I0412 14:19:55.851986 20203 upgrade_proto.cpp:1082] Attempting to upgrade input file specified using deprecated 'solver_type' field (enum)': /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210412-125840-4f61/solver.prototxt
I0412 14:19:55.852174 20203 upgrade_proto.cpp:1089] Successfully upgraded file specified using deprecated 'solver_type' field (enum) to 'type' field (string).
W0412 14:19:55.852181 20203 upgrade_proto.cpp:1091] Note that future Caffe releases will only support 'type' field (string) for a solver's type.
I0412 14:19:55.852253 20203 caffe.cpp:218] Using GPUs 3
I0412 14:19:55.867364 20203 caffe.cpp:223] GPU 3: GeForce GTX 1080 Ti
I0412 14:19:56.181569 20203 solver.cpp:44] Initializing solver from parameters:
test_iter: 51
test_interval: 102
base_lr: 0.01
display: 12
max_iter: 10200
lr_policy: "exp"
gamma: 0.99980193
momentum: 0.9
weight_decay: 0.0001
snapshot: 102
snapshot_prefix: "snapshot"
solver_mode: GPU
device_id: 3
net: "train_val.prototxt"
train_state {
level: 0
stage: ""
}
type: "SGD"
I0412 14:19:56.182649 20203 solver.cpp:87] Creating training net from net file: train_val.prototxt
I0412 14:19:56.183364 20203 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer val-data
I0412 14:19:56.183380 20203 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy
I0412 14:19:56.183549 20203 net.cpp:51] Initializing net from parameters:
state {
phase: TRAIN
level: 0
stage: ""
}
layer {
name: "train-data"
type: "Data"
top: "data"
top: "label"
include {
phase: TRAIN
}
transform_param {
mirror: true
crop_size: 227
mean_file: "/mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto"
}
data_param {
source: "/mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/train_db"
batch_size: 128
backend: LMDB
}
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 96
kernel_size: 11
stride: 4
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "conv1"
top: "conv1"
}
layer {
name: "norm1"
type: "LRN"
bottom: "conv1"
top: "norm1"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool1"
type: "Pooling"
bottom: "norm1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 2
kernel_size: 5
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu2"
type: "ReLU"
bottom: "conv2"
top: "conv2"
}
layer {
name: "norm2"
type: "LRN"
bottom: "conv2"
top: "norm2"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool2"
type: "Pooling"
bottom: "norm2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv3"
type: "Convolution"
bottom: "pool2"
top: "conv3"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu3"
type: "ReLU"
bottom: "conv3"
top: "conv3"
}
layer {
name: "conv4"
type: "Convolution"
bottom: "conv3"
top: "conv4"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 96
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: "conv4.2"
type: "Convolution"
bottom: "conv4"
top: "conv4.2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 96
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu4.2"
type: "ReLU"
bottom: "conv4.2"
top: "conv4.2"
}
layer {
name: "conv4.3"
type: "Convolution"
bottom: "conv4.2"
top: "conv4.3"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 96
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu4.3"
type: "ReLU"
bottom: "conv4.3"
top: "conv4.3"
}
layer {
name: "conv4.4"
type: "Convolution"
bottom: "conv4.3"
top: "conv4.4"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 96
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu4.4"
type: "ReLU"
bottom: "conv4.4"
top: "conv4.4"
}
layer {
name: "conv5"
type: "Convolution"
bottom: "conv4.4"
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"
}
I0412 14:19:56.183648 20203 layer_factory.hpp:77] Creating layer train-data
I0412 14:19:56.189752 20203 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/train_db
I0412 14:19:56.189978 20203 net.cpp:84] Creating Layer train-data
I0412 14:19:56.189990 20203 net.cpp:380] train-data -> data
I0412 14:19:56.190009 20203 net.cpp:380] train-data -> label
I0412 14:19:56.190021 20203 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto
I0412 14:19:56.195195 20203 data_layer.cpp:45] output data size: 128,3,227,227
I0412 14:19:56.347721 20203 net.cpp:122] Setting up train-data
I0412 14:19:56.347744 20203 net.cpp:129] Top shape: 128 3 227 227 (19787136)
I0412 14:19:56.347751 20203 net.cpp:129] Top shape: 128 (128)
I0412 14:19:56.347754 20203 net.cpp:137] Memory required for data: 79149056
I0412 14:19:56.347764 20203 layer_factory.hpp:77] Creating layer conv1
I0412 14:19:56.347784 20203 net.cpp:84] Creating Layer conv1
I0412 14:19:56.347790 20203 net.cpp:406] conv1 <- data
I0412 14:19:56.347802 20203 net.cpp:380] conv1 -> conv1
I0412 14:19:57.059183 20203 net.cpp:122] Setting up conv1
I0412 14:19:57.059213 20203 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0412 14:19:57.059221 20203 net.cpp:137] Memory required for data: 227833856
I0412 14:19:57.059247 20203 layer_factory.hpp:77] Creating layer relu1
I0412 14:19:57.059259 20203 net.cpp:84] Creating Layer relu1
I0412 14:19:57.059267 20203 net.cpp:406] relu1 <- conv1
I0412 14:19:57.059274 20203 net.cpp:367] relu1 -> conv1 (in-place)
I0412 14:19:57.059715 20203 net.cpp:122] Setting up relu1
I0412 14:19:57.059728 20203 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0412 14:19:57.059734 20203 net.cpp:137] Memory required for data: 376518656
I0412 14:19:57.059741 20203 layer_factory.hpp:77] Creating layer norm1
I0412 14:19:57.059752 20203 net.cpp:84] Creating Layer norm1
I0412 14:19:57.059758 20203 net.cpp:406] norm1 <- conv1
I0412 14:19:57.059767 20203 net.cpp:380] norm1 -> norm1
I0412 14:19:57.060418 20203 net.cpp:122] Setting up norm1
I0412 14:19:57.060433 20203 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0412 14:19:57.060439 20203 net.cpp:137] Memory required for data: 525203456
I0412 14:19:57.060444 20203 layer_factory.hpp:77] Creating layer pool1
I0412 14:19:57.060456 20203 net.cpp:84] Creating Layer pool1
I0412 14:19:57.060461 20203 net.cpp:406] pool1 <- norm1
I0412 14:19:57.060469 20203 net.cpp:380] pool1 -> pool1
I0412 14:19:57.060528 20203 net.cpp:122] Setting up pool1
I0412 14:19:57.060539 20203 net.cpp:129] Top shape: 128 96 27 27 (8957952)
I0412 14:19:57.060544 20203 net.cpp:137] Memory required for data: 561035264
I0412 14:19:57.060549 20203 layer_factory.hpp:77] Creating layer conv2
I0412 14:19:57.060564 20203 net.cpp:84] Creating Layer conv2
I0412 14:19:57.060571 20203 net.cpp:406] conv2 <- pool1
I0412 14:19:57.060580 20203 net.cpp:380] conv2 -> conv2
I0412 14:19:57.084409 20203 net.cpp:122] Setting up conv2
I0412 14:19:57.084430 20203 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0412 14:19:57.084434 20203 net.cpp:137] Memory required for data: 656586752
I0412 14:19:57.084447 20203 layer_factory.hpp:77] Creating layer relu2
I0412 14:19:57.084457 20203 net.cpp:84] Creating Layer relu2
I0412 14:19:57.084461 20203 net.cpp:406] relu2 <- conv2
I0412 14:19:57.084468 20203 net.cpp:367] relu2 -> conv2 (in-place)
I0412 14:19:57.085386 20203 net.cpp:122] Setting up relu2
I0412 14:19:57.085397 20203 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0412 14:19:57.085400 20203 net.cpp:137] Memory required for data: 752138240
I0412 14:19:57.085404 20203 layer_factory.hpp:77] Creating layer norm2
I0412 14:19:57.085413 20203 net.cpp:84] Creating Layer norm2
I0412 14:19:57.085417 20203 net.cpp:406] norm2 <- conv2
I0412 14:19:57.085424 20203 net.cpp:380] norm2 -> norm2
I0412 14:19:57.085721 20203 net.cpp:122] Setting up norm2
I0412 14:19:57.085729 20203 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0412 14:19:57.085734 20203 net.cpp:137] Memory required for data: 847689728
I0412 14:19:57.085737 20203 layer_factory.hpp:77] Creating layer pool2
I0412 14:19:57.085747 20203 net.cpp:84] Creating Layer pool2
I0412 14:19:57.085750 20203 net.cpp:406] pool2 <- norm2
I0412 14:19:57.085757 20203 net.cpp:380] pool2 -> pool2
I0412 14:19:57.085785 20203 net.cpp:122] Setting up pool2
I0412 14:19:57.085791 20203 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0412 14:19:57.085794 20203 net.cpp:137] Memory required for data: 869840896
I0412 14:19:57.085798 20203 layer_factory.hpp:77] Creating layer conv3
I0412 14:19:57.085829 20203 net.cpp:84] Creating Layer conv3
I0412 14:19:57.085834 20203 net.cpp:406] conv3 <- pool2
I0412 14:19:57.085841 20203 net.cpp:380] conv3 -> conv3
I0412 14:19:57.097656 20203 net.cpp:122] Setting up conv3
I0412 14:19:57.097671 20203 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0412 14:19:57.097673 20203 net.cpp:137] Memory required for data: 903067648
I0412 14:19:57.097684 20203 layer_factory.hpp:77] Creating layer relu3
I0412 14:19:57.097692 20203 net.cpp:84] Creating Layer relu3
I0412 14:19:57.097697 20203 net.cpp:406] relu3 <- conv3
I0412 14:19:57.097702 20203 net.cpp:367] relu3 -> conv3 (in-place)
I0412 14:19:57.098191 20203 net.cpp:122] Setting up relu3
I0412 14:19:57.098201 20203 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0412 14:19:57.098204 20203 net.cpp:137] Memory required for data: 936294400
I0412 14:19:57.098208 20203 layer_factory.hpp:77] Creating layer conv4
I0412 14:19:57.098218 20203 net.cpp:84] Creating Layer conv4
I0412 14:19:57.098222 20203 net.cpp:406] conv4 <- conv3
I0412 14:19:57.098229 20203 net.cpp:380] conv4 -> conv4
I0412 14:19:57.103674 20203 net.cpp:122] Setting up conv4
I0412 14:19:57.103686 20203 net.cpp:129] Top shape: 128 96 13 13 (2076672)
I0412 14:19:57.103690 20203 net.cpp:137] Memory required for data: 944601088
I0412 14:19:57.103698 20203 layer_factory.hpp:77] Creating layer relu4
I0412 14:19:57.103704 20203 net.cpp:84] Creating Layer relu4
I0412 14:19:57.103708 20203 net.cpp:406] relu4 <- conv4
I0412 14:19:57.103715 20203 net.cpp:367] relu4 -> conv4 (in-place)
I0412 14:19:57.104053 20203 net.cpp:122] Setting up relu4
I0412 14:19:57.104060 20203 net.cpp:129] Top shape: 128 96 13 13 (2076672)
I0412 14:19:57.104064 20203 net.cpp:137] Memory required for data: 952907776
I0412 14:19:57.104068 20203 layer_factory.hpp:77] Creating layer conv4.2
I0412 14:19:57.104077 20203 net.cpp:84] Creating Layer conv4.2
I0412 14:19:57.104080 20203 net.cpp:406] conv4.2 <- conv4
I0412 14:19:57.104087 20203 net.cpp:380] conv4.2 -> conv4.2
I0412 14:19:57.108472 20203 net.cpp:122] Setting up conv4.2
I0412 14:19:57.108484 20203 net.cpp:129] Top shape: 128 96 13 13 (2076672)
I0412 14:19:57.108489 20203 net.cpp:137] Memory required for data: 961214464
I0412 14:19:57.108500 20203 layer_factory.hpp:77] Creating layer relu4.2
I0412 14:19:57.108506 20203 net.cpp:84] Creating Layer relu4.2
I0412 14:19:57.108510 20203 net.cpp:406] relu4.2 <- conv4.2
I0412 14:19:57.108516 20203 net.cpp:367] relu4.2 -> conv4.2 (in-place)
I0412 14:19:57.108997 20203 net.cpp:122] Setting up relu4.2
I0412 14:19:57.109006 20203 net.cpp:129] Top shape: 128 96 13 13 (2076672)
I0412 14:19:57.109010 20203 net.cpp:137] Memory required for data: 969521152
I0412 14:19:57.109014 20203 layer_factory.hpp:77] Creating layer conv4.3
I0412 14:19:57.109025 20203 net.cpp:84] Creating Layer conv4.3
I0412 14:19:57.109028 20203 net.cpp:406] conv4.3 <- conv4.2
I0412 14:19:57.109035 20203 net.cpp:380] conv4.3 -> conv4.3
I0412 14:19:57.112463 20203 net.cpp:122] Setting up conv4.3
I0412 14:19:57.112473 20203 net.cpp:129] Top shape: 128 96 13 13 (2076672)
I0412 14:19:57.112478 20203 net.cpp:137] Memory required for data: 977827840
I0412 14:19:57.112484 20203 layer_factory.hpp:77] Creating layer relu4.3
I0412 14:19:57.112493 20203 net.cpp:84] Creating Layer relu4.3
I0412 14:19:57.112496 20203 net.cpp:406] relu4.3 <- conv4.3
I0412 14:19:57.112504 20203 net.cpp:367] relu4.3 -> conv4.3 (in-place)
I0412 14:19:57.112991 20203 net.cpp:122] Setting up relu4.3
I0412 14:19:57.113000 20203 net.cpp:129] Top shape: 128 96 13 13 (2076672)
I0412 14:19:57.113003 20203 net.cpp:137] Memory required for data: 986134528
I0412 14:19:57.113008 20203 layer_factory.hpp:77] Creating layer conv4.4
I0412 14:19:57.113016 20203 net.cpp:84] Creating Layer conv4.4
I0412 14:19:57.113020 20203 net.cpp:406] conv4.4 <- conv4.3
I0412 14:19:57.113027 20203 net.cpp:380] conv4.4 -> conv4.4
I0412 14:19:57.118353 20203 net.cpp:122] Setting up conv4.4
I0412 14:19:57.118371 20203 net.cpp:129] Top shape: 128 96 13 13 (2076672)
I0412 14:19:57.118376 20203 net.cpp:137] Memory required for data: 994441216
I0412 14:19:57.118407 20203 layer_factory.hpp:77] Creating layer relu4.4
I0412 14:19:57.118414 20203 net.cpp:84] Creating Layer relu4.4
I0412 14:19:57.118423 20203 net.cpp:406] relu4.4 <- conv4.4
I0412 14:19:57.118430 20203 net.cpp:367] relu4.4 -> conv4.4 (in-place)
I0412 14:19:57.119064 20203 net.cpp:122] Setting up relu4.4
I0412 14:19:57.119076 20203 net.cpp:129] Top shape: 128 96 13 13 (2076672)
I0412 14:19:57.119081 20203 net.cpp:137] Memory required for data: 1002747904
I0412 14:19:57.119086 20203 layer_factory.hpp:77] Creating layer conv5
I0412 14:19:57.119099 20203 net.cpp:84] Creating Layer conv5
I0412 14:19:57.119104 20203 net.cpp:406] conv5 <- conv4.4
I0412 14:19:57.119114 20203 net.cpp:380] conv5 -> conv5
I0412 14:19:57.125795 20203 net.cpp:122] Setting up conv5
I0412 14:19:57.125809 20203 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0412 14:19:57.125816 20203 net.cpp:137] Memory required for data: 1024899072
I0412 14:19:57.125826 20203 layer_factory.hpp:77] Creating layer relu5
I0412 14:19:57.125836 20203 net.cpp:84] Creating Layer relu5
I0412 14:19:57.125841 20203 net.cpp:406] relu5 <- conv5
I0412 14:19:57.125849 20203 net.cpp:367] relu5 -> conv5 (in-place)
I0412 14:19:57.126375 20203 net.cpp:122] Setting up relu5
I0412 14:19:57.126386 20203 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0412 14:19:57.126390 20203 net.cpp:137] Memory required for data: 1047050240
I0412 14:19:57.126394 20203 layer_factory.hpp:77] Creating layer pool5
I0412 14:19:57.126401 20203 net.cpp:84] Creating Layer pool5
I0412 14:19:57.126405 20203 net.cpp:406] pool5 <- conv5
I0412 14:19:57.126412 20203 net.cpp:380] pool5 -> pool5
I0412 14:19:57.126451 20203 net.cpp:122] Setting up pool5
I0412 14:19:57.126457 20203 net.cpp:129] Top shape: 128 256 6 6 (1179648)
I0412 14:19:57.126461 20203 net.cpp:137] Memory required for data: 1051768832
I0412 14:19:57.126464 20203 layer_factory.hpp:77] Creating layer fc6
I0412 14:19:57.126472 20203 net.cpp:84] Creating Layer fc6
I0412 14:19:57.126477 20203 net.cpp:406] fc6 <- pool5
I0412 14:19:57.126483 20203 net.cpp:380] fc6 -> fc6
I0412 14:19:57.557391 20203 net.cpp:122] Setting up fc6
I0412 14:19:57.557415 20203 net.cpp:129] Top shape: 128 4096 (524288)
I0412 14:19:57.557420 20203 net.cpp:137] Memory required for data: 1053865984
I0412 14:19:57.557432 20203 layer_factory.hpp:77] Creating layer relu6
I0412 14:19:57.557442 20203 net.cpp:84] Creating Layer relu6
I0412 14:19:57.557446 20203 net.cpp:406] relu6 <- fc6
I0412 14:19:57.557453 20203 net.cpp:367] relu6 -> fc6 (in-place)
I0412 14:19:57.558105 20203 net.cpp:122] Setting up relu6
I0412 14:19:57.558116 20203 net.cpp:129] Top shape: 128 4096 (524288)
I0412 14:19:57.558120 20203 net.cpp:137] Memory required for data: 1055963136
I0412 14:19:57.558125 20203 layer_factory.hpp:77] Creating layer drop6
I0412 14:19:57.558130 20203 net.cpp:84] Creating Layer drop6
I0412 14:19:57.558135 20203 net.cpp:406] drop6 <- fc6
I0412 14:19:57.558140 20203 net.cpp:367] drop6 -> fc6 (in-place)
I0412 14:19:57.558167 20203 net.cpp:122] Setting up drop6
I0412 14:19:57.558173 20203 net.cpp:129] Top shape: 128 4096 (524288)
I0412 14:19:57.558177 20203 net.cpp:137] Memory required for data: 1058060288
I0412 14:19:57.558180 20203 layer_factory.hpp:77] Creating layer fc7
I0412 14:19:57.558189 20203 net.cpp:84] Creating Layer fc7
I0412 14:19:57.558192 20203 net.cpp:406] fc7 <- fc6
I0412 14:19:57.558198 20203 net.cpp:380] fc7 -> fc7
I0412 14:19:57.737767 20203 net.cpp:122] Setting up fc7
I0412 14:19:57.737788 20203 net.cpp:129] Top shape: 128 4096 (524288)
I0412 14:19:57.737792 20203 net.cpp:137] Memory required for data: 1060157440
I0412 14:19:57.737802 20203 layer_factory.hpp:77] Creating layer relu7
I0412 14:19:57.737812 20203 net.cpp:84] Creating Layer relu7
I0412 14:19:57.737818 20203 net.cpp:406] relu7 <- fc7
I0412 14:19:57.737824 20203 net.cpp:367] relu7 -> fc7 (in-place)
I0412 14:19:57.738255 20203 net.cpp:122] Setting up relu7
I0412 14:19:57.738265 20203 net.cpp:129] Top shape: 128 4096 (524288)
I0412 14:19:57.738268 20203 net.cpp:137] Memory required for data: 1062254592
I0412 14:19:57.738291 20203 layer_factory.hpp:77] Creating layer drop7
I0412 14:19:57.738298 20203 net.cpp:84] Creating Layer drop7
I0412 14:19:57.738301 20203 net.cpp:406] drop7 <- fc7
I0412 14:19:57.738307 20203 net.cpp:367] drop7 -> fc7 (in-place)
I0412 14:19:57.738332 20203 net.cpp:122] Setting up drop7
I0412 14:19:57.738337 20203 net.cpp:129] Top shape: 128 4096 (524288)
I0412 14:19:57.738339 20203 net.cpp:137] Memory required for data: 1064351744
I0412 14:19:57.738343 20203 layer_factory.hpp:77] Creating layer fc8
I0412 14:19:57.738350 20203 net.cpp:84] Creating Layer fc8
I0412 14:19:57.738354 20203 net.cpp:406] fc8 <- fc7
I0412 14:19:57.738360 20203 net.cpp:380] fc8 -> fc8
I0412 14:19:57.746182 20203 net.cpp:122] Setting up fc8
I0412 14:19:57.746196 20203 net.cpp:129] Top shape: 128 196 (25088)
I0412 14:19:57.746198 20203 net.cpp:137] Memory required for data: 1064452096
I0412 14:19:57.746206 20203 layer_factory.hpp:77] Creating layer loss
I0412 14:19:57.746214 20203 net.cpp:84] Creating Layer loss
I0412 14:19:57.746218 20203 net.cpp:406] loss <- fc8
I0412 14:19:57.746223 20203 net.cpp:406] loss <- label
I0412 14:19:57.746230 20203 net.cpp:380] loss -> loss
I0412 14:19:57.746240 20203 layer_factory.hpp:77] Creating layer loss
I0412 14:19:57.747061 20203 net.cpp:122] Setting up loss
I0412 14:19:57.747071 20203 net.cpp:129] Top shape: (1)
I0412 14:19:57.747073 20203 net.cpp:132]     with loss weight 1
I0412 14:19:57.747089 20203 net.cpp:137] Memory required for data: 1064452100
I0412 14:19:57.747094 20203 net.cpp:198] loss needs backward computation.
I0412 14:19:57.747102 20203 net.cpp:198] fc8 needs backward computation.
I0412 14:19:57.747105 20203 net.cpp:198] drop7 needs backward computation.
I0412 14:19:57.747108 20203 net.cpp:198] relu7 needs backward computation.
I0412 14:19:57.747112 20203 net.cpp:198] fc7 needs backward computation.
I0412 14:19:57.747115 20203 net.cpp:198] drop6 needs backward computation.
I0412 14:19:57.747119 20203 net.cpp:198] relu6 needs backward computation.
I0412 14:19:57.747123 20203 net.cpp:198] fc6 needs backward computation.
I0412 14:19:57.747126 20203 net.cpp:198] pool5 needs backward computation.
I0412 14:19:57.747130 20203 net.cpp:198] relu5 needs backward computation.
I0412 14:19:57.747134 20203 net.cpp:198] conv5 needs backward computation.
I0412 14:19:57.747138 20203 net.cpp:198] relu4.4 needs backward computation.
I0412 14:19:57.747141 20203 net.cpp:198] conv4.4 needs backward computation.
I0412 14:19:57.747145 20203 net.cpp:198] relu4.3 needs backward computation.
I0412 14:19:57.747149 20203 net.cpp:198] conv4.3 needs backward computation.
I0412 14:19:57.747153 20203 net.cpp:198] relu4.2 needs backward computation.
I0412 14:19:57.747156 20203 net.cpp:198] conv4.2 needs backward computation.
I0412 14:19:57.747160 20203 net.cpp:198] relu4 needs backward computation.
I0412 14:19:57.747164 20203 net.cpp:198] conv4 needs backward computation.
I0412 14:19:57.747167 20203 net.cpp:198] relu3 needs backward computation.
I0412 14:19:57.747172 20203 net.cpp:198] conv3 needs backward computation.
I0412 14:19:57.747177 20203 net.cpp:198] pool2 needs backward computation.
I0412 14:19:57.747180 20203 net.cpp:198] norm2 needs backward computation.
I0412 14:19:57.747185 20203 net.cpp:198] relu2 needs backward computation.
I0412 14:19:57.747189 20203 net.cpp:198] conv2 needs backward computation.
I0412 14:19:57.747192 20203 net.cpp:198] pool1 needs backward computation.
I0412 14:19:57.747195 20203 net.cpp:198] norm1 needs backward computation.
I0412 14:19:57.747200 20203 net.cpp:198] relu1 needs backward computation.
I0412 14:19:57.747202 20203 net.cpp:198] conv1 needs backward computation.
I0412 14:19:57.747206 20203 net.cpp:200] train-data does not need backward computation.
I0412 14:19:57.747210 20203 net.cpp:242] This network produces output loss
I0412 14:19:57.747226 20203 net.cpp:255] Network initialization done.
I0412 14:19:57.754376 20203 solver.cpp:172] Creating test net (#0) specified by net file: train_val.prototxt
I0412 14:19:57.754411 20203 net.cpp:294] The NetState phase (1) differed from the phase (0) specified by a rule in layer train-data
I0412 14:19:57.754595 20203 net.cpp:51] Initializing net from parameters:
state {
phase: TEST
}
layer {
name: "val-data"
type: "Data"
top: "data"
top: "label"
include {
phase: TEST
}
transform_param {
crop_size: 227
mean_file: "/mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto"
}
data_param {
source: "/mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/val_db"
batch_size: 32
backend: LMDB
}
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 96
kernel_size: 11
stride: 4
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "conv1"
top: "conv1"
}
layer {
name: "norm1"
type: "LRN"
bottom: "conv1"
top: "norm1"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool1"
type: "Pooling"
bottom: "norm1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 2
kernel_size: 5
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu2"
type: "ReLU"
bottom: "conv2"
top: "conv2"
}
layer {
name: "norm2"
type: "LRN"
bottom: "conv2"
top: "norm2"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool2"
type: "Pooling"
bottom: "norm2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv3"
type: "Convolution"
bottom: "pool2"
top: "conv3"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu3"
type: "ReLU"
bottom: "conv3"
top: "conv3"
}
layer {
name: "conv4"
type: "Convolution"
bottom: "conv3"
top: "conv4"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 96
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: "conv4.2"
type: "Convolution"
bottom: "conv4"
top: "conv4.2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 96
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu4.2"
type: "ReLU"
bottom: "conv4.2"
top: "conv4.2"
}
layer {
name: "conv4.3"
type: "Convolution"
bottom: "conv4.2"
top: "conv4.3"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 96
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu4.3"
type: "ReLU"
bottom: "conv4.3"
top: "conv4.3"
}
layer {
name: "conv4.4"
type: "Convolution"
bottom: "conv4.3"
top: "conv4.4"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 96
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu4.4"
type: "ReLU"
bottom: "conv4.4"
top: "conv4.4"
}
layer {
name: "conv5"
type: "Convolution"
bottom: "conv4.4"
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"
}
I0412 14:19:57.754696 20203 layer_factory.hpp:77] Creating layer val-data
I0412 14:19:57.756206 20203 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/val_db
I0412 14:19:57.756412 20203 net.cpp:84] Creating Layer val-data
I0412 14:19:57.756420 20203 net.cpp:380] val-data -> data
I0412 14:19:57.756431 20203 net.cpp:380] val-data -> label
I0412 14:19:57.756438 20203 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto
I0412 14:19:57.760958 20203 data_layer.cpp:45] output data size: 32,3,227,227
I0412 14:19:57.800777 20203 net.cpp:122] Setting up val-data
I0412 14:19:57.800796 20203 net.cpp:129] Top shape: 32 3 227 227 (4946784)
I0412 14:19:57.800801 20203 net.cpp:129] Top shape: 32 (32)
I0412 14:19:57.800804 20203 net.cpp:137] Memory required for data: 19787264
I0412 14:19:57.800810 20203 layer_factory.hpp:77] Creating layer label_val-data_1_split
I0412 14:19:57.800822 20203 net.cpp:84] Creating Layer label_val-data_1_split
I0412 14:19:57.800827 20203 net.cpp:406] label_val-data_1_split <- label
I0412 14:19:57.800833 20203 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_0
I0412 14:19:57.800843 20203 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_1
I0412 14:19:57.800920 20203 net.cpp:122] Setting up label_val-data_1_split
I0412 14:19:57.800925 20203 net.cpp:129] Top shape: 32 (32)
I0412 14:19:57.800930 20203 net.cpp:129] Top shape: 32 (32)
I0412 14:19:57.800951 20203 net.cpp:137] Memory required for data: 19787520
I0412 14:19:57.800954 20203 layer_factory.hpp:77] Creating layer conv1
I0412 14:19:57.800966 20203 net.cpp:84] Creating Layer conv1
I0412 14:19:57.800969 20203 net.cpp:406] conv1 <- data
I0412 14:19:57.800976 20203 net.cpp:380] conv1 -> conv1
I0412 14:19:57.802868 20203 net.cpp:122] Setting up conv1
I0412 14:19:57.802881 20203 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0412 14:19:57.802884 20203 net.cpp:137] Memory required for data: 56958720
I0412 14:19:57.802894 20203 layer_factory.hpp:77] Creating layer relu1
I0412 14:19:57.802901 20203 net.cpp:84] Creating Layer relu1
I0412 14:19:57.802906 20203 net.cpp:406] relu1 <- conv1
I0412 14:19:57.802909 20203 net.cpp:367] relu1 -> conv1 (in-place)
I0412 14:19:57.803200 20203 net.cpp:122] Setting up relu1
I0412 14:19:57.803208 20203 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0412 14:19:57.803211 20203 net.cpp:137] Memory required for data: 94129920
I0412 14:19:57.803215 20203 layer_factory.hpp:77] Creating layer norm1
I0412 14:19:57.803223 20203 net.cpp:84] Creating Layer norm1
I0412 14:19:57.803227 20203 net.cpp:406] norm1 <- conv1
I0412 14:19:57.803232 20203 net.cpp:380] norm1 -> norm1
I0412 14:19:57.806309 20203 net.cpp:122] Setting up norm1
I0412 14:19:57.806321 20203 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0412 14:19:57.806325 20203 net.cpp:137] Memory required for data: 131301120
I0412 14:19:57.806329 20203 layer_factory.hpp:77] Creating layer pool1
I0412 14:19:57.806336 20203 net.cpp:84] Creating Layer pool1
I0412 14:19:57.806340 20203 net.cpp:406] pool1 <- norm1
I0412 14:19:57.806346 20203 net.cpp:380] pool1 -> pool1
I0412 14:19:57.806376 20203 net.cpp:122] Setting up pool1
I0412 14:19:57.806382 20203 net.cpp:129] Top shape: 32 96 27 27 (2239488)
I0412 14:19:57.806385 20203 net.cpp:137] Memory required for data: 140259072
I0412 14:19:57.806389 20203 layer_factory.hpp:77] Creating layer conv2
I0412 14:19:57.806398 20203 net.cpp:84] Creating Layer conv2
I0412 14:19:57.806401 20203 net.cpp:406] conv2 <- pool1
I0412 14:19:57.806406 20203 net.cpp:380] conv2 -> conv2
I0412 14:19:57.812628 20203 net.cpp:122] Setting up conv2
I0412 14:19:57.812642 20203 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0412 14:19:57.812646 20203 net.cpp:137] Memory required for data: 164146944
I0412 14:19:57.812656 20203 layer_factory.hpp:77] Creating layer relu2
I0412 14:19:57.812664 20203 net.cpp:84] Creating Layer relu2
I0412 14:19:57.812667 20203 net.cpp:406] relu2 <- conv2
I0412 14:19:57.812674 20203 net.cpp:367] relu2 -> conv2 (in-place)
I0412 14:19:57.813177 20203 net.cpp:122] Setting up relu2
I0412 14:19:57.813186 20203 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0412 14:19:57.813190 20203 net.cpp:137] Memory required for data: 188034816
I0412 14:19:57.813194 20203 layer_factory.hpp:77] Creating layer norm2
I0412 14:19:57.813205 20203 net.cpp:84] Creating Layer norm2
I0412 14:19:57.813208 20203 net.cpp:406] norm2 <- conv2
I0412 14:19:57.813215 20203 net.cpp:380] norm2 -> norm2
I0412 14:19:57.813580 20203 net.cpp:122] Setting up norm2
I0412 14:19:57.813589 20203 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0412 14:19:57.813592 20203 net.cpp:137] Memory required for data: 211922688
I0412 14:19:57.813596 20203 layer_factory.hpp:77] Creating layer pool2
I0412 14:19:57.813603 20203 net.cpp:84] Creating Layer pool2
I0412 14:19:57.813607 20203 net.cpp:406] pool2 <- norm2
I0412 14:19:57.813613 20203 net.cpp:380] pool2 -> pool2
I0412 14:19:57.813643 20203 net.cpp:122] Setting up pool2
I0412 14:19:57.813648 20203 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0412 14:19:57.813652 20203 net.cpp:137] Memory required for data: 217460480
I0412 14:19:57.813655 20203 layer_factory.hpp:77] Creating layer conv3
I0412 14:19:57.813666 20203 net.cpp:84] Creating Layer conv3
I0412 14:19:57.813670 20203 net.cpp:406] conv3 <- pool2
I0412 14:19:57.813675 20203 net.cpp:380] conv3 -> conv3
I0412 14:19:57.829823 20203 net.cpp:122] Setting up conv3
I0412 14:19:57.829846 20203 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0412 14:19:57.829870 20203 net.cpp:137] Memory required for data: 225767168
I0412 14:19:57.829885 20203 layer_factory.hpp:77] Creating layer relu3
I0412 14:19:57.829896 20203 net.cpp:84] Creating Layer relu3
I0412 14:19:57.829900 20203 net.cpp:406] relu3 <- conv3
I0412 14:19:57.829910 20203 net.cpp:367] relu3 -> conv3 (in-place)
I0412 14:19:57.830451 20203 net.cpp:122] Setting up relu3
I0412 14:19:57.830459 20203 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0412 14:19:57.830463 20203 net.cpp:137] Memory required for data: 234073856
I0412 14:19:57.830467 20203 layer_factory.hpp:77] Creating layer conv4
I0412 14:19:57.830479 20203 net.cpp:84] Creating Layer conv4
I0412 14:19:57.830483 20203 net.cpp:406] conv4 <- conv3
I0412 14:19:57.830490 20203 net.cpp:380] conv4 -> conv4
I0412 14:19:57.834941 20203 net.cpp:122] Setting up conv4
I0412 14:19:57.834954 20203 net.cpp:129] Top shape: 32 96 13 13 (519168)
I0412 14:19:57.834957 20203 net.cpp:137] Memory required for data: 236150528
I0412 14:19:57.834964 20203 layer_factory.hpp:77] Creating layer relu4
I0412 14:19:57.834971 20203 net.cpp:84] Creating Layer relu4
I0412 14:19:57.834975 20203 net.cpp:406] relu4 <- conv4
I0412 14:19:57.834982 20203 net.cpp:367] relu4 -> conv4 (in-place)
I0412 14:19:57.835481 20203 net.cpp:122] Setting up relu4
I0412 14:19:57.835490 20203 net.cpp:129] Top shape: 32 96 13 13 (519168)
I0412 14:19:57.835494 20203 net.cpp:137] Memory required for data: 238227200
I0412 14:19:57.835498 20203 layer_factory.hpp:77] Creating layer conv4.2
I0412 14:19:57.835508 20203 net.cpp:84] Creating Layer conv4.2
I0412 14:19:57.835512 20203 net.cpp:406] conv4.2 <- conv4
I0412 14:19:57.835520 20203 net.cpp:380] conv4.2 -> conv4.2
I0412 14:19:57.840180 20203 net.cpp:122] Setting up conv4.2
I0412 14:19:57.840196 20203 net.cpp:129] Top shape: 32 96 13 13 (519168)
I0412 14:19:57.840200 20203 net.cpp:137] Memory required for data: 240303872
I0412 14:19:57.840212 20203 layer_factory.hpp:77] Creating layer relu4.2
I0412 14:19:57.840220 20203 net.cpp:84] Creating Layer relu4.2
I0412 14:19:57.840224 20203 net.cpp:406] relu4.2 <- conv4.2
I0412 14:19:57.840230 20203 net.cpp:367] relu4.2 -> conv4.2 (in-place)
I0412 14:19:57.840734 20203 net.cpp:122] Setting up relu4.2
I0412 14:19:57.840744 20203 net.cpp:129] Top shape: 32 96 13 13 (519168)
I0412 14:19:57.840747 20203 net.cpp:137] Memory required for data: 242380544
I0412 14:19:57.840750 20203 layer_factory.hpp:77] Creating layer conv4.3
I0412 14:19:57.840765 20203 net.cpp:84] Creating Layer conv4.3
I0412 14:19:57.840770 20203 net.cpp:406] conv4.3 <- conv4.2
I0412 14:19:57.840775 20203 net.cpp:380] conv4.3 -> conv4.3
I0412 14:19:57.845449 20203 net.cpp:122] Setting up conv4.3
I0412 14:19:57.845466 20203 net.cpp:129] Top shape: 32 96 13 13 (519168)
I0412 14:19:57.845470 20203 net.cpp:137] Memory required for data: 244457216
I0412 14:19:57.845479 20203 layer_factory.hpp:77] Creating layer relu4.3
I0412 14:19:57.845487 20203 net.cpp:84] Creating Layer relu4.3
I0412 14:19:57.845492 20203 net.cpp:406] relu4.3 <- conv4.3
I0412 14:19:57.845499 20203 net.cpp:367] relu4.3 -> conv4.3 (in-place)
I0412 14:19:57.845846 20203 net.cpp:122] Setting up relu4.3
I0412 14:19:57.845856 20203 net.cpp:129] Top shape: 32 96 13 13 (519168)
I0412 14:19:57.845860 20203 net.cpp:137] Memory required for data: 246533888
I0412 14:19:57.845863 20203 layer_factory.hpp:77] Creating layer conv4.4
I0412 14:19:57.845875 20203 net.cpp:84] Creating Layer conv4.4
I0412 14:19:57.845880 20203 net.cpp:406] conv4.4 <- conv4.3
I0412 14:19:57.845886 20203 net.cpp:380] conv4.4 -> conv4.4
I0412 14:19:57.849527 20203 net.cpp:122] Setting up conv4.4
I0412 14:19:57.849540 20203 net.cpp:129] Top shape: 32 96 13 13 (519168)
I0412 14:19:57.849545 20203 net.cpp:137] Memory required for data: 248610560
I0412 14:19:57.849552 20203 layer_factory.hpp:77] Creating layer relu4.4
I0412 14:19:57.849558 20203 net.cpp:84] Creating Layer relu4.4
I0412 14:19:57.849562 20203 net.cpp:406] relu4.4 <- conv4.4
I0412 14:19:57.849568 20203 net.cpp:367] relu4.4 -> conv4.4 (in-place)
I0412 14:19:57.851155 20203 net.cpp:122] Setting up relu4.4
I0412 14:19:57.851184 20203 net.cpp:129] Top shape: 32 96 13 13 (519168)
I0412 14:19:57.851187 20203 net.cpp:137] Memory required for data: 250687232
I0412 14:19:57.851191 20203 layer_factory.hpp:77] Creating layer conv5
I0412 14:19:57.851203 20203 net.cpp:84] Creating Layer conv5
I0412 14:19:57.851207 20203 net.cpp:406] conv5 <- conv4.4
I0412 14:19:57.851214 20203 net.cpp:380] conv5 -> conv5
I0412 14:19:57.855340 20203 net.cpp:122] Setting up conv5
I0412 14:19:57.855350 20203 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0412 14:19:57.855355 20203 net.cpp:137] Memory required for data: 256225024
I0412 14:19:57.855361 20203 layer_factory.hpp:77] Creating layer relu5
I0412 14:19:57.855370 20203 net.cpp:84] Creating Layer relu5
I0412 14:19:57.855373 20203 net.cpp:406] relu5 <- conv5
I0412 14:19:57.855378 20203 net.cpp:367] relu5 -> conv5 (in-place)
I0412 14:19:57.855880 20203 net.cpp:122] Setting up relu5
I0412 14:19:57.855890 20203 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0412 14:19:57.855892 20203 net.cpp:137] Memory required for data: 261762816
I0412 14:19:57.855896 20203 layer_factory.hpp:77] Creating layer pool5
I0412 14:19:57.855903 20203 net.cpp:84] Creating Layer pool5
I0412 14:19:57.855907 20203 net.cpp:406] pool5 <- conv5
I0412 14:19:57.855913 20203 net.cpp:380] pool5 -> pool5
I0412 14:19:57.855957 20203 net.cpp:122] Setting up pool5
I0412 14:19:57.855963 20203 net.cpp:129] Top shape: 32 256 6 6 (294912)
I0412 14:19:57.855967 20203 net.cpp:137] Memory required for data: 262942464
I0412 14:19:57.855969 20203 layer_factory.hpp:77] Creating layer fc6
I0412 14:19:57.855976 20203 net.cpp:84] Creating Layer fc6
I0412 14:19:57.855980 20203 net.cpp:406] fc6 <- pool5
I0412 14:19:57.855986 20203 net.cpp:380] fc6 -> fc6
I0412 14:19:58.210417 20203 net.cpp:122] Setting up fc6
I0412 14:19:58.210438 20203 net.cpp:129] Top shape: 32 4096 (131072)
I0412 14:19:58.210443 20203 net.cpp:137] Memory required for data: 263466752
I0412 14:19:58.210458 20203 layer_factory.hpp:77] Creating layer relu6
I0412 14:19:58.210467 20203 net.cpp:84] Creating Layer relu6
I0412 14:19:58.210471 20203 net.cpp:406] relu6 <- fc6
I0412 14:19:58.210479 20203 net.cpp:367] relu6 -> fc6 (in-place)
I0412 14:19:58.210903 20203 net.cpp:122] Setting up relu6
I0412 14:19:58.210912 20203 net.cpp:129] Top shape: 32 4096 (131072)
I0412 14:19:58.210916 20203 net.cpp:137] Memory required for data: 263991040
I0412 14:19:58.210919 20203 layer_factory.hpp:77] Creating layer drop6
I0412 14:19:58.210925 20203 net.cpp:84] Creating Layer drop6
I0412 14:19:58.210929 20203 net.cpp:406] drop6 <- fc6
I0412 14:19:58.210935 20203 net.cpp:367] drop6 -> fc6 (in-place)
I0412 14:19:58.210958 20203 net.cpp:122] Setting up drop6
I0412 14:19:58.210964 20203 net.cpp:129] Top shape: 32 4096 (131072)
I0412 14:19:58.210968 20203 net.cpp:137] Memory required for data: 264515328
I0412 14:19:58.210970 20203 layer_factory.hpp:77] Creating layer fc7
I0412 14:19:58.210978 20203 net.cpp:84] Creating Layer fc7
I0412 14:19:58.210980 20203 net.cpp:406] fc7 <- fc6
I0412 14:19:58.210986 20203 net.cpp:380] fc7 -> fc7
I0412 14:19:58.371541 20203 net.cpp:122] Setting up fc7
I0412 14:19:58.371562 20203 net.cpp:129] Top shape: 32 4096 (131072)
I0412 14:19:58.371567 20203 net.cpp:137] Memory required for data: 265039616
I0412 14:19:58.371577 20203 layer_factory.hpp:77] Creating layer relu7
I0412 14:19:58.371585 20203 net.cpp:84] Creating Layer relu7
I0412 14:19:58.371590 20203 net.cpp:406] relu7 <- fc7
I0412 14:19:58.371598 20203 net.cpp:367] relu7 -> fc7 (in-place)
I0412 14:19:58.372228 20203 net.cpp:122] Setting up relu7
I0412 14:19:58.372238 20203 net.cpp:129] Top shape: 32 4096 (131072)
I0412 14:19:58.372241 20203 net.cpp:137] Memory required for data: 265563904
I0412 14:19:58.372246 20203 layer_factory.hpp:77] Creating layer drop7
I0412 14:19:58.372252 20203 net.cpp:84] Creating Layer drop7
I0412 14:19:58.372256 20203 net.cpp:406] drop7 <- fc7
I0412 14:19:58.372262 20203 net.cpp:367] drop7 -> fc7 (in-place)
I0412 14:19:58.372287 20203 net.cpp:122] Setting up drop7
I0412 14:19:58.372310 20203 net.cpp:129] Top shape: 32 4096 (131072)
I0412 14:19:58.372313 20203 net.cpp:137] Memory required for data: 266088192
I0412 14:19:58.372318 20203 layer_factory.hpp:77] Creating layer fc8
I0412 14:19:58.372325 20203 net.cpp:84] Creating Layer fc8
I0412 14:19:58.372328 20203 net.cpp:406] fc8 <- fc7
I0412 14:19:58.372335 20203 net.cpp:380] fc8 -> fc8
I0412 14:19:58.380040 20203 net.cpp:122] Setting up fc8
I0412 14:19:58.380053 20203 net.cpp:129] Top shape: 32 196 (6272)
I0412 14:19:58.380055 20203 net.cpp:137] Memory required for data: 266113280
I0412 14:19:58.380062 20203 layer_factory.hpp:77] Creating layer fc8_fc8_0_split
I0412 14:19:58.380071 20203 net.cpp:84] Creating Layer fc8_fc8_0_split
I0412 14:19:58.380074 20203 net.cpp:406] fc8_fc8_0_split <- fc8
I0412 14:19:58.380080 20203 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_0
I0412 14:19:58.380087 20203 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_1
I0412 14:19:58.380121 20203 net.cpp:122] Setting up fc8_fc8_0_split
I0412 14:19:58.380125 20203 net.cpp:129] Top shape: 32 196 (6272)
I0412 14:19:58.380129 20203 net.cpp:129] Top shape: 32 196 (6272)
I0412 14:19:58.380132 20203 net.cpp:137] Memory required for data: 266163456
I0412 14:19:58.380136 20203 layer_factory.hpp:77] Creating layer accuracy
I0412 14:19:58.380143 20203 net.cpp:84] Creating Layer accuracy
I0412 14:19:58.380146 20203 net.cpp:406] accuracy <- fc8_fc8_0_split_0
I0412 14:19:58.380151 20203 net.cpp:406] accuracy <- label_val-data_1_split_0
I0412 14:19:58.380156 20203 net.cpp:380] accuracy -> accuracy
I0412 14:19:58.380162 20203 net.cpp:122] Setting up accuracy
I0412 14:19:58.380167 20203 net.cpp:129] Top shape: (1)
I0412 14:19:58.380169 20203 net.cpp:137] Memory required for data: 266163460
I0412 14:19:58.380172 20203 layer_factory.hpp:77] Creating layer loss
I0412 14:19:58.380183 20203 net.cpp:84] Creating Layer loss
I0412 14:19:58.380187 20203 net.cpp:406] loss <- fc8_fc8_0_split_1
I0412 14:19:58.380192 20203 net.cpp:406] loss <- label_val-data_1_split_1
I0412 14:19:58.380195 20203 net.cpp:380] loss -> loss
I0412 14:19:58.380203 20203 layer_factory.hpp:77] Creating layer loss
I0412 14:19:58.391234 20203 net.cpp:122] Setting up loss
I0412 14:19:58.391250 20203 net.cpp:129] Top shape: (1)
I0412 14:19:58.391253 20203 net.cpp:132]     with loss weight 1
I0412 14:19:58.391264 20203 net.cpp:137] Memory required for data: 266163464
I0412 14:19:58.391270 20203 net.cpp:198] loss needs backward computation.
I0412 14:19:58.391276 20203 net.cpp:200] accuracy does not need backward computation.
I0412 14:19:58.391281 20203 net.cpp:198] fc8_fc8_0_split needs backward computation.
I0412 14:19:58.391285 20203 net.cpp:198] fc8 needs backward computation.
I0412 14:19:58.391289 20203 net.cpp:198] drop7 needs backward computation.
I0412 14:19:58.391292 20203 net.cpp:198] relu7 needs backward computation.
I0412 14:19:58.391297 20203 net.cpp:198] fc7 needs backward computation.
I0412 14:19:58.391301 20203 net.cpp:198] drop6 needs backward computation.
I0412 14:19:58.391305 20203 net.cpp:198] relu6 needs backward computation.
I0412 14:19:58.391309 20203 net.cpp:198] fc6 needs backward computation.
I0412 14:19:58.391314 20203 net.cpp:198] pool5 needs backward computation.
I0412 14:19:58.391319 20203 net.cpp:198] relu5 needs backward computation.
I0412 14:19:58.391321 20203 net.cpp:198] conv5 needs backward computation.
I0412 14:19:58.391325 20203 net.cpp:198] relu4.4 needs backward computation.
I0412 14:19:58.391330 20203 net.cpp:198] conv4.4 needs backward computation.
I0412 14:19:58.391333 20203 net.cpp:198] relu4.3 needs backward computation.
I0412 14:19:58.391337 20203 net.cpp:198] conv4.3 needs backward computation.
I0412 14:19:58.391341 20203 net.cpp:198] relu4.2 needs backward computation.
I0412 14:19:58.391347 20203 net.cpp:198] conv4.2 needs backward computation.
I0412 14:19:58.391351 20203 net.cpp:198] relu4 needs backward computation.
I0412 14:19:58.391355 20203 net.cpp:198] conv4 needs backward computation.
I0412 14:19:58.391360 20203 net.cpp:198] relu3 needs backward computation.
I0412 14:19:58.391381 20203 net.cpp:198] conv3 needs backward computation.
I0412 14:19:58.391386 20203 net.cpp:198] pool2 needs backward computation.
I0412 14:19:58.391391 20203 net.cpp:198] norm2 needs backward computation.
I0412 14:19:58.391394 20203 net.cpp:198] relu2 needs backward computation.
I0412 14:19:58.391398 20203 net.cpp:198] conv2 needs backward computation.
I0412 14:19:58.391402 20203 net.cpp:198] pool1 needs backward computation.
I0412 14:19:58.391407 20203 net.cpp:198] norm1 needs backward computation.
I0412 14:19:58.391410 20203 net.cpp:198] relu1 needs backward computation.
I0412 14:19:58.391415 20203 net.cpp:198] conv1 needs backward computation.
I0412 14:19:58.391419 20203 net.cpp:200] label_val-data_1_split does not need backward computation.
I0412 14:19:58.391423 20203 net.cpp:200] val-data does not need backward computation.
I0412 14:19:58.391427 20203 net.cpp:242] This network produces output accuracy
I0412 14:19:58.391431 20203 net.cpp:242] This network produces output loss
I0412 14:19:58.391453 20203 net.cpp:255] Network initialization done.
I0412 14:19:58.391537 20203 solver.cpp:56] Solver scaffolding done.
I0412 14:19:58.392206 20203 caffe.cpp:248] Starting Optimization
I0412 14:19:58.392217 20203 solver.cpp:272] Solving
I0412 14:19:58.392221 20203 solver.cpp:273] Learning Rate Policy: exp
I0412 14:19:58.393481 20203 solver.cpp:330] Iteration 0, Testing net (#0)
I0412 14:19:58.393491 20203 net.cpp:676] Ignoring source layer train-data
I0412 14:19:58.489135 20203 blocking_queue.cpp:49] Waiting for data
I0412 14:20:02.709460 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:20:02.754825 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00428922
I0412 14:20:02.754885 20203 solver.cpp:397]     Test net output #1: loss = 5.28069 (* 1 = 5.28069 loss)
I0412 14:20:02.850173 20203 solver.cpp:218] Iteration 0 (8.94696e+35 iter/s, 4.45774s/12 iters), loss = 5.2814
I0412 14:20:02.852138 20203 solver.cpp:237]     Train net output #0: loss = 5.2814 (* 1 = 5.2814 loss)
I0412 14:20:02.852159 20203 sgd_solver.cpp:105] Iteration 0, lr = 0.01
I0412 14:20:06.848487 20203 solver.cpp:218] Iteration 12 (3.00286 iter/s, 3.99618s/12 iters), loss = 5.27198
I0412 14:20:06.848541 20203 solver.cpp:237]     Train net output #0: loss = 5.27198 (* 1 = 5.27198 loss)
I0412 14:20:06.848554 20203 sgd_solver.cpp:105] Iteration 12, lr = 0.00997626
I0412 14:20:11.608510 20203 solver.cpp:218] Iteration 24 (2.52113 iter/s, 4.75977s/12 iters), loss = 5.27799
I0412 14:20:11.608573 20203 solver.cpp:237]     Train net output #0: loss = 5.27799 (* 1 = 5.27799 loss)
I0412 14:20:11.608590 20203 sgd_solver.cpp:105] Iteration 24, lr = 0.00995257
I0412 14:20:16.668282 20203 solver.cpp:218] Iteration 36 (2.37178 iter/s, 5.05949s/12 iters), loss = 5.2983
I0412 14:20:16.668331 20203 solver.cpp:237]     Train net output #0: loss = 5.2983 (* 1 = 5.2983 loss)
I0412 14:20:16.668341 20203 sgd_solver.cpp:105] Iteration 36, lr = 0.00992894
I0412 14:20:21.390647 20203 solver.cpp:218] Iteration 48 (2.54123 iter/s, 4.72212s/12 iters), loss = 5.3027
I0412 14:20:21.390698 20203 solver.cpp:237]     Train net output #0: loss = 5.3027 (* 1 = 5.3027 loss)
I0412 14:20:21.390712 20203 sgd_solver.cpp:105] Iteration 48, lr = 0.00990537
I0412 14:20:26.679602 20203 solver.cpp:218] Iteration 60 (2.26899 iter/s, 5.28869s/12 iters), loss = 5.29198
I0412 14:20:26.679792 20203 solver.cpp:237]     Train net output #0: loss = 5.29198 (* 1 = 5.29198 loss)
I0412 14:20:26.679806 20203 sgd_solver.cpp:105] Iteration 60, lr = 0.00988185
I0412 14:20:31.789865 20203 solver.cpp:218] Iteration 72 (2.3484 iter/s, 5.10987s/12 iters), loss = 5.29752
I0412 14:20:31.789916 20203 solver.cpp:237]     Train net output #0: loss = 5.29752 (* 1 = 5.29752 loss)
I0412 14:20:31.789928 20203 sgd_solver.cpp:105] Iteration 72, lr = 0.00985839
I0412 14:20:36.748898 20203 solver.cpp:218] Iteration 84 (2.41995 iter/s, 4.95878s/12 iters), loss = 5.29723
I0412 14:20:36.748940 20203 solver.cpp:237]     Train net output #0: loss = 5.29723 (* 1 = 5.29723 loss)
I0412 14:20:36.748950 20203 sgd_solver.cpp:105] Iteration 84, lr = 0.00983498
I0412 14:20:41.569759 20203 solver.cpp:218] Iteration 96 (2.48931 iter/s, 4.82062s/12 iters), loss = 5.31386
I0412 14:20:41.569813 20203 solver.cpp:237]     Train net output #0: loss = 5.31386 (* 1 = 5.31386 loss)
I0412 14:20:41.569826 20203 sgd_solver.cpp:105] Iteration 96, lr = 0.00981163
I0412 14:20:43.394351 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:20:43.730574 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_102.caffemodel
I0412 14:20:46.783524 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_102.solverstate
I0412 14:20:49.834396 20203 solver.cpp:330] Iteration 102, Testing net (#0)
I0412 14:20:49.834424 20203 net.cpp:676] Ignoring source layer train-data
I0412 14:20:54.319389 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:20:54.396864 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 14:20:54.396908 20203 solver.cpp:397]     Test net output #1: loss = 5.29109 (* 1 = 5.29109 loss)
I0412 14:20:56.282728 20203 solver.cpp:218] Iteration 108 (0.815641 iter/s, 14.7123s/12 iters), loss = 5.31561
I0412 14:20:56.282790 20203 solver.cpp:237]     Train net output #0: loss = 5.31561 (* 1 = 5.31561 loss)
I0412 14:20:56.282805 20203 sgd_solver.cpp:105] Iteration 108, lr = 0.00978834
I0412 14:21:01.116189 20203 solver.cpp:218] Iteration 120 (2.48282 iter/s, 4.8332s/12 iters), loss = 5.28988
I0412 14:21:01.116323 20203 solver.cpp:237]     Train net output #0: loss = 5.28988 (* 1 = 5.28988 loss)
I0412 14:21:01.116333 20203 sgd_solver.cpp:105] Iteration 120, lr = 0.0097651
I0412 14:21:06.081297 20203 solver.cpp:218] Iteration 132 (2.41703 iter/s, 4.96477s/12 iters), loss = 5.24655
I0412 14:21:06.081346 20203 solver.cpp:237]     Train net output #0: loss = 5.24655 (* 1 = 5.24655 loss)
I0412 14:21:06.081358 20203 sgd_solver.cpp:105] Iteration 132, lr = 0.00974192
I0412 14:21:10.773310 20203 solver.cpp:218] Iteration 144 (2.55767 iter/s, 4.69177s/12 iters), loss = 5.30975
I0412 14:21:10.773360 20203 solver.cpp:237]     Train net output #0: loss = 5.30975 (* 1 = 5.30975 loss)
I0412 14:21:10.773372 20203 sgd_solver.cpp:105] Iteration 144, lr = 0.00971879
I0412 14:21:15.683403 20203 solver.cpp:218] Iteration 156 (2.44407 iter/s, 4.90984s/12 iters), loss = 5.26393
I0412 14:21:15.683455 20203 solver.cpp:237]     Train net output #0: loss = 5.26393 (* 1 = 5.26393 loss)
I0412 14:21:15.683466 20203 sgd_solver.cpp:105] Iteration 156, lr = 0.00969571
I0412 14:21:20.401487 20203 solver.cpp:218] Iteration 168 (2.54354 iter/s, 4.71784s/12 iters), loss = 5.27205
I0412 14:21:20.401538 20203 solver.cpp:237]     Train net output #0: loss = 5.27205 (* 1 = 5.27205 loss)
I0412 14:21:20.401549 20203 sgd_solver.cpp:105] Iteration 168, lr = 0.00967269
I0412 14:21:25.212123 20203 solver.cpp:218] Iteration 180 (2.4946 iter/s, 4.81038s/12 iters), loss = 5.26786
I0412 14:21:25.212188 20203 solver.cpp:237]     Train net output #0: loss = 5.26786 (* 1 = 5.26786 loss)
I0412 14:21:25.212203 20203 sgd_solver.cpp:105] Iteration 180, lr = 0.00964973
I0412 14:21:30.054644 20203 solver.cpp:218] Iteration 192 (2.47818 iter/s, 4.84226s/12 iters), loss = 5.29224
I0412 14:21:30.054700 20203 solver.cpp:237]     Train net output #0: loss = 5.29224 (* 1 = 5.29224 loss)
I0412 14:21:30.054714 20203 sgd_solver.cpp:105] Iteration 192, lr = 0.00962682
I0412 14:21:33.608677 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:21:34.290046 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_204.caffemodel
I0412 14:21:37.403096 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_204.solverstate
I0412 14:21:39.857748 20203 solver.cpp:330] Iteration 204, Testing net (#0)
I0412 14:21:39.857782 20203 net.cpp:676] Ignoring source layer train-data
I0412 14:21:44.959813 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:21:45.092500 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 14:21:45.092531 20203 solver.cpp:397]     Test net output #1: loss = 5.28901 (* 1 = 5.28901 loss)
I0412 14:21:45.178843 20203 solver.cpp:218] Iteration 204 (0.793465 iter/s, 15.1235s/12 iters), loss = 5.27528
I0412 14:21:45.178885 20203 solver.cpp:237]     Train net output #0: loss = 5.27528 (* 1 = 5.27528 loss)
I0412 14:21:45.178894 20203 sgd_solver.cpp:105] Iteration 204, lr = 0.00960396
I0412 14:21:49.302356 20203 solver.cpp:218] Iteration 216 (2.91029 iter/s, 4.1233s/12 iters), loss = 5.28655
I0412 14:21:49.302399 20203 solver.cpp:237]     Train net output #0: loss = 5.28655 (* 1 = 5.28655 loss)
I0412 14:21:49.302412 20203 sgd_solver.cpp:105] Iteration 216, lr = 0.00958116
I0412 14:21:54.507835 20203 solver.cpp:218] Iteration 228 (2.30538 iter/s, 5.20522s/12 iters), loss = 5.26184
I0412 14:21:54.507877 20203 solver.cpp:237]     Train net output #0: loss = 5.26184 (* 1 = 5.26184 loss)
I0412 14:21:54.507885 20203 sgd_solver.cpp:105] Iteration 228, lr = 0.00955841
I0412 14:21:59.321933 20203 solver.cpp:218] Iteration 240 (2.49281 iter/s, 4.81385s/12 iters), loss = 5.30184
I0412 14:21:59.322007 20203 solver.cpp:237]     Train net output #0: loss = 5.30184 (* 1 = 5.30184 loss)
I0412 14:21:59.322021 20203 sgd_solver.cpp:105] Iteration 240, lr = 0.00953572
I0412 14:22:04.507977 20203 solver.cpp:218] Iteration 252 (2.31403 iter/s, 5.18576s/12 iters), loss = 5.28593
I0412 14:22:04.508108 20203 solver.cpp:237]     Train net output #0: loss = 5.28593 (* 1 = 5.28593 loss)
I0412 14:22:04.508122 20203 sgd_solver.cpp:105] Iteration 252, lr = 0.00951308
I0412 14:22:09.300354 20203 solver.cpp:218] Iteration 264 (2.50415 iter/s, 4.79205s/12 iters), loss = 5.27203
I0412 14:22:09.300410 20203 solver.cpp:237]     Train net output #0: loss = 5.27203 (* 1 = 5.27203 loss)
I0412 14:22:09.300423 20203 sgd_solver.cpp:105] Iteration 264, lr = 0.00949049
I0412 14:22:14.334714 20203 solver.cpp:218] Iteration 276 (2.38375 iter/s, 5.03409s/12 iters), loss = 5.29673
I0412 14:22:14.334767 20203 solver.cpp:237]     Train net output #0: loss = 5.29673 (* 1 = 5.29673 loss)
I0412 14:22:14.334780 20203 sgd_solver.cpp:105] Iteration 276, lr = 0.00946796
I0412 14:22:19.253551 20203 solver.cpp:218] Iteration 288 (2.43973 iter/s, 4.91858s/12 iters), loss = 5.29123
I0412 14:22:19.253600 20203 solver.cpp:237]     Train net output #0: loss = 5.29123 (* 1 = 5.29123 loss)
I0412 14:22:19.253612 20203 sgd_solver.cpp:105] Iteration 288, lr = 0.00944548
I0412 14:22:24.218992 20203 solver.cpp:218] Iteration 300 (2.41683 iter/s, 4.96518s/12 iters), loss = 5.28905
I0412 14:22:24.219045 20203 solver.cpp:237]     Train net output #0: loss = 5.28905 (* 1 = 5.28905 loss)
I0412 14:22:24.219058 20203 sgd_solver.cpp:105] Iteration 300, lr = 0.00942305
I0412 14:22:25.293226 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:22:26.432472 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_306.caffemodel
I0412 14:22:30.999752 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_306.solverstate
I0412 14:22:33.413734 20203 solver.cpp:330] Iteration 306, Testing net (#0)
I0412 14:22:33.413758 20203 net.cpp:676] Ignoring source layer train-data
I0412 14:22:37.711786 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:22:37.877427 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 14:22:37.877481 20203 solver.cpp:397]     Test net output #1: loss = 5.28766 (* 1 = 5.28766 loss)
I0412 14:22:39.743162 20203 solver.cpp:218] Iteration 312 (0.773022 iter/s, 15.5235s/12 iters), loss = 5.28861
I0412 14:22:39.743224 20203 solver.cpp:237]     Train net output #0: loss = 5.28861 (* 1 = 5.28861 loss)
I0412 14:22:39.743237 20203 sgd_solver.cpp:105] Iteration 312, lr = 0.00940068
I0412 14:22:44.634549 20203 solver.cpp:218] Iteration 324 (2.45343 iter/s, 4.89112s/12 iters), loss = 5.24988
I0412 14:22:44.634596 20203 solver.cpp:237]     Train net output #0: loss = 5.24988 (* 1 = 5.24988 loss)
I0412 14:22:44.634606 20203 sgd_solver.cpp:105] Iteration 324, lr = 0.00937836
I0412 14:22:49.578891 20203 solver.cpp:218] Iteration 336 (2.42714 iter/s, 4.94409s/12 iters), loss = 5.26598
I0412 14:22:49.578934 20203 solver.cpp:237]     Train net output #0: loss = 5.26598 (* 1 = 5.26598 loss)
I0412 14:22:49.578945 20203 sgd_solver.cpp:105] Iteration 336, lr = 0.0093561
I0412 14:22:54.403690 20203 solver.cpp:218] Iteration 348 (2.48728 iter/s, 4.82456s/12 iters), loss = 5.27186
I0412 14:22:54.403733 20203 solver.cpp:237]     Train net output #0: loss = 5.27186 (* 1 = 5.27186 loss)
I0412 14:22:54.403743 20203 sgd_solver.cpp:105] Iteration 348, lr = 0.00933388
I0412 14:22:59.204466 20203 solver.cpp:218] Iteration 360 (2.49973 iter/s, 4.80052s/12 iters), loss = 5.29809
I0412 14:22:59.204525 20203 solver.cpp:237]     Train net output #0: loss = 5.29809 (* 1 = 5.29809 loss)
I0412 14:22:59.204537 20203 sgd_solver.cpp:105] Iteration 360, lr = 0.00931172
I0412 14:23:04.021169 20203 solver.cpp:218] Iteration 372 (2.49147 iter/s, 4.81644s/12 iters), loss = 5.2789
I0412 14:23:04.021221 20203 solver.cpp:237]     Train net output #0: loss = 5.2789 (* 1 = 5.2789 loss)
I0412 14:23:04.021234 20203 sgd_solver.cpp:105] Iteration 372, lr = 0.00928961
I0412 14:23:09.750658 20203 solver.cpp:218] Iteration 384 (2.09453 iter/s, 5.7292s/12 iters), loss = 5.28752
I0412 14:23:09.750830 20203 solver.cpp:237]     Train net output #0: loss = 5.28752 (* 1 = 5.28752 loss)
I0412 14:23:09.750846 20203 sgd_solver.cpp:105] Iteration 384, lr = 0.00926756
I0412 14:23:14.568517 20203 solver.cpp:218] Iteration 396 (2.49093 iter/s, 4.81749s/12 iters), loss = 5.27361
I0412 14:23:14.568560 20203 solver.cpp:237]     Train net output #0: loss = 5.27361 (* 1 = 5.27361 loss)
I0412 14:23:14.568569 20203 sgd_solver.cpp:105] Iteration 396, lr = 0.00924556
I0412 14:23:17.685600 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:23:19.067333 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_408.caffemodel
I0412 14:23:23.403877 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_408.solverstate
I0412 14:23:27.090049 20203 solver.cpp:330] Iteration 408, Testing net (#0)
I0412 14:23:27.090078 20203 net.cpp:676] Ignoring source layer train-data
I0412 14:23:31.392374 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:23:31.595954 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 14:23:31.595997 20203 solver.cpp:397]     Test net output #1: loss = 5.28793 (* 1 = 5.28793 loss)
I0412 14:23:31.682664 20203 solver.cpp:218] Iteration 408 (0.701204 iter/s, 17.1134s/12 iters), loss = 5.28664
I0412 14:23:31.682710 20203 solver.cpp:237]     Train net output #0: loss = 5.28664 (* 1 = 5.28664 loss)
I0412 14:23:31.682720 20203 sgd_solver.cpp:105] Iteration 408, lr = 0.00922361
I0412 14:23:35.715111 20203 solver.cpp:218] Iteration 420 (2.97602 iter/s, 4.03222s/12 iters), loss = 5.28368
I0412 14:23:35.715170 20203 solver.cpp:237]     Train net output #0: loss = 5.28368 (* 1 = 5.28368 loss)
I0412 14:23:35.715188 20203 sgd_solver.cpp:105] Iteration 420, lr = 0.00920171
I0412 14:23:40.520694 20203 solver.cpp:218] Iteration 432 (2.49723 iter/s, 4.80532s/12 iters), loss = 5.27348
I0412 14:23:40.520817 20203 solver.cpp:237]     Train net output #0: loss = 5.27348 (* 1 = 5.27348 loss)
I0412 14:23:40.520829 20203 sgd_solver.cpp:105] Iteration 432, lr = 0.00917986
I0412 14:23:45.631803 20203 solver.cpp:218] Iteration 444 (2.34798 iter/s, 5.11077s/12 iters), loss = 5.28971
I0412 14:23:45.631846 20203 solver.cpp:237]     Train net output #0: loss = 5.28971 (* 1 = 5.28971 loss)
I0412 14:23:45.631855 20203 sgd_solver.cpp:105] Iteration 444, lr = 0.00915807
I0412 14:23:50.340682 20203 solver.cpp:218] Iteration 456 (2.54851 iter/s, 4.70863s/12 iters), loss = 5.2974
I0412 14:23:50.340739 20203 solver.cpp:237]     Train net output #0: loss = 5.2974 (* 1 = 5.2974 loss)
I0412 14:23:50.340754 20203 sgd_solver.cpp:105] Iteration 456, lr = 0.00913632
I0412 14:23:55.126292 20203 solver.cpp:218] Iteration 468 (2.50765 iter/s, 4.78536s/12 iters), loss = 5.29855
I0412 14:23:55.126335 20203 solver.cpp:237]     Train net output #0: loss = 5.29855 (* 1 = 5.29855 loss)
I0412 14:23:55.126343 20203 sgd_solver.cpp:105] Iteration 468, lr = 0.00911463
I0412 14:24:00.246696 20203 solver.cpp:218] Iteration 480 (2.34369 iter/s, 5.12014s/12 iters), loss = 5.27091
I0412 14:24:00.246753 20203 solver.cpp:237]     Train net output #0: loss = 5.27091 (* 1 = 5.27091 loss)
I0412 14:24:00.246767 20203 sgd_solver.cpp:105] Iteration 480, lr = 0.00909299
I0412 14:24:05.141503 20203 solver.cpp:218] Iteration 492 (2.45171 iter/s, 4.89455s/12 iters), loss = 5.29064
I0412 14:24:05.141541 20203 solver.cpp:237]     Train net output #0: loss = 5.29064 (* 1 = 5.29064 loss)
I0412 14:24:05.141548 20203 sgd_solver.cpp:105] Iteration 492, lr = 0.0090714
I0412 14:24:10.206341 20203 solver.cpp:218] Iteration 504 (2.3694 iter/s, 5.06457s/12 iters), loss = 5.27148
I0412 14:24:10.206401 20203 solver.cpp:237]     Train net output #0: loss = 5.27148 (* 1 = 5.27148 loss)
I0412 14:24:10.206414 20203 sgd_solver.cpp:105] Iteration 504, lr = 0.00904986
I0412 14:24:10.516408 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:24:12.361799 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_510.caffemodel
I0412 14:24:15.419447 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_510.solverstate
I0412 14:24:18.180577 20203 solver.cpp:330] Iteration 510, Testing net (#0)
I0412 14:24:18.180598 20203 net.cpp:676] Ignoring source layer train-data
I0412 14:24:22.428063 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:24:22.666286 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00612745
I0412 14:24:22.666335 20203 solver.cpp:397]     Test net output #1: loss = 5.28686 (* 1 = 5.28686 loss)
I0412 14:24:24.449348 20203 solver.cpp:218] Iteration 516 (0.842556 iter/s, 14.2424s/12 iters), loss = 5.28003
I0412 14:24:24.449400 20203 solver.cpp:237]     Train net output #0: loss = 5.28003 (* 1 = 5.28003 loss)
I0412 14:24:24.449412 20203 sgd_solver.cpp:105] Iteration 516, lr = 0.00902838
I0412 14:24:29.460561 20203 solver.cpp:218] Iteration 528 (2.39476 iter/s, 5.01095s/12 iters), loss = 5.28201
I0412 14:24:29.460609 20203 solver.cpp:237]     Train net output #0: loss = 5.28201 (* 1 = 5.28201 loss)
I0412 14:24:29.460621 20203 sgd_solver.cpp:105] Iteration 528, lr = 0.00900694
I0412 14:24:34.709429 20203 solver.cpp:218] Iteration 540 (2.28633 iter/s, 5.24859s/12 iters), loss = 5.28039
I0412 14:24:34.709470 20203 solver.cpp:237]     Train net output #0: loss = 5.28039 (* 1 = 5.28039 loss)
I0412 14:24:34.709479 20203 sgd_solver.cpp:105] Iteration 540, lr = 0.00898556
I0412 14:24:39.549939 20203 solver.cpp:218] Iteration 552 (2.4792 iter/s, 4.84026s/12 iters), loss = 5.27511
I0412 14:24:39.549988 20203 solver.cpp:237]     Train net output #0: loss = 5.27511 (* 1 = 5.27511 loss)
I0412 14:24:39.549995 20203 sgd_solver.cpp:105] Iteration 552, lr = 0.00896423
I0412 14:24:44.766876 20203 solver.cpp:218] Iteration 564 (2.30032 iter/s, 5.21667s/12 iters), loss = 5.26647
I0412 14:24:44.770224 20203 solver.cpp:237]     Train net output #0: loss = 5.26647 (* 1 = 5.26647 loss)
I0412 14:24:44.770233 20203 sgd_solver.cpp:105] Iteration 564, lr = 0.00894294
I0412 14:24:49.573875 20203 solver.cpp:218] Iteration 576 (2.49821 iter/s, 4.80344s/12 iters), loss = 5.28473
I0412 14:24:49.573930 20203 solver.cpp:237]     Train net output #0: loss = 5.28473 (* 1 = 5.28473 loss)
I0412 14:24:49.573943 20203 sgd_solver.cpp:105] Iteration 576, lr = 0.00892171
I0412 14:24:54.635025 20203 solver.cpp:218] Iteration 588 (2.37113 iter/s, 5.06088s/12 iters), loss = 5.27385
I0412 14:24:54.635076 20203 solver.cpp:237]     Train net output #0: loss = 5.27385 (* 1 = 5.27385 loss)
I0412 14:24:54.635089 20203 sgd_solver.cpp:105] Iteration 588, lr = 0.00890053
I0412 14:24:59.714468 20203 solver.cpp:218] Iteration 600 (2.36259 iter/s, 5.07917s/12 iters), loss = 5.2635
I0412 14:24:59.714519 20203 solver.cpp:237]     Train net output #0: loss = 5.2635 (* 1 = 5.2635 loss)
I0412 14:24:59.714529 20203 sgd_solver.cpp:105] Iteration 600, lr = 0.0088794
I0412 14:25:02.031126 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:25:04.113529 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_612.caffemodel
I0412 14:25:07.814806 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_612.solverstate
I0412 14:25:10.505007 20203 solver.cpp:330] Iteration 612, Testing net (#0)
I0412 14:25:10.505033 20203 net.cpp:676] Ignoring source layer train-data
I0412 14:25:14.863081 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:25:15.149114 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 14:25:15.149152 20203 solver.cpp:397]     Test net output #1: loss = 5.28639 (* 1 = 5.28639 loss)
I0412 14:25:15.235476 20203 solver.cpp:218] Iteration 612 (0.77318 iter/s, 15.5203s/12 iters), loss = 5.27685
I0412 14:25:15.235522 20203 solver.cpp:237]     Train net output #0: loss = 5.27685 (* 1 = 5.27685 loss)
I0412 14:25:15.235533 20203 sgd_solver.cpp:105] Iteration 612, lr = 0.00885831
I0412 14:25:19.068136 20203 solver.cpp:218] Iteration 624 (3.13117 iter/s, 3.83244s/12 iters), loss = 5.29367
I0412 14:25:19.068197 20203 solver.cpp:237]     Train net output #0: loss = 5.29367 (* 1 = 5.29367 loss)
I0412 14:25:19.068212 20203 sgd_solver.cpp:105] Iteration 624, lr = 0.00883728
I0412 14:25:23.912221 20203 solver.cpp:218] Iteration 636 (2.47738 iter/s, 4.84382s/12 iters), loss = 5.2848
I0412 14:25:23.912273 20203 solver.cpp:237]     Train net output #0: loss = 5.2848 (* 1 = 5.2848 loss)
I0412 14:25:23.912286 20203 sgd_solver.cpp:105] Iteration 636, lr = 0.0088163
I0412 14:25:28.903384 20203 solver.cpp:218] Iteration 648 (2.40438 iter/s, 4.9909s/12 iters), loss = 5.27838
I0412 14:25:28.903429 20203 solver.cpp:237]     Train net output #0: loss = 5.27838 (* 1 = 5.27838 loss)
I0412 14:25:28.903439 20203 sgd_solver.cpp:105] Iteration 648, lr = 0.00879537
I0412 14:25:34.228821 20203 solver.cpp:218] Iteration 660 (2.25346 iter/s, 5.32515s/12 iters), loss = 5.27062
I0412 14:25:34.228880 20203 solver.cpp:237]     Train net output #0: loss = 5.27062 (* 1 = 5.27062 loss)
I0412 14:25:34.228893 20203 sgd_solver.cpp:105] Iteration 660, lr = 0.00877449
I0412 14:25:39.182802 20203 solver.cpp:218] Iteration 672 (2.42243 iter/s, 4.95371s/12 iters), loss = 5.28096
I0412 14:25:39.182857 20203 solver.cpp:237]     Train net output #0: loss = 5.28096 (* 1 = 5.28096 loss)
I0412 14:25:39.182873 20203 sgd_solver.cpp:105] Iteration 672, lr = 0.00875366
I0412 14:25:44.318264 20203 solver.cpp:218] Iteration 684 (2.33682 iter/s, 5.13519s/12 iters), loss = 5.28366
I0412 14:25:44.318317 20203 solver.cpp:237]     Train net output #0: loss = 5.28366 (* 1 = 5.28366 loss)
I0412 14:25:44.318328 20203 sgd_solver.cpp:105] Iteration 684, lr = 0.00873287
I0412 14:25:45.114223 20203 blocking_queue.cpp:49] Waiting for data
I0412 14:25:49.197029 20203 solver.cpp:218] Iteration 696 (2.45977 iter/s, 4.8785s/12 iters), loss = 5.28247
I0412 14:25:49.197079 20203 solver.cpp:237]     Train net output #0: loss = 5.28247 (* 1 = 5.28247 loss)
I0412 14:25:49.197091 20203 sgd_solver.cpp:105] Iteration 696, lr = 0.00871214
I0412 14:25:53.637346 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:25:54.012367 20203 solver.cpp:218] Iteration 708 (2.49217 iter/s, 4.81507s/12 iters), loss = 5.26947
I0412 14:25:54.012428 20203 solver.cpp:237]     Train net output #0: loss = 5.26947 (* 1 = 5.26947 loss)
I0412 14:25:54.012442 20203 sgd_solver.cpp:105] Iteration 708, lr = 0.00869145
I0412 14:25:55.878196 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_714.caffemodel
I0412 14:25:58.900919 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_714.solverstate
I0412 14:26:01.195298 20203 solver.cpp:330] Iteration 714, Testing net (#0)
I0412 14:26:01.195319 20203 net.cpp:676] Ignoring source layer train-data
I0412 14:26:05.458324 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:26:05.779220 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 14:26:05.779271 20203 solver.cpp:397]     Test net output #1: loss = 5.28651 (* 1 = 5.28651 loss)
I0412 14:26:07.620666 20203 solver.cpp:218] Iteration 720 (0.881855 iter/s, 13.6077s/12 iters), loss = 5.27261
I0412 14:26:07.620719 20203 solver.cpp:237]     Train net output #0: loss = 5.27261 (* 1 = 5.27261 loss)
I0412 14:26:07.620731 20203 sgd_solver.cpp:105] Iteration 720, lr = 0.00867082
I0412 14:26:12.236428 20203 solver.cpp:218] Iteration 732 (2.59993 iter/s, 4.61551s/12 iters), loss = 5.27904
I0412 14:26:12.236480 20203 solver.cpp:237]     Train net output #0: loss = 5.27904 (* 1 = 5.27904 loss)
I0412 14:26:12.236492 20203 sgd_solver.cpp:105] Iteration 732, lr = 0.00865023
I0412 14:26:17.061066 20203 solver.cpp:218] Iteration 744 (2.48737 iter/s, 4.82438s/12 iters), loss = 5.28501
I0412 14:26:17.061245 20203 solver.cpp:237]     Train net output #0: loss = 5.28501 (* 1 = 5.28501 loss)
I0412 14:26:17.061261 20203 sgd_solver.cpp:105] Iteration 744, lr = 0.0086297
I0412 14:26:21.925262 20203 solver.cpp:218] Iteration 756 (2.4672 iter/s, 4.86381s/12 iters), loss = 5.28091
I0412 14:26:21.925309 20203 solver.cpp:237]     Train net output #0: loss = 5.28091 (* 1 = 5.28091 loss)
I0412 14:26:21.925318 20203 sgd_solver.cpp:105] Iteration 756, lr = 0.00860921
I0412 14:26:26.757680 20203 solver.cpp:218] Iteration 768 (2.48336 iter/s, 4.83216s/12 iters), loss = 5.28356
I0412 14:26:26.757736 20203 solver.cpp:237]     Train net output #0: loss = 5.28356 (* 1 = 5.28356 loss)
I0412 14:26:26.757748 20203 sgd_solver.cpp:105] Iteration 768, lr = 0.00858877
I0412 14:26:31.507895 20203 solver.cpp:218] Iteration 780 (2.52634 iter/s, 4.74995s/12 iters), loss = 5.27987
I0412 14:26:31.507966 20203 solver.cpp:237]     Train net output #0: loss = 5.27987 (* 1 = 5.27987 loss)
I0412 14:26:31.507982 20203 sgd_solver.cpp:105] Iteration 780, lr = 0.00856838
I0412 14:26:36.513116 20203 solver.cpp:218] Iteration 792 (2.39763 iter/s, 5.00495s/12 iters), loss = 5.27612
I0412 14:26:36.513159 20203 solver.cpp:237]     Train net output #0: loss = 5.27612 (* 1 = 5.27612 loss)
I0412 14:26:36.513166 20203 sgd_solver.cpp:105] Iteration 792, lr = 0.00854803
I0412 14:26:41.347577 20203 solver.cpp:218] Iteration 804 (2.48231 iter/s, 4.83421s/12 iters), loss = 5.29568
I0412 14:26:41.347623 20203 solver.cpp:237]     Train net output #0: loss = 5.29568 (* 1 = 5.29568 loss)
I0412 14:26:41.347633 20203 sgd_solver.cpp:105] Iteration 804, lr = 0.00852774
I0412 14:26:42.981989 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:26:45.734251 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_816.caffemodel
I0412 14:26:48.654001 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_816.solverstate
I0412 14:26:50.977082 20203 solver.cpp:330] Iteration 816, Testing net (#0)
I0412 14:26:50.977108 20203 net.cpp:676] Ignoring source layer train-data
I0412 14:26:55.215575 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:26:55.630540 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 14:26:55.630605 20203 solver.cpp:397]     Test net output #1: loss = 5.28606 (* 1 = 5.28606 loss)
I0412 14:26:55.716826 20203 solver.cpp:218] Iteration 816 (0.835154 iter/s, 14.3686s/12 iters), loss = 5.27491
I0412 14:26:55.716876 20203 solver.cpp:237]     Train net output #0: loss = 5.27491 (* 1 = 5.27491 loss)
I0412 14:26:55.716884 20203 sgd_solver.cpp:105] Iteration 816, lr = 0.00850749
I0412 14:26:59.769985 20203 solver.cpp:218] Iteration 828 (2.96082 iter/s, 4.05293s/12 iters), loss = 5.29088
I0412 14:26:59.770033 20203 solver.cpp:237]     Train net output #0: loss = 5.29088 (* 1 = 5.29088 loss)
I0412 14:26:59.770043 20203 sgd_solver.cpp:105] Iteration 828, lr = 0.00848729
I0412 14:27:04.572038 20203 solver.cpp:218] Iteration 840 (2.49906 iter/s, 4.8018s/12 iters), loss = 5.23848
I0412 14:27:04.572085 20203 solver.cpp:237]     Train net output #0: loss = 5.23848 (* 1 = 5.23848 loss)
I0412 14:27:04.572098 20203 sgd_solver.cpp:105] Iteration 840, lr = 0.00846714
I0412 14:27:09.391803 20203 solver.cpp:218] Iteration 852 (2.48988 iter/s, 4.81951s/12 iters), loss = 5.3071
I0412 14:27:09.391845 20203 solver.cpp:237]     Train net output #0: loss = 5.3071 (* 1 = 5.3071 loss)
I0412 14:27:09.391855 20203 sgd_solver.cpp:105] Iteration 852, lr = 0.00844704
I0412 14:27:14.223044 20203 solver.cpp:218] Iteration 864 (2.48396 iter/s, 4.83099s/12 iters), loss = 5.26
I0412 14:27:14.223094 20203 solver.cpp:237]     Train net output #0: loss = 5.26 (* 1 = 5.26 loss)
I0412 14:27:14.223106 20203 sgd_solver.cpp:105] Iteration 864, lr = 0.00842698
I0412 14:27:19.198624 20203 solver.cpp:218] Iteration 876 (2.41191 iter/s, 4.97532s/12 iters), loss = 5.27649
I0412 14:27:19.198803 20203 solver.cpp:237]     Train net output #0: loss = 5.27649 (* 1 = 5.27649 loss)
I0412 14:27:19.198824 20203 sgd_solver.cpp:105] Iteration 876, lr = 0.00840698
I0412 14:27:24.185992 20203 solver.cpp:218] Iteration 888 (2.40626 iter/s, 4.98699s/12 iters), loss = 5.27045
I0412 14:27:24.186043 20203 solver.cpp:237]     Train net output #0: loss = 5.27045 (* 1 = 5.27045 loss)
I0412 14:27:24.186053 20203 sgd_solver.cpp:105] Iteration 888, lr = 0.00838702
I0412 14:27:29.067260 20203 solver.cpp:218] Iteration 900 (2.45851 iter/s, 4.88101s/12 iters), loss = 5.27611
I0412 14:27:29.067304 20203 solver.cpp:237]     Train net output #0: loss = 5.27611 (* 1 = 5.27611 loss)
I0412 14:27:29.067314 20203 sgd_solver.cpp:105] Iteration 900, lr = 0.0083671
I0412 14:27:32.772558 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:27:33.966756 20203 solver.cpp:218] Iteration 912 (2.44936 iter/s, 4.89924s/12 iters), loss = 5.26233
I0412 14:27:33.966796 20203 solver.cpp:237]     Train net output #0: loss = 5.26233 (* 1 = 5.26233 loss)
I0412 14:27:33.966804 20203 sgd_solver.cpp:105] Iteration 912, lr = 0.00834724
I0412 14:27:36.077018 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_918.caffemodel
I0412 14:27:39.053488 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_918.solverstate
I0412 14:27:41.367806 20203 solver.cpp:330] Iteration 918, Testing net (#0)
I0412 14:27:41.367831 20203 net.cpp:676] Ignoring source layer train-data
I0412 14:27:45.759191 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:27:46.161813 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 14:27:46.161845 20203 solver.cpp:397]     Test net output #1: loss = 5.28633 (* 1 = 5.28633 loss)
I0412 14:27:47.910576 20203 solver.cpp:218] Iteration 924 (0.860635 iter/s, 13.9432s/12 iters), loss = 5.29051
I0412 14:27:47.910627 20203 solver.cpp:237]     Train net output #0: loss = 5.29051 (* 1 = 5.29051 loss)
I0412 14:27:47.910637 20203 sgd_solver.cpp:105] Iteration 924, lr = 0.00832742
I0412 14:27:53.038905 20203 solver.cpp:218] Iteration 936 (2.34007 iter/s, 5.12805s/12 iters), loss = 5.26703
I0412 14:27:53.039029 20203 solver.cpp:237]     Train net output #0: loss = 5.26703 (* 1 = 5.26703 loss)
I0412 14:27:53.039042 20203 sgd_solver.cpp:105] Iteration 936, lr = 0.00830765
I0412 14:27:57.675796 20203 solver.cpp:218] Iteration 948 (2.58812 iter/s, 4.63657s/12 iters), loss = 5.29046
I0412 14:27:57.675837 20203 solver.cpp:237]     Train net output #0: loss = 5.29046 (* 1 = 5.29046 loss)
I0412 14:27:57.675848 20203 sgd_solver.cpp:105] Iteration 948, lr = 0.00828793
I0412 14:28:02.650032 20203 solver.cpp:218] Iteration 960 (2.41256 iter/s, 4.97398s/12 iters), loss = 5.26992
I0412 14:28:02.650091 20203 solver.cpp:237]     Train net output #0: loss = 5.26992 (* 1 = 5.26992 loss)
I0412 14:28:02.650107 20203 sgd_solver.cpp:105] Iteration 960, lr = 0.00826825
I0412 14:28:08.047256 20203 solver.cpp:218] Iteration 972 (2.22348 iter/s, 5.39694s/12 iters), loss = 5.27182
I0412 14:28:08.047298 20203 solver.cpp:237]     Train net output #0: loss = 5.27182 (* 1 = 5.27182 loss)
I0412 14:28:08.047307 20203 sgd_solver.cpp:105] Iteration 972, lr = 0.00824862
I0412 14:28:13.169148 20203 solver.cpp:218] Iteration 984 (2.34301 iter/s, 5.12163s/12 iters), loss = 5.29486
I0412 14:28:13.169193 20203 solver.cpp:237]     Train net output #0: loss = 5.29486 (* 1 = 5.29486 loss)
I0412 14:28:13.169203 20203 sgd_solver.cpp:105] Iteration 984, lr = 0.00822903
I0412 14:28:18.197569 20203 solver.cpp:218] Iteration 996 (2.38657 iter/s, 5.02815s/12 iters), loss = 5.27459
I0412 14:28:18.197633 20203 solver.cpp:237]     Train net output #0: loss = 5.27459 (* 1 = 5.27459 loss)
I0412 14:28:18.197645 20203 sgd_solver.cpp:105] Iteration 996, lr = 0.0082095
I0412 14:28:23.084079 20203 solver.cpp:218] Iteration 1008 (2.45588 iter/s, 4.88623s/12 iters), loss = 5.29068
I0412 14:28:23.084233 20203 solver.cpp:237]     Train net output #0: loss = 5.29068 (* 1 = 5.29068 loss)
I0412 14:28:23.084247 20203 sgd_solver.cpp:105] Iteration 1008, lr = 0.00819001
I0412 14:28:24.121089 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:28:27.601756 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1020.caffemodel
I0412 14:28:31.486924 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1020.solverstate
I0412 14:28:33.830968 20203 solver.cpp:330] Iteration 1020, Testing net (#0)
I0412 14:28:33.830996 20203 net.cpp:676] Ignoring source layer train-data
I0412 14:28:37.987815 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:28:38.420653 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 14:28:38.420703 20203 solver.cpp:397]     Test net output #1: loss = 5.28596 (* 1 = 5.28596 loss)
I0412 14:28:38.504825 20203 solver.cpp:218] Iteration 1020 (0.778212 iter/s, 15.42s/12 iters), loss = 5.28923
I0412 14:28:38.504876 20203 solver.cpp:237]     Train net output #0: loss = 5.28923 (* 1 = 5.28923 loss)
I0412 14:28:38.504889 20203 sgd_solver.cpp:105] Iteration 1020, lr = 0.00817056
I0412 14:28:42.591905 20203 solver.cpp:218] Iteration 1032 (2.93625 iter/s, 4.08685s/12 iters), loss = 5.24977
I0412 14:28:42.591954 20203 solver.cpp:237]     Train net output #0: loss = 5.24977 (* 1 = 5.24977 loss)
I0412 14:28:42.591966 20203 sgd_solver.cpp:105] Iteration 1032, lr = 0.00815116
I0412 14:28:47.706737 20203 solver.cpp:218] Iteration 1044 (2.34624 iter/s, 5.11456s/12 iters), loss = 5.25973
I0412 14:28:47.706776 20203 solver.cpp:237]     Train net output #0: loss = 5.25973 (* 1 = 5.25973 loss)
I0412 14:28:47.706785 20203 sgd_solver.cpp:105] Iteration 1044, lr = 0.00813181
I0412 14:28:52.841341 20203 solver.cpp:218] Iteration 1056 (2.3372 iter/s, 5.13434s/12 iters), loss = 5.26493
I0412 14:28:52.841379 20203 solver.cpp:237]     Train net output #0: loss = 5.26493 (* 1 = 5.26493 loss)
I0412 14:28:52.841388 20203 sgd_solver.cpp:105] Iteration 1056, lr = 0.0081125
I0412 14:28:57.838786 20203 solver.cpp:218] Iteration 1068 (2.40135 iter/s, 4.99718s/12 iters), loss = 5.29016
I0412 14:28:57.838917 20203 solver.cpp:237]     Train net output #0: loss = 5.29016 (* 1 = 5.29016 loss)
I0412 14:28:57.838932 20203 sgd_solver.cpp:105] Iteration 1068, lr = 0.00809324
I0412 14:29:02.689520 20203 solver.cpp:218] Iteration 1080 (2.47402 iter/s, 4.8504s/12 iters), loss = 5.27217
I0412 14:29:02.689568 20203 solver.cpp:237]     Train net output #0: loss = 5.27217 (* 1 = 5.27217 loss)
I0412 14:29:02.689579 20203 sgd_solver.cpp:105] Iteration 1080, lr = 0.00807403
I0412 14:29:07.485769 20203 solver.cpp:218] Iteration 1092 (2.50209 iter/s, 4.79599s/12 iters), loss = 5.283
I0412 14:29:07.485819 20203 solver.cpp:237]     Train net output #0: loss = 5.283 (* 1 = 5.283 loss)
I0412 14:29:07.485832 20203 sgd_solver.cpp:105] Iteration 1092, lr = 0.00805486
I0412 14:29:12.297775 20203 solver.cpp:218] Iteration 1104 (2.4939 iter/s, 4.81175s/12 iters), loss = 5.27852
I0412 14:29:12.297824 20203 solver.cpp:237]     Train net output #0: loss = 5.27852 (* 1 = 5.27852 loss)
I0412 14:29:12.297837 20203 sgd_solver.cpp:105] Iteration 1104, lr = 0.00803573
I0412 14:29:15.380581 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:29:17.116842 20203 solver.cpp:218] Iteration 1116 (2.49024 iter/s, 4.81881s/12 iters), loss = 5.27309
I0412 14:29:17.116883 20203 solver.cpp:237]     Train net output #0: loss = 5.27309 (* 1 = 5.27309 loss)
I0412 14:29:17.116891 20203 sgd_solver.cpp:105] Iteration 1116, lr = 0.00801666
I0412 14:29:19.266387 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1122.caffemodel
I0412 14:29:22.247874 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1122.solverstate
I0412 14:29:24.562741 20203 solver.cpp:330] Iteration 1122, Testing net (#0)
I0412 14:29:24.562772 20203 net.cpp:676] Ignoring source layer train-data
I0412 14:29:28.643496 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:29:29.121688 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 14:29:29.121724 20203 solver.cpp:397]     Test net output #1: loss = 5.28629 (* 1 = 5.28629 loss)
I0412 14:29:31.114357 20203 solver.cpp:218] Iteration 1128 (0.857334 iter/s, 13.9969s/12 iters), loss = 5.27015
I0412 14:29:31.114403 20203 solver.cpp:237]     Train net output #0: loss = 5.27015 (* 1 = 5.27015 loss)
I0412 14:29:31.114411 20203 sgd_solver.cpp:105] Iteration 1128, lr = 0.00799762
I0412 14:29:36.382889 20203 solver.cpp:218] Iteration 1140 (2.27779 iter/s, 5.26825s/12 iters), loss = 5.27328
I0412 14:29:36.382952 20203 solver.cpp:237]     Train net output #0: loss = 5.27328 (* 1 = 5.27328 loss)
I0412 14:29:36.382968 20203 sgd_solver.cpp:105] Iteration 1140, lr = 0.00797863
I0412 14:29:41.129192 20203 solver.cpp:218] Iteration 1152 (2.52843 iter/s, 4.74603s/12 iters), loss = 5.28068
I0412 14:29:41.129256 20203 solver.cpp:237]     Train net output #0: loss = 5.28068 (* 1 = 5.28068 loss)
I0412 14:29:41.129272 20203 sgd_solver.cpp:105] Iteration 1152, lr = 0.00795969
I0412 14:29:45.789966 20203 solver.cpp:218] Iteration 1164 (2.57483 iter/s, 4.6605s/12 iters), loss = 5.27419
I0412 14:29:45.790009 20203 solver.cpp:237]     Train net output #0: loss = 5.27419 (* 1 = 5.27419 loss)
I0412 14:29:45.790019 20203 sgd_solver.cpp:105] Iteration 1164, lr = 0.00794079
I0412 14:29:50.526072 20203 solver.cpp:218] Iteration 1176 (2.53386 iter/s, 4.73585s/12 iters), loss = 5.29779
I0412 14:29:50.526134 20203 solver.cpp:237]     Train net output #0: loss = 5.29779 (* 1 = 5.29779 loss)
I0412 14:29:50.526154 20203 sgd_solver.cpp:105] Iteration 1176, lr = 0.00792194
I0412 14:29:55.356207 20203 solver.cpp:218] Iteration 1188 (2.48454 iter/s, 4.82987s/12 iters), loss = 5.2712
I0412 14:29:55.356261 20203 solver.cpp:237]     Train net output #0: loss = 5.2712 (* 1 = 5.2712 loss)
I0412 14:29:55.356272 20203 sgd_solver.cpp:105] Iteration 1188, lr = 0.00790313
I0412 14:30:00.385579 20203 solver.cpp:218] Iteration 1200 (2.38611 iter/s, 5.0291s/12 iters), loss = 5.29137
I0412 14:30:00.385702 20203 solver.cpp:237]     Train net output #0: loss = 5.29137 (* 1 = 5.29137 loss)
I0412 14:30:00.385715 20203 sgd_solver.cpp:105] Iteration 1200, lr = 0.00788437
I0412 14:30:05.306516 20203 solver.cpp:218] Iteration 1212 (2.43873 iter/s, 4.9206s/12 iters), loss = 5.26578
I0412 14:30:05.306571 20203 solver.cpp:237]     Train net output #0: loss = 5.26578 (* 1 = 5.26578 loss)
I0412 14:30:05.306581 20203 sgd_solver.cpp:105] Iteration 1212, lr = 0.00786565
I0412 14:30:05.605654 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:30:09.654474 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1224.caffemodel
I0412 14:30:12.658262 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1224.solverstate
I0412 14:30:14.975736 20203 solver.cpp:330] Iteration 1224, Testing net (#0)
I0412 14:30:14.975764 20203 net.cpp:676] Ignoring source layer train-data
I0412 14:30:18.829417 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:30:19.343155 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00612745
I0412 14:30:19.343204 20203 solver.cpp:397]     Test net output #1: loss = 5.28628 (* 1 = 5.28628 loss)
I0412 14:30:19.430436 20203 solver.cpp:218] Iteration 1224 (0.849661 iter/s, 14.1233s/12 iters), loss = 5.28775
I0412 14:30:19.430498 20203 solver.cpp:237]     Train net output #0: loss = 5.28775 (* 1 = 5.28775 loss)
I0412 14:30:19.430510 20203 sgd_solver.cpp:105] Iteration 1224, lr = 0.00784697
I0412 14:30:23.764019 20203 solver.cpp:218] Iteration 1236 (2.76923 iter/s, 4.33333s/12 iters), loss = 5.27237
I0412 14:30:23.764062 20203 solver.cpp:237]     Train net output #0: loss = 5.27237 (* 1 = 5.27237 loss)
I0412 14:30:23.764072 20203 sgd_solver.cpp:105] Iteration 1236, lr = 0.00782834
I0412 14:30:28.629045 20203 solver.cpp:218] Iteration 1248 (2.46671 iter/s, 4.86477s/12 iters), loss = 5.28905
I0412 14:30:28.629094 20203 solver.cpp:237]     Train net output #0: loss = 5.28905 (* 1 = 5.28905 loss)
I0412 14:30:28.629107 20203 sgd_solver.cpp:105] Iteration 1248, lr = 0.00780976
I0412 14:30:33.481115 20203 solver.cpp:218] Iteration 1260 (2.47331 iter/s, 4.85181s/12 iters), loss = 5.27449
I0412 14:30:33.481232 20203 solver.cpp:237]     Train net output #0: loss = 5.27449 (* 1 = 5.27449 loss)
I0412 14:30:33.481245 20203 sgd_solver.cpp:105] Iteration 1260, lr = 0.00779122
I0412 14:30:38.347671 20203 solver.cpp:218] Iteration 1272 (2.46598 iter/s, 4.86623s/12 iters), loss = 5.25238
I0412 14:30:38.347721 20203 solver.cpp:237]     Train net output #0: loss = 5.25238 (* 1 = 5.25238 loss)
I0412 14:30:38.347733 20203 sgd_solver.cpp:105] Iteration 1272, lr = 0.00777272
I0412 14:30:43.255817 20203 solver.cpp:218] Iteration 1284 (2.44505 iter/s, 4.90788s/12 iters), loss = 5.28613
I0412 14:30:43.255870 20203 solver.cpp:237]     Train net output #0: loss = 5.28613 (* 1 = 5.28613 loss)
I0412 14:30:43.255883 20203 sgd_solver.cpp:105] Iteration 1284, lr = 0.00775426
I0412 14:30:48.271441 20203 solver.cpp:218] Iteration 1296 (2.39265 iter/s, 5.01536s/12 iters), loss = 5.27272
I0412 14:30:48.271486 20203 solver.cpp:237]     Train net output #0: loss = 5.27272 (* 1 = 5.27272 loss)
I0412 14:30:48.271497 20203 sgd_solver.cpp:105] Iteration 1296, lr = 0.00773585
I0412 14:30:53.157065 20203 solver.cpp:218] Iteration 1308 (2.45632 iter/s, 4.88537s/12 iters), loss = 5.25763
I0412 14:30:53.157119 20203 solver.cpp:237]     Train net output #0: loss = 5.25763 (* 1 = 5.25763 loss)
I0412 14:30:53.157133 20203 sgd_solver.cpp:105] Iteration 1308, lr = 0.00771749
I0412 14:30:55.531392 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:30:57.906651 20203 solver.cpp:218] Iteration 1320 (2.52667 iter/s, 4.74933s/12 iters), loss = 5.28228
I0412 14:30:57.906699 20203 solver.cpp:237]     Train net output #0: loss = 5.28228 (* 1 = 5.28228 loss)
I0412 14:30:57.906710 20203 sgd_solver.cpp:105] Iteration 1320, lr = 0.00769916
I0412 14:30:59.858455 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1326.caffemodel
I0412 14:31:02.882943 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1326.solverstate
I0412 14:31:05.192968 20203 solver.cpp:330] Iteration 1326, Testing net (#0)
I0412 14:31:05.193050 20203 net.cpp:676] Ignoring source layer train-data
I0412 14:31:09.071890 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:31:09.629256 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 14:31:09.629307 20203 solver.cpp:397]     Test net output #1: loss = 5.28661 (* 1 = 5.28661 loss)
I0412 14:31:11.461581 20203 solver.cpp:218] Iteration 1332 (0.885327 iter/s, 13.5543s/12 iters), loss = 5.29231
I0412 14:31:11.461632 20203 solver.cpp:237]     Train net output #0: loss = 5.29231 (* 1 = 5.29231 loss)
I0412 14:31:11.461642 20203 sgd_solver.cpp:105] Iteration 1332, lr = 0.00768088
I0412 14:31:16.259505 20203 solver.cpp:218] Iteration 1344 (2.50122 iter/s, 4.79767s/12 iters), loss = 5.2909
I0412 14:31:16.259554 20203 solver.cpp:237]     Train net output #0: loss = 5.2909 (* 1 = 5.2909 loss)
I0412 14:31:16.259567 20203 sgd_solver.cpp:105] Iteration 1344, lr = 0.00766265
I0412 14:31:21.290009 20203 solver.cpp:218] Iteration 1356 (2.38557 iter/s, 5.03024s/12 iters), loss = 5.27759
I0412 14:31:21.290058 20203 solver.cpp:237]     Train net output #0: loss = 5.27759 (* 1 = 5.27759 loss)
I0412 14:31:21.290071 20203 sgd_solver.cpp:105] Iteration 1356, lr = 0.00764446
I0412 14:31:26.048923 20203 solver.cpp:218] Iteration 1368 (2.52172 iter/s, 4.75866s/12 iters), loss = 5.27604
I0412 14:31:26.048970 20203 solver.cpp:237]     Train net output #0: loss = 5.27604 (* 1 = 5.27604 loss)
I0412 14:31:26.048982 20203 sgd_solver.cpp:105] Iteration 1368, lr = 0.00762631
I0412 14:31:27.249073 20203 blocking_queue.cpp:49] Waiting for data
I0412 14:31:31.006644 20203 solver.cpp:218] Iteration 1380 (2.4206 iter/s, 4.95746s/12 iters), loss = 5.2784
I0412 14:31:31.006695 20203 solver.cpp:237]     Train net output #0: loss = 5.2784 (* 1 = 5.2784 loss)
I0412 14:31:31.006706 20203 sgd_solver.cpp:105] Iteration 1380, lr = 0.0076082
I0412 14:31:36.090852 20203 solver.cpp:218] Iteration 1392 (2.36038 iter/s, 5.08393s/12 iters), loss = 5.27204
I0412 14:31:36.091010 20203 solver.cpp:237]     Train net output #0: loss = 5.27204 (* 1 = 5.27204 loss)
I0412 14:31:36.091022 20203 sgd_solver.cpp:105] Iteration 1392, lr = 0.00759014
I0412 14:31:41.028558 20203 solver.cpp:218] Iteration 1404 (2.43046 iter/s, 4.93734s/12 iters), loss = 5.27922
I0412 14:31:41.028609 20203 solver.cpp:237]     Train net output #0: loss = 5.27922 (* 1 = 5.27922 loss)
I0412 14:31:41.028620 20203 sgd_solver.cpp:105] Iteration 1404, lr = 0.00757212
I0412 14:31:45.389554 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:31:45.734103 20203 solver.cpp:218] Iteration 1416 (2.55032 iter/s, 4.70529s/12 iters), loss = 5.27104
I0412 14:31:45.734146 20203 solver.cpp:237]     Train net output #0: loss = 5.27104 (* 1 = 5.27104 loss)
I0412 14:31:45.734155 20203 sgd_solver.cpp:105] Iteration 1416, lr = 0.00755414
I0412 14:31:50.153856 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1428.caffemodel
I0412 14:31:54.681999 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1428.solverstate
I0412 14:31:58.329341 20203 solver.cpp:330] Iteration 1428, Testing net (#0)
I0412 14:31:58.329370 20203 net.cpp:676] Ignoring source layer train-data
I0412 14:32:02.284226 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:32:02.876135 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 14:32:02.876201 20203 solver.cpp:397]     Test net output #1: loss = 5.28578 (* 1 = 5.28578 loss)
I0412 14:32:02.962880 20203 solver.cpp:218] Iteration 1428 (0.69654 iter/s, 17.228s/12 iters), loss = 5.27529
I0412 14:32:02.962935 20203 solver.cpp:237]     Train net output #0: loss = 5.27529 (* 1 = 5.27529 loss)
I0412 14:32:02.962947 20203 sgd_solver.cpp:105] Iteration 1428, lr = 0.0075362
I0412 14:32:07.152325 20203 solver.cpp:218] Iteration 1440 (2.86451 iter/s, 4.1892s/12 iters), loss = 5.28676
I0412 14:32:07.152431 20203 solver.cpp:237]     Train net output #0: loss = 5.28676 (* 1 = 5.28676 loss)
I0412 14:32:07.152441 20203 sgd_solver.cpp:105] Iteration 1440, lr = 0.00751831
I0412 14:32:11.789225 20203 solver.cpp:218] Iteration 1452 (2.58811 iter/s, 4.63659s/12 iters), loss = 5.28042
I0412 14:32:11.789279 20203 solver.cpp:237]     Train net output #0: loss = 5.28042 (* 1 = 5.28042 loss)
I0412 14:32:11.789292 20203 sgd_solver.cpp:105] Iteration 1452, lr = 0.00750046
I0412 14:32:16.618544 20203 solver.cpp:218] Iteration 1464 (2.48496 iter/s, 4.82906s/12 iters), loss = 5.28038
I0412 14:32:16.618594 20203 solver.cpp:237]     Train net output #0: loss = 5.28038 (* 1 = 5.28038 loss)
I0412 14:32:16.618607 20203 sgd_solver.cpp:105] Iteration 1464, lr = 0.00748265
I0412 14:32:21.563048 20203 solver.cpp:218] Iteration 1476 (2.42707 iter/s, 4.94424s/12 iters), loss = 5.27934
I0412 14:32:21.563108 20203 solver.cpp:237]     Train net output #0: loss = 5.27934 (* 1 = 5.27934 loss)
I0412 14:32:21.563125 20203 sgd_solver.cpp:105] Iteration 1476, lr = 0.00746489
I0412 14:32:26.286481 20203 solver.cpp:218] Iteration 1488 (2.54067 iter/s, 4.72317s/12 iters), loss = 5.25491
I0412 14:32:26.286535 20203 solver.cpp:237]     Train net output #0: loss = 5.25491 (* 1 = 5.25491 loss)
I0412 14:32:26.286547 20203 sgd_solver.cpp:105] Iteration 1488, lr = 0.00744716
I0412 14:32:30.961520 20203 solver.cpp:218] Iteration 1500 (2.56697 iter/s, 4.67478s/12 iters), loss = 5.27325
I0412 14:32:30.961571 20203 solver.cpp:237]     Train net output #0: loss = 5.27325 (* 1 = 5.27325 loss)
I0412 14:32:30.961585 20203 sgd_solver.cpp:105] Iteration 1500, lr = 0.00742948
I0412 14:32:35.818334 20203 solver.cpp:218] Iteration 1512 (2.47089 iter/s, 4.85655s/12 iters), loss = 5.29068
I0412 14:32:35.818383 20203 solver.cpp:237]     Train net output #0: loss = 5.29068 (* 1 = 5.29068 loss)
I0412 14:32:35.818394 20203 sgd_solver.cpp:105] Iteration 1512, lr = 0.00741184
I0412 14:32:37.425197 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:32:40.377233 20203 solver.cpp:218] Iteration 1524 (2.63236 iter/s, 4.55865s/12 iters), loss = 5.27764
I0412 14:32:40.377283 20203 solver.cpp:237]     Train net output #0: loss = 5.27764 (* 1 = 5.27764 loss)
I0412 14:32:40.377295 20203 sgd_solver.cpp:105] Iteration 1524, lr = 0.00739425
I0412 14:32:42.308516 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1530.caffemodel
I0412 14:32:46.726109 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1530.solverstate
I0412 14:32:49.479759 20203 solver.cpp:330] Iteration 1530, Testing net (#0)
I0412 14:32:49.479779 20203 net.cpp:676] Ignoring source layer train-data
I0412 14:32:53.311547 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:32:53.949318 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 14:32:53.949374 20203 solver.cpp:397]     Test net output #1: loss = 5.28582 (* 1 = 5.28582 loss)
I0412 14:32:55.816053 20203 solver.cpp:218] Iteration 1536 (0.777297 iter/s, 15.4381s/12 iters), loss = 5.27983
I0412 14:32:55.816107 20203 solver.cpp:237]     Train net output #0: loss = 5.27983 (* 1 = 5.27983 loss)
I0412 14:32:55.816119 20203 sgd_solver.cpp:105] Iteration 1536, lr = 0.00737669
I0412 14:33:00.606197 20203 solver.cpp:218] Iteration 1548 (2.50528 iter/s, 4.78988s/12 iters), loss = 5.23678
I0412 14:33:00.606249 20203 solver.cpp:237]     Train net output #0: loss = 5.23678 (* 1 = 5.23678 loss)
I0412 14:33:00.606262 20203 sgd_solver.cpp:105] Iteration 1548, lr = 0.00735918
I0412 14:33:05.346994 20203 solver.cpp:218] Iteration 1560 (2.53136 iter/s, 4.74054s/12 iters), loss = 5.29021
I0412 14:33:05.347040 20203 solver.cpp:237]     Train net output #0: loss = 5.29021 (* 1 = 5.29021 loss)
I0412 14:33:05.347052 20203 sgd_solver.cpp:105] Iteration 1560, lr = 0.00734171
I0412 14:33:10.176949 20203 solver.cpp:218] Iteration 1572 (2.48463 iter/s, 4.8297s/12 iters), loss = 5.25786
I0412 14:33:10.177074 20203 solver.cpp:237]     Train net output #0: loss = 5.25786 (* 1 = 5.25786 loss)
I0412 14:33:10.177088 20203 sgd_solver.cpp:105] Iteration 1572, lr = 0.00732427
I0412 14:33:14.950119 20203 solver.cpp:218] Iteration 1584 (2.51423 iter/s, 4.77283s/12 iters), loss = 5.27093
I0412 14:33:14.950173 20203 solver.cpp:237]     Train net output #0: loss = 5.27093 (* 1 = 5.27093 loss)
I0412 14:33:14.950184 20203 sgd_solver.cpp:105] Iteration 1584, lr = 0.00730688
I0412 14:33:19.822873 20203 solver.cpp:218] Iteration 1596 (2.46281 iter/s, 4.87248s/12 iters), loss = 5.26888
I0412 14:33:19.822922 20203 solver.cpp:237]     Train net output #0: loss = 5.26888 (* 1 = 5.26888 loss)
I0412 14:33:19.822934 20203 sgd_solver.cpp:105] Iteration 1596, lr = 0.00728954
I0412 14:33:24.672657 20203 solver.cpp:218] Iteration 1608 (2.47447 iter/s, 4.84952s/12 iters), loss = 5.26657
I0412 14:33:24.672708 20203 solver.cpp:237]     Train net output #0: loss = 5.26657 (* 1 = 5.26657 loss)
I0412 14:33:24.672721 20203 sgd_solver.cpp:105] Iteration 1608, lr = 0.00727223
I0412 14:33:28.400812 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:33:29.438808 20203 solver.cpp:218] Iteration 1620 (2.51789 iter/s, 4.76589s/12 iters), loss = 5.2565
I0412 14:33:29.438858 20203 solver.cpp:237]     Train net output #0: loss = 5.2565 (* 1 = 5.2565 loss)
I0412 14:33:29.438869 20203 sgd_solver.cpp:105] Iteration 1620, lr = 0.00725496
I0412 14:33:33.796377 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1632.caffemodel
I0412 14:33:37.637215 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1632.solverstate
I0412 14:33:40.060614 20203 solver.cpp:330] Iteration 1632, Testing net (#0)
I0412 14:33:40.060640 20203 net.cpp:676] Ignoring source layer train-data
I0412 14:33:44.128077 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:33:44.799063 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 14:33:44.799115 20203 solver.cpp:397]     Test net output #1: loss = 5.28602 (* 1 = 5.28602 loss)
I0412 14:33:44.885888 20203 solver.cpp:218] Iteration 1632 (0.776881 iter/s, 15.4464s/12 iters), loss = 5.29265
I0412 14:33:44.885939 20203 solver.cpp:237]     Train net output #0: loss = 5.29265 (* 1 = 5.29265 loss)
I0412 14:33:44.885973 20203 sgd_solver.cpp:105] Iteration 1632, lr = 0.00723774
I0412 14:33:49.394475 20203 solver.cpp:218] Iteration 1644 (2.66174 iter/s, 4.50834s/12 iters), loss = 5.25717
I0412 14:33:49.394528 20203 solver.cpp:237]     Train net output #0: loss = 5.25717 (* 1 = 5.25717 loss)
I0412 14:33:49.394542 20203 sgd_solver.cpp:105] Iteration 1644, lr = 0.00722056
I0412 14:33:54.316102 20203 solver.cpp:218] Iteration 1656 (2.43835 iter/s, 4.92136s/12 iters), loss = 5.29628
I0412 14:33:54.316150 20203 solver.cpp:237]     Train net output #0: loss = 5.29628 (* 1 = 5.29628 loss)
I0412 14:33:54.316162 20203 sgd_solver.cpp:105] Iteration 1656, lr = 0.00720341
I0412 14:33:59.331252 20203 solver.cpp:218] Iteration 1668 (2.39288 iter/s, 5.01488s/12 iters), loss = 5.26641
I0412 14:33:59.331315 20203 solver.cpp:237]     Train net output #0: loss = 5.26641 (* 1 = 5.26641 loss)
I0412 14:33:59.331332 20203 sgd_solver.cpp:105] Iteration 1668, lr = 0.00718631
I0412 14:34:04.147627 20203 solver.cpp:218] Iteration 1680 (2.49164 iter/s, 4.8161s/12 iters), loss = 5.27869
I0412 14:34:04.147681 20203 solver.cpp:237]     Train net output #0: loss = 5.27869 (* 1 = 5.27869 loss)
I0412 14:34:04.147696 20203 sgd_solver.cpp:105] Iteration 1680, lr = 0.00716925
I0412 14:34:08.971503 20203 solver.cpp:218] Iteration 1692 (2.48776 iter/s, 4.82362s/12 iters), loss = 5.29304
I0412 14:34:08.971542 20203 solver.cpp:237]     Train net output #0: loss = 5.29304 (* 1 = 5.29304 loss)
I0412 14:34:08.971550 20203 sgd_solver.cpp:105] Iteration 1692, lr = 0.00715223
I0412 14:34:13.766558 20203 solver.cpp:218] Iteration 1704 (2.50271 iter/s, 4.7948s/12 iters), loss = 5.27258
I0412 14:34:13.766613 20203 solver.cpp:237]     Train net output #0: loss = 5.27258 (* 1 = 5.27258 loss)
I0412 14:34:13.766625 20203 sgd_solver.cpp:105] Iteration 1704, lr = 0.00713525
I0412 14:34:18.469493 20203 solver.cpp:218] Iteration 1716 (2.55174 iter/s, 4.70268s/12 iters), loss = 5.28701
I0412 14:34:18.470150 20203 solver.cpp:237]     Train net output #0: loss = 5.28701 (* 1 = 5.28701 loss)
I0412 14:34:18.470160 20203 sgd_solver.cpp:105] Iteration 1716, lr = 0.00711831
I0412 14:34:19.511096 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:34:23.339465 20203 solver.cpp:218] Iteration 1728 (2.46452 iter/s, 4.8691s/12 iters), loss = 5.28606
I0412 14:34:23.339517 20203 solver.cpp:237]     Train net output #0: loss = 5.28606 (* 1 = 5.28606 loss)
I0412 14:34:23.339529 20203 sgd_solver.cpp:105] Iteration 1728, lr = 0.00710141
I0412 14:34:25.352851 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1734.caffemodel
I0412 14:34:28.371884 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1734.solverstate
I0412 14:34:30.701493 20203 solver.cpp:330] Iteration 1734, Testing net (#0)
I0412 14:34:30.701517 20203 net.cpp:676] Ignoring source layer train-data
I0412 14:34:34.509230 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:34:35.223407 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 14:34:35.223453 20203 solver.cpp:397]     Test net output #1: loss = 5.28587 (* 1 = 5.28587 loss)
I0412 14:34:37.035908 20203 solver.cpp:218] Iteration 1740 (0.876179 iter/s, 13.6958s/12 iters), loss = 5.25667
I0412 14:34:37.035948 20203 solver.cpp:237]     Train net output #0: loss = 5.25667 (* 1 = 5.25667 loss)
I0412 14:34:37.035955 20203 sgd_solver.cpp:105] Iteration 1740, lr = 0.00708455
I0412 14:34:41.833170 20203 solver.cpp:218] Iteration 1752 (2.50156 iter/s, 4.79701s/12 iters), loss = 5.27123
I0412 14:34:41.833218 20203 solver.cpp:237]     Train net output #0: loss = 5.27123 (* 1 = 5.27123 loss)
I0412 14:34:41.833231 20203 sgd_solver.cpp:105] Iteration 1752, lr = 0.00706773
I0412 14:34:46.643076 20203 solver.cpp:218] Iteration 1764 (2.49499 iter/s, 4.80965s/12 iters), loss = 5.26372
I0412 14:34:46.643126 20203 solver.cpp:237]     Train net output #0: loss = 5.26372 (* 1 = 5.26372 loss)
I0412 14:34:46.643137 20203 sgd_solver.cpp:105] Iteration 1764, lr = 0.00705094
I0412 14:34:51.496206 20203 solver.cpp:218] Iteration 1776 (2.47276 iter/s, 4.85287s/12 iters), loss = 5.27911
I0412 14:34:51.496328 20203 solver.cpp:237]     Train net output #0: loss = 5.27911 (* 1 = 5.27911 loss)
I0412 14:34:51.496342 20203 sgd_solver.cpp:105] Iteration 1776, lr = 0.0070342
I0412 14:34:56.451277 20203 solver.cpp:218] Iteration 1788 (2.42193 iter/s, 4.95474s/12 iters), loss = 5.27036
I0412 14:34:56.451323 20203 solver.cpp:237]     Train net output #0: loss = 5.27036 (* 1 = 5.27036 loss)
I0412 14:34:56.451335 20203 sgd_solver.cpp:105] Iteration 1788, lr = 0.0070175
I0412 14:35:01.353610 20203 solver.cpp:218] Iteration 1800 (2.44794 iter/s, 4.90207s/12 iters), loss = 5.28332
I0412 14:35:01.353652 20203 solver.cpp:237]     Train net output #0: loss = 5.28332 (* 1 = 5.28332 loss)
I0412 14:35:01.353659 20203 sgd_solver.cpp:105] Iteration 1800, lr = 0.00700084
I0412 14:35:06.310406 20203 solver.cpp:218] Iteration 1812 (2.42105 iter/s, 4.95653s/12 iters), loss = 5.26619
I0412 14:35:06.310458 20203 solver.cpp:237]     Train net output #0: loss = 5.26619 (* 1 = 5.26619 loss)
I0412 14:35:06.310470 20203 sgd_solver.cpp:105] Iteration 1812, lr = 0.00698422
I0412 14:35:09.421825 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:35:11.131248 20203 solver.cpp:218] Iteration 1824 (2.48933 iter/s, 4.82058s/12 iters), loss = 5.27572
I0412 14:35:11.131289 20203 solver.cpp:237]     Train net output #0: loss = 5.27572 (* 1 = 5.27572 loss)
I0412 14:35:11.131296 20203 sgd_solver.cpp:105] Iteration 1824, lr = 0.00696764
I0412 14:35:15.910704 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1836.caffemodel
I0412 14:35:18.864962 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1836.solverstate
I0412 14:35:21.150072 20203 solver.cpp:330] Iteration 1836, Testing net (#0)
I0412 14:35:21.150097 20203 net.cpp:676] Ignoring source layer train-data
I0412 14:35:24.835844 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:35:25.586643 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 14:35:25.586692 20203 solver.cpp:397]     Test net output #1: loss = 5.28561 (* 1 = 5.28561 loss)
I0412 14:35:25.673192 20203 solver.cpp:218] Iteration 1836 (0.825236 iter/s, 14.5413s/12 iters), loss = 5.28035
I0412 14:35:25.673240 20203 solver.cpp:237]     Train net output #0: loss = 5.28035 (* 1 = 5.28035 loss)
I0412 14:35:25.673252 20203 sgd_solver.cpp:105] Iteration 1836, lr = 0.0069511
I0412 14:35:29.852026 20203 solver.cpp:218] Iteration 1848 (2.87178 iter/s, 4.1786s/12 iters), loss = 5.27165
I0412 14:35:29.852078 20203 solver.cpp:237]     Train net output #0: loss = 5.27165 (* 1 = 5.27165 loss)
I0412 14:35:29.852092 20203 sgd_solver.cpp:105] Iteration 1848, lr = 0.00693459
I0412 14:35:34.608182 20203 solver.cpp:218] Iteration 1860 (2.52318 iter/s, 4.7559s/12 iters), loss = 5.28583
I0412 14:35:34.608232 20203 solver.cpp:237]     Train net output #0: loss = 5.28583 (* 1 = 5.28583 loss)
I0412 14:35:34.608244 20203 sgd_solver.cpp:105] Iteration 1860, lr = 0.00691813
I0412 14:35:39.225313 20203 solver.cpp:218] Iteration 1872 (2.59916 iter/s, 4.61688s/12 iters), loss = 5.27681
I0412 14:35:39.225365 20203 solver.cpp:237]     Train net output #0: loss = 5.27681 (* 1 = 5.27681 loss)
I0412 14:35:39.225378 20203 sgd_solver.cpp:105] Iteration 1872, lr = 0.0069017
I0412 14:35:43.821517 20203 solver.cpp:218] Iteration 1884 (2.61099 iter/s, 4.59595s/12 iters), loss = 5.28773
I0412 14:35:43.821568 20203 solver.cpp:237]     Train net output #0: loss = 5.28773 (* 1 = 5.28773 loss)
I0412 14:35:43.821580 20203 sgd_solver.cpp:105] Iteration 1884, lr = 0.00688532
I0412 14:35:48.655004 20203 solver.cpp:218] Iteration 1896 (2.48281 iter/s, 4.83323s/12 iters), loss = 5.26871
I0412 14:35:48.655050 20203 solver.cpp:237]     Train net output #0: loss = 5.26871 (* 1 = 5.26871 loss)
I0412 14:35:48.655061 20203 sgd_solver.cpp:105] Iteration 1896, lr = 0.00686897
I0412 14:35:53.530997 20203 solver.cpp:218] Iteration 1908 (2.46117 iter/s, 4.87574s/12 iters), loss = 5.28179
I0412 14:35:53.531049 20203 solver.cpp:237]     Train net output #0: loss = 5.28179 (* 1 = 5.28179 loss)
I0412 14:35:53.531062 20203 sgd_solver.cpp:105] Iteration 1908, lr = 0.00685266
I0412 14:35:58.581374 20203 solver.cpp:218] Iteration 1920 (2.37619 iter/s, 5.0501s/12 iters), loss = 5.27275
I0412 14:35:58.581554 20203 solver.cpp:237]     Train net output #0: loss = 5.27275 (* 1 = 5.27275 loss)
I0412 14:35:58.581575 20203 sgd_solver.cpp:105] Iteration 1920, lr = 0.00683639
I0412 14:35:58.935016 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:36:03.305214 20203 solver.cpp:218] Iteration 1932 (2.54051 iter/s, 4.72347s/12 iters), loss = 5.28012
I0412 14:36:03.305263 20203 solver.cpp:237]     Train net output #0: loss = 5.28012 (* 1 = 5.28012 loss)
I0412 14:36:03.305274 20203 sgd_solver.cpp:105] Iteration 1932, lr = 0.00682016
I0412 14:36:05.312589 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1938.caffemodel
I0412 14:36:08.427292 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1938.solverstate
I0412 14:36:10.738545 20203 solver.cpp:330] Iteration 1938, Testing net (#0)
I0412 14:36:10.738572 20203 net.cpp:676] Ignoring source layer train-data
I0412 14:36:14.470959 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:36:15.266052 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 14:36:15.266098 20203 solver.cpp:397]     Test net output #1: loss = 5.28591 (* 1 = 5.28591 loss)
I0412 14:36:17.193827 20203 solver.cpp:218] Iteration 1944 (0.864057 iter/s, 13.888s/12 iters), loss = 5.26979
I0412 14:36:17.193881 20203 solver.cpp:237]     Train net output #0: loss = 5.26979 (* 1 = 5.26979 loss)
I0412 14:36:17.193892 20203 sgd_solver.cpp:105] Iteration 1944, lr = 0.00680397
I0412 14:36:21.861022 20203 solver.cpp:218] Iteration 1956 (2.57128 iter/s, 4.66694s/12 iters), loss = 5.27996
I0412 14:36:21.861063 20203 solver.cpp:237]     Train net output #0: loss = 5.27996 (* 1 = 5.27996 loss)
I0412 14:36:21.861071 20203 sgd_solver.cpp:105] Iteration 1956, lr = 0.00678782
I0412 14:36:26.528232 20203 solver.cpp:218] Iteration 1968 (2.57126 iter/s, 4.66696s/12 iters), loss = 5.2725
I0412 14:36:26.528281 20203 solver.cpp:237]     Train net output #0: loss = 5.2725 (* 1 = 5.2725 loss)
I0412 14:36:26.528293 20203 sgd_solver.cpp:105] Iteration 1968, lr = 0.0067717
I0412 14:36:31.182345 20203 solver.cpp:218] Iteration 1980 (2.5785 iter/s, 4.65386s/12 iters), loss = 5.25428
I0412 14:36:31.182446 20203 solver.cpp:237]     Train net output #0: loss = 5.25428 (* 1 = 5.25428 loss)
I0412 14:36:31.182456 20203 sgd_solver.cpp:105] Iteration 1980, lr = 0.00675562
I0412 14:36:36.248524 20203 solver.cpp:218] Iteration 1992 (2.3688 iter/s, 5.06586s/12 iters), loss = 5.28647
I0412 14:36:36.248565 20203 solver.cpp:237]     Train net output #0: loss = 5.28647 (* 1 = 5.28647 loss)
I0412 14:36:36.248574 20203 sgd_solver.cpp:105] Iteration 1992, lr = 0.00673958
I0412 14:36:41.096223 20203 solver.cpp:218] Iteration 2004 (2.47553 iter/s, 4.84744s/12 iters), loss = 5.27591
I0412 14:36:41.096276 20203 solver.cpp:237]     Train net output #0: loss = 5.27591 (* 1 = 5.27591 loss)
I0412 14:36:41.096288 20203 sgd_solver.cpp:105] Iteration 2004, lr = 0.00672358
I0412 14:36:46.017612 20203 solver.cpp:218] Iteration 2016 (2.43847 iter/s, 4.92112s/12 iters), loss = 5.25972
I0412 14:36:46.017665 20203 solver.cpp:237]     Train net output #0: loss = 5.25972 (* 1 = 5.25972 loss)
I0412 14:36:46.017678 20203 sgd_solver.cpp:105] Iteration 2016, lr = 0.00670762
I0412 14:36:48.474326 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:36:50.825543 20203 solver.cpp:218] Iteration 2028 (2.49601 iter/s, 4.80767s/12 iters), loss = 5.27786
I0412 14:36:50.825598 20203 solver.cpp:237]     Train net output #0: loss = 5.27786 (* 1 = 5.27786 loss)
I0412 14:36:50.825610 20203 sgd_solver.cpp:105] Iteration 2028, lr = 0.00669169
I0412 14:36:55.010222 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2040.caffemodel
I0412 14:36:58.575744 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2040.solverstate
I0412 14:37:00.866569 20203 solver.cpp:330] Iteration 2040, Testing net (#0)
I0412 14:37:00.866592 20203 net.cpp:676] Ignoring source layer train-data
I0412 14:37:04.608561 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:37:05.437083 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00612745
I0412 14:37:05.437130 20203 solver.cpp:397]     Test net output #1: loss = 5.28541 (* 1 = 5.28541 loss)
I0412 14:37:05.523108 20203 solver.cpp:218] Iteration 2040 (0.816499 iter/s, 14.6969s/12 iters), loss = 5.28493
I0412 14:37:05.523155 20203 solver.cpp:237]     Train net output #0: loss = 5.28493 (* 1 = 5.28493 loss)
I0412 14:37:05.523166 20203 sgd_solver.cpp:105] Iteration 2040, lr = 0.00667581
I0412 14:37:09.676296 20203 solver.cpp:218] Iteration 2052 (2.8895 iter/s, 4.15296s/12 iters), loss = 5.28612
I0412 14:37:09.676340 20203 solver.cpp:237]     Train net output #0: loss = 5.28612 (* 1 = 5.28612 loss)
I0412 14:37:09.676350 20203 sgd_solver.cpp:105] Iteration 2052, lr = 0.00665996
I0412 14:37:11.453860 20203 blocking_queue.cpp:49] Waiting for data
I0412 14:37:14.769814 20203 solver.cpp:218] Iteration 2064 (2.35606 iter/s, 5.09325s/12 iters), loss = 5.27713
I0412 14:37:14.769855 20203 solver.cpp:237]     Train net output #0: loss = 5.27713 (* 1 = 5.27713 loss)
I0412 14:37:14.769865 20203 sgd_solver.cpp:105] Iteration 2064, lr = 0.00664414
I0412 14:37:19.740200 20203 solver.cpp:218] Iteration 2076 (2.41442 iter/s, 4.97013s/12 iters), loss = 5.2802
I0412 14:37:19.740242 20203 solver.cpp:237]     Train net output #0: loss = 5.2802 (* 1 = 5.2802 loss)
I0412 14:37:19.740250 20203 sgd_solver.cpp:105] Iteration 2076, lr = 0.00662837
I0412 14:37:24.508965 20203 solver.cpp:218] Iteration 2088 (2.51651 iter/s, 4.76851s/12 iters), loss = 5.2714
I0412 14:37:24.509021 20203 solver.cpp:237]     Train net output #0: loss = 5.2714 (* 1 = 5.2714 loss)
I0412 14:37:24.509034 20203 sgd_solver.cpp:105] Iteration 2088, lr = 0.00661263
I0412 14:37:29.046167 20203 solver.cpp:218] Iteration 2100 (2.64495 iter/s, 4.53695s/12 iters), loss = 5.27168
I0412 14:37:29.046216 20203 solver.cpp:237]     Train net output #0: loss = 5.27168 (* 1 = 5.27168 loss)
I0412 14:37:29.046226 20203 sgd_solver.cpp:105] Iteration 2100, lr = 0.00659693
I0412 14:37:33.790176 20203 solver.cpp:218] Iteration 2112 (2.52964 iter/s, 4.74375s/12 iters), loss = 5.27879
I0412 14:37:33.790227 20203 solver.cpp:237]     Train net output #0: loss = 5.27879 (* 1 = 5.27879 loss)
I0412 14:37:33.790241 20203 sgd_solver.cpp:105] Iteration 2112, lr = 0.00658127
I0412 14:37:38.342389 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:37:38.655593 20203 solver.cpp:218] Iteration 2124 (2.46652 iter/s, 4.86515s/12 iters), loss = 5.26005
I0412 14:37:38.655647 20203 solver.cpp:237]     Train net output #0: loss = 5.26005 (* 1 = 5.26005 loss)
I0412 14:37:38.655660 20203 sgd_solver.cpp:105] Iteration 2124, lr = 0.00656564
I0412 14:37:43.460351 20203 solver.cpp:218] Iteration 2136 (2.49766 iter/s, 4.8045s/12 iters), loss = 5.27404
I0412 14:37:43.460392 20203 solver.cpp:237]     Train net output #0: loss = 5.27404 (* 1 = 5.27404 loss)
I0412 14:37:43.460402 20203 sgd_solver.cpp:105] Iteration 2136, lr = 0.00655006
I0412 14:37:45.571499 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2142.caffemodel
I0412 14:37:51.160931 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2142.solverstate
I0412 14:37:58.725816 20203 solver.cpp:330] Iteration 2142, Testing net (#0)
I0412 14:37:58.725841 20203 net.cpp:676] Ignoring source layer train-data
I0412 14:38:02.298296 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:38:03.162250 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 14:38:03.162307 20203 solver.cpp:397]     Test net output #1: loss = 5.2857 (* 1 = 5.2857 loss)
I0412 14:38:04.897011 20203 solver.cpp:218] Iteration 2148 (0.559813 iter/s, 21.4357s/12 iters), loss = 5.2777
I0412 14:38:04.897053 20203 solver.cpp:237]     Train net output #0: loss = 5.2777 (* 1 = 5.2777 loss)
I0412 14:38:04.897063 20203 sgd_solver.cpp:105] Iteration 2148, lr = 0.00653451
I0412 14:38:09.715409 20203 solver.cpp:218] Iteration 2160 (2.49059 iter/s, 4.81814s/12 iters), loss = 5.28948
I0412 14:38:09.715518 20203 solver.cpp:237]     Train net output #0: loss = 5.28948 (* 1 = 5.28948 loss)
I0412 14:38:09.715530 20203 sgd_solver.cpp:105] Iteration 2160, lr = 0.00651899
I0412 14:38:14.981773 20203 solver.cpp:218] Iteration 2172 (2.27876 iter/s, 5.26603s/12 iters), loss = 5.27638
I0412 14:38:14.981814 20203 solver.cpp:237]     Train net output #0: loss = 5.27638 (* 1 = 5.27638 loss)
I0412 14:38:14.981823 20203 sgd_solver.cpp:105] Iteration 2172, lr = 0.00650351
I0412 14:38:20.175576 20203 solver.cpp:218] Iteration 2184 (2.31057 iter/s, 5.19353s/12 iters), loss = 5.27519
I0412 14:38:20.175617 20203 solver.cpp:237]     Train net output #0: loss = 5.27519 (* 1 = 5.27519 loss)
I0412 14:38:20.175627 20203 sgd_solver.cpp:105] Iteration 2184, lr = 0.00648807
I0412 14:38:25.056056 20203 solver.cpp:218] Iteration 2196 (2.45891 iter/s, 4.88022s/12 iters), loss = 5.25271
I0412 14:38:25.056118 20203 solver.cpp:237]     Train net output #0: loss = 5.25271 (* 1 = 5.25271 loss)
I0412 14:38:25.056134 20203 sgd_solver.cpp:105] Iteration 2196, lr = 0.00647267
I0412 14:38:29.964856 20203 solver.cpp:218] Iteration 2208 (2.44472 iter/s, 4.90853s/12 iters), loss = 5.27565
I0412 14:38:29.964897 20203 solver.cpp:237]     Train net output #0: loss = 5.27565 (* 1 = 5.27565 loss)
I0412 14:38:29.964905 20203 sgd_solver.cpp:105] Iteration 2208, lr = 0.0064573
I0412 14:38:34.673532 20203 solver.cpp:218] Iteration 2220 (2.54862 iter/s, 4.70843s/12 iters), loss = 5.28582
I0412 14:38:34.673583 20203 solver.cpp:237]     Train net output #0: loss = 5.28582 (* 1 = 5.28582 loss)
I0412 14:38:34.673596 20203 sgd_solver.cpp:105] Iteration 2220, lr = 0.00644197
I0412 14:38:36.473897 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:38:39.621718 20203 solver.cpp:218] Iteration 2232 (2.42526 iter/s, 4.94792s/12 iters), loss = 5.28663
I0412 14:38:39.621757 20203 solver.cpp:237]     Train net output #0: loss = 5.28663 (* 1 = 5.28663 loss)
I0412 14:38:39.621765 20203 sgd_solver.cpp:105] Iteration 2232, lr = 0.00642668
I0412 14:38:43.952095 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2244.caffemodel
I0412 14:38:47.013427 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2244.solverstate
I0412 14:38:49.316294 20203 solver.cpp:330] Iteration 2244, Testing net (#0)
I0412 14:38:49.316321 20203 net.cpp:676] Ignoring source layer train-data
I0412 14:38:52.899271 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:38:53.811206 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 14:38:53.811259 20203 solver.cpp:397]     Test net output #1: loss = 5.28555 (* 1 = 5.28555 loss)
I0412 14:38:53.895818 20203 solver.cpp:218] Iteration 2244 (0.840721 iter/s, 14.2735s/12 iters), loss = 5.28259
I0412 14:38:53.895869 20203 solver.cpp:237]     Train net output #0: loss = 5.28259 (* 1 = 5.28259 loss)
I0412 14:38:53.895881 20203 sgd_solver.cpp:105] Iteration 2244, lr = 0.00641142
I0412 14:38:58.043172 20203 solver.cpp:218] Iteration 2256 (2.89358 iter/s, 4.14711s/12 iters), loss = 5.24283
I0412 14:38:58.043228 20203 solver.cpp:237]     Train net output #0: loss = 5.24283 (* 1 = 5.24283 loss)
I0412 14:38:58.043243 20203 sgd_solver.cpp:105] Iteration 2256, lr = 0.0063962
I0412 14:39:02.933843 20203 solver.cpp:218] Iteration 2268 (2.45378 iter/s, 4.89041s/12 iters), loss = 5.28452
I0412 14:39:02.933881 20203 solver.cpp:237]     Train net output #0: loss = 5.28452 (* 1 = 5.28452 loss)
I0412 14:39:02.933890 20203 sgd_solver.cpp:105] Iteration 2268, lr = 0.00638101
I0412 14:39:07.765126 20203 solver.cpp:218] Iteration 2280 (2.48394 iter/s, 4.83103s/12 iters), loss = 5.2582
I0412 14:39:07.765170 20203 solver.cpp:237]     Train net output #0: loss = 5.2582 (* 1 = 5.2582 loss)
I0412 14:39:07.765180 20203 sgd_solver.cpp:105] Iteration 2280, lr = 0.00636586
I0412 14:39:12.598868 20203 solver.cpp:218] Iteration 2292 (2.48268 iter/s, 4.83348s/12 iters), loss = 5.27781
I0412 14:39:12.598917 20203 solver.cpp:237]     Train net output #0: loss = 5.27781 (* 1 = 5.27781 loss)
I0412 14:39:12.598927 20203 sgd_solver.cpp:105] Iteration 2292, lr = 0.00635075
I0412 14:39:17.713145 20203 solver.cpp:218] Iteration 2304 (2.3465 iter/s, 5.114s/12 iters), loss = 5.26953
I0412 14:39:17.713286 20203 solver.cpp:237]     Train net output #0: loss = 5.26953 (* 1 = 5.26953 loss)
I0412 14:39:17.713299 20203 sgd_solver.cpp:105] Iteration 2304, lr = 0.00633567
I0412 14:39:22.610473 20203 solver.cpp:218] Iteration 2316 (2.45049 iter/s, 4.89698s/12 iters), loss = 5.26379
I0412 14:39:22.610530 20203 solver.cpp:237]     Train net output #0: loss = 5.26379 (* 1 = 5.26379 loss)
I0412 14:39:22.610543 20203 sgd_solver.cpp:105] Iteration 2316, lr = 0.00632063
I0412 14:39:26.409194 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:39:27.433624 20203 solver.cpp:218] Iteration 2328 (2.48814 iter/s, 4.82288s/12 iters), loss = 5.26062
I0412 14:39:27.433673 20203 solver.cpp:237]     Train net output #0: loss = 5.26062 (* 1 = 5.26062 loss)
I0412 14:39:27.433686 20203 sgd_solver.cpp:105] Iteration 2328, lr = 0.00630562
I0412 14:39:32.645256 20203 solver.cpp:218] Iteration 2340 (2.30266 iter/s, 5.21136s/12 iters), loss = 5.29315
I0412 14:39:32.645306 20203 solver.cpp:237]     Train net output #0: loss = 5.29315 (* 1 = 5.29315 loss)
I0412 14:39:32.645320 20203 sgd_solver.cpp:105] Iteration 2340, lr = 0.00629065
I0412 14:39:34.611670 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2346.caffemodel
I0412 14:39:37.616854 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2346.solverstate
I0412 14:39:39.973153 20203 solver.cpp:330] Iteration 2346, Testing net (#0)
I0412 14:39:39.973176 20203 net.cpp:676] Ignoring source layer train-data
I0412 14:39:43.459493 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:39:44.403970 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 14:39:44.404024 20203 solver.cpp:397]     Test net output #1: loss = 5.28624 (* 1 = 5.28624 loss)
I0412 14:39:46.395582 20203 solver.cpp:218] Iteration 2352 (0.872746 iter/s, 13.7497s/12 iters), loss = 5.26079
I0412 14:39:46.395629 20203 solver.cpp:237]     Train net output #0: loss = 5.26079 (* 1 = 5.26079 loss)
I0412 14:39:46.395640 20203 sgd_solver.cpp:105] Iteration 2352, lr = 0.00627571
I0412 14:39:51.310840 20203 solver.cpp:218] Iteration 2364 (2.44151 iter/s, 4.915s/12 iters), loss = 5.30361
I0412 14:39:51.310989 20203 solver.cpp:237]     Train net output #0: loss = 5.30361 (* 1 = 5.30361 loss)
I0412 14:39:51.311000 20203 sgd_solver.cpp:105] Iteration 2364, lr = 0.00626081
I0412 14:39:56.197834 20203 solver.cpp:218] Iteration 2376 (2.45568 iter/s, 4.88663s/12 iters), loss = 5.26044
I0412 14:39:56.197887 20203 solver.cpp:237]     Train net output #0: loss = 5.26044 (* 1 = 5.26044 loss)
I0412 14:39:56.197899 20203 sgd_solver.cpp:105] Iteration 2376, lr = 0.00624595
I0412 14:40:01.093506 20203 solver.cpp:218] Iteration 2388 (2.45128 iter/s, 4.89541s/12 iters), loss = 5.27485
I0412 14:40:01.093552 20203 solver.cpp:237]     Train net output #0: loss = 5.27485 (* 1 = 5.27485 loss)
I0412 14:40:01.093562 20203 sgd_solver.cpp:105] Iteration 2388, lr = 0.00623112
I0412 14:40:05.957414 20203 solver.cpp:218] Iteration 2400 (2.46728 iter/s, 4.86365s/12 iters), loss = 5.28419
I0412 14:40:05.957468 20203 solver.cpp:237]     Train net output #0: loss = 5.28419 (* 1 = 5.28419 loss)
I0412 14:40:05.957481 20203 sgd_solver.cpp:105] Iteration 2400, lr = 0.00621633
I0412 14:40:10.825798 20203 solver.cpp:218] Iteration 2412 (2.46502 iter/s, 4.86811s/12 iters), loss = 5.27397
I0412 14:40:10.825850 20203 solver.cpp:237]     Train net output #0: loss = 5.27397 (* 1 = 5.27397 loss)
I0412 14:40:10.825861 20203 sgd_solver.cpp:105] Iteration 2412, lr = 0.00620157
I0412 14:40:15.617650 20203 solver.cpp:218] Iteration 2424 (2.50439 iter/s, 4.79159s/12 iters), loss = 5.27728
I0412 14:40:15.617699 20203 solver.cpp:237]     Train net output #0: loss = 5.27728 (* 1 = 5.27728 loss)
I0412 14:40:15.617712 20203 sgd_solver.cpp:105] Iteration 2424, lr = 0.00618684
I0412 14:40:16.635105 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:40:20.487936 20203 solver.cpp:218] Iteration 2436 (2.46406 iter/s, 4.87002s/12 iters), loss = 5.27936
I0412 14:40:20.487982 20203 solver.cpp:237]     Train net output #0: loss = 5.27936 (* 1 = 5.27936 loss)
I0412 14:40:20.487994 20203 sgd_solver.cpp:105] Iteration 2436, lr = 0.00617215
I0412 14:40:24.910033 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2448.caffemodel
I0412 14:40:29.047355 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2448.solverstate
I0412 14:40:31.353529 20203 solver.cpp:330] Iteration 2448, Testing net (#0)
I0412 14:40:31.353556 20203 net.cpp:676] Ignoring source layer train-data
I0412 14:40:34.852099 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:40:35.912473 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 14:40:35.912520 20203 solver.cpp:397]     Test net output #1: loss = 5.28574 (* 1 = 5.28574 loss)
I0412 14:40:35.998950 20203 solver.cpp:218] Iteration 2448 (0.773679 iter/s, 15.5103s/12 iters), loss = 5.25522
I0412 14:40:35.999001 20203 solver.cpp:237]     Train net output #0: loss = 5.25522 (* 1 = 5.25522 loss)
I0412 14:40:35.999012 20203 sgd_solver.cpp:105] Iteration 2448, lr = 0.0061575
I0412 14:40:40.088483 20203 solver.cpp:218] Iteration 2460 (2.93449 iter/s, 4.0893s/12 iters), loss = 5.26303
I0412 14:40:40.088526 20203 solver.cpp:237]     Train net output #0: loss = 5.26303 (* 1 = 5.26303 loss)
I0412 14:40:40.088534 20203 sgd_solver.cpp:105] Iteration 2460, lr = 0.00614288
I0412 14:40:44.876178 20203 solver.cpp:218] Iteration 2472 (2.50656 iter/s, 4.78743s/12 iters), loss = 5.27087
I0412 14:40:44.876224 20203 solver.cpp:237]     Train net output #0: loss = 5.27087 (* 1 = 5.27087 loss)
I0412 14:40:44.876233 20203 sgd_solver.cpp:105] Iteration 2472, lr = 0.0061283
I0412 14:40:49.960852 20203 solver.cpp:218] Iteration 2484 (2.36016 iter/s, 5.0844s/12 iters), loss = 5.27253
I0412 14:40:49.960904 20203 solver.cpp:237]     Train net output #0: loss = 5.27253 (* 1 = 5.27253 loss)
I0412 14:40:49.960917 20203 sgd_solver.cpp:105] Iteration 2484, lr = 0.00611375
I0412 14:40:55.014170 20203 solver.cpp:218] Iteration 2496 (2.3748 iter/s, 5.05305s/12 iters), loss = 5.27558
I0412 14:40:55.014307 20203 solver.cpp:237]     Train net output #0: loss = 5.27558 (* 1 = 5.27558 loss)
I0412 14:40:55.014320 20203 sgd_solver.cpp:105] Iteration 2496, lr = 0.00609923
I0412 14:40:59.853616 20203 solver.cpp:218] Iteration 2508 (2.4798 iter/s, 4.8391s/12 iters), loss = 5.28963
I0412 14:40:59.853729 20203 solver.cpp:237]     Train net output #0: loss = 5.28963 (* 1 = 5.28963 loss)
I0412 14:40:59.853745 20203 sgd_solver.cpp:105] Iteration 2508, lr = 0.00608475
I0412 14:41:04.822504 20203 solver.cpp:218] Iteration 2520 (2.41519 iter/s, 4.96855s/12 iters), loss = 5.2816
I0412 14:41:04.822559 20203 solver.cpp:237]     Train net output #0: loss = 5.2816 (* 1 = 5.2816 loss)
I0412 14:41:04.822572 20203 sgd_solver.cpp:105] Iteration 2520, lr = 0.0060703
I0412 14:41:07.968413 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:41:09.744685 20203 solver.cpp:218] Iteration 2532 (2.43808 iter/s, 4.92191s/12 iters), loss = 5.28331
I0412 14:41:09.744737 20203 solver.cpp:237]     Train net output #0: loss = 5.28331 (* 1 = 5.28331 loss)
I0412 14:41:09.744750 20203 sgd_solver.cpp:105] Iteration 2532, lr = 0.00605589
I0412 14:41:14.579466 20203 solver.cpp:218] Iteration 2544 (2.48215 iter/s, 4.83452s/12 iters), loss = 5.27082
I0412 14:41:14.579512 20203 solver.cpp:237]     Train net output #0: loss = 5.27082 (* 1 = 5.27082 loss)
I0412 14:41:14.579522 20203 sgd_solver.cpp:105] Iteration 2544, lr = 0.00604151
I0412 14:41:16.462687 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2550.caffemodel
I0412 14:41:21.469627 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2550.solverstate
I0412 14:41:25.455065 20203 solver.cpp:330] Iteration 2550, Testing net (#0)
I0412 14:41:25.455196 20203 net.cpp:676] Ignoring source layer train-data
I0412 14:41:28.996760 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:41:30.088969 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 14:41:30.089015 20203 solver.cpp:397]     Test net output #1: loss = 5.28602 (* 1 = 5.28602 loss)
I0412 14:41:31.812268 20203 solver.cpp:218] Iteration 2556 (0.696378 iter/s, 17.232s/12 iters), loss = 5.28323
I0412 14:41:31.812319 20203 solver.cpp:237]     Train net output #0: loss = 5.28323 (* 1 = 5.28323 loss)
I0412 14:41:31.812331 20203 sgd_solver.cpp:105] Iteration 2556, lr = 0.00602717
I0412 14:41:36.585675 20203 solver.cpp:218] Iteration 2568 (2.51406 iter/s, 4.77315s/12 iters), loss = 5.28542
I0412 14:41:36.585717 20203 solver.cpp:237]     Train net output #0: loss = 5.28542 (* 1 = 5.28542 loss)
I0412 14:41:36.585726 20203 sgd_solver.cpp:105] Iteration 2568, lr = 0.00601286
I0412 14:41:41.945900 20203 solver.cpp:218] Iteration 2580 (2.23883 iter/s, 5.35995s/12 iters), loss = 5.26985
I0412 14:41:41.945942 20203 solver.cpp:237]     Train net output #0: loss = 5.26985 (* 1 = 5.26985 loss)
I0412 14:41:41.945952 20203 sgd_solver.cpp:105] Iteration 2580, lr = 0.00599858
I0412 14:41:46.793969 20203 solver.cpp:218] Iteration 2592 (2.47535 iter/s, 4.8478s/12 iters), loss = 5.29046
I0412 14:41:46.794020 20203 solver.cpp:237]     Train net output #0: loss = 5.29046 (* 1 = 5.29046 loss)
I0412 14:41:46.794034 20203 sgd_solver.cpp:105] Iteration 2592, lr = 0.00598434
I0412 14:41:52.121423 20203 solver.cpp:218] Iteration 2604 (2.2526 iter/s, 5.32717s/12 iters), loss = 5.26128
I0412 14:41:52.121465 20203 solver.cpp:237]     Train net output #0: loss = 5.26128 (* 1 = 5.26128 loss)
I0412 14:41:52.121474 20203 sgd_solver.cpp:105] Iteration 2604, lr = 0.00597013
I0412 14:41:57.214807 20203 solver.cpp:218] Iteration 2616 (2.35612 iter/s, 5.09311s/12 iters), loss = 5.28052
I0412 14:41:57.215567 20203 solver.cpp:237]     Train net output #0: loss = 5.28052 (* 1 = 5.28052 loss)
I0412 14:41:57.215580 20203 sgd_solver.cpp:105] Iteration 2616, lr = 0.00595596
I0412 14:42:02.113315 20203 solver.cpp:218] Iteration 2628 (2.45021 iter/s, 4.89753s/12 iters), loss = 5.28175
I0412 14:42:02.113371 20203 solver.cpp:237]     Train net output #0: loss = 5.28175 (* 1 = 5.28175 loss)
I0412 14:42:02.113385 20203 sgd_solver.cpp:105] Iteration 2628, lr = 0.00594182
I0412 14:42:02.540132 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:42:07.015877 20203 solver.cpp:218] Iteration 2640 (2.44784 iter/s, 4.90229s/12 iters), loss = 5.28006
I0412 14:42:07.015934 20203 solver.cpp:237]     Train net output #0: loss = 5.28006 (* 1 = 5.28006 loss)
I0412 14:42:07.015946 20203 sgd_solver.cpp:105] Iteration 2640, lr = 0.00592771
I0412 14:42:11.467200 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2652.caffemodel
I0412 14:42:14.453658 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2652.solverstate
I0412 14:42:19.016959 20203 solver.cpp:330] Iteration 2652, Testing net (#0)
I0412 14:42:19.016986 20203 net.cpp:676] Ignoring source layer train-data
I0412 14:42:22.620229 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:42:23.685060 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 14:42:23.685101 20203 solver.cpp:397]     Test net output #1: loss = 5.28602 (* 1 = 5.28602 loss)
I0412 14:42:23.771387 20203 solver.cpp:218] Iteration 2652 (0.716215 iter/s, 16.7547s/12 iters), loss = 5.27352
I0412 14:42:23.771435 20203 solver.cpp:237]     Train net output #0: loss = 5.27352 (* 1 = 5.27352 loss)
I0412 14:42:23.771445 20203 sgd_solver.cpp:105] Iteration 2652, lr = 0.00591364
I0412 14:42:27.953744 20203 solver.cpp:218] Iteration 2664 (2.86936 iter/s, 4.18212s/12 iters), loss = 5.27857
I0412 14:42:27.962060 20203 solver.cpp:237]     Train net output #0: loss = 5.27857 (* 1 = 5.27857 loss)
I0412 14:42:27.962080 20203 sgd_solver.cpp:105] Iteration 2664, lr = 0.0058996
I0412 14:42:32.854450 20203 solver.cpp:218] Iteration 2676 (2.45289 iter/s, 4.89219s/12 iters), loss = 5.27085
I0412 14:42:32.854491 20203 solver.cpp:237]     Train net output #0: loss = 5.27085 (* 1 = 5.27085 loss)
I0412 14:42:32.854499 20203 sgd_solver.cpp:105] Iteration 2676, lr = 0.00588559
I0412 14:42:38.144275 20203 solver.cpp:218] Iteration 2688 (2.26862 iter/s, 5.28955s/12 iters), loss = 5.26354
I0412 14:42:38.144331 20203 solver.cpp:237]     Train net output #0: loss = 5.26354 (* 1 = 5.26354 loss)
I0412 14:42:38.144345 20203 sgd_solver.cpp:105] Iteration 2688, lr = 0.00587162
I0412 14:42:43.043107 20203 solver.cpp:218] Iteration 2700 (2.4497 iter/s, 4.89856s/12 iters), loss = 5.2793
I0412 14:42:43.043161 20203 solver.cpp:237]     Train net output #0: loss = 5.2793 (* 1 = 5.2793 loss)
I0412 14:42:43.043174 20203 sgd_solver.cpp:105] Iteration 2700, lr = 0.00585768
I0412 14:42:47.774209 20203 solver.cpp:218] Iteration 2712 (2.53655 iter/s, 4.73084s/12 iters), loss = 5.28654
I0412 14:42:47.774251 20203 solver.cpp:237]     Train net output #0: loss = 5.28654 (* 1 = 5.28654 loss)
I0412 14:42:47.774260 20203 sgd_solver.cpp:105] Iteration 2712, lr = 0.00584377
I0412 14:42:52.573827 20203 solver.cpp:218] Iteration 2724 (2.50033 iter/s, 4.79936s/12 iters), loss = 5.25994
I0412 14:42:52.573870 20203 solver.cpp:237]     Train net output #0: loss = 5.25994 (* 1 = 5.25994 loss)
I0412 14:42:52.573879 20203 sgd_solver.cpp:105] Iteration 2724, lr = 0.0058299
I0412 14:42:55.042593 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:42:57.315706 20203 solver.cpp:218] Iteration 2736 (2.53078 iter/s, 4.74163s/12 iters), loss = 5.28018
I0412 14:42:57.315753 20203 solver.cpp:237]     Train net output #0: loss = 5.28018 (* 1 = 5.28018 loss)
I0412 14:42:57.315764 20203 sgd_solver.cpp:105] Iteration 2736, lr = 0.00581605
I0412 14:43:02.048702 20203 solver.cpp:218] Iteration 2748 (2.53553 iter/s, 4.73274s/12 iters), loss = 5.27416
I0412 14:43:02.048789 20203 solver.cpp:237]     Train net output #0: loss = 5.27416 (* 1 = 5.27416 loss)
I0412 14:43:02.048801 20203 sgd_solver.cpp:105] Iteration 2748, lr = 0.00580225
I0412 14:43:04.112962 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2754.caffemodel
I0412 14:43:10.269196 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2754.solverstate
I0412 14:43:16.773481 20203 solver.cpp:330] Iteration 2754, Testing net (#0)
I0412 14:43:16.773509 20203 net.cpp:676] Ignoring source layer train-data
I0412 14:43:19.858124 20203 blocking_queue.cpp:49] Waiting for data
I0412 14:43:20.094673 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:43:21.204228 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00612745
I0412 14:43:21.204268 20203 solver.cpp:397]     Test net output #1: loss = 5.28608 (* 1 = 5.28608 loss)
I0412 14:43:23.108382 20203 solver.cpp:218] Iteration 2760 (0.569835 iter/s, 21.0587s/12 iters), loss = 5.27861
I0412 14:43:23.108436 20203 solver.cpp:237]     Train net output #0: loss = 5.27861 (* 1 = 5.27861 loss)
I0412 14:43:23.108449 20203 sgd_solver.cpp:105] Iteration 2760, lr = 0.00578847
I0412 14:43:28.046500 20203 solver.cpp:218] Iteration 2772 (2.43021 iter/s, 4.93785s/12 iters), loss = 5.27721
I0412 14:43:28.046559 20203 solver.cpp:237]     Train net output #0: loss = 5.27721 (* 1 = 5.27721 loss)
I0412 14:43:28.046571 20203 sgd_solver.cpp:105] Iteration 2772, lr = 0.00577473
I0412 14:43:33.310006 20203 solver.cpp:218] Iteration 2784 (2.27998 iter/s, 5.26321s/12 iters), loss = 5.27837
I0412 14:43:33.310155 20203 solver.cpp:237]     Train net output #0: loss = 5.27837 (* 1 = 5.27837 loss)
I0412 14:43:33.310168 20203 sgd_solver.cpp:105] Iteration 2784, lr = 0.00576102
I0412 14:43:38.172631 20203 solver.cpp:218] Iteration 2796 (2.46799 iter/s, 4.86226s/12 iters), loss = 5.27287
I0412 14:43:38.172679 20203 solver.cpp:237]     Train net output #0: loss = 5.27287 (* 1 = 5.27287 loss)
I0412 14:43:38.172691 20203 sgd_solver.cpp:105] Iteration 2796, lr = 0.00574734
I0412 14:43:43.298175 20203 solver.cpp:218] Iteration 2808 (2.34134 iter/s, 5.12527s/12 iters), loss = 5.26906
I0412 14:43:43.298228 20203 solver.cpp:237]     Train net output #0: loss = 5.26906 (* 1 = 5.26906 loss)
I0412 14:43:43.298240 20203 sgd_solver.cpp:105] Iteration 2808, lr = 0.00573369
I0412 14:43:48.256090 20203 solver.cpp:218] Iteration 2820 (2.42051 iter/s, 4.95764s/12 iters), loss = 5.27697
I0412 14:43:48.256163 20203 solver.cpp:237]     Train net output #0: loss = 5.27697 (* 1 = 5.27697 loss)
I0412 14:43:48.256182 20203 sgd_solver.cpp:105] Iteration 2820, lr = 0.00572008
I0412 14:43:52.870714 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:43:53.155030 20203 solver.cpp:218] Iteration 2832 (2.44965 iter/s, 4.89865s/12 iters), loss = 5.26313
I0412 14:43:53.155081 20203 solver.cpp:237]     Train net output #0: loss = 5.26313 (* 1 = 5.26313 loss)
I0412 14:43:53.155092 20203 sgd_solver.cpp:105] Iteration 2832, lr = 0.0057065
I0412 14:43:58.072513 20203 solver.cpp:218] Iteration 2844 (2.44041 iter/s, 4.91721s/12 iters), loss = 5.27177
I0412 14:43:58.072561 20203 solver.cpp:237]     Train net output #0: loss = 5.27177 (* 1 = 5.27177 loss)
I0412 14:43:58.072573 20203 sgd_solver.cpp:105] Iteration 2844, lr = 0.00569295
I0412 14:44:02.646754 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2856.caffemodel
I0412 14:44:05.657517 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2856.solverstate
I0412 14:44:07.991075 20203 solver.cpp:330] Iteration 2856, Testing net (#0)
I0412 14:44:07.991102 20203 net.cpp:676] Ignoring source layer train-data
I0412 14:44:11.251796 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:44:12.395519 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 14:44:12.395555 20203 solver.cpp:397]     Test net output #1: loss = 5.2865 (* 1 = 5.2865 loss)
I0412 14:44:12.481992 20203 solver.cpp:218] Iteration 2856 (0.832824 iter/s, 14.4088s/12 iters), loss = 5.28789
I0412 14:44:12.482046 20203 solver.cpp:237]     Train net output #0: loss = 5.28789 (* 1 = 5.28789 loss)
I0412 14:44:12.482057 20203 sgd_solver.cpp:105] Iteration 2856, lr = 0.00567944
I0412 14:44:17.139442 20203 solver.cpp:218] Iteration 2868 (2.57666 iter/s, 4.65719s/12 iters), loss = 5.28625
I0412 14:44:17.139485 20203 solver.cpp:237]     Train net output #0: loss = 5.28625 (* 1 = 5.28625 loss)
I0412 14:44:17.139494 20203 sgd_solver.cpp:105] Iteration 2868, lr = 0.00566595
I0412 14:44:21.853896 20203 solver.cpp:218] Iteration 2880 (2.5455 iter/s, 4.7142s/12 iters), loss = 5.28181
I0412 14:44:21.853979 20203 solver.cpp:237]     Train net output #0: loss = 5.28181 (* 1 = 5.28181 loss)
I0412 14:44:21.853994 20203 sgd_solver.cpp:105] Iteration 2880, lr = 0.0056525
I0412 14:44:26.685680 20203 solver.cpp:218] Iteration 2892 (2.48369 iter/s, 4.83151s/12 iters), loss = 5.26879
I0412 14:44:26.685734 20203 solver.cpp:237]     Train net output #0: loss = 5.26879 (* 1 = 5.26879 loss)
I0412 14:44:26.685745 20203 sgd_solver.cpp:105] Iteration 2892, lr = 0.00563908
I0412 14:44:31.674043 20203 solver.cpp:218] Iteration 2904 (2.40573 iter/s, 4.98809s/12 iters), loss = 5.25809
I0412 14:44:31.674096 20203 solver.cpp:237]     Train net output #0: loss = 5.25809 (* 1 = 5.25809 loss)
I0412 14:44:31.674108 20203 sgd_solver.cpp:105] Iteration 2904, lr = 0.00562569
I0412 14:44:36.648609 20203 solver.cpp:218] Iteration 2916 (2.4124 iter/s, 4.9743s/12 iters), loss = 5.27228
I0412 14:44:36.648751 20203 solver.cpp:237]     Train net output #0: loss = 5.27228 (* 1 = 5.27228 loss)
I0412 14:44:36.648764 20203 sgd_solver.cpp:105] Iteration 2916, lr = 0.00561233
I0412 14:44:41.723042 20203 solver.cpp:218] Iteration 2928 (2.36497 iter/s, 5.07406s/12 iters), loss = 5.27566
I0412 14:44:41.723099 20203 solver.cpp:237]     Train net output #0: loss = 5.27566 (* 1 = 5.27566 loss)
I0412 14:44:41.723112 20203 sgd_solver.cpp:105] Iteration 2928, lr = 0.00559901
I0412 14:44:43.545975 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:44:46.574826 20203 solver.cpp:218] Iteration 2940 (2.47345 iter/s, 4.85151s/12 iters), loss = 5.28488
I0412 14:44:46.574877 20203 solver.cpp:237]     Train net output #0: loss = 5.28488 (* 1 = 5.28488 loss)
I0412 14:44:46.574887 20203 sgd_solver.cpp:105] Iteration 2940, lr = 0.00558572
I0412 14:44:51.483800 20203 solver.cpp:218] Iteration 2952 (2.44464 iter/s, 4.9087s/12 iters), loss = 5.28018
I0412 14:44:51.483851 20203 solver.cpp:237]     Train net output #0: loss = 5.28018 (* 1 = 5.28018 loss)
I0412 14:44:51.483862 20203 sgd_solver.cpp:105] Iteration 2952, lr = 0.00557245
I0412 14:44:53.494640 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2958.caffemodel
I0412 14:44:56.485188 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2958.solverstate
I0412 14:44:58.794049 20203 solver.cpp:330] Iteration 2958, Testing net (#0)
I0412 14:44:58.794075 20203 net.cpp:676] Ignoring source layer train-data
I0412 14:45:02.182742 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:45:03.368870 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 14:45:03.368906 20203 solver.cpp:397]     Test net output #1: loss = 5.28589 (* 1 = 5.28589 loss)
I0412 14:45:05.444314 20203 solver.cpp:218] Iteration 2964 (0.859607 iter/s, 13.9599s/12 iters), loss = 5.24874
I0412 14:45:05.444365 20203 solver.cpp:237]     Train net output #0: loss = 5.24874 (* 1 = 5.24874 loss)
I0412 14:45:05.444375 20203 sgd_solver.cpp:105] Iteration 2964, lr = 0.00555922
I0412 14:45:10.483589 20203 solver.cpp:218] Iteration 2976 (2.38143 iter/s, 5.039s/12 iters), loss = 5.28481
I0412 14:45:10.483701 20203 solver.cpp:237]     Train net output #0: loss = 5.28481 (* 1 = 5.28481 loss)
I0412 14:45:10.483712 20203 sgd_solver.cpp:105] Iteration 2976, lr = 0.00554603
I0412 14:45:15.423889 20203 solver.cpp:218] Iteration 2988 (2.42916 iter/s, 4.93997s/12 iters), loss = 5.26172
I0412 14:45:15.423931 20203 solver.cpp:237]     Train net output #0: loss = 5.26172 (* 1 = 5.26172 loss)
I0412 14:45:15.423940 20203 sgd_solver.cpp:105] Iteration 2988, lr = 0.00553286
I0412 14:45:20.564025 20203 solver.cpp:218] Iteration 3000 (2.33469 iter/s, 5.13986s/12 iters), loss = 5.2692
I0412 14:45:20.564078 20203 solver.cpp:237]     Train net output #0: loss = 5.2692 (* 1 = 5.2692 loss)
I0412 14:45:20.564090 20203 sgd_solver.cpp:105] Iteration 3000, lr = 0.00551972
I0412 14:45:25.494819 20203 solver.cpp:218] Iteration 3012 (2.43382 iter/s, 4.93052s/12 iters), loss = 5.27872
I0412 14:45:25.494868 20203 solver.cpp:237]     Train net output #0: loss = 5.27872 (* 1 = 5.27872 loss)
I0412 14:45:25.494879 20203 sgd_solver.cpp:105] Iteration 3012, lr = 0.00550662
I0412 14:45:30.384016 20203 solver.cpp:218] Iteration 3024 (2.45453 iter/s, 4.88893s/12 iters), loss = 5.25896
I0412 14:45:30.384071 20203 solver.cpp:237]     Train net output #0: loss = 5.25896 (* 1 = 5.25896 loss)
I0412 14:45:30.384084 20203 sgd_solver.cpp:105] Iteration 3024, lr = 0.00549354
I0412 14:45:34.777765 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:45:35.805379 20203 solver.cpp:218] Iteration 3036 (2.21359 iter/s, 5.42107s/12 iters), loss = 5.25528
I0412 14:45:35.805421 20203 solver.cpp:237]     Train net output #0: loss = 5.25528 (* 1 = 5.25528 loss)
I0412 14:45:35.805430 20203 sgd_solver.cpp:105] Iteration 3036, lr = 0.0054805
I0412 14:45:40.600070 20203 solver.cpp:218] Iteration 3048 (2.50291 iter/s, 4.79443s/12 iters), loss = 5.29232
I0412 14:45:40.600245 20203 solver.cpp:237]     Train net output #0: loss = 5.29232 (* 1 = 5.29232 loss)
I0412 14:45:40.600260 20203 sgd_solver.cpp:105] Iteration 3048, lr = 0.00546749
I0412 14:45:44.896458 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3060.caffemodel
I0412 14:45:49.625411 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3060.solverstate
I0412 14:45:54.290982 20203 solver.cpp:330] Iteration 3060, Testing net (#0)
I0412 14:45:54.291007 20203 net.cpp:676] Ignoring source layer train-data
I0412 14:45:57.582274 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:45:58.966588 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00612745
I0412 14:45:58.966619 20203 solver.cpp:397]     Test net output #1: loss = 5.28565 (* 1 = 5.28565 loss)
I0412 14:45:59.053122 20203 solver.cpp:218] Iteration 3060 (0.650333 iter/s, 18.4521s/12 iters), loss = 5.26214
I0412 14:45:59.053169 20203 solver.cpp:237]     Train net output #0: loss = 5.26214 (* 1 = 5.26214 loss)
I0412 14:45:59.053179 20203 sgd_solver.cpp:105] Iteration 3060, lr = 0.00545451
I0412 14:46:03.186427 20203 solver.cpp:218] Iteration 3072 (2.90341 iter/s, 4.13306s/12 iters), loss = 5.30167
I0412 14:46:03.186497 20203 solver.cpp:237]     Train net output #0: loss = 5.30167 (* 1 = 5.30167 loss)
I0412 14:46:03.186517 20203 sgd_solver.cpp:105] Iteration 3072, lr = 0.00544156
I0412 14:46:08.309201 20203 solver.cpp:218] Iteration 3084 (2.34262 iter/s, 5.12248s/12 iters), loss = 5.27829
I0412 14:46:08.309253 20203 solver.cpp:237]     Train net output #0: loss = 5.27829 (* 1 = 5.27829 loss)
I0412 14:46:08.309263 20203 sgd_solver.cpp:105] Iteration 3084, lr = 0.00542864
I0412 14:46:13.458642 20203 solver.cpp:218] Iteration 3096 (2.33048 iter/s, 5.14916s/12 iters), loss = 5.27814
I0412 14:46:13.458748 20203 solver.cpp:237]     Train net output #0: loss = 5.27814 (* 1 = 5.27814 loss)
I0412 14:46:13.458761 20203 sgd_solver.cpp:105] Iteration 3096, lr = 0.00541575
I0412 14:46:18.644255 20203 solver.cpp:218] Iteration 3108 (2.31425 iter/s, 5.18528s/12 iters), loss = 5.27548
I0412 14:46:18.644302 20203 solver.cpp:237]     Train net output #0: loss = 5.27548 (* 1 = 5.27548 loss)
I0412 14:46:18.644311 20203 sgd_solver.cpp:105] Iteration 3108, lr = 0.00540289
I0412 14:46:23.381619 20203 solver.cpp:218] Iteration 3120 (2.53319 iter/s, 4.7371s/12 iters), loss = 5.27098
I0412 14:46:23.381666 20203 solver.cpp:237]     Train net output #0: loss = 5.27098 (* 1 = 5.27098 loss)
I0412 14:46:23.381676 20203 sgd_solver.cpp:105] Iteration 3120, lr = 0.00539006
I0412 14:46:28.160441 20203 solver.cpp:218] Iteration 3132 (2.51121 iter/s, 4.77858s/12 iters), loss = 5.28089
I0412 14:46:28.160491 20203 solver.cpp:237]     Train net output #0: loss = 5.28089 (* 1 = 5.28089 loss)
I0412 14:46:28.160502 20203 sgd_solver.cpp:105] Iteration 3132, lr = 0.00537727
I0412 14:46:29.190994 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:46:32.896806 20203 solver.cpp:218] Iteration 3144 (2.53371 iter/s, 4.73613s/12 iters), loss = 5.27633
I0412 14:46:32.896859 20203 solver.cpp:237]     Train net output #0: loss = 5.27633 (* 1 = 5.27633 loss)
I0412 14:46:32.896873 20203 sgd_solver.cpp:105] Iteration 3144, lr = 0.0053645
I0412 14:46:37.676002 20203 solver.cpp:218] Iteration 3156 (2.511 iter/s, 4.77896s/12 iters), loss = 5.25186
I0412 14:46:37.676051 20203 solver.cpp:237]     Train net output #0: loss = 5.25186 (* 1 = 5.25186 loss)
I0412 14:46:37.676065 20203 sgd_solver.cpp:105] Iteration 3156, lr = 0.00535176
I0412 14:46:39.512681 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3162.caffemodel
I0412 14:46:42.629886 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3162.solverstate
I0412 14:46:44.922453 20203 solver.cpp:330] Iteration 3162, Testing net (#0)
I0412 14:46:44.922530 20203 net.cpp:676] Ignoring source layer train-data
I0412 14:46:48.644763 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:46:49.980644 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 14:46:49.980693 20203 solver.cpp:397]     Test net output #1: loss = 5.2858 (* 1 = 5.2858 loss)
I0412 14:46:51.761515 20203 solver.cpp:218] Iteration 3168 (0.851973 iter/s, 14.085s/12 iters), loss = 5.2653
I0412 14:46:51.761574 20203 solver.cpp:237]     Train net output #0: loss = 5.2653 (* 1 = 5.2653 loss)
I0412 14:46:51.761587 20203 sgd_solver.cpp:105] Iteration 3168, lr = 0.00533906
I0412 14:46:56.581533 20203 solver.cpp:218] Iteration 3180 (2.48974 iter/s, 4.81978s/12 iters), loss = 5.27439
I0412 14:46:56.581571 20203 solver.cpp:237]     Train net output #0: loss = 5.27439 (* 1 = 5.27439 loss)
I0412 14:46:56.581579 20203 sgd_solver.cpp:105] Iteration 3180, lr = 0.00532638
I0412 14:47:01.329310 20203 solver.cpp:218] Iteration 3192 (2.52762 iter/s, 4.74756s/12 iters), loss = 5.28217
I0412 14:47:01.329355 20203 solver.cpp:237]     Train net output #0: loss = 5.28217 (* 1 = 5.28217 loss)
I0412 14:47:01.329365 20203 sgd_solver.cpp:105] Iteration 3192, lr = 0.00531374
I0412 14:47:06.015050 20203 solver.cpp:218] Iteration 3204 (2.5611 iter/s, 4.68549s/12 iters), loss = 5.26267
I0412 14:47:06.015096 20203 solver.cpp:237]     Train net output #0: loss = 5.26267 (* 1 = 5.26267 loss)
I0412 14:47:06.015105 20203 sgd_solver.cpp:105] Iteration 3204, lr = 0.00530112
I0412 14:47:10.977635 20203 solver.cpp:218] Iteration 3216 (2.41821 iter/s, 4.96234s/12 iters), loss = 5.28925
I0412 14:47:10.977689 20203 solver.cpp:237]     Train net output #0: loss = 5.28925 (* 1 = 5.28925 loss)
I0412 14:47:10.977699 20203 sgd_solver.cpp:105] Iteration 3216, lr = 0.00528853
I0412 14:47:15.925087 20203 solver.cpp:218] Iteration 3228 (2.42561 iter/s, 4.94721s/12 iters), loss = 5.2761
I0412 14:47:15.925205 20203 solver.cpp:237]     Train net output #0: loss = 5.2761 (* 1 = 5.2761 loss)
I0412 14:47:15.925218 20203 sgd_solver.cpp:105] Iteration 3228, lr = 0.00527598
I0412 14:47:19.064049 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:47:20.727052 20203 solver.cpp:218] Iteration 3240 (2.49913 iter/s, 4.80166s/12 iters), loss = 5.28372
I0412 14:47:20.727097 20203 solver.cpp:237]     Train net output #0: loss = 5.28372 (* 1 = 5.28372 loss)
I0412 14:47:20.727105 20203 sgd_solver.cpp:105] Iteration 3240, lr = 0.00526345
I0412 14:47:25.659549 20203 solver.cpp:218] Iteration 3252 (2.43296 iter/s, 4.93226s/12 iters), loss = 5.27204
I0412 14:47:25.659598 20203 solver.cpp:237]     Train net output #0: loss = 5.27204 (* 1 = 5.27204 loss)
I0412 14:47:25.659610 20203 sgd_solver.cpp:105] Iteration 3252, lr = 0.00525095
I0412 14:47:30.133180 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3264.caffemodel
I0412 14:47:33.212905 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3264.solverstate
I0412 14:47:35.547243 20203 solver.cpp:330] Iteration 3264, Testing net (#0)
I0412 14:47:35.547266 20203 net.cpp:676] Ignoring source layer train-data
I0412 14:47:38.643540 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:47:39.945683 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 14:47:39.945731 20203 solver.cpp:397]     Test net output #1: loss = 5.28614 (* 1 = 5.28614 loss)
I0412 14:47:40.030686 20203 solver.cpp:218] Iteration 3264 (0.83504 iter/s, 14.3706s/12 iters), loss = 5.2797
I0412 14:47:40.030735 20203 solver.cpp:237]     Train net output #0: loss = 5.2797 (* 1 = 5.2797 loss)
I0412 14:47:40.030746 20203 sgd_solver.cpp:105] Iteration 3264, lr = 0.00523849
I0412 14:47:44.119509 20203 solver.cpp:218] Iteration 3276 (2.93498 iter/s, 4.08861s/12 iters), loss = 5.28559
I0412 14:47:44.119562 20203 solver.cpp:237]     Train net output #0: loss = 5.28559 (* 1 = 5.28559 loss)
I0412 14:47:44.119571 20203 sgd_solver.cpp:105] Iteration 3276, lr = 0.00522605
I0412 14:47:48.904942 20203 solver.cpp:218] Iteration 3288 (2.50773 iter/s, 4.7852s/12 iters), loss = 5.2633
I0412 14:47:48.905110 20203 solver.cpp:237]     Train net output #0: loss = 5.2633 (* 1 = 5.2633 loss)
I0412 14:47:48.905124 20203 sgd_solver.cpp:105] Iteration 3288, lr = 0.00521364
I0412 14:47:54.097626 20203 solver.cpp:218] Iteration 3300 (2.3111 iter/s, 5.19233s/12 iters), loss = 5.28588
I0412 14:47:54.097667 20203 solver.cpp:237]     Train net output #0: loss = 5.28588 (* 1 = 5.28588 loss)
I0412 14:47:54.097676 20203 sgd_solver.cpp:105] Iteration 3300, lr = 0.00520126
I0412 14:47:59.129233 20203 solver.cpp:218] Iteration 3312 (2.38504 iter/s, 5.03137s/12 iters), loss = 5.25902
I0412 14:47:59.129284 20203 solver.cpp:237]     Train net output #0: loss = 5.25902 (* 1 = 5.25902 loss)
I0412 14:47:59.129297 20203 sgd_solver.cpp:105] Iteration 3312, lr = 0.00518892
I0412 14:48:04.114305 20203 solver.cpp:218] Iteration 3324 (2.4073 iter/s, 4.98483s/12 iters), loss = 5.28377
I0412 14:48:04.114359 20203 solver.cpp:237]     Train net output #0: loss = 5.28377 (* 1 = 5.28377 loss)
I0412 14:48:04.114373 20203 sgd_solver.cpp:105] Iteration 3324, lr = 0.0051766
I0412 14:48:08.986852 20203 solver.cpp:218] Iteration 3336 (2.4629 iter/s, 4.8723s/12 iters), loss = 5.27794
I0412 14:48:08.986902 20203 solver.cpp:237]     Train net output #0: loss = 5.27794 (* 1 = 5.27794 loss)
I0412 14:48:08.986913 20203 sgd_solver.cpp:105] Iteration 3336, lr = 0.00516431
I0412 14:48:09.518952 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:48:13.943917 20203 solver.cpp:218] Iteration 3348 (2.42091 iter/s, 4.95682s/12 iters), loss = 5.27595
I0412 14:48:13.943985 20203 solver.cpp:237]     Train net output #0: loss = 5.27595 (* 1 = 5.27595 loss)
I0412 14:48:13.944003 20203 sgd_solver.cpp:105] Iteration 3348, lr = 0.00515204
I0412 14:48:18.500420 20203 solver.cpp:218] Iteration 3360 (2.63374 iter/s, 4.55626s/12 iters), loss = 5.26283
I0412 14:48:18.500474 20203 solver.cpp:237]     Train net output #0: loss = 5.26283 (* 1 = 5.26283 loss)
I0412 14:48:18.500485 20203 sgd_solver.cpp:105] Iteration 3360, lr = 0.00513981
I0412 14:48:20.370951 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3366.caffemodel
I0412 14:48:27.654428 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3366.solverstate
I0412 14:48:31.370849 20203 solver.cpp:330] Iteration 3366, Testing net (#0)
I0412 14:48:31.370874 20203 net.cpp:676] Ignoring source layer train-data
I0412 14:48:34.453132 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:48:35.787911 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 14:48:35.787961 20203 solver.cpp:397]     Test net output #1: loss = 5.28584 (* 1 = 5.28584 loss)
I0412 14:48:37.566478 20203 solver.cpp:218] Iteration 3372 (0.629416 iter/s, 19.0653s/12 iters), loss = 5.28889
I0412 14:48:37.566522 20203 solver.cpp:237]     Train net output #0: loss = 5.28889 (* 1 = 5.28889 loss)
I0412 14:48:37.566531 20203 sgd_solver.cpp:105] Iteration 3372, lr = 0.00512761
I0412 14:48:42.596843 20203 solver.cpp:218] Iteration 3384 (2.38563 iter/s, 5.03012s/12 iters), loss = 5.26981
I0412 14:48:42.596885 20203 solver.cpp:237]     Train net output #0: loss = 5.26981 (* 1 = 5.26981 loss)
I0412 14:48:42.596895 20203 sgd_solver.cpp:105] Iteration 3384, lr = 0.00511544
I0412 14:48:47.502203 20203 solver.cpp:218] Iteration 3396 (2.44642 iter/s, 4.90512s/12 iters), loss = 5.26163
I0412 14:48:47.502257 20203 solver.cpp:237]     Train net output #0: loss = 5.26163 (* 1 = 5.26163 loss)
I0412 14:48:47.502270 20203 sgd_solver.cpp:105] Iteration 3396, lr = 0.00510329
I0412 14:48:52.297374 20203 solver.cpp:218] Iteration 3408 (2.50264 iter/s, 4.79493s/12 iters), loss = 5.28948
I0412 14:48:52.297544 20203 solver.cpp:237]     Train net output #0: loss = 5.28948 (* 1 = 5.28948 loss)
I0412 14:48:52.297557 20203 sgd_solver.cpp:105] Iteration 3408, lr = 0.00509117
I0412 14:48:57.303714 20203 solver.cpp:218] Iteration 3420 (2.39713 iter/s, 5.00598s/12 iters), loss = 5.27865
I0412 14:48:57.303764 20203 solver.cpp:237]     Train net output #0: loss = 5.27865 (* 1 = 5.27865 loss)
I0412 14:48:57.303776 20203 sgd_solver.cpp:105] Iteration 3420, lr = 0.00507909
I0412 14:49:02.317111 20203 solver.cpp:218] Iteration 3432 (2.3937 iter/s, 5.01315s/12 iters), loss = 5.26551
I0412 14:49:02.317157 20203 solver.cpp:237]     Train net output #0: loss = 5.26551 (* 1 = 5.26551 loss)
I0412 14:49:02.317167 20203 sgd_solver.cpp:105] Iteration 3432, lr = 0.00506703
I0412 14:49:04.806252 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:49:07.150910 20203 solver.cpp:218] Iteration 3444 (2.48264 iter/s, 4.83357s/12 iters), loss = 5.27926
I0412 14:49:07.150950 20203 solver.cpp:237]     Train net output #0: loss = 5.27926 (* 1 = 5.27926 loss)
I0412 14:49:07.150959 20203 sgd_solver.cpp:105] Iteration 3444, lr = 0.005055
I0412 14:49:12.050088 20203 solver.cpp:218] Iteration 3456 (2.44951 iter/s, 4.89894s/12 iters), loss = 5.2713
I0412 14:49:12.050141 20203 solver.cpp:237]     Train net output #0: loss = 5.2713 (* 1 = 5.2713 loss)
I0412 14:49:12.050156 20203 sgd_solver.cpp:105] Iteration 3456, lr = 0.005043
I0412 14:49:16.468194 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3468.caffemodel
I0412 14:49:20.183310 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3468.solverstate
I0412 14:49:23.766247 20203 solver.cpp:330] Iteration 3468, Testing net (#0)
I0412 14:49:23.766319 20203 net.cpp:676] Ignoring source layer train-data
I0412 14:49:24.222954 20203 blocking_queue.cpp:49] Waiting for data
I0412 14:49:26.844592 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:49:28.290660 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 14:49:28.290709 20203 solver.cpp:397]     Test net output #1: loss = 5.28602 (* 1 = 5.28602 loss)
I0412 14:49:28.377236 20203 solver.cpp:218] Iteration 3468 (0.735002 iter/s, 16.3265s/12 iters), loss = 5.27398
I0412 14:49:28.377285 20203 solver.cpp:237]     Train net output #0: loss = 5.27398 (* 1 = 5.27398 loss)
I0412 14:49:28.377296 20203 sgd_solver.cpp:105] Iteration 3468, lr = 0.00503102
I0412 14:49:32.350091 20203 solver.cpp:218] Iteration 3480 (3.02066 iter/s, 3.97264s/12 iters), loss = 5.27698
I0412 14:49:32.350142 20203 solver.cpp:237]     Train net output #0: loss = 5.27698 (* 1 = 5.27698 loss)
I0412 14:49:32.350154 20203 sgd_solver.cpp:105] Iteration 3480, lr = 0.00501908
I0412 14:49:37.083122 20203 solver.cpp:218] Iteration 3492 (2.5355 iter/s, 4.73279s/12 iters), loss = 5.2851
I0412 14:49:37.083169 20203 solver.cpp:237]     Train net output #0: loss = 5.2851 (* 1 = 5.2851 loss)
I0412 14:49:37.083180 20203 sgd_solver.cpp:105] Iteration 3492, lr = 0.00500716
I0412 14:49:41.867884 20203 solver.cpp:218] Iteration 3504 (2.50809 iter/s, 4.78452s/12 iters), loss = 5.27257
I0412 14:49:41.867945 20203 solver.cpp:237]     Train net output #0: loss = 5.27257 (* 1 = 5.27257 loss)
I0412 14:49:41.867961 20203 sgd_solver.cpp:105] Iteration 3504, lr = 0.00499527
I0412 14:49:46.674131 20203 solver.cpp:218] Iteration 3516 (2.49688 iter/s, 4.80599s/12 iters), loss = 5.26227
I0412 14:49:46.674185 20203 solver.cpp:237]     Train net output #0: loss = 5.26227 (* 1 = 5.26227 loss)
I0412 14:49:46.674199 20203 sgd_solver.cpp:105] Iteration 3516, lr = 0.00498341
I0412 14:49:51.365023 20203 solver.cpp:218] Iteration 3528 (2.55828 iter/s, 4.69065s/12 iters), loss = 5.27153
I0412 14:49:51.365073 20203 solver.cpp:237]     Train net output #0: loss = 5.27153 (* 1 = 5.27153 loss)
I0412 14:49:51.365084 20203 sgd_solver.cpp:105] Iteration 3528, lr = 0.00497158
I0412 14:49:55.929831 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:49:56.186518 20203 solver.cpp:218] Iteration 3540 (2.48898 iter/s, 4.82125s/12 iters), loss = 5.26048
I0412 14:49:56.186565 20203 solver.cpp:237]     Train net output #0: loss = 5.26048 (* 1 = 5.26048 loss)
I0412 14:49:56.186576 20203 sgd_solver.cpp:105] Iteration 3540, lr = 0.00495978
I0412 14:50:00.937678 20203 solver.cpp:218] Iteration 3552 (2.52582 iter/s, 4.75093s/12 iters), loss = 5.26698
I0412 14:50:00.937719 20203 solver.cpp:237]     Train net output #0: loss = 5.26698 (* 1 = 5.26698 loss)
I0412 14:50:00.937728 20203 sgd_solver.cpp:105] Iteration 3552, lr = 0.004948
I0412 14:50:05.851613 20203 solver.cpp:218] Iteration 3564 (2.44215 iter/s, 4.9137s/12 iters), loss = 5.29531
I0412 14:50:05.851657 20203 solver.cpp:237]     Train net output #0: loss = 5.29531 (* 1 = 5.29531 loss)
I0412 14:50:05.851666 20203 sgd_solver.cpp:105] Iteration 3564, lr = 0.00493626
I0412 14:50:07.787504 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3570.caffemodel
I0412 14:50:12.998208 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3570.solverstate
I0412 14:50:15.339449 20203 solver.cpp:330] Iteration 3570, Testing net (#0)
I0412 14:50:15.339471 20203 net.cpp:676] Ignoring source layer train-data
I0412 14:50:18.430682 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:50:19.845716 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 14:50:19.845762 20203 solver.cpp:397]     Test net output #1: loss = 5.2861 (* 1 = 5.2861 loss)
I0412 14:50:21.589823 20203 solver.cpp:218] Iteration 3576 (0.762507 iter/s, 15.7376s/12 iters), loss = 5.28651
I0412 14:50:21.589879 20203 solver.cpp:237]     Train net output #0: loss = 5.28651 (* 1 = 5.28651 loss)
I0412 14:50:21.589893 20203 sgd_solver.cpp:105] Iteration 3576, lr = 0.00492454
I0412 14:50:26.199734 20203 solver.cpp:218] Iteration 3588 (2.60322 iter/s, 4.60967s/12 iters), loss = 5.27851
I0412 14:50:26.199863 20203 solver.cpp:237]     Train net output #0: loss = 5.27851 (* 1 = 5.27851 loss)
I0412 14:50:26.199875 20203 sgd_solver.cpp:105] Iteration 3588, lr = 0.00491284
I0412 14:50:31.022326 20203 solver.cpp:218] Iteration 3600 (2.48846 iter/s, 4.82227s/12 iters), loss = 5.26795
I0412 14:50:31.022387 20203 solver.cpp:237]     Train net output #0: loss = 5.26795 (* 1 = 5.26795 loss)
I0412 14:50:31.022397 20203 sgd_solver.cpp:105] Iteration 3600, lr = 0.00490118
I0412 14:50:36.265947 20203 solver.cpp:218] Iteration 3612 (2.28861 iter/s, 5.24335s/12 iters), loss = 5.24729
I0412 14:50:36.266006 20203 solver.cpp:237]     Train net output #0: loss = 5.24729 (* 1 = 5.24729 loss)
I0412 14:50:36.266016 20203 sgd_solver.cpp:105] Iteration 3612, lr = 0.00488954
I0412 14:50:41.433216 20203 solver.cpp:218] Iteration 3624 (2.32243 iter/s, 5.167s/12 iters), loss = 5.27239
I0412 14:50:41.433260 20203 solver.cpp:237]     Train net output #0: loss = 5.27239 (* 1 = 5.27239 loss)
I0412 14:50:41.433269 20203 sgd_solver.cpp:105] Iteration 3624, lr = 0.00487793
I0412 14:50:46.347738 20203 solver.cpp:218] Iteration 3636 (2.44186 iter/s, 4.91428s/12 iters), loss = 5.28231
I0412 14:50:46.347793 20203 solver.cpp:237]     Train net output #0: loss = 5.28231 (* 1 = 5.28231 loss)
I0412 14:50:46.347806 20203 sgd_solver.cpp:105] Iteration 3636, lr = 0.00486635
I0412 14:50:48.216459 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:50:51.352028 20203 solver.cpp:218] Iteration 3648 (2.39807 iter/s, 5.00403s/12 iters), loss = 5.28548
I0412 14:50:51.352083 20203 solver.cpp:237]     Train net output #0: loss = 5.28548 (* 1 = 5.28548 loss)
I0412 14:50:51.352097 20203 sgd_solver.cpp:105] Iteration 3648, lr = 0.0048548
I0412 14:50:56.176306 20203 solver.cpp:218] Iteration 3660 (2.48754 iter/s, 4.82404s/12 iters), loss = 5.27446
I0412 14:50:56.176354 20203 solver.cpp:237]     Train net output #0: loss = 5.27446 (* 1 = 5.27446 loss)
I0412 14:50:56.176364 20203 sgd_solver.cpp:105] Iteration 3660, lr = 0.00484327
I0412 14:51:00.652572 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3672.caffemodel
I0412 14:51:04.887688 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3672.solverstate
I0412 14:51:09.717370 20203 solver.cpp:330] Iteration 3672, Testing net (#0)
I0412 14:51:09.717398 20203 net.cpp:676] Ignoring source layer train-data
I0412 14:51:12.776877 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:51:14.238708 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 14:51:14.238756 20203 solver.cpp:397]     Test net output #1: loss = 5.28578 (* 1 = 5.28578 loss)
I0412 14:51:14.325443 20203 solver.cpp:218] Iteration 3672 (0.661216 iter/s, 18.1484s/12 iters), loss = 5.25133
I0412 14:51:14.325491 20203 solver.cpp:237]     Train net output #0: loss = 5.25133 (* 1 = 5.25133 loss)
I0412 14:51:14.325502 20203 sgd_solver.cpp:105] Iteration 3672, lr = 0.00483177
I0412 14:51:18.526245 20203 solver.cpp:218] Iteration 3684 (2.85675 iter/s, 4.20058s/12 iters), loss = 5.27481
I0412 14:51:18.526302 20203 solver.cpp:237]     Train net output #0: loss = 5.27481 (* 1 = 5.27481 loss)
I0412 14:51:18.526316 20203 sgd_solver.cpp:105] Iteration 3684, lr = 0.0048203
I0412 14:51:23.315081 20203 solver.cpp:218] Iteration 3696 (2.50596 iter/s, 4.78858s/12 iters), loss = 5.26046
I0412 14:51:23.315150 20203 solver.cpp:237]     Train net output #0: loss = 5.26046 (* 1 = 5.26046 loss)
I0412 14:51:23.315167 20203 sgd_solver.cpp:105] Iteration 3696, lr = 0.00480886
I0412 14:51:28.175374 20203 solver.cpp:218] Iteration 3708 (2.46912 iter/s, 4.86003s/12 iters), loss = 5.2669
I0412 14:51:28.175426 20203 solver.cpp:237]     Train net output #0: loss = 5.2669 (* 1 = 5.2669 loss)
I0412 14:51:28.175436 20203 sgd_solver.cpp:105] Iteration 3708, lr = 0.00479744
I0412 14:51:33.106484 20203 solver.cpp:218] Iteration 3720 (2.43365 iter/s, 4.93086s/12 iters), loss = 5.26677
I0412 14:51:33.106629 20203 solver.cpp:237]     Train net output #0: loss = 5.26677 (* 1 = 5.26677 loss)
I0412 14:51:33.106642 20203 sgd_solver.cpp:105] Iteration 3720, lr = 0.00478605
I0412 14:51:38.219970 20203 solver.cpp:218] Iteration 3732 (2.34689 iter/s, 5.11314s/12 iters), loss = 5.25716
I0412 14:51:38.220019 20203 solver.cpp:237]     Train net output #0: loss = 5.25716 (* 1 = 5.25716 loss)
I0412 14:51:38.220031 20203 sgd_solver.cpp:105] Iteration 3732, lr = 0.00477469
I0412 14:51:42.109666 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:51:42.996583 20203 solver.cpp:218] Iteration 3744 (2.51237 iter/s, 4.77637s/12 iters), loss = 5.25704
I0412 14:51:42.996639 20203 solver.cpp:237]     Train net output #0: loss = 5.25704 (* 1 = 5.25704 loss)
I0412 14:51:42.996651 20203 sgd_solver.cpp:105] Iteration 3744, lr = 0.00476335
I0412 14:51:48.072458 20203 solver.cpp:218] Iteration 3756 (2.36425 iter/s, 5.07561s/12 iters), loss = 5.27789
I0412 14:51:48.072507 20203 solver.cpp:237]     Train net output #0: loss = 5.27789 (* 1 = 5.27789 loss)
I0412 14:51:48.072520 20203 sgd_solver.cpp:105] Iteration 3756, lr = 0.00475204
I0412 14:51:52.896070 20203 solver.cpp:218] Iteration 3768 (2.48789 iter/s, 4.82336s/12 iters), loss = 5.26562
I0412 14:51:52.896122 20203 solver.cpp:237]     Train net output #0: loss = 5.26562 (* 1 = 5.26562 loss)
I0412 14:51:52.896134 20203 sgd_solver.cpp:105] Iteration 3768, lr = 0.00474076
I0412 14:51:54.857054 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3774.caffemodel
I0412 14:51:57.815507 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3774.solverstate
I0412 14:52:01.596892 20203 solver.cpp:330] Iteration 3774, Testing net (#0)
I0412 14:52:01.596917 20203 net.cpp:676] Ignoring source layer train-data
I0412 14:52:04.718255 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:52:06.262342 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 14:52:06.262383 20203 solver.cpp:397]     Test net output #1: loss = 5.28645 (* 1 = 5.28645 loss)
I0412 14:52:08.108395 20203 solver.cpp:218] Iteration 3780 (0.788867 iter/s, 15.2117s/12 iters), loss = 5.30854
I0412 14:52:08.108440 20203 solver.cpp:237]     Train net output #0: loss = 5.30854 (* 1 = 5.30854 loss)
I0412 14:52:08.108449 20203 sgd_solver.cpp:105] Iteration 3780, lr = 0.00472951
I0412 14:52:12.971513 20203 solver.cpp:218] Iteration 3792 (2.46767 iter/s, 4.86288s/12 iters), loss = 5.27677
I0412 14:52:12.971556 20203 solver.cpp:237]     Train net output #0: loss = 5.27677 (* 1 = 5.27677 loss)
I0412 14:52:12.971565 20203 sgd_solver.cpp:105] Iteration 3792, lr = 0.00471828
I0412 14:52:17.817625 20203 solver.cpp:218] Iteration 3804 (2.47634 iter/s, 4.84587s/12 iters), loss = 5.2699
I0412 14:52:17.817672 20203 solver.cpp:237]     Train net output #0: loss = 5.2699 (* 1 = 5.2699 loss)
I0412 14:52:17.817682 20203 sgd_solver.cpp:105] Iteration 3804, lr = 0.00470707
I0412 14:52:22.670135 20203 solver.cpp:218] Iteration 3816 (2.47307 iter/s, 4.85227s/12 iters), loss = 5.27531
I0412 14:52:22.670173 20203 solver.cpp:237]     Train net output #0: loss = 5.27531 (* 1 = 5.27531 loss)
I0412 14:52:22.670181 20203 sgd_solver.cpp:105] Iteration 3816, lr = 0.0046959
I0412 14:52:27.708207 20203 solver.cpp:218] Iteration 3828 (2.38198 iter/s, 5.03782s/12 iters), loss = 5.2623
I0412 14:52:27.708266 20203 solver.cpp:237]     Train net output #0: loss = 5.2623 (* 1 = 5.2623 loss)
I0412 14:52:27.708279 20203 sgd_solver.cpp:105] Iteration 3828, lr = 0.00468475
I0412 14:52:32.528736 20203 solver.cpp:218] Iteration 3840 (2.48948 iter/s, 4.82028s/12 iters), loss = 5.27217
I0412 14:52:32.528786 20203 solver.cpp:237]     Train net output #0: loss = 5.27217 (* 1 = 5.27217 loss)
I0412 14:52:32.528797 20203 sgd_solver.cpp:105] Iteration 3840, lr = 0.00467363
I0412 14:52:33.518456 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:52:37.237802 20203 solver.cpp:218] Iteration 3852 (2.54841 iter/s, 4.70882s/12 iters), loss = 5.27034
I0412 14:52:37.237903 20203 solver.cpp:237]     Train net output #0: loss = 5.27034 (* 1 = 5.27034 loss)
I0412 14:52:37.237915 20203 sgd_solver.cpp:105] Iteration 3852, lr = 0.00466253
I0412 14:52:42.018863 20203 solver.cpp:218] Iteration 3864 (2.51006 iter/s, 4.78076s/12 iters), loss = 5.25783
I0412 14:52:42.018916 20203 solver.cpp:237]     Train net output #0: loss = 5.25783 (* 1 = 5.25783 loss)
I0412 14:52:42.018929 20203 sgd_solver.cpp:105] Iteration 3864, lr = 0.00465146
I0412 14:52:46.349892 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3876.caffemodel
I0412 14:52:49.328094 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3876.solverstate
I0412 14:52:53.485181 20203 solver.cpp:330] Iteration 3876, Testing net (#0)
I0412 14:52:53.485205 20203 net.cpp:676] Ignoring source layer train-data
I0412 14:52:56.784876 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:52:58.331938 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 14:52:58.331991 20203 solver.cpp:397]     Test net output #1: loss = 5.28589 (* 1 = 5.28589 loss)
I0412 14:52:58.418503 20203 solver.cpp:218] Iteration 3876 (0.731754 iter/s, 16.3989s/12 iters), loss = 5.27296
I0412 14:52:58.418551 20203 solver.cpp:237]     Train net output #0: loss = 5.27296 (* 1 = 5.27296 loss)
I0412 14:52:58.418562 20203 sgd_solver.cpp:105] Iteration 3876, lr = 0.00464042
I0412 14:53:02.387152 20203 solver.cpp:218] Iteration 3888 (3.02386 iter/s, 3.96844s/12 iters), loss = 5.26848
I0412 14:53:02.387203 20203 solver.cpp:237]     Train net output #0: loss = 5.26848 (* 1 = 5.26848 loss)
I0412 14:53:02.387217 20203 sgd_solver.cpp:105] Iteration 3888, lr = 0.0046294
I0412 14:53:07.043529 20203 solver.cpp:218] Iteration 3900 (2.57724 iter/s, 4.65614s/12 iters), loss = 5.27223
I0412 14:53:07.043570 20203 solver.cpp:237]     Train net output #0: loss = 5.27223 (* 1 = 5.27223 loss)
I0412 14:53:07.043578 20203 sgd_solver.cpp:105] Iteration 3900, lr = 0.00461841
I0412 14:53:11.973891 20203 solver.cpp:218] Iteration 3912 (2.43402 iter/s, 4.93011s/12 iters), loss = 5.26066
I0412 14:53:11.974048 20203 solver.cpp:237]     Train net output #0: loss = 5.26066 (* 1 = 5.26066 loss)
I0412 14:53:11.974061 20203 sgd_solver.cpp:105] Iteration 3912, lr = 0.00460744
I0412 14:53:17.124758 20203 solver.cpp:218] Iteration 3924 (2.32987 iter/s, 5.1505s/12 iters), loss = 5.29326
I0412 14:53:17.124815 20203 solver.cpp:237]     Train net output #0: loss = 5.29326 (* 1 = 5.29326 loss)
I0412 14:53:17.124830 20203 sgd_solver.cpp:105] Iteration 3924, lr = 0.0045965
I0412 14:53:21.915151 20203 solver.cpp:218] Iteration 3936 (2.50514 iter/s, 4.79015s/12 iters), loss = 5.27655
I0412 14:53:21.915202 20203 solver.cpp:237]     Train net output #0: loss = 5.27655 (* 1 = 5.27655 loss)
I0412 14:53:21.915215 20203 sgd_solver.cpp:105] Iteration 3936, lr = 0.00458559
I0412 14:53:25.106379 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:53:26.744109 20203 solver.cpp:218] Iteration 3948 (2.48513 iter/s, 4.82872s/12 iters), loss = 5.28535
I0412 14:53:26.744151 20203 solver.cpp:237]     Train net output #0: loss = 5.28535 (* 1 = 5.28535 loss)
I0412 14:53:26.744160 20203 sgd_solver.cpp:105] Iteration 3948, lr = 0.0045747
I0412 14:53:31.424352 20203 solver.cpp:218] Iteration 3960 (2.5641 iter/s, 4.68001s/12 iters), loss = 5.27473
I0412 14:53:31.424397 20203 solver.cpp:237]     Train net output #0: loss = 5.27473 (* 1 = 5.27473 loss)
I0412 14:53:31.424407 20203 sgd_solver.cpp:105] Iteration 3960, lr = 0.00456384
I0412 14:53:36.245138 20203 solver.cpp:218] Iteration 3972 (2.48935 iter/s, 4.82054s/12 iters), loss = 5.27741
I0412 14:53:36.245193 20203 solver.cpp:237]     Train net output #0: loss = 5.27741 (* 1 = 5.27741 loss)
I0412 14:53:36.245204 20203 sgd_solver.cpp:105] Iteration 3972, lr = 0.00455301
I0412 14:53:38.531733 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3978.caffemodel
I0412 14:53:41.512162 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3978.solverstate
I0412 14:53:47.046149 20203 solver.cpp:330] Iteration 3978, Testing net (#0)
I0412 14:53:47.046227 20203 net.cpp:676] Ignoring source layer train-data
I0412 14:53:49.940659 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:53:51.551798 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 14:53:51.551849 20203 solver.cpp:397]     Test net output #1: loss = 5.28564 (* 1 = 5.28564 loss)
I0412 14:53:53.499819 20203 solver.cpp:218] Iteration 3984 (0.695493 iter/s, 17.2539s/12 iters), loss = 5.27831
I0412 14:53:53.499877 20203 solver.cpp:237]     Train net output #0: loss = 5.27831 (* 1 = 5.27831 loss)
I0412 14:53:53.499938 20203 sgd_solver.cpp:105] Iteration 3984, lr = 0.0045422
I0412 14:53:58.714344 20203 solver.cpp:218] Iteration 3996 (2.30138 iter/s, 5.21426s/12 iters), loss = 5.26273
I0412 14:53:58.714393 20203 solver.cpp:237]     Train net output #0: loss = 5.26273 (* 1 = 5.26273 loss)
I0412 14:53:58.714404 20203 sgd_solver.cpp:105] Iteration 3996, lr = 0.00453141
I0412 14:54:03.494187 20203 solver.cpp:218] Iteration 4008 (2.51067 iter/s, 4.7796s/12 iters), loss = 5.28744
I0412 14:54:03.494235 20203 solver.cpp:237]     Train net output #0: loss = 5.28744 (* 1 = 5.28744 loss)
I0412 14:54:03.494246 20203 sgd_solver.cpp:105] Iteration 4008, lr = 0.00452066
I0412 14:54:08.568572 20203 solver.cpp:218] Iteration 4020 (2.36494 iter/s, 5.07412s/12 iters), loss = 5.2585
I0412 14:54:08.568634 20203 solver.cpp:237]     Train net output #0: loss = 5.2585 (* 1 = 5.2585 loss)
I0412 14:54:08.568645 20203 sgd_solver.cpp:105] Iteration 4020, lr = 0.00450992
I0412 14:54:13.484203 20203 solver.cpp:218] Iteration 4032 (2.44132 iter/s, 4.91537s/12 iters), loss = 5.27531
I0412 14:54:13.484258 20203 solver.cpp:237]     Train net output #0: loss = 5.27531 (* 1 = 5.27531 loss)
I0412 14:54:13.484272 20203 sgd_solver.cpp:105] Iteration 4032, lr = 0.00449921
I0412 14:54:18.370357 20203 solver.cpp:218] Iteration 4044 (2.45605 iter/s, 4.88589s/12 iters), loss = 5.27071
I0412 14:54:18.370529 20203 solver.cpp:237]     Train net output #0: loss = 5.27071 (* 1 = 5.27071 loss)
I0412 14:54:18.370543 20203 sgd_solver.cpp:105] Iteration 4044, lr = 0.00448853
I0412 14:54:18.858544 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:54:23.275230 20203 solver.cpp:218] Iteration 4056 (2.44673 iter/s, 4.9045s/12 iters), loss = 5.27854
I0412 14:54:23.275281 20203 solver.cpp:237]     Train net output #0: loss = 5.27854 (* 1 = 5.27854 loss)
I0412 14:54:23.275292 20203 sgd_solver.cpp:105] Iteration 4056, lr = 0.00447788
I0412 14:54:28.126752 20203 solver.cpp:218] Iteration 4068 (2.47358 iter/s, 4.85127s/12 iters), loss = 5.27489
I0412 14:54:28.126811 20203 solver.cpp:237]     Train net output #0: loss = 5.27489 (* 1 = 5.27489 loss)
I0412 14:54:28.126822 20203 sgd_solver.cpp:105] Iteration 4068, lr = 0.00446724
I0412 14:54:32.575544 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4080.caffemodel
I0412 14:54:39.450810 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4080.solverstate
I0412 14:54:41.756865 20203 solver.cpp:330] Iteration 4080, Testing net (#0)
I0412 14:54:41.756889 20203 net.cpp:676] Ignoring source layer train-data
I0412 14:54:44.652065 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:54:46.302225 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 14:54:46.302275 20203 solver.cpp:397]     Test net output #1: loss = 5.28612 (* 1 = 5.28612 loss)
I0412 14:54:46.388861 20203 solver.cpp:218] Iteration 4080 (0.657126 iter/s, 18.2613s/12 iters), loss = 5.28855
I0412 14:54:46.388914 20203 solver.cpp:237]     Train net output #0: loss = 5.28855 (* 1 = 5.28855 loss)
I0412 14:54:46.388926 20203 sgd_solver.cpp:105] Iteration 4080, lr = 0.00445664
I0412 14:54:50.541756 20203 solver.cpp:218] Iteration 4092 (2.88971 iter/s, 4.15266s/12 iters), loss = 5.2675
I0412 14:54:50.541890 20203 solver.cpp:237]     Train net output #0: loss = 5.2675 (* 1 = 5.2675 loss)
I0412 14:54:50.541908 20203 sgd_solver.cpp:105] Iteration 4092, lr = 0.00444606
I0412 14:54:55.237552 20203 solver.cpp:218] Iteration 4104 (2.55565 iter/s, 4.69547s/12 iters), loss = 5.26509
I0412 14:54:55.237597 20203 solver.cpp:237]     Train net output #0: loss = 5.26509 (* 1 = 5.26509 loss)
I0412 14:54:55.237608 20203 sgd_solver.cpp:105] Iteration 4104, lr = 0.0044355
I0412 14:55:00.017884 20203 solver.cpp:218] Iteration 4116 (2.51041 iter/s, 4.78009s/12 iters), loss = 5.29439
I0412 14:55:00.017942 20203 solver.cpp:237]     Train net output #0: loss = 5.29439 (* 1 = 5.29439 loss)
I0412 14:55:00.017972 20203 sgd_solver.cpp:105] Iteration 4116, lr = 0.00442497
I0412 14:55:04.723619 20203 solver.cpp:218] Iteration 4128 (2.55022 iter/s, 4.70548s/12 iters), loss = 5.26788
I0412 14:55:04.723667 20203 solver.cpp:237]     Train net output #0: loss = 5.26788 (* 1 = 5.26788 loss)
I0412 14:55:04.723677 20203 sgd_solver.cpp:105] Iteration 4128, lr = 0.00441447
I0412 14:55:09.548082 20203 solver.cpp:218] Iteration 4140 (2.48745 iter/s, 4.82422s/12 iters), loss = 5.25592
I0412 14:55:09.548125 20203 solver.cpp:237]     Train net output #0: loss = 5.25592 (* 1 = 5.25592 loss)
I0412 14:55:09.548135 20203 sgd_solver.cpp:105] Iteration 4140, lr = 0.00440398
I0412 14:55:12.014966 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:55:14.251574 20203 solver.cpp:218] Iteration 4152 (2.55143 iter/s, 4.70325s/12 iters), loss = 5.27164
I0412 14:55:14.251619 20203 solver.cpp:237]     Train net output #0: loss = 5.27164 (* 1 = 5.27164 loss)
I0412 14:55:14.251629 20203 sgd_solver.cpp:105] Iteration 4152, lr = 0.00439353
I0412 14:55:15.884907 20203 blocking_queue.cpp:49] Waiting for data
I0412 14:55:19.093286 20203 solver.cpp:218] Iteration 4164 (2.47859 iter/s, 4.84146s/12 iters), loss = 5.26667
I0412 14:55:19.093333 20203 solver.cpp:237]     Train net output #0: loss = 5.26667 (* 1 = 5.26667 loss)
I0412 14:55:19.093341 20203 sgd_solver.cpp:105] Iteration 4164, lr = 0.0043831
I0412 14:55:23.765028 20203 solver.cpp:218] Iteration 4176 (2.56877 iter/s, 4.6715s/12 iters), loss = 5.26513
I0412 14:55:23.765146 20203 solver.cpp:237]     Train net output #0: loss = 5.26513 (* 1 = 5.26513 loss)
I0412 14:55:23.765156 20203 sgd_solver.cpp:105] Iteration 4176, lr = 0.00437269
I0412 14:55:25.693063 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4182.caffemodel
I0412 14:55:33.821027 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4182.solverstate
I0412 14:55:38.367170 20203 solver.cpp:330] Iteration 4182, Testing net (#0)
I0412 14:55:38.367198 20203 net.cpp:676] Ignoring source layer train-data
I0412 14:55:41.374095 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:55:43.079970 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00612745
I0412 14:55:43.080001 20203 solver.cpp:397]     Test net output #1: loss = 5.28616 (* 1 = 5.28616 loss)
I0412 14:55:44.969589 20203 solver.cpp:218] Iteration 4188 (0.565942 iter/s, 21.2036s/12 iters), loss = 5.2698
I0412 14:55:44.969643 20203 solver.cpp:237]     Train net output #0: loss = 5.2698 (* 1 = 5.2698 loss)
I0412 14:55:44.969653 20203 sgd_solver.cpp:105] Iteration 4188, lr = 0.00436231
I0412 14:55:49.830840 20203 solver.cpp:218] Iteration 4200 (2.46863 iter/s, 4.86099s/12 iters), loss = 5.28446
I0412 14:55:49.830883 20203 solver.cpp:237]     Train net output #0: loss = 5.28446 (* 1 = 5.28446 loss)
I0412 14:55:49.830891 20203 sgd_solver.cpp:105] Iteration 4200, lr = 0.00435195
I0412 14:55:54.660923 20203 solver.cpp:218] Iteration 4212 (2.48455 iter/s, 4.82984s/12 iters), loss = 5.26906
I0412 14:55:54.665406 20203 solver.cpp:237]     Train net output #0: loss = 5.26906 (* 1 = 5.26906 loss)
I0412 14:55:54.665421 20203 sgd_solver.cpp:105] Iteration 4212, lr = 0.00434162
I0412 14:55:59.415195 20203 solver.cpp:218] Iteration 4224 (2.52653 iter/s, 4.74959s/12 iters), loss = 5.26589
I0412 14:55:59.415242 20203 solver.cpp:237]     Train net output #0: loss = 5.26589 (* 1 = 5.26589 loss)
I0412 14:55:59.415254 20203 sgd_solver.cpp:105] Iteration 4224, lr = 0.00433131
I0412 14:56:04.409756 20203 solver.cpp:218] Iteration 4236 (2.40274 iter/s, 4.99431s/12 iters), loss = 5.27137
I0412 14:56:04.409797 20203 solver.cpp:237]     Train net output #0: loss = 5.27137 (* 1 = 5.27137 loss)
I0412 14:56:04.409806 20203 sgd_solver.cpp:105] Iteration 4236, lr = 0.00432103
I0412 14:56:09.099552 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:56:09.338095 20203 solver.cpp:218] Iteration 4248 (2.43502 iter/s, 4.92809s/12 iters), loss = 5.24737
I0412 14:56:09.338148 20203 solver.cpp:237]     Train net output #0: loss = 5.24737 (* 1 = 5.24737 loss)
I0412 14:56:09.338162 20203 sgd_solver.cpp:105] Iteration 4248, lr = 0.00431077
I0412 14:56:14.373040 20203 solver.cpp:218] Iteration 4260 (2.38347 iter/s, 5.03468s/12 iters), loss = 5.27006
I0412 14:56:14.373090 20203 solver.cpp:237]     Train net output #0: loss = 5.27006 (* 1 = 5.27006 loss)
I0412 14:56:14.373103 20203 sgd_solver.cpp:105] Iteration 4260, lr = 0.00430053
I0412 14:56:19.271109 20203 solver.cpp:218] Iteration 4272 (2.45007 iter/s, 4.89781s/12 iters), loss = 5.29113
I0412 14:56:19.271162 20203 solver.cpp:237]     Train net output #0: loss = 5.29113 (* 1 = 5.29113 loss)
I0412 14:56:19.271174 20203 sgd_solver.cpp:105] Iteration 4272, lr = 0.00429032
I0412 14:56:23.763881 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4284.caffemodel
I0412 14:56:27.934703 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4284.solverstate
I0412 14:56:30.272567 20203 solver.cpp:330] Iteration 4284, Testing net (#0)
I0412 14:56:30.272593 20203 net.cpp:676] Ignoring source layer train-data
I0412 14:56:33.224823 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:56:34.975618 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 14:56:34.975652 20203 solver.cpp:397]     Test net output #1: loss = 5.28617 (* 1 = 5.28617 loss)
I0412 14:56:35.062312 20203 solver.cpp:218] Iteration 4284 (0.75995 iter/s, 15.7905s/12 iters), loss = 5.27539
I0412 14:56:35.062353 20203 solver.cpp:237]     Train net output #0: loss = 5.27539 (* 1 = 5.27539 loss)
I0412 14:56:35.062361 20203 sgd_solver.cpp:105] Iteration 4284, lr = 0.00428014
I0412 14:56:39.138587 20203 solver.cpp:218] Iteration 4296 (2.94402 iter/s, 4.07606s/12 iters), loss = 5.27735
I0412 14:56:39.138638 20203 solver.cpp:237]     Train net output #0: loss = 5.27735 (* 1 = 5.27735 loss)
I0412 14:56:39.138649 20203 sgd_solver.cpp:105] Iteration 4296, lr = 0.00426998
I0412 14:56:44.001407 20203 solver.cpp:218] Iteration 4308 (2.46783 iter/s, 4.86256s/12 iters), loss = 5.26227
I0412 14:56:44.001462 20203 solver.cpp:237]     Train net output #0: loss = 5.26227 (* 1 = 5.26227 loss)
I0412 14:56:44.001474 20203 sgd_solver.cpp:105] Iteration 4308, lr = 0.00425984
I0412 14:56:48.738699 20203 solver.cpp:218] Iteration 4320 (2.53323 iter/s, 4.73703s/12 iters), loss = 5.24807
I0412 14:56:48.738760 20203 solver.cpp:237]     Train net output #0: loss = 5.24807 (* 1 = 5.24807 loss)
I0412 14:56:48.738775 20203 sgd_solver.cpp:105] Iteration 4320, lr = 0.00424972
I0412 14:56:53.635237 20203 solver.cpp:218] Iteration 4332 (2.45085 iter/s, 4.89627s/12 iters), loss = 5.27875
I0412 14:56:53.635293 20203 solver.cpp:237]     Train net output #0: loss = 5.27875 (* 1 = 5.27875 loss)
I0412 14:56:53.635308 20203 sgd_solver.cpp:105] Iteration 4332, lr = 0.00423964
I0412 14:56:58.906800 20203 solver.cpp:218] Iteration 4344 (2.27648 iter/s, 5.2713s/12 iters), loss = 5.2827
I0412 14:56:58.906927 20203 solver.cpp:237]     Train net output #0: loss = 5.2827 (* 1 = 5.2827 loss)
I0412 14:56:58.906936 20203 sgd_solver.cpp:105] Iteration 4344, lr = 0.00422957
I0412 14:57:00.908341 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:57:03.890797 20203 solver.cpp:218] Iteration 4356 (2.40787 iter/s, 4.98366s/12 iters), loss = 5.28727
I0412 14:57:03.890849 20203 solver.cpp:237]     Train net output #0: loss = 5.28727 (* 1 = 5.28727 loss)
I0412 14:57:03.890861 20203 sgd_solver.cpp:105] Iteration 4356, lr = 0.00421953
I0412 14:57:08.881582 20203 solver.cpp:218] Iteration 4368 (2.40456 iter/s, 4.99052s/12 iters), loss = 5.27685
I0412 14:57:08.881626 20203 solver.cpp:237]     Train net output #0: loss = 5.27685 (* 1 = 5.27685 loss)
I0412 14:57:08.881636 20203 sgd_solver.cpp:105] Iteration 4368, lr = 0.00420951
I0412 14:57:13.747750 20203 solver.cpp:218] Iteration 4380 (2.46613 iter/s, 4.86591s/12 iters), loss = 5.26276
I0412 14:57:13.747808 20203 solver.cpp:237]     Train net output #0: loss = 5.26276 (* 1 = 5.26276 loss)
I0412 14:57:13.747820 20203 sgd_solver.cpp:105] Iteration 4380, lr = 0.00419952
I0412 14:57:15.716101 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4386.caffemodel
I0412 14:57:20.656735 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4386.solverstate
I0412 14:57:24.118881 20203 solver.cpp:330] Iteration 4386, Testing net (#0)
I0412 14:57:24.118906 20203 net.cpp:676] Ignoring source layer train-data
I0412 14:57:26.781018 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:57:28.553771 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 14:57:28.553823 20203 solver.cpp:397]     Test net output #1: loss = 5.28616 (* 1 = 5.28616 loss)
I0412 14:57:30.338500 20203 solver.cpp:218] Iteration 4392 (0.723326 iter/s, 16.59s/12 iters), loss = 5.27373
I0412 14:57:30.338595 20203 solver.cpp:237]     Train net output #0: loss = 5.27373 (* 1 = 5.27373 loss)
I0412 14:57:30.338604 20203 sgd_solver.cpp:105] Iteration 4392, lr = 0.00418954
I0412 14:57:35.149137 20203 solver.cpp:218] Iteration 4404 (2.49463 iter/s, 4.81034s/12 iters), loss = 5.26201
I0412 14:57:35.149184 20203 solver.cpp:237]     Train net output #0: loss = 5.26201 (* 1 = 5.26201 loss)
I0412 14:57:35.149195 20203 sgd_solver.cpp:105] Iteration 4404, lr = 0.0041796
I0412 14:57:40.010048 20203 solver.cpp:218] Iteration 4416 (2.4688 iter/s, 4.86066s/12 iters), loss = 5.26384
I0412 14:57:40.010107 20203 solver.cpp:237]     Train net output #0: loss = 5.26384 (* 1 = 5.26384 loss)
I0412 14:57:40.010118 20203 sgd_solver.cpp:105] Iteration 4416, lr = 0.00416967
I0412 14:57:44.855077 20203 solver.cpp:218] Iteration 4428 (2.4769 iter/s, 4.84477s/12 iters), loss = 5.2683
I0412 14:57:44.855134 20203 solver.cpp:237]     Train net output #0: loss = 5.2683 (* 1 = 5.2683 loss)
I0412 14:57:44.855147 20203 sgd_solver.cpp:105] Iteration 4428, lr = 0.00415977
I0412 14:57:49.757076 20203 solver.cpp:218] Iteration 4440 (2.44811 iter/s, 4.90173s/12 iters), loss = 5.263
I0412 14:57:49.757134 20203 solver.cpp:237]     Train net output #0: loss = 5.263 (* 1 = 5.263 loss)
I0412 14:57:49.757146 20203 sgd_solver.cpp:105] Iteration 4440, lr = 0.0041499
I0412 14:57:53.949612 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:57:54.845731 20203 solver.cpp:218] Iteration 4452 (2.35831 iter/s, 5.08838s/12 iters), loss = 5.26056
I0412 14:57:54.845783 20203 solver.cpp:237]     Train net output #0: loss = 5.26056 (* 1 = 5.26056 loss)
I0412 14:57:54.845794 20203 sgd_solver.cpp:105] Iteration 4452, lr = 0.00414005
I0412 14:57:59.712524 20203 solver.cpp:218] Iteration 4464 (2.46582 iter/s, 4.86653s/12 iters), loss = 5.28329
I0412 14:57:59.712570 20203 solver.cpp:237]     Train net output #0: loss = 5.28329 (* 1 = 5.28329 loss)
I0412 14:57:59.712579 20203 sgd_solver.cpp:105] Iteration 4464, lr = 0.00413022
I0412 14:58:04.673831 20203 solver.cpp:218] Iteration 4476 (2.41884 iter/s, 4.96105s/12 iters), loss = 5.25902
I0412 14:58:04.673951 20203 solver.cpp:237]     Train net output #0: loss = 5.25902 (* 1 = 5.25902 loss)
I0412 14:58:04.673980 20203 sgd_solver.cpp:105] Iteration 4476, lr = 0.00412041
I0412 14:58:09.064699 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4488.caffemodel
I0412 14:58:16.793843 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4488.solverstate
I0412 14:58:21.161331 20203 solver.cpp:330] Iteration 4488, Testing net (#0)
I0412 14:58:21.161356 20203 net.cpp:676] Ignoring source layer train-data
I0412 14:58:23.833767 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:58:25.606089 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00612745
I0412 14:58:25.606137 20203 solver.cpp:397]     Test net output #1: loss = 5.28581 (* 1 = 5.28581 loss)
I0412 14:58:25.691833 20203 solver.cpp:218] Iteration 4488 (0.570965 iter/s, 21.017s/12 iters), loss = 5.30791
I0412 14:58:25.691882 20203 solver.cpp:237]     Train net output #0: loss = 5.30791 (* 1 = 5.30791 loss)
I0412 14:58:25.691895 20203 sgd_solver.cpp:105] Iteration 4488, lr = 0.00411063
I0412 14:58:30.038074 20203 solver.cpp:218] Iteration 4500 (2.76116 iter/s, 4.34601s/12 iters), loss = 5.27005
I0412 14:58:30.038121 20203 solver.cpp:237]     Train net output #0: loss = 5.27005 (* 1 = 5.27005 loss)
I0412 14:58:30.038132 20203 sgd_solver.cpp:105] Iteration 4500, lr = 0.00410087
I0412 14:58:34.826153 20203 solver.cpp:218] Iteration 4512 (2.50636 iter/s, 4.78782s/12 iters), loss = 5.26949
I0412 14:58:34.826259 20203 solver.cpp:237]     Train net output #0: loss = 5.26949 (* 1 = 5.26949 loss)
I0412 14:58:34.826272 20203 sgd_solver.cpp:105] Iteration 4512, lr = 0.00409113
I0412 14:58:39.680774 20203 solver.cpp:218] Iteration 4524 (2.47203 iter/s, 4.85431s/12 iters), loss = 5.27617
I0412 14:58:39.680835 20203 solver.cpp:237]     Train net output #0: loss = 5.27617 (* 1 = 5.27617 loss)
I0412 14:58:39.680847 20203 sgd_solver.cpp:105] Iteration 4524, lr = 0.00408142
I0412 14:58:44.693204 20203 solver.cpp:218] Iteration 4536 (2.39418 iter/s, 5.01216s/12 iters), loss = 5.26682
I0412 14:58:44.693256 20203 solver.cpp:237]     Train net output #0: loss = 5.26682 (* 1 = 5.26682 loss)
I0412 14:58:44.693267 20203 sgd_solver.cpp:105] Iteration 4536, lr = 0.00407173
I0412 14:58:49.806044 20203 solver.cpp:218] Iteration 4548 (2.34716 iter/s, 5.11257s/12 iters), loss = 5.2646
I0412 14:58:49.806094 20203 solver.cpp:237]     Train net output #0: loss = 5.2646 (* 1 = 5.2646 loss)
I0412 14:58:49.806105 20203 sgd_solver.cpp:105] Iteration 4548, lr = 0.00406206
I0412 14:58:51.077560 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:58:54.735006 20203 solver.cpp:218] Iteration 4560 (2.43473 iter/s, 4.92868s/12 iters), loss = 5.27822
I0412 14:58:54.735080 20203 solver.cpp:237]     Train net output #0: loss = 5.27822 (* 1 = 5.27822 loss)
I0412 14:58:54.735100 20203 sgd_solver.cpp:105] Iteration 4560, lr = 0.00405242
I0412 14:58:59.509748 20203 solver.cpp:218] Iteration 4572 (2.51337 iter/s, 4.77447s/12 iters), loss = 5.26273
I0412 14:58:59.509791 20203 solver.cpp:237]     Train net output #0: loss = 5.26273 (* 1 = 5.26273 loss)
I0412 14:58:59.509801 20203 sgd_solver.cpp:105] Iteration 4572, lr = 0.0040428
I0412 14:59:04.298961 20203 solver.cpp:218] Iteration 4584 (2.50576 iter/s, 4.78896s/12 iters), loss = 5.27048
I0412 14:59:04.299011 20203 solver.cpp:237]     Train net output #0: loss = 5.27048 (* 1 = 5.27048 loss)
I0412 14:59:04.299022 20203 sgd_solver.cpp:105] Iteration 4584, lr = 0.0040332
I0412 14:59:06.529240 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4590.caffemodel
I0412 14:59:11.121978 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4590.solverstate
I0412 14:59:13.401685 20203 solver.cpp:330] Iteration 4590, Testing net (#0)
I0412 14:59:13.401710 20203 net.cpp:676] Ignoring source layer train-data
I0412 14:59:16.067224 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:59:17.926069 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 14:59:17.926110 20203 solver.cpp:397]     Test net output #1: loss = 5.28576 (* 1 = 5.28576 loss)
I0412 14:59:19.632757 20203 solver.cpp:218] Iteration 4596 (0.78262 iter/s, 15.3331s/12 iters), loss = 5.27272
I0412 14:59:19.632815 20203 solver.cpp:237]     Train net output #0: loss = 5.27272 (* 1 = 5.27272 loss)
I0412 14:59:19.632827 20203 sgd_solver.cpp:105] Iteration 4596, lr = 0.00402362
I0412 14:59:24.619987 20203 solver.cpp:218] Iteration 4608 (2.40628 iter/s, 4.98696s/12 iters), loss = 5.27387
I0412 14:59:24.620038 20203 solver.cpp:237]     Train net output #0: loss = 5.27387 (* 1 = 5.27387 loss)
I0412 14:59:24.620050 20203 sgd_solver.cpp:105] Iteration 4608, lr = 0.00401407
I0412 14:59:29.335048 20203 solver.cpp:218] Iteration 4620 (2.54517 iter/s, 4.71481s/12 iters), loss = 5.26328
I0412 14:59:29.335103 20203 solver.cpp:237]     Train net output #0: loss = 5.26328 (* 1 = 5.26328 loss)
I0412 14:59:29.335114 20203 sgd_solver.cpp:105] Iteration 4620, lr = 0.00400454
I0412 14:59:34.289870 20203 solver.cpp:218] Iteration 4632 (2.42201 iter/s, 4.95456s/12 iters), loss = 5.29502
I0412 14:59:34.289921 20203 solver.cpp:237]     Train net output #0: loss = 5.29502 (* 1 = 5.29502 loss)
I0412 14:59:34.289932 20203 sgd_solver.cpp:105] Iteration 4632, lr = 0.00399503
I0412 14:59:39.556891 20203 solver.cpp:218] Iteration 4644 (2.27845 iter/s, 5.26675s/12 iters), loss = 5.267
I0412 14:59:39.557014 20203 solver.cpp:237]     Train net output #0: loss = 5.267 (* 1 = 5.267 loss)
I0412 14:59:39.557027 20203 sgd_solver.cpp:105] Iteration 4644, lr = 0.00398555
I0412 14:59:42.990828 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:59:44.573364 20203 solver.cpp:218] Iteration 4656 (2.39228 iter/s, 5.01614s/12 iters), loss = 5.28178
I0412 14:59:44.573416 20203 solver.cpp:237]     Train net output #0: loss = 5.28178 (* 1 = 5.28178 loss)
I0412 14:59:44.573429 20203 sgd_solver.cpp:105] Iteration 4656, lr = 0.00397608
I0412 14:59:49.420660 20203 solver.cpp:218] Iteration 4668 (2.47574 iter/s, 4.84704s/12 iters), loss = 5.27092
I0412 14:59:49.420712 20203 solver.cpp:237]     Train net output #0: loss = 5.27092 (* 1 = 5.27092 loss)
I0412 14:59:49.420725 20203 sgd_solver.cpp:105] Iteration 4668, lr = 0.00396664
I0412 14:59:54.140960 20203 solver.cpp:218] Iteration 4680 (2.54235 iter/s, 4.72005s/12 iters), loss = 5.27902
I0412 14:59:54.141013 20203 solver.cpp:237]     Train net output #0: loss = 5.27902 (* 1 = 5.27902 loss)
I0412 14:59:54.141026 20203 sgd_solver.cpp:105] Iteration 4680, lr = 0.00395723
I0412 14:59:58.477815 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4692.caffemodel
I0412 15:00:01.541262 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4692.solverstate
I0412 15:00:03.840772 20203 solver.cpp:330] Iteration 4692, Testing net (#0)
I0412 15:00:03.840797 20203 net.cpp:676] Ignoring source layer train-data
I0412 15:00:06.452533 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:00:08.443712 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 15:00:08.443760 20203 solver.cpp:397]     Test net output #1: loss = 5.28608 (* 1 = 5.28608 loss)
I0412 15:00:08.529616 20203 solver.cpp:218] Iteration 4692 (0.834027 iter/s, 14.388s/12 iters), loss = 5.26709
I0412 15:00:08.529664 20203 solver.cpp:237]     Train net output #0: loss = 5.26709 (* 1 = 5.26709 loss)
I0412 15:00:08.529675 20203 sgd_solver.cpp:105] Iteration 4692, lr = 0.00394783
I0412 15:00:12.597398 20203 solver.cpp:218] Iteration 4704 (2.95017 iter/s, 4.06756s/12 iters), loss = 5.26967
I0412 15:00:12.597533 20203 solver.cpp:237]     Train net output #0: loss = 5.26967 (* 1 = 5.26967 loss)
I0412 15:00:12.597543 20203 sgd_solver.cpp:105] Iteration 4704, lr = 0.00393846
I0412 15:00:17.412139 20203 solver.cpp:218] Iteration 4716 (2.49252 iter/s, 4.8144s/12 iters), loss = 5.28253
I0412 15:00:17.412196 20203 solver.cpp:237]     Train net output #0: loss = 5.28253 (* 1 = 5.28253 loss)
I0412 15:00:17.412212 20203 sgd_solver.cpp:105] Iteration 4716, lr = 0.00392911
I0412 15:00:22.070240 20203 solver.cpp:218] Iteration 4728 (2.5763 iter/s, 4.65784s/12 iters), loss = 5.26835
I0412 15:00:22.070294 20203 solver.cpp:237]     Train net output #0: loss = 5.26835 (* 1 = 5.26835 loss)
I0412 15:00:22.070307 20203 sgd_solver.cpp:105] Iteration 4728, lr = 0.00391978
I0412 15:00:26.844648 20203 solver.cpp:218] Iteration 4740 (2.51354 iter/s, 4.77415s/12 iters), loss = 5.28019
I0412 15:00:26.844702 20203 solver.cpp:237]     Train net output #0: loss = 5.28019 (* 1 = 5.28019 loss)
I0412 15:00:26.844714 20203 sgd_solver.cpp:105] Iteration 4740, lr = 0.00391047
I0412 15:00:31.633894 20203 solver.cpp:218] Iteration 4752 (2.50575 iter/s, 4.78899s/12 iters), loss = 5.28123
I0412 15:00:31.633946 20203 solver.cpp:237]     Train net output #0: loss = 5.28123 (* 1 = 5.28123 loss)
I0412 15:00:31.633972 20203 sgd_solver.cpp:105] Iteration 4752, lr = 0.00390119
I0412 15:00:32.118827 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:00:36.385919 20203 solver.cpp:218] Iteration 4764 (2.52537 iter/s, 4.75177s/12 iters), loss = 5.28028
I0412 15:00:36.385996 20203 solver.cpp:237]     Train net output #0: loss = 5.28028 (* 1 = 5.28028 loss)
I0412 15:00:36.386013 20203 sgd_solver.cpp:105] Iteration 4764, lr = 0.00389193
I0412 15:00:41.193233 20203 solver.cpp:218] Iteration 4776 (2.49634 iter/s, 4.80703s/12 iters), loss = 5.27
I0412 15:00:41.193272 20203 solver.cpp:237]     Train net output #0: loss = 5.27 (* 1 = 5.27 loss)
I0412 15:00:41.193280 20203 sgd_solver.cpp:105] Iteration 4776, lr = 0.00388269
I0412 15:00:45.935868 20203 solver.cpp:218] Iteration 4788 (2.53037 iter/s, 4.74239s/12 iters), loss = 5.29238
I0412 15:00:45.935973 20203 solver.cpp:237]     Train net output #0: loss = 5.29238 (* 1 = 5.29238 loss)
I0412 15:00:45.935984 20203 sgd_solver.cpp:105] Iteration 4788, lr = 0.00387347
I0412 15:00:47.791832 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4794.caffemodel
I0412 15:00:50.842615 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4794.solverstate
I0412 15:00:53.411418 20203 solver.cpp:330] Iteration 4794, Testing net (#0)
I0412 15:00:53.411445 20203 net.cpp:676] Ignoring source layer train-data
I0412 15:00:55.876456 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:00:57.780153 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00612745
I0412 15:00:57.780186 20203 solver.cpp:397]     Test net output #1: loss = 5.28524 (* 1 = 5.28524 loss)
I0412 15:00:59.581398 20203 solver.cpp:218] Iteration 4800 (0.879452 iter/s, 13.6449s/12 iters), loss = 5.27439
I0412 15:00:59.581466 20203 solver.cpp:237]     Train net output #0: loss = 5.27439 (* 1 = 5.27439 loss)
I0412 15:00:59.581483 20203 sgd_solver.cpp:105] Iteration 4800, lr = 0.00386427
I0412 15:01:04.562119 20203 solver.cpp:218] Iteration 4812 (2.40942 iter/s, 4.98044s/12 iters), loss = 5.26806
I0412 15:01:04.562170 20203 solver.cpp:237]     Train net output #0: loss = 5.26806 (* 1 = 5.26806 loss)
I0412 15:01:04.562180 20203 sgd_solver.cpp:105] Iteration 4812, lr = 0.0038551
I0412 15:01:09.292068 20203 solver.cpp:218] Iteration 4824 (2.53716 iter/s, 4.72969s/12 iters), loss = 5.29218
I0412 15:01:09.292116 20203 solver.cpp:237]     Train net output #0: loss = 5.29218 (* 1 = 5.29218 loss)
I0412 15:01:09.292126 20203 sgd_solver.cpp:105] Iteration 4824, lr = 0.00384594
I0412 15:01:14.360018 20203 solver.cpp:218] Iteration 4836 (2.36795 iter/s, 5.06768s/12 iters), loss = 5.26653
I0412 15:01:14.360071 20203 solver.cpp:237]     Train net output #0: loss = 5.26653 (* 1 = 5.26653 loss)
I0412 15:01:14.360081 20203 sgd_solver.cpp:105] Iteration 4836, lr = 0.00383681
I0412 15:01:16.389457 20203 blocking_queue.cpp:49] Waiting for data
I0412 15:01:19.303280 20203 solver.cpp:218] Iteration 4848 (2.42768 iter/s, 4.943s/12 iters), loss = 5.26711
I0412 15:01:19.303330 20203 solver.cpp:237]     Train net output #0: loss = 5.26711 (* 1 = 5.26711 loss)
I0412 15:01:19.303342 20203 sgd_solver.cpp:105] Iteration 4848, lr = 0.0038277
I0412 15:01:21.750389 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:01:23.987020 20203 solver.cpp:218] Iteration 4860 (2.56219 iter/s, 4.68349s/12 iters), loss = 5.27117
I0412 15:01:23.987074 20203 solver.cpp:237]     Train net output #0: loss = 5.27117 (* 1 = 5.27117 loss)
I0412 15:01:23.987087 20203 sgd_solver.cpp:105] Iteration 4860, lr = 0.00381862
I0412 15:01:28.944484 20203 solver.cpp:218] Iteration 4872 (2.42072 iter/s, 4.9572s/12 iters), loss = 5.26447
I0412 15:01:28.944535 20203 solver.cpp:237]     Train net output #0: loss = 5.26447 (* 1 = 5.26447 loss)
I0412 15:01:28.944546 20203 sgd_solver.cpp:105] Iteration 4872, lr = 0.00380955
I0412 15:01:33.862982 20203 solver.cpp:218] Iteration 4884 (2.4399 iter/s, 4.91824s/12 iters), loss = 5.26722
I0412 15:01:33.863032 20203 solver.cpp:237]     Train net output #0: loss = 5.26722 (* 1 = 5.26722 loss)
I0412 15:01:33.863044 20203 sgd_solver.cpp:105] Iteration 4884, lr = 0.0038005
I0412 15:01:38.225490 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4896.caffemodel
I0412 15:01:41.237854 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4896.solverstate
I0412 15:01:44.710546 20203 solver.cpp:330] Iteration 4896, Testing net (#0)
I0412 15:01:44.710573 20203 net.cpp:676] Ignoring source layer train-data
I0412 15:01:47.361538 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:01:49.322149 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 15:01:49.322192 20203 solver.cpp:397]     Test net output #1: loss = 5.28624 (* 1 = 5.28624 loss)
I0412 15:01:49.408514 20203 solver.cpp:218] Iteration 4896 (0.77196 iter/s, 15.5448s/12 iters), loss = 5.27152
I0412 15:01:49.408561 20203 solver.cpp:237]     Train net output #0: loss = 5.27152 (* 1 = 5.27152 loss)
I0412 15:01:49.408572 20203 sgd_solver.cpp:105] Iteration 4896, lr = 0.00379148
I0412 15:01:53.453673 20203 solver.cpp:218] Iteration 4908 (2.96667 iter/s, 4.04493s/12 iters), loss = 5.28305
I0412 15:01:53.453716 20203 solver.cpp:237]     Train net output #0: loss = 5.28305 (* 1 = 5.28305 loss)
I0412 15:01:53.453724 20203 sgd_solver.cpp:105] Iteration 4908, lr = 0.00378248
I0412 15:01:58.543109 20203 solver.cpp:218] Iteration 4920 (2.35795 iter/s, 5.08917s/12 iters), loss = 5.27674
I0412 15:01:58.543151 20203 solver.cpp:237]     Train net output #0: loss = 5.27674 (* 1 = 5.27674 loss)
I0412 15:01:58.543162 20203 sgd_solver.cpp:105] Iteration 4920, lr = 0.0037735
I0412 15:02:03.786571 20203 solver.cpp:218] Iteration 4932 (2.28868 iter/s, 5.24319s/12 iters), loss = 5.26524
I0412 15:02:03.786625 20203 solver.cpp:237]     Train net output #0: loss = 5.26524 (* 1 = 5.26524 loss)
I0412 15:02:03.786639 20203 sgd_solver.cpp:105] Iteration 4932, lr = 0.00376454
I0412 15:02:08.709249 20203 solver.cpp:218] Iteration 4944 (2.43783 iter/s, 4.92241s/12 iters), loss = 5.26692
I0412 15:02:08.709297 20203 solver.cpp:237]     Train net output #0: loss = 5.26692 (* 1 = 5.26692 loss)
I0412 15:02:08.709306 20203 sgd_solver.cpp:105] Iteration 4944, lr = 0.0037556
I0412 15:02:13.403257 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:02:13.598959 20203 solver.cpp:218] Iteration 4956 (2.45427 iter/s, 4.88944s/12 iters), loss = 5.25214
I0412 15:02:13.599009 20203 solver.cpp:237]     Train net output #0: loss = 5.25214 (* 1 = 5.25214 loss)
I0412 15:02:13.599017 20203 sgd_solver.cpp:105] Iteration 4956, lr = 0.00374669
I0412 15:02:18.890955 20203 solver.cpp:218] Iteration 4968 (2.26769 iter/s, 5.29172s/12 iters), loss = 5.26454
I0412 15:02:18.891083 20203 solver.cpp:237]     Train net output #0: loss = 5.26454 (* 1 = 5.26454 loss)
I0412 15:02:18.891094 20203 sgd_solver.cpp:105] Iteration 4968, lr = 0.00373779
I0412 15:02:23.872885 20203 solver.cpp:218] Iteration 4980 (2.40887 iter/s, 4.98159s/12 iters), loss = 5.29201
I0412 15:02:23.872947 20203 solver.cpp:237]     Train net output #0: loss = 5.29201 (* 1 = 5.29201 loss)
I0412 15:02:23.872961 20203 sgd_solver.cpp:105] Iteration 4980, lr = 0.00372892
I0412 15:02:28.804330 20203 solver.cpp:218] Iteration 4992 (2.4335 iter/s, 4.93117s/12 iters), loss = 5.28927
I0412 15:02:28.804390 20203 solver.cpp:237]     Train net output #0: loss = 5.28927 (* 1 = 5.28927 loss)
I0412 15:02:28.804404 20203 sgd_solver.cpp:105] Iteration 4992, lr = 0.00372006
I0412 15:02:30.814451 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4998.caffemodel
I0412 15:02:39.254190 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4998.solverstate
I0412 15:02:42.673750 20203 solver.cpp:330] Iteration 4998, Testing net (#0)
I0412 15:02:42.673774 20203 net.cpp:676] Ignoring source layer train-data
I0412 15:02:45.194792 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:02:47.449172 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 15:02:47.449199 20203 solver.cpp:397]     Test net output #1: loss = 5.28597 (* 1 = 5.28597 loss)
I0412 15:02:49.148684 20203 solver.cpp:218] Iteration 5004 (0.58987 iter/s, 20.3435s/12 iters), loss = 5.27782
I0412 15:02:49.166072 20203 solver.cpp:237]     Train net output #0: loss = 5.27782 (* 1 = 5.27782 loss)
I0412 15:02:49.166090 20203 sgd_solver.cpp:105] Iteration 5004, lr = 0.00371123
I0412 15:02:54.301539 20203 solver.cpp:218] Iteration 5016 (2.33678 iter/s, 5.13526s/12 iters), loss = 5.26288
I0412 15:02:54.301586 20203 solver.cpp:237]     Train net output #0: loss = 5.26288 (* 1 = 5.26288 loss)
I0412 15:02:54.301596 20203 sgd_solver.cpp:105] Iteration 5016, lr = 0.00370242
I0412 15:02:59.770954 20203 solver.cpp:218] Iteration 5028 (2.19413 iter/s, 5.46913s/12 iters), loss = 5.24916
I0412 15:02:59.770999 20203 solver.cpp:237]     Train net output #0: loss = 5.24916 (* 1 = 5.24916 loss)
I0412 15:02:59.771009 20203 sgd_solver.cpp:105] Iteration 5028, lr = 0.00369363
I0412 15:03:04.767886 20203 solver.cpp:218] Iteration 5040 (2.4016 iter/s, 4.99667s/12 iters), loss = 5.2857
I0412 15:03:04.767936 20203 solver.cpp:237]     Train net output #0: loss = 5.2857 (* 1 = 5.2857 loss)
I0412 15:03:04.767948 20203 sgd_solver.cpp:105] Iteration 5040, lr = 0.00368486
I0412 15:03:09.854118 20203 solver.cpp:218] Iteration 5052 (2.35944 iter/s, 5.08596s/12 iters), loss = 5.27292
I0412 15:03:09.854171 20203 solver.cpp:237]     Train net output #0: loss = 5.27292 (* 1 = 5.27292 loss)
I0412 15:03:09.854182 20203 sgd_solver.cpp:105] Iteration 5052, lr = 0.00367611
I0412 15:03:11.925664 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:03:14.864961 20203 solver.cpp:218] Iteration 5064 (2.39493 iter/s, 5.01057s/12 iters), loss = 5.28993
I0412 15:03:14.865020 20203 solver.cpp:237]     Train net output #0: loss = 5.28993 (* 1 = 5.28993 loss)
I0412 15:03:14.865031 20203 sgd_solver.cpp:105] Iteration 5064, lr = 0.00366738
I0412 15:03:20.152359 20203 solver.cpp:218] Iteration 5076 (2.26967 iter/s, 5.28711s/12 iters), loss = 5.2772
I0412 15:03:20.152537 20203 solver.cpp:237]     Train net output #0: loss = 5.2772 (* 1 = 5.2772 loss)
I0412 15:03:20.152549 20203 sgd_solver.cpp:105] Iteration 5076, lr = 0.00365868
I0412 15:03:25.029428 20203 solver.cpp:218] Iteration 5088 (2.46069 iter/s, 4.87668s/12 iters), loss = 5.25972
I0412 15:03:25.029484 20203 solver.cpp:237]     Train net output #0: loss = 5.25972 (* 1 = 5.25972 loss)
I0412 15:03:25.029497 20203 sgd_solver.cpp:105] Iteration 5088, lr = 0.00364999
I0412 15:03:29.589427 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5100.caffemodel
I0412 15:03:32.679253 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5100.solverstate
I0412 15:03:36.015799 20203 solver.cpp:330] Iteration 5100, Testing net (#0)
I0412 15:03:36.015825 20203 net.cpp:676] Ignoring source layer train-data
I0412 15:03:38.835237 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:03:40.911414 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 15:03:40.911453 20203 solver.cpp:397]     Test net output #1: loss = 5.28577 (* 1 = 5.28577 loss)
I0412 15:03:40.997565 20203 solver.cpp:218] Iteration 5100 (0.751531 iter/s, 15.9674s/12 iters), loss = 5.26709
I0412 15:03:40.997617 20203 solver.cpp:237]     Train net output #0: loss = 5.26709 (* 1 = 5.26709 loss)
I0412 15:03:40.997627 20203 sgd_solver.cpp:105] Iteration 5100, lr = 0.00364132
I0412 15:03:45.363333 20203 solver.cpp:218] Iteration 5112 (2.74881 iter/s, 4.36552s/12 iters), loss = 5.26074
I0412 15:03:45.363376 20203 solver.cpp:237]     Train net output #0: loss = 5.26074 (* 1 = 5.26074 loss)
I0412 15:03:45.363385 20203 sgd_solver.cpp:105] Iteration 5112, lr = 0.00363268
I0412 15:03:50.332114 20203 solver.cpp:218] Iteration 5124 (2.41521 iter/s, 4.96852s/12 iters), loss = 5.27339
I0412 15:03:50.332258 20203 solver.cpp:237]     Train net output #0: loss = 5.27339 (* 1 = 5.27339 loss)
I0412 15:03:50.332270 20203 sgd_solver.cpp:105] Iteration 5124, lr = 0.00362405
I0412 15:03:55.449498 20203 solver.cpp:218] Iteration 5136 (2.34512 iter/s, 5.11702s/12 iters), loss = 5.26781
I0412 15:03:55.449551 20203 solver.cpp:237]     Train net output #0: loss = 5.26781 (* 1 = 5.26781 loss)
I0412 15:03:55.449563 20203 sgd_solver.cpp:105] Iteration 5136, lr = 0.00361545
I0412 15:04:01.050907 20203 solver.cpp:218] Iteration 5148 (2.14243 iter/s, 5.60111s/12 iters), loss = 5.26418
I0412 15:04:01.050966 20203 solver.cpp:237]     Train net output #0: loss = 5.26418 (* 1 = 5.26418 loss)
I0412 15:04:01.050978 20203 sgd_solver.cpp:105] Iteration 5148, lr = 0.00360687
I0412 15:04:05.369153 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:04:06.278863 20203 solver.cpp:218] Iteration 5160 (2.29548 iter/s, 5.22768s/12 iters), loss = 5.25651
I0412 15:04:06.278901 20203 solver.cpp:237]     Train net output #0: loss = 5.25651 (* 1 = 5.25651 loss)
I0412 15:04:06.278909 20203 sgd_solver.cpp:105] Iteration 5160, lr = 0.0035983
I0412 15:04:11.498035 20203 solver.cpp:218] Iteration 5172 (2.29933 iter/s, 5.2189s/12 iters), loss = 5.27884
I0412 15:04:11.498082 20203 solver.cpp:237]     Train net output #0: loss = 5.27884 (* 1 = 5.27884 loss)
I0412 15:04:11.498091 20203 sgd_solver.cpp:105] Iteration 5172, lr = 0.00358976
I0412 15:04:17.306514 20203 solver.cpp:218] Iteration 5184 (2.06605 iter/s, 5.80818s/12 iters), loss = 5.26932
I0412 15:04:17.306562 20203 solver.cpp:237]     Train net output #0: loss = 5.26932 (* 1 = 5.26932 loss)
I0412 15:04:17.306572 20203 sgd_solver.cpp:105] Iteration 5184, lr = 0.00358124
I0412 15:04:23.110273 20203 solver.cpp:218] Iteration 5196 (2.06773 iter/s, 5.80346s/12 iters), loss = 5.30902
I0412 15:04:23.110394 20203 solver.cpp:237]     Train net output #0: loss = 5.30902 (* 1 = 5.30902 loss)
I0412 15:04:23.110404 20203 sgd_solver.cpp:105] Iteration 5196, lr = 0.00357273
I0412 15:04:25.165647 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5202.caffemodel
I0412 15:04:28.315901 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5202.solverstate
I0412 15:04:30.757428 20203 solver.cpp:330] Iteration 5202, Testing net (#0)
I0412 15:04:30.757457 20203 net.cpp:676] Ignoring source layer train-data
I0412 15:04:33.149019 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:04:35.467224 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 15:04:35.467262 20203 solver.cpp:397]     Test net output #1: loss = 5.28581 (* 1 = 5.28581 loss)
I0412 15:04:37.281471 20203 solver.cpp:218] Iteration 5208 (0.84683 iter/s, 14.1705s/12 iters), loss = 5.27637
I0412 15:04:37.281512 20203 solver.cpp:237]     Train net output #0: loss = 5.27637 (* 1 = 5.27637 loss)
I0412 15:04:37.281520 20203 sgd_solver.cpp:105] Iteration 5208, lr = 0.00356425
I0412 15:04:42.789005 20203 solver.cpp:218] Iteration 5220 (2.17895 iter/s, 5.50725s/12 iters), loss = 5.2749
I0412 15:04:42.789047 20203 solver.cpp:237]     Train net output #0: loss = 5.2749 (* 1 = 5.2749 loss)
I0412 15:04:42.789057 20203 sgd_solver.cpp:105] Iteration 5220, lr = 0.00355579
I0412 15:04:48.159888 20203 solver.cpp:218] Iteration 5232 (2.23438 iter/s, 5.37061s/12 iters), loss = 5.2765
I0412 15:04:48.159930 20203 solver.cpp:237]     Train net output #0: loss = 5.2765 (* 1 = 5.2765 loss)
I0412 15:04:48.159938 20203 sgd_solver.cpp:105] Iteration 5232, lr = 0.00354735
I0412 15:04:53.618187 20203 solver.cpp:218] Iteration 5244 (2.1986 iter/s, 5.45802s/12 iters), loss = 5.27276
I0412 15:04:53.618305 20203 solver.cpp:237]     Train net output #0: loss = 5.27276 (* 1 = 5.27276 loss)
I0412 15:04:53.618319 20203 sgd_solver.cpp:105] Iteration 5244, lr = 0.00353892
I0412 15:04:59.507398 20203 solver.cpp:218] Iteration 5256 (2.03775 iter/s, 5.88884s/12 iters), loss = 5.26236
I0412 15:04:59.507452 20203 solver.cpp:237]     Train net output #0: loss = 5.26236 (* 1 = 5.26236 loss)
I0412 15:04:59.507462 20203 sgd_solver.cpp:105] Iteration 5256, lr = 0.00353052
I0412 15:05:00.948544 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:05:05.181013 20203 solver.cpp:218] Iteration 5268 (2.11517 iter/s, 5.67332s/12 iters), loss = 5.27716
I0412 15:05:05.181074 20203 solver.cpp:237]     Train net output #0: loss = 5.27716 (* 1 = 5.27716 loss)
I0412 15:05:05.181085 20203 sgd_solver.cpp:105] Iteration 5268, lr = 0.00352214
I0412 15:05:10.225071 20203 solver.cpp:218] Iteration 5280 (2.37917 iter/s, 5.04378s/12 iters), loss = 5.26403
I0412 15:05:10.225112 20203 solver.cpp:237]     Train net output #0: loss = 5.26403 (* 1 = 5.26403 loss)
I0412 15:05:10.225121 20203 sgd_solver.cpp:105] Iteration 5280, lr = 0.00351378
I0412 15:05:15.608893 20203 solver.cpp:218] Iteration 5292 (2.22901 iter/s, 5.38355s/12 iters), loss = 5.27839
I0412 15:05:15.608934 20203 solver.cpp:237]     Train net output #0: loss = 5.27839 (* 1 = 5.27839 loss)
I0412 15:05:15.608943 20203 sgd_solver.cpp:105] Iteration 5292, lr = 0.00350544
I0412 15:05:20.105391 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5304.caffemodel
I0412 15:05:23.170347 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5304.solverstate
I0412 15:05:25.576344 20203 solver.cpp:330] Iteration 5304, Testing net (#0)
I0412 15:05:25.576413 20203 net.cpp:676] Ignoring source layer train-data
I0412 15:05:28.213443 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:05:30.446103 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 15:05:30.446158 20203 solver.cpp:397]     Test net output #1: loss = 5.28552 (* 1 = 5.28552 loss)
I0412 15:05:30.532860 20203 solver.cpp:218] Iteration 5304 (0.804112 iter/s, 14.9233s/12 iters), loss = 5.27606
I0412 15:05:30.532922 20203 solver.cpp:237]     Train net output #0: loss = 5.27606 (* 1 = 5.27606 loss)
I0412 15:05:30.532932 20203 sgd_solver.cpp:105] Iteration 5304, lr = 0.00349711
I0412 15:05:35.006189 20203 solver.cpp:218] Iteration 5316 (2.68272 iter/s, 4.47307s/12 iters), loss = 5.27015
I0412 15:05:35.006239 20203 solver.cpp:237]     Train net output #0: loss = 5.27015 (* 1 = 5.27015 loss)
I0412 15:05:35.006250 20203 sgd_solver.cpp:105] Iteration 5316, lr = 0.00348881
I0412 15:05:40.552201 20203 solver.cpp:218] Iteration 5328 (2.16383 iter/s, 5.54572s/12 iters), loss = 5.26171
I0412 15:05:40.552251 20203 solver.cpp:237]     Train net output #0: loss = 5.26171 (* 1 = 5.26171 loss)
I0412 15:05:40.552263 20203 sgd_solver.cpp:105] Iteration 5328, lr = 0.00348053
I0412 15:05:45.669826 20203 solver.cpp:218] Iteration 5340 (2.34496 iter/s, 5.11735s/12 iters), loss = 5.30371
I0412 15:05:45.669888 20203 solver.cpp:237]     Train net output #0: loss = 5.30371 (* 1 = 5.30371 loss)
I0412 15:05:45.669900 20203 sgd_solver.cpp:105] Iteration 5340, lr = 0.00347226
I0412 15:05:50.844715 20203 solver.cpp:218] Iteration 5352 (2.31902 iter/s, 5.1746s/12 iters), loss = 5.27734
I0412 15:05:50.844767 20203 solver.cpp:237]     Train net output #0: loss = 5.27734 (* 1 = 5.27734 loss)
I0412 15:05:50.844777 20203 sgd_solver.cpp:105] Iteration 5352, lr = 0.00346402
I0412 15:05:54.330857 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:05:55.966794 20203 solver.cpp:218] Iteration 5364 (2.34292 iter/s, 5.1218s/12 iters), loss = 5.27548
I0412 15:05:55.966989 20203 solver.cpp:237]     Train net output #0: loss = 5.27548 (* 1 = 5.27548 loss)
I0412 15:05:55.967010 20203 sgd_solver.cpp:105] Iteration 5364, lr = 0.0034558
I0412 15:06:01.249897 20203 solver.cpp:218] Iteration 5376 (2.27157 iter/s, 5.28269s/12 iters), loss = 5.26503
I0412 15:06:01.249977 20203 solver.cpp:237]     Train net output #0: loss = 5.26503 (* 1 = 5.26503 loss)
I0412 15:06:01.249990 20203 sgd_solver.cpp:105] Iteration 5376, lr = 0.00344759
I0412 15:06:06.717947 20203 solver.cpp:218] Iteration 5388 (2.19469 iter/s, 5.46775s/12 iters), loss = 5.2692
I0412 15:06:06.718041 20203 solver.cpp:237]     Train net output #0: loss = 5.2692 (* 1 = 5.2692 loss)
I0412 15:06:06.718055 20203 sgd_solver.cpp:105] Iteration 5388, lr = 0.00343941
I0412 15:06:11.752297 20203 solver.cpp:218] Iteration 5400 (2.38377 iter/s, 5.03404s/12 iters), loss = 5.27035
I0412 15:06:11.752352 20203 solver.cpp:237]     Train net output #0: loss = 5.27035 (* 1 = 5.27035 loss)
I0412 15:06:11.752364 20203 sgd_solver.cpp:105] Iteration 5400, lr = 0.00343124
I0412 15:06:13.767859 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5406.caffemodel
I0412 15:06:17.129055 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5406.solverstate
I0412 15:06:19.408448 20203 solver.cpp:330] Iteration 5406, Testing net (#0)
I0412 15:06:19.408468 20203 net.cpp:676] Ignoring source layer train-data
I0412 15:06:21.958220 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:06:24.432535 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 15:06:24.432572 20203 solver.cpp:397]     Test net output #1: loss = 5.28571 (* 1 = 5.28571 loss)
I0412 15:06:26.791669 20203 solver.cpp:218] Iteration 5412 (0.797942 iter/s, 15.0387s/12 iters), loss = 5.26686
I0412 15:06:26.791774 20203 solver.cpp:237]     Train net output #0: loss = 5.26686 (* 1 = 5.26686 loss)
I0412 15:06:26.791783 20203 sgd_solver.cpp:105] Iteration 5412, lr = 0.00342309
I0412 15:06:32.200675 20203 solver.cpp:218] Iteration 5424 (2.21866 iter/s, 5.40867s/12 iters), loss = 5.27893
I0412 15:06:32.200733 20203 solver.cpp:237]     Train net output #0: loss = 5.27893 (* 1 = 5.27893 loss)
I0412 15:06:32.200745 20203 sgd_solver.cpp:105] Iteration 5424, lr = 0.00341497
I0412 15:06:38.303383 20203 solver.cpp:218] Iteration 5436 (1.96644 iter/s, 6.10239s/12 iters), loss = 5.26305
I0412 15:06:38.303429 20203 solver.cpp:237]     Train net output #0: loss = 5.26305 (* 1 = 5.26305 loss)
I0412 15:06:38.303437 20203 sgd_solver.cpp:105] Iteration 5436, lr = 0.00340686
I0412 15:06:43.276777 20203 solver.cpp:218] Iteration 5448 (2.41297 iter/s, 4.97313s/12 iters), loss = 5.27688
I0412 15:06:43.276842 20203 solver.cpp:237]     Train net output #0: loss = 5.27688 (* 1 = 5.27688 loss)
I0412 15:06:43.276854 20203 sgd_solver.cpp:105] Iteration 5448, lr = 0.00339877
I0412 15:06:48.910598 20203 solver.cpp:218] Iteration 5460 (2.13011 iter/s, 5.63351s/12 iters), loss = 5.27875
I0412 15:06:48.910643 20203 solver.cpp:237]     Train net output #0: loss = 5.27875 (* 1 = 5.27875 loss)
I0412 15:06:48.910653 20203 sgd_solver.cpp:105] Iteration 5460, lr = 0.0033907
I0412 15:06:49.641880 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:06:54.589007 20203 solver.cpp:218] Iteration 5472 (2.11338 iter/s, 5.67812s/12 iters), loss = 5.27766
I0412 15:06:54.589063 20203 solver.cpp:237]     Train net output #0: loss = 5.27766 (* 1 = 5.27766 loss)
I0412 15:06:54.589076 20203 sgd_solver.cpp:105] Iteration 5472, lr = 0.00338265
I0412 15:06:59.702699 20203 solver.cpp:218] Iteration 5484 (2.34677 iter/s, 5.11341s/12 iters), loss = 5.27404
I0412 15:06:59.702879 20203 solver.cpp:237]     Train net output #0: loss = 5.27404 (* 1 = 5.27404 loss)
I0412 15:06:59.702894 20203 sgd_solver.cpp:105] Iteration 5484, lr = 0.00337462
I0412 15:07:04.730926 20203 solver.cpp:218] Iteration 5496 (2.38671 iter/s, 5.02783s/12 iters), loss = 5.29195
I0412 15:07:04.730983 20203 solver.cpp:237]     Train net output #0: loss = 5.29195 (* 1 = 5.29195 loss)
I0412 15:07:04.730993 20203 sgd_solver.cpp:105] Iteration 5496, lr = 0.00336661
I0412 15:07:09.359189 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5508.caffemodel
I0412 15:07:15.407721 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5508.solverstate
I0412 15:07:18.463213 20203 solver.cpp:330] Iteration 5508, Testing net (#0)
I0412 15:07:18.463238 20203 net.cpp:676] Ignoring source layer train-data
I0412 15:07:20.721006 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:07:23.206770 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 15:07:23.206800 20203 solver.cpp:397]     Test net output #1: loss = 5.28651 (* 1 = 5.28651 loss)
I0412 15:07:23.293498 20203 solver.cpp:218] Iteration 5508 (0.646491 iter/s, 18.5617s/12 iters), loss = 5.27538
I0412 15:07:23.293555 20203 solver.cpp:237]     Train net output #0: loss = 5.27538 (* 1 = 5.27538 loss)
I0412 15:07:23.293567 20203 sgd_solver.cpp:105] Iteration 5508, lr = 0.00335861
I0412 15:07:27.743160 20203 solver.cpp:218] Iteration 5520 (2.69699 iter/s, 4.44941s/12 iters), loss = 5.27501
I0412 15:07:27.743201 20203 solver.cpp:237]     Train net output #0: loss = 5.27501 (* 1 = 5.27501 loss)
I0412 15:07:27.743211 20203 sgd_solver.cpp:105] Iteration 5520, lr = 0.00335064
I0412 15:07:30.778192 20203 blocking_queue.cpp:49] Waiting for data
I0412 15:07:33.317720 20203 solver.cpp:218] Iteration 5532 (2.15275 iter/s, 5.57428s/12 iters), loss = 5.29159
I0412 15:07:33.317771 20203 solver.cpp:237]     Train net output #0: loss = 5.29159 (* 1 = 5.29159 loss)
I0412 15:07:33.317781 20203 sgd_solver.cpp:105] Iteration 5532, lr = 0.00334268
I0412 15:07:38.282330 20203 solver.cpp:218] Iteration 5544 (2.41724 iter/s, 4.96434s/12 iters), loss = 5.26163
I0412 15:07:38.282382 20203 solver.cpp:237]     Train net output #0: loss = 5.26163 (* 1 = 5.26163 loss)
I0412 15:07:38.282390 20203 sgd_solver.cpp:105] Iteration 5544, lr = 0.00333475
I0412 15:07:43.272122 20203 solver.cpp:218] Iteration 5556 (2.40504 iter/s, 4.98952s/12 iters), loss = 5.27042
I0412 15:07:43.272176 20203 solver.cpp:237]     Train net output #0: loss = 5.27042 (* 1 = 5.27042 loss)
I0412 15:07:43.272188 20203 sgd_solver.cpp:105] Iteration 5556, lr = 0.00332683
I0412 15:07:45.918943 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:07:48.180132 20203 solver.cpp:218] Iteration 5568 (2.44512 iter/s, 4.90774s/12 iters), loss = 5.2793
I0412 15:07:48.180187 20203 solver.cpp:237]     Train net output #0: loss = 5.2793 (* 1 = 5.2793 loss)
I0412 15:07:48.180198 20203 sgd_solver.cpp:105] Iteration 5568, lr = 0.00331893
I0412 15:07:53.058053 20203 solver.cpp:218] Iteration 5580 (2.4602 iter/s, 4.87765s/12 iters), loss = 5.25813
I0412 15:07:53.058111 20203 solver.cpp:237]     Train net output #0: loss = 5.25813 (* 1 = 5.25813 loss)
I0412 15:07:53.058125 20203 sgd_solver.cpp:105] Iteration 5580, lr = 0.00331105
I0412 15:07:57.909446 20203 solver.cpp:218] Iteration 5592 (2.47365 iter/s, 4.85112s/12 iters), loss = 5.27008
I0412 15:07:57.909505 20203 solver.cpp:237]     Train net output #0: loss = 5.27008 (* 1 = 5.27008 loss)
I0412 15:07:57.909518 20203 sgd_solver.cpp:105] Iteration 5592, lr = 0.00330319
I0412 15:08:02.785001 20203 solver.cpp:218] Iteration 5604 (2.4614 iter/s, 4.87528s/12 iters), loss = 5.26127
I0412 15:08:02.785151 20203 solver.cpp:237]     Train net output #0: loss = 5.26127 (* 1 = 5.26127 loss)
I0412 15:08:02.785162 20203 sgd_solver.cpp:105] Iteration 5604, lr = 0.00329535
I0412 15:08:04.750550 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5610.caffemodel
I0412 15:08:08.385978 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5610.solverstate
I0412 15:08:10.699860 20203 solver.cpp:330] Iteration 5610, Testing net (#0)
I0412 15:08:10.699890 20203 net.cpp:676] Ignoring source layer train-data
I0412 15:08:13.099514 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:08:15.384557 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 15:08:15.384598 20203 solver.cpp:397]     Test net output #1: loss = 5.28594 (* 1 = 5.28594 loss)
I0412 15:08:17.159121 20203 solver.cpp:218] Iteration 5616 (0.834877 iter/s, 14.3734s/12 iters), loss = 5.29069
I0412 15:08:17.159184 20203 solver.cpp:237]     Train net output #0: loss = 5.29069 (* 1 = 5.29069 loss)
I0412 15:08:17.159196 20203 sgd_solver.cpp:105] Iteration 5616, lr = 0.00328752
I0412 15:08:22.994629 20203 solver.cpp:218] Iteration 5628 (2.05649 iter/s, 5.83519s/12 iters), loss = 5.27412
I0412 15:08:22.994683 20203 solver.cpp:237]     Train net output #0: loss = 5.27412 (* 1 = 5.27412 loss)
I0412 15:08:22.994693 20203 sgd_solver.cpp:105] Iteration 5628, lr = 0.00327972
I0412 15:08:27.984586 20203 solver.cpp:218] Iteration 5640 (2.40496 iter/s, 4.98968s/12 iters), loss = 5.26035
I0412 15:08:27.984640 20203 solver.cpp:237]     Train net output #0: loss = 5.26035 (* 1 = 5.26035 loss)
I0412 15:08:27.984652 20203 sgd_solver.cpp:105] Iteration 5640, lr = 0.00327193
I0412 15:08:33.300854 20203 solver.cpp:218] Iteration 5652 (2.25734 iter/s, 5.31598s/12 iters), loss = 5.26852
I0412 15:08:33.300987 20203 solver.cpp:237]     Train net output #0: loss = 5.26852 (* 1 = 5.26852 loss)
I0412 15:08:33.300998 20203 sgd_solver.cpp:105] Iteration 5652, lr = 0.00326416
I0412 15:08:38.246376 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:08:38.412521 20203 solver.cpp:218] Iteration 5664 (2.34773 iter/s, 5.11132s/12 iters), loss = 5.25082
I0412 15:08:38.412572 20203 solver.cpp:237]     Train net output #0: loss = 5.25082 (* 1 = 5.25082 loss)
I0412 15:08:38.412582 20203 sgd_solver.cpp:105] Iteration 5664, lr = 0.00325641
I0412 15:08:43.553787 20203 solver.cpp:218] Iteration 5676 (2.33418 iter/s, 5.14099s/12 iters), loss = 5.26528
I0412 15:08:43.553839 20203 solver.cpp:237]     Train net output #0: loss = 5.26528 (* 1 = 5.26528 loss)
I0412 15:08:43.553851 20203 sgd_solver.cpp:105] Iteration 5676, lr = 0.00324868
I0412 15:08:48.392977 20203 solver.cpp:218] Iteration 5688 (2.47989 iter/s, 4.83893s/12 iters), loss = 5.29343
I0412 15:08:48.393028 20203 solver.cpp:237]     Train net output #0: loss = 5.29343 (* 1 = 5.29343 loss)
I0412 15:08:48.393038 20203 sgd_solver.cpp:105] Iteration 5688, lr = 0.00324097
I0412 15:08:53.364964 20203 solver.cpp:218] Iteration 5700 (2.41365 iter/s, 4.97171s/12 iters), loss = 5.2857
I0412 15:08:53.365018 20203 solver.cpp:237]     Train net output #0: loss = 5.2857 (* 1 = 5.2857 loss)
I0412 15:08:53.365029 20203 sgd_solver.cpp:105] Iteration 5700, lr = 0.00323328
I0412 15:08:57.733935 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5712.caffemodel
I0412 15:09:00.705591 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5712.solverstate
I0412 15:09:02.993194 20203 solver.cpp:330] Iteration 5712, Testing net (#0)
I0412 15:09:02.993216 20203 net.cpp:676] Ignoring source layer train-data
I0412 15:09:05.279351 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:09:07.638283 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 15:09:07.638317 20203 solver.cpp:397]     Test net output #1: loss = 5.28586 (* 1 = 5.28586 loss)
I0412 15:09:07.724763 20203 solver.cpp:218] Iteration 5712 (0.835704 iter/s, 14.3591s/12 iters), loss = 5.27599
I0412 15:09:07.724804 20203 solver.cpp:237]     Train net output #0: loss = 5.27599 (* 1 = 5.27599 loss)
I0412 15:09:07.724812 20203 sgd_solver.cpp:105] Iteration 5712, lr = 0.0032256
I0412 15:09:12.038497 20203 solver.cpp:218] Iteration 5724 (2.78196 iter/s, 4.3135s/12 iters), loss = 5.27002
I0412 15:09:12.038553 20203 solver.cpp:237]     Train net output #0: loss = 5.27002 (* 1 = 5.27002 loss)
I0412 15:09:12.038563 20203 sgd_solver.cpp:105] Iteration 5724, lr = 0.00321794
I0412 15:09:17.408769 20203 solver.cpp:218] Iteration 5736 (2.23465 iter/s, 5.36998s/12 iters), loss = 5.24681
I0412 15:09:17.408824 20203 solver.cpp:237]     Train net output #0: loss = 5.24681 (* 1 = 5.24681 loss)
I0412 15:09:17.408836 20203 sgd_solver.cpp:105] Iteration 5736, lr = 0.0032103
I0412 15:09:23.447253 20203 solver.cpp:218] Iteration 5748 (1.98736 iter/s, 6.03817s/12 iters), loss = 5.27554
I0412 15:09:23.447297 20203 solver.cpp:237]     Train net output #0: loss = 5.27554 (* 1 = 5.27554 loss)
I0412 15:09:23.447306 20203 sgd_solver.cpp:105] Iteration 5748, lr = 0.00320268
I0412 15:09:28.744179 20203 solver.cpp:218] Iteration 5760 (2.26558 iter/s, 5.29665s/12 iters), loss = 5.26676
I0412 15:09:28.744240 20203 solver.cpp:237]     Train net output #0: loss = 5.26676 (* 1 = 5.26676 loss)
I0412 15:09:28.744252 20203 sgd_solver.cpp:105] Iteration 5760, lr = 0.00319508
I0412 15:09:30.881094 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:09:33.766916 20203 solver.cpp:218] Iteration 5772 (2.38927 iter/s, 5.02246s/12 iters), loss = 5.29131
I0412 15:09:33.766966 20203 solver.cpp:237]     Train net output #0: loss = 5.29131 (* 1 = 5.29131 loss)
I0412 15:09:33.766978 20203 sgd_solver.cpp:105] Iteration 5772, lr = 0.00318749
I0412 15:09:38.726817 20203 solver.cpp:218] Iteration 5784 (2.41953 iter/s, 4.95964s/12 iters), loss = 5.27278
I0412 15:09:38.729938 20203 solver.cpp:237]     Train net output #0: loss = 5.27278 (* 1 = 5.27278 loss)
I0412 15:09:38.729949 20203 sgd_solver.cpp:105] Iteration 5784, lr = 0.00317992
I0412 15:09:43.672003 20203 solver.cpp:218] Iteration 5796 (2.42824 iter/s, 4.94185s/12 iters), loss = 5.26662
I0412 15:09:43.672060 20203 solver.cpp:237]     Train net output #0: loss = 5.26662 (* 1 = 5.26662 loss)
I0412 15:09:43.672070 20203 sgd_solver.cpp:105] Iteration 5796, lr = 0.00317237
I0412 15:09:48.795171 20203 solver.cpp:218] Iteration 5808 (2.34243 iter/s, 5.12289s/12 iters), loss = 5.26453
I0412 15:09:48.795228 20203 solver.cpp:237]     Train net output #0: loss = 5.26453 (* 1 = 5.26453 loss)
I0412 15:09:48.795241 20203 sgd_solver.cpp:105] Iteration 5808, lr = 0.00316484
I0412 15:09:51.475117 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5814.caffemodel
I0412 15:09:55.882344 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5814.solverstate
I0412 15:10:00.256659 20203 solver.cpp:330] Iteration 5814, Testing net (#0)
I0412 15:10:00.256685 20203 net.cpp:676] Ignoring source layer train-data
I0412 15:10:02.514200 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:10:04.946825 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 15:10:04.946864 20203 solver.cpp:397]     Test net output #1: loss = 5.28588 (* 1 = 5.28588 loss)
I0412 15:10:06.705030 20203 solver.cpp:218] Iteration 5820 (0.670052 iter/s, 17.9091s/12 iters), loss = 5.27597
I0412 15:10:06.705073 20203 solver.cpp:237]     Train net output #0: loss = 5.27597 (* 1 = 5.27597 loss)
I0412 15:10:06.705081 20203 sgd_solver.cpp:105] Iteration 5820, lr = 0.00315733
I0412 15:10:12.215631 20203 solver.cpp:218] Iteration 5832 (2.17774 iter/s, 5.51031s/12 iters), loss = 5.27642
I0412 15:10:12.215778 20203 solver.cpp:237]     Train net output #0: loss = 5.27642 (* 1 = 5.27642 loss)
I0412 15:10:12.215788 20203 sgd_solver.cpp:105] Iteration 5832, lr = 0.00314983
I0412 15:10:17.341519 20203 solver.cpp:218] Iteration 5844 (2.34123 iter/s, 5.12552s/12 iters), loss = 5.26281
I0412 15:10:17.341574 20203 solver.cpp:237]     Train net output #0: loss = 5.26281 (* 1 = 5.26281 loss)
I0412 15:10:17.341586 20203 sgd_solver.cpp:105] Iteration 5844, lr = 0.00314235
I0412 15:10:22.270220 20203 solver.cpp:218] Iteration 5856 (2.43485 iter/s, 4.92843s/12 iters), loss = 5.26164
I0412 15:10:22.270272 20203 solver.cpp:237]     Train net output #0: loss = 5.26164 (* 1 = 5.26164 loss)
I0412 15:10:22.270282 20203 sgd_solver.cpp:105] Iteration 5856, lr = 0.00313489
I0412 15:10:26.239890 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:10:27.050088 20203 solver.cpp:218] Iteration 5868 (2.51066 iter/s, 4.77961s/12 iters), loss = 5.25292
I0412 15:10:27.050138 20203 solver.cpp:237]     Train net output #0: loss = 5.25292 (* 1 = 5.25292 loss)
I0412 15:10:27.050150 20203 sgd_solver.cpp:105] Iteration 5868, lr = 0.00312745
I0412 15:10:31.852205 20203 solver.cpp:218] Iteration 5880 (2.49903 iter/s, 4.80186s/12 iters), loss = 5.27897
I0412 15:10:31.852265 20203 solver.cpp:237]     Train net output #0: loss = 5.27897 (* 1 = 5.27897 loss)
I0412 15:10:31.852278 20203 sgd_solver.cpp:105] Iteration 5880, lr = 0.00312002
I0412 15:10:36.817564 20203 solver.cpp:218] Iteration 5892 (2.41688 iter/s, 4.96507s/12 iters), loss = 5.27429
I0412 15:10:36.817629 20203 solver.cpp:237]     Train net output #0: loss = 5.27429 (* 1 = 5.27429 loss)
I0412 15:10:36.817641 20203 sgd_solver.cpp:105] Iteration 5892, lr = 0.00311262
I0412 15:10:42.873823 20203 solver.cpp:218] Iteration 5904 (1.98153 iter/s, 6.05592s/12 iters), loss = 5.30286
I0412 15:10:42.876641 20203 solver.cpp:237]     Train net output #0: loss = 5.30286 (* 1 = 5.30286 loss)
I0412 15:10:42.876660 20203 sgd_solver.cpp:105] Iteration 5904, lr = 0.00310523
I0412 15:10:51.424916 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5916.caffemodel
I0412 15:10:55.629880 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5916.solverstate
I0412 15:10:58.788185 20203 solver.cpp:330] Iteration 5916, Testing net (#0)
I0412 15:10:58.788210 20203 net.cpp:676] Ignoring source layer train-data
I0412 15:11:02.889050 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:11:07.237462 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 15:11:07.237499 20203 solver.cpp:397]     Test net output #1: loss = 5.2859 (* 1 = 5.2859 loss)
I0412 15:11:07.354224 20203 solver.cpp:218] Iteration 5916 (0.490265 iter/s, 24.4766s/12 iters), loss = 5.26934
I0412 15:11:07.354276 20203 solver.cpp:237]     Train net output #0: loss = 5.26934 (* 1 = 5.26934 loss)
I0412 15:11:07.354288 20203 sgd_solver.cpp:105] Iteration 5916, lr = 0.00309785
I0412 15:11:12.424994 20203 solver.cpp:218] Iteration 5928 (2.36663 iter/s, 5.07049s/12 iters), loss = 5.26841
I0412 15:11:12.425047 20203 solver.cpp:237]     Train net output #0: loss = 5.26841 (* 1 = 5.26841 loss)
I0412 15:11:12.425060 20203 sgd_solver.cpp:105] Iteration 5928, lr = 0.0030905
I0412 15:11:18.995956 20203 solver.cpp:218] Iteration 5940 (1.82631 iter/s, 6.57062s/12 iters), loss = 5.28297
I0412 15:11:18.996115 20203 solver.cpp:237]     Train net output #0: loss = 5.28297 (* 1 = 5.28297 loss)
I0412 15:11:18.996129 20203 sgd_solver.cpp:105] Iteration 5940, lr = 0.00308316
I0412 15:11:25.401264 20203 solver.cpp:218] Iteration 5952 (1.87357 iter/s, 6.40487s/12 iters), loss = 5.27539
I0412 15:11:25.407430 20203 solver.cpp:237]     Train net output #0: loss = 5.27539 (* 1 = 5.27539 loss)
I0412 15:11:25.407457 20203 sgd_solver.cpp:105] Iteration 5952, lr = 0.00307584
I0412 15:11:31.913019 20203 solver.cpp:218] Iteration 5964 (1.84464 iter/s, 6.50532s/12 iters), loss = 5.26104
I0412 15:11:31.913076 20203 solver.cpp:237]     Train net output #0: loss = 5.26104 (* 1 = 5.26104 loss)
I0412 15:11:31.913087 20203 sgd_solver.cpp:105] Iteration 5964, lr = 0.00306854
I0412 15:11:33.537186 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:11:38.177057 20203 solver.cpp:218] Iteration 5976 (1.9158 iter/s, 6.26371s/12 iters), loss = 5.27749
I0412 15:11:38.177112 20203 solver.cpp:237]     Train net output #0: loss = 5.27749 (* 1 = 5.27749 loss)
I0412 15:11:38.177124 20203 sgd_solver.cpp:105] Iteration 5976, lr = 0.00306125
I0412 15:11:44.562204 20203 solver.cpp:218] Iteration 5988 (1.87946 iter/s, 6.38481s/12 iters), loss = 5.26529
I0412 15:11:44.562265 20203 solver.cpp:237]     Train net output #0: loss = 5.26529 (* 1 = 5.26529 loss)
I0412 15:11:44.562278 20203 sgd_solver.cpp:105] Iteration 5988, lr = 0.00305398
I0412 15:11:51.130053 20203 solver.cpp:218] Iteration 6000 (1.82718 iter/s, 6.5675s/12 iters), loss = 5.27904
I0412 15:11:51.130182 20203 solver.cpp:237]     Train net output #0: loss = 5.27904 (* 1 = 5.27904 loss)
I0412 15:11:51.130195 20203 sgd_solver.cpp:105] Iteration 6000, lr = 0.00304673
I0412 15:11:57.262058 20203 solver.cpp:218] Iteration 6012 (1.95707 iter/s, 6.13161s/12 iters), loss = 5.27328
I0412 15:11:57.262110 20203 solver.cpp:237]     Train net output #0: loss = 5.27328 (* 1 = 5.27328 loss)
I0412 15:11:57.262122 20203 sgd_solver.cpp:105] Iteration 6012, lr = 0.0030395
I0412 15:12:00.006283 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6018.caffemodel
I0412 15:12:03.511188 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6018.solverstate
I0412 15:12:06.355477 20203 solver.cpp:330] Iteration 6018, Testing net (#0)
I0412 15:12:06.355499 20203 net.cpp:676] Ignoring source layer train-data
I0412 15:12:08.968866 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:12:11.423851 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 15:12:11.423900 20203 solver.cpp:397]     Test net output #1: loss = 5.28585 (* 1 = 5.28585 loss)
I0412 15:12:13.168936 20203 solver.cpp:218] Iteration 6024 (0.754425 iter/s, 15.9062s/12 iters), loss = 5.26337
I0412 15:12:13.168989 20203 solver.cpp:237]     Train net output #0: loss = 5.26337 (* 1 = 5.26337 loss)
I0412 15:12:13.169001 20203 sgd_solver.cpp:105] Iteration 6024, lr = 0.00303228
I0412 15:12:17.929513 20203 solver.cpp:218] Iteration 6036 (2.52084 iter/s, 4.76031s/12 iters), loss = 5.26211
I0412 15:12:17.929566 20203 solver.cpp:237]     Train net output #0: loss = 5.26211 (* 1 = 5.26211 loss)
I0412 15:12:17.929577 20203 sgd_solver.cpp:105] Iteration 6036, lr = 0.00302508
I0412 15:12:23.013442 20203 solver.cpp:218] Iteration 6048 (2.36051 iter/s, 5.08365s/12 iters), loss = 5.30267
I0412 15:12:23.013556 20203 solver.cpp:237]     Train net output #0: loss = 5.30267 (* 1 = 5.30267 loss)
I0412 15:12:23.013568 20203 sgd_solver.cpp:105] Iteration 6048, lr = 0.0030179
I0412 15:12:27.942811 20203 solver.cpp:218] Iteration 6060 (2.43455 iter/s, 4.92904s/12 iters), loss = 5.27998
I0412 15:12:27.942862 20203 solver.cpp:237]     Train net output #0: loss = 5.27998 (* 1 = 5.27998 loss)
I0412 15:12:27.942874 20203 sgd_solver.cpp:105] Iteration 6060, lr = 0.00301074
I0412 15:12:31.295197 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:12:32.837276 20203 solver.cpp:218] Iteration 6072 (2.45188 iter/s, 4.89419s/12 iters), loss = 5.27621
I0412 15:12:32.837357 20203 solver.cpp:237]     Train net output #0: loss = 5.27621 (* 1 = 5.27621 loss)
I0412 15:12:32.837375 20203 sgd_solver.cpp:105] Iteration 6072, lr = 0.00300359
I0412 15:12:37.868114 20203 solver.cpp:218] Iteration 6084 (2.38543 iter/s, 5.03054s/12 iters), loss = 5.25428
I0412 15:12:37.868167 20203 solver.cpp:237]     Train net output #0: loss = 5.25428 (* 1 = 5.25428 loss)
I0412 15:12:37.868180 20203 sgd_solver.cpp:105] Iteration 6084, lr = 0.00299646
I0412 15:12:42.742688 20203 solver.cpp:218] Iteration 6096 (2.46189 iter/s, 4.87431s/12 iters), loss = 5.26106
I0412 15:12:42.742745 20203 solver.cpp:237]     Train net output #0: loss = 5.26106 (* 1 = 5.26106 loss)
I0412 15:12:42.742758 20203 sgd_solver.cpp:105] Iteration 6096, lr = 0.00298934
I0412 15:12:47.594183 20203 solver.cpp:218] Iteration 6108 (2.4736 iter/s, 4.85123s/12 iters), loss = 5.2757
I0412 15:12:47.594238 20203 solver.cpp:237]     Train net output #0: loss = 5.2757 (* 1 = 5.2757 loss)
I0412 15:12:47.594250 20203 sgd_solver.cpp:105] Iteration 6108, lr = 0.00298225
I0412 15:12:52.057535 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6120.caffemodel
I0412 15:13:03.137260 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6120.solverstate
I0412 15:13:07.030314 20203 solver.cpp:330] Iteration 6120, Testing net (#0)
I0412 15:13:07.030342 20203 net.cpp:676] Ignoring source layer train-data
I0412 15:13:09.240321 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:13:11.829012 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 15:13:11.829066 20203 solver.cpp:397]     Test net output #1: loss = 5.28584 (* 1 = 5.28584 loss)
I0412 15:13:11.915617 20203 solver.cpp:218] Iteration 6120 (0.493414 iter/s, 24.3204s/12 iters), loss = 5.26584
I0412 15:13:11.915669 20203 solver.cpp:237]     Train net output #0: loss = 5.26584 (* 1 = 5.26584 loss)
I0412 15:13:11.915681 20203 sgd_solver.cpp:105] Iteration 6120, lr = 0.00297517
I0412 15:13:16.251457 20203 solver.cpp:218] Iteration 6132 (2.76779 iter/s, 4.3356s/12 iters), loss = 5.27315
I0412 15:13:16.251510 20203 solver.cpp:237]     Train net output #0: loss = 5.27315 (* 1 = 5.27315 loss)
I0412 15:13:16.251523 20203 sgd_solver.cpp:105] Iteration 6132, lr = 0.0029681
I0412 15:13:20.876045 20203 solver.cpp:218] Iteration 6144 (2.59497 iter/s, 4.62433s/12 iters), loss = 5.27318
I0412 15:13:20.876101 20203 solver.cpp:237]     Train net output #0: loss = 5.27318 (* 1 = 5.27318 loss)
I0412 15:13:20.876116 20203 sgd_solver.cpp:105] Iteration 6144, lr = 0.00296105
I0412 15:13:25.732228 20203 solver.cpp:218] Iteration 6156 (2.47122 iter/s, 4.8559s/12 iters), loss = 5.2791
I0412 15:13:25.732283 20203 solver.cpp:237]     Train net output #0: loss = 5.2791 (* 1 = 5.2791 loss)
I0412 15:13:25.732296 20203 sgd_solver.cpp:105] Iteration 6156, lr = 0.00295402
I0412 15:13:30.565249 20203 solver.cpp:218] Iteration 6168 (2.48306 iter/s, 4.83275s/12 iters), loss = 5.28819
I0412 15:13:30.565315 20203 solver.cpp:237]     Train net output #0: loss = 5.28819 (* 1 = 5.28819 loss)
I0412 15:13:30.565330 20203 sgd_solver.cpp:105] Iteration 6168, lr = 0.00294701
I0412 15:13:31.148136 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:13:35.511445 20203 solver.cpp:218] Iteration 6180 (2.42625 iter/s, 4.94591s/12 iters), loss = 5.28258
I0412 15:13:35.511574 20203 solver.cpp:237]     Train net output #0: loss = 5.28258 (* 1 = 5.28258 loss)
I0412 15:13:35.511590 20203 sgd_solver.cpp:105] Iteration 6180, lr = 0.00294001
I0412 15:13:40.476969 20203 solver.cpp:218] Iteration 6192 (2.41683 iter/s, 4.96518s/12 iters), loss = 5.26933
I0412 15:13:40.477020 20203 solver.cpp:237]     Train net output #0: loss = 5.26933 (* 1 = 5.26933 loss)
I0412 15:13:40.477030 20203 sgd_solver.cpp:105] Iteration 6192, lr = 0.00293303
I0412 15:13:45.623039 20203 solver.cpp:218] Iteration 6204 (2.33201 iter/s, 5.14579s/12 iters), loss = 5.28742
I0412 15:13:45.623095 20203 solver.cpp:237]     Train net output #0: loss = 5.28742 (* 1 = 5.28742 loss)
I0412 15:13:45.623108 20203 sgd_solver.cpp:105] Iteration 6204, lr = 0.00292607
I0412 15:13:50.878185 20203 solver.cpp:218] Iteration 6216 (2.2836 iter/s, 5.25486s/12 iters), loss = 5.27879
I0412 15:13:50.878238 20203 solver.cpp:237]     Train net output #0: loss = 5.27879 (* 1 = 5.27879 loss)
I0412 15:13:50.878250 20203 sgd_solver.cpp:105] Iteration 6216, lr = 0.00291912
I0412 15:13:53.087788 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6222.caffemodel
I0412 15:13:56.052088 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6222.solverstate
I0412 15:13:58.334937 20203 solver.cpp:330] Iteration 6222, Testing net (#0)
I0412 15:13:58.334959 20203 net.cpp:676] Ignoring source layer train-data
I0412 15:14:00.526413 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:14:01.874378 20203 blocking_queue.cpp:49] Waiting for data
I0412 15:14:03.202276 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 15:14:03.202312 20203 solver.cpp:397]     Test net output #1: loss = 5.28585 (* 1 = 5.28585 loss)
I0412 15:14:05.057821 20203 solver.cpp:218] Iteration 6228 (0.846323 iter/s, 14.179s/12 iters), loss = 5.27481
I0412 15:14:05.057885 20203 solver.cpp:237]     Train net output #0: loss = 5.27481 (* 1 = 5.27481 loss)
I0412 15:14:05.057901 20203 sgd_solver.cpp:105] Iteration 6228, lr = 0.00291219
I0412 15:14:10.107360 20203 solver.cpp:218] Iteration 6240 (2.37659 iter/s, 5.04925s/12 iters), loss = 5.28006
I0412 15:14:10.107484 20203 solver.cpp:237]     Train net output #0: loss = 5.28006 (* 1 = 5.28006 loss)
I0412 15:14:10.107496 20203 sgd_solver.cpp:105] Iteration 6240, lr = 0.00290528
I0412 15:14:15.176575 20203 solver.cpp:218] Iteration 6252 (2.36739 iter/s, 5.06887s/12 iters), loss = 5.25641
I0412 15:14:15.176626 20203 solver.cpp:237]     Train net output #0: loss = 5.25641 (* 1 = 5.25641 loss)
I0412 15:14:15.176637 20203 sgd_solver.cpp:105] Iteration 6252, lr = 0.00289838
I0412 15:14:20.357657 20203 solver.cpp:218] Iteration 6264 (2.31624 iter/s, 5.18081s/12 iters), loss = 5.26748
I0412 15:14:20.357707 20203 solver.cpp:237]     Train net output #0: loss = 5.26748 (* 1 = 5.26748 loss)
I0412 15:14:20.357719 20203 sgd_solver.cpp:105] Iteration 6264, lr = 0.0028915
I0412 15:14:23.155205 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:14:25.264369 20203 solver.cpp:218] Iteration 6276 (2.44576 iter/s, 4.90645s/12 iters), loss = 5.27923
I0412 15:14:25.264425 20203 solver.cpp:237]     Train net output #0: loss = 5.27923 (* 1 = 5.27923 loss)
I0412 15:14:25.264437 20203 sgd_solver.cpp:105] Iteration 6276, lr = 0.00288463
I0412 15:14:30.250931 20203 solver.cpp:218] Iteration 6288 (2.4066 iter/s, 4.98629s/12 iters), loss = 5.26172
I0412 15:14:30.250985 20203 solver.cpp:237]     Train net output #0: loss = 5.26172 (* 1 = 5.26172 loss)
I0412 15:14:30.250998 20203 sgd_solver.cpp:105] Iteration 6288, lr = 0.00287779
I0412 15:14:35.493786 20203 solver.cpp:218] Iteration 6300 (2.28895 iter/s, 5.24257s/12 iters), loss = 5.27286
I0412 15:14:35.493847 20203 solver.cpp:237]     Train net output #0: loss = 5.27286 (* 1 = 5.27286 loss)
I0412 15:14:35.493863 20203 sgd_solver.cpp:105] Iteration 6300, lr = 0.00287095
I0412 15:14:40.372469 20203 solver.cpp:218] Iteration 6312 (2.45982 iter/s, 4.87841s/12 iters), loss = 5.26009
I0412 15:14:40.373884 20203 solver.cpp:237]     Train net output #0: loss = 5.26009 (* 1 = 5.26009 loss)
I0412 15:14:40.373898 20203 sgd_solver.cpp:105] Iteration 6312, lr = 0.00286414
I0412 15:14:44.808774 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6324.caffemodel
I0412 15:14:47.829231 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6324.solverstate
I0412 15:14:50.136231 20203 solver.cpp:330] Iteration 6324, Testing net (#0)
I0412 15:14:50.136260 20203 net.cpp:676] Ignoring source layer train-data
I0412 15:14:52.106772 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:14:54.597656 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 15:14:54.597707 20203 solver.cpp:397]     Test net output #1: loss = 5.28604 (* 1 = 5.28604 loss)
I0412 15:14:54.684273 20203 solver.cpp:218] Iteration 6324 (0.838586 iter/s, 14.3098s/12 iters), loss = 5.29723
I0412 15:14:54.684314 20203 solver.cpp:237]     Train net output #0: loss = 5.29723 (* 1 = 5.29723 loss)
I0412 15:14:54.684322 20203 sgd_solver.cpp:105] Iteration 6324, lr = 0.00285734
I0412 15:14:59.129686 20203 solver.cpp:218] Iteration 6336 (2.69956 iter/s, 4.44517s/12 iters), loss = 5.27196
I0412 15:14:59.129735 20203 solver.cpp:237]     Train net output #0: loss = 5.27196 (* 1 = 5.27196 loss)
I0412 15:14:59.129745 20203 sgd_solver.cpp:105] Iteration 6336, lr = 0.00285055
I0412 15:15:03.914705 20203 solver.cpp:218] Iteration 6348 (2.50796 iter/s, 4.78476s/12 iters), loss = 5.26092
I0412 15:15:03.914752 20203 solver.cpp:237]     Train net output #0: loss = 5.26092 (* 1 = 5.26092 loss)
I0412 15:15:03.914763 20203 sgd_solver.cpp:105] Iteration 6348, lr = 0.00284379
I0412 15:15:08.853554 20203 solver.cpp:218] Iteration 6360 (2.42985 iter/s, 4.93858s/12 iters), loss = 5.2661
I0412 15:15:08.853605 20203 solver.cpp:237]     Train net output #0: loss = 5.2661 (* 1 = 5.2661 loss)
I0412 15:15:08.853615 20203 sgd_solver.cpp:105] Iteration 6360, lr = 0.00283703
I0412 15:15:13.550822 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:15:13.686445 20203 solver.cpp:218] Iteration 6372 (2.48312 iter/s, 4.83263s/12 iters), loss = 5.2527
I0412 15:15:13.686497 20203 solver.cpp:237]     Train net output #0: loss = 5.2527 (* 1 = 5.2527 loss)
I0412 15:15:13.686509 20203 sgd_solver.cpp:105] Iteration 6372, lr = 0.0028303
I0412 15:15:18.532565 20203 solver.cpp:218] Iteration 6384 (2.47634 iter/s, 4.84586s/12 iters), loss = 5.26777
I0412 15:15:18.532606 20203 solver.cpp:237]     Train net output #0: loss = 5.26777 (* 1 = 5.26777 loss)
I0412 15:15:18.532614 20203 sgd_solver.cpp:105] Iteration 6384, lr = 0.00282358
I0412 15:15:23.672044 20203 solver.cpp:218] Iteration 6396 (2.33499 iter/s, 5.13921s/12 iters), loss = 5.29545
I0412 15:15:23.672086 20203 solver.cpp:237]     Train net output #0: loss = 5.29545 (* 1 = 5.29545 loss)
I0412 15:15:23.672094 20203 sgd_solver.cpp:105] Iteration 6396, lr = 0.00281687
I0412 15:15:28.917498 20203 solver.cpp:218] Iteration 6408 (2.28782 iter/s, 5.24518s/12 iters), loss = 5.28598
I0412 15:15:28.917548 20203 solver.cpp:237]     Train net output #0: loss = 5.28598 (* 1 = 5.28598 loss)
I0412 15:15:28.917558 20203 sgd_solver.cpp:105] Iteration 6408, lr = 0.00281019
I0412 15:15:33.723011 20203 solver.cpp:218] Iteration 6420 (2.49727 iter/s, 4.80525s/12 iters), loss = 5.28046
I0412 15:15:33.723065 20203 solver.cpp:237]     Train net output #0: loss = 5.28046 (* 1 = 5.28046 loss)
I0412 15:15:33.723080 20203 sgd_solver.cpp:105] Iteration 6420, lr = 0.00280351
I0412 15:15:35.687567 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6426.caffemodel
I0412 15:15:38.679883 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6426.solverstate
I0412 15:15:40.967046 20203 solver.cpp:330] Iteration 6426, Testing net (#0)
I0412 15:15:40.967072 20203 net.cpp:676] Ignoring source layer train-data
I0412 15:15:42.975662 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:15:45.597362 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 15:15:45.597491 20203 solver.cpp:397]     Test net output #1: loss = 5.28593 (* 1 = 5.28593 loss)
I0412 15:15:47.429556 20203 solver.cpp:218] Iteration 6432 (0.875534 iter/s, 13.7059s/12 iters), loss = 5.26904
I0412 15:15:47.429600 20203 solver.cpp:237]     Train net output #0: loss = 5.26904 (* 1 = 5.26904 loss)
I0412 15:15:47.429610 20203 sgd_solver.cpp:105] Iteration 6432, lr = 0.00279686
I0412 15:15:52.174702 20203 solver.cpp:218] Iteration 6444 (2.52904 iter/s, 4.74489s/12 iters), loss = 5.24683
I0412 15:15:52.174752 20203 solver.cpp:237]     Train net output #0: loss = 5.24683 (* 1 = 5.24683 loss)
I0412 15:15:52.174764 20203 sgd_solver.cpp:105] Iteration 6444, lr = 0.00279022
I0412 15:15:56.892982 20203 solver.cpp:218] Iteration 6456 (2.54344 iter/s, 4.71802s/12 iters), loss = 5.27312
I0412 15:15:56.893028 20203 solver.cpp:237]     Train net output #0: loss = 5.27312 (* 1 = 5.27312 loss)
I0412 15:15:56.893038 20203 sgd_solver.cpp:105] Iteration 6456, lr = 0.00278359
I0412 15:16:01.608652 20203 solver.cpp:218] Iteration 6468 (2.54484 iter/s, 4.71542s/12 iters), loss = 5.26983
I0412 15:16:01.608693 20203 solver.cpp:237]     Train net output #0: loss = 5.26983 (* 1 = 5.26983 loss)
I0412 15:16:01.608702 20203 sgd_solver.cpp:105] Iteration 6468, lr = 0.00277698
I0412 15:16:03.558606 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:16:06.579228 20203 solver.cpp:218] Iteration 6480 (2.41433 iter/s, 4.97031s/12 iters), loss = 5.28773
I0412 15:16:06.579277 20203 solver.cpp:237]     Train net output #0: loss = 5.28773 (* 1 = 5.28773 loss)
I0412 15:16:06.579288 20203 sgd_solver.cpp:105] Iteration 6480, lr = 0.00277039
I0412 15:16:11.716884 20203 solver.cpp:218] Iteration 6492 (2.33582 iter/s, 5.13738s/12 iters), loss = 5.27215
I0412 15:16:11.716926 20203 solver.cpp:237]     Train net output #0: loss = 5.27215 (* 1 = 5.27215 loss)
I0412 15:16:11.716935 20203 sgd_solver.cpp:105] Iteration 6492, lr = 0.00276381
I0412 15:16:16.743374 20203 solver.cpp:218] Iteration 6504 (2.38748 iter/s, 5.02623s/12 iters), loss = 5.27113
I0412 15:16:16.743528 20203 solver.cpp:237]     Train net output #0: loss = 5.27113 (* 1 = 5.27113 loss)
I0412 15:16:16.743542 20203 sgd_solver.cpp:105] Iteration 6504, lr = 0.00275725
I0412 15:16:21.462601 20203 solver.cpp:218] Iteration 6516 (2.54298 iter/s, 4.71887s/12 iters), loss = 5.26386
I0412 15:16:21.462642 20203 solver.cpp:237]     Train net output #0: loss = 5.26386 (* 1 = 5.26386 loss)
I0412 15:16:21.462653 20203 sgd_solver.cpp:105] Iteration 6516, lr = 0.00275071
I0412 15:16:26.042106 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6528.caffemodel
I0412 15:16:29.075585 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6528.solverstate
I0412 15:16:31.403976 20203 solver.cpp:330] Iteration 6528, Testing net (#0)
I0412 15:16:31.404000 20203 net.cpp:676] Ignoring source layer train-data
I0412 15:16:33.290187 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:16:35.954200 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 15:16:35.954249 20203 solver.cpp:397]     Test net output #1: loss = 5.28599 (* 1 = 5.28599 loss)
I0412 15:16:36.040585 20203 solver.cpp:218] Iteration 6528 (0.823196 iter/s, 14.5773s/12 iters), loss = 5.27058
I0412 15:16:36.040637 20203 solver.cpp:237]     Train net output #0: loss = 5.27058 (* 1 = 5.27058 loss)
I0412 15:16:36.040649 20203 sgd_solver.cpp:105] Iteration 6528, lr = 0.00274418
I0412 15:16:40.080123 20203 solver.cpp:218] Iteration 6540 (2.97081 iter/s, 4.0393s/12 iters), loss = 5.2722
I0412 15:16:40.080178 20203 solver.cpp:237]     Train net output #0: loss = 5.2722 (* 1 = 5.2722 loss)
I0412 15:16:40.080189 20203 sgd_solver.cpp:105] Iteration 6540, lr = 0.00273766
I0412 15:16:44.821236 20203 solver.cpp:218] Iteration 6552 (2.53119 iter/s, 4.74086s/12 iters), loss = 5.26695
I0412 15:16:44.821278 20203 solver.cpp:237]     Train net output #0: loss = 5.26695 (* 1 = 5.26695 loss)
I0412 15:16:44.821290 20203 sgd_solver.cpp:105] Iteration 6552, lr = 0.00273116
I0412 15:16:49.528018 20203 solver.cpp:218] Iteration 6564 (2.54965 iter/s, 4.70653s/12 iters), loss = 5.2595
I0412 15:16:49.528136 20203 solver.cpp:237]     Train net output #0: loss = 5.2595 (* 1 = 5.2595 loss)
I0412 15:16:49.528149 20203 sgd_solver.cpp:105] Iteration 6564, lr = 0.00272468
I0412 15:16:53.760479 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:16:54.553908 20203 solver.cpp:218] Iteration 6576 (2.38779 iter/s, 5.02556s/12 iters), loss = 5.25543
I0412 15:16:54.553951 20203 solver.cpp:237]     Train net output #0: loss = 5.25543 (* 1 = 5.25543 loss)
I0412 15:16:54.553978 20203 sgd_solver.cpp:105] Iteration 6576, lr = 0.00271821
I0412 15:16:59.583329 20203 solver.cpp:218] Iteration 6588 (2.38608 iter/s, 5.02916s/12 iters), loss = 5.28269
I0412 15:16:59.583376 20203 solver.cpp:237]     Train net output #0: loss = 5.28269 (* 1 = 5.28269 loss)
I0412 15:16:59.583389 20203 sgd_solver.cpp:105] Iteration 6588, lr = 0.00271175
I0412 15:17:04.138859 20203 solver.cpp:218] Iteration 6600 (2.63431 iter/s, 4.55528s/12 iters), loss = 5.27304
I0412 15:17:04.138916 20203 solver.cpp:237]     Train net output #0: loss = 5.27304 (* 1 = 5.27304 loss)
I0412 15:17:04.138929 20203 sgd_solver.cpp:105] Iteration 6600, lr = 0.00270532
I0412 15:17:09.021381 20203 solver.cpp:218] Iteration 6612 (2.45788 iter/s, 4.88225s/12 iters), loss = 5.30456
I0412 15:17:09.021425 20203 solver.cpp:237]     Train net output #0: loss = 5.30456 (* 1 = 5.30456 loss)
I0412 15:17:09.021433 20203 sgd_solver.cpp:105] Iteration 6612, lr = 0.00269889
I0412 15:17:13.965400 20203 solver.cpp:218] Iteration 6624 (2.4273 iter/s, 4.94375s/12 iters), loss = 5.27182
I0412 15:17:13.965452 20203 solver.cpp:237]     Train net output #0: loss = 5.27182 (* 1 = 5.27182 loss)
I0412 15:17:13.965463 20203 sgd_solver.cpp:105] Iteration 6624, lr = 0.00269248
I0412 15:17:16.007659 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6630.caffemodel
I0412 15:17:20.525290 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6630.solverstate
I0412 15:17:24.314333 20203 solver.cpp:330] Iteration 6630, Testing net (#0)
I0412 15:17:24.314360 20203 net.cpp:676] Ignoring source layer train-data
I0412 15:17:26.180444 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:17:28.786491 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 15:17:28.786520 20203 solver.cpp:397]     Test net output #1: loss = 5.28602 (* 1 = 5.28602 loss)
I0412 15:17:30.766371 20203 solver.cpp:218] Iteration 6636 (0.714276 iter/s, 16.8002s/12 iters), loss = 5.27259
I0412 15:17:30.766418 20203 solver.cpp:237]     Train net output #0: loss = 5.27259 (* 1 = 5.27259 loss)
I0412 15:17:30.766428 20203 sgd_solver.cpp:105] Iteration 6636, lr = 0.00268609
I0412 15:17:35.699987 20203 solver.cpp:218] Iteration 6648 (2.43242 iter/s, 4.93335s/12 iters), loss = 5.27591
I0412 15:17:35.700028 20203 solver.cpp:237]     Train net output #0: loss = 5.27591 (* 1 = 5.27591 loss)
I0412 15:17:35.700038 20203 sgd_solver.cpp:105] Iteration 6648, lr = 0.00267971
I0412 15:17:40.811573 20203 solver.cpp:218] Iteration 6660 (2.34773 iter/s, 5.11132s/12 iters), loss = 5.27999
I0412 15:17:40.811621 20203 solver.cpp:237]     Train net output #0: loss = 5.27999 (* 1 = 5.27999 loss)
I0412 15:17:40.811631 20203 sgd_solver.cpp:105] Iteration 6660, lr = 0.00267335
I0412 15:17:45.738360 20203 solver.cpp:218] Iteration 6672 (2.43579 iter/s, 4.92653s/12 iters), loss = 5.26383
I0412 15:17:45.738405 20203 solver.cpp:237]     Train net output #0: loss = 5.26383 (* 1 = 5.26383 loss)
I0412 15:17:45.738415 20203 sgd_solver.cpp:105] Iteration 6672, lr = 0.00266701
I0412 15:17:47.061370 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:17:50.536304 20203 solver.cpp:218] Iteration 6684 (2.5012 iter/s, 4.79769s/12 iters), loss = 5.27217
I0412 15:17:50.536399 20203 solver.cpp:237]     Train net output #0: loss = 5.27217 (* 1 = 5.27217 loss)
I0412 15:17:50.536409 20203 sgd_solver.cpp:105] Iteration 6684, lr = 0.00266067
I0412 15:17:55.469942 20203 solver.cpp:218] Iteration 6696 (2.43244 iter/s, 4.93333s/12 iters), loss = 5.26558
I0412 15:17:55.470012 20203 solver.cpp:237]     Train net output #0: loss = 5.26558 (* 1 = 5.26558 loss)
I0412 15:17:55.470029 20203 sgd_solver.cpp:105] Iteration 6696, lr = 0.00265436
I0412 15:18:00.346585 20203 solver.cpp:218] Iteration 6708 (2.46085 iter/s, 4.87636s/12 iters), loss = 5.27295
I0412 15:18:00.346643 20203 solver.cpp:237]     Train net output #0: loss = 5.27295 (* 1 = 5.27295 loss)
I0412 15:18:00.346657 20203 sgd_solver.cpp:105] Iteration 6708, lr = 0.00264805
I0412 15:18:05.114842 20203 solver.cpp:218] Iteration 6720 (2.51678 iter/s, 4.76799s/12 iters), loss = 5.27062
I0412 15:18:05.114894 20203 solver.cpp:237]     Train net output #0: loss = 5.27062 (* 1 = 5.27062 loss)
I0412 15:18:05.114905 20203 sgd_solver.cpp:105] Iteration 6720, lr = 0.00264177
I0412 15:18:09.709233 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6732.caffemodel
I0412 15:18:12.695565 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6732.solverstate
I0412 15:18:14.988339 20203 solver.cpp:330] Iteration 6732, Testing net (#0)
I0412 15:18:14.988365 20203 net.cpp:676] Ignoring source layer train-data
I0412 15:18:16.853916 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:18:19.527776 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 15:18:19.527815 20203 solver.cpp:397]     Test net output #1: loss = 5.28573 (* 1 = 5.28573 loss)
I0412 15:18:19.614166 20203 solver.cpp:218] Iteration 6732 (0.827663 iter/s, 14.4987s/12 iters), loss = 5.27075
I0412 15:18:19.614220 20203 solver.cpp:237]     Train net output #0: loss = 5.27075 (* 1 = 5.27075 loss)
I0412 15:18:19.614231 20203 sgd_solver.cpp:105] Iteration 6732, lr = 0.0026355
I0412 15:18:23.796676 20203 solver.cpp:218] Iteration 6744 (2.86925 iter/s, 4.18227s/12 iters), loss = 5.26518
I0412 15:18:23.796790 20203 solver.cpp:237]     Train net output #0: loss = 5.26518 (* 1 = 5.26518 loss)
I0412 15:18:23.796800 20203 sgd_solver.cpp:105] Iteration 6744, lr = 0.00262924
I0412 15:18:28.865465 20203 solver.cpp:218] Iteration 6756 (2.36759 iter/s, 5.06846s/12 iters), loss = 5.28919
I0412 15:18:28.865505 20203 solver.cpp:237]     Train net output #0: loss = 5.28919 (* 1 = 5.28919 loss)
I0412 15:18:28.865514 20203 sgd_solver.cpp:105] Iteration 6756, lr = 0.002623
I0412 15:18:34.171104 20203 solver.cpp:218] Iteration 6768 (2.26186 iter/s, 5.30537s/12 iters), loss = 5.2726
I0412 15:18:34.171146 20203 solver.cpp:237]     Train net output #0: loss = 5.2726 (* 1 = 5.2726 loss)
I0412 15:18:34.171155 20203 sgd_solver.cpp:105] Iteration 6768, lr = 0.00261677
I0412 15:18:37.657506 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:18:39.101259 20203 solver.cpp:218] Iteration 6780 (2.43413 iter/s, 4.9299s/12 iters), loss = 5.27815
I0412 15:18:39.101305 20203 solver.cpp:237]     Train net output #0: loss = 5.27815 (* 1 = 5.27815 loss)
I0412 15:18:39.101317 20203 sgd_solver.cpp:105] Iteration 6780, lr = 0.00261056
I0412 15:18:44.173755 20203 solver.cpp:218] Iteration 6792 (2.36582 iter/s, 5.07223s/12 iters), loss = 5.25734
I0412 15:18:44.173817 20203 solver.cpp:237]     Train net output #0: loss = 5.25734 (* 1 = 5.25734 loss)
I0412 15:18:44.173835 20203 sgd_solver.cpp:105] Iteration 6792, lr = 0.00260436
I0412 15:18:49.016892 20203 solver.cpp:218] Iteration 6804 (2.47787 iter/s, 4.84287s/12 iters), loss = 5.26586
I0412 15:18:49.016947 20203 solver.cpp:237]     Train net output #0: loss = 5.26586 (* 1 = 5.26586 loss)
I0412 15:18:49.016960 20203 sgd_solver.cpp:105] Iteration 6804, lr = 0.00259817
I0412 15:18:54.165926 20203 solver.cpp:218] Iteration 6816 (2.33066 iter/s, 5.14876s/12 iters), loss = 5.28059
I0412 15:18:54.166040 20203 solver.cpp:237]     Train net output #0: loss = 5.28059 (* 1 = 5.28059 loss)
I0412 15:18:54.166055 20203 sgd_solver.cpp:105] Iteration 6816, lr = 0.00259201
I0412 15:18:59.201159 20203 solver.cpp:218] Iteration 6828 (2.38336 iter/s, 5.0349s/12 iters), loss = 5.26984
I0412 15:18:59.201215 20203 solver.cpp:237]     Train net output #0: loss = 5.26984 (* 1 = 5.26984 loss)
I0412 15:18:59.201228 20203 sgd_solver.cpp:105] Iteration 6828, lr = 0.00258585
I0412 15:19:01.145642 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6834.caffemodel
I0412 15:19:08.688565 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6834.solverstate
I0412 15:19:14.451493 20203 solver.cpp:330] Iteration 6834, Testing net (#0)
I0412 15:19:14.451514 20203 net.cpp:676] Ignoring source layer train-data
I0412 15:19:16.353924 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:19:19.298652 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 15:19:19.298702 20203 solver.cpp:397]     Test net output #1: loss = 5.28626 (* 1 = 5.28626 loss)
I0412 15:19:21.288164 20203 solver.cpp:218] Iteration 6840 (0.54333 iter/s, 22.086s/12 iters), loss = 5.27379
I0412 15:19:21.288210 20203 solver.cpp:237]     Train net output #0: loss = 5.27379 (* 1 = 5.27379 loss)
I0412 15:19:21.288219 20203 sgd_solver.cpp:105] Iteration 6840, lr = 0.00257971
I0412 15:19:26.223847 20203 solver.cpp:218] Iteration 6852 (2.43141 iter/s, 4.93542s/12 iters), loss = 5.27443
I0412 15:19:26.224005 20203 solver.cpp:237]     Train net output #0: loss = 5.27443 (* 1 = 5.27443 loss)
I0412 15:19:26.224020 20203 sgd_solver.cpp:105] Iteration 6852, lr = 0.00257359
I0412 15:19:31.445181 20203 solver.cpp:218] Iteration 6864 (2.29843 iter/s, 5.22095s/12 iters), loss = 5.27451
I0412 15:19:31.445225 20203 solver.cpp:237]     Train net output #0: loss = 5.27451 (* 1 = 5.27451 loss)
I0412 15:19:31.445235 20203 sgd_solver.cpp:105] Iteration 6864, lr = 0.00256748
I0412 15:19:36.634001 20203 solver.cpp:218] Iteration 6876 (2.31279 iter/s, 5.18855s/12 iters), loss = 5.28536
I0412 15:19:36.634043 20203 solver.cpp:237]     Train net output #0: loss = 5.28536 (* 1 = 5.28536 loss)
I0412 15:19:36.634052 20203 sgd_solver.cpp:105] Iteration 6876, lr = 0.00256138
I0412 15:19:37.310127 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:19:41.766739 20203 solver.cpp:218] Iteration 6888 (2.33806 iter/s, 5.13246s/12 iters), loss = 5.28041
I0412 15:19:41.766793 20203 solver.cpp:237]     Train net output #0: loss = 5.28041 (* 1 = 5.28041 loss)
I0412 15:19:41.766803 20203 sgd_solver.cpp:105] Iteration 6888, lr = 0.0025553
I0412 15:19:46.786130 20203 solver.cpp:218] Iteration 6900 (2.39086 iter/s, 5.01912s/12 iters), loss = 5.26838
I0412 15:19:46.786186 20203 solver.cpp:237]     Train net output #0: loss = 5.26838 (* 1 = 5.26838 loss)
I0412 15:19:46.786199 20203 sgd_solver.cpp:105] Iteration 6900, lr = 0.00254923
I0412 15:19:51.526223 20203 solver.cpp:218] Iteration 6912 (2.53174 iter/s, 4.73983s/12 iters), loss = 5.28349
I0412 15:19:51.526285 20203 solver.cpp:237]     Train net output #0: loss = 5.28349 (* 1 = 5.28349 loss)
I0412 15:19:51.526299 20203 sgd_solver.cpp:105] Iteration 6912, lr = 0.00254318
I0412 15:19:56.466756 20203 solver.cpp:218] Iteration 6924 (2.42902 iter/s, 4.94026s/12 iters), loss = 5.27887
I0412 15:19:56.466827 20203 solver.cpp:237]     Train net output #0: loss = 5.27887 (* 1 = 5.27887 loss)
I0412 15:19:56.466838 20203 sgd_solver.cpp:105] Iteration 6924, lr = 0.00253714
I0412 15:20:01.082650 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6936.caffemodel
I0412 15:20:04.340893 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6936.solverstate
I0412 15:20:09.051261 20203 solver.cpp:330] Iteration 6936, Testing net (#0)
I0412 15:20:09.051291 20203 net.cpp:676] Ignoring source layer train-data
I0412 15:20:09.730937 20203 blocking_queue.cpp:49] Waiting for data
I0412 15:20:10.825984 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:20:13.546314 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 15:20:13.546355 20203 solver.cpp:397]     Test net output #1: loss = 5.28607 (* 1 = 5.28607 loss)
I0412 15:20:13.633729 20203 solver.cpp:218] Iteration 6936 (0.699049 iter/s, 17.1662s/12 iters), loss = 5.28635
I0412 15:20:13.633785 20203 solver.cpp:237]     Train net output #0: loss = 5.28635 (* 1 = 5.28635 loss)
I0412 15:20:13.633798 20203 sgd_solver.cpp:105] Iteration 6936, lr = 0.00253112
I0412 15:20:17.614516 20203 solver.cpp:218] Iteration 6948 (3.01466 iter/s, 3.98055s/12 iters), loss = 5.27672
I0412 15:20:17.614562 20203 solver.cpp:237]     Train net output #0: loss = 5.27672 (* 1 = 5.27672 loss)
I0412 15:20:17.614571 20203 sgd_solver.cpp:105] Iteration 6948, lr = 0.00252511
I0412 15:20:22.467267 20203 solver.cpp:218] Iteration 6960 (2.47296 iter/s, 4.85249s/12 iters), loss = 5.26537
I0412 15:20:22.467316 20203 solver.cpp:237]     Train net output #0: loss = 5.26537 (* 1 = 5.26537 loss)
I0412 15:20:22.467329 20203 sgd_solver.cpp:105] Iteration 6960, lr = 0.00251911
I0412 15:20:27.439775 20203 solver.cpp:218] Iteration 6972 (2.4134 iter/s, 4.97224s/12 iters), loss = 5.26719
I0412 15:20:27.439916 20203 solver.cpp:237]     Train net output #0: loss = 5.26719 (* 1 = 5.26719 loss)
I0412 15:20:27.439931 20203 sgd_solver.cpp:105] Iteration 6972, lr = 0.00251313
I0412 15:20:29.870409 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:20:31.877635 20203 solver.cpp:218] Iteration 6984 (2.70421 iter/s, 4.43753s/12 iters), loss = 5.27756
I0412 15:20:31.877677 20203 solver.cpp:237]     Train net output #0: loss = 5.27756 (* 1 = 5.27756 loss)
I0412 15:20:31.877686 20203 sgd_solver.cpp:105] Iteration 6984, lr = 0.00250717
I0412 15:20:36.882795 20203 solver.cpp:218] Iteration 6996 (2.39765 iter/s, 5.00491s/12 iters), loss = 5.25855
I0412 15:20:36.882846 20203 solver.cpp:237]     Train net output #0: loss = 5.25855 (* 1 = 5.25855 loss)
I0412 15:20:36.882859 20203 sgd_solver.cpp:105] Iteration 6996, lr = 0.00250121
I0412 15:20:41.555073 20203 solver.cpp:218] Iteration 7008 (2.56847 iter/s, 4.67204s/12 iters), loss = 5.25928
I0412 15:20:41.555128 20203 solver.cpp:237]     Train net output #0: loss = 5.25928 (* 1 = 5.25928 loss)
I0412 15:20:41.555142 20203 sgd_solver.cpp:105] Iteration 7008, lr = 0.00249528
I0412 15:20:46.500147 20203 solver.cpp:218] Iteration 7020 (2.42678 iter/s, 4.94482s/12 iters), loss = 5.25792
I0412 15:20:46.500197 20203 solver.cpp:237]     Train net output #0: loss = 5.25792 (* 1 = 5.25792 loss)
I0412 15:20:46.500209 20203 sgd_solver.cpp:105] Iteration 7020, lr = 0.00248935
I0412 15:20:51.363879 20203 solver.cpp:218] Iteration 7032 (2.46737 iter/s, 4.86348s/12 iters), loss = 5.30613
I0412 15:20:51.363934 20203 solver.cpp:237]     Train net output #0: loss = 5.30613 (* 1 = 5.30613 loss)
I0412 15:20:51.363945 20203 sgd_solver.cpp:105] Iteration 7032, lr = 0.00248344
I0412 15:20:53.381276 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7038.caffemodel
I0412 15:20:59.086354 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7038.solverstate
I0412 15:21:02.318563 20203 solver.cpp:330] Iteration 7038, Testing net (#0)
I0412 15:21:02.318590 20203 net.cpp:676] Ignoring source layer train-data
I0412 15:21:04.068678 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:21:06.881011 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 15:21:06.881049 20203 solver.cpp:397]     Test net output #1: loss = 5.28582 (* 1 = 5.28582 loss)
I0412 15:21:08.811785 20203 solver.cpp:218] Iteration 7044 (0.68779 iter/s, 17.4472s/12 iters), loss = 5.27164
I0412 15:21:08.811837 20203 solver.cpp:237]     Train net output #0: loss = 5.27164 (* 1 = 5.27164 loss)
I0412 15:21:08.811848 20203 sgd_solver.cpp:105] Iteration 7044, lr = 0.00247755
I0412 15:21:13.964144 20203 solver.cpp:218] Iteration 7056 (2.32915 iter/s, 5.1521s/12 iters), loss = 5.27084
I0412 15:21:13.964186 20203 solver.cpp:237]     Train net output #0: loss = 5.27084 (* 1 = 5.27084 loss)
I0412 15:21:13.964195 20203 sgd_solver.cpp:105] Iteration 7056, lr = 0.00247166
I0412 15:21:18.752666 20203 solver.cpp:218] Iteration 7068 (2.50612 iter/s, 4.78829s/12 iters), loss = 5.27059
I0412 15:21:18.752712 20203 solver.cpp:237]     Train net output #0: loss = 5.27059 (* 1 = 5.27059 loss)
I0412 15:21:18.752722 20203 sgd_solver.cpp:105] Iteration 7068, lr = 0.0024658
I0412 15:21:23.505137 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:21:23.609208 20203 solver.cpp:218] Iteration 7080 (2.47102 iter/s, 4.8563s/12 iters), loss = 5.25027
I0412 15:21:23.609259 20203 solver.cpp:237]     Train net output #0: loss = 5.25027 (* 1 = 5.25027 loss)
I0412 15:21:23.609272 20203 sgd_solver.cpp:105] Iteration 7080, lr = 0.00245994
I0412 15:21:28.791160 20203 solver.cpp:218] Iteration 7092 (2.31584 iter/s, 5.18169s/12 iters), loss = 5.26595
I0412 15:21:28.791203 20203 solver.cpp:237]     Train net output #0: loss = 5.26595 (* 1 = 5.26595 loss)
I0412 15:21:28.791213 20203 sgd_solver.cpp:105] Iteration 7092, lr = 0.0024541
I0412 15:21:33.951927 20203 solver.cpp:218] Iteration 7104 (2.32535 iter/s, 5.16051s/12 iters), loss = 5.29585
I0412 15:21:33.952080 20203 solver.cpp:237]     Train net output #0: loss = 5.29585 (* 1 = 5.29585 loss)
I0412 15:21:33.952095 20203 sgd_solver.cpp:105] Iteration 7104, lr = 0.00244827
I0412 15:21:38.991907 20203 solver.cpp:218] Iteration 7116 (2.38113 iter/s, 5.03963s/12 iters), loss = 5.28198
I0412 15:21:38.991947 20203 solver.cpp:237]     Train net output #0: loss = 5.28198 (* 1 = 5.28198 loss)
I0412 15:21:38.991957 20203 sgd_solver.cpp:105] Iteration 7116, lr = 0.00244246
I0412 15:21:43.789260 20203 solver.cpp:218] Iteration 7128 (2.50151 iter/s, 4.79711s/12 iters), loss = 5.27304
I0412 15:21:43.789316 20203 solver.cpp:237]     Train net output #0: loss = 5.27304 (* 1 = 5.27304 loss)
I0412 15:21:43.789328 20203 sgd_solver.cpp:105] Iteration 7128, lr = 0.00243666
I0412 15:21:48.393666 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7140.caffemodel
I0412 15:21:54.136540 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7140.solverstate
I0412 15:21:58.029908 20203 solver.cpp:330] Iteration 7140, Testing net (#0)
I0412 15:21:58.029937 20203 net.cpp:676] Ignoring source layer train-data
I0412 15:21:59.679991 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:22:02.685871 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 15:22:02.685909 20203 solver.cpp:397]     Test net output #1: loss = 5.28604 (* 1 = 5.28604 loss)
I0412 15:22:02.772428 20203 solver.cpp:218] Iteration 7140 (0.632165 iter/s, 18.9824s/12 iters), loss = 5.26488
I0412 15:22:02.772480 20203 solver.cpp:237]     Train net output #0: loss = 5.26488 (* 1 = 5.26488 loss)
I0412 15:22:02.772491 20203 sgd_solver.cpp:105] Iteration 7140, lr = 0.00243088
I0412 15:22:07.285167 20203 solver.cpp:218] Iteration 7152 (2.65928 iter/s, 4.5125s/12 iters), loss = 5.24827
I0412 15:22:07.285292 20203 solver.cpp:237]     Train net output #0: loss = 5.24827 (* 1 = 5.24827 loss)
I0412 15:22:07.285307 20203 sgd_solver.cpp:105] Iteration 7152, lr = 0.00242511
I0412 15:22:12.164218 20203 solver.cpp:218] Iteration 7164 (2.45966 iter/s, 4.87873s/12 iters), loss = 5.27281
I0412 15:22:12.164276 20203 solver.cpp:237]     Train net output #0: loss = 5.27281 (* 1 = 5.27281 loss)
I0412 15:22:12.164288 20203 sgd_solver.cpp:105] Iteration 7164, lr = 0.00241935
I0412 15:22:17.244938 20203 solver.cpp:218] Iteration 7176 (2.36199 iter/s, 5.08045s/12 iters), loss = 5.25898
I0412 15:22:17.244985 20203 solver.cpp:237]     Train net output #0: loss = 5.25898 (* 1 = 5.25898 loss)
I0412 15:22:17.244994 20203 sgd_solver.cpp:105] Iteration 7176, lr = 0.0024136
I0412 15:22:19.370633 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:22:22.260440 20203 solver.cpp:218] Iteration 7188 (2.3927 iter/s, 5.01525s/12 iters), loss = 5.27401
I0412 15:22:22.260478 20203 solver.cpp:237]     Train net output #0: loss = 5.27401 (* 1 = 5.27401 loss)
I0412 15:22:22.260488 20203 sgd_solver.cpp:105] Iteration 7188, lr = 0.00240787
I0412 15:22:27.144281 20203 solver.cpp:218] Iteration 7200 (2.4572 iter/s, 4.8836s/12 iters), loss = 5.27461
I0412 15:22:27.144338 20203 solver.cpp:237]     Train net output #0: loss = 5.27461 (* 1 = 5.27461 loss)
I0412 15:22:27.144351 20203 sgd_solver.cpp:105] Iteration 7200, lr = 0.00240216
I0412 15:22:32.123286 20203 solver.cpp:218] Iteration 7212 (2.41025 iter/s, 4.97874s/12 iters), loss = 5.27992
I0412 15:22:32.123347 20203 solver.cpp:237]     Train net output #0: loss = 5.27992 (* 1 = 5.27992 loss)
I0412 15:22:32.123359 20203 sgd_solver.cpp:105] Iteration 7212, lr = 0.00239645
I0412 15:22:37.158792 20203 solver.cpp:218] Iteration 7224 (2.3832 iter/s, 5.03524s/12 iters), loss = 5.26648
I0412 15:22:37.158834 20203 solver.cpp:237]     Train net output #0: loss = 5.26648 (* 1 = 5.26648 loss)
I0412 15:22:37.158844 20203 sgd_solver.cpp:105] Iteration 7224, lr = 0.00239076
I0412 15:22:41.901510 20203 solver.cpp:218] Iteration 7236 (2.53032 iter/s, 4.74248s/12 iters), loss = 5.27598
I0412 15:22:41.906646 20203 solver.cpp:237]     Train net output #0: loss = 5.27598 (* 1 = 5.27598 loss)
I0412 15:22:41.906661 20203 sgd_solver.cpp:105] Iteration 7236, lr = 0.00238509
I0412 15:22:43.769623 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7242.caffemodel
I0412 15:22:46.980473 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7242.solverstate
I0412 15:22:50.691936 20203 solver.cpp:330] Iteration 7242, Testing net (#0)
I0412 15:22:50.691958 20203 net.cpp:676] Ignoring source layer train-data
I0412 15:22:52.336665 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:22:55.246462 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 15:22:55.246507 20203 solver.cpp:397]     Test net output #1: loss = 5.28578 (* 1 = 5.28578 loss)
I0412 15:22:57.209103 20203 solver.cpp:218] Iteration 7248 (0.784218 iter/s, 15.3019s/12 iters), loss = 5.27139
I0412 15:22:57.209154 20203 solver.cpp:237]     Train net output #0: loss = 5.27139 (* 1 = 5.27139 loss)
I0412 15:22:57.209164 20203 sgd_solver.cpp:105] Iteration 7248, lr = 0.00237942
I0412 15:23:02.110956 20203 solver.cpp:218] Iteration 7260 (2.44818 iter/s, 4.9016s/12 iters), loss = 5.27242
I0412 15:23:02.111008 20203 solver.cpp:237]     Train net output #0: loss = 5.27242 (* 1 = 5.27242 loss)
I0412 15:23:02.111018 20203 sgd_solver.cpp:105] Iteration 7260, lr = 0.00237378
I0412 15:23:07.260648 20203 solver.cpp:218] Iteration 7272 (2.33035 iter/s, 5.14943s/12 iters), loss = 5.25625
I0412 15:23:07.260687 20203 solver.cpp:237]     Train net output #0: loss = 5.25625 (* 1 = 5.25625 loss)
I0412 15:23:07.260696 20203 sgd_solver.cpp:105] Iteration 7272, lr = 0.00236814
I0412 15:23:11.279084 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:23:12.026916 20203 solver.cpp:218] Iteration 7284 (2.51782 iter/s, 4.76603s/12 iters), loss = 5.25884
I0412 15:23:12.027029 20203 solver.cpp:237]     Train net output #0: loss = 5.25884 (* 1 = 5.25884 loss)
I0412 15:23:12.027041 20203 sgd_solver.cpp:105] Iteration 7284, lr = 0.00236252
I0412 15:23:16.948856 20203 solver.cpp:218] Iteration 7296 (2.43822 iter/s, 4.92163s/12 iters), loss = 5.28673
I0412 15:23:16.948912 20203 solver.cpp:237]     Train net output #0: loss = 5.28673 (* 1 = 5.28673 loss)
I0412 15:23:16.948926 20203 sgd_solver.cpp:105] Iteration 7296, lr = 0.00235691
I0412 15:23:21.598446 20203 solver.cpp:218] Iteration 7308 (2.58101 iter/s, 4.64934s/12 iters), loss = 5.28278
I0412 15:23:21.598505 20203 solver.cpp:237]     Train net output #0: loss = 5.28278 (* 1 = 5.28278 loss)
I0412 15:23:21.598520 20203 sgd_solver.cpp:105] Iteration 7308, lr = 0.00235131
I0412 15:23:26.366905 20203 solver.cpp:218] Iteration 7320 (2.51667 iter/s, 4.7682s/12 iters), loss = 5.29413
I0412 15:23:26.366963 20203 solver.cpp:237]     Train net output #0: loss = 5.29413 (* 1 = 5.29413 loss)
I0412 15:23:26.366976 20203 sgd_solver.cpp:105] Iteration 7320, lr = 0.00234573
I0412 15:23:31.306993 20203 solver.cpp:218] Iteration 7332 (2.42924 iter/s, 4.93982s/12 iters), loss = 5.2685
I0412 15:23:31.307046 20203 solver.cpp:237]     Train net output #0: loss = 5.2685 (* 1 = 5.2685 loss)
I0412 15:23:31.307057 20203 sgd_solver.cpp:105] Iteration 7332, lr = 0.00234016
I0412 15:23:35.648049 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7344.caffemodel
I0412 15:23:38.678447 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7344.solverstate
I0412 15:23:42.202976 20203 solver.cpp:330] Iteration 7344, Testing net (#0)
I0412 15:23:42.203078 20203 net.cpp:676] Ignoring source layer train-data
I0412 15:23:43.836886 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:23:46.745597 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 15:23:46.745663 20203 solver.cpp:397]     Test net output #1: loss = 5.28648 (* 1 = 5.28648 loss)
I0412 15:23:46.832314 20203 solver.cpp:218] Iteration 7344 (0.772964 iter/s, 15.5247s/12 iters), loss = 5.27464
I0412 15:23:46.832376 20203 solver.cpp:237]     Train net output #0: loss = 5.27464 (* 1 = 5.27464 loss)
I0412 15:23:46.832387 20203 sgd_solver.cpp:105] Iteration 7344, lr = 0.0023346
I0412 15:23:50.609112 20203 solver.cpp:218] Iteration 7356 (3.17748 iter/s, 3.77657s/12 iters), loss = 5.28521
I0412 15:23:50.609163 20203 solver.cpp:237]     Train net output #0: loss = 5.28521 (* 1 = 5.28521 loss)
I0412 15:23:50.609175 20203 sgd_solver.cpp:105] Iteration 7356, lr = 0.00232906
I0412 15:23:55.763861 20203 solver.cpp:218] Iteration 7368 (2.32807 iter/s, 5.15448s/12 iters), loss = 5.27921
I0412 15:23:55.763918 20203 solver.cpp:237]     Train net output #0: loss = 5.27921 (* 1 = 5.27921 loss)
I0412 15:23:55.763931 20203 sgd_solver.cpp:105] Iteration 7368, lr = 0.00232353
I0412 15:24:00.404268 20203 solver.cpp:218] Iteration 7380 (2.58612 iter/s, 4.64016s/12 iters), loss = 5.26507
I0412 15:24:00.404320 20203 solver.cpp:237]     Train net output #0: loss = 5.26507 (* 1 = 5.26507 loss)
I0412 15:24:00.404330 20203 sgd_solver.cpp:105] Iteration 7380, lr = 0.00231802
I0412 15:24:01.732424 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:24:05.238771 20203 solver.cpp:218] Iteration 7392 (2.48229 iter/s, 4.83425s/12 iters), loss = 5.27383
I0412 15:24:05.238817 20203 solver.cpp:237]     Train net output #0: loss = 5.27383 (* 1 = 5.27383 loss)
I0412 15:24:05.238828 20203 sgd_solver.cpp:105] Iteration 7392, lr = 0.00231251
I0412 15:24:10.091218 20203 solver.cpp:218] Iteration 7404 (2.4731 iter/s, 4.8522s/12 iters), loss = 5.27209
I0412 15:24:10.091257 20203 solver.cpp:237]     Train net output #0: loss = 5.27209 (* 1 = 5.27209 loss)
I0412 15:24:10.091266 20203 sgd_solver.cpp:105] Iteration 7404, lr = 0.00230702
I0412 15:24:15.152302 20203 solver.cpp:218] Iteration 7416 (2.37115 iter/s, 5.06084s/12 iters), loss = 5.26952
I0412 15:24:15.152431 20203 solver.cpp:237]     Train net output #0: loss = 5.26952 (* 1 = 5.26952 loss)
I0412 15:24:15.152441 20203 sgd_solver.cpp:105] Iteration 7416, lr = 0.00230154
I0412 15:24:19.913354 20203 solver.cpp:218] Iteration 7428 (2.52062 iter/s, 4.76073s/12 iters), loss = 5.27948
I0412 15:24:19.913398 20203 solver.cpp:237]     Train net output #0: loss = 5.27948 (* 1 = 5.27948 loss)
I0412 15:24:19.913408 20203 sgd_solver.cpp:105] Iteration 7428, lr = 0.00229608
I0412 15:24:24.829066 20203 solver.cpp:218] Iteration 7440 (2.44128 iter/s, 4.91546s/12 iters), loss = 5.26439
I0412 15:24:24.829119 20203 solver.cpp:237]     Train net output #0: loss = 5.26439 (* 1 = 5.26439 loss)
I0412 15:24:24.829133 20203 sgd_solver.cpp:105] Iteration 7440, lr = 0.00229063
I0412 15:24:26.777163 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7446.caffemodel
I0412 15:24:29.888963 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7446.solverstate
I0412 15:24:32.309526 20203 solver.cpp:330] Iteration 7446, Testing net (#0)
I0412 15:24:32.309547 20203 net.cpp:676] Ignoring source layer train-data
I0412 15:24:33.801976 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:24:36.930061 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 15:24:36.930109 20203 solver.cpp:397]     Test net output #1: loss = 5.28593 (* 1 = 5.28593 loss)
I0412 15:24:38.757683 20203 solver.cpp:218] Iteration 7452 (0.861573 iter/s, 13.928s/12 iters), loss = 5.26301
I0412 15:24:38.757741 20203 solver.cpp:237]     Train net output #0: loss = 5.26301 (* 1 = 5.26301 loss)
I0412 15:24:38.757755 20203 sgd_solver.cpp:105] Iteration 7452, lr = 0.00228519
I0412 15:24:43.598531 20203 solver.cpp:218] Iteration 7464 (2.47904 iter/s, 4.84059s/12 iters), loss = 5.28348
I0412 15:24:43.598572 20203 solver.cpp:237]     Train net output #0: loss = 5.28348 (* 1 = 5.28348 loss)
I0412 15:24:43.598582 20203 sgd_solver.cpp:105] Iteration 7464, lr = 0.00227976
I0412 15:24:48.558331 20203 solver.cpp:218] Iteration 7476 (2.41957 iter/s, 4.95955s/12 iters), loss = 5.27815
I0412 15:24:48.558483 20203 solver.cpp:237]     Train net output #0: loss = 5.27815 (* 1 = 5.27815 loss)
I0412 15:24:48.558497 20203 sgd_solver.cpp:105] Iteration 7476, lr = 0.00227435
I0412 15:24:51.899344 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:24:53.377063 20203 solver.cpp:218] Iteration 7488 (2.49046 iter/s, 4.81838s/12 iters), loss = 5.27258
I0412 15:24:53.377115 20203 solver.cpp:237]     Train net output #0: loss = 5.27258 (* 1 = 5.27258 loss)
I0412 15:24:53.377126 20203 sgd_solver.cpp:105] Iteration 7488, lr = 0.00226895
I0412 15:24:58.192416 20203 solver.cpp:218] Iteration 7500 (2.49216 iter/s, 4.8151s/12 iters), loss = 5.25784
I0412 15:24:58.192462 20203 solver.cpp:237]     Train net output #0: loss = 5.25784 (* 1 = 5.25784 loss)
I0412 15:24:58.192471 20203 sgd_solver.cpp:105] Iteration 7500, lr = 0.00226357
I0412 15:25:02.953354 20203 solver.cpp:218] Iteration 7512 (2.52064 iter/s, 4.76069s/12 iters), loss = 5.26093
I0412 15:25:02.953418 20203 solver.cpp:237]     Train net output #0: loss = 5.26093 (* 1 = 5.26093 loss)
I0412 15:25:02.953433 20203 sgd_solver.cpp:105] Iteration 7512, lr = 0.00225819
I0412 15:25:08.070124 20203 solver.cpp:218] Iteration 7524 (2.34535 iter/s, 5.1165s/12 iters), loss = 5.27204
I0412 15:25:08.070164 20203 solver.cpp:237]     Train net output #0: loss = 5.27204 (* 1 = 5.27204 loss)
I0412 15:25:08.070173 20203 sgd_solver.cpp:105] Iteration 7524, lr = 0.00225283
I0412 15:25:12.983968 20203 solver.cpp:218] Iteration 7536 (2.4422 iter/s, 4.9136s/12 iters), loss = 5.26028
I0412 15:25:12.984009 20203 solver.cpp:237]     Train net output #0: loss = 5.26028 (* 1 = 5.26028 loss)
I0412 15:25:12.984019 20203 sgd_solver.cpp:105] Iteration 7536, lr = 0.00224748
I0412 15:25:17.416427 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7548.caffemodel
I0412 15:25:20.427570 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7548.solverstate
I0412 15:25:22.734510 20203 solver.cpp:330] Iteration 7548, Testing net (#0)
I0412 15:25:22.734537 20203 net.cpp:676] Ignoring source layer train-data
I0412 15:25:24.184075 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:25:27.144192 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 15:25:27.144243 20203 solver.cpp:397]     Test net output #1: loss = 5.28602 (* 1 = 5.28602 loss)
I0412 15:25:27.230922 20203 solver.cpp:218] Iteration 7548 (0.842321 iter/s, 14.2463s/12 iters), loss = 5.27983
I0412 15:25:27.230970 20203 solver.cpp:237]     Train net output #0: loss = 5.27983 (* 1 = 5.27983 loss)
I0412 15:25:27.230980 20203 sgd_solver.cpp:105] Iteration 7548, lr = 0.00224215
I0412 15:25:31.504401 20203 solver.cpp:218] Iteration 7560 (2.80817 iter/s, 4.27325s/12 iters), loss = 5.2711
I0412 15:25:31.504456 20203 solver.cpp:237]     Train net output #0: loss = 5.2711 (* 1 = 5.2711 loss)
I0412 15:25:31.504467 20203 sgd_solver.cpp:105] Iteration 7560, lr = 0.00223682
I0412 15:25:36.717687 20203 solver.cpp:218] Iteration 7572 (2.30193 iter/s, 5.21302s/12 iters), loss = 5.28055
I0412 15:25:36.717738 20203 solver.cpp:237]     Train net output #0: loss = 5.28055 (* 1 = 5.28055 loss)
I0412 15:25:36.717748 20203 sgd_solver.cpp:105] Iteration 7572, lr = 0.00223151
I0412 15:25:41.731796 20203 solver.cpp:218] Iteration 7584 (2.39337 iter/s, 5.01385s/12 iters), loss = 5.28933
I0412 15:25:41.731854 20203 solver.cpp:237]     Train net output #0: loss = 5.28933 (* 1 = 5.28933 loss)
I0412 15:25:41.731870 20203 sgd_solver.cpp:105] Iteration 7584, lr = 0.00222621
I0412 15:25:42.336432 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:25:46.559906 20203 solver.cpp:218] Iteration 7596 (2.48558 iter/s, 4.82785s/12 iters), loss = 5.27866
I0412 15:25:46.559960 20203 solver.cpp:237]     Train net output #0: loss = 5.27866 (* 1 = 5.27866 loss)
I0412 15:25:46.559975 20203 sgd_solver.cpp:105] Iteration 7596, lr = 0.00222093
I0412 15:25:51.340461 20203 solver.cpp:218] Iteration 7608 (2.5103 iter/s, 4.7803s/12 iters), loss = 5.26217
I0412 15:25:51.340612 20203 solver.cpp:237]     Train net output #0: loss = 5.26217 (* 1 = 5.26217 loss)
I0412 15:25:51.340624 20203 sgd_solver.cpp:105] Iteration 7608, lr = 0.00221565
I0412 15:25:56.209631 20203 solver.cpp:218] Iteration 7620 (2.46466 iter/s, 4.86882s/12 iters), loss = 5.27488
I0412 15:25:56.209681 20203 solver.cpp:237]     Train net output #0: loss = 5.27488 (* 1 = 5.27488 loss)
I0412 15:25:56.209692 20203 sgd_solver.cpp:105] Iteration 7620, lr = 0.00221039
I0412 15:25:58.612236 20203 blocking_queue.cpp:49] Waiting for data
I0412 15:26:01.021366 20203 solver.cpp:218] Iteration 7632 (2.49403 iter/s, 4.81149s/12 iters), loss = 5.27833
I0412 15:26:01.021395 20203 solver.cpp:237]     Train net output #0: loss = 5.27833 (* 1 = 5.27833 loss)
I0412 15:26:01.021404 20203 sgd_solver.cpp:105] Iteration 7632, lr = 0.00220515
I0412 15:26:05.703889 20203 solver.cpp:218] Iteration 7644 (2.56285 iter/s, 4.68229s/12 iters), loss = 5.28349
I0412 15:26:05.703943 20203 solver.cpp:237]     Train net output #0: loss = 5.28349 (* 1 = 5.28349 loss)
I0412 15:26:05.703954 20203 sgd_solver.cpp:105] Iteration 7644, lr = 0.00219991
I0412 15:26:07.529872 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7650.caffemodel
I0412 15:26:10.592854 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7650.solverstate
I0412 15:26:12.872681 20203 solver.cpp:330] Iteration 7650, Testing net (#0)
I0412 15:26:12.872704 20203 net.cpp:676] Ignoring source layer train-data
I0412 15:26:14.431280 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:26:17.592679 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 15:26:17.592715 20203 solver.cpp:397]     Test net output #1: loss = 5.28626 (* 1 = 5.28626 loss)
I0412 15:26:19.603857 20203 solver.cpp:218] Iteration 7656 (0.86335 iter/s, 13.8994s/12 iters), loss = 5.2753
I0412 15:26:19.603915 20203 solver.cpp:237]     Train net output #0: loss = 5.2753 (* 1 = 5.2753 loss)
I0412 15:26:19.603929 20203 sgd_solver.cpp:105] Iteration 7656, lr = 0.00219469
I0412 15:26:24.785828 20203 solver.cpp:218] Iteration 7668 (2.31585 iter/s, 5.18169s/12 iters), loss = 5.27036
I0412 15:26:24.785944 20203 solver.cpp:237]     Train net output #0: loss = 5.27036 (* 1 = 5.27036 loss)
I0412 15:26:24.785980 20203 sgd_solver.cpp:105] Iteration 7668, lr = 0.00218948
I0412 15:26:29.627094 20203 solver.cpp:218] Iteration 7680 (2.47885 iter/s, 4.84095s/12 iters), loss = 5.26028
I0412 15:26:29.627136 20203 solver.cpp:237]     Train net output #0: loss = 5.26028 (* 1 = 5.26028 loss)
I0412 15:26:29.627146 20203 sgd_solver.cpp:105] Iteration 7680, lr = 0.00218428
I0412 15:26:32.349359 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:26:34.457772 20203 solver.cpp:218] Iteration 7692 (2.48425 iter/s, 4.83043s/12 iters), loss = 5.2697
I0412 15:26:34.457816 20203 solver.cpp:237]     Train net output #0: loss = 5.2697 (* 1 = 5.2697 loss)
I0412 15:26:34.457826 20203 sgd_solver.cpp:105] Iteration 7692, lr = 0.00217909
I0412 15:26:39.309610 20203 solver.cpp:218] Iteration 7704 (2.47342 iter/s, 4.85159s/12 iters), loss = 5.25373
I0412 15:26:39.309653 20203 solver.cpp:237]     Train net output #0: loss = 5.25373 (* 1 = 5.25373 loss)
I0412 15:26:39.309660 20203 sgd_solver.cpp:105] Iteration 7704, lr = 0.00217392
I0412 15:26:43.973358 20203 solver.cpp:218] Iteration 7716 (2.57317 iter/s, 4.66351s/12 iters), loss = 5.25624
I0412 15:26:43.973412 20203 solver.cpp:237]     Train net output #0: loss = 5.25624 (* 1 = 5.25624 loss)
I0412 15:26:43.973428 20203 sgd_solver.cpp:105] Iteration 7716, lr = 0.00216876
I0412 15:26:48.737704 20203 solver.cpp:218] Iteration 7728 (2.51884 iter/s, 4.76409s/12 iters), loss = 5.25984
I0412 15:26:48.737762 20203 solver.cpp:237]     Train net output #0: loss = 5.25984 (* 1 = 5.25984 loss)
I0412 15:26:48.737779 20203 sgd_solver.cpp:105] Iteration 7728, lr = 0.00216361
I0412 15:26:53.783044 20203 solver.cpp:218] Iteration 7740 (2.37856 iter/s, 5.04508s/12 iters), loss = 5.29924
I0412 15:26:53.783087 20203 solver.cpp:237]     Train net output #0: loss = 5.29924 (* 1 = 5.29924 loss)
I0412 15:26:53.783097 20203 sgd_solver.cpp:105] Iteration 7740, lr = 0.00215847
I0412 15:26:58.224647 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7752.caffemodel
I0412 15:27:01.487565 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7752.solverstate
I0412 15:27:05.994937 20203 solver.cpp:330] Iteration 7752, Testing net (#0)
I0412 15:27:05.994964 20203 net.cpp:676] Ignoring source layer train-data
I0412 15:27:07.371959 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:27:10.499015 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 15:27:10.499084 20203 solver.cpp:397]     Test net output #1: loss = 5.28598 (* 1 = 5.28598 loss)
I0412 15:27:10.586448 20203 solver.cpp:218] Iteration 7752 (0.714173 iter/s, 16.8027s/12 iters), loss = 5.27397
I0412 15:27:10.586505 20203 solver.cpp:237]     Train net output #0: loss = 5.27397 (* 1 = 5.27397 loss)
I0412 15:27:10.586520 20203 sgd_solver.cpp:105] Iteration 7752, lr = 0.00215335
I0412 15:27:14.778522 20203 solver.cpp:218] Iteration 7764 (2.86271 iter/s, 4.19184s/12 iters), loss = 5.2773
I0412 15:27:14.778576 20203 solver.cpp:237]     Train net output #0: loss = 5.2773 (* 1 = 5.2773 loss)
I0412 15:27:14.778587 20203 sgd_solver.cpp:105] Iteration 7764, lr = 0.00214823
I0412 15:27:19.666931 20203 solver.cpp:218] Iteration 7776 (2.45492 iter/s, 4.88815s/12 iters), loss = 5.27137
I0412 15:27:19.666985 20203 solver.cpp:237]     Train net output #0: loss = 5.27137 (* 1 = 5.27137 loss)
I0412 15:27:19.666998 20203 sgd_solver.cpp:105] Iteration 7776, lr = 0.00214313
I0412 15:27:24.572911 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:27:24.589131 20203 solver.cpp:218] Iteration 7788 (2.43807 iter/s, 4.92193s/12 iters), loss = 5.2459
I0412 15:27:24.589200 20203 solver.cpp:237]     Train net output #0: loss = 5.2459 (* 1 = 5.2459 loss)
I0412 15:27:24.589217 20203 sgd_solver.cpp:105] Iteration 7788, lr = 0.00213805
I0412 15:27:29.628073 20203 solver.cpp:218] Iteration 7800 (2.38158 iter/s, 5.03866s/12 iters), loss = 5.26702
I0412 15:27:29.628190 20203 solver.cpp:237]     Train net output #0: loss = 5.26702 (* 1 = 5.26702 loss)
I0412 15:27:29.628202 20203 sgd_solver.cpp:105] Iteration 7800, lr = 0.00213297
I0412 15:27:34.889263 20203 solver.cpp:218] Iteration 7812 (2.28099 iter/s, 5.26086s/12 iters), loss = 5.29488
I0412 15:27:34.889302 20203 solver.cpp:237]     Train net output #0: loss = 5.29488 (* 1 = 5.29488 loss)
I0412 15:27:34.889312 20203 sgd_solver.cpp:105] Iteration 7812, lr = 0.00212791
I0412 15:27:39.765229 20203 solver.cpp:218] Iteration 7824 (2.46118 iter/s, 4.87572s/12 iters), loss = 5.27232
I0412 15:27:39.765275 20203 solver.cpp:237]     Train net output #0: loss = 5.27232 (* 1 = 5.27232 loss)
I0412 15:27:39.765286 20203 sgd_solver.cpp:105] Iteration 7824, lr = 0.00212285
I0412 15:27:44.594676 20203 solver.cpp:218] Iteration 7836 (2.48489 iter/s, 4.82919s/12 iters), loss = 5.27469
I0412 15:27:44.594727 20203 solver.cpp:237]     Train net output #0: loss = 5.27469 (* 1 = 5.27469 loss)
I0412 15:27:44.594738 20203 sgd_solver.cpp:105] Iteration 7836, lr = 0.00211781
I0412 15:27:49.403061 20203 solver.cpp:218] Iteration 7848 (2.49578 iter/s, 4.80813s/12 iters), loss = 5.26066
I0412 15:27:49.403134 20203 solver.cpp:237]     Train net output #0: loss = 5.26066 (* 1 = 5.26066 loss)
I0412 15:27:49.403152 20203 sgd_solver.cpp:105] Iteration 7848, lr = 0.00211279
I0412 15:27:51.398577 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7854.caffemodel
I0412 15:27:55.997890 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7854.solverstate
I0412 15:28:00.172044 20203 solver.cpp:330] Iteration 7854, Testing net (#0)
I0412 15:28:00.172122 20203 net.cpp:676] Ignoring source layer train-data
I0412 15:28:01.533481 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:28:04.616704 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 15:28:04.616757 20203 solver.cpp:397]     Test net output #1: loss = 5.28591 (* 1 = 5.28591 loss)
I0412 15:28:06.289708 20203 solver.cpp:218] Iteration 7860 (0.710652 iter/s, 16.8859s/12 iters), loss = 5.24535
I0412 15:28:06.289767 20203 solver.cpp:237]     Train net output #0: loss = 5.24535 (* 1 = 5.24535 loss)
I0412 15:28:06.289779 20203 sgd_solver.cpp:105] Iteration 7860, lr = 0.00210777
I0412 15:28:11.140014 20203 solver.cpp:218] Iteration 7872 (2.4742 iter/s, 4.85005s/12 iters), loss = 5.26984
I0412 15:28:11.140058 20203 solver.cpp:237]     Train net output #0: loss = 5.26984 (* 1 = 5.26984 loss)
I0412 15:28:11.140066 20203 sgd_solver.cpp:105] Iteration 7872, lr = 0.00210277
I0412 15:28:16.251185 20203 solver.cpp:218] Iteration 7884 (2.34792 iter/s, 5.11091s/12 iters), loss = 5.26037
I0412 15:28:16.251240 20203 solver.cpp:237]     Train net output #0: loss = 5.26037 (* 1 = 5.26037 loss)
I0412 15:28:16.251255 20203 sgd_solver.cpp:105] Iteration 7884, lr = 0.00209777
I0412 15:28:18.213865 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:28:21.166421 20203 solver.cpp:218] Iteration 7896 (2.44152 iter/s, 4.91497s/12 iters), loss = 5.2722
I0412 15:28:21.166482 20203 solver.cpp:237]     Train net output #0: loss = 5.2722 (* 1 = 5.2722 loss)
I0412 15:28:21.166493 20203 sgd_solver.cpp:105] Iteration 7896, lr = 0.00209279
I0412 15:28:26.242022 20203 solver.cpp:218] Iteration 7908 (2.36438 iter/s, 5.07534s/12 iters), loss = 5.27271
I0412 15:28:26.242074 20203 solver.cpp:237]     Train net output #0: loss = 5.27271 (* 1 = 5.27271 loss)
I0412 15:28:26.242087 20203 sgd_solver.cpp:105] Iteration 7908, lr = 0.00208782
I0412 15:28:31.073557 20203 solver.cpp:218] Iteration 7920 (2.48382 iter/s, 4.83128s/12 iters), loss = 5.28665
I0412 15:28:31.073710 20203 solver.cpp:237]     Train net output #0: loss = 5.28665 (* 1 = 5.28665 loss)
I0412 15:28:31.073724 20203 sgd_solver.cpp:105] Iteration 7920, lr = 0.00208287
I0412 15:28:36.165166 20203 solver.cpp:218] Iteration 7932 (2.35699 iter/s, 5.09124s/12 iters), loss = 5.26871
I0412 15:28:36.165226 20203 solver.cpp:237]     Train net output #0: loss = 5.26871 (* 1 = 5.26871 loss)
I0412 15:28:36.165242 20203 sgd_solver.cpp:105] Iteration 7932, lr = 0.00207792
I0412 15:28:41.147238 20203 solver.cpp:218] Iteration 7944 (2.40877 iter/s, 4.9818s/12 iters), loss = 5.27035
I0412 15:28:41.147281 20203 solver.cpp:237]     Train net output #0: loss = 5.27035 (* 1 = 5.27035 loss)
I0412 15:28:41.147290 20203 sgd_solver.cpp:105] Iteration 7944, lr = 0.00207299
I0412 15:28:45.595288 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7956.caffemodel
I0412 15:28:49.420202 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7956.solverstate
I0412 15:28:51.724900 20203 solver.cpp:330] Iteration 7956, Testing net (#0)
I0412 15:28:51.724920 20203 net.cpp:676] Ignoring source layer train-data
I0412 15:28:53.070358 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:28:56.193133 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 15:28:56.193189 20203 solver.cpp:397]     Test net output #1: loss = 5.28644 (* 1 = 5.28644 loss)
I0412 15:28:56.279801 20203 solver.cpp:218] Iteration 7956 (0.793027 iter/s, 15.1319s/12 iters), loss = 5.27586
I0412 15:28:56.279870 20203 solver.cpp:237]     Train net output #0: loss = 5.27586 (* 1 = 5.27586 loss)
I0412 15:28:56.279886 20203 sgd_solver.cpp:105] Iteration 7956, lr = 0.00206807
I0412 15:29:00.521121 20203 solver.cpp:218] Iteration 7968 (2.82947 iter/s, 4.24107s/12 iters), loss = 5.27508
I0412 15:29:00.521167 20203 solver.cpp:237]     Train net output #0: loss = 5.27508 (* 1 = 5.27508 loss)
I0412 15:29:00.521175 20203 sgd_solver.cpp:105] Iteration 7968, lr = 0.00206316
I0412 15:29:05.363358 20203 solver.cpp:218] Iteration 7980 (2.47832 iter/s, 4.84199s/12 iters), loss = 5.2579
I0412 15:29:05.363492 20203 solver.cpp:237]     Train net output #0: loss = 5.2579 (* 1 = 5.2579 loss)
I0412 15:29:05.363502 20203 sgd_solver.cpp:105] Iteration 7980, lr = 0.00205826
I0412 15:29:09.552873 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:29:10.262848 20203 solver.cpp:218] Iteration 7992 (2.4494 iter/s, 4.89915s/12 iters), loss = 5.25757
I0412 15:29:10.262892 20203 solver.cpp:237]     Train net output #0: loss = 5.25757 (* 1 = 5.25757 loss)
I0412 15:29:10.262902 20203 sgd_solver.cpp:105] Iteration 7992, lr = 0.00205337
I0412 15:29:15.138448 20203 solver.cpp:218] Iteration 8004 (2.46136 iter/s, 4.87535s/12 iters), loss = 5.27874
I0412 15:29:15.138486 20203 solver.cpp:237]     Train net output #0: loss = 5.27874 (* 1 = 5.27874 loss)
I0412 15:29:15.138494 20203 sgd_solver.cpp:105] Iteration 8004, lr = 0.0020485
I0412 15:29:20.146670 20203 solver.cpp:218] Iteration 8016 (2.39618 iter/s, 5.00797s/12 iters), loss = 5.27812
I0412 15:29:20.146723 20203 solver.cpp:237]     Train net output #0: loss = 5.27812 (* 1 = 5.27812 loss)
I0412 15:29:20.146736 20203 sgd_solver.cpp:105] Iteration 8016, lr = 0.00204363
I0412 15:29:24.800217 20203 solver.cpp:218] Iteration 8028 (2.57882 iter/s, 4.65329s/12 iters), loss = 5.29416
I0412 15:29:24.800264 20203 solver.cpp:237]     Train net output #0: loss = 5.29416 (* 1 = 5.29416 loss)
I0412 15:29:24.800276 20203 sgd_solver.cpp:105] Iteration 8028, lr = 0.00203878
I0412 15:29:29.701675 20203 solver.cpp:218] Iteration 8040 (2.44838 iter/s, 4.9012s/12 iters), loss = 5.26554
I0412 15:29:29.701719 20203 solver.cpp:237]     Train net output #0: loss = 5.26554 (* 1 = 5.26554 loss)
I0412 15:29:29.701727 20203 sgd_solver.cpp:105] Iteration 8040, lr = 0.00203394
I0412 15:29:34.649802 20203 solver.cpp:218] Iteration 8052 (2.42528 iter/s, 4.94788s/12 iters), loss = 5.28053
I0412 15:29:34.649844 20203 solver.cpp:237]     Train net output #0: loss = 5.28053 (* 1 = 5.28053 loss)
I0412 15:29:34.649853 20203 sgd_solver.cpp:105] Iteration 8052, lr = 0.00202911
I0412 15:29:36.665815 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8058.caffemodel
I0412 15:29:43.189147 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8058.solverstate
I0412 15:29:46.364116 20203 solver.cpp:330] Iteration 8058, Testing net (#0)
I0412 15:29:46.364143 20203 net.cpp:676] Ignoring source layer train-data
I0412 15:29:47.781121 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:29:51.213224 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 15:29:51.213265 20203 solver.cpp:397]     Test net output #1: loss = 5.28637 (* 1 = 5.28637 loss)
I0412 15:29:53.001890 20203 solver.cpp:218] Iteration 8064 (0.653905 iter/s, 18.3513s/12 iters), loss = 5.27781
I0412 15:29:53.001940 20203 solver.cpp:237]     Train net output #0: loss = 5.27781 (* 1 = 5.27781 loss)
I0412 15:29:53.001950 20203 sgd_solver.cpp:105] Iteration 8064, lr = 0.00202429
I0412 15:29:58.102268 20203 solver.cpp:218] Iteration 8076 (2.35289 iter/s, 5.1001s/12 iters), loss = 5.27321
I0412 15:29:58.102317 20203 solver.cpp:237]     Train net output #0: loss = 5.27321 (* 1 = 5.27321 loss)
I0412 15:29:58.102329 20203 sgd_solver.cpp:105] Iteration 8076, lr = 0.00201949
I0412 15:30:03.117197 20203 solver.cpp:218] Iteration 8088 (2.39298 iter/s, 5.01466s/12 iters), loss = 5.26334
I0412 15:30:03.117262 20203 solver.cpp:237]     Train net output #0: loss = 5.26334 (* 1 = 5.26334 loss)
I0412 15:30:03.117277 20203 sgd_solver.cpp:105] Iteration 8088, lr = 0.00201469
I0412 15:30:04.555284 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:30:08.070325 20203 solver.cpp:218] Iteration 8100 (2.42285 iter/s, 4.95285s/12 iters), loss = 5.26411
I0412 15:30:08.070446 20203 solver.cpp:237]     Train net output #0: loss = 5.26411 (* 1 = 5.26411 loss)
I0412 15:30:08.070461 20203 sgd_solver.cpp:105] Iteration 8100, lr = 0.00200991
I0412 15:30:12.955540 20203 solver.cpp:218] Iteration 8112 (2.45656 iter/s, 4.88489s/12 iters), loss = 5.26587
I0412 15:30:12.955591 20203 solver.cpp:237]     Train net output #0: loss = 5.26587 (* 1 = 5.26587 loss)
I0412 15:30:12.955602 20203 sgd_solver.cpp:105] Iteration 8112, lr = 0.00200514
I0412 15:30:17.843149 20203 solver.cpp:218] Iteration 8124 (2.45532 iter/s, 4.88735s/12 iters), loss = 5.27273
I0412 15:30:17.843205 20203 solver.cpp:237]     Train net output #0: loss = 5.27273 (* 1 = 5.27273 loss)
I0412 15:30:17.843219 20203 sgd_solver.cpp:105] Iteration 8124, lr = 0.00200038
I0412 15:30:22.670099 20203 solver.cpp:218] Iteration 8136 (2.48618 iter/s, 4.82668s/12 iters), loss = 5.28503
I0412 15:30:22.670150 20203 solver.cpp:237]     Train net output #0: loss = 5.28503 (* 1 = 5.28503 loss)
I0412 15:30:22.670161 20203 sgd_solver.cpp:105] Iteration 8136, lr = 0.00199563
I0412 15:30:27.601545 20203 solver.cpp:218] Iteration 8148 (2.43349 iter/s, 4.93119s/12 iters), loss = 5.25097
I0412 15:30:27.601595 20203 solver.cpp:237]     Train net output #0: loss = 5.25097 (* 1 = 5.25097 loss)
I0412 15:30:27.601606 20203 sgd_solver.cpp:105] Iteration 8148, lr = 0.00199089
I0412 15:30:32.190500 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8160.caffemodel
I0412 15:30:36.424566 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8160.solverstate
I0412 15:30:40.116905 20203 solver.cpp:330] Iteration 8160, Testing net (#0)
I0412 15:30:40.117022 20203 net.cpp:676] Ignoring source layer train-data
I0412 15:30:41.387616 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:30:44.633303 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 15:30:44.633353 20203 solver.cpp:397]     Test net output #1: loss = 5.28623 (* 1 = 5.28623 loss)
I0412 15:30:44.720098 20203 solver.cpp:218] Iteration 8160 (0.701025 iter/s, 17.1178s/12 iters), loss = 5.26621
I0412 15:30:44.720172 20203 solver.cpp:237]     Train net output #0: loss = 5.26621 (* 1 = 5.26621 loss)
I0412 15:30:44.720188 20203 sgd_solver.cpp:105] Iteration 8160, lr = 0.00198616
I0412 15:30:48.657713 20203 solver.cpp:218] Iteration 8172 (3.04772 iter/s, 3.93737s/12 iters), loss = 5.2843
I0412 15:30:48.657764 20203 solver.cpp:237]     Train net output #0: loss = 5.2843 (* 1 = 5.2843 loss)
I0412 15:30:48.657775 20203 sgd_solver.cpp:105] Iteration 8172, lr = 0.00198145
I0412 15:30:53.505867 20203 solver.cpp:218] Iteration 8184 (2.4753 iter/s, 4.84789s/12 iters), loss = 5.27292
I0412 15:30:53.505920 20203 solver.cpp:237]     Train net output #0: loss = 5.27292 (* 1 = 5.27292 loss)
I0412 15:30:53.505930 20203 sgd_solver.cpp:105] Iteration 8184, lr = 0.00197674
I0412 15:30:56.974884 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:30:58.364113 20203 solver.cpp:218] Iteration 8196 (2.47016 iter/s, 4.85799s/12 iters), loss = 5.27504
I0412 15:30:58.364163 20203 solver.cpp:237]     Train net output #0: loss = 5.27504 (* 1 = 5.27504 loss)
I0412 15:30:58.364176 20203 sgd_solver.cpp:105] Iteration 8196, lr = 0.00197205
I0412 15:31:03.509696 20203 solver.cpp:218] Iteration 8208 (2.33222 iter/s, 5.14531s/12 iters), loss = 5.25898
I0412 15:31:03.509749 20203 solver.cpp:237]     Train net output #0: loss = 5.25898 (* 1 = 5.25898 loss)
I0412 15:31:03.509763 20203 sgd_solver.cpp:105] Iteration 8208, lr = 0.00196737
I0412 15:31:08.389653 20203 solver.cpp:218] Iteration 8220 (2.45917 iter/s, 4.87969s/12 iters), loss = 5.26223
I0412 15:31:08.389695 20203 solver.cpp:237]     Train net output #0: loss = 5.26223 (* 1 = 5.26223 loss)
I0412 15:31:08.389704 20203 sgd_solver.cpp:105] Iteration 8220, lr = 0.0019627
I0412 15:31:13.456418 20203 solver.cpp:218] Iteration 8232 (2.3685 iter/s, 5.06651s/12 iters), loss = 5.27131
I0412 15:31:13.456496 20203 solver.cpp:237]     Train net output #0: loss = 5.27131 (* 1 = 5.27131 loss)
I0412 15:31:13.456506 20203 sgd_solver.cpp:105] Iteration 8232, lr = 0.00195804
I0412 15:31:18.252421 20203 solver.cpp:218] Iteration 8244 (2.50223 iter/s, 4.79572s/12 iters), loss = 5.25447
I0412 15:31:18.252467 20203 solver.cpp:237]     Train net output #0: loss = 5.25447 (* 1 = 5.25447 loss)
I0412 15:31:18.252478 20203 sgd_solver.cpp:105] Iteration 8244, lr = 0.00195339
I0412 15:31:22.939213 20203 solver.cpp:218] Iteration 8256 (2.56052 iter/s, 4.68654s/12 iters), loss = 5.26917
I0412 15:31:22.939270 20203 solver.cpp:237]     Train net output #0: loss = 5.26917 (* 1 = 5.26917 loss)
I0412 15:31:22.939282 20203 sgd_solver.cpp:105] Iteration 8256, lr = 0.00194875
I0412 15:31:24.904070 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8262.caffemodel
I0412 15:31:27.929849 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8262.solverstate
I0412 15:31:30.228148 20203 solver.cpp:330] Iteration 8262, Testing net (#0)
I0412 15:31:30.228173 20203 net.cpp:676] Ignoring source layer train-data
I0412 15:31:31.455454 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:31:34.845175 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00612745
I0412 15:31:34.845227 20203 solver.cpp:397]     Test net output #1: loss = 5.28626 (* 1 = 5.28626 loss)
I0412 15:31:36.490883 20203 solver.cpp:218] Iteration 8268 (0.885539 iter/s, 13.5511s/12 iters), loss = 5.27932
I0412 15:31:36.490926 20203 solver.cpp:237]     Train net output #0: loss = 5.27932 (* 1 = 5.27932 loss)
I0412 15:31:36.490934 20203 sgd_solver.cpp:105] Iteration 8268, lr = 0.00194412
I0412 15:31:41.468539 20203 solver.cpp:218] Iteration 8280 (2.4109 iter/s, 4.9774s/12 iters), loss = 5.28372
I0412 15:31:41.468592 20203 solver.cpp:237]     Train net output #0: loss = 5.28372 (* 1 = 5.28372 loss)
I0412 15:31:41.468605 20203 sgd_solver.cpp:105] Iteration 8280, lr = 0.00193951
I0412 15:31:46.378732 20203 solver.cpp:218] Iteration 8292 (2.44403 iter/s, 4.90993s/12 iters), loss = 5.29387
I0412 15:31:46.378890 20203 solver.cpp:237]     Train net output #0: loss = 5.29387 (* 1 = 5.29387 loss)
I0412 15:31:46.378903 20203 sgd_solver.cpp:105] Iteration 8292, lr = 0.0019349
I0412 15:31:47.019415 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:31:51.257050 20203 solver.cpp:218] Iteration 8304 (2.46005 iter/s, 4.87795s/12 iters), loss = 5.27946
I0412 15:31:51.257093 20203 solver.cpp:237]     Train net output #0: loss = 5.27946 (* 1 = 5.27946 loss)
I0412 15:31:51.257102 20203 sgd_solver.cpp:105] Iteration 8304, lr = 0.00193031
I0412 15:31:54.038179 20203 blocking_queue.cpp:49] Waiting for data
I0412 15:31:56.086194 20203 solver.cpp:218] Iteration 8316 (2.48504 iter/s, 4.82889s/12 iters), loss = 5.2742
I0412 15:31:56.086249 20203 solver.cpp:237]     Train net output #0: loss = 5.2742 (* 1 = 5.2742 loss)
I0412 15:31:56.086262 20203 sgd_solver.cpp:105] Iteration 8316, lr = 0.00192573
I0412 15:32:00.718389 20203 solver.cpp:218] Iteration 8328 (2.59072 iter/s, 4.63193s/12 iters), loss = 5.28222
I0412 15:32:00.718444 20203 solver.cpp:237]     Train net output #0: loss = 5.28222 (* 1 = 5.28222 loss)
I0412 15:32:00.718456 20203 sgd_solver.cpp:105] Iteration 8328, lr = 0.00192115
I0412 15:32:05.498267 20203 solver.cpp:218] Iteration 8340 (2.51066 iter/s, 4.77962s/12 iters), loss = 5.27299
I0412 15:32:05.498309 20203 solver.cpp:237]     Train net output #0: loss = 5.27299 (* 1 = 5.27299 loss)
I0412 15:32:05.498318 20203 sgd_solver.cpp:105] Iteration 8340, lr = 0.00191659
I0412 15:32:10.242534 20203 solver.cpp:218] Iteration 8352 (2.5295 iter/s, 4.74402s/12 iters), loss = 5.28568
I0412 15:32:10.242578 20203 solver.cpp:237]     Train net output #0: loss = 5.28568 (* 1 = 5.28568 loss)
I0412 15:32:10.242586 20203 sgd_solver.cpp:105] Iteration 8352, lr = 0.00191204
I0412 15:32:14.787986 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8364.caffemodel
I0412 15:32:18.406054 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8364.solverstate
I0412 15:32:21.938247 20203 solver.cpp:330] Iteration 8364, Testing net (#0)
I0412 15:32:21.938275 20203 net.cpp:676] Ignoring source layer train-data
I0412 15:32:23.009761 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:32:26.294385 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 15:32:26.294445 20203 solver.cpp:397]     Test net output #1: loss = 5.28601 (* 1 = 5.28601 loss)
I0412 15:32:26.380964 20203 solver.cpp:218] Iteration 8364 (0.743599 iter/s, 16.1377s/12 iters), loss = 5.2667
I0412 15:32:26.381012 20203 solver.cpp:237]     Train net output #0: loss = 5.2667 (* 1 = 5.2667 loss)
I0412 15:32:26.381023 20203 sgd_solver.cpp:105] Iteration 8364, lr = 0.0019075
I0412 15:32:30.752957 20203 solver.cpp:218] Iteration 8376 (2.74489 iter/s, 4.37175s/12 iters), loss = 5.26808
I0412 15:32:30.752998 20203 solver.cpp:237]     Train net output #0: loss = 5.26808 (* 1 = 5.26808 loss)
I0412 15:32:30.753008 20203 sgd_solver.cpp:105] Iteration 8376, lr = 0.00190297
I0412 15:32:35.622118 20203 solver.cpp:218] Iteration 8388 (2.46462 iter/s, 4.86891s/12 iters), loss = 5.26135
I0412 15:32:35.622157 20203 solver.cpp:237]     Train net output #0: loss = 5.26135 (* 1 = 5.26135 loss)
I0412 15:32:35.622165 20203 sgd_solver.cpp:105] Iteration 8388, lr = 0.00189846
I0412 15:32:38.273592 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:32:40.358208 20203 solver.cpp:218] Iteration 8400 (2.53387 iter/s, 4.73585s/12 iters), loss = 5.26284
I0412 15:32:40.358254 20203 solver.cpp:237]     Train net output #0: loss = 5.26284 (* 1 = 5.26284 loss)
I0412 15:32:40.358265 20203 sgd_solver.cpp:105] Iteration 8400, lr = 0.00189395
I0412 15:32:45.132910 20203 solver.cpp:218] Iteration 8412 (2.51338 iter/s, 4.77445s/12 iters), loss = 5.24944
I0412 15:32:45.132952 20203 solver.cpp:237]     Train net output #0: loss = 5.24944 (* 1 = 5.24944 loss)
I0412 15:32:45.132962 20203 sgd_solver.cpp:105] Iteration 8412, lr = 0.00188945
I0412 15:32:50.062273 20203 solver.cpp:218] Iteration 8424 (2.43452 iter/s, 4.92911s/12 iters), loss = 5.25725
I0412 15:32:50.062381 20203 solver.cpp:237]     Train net output #0: loss = 5.25725 (* 1 = 5.25725 loss)
I0412 15:32:50.062391 20203 sgd_solver.cpp:105] Iteration 8424, lr = 0.00188497
I0412 15:32:54.860025 20203 solver.cpp:218] Iteration 8436 (2.50134 iter/s, 4.79744s/12 iters), loss = 5.25387
I0412 15:32:54.860078 20203 solver.cpp:237]     Train net output #0: loss = 5.25387 (* 1 = 5.25387 loss)
I0412 15:32:54.860093 20203 sgd_solver.cpp:105] Iteration 8436, lr = 0.00188049
I0412 15:32:59.479420 20203 solver.cpp:218] Iteration 8448 (2.59788 iter/s, 4.61914s/12 iters), loss = 5.29134
I0412 15:32:59.479470 20203 solver.cpp:237]     Train net output #0: loss = 5.29134 (* 1 = 5.29134 loss)
I0412 15:32:59.479481 20203 sgd_solver.cpp:105] Iteration 8448, lr = 0.00187603
I0412 15:33:04.318562 20203 solver.cpp:218] Iteration 8460 (2.47991 iter/s, 4.83888s/12 iters), loss = 5.27586
I0412 15:33:04.318620 20203 solver.cpp:237]     Train net output #0: loss = 5.27586 (* 1 = 5.27586 loss)
I0412 15:33:04.318634 20203 sgd_solver.cpp:105] Iteration 8460, lr = 0.00187157
I0412 15:33:06.245391 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8466.caffemodel
I0412 15:33:09.206806 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8466.solverstate
I0412 15:33:12.582526 20203 solver.cpp:330] Iteration 8466, Testing net (#0)
I0412 15:33:12.582551 20203 net.cpp:676] Ignoring source layer train-data
I0412 15:33:13.614732 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:33:17.048444 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 15:33:17.048496 20203 solver.cpp:397]     Test net output #1: loss = 5.28604 (* 1 = 5.28604 loss)
I0412 15:33:19.035228 20203 solver.cpp:218] Iteration 8472 (0.815439 iter/s, 14.716s/12 iters), loss = 5.27412
I0412 15:33:19.035275 20203 solver.cpp:237]     Train net output #0: loss = 5.27412 (* 1 = 5.27412 loss)
I0412 15:33:19.035284 20203 sgd_solver.cpp:105] Iteration 8472, lr = 0.00186713
I0412 15:33:23.973507 20203 solver.cpp:218] Iteration 8484 (2.43012 iter/s, 4.93802s/12 iters), loss = 5.26998
I0412 15:33:23.973603 20203 solver.cpp:237]     Train net output #0: loss = 5.26998 (* 1 = 5.26998 loss)
I0412 15:33:23.973613 20203 sgd_solver.cpp:105] Iteration 8484, lr = 0.0018627
I0412 15:33:28.763748 20203 solver.cpp:218] Iteration 8496 (2.50525 iter/s, 4.78993s/12 iters), loss = 5.25916
I0412 15:33:28.763800 20203 solver.cpp:237]     Train net output #0: loss = 5.25916 (* 1 = 5.25916 loss)
I0412 15:33:28.763813 20203 sgd_solver.cpp:105] Iteration 8496, lr = 0.00185827
I0412 15:33:28.766341 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:33:33.770190 20203 solver.cpp:218] Iteration 8508 (2.39704 iter/s, 5.00617s/12 iters), loss = 5.27625
I0412 15:33:33.770234 20203 solver.cpp:237]     Train net output #0: loss = 5.27625 (* 1 = 5.27625 loss)
I0412 15:33:33.770243 20203 sgd_solver.cpp:105] Iteration 8508, lr = 0.00185386
I0412 15:33:38.517611 20203 solver.cpp:218] Iteration 8520 (2.52782 iter/s, 4.74717s/12 iters), loss = 5.29513
I0412 15:33:38.517663 20203 solver.cpp:237]     Train net output #0: loss = 5.29513 (* 1 = 5.29513 loss)
I0412 15:33:38.517676 20203 sgd_solver.cpp:105] Iteration 8520, lr = 0.00184946
I0412 15:33:43.649036 20203 solver.cpp:218] Iteration 8532 (2.33866 iter/s, 5.13115s/12 iters), loss = 5.27167
I0412 15:33:43.649086 20203 solver.cpp:237]     Train net output #0: loss = 5.27167 (* 1 = 5.27167 loss)
I0412 15:33:43.649098 20203 sgd_solver.cpp:105] Iteration 8532, lr = 0.00184507
I0412 15:33:48.430992 20203 solver.cpp:218] Iteration 8544 (2.50957 iter/s, 4.7817s/12 iters), loss = 5.27323
I0412 15:33:48.431046 20203 solver.cpp:237]     Train net output #0: loss = 5.27323 (* 1 = 5.27323 loss)
I0412 15:33:48.431057 20203 sgd_solver.cpp:105] Iteration 8544, lr = 0.00184069
I0412 15:33:53.551508 20203 solver.cpp:218] Iteration 8556 (2.34364 iter/s, 5.12024s/12 iters), loss = 5.26213
I0412 15:33:53.551559 20203 solver.cpp:237]     Train net output #0: loss = 5.26213 (* 1 = 5.26213 loss)
I0412 15:33:53.551573 20203 sgd_solver.cpp:105] Iteration 8556, lr = 0.00183632
I0412 15:33:57.949862 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8568.caffemodel
I0412 15:34:04.425137 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8568.solverstate
I0412 15:34:08.550441 20203 solver.cpp:330] Iteration 8568, Testing net (#0)
I0412 15:34:08.550465 20203 net.cpp:676] Ignoring source layer train-data
I0412 15:34:09.676143 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:34:13.256289 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 15:34:13.256335 20203 solver.cpp:397]     Test net output #1: loss = 5.28607 (* 1 = 5.28607 loss)
I0412 15:34:13.343042 20203 solver.cpp:218] Iteration 8568 (0.606346 iter/s, 19.7907s/12 iters), loss = 5.24684
I0412 15:34:13.343086 20203 solver.cpp:237]     Train net output #0: loss = 5.24684 (* 1 = 5.24684 loss)
I0412 15:34:13.343096 20203 sgd_solver.cpp:105] Iteration 8568, lr = 0.00183196
I0412 15:34:17.618865 20203 solver.cpp:218] Iteration 8580 (2.80663 iter/s, 4.27559s/12 iters), loss = 5.26182
I0412 15:34:17.618911 20203 solver.cpp:237]     Train net output #0: loss = 5.26182 (* 1 = 5.26182 loss)
I0412 15:34:17.618922 20203 sgd_solver.cpp:105] Iteration 8580, lr = 0.00182761
I0412 15:34:22.363010 20203 solver.cpp:218] Iteration 8592 (2.52957 iter/s, 4.7439s/12 iters), loss = 5.24997
I0412 15:34:22.363056 20203 solver.cpp:237]     Train net output #0: loss = 5.24997 (* 1 = 5.24997 loss)
I0412 15:34:22.363067 20203 sgd_solver.cpp:105] Iteration 8592, lr = 0.00182327
I0412 15:34:24.444769 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:34:27.245273 20203 solver.cpp:218] Iteration 8604 (2.45801 iter/s, 4.882s/12 iters), loss = 5.27126
I0412 15:34:27.245327 20203 solver.cpp:237]     Train net output #0: loss = 5.27126 (* 1 = 5.27126 loss)
I0412 15:34:27.245338 20203 sgd_solver.cpp:105] Iteration 8604, lr = 0.00181894
I0412 15:34:32.360107 20203 solver.cpp:218] Iteration 8616 (2.34624 iter/s, 5.11456s/12 iters), loss = 5.26403
I0412 15:34:32.360210 20203 solver.cpp:237]     Train net output #0: loss = 5.26403 (* 1 = 5.26403 loss)
I0412 15:34:32.360221 20203 sgd_solver.cpp:105] Iteration 8616, lr = 0.00181462
I0412 15:34:37.478904 20203 solver.cpp:218] Iteration 8628 (2.34445 iter/s, 5.11847s/12 iters), loss = 5.28819
I0412 15:34:37.478962 20203 solver.cpp:237]     Train net output #0: loss = 5.28819 (* 1 = 5.28819 loss)
I0412 15:34:37.478976 20203 sgd_solver.cpp:105] Iteration 8628, lr = 0.00181031
I0412 15:34:42.141993 20203 solver.cpp:218] Iteration 8640 (2.57355 iter/s, 4.66283s/12 iters), loss = 5.26465
I0412 15:34:42.142038 20203 solver.cpp:237]     Train net output #0: loss = 5.26465 (* 1 = 5.26465 loss)
I0412 15:34:42.142048 20203 sgd_solver.cpp:105] Iteration 8640, lr = 0.00180602
I0412 15:34:47.088464 20203 solver.cpp:218] Iteration 8652 (2.4261 iter/s, 4.94622s/12 iters), loss = 5.26606
I0412 15:34:47.088500 20203 solver.cpp:237]     Train net output #0: loss = 5.26606 (* 1 = 5.26606 loss)
I0412 15:34:47.088508 20203 sgd_solver.cpp:105] Iteration 8652, lr = 0.00180173
I0412 15:34:52.274624 20203 solver.cpp:218] Iteration 8664 (2.31397 iter/s, 5.1859s/12 iters), loss = 5.27519
I0412 15:34:52.274670 20203 solver.cpp:237]     Train net output #0: loss = 5.27519 (* 1 = 5.27519 loss)
I0412 15:34:52.274682 20203 sgd_solver.cpp:105] Iteration 8664, lr = 0.00179745
I0412 15:34:54.246556 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8670.caffemodel
I0412 15:34:57.221943 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8670.solverstate
I0412 15:34:59.538867 20203 solver.cpp:330] Iteration 8670, Testing net (#0)
I0412 15:34:59.538894 20203 net.cpp:676] Ignoring source layer train-data
I0412 15:35:00.529345 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:35:04.033752 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 15:35:04.033931 20203 solver.cpp:397]     Test net output #1: loss = 5.28632 (* 1 = 5.28632 loss)
I0412 15:35:05.815874 20203 solver.cpp:218] Iteration 8676 (0.886221 iter/s, 13.5406s/12 iters), loss = 5.27559
I0412 15:35:05.815928 20203 solver.cpp:237]     Train net output #0: loss = 5.27559 (* 1 = 5.27559 loss)
I0412 15:35:05.815941 20203 sgd_solver.cpp:105] Iteration 8676, lr = 0.00179318
I0412 15:35:10.577661 20203 solver.cpp:218] Iteration 8688 (2.5202 iter/s, 4.76152s/12 iters), loss = 5.26459
I0412 15:35:10.577709 20203 solver.cpp:237]     Train net output #0: loss = 5.26459 (* 1 = 5.26459 loss)
I0412 15:35:10.577720 20203 sgd_solver.cpp:105] Iteration 8688, lr = 0.00178893
I0412 15:35:14.997424 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:35:15.705168 20203 solver.cpp:218] Iteration 8700 (2.34044 iter/s, 5.12724s/12 iters), loss = 5.26724
I0412 15:35:15.705217 20203 solver.cpp:237]     Train net output #0: loss = 5.26724 (* 1 = 5.26724 loss)
I0412 15:35:15.705230 20203 sgd_solver.cpp:105] Iteration 8700, lr = 0.00178468
I0412 15:35:20.707196 20203 solver.cpp:218] Iteration 8712 (2.39915 iter/s, 5.00177s/12 iters), loss = 5.28004
I0412 15:35:20.707247 20203 solver.cpp:237]     Train net output #0: loss = 5.28004 (* 1 = 5.28004 loss)
I0412 15:35:20.707257 20203 sgd_solver.cpp:105] Iteration 8712, lr = 0.00178044
I0412 15:35:25.370041 20203 solver.cpp:218] Iteration 8724 (2.57368 iter/s, 4.66259s/12 iters), loss = 5.2823
I0412 15:35:25.370093 20203 solver.cpp:237]     Train net output #0: loss = 5.2823 (* 1 = 5.2823 loss)
I0412 15:35:25.370105 20203 sgd_solver.cpp:105] Iteration 8724, lr = 0.00177621
I0412 15:35:30.057395 20203 solver.cpp:218] Iteration 8736 (2.56022 iter/s, 4.6871s/12 iters), loss = 5.29202
I0412 15:35:30.057436 20203 solver.cpp:237]     Train net output #0: loss = 5.29202 (* 1 = 5.29202 loss)
I0412 15:35:30.057446 20203 sgd_solver.cpp:105] Iteration 8736, lr = 0.001772
I0412 15:35:34.972498 20203 solver.cpp:218] Iteration 8748 (2.44158 iter/s, 4.91485s/12 iters), loss = 5.26968
I0412 15:35:34.972568 20203 solver.cpp:237]     Train net output #0: loss = 5.26968 (* 1 = 5.26968 loss)
I0412 15:35:34.972576 20203 sgd_solver.cpp:105] Iteration 8748, lr = 0.00176779
I0412 15:35:39.936558 20203 solver.cpp:218] Iteration 8760 (2.41752 iter/s, 4.96377s/12 iters), loss = 5.27703
I0412 15:35:39.936607 20203 solver.cpp:237]     Train net output #0: loss = 5.27703 (* 1 = 5.27703 loss)
I0412 15:35:39.936617 20203 sgd_solver.cpp:105] Iteration 8760, lr = 0.00176359
I0412 15:35:44.471428 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8772.caffemodel
I0412 15:35:47.509896 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8772.solverstate
I0412 15:35:49.804253 20203 solver.cpp:330] Iteration 8772, Testing net (#0)
I0412 15:35:49.804280 20203 net.cpp:676] Ignoring source layer train-data
I0412 15:35:50.805224 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:35:54.324822 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 15:35:54.324872 20203 solver.cpp:397]     Test net output #1: loss = 5.28633 (* 1 = 5.28633 loss)
I0412 15:35:54.411244 20203 solver.cpp:218] Iteration 8772 (0.829071 iter/s, 14.474s/12 iters), loss = 5.2774
I0412 15:35:54.411295 20203 solver.cpp:237]     Train net output #0: loss = 5.2774 (* 1 = 5.2774 loss)
I0412 15:35:54.411306 20203 sgd_solver.cpp:105] Iteration 8772, lr = 0.00175941
I0412 15:35:58.591754 20203 solver.cpp:218] Iteration 8784 (2.87062 iter/s, 4.18028s/12 iters), loss = 5.27447
I0412 15:35:58.591804 20203 solver.cpp:237]     Train net output #0: loss = 5.27447 (* 1 = 5.27447 loss)
I0412 15:35:58.591816 20203 sgd_solver.cpp:105] Iteration 8784, lr = 0.00175523
I0412 15:36:03.398241 20203 solver.cpp:218] Iteration 8796 (2.49676 iter/s, 4.80623s/12 iters), loss = 5.25981
I0412 15:36:03.398288 20203 solver.cpp:237]     Train net output #0: loss = 5.25981 (* 1 = 5.25981 loss)
I0412 15:36:03.398298 20203 sgd_solver.cpp:105] Iteration 8796, lr = 0.00175106
I0412 15:36:04.694408 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:36:08.093032 20203 solver.cpp:218] Iteration 8808 (2.55616 iter/s, 4.69454s/12 iters), loss = 5.26536
I0412 15:36:08.093190 20203 solver.cpp:237]     Train net output #0: loss = 5.26536 (* 1 = 5.26536 loss)
I0412 15:36:08.093204 20203 sgd_solver.cpp:105] Iteration 8808, lr = 0.0017469
I0412 15:36:12.913491 20203 solver.cpp:218] Iteration 8820 (2.48958 iter/s, 4.8201s/12 iters), loss = 5.265
I0412 15:36:12.913539 20203 solver.cpp:237]     Train net output #0: loss = 5.265 (* 1 = 5.265 loss)
I0412 15:36:12.913550 20203 sgd_solver.cpp:105] Iteration 8820, lr = 0.00174276
I0412 15:36:17.982472 20203 solver.cpp:218] Iteration 8832 (2.36746 iter/s, 5.06871s/12 iters), loss = 5.26538
I0412 15:36:17.982515 20203 solver.cpp:237]     Train net output #0: loss = 5.26538 (* 1 = 5.26538 loss)
I0412 15:36:17.982524 20203 sgd_solver.cpp:105] Iteration 8832, lr = 0.00173862
I0412 15:36:22.765110 20203 solver.cpp:218] Iteration 8844 (2.50921 iter/s, 4.78238s/12 iters), loss = 5.29729
I0412 15:36:22.765156 20203 solver.cpp:237]     Train net output #0: loss = 5.29729 (* 1 = 5.29729 loss)
I0412 15:36:22.765166 20203 sgd_solver.cpp:105] Iteration 8844, lr = 0.00173449
I0412 15:36:27.662655 20203 solver.cpp:218] Iteration 8856 (2.45034 iter/s, 4.89728s/12 iters), loss = 5.2579
I0412 15:36:27.662725 20203 solver.cpp:237]     Train net output #0: loss = 5.2579 (* 1 = 5.2579 loss)
I0412 15:36:27.662742 20203 sgd_solver.cpp:105] Iteration 8856, lr = 0.00173037
I0412 15:36:32.581202 20203 solver.cpp:218] Iteration 8868 (2.43988 iter/s, 4.91827s/12 iters), loss = 5.25932
I0412 15:36:32.581243 20203 solver.cpp:237]     Train net output #0: loss = 5.25932 (* 1 = 5.25932 loss)
I0412 15:36:32.581254 20203 sgd_solver.cpp:105] Iteration 8868, lr = 0.00172626
I0412 15:36:34.682504 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8874.caffemodel
I0412 15:36:37.649307 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8874.solverstate
I0412 15:36:39.966547 20203 solver.cpp:330] Iteration 8874, Testing net (#0)
I0412 15:36:39.966607 20203 net.cpp:676] Ignoring source layer train-data
I0412 15:36:40.969674 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:36:44.709307 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 15:36:44.709352 20203 solver.cpp:397]     Test net output #1: loss = 5.28609 (* 1 = 5.28609 loss)
I0412 15:36:46.646832 20203 solver.cpp:218] Iteration 8880 (0.853182 iter/s, 14.065s/12 iters), loss = 5.27805
I0412 15:36:46.646888 20203 solver.cpp:237]     Train net output #0: loss = 5.27805 (* 1 = 5.27805 loss)
I0412 15:36:46.646900 20203 sgd_solver.cpp:105] Iteration 8880, lr = 0.00172217
I0412 15:36:51.541565 20203 solver.cpp:218] Iteration 8892 (2.45175 iter/s, 4.89446s/12 iters), loss = 5.27673
I0412 15:36:51.541617 20203 solver.cpp:237]     Train net output #0: loss = 5.27673 (* 1 = 5.27673 loss)
I0412 15:36:51.541630 20203 sgd_solver.cpp:105] Iteration 8892, lr = 0.00171808
I0412 15:36:54.863018 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:36:56.264587 20203 solver.cpp:218] Iteration 8904 (2.54089 iter/s, 4.72276s/12 iters), loss = 5.27711
I0412 15:36:56.264642 20203 solver.cpp:237]     Train net output #0: loss = 5.27711 (* 1 = 5.27711 loss)
I0412 15:36:56.264652 20203 sgd_solver.cpp:105] Iteration 8904, lr = 0.001714
I0412 15:37:00.999433 20203 solver.cpp:218] Iteration 8916 (2.53454 iter/s, 4.73458s/12 iters), loss = 5.26748
I0412 15:37:00.999491 20203 solver.cpp:237]     Train net output #0: loss = 5.26748 (* 1 = 5.26748 loss)
I0412 15:37:00.999505 20203 sgd_solver.cpp:105] Iteration 8916, lr = 0.00170993
I0412 15:37:05.579375 20203 solver.cpp:218] Iteration 8928 (2.62027 iter/s, 4.57968s/12 iters), loss = 5.25884
I0412 15:37:05.579428 20203 solver.cpp:237]     Train net output #0: loss = 5.25884 (* 1 = 5.25884 loss)
I0412 15:37:05.579439 20203 sgd_solver.cpp:105] Iteration 8928, lr = 0.00170587
I0412 15:37:10.144542 20203 solver.cpp:218] Iteration 8940 (2.62874 iter/s, 4.56492s/12 iters), loss = 5.2647
I0412 15:37:10.144676 20203 solver.cpp:237]     Train net output #0: loss = 5.2647 (* 1 = 5.2647 loss)
I0412 15:37:10.144687 20203 sgd_solver.cpp:105] Iteration 8940, lr = 0.00170182
I0412 15:37:14.980597 20203 solver.cpp:218] Iteration 8952 (2.48154 iter/s, 4.83571s/12 iters), loss = 5.25694
I0412 15:37:14.980639 20203 solver.cpp:237]     Train net output #0: loss = 5.25694 (* 1 = 5.25694 loss)
I0412 15:37:14.980649 20203 sgd_solver.cpp:105] Iteration 8952, lr = 0.00169778
I0412 15:37:19.825624 20203 solver.cpp:218] Iteration 8964 (2.4769 iter/s, 4.84477s/12 iters), loss = 5.27773
I0412 15:37:19.825685 20203 solver.cpp:237]     Train net output #0: loss = 5.27773 (* 1 = 5.27773 loss)
I0412 15:37:19.825700 20203 sgd_solver.cpp:105] Iteration 8964, lr = 0.00169375
I0412 15:37:24.107930 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8976.caffemodel
I0412 15:37:28.482040 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8976.solverstate
I0412 15:37:31.594421 20203 solver.cpp:330] Iteration 8976, Testing net (#0)
I0412 15:37:31.594449 20203 net.cpp:676] Ignoring source layer train-data
I0412 15:37:32.545554 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:37:36.053890 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 15:37:36.053937 20203 solver.cpp:397]     Test net output #1: loss = 5.28619 (* 1 = 5.28619 loss)
I0412 15:37:36.140065 20203 solver.cpp:218] Iteration 8976 (0.735578 iter/s, 16.3137s/12 iters), loss = 5.27676
I0412 15:37:36.140115 20203 solver.cpp:237]     Train net output #0: loss = 5.27676 (* 1 = 5.27676 loss)
I0412 15:37:36.140125 20203 sgd_solver.cpp:105] Iteration 8976, lr = 0.00168973
I0412 15:37:40.579262 20203 solver.cpp:218] Iteration 8988 (2.70334 iter/s, 4.43895s/12 iters), loss = 5.28436
I0412 15:37:40.579392 20203 solver.cpp:237]     Train net output #0: loss = 5.28436 (* 1 = 5.28436 loss)
I0412 15:37:40.579406 20203 sgd_solver.cpp:105] Iteration 8988, lr = 0.00168571
I0412 15:37:43.690866 20203 blocking_queue.cpp:49] Waiting for data
I0412 15:37:45.393451 20203 solver.cpp:218] Iteration 9000 (2.49281 iter/s, 4.81385s/12 iters), loss = 5.28965
I0412 15:37:45.393501 20203 solver.cpp:237]     Train net output #0: loss = 5.28965 (* 1 = 5.28965 loss)
I0412 15:37:45.393513 20203 sgd_solver.cpp:105] Iteration 9000, lr = 0.00168171
I0412 15:37:46.054184 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:37:50.202512 20203 solver.cpp:218] Iteration 9012 (2.49543 iter/s, 4.80879s/12 iters), loss = 5.28532
I0412 15:37:50.202569 20203 solver.cpp:237]     Train net output #0: loss = 5.28532 (* 1 = 5.28532 loss)
I0412 15:37:50.202584 20203 sgd_solver.cpp:105] Iteration 9012, lr = 0.00167772
I0412 15:37:55.127367 20203 solver.cpp:218] Iteration 9024 (2.43675 iter/s, 4.92459s/12 iters), loss = 5.26774
I0412 15:37:55.127410 20203 solver.cpp:237]     Train net output #0: loss = 5.26774 (* 1 = 5.26774 loss)
I0412 15:37:55.127419 20203 sgd_solver.cpp:105] Iteration 9024, lr = 0.00167374
I0412 15:38:00.024955 20203 solver.cpp:218] Iteration 9036 (2.45031 iter/s, 4.89734s/12 iters), loss = 5.27078
I0412 15:38:00.025002 20203 solver.cpp:237]     Train net output #0: loss = 5.27078 (* 1 = 5.27078 loss)
I0412 15:38:00.025012 20203 sgd_solver.cpp:105] Iteration 9036, lr = 0.00166976
I0412 15:38:04.854529 20203 solver.cpp:218] Iteration 9048 (2.48482 iter/s, 4.82932s/12 iters), loss = 5.27458
I0412 15:38:04.854573 20203 solver.cpp:237]     Train net output #0: loss = 5.27458 (* 1 = 5.27458 loss)
I0412 15:38:04.854583 20203 sgd_solver.cpp:105] Iteration 9048, lr = 0.0016658
I0412 15:38:09.793200 20203 solver.cpp:218] Iteration 9060 (2.42993 iter/s, 4.93841s/12 iters), loss = 5.28836
I0412 15:38:09.793257 20203 solver.cpp:237]     Train net output #0: loss = 5.28836 (* 1 = 5.28836 loss)
I0412 15:38:09.793270 20203 sgd_solver.cpp:105] Iteration 9060, lr = 0.00166184
I0412 15:38:14.546180 20203 solver.cpp:218] Iteration 9072 (2.52487 iter/s, 4.75272s/12 iters), loss = 5.26691
I0412 15:38:14.546298 20203 solver.cpp:237]     Train net output #0: loss = 5.26691 (* 1 = 5.26691 loss)
I0412 15:38:14.546306 20203 sgd_solver.cpp:105] Iteration 9072, lr = 0.0016579
I0412 15:38:16.609436 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9078.caffemodel
I0412 15:38:27.333056 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9078.solverstate
I0412 15:38:34.075868 20203 solver.cpp:330] Iteration 9078, Testing net (#0)
I0412 15:38:34.075898 20203 net.cpp:676] Ignoring source layer train-data
I0412 15:38:35.033079 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:38:38.569547 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 15:38:38.569595 20203 solver.cpp:397]     Test net output #1: loss = 5.28643 (* 1 = 5.28643 loss)
I0412 15:38:40.148409 20203 solver.cpp:218] Iteration 9084 (0.468731 iter/s, 25.6011s/12 iters), loss = 5.25392
I0412 15:38:40.148463 20203 solver.cpp:237]     Train net output #0: loss = 5.25392 (* 1 = 5.25392 loss)
I0412 15:38:40.148475 20203 sgd_solver.cpp:105] Iteration 9084, lr = 0.00165396
I0412 15:38:44.904762 20203 solver.cpp:218] Iteration 9096 (2.52308 iter/s, 4.7561s/12 iters), loss = 5.2639
I0412 15:38:44.904844 20203 solver.cpp:237]     Train net output #0: loss = 5.2639 (* 1 = 5.2639 loss)
I0412 15:38:44.904855 20203 sgd_solver.cpp:105] Iteration 9096, lr = 0.00165003
I0412 15:38:47.712978 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:38:49.754688 20203 solver.cpp:218] Iteration 9108 (2.47441 iter/s, 4.84964s/12 iters), loss = 5.2596
I0412 15:38:49.754732 20203 solver.cpp:237]     Train net output #0: loss = 5.2596 (* 1 = 5.2596 loss)
I0412 15:38:49.754743 20203 sgd_solver.cpp:105] Iteration 9108, lr = 0.00164612
I0412 15:38:54.622622 20203 solver.cpp:218] Iteration 9120 (2.46524 iter/s, 4.86768s/12 iters), loss = 5.25133
I0412 15:38:54.622670 20203 solver.cpp:237]     Train net output #0: loss = 5.25133 (* 1 = 5.25133 loss)
I0412 15:38:54.622682 20203 sgd_solver.cpp:105] Iteration 9120, lr = 0.00164221
I0412 15:38:59.382162 20203 solver.cpp:218] Iteration 9132 (2.52139 iter/s, 4.75929s/12 iters), loss = 5.25209
I0412 15:38:59.382212 20203 solver.cpp:237]     Train net output #0: loss = 5.25209 (* 1 = 5.25209 loss)
I0412 15:38:59.382225 20203 sgd_solver.cpp:105] Iteration 9132, lr = 0.00163831
I0412 15:39:04.078800 20203 solver.cpp:218] Iteration 9144 (2.55515 iter/s, 4.69639s/12 iters), loss = 5.25975
I0412 15:39:04.078837 20203 solver.cpp:237]     Train net output #0: loss = 5.25975 (* 1 = 5.25975 loss)
I0412 15:39:04.078845 20203 sgd_solver.cpp:105] Iteration 9144, lr = 0.00163442
I0412 15:39:08.769277 20203 solver.cpp:218] Iteration 9156 (2.55851 iter/s, 4.69023s/12 iters), loss = 5.2868
I0412 15:39:08.769331 20203 solver.cpp:237]     Train net output #0: loss = 5.2868 (* 1 = 5.2868 loss)
I0412 15:39:08.769345 20203 sgd_solver.cpp:105] Iteration 9156, lr = 0.00163054
I0412 15:39:13.884618 20203 solver.cpp:218] Iteration 9168 (2.34601 iter/s, 5.11506s/12 iters), loss = 5.27301
I0412 15:39:13.884661 20203 solver.cpp:237]     Train net output #0: loss = 5.27301 (* 1 = 5.27301 loss)
I0412 15:39:13.884670 20203 sgd_solver.cpp:105] Iteration 9168, lr = 0.00162667
I0412 15:39:18.312557 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9180.caffemodel
I0412 15:39:21.322760 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9180.solverstate
I0412 15:39:24.496577 20203 solver.cpp:330] Iteration 9180, Testing net (#0)
I0412 15:39:24.496605 20203 net.cpp:676] Ignoring source layer train-data
I0412 15:39:25.364827 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:39:28.998241 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 15:39:28.998283 20203 solver.cpp:397]     Test net output #1: loss = 5.28668 (* 1 = 5.28668 loss)
I0412 15:39:29.084628 20203 solver.cpp:218] Iteration 9180 (0.789508 iter/s, 15.1993s/12 iters), loss = 5.27212
I0412 15:39:29.086153 20203 solver.cpp:237]     Train net output #0: loss = 5.27212 (* 1 = 5.27212 loss)
I0412 15:39:29.086165 20203 sgd_solver.cpp:105] Iteration 9180, lr = 0.00162281
I0412 15:39:33.024775 20203 solver.cpp:218] Iteration 9192 (3.04688 iter/s, 3.93846s/12 iters), loss = 5.27496
I0412 15:39:33.024816 20203 solver.cpp:237]     Train net output #0: loss = 5.27496 (* 1 = 5.27496 loss)
I0412 15:39:33.024824 20203 sgd_solver.cpp:105] Iteration 9192, lr = 0.00161895
I0412 15:39:37.779979 20203 solver.cpp:218] Iteration 9204 (2.52368 iter/s, 4.75496s/12 iters), loss = 5.26664
I0412 15:39:37.780026 20203 solver.cpp:237]     Train net output #0: loss = 5.26664 (* 1 = 5.26664 loss)
I0412 15:39:37.780036 20203 sgd_solver.cpp:105] Iteration 9204, lr = 0.00161511
I0412 15:39:37.841830 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:39:42.560645 20203 solver.cpp:218] Iteration 9216 (2.51025 iter/s, 4.7804s/12 iters), loss = 5.27821
I0412 15:39:42.560696 20203 solver.cpp:237]     Train net output #0: loss = 5.27821 (* 1 = 5.27821 loss)
I0412 15:39:42.560708 20203 sgd_solver.cpp:105] Iteration 9216, lr = 0.00161128
I0412 15:39:47.419960 20203 solver.cpp:218] Iteration 9228 (2.46962 iter/s, 4.85906s/12 iters), loss = 5.28463
I0412 15:39:47.420006 20203 solver.cpp:237]     Train net output #0: loss = 5.28463 (* 1 = 5.28463 loss)
I0412 15:39:47.420017 20203 sgd_solver.cpp:105] Iteration 9228, lr = 0.00160745
I0412 15:39:52.107452 20203 solver.cpp:218] Iteration 9240 (2.56014 iter/s, 4.68724s/12 iters), loss = 5.26042
I0412 15:39:52.107648 20203 solver.cpp:237]     Train net output #0: loss = 5.26042 (* 1 = 5.26042 loss)
I0412 15:39:52.107659 20203 sgd_solver.cpp:105] Iteration 9240, lr = 0.00160363
I0412 15:39:56.958048 20203 solver.cpp:218] Iteration 9252 (2.47413 iter/s, 4.85019s/12 iters), loss = 5.27391
I0412 15:39:56.958104 20203 solver.cpp:237]     Train net output #0: loss = 5.27391 (* 1 = 5.27391 loss)
I0412 15:39:56.958117 20203 sgd_solver.cpp:105] Iteration 9252, lr = 0.00159983
I0412 15:40:01.711480 20203 solver.cpp:218] Iteration 9264 (2.52463 iter/s, 4.75317s/12 iters), loss = 5.26001
I0412 15:40:01.711532 20203 solver.cpp:237]     Train net output #0: loss = 5.26001 (* 1 = 5.26001 loss)
I0412 15:40:01.711544 20203 sgd_solver.cpp:105] Iteration 9264, lr = 0.00159603
I0412 15:40:06.389189 20203 solver.cpp:218] Iteration 9276 (2.5655 iter/s, 4.67746s/12 iters), loss = 5.24845
I0412 15:40:06.389230 20203 solver.cpp:237]     Train net output #0: loss = 5.24845 (* 1 = 5.24845 loss)
I0412 15:40:06.389240 20203 sgd_solver.cpp:105] Iteration 9276, lr = 0.00159224
I0412 15:40:08.329900 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9282.caffemodel
I0412 15:40:12.255445 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9282.solverstate
I0412 15:40:15.439523 20203 solver.cpp:330] Iteration 9282, Testing net (#0)
I0412 15:40:15.439549 20203 net.cpp:676] Ignoring source layer train-data
I0412 15:40:16.240686 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:40:19.865128 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 15:40:19.865171 20203 solver.cpp:397]     Test net output #1: loss = 5.28618 (* 1 = 5.28618 loss)
I0412 15:40:21.581295 20203 solver.cpp:218] Iteration 9288 (0.789919 iter/s, 15.1914s/12 iters), loss = 5.2657
I0412 15:40:21.581349 20203 solver.cpp:237]     Train net output #0: loss = 5.2657 (* 1 = 5.2657 loss)
I0412 15:40:21.581360 20203 sgd_solver.cpp:105] Iteration 9288, lr = 0.00158846
I0412 15:40:26.377923 20203 solver.cpp:218] Iteration 9300 (2.50189 iter/s, 4.79637s/12 iters), loss = 5.25138
I0412 15:40:26.378087 20203 solver.cpp:237]     Train net output #0: loss = 5.25138 (* 1 = 5.25138 loss)
I0412 15:40:26.378103 20203 sgd_solver.cpp:105] Iteration 9300, lr = 0.00158469
I0412 15:40:28.499367 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:40:31.078605 20203 solver.cpp:218] Iteration 9312 (2.55302 iter/s, 4.70032s/12 iters), loss = 5.27112
I0412 15:40:31.078657 20203 solver.cpp:237]     Train net output #0: loss = 5.27112 (* 1 = 5.27112 loss)
I0412 15:40:31.078670 20203 sgd_solver.cpp:105] Iteration 9312, lr = 0.00158092
I0412 15:40:35.930605 20203 solver.cpp:218] Iteration 9324 (2.47334 iter/s, 4.85174s/12 iters), loss = 5.28037
I0412 15:40:35.930647 20203 solver.cpp:237]     Train net output #0: loss = 5.28037 (* 1 = 5.28037 loss)
I0412 15:40:35.930655 20203 sgd_solver.cpp:105] Iteration 9324, lr = 0.00157717
I0412 15:40:40.742200 20203 solver.cpp:218] Iteration 9336 (2.49411 iter/s, 4.81134s/12 iters), loss = 5.28342
I0412 15:40:40.742254 20203 solver.cpp:237]     Train net output #0: loss = 5.28342 (* 1 = 5.28342 loss)
I0412 15:40:40.742267 20203 sgd_solver.cpp:105] Iteration 9336, lr = 0.00157343
I0412 15:40:45.426623 20203 solver.cpp:218] Iteration 9348 (2.56182 iter/s, 4.68416s/12 iters), loss = 5.26963
I0412 15:40:45.426678 20203 solver.cpp:237]     Train net output #0: loss = 5.26963 (* 1 = 5.26963 loss)
I0412 15:40:45.426689 20203 sgd_solver.cpp:105] Iteration 9348, lr = 0.00156969
I0412 15:40:50.269573 20203 solver.cpp:218] Iteration 9360 (2.47796 iter/s, 4.84269s/12 iters), loss = 5.27046
I0412 15:40:50.269615 20203 solver.cpp:237]     Train net output #0: loss = 5.27046 (* 1 = 5.27046 loss)
I0412 15:40:50.269625 20203 sgd_solver.cpp:105] Iteration 9360, lr = 0.00156596
I0412 15:40:55.259543 20203 solver.cpp:218] Iteration 9372 (2.40495 iter/s, 4.98971s/12 iters), loss = 5.27356
I0412 15:40:55.259596 20203 solver.cpp:237]     Train net output #0: loss = 5.27356 (* 1 = 5.27356 loss)
I0412 15:40:55.259609 20203 sgd_solver.cpp:105] Iteration 9372, lr = 0.00156225
I0412 15:40:59.436190 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9384.caffemodel
I0412 15:41:03.260964 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9384.solverstate
I0412 15:41:08.495152 20203 solver.cpp:330] Iteration 9384, Testing net (#0)
I0412 15:41:08.495178 20203 net.cpp:676] Ignoring source layer train-data
I0412 15:41:09.271287 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:41:12.935277 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 15:41:12.935328 20203 solver.cpp:397]     Test net output #1: loss = 5.28619 (* 1 = 5.28619 loss)
I0412 15:41:13.021574 20203 solver.cpp:218] Iteration 9384 (0.675628 iter/s, 17.7612s/12 iters), loss = 5.2775
I0412 15:41:13.021631 20203 solver.cpp:237]     Train net output #0: loss = 5.2775 (* 1 = 5.2775 loss)
I0412 15:41:13.021643 20203 sgd_solver.cpp:105] Iteration 9384, lr = 0.00155854
I0412 15:41:17.201077 20203 solver.cpp:218] Iteration 9396 (2.87132 iter/s, 4.17926s/12 iters), loss = 5.26747
I0412 15:41:17.201124 20203 solver.cpp:237]     Train net output #0: loss = 5.26747 (* 1 = 5.26747 loss)
I0412 15:41:17.201134 20203 sgd_solver.cpp:105] Iteration 9396, lr = 0.00155484
I0412 15:41:21.432715 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:41:22.085342 20203 solver.cpp:218] Iteration 9408 (2.457 iter/s, 4.88401s/12 iters), loss = 5.27144
I0412 15:41:22.085388 20203 solver.cpp:237]     Train net output #0: loss = 5.27144 (* 1 = 5.27144 loss)
I0412 15:41:22.085397 20203 sgd_solver.cpp:105] Iteration 9408, lr = 0.00155114
I0412 15:41:26.962070 20203 solver.cpp:218] Iteration 9420 (2.46079 iter/s, 4.87648s/12 iters), loss = 5.27622
I0412 15:41:26.962118 20203 solver.cpp:237]     Train net output #0: loss = 5.27622 (* 1 = 5.27622 loss)
I0412 15:41:26.962128 20203 sgd_solver.cpp:105] Iteration 9420, lr = 0.00154746
I0412 15:41:31.764657 20203 solver.cpp:218] Iteration 9432 (2.49879 iter/s, 4.80233s/12 iters), loss = 5.28189
I0412 15:41:31.764786 20203 solver.cpp:237]     Train net output #0: loss = 5.28189 (* 1 = 5.28189 loss)
I0412 15:41:31.764799 20203 sgd_solver.cpp:105] Iteration 9432, lr = 0.00154379
I0412 15:41:36.444387 20203 solver.cpp:218] Iteration 9444 (2.56443 iter/s, 4.6794s/12 iters), loss = 5.2884
I0412 15:41:36.444444 20203 solver.cpp:237]     Train net output #0: loss = 5.2884 (* 1 = 5.2884 loss)
I0412 15:41:36.444458 20203 sgd_solver.cpp:105] Iteration 9444, lr = 0.00154012
I0412 15:41:41.095502 20203 solver.cpp:218] Iteration 9456 (2.58017 iter/s, 4.65086s/12 iters), loss = 5.26404
I0412 15:41:41.095549 20203 solver.cpp:237]     Train net output #0: loss = 5.26404 (* 1 = 5.26404 loss)
I0412 15:41:41.095561 20203 sgd_solver.cpp:105] Iteration 9456, lr = 0.00153647
I0412 15:41:45.975881 20203 solver.cpp:218] Iteration 9468 (2.45895 iter/s, 4.88012s/12 iters), loss = 5.27535
I0412 15:41:45.975932 20203 solver.cpp:237]     Train net output #0: loss = 5.27535 (* 1 = 5.27535 loss)
I0412 15:41:45.975945 20203 sgd_solver.cpp:105] Iteration 9468, lr = 0.00153282
I0412 15:41:50.915442 20203 solver.cpp:218] Iteration 9480 (2.4295 iter/s, 4.9393s/12 iters), loss = 5.27597
I0412 15:41:50.915498 20203 solver.cpp:237]     Train net output #0: loss = 5.27597 (* 1 = 5.27597 loss)
I0412 15:41:50.915510 20203 sgd_solver.cpp:105] Iteration 9480, lr = 0.00152918
I0412 15:41:52.786736 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9486.caffemodel
I0412 15:41:57.502929 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9486.solverstate
I0412 15:42:01.163645 20203 solver.cpp:330] Iteration 9486, Testing net (#0)
I0412 15:42:01.163671 20203 net.cpp:676] Ignoring source layer train-data
I0412 15:42:01.878803 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:42:05.595094 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 15:42:05.595144 20203 solver.cpp:397]     Test net output #1: loss = 5.28572 (* 1 = 5.28572 loss)
I0412 15:42:07.465207 20203 solver.cpp:218] Iteration 9492 (0.725118 iter/s, 16.549s/12 iters), loss = 5.26883
I0412 15:42:07.465252 20203 solver.cpp:237]     Train net output #0: loss = 5.26883 (* 1 = 5.26883 loss)
I0412 15:42:07.465261 20203 sgd_solver.cpp:105] Iteration 9492, lr = 0.00152555
I0412 15:42:12.328616 20203 solver.cpp:218] Iteration 9504 (2.46754 iter/s, 4.86315s/12 iters), loss = 5.262
I0412 15:42:12.328665 20203 solver.cpp:237]     Train net output #0: loss = 5.262 (* 1 = 5.262 loss)
I0412 15:42:12.328673 20203 sgd_solver.cpp:105] Iteration 9504, lr = 0.00152193
I0412 15:42:13.752477 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:42:17.303251 20203 solver.cpp:218] Iteration 9516 (2.41237 iter/s, 4.97437s/12 iters), loss = 5.26309
I0412 15:42:17.303303 20203 solver.cpp:237]     Train net output #0: loss = 5.26309 (* 1 = 5.26309 loss)
I0412 15:42:17.303315 20203 sgd_solver.cpp:105] Iteration 9516, lr = 0.00151831
I0412 15:42:22.278666 20203 solver.cpp:218] Iteration 9528 (2.41199 iter/s, 4.97514s/12 iters), loss = 5.26606
I0412 15:42:22.278734 20203 solver.cpp:237]     Train net output #0: loss = 5.26606 (* 1 = 5.26606 loss)
I0412 15:42:22.278751 20203 sgd_solver.cpp:105] Iteration 9528, lr = 0.00151471
I0412 15:42:27.099793 20203 solver.cpp:218] Iteration 9540 (2.48918 iter/s, 4.82086s/12 iters), loss = 5.24732
I0412 15:42:27.099836 20203 solver.cpp:237]     Train net output #0: loss = 5.24732 (* 1 = 5.24732 loss)
I0412 15:42:27.099845 20203 sgd_solver.cpp:105] Iteration 9540, lr = 0.00151111
I0412 15:42:32.020284 20203 solver.cpp:218] Iteration 9552 (2.43891 iter/s, 4.92023s/12 iters), loss = 5.29814
I0412 15:42:32.020416 20203 solver.cpp:237]     Train net output #0: loss = 5.29814 (* 1 = 5.29814 loss)
I0412 15:42:32.020424 20203 sgd_solver.cpp:105] Iteration 9552, lr = 0.00150752
I0412 15:42:36.844597 20203 solver.cpp:218] Iteration 9564 (2.48758 iter/s, 4.82397s/12 iters), loss = 5.26019
I0412 15:42:36.844663 20203 solver.cpp:237]     Train net output #0: loss = 5.26019 (* 1 = 5.26019 loss)
I0412 15:42:36.844676 20203 sgd_solver.cpp:105] Iteration 9564, lr = 0.00150395
I0412 15:42:41.707489 20203 solver.cpp:218] Iteration 9576 (2.4678 iter/s, 4.86262s/12 iters), loss = 5.26352
I0412 15:42:41.707535 20203 solver.cpp:237]     Train net output #0: loss = 5.26352 (* 1 = 5.26352 loss)
I0412 15:42:41.707545 20203 sgd_solver.cpp:105] Iteration 9576, lr = 0.00150037
I0412 15:42:46.282269 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9588.caffemodel
I0412 15:42:53.667389 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9588.solverstate
I0412 15:42:58.544469 20203 solver.cpp:330] Iteration 9588, Testing net (#0)
I0412 15:42:58.544494 20203 net.cpp:676] Ignoring source layer train-data
I0412 15:42:59.192852 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:43:02.940796 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 15:43:02.940955 20203 solver.cpp:397]     Test net output #1: loss = 5.28592 (* 1 = 5.28592 loss)
I0412 15:43:03.027034 20203 solver.cpp:218] Iteration 9588 (0.562888 iter/s, 21.3186s/12 iters), loss = 5.27456
I0412 15:43:03.027093 20203 solver.cpp:237]     Train net output #0: loss = 5.27456 (* 1 = 5.27456 loss)
I0412 15:43:03.027107 20203 sgd_solver.cpp:105] Iteration 9588, lr = 0.00149681
I0412 15:43:07.110119 20203 solver.cpp:218] Iteration 9600 (2.93913 iter/s, 4.08285s/12 iters), loss = 5.27407
I0412 15:43:07.110173 20203 solver.cpp:237]     Train net output #0: loss = 5.27407 (* 1 = 5.27407 loss)
I0412 15:43:07.110188 20203 sgd_solver.cpp:105] Iteration 9600, lr = 0.00149326
I0412 15:43:10.591666 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:43:11.897214 20203 solver.cpp:218] Iteration 9612 (2.50688 iter/s, 4.78683s/12 iters), loss = 5.27317
I0412 15:43:11.897262 20203 solver.cpp:237]     Train net output #0: loss = 5.27317 (* 1 = 5.27317 loss)
I0412 15:43:11.897274 20203 sgd_solver.cpp:105] Iteration 9612, lr = 0.00148971
I0412 15:43:16.754258 20203 solver.cpp:218] Iteration 9624 (2.47077 iter/s, 4.85679s/12 iters), loss = 5.26776
I0412 15:43:16.754312 20203 solver.cpp:237]     Train net output #0: loss = 5.26776 (* 1 = 5.26776 loss)
I0412 15:43:16.754324 20203 sgd_solver.cpp:105] Iteration 9624, lr = 0.00148618
I0412 15:43:21.473009 20203 solver.cpp:218] Iteration 9636 (2.54318 iter/s, 4.7185s/12 iters), loss = 5.25605
I0412 15:43:21.473050 20203 solver.cpp:237]     Train net output #0: loss = 5.25605 (* 1 = 5.25605 loss)
I0412 15:43:21.473059 20203 sgd_solver.cpp:105] Iteration 9636, lr = 0.00148265
I0412 15:43:26.252184 20203 solver.cpp:218] Iteration 9648 (2.51103 iter/s, 4.77892s/12 iters), loss = 5.26528
I0412 15:43:26.252241 20203 solver.cpp:237]     Train net output #0: loss = 5.26528 (* 1 = 5.26528 loss)
I0412 15:43:26.252256 20203 sgd_solver.cpp:105] Iteration 9648, lr = 0.00147913
I0412 15:43:31.117851 20203 solver.cpp:218] Iteration 9660 (2.46639 iter/s, 4.8654s/12 iters), loss = 5.25527
I0412 15:43:31.117897 20203 solver.cpp:237]     Train net output #0: loss = 5.25527 (* 1 = 5.25527 loss)
I0412 15:43:31.117908 20203 sgd_solver.cpp:105] Iteration 9660, lr = 0.00147562
I0412 15:43:35.917219 20203 solver.cpp:218] Iteration 9672 (2.50046 iter/s, 4.79912s/12 iters), loss = 5.27395
I0412 15:43:35.917366 20203 solver.cpp:237]     Train net output #0: loss = 5.27395 (* 1 = 5.27395 loss)
I0412 15:43:35.917378 20203 sgd_solver.cpp:105] Iteration 9672, lr = 0.00147211
I0412 15:43:40.773049 20203 solver.cpp:218] Iteration 9684 (2.47144 iter/s, 4.85547s/12 iters), loss = 5.29045
I0412 15:43:40.773099 20203 solver.cpp:237]     Train net output #0: loss = 5.29045 (* 1 = 5.29045 loss)
I0412 15:43:40.773110 20203 sgd_solver.cpp:105] Iteration 9684, lr = 0.00146862
I0412 15:43:42.720353 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9690.caffemodel
I0412 15:43:45.690094 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9690.solverstate
I0412 15:43:49.930917 20203 solver.cpp:330] Iteration 9690, Testing net (#0)
I0412 15:43:49.930943 20203 net.cpp:676] Ignoring source layer train-data
I0412 15:43:50.660697 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:43:53.498392 20203 blocking_queue.cpp:49] Waiting for data
I0412 15:43:54.548525 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 15:43:54.548563 20203 solver.cpp:397]     Test net output #1: loss = 5.28612 (* 1 = 5.28612 loss)
I0412 15:43:56.379498 20203 solver.cpp:218] Iteration 9696 (0.768947 iter/s, 15.6058s/12 iters), loss = 5.28464
I0412 15:43:56.379560 20203 solver.cpp:237]     Train net output #0: loss = 5.28464 (* 1 = 5.28464 loss)
I0412 15:43:56.379575 20203 sgd_solver.cpp:105] Iteration 9696, lr = 0.00146513
I0412 15:44:01.190232 20203 solver.cpp:218] Iteration 9708 (2.49456 iter/s, 4.81047s/12 iters), loss = 5.28731
I0412 15:44:01.190292 20203 solver.cpp:237]     Train net output #0: loss = 5.28731 (* 1 = 5.28731 loss)
I0412 15:44:01.190305 20203 sgd_solver.cpp:105] Iteration 9708, lr = 0.00146165
I0412 15:44:01.881536 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:44:05.950599 20203 solver.cpp:218] Iteration 9720 (2.52095 iter/s, 4.76011s/12 iters), loss = 5.28671
I0412 15:44:05.950667 20203 solver.cpp:237]     Train net output #0: loss = 5.28671 (* 1 = 5.28671 loss)
I0412 15:44:05.950676 20203 sgd_solver.cpp:105] Iteration 9720, lr = 0.00145818
I0412 15:44:10.540201 20203 solver.cpp:218] Iteration 9732 (2.61476 iter/s, 4.58933s/12 iters), loss = 5.26464
I0412 15:44:10.540254 20203 solver.cpp:237]     Train net output #0: loss = 5.26464 (* 1 = 5.26464 loss)
I0412 15:44:10.540267 20203 sgd_solver.cpp:105] Iteration 9732, lr = 0.00145472
I0412 15:44:15.464294 20203 solver.cpp:218] Iteration 9744 (2.43713 iter/s, 4.92383s/12 iters), loss = 5.26843
I0412 15:44:15.464351 20203 solver.cpp:237]     Train net output #0: loss = 5.26843 (* 1 = 5.26843 loss)
I0412 15:44:15.464365 20203 sgd_solver.cpp:105] Iteration 9744, lr = 0.00145127
I0412 15:44:20.221170 20203 solver.cpp:218] Iteration 9756 (2.5228 iter/s, 4.75661s/12 iters), loss = 5.27298
I0412 15:44:20.221228 20203 solver.cpp:237]     Train net output #0: loss = 5.27298 (* 1 = 5.27298 loss)
I0412 15:44:20.221242 20203 sgd_solver.cpp:105] Iteration 9756, lr = 0.00144782
I0412 15:44:25.249290 20203 solver.cpp:218] Iteration 9768 (2.38671 iter/s, 5.02785s/12 iters), loss = 5.28843
I0412 15:44:25.249341 20203 solver.cpp:237]     Train net output #0: loss = 5.28843 (* 1 = 5.28843 loss)
I0412 15:44:25.249354 20203 sgd_solver.cpp:105] Iteration 9768, lr = 0.00144438
I0412 15:44:30.082895 20203 solver.cpp:218] Iteration 9780 (2.48275 iter/s, 4.83334s/12 iters), loss = 5.26947
I0412 15:44:30.082953 20203 solver.cpp:237]     Train net output #0: loss = 5.26947 (* 1 = 5.26947 loss)
I0412 15:44:30.082970 20203 sgd_solver.cpp:105] Iteration 9780, lr = 0.00144095
I0412 15:44:34.764575 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9792.caffemodel
I0412 15:44:37.860040 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9792.solverstate
I0412 15:44:40.212739 20203 solver.cpp:330] Iteration 9792, Testing net (#0)
I0412 15:44:40.212765 20203 net.cpp:676] Ignoring source layer train-data
I0412 15:44:40.782757 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:44:44.630259 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 15:44:44.630312 20203 solver.cpp:397]     Test net output #1: loss = 5.28592 (* 1 = 5.28592 loss)
I0412 15:44:44.716943 20203 solver.cpp:218] Iteration 9792 (0.820043 iter/s, 14.6334s/12 iters), loss = 5.25462
I0412 15:44:44.717007 20203 solver.cpp:237]     Train net output #0: loss = 5.25462 (* 1 = 5.25462 loss)
I0412 15:44:44.717021 20203 sgd_solver.cpp:105] Iteration 9792, lr = 0.00143753
I0412 15:44:48.630271 20203 solver.cpp:218] Iteration 9804 (3.06662 iter/s, 3.9131s/12 iters), loss = 5.27171
I0412 15:44:48.630312 20203 solver.cpp:237]     Train net output #0: loss = 5.27171 (* 1 = 5.27171 loss)
I0412 15:44:48.630322 20203 sgd_solver.cpp:105] Iteration 9804, lr = 0.00143412
I0412 15:44:51.571174 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:44:53.551941 20203 solver.cpp:218] Iteration 9816 (2.43833 iter/s, 4.92141s/12 iters), loss = 5.26084
I0412 15:44:53.552001 20203 solver.cpp:237]     Train net output #0: loss = 5.26084 (* 1 = 5.26084 loss)
I0412 15:44:53.552014 20203 sgd_solver.cpp:105] Iteration 9816, lr = 0.00143072
I0412 15:44:58.042928 20203 solver.cpp:218] Iteration 9828 (2.67217 iter/s, 4.49074s/12 iters), loss = 5.25629
I0412 15:44:58.042970 20203 solver.cpp:237]     Train net output #0: loss = 5.25629 (* 1 = 5.25629 loss)
I0412 15:44:58.042979 20203 sgd_solver.cpp:105] Iteration 9828, lr = 0.00142732
I0412 15:45:02.845257 20203 solver.cpp:218] Iteration 9840 (2.49892 iter/s, 4.80208s/12 iters), loss = 5.24957
I0412 15:45:02.845302 20203 solver.cpp:237]     Train net output #0: loss = 5.24957 (* 1 = 5.24957 loss)
I0412 15:45:02.845312 20203 sgd_solver.cpp:105] Iteration 9840, lr = 0.00142393
I0412 15:45:07.517685 20203 solver.cpp:218] Iteration 9852 (2.56839 iter/s, 4.67218s/12 iters), loss = 5.2702
I0412 15:45:07.517727 20203 solver.cpp:237]     Train net output #0: loss = 5.2702 (* 1 = 5.2702 loss)
I0412 15:45:07.517736 20203 sgd_solver.cpp:105] Iteration 9852, lr = 0.00142055
I0412 15:45:12.339273 20203 solver.cpp:218] Iteration 9864 (2.48894 iter/s, 4.82134s/12 iters), loss = 5.28898
I0412 15:45:12.339381 20203 solver.cpp:237]     Train net output #0: loss = 5.28898 (* 1 = 5.28898 loss)
I0412 15:45:12.339393 20203 sgd_solver.cpp:105] Iteration 9864, lr = 0.00141718
I0412 15:45:17.120802 20203 solver.cpp:218] Iteration 9876 (2.50982 iter/s, 4.78122s/12 iters), loss = 5.27031
I0412 15:45:17.120852 20203 solver.cpp:237]     Train net output #0: loss = 5.27031 (* 1 = 5.27031 loss)
I0412 15:45:17.120863 20203 sgd_solver.cpp:105] Iteration 9876, lr = 0.00141381
I0412 15:45:21.711014 20203 solver.cpp:218] Iteration 9888 (2.6144 iter/s, 4.58996s/12 iters), loss = 5.2793
I0412 15:45:21.711064 20203 solver.cpp:237]     Train net output #0: loss = 5.2793 (* 1 = 5.2793 loss)
I0412 15:45:21.711078 20203 sgd_solver.cpp:105] Iteration 9888, lr = 0.00141045
I0412 15:45:23.549007 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9894.caffemodel
I0412 15:45:28.412320 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9894.solverstate
I0412 15:45:31.135496 20203 solver.cpp:330] Iteration 9894, Testing net (#0)
I0412 15:45:31.135524 20203 net.cpp:676] Ignoring source layer train-data
I0412 15:45:31.685168 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:45:35.564921 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 15:45:35.564967 20203 solver.cpp:397]     Test net output #1: loss = 5.28619 (* 1 = 5.28619 loss)
I0412 15:45:37.482803 20203 solver.cpp:218] Iteration 9900 (0.760886 iter/s, 15.7711s/12 iters), loss = 5.28127
I0412 15:45:37.482851 20203 solver.cpp:237]     Train net output #0: loss = 5.28127 (* 1 = 5.28127 loss)
I0412 15:45:37.482862 20203 sgd_solver.cpp:105] Iteration 9900, lr = 0.00140711
I0412 15:45:42.263310 20203 solver.cpp:218] Iteration 9912 (2.51033 iter/s, 4.78025s/12 iters), loss = 5.25773
I0412 15:45:42.263361 20203 solver.cpp:237]     Train net output #0: loss = 5.25773 (* 1 = 5.25773 loss)
I0412 15:45:42.263375 20203 sgd_solver.cpp:105] Iteration 9912, lr = 0.00140377
I0412 15:45:42.322026 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:45:46.989665 20203 solver.cpp:218] Iteration 9924 (2.53909 iter/s, 4.7261s/12 iters), loss = 5.27642
I0412 15:45:46.989820 20203 solver.cpp:237]     Train net output #0: loss = 5.27642 (* 1 = 5.27642 loss)
I0412 15:45:46.989835 20203 sgd_solver.cpp:105] Iteration 9924, lr = 0.00140043
I0412 15:45:52.200415 20203 solver.cpp:218] Iteration 9936 (2.3031 iter/s, 5.21038s/12 iters), loss = 5.28919
I0412 15:45:52.200471 20203 solver.cpp:237]     Train net output #0: loss = 5.28919 (* 1 = 5.28919 loss)
I0412 15:45:52.200484 20203 sgd_solver.cpp:105] Iteration 9936, lr = 0.00139711
I0412 15:45:57.135111 20203 solver.cpp:218] Iteration 9948 (2.43189 iter/s, 4.93443s/12 iters), loss = 5.2615
I0412 15:45:57.135159 20203 solver.cpp:237]     Train net output #0: loss = 5.2615 (* 1 = 5.2615 loss)
I0412 15:45:57.135172 20203 sgd_solver.cpp:105] Iteration 9948, lr = 0.00139379
I0412 15:46:02.045898 20203 solver.cpp:218] Iteration 9960 (2.44373 iter/s, 4.91052s/12 iters), loss = 5.27054
I0412 15:46:02.045969 20203 solver.cpp:237]     Train net output #0: loss = 5.27054 (* 1 = 5.27054 loss)
I0412 15:46:02.045985 20203 sgd_solver.cpp:105] Iteration 9960, lr = 0.00139048
I0412 15:46:06.795205 20203 solver.cpp:218] Iteration 9972 (2.52682 iter/s, 4.74904s/12 iters), loss = 5.26548
I0412 15:46:06.795251 20203 solver.cpp:237]     Train net output #0: loss = 5.26548 (* 1 = 5.26548 loss)
I0412 15:46:06.795264 20203 sgd_solver.cpp:105] Iteration 9972, lr = 0.00138718
I0412 15:46:11.692808 20203 solver.cpp:218] Iteration 9984 (2.45031 iter/s, 4.89735s/12 iters), loss = 5.24676
I0412 15:46:11.692857 20203 solver.cpp:237]     Train net output #0: loss = 5.24676 (* 1 = 5.24676 loss)
I0412 15:46:11.692867 20203 sgd_solver.cpp:105] Iteration 9984, lr = 0.00138389
I0412 15:46:15.958686 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9996.caffemodel
I0412 15:46:20.687606 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9996.solverstate
I0412 15:46:23.423405 20203 solver.cpp:330] Iteration 9996, Testing net (#0)
I0412 15:46:23.423431 20203 net.cpp:676] Ignoring source layer train-data
I0412 15:46:23.882230 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:46:27.774756 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 15:46:27.774807 20203 solver.cpp:397]     Test net output #1: loss = 5.28659 (* 1 = 5.28659 loss)
I0412 15:46:27.859943 20203 solver.cpp:218] Iteration 9996 (0.742279 iter/s, 16.1664s/12 iters), loss = 5.26978
I0412 15:46:27.859994 20203 solver.cpp:237]     Train net output #0: loss = 5.26978 (* 1 = 5.26978 loss)
I0412 15:46:27.860005 20203 sgd_solver.cpp:105] Iteration 9996, lr = 0.0013806
I0412 15:46:31.877318 20203 solver.cpp:218] Iteration 10008 (2.98719 iter/s, 4.01715s/12 iters), loss = 5.24806
I0412 15:46:31.877368 20203 solver.cpp:237]     Train net output #0: loss = 5.24806 (* 1 = 5.24806 loss)
I0412 15:46:31.877379 20203 sgd_solver.cpp:105] Iteration 10008, lr = 0.00137732
I0412 15:46:33.972394 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:46:36.597298 20203 solver.cpp:218] Iteration 10020 (2.54252 iter/s, 4.71972s/12 iters), loss = 5.26702
I0412 15:46:36.597355 20203 solver.cpp:237]     Train net output #0: loss = 5.26702 (* 1 = 5.26702 loss)
I0412 15:46:36.597369 20203 sgd_solver.cpp:105] Iteration 10020, lr = 0.00137405
I0412 15:46:41.411087 20203 solver.cpp:218] Iteration 10032 (2.49298 iter/s, 4.81353s/12 iters), loss = 5.27722
I0412 15:46:41.411129 20203 solver.cpp:237]     Train net output #0: loss = 5.27722 (* 1 = 5.27722 loss)
I0412 15:46:41.411137 20203 sgd_solver.cpp:105] Iteration 10032, lr = 0.00137079
I0412 15:46:46.493856 20203 solver.cpp:218] Iteration 10044 (2.36104 iter/s, 5.0825s/12 iters), loss = 5.2859
I0412 15:46:46.493908 20203 solver.cpp:237]     Train net output #0: loss = 5.2859 (* 1 = 5.2859 loss)
I0412 15:46:46.493921 20203 sgd_solver.cpp:105] Iteration 10044, lr = 0.00136754
I0412 15:46:51.321216 20203 solver.cpp:218] Iteration 10056 (2.48596 iter/s, 4.8271s/12 iters), loss = 5.27758
I0412 15:46:51.321374 20203 solver.cpp:237]     Train net output #0: loss = 5.27758 (* 1 = 5.27758 loss)
I0412 15:46:51.321385 20203 sgd_solver.cpp:105] Iteration 10056, lr = 0.00136429
I0412 15:46:56.216876 20203 solver.cpp:218] Iteration 10068 (2.45134 iter/s, 4.89529s/12 iters), loss = 5.27808
I0412 15:46:56.216934 20203 solver.cpp:237]     Train net output #0: loss = 5.27808 (* 1 = 5.27808 loss)
I0412 15:46:56.216946 20203 sgd_solver.cpp:105] Iteration 10068, lr = 0.00136105
I0412 15:47:01.092988 20203 solver.cpp:218] Iteration 10080 (2.46111 iter/s, 4.87584s/12 iters), loss = 5.2633
I0412 15:47:01.093042 20203 solver.cpp:237]     Train net output #0: loss = 5.2633 (* 1 = 5.2633 loss)
I0412 15:47:01.093053 20203 sgd_solver.cpp:105] Iteration 10080, lr = 0.00135782
I0412 15:47:06.103775 20203 solver.cpp:218] Iteration 10092 (2.39496 iter/s, 5.01052s/12 iters), loss = 5.27904
I0412 15:47:06.103829 20203 solver.cpp:237]     Train net output #0: loss = 5.27904 (* 1 = 5.27904 loss)
I0412 15:47:06.103842 20203 sgd_solver.cpp:105] Iteration 10092, lr = 0.0013546
I0412 15:47:08.086088 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10098.caffemodel
I0412 15:47:19.575757 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10098.solverstate
I0412 15:47:31.936216 20203 solver.cpp:330] Iteration 10098, Testing net (#0)
I0412 15:47:31.936273 20203 net.cpp:676] Ignoring source layer train-data
I0412 15:47:32.412896 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:47:36.365536 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 15:47:36.365576 20203 solver.cpp:397]     Test net output #1: loss = 5.2863 (* 1 = 5.2863 loss)
I0412 15:47:38.130440 20203 solver.cpp:218] Iteration 10104 (0.374704 iter/s, 32.0253s/12 iters), loss = 5.26893
I0412 15:47:38.130502 20203 solver.cpp:237]     Train net output #0: loss = 5.26893 (* 1 = 5.26893 loss)
I0412 15:47:38.130515 20203 sgd_solver.cpp:105] Iteration 10104, lr = 0.00135138
I0412 15:47:42.194747 20207 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:47:42.767423 20203 solver.cpp:218] Iteration 10116 (2.58804 iter/s, 4.63672s/12 iters), loss = 5.26199
I0412 15:47:42.767486 20203 solver.cpp:237]     Train net output #0: loss = 5.26199 (* 1 = 5.26199 loss)
I0412 15:47:42.767500 20203 sgd_solver.cpp:105] Iteration 10116, lr = 0.00134817
I0412 15:47:47.594655 20203 solver.cpp:218] Iteration 10128 (2.48604 iter/s, 4.82696s/12 iters), loss = 5.27257
I0412 15:47:47.594708 20203 solver.cpp:237]     Train net output #0: loss = 5.27257 (* 1 = 5.27257 loss)
I0412 15:47:47.594722 20203 sgd_solver.cpp:105] Iteration 10128, lr = 0.00134497
I0412 15:47:52.280985 20203 solver.cpp:218] Iteration 10140 (2.56078 iter/s, 4.68608s/12 iters), loss = 5.28286
I0412 15:47:52.281026 20203 solver.cpp:237]     Train net output #0: loss = 5.28286 (* 1 = 5.28286 loss)
I0412 15:47:52.281034 20203 sgd_solver.cpp:105] Iteration 10140, lr = 0.00134178
I0412 15:47:57.104593 20203 solver.cpp:218] Iteration 10152 (2.48789 iter/s, 4.82336s/12 iters), loss = 5.27932
I0412 15:47:57.104636 20203 solver.cpp:237]     Train net output #0: loss = 5.27932 (* 1 = 5.27932 loss)
I0412 15:47:57.104647 20203 sgd_solver.cpp:105] Iteration 10152, lr = 0.00133859
I0412 15:48:01.756955 20203 solver.cpp:218] Iteration 10164 (2.57947 iter/s, 4.65212s/12 iters), loss = 5.26393
I0412 15:48:01.757009 20203 solver.cpp:237]     Train net output #0: loss = 5.26393 (* 1 = 5.26393 loss)
I0412 15:48:01.757022 20203 sgd_solver.cpp:105] Iteration 10164, lr = 0.00133541
I0412 15:48:06.566059 20203 solver.cpp:218] Iteration 10176 (2.4954 iter/s, 4.80885s/12 iters), loss = 5.27173
I0412 15:48:06.566156 20203 solver.cpp:237]     Train net output #0: loss = 5.27173 (* 1 = 5.27173 loss)
I0412 15:48:06.566164 20203 sgd_solver.cpp:105] Iteration 10176, lr = 0.00133224
I0412 15:48:11.118269 20203 solver.cpp:218] Iteration 10188 (2.63625 iter/s, 4.55191s/12 iters), loss = 5.27859
I0412 15:48:11.118331 20203 solver.cpp:237]     Train net output #0: loss = 5.27859 (* 1 = 5.27859 loss)
I0412 15:48:11.118347 20203 sgd_solver.cpp:105] Iteration 10188, lr = 0.00132908
I0412 15:48:15.393011 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10200.caffemodel
I0412 15:48:18.434180 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10200.solverstate
I0412 15:48:24.292459 20203 solver.cpp:310] Iteration 10200, loss = 5.26314
I0412 15:48:24.292495 20203 solver.cpp:330] Iteration 10200, Testing net (#0)
I0412 15:48:24.292505 20203 net.cpp:676] Ignoring source layer train-data
I0412 15:48:24.710184 20208 data_layer.cpp:73] Restarting data prefetching from start.
I0412 15:48:28.723512 20203 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 15:48:28.723559 20203 solver.cpp:397]     Test net output #1: loss = 5.28573 (* 1 = 5.28573 loss)
I0412 15:48:28.723572 20203 solver.cpp:315] Optimization Done.
I0412 15:48:28.723579 20203 caffe.cpp:259] Optimization Done.