/s, 5.24834s/12 iters), loss = 5.1649
I0406 15:11:24.453766 21485 solver.cpp:237]     Train net output #0: loss = 5.1649 (* 1 = 5.1649 loss)
I0406 15:11:24.453773 21485 sgd_solver.cpp:105] Iteration 372, lr = 0.05
I0406 15:11:29.651284 21485 solver.cpp:218] Iteration 384 (2.3088 iter/s, 5.1975s/12 iters), loss = 5.22085
I0406 15:11:29.651435 21485 solver.cpp:237]     Train net output #0: loss = 5.22085 (* 1 = 5.22085 loss)
I0406 15:11:29.651444 21485 sgd_solver.cpp:105] Iteration 384, lr = 0.05
I0406 15:11:35.019542 21485 solver.cpp:218] Iteration 396 (2.23543 iter/s, 5.3681s/12 iters), loss = 5.21182
I0406 15:11:35.019585 21485 solver.cpp:237]     Train net output #0: loss = 5.21182 (* 1 = 5.21182 loss)
I0406 15:11:35.019590 21485 sgd_solver.cpp:105] Iteration 396, lr = 0.05
I0406 15:11:38.214394 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:11:39.637035 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_408.caffemodel
I0406 15:11:42.726554 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_408.solverstate
I0406 15:11:45.058364 21485 solver.cpp:330] Iteration 408, Testing net (#0)
I0406 15:11:45.058383 21485 net.cpp:676] Ignoring source layer train-data
I0406 15:11:49.163015 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:11:49.368849 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0104167
I0406 15:11:49.368892 21485 solver.cpp:397]     Test net output #1: loss = 5.1757 (* 1 = 5.1757 loss)
I0406 15:11:49.503995 21485 solver.cpp:218] Iteration 408 (0.828477 iter/s, 14.4844s/12 iters), loss = 5.1643
I0406 15:11:49.504056 21485 solver.cpp:237]     Train net output #0: loss = 5.1643 (* 1 = 5.1643 loss)
I0406 15:11:49.504065 21485 sgd_solver.cpp:105] Iteration 408, lr = 0.05
I0406 15:11:53.712482 21485 solver.cpp:218] Iteration 420 (2.85143 iter/s, 4.20841s/12 iters), loss = 5.14617
I0406 15:11:53.712541 21485 solver.cpp:237]     Train net output #0: loss = 5.14617 (* 1 = 5.14617 loss)
I0406 15:11:53.712549 21485 sgd_solver.cpp:105] Iteration 420, lr = 0.05
I0406 15:11:58.907546 21485 solver.cpp:218] Iteration 432 (2.30992 iter/s, 5.19499s/12 iters), loss = 5.13934
I0406 15:11:58.907595 21485 solver.cpp:237]     Train net output #0: loss = 5.13934 (* 1 = 5.13934 loss)
I0406 15:11:58.907601 21485 sgd_solver.cpp:105] Iteration 432, lr = 0.05
I0406 15:12:04.218502 21485 solver.cpp:218] Iteration 444 (2.2596 iter/s, 5.31067s/12 iters), loss = 5.16647
I0406 15:12:04.218591 21485 solver.cpp:237]     Train net output #0: loss = 5.16647 (* 1 = 5.16647 loss)
I0406 15:12:04.218597 21485 sgd_solver.cpp:105] Iteration 444, lr = 0.05
I0406 15:12:09.397869 21485 solver.cpp:218] Iteration 456 (2.31693 iter/s, 5.17927s/12 iters), loss = 5.18544
I0406 15:12:09.397909 21485 solver.cpp:237]     Train net output #0: loss = 5.18544 (* 1 = 5.18544 loss)
I0406 15:12:09.397915 21485 sgd_solver.cpp:105] Iteration 456, lr = 0.05
I0406 15:12:14.676488 21485 solver.cpp:218] Iteration 468 (2.27335 iter/s, 5.27856s/12 iters), loss = 5.17787
I0406 15:12:14.676546 21485 solver.cpp:237]     Train net output #0: loss = 5.17787 (* 1 = 5.17787 loss)
I0406 15:12:14.676555 21485 sgd_solver.cpp:105] Iteration 468, lr = 0.05
I0406 15:12:19.923023 21485 solver.cpp:218] Iteration 480 (2.28725 iter/s, 5.24647s/12 iters), loss = 5.13401
I0406 15:12:19.923071 21485 solver.cpp:237]     Train net output #0: loss = 5.13401 (* 1 = 5.13401 loss)
I0406 15:12:19.923077 21485 sgd_solver.cpp:105] Iteration 480, lr = 0.05
I0406 15:12:25.176568 21485 solver.cpp:218] Iteration 492 (2.2842 iter/s, 5.25348s/12 iters), loss = 5.14929
I0406 15:12:25.176610 21485 solver.cpp:237]     Train net output #0: loss = 5.14929 (* 1 = 5.14929 loss)
I0406 15:12:25.176615 21485 sgd_solver.cpp:105] Iteration 492, lr = 0.05
I0406 15:12:30.215886 21485 solver.cpp:218] Iteration 504 (2.3813 iter/s, 5.03927s/12 iters), loss = 5.0775
I0406 15:12:30.215926 21485 solver.cpp:237]     Train net output #0: loss = 5.0775 (* 1 = 5.0775 loss)
I0406 15:12:30.215931 21485 sgd_solver.cpp:105] Iteration 504, lr = 0.05
I0406 15:12:30.462765 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:12:32.389694 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_510.caffemodel
I0406 15:12:35.399860 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_510.solverstate
I0406 15:12:37.707021 21485 solver.cpp:330] Iteration 510, Testing net (#0)
I0406 15:12:37.707042 21485 net.cpp:676] Ignoring source layer train-data
I0406 15:12:41.794788 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:12:42.036931 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0147059
I0406 15:12:42.036960 21485 solver.cpp:397]     Test net output #1: loss = 5.13855 (* 1 = 5.13855 loss)
I0406 15:12:43.870752 21485 solver.cpp:218] Iteration 516 (0.878811 iter/s, 13.6548s/12 iters), loss = 5.15409
I0406 15:12:43.870796 21485 solver.cpp:237]     Train net output #0: loss = 5.15409 (* 1 = 5.15409 loss)
I0406 15:12:43.870801 21485 sgd_solver.cpp:105] Iteration 516, lr = 0.05
I0406 15:12:48.890126 21485 solver.cpp:218] Iteration 528 (2.39076 iter/s, 5.01932s/12 iters), loss = 5.17879
I0406 15:12:48.890167 21485 solver.cpp:237]     Train net output #0: loss = 5.17879 (* 1 = 5.17879 loss)
I0406 15:12:48.890173 21485 sgd_solver.cpp:105] Iteration 528, lr = 0.05
I0406 15:12:54.086252 21485 solver.cpp:218] Iteration 540 (2.30944 iter/s, 5.19607s/12 iters), loss = 5.12052
I0406 15:12:54.086300 21485 solver.cpp:237]     Train net output #0: loss = 5.12052 (* 1 = 5.12052 loss)
I0406 15:12:54.086306 21485 sgd_solver.cpp:105] Iteration 540, lr = 0.05
I0406 15:12:59.046283 21485 solver.cpp:218] Iteration 552 (2.41937 iter/s, 4.95996s/12 iters), loss = 5.21366
I0406 15:12:59.046341 21485 solver.cpp:237]     Train net output #0: loss = 5.21366 (* 1 = 5.21366 loss)
I0406 15:12:59.046350 21485 sgd_solver.cpp:105] Iteration 552, lr = 0.05
I0406 15:13:04.329288 21485 solver.cpp:218] Iteration 564 (2.27146 iter/s, 5.28294s/12 iters), loss = 5.10962
I0406 15:13:04.329324 21485 solver.cpp:237]     Train net output #0: loss = 5.10962 (* 1 = 5.10962 loss)
I0406 15:13:04.329329 21485 sgd_solver.cpp:105] Iteration 564, lr = 0.05
I0406 15:13:09.544808 21485 solver.cpp:218] Iteration 576 (2.30085 iter/s, 5.21547s/12 iters), loss = 5.03162
I0406 15:13:09.544924 21485 solver.cpp:237]     Train net output #0: loss = 5.03162 (* 1 = 5.03162 loss)
I0406 15:13:09.544932 21485 sgd_solver.cpp:105] Iteration 576, lr = 0.05
I0406 15:13:14.541416 21485 solver.cpp:218] Iteration 588 (2.40169 iter/s, 4.99648s/12 iters), loss = 5.09594
I0406 15:13:14.541474 21485 solver.cpp:237]     Train net output #0: loss = 5.09594 (* 1 = 5.09594 loss)
I0406 15:13:14.541483 21485 sgd_solver.cpp:105] Iteration 588, lr = 0.05
I0406 15:13:19.866767 21485 solver.cpp:218] Iteration 600 (2.2534 iter/s, 5.32528s/12 iters), loss = 5.1131
I0406 15:13:19.866813 21485 solver.cpp:237]     Train net output #0: loss = 5.1131 (* 1 = 5.1131 loss)
I0406 15:13:19.866818 21485 sgd_solver.cpp:105] Iteration 600, lr = 0.05
I0406 15:13:22.412928 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:13:24.715348 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_612.caffemodel
I0406 15:13:27.725091 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_612.solverstate
I0406 15:13:30.051942 21485 solver.cpp:330] Iteration 612, Testing net (#0)
I0406 15:13:30.051967 21485 net.cpp:676] Ignoring source layer train-data
I0406 15:13:34.162393 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:13:34.486264 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0189951
I0406 15:13:34.486300 21485 solver.cpp:397]     Test net output #1: loss = 5.10721 (* 1 = 5.10721 loss)
I0406 15:13:34.627125 21485 solver.cpp:218] Iteration 612 (0.812992 iter/s, 14.7603s/12 iters), loss = 5.07443
I0406 15:13:34.629392 21485 solver.cpp:237]     Train net output #0: loss = 5.07443 (* 1 = 5.07443 loss)
I0406 15:13:34.629405 21485 sgd_solver.cpp:105] Iteration 612, lr = 0.05
I0406 15:13:38.983664 21485 solver.cpp:218] Iteration 624 (2.75592 iter/s, 4.35426s/12 iters), loss = 5.06032
I0406 15:13:38.983711 21485 solver.cpp:237]     Train net output #0: loss = 5.06032 (* 1 = 5.06032 loss)
I0406 15:13:38.983716 21485 sgd_solver.cpp:105] Iteration 624, lr = 0.05
I0406 15:13:44.060494 21485 solver.cpp:218] Iteration 636 (2.36371 iter/s, 5.07677s/12 iters), loss = 5.09509
I0406 15:13:44.060621 21485 solver.cpp:237]     Train net output #0: loss = 5.09509 (* 1 = 5.09509 loss)
I0406 15:13:44.060626 21485 sgd_solver.cpp:105] Iteration 636, lr = 0.05
I0406 15:13:49.306241 21485 solver.cpp:218] Iteration 648 (2.28763 iter/s, 5.24561s/12 iters), loss = 4.9874
I0406 15:13:49.306288 21485 solver.cpp:237]     Train net output #0: loss = 4.9874 (* 1 = 4.9874 loss)
I0406 15:13:49.306293 21485 sgd_solver.cpp:105] Iteration 648, lr = 0.05
I0406 15:13:54.604457 21485 solver.cpp:218] Iteration 660 (2.26494 iter/s, 5.29815s/12 iters), loss = 5.08965
I0406 15:13:54.604504 21485 solver.cpp:237]     Train net output #0: loss = 5.08965 (* 1 = 5.08965 loss)
I0406 15:13:54.604511 21485 sgd_solver.cpp:105] Iteration 660, lr = 0.05
I0406 15:13:59.766490 21485 solver.cpp:218] Iteration 672 (2.32469 iter/s, 5.16197s/12 iters), loss = 5.1848
I0406 15:13:59.766547 21485 solver.cpp:237]     Train net output #0: loss = 5.1848 (* 1 = 5.1848 loss)
I0406 15:13:59.766556 21485 sgd_solver.cpp:105] Iteration 672, lr = 0.05
I0406 15:14:04.988931 21485 solver.cpp:218] Iteration 684 (2.29781 iter/s, 5.22237s/12 iters), loss = 5.02285
I0406 15:14:04.988974 21485 solver.cpp:237]     Train net output #0: loss = 5.02285 (* 1 = 5.02285 loss)
I0406 15:14:04.988981 21485 sgd_solver.cpp:105] Iteration 684, lr = 0.05
I0406 15:14:05.792582 21485 blocking_queue.cpp:49] Waiting for data
I0406 15:14:10.136963 21485 solver.cpp:218] Iteration 696 (2.33102 iter/s, 5.14797s/12 iters), loss = 5.03837
I0406 15:14:10.137008 21485 solver.cpp:237]     Train net output #0: loss = 5.03837 (* 1 = 5.03837 loss)
I0406 15:14:10.137013 21485 sgd_solver.cpp:105] Iteration 696, lr = 0.05
I0406 15:14:14.959697 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:14:15.372437 21485 solver.cpp:218] Iteration 708 (2.29208 iter/s, 5.23541s/12 iters), loss = 5.10078
I0406 15:14:15.372481 21485 solver.cpp:237]     Train net output #0: loss = 5.10078 (* 1 = 5.10078 loss)
I0406 15:14:15.372488 21485 sgd_solver.cpp:105] Iteration 708, lr = 0.05
I0406 15:14:17.458611 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_714.caffemodel
I0406 15:14:20.483410 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_714.solverstate
I0406 15:14:22.795910 21485 solver.cpp:330] Iteration 714, Testing net (#0)
I0406 15:14:22.795933 21485 net.cpp:676] Ignoring source layer train-data
I0406 15:14:26.889657 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:14:27.206188 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0220588
I0406 15:14:27.206228 21485 solver.cpp:397]     Test net output #1: loss = 5.0856 (* 1 = 5.0856 loss)
I0406 15:14:28.994653 21485 solver.cpp:218] Iteration 720 (0.880917 iter/s, 13.6222s/12 iters), loss = 5.08586
I0406 15:14:28.994693 21485 solver.cpp:237]     Train net output #0: loss = 5.08586 (* 1 = 5.08586 loss)
I0406 15:14:28.994699 21485 sgd_solver.cpp:105] Iteration 720, lr = 0.05
I0406 15:14:34.302996 21485 solver.cpp:218] Iteration 732 (2.26062 iter/s, 5.30829s/12 iters), loss = 5.05875
I0406 15:14:34.303041 21485 solver.cpp:237]     Train net output #0: loss = 5.05875 (* 1 = 5.05875 loss)
I0406 15:14:34.303047 21485 sgd_solver.cpp:105] Iteration 732, lr = 0.05
I0406 15:14:39.960498 21485 solver.cpp:218] Iteration 744 (2.1211 iter/s, 5.65744s/12 iters), loss = 5.07692
I0406 15:14:39.960541 21485 solver.cpp:237]     Train net output #0: loss = 5.07692 (* 1 = 5.07692 loss)
I0406 15:14:39.960546 21485 sgd_solver.cpp:105] Iteration 744, lr = 0.05
I0406 15:14:45.103425 21485 solver.cpp:218] Iteration 756 (2.33333 iter/s, 5.14287s/12 iters), loss = 4.96154
I0406 15:14:45.103519 21485 solver.cpp:237]     Train net output #0: loss = 4.96154 (* 1 = 4.96154 loss)
I0406 15:14:45.103525 21485 sgd_solver.cpp:105] Iteration 756, lr = 0.05
I0406 15:14:50.392010 21485 solver.cpp:218] Iteration 768 (2.26908 iter/s, 5.28848s/12 iters), loss = 5.1017
I0406 15:14:50.392053 21485 solver.cpp:237]     Train net output #0: loss = 5.1017 (* 1 = 5.1017 loss)
I0406 15:14:50.392058 21485 sgd_solver.cpp:105] Iteration 768, lr = 0.05
I0406 15:14:55.702762 21485 solver.cpp:218] Iteration 780 (2.25959 iter/s, 5.31069s/12 iters), loss = 5.02522
I0406 15:14:55.702803 21485 solver.cpp:237]     Train net output #0: loss = 5.02522 (* 1 = 5.02522 loss)
I0406 15:14:55.702808 21485 sgd_solver.cpp:105] Iteration 780, lr = 0.05
I0406 15:15:01.082459 21485 solver.cpp:218] Iteration 792 (2.23063 iter/s, 5.37964s/12 iters), loss = 5.07376
I0406 15:15:01.082511 21485 solver.cpp:237]     Train net output #0: loss = 5.07376 (* 1 = 5.07376 loss)
I0406 15:15:01.082520 21485 sgd_solver.cpp:105] Iteration 792, lr = 0.05
I0406 15:15:06.467738 21485 solver.cpp:218] Iteration 804 (2.22832 iter/s, 5.38521s/12 iters), loss = 5.10791
I0406 15:15:06.467790 21485 solver.cpp:237]     Train net output #0: loss = 5.10791 (* 1 = 5.10791 loss)
I0406 15:15:06.467799 21485 sgd_solver.cpp:105] Iteration 804, lr = 0.05
I0406 15:15:08.325907 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:15:11.282770 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_816.caffemodel
I0406 15:15:14.272495 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_816.solverstate
I0406 15:15:16.582769 21485 solver.cpp:330] Iteration 816, Testing net (#0)
I0406 15:15:16.582885 21485 net.cpp:676] Ignoring source layer train-data
I0406 15:15:20.580602 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:15:20.928107 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0208333
I0406 15:15:20.928140 21485 solver.cpp:397]     Test net output #1: loss = 5.06867 (* 1 = 5.06867 loss)
I0406 15:15:21.066696 21485 solver.cpp:218] Iteration 816 (0.82198 iter/s, 14.5989s/12 iters), loss = 5.02349
I0406 15:15:21.066748 21485 solver.cpp:237]     Train net output #0: loss = 5.02349 (* 1 = 5.02349 loss)
I0406 15:15:21.066756 21485 sgd_solver.cpp:105] Iteration 816, lr = 0.05
I0406 15:15:25.403669 21485 solver.cpp:218] Iteration 828 (2.76695 iter/s, 4.3369s/12 iters), loss = 5.03398
I0406 15:15:25.403724 21485 solver.cpp:237]     Train net output #0: loss = 5.03398 (* 1 = 5.03398 loss)
I0406 15:15:25.403733 21485 sgd_solver.cpp:105] Iteration 828, lr = 0.05
I0406 15:15:30.493893 21485 solver.cpp:218] Iteration 840 (2.35749 iter/s, 5.09015s/12 iters), loss = 5.01809
I0406 15:15:30.493955 21485 solver.cpp:237]     Train net output #0: loss = 5.01809 (* 1 = 5.01809 loss)
I0406 15:15:30.493964 21485 sgd_solver.cpp:105] Iteration 840, lr = 0.05
I0406 15:15:35.662396 21485 solver.cpp:218] Iteration 852 (2.32179 iter/s, 5.16843s/12 iters), loss = 4.91331
I0406 15:15:35.662436 21485 solver.cpp:237]     Train net output #0: loss = 4.91331 (* 1 = 4.91331 loss)
I0406 15:15:35.662441 21485 sgd_solver.cpp:105] Iteration 852, lr = 0.05
I0406 15:15:41.002450 21485 solver.cpp:218] Iteration 864 (2.24719 iter/s, 5.34s/12 iters), loss = 5.01436
I0406 15:15:41.002506 21485 solver.cpp:237]     Train net output #0: loss = 5.01436 (* 1 = 5.01436 loss)
I0406 15:15:41.002516 21485 sgd_solver.cpp:105] Iteration 864, lr = 0.05
I0406 15:15:46.294524 21485 solver.cpp:218] Iteration 876 (2.26757 iter/s, 5.29201s/12 iters), loss = 5.06322
I0406 15:15:46.294585 21485 solver.cpp:237]     Train net output #0: loss = 5.06322 (* 1 = 5.06322 loss)
I0406 15:15:46.294593 21485 sgd_solver.cpp:105] Iteration 876, lr = 0.05
I0406 15:15:51.686815 21485 solver.cpp:218] Iteration 888 (2.22543 iter/s, 5.39223s/12 iters), loss = 4.96893
I0406 15:15:51.686923 21485 solver.cpp:237]     Train net output #0: loss = 4.96893 (* 1 = 4.96893 loss)
I0406 15:15:51.686930 21485 sgd_solver.cpp:105] Iteration 888, lr = 0.05
I0406 15:15:56.947007 21485 solver.cpp:218] Iteration 900 (2.28134 iter/s, 5.26007s/12 iters), loss = 5.01625
I0406 15:15:56.947057 21485 solver.cpp:237]     Train net output #0: loss = 5.01625 (* 1 = 5.01625 loss)
I0406 15:15:56.947063 21485 sgd_solver.cpp:105] Iteration 900, lr = 0.05
I0406 15:16:01.039501 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:16:02.222800 21485 solver.cpp:218] Iteration 912 (2.27458 iter/s, 5.27571s/12 iters), loss = 4.96216
I0406 15:16:02.222927 21485 solver.cpp:237]     Train net output #0: loss = 4.96216 (* 1 = 4.96216 loss)
I0406 15:16:02.222947 21485 sgd_solver.cpp:105] Iteration 912, lr = 0.05
I0406 15:16:04.315472 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_918.caffemodel
I0406 15:16:07.615815 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_918.solverstate
I0406 15:16:11.109659 21485 solver.cpp:330] Iteration 918, Testing net (#0)
I0406 15:16:11.109679 21485 net.cpp:676] Ignoring source layer train-data
I0406 15:16:15.133193 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:16:15.532999 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0281863
I0406 15:16:15.533036 21485 solver.cpp:397]     Test net output #1: loss = 5.00476 (* 1 = 5.00476 loss)
I0406 15:16:17.293860 21485 solver.cpp:218] Iteration 924 (0.796234 iter/s, 15.0709s/12 iters), loss = 4.98419
I0406 15:16:17.293920 21485 solver.cpp:237]     Train net output #0: loss = 4.98419 (* 1 = 4.98419 loss)
I0406 15:16:17.293928 21485 sgd_solver.cpp:105] Iteration 924, lr = 0.05
I0406 15:16:22.460914 21485 solver.cpp:218] Iteration 936 (2.32244 iter/s, 5.16699s/12 iters), loss = 5.08497
I0406 15:16:22.461031 21485 solver.cpp:237]     Train net output #0: loss = 5.08497 (* 1 = 5.08497 loss)
I0406 15:16:22.461037 21485 sgd_solver.cpp:105] Iteration 936, lr = 0.05
I0406 15:16:27.433929 21485 solver.cpp:218] Iteration 948 (2.41308 iter/s, 4.97289s/12 iters), loss = 4.83643
I0406 15:16:27.433969 21485 solver.cpp:237]     Train net output #0: loss = 4.83643 (* 1 = 4.83643 loss)
I0406 15:16:27.433974 21485 sgd_solver.cpp:105] Iteration 948, lr = 0.05
I0406 15:16:32.780438 21485 solver.cpp:218] Iteration 960 (2.24448 iter/s, 5.34646s/12 iters), loss = 5.06443
I0406 15:16:32.780476 21485 solver.cpp:237]     Train net output #0: loss = 5.06443 (* 1 = 5.06443 loss)
I0406 15:16:32.780481 21485 sgd_solver.cpp:105] Iteration 960, lr = 0.05
I0406 15:16:38.108893 21485 solver.cpp:218] Iteration 972 (2.25209 iter/s, 5.32838s/12 iters), loss = 4.82589
I0406 15:16:38.108959 21485 solver.cpp:237]     Train net output #0: loss = 4.82589 (* 1 = 4.82589 loss)
I0406 15:16:38.108969 21485 sgd_solver.cpp:105] Iteration 972, lr = 0.05
I0406 15:16:43.483953 21485 solver.cpp:218] Iteration 984 (2.23256 iter/s, 5.37499s/12 iters), loss = 4.97014
I0406 15:16:43.483999 21485 solver.cpp:237]     Train net output #0: loss = 4.97014 (* 1 = 4.97014 loss)
I0406 15:16:43.484004 21485 sgd_solver.cpp:105] Iteration 984, lr = 0.05
I0406 15:16:48.805773 21485 solver.cpp:218] Iteration 996 (2.25489 iter/s, 5.32176s/12 iters), loss = 4.83803
I0406 15:16:48.805819 21485 solver.cpp:237]     Train net output #0: loss = 4.83803 (* 1 = 4.83803 loss)
I0406 15:16:48.805824 21485 sgd_solver.cpp:105] Iteration 996, lr = 0.05
I0406 15:16:54.104779 21485 solver.cpp:218] Iteration 1008 (2.2646 iter/s, 5.29895s/12 iters), loss = 5.05595
I0406 15:16:54.105033 21485 solver.cpp:237]     Train net output #0: loss = 5.05595 (* 1 = 5.05595 loss)
I0406 15:16:54.105041 21485 sgd_solver.cpp:105] Iteration 1008, lr = 0.05
I0406 15:16:55.212213 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:16:58.984860 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1020.caffemodel
I0406 15:17:02.900561 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1020.solverstate
I0406 15:17:05.299165 21485 solver.cpp:330] Iteration 1020, Testing net (#0)
I0406 15:17:05.299182 21485 net.cpp:676] Ignoring source layer train-data
I0406 15:17:09.208523 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:17:09.633289 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0300245
I0406 15:17:09.633328 21485 solver.cpp:397]     Test net output #1: loss = 4.96588 (* 1 = 4.96588 loss)
I0406 15:17:09.773164 21485 solver.cpp:218] Iteration 1020 (0.765886 iter/s, 15.6681s/12 iters), loss = 4.86033
I0406 15:17:09.773224 21485 solver.cpp:237]     Train net output #0: loss = 4.86033 (* 1 = 4.86033 loss)
I0406 15:17:09.773232 21485 sgd_solver.cpp:105] Iteration 1020, lr = 0.05
I0406 15:17:14.143365 21485 solver.cpp:218] Iteration 1032 (2.74591 iter/s, 4.37013s/12 iters), loss = 5.226
I0406 15:17:14.143404 21485 solver.cpp:237]     Train net output #0: loss = 5.226 (* 1 = 5.226 loss)
I0406 15:17:14.143409 21485 sgd_solver.cpp:105] Iteration 1032, lr = 0.05
I0406 15:17:19.342032 21485 solver.cpp:218] Iteration 1044 (2.30831 iter/s, 5.19861s/12 iters), loss = 4.98183
I0406 15:17:19.342085 21485 solver.cpp:237]     Train net output #0: loss = 4.98183 (* 1 = 4.98183 loss)
I0406 15:17:19.342094 21485 sgd_solver.cpp:105] Iteration 1044, lr = 0.05
I0406 15:17:24.614415 21485 solver.cpp:218] Iteration 1056 (2.27604 iter/s, 5.27232s/12 iters), loss = 4.92784
I0406 15:17:24.614583 21485 solver.cpp:237]     Train net output #0: loss = 4.92784 (* 1 = 4.92784 loss)
I0406 15:17:24.614590 21485 sgd_solver.cpp:105] Iteration 1056, lr = 0.05
I0406 15:17:29.931363 21485 solver.cpp:218] Iteration 1068 (2.25701 iter/s, 5.31677s/12 iters), loss = 4.87195
I0406 15:17:29.931407 21485 solver.cpp:237]     Train net output #0: loss = 4.87195 (* 1 = 4.87195 loss)
I0406 15:17:29.931413 21485 sgd_solver.cpp:105] Iteration 1068, lr = 0.05
I0406 15:17:34.778581 21485 solver.cpp:218] Iteration 1080 (2.47568 iter/s, 4.84716s/12 iters), loss = 4.95782
I0406 15:17:34.778628 21485 solver.cpp:237]     Train net output #0: loss = 4.95782 (* 1 = 4.95782 loss)
I0406 15:17:34.778633 21485 sgd_solver.cpp:105] Iteration 1080, lr = 0.05
I0406 15:17:39.873879 21485 solver.cpp:218] Iteration 1092 (2.35514 iter/s, 5.09524s/12 iters), loss = 5.00058
I0406 15:17:39.873920 21485 solver.cpp:237]     Train net output #0: loss = 5.00058 (* 1 = 5.00058 loss)
I0406 15:17:39.873926 21485 sgd_solver.cpp:105] Iteration 1092, lr = 0.05
I0406 15:17:45.228755 21485 solver.cpp:218] Iteration 1104 (2.24097 iter/s, 5.35482s/12 iters), loss = 4.89753
I0406 15:17:45.228808 21485 solver.cpp:237]     Train net output #0: loss = 4.89753 (* 1 = 4.89753 loss)
I0406 15:17:45.228817 21485 sgd_solver.cpp:105] Iteration 1104, lr = 0.05
I0406 15:17:48.672873 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:17:50.694008 21485 solver.cpp:218] Iteration 1116 (2.19572 iter/s, 5.46519s/12 iters), loss = 4.83006
I0406 15:17:50.694067 21485 solver.cpp:237]     Train net output #0: loss = 4.83006 (* 1 = 4.83006 loss)
I0406 15:17:50.694074 21485 sgd_solver.cpp:105] Iteration 1116, lr = 0.05
I0406 15:17:52.841435 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1122.caffemodel
I0406 15:17:56.163669 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1122.solverstate
I0406 15:17:58.465502 21485 solver.cpp:330] Iteration 1122, Testing net (#0)
I0406 15:17:58.465528 21485 net.cpp:676] Ignoring source layer train-data
I0406 15:18:02.415403 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:18:02.894476 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0281863
I0406 15:18:02.894511 21485 solver.cpp:397]     Test net output #1: loss = 4.92939 (* 1 = 4.92939 loss)
I0406 15:18:04.977303 21485 solver.cpp:218] Iteration 1128 (0.840146 iter/s, 14.2832s/12 iters), loss = 4.93501
I0406 15:18:04.977345 21485 solver.cpp:237]     Train net output #0: loss = 4.93501 (* 1 = 4.93501 loss)
I0406 15:18:04.977351 21485 sgd_solver.cpp:105] Iteration 1128, lr = 0.05
I0406 15:18:10.009783 21485 solver.cpp:218] Iteration 1140 (2.38454 iter/s, 5.03242s/12 iters), loss = 4.84719
I0406 15:18:10.009829 21485 solver.cpp:237]     Train net output #0: loss = 4.84719 (* 1 = 4.84719 loss)
I0406 15:18:10.009835 21485 sgd_solver.cpp:105] Iteration 1140, lr = 0.05
I0406 15:18:15.325248 21485 solver.cpp:218] Iteration 1152 (2.25759 iter/s, 5.31541s/12 iters), loss = 4.87068
I0406 15:18:15.325284 21485 solver.cpp:237]     Train net output #0: loss = 4.87068 (* 1 = 4.87068 loss)
I0406 15:18:15.325289 21485 sgd_solver.cpp:105] Iteration 1152, lr = 0.05
I0406 15:18:20.416347 21485 solver.cpp:218] Iteration 1164 (2.35708 iter/s, 5.09104s/12 iters), loss = 5.01842
I0406 15:18:20.416401 21485 solver.cpp:237]     Train net output #0: loss = 5.01842 (* 1 = 5.01842 loss)
I0406 15:18:20.416409 21485 sgd_solver.cpp:105] Iteration 1164, lr = 0.05
I0406 15:18:25.512089 21485 solver.cpp:218] Iteration 1176 (2.35494 iter/s, 5.09568s/12 iters), loss = 4.95037
I0406 15:18:25.512126 21485 solver.cpp:237]     Train net output #0: loss = 4.95037 (* 1 = 4.95037 loss)
I0406 15:18:25.512131 21485 sgd_solver.cpp:105] Iteration 1176, lr = 0.05
I0406 15:18:30.481670 21485 solver.cpp:218] Iteration 1188 (2.41472 iter/s, 4.96953s/12 iters), loss = 4.85254
I0406 15:18:30.481797 21485 solver.cpp:237]     Train net output #0: loss = 4.85254 (* 1 = 4.85254 loss)
I0406 15:18:30.481803 21485 sgd_solver.cpp:105] Iteration 1188, lr = 0.05
I0406 15:18:35.859391 21485 solver.cpp:218] Iteration 1200 (2.23149 iter/s, 5.37758s/12 iters), loss = 4.84204
I0406 15:18:35.859432 21485 solver.cpp:237]     Train net output #0: loss = 4.84204 (* 1 = 4.84204 loss)
I0406 15:18:35.859437 21485 sgd_solver.cpp:105] Iteration 1200, lr = 0.05
I0406 15:18:41.180115 21485 solver.cpp:218] Iteration 1212 (2.25535 iter/s, 5.32067s/12 iters), loss = 4.88046
I0406 15:18:41.180167 21485 solver.cpp:237]     Train net output #0: loss = 4.88046 (* 1 = 4.88046 loss)
I0406 15:18:41.180177 21485 sgd_solver.cpp:105] Iteration 1212, lr = 0.05
I0406 15:18:41.400990 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:18:45.753295 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1224.caffemodel
I0406 15:18:48.793982 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1224.solverstate
I0406 15:18:51.110842 21485 solver.cpp:330] Iteration 1224, Testing net (#0)
I0406 15:18:51.110862 21485 net.cpp:676] Ignoring source layer train-data
I0406 15:18:54.899061 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:18:55.406986 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0306373
I0406 15:18:55.407021 21485 solver.cpp:397]     Test net output #1: loss = 4.88867 (* 1 = 4.88867 loss)
I0406 15:18:55.542613 21485 solver.cpp:218] Iteration 1224 (0.835513 iter/s, 14.3624s/12 iters), loss = 4.85375
I0406 15:18:55.542660 21485 solver.cpp:237]     Train net output #0: loss = 4.85375 (* 1 = 4.85375 loss)
I0406 15:18:55.542666 21485 sgd_solver.cpp:105] Iteration 1224, lr = 0.05
I0406 15:18:59.869623 21485 solver.cpp:218] Iteration 1236 (2.77332 iter/s, 4.32694s/12 iters), loss = 4.97754
I0406 15:18:59.869674 21485 solver.cpp:237]     Train net output #0: loss = 4.97754 (* 1 = 4.97754 loss)
I0406 15:18:59.869683 21485 sgd_solver.cpp:105] Iteration 1236, lr = 0.05
I0406 15:19:05.116094 21485 solver.cpp:218] Iteration 1248 (2.28728 iter/s, 5.24641s/12 iters), loss = 4.88918
I0406 15:19:05.116206 21485 solver.cpp:237]     Train net output #0: loss = 4.88918 (* 1 = 4.88918 loss)
I0406 15:19:05.116214 21485 sgd_solver.cpp:105] Iteration 1248, lr = 0.05
I0406 15:19:10.375269 21485 solver.cpp:218] Iteration 1260 (2.28178 iter/s, 5.25905s/12 iters), loss = 4.93341
I0406 15:19:10.375320 21485 solver.cpp:237]     Train net output #0: loss = 4.93341 (* 1 = 4.93341 loss)
I0406 15:19:10.375329 21485 sgd_solver.cpp:105] Iteration 1260, lr = 0.05
I0406 15:19:15.641366 21485 solver.cpp:218] Iteration 1272 (2.27876 iter/s, 5.26603s/12 iters), loss = 4.85512
I0406 15:19:15.641427 21485 solver.cpp:237]     Train net output #0: loss = 4.85512 (* 1 = 4.85512 loss)
I0406 15:19:15.641435 21485 sgd_solver.cpp:105] Iteration 1272, lr = 0.05
I0406 15:19:20.983481 21485 solver.cpp:218] Iteration 1284 (2.24633 iter/s, 5.34204s/12 iters), loss = 4.81148
I0406 15:19:20.983536 21485 solver.cpp:237]     Train net output #0: loss = 4.81148 (* 1 = 4.81148 loss)
I0406 15:19:20.983542 21485 sgd_solver.cpp:105] Iteration 1284, lr = 0.05
I0406 15:19:26.322165 21485 solver.cpp:218] Iteration 1296 (2.24777 iter/s, 5.33862s/12 iters), loss = 4.95532
I0406 15:19:26.322222 21485 solver.cpp:237]     Train net output #0: loss = 4.95532 (* 1 = 4.95532 loss)
I0406 15:19:26.322230 21485 sgd_solver.cpp:105] Iteration 1296, lr = 0.05
I0406 15:19:31.774082 21485 solver.cpp:218] Iteration 1308 (2.20109 iter/s, 5.45185s/12 iters), loss = 4.88601
I0406 15:19:31.774124 21485 solver.cpp:237]     Train net output #0: loss = 4.88601 (* 1 = 4.88601 loss)
I0406 15:19:31.774130 21485 sgd_solver.cpp:105] Iteration 1308, lr = 0.05
I0406 15:19:34.461810 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:19:37.143586 21485 solver.cpp:218] Iteration 1320 (2.23487 iter/s, 5.36945s/12 iters), loss = 5.03049
I0406 15:19:37.143708 21485 solver.cpp:237]     Train net output #0: loss = 5.03049 (* 1 = 5.03049 loss)
I0406 15:19:37.143715 21485 sgd_solver.cpp:105] Iteration 1320, lr = 0.05
I0406 15:19:39.304301 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1326.caffemodel
I0406 15:19:42.369016 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1326.solverstate
I0406 15:19:44.720207 21485 solver.cpp:330] Iteration 1326, Testing net (#0)
I0406 15:19:44.720227 21485 net.cpp:676] Ignoring source layer train-data
I0406 15:19:48.483670 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:19:49.035362 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0269608
I0406 15:19:49.035392 21485 solver.cpp:397]     Test net output #1: loss = 4.9383 (* 1 = 4.9383 loss)
I0406 15:19:50.990556 21485 solver.cpp:218] Iteration 1332 (0.866624 iter/s, 13.8468s/12 iters), loss = 4.85393
I0406 15:19:50.990600 21485 solver.cpp:237]     Train net output #0: loss = 4.85393 (* 1 = 4.85393 loss)
I0406 15:19:50.990607 21485 sgd_solver.cpp:105] Iteration 1332, lr = 0.05
I0406 15:19:56.086318 21485 solver.cpp:218] Iteration 1344 (2.35492 iter/s, 5.09571s/12 iters), loss = 4.85432
I0406 15:19:56.086359 21485 solver.cpp:237]     Train net output #0: loss = 4.85432 (* 1 = 4.85432 loss)
I0406 15:19:56.086364 21485 sgd_solver.cpp:105] Iteration 1344, lr = 0.05
I0406 15:20:01.379019 21485 solver.cpp:218] Iteration 1356 (2.2673 iter/s, 5.29264s/12 iters), loss = 4.75796
I0406 15:20:01.379073 21485 solver.cpp:237]     Train net output #0: loss = 4.75796 (* 1 = 4.75796 loss)
I0406 15:20:01.379082 21485 sgd_solver.cpp:105] Iteration 1356, lr = 0.05
I0406 15:20:06.709372 21485 solver.cpp:218] Iteration 1368 (2.25129 iter/s, 5.33028s/12 iters), loss = 4.8551
I0406 15:20:06.709420 21485 solver.cpp:237]     Train net output #0: loss = 4.8551 (* 1 = 4.8551 loss)
I0406 15:20:06.709426 21485 sgd_solver.cpp:105] Iteration 1368, lr = 0.05
I0406 15:20:08.011395 21485 blocking_queue.cpp:49] Waiting for data
I0406 15:20:12.058156 21485 solver.cpp:218] Iteration 1380 (2.24352 iter/s, 5.34873s/12 iters), loss = 4.93051
I0406 15:20:12.058188 21485 solver.cpp:237]     Train net output #0: loss = 4.93051 (* 1 = 4.93051 loss)
I0406 15:20:12.058193 21485 sgd_solver.cpp:105] Iteration 1380, lr = 0.05
I0406 15:20:17.044054 21485 solver.cpp:218] Iteration 1392 (2.40681 iter/s, 4.98585s/12 iters), loss = 4.90207
I0406 15:20:17.044104 21485 solver.cpp:237]     Train net output #0: loss = 4.90207 (* 1 = 4.90207 loss)
I0406 15:20:17.044111 21485 sgd_solver.cpp:105] Iteration 1392, lr = 0.05
I0406 15:20:22.087486 21485 solver.cpp:218] Iteration 1404 (2.37937 iter/s, 5.04336s/12 iters), loss = 4.78255
I0406 15:20:22.087540 21485 solver.cpp:237]     Train net output #0: loss = 4.78255 (* 1 = 4.78255 loss)
I0406 15:20:22.087548 21485 sgd_solver.cpp:105] Iteration 1404, lr = 0.05
I0406 15:20:26.790796 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:20:27.175983 21485 solver.cpp:218] Iteration 1416 (2.35829 iter/s, 5.08842s/12 iters), loss = 5.00893
I0406 15:20:27.176040 21485 solver.cpp:237]     Train net output #0: loss = 5.00893 (* 1 = 5.00893 loss)
I0406 15:20:27.176048 21485 sgd_solver.cpp:105] Iteration 1416, lr = 0.05
I0406 15:20:32.088397 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1428.caffemodel
I0406 15:20:35.049594 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1428.solverstate
I0406 15:20:37.361171 21485 solver.cpp:330] Iteration 1428, Testing net (#0)
I0406 15:20:37.361191 21485 net.cpp:676] Ignoring source layer train-data
I0406 15:20:41.057495 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:20:41.642007 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0281863
I0406 15:20:41.642041 21485 solver.cpp:397]     Test net output #1: loss = 4.8488 (* 1 = 4.8488 loss)
I0406 15:20:41.776062 21485 solver.cpp:218] Iteration 1428 (0.821917 iter/s, 14.6s/12 iters), loss = 4.77593
I0406 15:20:41.776118 21485 solver.cpp:237]     Train net output #0: loss = 4.77593 (* 1 = 4.77593 loss)
I0406 15:20:41.776125 21485 sgd_solver.cpp:105] Iteration 1428, lr = 0.05
I0406 15:20:46.108986 21485 solver.cpp:218] Iteration 1440 (2.76953 iter/s, 4.33286s/12 iters), loss = 4.78346
I0406 15:20:46.109050 21485 solver.cpp:237]     Train net output #0: loss = 4.78346 (* 1 = 4.78346 loss)
I0406 15:20:46.109059 21485 sgd_solver.cpp:105] Iteration 1440, lr = 0.05
I0406 15:20:51.300963 21485 solver.cpp:218] Iteration 1452 (2.31129 iter/s, 5.1919s/12 iters), loss = 4.71199
I0406 15:20:51.301025 21485 solver.cpp:237]     Train net output #0: loss = 4.71199 (* 1 = 4.71199 loss)
I0406 15:20:51.301033 21485 sgd_solver.cpp:105] Iteration 1452, lr = 0.05
I0406 15:20:56.593387 21485 solver.cpp:218] Iteration 1464 (2.26742 iter/s, 5.29235s/12 iters), loss = 4.87681
I0406 15:20:56.593432 21485 solver.cpp:237]     Train net output #0: loss = 4.87681 (* 1 = 4.87681 loss)
I0406 15:20:56.593438 21485 sgd_solver.cpp:105] Iteration 1464, lr = 0.05
I0406 15:21:01.738816 21485 solver.cpp:218] Iteration 1476 (2.3322 iter/s, 5.14536s/12 iters), loss = 4.85112
I0406 15:21:01.738878 21485 solver.cpp:237]     Train net output #0: loss = 4.85112 (* 1 = 4.85112 loss)
I0406 15:21:01.738888 21485 sgd_solver.cpp:105] Iteration 1476, lr = 0.05
I0406 15:21:06.686686 21485 solver.cpp:218] Iteration 1488 (2.42533 iter/s, 4.94779s/12 iters), loss = 4.69461
I0406 15:21:06.686744 21485 solver.cpp:237]     Train net output #0: loss = 4.69461 (* 1 = 4.69461 loss)
I0406 15:21:06.686753 21485 sgd_solver.cpp:105] Iteration 1488, lr = 0.05
I0406 15:21:12.073951 21485 solver.cpp:218] Iteration 1500 (2.2275 iter/s, 5.3872s/12 iters), loss = 4.87466
I0406 15:21:12.074061 21485 solver.cpp:237]     Train net output #0: loss = 4.87466 (* 1 = 4.87466 loss)
I0406 15:21:12.074069 21485 sgd_solver.cpp:105] Iteration 1500, lr = 0.05
I0406 15:21:17.458768 21485 solver.cpp:218] Iteration 1512 (2.22854 iter/s, 5.3847s/12 iters), loss = 4.76131
I0406 15:21:17.458806 21485 solver.cpp:237]     Train net output #0: loss = 4.76131 (* 1 = 4.76131 loss)
I0406 15:21:17.458811 21485 sgd_solver.cpp:105] Iteration 1512, lr = 0.05
I0406 15:21:19.383898 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:21:22.787413 21485 solver.cpp:218] Iteration 1524 (2.252 iter/s, 5.32859s/12 iters), loss = 4.96238
I0406 15:21:22.787459 21485 solver.cpp:237]     Train net output #0: loss = 4.96238 (* 1 = 4.96238 loss)
I0406 15:21:22.787464 21485 sgd_solver.cpp:105] Iteration 1524, lr = 0.05
I0406 15:21:24.806200 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1530.caffemodel
I0406 15:21:27.841825 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1530.solverstate
I0406 15:21:30.147847 21485 solver.cpp:330] Iteration 1530, Testing net (#0)
I0406 15:21:30.147871 21485 net.cpp:676] Ignoring source layer train-data
I0406 15:21:33.819208 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:21:34.450675 21485 solver.cpp:397]     Test net output #0: accuracy = 0.033701
I0406 15:21:34.450711 21485 solver.cpp:397]     Test net output #1: loss = 4.83927 (* 1 = 4.83927 loss)
I0406 15:21:36.310760 21485 solver.cpp:218] Iteration 1536 (0.887358 iter/s, 13.5233s/12 iters), loss = 4.73962
I0406 15:21:36.310798 21485 solver.cpp:237]     Train net output #0: loss = 4.73962 (* 1 = 4.73962 loss)
I0406 15:21:36.310803 21485 sgd_solver.cpp:105] Iteration 1536, lr = 0.05
I0406 15:21:41.457087 21485 solver.cpp:218] Iteration 1548 (2.33178 iter/s, 5.14627s/12 iters), loss = 4.67147
I0406 15:21:41.457129 21485 solver.cpp:237]     Train net output #0: loss = 4.67147 (* 1 = 4.67147 loss)
I0406 15:21:41.457135 21485 sgd_solver.cpp:105] Iteration 1548, lr = 0.05
I0406 15:21:46.745815 21485 solver.cpp:218] Iteration 1560 (2.269 iter/s, 5.28866s/12 iters), loss = 4.66778
I0406 15:21:46.745991 21485 solver.cpp:237]     Train net output #0: loss = 4.66778 (* 1 = 4.66778 loss)
I0406 15:21:46.746002 21485 sgd_solver.cpp:105] Iteration 1560, lr = 0.05
I0406 15:21:52.096321 21485 solver.cpp:218] Iteration 1572 (2.24286 iter/s, 5.35032s/12 iters), loss = 4.84094
I0406 15:21:52.096374 21485 solver.cpp:237]     Train net output #0: loss = 4.84094 (* 1 = 4.84094 loss)
I0406 15:21:52.096382 21485 sgd_solver.cpp:105] Iteration 1572, lr = 0.05
I0406 15:21:57.421044 21485 solver.cpp:218] Iteration 1584 (2.25367 iter/s, 5.32466s/12 iters), loss = 4.8856
I0406 15:21:57.421095 21485 solver.cpp:237]     Train net output #0: loss = 4.8856 (* 1 = 4.8856 loss)
I0406 15:21:57.421103 21485 sgd_solver.cpp:105] Iteration 1584, lr = 0.05
I0406 15:22:02.681540 21485 solver.cpp:218] Iteration 1596 (2.28118 iter/s, 5.26043s/12 iters), loss = 4.77535
I0406 15:22:02.681582 21485 solver.cpp:237]     Train net output #0: loss = 4.77535 (* 1 = 4.77535 loss)
I0406 15:22:02.681587 21485 sgd_solver.cpp:105] Iteration 1596, lr = 0.05
I0406 15:22:08.074074 21485 solver.cpp:218] Iteration 1608 (2.22532 iter/s, 5.39248s/12 iters), loss = 4.56419
I0406 15:22:08.074110 21485 solver.cpp:237]     Train net output #0: loss = 4.56419 (* 1 = 4.56419 loss)
I0406 15:22:08.074115 21485 sgd_solver.cpp:105] Iteration 1608, lr = 0.05
I0406 15:22:12.110107 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:22:13.239056 21485 solver.cpp:218] Iteration 1620 (2.32336 iter/s, 5.16493s/12 iters), loss = 4.65284
I0406 15:22:13.239091 21485 solver.cpp:237]     Train net output #0: loss = 4.65284 (* 1 = 4.65284 loss)
I0406 15:22:13.239097 21485 sgd_solver.cpp:105] Iteration 1620, lr = 0.05
I0406 15:22:17.896425 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1632.caffemodel
I0406 15:22:20.934690 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1632.solverstate
I0406 15:22:23.230724 21485 solver.cpp:330] Iteration 1632, Testing net (#0)
I0406 15:22:23.230742 21485 net.cpp:676] Ignoring source layer train-data
I0406 15:22:27.057381 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:22:27.722996 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0355392
I0406 15:22:27.723048 21485 solver.cpp:397]     Test net output #1: loss = 4.81623 (* 1 = 4.81623 loss)
I0406 15:22:27.864032 21485 solver.cpp:218] Iteration 1632 (0.820517 iter/s, 14.6249s/12 iters), loss = 4.80484
I0406 15:22:27.865664 21485 solver.cpp:237]     Train net output #0: loss = 4.80484 (* 1 = 4.80484 loss)
I0406 15:22:27.865679 21485 sgd_solver.cpp:105] Iteration 1632, lr = 0.05
I0406 15:22:32.245811 21485 solver.cpp:218] Iteration 1644 (2.73964 iter/s, 4.38014s/12 iters), loss = 4.93825
I0406 15:22:32.245868 21485 solver.cpp:237]     Train net output #0: loss = 4.93825 (* 1 = 4.93825 loss)
I0406 15:22:32.245877 21485 sgd_solver.cpp:105] Iteration 1644, lr = 0.05
I0406 15:22:37.504626 21485 solver.cpp:218] Iteration 1656 (2.28191 iter/s, 5.25874s/12 iters), loss = 4.78295
I0406 15:22:37.504674 21485 solver.cpp:237]     Train net output #0: loss = 4.78295 (* 1 = 4.78295 loss)
I0406 15:22:37.504681 21485 sgd_solver.cpp:105] Iteration 1656, lr = 0.05
I0406 15:22:42.428952 21485 solver.cpp:218] Iteration 1668 (2.43691 iter/s, 4.92426s/12 iters), loss = 4.79393
I0406 15:22:42.429005 21485 solver.cpp:237]     Train net output #0: loss = 4.79393 (* 1 = 4.79393 loss)
I0406 15:22:42.429013 21485 sgd_solver.cpp:105] Iteration 1668, lr = 0.05
I0406 15:22:47.522830 21485 solver.cpp:218] Iteration 1680 (2.3558 iter/s, 5.09381s/12 iters), loss = 4.7057
I0406 15:22:47.522886 21485 solver.cpp:237]     Train net output #0: loss = 4.7057 (* 1 = 4.7057 loss)
I0406 15:22:47.522893 21485 sgd_solver.cpp:105] Iteration 1680, lr = 0.05
I0406 15:22:52.887462 21485 solver.cpp:218] Iteration 1692 (2.2369 iter/s, 5.36457s/12 iters), loss = 4.61051
I0406 15:22:52.887643 21485 solver.cpp:237]     Train net output #0: loss = 4.61051 (* 1 = 4.61051 loss)
I0406 15:22:52.887652 21485 sgd_solver.cpp:105] Iteration 1692, lr = 0.05
I0406 15:22:57.950980 21485 solver.cpp:218] Iteration 1704 (2.36998 iter/s, 5.06332s/12 iters), loss = 4.81225
I0406 15:22:57.957367 21485 solver.cpp:237]     Train net output #0: loss = 4.81225 (* 1 = 4.81225 loss)
I0406 15:22:57.957382 21485 sgd_solver.cpp:105] Iteration 1704, lr = 0.05
I0406 15:23:03.317739 21485 solver.cpp:218] Iteration 1716 (2.23865 iter/s, 5.36037s/12 iters), loss = 4.90579
I0406 15:23:03.317800 21485 solver.cpp:237]     Train net output #0: loss = 4.90579 (* 1 = 4.90579 loss)
I0406 15:23:03.317808 21485 sgd_solver.cpp:105] Iteration 1716, lr = 0.05
I0406 15:23:04.409340 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:23:08.716862 21485 solver.cpp:218] Iteration 1728 (2.22261 iter/s, 5.39905s/12 iters), loss = 4.85799
I0406 15:23:08.716925 21485 solver.cpp:237]     Train net output #0: loss = 4.85799 (* 1 = 4.85799 loss)
I0406 15:23:08.716934 21485 sgd_solver.cpp:105] Iteration 1728, lr = 0.05
I0406 15:23:10.804108 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1734.caffemodel
I0406 15:23:13.891909 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1734.solverstate
I0406 15:23:16.242496 21485 solver.cpp:330] Iteration 1734, Testing net (#0)
I0406 15:23:16.242516 21485 net.cpp:676] Ignoring source layer train-data
I0406 15:23:20.004070 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:23:20.699466 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0386029
I0406 15:23:20.699498 21485 solver.cpp:397]     Test net output #1: loss = 4.81204 (* 1 = 4.81204 loss)
I0406 15:23:22.546355 21485 solver.cpp:218] Iteration 1740 (0.867715 iter/s, 13.8294s/12 iters), loss = 4.97864
I0406 15:23:22.546399 21485 solver.cpp:237]     Train net output #0: loss = 4.97864 (* 1 = 4.97864 loss)
I0406 15:23:22.546406 21485 sgd_solver.cpp:105] Iteration 1740, lr = 0.05
I0406 15:23:27.673173 21485 solver.cpp:218] Iteration 1752 (2.34066 iter/s, 5.12676s/12 iters), loss = 4.76663
I0406 15:23:27.673267 21485 solver.cpp:237]     Train net output #0: loss = 4.76663 (* 1 = 4.76663 loss)
I0406 15:23:27.673274 21485 sgd_solver.cpp:105] Iteration 1752, lr = 0.05
I0406 15:23:32.980427 21485 solver.cpp:218] Iteration 1764 (2.2611 iter/s, 5.30715s/12 iters), loss = 4.83315
I0406 15:23:32.980466 21485 solver.cpp:237]     Train net output #0: loss = 4.83315 (* 1 = 4.83315 loss)
I0406 15:23:32.980471 21485 sgd_solver.cpp:105] Iteration 1764, lr = 0.05
I0406 15:23:38.075557 21485 solver.cpp:218] Iteration 1776 (2.35522 iter/s, 5.09508s/12 iters), loss = 4.63969
I0406 15:23:38.075598 21485 solver.cpp:237]     Train net output #0: loss = 4.63969 (* 1 = 4.63969 loss)
I0406 15:23:38.075603 21485 sgd_solver.cpp:105] Iteration 1776, lr = 0.05
I0406 15:23:43.354315 21485 solver.cpp:218] Iteration 1788 (2.27329 iter/s, 5.2787s/12 iters), loss = 4.69299
I0406 15:23:43.354362 21485 solver.cpp:237]     Train net output #0: loss = 4.69299 (* 1 = 4.69299 loss)
I0406 15:23:43.354367 21485 sgd_solver.cpp:105] Iteration 1788, lr = 0.05
I0406 15:23:48.714969 21485 solver.cpp:218] Iteration 1800 (2.23856 iter/s, 5.3606s/12 iters), loss = 4.87584
I0406 15:23:48.715010 21485 solver.cpp:237]     Train net output #0: loss = 4.87584 (* 1 = 4.87584 loss)
I0406 15:23:48.715015 21485 sgd_solver.cpp:105] Iteration 1800, lr = 0.05
I0406 15:23:54.013165 21485 solver.cpp:218] Iteration 1812 (2.26495 iter/s, 5.29814s/12 iters), loss = 4.75619
I0406 15:23:54.013227 21485 solver.cpp:237]     Train net output #0: loss = 4.75619 (* 1 = 4.75619 loss)
I0406 15:23:54.013236 21485 sgd_solver.cpp:105] Iteration 1812, lr = 0.05
I0406 15:23:57.362625 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:23:59.268234 21485 solver.cpp:218] Iteration 1824 (2.28354 iter/s, 5.25499s/12 iters), loss = 4.80919
I0406 15:23:59.268378 21485 solver.cpp:237]     Train net output #0: loss = 4.80919 (* 1 = 4.80919 loss)
I0406 15:23:59.268384 21485 sgd_solver.cpp:105] Iteration 1824, lr = 0.05
I0406 15:24:03.768471 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1836.caffemodel
I0406 15:24:06.753562 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1836.solverstate
I0406 15:24:09.099438 21485 solver.cpp:330] Iteration 1836, Testing net (#0)
I0406 15:24:09.099457 21485 net.cpp:676] Ignoring source layer train-data
I0406 15:24:12.730497 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:24:13.498700 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0318627
I0406 15:24:13.498725 21485 solver.cpp:397]     Test net output #1: loss = 4.8089 (* 1 = 4.8089 loss)
I0406 15:24:13.639101 21485 solver.cpp:218] Iteration 1836 (0.835032 iter/s, 14.3707s/12 iters), loss = 4.6128
I0406 15:24:13.639158 21485 solver.cpp:237]     Train net output #0: loss = 4.6128 (* 1 = 4.6128 loss)
I0406 15:24:13.639166 21485 sgd_solver.cpp:105] Iteration 1836, lr = 0.05
I0406 15:24:17.919734 21485 solver.cpp:218] Iteration 1848 (2.80337 iter/s, 4.28057s/12 iters), loss = 4.64949
I0406 15:24:17.919773 21485 solver.cpp:237]     Train net output #0: loss = 4.64949 (* 1 = 4.64949 loss)
I0406 15:24:17.919778 21485 sgd_solver.cpp:105] Iteration 1848, lr = 0.05
I0406 15:24:23.129611 21485 solver.cpp:218] Iteration 1860 (2.30334 iter/s, 5.20982s/12 iters), loss = 4.81189
I0406 15:24:23.129650 21485 solver.cpp:237]     Train net output #0: loss = 4.81189 (* 1 = 4.81189 loss)
I0406 15:24:23.129655 21485 sgd_solver.cpp:105] Iteration 1860, lr = 0.05
I0406 15:24:28.489333 21485 solver.cpp:218] Iteration 1872 (2.23894 iter/s, 5.35967s/12 iters), loss = 4.83952
I0406 15:24:28.489369 21485 solver.cpp:237]     Train net output #0: loss = 4.83952 (* 1 = 4.83952 loss)
I0406 15:24:28.489374 21485 sgd_solver.cpp:105] Iteration 1872, lr = 0.05
I0406 15:24:33.801493 21485 solver.cpp:218] Iteration 1884 (2.25899 iter/s, 5.31211s/12 iters), loss = 4.72243
I0406 15:24:33.801589 21485 solver.cpp:237]     Train net output #0: loss = 4.72243 (* 1 = 4.72243 loss)
I0406 15:24:33.801594 21485 sgd_solver.cpp:105] Iteration 1884, lr = 0.05
I0406 15:24:38.989158 21485 solver.cpp:218] Iteration 1896 (2.31323 iter/s, 5.18755s/12 iters), loss = 4.63519
I0406 15:24:38.989217 21485 solver.cpp:237]     Train net output #0: loss = 4.63519 (* 1 = 4.63519 loss)
I0406 15:24:38.989226 21485 sgd_solver.cpp:105] Iteration 1896, lr = 0.05
I0406 15:24:44.318879 21485 solver.cpp:218] Iteration 1908 (2.25156 iter/s, 5.32965s/12 iters), loss = 4.75342
I0406 15:24:44.318925 21485 solver.cpp:237]     Train net output #0: loss = 4.75342 (* 1 = 4.75342 loss)
I0406 15:24:44.318933 21485 sgd_solver.cpp:105] Iteration 1908, lr = 0.05
I0406 15:24:49.452190 21485 solver.cpp:218] Iteration 1920 (2.3377 iter/s, 5.13325s/12 iters), loss = 4.79307
I0406 15:24:49.452242 21485 solver.cpp:237]     Train net output #0: loss = 4.79307 (* 1 = 4.79307 loss)
I0406 15:24:49.452250 21485 sgd_solver.cpp:105] Iteration 1920, lr = 0.05
I0406 15:24:49.752959 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:24:54.754441 21485 solver.cpp:218] Iteration 1932 (2.26322 iter/s, 5.30218s/12 iters), loss = 4.7736
I0406 15:24:54.754503 21485 solver.cpp:237]     Train net output #0: loss = 4.7736 (* 1 = 4.7736 loss)
I0406 15:24:54.754510 21485 sgd_solver.cpp:105] Iteration 1932, lr = 0.05
I0406 15:24:56.798419 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1938.caffemodel
I0406 15:24:59.849184 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1938.solverstate
I0406 15:25:02.165634 21485 solver.cpp:330] Iteration 1938, Testing net (#0)
I0406 15:25:02.165652 21485 net.cpp:676] Ignoring source layer train-data
I0406 15:25:05.767546 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:25:06.543493 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0367647
I0406 15:25:06.543527 21485 solver.cpp:397]     Test net output #1: loss = 4.78614 (* 1 = 4.78614 loss)
I0406 15:25:08.442744 21485 solver.cpp:218] Iteration 1944 (0.876665 iter/s, 13.6882s/12 iters), loss = 5.05915
I0406 15:25:08.442795 21485 solver.cpp:237]     Train net output #0: loss = 5.05915 (* 1 = 5.05915 loss)
I0406 15:25:08.442802 21485 sgd_solver.cpp:105] Iteration 1944, lr = 0.05
I0406 15:25:13.397287 21485 solver.cpp:218] Iteration 1956 (2.42205 iter/s, 4.95448s/12 iters), loss = 4.77395
I0406 15:25:13.397333 21485 solver.cpp:237]     Train net output #0: loss = 4.77395 (* 1 = 4.77395 loss)
I0406 15:25:13.397339 21485 sgd_solver.cpp:105] Iteration 1956, lr = 0.05
I0406 15:25:18.766206 21485 solver.cpp:218] Iteration 1968 (2.23511 iter/s, 5.36886s/12 iters), loss = 4.7622
I0406 15:25:18.766249 21485 solver.cpp:237]     Train net output #0: loss = 4.7622 (* 1 = 4.7622 loss)
I0406 15:25:18.766255 21485 sgd_solver.cpp:105] Iteration 1968, lr = 0.05
I0406 15:25:23.601737 21485 solver.cpp:218] Iteration 1980 (2.48166 iter/s, 4.83547s/12 iters), loss = 4.73224
I0406 15:25:23.601779 21485 solver.cpp:237]     Train net output #0: loss = 4.73224 (* 1 = 4.73224 loss)
I0406 15:25:23.601784 21485 sgd_solver.cpp:105] Iteration 1980, lr = 0.05
I0406 15:25:28.780898 21485 solver.cpp:218] Iteration 1992 (2.31701 iter/s, 5.1791s/12 iters), loss = 4.6007
I0406 15:25:28.780948 21485 solver.cpp:237]     Train net output #0: loss = 4.6007 (* 1 = 4.6007 loss)
I0406 15:25:28.780956 21485 sgd_solver.cpp:105] Iteration 1992, lr = 0.05
I0406 15:25:34.207461 21485 solver.cpp:218] Iteration 2004 (2.21137 iter/s, 5.4265s/12 iters), loss = 4.69809
I0406 15:25:34.207520 21485 solver.cpp:237]     Train net output #0: loss = 4.69809 (* 1 = 4.69809 loss)
I0406 15:25:34.207528 21485 sgd_solver.cpp:105] Iteration 2004, lr = 0.05
I0406 15:25:39.465637 21485 solver.cpp:218] Iteration 2016 (2.28219 iter/s, 5.25811s/12 iters), loss = 4.66722
I0406 15:25:39.465741 21485 solver.cpp:237]     Train net output #0: loss = 4.66722 (* 1 = 4.66722 loss)
I0406 15:25:39.465747 21485 sgd_solver.cpp:105] Iteration 2016, lr = 0.05
I0406 15:25:42.109467 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:25:44.793453 21485 solver.cpp:218] Iteration 2028 (2.25246 iter/s, 5.32751s/12 iters), loss = 4.83254
I0406 15:25:44.793526 21485 solver.cpp:237]     Train net output #0: loss = 4.83254 (* 1 = 4.83254 loss)
I0406 15:25:44.793532 21485 sgd_solver.cpp:105] Iteration 2028, lr = 0.05
I0406 15:25:49.385910 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2040.caffemodel
I0406 15:25:52.452759 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2040.solverstate
I0406 15:25:54.759886 21485 solver.cpp:330] Iteration 2040, Testing net (#0)
I0406 15:25:54.759907 21485 net.cpp:676] Ignoring source layer train-data
I0406 15:25:58.338058 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:25:59.195935 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0349265
I0406 15:25:59.195977 21485 solver.cpp:397]     Test net output #1: loss = 4.8206 (* 1 = 4.8206 loss)
I0406 15:25:59.336313 21485 solver.cpp:218] Iteration 2040 (0.825152 iter/s, 14.5428s/12 iters), loss = 4.89918
I0406 15:25:59.336367 21485 solver.cpp:237]     Train net output #0: loss = 4.89918 (* 1 = 4.89918 loss)
I0406 15:25:59.336374 21485 sgd_solver.cpp:105] Iteration 2040, lr = 0.05
I0406 15:26:03.470360 21485 solver.cpp:218] Iteration 2052 (2.90278 iter/s, 4.13398s/12 iters), loss = 4.6944
I0406 15:26:03.470418 21485 solver.cpp:237]     Train net output #0: loss = 4.6944 (* 1 = 4.6944 loss)
I0406 15:26:03.470427 21485 sgd_solver.cpp:105] Iteration 2052, lr = 0.05
I0406 15:26:05.173344 21485 blocking_queue.cpp:49] Waiting for data
I0406 15:26:08.695721 21485 solver.cpp:218] Iteration 2064 (2.29652 iter/s, 5.22529s/12 iters), loss = 4.63933
I0406 15:26:08.695771 21485 solver.cpp:237]     Train net output #0: loss = 4.63933 (* 1 = 4.63933 loss)
I0406 15:26:08.695777 21485 sgd_solver.cpp:105] Iteration 2064, lr = 0.05
I0406 15:26:13.929287 21485 solver.cpp:218] Iteration 2076 (2.29292 iter/s, 5.2335s/12 iters), loss = 4.7379
I0406 15:26:13.929425 21485 solver.cpp:237]     Train net output #0: loss = 4.7379 (* 1 = 4.7379 loss)
I0406 15:26:13.929432 21485 sgd_solver.cpp:105] Iteration 2076, lr = 0.05
I0406 15:26:19.293860 21485 solver.cpp:218] Iteration 2088 (2.23696 iter/s, 5.36442s/12 iters), loss = 4.73083
I0406 15:26:19.293910 21485 solver.cpp:237]     Train net output #0: loss = 4.73083 (* 1 = 4.73083 loss)
I0406 15:26:19.293917 21485 sgd_solver.cpp:105] Iteration 2088, lr = 0.05
I0406 15:26:24.413144 21485 solver.cpp:218] Iteration 2100 (2.3441 iter/s, 5.11923s/12 iters), loss = 4.8127
I0406 15:26:24.413180 21485 solver.cpp:237]     Train net output #0: loss = 4.8127 (* 1 = 4.8127 loss)
I0406 15:26:24.413187 21485 sgd_solver.cpp:105] Iteration 2100, lr = 0.05
I0406 15:26:29.809155 21485 solver.cpp:218] Iteration 2112 (2.22389 iter/s, 5.39596s/12 iters), loss = 4.57044
I0406 15:26:29.809202 21485 solver.cpp:237]     Train net output #0: loss = 4.57044 (* 1 = 4.57044 loss)
I0406 15:26:29.809208 21485 sgd_solver.cpp:105] Iteration 2112, lr = 0.05
I0406 15:26:34.422497 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:26:34.780359 21485 solver.cpp:218] Iteration 2124 (2.41393 iter/s, 4.97114s/12 iters), loss = 4.74124
I0406 15:26:34.780405 21485 solver.cpp:237]     Train net output #0: loss = 4.74124 (* 1 = 4.74124 loss)
I0406 15:26:34.780411 21485 sgd_solver.cpp:105] Iteration 2124, lr = 0.05
I0406 15:26:40.080255 21485 solver.cpp:218] Iteration 2136 (2.26422 iter/s, 5.29984s/12 iters), loss = 4.7177
I0406 15:26:40.080296 21485 solver.cpp:237]     Train net output #0: loss = 4.7177 (* 1 = 4.7177 loss)
I0406 15:26:40.080300 21485 sgd_solver.cpp:105] Iteration 2136, lr = 0.05
I0406 15:26:42.212750 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2142.caffemodel
I0406 15:26:45.193835 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2142.solverstate
I0406 15:26:48.075512 21485 solver.cpp:330] Iteration 2142, Testing net (#0)
I0406 15:26:48.075531 21485 net.cpp:676] Ignoring source layer train-data
I0406 15:26:51.511698 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:26:52.366498 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0330882
I0406 15:26:52.366533 21485 solver.cpp:397]     Test net output #1: loss = 4.83808 (* 1 = 4.83808 loss)
I0406 15:26:54.150079 21485 solver.cpp:218] Iteration 2148 (0.852892 iter/s, 14.0698s/12 iters), loss = 4.81767
I0406 15:26:54.150123 21485 solver.cpp:237]     Train net output #0: loss = 4.81767 (* 1 = 4.81767 loss)
I0406 15:26:54.150130 21485 sgd_solver.cpp:105] Iteration 2148, lr = 0.05
I0406 15:26:59.363366 21485 solver.cpp:218] Iteration 2160 (2.30184 iter/s, 5.21323s/12 iters), loss = 4.70372
I0406 15:26:59.363418 21485 solver.cpp:237]     Train net output #0: loss = 4.70372 (* 1 = 4.70372 loss)
I0406 15:26:59.363426 21485 sgd_solver.cpp:105] Iteration 2160, lr = 0.05
I0406 15:27:04.494252 21485 solver.cpp:218] Iteration 2172 (2.33881 iter/s, 5.13082s/12 iters), loss = 4.63506
I0406 15:27:04.494304 21485 solver.cpp:237]     Train net output #0: loss = 4.63506 (* 1 = 4.63506 loss)
I0406 15:27:04.494313 21485 sgd_solver.cpp:105] Iteration 2172, lr = 0.05
I0406 15:27:09.649663 21485 solver.cpp:218] Iteration 2184 (2.32768 iter/s, 5.15535s/12 iters), loss = 4.8525
I0406 15:27:09.649708 21485 solver.cpp:237]     Train net output #0: loss = 4.8525 (* 1 = 4.8525 loss)
I0406 15:27:09.649716 21485 sgd_solver.cpp:105] Iteration 2184, lr = 0.05
I0406 15:27:14.929558 21485 solver.cpp:218] Iteration 2196 (2.2728 iter/s, 5.27983s/12 iters), loss = 4.62598
I0406 15:27:14.929618 21485 solver.cpp:237]     Train net output #0: loss = 4.62598 (* 1 = 4.62598 loss)
I0406 15:27:14.929627 21485 sgd_solver.cpp:105] Iteration 2196, lr = 0.05
I0406 15:27:20.256327 21485 solver.cpp:218] Iteration 2208 (2.2528 iter/s, 5.3267s/12 iters), loss = 4.73535
I0406 15:27:20.256477 21485 solver.cpp:237]     Train net output #0: loss = 4.73535 (* 1 = 4.73535 loss)
I0406 15:27:20.256486 21485 sgd_solver.cpp:105] Iteration 2208, lr = 0.05
I0406 15:27:25.662276 21485 solver.cpp:218] Iteration 2220 (2.21985 iter/s, 5.40578s/12 iters), loss = 4.69515
I0406 15:27:25.662331 21485 solver.cpp:237]     Train net output #0: loss = 4.69515 (* 1 = 4.69515 loss)
I0406 15:27:25.662339 21485 sgd_solver.cpp:105] Iteration 2220, lr = 0.05
I0406 15:27:27.588099 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:27:31.039741 21485 solver.cpp:218] Iteration 2232 (2.23156 iter/s, 5.3774s/12 iters), loss = 4.79069
I0406 15:27:31.039790 21485 solver.cpp:237]     Train net output #0: loss = 4.79069 (* 1 = 4.79069 loss)
I0406 15:27:31.039796 21485 sgd_solver.cpp:105] Iteration 2232, lr = 0.05
I0406 15:27:35.956195 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2244.caffemodel
I0406 15:27:38.977010 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2244.solverstate
I0406 15:27:42.331748 21485 solver.cpp:330] Iteration 2244, Testing net (#0)
I0406 15:27:42.331769 21485 net.cpp:676] Ignoring source layer train-data
I0406 15:27:45.902400 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:27:46.804456 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0490196
I0406 15:27:46.804495 21485 solver.cpp:397]     Test net output #1: loss = 4.75506 (* 1 = 4.75506 loss)
I0406 15:27:46.942559 21485 solver.cpp:218] Iteration 2244 (0.754586 iter/s, 15.9028s/12 iters), loss = 4.5475
I0406 15:27:46.942601 21485 solver.cpp:237]     Train net output #0: loss = 4.5475 (* 1 = 4.5475 loss)
I0406 15:27:46.942607 21485 sgd_solver.cpp:105] Iteration 2244, lr = 0.05
I0406 15:27:51.249500 21485 solver.cpp:218] Iteration 2256 (2.78624 iter/s, 4.30688s/12 iters), loss = 4.58733
I0406 15:27:51.249601 21485 solver.cpp:237]     Train net output #0: loss = 4.58733 (* 1 = 4.58733 loss)
I0406 15:27:51.249608 21485 sgd_solver.cpp:105] Iteration 2256, lr = 0.05
I0406 15:27:56.441871 21485 solver.cpp:218] Iteration 2268 (2.31114 iter/s, 5.19225s/12 iters), loss = 4.69823
I0406 15:27:56.441933 21485 solver.cpp:237]     Train net output #0: loss = 4.69823 (* 1 = 4.69823 loss)
I0406 15:27:56.441941 21485 sgd_solver.cpp:105] Iteration 2268, lr = 0.05
I0406 15:28:01.699990 21485 solver.cpp:218] Iteration 2280 (2.28222 iter/s, 5.25805s/12 iters), loss = 4.63208
I0406 15:28:01.700047 21485 solver.cpp:237]     Train net output #0: loss = 4.63208 (* 1 = 4.63208 loss)
I0406 15:28:01.700054 21485 sgd_solver.cpp:105] Iteration 2280, lr = 0.05
I0406 15:28:07.031118 21485 solver.cpp:218] Iteration 2292 (2.25096 iter/s, 5.33106s/12 iters), loss = 4.87512
I0406 15:28:07.031162 21485 solver.cpp:237]     Train net output #0: loss = 4.87512 (* 1 = 4.87512 loss)
I0406 15:28:07.031167 21485 sgd_solver.cpp:105] Iteration 2292, lr = 0.05
I0406 15:28:12.218084 21485 solver.cpp:218] Iteration 2304 (2.31352 iter/s, 5.18691s/12 iters), loss = 4.89552
I0406 15:28:12.218127 21485 solver.cpp:237]     Train net output #0: loss = 4.89552 (* 1 = 4.89552 loss)
I0406 15:28:12.218133 21485 sgd_solver.cpp:105] Iteration 2304, lr = 0.05
I0406 15:28:17.124208 21485 solver.cpp:218] Iteration 2316 (2.44595 iter/s, 4.90607s/12 iters), loss = 4.66023
I0406 15:28:17.124248 21485 solver.cpp:237]     Train net output #0: loss = 4.66023 (* 1 = 4.66023 loss)
I0406 15:28:17.124253 21485 sgd_solver.cpp:105] Iteration 2316, lr = 0.05
I0406 15:28:21.400709 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:28:22.596310 21485 solver.cpp:218] Iteration 2328 (2.19296 iter/s, 5.47205s/12 iters), loss = 4.66815
I0406 15:28:22.596355 21485 solver.cpp:237]     Train net output #0: loss = 4.66815 (* 1 = 4.66815 loss)
I0406 15:28:22.596361 21485 sgd_solver.cpp:105] Iteration 2328, lr = 0.05
I0406 15:28:27.719579 21485 solver.cpp:218] Iteration 2340 (2.34228 iter/s, 5.12321s/12 iters), loss = 4.78202
I0406 15:28:27.719620 21485 solver.cpp:237]     Train net output #0: loss = 4.78202 (* 1 = 4.78202 loss)
I0406 15:28:27.719626 21485 sgd_solver.cpp:105] Iteration 2340, lr = 0.05
I0406 15:28:29.703759 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2346.caffemodel
I0406 15:28:34.343917 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2346.solverstate
I0406 15:28:36.733603 21485 solver.cpp:330] Iteration 2346, Testing net (#0)
I0406 15:28:36.733621 21485 net.cpp:676] Ignoring source layer train-data
I0406 15:28:40.307956 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:28:41.237053 21485 solver.cpp:397]     Test net output #0: accuracy = 0.036152
I0406 15:28:41.237087 21485 solver.cpp:397]     Test net output #1: loss = 4.81969 (* 1 = 4.81969 loss)
I0406 15:28:43.125391 21485 solver.cpp:218] Iteration 2352 (0.778929 iter/s, 15.4058s/12 iters), loss = 4.83637
I0406 15:28:43.125433 21485 solver.cpp:237]     Train net output #0: loss = 4.83637 (* 1 = 4.83637 loss)
I0406 15:28:43.125438 21485 sgd_solver.cpp:105] Iteration 2352, lr = 0.05
I0406 15:28:48.231051 21485 solver.cpp:218] Iteration 2364 (2.35036 iter/s, 5.10561s/12 iters), loss = 4.59681
I0406 15:28:48.231093 21485 solver.cpp:237]     Train net output #0: loss = 4.59681 (* 1 = 4.59681 loss)
I0406 15:28:48.231101 21485 sgd_solver.cpp:105] Iteration 2364, lr = 0.05
I0406 15:28:53.406273 21485 solver.cpp:218] Iteration 2376 (2.31877 iter/s, 5.17516s/12 iters), loss = 4.81126
I0406 15:28:53.406427 21485 solver.cpp:237]     Train net output #0: loss = 4.81126 (* 1 = 4.81126 loss)
I0406 15:28:53.406437 21485 sgd_solver.cpp:105] Iteration 2376, lr = 0.05
I0406 15:28:58.539885 21485 solver.cpp:218] Iteration 2388 (2.33761 iter/s, 5.13344s/12 iters), loss = 4.59916
I0406 15:28:58.539937 21485 solver.cpp:237]     Train net output #0: loss = 4.59916 (* 1 = 4.59916 loss)
I0406 15:28:58.539944 21485 sgd_solver.cpp:105] Iteration 2388, lr = 0.05
I0406 15:29:03.777087 21485 solver.cpp:218] Iteration 2400 (2.29132 iter/s, 5.23715s/12 iters), loss = 4.52126
I0406 15:29:03.777123 21485 solver.cpp:237]     Train net output #0: loss = 4.52126 (* 1 = 4.52126 loss)
I0406 15:29:03.777128 21485 sgd_solver.cpp:105] Iteration 2400, lr = 0.05
I0406 15:29:09.011226 21485 solver.cpp:218] Iteration 2412 (2.29266 iter/s, 5.23409s/12 iters), loss = 4.60739
I0406 15:29:09.011263 21485 solver.cpp:237]     Train net output #0: loss = 4.60739 (* 1 = 4.60739 loss)
I0406 15:29:09.011269 21485 sgd_solver.cpp:105] Iteration 2412, lr = 0.05
I0406 15:29:14.440196 21485 solver.cpp:218] Iteration 2424 (2.21039 iter/s, 5.42891s/12 iters), loss = 4.77977
I0406 15:29:14.440240 21485 solver.cpp:237]     Train net output #0: loss = 4.77977 (* 1 = 4.77977 loss)
I0406 15:29:14.440246 21485 sgd_solver.cpp:105] Iteration 2424, lr = 0.05
I0406 15:29:15.595620 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:29:19.869832 21485 solver.cpp:218] Iteration 2436 (2.21012 iter/s, 5.42957s/12 iters), loss = 4.67229
I0406 15:29:19.869875 21485 solver.cpp:237]     Train net output #0: loss = 4.67229 (* 1 = 4.67229 loss)
I0406 15:29:19.869880 21485 sgd_solver.cpp:105] Iteration 2436, lr = 0.05
I0406 15:29:24.747119 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2448.caffemodel
I0406 15:29:27.756361 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2448.solverstate
I0406 15:29:30.055860 21485 solver.cpp:330] Iteration 2448, Testing net (#0)
I0406 15:29:30.055886 21485 net.cpp:676] Ignoring source layer train-data
I0406 15:29:33.455590 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:29:34.424844 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0386029
I0406 15:29:34.424890 21485 solver.cpp:397]     Test net output #1: loss = 4.81583 (* 1 = 4.81583 loss)
I0406 15:29:34.561519 21485 solver.cpp:218] Iteration 2448 (0.816791 iter/s, 14.6916s/12 iters), loss = 4.88133
I0406 15:29:34.561561 21485 solver.cpp:237]     Train net output #0: loss = 4.88133 (* 1 = 4.88133 loss)
I0406 15:29:34.561566 21485 sgd_solver.cpp:105] Iteration 2448, lr = 0.05
I0406 15:29:38.887661 21485 solver.cpp:218] Iteration 2460 (2.77387 iter/s, 4.32608s/12 iters), loss = 4.74426
I0406 15:29:38.887710 21485 solver.cpp:237]     Train net output #0: loss = 4.74426 (* 1 = 4.74426 loss)
I0406 15:29:38.887717 21485 sgd_solver.cpp:105] Iteration 2460, lr = 0.05
I0406 15:29:44.107779 21485 solver.cpp:218] Iteration 2472 (2.29883 iter/s, 5.22005s/12 iters), loss = 4.69989
I0406 15:29:44.107832 21485 solver.cpp:237]     Train net output #0: loss = 4.69989 (* 1 = 4.69989 loss)
I0406 15:29:44.107839 21485 sgd_solver.cpp:105] Iteration 2472, lr = 0.05
I0406 15:29:49.225157 21485 solver.cpp:218] Iteration 2484 (2.34498 iter/s, 5.11731s/12 iters), loss = 4.65701
I0406 15:29:49.225203 21485 solver.cpp:237]     Train net output #0: loss = 4.65701 (* 1 = 4.65701 loss)
I0406 15:29:49.225210 21485 sgd_solver.cpp:105] Iteration 2484, lr = 0.05
I0406 15:29:54.513860 21485 solver.cpp:218] Iteration 2496 (2.26901 iter/s, 5.28864s/12 iters), loss = 4.56327
I0406 15:29:54.513914 21485 solver.cpp:237]     Train net output #0: loss = 4.56327 (* 1 = 4.56327 loss)
I0406 15:29:54.513922 21485 sgd_solver.cpp:105] Iteration 2496, lr = 0.05
I0406 15:29:59.849503 21485 solver.cpp:218] Iteration 2508 (2.24906 iter/s, 5.33557s/12 iters), loss = 4.6312
I0406 15:29:59.849655 21485 solver.cpp:237]     Train net output #0: loss = 4.6312 (* 1 = 4.6312 loss)
I0406 15:29:59.849661 21485 sgd_solver.cpp:105] Iteration 2508, lr = 0.05
I0406 15:30:05.070389 21485 solver.cpp:218] Iteration 2520 (2.29853 iter/s, 5.22072s/12 iters), loss = 4.63247
I0406 15:30:05.070438 21485 solver.cpp:237]     Train net output #0: loss = 4.63247 (* 1 = 4.63247 loss)
I0406 15:30:05.070446 21485 sgd_solver.cpp:105] Iteration 2520, lr = 0.05
I0406 15:30:08.137001 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:30:10.101356 21485 solver.cpp:218] Iteration 2532 (2.38526 iter/s, 5.03091s/12 iters), loss = 4.64622
I0406 15:30:10.101402 21485 solver.cpp:237]     Train net output #0: loss = 4.64622 (* 1 = 4.64622 loss)
I0406 15:30:10.101408 21485 sgd_solver.cpp:105] Iteration 2532, lr = 0.05
I0406 15:30:15.621719 21485 solver.cpp:218] Iteration 2544 (2.17379 iter/s, 5.5203s/12 iters), loss = 4.65274
I0406 15:30:15.621754 21485 solver.cpp:237]     Train net output #0: loss = 4.65274 (* 1 = 4.65274 loss)
I0406 15:30:15.621760 21485 sgd_solver.cpp:105] Iteration 2544, lr = 0.05
I0406 15:30:17.900429 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2550.caffemodel
I0406 15:30:20.880345 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2550.solverstate
I0406 15:30:23.271306 21485 solver.cpp:330] Iteration 2550, Testing net (#0)
I0406 15:30:23.271325 21485 net.cpp:676] Ignoring source layer train-data
I0406 15:30:26.579746 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:30:27.688344 21485 solver.cpp:397]     Test net output #0: accuracy = 0.033701
I0406 15:30:27.688372 21485 solver.cpp:397]     Test net output #1: loss = 4.8536 (* 1 = 4.8536 loss)
I0406 15:30:29.535990 21485 solver.cpp:218] Iteration 2556 (0.862427 iter/s, 13.9142s/12 iters), loss = 4.741
I0406 15:30:29.536033 21485 solver.cpp:237]     Train net output #0: loss = 4.741 (* 1 = 4.741 loss)
I0406 15:30:29.536038 21485 sgd_solver.cpp:105] Iteration 2556, lr = 0.05
I0406 15:30:34.579860 21485 solver.cpp:218] Iteration 2568 (2.37916 iter/s, 5.04381s/12 iters), loss = 4.73723
I0406 15:30:34.579996 21485 solver.cpp:237]     Train net output #0: loss = 4.73723 (* 1 = 4.73723 loss)
I0406 15:30:34.580004 21485 sgd_solver.cpp:105] Iteration 2568, lr = 0.05
I0406 15:30:39.974114 21485 solver.cpp:218] Iteration 2580 (2.22465 iter/s, 5.39411s/12 iters), loss = 4.73331
I0406 15:30:39.974153 21485 solver.cpp:237]     Train net output #0: loss = 4.73331 (* 1 = 4.73331 loss)
I0406 15:30:39.974159 21485 sgd_solver.cpp:105] Iteration 2580, lr = 0.05
I0406 15:30:45.228422 21485 solver.cpp:218] Iteration 2592 (2.28386 iter/s, 5.25426s/12 iters), loss = 4.69741
I0406 15:30:45.228464 21485 solver.cpp:237]     Train net output #0: loss = 4.69741 (* 1 = 4.69741 loss)
I0406 15:30:45.228471 21485 sgd_solver.cpp:105] Iteration 2592, lr = 0.05
I0406 15:30:50.356720 21485 solver.cpp:218] Iteration 2604 (2.33999 iter/s, 5.12824s/12 iters), loss = 4.56355
I0406 15:30:50.356773 21485 solver.cpp:237]     Train net output #0: loss = 4.56355 (* 1 = 4.56355 loss)
I0406 15:30:50.356782 21485 sgd_solver.cpp:105] Iteration 2604, lr = 0.05
I0406 15:30:55.721513 21485 solver.cpp:218] Iteration 2616 (2.23683 iter/s, 5.36473s/12 iters), loss = 4.62076
I0406 15:30:55.721554 21485 solver.cpp:237]     Train net output #0: loss = 4.62076 (* 1 = 4.62076 loss)
I0406 15:30:55.721558 21485 sgd_solver.cpp:105] Iteration 2616, lr = 0.05
I0406 15:31:00.913473 21485 solver.cpp:218] Iteration 2628 (2.31129 iter/s, 5.1919s/12 iters), loss = 4.76172
I0406 15:31:00.913520 21485 solver.cpp:237]     Train net output #0: loss = 4.76172 (* 1 = 4.76172 loss)
I0406 15:31:00.913527 21485 sgd_solver.cpp:105] Iteration 2628, lr = 0.05
I0406 15:31:01.338841 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:31:06.109009 21485 solver.cpp:218] Iteration 2640 (2.30971 iter/s, 5.19547s/12 iters), loss = 4.71008
I0406 15:31:06.109138 21485 solver.cpp:237]     Train net output #0: loss = 4.71008 (* 1 = 4.71008 loss)
I0406 15:31:06.109145 21485 sgd_solver.cpp:105] Iteration 2640, lr = 0.05
I0406 15:31:10.886346 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2652.caffemodel
I0406 15:31:14.010401 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2652.solverstate
I0406 15:31:16.324829 21485 solver.cpp:330] Iteration 2652, Testing net (#0)
I0406 15:31:16.324847 21485 net.cpp:676] Ignoring source layer train-data
I0406 15:31:19.687785 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:31:20.725669 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0355392
I0406 15:31:20.725704 21485 solver.cpp:397]     Test net output #1: loss = 4.77518 (* 1 = 4.77518 loss)
I0406 15:31:20.865676 21485 solver.cpp:218] Iteration 2652 (0.813199 iter/s, 14.7565s/12 iters), loss = 4.82103
I0406 15:31:20.865729 21485 solver.cpp:237]     Train net output #0: loss = 4.82103 (* 1 = 4.82103 loss)
I0406 15:31:20.865736 21485 sgd_solver.cpp:105] Iteration 2652, lr = 0.05
I0406 15:31:24.933095 21485 solver.cpp:218] Iteration 2664 (2.95032 iter/s, 4.06735s/12 iters), loss = 4.53045
I0406 15:31:24.933135 21485 solver.cpp:237]     Train net output #0: loss = 4.53045 (* 1 = 4.53045 loss)
I0406 15:31:24.933140 21485 sgd_solver.cpp:105] Iteration 2664, lr = 0.05
I0406 15:31:30.158440 21485 solver.cpp:218] Iteration 2676 (2.29652 iter/s, 5.22529s/12 iters), loss = 4.68311
I0406 15:31:30.158490 21485 solver.cpp:237]     Train net output #0: loss = 4.68311 (* 1 = 4.68311 loss)
I0406 15:31:30.158496 21485 sgd_solver.cpp:105] Iteration 2676, lr = 0.05
I0406 15:31:35.542444 21485 solver.cpp:218] Iteration 2688 (2.22885 iter/s, 5.38393s/12 iters), loss = 4.55726
I0406 15:31:35.542488 21485 solver.cpp:237]     Train net output #0: loss = 4.55726 (* 1 = 4.55726 loss)
I0406 15:31:35.542495 21485 sgd_solver.cpp:105] Iteration 2688, lr = 0.05
I0406 15:31:40.874446 21485 solver.cpp:218] Iteration 2700 (2.25059 iter/s, 5.33194s/12 iters), loss = 4.51494
I0406 15:31:40.874567 21485 solver.cpp:237]     Train net output #0: loss = 4.51494 (* 1 = 4.51494 loss)
I0406 15:31:40.874575 21485 sgd_solver.cpp:105] Iteration 2700, lr = 0.05
I0406 15:31:46.135659 21485 solver.cpp:218] Iteration 2712 (2.2809 iter/s, 5.26109s/12 iters), loss = 4.60739
I0406 15:31:46.135699 21485 solver.cpp:237]     Train net output #0: loss = 4.60739 (* 1 = 4.60739 loss)
I0406 15:31:46.135704 21485 sgd_solver.cpp:105] Iteration 2712, lr = 0.05
I0406 15:31:51.269858 21485 solver.cpp:218] Iteration 2724 (2.3373 iter/s, 5.13414s/12 iters), loss = 4.6835
I0406 15:31:51.269919 21485 solver.cpp:237]     Train net output #0: loss = 4.6835 (* 1 = 4.6835 loss)
I0406 15:31:51.269927 21485 sgd_solver.cpp:105] Iteration 2724, lr = 0.05
I0406 15:31:54.015046 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:31:56.610236 21485 solver.cpp:218] Iteration 2736 (2.24706 iter/s, 5.3403s/12 iters), loss = 4.58502
I0406 15:31:56.610296 21485 solver.cpp:237]     Train net output #0: loss = 4.58502 (* 1 = 4.58502 loss)
I0406 15:31:56.610306 21485 sgd_solver.cpp:105] Iteration 2736, lr = 0.05
I0406 15:32:01.928977 21485 solver.cpp:218] Iteration 2748 (2.25621 iter/s, 5.31866s/12 iters), loss = 4.76796
I0406 15:32:01.929034 21485 solver.cpp:237]     Train net output #0: loss = 4.76796 (* 1 = 4.76796 loss)
I0406 15:32:01.929041 21485 sgd_solver.cpp:105] Iteration 2748, lr = 0.05
I0406 15:32:04.012099 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2754.caffemodel
I0406 15:32:07.083673 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2754.solverstate
I0406 15:32:09.387543 21485 solver.cpp:330] Iteration 2754, Testing net (#0)
I0406 15:32:09.387564 21485 net.cpp:676] Ignoring source layer train-data
I0406 15:32:12.437230 21485 blocking_queue.cpp:49] Waiting for data
I0406 15:32:12.670817 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:32:13.779513 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0459559
I0406 15:32:13.779551 21485 solver.cpp:397]     Test net output #1: loss = 4.71929 (* 1 = 4.71929 loss)
I0406 15:32:15.575652 21485 solver.cpp:218] Iteration 2760 (0.87934 iter/s, 13.6466s/12 iters), loss = 4.62504
I0406 15:32:15.575709 21485 solver.cpp:237]     Train net output #0: loss = 4.62504 (* 1 = 4.62504 loss)
I0406 15:32:15.575718 21485 sgd_solver.cpp:105] Iteration 2760, lr = 0.05
I0406 15:32:20.706043 21485 solver.cpp:218] Iteration 2772 (2.33904 iter/s, 5.13032s/12 iters), loss = 4.51485
I0406 15:32:20.706085 21485 solver.cpp:237]     Train net output #0: loss = 4.51485 (* 1 = 4.51485 loss)
I0406 15:32:20.706091 21485 sgd_solver.cpp:105] Iteration 2772, lr = 0.05
I0406 15:32:26.022006 21485 solver.cpp:218] Iteration 2784 (2.25738 iter/s, 5.3159s/12 iters), loss = 4.63568
I0406 15:32:26.022064 21485 solver.cpp:237]     Train net output #0: loss = 4.63568 (* 1 = 4.63568 loss)
I0406 15:32:26.022073 21485 sgd_solver.cpp:105] Iteration 2784, lr = 0.05
I0406 15:32:31.368609 21485 solver.cpp:218] Iteration 2796 (2.24444 iter/s, 5.34654s/12 iters), loss = 4.66123
I0406 15:32:31.368651 21485 solver.cpp:237]     Train net output #0: loss = 4.66123 (* 1 = 4.66123 loss)
I0406 15:32:31.368656 21485 sgd_solver.cpp:105] Iteration 2796, lr = 0.05
I0406 15:32:36.662798 21485 solver.cpp:218] Iteration 2808 (2.26666 iter/s, 5.29413s/12 iters), loss = 4.52916
I0406 15:32:36.662842 21485 solver.cpp:237]     Train net output #0: loss = 4.52916 (* 1 = 4.52916 loss)
I0406 15:32:36.662848 21485 sgd_solver.cpp:105] Iteration 2808, lr = 0.05
I0406 15:32:41.858940 21485 solver.cpp:218] Iteration 2820 (2.30943 iter/s, 5.19608s/12 iters), loss = 4.41518
I0406 15:32:41.858996 21485 solver.cpp:237]     Train net output #0: loss = 4.41518 (* 1 = 4.41518 loss)
I0406 15:32:41.859006 21485 sgd_solver.cpp:105] Iteration 2820, lr = 0.05
I0406 15:32:46.802009 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:32:47.130450 21485 solver.cpp:218] Iteration 2832 (2.27642 iter/s, 5.27144s/12 iters), loss = 4.67982
I0406 15:32:47.130488 21485 solver.cpp:237]     Train net output #0: loss = 4.67982 (* 1 = 4.67982 loss)
I0406 15:32:47.130493 21485 sgd_solver.cpp:105] Iteration 2832, lr = 0.05
I0406 15:32:52.177650 21485 solver.cpp:218] Iteration 2844 (2.37758 iter/s, 5.04714s/12 iters), loss = 4.6148
I0406 15:32:52.177695 21485 solver.cpp:237]     Train net output #0: loss = 4.6148 (* 1 = 4.6148 loss)
I0406 15:32:52.177700 21485 sgd_solver.cpp:105] Iteration 2844, lr = 0.05
I0406 15:32:56.722116 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2856.caffemodel
I0406 15:32:59.775338 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2856.solverstate
I0406 15:33:02.143208 21485 solver.cpp:330] Iteration 2856, Testing net (#0)
I0406 15:33:02.143232 21485 net.cpp:676] Ignoring source layer train-data
I0406 15:33:05.393795 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:33:06.600528 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0471814
I0406 15:33:06.600567 21485 solver.cpp:397]     Test net output #1: loss = 4.66627 (* 1 = 4.66627 loss)
I0406 15:33:06.743644 21485 solver.cpp:218] Iteration 2856 (0.82384 iter/s, 14.5659s/12 iters), loss = 4.45541
I0406 15:33:06.745250 21485 solver.cpp:237]     Train net output #0: loss = 4.45541 (* 1 = 4.45541 loss)
I0406 15:33:06.745262 21485 sgd_solver.cpp:105] Iteration 2856, lr = 0.05
I0406 15:33:10.979579 21485 solver.cpp:218] Iteration 2868 (2.83398 iter/s, 4.23432s/12 iters), loss = 4.66518
I0406 15:33:10.979622 21485 solver.cpp:237]     Train net output #0: loss = 4.66518 (* 1 = 4.66518 loss)
I0406 15:33:10.979627 21485 sgd_solver.cpp:105] Iteration 2868, lr = 0.05
I0406 15:33:16.281574 21485 solver.cpp:218] Iteration 2880 (2.26332 iter/s, 5.30194s/12 iters), loss = 4.46405
I0406 15:33:16.281615 21485 solver.cpp:237]     Train net output #0: loss = 4.46405 (* 1 = 4.46405 loss)
I0406 15:33:16.281621 21485 sgd_solver.cpp:105] Iteration 2880, lr = 0.05
I0406 15:33:21.466980 21485 solver.cpp:218] Iteration 2892 (2.31421 iter/s, 5.18535s/12 iters), loss = 4.69565
I0406 15:33:21.467116 21485 solver.cpp:237]     Train net output #0: loss = 4.69565 (* 1 = 4.69565 loss)
I0406 15:33:21.467123 21485 sgd_solver.cpp:105] Iteration 2892, lr = 0.05
I0406 15:33:26.354645 21485 solver.cpp:218] Iteration 2904 (2.45524 iter/s, 4.88751s/12 iters), loss = 4.59509
I0406 15:33:26.354701 21485 solver.cpp:237]     Train net output #0: loss = 4.59509 (* 1 = 4.59509 loss)
I0406 15:33:26.354710 21485 sgd_solver.cpp:105] Iteration 2904, lr = 0.05
I0406 15:33:31.448438 21485 solver.cpp:218] Iteration 2916 (2.35584 iter/s, 5.09372s/12 iters), loss = 4.44937
I0406 15:33:31.448493 21485 solver.cpp:237]     Train net output #0: loss = 4.44937 (* 1 = 4.44937 loss)
I0406 15:33:31.448500 21485 sgd_solver.cpp:105] Iteration 2916, lr = 0.05
I0406 15:33:36.546670 21485 solver.cpp:218] Iteration 2928 (2.35379 iter/s, 5.09817s/12 iters), loss = 4.59015
I0406 15:33:36.546715 21485 solver.cpp:237]     Train net output #0: loss = 4.59015 (* 1 = 4.59015 loss)
I0406 15:33:36.546721 21485 sgd_solver.cpp:105] Iteration 2928, lr = 0.05
I0406 15:33:38.453531 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:33:41.851048 21485 solver.cpp:218] Iteration 2940 (2.26231 iter/s, 5.30432s/12 iters), loss = 4.71038
I0406 15:33:41.851104 21485 solver.cpp:237]     Train net output #0: loss = 4.71038 (* 1 = 4.71038 loss)
I0406 15:33:41.851112 21485 sgd_solver.cpp:105] Iteration 2940, lr = 0.05
I0406 15:33:47.106025 21485 solver.cpp:218] Iteration 2952 (2.28358 iter/s, 5.25491s/12 iters), loss = 4.5824
I0406 15:33:47.106076 21485 solver.cpp:237]     Train net output #0: loss = 4.5824 (* 1 = 4.5824 loss)
I0406 15:33:47.106083 21485 sgd_solver.cpp:105] Iteration 2952, lr = 0.05
I0406 15:33:49.263691 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2958.caffemodel
I0406 15:33:52.384445 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2958.solverstate
I0406 15:33:54.696697 21485 solver.cpp:330] Iteration 2958, Testing net (#0)
I0406 15:33:54.696718 21485 net.cpp:676] Ignoring source layer train-data
I0406 15:33:57.984403 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:33:59.197366 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0453431
I0406 15:33:59.197399 21485 solver.cpp:397]     Test net output #1: loss = 4.67824 (* 1 = 4.67824 loss)
I0406 15:34:00.960932 21485 solver.cpp:218] Iteration 2964 (0.866123 iter/s, 13.8548s/12 iters), loss = 4.52838
I0406 15:34:00.960979 21485 solver.cpp:237]     Train net output #0: loss = 4.52838 (* 1 = 4.52838 loss)
I0406 15:34:00.960984 21485 sgd_solver.cpp:105] Iteration 2964, lr = 0.05
I0406 15:34:06.226665 21485 solver.cpp:218] Iteration 2976 (2.27891 iter/s, 5.26567s/12 iters), loss = 4.68366
I0406 15:34:06.226703 21485 solver.cpp:237]     Train net output #0: loss = 4.68366 (* 1 = 4.68366 loss)
I0406 15:34:06.226709 21485 sgd_solver.cpp:105] Iteration 2976, lr = 0.05
I0406 15:34:11.384356 21485 solver.cpp:218] Iteration 2988 (2.32665 iter/s, 5.15763s/12 iters), loss = 4.75193
I0406 15:34:11.384400 21485 solver.cpp:237]     Train net output #0: loss = 4.75193 (* 1 = 4.75193 loss)
I0406 15:34:11.384407 21485 sgd_solver.cpp:105] Iteration 2988, lr = 0.05
I0406 15:34:16.692885 21485 solver.cpp:218] Iteration 3000 (2.26054 iter/s, 5.30847s/12 iters), loss = 4.57983
I0406 15:34:16.692932 21485 solver.cpp:237]     Train net output #0: loss = 4.57983 (* 1 = 4.57983 loss)
I0406 15:34:16.692939 21485 sgd_solver.cpp:105] Iteration 3000, lr = 0.05
I0406 15:34:21.734203 21485 solver.cpp:218] Iteration 3012 (2.38036 iter/s, 5.04126s/12 iters), loss = 4.72305
I0406 15:34:21.734243 21485 solver.cpp:237]     Train net output #0: loss = 4.72305 (* 1 = 4.72305 loss)
I0406 15:34:21.734248 21485 sgd_solver.cpp:105] Iteration 3012, lr = 0.05
I0406 15:34:26.897588 21485 solver.cpp:218] Iteration 3024 (2.32408 iter/s, 5.16333s/12 iters), loss = 4.45454
I0406 15:34:26.897734 21485 solver.cpp:237]     Train net output #0: loss = 4.45454 (* 1 = 4.45454 loss)
I0406 15:34:26.897742 21485 sgd_solver.cpp:105] Iteration 3024, lr = 0.05
I0406 15:34:31.048563 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:34:32.185729 21485 solver.cpp:218] Iteration 3036 (2.2693 iter/s, 5.28798s/12 iters), loss = 4.5186
I0406 15:34:32.185782 21485 solver.cpp:237]     Train net output #0: loss = 4.5186 (* 1 = 4.5186 loss)
I0406 15:34:32.185791 21485 sgd_solver.cpp:105] Iteration 3036, lr = 0.05
I0406 15:34:37.377542 21485 solver.cpp:218] Iteration 3048 (2.31136 iter/s, 5.19174s/12 iters), loss = 4.71343
I0406 15:34:37.377583 21485 solver.cpp:237]     Train net output #0: loss = 4.71343 (* 1 = 4.71343 loss)
I0406 15:34:37.377588 21485 sgd_solver.cpp:105] Iteration 3048, lr = 0.05
I0406 15:34:41.832953 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3060.caffemodel
I0406 15:34:45.067175 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3060.solverstate
I0406 15:34:47.440492 21485 solver.cpp:330] Iteration 3060, Testing net (#0)
I0406 15:34:47.440516 21485 net.cpp:676] Ignoring source layer train-data
I0406 15:34:50.646839 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:34:51.848135 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0490196
I0406 15:34:51.848167 21485 solver.cpp:397]     Test net output #1: loss = 4.76056 (* 1 = 4.76056 loss)
I0406 15:34:51.986428 21485 solver.cpp:218] Iteration 3060 (0.821421 iter/s, 14.6088s/12 iters), loss = 4.8208
I0406 15:34:51.986469 21485 solver.cpp:237]     Train net output #0: loss = 4.8208 (* 1 = 4.8208 loss)
I0406 15:34:51.986474 21485 sgd_solver.cpp:105] Iteration 3060, lr = 0.05
I0406 15:34:56.155120 21485 solver.cpp:218] Iteration 3072 (2.87864 iter/s, 4.16863s/12 iters), loss = 4.60509
I0406 15:34:56.155158 21485 solver.cpp:237]     Train net output #0: loss = 4.60509 (* 1 = 4.60509 loss)
I0406 15:34:56.155164 21485 sgd_solver.cpp:105] Iteration 3072, lr = 0.05
I0406 15:35:01.400956 21485 solver.cpp:218] Iteration 3084 (2.28755 iter/s, 5.24579s/12 iters), loss = 4.73727
I0406 15:35:01.401083 21485 solver.cpp:237]     Train net output #0: loss = 4.73727 (* 1 = 4.73727 loss)
I0406 15:35:01.401089 21485 sgd_solver.cpp:105] Iteration 3084, lr = 0.05
I0406 15:35:06.764535 21485 solver.cpp:218] Iteration 3096 (2.23737 iter/s, 5.36344s/12 iters), loss = 4.47725
I0406 15:35:06.764582 21485 solver.cpp:237]     Train net output #0: loss = 4.47725 (* 1 = 4.47725 loss)
I0406 15:35:06.764588 21485 sgd_solver.cpp:105] Iteration 3096, lr = 0.05
I0406 15:35:11.955916 21485 solver.cpp:218] Iteration 3108 (2.31155 iter/s, 5.19132s/12 iters), loss = 4.40518
I0406 15:35:11.955957 21485 solver.cpp:237]     Train net output #0: loss = 4.40518 (* 1 = 4.40518 loss)
I0406 15:35:11.955963 21485 sgd_solver.cpp:105] Iteration 3108, lr = 0.05
I0406 15:35:17.036830 21485 solver.cpp:218] Iteration 3120 (2.36181 iter/s, 5.08086s/12 iters), loss = 4.54499
I0406 15:35:17.036873 21485 solver.cpp:237]     Train net output #0: loss = 4.54499 (* 1 = 4.54499 loss)
I0406 15:35:17.036878 21485 sgd_solver.cpp:105] Iteration 3120, lr = 0.05
I0406 15:35:22.197209 21485 solver.cpp:218] Iteration 3132 (2.32544 iter/s, 5.16032s/12 iters), loss = 4.67374
I0406 15:35:22.197253 21485 solver.cpp:237]     Train net output #0: loss = 4.67374 (* 1 = 4.67374 loss)
I0406 15:35:22.197258 21485 sgd_solver.cpp:105] Iteration 3132, lr = 0.05
I0406 15:35:23.264330 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:35:27.341069 21485 solver.cpp:218] Iteration 3144 (2.33291 iter/s, 5.1438s/12 iters), loss = 4.74774
I0406 15:35:27.341115 21485 solver.cpp:237]     Train net output #0: loss = 4.74774 (* 1 = 4.74774 loss)
I0406 15:35:27.341120 21485 sgd_solver.cpp:105] Iteration 3144, lr = 0.05
I0406 15:35:32.848038 21485 solver.cpp:218] Iteration 3156 (2.17908 iter/s, 5.50691s/12 iters), loss = 4.85383
I0406 15:35:32.848171 21485 solver.cpp:237]     Train net output #0: loss = 4.85383 (* 1 = 4.85383 loss)
I0406 15:35:32.848178 21485 sgd_solver.cpp:105] Iteration 3156, lr = 0.05
I0406 15:35:34.946066 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3162.caffemodel
I0406 15:35:38.055184 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3162.solverstate
I0406 15:35:40.381963 21485 solver.cpp:330] Iteration 3162, Testing net (#0)
I0406 15:35:40.381984 21485 net.cpp:676] Ignoring source layer train-data
I0406 15:35:43.513733 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:35:44.846235 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0416667
I0406 15:35:44.846272 21485 solver.cpp:397]     Test net output #1: loss = 4.80338 (* 1 = 4.80338 loss)
I0406 15:35:46.706802 21485 solver.cpp:218] Iteration 3168 (0.865887 iter/s, 13.8586s/12 iters), loss = 4.54373
I0406 15:35:46.706847 21485 solver.cpp:237]     Train net output #0: loss = 4.54373 (* 1 = 4.54373 loss)
I0406 15:35:46.706852 21485 sgd_solver.cpp:105] Iteration 3168, lr = 0.05
I0406 15:35:51.911747 21485 solver.cpp:218] Iteration 3180 (2.30553 iter/s, 5.20488s/12 iters), loss = 4.6122
I0406 15:35:51.911795 21485 solver.cpp:237]     Train net output #0: loss = 4.6122 (* 1 = 4.6122 loss)
I0406 15:35:51.911803 21485 sgd_solver.cpp:105] Iteration 3180, lr = 0.05
I0406 15:35:57.368073 21485 solver.cpp:218] Iteration 3192 (2.19931 iter/s, 5.45626s/12 iters), loss = 4.53611
I0406 15:35:57.368113 21485 solver.cpp:237]     Train net output #0: loss = 4.53611 (* 1 = 4.53611 loss)
I0406 15:35:57.368119 21485 sgd_solver.cpp:105] Iteration 3192, lr = 0.05
I0406 15:36:02.575176 21485 solver.cpp:218] Iteration 3204 (2.30457 iter/s, 5.20704s/12 iters), loss = 4.68456
I0406 15:36:02.575223 21485 solver.cpp:237]     Train net output #0: loss = 4.68456 (* 1 = 4.68456 loss)
I0406 15:36:02.575230 21485 sgd_solver.cpp:105] Iteration 3204, lr = 0.05
I0406 15:36:07.945955 21485 solver.cpp:218] Iteration 3216 (2.23434 iter/s, 5.37072s/12 iters), loss = 4.58833
I0406 15:36:07.946080 21485 solver.cpp:237]     Train net output #0: loss = 4.58833 (* 1 = 4.58833 loss)
I0406 15:36:07.946087 21485 sgd_solver.cpp:105] Iteration 3216, lr = 0.05
I0406 15:36:13.133574 21485 solver.cpp:218] Iteration 3228 (2.31326 iter/s, 5.18749s/12 iters), loss = 4.31844
I0406 15:36:13.133610 21485 solver.cpp:237]     Train net output #0: loss = 4.31844 (* 1 = 4.31844 loss)
I0406 15:36:13.133615 21485 sgd_solver.cpp:105] Iteration 3228, lr = 0.05
I0406 15:36:16.324278 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:36:18.166352 21485 solver.cpp:218] Iteration 3240 (2.38439 iter/s, 5.03273s/12 iters), loss = 4.41432
I0406 15:36:18.166394 21485 solver.cpp:237]     Train net output #0: loss = 4.41432 (* 1 = 4.41432 loss)
I0406 15:36:18.166401 21485 sgd_solver.cpp:105] Iteration 3240, lr = 0.05
I0406 15:36:23.493058 21485 solver.cpp:218] Iteration 3252 (2.25282 iter/s, 5.32665s/12 iters), loss = 4.49306
I0406 15:36:23.493106 21485 solver.cpp:237]     Train net output #0: loss = 4.49306 (* 1 = 4.49306 loss)
I0406 15:36:23.493111 21485 sgd_solver.cpp:105] Iteration 3252, lr = 0.05
I0406 15:36:28.045536 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3264.caffemodel
I0406 15:36:31.056383 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3264.solverstate
I0406 15:36:33.358052 21485 solver.cpp:330] Iteration 3264, Testing net (#0)
I0406 15:36:33.358069 21485 net.cpp:676] Ignoring source layer train-data
I0406 15:36:36.411546 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:36:37.691336 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0416667
I0406 15:36:37.691375 21485 solver.cpp:397]     Test net output #1: loss = 4.78879 (* 1 = 4.78879 loss)
I0406 15:36:37.833564 21485 solver.cpp:218] Iteration 3264 (0.836795 iter/s, 14.3404s/12 iters), loss = 4.4904
I0406 15:36:37.833617 21485 solver.cpp:237]     Train net output #0: loss = 4.4904 (* 1 = 4.4904 loss)
I0406 15:36:37.833626 21485 sgd_solver.cpp:105] Iteration 3264, lr = 0.05
I0406 15:36:42.119617 21485 solver.cpp:218] Iteration 3276 (2.79983 iter/s, 4.28598s/12 iters), loss = 4.63039
I0406 15:36:42.119757 21485 solver.cpp:237]     Train net output #0: loss = 4.63039 (* 1 = 4.63039 loss)
I0406 15:36:42.119765 21485 sgd_solver.cpp:105] Iteration 3276, lr = 0.05
I0406 15:36:47.229044 21485 solver.cpp:218] Iteration 3288 (2.34867 iter/s, 5.10928s/12 iters), loss = 4.95186
I0406 15:36:47.229086 21485 solver.cpp:237]     Train net output #0: loss = 4.95186 (* 1 = 4.95186 loss)
I0406 15:36:47.229091 21485 sgd_solver.cpp:105] Iteration 3288, lr = 0.05
I0406 15:36:52.550019 21485 solver.cpp:218] Iteration 3300 (2.25525 iter/s, 5.32092s/12 iters), loss = 4.42129
I0406 15:36:52.550071 21485 solver.cpp:237]     Train net output #0: loss = 4.42129 (* 1 = 4.42129 loss)
I0406 15:36:52.550077 21485 sgd_solver.cpp:105] Iteration 3300, lr = 0.05
I0406 15:36:57.907827 21485 solver.cpp:218] Iteration 3312 (2.23975 iter/s, 5.35774s/12 iters), loss = 4.48728
I0406 15:36:57.907873 21485 solver.cpp:237]     Train net output #0: loss = 4.48728 (* 1 = 4.48728 loss)
I0406 15:36:57.907878 21485 sgd_solver.cpp:105] Iteration 3312, lr = 0.05
I0406 15:37:02.937074 21485 solver.cpp:218] Iteration 3324 (2.38607 iter/s, 5.02918s/12 iters), loss = 4.52328
I0406 15:37:02.937129 21485 solver.cpp:237]     Train net output #0: loss = 4.52328 (* 1 = 4.52328 loss)
I0406 15:37:02.937135 21485 sgd_solver.cpp:105] Iteration 3324, lr = 0.05
I0406 15:37:08.228363 21485 solver.cpp:218] Iteration 3336 (2.26791 iter/s, 5.29122s/12 iters), loss = 4.50638
I0406 15:37:08.228415 21485 solver.cpp:237]     Train net output #0: loss = 4.50638 (* 1 = 4.50638 loss)
I0406 15:37:08.228421 21485 sgd_solver.cpp:105] Iteration 3336, lr = 0.05
I0406 15:37:08.729353 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:37:13.619289 21485 solver.cpp:218] Iteration 3348 (2.22599 iter/s, 5.39087s/12 iters), loss = 4.63511
I0406 15:37:13.619398 21485 solver.cpp:237]     Train net output #0: loss = 4.63511 (* 1 = 4.63511 loss)
I0406 15:37:13.619405 21485 sgd_solver.cpp:105] Iteration 3348, lr = 0.05
I0406 15:37:19.041849 21485 solver.cpp:218] Iteration 3360 (2.21303 iter/s, 5.42243s/12 iters), loss = 4.68945
I0406 15:37:19.041898 21485 solver.cpp:237]     Train net output #0: loss = 4.68945 (* 1 = 4.68945 loss)
I0406 15:37:19.041904 21485 sgd_solver.cpp:105] Iteration 3360, lr = 0.05
I0406 15:37:21.197674 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3366.caffemodel
I0406 15:37:24.309808 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3366.solverstate
I0406 15:37:26.618851 21485 solver.cpp:330] Iteration 3366, Testing net (#0)
I0406 15:37:26.618876 21485 net.cpp:676] Ignoring source layer train-data
I0406 15:37:29.694324 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:37:31.099567 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0441176
I0406 15:37:31.099606 21485 solver.cpp:397]     Test net output #1: loss = 4.72279 (* 1 = 4.72279 loss)
I0406 15:37:32.943259 21485 solver.cpp:218] Iteration 3372 (0.863225 iter/s, 13.9014s/12 iters), loss = 4.57625
I0406 15:37:32.943308 21485 solver.cpp:237]     Train net output #0: loss = 4.57625 (* 1 = 4.57625 loss)
I0406 15:37:32.943316 21485 sgd_solver.cpp:105] Iteration 3372, lr = 0.05
I0406 15:37:38.179216 21485 solver.cpp:218] Iteration 3384 (2.29187 iter/s, 5.2359s/12 iters), loss = 4.49863
I0406 15:37:38.179256 21485 solver.cpp:237]     Train net output #0: loss = 4.49863 (* 1 = 4.49863 loss)
I0406 15:37:38.179263 21485 sgd_solver.cpp:105] Iteration 3384, lr = 0.05
I0406 15:37:43.440783 21485 solver.cpp:218] Iteration 3396 (2.28072 iter/s, 5.2615s/12 iters), loss = 4.59812
I0406 15:37:43.440830 21485 solver.cpp:237]     Train net output #0: loss = 4.59812 (* 1 = 4.59812 loss)
I0406 15:37:43.440837 21485 sgd_solver.cpp:105] Iteration 3396, lr = 0.05
I0406 15:37:48.440258 21485 solver.cpp:218] Iteration 3408 (2.40028 iter/s, 4.99941s/12 iters), loss = 4.39136
I0406 15:37:48.440435 21485 solver.cpp:237]     Train net output #0: loss = 4.39136 (* 1 = 4.39136 loss)
I0406 15:37:48.440444 21485 sgd_solver.cpp:105] Iteration 3408, lr = 0.05
I0406 15:37:53.735672 21485 solver.cpp:218] Iteration 3420 (2.26619 iter/s, 5.29522s/12 iters), loss = 4.41373
I0406 15:37:53.735721 21485 solver.cpp:237]     Train net output #0: loss = 4.41373 (* 1 = 4.41373 loss)
I0406 15:37:53.735726 21485 sgd_solver.cpp:105] Iteration 3420, lr = 0.05
I0406 15:37:58.995705 21485 solver.cpp:218] Iteration 3432 (2.28138 iter/s, 5.25997s/12 iters), loss = 4.47165
I0406 15:37:58.995765 21485 solver.cpp:237]     Train net output #0: loss = 4.47165 (* 1 = 4.47165 loss)
I0406 15:37:58.995774 21485 sgd_solver.cpp:105] Iteration 3432, lr = 0.05
I0406 15:38:01.759143 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:38:04.323302 21485 solver.cpp:218] Iteration 3444 (2.25245 iter/s, 5.32752s/12 iters), loss = 4.63653
I0406 15:38:04.323343 21485 solver.cpp:237]     Train net output #0: loss = 4.63653 (* 1 = 4.63653 loss)
I0406 15:38:04.323348 21485 sgd_solver.cpp:105] Iteration 3444, lr = 0.05
I0406 15:38:09.504496 21485 solver.cpp:218] Iteration 3456 (2.31609 iter/s, 5.18114s/12 iters), loss = 4.68816
I0406 15:38:09.504534 21485 solver.cpp:237]     Train net output #0: loss = 4.68816 (* 1 = 4.68816 loss)
I0406 15:38:09.504539 21485 sgd_solver.cpp:105] Iteration 3456, lr = 0.05
I0406 15:38:14.301463 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3468.caffemodel
I0406 15:38:17.374629 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3468.solverstate
I0406 15:38:19.712064 21485 solver.cpp:330] Iteration 3468, Testing net (#0)
I0406 15:38:19.712126 21485 net.cpp:676] Ignoring source layer train-data
I0406 15:38:20.113919 21485 blocking_queue.cpp:49] Waiting for data
I0406 15:38:22.669513 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:38:24.027424 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0404412
I0406 15:38:24.027456 21485 solver.cpp:397]     Test net output #1: loss = 4.77741 (* 1 = 4.77741 loss)
I0406 15:38:24.167568 21485 solver.cpp:218] Iteration 3468 (0.818385 iter/s, 14.663s/12 iters), loss = 4.47586
I0406 15:38:24.167618 21485 solver.cpp:237]     Train net output #0: loss = 4.47586 (* 1 = 4.47586 loss)
I0406 15:38:24.167625 21485 sgd_solver.cpp:105] Iteration 3468, lr = 0.05
I0406 15:38:28.581717 21485 solver.cpp:218] Iteration 3480 (2.71857 iter/s, 4.41408s/12 iters), loss = 4.61309
I0406 15:38:28.581780 21485 solver.cpp:237]     Train net output #0: loss = 4.61309 (* 1 = 4.61309 loss)
I0406 15:38:28.581789 21485 sgd_solver.cpp:105] Iteration 3480, lr = 0.05
I0406 15:38:33.749577 21485 solver.cpp:218] Iteration 3492 (2.32208 iter/s, 5.16778s/12 iters), loss = 4.46609
I0406 15:38:33.749635 21485 solver.cpp:237]     Train net output #0: loss = 4.46609 (* 1 = 4.46609 loss)
I0406 15:38:33.749645 21485 sgd_solver.cpp:105] Iteration 3492, lr = 0.05
I0406 15:38:38.981448 21485 solver.cpp:218] Iteration 3504 (2.29367 iter/s, 5.2318s/12 iters), loss = 4.62101
I0406 15:38:38.981489 21485 solver.cpp:237]     Train net output #0: loss = 4.62101 (* 1 = 4.62101 loss)
I0406 15:38:38.981494 21485 sgd_solver.cpp:105] Iteration 3504, lr = 0.05
I0406 15:38:44.242877 21485 solver.cpp:218] Iteration 3516 (2.28077 iter/s, 5.26137s/12 iters), loss = 4.59298
I0406 15:38:44.242926 21485 solver.cpp:237]     Train net output #0: loss = 4.59298 (* 1 = 4.59298 loss)
I0406 15:38:44.242934 21485 sgd_solver.cpp:105] Iteration 3516, lr = 0.05
I0406 15:38:49.414471 21485 solver.cpp:218] Iteration 3528 (2.32039 iter/s, 5.17154s/12 iters), loss = 4.3629
I0406 15:38:49.414517 21485 solver.cpp:237]     Train net output #0: loss = 4.3629 (* 1 = 4.3629 loss)
I0406 15:38:49.414525 21485 sgd_solver.cpp:105] Iteration 3528, lr = 0.05
I0406 15:38:54.450292 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:38:54.749892 21485 solver.cpp:218] Iteration 3540 (2.24914 iter/s, 5.33536s/12 iters), loss = 4.5011
I0406 15:38:54.749941 21485 solver.cpp:237]     Train net output #0: loss = 4.5011 (* 1 = 4.5011 loss)
I0406 15:38:54.749949 21485 sgd_solver.cpp:105] Iteration 3540, lr = 0.05
I0406 15:39:00.019076 21485 solver.cpp:218] Iteration 3552 (2.27742 iter/s, 5.26912s/12 iters), loss = 4.63919
I0406 15:39:00.019115 21485 solver.cpp:237]     Train net output #0: loss = 4.63919 (* 1 = 4.63919 loss)
I0406 15:39:00.019121 21485 sgd_solver.cpp:105] Iteration 3552, lr = 0.05
I0406 15:39:05.322647 21485 solver.cpp:218] Iteration 3564 (2.26265 iter/s, 5.30352s/12 iters), loss = 4.55449
I0406 15:39:05.322692 21485 solver.cpp:237]     Train net output #0: loss = 4.55449 (* 1 = 4.55449 loss)
I0406 15:39:05.322698 21485 sgd_solver.cpp:105] Iteration 3564, lr = 0.05
I0406 15:39:07.257369 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3570.caffemodel
I0406 15:39:10.569772 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3570.solverstate
I0406 15:39:14.555459 21485 solver.cpp:330] Iteration 3570, Testing net (#0)
I0406 15:39:14.555480 21485 net.cpp:676] Ignoring source layer train-data
I0406 15:39:17.462983 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:39:18.853040 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0386029
I0406 15:39:18.853075 21485 solver.cpp:397]     Test net output #1: loss = 4.8055 (* 1 = 4.8055 loss)
I0406 15:39:20.708214 21485 solver.cpp:218] Iteration 3576 (0.779954 iter/s, 15.3855s/12 iters), loss = 4.82203
I0406 15:39:20.708252 21485 solver.cpp:237]     Train net output #0: loss = 4.82203 (* 1 = 4.82203 loss)
I0406 15:39:20.708258 21485 sgd_solver.cpp:105] Iteration 3576, lr = 0.05
I0406 15:39:25.720377 21485 solver.cpp:218] Iteration 3588 (2.3942 iter/s, 5.01211s/12 iters), loss = 4.64874
I0406 15:39:25.720477 21485 solver.cpp:237]     Train net output #0: loss = 4.64874 (* 1 = 4.64874 loss)
I0406 15:39:25.720484 21485 sgd_solver.cpp:105] Iteration 3588, lr = 0.05
I0406 15:39:31.081694 21485 solver.cpp:218] Iteration 3600 (2.2383 iter/s, 5.3612s/12 iters), loss = 4.8355
I0406 15:39:31.081749 21485 solver.cpp:237]     Train net output #0: loss = 4.8355 (* 1 = 4.8355 loss)
I0406 15:39:31.081758 21485 sgd_solver.cpp:105] Iteration 3600, lr = 0.05
I0406 15:39:36.224287 21485 solver.cpp:218] Iteration 3612 (2.33348 iter/s, 5.14252s/12 iters), loss = 4.6213
I0406 15:39:36.224330 21485 solver.cpp:237]     Train net output #0: loss = 4.6213 (* 1 = 4.6213 loss)
I0406 15:39:36.224336 21485 sgd_solver.cpp:105] Iteration 3612, lr = 0.05
I0406 15:39:41.451457 21485 solver.cpp:218] Iteration 3624 (2.29572 iter/s, 5.22712s/12 iters), loss = 4.54479
I0406 15:39:41.451493 21485 solver.cpp:237]     Train net output #0: loss = 4.54479 (* 1 = 4.54479 loss)
I0406 15:39:41.451498 21485 sgd_solver.cpp:105] Iteration 3624, lr = 0.05
I0406 15:39:46.635205 21485 solver.cpp:218] Iteration 3636 (2.31495 iter/s, 5.18369s/12 iters), loss = 4.50876
I0406 15:39:46.635264 21485 solver.cpp:237]     Train net output #0: loss = 4.50876 (* 1 = 4.50876 loss)
I0406 15:39:46.635272 21485 sgd_solver.cpp:105] Iteration 3636, lr = 0.05
I0406 15:39:48.639367 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:39:51.938149 21485 solver.cpp:218] Iteration 3648 (2.26293 iter/s, 5.30287s/12 iters), loss = 4.62186
I0406 15:39:51.938199 21485 solver.cpp:237]     Train net output #0: loss = 4.62186 (* 1 = 4.62186 loss)
I0406 15:39:51.938206 21485 sgd_solver.cpp:105] Iteration 3648, lr = 0.05
I0406 15:39:57.146766 21485 solver.cpp:218] Iteration 3660 (2.3039 iter/s, 5.20855s/12 iters), loss = 4.65313
I0406 15:39:57.146899 21485 solver.cpp:237]     Train net output #0: loss = 4.65313 (* 1 = 4.65313 loss)
I0406 15:39:57.146908 21485 sgd_solver.cpp:105] Iteration 3660, lr = 0.05
I0406 15:40:01.847918 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3672.caffemodel
I0406 15:40:05.979321 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3672.solverstate
I0406 15:40:08.288419 21485 solver.cpp:330] Iteration 3672, Testing net (#0)
I0406 15:40:08.288434 21485 net.cpp:676] Ignoring source layer train-data
I0406 15:40:11.265213 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:40:12.701117 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0471814
I0406 15:40:12.701148 21485 solver.cpp:397]     Test net output #1: loss = 4.76239 (* 1 = 4.76239 loss)
I0406 15:40:12.837107 21485 solver.cpp:218] Iteration 3672 (0.764809 iter/s, 15.6902s/12 iters), loss = 4.43515
I0406 15:40:12.837158 21485 solver.cpp:237]     Train net output #0: loss = 4.43515 (* 1 = 4.43515 loss)
I0406 15:40:12.837167 21485 sgd_solver.cpp:105] Iteration 3672, lr = 0.05
I0406 15:40:17.013689 21485 solver.cpp:218] Iteration 3684 (2.87321 iter/s, 4.17652s/12 iters), loss = 4.77972
I0406 15:40:17.013732 21485 solver.cpp:237]     Train net output #0: loss = 4.77972 (* 1 = 4.77972 loss)
I0406 15:40:17.013737 21485 sgd_solver.cpp:105] Iteration 3684, lr = 0.05
I0406 15:40:22.186017 21485 solver.cpp:218] Iteration 3696 (2.32007 iter/s, 5.17227s/12 iters), loss = 4.61876
I0406 15:40:22.186071 21485 solver.cpp:237]     Train net output #0: loss = 4.61876 (* 1 = 4.61876 loss)
I0406 15:40:22.186080 21485 sgd_solver.cpp:105] Iteration 3696, lr = 0.05
I0406 15:40:27.484632 21485 solver.cpp:218] Iteration 3708 (2.26477 iter/s, 5.29854s/12 iters), loss = 4.95605
I0406 15:40:27.484769 21485 solver.cpp:237]     Train net output #0: loss = 4.95605 (* 1 = 4.95605 loss)
I0406 15:40:27.484776 21485 sgd_solver.cpp:105] Iteration 3708, lr = 0.05
I0406 15:40:32.840226 21485 solver.cpp:218] Iteration 3720 (2.24071 iter/s, 5.35544s/12 iters), loss = 5.02528
I0406 15:40:32.840271 21485 solver.cpp:237]     Train net output #0: loss = 5.02528 (* 1 = 5.02528 loss)
I0406 15:40:32.840276 21485 sgd_solver.cpp:105] Iteration 3720, lr = 0.05
I0406 15:40:38.117835 21485 solver.cpp:218] Iteration 3732 (2.27378 iter/s, 5.27756s/12 iters), loss = 4.68083
I0406 15:40:38.117869 21485 solver.cpp:237]     Train net output #0: loss = 4.68083 (* 1 = 4.68083 loss)
I0406 15:40:38.117875 21485 sgd_solver.cpp:105] Iteration 3732, lr = 0.05
I0406 15:40:42.179441 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:40:43.290014 21485 solver.cpp:218] Iteration 3744 (2.32013 iter/s, 5.17212s/12 iters), loss = 4.60998
I0406 15:40:43.290078 21485 solver.cpp:237]     Train net output #0: loss = 4.60998 (* 1 = 4.60998 loss)
I0406 15:40:43.290087 21485 sgd_solver.cpp:105] Iteration 3744, lr = 0.05
I0406 15:40:48.511137 21485 solver.cpp:218] Iteration 3756 (2.29839 iter/s, 5.22104s/12 iters), loss = 4.94301
I0406 15:40:48.511189 21485 solver.cpp:237]     Train net output #0: loss = 4.94301 (* 1 = 4.94301 loss)
I0406 15:40:48.511196 21485 sgd_solver.cpp:105] Iteration 3756, lr = 0.05
I0406 15:40:53.742012 21485 solver.cpp:218] Iteration 3768 (2.29411 iter/s, 5.2308s/12 iters), loss = 4.82986
I0406 15:40:53.742082 21485 solver.cpp:237]     Train net output #0: loss = 4.82986 (* 1 = 4.82986 loss)
I0406 15:40:53.742094 21485 sgd_solver.cpp:105] Iteration 3768, lr = 0.05
I0406 15:40:55.778591 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3774.caffemodel
I0406 15:40:59.384471 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3774.solverstate
I0406 15:41:01.687196 21485 solver.cpp:330] Iteration 3774, Testing net (#0)
I0406 15:41:01.687217 21485 net.cpp:676] Ignoring source layer train-data
I0406 15:41:04.645586 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:41:06.141405 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0398284
I0406 15:41:06.141443 21485 solver.cpp:397]     Test net output #1: loss = 4.83333 (* 1 = 4.83333 loss)
I0406 15:41:08.073650 21485 solver.cpp:218] Iteration 3780 (0.837313 iter/s, 14.3316s/12 iters), loss = 4.62004
I0406 15:41:08.073699 21485 solver.cpp:237]     Train net output #0: loss = 4.62004 (* 1 = 4.62004 loss)
I0406 15:41:08.073706 21485 sgd_solver.cpp:105] Iteration 3780, lr = 0.05
I0406 15:41:13.046391 21485 solver.cpp:218] Iteration 3792 (2.41318 iter/s, 4.97268s/12 iters), loss = 4.72673
I0406 15:41:13.046432 21485 solver.cpp:237]     Train net output #0: loss = 4.72673 (* 1 = 4.72673 loss)
I0406 15:41:13.046437 21485 sgd_solver.cpp:105] Iteration 3792, lr = 0.05
I0406 15:41:18.463089 21485 solver.cpp:218] Iteration 3804 (2.2154 iter/s, 5.41664s/12 iters), loss = 4.65313
I0406 15:41:18.463146 21485 solver.cpp:237]     Train net output #0: loss = 4.65313 (* 1 = 4.65313 loss)
I0406 15:41:18.463155 21485 sgd_solver.cpp:105] Iteration 3804, lr = 0.05
I0406 15:41:23.823253 21485 solver.cpp:218] Iteration 3816 (2.23877 iter/s, 5.36009s/12 iters), loss = 4.62998
I0406 15:41:23.823307 21485 solver.cpp:237]     Train net output #0: loss = 4.62998 (* 1 = 4.62998 loss)
I0406 15:41:23.823314 21485 sgd_solver.cpp:105] Iteration 3816, lr = 0.05
I0406 15:41:29.003337 21485 solver.cpp:218] Iteration 3828 (2.31659 iter/s, 5.18002s/12 iters), loss = 4.46856
I0406 15:41:29.003377 21485 solver.cpp:237]     Train net output #0: loss = 4.46856 (* 1 = 4.46856 loss)
I0406 15:41:29.003383 21485 sgd_solver.cpp:105] Iteration 3828, lr = 0.05
I0406 15:41:34.343314 21485 solver.cpp:218] Iteration 3840 (2.24723 iter/s, 5.33992s/12 iters), loss = 4.63958
I0406 15:41:34.343461 21485 solver.cpp:237]     Train net output #0: loss = 4.63958 (* 1 = 4.63958 loss)
I0406 15:41:34.343470 21485 sgd_solver.cpp:105] Iteration 3840, lr = 0.05
I0406 15:41:35.442251 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:41:39.582330 21485 solver.cpp:218] Iteration 3852 (2.29058 iter/s, 5.23886s/12 iters), loss = 4.73045
I0406 15:41:39.582376 21485 solver.cpp:237]     Train net output #0: loss = 4.73045 (* 1 = 4.73045 loss)
I0406 15:41:39.582381 21485 sgd_solver.cpp:105] Iteration 3852, lr = 0.05
I0406 15:41:44.922993 21485 solver.cpp:218] Iteration 3864 (2.24694 iter/s, 5.34061s/12 iters), loss = 4.79013
I0406 15:41:44.923036 21485 solver.cpp:237]     Train net output #0: loss = 4.79013 (* 1 = 4.79013 loss)
I0406 15:41:44.923043 21485 sgd_solver.cpp:105] Iteration 3864, lr = 0.05
I0406 15:41:49.508580 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3876.caffemodel
I0406 15:41:52.790735 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3876.solverstate
I0406 15:41:55.097442 21485 solver.cpp:330] Iteration 3876, Testing net (#0)
I0406 15:41:55.097460 21485 net.cpp:676] Ignoring source layer train-data
I0406 15:41:57.904994 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:41:59.452728 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0355392
I0406 15:41:59.452764 21485 solver.cpp:397]     Test net output #1: loss = 4.82947 (* 1 = 4.82947 loss)
I0406 15:41:59.584652 21485 solver.cpp:218] Iteration 3876 (0.818464 iter/s, 14.6616s/12 iters), loss = 4.6061
I0406 15:41:59.586252 21485 solver.cpp:237]     Train net output #0: loss = 4.6061 (* 1 = 4.6061 loss)
I0406 15:41:59.586264 21485 sgd_solver.cpp:105] Iteration 3876, lr = 0.05
I0406 15:42:03.727625 21485 solver.cpp:218] Iteration 3888 (2.8976 iter/s, 4.14136s/12 iters), loss = 4.73259
I0406 15:42:03.727674 21485 solver.cpp:237]     Train net output #0: loss = 4.73259 (* 1 = 4.73259 loss)
I0406 15:42:03.727681 21485 sgd_solver.cpp:105] Iteration 3888, lr = 0.05
I0406 15:42:09.097067 21485 solver.cpp:218] Iteration 3900 (2.23489 iter/s, 5.36938s/12 iters), loss = 4.68409
I0406 15:42:09.097254 21485 solver.cpp:237]     Train net output #0: loss = 4.68409 (* 1 = 4.68409 loss)
I0406 15:42:09.097265 21485 sgd_solver.cpp:105] Iteration 3900, lr = 0.05
I0406 15:42:14.331755 21485 solver.cpp:218] Iteration 3912 (2.29249 iter/s, 5.23449s/12 iters), loss = 4.55579
I0406 15:42:14.331809 21485 solver.cpp:237]     Train net output #0: loss = 4.55579 (* 1 = 4.55579 loss)
I0406 15:42:14.331816 21485 sgd_solver.cpp:105] Iteration 3912, lr = 0.05
I0406 15:42:19.627492 21485 solver.cpp:218] Iteration 3924 (2.266 iter/s, 5.29567s/12 iters), loss = 4.6496
I0406 15:42:19.627533 21485 solver.cpp:237]     Train net output #0: loss = 4.6496 (* 1 = 4.6496 loss)
I0406 15:42:19.627539 21485 sgd_solver.cpp:105] Iteration 3924, lr = 0.05
I0406 15:42:25.048003 21485 solver.cpp:218] Iteration 3936 (2.21384 iter/s, 5.42045s/12 iters), loss = 4.59109
I0406 15:42:25.048059 21485 solver.cpp:237]     Train net output #0: loss = 4.59109 (* 1 = 4.59109 loss)
I0406 15:42:25.048067 21485 sgd_solver.cpp:105] Iteration 3936, lr = 0.05
I0406 15:42:28.558634 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:42:30.302181 21485 solver.cpp:218] Iteration 3948 (2.28393 iter/s, 5.25411s/12 iters), loss = 4.5769
I0406 15:42:30.302222 21485 solver.cpp:237]     Train net output #0: loss = 4.5769 (* 1 = 4.5769 loss)
I0406 15:42:30.302227 21485 sgd_solver.cpp:105] Iteration 3948, lr = 0.05
I0406 15:42:35.660715 21485 solver.cpp:218] Iteration 3960 (2.23944 iter/s, 5.35848s/12 iters), loss = 4.52265
I0406 15:42:35.660753 21485 solver.cpp:237]     Train net output #0: loss = 4.52265 (* 1 = 4.52265 loss)
I0406 15:42:35.660759 21485 sgd_solver.cpp:105] Iteration 3960, lr = 0.05
I0406 15:42:40.837383 21485 solver.cpp:218] Iteration 3972 (2.31812 iter/s, 5.17661s/12 iters), loss = 4.83609
I0406 15:42:40.837507 21485 solver.cpp:237]     Train net output #0: loss = 4.83609 (* 1 = 4.83609 loss)
I0406 15:42:40.837513 21485 sgd_solver.cpp:105] Iteration 3972, lr = 0.05
I0406 15:42:42.918355 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3978.caffemodel
I0406 15:42:46.035003 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3978.solverstate
I0406 15:42:48.350592 21485 solver.cpp:330] Iteration 3978, Testing net (#0)
I0406 15:42:48.350610 21485 net.cpp:676] Ignoring source layer train-data
I0406 15:42:51.135583 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:42:52.681110 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0441176
I0406 15:42:52.681149 21485 solver.cpp:397]     Test net output #1: loss = 4.81967 (* 1 = 4.81967 loss)
I0406 15:42:54.560485 21485 solver.cpp:218] Iteration 3984 (0.874446 iter/s, 13.723s/12 iters), loss = 4.53556
I0406 15:42:54.560526 21485 solver.cpp:237]     Train net output #0: loss = 4.53556 (* 1 = 4.53556 loss)
I0406 15:42:54.560531 21485 sgd_solver.cpp:105] Iteration 3984, lr = 0.05
I0406 15:42:59.799268 21485 solver.cpp:218] Iteration 3996 (2.29063 iter/s, 5.23872s/12 iters), loss = 4.74997
I0406 15:42:59.799324 21485 solver.cpp:237]     Train net output #0: loss = 4.74997 (* 1 = 4.74997 loss)
I0406 15:42:59.799332 21485 sgd_solver.cpp:105] Iteration 3996, lr = 0.05
I0406 15:43:05.055094 21485 solver.cpp:218] Iteration 4008 (2.28321 iter/s, 5.25576s/12 iters), loss = 4.59834
I0406 15:43:05.055131 21485 solver.cpp:237]     Train net output #0: loss = 4.59834 (* 1 = 4.59834 loss)
I0406 15:43:05.055136 21485 sgd_solver.cpp:105] Iteration 4008, lr = 0.05
I0406 15:43:10.348842 21485 solver.cpp:218] Iteration 4020 (2.26685 iter/s, 5.29369s/12 iters), loss = 4.69887
I0406 15:43:10.348899 21485 solver.cpp:237]     Train net output #0: loss = 4.69887 (* 1 = 4.69887 loss)
I0406 15:43:10.348906 21485 sgd_solver.cpp:105] Iteration 4020, lr = 0.05
I0406 15:43:15.624523 21485 solver.cpp:218] Iteration 4032 (2.27462 iter/s, 5.27561s/12 iters), loss = 4.47063
I0406 15:43:15.624711 21485 solver.cpp:237]     Train net output #0: loss = 4.47063 (* 1 = 4.47063 loss)
I0406 15:43:15.624719 21485 sgd_solver.cpp:105] Iteration 4032, lr = 0.05
I0406 15:43:20.938107 21485 solver.cpp:218] Iteration 4044 (2.25845 iter/s, 5.31339s/12 iters), loss = 4.73639
I0406 15:43:20.938144 21485 solver.cpp:237]     Train net output #0: loss = 4.73639 (* 1 = 4.73639 loss)
I0406 15:43:20.938149 21485 sgd_solver.cpp:105] Iteration 4044, lr = 0.05
I0406 15:43:21.490504 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:43:26.231170 21485 solver.cpp:218] Iteration 4056 (2.26714 iter/s, 5.29301s/12 iters), loss = 4.72954
I0406 15:43:26.231215 21485 solver.cpp:237]     Train net output #0: loss = 4.72954 (* 1 = 4.72954 loss)
I0406 15:43:26.231221 21485 sgd_solver.cpp:105] Iteration 4056, lr = 0.05
I0406 15:43:31.301678 21485 solver.cpp:218] Iteration 4068 (2.36666 iter/s, 5.07044s/12 iters), loss = 4.55442
I0406 15:43:31.301736 21485 solver.cpp:237]     Train net output #0: loss = 4.55442 (* 1 = 4.55442 loss)
I0406 15:43:31.301744 21485 sgd_solver.cpp:105] Iteration 4068, lr = 0.05
I0406 15:43:36.080577 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4080.caffemodel
I0406 15:43:39.162318 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4080.solverstate
I0406 15:43:41.485282 21485 solver.cpp:330] Iteration 4080, Testing net (#0)
I0406 15:43:41.485303 21485 net.cpp:676] Ignoring source layer train-data
I0406 15:43:44.242305 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:43:45.818320 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0410539
I0406 15:43:45.818429 21485 solver.cpp:397]     Test net output #1: loss = 4.77044 (* 1 = 4.77044 loss)
I0406 15:43:45.954129 21485 solver.cpp:218] Iteration 4080 (0.818979 iter/s, 14.6524s/12 iters), loss = 4.60208
I0406 15:43:45.954187 21485 solver.cpp:237]     Train net output #0: loss = 4.60208 (* 1 = 4.60208 loss)
I0406 15:43:45.954197 21485 sgd_solver.cpp:105] Iteration 4080, lr = 0.05
I0406 15:43:50.306989 21485 solver.cpp:218] Iteration 4092 (2.75685 iter/s, 4.35279s/12 iters), loss = 4.59888
I0406 15:43:50.307039 21485 solver.cpp:237]     Train net output #0: loss = 4.59888 (* 1 = 4.59888 loss)
I0406 15:43:50.307046 21485 sgd_solver.cpp:105] Iteration 4092, lr = 0.05
I0406 15:43:55.495573 21485 solver.cpp:218] Iteration 4104 (2.3128 iter/s, 5.18852s/12 iters), loss = 4.51108
I0406 15:43:55.495611 21485 solver.cpp:237]     Train net output #0: loss = 4.51108 (* 1 = 4.51108 loss)
I0406 15:43:55.495617 21485 sgd_solver.cpp:105] Iteration 4104, lr = 0.05
I0406 15:44:00.625506 21485 solver.cpp:218] Iteration 4116 (2.33924 iter/s, 5.12988s/12 iters), loss = 4.47843
I0406 15:44:00.625561 21485 solver.cpp:237]     Train net output #0: loss = 4.47843 (* 1 = 4.47843 loss)
I0406 15:44:00.625571 21485 sgd_solver.cpp:105] Iteration 4116, lr = 0.05
I0406 15:44:05.899435 21485 solver.cpp:218] Iteration 4128 (2.27537 iter/s, 5.27386s/12 iters), loss = 4.69321
I0406 15:44:05.899488 21485 solver.cpp:237]     Train net output #0: loss = 4.69321 (* 1 = 4.69321 loss)
I0406 15:44:05.899497 21485 sgd_solver.cpp:105] Iteration 4128, lr = 0.05
I0406 15:44:10.923964 21485 solver.cpp:218] Iteration 4140 (2.38832 iter/s, 5.02446s/12 iters), loss = 4.70055
I0406 15:44:10.924005 21485 solver.cpp:237]     Train net output #0: loss = 4.70055 (* 1 = 4.70055 loss)
I0406 15:44:10.924010 21485 sgd_solver.cpp:105] Iteration 4140, lr = 0.05
I0406 15:44:13.724123 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:44:16.258041 21485 solver.cpp:218] Iteration 4152 (2.24971 iter/s, 5.33402s/12 iters), loss = 4.69467
I0406 15:44:16.258195 21485 solver.cpp:237]     Train net output #0: loss = 4.69467 (* 1 = 4.69467 loss)
I0406 15:44:16.258204 21485 sgd_solver.cpp:105] Iteration 4152, lr = 0.05
I0406 15:44:17.938647 21485 blocking_queue.cpp:49] Waiting for data
I0406 15:44:21.545543 21485 solver.cpp:218] Iteration 4164 (2.26957 iter/s, 5.28734s/12 iters), loss = 4.6404
I0406 15:44:21.545585 21485 solver.cpp:237]     Train net output #0: loss = 4.6404 (* 1 = 4.6404 loss)
I0406 15:44:21.545590 21485 sgd_solver.cpp:105] Iteration 4164, lr = 0.05
I0406 15:44:26.542527 21485 solver.cpp:218] Iteration 4176 (2.40148 iter/s, 4.99692s/12 iters), loss = 4.67501
I0406 15:44:26.542573 21485 solver.cpp:237]     Train net output #0: loss = 4.67501 (* 1 = 4.67501 loss)
I0406 15:44:26.542579 21485 sgd_solver.cpp:105] Iteration 4176, lr = 0.05
I0406 15:44:28.602120 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4182.caffemodel
I0406 15:44:32.007889 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4182.solverstate
I0406 15:44:35.054404 21485 solver.cpp:330] Iteration 4182, Testing net (#0)
I0406 15:44:35.054425 21485 net.cpp:676] Ignoring source layer train-data
I0406 15:44:37.796602 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:44:39.421151 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0422794
I0406 15:44:39.421187 21485 solver.cpp:397]     Test net output #1: loss = 4.79612 (* 1 = 4.79612 loss)
I0406 15:44:41.283433 21485 solver.cpp:218] Iteration 4188 (0.814064 iter/s, 14.7408s/12 iters), loss = 4.66549
I0406 15:44:41.283478 21485 solver.cpp:237]     Train net output #0: loss = 4.66549 (* 1 = 4.66549 loss)
I0406 15:44:41.283483 21485 sgd_solver.cpp:105] Iteration 4188, lr = 0.05
I0406 15:44:46.439859 21485 solver.cpp:218] Iteration 4200 (2.32722 iter/s, 5.15636s/12 iters), loss = 4.45638
I0406 15:44:46.439986 21485 solver.cpp:237]     Train net output #0: loss = 4.45638 (* 1 = 4.45638 loss)
I0406 15:44:46.439996 21485 sgd_solver.cpp:105] Iteration 4200, lr = 0.05
I0406 15:44:51.794934 21485 solver.cpp:218] Iteration 4212 (2.24092 iter/s, 5.35494s/12 iters), loss = 4.61784
I0406 15:44:51.794972 21485 solver.cpp:237]     Train net output #0: loss = 4.61784 (* 1 = 4.61784 loss)
I0406 15:44:51.794977 21485 sgd_solver.cpp:105] Iteration 4212, lr = 0.05
I0406 15:44:57.209821 21485 solver.cpp:218] Iteration 4224 (2.21613 iter/s, 5.41483s/12 iters), loss = 4.46816
I0406 15:44:57.209877 21485 solver.cpp:237]     Train net output #0: loss = 4.46816 (* 1 = 4.46816 loss)
I0406 15:44:57.209884 21485 sgd_solver.cpp:105] Iteration 4224, lr = 0.05
I0406 15:45:02.561795 21485 solver.cpp:218] Iteration 4236 (2.24219 iter/s, 5.3519s/12 iters), loss = 4.35756
I0406 15:45:02.561857 21485 solver.cpp:237]     Train net output #0: loss = 4.35756 (* 1 = 4.35756 loss)
I0406 15:45:02.561866 21485 sgd_solver.cpp:105] Iteration 4236, lr = 0.05
I0406 15:45:07.525051 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:45:07.801029 21485 solver.cpp:218] Iteration 4248 (2.29044 iter/s, 5.23916s/12 iters), loss = 4.6846
I0406 15:45:07.801070 21485 solver.cpp:237]     Train net output #0: loss = 4.6846 (* 1 = 4.6846 loss)
I0406 15:45:07.801077 21485 sgd_solver.cpp:105] Iteration 4248, lr = 0.05
I0406 15:45:13.031734 21485 solver.cpp:218] Iteration 4260 (2.29417 iter/s, 5.23065s/12 iters), loss = 4.62131
I0406 15:45:13.031776 21485 solver.cpp:237]     Train net output #0: loss = 4.62131 (* 1 = 4.62131 loss)
I0406 15:45:13.031782 21485 sgd_solver.cpp:105] Iteration 4260, lr = 0.05
I0406 15:45:18.238564 21485 solver.cpp:218] Iteration 4272 (2.30469 iter/s, 5.20677s/12 iters), loss = 4.53708
I0406 15:45:18.238699 21485 solver.cpp:237]     Train net output #0: loss = 4.53708 (* 1 = 4.53708 loss)
I0406 15:45:18.238705 21485 sgd_solver.cpp:105] Iteration 4272, lr = 0.05
I0406 15:45:23.139664 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4284.caffemodel
I0406 15:45:26.164710 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4284.solverstate
I0406 15:45:28.479372 21485 solver.cpp:330] Iteration 4284, Testing net (#0)
I0406 15:45:28.479391 21485 net.cpp:676] Ignoring source layer train-data
I0406 15:45:31.087354 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:45:32.743685 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0520833
I0406 15:45:32.743721 21485 solver.cpp:397]     Test net output #1: loss = 4.77621 (* 1 = 4.77621 loss)
I0406 15:45:32.884528 21485 solver.cpp:218] Iteration 4284 (0.819346 iter/s, 14.6458s/12 iters), loss = 4.71734
I0406 15:45:32.884593 21485 solver.cpp:237]     Train net output #0: loss = 4.71734 (* 1 = 4.71734 loss)
I0406 15:45:32.884600 21485 sgd_solver.cpp:105] Iteration 4284, lr = 0.05
I0406 15:45:37.136729 21485 solver.cpp:218] Iteration 4296 (2.82212 iter/s, 4.25212s/12 iters), loss = 4.74649
I0406 15:45:37.136770 21485 solver.cpp:237]     Train net output #0: loss = 4.74649 (* 1 = 4.74649 loss)
I0406 15:45:37.136775 21485 sgd_solver.cpp:105] Iteration 4296, lr = 0.05
I0406 15:45:42.410988 21485 solver.cpp:218] Iteration 4308 (2.27523 iter/s, 5.2742s/12 iters), loss = 4.67558
I0406 15:45:42.411027 21485 solver.cpp:237]     Train net output #0: loss = 4.67558 (* 1 = 4.67558 loss)
I0406 15:45:42.411033 21485 sgd_solver.cpp:105] Iteration 4308, lr = 0.05
I0406 15:45:47.552783 21485 solver.cpp:218] Iteration 4320 (2.33384 iter/s, 5.14174s/12 iters), loss = 4.62585
I0406 15:45:47.552824 21485 solver.cpp:237]     Train net output #0: loss = 4.62585 (* 1 = 4.62585 loss)
I0406 15:45:47.552830 21485 sgd_solver.cpp:105] Iteration 4320, lr = 0.05
I0406 15:45:52.977623 21485 solver.cpp:218] Iteration 4332 (2.21207 iter/s, 5.42478s/12 iters), loss = 4.68178
I0406 15:45:52.977730 21485 solver.cpp:237]     Train net output #0: loss = 4.68178 (* 1 = 4.68178 loss)
I0406 15:45:52.977735 21485 sgd_solver.cpp:105] Iteration 4332, lr = 0.05
I0406 15:45:58.069284 21485 solver.cpp:218] Iteration 4344 (2.35685 iter/s, 5.09154s/12 iters), loss = 4.60332
I0406 15:45:58.069341 21485 solver.cpp:237]     Train net output #0: loss = 4.60332 (* 1 = 4.60332 loss)
I0406 15:45:58.069350 21485 sgd_solver.cpp:105] Iteration 4344, lr = 0.05
I0406 15:46:00.078069 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:46:03.357451 21485 solver.cpp:218] Iteration 4356 (2.26925 iter/s, 5.2881s/12 iters), loss = 4.88534
I0406 15:46:03.357491 21485 solver.cpp:237]     Train net output #0: loss = 4.88534 (* 1 = 4.88534 loss)
I0406 15:46:03.357496 21485 sgd_solver.cpp:105] Iteration 4356, lr = 0.05
I0406 15:46:08.656036 21485 solver.cpp:218] Iteration 4368 (2.26478 iter/s, 5.29853s/12 iters), loss = 4.66389
I0406 15:46:08.656090 21485 solver.cpp:237]     Train net output #0: loss = 4.66389 (* 1 = 4.66389 loss)
I0406 15:46:08.656100 21485 sgd_solver.cpp:105] Iteration 4368, lr = 0.05
I0406 15:46:13.944353 21485 solver.cpp:218] Iteration 4380 (2.26918 iter/s, 5.28825s/12 iters), loss = 4.40677
I0406 15:46:13.944413 21485 solver.cpp:237]     Train net output #0: loss = 4.40677 (* 1 = 4.40677 loss)
I0406 15:46:13.944422 21485 sgd_solver.cpp:105] Iteration 4380, lr = 0.05
I0406 15:46:16.003619 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4386.caffemodel
I0406 15:46:19.014021 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4386.solverstate
I0406 15:46:21.312019 21485 solver.cpp:330] Iteration 4386, Testing net (#0)
I0406 15:46:21.312039 21485 net.cpp:676] Ignoring source layer train-data
I0406 15:46:23.878870 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:46:25.618702 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0386029
I0406 15:46:25.618731 21485 solver.cpp:397]     Test net output #1: loss = 4.84259 (* 1 = 4.84259 loss)
I0406 15:46:27.452463 21485 solver.cpp:218] Iteration 4392 (0.88836 iter/s, 13.508s/12 iters), loss = 4.63198
I0406 15:46:27.452507 21485 solver.cpp:237]     Train net output #0: loss = 4.63198 (* 1 = 4.63198 loss)
I0406 15:46:27.452513 21485 sgd_solver.cpp:105] Iteration 4392, lr = 0.05
I0406 15:46:32.535544 21485 solver.cpp:218] Iteration 4404 (2.3608 iter/s, 5.08302s/12 iters), loss = 4.54564
I0406 15:46:32.535604 21485 solver.cpp:237]     Train net output #0: loss = 4.54564 (* 1 = 4.54564 loss)
I0406 15:46:32.535614 21485 sgd_solver.cpp:105] Iteration 4404, lr = 0.05
I0406 15:46:37.673548 21485 solver.cpp:218] Iteration 4416 (2.33557 iter/s, 5.13793s/12 iters), loss = 4.59951
I0406 15:46:37.673601 21485 solver.cpp:237]     Train net output #0: loss = 4.59951 (* 1 = 4.59951 loss)
I0406 15:46:37.673611 21485 sgd_solver.cpp:105] Iteration 4416, lr = 0.05
I0406 15:46:43.021605 21485 solver.cpp:218] Iteration 4428 (2.24383 iter/s, 5.34799s/12 iters), loss = 4.57842
I0406 15:46:43.021649 21485 solver.cpp:237]     Train net output #0: loss = 4.57842 (* 1 = 4.57842 loss)
I0406 15:46:43.021656 21485 sgd_solver.cpp:105] Iteration 4428, lr = 0.05
I0406 15:46:47.957764 21485 solver.cpp:218] Iteration 4440 (2.43107 iter/s, 4.9361s/12 iters), loss = 4.43751
I0406 15:46:47.957806 21485 solver.cpp:237]     Train net output #0: loss = 4.43751 (* 1 = 4.43751 loss)
I0406 15:46:47.957813 21485 sgd_solver.cpp:105] Iteration 4440, lr = 0.05
I0406 15:46:52.211563 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:46:53.308146 21485 solver.cpp:218] Iteration 4452 (2.24285 iter/s, 5.35033s/12 iters), loss = 4.35688
I0406 15:46:53.308185 21485 solver.cpp:237]     Train net output #0: loss = 4.35688 (* 1 = 4.35688 loss)
I0406 15:46:53.308190 21485 sgd_solver.cpp:105] Iteration 4452, lr = 0.05
I0406 15:46:58.628834 21485 solver.cpp:218] Iteration 4464 (2.25537 iter/s, 5.32063s/12 iters), loss = 4.56781
I0406 15:46:58.628962 21485 solver.cpp:237]     Train net output #0: loss = 4.56781 (* 1 = 4.56781 loss)
I0406 15:46:58.628969 21485 sgd_solver.cpp:105] Iteration 4464, lr = 0.05
I0406 15:47:03.860435 21485 solver.cpp:218] Iteration 4476 (2.29382 iter/s, 5.23146s/12 iters), loss = 4.7739
I0406 15:47:03.860479 21485 solver.cpp:237]     Train net output #0: loss = 4.7739 (* 1 = 4.7739 loss)
I0406 15:47:03.860484 21485 sgd_solver.cpp:105] Iteration 4476, lr = 0.05
I0406 15:47:08.515075 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4488.caffemodel
I0406 15:47:12.391059 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4488.solverstate
I0406 15:47:14.689492 21485 solver.cpp:330] Iteration 4488, Testing net (#0)
I0406 15:47:14.689512 21485 net.cpp:676] Ignoring source layer train-data
I0406 15:47:17.356070 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:47:19.120391 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0398284
I0406 15:47:19.120419 21485 solver.cpp:397]     Test net output #1: loss = 4.77949 (* 1 = 4.77949 loss)
I0406 15:47:19.251760 21485 solver.cpp:218] Iteration 4488 (0.779663 iter/s, 15.3913s/12 iters), loss = 4.42785
I0406 15:47:19.251816 21485 solver.cpp:237]     Train net output #0: loss = 4.42785 (* 1 = 4.42785 loss)
I0406 15:47:19.251825 21485 sgd_solver.cpp:105] Iteration 4488, lr = 0.05
I0406 15:47:23.634215 21485 solver.cpp:218] Iteration 4500 (2.73824 iter/s, 4.38238s/12 iters), loss = 4.83158
I0406 15:47:23.634258 21485 solver.cpp:237]     Train net output #0: loss = 4.83158 (* 1 = 4.83158 loss)
I0406 15:47:23.634263 21485 sgd_solver.cpp:105] Iteration 4500, lr = 0.05
I0406 15:47:28.798187 21485 solver.cpp:218] Iteration 4512 (2.32382 iter/s, 5.16391s/12 iters), loss = 4.60551
I0406 15:47:28.798290 21485 solver.cpp:237]     Train net output #0: loss = 4.60551 (* 1 = 4.60551 loss)
I0406 15:47:28.798296 21485 sgd_solver.cpp:105] Iteration 4512, lr = 0.05
I0406 15:47:34.161883 21485 solver.cpp:218] Iteration 4524 (2.23731 iter/s, 5.36358s/12 iters), loss = 4.51778
I0406 15:47:34.161926 21485 solver.cpp:237]     Train net output #0: loss = 4.51778 (* 1 = 4.51778 loss)
I0406 15:47:34.161932 21485 sgd_solver.cpp:105] Iteration 4524, lr = 0.05
I0406 15:47:39.267479 21485 solver.cpp:218] Iteration 4536 (2.35039 iter/s, 5.10554s/12 iters), loss = 4.57586
I0406 15:47:39.267529 21485 solver.cpp:237]     Train net output #0: loss = 4.57586 (* 1 = 4.57586 loss)
I0406 15:47:39.267534 21485 sgd_solver.cpp:105] Iteration 4536, lr = 0.05
I0406 15:47:44.497993 21485 solver.cpp:218] Iteration 4548 (2.29426 iter/s, 5.23045s/12 iters), loss = 4.60885
I0406 15:47:44.498031 21485 solver.cpp:237]     Train net output #0: loss = 4.60885 (* 1 = 4.60885 loss)
I0406 15:47:44.498036 21485 sgd_solver.cpp:105] Iteration 4548, lr = 0.05
I0406 15:47:45.809240 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:47:49.748864 21485 solver.cpp:218] Iteration 4560 (2.28536 iter/s, 5.25082s/12 iters), loss = 4.61483
I0406 15:47:49.748915 21485 solver.cpp:237]     Train net output #0: loss = 4.61483 (* 1 = 4.61483 loss)
I0406 15:47:49.748921 21485 sgd_solver.cpp:105] Iteration 4560, lr = 0.05
I0406 15:47:55.034945 21485 solver.cpp:218] Iteration 4572 (2.27014 iter/s, 5.28601s/12 iters), loss = 4.57727
I0406 15:47:55.034988 21485 solver.cpp:237]     Train net output #0: loss = 4.57727 (* 1 = 4.57727 loss)
I0406 15:47:55.034994 21485 sgd_solver.cpp:105] Iteration 4572, lr = 0.05
I0406 15:48:00.132812 21485 solver.cpp:218] Iteration 4584 (2.35395 iter/s, 5.09781s/12 iters), loss = 4.53089
I0406 15:48:00.132902 21485 solver.cpp:237]     Train net output #0: loss = 4.53089 (* 1 = 4.53089 loss)
I0406 15:48:00.132910 21485 sgd_solver.cpp:105] Iteration 4584, lr = 0.05
I0406 15:48:02.193547 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4590.caffemodel
I0406 15:48:06.173442 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4590.solverstate
I0406 15:48:08.474980 21485 solver.cpp:330] Iteration 4590, Testing net (#0)
I0406 15:48:08.474998 21485 net.cpp:676] Ignoring source layer train-data
I0406 15:48:11.161074 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:48:12.975818 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0343137
I0406 15:48:12.975852 21485 solver.cpp:397]     Test net output #1: loss = 4.79809 (* 1 = 4.79809 loss)
I0406 15:48:14.755776 21485 solver.cpp:218] Iteration 4596 (0.820632 iter/s, 14.6229s/12 iters), loss = 4.48754
I0406 15:48:14.755820 21485 solver.cpp:237]     Train net output #0: loss = 4.48754 (* 1 = 4.48754 loss)
I0406 15:48:14.755826 21485 sgd_solver.cpp:105] Iteration 4596, lr = 0.05
I0406 15:48:19.994679 21485 solver.cpp:218] Iteration 4608 (2.29058 iter/s, 5.23884s/12 iters), loss = 4.60508
I0406 15:48:19.994735 21485 solver.cpp:237]     Train net output #0: loss = 4.60508 (* 1 = 4.60508 loss)
I0406 15:48:19.994743 21485 sgd_solver.cpp:105] Iteration 4608, lr = 0.05
I0406 15:48:25.273342 21485 solver.cpp:218] Iteration 4620 (2.27333 iter/s, 5.27859s/12 iters), loss = 4.84771
I0406 15:48:25.273388 21485 solver.cpp:237]     Train net output #0: loss = 4.84771 (* 1 = 4.84771 loss)
I0406 15:48:25.273393 21485 sgd_solver.cpp:105] Iteration 4620, lr = 0.05
I0406 15:48:30.121738 21485 solver.cpp:218] Iteration 4632 (2.47508 iter/s, 4.84834s/12 iters), loss = 4.53696
I0406 15:48:30.121783 21485 solver.cpp:237]     Train net output #0: loss = 4.53696 (* 1 = 4.53696 loss)
I0406 15:48:30.121788 21485 sgd_solver.cpp:105] Iteration 4632, lr = 0.05
I0406 15:48:35.324076 21485 solver.cpp:218] Iteration 4644 (2.30668 iter/s, 5.20228s/12 iters), loss = 4.53158
I0406 15:48:35.324169 21485 solver.cpp:237]     Train net output #0: loss = 4.53158 (* 1 = 4.53158 loss)
I0406 15:48:35.324177 21485 sgd_solver.cpp:105] Iteration 4644, lr = 0.05
I0406 15:48:38.835651 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:48:40.530812 21485 solver.cpp:218] Iteration 4656 (2.30475 iter/s, 5.20663s/12 iters), loss = 4.54536
I0406 15:48:40.530848 21485 solver.cpp:237]     Train net output #0: loss = 4.54536 (* 1 = 4.54536 loss)
I0406 15:48:40.530853 21485 sgd_solver.cpp:105] Iteration 4656, lr = 0.05
I0406 15:48:45.869171 21485 solver.cpp:218] Iteration 4668 (2.2479 iter/s, 5.33831s/12 iters), loss = 4.5769
I0406 15:48:45.869226 21485 solver.cpp:237]     Train net output #0: loss = 4.5769 (* 1 = 4.5769 loss)
I0406 15:48:45.869235 21485 sgd_solver.cpp:105] Iteration 4668, lr = 0.05
I0406 15:48:51.130673 21485 solver.cpp:218] Iteration 4680 (2.28075 iter/s, 5.26143s/12 iters), loss = 4.56035
I0406 15:48:51.130728 21485 solver.cpp:237]     Train net output #0: loss = 4.56035 (* 1 = 4.56035 loss)
I0406 15:48:51.130736 21485 sgd_solver.cpp:105] Iteration 4680, lr = 0.05
I0406 15:48:55.798913 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4692.caffemodel
I0406 15:48:59.763742 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4692.solverstate
I0406 15:49:02.071295 21485 solver.cpp:330] Iteration 4692, Testing net (#0)
I0406 15:49:02.071316 21485 net.cpp:676] Ignoring source layer train-data
I0406 15:49:04.554219 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:49:06.375412 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0355392
I0406 15:49:06.375550 21485 solver.cpp:397]     Test net output #1: loss = 4.84139 (* 1 = 4.84139 loss)
I0406 15:49:06.516183 21485 solver.cpp:218] Iteration 4692 (0.779958 iter/s, 15.3854s/12 iters), loss = 4.54041
I0406 15:49:06.516239 21485 solver.cpp:237]     Train net output #0: loss = 4.54041 (* 1 = 4.54041 loss)
I0406 15:49:06.516247 21485 sgd_solver.cpp:105] Iteration 4692, lr = 0.05
I0406 15:49:10.955683 21485 solver.cpp:218] Iteration 4704 (2.70305 iter/s, 4.43943s/12 iters), loss = 4.69493
I0406 15:49:10.955729 21485 solver.cpp:237]     Train net output #0: loss = 4.69493 (* 1 = 4.69493 loss)
I0406 15:49:10.955734 21485 sgd_solver.cpp:105] Iteration 4704, lr = 0.05
I0406 15:49:16.085053 21485 solver.cpp:218] Iteration 4716 (2.3395 iter/s, 5.1293s/12 iters), loss = 4.52626
I0406 15:49:16.085108 21485 solver.cpp:237]     Train net output #0: loss = 4.52626 (* 1 = 4.52626 loss)
I0406 15:49:16.085116 21485 sgd_solver.cpp:105] Iteration 4716, lr = 0.05
I0406 15:49:21.292661 21485 solver.cpp:218] Iteration 4728 (2.30435 iter/s, 5.20753s/12 iters), loss = 4.48835
I0406 15:49:21.292721 21485 solver.cpp:237]     Train net output #0: loss = 4.48835 (* 1 = 4.48835 loss)
I0406 15:49:21.292729 21485 sgd_solver.cpp:105] Iteration 4728, lr = 0.05
I0406 15:49:26.756768 21485 solver.cpp:218] Iteration 4740 (2.19618 iter/s, 5.46404s/12 iters), loss = 4.47935
I0406 15:49:26.756829 21485 solver.cpp:237]     Train net output #0: loss = 4.47935 (* 1 = 4.47935 loss)
I0406 15:49:26.756837 21485 sgd_solver.cpp:105] Iteration 4740, lr = 0.05
I0406 15:49:32.044303 21485 solver.cpp:218] Iteration 4752 (2.26952 iter/s, 5.28747s/12 iters), loss = 4.45498
I0406 15:49:32.044342 21485 solver.cpp:237]     Train net output #0: loss = 4.45498 (* 1 = 4.45498 loss)
I0406 15:49:32.044348 21485 sgd_solver.cpp:105] Iteration 4752, lr = 0.05
I0406 15:49:32.630276 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:49:37.372643 21485 solver.cpp:218] Iteration 4764 (2.25213 iter/s, 5.32828s/12 iters), loss = 4.55065
I0406 15:49:37.372738 21485 solver.cpp:237]     Train net output #0: loss = 4.55065 (* 1 = 4.55065 loss)
I0406 15:49:37.372745 21485 sgd_solver.cpp:105] Iteration 4764, lr = 0.05
I0406 15:49:42.573360 21485 solver.cpp:218] Iteration 4776 (2.30742 iter/s, 5.20061s/12 iters), loss = 4.72723
I0406 15:49:42.573405 21485 solver.cpp:237]     Train net output #0: loss = 4.72723 (* 1 = 4.72723 loss)
I0406 15:49:42.573411 21485 sgd_solver.cpp:105] Iteration 4776, lr = 0.05
I0406 15:49:47.784180 21485 solver.cpp:218] Iteration 4788 (2.30293 iter/s, 5.21076s/12 iters), loss = 4.5287
I0406 15:49:47.784222 21485 solver.cpp:237]     Train net output #0: loss = 4.5287 (* 1 = 4.5287 loss)
I0406 15:49:47.784227 21485 sgd_solver.cpp:105] Iteration 4788, lr = 0.05
I0406 15:49:49.930356 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4794.caffemodel
I0406 15:49:54.459216 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4794.solverstate
I0406 15:49:57.716784 21485 solver.cpp:330] Iteration 4794, Testing net (#0)
I0406 15:49:57.716804 21485 net.cpp:676] Ignoring source layer train-data
I0406 15:50:00.257537 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:50:02.200582 21485 solver.cpp:397]     Test net output #0: accuracy = 0.036152
I0406 15:50:02.200618 21485 solver.cpp:397]     Test net output #1: loss = 4.80964 (* 1 = 4.80964 loss)
I0406 15:50:04.187505 21485 solver.cpp:218] Iteration 4800 (0.731561 iter/s, 16.4033s/12 iters), loss = 4.36235
I0406 15:50:04.187554 21485 solver.cpp:237]     Train net output #0: loss = 4.36235 (* 1 = 4.36235 loss)
I0406 15:50:04.187561 21485 sgd_solver.cpp:105] Iteration 4800, lr = 0.05
I0406 15:50:09.211943 21485 solver.cpp:218] Iteration 4812 (2.38836 iter/s, 5.02438s/12 iters), loss = 4.7223
I0406 15:50:09.212085 21485 solver.cpp:237]     Train net output #0: loss = 4.7223 (* 1 = 4.7223 loss)
I0406 15:50:09.212091 21485 sgd_solver.cpp:105] Iteration 4812, lr = 0.05
I0406 15:50:14.413492 21485 solver.cpp:218] Iteration 4824 (2.30707 iter/s, 5.20139s/12 iters), loss = 4.27681
I0406 15:50:14.413537 21485 solver.cpp:237]     Train net output #0: loss = 4.27681 (* 1 = 4.27681 loss)
I0406 15:50:14.413542 21485 sgd_solver.cpp:105] Iteration 4824, lr = 0.05
I0406 15:50:19.660658 21485 solver.cpp:218] Iteration 4836 (2.28697 iter/s, 5.24711s/12 iters), loss = 4.67159
I0406 15:50:19.660692 21485 solver.cpp:237]     Train net output #0: loss = 4.67159 (* 1 = 4.67159 loss)
I0406 15:50:19.660697 21485 sgd_solver.cpp:105] Iteration 4836, lr = 0.05
I0406 15:50:21.766880 21485 blocking_queue.cpp:49] Waiting for data
I0406 15:50:24.979501 21485 solver.cpp:218] Iteration 4848 (2.25615 iter/s, 5.31879s/12 iters), loss = 4.54056
I0406 15:50:24.979542 21485 solver.cpp:237]     Train net output #0: loss = 4.54056 (* 1 = 4.54056 loss)
I0406 15:50:24.979548 21485 sgd_solver.cpp:105] Iteration 4848, lr = 0.05
I0406 15:50:27.818001 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:50:30.242468 21485 solver.cpp:218] Iteration 4860 (2.28011 iter/s, 5.26291s/12 iters), loss = 4.72214
I0406 15:50:30.242511 21485 solver.cpp:237]     Train net output #0: loss = 4.72214 (* 1 = 4.72214 loss)
I0406 15:50:30.242516 21485 sgd_solver.cpp:105] Iteration 4860, lr = 0.05
I0406 15:50:35.414018 21485 solver.cpp:218] Iteration 4872 (2.32041 iter/s, 5.17149s/12 iters), loss = 4.68005
I0406 15:50:35.414058 21485 solver.cpp:237]     Train net output #0: loss = 4.68005 (* 1 = 4.68005 loss)
I0406 15:50:35.414063 21485 sgd_solver.cpp:105] Iteration 4872, lr = 0.05
I0406 15:50:40.556795 21485 solver.cpp:218] Iteration 4884 (2.33339 iter/s, 5.14273s/12 iters), loss = 4.49441
I0406 15:50:40.556864 21485 solver.cpp:237]     Train net output #0: loss = 4.49441 (* 1 = 4.49441 loss)
I0406 15:50:40.556869 21485 sgd_solver.cpp:105] Iteration 4884, lr = 0.05
I0406 15:50:45.400540 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4896.caffemodel
I0406 15:50:49.288362 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4896.solverstate
I0406 15:50:51.631295 21485 solver.cpp:330] Iteration 4896, Testing net (#0)
I0406 15:50:51.631315 21485 net.cpp:676] Ignoring source layer train-data
I0406 15:50:54.020711 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:50:55.953020 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0355392
I0406 15:50:55.953054 21485 solver.cpp:397]     Test net output #1: loss = 4.83792 (* 1 = 4.83792 loss)
I0406 15:50:56.094803 21485 solver.cpp:218] Iteration 4896 (0.772304 iter/s, 15.5379s/12 iters), loss = 4.56806
I0406 15:50:56.094856 21485 solver.cpp:237]     Train net output #0: loss = 4.56806 (* 1 = 4.56806 loss)
I0406 15:50:56.094863 21485 sgd_solver.cpp:105] Iteration 4896, lr = 0.05
I0406 15:51:00.321717 21485 solver.cpp:218] Iteration 4908 (2.839 iter/s, 4.22684s/12 iters), loss = 4.76618
I0406 15:51:00.321758 21485 solver.cpp:237]     Train net output #0: loss = 4.76618 (* 1 = 4.76618 loss)
I0406 15:51:00.321763 21485 sgd_solver.cpp:105] Iteration 4908, lr = 0.05
I0406 15:51:05.335685 21485 solver.cpp:218] Iteration 4920 (2.39334 iter/s, 5.01392s/12 iters), loss = 4.71983
I0406 15:51:05.335723 21485 solver.cpp:237]     Train net output #0: loss = 4.71983 (* 1 = 4.71983 loss)
I0406 15:51:05.335728 21485 sgd_solver.cpp:105] Iteration 4920, lr = 0.05
I0406 15:51:10.553357 21485 solver.cpp:218] Iteration 4932 (2.2999 iter/s, 5.21762s/12 iters), loss = 4.59164
I0406 15:51:10.553400 21485 solver.cpp:237]     Train net output #0: loss = 4.59164 (* 1 = 4.59164 loss)
I0406 15:51:10.553406 21485 sgd_solver.cpp:105] Iteration 4932, lr = 0.05
I0406 15:51:15.819296 21485 solver.cpp:218] Iteration 4944 (2.27882 iter/s, 5.26588s/12 iters), loss = 4.61993
I0406 15:51:15.819439 21485 solver.cpp:237]     Train net output #0: loss = 4.61993 (* 1 = 4.61993 loss)
I0406 15:51:15.819447 21485 sgd_solver.cpp:105] Iteration 4944, lr = 0.05
I0406 15:51:20.905845 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:51:21.155213 21485 solver.cpp:218] Iteration 4956 (2.24897 iter/s, 5.33577s/12 iters), loss = 4.73009
I0406 15:51:21.155256 21485 solver.cpp:237]     Train net output #0: loss = 4.73009 (* 1 = 4.73009 loss)
I0406 15:51:21.155261 21485 sgd_solver.cpp:105] Iteration 4956, lr = 0.05
I0406 15:51:26.416934 21485 solver.cpp:218] Iteration 4968 (2.28065 iter/s, 5.26166s/12 iters), loss = 4.60991
I0406 15:51:26.416985 21485 solver.cpp:237]     Train net output #0: loss = 4.60991 (* 1 = 4.60991 loss)
I0406 15:51:26.416991 21485 sgd_solver.cpp:105] Iteration 4968, lr = 0.05
I0406 15:51:31.431939 21485 solver.cpp:218] Iteration 4980 (2.39285 iter/s, 5.01494s/12 iters), loss = 4.61324
I0406 15:51:31.431982 21485 solver.cpp:237]     Train net output #0: loss = 4.61324 (* 1 = 4.61324 loss)
I0406 15:51:31.431988 21485 sgd_solver.cpp:105] Iteration 4980, lr = 0.05
I0406 15:51:36.697973 21485 solver.cpp:218] Iteration 4992 (2.27878 iter/s, 5.26598s/12 iters), loss = 4.76986
I0406 15:51:36.698011 21485 solver.cpp:237]     Train net output #0: loss = 4.76986 (* 1 = 4.76986 loss)
I0406 15:51:36.698016 21485 sgd_solver.cpp:105] Iteration 4992, lr = 0.05
I0406 15:51:38.787850 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4998.caffemodel
I0406 15:51:43.066141 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4998.solverstate
I0406 15:51:45.426434 21485 solver.cpp:330] Iteration 4998, Testing net (#0)
I0406 15:51:45.426457 21485 net.cpp:676] Ignoring source layer train-data
I0406 15:51:47.826800 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:51:49.756722 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0392157
I0406 15:51:49.756759 21485 solver.cpp:397]     Test net output #1: loss = 4.87709 (* 1 = 4.87709 loss)
I0406 15:51:51.687270 21485 solver.cpp:218] Iteration 5004 (0.800574 iter/s, 14.9892s/12 iters), loss = 4.66018
I0406 15:51:51.687327 21485 solver.cpp:237]     Train net output #0: loss = 4.66018 (* 1 = 4.66018 loss)
I0406 15:51:51.687336 21485 sgd_solver.cpp:105] Iteration 5004, lr = 0.05
I0406 15:51:56.902027 21485 solver.cpp:218] Iteration 5016 (2.30119 iter/s, 5.21469s/12 iters), loss = 4.65264
I0406 15:51:56.902082 21485 solver.cpp:237]     Train net output #0: loss = 4.65264 (* 1 = 4.65264 loss)
I0406 15:51:56.902091 21485 sgd_solver.cpp:105] Iteration 5016, lr = 0.05
I0406 15:52:02.234108 21485 solver.cpp:218] Iteration 5028 (2.25055 iter/s, 5.33202s/12 iters), loss = 4.55888
I0406 15:52:02.234151 21485 solver.cpp:237]     Train net output #0: loss = 4.55888 (* 1 = 4.55888 loss)
I0406 15:52:02.234156 21485 sgd_solver.cpp:105] Iteration 5028, lr = 0.05
I0406 15:52:07.630326 21485 solver.cpp:218] Iteration 5040 (2.2238 iter/s, 5.39616s/12 iters), loss = 4.85596
I0406 15:52:07.630369 21485 solver.cpp:237]     Train net output #0: loss = 4.85596 (* 1 = 4.85596 loss)
I0406 15:52:07.630374 21485 sgd_solver.cpp:105] Iteration 5040, lr = 0.05
I0406 15:52:12.960409 21485 solver.cpp:218] Iteration 5052 (2.2514 iter/s, 5.33002s/12 iters), loss = 4.82882
I0406 15:52:12.960460 21485 solver.cpp:237]     Train net output #0: loss = 4.82882 (* 1 = 4.82882 loss)
I0406 15:52:12.960466 21485 sgd_solver.cpp:105] Iteration 5052, lr = 0.05
I0406 15:52:14.899178 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:52:18.067160 21485 solver.cpp:218] Iteration 5064 (2.34986 iter/s, 5.10669s/12 iters), loss = 5.03373
I0406 15:52:18.067273 21485 solver.cpp:237]     Train net output #0: loss = 5.03373 (* 1 = 5.03373 loss)
I0406 15:52:18.067281 21485 sgd_solver.cpp:105] Iteration 5064, lr = 0.05
I0406 15:52:23.053515 21485 solver.cpp:218] Iteration 5076 (2.40663 iter/s, 4.98622s/12 iters), loss = 4.78369
I0406 15:52:23.053570 21485 solver.cpp:237]     Train net output #0: loss = 4.78369 (* 1 = 4.78369 loss)
I0406 15:52:23.053578 21485 sgd_solver.cpp:105] Iteration 5076, lr = 0.05
I0406 15:52:28.239491 21485 solver.cpp:218] Iteration 5088 (2.31396 iter/s, 5.18591s/12 iters), loss = 4.64025
I0406 15:52:28.239531 21485 solver.cpp:237]     Train net output #0: loss = 4.64025 (* 1 = 4.64025 loss)
I0406 15:52:28.239537 21485 sgd_solver.cpp:105] Iteration 5088, lr = 0.05
I0406 15:52:32.993477 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5100.caffemodel
I0406 15:52:37.105099 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5100.solverstate
I0406 15:52:39.412513 21485 solver.cpp:330] Iteration 5100, Testing net (#0)
I0406 15:52:39.412531 21485 net.cpp:676] Ignoring source layer train-data
I0406 15:52:41.729210 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:52:43.710116 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0367647
I0406 15:52:43.710152 21485 solver.cpp:397]     Test net output #1: loss = 4.89095 (* 1 = 4.89095 loss)
I0406 15:52:43.850257 21485 solver.cpp:218] Iteration 5100 (0.768703 iter/s, 15.6107s/12 iters), loss = 4.73774
I0406 15:52:43.850301 21485 solver.cpp:237]     Train net output #0: loss = 4.73774 (* 1 = 4.73774 loss)
I0406 15:52:43.850306 21485 sgd_solver.cpp:105] Iteration 5100, lr = 0.05
I0406 15:52:48.188045 21485 solver.cpp:218] Iteration 5112 (2.76643 iter/s, 4.33772s/12 iters), loss = 4.63532
I0406 15:52:48.188179 21485 solver.cpp:237]     Train net output #0: loss = 4.63532 (* 1 = 4.63532 loss)
I0406 15:52:48.188190 21485 sgd_solver.cpp:105] Iteration 5112, lr = 0.05
I0406 15:52:53.383710 21485 solver.cpp:218] Iteration 5124 (2.30968 iter/s, 5.19552s/12 iters), loss = 4.93855
I0406 15:52:53.383764 21485 solver.cpp:237]     Train net output #0: loss = 4.93855 (* 1 = 4.93855 loss)
I0406 15:52:53.383772 21485 sgd_solver.cpp:105] Iteration 5124, lr = 0.05
I0406 15:52:58.689311 21485 solver.cpp:218] Iteration 5136 (2.26179 iter/s, 5.30553s/12 iters), loss = 4.79701
I0406 15:52:58.689355 21485 solver.cpp:237]     Train net output #0: loss = 4.79701 (* 1 = 4.79701 loss)
I0406 15:52:58.689361 21485 sgd_solver.cpp:105] Iteration 5136, lr = 0.05
I0406 15:53:03.705224 21485 solver.cpp:218] Iteration 5148 (2.39241 iter/s, 5.01586s/12 iters), loss = 4.90447
I0406 15:53:03.705265 21485 solver.cpp:237]     Train net output #0: loss = 4.90447 (* 1 = 4.90447 loss)
I0406 15:53:03.705271 21485 sgd_solver.cpp:105] Iteration 5148, lr = 0.05
I0406 15:53:07.828626 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:53:08.891752 21485 solver.cpp:218] Iteration 5160 (2.31371 iter/s, 5.18647s/12 iters), loss = 4.90154
I0406 15:53:08.891801 21485 solver.cpp:237]     Train net output #0: loss = 4.90154 (* 1 = 4.90154 loss)
I0406 15:53:08.891808 21485 sgd_solver.cpp:105] Iteration 5160, lr = 0.05
I0406 15:53:14.048568 21485 solver.cpp:218] Iteration 5172 (2.32705 iter/s, 5.15675s/12 iters), loss = 4.87124
I0406 15:53:14.048610 21485 solver.cpp:237]     Train net output #0: loss = 4.87124 (* 1 = 4.87124 loss)
I0406 15:53:14.048616 21485 sgd_solver.cpp:105] Iteration 5172, lr = 0.05
I0406 15:53:19.299552 21485 solver.cpp:218] Iteration 5184 (2.28531 iter/s, 5.25092s/12 iters), loss = 5.05685
I0406 15:53:19.299690 21485 solver.cpp:237]     Train net output #0: loss = 5.05685 (* 1 = 5.05685 loss)
I0406 15:53:19.299700 21485 sgd_solver.cpp:105] Iteration 5184, lr = 0.05
I0406 15:53:24.500689 21485 solver.cpp:218] Iteration 5196 (2.30725 iter/s, 5.20099s/12 iters), loss = 4.8548
I0406 15:53:24.500748 21485 solver.cpp:237]     Train net output #0: loss = 4.8548 (* 1 = 4.8548 loss)
I0406 15:53:24.500757 21485 sgd_solver.cpp:105] Iteration 5196, lr = 0.05
I0406 15:53:26.655875 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5202.caffemodel
I0406 15:53:32.055198 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5202.solverstate
I0406 15:53:34.403210 21485 solver.cpp:330] Iteration 5202, Testing net (#0)
I0406 15:53:34.403232 21485 net.cpp:676] Ignoring source layer train-data
I0406 15:53:36.705127 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:53:38.721948 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0257353
I0406 15:53:38.721984 21485 solver.cpp:397]     Test net output #1: loss = 4.99116 (* 1 = 4.99116 loss)
I0406 15:53:40.456418 21485 solver.cpp:218] Iteration 5208 (0.752084 iter/s, 15.9557s/12 iters), loss = 4.97607
I0406 15:53:40.456467 21485 solver.cpp:237]     Train net output #0: loss = 4.97607 (* 1 = 4.97607 loss)
I0406 15:53:40.456472 21485 sgd_solver.cpp:105] Iteration 5208, lr = 0.05
I0406 15:53:45.406322 21485 solver.cpp:218] Iteration 5220 (2.42432 iter/s, 4.94984s/12 iters), loss = 4.86697
I0406 15:53:45.406359 21485 solver.cpp:237]     Train net output #0: loss = 4.86697 (* 1 = 4.86697 loss)
I0406 15:53:45.406364 21485 sgd_solver.cpp:105] Iteration 5220, lr = 0.05
I0406 15:53:50.695818 21485 solver.cpp:218] Iteration 5232 (2.26867 iter/s, 5.28944s/12 iters), loss = 5.01259
I0406 15:53:50.695940 21485 solver.cpp:237]     Train net output #0: loss = 5.01259 (* 1 = 5.01259 loss)
I0406 15:53:50.695947 21485 sgd_solver.cpp:105] Iteration 5232, lr = 0.05
I0406 15:53:55.989414 21485 solver.cpp:218] Iteration 5244 (2.26695 iter/s, 5.29346s/12 iters), loss = 5.06604
I0406 15:53:55.989454 21485 solver.cpp:237]     Train net output #0: loss = 5.06604 (* 1 = 5.06604 loss)
I0406 15:53:55.989459 21485 sgd_solver.cpp:105] Iteration 5244, lr = 0.05
I0406 15:54:01.242338 21485 solver.cpp:218] Iteration 5256 (2.28447 iter/s, 5.25287s/12 iters), loss = 5.02814
I0406 15:54:01.242380 21485 solver.cpp:237]     Train net output #0: loss = 5.02814 (* 1 = 5.02814 loss)
I0406 15:54:01.242386 21485 sgd_solver.cpp:105] Iteration 5256, lr = 0.05
I0406 15:54:02.629767 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:54:06.525106 21485 solver.cpp:218] Iteration 5268 (2.27156 iter/s, 5.2827s/12 iters), loss = 4.94015
I0406 15:54:06.525163 21485 solver.cpp:237]     Train net output #0: loss = 4.94015 (* 1 = 4.94015 loss)
I0406 15:54:06.525171 21485 sgd_solver.cpp:105] Iteration 5268, lr = 0.05
I0406 15:54:11.840041 21485 solver.cpp:218] Iteration 5280 (2.25782 iter/s, 5.31487s/12 iters), loss = 4.9879
I0406 15:54:11.840086 21485 solver.cpp:237]     Train net output #0: loss = 4.9879 (* 1 = 4.9879 loss)
I0406 15:54:11.840093 21485 sgd_solver.cpp:105] Iteration 5280, lr = 0.05
I0406 15:54:16.854902 21485 solver.cpp:218] Iteration 5292 (2.39292 iter/s, 5.0148s/12 iters), loss = 4.91219
I0406 15:54:16.854949 21485 solver.cpp:237]     Train net output #0: loss = 4.91219 (* 1 = 4.91219 loss)
I0406 15:54:16.854954 21485 sgd_solver.cpp:105] Iteration 5292, lr = 0.05
I0406 15:54:21.612366 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5304.caffemodel
I0406 15:54:26.055125 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5304.solverstate
I0406 15:54:29.286746 21485 solver.cpp:330] Iteration 5304, Testing net (#0)
I0406 15:54:29.286767 21485 net.cpp:676] Ignoring source layer train-data
I0406 15:54:31.487540 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:54:33.587214 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0269608
I0406 15:54:33.587258 21485 solver.cpp:397]     Test net output #1: loss = 5.01117 (* 1 = 5.01117 loss)
I0406 15:54:33.728289 21485 solver.cpp:218] Iteration 5304 (0.711182 iter/s, 16.8733s/12 iters), loss = 4.87011
I0406 15:54:33.728348 21485 solver.cpp:237]     Train net output #0: loss = 4.87011 (* 1 = 4.87011 loss)
I0406 15:54:33.728358 21485 sgd_solver.cpp:105] Iteration 5304, lr = 0.05
I0406 15:54:37.967590 21485 solver.cpp:218] Iteration 5316 (2.83071 iter/s, 4.23922s/12 iters), loss = 4.86618
I0406 15:54:37.967643 21485 solver.cpp:237]     Train net output #0: loss = 4.86618 (* 1 = 4.86618 loss)
I0406 15:54:37.967651 21485 sgd_solver.cpp:105] Iteration 5316, lr = 0.05
I0406 15:54:43.202718 21485 solver.cpp:218] Iteration 5328 (2.29224 iter/s, 5.23505s/12 iters), loss = 4.89904
I0406 15:54:43.202776 21485 solver.cpp:237]     Train net output #0: loss = 4.89904 (* 1 = 4.89904 loss)
I0406 15:54:43.202785 21485 sgd_solver.cpp:105] Iteration 5328, lr = 0.05
I0406 15:54:48.320875 21485 solver.cpp:218] Iteration 5340 (2.34463 iter/s, 5.11808s/12 iters), loss = 4.81278
I0406 15:54:48.320930 21485 solver.cpp:237]     Train net output #0: loss = 4.81278 (* 1 = 4.81278 loss)
I0406 15:54:48.320938 21485 sgd_solver.cpp:105] Iteration 5340, lr = 0.05
I0406 15:54:53.575057 21485 solver.cpp:218] Iteration 5352 (2.28393 iter/s, 5.25411s/12 iters), loss = 4.79169
I0406 15:54:53.575176 21485 solver.cpp:237]     Train net output #0: loss = 4.79169 (* 1 = 4.79169 loss)
I0406 15:54:53.575182 21485 sgd_solver.cpp:105] Iteration 5352, lr = 0.05
I0406 15:54:57.255498 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:54:58.963809 21485 solver.cpp:218] Iteration 5364 (2.22692 iter/s, 5.38862s/12 iters), loss = 4.87277
I0406 15:54:58.963860 21485 solver.cpp:237]     Train net output #0: loss = 4.87277 (* 1 = 4.87277 loss)
I0406 15:54:58.963869 21485 sgd_solver.cpp:105] Iteration 5364, lr = 0.05
I0406 15:55:04.272797 21485 solver.cpp:218] Iteration 5376 (2.26034 iter/s, 5.30893s/12 iters), loss = 4.79002
I0406 15:55:04.272840 21485 solver.cpp:237]     Train net output #0: loss = 4.79002 (* 1 = 4.79002 loss)
I0406 15:55:04.272846 21485 sgd_solver.cpp:105] Iteration 5376, lr = 0.05
I0406 15:55:09.417807 21485 solver.cpp:218] Iteration 5388 (2.33238 iter/s, 5.14495s/12 iters), loss = 4.94953
I0406 15:55:09.417846 21485 solver.cpp:237]     Train net output #0: loss = 4.94953 (* 1 = 4.94953 loss)
I0406 15:55:09.417852 21485 sgd_solver.cpp:105] Iteration 5388, lr = 0.05
I0406 15:55:14.484508 21485 solver.cpp:218] Iteration 5400 (2.36843 iter/s, 5.06665s/12 iters), loss = 4.75338
I0406 15:55:14.484552 21485 solver.cpp:237]     Train net output #0: loss = 4.75338 (* 1 = 4.75338 loss)
I0406 15:55:14.484558 21485 sgd_solver.cpp:105] Iteration 5400, lr = 0.05
I0406 15:55:16.553242 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5406.caffemodel
I0406 15:55:21.040020 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5406.solverstate
I0406 15:55:23.452410 21485 solver.cpp:330] Iteration 5406, Testing net (#0)
I0406 15:55:23.452433 21485 net.cpp:676] Ignoring source layer train-data
I0406 15:55:25.631578 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:55:27.833750 21485 solver.cpp:397]     Test net output #0: accuracy = 0.028799
I0406 15:55:27.833784 21485 solver.cpp:397]     Test net output #1: loss = 4.99995 (* 1 = 4.99995 loss)
I0406 15:55:29.618789 21485 solver.cpp:218] Iteration 5412 (0.793019 iter/s, 15.1321s/12 iters), loss = 4.96244
I0406 15:55:29.618841 21485 solver.cpp:237]     Train net output #0: loss = 4.96244 (* 1 = 4.96244 loss)
I0406 15:55:29.618849 21485 sgd_solver.cpp:105] Iteration 5412, lr = 0.05
I0406 15:55:34.804536 21485 solver.cpp:218] Iteration 5424 (2.31406 iter/s, 5.18568s/12 iters), loss = 4.75851
I0406 15:55:34.804600 21485 solver.cpp:237]     Train net output #0: loss = 4.75851 (* 1 = 4.75851 loss)
I0406 15:55:34.804608 21485 sgd_solver.cpp:105] Iteration 5424, lr = 0.05
I0406 15:55:40.080504 21485 solver.cpp:218] Iteration 5436 (2.2745 iter/s, 5.27589s/12 iters), loss = 4.52351
I0406 15:55:40.080545 21485 solver.cpp:237]     Train net output #0: loss = 4.52351 (* 1 = 4.52351 loss)
I0406 15:55:40.080551 21485 sgd_solver.cpp:105] Iteration 5436, lr = 0.05
I0406 15:55:45.355031 21485 solver.cpp:218] Iteration 5448 (2.27511 iter/s, 5.27447s/12 iters), loss = 4.88783
I0406 15:55:45.355069 21485 solver.cpp:237]     Train net output #0: loss = 4.88783 (* 1 = 4.88783 loss)
I0406 15:55:45.355074 21485 sgd_solver.cpp:105] Iteration 5448, lr = 0.05
I0406 15:55:50.454488 21485 solver.cpp:218] Iteration 5460 (2.35322 iter/s, 5.0994s/12 iters), loss = 4.79796
I0406 15:55:50.454548 21485 solver.cpp:237]     Train net output #0: loss = 4.79796 (* 1 = 4.79796 loss)
I0406 15:55:50.454557 21485 sgd_solver.cpp:105] Iteration 5460, lr = 0.05
I0406 15:55:51.040477 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:55:55.820060 21485 solver.cpp:218] Iteration 5472 (2.23651 iter/s, 5.3655s/12 iters), loss = 4.91696
I0406 15:55:55.820196 21485 solver.cpp:237]     Train net output #0: loss = 4.91696 (* 1 = 4.91696 loss)
I0406 15:55:55.820205 21485 sgd_solver.cpp:105] Iteration 5472, lr = 0.05
I0406 15:56:01.119601 21485 solver.cpp:218] Iteration 5484 (2.26441 iter/s, 5.29939s/12 iters), loss = 4.94989
I0406 15:56:01.119657 21485 solver.cpp:237]     Train net output #0: loss = 4.94989 (* 1 = 4.94989 loss)
I0406 15:56:01.119665 21485 sgd_solver.cpp:105] Iteration 5484, lr = 0.05
I0406 15:56:06.371817 21485 solver.cpp:218] Iteration 5496 (2.28478 iter/s, 5.25215s/12 iters), loss = 4.79549
I0406 15:56:06.371863 21485 solver.cpp:237]     Train net output #0: loss = 4.79549 (* 1 = 4.79549 loss)
I0406 15:56:06.371870 21485 sgd_solver.cpp:105] Iteration 5496, lr = 0.05
I0406 15:56:11.126205 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5508.caffemodel
I0406 15:56:15.599229 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5508.solverstate
I0406 15:56:18.096787 21485 solver.cpp:330] Iteration 5508, Testing net (#0)
I0406 15:56:18.096807 21485 net.cpp:676] Ignoring source layer train-data
I0406 15:56:20.258236 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:56:22.411180 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0318627
I0406 15:56:22.411217 21485 solver.cpp:397]     Test net output #1: loss = 5.00932 (* 1 = 5.00932 loss)
I0406 15:56:22.548033 21485 solver.cpp:218] Iteration 5508 (0.741833 iter/s, 16.1762s/12 iters), loss = 4.87342
I0406 15:56:22.548101 21485 solver.cpp:237]     Train net output #0: loss = 4.87342 (* 1 = 4.87342 loss)
I0406 15:56:22.548110 21485 sgd_solver.cpp:105] Iteration 5508, lr = 0.05
I0406 15:56:26.792512 21485 solver.cpp:218] Iteration 5520 (2.82725 iter/s, 4.2444s/12 iters), loss = 4.90169
I0406 15:56:26.792604 21485 solver.cpp:237]     Train net output #0: loss = 4.90169 (* 1 = 4.90169 loss)
I0406 15:56:26.792611 21485 sgd_solver.cpp:105] Iteration 5520, lr = 0.05
I0406 15:56:29.187708 21485 blocking_queue.cpp:49] Waiting for data
I0406 15:56:31.842509 21485 solver.cpp:218] Iteration 5532 (2.37629 iter/s, 5.0499s/12 iters), loss = 4.71579
I0406 15:56:31.842546 21485 solver.cpp:237]     Train net output #0: loss = 4.71579 (* 1 = 4.71579 loss)
I0406 15:56:31.842551 21485 sgd_solver.cpp:105] Iteration 5532, lr = 0.05
I0406 15:56:37.228677 21485 solver.cpp:218] Iteration 5544 (2.22795 iter/s, 5.38611s/12 iters), loss = 4.87009
I0406 15:56:37.228735 21485 solver.cpp:237]     Train net output #0: loss = 4.87009 (* 1 = 4.87009 loss)
I0406 15:56:37.228744 21485 sgd_solver.cpp:105] Iteration 5544, lr = 0.05
I0406 15:56:42.556660 21485 solver.cpp:218] Iteration 5556 (2.25229 iter/s, 5.32792s/12 iters), loss = 4.80435
I0406 15:56:42.556701 21485 solver.cpp:237]     Train net output #0: loss = 4.80435 (* 1 = 4.80435 loss)
I0406 15:56:42.556707 21485 sgd_solver.cpp:105] Iteration 5556, lr = 0.05
I0406 15:56:45.293756 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:56:47.816758 21485 solver.cpp:218] Iteration 5568 (2.28135 iter/s, 5.26005s/12 iters), loss = 4.84535
I0406 15:56:47.816804 21485 solver.cpp:237]     Train net output #0: loss = 4.84535 (* 1 = 4.84535 loss)
I0406 15:56:47.816812 21485 sgd_solver.cpp:105] Iteration 5568, lr = 0.05
I0406 15:56:52.908149 21485 solver.cpp:218] Iteration 5580 (2.35695 iter/s, 5.09133s/12 iters), loss = 4.92835
I0406 15:56:52.908196 21485 solver.cpp:237]     Train net output #0: loss = 4.92835 (* 1 = 4.92835 loss)
I0406 15:56:52.908202 21485 sgd_solver.cpp:105] Iteration 5580, lr = 0.05
I0406 15:56:58.162078 21485 solver.cpp:218] Iteration 5592 (2.28403 iter/s, 5.25387s/12 iters), loss = 4.92598
I0406 15:56:58.162199 21485 solver.cpp:237]     Train net output #0: loss = 4.92598 (* 1 = 4.92598 loss)
I0406 15:56:58.162205 21485 sgd_solver.cpp:105] Iteration 5592, lr = 0.05
I0406 15:57:03.383546 21485 solver.cpp:218] Iteration 5604 (2.29826 iter/s, 5.22134s/12 iters), loss = 4.8881
I0406 15:57:03.383605 21485 solver.cpp:237]     Train net output #0: loss = 4.8881 (* 1 = 4.8881 loss)
I0406 15:57:03.383615 21485 sgd_solver.cpp:105] Iteration 5604, lr = 0.05
I0406 15:57:05.399328 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5610.caffemodel
I0406 15:57:09.807318 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5610.solverstate
I0406 15:57:12.668865 21485 solver.cpp:330] Iteration 5610, Testing net (#0)
I0406 15:57:12.668892 21485 net.cpp:676] Ignoring source layer train-data
I0406 15:57:14.811218 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:57:17.020437 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0238971
I0406 15:57:17.020483 21485 solver.cpp:397]     Test net output #1: loss = 5.04399 (* 1 = 5.04399 loss)
I0406 15:57:18.916409 21485 solver.cpp:218] Iteration 5616 (0.772559 iter/s, 15.5328s/12 iters), loss = 4.8409
I0406 15:57:18.916460 21485 solver.cpp:237]     Train net output #0: loss = 4.8409 (* 1 = 4.8409 loss)
I0406 15:57:18.916468 21485 sgd_solver.cpp:105] Iteration 5616, lr = 0.05
I0406 15:57:24.139127 21485 solver.cpp:218] Iteration 5628 (2.29768 iter/s, 5.22265s/12 iters), loss = 4.90808
I0406 15:57:24.139175 21485 solver.cpp:237]     Train net output #0: loss = 4.90808 (* 1 = 4.90808 loss)
I0406 15:57:24.139180 21485 sgd_solver.cpp:105] Iteration 5628, lr = 0.05
I0406 15:57:29.399001 21485 solver.cpp:218] Iteration 5640 (2.28145 iter/s, 5.25981s/12 iters), loss = 5.03961
I0406 15:57:29.399127 21485 solver.cpp:237]     Train net output #0: loss = 5.03961 (* 1 = 5.03961 loss)
I0406 15:57:29.399137 21485 sgd_solver.cpp:105] Iteration 5640, lr = 0.05
I0406 15:57:34.560561 21485 solver.cpp:218] Iteration 5652 (2.32494 iter/s, 5.16142s/12 iters), loss = 4.72235
I0406 15:57:34.560604 21485 solver.cpp:237]     Train net output #0: loss = 4.72235 (* 1 = 4.72235 loss)
I0406 15:57:34.560611 21485 sgd_solver.cpp:105] Iteration 5652, lr = 0.05
I0406 15:57:39.570423 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:57:39.787683 21485 solver.cpp:218] Iteration 5664 (2.29575 iter/s, 5.22706s/12 iters), loss = 5.08263
I0406 15:57:39.787742 21485 solver.cpp:237]     Train net output #0: loss = 5.08263 (* 1 = 5.08263 loss)
I0406 15:57:39.787750 21485 sgd_solver.cpp:105] Iteration 5664, lr = 0.05
I0406 15:57:45.002705 21485 solver.cpp:218] Iteration 5676 (2.30108 iter/s, 5.21495s/12 iters), loss = 4.81907
I0406 15:57:45.002745 21485 solver.cpp:237]     Train net output #0: loss = 4.81907 (* 1 = 4.81907 loss)
I0406 15:57:45.002750 21485 sgd_solver.cpp:105] Iteration 5676, lr = 0.05
I0406 15:57:50.372666 21485 solver.cpp:218] Iteration 5688 (2.23468 iter/s, 5.3699s/12 iters), loss = 4.84136
I0406 15:57:50.372710 21485 solver.cpp:237]     Train net output #0: loss = 4.84136 (* 1 = 4.84136 loss)
I0406 15:57:50.372716 21485 sgd_solver.cpp:105] Iteration 5688, lr = 0.05
I0406 15:57:55.683903 21485 solver.cpp:218] Iteration 5700 (2.25939 iter/s, 5.31118s/12 iters), loss = 5.02993
I0406 15:57:55.683959 21485 solver.cpp:237]     Train net output #0: loss = 5.02993 (* 1 = 5.02993 loss)
I0406 15:57:55.683967 21485 sgd_solver.cpp:105] Iteration 5700, lr = 0.05
I0406 15:58:00.496634 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5712.caffemodel
I0406 15:58:04.918210 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5712.solverstate
I0406 15:58:07.608628 21485 solver.cpp:330] Iteration 5712, Testing net (#0)
I0406 15:58:07.608649 21485 net.cpp:676] Ignoring source layer train-data
I0406 15:58:09.748247 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:58:12.050449 21485 solver.cpp:397]     Test net output #0: accuracy = 0.036152
I0406 15:58:12.050482 21485 solver.cpp:397]     Test net output #1: loss = 4.98201 (* 1 = 4.98201 loss)
I0406 15:58:12.191169 21485 solver.cpp:218] Iteration 5712 (0.726956 iter/s, 16.5072s/12 iters), loss = 4.84611
I0406 15:58:12.191208 21485 solver.cpp:237]     Train net output #0: loss = 4.84611 (* 1 = 4.84611 loss)
I0406 15:58:12.191213 21485 sgd_solver.cpp:105] Iteration 5712, lr = 0.05
I0406 15:58:16.380674 21485 solver.cpp:218] Iteration 5724 (2.86434 iter/s, 4.18945s/12 iters), loss = 4.85953
I0406 15:58:16.380722 21485 solver.cpp:237]     Train net output #0: loss = 4.85953 (* 1 = 4.85953 loss)
I0406 15:58:16.380728 21485 sgd_solver.cpp:105] Iteration 5724, lr = 0.05
I0406 15:58:21.581980 21485 solver.cpp:218] Iteration 5736 (2.30714 iter/s, 5.20125s/12 iters), loss = 4.91435
I0406 15:58:21.582015 21485 solver.cpp:237]     Train net output #0: loss = 4.91435 (* 1 = 4.91435 loss)
I0406 15:58:21.582020 21485 sgd_solver.cpp:105] Iteration 5736, lr = 0.05
I0406 15:58:26.886777 21485 solver.cpp:218] Iteration 5748 (2.26213 iter/s, 5.30475s/12 iters), loss = 4.83676
I0406 15:58:26.886819 21485 solver.cpp:237]     Train net output #0: loss = 4.83676 (* 1 = 4.83676 loss)
I0406 15:58:26.886824 21485 sgd_solver.cpp:105] Iteration 5748, lr = 0.05
I0406 15:58:32.188169 21485 solver.cpp:218] Iteration 5760 (2.26358 iter/s, 5.30133s/12 iters), loss = 4.82863
I0406 15:58:32.188277 21485 solver.cpp:237]     Train net output #0: loss = 4.82863 (* 1 = 4.82863 loss)
I0406 15:58:32.188283 21485 sgd_solver.cpp:105] Iteration 5760, lr = 0.05
I0406 15:58:34.103209 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:58:37.198158 21485 solver.cpp:218] Iteration 5772 (2.39527 iter/s, 5.00986s/12 iters), loss = 4.87272
I0406 15:58:37.198211 21485 solver.cpp:237]     Train net output #0: loss = 4.87272 (* 1 = 4.87272 loss)
I0406 15:58:37.198221 21485 sgd_solver.cpp:105] Iteration 5772, lr = 0.05
I0406 15:58:42.326509 21485 solver.cpp:218] Iteration 5784 (2.33996 iter/s, 5.12828s/12 iters), loss = 4.80044
I0406 15:58:42.326557 21485 solver.cpp:237]     Train net output #0: loss = 4.80044 (* 1 = 4.80044 loss)
I0406 15:58:42.326563 21485 sgd_solver.cpp:105] Iteration 5784, lr = 0.05
I0406 15:58:47.742033 21485 solver.cpp:218] Iteration 5796 (2.21588 iter/s, 5.41546s/12 iters), loss = 4.86475
I0406 15:58:47.742070 21485 solver.cpp:237]     Train net output #0: loss = 4.86475 (* 1 = 4.86475 loss)
I0406 15:58:47.742075 21485 sgd_solver.cpp:105] Iteration 5796, lr = 0.05
I0406 15:58:52.837323 21485 solver.cpp:218] Iteration 5808 (2.35514 iter/s, 5.09524s/12 iters), loss = 4.77976
I0406 15:58:52.837370 21485 solver.cpp:237]     Train net output #0: loss = 4.77976 (* 1 = 4.77976 loss)
I0406 15:58:52.837376 21485 sgd_solver.cpp:105] Iteration 5808, lr = 0.05
I0406 15:58:54.898633 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5814.caffemodel
I0406 15:58:59.403504 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5814.solverstate
I0406 15:59:03.408694 21485 solver.cpp:330] Iteration 5814, Testing net (#0)
I0406 15:59:03.408778 21485 net.cpp:676] Ignoring source layer train-data
I0406 15:59:05.518107 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:59:07.804708 21485 solver.cpp:397]     Test net output #0: accuracy = 0.036152
I0406 15:59:07.804738 21485 solver.cpp:397]     Test net output #1: loss = 4.95364 (* 1 = 4.95364 loss)
I0406 15:59:09.713311 21485 solver.cpp:218] Iteration 5820 (0.711072 iter/s, 16.8759s/12 iters), loss = 4.59448
I0406 15:59:09.713367 21485 solver.cpp:237]     Train net output #0: loss = 4.59448 (* 1 = 4.59448 loss)
I0406 15:59:09.713374 21485 sgd_solver.cpp:105] Iteration 5820, lr = 0.05
I0406 15:59:14.778772 21485 solver.cpp:218] Iteration 5832 (2.36902 iter/s, 5.06539s/12 iters), loss = 4.9981
I0406 15:59:14.778817 21485 solver.cpp:237]     Train net output #0: loss = 4.9981 (* 1 = 4.9981 loss)
I0406 15:59:14.778823 21485 sgd_solver.cpp:105] Iteration 5832, lr = 0.05
I0406 15:59:19.799625 21485 solver.cpp:218] Iteration 5844 (2.39006 iter/s, 5.02079s/12 iters), loss = 4.75401
I0406 15:59:19.799669 21485 solver.cpp:237]     Train net output #0: loss = 4.75401 (* 1 = 4.75401 loss)
I0406 15:59:19.799674 21485 sgd_solver.cpp:105] Iteration 5844, lr = 0.05
I0406 15:59:25.214968 21485 solver.cpp:218] Iteration 5856 (2.21595 iter/s, 5.41528s/12 iters), loss = 4.77447
I0406 15:59:25.215029 21485 solver.cpp:237]     Train net output #0: loss = 4.77447 (* 1 = 4.77447 loss)
I0406 15:59:25.215039 21485 sgd_solver.cpp:105] Iteration 5856, lr = 0.05
I0406 15:59:29.520712 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 15:59:30.387368 21485 solver.cpp:218] Iteration 5868 (2.32004 iter/s, 5.17233s/12 iters), loss = 4.71141
I0406 15:59:30.387421 21485 solver.cpp:237]     Train net output #0: loss = 4.71141 (* 1 = 4.71141 loss)
I0406 15:59:30.387430 21485 sgd_solver.cpp:105] Iteration 5868, lr = 0.05
I0406 15:59:35.651343 21485 solver.cpp:218] Iteration 5880 (2.27967 iter/s, 5.26391s/12 iters), loss = 4.82674
I0406 15:59:35.651496 21485 solver.cpp:237]     Train net output #0: loss = 4.82674 (* 1 = 4.82674 loss)
I0406 15:59:35.651504 21485 sgd_solver.cpp:105] Iteration 5880, lr = 0.05
I0406 15:59:40.652993 21485 solver.cpp:218] Iteration 5892 (2.39929 iter/s, 5.00149s/12 iters), loss = 4.84466
I0406 15:59:40.653049 21485 solver.cpp:237]     Train net output #0: loss = 4.84466 (* 1 = 4.84466 loss)
I0406 15:59:40.653059 21485 sgd_solver.cpp:105] Iteration 5892, lr = 0.05
I0406 15:59:45.908279 21485 solver.cpp:218] Iteration 5904 (2.28345 iter/s, 5.25522s/12 iters), loss = 4.5697
I0406 15:59:45.908324 21485 solver.cpp:237]     Train net output #0: loss = 4.5697 (* 1 = 4.5697 loss)
I0406 15:59:45.908330 21485 sgd_solver.cpp:105] Iteration 5904, lr = 0.05
I0406 15:59:50.447566 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5916.caffemodel
I0406 15:59:57.081223 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5916.solverstate
I0406 15:59:59.411123 21485 solver.cpp:330] Iteration 5916, Testing net (#0)
I0406 15:59:59.411144 21485 net.cpp:676] Ignoring source layer train-data
I0406 16:00:01.433929 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:00:03.720295 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0306373
I0406 16:00:03.720340 21485 solver.cpp:397]     Test net output #1: loss = 4.93952 (* 1 = 4.93952 loss)
I0406 16:00:03.861047 21485 solver.cpp:218] Iteration 5916 (0.668423 iter/s, 17.9527s/12 iters), loss = 4.76997
I0406 16:00:03.861096 21485 solver.cpp:237]     Train net output #0: loss = 4.76997 (* 1 = 4.76997 loss)
I0406 16:00:03.861104 21485 sgd_solver.cpp:105] Iteration 5916, lr = 0.05
I0406 16:00:08.173316 21485 solver.cpp:218] Iteration 5928 (2.7828 iter/s, 4.3122s/12 iters), loss = 4.68778
I0406 16:00:08.173406 21485 solver.cpp:237]     Train net output #0: loss = 4.68778 (* 1 = 4.68778 loss)
I0406 16:00:08.173413 21485 sgd_solver.cpp:105] Iteration 5928, lr = 0.05
I0406 16:00:13.318650 21485 solver.cpp:218] Iteration 5940 (2.33226 iter/s, 5.14523s/12 iters), loss = 4.75355
I0406 16:00:13.318692 21485 solver.cpp:237]     Train net output #0: loss = 4.75355 (* 1 = 4.75355 loss)
I0406 16:00:13.318697 21485 sgd_solver.cpp:105] Iteration 5940, lr = 0.05
I0406 16:00:18.291608 21485 solver.cpp:218] Iteration 5952 (2.41308 iter/s, 4.97289s/12 iters), loss = 4.76438
I0406 16:00:18.291659 21485 solver.cpp:237]     Train net output #0: loss = 4.76438 (* 1 = 4.76438 loss)
I0406 16:00:18.291668 21485 sgd_solver.cpp:105] Iteration 5952, lr = 0.05
I0406 16:00:23.633153 21485 solver.cpp:218] Iteration 5964 (2.24657 iter/s, 5.34148s/12 iters), loss = 4.83376
I0406 16:00:23.633195 21485 solver.cpp:237]     Train net output #0: loss = 4.83376 (* 1 = 4.83376 loss)
I0406 16:00:23.633200 21485 sgd_solver.cpp:105] Iteration 5964, lr = 0.05
I0406 16:00:25.036788 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:00:28.850143 21485 solver.cpp:218] Iteration 5976 (2.3002 iter/s, 5.21693s/12 iters), loss = 4.78333
I0406 16:00:28.850190 21485 solver.cpp:237]     Train net output #0: loss = 4.78333 (* 1 = 4.78333 loss)
I0406 16:00:28.850198 21485 sgd_solver.cpp:105] Iteration 5976, lr = 0.05
I0406 16:00:34.097513 21485 solver.cpp:218] Iteration 5988 (2.28688 iter/s, 5.24731s/12 iters), loss = 4.86242
I0406 16:00:34.097553 21485 solver.cpp:237]     Train net output #0: loss = 4.86242 (* 1 = 4.86242 loss)
I0406 16:00:34.097559 21485 sgd_solver.cpp:105] Iteration 5988, lr = 0.05
I0406 16:00:39.321913 21485 solver.cpp:218] Iteration 6000 (2.29694 iter/s, 5.22435s/12 iters), loss = 4.74106
I0406 16:00:39.322036 21485 solver.cpp:237]     Train net output #0: loss = 4.74106 (* 1 = 4.74106 loss)
I0406 16:00:39.322042 21485 sgd_solver.cpp:105] Iteration 6000, lr = 0.05
I0406 16:00:44.444820 21485 solver.cpp:218] Iteration 6012 (2.34248 iter/s, 5.12278s/12 iters), loss = 4.73686
I0406 16:00:44.444864 21485 solver.cpp:237]     Train net output #0: loss = 4.73686 (* 1 = 4.73686 loss)
I0406 16:00:44.444870 21485 sgd_solver.cpp:105] Iteration 6012, lr = 0.05
I0406 16:00:46.494715 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6018.caffemodel
I0406 16:00:51.176219 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6018.solverstate
I0406 16:00:54.001186 21485 solver.cpp:330] Iteration 6018, Testing net (#0)
I0406 16:00:54.001209 21485 net.cpp:676] Ignoring source layer train-data
I0406 16:00:56.009662 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:00:58.390285 21485 solver.cpp:397]     Test net output #0: accuracy = 0.03125
I0406 16:00:58.390321 21485 solver.cpp:397]     Test net output #1: loss = 4.96512 (* 1 = 4.96512 loss)
I0406 16:01:00.213251 21485 solver.cpp:218] Iteration 6024 (0.761017 iter/s, 15.7684s/12 iters), loss = 4.70246
I0406 16:01:00.213312 21485 solver.cpp:237]     Train net output #0: loss = 4.70246 (* 1 = 4.70246 loss)
I0406 16:01:00.213322 21485 sgd_solver.cpp:105] Iteration 6024, lr = 0.05
I0406 16:01:05.290900 21485 solver.cpp:218] Iteration 6036 (2.36333 iter/s, 5.07757s/12 iters), loss = 4.60168
I0406 16:01:05.290956 21485 solver.cpp:237]     Train net output #0: loss = 4.60168 (* 1 = 4.60168 loss)
I0406 16:01:05.290963 21485 sgd_solver.cpp:105] Iteration 6036, lr = 0.05
I0406 16:01:10.571754 21485 solver.cpp:218] Iteration 6048 (2.27239 iter/s, 5.28078s/12 iters), loss = 4.63538
I0406 16:01:10.571887 21485 solver.cpp:237]     Train net output #0: loss = 4.63538 (* 1 = 4.63538 loss)
I0406 16:01:10.571895 21485 sgd_solver.cpp:105] Iteration 6048, lr = 0.05
I0406 16:01:15.581670 21485 solver.cpp:218] Iteration 6060 (2.39532 iter/s, 5.00977s/12 iters), loss = 4.80528
I0406 16:01:15.581722 21485 solver.cpp:237]     Train net output #0: loss = 4.80528 (* 1 = 4.80528 loss)
I0406 16:01:15.581730 21485 sgd_solver.cpp:105] Iteration 6060, lr = 0.05
I0406 16:01:19.212684 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:01:20.709736 21485 solver.cpp:218] Iteration 6072 (2.34009 iter/s, 5.12801s/12 iters), loss = 4.56328
I0406 16:01:20.709775 21485 solver.cpp:237]     Train net output #0: loss = 4.56328 (* 1 = 4.56328 loss)
I0406 16:01:20.709781 21485 sgd_solver.cpp:105] Iteration 6072, lr = 0.05
I0406 16:01:26.098073 21485 solver.cpp:218] Iteration 6084 (2.22705 iter/s, 5.38828s/12 iters), loss = 4.74889
I0406 16:01:26.098130 21485 solver.cpp:237]     Train net output #0: loss = 4.74889 (* 1 = 4.74889 loss)
I0406 16:01:26.098140 21485 sgd_solver.cpp:105] Iteration 6084, lr = 0.05
I0406 16:01:31.305562 21485 solver.cpp:218] Iteration 6096 (2.30441 iter/s, 5.20742s/12 iters), loss = 4.66797
I0406 16:01:31.305615 21485 solver.cpp:237]     Train net output #0: loss = 4.66797 (* 1 = 4.66797 loss)
I0406 16:01:31.305624 21485 sgd_solver.cpp:105] Iteration 6096, lr = 0.05
I0406 16:01:36.634905 21485 solver.cpp:218] Iteration 6108 (2.25171 iter/s, 5.32928s/12 iters), loss = 4.61671
I0406 16:01:36.634951 21485 solver.cpp:237]     Train net output #0: loss = 4.61671 (* 1 = 4.61671 loss)
I0406 16:01:36.634956 21485 sgd_solver.cpp:105] Iteration 6108, lr = 0.05
I0406 16:01:41.299471 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6120.caffemodel
I0406 16:01:45.712379 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6120.solverstate
I0406 16:01:48.913028 21485 solver.cpp:330] Iteration 6120, Testing net (#0)
I0406 16:01:48.913048 21485 net.cpp:676] Ignoring source layer train-data
I0406 16:01:50.888993 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:01:53.289119 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0355392
I0406 16:01:53.289152 21485 solver.cpp:397]     Test net output #1: loss = 4.95558 (* 1 = 4.95558 loss)
I0406 16:01:53.429451 21485 solver.cpp:218] Iteration 6120 (0.71452 iter/s, 16.7945s/12 iters), loss = 4.86869
I0406 16:01:53.429507 21485 solver.cpp:237]     Train net output #0: loss = 4.86869 (* 1 = 4.86869 loss)
I0406 16:01:53.429513 21485 sgd_solver.cpp:105] Iteration 6120, lr = 0.05
I0406 16:01:57.698280 21485 solver.cpp:218] Iteration 6132 (2.81112 iter/s, 4.26876s/12 iters), loss = 4.62018
I0406 16:01:57.698321 21485 solver.cpp:237]     Train net output #0: loss = 4.62018 (* 1 = 4.62018 loss)
I0406 16:01:57.698328 21485 sgd_solver.cpp:105] Iteration 6132, lr = 0.05
I0406 16:02:02.810006 21485 solver.cpp:218] Iteration 6144 (2.34757 iter/s, 5.11167s/12 iters), loss = 4.36792
I0406 16:02:02.810055 21485 solver.cpp:237]     Train net output #0: loss = 4.36792 (* 1 = 4.36792 loss)
I0406 16:02:02.810061 21485 sgd_solver.cpp:105] Iteration 6144, lr = 0.05
I0406 16:02:07.909217 21485 solver.cpp:218] Iteration 6156 (2.35333 iter/s, 5.09915s/12 iters), loss = 4.80279
I0406 16:02:07.909256 21485 solver.cpp:237]     Train net output #0: loss = 4.80279 (* 1 = 4.80279 loss)
I0406 16:02:07.909261 21485 sgd_solver.cpp:105] Iteration 6156, lr = 0.05
I0406 16:02:13.234341 21485 solver.cpp:218] Iteration 6168 (2.25349 iter/s, 5.32507s/12 iters), loss = 4.71695
I0406 16:02:13.234445 21485 solver.cpp:237]     Train net output #0: loss = 4.71695 (* 1 = 4.71695 loss)
I0406 16:02:13.234452 21485 sgd_solver.cpp:105] Iteration 6168, lr = 0.05
I0406 16:02:13.848285 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:02:18.447966 21485 solver.cpp:218] Iteration 6180 (2.30171 iter/s, 5.21351s/12 iters), loss = 4.97519
I0406 16:02:18.448017 21485 solver.cpp:237]     Train net output #0: loss = 4.97519 (* 1 = 4.97519 loss)
I0406 16:02:18.448024 21485 sgd_solver.cpp:105] Iteration 6180, lr = 0.05
I0406 16:02:23.876010 21485 solver.cpp:218] Iteration 6192 (2.21077 iter/s, 5.42798s/12 iters), loss = 4.79465
I0406 16:02:23.876060 21485 solver.cpp:237]     Train net output #0: loss = 4.79465 (* 1 = 4.79465 loss)
I0406 16:02:23.876067 21485 sgd_solver.cpp:105] Iteration 6192, lr = 0.05
I0406 16:02:29.085404 21485 solver.cpp:218] Iteration 6204 (2.30356 iter/s, 5.20933s/12 iters), loss = 4.63935
I0406 16:02:29.085461 21485 solver.cpp:237]     Train net output #0: loss = 4.63935 (* 1 = 4.63935 loss)
I0406 16:02:29.085469 21485 sgd_solver.cpp:105] Iteration 6204, lr = 0.05
I0406 16:02:34.443614 21485 solver.cpp:218] Iteration 6216 (2.23958 iter/s, 5.35815s/12 iters), loss = 4.84211
I0406 16:02:34.443660 21485 solver.cpp:237]     Train net output #0: loss = 4.84211 (* 1 = 4.84211 loss)
I0406 16:02:34.443666 21485 sgd_solver.cpp:105] Iteration 6216, lr = 0.05
I0406 16:02:36.648300 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6222.caffemodel
I0406 16:02:41.070329 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6222.solverstate
I0406 16:02:44.060185 21485 solver.cpp:330] Iteration 6222, Testing net (#0)
I0406 16:02:44.060314 21485 net.cpp:676] Ignoring source layer train-data
I0406 16:02:45.965708 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:02:47.267392 21485 blocking_queue.cpp:49] Waiting for data
I0406 16:02:48.431246 21485 solver.cpp:397]     Test net output #0: accuracy = 0.036152
I0406 16:02:48.431285 21485 solver.cpp:397]     Test net output #1: loss = 4.96199 (* 1 = 4.96199 loss)
I0406 16:02:50.186211 21485 solver.cpp:218] Iteration 6228 (0.762266 iter/s, 15.7425s/12 iters), loss = 4.58215
I0406 16:02:50.186262 21485 solver.cpp:237]     Train net output #0: loss = 4.58215 (* 1 = 4.58215 loss)
I0406 16:02:50.186269 21485 sgd_solver.cpp:105] Iteration 6228, lr = 0.05
I0406 16:02:55.152570 21485 solver.cpp:218] Iteration 6240 (2.41629 iter/s, 4.96629s/12 iters), loss = 4.65025
I0406 16:02:55.152623 21485 solver.cpp:237]     Train net output #0: loss = 4.65025 (* 1 = 4.65025 loss)
I0406 16:02:55.152631 21485 sgd_solver.cpp:105] Iteration 6240, lr = 0.05
I0406 16:03:00.289350 21485 solver.cpp:218] Iteration 6252 (2.33612 iter/s, 5.13671s/12 iters), loss = 4.60209
I0406 16:03:00.289397 21485 solver.cpp:237]     Train net output #0: loss = 4.60209 (* 1 = 4.60209 loss)
I0406 16:03:00.289402 21485 sgd_solver.cpp:105] Iteration 6252, lr = 0.05
I0406 16:03:05.486687 21485 solver.cpp:218] Iteration 6264 (2.3089 iter/s, 5.19728s/12 iters), loss = 4.7569
I0406 16:03:05.486729 21485 solver.cpp:237]     Train net output #0: loss = 4.7569 (* 1 = 4.7569 loss)
I0406 16:03:05.486735 21485 sgd_solver.cpp:105] Iteration 6264, lr = 0.05
I0406 16:03:08.323479 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:03:10.694375 21485 solver.cpp:218] Iteration 6276 (2.30431 iter/s, 5.20763s/12 iters), loss = 4.73675
I0406 16:03:10.694420 21485 solver.cpp:237]     Train net output #0: loss = 4.73675 (* 1 = 4.73675 loss)
I0406 16:03:10.694427 21485 sgd_solver.cpp:105] Iteration 6276, lr = 0.05
I0406 16:03:16.195137 21485 solver.cpp:218] Iteration 6288 (2.18154 iter/s, 5.5007s/12 iters), loss = 4.65844
I0406 16:03:16.195251 21485 solver.cpp:237]     Train net output #0: loss = 4.65844 (* 1 = 4.65844 loss)
I0406 16:03:16.195259 21485 sgd_solver.cpp:105] Iteration 6288, lr = 0.05
I0406 16:03:21.299737 21485 solver.cpp:218] Iteration 6300 (2.35088 iter/s, 5.10448s/12 iters), loss = 4.77809
I0406 16:03:21.299783 21485 solver.cpp:237]     Train net output #0: loss = 4.77809 (* 1 = 4.77809 loss)
I0406 16:03:21.299789 21485 sgd_solver.cpp:105] Iteration 6300, lr = 0.05
I0406 16:03:26.740453 21485 solver.cpp:218] Iteration 6312 (2.20561 iter/s, 5.44066s/12 iters), loss = 4.68276
I0406 16:03:26.740505 21485 solver.cpp:237]     Train net output #0: loss = 4.68276 (* 1 = 4.68276 loss)
I0406 16:03:26.740511 21485 sgd_solver.cpp:105] Iteration 6312, lr = 0.05
I0406 16:03:31.450011 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6324.caffemodel
I0406 16:03:36.176405 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6324.solverstate
I0406 16:03:40.092682 21485 solver.cpp:330] Iteration 6324, Testing net (#0)
I0406 16:03:40.092701 21485 net.cpp:676] Ignoring source layer train-data
I0406 16:03:41.936517 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:03:44.422634 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0373775
I0406 16:03:44.422670 21485 solver.cpp:397]     Test net output #1: loss = 4.96153 (* 1 = 4.96153 loss)
I0406 16:03:44.560933 21485 solver.cpp:218] Iteration 6324 (0.673385 iter/s, 17.8204s/12 iters), loss = 4.68935
I0406 16:03:44.560974 21485 solver.cpp:237]     Train net output #0: loss = 4.68935 (* 1 = 4.68935 loss)
I0406 16:03:44.560981 21485 sgd_solver.cpp:105] Iteration 6324, lr = 0.05
I0406 16:03:48.718780 21485 solver.cpp:218] Iteration 6336 (2.88615 iter/s, 4.15779s/12 iters), loss = 4.82615
I0406 16:03:48.718984 21485 solver.cpp:237]     Train net output #0: loss = 4.82615 (* 1 = 4.82615 loss)
I0406 16:03:48.718998 21485 sgd_solver.cpp:105] Iteration 6336, lr = 0.05
I0406 16:03:53.910948 21485 solver.cpp:218] Iteration 6348 (2.31127 iter/s, 5.19195s/12 iters), loss = 4.78385
I0406 16:03:53.911007 21485 solver.cpp:237]     Train net output #0: loss = 4.78385 (* 1 = 4.78385 loss)
I0406 16:03:53.911016 21485 sgd_solver.cpp:105] Iteration 6348, lr = 0.05
I0406 16:03:59.149271 21485 solver.cpp:218] Iteration 6360 (2.29084 iter/s, 5.23825s/12 iters), loss = 4.67931
I0406 16:03:59.149329 21485 solver.cpp:237]     Train net output #0: loss = 4.67931 (* 1 = 4.67931 loss)
I0406 16:03:59.149338 21485 sgd_solver.cpp:105] Iteration 6360, lr = 0.05
I0406 16:04:04.011855 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:04:04.205278 21485 solver.cpp:218] Iteration 6372 (2.37345 iter/s, 5.05593s/12 iters), loss = 4.76228
I0406 16:04:04.205320 21485 solver.cpp:237]     Train net output #0: loss = 4.76228 (* 1 = 4.76228 loss)
I0406 16:04:04.205327 21485 sgd_solver.cpp:105] Iteration 6372, lr = 0.05
I0406 16:04:09.575304 21485 solver.cpp:218] Iteration 6384 (2.23465 iter/s, 5.36997s/12 iters), loss = 4.83831
I0406 16:04:09.575342 21485 solver.cpp:237]     Train net output #0: loss = 4.83831 (* 1 = 4.83831 loss)
I0406 16:04:09.575347 21485 sgd_solver.cpp:105] Iteration 6384, lr = 0.05
I0406 16:04:14.924998 21485 solver.cpp:218] Iteration 6396 (2.24314 iter/s, 5.34964s/12 iters), loss = 4.85286
I0406 16:04:14.925052 21485 solver.cpp:237]     Train net output #0: loss = 4.85286 (* 1 = 4.85286 loss)
I0406 16:04:14.925060 21485 sgd_solver.cpp:105] Iteration 6396, lr = 0.05
I0406 16:04:20.029289 21485 solver.cpp:218] Iteration 6408 (2.35099 iter/s, 5.10423s/12 iters), loss = 5.24501
I0406 16:04:20.029382 21485 solver.cpp:237]     Train net output #0: loss = 5.24501 (* 1 = 5.24501 loss)
I0406 16:04:20.029388 21485 sgd_solver.cpp:105] Iteration 6408, lr = 0.05
I0406 16:04:25.098186 21485 solver.cpp:218] Iteration 6420 (2.36743 iter/s, 5.06879s/12 iters), loss = 4.97544
I0406 16:04:25.098233 21485 solver.cpp:237]     Train net output #0: loss = 4.97544 (* 1 = 4.97544 loss)
I0406 16:04:25.098240 21485 sgd_solver.cpp:105] Iteration 6420, lr = 0.05
I0406 16:04:27.274173 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6426.caffemodel
I0406 16:04:31.711730 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6426.solverstate
I0406 16:04:35.524389 21485 solver.cpp:330] Iteration 6426, Testing net (#0)
I0406 16:04:35.524410 21485 net.cpp:676] Ignoring source layer train-data
I0406 16:04:37.321841 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:04:39.864552 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0245098
I0406 16:04:39.864895 21485 solver.cpp:397]     Test net output #1: loss = 5.04787 (* 1 = 5.04787 loss)
I0406 16:04:41.646263 21485 solver.cpp:218] Iteration 6432 (0.725162 iter/s, 16.548s/12 iters), loss = 4.89469
I0406 16:04:41.646324 21485 solver.cpp:237]     Train net output #0: loss = 4.89469 (* 1 = 4.89469 loss)
I0406 16:04:41.646335 21485 sgd_solver.cpp:105] Iteration 6432, lr = 0.05
I0406 16:04:46.813553 21485 solver.cpp:218] Iteration 6444 (2.32234 iter/s, 5.16721s/12 iters), loss = 4.85105
I0406 16:04:46.813611 21485 solver.cpp:237]     Train net output #0: loss = 4.85105 (* 1 = 4.85105 loss)
I0406 16:04:46.813621 21485 sgd_solver.cpp:105] Iteration 6444, lr = 0.05
I0406 16:04:52.273049 21485 solver.cpp:218] Iteration 6456 (2.19803 iter/s, 5.45943s/12 iters), loss = 4.90593
I0406 16:04:52.273147 21485 solver.cpp:237]     Train net output #0: loss = 4.90593 (* 1 = 4.90593 loss)
I0406 16:04:52.273154 21485 sgd_solver.cpp:105] Iteration 6456, lr = 0.05
I0406 16:04:57.373320 21485 solver.cpp:218] Iteration 6468 (2.35287 iter/s, 5.10016s/12 iters), loss = 4.71962
I0406 16:04:57.373358 21485 solver.cpp:237]     Train net output #0: loss = 4.71962 (* 1 = 4.71962 loss)
I0406 16:04:57.373363 21485 sgd_solver.cpp:105] Iteration 6468, lr = 0.05
I0406 16:04:59.372982 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:05:02.602625 21485 solver.cpp:218] Iteration 6480 (2.29478 iter/s, 5.22925s/12 iters), loss = 4.94697
I0406 16:05:02.602671 21485 solver.cpp:237]     Train net output #0: loss = 4.94697 (* 1 = 4.94697 loss)
I0406 16:05:02.602677 21485 sgd_solver.cpp:105] Iteration 6480, lr = 0.05
I0406 16:05:07.857487 21485 solver.cpp:218] Iteration 6492 (2.28363 iter/s, 5.2548s/12 iters), loss = 5.0932
I0406 16:05:07.857535 21485 solver.cpp:237]     Train net output #0: loss = 5.0932 (* 1 = 5.0932 loss)
I0406 16:05:07.857543 21485 sgd_solver.cpp:105] Iteration 6492, lr = 0.05
I0406 16:05:13.053434 21485 solver.cpp:218] Iteration 6504 (2.30952 iter/s, 5.19589s/12 iters), loss = 4.96543
I0406 16:05:13.053472 21485 solver.cpp:237]     Train net output #0: loss = 4.96543 (* 1 = 4.96543 loss)
I0406 16:05:13.053478 21485 sgd_solver.cpp:105] Iteration 6504, lr = 0.05
I0406 16:05:18.336004 21485 solver.cpp:218] Iteration 6516 (2.27165 iter/s, 5.28251s/12 iters), loss = 4.80222
I0406 16:05:18.336056 21485 solver.cpp:237]     Train net output #0: loss = 4.80222 (* 1 = 4.80222 loss)
I0406 16:05:18.336063 21485 sgd_solver.cpp:105] Iteration 6516, lr = 0.05
I0406 16:05:23.161267 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6528.caffemodel
I0406 16:05:27.379691 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6528.solverstate
I0406 16:05:31.134496 21485 solver.cpp:330] Iteration 6528, Testing net (#0)
I0406 16:05:31.134521 21485 net.cpp:676] Ignoring source layer train-data
I0406 16:05:32.915493 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:05:35.479100 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0294118
I0406 16:05:35.479135 21485 solver.cpp:397]     Test net output #1: loss = 5.02379 (* 1 = 5.02379 loss)
I0406 16:05:35.617885 21485 solver.cpp:218] Iteration 6528 (0.694371 iter/s, 17.2818s/12 iters), loss = 4.79874
I0406 16:05:35.617940 21485 solver.cpp:237]     Train net output #0: loss = 4.79874 (* 1 = 4.79874 loss)
I0406 16:05:35.617950 21485 sgd_solver.cpp:105] Iteration 6528, lr = 0.05
I0406 16:05:39.763273 21485 solver.cpp:218] Iteration 6540 (2.89483 iter/s, 4.14532s/12 iters), loss = 5.01303
I0406 16:05:39.763315 21485 solver.cpp:237]     Train net output #0: loss = 5.01303 (* 1 = 5.01303 loss)
I0406 16:05:39.763321 21485 sgd_solver.cpp:105] Iteration 6540, lr = 0.05
I0406 16:05:44.952109 21485 solver.cpp:218] Iteration 6552 (2.31269 iter/s, 5.18877s/12 iters), loss = 4.69908
I0406 16:05:44.952169 21485 solver.cpp:237]     Train net output #0: loss = 4.69908 (* 1 = 4.69908 loss)
I0406 16:05:44.952178 21485 sgd_solver.cpp:105] Iteration 6552, lr = 0.05
I0406 16:05:50.260704 21485 solver.cpp:218] Iteration 6564 (2.26052 iter/s, 5.30852s/12 iters), loss = 4.76639
I0406 16:05:50.260756 21485 solver.cpp:237]     Train net output #0: loss = 4.76639 (* 1 = 4.76639 loss)
I0406 16:05:50.260764 21485 sgd_solver.cpp:105] Iteration 6564, lr = 0.05
I0406 16:05:54.570866 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:05:55.397735 21485 solver.cpp:218] Iteration 6576 (2.33601 iter/s, 5.13696s/12 iters), loss = 4.82717
I0406 16:05:55.397786 21485 solver.cpp:237]     Train net output #0: loss = 4.82717 (* 1 = 4.82717 loss)
I0406 16:05:55.397794 21485 sgd_solver.cpp:105] Iteration 6576, lr = 0.05
I0406 16:06:00.710530 21485 solver.cpp:218] Iteration 6588 (2.25872 iter/s, 5.31273s/12 iters), loss = 4.86842
I0406 16:06:00.710572 21485 solver.cpp:237]     Train net output #0: loss = 4.86842 (* 1 = 4.86842 loss)
I0406 16:06:00.710578 21485 sgd_solver.cpp:105] Iteration 6588, lr = 0.05
I0406 16:06:05.859035 21485 solver.cpp:218] Iteration 6600 (2.3308 iter/s, 5.14844s/12 iters), loss = 4.98533
I0406 16:06:05.859094 21485 solver.cpp:237]     Train net output #0: loss = 4.98533 (* 1 = 4.98533 loss)
I0406 16:06:05.859102 21485 sgd_solver.cpp:105] Iteration 6600, lr = 0.05
I0406 16:06:11.170904 21485 solver.cpp:218] Iteration 6612 (2.25912 iter/s, 5.3118s/12 iters), loss = 4.93716
I0406 16:06:11.170958 21485 solver.cpp:237]     Train net output #0: loss = 4.93716 (* 1 = 4.93716 loss)
I0406 16:06:11.170966 21485 sgd_solver.cpp:105] Iteration 6612, lr = 0.05
I0406 16:06:16.181753 21485 solver.cpp:218] Iteration 6624 (2.39484 iter/s, 5.01078s/12 iters), loss = 4.75478
I0406 16:06:16.181809 21485 solver.cpp:237]     Train net output #0: loss = 4.75478 (* 1 = 4.75478 loss)
I0406 16:06:16.181818 21485 sgd_solver.cpp:105] Iteration 6624, lr = 0.05
I0406 16:06:18.275794 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6630.caffemodel
I0406 16:06:23.260030 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6630.solverstate
I0406 16:06:27.007992 21485 solver.cpp:330] Iteration 6630, Testing net (#0)
I0406 16:06:27.008116 21485 net.cpp:676] Ignoring source layer train-data
I0406 16:06:28.750921 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:06:31.427031 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0275735
I0406 16:06:31.427059 21485 solver.cpp:397]     Test net output #1: loss = 4.99021 (* 1 = 4.99021 loss)
I0406 16:06:33.477236 21485 solver.cpp:218] Iteration 6636 (0.693825 iter/s, 17.2954s/12 iters), loss = 5.00706
I0406 16:06:33.477281 21485 solver.cpp:237]     Train net output #0: loss = 5.00706 (* 1 = 5.00706 loss)
I0406 16:06:33.477286 21485 sgd_solver.cpp:105] Iteration 6636, lr = 0.05
I0406 16:06:38.664989 21485 solver.cpp:218] Iteration 6648 (2.31317 iter/s, 5.18769s/12 iters), loss = 4.86699
I0406 16:06:38.665043 21485 solver.cpp:237]     Train net output #0: loss = 4.86699 (* 1 = 4.86699 loss)
I0406 16:06:38.665052 21485 sgd_solver.cpp:105] Iteration 6648, lr = 0.05
I0406 16:06:43.953985 21485 solver.cpp:218] Iteration 6660 (2.26889 iter/s, 5.28893s/12 iters), loss = 4.88915
I0406 16:06:43.954041 21485 solver.cpp:237]     Train net output #0: loss = 4.88915 (* 1 = 4.88915 loss)
I0406 16:06:43.954048 21485 sgd_solver.cpp:105] Iteration 6660, lr = 0.05
I0406 16:06:49.209774 21485 solver.cpp:218] Iteration 6672 (2.28323 iter/s, 5.25572s/12 iters), loss = 4.86925
I0406 16:06:49.209815 21485 solver.cpp:237]     Train net output #0: loss = 4.86925 (* 1 = 4.86925 loss)
I0406 16:06:49.209820 21485 sgd_solver.cpp:105] Iteration 6672, lr = 0.05
I0406 16:06:50.706331 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:06:54.608119 21485 solver.cpp:218] Iteration 6684 (2.22293 iter/s, 5.39828s/12 iters), loss = 4.82649
I0406 16:06:54.608176 21485 solver.cpp:237]     Train net output #0: loss = 4.82649 (* 1 = 4.82649 loss)
I0406 16:06:54.608184 21485 sgd_solver.cpp:105] Iteration 6684, lr = 0.05
I0406 16:06:59.935156 21485 solver.cpp:218] Iteration 6696 (2.25269 iter/s, 5.32697s/12 iters), loss = 5.08841
I0406 16:06:59.935261 21485 solver.cpp:237]     Train net output #0: loss = 5.08841 (* 1 = 5.08841 loss)
I0406 16:06:59.935266 21485 sgd_solver.cpp:105] Iteration 6696, lr = 0.05
I0406 16:07:04.849457 21485 solver.cpp:218] Iteration 6708 (2.44191 iter/s, 4.91419s/12 iters), loss = 4.78233
I0406 16:07:04.849496 21485 solver.cpp:237]     Train net output #0: loss = 4.78233 (* 1 = 4.78233 loss)
I0406 16:07:04.849501 21485 sgd_solver.cpp:105] Iteration 6708, lr = 0.05
I0406 16:07:10.061862 21485 solver.cpp:218] Iteration 6720 (2.30223 iter/s, 5.21235s/12 iters), loss = 4.78191
I0406 16:07:10.061919 21485 solver.cpp:237]     Train net output #0: loss = 4.78191 (* 1 = 4.78191 loss)
I0406 16:07:10.061928 21485 sgd_solver.cpp:105] Iteration 6720, lr = 0.05
I0406 16:07:14.756222 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6732.caffemodel
I0406 16:07:18.724941 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6732.solverstate
I0406 16:07:25.076540 21485 solver.cpp:330] Iteration 6732, Testing net (#0)
I0406 16:07:25.076557 21485 net.cpp:676] Ignoring source layer train-data
I0406 16:07:26.799170 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:07:29.418349 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0269608
I0406 16:07:29.418380 21485 solver.cpp:397]     Test net output #1: loss = 5.03329 (* 1 = 5.03329 loss)
I0406 16:07:29.553745 21485 solver.cpp:218] Iteration 6732 (0.615643 iter/s, 19.4918s/12 iters), loss = 4.80966
I0406 16:07:29.555362 21485 solver.cpp:237]     Train net output #0: loss = 4.80966 (* 1 = 4.80966 loss)
I0406 16:07:29.555375 21485 sgd_solver.cpp:105] Iteration 6732, lr = 0.05
I0406 16:07:33.704386 21485 solver.cpp:218] Iteration 6744 (2.89225 iter/s, 4.14901s/12 iters), loss = 4.84992
I0406 16:07:33.704509 21485 solver.cpp:237]     Train net output #0: loss = 4.84992 (* 1 = 4.84992 loss)
I0406 16:07:33.704516 21485 sgd_solver.cpp:105] Iteration 6744, lr = 0.05
I0406 16:07:38.838071 21485 solver.cpp:218] Iteration 6756 (2.33757 iter/s, 5.13354s/12 iters), loss = 4.88401
I0406 16:07:38.838125 21485 solver.cpp:237]     Train net output #0: loss = 4.88401 (* 1 = 4.88401 loss)
I0406 16:07:38.838133 21485 sgd_solver.cpp:105] Iteration 6756, lr = 0.05
I0406 16:07:43.902251 21485 solver.cpp:218] Iteration 6768 (2.36962 iter/s, 5.06411s/12 iters), loss = 4.92124
I0406 16:07:43.902289 21485 solver.cpp:237]     Train net output #0: loss = 4.92124 (* 1 = 4.92124 loss)
I0406 16:07:43.902295 21485 sgd_solver.cpp:105] Iteration 6768, lr = 0.05
I0406 16:07:47.487254 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:07:49.116569 21485 solver.cpp:218] Iteration 6780 (2.30138 iter/s, 5.21427s/12 iters), loss = 4.90228
I0406 16:07:49.116607 21485 solver.cpp:237]     Train net output #0: loss = 4.90228 (* 1 = 4.90228 loss)
I0406 16:07:49.116613 21485 sgd_solver.cpp:105] Iteration 6780, lr = 0.05
I0406 16:07:54.484302 21485 solver.cpp:218] Iteration 6792 (2.2356 iter/s, 5.36768s/12 iters), loss = 4.90257
I0406 16:07:54.484347 21485 solver.cpp:237]     Train net output #0: loss = 4.90257 (* 1 = 4.90257 loss)
I0406 16:07:54.484354 21485 sgd_solver.cpp:105] Iteration 6792, lr = 0.05
I0406 16:07:59.860468 21485 solver.cpp:218] Iteration 6804 (2.2321 iter/s, 5.3761s/12 iters), loss = 5.00832
I0406 16:07:59.860512 21485 solver.cpp:237]     Train net output #0: loss = 5.00832 (* 1 = 5.00832 loss)
I0406 16:07:59.860517 21485 sgd_solver.cpp:105] Iteration 6804, lr = 0.05
I0406 16:08:05.227980 21485 solver.cpp:218] Iteration 6816 (2.2357 iter/s, 5.36744s/12 iters), loss = 4.84086
I0406 16:08:05.228101 21485 solver.cpp:237]     Train net output #0: loss = 4.84086 (* 1 = 4.84086 loss)
I0406 16:08:05.228111 21485 sgd_solver.cpp:105] Iteration 6816, lr = 0.05
I0406 16:08:10.580520 21485 solver.cpp:218] Iteration 6828 (2.24198 iter/s, 5.3524s/12 iters), loss = 4.81443
I0406 16:08:10.580581 21485 solver.cpp:237]     Train net output #0: loss = 4.81443 (* 1 = 4.81443 loss)
I0406 16:08:10.580590 21485 sgd_solver.cpp:105] Iteration 6828, lr = 0.05
I0406 16:08:12.720500 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6834.caffemodel
I0406 16:08:18.770679 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6834.solverstate
I0406 16:08:23.408131 21485 solver.cpp:330] Iteration 6834, Testing net (#0)
I0406 16:08:23.408154 21485 net.cpp:676] Ignoring source layer train-data
I0406 16:08:25.093096 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:08:27.805506 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0306373
I0406 16:08:27.805531 21485 solver.cpp:397]     Test net output #1: loss = 5.02058 (* 1 = 5.02058 loss)
I0406 16:08:29.686731 21485 solver.cpp:218] Iteration 6840 (0.628071 iter/s, 19.1061s/12 iters), loss = 4.59623
I0406 16:08:29.686789 21485 solver.cpp:237]     Train net output #0: loss = 4.59623 (* 1 = 4.59623 loss)
I0406 16:08:29.686796 21485 sgd_solver.cpp:105] Iteration 6840, lr = 0.05
I0406 16:08:34.800411 21485 solver.cpp:218] Iteration 6852 (2.34668 iter/s, 5.11361s/12 iters), loss = 4.6691
I0406 16:08:34.800457 21485 solver.cpp:237]     Train net output #0: loss = 4.6691 (* 1 = 4.6691 loss)
I0406 16:08:34.800464 21485 sgd_solver.cpp:105] Iteration 6852, lr = 0.05
I0406 16:08:40.014555 21485 solver.cpp:218] Iteration 6864 (2.30146 iter/s, 5.21408s/12 iters), loss = 4.80472
I0406 16:08:40.014695 21485 solver.cpp:237]     Train net output #0: loss = 4.80472 (* 1 = 4.80472 loss)
I0406 16:08:40.014703 21485 sgd_solver.cpp:105] Iteration 6864, lr = 0.05
I0406 16:08:45.103127 21485 solver.cpp:218] Iteration 6876 (2.3583 iter/s, 5.08842s/12 iters), loss = 4.86679
I0406 16:08:45.103168 21485 solver.cpp:237]     Train net output #0: loss = 4.86679 (* 1 = 4.86679 loss)
I0406 16:08:45.103173 21485 sgd_solver.cpp:105] Iteration 6876, lr = 0.05
I0406 16:08:45.666004 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:08:50.074534 21485 solver.cpp:218] Iteration 6888 (2.41383 iter/s, 4.97135s/12 iters), loss = 4.82667
I0406 16:08:50.074579 21485 solver.cpp:237]     Train net output #0: loss = 4.82667 (* 1 = 4.82667 loss)
I0406 16:08:50.074586 21485 sgd_solver.cpp:105] Iteration 6888, lr = 0.05
I0406 16:08:55.360766 21485 solver.cpp:218] Iteration 6900 (2.27008 iter/s, 5.28616s/12 iters), loss = 5.00593
I0406 16:08:55.360819 21485 solver.cpp:237]     Train net output #0: loss = 5.00593 (* 1 = 5.00593 loss)
I0406 16:08:55.360827 21485 sgd_solver.cpp:105] Iteration 6900, lr = 0.05
I0406 16:09:00.724370 21485 solver.cpp:218] Iteration 6912 (2.23733 iter/s, 5.36354s/12 iters), loss = 4.92618
I0406 16:09:00.724412 21485 solver.cpp:237]     Train net output #0: loss = 4.92618 (* 1 = 4.92618 loss)
I0406 16:09:00.724417 21485 sgd_solver.cpp:105] Iteration 6912, lr = 0.05
I0406 16:09:06.078590 21485 solver.cpp:218] Iteration 6924 (2.24125 iter/s, 5.35416s/12 iters), loss = 4.62425
I0406 16:09:06.078631 21485 solver.cpp:237]     Train net output #0: loss = 4.62425 (* 1 = 4.62425 loss)
I0406 16:09:06.078637 21485 sgd_solver.cpp:105] Iteration 6924, lr = 0.05
I0406 16:09:10.680253 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6936.caffemodel
I0406 16:09:15.515848 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6936.solverstate
I0406 16:09:19.498446 21485 solver.cpp:330] Iteration 6936, Testing net (#0)
I0406 16:09:19.498466 21485 net.cpp:676] Ignoring source layer train-data
I0406 16:09:20.057418 21485 blocking_queue.cpp:49] Waiting for data
I0406 16:09:21.088768 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:09:23.896760 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0355392
I0406 16:09:23.896793 21485 solver.cpp:397]     Test net output #1: loss = 5.02338 (* 1 = 5.02338 loss)
I0406 16:09:24.037336 21485 solver.cpp:218] Iteration 6936 (0.6682 iter/s, 17.9587s/12 iters), loss = 4.62947
I0406 16:09:24.037392 21485 solver.cpp:237]     Train net output #0: loss = 4.62947 (* 1 = 4.62947 loss)
I0406 16:09:24.037401 21485 sgd_solver.cpp:105] Iteration 6936, lr = 0.05
I0406 16:09:28.400382 21485 solver.cpp:218] Iteration 6948 (2.75042 iter/s, 4.36297s/12 iters), loss = 4.77062
I0406 16:09:28.400424 21485 solver.cpp:237]     Train net output #0: loss = 4.77062 (* 1 = 4.77062 loss)
I0406 16:09:28.400429 21485 sgd_solver.cpp:105] Iteration 6948, lr = 0.05
I0406 16:09:33.487001 21485 solver.cpp:218] Iteration 6960 (2.35916 iter/s, 5.08656s/12 iters), loss = 4.71779
I0406 16:09:33.487040 21485 solver.cpp:237]     Train net output #0: loss = 4.71779 (* 1 = 4.71779 loss)
I0406 16:09:33.487046 21485 sgd_solver.cpp:105] Iteration 6960, lr = 0.05
I0406 16:09:38.797679 21485 solver.cpp:218] Iteration 6972 (2.25962 iter/s, 5.31062s/12 iters), loss = 4.8407
I0406 16:09:38.797730 21485 solver.cpp:237]     Train net output #0: loss = 4.8407 (* 1 = 4.8407 loss)
I0406 16:09:38.797735 21485 sgd_solver.cpp:105] Iteration 6972, lr = 0.05
I0406 16:09:41.813524 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:09:44.170532 21485 solver.cpp:218] Iteration 6984 (2.23348 iter/s, 5.37279s/12 iters), loss = 4.72055
I0406 16:09:44.170574 21485 solver.cpp:237]     Train net output #0: loss = 4.72055 (* 1 = 4.72055 loss)
I0406 16:09:44.170580 21485 sgd_solver.cpp:105] Iteration 6984, lr = 0.05
I0406 16:09:49.496214 21485 solver.cpp:218] Iteration 6996 (2.25326 iter/s, 5.32562s/12 iters), loss = 4.78033
I0406 16:09:49.496263 21485 solver.cpp:237]     Train net output #0: loss = 4.78033 (* 1 = 4.78033 loss)
I0406 16:09:49.496271 21485 sgd_solver.cpp:105] Iteration 6996, lr = 0.05
I0406 16:09:54.753644 21485 solver.cpp:218] Iteration 7008 (2.28252 iter/s, 5.25735s/12 iters), loss = 4.91639
I0406 16:09:54.753695 21485 solver.cpp:237]     Train net output #0: loss = 4.91639 (* 1 = 4.91639 loss)
I0406 16:09:54.753703 21485 sgd_solver.cpp:105] Iteration 7008, lr = 0.05
I0406 16:10:00.005946 21485 solver.cpp:218] Iteration 7020 (2.28474 iter/s, 5.25224s/12 iters), loss = 4.79615
I0406 16:10:00.005986 21485 solver.cpp:237]     Train net output #0: loss = 4.79615 (* 1 = 4.79615 loss)
I0406 16:10:00.005995 21485 sgd_solver.cpp:105] Iteration 7020, lr = 0.05
I0406 16:10:05.275513 21485 solver.cpp:218] Iteration 7032 (2.27725 iter/s, 5.26951s/12 iters), loss = 4.8165
I0406 16:10:05.275555 21485 solver.cpp:237]     Train net output #0: loss = 4.8165 (* 1 = 4.8165 loss)
I0406 16:10:05.275561 21485 sgd_solver.cpp:105] Iteration 7032, lr = 0.05
I0406 16:10:07.455195 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7038.caffemodel
I0406 16:10:11.872606 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7038.solverstate
I0406 16:10:15.792136 21485 solver.cpp:330] Iteration 7038, Testing net (#0)
I0406 16:10:15.792155 21485 net.cpp:676] Ignoring source layer train-data
I0406 16:10:17.365468 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:10:20.170729 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0367647
I0406 16:10:20.170764 21485 solver.cpp:397]     Test net output #1: loss = 5.00365 (* 1 = 5.00365 loss)
I0406 16:10:22.171306 21485 solver.cpp:218] Iteration 7044 (0.710238 iter/s, 16.8957s/12 iters), loss = 5.03813
I0406 16:10:22.171350 21485 solver.cpp:237]     Train net output #0: loss = 5.03813 (* 1 = 5.03813 loss)
I0406 16:10:22.171355 21485 sgd_solver.cpp:105] Iteration 7044, lr = 0.05
I0406 16:10:27.279472 21485 solver.cpp:218] Iteration 7056 (2.34921 iter/s, 5.1081s/12 iters), loss = 4.76311
I0406 16:10:27.279518 21485 solver.cpp:237]     Train net output #0: loss = 4.76311 (* 1 = 4.76311 loss)
I0406 16:10:27.279523 21485 sgd_solver.cpp:105] Iteration 7056, lr = 0.05
I0406 16:10:32.388015 21485 solver.cpp:218] Iteration 7068 (2.34904 iter/s, 5.10848s/12 iters), loss = 4.72955
I0406 16:10:32.388064 21485 solver.cpp:237]     Train net output #0: loss = 4.72955 (* 1 = 4.72955 loss)
I0406 16:10:32.388072 21485 sgd_solver.cpp:105] Iteration 7068, lr = 0.05
I0406 16:10:37.510695 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:10:37.673401 21485 solver.cpp:218] Iteration 7080 (2.27044 iter/s, 5.28532s/12 iters), loss = 4.80181
I0406 16:10:37.673441 21485 solver.cpp:237]     Train net output #0: loss = 4.80181 (* 1 = 4.80181 loss)
I0406 16:10:37.673447 21485 sgd_solver.cpp:105] Iteration 7080, lr = 0.05
I0406 16:10:42.884090 21485 solver.cpp:218] Iteration 7092 (2.30298 iter/s, 5.21063s/12 iters), loss = 4.85764
I0406 16:10:42.884179 21485 solver.cpp:237]     Train net output #0: loss = 4.85764 (* 1 = 4.85764 loss)
I0406 16:10:42.884186 21485 sgd_solver.cpp:105] Iteration 7092, lr = 0.05
I0406 16:10:47.892788 21485 solver.cpp:218] Iteration 7104 (2.39588 iter/s, 5.00859s/12 iters), loss = 4.82698
I0406 16:10:47.892840 21485 solver.cpp:237]     Train net output #0: loss = 4.82698 (* 1 = 4.82698 loss)
I0406 16:10:47.892848 21485 sgd_solver.cpp:105] Iteration 7104, lr = 0.05
I0406 16:10:53.063143 21485 solver.cpp:218] Iteration 7116 (2.32095 iter/s, 5.17029s/12 iters), loss = 4.73537
I0406 16:10:53.063186 21485 solver.cpp:237]     Train net output #0: loss = 4.73537 (* 1 = 4.73537 loss)
I0406 16:10:53.063191 21485 sgd_solver.cpp:105] Iteration 7116, lr = 0.05
I0406 16:10:58.384415 21485 solver.cpp:218] Iteration 7128 (2.25512 iter/s, 5.32121s/12 iters), loss = 4.8666
I0406 16:10:58.384467 21485 solver.cpp:237]     Train net output #0: loss = 4.8666 (* 1 = 4.8666 loss)
I0406 16:10:58.384475 21485 sgd_solver.cpp:105] Iteration 7128, lr = 0.05
I0406 16:11:03.163136 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7140.caffemodel
I0406 16:11:07.627478 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7140.solverstate
I0406 16:11:11.369338 21485 solver.cpp:330] Iteration 7140, Testing net (#0)
I0406 16:11:11.369359 21485 net.cpp:676] Ignoring source layer train-data
I0406 16:11:12.905019 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:11:15.696703 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0232843
I0406 16:11:15.696730 21485 solver.cpp:397]     Test net output #1: loss = 5.05807 (* 1 = 5.05807 loss)
I0406 16:11:15.834146 21485 solver.cpp:218] Iteration 7140 (0.687692 iter/s, 17.4497s/12 iters), loss = 4.92262
I0406 16:11:15.835754 21485 solver.cpp:237]     Train net output #0: loss = 4.92262 (* 1 = 4.92262 loss)
I0406 16:11:15.835768 21485 sgd_solver.cpp:105] Iteration 7140, lr = 0.05
I0406 16:11:20.016216 21485 solver.cpp:218] Iteration 7152 (2.8705 iter/s, 4.18045s/12 iters), loss = 4.8962
I0406 16:11:20.016258 21485 solver.cpp:237]     Train net output #0: loss = 4.8962 (* 1 = 4.8962 loss)
I0406 16:11:20.016263 21485 sgd_solver.cpp:105] Iteration 7152, lr = 0.05
I0406 16:11:25.279783 21485 solver.cpp:218] Iteration 7164 (2.27984 iter/s, 5.26352s/12 iters), loss = 4.8888
I0406 16:11:25.279819 21485 solver.cpp:237]     Train net output #0: loss = 4.8888 (* 1 = 4.8888 loss)
I0406 16:11:25.279825 21485 sgd_solver.cpp:105] Iteration 7164, lr = 0.05
I0406 16:11:30.475149 21485 solver.cpp:218] Iteration 7176 (2.30978 iter/s, 5.19531s/12 iters), loss = 4.81453
I0406 16:11:30.475219 21485 solver.cpp:237]     Train net output #0: loss = 4.81453 (* 1 = 4.81453 loss)
I0406 16:11:30.475229 21485 sgd_solver.cpp:105] Iteration 7176, lr = 0.05
I0406 16:11:32.755679 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:11:35.821445 21485 solver.cpp:218] Iteration 7188 (2.24458 iter/s, 5.34622s/12 iters), loss = 5.00012
I0406 16:11:35.821481 21485 solver.cpp:237]     Train net output #0: loss = 5.00012 (* 1 = 5.00012 loss)
I0406 16:11:35.821486 21485 sgd_solver.cpp:105] Iteration 7188, lr = 0.05
I0406 16:11:40.978426 21485 solver.cpp:218] Iteration 7200 (2.32697 iter/s, 5.15693s/12 iters), loss = 4.91469
I0406 16:11:40.978480 21485 solver.cpp:237]     Train net output #0: loss = 4.91469 (* 1 = 4.91469 loss)
I0406 16:11:40.978488 21485 sgd_solver.cpp:105] Iteration 7200, lr = 0.05
I0406 16:11:46.107522 21485 solver.cpp:218] Iteration 7212 (2.33963 iter/s, 5.12902s/12 iters), loss = 4.89359
I0406 16:11:46.107650 21485 solver.cpp:237]     Train net output #0: loss = 4.89359 (* 1 = 4.89359 loss)
I0406 16:11:46.107659 21485 sgd_solver.cpp:105] Iteration 7212, lr = 0.05
I0406 16:11:51.266320 21485 solver.cpp:218] Iteration 7224 (2.32619 iter/s, 5.15866s/12 iters), loss = 4.80486
I0406 16:11:51.266363 21485 solver.cpp:237]     Train net output #0: loss = 4.80486 (* 1 = 4.80486 loss)
I0406 16:11:51.266368 21485 sgd_solver.cpp:105] Iteration 7224, lr = 0.05
I0406 16:11:56.344213 21485 solver.cpp:218] Iteration 7236 (2.36321 iter/s, 5.07784s/12 iters), loss = 4.73209
I0406 16:11:56.344254 21485 solver.cpp:237]     Train net output #0: loss = 4.73209 (* 1 = 4.73209 loss)
I0406 16:11:56.344259 21485 sgd_solver.cpp:105] Iteration 7236, lr = 0.05
I0406 16:11:58.472836 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7242.caffemodel
I0406 16:12:03.044502 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7242.solverstate
I0406 16:12:07.126152 21485 solver.cpp:330] Iteration 7242, Testing net (#0)
I0406 16:12:07.126168 21485 net.cpp:676] Ignoring source layer train-data
I0406 16:12:08.662160 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:12:11.495478 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0275735
I0406 16:12:11.495512 21485 solver.cpp:397]     Test net output #1: loss = 5.04335 (* 1 = 5.04335 loss)
I0406 16:12:13.255494 21485 solver.cpp:218] Iteration 7248 (0.709588 iter/s, 16.9112s/12 iters), loss = 4.99855
I0406 16:12:13.255542 21485 solver.cpp:237]     Train net output #0: loss = 4.99855 (* 1 = 4.99855 loss)
I0406 16:12:13.255548 21485 sgd_solver.cpp:105] Iteration 7248, lr = 0.05
I0406 16:12:18.338948 21485 solver.cpp:218] Iteration 7260 (2.36063 iter/s, 5.08339s/12 iters), loss = 4.83227
I0406 16:12:18.339102 21485 solver.cpp:237]     Train net output #0: loss = 4.83227 (* 1 = 4.83227 loss)
I0406 16:12:18.339112 21485 sgd_solver.cpp:105] Iteration 7260, lr = 0.05
I0406 16:12:23.650626 21485 solver.cpp:218] Iteration 7272 (2.25924 iter/s, 5.31152s/12 iters), loss = 4.64705
I0406 16:12:23.650662 21485 solver.cpp:237]     Train net output #0: loss = 4.64705 (* 1 = 4.64705 loss)
I0406 16:12:23.650667 21485 sgd_solver.cpp:105] Iteration 7272, lr = 0.05
I0406 16:12:28.226105 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:12:29.028550 21485 solver.cpp:218] Iteration 7284 (2.23137 iter/s, 5.37787s/12 iters), loss = 4.83565
I0406 16:12:29.028602 21485 solver.cpp:237]     Train net output #0: loss = 4.83565 (* 1 = 4.83565 loss)
I0406 16:12:29.028610 21485 sgd_solver.cpp:105] Iteration 7284, lr = 0.05
I0406 16:12:34.304927 21485 solver.cpp:218] Iteration 7296 (2.27482 iter/s, 5.27515s/12 iters), loss = 4.80104
I0406 16:12:34.304976 21485 solver.cpp:237]     Train net output #0: loss = 4.80104 (* 1 = 4.80104 loss)
I0406 16:12:34.304983 21485 sgd_solver.cpp:105] Iteration 7296, lr = 0.05
I0406 16:12:39.679337 21485 solver.cpp:218] Iteration 7308 (2.23283 iter/s, 5.37434s/12 iters), loss = 4.93701
I0406 16:12:39.679387 21485 solver.cpp:237]     Train net output #0: loss = 4.93701 (* 1 = 4.93701 loss)
I0406 16:12:39.679395 21485 sgd_solver.cpp:105] Iteration 7308, lr = 0.05
I0406 16:12:44.830727 21485 solver.cpp:218] Iteration 7320 (2.3295 iter/s, 5.15133s/12 iters), loss = 4.86818
I0406 16:12:44.830770 21485 solver.cpp:237]     Train net output #0: loss = 4.86818 (* 1 = 4.86818 loss)
I0406 16:12:44.830776 21485 sgd_solver.cpp:105] Iteration 7320, lr = 0.05
I0406 16:12:50.143939 21485 solver.cpp:218] Iteration 7332 (2.25855 iter/s, 5.31315s/12 iters), loss = 4.88937
I0406 16:12:50.144034 21485 solver.cpp:237]     Train net output #0: loss = 4.88937 (* 1 = 4.88937 loss)
I0406 16:12:50.144040 21485 sgd_solver.cpp:105] Iteration 7332, lr = 0.05
I0406 16:12:55.021106 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7344.caffemodel
I0406 16:13:00.145390 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7344.solverstate
I0406 16:13:04.489050 21485 solver.cpp:330] Iteration 7344, Testing net (#0)
I0406 16:13:04.489078 21485 net.cpp:676] Ignoring source layer train-data
I0406 16:13:06.091270 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:13:08.925065 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0275735
I0406 16:13:08.925099 21485 solver.cpp:397]     Test net output #1: loss = 5.02164 (* 1 = 5.02164 loss)
I0406 16:13:09.063397 21485 solver.cpp:218] Iteration 7344 (0.634271 iter/s, 18.9194s/12 iters), loss = 4.89512
I0406 16:13:09.065021 21485 solver.cpp:237]     Train net output #0: loss = 4.89512 (* 1 = 4.89512 loss)
I0406 16:13:09.065034 21485 sgd_solver.cpp:105] Iteration 7344, lr = 0.05
I0406 16:13:13.523584 21485 solver.cpp:218] Iteration 7356 (2.69145 iter/s, 4.45856s/12 iters), loss = 4.77499
I0406 16:13:13.523628 21485 solver.cpp:237]     Train net output #0: loss = 4.77499 (* 1 = 4.77499 loss)
I0406 16:13:13.523634 21485 sgd_solver.cpp:105] Iteration 7356, lr = 0.05
I0406 16:13:18.707926 21485 solver.cpp:218] Iteration 7368 (2.31469 iter/s, 5.18428s/12 iters), loss = 4.87625
I0406 16:13:18.707968 21485 solver.cpp:237]     Train net output #0: loss = 4.87625 (* 1 = 4.87625 loss)
I0406 16:13:18.707974 21485 sgd_solver.cpp:105] Iteration 7368, lr = 0.05
I0406 16:13:23.660667 21485 solver.cpp:218] Iteration 7380 (2.42293 iter/s, 4.95268s/12 iters), loss = 4.8908
I0406 16:13:23.660801 21485 solver.cpp:237]     Train net output #0: loss = 4.8908 (* 1 = 4.8908 loss)
I0406 16:13:23.660809 21485 sgd_solver.cpp:105] Iteration 7380, lr = 0.05
I0406 16:13:25.006590 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:13:28.809412 21485 solver.cpp:218] Iteration 7392 (2.33073 iter/s, 5.1486s/12 iters), loss = 4.78447
I0406 16:13:28.809448 21485 solver.cpp:237]     Train net output #0: loss = 4.78447 (* 1 = 4.78447 loss)
I0406 16:13:28.809453 21485 sgd_solver.cpp:105] Iteration 7392, lr = 0.05
I0406 16:13:34.047940 21485 solver.cpp:218] Iteration 7404 (2.29074 iter/s, 5.23848s/12 iters), loss = 4.97016
I0406 16:13:34.047981 21485 solver.cpp:237]     Train net output #0: loss = 4.97016 (* 1 = 4.97016 loss)
I0406 16:13:34.047987 21485 sgd_solver.cpp:105] Iteration 7404, lr = 0.05
I0406 16:13:39.303222 21485 solver.cpp:218] Iteration 7416 (2.28344 iter/s, 5.25522s/12 iters), loss = 4.8972
I0406 16:13:39.303282 21485 solver.cpp:237]     Train net output #0: loss = 4.8972 (* 1 = 4.8972 loss)
I0406 16:13:39.303290 21485 sgd_solver.cpp:105] Iteration 7416, lr = 0.05
I0406 16:13:44.474475 21485 solver.cpp:218] Iteration 7428 (2.32055 iter/s, 5.17119s/12 iters), loss = 4.64751
I0406 16:13:44.474511 21485 solver.cpp:237]     Train net output #0: loss = 4.64751 (* 1 = 4.64751 loss)
I0406 16:13:44.474517 21485 sgd_solver.cpp:105] Iteration 7428, lr = 0.05
I0406 16:13:49.877813 21485 solver.cpp:218] Iteration 7440 (2.22087 iter/s, 5.40329s/12 iters), loss = 4.66658
I0406 16:13:49.877858 21485 solver.cpp:237]     Train net output #0: loss = 4.66658 (* 1 = 4.66658 loss)
I0406 16:13:49.877866 21485 sgd_solver.cpp:105] Iteration 7440, lr = 0.05
I0406 16:13:52.039006 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7446.caffemodel
I0406 16:13:56.641795 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7446.solverstate
I0406 16:14:00.695747 21485 solver.cpp:330] Iteration 7446, Testing net (#0)
I0406 16:14:00.695771 21485 net.cpp:676] Ignoring source layer train-data
I0406 16:14:02.105454 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:14:04.998003 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0343137
I0406 16:14:04.998030 21485 solver.cpp:397]     Test net output #1: loss = 4.99892 (* 1 = 4.99892 loss)
I0406 16:14:06.811352 21485 solver.cpp:218] Iteration 7452 (0.708655 iter/s, 16.9335s/12 iters), loss = 4.94887
I0406 16:14:06.811398 21485 solver.cpp:237]     Train net output #0: loss = 4.94887 (* 1 = 4.94887 loss)
I0406 16:14:06.811404 21485 sgd_solver.cpp:105] Iteration 7452, lr = 0.05
I0406 16:14:11.961681 21485 solver.cpp:218] Iteration 7464 (2.32998 iter/s, 5.15027s/12 iters), loss = 4.67907
I0406 16:14:11.961720 21485 solver.cpp:237]     Train net output #0: loss = 4.67907 (* 1 = 4.67907 loss)
I0406 16:14:11.961725 21485 sgd_solver.cpp:105] Iteration 7464, lr = 0.05
I0406 16:14:17.252854 21485 solver.cpp:218] Iteration 7476 (2.26795 iter/s, 5.29112s/12 iters), loss = 4.64732
I0406 16:14:17.252904 21485 solver.cpp:237]     Train net output #0: loss = 4.64732 (* 1 = 4.64732 loss)
I0406 16:14:17.252912 21485 sgd_solver.cpp:105] Iteration 7476, lr = 0.05
I0406 16:14:20.863798 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:14:22.479602 21485 solver.cpp:218] Iteration 7488 (2.29591 iter/s, 5.22668s/12 iters), loss = 4.74053
I0406 16:14:22.479647 21485 solver.cpp:237]     Train net output #0: loss = 4.74053 (* 1 = 4.74053 loss)
I0406 16:14:22.479653 21485 sgd_solver.cpp:105] Iteration 7488, lr = 0.05
I0406 16:14:27.663357 21485 solver.cpp:218] Iteration 7500 (2.31495 iter/s, 5.1837s/12 iters), loss = 4.84967
I0406 16:14:27.663444 21485 solver.cpp:237]     Train net output #0: loss = 4.84967 (* 1 = 4.84967 loss)
I0406 16:14:27.663451 21485 sgd_solver.cpp:105] Iteration 7500, lr = 0.05
I0406 16:14:33.020956 21485 solver.cpp:218] Iteration 7512 (2.23985 iter/s, 5.35749s/12 iters), loss = 4.98403
I0406 16:14:33.021003 21485 solver.cpp:237]     Train net output #0: loss = 4.98403 (* 1 = 4.98403 loss)
I0406 16:14:33.021010 21485 sgd_solver.cpp:105] Iteration 7512, lr = 0.05
I0406 16:14:38.287883 21485 solver.cpp:218] Iteration 7524 (2.2784 iter/s, 5.26686s/12 iters), loss = 4.76232
I0406 16:14:38.287930 21485 solver.cpp:237]     Train net output #0: loss = 4.76232 (* 1 = 4.76232 loss)
I0406 16:14:38.287935 21485 sgd_solver.cpp:105] Iteration 7524, lr = 0.05
I0406 16:14:43.517602 21485 solver.cpp:218] Iteration 7536 (2.29461 iter/s, 5.22966s/12 iters), loss = 4.67965
I0406 16:14:43.517643 21485 solver.cpp:237]     Train net output #0: loss = 4.67965 (* 1 = 4.67965 loss)
I0406 16:14:43.517649 21485 sgd_solver.cpp:105] Iteration 7536, lr = 0.05
I0406 16:14:47.940596 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7548.caffemodel
I0406 16:14:53.910606 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7548.solverstate
I0406 16:14:58.973192 21485 solver.cpp:330] Iteration 7548, Testing net (#0)
I0406 16:14:58.973305 21485 net.cpp:676] Ignoring source layer train-data
I0406 16:15:00.372486 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:15:03.273856 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0251225
I0406 16:15:03.273903 21485 solver.cpp:397]     Test net output #1: loss = 5.02616 (* 1 = 5.02616 loss)
I0406 16:15:03.414587 21485 solver.cpp:218] Iteration 7548 (0.603108 iter/s, 19.8969s/12 iters), loss = 4.63751
I0406 16:15:03.414641 21485 solver.cpp:237]     Train net output #0: loss = 4.63751 (* 1 = 4.63751 loss)
I0406 16:15:03.414650 21485 sgd_solver.cpp:105] Iteration 7548, lr = 0.05
I0406 16:15:07.681474 21485 solver.cpp:218] Iteration 7560 (2.8124 iter/s, 4.26682s/12 iters), loss = 4.67708
I0406 16:15:07.681512 21485 solver.cpp:237]     Train net output #0: loss = 4.67708 (* 1 = 4.67708 loss)
I0406 16:15:07.681517 21485 sgd_solver.cpp:105] Iteration 7560, lr = 0.05
I0406 16:15:12.858461 21485 solver.cpp:218] Iteration 7572 (2.31798 iter/s, 5.17693s/12 iters), loss = 4.86715
I0406 16:15:12.858510 21485 solver.cpp:237]     Train net output #0: loss = 4.86715 (* 1 = 4.86715 loss)
I0406 16:15:12.858518 21485 sgd_solver.cpp:105] Iteration 7572, lr = 0.05
I0406 16:15:18.076103 21485 solver.cpp:218] Iteration 7584 (2.29992 iter/s, 5.21757s/12 iters), loss = 4.67414
I0406 16:15:18.076160 21485 solver.cpp:237]     Train net output #0: loss = 4.67414 (* 1 = 4.67414 loss)
I0406 16:15:18.076169 21485 sgd_solver.cpp:105] Iteration 7584, lr = 0.05
I0406 16:15:18.669098 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:15:23.184897 21485 solver.cpp:218] Iteration 7596 (2.34893 iter/s, 5.10872s/12 iters), loss = 4.85053
I0406 16:15:23.184942 21485 solver.cpp:237]     Train net output #0: loss = 4.85053 (* 1 = 4.85053 loss)
I0406 16:15:23.184948 21485 sgd_solver.cpp:105] Iteration 7596, lr = 0.05
I0406 16:15:28.254716 21485 solver.cpp:218] Iteration 7608 (2.36698 iter/s, 5.06975s/12 iters), loss = 4.79304
I0406 16:15:28.254771 21485 solver.cpp:237]     Train net output #0: loss = 4.79304 (* 1 = 4.79304 loss)
I0406 16:15:28.254779 21485 sgd_solver.cpp:105] Iteration 7608, lr = 0.05
I0406 16:15:33.607223 21485 solver.cpp:218] Iteration 7620 (2.24197 iter/s, 5.35244s/12 iters), loss = 4.90299
I0406 16:15:33.607368 21485 solver.cpp:237]     Train net output #0: loss = 4.90299 (* 1 = 4.90299 loss)
I0406 16:15:33.607376 21485 sgd_solver.cpp:105] Iteration 7620, lr = 0.05
I0406 16:15:36.209483 21485 blocking_queue.cpp:49] Waiting for data
I0406 16:15:38.663017 21485 solver.cpp:218] Iteration 7632 (2.37359 iter/s, 5.05564s/12 iters), loss = 4.63415
I0406 16:15:38.663074 21485 solver.cpp:237]     Train net output #0: loss = 4.63415 (* 1 = 4.63415 loss)
I0406 16:15:38.663081 21485 sgd_solver.cpp:105] Iteration 7632, lr = 0.05
I0406 16:15:43.858945 21485 solver.cpp:218] Iteration 7644 (2.30954 iter/s, 5.19585s/12 iters), loss = 4.73653
I0406 16:15:43.859004 21485 solver.cpp:237]     Train net output #0: loss = 4.73653 (* 1 = 4.73653 loss)
I0406 16:15:43.859011 21485 sgd_solver.cpp:105] Iteration 7644, lr = 0.05
I0406 16:15:45.728963 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7650.caffemodel
I0406 16:15:49.978349 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7650.solverstate
I0406 16:15:53.716136 21485 solver.cpp:330] Iteration 7650, Testing net (#0)
I0406 16:15:53.716159 21485 net.cpp:676] Ignoring source layer train-data
I0406 16:15:55.129163 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:15:58.085245 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0245098
I0406 16:15:58.085281 21485 solver.cpp:397]     Test net output #1: loss = 5.02549 (* 1 = 5.02549 loss)
I0406 16:15:59.958549 21485 solver.cpp:218] Iteration 7656 (0.745363 iter/s, 16.0995s/12 iters), loss = 4.59157
I0406 16:15:59.958609 21485 solver.cpp:237]     Train net output #0: loss = 4.59157 (* 1 = 4.59157 loss)
I0406 16:15:59.958618 21485 sgd_solver.cpp:105] Iteration 7656, lr = 0.05
I0406 16:16:05.194828 21485 solver.cpp:218] Iteration 7668 (2.29174 iter/s, 5.2362s/12 iters), loss = 4.94911
I0406 16:16:05.194974 21485 solver.cpp:237]     Train net output #0: loss = 4.94911 (* 1 = 4.94911 loss)
I0406 16:16:05.194983 21485 sgd_solver.cpp:105] Iteration 7668, lr = 0.05
I0406 16:16:10.351330 21485 solver.cpp:218] Iteration 7680 (2.32723 iter/s, 5.15635s/12 iters), loss = 4.68345
I0406 16:16:10.351373 21485 solver.cpp:237]     Train net output #0: loss = 4.68345 (* 1 = 4.68345 loss)
I0406 16:16:10.351378 21485 sgd_solver.cpp:105] Iteration 7680, lr = 0.05
I0406 16:16:13.313491 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:16:15.720185 21485 solver.cpp:218] Iteration 7692 (2.23514 iter/s, 5.36879s/12 iters), loss = 4.76839
I0406 16:16:15.720228 21485 solver.cpp:237]     Train net output #0: loss = 4.76839 (* 1 = 4.76839 loss)
I0406 16:16:15.720233 21485 sgd_solver.cpp:105] Iteration 7692, lr = 0.05
I0406 16:16:20.984479 21485 solver.cpp:218] Iteration 7704 (2.27953 iter/s, 5.26424s/12 iters), loss = 4.82995
I0406 16:16:20.984516 21485 solver.cpp:237]     Train net output #0: loss = 4.82995 (* 1 = 4.82995 loss)
I0406 16:16:20.984521 21485 sgd_solver.cpp:105] Iteration 7704, lr = 0.05
I0406 16:16:26.202000 21485 solver.cpp:218] Iteration 7716 (2.29997 iter/s, 5.21747s/12 iters), loss = 4.94609
I0406 16:16:26.202044 21485 solver.cpp:237]     Train net output #0: loss = 4.94609 (* 1 = 4.94609 loss)
I0406 16:16:26.202049 21485 sgd_solver.cpp:105] Iteration 7716, lr = 0.05
I0406 16:16:31.382620 21485 solver.cpp:218] Iteration 7728 (2.31635 iter/s, 5.18056s/12 iters), loss = 4.83571
I0406 16:16:31.382658 21485 solver.cpp:237]     Train net output #0: loss = 4.83571 (* 1 = 4.83571 loss)
I0406 16:16:31.382663 21485 sgd_solver.cpp:105] Iteration 7728, lr = 0.05
I0406 16:16:36.809094 21485 solver.cpp:218] Iteration 7740 (2.2114 iter/s, 5.42642s/12 iters), loss = 4.79009
I0406 16:16:36.809212 21485 solver.cpp:237]     Train net output #0: loss = 4.79009 (* 1 = 4.79009 loss)
I0406 16:16:36.809221 21485 sgd_solver.cpp:105] Iteration 7740, lr = 0.05
I0406 16:16:41.590903 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7752.caffemodel
I0406 16:16:45.981312 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7752.solverstate
I0406 16:16:49.717872 21485 solver.cpp:330] Iteration 7752, Testing net (#0)
I0406 16:16:49.717892 21485 net.cpp:676] Ignoring source layer train-data
I0406 16:16:51.056011 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:16:54.034431 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0257353
I0406 16:16:54.034466 21485 solver.cpp:397]     Test net output #1: loss = 5.03935 (* 1 = 5.03935 loss)
I0406 16:16:54.174443 21485 solver.cpp:218] Iteration 7752 (0.691036 iter/s, 17.3652s/12 iters), loss = 4.79685
I0406 16:16:54.174495 21485 solver.cpp:237]     Train net output #0: loss = 4.79685 (* 1 = 4.79685 loss)
I0406 16:16:54.174502 21485 sgd_solver.cpp:105] Iteration 7752, lr = 0.05
I0406 16:16:58.518180 21485 solver.cpp:218] Iteration 7764 (2.76264 iter/s, 4.34367s/12 iters), loss = 4.72132
I0406 16:16:58.518220 21485 solver.cpp:237]     Train net output #0: loss = 4.72132 (* 1 = 4.72132 loss)
I0406 16:16:58.518225 21485 sgd_solver.cpp:105] Iteration 7764, lr = 0.05
I0406 16:17:03.709745 21485 solver.cpp:218] Iteration 7776 (2.31147 iter/s, 5.19151s/12 iters), loss = 4.75068
I0406 16:17:03.709792 21485 solver.cpp:237]     Train net output #0: loss = 4.75068 (* 1 = 4.75068 loss)
I0406 16:17:03.709800 21485 sgd_solver.cpp:105] Iteration 7776, lr = 0.05
I0406 16:17:08.996799 21485 solver.cpp:218] Iteration 7788 (2.26972 iter/s, 5.28699s/12 iters), loss = 4.80662
I0406 16:17:08.996956 21485 solver.cpp:237]     Train net output #0: loss = 4.80662 (* 1 = 4.80662 loss)
I0406 16:17:08.996965 21485 sgd_solver.cpp:105] Iteration 7788, lr = 0.05
I0406 16:17:09.003296 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:17:14.327581 21485 solver.cpp:218] Iteration 7800 (2.25115 iter/s, 5.33061s/12 iters), loss = 4.7777
I0406 16:17:14.327643 21485 solver.cpp:237]     Train net output #0: loss = 4.7777 (* 1 = 4.7777 loss)
I0406 16:17:14.327651 21485 sgd_solver.cpp:105] Iteration 7800, lr = 0.05
I0406 16:17:19.646728 21485 solver.cpp:218] Iteration 7812 (2.25603 iter/s, 5.31907s/12 iters), loss = 4.86816
I0406 16:17:19.646776 21485 solver.cpp:237]     Train net output #0: loss = 4.86816 (* 1 = 4.86816 loss)
I0406 16:17:19.646785 21485 sgd_solver.cpp:105] Iteration 7812, lr = 0.05
I0406 16:17:24.803393 21485 solver.cpp:218] Iteration 7824 (2.32711 iter/s, 5.1566s/12 iters), loss = 4.72729
I0406 16:17:24.803437 21485 solver.cpp:237]     Train net output #0: loss = 4.72729 (* 1 = 4.72729 loss)
I0406 16:17:24.803443 21485 sgd_solver.cpp:105] Iteration 7824, lr = 0.05
I0406 16:17:30.123975 21485 solver.cpp:218] Iteration 7836 (2.25542 iter/s, 5.32052s/12 iters), loss = 4.79265
I0406 16:17:30.124014 21485 solver.cpp:237]     Train net output #0: loss = 4.79265 (* 1 = 4.79265 loss)
I0406 16:17:30.124020 21485 sgd_solver.cpp:105] Iteration 7836, lr = 0.05
I0406 16:17:35.440796 21485 solver.cpp:218] Iteration 7848 (2.25701 iter/s, 5.31676s/12 iters), loss = 4.74436
I0406 16:17:35.440848 21485 solver.cpp:237]     Train net output #0: loss = 4.74436 (* 1 = 4.74436 loss)
I0406 16:17:35.440856 21485 sgd_solver.cpp:105] Iteration 7848, lr = 0.05
I0406 16:17:37.596643 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7854.caffemodel
I0406 16:17:41.756948 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7854.solverstate
I0406 16:17:45.549121 21485 solver.cpp:330] Iteration 7854, Testing net (#0)
I0406 16:17:45.549149 21485 net.cpp:676] Ignoring source layer train-data
I0406 16:17:46.858738 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:17:49.962018 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0281863
I0406 16:17:49.962054 21485 solver.cpp:397]     Test net output #1: loss = 5.00781 (* 1 = 5.00781 loss)
I0406 16:17:51.913017 21485 solver.cpp:218] Iteration 7860 (0.728502 iter/s, 16.4722s/12 iters), loss = 4.7406
I0406 16:17:51.913062 21485 solver.cpp:237]     Train net output #0: loss = 4.7406 (* 1 = 4.7406 loss)
I0406 16:17:51.913069 21485 sgd_solver.cpp:105] Iteration 7860, lr = 0.05
I0406 16:17:56.808403 21485 solver.cpp:218] Iteration 7872 (2.45132 iter/s, 4.89532s/12 iters), loss = 4.69664
I0406 16:17:56.808449 21485 solver.cpp:237]     Train net output #0: loss = 4.69664 (* 1 = 4.69664 loss)
I0406 16:17:56.808455 21485 sgd_solver.cpp:105] Iteration 7872, lr = 0.05
I0406 16:18:01.997437 21485 solver.cpp:218] Iteration 7884 (2.3126 iter/s, 5.18897s/12 iters), loss = 4.78953
I0406 16:18:01.997503 21485 solver.cpp:237]     Train net output #0: loss = 4.78953 (* 1 = 4.78953 loss)
I0406 16:18:01.997512 21485 sgd_solver.cpp:105] Iteration 7884, lr = 0.05
I0406 16:18:04.300209 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:18:07.361620 21485 solver.cpp:218] Iteration 7896 (2.23709 iter/s, 5.36411s/12 iters), loss = 4.72706
I0406 16:18:07.361677 21485 solver.cpp:237]     Train net output #0: loss = 4.72706 (* 1 = 4.72706 loss)
I0406 16:18:07.361685 21485 sgd_solver.cpp:105] Iteration 7896, lr = 0.05
I0406 16:18:12.410933 21485 solver.cpp:218] Iteration 7908 (2.37659 iter/s, 5.04925s/12 iters), loss = 5.03775
I0406 16:18:12.411034 21485 solver.cpp:237]     Train net output #0: loss = 5.03775 (* 1 = 5.03775 loss)
I0406 16:18:12.411041 21485 sgd_solver.cpp:105] Iteration 7908, lr = 0.05
I0406 16:18:17.585312 21485 solver.cpp:218] Iteration 7920 (2.31917 iter/s, 5.17426s/12 iters), loss = 4.73488
I0406 16:18:17.585352 21485 solver.cpp:237]     Train net output #0: loss = 4.73488 (* 1 = 4.73488 loss)
I0406 16:18:17.585358 21485 sgd_solver.cpp:105] Iteration 7920, lr = 0.05
I0406 16:18:22.843554 21485 solver.cpp:218] Iteration 7932 (2.28216 iter/s, 5.25818s/12 iters), loss = 4.76296
I0406 16:18:22.843621 21485 solver.cpp:237]     Train net output #0: loss = 4.76296 (* 1 = 4.76296 loss)
I0406 16:18:22.843631 21485 sgd_solver.cpp:105] Iteration 7932, lr = 0.05
I0406 16:18:28.181017 21485 solver.cpp:218] Iteration 7944 (2.24829 iter/s, 5.33739s/12 iters), loss = 4.66687
I0406 16:18:28.181063 21485 solver.cpp:237]     Train net output #0: loss = 4.66687 (* 1 = 4.66687 loss)
I0406 16:18:28.181071 21485 sgd_solver.cpp:105] Iteration 7944, lr = 0.05
I0406 16:18:32.661368 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7956.caffemodel
I0406 16:18:35.654911 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7956.solverstate
I0406 16:18:39.446377 21485 solver.cpp:330] Iteration 7956, Testing net (#0)
I0406 16:18:39.446398 21485 net.cpp:676] Ignoring source layer train-data
I0406 16:18:40.653761 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:18:43.720605 21485 solver.cpp:397]     Test net output #0: accuracy = 0.026348
I0406 16:18:43.720711 21485 solver.cpp:397]     Test net output #1: loss = 5.06211 (* 1 = 5.06211 loss)
I0406 16:18:43.861183 21485 solver.cpp:218] Iteration 7956 (0.765301 iter/s, 15.6801s/12 iters), loss = 4.89482
I0406 16:18:43.861229 21485 solver.cpp:237]     Train net output #0: loss = 4.89482 (* 1 = 4.89482 loss)
I0406 16:18:43.861238 21485 sgd_solver.cpp:105] Iteration 7956, lr = 0.05
I0406 16:18:48.146525 21485 solver.cpp:218] Iteration 7968 (2.80029 iter/s, 4.28527s/12 iters), loss = 4.7498
I0406 16:18:48.146566 21485 solver.cpp:237]     Train net output #0: loss = 4.7498 (* 1 = 4.7498 loss)
I0406 16:18:48.146572 21485 sgd_solver.cpp:105] Iteration 7968, lr = 0.05
I0406 16:18:53.217006 21485 solver.cpp:218] Iteration 7980 (2.36667 iter/s, 5.07043s/12 iters), loss = 4.63799
I0406 16:18:53.217046 21485 solver.cpp:237]     Train net output #0: loss = 4.63799 (* 1 = 4.63799 loss)
I0406 16:18:53.217051 21485 sgd_solver.cpp:105] Iteration 7980, lr = 0.05
I0406 16:18:57.743621 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:18:58.532050 21485 solver.cpp:218] Iteration 7992 (2.25777 iter/s, 5.31498s/12 iters), loss = 4.65752
I0406 16:18:58.532106 21485 solver.cpp:237]     Train net output #0: loss = 4.65752 (* 1 = 4.65752 loss)
I0406 16:18:58.532114 21485 sgd_solver.cpp:105] Iteration 7992, lr = 0.05
I0406 16:19:03.637042 21485 solver.cpp:218] Iteration 8004 (2.35067 iter/s, 5.10492s/12 iters), loss = 4.72403
I0406 16:19:03.637080 21485 solver.cpp:237]     Train net output #0: loss = 4.72403 (* 1 = 4.72403 loss)
I0406 16:19:03.637085 21485 sgd_solver.cpp:105] Iteration 8004, lr = 0.05
I0406 16:19:08.887230 21485 solver.cpp:218] Iteration 8016 (2.28565 iter/s, 5.25014s/12 iters), loss = 4.82356
I0406 16:19:08.887276 21485 solver.cpp:237]     Train net output #0: loss = 4.82356 (* 1 = 4.82356 loss)
I0406 16:19:08.887281 21485 sgd_solver.cpp:105] Iteration 8016, lr = 0.05
I0406 16:19:14.013464 21485 solver.cpp:218] Iteration 8028 (2.34093 iter/s, 5.12617s/12 iters), loss = 4.73876
I0406 16:19:14.013588 21485 solver.cpp:237]     Train net output #0: loss = 4.73876 (* 1 = 4.73876 loss)
I0406 16:19:14.013595 21485 sgd_solver.cpp:105] Iteration 8028, lr = 0.05
I0406 16:19:19.097648 21485 solver.cpp:218] Iteration 8040 (2.36033 iter/s, 5.08404s/12 iters), loss = 4.62699
I0406 16:19:19.097703 21485 solver.cpp:237]     Train net output #0: loss = 4.62699 (* 1 = 4.62699 loss)
I0406 16:19:19.097712 21485 sgd_solver.cpp:105] Iteration 8040, lr = 0.05
I0406 16:19:24.306181 21485 solver.cpp:218] Iteration 8052 (2.30394 iter/s, 5.20847s/12 iters), loss = 4.69511
I0406 16:19:24.306216 21485 solver.cpp:237]     Train net output #0: loss = 4.69511 (* 1 = 4.69511 loss)
I0406 16:19:24.306221 21485 sgd_solver.cpp:105] Iteration 8052, lr = 0.05
I0406 16:19:26.610827 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8058.caffemodel
I0406 16:19:29.549685 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8058.solverstate
I0406 16:19:33.771308 21485 solver.cpp:330] Iteration 8058, Testing net (#0)
I0406 16:19:33.771339 21485 net.cpp:676] Ignoring source layer train-data
I0406 16:19:35.017464 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:19:38.196359 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0306373
I0406 16:19:38.196394 21485 solver.cpp:397]     Test net output #1: loss = 5.02239 (* 1 = 5.02239 loss)
I0406 16:19:39.981741 21485 solver.cpp:218] Iteration 8064 (0.765525 iter/s, 15.6755s/12 iters), loss = 4.79484
I0406 16:19:39.981786 21485 solver.cpp:237]     Train net output #0: loss = 4.79484 (* 1 = 4.79484 loss)
I0406 16:19:39.981793 21485 sgd_solver.cpp:105] Iteration 8064, lr = 0.05
I0406 16:19:45.011123 21485 solver.cpp:218] Iteration 8076 (2.38601 iter/s, 5.02932s/12 iters), loss = 4.98933
I0406 16:19:45.011214 21485 solver.cpp:237]     Train net output #0: loss = 4.98933 (* 1 = 4.98933 loss)
I0406 16:19:45.011220 21485 sgd_solver.cpp:105] Iteration 8076, lr = 0.05
I0406 16:19:50.243479 21485 solver.cpp:218] Iteration 8088 (2.29347 iter/s, 5.23225s/12 iters), loss = 4.67823
I0406 16:19:50.243526 21485 solver.cpp:237]     Train net output #0: loss = 4.67823 (* 1 = 4.67823 loss)
I0406 16:19:50.243531 21485 sgd_solver.cpp:105] Iteration 8088, lr = 0.05
I0406 16:19:51.697149 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:19:55.453755 21485 solver.cpp:218] Iteration 8100 (2.30317 iter/s, 5.21021s/12 iters), loss = 4.72237
I0406 16:19:55.453796 21485 solver.cpp:237]     Train net output #0: loss = 4.72237 (* 1 = 4.72237 loss)
I0406 16:19:55.453802 21485 sgd_solver.cpp:105] Iteration 8100, lr = 0.05
I0406 16:20:00.661600 21485 solver.cpp:218] Iteration 8112 (2.30424 iter/s, 5.20779s/12 iters), loss = 4.80552
I0406 16:20:00.661638 21485 solver.cpp:237]     Train net output #0: loss = 4.80552 (* 1 = 4.80552 loss)
I0406 16:20:00.661643 21485 sgd_solver.cpp:105] Iteration 8112, lr = 0.05
I0406 16:20:05.889197 21485 solver.cpp:218] Iteration 8124 (2.29554 iter/s, 5.22753s/12 iters), loss = 4.98291
I0406 16:20:05.889250 21485 solver.cpp:237]     Train net output #0: loss = 4.98291 (* 1 = 4.98291 loss)
I0406 16:20:05.889257 21485 sgd_solver.cpp:105] Iteration 8124, lr = 0.05
I0406 16:20:11.248028 21485 solver.cpp:218] Iteration 8136 (2.23932 iter/s, 5.35876s/12 iters), loss = 4.842
I0406 16:20:11.248081 21485 solver.cpp:237]     Train net output #0: loss = 4.842 (* 1 = 4.842 loss)
I0406 16:20:11.248090 21485 sgd_solver.cpp:105] Iteration 8136, lr = 0.05
I0406 16:20:16.536692 21485 solver.cpp:218] Iteration 8148 (2.26903 iter/s, 5.28859s/12 iters), loss = 4.81357
I0406 16:20:16.536814 21485 solver.cpp:237]     Train net output #0: loss = 4.81357 (* 1 = 4.81357 loss)
I0406 16:20:16.536823 21485 sgd_solver.cpp:105] Iteration 8148, lr = 0.05
I0406 16:20:21.171727 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8160.caffemodel
I0406 16:20:24.189838 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8160.solverstate
I0406 16:20:28.154728 21485 solver.cpp:330] Iteration 8160, Testing net (#0)
I0406 16:20:28.154745 21485 net.cpp:676] Ignoring source layer train-data
I0406 16:20:29.335089 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:20:32.685025 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0238971
I0406 16:20:32.685062 21485 solver.cpp:397]     Test net output #1: loss = 5.01199 (* 1 = 5.01199 loss)
I0406 16:20:32.822258 21485 solver.cpp:218] Iteration 8160 (0.736855 iter/s, 16.2854s/12 iters), loss = 4.76279
I0406 16:20:32.822322 21485 solver.cpp:237]     Train net output #0: loss = 4.76279 (* 1 = 4.76279 loss)
I0406 16:20:32.822330 21485 sgd_solver.cpp:105] Iteration 8160, lr = 0.05
I0406 16:20:37.040911 21485 solver.cpp:218] Iteration 8172 (2.84456 iter/s, 4.21858s/12 iters), loss = 4.72182
I0406 16:20:37.040939 21485 solver.cpp:237]     Train net output #0: loss = 4.72182 (* 1 = 4.72182 loss)
I0406 16:20:37.040943 21485 sgd_solver.cpp:105] Iteration 8172, lr = 0.05
I0406 16:20:42.237746 21485 solver.cpp:218] Iteration 8184 (2.30912 iter/s, 5.19678s/12 iters), loss = 4.71431
I0406 16:20:42.237802 21485 solver.cpp:237]     Train net output #0: loss = 4.71431 (* 1 = 4.71431 loss)
I0406 16:20:42.237810 21485 sgd_solver.cpp:105] Iteration 8184, lr = 0.05
I0406 16:20:45.950913 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:20:47.525125 21485 solver.cpp:218] Iteration 8196 (2.26959 iter/s, 5.28731s/12 iters), loss = 4.76771
I0406 16:20:47.525276 21485 solver.cpp:237]     Train net output #0: loss = 4.76771 (* 1 = 4.76771 loss)
I0406 16:20:47.525283 21485 sgd_solver.cpp:105] Iteration 8196, lr = 0.05
I0406 16:20:52.567698 21485 solver.cpp:218] Iteration 8208 (2.37982 iter/s, 5.04241s/12 iters), loss = 4.71191
I0406 16:20:52.567749 21485 solver.cpp:237]     Train net output #0: loss = 4.71191 (* 1 = 4.71191 loss)
I0406 16:20:52.567754 21485 sgd_solver.cpp:105] Iteration 8208, lr = 0.05
I0406 16:20:57.694005 21485 solver.cpp:218] Iteration 8220 (2.3409 iter/s, 5.12624s/12 iters), loss = 4.94678
I0406 16:20:57.694052 21485 solver.cpp:237]     Train net output #0: loss = 4.94678 (* 1 = 4.94678 loss)
I0406 16:20:57.694058 21485 sgd_solver.cpp:105] Iteration 8220, lr = 0.05
I0406 16:21:02.990494 21485 solver.cpp:218] Iteration 8232 (2.26568 iter/s, 5.29643s/12 iters), loss = 4.88307
I0406 16:21:02.990545 21485 solver.cpp:237]     Train net output #0: loss = 4.88307 (* 1 = 4.88307 loss)
I0406 16:21:02.990551 21485 sgd_solver.cpp:105] Iteration 8232, lr = 0.05
I0406 16:21:08.349371 21485 solver.cpp:218] Iteration 8244 (2.2393 iter/s, 5.35881s/12 iters), loss = 4.59808
I0406 16:21:08.349414 21485 solver.cpp:237]     Train net output #0: loss = 4.59808 (* 1 = 4.59808 loss)
I0406 16:21:08.349419 21485 sgd_solver.cpp:105] Iteration 8244, lr = 0.05
I0406 16:21:13.455994 21485 solver.cpp:218] Iteration 8256 (2.34992 iter/s, 5.10656s/12 iters), loss = 4.57668
I0406 16:21:13.456045 21485 solver.cpp:237]     Train net output #0: loss = 4.57668 (* 1 = 4.57668 loss)
I0406 16:21:13.456053 21485 sgd_solver.cpp:105] Iteration 8256, lr = 0.05
I0406 16:21:15.542865 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8262.caffemodel
I0406 16:21:18.571408 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8262.solverstate
I0406 16:21:22.024075 21485 solver.cpp:330] Iteration 8262, Testing net (#0)
I0406 16:21:22.024097 21485 net.cpp:676] Ignoring source layer train-data
I0406 16:21:23.155879 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:21:26.326784 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0300245
I0406 16:21:26.326820 21485 solver.cpp:397]     Test net output #1: loss = 5.0172 (* 1 = 5.0172 loss)
I0406 16:21:28.278934 21485 solver.cpp:218] Iteration 8268 (0.80956 iter/s, 14.8229s/12 iters), loss = 4.45526
I0406 16:21:28.278981 21485 solver.cpp:237]     Train net output #0: loss = 4.45526 (* 1 = 4.45526 loss)
I0406 16:21:28.278990 21485 sgd_solver.cpp:105] Iteration 8268, lr = 0.05
I0406 16:21:33.408038 21485 solver.cpp:218] Iteration 8280 (2.33962 iter/s, 5.12904s/12 iters), loss = 4.47204
I0406 16:21:33.408093 21485 solver.cpp:237]     Train net output #0: loss = 4.47204 (* 1 = 4.47204 loss)
I0406 16:21:33.408100 21485 sgd_solver.cpp:105] Iteration 8280, lr = 0.05
I0406 16:21:38.703919 21485 solver.cpp:218] Iteration 8292 (2.26594 iter/s, 5.29582s/12 iters), loss = 4.60374
I0406 16:21:38.703958 21485 solver.cpp:237]     Train net output #0: loss = 4.60374 (* 1 = 4.60374 loss)
I0406 16:21:38.703963 21485 sgd_solver.cpp:105] Iteration 8292, lr = 0.05
I0406 16:21:39.457684 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:21:43.925591 21485 solver.cpp:218] Iteration 8304 (2.29814 iter/s, 5.22162s/12 iters), loss = 4.80935
I0406 16:21:43.925635 21485 solver.cpp:237]     Train net output #0: loss = 4.80935 (* 1 = 4.80935 loss)
I0406 16:21:43.925642 21485 sgd_solver.cpp:105] Iteration 8304, lr = 0.05
I0406 16:21:46.783980 21485 blocking_queue.cpp:49] Waiting for data
I0406 16:21:49.101475 21485 solver.cpp:218] Iteration 8316 (2.31847 iter/s, 5.17583s/12 iters), loss = 4.74604
I0406 16:21:49.101608 21485 solver.cpp:237]     Train net output #0: loss = 4.74604 (* 1 = 4.74604 loss)
I0406 16:21:49.101613 21485 sgd_solver.cpp:105] Iteration 8316, lr = 0.05
I0406 16:21:54.347314 21485 solver.cpp:218] Iteration 8328 (2.28759 iter/s, 5.2457s/12 iters), loss = 4.83985
I0406 16:21:54.347353 21485 solver.cpp:237]     Train net output #0: loss = 4.83985 (* 1 = 4.83985 loss)
I0406 16:21:54.347359 21485 sgd_solver.cpp:105] Iteration 8328, lr = 0.05
I0406 16:21:59.461213 21485 solver.cpp:218] Iteration 8340 (2.34657 iter/s, 5.11385s/12 iters), loss = 4.68238
I0406 16:21:59.461251 21485 solver.cpp:237]     Train net output #0: loss = 4.68238 (* 1 = 4.68238 loss)
I0406 16:21:59.461256 21485 sgd_solver.cpp:105] Iteration 8340, lr = 0.05
I0406 16:22:04.655227 21485 solver.cpp:218] Iteration 8352 (2.31037 iter/s, 5.19397s/12 iters), loss = 4.50845
I0406 16:22:04.655257 21485 solver.cpp:237]     Train net output #0: loss = 4.50845 (* 1 = 4.50845 loss)
I0406 16:22:04.655262 21485 sgd_solver.cpp:105] Iteration 8352, lr = 0.05
I0406 16:22:09.219027 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8364.caffemodel
I0406 16:22:13.054756 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8364.solverstate
I0406 16:22:16.372968 21485 solver.cpp:330] Iteration 8364, Testing net (#0)
I0406 16:22:16.372987 21485 net.cpp:676] Ignoring source layer train-data
I0406 16:22:17.422653 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:22:20.700069 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0208333
I0406 16:22:20.700198 21485 solver.cpp:397]     Test net output #1: loss = 5.03404 (* 1 = 5.03404 loss)
I0406 16:22:20.841032 21485 solver.cpp:218] Iteration 8364 (0.741392 iter/s, 16.1858s/12 iters), loss = 4.54505
I0406 16:22:20.841085 21485 solver.cpp:237]     Train net output #0: loss = 4.54505 (* 1 = 4.54505 loss)
I0406 16:22:20.841094 21485 sgd_solver.cpp:105] Iteration 8364, lr = 0.05
I0406 16:22:25.209901 21485 solver.cpp:218] Iteration 8376 (2.74675 iter/s, 4.3688s/12 iters), loss = 4.91712
I0406 16:22:25.209949 21485 solver.cpp:237]     Train net output #0: loss = 4.91712 (* 1 = 4.91712 loss)
I0406 16:22:25.209957 21485 sgd_solver.cpp:105] Iteration 8376, lr = 0.05
I0406 16:22:30.457252 21485 solver.cpp:218] Iteration 8388 (2.28689 iter/s, 5.24729s/12 iters), loss = 4.60058
I0406 16:22:30.457293 21485 solver.cpp:237]     Train net output #0: loss = 4.60058 (* 1 = 4.60058 loss)
I0406 16:22:30.457298 21485 sgd_solver.cpp:105] Iteration 8388, lr = 0.05
I0406 16:22:33.363610 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:22:35.805362 21485 solver.cpp:218] Iteration 8400 (2.24381 iter/s, 5.34805s/12 iters), loss = 4.78826
I0406 16:22:35.805436 21485 solver.cpp:237]     Train net output #0: loss = 4.78826 (* 1 = 4.78826 loss)
I0406 16:22:35.805449 21485 sgd_solver.cpp:105] Iteration 8400, lr = 0.05
I0406 16:22:40.967665 21485 solver.cpp:218] Iteration 8412 (2.32458 iter/s, 5.16222s/12 iters), loss = 4.68189
I0406 16:22:40.967702 21485 solver.cpp:237]     Train net output #0: loss = 4.68189 (* 1 = 4.68189 loss)
I0406 16:22:40.967707 21485 sgd_solver.cpp:105] Iteration 8412, lr = 0.05
I0406 16:22:46.194013 21485 solver.cpp:218] Iteration 8424 (2.29608 iter/s, 5.2263s/12 iters), loss = 4.80883
I0406 16:22:46.194054 21485 solver.cpp:237]     Train net output #0: loss = 4.80883 (* 1 = 4.80883 loss)
I0406 16:22:46.194059 21485 sgd_solver.cpp:105] Iteration 8424, lr = 0.05
I0406 16:22:51.460500 21485 solver.cpp:218] Iteration 8436 (2.27858 iter/s, 5.26643s/12 iters), loss = 4.83004
I0406 16:22:51.460623 21485 solver.cpp:237]     Train net output #0: loss = 4.83004 (* 1 = 4.83004 loss)
I0406 16:22:51.460629 21485 sgd_solver.cpp:105] Iteration 8436, lr = 0.05
I0406 16:22:56.739215 21485 solver.cpp:218] Iteration 8448 (2.27334 iter/s, 5.27858s/12 iters), loss = 4.81287
I0406 16:22:56.739265 21485 solver.cpp:237]     Train net output #0: loss = 4.81287 (* 1 = 4.81287 loss)
I0406 16:22:56.739274 21485 sgd_solver.cpp:105] Iteration 8448, lr = 0.05
I0406 16:23:01.970835 21485 solver.cpp:218] Iteration 8460 (2.29377 iter/s, 5.23156s/12 iters), loss = 4.73534
I0406 16:23:01.970881 21485 solver.cpp:237]     Train net output #0: loss = 4.73534 (* 1 = 4.73534 loss)
I0406 16:23:01.970888 21485 sgd_solver.cpp:105] Iteration 8460, lr = 0.05
I0406 16:23:04.101586 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8466.caffemodel
I0406 16:23:07.139319 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8466.solverstate
I0406 16:23:10.394625 21485 solver.cpp:330] Iteration 8466, Testing net (#0)
I0406 16:23:10.394645 21485 net.cpp:676] Ignoring source layer train-data
I0406 16:23:11.421459 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:23:14.777560 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0306373
I0406 16:23:14.777582 21485 solver.cpp:397]     Test net output #1: loss = 5.03329 (* 1 = 5.03329 loss)
I0406 16:23:16.694674 21485 solver.cpp:218] Iteration 8472 (0.815008 iter/s, 14.7238s/12 iters), loss = 4.97409
I0406 16:23:16.694722 21485 solver.cpp:237]     Train net output #0: loss = 4.97409 (* 1 = 4.97409 loss)
I0406 16:23:16.694730 21485 sgd_solver.cpp:105] Iteration 8472, lr = 0.05
I0406 16:23:21.792245 21485 solver.cpp:218] Iteration 8484 (2.35409 iter/s, 5.09751s/12 iters), loss = 4.81917
I0406 16:23:21.792464 21485 solver.cpp:237]     Train net output #0: loss = 4.81917 (* 1 = 4.81917 loss)
I0406 16:23:21.792469 21485 sgd_solver.cpp:105] Iteration 8484, lr = 0.05
I0406 16:23:27.250560 21485 solver.cpp:218] Iteration 8496 (2.19857 iter/s, 5.45808s/12 iters), loss = 4.9086
I0406 16:23:27.250612 21485 solver.cpp:237]     Train net output #0: loss = 4.9086 (* 1 = 4.9086 loss)
I0406 16:23:27.250622 21485 sgd_solver.cpp:105] Iteration 8496, lr = 0.05
I0406 16:23:27.285643 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:23:32.553485 21485 solver.cpp:218] Iteration 8508 (2.26293 iter/s, 5.30286s/12 iters), loss = 4.85266
I0406 16:23:32.553529 21485 solver.cpp:237]     Train net output #0: loss = 4.85266 (* 1 = 4.85266 loss)
I0406 16:23:32.553535 21485 sgd_solver.cpp:105] Iteration 8508, lr = 0.05
I0406 16:23:37.789492 21485 solver.cpp:218] Iteration 8520 (2.29185 iter/s, 5.23595s/12 iters), loss = 4.8878
I0406 16:23:37.789553 21485 solver.cpp:237]     Train net output #0: loss = 4.8878 (* 1 = 4.8878 loss)
I0406 16:23:37.789561 21485 sgd_solver.cpp:105] Iteration 8520, lr = 0.05
I0406 16:23:43.126222 21485 solver.cpp:218] Iteration 8532 (2.2486 iter/s, 5.33666s/12 iters), loss = 5.02446
I0406 16:23:43.126261 21485 solver.cpp:237]     Train net output #0: loss = 5.02446 (* 1 = 5.02446 loss)
I0406 16:23:43.126266 21485 sgd_solver.cpp:105] Iteration 8532, lr = 0.05
I0406 16:23:48.419356 21485 solver.cpp:218] Iteration 8544 (2.26711 iter/s, 5.29308s/12 iters), loss = 5.24216
I0406 16:23:48.419414 21485 solver.cpp:237]     Train net output #0: loss = 5.24216 (* 1 = 5.24216 loss)
I0406 16:23:48.419422 21485 sgd_solver.cpp:105] Iteration 8544, lr = 0.05
I0406 16:23:53.760043 21485 solver.cpp:218] Iteration 8556 (2.24693 iter/s, 5.34062s/12 iters), loss = 5.0743
I0406 16:23:53.760215 21485 solver.cpp:237]     Train net output #0: loss = 5.0743 (* 1 = 5.0743 loss)
I0406 16:23:53.760221 21485 sgd_solver.cpp:105] Iteration 8556, lr = 0.05
I0406 16:23:58.626871 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8568.caffemodel
I0406 16:24:01.668995 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8568.solverstate
I0406 16:24:04.741573 21485 solver.cpp:330] Iteration 8568, Testing net (#0)
I0406 16:24:04.741592 21485 net.cpp:676] Ignoring source layer train-data
I0406 16:24:05.771198 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:24:09.089881 21485 solver.cpp:397]     Test net output #0: accuracy = 0.026348
I0406 16:24:09.089917 21485 solver.cpp:397]     Test net output #1: loss = 5.06785 (* 1 = 5.06785 loss)
I0406 16:24:09.230664 21485 solver.cpp:218] Iteration 8568 (0.775673 iter/s, 15.4704s/12 iters), loss = 4.94414
I0406 16:24:09.230715 21485 solver.cpp:237]     Train net output #0: loss = 4.94414 (* 1 = 4.94414 loss)
I0406 16:24:09.230722 21485 sgd_solver.cpp:105] Iteration 8568, lr = 0.05
I0406 16:24:13.570549 21485 solver.cpp:218] Iteration 8580 (2.76509 iter/s, 4.33982s/12 iters), loss = 4.88334
I0406 16:24:13.570595 21485 solver.cpp:237]     Train net output #0: loss = 4.88334 (* 1 = 4.88334 loss)
I0406 16:24:13.570600 21485 sgd_solver.cpp:105] Iteration 8580, lr = 0.05
I0406 16:24:18.877703 21485 solver.cpp:218] Iteration 8592 (2.26113 iter/s, 5.30709s/12 iters), loss = 4.66544
I0406 16:24:18.877758 21485 solver.cpp:237]     Train net output #0: loss = 4.66544 (* 1 = 4.66544 loss)
I0406 16:24:18.877765 21485 sgd_solver.cpp:105] Iteration 8592, lr = 0.05
I0406 16:24:21.137384 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:24:24.197574 21485 solver.cpp:218] Iteration 8604 (2.25572 iter/s, 5.3198s/12 iters), loss = 4.81933
I0406 16:24:24.197712 21485 solver.cpp:237]     Train net output #0: loss = 4.81933 (* 1 = 4.81933 loss)
I0406 16:24:24.197727 21485 sgd_solver.cpp:105] Iteration 8604, lr = 0.05
I0406 16:24:29.461480 21485 solver.cpp:218] Iteration 8616 (2.27974 iter/s, 5.26376s/12 iters), loss = 5.09377
I0406 16:24:29.461519 21485 solver.cpp:237]     Train net output #0: loss = 5.09377 (* 1 = 5.09377 loss)
I0406 16:24:29.461524 21485 sgd_solver.cpp:105] Iteration 8616, lr = 0.05
I0406 16:24:34.684199 21485 solver.cpp:218] Iteration 8628 (2.29768 iter/s, 5.22266s/12 iters), loss = 4.93473
I0406 16:24:34.684254 21485 solver.cpp:237]     Train net output #0: loss = 4.93473 (* 1 = 4.93473 loss)
I0406 16:24:34.684262 21485 sgd_solver.cpp:105] Iteration 8628, lr = 0.05
I0406 16:24:39.997634 21485 solver.cpp:218] Iteration 8640 (2.25845 iter/s, 5.31337s/12 iters), loss = 4.77682
I0406 16:24:39.997678 21485 solver.cpp:237]     Train net output #0: loss = 4.77682 (* 1 = 4.77682 loss)
I0406 16:24:39.997682 21485 sgd_solver.cpp:105] Iteration 8640, lr = 0.05
I0406 16:24:45.111378 21485 solver.cpp:218] Iteration 8652 (2.34664 iter/s, 5.11368s/12 iters), loss = 4.71301
I0406 16:24:45.111418 21485 solver.cpp:237]     Train net output #0: loss = 4.71301 (* 1 = 4.71301 loss)
I0406 16:24:45.111424 21485 sgd_solver.cpp:105] Iteration 8652, lr = 0.05
I0406 16:24:50.499078 21485 solver.cpp:218] Iteration 8664 (2.22732 iter/s, 5.38764s/12 iters), loss = 4.99456
I0406 16:24:50.499130 21485 solver.cpp:237]     Train net output #0: loss = 4.99456 (* 1 = 4.99456 loss)
I0406 16:24:50.499137 21485 sgd_solver.cpp:105] Iteration 8664, lr = 0.05
I0406 16:24:52.516806 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8670.caffemodel
I0406 16:24:55.526983 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8670.solverstate
I0406 16:24:58.924343 21485 solver.cpp:330] Iteration 8670, Testing net (#0)
I0406 16:24:58.924365 21485 net.cpp:676] Ignoring source layer train-data
I0406 16:24:59.899158 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:25:03.371352 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0183824
I0406 16:25:03.371381 21485 solver.cpp:397]     Test net output #1: loss = 5.0929 (* 1 = 5.0929 loss)
I0406 16:25:05.214663 21485 solver.cpp:218] Iteration 8676 (0.815465 iter/s, 14.7155s/12 iters), loss = 4.72498
I0406 16:25:05.214699 21485 solver.cpp:237]     Train net output #0: loss = 4.72498 (* 1 = 4.72498 loss)
I0406 16:25:05.214705 21485 sgd_solver.cpp:105] Iteration 8676, lr = 0.05
I0406 16:25:10.408694 21485 solver.cpp:218] Iteration 8688 (2.31037 iter/s, 5.19397s/12 iters), loss = 4.78638
I0406 16:25:10.408744 21485 solver.cpp:237]     Train net output #0: loss = 4.78638 (* 1 = 4.78638 loss)
I0406 16:25:10.408751 21485 sgd_solver.cpp:105] Iteration 8688, lr = 0.05
I0406 16:25:14.959673 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:25:15.648500 21485 solver.cpp:218] Iteration 8700 (2.29019 iter/s, 5.23974s/12 iters), loss = 4.69589
I0406 16:25:15.648543 21485 solver.cpp:237]     Train net output #0: loss = 4.69589 (* 1 = 4.69589 loss)
I0406 16:25:15.648548 21485 sgd_solver.cpp:105] Iteration 8700, lr = 0.05
I0406 16:25:20.610332 21485 solver.cpp:218] Iteration 8712 (2.41849 iter/s, 4.96177s/12 iters), loss = 4.82524
I0406 16:25:20.610378 21485 solver.cpp:237]     Train net output #0: loss = 4.82524 (* 1 = 4.82524 loss)
I0406 16:25:20.610383 21485 sgd_solver.cpp:105] Iteration 8712, lr = 0.05
I0406 16:25:25.982447 21485 solver.cpp:218] Iteration 8724 (2.23378 iter/s, 5.37205s/12 iters), loss = 4.89581
I0406 16:25:25.982586 21485 solver.cpp:237]     Train net output #0: loss = 4.89581 (* 1 = 4.89581 loss)
I0406 16:25:25.982595 21485 sgd_solver.cpp:105] Iteration 8724, lr = 0.05
I0406 16:25:31.160403 21485 solver.cpp:218] Iteration 8736 (2.31758 iter/s, 5.1778s/12 iters), loss = 4.74476
I0406 16:25:31.160441 21485 solver.cpp:237]     Train net output #0: loss = 4.74476 (* 1 = 4.74476 loss)
I0406 16:25:31.160447 21485 sgd_solver.cpp:105] Iteration 8736, lr = 0.05
I0406 16:25:36.251721 21485 solver.cpp:218] Iteration 8748 (2.35698 iter/s, 5.09126s/12 iters), loss = 4.80009
I0406 16:25:36.251780 21485 solver.cpp:237]     Train net output #0: loss = 4.80009 (* 1 = 4.80009 loss)
I0406 16:25:36.251788 21485 sgd_solver.cpp:105] Iteration 8748, lr = 0.05
I0406 16:25:41.417346 21485 solver.cpp:218] Iteration 8760 (2.32308 iter/s, 5.16555s/12 iters), loss = 4.94514
I0406 16:25:41.417387 21485 solver.cpp:237]     Train net output #0: loss = 4.94514 (* 1 = 4.94514 loss)
I0406 16:25:41.417393 21485 sgd_solver.cpp:105] Iteration 8760, lr = 0.05
I0406 16:25:46.074075 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8772.caffemodel
I0406 16:25:49.093180 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8772.solverstate
I0406 16:25:51.706696 21485 solver.cpp:330] Iteration 8772, Testing net (#0)
I0406 16:25:51.706717 21485 net.cpp:676] Ignoring source layer train-data
I0406 16:25:52.758890 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:25:56.283080 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0214461
I0406 16:25:56.283200 21485 solver.cpp:397]     Test net output #1: loss = 5.06482 (* 1 = 5.06482 loss)
I0406 16:25:56.423300 21485 solver.cpp:218] Iteration 8772 (0.799685 iter/s, 15.0059s/12 iters), loss = 4.77766
I0406 16:25:56.423357 21485 solver.cpp:237]     Train net output #0: loss = 4.77766 (* 1 = 4.77766 loss)
I0406 16:25:56.423364 21485 sgd_solver.cpp:105] Iteration 8772, lr = 0.05
I0406 16:26:00.749267 21485 solver.cpp:218] Iteration 8784 (2.77399 iter/s, 4.3259s/12 iters), loss = 4.99382
I0406 16:26:00.749308 21485 solver.cpp:237]     Train net output #0: loss = 4.99382 (* 1 = 4.99382 loss)
I0406 16:26:00.749313 21485 sgd_solver.cpp:105] Iteration 8784, lr = 0.05
I0406 16:26:05.924929 21485 solver.cpp:218] Iteration 8796 (2.31857 iter/s, 5.17561s/12 iters), loss = 4.61914
I0406 16:26:05.924969 21485 solver.cpp:237]     Train net output #0: loss = 4.61914 (* 1 = 4.61914 loss)
I0406 16:26:05.924975 21485 sgd_solver.cpp:105] Iteration 8796, lr = 0.05
I0406 16:26:07.400923 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:26:11.298638 21485 solver.cpp:218] Iteration 8808 (2.23312 iter/s, 5.37365s/12 iters), loss = 4.63461
I0406 16:26:11.298678 21485 solver.cpp:237]     Train net output #0: loss = 4.63461 (* 1 = 4.63461 loss)
I0406 16:26:11.298683 21485 sgd_solver.cpp:105] Iteration 8808, lr = 0.05
I0406 16:26:16.487870 21485 solver.cpp:218] Iteration 8820 (2.31251 iter/s, 5.18917s/12 iters), loss = 4.72563
I0406 16:26:16.487920 21485 solver.cpp:237]     Train net output #0: loss = 4.72563 (* 1 = 4.72563 loss)
I0406 16:26:16.487926 21485 sgd_solver.cpp:105] Iteration 8820, lr = 0.05
I0406 16:26:21.638986 21485 solver.cpp:218] Iteration 8832 (2.32962 iter/s, 5.15105s/12 iters), loss = 4.89244
I0406 16:26:21.639039 21485 solver.cpp:237]     Train net output #0: loss = 4.89244 (* 1 = 4.89244 loss)
I0406 16:26:21.639047 21485 sgd_solver.cpp:105] Iteration 8832, lr = 0.05
I0406 16:26:27.022418 21485 solver.cpp:218] Iteration 8844 (2.22909 iter/s, 5.38336s/12 iters), loss = 4.66421
I0406 16:26:27.022671 21485 solver.cpp:237]     Train net output #0: loss = 4.66421 (* 1 = 4.66421 loss)
I0406 16:26:27.022680 21485 sgd_solver.cpp:105] Iteration 8844, lr = 0.05
I0406 16:26:32.161329 21485 solver.cpp:218] Iteration 8856 (2.33525 iter/s, 5.13865s/12 iters), loss = 4.63568
I0406 16:26:32.161375 21485 solver.cpp:237]     Train net output #0: loss = 4.63568 (* 1 = 4.63568 loss)
I0406 16:26:32.161381 21485 sgd_solver.cpp:105] Iteration 8856, lr = 0.05
I0406 16:26:37.292418 21485 solver.cpp:218] Iteration 8868 (2.33871 iter/s, 5.13103s/12 iters), loss = 4.7503
I0406 16:26:37.292457 21485 solver.cpp:237]     Train net output #0: loss = 4.7503 (* 1 = 4.7503 loss)
I0406 16:26:37.292462 21485 sgd_solver.cpp:105] Iteration 8868, lr = 0.05
I0406 16:26:39.278954 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8874.caffemodel
I0406 16:26:42.327783 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8874.solverstate
I0406 16:26:44.665957 21485 solver.cpp:330] Iteration 8874, Testing net (#0)
I0406 16:26:44.665983 21485 net.cpp:676] Ignoring source layer train-data
I0406 16:26:45.553048 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:26:49.004750 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0294118
I0406 16:26:49.004787 21485 solver.cpp:397]     Test net output #1: loss = 5.08343 (* 1 = 5.08343 loss)
I0406 16:26:50.931782 21485 solver.cpp:218] Iteration 8880 (0.87981 iter/s, 13.6393s/12 iters), loss = 4.92732
I0406 16:26:50.931828 21485 solver.cpp:237]     Train net output #0: loss = 4.92732 (* 1 = 4.92732 loss)
I0406 16:26:50.931834 21485 sgd_solver.cpp:105] Iteration 8880, lr = 0.05
I0406 16:26:56.201308 21485 solver.cpp:218] Iteration 8892 (2.27727 iter/s, 5.26946s/12 iters), loss = 4.97497
I0406 16:26:56.201352 21485 solver.cpp:237]     Train net output #0: loss = 4.97497 (* 1 = 4.97497 loss)
I0406 16:26:56.201359 21485 sgd_solver.cpp:105] Iteration 8892, lr = 0.05
I0406 16:26:59.974229 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:27:01.578603 21485 solver.cpp:218] Iteration 8904 (2.23163 iter/s, 5.37723s/12 iters), loss = 4.89348
I0406 16:27:01.578644 21485 solver.cpp:237]     Train net output #0: loss = 4.89348 (* 1 = 4.89348 loss)
I0406 16:27:01.578650 21485 sgd_solver.cpp:105] Iteration 8904, lr = 0.05
I0406 16:27:06.952147 21485 solver.cpp:218] Iteration 8916 (2.23319 iter/s, 5.37348s/12 iters), loss = 4.68606
I0406 16:27:06.952204 21485 solver.cpp:237]     Train net output #0: loss = 4.68606 (* 1 = 4.68606 loss)
I0406 16:27:06.952212 21485 sgd_solver.cpp:105] Iteration 8916, lr = 0.05
I0406 16:27:12.287274 21485 solver.cpp:218] Iteration 8928 (2.24927 iter/s, 5.33506s/12 iters), loss = 4.81701
I0406 16:27:12.287315 21485 solver.cpp:237]     Train net output #0: loss = 4.81701 (* 1 = 4.81701 loss)
I0406 16:27:12.287322 21485 sgd_solver.cpp:105] Iteration 8928, lr = 0.05
I0406 16:27:17.350698 21485 solver.cpp:218] Iteration 8940 (2.36996 iter/s, 5.06337s/12 iters), loss = 4.69321
I0406 16:27:17.350739 21485 solver.cpp:237]     Train net output #0: loss = 4.69321 (* 1 = 4.69321 loss)
I0406 16:27:17.350745 21485 sgd_solver.cpp:105] Iteration 8940, lr = 0.05
I0406 16:27:22.607625 21485 solver.cpp:218] Iteration 8952 (2.28273 iter/s, 5.25687s/12 iters), loss = 4.73896
I0406 16:27:22.607671 21485 solver.cpp:237]     Train net output #0: loss = 4.73896 (* 1 = 4.73896 loss)
I0406 16:27:22.607676 21485 sgd_solver.cpp:105] Iteration 8952, lr = 0.05
I0406 16:27:28.041491 21485 solver.cpp:218] Iteration 8964 (2.2084 iter/s, 5.4338s/12 iters), loss = 4.46298
I0406 16:27:28.041538 21485 solver.cpp:237]     Train net output #0: loss = 4.46298 (* 1 = 4.46298 loss)
I0406 16:27:28.041544 21485 sgd_solver.cpp:105] Iteration 8964, lr = 0.05
I0406 16:27:32.716917 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8976.caffemodel
I0406 16:27:35.746112 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8976.solverstate
I0406 16:27:38.477030 21485 solver.cpp:330] Iteration 8976, Testing net (#0)
I0406 16:27:38.477052 21485 net.cpp:676] Ignoring source layer train-data
I0406 16:27:39.377272 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:27:42.900610 21485 solver.cpp:397]     Test net output #0: accuracy = 0.028799
I0406 16:27:42.900645 21485 solver.cpp:397]     Test net output #1: loss = 5.08424 (* 1 = 5.08424 loss)
I0406 16:27:43.043798 21485 solver.cpp:218] Iteration 8976 (0.79988 iter/s, 15.0022s/12 iters), loss = 4.60735
I0406 16:27:43.045414 21485 solver.cpp:237]     Train net output #0: loss = 4.60735 (* 1 = 4.60735 loss)
I0406 16:27:43.045428 21485 sgd_solver.cpp:105] Iteration 8976, lr = 0.05
I0406 16:27:47.302022 21485 solver.cpp:218] Iteration 8988 (2.81915 iter/s, 4.2566s/12 iters), loss = 4.67298
I0406 16:27:47.302071 21485 solver.cpp:237]     Train net output #0: loss = 4.67298 (* 1 = 4.67298 loss)
I0406 16:27:47.302078 21485 sgd_solver.cpp:105] Iteration 8988, lr = 0.05
I0406 16:27:50.709554 21485 blocking_queue.cpp:49] Waiting for data
I0406 16:27:52.401439 21485 solver.cpp:218] Iteration 9000 (2.35324 iter/s, 5.09935s/12 iters), loss = 4.63736
I0406 16:27:52.401480 21485 solver.cpp:237]     Train net output #0: loss = 4.63736 (* 1 = 4.63736 loss)
I0406 16:27:52.401485 21485 sgd_solver.cpp:105] Iteration 9000, lr = 0.05
I0406 16:27:53.138100 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:27:57.773970 21485 solver.cpp:218] Iteration 9012 (2.23361 iter/s, 5.37247s/12 iters), loss = 4.68208
I0406 16:27:57.774024 21485 solver.cpp:237]     Train net output #0: loss = 4.68208 (* 1 = 4.68208 loss)
I0406 16:27:57.774032 21485 sgd_solver.cpp:105] Iteration 9012, lr = 0.05
I0406 16:28:03.069391 21485 solver.cpp:218] Iteration 9024 (2.26614 iter/s, 5.29535s/12 iters), loss = 4.72817
I0406 16:28:03.069505 21485 solver.cpp:237]     Train net output #0: loss = 4.72817 (* 1 = 4.72817 loss)
I0406 16:28:03.069514 21485 sgd_solver.cpp:105] Iteration 9024, lr = 0.05
I0406 16:28:08.377599 21485 solver.cpp:218] Iteration 9036 (2.26071 iter/s, 5.30808s/12 iters), loss = 4.81078
I0406 16:28:08.377651 21485 solver.cpp:237]     Train net output #0: loss = 4.81078 (* 1 = 4.81078 loss)
I0406 16:28:08.377661 21485 sgd_solver.cpp:105] Iteration 9036, lr = 0.05
I0406 16:28:13.491847 21485 solver.cpp:218] Iteration 9048 (2.34642 iter/s, 5.11418s/12 iters), loss = 4.76284
I0406 16:28:13.491886 21485 solver.cpp:237]     Train net output #0: loss = 4.76284 (* 1 = 4.76284 loss)
I0406 16:28:13.491891 21485 sgd_solver.cpp:105] Iteration 9048, lr = 0.05
I0406 16:28:18.611109 21485 solver.cpp:218] Iteration 9060 (2.34412 iter/s, 5.1192s/12 iters), loss = 4.74991
I0406 16:28:18.611164 21485 solver.cpp:237]     Train net output #0: loss = 4.74991 (* 1 = 4.74991 loss)
I0406 16:28:18.611172 21485 sgd_solver.cpp:105] Iteration 9060, lr = 0.05
I0406 16:28:23.877611 21485 solver.cpp:218] Iteration 9072 (2.27858 iter/s, 5.26643s/12 iters), loss = 4.77348
I0406 16:28:23.877668 21485 solver.cpp:237]     Train net output #0: loss = 4.77348 (* 1 = 4.77348 loss)
I0406 16:28:23.877676 21485 sgd_solver.cpp:105] Iteration 9072, lr = 0.05
I0406 16:28:25.986410 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9078.caffemodel
I0406 16:28:29.062702 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9078.solverstate
I0406 16:28:31.432252 21485 solver.cpp:330] Iteration 9078, Testing net (#0)
I0406 16:28:31.432273 21485 net.cpp:676] Ignoring source layer train-data
I0406 16:28:32.255818 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:28:35.820648 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0220588
I0406 16:28:35.821066 21485 solver.cpp:397]     Test net output #1: loss = 5.11319 (* 1 = 5.11319 loss)
I0406 16:28:37.600605 21485 solver.cpp:218] Iteration 9084 (0.874449 iter/s, 13.7229s/12 iters), loss = 4.94101
I0406 16:28:37.600651 21485 solver.cpp:237]     Train net output #0: loss = 4.94101 (* 1 = 4.94101 loss)
I0406 16:28:37.600656 21485 sgd_solver.cpp:105] Iteration 9084, lr = 0.05
I0406 16:28:42.799149 21485 solver.cpp:218] Iteration 9096 (2.30837 iter/s, 5.19848s/12 iters), loss = 4.69818
I0406 16:28:42.799196 21485 solver.cpp:237]     Train net output #0: loss = 4.69818 (* 1 = 4.69818 loss)
I0406 16:28:42.799204 21485 sgd_solver.cpp:105] Iteration 9096, lr = 0.05
I0406 16:28:45.634836 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:28:47.956946 21485 solver.cpp:218] Iteration 9108 (2.3266 iter/s, 5.15773s/12 iters), loss = 4.61792
I0406 16:28:47.956988 21485 solver.cpp:237]     Train net output #0: loss = 4.61792 (* 1 = 4.61792 loss)
I0406 16:28:47.956995 21485 sgd_solver.cpp:105] Iteration 9108, lr = 0.05
I0406 16:28:53.080384 21485 solver.cpp:218] Iteration 9120 (2.3422 iter/s, 5.12338s/12 iters), loss = 4.78197
I0406 16:28:53.080423 21485 solver.cpp:237]     Train net output #0: loss = 4.78197 (* 1 = 4.78197 loss)
I0406 16:28:53.080430 21485 sgd_solver.cpp:105] Iteration 9120, lr = 0.05
I0406 16:28:58.112640 21485 solver.cpp:218] Iteration 9132 (2.38464 iter/s, 5.0322s/12 iters), loss = 4.69655
I0406 16:28:58.112685 21485 solver.cpp:237]     Train net output #0: loss = 4.69655 (* 1 = 4.69655 loss)
I0406 16:28:58.112691 21485 sgd_solver.cpp:105] Iteration 9132, lr = 0.05
I0406 16:29:03.487725 21485 solver.cpp:218] Iteration 9144 (2.23255 iter/s, 5.37502s/12 iters), loss = 4.89294
I0406 16:29:03.487776 21485 solver.cpp:237]     Train net output #0: loss = 4.89294 (* 1 = 4.89294 loss)
I0406 16:29:03.487782 21485 sgd_solver.cpp:105] Iteration 9144, lr = 0.05
I0406 16:29:08.897980 21485 solver.cpp:218] Iteration 9156 (2.21803 iter/s, 5.41019s/12 iters), loss = 4.80234
I0406 16:29:08.898087 21485 solver.cpp:237]     Train net output #0: loss = 4.80234 (* 1 = 4.80234 loss)
I0406 16:29:08.898094 21485 sgd_solver.cpp:105] Iteration 9156, lr = 0.05
I0406 16:29:13.934002 21485 solver.cpp:218] Iteration 9168 (2.38289 iter/s, 5.0359s/12 iters), loss = 4.84006
I0406 16:29:13.934044 21485 solver.cpp:237]     Train net output #0: loss = 4.84006 (* 1 = 4.84006 loss)
I0406 16:29:13.934051 21485 sgd_solver.cpp:105] Iteration 9168, lr = 0.05
I0406 16:29:18.773175 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9180.caffemodel
I0406 16:29:21.796181 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9180.solverstate
I0406 16:29:24.094658 21485 solver.cpp:330] Iteration 9180, Testing net (#0)
I0406 16:29:24.094678 21485 net.cpp:676] Ignoring source layer train-data
I0406 16:29:24.862272 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:29:28.511754 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0294118
I0406 16:29:28.511783 21485 solver.cpp:397]     Test net output #1: loss = 5.06852 (* 1 = 5.06852 loss)
I0406 16:29:28.642822 21485 solver.cpp:218] Iteration 9180 (0.81584 iter/s, 14.7088s/12 iters), loss = 4.77874
I0406 16:29:28.642886 21485 solver.cpp:237]     Train net output #0: loss = 4.77874 (* 1 = 4.77874 loss)
I0406 16:29:28.642894 21485 sgd_solver.cpp:105] Iteration 9180, lr = 0.05
I0406 16:29:32.855479 21485 solver.cpp:218] Iteration 9192 (2.84862 iter/s, 4.21257s/12 iters), loss = 4.80573
I0406 16:29:32.855532 21485 solver.cpp:237]     Train net output #0: loss = 4.80573 (* 1 = 4.80573 loss)
I0406 16:29:32.855540 21485 sgd_solver.cpp:105] Iteration 9192, lr = 0.05
I0406 16:29:38.154975 21485 solver.cpp:218] Iteration 9204 (2.2644 iter/s, 5.29943s/12 iters), loss = 4.8687
I0406 16:29:38.155023 21485 solver.cpp:237]     Train net output #0: loss = 4.8687 (* 1 = 4.8687 loss)
I0406 16:29:38.155028 21485 sgd_solver.cpp:105] Iteration 9204, lr = 0.05
I0406 16:29:38.217458 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:29:43.379179 21485 solver.cpp:218] Iteration 9216 (2.29703 iter/s, 5.22414s/12 iters), loss = 4.69429
I0406 16:29:43.379354 21485 solver.cpp:237]     Train net output #0: loss = 4.69429 (* 1 = 4.69429 loss)
I0406 16:29:43.379362 21485 sgd_solver.cpp:105] Iteration 9216, lr = 0.05
I0406 16:29:48.684690 21485 solver.cpp:218] Iteration 9228 (2.26188 iter/s, 5.30533s/12 iters), loss = 4.75665
I0406 16:29:48.684739 21485 solver.cpp:237]     Train net output #0: loss = 4.75665 (* 1 = 4.75665 loss)
I0406 16:29:48.684746 21485 sgd_solver.cpp:105] Iteration 9228, lr = 0.05
I0406 16:29:53.916332 21485 solver.cpp:218] Iteration 9240 (2.29376 iter/s, 5.23158s/12 iters), loss = 4.81492
I0406 16:29:53.916378 21485 solver.cpp:237]     Train net output #0: loss = 4.81492 (* 1 = 4.81492 loss)
I0406 16:29:53.916383 21485 sgd_solver.cpp:105] Iteration 9240, lr = 0.05
I0406 16:29:59.216001 21485 solver.cpp:218] Iteration 9252 (2.26432 iter/s, 5.29961s/12 iters), loss = 4.88161
I0406 16:29:59.216055 21485 solver.cpp:237]     Train net output #0: loss = 4.88161 (* 1 = 4.88161 loss)
I0406 16:29:59.216063 21485 sgd_solver.cpp:105] Iteration 9252, lr = 0.05
I0406 16:30:04.530309 21485 solver.cpp:218] Iteration 9264 (2.25808 iter/s, 5.31424s/12 iters), loss = 4.5496
I0406 16:30:04.530349 21485 solver.cpp:237]     Train net output #0: loss = 4.5496 (* 1 = 4.5496 loss)
I0406 16:30:04.530354 21485 sgd_solver.cpp:105] Iteration 9264, lr = 0.05
I0406 16:30:09.782138 21485 solver.cpp:218] Iteration 9276 (2.28494 iter/s, 5.25177s/12 iters), loss = 4.73877
I0406 16:30:09.782179 21485 solver.cpp:237]     Train net output #0: loss = 4.73877 (* 1 = 4.73877 loss)
I0406 16:30:09.782184 21485 sgd_solver.cpp:105] Iteration 9276, lr = 0.05
I0406 16:30:11.808640 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9282.caffemodel
I0406 16:30:14.848819 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9282.solverstate
I0406 16:30:17.208025 21485 solver.cpp:330] Iteration 9282, Testing net (#0)
I0406 16:30:17.208047 21485 net.cpp:676] Ignoring source layer train-data
I0406 16:30:17.958443 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:30:21.583577 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0220588
I0406 16:30:21.583604 21485 solver.cpp:397]     Test net output #1: loss = 5.0682 (* 1 = 5.0682 loss)
I0406 16:30:23.480453 21485 solver.cpp:218] Iteration 9288 (0.876024 iter/s, 13.6983s/12 iters), loss = 4.79409
I0406 16:30:23.480497 21485 solver.cpp:237]     Train net output #0: loss = 4.79409 (* 1 = 4.79409 loss)
I0406 16:30:23.480504 21485 sgd_solver.cpp:105] Iteration 9288, lr = 0.05
I0406 16:30:28.492043 21485 solver.cpp:218] Iteration 9300 (2.39448 iter/s, 5.01153s/12 iters), loss = 4.60967
I0406 16:30:28.492086 21485 solver.cpp:237]     Train net output #0: loss = 4.60967 (* 1 = 4.60967 loss)
I0406 16:30:28.492092 21485 sgd_solver.cpp:105] Iteration 9300, lr = 0.05
I0406 16:30:30.817623 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:30:33.487097 21485 solver.cpp:218] Iteration 9312 (2.40241 iter/s, 4.99499s/12 iters), loss = 4.63527
I0406 16:30:33.487140 21485 solver.cpp:237]     Train net output #0: loss = 4.63527 (* 1 = 4.63527 loss)
I0406 16:30:33.487146 21485 sgd_solver.cpp:105] Iteration 9312, lr = 0.05
I0406 16:30:38.694341 21485 solver.cpp:218] Iteration 9324 (2.30451 iter/s, 5.20719s/12 iters), loss = 4.68187
I0406 16:30:38.694380 21485 solver.cpp:237]     Train net output #0: loss = 4.68187 (* 1 = 4.68187 loss)
I0406 16:30:38.694386 21485 sgd_solver.cpp:105] Iteration 9324, lr = 0.05
I0406 16:30:43.939299 21485 solver.cpp:218] Iteration 9336 (2.28794 iter/s, 5.2449s/12 iters), loss = 4.82169
I0406 16:30:43.939349 21485 solver.cpp:237]     Train net output #0: loss = 4.82169 (* 1 = 4.82169 loss)
I0406 16:30:43.939355 21485 sgd_solver.cpp:105] Iteration 9336, lr = 0.05
I0406 16:30:49.187707 21485 solver.cpp:218] Iteration 9348 (2.28644 iter/s, 5.24834s/12 iters), loss = 4.66252
I0406 16:30:49.187850 21485 solver.cpp:237]     Train net output #0: loss = 4.66252 (* 1 = 4.66252 loss)
I0406 16:30:49.187857 21485 sgd_solver.cpp:105] Iteration 9348, lr = 0.05
I0406 16:30:54.584589 21485 solver.cpp:218] Iteration 9360 (2.22357 iter/s, 5.39673s/12 iters), loss = 4.81821
I0406 16:30:54.584632 21485 solver.cpp:237]     Train net output #0: loss = 4.81821 (* 1 = 4.81821 loss)
I0406 16:30:54.584638 21485 sgd_solver.cpp:105] Iteration 9360, lr = 0.05
I0406 16:31:00.017625 21485 solver.cpp:218] Iteration 9372 (2.20873 iter/s, 5.43298s/12 iters), loss = 5.04011
I0406 16:31:00.017666 21485 solver.cpp:237]     Train net output #0: loss = 5.04011 (* 1 = 5.04011 loss)
I0406 16:31:00.017671 21485 sgd_solver.cpp:105] Iteration 9372, lr = 0.05
I0406 16:31:04.596536 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9384.caffemodel
I0406 16:31:08.280746 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9384.solverstate
I0406 16:31:11.013334 21485 solver.cpp:330] Iteration 9384, Testing net (#0)
I0406 16:31:11.013357 21485 net.cpp:676] Ignoring source layer train-data
I0406 16:31:11.769372 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:31:15.523842 21485 solver.cpp:397]     Test net output #0: accuracy = 0.026348
I0406 16:31:15.523877 21485 solver.cpp:397]     Test net output #1: loss = 5.09412 (* 1 = 5.09412 loss)
I0406 16:31:15.660109 21485 solver.cpp:218] Iteration 9384 (0.767144 iter/s, 15.6424s/12 iters), loss = 4.51562
I0406 16:31:15.660152 21485 solver.cpp:237]     Train net output #0: loss = 4.51562 (* 1 = 4.51562 loss)
I0406 16:31:15.660157 21485 sgd_solver.cpp:105] Iteration 9384, lr = 0.05
I0406 16:31:19.978273 21485 solver.cpp:218] Iteration 9396 (2.779 iter/s, 4.3181s/12 iters), loss = 4.57633
I0406 16:31:19.978377 21485 solver.cpp:237]     Train net output #0: loss = 4.57633 (* 1 = 4.57633 loss)
I0406 16:31:19.978387 21485 sgd_solver.cpp:105] Iteration 9396, lr = 0.05
I0406 16:31:24.248282 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:31:24.968911 21485 solver.cpp:218] Iteration 9408 (2.40456 iter/s, 4.99052s/12 iters), loss = 4.68956
I0406 16:31:24.968961 21485 solver.cpp:237]     Train net output #0: loss = 4.68956 (* 1 = 4.68956 loss)
I0406 16:31:24.968967 21485 sgd_solver.cpp:105] Iteration 9408, lr = 0.05
I0406 16:31:30.258769 21485 solver.cpp:218] Iteration 9420 (2.26852 iter/s, 5.28979s/12 iters), loss = 4.76899
I0406 16:31:30.258811 21485 solver.cpp:237]     Train net output #0: loss = 4.76899 (* 1 = 4.76899 loss)
I0406 16:31:30.258817 21485 sgd_solver.cpp:105] Iteration 9420, lr = 0.05
I0406 16:31:35.622371 21485 solver.cpp:218] Iteration 9432 (2.23733 iter/s, 5.36354s/12 iters), loss = 4.73099
I0406 16:31:35.622419 21485 solver.cpp:237]     Train net output #0: loss = 4.73099 (* 1 = 4.73099 loss)
I0406 16:31:35.622426 21485 sgd_solver.cpp:105] Iteration 9432, lr = 0.05
I0406 16:31:40.788998 21485 solver.cpp:218] Iteration 9444 (2.32263 iter/s, 5.16656s/12 iters), loss = 4.67229
I0406 16:31:40.789041 21485 solver.cpp:237]     Train net output #0: loss = 4.67229 (* 1 = 4.67229 loss)
I0406 16:31:40.789050 21485 sgd_solver.cpp:105] Iteration 9444, lr = 0.05
I0406 16:31:46.076980 21485 solver.cpp:218] Iteration 9456 (2.26932 iter/s, 5.28792s/12 iters), loss = 4.69158
I0406 16:31:46.077028 21485 solver.cpp:237]     Train net output #0: loss = 4.69158 (* 1 = 4.69158 loss)
I0406 16:31:46.077034 21485 sgd_solver.cpp:105] Iteration 9456, lr = 0.05
I0406 16:31:51.380535 21485 solver.cpp:218] Iteration 9468 (2.26266 iter/s, 5.30349s/12 iters), loss = 4.80465
I0406 16:31:51.380661 21485 solver.cpp:237]     Train net output #0: loss = 4.80465 (* 1 = 4.80465 loss)
I0406 16:31:51.380667 21485 sgd_solver.cpp:105] Iteration 9468, lr = 0.05
I0406 16:31:56.563910 21485 solver.cpp:218] Iteration 9480 (2.31516 iter/s, 5.18324s/12 iters), loss = 4.81652
I0406 16:31:56.563962 21485 solver.cpp:237]     Train net output #0: loss = 4.81652 (* 1 = 4.81652 loss)
I0406 16:31:56.563971 21485 sgd_solver.cpp:105] Iteration 9480, lr = 0.05
I0406 16:31:58.604902 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9486.caffemodel
I0406 16:32:01.660928 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9486.solverstate
I0406 16:32:04.249348 21485 solver.cpp:330] Iteration 9486, Testing net (#0)
I0406 16:32:04.249366 21485 net.cpp:676] Ignoring source layer train-data
I0406 16:32:04.890914 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:32:08.525768 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0232843
I0406 16:32:08.525804 21485 solver.cpp:397]     Test net output #1: loss = 5.07469 (* 1 = 5.07469 loss)
I0406 16:32:10.410665 21485 solver.cpp:218] Iteration 9492 (0.866633 iter/s, 13.8467s/12 iters), loss = 4.94629
I0406 16:32:10.410710 21485 solver.cpp:237]     Train net output #0: loss = 4.94629 (* 1 = 4.94629 loss)
I0406 16:32:10.410715 21485 sgd_solver.cpp:105] Iteration 9492, lr = 0.05
I0406 16:32:15.648656 21485 solver.cpp:218] Iteration 9504 (2.29098 iter/s, 5.23793s/12 iters), loss = 4.66553
I0406 16:32:15.648691 21485 solver.cpp:237]     Train net output #0: loss = 4.66553 (* 1 = 4.66553 loss)
I0406 16:32:15.648696 21485 sgd_solver.cpp:105] Iteration 9504, lr = 0.05
I0406 16:32:17.129338 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:32:20.872071 21485 solver.cpp:218] Iteration 9516 (2.29737 iter/s, 5.22336s/12 iters), loss = 4.59325
I0406 16:32:20.872123 21485 solver.cpp:237]     Train net output #0: loss = 4.59325 (* 1 = 4.59325 loss)
I0406 16:32:20.872130 21485 sgd_solver.cpp:105] Iteration 9516, lr = 0.05
I0406 16:32:26.154311 21485 solver.cpp:218] Iteration 9528 (2.27179 iter/s, 5.28217s/12 iters), loss = 4.75289
I0406 16:32:26.154417 21485 solver.cpp:237]     Train net output #0: loss = 4.75289 (* 1 = 4.75289 loss)
I0406 16:32:26.154424 21485 sgd_solver.cpp:105] Iteration 9528, lr = 0.05
I0406 16:32:31.131278 21485 solver.cpp:218] Iteration 9540 (2.41116 iter/s, 4.97685s/12 iters), loss = 4.75679
I0406 16:32:31.131320 21485 solver.cpp:237]     Train net output #0: loss = 4.75679 (* 1 = 4.75679 loss)
I0406 16:32:31.131325 21485 sgd_solver.cpp:105] Iteration 9540, lr = 0.05
I0406 16:32:36.507427 21485 solver.cpp:218] Iteration 9552 (2.2321 iter/s, 5.37609s/12 iters), loss = 4.68773
I0406 16:32:36.507472 21485 solver.cpp:237]     Train net output #0: loss = 4.68773 (* 1 = 4.68773 loss)
I0406 16:32:36.507477 21485 sgd_solver.cpp:105] Iteration 9552, lr = 0.05
I0406 16:32:41.604118 21485 solver.cpp:218] Iteration 9564 (2.3545 iter/s, 5.09663s/12 iters), loss = 4.73305
I0406 16:32:41.604168 21485 solver.cpp:237]     Train net output #0: loss = 4.73305 (* 1 = 4.73305 loss)
I0406 16:32:41.604177 21485 sgd_solver.cpp:105] Iteration 9564, lr = 0.05
I0406 16:32:46.905880 21485 solver.cpp:218] Iteration 9576 (2.26343 iter/s, 5.3017s/12 iters), loss = 4.85186
I0406 16:32:46.905934 21485 solver.cpp:237]     Train net output #0: loss = 4.85186 (* 1 = 4.85186 loss)
I0406 16:32:46.905942 21485 sgd_solver.cpp:105] Iteration 9576, lr = 0.05
I0406 16:32:51.569902 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9588.caffemodel
I0406 16:32:54.599932 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9588.solverstate
I0406 16:32:56.904443 21485 solver.cpp:330] Iteration 9588, Testing net (#0)
I0406 16:32:56.904564 21485 net.cpp:676] Ignoring source layer train-data
I0406 16:32:57.569862 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:33:01.341425 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0214461
I0406 16:33:01.341459 21485 solver.cpp:397]     Test net output #1: loss = 5.1354 (* 1 = 5.1354 loss)
I0406 16:33:01.481858 21485 solver.cpp:218] Iteration 9588 (0.823276 iter/s, 14.5759s/12 iters), loss = 4.85205
I0406 16:33:01.481914 21485 solver.cpp:237]     Train net output #0: loss = 4.85205 (* 1 = 4.85205 loss)
I0406 16:33:01.481921 21485 sgd_solver.cpp:105] Iteration 9588, lr = 0.05
I0406 16:33:05.771374 21485 solver.cpp:218] Iteration 9600 (2.79757 iter/s, 4.28944s/12 iters), loss = 4.85012
I0406 16:33:05.771422 21485 solver.cpp:237]     Train net output #0: loss = 4.85012 (* 1 = 4.85012 loss)
I0406 16:33:05.771430 21485 sgd_solver.cpp:105] Iteration 9600, lr = 0.05
I0406 16:33:09.488569 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:33:11.016052 21485 solver.cpp:218] Iteration 9612 (2.28806 iter/s, 5.24461s/12 iters), loss = 4.85901
I0406 16:33:11.016109 21485 solver.cpp:237]     Train net output #0: loss = 4.85901 (* 1 = 4.85901 loss)
I0406 16:33:11.016117 21485 sgd_solver.cpp:105] Iteration 9612, lr = 0.05
I0406 16:33:16.442428 21485 solver.cpp:218] Iteration 9624 (2.21145 iter/s, 5.4263s/12 iters), loss = 4.76015
I0406 16:33:16.442487 21485 solver.cpp:237]     Train net output #0: loss = 4.76015 (* 1 = 4.76015 loss)
I0406 16:33:16.442494 21485 sgd_solver.cpp:105] Iteration 9624, lr = 0.05
I0406 16:33:21.760013 21485 solver.cpp:218] Iteration 9636 (2.25669 iter/s, 5.31751s/12 iters), loss = 4.85233
I0406 16:33:21.760071 21485 solver.cpp:237]     Train net output #0: loss = 4.85233 (* 1 = 4.85233 loss)
I0406 16:33:21.760079 21485 sgd_solver.cpp:105] Iteration 9636, lr = 0.05
I0406 16:33:26.996876 21485 solver.cpp:218] Iteration 9648 (2.29148 iter/s, 5.23679s/12 iters), loss = 4.51812
I0406 16:33:26.996991 21485 solver.cpp:237]     Train net output #0: loss = 4.51812 (* 1 = 4.51812 loss)
I0406 16:33:26.996997 21485 sgd_solver.cpp:105] Iteration 9648, lr = 0.05
I0406 16:33:32.170601 21485 solver.cpp:218] Iteration 9660 (2.31947 iter/s, 5.17359s/12 iters), loss = 4.711
I0406 16:33:32.170651 21485 solver.cpp:237]     Train net output #0: loss = 4.711 (* 1 = 4.711 loss)
I0406 16:33:32.170660 21485 sgd_solver.cpp:105] Iteration 9660, lr = 0.05
I0406 16:33:37.419550 21485 solver.cpp:218] Iteration 9672 (2.2862 iter/s, 5.24888s/12 iters), loss = 4.65301
I0406 16:33:37.419601 21485 solver.cpp:237]     Train net output #0: loss = 4.65301 (* 1 = 4.65301 loss)
I0406 16:33:37.419608 21485 sgd_solver.cpp:105] Iteration 9672, lr = 0.05
I0406 16:33:42.718945 21485 solver.cpp:218] Iteration 9684 (2.26444 iter/s, 5.29933s/12 iters), loss = 4.73432
I0406 16:33:42.718992 21485 solver.cpp:237]     Train net output #0: loss = 4.73432 (* 1 = 4.73432 loss)
I0406 16:33:42.719000 21485 sgd_solver.cpp:105] Iteration 9684, lr = 0.05
I0406 16:33:44.839480 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9690.caffemodel
I0406 16:33:47.856163 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9690.solverstate
I0406 16:33:50.172996 21485 solver.cpp:330] Iteration 9690, Testing net (#0)
I0406 16:33:50.173018 21485 net.cpp:676] Ignoring source layer train-data
I0406 16:33:50.781419 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:33:53.678292 21485 blocking_queue.cpp:49] Waiting for data
I0406 16:33:54.667847 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0238971
I0406 16:33:54.667877 21485 solver.cpp:397]     Test net output #1: loss = 5.08937 (* 1 = 5.08937 loss)
I0406 16:33:56.582356 21485 solver.cpp:218] Iteration 9696 (0.865592 iter/s, 13.8634s/12 iters), loss = 4.79762
I0406 16:33:56.582398 21485 solver.cpp:237]     Train net output #0: loss = 4.79762 (* 1 = 4.79762 loss)
I0406 16:33:56.582404 21485 sgd_solver.cpp:105] Iteration 9696, lr = 0.05
I0406 16:34:01.692625 21485 solver.cpp:218] Iteration 9708 (2.34824 iter/s, 5.11021s/12 iters), loss = 4.83536
I0406 16:34:01.692754 21485 solver.cpp:237]     Train net output #0: loss = 4.83536 (* 1 = 4.83536 loss)
I0406 16:34:01.692760 21485 sgd_solver.cpp:105] Iteration 9708, lr = 0.05
I0406 16:34:02.453251 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:34:07.204799 21485 solver.cpp:218] Iteration 9720 (2.17706 iter/s, 5.51203s/12 iters), loss = 4.7253
I0406 16:34:07.204857 21485 solver.cpp:237]     Train net output #0: loss = 4.7253 (* 1 = 4.7253 loss)
I0406 16:34:07.204865 21485 sgd_solver.cpp:105] Iteration 9720, lr = 0.05
I0406 16:34:12.438531 21485 solver.cpp:218] Iteration 9732 (2.29285 iter/s, 5.23366s/12 iters), loss = 4.83169
I0406 16:34:12.438575 21485 solver.cpp:237]     Train net output #0: loss = 4.83169 (* 1 = 4.83169 loss)
I0406 16:34:12.438580 21485 sgd_solver.cpp:105] Iteration 9732, lr = 0.05
I0406 16:34:17.797078 21485 solver.cpp:218] Iteration 9744 (2.23944 iter/s, 5.35849s/12 iters), loss = 4.84033
I0406 16:34:17.797117 21485 solver.cpp:237]     Train net output #0: loss = 4.84033 (* 1 = 4.84033 loss)
I0406 16:34:17.797123 21485 sgd_solver.cpp:105] Iteration 9744, lr = 0.05
I0406 16:34:23.018245 21485 solver.cpp:218] Iteration 9756 (2.29836 iter/s, 5.22111s/12 iters), loss = 4.78103
I0406 16:34:23.018285 21485 solver.cpp:237]     Train net output #0: loss = 4.78103 (* 1 = 4.78103 loss)
I0406 16:34:23.018291 21485 sgd_solver.cpp:105] Iteration 9756, lr = 0.05
I0406 16:34:28.493146 21485 solver.cpp:218] Iteration 9768 (2.19185 iter/s, 5.47484s/12 iters), loss = 4.7697
I0406 16:34:28.493206 21485 solver.cpp:237]     Train net output #0: loss = 4.7697 (* 1 = 4.7697 loss)
I0406 16:34:28.493216 21485 sgd_solver.cpp:105] Iteration 9768, lr = 0.05
I0406 16:34:33.831499 21485 solver.cpp:218] Iteration 9780 (2.24792 iter/s, 5.33828s/12 iters), loss = 4.70296
I0406 16:34:33.831624 21485 solver.cpp:237]     Train net output #0: loss = 4.70296 (* 1 = 4.70296 loss)
I0406 16:34:33.831632 21485 sgd_solver.cpp:105] Iteration 9780, lr = 0.05
I0406 16:34:38.498720 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9792.caffemodel
I0406 16:34:41.481146 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9792.solverstate
I0406 16:34:43.795348 21485 solver.cpp:330] Iteration 9792, Testing net (#0)
I0406 16:34:43.795375 21485 net.cpp:676] Ignoring source layer train-data
I0406 16:34:44.341570 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:34:48.273903 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0306373
I0406 16:34:48.273928 21485 solver.cpp:397]     Test net output #1: loss = 5.09192 (* 1 = 5.09192 loss)
I0406 16:34:48.412325 21485 solver.cpp:218] Iteration 9792 (0.823006 iter/s, 14.5807s/12 iters), loss = 4.8311
I0406 16:34:48.412364 21485 solver.cpp:237]     Train net output #0: loss = 4.8311 (* 1 = 4.8311 loss)
I0406 16:34:48.412369 21485 sgd_solver.cpp:105] Iteration 9792, lr = 0.05
I0406 16:34:52.690714 21485 solver.cpp:218] Iteration 9804 (2.80483 iter/s, 4.27833s/12 iters), loss = 4.6869
I0406 16:34:52.690752 21485 solver.cpp:237]     Train net output #0: loss = 4.6869 (* 1 = 4.6869 loss)
I0406 16:34:52.690757 21485 sgd_solver.cpp:105] Iteration 9804, lr = 0.05
I0406 16:34:55.846698 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:34:58.080312 21485 solver.cpp:218] Iteration 9816 (2.22653 iter/s, 5.38955s/12 iters), loss = 4.60298
I0406 16:34:58.080353 21485 solver.cpp:237]     Train net output #0: loss = 4.60298 (* 1 = 4.60298 loss)
I0406 16:34:58.080358 21485 sgd_solver.cpp:105] Iteration 9816, lr = 0.05
I0406 16:35:03.277819 21485 solver.cpp:218] Iteration 9828 (2.30883 iter/s, 5.19745s/12 iters), loss = 4.66361
I0406 16:35:03.277877 21485 solver.cpp:237]     Train net output #0: loss = 4.66361 (* 1 = 4.66361 loss)
I0406 16:35:03.277886 21485 sgd_solver.cpp:105] Iteration 9828, lr = 0.05
I0406 16:35:08.597033 21485 solver.cpp:218] Iteration 9840 (2.256 iter/s, 5.31914s/12 iters), loss = 4.66975
I0406 16:35:08.597165 21485 solver.cpp:237]     Train net output #0: loss = 4.66975 (* 1 = 4.66975 loss)
I0406 16:35:08.597172 21485 sgd_solver.cpp:105] Iteration 9840, lr = 0.05
I0406 16:35:13.765687 21485 solver.cpp:218] Iteration 9852 (2.32175 iter/s, 5.16851s/12 iters), loss = 4.76121
I0406 16:35:13.765743 21485 solver.cpp:237]     Train net output #0: loss = 4.76121 (* 1 = 4.76121 loss)
I0406 16:35:13.765751 21485 sgd_solver.cpp:105] Iteration 9852, lr = 0.05
I0406 16:35:19.052078 21485 solver.cpp:218] Iteration 9864 (2.27001 iter/s, 5.28632s/12 iters), loss = 4.65568
I0406 16:35:19.052117 21485 solver.cpp:237]     Train net output #0: loss = 4.65568 (* 1 = 4.65568 loss)
I0406 16:35:19.052122 21485 sgd_solver.cpp:105] Iteration 9864, lr = 0.05
I0406 16:35:24.184095 21485 solver.cpp:218] Iteration 9876 (2.33829 iter/s, 5.13196s/12 iters), loss = 4.9181
I0406 16:35:24.184139 21485 solver.cpp:237]     Train net output #0: loss = 4.9181 (* 1 = 4.9181 loss)
I0406 16:35:24.184146 21485 sgd_solver.cpp:105] Iteration 9876, lr = 0.05
I0406 16:35:29.401590 21485 solver.cpp:218] Iteration 9888 (2.29998 iter/s, 5.21744s/12 iters), loss = 4.82196
I0406 16:35:29.401631 21485 solver.cpp:237]     Train net output #0: loss = 4.82196 (* 1 = 4.82196 loss)
I0406 16:35:29.401636 21485 sgd_solver.cpp:105] Iteration 9888, lr = 0.05
I0406 16:35:31.518966 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9894.caffemodel
I0406 16:35:34.506825 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9894.solverstate
I0406 16:35:36.816246 21485 solver.cpp:330] Iteration 9894, Testing net (#0)
I0406 16:35:36.816267 21485 net.cpp:676] Ignoring source layer train-data
I0406 16:35:37.349000 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:35:41.139575 21485 solver.cpp:397]     Test net output #0: accuracy = 0.026348
I0406 16:35:41.139679 21485 solver.cpp:397]     Test net output #1: loss = 5.09098 (* 1 = 5.09098 loss)
I0406 16:35:43.005663 21485 solver.cpp:218] Iteration 9900 (0.882092 iter/s, 13.604s/12 iters), loss = 4.7164
I0406 16:35:43.005709 21485 solver.cpp:237]     Train net output #0: loss = 4.7164 (* 1 = 4.7164 loss)
I0406 16:35:43.005714 21485 sgd_solver.cpp:105] Iteration 9900, lr = 0.05
I0406 16:35:48.116063 21485 solver.cpp:218] Iteration 9912 (2.34818 iter/s, 5.11033s/12 iters), loss = 4.90309
I0406 16:35:48.116111 21485 solver.cpp:237]     Train net output #0: loss = 4.90309 (* 1 = 4.90309 loss)
I0406 16:35:48.116117 21485 sgd_solver.cpp:105] Iteration 9912, lr = 0.05
I0406 16:35:48.204365 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:35:53.504585 21485 solver.cpp:218] Iteration 9924 (2.22698 iter/s, 5.38847s/12 iters), loss = 5.05231
I0406 16:35:53.504624 21485 solver.cpp:237]     Train net output #0: loss = 5.05231 (* 1 = 5.05231 loss)
I0406 16:35:53.504629 21485 sgd_solver.cpp:105] Iteration 9924, lr = 0.05
I0406 16:35:58.897228 21485 solver.cpp:218] Iteration 9936 (2.22528 iter/s, 5.39258s/12 iters), loss = 4.9137
I0406 16:35:58.897287 21485 solver.cpp:237]     Train net output #0: loss = 4.9137 (* 1 = 4.9137 loss)
I0406 16:35:58.897295 21485 sgd_solver.cpp:105] Iteration 9936, lr = 0.05
I0406 16:36:04.200596 21485 solver.cpp:218] Iteration 9948 (2.26274 iter/s, 5.3033s/12 iters), loss = 5.21966
I0406 16:36:04.200637 21485 solver.cpp:237]     Train net output #0: loss = 5.21966 (* 1 = 5.21966 loss)
I0406 16:36:04.200644 21485 sgd_solver.cpp:105] Iteration 9948, lr = 0.05
I0406 16:36:09.466754 21485 solver.cpp:218] Iteration 9960 (2.27873 iter/s, 5.2661s/12 iters), loss = 4.91581
I0406 16:36:09.466795 21485 solver.cpp:237]     Train net output #0: loss = 4.91581 (* 1 = 4.91581 loss)
I0406 16:36:09.466800 21485 sgd_solver.cpp:105] Iteration 9960, lr = 0.05
I0406 16:36:14.698240 21485 solver.cpp:218] Iteration 9972 (2.29383 iter/s, 5.23143s/12 iters), loss = 4.67116
I0406 16:36:14.698359 21485 solver.cpp:237]     Train net output #0: loss = 4.67116 (* 1 = 4.67116 loss)
I0406 16:36:14.698364 21485 sgd_solver.cpp:105] Iteration 9972, lr = 0.05
I0406 16:36:19.976095 21485 solver.cpp:218] Iteration 9984 (2.27371 iter/s, 5.27772s/12 iters), loss = 4.68814
I0406 16:36:19.976148 21485 solver.cpp:237]     Train net output #0: loss = 4.68814 (* 1 = 4.68814 loss)
I0406 16:36:19.976156 21485 sgd_solver.cpp:105] Iteration 9984, lr = 0.05
I0406 16:36:24.842458 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9996.caffemodel
I0406 16:36:27.759949 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9996.solverstate
I0406 16:36:30.063339 21485 solver.cpp:330] Iteration 9996, Testing net (#0)
I0406 16:36:30.063359 21485 net.cpp:676] Ignoring source layer train-data
I0406 16:36:30.540475 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:36:34.525882 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0300245
I0406 16:36:34.525909 21485 solver.cpp:397]     Test net output #1: loss = 5.04555 (* 1 = 5.04555 loss)
I0406 16:36:34.661396 21485 solver.cpp:218] Iteration 9996 (0.817147 iter/s, 14.6852s/12 iters), loss = 4.71293
I0406 16:36:34.661438 21485 solver.cpp:237]     Train net output #0: loss = 4.71293 (* 1 = 4.71293 loss)
I0406 16:36:34.661444 21485 sgd_solver.cpp:105] Iteration 9996, lr = 0.05
I0406 16:36:38.994172 21485 solver.cpp:218] Iteration 10008 (2.76963 iter/s, 4.33271s/12 iters), loss = 4.66968
I0406 16:36:38.994220 21485 solver.cpp:237]     Train net output #0: loss = 4.66968 (* 1 = 4.66968 loss)
I0406 16:36:38.994225 21485 sgd_solver.cpp:105] Iteration 10008, lr = 0.05
I0406 16:36:41.362099 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:36:44.365334 21485 solver.cpp:218] Iteration 10020 (2.23418 iter/s, 5.3711s/12 iters), loss = 4.65015
I0406 16:36:44.365388 21485 solver.cpp:237]     Train net output #0: loss = 4.65015 (* 1 = 4.65015 loss)
I0406 16:36:44.365396 21485 sgd_solver.cpp:105] Iteration 10020, lr = 0.05
I0406 16:36:49.622759 21485 solver.cpp:218] Iteration 10032 (2.28252 iter/s, 5.25736s/12 iters), loss = 4.76537
I0406 16:36:49.622879 21485 solver.cpp:237]     Train net output #0: loss = 4.76537 (* 1 = 4.76537 loss)
I0406 16:36:49.622886 21485 sgd_solver.cpp:105] Iteration 10032, lr = 0.05
I0406 16:36:54.706354 21485 solver.cpp:218] Iteration 10044 (2.3606 iter/s, 5.08346s/12 iters), loss = 4.84298
I0406 16:36:54.706408 21485 solver.cpp:237]     Train net output #0: loss = 4.84298 (* 1 = 4.84298 loss)
I0406 16:36:54.706416 21485 sgd_solver.cpp:105] Iteration 10044, lr = 0.05
I0406 16:37:00.003846 21485 solver.cpp:218] Iteration 10056 (2.26525 iter/s, 5.29742s/12 iters), loss = 4.86344
I0406 16:37:00.003904 21485 solver.cpp:237]     Train net output #0: loss = 4.86344 (* 1 = 4.86344 loss)
I0406 16:37:00.003913 21485 sgd_solver.cpp:105] Iteration 10056, lr = 0.05
I0406 16:37:05.326835 21485 solver.cpp:218] Iteration 10068 (2.2544 iter/s, 5.32292s/12 iters), loss = 4.75896
I0406 16:37:05.326875 21485 solver.cpp:237]     Train net output #0: loss = 4.75896 (* 1 = 4.75896 loss)
I0406 16:37:05.326880 21485 sgd_solver.cpp:105] Iteration 10068, lr = 0.05
I0406 16:37:10.820325 21485 solver.cpp:218] Iteration 10080 (2.18442 iter/s, 5.49344s/12 iters), loss = 4.69292
I0406 16:37:10.820363 21485 solver.cpp:237]     Train net output #0: loss = 4.69292 (* 1 = 4.69292 loss)
I0406 16:37:10.820367 21485 sgd_solver.cpp:105] Iteration 10080, lr = 0.05
I0406 16:37:16.060672 21485 solver.cpp:218] Iteration 10092 (2.28995 iter/s, 5.24029s/12 iters), loss = 4.55164
I0406 16:37:16.060731 21485 solver.cpp:237]     Train net output #0: loss = 4.55164 (* 1 = 4.55164 loss)
I0406 16:37:16.060740 21485 sgd_solver.cpp:105] Iteration 10092, lr = 0.05
I0406 16:37:18.197108 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10098.caffemodel
I0406 16:37:21.249166 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10098.solverstate
I0406 16:37:23.562362 21485 solver.cpp:330] Iteration 10098, Testing net (#0)
I0406 16:37:23.562387 21485 net.cpp:676] Ignoring source layer train-data
I0406 16:37:23.971838 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:37:27.991492 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0232843
I0406 16:37:27.991534 21485 solver.cpp:397]     Test net output #1: loss = 5.13101 (* 1 = 5.13101 loss)
I0406 16:37:29.875385 21485 solver.cpp:218] Iteration 10104 (0.868644 iter/s, 13.8146s/12 iters), loss = 4.6366
I0406 16:37:29.875435 21485 solver.cpp:237]     Train net output #0: loss = 4.6366 (* 1 = 4.6366 loss)
I0406 16:37:29.875442 21485 sgd_solver.cpp:105] Iteration 10104, lr = 0.05
I0406 16:37:34.356711 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:37:35.041752 21485 solver.cpp:218] Iteration 10116 (2.32275 iter/s, 5.1663s/12 iters), loss = 4.75993
I0406 16:37:35.041796 21485 solver.cpp:237]     Train net output #0: loss = 4.75993 (* 1 = 4.75993 loss)
I0406 16:37:35.041802 21485 sgd_solver.cpp:105] Iteration 10116, lr = 0.05
I0406 16:37:40.248095 21485 solver.cpp:218] Iteration 10128 (2.30491 iter/s, 5.20628s/12 iters), loss = 4.66182
I0406 16:37:40.248136 21485 solver.cpp:237]     Train net output #0: loss = 4.66182 (* 1 = 4.66182 loss)
I0406 16:37:40.248142 21485 sgd_solver.cpp:105] Iteration 10128, lr = 0.05
I0406 16:37:45.310475 21485 solver.cpp:218] Iteration 10140 (2.37045 iter/s, 5.06233s/12 iters), loss = 4.51335
I0406 16:37:45.310515 21485 solver.cpp:237]     Train net output #0: loss = 4.51335 (* 1 = 4.51335 loss)
I0406 16:37:45.310520 21485 sgd_solver.cpp:105] Iteration 10140, lr = 0.05
I0406 16:37:50.623953 21485 solver.cpp:218] Iteration 10152 (2.25843 iter/s, 5.31342s/12 iters), loss = 4.55971
I0406 16:37:50.623997 21485 solver.cpp:237]     Train net output #0: loss = 4.55971 (* 1 = 4.55971 loss)
I0406 16:37:50.624002 21485 sgd_solver.cpp:105] Iteration 10152, lr = 0.05
I0406 16:37:55.739390 21485 solver.cpp:218] Iteration 10164 (2.34587 iter/s, 5.11538s/12 iters), loss = 4.98953
I0406 16:37:55.739490 21485 solver.cpp:237]     Train net output #0: loss = 4.98953 (* 1 = 4.98953 loss)
I0406 16:37:55.739497 21485 sgd_solver.cpp:105] Iteration 10164, lr = 0.05
I0406 16:38:00.965988 21485 solver.cpp:218] Iteration 10176 (2.296 iter/s, 5.22648s/12 iters), loss = 4.86995
I0406 16:38:00.966049 21485 solver.cpp:237]     Train net output #0: loss = 4.86995 (* 1 = 4.86995 loss)
I0406 16:38:00.966058 21485 sgd_solver.cpp:105] Iteration 10176, lr = 0.05
I0406 16:38:06.111033 21485 solver.cpp:218] Iteration 10188 (2.33237 iter/s, 5.14497s/12 iters), loss = 4.77164
I0406 16:38:06.111073 21485 solver.cpp:237]     Train net output #0: loss = 4.77164 (* 1 = 4.77164 loss)
I0406 16:38:06.111078 21485 sgd_solver.cpp:105] Iteration 10188, lr = 0.05
I0406 16:38:10.925204 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10200.caffemodel
I0406 16:38:13.937320 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10200.solverstate
I0406 16:38:16.251883 21485 solver.cpp:330] Iteration 10200, Testing net (#0)
I0406 16:38:16.251902 21485 net.cpp:676] Ignoring source layer train-data
I0406 16:38:16.688344 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:38:20.828337 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0214461
I0406 16:38:20.828366 21485 solver.cpp:397]     Test net output #1: loss = 5.0703 (* 1 = 5.0703 loss)
I0406 16:38:20.968569 21485 solver.cpp:218] Iteration 10200 (0.807674 iter/s, 14.8575s/12 iters), loss = 4.95435
I0406 16:38:20.968611 21485 solver.cpp:237]     Train net output #0: loss = 4.95435 (* 1 = 4.95435 loss)
I0406 16:38:20.968616 21485 sgd_solver.cpp:105] Iteration 10200, lr = 0.05
I0406 16:38:25.168308 21485 solver.cpp:218] Iteration 10212 (2.85736 iter/s, 4.19968s/12 iters), loss = 4.59584
I0406 16:38:25.168345 21485 solver.cpp:237]     Train net output #0: loss = 4.59584 (* 1 = 4.59584 loss)
I0406 16:38:25.168350 21485 sgd_solver.cpp:105] Iteration 10212, lr = 0.05
I0406 16:38:26.719305 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:38:30.467137 21485 solver.cpp:218] Iteration 10224 (2.26467 iter/s, 5.29877s/12 iters), loss = 4.62717
I0406 16:38:30.467181 21485 solver.cpp:237]     Train net output #0: loss = 4.62717 (* 1 = 4.62717 loss)
I0406 16:38:30.467186 21485 sgd_solver.cpp:105] Iteration 10224, lr = 0.05
I0406 16:38:35.806977 21485 solver.cpp:218] Iteration 10236 (2.24728 iter/s, 5.33978s/12 iters), loss = 4.69015
I0406 16:38:35.807025 21485 solver.cpp:237]     Train net output #0: loss = 4.69015 (* 1 = 4.69015 loss)
I0406 16:38:35.807032 21485 sgd_solver.cpp:105] Iteration 10236, lr = 0.05
I0406 16:38:41.140077 21485 solver.cpp:218] Iteration 10248 (2.25013 iter/s, 5.33303s/12 iters), loss = 4.49603
I0406 16:38:41.140128 21485 solver.cpp:237]     Train net output #0: loss = 4.49603 (* 1 = 4.49603 loss)
I0406 16:38:41.140136 21485 sgd_solver.cpp:105] Iteration 10248, lr = 0.05
I0406 16:38:46.391448 21485 solver.cpp:218] Iteration 10260 (2.28514 iter/s, 5.25131s/12 iters), loss = 4.36771
I0406 16:38:46.391489 21485 solver.cpp:237]     Train net output #0: loss = 4.36771 (* 1 = 4.36771 loss)
I0406 16:38:46.391495 21485 sgd_solver.cpp:105] Iteration 10260, lr = 0.05
I0406 16:38:51.496500 21485 solver.cpp:218] Iteration 10272 (2.35064 iter/s, 5.105s/12 iters), loss = 5.2299
I0406 16:38:51.496546 21485 solver.cpp:237]     Train net output #0: loss = 5.2299 (* 1 = 5.2299 loss)
I0406 16:38:51.496551 21485 sgd_solver.cpp:105] Iteration 10272, lr = 0.05
I0406 16:38:56.837754 21485 solver.cpp:218] Iteration 10284 (2.24669 iter/s, 5.3412s/12 iters), loss = 5.06068
I0406 16:38:56.837855 21485 solver.cpp:237]     Train net output #0: loss = 5.06068 (* 1 = 5.06068 loss)
I0406 16:38:56.837862 21485 sgd_solver.cpp:105] Iteration 10284, lr = 0.05
I0406 16:39:02.025005 21485 solver.cpp:218] Iteration 10296 (2.31341 iter/s, 5.18714s/12 iters), loss = 5.07189
I0406 16:39:02.025045 21485 solver.cpp:237]     Train net output #0: loss = 5.07189 (* 1 = 5.07189 loss)
I0406 16:39:02.025050 21485 sgd_solver.cpp:105] Iteration 10296, lr = 0.05
I0406 16:39:04.213747 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10302.caffemodel
I0406 16:39:07.218575 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10302.solverstate
I0406 16:39:09.542189 21485 solver.cpp:330] Iteration 10302, Testing net (#0)
I0406 16:39:09.542208 21485 net.cpp:676] Ignoring source layer train-data
I0406 16:39:09.872808 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:39:13.899493 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0171569
I0406 16:39:13.899521 21485 solver.cpp:397]     Test net output #1: loss = 5.12305 (* 1 = 5.12305 loss)
I0406 16:39:15.853816 21485 solver.cpp:218] Iteration 10308 (0.867757 iter/s, 13.8288s/12 iters), loss = 4.94835
I0406 16:39:15.853857 21485 solver.cpp:237]     Train net output #0: loss = 4.94835 (* 1 = 4.94835 loss)
I0406 16:39:15.853863 21485 sgd_solver.cpp:105] Iteration 10308, lr = 0.05
I0406 16:39:19.600380 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:39:21.070423 21485 solver.cpp:218] Iteration 10320 (2.30037 iter/s, 5.21655s/12 iters), loss = 5.07361
I0406 16:39:21.070463 21485 solver.cpp:237]     Train net output #0: loss = 5.07361 (* 1 = 5.07361 loss)
I0406 16:39:21.070469 21485 sgd_solver.cpp:105] Iteration 10320, lr = 0.05
I0406 16:39:26.422451 21485 solver.cpp:218] Iteration 10332 (2.24217 iter/s, 5.35197s/12 iters), loss = 4.87526
I0406 16:39:26.422508 21485 solver.cpp:237]     Train net output #0: loss = 4.87526 (* 1 = 4.87526 loss)
I0406 16:39:26.422515 21485 sgd_solver.cpp:105] Iteration 10332, lr = 0.05
I0406 16:39:31.558614 21485 solver.cpp:218] Iteration 10344 (2.33641 iter/s, 5.13609s/12 iters), loss = 4.95647
I0406 16:39:31.558763 21485 solver.cpp:237]     Train net output #0: loss = 4.95647 (* 1 = 4.95647 loss)
I0406 16:39:31.558769 21485 sgd_solver.cpp:105] Iteration 10344, lr = 0.05
I0406 16:39:36.477898 21485 solver.cpp:218] Iteration 10356 (2.43946 iter/s, 4.91912s/12 iters), loss = 4.91223
I0406 16:39:36.477944 21485 solver.cpp:237]     Train net output #0: loss = 4.91223 (* 1 = 4.91223 loss)
I0406 16:39:36.477950 21485 sgd_solver.cpp:105] Iteration 10356, lr = 0.05
I0406 16:39:41.851495 21485 solver.cpp:218] Iteration 10368 (2.23317 iter/s, 5.37353s/12 iters), loss = 4.90404
I0406 16:39:41.851552 21485 solver.cpp:237]     Train net output #0: loss = 4.90404 (* 1 = 4.90404 loss)
I0406 16:39:41.851562 21485 sgd_solver.cpp:105] Iteration 10368, lr = 0.05
I0406 16:39:47.009512 21485 solver.cpp:218] Iteration 10380 (2.32651 iter/s, 5.15795s/12 iters), loss = 4.94679
I0406 16:39:47.009559 21485 solver.cpp:237]     Train net output #0: loss = 4.94679 (* 1 = 4.94679 loss)
I0406 16:39:47.009564 21485 sgd_solver.cpp:105] Iteration 10380, lr = 0.05
I0406 16:39:52.305511 21485 solver.cpp:218] Iteration 10392 (2.26589 iter/s, 5.29594s/12 iters), loss = 4.83685
I0406 16:39:52.305552 21485 solver.cpp:237]     Train net output #0: loss = 4.83685 (* 1 = 4.83685 loss)
I0406 16:39:52.305558 21485 sgd_solver.cpp:105] Iteration 10392, lr = 0.05
I0406 16:39:56.976197 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10404.caffemodel
I0406 16:39:59.988134 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10404.solverstate
I0406 16:40:02.770438 21485 solver.cpp:330] Iteration 10404, Testing net (#0)
I0406 16:40:02.770543 21485 net.cpp:676] Ignoring source layer train-data
I0406 16:40:03.058373 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:40:03.569711 21485 blocking_queue.cpp:49] Waiting for data
I0406 16:40:07.329916 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0165441
I0406 16:40:07.329950 21485 solver.cpp:397]     Test net output #1: loss = 5.09566 (* 1 = 5.09566 loss)
I0406 16:40:07.463225 21485 solver.cpp:218] Iteration 10404 (0.791679 iter/s, 15.1577s/12 iters), loss = 4.76605
I0406 16:40:07.463269 21485 solver.cpp:237]     Train net output #0: loss = 4.76605 (* 1 = 4.76605 loss)
I0406 16:40:07.463274 21485 sgd_solver.cpp:105] Iteration 10404, lr = 0.05
I0406 16:40:11.680977 21485 solver.cpp:218] Iteration 10416 (2.84516 iter/s, 4.21769s/12 iters), loss = 4.97099
I0406 16:40:11.681020 21485 solver.cpp:237]     Train net output #0: loss = 4.97099 (* 1 = 4.97099 loss)
I0406 16:40:11.681026 21485 sgd_solver.cpp:105] Iteration 10416, lr = 0.05
I0406 16:40:12.562799 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:40:16.837998 21485 solver.cpp:218] Iteration 10428 (2.32695 iter/s, 5.15696s/12 iters), loss = 4.98424
I0406 16:40:16.838037 21485 solver.cpp:237]     Train net output #0: loss = 4.98424 (* 1 = 4.98424 loss)
I0406 16:40:16.838042 21485 sgd_solver.cpp:105] Iteration 10428, lr = 0.05
I0406 16:40:22.004426 21485 solver.cpp:218] Iteration 10440 (2.32271 iter/s, 5.16637s/12 iters), loss = 4.9031
I0406 16:40:22.004477 21485 solver.cpp:237]     Train net output #0: loss = 4.9031 (* 1 = 4.9031 loss)
I0406 16:40:22.004484 21485 sgd_solver.cpp:105] Iteration 10440, lr = 0.05
I0406 16:40:27.238984 21485 solver.cpp:218] Iteration 10452 (2.29249 iter/s, 5.23449s/12 iters), loss = 5.01553
I0406 16:40:27.239030 21485 solver.cpp:237]     Train net output #0: loss = 5.01553 (* 1 = 5.01553 loss)
I0406 16:40:27.239035 21485 sgd_solver.cpp:105] Iteration 10452, lr = 0.05
I0406 16:40:32.435791 21485 solver.cpp:218] Iteration 10464 (2.30914 iter/s, 5.19674s/12 iters), loss = 5.03807
I0406 16:40:32.435832 21485 solver.cpp:237]     Train net output #0: loss = 5.03807 (* 1 = 5.03807 loss)
I0406 16:40:32.435837 21485 sgd_solver.cpp:105] Iteration 10464, lr = 0.05
I0406 16:40:37.659442 21485 solver.cpp:218] Iteration 10476 (2.29727 iter/s, 5.2236s/12 iters), loss = 5.0056
I0406 16:40:37.659561 21485 solver.cpp:237]     Train net output #0: loss = 5.0056 (* 1 = 5.0056 loss)
I0406 16:40:37.659569 21485 sgd_solver.cpp:105] Iteration 10476, lr = 0.05
I0406 16:40:42.810143 21485 solver.cpp:218] Iteration 10488 (2.32984 iter/s, 5.15056s/12 iters), loss = 4.90699
I0406 16:40:42.810199 21485 solver.cpp:237]     Train net output #0: loss = 4.90699 (* 1 = 4.90699 loss)
I0406 16:40:42.810206 21485 sgd_solver.cpp:105] Iteration 10488, lr = 0.05
I0406 16:40:48.115607 21485 solver.cpp:218] Iteration 10500 (2.26185 iter/s, 5.3054s/12 iters), loss = 4.97388
I0406 16:40:48.115646 21485 solver.cpp:237]     Train net output #0: loss = 4.97388 (* 1 = 4.97388 loss)
I0406 16:40:48.115651 21485 sgd_solver.cpp:105] Iteration 10500, lr = 0.05
I0406 16:40:50.170352 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10506.caffemodel
I0406 16:40:53.256458 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10506.solverstate
I0406 16:40:55.574332 21485 solver.cpp:330] Iteration 10506, Testing net (#0)
I0406 16:40:55.574353 21485 net.cpp:676] Ignoring source layer train-data
I0406 16:40:55.818224 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:40:59.890638 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0165441
I0406 16:40:59.890666 21485 solver.cpp:397]     Test net output #1: loss = 5.13197 (* 1 = 5.13197 loss)
I0406 16:41:01.797752 21485 solver.cpp:218] Iteration 10512 (0.877059 iter/s, 13.6821s/12 iters), loss = 4.90845
I0406 16:41:01.797804 21485 solver.cpp:237]     Train net output #0: loss = 4.90845 (* 1 = 4.90845 loss)
I0406 16:41:01.797809 21485 sgd_solver.cpp:105] Iteration 10512, lr = 0.05
I0406 16:41:04.968631 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:41:07.200256 21485 solver.cpp:218] Iteration 10524 (2.22122 iter/s, 5.40244s/12 iters), loss = 4.98645
I0406 16:41:07.200309 21485 solver.cpp:237]     Train net output #0: loss = 4.98645 (* 1 = 4.98645 loss)
I0406 16:41:07.200316 21485 sgd_solver.cpp:105] Iteration 10524, lr = 0.05
I0406 16:41:12.568284 21485 solver.cpp:218] Iteration 10536 (2.23549 iter/s, 5.36796s/12 iters), loss = 5.0419
I0406 16:41:12.568410 21485 solver.cpp:237]     Train net output #0: loss = 5.0419 (* 1 = 5.0419 loss)
I0406 16:41:12.568416 21485 sgd_solver.cpp:105] Iteration 10536, lr = 0.05
I0406 16:41:17.882491 21485 solver.cpp:218] Iteration 10548 (2.25816 iter/s, 5.31406s/12 iters), loss = 4.91225
I0406 16:41:17.882545 21485 solver.cpp:237]     Train net output #0: loss = 4.91225 (* 1 = 4.91225 loss)
I0406 16:41:17.882552 21485 sgd_solver.cpp:105] Iteration 10548, lr = 0.05
I0406 16:41:23.171761 21485 solver.cpp:218] Iteration 10560 (2.26877 iter/s, 5.2892s/12 iters), loss = 4.95219
I0406 16:41:23.171804 21485 solver.cpp:237]     Train net output #0: loss = 4.95219 (* 1 = 4.95219 loss)
I0406 16:41:23.171809 21485 sgd_solver.cpp:105] Iteration 10560, lr = 0.05
I0406 16:41:28.347805 21485 solver.cpp:218] Iteration 10572 (2.3184 iter/s, 5.17599s/12 iters), loss = 5.05895
I0406 16:41:28.347847 21485 solver.cpp:237]     Train net output #0: loss = 5.05895 (* 1 = 5.05895 loss)
I0406 16:41:28.347853 21485 sgd_solver.cpp:105] Iteration 10572, lr = 0.05
I0406 16:41:33.541090 21485 solver.cpp:218] Iteration 10584 (2.31071 iter/s, 5.19322s/12 iters), loss = 5.0854
I0406 16:41:33.541149 21485 solver.cpp:237]     Train net output #0: loss = 5.0854 (* 1 = 5.0854 loss)
I0406 16:41:33.541157 21485 sgd_solver.cpp:105] Iteration 10584, lr = 0.05
I0406 16:41:38.793921 21485 solver.cpp:218] Iteration 10596 (2.28451 iter/s, 5.25277s/12 iters), loss = 5.00381
I0406 16:41:38.793962 21485 solver.cpp:237]     Train net output #0: loss = 5.00381 (* 1 = 5.00381 loss)
I0406 16:41:38.793967 21485 sgd_solver.cpp:105] Iteration 10596, lr = 0.05
I0406 16:41:43.559299 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10608.caffemodel
I0406 16:41:47.438727 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10608.solverstate
I0406 16:41:49.769717 21485 solver.cpp:330] Iteration 10608, Testing net (#0)
I0406 16:41:49.769737 21485 net.cpp:676] Ignoring source layer train-data
I0406 16:41:49.985685 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:41:54.058252 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0208333
I0406 16:41:54.058291 21485 solver.cpp:397]     Test net output #1: loss = 5.08219 (* 1 = 5.08219 loss)
I0406 16:41:54.198484 21485 solver.cpp:218] Iteration 10608 (0.778993 iter/s, 15.4045s/12 iters), loss = 4.92467
I0406 16:41:54.198535 21485 solver.cpp:237]     Train net output #0: loss = 4.92467 (* 1 = 4.92467 loss)
I0406 16:41:54.198546 21485 sgd_solver.cpp:105] Iteration 10608, lr = 0.05
I0406 16:41:58.567188 21485 solver.cpp:218] Iteration 10620 (2.74685 iter/s, 4.36864s/12 iters), loss = 5.15208
I0406 16:41:58.567236 21485 solver.cpp:237]     Train net output #0: loss = 5.15208 (* 1 = 5.15208 loss)
I0406 16:41:58.567245 21485 sgd_solver.cpp:105] Iteration 10620, lr = 0.05
I0406 16:41:58.692193 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:42:03.903723 21485 solver.cpp:218] Iteration 10632 (2.24868 iter/s, 5.33647s/12 iters), loss = 5.11946
I0406 16:42:03.903762 21485 solver.cpp:237]     Train net output #0: loss = 5.11946 (* 1 = 5.11946 loss)
I0406 16:42:03.903769 21485 sgd_solver.cpp:105] Iteration 10632, lr = 0.05
I0406 16:42:08.954026 21485 solver.cpp:218] Iteration 10644 (2.37612 iter/s, 5.05025s/12 iters), loss = 4.88315
I0406 16:42:08.954068 21485 solver.cpp:237]     Train net output #0: loss = 4.88315 (* 1 = 4.88315 loss)
I0406 16:42:08.954073 21485 sgd_solver.cpp:105] Iteration 10644, lr = 0.05
I0406 16:42:14.143097 21485 solver.cpp:218] Iteration 10656 (2.31258 iter/s, 5.18901s/12 iters), loss = 5.05057
I0406 16:42:14.143224 21485 solver.cpp:237]     Train net output #0: loss = 5.05057 (* 1 = 5.05057 loss)
I0406 16:42:14.143231 21485 sgd_solver.cpp:105] Iteration 10656, lr = 0.05
I0406 16:42:19.539667 21485 solver.cpp:218] Iteration 10668 (2.22369 iter/s, 5.39643s/12 iters), loss = 5.04249
I0406 16:42:19.539719 21485 solver.cpp:237]     Train net output #0: loss = 5.04249 (* 1 = 5.04249 loss)
I0406 16:42:19.539726 21485 sgd_solver.cpp:105] Iteration 10668, lr = 0.05
I0406 16:42:24.859570 21485 solver.cpp:218] Iteration 10680 (2.25571 iter/s, 5.31984s/12 iters), loss = 4.92846
I0406 16:42:24.859612 21485 solver.cpp:237]     Train net output #0: loss = 4.92846 (* 1 = 4.92846 loss)
I0406 16:42:24.859618 21485 sgd_solver.cpp:105] Iteration 10680, lr = 0.05
I0406 16:42:29.991832 21485 solver.cpp:218] Iteration 10692 (2.33818 iter/s, 5.1322s/12 iters), loss = 4.83686
I0406 16:42:29.991892 21485 solver.cpp:237]     Train net output #0: loss = 4.83686 (* 1 = 4.83686 loss)
I0406 16:42:29.991900 21485 sgd_solver.cpp:105] Iteration 10692, lr = 0.05
I0406 16:42:35.322654 21485 solver.cpp:218] Iteration 10704 (2.25109 iter/s, 5.33074s/12 iters), loss = 4.81931
I0406 16:42:35.322717 21485 solver.cpp:237]     Train net output #0: loss = 4.81931 (* 1 = 4.81931 loss)
I0406 16:42:35.322726 21485 sgd_solver.cpp:105] Iteration 10704, lr = 0.05
I0406 16:42:37.458696 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10710.caffemodel
I0406 16:42:40.444160 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10710.solverstate
I0406 16:42:42.760396 21485 solver.cpp:330] Iteration 10710, Testing net (#0)
I0406 16:42:42.760414 21485 net.cpp:676] Ignoring source layer train-data
I0406 16:42:42.960402 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:42:47.214226 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0226716
I0406 16:42:47.214291 21485 solver.cpp:397]     Test net output #1: loss = 5.08213 (* 1 = 5.08213 loss)
I0406 16:42:49.150748 21485 solver.cpp:218] Iteration 10716 (0.867803 iter/s, 13.828s/12 iters), loss = 4.97106
I0406 16:42:49.150810 21485 solver.cpp:237]     Train net output #0: loss = 4.97106 (* 1 = 4.97106 loss)
I0406 16:42:49.150822 21485 sgd_solver.cpp:105] Iteration 10716, lr = 0.05
I0406 16:42:51.456557 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:42:54.310532 21485 solver.cpp:218] Iteration 10728 (2.32571 iter/s, 5.15971s/12 iters), loss = 5.00341
I0406 16:42:54.310577 21485 solver.cpp:237]     Train net output #0: loss = 5.00341 (* 1 = 5.00341 loss)
I0406 16:42:54.310583 21485 sgd_solver.cpp:105] Iteration 10728, lr = 0.05
I0406 16:42:59.771186 21485 solver.cpp:218] Iteration 10740 (2.19756 iter/s, 5.46059s/12 iters), loss = 4.92736
I0406 16:42:59.771229 21485 solver.cpp:237]     Train net output #0: loss = 4.92736 (* 1 = 4.92736 loss)
I0406 16:42:59.771235 21485 sgd_solver.cpp:105] Iteration 10740, lr = 0.05
I0406 16:43:05.034776 21485 solver.cpp:218] Iteration 10752 (2.27984 iter/s, 5.26353s/12 iters), loss = 5.11878
I0406 16:43:05.034830 21485 solver.cpp:237]     Train net output #0: loss = 5.11878 (* 1 = 5.11878 loss)
I0406 16:43:05.034839 21485 sgd_solver.cpp:105] Iteration 10752, lr = 0.05
I0406 16:43:10.332479 21485 solver.cpp:218] Iteration 10764 (2.26516 iter/s, 5.29763s/12 iters), loss = 4.83317
I0406 16:43:10.332531 21485 solver.cpp:237]     Train net output #0: loss = 4.83317 (* 1 = 4.83317 loss)
I0406 16:43:10.332540 21485 sgd_solver.cpp:105] Iteration 10764, lr = 0.05
I0406 16:43:15.566730 21485 solver.cpp:218] Iteration 10776 (2.29262 iter/s, 5.23419s/12 iters), loss = 4.92167
I0406 16:43:15.566785 21485 solver.cpp:237]     Train net output #0: loss = 4.92167 (* 1 = 4.92167 loss)
I0406 16:43:15.566794 21485 sgd_solver.cpp:105] Iteration 10776, lr = 0.05
I0406 16:43:20.900537 21485 solver.cpp:218] Iteration 10788 (2.24983 iter/s, 5.33374s/12 iters), loss = 4.9054
I0406 16:43:20.900660 21485 solver.cpp:237]     Train net output #0: loss = 4.9054 (* 1 = 4.9054 loss)
I0406 16:43:20.900669 21485 sgd_solver.cpp:105] Iteration 10788, lr = 0.05
I0406 16:43:26.361771 21485 solver.cpp:218] Iteration 10800 (2.19736 iter/s, 5.4611s/12 iters), loss = 4.69371
I0406 16:43:26.361837 21485 solver.cpp:237]     Train net output #0: loss = 4.69371 (* 1 = 4.69371 loss)
I0406 16:43:26.361847 21485 sgd_solver.cpp:105] Iteration 10800, lr = 0.05
I0406 16:43:31.157577 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10812.caffemodel
I0406 16:43:34.775049 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10812.solverstate
I0406 16:43:37.883580 21485 solver.cpp:330] Iteration 10812, Testing net (#0)
I0406 16:43:37.883601 21485 net.cpp:676] Ignoring source layer train-data
I0406 16:43:38.039992 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:43:42.358814 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0214461
I0406 16:43:42.358855 21485 solver.cpp:397]     Test net output #1: loss = 5.09684 (* 1 = 5.09684 loss)
I0406 16:43:42.498692 21485 solver.cpp:218] Iteration 10812 (0.74364 iter/s, 16.1369s/12 iters), loss = 4.89683
I0406 16:43:42.498736 21485 solver.cpp:237]     Train net output #0: loss = 4.89683 (* 1 = 4.89683 loss)
I0406 16:43:42.498741 21485 sgd_solver.cpp:105] Iteration 10812, lr = 0.05
I0406 16:43:46.118445 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:43:46.771853 21485 solver.cpp:218] Iteration 10824 (2.80826 iter/s, 4.2731s/12 iters), loss = 4.89141
I0406 16:43:46.771893 21485 solver.cpp:237]     Train net output #0: loss = 4.89141 (* 1 = 4.89141 loss)
I0406 16:43:46.771898 21485 sgd_solver.cpp:105] Iteration 10824, lr = 0.05
I0406 16:43:51.972121 21485 solver.cpp:218] Iteration 10836 (2.3076 iter/s, 5.20022s/12 iters), loss = 5.0053
I0406 16:43:51.972205 21485 solver.cpp:237]     Train net output #0: loss = 5.0053 (* 1 = 5.0053 loss)
I0406 16:43:51.972211 21485 sgd_solver.cpp:105] Iteration 10836, lr = 0.05
I0406 16:43:57.186262 21485 solver.cpp:218] Iteration 10848 (2.30148 iter/s, 5.21404s/12 iters), loss = 4.77561
I0406 16:43:57.186313 21485 solver.cpp:237]     Train net output #0: loss = 4.77561 (* 1 = 4.77561 loss)
I0406 16:43:57.186321 21485 sgd_solver.cpp:105] Iteration 10848, lr = 0.05
I0406 16:44:02.705497 21485 solver.cpp:218] Iteration 10860 (2.17424 iter/s, 5.51917s/12 iters), loss = 4.81376
I0406 16:44:02.705539 21485 solver.cpp:237]     Train net output #0: loss = 4.81376 (* 1 = 4.81376 loss)
I0406 16:44:02.705544 21485 sgd_solver.cpp:105] Iteration 10860, lr = 0.05
I0406 16:44:07.892347 21485 solver.cpp:218] Iteration 10872 (2.31357 iter/s, 5.18679s/12 iters), loss = 4.81327
I0406 16:44:07.892382 21485 solver.cpp:237]     Train net output #0: loss = 4.81327 (* 1 = 4.81327 loss)
I0406 16:44:07.892387 21485 sgd_solver.cpp:105] Iteration 10872, lr = 0.05
I0406 16:44:13.037132 21485 solver.cpp:218] Iteration 10884 (2.33248 iter/s, 5.14473s/12 iters), loss = 4.99067
I0406 16:44:13.037245 21485 solver.cpp:237]     Train net output #0: loss = 4.99067 (* 1 = 4.99067 loss)
I0406 16:44:13.037253 21485 sgd_solver.cpp:105] Iteration 10884, lr = 0.05
I0406 16:44:18.034240 21485 solver.cpp:218] Iteration 10896 (2.40145 iter/s, 4.99698s/12 iters), loss = 4.87512
I0406 16:44:18.034286 21485 solver.cpp:237]     Train net output #0: loss = 4.87512 (* 1 = 4.87512 loss)
I0406 16:44:18.034292 21485 sgd_solver.cpp:105] Iteration 10896, lr = 0.05
I0406 16:44:23.360364 21485 solver.cpp:218] Iteration 10908 (2.25307 iter/s, 5.32606s/12 iters), loss = 4.9448
I0406 16:44:23.360536 21485 solver.cpp:237]     Train net output #0: loss = 4.9448 (* 1 = 4.9448 loss)
I0406 16:44:23.360545 21485 sgd_solver.cpp:105] Iteration 10908, lr = 0.05
I0406 16:44:25.436491 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10914.caffemodel
I0406 16:44:28.489359 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10914.solverstate
I0406 16:44:31.167712 21485 solver.cpp:330] Iteration 10914, Testing net (#0)
I0406 16:44:31.167733 21485 net.cpp:676] Ignoring source layer train-data
I0406 16:44:31.271100 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:44:35.610380 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0251225
I0406 16:44:35.610417 21485 solver.cpp:397]     Test net output #1: loss = 5.07708 (* 1 = 5.07708 loss)
I0406 16:44:37.419096 21485 solver.cpp:218] Iteration 10920 (0.853573 iter/s, 14.0586s/12 iters), loss = 4.80253
I0406 16:44:37.419133 21485 solver.cpp:237]     Train net output #0: loss = 4.80253 (* 1 = 4.80253 loss)
I0406 16:44:37.419139 21485 sgd_solver.cpp:105] Iteration 10920, lr = 0.05
I0406 16:44:38.919431 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:44:42.533409 21485 solver.cpp:218] Iteration 10932 (2.34638 iter/s, 5.11426s/12 iters), loss = 4.69957
I0406 16:44:42.533471 21485 solver.cpp:237]     Train net output #0: loss = 4.69957 (* 1 = 4.69957 loss)
I0406 16:44:42.533483 21485 sgd_solver.cpp:105] Iteration 10932, lr = 0.05
I0406 16:44:47.793409 21485 solver.cpp:218] Iteration 10944 (2.2814 iter/s, 5.25992s/12 iters), loss = 4.84012
I0406 16:44:47.793474 21485 solver.cpp:237]     Train net output #0: loss = 4.84012 (* 1 = 4.84012 loss)
I0406 16:44:47.793483 21485 sgd_solver.cpp:105] Iteration 10944, lr = 0.05
I0406 16:44:53.083197 21485 solver.cpp:218] Iteration 10956 (2.26856 iter/s, 5.28971s/12 iters), loss = 4.68323
I0406 16:44:53.083245 21485 solver.cpp:237]     Train net output #0: loss = 4.68323 (* 1 = 4.68323 loss)
I0406 16:44:53.083252 21485 sgd_solver.cpp:105] Iteration 10956, lr = 0.05
I0406 16:44:58.454782 21485 solver.cpp:218] Iteration 10968 (2.23401 iter/s, 5.37152s/12 iters), loss = 4.76404
I0406 16:44:58.454928 21485 solver.cpp:237]     Train net output #0: loss = 4.76404 (* 1 = 4.76404 loss)
I0406 16:44:58.454937 21485 sgd_solver.cpp:105] Iteration 10968, lr = 0.05
I0406 16:45:03.657361 21485 solver.cpp:218] Iteration 10980 (2.30662 iter/s, 5.20242s/12 iters), loss = 5.01923
I0406 16:45:03.657413 21485 solver.cpp:237]     Train net output #0: loss = 5.01923 (* 1 = 5.01923 loss)
I0406 16:45:03.657423 21485 sgd_solver.cpp:105] Iteration 10980, lr = 0.05
I0406 16:45:08.894094 21485 solver.cpp:218] Iteration 10992 (2.29153 iter/s, 5.23667s/12 iters), loss = 4.95742
I0406 16:45:08.894137 21485 solver.cpp:237]     Train net output #0: loss = 4.95742 (* 1 = 4.95742 loss)
I0406 16:45:08.894142 21485 sgd_solver.cpp:105] Iteration 10992, lr = 0.05
I0406 16:45:13.997256 21485 solver.cpp:218] Iteration 11004 (2.35151 iter/s, 5.1031s/12 iters), loss = 5.05076
I0406 16:45:13.997318 21485 solver.cpp:237]     Train net output #0: loss = 5.05076 (* 1 = 5.05076 loss)
I0406 16:45:13.997326 21485 sgd_solver.cpp:105] Iteration 11004, lr = 0.05
I0406 16:45:18.805819 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_11016.caffemodel
I0406 16:45:23.444128 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_11016.solverstate
I0406 16:45:27.743942 21485 solver.cpp:330] Iteration 11016, Testing net (#0)
I0406 16:45:27.743960 21485 net.cpp:676] Ignoring source layer train-data
I0406 16:45:27.803247 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:45:32.054500 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0208333
I0406 16:45:32.054649 21485 solver.cpp:397]     Test net output #1: loss = 5.10683 (* 1 = 5.10683 loss)
I0406 16:45:32.195183 21485 solver.cpp:218] Iteration 11016 (0.659418 iter/s, 18.1979s/12 iters), loss = 4.89856
I0406 16:45:32.195232 21485 solver.cpp:237]     Train net output #0: loss = 4.89856 (* 1 = 4.89856 loss)
I0406 16:45:32.195240 21485 sgd_solver.cpp:105] Iteration 11016, lr = 0.05
I0406 16:45:32.687896 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:45:35.262648 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:45:36.561061 21485 solver.cpp:218] Iteration 11028 (2.74863 iter/s, 4.36582s/12 iters), loss = 5.13023
I0406 16:45:36.561100 21485 solver.cpp:237]     Train net output #0: loss = 5.13023 (* 1 = 5.13023 loss)
I0406 16:45:36.561105 21485 sgd_solver.cpp:105] Iteration 11028, lr = 0.05
I0406 16:45:41.970306 21485 solver.cpp:218] Iteration 11040 (2.21845 iter/s, 5.40919s/12 iters), loss = 4.88905
I0406 16:45:41.970341 21485 solver.cpp:237]     Train net output #0: loss = 4.88905 (* 1 = 4.88905 loss)
I0406 16:45:41.970347 21485 sgd_solver.cpp:105] Iteration 11040, lr = 0.05
I0406 16:45:47.268867 21485 solver.cpp:218] Iteration 11052 (2.26479 iter/s, 5.29851s/12 iters), loss = 5.004
I0406 16:45:47.268918 21485 solver.cpp:237]     Train net output #0: loss = 5.004 (* 1 = 5.004 loss)
I0406 16:45:47.268923 21485 sgd_solver.cpp:105] Iteration 11052, lr = 0.05
I0406 16:45:52.545915 21485 solver.cpp:218] Iteration 11064 (2.27403 iter/s, 5.27698s/12 iters), loss = 4.99615
I0406 16:45:52.545967 21485 solver.cpp:237]     Train net output #0: loss = 4.99615 (* 1 = 4.99615 loss)
I0406 16:45:52.545974 21485 sgd_solver.cpp:105] Iteration 11064, lr = 0.05
I0406 16:45:57.980789 21485 solver.cpp:218] Iteration 11076 (2.20799 iter/s, 5.43481s/12 iters), loss = 4.89091
I0406 16:45:57.980840 21485 solver.cpp:237]     Train net output #0: loss = 4.89091 (* 1 = 4.89091 loss)
I0406 16:45:57.980847 21485 sgd_solver.cpp:105] Iteration 11076, lr = 0.05
I0406 16:46:03.129230 21485 solver.cpp:218] Iteration 11088 (2.33083 iter/s, 5.14838s/12 iters), loss = 4.85691
I0406 16:46:03.129336 21485 solver.cpp:237]     Train net output #0: loss = 4.85691 (* 1 = 4.85691 loss)
I0406 16:46:03.129343 21485 sgd_solver.cpp:105] Iteration 11088, lr = 0.05
I0406 16:46:06.551662 21485 blocking_queue.cpp:49] Waiting for data
I0406 16:46:08.142705 21485 solver.cpp:218] Iteration 11100 (2.39361 iter/s, 5.01335s/12 iters), loss = 4.6298
I0406 16:46:08.142756 21485 solver.cpp:237]     Train net output #0: loss = 4.6298 (* 1 = 4.6298 loss)
I0406 16:46:08.142762 21485 sgd_solver.cpp:105] Iteration 11100, lr = 0.05
I0406 16:46:13.130502 21485 solver.cpp:218] Iteration 11112 (2.4059 iter/s, 4.98773s/12 iters), loss = 4.83949
I0406 16:46:13.130555 21485 solver.cpp:237]     Train net output #0: loss = 4.83949 (* 1 = 4.83949 loss)
I0406 16:46:13.130563 21485 sgd_solver.cpp:105] Iteration 11112, lr = 0.05
I0406 16:46:15.159051 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_11118.caffemodel
I0406 16:46:18.216517 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_11118.solverstate
I0406 16:46:21.667647 21485 solver.cpp:330] Iteration 11118, Testing net (#0)
I0406 16:46:21.667670 21485 net.cpp:676] Ignoring source layer train-data
I0406 16:46:25.971324 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0220588
I0406 16:46:25.971362 21485 solver.cpp:397]     Test net output #1: loss = 5.09528 (* 1 = 5.09528 loss)
I0406 16:46:26.500389 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:46:27.797307 21485 solver.cpp:218] Iteration 11124 (0.818178 iter/s, 14.6667s/12 iters), loss = 4.78146
I0406 16:46:27.797369 21485 solver.cpp:237]     Train net output #0: loss = 4.78146 (* 1 = 4.78146 loss)
I0406 16:46:27.797376 21485 sgd_solver.cpp:105] Iteration 11124, lr = 0.05
I0406 16:46:28.706346 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:46:32.924866 21485 solver.cpp:218] Iteration 11136 (2.34033 iter/s, 5.12748s/12 iters), loss = 4.92422
I0406 16:46:32.924922 21485 solver.cpp:237]     Train net output #0: loss = 4.92422 (* 1 = 4.92422 loss)
I0406 16:46:32.924929 21485 sgd_solver.cpp:105] Iteration 11136, lr = 0.05
I0406 16:46:37.972803 21485 solver.cpp:218] Iteration 11148 (2.37724 iter/s, 5.04786s/12 iters), loss = 4.88673
I0406 16:46:37.972929 21485 solver.cpp:237]     Train net output #0: loss = 4.88673 (* 1 = 4.88673 loss)
I0406 16:46:37.972936 21485 sgd_solver.cpp:105] Iteration 11148, lr = 0.05
I0406 16:46:43.358085 21485 solver.cpp:218] Iteration 11160 (2.22835 iter/s, 5.38514s/12 iters), loss = 4.88569
I0406 16:46:43.358139 21485 solver.cpp:237]     Train net output #0: loss = 4.88569 (* 1 = 4.88569 loss)
I0406 16:46:43.358146 21485 sgd_solver.cpp:105] Iteration 11160, lr = 0.05
I0406 16:46:48.737018 21485 solver.cpp:218] Iteration 11172 (2.23095 iter/s, 5.37887s/12 iters), loss = 4.77631
I0406 16:46:48.737066 21485 solver.cpp:237]     Train net output #0: loss = 4.77631 (* 1 = 4.77631 loss)
I0406 16:46:48.737071 21485 sgd_solver.cpp:105] Iteration 11172, lr = 0.05
I0406 16:46:54.092339 21485 solver.cpp:218] Iteration 11184 (2.24079 iter/s, 5.35526s/12 iters), loss = 5.00537
I0406 16:46:54.092399 21485 solver.cpp:237]     Train net output #0: loss = 5.00537 (* 1 = 5.00537 loss)
I0406 16:46:54.092407 21485 sgd_solver.cpp:105] Iteration 11184, lr = 0.05
I0406 16:46:59.177919 21485 solver.cpp:218] Iteration 11196 (2.35965 iter/s, 5.08551s/12 iters), loss = 4.68648
I0406 16:46:59.177959 21485 solver.cpp:237]     Train net output #0: loss = 4.68648 (* 1 = 4.68648 loss)
I0406 16:46:59.177965 21485 sgd_solver.cpp:105] Iteration 11196, lr = 0.05
I0406 16:47:04.376683 21485 solver.cpp:218] Iteration 11208 (2.30827 iter/s, 5.1987s/12 iters), loss = 4.80013
I0406 16:47:04.376739 21485 solver.cpp:237]     Train net output #0: loss = 4.80013 (* 1 = 4.80013 loss)
I0406 16:47:04.376747 21485 sgd_solver.cpp:105] Iteration 11208, lr = 0.05
I0406 16:47:08.952430 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_11220.caffemodel
I0406 16:47:13.361392 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_11220.solverstate
I0406 16:47:17.473845 21485 solver.cpp:330] Iteration 11220, Testing net (#0)
I0406 16:47:17.473867 21485 net.cpp:676] Ignoring source layer train-data
I0406 16:47:21.874421 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0208333
I0406 16:47:21.874459 21485 solver.cpp:397]     Test net output #1: loss = 5.07312 (* 1 = 5.07312 loss)
I0406 16:47:22.008987 21485 solver.cpp:218] Iteration 11220 (0.680571 iter/s, 17.6322s/12 iters), loss = 4.71803
I0406 16:47:22.009045 21485 solver.cpp:237]     Train net output #0: loss = 4.71803 (* 1 = 4.71803 loss)
I0406 16:47:22.009053 21485 sgd_solver.cpp:105] Iteration 11220, lr = 0.05
I0406 16:47:22.360436 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:47:24.299986 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:47:26.447176 21485 solver.cpp:218] Iteration 11232 (2.70385 iter/s, 4.43812s/12 iters), loss = 4.88593
I0406 16:47:26.447221 21485 solver.cpp:237]     Train net output #0: loss = 4.88593 (* 1 = 4.88593 loss)
I0406 16:47:26.447227 21485 sgd_solver.cpp:105] Iteration 11232, lr = 0.05
I0406 16:47:31.602874 21485 solver.cpp:218] Iteration 11244 (2.32755 iter/s, 5.15564s/12 iters), loss = 4.7572
I0406 16:47:31.602950 21485 solver.cpp:237]     Train net output #0: loss = 4.7572 (* 1 = 4.7572 loss)
I0406 16:47:31.602960 21485 sgd_solver.cpp:105] Iteration 11244, lr = 0.05
I0406 16:47:36.760721 21485 solver.cpp:218] Iteration 11256 (2.32659 iter/s, 5.15776s/12 iters), loss = 5.1515
I0406 16:47:36.760774 21485 solver.cpp:237]     Train net output #0: loss = 5.1515 (* 1 = 5.1515 loss)
I0406 16:47:36.760782 21485 sgd_solver.cpp:105] Iteration 11256, lr = 0.05
I0406 16:47:42.114962 21485 solver.cpp:218] Iteration 11268 (2.24124 iter/s, 5.35418s/12 iters), loss = 5.15252
I0406 16:47:42.115104 21485 solver.cpp:237]     Train net output #0: loss = 5.15252 (* 1 = 5.15252 loss)
I0406 16:47:42.115111 21485 sgd_solver.cpp:105] Iteration 11268, lr = 0.05
I0406 16:47:47.324120 21485 solver.cpp:218] Iteration 11280 (2.3037 iter/s, 5.209s/12 iters), loss = 4.9285
I0406 16:47:47.324167 21485 solver.cpp:237]     Train net output #0: loss = 4.9285 (* 1 = 4.9285 loss)
I0406 16:47:47.324173 21485 sgd_solver.cpp:105] Iteration 11280, lr = 0.05
I0406 16:47:52.393725 21485 solver.cpp:218] Iteration 11292 (2.36708 iter/s, 5.06954s/12 iters), loss = 4.97037
I0406 16:47:52.393767 21485 solver.cpp:237]     Train net output #0: loss = 4.97037 (* 1 = 4.97037 loss)
I0406 16:47:52.393772 21485 sgd_solver.cpp:105] Iteration 11292, lr = 0.05
I0406 16:47:57.697445 21485 solver.cpp:218] Iteration 11304 (2.26259 iter/s, 5.30366s/12 iters), loss = 4.95015
I0406 16:47:57.697487 21485 solver.cpp:237]     Train net output #0: loss = 4.95015 (* 1 = 4.95015 loss)
I0406 16:47:57.697492 21485 sgd_solver.cpp:105] Iteration 11304, lr = 0.05
I0406 16:48:02.978778 21485 solver.cpp:218] Iteration 11316 (2.27218 iter/s, 5.28127s/12 iters), loss = 4.79196
I0406 16:48:02.978827 21485 solver.cpp:237]     Train net output #0: loss = 4.79196 (* 1 = 4.79196 loss)
I0406 16:48:02.978834 21485 sgd_solver.cpp:105] Iteration 11316, lr = 0.05
I0406 16:48:05.136283 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_11322.caffemodel
I0406 16:48:08.206812 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_11322.solverstate
I0406 16:48:12.427942 21485 solver.cpp:330] Iteration 11322, Testing net (#0)
I0406 16:48:12.428031 21485 net.cpp:676] Ignoring source layer train-data
I0406 16:48:16.899150 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0220588
I0406 16:48:16.899199 21485 solver.cpp:397]     Test net output #1: loss = 5.10685 (* 1 = 5.10685 loss)
I0406 16:48:17.271005 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:48:18.745864 21485 solver.cpp:218] Iteration 11328 (0.761082 iter/s, 15.767s/12 iters), loss = 4.91709
I0406 16:48:18.745906 21485 solver.cpp:237]     Train net output #0: loss = 4.91709 (* 1 = 4.91709 loss)
I0406 16:48:18.745913 21485 sgd_solver.cpp:105] Iteration 11328, lr = 0.05
I0406 16:48:18.895282 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:48:23.884857 21485 solver.cpp:218] Iteration 11340 (2.33511 iter/s, 5.13894s/12 iters), loss = 4.9457
I0406 16:48:23.884900 21485 solver.cpp:237]     Train net output #0: loss = 4.9457 (* 1 = 4.9457 loss)
I0406 16:48:23.884907 21485 sgd_solver.cpp:105] Iteration 11340, lr = 0.05
I0406 16:48:28.986408 21485 solver.cpp:218] Iteration 11352 (2.35225 iter/s, 5.10149s/12 iters), loss = 4.85102
I0406 16:48:28.986460 21485 solver.cpp:237]     Train net output #0: loss = 4.85102 (* 1 = 4.85102 loss)
I0406 16:48:28.986469 21485 sgd_solver.cpp:105] Iteration 11352, lr = 0.05
I0406 16:48:34.185321 21485 solver.cpp:218] Iteration 11364 (2.30821 iter/s, 5.19884s/12 iters), loss = 4.78828
I0406 16:48:34.185375 21485 solver.cpp:237]     Train net output #0: loss = 4.78828 (* 1 = 4.78828 loss)
I0406 16:48:34.185384 21485 sgd_solver.cpp:105] Iteration 11364, lr = 0.05
I0406 16:48:39.448084 21485 solver.cpp:218] Iteration 11376 (2.2802 iter/s, 5.2627s/12 iters), loss = 4.8441
I0406 16:48:39.448140 21485 solver.cpp:237]     Train net output #0: loss = 4.8441 (* 1 = 4.8441 loss)
I0406 16:48:39.448148 21485 sgd_solver.cpp:105] Iteration 11376, lr = 0.05
I0406 16:48:44.470885 21485 solver.cpp:218] Iteration 11388 (2.38913 iter/s, 5.02274s/12 iters), loss = 4.81728
I0406 16:48:44.471005 21485 solver.cpp:237]     Train net output #0: loss = 4.81728 (* 1 = 4.81728 loss)
I0406 16:48:44.471012 21485 sgd_solver.cpp:105] Iteration 11388, lr = 0.05
I0406 16:48:49.700475 21485 solver.cpp:218] Iteration 11400 (2.29469 iter/s, 5.22946s/12 iters), loss = 4.85608
I0406 16:48:49.700517 21485 solver.cpp:237]     Train net output #0: loss = 4.85608 (* 1 = 4.85608 loss)
I0406 16:48:49.700522 21485 sgd_solver.cpp:105] Iteration 11400, lr = 0.05
I0406 16:48:54.972898 21485 solver.cpp:218] Iteration 11412 (2.27602 iter/s, 5.27236s/12 iters), loss = 5.02921
I0406 16:48:54.972944 21485 solver.cpp:237]     Train net output #0: loss = 5.02921 (* 1 = 5.02921 loss)
I0406 16:48:54.972952 21485 sgd_solver.cpp:105] Iteration 11412, lr = 0.05
I0406 16:48:59.826858 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_11424.caffemodel
I0406 16:49:03.773907 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_11424.solverstate
I0406 16:49:07.476107 21485 solver.cpp:330] Iteration 11424, Testing net (#0)
I0406 16:49:07.476128 21485 net.cpp:676] Ignoring source layer train-data
I0406 16:49:11.841023 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0214461
I0406 16:49:11.841058 21485 solver.cpp:397]     Test net output #1: loss = 5.15095 (* 1 = 5.15095 loss)
I0406 16:49:11.981285 21485 solver.cpp:218] Iteration 11424 (0.705537 iter/s, 17.0083s/12 iters), loss = 5.02655
I0406 16:49:11.982887 21485 solver.cpp:237]     Train net output #0: loss = 5.02655 (* 1 = 5.02655 loss)
I0406 16:49:11.982899 21485 sgd_solver.cpp:105] Iteration 11424, lr = 0.05
I0406 16:49:12.045225 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:49:13.401607 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:49:16.296602 21485 solver.cpp:218] Iteration 11436 (2.78183 iter/s, 4.31371s/12 iters), loss = 5.01173
I0406 16:49:16.296720 21485 solver.cpp:237]     Train net output #0: loss = 5.01173 (* 1 = 5.01173 loss)
I0406 16:49:16.296730 21485 sgd_solver.cpp:105] Iteration 11436, lr = 0.05
I0406 16:49:21.357718 21485 solver.cpp:218] Iteration 11448 (2.37108 iter/s, 5.06098s/12 iters), loss = 5.02281
I0406 16:49:21.357761 21485 solver.cpp:237]     Train net output #0: loss = 5.02281 (* 1 = 5.02281 loss)
I0406 16:49:21.357767 21485 sgd_solver.cpp:105] Iteration 11448, lr = 0.05
I0406 16:49:26.623759 21485 solver.cpp:218] Iteration 11460 (2.27878 iter/s, 5.26597s/12 iters), loss = 5.00206
I0406 16:49:26.623813 21485 solver.cpp:237]     Train net output #0: loss = 5.00206 (* 1 = 5.00206 loss)
I0406 16:49:26.623822 21485 sgd_solver.cpp:105] Iteration 11460, lr = 0.05
I0406 16:49:32.121351 21485 solver.cpp:218] Iteration 11472 (2.1828 iter/s, 5.49752s/12 iters), loss = 4.87566
I0406 16:49:32.121419 21485 solver.cpp:237]     Train net output #0: loss = 4.87566 (* 1 = 4.87566 loss)
I0406 16:49:32.121428 21485 sgd_solver.cpp:105] Iteration 11472, lr = 0.05
I0406 16:49:37.396275 21485 solver.cpp:218] Iteration 11484 (2.27495 iter/s, 5.27484s/12 iters), loss = 4.85077
I0406 16:49:37.396317 21485 solver.cpp:237]     Train net output #0: loss = 4.85077 (* 1 = 4.85077 loss)
I0406 16:49:37.396322 21485 sgd_solver.cpp:105] Iteration 11484, lr = 0.05
I0406 16:49:42.660478 21485 solver.cpp:218] Iteration 11496 (2.27957 iter/s, 5.26415s/12 iters), loss = 4.75866
I0406 16:49:42.660526 21485 solver.cpp:237]     Train net output #0: loss = 4.75866 (* 1 = 4.75866 loss)
I0406 16:49:42.660534 21485 sgd_solver.cpp:105] Iteration 11496, lr = 0.05
I0406 16:49:47.728293 21485 solver.cpp:218] Iteration 11508 (2.36791 iter/s, 5.06776s/12 iters), loss = 4.80657
I0406 16:49:47.728396 21485 solver.cpp:237]     Train net output #0: loss = 4.80657 (* 1 = 4.80657 loss)
I0406 16:49:47.728404 21485 sgd_solver.cpp:105] Iteration 11508, lr = 0.05
I0406 16:49:52.897555 21485 solver.cpp:218] Iteration 11520 (2.32147 iter/s, 5.16915s/12 iters), loss = 4.89843
I0406 16:49:52.897603 21485 solver.cpp:237]     Train net output #0: loss = 4.89843 (* 1 = 4.89843 loss)
I0406 16:49:52.897608 21485 sgd_solver.cpp:105] Iteration 11520, lr = 0.05
I0406 16:49:54.959946 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_11526.caffemodel
I0406 16:49:58.027729 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_11526.solverstate
I0406 16:50:01.753736 21485 solver.cpp:330] Iteration 11526, Testing net (#0)
I0406 16:50:01.753757 21485 net.cpp:676] Ignoring source layer train-data
I0406 16:50:06.103677 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0165441
I0406 16:50:06.103714 21485 solver.cpp:397]     Test net output #1: loss = 5.14392 (* 1 = 5.14392 loss)
I0406 16:50:06.196110 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:50:07.386763 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:50:08.011970 21485 solver.cpp:218] Iteration 11532 (0.793947 iter/s, 15.1144s/12 iters), loss = 5.00808
I0406 16:50:08.012012 21485 solver.cpp:237]     Train net output #0: loss = 5.00808 (* 1 = 5.00808 loss)
I0406 16:50:08.012017 21485 sgd_solver.cpp:105] Iteration 11532, lr = 0.05
I0406 16:50:13.073493 21485 solver.cpp:218] Iteration 11544 (2.37085 iter/s, 5.06147s/12 iters), loss = 4.9969
I0406 16:50:13.073541 21485 solver.cpp:237]     Train net output #0: loss = 4.9969 (* 1 = 4.9969 loss)
I0406 16:50:13.073549 21485 sgd_solver.cpp:105] Iteration 11544, lr = 0.05
I0406 16:50:18.236343 21485 solver.cpp:218] Iteration 11556 (2.32433 iter/s, 5.16279s/12 iters), loss = 4.77916
I0406 16:50:18.236475 21485 solver.cpp:237]     Train net output #0: loss = 4.77916 (* 1 = 4.77916 loss)
I0406 16:50:18.236482 21485 sgd_solver.cpp:105] Iteration 11556, lr = 0.05
I0406 16:50:23.372458 21485 solver.cpp:218] Iteration 11568 (2.33646 iter/s, 5.13597s/12 iters), loss = 4.67521
I0406 16:50:23.372514 21485 solver.cpp:237]     Train net output #0: loss = 4.67521 (* 1 = 4.67521 loss)
I0406 16:50:23.372520 21485 sgd_solver.cpp:105] Iteration 11568, lr = 0.05
I0406 16:50:28.373021 21485 solver.cpp:218] Iteration 11580 (2.39976 iter/s, 5.00049s/12 iters), loss = 4.71296
I0406 16:50:28.373066 21485 solver.cpp:237]     Train net output #0: loss = 4.71296 (* 1 = 4.71296 loss)
I0406 16:50:28.373072 21485 sgd_solver.cpp:105] Iteration 11580, lr = 0.05
I0406 16:50:33.555709 21485 solver.cpp:218] Iteration 11592 (2.31543 iter/s, 5.18263s/12 iters), loss = 4.89722
I0406 16:50:33.555752 21485 solver.cpp:237]     Train net output #0: loss = 4.89722 (* 1 = 4.89722 loss)
I0406 16:50:33.555758 21485 sgd_solver.cpp:105] Iteration 11592, lr = 0.05
I0406 16:50:38.900687 21485 solver.cpp:218] Iteration 11604 (2.24512 iter/s, 5.34492s/12 iters), loss = 4.81459
I0406 16:50:38.900730 21485 solver.cpp:237]     Train net output #0: loss = 4.81459 (* 1 = 4.81459 loss)
I0406 16:50:38.900736 21485 sgd_solver.cpp:105] Iteration 11604, lr = 0.05
I0406 16:50:44.208767 21485 solver.cpp:218] Iteration 11616 (2.26073 iter/s, 5.30802s/12 iters), loss = 5.00885
I0406 16:50:44.208807 21485 solver.cpp:237]     Train net output #0: loss = 5.00885 (* 1 = 5.00885 loss)
I0406 16:50:44.208813 21485 sgd_solver.cpp:105] Iteration 11616, lr = 0.05
I0406 16:50:48.994587 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_11628.caffemodel
I0406 16:50:54.123111 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_11628.solverstate
I0406 16:50:58.216810 21485 solver.cpp:330] Iteration 11628, Testing net (#0)
I0406 16:50:58.216832 21485 net.cpp:676] Ignoring source layer train-data
I0406 16:51:02.517889 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0202206
I0406 16:51:02.517921 21485 solver.cpp:397]     Test net output #1: loss = 5.09763 (* 1 = 5.09763 loss)
I0406 16:51:02.592483 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:51:02.651391 21485 solver.cpp:218] Iteration 11628 (0.650669 iter/s, 18.4426s/12 iters), loss = 4.85925
I0406 16:51:02.651448 21485 solver.cpp:237]     Train net output #0: loss = 4.85925 (* 1 = 4.85925 loss)
I0406 16:51:02.651458 21485 sgd_solver.cpp:105] Iteration 11628, lr = 0.05
I0406 16:51:03.429179 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:51:06.887516 21485 solver.cpp:218] Iteration 11640 (2.83283 iter/s, 4.23605s/12 iters), loss = 5.15094
I0406 16:51:06.887565 21485 solver.cpp:237]     Train net output #0: loss = 5.15094 (* 1 = 5.15094 loss)
I0406 16:51:06.887571 21485 sgd_solver.cpp:105] Iteration 11640, lr = 0.05
I0406 16:51:11.968549 21485 solver.cpp:218] Iteration 11652 (2.36175 iter/s, 5.08097s/12 iters), loss = 4.90543
I0406 16:51:11.968588 21485 solver.cpp:237]     Train net output #0: loss = 4.90543 (* 1 = 4.90543 loss)
I0406 16:51:11.968595 21485 sgd_solver.cpp:105] Iteration 11652, lr = 0.05
I0406 16:51:17.242343 21485 solver.cpp:218] Iteration 11664 (2.27543 iter/s, 5.27374s/12 iters), loss = 4.86179
I0406 16:51:17.242389 21485 solver.cpp:237]     Train net output #0: loss = 4.86179 (* 1 = 4.86179 loss)
I0406 16:51:17.242394 21485 sgd_solver.cpp:105] Iteration 11664, lr = 0.05
I0406 16:51:22.486284 21485 solver.cpp:218] Iteration 11676 (2.28838 iter/s, 5.24388s/12 iters), loss = 4.88906
I0406 16:51:22.486434 21485 solver.cpp:237]     Train net output #0: loss = 4.88906 (* 1 = 4.88906 loss)
I0406 16:51:22.486444 21485 sgd_solver.cpp:105] Iteration 11676, lr = 0.05
I0406 16:51:27.807271 21485 solver.cpp:218] Iteration 11688 (2.25529 iter/s, 5.32083s/12 iters), loss = 4.92874
I0406 16:51:27.807313 21485 solver.cpp:237]     Train net output #0: loss = 4.92874 (* 1 = 4.92874 loss)
I0406 16:51:27.807318 21485 sgd_solver.cpp:105] Iteration 11688, lr = 0.05
I0406 16:51:33.054044 21485 solver.cpp:218] Iteration 11700 (2.28715 iter/s, 5.24671s/12 iters), loss = 4.97072
I0406 16:51:33.054081 21485 solver.cpp:237]     Train net output #0: loss = 4.97072 (* 1 = 4.97072 loss)
I0406 16:51:33.054086 21485 sgd_solver.cpp:105] Iteration 11700, lr = 0.05
I0406 16:51:38.481768 21485 solver.cpp:218] Iteration 11712 (2.2109 iter/s, 5.42767s/12 iters), loss = 4.74545
I0406 16:51:38.481824 21485 solver.cpp:237]     Train net output #0: loss = 4.74545 (* 1 = 4.74545 loss)
I0406 16:51:38.481832 21485 sgd_solver.cpp:105] Iteration 11712, lr = 0.05
I0406 16:51:44.150928 21485 solver.cpp:218] Iteration 11724 (2.11674 iter/s, 5.6691s/12 iters), loss = 4.69131
I0406 16:51:44.150965 21485 solver.cpp:237]     Train net output #0: loss = 4.69131 (* 1 = 4.69131 loss)
I0406 16:51:44.150971 21485 sgd_solver.cpp:105] Iteration 11724, lr = 0.05
I0406 16:51:46.284049 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_11730.caffemodel
I0406 16:51:49.367184 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_11730.solverstate
I0406 16:51:53.249917 21485 solver.cpp:330] Iteration 11730, Testing net (#0)
I0406 16:51:53.249996 21485 net.cpp:676] Ignoring source layer train-data
I0406 16:51:57.506404 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:51:57.536507 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0251225
I0406 16:51:57.536540 21485 solver.cpp:397]     Test net output #1: loss = 5.07862 (* 1 = 5.07862 loss)
I0406 16:51:58.132997 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:51:59.436141 21485 solver.cpp:218] Iteration 11736 (0.785075 iter/s, 15.2852s/12 iters), loss = 4.93781
I0406 16:51:59.436199 21485 solver.cpp:237]     Train net output #0: loss = 4.93781 (* 1 = 4.93781 loss)
I0406 16:51:59.436208 21485 sgd_solver.cpp:105] Iteration 11736, lr = 0.05
I0406 16:52:04.772002 21485 solver.cpp:218] Iteration 11748 (2.24896 iter/s, 5.33579s/12 iters), loss = 5.0898
I0406 16:52:04.772058 21485 solver.cpp:237]     Train net output #0: loss = 5.0898 (* 1 = 5.0898 loss)
I0406 16:52:04.772066 21485 sgd_solver.cpp:105] Iteration 11748, lr = 0.05
I0406 16:52:10.142303 21485 solver.cpp:218] Iteration 11760 (2.23454 iter/s, 5.37023s/12 iters), loss = 5.03324
I0406 16:52:10.142356 21485 solver.cpp:237]     Train net output #0: loss = 5.03324 (* 1 = 5.03324 loss)
I0406 16:52:10.142365 21485 sgd_solver.cpp:105] Iteration 11760, lr = 0.05
I0406 16:52:15.343772 21485 solver.cpp:218] Iteration 11772 (2.30707 iter/s, 5.2014s/12 iters), loss = 5.05431
I0406 16:52:15.343817 21485 solver.cpp:237]     Train net output #0: loss = 5.05431 (* 1 = 5.05431 loss)
I0406 16:52:15.343823 21485 sgd_solver.cpp:105] Iteration 11772, lr = 0.05
I0406 16:52:19.156052 21485 blocking_queue.cpp:49] Waiting for data
I0406 16:52:20.611996 21485 solver.cpp:218] Iteration 11784 (2.27783 iter/s, 5.26817s/12 iters), loss = 5.18318
I0406 16:52:20.612051 21485 solver.cpp:237]     Train net output #0: loss = 5.18318 (* 1 = 5.18318 loss)
I0406 16:52:20.612061 21485 sgd_solver.cpp:105] Iteration 11784, lr = 0.05
I0406 16:52:25.968384 21485 solver.cpp:218] Iteration 11796 (2.24035 iter/s, 5.35632s/12 iters), loss = 4.95401
I0406 16:52:25.968559 21485 solver.cpp:237]     Train net output #0: loss = 4.95401 (* 1 = 4.95401 loss)
I0406 16:52:25.968567 21485 sgd_solver.cpp:105] Iteration 11796, lr = 0.05
I0406 16:52:31.219746 21485 solver.cpp:218] Iteration 11808 (2.2852 iter/s, 5.25118s/12 iters), loss = 5.01729
I0406 16:52:31.219787 21485 solver.cpp:237]     Train net output #0: loss = 5.01729 (* 1 = 5.01729 loss)
I0406 16:52:31.219794 21485 sgd_solver.cpp:105] Iteration 11808, lr = 0.05
I0406 16:52:36.343703 21485 solver.cpp:218] Iteration 11820 (2.34197 iter/s, 5.1239s/12 iters), loss = 5.1221
I0406 16:52:36.343744 21485 solver.cpp:237]     Train net output #0: loss = 5.1221 (* 1 = 5.1221 loss)
I0406 16:52:36.343750 21485 sgd_solver.cpp:105] Iteration 11820, lr = 0.05
I0406 16:52:40.949729 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_11832.caffemodel
I0406 16:52:44.032734 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_11832.solverstate
I0406 16:52:46.339577 21485 solver.cpp:330] Iteration 11832, Testing net (#0)
I0406 16:52:46.339596 21485 net.cpp:676] Ignoring source layer train-data
I0406 16:52:50.854148 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:52:50.929324 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0183824
I0406 16:52:50.929354 21485 solver.cpp:397]     Test net output #1: loss = 5.11901 (* 1 = 5.11901 loss)
I0406 16:52:51.070366 21485 solver.cpp:218] Iteration 11832 (0.814852 iter/s, 14.7266s/12 iters), loss = 5.04797
I0406 16:52:51.070425 21485 solver.cpp:237]     Train net output #0: loss = 5.04797 (* 1 = 5.04797 loss)
I0406 16:52:51.070433 21485 sgd_solver.cpp:105] Iteration 11832, lr = 0.05
I0406 16:52:51.170974 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:52:55.504839 21485 solver.cpp:218] Iteration 11844 (2.70612 iter/s, 4.4344s/12 iters), loss = 5.06656
I0406 16:52:55.504889 21485 solver.cpp:237]     Train net output #0: loss = 5.06656 (* 1 = 5.06656 loss)
I0406 16:52:55.504895 21485 sgd_solver.cpp:105] Iteration 11844, lr = 0.05
I0406 16:53:00.670820 21485 solver.cpp:218] Iteration 11856 (2.32292 iter/s, 5.16592s/12 iters), loss = 5.09099
I0406 16:53:00.670925 21485 solver.cpp:237]     Train net output #0: loss = 5.09099 (* 1 = 5.09099 loss)
I0406 16:53:00.670934 21485 sgd_solver.cpp:105] Iteration 11856, lr = 0.05
I0406 16:53:05.770469 21485 solver.cpp:218] Iteration 11868 (2.35316 iter/s, 5.09953s/12 iters), loss = 4.99688
I0406 16:53:05.770519 21485 solver.cpp:237]     Train net output #0: loss = 4.99688 (* 1 = 4.99688 loss)
I0406 16:53:05.770526 21485 sgd_solver.cpp:105] Iteration 11868, lr = 0.05
I0406 16:53:11.121196 21485 solver.cpp:218] Iteration 11880 (2.24272 iter/s, 5.35066s/12 iters), loss = 4.96841
I0406 16:53:11.121260 21485 solver.cpp:237]     Train net output #0: loss = 4.96841 (* 1 = 4.96841 loss)
I0406 16:53:11.121269 21485 sgd_solver.cpp:105] Iteration 11880, lr = 0.05
I0406 16:53:16.437794 21485 solver.cpp:218] Iteration 11892 (2.25712 iter/s, 5.31652s/12 iters), loss = 5.12399
I0406 16:53:16.437851 21485 solver.cpp:237]     Train net output #0: loss = 5.12399 (* 1 = 5.12399 loss)
I0406 16:53:16.437861 21485 sgd_solver.cpp:105] Iteration 11892, lr = 0.05
I0406 16:53:21.720404 21485 solver.cpp:218] Iteration 11904 (2.27164 iter/s, 5.28254s/12 iters), loss = 4.97904
I0406 16:53:21.720459 21485 solver.cpp:237]     Train net output #0: loss = 4.97904 (* 1 = 4.97904 loss)
I0406 16:53:21.720468 21485 sgd_solver.cpp:105] Iteration 11904, lr = 0.05
I0406 16:53:27.200193 21485 solver.cpp:218] Iteration 11916 (2.18989 iter/s, 5.47972s/12 iters), loss = 4.95693
I0406 16:53:27.200249 21485 solver.cpp:237]     Train net output #0: loss = 4.95693 (* 1 = 4.95693 loss)
I0406 16:53:27.200258 21485 sgd_solver.cpp:105] Iteration 11916, lr = 0.05
I0406 16:53:32.570019 21485 solver.cpp:218] Iteration 11928 (2.23474 iter/s, 5.36976s/12 iters), loss = 4.88327
I0406 16:53:32.570144 21485 solver.cpp:237]     Train net output #0: loss = 4.88327 (* 1 = 4.88327 loss)
I0406 16:53:32.570150 21485 sgd_solver.cpp:105] Iteration 11928, lr = 0.05
I0406 16:53:34.796249 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_11934.caffemodel
I0406 16:53:35.832674 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:53:37.978165 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_11934.solverstate
I0406 16:53:40.307374 21485 solver.cpp:330] Iteration 11934, Testing net (#0)
I0406 16:53:40.307396 21485 net.cpp:676] Ignoring source layer train-data
I0406 16:53:44.950860 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:53:45.056547 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0147059
I0406 16:53:45.056573 21485 solver.cpp:397]     Test net output #1: loss = 5.1477 (* 1 = 5.1477 loss)
I0406 16:53:47.050935 21485 solver.cpp:218] Iteration 11940 (0.828685 iter/s, 14.4808s/12 iters), loss = 4.81253
I0406 16:53:47.050989 21485 solver.cpp:237]     Train net output #0: loss = 4.81253 (* 1 = 4.81253 loss)
I0406 16:53:47.050997 21485 sgd_solver.cpp:105] Iteration 11940, lr = 0.05
I0406 16:53:52.420359 21485 solver.cpp:218] Iteration 11952 (2.23491 iter/s, 5.36936s/12 iters), loss = 4.97367
I0406 16:53:52.420408 21485 solver.cpp:237]     Train net output #0: loss = 4.97367 (* 1 = 4.97367 loss)
I0406 16:53:52.420415 21485 sgd_solver.cpp:105] Iteration 11952, lr = 0.05
I0406 16:53:57.800266 21485 solver.cpp:218] Iteration 11964 (2.23055 iter/s, 5.37984s/12 iters), loss = 5.06097
I0406 16:53:57.800321 21485 solver.cpp:237]     Train net output #0: loss = 5.06097 (* 1 = 5.06097 loss)
I0406 16:53:57.800330 21485 sgd_solver.cpp:105] Iteration 11964, lr = 0.05
I0406 16:54:03.329421 21485 solver.cpp:218] Iteration 11976 (2.17034 iter/s, 5.52909s/12 iters), loss = 5.08543
I0406 16:54:03.329517 21485 solver.cpp:237]     Train net output #0: loss = 5.08543 (* 1 = 5.08543 loss)
I0406 16:54:03.329524 21485 sgd_solver.cpp:105] Iteration 11976, lr = 0.05
I0406 16:54:08.722050 21485 solver.cpp:218] Iteration 11988 (2.22531 iter/s, 5.39252s/12 iters), loss = 5.00866
I0406 16:54:08.722096 21485 solver.cpp:237]     Train net output #0: loss = 5.00866 (* 1 = 5.00866 loss)
I0406 16:54:08.722101 21485 sgd_solver.cpp:105] Iteration 11988, lr = 0.05
I0406 16:54:14.181841 21485 solver.cpp:218] Iteration 12000 (2.19791 iter/s, 5.45973s/12 iters), loss = 5.03359
I0406 16:54:14.181877 21485 solver.cpp:237]     Train net output #0: loss = 5.03359 (* 1 = 5.03359 loss)
I0406 16:54:14.181882 21485 sgd_solver.cpp:105] Iteration 12000, lr = 0.05
I0406 16:54:19.545433 21485 solver.cpp:218] Iteration 12012 (2.23733 iter/s, 5.36353s/12 iters), loss = 4.97623
I0406 16:54:19.545491 21485 solver.cpp:237]     Train net output #0: loss = 4.97623 (* 1 = 4.97623 loss)
I0406 16:54:19.545500 21485 sgd_solver.cpp:105] Iteration 12012, lr = 0.05
I0406 16:54:24.947935 21485 solver.cpp:218] Iteration 12024 (2.22122 iter/s, 5.40243s/12 iters), loss = 4.99738
I0406 16:54:24.947993 21485 solver.cpp:237]     Train net output #0: loss = 4.99738 (* 1 = 4.99738 loss)
I0406 16:54:24.948001 21485 sgd_solver.cpp:105] Iteration 12024, lr = 0.05
I0406 16:54:29.636188 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_12036.caffemodel
I0406 16:54:30.359390 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:54:32.698650 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_12036.solverstate
I0406 16:54:35.000413 21485 solver.cpp:330] Iteration 12036, Testing net (#0)
I0406 16:54:35.000507 21485 net.cpp:676] Ignoring source layer train-data
I0406 16:54:39.321954 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:54:39.460283 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0202206
I0406 16:54:39.460322 21485 solver.cpp:397]     Test net output #1: loss = 5.10774 (* 1 = 5.10774 loss)
I0406 16:54:39.591603 21485 solver.cpp:218] Iteration 12036 (0.81947 iter/s, 14.6436s/12 iters), loss = 4.91387
I0406 16:54:39.593201 21485 solver.cpp:237]     Train net output #0: loss = 4.91387 (* 1 = 4.91387 loss)
I0406 16:54:39.593214 21485 sgd_solver.cpp:105] Iteration 12036, lr = 0.05
I0406 16:54:43.815551 21485 solver.cpp:218] Iteration 12048 (2.84202 iter/s, 4.22234s/12 iters), loss = 5.00908
I0406 16:54:43.815588 21485 solver.cpp:237]     Train net output #0: loss = 5.00908 (* 1 = 5.00908 loss)
I0406 16:54:43.815593 21485 sgd_solver.cpp:105] Iteration 12048, lr = 0.05
I0406 16:54:48.886716 21485 solver.cpp:218] Iteration 12060 (2.36634 iter/s, 5.07111s/12 iters), loss = 5.056
I0406 16:54:48.886765 21485 solver.cpp:237]     Train net output #0: loss = 5.056 (* 1 = 5.056 loss)
I0406 16:54:48.886773 21485 sgd_solver.cpp:105] Iteration 12060, lr = 0.05
I0406 16:54:54.385696 21485 solver.cpp:218] Iteration 12072 (2.18225 iter/s, 5.49891s/12 iters), loss = 4.99488
I0406 16:54:54.385756 21485 solver.cpp:237]     Train net output #0: loss = 4.99488 (* 1 = 4.99488 loss)
I0406 16:54:54.385766 21485 sgd_solver.cpp:105] Iteration 12072, lr = 0.05
I0406 16:54:59.855294 21485 solver.cpp:218] Iteration 12084 (2.19397 iter/s, 5.46953s/12 iters), loss = 4.95154
I0406 16:54:59.861696 21485 solver.cpp:237]     Train net output #0: loss = 4.95154 (* 1 = 4.95154 loss)
I0406 16:54:59.861708 21485 sgd_solver.cpp:105] Iteration 12084, lr = 0.05
I0406 16:55:05.133109 21485 solver.cpp:218] Iteration 12096 (2.27643 iter/s, 5.27141s/12 iters), loss = 4.94769
I0406 16:55:05.133229 21485 solver.cpp:237]     Train net output #0: loss = 4.94769 (* 1 = 4.94769 loss)
I0406 16:55:05.133237 21485 sgd_solver.cpp:105] Iteration 12096, lr = 0.05
I0406 16:55:10.554306 21485 solver.cpp:218] Iteration 12108 (2.21359 iter/s, 5.42106s/12 iters), loss = 4.97888
I0406 16:55:10.554360 21485 solver.cpp:237]     Train net output #0: loss = 4.97888 (* 1 = 4.97888 loss)
I0406 16:55:10.554368 21485 sgd_solver.cpp:105] Iteration 12108, lr = 0.05
I0406 16:55:15.914500 21485 solver.cpp:218] Iteration 12120 (2.23875 iter/s, 5.36013s/12 iters), loss = 5.14271
I0406 16:55:15.914553 21485 solver.cpp:237]     Train net output #0: loss = 5.14271 (* 1 = 5.14271 loss)
I0406 16:55:15.914562 21485 sgd_solver.cpp:105] Iteration 12120, lr = 0.05
I0406 16:55:21.099751 21485 solver.cpp:218] Iteration 12132 (2.31429 iter/s, 5.18518s/12 iters), loss = 5.07828
I0406 16:55:21.099802 21485 solver.cpp:237]     Train net output #0: loss = 5.07828 (* 1 = 5.07828 loss)
I0406 16:55:21.099812 21485 sgd_solver.cpp:105] Iteration 12132, lr = 0.05
I0406 16:55:23.318233 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_12138.caffemodel
I0406 16:55:23.664793 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:55:26.373555 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_12138.solverstate
I0406 16:55:28.713192 21485 solver.cpp:330] Iteration 12138, Testing net (#0)
I0406 16:55:28.713212 21485 net.cpp:676] Ignoring source layer train-data
I0406 16:55:33.113194 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:55:33.312242 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0183824
I0406 16:55:33.312273 21485 solver.cpp:397]     Test net output #1: loss = 5.139 (* 1 = 5.139 loss)
I0406 16:55:35.314945 21485 solver.cpp:218] Iteration 12144 (0.844171 iter/s, 14.2151s/12 iters), loss = 4.99851
I0406 16:55:35.315111 21485 solver.cpp:237]     Train net output #0: loss = 4.99851 (* 1 = 4.99851 loss)
I0406 16:55:35.315121 21485 sgd_solver.cpp:105] Iteration 12144, lr = 0.05
I0406 16:55:40.475940 21485 solver.cpp:218] Iteration 12156 (2.32521 iter/s, 5.16082s/12 iters), loss = 4.84943
I0406 16:55:40.475989 21485 solver.cpp:237]     Train net output #0: loss = 4.84943 (* 1 = 4.84943 loss)
I0406 16:55:40.475996 21485 sgd_solver.cpp:105] Iteration 12156, lr = 0.05
I0406 16:55:45.800829 21485 solver.cpp:218] Iteration 12168 (2.2536 iter/s, 5.32482s/12 iters), loss = 4.95718
I0406 16:55:45.800873 21485 solver.cpp:237]     Train net output #0: loss = 4.95718 (* 1 = 4.95718 loss)
I0406 16:55:45.800879 21485 sgd_solver.cpp:105] Iteration 12168, lr = 0.05
I0406 16:55:51.272153 21485 solver.cpp:218] Iteration 12180 (2.19328 iter/s, 5.47126s/12 iters), loss = 4.93126
I0406 16:55:51.272214 21485 solver.cpp:237]     Train net output #0: loss = 4.93126 (* 1 = 4.93126 loss)
I0406 16:55:51.272222 21485 sgd_solver.cpp:105] Iteration 12180, lr = 0.05
I0406 16:55:56.621680 21485 solver.cpp:218] Iteration 12192 (2.24322 iter/s, 5.34946s/12 iters), loss = 4.93368
I0406 16:55:56.621718 21485 solver.cpp:237]     Train net output #0: loss = 4.93368 (* 1 = 4.93368 loss)
I0406 16:55:56.621727 21485 sgd_solver.cpp:105] Iteration 12192, lr = 0.05
I0406 16:56:01.887408 21485 solver.cpp:218] Iteration 12204 (2.27891 iter/s, 5.26567s/12 iters), loss = 4.99858
I0406 16:56:01.887455 21485 solver.cpp:237]     Train net output #0: loss = 4.99858 (* 1 = 4.99858 loss)
I0406 16:56:01.887460 21485 sgd_solver.cpp:105] Iteration 12204, lr = 0.05
I0406 16:56:07.245179 21485 solver.cpp:218] Iteration 12216 (2.23976 iter/s, 5.35771s/12 iters), loss = 4.82524
I0406 16:56:07.245303 21485 solver.cpp:237]     Train net output #0: loss = 4.82524 (* 1 = 4.82524 loss)
I0406 16:56:07.245312 21485 sgd_solver.cpp:105] Iteration 12216, lr = 0.05
I0406 16:56:12.630376 21485 solver.cpp:218] Iteration 12228 (2.22839 iter/s, 5.38506s/12 iters), loss = 5.04684
I0406 16:56:12.630434 21485 solver.cpp:237]     Train net output #0: loss = 5.04684 (* 1 = 5.04684 loss)
I0406 16:56:12.630441 21485 sgd_solver.cpp:105] Iteration 12228, lr = 0.05
I0406 16:56:17.277230 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:56:17.365742 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_12240.caffemodel
I0406 16:56:20.418529 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_12240.solverstate
I0406 16:56:22.729048 21485 solver.cpp:330] Iteration 12240, Testing net (#0)
I0406 16:56:22.729071 21485 net.cpp:676] Ignoring source layer train-data
I0406 16:56:26.975539 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:56:27.200803 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0165441
I0406 16:56:27.200840 21485 solver.cpp:397]     Test net output #1: loss = 5.15074 (* 1 = 5.15074 loss)
I0406 16:56:27.341328 21485 solver.cpp:218] Iteration 12240 (0.815722 iter/s, 14.7109s/12 iters), loss = 5.11868
I0406 16:56:27.341372 21485 solver.cpp:237]     Train net output #0: loss = 5.11868 (* 1 = 5.11868 loss)
I0406 16:56:27.341377 21485 sgd_solver.cpp:105] Iteration 12240, lr = 0.05
I0406 16:56:31.765101 21485 solver.cpp:218] Iteration 12252 (2.71265 iter/s, 4.42371s/12 iters), loss = 4.99103
I0406 16:56:31.765154 21485 solver.cpp:237]     Train net output #0: loss = 4.99103 (* 1 = 4.99103 loss)
I0406 16:56:31.765164 21485 sgd_solver.cpp:105] Iteration 12252, lr = 0.05
I0406 16:56:37.015503 21485 solver.cpp:218] Iteration 12264 (2.28557 iter/s, 5.25034s/12 iters), loss = 4.8233
I0406 16:56:37.015542 21485 solver.cpp:237]     Train net output #0: loss = 4.8233 (* 1 = 4.8233 loss)
I0406 16:56:37.015547 21485 sgd_solver.cpp:105] Iteration 12264, lr = 0.05
I0406 16:56:42.403676 21485 solver.cpp:218] Iteration 12276 (2.22713 iter/s, 5.38811s/12 iters), loss = 4.86836
I0406 16:56:42.403821 21485 solver.cpp:237]     Train net output #0: loss = 4.86836 (* 1 = 4.86836 loss)
I0406 16:56:42.403831 21485 sgd_solver.cpp:105] Iteration 12276, lr = 0.05
I0406 16:56:47.671228 21485 solver.cpp:218] Iteration 12288 (2.27816 iter/s, 5.2674s/12 iters), loss = 4.88955
I0406 16:56:47.671267 21485 solver.cpp:237]     Train net output #0: loss = 4.88955 (* 1 = 4.88955 loss)
I0406 16:56:47.671273 21485 sgd_solver.cpp:105] Iteration 12288, lr = 0.05
I0406 16:56:52.922149 21485 solver.cpp:218] Iteration 12300 (2.28534 iter/s, 5.25087s/12 iters), loss = 4.87612
I0406 16:56:52.922196 21485 solver.cpp:237]     Train net output #0: loss = 4.87612 (* 1 = 4.87612 loss)
I0406 16:56:52.922201 21485 sgd_solver.cpp:105] Iteration 12300, lr = 0.05
I0406 16:56:58.337662 21485 solver.cpp:218] Iteration 12312 (2.21603 iter/s, 5.41509s/12 iters), loss = 4.79207
I0406 16:56:58.337716 21485 solver.cpp:237]     Train net output #0: loss = 4.79207 (* 1 = 4.79207 loss)
I0406 16:56:58.337723 21485 sgd_solver.cpp:105] Iteration 12312, lr = 0.05
I0406 16:57:03.721959 21485 solver.cpp:218] Iteration 12324 (2.22873 iter/s, 5.38423s/12 iters), loss = 4.84296
I0406 16:57:03.721997 21485 solver.cpp:237]     Train net output #0: loss = 4.84296 (* 1 = 4.84296 loss)
I0406 16:57:03.722002 21485 sgd_solver.cpp:105] Iteration 12324, lr = 0.05
I0406 16:57:09.384018 21485 solver.cpp:218] Iteration 12336 (2.11939 iter/s, 5.66201s/12 iters), loss = 4.83919
I0406 16:57:09.384065 21485 solver.cpp:237]     Train net output #0: loss = 4.83919 (* 1 = 4.83919 loss)
I0406 16:57:09.384073 21485 sgd_solver.cpp:105] Iteration 12336, lr = 0.05
I0406 16:57:11.283044 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:57:11.743671 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_12342.caffemodel
I0406 16:57:14.844664 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_12342.solverstate
I0406 16:57:17.180600 21485 solver.cpp:330] Iteration 12342, Testing net (#0)
I0406 16:57:17.180621 21485 net.cpp:676] Ignoring source layer train-data
I0406 16:57:21.709414 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:57:21.962919 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0189951
I0406 16:57:21.962958 21485 solver.cpp:397]     Test net output #1: loss = 5.07652 (* 1 = 5.07652 loss)
I0406 16:57:24.020107 21485 solver.cpp:218] Iteration 12348 (0.819894 iter/s, 14.636s/12 iters), loss = 4.90952
I0406 16:57:24.020153 21485 solver.cpp:237]     Train net output #0: loss = 4.90952 (* 1 = 4.90952 loss)
I0406 16:57:24.020159 21485 sgd_solver.cpp:105] Iteration 12348, lr = 0.05
I0406 16:57:29.523545 21485 solver.cpp:218] Iteration 12360 (2.18048 iter/s, 5.50337s/12 iters), loss = 4.69091
I0406 16:57:29.523602 21485 solver.cpp:237]     Train net output #0: loss = 4.69091 (* 1 = 4.69091 loss)
I0406 16:57:29.523609 21485 sgd_solver.cpp:105] Iteration 12360, lr = 0.05
I0406 16:57:34.828908 21485 solver.cpp:218] Iteration 12372 (2.2619 iter/s, 5.30528s/12 iters), loss = 4.8471
I0406 16:57:34.828959 21485 solver.cpp:237]     Train net output #0: loss = 4.8471 (* 1 = 4.8471 loss)
I0406 16:57:34.828966 21485 sgd_solver.cpp:105] Iteration 12372, lr = 0.05
I0406 16:57:40.368181 21485 solver.cpp:218] Iteration 12384 (2.16637 iter/s, 5.53921s/12 iters), loss = 4.81184
I0406 16:57:40.368227 21485 solver.cpp:237]     Train net output #0: loss = 4.81184 (* 1 = 4.81184 loss)
I0406 16:57:40.368232 21485 sgd_solver.cpp:105] Iteration 12384, lr = 0.05
I0406 16:57:46.216136 21485 solver.cpp:218] Iteration 12396 (2.05202 iter/s, 5.8479s/12 iters), loss = 4.87659
I0406 16:57:46.216266 21485 solver.cpp:237]     Train net output #0: loss = 4.87659 (* 1 = 4.87659 loss)
I0406 16:57:46.216275 21485 sgd_solver.cpp:105] Iteration 12396, lr = 0.05
I0406 16:57:51.819447 21485 solver.cpp:218] Iteration 12408 (2.14165 iter/s, 5.60317s/12 iters), loss = 4.9451
I0406 16:57:51.819500 21485 solver.cpp:237]     Train net output #0: loss = 4.9451 (* 1 = 4.9451 loss)
I0406 16:57:51.819507 21485 sgd_solver.cpp:105] Iteration 12408, lr = 0.05
I0406 16:57:57.344532 21485 solver.cpp:218] Iteration 12420 (2.17194 iter/s, 5.52502s/12 iters), loss = 5.02838
I0406 16:57:57.344588 21485 solver.cpp:237]     Train net output #0: loss = 5.02838 (* 1 = 5.02838 loss)
I0406 16:57:57.344596 21485 sgd_solver.cpp:105] Iteration 12420, lr = 0.05
I0406 16:58:02.743655 21485 solver.cpp:218] Iteration 12432 (2.22261 iter/s, 5.39906s/12 iters), loss = 5.41065
I0406 16:58:02.743695 21485 solver.cpp:237]     Train net output #0: loss = 5.41065 (* 1 = 5.41065 loss)
I0406 16:58:02.743700 21485 sgd_solver.cpp:105] Iteration 12432, lr = 0.05
I0406 16:58:07.208570 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:58:08.073679 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_12444.caffemodel
I0406 16:58:11.130846 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_12444.solverstate
I0406 16:58:13.541539 21485 solver.cpp:330] Iteration 12444, Testing net (#0)
I0406 16:58:13.541563 21485 net.cpp:676] Ignoring source layer train-data
I0406 16:58:18.347209 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:58:18.673967 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0183824
I0406 16:58:18.674003 21485 solver.cpp:397]     Test net output #1: loss = 5.09021 (* 1 = 5.09021 loss)
I0406 16:58:18.813817 21485 solver.cpp:218] Iteration 12444 (0.746728 iter/s, 16.0701s/12 iters), loss = 4.9416
I0406 16:58:18.813872 21485 solver.cpp:237]     Train net output #0: loss = 4.9416 (* 1 = 4.9416 loss)
I0406 16:58:18.813880 21485 sgd_solver.cpp:105] Iteration 12444, lr = 0.05
I0406 16:58:23.476166 21485 solver.cpp:218] Iteration 12456 (2.57385 iter/s, 4.66227s/12 iters), loss = 4.83375
I0406 16:58:23.476225 21485 solver.cpp:237]     Train net output #0: loss = 4.83375 (* 1 = 4.83375 loss)
I0406 16:58:23.476233 21485 sgd_solver.cpp:105] Iteration 12456, lr = 0.05
I0406 16:58:28.020865 21485 blocking_queue.cpp:49] Waiting for data
I0406 16:58:29.046651 21485 solver.cpp:218] Iteration 12468 (2.15424 iter/s, 5.57042s/12 iters), loss = 4.73704
I0406 16:58:29.046694 21485 solver.cpp:237]     Train net output #0: loss = 4.73704 (* 1 = 4.73704 loss)
I0406 16:58:29.046698 21485 sgd_solver.cpp:105] Iteration 12468, lr = 0.05
I0406 16:58:34.704732 21485 solver.cpp:218] Iteration 12480 (2.12088 iter/s, 5.65802s/12 iters), loss = 4.84092
I0406 16:58:34.704792 21485 solver.cpp:237]     Train net output #0: loss = 4.84092 (* 1 = 4.84092 loss)
I0406 16:58:34.704800 21485 sgd_solver.cpp:105] Iteration 12480, lr = 0.05
I0406 16:58:40.556934 21485 solver.cpp:218] Iteration 12492 (2.05131 iter/s, 5.84992s/12 iters), loss = 4.67211
I0406 16:58:40.556989 21485 solver.cpp:237]     Train net output #0: loss = 4.67211 (* 1 = 4.67211 loss)
I0406 16:58:40.556998 21485 sgd_solver.cpp:105] Iteration 12492, lr = 0.05
I0406 16:58:46.172253 21485 solver.cpp:218] Iteration 12504 (2.13704 iter/s, 5.61525s/12 iters), loss = 4.65839
I0406 16:58:46.172304 21485 solver.cpp:237]     Train net output #0: loss = 4.65839 (* 1 = 4.65839 loss)
I0406 16:58:46.172312 21485 sgd_solver.cpp:105] Iteration 12504, lr = 0.05
I0406 16:58:51.820264 21485 solver.cpp:218] Iteration 12516 (2.12467 iter/s, 5.64795s/12 iters), loss = 4.531
I0406 16:58:51.820387 21485 solver.cpp:237]     Train net output #0: loss = 4.531 (* 1 = 4.531 loss)
I0406 16:58:51.820395 21485 sgd_solver.cpp:105] Iteration 12516, lr = 0.05
I0406 16:58:57.210325 21485 solver.cpp:218] Iteration 12528 (2.22638 iter/s, 5.38993s/12 iters), loss = 4.84041
I0406 16:58:57.210372 21485 solver.cpp:237]     Train net output #0: loss = 4.84041 (* 1 = 4.84041 loss)
I0406 16:58:57.210378 21485 sgd_solver.cpp:105] Iteration 12528, lr = 0.05
I0406 16:59:02.702080 21485 solver.cpp:218] Iteration 12540 (2.18512 iter/s, 5.49169s/12 iters), loss = 4.84282
I0406 16:59:02.708362 21485 solver.cpp:237]     Train net output #0: loss = 4.84282 (* 1 = 4.84282 loss)
I0406 16:59:02.708379 21485 sgd_solver.cpp:105] Iteration 12540, lr = 0.05
I0406 16:59:03.760496 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:59:04.982771 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_12546.caffemodel
I0406 16:59:08.145285 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_12546.solverstate
I0406 16:59:10.512022 21485 solver.cpp:330] Iteration 12546, Testing net (#0)
I0406 16:59:10.512040 21485 net.cpp:676] Ignoring source layer train-data
I0406 16:59:15.113512 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 16:59:15.501498 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0269608
I0406 16:59:15.501538 21485 solver.cpp:397]     Test net output #1: loss = 5.08926 (* 1 = 5.08926 loss)
I0406 16:59:17.425164 21485 solver.cpp:218] Iteration 12552 (0.815395 iter/s, 14.7168s/12 iters), loss = 4.95156
I0406 16:59:17.425220 21485 solver.cpp:237]     Train net output #0: loss = 4.95156 (* 1 = 4.95156 loss)
I0406 16:59:17.425228 21485 sgd_solver.cpp:105] Iteration 12552, lr = 0.05
I0406 16:59:23.347188 21485 solver.cpp:218] Iteration 12564 (2.02636 iter/s, 5.92195s/12 iters), loss = 4.69037
I0406 16:59:23.353751 21485 solver.cpp:237]     Train net output #0: loss = 4.69037 (* 1 = 4.69037 loss)
I0406 16:59:23.353775 21485 sgd_solver.cpp:105] Iteration 12564, lr = 0.05
I0406 16:59:28.956600 21485 solver.cpp:218] Iteration 12576 (2.14177 iter/s, 5.60285s/12 iters), loss = 5.05041
I0406 16:59:28.956646 21485 solver.cpp:237]     Train net output #0: loss = 5.05041 (* 1 = 5.05041 loss)
I0406 16:59:28.956655 21485 sgd_solver.cpp:105] Iteration 12576, lr = 0.05
I0406 16:59:34.378829 21485 solver.cpp:218] Iteration 12588 (2.21314 iter/s, 5.42216s/12 iters), loss = 4.94337
I0406 16:59:34.378885 21485 solver.cpp:237]     Train net output #0: loss = 4.94337 (* 1 = 4.94337 loss)
I0406 16:59:34.378892 21485 sgd_solver.cpp:105] Iteration 12588, lr = 0.05
I0406 16:59:39.854063 21485 solver.cpp:218] Iteration 12600 (2.19171 iter/s, 5.47517s/12 iters), loss = 4.97227
I0406 16:59:39.854112 21485 solver.cpp:237]     Train net output #0: loss = 4.97227 (* 1 = 4.97227 loss)
I0406 16:59:39.854120 21485 sgd_solver.cpp:105] Iteration 12600, lr = 0.05
I0406 16:59:45.561307 21485 solver.cpp:218] Iteration 12612 (2.10261 iter/s, 5.70718s/12 iters), loss = 4.96055
I0406 16:59:45.561350 21485 solver.cpp:237]     Train net output #0: loss = 4.96055 (* 1 = 4.96055 loss)
I0406 16:59:45.561357 21485 sgd_solver.cpp:105] Iteration 12612, lr = 0.05
I0406 16:59:50.971547 21485 solver.cpp:218] Iteration 12624 (2.21804 iter/s, 5.41018s/12 iters), loss = 4.96349
I0406 16:59:50.971597 21485 solver.cpp:237]     Train net output #0: loss = 4.96349 (* 1 = 4.96349 loss)
I0406 16:59:50.971604 21485 sgd_solver.cpp:105] Iteration 12624, lr = 0.05
I0406 16:59:56.696635 21485 solver.cpp:218] Iteration 12636 (2.09606 iter/s, 5.72502s/12 iters), loss = 4.72662
I0406 16:59:56.703413 21485 solver.cpp:237]     Train net output #0: loss = 4.72662 (* 1 = 4.72662 loss)
I0406 16:59:56.703428 21485 sgd_solver.cpp:105] Iteration 12636, lr = 0.05
I0406 17:00:00.254901 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:00:01.903684 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_12648.caffemodel
I0406 17:00:06.511130 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_12648.solverstate
I0406 17:00:10.148758 21485 solver.cpp:330] Iteration 12648, Testing net (#0)
I0406 17:00:10.148779 21485 net.cpp:676] Ignoring source layer train-data
I0406 17:00:14.620931 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:00:15.031766 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0202206
I0406 17:00:15.031803 21485 solver.cpp:397]     Test net output #1: loss = 5.09417 (* 1 = 5.09417 loss)
I0406 17:00:15.172027 21485 solver.cpp:218] Iteration 12648 (0.649751 iter/s, 18.4686s/12 iters), loss = 4.75337
I0406 17:00:15.172086 21485 solver.cpp:237]     Train net output #0: loss = 4.75337 (* 1 = 4.75337 loss)
I0406 17:00:15.172092 21485 sgd_solver.cpp:105] Iteration 12648, lr = 0.05
I0406 17:00:19.682366 21485 solver.cpp:218] Iteration 12660 (2.6606 iter/s, 4.51026s/12 iters), loss = 4.82843
I0406 17:00:19.682425 21485 solver.cpp:237]     Train net output #0: loss = 4.82843 (* 1 = 4.82843 loss)
I0406 17:00:19.682435 21485 sgd_solver.cpp:105] Iteration 12660, lr = 0.05
I0406 17:00:25.115873 21485 solver.cpp:218] Iteration 12672 (2.20855 iter/s, 5.43343s/12 iters), loss = 4.89603
I0406 17:00:25.115928 21485 solver.cpp:237]     Train net output #0: loss = 4.89603 (* 1 = 4.89603 loss)
I0406 17:00:25.115937 21485 sgd_solver.cpp:105] Iteration 12672, lr = 0.05
I0406 17:00:30.805037 21485 solver.cpp:218] Iteration 12684 (2.1093 iter/s, 5.68909s/12 iters), loss = 4.96709
I0406 17:00:30.805178 21485 solver.cpp:237]     Train net output #0: loss = 4.96709 (* 1 = 4.96709 loss)
I0406 17:00:30.805187 21485 sgd_solver.cpp:105] Iteration 12684, lr = 0.05
I0406 17:00:36.352583 21485 solver.cpp:218] Iteration 12696 (2.16318 iter/s, 5.54739s/12 iters), loss = 4.83016
I0406 17:00:36.352632 21485 solver.cpp:237]     Train net output #0: loss = 4.83016 (* 1 = 4.83016 loss)
I0406 17:00:36.352639 21485 sgd_solver.cpp:105] Iteration 12696, lr = 0.05
I0406 17:00:41.848326 21485 solver.cpp:218] Iteration 12708 (2.18353 iter/s, 5.49568s/12 iters), loss = 4.9195
I0406 17:00:41.848379 21485 solver.cpp:237]     Train net output #0: loss = 4.9195 (* 1 = 4.9195 loss)
I0406 17:00:41.848387 21485 sgd_solver.cpp:105] Iteration 12708, lr = 0.05
I0406 17:00:47.274896 21485 solver.cpp:218] Iteration 12720 (2.21137 iter/s, 5.4265s/12 iters), loss = 4.73798
I0406 17:00:47.274945 21485 solver.cpp:237]     Train net output #0: loss = 4.73798 (* 1 = 4.73798 loss)
I0406 17:00:47.274952 21485 sgd_solver.cpp:105] Iteration 12720, lr = 0.05
I0406 17:00:52.438685 21485 solver.cpp:218] Iteration 12732 (2.3239 iter/s, 5.16372s/12 iters), loss = 4.89552
I0406 17:00:52.438724 21485 solver.cpp:237]     Train net output #0: loss = 4.89552 (* 1 = 4.89552 loss)
I0406 17:00:52.438730 21485 sgd_solver.cpp:105] Iteration 12732, lr = 0.05
I0406 17:00:57.921268 21485 solver.cpp:218] Iteration 12744 (2.18877 iter/s, 5.48253s/12 iters), loss = 5.0545
I0406 17:00:57.921314 21485 solver.cpp:237]     Train net output #0: loss = 5.0545 (* 1 = 5.0545 loss)
I0406 17:00:57.921320 21485 sgd_solver.cpp:105] Iteration 12744, lr = 0.05
I0406 17:00:58.177551 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:01:00.198205 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_12750.caffemodel
I0406 17:01:03.226533 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_12750.solverstate
I0406 17:01:05.581548 21485 solver.cpp:330] Iteration 12750, Testing net (#0)
I0406 17:01:05.581571 21485 net.cpp:676] Ignoring source layer train-data
I0406 17:01:09.703508 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:01:10.170054 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0245098
I0406 17:01:10.170083 21485 solver.cpp:397]     Test net output #1: loss = 5.09428 (* 1 = 5.09428 loss)
I0406 17:01:12.117595 21485 solver.cpp:218] Iteration 12756 (0.845292 iter/s, 14.1963s/12 iters), loss = 4.9178
I0406 17:01:12.117651 21485 solver.cpp:237]     Train net output #0: loss = 4.9178 (* 1 = 4.9178 loss)
I0406 17:01:12.117660 21485 sgd_solver.cpp:105] Iteration 12756, lr = 0.05
I0406 17:01:17.381727 21485 solver.cpp:218] Iteration 12768 (2.27961 iter/s, 5.26406s/12 iters), loss = 4.72749
I0406 17:01:17.381778 21485 solver.cpp:237]     Train net output #0: loss = 4.72749 (* 1 = 4.72749 loss)
I0406 17:01:17.381785 21485 sgd_solver.cpp:105] Iteration 12768, lr = 0.05
I0406 17:01:22.768566 21485 solver.cpp:218] Iteration 12780 (2.22768 iter/s, 5.38677s/12 iters), loss = 4.89236
I0406 17:01:22.768620 21485 solver.cpp:237]     Train net output #0: loss = 4.89236 (* 1 = 4.89236 loss)
I0406 17:01:22.768626 21485 sgd_solver.cpp:105] Iteration 12780, lr = 0.05
I0406 17:01:27.976248 21485 solver.cpp:218] Iteration 12792 (2.30432 iter/s, 5.20762s/12 iters), loss = 5.10405
I0406 17:01:27.976289 21485 solver.cpp:237]     Train net output #0: loss = 5.10405 (* 1 = 5.10405 loss)
I0406 17:01:27.976294 21485 sgd_solver.cpp:105] Iteration 12792, lr = 0.05
I0406 17:01:33.416734 21485 solver.cpp:218] Iteration 12804 (2.20571 iter/s, 5.44043s/12 iters), loss = 4.76845
I0406 17:01:33.416860 21485 solver.cpp:237]     Train net output #0: loss = 4.76845 (* 1 = 4.76845 loss)
I0406 17:01:33.416867 21485 sgd_solver.cpp:105] Iteration 12804, lr = 0.05
I0406 17:01:38.950868 21485 solver.cpp:218] Iteration 12816 (2.16842 iter/s, 5.53399s/12 iters), loss = 4.61046
I0406 17:01:38.950935 21485 solver.cpp:237]     Train net output #0: loss = 4.61046 (* 1 = 4.61046 loss)
I0406 17:01:38.950944 21485 sgd_solver.cpp:105] Iteration 12816, lr = 0.05
I0406 17:01:44.337215 21485 solver.cpp:218] Iteration 12828 (2.22789 iter/s, 5.38627s/12 iters), loss = 4.76234
I0406 17:01:44.337273 21485 solver.cpp:237]     Train net output #0: loss = 4.76234 (* 1 = 4.76234 loss)
I0406 17:01:44.337281 21485 sgd_solver.cpp:105] Iteration 12828, lr = 0.05
I0406 17:01:49.462136 21485 solver.cpp:218] Iteration 12840 (2.34153 iter/s, 5.12485s/12 iters), loss = 4.83249
I0406 17:01:49.462183 21485 solver.cpp:237]     Train net output #0: loss = 4.83249 (* 1 = 4.83249 loss)
I0406 17:01:49.462189 21485 sgd_solver.cpp:105] Iteration 12840, lr = 0.05
I0406 17:01:51.993347 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:01:54.324268 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_12852.caffemodel
I0406 17:01:58.225570 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_12852.solverstate
I0406 17:02:00.608296 21485 solver.cpp:330] Iteration 12852, Testing net (#0)
I0406 17:02:00.608319 21485 net.cpp:676] Ignoring source layer train-data
I0406 17:02:04.795334 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:02:05.270388 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0196078
I0406 17:02:05.270422 21485 solver.cpp:397]     Test net output #1: loss = 5.09136 (* 1 = 5.09136 loss)
I0406 17:02:05.410930 21485 solver.cpp:218] Iteration 12852 (0.752411 iter/s, 15.9487s/12 iters), loss = 4.8912
I0406 17:02:05.411011 21485 solver.cpp:237]     Train net output #0: loss = 4.8912 (* 1 = 4.8912 loss)
I0406 17:02:05.411020 21485 sgd_solver.cpp:105] Iteration 12852, lr = 0.05
I0406 17:02:09.787369 21485 solver.cpp:218] Iteration 12864 (2.74202 iter/s, 4.37634s/12 iters), loss = 4.63355
I0406 17:02:09.787422 21485 solver.cpp:237]     Train net output #0: loss = 4.63355 (* 1 = 4.63355 loss)
I0406 17:02:09.787431 21485 sgd_solver.cpp:105] Iteration 12864, lr = 0.05
I0406 17:02:15.029551 21485 solver.cpp:218] Iteration 12876 (2.28915 iter/s, 5.24212s/12 iters), loss = 4.92087
I0406 17:02:15.029592 21485 solver.cpp:237]     Train net output #0: loss = 4.92087 (* 1 = 4.92087 loss)
I0406 17:02:15.029597 21485 sgd_solver.cpp:105] Iteration 12876, lr = 0.05
I0406 17:02:20.399765 21485 solver.cpp:218] Iteration 12888 (2.23457 iter/s, 5.37016s/12 iters), loss = 4.85967
I0406 17:02:20.399803 21485 solver.cpp:237]     Train net output #0: loss = 4.85967 (* 1 = 4.85967 loss)
I0406 17:02:20.399808 21485 sgd_solver.cpp:105] Iteration 12888, lr = 0.05
I0406 17:02:25.967339 21485 solver.cpp:218] Iteration 12900 (2.15536 iter/s, 5.56752s/12 iters), loss = 4.83065
I0406 17:02:25.967388 21485 solver.cpp:237]     Train net output #0: loss = 4.83065 (* 1 = 4.83065 loss)
I0406 17:02:25.967396 21485 sgd_solver.cpp:105] Iteration 12900, lr = 0.05
I0406 17:02:31.458737 21485 solver.cpp:218] Iteration 12912 (2.18526 iter/s, 5.49133s/12 iters), loss = 4.67962
I0406 17:02:31.458794 21485 solver.cpp:237]     Train net output #0: loss = 4.67962 (* 1 = 4.67962 loss)
I0406 17:02:31.458801 21485 sgd_solver.cpp:105] Iteration 12912, lr = 0.05
I0406 17:02:36.914021 21485 solver.cpp:218] Iteration 12924 (2.19973 iter/s, 5.45522s/12 iters), loss = 4.68474
I0406 17:02:36.914109 21485 solver.cpp:237]     Train net output #0: loss = 4.68474 (* 1 = 4.68474 loss)
I0406 17:02:36.914115 21485 sgd_solver.cpp:105] Iteration 12924, lr = 0.05
I0406 17:02:42.116823 21485 solver.cpp:218] Iteration 12936 (2.3065 iter/s, 5.20269s/12 iters), loss = 4.68262
I0406 17:02:42.116890 21485 solver.cpp:237]     Train net output #0: loss = 4.68262 (* 1 = 4.68262 loss)
I0406 17:02:42.116899 21485 sgd_solver.cpp:105] Iteration 12936, lr = 0.05
I0406 17:02:46.831817 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:02:47.406644 21485 solver.cpp:218] Iteration 12948 (2.26854 iter/s, 5.28975s/12 iters), loss = 4.79901
I0406 17:02:47.406703 21485 solver.cpp:237]     Train net output #0: loss = 4.79901 (* 1 = 4.79901 loss)
I0406 17:02:47.406713 21485 sgd_solver.cpp:105] Iteration 12948, lr = 0.05
I0406 17:02:49.546051 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_12954.caffemodel
I0406 17:02:52.596376 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_12954.solverstate
I0406 17:02:54.910682 21485 solver.cpp:330] Iteration 12954, Testing net (#0)
I0406 17:02:54.910706 21485 net.cpp:676] Ignoring source layer train-data
I0406 17:02:58.876080 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:02:59.400830 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0269608
I0406 17:02:59.400866 21485 solver.cpp:397]     Test net output #1: loss = 5.10796 (* 1 = 5.10796 loss)
I0406 17:03:01.159312 21485 solver.cpp:218] Iteration 12960 (0.872562 iter/s, 13.7526s/12 iters), loss = 4.7717
I0406 17:03:01.159350 21485 solver.cpp:237]     Train net output #0: loss = 4.7717 (* 1 = 4.7717 loss)
I0406 17:03:01.159355 21485 sgd_solver.cpp:105] Iteration 12960, lr = 0.05
I0406 17:03:06.429462 21485 solver.cpp:218] Iteration 12972 (2.277 iter/s, 5.27009s/12 iters), loss = 4.67202
I0406 17:03:06.429515 21485 solver.cpp:237]     Train net output #0: loss = 4.67202 (* 1 = 4.67202 loss)
I0406 17:03:06.429523 21485 sgd_solver.cpp:105] Iteration 12972, lr = 0.05
I0406 17:03:11.840520 21485 solver.cpp:218] Iteration 12984 (2.21771 iter/s, 5.411s/12 iters), loss = 4.74798
I0406 17:03:11.840638 21485 solver.cpp:237]     Train net output #0: loss = 4.74798 (* 1 = 4.74798 loss)
I0406 17:03:11.840646 21485 sgd_solver.cpp:105] Iteration 12984, lr = 0.05
I0406 17:03:17.120075 21485 solver.cpp:218] Iteration 12996 (2.27297 iter/s, 5.27943s/12 iters), loss = 4.68444
I0406 17:03:17.120113 21485 solver.cpp:237]     Train net output #0: loss = 4.68444 (* 1 = 4.68444 loss)
I0406 17:03:17.120118 21485 sgd_solver.cpp:105] Iteration 12996, lr = 0.05
I0406 17:03:22.267428 21485 solver.cpp:218] Iteration 13008 (2.33132 iter/s, 5.1473s/12 iters), loss = 4.78251
I0406 17:03:22.267472 21485 solver.cpp:237]     Train net output #0: loss = 4.78251 (* 1 = 4.78251 loss)
I0406 17:03:22.267477 21485 sgd_solver.cpp:105] Iteration 13008, lr = 0.05
I0406 17:03:27.380167 21485 solver.cpp:218] Iteration 13020 (2.34711 iter/s, 5.11268s/12 iters), loss = 4.70102
I0406 17:03:27.380210 21485 solver.cpp:237]     Train net output #0: loss = 4.70102 (* 1 = 4.70102 loss)
I0406 17:03:27.380215 21485 sgd_solver.cpp:105] Iteration 13020, lr = 0.05
I0406 17:03:32.305223 21485 solver.cpp:218] Iteration 13032 (2.43655 iter/s, 4.925s/12 iters), loss = 4.86184
I0406 17:03:32.305263 21485 solver.cpp:237]     Train net output #0: loss = 4.86184 (* 1 = 4.86184 loss)
I0406 17:03:32.305269 21485 sgd_solver.cpp:105] Iteration 13032, lr = 0.05
I0406 17:03:37.779398 21485 solver.cpp:218] Iteration 13044 (2.19213 iter/s, 5.47412s/12 iters), loss = 4.71187
I0406 17:03:37.779449 21485 solver.cpp:237]     Train net output #0: loss = 4.71187 (* 1 = 4.71187 loss)
I0406 17:03:37.779458 21485 sgd_solver.cpp:105] Iteration 13044, lr = 0.05
I0406 17:03:39.668872 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:03:42.658107 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_13056.caffemodel
I0406 17:03:45.797897 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_13056.solverstate
I0406 17:03:48.134979 21485 solver.cpp:330] Iteration 13056, Testing net (#0)
I0406 17:03:48.135000 21485 net.cpp:676] Ignoring source layer train-data
I0406 17:03:52.000706 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:03:52.518079 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0232843
I0406 17:03:52.518115 21485 solver.cpp:397]     Test net output #1: loss = 5.08178 (* 1 = 5.08178 loss)
I0406 17:03:52.658463 21485 solver.cpp:218] Iteration 13056 (0.806506 iter/s, 14.879s/12 iters), loss = 4.70724
I0406 17:03:52.658506 21485 solver.cpp:237]     Train net output #0: loss = 4.70724 (* 1 = 4.70724 loss)
I0406 17:03:52.658512 21485 sgd_solver.cpp:105] Iteration 13056, lr = 0.05
I0406 17:03:56.893054 21485 solver.cpp:218] Iteration 13068 (2.83384 iter/s, 4.23454s/12 iters), loss = 4.80412
I0406 17:03:56.893093 21485 solver.cpp:237]     Train net output #0: loss = 4.80412 (* 1 = 4.80412 loss)
I0406 17:03:56.893098 21485 sgd_solver.cpp:105] Iteration 13068, lr = 0.05
I0406 17:04:02.234575 21485 solver.cpp:218] Iteration 13080 (2.24658 iter/s, 5.34146s/12 iters), loss = 4.73972
I0406 17:04:02.234637 21485 solver.cpp:237]     Train net output #0: loss = 4.73972 (* 1 = 4.73972 loss)
I0406 17:04:02.234647 21485 sgd_solver.cpp:105] Iteration 13080, lr = 0.05
I0406 17:04:07.657060 21485 solver.cpp:218] Iteration 13092 (2.21304 iter/s, 5.42241s/12 iters), loss = 4.74512
I0406 17:04:07.657105 21485 solver.cpp:237]     Train net output #0: loss = 4.74512 (* 1 = 4.74512 loss)
I0406 17:04:07.657110 21485 sgd_solver.cpp:105] Iteration 13092, lr = 0.05
I0406 17:04:12.909370 21485 solver.cpp:218] Iteration 13104 (2.28473 iter/s, 5.25225s/12 iters), loss = 4.78085
I0406 17:04:12.909489 21485 solver.cpp:237]     Train net output #0: loss = 4.78085 (* 1 = 4.78085 loss)
I0406 17:04:12.909497 21485 sgd_solver.cpp:105] Iteration 13104, lr = 0.05
I0406 17:04:18.194535 21485 solver.cpp:218] Iteration 13116 (2.27056 iter/s, 5.28503s/12 iters), loss = 4.83568
I0406 17:04:18.194578 21485 solver.cpp:237]     Train net output #0: loss = 4.83568 (* 1 = 4.83568 loss)
I0406 17:04:18.194583 21485 sgd_solver.cpp:105] Iteration 13116, lr = 0.05
I0406 17:04:23.502322 21485 solver.cpp:218] Iteration 13128 (2.26085 iter/s, 5.30773s/12 iters), loss = 4.89681
I0406 17:04:23.502362 21485 solver.cpp:237]     Train net output #0: loss = 4.89681 (* 1 = 4.89681 loss)
I0406 17:04:23.502367 21485 sgd_solver.cpp:105] Iteration 13128, lr = 0.05
I0406 17:04:30.833580 21485 solver.cpp:218] Iteration 13140 (1.63684 iter/s, 7.3312s/12 iters), loss = 4.72586
I0406 17:04:30.833636 21485 solver.cpp:237]     Train net output #0: loss = 4.72586 (* 1 = 4.72586 loss)
I0406 17:04:30.833644 21485 sgd_solver.cpp:105] Iteration 13140, lr = 0.05
I0406 17:04:36.417955 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:04:38.221524 21485 solver.cpp:218] Iteration 13152 (1.62428 iter/s, 7.38787s/12 iters), loss = 4.83969
I0406 17:04:38.221578 21485 solver.cpp:237]     Train net output #0: loss = 4.83969 (* 1 = 4.83969 loss)
I0406 17:04:38.221586 21485 sgd_solver.cpp:105] Iteration 13152, lr = 0.05
I0406 17:04:41.463663 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_13158.caffemodel
I0406 17:04:45.685369 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_13158.solverstate
I0406 17:04:48.417539 21485 solver.cpp:330] Iteration 13158, Testing net (#0)
I0406 17:04:48.417560 21485 net.cpp:676] Ignoring source layer train-data
I0406 17:04:53.397452 21485 blocking_queue.cpp:49] Waiting for data
I0406 17:04:53.718436 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:04:54.515453 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0245098
I0406 17:04:54.515492 21485 solver.cpp:397]     Test net output #1: loss = 5.12745 (* 1 = 5.12745 loss)
I0406 17:04:56.957536 21485 solver.cpp:218] Iteration 13164 (0.64048 iter/s, 18.7359s/12 iters), loss = 4.911
I0406 17:04:56.957597 21485 solver.cpp:237]     Train net output #0: loss = 4.911 (* 1 = 4.911 loss)
I0406 17:04:56.957603 21485 sgd_solver.cpp:105] Iteration 13164, lr = 0.05
I0406 17:05:03.476164 21485 solver.cpp:218] Iteration 13176 (1.8409 iter/s, 6.51856s/12 iters), loss = 4.7932
I0406 17:05:03.476213 21485 solver.cpp:237]     Train net output #0: loss = 4.7932 (* 1 = 4.7932 loss)
I0406 17:05:03.476222 21485 sgd_solver.cpp:105] Iteration 13176, lr = 0.05
I0406 17:05:09.711027 21485 solver.cpp:218] Iteration 13188 (1.92468 iter/s, 6.23479s/12 iters), loss = 4.70209
I0406 17:05:09.711084 21485 solver.cpp:237]     Train net output #0: loss = 4.70209 (* 1 = 4.70209 loss)
I0406 17:05:09.711092 21485 sgd_solver.cpp:105] Iteration 13188, lr = 0.05
I0406 17:05:16.187155 21485 solver.cpp:218] Iteration 13200 (1.85298 iter/s, 6.47605s/12 iters), loss = 4.83177
I0406 17:05:16.196943 21485 solver.cpp:237]     Train net output #0: loss = 4.83177 (* 1 = 4.83177 loss)
I0406 17:05:16.196964 21485 sgd_solver.cpp:105] Iteration 13200, lr = 0.05
I0406 17:05:22.721052 21485 solver.cpp:218] Iteration 13212 (1.83933 iter/s, 6.52412s/12 iters), loss = 4.81137
I0406 17:05:22.721103 21485 solver.cpp:237]     Train net output #0: loss = 4.81137 (* 1 = 4.81137 loss)
I0406 17:05:22.721110 21485 sgd_solver.cpp:105] Iteration 13212, lr = 0.05
I0406 17:05:29.266978 21485 solver.cpp:218] Iteration 13224 (1.83322 iter/s, 6.54585s/12 iters), loss = 4.78572
I0406 17:05:29.267038 21485 solver.cpp:237]     Train net output #0: loss = 4.78572 (* 1 = 4.78572 loss)
I0406 17:05:29.267047 21485 sgd_solver.cpp:105] Iteration 13224, lr = 0.05
I0406 17:05:35.644932 21485 solver.cpp:218] Iteration 13236 (1.88459 iter/s, 6.36744s/12 iters), loss = 4.77235
I0406 17:05:35.644989 21485 solver.cpp:237]     Train net output #0: loss = 4.77235 (* 1 = 4.77235 loss)
I0406 17:05:35.644997 21485 sgd_solver.cpp:105] Iteration 13236, lr = 0.05
I0406 17:05:41.861297 21485 solver.cpp:218] Iteration 13248 (1.93041 iter/s, 6.21629s/12 iters), loss = 4.90994
I0406 17:05:41.861349 21485 solver.cpp:237]     Train net output #0: loss = 4.90994 (* 1 = 4.90994 loss)
I0406 17:05:41.861359 21485 sgd_solver.cpp:105] Iteration 13248, lr = 0.05
I0406 17:05:43.210201 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:05:48.007591 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_13260.caffemodel
I0406 17:05:51.781081 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_13260.solverstate
I0406 17:05:54.577184 21485 solver.cpp:330] Iteration 13260, Testing net (#0)
I0406 17:05:54.577209 21485 net.cpp:676] Ignoring source layer train-data
I0406 17:05:59.619685 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:06:00.482478 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0226716
I0406 17:06:00.482515 21485 solver.cpp:397]     Test net output #1: loss = 5.11901 (* 1 = 5.11901 loss)
I0406 17:06:00.634685 21485 solver.cpp:218] Iteration 13260 (0.639205 iter/s, 18.7733s/12 iters), loss = 4.83077
I0406 17:06:00.634742 21485 solver.cpp:237]     Train net output #0: loss = 4.83077 (* 1 = 4.83077 loss)
I0406 17:06:00.634749 21485 sgd_solver.cpp:105] Iteration 13260, lr = 0.05
I0406 17:06:05.640581 21485 solver.cpp:218] Iteration 13272 (2.39721 iter/s, 5.00582s/12 iters), loss = 4.7926
I0406 17:06:05.640643 21485 solver.cpp:237]     Train net output #0: loss = 4.7926 (* 1 = 4.7926 loss)
I0406 17:06:05.640652 21485 sgd_solver.cpp:105] Iteration 13272, lr = 0.05
I0406 17:06:12.078666 21485 solver.cpp:218] Iteration 13284 (1.86393 iter/s, 6.43801s/12 iters), loss = 4.87949
I0406 17:06:12.078719 21485 solver.cpp:237]     Train net output #0: loss = 4.87949 (* 1 = 4.87949 loss)
I0406 17:06:12.078727 21485 sgd_solver.cpp:105] Iteration 13284, lr = 0.05
I0406 17:06:17.763684 21485 solver.cpp:218] Iteration 13296 (2.11084 iter/s, 5.68495s/12 iters), loss = 4.82597
I0406 17:06:17.763741 21485 solver.cpp:237]     Train net output #0: loss = 4.82597 (* 1 = 4.82597 loss)
I0406 17:06:17.763747 21485 sgd_solver.cpp:105] Iteration 13296, lr = 0.05
I0406 17:06:23.094139 21485 solver.cpp:218] Iteration 13308 (2.25125 iter/s, 5.33038s/12 iters), loss = 4.91498
I0406 17:06:23.094257 21485 solver.cpp:237]     Train net output #0: loss = 4.91498 (* 1 = 4.91498 loss)
I0406 17:06:23.094264 21485 sgd_solver.cpp:105] Iteration 13308, lr = 0.05
I0406 17:06:28.404784 21485 solver.cpp:218] Iteration 13320 (2.25967 iter/s, 5.3105s/12 iters), loss = 4.72005
I0406 17:06:28.404834 21485 solver.cpp:237]     Train net output #0: loss = 4.72005 (* 1 = 4.72005 loss)
I0406 17:06:28.404845 21485 sgd_solver.cpp:105] Iteration 13320, lr = 0.05
I0406 17:06:33.492619 21485 solver.cpp:218] Iteration 13332 (2.35859 iter/s, 5.08778s/12 iters), loss = 4.72931
I0406 17:06:33.492656 21485 solver.cpp:237]     Train net output #0: loss = 4.72931 (* 1 = 4.72931 loss)
I0406 17:06:33.492662 21485 sgd_solver.cpp:105] Iteration 13332, lr = 0.05
I0406 17:06:38.895591 21485 solver.cpp:218] Iteration 13344 (2.22102 iter/s, 5.40292s/12 iters), loss = 4.68795
I0406 17:06:38.895637 21485 solver.cpp:237]     Train net output #0: loss = 4.68795 (* 1 = 4.68795 loss)
I0406 17:06:38.895643 21485 sgd_solver.cpp:105] Iteration 13344, lr = 0.05
I0406 17:06:41.979562 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:06:44.084172 21485 solver.cpp:218] Iteration 13356 (2.3128 iter/s, 5.18852s/12 iters), loss = 4.69872
I0406 17:06:44.084218 21485 solver.cpp:237]     Train net output #0: loss = 4.69872 (* 1 = 4.69872 loss)
I0406 17:06:44.084223 21485 sgd_solver.cpp:105] Iteration 13356, lr = 0.05
I0406 17:06:46.319084 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_13362.caffemodel
I0406 17:06:49.304960 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_13362.solverstate
I0406 17:06:51.614091 21485 solver.cpp:330] Iteration 13362, Testing net (#0)
I0406 17:06:51.614110 21485 net.cpp:676] Ignoring source layer train-data
I0406 17:06:55.386785 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:06:56.031576 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0245098
I0406 17:06:56.031612 21485 solver.cpp:397]     Test net output #1: loss = 5.09933 (* 1 = 5.09933 loss)
I0406 17:06:58.108613 21485 solver.cpp:218] Iteration 13368 (0.855653 iter/s, 14.0244s/12 iters), loss = 4.72914
I0406 17:06:58.108657 21485 solver.cpp:237]     Train net output #0: loss = 4.72914 (* 1 = 4.72914 loss)
I0406 17:06:58.108662 21485 sgd_solver.cpp:105] Iteration 13368, lr = 0.05
I0406 17:07:03.308876 21485 solver.cpp:218] Iteration 13380 (2.3076 iter/s, 5.2002s/12 iters), loss = 4.9823
I0406 17:07:03.308928 21485 solver.cpp:237]     Train net output #0: loss = 4.9823 (* 1 = 4.9823 loss)
I0406 17:07:03.308934 21485 sgd_solver.cpp:105] Iteration 13380, lr = 0.05
I0406 17:07:08.615758 21485 solver.cpp:218] Iteration 13392 (2.26124 iter/s, 5.30682s/12 iters), loss = 5.07296
I0406 17:07:08.615794 21485 solver.cpp:237]     Train net output #0: loss = 5.07296 (* 1 = 5.07296 loss)
I0406 17:07:08.615800 21485 sgd_solver.cpp:105] Iteration 13392, lr = 0.05
I0406 17:07:13.863713 21485 solver.cpp:218] Iteration 13404 (2.28663 iter/s, 5.2479s/12 iters), loss = 4.88373
I0406 17:07:13.863770 21485 solver.cpp:237]     Train net output #0: loss = 4.88373 (* 1 = 4.88373 loss)
I0406 17:07:13.863778 21485 sgd_solver.cpp:105] Iteration 13404, lr = 0.05
I0406 17:07:19.126209 21485 solver.cpp:218] Iteration 13416 (2.28032 iter/s, 5.26243s/12 iters), loss = 4.58992
I0406 17:07:19.126269 21485 solver.cpp:237]     Train net output #0: loss = 4.58992 (* 1 = 4.58992 loss)
I0406 17:07:19.126278 21485 sgd_solver.cpp:105] Iteration 13416, lr = 0.05
I0406 17:07:24.185497 21485 solver.cpp:218] Iteration 13428 (2.37191 iter/s, 5.05921s/12 iters), loss = 4.95624
I0406 17:07:24.185542 21485 solver.cpp:237]     Train net output #0: loss = 4.95624 (* 1 = 4.95624 loss)
I0406 17:07:24.185549 21485 sgd_solver.cpp:105] Iteration 13428, lr = 0.05
I0406 17:07:29.563585 21485 solver.cpp:218] Iteration 13440 (2.2313 iter/s, 5.37803s/12 iters), loss = 4.99147
I0406 17:07:29.563678 21485 solver.cpp:237]     Train net output #0: loss = 4.99147 (* 1 = 4.99147 loss)
I0406 17:07:29.563683 21485 sgd_solver.cpp:105] Iteration 13440, lr = 0.05
I0406 17:07:34.822727 21485 solver.cpp:218] Iteration 13452 (2.28179 iter/s, 5.25903s/12 iters), loss = 4.91816
I0406 17:07:34.822784 21485 solver.cpp:237]     Train net output #0: loss = 4.91816 (* 1 = 4.91816 loss)
I0406 17:07:34.822791 21485 sgd_solver.cpp:105] Iteration 13452, lr = 0.05
I0406 17:07:34.970430 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:07:39.358497 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_13464.caffemodel
I0406 17:07:42.354027 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_13464.solverstate
I0406 17:07:44.680377 21485 solver.cpp:330] Iteration 13464, Testing net (#0)
I0406 17:07:44.680395 21485 net.cpp:676] Ignoring source layer train-data
I0406 17:07:48.314492 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:07:49.009413 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0226716
I0406 17:07:49.009440 21485 solver.cpp:397]     Test net output #1: loss = 5.11517 (* 1 = 5.11517 loss)
I0406 17:07:49.149260 21485 solver.cpp:218] Iteration 13464 (0.83761 iter/s, 14.3265s/12 iters), loss = 4.91337
I0406 17:07:49.149308 21485 solver.cpp:237]     Train net output #0: loss = 4.91337 (* 1 = 4.91337 loss)
I0406 17:07:49.149313 21485 sgd_solver.cpp:105] Iteration 13464, lr = 0.05
I0406 17:07:53.530989 21485 solver.cpp:218] Iteration 13476 (2.73869 iter/s, 4.38166s/12 iters), loss = 4.72702
I0406 17:07:53.531036 21485 solver.cpp:237]     Train net output #0: loss = 4.72702 (* 1 = 4.72702 loss)
I0406 17:07:53.531044 21485 sgd_solver.cpp:105] Iteration 13476, lr = 0.05
I0406 17:07:58.739013 21485 solver.cpp:218] Iteration 13488 (2.30416 iter/s, 5.20796s/12 iters), loss = 4.86533
I0406 17:07:58.739055 21485 solver.cpp:237]     Train net output #0: loss = 4.86533 (* 1 = 4.86533 loss)
I0406 17:07:58.739061 21485 sgd_solver.cpp:105] Iteration 13488, lr = 0.05
I0406 17:08:03.750488 21485 solver.cpp:218] Iteration 13500 (2.39453 iter/s, 5.01142s/12 iters), loss = 4.92569
I0406 17:08:03.750608 21485 solver.cpp:237]     Train net output #0: loss = 4.92569 (* 1 = 4.92569 loss)
I0406 17:08:03.750614 21485 sgd_solver.cpp:105] Iteration 13500, lr = 0.05
I0406 17:08:09.062397 21485 solver.cpp:218] Iteration 13512 (2.25913 iter/s, 5.31178s/12 iters), loss = 4.85211
I0406 17:08:09.062438 21485 solver.cpp:237]     Train net output #0: loss = 4.85211 (* 1 = 4.85211 loss)
I0406 17:08:09.062443 21485 sgd_solver.cpp:105] Iteration 13512, lr = 0.05
I0406 17:08:14.396867 21485 solver.cpp:218] Iteration 13524 (2.24954 iter/s, 5.33441s/12 iters), loss = 4.85271
I0406 17:08:14.396915 21485 solver.cpp:237]     Train net output #0: loss = 4.85271 (* 1 = 4.85271 loss)
I0406 17:08:14.396921 21485 sgd_solver.cpp:105] Iteration 13524, lr = 0.05
I0406 17:08:19.225138 21485 solver.cpp:218] Iteration 13536 (2.4854 iter/s, 4.8282s/12 iters), loss = 4.85346
I0406 17:08:19.225200 21485 solver.cpp:237]     Train net output #0: loss = 4.85346 (* 1 = 4.85346 loss)
I0406 17:08:19.225209 21485 sgd_solver.cpp:105] Iteration 13536, lr = 0.05
I0406 17:08:24.458570 21485 solver.cpp:218] Iteration 13548 (2.29298 iter/s, 5.23336s/12 iters), loss = 4.76304
I0406 17:08:24.458611 21485 solver.cpp:237]     Train net output #0: loss = 4.76304 (* 1 = 4.76304 loss)
I0406 17:08:24.458616 21485 sgd_solver.cpp:105] Iteration 13548, lr = 0.05
I0406 17:08:27.035333 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:08:29.926026 21485 solver.cpp:218] Iteration 13560 (2.19483 iter/s, 5.4674s/12 iters), loss = 4.65067
I0406 17:08:29.926066 21485 solver.cpp:237]     Train net output #0: loss = 4.65067 (* 1 = 4.65067 loss)
I0406 17:08:29.926074 21485 sgd_solver.cpp:105] Iteration 13560, lr = 0.05
I0406 17:08:32.041646 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_13566.caffemodel
I0406 17:08:35.061364 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_13566.solverstate
I0406 17:08:37.394526 21485 solver.cpp:330] Iteration 13566, Testing net (#0)
I0406 17:08:37.394546 21485 net.cpp:676] Ignoring source layer train-data
I0406 17:08:41.167474 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:08:41.906883 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0232843
I0406 17:08:41.906913 21485 solver.cpp:397]     Test net output #1: loss = 5.09998 (* 1 = 5.09998 loss)
I0406 17:08:43.847676 21485 solver.cpp:218] Iteration 13572 (0.86197 iter/s, 13.9216s/12 iters), loss = 4.72526
I0406 17:08:43.847728 21485 solver.cpp:237]     Train net output #0: loss = 4.72526 (* 1 = 4.72526 loss)
I0406 17:08:43.847735 21485 sgd_solver.cpp:105] Iteration 13572, lr = 0.05
I0406 17:08:48.779868 21485 solver.cpp:218] Iteration 13584 (2.43303 iter/s, 4.93213s/12 iters), loss = 4.84124
I0406 17:08:48.779915 21485 solver.cpp:237]     Train net output #0: loss = 4.84124 (* 1 = 4.84124 loss)
I0406 17:08:48.779922 21485 sgd_solver.cpp:105] Iteration 13584, lr = 0.05
I0406 17:08:53.881253 21485 solver.cpp:218] Iteration 13596 (2.35233 iter/s, 5.10132s/12 iters), loss = 4.77411
I0406 17:08:53.881297 21485 solver.cpp:237]     Train net output #0: loss = 4.77411 (* 1 = 4.77411 loss)
I0406 17:08:53.881302 21485 sgd_solver.cpp:105] Iteration 13596, lr = 0.05
I0406 17:08:59.203794 21485 solver.cpp:218] Iteration 13608 (2.25459 iter/s, 5.32248s/12 iters), loss = 4.79018
I0406 17:08:59.203838 21485 solver.cpp:237]     Train net output #0: loss = 4.79018 (* 1 = 4.79018 loss)
I0406 17:08:59.203845 21485 sgd_solver.cpp:105] Iteration 13608, lr = 0.05
I0406 17:09:04.525171 21485 solver.cpp:218] Iteration 13620 (2.25508 iter/s, 5.32132s/12 iters), loss = 4.65761
I0406 17:09:04.525213 21485 solver.cpp:237]     Train net output #0: loss = 4.65761 (* 1 = 4.65761 loss)
I0406 17:09:04.525218 21485 sgd_solver.cpp:105] Iteration 13620, lr = 0.05
I0406 17:09:09.760807 21485 solver.cpp:218] Iteration 13632 (2.29201 iter/s, 5.23558s/12 iters), loss = 4.49522
I0406 17:09:09.760964 21485 solver.cpp:237]     Train net output #0: loss = 4.49522 (* 1 = 4.49522 loss)
I0406 17:09:09.760974 21485 sgd_solver.cpp:105] Iteration 13632, lr = 0.05
I0406 17:09:15.111707 21485 solver.cpp:218] Iteration 13644 (2.24268 iter/s, 5.35073s/12 iters), loss = 4.78289
I0406 17:09:15.111768 21485 solver.cpp:237]     Train net output #0: loss = 4.78289 (* 1 = 4.78289 loss)
I0406 17:09:15.111775 21485 sgd_solver.cpp:105] Iteration 13644, lr = 0.05
I0406 17:09:19.942577 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:09:20.351882 21485 solver.cpp:218] Iteration 13656 (2.29003 iter/s, 5.2401s/12 iters), loss = 4.60924
I0406 17:09:20.351933 21485 solver.cpp:237]     Train net output #0: loss = 4.60924 (* 1 = 4.60924 loss)
I0406 17:09:20.351940 21485 sgd_solver.cpp:105] Iteration 13656, lr = 0.05
I0406 17:09:25.098356 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_13668.caffemodel
I0406 17:09:28.114137 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_13668.solverstate
I0406 17:09:30.414958 21485 solver.cpp:330] Iteration 13668, Testing net (#0)
I0406 17:09:30.414979 21485 net.cpp:676] Ignoring source layer train-data
I0406 17:09:34.080417 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:09:34.846617 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0257353
I0406 17:09:34.846648 21485 solver.cpp:397]     Test net output #1: loss = 5.13036 (* 1 = 5.13036 loss)
I0406 17:09:34.989818 21485 solver.cpp:218] Iteration 13668 (0.819791 iter/s, 14.6379s/12 iters), loss = 4.7223
I0406 17:09:34.989881 21485 solver.cpp:237]     Train net output #0: loss = 4.7223 (* 1 = 4.7223 loss)
I0406 17:09:34.989888 21485 sgd_solver.cpp:105] Iteration 13668, lr = 0.05
I0406 17:09:39.259189 21485 solver.cpp:218] Iteration 13680 (2.81077 iter/s, 4.2693s/12 iters), loss = 4.76439
I0406 17:09:39.259248 21485 solver.cpp:237]     Train net output #0: loss = 4.76439 (* 1 = 4.76439 loss)
I0406 17:09:39.259258 21485 sgd_solver.cpp:105] Iteration 13680, lr = 0.05
I0406 17:09:44.519205 21485 solver.cpp:218] Iteration 13692 (2.28139 iter/s, 5.25994s/12 iters), loss = 4.80754
I0406 17:09:44.519331 21485 solver.cpp:237]     Train net output #0: loss = 4.80754 (* 1 = 4.80754 loss)
I0406 17:09:44.519340 21485 sgd_solver.cpp:105] Iteration 13692, lr = 0.05
I0406 17:09:49.787863 21485 solver.cpp:218] Iteration 13704 (2.27768 iter/s, 5.26852s/12 iters), loss = 5.01039
I0406 17:09:49.787905 21485 solver.cpp:237]     Train net output #0: loss = 5.01039 (* 1 = 5.01039 loss)
I0406 17:09:49.787912 21485 sgd_solver.cpp:105] Iteration 13704, lr = 0.05
I0406 17:09:55.087154 21485 solver.cpp:218] Iteration 13716 (2.26448 iter/s, 5.29923s/12 iters), loss = 4.99778
I0406 17:09:55.087201 21485 solver.cpp:237]     Train net output #0: loss = 4.99778 (* 1 = 4.99778 loss)
I0406 17:09:55.087208 21485 sgd_solver.cpp:105] Iteration 13716, lr = 0.05
I0406 17:10:00.219506 21485 solver.cpp:218] Iteration 13728 (2.33814 iter/s, 5.13229s/12 iters), loss = 4.9916
I0406 17:10:00.219552 21485 solver.cpp:237]     Train net output #0: loss = 4.9916 (* 1 = 4.9916 loss)
I0406 17:10:00.219556 21485 sgd_solver.cpp:105] Iteration 13728, lr = 0.05
I0406 17:10:05.526034 21485 solver.cpp:218] Iteration 13740 (2.26139 iter/s, 5.30647s/12 iters), loss = 5.04896
I0406 17:10:05.526075 21485 solver.cpp:237]     Train net output #0: loss = 5.04896 (* 1 = 5.04896 loss)
I0406 17:10:05.526082 21485 sgd_solver.cpp:105] Iteration 13740, lr = 0.05
I0406 17:10:10.887382 21485 solver.cpp:218] Iteration 13752 (2.23827 iter/s, 5.36129s/12 iters), loss = 4.90954
I0406 17:10:10.887423 21485 solver.cpp:237]     Train net output #0: loss = 4.90954 (* 1 = 4.90954 loss)
I0406 17:10:10.887428 21485 sgd_solver.cpp:105] Iteration 13752, lr = 0.05
I0406 17:10:12.638814 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:10:16.087277 21485 solver.cpp:218] Iteration 13764 (2.30777 iter/s, 5.19984s/12 iters), loss = 4.79882
I0406 17:10:16.087431 21485 solver.cpp:237]     Train net output #0: loss = 4.79882 (* 1 = 4.79882 loss)
I0406 17:10:16.087440 21485 sgd_solver.cpp:105] Iteration 13764, lr = 0.05
I0406 17:10:18.277673 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_13770.caffemodel
I0406 17:10:23.113690 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_13770.solverstate
I0406 17:10:25.403095 21485 solver.cpp:330] Iteration 13770, Testing net (#0)
I0406 17:10:25.403113 21485 net.cpp:676] Ignoring source layer train-data
I0406 17:10:29.031205 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:10:29.823055 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0238971
I0406 17:10:29.823086 21485 solver.cpp:397]     Test net output #1: loss = 5.16717 (* 1 = 5.16717 loss)
I0406 17:10:31.745833 21485 solver.cpp:218] Iteration 13776 (0.766362 iter/s, 15.6584s/12 iters), loss = 5.06947
I0406 17:10:31.745879 21485 solver.cpp:237]     Train net output #0: loss = 5.06947 (* 1 = 5.06947 loss)
I0406 17:10:31.745884 21485 sgd_solver.cpp:105] Iteration 13776, lr = 0.05
I0406 17:10:37.077111 21485 solver.cpp:218] Iteration 13788 (2.25089 iter/s, 5.33121s/12 iters), loss = 5.19221
I0406 17:10:37.077154 21485 solver.cpp:237]     Train net output #0: loss = 5.19221 (* 1 = 5.19221 loss)
I0406 17:10:37.077160 21485 sgd_solver.cpp:105] Iteration 13788, lr = 0.05
I0406 17:10:42.244956 21485 solver.cpp:218] Iteration 13800 (2.32208 iter/s, 5.16779s/12 iters), loss = 5.13849
I0406 17:10:42.244995 21485 solver.cpp:237]     Train net output #0: loss = 5.13849 (* 1 = 5.13849 loss)
I0406 17:10:42.245000 21485 sgd_solver.cpp:105] Iteration 13800, lr = 0.05
I0406 17:10:47.443065 21485 solver.cpp:218] Iteration 13812 (2.30856 iter/s, 5.19805s/12 iters), loss = 5.60355
I0406 17:10:47.443187 21485 solver.cpp:237]     Train net output #0: loss = 5.60355 (* 1 = 5.60355 loss)
I0406 17:10:47.443197 21485 sgd_solver.cpp:105] Iteration 13812, lr = 0.05
I0406 17:10:52.713591 21485 solver.cpp:218] Iteration 13824 (2.27687 iter/s, 5.2704s/12 iters), loss = 5.16337
I0406 17:10:52.713629 21485 solver.cpp:237]     Train net output #0: loss = 5.16337 (* 1 = 5.16337 loss)
I0406 17:10:52.713634 21485 sgd_solver.cpp:105] Iteration 13824, lr = 0.05
I0406 17:10:57.916452 21485 solver.cpp:218] Iteration 13836 (2.30645 iter/s, 5.20281s/12 iters), loss = 5.19653
I0406 17:10:57.916512 21485 solver.cpp:237]     Train net output #0: loss = 5.19653 (* 1 = 5.19653 loss)
I0406 17:10:57.916520 21485 sgd_solver.cpp:105] Iteration 13836, lr = 0.05
I0406 17:11:03.059191 21485 solver.cpp:218] Iteration 13848 (2.33342 iter/s, 5.14267s/12 iters), loss = 5.17074
I0406 17:11:03.059248 21485 solver.cpp:237]     Train net output #0: loss = 5.17074 (* 1 = 5.17074 loss)
I0406 17:11:03.059257 21485 sgd_solver.cpp:105] Iteration 13848, lr = 0.05
I0406 17:11:07.180294 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:11:08.395145 21485 solver.cpp:218] Iteration 13860 (2.24893 iter/s, 5.33588s/12 iters), loss = 5.12604
I0406 17:11:08.395200 21485 solver.cpp:237]     Train net output #0: loss = 5.12604 (* 1 = 5.12604 loss)
I0406 17:11:08.395208 21485 sgd_solver.cpp:105] Iteration 13860, lr = 0.05
I0406 17:11:13.182322 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_13872.caffemodel
I0406 17:11:16.190495 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_13872.solverstate
I0406 17:11:20.306247 21485 solver.cpp:330] Iteration 13872, Testing net (#0)
I0406 17:11:20.306375 21485 net.cpp:676] Ignoring source layer train-data
I0406 17:11:21.224967 21485 blocking_queue.cpp:49] Waiting for data
I0406 17:11:23.746354 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:11:24.580893 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0116422
I0406 17:11:24.580925 21485 solver.cpp:397]     Test net output #1: loss = 5.22027 (* 1 = 5.22027 loss)
I0406 17:11:24.721603 21485 solver.cpp:218] Iteration 13872 (0.735006 iter/s, 16.3264s/12 iters), loss = 5.16419
I0406 17:11:24.721663 21485 solver.cpp:237]     Train net output #0: loss = 5.16419 (* 1 = 5.16419 loss)
I0406 17:11:24.721671 21485 sgd_solver.cpp:105] Iteration 13872, lr = 0.05
I0406 17:11:28.927469 21485 solver.cpp:218] Iteration 13884 (2.85321 iter/s, 4.20579s/12 iters), loss = 5.23527
I0406 17:11:28.927518 21485 solver.cpp:237]     Train net output #0: loss = 5.23527 (* 1 = 5.23527 loss)
I0406 17:11:28.927525 21485 sgd_solver.cpp:105] Iteration 13884, lr = 0.05
I0406 17:11:34.010113 21485 solver.cpp:218] Iteration 13896 (2.36101 iter/s, 5.08258s/12 iters), loss = 5.13385
I0406 17:11:34.010159 21485 solver.cpp:237]     Train net output #0: loss = 5.13385 (* 1 = 5.13385 loss)
I0406 17:11:34.010165 21485 sgd_solver.cpp:105] Iteration 13896, lr = 0.05
I0406 17:11:39.383797 21485 solver.cpp:218] Iteration 13908 (2.23313 iter/s, 5.37362s/12 iters), loss = 5.27122
I0406 17:11:39.383838 21485 solver.cpp:237]     Train net output #0: loss = 5.27122 (* 1 = 5.27122 loss)
I0406 17:11:39.383844 21485 sgd_solver.cpp:105] Iteration 13908, lr = 0.05
I0406 17:11:44.557911 21485 solver.cpp:218] Iteration 13920 (2.31926 iter/s, 5.17406s/12 iters), loss = 5.02834
I0406 17:11:44.557968 21485 solver.cpp:237]     Train net output #0: loss = 5.02834 (* 1 = 5.02834 loss)
I0406 17:11:44.557977 21485 sgd_solver.cpp:105] Iteration 13920, lr = 0.05
I0406 17:11:49.922253 21485 solver.cpp:218] Iteration 13932 (2.23702 iter/s, 5.36427s/12 iters), loss = 5.09215
I0406 17:11:49.922314 21485 solver.cpp:237]     Train net output #0: loss = 5.09215 (* 1 = 5.09215 loss)
I0406 17:11:49.922323 21485 sgd_solver.cpp:105] Iteration 13932, lr = 0.05
I0406 17:11:55.297616 21485 solver.cpp:218] Iteration 13944 (2.23244 iter/s, 5.37528s/12 iters), loss = 5.0378
I0406 17:11:55.297749 21485 solver.cpp:237]     Train net output #0: loss = 5.0378 (* 1 = 5.0378 loss)
I0406 17:11:55.297758 21485 sgd_solver.cpp:105] Iteration 13944, lr = 0.05
I0406 17:12:00.429147 21485 solver.cpp:218] Iteration 13956 (2.33855 iter/s, 5.13139s/12 iters), loss = 5.1696
I0406 17:12:00.429183 21485 solver.cpp:237]     Train net output #0: loss = 5.1696 (* 1 = 5.1696 loss)
I0406 17:12:00.429188 21485 sgd_solver.cpp:105] Iteration 13956, lr = 0.05
I0406 17:12:01.487903 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:12:05.725368 21485 solver.cpp:218] Iteration 13968 (2.26579 iter/s, 5.29617s/12 iters), loss = 5.10667
I0406 17:12:05.725406 21485 solver.cpp:237]     Train net output #0: loss = 5.10667 (* 1 = 5.10667 loss)
I0406 17:12:05.725411 21485 sgd_solver.cpp:105] Iteration 13968, lr = 0.05
I0406 17:12:07.978515 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_13974.caffemodel
I0406 17:12:10.956638 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_13974.solverstate
I0406 17:12:13.251349 21485 solver.cpp:330] Iteration 13974, Testing net (#0)
I0406 17:12:13.251368 21485 net.cpp:676] Ignoring source layer train-data
I0406 17:12:16.781744 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:12:17.736325 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0128676
I0406 17:12:17.736357 21485 solver.cpp:397]     Test net output #1: loss = 5.16714 (* 1 = 5.16714 loss)
I0406 17:12:19.633419 21485 solver.cpp:218] Iteration 13980 (0.862813 iter/s, 13.908s/12 iters), loss = 5.24238
I0406 17:12:19.633468 21485 solver.cpp:237]     Train net output #0: loss = 5.24238 (* 1 = 5.24238 loss)
I0406 17:12:19.633476 21485 sgd_solver.cpp:105] Iteration 13980, lr = 0.05
I0406 17:12:24.797622 21485 solver.cpp:218] Iteration 13992 (2.32372 iter/s, 5.16413s/12 iters), loss = 5.17175
I0406 17:12:24.797685 21485 solver.cpp:237]     Train net output #0: loss = 5.17175 (* 1 = 5.17175 loss)
I0406 17:12:24.797694 21485 sgd_solver.cpp:105] Iteration 13992, lr = 0.05
I0406 17:12:29.936786 21485 solver.cpp:218] Iteration 14004 (2.33505 iter/s, 5.13908s/12 iters), loss = 5.13789
I0406 17:12:29.936965 21485 solver.cpp:237]     Train net output #0: loss = 5.13789 (* 1 = 5.13789 loss)
I0406 17:12:29.936976 21485 sgd_solver.cpp:105] Iteration 14004, lr = 0.05
I0406 17:12:35.130193 21485 solver.cpp:218] Iteration 14016 (2.3107 iter/s, 5.19322s/12 iters), loss = 5.08081
I0406 17:12:35.130234 21485 solver.cpp:237]     Train net output #0: loss = 5.08081 (* 1 = 5.08081 loss)
I0406 17:12:35.130240 21485 sgd_solver.cpp:105] Iteration 14016, lr = 0.05
I0406 17:12:40.324995 21485 solver.cpp:218] Iteration 14028 (2.31003 iter/s, 5.19474s/12 iters), loss = 5.1388
I0406 17:12:40.325062 21485 solver.cpp:237]     Train net output #0: loss = 5.1388 (* 1 = 5.1388 loss)
I0406 17:12:40.325074 21485 sgd_solver.cpp:105] Iteration 14028, lr = 0.05
I0406 17:12:45.597862 21485 solver.cpp:218] Iteration 14040 (2.27583 iter/s, 5.27279s/12 iters), loss = 5.21343
I0406 17:12:45.597898 21485 solver.cpp:237]     Train net output #0: loss = 5.21343 (* 1 = 5.21343 loss)
I0406 17:12:45.597903 21485 sgd_solver.cpp:105] Iteration 14040, lr = 0.05
I0406 17:12:50.962925 21485 solver.cpp:218] Iteration 14052 (2.23672 iter/s, 5.36501s/12 iters), loss = 5.04606
I0406 17:12:50.962975 21485 solver.cpp:237]     Train net output #0: loss = 5.04606 (* 1 = 5.04606 loss)
I0406 17:12:50.962983 21485 sgd_solver.cpp:105] Iteration 14052, lr = 0.05
I0406 17:12:54.149529 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:12:55.957324 21485 solver.cpp:218] Iteration 14064 (2.40272 iter/s, 4.99434s/12 iters), loss = 5.03521
I0406 17:12:55.957363 21485 solver.cpp:237]     Train net output #0: loss = 5.03521 (* 1 = 5.03521 loss)
I0406 17:12:55.957370 21485 sgd_solver.cpp:105] Iteration 14064, lr = 0.05
I0406 17:13:00.658666 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_14076.caffemodel
I0406 17:13:03.686494 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_14076.solverstate
I0406 17:13:05.989640 21485 solver.cpp:330] Iteration 14076, Testing net (#0)
I0406 17:13:05.989665 21485 net.cpp:676] Ignoring source layer train-data
I0406 17:13:09.518409 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:13:10.434118 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0208333
I0406 17:13:10.434154 21485 solver.cpp:397]     Test net output #1: loss = 5.13589 (* 1 = 5.13589 loss)
I0406 17:13:10.574563 21485 solver.cpp:218] Iteration 14076 (0.820951 iter/s, 14.6172s/12 iters), loss = 4.95742
I0406 17:13:10.574620 21485 solver.cpp:237]     Train net output #0: loss = 4.95742 (* 1 = 4.95742 loss)
I0406 17:13:10.574630 21485 sgd_solver.cpp:105] Iteration 14076, lr = 0.05
I0406 17:13:14.976543 21485 solver.cpp:218] Iteration 14088 (2.72609 iter/s, 4.4019s/12 iters), loss = 5.09352
I0406 17:13:14.976603 21485 solver.cpp:237]     Train net output #0: loss = 5.09352 (* 1 = 5.09352 loss)
I0406 17:13:14.976610 21485 sgd_solver.cpp:105] Iteration 14088, lr = 0.05
I0406 17:13:20.120043 21485 solver.cpp:218] Iteration 14100 (2.33307 iter/s, 5.14343s/12 iters), loss = 5.14287
I0406 17:13:20.120090 21485 solver.cpp:237]     Train net output #0: loss = 5.14287 (* 1 = 5.14287 loss)
I0406 17:13:20.120096 21485 sgd_solver.cpp:105] Iteration 14100, lr = 0.05
I0406 17:13:25.134505 21485 solver.cpp:218] Iteration 14112 (2.39311 iter/s, 5.0144s/12 iters), loss = 5.20305
I0406 17:13:25.134548 21485 solver.cpp:237]     Train net output #0: loss = 5.20305 (* 1 = 5.20305 loss)
I0406 17:13:25.134553 21485 sgd_solver.cpp:105] Iteration 14112, lr = 0.05
I0406 17:13:30.112383 21485 solver.cpp:218] Iteration 14124 (2.41069 iter/s, 4.97782s/12 iters), loss = 5.0558
I0406 17:13:30.112426 21485 solver.cpp:237]     Train net output #0: loss = 5.0558 (* 1 = 5.0558 loss)
I0406 17:13:30.112432 21485 sgd_solver.cpp:105] Iteration 14124, lr = 0.05
I0406 17:13:35.492550 21485 solver.cpp:218] Iteration 14136 (2.23044 iter/s, 5.38011s/12 iters), loss = 5.15064
I0406 17:13:35.492723 21485 solver.cpp:237]     Train net output #0: loss = 5.15064 (* 1 = 5.15064 loss)
I0406 17:13:35.492733 21485 sgd_solver.cpp:105] Iteration 14136, lr = 0.05
I0406 17:13:40.625730 21485 solver.cpp:218] Iteration 14148 (2.33781 iter/s, 5.133s/12 iters), loss = 5.18052
I0406 17:13:40.625769 21485 solver.cpp:237]     Train net output #0: loss = 5.18052 (* 1 = 5.18052 loss)
I0406 17:13:40.625775 21485 sgd_solver.cpp:105] Iteration 14148, lr = 0.05
I0406 17:13:45.774904 21485 solver.cpp:218] Iteration 14160 (2.3305 iter/s, 5.14912s/12 iters), loss = 5.16782
I0406 17:13:45.774945 21485 solver.cpp:237]     Train net output #0: loss = 5.16782 (* 1 = 5.16782 loss)
I0406 17:13:45.774950 21485 sgd_solver.cpp:105] Iteration 14160, lr = 0.05
I0406 17:13:46.041016 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:13:51.133996 21485 solver.cpp:218] Iteration 14172 (2.23921 iter/s, 5.35904s/12 iters), loss = 5.11064
I0406 17:13:51.134037 21485 solver.cpp:237]     Train net output #0: loss = 5.11064 (* 1 = 5.11064 loss)
I0406 17:13:51.134042 21485 sgd_solver.cpp:105] Iteration 14172, lr = 0.05
I0406 17:13:53.255232 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_14178.caffemodel
I0406 17:13:56.397390 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_14178.solverstate
I0406 17:13:58.856724 21485 solver.cpp:330] Iteration 14178, Testing net (#0)
I0406 17:13:58.856745 21485 net.cpp:676] Ignoring source layer train-data
I0406 17:14:02.229868 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:14:03.178669 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0165441
I0406 17:14:03.178705 21485 solver.cpp:397]     Test net output #1: loss = 5.13941 (* 1 = 5.13941 loss)
I0406 17:14:04.950621 21485 solver.cpp:218] Iteration 14184 (0.868522 iter/s, 13.8166s/12 iters), loss = 5.1081
I0406 17:14:04.950682 21485 solver.cpp:237]     Train net output #0: loss = 5.1081 (* 1 = 5.1081 loss)
I0406 17:14:04.950691 21485 sgd_solver.cpp:105] Iteration 14184, lr = 0.05
I0406 17:14:10.154222 21485 solver.cpp:218] Iteration 14196 (2.30613 iter/s, 5.20352s/12 iters), loss = 5.0589
I0406 17:14:10.154345 21485 solver.cpp:237]     Train net output #0: loss = 5.0589 (* 1 = 5.0589 loss)
I0406 17:14:10.154354 21485 sgd_solver.cpp:105] Iteration 14196, lr = 0.05
I0406 17:14:15.460357 21485 solver.cpp:218] Iteration 14208 (2.26159 iter/s, 5.306s/12 iters), loss = 5.11923
I0406 17:14:15.460400 21485 solver.cpp:237]     Train net output #0: loss = 5.11923 (* 1 = 5.11923 loss)
I0406 17:14:15.460405 21485 sgd_solver.cpp:105] Iteration 14208, lr = 0.05
I0406 17:14:20.692804 21485 solver.cpp:218] Iteration 14220 (2.29341 iter/s, 5.23239s/12 iters), loss = 5.0973
I0406 17:14:20.692854 21485 solver.cpp:237]     Train net output #0: loss = 5.0973 (* 1 = 5.0973 loss)
I0406 17:14:20.692862 21485 sgd_solver.cpp:105] Iteration 14220, lr = 0.05
I0406 17:14:25.912142 21485 solver.cpp:218] Iteration 14232 (2.29917 iter/s, 5.21927s/12 iters), loss = 5.04104
I0406 17:14:25.912199 21485 solver.cpp:237]     Train net output #0: loss = 5.04104 (* 1 = 5.04104 loss)
I0406 17:14:25.912207 21485 sgd_solver.cpp:105] Iteration 14232, lr = 0.05
I0406 17:14:31.260394 21485 solver.cpp:218] Iteration 14244 (2.24375 iter/s, 5.34819s/12 iters), loss = 5.02259
I0406 17:14:31.260439 21485 solver.cpp:237]     Train net output #0: loss = 5.02259 (* 1 = 5.02259 loss)
I0406 17:14:31.260444 21485 sgd_solver.cpp:105] Iteration 14244, lr = 0.05
I0406 17:14:36.309803 21485 solver.cpp:218] Iteration 14256 (2.37654 iter/s, 5.04935s/12 iters), loss = 5.10634
I0406 17:14:36.309844 21485 solver.cpp:237]     Train net output #0: loss = 5.10634 (* 1 = 5.10634 loss)
I0406 17:14:36.309849 21485 sgd_solver.cpp:105] Iteration 14256, lr = 0.05
I0406 17:14:38.636090 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:14:41.326639 21485 solver.cpp:218] Iteration 14268 (2.39197 iter/s, 5.01678s/12 iters), loss = 5.08807
I0406 17:14:41.326763 21485 solver.cpp:237]     Train net output #0: loss = 5.08807 (* 1 = 5.08807 loss)
I0406 17:14:41.326769 21485 sgd_solver.cpp:105] Iteration 14268, lr = 0.05
I0406 17:14:46.045522 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_14280.caffemodel
I0406 17:14:49.094774 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_14280.solverstate
I0406 17:14:51.445894 21485 solver.cpp:330] Iteration 14280, Testing net (#0)
I0406 17:14:51.445910 21485 net.cpp:676] Ignoring source layer train-data
I0406 17:14:54.908277 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:14:55.909601 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0189951
I0406 17:14:55.909638 21485 solver.cpp:397]     Test net output #1: loss = 5.16302 (* 1 = 5.16302 loss)
I0406 17:14:56.048295 21485 solver.cpp:218] Iteration 14280 (0.815133 iter/s, 14.7215s/12 iters), loss = 5.146
I0406 17:14:56.048336 21485 solver.cpp:237]     Train net output #0: loss = 5.146 (* 1 = 5.146 loss)
I0406 17:14:56.048341 21485 sgd_solver.cpp:105] Iteration 14280, lr = 0.05
I0406 17:15:00.165043 21485 solver.cpp:218] Iteration 14292 (2.91496 iter/s, 4.11669s/12 iters), loss = 5.18524
I0406 17:15:00.165098 21485 solver.cpp:237]     Train net output #0: loss = 5.18524 (* 1 = 5.18524 loss)
I0406 17:15:00.165107 21485 sgd_solver.cpp:105] Iteration 14292, lr = 0.05
I0406 17:15:05.337460 21485 solver.cpp:218] Iteration 14304 (2.32003 iter/s, 5.17235s/12 iters), loss = 4.99018
I0406 17:15:05.337497 21485 solver.cpp:237]     Train net output #0: loss = 4.99018 (* 1 = 4.99018 loss)
I0406 17:15:05.337503 21485 sgd_solver.cpp:105] Iteration 14304, lr = 0.05
I0406 17:15:10.922389 21485 solver.cpp:218] Iteration 14316 (2.14866 iter/s, 5.58487s/12 iters), loss = 5.12937
I0406 17:15:10.922443 21485 solver.cpp:237]     Train net output #0: loss = 5.12937 (* 1 = 5.12937 loss)
I0406 17:15:10.922451 21485 sgd_solver.cpp:105] Iteration 14316, lr = 0.05
I0406 17:15:16.106500 21485 solver.cpp:218] Iteration 14328 (2.3148 iter/s, 5.18404s/12 iters), loss = 5.11378
I0406 17:15:16.106607 21485 solver.cpp:237]     Train net output #0: loss = 5.11378 (* 1 = 5.11378 loss)
I0406 17:15:16.106614 21485 sgd_solver.cpp:105] Iteration 14328, lr = 0.05
I0406 17:15:21.110729 21485 solver.cpp:218] Iteration 14340 (2.39803 iter/s, 5.00411s/12 iters), loss = 5.01024
I0406 17:15:21.110771 21485 solver.cpp:237]     Train net output #0: loss = 5.01024 (* 1 = 5.01024 loss)
I0406 17:15:21.110777 21485 sgd_solver.cpp:105] Iteration 14340, lr = 0.05
I0406 17:15:26.118556 21485 solver.cpp:218] Iteration 14352 (2.39628 iter/s, 5.00776s/12 iters), loss = 5.02858
I0406 17:15:26.118613 21485 solver.cpp:237]     Train net output #0: loss = 5.02858 (* 1 = 5.02858 loss)
I0406 17:15:26.118623 21485 sgd_solver.cpp:105] Iteration 14352, lr = 0.05
I0406 17:15:31.011147 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:15:31.419162 21485 solver.cpp:218] Iteration 14364 (2.26392 iter/s, 5.30053s/12 iters), loss = 4.97562
I0406 17:15:31.419214 21485 solver.cpp:237]     Train net output #0: loss = 4.97562 (* 1 = 4.97562 loss)
I0406 17:15:31.419224 21485 sgd_solver.cpp:105] Iteration 14364, lr = 0.05
I0406 17:15:36.695765 21485 solver.cpp:218] Iteration 14376 (2.27422 iter/s, 5.27653s/12 iters), loss = 5.08689
I0406 17:15:36.695822 21485 solver.cpp:237]     Train net output #0: loss = 5.08689 (* 1 = 5.08689 loss)
I0406 17:15:36.695829 21485 sgd_solver.cpp:105] Iteration 14376, lr = 0.05
I0406 17:15:38.857623 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_14382.caffemodel
I0406 17:15:41.887794 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_14382.solverstate
I0406 17:15:44.238554 21485 solver.cpp:330] Iteration 14382, Testing net (#0)
I0406 17:15:44.238579 21485 net.cpp:676] Ignoring source layer train-data
I0406 17:15:47.667913 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:15:48.699358 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0196078
I0406 17:15:48.699390 21485 solver.cpp:397]     Test net output #1: loss = 5.14656 (* 1 = 5.14656 loss)
I0406 17:15:50.497669 21485 solver.cpp:218] Iteration 14388 (0.869449 iter/s, 13.8018s/12 iters), loss = 4.98842
I0406 17:15:50.497714 21485 solver.cpp:237]     Train net output #0: loss = 4.98842 (* 1 = 4.98842 loss)
I0406 17:15:50.497720 21485 sgd_solver.cpp:105] Iteration 14388, lr = 0.05
I0406 17:15:55.700579 21485 solver.cpp:218] Iteration 14400 (2.30643 iter/s, 5.20285s/12 iters), loss = 5.06465
I0406 17:15:55.700621 21485 solver.cpp:237]     Train net output #0: loss = 5.06465 (* 1 = 5.06465 loss)
I0406 17:15:55.700626 21485 sgd_solver.cpp:105] Iteration 14400, lr = 0.05
I0406 17:16:01.055541 21485 solver.cpp:218] Iteration 14412 (2.24094 iter/s, 5.3549s/12 iters), loss = 5.05425
I0406 17:16:01.055599 21485 solver.cpp:237]     Train net output #0: loss = 5.05425 (* 1 = 5.05425 loss)
I0406 17:16:01.055608 21485 sgd_solver.cpp:105] Iteration 14412, lr = 0.05
I0406 17:16:06.209462 21485 solver.cpp:218] Iteration 14424 (2.32835 iter/s, 5.15385s/12 iters), loss = 5.05777
I0406 17:16:06.209504 21485 solver.cpp:237]     Train net output #0: loss = 5.05777 (* 1 = 5.05777 loss)
I0406 17:16:06.209511 21485 sgd_solver.cpp:105] Iteration 14424, lr = 0.05
I0406 17:16:11.185858 21485 solver.cpp:218] Iteration 14436 (2.41141 iter/s, 4.97633s/12 iters), loss = 5.00611
I0406 17:16:11.185905 21485 solver.cpp:237]     Train net output #0: loss = 5.00611 (* 1 = 5.00611 loss)
I0406 17:16:11.185910 21485 sgd_solver.cpp:105] Iteration 14436, lr = 0.05
I0406 17:16:16.434453 21485 solver.cpp:218] Iteration 14448 (2.28635 iter/s, 5.24853s/12 iters), loss = 4.98892
I0406 17:16:16.434504 21485 solver.cpp:237]     Train net output #0: loss = 4.98892 (* 1 = 4.98892 loss)
I0406 17:16:16.434514 21485 sgd_solver.cpp:105] Iteration 14448, lr = 0.05
I0406 17:16:21.625203 21485 solver.cpp:218] Iteration 14460 (2.31183 iter/s, 5.19069s/12 iters), loss = 5.06389
I0406 17:16:21.625303 21485 solver.cpp:237]     Train net output #0: loss = 5.06389 (* 1 = 5.06389 loss)
I0406 17:16:21.625309 21485 sgd_solver.cpp:105] Iteration 14460, lr = 0.05
I0406 17:16:23.392706 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:16:26.538750 21485 solver.cpp:218] Iteration 14472 (2.44228 iter/s, 4.91344s/12 iters), loss = 5.13223
I0406 17:16:26.538789 21485 solver.cpp:237]     Train net output #0: loss = 5.13223 (* 1 = 5.13223 loss)
I0406 17:16:26.538795 21485 sgd_solver.cpp:105] Iteration 14472, lr = 0.05
I0406 17:16:31.180119 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_14484.caffemodel
I0406 17:16:34.266603 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_14484.solverstate
I0406 17:16:36.585557 21485 solver.cpp:330] Iteration 14484, Testing net (#0)
I0406 17:16:36.585579 21485 net.cpp:676] Ignoring source layer train-data
I0406 17:16:39.969633 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:16:41.064354 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0134804
I0406 17:16:41.064414 21485 solver.cpp:397]     Test net output #1: loss = 5.16764 (* 1 = 5.16764 loss)
I0406 17:16:41.204813 21485 solver.cpp:218] Iteration 14484 (0.818219 iter/s, 14.666s/12 iters), loss = 5.08235
I0406 17:16:41.204860 21485 solver.cpp:237]     Train net output #0: loss = 5.08235 (* 1 = 5.08235 loss)
I0406 17:16:41.204867 21485 sgd_solver.cpp:105] Iteration 14484, lr = 0.05
I0406 17:16:45.339334 21485 solver.cpp:218] Iteration 14496 (2.90244 iter/s, 4.13446s/12 iters), loss = 5.05665
I0406 17:16:45.339375 21485 solver.cpp:237]     Train net output #0: loss = 5.05665 (* 1 = 5.05665 loss)
I0406 17:16:45.339380 21485 sgd_solver.cpp:105] Iteration 14496, lr = 0.05
I0406 17:16:50.592816 21485 solver.cpp:218] Iteration 14508 (2.28422 iter/s, 5.25342s/12 iters), loss = 5.05251
I0406 17:16:50.592865 21485 solver.cpp:237]     Train net output #0: loss = 5.05251 (* 1 = 5.05251 loss)
I0406 17:16:50.592872 21485 sgd_solver.cpp:105] Iteration 14508, lr = 0.05
I0406 17:16:55.790684 21485 solver.cpp:218] Iteration 14520 (2.30867 iter/s, 5.1978s/12 iters), loss = 5.06842
I0406 17:16:55.790843 21485 solver.cpp:237]     Train net output #0: loss = 5.06842 (* 1 = 5.06842 loss)
I0406 17:16:55.790853 21485 sgd_solver.cpp:105] Iteration 14520, lr = 0.05
I0406 17:17:01.026494 21485 solver.cpp:218] Iteration 14532 (2.29198 iter/s, 5.23564s/12 iters), loss = 5.08168
I0406 17:17:01.026541 21485 solver.cpp:237]     Train net output #0: loss = 5.08168 (* 1 = 5.08168 loss)
I0406 17:17:01.026549 21485 sgd_solver.cpp:105] Iteration 14532, lr = 0.05
I0406 17:17:06.212862 21485 solver.cpp:218] Iteration 14544 (2.31379 iter/s, 5.1863s/12 iters), loss = 5.05589
I0406 17:17:06.212934 21485 solver.cpp:237]     Train net output #0: loss = 5.05589 (* 1 = 5.05589 loss)
I0406 17:17:06.212944 21485 sgd_solver.cpp:105] Iteration 14544, lr = 0.05
I0406 17:17:11.689265 21485 solver.cpp:218] Iteration 14556 (2.19125 iter/s, 5.47632s/12 iters), loss = 5.07694
I0406 17:17:11.689306 21485 solver.cpp:237]     Train net output #0: loss = 5.07694 (* 1 = 5.07694 loss)
I0406 17:17:11.689312 21485 sgd_solver.cpp:105] Iteration 14556, lr = 0.05
I0406 17:17:15.779170 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:17:15.985885 21485 blocking_queue.cpp:49] Waiting for data
I0406 17:17:16.966740 21485 solver.cpp:218] Iteration 14568 (2.27384 iter/s, 5.27742s/12 iters), loss = 5.07682
I0406 17:17:16.966797 21485 solver.cpp:237]     Train net output #0: loss = 5.07682 (* 1 = 5.07682 loss)
I0406 17:17:16.966806 21485 sgd_solver.cpp:105] Iteration 14568, lr = 0.05
I0406 17:17:22.211046 21485 solver.cpp:218] Iteration 14580 (2.28823 iter/s, 5.24424s/12 iters), loss = 5.07408
I0406 17:17:22.211103 21485 solver.cpp:237]     Train net output #0: loss = 5.07408 (* 1 = 5.07408 loss)
I0406 17:17:22.211112 21485 sgd_solver.cpp:105] Iteration 14580, lr = 0.05
I0406 17:17:24.315613 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_14586.caffemodel
I0406 17:17:27.307358 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_14586.solverstate
I0406 17:17:29.614918 21485 solver.cpp:330] Iteration 14586, Testing net (#0)
I0406 17:17:29.614941 21485 net.cpp:676] Ignoring source layer train-data
I0406 17:17:33.074520 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:17:34.182597 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0196078
I0406 17:17:34.182634 21485 solver.cpp:397]     Test net output #1: loss = 5.15014 (* 1 = 5.15014 loss)
I0406 17:17:36.053174 21485 solver.cpp:218] Iteration 14592 (0.866923 iter/s, 13.8421s/12 iters), loss = 5.1051
I0406 17:17:36.053220 21485 solver.cpp:237]     Train net output #0: loss = 5.1051 (* 1 = 5.1051 loss)
I0406 17:17:36.053225 21485 sgd_solver.cpp:105] Iteration 14592, lr = 0.05
I0406 17:17:41.314486 21485 solver.cpp:218] Iteration 14604 (2.28083 iter/s, 5.26125s/12 iters), loss = 5.05713
I0406 17:17:41.314532 21485 solver.cpp:237]     Train net output #0: loss = 5.05713 (* 1 = 5.05713 loss)
I0406 17:17:41.314540 21485 sgd_solver.cpp:105] Iteration 14604, lr = 0.05
I0406 17:17:46.446816 21485 solver.cpp:218] Iteration 14616 (2.33815 iter/s, 5.13227s/12 iters), loss = 5.04684
I0406 17:17:46.446864 21485 solver.cpp:237]     Train net output #0: loss = 5.04684 (* 1 = 5.04684 loss)
I0406 17:17:46.446872 21485 sgd_solver.cpp:105] Iteration 14616, lr = 0.05
I0406 17:17:51.667603 21485 solver.cpp:218] Iteration 14628 (2.29853 iter/s, 5.22072s/12 iters), loss = 4.99573
I0406 17:17:51.667649 21485 solver.cpp:237]     Train net output #0: loss = 4.99573 (* 1 = 4.99573 loss)
I0406 17:17:51.667654 21485 sgd_solver.cpp:105] Iteration 14628, lr = 0.05
I0406 17:17:57.080912 21485 solver.cpp:218] Iteration 14640 (2.21679 iter/s, 5.41324s/12 iters), loss = 4.95282
I0406 17:17:57.080968 21485 solver.cpp:237]     Train net output #0: loss = 4.95282 (* 1 = 4.95282 loss)
I0406 17:17:57.080976 21485 sgd_solver.cpp:105] Iteration 14640, lr = 0.05
I0406 17:18:02.331599 21485 solver.cpp:218] Iteration 14652 (2.28545 iter/s, 5.25061s/12 iters), loss = 5.01835
I0406 17:18:02.331728 21485 solver.cpp:237]     Train net output #0: loss = 5.01835 (* 1 = 5.01835 loss)
I0406 17:18:02.331735 21485 sgd_solver.cpp:105] Iteration 14652, lr = 0.05
I0406 17:18:07.664851 21485 solver.cpp:218] Iteration 14664 (2.25009 iter/s, 5.33311s/12 iters), loss = 5.02683
I0406 17:18:07.664898 21485 solver.cpp:237]     Train net output #0: loss = 5.02683 (* 1 = 5.02683 loss)
I0406 17:18:07.664904 21485 sgd_solver.cpp:105] Iteration 14664, lr = 0.05
I0406 17:18:08.656603 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:18:12.791862 21485 solver.cpp:218] Iteration 14676 (2.34057 iter/s, 5.12695s/12 iters), loss = 5.11024
I0406 17:18:12.791898 21485 solver.cpp:237]     Train net output #0: loss = 5.11024 (* 1 = 5.11024 loss)
I0406 17:18:12.791903 21485 sgd_solver.cpp:105] Iteration 14676, lr = 0.05
I0406 17:18:17.450212 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_14688.caffemodel
I0406 17:18:20.530581 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_14688.solverstate
I0406 17:18:23.377161 21485 solver.cpp:330] Iteration 14688, Testing net (#0)
I0406 17:18:23.377180 21485 net.cpp:676] Ignoring source layer train-data
I0406 17:18:26.535347 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:18:27.755853 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0140931
I0406 17:18:27.755889 21485 solver.cpp:397]     Test net output #1: loss = 5.1586 (* 1 = 5.1586 loss)
I0406 17:18:27.897027 21485 solver.cpp:218] Iteration 14688 (0.794433 iter/s, 15.1051s/12 iters), loss = 5.10954
I0406 17:18:27.897078 21485 solver.cpp:237]     Train net output #0: loss = 5.10954 (* 1 = 5.10954 loss)
I0406 17:18:27.897085 21485 sgd_solver.cpp:105] Iteration 14688, lr = 0.05
I0406 17:18:32.218473 21485 solver.cpp:218] Iteration 14700 (2.77689 iter/s, 4.32138s/12 iters), loss = 5.13825
I0406 17:18:32.218515 21485 solver.cpp:237]     Train net output #0: loss = 5.13825 (* 1 = 5.13825 loss)
I0406 17:18:32.218520 21485 sgd_solver.cpp:105] Iteration 14700, lr = 0.05
I0406 17:18:37.504436 21485 solver.cpp:218] Iteration 14712 (2.27019 iter/s, 5.28591s/12 iters), loss = 5.06529
I0406 17:18:37.504521 21485 solver.cpp:237]     Train net output #0: loss = 5.06529 (* 1 = 5.06529 loss)
I0406 17:18:37.504528 21485 sgd_solver.cpp:105] Iteration 14712, lr = 0.05
I0406 17:18:42.652669 21485 solver.cpp:218] Iteration 14724 (2.33094 iter/s, 5.14813s/12 iters), loss = 5.10513
I0406 17:18:42.652714 21485 solver.cpp:237]     Train net output #0: loss = 5.10513 (* 1 = 5.10513 loss)
I0406 17:18:42.652720 21485 sgd_solver.cpp:105] Iteration 14724, lr = 0.05
I0406 17:18:47.733896 21485 solver.cpp:218] Iteration 14736 (2.36167 iter/s, 5.08116s/12 iters), loss = 5.13412
I0406 17:18:47.733959 21485 solver.cpp:237]     Train net output #0: loss = 5.13412 (* 1 = 5.13412 loss)
I0406 17:18:47.733969 21485 sgd_solver.cpp:105] Iteration 14736, lr = 0.05
I0406 17:18:52.931197 21485 solver.cpp:218] Iteration 14748 (2.30893 iter/s, 5.19722s/12 iters), loss = 5.13595
I0406 17:18:52.931252 21485 solver.cpp:237]     Train net output #0: loss = 5.13595 (* 1 = 5.13595 loss)
I0406 17:18:52.931258 21485 sgd_solver.cpp:105] Iteration 14748, lr = 0.05
I0406 17:18:58.240676 21485 solver.cpp:218] Iteration 14760 (2.26014 iter/s, 5.30941s/12 iters), loss = 5.13476
I0406 17:18:58.240720 21485 solver.cpp:237]     Train net output #0: loss = 5.13476 (* 1 = 5.13476 loss)
I0406 17:18:58.240725 21485 sgd_solver.cpp:105] Iteration 14760, lr = 0.05
I0406 17:19:01.534448 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:19:03.626142 21485 solver.cpp:218] Iteration 14772 (2.22824 iter/s, 5.38541s/12 iters), loss = 5.10028
I0406 17:19:03.626188 21485 solver.cpp:237]     Train net output #0: loss = 5.10028 (* 1 = 5.10028 loss)
I0406 17:19:03.626194 21485 sgd_solver.cpp:105] Iteration 14772, lr = 0.05
I0406 17:19:08.395819 21485 solver.cpp:218] Iteration 14784 (2.51593 iter/s, 4.76962s/12 iters), loss = 5.05094
I0406 17:19:08.395946 21485 solver.cpp:237]     Train net output #0: loss = 5.05094 (* 1 = 5.05094 loss)
I0406 17:19:08.395951 21485 sgd_solver.cpp:105] Iteration 14784, lr = 0.05
I0406 17:19:10.496503 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_14790.caffemodel
I0406 17:19:13.646291 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_14790.solverstate
I0406 17:19:15.953256 21485 solver.cpp:330] Iteration 14790, Testing net (#0)
I0406 17:19:15.953277 21485 net.cpp:676] Ignoring source layer train-data
I0406 17:19:19.099320 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:19:20.311565 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0140931
I0406 17:19:20.311609 21485 solver.cpp:397]     Test net output #1: loss = 5.14892 (* 1 = 5.14892 loss)
I0406 17:19:22.109112 21485 solver.cpp:218] Iteration 14796 (0.875072 iter/s, 13.7132s/12 iters), loss = 5.03219
I0406 17:19:22.109155 21485 solver.cpp:237]     Train net output #0: loss = 5.03219 (* 1 = 5.03219 loss)
I0406 17:19:22.109160 21485 sgd_solver.cpp:105] Iteration 14796, lr = 0.05
I0406 17:19:27.398960 21485 solver.cpp:218] Iteration 14808 (2.26852 iter/s, 5.28979s/12 iters), loss = 5.13154
I0406 17:19:27.399003 21485 solver.cpp:237]     Train net output #0: loss = 5.13154 (* 1 = 5.13154 loss)
I0406 17:19:27.399008 21485 sgd_solver.cpp:105] Iteration 14808, lr = 0.05
I0406 17:19:32.477744 21485 solver.cpp:218] Iteration 14820 (2.3628 iter/s, 5.07872s/12 iters), loss = 5.07161
I0406 17:19:32.477795 21485 solver.cpp:237]     Train net output #0: loss = 5.07161 (* 1 = 5.07161 loss)
I0406 17:19:32.477805 21485 sgd_solver.cpp:105] Iteration 14820, lr = 0.05
I0406 17:19:37.683898 21485 solver.cpp:218] Iteration 14832 (2.30499 iter/s, 5.20609s/12 iters), loss = 5.01772
I0406 17:19:37.683948 21485 solver.cpp:237]     Train net output #0: loss = 5.01772 (* 1 = 5.01772 loss)
I0406 17:19:37.683957 21485 sgd_solver.cpp:105] Iteration 14832, lr = 0.05
I0406 17:19:42.913933 21485 solver.cpp:218] Iteration 14844 (2.29447 iter/s, 5.22997s/12 iters), loss = 5.06881
I0406 17:19:42.914039 21485 solver.cpp:237]     Train net output #0: loss = 5.06881 (* 1 = 5.06881 loss)
I0406 17:19:42.914045 21485 sgd_solver.cpp:105] Iteration 14844, lr = 0.05
I0406 17:19:48.316072 21485 solver.cpp:218] Iteration 14856 (2.22139 iter/s, 5.40202s/12 iters), loss = 5.09774
I0406 17:19:48.316120 21485 solver.cpp:237]     Train net output #0: loss = 5.09774 (* 1 = 5.09774 loss)
I0406 17:19:48.316128 21485 sgd_solver.cpp:105] Iteration 14856, lr = 0.05
I0406 17:19:53.508649 21485 solver.cpp:218] Iteration 14868 (2.31102 iter/s, 5.19252s/12 iters), loss = 5.03644
I0406 17:19:53.508689 21485 solver.cpp:237]     Train net output #0: loss = 5.03644 (* 1 = 5.03644 loss)
I0406 17:19:53.508695 21485 sgd_solver.cpp:105] Iteration 14868, lr = 0.05
I0406 17:19:53.796145 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:19:58.628311 21485 solver.cpp:218] Iteration 14880 (2.34393 iter/s, 5.11961s/12 iters), loss = 5.1619
I0406 17:19:58.628353 21485 solver.cpp:237]     Train net output #0: loss = 5.1619 (* 1 = 5.1619 loss)
I0406 17:19:58.628360 21485 sgd_solver.cpp:105] Iteration 14880, lr = 0.05
I0406 17:20:03.464668 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_14892.caffemodel
I0406 17:20:07.293102 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_14892.solverstate
I0406 17:20:09.600185 21485 solver.cpp:330] Iteration 14892, Testing net (#0)
I0406 17:20:09.600204 21485 net.cpp:676] Ignoring source layer train-data
I0406 17:20:12.773346 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:20:14.048585 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0122549
I0406 17:20:14.048739 21485 solver.cpp:397]     Test net output #1: loss = 5.16901 (* 1 = 5.16901 loss)
I0406 17:20:14.189011 21485 solver.cpp:218] Iteration 14892 (0.771176 iter/s, 15.5606s/12 iters), loss = 5.1368
I0406 17:20:14.189067 21485 solver.cpp:237]     Train net output #0: loss = 5.1368 (* 1 = 5.1368 loss)
I0406 17:20:14.189074 21485 sgd_solver.cpp:105] Iteration 14892, lr = 0.05
I0406 17:20:18.529042 21485 solver.cpp:218] Iteration 14904 (2.765 iter/s, 4.33996s/12 iters), loss = 5.05443
I0406 17:20:18.529093 21485 solver.cpp:237]     Train net output #0: loss = 5.05443 (* 1 = 5.05443 loss)
I0406 17:20:18.529098 21485 sgd_solver.cpp:105] Iteration 14904, lr = 0.05
I0406 17:20:23.664070 21485 solver.cpp:218] Iteration 14916 (2.33692 iter/s, 5.13496s/12 iters), loss = 4.95502
I0406 17:20:23.664113 21485 solver.cpp:237]     Train net output #0: loss = 4.95502 (* 1 = 4.95502 loss)
I0406 17:20:23.664119 21485 sgd_solver.cpp:105] Iteration 14916, lr = 0.05
I0406 17:20:28.882583 21485 solver.cpp:218] Iteration 14928 (2.29953 iter/s, 5.21846s/12 iters), loss = 5.02653
I0406 17:20:28.882620 21485 solver.cpp:237]     Train net output #0: loss = 5.02653 (* 1 = 5.02653 loss)
I0406 17:20:28.882625 21485 sgd_solver.cpp:105] Iteration 14928, lr = 0.05
I0406 17:20:34.238711 21485 solver.cpp:218] Iteration 14940 (2.24045 iter/s, 5.35607s/12 iters), loss = 5.0394
I0406 17:20:34.238760 21485 solver.cpp:237]     Train net output #0: loss = 5.0394 (* 1 = 5.0394 loss)
I0406 17:20:34.238770 21485 sgd_solver.cpp:105] Iteration 14940, lr = 0.05
I0406 17:20:39.525481 21485 solver.cpp:218] Iteration 14952 (2.26985 iter/s, 5.2867s/12 iters), loss = 5.14894
I0406 17:20:39.525535 21485 solver.cpp:237]     Train net output #0: loss = 5.14894 (* 1 = 5.14894 loss)
I0406 17:20:39.525542 21485 sgd_solver.cpp:105] Iteration 14952, lr = 0.05
I0406 17:20:44.736728 21485 solver.cpp:218] Iteration 14964 (2.30274 iter/s, 5.21118s/12 iters), loss = 5.12312
I0406 17:20:44.736816 21485 solver.cpp:237]     Train net output #0: loss = 5.12312 (* 1 = 5.12312 loss)
I0406 17:20:44.736822 21485 sgd_solver.cpp:105] Iteration 14964, lr = 0.05
I0406 17:20:47.251152 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:20:49.966034 21485 solver.cpp:218] Iteration 14976 (2.29481 iter/s, 5.2292s/12 iters), loss = 5.08995
I0406 17:20:49.966076 21485 solver.cpp:237]     Train net output #0: loss = 5.08995 (* 1 = 5.08995 loss)
I0406 17:20:49.966082 21485 sgd_solver.cpp:105] Iteration 14976, lr = 0.05
I0406 17:20:55.413404 21485 solver.cpp:218] Iteration 14988 (2.20292 iter/s, 5.44731s/12 iters), loss = 5.00594
I0406 17:20:55.413461 21485 solver.cpp:237]     Train net output #0: loss = 5.00594 (* 1 = 5.00594 loss)
I0406 17:20:55.413470 21485 sgd_solver.cpp:105] Iteration 14988, lr = 0.05
I0406 17:20:57.542335 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_14994.caffemodel
I0406 17:21:01.572976 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_14994.solverstate
I0406 17:21:05.299012 21485 solver.cpp:330] Iteration 14994, Testing net (#0)
I0406 17:21:05.299032 21485 net.cpp:676] Ignoring source layer train-data
I0406 17:21:08.320966 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:21:09.580787 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0202206
I0406 17:21:09.580821 21485 solver.cpp:397]     Test net output #1: loss = 5.13624 (* 1 = 5.13624 loss)
I0406 17:21:11.443821 21485 solver.cpp:218] Iteration 15000 (0.74858 iter/s, 16.0303s/12 iters), loss = 5.13593
I0406 17:21:11.443878 21485 solver.cpp:237]     Train net output #0: loss = 5.13593 (* 1 = 5.13593 loss)
I0406 17:21:11.443886 21485 sgd_solver.cpp:105] Iteration 15000, lr = 0.05
I0406 17:21:16.603107 21485 solver.cpp:218] Iteration 15012 (2.32594 iter/s, 5.15921s/12 iters), loss = 4.97601
I0406 17:21:16.603281 21485 solver.cpp:237]     Train net output #0: loss = 4.97601 (* 1 = 4.97601 loss)
I0406 17:21:16.603289 21485 sgd_solver.cpp:105] Iteration 15012, lr = 0.05
I0406 17:21:21.911342 21485 solver.cpp:218] Iteration 15024 (2.26072 iter/s, 5.30804s/12 iters), loss = 5.11584
I0406 17:21:21.911705 21485 solver.cpp:237]     Train net output #0: loss = 5.11584 (* 1 = 5.11584 loss)
I0406 17:21:21.911713 21485 sgd_solver.cpp:105] Iteration 15024, lr = 0.05
I0406 17:21:27.050977 21485 solver.cpp:218] Iteration 15036 (2.33497 iter/s, 5.13926s/12 iters), loss = 5.08934
I0406 17:21:27.051020 21485 solver.cpp:237]     Train net output #0: loss = 5.08934 (* 1 = 5.08934 loss)
I0406 17:21:27.051025 21485 sgd_solver.cpp:105] Iteration 15036, lr = 0.05
I0406 17:21:32.172000 21485 solver.cpp:218] Iteration 15048 (2.34331 iter/s, 5.12097s/12 iters), loss = 4.97518
I0406 17:21:32.172046 21485 solver.cpp:237]     Train net output #0: loss = 4.97518 (* 1 = 4.97518 loss)
I0406 17:21:32.172051 21485 sgd_solver.cpp:105] Iteration 15048, lr = 0.05
I0406 17:21:37.394220 21485 solver.cpp:218] Iteration 15060 (2.2979 iter/s, 5.22216s/12 iters), loss = 5.04194
I0406 17:21:37.394264 21485 solver.cpp:237]     Train net output #0: loss = 5.04194 (* 1 = 5.04194 loss)
I0406 17:21:37.394270 21485 sgd_solver.cpp:105] Iteration 15060, lr = 0.05
I0406 17:21:42.140370 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:21:42.498131 21485 solver.cpp:218] Iteration 15072 (2.35117 iter/s, 5.10385s/12 iters), loss = 5.07834
I0406 17:21:42.498168 21485 solver.cpp:237]     Train net output #0: loss = 5.07834 (* 1 = 5.07834 loss)
I0406 17:21:42.498173 21485 sgd_solver.cpp:105] Iteration 15072, lr = 0.05
I0406 17:21:47.595249 21485 solver.cpp:218] Iteration 15084 (2.3543 iter/s, 5.09707s/12 iters), loss = 5.00179
I0406 17:21:47.595366 21485 solver.cpp:237]     Train net output #0: loss = 5.00179 (* 1 = 5.00179 loss)
I0406 17:21:47.595376 21485 sgd_solver.cpp:105] Iteration 15084, lr = 0.05
I0406 17:21:52.186306 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_15096.caffemodel
I0406 17:21:55.210172 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_15096.solverstate
I0406 17:21:57.610000 21485 solver.cpp:330] Iteration 15096, Testing net (#0)
I0406 17:21:57.610020 21485 net.cpp:676] Ignoring source layer train-data
I0406 17:22:00.706355 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:22:01.999837 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0122549
I0406 17:22:01.999867 21485 solver.cpp:397]     Test net output #1: loss = 5.18833 (* 1 = 5.18833 loss)
I0406 17:22:02.134631 21485 solver.cpp:218] Iteration 15096 (0.825352 iter/s, 14.5393s/12 iters), loss = 5.07293
I0406 17:22:02.134683 21485 solver.cpp:237]     Train net output #0: loss = 5.07293 (* 1 = 5.07293 loss)
I0406 17:22:02.134692 21485 sgd_solver.cpp:105] Iteration 15096, lr = 0.05
I0406 17:22:06.543870 21485 solver.cpp:218] Iteration 15108 (2.7216 iter/s, 4.40917s/12 iters), loss = 5.13284
I0406 17:22:06.543920 21485 solver.cpp:237]     Train net output #0: loss = 5.13284 (* 1 = 5.13284 loss)
I0406 17:22:06.543927 21485 sgd_solver.cpp:105] Iteration 15108, lr = 0.05
I0406 17:22:11.678653 21485 solver.cpp:218] Iteration 15120 (2.33703 iter/s, 5.13472s/12 iters), loss = 5.02319
I0406 17:22:11.678691 21485 solver.cpp:237]     Train net output #0: loss = 5.02319 (* 1 = 5.02319 loss)
I0406 17:22:11.678696 21485 sgd_solver.cpp:105] Iteration 15120, lr = 0.05
I0406 17:22:16.620066 21485 solver.cpp:218] Iteration 15132 (2.42848 iter/s, 4.94136s/12 iters), loss = 5.04876
I0406 17:22:16.620113 21485 solver.cpp:237]     Train net output #0: loss = 5.04876 (* 1 = 5.04876 loss)
I0406 17:22:16.620121 21485 sgd_solver.cpp:105] Iteration 15132, lr = 0.05
I0406 17:22:21.820513 21485 solver.cpp:218] Iteration 15144 (2.30752 iter/s, 5.20038s/12 iters), loss = 5.07548
I0406 17:22:21.820672 21485 solver.cpp:237]     Train net output #0: loss = 5.07548 (* 1 = 5.07548 loss)
I0406 17:22:21.820680 21485 sgd_solver.cpp:105] Iteration 15144, lr = 0.05
I0406 17:22:27.270625 21485 solver.cpp:218] Iteration 15156 (2.20186 iter/s, 5.44994s/12 iters), loss = 4.91052
I0406 17:22:27.270674 21485 solver.cpp:237]     Train net output #0: loss = 4.91052 (* 1 = 4.91052 loss)
I0406 17:22:27.270682 21485 sgd_solver.cpp:105] Iteration 15156, lr = 0.05
I0406 17:22:32.514586 21485 solver.cpp:218] Iteration 15168 (2.28838 iter/s, 5.2439s/12 iters), loss = 4.94134
I0406 17:22:32.514636 21485 solver.cpp:237]     Train net output #0: loss = 4.94134 (* 1 = 4.94134 loss)
I0406 17:22:32.514642 21485 sgd_solver.cpp:105] Iteration 15168, lr = 0.05
I0406 17:22:34.391695 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:22:37.581867 21485 solver.cpp:218] Iteration 15180 (2.36816 iter/s, 5.06722s/12 iters), loss = 5.03959
I0406 17:22:37.581913 21485 solver.cpp:237]     Train net output #0: loss = 5.03959 (* 1 = 5.03959 loss)
I0406 17:22:37.581923 21485 sgd_solver.cpp:105] Iteration 15180, lr = 0.05
I0406 17:22:42.919006 21485 solver.cpp:218] Iteration 15192 (2.24842 iter/s, 5.33708s/12 iters), loss = 5.00865
I0406 17:22:42.919045 21485 solver.cpp:237]     Train net output #0: loss = 5.00865 (* 1 = 5.00865 loss)
I0406 17:22:42.919051 21485 sgd_solver.cpp:105] Iteration 15192, lr = 0.05
I0406 17:22:45.073540 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_15198.caffemodel
I0406 17:22:48.315680 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_15198.solverstate
I0406 17:22:50.611033 21485 solver.cpp:330] Iteration 15198, Testing net (#0)
I0406 17:22:50.611053 21485 net.cpp:676] Ignoring source layer train-data
I0406 17:22:53.626634 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:22:55.119859 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0177696
I0406 17:22:55.119899 21485 solver.cpp:397]     Test net output #1: loss = 5.16127 (* 1 = 5.16127 loss)
I0406 17:22:56.981773 21485 solver.cpp:218] Iteration 15204 (0.85332 iter/s, 14.0627s/12 iters), loss = 4.99674
I0406 17:22:56.981817 21485 solver.cpp:237]     Train net output #0: loss = 4.99674 (* 1 = 4.99674 loss)
I0406 17:22:56.981823 21485 sgd_solver.cpp:105] Iteration 15204, lr = 0.05
I0406 17:23:02.216226 21485 solver.cpp:218] Iteration 15216 (2.29256 iter/s, 5.23433s/12 iters), loss = 4.98371
I0406 17:23:02.216336 21485 solver.cpp:237]     Train net output #0: loss = 4.98371 (* 1 = 4.98371 loss)
I0406 17:23:02.216347 21485 sgd_solver.cpp:105] Iteration 15216, lr = 0.05
I0406 17:23:07.479352 21485 solver.cpp:218] Iteration 15228 (2.28007 iter/s, 5.26301s/12 iters), loss = 5.0514
I0406 17:23:07.479410 21485 solver.cpp:237]     Train net output #0: loss = 5.0514 (* 1 = 5.0514 loss)
I0406 17:23:07.479419 21485 sgd_solver.cpp:105] Iteration 15228, lr = 0.05
I0406 17:23:12.756450 21485 solver.cpp:218] Iteration 15240 (2.27401 iter/s, 5.27703s/12 iters), loss = 5.14489
I0406 17:23:12.756489 21485 solver.cpp:237]     Train net output #0: loss = 5.14489 (* 1 = 5.14489 loss)
I0406 17:23:12.756495 21485 sgd_solver.cpp:105] Iteration 15240, lr = 0.05
I0406 17:23:17.539731 21485 blocking_queue.cpp:49] Waiting for data
I0406 17:23:18.071508 21485 solver.cpp:218] Iteration 15252 (2.25776 iter/s, 5.31501s/12 iters), loss = 4.99829
I0406 17:23:18.071554 21485 solver.cpp:237]     Train net output #0: loss = 4.99829 (* 1 = 4.99829 loss)
I0406 17:23:18.071560 21485 sgd_solver.cpp:105] Iteration 15252, lr = 0.05
I0406 17:23:23.249267 21485 solver.cpp:218] Iteration 15264 (2.31763 iter/s, 5.1777s/12 iters), loss = 4.9451
I0406 17:23:23.249330 21485 solver.cpp:237]     Train net output #0: loss = 4.9451 (* 1 = 4.9451 loss)
I0406 17:23:23.249339 21485 sgd_solver.cpp:105] Iteration 15264, lr = 0.05
I0406 17:23:27.433198 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:23:28.593044 21485 solver.cpp:218] Iteration 15276 (2.24563 iter/s, 5.3437s/12 iters), loss = 5.03559
I0406 17:23:28.593101 21485 solver.cpp:237]     Train net output #0: loss = 5.03559 (* 1 = 5.03559 loss)
I0406 17:23:28.593111 21485 sgd_solver.cpp:105] Iteration 15276, lr = 0.05
I0406 17:23:33.886488 21485 solver.cpp:218] Iteration 15288 (2.26699 iter/s, 5.29337s/12 iters), loss = 5.02886
I0406 17:23:33.886548 21485 solver.cpp:237]     Train net output #0: loss = 5.02886 (* 1 = 5.02886 loss)
I0406 17:23:33.886555 21485 sgd_solver.cpp:105] Iteration 15288, lr = 0.05
I0406 17:23:38.650635 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_15300.caffemodel
I0406 17:23:41.681862 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_15300.solverstate
I0406 17:23:43.974051 21485 solver.cpp:330] Iteration 15300, Testing net (#0)
I0406 17:23:43.974071 21485 net.cpp:676] Ignoring source layer train-data
I0406 17:23:47.062064 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:23:48.434991 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0177696
I0406 17:23:48.435025 21485 solver.cpp:397]     Test net output #1: loss = 5.14985 (* 1 = 5.14985 loss)
I0406 17:23:48.574918 21485 solver.cpp:218] Iteration 15300 (0.816973 iter/s, 14.6884s/12 iters), loss = 5.11592
I0406 17:23:48.574965 21485 solver.cpp:237]     Train net output #0: loss = 5.11592 (* 1 = 5.11592 loss)
I0406 17:23:48.574970 21485 sgd_solver.cpp:105] Iteration 15300, lr = 0.05
I0406 17:23:52.821859 21485 solver.cpp:218] Iteration 15312 (2.82561 iter/s, 4.24688s/12 iters), loss = 5.11034
I0406 17:23:52.821908 21485 solver.cpp:237]     Train net output #0: loss = 5.11034 (* 1 = 5.11034 loss)
I0406 17:23:52.821916 21485 sgd_solver.cpp:105] Iteration 15312, lr = 0.05
I0406 17:23:57.867785 21485 solver.cpp:218] Iteration 15324 (2.37819 iter/s, 5.04586s/12 iters), loss = 5.07902
I0406 17:23:57.867902 21485 solver.cpp:237]     Train net output #0: loss = 5.07902 (* 1 = 5.07902 loss)
I0406 17:23:57.867911 21485 sgd_solver.cpp:105] Iteration 15324, lr = 0.05
I0406 17:24:03.058452 21485 solver.cpp:218] Iteration 15336 (2.3119 iter/s, 5.19054s/12 iters), loss = 5.02402
I0406 17:24:03.058495 21485 solver.cpp:237]     Train net output #0: loss = 5.02402 (* 1 = 5.02402 loss)
I0406 17:24:03.058501 21485 sgd_solver.cpp:105] Iteration 15336, lr = 0.05
I0406 17:24:08.247534 21485 solver.cpp:218] Iteration 15348 (2.31258 iter/s, 5.18902s/12 iters), loss = 5.07602
I0406 17:24:08.247591 21485 solver.cpp:237]     Train net output #0: loss = 5.07602 (* 1 = 5.07602 loss)
I0406 17:24:08.247599 21485 sgd_solver.cpp:105] Iteration 15348, lr = 0.05
I0406 17:24:13.471112 21485 solver.cpp:218] Iteration 15360 (2.29731 iter/s, 5.22351s/12 iters), loss = 4.93383
I0406 17:24:13.471171 21485 solver.cpp:237]     Train net output #0: loss = 4.93383 (* 1 = 4.93383 loss)
I0406 17:24:13.471179 21485 sgd_solver.cpp:105] Iteration 15360, lr = 0.05
I0406 17:24:18.792022 21485 solver.cpp:218] Iteration 15372 (2.25528 iter/s, 5.32084s/12 iters), loss = 5.16667
I0406 17:24:18.792068 21485 solver.cpp:237]     Train net output #0: loss = 5.16667 (* 1 = 5.16667 loss)
I0406 17:24:18.792074 21485 sgd_solver.cpp:105] Iteration 15372, lr = 0.05
I0406 17:24:19.918360 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:24:24.147509 21485 solver.cpp:218] Iteration 15384 (2.24072 iter/s, 5.35543s/12 iters), loss = 5.14192
I0406 17:24:24.147552 21485 solver.cpp:237]     Train net output #0: loss = 5.14192 (* 1 = 5.14192 loss)
I0406 17:24:24.147557 21485 sgd_solver.cpp:105] Iteration 15384, lr = 0.05
I0406 17:24:29.411226 21485 solver.cpp:218] Iteration 15396 (2.27978 iter/s, 5.26366s/12 iters), loss = 5.18342
I0406 17:24:29.411339 21485 solver.cpp:237]     Train net output #0: loss = 5.18342 (* 1 = 5.18342 loss)
I0406 17:24:29.411345 21485 sgd_solver.cpp:105] Iteration 15396, lr = 0.05
I0406 17:24:31.618971 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_15402.caffemodel
I0406 17:24:34.637642 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_15402.solverstate
I0406 17:24:36.951447 21485 solver.cpp:330] Iteration 15402, Testing net (#0)
I0406 17:24:36.951468 21485 net.cpp:676] Ignoring source layer train-data
I0406 17:24:39.950338 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:24:41.428436 21485 solver.cpp:397]     Test net output #0: accuracy = 0.00919118
I0406 17:24:41.428469 21485 solver.cpp:397]     Test net output #1: loss = 5.19744 (* 1 = 5.19744 loss)
I0406 17:24:43.341847 21485 solver.cpp:218] Iteration 15408 (0.86142 iter/s, 13.9305s/12 iters), loss = 5.19634
I0406 17:24:43.341907 21485 solver.cpp:237]     Train net output #0: loss = 5.19634 (* 1 = 5.19634 loss)
I0406 17:24:43.341914 21485 sgd_solver.cpp:105] Iteration 15408, lr = 0.05
I0406 17:24:48.559969 21485 solver.cpp:218] Iteration 15420 (2.29971 iter/s, 5.21805s/12 iters), loss = 5.15767
I0406 17:24:48.560009 21485 solver.cpp:237]     Train net output #0: loss = 5.15767 (* 1 = 5.15767 loss)
I0406 17:24:48.560014 21485 sgd_solver.cpp:105] Iteration 15420, lr = 0.05
I0406 17:24:53.917904 21485 solver.cpp:218] Iteration 15432 (2.23969 iter/s, 5.35788s/12 iters), loss = 5.21031
I0406 17:24:53.917950 21485 solver.cpp:237]     Train net output #0: loss = 5.21031 (* 1 = 5.21031 loss)
I0406 17:24:53.917956 21485 sgd_solver.cpp:105] Iteration 15432, lr = 0.05
I0406 17:24:59.315271 21485 solver.cpp:218] Iteration 15444 (2.22333 iter/s, 5.3973s/12 iters), loss = 5.08923
I0406 17:24:59.315320 21485 solver.cpp:237]     Train net output #0: loss = 5.08923 (* 1 = 5.08923 loss)
I0406 17:24:59.315325 21485 sgd_solver.cpp:105] Iteration 15444, lr = 0.05
I0406 17:25:04.471539 21485 solver.cpp:218] Iteration 15456 (2.32729 iter/s, 5.1562s/12 iters), loss = 5.08105
I0406 17:25:04.471675 21485 solver.cpp:237]     Train net output #0: loss = 5.08105 (* 1 = 5.08105 loss)
I0406 17:25:04.471684 21485 sgd_solver.cpp:105] Iteration 15456, lr = 0.05
I0406 17:25:09.741427 21485 solver.cpp:218] Iteration 15468 (2.27715 iter/s, 5.26974s/12 iters), loss = 5.10898
I0406 17:25:09.741466 21485 solver.cpp:237]     Train net output #0: loss = 5.10898 (* 1 = 5.10898 loss)
I0406 17:25:09.741472 21485 sgd_solver.cpp:105] Iteration 15468, lr = 0.05
I0406 17:25:12.962208 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:25:14.965795 21485 solver.cpp:218] Iteration 15480 (2.29695 iter/s, 5.22431s/12 iters), loss = 5.03151
I0406 17:25:14.965838 21485 solver.cpp:237]     Train net output #0: loss = 5.03151 (* 1 = 5.03151 loss)
I0406 17:25:14.965843 21485 sgd_solver.cpp:105] Iteration 15480, lr = 0.05
I0406 17:25:20.259707 21485 solver.cpp:218] Iteration 15492 (2.26678 iter/s, 5.29385s/12 iters), loss = 5.14517
I0406 17:25:20.259758 21485 solver.cpp:237]     Train net output #0: loss = 5.14517 (* 1 = 5.14517 loss)
I0406 17:25:20.259765 21485 sgd_solver.cpp:105] Iteration 15492, lr = 0.05
I0406 17:25:25.088660 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_15504.caffemodel
I0406 17:25:28.091581 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_15504.solverstate
I0406 17:25:30.389227 21485 solver.cpp:330] Iteration 15504, Testing net (#0)
I0406 17:25:30.389246 21485 net.cpp:676] Ignoring source layer train-data
I0406 17:25:33.219393 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:25:34.673596 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0159314
I0406 17:25:34.673745 21485 solver.cpp:397]     Test net output #1: loss = 5.15537 (* 1 = 5.15537 loss)
I0406 17:25:34.814162 21485 solver.cpp:218] Iteration 15504 (0.824493 iter/s, 14.5544s/12 iters), loss = 5.07336
I0406 17:25:34.814224 21485 solver.cpp:237]     Train net output #0: loss = 5.07336 (* 1 = 5.07336 loss)
I0406 17:25:34.814234 21485 sgd_solver.cpp:105] Iteration 15504, lr = 0.05
I0406 17:25:38.926919 21485 solver.cpp:218] Iteration 15516 (2.91781 iter/s, 4.11268s/12 iters), loss = 5.09219
I0406 17:25:38.926981 21485 solver.cpp:237]     Train net output #0: loss = 5.09219 (* 1 = 5.09219 loss)
I0406 17:25:38.926991 21485 sgd_solver.cpp:105] Iteration 15516, lr = 0.05
I0406 17:25:44.286540 21485 solver.cpp:218] Iteration 15528 (2.239 iter/s, 5.35954s/12 iters), loss = 5.10342
I0406 17:25:44.286581 21485 solver.cpp:237]     Train net output #0: loss = 5.10342 (* 1 = 5.10342 loss)
I0406 17:25:44.286586 21485 sgd_solver.cpp:105] Iteration 15528, lr = 0.05
I0406 17:25:49.446281 21485 solver.cpp:218] Iteration 15540 (2.32573 iter/s, 5.15968s/12 iters), loss = 5.05493
I0406 17:25:49.446338 21485 solver.cpp:237]     Train net output #0: loss = 5.05493 (* 1 = 5.05493 loss)
I0406 17:25:49.446346 21485 sgd_solver.cpp:105] Iteration 15540, lr = 0.05
I0406 17:25:54.588892 21485 solver.cpp:218] Iteration 15552 (2.33348 iter/s, 5.14254s/12 iters), loss = 5.09164
I0406 17:25:54.588938 21485 solver.cpp:237]     Train net output #0: loss = 5.09164 (* 1 = 5.09164 loss)
I0406 17:25:54.588944 21485 sgd_solver.cpp:105] Iteration 15552, lr = 0.05
I0406 17:25:59.668653 21485 solver.cpp:218] Iteration 15564 (2.36234 iter/s, 5.0797s/12 iters), loss = 5.12625
I0406 17:25:59.668689 21485 solver.cpp:237]     Train net output #0: loss = 5.12625 (* 1 = 5.12625 loss)
I0406 17:25:59.668694 21485 sgd_solver.cpp:105] Iteration 15564, lr = 0.05
I0406 17:26:04.778049 21485 solver.cpp:218] Iteration 15576 (2.34864 iter/s, 5.10935s/12 iters), loss = 5.1644
I0406 17:26:04.778151 21485 solver.cpp:237]     Train net output #0: loss = 5.1644 (* 1 = 5.1644 loss)
I0406 17:26:04.778158 21485 sgd_solver.cpp:105] Iteration 15576, lr = 0.05
I0406 17:26:05.232851 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:26:09.980943 21485 solver.cpp:218] Iteration 15588 (2.30646 iter/s, 5.20278s/12 iters), loss = 5.25433
I0406 17:26:09.980993 21485 solver.cpp:237]     Train net output #0: loss = 5.25433 (* 1 = 5.25433 loss)
I0406 17:26:09.980999 21485 sgd_solver.cpp:105] Iteration 15588, lr = 0.05
I0406 17:26:15.232774 21485 solver.cpp:218] Iteration 15600 (2.28495 iter/s, 5.25176s/12 iters), loss = 5.19906
I0406 17:26:15.232831 21485 solver.cpp:237]     Train net output #0: loss = 5.19906 (* 1 = 5.19906 loss)
I0406 17:26:15.232838 21485 sgd_solver.cpp:105] Iteration 15600, lr = 0.05
I0406 17:26:17.365622 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_15606.caffemodel
I0406 17:26:20.379755 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_15606.solverstate
I0406 17:26:22.681183 21485 solver.cpp:330] Iteration 15606, Testing net (#0)
I0406 17:26:22.681202 21485 net.cpp:676] Ignoring source layer train-data
I0406 17:26:25.537499 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:26:27.199676 21485 solver.cpp:397]     Test net output #0: accuracy = 0.00919118
I0406 17:26:27.199712 21485 solver.cpp:397]     Test net output #1: loss = 5.18272 (* 1 = 5.18272 loss)
I0406 17:26:29.097564 21485 solver.cpp:218] Iteration 15612 (0.865506 iter/s, 13.8647s/12 iters), loss = 5.17301
I0406 17:26:29.097605 21485 solver.cpp:237]     Train net output #0: loss = 5.17301 (* 1 = 5.17301 loss)
I0406 17:26:29.097610 21485 sgd_solver.cpp:105] Iteration 15612, lr = 0.05
I0406 17:26:34.160276 21485 solver.cpp:218] Iteration 15624 (2.3703 iter/s, 5.06265s/12 iters), loss = 5.09258
I0406 17:26:34.160327 21485 solver.cpp:237]     Train net output #0: loss = 5.09258 (* 1 = 5.09258 loss)
I0406 17:26:34.160334 21485 sgd_solver.cpp:105] Iteration 15624, lr = 0.05
I0406 17:26:39.486824 21485 solver.cpp:218] Iteration 15636 (2.2529 iter/s, 5.32648s/12 iters), loss = 5.04478
I0406 17:26:39.486986 21485 solver.cpp:237]     Train net output #0: loss = 5.04478 (* 1 = 5.04478 loss)
I0406 17:26:39.486996 21485 sgd_solver.cpp:105] Iteration 15636, lr = 0.05
I0406 17:26:44.882448 21485 solver.cpp:218] Iteration 15648 (2.2241 iter/s, 5.39545s/12 iters), loss = 5.05624
I0406 17:26:44.882491 21485 solver.cpp:237]     Train net output #0: loss = 5.05624 (* 1 = 5.05624 loss)
I0406 17:26:44.882498 21485 sgd_solver.cpp:105] Iteration 15648, lr = 0.05
I0406 17:26:50.135708 21485 solver.cpp:218] Iteration 15660 (2.28432 iter/s, 5.2532s/12 iters), loss = 5.18527
I0406 17:26:50.135758 21485 solver.cpp:237]     Train net output #0: loss = 5.18527 (* 1 = 5.18527 loss)
I0406 17:26:50.135766 21485 sgd_solver.cpp:105] Iteration 15660, lr = 0.05
I0406 17:26:55.393705 21485 solver.cpp:218] Iteration 15672 (2.28226 iter/s, 5.25793s/12 iters), loss = 5.09511
I0406 17:26:55.393746 21485 solver.cpp:237]     Train net output #0: loss = 5.09511 (* 1 = 5.09511 loss)
I0406 17:26:55.393752 21485 sgd_solver.cpp:105] Iteration 15672, lr = 0.05
I0406 17:26:58.269677 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:27:00.882285 21485 solver.cpp:218] Iteration 15684 (2.18638 iter/s, 5.48852s/12 iters), loss = 5.09714
I0406 17:27:00.882347 21485 solver.cpp:237]     Train net output #0: loss = 5.09714 (* 1 = 5.09714 loss)
I0406 17:27:00.882355 21485 sgd_solver.cpp:105] Iteration 15684, lr = 0.05
I0406 17:27:06.230532 21485 solver.cpp:218] Iteration 15696 (2.24376 iter/s, 5.34818s/12 iters), loss = 5.04675
I0406 17:27:06.230589 21485 solver.cpp:237]     Train net output #0: loss = 5.04675 (* 1 = 5.04675 loss)
I0406 17:27:06.230597 21485 sgd_solver.cpp:105] Iteration 15696, lr = 0.05
I0406 17:27:11.027016 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_15708.caffemodel
I0406 17:27:14.064031 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_15708.solverstate
I0406 17:27:16.371008 21485 solver.cpp:330] Iteration 15708, Testing net (#0)
I0406 17:27:16.371028 21485 net.cpp:676] Ignoring source layer train-data
I0406 17:27:19.118818 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:27:20.649590 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0171569
I0406 17:27:20.649624 21485 solver.cpp:397]     Test net output #1: loss = 5.14063 (* 1 = 5.14063 loss)
I0406 17:27:20.785492 21485 solver.cpp:218] Iteration 15708 (0.824465 iter/s, 14.5549s/12 iters), loss = 5.10439
I0406 17:27:20.785544 21485 solver.cpp:237]     Train net output #0: loss = 5.10439 (* 1 = 5.10439 loss)
I0406 17:27:20.785552 21485 sgd_solver.cpp:105] Iteration 15708, lr = 0.05
I0406 17:27:25.133806 21485 solver.cpp:218] Iteration 15720 (2.75973 iter/s, 4.34825s/12 iters), loss = 5.10351
I0406 17:27:25.133849 21485 solver.cpp:237]     Train net output #0: loss = 5.10351 (* 1 = 5.10351 loss)
I0406 17:27:25.133855 21485 sgd_solver.cpp:105] Iteration 15720, lr = 0.05
I0406 17:27:30.337831 21485 solver.cpp:218] Iteration 15732 (2.30594 iter/s, 5.20396s/12 iters), loss = 5.12296
I0406 17:27:30.337880 21485 solver.cpp:237]     Train net output #0: loss = 5.12296 (* 1 = 5.12296 loss)
I0406 17:27:30.337888 21485 sgd_solver.cpp:105] Iteration 15732, lr = 0.05
I0406 17:27:35.692473 21485 solver.cpp:218] Iteration 15744 (2.24107 iter/s, 5.35458s/12 iters), loss = 5.12242
I0406 17:27:35.692515 21485 solver.cpp:237]     Train net output #0: loss = 5.12242 (* 1 = 5.12242 loss)
I0406 17:27:35.692520 21485 sgd_solver.cpp:105] Iteration 15744, lr = 0.05
I0406 17:27:40.856117 21485 solver.cpp:218] Iteration 15756 (2.32397 iter/s, 5.16358s/12 iters), loss = 5.0712
I0406 17:27:40.856173 21485 solver.cpp:237]     Train net output #0: loss = 5.0712 (* 1 = 5.0712 loss)
I0406 17:27:40.856182 21485 sgd_solver.cpp:105] Iteration 15756, lr = 0.05
I0406 17:27:45.860739 21485 solver.cpp:218] Iteration 15768 (2.39782 iter/s, 5.00455s/12 iters), loss = 4.99995
I0406 17:27:45.860918 21485 solver.cpp:237]     Train net output #0: loss = 4.99995 (* 1 = 4.99995 loss)
I0406 17:27:45.860927 21485 sgd_solver.cpp:105] Iteration 15768, lr = 0.05
I0406 17:27:50.682297 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:27:51.008824 21485 solver.cpp:218] Iteration 15780 (2.33105 iter/s, 5.14789s/12 iters), loss = 5.10676
I0406 17:27:51.008895 21485 solver.cpp:237]     Train net output #0: loss = 5.10676 (* 1 = 5.10676 loss)
I0406 17:27:51.008904 21485 sgd_solver.cpp:105] Iteration 15780, lr = 0.05
I0406 17:27:56.305234 21485 solver.cpp:218] Iteration 15792 (2.26572 iter/s, 5.29634s/12 iters), loss = 4.93683
I0406 17:27:56.305269 21485 solver.cpp:237]     Train net output #0: loss = 4.93683 (* 1 = 4.93683 loss)
I0406 17:27:56.305274 21485 sgd_solver.cpp:105] Iteration 15792, lr = 0.05
I0406 17:28:01.492702 21485 solver.cpp:218] Iteration 15804 (2.31329 iter/s, 5.18742s/12 iters), loss = 4.95856
I0406 17:28:01.492748 21485 solver.cpp:237]     Train net output #0: loss = 4.95856 (* 1 = 4.95856 loss)
I0406 17:28:01.492753 21485 sgd_solver.cpp:105] Iteration 15804, lr = 0.05
I0406 17:28:03.600102 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_15810.caffemodel
I0406 17:28:06.616323 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_15810.solverstate
I0406 17:28:08.918510 21485 solver.cpp:330] Iteration 15810, Testing net (#0)
I0406 17:28:08.918534 21485 net.cpp:676] Ignoring source layer train-data
I0406 17:28:11.650918 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:28:13.210798 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0171569
I0406 17:28:13.210834 21485 solver.cpp:397]     Test net output #1: loss = 5.15953 (* 1 = 5.15953 loss)
I0406 17:28:14.986460 21485 solver.cpp:218] Iteration 15816 (0.889304 iter/s, 13.4937s/12 iters), loss = 5.10931
I0406 17:28:14.986521 21485 solver.cpp:237]     Train net output #0: loss = 5.10931 (* 1 = 5.10931 loss)
I0406 17:28:14.986528 21485 sgd_solver.cpp:105] Iteration 15816, lr = 0.05
I0406 17:28:20.116957 21485 solver.cpp:218] Iteration 15828 (2.33899 iter/s, 5.13042s/12 iters), loss = 5.12053
I0406 17:28:20.117051 21485 solver.cpp:237]     Train net output #0: loss = 5.12053 (* 1 = 5.12053 loss)
I0406 17:28:20.117058 21485 sgd_solver.cpp:105] Iteration 15828, lr = 0.05
I0406 17:28:25.477984 21485 solver.cpp:218] Iteration 15840 (2.23842 iter/s, 5.36092s/12 iters), loss = 5.06109
I0406 17:28:25.478029 21485 solver.cpp:237]     Train net output #0: loss = 5.06109 (* 1 = 5.06109 loss)
I0406 17:28:25.478034 21485 sgd_solver.cpp:105] Iteration 15840, lr = 0.05
I0406 17:28:30.680212 21485 solver.cpp:218] Iteration 15852 (2.30673 iter/s, 5.20217s/12 iters), loss = 4.9689
I0406 17:28:30.680255 21485 solver.cpp:237]     Train net output #0: loss = 4.9689 (* 1 = 4.9689 loss)
I0406 17:28:30.680260 21485 sgd_solver.cpp:105] Iteration 15852, lr = 0.05
I0406 17:28:35.616259 21485 solver.cpp:218] Iteration 15864 (2.43112 iter/s, 4.93599s/12 iters), loss = 4.88771
I0406 17:28:35.616303 21485 solver.cpp:237]     Train net output #0: loss = 4.88771 (* 1 = 4.88771 loss)
I0406 17:28:35.616309 21485 sgd_solver.cpp:105] Iteration 15864, lr = 0.05
I0406 17:28:40.707249 21485 solver.cpp:218] Iteration 15876 (2.35713 iter/s, 5.09094s/12 iters), loss = 4.9932
I0406 17:28:40.707286 21485 solver.cpp:237]     Train net output #0: loss = 4.9932 (* 1 = 4.9932 loss)
I0406 17:28:40.707293 21485 sgd_solver.cpp:105] Iteration 15876, lr = 0.05
I0406 17:28:42.649055 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:28:46.069715 21485 solver.cpp:218] Iteration 15888 (2.23781 iter/s, 5.36239s/12 iters), loss = 5.05881
I0406 17:28:46.069769 21485 solver.cpp:237]     Train net output #0: loss = 5.05881 (* 1 = 5.05881 loss)
I0406 17:28:46.069777 21485 sgd_solver.cpp:105] Iteration 15888, lr = 0.05
I0406 17:28:51.185014 21485 solver.cpp:218] Iteration 15900 (2.34594 iter/s, 5.11523s/12 iters), loss = 5.1399
I0406 17:28:51.185133 21485 solver.cpp:237]     Train net output #0: loss = 5.1399 (* 1 = 5.1399 loss)
I0406 17:28:51.185139 21485 sgd_solver.cpp:105] Iteration 15900, lr = 0.05
I0406 17:28:55.932638 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_15912.caffemodel
I0406 17:28:58.956626 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_15912.solverstate
I0406 17:29:01.302290 21485 solver.cpp:330] Iteration 15912, Testing net (#0)
I0406 17:29:01.302322 21485 net.cpp:676] Ignoring source layer train-data
I0406 17:29:04.086308 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:29:05.716559 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0189951
I0406 17:29:05.716591 21485 solver.cpp:397]     Test net output #1: loss = 5.13077 (* 1 = 5.13077 loss)
I0406 17:29:05.851001 21485 solver.cpp:218] Iteration 15912 (0.818227 iter/s, 14.6659s/12 iters), loss = 5.00745
I0406 17:29:05.851058 21485 solver.cpp:237]     Train net output #0: loss = 5.00745 (* 1 = 5.00745 loss)
I0406 17:29:05.851063 21485 sgd_solver.cpp:105] Iteration 15912, lr = 0.05
I0406 17:29:10.213601 21485 solver.cpp:218] Iteration 15924 (2.7507 iter/s, 4.36252s/12 iters), loss = 5.02831
I0406 17:29:10.213654 21485 solver.cpp:237]     Train net output #0: loss = 5.02831 (* 1 = 5.02831 loss)
I0406 17:29:10.213661 21485 sgd_solver.cpp:105] Iteration 15924, lr = 0.05
I0406 17:29:15.420365 21485 solver.cpp:218] Iteration 15936 (2.30473 iter/s, 5.20669s/12 iters), loss = 5.05322
I0406 17:29:15.420413 21485 solver.cpp:237]     Train net output #0: loss = 5.05322 (* 1 = 5.05322 loss)
I0406 17:29:15.420419 21485 sgd_solver.cpp:105] Iteration 15936, lr = 0.05
I0406 17:29:15.420617 21485 blocking_queue.cpp:49] Waiting for data
I0406 17:29:20.630936 21485 solver.cpp:218] Iteration 15948 (2.30304 iter/s, 5.21051s/12 iters), loss = 5.13739
I0406 17:29:20.630968 21485 solver.cpp:237]     Train net output #0: loss = 5.13739 (* 1 = 5.13739 loss)
I0406 17:29:20.630975 21485 sgd_solver.cpp:105] Iteration 15948, lr = 0.05
I0406 17:29:25.787818 21485 solver.cpp:218] Iteration 15960 (2.32701 iter/s, 5.15683s/12 iters), loss = 5.03938
I0406 17:29:25.787904 21485 solver.cpp:237]     Train net output #0: loss = 5.03938 (* 1 = 5.03938 loss)
I0406 17:29:25.787910 21485 sgd_solver.cpp:105] Iteration 15960, lr = 0.05
I0406 17:29:31.243489 21485 solver.cpp:218] Iteration 15972 (2.19959 iter/s, 5.45557s/12 iters), loss = 4.9579
I0406 17:29:31.243541 21485 solver.cpp:237]     Train net output #0: loss = 4.9579 (* 1 = 4.9579 loss)
I0406 17:29:31.243549 21485 sgd_solver.cpp:105] Iteration 15972, lr = 0.05
I0406 17:29:35.556082 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:29:36.715979 21485 solver.cpp:218] Iteration 15984 (2.19281 iter/s, 5.47243s/12 iters), loss = 5.07212
I0406 17:29:36.716017 21485 solver.cpp:237]     Train net output #0: loss = 5.07212 (* 1 = 5.07212 loss)
I0406 17:29:36.716022 21485 sgd_solver.cpp:105] Iteration 15984, lr = 0.05
I0406 17:29:41.665776 21485 solver.cpp:218] Iteration 15996 (2.42437 iter/s, 4.94974s/12 iters), loss = 5.03598
I0406 17:29:41.665822 21485 solver.cpp:237]     Train net output #0: loss = 5.03598 (* 1 = 5.03598 loss)
I0406 17:29:41.665827 21485 sgd_solver.cpp:105] Iteration 15996, lr = 0.05
I0406 17:29:46.957340 21485 solver.cpp:218] Iteration 16008 (2.26779 iter/s, 5.2915s/12 iters), loss = 5.05944
I0406 17:29:46.957389 21485 solver.cpp:237]     Train net output #0: loss = 5.05944 (* 1 = 5.05944 loss)
I0406 17:29:46.957394 21485 sgd_solver.cpp:105] Iteration 16008, lr = 0.05
I0406 17:29:49.101680 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_16014.caffemodel
I0406 17:29:53.448664 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_16014.solverstate
I0406 17:29:55.756474 21485 solver.cpp:330] Iteration 16014, Testing net (#0)
I0406 17:29:55.756494 21485 net.cpp:676] Ignoring source layer train-data
I0406 17:29:58.497830 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:30:00.189393 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0165441
I0406 17:30:00.189430 21485 solver.cpp:397]     Test net output #1: loss = 5.14508 (* 1 = 5.14508 loss)
I0406 17:30:02.203693 21485 solver.cpp:218] Iteration 16020 (0.787077 iter/s, 15.2463s/12 iters), loss = 5.02883
I0406 17:30:02.203743 21485 solver.cpp:237]     Train net output #0: loss = 5.02883 (* 1 = 5.02883 loss)
I0406 17:30:02.203752 21485 sgd_solver.cpp:105] Iteration 16020, lr = 0.05
I0406 17:30:07.459122 21485 solver.cpp:218] Iteration 16032 (2.28338 iter/s, 5.25537s/12 iters), loss = 5.1028
I0406 17:30:07.459162 21485 solver.cpp:237]     Train net output #0: loss = 5.1028 (* 1 = 5.1028 loss)
I0406 17:30:07.459167 21485 sgd_solver.cpp:105] Iteration 16032, lr = 0.05
I0406 17:30:12.853231 21485 solver.cpp:218] Iteration 16044 (2.22467 iter/s, 5.39406s/12 iters), loss = 5.0351
I0406 17:30:12.853271 21485 solver.cpp:237]     Train net output #0: loss = 5.0351 (* 1 = 5.0351 loss)
I0406 17:30:12.853276 21485 sgd_solver.cpp:105] Iteration 16044, lr = 0.05
I0406 17:30:18.069092 21485 solver.cpp:218] Iteration 16056 (2.3007 iter/s, 5.2158s/12 iters), loss = 5.05586
I0406 17:30:18.069140 21485 solver.cpp:237]     Train net output #0: loss = 5.05586 (* 1 = 5.05586 loss)
I0406 17:30:18.069145 21485 sgd_solver.cpp:105] Iteration 16056, lr = 0.05
I0406 17:30:23.389267 21485 solver.cpp:218] Iteration 16068 (2.25559 iter/s, 5.32012s/12 iters), loss = 5.09418
I0406 17:30:23.389304 21485 solver.cpp:237]     Train net output #0: loss = 5.09418 (* 1 = 5.09418 loss)
I0406 17:30:23.389308 21485 sgd_solver.cpp:105] Iteration 16068, lr = 0.05
I0406 17:30:28.371309 21485 solver.cpp:218] Iteration 16080 (2.40867 iter/s, 4.98199s/12 iters), loss = 5.17043
I0406 17:30:28.371349 21485 solver.cpp:237]     Train net output #0: loss = 5.17043 (* 1 = 5.17043 loss)
I0406 17:30:28.371356 21485 sgd_solver.cpp:105] Iteration 16080, lr = 0.05
I0406 17:30:29.524327 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:30:33.682215 21485 solver.cpp:218] Iteration 16092 (2.25953 iter/s, 5.31085s/12 iters), loss = 5.1729
I0406 17:30:33.682265 21485 solver.cpp:237]     Train net output #0: loss = 5.1729 (* 1 = 5.1729 loss)
I0406 17:30:33.682271 21485 sgd_solver.cpp:105] Iteration 16092, lr = 0.05
I0406 17:30:38.980376 21485 solver.cpp:218] Iteration 16104 (2.26496 iter/s, 5.2981s/12 iters), loss = 5.17427
I0406 17:30:38.980415 21485 solver.cpp:237]     Train net output #0: loss = 5.17427 (* 1 = 5.17427 loss)
I0406 17:30:38.980420 21485 sgd_solver.cpp:105] Iteration 16104, lr = 0.05
I0406 17:30:43.611055 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_16116.caffemodel
I0406 17:30:47.124500 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_16116.solverstate
I0406 17:30:50.860455 21485 solver.cpp:330] Iteration 16116, Testing net (#0)
I0406 17:30:50.860478 21485 net.cpp:676] Ignoring source layer train-data
I0406 17:30:53.472960 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:30:55.316728 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0134804
I0406 17:30:55.316776 21485 solver.cpp:397]     Test net output #1: loss = 5.18279 (* 1 = 5.18279 loss)
I0406 17:30:55.457228 21485 solver.cpp:218] Iteration 16116 (0.728297 iter/s, 16.4768s/12 iters), loss = 5.16712
I0406 17:30:55.457269 21485 solver.cpp:237]     Train net output #0: loss = 5.16712 (* 1 = 5.16712 loss)
I0406 17:30:55.457274 21485 sgd_solver.cpp:105] Iteration 16116, lr = 0.05
I0406 17:30:59.626418 21485 solver.cpp:218] Iteration 16128 (2.87829 iter/s, 4.16914s/12 iters), loss = 5.10644
I0406 17:30:59.626500 21485 solver.cpp:237]     Train net output #0: loss = 5.10644 (* 1 = 5.10644 loss)
I0406 17:30:59.626507 21485 sgd_solver.cpp:105] Iteration 16128, lr = 0.05
I0406 17:31:04.728478 21485 solver.cpp:218] Iteration 16140 (2.35204 iter/s, 5.10196s/12 iters), loss = 5.16322
I0406 17:31:04.728523 21485 solver.cpp:237]     Train net output #0: loss = 5.16322 (* 1 = 5.16322 loss)
I0406 17:31:04.728531 21485 sgd_solver.cpp:105] Iteration 16140, lr = 0.05
I0406 17:31:10.101706 21485 solver.cpp:218] Iteration 16152 (2.23332 iter/s, 5.37317s/12 iters), loss = 5.14843
I0406 17:31:10.101765 21485 solver.cpp:237]     Train net output #0: loss = 5.14843 (* 1 = 5.14843 loss)
I0406 17:31:10.101774 21485 sgd_solver.cpp:105] Iteration 16152, lr = 0.05
I0406 17:31:15.428180 21485 solver.cpp:218] Iteration 16164 (2.25293 iter/s, 5.3264s/12 iters), loss = 5.04834
I0406 17:31:15.428231 21485 solver.cpp:237]     Train net output #0: loss = 5.04834 (* 1 = 5.04834 loss)
I0406 17:31:15.428241 21485 sgd_solver.cpp:105] Iteration 16164, lr = 0.05
I0406 17:31:20.699502 21485 solver.cpp:218] Iteration 16176 (2.2765 iter/s, 5.27126s/12 iters), loss = 5.07289
I0406 17:31:20.699545 21485 solver.cpp:237]     Train net output #0: loss = 5.07289 (* 1 = 5.07289 loss)
I0406 17:31:20.699550 21485 sgd_solver.cpp:105] Iteration 16176, lr = 0.05
I0406 17:31:24.138588 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:31:26.089952 21485 solver.cpp:218] Iteration 16188 (2.22618 iter/s, 5.39039s/12 iters), loss = 5.03932
I0406 17:31:26.089998 21485 solver.cpp:237]     Train net output #0: loss = 5.03932 (* 1 = 5.03932 loss)
I0406 17:31:26.090003 21485 sgd_solver.cpp:105] Iteration 16188, lr = 0.05
I0406 17:31:31.280767 21485 solver.cpp:218] Iteration 16200 (2.3118 iter/s, 5.19075s/12 iters), loss = 5.07396
I0406 17:31:31.280903 21485 solver.cpp:237]     Train net output #0: loss = 5.07396 (* 1 = 5.07396 loss)
I0406 17:31:31.280912 21485 sgd_solver.cpp:105] Iteration 16200, lr = 0.05
I0406 17:31:36.532546 21485 solver.cpp:218] Iteration 16212 (2.285 iter/s, 5.25164s/12 iters), loss = 5.083
I0406 17:31:36.532583 21485 solver.cpp:237]     Train net output #0: loss = 5.083 (* 1 = 5.083 loss)
I0406 17:31:36.532588 21485 sgd_solver.cpp:105] Iteration 16212, lr = 0.05
I0406 17:31:38.645546 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_16218.caffemodel
I0406 17:31:41.684965 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_16218.solverstate
I0406 17:31:44.031440 21485 solver.cpp:330] Iteration 16218, Testing net (#0)
I0406 17:31:44.031462 21485 net.cpp:676] Ignoring source layer train-data
I0406 17:31:46.663714 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:31:48.471123 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0134804
I0406 17:31:48.471267 21485 solver.cpp:397]     Test net output #1: loss = 5.14609 (* 1 = 5.14609 loss)
I0406 17:31:50.226874 21485 solver.cpp:218] Iteration 16224 (0.876279 iter/s, 13.6943s/12 iters), loss = 5.18444
I0406 17:31:50.226922 21485 solver.cpp:237]     Train net output #0: loss = 5.18444 (* 1 = 5.18444 loss)
I0406 17:31:50.226927 21485 sgd_solver.cpp:105] Iteration 16224, lr = 0.05
I0406 17:31:55.437844 21485 solver.cpp:218] Iteration 16236 (2.30286 iter/s, 5.21091s/12 iters), loss = 5.14866
I0406 17:31:55.437891 21485 solver.cpp:237]     Train net output #0: loss = 5.14866 (* 1 = 5.14866 loss)
I0406 17:31:55.437896 21485 sgd_solver.cpp:105] Iteration 16236, lr = 0.05
I0406 17:32:00.751631 21485 solver.cpp:218] Iteration 16248 (2.2583 iter/s, 5.31372s/12 iters), loss = 5.03778
I0406 17:32:00.751677 21485 solver.cpp:237]     Train net output #0: loss = 5.03778 (* 1 = 5.03778 loss)
I0406 17:32:00.751682 21485 sgd_solver.cpp:105] Iteration 16248, lr = 0.05
I0406 17:32:05.866832 21485 solver.cpp:218] Iteration 16260 (2.34598 iter/s, 5.11514s/12 iters), loss = 5.08744
I0406 17:32:05.866953 21485 solver.cpp:237]     Train net output #0: loss = 5.08744 (* 1 = 5.08744 loss)
I0406 17:32:05.866961 21485 sgd_solver.cpp:105] Iteration 16260, lr = 0.05
I0406 17:32:11.120810 21485 solver.cpp:218] Iteration 16272 (2.28404 iter/s, 5.25384s/12 iters), loss = 5.09164
I0406 17:32:11.120864 21485 solver.cpp:237]     Train net output #0: loss = 5.09164 (* 1 = 5.09164 loss)
I0406 17:32:11.120872 21485 sgd_solver.cpp:105] Iteration 16272, lr = 0.05
I0406 17:32:16.482379 21485 solver.cpp:218] Iteration 16284 (2.23818 iter/s, 5.3615s/12 iters), loss = 5.09128
I0406 17:32:16.482419 21485 solver.cpp:237]     Train net output #0: loss = 5.09128 (* 1 = 5.09128 loss)
I0406 17:32:16.482424 21485 sgd_solver.cpp:105] Iteration 16284, lr = 0.05
I0406 17:32:16.980851 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:32:21.835769 21485 solver.cpp:218] Iteration 16296 (2.24159 iter/s, 5.35334s/12 iters), loss = 5.06933
I0406 17:32:21.835808 21485 solver.cpp:237]     Train net output #0: loss = 5.06933 (* 1 = 5.06933 loss)
I0406 17:32:21.835814 21485 sgd_solver.cpp:105] Iteration 16296, lr = 0.05
I0406 17:32:26.731418 21485 solver.cpp:218] Iteration 16308 (2.45118 iter/s, 4.89559s/12 iters), loss = 5.08045
I0406 17:32:26.731459 21485 solver.cpp:237]     Train net output #0: loss = 5.08045 (* 1 = 5.08045 loss)
I0406 17:32:26.731465 21485 sgd_solver.cpp:105] Iteration 16308, lr = 0.05
I0406 17:32:31.581310 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_16320.caffemodel
I0406 17:32:34.622479 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_16320.solverstate
I0406 17:32:36.963886 21485 solver.cpp:330] Iteration 16320, Testing net (#0)
I0406 17:32:36.963990 21485 net.cpp:676] Ignoring source layer train-data
I0406 17:32:39.529980 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:32:41.307919 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0134804
I0406 17:32:41.307953 21485 solver.cpp:397]     Test net output #1: loss = 5.13718 (* 1 = 5.13718 loss)
I0406 17:32:41.444401 21485 solver.cpp:218] Iteration 16320 (0.815609 iter/s, 14.7129s/12 iters), loss = 5.07718
I0406 17:32:41.444442 21485 solver.cpp:237]     Train net output #0: loss = 5.07718 (* 1 = 5.07718 loss)
I0406 17:32:41.444449 21485 sgd_solver.cpp:105] Iteration 16320, lr = 0.05
I0406 17:32:45.695835 21485 solver.cpp:218] Iteration 16332 (2.82262 iter/s, 4.25137s/12 iters), loss = 5.06192
I0406 17:32:45.695888 21485 solver.cpp:237]     Train net output #0: loss = 5.06192 (* 1 = 5.06192 loss)
I0406 17:32:45.695899 21485 sgd_solver.cpp:105] Iteration 16332, lr = 0.05
I0406 17:32:50.860731 21485 solver.cpp:218] Iteration 16344 (2.32341 iter/s, 5.16483s/12 iters), loss = 4.95115
I0406 17:32:50.860772 21485 solver.cpp:237]     Train net output #0: loss = 4.95115 (* 1 = 4.95115 loss)
I0406 17:32:50.860778 21485 sgd_solver.cpp:105] Iteration 16344, lr = 0.05
I0406 17:32:56.038095 21485 solver.cpp:218] Iteration 16356 (2.31781 iter/s, 5.17731s/12 iters), loss = 5.0721
I0406 17:32:56.038136 21485 solver.cpp:237]     Train net output #0: loss = 5.0721 (* 1 = 5.0721 loss)
I0406 17:32:56.038143 21485 sgd_solver.cpp:105] Iteration 16356, lr = 0.05
I0406 17:33:01.518240 21485 solver.cpp:218] Iteration 16368 (2.18975 iter/s, 5.48009s/12 iters), loss = 5.13177
I0406 17:33:01.518290 21485 solver.cpp:237]     Train net output #0: loss = 5.13177 (* 1 = 5.13177 loss)
I0406 17:33:01.518297 21485 sgd_solver.cpp:105] Iteration 16368, lr = 0.05
I0406 17:33:06.865584 21485 solver.cpp:218] Iteration 16380 (2.24413 iter/s, 5.34728s/12 iters), loss = 5.04876
I0406 17:33:06.865630 21485 solver.cpp:237]     Train net output #0: loss = 5.04876 (* 1 = 5.04876 loss)
I0406 17:33:06.865636 21485 sgd_solver.cpp:105] Iteration 16380, lr = 0.05
I0406 17:33:09.597965 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:33:12.171875 21485 solver.cpp:218] Iteration 16392 (2.26149 iter/s, 5.30623s/12 iters), loss = 5.14961
I0406 17:33:12.171928 21485 solver.cpp:237]     Train net output #0: loss = 5.14961 (* 1 = 5.14961 loss)
I0406 17:33:12.171936 21485 sgd_solver.cpp:105] Iteration 16392, lr = 0.05
I0406 17:33:17.514369 21485 solver.cpp:218] Iteration 16404 (2.24617 iter/s, 5.34243s/12 iters), loss = 5.04328
I0406 17:33:17.514406 21485 solver.cpp:237]     Train net output #0: loss = 5.04328 (* 1 = 5.04328 loss)
I0406 17:33:17.514412 21485 sgd_solver.cpp:105] Iteration 16404, lr = 0.05
I0406 17:33:22.721576 21485 solver.cpp:218] Iteration 16416 (2.30452 iter/s, 5.20715s/12 iters), loss = 5.06917
I0406 17:33:22.727845 21485 solver.cpp:237]     Train net output #0: loss = 5.06917 (* 1 = 5.06917 loss)
I0406 17:33:22.727865 21485 sgd_solver.cpp:105] Iteration 16416, lr = 0.05
I0406 17:33:24.931646 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_16422.caffemodel
I0406 17:33:27.940995 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_16422.solverstate
I0406 17:33:30.244643 21485 solver.cpp:330] Iteration 16422, Testing net (#0)
I0406 17:33:30.244663 21485 net.cpp:676] Ignoring source layer train-data
I0406 17:33:32.754575 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:33:34.553860 21485 solver.cpp:397]     Test net output #0: accuracy = 0.00980392
I0406 17:33:34.553898 21485 solver.cpp:397]     Test net output #1: loss = 5.13795 (* 1 = 5.13795 loss)
I0406 17:33:36.444173 21485 solver.cpp:218] Iteration 16428 (0.874869 iter/s, 13.7163s/12 iters), loss = 5.08049
I0406 17:33:36.444213 21485 solver.cpp:237]     Train net output #0: loss = 5.08049 (* 1 = 5.08049 loss)
I0406 17:33:36.444218 21485 sgd_solver.cpp:105] Iteration 16428, lr = 0.05
I0406 17:33:41.588081 21485 solver.cpp:218] Iteration 16440 (2.33288 iter/s, 5.14385s/12 iters), loss = 5.06965
I0406 17:33:41.588238 21485 solver.cpp:237]     Train net output #0: loss = 5.06965 (* 1 = 5.06965 loss)
I0406 17:33:41.588245 21485 sgd_solver.cpp:105] Iteration 16440, lr = 0.05
I0406 17:33:46.545116 21485 solver.cpp:218] Iteration 16452 (2.42088 iter/s, 4.95687s/12 iters), loss = 5.09556
I0406 17:33:46.545161 21485 solver.cpp:237]     Train net output #0: loss = 5.09556 (* 1 = 5.09556 loss)
I0406 17:33:46.545166 21485 sgd_solver.cpp:105] Iteration 16452, lr = 0.05
I0406 17:33:51.682260 21485 solver.cpp:218] Iteration 16464 (2.33595 iter/s, 5.13709s/12 iters), loss = 5.23212
I0406 17:33:51.682296 21485 solver.cpp:237]     Train net output #0: loss = 5.23212 (* 1 = 5.23212 loss)
I0406 17:33:51.682301 21485 sgd_solver.cpp:105] Iteration 16464, lr = 0.05
I0406 17:33:56.718413 21485 solver.cpp:218] Iteration 16476 (2.3828 iter/s, 5.0361s/12 iters), loss = 5.16638
I0406 17:33:56.718467 21485 solver.cpp:237]     Train net output #0: loss = 5.16638 (* 1 = 5.16638 loss)
I0406 17:33:56.718477 21485 sgd_solver.cpp:105] Iteration 16476, lr = 0.05
I0406 17:34:01.790272 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:34:02.094910 21485 solver.cpp:218] Iteration 16488 (2.23196 iter/s, 5.37643s/12 iters), loss = 5.1567
I0406 17:34:02.094959 21485 solver.cpp:237]     Train net output #0: loss = 5.1567 (* 1 = 5.1567 loss)
I0406 17:34:02.094964 21485 sgd_solver.cpp:105] Iteration 16488, lr = 0.05
I0406 17:34:07.248577 21485 solver.cpp:218] Iteration 16500 (2.32847 iter/s, 5.15361s/12 iters), loss = 5.01657
I0406 17:34:07.248616 21485 solver.cpp:237]     Train net output #0: loss = 5.01657 (* 1 = 5.01657 loss)
I0406 17:34:07.248622 21485 sgd_solver.cpp:105] Iteration 16500, lr = 0.05
I0406 17:34:12.521414 21485 solver.cpp:218] Iteration 16512 (2.27584 iter/s, 5.27278s/12 iters), loss = 4.98507
I0406 17:34:12.521553 21485 solver.cpp:237]     Train net output #0: loss = 4.98507 (* 1 = 4.98507 loss)
I0406 17:34:12.521562 21485 sgd_solver.cpp:105] Iteration 16512, lr = 0.05
I0406 17:34:17.300161 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_16524.caffemodel
I0406 17:34:20.319952 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_16524.solverstate
I0406 17:34:22.615242 21485 solver.cpp:330] Iteration 16524, Testing net (#0)
I0406 17:34:22.615262 21485 net.cpp:676] Ignoring source layer train-data
I0406 17:34:25.063359 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:34:26.964419 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0171569
I0406 17:34:26.964459 21485 solver.cpp:397]     Test net output #1: loss = 5.12866 (* 1 = 5.12866 loss)
I0406 17:34:27.105046 21485 solver.cpp:218] Iteration 16524 (0.822848 iter/s, 14.5835s/12 iters), loss = 5.03724
I0406 17:34:27.105091 21485 solver.cpp:237]     Train net output #0: loss = 5.03724 (* 1 = 5.03724 loss)
I0406 17:34:27.105098 21485 sgd_solver.cpp:105] Iteration 16524, lr = 0.05
I0406 17:34:31.327445 21485 solver.cpp:218] Iteration 16536 (2.84203 iter/s, 4.22233s/12 iters), loss = 5.07947
I0406 17:34:31.327500 21485 solver.cpp:237]     Train net output #0: loss = 5.07947 (* 1 = 5.07947 loss)
I0406 17:34:31.327508 21485 sgd_solver.cpp:105] Iteration 16536, lr = 0.05
I0406 17:34:36.534888 21485 solver.cpp:218] Iteration 16548 (2.30442 iter/s, 5.20739s/12 iters), loss = 5.06497
I0406 17:34:36.534924 21485 solver.cpp:237]     Train net output #0: loss = 5.06497 (* 1 = 5.06497 loss)
I0406 17:34:36.534929 21485 sgd_solver.cpp:105] Iteration 16548, lr = 0.05
I0406 17:34:41.697368 21485 solver.cpp:218] Iteration 16560 (2.32449 iter/s, 5.16243s/12 iters), loss = 5.08326
I0406 17:34:41.697409 21485 solver.cpp:237]     Train net output #0: loss = 5.08326 (* 1 = 5.08326 loss)
I0406 17:34:41.697415 21485 sgd_solver.cpp:105] Iteration 16560, lr = 0.05
I0406 17:34:46.842459 21485 solver.cpp:218] Iteration 16572 (2.33235 iter/s, 5.14503s/12 iters), loss = 4.97831
I0406 17:34:46.842622 21485 solver.cpp:237]     Train net output #0: loss = 4.97831 (* 1 = 4.97831 loss)
I0406 17:34:46.842630 21485 sgd_solver.cpp:105] Iteration 16572, lr = 0.05
I0406 17:34:52.159056 21485 solver.cpp:218] Iteration 16584 (2.25716 iter/s, 5.31642s/12 iters), loss = 5.08502
I0406 17:34:52.159116 21485 solver.cpp:237]     Train net output #0: loss = 5.08502 (* 1 = 5.08502 loss)
I0406 17:34:52.159126 21485 sgd_solver.cpp:105] Iteration 16584, lr = 0.05
I0406 17:34:54.127625 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:34:57.510987 21485 solver.cpp:218] Iteration 16596 (2.24221 iter/s, 5.35186s/12 iters), loss = 5.04466
I0406 17:34:57.511027 21485 solver.cpp:237]     Train net output #0: loss = 5.04466 (* 1 = 5.04466 loss)
I0406 17:34:57.511034 21485 sgd_solver.cpp:105] Iteration 16596, lr = 0.05
I0406 17:35:02.771425 21485 solver.cpp:218] Iteration 16608 (2.2812 iter/s, 5.26038s/12 iters), loss = 5.0601
I0406 17:35:02.771487 21485 solver.cpp:237]     Train net output #0: loss = 5.0601 (* 1 = 5.0601 loss)
I0406 17:35:02.771494 21485 sgd_solver.cpp:105] Iteration 16608, lr = 0.05
I0406 17:35:07.998821 21485 solver.cpp:218] Iteration 16620 (2.29563 iter/s, 5.22732s/12 iters), loss = 5.07114
I0406 17:35:07.998869 21485 solver.cpp:237]     Train net output #0: loss = 5.07114 (* 1 = 5.07114 loss)
I0406 17:35:07.998878 21485 sgd_solver.cpp:105] Iteration 16620, lr = 0.05
I0406 17:35:10.133570 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_16626.caffemodel
I0406 17:35:13.134795 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_16626.solverstate
I0406 17:35:15.440279 21485 solver.cpp:330] Iteration 16626, Testing net (#0)
I0406 17:35:15.440299 21485 net.cpp:676] Ignoring source layer train-data
I0406 17:35:17.868793 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:35:19.153393 21485 blocking_queue.cpp:49] Waiting for data
I0406 17:35:19.785248 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0153186
I0406 17:35:19.785281 21485 solver.cpp:397]     Test net output #1: loss = 5.15958 (* 1 = 5.15958 loss)
I0406 17:35:21.644470 21485 solver.cpp:218] Iteration 16632 (0.879405 iter/s, 13.6456s/12 iters), loss = 5.0052
I0406 17:35:21.644517 21485 solver.cpp:237]     Train net output #0: loss = 5.0052 (* 1 = 5.0052 loss)
I0406 17:35:21.644523 21485 sgd_solver.cpp:105] Iteration 16632, lr = 0.05
I0406 17:35:26.491458 21485 solver.cpp:218] Iteration 16644 (2.4758 iter/s, 4.84692s/12 iters), loss = 5.02135
I0406 17:35:26.491518 21485 solver.cpp:237]     Train net output #0: loss = 5.02135 (* 1 = 5.02135 loss)
I0406 17:35:26.491525 21485 sgd_solver.cpp:105] Iteration 16644, lr = 0.05
I0406 17:35:31.781047 21485 solver.cpp:218] Iteration 16656 (2.26864 iter/s, 5.28952s/12 iters), loss = 5.07041
I0406 17:35:31.781090 21485 solver.cpp:237]     Train net output #0: loss = 5.07041 (* 1 = 5.07041 loss)
I0406 17:35:31.781095 21485 sgd_solver.cpp:105] Iteration 16656, lr = 0.05
I0406 17:35:36.857494 21485 solver.cpp:218] Iteration 16668 (2.36388 iter/s, 5.07639s/12 iters), loss = 5.02221
I0406 17:35:36.857537 21485 solver.cpp:237]     Train net output #0: loss = 5.02221 (* 1 = 5.02221 loss)
I0406 17:35:36.857542 21485 sgd_solver.cpp:105] Iteration 16668, lr = 0.05
I0406 17:35:42.269142 21485 solver.cpp:218] Iteration 16680 (2.21746 iter/s, 5.41159s/12 iters), loss = 4.97941
I0406 17:35:42.269182 21485 solver.cpp:237]     Train net output #0: loss = 4.97941 (* 1 = 4.97941 loss)
I0406 17:35:42.269187 21485 sgd_solver.cpp:105] Iteration 16680, lr = 0.05
I0406 17:35:46.451624 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:35:47.571161 21485 solver.cpp:218] Iteration 16692 (2.26331 iter/s, 5.30197s/12 iters), loss = 5.09296
I0406 17:35:47.571197 21485 solver.cpp:237]     Train net output #0: loss = 5.09296 (* 1 = 5.09296 loss)
I0406 17:35:47.571202 21485 sgd_solver.cpp:105] Iteration 16692, lr = 0.05
I0406 17:35:52.928921 21485 solver.cpp:218] Iteration 16704 (2.23977 iter/s, 5.3577s/12 iters), loss = 5.04501
I0406 17:35:52.929067 21485 solver.cpp:237]     Train net output #0: loss = 5.04501 (* 1 = 5.04501 loss)
I0406 17:35:52.929075 21485 sgd_solver.cpp:105] Iteration 16704, lr = 0.05
I0406 17:35:58.103302 21485 solver.cpp:218] Iteration 16716 (2.31919 iter/s, 5.17422s/12 iters), loss = 5.07155
I0406 17:35:58.103350 21485 solver.cpp:237]     Train net output #0: loss = 5.07155 (* 1 = 5.07155 loss)
I0406 17:35:58.103356 21485 sgd_solver.cpp:105] Iteration 16716, lr = 0.05
I0406 17:36:02.826248 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_16728.caffemodel
I0406 17:36:05.753019 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_16728.solverstate
I0406 17:36:08.052917 21485 solver.cpp:330] Iteration 16728, Testing net (#0)
I0406 17:36:08.052935 21485 net.cpp:676] Ignoring source layer train-data
I0406 17:36:10.443543 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:36:12.423888 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0122549
I0406 17:36:12.423921 21485 solver.cpp:397]     Test net output #1: loss = 5.13238 (* 1 = 5.13238 loss)
I0406 17:36:12.559419 21485 solver.cpp:218] Iteration 16728 (0.830102 iter/s, 14.4561s/12 iters), loss = 4.9042
I0406 17:36:12.561012 21485 solver.cpp:237]     Train net output #0: loss = 4.9042 (* 1 = 4.9042 loss)
I0406 17:36:12.561026 21485 sgd_solver.cpp:105] Iteration 16728, lr = 0.05
I0406 17:36:16.665812 21485 solver.cpp:218] Iteration 16740 (2.92341 iter/s, 4.1048s/12 iters), loss = 5.06345
I0406 17:36:16.665858 21485 solver.cpp:237]     Train net output #0: loss = 5.06345 (* 1 = 5.06345 loss)
I0406 17:36:16.665864 21485 sgd_solver.cpp:105] Iteration 16740, lr = 0.05
I0406 17:36:21.921767 21485 solver.cpp:218] Iteration 16752 (2.28315 iter/s, 5.25589s/12 iters), loss = 4.99407
I0406 17:36:21.921811 21485 solver.cpp:237]     Train net output #0: loss = 4.99407 (* 1 = 4.99407 loss)
I0406 17:36:21.921816 21485 sgd_solver.cpp:105] Iteration 16752, lr = 0.05
I0406 17:36:27.334808 21485 solver.cpp:218] Iteration 16764 (2.21689 iter/s, 5.41298s/12 iters), loss = 4.88318
I0406 17:36:27.334941 21485 solver.cpp:237]     Train net output #0: loss = 4.88318 (* 1 = 4.88318 loss)
I0406 17:36:27.334951 21485 sgd_solver.cpp:105] Iteration 16764, lr = 0.05
I0406 17:36:32.309023 21485 solver.cpp:218] Iteration 16776 (2.41251 iter/s, 4.97407s/12 iters), loss = 4.95419
I0406 17:36:32.309069 21485 solver.cpp:237]     Train net output #0: loss = 4.95419 (* 1 = 4.95419 loss)
I0406 17:36:32.309075 21485 sgd_solver.cpp:105] Iteration 16776, lr = 0.05
I0406 17:36:37.631537 21485 solver.cpp:218] Iteration 16788 (2.2546 iter/s, 5.32245s/12 iters), loss = 5.03886
I0406 17:36:37.631582 21485 solver.cpp:237]     Train net output #0: loss = 5.03886 (* 1 = 5.03886 loss)
I0406 17:36:37.631587 21485 sgd_solver.cpp:105] Iteration 16788, lr = 0.05
I0406 17:36:38.790185 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:36:43.005250 21485 solver.cpp:218] Iteration 16800 (2.23312 iter/s, 5.37365s/12 iters), loss = 5.01432
I0406 17:36:43.005304 21485 solver.cpp:237]     Train net output #0: loss = 5.01432 (* 1 = 5.01432 loss)
I0406 17:36:43.005312 21485 sgd_solver.cpp:105] Iteration 16800, lr = 0.05
I0406 17:36:48.285619 21485 solver.cpp:218] Iteration 16812 (2.2726 iter/s, 5.2803s/12 iters), loss = 5.09409
I0406 17:36:48.285672 21485 solver.cpp:237]     Train net output #0: loss = 5.09409 (* 1 = 5.09409 loss)
I0406 17:36:48.285681 21485 sgd_solver.cpp:105] Iteration 16812, lr = 0.05
I0406 17:36:53.338235 21485 solver.cpp:218] Iteration 16824 (2.37504 iter/s, 5.05255s/12 iters), loss = 5.02697
I0406 17:36:53.338294 21485 solver.cpp:237]     Train net output #0: loss = 5.02697 (* 1 = 5.02697 loss)
I0406 17:36:53.338301 21485 sgd_solver.cpp:105] Iteration 16824, lr = 0.05
I0406 17:36:55.431661 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_16830.caffemodel
I0406 17:36:58.474233 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_16830.solverstate
I0406 17:37:00.808672 21485 solver.cpp:330] Iteration 16830, Testing net (#0)
I0406 17:37:00.808692 21485 net.cpp:676] Ignoring source layer train-data
I0406 17:37:03.207456 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:37:05.190567 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0128676
I0406 17:37:05.190600 21485 solver.cpp:397]     Test net output #1: loss = 5.13379 (* 1 = 5.13379 loss)
I0406 17:37:07.200984 21485 solver.cpp:218] Iteration 16836 (0.865634 iter/s, 13.8627s/12 iters), loss = 4.96771
I0406 17:37:07.201051 21485 solver.cpp:237]     Train net output #0: loss = 4.96771 (* 1 = 4.96771 loss)
I0406 17:37:07.201061 21485 sgd_solver.cpp:105] Iteration 16836, lr = 0.05
I0406 17:37:12.352344 21485 solver.cpp:218] Iteration 16848 (2.32952 iter/s, 5.15129s/12 iters), loss = 5.10282
I0406 17:37:12.352382 21485 solver.cpp:237]     Train net output #0: loss = 5.10282 (* 1 = 5.10282 loss)
I0406 17:37:12.352387 21485 sgd_solver.cpp:105] Iteration 16848, lr = 0.05
I0406 17:37:17.405283 21485 solver.cpp:218] Iteration 16860 (2.37488 iter/s, 5.05289s/12 iters), loss = 5.0897
I0406 17:37:17.405340 21485 solver.cpp:237]     Train net output #0: loss = 5.0897 (* 1 = 5.0897 loss)
I0406 17:37:17.405351 21485 sgd_solver.cpp:105] Iteration 16860, lr = 0.05
I0406 17:37:22.387971 21485 solver.cpp:218] Iteration 16872 (2.40837 iter/s, 4.98262s/12 iters), loss = 4.96411
I0406 17:37:22.388018 21485 solver.cpp:237]     Train net output #0: loss = 4.96411 (* 1 = 4.96411 loss)
I0406 17:37:22.388023 21485 sgd_solver.cpp:105] Iteration 16872, lr = 0.05
I0406 17:37:27.595511 21485 solver.cpp:218] Iteration 16884 (2.30438 iter/s, 5.20747s/12 iters), loss = 4.94998
I0406 17:37:27.595563 21485 solver.cpp:237]     Train net output #0: loss = 4.94998 (* 1 = 4.94998 loss)
I0406 17:37:27.595571 21485 sgd_solver.cpp:105] Iteration 16884, lr = 0.05
I0406 17:37:31.194242 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:37:32.880909 21485 solver.cpp:218] Iteration 16896 (2.27043 iter/s, 5.28533s/12 iters), loss = 4.99305
I0406 17:37:32.880949 21485 solver.cpp:237]     Train net output #0: loss = 4.99305 (* 1 = 4.99305 loss)
I0406 17:37:32.880955 21485 sgd_solver.cpp:105] Iteration 16896, lr = 0.05
I0406 17:37:38.220715 21485 solver.cpp:218] Iteration 16908 (2.2473 iter/s, 5.33975s/12 iters), loss = 5.00337
I0406 17:37:38.220767 21485 solver.cpp:237]     Train net output #0: loss = 5.00337 (* 1 = 5.00337 loss)
I0406 17:37:38.220774 21485 sgd_solver.cpp:105] Iteration 16908, lr = 0.05
I0406 17:37:43.676028 21485 solver.cpp:218] Iteration 16920 (2.19972 iter/s, 5.45525s/12 iters), loss = 5.1695
I0406 17:37:43.676066 21485 solver.cpp:237]     Train net output #0: loss = 5.1695 (* 1 = 5.1695 loss)
I0406 17:37:43.676072 21485 sgd_solver.cpp:105] Iteration 16920, lr = 0.05
I0406 17:37:48.535867 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_16932.caffemodel
I0406 17:37:51.563472 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_16932.solverstate
I0406 17:37:53.859860 21485 solver.cpp:330] Iteration 16932, Testing net (#0)
I0406 17:37:53.859879 21485 net.cpp:676] Ignoring source layer train-data
I0406 17:37:56.189841 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:37:58.273315 21485 solver.cpp:397]     Test net output #0: accuracy = 0.00735294
I0406 17:37:58.273355 21485 solver.cpp:397]     Test net output #1: loss = 5.18039 (* 1 = 5.18039 loss)
I0406 17:37:58.411558 21485 solver.cpp:218] Iteration 16932 (0.814361 iter/s, 14.7355s/12 iters), loss = 5.10067
I0406 17:37:58.411618 21485 solver.cpp:237]     Train net output #0: loss = 5.10067 (* 1 = 5.10067 loss)
I0406 17:37:58.411626 21485 sgd_solver.cpp:105] Iteration 16932, lr = 0.05
I0406 17:38:02.778059 21485 solver.cpp:218] Iteration 16944 (2.74825 iter/s, 4.36642s/12 iters), loss = 5.01518
I0406 17:38:02.778219 21485 solver.cpp:237]     Train net output #0: loss = 5.01518 (* 1 = 5.01518 loss)
I0406 17:38:02.778228 21485 sgd_solver.cpp:105] Iteration 16944, lr = 0.05
I0406 17:38:08.081774 21485 solver.cpp:218] Iteration 16956 (2.26264 iter/s, 5.30354s/12 iters), loss = 4.89968
I0406 17:38:08.081815 21485 solver.cpp:237]     Train net output #0: loss = 4.89968 (* 1 = 4.89968 loss)
I0406 17:38:08.081820 21485 sgd_solver.cpp:105] Iteration 16956, lr = 0.05
I0406 17:38:13.431911 21485 solver.cpp:218] Iteration 16968 (2.24296 iter/s, 5.35008s/12 iters), loss = 5.00347
I0406 17:38:13.431948 21485 solver.cpp:237]     Train net output #0: loss = 5.00347 (* 1 = 5.00347 loss)
I0406 17:38:13.431954 21485 sgd_solver.cpp:105] Iteration 16968, lr = 0.05
I0406 17:38:18.813570 21485 solver.cpp:218] Iteration 16980 (2.22982 iter/s, 5.3816s/12 iters), loss = 5.0223
I0406 17:38:18.813633 21485 solver.cpp:237]     Train net output #0: loss = 5.0223 (* 1 = 5.0223 loss)
I0406 17:38:18.813642 21485 sgd_solver.cpp:105] Iteration 16980, lr = 0.05
I0406 17:38:24.130407 21485 solver.cpp:218] Iteration 16992 (2.25701 iter/s, 5.31676s/12 iters), loss = 5.03658
I0406 17:38:24.130468 21485 solver.cpp:237]     Train net output #0: loss = 5.03658 (* 1 = 5.03658 loss)
I0406 17:38:24.130477 21485 sgd_solver.cpp:105] Iteration 16992, lr = 0.05
I0406 17:38:24.653210 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:38:29.438277 21485 solver.cpp:218] Iteration 17004 (2.26083 iter/s, 5.30779s/12 iters), loss = 5.00424
I0406 17:38:29.438336 21485 solver.cpp:237]     Train net output #0: loss = 5.00424 (* 1 = 5.00424 loss)
I0406 17:38:29.438344 21485 sgd_solver.cpp:105] Iteration 17004, lr = 0.05
I0406 17:38:34.776851 21485 solver.cpp:218] Iteration 17016 (2.24782 iter/s, 5.3385s/12 iters), loss = 5.0621
I0406 17:38:34.776974 21485 solver.cpp:237]     Train net output #0: loss = 5.0621 (* 1 = 5.0621 loss)
I0406 17:38:34.776983 21485 sgd_solver.cpp:105] Iteration 17016, lr = 0.05
I0406 17:38:40.127490 21485 solver.cpp:218] Iteration 17028 (2.24278 iter/s, 5.3505s/12 iters), loss = 5.07894
I0406 17:38:40.127537 21485 solver.cpp:237]     Train net output #0: loss = 5.07894 (* 1 = 5.07894 loss)
I0406 17:38:40.127544 21485 sgd_solver.cpp:105] Iteration 17028, lr = 0.05
I0406 17:38:42.090663 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_17034.caffemodel
I0406 17:38:45.100615 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_17034.solverstate
I0406 17:38:47.420049 21485 solver.cpp:330] Iteration 17034, Testing net (#0)
I0406 17:38:47.420066 21485 net.cpp:676] Ignoring source layer train-data
I0406 17:38:49.736433 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:38:51.911195 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0202206
I0406 17:38:51.911227 21485 solver.cpp:397]     Test net output #1: loss = 5.12289 (* 1 = 5.12289 loss)
I0406 17:38:53.697553 21485 solver.cpp:218] Iteration 17040 (0.884303 iter/s, 13.57s/12 iters), loss = 4.93388
I0406 17:38:53.697595 21485 solver.cpp:237]     Train net output #0: loss = 4.93388 (* 1 = 4.93388 loss)
I0406 17:38:53.697602 21485 sgd_solver.cpp:105] Iteration 17040, lr = 0.05
I0406 17:38:58.874594 21485 solver.cpp:218] Iteration 17052 (2.31795 iter/s, 5.17698s/12 iters), loss = 4.97031
I0406 17:38:58.874639 21485 solver.cpp:237]     Train net output #0: loss = 4.97031 (* 1 = 4.97031 loss)
I0406 17:38:58.874645 21485 sgd_solver.cpp:105] Iteration 17052, lr = 0.05
I0406 17:39:04.106221 21485 solver.cpp:218] Iteration 17064 (2.29377 iter/s, 5.23157s/12 iters), loss = 4.99841
I0406 17:39:04.106266 21485 solver.cpp:237]     Train net output #0: loss = 4.99841 (* 1 = 4.99841 loss)
I0406 17:39:04.106271 21485 sgd_solver.cpp:105] Iteration 17064, lr = 0.05
I0406 17:39:09.397737 21485 solver.cpp:218] Iteration 17076 (2.26781 iter/s, 5.29145s/12 iters), loss = 5.17813
I0406 17:39:09.397895 21485 solver.cpp:237]     Train net output #0: loss = 5.17813 (* 1 = 5.17813 loss)
I0406 17:39:09.397904 21485 sgd_solver.cpp:105] Iteration 17076, lr = 0.05
I0406 17:39:14.584046 21485 solver.cpp:218] Iteration 17088 (2.31386 iter/s, 5.18613s/12 iters), loss = 5.16042
I0406 17:39:14.584105 21485 solver.cpp:237]     Train net output #0: loss = 5.16042 (* 1 = 5.16042 loss)
I0406 17:39:14.584113 21485 sgd_solver.cpp:105] Iteration 17088, lr = 0.05
I0406 17:39:17.373337 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:39:19.877902 21485 solver.cpp:218] Iteration 17100 (2.26681 iter/s, 5.29379s/12 iters), loss = 5.22011
I0406 17:39:19.877943 21485 solver.cpp:237]     Train net output #0: loss = 5.22011 (* 1 = 5.22011 loss)
I0406 17:39:19.877948 21485 sgd_solver.cpp:105] Iteration 17100, lr = 0.05
I0406 17:39:25.060976 21485 solver.cpp:218] Iteration 17112 (2.31525 iter/s, 5.18302s/12 iters), loss = 5.10213
I0406 17:39:25.061018 21485 solver.cpp:237]     Train net output #0: loss = 5.10213 (* 1 = 5.10213 loss)
I0406 17:39:25.061024 21485 sgd_solver.cpp:105] Iteration 17112, lr = 0.05
I0406 17:39:29.959208 21485 solver.cpp:218] Iteration 17124 (2.44989 iter/s, 4.89818s/12 iters), loss = 5.14556
I0406 17:39:29.959249 21485 solver.cpp:237]     Train net output #0: loss = 5.14556 (* 1 = 5.14556 loss)
I0406 17:39:29.959254 21485 sgd_solver.cpp:105] Iteration 17124, lr = 0.05
I0406 17:39:34.750982 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_17136.caffemodel
I0406 17:39:39.036398 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_17136.solverstate
I0406 17:39:41.358263 21485 solver.cpp:330] Iteration 17136, Testing net (#0)
I0406 17:39:41.358350 21485 net.cpp:676] Ignoring source layer train-data
I0406 17:39:43.629629 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:39:45.734273 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0122549
I0406 17:39:45.734299 21485 solver.cpp:397]     Test net output #1: loss = 5.17288 (* 1 = 5.17288 loss)
I0406 17:39:45.874590 21485 solver.cpp:218] Iteration 17136 (0.75399 iter/s, 15.9153s/12 iters), loss = 5.12009
I0406 17:39:45.874650 21485 solver.cpp:237]     Train net output #0: loss = 5.12009 (* 1 = 5.12009 loss)
I0406 17:39:45.874660 21485 sgd_solver.cpp:105] Iteration 17136, lr = 0.05
I0406 17:39:50.135792 21485 solver.cpp:218] Iteration 17148 (2.81616 iter/s, 4.26113s/12 iters), loss = 5.13449
I0406 17:39:50.135838 21485 solver.cpp:237]     Train net output #0: loss = 5.13449 (* 1 = 5.13449 loss)
I0406 17:39:50.135844 21485 sgd_solver.cpp:105] Iteration 17148, lr = 0.05
I0406 17:39:55.262742 21485 solver.cpp:218] Iteration 17160 (2.3406 iter/s, 5.12689s/12 iters), loss = 5.1786
I0406 17:39:55.262787 21485 solver.cpp:237]     Train net output #0: loss = 5.1786 (* 1 = 5.1786 loss)
I0406 17:39:55.262794 21485 sgd_solver.cpp:105] Iteration 17160, lr = 0.05
I0406 17:40:00.589726 21485 solver.cpp:218] Iteration 17172 (2.25271 iter/s, 5.32692s/12 iters), loss = 5.09146
I0406 17:40:00.589789 21485 solver.cpp:237]     Train net output #0: loss = 5.09146 (* 1 = 5.09146 loss)
I0406 17:40:00.589802 21485 sgd_solver.cpp:105] Iteration 17172, lr = 0.05
I0406 17:40:05.769557 21485 solver.cpp:218] Iteration 17184 (2.31671 iter/s, 5.17976s/12 iters), loss = 5.16518
I0406 17:40:05.769598 21485 solver.cpp:237]     Train net output #0: loss = 5.16518 (* 1 = 5.16518 loss)
I0406 17:40:05.769603 21485 sgd_solver.cpp:105] Iteration 17184, lr = 0.05
I0406 17:40:10.761723 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:40:11.044792 21485 solver.cpp:218] Iteration 17196 (2.27481 iter/s, 5.27518s/12 iters), loss = 5.15553
I0406 17:40:11.044833 21485 solver.cpp:237]     Train net output #0: loss = 5.15553 (* 1 = 5.15553 loss)
I0406 17:40:11.044839 21485 sgd_solver.cpp:105] Iteration 17196, lr = 0.05
I0406 17:40:16.322487 21485 solver.cpp:218] Iteration 17208 (2.27374 iter/s, 5.27764s/12 iters), loss = 5.17525
I0406 17:40:16.322636 21485 solver.cpp:237]     Train net output #0: loss = 5.17525 (* 1 = 5.17525 loss)
I0406 17:40:16.322644 21485 sgd_solver.cpp:105] Iteration 17208, lr = 0.05
I0406 17:40:21.611802 21485 solver.cpp:218] Iteration 17220 (2.26879 iter/s, 5.28915s/12 iters), loss = 5.06325
I0406 17:40:21.611857 21485 solver.cpp:237]     Train net output #0: loss = 5.06325 (* 1 = 5.06325 loss)
I0406 17:40:21.611865 21485 sgd_solver.cpp:105] Iteration 17220, lr = 0.05
I0406 17:40:26.999744 21485 solver.cpp:218] Iteration 17232 (2.22723 iter/s, 5.38787s/12 iters), loss = 5.10722
I0406 17:40:26.999799 21485 solver.cpp:237]     Train net output #0: loss = 5.10722 (* 1 = 5.10722 loss)
I0406 17:40:26.999807 21485 sgd_solver.cpp:105] Iteration 17232, lr = 0.05
I0406 17:40:29.140452 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_17238.caffemodel
I0406 17:40:32.370437 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_17238.solverstate
I0406 17:40:36.101490 21485 solver.cpp:330] Iteration 17238, Testing net (#0)
I0406 17:40:36.101507 21485 net.cpp:676] Ignoring source layer train-data
I0406 17:40:38.296144 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:40:40.551699 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0147059
I0406 17:40:40.551738 21485 solver.cpp:397]     Test net output #1: loss = 5.1693 (* 1 = 5.1693 loss)
I0406 17:40:42.324676 21485 solver.cpp:218] Iteration 17244 (0.783041 iter/s, 15.3249s/12 iters), loss = 5.30558
I0406 17:40:42.324724 21485 solver.cpp:237]     Train net output #0: loss = 5.30558 (* 1 = 5.30558 loss)
I0406 17:40:42.324730 21485 sgd_solver.cpp:105] Iteration 17244, lr = 0.05
I0406 17:40:47.614832 21485 solver.cpp:218] Iteration 17256 (2.26839 iter/s, 5.29009s/12 iters), loss = 5.07515
I0406 17:40:47.614945 21485 solver.cpp:237]     Train net output #0: loss = 5.07515 (* 1 = 5.07515 loss)
I0406 17:40:47.614954 21485 sgd_solver.cpp:105] Iteration 17256, lr = 0.05
I0406 17:40:52.829058 21485 solver.cpp:218] Iteration 17268 (2.30145 iter/s, 5.2141s/12 iters), loss = 5.17
I0406 17:40:52.829106 21485 solver.cpp:237]     Train net output #0: loss = 5.17 (* 1 = 5.17 loss)
I0406 17:40:52.829114 21485 sgd_solver.cpp:105] Iteration 17268, lr = 0.05
I0406 17:40:58.001703 21485 solver.cpp:218] Iteration 17280 (2.31992 iter/s, 5.17258s/12 iters), loss = 5.07561
I0406 17:40:58.001755 21485 solver.cpp:237]     Train net output #0: loss = 5.07561 (* 1 = 5.07561 loss)
I0406 17:40:58.001763 21485 sgd_solver.cpp:105] Iteration 17280, lr = 0.05
I0406 17:41:03.270810 21485 solver.cpp:218] Iteration 17292 (2.27745 iter/s, 5.26904s/12 iters), loss = 5.07925
I0406 17:41:03.270856 21485 solver.cpp:237]     Train net output #0: loss = 5.07925 (* 1 = 5.07925 loss)
I0406 17:41:03.270861 21485 sgd_solver.cpp:105] Iteration 17292, lr = 0.05
I0406 17:41:05.284799 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:41:08.548287 21485 solver.cpp:218] Iteration 17304 (2.27384 iter/s, 5.27742s/12 iters), loss = 5.39628
I0406 17:41:08.548336 21485 solver.cpp:237]     Train net output #0: loss = 5.39628 (* 1 = 5.39628 loss)
I0406 17:41:08.548344 21485 sgd_solver.cpp:105] Iteration 17304, lr = 0.05
I0406 17:41:13.835377 21485 solver.cpp:218] Iteration 17316 (2.26971 iter/s, 5.28702s/12 iters), loss = 5.16984
I0406 17:41:13.835433 21485 solver.cpp:237]     Train net output #0: loss = 5.16984 (* 1 = 5.16984 loss)
I0406 17:41:13.835441 21485 sgd_solver.cpp:105] Iteration 17316, lr = 0.05
I0406 17:41:19.116559 21485 solver.cpp:218] Iteration 17328 (2.27225 iter/s, 5.28112s/12 iters), loss = 5.11248
I0406 17:41:19.116686 21485 solver.cpp:237]     Train net output #0: loss = 5.11248 (* 1 = 5.11248 loss)
I0406 17:41:19.116693 21485 sgd_solver.cpp:105] Iteration 17328, lr = 0.05
I0406 17:41:23.875761 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_17340.caffemodel
I0406 17:41:26.897193 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_17340.solverstate
I0406 17:41:29.762553 21485 solver.cpp:330] Iteration 17340, Testing net (#0)
I0406 17:41:29.762575 21485 net.cpp:676] Ignoring source layer train-data
I0406 17:41:30.971308 21485 blocking_queue.cpp:49] Waiting for data
I0406 17:41:32.054177 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:41:34.430112 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0147059
I0406 17:41:34.430140 21485 solver.cpp:397]     Test net output #1: loss = 5.14217 (* 1 = 5.14217 loss)
I0406 17:41:34.564373 21485 solver.cpp:218] Iteration 17340 (0.776816 iter/s, 15.4477s/12 iters), loss = 5.04026
I0406 17:41:34.564424 21485 solver.cpp:237]     Train net output #0: loss = 5.04026 (* 1 = 5.04026 loss)
I0406 17:41:34.564431 21485 sgd_solver.cpp:105] Iteration 17340, lr = 0.05
I0406 17:41:38.829283 21485 solver.cpp:218] Iteration 17352 (2.8137 iter/s, 4.26485s/12 iters), loss = 5.03906
I0406 17:41:38.829324 21485 solver.cpp:237]     Train net output #0: loss = 5.03906 (* 1 = 5.03906 loss)
I0406 17:41:38.829329 21485 sgd_solver.cpp:105] Iteration 17352, lr = 0.05
I0406 17:41:44.088729 21485 solver.cpp:218] Iteration 17364 (2.28163 iter/s, 5.25939s/12 iters), loss = 5.14139
I0406 17:41:44.088786 21485 solver.cpp:237]     Train net output #0: loss = 5.14139 (* 1 = 5.14139 loss)
I0406 17:41:44.088794 21485 sgd_solver.cpp:105] Iteration 17364, lr = 0.05
I0406 17:41:49.199767 21485 solver.cpp:218] Iteration 17376 (2.34789 iter/s, 5.11097s/12 iters), loss = 5.09798
I0406 17:41:49.199851 21485 solver.cpp:237]     Train net output #0: loss = 5.09798 (* 1 = 5.09798 loss)
I0406 17:41:49.199857 21485 sgd_solver.cpp:105] Iteration 17376, lr = 0.05
I0406 17:41:54.389575 21485 solver.cpp:218] Iteration 17388 (2.31227 iter/s, 5.18971s/12 iters), loss = 5.08524
I0406 17:41:54.389624 21485 solver.cpp:237]     Train net output #0: loss = 5.08524 (* 1 = 5.08524 loss)
I0406 17:41:54.389629 21485 sgd_solver.cpp:105] Iteration 17388, lr = 0.05
I0406 17:41:58.712057 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:41:59.679769 21485 solver.cpp:218] Iteration 17400 (2.26838 iter/s, 5.29012s/12 iters), loss = 4.99807
I0406 17:41:59.679826 21485 solver.cpp:237]     Train net output #0: loss = 4.99807 (* 1 = 4.99807 loss)
I0406 17:41:59.679834 21485 sgd_solver.cpp:105] Iteration 17400, lr = 0.05
I0406 17:42:04.770915 21485 solver.cpp:218] Iteration 17412 (2.35706 iter/s, 5.09108s/12 iters), loss = 5.17197
I0406 17:42:04.770952 21485 solver.cpp:237]     Train net output #0: loss = 5.17197 (* 1 = 5.17197 loss)
I0406 17:42:04.770958 21485 sgd_solver.cpp:105] Iteration 17412, lr = 0.05
I0406 17:42:10.020478 21485 solver.cpp:218] Iteration 17424 (2.28593 iter/s, 5.24951s/12 iters), loss = 5.12347
I0406 17:42:10.020532 21485 solver.cpp:237]     Train net output #0: loss = 5.12347 (* 1 = 5.12347 loss)
I0406 17:42:10.020540 21485 sgd_solver.cpp:105] Iteration 17424, lr = 0.05
I0406 17:42:15.336357 21485 solver.cpp:218] Iteration 17436 (2.25742 iter/s, 5.31581s/12 iters), loss = 5.0685
I0406 17:42:15.336410 21485 solver.cpp:237]     Train net output #0: loss = 5.0685 (* 1 = 5.0685 loss)
I0406 17:42:15.336419 21485 sgd_solver.cpp:105] Iteration 17436, lr = 0.05
I0406 17:42:17.374991 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_17442.caffemodel
I0406 17:42:20.449915 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_17442.solverstate
I0406 17:42:22.745213 21485 solver.cpp:330] Iteration 17442, Testing net (#0)
I0406 17:42:22.745231 21485 net.cpp:676] Ignoring source layer train-data
I0406 17:42:24.913650 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:42:27.190470 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0165441
I0406 17:42:27.190510 21485 solver.cpp:397]     Test net output #1: loss = 5.15614 (* 1 = 5.15614 loss)
I0406 17:42:29.057761 21485 solver.cpp:218] Iteration 17448 (0.87455 iter/s, 13.7213s/12 iters), loss = 5.09493
I0406 17:42:29.057803 21485 solver.cpp:237]     Train net output #0: loss = 5.09493 (* 1 = 5.09493 loss)
I0406 17:42:29.057808 21485 sgd_solver.cpp:105] Iteration 17448, lr = 0.05
I0406 17:42:34.305795 21485 solver.cpp:218] Iteration 17460 (2.28659 iter/s, 5.24798s/12 iters), loss = 5.06108
I0406 17:42:34.305831 21485 solver.cpp:237]     Train net output #0: loss = 5.06108 (* 1 = 5.06108 loss)
I0406 17:42:34.305836 21485 sgd_solver.cpp:105] Iteration 17460, lr = 0.05
I0406 17:42:39.499974 21485 solver.cpp:218] Iteration 17472 (2.3103 iter/s, 5.19412s/12 iters), loss = 5.05149
I0406 17:42:39.500023 21485 solver.cpp:237]     Train net output #0: loss = 5.05149 (* 1 = 5.05149 loss)
I0406 17:42:39.500031 21485 sgd_solver.cpp:105] Iteration 17472, lr = 0.05
I0406 17:42:44.791167 21485 solver.cpp:218] Iteration 17484 (2.26795 iter/s, 5.29113s/12 iters), loss = 5.00307
I0406 17:42:44.791225 21485 solver.cpp:237]     Train net output #0: loss = 5.00307 (* 1 = 5.00307 loss)
I0406 17:42:44.791234 21485 sgd_solver.cpp:105] Iteration 17484, lr = 0.05
I0406 17:42:49.931212 21485 solver.cpp:218] Iteration 17496 (2.33464 iter/s, 5.13998s/12 iters), loss = 5.06728
I0406 17:42:49.931260 21485 solver.cpp:237]     Train net output #0: loss = 5.06728 (* 1 = 5.06728 loss)
I0406 17:42:49.931267 21485 sgd_solver.cpp:105] Iteration 17496, lr = 0.05
I0406 17:42:51.242142 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:42:55.112637 21485 solver.cpp:218] Iteration 17508 (2.316 iter/s, 5.18136s/12 iters), loss = 5.0441
I0406 17:42:55.112685 21485 solver.cpp:237]     Train net output #0: loss = 5.0441 (* 1 = 5.0441 loss)
I0406 17:42:55.112692 21485 sgd_solver.cpp:105] Iteration 17508, lr = 0.05
I0406 17:43:00.373872 21485 solver.cpp:218] Iteration 17520 (2.28086 iter/s, 5.26118s/12 iters), loss = 5.16036
I0406 17:43:00.373912 21485 solver.cpp:237]     Train net output #0: loss = 5.16036 (* 1 = 5.16036 loss)
I0406 17:43:00.373919 21485 sgd_solver.cpp:105] Iteration 17520, lr = 0.05
I0406 17:43:05.441959 21485 solver.cpp:218] Iteration 17532 (2.36778 iter/s, 5.06803s/12 iters), loss = 5.08518
I0406 17:43:05.442005 21485 solver.cpp:237]     Train net output #0: loss = 5.08518 (* 1 = 5.08518 loss)
I0406 17:43:05.442011 21485 sgd_solver.cpp:105] Iteration 17532, lr = 0.05
I0406 17:43:10.092334 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_17544.caffemodel
I0406 17:43:13.154386 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_17544.solverstate
I0406 17:43:15.454620 21485 solver.cpp:330] Iteration 17544, Testing net (#0)
I0406 17:43:15.454644 21485 net.cpp:676] Ignoring source layer train-data
I0406 17:43:17.498041 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:43:19.770292 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0189951
I0406 17:43:19.770318 21485 solver.cpp:397]     Test net output #1: loss = 5.11941 (* 1 = 5.11941 loss)
I0406 17:43:19.908262 21485 solver.cpp:218] Iteration 17544 (0.829517 iter/s, 14.4663s/12 iters), loss = 4.94566
I0406 17:43:19.908306 21485 solver.cpp:237]     Train net output #0: loss = 4.94566 (* 1 = 4.94566 loss)
I0406 17:43:19.908311 21485 sgd_solver.cpp:105] Iteration 17544, lr = 0.05
I0406 17:43:24.192155 21485 solver.cpp:218] Iteration 17556 (2.80123 iter/s, 4.28383s/12 iters), loss = 4.96085
I0406 17:43:24.192284 21485 solver.cpp:237]     Train net output #0: loss = 4.96085 (* 1 = 4.96085 loss)
I0406 17:43:24.192291 21485 sgd_solver.cpp:105] Iteration 17556, lr = 0.05
I0406 17:43:29.392902 21485 solver.cpp:218] Iteration 17568 (2.30742 iter/s, 5.20061s/12 iters), loss = 5.05187
I0406 17:43:29.392943 21485 solver.cpp:237]     Train net output #0: loss = 5.05187 (* 1 = 5.05187 loss)
I0406 17:43:29.392949 21485 sgd_solver.cpp:105] Iteration 17568, lr = 0.05
I0406 17:43:34.635023 21485 solver.cpp:218] Iteration 17580 (2.28918 iter/s, 5.24206s/12 iters), loss = 5.00964
I0406 17:43:34.635076 21485 solver.cpp:237]     Train net output #0: loss = 5.00964 (* 1 = 5.00964 loss)
I0406 17:43:34.635083 21485 sgd_solver.cpp:105] Iteration 17580, lr = 0.05
I0406 17:43:39.749416 21485 solver.cpp:218] Iteration 17592 (2.34635 iter/s, 5.11433s/12 iters), loss = 5.02713
I0406 17:43:39.749460 21485 solver.cpp:237]     Train net output #0: loss = 5.02713 (* 1 = 5.02713 loss)
I0406 17:43:39.749467 21485 sgd_solver.cpp:105] Iteration 17592, lr = 0.05
I0406 17:43:43.340184 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:43:45.062320 21485 solver.cpp:218] Iteration 17604 (2.25868 iter/s, 5.31284s/12 iters), loss = 4.98466
I0406 17:43:45.062373 21485 solver.cpp:237]     Train net output #0: loss = 4.98466 (* 1 = 4.98466 loss)
I0406 17:43:45.062382 21485 sgd_solver.cpp:105] Iteration 17604, lr = 0.05
I0406 17:43:50.502030 21485 solver.cpp:218] Iteration 17616 (2.20603 iter/s, 5.43965s/12 iters), loss = 5.01827
I0406 17:43:50.502084 21485 solver.cpp:237]     Train net output #0: loss = 5.01827 (* 1 = 5.01827 loss)
I0406 17:43:50.502094 21485 sgd_solver.cpp:105] Iteration 17616, lr = 0.05
I0406 17:43:55.545347 21485 solver.cpp:218] Iteration 17628 (2.37942 iter/s, 5.04326s/12 iters), loss = 5.06441
I0406 17:43:55.545455 21485 solver.cpp:237]     Train net output #0: loss = 5.06441 (* 1 = 5.06441 loss)
I0406 17:43:55.545462 21485 sgd_solver.cpp:105] Iteration 17628, lr = 0.05
I0406 17:44:00.816123 21485 solver.cpp:218] Iteration 17640 (2.27676 iter/s, 5.27065s/12 iters), loss = 5.05183
I0406 17:44:00.816159 21485 solver.cpp:237]     Train net output #0: loss = 5.05183 (* 1 = 5.05183 loss)
I0406 17:44:00.816164 21485 sgd_solver.cpp:105] Iteration 17640, lr = 0.05
I0406 17:44:02.986507 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_17646.caffemodel
I0406 17:44:06.011317 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_17646.solverstate
I0406 17:44:08.338153 21485 solver.cpp:330] Iteration 17646, Testing net (#0)
I0406 17:44:08.338172 21485 net.cpp:676] Ignoring source layer train-data
I0406 17:44:10.384402 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:44:12.801175 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0183824
I0406 17:44:12.801208 21485 solver.cpp:397]     Test net output #1: loss = 5.13716 (* 1 = 5.13716 loss)
I0406 17:44:14.626318 21485 solver.cpp:218] Iteration 17652 (0.868926 iter/s, 13.8101s/12 iters), loss = 5.12159
I0406 17:44:14.626360 21485 solver.cpp:237]     Train net output #0: loss = 5.12159 (* 1 = 5.12159 loss)
I0406 17:44:14.626366 21485 sgd_solver.cpp:105] Iteration 17652, lr = 0.05
I0406 17:44:19.842485 21485 solver.cpp:218] Iteration 17664 (2.30057 iter/s, 5.21611s/12 iters), loss = 4.94841
I0406 17:44:19.842545 21485 solver.cpp:237]     Train net output #0: loss = 4.94841 (* 1 = 4.94841 loss)
I0406 17:44:19.842553 21485 sgd_solver.cpp:105] Iteration 17664, lr = 0.05
I0406 17:44:25.138590 21485 solver.cpp:218] Iteration 17676 (2.26585 iter/s, 5.29603s/12 iters), loss = 4.82216
I0406 17:44:25.138630 21485 solver.cpp:237]     Train net output #0: loss = 4.82216 (* 1 = 4.82216 loss)
I0406 17:44:25.138635 21485 sgd_solver.cpp:105] Iteration 17676, lr = 0.05
I0406 17:44:30.219568 21485 solver.cpp:218] Iteration 17688 (2.36178 iter/s, 5.08092s/12 iters), loss = 5.04982
I0406 17:44:30.219630 21485 solver.cpp:237]     Train net output #0: loss = 5.04982 (* 1 = 5.04982 loss)
I0406 17:44:30.219636 21485 sgd_solver.cpp:105] Iteration 17688, lr = 0.05
I0406 17:44:35.562763 21485 solver.cpp:218] Iteration 17700 (2.24588 iter/s, 5.34312s/12 iters), loss = 5.05387
I0406 17:44:35.562824 21485 solver.cpp:237]     Train net output #0: loss = 5.05387 (* 1 = 5.05387 loss)
I0406 17:44:35.562834 21485 sgd_solver.cpp:105] Iteration 17700, lr = 0.05
I0406 17:44:36.118678 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:44:40.921932 21485 solver.cpp:218] Iteration 17712 (2.23919 iter/s, 5.35909s/12 iters), loss = 5.09539
I0406 17:44:40.921983 21485 solver.cpp:237]     Train net output #0: loss = 5.09539 (* 1 = 5.09539 loss)
I0406 17:44:40.921990 21485 sgd_solver.cpp:105] Iteration 17712, lr = 0.05
I0406 17:44:46.286859 21485 solver.cpp:218] Iteration 17724 (2.23678 iter/s, 5.36486s/12 iters), loss = 5.10857
I0406 17:44:46.286918 21485 solver.cpp:237]     Train net output #0: loss = 5.10857 (* 1 = 5.10857 loss)
I0406 17:44:46.286927 21485 sgd_solver.cpp:105] Iteration 17724, lr = 0.05
I0406 17:44:51.542662 21485 solver.cpp:218] Iteration 17736 (2.28322 iter/s, 5.25573s/12 iters), loss = 4.97926
I0406 17:44:51.542711 21485 solver.cpp:237]     Train net output #0: loss = 4.97926 (* 1 = 4.97926 loss)
I0406 17:44:51.542718 21485 sgd_solver.cpp:105] Iteration 17736, lr = 0.05
I0406 17:44:56.357028 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_17748.caffemodel
I0406 17:44:59.391381 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_17748.solverstate
I0406 17:45:02.143151 21485 solver.cpp:330] Iteration 17748, Testing net (#0)
I0406 17:45:02.143232 21485 net.cpp:676] Ignoring source layer train-data
I0406 17:45:04.170385 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:45:06.521009 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0153186
I0406 17:45:06.521037 21485 solver.cpp:397]     Test net output #1: loss = 5.17297 (* 1 = 5.17297 loss)
I0406 17:45:06.657464 21485 solver.cpp:218] Iteration 17748 (0.793927 iter/s, 15.1147s/12 iters), loss = 5.13603
I0406 17:45:06.657523 21485 solver.cpp:237]     Train net output #0: loss = 5.13603 (* 1 = 5.13603 loss)
I0406 17:45:06.657532 21485 sgd_solver.cpp:105] Iteration 17748, lr = 0.05
I0406 17:45:10.910745 21485 solver.cpp:218] Iteration 17760 (2.8214 iter/s, 4.25321s/12 iters), loss = 5.0639
I0406 17:45:10.910794 21485 solver.cpp:237]     Train net output #0: loss = 5.0639 (* 1 = 5.0639 loss)
I0406 17:45:10.910799 21485 sgd_solver.cpp:105] Iteration 17760, lr = 0.05
I0406 17:45:16.135576 21485 solver.cpp:218] Iteration 17772 (2.29675 iter/s, 5.22477s/12 iters), loss = 5.01382
I0406 17:45:16.135619 21485 solver.cpp:237]     Train net output #0: loss = 5.01382 (* 1 = 5.01382 loss)
I0406 17:45:16.135624 21485 sgd_solver.cpp:105] Iteration 17772, lr = 0.05
I0406 17:45:21.462868 21485 solver.cpp:218] Iteration 17784 (2.25258 iter/s, 5.32723s/12 iters), loss = 5.01532
I0406 17:45:21.462909 21485 solver.cpp:237]     Train net output #0: loss = 5.01532 (* 1 = 5.01532 loss)
I0406 17:45:21.462915 21485 sgd_solver.cpp:105] Iteration 17784, lr = 0.05
I0406 17:45:26.724105 21485 solver.cpp:218] Iteration 17796 (2.28086 iter/s, 5.26118s/12 iters), loss = 5.04674
I0406 17:45:26.724148 21485 solver.cpp:237]     Train net output #0: loss = 5.04674 (* 1 = 5.04674 loss)
I0406 17:45:26.724153 21485 sgd_solver.cpp:105] Iteration 17796, lr = 0.05
I0406 17:45:29.524145 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:45:31.735872 21485 solver.cpp:218] Iteration 17808 (2.39439 iter/s, 5.01171s/12 iters), loss = 5.06191
I0406 17:45:31.735910 21485 solver.cpp:237]     Train net output #0: loss = 5.06191 (* 1 = 5.06191 loss)
I0406 17:45:31.735916 21485 sgd_solver.cpp:105] Iteration 17808, lr = 0.05
I0406 17:45:36.594377 21485 solver.cpp:218] Iteration 17820 (2.46992 iter/s, 4.85845s/12 iters), loss = 4.99902
I0406 17:45:36.594485 21485 solver.cpp:237]     Train net output #0: loss = 4.99902 (* 1 = 4.99902 loss)
I0406 17:45:36.594491 21485 sgd_solver.cpp:105] Iteration 17820, lr = 0.05
I0406 17:45:41.711187 21485 solver.cpp:218] Iteration 17832 (2.34526 iter/s, 5.1167s/12 iters), loss = 5.23727
I0406 17:45:41.711233 21485 solver.cpp:237]     Train net output #0: loss = 5.23727 (* 1 = 5.23727 loss)
I0406 17:45:41.711238 21485 sgd_solver.cpp:105] Iteration 17832, lr = 0.05
I0406 17:45:47.043521 21485 solver.cpp:218] Iteration 17844 (2.25045 iter/s, 5.33227s/12 iters), loss = 5.0635
I0406 17:45:47.043570 21485 solver.cpp:237]     Train net output #0: loss = 5.0635 (* 1 = 5.0635 loss)
I0406 17:45:47.043576 21485 sgd_solver.cpp:105] Iteration 17844, lr = 0.05
I0406 17:45:49.065660 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_17850.caffemodel
I0406 17:45:52.057914 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_17850.solverstate
I0406 17:45:55.915174 21485 solver.cpp:330] Iteration 17850, Testing net (#0)
I0406 17:45:55.915194 21485 net.cpp:676] Ignoring source layer train-data
I0406 17:45:57.949154 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:46:00.431999 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0147059
I0406 17:46:00.432036 21485 solver.cpp:397]     Test net output #1: loss = 5.15401 (* 1 = 5.15401 loss)
I0406 17:46:02.407341 21485 solver.cpp:218] Iteration 17856 (0.781059 iter/s, 15.3638s/12 iters), loss = 5.05672
I0406 17:46:02.407382 21485 solver.cpp:237]     Train net output #0: loss = 5.05672 (* 1 = 5.05672 loss)
I0406 17:46:02.407388 21485 sgd_solver.cpp:105] Iteration 17856, lr = 0.05
I0406 17:46:07.424719 21485 solver.cpp:218] Iteration 17868 (2.39172 iter/s, 5.01732s/12 iters), loss = 5.09241
I0406 17:46:07.424891 21485 solver.cpp:237]     Train net output #0: loss = 5.09241 (* 1 = 5.09241 loss)
I0406 17:46:07.424901 21485 sgd_solver.cpp:105] Iteration 17868, lr = 0.05
I0406 17:46:12.749101 21485 solver.cpp:218] Iteration 17880 (2.25386 iter/s, 5.32421s/12 iters), loss = 5.04604
I0406 17:46:12.749148 21485 solver.cpp:237]     Train net output #0: loss = 5.04604 (* 1 = 5.04604 loss)
I0406 17:46:12.749155 21485 sgd_solver.cpp:105] Iteration 17880, lr = 0.05
I0406 17:46:18.021292 21485 solver.cpp:218] Iteration 17892 (2.27612 iter/s, 5.27213s/12 iters), loss = 5.06708
I0406 17:46:18.021342 21485 solver.cpp:237]     Train net output #0: loss = 5.06708 (* 1 = 5.06708 loss)
I0406 17:46:18.021349 21485 sgd_solver.cpp:105] Iteration 17892, lr = 0.05
I0406 17:46:23.038707 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:46:23.307770 21485 solver.cpp:218] Iteration 17904 (2.26997 iter/s, 5.28641s/12 iters), loss = 4.98488
I0406 17:46:23.307817 21485 solver.cpp:237]     Train net output #0: loss = 4.98488 (* 1 = 4.98488 loss)
I0406 17:46:23.307823 21485 sgd_solver.cpp:105] Iteration 17904, lr = 0.05
I0406 17:46:28.515275 21485 solver.cpp:218] Iteration 17916 (2.3044 iter/s, 5.20743s/12 iters), loss = 5.05276
I0406 17:46:28.515334 21485 solver.cpp:237]     Train net output #0: loss = 5.05276 (* 1 = 5.05276 loss)
I0406 17:46:28.515343 21485 sgd_solver.cpp:105] Iteration 17916, lr = 0.05
I0406 17:46:33.859581 21485 solver.cpp:218] Iteration 17928 (2.24541 iter/s, 5.34423s/12 iters), loss = 4.96783
I0406 17:46:33.859640 21485 solver.cpp:237]     Train net output #0: loss = 4.96783 (* 1 = 4.96783 loss)
I0406 17:46:33.859649 21485 sgd_solver.cpp:105] Iteration 17928, lr = 0.05
I0406 17:46:39.198451 21485 solver.cpp:218] Iteration 17940 (2.2477 iter/s, 5.3388s/12 iters), loss = 5.10844
I0406 17:46:39.198547 21485 solver.cpp:237]     Train net output #0: loss = 5.10844 (* 1 = 5.10844 loss)
I0406 17:46:39.198554 21485 sgd_solver.cpp:105] Iteration 17940, lr = 0.05
I0406 17:46:44.008487 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_17952.caffemodel
I0406 17:46:47.028816 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_17952.solverstate
I0406 17:46:49.917217 21485 solver.cpp:330] Iteration 17952, Testing net (#0)
I0406 17:46:49.917237 21485 net.cpp:676] Ignoring source layer train-data
I0406 17:46:51.913374 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:46:54.406175 21485 solver.cpp:397]     Test net output #0: accuracy = 0.00980392
I0406 17:46:54.406211 21485 solver.cpp:397]     Test net output #1: loss = 5.17529 (* 1 = 5.17529 loss)
I0406 17:46:54.542286 21485 solver.cpp:218] Iteration 17952 (0.782078 iter/s, 15.3437s/12 iters), loss = 5.07943
I0406 17:46:54.542353 21485 solver.cpp:237]     Train net output #0: loss = 5.07943 (* 1 = 5.07943 loss)
I0406 17:46:54.542361 21485 sgd_solver.cpp:105] Iteration 17952, lr = 0.05
I0406 17:46:58.741828 21485 solver.cpp:218] Iteration 17964 (2.85751 iter/s, 4.19946s/12 iters), loss = 5.14188
I0406 17:46:58.741873 21485 solver.cpp:237]     Train net output #0: loss = 5.14188 (* 1 = 5.14188 loss)
I0406 17:46:58.741878 21485 sgd_solver.cpp:105] Iteration 17964, lr = 0.05
I0406 17:47:04.027001 21485 solver.cpp:218] Iteration 17976 (2.27053 iter/s, 5.28511s/12 iters), loss = 5.0548
I0406 17:47:04.027040 21485 solver.cpp:237]     Train net output #0: loss = 5.0548 (* 1 = 5.0548 loss)
I0406 17:47:04.027047 21485 sgd_solver.cpp:105] Iteration 17976, lr = 0.05
I0406 17:47:09.339169 21485 solver.cpp:218] Iteration 17988 (2.25899 iter/s, 5.31211s/12 iters), loss = 4.95268
I0406 17:47:09.339280 21485 solver.cpp:237]     Train net output #0: loss = 4.95268 (* 1 = 4.95268 loss)
I0406 17:47:09.339287 21485 sgd_solver.cpp:105] Iteration 17988, lr = 0.05
I0406 17:47:14.667497 21485 solver.cpp:218] Iteration 18000 (2.25217 iter/s, 5.3282s/12 iters), loss = 5.05015
I0406 17:47:14.667543 21485 solver.cpp:237]     Train net output #0: loss = 5.05015 (* 1 = 5.05015 loss)
I0406 17:47:14.667549 21485 sgd_solver.cpp:105] Iteration 18000, lr = 0.05
I0406 17:47:16.615167 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:47:19.719470 21485 solver.cpp:218] Iteration 18012 (2.37534 iter/s, 5.05191s/12 iters), loss = 5.02596
I0406 17:47:19.719529 21485 solver.cpp:237]     Train net output #0: loss = 5.02596 (* 1 = 5.02596 loss)
I0406 17:47:19.719537 21485 sgd_solver.cpp:105] Iteration 18012, lr = 0.05
I0406 17:47:24.581353 21485 solver.cpp:218] Iteration 18024 (2.46822 iter/s, 4.86181s/12 iters), loss = 5.06634
I0406 17:47:24.581394 21485 solver.cpp:237]     Train net output #0: loss = 5.06634 (* 1 = 5.06634 loss)
I0406 17:47:24.581400 21485 sgd_solver.cpp:105] Iteration 18024, lr = 0.05
I0406 17:47:29.592149 21485 solver.cpp:218] Iteration 18036 (2.39486 iter/s, 5.01073s/12 iters), loss = 5.07529
I0406 17:47:29.592208 21485 solver.cpp:237]     Train net output #0: loss = 5.07529 (* 1 = 5.07529 loss)
I0406 17:47:29.592219 21485 sgd_solver.cpp:105] Iteration 18036, lr = 0.05
I0406 17:47:29.592600 21485 blocking_queue.cpp:49] Waiting for data
I0406 17:47:34.897704 21485 solver.cpp:218] Iteration 18048 (2.26181 iter/s, 5.30549s/12 iters), loss = 4.97872
I0406 17:47:34.897756 21485 solver.cpp:237]     Train net output #0: loss = 4.97872 (* 1 = 4.97872 loss)
I0406 17:47:34.897764 21485 sgd_solver.cpp:105] Iteration 18048, lr = 0.05
I0406 17:47:36.968282 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_18054.caffemodel
I0406 17:47:39.954074 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_18054.solverstate
I0406 17:47:42.846702 21485 solver.cpp:330] Iteration 18054, Testing net (#0)
I0406 17:47:42.846721 21485 net.cpp:676] Ignoring source layer train-data
I0406 17:47:44.751922 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:47:47.192961 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0110294
I0406 17:47:47.192996 21485 solver.cpp:397]     Test net output #1: loss = 5.14009 (* 1 = 5.14009 loss)
I0406 17:47:48.982985 21485 solver.cpp:218] Iteration 18060 (0.851957 iter/s, 14.0852s/12 iters), loss = 5.0345
I0406 17:47:48.983031 21485 solver.cpp:237]     Train net output #0: loss = 5.0345 (* 1 = 5.0345 loss)
I0406 17:47:48.983037 21485 sgd_solver.cpp:105] Iteration 18060, lr = 0.05
I0406 17:47:54.166110 21485 solver.cpp:218] Iteration 18072 (2.31523 iter/s, 5.18306s/12 iters), loss = 5.17115
I0406 17:47:54.166163 21485 solver.cpp:237]     Train net output #0: loss = 5.17115 (* 1 = 5.17115 loss)
I0406 17:47:54.166172 21485 sgd_solver.cpp:105] Iteration 18072, lr = 0.05
I0406 17:47:59.425390 21485 solver.cpp:218] Iteration 18084 (2.28171 iter/s, 5.25921s/12 iters), loss = 5.0229
I0406 17:47:59.425442 21485 solver.cpp:237]     Train net output #0: loss = 5.0229 (* 1 = 5.0229 loss)
I0406 17:47:59.425449 21485 sgd_solver.cpp:105] Iteration 18084, lr = 0.05
I0406 17:48:04.705199 21485 solver.cpp:218] Iteration 18096 (2.27284 iter/s, 5.27974s/12 iters), loss = 4.97288
I0406 17:48:04.705252 21485 solver.cpp:237]     Train net output #0: loss = 4.97288 (* 1 = 4.97288 loss)
I0406 17:48:04.705261 21485 sgd_solver.cpp:105] Iteration 18096, lr = 0.05
I0406 17:48:09.006911 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:48:10.047868 21485 solver.cpp:218] Iteration 18108 (2.2461 iter/s, 5.3426s/12 iters), loss = 5.05739
I0406 17:48:10.048043 21485 solver.cpp:237]     Train net output #0: loss = 5.05739 (* 1 = 5.05739 loss)
I0406 17:48:10.048053 21485 sgd_solver.cpp:105] Iteration 18108, lr = 0.05
I0406 17:48:15.345005 21485 solver.cpp:218] Iteration 18120 (2.26545 iter/s, 5.29695s/12 iters), loss = 5.07438
I0406 17:48:15.345053 21485 solver.cpp:237]     Train net output #0: loss = 5.07438 (* 1 = 5.07438 loss)
I0406 17:48:15.345060 21485 sgd_solver.cpp:105] Iteration 18120, lr = 0.05
I0406 17:48:20.726377 21485 solver.cpp:218] Iteration 18132 (2.22994 iter/s, 5.38132s/12 iters), loss = 5.0483
I0406 17:48:20.726418 21485 solver.cpp:237]     Train net output #0: loss = 5.0483 (* 1 = 5.0483 loss)
I0406 17:48:20.726423 21485 sgd_solver.cpp:105] Iteration 18132, lr = 0.05
I0406 17:48:25.836068 21485 solver.cpp:218] Iteration 18144 (2.34851 iter/s, 5.10963s/12 iters), loss = 4.88238
I0406 17:48:25.836119 21485 solver.cpp:237]     Train net output #0: loss = 4.88238 (* 1 = 4.88238 loss)
I0406 17:48:25.836127 21485 sgd_solver.cpp:105] Iteration 18144, lr = 0.05
I0406 17:48:30.603533 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_18156.caffemodel
I0406 17:48:34.061306 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_18156.solverstate
I0406 17:48:36.414142 21485 solver.cpp:330] Iteration 18156, Testing net (#0)
I0406 17:48:36.414162 21485 net.cpp:676] Ignoring source layer train-data
I0406 17:48:38.324982 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:48:40.795671 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0110294
I0406 17:48:40.795747 21485 solver.cpp:397]     Test net output #1: loss = 5.14423 (* 1 = 5.14423 loss)
I0406 17:48:40.931571 21485 solver.cpp:218] Iteration 18156 (0.794942 iter/s, 15.0954s/12 iters), loss = 5.08386
I0406 17:48:40.931628 21485 solver.cpp:237]     Train net output #0: loss = 5.08386 (* 1 = 5.08386 loss)
I0406 17:48:40.931635 21485 sgd_solver.cpp:105] Iteration 18156, lr = 0.05
I0406 17:48:45.231781 21485 solver.cpp:218] Iteration 18168 (2.79061 iter/s, 4.30014s/12 iters), loss = 4.95497
I0406 17:48:45.231828 21485 solver.cpp:237]     Train net output #0: loss = 4.95497 (* 1 = 4.95497 loss)
I0406 17:48:45.231834 21485 sgd_solver.cpp:105] Iteration 18168, lr = 0.05
I0406 17:48:50.571624 21485 solver.cpp:218] Iteration 18180 (2.24728 iter/s, 5.33978s/12 iters), loss = 4.96892
I0406 17:48:50.571669 21485 solver.cpp:237]     Train net output #0: loss = 4.96892 (* 1 = 4.96892 loss)
I0406 17:48:50.571676 21485 sgd_solver.cpp:105] Iteration 18180, lr = 0.05
I0406 17:48:55.779253 21485 solver.cpp:218] Iteration 18192 (2.30434 iter/s, 5.20757s/12 iters), loss = 4.98032
I0406 17:48:55.779295 21485 solver.cpp:237]     Train net output #0: loss = 4.98032 (* 1 = 4.98032 loss)
I0406 17:48:55.779300 21485 sgd_solver.cpp:105] Iteration 18192, lr = 0.05
I0406 17:49:01.038600 21485 solver.cpp:218] Iteration 18204 (2.28168 iter/s, 5.25929s/12 iters), loss = 5.03357
I0406 17:49:01.038640 21485 solver.cpp:237]     Train net output #0: loss = 5.03357 (* 1 = 5.03357 loss)
I0406 17:49:01.038646 21485 sgd_solver.cpp:105] Iteration 18204, lr = 0.05
I0406 17:49:02.382946 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:49:06.284626 21485 solver.cpp:218] Iteration 18216 (2.28747 iter/s, 5.24596s/12 iters), loss = 5.22602
I0406 17:49:06.284679 21485 solver.cpp:237]     Train net output #0: loss = 5.22602 (* 1 = 5.22602 loss)
I0406 17:49:06.284687 21485 sgd_solver.cpp:105] Iteration 18216, lr = 0.05
I0406 17:49:11.469141 21485 solver.cpp:218] Iteration 18228 (2.31462 iter/s, 5.18444s/12 iters), loss = 5.2717
I0406 17:49:11.469274 21485 solver.cpp:237]     Train net output #0: loss = 5.2717 (* 1 = 5.2717 loss)
I0406 17:49:11.469282 21485 sgd_solver.cpp:105] Iteration 18228, lr = 0.05
I0406 17:49:16.894946 21485 solver.cpp:218] Iteration 18240 (2.21171 iter/s, 5.42566s/12 iters), loss = 5.24479
I0406 17:49:16.895002 21485 solver.cpp:237]     Train net output #0: loss = 5.24479 (* 1 = 5.24479 loss)
I0406 17:49:16.895011 21485 sgd_solver.cpp:105] Iteration 18240, lr = 0.05
I0406 17:49:22.026571 21485 solver.cpp:218] Iteration 18252 (2.33847 iter/s, 5.13155s/12 iters), loss = 5.30556
I0406 17:49:22.026616 21485 solver.cpp:237]     Train net output #0: loss = 5.30556 (* 1 = 5.30556 loss)
I0406 17:49:22.026623 21485 sgd_solver.cpp:105] Iteration 18252, lr = 0.05
I0406 17:49:24.189767 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_18258.caffemodel
I0406 17:49:28.002035 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_18258.solverstate
I0406 17:49:30.329154 21485 solver.cpp:330] Iteration 18258, Testing net (#0)
I0406 17:49:30.329174 21485 net.cpp:676] Ignoring source layer train-data
I0406 17:49:32.171432 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:49:34.684101 21485 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 17:49:34.684135 21485 solver.cpp:397]     Test net output #1: loss = 5.28318 (* 1 = 5.28318 loss)
I0406 17:49:36.520012 21485 solver.cpp:218] Iteration 18264 (0.827964 iter/s, 14.4934s/12 iters), loss = 5.31195
I0406 17:49:36.520066 21485 solver.cpp:237]     Train net output #0: loss = 5.31195 (* 1 = 5.31195 loss)
I0406 17:49:36.520073 21485 sgd_solver.cpp:105] Iteration 18264, lr = 0.05
I0406 17:49:41.586704 21485 solver.cpp:218] Iteration 18276 (2.36844 iter/s, 5.06663s/12 iters), loss = 5.26603
I0406 17:49:41.586802 21485 solver.cpp:237]     Train net output #0: loss = 5.26603 (* 1 = 5.26603 loss)
I0406 17:49:41.586808 21485 sgd_solver.cpp:105] Iteration 18276, lr = 0.05
I0406 17:49:46.740674 21485 solver.cpp:218] Iteration 18288 (2.32835 iter/s, 5.15386s/12 iters), loss = 5.2995
I0406 17:49:46.740718 21485 solver.cpp:237]     Train net output #0: loss = 5.2995 (* 1 = 5.2995 loss)
I0406 17:49:46.740725 21485 sgd_solver.cpp:105] Iteration 18288, lr = 0.05
I0406 17:49:51.944433 21485 solver.cpp:218] Iteration 18300 (2.30605 iter/s, 5.2037s/12 iters), loss = 5.30276
I0406 17:49:51.944473 21485 solver.cpp:237]     Train net output #0: loss = 5.30276 (* 1 = 5.30276 loss)
I0406 17:49:51.944479 21485 sgd_solver.cpp:105] Iteration 18300, lr = 0.05
I0406 17:49:55.415097 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:49:57.108686 21485 solver.cpp:218] Iteration 18312 (2.32369 iter/s, 5.16419s/12 iters), loss = 5.23747
I0406 17:49:57.108738 21485 solver.cpp:237]     Train net output #0: loss = 5.23747 (* 1 = 5.23747 loss)
I0406 17:49:57.108744 21485 sgd_solver.cpp:105] Iteration 18312, lr = 0.05
I0406 17:50:02.362700 21485 solver.cpp:218] Iteration 18324 (2.284 iter/s, 5.25395s/12 iters), loss = 5.27902
I0406 17:50:02.362742 21485 solver.cpp:237]     Train net output #0: loss = 5.27902 (* 1 = 5.27902 loss)
I0406 17:50:02.362747 21485 sgd_solver.cpp:105] Iteration 18324, lr = 0.05
I0406 17:50:07.612668 21485 solver.cpp:218] Iteration 18336 (2.28575 iter/s, 5.24992s/12 iters), loss = 5.28413
I0406 17:50:07.612704 21485 solver.cpp:237]     Train net output #0: loss = 5.28413 (* 1 = 5.28413 loss)
I0406 17:50:07.612710 21485 sgd_solver.cpp:105] Iteration 18336, lr = 0.05
I0406 17:50:12.851632 21485 solver.cpp:218] Iteration 18348 (2.29055 iter/s, 5.23891s/12 iters), loss = 5.26146
I0406 17:50:12.851768 21485 solver.cpp:237]     Train net output #0: loss = 5.26146 (* 1 = 5.26146 loss)
I0406 17:50:12.851775 21485 sgd_solver.cpp:105] Iteration 18348, lr = 0.05
I0406 17:50:17.648995 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_18360.caffemodel
I0406 17:50:23.261176 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_18360.solverstate
I0406 17:50:25.577728 21485 solver.cpp:330] Iteration 18360, Testing net (#0)
I0406 17:50:25.577750 21485 net.cpp:676] Ignoring source layer train-data
I0406 17:50:27.396270 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:50:30.091574 21485 solver.cpp:397]     Test net output #0: accuracy = 0.00612745
I0406 17:50:30.091609 21485 solver.cpp:397]     Test net output #1: loss = 5.25737 (* 1 = 5.25737 loss)
I0406 17:50:30.231698 21485 solver.cpp:218] Iteration 18360 (0.690452 iter/s, 17.3799s/12 iters), loss = 5.26601
I0406 17:50:30.231747 21485 solver.cpp:237]     Train net output #0: loss = 5.26601 (* 1 = 5.26601 loss)
I0406 17:50:30.231753 21485 sgd_solver.cpp:105] Iteration 18360, lr = 0.05
I0406 17:50:34.665722 21485 solver.cpp:218] Iteration 18372 (2.70639 iter/s, 4.43396s/12 iters), loss = 5.24955
I0406 17:50:34.665766 21485 solver.cpp:237]     Train net output #0: loss = 5.24955 (* 1 = 5.24955 loss)
I0406 17:50:34.665771 21485 sgd_solver.cpp:105] Iteration 18372, lr = 0.05
I0406 17:50:39.632956 21485 solver.cpp:218] Iteration 18384 (2.41586 iter/s, 4.96718s/12 iters), loss = 5.20099
I0406 17:50:39.632997 21485 solver.cpp:237]     Train net output #0: loss = 5.20099 (* 1 = 5.20099 loss)
I0406 17:50:39.633002 21485 sgd_solver.cpp:105] Iteration 18384, lr = 0.05
I0406 17:50:44.859002 21485 solver.cpp:218] Iteration 18396 (2.29622 iter/s, 5.22599s/12 iters), loss = 5.23893
I0406 17:50:44.859117 21485 solver.cpp:237]     Train net output #0: loss = 5.23893 (* 1 = 5.23893 loss)
I0406 17:50:44.859127 21485 sgd_solver.cpp:105] Iteration 18396, lr = 0.05
I0406 17:50:50.179615 21485 solver.cpp:218] Iteration 18408 (2.25543 iter/s, 5.32048s/12 iters), loss = 5.29059
I0406 17:50:50.179666 21485 solver.cpp:237]     Train net output #0: loss = 5.29059 (* 1 = 5.29059 loss)
I0406 17:50:50.179674 21485 sgd_solver.cpp:105] Iteration 18408, lr = 0.05
I0406 17:50:50.761549 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:50:55.582255 21485 solver.cpp:218] Iteration 18420 (2.22116 iter/s, 5.40257s/12 iters), loss = 5.22384
I0406 17:50:55.582302 21485 solver.cpp:237]     Train net output #0: loss = 5.22384 (* 1 = 5.22384 loss)
I0406 17:50:55.582309 21485 sgd_solver.cpp:105] Iteration 18420, lr = 0.05
I0406 17:51:00.989826 21485 solver.cpp:218] Iteration 18432 (2.21914 iter/s, 5.40751s/12 iters), loss = 5.23472
I0406 17:51:00.989866 21485 solver.cpp:237]     Train net output #0: loss = 5.23472 (* 1 = 5.23472 loss)
I0406 17:51:00.989871 21485 sgd_solver.cpp:105] Iteration 18432, lr = 0.05
I0406 17:51:06.381579 21485 solver.cpp:218] Iteration 18444 (2.22565 iter/s, 5.39169s/12 iters), loss = 5.21773
I0406 17:51:06.381631 21485 solver.cpp:237]     Train net output #0: loss = 5.21773 (* 1 = 5.21773 loss)
I0406 17:51:06.381639 21485 sgd_solver.cpp:105] Iteration 18444, lr = 0.05
I0406 17:51:11.707041 21485 solver.cpp:218] Iteration 18456 (2.25335 iter/s, 5.3254s/12 iters), loss = 5.16866
I0406 17:51:11.707087 21485 solver.cpp:237]     Train net output #0: loss = 5.16866 (* 1 = 5.16866 loss)
I0406 17:51:11.707093 21485 sgd_solver.cpp:105] Iteration 18456, lr = 0.05
I0406 17:51:13.859062 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_18462.caffemodel
I0406 17:51:16.988351 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_18462.solverstate
I0406 17:51:20.748066 21485 solver.cpp:330] Iteration 18462, Testing net (#0)
I0406 17:51:20.748085 21485 net.cpp:676] Ignoring source layer train-data
I0406 17:51:22.675338 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:51:25.461275 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0110294
I0406 17:51:25.461310 21485 solver.cpp:397]     Test net output #1: loss = 5.21554 (* 1 = 5.21554 loss)
I0406 17:51:27.664804 21485 solver.cpp:218] Iteration 18468 (0.751988 iter/s, 15.9577s/12 iters), loss = 5.15627
I0406 17:51:27.664844 21485 solver.cpp:237]     Train net output #0: loss = 5.15627 (* 1 = 5.15627 loss)
I0406 17:51:27.664850 21485 sgd_solver.cpp:105] Iteration 18468, lr = 0.05
I0406 17:51:32.890395 21485 solver.cpp:218] Iteration 18480 (2.29641 iter/s, 5.22554s/12 iters), loss = 5.16259
I0406 17:51:32.890442 21485 solver.cpp:237]     Train net output #0: loss = 5.16259 (* 1 = 5.16259 loss)
I0406 17:51:32.890447 21485 sgd_solver.cpp:105] Iteration 18480, lr = 0.05
I0406 17:51:38.198103 21485 solver.cpp:218] Iteration 18492 (2.26089 iter/s, 5.30764s/12 iters), loss = 5.18471
I0406 17:51:38.198153 21485 solver.cpp:237]     Train net output #0: loss = 5.18471 (* 1 = 5.18471 loss)
I0406 17:51:38.198158 21485 sgd_solver.cpp:105] Iteration 18492, lr = 0.05
I0406 17:51:43.769086 21485 solver.cpp:218] Iteration 18504 (2.15404 iter/s, 5.57092s/12 iters), loss = 5.23432
I0406 17:51:43.769129 21485 solver.cpp:237]     Train net output #0: loss = 5.23432 (* 1 = 5.23432 loss)
I0406 17:51:43.769135 21485 sgd_solver.cpp:105] Iteration 18504, lr = 0.05
I0406 17:51:46.566033 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:51:48.976799 21485 solver.cpp:218] Iteration 18516 (2.3043 iter/s, 5.20765s/12 iters), loss = 5.20663
I0406 17:51:48.976938 21485 solver.cpp:237]     Train net output #0: loss = 5.20663 (* 1 = 5.20663 loss)
I0406 17:51:48.976946 21485 sgd_solver.cpp:105] Iteration 18516, lr = 0.05
I0406 17:51:54.340279 21485 solver.cpp:218] Iteration 18528 (2.23741 iter/s, 5.36333s/12 iters), loss = 5.13991
I0406 17:51:54.340327 21485 solver.cpp:237]     Train net output #0: loss = 5.13991 (* 1 = 5.13991 loss)
I0406 17:51:54.340332 21485 sgd_solver.cpp:105] Iteration 18528, lr = 0.05
I0406 17:51:59.904176 21485 solver.cpp:218] Iteration 18540 (2.15679 iter/s, 5.56383s/12 iters), loss = 5.20023
I0406 17:51:59.904238 21485 solver.cpp:237]     Train net output #0: loss = 5.20023 (* 1 = 5.20023 loss)
I0406 17:51:59.904247 21485 sgd_solver.cpp:105] Iteration 18540, lr = 0.05
I0406 17:52:05.232553 21485 solver.cpp:218] Iteration 18552 (2.25212 iter/s, 5.3283s/12 iters), loss = 5.23857
I0406 17:52:05.232604 21485 solver.cpp:237]     Train net output #0: loss = 5.23857 (* 1 = 5.23857 loss)
I0406 17:52:05.232611 21485 sgd_solver.cpp:105] Iteration 18552, lr = 0.05
I0406 17:52:09.974671 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_18564.caffemodel
I0406 17:52:13.160305 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_18564.solverstate
I0406 17:52:15.479902 21485 solver.cpp:330] Iteration 18564, Testing net (#0)
I0406 17:52:15.479921 21485 net.cpp:676] Ignoring source layer train-data
I0406 17:52:17.146107 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:52:20.061590 21485 solver.cpp:397]     Test net output #0: accuracy = 0.00796569
I0406 17:52:20.061712 21485 solver.cpp:397]     Test net output #1: loss = 5.25053 (* 1 = 5.25053 loss)
I0406 17:52:20.199151 21485 solver.cpp:218] Iteration 18564 (0.801789 iter/s, 14.9665s/12 iters), loss = 5.22403
I0406 17:52:20.199205 21485 solver.cpp:237]     Train net output #0: loss = 5.22403 (* 1 = 5.22403 loss)
I0406 17:52:20.199213 21485 sgd_solver.cpp:105] Iteration 18564, lr = 0.05
I0406 17:52:24.348776 21485 solver.cpp:218] Iteration 18576 (2.89188 iter/s, 4.14955s/12 iters), loss = 5.28006
I0406 17:52:24.348846 21485 solver.cpp:237]     Train net output #0: loss = 5.28006 (* 1 = 5.28006 loss)
I0406 17:52:24.348855 21485 sgd_solver.cpp:105] Iteration 18576, lr = 0.05
I0406 17:52:29.667243 21485 solver.cpp:218] Iteration 18588 (2.25632 iter/s, 5.31839s/12 iters), loss = 5.30373
I0406 17:52:29.667282 21485 solver.cpp:237]     Train net output #0: loss = 5.30373 (* 1 = 5.30373 loss)
I0406 17:52:29.667289 21485 sgd_solver.cpp:105] Iteration 18588, lr = 0.05
I0406 17:52:35.012084 21485 solver.cpp:218] Iteration 18600 (2.24518 iter/s, 5.34478s/12 iters), loss = 5.30214
I0406 17:52:35.012140 21485 solver.cpp:237]     Train net output #0: loss = 5.30214 (* 1 = 5.30214 loss)
I0406 17:52:35.012146 21485 sgd_solver.cpp:105] Iteration 18600, lr = 0.05
I0406 17:52:40.320212 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:52:40.540309 21485 solver.cpp:218] Iteration 18612 (2.17071 iter/s, 5.52816s/12 iters), loss = 5.2834
I0406 17:52:40.540352 21485 solver.cpp:237]     Train net output #0: loss = 5.2834 (* 1 = 5.2834 loss)
I0406 17:52:40.540357 21485 sgd_solver.cpp:105] Iteration 18612, lr = 0.05
I0406 17:52:45.667621 21485 solver.cpp:218] Iteration 18624 (2.34043 iter/s, 5.12726s/12 iters), loss = 5.2835
I0406 17:52:45.667665 21485 solver.cpp:237]     Train net output #0: loss = 5.2835 (* 1 = 5.2835 loss)
I0406 17:52:45.667670 21485 sgd_solver.cpp:105] Iteration 18624, lr = 0.05
I0406 17:52:50.962105 21485 solver.cpp:218] Iteration 18636 (2.26654 iter/s, 5.29443s/12 iters), loss = 5.27382
I0406 17:52:50.962235 21485 solver.cpp:237]     Train net output #0: loss = 5.27382 (* 1 = 5.27382 loss)
I0406 17:52:50.962241 21485 sgd_solver.cpp:105] Iteration 18636, lr = 0.05
I0406 17:52:56.436620 21485 solver.cpp:218] Iteration 18648 (2.19203 iter/s, 5.47437s/12 iters), loss = 5.28437
I0406 17:52:56.436666 21485 solver.cpp:237]     Train net output #0: loss = 5.28437 (* 1 = 5.28437 loss)
I0406 17:52:56.436671 21485 sgd_solver.cpp:105] Iteration 18648, lr = 0.05
I0406 17:53:01.560539 21485 solver.cpp:218] Iteration 18660 (2.34198 iter/s, 5.12386s/12 iters), loss = 5.26573
I0406 17:53:01.560590 21485 solver.cpp:237]     Train net output #0: loss = 5.26573 (* 1 = 5.26573 loss)
I0406 17:53:01.560596 21485 sgd_solver.cpp:105] Iteration 18660, lr = 0.05
I0406 17:53:03.835780 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_18666.caffemodel
I0406 17:53:06.839452 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_18666.solverstate
I0406 17:53:09.199880 21485 solver.cpp:330] Iteration 18666, Testing net (#0)
I0406 17:53:09.199899 21485 net.cpp:676] Ignoring source layer train-data
I0406 17:53:11.052186 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:53:13.815505 21485 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 17:53:13.815541 21485 solver.cpp:397]     Test net output #1: loss = 5.27591 (* 1 = 5.27591 loss)
I0406 17:53:15.864557 21485 solver.cpp:218] Iteration 18672 (0.838929 iter/s, 14.304s/12 iters), loss = 5.24688
I0406 17:53:15.864611 21485 solver.cpp:237]     Train net output #0: loss = 5.24688 (* 1 = 5.24688 loss)
I0406 17:53:15.864619 21485 sgd_solver.cpp:105] Iteration 18672, lr = 0.05
I0406 17:53:21.040591 21485 solver.cpp:218] Iteration 18684 (2.31849 iter/s, 5.17579s/12 iters), loss = 5.19211
I0406 17:53:21.040696 21485 solver.cpp:237]     Train net output #0: loss = 5.19211 (* 1 = 5.19211 loss)
I0406 17:53:21.040704 21485 sgd_solver.cpp:105] Iteration 18684, lr = 0.05
I0406 17:53:26.469952 21485 solver.cpp:218] Iteration 18696 (2.21025 iter/s, 5.42924s/12 iters), loss = 5.25818
I0406 17:53:26.469990 21485 solver.cpp:237]     Train net output #0: loss = 5.25818 (* 1 = 5.25818 loss)
I0406 17:53:26.469995 21485 sgd_solver.cpp:105] Iteration 18696, lr = 0.05
I0406 17:53:31.884631 21485 solver.cpp:218] Iteration 18708 (2.21622 iter/s, 5.41463s/12 iters), loss = 5.21003
I0406 17:53:31.884675 21485 solver.cpp:237]     Train net output #0: loss = 5.21003 (* 1 = 5.21003 loss)
I0406 17:53:31.884683 21485 sgd_solver.cpp:105] Iteration 18708, lr = 0.05
I0406 17:53:33.736943 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:53:37.258219 21485 solver.cpp:218] Iteration 18720 (2.23317 iter/s, 5.37353s/12 iters), loss = 5.28639
I0406 17:53:37.258265 21485 solver.cpp:237]     Train net output #0: loss = 5.28639 (* 1 = 5.28639 loss)
I0406 17:53:37.258270 21485 sgd_solver.cpp:105] Iteration 18720, lr = 0.05
I0406 17:53:37.698077 21485 blocking_queue.cpp:49] Waiting for data
I0406 17:53:42.638479 21485 solver.cpp:218] Iteration 18732 (2.2304 iter/s, 5.3802s/12 iters), loss = 5.2055
I0406 17:53:42.644881 21485 solver.cpp:237]     Train net output #0: loss = 5.2055 (* 1 = 5.2055 loss)
I0406 17:53:42.644913 21485 sgd_solver.cpp:105] Iteration 18732, lr = 0.05
I0406 17:53:48.038378 21485 solver.cpp:218] Iteration 18744 (2.2249 iter/s, 5.3935s/12 iters), loss = 5.21964
I0406 17:53:48.038431 21485 solver.cpp:237]     Train net output #0: loss = 5.21964 (* 1 = 5.21964 loss)
I0406 17:53:48.038439 21485 sgd_solver.cpp:105] Iteration 18744, lr = 0.05
I0406 17:53:53.538266 21485 solver.cpp:218] Iteration 18756 (2.18189 iter/s, 5.49982s/12 iters), loss = 5.09995
I0406 17:53:53.538429 21485 solver.cpp:237]     Train net output #0: loss = 5.09995 (* 1 = 5.09995 loss)
I0406 17:53:53.538437 21485 sgd_solver.cpp:105] Iteration 18756, lr = 0.05
I0406 17:53:58.349020 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_18768.caffemodel
I0406 17:54:01.604382 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_18768.solverstate
I0406 17:54:03.917184 21485 solver.cpp:330] Iteration 18768, Testing net (#0)
I0406 17:54:03.917204 21485 net.cpp:676] Ignoring source layer train-data
I0406 17:54:05.603945 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:54:08.613916 21485 solver.cpp:397]     Test net output #0: accuracy = 0.00980392
I0406 17:54:08.613957 21485 solver.cpp:397]     Test net output #1: loss = 5.17271 (* 1 = 5.17271 loss)
I0406 17:54:08.752009 21485 solver.cpp:218] Iteration 18768 (0.788769 iter/s, 15.2136s/12 iters), loss = 5.15677
I0406 17:54:08.752064 21485 solver.cpp:237]     Train net output #0: loss = 5.15677 (* 1 = 5.15677 loss)
I0406 17:54:08.752074 21485 sgd_solver.cpp:105] Iteration 18768, lr = 0.05
I0406 17:54:13.156374 21485 solver.cpp:218] Iteration 18780 (2.72461 iter/s, 4.40429s/12 iters), loss = 5.19026
I0406 17:54:13.156419 21485 solver.cpp:237]     Train net output #0: loss = 5.19026 (* 1 = 5.19026 loss)
I0406 17:54:13.156425 21485 sgd_solver.cpp:105] Iteration 18780, lr = 0.05
I0406 17:54:18.407977 21485 solver.cpp:218] Iteration 18792 (2.28504 iter/s, 5.25154s/12 iters), loss = 5.0579
I0406 17:54:18.408020 21485 solver.cpp:237]     Train net output #0: loss = 5.0579 (* 1 = 5.0579 loss)
I0406 17:54:18.408025 21485 sgd_solver.cpp:105] Iteration 18792, lr = 0.05
I0406 17:54:23.807369 21485 solver.cpp:218] Iteration 18804 (2.2225 iter/s, 5.39933s/12 iters), loss = 5.16882
I0406 17:54:23.807474 21485 solver.cpp:237]     Train net output #0: loss = 5.16882 (* 1 = 5.16882 loss)
I0406 17:54:23.807482 21485 sgd_solver.cpp:105] Iteration 18804, lr = 0.05
I0406 17:54:28.276000 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:54:29.149327 21485 solver.cpp:218] Iteration 18816 (2.24642 iter/s, 5.34184s/12 iters), loss = 5.0875
I0406 17:54:29.149367 21485 solver.cpp:237]     Train net output #0: loss = 5.0875 (* 1 = 5.0875 loss)
I0406 17:54:29.149372 21485 sgd_solver.cpp:105] Iteration 18816, lr = 0.05
I0406 17:54:34.460938 21485 solver.cpp:218] Iteration 18828 (2.2601 iter/s, 5.30951s/12 iters), loss = 5.1313
I0406 17:54:34.460995 21485 solver.cpp:237]     Train net output #0: loss = 5.1313 (* 1 = 5.1313 loss)
I0406 17:54:34.461004 21485 sgd_solver.cpp:105] Iteration 18828, lr = 0.05
I0406 17:54:39.949462 21485 solver.cpp:218] Iteration 18840 (2.18641 iter/s, 5.48845s/12 iters), loss = 5.20205
I0406 17:54:39.949515 21485 solver.cpp:237]     Train net output #0: loss = 5.20205 (* 1 = 5.20205 loss)
I0406 17:54:39.949523 21485 sgd_solver.cpp:105] Iteration 18840, lr = 0.05
I0406 17:54:45.147006 21485 solver.cpp:218] Iteration 18852 (2.30881 iter/s, 5.19748s/12 iters), loss = 5.11012
I0406 17:54:45.147047 21485 solver.cpp:237]     Train net output #0: loss = 5.11012 (* 1 = 5.11012 loss)
I0406 17:54:45.147053 21485 sgd_solver.cpp:105] Iteration 18852, lr = 0.05
I0406 17:54:50.517213 21485 solver.cpp:218] Iteration 18864 (2.23457 iter/s, 5.37015s/12 iters), loss = 5.11374
I0406 17:54:50.517259 21485 solver.cpp:237]     Train net output #0: loss = 5.11374 (* 1 = 5.11374 loss)
I0406 17:54:50.517266 21485 sgd_solver.cpp:105] Iteration 18864, lr = 0.05
I0406 17:54:52.796442 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_18870.caffemodel
I0406 17:54:55.837898 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_18870.solverstate
I0406 17:54:58.157699 21485 solver.cpp:330] Iteration 18870, Testing net (#0)
I0406 17:54:58.157721 21485 net.cpp:676] Ignoring source layer train-data
I0406 17:54:59.848867 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:55:02.853761 21485 solver.cpp:397]     Test net output #0: accuracy = 0.00919118
I0406 17:55:02.853799 21485 solver.cpp:397]     Test net output #1: loss = 5.14509 (* 1 = 5.14509 loss)
I0406 17:55:04.825698 21485 solver.cpp:218] Iteration 18876 (0.838666 iter/s, 14.3084s/12 iters), loss = 5.12355
I0406 17:55:04.825754 21485 solver.cpp:237]     Train net output #0: loss = 5.12355 (* 1 = 5.12355 loss)
I0406 17:55:04.825762 21485 sgd_solver.cpp:105] Iteration 18876, lr = 0.05
I0406 17:55:10.151389 21485 solver.cpp:218] Iteration 18888 (2.25326 iter/s, 5.32561s/12 iters), loss = 5.13278
I0406 17:55:10.151451 21485 solver.cpp:237]     Train net output #0: loss = 5.13278 (* 1 = 5.13278 loss)
I0406 17:55:10.151459 21485 sgd_solver.cpp:105] Iteration 18888, lr = 0.05
I0406 17:55:15.610643 21485 solver.cpp:218] Iteration 18900 (2.19813 iter/s, 5.45918s/12 iters), loss = 5.03181
I0406 17:55:15.610688 21485 solver.cpp:237]     Train net output #0: loss = 5.03181 (* 1 = 5.03181 loss)
I0406 17:55:15.610694 21485 sgd_solver.cpp:105] Iteration 18900, lr = 0.05
I0406 17:55:21.069454 21485 solver.cpp:218] Iteration 18912 (2.1983 iter/s, 5.45875s/12 iters), loss = 5.11714
I0406 17:55:21.069505 21485 solver.cpp:237]     Train net output #0: loss = 5.11714 (* 1 = 5.11714 loss)
I0406 17:55:21.069514 21485 sgd_solver.cpp:105] Iteration 18912, lr = 0.05
I0406 17:55:22.508561 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:55:26.425995 21485 solver.cpp:218] Iteration 18924 (2.24028 iter/s, 5.35647s/12 iters), loss = 5.02742
I0406 17:55:26.426124 21485 solver.cpp:237]     Train net output #0: loss = 5.02742 (* 1 = 5.02742 loss)
I0406 17:55:26.426132 21485 sgd_solver.cpp:105] Iteration 18924, lr = 0.05
I0406 17:55:31.670547 21485 solver.cpp:218] Iteration 18936 (2.28815 iter/s, 5.24441s/12 iters), loss = 5.10524
I0406 17:55:31.670606 21485 solver.cpp:237]     Train net output #0: loss = 5.10524 (* 1 = 5.10524 loss)
I0406 17:55:31.670615 21485 sgd_solver.cpp:105] Iteration 18936, lr = 0.05
I0406 17:55:36.997956 21485 solver.cpp:218] Iteration 18948 (2.25253 iter/s, 5.32734s/12 iters), loss = 5.10626
I0406 17:55:36.998003 21485 solver.cpp:237]     Train net output #0: loss = 5.10626 (* 1 = 5.10626 loss)
I0406 17:55:36.998010 21485 sgd_solver.cpp:105] Iteration 18948, lr = 0.05
I0406 17:55:42.103876 21485 solver.cpp:218] Iteration 18960 (2.35024 iter/s, 5.10586s/12 iters), loss = 4.99593
I0406 17:55:42.103935 21485 solver.cpp:237]     Train net output #0: loss = 4.99593 (* 1 = 4.99593 loss)
I0406 17:55:42.103943 21485 sgd_solver.cpp:105] Iteration 18960, lr = 0.05
I0406 17:55:47.145992 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_18972.caffemodel
I0406 17:55:50.317329 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_18972.solverstate
I0406 17:55:52.982112 21485 solver.cpp:330] Iteration 18972, Testing net (#0)
I0406 17:55:52.982136 21485 net.cpp:676] Ignoring source layer train-data
I0406 17:55:54.679453 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:55:57.654507 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0116422
I0406 17:55:57.654582 21485 solver.cpp:397]     Test net output #1: loss = 5.1372 (* 1 = 5.1372 loss)
I0406 17:55:57.794116 21485 solver.cpp:218] Iteration 18972 (0.764809 iter/s, 15.6902s/12 iters), loss = 5.0633
I0406 17:55:57.794152 21485 solver.cpp:237]     Train net output #0: loss = 5.0633 (* 1 = 5.0633 loss)
I0406 17:55:57.794158 21485 sgd_solver.cpp:105] Iteration 18972, lr = 0.05
I0406 17:56:02.128013 21485 solver.cpp:218] Iteration 18984 (2.76891 iter/s, 4.33384s/12 iters), loss = 5.28167
I0406 17:56:02.128074 21485 solver.cpp:237]     Train net output #0: loss = 5.28167 (* 1 = 5.28167 loss)
I0406 17:56:02.128082 21485 sgd_solver.cpp:105] Iteration 18984, lr = 0.05
I0406 17:56:07.319238 21485 solver.cpp:218] Iteration 18996 (2.31162 iter/s, 5.19115s/12 iters), loss = 5.04783
I0406 17:56:07.319279 21485 solver.cpp:237]     Train net output #0: loss = 5.04783 (* 1 = 5.04783 loss)
I0406 17:56:07.319285 21485 sgd_solver.cpp:105] Iteration 18996, lr = 0.05
I0406 17:56:12.569293 21485 solver.cpp:218] Iteration 19008 (2.28572 iter/s, 5.25s/12 iters), loss = 5.16223
I0406 17:56:12.569344 21485 solver.cpp:237]     Train net output #0: loss = 5.16223 (* 1 = 5.16223 loss)
I0406 17:56:12.569351 21485 sgd_solver.cpp:105] Iteration 19008, lr = 0.05
I0406 17:56:16.135048 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:56:17.709182 21485 solver.cpp:218] Iteration 19020 (2.33471 iter/s, 5.13982s/12 iters), loss = 4.97762
I0406 17:56:17.709224 21485 solver.cpp:237]     Train net output #0: loss = 4.97762 (* 1 = 4.97762 loss)
I0406 17:56:17.709230 21485 sgd_solver.cpp:105] Iteration 19020, lr = 0.05
I0406 17:56:22.787391 21485 solver.cpp:218] Iteration 19032 (2.36306 iter/s, 5.07816s/12 iters), loss = 5.07426
I0406 17:56:22.787436 21485 solver.cpp:237]     Train net output #0: loss = 5.07426 (* 1 = 5.07426 loss)
I0406 17:56:22.787442 21485 sgd_solver.cpp:105] Iteration 19032, lr = 0.05
I0406 17:56:27.990130 21485 solver.cpp:218] Iteration 19044 (2.3065 iter/s, 5.20268s/12 iters), loss = 5.12862
I0406 17:56:27.990252 21485 solver.cpp:237]     Train net output #0: loss = 5.12862 (* 1 = 5.12862 loss)
I0406 17:56:27.990259 21485 sgd_solver.cpp:105] Iteration 19044, lr = 0.05
I0406 17:56:32.743983 21485 solver.cpp:218] Iteration 19056 (2.52434 iter/s, 4.75372s/12 iters), loss = 5.08678
I0406 17:56:32.744019 21485 solver.cpp:237]     Train net output #0: loss = 5.08678 (* 1 = 5.08678 loss)
I0406 17:56:32.744025 21485 sgd_solver.cpp:105] Iteration 19056, lr = 0.05
I0406 17:56:37.975879 21485 solver.cpp:218] Iteration 19068 (2.29365 iter/s, 5.23184s/12 iters), loss = 5.10472
I0406 17:56:37.975937 21485 solver.cpp:237]     Train net output #0: loss = 5.10472 (* 1 = 5.10472 loss)
I0406 17:56:37.975945 21485 sgd_solver.cpp:105] Iteration 19068, lr = 0.05
I0406 17:56:40.136379 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_19074.caffemodel
I0406 17:56:43.182138 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_19074.solverstate
I0406 17:56:45.507727 21485 solver.cpp:330] Iteration 19074, Testing net (#0)
I0406 17:56:45.507750 21485 net.cpp:676] Ignoring source layer train-data
I0406 17:56:47.081331 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:56:49.923589 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0116422
I0406 17:56:49.923619 21485 solver.cpp:397]     Test net output #1: loss = 5.13678 (* 1 = 5.13678 loss)
I0406 17:56:51.726953 21485 solver.cpp:218] Iteration 19080 (0.872663 iter/s, 13.751s/12 iters), loss = 4.95332
I0406 17:56:51.727010 21485 solver.cpp:237]     Train net output #0: loss = 4.95332 (* 1 = 4.95332 loss)
I0406 17:56:51.727018 21485 sgd_solver.cpp:105] Iteration 19080, lr = 0.05
I0406 17:56:56.926890 21485 solver.cpp:218] Iteration 19092 (2.30775 iter/s, 5.19987s/12 iters), loss = 5.11039
I0406 17:56:56.926931 21485 solver.cpp:237]     Train net output #0: loss = 5.11039 (* 1 = 5.11039 loss)
I0406 17:56:56.926936 21485 sgd_solver.cpp:105] Iteration 19092, lr = 0.05
I0406 17:57:02.243216 21485 solver.cpp:218] Iteration 19104 (2.25722 iter/s, 5.31627s/12 iters), loss = 5.05568
I0406 17:57:02.243320 21485 solver.cpp:237]     Train net output #0: loss = 5.05568 (* 1 = 5.05568 loss)
I0406 17:57:02.243326 21485 sgd_solver.cpp:105] Iteration 19104, lr = 0.05
I0406 17:57:07.454181 21485 solver.cpp:218] Iteration 19116 (2.30289 iter/s, 5.21085s/12 iters), loss = 5.10847
I0406 17:57:07.454228 21485 solver.cpp:237]     Train net output #0: loss = 5.10847 (* 1 = 5.10847 loss)
I0406 17:57:07.454236 21485 sgd_solver.cpp:105] Iteration 19116, lr = 0.05
I0406 17:57:08.050734 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:57:12.807210 21485 solver.cpp:218] Iteration 19128 (2.24175 iter/s, 5.35297s/12 iters), loss = 5.19842
I0406 17:57:12.807255 21485 solver.cpp:237]     Train net output #0: loss = 5.19842 (* 1 = 5.19842 loss)
I0406 17:57:12.807260 21485 sgd_solver.cpp:105] Iteration 19128, lr = 0.05
I0406 17:57:18.098191 21485 solver.cpp:218] Iteration 19140 (2.26804 iter/s, 5.29092s/12 iters), loss = 5.12916
I0406 17:57:18.098237 21485 solver.cpp:237]     Train net output #0: loss = 5.12916 (* 1 = 5.12916 loss)
I0406 17:57:18.098242 21485 sgd_solver.cpp:105] Iteration 19140, lr = 0.05
I0406 17:57:23.180789 21485 solver.cpp:218] Iteration 19152 (2.36102 iter/s, 5.08254s/12 iters), loss = 5.08565
I0406 17:57:23.180831 21485 solver.cpp:237]     Train net output #0: loss = 5.08565 (* 1 = 5.08565 loss)
I0406 17:57:23.180837 21485 sgd_solver.cpp:105] Iteration 19152, lr = 0.05
I0406 17:57:28.690824 21485 solver.cpp:218] Iteration 19164 (2.17787 iter/s, 5.50997s/12 iters), loss = 5.09763
I0406 17:57:28.690877 21485 solver.cpp:237]     Train net output #0: loss = 5.09763 (* 1 = 5.09763 loss)
I0406 17:57:28.690884 21485 sgd_solver.cpp:105] Iteration 19164, lr = 0.05
I0406 17:57:33.275698 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_19176.caffemodel
I0406 17:57:36.306638 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_19176.solverstate
I0406 17:57:38.608650 21485 solver.cpp:330] Iteration 19176, Testing net (#0)
I0406 17:57:38.608675 21485 net.cpp:676] Ignoring source layer train-data
I0406 17:57:40.064643 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:57:42.943971 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0116422
I0406 17:57:42.944006 21485 solver.cpp:397]     Test net output #1: loss = 5.13158 (* 1 = 5.13158 loss)
I0406 17:57:43.074589 21485 solver.cpp:218] Iteration 19176 (0.834278 iter/s, 14.3837s/12 iters), loss = 5.04437
I0406 17:57:43.076192 21485 solver.cpp:237]     Train net output #0: loss = 5.04437 (* 1 = 5.04437 loss)
I0406 17:57:43.076203 21485 sgd_solver.cpp:105] Iteration 19176, lr = 0.05
I0406 17:57:47.059643 21485 solver.cpp:218] Iteration 19188 (3.01247 iter/s, 3.98344s/12 iters), loss = 5.01585
I0406 17:57:47.059686 21485 solver.cpp:237]     Train net output #0: loss = 5.01585 (* 1 = 5.01585 loss)
I0406 17:57:47.059691 21485 sgd_solver.cpp:105] Iteration 19188, lr = 0.05
I0406 17:57:52.149474 21485 solver.cpp:218] Iteration 19200 (2.35767 iter/s, 5.08977s/12 iters), loss = 4.99002
I0406 17:57:52.149518 21485 solver.cpp:237]     Train net output #0: loss = 4.99002 (* 1 = 4.99002 loss)
I0406 17:57:52.149523 21485 sgd_solver.cpp:105] Iteration 19200, lr = 0.05
I0406 17:57:57.441716 21485 solver.cpp:218] Iteration 19212 (2.2675 iter/s, 5.29218s/12 iters), loss = 5.04016
I0406 17:57:57.441767 21485 solver.cpp:237]     Train net output #0: loss = 5.04016 (* 1 = 5.04016 loss)
I0406 17:57:57.441776 21485 sgd_solver.cpp:105] Iteration 19212, lr = 0.05
I0406 17:58:00.315846 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:58:02.738257 21485 solver.cpp:218] Iteration 19224 (2.26566 iter/s, 5.29648s/12 iters), loss = 4.9996
I0406 17:58:02.738304 21485 solver.cpp:237]     Train net output #0: loss = 4.9996 (* 1 = 4.9996 loss)
I0406 17:58:02.738312 21485 sgd_solver.cpp:105] Iteration 19224, lr = 0.05
I0406 17:58:07.966521 21485 solver.cpp:218] Iteration 19236 (2.29524 iter/s, 5.2282s/12 iters), loss = 5.02261
I0406 17:58:07.966639 21485 solver.cpp:237]     Train net output #0: loss = 5.02261 (* 1 = 5.02261 loss)
I0406 17:58:07.966645 21485 sgd_solver.cpp:105] Iteration 19236, lr = 0.05
I0406 17:58:13.362612 21485 solver.cpp:218] Iteration 19248 (2.22388 iter/s, 5.39596s/12 iters), loss = 5.03093
I0406 17:58:13.362649 21485 solver.cpp:237]     Train net output #0: loss = 5.03093 (* 1 = 5.03093 loss)
I0406 17:58:13.362654 21485 sgd_solver.cpp:105] Iteration 19248, lr = 0.05
I0406 17:58:18.812731 21485 solver.cpp:218] Iteration 19260 (2.20181 iter/s, 5.45007s/12 iters), loss = 5.04163
I0406 17:58:18.812791 21485 solver.cpp:237]     Train net output #0: loss = 5.04163 (* 1 = 5.04163 loss)
I0406 17:58:18.812800 21485 sgd_solver.cpp:105] Iteration 19260, lr = 0.05
I0406 17:58:24.090386 21485 solver.cpp:218] Iteration 19272 (2.27377 iter/s, 5.27759s/12 iters), loss = 5.02611
I0406 17:58:24.090422 21485 solver.cpp:237]     Train net output #0: loss = 5.02611 (* 1 = 5.02611 loss)
I0406 17:58:24.090427 21485 sgd_solver.cpp:105] Iteration 19272, lr = 0.05
I0406 17:58:26.122836 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_19278.caffemodel
I0406 17:58:29.221534 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_19278.solverstate
I0406 17:58:31.527063 21485 solver.cpp:330] Iteration 19278, Testing net (#0)
I0406 17:58:31.527083 21485 net.cpp:676] Ignoring source layer train-data
I0406 17:58:32.942546 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:58:35.895191 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0116422
I0406 17:58:35.895220 21485 solver.cpp:397]     Test net output #1: loss = 5.13244 (* 1 = 5.13244 loss)
I0406 17:58:37.839722 21485 solver.cpp:218] Iteration 19284 (0.872772 iter/s, 13.7493s/12 iters), loss = 5.14091
I0406 17:58:37.839766 21485 solver.cpp:237]     Train net output #0: loss = 5.14091 (* 1 = 5.14091 loss)
I0406 17:58:37.839771 21485 sgd_solver.cpp:105] Iteration 19284, lr = 0.05
I0406 17:58:42.998072 21485 solver.cpp:218] Iteration 19296 (2.32635 iter/s, 5.1583s/12 iters), loss = 5.10792
I0406 17:58:42.998245 21485 solver.cpp:237]     Train net output #0: loss = 5.10792 (* 1 = 5.10792 loss)
I0406 17:58:42.998253 21485 sgd_solver.cpp:105] Iteration 19296, lr = 0.05
I0406 17:58:48.255861 21485 solver.cpp:218] Iteration 19308 (2.28241 iter/s, 5.25761s/12 iters), loss = 5.03119
I0406 17:58:48.255903 21485 solver.cpp:237]     Train net output #0: loss = 5.03119 (* 1 = 5.03119 loss)
I0406 17:58:48.255908 21485 sgd_solver.cpp:105] Iteration 19308, lr = 0.05
I0406 17:58:52.997228 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:58:53.187674 21485 solver.cpp:218] Iteration 19320 (2.43321 iter/s, 4.93176s/12 iters), loss = 5.04931
I0406 17:58:53.187711 21485 solver.cpp:237]     Train net output #0: loss = 5.04931 (* 1 = 5.04931 loss)
I0406 17:58:53.187716 21485 sgd_solver.cpp:105] Iteration 19320, lr = 0.05
I0406 17:58:58.505645 21485 solver.cpp:218] Iteration 19332 (2.25652 iter/s, 5.31792s/12 iters), loss = 5.05468
I0406 17:58:58.505693 21485 solver.cpp:237]     Train net output #0: loss = 5.05468 (* 1 = 5.05468 loss)
I0406 17:58:58.505700 21485 sgd_solver.cpp:105] Iteration 19332, lr = 0.05
I0406 17:59:03.755084 21485 solver.cpp:218] Iteration 19344 (2.28599 iter/s, 5.24938s/12 iters), loss = 4.97599
I0406 17:59:03.755129 21485 solver.cpp:237]     Train net output #0: loss = 4.97599 (* 1 = 4.97599 loss)
I0406 17:59:03.755136 21485 sgd_solver.cpp:105] Iteration 19344, lr = 0.05
I0406 17:59:09.018045 21485 solver.cpp:218] Iteration 19356 (2.28011 iter/s, 5.2629s/12 iters), loss = 5.16553
I0406 17:59:09.018101 21485 solver.cpp:237]     Train net output #0: loss = 5.16553 (* 1 = 5.16553 loss)
I0406 17:59:09.018111 21485 sgd_solver.cpp:105] Iteration 19356, lr = 0.05
I0406 17:59:14.618733 21485 solver.cpp:218] Iteration 19368 (2.14262 iter/s, 5.60063s/12 iters), loss = 5.11534
I0406 17:59:14.618809 21485 solver.cpp:237]     Train net output #0: loss = 5.11534 (* 1 = 5.11534 loss)
I0406 17:59:14.618816 21485 sgd_solver.cpp:105] Iteration 19368, lr = 0.05
I0406 17:59:19.266285 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_19380.caffemodel
I0406 17:59:22.340596 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_19380.solverstate
I0406 17:59:24.677244 21485 solver.cpp:330] Iteration 19380, Testing net (#0)
I0406 17:59:24.677269 21485 net.cpp:676] Ignoring source layer train-data
I0406 17:59:26.068943 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:59:29.053895 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0110294
I0406 17:59:29.053933 21485 solver.cpp:397]     Test net output #1: loss = 5.1644 (* 1 = 5.1644 loss)
I0406 17:59:29.194317 21485 solver.cpp:218] Iteration 19380 (0.823299 iter/s, 14.5755s/12 iters), loss = 5.08924
I0406 17:59:29.194361 21485 solver.cpp:237]     Train net output #0: loss = 5.08924 (* 1 = 5.08924 loss)
I0406 17:59:29.194366 21485 sgd_solver.cpp:105] Iteration 19380, lr = 0.05
I0406 17:59:33.524202 21485 solver.cpp:218] Iteration 19392 (2.77147 iter/s, 4.32983s/12 iters), loss = 5.04191
I0406 17:59:33.524247 21485 solver.cpp:237]     Train net output #0: loss = 5.04191 (* 1 = 5.04191 loss)
I0406 17:59:33.524252 21485 sgd_solver.cpp:105] Iteration 19392, lr = 0.05
I0406 17:59:38.901487 21485 solver.cpp:218] Iteration 19404 (2.23164 iter/s, 5.37722s/12 iters), loss = 4.9497
I0406 17:59:38.901544 21485 solver.cpp:237]     Train net output #0: loss = 4.9497 (* 1 = 4.9497 loss)
I0406 17:59:38.901554 21485 sgd_solver.cpp:105] Iteration 19404, lr = 0.05
I0406 17:59:39.718919 21485 blocking_queue.cpp:49] Waiting for data
I0406 17:59:44.305243 21485 solver.cpp:218] Iteration 19416 (2.22071 iter/s, 5.40369s/12 iters), loss = 4.96807
I0406 17:59:44.305294 21485 solver.cpp:237]     Train net output #0: loss = 4.96807 (* 1 = 4.96807 loss)
I0406 17:59:44.305303 21485 sgd_solver.cpp:105] Iteration 19416, lr = 0.05
I0406 17:59:46.398358 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 17:59:49.691764 21485 solver.cpp:218] Iteration 19428 (2.22781 iter/s, 5.38646s/12 iters), loss = 4.97945
I0406 17:59:49.691802 21485 solver.cpp:237]     Train net output #0: loss = 4.97945 (* 1 = 4.97945 loss)
I0406 17:59:49.691807 21485 sgd_solver.cpp:105] Iteration 19428, lr = 0.05
I0406 17:59:54.898265 21485 solver.cpp:218] Iteration 19440 (2.30484 iter/s, 5.20644s/12 iters), loss = 5.11889
I0406 17:59:54.898329 21485 solver.cpp:237]     Train net output #0: loss = 5.11889 (* 1 = 5.11889 loss)
I0406 17:59:54.898339 21485 sgd_solver.cpp:105] Iteration 19440, lr = 0.05
I0406 18:00:00.114531 21485 solver.cpp:218] Iteration 19452 (2.30053 iter/s, 5.21619s/12 iters), loss = 5.12273
I0406 18:00:00.114588 21485 solver.cpp:237]     Train net output #0: loss = 5.12273 (* 1 = 5.12273 loss)
I0406 18:00:00.114595 21485 sgd_solver.cpp:105] Iteration 19452, lr = 0.05
I0406 18:00:05.310509 21485 solver.cpp:218] Iteration 19464 (2.30951 iter/s, 5.19591s/12 iters), loss = 4.99121
I0406 18:00:05.310552 21485 solver.cpp:237]     Train net output #0: loss = 4.99121 (* 1 = 4.99121 loss)
I0406 18:00:05.310559 21485 sgd_solver.cpp:105] Iteration 19464, lr = 0.05
I0406 18:00:10.505427 21485 solver.cpp:218] Iteration 19476 (2.30998 iter/s, 5.19486s/12 iters), loss = 5.04855
I0406 18:00:10.505487 21485 solver.cpp:237]     Train net output #0: loss = 5.04855 (* 1 = 5.04855 loss)
I0406 18:00:10.505496 21485 sgd_solver.cpp:105] Iteration 19476, lr = 0.05
I0406 18:00:12.654458 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_19482.caffemodel
I0406 18:00:17.152873 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_19482.solverstate
I0406 18:00:19.491753 21485 solver.cpp:330] Iteration 19482, Testing net (#0)
I0406 18:00:19.491778 21485 net.cpp:676] Ignoring source layer train-data
I0406 18:00:20.802723 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 18:00:23.872638 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0140931
I0406 18:00:23.872673 21485 solver.cpp:397]     Test net output #1: loss = 5.12596 (* 1 = 5.12596 loss)
I0406 18:00:25.480266 21485 solver.cpp:218] Iteration 19488 (0.801348 iter/s, 14.9748s/12 iters), loss = 5.08813
I0406 18:00:25.480317 21485 solver.cpp:237]     Train net output #0: loss = 5.08813 (* 1 = 5.08813 loss)
I0406 18:00:25.480325 21485 sgd_solver.cpp:105] Iteration 19488, lr = 0.05
I0406 18:00:30.434068 21485 solver.cpp:218] Iteration 19500 (2.42241 iter/s, 4.95374s/12 iters), loss = 4.99688
I0406 18:00:30.434110 21485 solver.cpp:237]     Train net output #0: loss = 4.99688 (* 1 = 4.99688 loss)
I0406 18:00:30.434115 21485 sgd_solver.cpp:105] Iteration 19500, lr = 0.05
I0406 18:00:35.757390 21485 solver.cpp:218] Iteration 19512 (2.25426 iter/s, 5.32327s/12 iters), loss = 4.98729
I0406 18:00:35.757428 21485 solver.cpp:237]     Train net output #0: loss = 4.98729 (* 1 = 4.98729 loss)
I0406 18:00:35.757433 21485 sgd_solver.cpp:105] Iteration 19512, lr = 0.05
I0406 18:00:40.172072 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 18:00:41.024516 21485 solver.cpp:218] Iteration 19524 (2.2783 iter/s, 5.26708s/12 iters), loss = 4.90058
I0406 18:00:41.024554 21485 solver.cpp:237]     Train net output #0: loss = 4.90058 (* 1 = 4.90058 loss)
I0406 18:00:41.024559 21485 sgd_solver.cpp:105] Iteration 19524, lr = 0.05
I0406 18:00:46.227257 21485 solver.cpp:218] Iteration 19536 (2.3065 iter/s, 5.20268s/12 iters), loss = 5.02386
I0406 18:00:46.227313 21485 solver.cpp:237]     Train net output #0: loss = 5.02386 (* 1 = 5.02386 loss)
I0406 18:00:46.227320 21485 sgd_solver.cpp:105] Iteration 19536, lr = 0.05
I0406 18:00:51.405611 21485 solver.cpp:218] Iteration 19548 (2.31737 iter/s, 5.17829s/12 iters), loss = 5.06713
I0406 18:00:51.405735 21485 solver.cpp:237]     Train net output #0: loss = 5.06713 (* 1 = 5.06713 loss)
I0406 18:00:51.405741 21485 sgd_solver.cpp:105] Iteration 19548, lr = 0.05
I0406 18:00:56.444398 21485 solver.cpp:218] Iteration 19560 (2.38159 iter/s, 5.03865s/12 iters), loss = 5.13121
I0406 18:00:56.444447 21485 solver.cpp:237]     Train net output #0: loss = 5.13121 (* 1 = 5.13121 loss)
I0406 18:00:56.444453 21485 sgd_solver.cpp:105] Iteration 19560, lr = 0.05
I0406 18:01:01.444720 21485 solver.cpp:218] Iteration 19572 (2.39987 iter/s, 5.00026s/12 iters), loss = 5.0904
I0406 18:01:01.444772 21485 solver.cpp:237]     Train net output #0: loss = 5.0904 (* 1 = 5.0904 loss)
I0406 18:01:01.444780 21485 sgd_solver.cpp:105] Iteration 19572, lr = 0.05
I0406 18:01:05.861150 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_19584.caffemodel
I0406 18:01:09.133353 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_19584.solverstate
I0406 18:01:12.876777 21485 solver.cpp:330] Iteration 19584, Testing net (#0)
I0406 18:01:12.876802 21485 net.cpp:676] Ignoring source layer train-data
I0406 18:01:14.171300 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 18:01:17.279336 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0147059
I0406 18:01:17.279376 21485 solver.cpp:397]     Test net output #1: loss = 5.17228 (* 1 = 5.17228 loss)
I0406 18:01:17.419798 21485 solver.cpp:218] Iteration 19584 (0.751173 iter/s, 15.975s/12 iters), loss = 5.11299
I0406 18:01:17.419836 21485 solver.cpp:237]     Train net output #0: loss = 5.11299 (* 1 = 5.11299 loss)
I0406 18:01:17.419842 21485 sgd_solver.cpp:105] Iteration 19584, lr = 0.05
I0406 18:01:21.701427 21485 solver.cpp:218] Iteration 19596 (2.8027 iter/s, 4.28158s/12 iters), loss = 5.076
I0406 18:01:21.701516 21485 solver.cpp:237]     Train net output #0: loss = 5.076 (* 1 = 5.076 loss)
I0406 18:01:21.701524 21485 sgd_solver.cpp:105] Iteration 19596, lr = 0.05
I0406 18:01:27.028807 21485 solver.cpp:218] Iteration 19608 (2.25256 iter/s, 5.32728s/12 iters), loss = 5.00026
I0406 18:01:27.028865 21485 solver.cpp:237]     Train net output #0: loss = 5.00026 (* 1 = 5.00026 loss)
I0406 18:01:27.028874 21485 sgd_solver.cpp:105] Iteration 19608, lr = 0.05
I0406 18:01:32.308634 21485 solver.cpp:218] Iteration 19620 (2.27283 iter/s, 5.27976s/12 iters), loss = 4.97264
I0406 18:01:32.308676 21485 solver.cpp:237]     Train net output #0: loss = 4.97264 (* 1 = 4.97264 loss)
I0406 18:01:32.308681 21485 sgd_solver.cpp:105] Iteration 19620, lr = 0.05
I0406 18:01:33.766405 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 18:01:37.737681 21485 solver.cpp:218] Iteration 19632 (2.21036 iter/s, 5.42899s/12 iters), loss = 4.97689
I0406 18:01:37.737735 21485 solver.cpp:237]     Train net output #0: loss = 4.97689 (* 1 = 4.97689 loss)
I0406 18:01:37.737742 21485 sgd_solver.cpp:105] Iteration 19632, lr = 0.05
I0406 18:01:43.048874 21485 solver.cpp:218] Iteration 19644 (2.25941 iter/s, 5.31113s/12 iters), loss = 5.02494
I0406 18:01:43.048923 21485 solver.cpp:237]     Train net output #0: loss = 5.02494 (* 1 = 5.02494 loss)
I0406 18:01:43.048928 21485 sgd_solver.cpp:105] Iteration 19644, lr = 0.05
I0406 18:01:48.343847 21485 solver.cpp:218] Iteration 19656 (2.26633 iter/s, 5.29491s/12 iters), loss = 5.0961
I0406 18:01:48.343888 21485 solver.cpp:237]     Train net output #0: loss = 5.0961 (* 1 = 5.0961 loss)
I0406 18:01:48.343894 21485 sgd_solver.cpp:105] Iteration 19656, lr = 0.05
I0406 18:01:53.696470 21485 solver.cpp:218] Iteration 19668 (2.24191 iter/s, 5.35257s/12 iters), loss = 4.93473
I0406 18:01:53.696592 21485 solver.cpp:237]     Train net output #0: loss = 4.93473 (* 1 = 4.93473 loss)
I0406 18:01:53.696599 21485 sgd_solver.cpp:105] Iteration 19668, lr = 0.05
I0406 18:01:58.873112 21485 solver.cpp:218] Iteration 19680 (2.31816 iter/s, 5.17651s/12 iters), loss = 4.969
I0406 18:01:58.873154 21485 solver.cpp:237]     Train net output #0: loss = 4.969 (* 1 = 4.969 loss)
I0406 18:01:58.873160 21485 sgd_solver.cpp:105] Iteration 19680, lr = 0.05
I0406 18:02:00.813279 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_19686.caffemodel
I0406 18:02:03.860643 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_19686.solverstate
I0406 18:02:06.167369 21485 solver.cpp:330] Iteration 19686, Testing net (#0)
I0406 18:02:06.167389 21485 net.cpp:676] Ignoring source layer train-data
I0406 18:02:07.434306 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 18:02:10.547737 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0140931
I0406 18:02:10.547766 21485 solver.cpp:397]     Test net output #1: loss = 5.11922 (* 1 = 5.11922 loss)
I0406 18:02:12.483711 21485 solver.cpp:218] Iteration 19692 (0.881669 iter/s, 13.6106s/12 iters), loss = 5.08785
I0406 18:02:12.483750 21485 solver.cpp:237]     Train net output #0: loss = 5.08785 (* 1 = 5.08785 loss)
I0406 18:02:12.483757 21485 sgd_solver.cpp:105] Iteration 19692, lr = 0.05
I0406 18:02:17.646803 21485 solver.cpp:218] Iteration 19704 (2.32421 iter/s, 5.16304s/12 iters), loss = 4.93165
I0406 18:02:17.646852 21485 solver.cpp:237]     Train net output #0: loss = 4.93165 (* 1 = 4.93165 loss)
I0406 18:02:17.646858 21485 sgd_solver.cpp:105] Iteration 19704, lr = 0.05
I0406 18:02:22.882210 21485 solver.cpp:218] Iteration 19716 (2.29211 iter/s, 5.23534s/12 iters), loss = 4.9064
I0406 18:02:22.882256 21485 solver.cpp:237]     Train net output #0: loss = 4.9064 (* 1 = 4.9064 loss)
I0406 18:02:22.882262 21485 sgd_solver.cpp:105] Iteration 19716, lr = 0.05
I0406 18:02:26.729334 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 18:02:28.381038 21485 solver.cpp:218] Iteration 19728 (2.18231 iter/s, 5.49877s/12 iters), loss = 4.98363
I0406 18:02:28.381083 21485 solver.cpp:237]     Train net output #0: loss = 4.98363 (* 1 = 4.98363 loss)
I0406 18:02:28.381089 21485 sgd_solver.cpp:105] Iteration 19728, lr = 0.05
I0406 18:02:33.695765 21485 solver.cpp:218] Iteration 19740 (2.2579 iter/s, 5.31467s/12 iters), loss = 4.89168
I0406 18:02:33.695801 21485 solver.cpp:237]     Train net output #0: loss = 4.89168 (* 1 = 4.89168 loss)
I0406 18:02:33.695807 21485 sgd_solver.cpp:105] Iteration 19740, lr = 0.05
I0406 18:02:39.023084 21485 solver.cpp:218] Iteration 19752 (2.25256 iter/s, 5.32726s/12 iters), loss = 5.00546
I0406 18:02:39.023140 21485 solver.cpp:237]     Train net output #0: loss = 5.00546 (* 1 = 5.00546 loss)
I0406 18:02:39.023149 21485 sgd_solver.cpp:105] Iteration 19752, lr = 0.05
I0406 18:02:44.127912 21485 solver.cpp:218] Iteration 19764 (2.35075 iter/s, 5.10476s/12 iters), loss = 4.96543
I0406 18:02:44.127961 21485 solver.cpp:237]     Train net output #0: loss = 4.96543 (* 1 = 4.96543 loss)
I0406 18:02:44.127969 21485 sgd_solver.cpp:105] Iteration 19764, lr = 0.05
I0406 18:02:49.493377 21485 solver.cpp:218] Iteration 19776 (2.23655 iter/s, 5.3654s/12 iters), loss = 4.93103
I0406 18:02:49.493418 21485 solver.cpp:237]     Train net output #0: loss = 4.93103 (* 1 = 4.93103 loss)
I0406 18:02:49.493424 21485 sgd_solver.cpp:105] Iteration 19776, lr = 0.05
I0406 18:02:53.967151 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_19788.caffemodel
I0406 18:02:57.007053 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_19788.solverstate
I0406 18:02:59.323554 21485 solver.cpp:330] Iteration 19788, Testing net (#0)
I0406 18:02:59.323576 21485 net.cpp:676] Ignoring source layer train-data
I0406 18:03:00.522541 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 18:03:03.636332 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0189951
I0406 18:03:03.636368 21485 solver.cpp:397]     Test net output #1: loss = 5.11141 (* 1 = 5.11141 loss)
I0406 18:03:03.776612 21485 solver.cpp:218] Iteration 19788 (0.840149 iter/s, 14.2832s/12 iters), loss = 4.7814
I0406 18:03:03.776672 21485 solver.cpp:237]     Train net output #0: loss = 4.7814 (* 1 = 4.7814 loss)
I0406 18:03:03.776681 21485 sgd_solver.cpp:105] Iteration 19788, lr = 0.05
I0406 18:03:08.071732 21485 solver.cpp:218] Iteration 19800 (2.79392 iter/s, 4.29505s/12 iters), loss = 4.93997
I0406 18:03:08.071782 21485 solver.cpp:237]     Train net output #0: loss = 4.93997 (* 1 = 4.93997 loss)
I0406 18:03:08.071791 21485 sgd_solver.cpp:105] Iteration 19800, lr = 0.05
I0406 18:03:13.200382 21485 solver.cpp:218] Iteration 19812 (2.33982 iter/s, 5.12859s/12 iters), loss = 4.85424
I0406 18:03:13.200433 21485 solver.cpp:237]     Train net output #0: loss = 4.85424 (* 1 = 4.85424 loss)
I0406 18:03:13.200440 21485 sgd_solver.cpp:105] Iteration 19812, lr = 0.05
I0406 18:03:18.522296 21485 solver.cpp:218] Iteration 19824 (2.25486 iter/s, 5.32185s/12 iters), loss = 4.99376
I0406 18:03:18.522356 21485 solver.cpp:237]     Train net output #0: loss = 4.99376 (* 1 = 4.99376 loss)
I0406 18:03:18.522367 21485 sgd_solver.cpp:105] Iteration 19824, lr = 0.05
I0406 18:03:19.159340 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 18:03:23.849782 21485 solver.cpp:218] Iteration 19836 (2.2525 iter/s, 5.32742s/12 iters), loss = 4.97379
I0406 18:03:23.849822 21485 solver.cpp:237]     Train net output #0: loss = 4.97379 (* 1 = 4.97379 loss)
I0406 18:03:23.849826 21485 sgd_solver.cpp:105] Iteration 19836, lr = 0.05
I0406 18:03:28.998833 21485 solver.cpp:218] Iteration 19848 (2.33055 iter/s, 5.149s/12 iters), loss = 4.99148
I0406 18:03:28.998924 21485 solver.cpp:237]     Train net output #0: loss = 4.99148 (* 1 = 4.99148 loss)
I0406 18:03:28.998930 21485 sgd_solver.cpp:105] Iteration 19848, lr = 0.05
I0406 18:03:34.351322 21485 solver.cpp:218] Iteration 19860 (2.24199 iter/s, 5.35239s/12 iters), loss = 5.01303
I0406 18:03:34.351363 21485 solver.cpp:237]     Train net output #0: loss = 5.01303 (* 1 = 5.01303 loss)
I0406 18:03:34.351369 21485 sgd_solver.cpp:105] Iteration 19860, lr = 0.05
I0406 18:03:39.754379 21485 solver.cpp:218] Iteration 19872 (2.22099 iter/s, 5.403s/12 iters), loss = 4.86264
I0406 18:03:39.754436 21485 solver.cpp:237]     Train net output #0: loss = 4.86264 (* 1 = 4.86264 loss)
I0406 18:03:39.754444 21485 sgd_solver.cpp:105] Iteration 19872, lr = 0.05
I0406 18:03:45.051914 21485 solver.cpp:218] Iteration 19884 (2.26523 iter/s, 5.29747s/12 iters), loss = 4.99727
I0406 18:03:45.051952 21485 solver.cpp:237]     Train net output #0: loss = 4.99727 (* 1 = 4.99727 loss)
I0406 18:03:45.051957 21485 sgd_solver.cpp:105] Iteration 19884, lr = 0.05
I0406 18:03:47.217933 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_19890.caffemodel
I0406 18:03:50.206653 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_19890.solverstate
I0406 18:03:52.508548 21485 solver.cpp:330] Iteration 19890, Testing net (#0)
I0406 18:03:52.508569 21485 net.cpp:676] Ignoring source layer train-data
I0406 18:03:53.674686 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 18:03:56.861714 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0110294
I0406 18:03:56.861752 21485 solver.cpp:397]     Test net output #1: loss = 5.1413 (* 1 = 5.1413 loss)
I0406 18:03:58.728358 21485 solver.cpp:218] Iteration 19896 (0.877424 iter/s, 13.6764s/12 iters), loss = 4.83476
I0406 18:03:58.728411 21485 solver.cpp:237]     Train net output #0: loss = 4.83476 (* 1 = 4.83476 loss)
I0406 18:03:58.728420 21485 sgd_solver.cpp:105] Iteration 19896, lr = 0.05
I0406 18:04:03.833614 21485 solver.cpp:218] Iteration 19908 (2.35055 iter/s, 5.10519s/12 iters), loss = 5.02642
I0406 18:04:03.833741 21485 solver.cpp:237]     Train net output #0: loss = 5.02642 (* 1 = 5.02642 loss)
I0406 18:04:03.833748 21485 sgd_solver.cpp:105] Iteration 19908, lr = 0.05
I0406 18:04:09.163273 21485 solver.cpp:218] Iteration 19920 (2.25161 iter/s, 5.32952s/12 iters), loss = 4.93624
I0406 18:04:09.163311 21485 solver.cpp:237]     Train net output #0: loss = 4.93624 (* 1 = 4.93624 loss)
I0406 18:04:09.163316 21485 sgd_solver.cpp:105] Iteration 19920, lr = 0.05
I0406 18:04:12.034590 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 18:04:14.470078 21485 solver.cpp:218] Iteration 19932 (2.26127 iter/s, 5.30675s/12 iters), loss = 4.95165
I0406 18:04:14.470132 21485 solver.cpp:237]     Train net output #0: loss = 4.95165 (* 1 = 4.95165 loss)
I0406 18:04:14.470139 21485 sgd_solver.cpp:105] Iteration 19932, lr = 0.05
I0406 18:04:19.809255 21485 solver.cpp:218] Iteration 19944 (2.24757 iter/s, 5.33911s/12 iters), loss = 5.07904
I0406 18:04:19.809307 21485 solver.cpp:237]     Train net output #0: loss = 5.07904 (* 1 = 5.07904 loss)
I0406 18:04:19.809314 21485 sgd_solver.cpp:105] Iteration 19944, lr = 0.05
I0406 18:04:24.808235 21485 solver.cpp:218] Iteration 19956 (2.40052 iter/s, 4.99892s/12 iters), loss = 4.89258
I0406 18:04:24.808279 21485 solver.cpp:237]     Train net output #0: loss = 4.89258 (* 1 = 4.89258 loss)
I0406 18:04:24.808285 21485 sgd_solver.cpp:105] Iteration 19956, lr = 0.05
I0406 18:04:30.151484 21485 solver.cpp:218] Iteration 19968 (2.24585 iter/s, 5.34319s/12 iters), loss = 4.99569
I0406 18:04:30.151546 21485 solver.cpp:237]     Train net output #0: loss = 4.99569 (* 1 = 4.99569 loss)
I0406 18:04:30.151553 21485 sgd_solver.cpp:105] Iteration 19968, lr = 0.05
I0406 18:04:35.466840 21485 solver.cpp:218] Iteration 19980 (2.25764 iter/s, 5.31528s/12 iters), loss = 5.06485
I0406 18:04:35.466951 21485 solver.cpp:237]     Train net output #0: loss = 5.06485 (* 1 = 5.06485 loss)
I0406 18:04:35.466961 21485 sgd_solver.cpp:105] Iteration 19980, lr = 0.05
I0406 18:04:40.213699 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_19992.caffemodel
I0406 18:04:43.216557 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_19992.solverstate
I0406 18:04:45.568444 21485 solver.cpp:330] Iteration 19992, Testing net (#0)
I0406 18:04:45.568465 21485 net.cpp:676] Ignoring source layer train-data
I0406 18:04:46.742352 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 18:04:49.983960 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0140931
I0406 18:04:49.983994 21485 solver.cpp:397]     Test net output #1: loss = 5.12809 (* 1 = 5.12809 loss)
I0406 18:04:50.118583 21485 solver.cpp:218] Iteration 19992 (0.819021 iter/s, 14.6516s/12 iters), loss = 5.09146
I0406 18:04:50.120168 21485 solver.cpp:237]     Train net output #0: loss = 5.09146 (* 1 = 5.09146 loss)
I0406 18:04:50.120182 21485 sgd_solver.cpp:105] Iteration 19992, lr = 0.05
I0406 18:04:54.420518 21485 solver.cpp:218] Iteration 20004 (2.79047 iter/s, 4.30035s/12 iters), loss = 4.88547
I0406 18:04:54.420572 21485 solver.cpp:237]     Train net output #0: loss = 4.88547 (* 1 = 4.88547 loss)
I0406 18:04:54.420580 21485 sgd_solver.cpp:105] Iteration 20004, lr = 0.05
I0406 18:04:59.605898 21485 solver.cpp:218] Iteration 20016 (2.31423 iter/s, 5.18531s/12 iters), loss = 4.859
I0406 18:04:59.605955 21485 solver.cpp:237]     Train net output #0: loss = 4.859 (* 1 = 4.859 loss)
I0406 18:04:59.605963 21485 sgd_solver.cpp:105] Iteration 20016, lr = 0.05
I0406 18:05:04.644412 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 18:05:04.809903 21485 solver.cpp:218] Iteration 20028 (2.30595 iter/s, 5.20394s/12 iters), loss = 5.10143
I0406 18:05:04.809940 21485 solver.cpp:237]     Train net output #0: loss = 5.10143 (* 1 = 5.10143 loss)
I0406 18:05:04.809945 21485 sgd_solver.cpp:105] Iteration 20028, lr = 0.05
I0406 18:05:10.035554 21485 solver.cpp:218] Iteration 20040 (2.29639 iter/s, 5.2256s/12 iters), loss = 4.96572
I0406 18:05:10.035709 21485 solver.cpp:237]     Train net output #0: loss = 4.96572 (* 1 = 4.96572 loss)
I0406 18:05:10.035719 21485 sgd_solver.cpp:105] Iteration 20040, lr = 0.05
I0406 18:05:15.255156 21485 solver.cpp:218] Iteration 20052 (2.2991 iter/s, 5.21944s/12 iters), loss = 4.9105
I0406 18:05:15.255199 21485 solver.cpp:237]     Train net output #0: loss = 4.9105 (* 1 = 4.9105 loss)
I0406 18:05:15.255206 21485 sgd_solver.cpp:105] Iteration 20052, lr = 0.05
I0406 18:05:20.289394 21485 solver.cpp:218] Iteration 20064 (2.3837 iter/s, 5.03418s/12 iters), loss = 4.94722
I0406 18:05:20.289429 21485 solver.cpp:237]     Train net output #0: loss = 4.94722 (* 1 = 4.94722 loss)
I0406 18:05:20.289435 21485 sgd_solver.cpp:105] Iteration 20064, lr = 0.05
I0406 18:05:25.501442 21485 solver.cpp:218] Iteration 20076 (2.30238 iter/s, 5.212s/12 iters), loss = 5.00756
I0406 18:05:25.501484 21485 solver.cpp:237]     Train net output #0: loss = 5.00756 (* 1 = 5.00756 loss)
I0406 18:05:25.501490 21485 sgd_solver.cpp:105] Iteration 20076, lr = 0.05
I0406 18:05:30.833070 21485 solver.cpp:218] Iteration 20088 (2.25074 iter/s, 5.33158s/12 iters), loss = 4.94134
I0406 18:05:30.833112 21485 solver.cpp:237]     Train net output #0: loss = 4.94134 (* 1 = 4.94134 loss)
I0406 18:05:30.833118 21485 sgd_solver.cpp:105] Iteration 20088, lr = 0.05
I0406 18:05:32.942912 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_20094.caffemodel
I0406 18:05:35.953291 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_20094.solverstate
I0406 18:05:38.256971 21485 solver.cpp:330] Iteration 20094, Testing net (#0)
I0406 18:05:38.256991 21485 net.cpp:676] Ignoring source layer train-data
I0406 18:05:39.468891 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 18:05:42.236276 21485 blocking_queue.cpp:49] Waiting for data
I0406 18:05:42.680673 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0183824
I0406 18:05:42.680711 21485 solver.cpp:397]     Test net output #1: loss = 5.12566 (* 1 = 5.12566 loss)
I0406 18:05:44.440424 21485 solver.cpp:218] Iteration 20100 (0.881879 iter/s, 13.6073s/12 iters), loss = 4.93389
I0406 18:05:44.440472 21485 solver.cpp:237]     Train net output #0: loss = 4.93389 (* 1 = 4.93389 loss)
I0406 18:05:44.440479 21485 sgd_solver.cpp:105] Iteration 20100, lr = 0.05
I0406 18:05:49.782403 21485 solver.cpp:218] Iteration 20112 (2.24638 iter/s, 5.34192s/12 iters), loss = 4.85923
I0406 18:05:49.782439 21485 solver.cpp:237]     Train net output #0: loss = 4.85923 (* 1 = 4.85923 loss)
I0406 18:05:49.782445 21485 sgd_solver.cpp:105] Iteration 20112, lr = 0.05
I0406 18:05:54.810292 21485 solver.cpp:218] Iteration 20124 (2.38671 iter/s, 5.02783s/12 iters), loss = 4.89605
I0406 18:05:54.810338 21485 solver.cpp:237]     Train net output #0: loss = 4.89605 (* 1 = 4.89605 loss)
I0406 18:05:54.810343 21485 sgd_solver.cpp:105] Iteration 20124, lr = 0.05
I0406 18:05:57.085278 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 18:06:00.190634 21485 solver.cpp:218] Iteration 20136 (2.23037 iter/s, 5.38028s/12 iters), loss = 4.90747
I0406 18:06:00.190696 21485 solver.cpp:237]     Train net output #0: loss = 4.90747 (* 1 = 4.90747 loss)
I0406 18:06:00.190706 21485 sgd_solver.cpp:105] Iteration 20136, lr = 0.05
I0406 18:06:05.305126 21485 solver.cpp:218] Iteration 20148 (2.34631 iter/s, 5.11442s/12 iters), loss = 5.03598
I0406 18:06:05.305171 21485 solver.cpp:237]     Train net output #0: loss = 5.03598 (* 1 = 5.03598 loss)
I0406 18:06:05.305176 21485 sgd_solver.cpp:105] Iteration 20148, lr = 0.05
I0406 18:06:10.717831 21485 solver.cpp:218] Iteration 20160 (2.21703 iter/s, 5.41265s/12 iters), loss = 4.92675
I0406 18:06:10.717880 21485 solver.cpp:237]     Train net output #0: loss = 4.92675 (* 1 = 4.92675 loss)
I0406 18:06:10.717887 21485 sgd_solver.cpp:105] Iteration 20160, lr = 0.05
I0406 18:06:15.896924 21485 solver.cpp:218] Iteration 20172 (2.31704 iter/s, 5.17903s/12 iters), loss = 4.95313
I0406 18:06:15.898078 21485 solver.cpp:237]     Train net output #0: loss = 4.95313 (* 1 = 4.95313 loss)
I0406 18:06:15.898090 21485 sgd_solver.cpp:105] Iteration 20172, lr = 0.05
I0406 18:06:20.971181 21485 solver.cpp:218] Iteration 20184 (2.36542 iter/s, 5.0731s/12 iters), loss = 5.16474
I0406 18:06:20.971225 21485 solver.cpp:237]     Train net output #0: loss = 5.16474 (* 1 = 5.16474 loss)
I0406 18:06:20.971230 21485 sgd_solver.cpp:105] Iteration 20184, lr = 0.05
I0406 18:06:25.578465 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_20196.caffemodel
I0406 18:06:28.659838 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_20196.solverstate
I0406 18:06:30.957707 21485 solver.cpp:330] Iteration 20196, Testing net (#0)
I0406 18:06:30.957727 21485 net.cpp:676] Ignoring source layer train-data
I0406 18:06:32.039223 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 18:06:35.284818 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0140931
I0406 18:06:35.284848 21485 solver.cpp:397]     Test net output #1: loss = 5.18398 (* 1 = 5.18398 loss)
I0406 18:06:35.425599 21485 solver.cpp:218] Iteration 20196 (0.830199 iter/s, 14.4544s/12 iters), loss = 5.15568
I0406 18:06:35.425657 21485 solver.cpp:237]     Train net output #0: loss = 5.15568 (* 1 = 5.15568 loss)
I0406 18:06:35.425665 21485 sgd_solver.cpp:105] Iteration 20196, lr = 0.05
I0406 18:06:39.725519 21485 solver.cpp:218] Iteration 20208 (2.7908 iter/s, 4.29985s/12 iters), loss = 5.00423
I0406 18:06:39.725558 21485 solver.cpp:237]     Train net output #0: loss = 5.00423 (* 1 = 5.00423 loss)
I0406 18:06:39.725564 21485 sgd_solver.cpp:105] Iteration 20208, lr = 0.05
I0406 18:06:44.946429 21485 solver.cpp:218] Iteration 20220 (2.29847 iter/s, 5.22085s/12 iters), loss = 4.81189
I0406 18:06:44.946486 21485 solver.cpp:237]     Train net output #0: loss = 4.81189 (* 1 = 4.81189 loss)
I0406 18:06:44.946496 21485 sgd_solver.cpp:105] Iteration 20220, lr = 0.05
I0406 18:06:49.517472 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 18:06:50.333359 21485 solver.cpp:218] Iteration 20232 (2.22764 iter/s, 5.38686s/12 iters), loss = 4.90194
I0406 18:06:50.333400 21485 solver.cpp:237]     Train net output #0: loss = 4.90194 (* 1 = 4.90194 loss)
I0406 18:06:50.333406 21485 sgd_solver.cpp:105] Iteration 20232, lr = 0.05
I0406 18:06:55.674367 21485 solver.cpp:218] Iteration 20244 (2.24679 iter/s, 5.34096s/12 iters), loss = 4.98793
I0406 18:06:55.674403 21485 solver.cpp:237]     Train net output #0: loss = 4.98793 (* 1 = 4.98793 loss)
I0406 18:06:55.674408 21485 sgd_solver.cpp:105] Iteration 20244, lr = 0.05
I0406 18:07:00.980702 21485 solver.cpp:218] Iteration 20256 (2.26147 iter/s, 5.30628s/12 iters), loss = 5.03808
I0406 18:07:00.980752 21485 solver.cpp:237]     Train net output #0: loss = 5.03808 (* 1 = 5.03808 loss)
I0406 18:07:00.980762 21485 sgd_solver.cpp:105] Iteration 20256, lr = 0.05
I0406 18:07:06.100687 21485 solver.cpp:218] Iteration 20268 (2.34379 iter/s, 5.11992s/12 iters), loss = 5.01506
I0406 18:07:06.100736 21485 solver.cpp:237]     Train net output #0: loss = 5.01506 (* 1 = 5.01506 loss)
I0406 18:07:06.100741 21485 sgd_solver.cpp:105] Iteration 20268, lr = 0.05
I0406 18:07:11.337435 21485 solver.cpp:218] Iteration 20280 (2.29152 iter/s, 5.23669s/12 iters), loss = 4.99733
I0406 18:07:11.337474 21485 solver.cpp:237]     Train net output #0: loss = 4.99733 (* 1 = 4.99733 loss)
I0406 18:07:11.337479 21485 sgd_solver.cpp:105] Iteration 20280, lr = 0.05
I0406 18:07:16.485281 21485 solver.cpp:218] Iteration 20292 (2.3311 iter/s, 5.14779s/12 iters), loss = 4.94075
I0406 18:07:16.485332 21485 solver.cpp:237]     Train net output #0: loss = 4.94075 (* 1 = 4.94075 loss)
I0406 18:07:16.485340 21485 sgd_solver.cpp:105] Iteration 20292, lr = 0.05
I0406 18:07:18.543710 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_20298.caffemodel
I0406 18:07:21.631417 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_20298.solverstate
I0406 18:07:23.944252 21485 solver.cpp:330] Iteration 20298, Testing net (#0)
I0406 18:07:23.944272 21485 net.cpp:676] Ignoring source layer train-data
I0406 18:07:24.973136 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 18:07:28.332028 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0147059
I0406 18:07:28.332062 21485 solver.cpp:397]     Test net output #1: loss = 5.13993 (* 1 = 5.13993 loss)
I0406 18:07:30.218163 21485 solver.cpp:218] Iteration 20304 (0.873819 iter/s, 13.7328s/12 iters), loss = 5.01333
I0406 18:07:30.218217 21485 solver.cpp:237]     Train net output #0: loss = 5.01333 (* 1 = 5.01333 loss)
I0406 18:07:30.218225 21485 sgd_solver.cpp:105] Iteration 20304, lr = 0.05
I0406 18:07:35.405804 21485 solver.cpp:218] Iteration 20316 (2.31322 iter/s, 5.18757s/12 iters), loss = 4.88701
I0406 18:07:35.405856 21485 solver.cpp:237]     Train net output #0: loss = 4.88701 (* 1 = 4.88701 loss)
I0406 18:07:35.405864 21485 sgd_solver.cpp:105] Iteration 20316, lr = 0.05
I0406 18:07:40.489851 21485 solver.cpp:218] Iteration 20328 (2.36036 iter/s, 5.08398s/12 iters), loss = 4.86687
I0406 18:07:40.489904 21485 solver.cpp:237]     Train net output #0: loss = 4.86687 (* 1 = 4.86687 loss)
I0406 18:07:40.489912 21485 sgd_solver.cpp:105] Iteration 20328, lr = 0.05
I0406 18:07:41.952535 21507 data_layer.cpp:73] Restarting data prefetching from start.
I0406 18:07:45.777395 21485 solver.cpp:218] Iteration 20340 (2.26951 iter/s, 5.28747s/12 iters), loss = 4.88545
I0406 18:07:45.777447 21485 solver.cpp:237]     Train net output #0: loss = 4.88545 (* 1 = 4.88545 loss)
I0406 18:07:45.777454 21485 sgd_solver.cpp:105] Iteration 20340, lr = 0.05
I0406 18:07:50.811077 21485 solver.cpp:218] Iteration 20352 (2.38397 iter/s, 5.03362s/12 iters), loss = 4.84137
I0406 18:07:50.811115 21485 solver.cpp:237]     Train net output #0: loss = 4.84137 (* 1 = 4.84137 loss)
I0406 18:07:50.811121 21485 sgd_solver.cpp:105] Iteration 20352, lr = 0.05
I0406 18:07:55.921975 21485 solver.cpp:218] Iteration 20364 (2.34794 iter/s, 5.11085s/12 iters), loss = 4.97268
I0406 18:07:55.922065 21485 solver.cpp:237]     Train net output #0: loss = 4.97268 (* 1 = 4.97268 loss)
I0406 18:07:55.922071 21485 sgd_solver.cpp:105] Iteration 20364, lr = 0.05
I0406 18:08:01.075464 21485 solver.cpp:218] Iteration 20376 (2.32856 iter/s, 5.15339s/12 iters), loss = 4.80685
I0406 18:08:01.075505 21485 solver.cpp:237]     Train net output #0: loss = 4.80685 (* 1 = 4.80685 loss)
I0406 18:08:01.075510 21485 sgd_solver.cpp:105] Iteration 20376, lr = 0.05
I0406 18:08:06.522729 21485 solver.cpp:218] Iteration 20388 (2.20296 iter/s, 5.44721s/12 iters), loss = 4.94431
I0406 18:08:06.522783 21485 solver.cpp:237]     Train net output #0: loss = 4.94431 (* 1 = 4.94431 loss)
I0406 18:08:06.522790 21485 sgd_solver.cpp:105] Iteration 20388, lr = 0.05
I0406 18:08:11.308043 21485 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_20400.caffemodel
I0406 18:08:14.337035 21485 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_20400.solverstate
I0406 18:08:16.724452 21485 solver.cpp:310] Iteration 20400, loss = 4.99714
I0406 18:08:16.724479 21485 solver.cpp:330] Iteration 20400, Testing net (#0)
I0406 18:08:16.724484 21485 net.cpp:676] Ignoring source layer train-data
I0406 18:08:17.743319 21539 data_layer.cpp:73] Restarting data prefetching from start.
I0406 18:08:21.104001 21485 solver.cpp:397]     Test net output #0: accuracy = 0.0177696
I0406 18:08:21.104038 21485 solver.cpp:397]     Test net output #1: loss = 5.13279 (* 1 = 5.13279 loss)
I0406 18:08:21.104043 21485 solver.cpp:315] Optimization Done.
I0406 18:08:21.104048 21485 caffe.cpp:259] Optimization Done.