DIGITS-CNN/cars/architecture-investigations/fc/1-layer/256/caffe_output.log

I0410 13:28:45.599097 18353 upgrade_proto.cpp:1082] Attempting to upgrade input file specified using deprecated 'solver_type' field (enum)': /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210410-132843-8f34/solver.prototxt
I0410 13:28:45.599355 18353 upgrade_proto.cpp:1089] Successfully upgraded file specified using deprecated 'solver_type' field (enum) to 'type' field (string).
W0410 13:28:45.599366 18353 upgrade_proto.cpp:1091] Note that future Caffe releases will only support 'type' field (string) for a solver's type.
I0410 13:28:45.599481 18353 caffe.cpp:218] Using GPUs 0
I0410 13:28:45.626740 18353 caffe.cpp:223] GPU 0: GeForce GTX 1080 Ti
I0410 13:28:45.921651 18353 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: 0
net: "train_val.prototxt"
train_state {
level: 0
stage: ""
}
type: "SGD"
I0410 13:28:45.922817 18353 solver.cpp:87] Creating training net from net file: train_val.prototxt
I0410 13:28:45.923482 18353 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer val-data
I0410 13:28:45.923497 18353 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy
I0410 13:28:45.923630 18353 net.cpp:51] Initializing net from parameters:
state {
phase: TRAIN
level: 0
stage: ""
}
layer {
name: "train-data"
type: "Data"
top: "data"
top: "label"
include {
phase: TRAIN
}
transform_param {
mirror: true
crop_size: 227
mean_file: "/mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto"
}
data_param {
source: "/mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/train_db"
batch_size: 128
backend: LMDB
}
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 96
kernel_size: 11
stride: 4
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "conv1"
top: "conv1"
}
layer {
name: "norm1"
type: "LRN"
bottom: "conv1"
top: "norm1"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool1"
type: "Pooling"
bottom: "norm1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 2
kernel_size: 5
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu2"
type: "ReLU"
bottom: "conv2"
top: "conv2"
}
layer {
name: "norm2"
type: "LRN"
bottom: "conv2"
top: "norm2"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool2"
type: "Pooling"
bottom: "norm2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv3"
type: "Convolution"
bottom: "pool2"
top: "conv3"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu3"
type: "ReLU"
bottom: "conv3"
top: "conv3"
}
layer {
name: "conv4"
type: "Convolution"
bottom: "conv3"
top: "conv4"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu4"
type: "ReLU"
bottom: "conv4"
top: "conv4"
}
layer {
name: "conv5"
type: "Convolution"
bottom: "conv4"
top: "conv5"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu5"
type: "ReLU"
bottom: "conv5"
top: "conv5"
}
layer {
name: "pool5"
type: "Pooling"
bottom: "conv5"
top: "pool5"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "fc6"
type: "InnerProduct"
bottom: "pool5"
top: "fc6"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 256
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: "fc8"
type: "InnerProduct"
bottom: "fc6"
top: "fc8"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 196
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "loss"
type: "SoftmaxWithLoss"
bottom: "fc8"
bottom: "label"
top: "loss"
}
I0410 13:28:45.923720 18353 layer_factory.hpp:77] Creating layer train-data
I0410 13:28:46.007031 18353 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/train_db
I0410 13:28:46.007316 18353 net.cpp:84] Creating Layer train-data
I0410 13:28:46.007344 18353 net.cpp:380] train-data -> data
I0410 13:28:46.007385 18353 net.cpp:380] train-data -> label
I0410 13:28:46.007411 18353 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto
I0410 13:28:46.018450 18353 data_layer.cpp:45] output data size: 128,3,227,227
I0410 13:28:46.212792 18353 net.cpp:122] Setting up train-data
I0410 13:28:46.212821 18353 net.cpp:129] Top shape: 128 3 227 227 (19787136)
I0410 13:28:46.212828 18353 net.cpp:129] Top shape: 128 (128)
I0410 13:28:46.212831 18353 net.cpp:137] Memory required for data: 79149056
I0410 13:28:46.212844 18353 layer_factory.hpp:77] Creating layer conv1
I0410 13:28:46.212869 18353 net.cpp:84] Creating Layer conv1
I0410 13:28:46.212875 18353 net.cpp:406] conv1 <- data
I0410 13:28:46.212890 18353 net.cpp:380] conv1 -> conv1
I0410 13:28:46.798507 18353 net.cpp:122] Setting up conv1
I0410 13:28:46.798530 18353 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0410 13:28:46.798535 18353 net.cpp:137] Memory required for data: 227833856
I0410 13:28:46.798557 18353 layer_factory.hpp:77] Creating layer relu1
I0410 13:28:46.798568 18353 net.cpp:84] Creating Layer relu1
I0410 13:28:46.798573 18353 net.cpp:406] relu1 <- conv1
I0410 13:28:46.798581 18353 net.cpp:367] relu1 -> conv1 (in-place)
I0410 13:28:46.798899 18353 net.cpp:122] Setting up relu1
I0410 13:28:46.798909 18353 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0410 13:28:46.798913 18353 net.cpp:137] Memory required for data: 376518656
I0410 13:28:46.798918 18353 layer_factory.hpp:77] Creating layer norm1
I0410 13:28:46.798928 18353 net.cpp:84] Creating Layer norm1
I0410 13:28:46.798933 18353 net.cpp:406] norm1 <- conv1
I0410 13:28:46.798938 18353 net.cpp:380] norm1 -> norm1
I0410 13:28:46.799434 18353 net.cpp:122] Setting up norm1
I0410 13:28:46.799445 18353 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0410 13:28:46.799449 18353 net.cpp:137] Memory required for data: 525203456
I0410 13:28:46.799454 18353 layer_factory.hpp:77] Creating layer pool1
I0410 13:28:46.799463 18353 net.cpp:84] Creating Layer pool1
I0410 13:28:46.799468 18353 net.cpp:406] pool1 <- norm1
I0410 13:28:46.799472 18353 net.cpp:380] pool1 -> pool1
I0410 13:28:46.799538 18353 net.cpp:122] Setting up pool1
I0410 13:28:46.799546 18353 net.cpp:129] Top shape: 128 96 27 27 (8957952)
I0410 13:28:46.799549 18353 net.cpp:137] Memory required for data: 561035264
I0410 13:28:46.799553 18353 layer_factory.hpp:77] Creating layer conv2
I0410 13:28:46.799566 18353 net.cpp:84] Creating Layer conv2
I0410 13:28:46.799569 18353 net.cpp:406] conv2 <- pool1
I0410 13:28:46.799576 18353 net.cpp:380] conv2 -> conv2
I0410 13:28:46.807049 18353 net.cpp:122] Setting up conv2
I0410 13:28:46.807062 18353 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0410 13:28:46.807066 18353 net.cpp:137] Memory required for data: 656586752
I0410 13:28:46.807076 18353 layer_factory.hpp:77] Creating layer relu2
I0410 13:28:46.807083 18353 net.cpp:84] Creating Layer relu2
I0410 13:28:46.807087 18353 net.cpp:406] relu2 <- conv2
I0410 13:28:46.807093 18353 net.cpp:367] relu2 -> conv2 (in-place)
I0410 13:28:46.807570 18353 net.cpp:122] Setting up relu2
I0410 13:28:46.807580 18353 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0410 13:28:46.807585 18353 net.cpp:137] Memory required for data: 752138240
I0410 13:28:46.807590 18353 layer_factory.hpp:77] Creating layer norm2
I0410 13:28:46.807596 18353 net.cpp:84] Creating Layer norm2
I0410 13:28:46.807600 18353 net.cpp:406] norm2 <- conv2
I0410 13:28:46.807606 18353 net.cpp:380] norm2 -> norm2
I0410 13:28:46.807969 18353 net.cpp:122] Setting up norm2
I0410 13:28:46.807978 18353 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0410 13:28:46.807982 18353 net.cpp:137] Memory required for data: 847689728
I0410 13:28:46.807986 18353 layer_factory.hpp:77] Creating layer pool2
I0410 13:28:46.807996 18353 net.cpp:84] Creating Layer pool2
I0410 13:28:46.808001 18353 net.cpp:406] pool2 <- norm2
I0410 13:28:46.808007 18353 net.cpp:380] pool2 -> pool2
I0410 13:28:46.808039 18353 net.cpp:122] Setting up pool2
I0410 13:28:46.808046 18353 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0410 13:28:46.808049 18353 net.cpp:137] Memory required for data: 869840896
I0410 13:28:46.808053 18353 layer_factory.hpp:77] Creating layer conv3
I0410 13:28:46.808064 18353 net.cpp:84] Creating Layer conv3
I0410 13:28:46.808068 18353 net.cpp:406] conv3 <- pool2
I0410 13:28:46.808075 18353 net.cpp:380] conv3 -> conv3
I0410 13:28:46.819180 18353 net.cpp:122] Setting up conv3
I0410 13:28:46.819192 18353 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0410 13:28:46.819196 18353 net.cpp:137] Memory required for data: 903067648
I0410 13:28:46.819207 18353 layer_factory.hpp:77] Creating layer relu3
I0410 13:28:46.819214 18353 net.cpp:84] Creating Layer relu3
I0410 13:28:46.819218 18353 net.cpp:406] relu3 <- conv3
I0410 13:28:46.819226 18353 net.cpp:367] relu3 -> conv3 (in-place)
I0410 13:28:46.819773 18353 net.cpp:122] Setting up relu3
I0410 13:28:46.819784 18353 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0410 13:28:46.819788 18353 net.cpp:137] Memory required for data: 936294400
I0410 13:28:46.819792 18353 layer_factory.hpp:77] Creating layer conv4
I0410 13:28:46.819803 18353 net.cpp:84] Creating Layer conv4
I0410 13:28:46.819808 18353 net.cpp:406] conv4 <- conv3
I0410 13:28:46.819815 18353 net.cpp:380] conv4 -> conv4
I0410 13:28:46.831454 18353 net.cpp:122] Setting up conv4
I0410 13:28:46.831467 18353 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0410 13:28:46.831471 18353 net.cpp:137] Memory required for data: 969521152
I0410 13:28:46.831480 18353 layer_factory.hpp:77] Creating layer relu4
I0410 13:28:46.831486 18353 net.cpp:84] Creating Layer relu4
I0410 13:28:46.831490 18353 net.cpp:406] relu4 <- conv4
I0410 13:28:46.831497 18353 net.cpp:367] relu4 -> conv4 (in-place)
I0410 13:28:46.831881 18353 net.cpp:122] Setting up relu4
I0410 13:28:46.831892 18353 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0410 13:28:46.831897 18353 net.cpp:137] Memory required for data: 1002747904
I0410 13:28:46.831900 18353 layer_factory.hpp:77] Creating layer conv5
I0410 13:28:46.831910 18353 net.cpp:84] Creating Layer conv5
I0410 13:28:46.831914 18353 net.cpp:406] conv5 <- conv4
I0410 13:28:46.831943 18353 net.cpp:380] conv5 -> conv5
I0410 13:28:46.841342 18353 net.cpp:122] Setting up conv5
I0410 13:28:46.841356 18353 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0410 13:28:46.841361 18353 net.cpp:137] Memory required for data: 1024899072
I0410 13:28:46.841372 18353 layer_factory.hpp:77] Creating layer relu5
I0410 13:28:46.841378 18353 net.cpp:84] Creating Layer relu5
I0410 13:28:46.841382 18353 net.cpp:406] relu5 <- conv5
I0410 13:28:46.841389 18353 net.cpp:367] relu5 -> conv5 (in-place)
I0410 13:28:46.841940 18353 net.cpp:122] Setting up relu5
I0410 13:28:46.841950 18353 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0410 13:28:46.841964 18353 net.cpp:137] Memory required for data: 1047050240
I0410 13:28:46.841969 18353 layer_factory.hpp:77] Creating layer pool5
I0410 13:28:46.841979 18353 net.cpp:84] Creating Layer pool5
I0410 13:28:46.841982 18353 net.cpp:406] pool5 <- conv5
I0410 13:28:46.841989 18353 net.cpp:380] pool5 -> pool5
I0410 13:28:46.842033 18353 net.cpp:122] Setting up pool5
I0410 13:28:46.842041 18353 net.cpp:129] Top shape: 128 256 6 6 (1179648)
I0410 13:28:46.842044 18353 net.cpp:137] Memory required for data: 1051768832
I0410 13:28:46.842047 18353 layer_factory.hpp:77] Creating layer fc6
I0410 13:28:46.842058 18353 net.cpp:84] Creating Layer fc6
I0410 13:28:46.842062 18353 net.cpp:406] fc6 <- pool5
I0410 13:28:46.842069 18353 net.cpp:380] fc6 -> fc6
I0410 13:28:46.866420 18353 net.cpp:122] Setting up fc6
I0410 13:28:46.866436 18353 net.cpp:129] Top shape: 128 256 (32768)
I0410 13:28:46.866441 18353 net.cpp:137] Memory required for data: 1051899904
I0410 13:28:46.866449 18353 layer_factory.hpp:77] Creating layer relu6
I0410 13:28:46.866457 18353 net.cpp:84] Creating Layer relu6
I0410 13:28:46.866462 18353 net.cpp:406] relu6 <- fc6
I0410 13:28:46.866468 18353 net.cpp:367] relu6 -> fc6 (in-place)
I0410 13:28:46.867090 18353 net.cpp:122] Setting up relu6
I0410 13:28:46.867100 18353 net.cpp:129] Top shape: 128 256 (32768)
I0410 13:28:46.867105 18353 net.cpp:137] Memory required for data: 1052030976
I0410 13:28:46.867108 18353 layer_factory.hpp:77] Creating layer drop6
I0410 13:28:46.867115 18353 net.cpp:84] Creating Layer drop6
I0410 13:28:46.867120 18353 net.cpp:406] drop6 <- fc6
I0410 13:28:46.867127 18353 net.cpp:367] drop6 -> fc6 (in-place)
I0410 13:28:46.867157 18353 net.cpp:122] Setting up drop6
I0410 13:28:46.867164 18353 net.cpp:129] Top shape: 128 256 (32768)
I0410 13:28:46.867168 18353 net.cpp:137] Memory required for data: 1052162048
I0410 13:28:46.867172 18353 layer_factory.hpp:77] Creating layer fc8
I0410 13:28:46.867179 18353 net.cpp:84] Creating Layer fc8
I0410 13:28:46.867183 18353 net.cpp:406] fc8 <- fc6
I0410 13:28:46.867190 18353 net.cpp:380] fc8 -> fc8
I0410 13:28:46.867763 18353 net.cpp:122] Setting up fc8
I0410 13:28:46.867769 18353 net.cpp:129] Top shape: 128 196 (25088)
I0410 13:28:46.867772 18353 net.cpp:137] Memory required for data: 1052262400
I0410 13:28:46.867779 18353 layer_factory.hpp:77] Creating layer loss
I0410 13:28:46.867787 18353 net.cpp:84] Creating Layer loss
I0410 13:28:46.867791 18353 net.cpp:406] loss <- fc8
I0410 13:28:46.867795 18353 net.cpp:406] loss <- label
I0410 13:28:46.867802 18353 net.cpp:380] loss -> loss
I0410 13:28:46.867811 18353 layer_factory.hpp:77] Creating layer loss
I0410 13:28:46.868445 18353 net.cpp:122] Setting up loss
I0410 13:28:46.868455 18353 net.cpp:129] Top shape: (1)
I0410 13:28:46.868459 18353 net.cpp:132]     with loss weight 1
I0410 13:28:46.868479 18353 net.cpp:137] Memory required for data: 1052262404
I0410 13:28:46.868484 18353 net.cpp:198] loss needs backward computation.
I0410 13:28:46.868490 18353 net.cpp:198] fc8 needs backward computation.
I0410 13:28:46.868494 18353 net.cpp:198] drop6 needs backward computation.
I0410 13:28:46.868499 18353 net.cpp:198] relu6 needs backward computation.
I0410 13:28:46.868501 18353 net.cpp:198] fc6 needs backward computation.
I0410 13:28:46.868505 18353 net.cpp:198] pool5 needs backward computation.
I0410 13:28:46.868510 18353 net.cpp:198] relu5 needs backward computation.
I0410 13:28:46.868532 18353 net.cpp:198] conv5 needs backward computation.
I0410 13:28:46.868536 18353 net.cpp:198] relu4 needs backward computation.
I0410 13:28:46.868541 18353 net.cpp:198] conv4 needs backward computation.
I0410 13:28:46.868543 18353 net.cpp:198] relu3 needs backward computation.
I0410 13:28:46.868547 18353 net.cpp:198] conv3 needs backward computation.
I0410 13:28:46.868551 18353 net.cpp:198] pool2 needs backward computation.
I0410 13:28:46.868556 18353 net.cpp:198] norm2 needs backward computation.
I0410 13:28:46.868559 18353 net.cpp:198] relu2 needs backward computation.
I0410 13:28:46.868563 18353 net.cpp:198] conv2 needs backward computation.
I0410 13:28:46.868567 18353 net.cpp:198] pool1 needs backward computation.
I0410 13:28:46.868571 18353 net.cpp:198] norm1 needs backward computation.
I0410 13:28:46.868577 18353 net.cpp:198] relu1 needs backward computation.
I0410 13:28:46.868580 18353 net.cpp:198] conv1 needs backward computation.
I0410 13:28:46.868584 18353 net.cpp:200] train-data does not need backward computation.
I0410 13:28:46.868588 18353 net.cpp:242] This network produces output loss
I0410 13:28:46.868602 18353 net.cpp:255] Network initialization done.
I0410 13:28:46.869153 18353 solver.cpp:172] Creating test net (#0) specified by net file: train_val.prototxt
I0410 13:28:46.869184 18353 net.cpp:294] The NetState phase (1) differed from the phase (0) specified by a rule in layer train-data
I0410 13:28:46.869329 18353 net.cpp:51] Initializing net from parameters:
state {
phase: TEST
}
layer {
name: "val-data"
type: "Data"
top: "data"
top: "label"
include {
phase: TEST
}
transform_param {
crop_size: 227
mean_file: "/mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto"
}
data_param {
source: "/mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/val_db"
batch_size: 32
backend: LMDB
}
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 96
kernel_size: 11
stride: 4
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "conv1"
top: "conv1"
}
layer {
name: "norm1"
type: "LRN"
bottom: "conv1"
top: "norm1"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool1"
type: "Pooling"
bottom: "norm1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 2
kernel_size: 5
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu2"
type: "ReLU"
bottom: "conv2"
top: "conv2"
}
layer {
name: "norm2"
type: "LRN"
bottom: "conv2"
top: "norm2"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool2"
type: "Pooling"
bottom: "norm2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv3"
type: "Convolution"
bottom: "pool2"
top: "conv3"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu3"
type: "ReLU"
bottom: "conv3"
top: "conv3"
}
layer {
name: "conv4"
type: "Convolution"
bottom: "conv3"
top: "conv4"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu4"
type: "ReLU"
bottom: "conv4"
top: "conv4"
}
layer {
name: "conv5"
type: "Convolution"
bottom: "conv4"
top: "conv5"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu5"
type: "ReLU"
bottom: "conv5"
top: "conv5"
}
layer {
name: "pool5"
type: "Pooling"
bottom: "conv5"
top: "pool5"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "fc6"
type: "InnerProduct"
bottom: "pool5"
top: "fc6"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 256
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: "fc8"
type: "InnerProduct"
bottom: "fc6"
top: "fc8"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 196
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "accuracy"
type: "Accuracy"
bottom: "fc8"
bottom: "label"
top: "accuracy"
include {
phase: TEST
}
}
layer {
name: "loss"
type: "SoftmaxWithLoss"
bottom: "fc8"
bottom: "label"
top: "loss"
}
I0410 13:28:46.869428 18353 layer_factory.hpp:77] Creating layer val-data
I0410 13:28:46.879921 18353 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/val_db
I0410 13:28:46.880173 18353 net.cpp:84] Creating Layer val-data
I0410 13:28:46.880183 18353 net.cpp:380] val-data -> data
I0410 13:28:46.880195 18353 net.cpp:380] val-data -> label
I0410 13:28:46.880203 18353 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto
I0410 13:28:46.884544 18353 data_layer.cpp:45] output data size: 32,3,227,227
I0410 13:28:46.918799 18353 net.cpp:122] Setting up val-data
I0410 13:28:46.918821 18353 net.cpp:129] Top shape: 32 3 227 227 (4946784)
I0410 13:28:46.918826 18353 net.cpp:129] Top shape: 32 (32)
I0410 13:28:46.918830 18353 net.cpp:137] Memory required for data: 19787264
I0410 13:28:46.918838 18353 layer_factory.hpp:77] Creating layer label_val-data_1_split
I0410 13:28:46.918851 18353 net.cpp:84] Creating Layer label_val-data_1_split
I0410 13:28:46.918856 18353 net.cpp:406] label_val-data_1_split <- label
I0410 13:28:46.918864 18353 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_0
I0410 13:28:46.918874 18353 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_1
I0410 13:28:46.918977 18353 net.cpp:122] Setting up label_val-data_1_split
I0410 13:28:46.918983 18353 net.cpp:129] Top shape: 32 (32)
I0410 13:28:46.918987 18353 net.cpp:129] Top shape: 32 (32)
I0410 13:28:46.918992 18353 net.cpp:137] Memory required for data: 19787520
I0410 13:28:46.918994 18353 layer_factory.hpp:77] Creating layer conv1
I0410 13:28:46.919008 18353 net.cpp:84] Creating Layer conv1
I0410 13:28:46.919011 18353 net.cpp:406] conv1 <- data
I0410 13:28:46.919018 18353 net.cpp:380] conv1 -> conv1
I0410 13:28:46.921309 18353 net.cpp:122] Setting up conv1
I0410 13:28:46.921321 18353 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0410 13:28:46.921325 18353 net.cpp:137] Memory required for data: 56958720
I0410 13:28:46.921336 18353 layer_factory.hpp:77] Creating layer relu1
I0410 13:28:46.921344 18353 net.cpp:84] Creating Layer relu1
I0410 13:28:46.921368 18353 net.cpp:406] relu1 <- conv1
I0410 13:28:46.921375 18353 net.cpp:367] relu1 -> conv1 (in-place)
I0410 13:28:46.921885 18353 net.cpp:122] Setting up relu1
I0410 13:28:46.921896 18353 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0410 13:28:46.921900 18353 net.cpp:137] Memory required for data: 94129920
I0410 13:28:46.921905 18353 layer_factory.hpp:77] Creating layer norm1
I0410 13:28:46.921913 18353 net.cpp:84] Creating Layer norm1
I0410 13:28:46.921918 18353 net.cpp:406] norm1 <- conv1
I0410 13:28:46.921924 18353 net.cpp:380] norm1 -> norm1
I0410 13:28:46.922299 18353 net.cpp:122] Setting up norm1
I0410 13:28:46.922309 18353 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0410 13:28:46.922313 18353 net.cpp:137] Memory required for data: 131301120
I0410 13:28:46.922322 18353 layer_factory.hpp:77] Creating layer pool1
I0410 13:28:46.922331 18353 net.cpp:84] Creating Layer pool1
I0410 13:28:46.922335 18353 net.cpp:406] pool1 <- norm1
I0410 13:28:46.922341 18353 net.cpp:380] pool1 -> pool1
I0410 13:28:46.922379 18353 net.cpp:122] Setting up pool1
I0410 13:28:46.922384 18353 net.cpp:129] Top shape: 32 96 27 27 (2239488)
I0410 13:28:46.922389 18353 net.cpp:137] Memory required for data: 140259072
I0410 13:28:46.922391 18353 layer_factory.hpp:77] Creating layer conv2
I0410 13:28:46.922400 18353 net.cpp:84] Creating Layer conv2
I0410 13:28:46.922405 18353 net.cpp:406] conv2 <- pool1
I0410 13:28:46.922410 18353 net.cpp:380] conv2 -> conv2
I0410 13:28:46.930526 18353 net.cpp:122] Setting up conv2
I0410 13:28:46.930541 18353 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0410 13:28:46.930546 18353 net.cpp:137] Memory required for data: 164146944
I0410 13:28:46.930557 18353 layer_factory.hpp:77] Creating layer relu2
I0410 13:28:46.930565 18353 net.cpp:84] Creating Layer relu2
I0410 13:28:46.930569 18353 net.cpp:406] relu2 <- conv2
I0410 13:28:46.930577 18353 net.cpp:367] relu2 -> conv2 (in-place)
I0410 13:28:46.931138 18353 net.cpp:122] Setting up relu2
I0410 13:28:46.931147 18353 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0410 13:28:46.931151 18353 net.cpp:137] Memory required for data: 188034816
I0410 13:28:46.931155 18353 layer_factory.hpp:77] Creating layer norm2
I0410 13:28:46.931165 18353 net.cpp:84] Creating Layer norm2
I0410 13:28:46.931170 18353 net.cpp:406] norm2 <- conv2
I0410 13:28:46.931176 18353 net.cpp:380] norm2 -> norm2
I0410 13:28:46.931762 18353 net.cpp:122] Setting up norm2
I0410 13:28:46.931772 18353 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0410 13:28:46.931777 18353 net.cpp:137] Memory required for data: 211922688
I0410 13:28:46.931780 18353 layer_factory.hpp:77] Creating layer pool2
I0410 13:28:46.931788 18353 net.cpp:84] Creating Layer pool2
I0410 13:28:46.931792 18353 net.cpp:406] pool2 <- norm2
I0410 13:28:46.931799 18353 net.cpp:380] pool2 -> pool2
I0410 13:28:46.931833 18353 net.cpp:122] Setting up pool2
I0410 13:28:46.931840 18353 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0410 13:28:46.931843 18353 net.cpp:137] Memory required for data: 217460480
I0410 13:28:46.931847 18353 layer_factory.hpp:77] Creating layer conv3
I0410 13:28:46.931859 18353 net.cpp:84] Creating Layer conv3
I0410 13:28:46.931862 18353 net.cpp:406] conv3 <- pool2
I0410 13:28:46.931869 18353 net.cpp:380] conv3 -> conv3
I0410 13:28:46.942800 18353 net.cpp:122] Setting up conv3
I0410 13:28:46.942814 18353 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0410 13:28:46.942818 18353 net.cpp:137] Memory required for data: 225767168
I0410 13:28:46.942828 18353 layer_factory.hpp:77] Creating layer relu3
I0410 13:28:46.942835 18353 net.cpp:84] Creating Layer relu3
I0410 13:28:46.942839 18353 net.cpp:406] relu3 <- conv3
I0410 13:28:46.942848 18353 net.cpp:367] relu3 -> conv3 (in-place)
I0410 13:28:46.944607 18353 net.cpp:122] Setting up relu3
I0410 13:28:46.944618 18353 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0410 13:28:46.944622 18353 net.cpp:137] Memory required for data: 234073856
I0410 13:28:46.944626 18353 layer_factory.hpp:77] Creating layer conv4
I0410 13:28:46.944658 18353 net.cpp:84] Creating Layer conv4
I0410 13:28:46.944664 18353 net.cpp:406] conv4 <- conv3
I0410 13:28:46.944671 18353 net.cpp:380] conv4 -> conv4
I0410 13:28:46.955147 18353 net.cpp:122] Setting up conv4
I0410 13:28:46.955159 18353 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0410 13:28:46.955165 18353 net.cpp:137] Memory required for data: 242380544
I0410 13:28:46.955173 18353 layer_factory.hpp:77] Creating layer relu4
I0410 13:28:46.955180 18353 net.cpp:84] Creating Layer relu4
I0410 13:28:46.955184 18353 net.cpp:406] relu4 <- conv4
I0410 13:28:46.955190 18353 net.cpp:367] relu4 -> conv4 (in-place)
I0410 13:28:46.955739 18353 net.cpp:122] Setting up relu4
I0410 13:28:46.955749 18353 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0410 13:28:46.955754 18353 net.cpp:137] Memory required for data: 250687232
I0410 13:28:46.955757 18353 layer_factory.hpp:77] Creating layer conv5
I0410 13:28:46.955768 18353 net.cpp:84] Creating Layer conv5
I0410 13:28:46.955773 18353 net.cpp:406] conv5 <- conv4
I0410 13:28:46.955780 18353 net.cpp:380] conv5 -> conv5
I0410 13:28:46.965056 18353 net.cpp:122] Setting up conv5
I0410 13:28:46.965070 18353 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0410 13:28:46.965075 18353 net.cpp:137] Memory required for data: 256225024
I0410 13:28:46.965087 18353 layer_factory.hpp:77] Creating layer relu5
I0410 13:28:46.965095 18353 net.cpp:84] Creating Layer relu5
I0410 13:28:46.965098 18353 net.cpp:406] relu5 <- conv5
I0410 13:28:46.965106 18353 net.cpp:367] relu5 -> conv5 (in-place)
I0410 13:28:46.965648 18353 net.cpp:122] Setting up relu5
I0410 13:28:46.965658 18353 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0410 13:28:46.965662 18353 net.cpp:137] Memory required for data: 261762816
I0410 13:28:46.965667 18353 layer_factory.hpp:77] Creating layer pool5
I0410 13:28:46.965677 18353 net.cpp:84] Creating Layer pool5
I0410 13:28:46.965682 18353 net.cpp:406] pool5 <- conv5
I0410 13:28:46.965687 18353 net.cpp:380] pool5 -> pool5
I0410 13:28:46.965730 18353 net.cpp:122] Setting up pool5
I0410 13:28:46.965736 18353 net.cpp:129] Top shape: 32 256 6 6 (294912)
I0410 13:28:46.965740 18353 net.cpp:137] Memory required for data: 262942464
I0410 13:28:46.965744 18353 layer_factory.hpp:77] Creating layer fc6
I0410 13:28:46.965754 18353 net.cpp:84] Creating Layer fc6
I0410 13:28:46.965759 18353 net.cpp:406] fc6 <- pool5
I0410 13:28:46.965765 18353 net.cpp:380] fc6 -> fc6
I0410 13:28:46.989315 18353 net.cpp:122] Setting up fc6
I0410 13:28:46.989332 18353 net.cpp:129] Top shape: 32 256 (8192)
I0410 13:28:46.989336 18353 net.cpp:137] Memory required for data: 262975232
I0410 13:28:46.989346 18353 layer_factory.hpp:77] Creating layer relu6
I0410 13:28:46.989354 18353 net.cpp:84] Creating Layer relu6
I0410 13:28:46.989359 18353 net.cpp:406] relu6 <- fc6
I0410 13:28:46.989367 18353 net.cpp:367] relu6 -> fc6 (in-place)
I0410 13:28:46.990041 18353 net.cpp:122] Setting up relu6
I0410 13:28:46.990051 18353 net.cpp:129] Top shape: 32 256 (8192)
I0410 13:28:46.990054 18353 net.cpp:137] Memory required for data: 263008000
I0410 13:28:46.990058 18353 layer_factory.hpp:77] Creating layer drop6
I0410 13:28:46.990065 18353 net.cpp:84] Creating Layer drop6
I0410 13:28:46.990069 18353 net.cpp:406] drop6 <- fc6
I0410 13:28:46.990077 18353 net.cpp:367] drop6 -> fc6 (in-place)
I0410 13:28:46.990104 18353 net.cpp:122] Setting up drop6
I0410 13:28:46.990110 18353 net.cpp:129] Top shape: 32 256 (8192)
I0410 13:28:46.990113 18353 net.cpp:137] Memory required for data: 263040768
I0410 13:28:46.990118 18353 layer_factory.hpp:77] Creating layer fc8
I0410 13:28:46.990124 18353 net.cpp:84] Creating Layer fc8
I0410 13:28:46.990128 18353 net.cpp:406] fc8 <- fc6
I0410 13:28:46.990135 18353 net.cpp:380] fc8 -> fc8
I0410 13:28:46.990672 18353 net.cpp:122] Setting up fc8
I0410 13:28:46.990679 18353 net.cpp:129] Top shape: 32 196 (6272)
I0410 13:28:46.990682 18353 net.cpp:137] Memory required for data: 263065856
I0410 13:28:46.990689 18353 layer_factory.hpp:77] Creating layer fc8_fc8_0_split
I0410 13:28:46.990696 18353 net.cpp:84] Creating Layer fc8_fc8_0_split
I0410 13:28:46.990717 18353 net.cpp:406] fc8_fc8_0_split <- fc8
I0410 13:28:46.990723 18353 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_0
I0410 13:28:46.990733 18353 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_1
I0410 13:28:46.990767 18353 net.cpp:122] Setting up fc8_fc8_0_split
I0410 13:28:46.990773 18353 net.cpp:129] Top shape: 32 196 (6272)
I0410 13:28:46.990777 18353 net.cpp:129] Top shape: 32 196 (6272)
I0410 13:28:46.990780 18353 net.cpp:137] Memory required for data: 263116032
I0410 13:28:46.990783 18353 layer_factory.hpp:77] Creating layer accuracy
I0410 13:28:46.990790 18353 net.cpp:84] Creating Layer accuracy
I0410 13:28:46.990794 18353 net.cpp:406] accuracy <- fc8_fc8_0_split_0
I0410 13:28:46.990799 18353 net.cpp:406] accuracy <- label_val-data_1_split_0
I0410 13:28:46.990805 18353 net.cpp:380] accuracy -> accuracy
I0410 13:28:46.990813 18353 net.cpp:122] Setting up accuracy
I0410 13:28:46.990818 18353 net.cpp:129] Top shape: (1)
I0410 13:28:46.990820 18353 net.cpp:137] Memory required for data: 263116036
I0410 13:28:46.990823 18353 layer_factory.hpp:77] Creating layer loss
I0410 13:28:46.990829 18353 net.cpp:84] Creating Layer loss
I0410 13:28:46.990833 18353 net.cpp:406] loss <- fc8_fc8_0_split_1
I0410 13:28:46.990837 18353 net.cpp:406] loss <- label_val-data_1_split_1
I0410 13:28:46.990842 18353 net.cpp:380] loss -> loss
I0410 13:28:46.990849 18353 layer_factory.hpp:77] Creating layer loss
I0410 13:28:46.991662 18353 net.cpp:122] Setting up loss
I0410 13:28:46.991672 18353 net.cpp:129] Top shape: (1)
I0410 13:28:46.991674 18353 net.cpp:132]     with loss weight 1
I0410 13:28:46.991686 18353 net.cpp:137] Memory required for data: 263116040
I0410 13:28:46.991690 18353 net.cpp:198] loss needs backward computation.
I0410 13:28:46.991695 18353 net.cpp:200] accuracy does not need backward computation.
I0410 13:28:46.991700 18353 net.cpp:198] fc8_fc8_0_split needs backward computation.
I0410 13:28:46.991703 18353 net.cpp:198] fc8 needs backward computation.
I0410 13:28:46.991708 18353 net.cpp:198] drop6 needs backward computation.
I0410 13:28:46.991710 18353 net.cpp:198] relu6 needs backward computation.
I0410 13:28:46.991714 18353 net.cpp:198] fc6 needs backward computation.
I0410 13:28:46.991717 18353 net.cpp:198] pool5 needs backward computation.
I0410 13:28:46.991722 18353 net.cpp:198] relu5 needs backward computation.
I0410 13:28:46.991725 18353 net.cpp:198] conv5 needs backward computation.
I0410 13:28:46.991729 18353 net.cpp:198] relu4 needs backward computation.
I0410 13:28:46.991732 18353 net.cpp:198] conv4 needs backward computation.
I0410 13:28:46.991736 18353 net.cpp:198] relu3 needs backward computation.
I0410 13:28:46.991739 18353 net.cpp:198] conv3 needs backward computation.
I0410 13:28:46.991744 18353 net.cpp:198] pool2 needs backward computation.
I0410 13:28:46.991747 18353 net.cpp:198] norm2 needs backward computation.
I0410 13:28:46.991750 18353 net.cpp:198] relu2 needs backward computation.
I0410 13:28:46.991755 18353 net.cpp:198] conv2 needs backward computation.
I0410 13:28:46.991757 18353 net.cpp:198] pool1 needs backward computation.
I0410 13:28:46.991761 18353 net.cpp:198] norm1 needs backward computation.
I0410 13:28:46.991765 18353 net.cpp:198] relu1 needs backward computation.
I0410 13:28:46.991768 18353 net.cpp:198] conv1 needs backward computation.
I0410 13:28:46.991772 18353 net.cpp:200] label_val-data_1_split does not need backward computation.
I0410 13:28:46.991776 18353 net.cpp:200] val-data does not need backward computation.
I0410 13:28:46.991780 18353 net.cpp:242] This network produces output accuracy
I0410 13:28:46.991784 18353 net.cpp:242] This network produces output loss
I0410 13:28:46.991799 18353 net.cpp:255] Network initialization done.
I0410 13:28:46.991868 18353 solver.cpp:56] Solver scaffolding done.
I0410 13:28:46.992254 18353 caffe.cpp:248] Starting Optimization
I0410 13:28:46.992264 18353 solver.cpp:272] Solving
I0410 13:28:46.992267 18353 solver.cpp:273] Learning Rate Policy: exp
I0410 13:28:46.993083 18353 solver.cpp:330] Iteration 0, Testing net (#0)
I0410 13:28:46.993103 18353 net.cpp:676] Ignoring source layer train-data
I0410 13:28:46.995333 18353 blocking_queue.cpp:49] Waiting for data
I0410 13:28:51.467716 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:28:51.511610 18353 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0410 13:28:51.511653 18353 solver.cpp:397]     Test net output #1: loss = 5.27914 (* 1 = 5.27914 loss)
I0410 13:28:51.596724 18353 solver.cpp:218] Iteration 0 (-5.2899e-30 iter/s, 4.60427s/12 iters), loss = 5.27351
I0410 13:28:51.596762 18353 solver.cpp:237]     Train net output #0: loss = 5.27351 (* 1 = 5.27351 loss)
I0410 13:28:51.596786 18353 sgd_solver.cpp:105] Iteration 0, lr = 0.01
I0410 13:28:55.494565 18353 solver.cpp:218] Iteration 12 (3.07878 iter/s, 3.89765s/12 iters), loss = 5.27118
I0410 13:28:55.494608 18353 solver.cpp:237]     Train net output #0: loss = 5.27118 (* 1 = 5.27118 loss)
I0410 13:28:55.494619 18353 sgd_solver.cpp:105] Iteration 12, lr = 0.00997626
I0410 13:29:00.241415 18353 solver.cpp:218] Iteration 24 (2.52811 iter/s, 4.74663s/12 iters), loss = 5.28136
I0410 13:29:00.241461 18353 solver.cpp:237]     Train net output #0: loss = 5.28136 (* 1 = 5.28136 loss)
I0410 13:29:00.241472 18353 sgd_solver.cpp:105] Iteration 24, lr = 0.00995257
I0410 13:29:05.047654 18353 solver.cpp:218] Iteration 36 (2.49687 iter/s, 4.80602s/12 iters), loss = 5.28017
I0410 13:29:05.047699 18353 solver.cpp:237]     Train net output #0: loss = 5.28017 (* 1 = 5.28017 loss)
I0410 13:29:05.047713 18353 sgd_solver.cpp:105] Iteration 36, lr = 0.00992894
I0410 13:29:09.879206 18353 solver.cpp:218] Iteration 48 (2.48379 iter/s, 4.83133s/12 iters), loss = 5.28742
I0410 13:29:09.879246 18353 solver.cpp:237]     Train net output #0: loss = 5.28742 (* 1 = 5.28742 loss)
I0410 13:29:09.879258 18353 sgd_solver.cpp:105] Iteration 48, lr = 0.00990537
I0410 13:29:14.691164 18353 solver.cpp:218] Iteration 60 (2.4939 iter/s, 4.81174s/12 iters), loss = 5.28094
I0410 13:29:14.691207 18353 solver.cpp:237]     Train net output #0: loss = 5.28094 (* 1 = 5.28094 loss)
I0410 13:29:14.691217 18353 sgd_solver.cpp:105] Iteration 60, lr = 0.00988185
I0410 13:29:19.510717 18353 solver.cpp:218] Iteration 72 (2.48997 iter/s, 4.81933s/12 iters), loss = 5.2808
I0410 13:29:19.510808 18353 solver.cpp:237]     Train net output #0: loss = 5.2808 (* 1 = 5.2808 loss)
I0410 13:29:19.510821 18353 sgd_solver.cpp:105] Iteration 72, lr = 0.00985839
I0410 13:29:24.294188 18353 solver.cpp:218] Iteration 84 (2.50878 iter/s, 4.78321s/12 iters), loss = 5.28572
I0410 13:29:24.294235 18353 solver.cpp:237]     Train net output #0: loss = 5.28572 (* 1 = 5.28572 loss)
I0410 13:29:24.294248 18353 sgd_solver.cpp:105] Iteration 84, lr = 0.00983498
I0410 13:29:29.128234 18353 solver.cpp:218] Iteration 96 (2.4825 iter/s, 4.83383s/12 iters), loss = 5.28377
I0410 13:29:29.128278 18353 solver.cpp:237]     Train net output #0: loss = 5.28377 (* 1 = 5.28377 loss)
I0410 13:29:29.128288 18353 sgd_solver.cpp:105] Iteration 96, lr = 0.00981163
I0410 13:29:30.760265 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:29:31.065268 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_102.caffemodel
I0410 13:29:31.652758 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_102.solverstate
I0410 13:29:32.011798 18353 solver.cpp:330] Iteration 102, Testing net (#0)
I0410 13:29:32.011826 18353 net.cpp:676] Ignoring source layer train-data
I0410 13:29:36.362304 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:29:36.438158 18353 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 13:29:36.438201 18353 solver.cpp:397]     Test net output #1: loss = 5.27879 (* 1 = 5.27879 loss)
I0410 13:29:38.203984 18353 solver.cpp:218] Iteration 108 (1.32226 iter/s, 9.07539s/12 iters), loss = 5.27684
I0410 13:29:38.204031 18353 solver.cpp:237]     Train net output #0: loss = 5.27684 (* 1 = 5.27684 loss)
I0410 13:29:38.204043 18353 sgd_solver.cpp:105] Iteration 108, lr = 0.00978834
I0410 13:29:42.993652 18353 solver.cpp:218] Iteration 120 (2.50551 iter/s, 4.78945s/12 iters), loss = 5.27292
I0410 13:29:42.993701 18353 solver.cpp:237]     Train net output #0: loss = 5.27292 (* 1 = 5.27292 loss)
I0410 13:29:42.993713 18353 sgd_solver.cpp:105] Iteration 120, lr = 0.0097651
I0410 13:29:47.763233 18353 solver.cpp:218] Iteration 132 (2.51606 iter/s, 4.76936s/12 iters), loss = 5.24752
I0410 13:29:47.763283 18353 solver.cpp:237]     Train net output #0: loss = 5.24752 (* 1 = 5.24752 loss)
I0410 13:29:47.763296 18353 sgd_solver.cpp:105] Iteration 132, lr = 0.00974192
I0410 13:29:52.570581 18353 solver.cpp:218] Iteration 144 (2.49629 iter/s, 4.80713s/12 iters), loss = 5.29731
I0410 13:29:52.570744 18353 solver.cpp:237]     Train net output #0: loss = 5.29731 (* 1 = 5.29731 loss)
I0410 13:29:52.570758 18353 sgd_solver.cpp:105] Iteration 144, lr = 0.00971879
I0410 13:29:57.389384 18353 solver.cpp:218] Iteration 156 (2.49042 iter/s, 4.81847s/12 iters), loss = 5.26402
I0410 13:29:57.389434 18353 solver.cpp:237]     Train net output #0: loss = 5.26402 (* 1 = 5.26402 loss)
I0410 13:29:57.389447 18353 sgd_solver.cpp:105] Iteration 156, lr = 0.00969571
I0410 13:30:02.180932 18353 solver.cpp:218] Iteration 168 (2.50453 iter/s, 4.79133s/12 iters), loss = 5.26406
I0410 13:30:02.180980 18353 solver.cpp:237]     Train net output #0: loss = 5.26406 (* 1 = 5.26406 loss)
I0410 13:30:02.180992 18353 sgd_solver.cpp:105] Iteration 168, lr = 0.00967269
I0410 13:30:06.979442 18353 solver.cpp:218] Iteration 180 (2.50089 iter/s, 4.79829s/12 iters), loss = 5.25816
I0410 13:30:06.979494 18353 solver.cpp:237]     Train net output #0: loss = 5.25816 (* 1 = 5.25816 loss)
I0410 13:30:06.979504 18353 sgd_solver.cpp:105] Iteration 180, lr = 0.00964973
I0410 13:30:11.786164 18353 solver.cpp:218] Iteration 192 (2.49662 iter/s, 4.80649s/12 iters), loss = 5.26288
I0410 13:30:11.786226 18353 solver.cpp:237]     Train net output #0: loss = 5.26288 (* 1 = 5.26288 loss)
I0410 13:30:11.786242 18353 sgd_solver.cpp:105] Iteration 192, lr = 0.00962682
I0410 13:30:15.648447 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:30:16.300645 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_204.caffemodel
I0410 13:30:16.603500 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_204.solverstate
I0410 13:30:16.807330 18353 solver.cpp:330] Iteration 204, Testing net (#0)
I0410 13:30:16.807361 18353 net.cpp:676] Ignoring source layer train-data
I0410 13:30:21.316921 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:30:21.438299 18353 solver.cpp:397]     Test net output #0: accuracy = 0.00735294
I0410 13:30:21.438351 18353 solver.cpp:397]     Test net output #1: loss = 5.21164 (* 1 = 5.21164 loss)
I0410 13:30:21.519675 18353 solver.cpp:218] Iteration 204 (1.2329 iter/s, 9.73312s/12 iters), loss = 5.19107
I0410 13:30:21.519727 18353 solver.cpp:237]     Train net output #0: loss = 5.19107 (* 1 = 5.19107 loss)
I0410 13:30:21.519738 18353 sgd_solver.cpp:105] Iteration 204, lr = 0.00960396
I0410 13:30:25.610288 18353 solver.cpp:218] Iteration 216 (2.93369 iter/s, 4.09041s/12 iters), loss = 5.21252
I0410 13:30:25.610414 18353 solver.cpp:237]     Train net output #0: loss = 5.21252 (* 1 = 5.21252 loss)
I0410 13:30:25.610425 18353 sgd_solver.cpp:105] Iteration 216, lr = 0.00958116
I0410 13:30:30.402014 18353 solver.cpp:218] Iteration 228 (2.50447 iter/s, 4.79142s/12 iters), loss = 5.22201
I0410 13:30:30.402065 18353 solver.cpp:237]     Train net output #0: loss = 5.22201 (* 1 = 5.22201 loss)
I0410 13:30:30.402076 18353 sgd_solver.cpp:105] Iteration 228, lr = 0.00955841
I0410 13:30:35.195777 18353 solver.cpp:218] Iteration 240 (2.50337 iter/s, 4.79354s/12 iters), loss = 5.22456
I0410 13:30:35.195832 18353 solver.cpp:237]     Train net output #0: loss = 5.22456 (* 1 = 5.22456 loss)
I0410 13:30:35.195843 18353 sgd_solver.cpp:105] Iteration 240, lr = 0.00953572
I0410 13:30:40.018483 18353 solver.cpp:218] Iteration 252 (2.48835 iter/s, 4.82247s/12 iters), loss = 5.16041
I0410 13:30:40.018541 18353 solver.cpp:237]     Train net output #0: loss = 5.16041 (* 1 = 5.16041 loss)
I0410 13:30:40.018555 18353 sgd_solver.cpp:105] Iteration 252, lr = 0.00951308
I0410 13:30:44.863235 18353 solver.cpp:218] Iteration 264 (2.47702 iter/s, 4.84452s/12 iters), loss = 5.29866
I0410 13:30:44.863277 18353 solver.cpp:237]     Train net output #0: loss = 5.29866 (* 1 = 5.29866 loss)
I0410 13:30:44.863287 18353 sgd_solver.cpp:105] Iteration 264, lr = 0.00949049
I0410 13:30:49.661846 18353 solver.cpp:218] Iteration 276 (2.50084 iter/s, 4.79839s/12 iters), loss = 5.20624
I0410 13:30:49.661901 18353 solver.cpp:237]     Train net output #0: loss = 5.20624 (* 1 = 5.20624 loss)
I0410 13:30:49.661912 18353 sgd_solver.cpp:105] Iteration 276, lr = 0.00946796
I0410 13:30:54.430438 18353 solver.cpp:218] Iteration 288 (2.51659 iter/s, 4.76836s/12 iters), loss = 5.08793
I0410 13:30:54.430500 18353 solver.cpp:237]     Train net output #0: loss = 5.08793 (* 1 = 5.08793 loss)
I0410 13:30:54.430514 18353 sgd_solver.cpp:105] Iteration 288, lr = 0.00944548
I0410 13:30:59.202740 18353 solver.cpp:218] Iteration 300 (2.51463 iter/s, 4.77206s/12 iters), loss = 5.17502
I0410 13:30:59.202906 18353 solver.cpp:237]     Train net output #0: loss = 5.17502 (* 1 = 5.17502 loss)
I0410 13:30:59.202919 18353 sgd_solver.cpp:105] Iteration 300, lr = 0.00942305
I0410 13:31:00.147606 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:31:01.149010 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_306.caffemodel
I0410 13:31:01.638471 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_306.solverstate
I0410 13:31:02.298786 18353 solver.cpp:330] Iteration 306, Testing net (#0)
I0410 13:31:02.298818 18353 net.cpp:676] Ignoring source layer train-data
I0410 13:31:06.590077 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:31:06.747010 18353 solver.cpp:397]     Test net output #0: accuracy = 0.00735294
I0410 13:31:06.747061 18353 solver.cpp:397]     Test net output #1: loss = 5.14714 (* 1 = 5.14714 loss)
I0410 13:31:08.551136 18353 solver.cpp:218] Iteration 312 (1.28371 iter/s, 9.34791s/12 iters), loss = 5.11782
I0410 13:31:08.551187 18353 solver.cpp:237]     Train net output #0: loss = 5.11782 (* 1 = 5.11782 loss)
I0410 13:31:08.551200 18353 sgd_solver.cpp:105] Iteration 312, lr = 0.00940068
I0410 13:31:13.352361 18353 solver.cpp:218] Iteration 324 (2.49948 iter/s, 4.801s/12 iters), loss = 5.1685
I0410 13:31:13.352419 18353 solver.cpp:237]     Train net output #0: loss = 5.1685 (* 1 = 5.1685 loss)
I0410 13:31:13.352432 18353 sgd_solver.cpp:105] Iteration 324, lr = 0.00937836
I0410 13:31:18.135365 18353 solver.cpp:218] Iteration 336 (2.509 iter/s, 4.78278s/12 iters), loss = 5.12779
I0410 13:31:18.135421 18353 solver.cpp:237]     Train net output #0: loss = 5.12779 (* 1 = 5.12779 loss)
I0410 13:31:18.135432 18353 sgd_solver.cpp:105] Iteration 336, lr = 0.0093561
I0410 13:31:22.922883 18353 solver.cpp:218] Iteration 348 (2.50664 iter/s, 4.78729s/12 iters), loss = 5.12706
I0410 13:31:22.922940 18353 solver.cpp:237]     Train net output #0: loss = 5.12706 (* 1 = 5.12706 loss)
I0410 13:31:22.922953 18353 sgd_solver.cpp:105] Iteration 348, lr = 0.00933388
I0410 13:31:27.818292 18353 solver.cpp:218] Iteration 360 (2.45139 iter/s, 4.89518s/12 iters), loss = 5.15952
I0410 13:31:27.818341 18353 solver.cpp:237]     Train net output #0: loss = 5.15952 (* 1 = 5.15952 loss)
I0410 13:31:27.818349 18353 sgd_solver.cpp:105] Iteration 360, lr = 0.00931172
I0410 13:31:32.648501 18353 solver.cpp:218] Iteration 372 (2.48448 iter/s, 4.82999s/12 iters), loss = 5.10923
I0410 13:31:32.648577 18353 solver.cpp:237]     Train net output #0: loss = 5.10923 (* 1 = 5.10923 loss)
I0410 13:31:32.648587 18353 sgd_solver.cpp:105] Iteration 372, lr = 0.00928961
I0410 13:31:37.440991 18353 solver.cpp:218] Iteration 384 (2.50405 iter/s, 4.79224s/12 iters), loss = 5.09812
I0410 13:31:37.441048 18353 solver.cpp:237]     Train net output #0: loss = 5.09812 (* 1 = 5.09812 loss)
I0410 13:31:37.441061 18353 sgd_solver.cpp:105] Iteration 384, lr = 0.00926756
I0410 13:31:42.235280 18353 solver.cpp:218] Iteration 396 (2.50309 iter/s, 4.79407s/12 iters), loss = 5.06009
I0410 13:31:42.235325 18353 solver.cpp:237]     Train net output #0: loss = 5.06009 (* 1 = 5.06009 loss)
I0410 13:31:42.235334 18353 sgd_solver.cpp:105] Iteration 396, lr = 0.00924556
I0410 13:31:45.253728 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:31:46.600811 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_408.caffemodel
I0410 13:31:47.486912 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_408.solverstate
I0410 13:31:47.904908 18353 solver.cpp:330] Iteration 408, Testing net (#0)
I0410 13:31:47.904937 18353 net.cpp:676] Ignoring source layer train-data
I0410 13:31:52.084388 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:31:52.286082 18353 solver.cpp:397]     Test net output #0: accuracy = 0.0159314
I0410 13:31:52.286114 18353 solver.cpp:397]     Test net output #1: loss = 5.09138 (* 1 = 5.09138 loss)
I0410 13:31:52.367595 18353 solver.cpp:218] Iteration 408 (1.18437 iter/s, 10.1319s/12 iters), loss = 5.17838
I0410 13:31:52.367662 18353 solver.cpp:237]     Train net output #0: loss = 5.17838 (* 1 = 5.17838 loss)
I0410 13:31:52.367676 18353 sgd_solver.cpp:105] Iteration 408, lr = 0.00922361
I0410 13:31:56.396499 18353 solver.cpp:218] Iteration 420 (2.97863 iter/s, 4.0287s/12 iters), loss = 5.13791
I0410 13:31:56.396557 18353 solver.cpp:237]     Train net output #0: loss = 5.13791 (* 1 = 5.13791 loss)
I0410 13:31:56.396569 18353 sgd_solver.cpp:105] Iteration 420, lr = 0.00920171
I0410 13:32:01.152849 18353 solver.cpp:218] Iteration 432 (2.52306 iter/s, 4.75613s/12 iters), loss = 5.16788
I0410 13:32:01.152911 18353 solver.cpp:237]     Train net output #0: loss = 5.16788 (* 1 = 5.16788 loss)
I0410 13:32:01.152925 18353 sgd_solver.cpp:105] Iteration 432, lr = 0.00917986
I0410 13:32:06.051076 18353 solver.cpp:218] Iteration 444 (2.44998 iter/s, 4.898s/12 iters), loss = 5.04653
I0410 13:32:06.051204 18353 solver.cpp:237]     Train net output #0: loss = 5.04653 (* 1 = 5.04653 loss)
I0410 13:32:06.051218 18353 sgd_solver.cpp:105] Iteration 444, lr = 0.00915807
I0410 13:32:10.883579 18353 solver.cpp:218] Iteration 456 (2.48334 iter/s, 4.83221s/12 iters), loss = 5.0797
I0410 13:32:10.883632 18353 solver.cpp:237]     Train net output #0: loss = 5.0797 (* 1 = 5.0797 loss)
I0410 13:32:10.883643 18353 sgd_solver.cpp:105] Iteration 456, lr = 0.00913632
I0410 13:32:15.690878 18353 solver.cpp:218] Iteration 468 (2.49631 iter/s, 4.80709s/12 iters), loss = 5.10949
I0410 13:32:15.690922 18353 solver.cpp:237]     Train net output #0: loss = 5.10949 (* 1 = 5.10949 loss)
I0410 13:32:15.690932 18353 sgd_solver.cpp:105] Iteration 468, lr = 0.00911463
I0410 13:32:20.507534 18353 solver.cpp:218] Iteration 480 (2.49146 iter/s, 4.81645s/12 iters), loss = 5.04724
I0410 13:32:20.507589 18353 solver.cpp:237]     Train net output #0: loss = 5.04724 (* 1 = 5.04724 loss)
I0410 13:32:20.507602 18353 sgd_solver.cpp:105] Iteration 480, lr = 0.00909299
I0410 13:32:25.362396 18353 solver.cpp:218] Iteration 492 (2.47186 iter/s, 4.85464s/12 iters), loss = 5.11802
I0410 13:32:25.362447 18353 solver.cpp:237]     Train net output #0: loss = 5.11802 (* 1 = 5.11802 loss)
I0410 13:32:25.362459 18353 sgd_solver.cpp:105] Iteration 492, lr = 0.0090714
I0410 13:32:30.133383 18353 solver.cpp:218] Iteration 504 (2.51531 iter/s, 4.77078s/12 iters), loss = 5.11083
I0410 13:32:30.133442 18353 solver.cpp:237]     Train net output #0: loss = 5.11083 (* 1 = 5.11083 loss)
I0410 13:32:30.133455 18353 sgd_solver.cpp:105] Iteration 504, lr = 0.00904986
I0410 13:32:30.381103 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:32:32.116602 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_510.caffemodel
I0410 13:32:32.442308 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_510.solverstate
I0410 13:32:32.644781 18353 solver.cpp:330] Iteration 510, Testing net (#0)
I0410 13:32:32.644809 18353 net.cpp:676] Ignoring source layer train-data
I0410 13:32:36.983175 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:32:37.219594 18353 solver.cpp:397]     Test net output #0: accuracy = 0.0232843
I0410 13:32:37.219626 18353 solver.cpp:397]     Test net output #1: loss = 5.03098 (* 1 = 5.03098 loss)
I0410 13:32:39.060112 18353 solver.cpp:218] Iteration 516 (1.34433 iter/s, 8.92639s/12 iters), loss = 4.96743
I0410 13:32:39.060158 18353 solver.cpp:237]     Train net output #0: loss = 4.96743 (* 1 = 4.96743 loss)
I0410 13:32:39.060169 18353 sgd_solver.cpp:105] Iteration 516, lr = 0.00902838
I0410 13:32:43.856173 18353 solver.cpp:218] Iteration 528 (2.50216 iter/s, 4.79585s/12 iters), loss = 5.06687
I0410 13:32:43.856220 18353 solver.cpp:237]     Train net output #0: loss = 5.06687 (* 1 = 5.06687 loss)
I0410 13:32:43.856230 18353 sgd_solver.cpp:105] Iteration 528, lr = 0.00900694
I0410 13:32:48.847501 18353 solver.cpp:218] Iteration 540 (2.40427 iter/s, 4.99111s/12 iters), loss = 4.9997
I0410 13:32:48.847553 18353 solver.cpp:237]     Train net output #0: loss = 4.9997 (* 1 = 4.9997 loss)
I0410 13:32:48.847566 18353 sgd_solver.cpp:105] Iteration 540, lr = 0.00898556
I0410 13:32:53.647042 18353 solver.cpp:218] Iteration 552 (2.50035 iter/s, 4.79933s/12 iters), loss = 5.08308
I0410 13:32:53.647094 18353 solver.cpp:237]     Train net output #0: loss = 5.08308 (* 1 = 5.08308 loss)
I0410 13:32:53.647106 18353 sgd_solver.cpp:105] Iteration 552, lr = 0.00896423
I0410 13:32:58.454965 18353 solver.cpp:218] Iteration 564 (2.49599 iter/s, 4.80771s/12 iters), loss = 4.99543
I0410 13:32:58.455015 18353 solver.cpp:237]     Train net output #0: loss = 4.99543 (* 1 = 4.99543 loss)
I0410 13:32:58.455029 18353 sgd_solver.cpp:105] Iteration 564, lr = 0.00894294
I0410 13:33:03.214296 18353 solver.cpp:218] Iteration 576 (2.52147 iter/s, 4.75912s/12 iters), loss = 5.0517
I0410 13:33:03.214347 18353 solver.cpp:237]     Train net output #0: loss = 5.0517 (* 1 = 5.0517 loss)
I0410 13:33:03.214359 18353 sgd_solver.cpp:105] Iteration 576, lr = 0.00892171
I0410 13:33:08.054003 18353 solver.cpp:218] Iteration 588 (2.4796 iter/s, 4.83949s/12 iters), loss = 4.96009
I0410 13:33:08.054117 18353 solver.cpp:237]     Train net output #0: loss = 4.96009 (* 1 = 4.96009 loss)
I0410 13:33:08.054131 18353 sgd_solver.cpp:105] Iteration 588, lr = 0.00890053
I0410 13:33:12.873062 18353 solver.cpp:218] Iteration 600 (2.49025 iter/s, 4.81879s/12 iters), loss = 5.02712
I0410 13:33:12.873113 18353 solver.cpp:237]     Train net output #0: loss = 5.02712 (* 1 = 5.02712 loss)
I0410 13:33:12.873126 18353 sgd_solver.cpp:105] Iteration 600, lr = 0.0088794
I0410 13:33:15.161584 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:33:17.370432 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_612.caffemodel
I0410 13:33:17.692783 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_612.solverstate
I0410 13:33:17.900879 18353 solver.cpp:330] Iteration 612, Testing net (#0)
I0410 13:33:17.900902 18353 net.cpp:676] Ignoring source layer train-data
I0410 13:33:22.024000 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:33:22.307170 18353 solver.cpp:397]     Test net output #0: accuracy = 0.0208333
I0410 13:33:22.307219 18353 solver.cpp:397]     Test net output #1: loss = 4.96715 (* 1 = 4.96715 loss)
I0410 13:33:22.389060 18353 solver.cpp:218] Iteration 612 (1.26108 iter/s, 9.51565s/12 iters), loss = 5.00101
I0410 13:33:22.389107 18353 solver.cpp:237]     Train net output #0: loss = 5.00101 (* 1 = 5.00101 loss)
I0410 13:33:22.389119 18353 sgd_solver.cpp:105] Iteration 612, lr = 0.00885831
I0410 13:33:26.586331 18353 solver.cpp:218] Iteration 624 (2.85913 iter/s, 4.19708s/12 iters), loss = 4.88992
I0410 13:33:26.586385 18353 solver.cpp:237]     Train net output #0: loss = 4.88992 (* 1 = 4.88992 loss)
I0410 13:33:26.586397 18353 sgd_solver.cpp:105] Iteration 624, lr = 0.00883728
I0410 13:33:31.612911 18353 solver.cpp:218] Iteration 636 (2.38741 iter/s, 5.02636s/12 iters), loss = 4.9153
I0410 13:33:31.612969 18353 solver.cpp:237]     Train net output #0: loss = 4.9153 (* 1 = 4.9153 loss)
I0410 13:33:31.612982 18353 sgd_solver.cpp:105] Iteration 636, lr = 0.0088163
I0410 13:33:36.421813 18353 solver.cpp:218] Iteration 648 (2.49548 iter/s, 4.80869s/12 iters), loss = 5.13299
I0410 13:33:36.421856 18353 solver.cpp:237]     Train net output #0: loss = 5.13299 (* 1 = 5.13299 loss)
I0410 13:33:36.421865 18353 sgd_solver.cpp:105] Iteration 648, lr = 0.00879537
I0410 13:33:41.234724 18353 solver.cpp:218] Iteration 660 (2.4934 iter/s, 4.8127s/12 iters), loss = 5.07361
I0410 13:33:41.234858 18353 solver.cpp:237]     Train net output #0: loss = 5.07361 (* 1 = 5.07361 loss)
I0410 13:33:41.234874 18353 sgd_solver.cpp:105] Iteration 660, lr = 0.00877449
I0410 13:33:45.991466 18353 solver.cpp:218] Iteration 672 (2.52288 iter/s, 4.75646s/12 iters), loss = 4.90382
I0410 13:33:45.991503 18353 solver.cpp:237]     Train net output #0: loss = 4.90382 (* 1 = 4.90382 loss)
I0410 13:33:45.991515 18353 sgd_solver.cpp:105] Iteration 672, lr = 0.00875366
I0410 13:33:49.948544 18353 blocking_queue.cpp:49] Waiting for data
I0410 13:33:50.813939 18353 solver.cpp:218] Iteration 684 (2.48845 iter/s, 4.82227s/12 iters), loss = 4.77979
I0410 13:33:50.814013 18353 solver.cpp:237]     Train net output #0: loss = 4.77979 (* 1 = 4.77979 loss)
I0410 13:33:50.814024 18353 sgd_solver.cpp:105] Iteration 684, lr = 0.00873287
I0410 13:33:55.640511 18353 solver.cpp:218] Iteration 696 (2.48636 iter/s, 4.82634s/12 iters), loss = 4.88568
I0410 13:33:55.640564 18353 solver.cpp:237]     Train net output #0: loss = 4.88568 (* 1 = 4.88568 loss)
I0410 13:33:55.640578 18353 sgd_solver.cpp:105] Iteration 696, lr = 0.00871214
I0410 13:34:00.095309 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:34:00.466298 18353 solver.cpp:218] Iteration 708 (2.48675 iter/s, 4.82557s/12 iters), loss = 5.00854
I0410 13:34:00.466358 18353 solver.cpp:237]     Train net output #0: loss = 5.00854 (* 1 = 5.00854 loss)
I0410 13:34:00.466372 18353 sgd_solver.cpp:105] Iteration 708, lr = 0.00869145
I0410 13:34:02.622711 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_714.caffemodel
I0410 13:34:02.936161 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_714.solverstate
I0410 13:34:03.148313 18353 solver.cpp:330] Iteration 714, Testing net (#0)
I0410 13:34:03.148342 18353 net.cpp:676] Ignoring source layer train-data
I0410 13:34:07.349382 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:34:07.673627 18353 solver.cpp:397]     Test net output #0: accuracy = 0.0300245
I0410 13:34:07.673676 18353 solver.cpp:397]     Test net output #1: loss = 4.93297 (* 1 = 4.93297 loss)
I0410 13:34:09.504738 18353 solver.cpp:218] Iteration 720 (1.32771 iter/s, 9.0381s/12 iters), loss = 5.03595
I0410 13:34:09.504794 18353 solver.cpp:237]     Train net output #0: loss = 5.03595 (* 1 = 5.03595 loss)
I0410 13:34:09.504806 18353 sgd_solver.cpp:105] Iteration 720, lr = 0.00867082
I0410 13:34:14.545984 18353 solver.cpp:218] Iteration 732 (2.38047 iter/s, 5.04101s/12 iters), loss = 4.84105
I0410 13:34:14.546103 18353 solver.cpp:237]     Train net output #0: loss = 4.84105 (* 1 = 4.84105 loss)
I0410 13:34:14.546118 18353 sgd_solver.cpp:105] Iteration 732, lr = 0.00865023
I0410 13:34:19.344077 18353 solver.cpp:218] Iteration 744 (2.50113 iter/s, 4.79782s/12 iters), loss = 4.9522
I0410 13:34:19.344130 18353 solver.cpp:237]     Train net output #0: loss = 4.9522 (* 1 = 4.9522 loss)
I0410 13:34:19.344141 18353 sgd_solver.cpp:105] Iteration 744, lr = 0.0086297
I0410 13:34:24.181560 18353 solver.cpp:218] Iteration 756 (2.48073 iter/s, 4.83728s/12 iters), loss = 4.96332
I0410 13:34:24.181602 18353 solver.cpp:237]     Train net output #0: loss = 4.96332 (* 1 = 4.96332 loss)
I0410 13:34:24.181612 18353 sgd_solver.cpp:105] Iteration 756, lr = 0.00860921
I0410 13:34:29.019403 18353 solver.cpp:218] Iteration 768 (2.48054 iter/s, 4.83765s/12 iters), loss = 4.87163
I0410 13:34:29.019444 18353 solver.cpp:237]     Train net output #0: loss = 4.87163 (* 1 = 4.87163 loss)
I0410 13:34:29.019452 18353 sgd_solver.cpp:105] Iteration 768, lr = 0.00858877
I0410 13:34:33.830617 18353 solver.cpp:218] Iteration 780 (2.49428 iter/s, 4.81101s/12 iters), loss = 4.85705
I0410 13:34:33.830667 18353 solver.cpp:237]     Train net output #0: loss = 4.85705 (* 1 = 4.85705 loss)
I0410 13:34:33.830677 18353 sgd_solver.cpp:105] Iteration 780, lr = 0.00856838
I0410 13:34:38.681201 18353 solver.cpp:218] Iteration 792 (2.47403 iter/s, 4.85038s/12 iters), loss = 4.69125
I0410 13:34:38.681259 18353 solver.cpp:237]     Train net output #0: loss = 4.69125 (* 1 = 4.69125 loss)
I0410 13:34:38.681272 18353 sgd_solver.cpp:105] Iteration 792, lr = 0.00854803
I0410 13:34:43.579320 18353 solver.cpp:218] Iteration 804 (2.45003 iter/s, 4.89791s/12 iters), loss = 4.98446
I0410 13:34:43.579371 18353 solver.cpp:237]     Train net output #0: loss = 4.98446 (* 1 = 4.98446 loss)
I0410 13:34:43.579383 18353 sgd_solver.cpp:105] Iteration 804, lr = 0.00852774
I0410 13:34:45.254433 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:34:47.964468 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_816.caffemodel
I0410 13:34:48.275637 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_816.solverstate
I0410 13:34:48.481784 18353 solver.cpp:330] Iteration 816, Testing net (#0)
I0410 13:34:48.481817 18353 net.cpp:676] Ignoring source layer train-data
I0410 13:34:52.561791 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:34:52.913844 18353 solver.cpp:397]     Test net output #0: accuracy = 0.0343137
I0410 13:34:52.913885 18353 solver.cpp:397]     Test net output #1: loss = 4.85525 (* 1 = 4.85525 loss)
I0410 13:34:52.995776 18353 solver.cpp:218] Iteration 816 (1.27441 iter/s, 9.41611s/12 iters), loss = 4.96716
I0410 13:34:52.995837 18353 solver.cpp:237]     Train net output #0: loss = 4.96716 (* 1 = 4.96716 loss)
I0410 13:34:52.995851 18353 sgd_solver.cpp:105] Iteration 816, lr = 0.00850749
I0410 13:34:57.119913 18353 solver.cpp:218] Iteration 828 (2.90984 iter/s, 4.12393s/12 iters), loss = 4.88911
I0410 13:34:57.119980 18353 solver.cpp:237]     Train net output #0: loss = 4.88911 (* 1 = 4.88911 loss)
I0410 13:34:57.119997 18353 sgd_solver.cpp:105] Iteration 828, lr = 0.00848729
I0410 13:35:01.931084 18353 solver.cpp:218] Iteration 840 (2.49431 iter/s, 4.81095s/12 iters), loss = 4.71537
I0410 13:35:01.931143 18353 solver.cpp:237]     Train net output #0: loss = 4.71537 (* 1 = 4.71537 loss)
I0410 13:35:01.931156 18353 sgd_solver.cpp:105] Iteration 840, lr = 0.00846714
I0410 13:35:06.700747 18353 solver.cpp:218] Iteration 852 (2.51601 iter/s, 4.76945s/12 iters), loss = 4.80388
I0410 13:35:06.700796 18353 solver.cpp:237]     Train net output #0: loss = 4.80388 (* 1 = 4.80388 loss)
I0410 13:35:06.700806 18353 sgd_solver.cpp:105] Iteration 852, lr = 0.00844704
I0410 13:35:11.491523 18353 solver.cpp:218] Iteration 864 (2.50492 iter/s, 4.79057s/12 iters), loss = 4.85414
I0410 13:35:11.491564 18353 solver.cpp:237]     Train net output #0: loss = 4.85414 (* 1 = 4.85414 loss)
I0410 13:35:11.491572 18353 sgd_solver.cpp:105] Iteration 864, lr = 0.00842698
I0410 13:35:16.550673 18353 solver.cpp:218] Iteration 876 (2.37203 iter/s, 5.05895s/12 iters), loss = 4.75736
I0410 13:35:16.550779 18353 solver.cpp:237]     Train net output #0: loss = 4.75736 (* 1 = 4.75736 loss)
I0410 13:35:16.550792 18353 sgd_solver.cpp:105] Iteration 876, lr = 0.00840698
I0410 13:35:21.387084 18353 solver.cpp:218] Iteration 888 (2.48131 iter/s, 4.83615s/12 iters), loss = 4.78754
I0410 13:35:21.387136 18353 solver.cpp:237]     Train net output #0: loss = 4.78754 (* 1 = 4.78754 loss)
I0410 13:35:21.387148 18353 sgd_solver.cpp:105] Iteration 888, lr = 0.00838702
I0410 13:35:26.289463 18353 solver.cpp:218] Iteration 900 (2.44789 iter/s, 4.90217s/12 iters), loss = 4.81667
I0410 13:35:26.289520 18353 solver.cpp:237]     Train net output #0: loss = 4.81667 (* 1 = 4.81667 loss)
I0410 13:35:26.289533 18353 sgd_solver.cpp:105] Iteration 900, lr = 0.0083671
I0410 13:35:30.065737 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:35:31.124670 18353 solver.cpp:218] Iteration 912 (2.48191 iter/s, 4.835s/12 iters), loss = 4.73907
I0410 13:35:31.124722 18353 solver.cpp:237]     Train net output #0: loss = 4.73907 (* 1 = 4.73907 loss)
I0410 13:35:31.124734 18353 sgd_solver.cpp:105] Iteration 912, lr = 0.00834724
I0410 13:35:33.094696 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_918.caffemodel
I0410 13:35:33.394491 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_918.solverstate
I0410 13:35:33.596521 18353 solver.cpp:330] Iteration 918, Testing net (#0)
I0410 13:35:33.596544 18353 net.cpp:676] Ignoring source layer train-data
I0410 13:35:37.711308 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:35:38.109664 18353 solver.cpp:397]     Test net output #0: accuracy = 0.0373775
I0410 13:35:38.109706 18353 solver.cpp:397]     Test net output #1: loss = 4.82748 (* 1 = 4.82748 loss)
I0410 13:35:39.923985 18353 solver.cpp:218] Iteration 924 (1.36379 iter/s, 8.799s/12 iters), loss = 4.6773
I0410 13:35:39.924044 18353 solver.cpp:237]     Train net output #0: loss = 4.6773 (* 1 = 4.6773 loss)
I0410 13:35:39.924057 18353 sgd_solver.cpp:105] Iteration 924, lr = 0.00832742
I0410 13:35:44.744292 18353 solver.cpp:218] Iteration 936 (2.48958 iter/s, 4.82009s/12 iters), loss = 4.85176
I0410 13:35:44.744346 18353 solver.cpp:237]     Train net output #0: loss = 4.85176 (* 1 = 4.85176 loss)
I0410 13:35:44.744359 18353 sgd_solver.cpp:105] Iteration 936, lr = 0.00830765
I0410 13:35:49.588932 18353 solver.cpp:218] Iteration 948 (2.47707 iter/s, 4.84443s/12 iters), loss = 4.65436
I0410 13:35:49.589080 18353 solver.cpp:237]     Train net output #0: loss = 4.65436 (* 1 = 4.65436 loss)
I0410 13:35:49.589095 18353 sgd_solver.cpp:105] Iteration 948, lr = 0.00828793
I0410 13:35:54.423965 18353 solver.cpp:218] Iteration 960 (2.48204 iter/s, 4.83474s/12 iters), loss = 4.54211
I0410 13:35:54.424005 18353 solver.cpp:237]     Train net output #0: loss = 4.54211 (* 1 = 4.54211 loss)
I0410 13:35:54.424015 18353 sgd_solver.cpp:105] Iteration 960, lr = 0.00826825
I0410 13:35:59.458235 18353 solver.cpp:218] Iteration 972 (2.38376 iter/s, 5.03407s/12 iters), loss = 4.77122
I0410 13:35:59.458292 18353 solver.cpp:237]     Train net output #0: loss = 4.77122 (* 1 = 4.77122 loss)
I0410 13:35:59.458305 18353 sgd_solver.cpp:105] Iteration 972, lr = 0.00824862
I0410 13:36:04.366421 18353 solver.cpp:218] Iteration 984 (2.445 iter/s, 4.90798s/12 iters), loss = 4.70817
I0410 13:36:04.366470 18353 solver.cpp:237]     Train net output #0: loss = 4.70817 (* 1 = 4.70817 loss)
I0410 13:36:04.366480 18353 sgd_solver.cpp:105] Iteration 984, lr = 0.00822903
I0410 13:36:09.243734 18353 solver.cpp:218] Iteration 996 (2.46048 iter/s, 4.87711s/12 iters), loss = 4.64167
I0410 13:36:09.243793 18353 solver.cpp:237]     Train net output #0: loss = 4.64167 (* 1 = 4.64167 loss)
I0410 13:36:09.243805 18353 sgd_solver.cpp:105] Iteration 996, lr = 0.0082095
I0410 13:36:14.046546 18353 solver.cpp:218] Iteration 1008 (2.49864 iter/s, 4.8026s/12 iters), loss = 4.67439
I0410 13:36:14.046598 18353 solver.cpp:237]     Train net output #0: loss = 4.67439 (* 1 = 4.67439 loss)
I0410 13:36:14.046610 18353 sgd_solver.cpp:105] Iteration 1008, lr = 0.00819001
I0410 13:36:15.039682 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:36:18.497021 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1020.caffemodel
I0410 13:36:18.807870 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1020.solverstate
I0410 13:36:19.018946 18353 solver.cpp:330] Iteration 1020, Testing net (#0)
I0410 13:36:19.018976 18353 net.cpp:676] Ignoring source layer train-data
I0410 13:36:23.132321 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:36:23.568559 18353 solver.cpp:397]     Test net output #0: accuracy = 0.0533088
I0410 13:36:23.568611 18353 solver.cpp:397]     Test net output #1: loss = 4.59366 (* 1 = 4.59366 loss)
I0410 13:36:23.650089 18353 solver.cpp:218] Iteration 1020 (1.24958 iter/s, 9.6032s/12 iters), loss = 4.48172
I0410 13:36:23.650142 18353 solver.cpp:237]     Train net output #0: loss = 4.48172 (* 1 = 4.48172 loss)
I0410 13:36:23.650156 18353 sgd_solver.cpp:105] Iteration 1020, lr = 0.00817056
I0410 13:36:27.798918 18353 solver.cpp:218] Iteration 1032 (2.89251 iter/s, 4.14864s/12 iters), loss = 4.64712
I0410 13:36:27.798966 18353 solver.cpp:237]     Train net output #0: loss = 4.64712 (* 1 = 4.64712 loss)
I0410 13:36:27.798979 18353 sgd_solver.cpp:105] Iteration 1032, lr = 0.00815116
I0410 13:36:32.693496 18353 solver.cpp:218] Iteration 1044 (2.45179 iter/s, 4.89438s/12 iters), loss = 4.70108
I0410 13:36:32.693550 18353 solver.cpp:237]     Train net output #0: loss = 4.70108 (* 1 = 4.70108 loss)
I0410 13:36:32.693563 18353 sgd_solver.cpp:105] Iteration 1044, lr = 0.00813181
I0410 13:36:37.619858 18353 solver.cpp:218] Iteration 1056 (2.43598 iter/s, 4.92615s/12 iters), loss = 4.6028
I0410 13:36:37.619915 18353 solver.cpp:237]     Train net output #0: loss = 4.6028 (* 1 = 4.6028 loss)
I0410 13:36:37.619926 18353 sgd_solver.cpp:105] Iteration 1056, lr = 0.0081125
I0410 13:36:42.535174 18353 solver.cpp:218] Iteration 1068 (2.44145 iter/s, 4.91511s/12 iters), loss = 4.64417
I0410 13:36:42.535228 18353 solver.cpp:237]     Train net output #0: loss = 4.64417 (* 1 = 4.64417 loss)
I0410 13:36:42.535241 18353 sgd_solver.cpp:105] Iteration 1068, lr = 0.00809324
I0410 13:36:47.451942 18353 solver.cpp:218] Iteration 1080 (2.44073 iter/s, 4.91656s/12 iters), loss = 4.53593
I0410 13:36:47.451985 18353 solver.cpp:237]     Train net output #0: loss = 4.53593 (* 1 = 4.53593 loss)
I0410 13:36:47.451994 18353 sgd_solver.cpp:105] Iteration 1080, lr = 0.00807403
I0410 13:36:52.483651 18353 solver.cpp:218] Iteration 1092 (2.38497 iter/s, 5.03151s/12 iters), loss = 4.45602
I0410 13:36:52.483705 18353 solver.cpp:237]     Train net output #0: loss = 4.45602 (* 1 = 4.45602 loss)
I0410 13:36:52.483717 18353 sgd_solver.cpp:105] Iteration 1092, lr = 0.00805486
I0410 13:36:57.492077 18353 solver.cpp:218] Iteration 1104 (2.39606 iter/s, 5.00822s/12 iters), loss = 4.59711
I0410 13:36:57.492154 18353 solver.cpp:237]     Train net output #0: loss = 4.59711 (* 1 = 4.59711 loss)
I0410 13:36:57.492166 18353 sgd_solver.cpp:105] Iteration 1104, lr = 0.00803573
I0410 13:37:00.640162 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:37:02.453686 18353 solver.cpp:218] Iteration 1116 (2.41869 iter/s, 4.96137s/12 iters), loss = 4.62401
I0410 13:37:02.453737 18353 solver.cpp:237]     Train net output #0: loss = 4.62401 (* 1 = 4.62401 loss)
I0410 13:37:02.453748 18353 sgd_solver.cpp:105] Iteration 1116, lr = 0.00801666
I0410 13:37:04.474838 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1122.caffemodel
I0410 13:37:04.754909 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1122.solverstate
I0410 13:37:04.949824 18353 solver.cpp:330] Iteration 1122, Testing net (#0)
I0410 13:37:04.949854 18353 net.cpp:676] Ignoring source layer train-data
I0410 13:37:08.948029 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:37:09.423416 18353 solver.cpp:397]     Test net output #0: accuracy = 0.0618873
I0410 13:37:09.423453 18353 solver.cpp:397]     Test net output #1: loss = 4.50515 (* 1 = 4.50515 loss)
I0410 13:37:11.176360 18353 solver.cpp:218] Iteration 1128 (1.37577 iter/s, 8.72237s/12 iters), loss = 4.68809
I0410 13:37:11.176407 18353 solver.cpp:237]     Train net output #0: loss = 4.68809 (* 1 = 4.68809 loss)
I0410 13:37:11.176416 18353 sgd_solver.cpp:105] Iteration 1128, lr = 0.00799762
I0410 13:37:16.089619 18353 solver.cpp:218] Iteration 1140 (2.44247 iter/s, 4.91305s/12 iters), loss = 4.60572
I0410 13:37:16.089673 18353 solver.cpp:237]     Train net output #0: loss = 4.60572 (* 1 = 4.60572 loss)
I0410 13:37:16.089686 18353 sgd_solver.cpp:105] Iteration 1140, lr = 0.00797863
I0410 13:37:20.911170 18353 solver.cpp:218] Iteration 1152 (2.48893 iter/s, 4.82135s/12 iters), loss = 4.48119
I0410 13:37:20.911224 18353 solver.cpp:237]     Train net output #0: loss = 4.48119 (* 1 = 4.48119 loss)
I0410 13:37:20.911235 18353 sgd_solver.cpp:105] Iteration 1152, lr = 0.00795969
I0410 13:37:25.809062 18353 solver.cpp:218] Iteration 1164 (2.45014 iter/s, 4.89768s/12 iters), loss = 4.35876
I0410 13:37:25.809104 18353 solver.cpp:237]     Train net output #0: loss = 4.35876 (* 1 = 4.35876 loss)
I0410 13:37:25.809114 18353 sgd_solver.cpp:105] Iteration 1164, lr = 0.00794079
I0410 13:37:30.644976 18353 solver.cpp:218] Iteration 1176 (2.48154 iter/s, 4.83571s/12 iters), loss = 4.37241
I0410 13:37:30.645107 18353 solver.cpp:237]     Train net output #0: loss = 4.37241 (* 1 = 4.37241 loss)
I0410 13:37:30.645121 18353 sgd_solver.cpp:105] Iteration 1176, lr = 0.00792194
I0410 13:37:35.573251 18353 solver.cpp:218] Iteration 1188 (2.43507 iter/s, 4.928s/12 iters), loss = 4.40613
I0410 13:37:35.573300 18353 solver.cpp:237]     Train net output #0: loss = 4.40613 (* 1 = 4.40613 loss)
I0410 13:37:35.573312 18353 sgd_solver.cpp:105] Iteration 1188, lr = 0.00790313
I0410 13:37:40.533994 18353 solver.cpp:218] Iteration 1200 (2.41909 iter/s, 4.96054s/12 iters), loss = 4.58569
I0410 13:37:40.534050 18353 solver.cpp:237]     Train net output #0: loss = 4.58569 (* 1 = 4.58569 loss)
I0410 13:37:40.534062 18353 sgd_solver.cpp:105] Iteration 1200, lr = 0.00788437
I0410 13:37:45.471169 18353 solver.cpp:218] Iteration 1212 (2.43065 iter/s, 4.93696s/12 iters), loss = 4.41038
I0410 13:37:45.471222 18353 solver.cpp:237]     Train net output #0: loss = 4.41038 (* 1 = 4.41038 loss)
I0410 13:37:45.471232 18353 sgd_solver.cpp:105] Iteration 1212, lr = 0.00786565
I0410 13:37:45.757951 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:37:49.949860 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1224.caffemodel
I0410 13:37:50.502624 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1224.solverstate
I0410 13:37:50.986214 18353 solver.cpp:330] Iteration 1224, Testing net (#0)
I0410 13:37:50.986243 18353 net.cpp:676] Ignoring source layer train-data
I0410 13:37:55.041323 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:37:55.559275 18353 solver.cpp:397]     Test net output #0: accuracy = 0.0821078
I0410 13:37:55.559316 18353 solver.cpp:397]     Test net output #1: loss = 4.33798 (* 1 = 4.33798 loss)
I0410 13:37:55.640758 18353 solver.cpp:218] Iteration 1224 (1.18003 iter/s, 10.1692s/12 iters), loss = 4.43361
I0410 13:37:55.640801 18353 solver.cpp:237]     Train net output #0: loss = 4.43361 (* 1 = 4.43361 loss)
I0410 13:37:55.640811 18353 sgd_solver.cpp:105] Iteration 1224, lr = 0.00784697
I0410 13:37:59.775192 18353 solver.cpp:218] Iteration 1236 (2.90257 iter/s, 4.13426s/12 iters), loss = 4.6099
I0410 13:37:59.775240 18353 solver.cpp:237]     Train net output #0: loss = 4.6099 (* 1 = 4.6099 loss)
I0410 13:37:59.775251 18353 sgd_solver.cpp:105] Iteration 1236, lr = 0.00782834
I0410 13:38:04.626315 18353 solver.cpp:218] Iteration 1248 (2.47376 iter/s, 4.85092s/12 iters), loss = 4.18499
I0410 13:38:04.626439 18353 solver.cpp:237]     Train net output #0: loss = 4.18499 (* 1 = 4.18499 loss)
I0410 13:38:04.626456 18353 sgd_solver.cpp:105] Iteration 1248, lr = 0.00780976
I0410 13:38:09.543071 18353 solver.cpp:218] Iteration 1260 (2.44077 iter/s, 4.91648s/12 iters), loss = 4.39298
I0410 13:38:09.543125 18353 solver.cpp:237]     Train net output #0: loss = 4.39298 (* 1 = 4.39298 loss)
I0410 13:38:09.543138 18353 sgd_solver.cpp:105] Iteration 1260, lr = 0.00779122
I0410 13:38:14.472416 18353 solver.cpp:218] Iteration 1272 (2.4345 iter/s, 4.92914s/12 iters), loss = 4.3046
I0410 13:38:14.472456 18353 solver.cpp:237]     Train net output #0: loss = 4.3046 (* 1 = 4.3046 loss)
I0410 13:38:14.472465 18353 sgd_solver.cpp:105] Iteration 1272, lr = 0.00777272
I0410 13:38:19.426360 18353 solver.cpp:218] Iteration 1284 (2.42241 iter/s, 4.95375s/12 iters), loss = 4.3607
I0410 13:38:19.426411 18353 solver.cpp:237]     Train net output #0: loss = 4.3607 (* 1 = 4.3607 loss)
I0410 13:38:19.426425 18353 sgd_solver.cpp:105] Iteration 1284, lr = 0.00775426
I0410 13:38:24.272773 18353 solver.cpp:218] Iteration 1296 (2.47616 iter/s, 4.84621s/12 iters), loss = 4.22866
I0410 13:38:24.272830 18353 solver.cpp:237]     Train net output #0: loss = 4.22866 (* 1 = 4.22866 loss)
I0410 13:38:24.272841 18353 sgd_solver.cpp:105] Iteration 1296, lr = 0.00773585
I0410 13:38:29.202837 18353 solver.cpp:218] Iteration 1308 (2.43415 iter/s, 4.92985s/12 iters), loss = 4.39708
I0410 13:38:29.202890 18353 solver.cpp:237]     Train net output #0: loss = 4.39708 (* 1 = 4.39708 loss)
I0410 13:38:29.202904 18353 sgd_solver.cpp:105] Iteration 1308, lr = 0.00771749
I0410 13:38:31.676445 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:38:34.110857 18353 solver.cpp:218] Iteration 1320 (2.44508 iter/s, 4.90781s/12 iters), loss = 4.19353
I0410 13:38:34.110908 18353 solver.cpp:237]     Train net output #0: loss = 4.19353 (* 1 = 4.19353 loss)
I0410 13:38:34.110919 18353 sgd_solver.cpp:105] Iteration 1320, lr = 0.00769916
I0410 13:38:36.096741 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1326.caffemodel
I0410 13:38:36.396412 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1326.solverstate
I0410 13:38:36.589812 18353 solver.cpp:330] Iteration 1326, Testing net (#0)
I0410 13:38:36.589830 18353 net.cpp:676] Ignoring source layer train-data
I0410 13:38:40.607465 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:38:41.160753 18353 solver.cpp:397]     Test net output #0: accuracy = 0.0925245
I0410 13:38:41.160801 18353 solver.cpp:397]     Test net output #1: loss = 4.22254 (* 1 = 4.22254 loss)
I0410 13:38:43.092739 18353 solver.cpp:218] Iteration 1332 (1.33607 iter/s, 8.98156s/12 iters), loss = 4.08108
I0410 13:38:43.092788 18353 solver.cpp:237]     Train net output #0: loss = 4.08108 (* 1 = 4.08108 loss)
I0410 13:38:43.092798 18353 sgd_solver.cpp:105] Iteration 1332, lr = 0.00768088
I0410 13:38:48.032351 18353 solver.cpp:218] Iteration 1344 (2.42944 iter/s, 4.9394s/12 iters), loss = 4.26138
I0410 13:38:48.032408 18353 solver.cpp:237]     Train net output #0: loss = 4.26138 (* 1 = 4.26138 loss)
I0410 13:38:48.032421 18353 sgd_solver.cpp:105] Iteration 1344, lr = 0.00766265
I0410 13:38:52.917798 18353 solver.cpp:218] Iteration 1356 (2.45638 iter/s, 4.88524s/12 iters), loss = 4.31353
I0410 13:38:52.917843 18353 solver.cpp:237]     Train net output #0: loss = 4.31353 (* 1 = 4.31353 loss)
I0410 13:38:52.917852 18353 sgd_solver.cpp:105] Iteration 1356, lr = 0.00764446
I0410 13:38:57.325448 18353 blocking_queue.cpp:49] Waiting for data
I0410 13:38:57.770942 18353 solver.cpp:218] Iteration 1368 (2.47273 iter/s, 4.85294s/12 iters), loss = 4.20193
I0410 13:38:57.770992 18353 solver.cpp:237]     Train net output #0: loss = 4.20193 (* 1 = 4.20193 loss)
I0410 13:38:57.771003 18353 sgd_solver.cpp:105] Iteration 1368, lr = 0.00762631
I0410 13:39:02.608240 18353 solver.cpp:218] Iteration 1380 (2.48082 iter/s, 4.8371s/12 iters), loss = 4.21117
I0410 13:39:02.608279 18353 solver.cpp:237]     Train net output #0: loss = 4.21117 (* 1 = 4.21117 loss)
I0410 13:39:02.608289 18353 sgd_solver.cpp:105] Iteration 1380, lr = 0.0076082
I0410 13:39:07.547163 18353 solver.cpp:218] Iteration 1392 (2.42978 iter/s, 4.93873s/12 iters), loss = 4.16942
I0410 13:39:07.547271 18353 solver.cpp:237]     Train net output #0: loss = 4.16942 (* 1 = 4.16942 loss)
I0410 13:39:07.547281 18353 sgd_solver.cpp:105] Iteration 1392, lr = 0.00759014
I0410 13:39:12.453647 18353 solver.cpp:218] Iteration 1404 (2.44587 iter/s, 4.90622s/12 iters), loss = 4.22309
I0410 13:39:12.453701 18353 solver.cpp:237]     Train net output #0: loss = 4.22309 (* 1 = 4.22309 loss)
I0410 13:39:12.453712 18353 sgd_solver.cpp:105] Iteration 1404, lr = 0.00757212
I0410 13:39:17.010244 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:39:17.359277 18353 solver.cpp:218] Iteration 1416 (2.44627 iter/s, 4.90542s/12 iters), loss = 3.94939
I0410 13:39:17.359330 18353 solver.cpp:237]     Train net output #0: loss = 3.94939 (* 1 = 3.94939 loss)
I0410 13:39:17.359344 18353 sgd_solver.cpp:105] Iteration 1416, lr = 0.00755414
I0410 13:39:21.817418 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1428.caffemodel
I0410 13:39:22.135938 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1428.solverstate
I0410 13:39:22.343071 18353 solver.cpp:330] Iteration 1428, Testing net (#0)
I0410 13:39:22.343102 18353 net.cpp:676] Ignoring source layer train-data
I0410 13:39:26.068014 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:39:26.656471 18353 solver.cpp:397]     Test net output #0: accuracy = 0.113358
I0410 13:39:26.656498 18353 solver.cpp:397]     Test net output #1: loss = 4.07387 (* 1 = 4.07387 loss)
I0410 13:39:26.737921 18353 solver.cpp:218] Iteration 1428 (1.27955 iter/s, 9.37831s/12 iters), loss = 3.9925
I0410 13:39:26.737987 18353 solver.cpp:237]     Train net output #0: loss = 3.9925 (* 1 = 3.9925 loss)
I0410 13:39:26.737998 18353 sgd_solver.cpp:105] Iteration 1428, lr = 0.0075362
I0410 13:39:30.876267 18353 solver.cpp:218] Iteration 1440 (2.89985 iter/s, 4.13815s/12 iters), loss = 4.00927
I0410 13:39:30.876308 18353 solver.cpp:237]     Train net output #0: loss = 4.00927 (* 1 = 4.00927 loss)
I0410 13:39:30.876318 18353 sgd_solver.cpp:105] Iteration 1440, lr = 0.00751831
I0410 13:39:35.772658 18353 solver.cpp:218] Iteration 1452 (2.45088 iter/s, 4.8962s/12 iters), loss = 4.25292
I0410 13:39:35.772702 18353 solver.cpp:237]     Train net output #0: loss = 4.25292 (* 1 = 4.25292 loss)
I0410 13:39:35.772711 18353 sgd_solver.cpp:105] Iteration 1452, lr = 0.00750046
I0410 13:39:40.680783 18353 solver.cpp:218] Iteration 1464 (2.44503 iter/s, 4.90792s/12 iters), loss = 4.13796
I0410 13:39:40.680928 18353 solver.cpp:237]     Train net output #0: loss = 4.13796 (* 1 = 4.13796 loss)
I0410 13:39:40.680941 18353 sgd_solver.cpp:105] Iteration 1464, lr = 0.00748265
I0410 13:39:45.617790 18353 solver.cpp:218] Iteration 1476 (2.43077 iter/s, 4.93671s/12 iters), loss = 3.93171
I0410 13:39:45.617837 18353 solver.cpp:237]     Train net output #0: loss = 3.93171 (* 1 = 3.93171 loss)
I0410 13:39:45.617847 18353 sgd_solver.cpp:105] Iteration 1476, lr = 0.00746489
I0410 13:39:50.546298 18353 solver.cpp:218] Iteration 1488 (2.43492 iter/s, 4.9283s/12 iters), loss = 3.89786
I0410 13:39:50.546350 18353 solver.cpp:237]     Train net output #0: loss = 3.89786 (* 1 = 3.89786 loss)
I0410 13:39:50.546362 18353 sgd_solver.cpp:105] Iteration 1488, lr = 0.00744716
I0410 13:39:55.458495 18353 solver.cpp:218] Iteration 1500 (2.443 iter/s, 4.91199s/12 iters), loss = 3.89558
I0410 13:39:55.458544 18353 solver.cpp:237]     Train net output #0: loss = 3.89558 (* 1 = 3.89558 loss)
I0410 13:39:55.458556 18353 sgd_solver.cpp:105] Iteration 1500, lr = 0.00742948
I0410 13:40:00.541422 18353 solver.cpp:218] Iteration 1512 (2.36094 iter/s, 5.08272s/12 iters), loss = 4.24201
I0410 13:40:00.541481 18353 solver.cpp:237]     Train net output #0: loss = 4.24201 (* 1 = 4.24201 loss)
I0410 13:40:00.541493 18353 sgd_solver.cpp:105] Iteration 1512, lr = 0.00741184
I0410 13:40:02.387243 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:40:05.494568 18353 solver.cpp:218] Iteration 1524 (2.42281 iter/s, 4.95294s/12 iters), loss = 3.88614
I0410 13:40:05.494621 18353 solver.cpp:237]     Train net output #0: loss = 3.88614 (* 1 = 3.88614 loss)
I0410 13:40:05.494635 18353 sgd_solver.cpp:105] Iteration 1524, lr = 0.00739425
I0410 13:40:07.469264 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1530.caffemodel
I0410 13:40:07.804294 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1530.solverstate
I0410 13:40:08.016763 18353 solver.cpp:330] Iteration 1530, Testing net (#0)
I0410 13:40:08.016788 18353 net.cpp:676] Ignoring source layer train-data
I0410 13:40:11.729600 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:40:12.464538 18353 solver.cpp:397]     Test net output #0: accuracy = 0.137255
I0410 13:40:12.464588 18353 solver.cpp:397]     Test net output #1: loss = 3.90476 (* 1 = 3.90476 loss)
I0410 13:40:14.335297 18353 solver.cpp:218] Iteration 1536 (1.3574 iter/s, 8.84041s/12 iters), loss = 3.96422
I0410 13:40:14.335341 18353 solver.cpp:237]     Train net output #0: loss = 3.96422 (* 1 = 3.96422 loss)
I0410 13:40:14.335351 18353 sgd_solver.cpp:105] Iteration 1536, lr = 0.00737669
I0410 13:40:19.278291 18353 solver.cpp:218] Iteration 1548 (2.42778 iter/s, 4.94279s/12 iters), loss = 3.58958
I0410 13:40:19.278337 18353 solver.cpp:237]     Train net output #0: loss = 3.58958 (* 1 = 3.58958 loss)
I0410 13:40:19.278347 18353 sgd_solver.cpp:105] Iteration 1548, lr = 0.00735918
I0410 13:40:24.118413 18353 solver.cpp:218] Iteration 1560 (2.47938 iter/s, 4.83992s/12 iters), loss = 3.97728
I0410 13:40:24.118463 18353 solver.cpp:237]     Train net output #0: loss = 3.97728 (* 1 = 3.97728 loss)
I0410 13:40:24.118472 18353 sgd_solver.cpp:105] Iteration 1560, lr = 0.00734171
I0410 13:40:29.011102 18353 solver.cpp:218] Iteration 1572 (2.45274 iter/s, 4.89249s/12 iters), loss = 3.94714
I0410 13:40:29.011143 18353 solver.cpp:237]     Train net output #0: loss = 3.94714 (* 1 = 3.94714 loss)
I0410 13:40:29.011153 18353 sgd_solver.cpp:105] Iteration 1572, lr = 0.00732427
I0410 13:40:33.987452 18353 solver.cpp:218] Iteration 1584 (2.4115 iter/s, 4.97615s/12 iters), loss = 3.82087
I0410 13:40:33.987505 18353 solver.cpp:237]     Train net output #0: loss = 3.82087 (* 1 = 3.82087 loss)
I0410 13:40:33.987517 18353 sgd_solver.cpp:105] Iteration 1584, lr = 0.00730688
I0410 13:40:38.878487 18353 solver.cpp:218] Iteration 1596 (2.45357 iter/s, 4.89083s/12 iters), loss = 4.012
I0410 13:40:38.878545 18353 solver.cpp:237]     Train net output #0: loss = 4.012 (* 1 = 4.012 loss)
I0410 13:40:38.878556 18353 sgd_solver.cpp:105] Iteration 1596, lr = 0.00728954
I0410 13:40:43.726186 18353 solver.cpp:218] Iteration 1608 (2.47551 iter/s, 4.84749s/12 iters), loss = 3.94032
I0410 13:40:43.726284 18353 solver.cpp:237]     Train net output #0: loss = 3.94032 (* 1 = 3.94032 loss)
I0410 13:40:43.726296 18353 sgd_solver.cpp:105] Iteration 1608, lr = 0.00727223
I0410 13:40:47.515481 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:40:48.551498 18353 solver.cpp:218] Iteration 1620 (2.48702 iter/s, 4.82506s/12 iters), loss = 3.70949
I0410 13:40:48.551556 18353 solver.cpp:237]     Train net output #0: loss = 3.70949 (* 1 = 3.70949 loss)
I0410 13:40:48.551569 18353 sgd_solver.cpp:105] Iteration 1620, lr = 0.00725496
I0410 13:40:53.048491 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1632.caffemodel
I0410 13:40:53.587656 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1632.solverstate
I0410 13:40:54.727617 18353 solver.cpp:330] Iteration 1632, Testing net (#0)
I0410 13:40:54.727634 18353 net.cpp:676] Ignoring source layer train-data
I0410 13:40:58.506417 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:40:59.173230 18353 solver.cpp:397]     Test net output #0: accuracy = 0.134804
I0410 13:40:59.173285 18353 solver.cpp:397]     Test net output #1: loss = 3.85261 (* 1 = 3.85261 loss)
I0410 13:40:59.254660 18353 solver.cpp:218] Iteration 1632 (1.1212 iter/s, 10.7028s/12 iters), loss = 3.87986
I0410 13:40:59.254710 18353 solver.cpp:237]     Train net output #0: loss = 3.87986 (* 1 = 3.87986 loss)
I0410 13:40:59.254721 18353 sgd_solver.cpp:105] Iteration 1632, lr = 0.00723774
I0410 13:41:03.418031 18353 solver.cpp:218] Iteration 1644 (2.88241 iter/s, 4.16318s/12 iters), loss = 3.79865
I0410 13:41:03.418089 18353 solver.cpp:237]     Train net output #0: loss = 3.79865 (* 1 = 3.79865 loss)
I0410 13:41:03.418102 18353 sgd_solver.cpp:105] Iteration 1644, lr = 0.00722056
I0410 13:41:08.300027 18353 solver.cpp:218] Iteration 1656 (2.45811 iter/s, 4.88179s/12 iters), loss = 3.72162
I0410 13:41:08.300071 18353 solver.cpp:237]     Train net output #0: loss = 3.72162 (* 1 = 3.72162 loss)
I0410 13:41:08.300081 18353 sgd_solver.cpp:105] Iteration 1656, lr = 0.00720341
I0410 13:41:13.156759 18353 solver.cpp:218] Iteration 1668 (2.4709 iter/s, 4.85653s/12 iters), loss = 3.82464
I0410 13:41:13.156822 18353 solver.cpp:237]     Train net output #0: loss = 3.82464 (* 1 = 3.82464 loss)
I0410 13:41:13.156836 18353 sgd_solver.cpp:105] Iteration 1668, lr = 0.00718631
I0410 13:41:17.953485 18353 solver.cpp:218] Iteration 1680 (2.50182 iter/s, 4.79651s/12 iters), loss = 3.6154
I0410 13:41:17.953616 18353 solver.cpp:237]     Train net output #0: loss = 3.6154 (* 1 = 3.6154 loss)
I0410 13:41:17.953629 18353 sgd_solver.cpp:105] Iteration 1680, lr = 0.00716925
I0410 13:41:22.817329 18353 solver.cpp:218] Iteration 1692 (2.46733 iter/s, 4.86356s/12 iters), loss = 3.70805
I0410 13:41:22.817382 18353 solver.cpp:237]     Train net output #0: loss = 3.70805 (* 1 = 3.70805 loss)
I0410 13:41:22.817394 18353 sgd_solver.cpp:105] Iteration 1692, lr = 0.00715223
I0410 13:41:28.040139 18353 solver.cpp:218] Iteration 1704 (2.29771 iter/s, 5.2226s/12 iters), loss = 3.63719
I0410 13:41:28.040184 18353 solver.cpp:237]     Train net output #0: loss = 3.63719 (* 1 = 3.63719 loss)
I0410 13:41:28.040195 18353 sgd_solver.cpp:105] Iteration 1704, lr = 0.00713525
I0410 13:41:32.973126 18353 solver.cpp:218] Iteration 1716 (2.4327 iter/s, 4.93279s/12 iters), loss = 3.76967
I0410 13:41:32.973171 18353 solver.cpp:237]     Train net output #0: loss = 3.76967 (* 1 = 3.76967 loss)
I0410 13:41:32.973183 18353 sgd_solver.cpp:105] Iteration 1716, lr = 0.00711831
I0410 13:41:34.024582 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:41:37.854669 18353 solver.cpp:218] Iteration 1728 (2.45834 iter/s, 4.88134s/12 iters), loss = 3.58911
I0410 13:41:37.854710 18353 solver.cpp:237]     Train net output #0: loss = 3.58911 (* 1 = 3.58911 loss)
I0410 13:41:37.854719 18353 sgd_solver.cpp:105] Iteration 1728, lr = 0.00710141
I0410 13:41:39.882814 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1734.caffemodel
I0410 13:41:40.201253 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1734.solverstate
I0410 13:41:40.414444 18353 solver.cpp:330] Iteration 1734, Testing net (#0)
I0410 13:41:40.414474 18353 net.cpp:676] Ignoring source layer train-data
I0410 13:41:44.277782 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:41:44.980921 18353 solver.cpp:397]     Test net output #0: accuracy = 0.14277
I0410 13:41:44.980959 18353 solver.cpp:397]     Test net output #1: loss = 3.87625 (* 1 = 3.87625 loss)
I0410 13:41:46.837123 18353 solver.cpp:218] Iteration 1740 (1.33598 iter/s, 8.98214s/12 iters), loss = 3.84722
I0410 13:41:46.837177 18353 solver.cpp:237]     Train net output #0: loss = 3.84722 (* 1 = 3.84722 loss)
I0410 13:41:46.837188 18353 sgd_solver.cpp:105] Iteration 1740, lr = 0.00708455
I0410 13:41:51.844434 18353 solver.cpp:218] Iteration 1752 (2.3966 iter/s, 5.0071s/12 iters), loss = 3.40985
I0410 13:41:51.844528 18353 solver.cpp:237]     Train net output #0: loss = 3.40985 (* 1 = 3.40985 loss)
I0410 13:41:51.844542 18353 sgd_solver.cpp:105] Iteration 1752, lr = 0.00706773
I0410 13:41:56.829865 18353 solver.cpp:218] Iteration 1764 (2.40713 iter/s, 4.98519s/12 iters), loss = 3.97024
I0410 13:41:56.829910 18353 solver.cpp:237]     Train net output #0: loss = 3.97024 (* 1 = 3.97024 loss)
I0410 13:41:56.829921 18353 sgd_solver.cpp:105] Iteration 1764, lr = 0.00705094
I0410 13:42:02.075752 18353 solver.cpp:218] Iteration 1776 (2.2876 iter/s, 5.24567s/12 iters), loss = 3.77286
I0410 13:42:02.075809 18353 solver.cpp:237]     Train net output #0: loss = 3.77286 (* 1 = 3.77286 loss)
I0410 13:42:02.075822 18353 sgd_solver.cpp:105] Iteration 1776, lr = 0.0070342
I0410 13:42:06.938032 18353 solver.cpp:218] Iteration 1788 (2.46808 iter/s, 4.86208s/12 iters), loss = 3.76638
I0410 13:42:06.938074 18353 solver.cpp:237]     Train net output #0: loss = 3.76638 (* 1 = 3.76638 loss)
I0410 13:42:06.938084 18353 sgd_solver.cpp:105] Iteration 1788, lr = 0.0070175
I0410 13:42:11.837436 18353 solver.cpp:218] Iteration 1800 (2.44938 iter/s, 4.89921s/12 iters), loss = 3.57106
I0410 13:42:11.837489 18353 solver.cpp:237]     Train net output #0: loss = 3.57106 (* 1 = 3.57106 loss)
I0410 13:42:11.837500 18353 sgd_solver.cpp:105] Iteration 1800, lr = 0.00700084
I0410 13:42:16.718386 18353 solver.cpp:218] Iteration 1812 (2.45864 iter/s, 4.88075s/12 iters), loss = 3.52729
I0410 13:42:16.718434 18353 solver.cpp:237]     Train net output #0: loss = 3.52729 (* 1 = 3.52729 loss)
I0410 13:42:16.718444 18353 sgd_solver.cpp:105] Iteration 1812, lr = 0.00698422
I0410 13:42:19.894408 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:42:21.687791 18353 solver.cpp:218] Iteration 1824 (2.41488 iter/s, 4.9692s/12 iters), loss = 3.67844
I0410 13:42:21.687844 18353 solver.cpp:237]     Train net output #0: loss = 3.67844 (* 1 = 3.67844 loss)
I0410 13:42:21.687856 18353 sgd_solver.cpp:105] Iteration 1824, lr = 0.00696764
I0410 13:42:26.144304 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1836.caffemodel
I0410 13:42:26.472425 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1836.solverstate
I0410 13:42:26.679661 18353 solver.cpp:330] Iteration 1836, Testing net (#0)
I0410 13:42:26.679682 18353 net.cpp:676] Ignoring source layer train-data
I0410 13:42:30.356231 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:42:31.101539 18353 solver.cpp:397]     Test net output #0: accuracy = 0.181985
I0410 13:42:31.101572 18353 solver.cpp:397]     Test net output #1: loss = 3.57133 (* 1 = 3.57133 loss)
I0410 13:42:31.182651 18353 solver.cpp:218] Iteration 1836 (1.26389 iter/s, 9.49453s/12 iters), loss = 3.61837
I0410 13:42:31.182695 18353 solver.cpp:237]     Train net output #0: loss = 3.61837 (* 1 = 3.61837 loss)
I0410 13:42:31.182705 18353 sgd_solver.cpp:105] Iteration 1836, lr = 0.0069511
I0410 13:42:35.476176 18353 solver.cpp:218] Iteration 1848 (2.79502 iter/s, 4.29334s/12 iters), loss = 3.5673
I0410 13:42:35.476222 18353 solver.cpp:237]     Train net output #0: loss = 3.5673 (* 1 = 3.5673 loss)
I0410 13:42:35.476231 18353 sgd_solver.cpp:105] Iteration 1848, lr = 0.00693459
I0410 13:42:40.317862 18353 solver.cpp:218] Iteration 1860 (2.47858 iter/s, 4.84148s/12 iters), loss = 3.50609
I0410 13:42:40.317921 18353 solver.cpp:237]     Train net output #0: loss = 3.50609 (* 1 = 3.50609 loss)
I0410 13:42:40.317936 18353 sgd_solver.cpp:105] Iteration 1860, lr = 0.00691813
I0410 13:42:45.256816 18353 solver.cpp:218] Iteration 1872 (2.42977 iter/s, 4.93875s/12 iters), loss = 3.50338
I0410 13:42:45.256868 18353 solver.cpp:237]     Train net output #0: loss = 3.50338 (* 1 = 3.50338 loss)
I0410 13:42:45.256880 18353 sgd_solver.cpp:105] Iteration 1872, lr = 0.0069017
I0410 13:42:50.117719 18353 solver.cpp:218] Iteration 1884 (2.46878 iter/s, 4.8607s/12 iters), loss = 3.72516
I0410 13:42:50.117775 18353 solver.cpp:237]     Train net output #0: loss = 3.72516 (* 1 = 3.72516 loss)
I0410 13:42:50.117789 18353 sgd_solver.cpp:105] Iteration 1884, lr = 0.00688532
I0410 13:42:55.027396 18353 solver.cpp:218] Iteration 1896 (2.44425 iter/s, 4.90948s/12 iters), loss = 3.54669
I0410 13:42:55.027437 18353 solver.cpp:237]     Train net output #0: loss = 3.54669 (* 1 = 3.54669 loss)
I0410 13:42:55.027446 18353 sgd_solver.cpp:105] Iteration 1896, lr = 0.00686897
I0410 13:42:59.966117 18353 solver.cpp:218] Iteration 1908 (2.42988 iter/s, 4.93852s/12 iters), loss = 3.47008
I0410 13:42:59.966264 18353 solver.cpp:237]     Train net output #0: loss = 3.47008 (* 1 = 3.47008 loss)
I0410 13:42:59.966279 18353 sgd_solver.cpp:105] Iteration 1908, lr = 0.00685266
I0410 13:43:04.878981 18353 solver.cpp:218] Iteration 1920 (2.44271 iter/s, 4.91257s/12 iters), loss = 3.47177
I0410 13:43:04.879034 18353 solver.cpp:237]     Train net output #0: loss = 3.47177 (* 1 = 3.47177 loss)
I0410 13:43:04.879047 18353 sgd_solver.cpp:105] Iteration 1920, lr = 0.00683639
I0410 13:43:05.200683 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:43:09.914070 18353 solver.cpp:218] Iteration 1932 (2.38337 iter/s, 5.03488s/12 iters), loss = 3.45598
I0410 13:43:09.914124 18353 solver.cpp:237]     Train net output #0: loss = 3.45598 (* 1 = 3.45598 loss)
I0410 13:43:09.914136 18353 sgd_solver.cpp:105] Iteration 1932, lr = 0.00682016
I0410 13:43:11.965885 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1938.caffemodel
I0410 13:43:12.583484 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1938.solverstate
I0410 13:43:12.794668 18353 solver.cpp:330] Iteration 1938, Testing net (#0)
I0410 13:43:12.794689 18353 net.cpp:676] Ignoring source layer train-data
I0410 13:43:16.462787 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:43:17.245687 18353 solver.cpp:397]     Test net output #0: accuracy = 0.205882
I0410 13:43:17.245723 18353 solver.cpp:397]     Test net output #1: loss = 3.39654 (* 1 = 3.39654 loss)
I0410 13:43:19.125082 18353 solver.cpp:218] Iteration 1944 (1.30283 iter/s, 9.21069s/12 iters), loss = 3.3937
I0410 13:43:19.125131 18353 solver.cpp:237]     Train net output #0: loss = 3.3937 (* 1 = 3.3937 loss)
I0410 13:43:19.125142 18353 sgd_solver.cpp:105] Iteration 1944, lr = 0.00680397
I0410 13:43:24.038681 18353 solver.cpp:218] Iteration 1956 (2.4423 iter/s, 4.91339s/12 iters), loss = 3.51245
I0410 13:43:24.038736 18353 solver.cpp:237]     Train net output #0: loss = 3.51245 (* 1 = 3.51245 loss)
I0410 13:43:24.038748 18353 sgd_solver.cpp:105] Iteration 1956, lr = 0.00678782
I0410 13:43:28.933876 18353 solver.cpp:218] Iteration 1968 (2.45149 iter/s, 4.89498s/12 iters), loss = 3.3223
I0410 13:43:28.933933 18353 solver.cpp:237]     Train net output #0: loss = 3.3223 (* 1 = 3.3223 loss)
I0410 13:43:28.933944 18353 sgd_solver.cpp:105] Iteration 1968, lr = 0.0067717
I0410 13:43:33.843350 18353 solver.cpp:218] Iteration 1980 (2.44435 iter/s, 4.90927s/12 iters), loss = 3.47218
I0410 13:43:33.843729 18353 solver.cpp:237]     Train net output #0: loss = 3.47218 (* 1 = 3.47218 loss)
I0410 13:43:33.843742 18353 sgd_solver.cpp:105] Iteration 1980, lr = 0.00675562
I0410 13:43:38.981032 18353 solver.cpp:218] Iteration 1992 (2.33593 iter/s, 5.13715s/12 iters), loss = 3.44798
I0410 13:43:38.981092 18353 solver.cpp:237]     Train net output #0: loss = 3.44798 (* 1 = 3.44798 loss)
I0410 13:43:38.981106 18353 sgd_solver.cpp:105] Iteration 1992, lr = 0.00673958
I0410 13:43:43.805471 18353 solver.cpp:218] Iteration 2004 (2.48744 iter/s, 4.82423s/12 iters), loss = 3.23588
I0410 13:43:43.805527 18353 solver.cpp:237]     Train net output #0: loss = 3.23588 (* 1 = 3.23588 loss)
I0410 13:43:43.805541 18353 sgd_solver.cpp:105] Iteration 2004, lr = 0.00672358
I0410 13:43:48.728796 18353 solver.cpp:218] Iteration 2016 (2.43748 iter/s, 4.92312s/12 iters), loss = 3.44223
I0410 13:43:48.728847 18353 solver.cpp:237]     Train net output #0: loss = 3.44223 (* 1 = 3.44223 loss)
I0410 13:43:48.728861 18353 sgd_solver.cpp:105] Iteration 2016, lr = 0.00670762
I0410 13:43:51.216867 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:43:53.614385 18353 solver.cpp:218] Iteration 2028 (2.4563 iter/s, 4.88539s/12 iters), loss = 2.93948
I0410 13:43:53.614442 18353 solver.cpp:237]     Train net output #0: loss = 2.93948 (* 1 = 2.93948 loss)
I0410 13:43:53.614454 18353 sgd_solver.cpp:105] Iteration 2028, lr = 0.00669169
I0410 13:43:58.062755 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2040.caffemodel
I0410 13:43:58.346411 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2040.solverstate
I0410 13:43:58.540051 18353 solver.cpp:330] Iteration 2040, Testing net (#0)
I0410 13:43:58.540076 18353 net.cpp:676] Ignoring source layer train-data
I0410 13:44:02.197772 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:44:03.036087 18353 solver.cpp:397]     Test net output #0: accuracy = 0.224265
I0410 13:44:03.036150 18353 solver.cpp:397]     Test net output #1: loss = 3.28762 (* 1 = 3.28762 loss)
I0410 13:44:03.117566 18353 solver.cpp:218] Iteration 2040 (1.26278 iter/s, 9.50285s/12 iters), loss = 3.17692
I0410 13:44:03.117614 18353 solver.cpp:237]     Train net output #0: loss = 3.17692 (* 1 = 3.17692 loss)
I0410 13:44:03.117627 18353 sgd_solver.cpp:105] Iteration 2040, lr = 0.00667581
I0410 13:44:07.486397 18353 solver.cpp:218] Iteration 2052 (2.74684 iter/s, 4.36865s/12 iters), loss = 3.36455
I0410 13:44:07.486559 18353 solver.cpp:237]     Train net output #0: loss = 3.36455 (* 1 = 3.36455 loss)
I0410 13:44:07.486572 18353 sgd_solver.cpp:105] Iteration 2052, lr = 0.00665996
I0410 13:44:07.486806 18353 blocking_queue.cpp:49] Waiting for data
I0410 13:44:12.308060 18353 solver.cpp:218] Iteration 2064 (2.48893 iter/s, 4.82136s/12 iters), loss = 3.27496
I0410 13:44:12.308107 18353 solver.cpp:237]     Train net output #0: loss = 3.27496 (* 1 = 3.27496 loss)
I0410 13:44:12.308117 18353 sgd_solver.cpp:105] Iteration 2064, lr = 0.00664414
I0410 13:44:17.437088 18353 solver.cpp:218] Iteration 2076 (2.33972 iter/s, 5.12882s/12 iters), loss = 3.28948
I0410 13:44:17.437134 18353 solver.cpp:237]     Train net output #0: loss = 3.28948 (* 1 = 3.28948 loss)
I0410 13:44:17.437145 18353 sgd_solver.cpp:105] Iteration 2076, lr = 0.00662837
I0410 13:44:22.360395 18353 solver.cpp:218] Iteration 2088 (2.43749 iter/s, 4.9231s/12 iters), loss = 3.16393
I0410 13:44:22.360450 18353 solver.cpp:237]     Train net output #0: loss = 3.16393 (* 1 = 3.16393 loss)
I0410 13:44:22.360462 18353 sgd_solver.cpp:105] Iteration 2088, lr = 0.00661263
I0410 13:44:27.189093 18353 solver.cpp:218] Iteration 2100 (2.48525 iter/s, 4.82849s/12 iters), loss = 3.349
I0410 13:44:27.189141 18353 solver.cpp:237]     Train net output #0: loss = 3.349 (* 1 = 3.349 loss)
I0410 13:44:27.189149 18353 sgd_solver.cpp:105] Iteration 2100, lr = 0.00659693
I0410 13:44:32.079792 18353 solver.cpp:218] Iteration 2112 (2.45374 iter/s, 4.8905s/12 iters), loss = 3.25057
I0410 13:44:32.079834 18353 solver.cpp:237]     Train net output #0: loss = 3.25057 (* 1 = 3.25057 loss)
I0410 13:44:32.079843 18353 sgd_solver.cpp:105] Iteration 2112, lr = 0.00658127
I0410 13:44:36.593744 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:44:36.906211 18353 solver.cpp:218] Iteration 2124 (2.48642 iter/s, 4.82622s/12 iters), loss = 3.05907
I0410 13:44:36.906261 18353 solver.cpp:237]     Train net output #0: loss = 3.05907 (* 1 = 3.05907 loss)
I0410 13:44:36.906272 18353 sgd_solver.cpp:105] Iteration 2124, lr = 0.00656564
I0410 13:44:41.836755 18353 solver.cpp:218] Iteration 2136 (2.43391 iter/s, 4.93034s/12 iters), loss = 3.15181
I0410 13:44:41.836851 18353 solver.cpp:237]     Train net output #0: loss = 3.15181 (* 1 = 3.15181 loss)
I0410 13:44:41.836861 18353 sgd_solver.cpp:105] Iteration 2136, lr = 0.00655006
I0410 13:44:43.828114 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2142.caffemodel
I0410 13:44:44.131263 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2142.solverstate
I0410 13:44:44.325698 18353 solver.cpp:330] Iteration 2142, Testing net (#0)
I0410 13:44:44.325721 18353 net.cpp:676] Ignoring source layer train-data
I0410 13:44:47.943614 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:44:48.830090 18353 solver.cpp:397]     Test net output #0: accuracy = 0.253676
I0410 13:44:48.830140 18353 solver.cpp:397]     Test net output #1: loss = 3.22863 (* 1 = 3.22863 loss)
I0410 13:44:50.762620 18353 solver.cpp:218] Iteration 2148 (1.34446 iter/s, 8.9255s/12 iters), loss = 3.216
I0410 13:44:50.762666 18353 solver.cpp:237]     Train net output #0: loss = 3.216 (* 1 = 3.216 loss)
I0410 13:44:50.762678 18353 sgd_solver.cpp:105] Iteration 2148, lr = 0.00653451
I0410 13:44:55.708389 18353 solver.cpp:218] Iteration 2160 (2.42642 iter/s, 4.94557s/12 iters), loss = 3.47914
I0410 13:44:55.708444 18353 solver.cpp:237]     Train net output #0: loss = 3.47914 (* 1 = 3.47914 loss)
I0410 13:44:55.708458 18353 sgd_solver.cpp:105] Iteration 2160, lr = 0.00651899
I0410 13:45:00.649972 18353 solver.cpp:218] Iteration 2172 (2.42848 iter/s, 4.94136s/12 iters), loss = 3.01132
I0410 13:45:00.650030 18353 solver.cpp:237]     Train net output #0: loss = 3.01132 (* 1 = 3.01132 loss)
I0410 13:45:00.650043 18353 sgd_solver.cpp:105] Iteration 2172, lr = 0.00650351
I0410 13:45:05.515100 18353 solver.cpp:218] Iteration 2184 (2.46664 iter/s, 4.86492s/12 iters), loss = 2.95343
I0410 13:45:05.515154 18353 solver.cpp:237]     Train net output #0: loss = 2.95343 (* 1 = 2.95343 loss)
I0410 13:45:05.515166 18353 sgd_solver.cpp:105] Iteration 2184, lr = 0.00648807
I0410 13:45:10.449338 18353 solver.cpp:218] Iteration 2196 (2.43209 iter/s, 4.93403s/12 iters), loss = 2.96801
I0410 13:45:10.449391 18353 solver.cpp:237]     Train net output #0: loss = 2.96801 (* 1 = 2.96801 loss)
I0410 13:45:10.449404 18353 sgd_solver.cpp:105] Iteration 2196, lr = 0.00647267
I0410 13:45:15.373298 18353 solver.cpp:218] Iteration 2208 (2.43718 iter/s, 4.92373s/12 iters), loss = 2.91968
I0410 13:45:15.373440 18353 solver.cpp:237]     Train net output #0: loss = 2.91968 (* 1 = 2.91968 loss)
I0410 13:45:15.373454 18353 sgd_solver.cpp:105] Iteration 2208, lr = 0.0064573
I0410 13:45:20.264772 18353 solver.cpp:218] Iteration 2220 (2.45339 iter/s, 4.89119s/12 iters), loss = 2.85161
I0410 13:45:20.264813 18353 solver.cpp:237]     Train net output #0: loss = 2.85161 (* 1 = 2.85161 loss)
I0410 13:45:20.264823 18353 sgd_solver.cpp:105] Iteration 2220, lr = 0.00644197
I0410 13:45:22.042301 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:45:25.180999 18353 solver.cpp:218] Iteration 2232 (2.44099 iter/s, 4.91604s/12 iters), loss = 2.79306
I0410 13:45:25.181052 18353 solver.cpp:237]     Train net output #0: loss = 2.79306 (* 1 = 2.79306 loss)
I0410 13:45:25.181066 18353 sgd_solver.cpp:105] Iteration 2232, lr = 0.00642668
I0410 13:45:29.647581 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2244.caffemodel
I0410 13:45:29.967653 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2244.solverstate
I0410 13:45:30.175951 18353 solver.cpp:330] Iteration 2244, Testing net (#0)
I0410 13:45:30.175974 18353 net.cpp:676] Ignoring source layer train-data
I0410 13:45:33.837415 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:45:34.765846 18353 solver.cpp:397]     Test net output #0: accuracy = 0.266544
I0410 13:45:34.765890 18353 solver.cpp:397]     Test net output #1: loss = 3.08475 (* 1 = 3.08475 loss)
I0410 13:45:34.847196 18353 solver.cpp:218] Iteration 2244 (1.24148 iter/s, 9.66586s/12 iters), loss = 3.07465
I0410 13:45:34.847245 18353 solver.cpp:237]     Train net output #0: loss = 3.07465 (* 1 = 3.07465 loss)
I0410 13:45:34.847255 18353 sgd_solver.cpp:105] Iteration 2244, lr = 0.00641142
I0410 13:45:39.102602 18353 solver.cpp:218] Iteration 2256 (2.82007 iter/s, 4.25522s/12 iters), loss = 2.99262
I0410 13:45:39.102654 18353 solver.cpp:237]     Train net output #0: loss = 2.99262 (* 1 = 2.99262 loss)
I0410 13:45:39.102666 18353 sgd_solver.cpp:105] Iteration 2256, lr = 0.0063962
I0410 13:45:44.015529 18353 solver.cpp:218] Iteration 2268 (2.44264 iter/s, 4.91272s/12 iters), loss = 3.19675
I0410 13:45:44.015578 18353 solver.cpp:237]     Train net output #0: loss = 3.19675 (* 1 = 3.19675 loss)
I0410 13:45:44.015589 18353 sgd_solver.cpp:105] Iteration 2268, lr = 0.00638101
I0410 13:45:48.911358 18353 solver.cpp:218] Iteration 2280 (2.45117 iter/s, 4.89563s/12 iters), loss = 2.86009
I0410 13:45:48.911422 18353 solver.cpp:237]     Train net output #0: loss = 2.86009 (* 1 = 2.86009 loss)
I0410 13:45:48.911432 18353 sgd_solver.cpp:105] Iteration 2280, lr = 0.00636586
I0410 13:45:53.847028 18353 solver.cpp:218] Iteration 2292 (2.43139 iter/s, 4.93545s/12 iters), loss = 2.92611
I0410 13:45:53.847075 18353 solver.cpp:237]     Train net output #0: loss = 2.92611 (* 1 = 2.92611 loss)
I0410 13:45:53.847086 18353 sgd_solver.cpp:105] Iteration 2292, lr = 0.00635075
I0410 13:45:58.773135 18353 solver.cpp:218] Iteration 2304 (2.4361 iter/s, 4.9259s/12 iters), loss = 2.84746
I0410 13:45:58.773195 18353 solver.cpp:237]     Train net output #0: loss = 2.84746 (* 1 = 2.84746 loss)
I0410 13:45:58.773206 18353 sgd_solver.cpp:105] Iteration 2304, lr = 0.00633567
I0410 13:46:03.703402 18353 solver.cpp:218] Iteration 2316 (2.43405 iter/s, 4.93005s/12 iters), loss = 2.95571
I0410 13:46:03.703456 18353 solver.cpp:237]     Train net output #0: loss = 2.95571 (* 1 = 2.95571 loss)
I0410 13:46:03.703469 18353 sgd_solver.cpp:105] Iteration 2316, lr = 0.00632063
I0410 13:46:07.520294 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:46:08.556756 18353 solver.cpp:218] Iteration 2328 (2.47262 iter/s, 4.85315s/12 iters), loss = 2.97204
I0410 13:46:08.556798 18353 solver.cpp:237]     Train net output #0: loss = 2.97204 (* 1 = 2.97204 loss)
I0410 13:46:08.556807 18353 sgd_solver.cpp:105] Iteration 2328, lr = 0.00630562
I0410 13:46:13.498801 18353 solver.cpp:218] Iteration 2340 (2.42824 iter/s, 4.94185s/12 iters), loss = 2.68723
I0410 13:46:13.498847 18353 solver.cpp:237]     Train net output #0: loss = 2.68723 (* 1 = 2.68723 loss)
I0410 13:46:13.498860 18353 sgd_solver.cpp:105] Iteration 2340, lr = 0.00629065
I0410 13:46:15.509204 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2346.caffemodel
I0410 13:46:15.916226 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2346.solverstate
I0410 13:46:16.279402 18353 solver.cpp:330] Iteration 2346, Testing net (#0)
I0410 13:46:16.279431 18353 net.cpp:676] Ignoring source layer train-data
I0410 13:46:19.828017 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:46:20.790210 18353 solver.cpp:397]     Test net output #0: accuracy = 0.290441
I0410 13:46:20.790267 18353 solver.cpp:397]     Test net output #1: loss = 2.96096 (* 1 = 2.96096 loss)
I0410 13:46:22.695128 18353 solver.cpp:218] Iteration 2352 (1.30491 iter/s, 9.19601s/12 iters), loss = 2.67998
I0410 13:46:22.695179 18353 solver.cpp:237]     Train net output #0: loss = 2.67998 (* 1 = 2.67998 loss)
I0410 13:46:22.695189 18353 sgd_solver.cpp:105] Iteration 2352, lr = 0.00627571
I0410 13:46:27.556747 18353 solver.cpp:218] Iteration 2364 (2.46842 iter/s, 4.86141s/12 iters), loss = 2.76407
I0410 13:46:27.556802 18353 solver.cpp:237]     Train net output #0: loss = 2.76407 (* 1 = 2.76407 loss)
I0410 13:46:27.556815 18353 sgd_solver.cpp:105] Iteration 2364, lr = 0.00626081
I0410 13:46:32.363173 18353 solver.cpp:218] Iteration 2376 (2.49677 iter/s, 4.80622s/12 iters), loss = 2.76061
I0410 13:46:32.363231 18353 solver.cpp:237]     Train net output #0: loss = 2.76061 (* 1 = 2.76061 loss)
I0410 13:46:32.363242 18353 sgd_solver.cpp:105] Iteration 2376, lr = 0.00624595
I0410 13:46:37.229712 18353 solver.cpp:218] Iteration 2388 (2.46593 iter/s, 4.86633s/12 iters), loss = 2.80992
I0410 13:46:37.229773 18353 solver.cpp:237]     Train net output #0: loss = 2.80992 (* 1 = 2.80992 loss)
I0410 13:46:37.229785 18353 sgd_solver.cpp:105] Iteration 2388, lr = 0.00623112
I0410 13:46:42.184217 18353 solver.cpp:218] Iteration 2400 (2.42214 iter/s, 4.9543s/12 iters), loss = 2.61507
I0410 13:46:42.184260 18353 solver.cpp:237]     Train net output #0: loss = 2.61507 (* 1 = 2.61507 loss)
I0410 13:46:42.184270 18353 sgd_solver.cpp:105] Iteration 2400, lr = 0.00621633
I0410 13:46:47.351087 18353 solver.cpp:218] Iteration 2412 (2.32258 iter/s, 5.16667s/12 iters), loss = 2.5541
I0410 13:46:47.351135 18353 solver.cpp:237]     Train net output #0: loss = 2.5541 (* 1 = 2.5541 loss)
I0410 13:46:47.351145 18353 sgd_solver.cpp:105] Iteration 2412, lr = 0.00620157
I0410 13:46:52.294188 18353 solver.cpp:218] Iteration 2424 (2.42772 iter/s, 4.9429s/12 iters), loss = 2.89204
I0410 13:46:52.294365 18353 solver.cpp:237]     Train net output #0: loss = 2.89204 (* 1 = 2.89204 loss)
I0410 13:46:52.294381 18353 sgd_solver.cpp:105] Iteration 2424, lr = 0.00618684
I0410 13:46:53.339831 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:46:57.144178 18353 solver.cpp:218] Iteration 2436 (2.4744 iter/s, 4.84967s/12 iters), loss = 2.55621
I0410 13:46:57.144228 18353 solver.cpp:237]     Train net output #0: loss = 2.55621 (* 1 = 2.55621 loss)
I0410 13:46:57.144239 18353 sgd_solver.cpp:105] Iteration 2436, lr = 0.00617215
I0410 13:47:01.594825 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2448.caffemodel
I0410 13:47:01.907812 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2448.solverstate
I0410 13:47:02.113054 18353 solver.cpp:330] Iteration 2448, Testing net (#0)
I0410 13:47:02.113085 18353 net.cpp:676] Ignoring source layer train-data
I0410 13:47:05.662081 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:47:06.640868 18353 solver.cpp:397]     Test net output #0: accuracy = 0.310049
I0410 13:47:06.640920 18353 solver.cpp:397]     Test net output #1: loss = 2.84895 (* 1 = 2.84895 loss)
I0410 13:47:06.722532 18353 solver.cpp:218] Iteration 2448 (1.25287 iter/s, 9.57802s/12 iters), loss = 2.48788
I0410 13:47:06.722584 18353 solver.cpp:237]     Train net output #0: loss = 2.48788 (* 1 = 2.48788 loss)
I0410 13:47:06.722596 18353 sgd_solver.cpp:105] Iteration 2448, lr = 0.0061575
I0410 13:47:10.848481 18353 solver.cpp:218] Iteration 2460 (2.90855 iter/s, 4.12577s/12 iters), loss = 2.25917
I0410 13:47:10.848520 18353 solver.cpp:237]     Train net output #0: loss = 2.25917 (* 1 = 2.25917 loss)
I0410 13:47:10.848529 18353 sgd_solver.cpp:105] Iteration 2460, lr = 0.00614288
I0410 13:47:15.772990 18353 solver.cpp:218] Iteration 2472 (2.43689 iter/s, 4.92431s/12 iters), loss = 2.79266
I0410 13:47:15.773051 18353 solver.cpp:237]     Train net output #0: loss = 2.79266 (* 1 = 2.79266 loss)
I0410 13:47:15.773063 18353 sgd_solver.cpp:105] Iteration 2472, lr = 0.0061283
I0410 13:47:20.874701 18353 solver.cpp:218] Iteration 2484 (2.35225 iter/s, 5.10149s/12 iters), loss = 2.94248
I0410 13:47:20.874752 18353 solver.cpp:237]     Train net output #0: loss = 2.94248 (* 1 = 2.94248 loss)
I0410 13:47:20.874763 18353 sgd_solver.cpp:105] Iteration 2484, lr = 0.00611375
I0410 13:47:25.965553 18353 solver.cpp:218] Iteration 2496 (2.35727 iter/s, 5.09064s/12 iters), loss = 2.91343
I0410 13:47:25.965658 18353 solver.cpp:237]     Train net output #0: loss = 2.91343 (* 1 = 2.91343 loss)
I0410 13:47:25.965672 18353 sgd_solver.cpp:105] Iteration 2496, lr = 0.00609923
I0410 13:47:30.886927 18353 solver.cpp:218] Iteration 2508 (2.43847 iter/s, 4.92112s/12 iters), loss = 3.05394
I0410 13:47:30.886978 18353 solver.cpp:237]     Train net output #0: loss = 3.05394 (* 1 = 3.05394 loss)
I0410 13:47:30.886991 18353 sgd_solver.cpp:105] Iteration 2508, lr = 0.00608475
I0410 13:47:35.834262 18353 solver.cpp:218] Iteration 2520 (2.42565 iter/s, 4.94713s/12 iters), loss = 2.31371
I0410 13:47:35.834304 18353 solver.cpp:237]     Train net output #0: loss = 2.31371 (* 1 = 2.31371 loss)
I0410 13:47:35.834313 18353 sgd_solver.cpp:105] Iteration 2520, lr = 0.0060703
I0410 13:47:39.019659 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:47:40.786224 18353 solver.cpp:218] Iteration 2532 (2.42338 iter/s, 4.95177s/12 iters), loss = 2.70147
I0410 13:47:40.786268 18353 solver.cpp:237]     Train net output #0: loss = 2.70147 (* 1 = 2.70147 loss)
I0410 13:47:40.786278 18353 sgd_solver.cpp:105] Iteration 2532, lr = 0.00605589
I0410 13:47:45.714474 18353 solver.cpp:218] Iteration 2544 (2.43504 iter/s, 4.92805s/12 iters), loss = 2.56679
I0410 13:47:45.714519 18353 solver.cpp:237]     Train net output #0: loss = 2.56679 (* 1 = 2.56679 loss)
I0410 13:47:45.714529 18353 sgd_solver.cpp:105] Iteration 2544, lr = 0.00604151
I0410 13:47:47.657984 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2550.caffemodel
I0410 13:47:47.942725 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2550.solverstate
I0410 13:47:48.142531 18353 solver.cpp:330] Iteration 2550, Testing net (#0)
I0410 13:47:48.142551 18353 net.cpp:676] Ignoring source layer train-data
I0410 13:47:51.571621 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:47:52.589107 18353 solver.cpp:397]     Test net output #0: accuracy = 0.298407
I0410 13:47:52.589149 18353 solver.cpp:397]     Test net output #1: loss = 2.89551 (* 1 = 2.89551 loss)
I0410 13:47:54.536147 18353 solver.cpp:218] Iteration 2556 (1.36033 iter/s, 8.82136s/12 iters), loss = 2.67895
I0410 13:47:54.536198 18353 solver.cpp:237]     Train net output #0: loss = 2.67895 (* 1 = 2.67895 loss)
I0410 13:47:54.536211 18353 sgd_solver.cpp:105] Iteration 2556, lr = 0.00602717
I0410 13:47:59.568100 18353 solver.cpp:218] Iteration 2568 (2.38486 iter/s, 5.03175s/12 iters), loss = 2.54045
I0410 13:47:59.568255 18353 solver.cpp:237]     Train net output #0: loss = 2.54045 (* 1 = 2.54045 loss)
I0410 13:47:59.568269 18353 sgd_solver.cpp:105] Iteration 2568, lr = 0.00601286
I0410 13:48:04.441026 18353 solver.cpp:218] Iteration 2580 (2.46274 iter/s, 4.87262s/12 iters), loss = 2.31302
I0410 13:48:04.441087 18353 solver.cpp:237]     Train net output #0: loss = 2.31302 (* 1 = 2.31302 loss)
I0410 13:48:04.441099 18353 sgd_solver.cpp:105] Iteration 2580, lr = 0.00599858
I0410 13:48:09.240120 18353 solver.cpp:218] Iteration 2592 (2.50058 iter/s, 4.79888s/12 iters), loss = 2.77194
I0410 13:48:09.240180 18353 solver.cpp:237]     Train net output #0: loss = 2.77194 (* 1 = 2.77194 loss)
I0410 13:48:09.240192 18353 sgd_solver.cpp:105] Iteration 2592, lr = 0.00598434
I0410 13:48:14.091184 18353 solver.cpp:218] Iteration 2604 (2.47379 iter/s, 4.85085s/12 iters), loss = 2.83817
I0410 13:48:14.091241 18353 solver.cpp:237]     Train net output #0: loss = 2.83817 (* 1 = 2.83817 loss)
I0410 13:48:14.091254 18353 sgd_solver.cpp:105] Iteration 2604, lr = 0.00597013
I0410 13:48:18.946583 18353 solver.cpp:218] Iteration 2616 (2.47158 iter/s, 4.85519s/12 iters), loss = 2.83714
I0410 13:48:18.946645 18353 solver.cpp:237]     Train net output #0: loss = 2.83714 (* 1 = 2.83714 loss)
I0410 13:48:18.946656 18353 sgd_solver.cpp:105] Iteration 2616, lr = 0.00595596
I0410 13:48:23.746433 18353 solver.cpp:218] Iteration 2628 (2.50019 iter/s, 4.79964s/12 iters), loss = 2.34667
I0410 13:48:23.746491 18353 solver.cpp:237]     Train net output #0: loss = 2.34667 (* 1 = 2.34667 loss)
I0410 13:48:23.746503 18353 sgd_solver.cpp:105] Iteration 2628, lr = 0.00594182
I0410 13:48:24.170904 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:48:28.613706 18353 solver.cpp:218] Iteration 2640 (2.46555 iter/s, 4.86706s/12 iters), loss = 2.43352
I0410 13:48:28.613770 18353 solver.cpp:237]     Train net output #0: loss = 2.43352 (* 1 = 2.43352 loss)
I0410 13:48:28.613783 18353 sgd_solver.cpp:105] Iteration 2640, lr = 0.00592771
I0410 13:48:32.988857 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2652.caffemodel
I0410 13:48:33.295639 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2652.solverstate
I0410 13:48:33.594156 18353 solver.cpp:330] Iteration 2652, Testing net (#0)
I0410 13:48:33.594184 18353 net.cpp:676] Ignoring source layer train-data
I0410 13:48:37.117542 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:48:38.170817 18353 solver.cpp:397]     Test net output #0: accuracy = 0.343137
I0410 13:48:38.170872 18353 solver.cpp:397]     Test net output #1: loss = 2.71235 (* 1 = 2.71235 loss)
I0410 13:48:38.252375 18353 solver.cpp:218] Iteration 2652 (1.24503 iter/s, 9.63832s/12 iters), loss = 2.44637
I0410 13:48:38.252424 18353 solver.cpp:237]     Train net output #0: loss = 2.44637 (* 1 = 2.44637 loss)
I0410 13:48:38.252436 18353 sgd_solver.cpp:105] Iteration 2652, lr = 0.00591364
I0410 13:48:42.436259 18353 solver.cpp:218] Iteration 2664 (2.86828 iter/s, 4.1837s/12 iters), loss = 2.51245
I0410 13:48:42.436314 18353 solver.cpp:237]     Train net output #0: loss = 2.51245 (* 1 = 2.51245 loss)
I0410 13:48:42.436331 18353 sgd_solver.cpp:105] Iteration 2664, lr = 0.0058996
I0410 13:48:47.315321 18353 solver.cpp:218] Iteration 2676 (2.45959 iter/s, 4.87886s/12 iters), loss = 2.49234
I0410 13:48:47.315374 18353 solver.cpp:237]     Train net output #0: loss = 2.49234 (* 1 = 2.49234 loss)
I0410 13:48:47.315385 18353 sgd_solver.cpp:105] Iteration 2676, lr = 0.00588559
I0410 13:48:52.233330 18353 solver.cpp:218] Iteration 2688 (2.44011 iter/s, 4.91781s/12 iters), loss = 2.73496
I0410 13:48:52.233376 18353 solver.cpp:237]     Train net output #0: loss = 2.73496 (* 1 = 2.73496 loss)
I0410 13:48:52.233386 18353 sgd_solver.cpp:105] Iteration 2688, lr = 0.00587162
I0410 13:48:57.113040 18353 solver.cpp:218] Iteration 2700 (2.45926 iter/s, 4.87951s/12 iters), loss = 2.3454
I0410 13:48:57.113092 18353 solver.cpp:237]     Train net output #0: loss = 2.3454 (* 1 = 2.3454 loss)
I0410 13:48:57.113104 18353 sgd_solver.cpp:105] Iteration 2700, lr = 0.00585768
I0410 13:49:02.045753 18353 solver.cpp:218] Iteration 2712 (2.43284 iter/s, 4.93251s/12 iters), loss = 2.48152
I0410 13:49:02.045807 18353 solver.cpp:237]     Train net output #0: loss = 2.48152 (* 1 = 2.48152 loss)
I0410 13:49:02.045819 18353 sgd_solver.cpp:105] Iteration 2712, lr = 0.00584377
I0410 13:49:06.977442 18353 solver.cpp:218] Iteration 2724 (2.43335 iter/s, 4.93148s/12 iters), loss = 2.35516
I0410 13:49:06.977607 18353 solver.cpp:237]     Train net output #0: loss = 2.35516 (* 1 = 2.35516 loss)
I0410 13:49:06.977622 18353 sgd_solver.cpp:105] Iteration 2724, lr = 0.0058299
I0410 13:49:09.560894 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:49:11.911739 18353 solver.cpp:218] Iteration 2736 (2.43211 iter/s, 4.93399s/12 iters), loss = 2.42899
I0410 13:49:11.911792 18353 solver.cpp:237]     Train net output #0: loss = 2.42899 (* 1 = 2.42899 loss)
I0410 13:49:11.911805 18353 sgd_solver.cpp:105] Iteration 2736, lr = 0.00581605
I0410 13:49:16.800308 18353 solver.cpp:218] Iteration 2748 (2.45481 iter/s, 4.88837s/12 iters), loss = 2.46431
I0410 13:49:16.800359 18353 solver.cpp:237]     Train net output #0: loss = 2.46431 (* 1 = 2.46431 loss)
I0410 13:49:16.800370 18353 sgd_solver.cpp:105] Iteration 2748, lr = 0.00580225
I0410 13:49:18.785130 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2754.caffemodel
I0410 13:49:19.079620 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2754.solverstate
I0410 13:49:19.272706 18353 solver.cpp:330] Iteration 2754, Testing net (#0)
I0410 13:49:19.272732 18353 net.cpp:676] Ignoring source layer train-data
I0410 13:49:21.944546 18353 blocking_queue.cpp:49] Waiting for data
I0410 13:49:22.541194 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:49:23.684855 18353 solver.cpp:397]     Test net output #0: accuracy = 0.337623
I0410 13:49:23.684897 18353 solver.cpp:397]     Test net output #1: loss = 2.74954 (* 1 = 2.74954 loss)
I0410 13:49:25.606098 18353 solver.cpp:218] Iteration 2760 (1.36279 iter/s, 8.80548s/12 iters), loss = 2.18205
I0410 13:49:25.606155 18353 solver.cpp:237]     Train net output #0: loss = 2.18205 (* 1 = 2.18205 loss)
I0410 13:49:25.606169 18353 sgd_solver.cpp:105] Iteration 2760, lr = 0.00578847
I0410 13:49:30.556263 18353 solver.cpp:218] Iteration 2772 (2.42426 iter/s, 4.94996s/12 iters), loss = 2.6785
I0410 13:49:30.556321 18353 solver.cpp:237]     Train net output #0: loss = 2.6785 (* 1 = 2.6785 loss)
I0410 13:49:30.556335 18353 sgd_solver.cpp:105] Iteration 2772, lr = 0.00577473
I0410 13:49:35.814471 18353 solver.cpp:218] Iteration 2784 (2.28224 iter/s, 5.25799s/12 iters), loss = 2.60569
I0410 13:49:35.814530 18353 solver.cpp:237]     Train net output #0: loss = 2.60569 (* 1 = 2.60569 loss)
I0410 13:49:35.814543 18353 sgd_solver.cpp:105] Iteration 2784, lr = 0.00576102
I0410 13:49:40.761415 18353 solver.cpp:218] Iteration 2796 (2.42584 iter/s, 4.94673s/12 iters), loss = 2.38172
I0410 13:49:40.761518 18353 solver.cpp:237]     Train net output #0: loss = 2.38172 (* 1 = 2.38172 loss)
I0410 13:49:40.761529 18353 sgd_solver.cpp:105] Iteration 2796, lr = 0.00574734
I0410 13:49:45.690212 18353 solver.cpp:218] Iteration 2808 (2.4348 iter/s, 4.92854s/12 iters), loss = 2.18728
I0410 13:49:45.690259 18353 solver.cpp:237]     Train net output #0: loss = 2.18728 (* 1 = 2.18728 loss)
I0410 13:49:45.690269 18353 sgd_solver.cpp:105] Iteration 2808, lr = 0.00573369
I0410 13:49:50.616744 18353 solver.cpp:218] Iteration 2820 (2.43589 iter/s, 4.92633s/12 iters), loss = 2.41611
I0410 13:49:50.616793 18353 solver.cpp:237]     Train net output #0: loss = 2.41611 (* 1 = 2.41611 loss)
I0410 13:49:50.616803 18353 sgd_solver.cpp:105] Iteration 2820, lr = 0.00572008
I0410 13:49:55.260185 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:49:55.544586 18353 solver.cpp:218] Iteration 2832 (2.43524 iter/s, 4.92764s/12 iters), loss = 2.41694
I0410 13:49:55.544631 18353 solver.cpp:237]     Train net output #0: loss = 2.41694 (* 1 = 2.41694 loss)
I0410 13:49:55.544641 18353 sgd_solver.cpp:105] Iteration 2832, lr = 0.0057065
I0410 13:50:00.759503 18353 solver.cpp:218] Iteration 2844 (2.30119 iter/s, 5.2147s/12 iters), loss = 2.38468
I0410 13:50:00.759562 18353 solver.cpp:237]     Train net output #0: loss = 2.38468 (* 1 = 2.38468 loss)
I0410 13:50:00.759575 18353 sgd_solver.cpp:105] Iteration 2844, lr = 0.00569295
I0410 13:50:06.360571 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2856.caffemodel
I0410 13:50:06.669847 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2856.solverstate
I0410 13:50:06.883953 18353 solver.cpp:330] Iteration 2856, Testing net (#0)
I0410 13:50:06.883972 18353 net.cpp:676] Ignoring source layer train-data
I0410 13:50:10.161761 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:50:11.308306 18353 solver.cpp:397]     Test net output #0: accuracy = 0.342524
I0410 13:50:11.308468 18353 solver.cpp:397]     Test net output #1: loss = 2.68798 (* 1 = 2.68798 loss)
I0410 13:50:11.389681 18353 solver.cpp:218] Iteration 2856 (1.1289 iter/s, 10.6298s/12 iters), loss = 2.29058
I0410 13:50:11.389734 18353 solver.cpp:237]     Train net output #0: loss = 2.29058 (* 1 = 2.29058 loss)
I0410 13:50:11.389744 18353 sgd_solver.cpp:105] Iteration 2856, lr = 0.00567944
I0410 13:50:15.558637 18353 solver.cpp:218] Iteration 2868 (2.87855 iter/s, 4.16877s/12 iters), loss = 2.34681
I0410 13:50:15.558686 18353 solver.cpp:237]     Train net output #0: loss = 2.34681 (* 1 = 2.34681 loss)
I0410 13:50:15.558696 18353 sgd_solver.cpp:105] Iteration 2868, lr = 0.00566595
I0410 13:50:20.501816 18353 solver.cpp:218] Iteration 2880 (2.42769 iter/s, 4.94297s/12 iters), loss = 2.01662
I0410 13:50:20.501869 18353 solver.cpp:237]     Train net output #0: loss = 2.01662 (* 1 = 2.01662 loss)
I0410 13:50:20.501883 18353 sgd_solver.cpp:105] Iteration 2880, lr = 0.0056525
I0410 13:50:25.443928 18353 solver.cpp:218] Iteration 2892 (2.42821 iter/s, 4.9419s/12 iters), loss = 2.27543
I0410 13:50:25.443987 18353 solver.cpp:237]     Train net output #0: loss = 2.27543 (* 1 = 2.27543 loss)
I0410 13:50:25.444000 18353 sgd_solver.cpp:105] Iteration 2892, lr = 0.00563908
I0410 13:50:30.356631 18353 solver.cpp:218] Iteration 2904 (2.44275 iter/s, 4.91249s/12 iters), loss = 2.11401
I0410 13:50:30.356681 18353 solver.cpp:237]     Train net output #0: loss = 2.11401 (* 1 = 2.11401 loss)
I0410 13:50:30.356693 18353 sgd_solver.cpp:105] Iteration 2904, lr = 0.00562569
I0410 13:50:35.254042 18353 solver.cpp:218] Iteration 2916 (2.45038 iter/s, 4.89721s/12 iters), loss = 2.25554
I0410 13:50:35.254094 18353 solver.cpp:237]     Train net output #0: loss = 2.25554 (* 1 = 2.25554 loss)
I0410 13:50:35.254106 18353 sgd_solver.cpp:105] Iteration 2916, lr = 0.00561233
I0410 13:50:40.144529 18353 solver.cpp:218] Iteration 2928 (2.45383 iter/s, 4.89031s/12 iters), loss = 2.07217
I0410 13:50:40.144588 18353 solver.cpp:237]     Train net output #0: loss = 2.07217 (* 1 = 2.07217 loss)
I0410 13:50:40.144599 18353 sgd_solver.cpp:105] Iteration 2928, lr = 0.00559901
I0410 13:50:41.961776 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:50:45.095649 18353 solver.cpp:218] Iteration 2940 (2.42377 iter/s, 4.95095s/12 iters), loss = 2.37838
I0410 13:50:45.095701 18353 solver.cpp:237]     Train net output #0: loss = 2.37838 (* 1 = 2.37838 loss)
I0410 13:50:45.095712 18353 sgd_solver.cpp:105] Iteration 2940, lr = 0.00558572
I0410 13:50:50.091406 18353 solver.cpp:218] Iteration 2952 (2.40211 iter/s, 4.9956s/12 iters), loss = 2.23771
I0410 13:50:50.091457 18353 solver.cpp:237]     Train net output #0: loss = 2.23771 (* 1 = 2.23771 loss)
I0410 13:50:50.091468 18353 sgd_solver.cpp:105] Iteration 2952, lr = 0.00557245
I0410 13:50:52.095760 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2958.caffemodel
I0410 13:50:52.778113 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2958.solverstate
I0410 13:50:52.985327 18353 solver.cpp:330] Iteration 2958, Testing net (#0)
I0410 13:50:52.985353 18353 net.cpp:676] Ignoring source layer train-data
I0410 13:50:56.255712 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:50:57.432770 18353 solver.cpp:397]     Test net output #0: accuracy = 0.343137
I0410 13:50:57.432822 18353 solver.cpp:397]     Test net output #1: loss = 2.7177 (* 1 = 2.7177 loss)
I0410 13:50:59.406410 18353 solver.cpp:218] Iteration 2964 (1.28828 iter/s, 9.31477s/12 iters), loss = 2.3574
I0410 13:50:59.406458 18353 solver.cpp:237]     Train net output #0: loss = 2.3574 (* 1 = 2.3574 loss)
I0410 13:50:59.406467 18353 sgd_solver.cpp:105] Iteration 2964, lr = 0.00555922
I0410 13:51:04.379737 18353 solver.cpp:218] Iteration 2976 (2.41295 iter/s, 4.97317s/12 iters), loss = 2.07225
I0410 13:51:04.379784 18353 solver.cpp:237]     Train net output #0: loss = 2.07225 (* 1 = 2.07225 loss)
I0410 13:51:04.379796 18353 sgd_solver.cpp:105] Iteration 2976, lr = 0.00554603
I0410 13:51:09.267414 18353 solver.cpp:218] Iteration 2988 (2.45523 iter/s, 4.88753s/12 iters), loss = 2.18195
I0410 13:51:09.267460 18353 solver.cpp:237]     Train net output #0: loss = 2.18195 (* 1 = 2.18195 loss)
I0410 13:51:09.267472 18353 sgd_solver.cpp:105] Iteration 2988, lr = 0.00553286
I0410 13:51:14.215021 18353 solver.cpp:218] Iteration 3000 (2.42549 iter/s, 4.94746s/12 iters), loss = 2.469
I0410 13:51:14.215113 18353 solver.cpp:237]     Train net output #0: loss = 2.469 (* 1 = 2.469 loss)
I0410 13:51:14.215126 18353 sgd_solver.cpp:105] Iteration 3000, lr = 0.00551972
I0410 13:51:19.091048 18353 solver.cpp:218] Iteration 3012 (2.46112 iter/s, 4.87583s/12 iters), loss = 2.20564
I0410 13:51:19.091104 18353 solver.cpp:237]     Train net output #0: loss = 2.20564 (* 1 = 2.20564 loss)
I0410 13:51:19.091117 18353 sgd_solver.cpp:105] Iteration 3012, lr = 0.00550662
I0410 13:51:24.201122 18353 solver.cpp:218] Iteration 3024 (2.34838 iter/s, 5.1099s/12 iters), loss = 2.10652
I0410 13:51:24.201181 18353 solver.cpp:237]     Train net output #0: loss = 2.10652 (* 1 = 2.10652 loss)
I0410 13:51:24.201195 18353 sgd_solver.cpp:105] Iteration 3024, lr = 0.00549354
I0410 13:51:28.093194 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:51:29.106462 18353 solver.cpp:218] Iteration 3036 (2.44639 iter/s, 4.90518s/12 iters), loss = 1.85562
I0410 13:51:29.106510 18353 solver.cpp:237]     Train net output #0: loss = 1.85562 (* 1 = 1.85562 loss)
I0410 13:51:29.106520 18353 sgd_solver.cpp:105] Iteration 3036, lr = 0.0054805
I0410 13:51:34.059108 18353 solver.cpp:218] Iteration 3048 (2.42303 iter/s, 4.95249s/12 iters), loss = 2.22064
I0410 13:51:34.059160 18353 solver.cpp:237]     Train net output #0: loss = 2.22064 (* 1 = 2.22064 loss)
I0410 13:51:34.059170 18353 sgd_solver.cpp:105] Iteration 3048, lr = 0.00546749
I0410 13:51:38.504328 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3060.caffemodel
I0410 13:51:38.809418 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3060.solverstate
I0410 13:51:39.007701 18353 solver.cpp:330] Iteration 3060, Testing net (#0)
I0410 13:51:39.007719 18353 net.cpp:676] Ignoring source layer train-data
I0410 13:51:42.407663 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:51:43.624295 18353 solver.cpp:397]     Test net output #0: accuracy = 0.363971
I0410 13:51:43.624346 18353 solver.cpp:397]     Test net output #1: loss = 2.66376 (* 1 = 2.66376 loss)
I0410 13:51:43.705695 18353 solver.cpp:218] Iteration 3060 (1.24399 iter/s, 9.64634s/12 iters), loss = 2.17616
I0410 13:51:43.705742 18353 solver.cpp:237]     Train net output #0: loss = 2.17616 (* 1 = 2.17616 loss)
I0410 13:51:43.705754 18353 sgd_solver.cpp:105] Iteration 3060, lr = 0.00545451
I0410 13:51:47.852028 18353 solver.cpp:218] Iteration 3072 (2.89422 iter/s, 4.14619s/12 iters), loss = 2.25789
I0410 13:51:47.852171 18353 solver.cpp:237]     Train net output #0: loss = 2.25789 (* 1 = 2.25789 loss)
I0410 13:51:47.852183 18353 sgd_solver.cpp:105] Iteration 3072, lr = 0.00544156
I0410 13:51:52.764294 18353 solver.cpp:218] Iteration 3084 (2.44299 iter/s, 4.91201s/12 iters), loss = 2.22512
I0410 13:51:52.764350 18353 solver.cpp:237]     Train net output #0: loss = 2.22512 (* 1 = 2.22512 loss)
I0410 13:51:52.764364 18353 sgd_solver.cpp:105] Iteration 3084, lr = 0.00542864
I0410 13:51:57.550388 18353 solver.cpp:218] Iteration 3096 (2.50735 iter/s, 4.78593s/12 iters), loss = 2.21929
I0410 13:51:57.550441 18353 solver.cpp:237]     Train net output #0: loss = 2.21929 (* 1 = 2.21929 loss)
I0410 13:51:57.550453 18353 sgd_solver.cpp:105] Iteration 3096, lr = 0.00541575
I0410 13:52:02.533702 18353 solver.cpp:218] Iteration 3108 (2.40811 iter/s, 4.98315s/12 iters), loss = 1.79954
I0410 13:52:02.533751 18353 solver.cpp:237]     Train net output #0: loss = 1.79954 (* 1 = 1.79954 loss)
I0410 13:52:02.533763 18353 sgd_solver.cpp:105] Iteration 3108, lr = 0.00540289
I0410 13:52:07.485780 18353 solver.cpp:218] Iteration 3120 (2.42331 iter/s, 4.95191s/12 iters), loss = 1.85196
I0410 13:52:07.485839 18353 solver.cpp:237]     Train net output #0: loss = 1.85196 (* 1 = 1.85196 loss)
I0410 13:52:07.485853 18353 sgd_solver.cpp:105] Iteration 3120, lr = 0.00539006
I0410 13:52:12.368455 18353 solver.cpp:218] Iteration 3132 (2.45775 iter/s, 4.88251s/12 iters), loss = 2.52653
I0410 13:52:12.368516 18353 solver.cpp:237]     Train net output #0: loss = 2.52653 (* 1 = 2.52653 loss)
I0410 13:52:12.368530 18353 sgd_solver.cpp:105] Iteration 3132, lr = 0.00537727
I0410 13:52:13.482409 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:52:17.305106 18353 solver.cpp:218] Iteration 3144 (2.43088 iter/s, 4.93648s/12 iters), loss = 1.9413
I0410 13:52:17.305160 18353 solver.cpp:237]     Train net output #0: loss = 1.9413 (* 1 = 1.9413 loss)
I0410 13:52:17.305174 18353 sgd_solver.cpp:105] Iteration 3144, lr = 0.0053645
I0410 13:52:22.390087 18353 solver.cpp:218] Iteration 3156 (2.35997 iter/s, 5.08481s/12 iters), loss = 2.11867
I0410 13:52:22.390170 18353 solver.cpp:237]     Train net output #0: loss = 2.11867 (* 1 = 2.11867 loss)
I0410 13:52:22.390182 18353 sgd_solver.cpp:105] Iteration 3156, lr = 0.00535176
I0410 13:52:24.386270 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3162.caffemodel
I0410 13:52:24.845896 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3162.solverstate
I0410 13:52:25.054814 18353 solver.cpp:330] Iteration 3162, Testing net (#0)
I0410 13:52:25.054836 18353 net.cpp:676] Ignoring source layer train-data
I0410 13:52:28.301630 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:52:29.677714 18353 solver.cpp:397]     Test net output #0: accuracy = 0.373774
I0410 13:52:29.677762 18353 solver.cpp:397]     Test net output #1: loss = 2.57004 (* 1 = 2.57004 loss)
I0410 13:52:31.425832 18353 solver.cpp:218] Iteration 3168 (1.3281 iter/s, 9.03547s/12 iters), loss = 1.68413
I0410 13:52:31.425894 18353 solver.cpp:237]     Train net output #0: loss = 1.68413 (* 1 = 1.68413 loss)
I0410 13:52:31.425907 18353 sgd_solver.cpp:105] Iteration 3168, lr = 0.00533906
I0410 13:52:36.322515 18353 solver.cpp:218] Iteration 3180 (2.45072 iter/s, 4.89651s/12 iters), loss = 2.2834
I0410 13:52:36.322571 18353 solver.cpp:237]     Train net output #0: loss = 2.2834 (* 1 = 2.2834 loss)
I0410 13:52:36.322583 18353 sgd_solver.cpp:105] Iteration 3180, lr = 0.00532638
I0410 13:52:41.203194 18353 solver.cpp:218] Iteration 3192 (2.45876 iter/s, 4.88052s/12 iters), loss = 2.0547
I0410 13:52:41.203248 18353 solver.cpp:237]     Train net output #0: loss = 2.0547 (* 1 = 2.0547 loss)
I0410 13:52:41.203258 18353 sgd_solver.cpp:105] Iteration 3192, lr = 0.00531374
I0410 13:52:46.118225 18353 solver.cpp:218] Iteration 3204 (2.44157 iter/s, 4.91487s/12 iters), loss = 1.89946
I0410 13:52:46.118273 18353 solver.cpp:237]     Train net output #0: loss = 1.89946 (* 1 = 1.89946 loss)
I0410 13:52:46.118286 18353 sgd_solver.cpp:105] Iteration 3204, lr = 0.00530112
I0410 13:52:51.016945 18353 solver.cpp:218] Iteration 3216 (2.4497 iter/s, 4.89856s/12 iters), loss = 2.08793
I0410 13:52:51.017007 18353 solver.cpp:237]     Train net output #0: loss = 2.08793 (* 1 = 2.08793 loss)
I0410 13:52:51.017021 18353 sgd_solver.cpp:105] Iteration 3216, lr = 0.00528853
I0410 13:52:55.935194 18353 solver.cpp:218] Iteration 3228 (2.43998 iter/s, 4.91808s/12 iters), loss = 2.23101
I0410 13:52:55.935322 18353 solver.cpp:237]     Train net output #0: loss = 2.23101 (* 1 = 2.23101 loss)
I0410 13:52:55.935333 18353 sgd_solver.cpp:105] Iteration 3228, lr = 0.00527598
I0410 13:52:59.150748 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:53:00.890262 18353 solver.cpp:218] Iteration 3240 (2.42188 iter/s, 4.95482s/12 iters), loss = 1.91822
I0410 13:53:00.890316 18353 solver.cpp:237]     Train net output #0: loss = 1.91822 (* 1 = 1.91822 loss)
I0410 13:53:00.890326 18353 sgd_solver.cpp:105] Iteration 3240, lr = 0.00526345
I0410 13:53:05.824908 18353 solver.cpp:218] Iteration 3252 (2.43187 iter/s, 4.93448s/12 iters), loss = 2.00403
I0410 13:53:05.824966 18353 solver.cpp:237]     Train net output #0: loss = 2.00403 (* 1 = 2.00403 loss)
I0410 13:53:05.824980 18353 sgd_solver.cpp:105] Iteration 3252, lr = 0.00525095
I0410 13:53:10.252840 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3264.caffemodel
I0410 13:53:10.534040 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3264.solverstate
I0410 13:53:10.725033 18353 solver.cpp:330] Iteration 3264, Testing net (#0)
I0410 13:53:10.725059 18353 net.cpp:676] Ignoring source layer train-data
I0410 13:53:13.790335 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:53:15.118892 18353 solver.cpp:397]     Test net output #0: accuracy = 0.393382
I0410 13:53:15.118928 18353 solver.cpp:397]     Test net output #1: loss = 2.54084 (* 1 = 2.54084 loss)
I0410 13:53:15.200150 18353 solver.cpp:218] Iteration 3264 (1.28 iter/s, 9.37498s/12 iters), loss = 2.09418
I0410 13:53:15.200201 18353 solver.cpp:237]     Train net output #0: loss = 2.09418 (* 1 = 2.09418 loss)
I0410 13:53:15.200210 18353 sgd_solver.cpp:105] Iteration 3264, lr = 0.00523849
I0410 13:53:19.330528 18353 solver.cpp:218] Iteration 3276 (2.90541 iter/s, 4.13023s/12 iters), loss = 1.91352
I0410 13:53:19.330585 18353 solver.cpp:237]     Train net output #0: loss = 1.91352 (* 1 = 1.91352 loss)
I0410 13:53:19.330598 18353 sgd_solver.cpp:105] Iteration 3276, lr = 0.00522605
I0410 13:53:24.216610 18353 solver.cpp:218] Iteration 3288 (2.45604 iter/s, 4.8859s/12 iters), loss = 1.989
I0410 13:53:24.216675 18353 solver.cpp:237]     Train net output #0: loss = 1.989 (* 1 = 1.989 loss)
I0410 13:53:24.216689 18353 sgd_solver.cpp:105] Iteration 3288, lr = 0.00521364
I0410 13:53:29.127517 18353 solver.cpp:218] Iteration 3300 (2.44363 iter/s, 4.91073s/12 iters), loss = 2.23016
I0410 13:53:29.127601 18353 solver.cpp:237]     Train net output #0: loss = 2.23016 (* 1 = 2.23016 loss)
I0410 13:53:29.127614 18353 sgd_solver.cpp:105] Iteration 3300, lr = 0.00520126
I0410 13:53:34.111251 18353 solver.cpp:218] Iteration 3312 (2.40793 iter/s, 4.98354s/12 iters), loss = 1.89274
I0410 13:53:34.111292 18353 solver.cpp:237]     Train net output #0: loss = 1.89274 (* 1 = 1.89274 loss)
I0410 13:53:34.111302 18353 sgd_solver.cpp:105] Iteration 3312, lr = 0.00518892
I0410 13:53:39.045847 18353 solver.cpp:218] Iteration 3324 (2.43189 iter/s, 4.93444s/12 iters), loss = 1.87024
I0410 13:53:39.045898 18353 solver.cpp:237]     Train net output #0: loss = 1.87024 (* 1 = 1.87024 loss)
I0410 13:53:39.045909 18353 sgd_solver.cpp:105] Iteration 3324, lr = 0.0051766
I0410 13:53:43.934546 18353 solver.cpp:218] Iteration 3336 (2.45472 iter/s, 4.88853s/12 iters), loss = 1.85678
I0410 13:53:43.934603 18353 solver.cpp:237]     Train net output #0: loss = 1.85678 (* 1 = 1.85678 loss)
I0410 13:53:43.934617 18353 sgd_solver.cpp:105] Iteration 3336, lr = 0.00516431
I0410 13:53:44.397042 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:53:48.860785 18353 solver.cpp:218] Iteration 3348 (2.43602 iter/s, 4.92607s/12 iters), loss = 1.69983
I0410 13:53:48.860841 18353 solver.cpp:237]     Train net output #0: loss = 1.69983 (* 1 = 1.69983 loss)
I0410 13:53:48.860854 18353 sgd_solver.cpp:105] Iteration 3348, lr = 0.00515204
I0410 13:53:53.766647 18353 solver.cpp:218] Iteration 3360 (2.44614 iter/s, 4.9057s/12 iters), loss = 1.6535
I0410 13:53:53.766696 18353 solver.cpp:237]     Train net output #0: loss = 1.6535 (* 1 = 1.6535 loss)
I0410 13:53:53.766707 18353 sgd_solver.cpp:105] Iteration 3360, lr = 0.00513981
I0410 13:53:55.775666 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3366.caffemodel
I0410 13:53:56.073750 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3366.solverstate
I0410 13:53:56.272873 18353 solver.cpp:330] Iteration 3366, Testing net (#0)
I0410 13:53:56.272895 18353 net.cpp:676] Ignoring source layer train-data
I0410 13:53:59.420053 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:54:00.779429 18353 solver.cpp:397]     Test net output #0: accuracy = 0.412377
I0410 13:54:00.779466 18353 solver.cpp:397]     Test net output #1: loss = 2.40072 (* 1 = 2.40072 loss)
I0410 13:54:02.531808 18353 solver.cpp:218] Iteration 3372 (1.3691 iter/s, 8.76491s/12 iters), loss = 2.01821
I0410 13:54:02.531865 18353 solver.cpp:237]     Train net output #0: loss = 2.01821 (* 1 = 2.01821 loss)
I0410 13:54:02.531877 18353 sgd_solver.cpp:105] Iteration 3372, lr = 0.00512761
I0410 13:54:07.417285 18353 solver.cpp:218] Iteration 3384 (2.45635 iter/s, 4.8853s/12 iters), loss = 1.61832
I0410 13:54:07.417346 18353 solver.cpp:237]     Train net output #0: loss = 1.61832 (* 1 = 1.61832 loss)
I0410 13:54:07.417359 18353 sgd_solver.cpp:105] Iteration 3384, lr = 0.00511544
I0410 13:54:12.370230 18353 solver.cpp:218] Iteration 3396 (2.42288 iter/s, 4.95277s/12 iters), loss = 2.01214
I0410 13:54:12.370275 18353 solver.cpp:237]     Train net output #0: loss = 2.01214 (* 1 = 2.01214 loss)
I0410 13:54:12.370285 18353 sgd_solver.cpp:105] Iteration 3396, lr = 0.00510329
I0410 13:54:17.276569 18353 solver.cpp:218] Iteration 3408 (2.4459 iter/s, 4.90617s/12 iters), loss = 2.1487
I0410 13:54:17.276628 18353 solver.cpp:237]     Train net output #0: loss = 2.1487 (* 1 = 2.1487 loss)
I0410 13:54:17.276641 18353 sgd_solver.cpp:105] Iteration 3408, lr = 0.00509117
I0410 13:54:22.184651 18353 solver.cpp:218] Iteration 3420 (2.44503 iter/s, 4.90791s/12 iters), loss = 1.68488
I0410 13:54:22.184701 18353 solver.cpp:237]     Train net output #0: loss = 1.68488 (* 1 = 1.68488 loss)
I0410 13:54:22.184711 18353 sgd_solver.cpp:105] Iteration 3420, lr = 0.00507909
I0410 13:54:27.112494 18353 solver.cpp:218] Iteration 3432 (2.43523 iter/s, 4.92768s/12 iters), loss = 1.76902
I0410 13:54:27.112547 18353 solver.cpp:237]     Train net output #0: loss = 1.76902 (* 1 = 1.76902 loss)
I0410 13:54:27.112560 18353 sgd_solver.cpp:105] Iteration 3432, lr = 0.00506703
I0410 13:54:29.699939 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:54:32.011375 18353 solver.cpp:218] Iteration 3444 (2.44962 iter/s, 4.89871s/12 iters), loss = 1.58958
I0410 13:54:32.011426 18353 solver.cpp:237]     Train net output #0: loss = 1.58958 (* 1 = 1.58958 loss)
I0410 13:54:32.011438 18353 sgd_solver.cpp:105] Iteration 3444, lr = 0.005055
I0410 13:54:36.981011 18353 solver.cpp:218] Iteration 3456 (2.41475 iter/s, 4.96946s/12 iters), loss = 1.92716
I0410 13:54:36.981061 18353 solver.cpp:237]     Train net output #0: loss = 1.92716 (* 1 = 1.92716 loss)
I0410 13:54:36.981073 18353 sgd_solver.cpp:105] Iteration 3456, lr = 0.005043
I0410 13:54:41.391474 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3468.caffemodel
I0410 13:54:42.255647 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3468.solverstate
I0410 13:54:42.476663 18353 solver.cpp:330] Iteration 3468, Testing net (#0)
I0410 13:54:42.476692 18353 net.cpp:676] Ignoring source layer train-data
I0410 13:54:42.499825 18353 blocking_queue.cpp:49] Waiting for data
I0410 13:54:45.580359 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:54:46.987639 18353 solver.cpp:397]     Test net output #0: accuracy = 0.40625
I0410 13:54:46.987687 18353 solver.cpp:397]     Test net output #1: loss = 2.41752 (* 1 = 2.41752 loss)
I0410 13:54:47.068944 18353 solver.cpp:218] Iteration 3468 (1.18957 iter/s, 10.0877s/12 iters), loss = 1.85254
I0410 13:54:47.068997 18353 solver.cpp:237]     Train net output #0: loss = 1.85254 (* 1 = 1.85254 loss)
I0410 13:54:47.069010 18353 sgd_solver.cpp:105] Iteration 3468, lr = 0.00503102
I0410 13:54:51.150470 18353 solver.cpp:218] Iteration 3480 (2.94019 iter/s, 4.08137s/12 iters), loss = 1.86459
I0410 13:54:51.150511 18353 solver.cpp:237]     Train net output #0: loss = 1.86459 (* 1 = 1.86459 loss)
I0410 13:54:51.150521 18353 sgd_solver.cpp:105] Iteration 3480, lr = 0.00501908
I0410 13:54:55.967957 18353 solver.cpp:218] Iteration 3492 (2.49101 iter/s, 4.81733s/12 iters), loss = 1.83596
I0410 13:54:55.968011 18353 solver.cpp:237]     Train net output #0: loss = 1.83596 (* 1 = 1.83596 loss)
I0410 13:54:55.968024 18353 sgd_solver.cpp:105] Iteration 3492, lr = 0.00500716
I0410 13:55:00.860098 18353 solver.cpp:218] Iteration 3504 (2.453 iter/s, 4.89197s/12 iters), loss = 1.84695
I0410 13:55:00.860224 18353 solver.cpp:237]     Train net output #0: loss = 1.84695 (* 1 = 1.84695 loss)
I0410 13:55:00.860234 18353 sgd_solver.cpp:105] Iteration 3504, lr = 0.00499527
I0410 13:55:05.782624 18353 solver.cpp:218] Iteration 3516 (2.43789 iter/s, 4.92228s/12 iters), loss = 1.5922
I0410 13:55:05.782680 18353 solver.cpp:237]     Train net output #0: loss = 1.5922 (* 1 = 1.5922 loss)
I0410 13:55:05.782693 18353 sgd_solver.cpp:105] Iteration 3516, lr = 0.00498341
I0410 13:55:10.745411 18353 solver.cpp:218] Iteration 3528 (2.41808 iter/s, 4.96261s/12 iters), loss = 1.6908
I0410 13:55:10.745452 18353 solver.cpp:237]     Train net output #0: loss = 1.6908 (* 1 = 1.6908 loss)
I0410 13:55:10.745461 18353 sgd_solver.cpp:105] Iteration 3528, lr = 0.00497158
I0410 13:55:15.355926 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:55:15.612751 18353 solver.cpp:218] Iteration 3540 (2.4655 iter/s, 4.86717s/12 iters), loss = 1.6195
I0410 13:55:15.612808 18353 solver.cpp:237]     Train net output #0: loss = 1.6195 (* 1 = 1.6195 loss)
I0410 13:55:15.612820 18353 sgd_solver.cpp:105] Iteration 3540, lr = 0.00495978
I0410 13:55:20.524966 18353 solver.cpp:218] Iteration 3552 (2.44298 iter/s, 4.91204s/12 iters), loss = 1.7352
I0410 13:55:20.525017 18353 solver.cpp:237]     Train net output #0: loss = 1.7352 (* 1 = 1.7352 loss)
I0410 13:55:20.525030 18353 sgd_solver.cpp:105] Iteration 3552, lr = 0.004948
I0410 13:55:25.498132 18353 solver.cpp:218] Iteration 3564 (2.41304 iter/s, 4.97298s/12 iters), loss = 1.50465
I0410 13:55:25.498184 18353 solver.cpp:237]     Train net output #0: loss = 1.50465 (* 1 = 1.50465 loss)
I0410 13:55:25.498195 18353 sgd_solver.cpp:105] Iteration 3564, lr = 0.00493626
I0410 13:55:27.530716 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3570.caffemodel
I0410 13:55:27.835461 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3570.solverstate
I0410 13:55:28.031257 18353 solver.cpp:330] Iteration 3570, Testing net (#0)
I0410 13:55:28.031289 18353 net.cpp:676] Ignoring source layer train-data
I0410 13:55:31.207485 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:55:32.614687 18353 solver.cpp:397]     Test net output #0: accuracy = 0.409314
I0410 13:55:32.614737 18353 solver.cpp:397]     Test net output #1: loss = 2.48611 (* 1 = 2.48611 loss)
I0410 13:55:34.432185 18353 solver.cpp:218] Iteration 3576 (1.34321 iter/s, 8.93379s/12 iters), loss = 1.88175
I0410 13:55:34.432238 18353 solver.cpp:237]     Train net output #0: loss = 1.88175 (* 1 = 1.88175 loss)
I0410 13:55:34.432250 18353 sgd_solver.cpp:105] Iteration 3576, lr = 0.00492454
I0410 13:55:39.376101 18353 solver.cpp:218] Iteration 3588 (2.42731 iter/s, 4.94374s/12 iters), loss = 1.56805
I0410 13:55:39.376155 18353 solver.cpp:237]     Train net output #0: loss = 1.56805 (* 1 = 1.56805 loss)
I0410 13:55:39.376168 18353 sgd_solver.cpp:105] Iteration 3588, lr = 0.00491284
I0410 13:55:44.306408 18353 solver.cpp:218] Iteration 3600 (2.43401 iter/s, 4.93013s/12 iters), loss = 1.85162
I0410 13:55:44.306466 18353 solver.cpp:237]     Train net output #0: loss = 1.85162 (* 1 = 1.85162 loss)
I0410 13:55:44.306479 18353 sgd_solver.cpp:105] Iteration 3600, lr = 0.00490118
I0410 13:55:49.227891 18353 solver.cpp:218] Iteration 3612 (2.43838 iter/s, 4.9213s/12 iters), loss = 1.7518
I0410 13:55:49.227950 18353 solver.cpp:237]     Train net output #0: loss = 1.7518 (* 1 = 1.7518 loss)
I0410 13:55:49.227964 18353 sgd_solver.cpp:105] Iteration 3612, lr = 0.00488954
I0410 13:55:54.148192 18353 solver.cpp:218] Iteration 3624 (2.43896 iter/s, 4.92012s/12 iters), loss = 1.99102
I0410 13:55:54.148245 18353 solver.cpp:237]     Train net output #0: loss = 1.99102 (* 1 = 1.99102 loss)
I0410 13:55:54.148258 18353 sgd_solver.cpp:105] Iteration 3624, lr = 0.00487793
I0410 13:55:59.075017 18353 solver.cpp:218] Iteration 3636 (2.43573 iter/s, 4.92665s/12 iters), loss = 1.64064
I0410 13:55:59.075067 18353 solver.cpp:237]     Train net output #0: loss = 1.64064 (* 1 = 1.64064 loss)
I0410 13:55:59.075079 18353 sgd_solver.cpp:105] Iteration 3636, lr = 0.00486635
I0410 13:56:00.960935 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:56:04.060689 18353 solver.cpp:218] Iteration 3648 (2.40698 iter/s, 4.9855s/12 iters), loss = 1.48346
I0410 13:56:04.060797 18353 solver.cpp:237]     Train net output #0: loss = 1.48346 (* 1 = 1.48346 loss)
I0410 13:56:04.060811 18353 sgd_solver.cpp:105] Iteration 3648, lr = 0.0048548
I0410 13:56:08.946457 18353 solver.cpp:218] Iteration 3660 (2.45623 iter/s, 4.88554s/12 iters), loss = 1.68004
I0410 13:56:08.946514 18353 solver.cpp:237]     Train net output #0: loss = 1.68004 (* 1 = 1.68004 loss)
I0410 13:56:08.946527 18353 sgd_solver.cpp:105] Iteration 3660, lr = 0.00484327
I0410 13:56:13.347187 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3672.caffemodel
I0410 13:56:13.679590 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3672.solverstate
I0410 13:56:13.883628 18353 solver.cpp:330] Iteration 3672, Testing net (#0)
I0410 13:56:13.883657 18353 net.cpp:676] Ignoring source layer train-data
I0410 13:56:16.840193 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:56:18.293134 18353 solver.cpp:397]     Test net output #0: accuracy = 0.411152
I0410 13:56:18.293182 18353 solver.cpp:397]     Test net output #1: loss = 2.42925 (* 1 = 2.42925 loss)
I0410 13:56:18.374298 18353 solver.cpp:218] Iteration 3672 (1.27286 iter/s, 9.42756s/12 iters), loss = 1.614
I0410 13:56:18.374352 18353 solver.cpp:237]     Train net output #0: loss = 1.614 (* 1 = 1.614 loss)
I0410 13:56:18.374364 18353 sgd_solver.cpp:105] Iteration 3672, lr = 0.00483177
I0410 13:56:22.634196 18353 solver.cpp:218] Iteration 3684 (2.81707 iter/s, 4.25974s/12 iters), loss = 1.79051
I0410 13:56:22.634235 18353 solver.cpp:237]     Train net output #0: loss = 1.79051 (* 1 = 1.79051 loss)
I0410 13:56:22.634244 18353 sgd_solver.cpp:105] Iteration 3684, lr = 0.0048203
I0410 13:56:27.507311 18353 solver.cpp:218] Iteration 3696 (2.46257 iter/s, 4.87295s/12 iters), loss = 1.58496
I0410 13:56:27.507366 18353 solver.cpp:237]     Train net output #0: loss = 1.58496 (* 1 = 1.58496 loss)
I0410 13:56:27.507377 18353 sgd_solver.cpp:105] Iteration 3696, lr = 0.00480886
I0410 13:56:32.436619 18353 solver.cpp:218] Iteration 3708 (2.43451 iter/s, 4.92913s/12 iters), loss = 1.81088
I0410 13:56:32.436667 18353 solver.cpp:237]     Train net output #0: loss = 1.81088 (* 1 = 1.81088 loss)
I0410 13:56:32.436679 18353 sgd_solver.cpp:105] Iteration 3708, lr = 0.00479744
I0410 13:56:37.354802 18353 solver.cpp:218] Iteration 3720 (2.44001 iter/s, 4.91801s/12 iters), loss = 1.87532
I0410 13:56:37.354956 18353 solver.cpp:237]     Train net output #0: loss = 1.87532 (* 1 = 1.87532 loss)
I0410 13:56:37.354969 18353 sgd_solver.cpp:105] Iteration 3720, lr = 0.00478605
I0410 13:56:42.326727 18353 solver.cpp:218] Iteration 3732 (2.41369 iter/s, 4.97165s/12 iters), loss = 1.50534
I0410 13:56:42.326781 18353 solver.cpp:237]     Train net output #0: loss = 1.50534 (* 1 = 1.50534 loss)
I0410 13:56:42.326794 18353 sgd_solver.cpp:105] Iteration 3732, lr = 0.00477469
I0410 13:56:46.374681 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:56:47.372406 18353 solver.cpp:218] Iteration 3744 (2.37836 iter/s, 5.04549s/12 iters), loss = 1.57691
I0410 13:56:47.372457 18353 solver.cpp:237]     Train net output #0: loss = 1.57691 (* 1 = 1.57691 loss)
I0410 13:56:47.372467 18353 sgd_solver.cpp:105] Iteration 3744, lr = 0.00476335
I0410 13:56:52.314335 18353 solver.cpp:218] Iteration 3756 (2.42829 iter/s, 4.94175s/12 iters), loss = 1.75745
I0410 13:56:52.314376 18353 solver.cpp:237]     Train net output #0: loss = 1.75745 (* 1 = 1.75745 loss)
I0410 13:56:52.314385 18353 sgd_solver.cpp:105] Iteration 3756, lr = 0.00475204
I0410 13:56:57.231103 18353 solver.cpp:218] Iteration 3768 (2.44071 iter/s, 4.9166s/12 iters), loss = 1.90461
I0410 13:56:57.231166 18353 solver.cpp:237]     Train net output #0: loss = 1.90461 (* 1 = 1.90461 loss)
I0410 13:56:57.231179 18353 sgd_solver.cpp:105] Iteration 3768, lr = 0.00474076
I0410 13:56:59.190728 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3774.caffemodel
I0410 13:56:59.848824 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3774.solverstate
I0410 13:57:00.058601 18353 solver.cpp:330] Iteration 3774, Testing net (#0)
I0410 13:57:00.058629 18353 net.cpp:676] Ignoring source layer train-data
I0410 13:57:03.063194 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:57:04.579468 18353 solver.cpp:397]     Test net output #0: accuracy = 0.448529
I0410 13:57:04.579521 18353 solver.cpp:397]     Test net output #1: loss = 2.34371 (* 1 = 2.34371 loss)
I0410 13:57:06.401641 18353 solver.cpp:218] Iteration 3780 (1.30858 iter/s, 9.17025s/12 iters), loss = 1.28139
I0410 13:57:06.401700 18353 solver.cpp:237]     Train net output #0: loss = 1.28139 (* 1 = 1.28139 loss)
I0410 13:57:06.401712 18353 sgd_solver.cpp:105] Iteration 3780, lr = 0.00472951
I0410 13:57:11.357578 18353 solver.cpp:218] Iteration 3792 (2.42143 iter/s, 4.95576s/12 iters), loss = 1.6356
I0410 13:57:11.357677 18353 solver.cpp:237]     Train net output #0: loss = 1.6356 (* 1 = 1.6356 loss)
I0410 13:57:11.357689 18353 sgd_solver.cpp:105] Iteration 3792, lr = 0.00471828
I0410 13:57:16.341289 18353 solver.cpp:218] Iteration 3804 (2.40796 iter/s, 4.98348s/12 iters), loss = 1.6767
I0410 13:57:16.341347 18353 solver.cpp:237]     Train net output #0: loss = 1.6767 (* 1 = 1.6767 loss)
I0410 13:57:16.341361 18353 sgd_solver.cpp:105] Iteration 3804, lr = 0.00470707
I0410 13:57:21.255390 18353 solver.cpp:218] Iteration 3816 (2.44204 iter/s, 4.91392s/12 iters), loss = 1.29898
I0410 13:57:21.255437 18353 solver.cpp:237]     Train net output #0: loss = 1.29898 (* 1 = 1.29898 loss)
I0410 13:57:21.255447 18353 sgd_solver.cpp:105] Iteration 3816, lr = 0.0046959
I0410 13:57:26.183518 18353 solver.cpp:218] Iteration 3828 (2.43509 iter/s, 4.92795s/12 iters), loss = 1.66493
I0410 13:57:26.183570 18353 solver.cpp:237]     Train net output #0: loss = 1.66493 (* 1 = 1.66493 loss)
I0410 13:57:26.183579 18353 sgd_solver.cpp:105] Iteration 3828, lr = 0.00468475
I0410 13:57:31.167183 18353 solver.cpp:218] Iteration 3840 (2.40796 iter/s, 4.98348s/12 iters), loss = 1.42886
I0410 13:57:31.167254 18353 solver.cpp:237]     Train net output #0: loss = 1.42886 (* 1 = 1.42886 loss)
I0410 13:57:31.167271 18353 sgd_solver.cpp:105] Iteration 3840, lr = 0.00467363
I0410 13:57:32.300902 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:57:36.142433 18353 solver.cpp:218] Iteration 3852 (2.41203 iter/s, 4.97506s/12 iters), loss = 1.49186
I0410 13:57:36.142482 18353 solver.cpp:237]     Train net output #0: loss = 1.49186 (* 1 = 1.49186 loss)
I0410 13:57:36.142491 18353 sgd_solver.cpp:105] Iteration 3852, lr = 0.00466253
I0410 13:57:41.096594 18353 solver.cpp:218] Iteration 3864 (2.42229 iter/s, 4.95399s/12 iters), loss = 1.36063
I0410 13:57:41.096639 18353 solver.cpp:237]     Train net output #0: loss = 1.36063 (* 1 = 1.36063 loss)
I0410 13:57:41.096649 18353 sgd_solver.cpp:105] Iteration 3864, lr = 0.00465146
I0410 13:57:45.531167 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3876.caffemodel
I0410 13:57:45.849758 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3876.solverstate
I0410 13:57:46.059979 18353 solver.cpp:330] Iteration 3876, Testing net (#0)
I0410 13:57:46.060007 18353 net.cpp:676] Ignoring source layer train-data
I0410 13:57:48.934324 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:57:50.474426 18353 solver.cpp:397]     Test net output #0: accuracy = 0.406863
I0410 13:57:50.474465 18353 solver.cpp:397]     Test net output #1: loss = 2.56164 (* 1 = 2.56164 loss)
I0410 13:57:50.555913 18353 solver.cpp:218] Iteration 3876 (1.26863 iter/s, 9.45904s/12 iters), loss = 1.44307
I0410 13:57:50.555967 18353 solver.cpp:237]     Train net output #0: loss = 1.44307 (* 1 = 1.44307 loss)
I0410 13:57:50.555977 18353 sgd_solver.cpp:105] Iteration 3876, lr = 0.00464042
I0410 13:57:54.755039 18353 solver.cpp:218] Iteration 3888 (2.85785 iter/s, 4.19896s/12 iters), loss = 1.67879
I0410 13:57:54.755089 18353 solver.cpp:237]     Train net output #0: loss = 1.67879 (* 1 = 1.67879 loss)
I0410 13:57:54.755100 18353 sgd_solver.cpp:105] Iteration 3888, lr = 0.0046294
I0410 13:57:59.678004 18353 solver.cpp:218] Iteration 3900 (2.43765 iter/s, 4.92278s/12 iters), loss = 1.50739
I0410 13:57:59.678053 18353 solver.cpp:237]     Train net output #0: loss = 1.50739 (* 1 = 1.50739 loss)
I0410 13:57:59.678063 18353 sgd_solver.cpp:105] Iteration 3900, lr = 0.00461841
I0410 13:58:04.604776 18353 solver.cpp:218] Iteration 3912 (2.43576 iter/s, 4.92659s/12 iters), loss = 1.50933
I0410 13:58:04.604821 18353 solver.cpp:237]     Train net output #0: loss = 1.50933 (* 1 = 1.50933 loss)
I0410 13:58:04.604832 18353 sgd_solver.cpp:105] Iteration 3912, lr = 0.00460744
I0410 13:58:09.518885 18353 solver.cpp:218] Iteration 3924 (2.44203 iter/s, 4.91394s/12 iters), loss = 1.58535
I0410 13:58:09.518927 18353 solver.cpp:237]     Train net output #0: loss = 1.58535 (* 1 = 1.58535 loss)
I0410 13:58:09.518937 18353 sgd_solver.cpp:105] Iteration 3924, lr = 0.0045965
I0410 13:58:14.403676 18353 solver.cpp:218] Iteration 3936 (2.45669 iter/s, 4.88462s/12 iters), loss = 1.35585
I0410 13:58:14.403717 18353 solver.cpp:237]     Train net output #0: loss = 1.35585 (* 1 = 1.35585 loss)
I0410 13:58:14.403725 18353 sgd_solver.cpp:105] Iteration 3936, lr = 0.00458559
I0410 13:58:17.756521 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:58:19.393201 18353 solver.cpp:218] Iteration 3948 (2.40512 iter/s, 4.98935s/12 iters), loss = 1.36038
I0410 13:58:19.393255 18353 solver.cpp:237]     Train net output #0: loss = 1.36038 (* 1 = 1.36038 loss)
I0410 13:58:19.393270 18353 sgd_solver.cpp:105] Iteration 3948, lr = 0.0045747
I0410 13:58:24.301466 18353 solver.cpp:218] Iteration 3960 (2.44495 iter/s, 4.90808s/12 iters), loss = 1.20048
I0410 13:58:24.301517 18353 solver.cpp:237]     Train net output #0: loss = 1.20048 (* 1 = 1.20048 loss)
I0410 13:58:24.301527 18353 sgd_solver.cpp:105] Iteration 3960, lr = 0.00456384
I0410 13:58:29.203214 18353 solver.cpp:218] Iteration 3972 (2.4482 iter/s, 4.90157s/12 iters), loss = 1.30836
I0410 13:58:29.203271 18353 solver.cpp:237]     Train net output #0: loss = 1.30836 (* 1 = 1.30836 loss)
I0410 13:58:29.203284 18353 sgd_solver.cpp:105] Iteration 3972, lr = 0.00455301
I0410 13:58:31.166640 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3978.caffemodel
I0410 13:58:31.473456 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3978.solverstate
I0410 13:58:31.682641 18353 solver.cpp:330] Iteration 3978, Testing net (#0)
I0410 13:58:31.682668 18353 net.cpp:676] Ignoring source layer train-data
I0410 13:58:34.520434 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:58:36.110832 18353 solver.cpp:397]     Test net output #0: accuracy = 0.418505
I0410 13:58:36.110883 18353 solver.cpp:397]     Test net output #1: loss = 2.59685 (* 1 = 2.59685 loss)
I0410 13:58:37.946161 18353 solver.cpp:218] Iteration 3984 (1.37258 iter/s, 8.74267s/12 iters), loss = 1.35089
I0410 13:58:37.946220 18353 solver.cpp:237]     Train net output #0: loss = 1.35089 (* 1 = 1.35089 loss)
I0410 13:58:37.946233 18353 sgd_solver.cpp:105] Iteration 3984, lr = 0.0045422
I0410 13:58:42.942209 18353 solver.cpp:218] Iteration 3996 (2.40199 iter/s, 4.99585s/12 iters), loss = 1.5281
I0410 13:58:42.942260 18353 solver.cpp:237]     Train net output #0: loss = 1.5281 (* 1 = 1.5281 loss)
I0410 13:58:42.942272 18353 sgd_solver.cpp:105] Iteration 3996, lr = 0.00453141
I0410 13:58:47.826309 18353 solver.cpp:218] Iteration 4008 (2.45704 iter/s, 4.88392s/12 iters), loss = 1.54976
I0410 13:58:47.826426 18353 solver.cpp:237]     Train net output #0: loss = 1.54976 (* 1 = 1.54976 loss)
I0410 13:58:47.826437 18353 sgd_solver.cpp:105] Iteration 4008, lr = 0.00452066
I0410 13:58:52.756757 18353 solver.cpp:218] Iteration 4020 (2.43398 iter/s, 4.9302s/12 iters), loss = 1.55771
I0410 13:58:52.756804 18353 solver.cpp:237]     Train net output #0: loss = 1.55771 (* 1 = 1.55771 loss)
I0410 13:58:52.756814 18353 sgd_solver.cpp:105] Iteration 4020, lr = 0.00450992
I0410 13:58:57.678865 18353 solver.cpp:218] Iteration 4032 (2.43807 iter/s, 4.92193s/12 iters), loss = 1.75936
I0410 13:58:57.678916 18353 solver.cpp:237]     Train net output #0: loss = 1.75936 (* 1 = 1.75936 loss)
I0410 13:58:57.678928 18353 sgd_solver.cpp:105] Iteration 4032, lr = 0.00449921
I0410 13:59:02.567752 18353 solver.cpp:218] Iteration 4044 (2.45464 iter/s, 4.8887s/12 iters), loss = 1.46169
I0410 13:59:02.567806 18353 solver.cpp:237]     Train net output #0: loss = 1.46169 (* 1 = 1.46169 loss)
I0410 13:59:02.567817 18353 sgd_solver.cpp:105] Iteration 4044, lr = 0.00448853
I0410 13:59:03.060986 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:59:07.497151 18353 solver.cpp:218] Iteration 4056 (2.43447 iter/s, 4.92921s/12 iters), loss = 1.44946
I0410 13:59:07.497254 18353 solver.cpp:237]     Train net output #0: loss = 1.44946 (* 1 = 1.44946 loss)
I0410 13:59:07.497269 18353 sgd_solver.cpp:105] Iteration 4056, lr = 0.00447788
I0410 13:59:12.344678 18353 solver.cpp:218] Iteration 4068 (2.4756 iter/s, 4.8473s/12 iters), loss = 1.49567
I0410 13:59:12.344731 18353 solver.cpp:237]     Train net output #0: loss = 1.49567 (* 1 = 1.49567 loss)
I0410 13:59:12.344744 18353 sgd_solver.cpp:105] Iteration 4068, lr = 0.00446724
I0410 13:59:16.905570 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4080.caffemodel
I0410 13:59:17.223898 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4080.solverstate
I0410 13:59:17.431499 18353 solver.cpp:330] Iteration 4080, Testing net (#0)
I0410 13:59:17.431525 18353 net.cpp:676] Ignoring source layer train-data
I0410 13:59:20.236210 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:59:21.981973 18353 solver.cpp:397]     Test net output #0: accuracy = 0.426471
I0410 13:59:21.982024 18353 solver.cpp:397]     Test net output #1: loss = 2.44497 (* 1 = 2.44497 loss)
I0410 13:59:22.064462 18353 solver.cpp:218] Iteration 4080 (1.23463 iter/s, 9.71949s/12 iters), loss = 1.45275
I0410 13:59:22.064512 18353 solver.cpp:237]     Train net output #0: loss = 1.45275 (* 1 = 1.45275 loss)
I0410 13:59:22.064522 18353 sgd_solver.cpp:105] Iteration 4080, lr = 0.00445664
I0410 13:59:26.223518 18353 solver.cpp:218] Iteration 4092 (2.88538 iter/s, 4.1589s/12 iters), loss = 1.49643
I0410 13:59:26.223572 18353 solver.cpp:237]     Train net output #0: loss = 1.49643 (* 1 = 1.49643 loss)
I0410 13:59:26.223585 18353 sgd_solver.cpp:105] Iteration 4092, lr = 0.00444606
I0410 13:59:31.279194 18353 solver.cpp:218] Iteration 4104 (2.37366 iter/s, 5.05549s/12 iters), loss = 1.36383
I0410 13:59:31.279244 18353 solver.cpp:237]     Train net output #0: loss = 1.36383 (* 1 = 1.36383 loss)
I0410 13:59:31.279256 18353 sgd_solver.cpp:105] Iteration 4104, lr = 0.0044355
I0410 13:59:36.147090 18353 solver.cpp:218] Iteration 4116 (2.46522 iter/s, 4.86772s/12 iters), loss = 1.6937
I0410 13:59:36.147138 18353 solver.cpp:237]     Train net output #0: loss = 1.6937 (* 1 = 1.6937 loss)
I0410 13:59:36.147147 18353 sgd_solver.cpp:105] Iteration 4116, lr = 0.00442497
I0410 13:59:41.048689 18353 solver.cpp:218] Iteration 4128 (2.44827 iter/s, 4.90142s/12 iters), loss = 1.28422
I0410 13:59:41.048734 18353 solver.cpp:237]     Train net output #0: loss = 1.28422 (* 1 = 1.28422 loss)
I0410 13:59:41.048744 18353 sgd_solver.cpp:105] Iteration 4128, lr = 0.00441447
I0410 13:59:46.039047 18353 solver.cpp:218] Iteration 4140 (2.40473 iter/s, 4.99017s/12 iters), loss = 1.37031
I0410 13:59:46.039096 18353 solver.cpp:237]     Train net output #0: loss = 1.37031 (* 1 = 1.37031 loss)
I0410 13:59:46.039108 18353 sgd_solver.cpp:105] Iteration 4140, lr = 0.00440398
I0410 13:59:48.612550 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:59:50.927711 18353 solver.cpp:218] Iteration 4152 (2.45475 iter/s, 4.88849s/12 iters), loss = 1.30111
I0410 13:59:50.927805 18353 solver.cpp:237]     Train net output #0: loss = 1.30111 (* 1 = 1.30111 loss)
I0410 13:59:50.927816 18353 sgd_solver.cpp:105] Iteration 4152, lr = 0.00439353
I0410 13:59:50.927991 18353 blocking_queue.cpp:49] Waiting for data
I0410 13:59:55.949334 18353 solver.cpp:218] Iteration 4164 (2.38977 iter/s, 5.0214s/12 iters), loss = 1.51898
I0410 13:59:55.949381 18353 solver.cpp:237]     Train net output #0: loss = 1.51898 (* 1 = 1.51898 loss)
I0410 13:59:55.949391 18353 sgd_solver.cpp:105] Iteration 4164, lr = 0.0043831
I0410 14:00:01.312629 18353 solver.cpp:218] Iteration 4176 (2.23751 iter/s, 5.3631s/12 iters), loss = 1.35523
I0410 14:00:01.312678 18353 solver.cpp:237]     Train net output #0: loss = 1.35523 (* 1 = 1.35523 loss)
I0410 14:00:01.312688 18353 sgd_solver.cpp:105] Iteration 4176, lr = 0.00437269
I0410 14:00:03.327309 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4182.caffemodel
I0410 14:00:04.197429 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4182.solverstate
I0410 14:00:04.541380 18353 solver.cpp:330] Iteration 4182, Testing net (#0)
I0410 14:00:04.541406 18353 net.cpp:676] Ignoring source layer train-data
I0410 14:00:07.505839 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:00:09.160324 18353 solver.cpp:397]     Test net output #0: accuracy = 0.426471
I0410 14:00:09.160356 18353 solver.cpp:397]     Test net output #1: loss = 2.54183 (* 1 = 2.54183 loss)
I0410 14:00:11.015223 18353 solver.cpp:218] Iteration 4188 (1.23682 iter/s, 9.7023s/12 iters), loss = 1.16912
I0410 14:00:11.015265 18353 solver.cpp:237]     Train net output #0: loss = 1.16912 (* 1 = 1.16912 loss)
I0410 14:00:11.015275 18353 sgd_solver.cpp:105] Iteration 4188, lr = 0.00436231
I0410 14:00:15.968037 18353 solver.cpp:218] Iteration 4200 (2.42295 iter/s, 4.95264s/12 iters), loss = 1.55696
I0410 14:00:15.968091 18353 solver.cpp:237]     Train net output #0: loss = 1.55696 (* 1 = 1.55696 loss)
I0410 14:00:15.968102 18353 sgd_solver.cpp:105] Iteration 4200, lr = 0.00435195
I0410 14:00:20.809840 18353 solver.cpp:218] Iteration 4212 (2.47851 iter/s, 4.84162s/12 iters), loss = 1.26365
I0410 14:00:20.809885 18353 solver.cpp:237]     Train net output #0: loss = 1.26365 (* 1 = 1.26365 loss)
I0410 14:00:20.809895 18353 sgd_solver.cpp:105] Iteration 4212, lr = 0.00434162
I0410 14:00:25.698807 18353 solver.cpp:218] Iteration 4224 (2.45459 iter/s, 4.88879s/12 iters), loss = 1.31941
I0410 14:00:25.698931 18353 solver.cpp:237]     Train net output #0: loss = 1.31941 (* 1 = 1.31941 loss)
I0410 14:00:25.698943 18353 sgd_solver.cpp:105] Iteration 4224, lr = 0.00433131
I0410 14:00:30.642099 18353 solver.cpp:218] Iteration 4236 (2.42766 iter/s, 4.94303s/12 iters), loss = 1.40409
I0410 14:00:30.642158 18353 solver.cpp:237]     Train net output #0: loss = 1.40409 (* 1 = 1.40409 loss)
I0410 14:00:30.642170 18353 sgd_solver.cpp:105] Iteration 4236, lr = 0.00432103
I0410 14:00:35.275243 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:00:35.501474 18353 solver.cpp:218] Iteration 4248 (2.46955 iter/s, 4.85919s/12 iters), loss = 1.36637
I0410 14:00:35.501535 18353 solver.cpp:237]     Train net output #0: loss = 1.36637 (* 1 = 1.36637 loss)
I0410 14:00:35.501549 18353 sgd_solver.cpp:105] Iteration 4248, lr = 0.00431077
I0410 14:00:40.464879 18353 solver.cpp:218] Iteration 4260 (2.41779 iter/s, 4.96321s/12 iters), loss = 1.36796
I0410 14:00:40.464934 18353 solver.cpp:237]     Train net output #0: loss = 1.36796 (* 1 = 1.36796 loss)
I0410 14:00:40.464948 18353 sgd_solver.cpp:105] Iteration 4260, lr = 0.00430053
I0410 14:00:45.368072 18353 solver.cpp:218] Iteration 4272 (2.44748 iter/s, 4.903s/12 iters), loss = 1.1717
I0410 14:00:45.368124 18353 solver.cpp:237]     Train net output #0: loss = 1.1717 (* 1 = 1.1717 loss)
I0410 14:00:45.368135 18353 sgd_solver.cpp:105] Iteration 4272, lr = 0.00429032
I0410 14:00:49.853034 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4284.caffemodel
I0410 14:00:50.170987 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4284.solverstate
I0410 14:00:50.381084 18353 solver.cpp:330] Iteration 4284, Testing net (#0)
I0410 14:00:50.381114 18353 net.cpp:676] Ignoring source layer train-data
I0410 14:00:53.216172 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:00:54.904783 18353 solver.cpp:397]     Test net output #0: accuracy = 0.430147
I0410 14:00:54.904835 18353 solver.cpp:397]     Test net output #1: loss = 2.39067 (* 1 = 2.39067 loss)
I0410 14:00:54.986205 18353 solver.cpp:218] Iteration 4284 (1.24768 iter/s, 9.61784s/12 iters), loss = 1.4687
I0410 14:00:54.986254 18353 solver.cpp:237]     Train net output #0: loss = 1.4687 (* 1 = 1.4687 loss)
I0410 14:00:54.986268 18353 sgd_solver.cpp:105] Iteration 4284, lr = 0.00428014
I0410 14:00:59.266633 18353 solver.cpp:218] Iteration 4296 (2.80357 iter/s, 4.28026s/12 iters), loss = 1.66026
I0410 14:00:59.266717 18353 solver.cpp:237]     Train net output #0: loss = 1.66026 (* 1 = 1.66026 loss)
I0410 14:00:59.266731 18353 sgd_solver.cpp:105] Iteration 4296, lr = 0.00426998
I0410 14:01:04.187674 18353 solver.cpp:218] Iteration 4308 (2.43862 iter/s, 4.92083s/12 iters), loss = 1.37067
I0410 14:01:04.187721 18353 solver.cpp:237]     Train net output #0: loss = 1.37067 (* 1 = 1.37067 loss)
I0410 14:01:04.187731 18353 sgd_solver.cpp:105] Iteration 4308, lr = 0.00425984
I0410 14:01:09.142001 18353 solver.cpp:218] Iteration 4320 (2.42221 iter/s, 4.95415s/12 iters), loss = 1.41673
I0410 14:01:09.142048 18353 solver.cpp:237]     Train net output #0: loss = 1.41673 (* 1 = 1.41673 loss)
I0410 14:01:09.142058 18353 sgd_solver.cpp:105] Iteration 4320, lr = 0.00424972
I0410 14:01:14.021447 18353 solver.cpp:218] Iteration 4332 (2.45939 iter/s, 4.87926s/12 iters), loss = 1.48246
I0410 14:01:14.021507 18353 solver.cpp:237]     Train net output #0: loss = 1.48246 (* 1 = 1.48246 loss)
I0410 14:01:14.021519 18353 sgd_solver.cpp:105] Iteration 4332, lr = 0.00423964
I0410 14:01:19.020776 18353 solver.cpp:218] Iteration 4344 (2.40042 iter/s, 4.99913s/12 iters), loss = 1.25341
I0410 14:01:19.020834 18353 solver.cpp:237]     Train net output #0: loss = 1.25341 (* 1 = 1.25341 loss)
I0410 14:01:19.020848 18353 sgd_solver.cpp:105] Iteration 4344, lr = 0.00422957
I0410 14:01:20.851737 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:01:23.894788 18353 solver.cpp:218] Iteration 4356 (2.46213 iter/s, 4.87383s/12 iters), loss = 1.38645
I0410 14:01:23.894840 18353 solver.cpp:237]     Train net output #0: loss = 1.38645 (* 1 = 1.38645 loss)
I0410 14:01:23.894851 18353 sgd_solver.cpp:105] Iteration 4356, lr = 0.00421953
I0410 14:01:28.794024 18353 solver.cpp:218] Iteration 4368 (2.44946 iter/s, 4.89905s/12 iters), loss = 1.18445
I0410 14:01:28.794078 18353 solver.cpp:237]     Train net output #0: loss = 1.18445 (* 1 = 1.18445 loss)
I0410 14:01:28.794091 18353 sgd_solver.cpp:105] Iteration 4368, lr = 0.00420951
I0410 14:01:34.146457 18353 solver.cpp:218] Iteration 4380 (2.24205 iter/s, 5.35224s/12 iters), loss = 1.42162
I0410 14:01:34.157761 18353 solver.cpp:237]     Train net output #0: loss = 1.42162 (* 1 = 1.42162 loss)
I0410 14:01:34.157774 18353 sgd_solver.cpp:105] Iteration 4380, lr = 0.00419952
I0410 14:01:36.129391 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4386.caffemodel
I0410 14:01:36.461992 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4386.solverstate
I0410 14:01:36.671322 18353 solver.cpp:330] Iteration 4386, Testing net (#0)
I0410 14:01:36.671345 18353 net.cpp:676] Ignoring source layer train-data
I0410 14:01:39.407163 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:01:41.223201 18353 solver.cpp:397]     Test net output #0: accuracy = 0.463848
I0410 14:01:41.223235 18353 solver.cpp:397]     Test net output #1: loss = 2.31559 (* 1 = 2.31559 loss)
I0410 14:01:43.116909 18353 solver.cpp:218] Iteration 4392 (1.33945 iter/s, 8.95892s/12 iters), loss = 1.15617
I0410 14:01:43.116966 18353 solver.cpp:237]     Train net output #0: loss = 1.15617 (* 1 = 1.15617 loss)
I0410 14:01:43.116978 18353 sgd_solver.cpp:105] Iteration 4392, lr = 0.00418954
I0410 14:01:48.070022 18353 solver.cpp:218] Iteration 4404 (2.42281 iter/s, 4.95292s/12 iters), loss = 1.40094
I0410 14:01:48.070076 18353 solver.cpp:237]     Train net output #0: loss = 1.40094 (* 1 = 1.40094 loss)
I0410 14:01:48.070088 18353 sgd_solver.cpp:105] Iteration 4404, lr = 0.0041796
I0410 14:01:52.962445 18353 solver.cpp:218] Iteration 4416 (2.45287 iter/s, 4.89223s/12 iters), loss = 1.1402
I0410 14:01:52.962500 18353 solver.cpp:237]     Train net output #0: loss = 1.1402 (* 1 = 1.1402 loss)
I0410 14:01:52.962512 18353 sgd_solver.cpp:105] Iteration 4416, lr = 0.00416967
I0410 14:01:57.905905 18353 solver.cpp:218] Iteration 4428 (2.42754 iter/s, 4.94327s/12 iters), loss = 1.15563
I0410 14:01:57.905968 18353 solver.cpp:237]     Train net output #0: loss = 1.15563 (* 1 = 1.15563 loss)
I0410 14:01:57.905978 18353 sgd_solver.cpp:105] Iteration 4428, lr = 0.00415977
I0410 14:02:02.911180 18353 solver.cpp:218] Iteration 4440 (2.39756 iter/s, 5.00509s/12 iters), loss = 1.26739
I0410 14:02:02.911234 18353 solver.cpp:237]     Train net output #0: loss = 1.26739 (* 1 = 1.26739 loss)
I0410 14:02:02.911247 18353 sgd_solver.cpp:105] Iteration 4440, lr = 0.0041499
I0410 14:02:06.895454 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:02:07.842903 18353 solver.cpp:218] Iteration 4452 (2.43332 iter/s, 4.93153s/12 iters), loss = 1.07145
I0410 14:02:07.842962 18353 solver.cpp:237]     Train net output #0: loss = 1.07145 (* 1 = 1.07145 loss)
I0410 14:02:07.842975 18353 sgd_solver.cpp:105] Iteration 4452, lr = 0.00414005
I0410 14:02:12.805490 18353 solver.cpp:218] Iteration 4464 (2.41819 iter/s, 4.96239s/12 iters), loss = 1.42448
I0410 14:02:12.805548 18353 solver.cpp:237]     Train net output #0: loss = 1.42448 (* 1 = 1.42448 loss)
I0410 14:02:12.805562 18353 sgd_solver.cpp:105] Iteration 4464, lr = 0.00413022
I0410 14:02:17.730872 18353 solver.cpp:218] Iteration 4476 (2.43645 iter/s, 4.92519s/12 iters), loss = 1.12103
I0410 14:02:17.730918 18353 solver.cpp:237]     Train net output #0: loss = 1.12103 (* 1 = 1.12103 loss)
I0410 14:02:17.730927 18353 sgd_solver.cpp:105] Iteration 4476, lr = 0.00412041
I0410 14:02:22.153439 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4488.caffemodel
I0410 14:02:22.511227 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4488.solverstate
I0410 14:02:22.716478 18353 solver.cpp:330] Iteration 4488, Testing net (#0)
I0410 14:02:22.716501 18353 net.cpp:676] Ignoring source layer train-data
I0410 14:02:25.503085 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:02:27.305495 18353 solver.cpp:397]     Test net output #0: accuracy = 0.45098
I0410 14:02:27.305538 18353 solver.cpp:397]     Test net output #1: loss = 2.4038 (* 1 = 2.4038 loss)
I0410 14:02:27.387152 18353 solver.cpp:218] Iteration 4488 (1.24275 iter/s, 9.65598s/12 iters), loss = 1.08103
I0410 14:02:27.387203 18353 solver.cpp:237]     Train net output #0: loss = 1.08103 (* 1 = 1.08103 loss)
I0410 14:02:27.387214 18353 sgd_solver.cpp:105] Iteration 4488, lr = 0.00411063
I0410 14:02:31.719975 18353 solver.cpp:218] Iteration 4500 (2.76967 iter/s, 4.33265s/12 iters), loss = 1.08215
I0410 14:02:31.720031 18353 solver.cpp:237]     Train net output #0: loss = 1.08215 (* 1 = 1.08215 loss)
I0410 14:02:31.720041 18353 sgd_solver.cpp:105] Iteration 4500, lr = 0.00410087
I0410 14:02:36.633400 18353 solver.cpp:218] Iteration 4512 (2.44238 iter/s, 4.91323s/12 iters), loss = 1.22718
I0410 14:02:36.633452 18353 solver.cpp:237]     Train net output #0: loss = 1.22718 (* 1 = 1.22718 loss)
I0410 14:02:36.633466 18353 sgd_solver.cpp:105] Iteration 4512, lr = 0.00409113
I0410 14:02:41.555280 18353 solver.cpp:218] Iteration 4524 (2.43819 iter/s, 4.92169s/12 iters), loss = 1.15856
I0410 14:02:41.555425 18353 solver.cpp:237]     Train net output #0: loss = 1.15856 (* 1 = 1.15856 loss)
I0410 14:02:41.555438 18353 sgd_solver.cpp:105] Iteration 4524, lr = 0.00408142
I0410 14:02:46.421232 18353 solver.cpp:218] Iteration 4536 (2.46626 iter/s, 4.86567s/12 iters), loss = 0.911608
I0410 14:02:46.421283 18353 solver.cpp:237]     Train net output #0: loss = 0.911608 (* 1 = 0.911608 loss)
I0410 14:02:46.421293 18353 sgd_solver.cpp:105] Iteration 4536, lr = 0.00407173
I0410 14:02:51.401795 18353 solver.cpp:218] Iteration 4548 (2.40946 iter/s, 4.98037s/12 iters), loss = 0.927532
I0410 14:02:51.401850 18353 solver.cpp:237]     Train net output #0: loss = 0.927532 (* 1 = 0.927532 loss)
I0410 14:02:51.401861 18353 sgd_solver.cpp:105] Iteration 4548, lr = 0.00406206
I0410 14:02:52.620151 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:02:56.246757 18353 solver.cpp:218] Iteration 4560 (2.4769 iter/s, 4.84477s/12 iters), loss = 1.13474
I0410 14:02:56.246799 18353 solver.cpp:237]     Train net output #0: loss = 1.13474 (* 1 = 1.13474 loss)
I0410 14:02:56.246809 18353 sgd_solver.cpp:105] Iteration 4560, lr = 0.00405242
I0410 14:03:01.207598 18353 solver.cpp:218] Iteration 4572 (2.41903 iter/s, 4.96066s/12 iters), loss = 1.15158
I0410 14:03:01.207650 18353 solver.cpp:237]     Train net output #0: loss = 1.15158 (* 1 = 1.15158 loss)
I0410 14:03:01.207661 18353 sgd_solver.cpp:105] Iteration 4572, lr = 0.0040428
I0410 14:03:06.064555 18353 solver.cpp:218] Iteration 4584 (2.47078 iter/s, 4.85677s/12 iters), loss = 1.40769
I0410 14:03:06.064604 18353 solver.cpp:237]     Train net output #0: loss = 1.40769 (* 1 = 1.40769 loss)
I0410 14:03:06.064615 18353 sgd_solver.cpp:105] Iteration 4584, lr = 0.0040332
I0410 14:03:08.181063 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4590.caffemodel
I0410 14:03:08.903422 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4590.solverstate
I0410 14:03:09.106135 18353 solver.cpp:330] Iteration 4590, Testing net (#0)
I0410 14:03:09.106156 18353 net.cpp:676] Ignoring source layer train-data
I0410 14:03:11.678071 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:03:13.489878 18353 solver.cpp:397]     Test net output #0: accuracy = 0.452206
I0410 14:03:13.489923 18353 solver.cpp:397]     Test net output #1: loss = 2.36515 (* 1 = 2.36515 loss)
I0410 14:03:15.362236 18353 solver.cpp:218] Iteration 4596 (1.29069 iter/s, 9.29739s/12 iters), loss = 1.24643
I0410 14:03:15.362287 18353 solver.cpp:237]     Train net output #0: loss = 1.24643 (* 1 = 1.24643 loss)
I0410 14:03:15.362298 18353 sgd_solver.cpp:105] Iteration 4596, lr = 0.00402362
I0410 14:03:20.241621 18353 solver.cpp:218] Iteration 4608 (2.45942 iter/s, 4.8792s/12 iters), loss = 1.01562
I0410 14:03:20.241662 18353 solver.cpp:237]     Train net output #0: loss = 1.01562 (* 1 = 1.01562 loss)
I0410 14:03:20.241670 18353 sgd_solver.cpp:105] Iteration 4608, lr = 0.00401407
I0410 14:03:25.085870 18353 solver.cpp:218] Iteration 4620 (2.47726 iter/s, 4.84407s/12 iters), loss = 0.904701
I0410 14:03:25.085923 18353 solver.cpp:237]     Train net output #0: loss = 0.904701 (* 1 = 0.904701 loss)
I0410 14:03:25.085935 18353 sgd_solver.cpp:105] Iteration 4620, lr = 0.00400454
I0410 14:03:29.976976 18353 solver.cpp:218] Iteration 4632 (2.45353 iter/s, 4.89091s/12 iters), loss = 1.254
I0410 14:03:29.977035 18353 solver.cpp:237]     Train net output #0: loss = 1.254 (* 1 = 1.254 loss)
I0410 14:03:29.977047 18353 sgd_solver.cpp:105] Iteration 4632, lr = 0.00399503
I0410 14:03:34.946321 18353 solver.cpp:218] Iteration 4644 (2.4149 iter/s, 4.96915s/12 iters), loss = 1.26888
I0410 14:03:34.946374 18353 solver.cpp:237]     Train net output #0: loss = 1.26888 (* 1 = 1.26888 loss)
I0410 14:03:34.946384 18353 sgd_solver.cpp:105] Iteration 4644, lr = 0.00398555
I0410 14:03:38.316803 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:03:39.879413 18353 solver.cpp:218] Iteration 4656 (2.43265 iter/s, 4.9329s/12 iters), loss = 1.00222
I0410 14:03:39.879464 18353 solver.cpp:237]     Train net output #0: loss = 1.00222 (* 1 = 1.00222 loss)
I0410 14:03:39.879477 18353 sgd_solver.cpp:105] Iteration 4656, lr = 0.00397608
I0410 14:03:44.789042 18353 solver.cpp:218] Iteration 4668 (2.44427 iter/s, 4.90944s/12 iters), loss = 1.165
I0410 14:03:44.789157 18353 solver.cpp:237]     Train net output #0: loss = 1.165 (* 1 = 1.165 loss)
I0410 14:03:44.789170 18353 sgd_solver.cpp:105] Iteration 4668, lr = 0.00396664
I0410 14:03:50.016512 18353 solver.cpp:218] Iteration 4680 (2.29568 iter/s, 5.22721s/12 iters), loss = 1.05062
I0410 14:03:50.016557 18353 solver.cpp:237]     Train net output #0: loss = 1.05062 (* 1 = 1.05062 loss)
I0410 14:03:50.016567 18353 sgd_solver.cpp:105] Iteration 4680, lr = 0.00395723
I0410 14:03:54.453541 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4692.caffemodel
I0410 14:03:54.753439 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4692.solverstate
I0410 14:03:54.945390 18353 solver.cpp:330] Iteration 4692, Testing net (#0)
I0410 14:03:54.945410 18353 net.cpp:676] Ignoring source layer train-data
I0410 14:03:57.424165 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:03:59.271379 18353 solver.cpp:397]     Test net output #0: accuracy = 0.455882
I0410 14:03:59.271418 18353 solver.cpp:397]     Test net output #1: loss = 2.4423 (* 1 = 2.4423 loss)
I0410 14:03:59.352795 18353 solver.cpp:218] Iteration 4692 (1.28535 iter/s, 9.33599s/12 iters), loss = 1.06474
I0410 14:03:59.352859 18353 solver.cpp:237]     Train net output #0: loss = 1.06474 (* 1 = 1.06474 loss)
I0410 14:03:59.352874 18353 sgd_solver.cpp:105] Iteration 4692, lr = 0.00394783
I0410 14:04:03.622735 18353 solver.cpp:218] Iteration 4704 (2.81046 iter/s, 4.26976s/12 iters), loss = 1.38137
I0410 14:04:03.622773 18353 solver.cpp:237]     Train net output #0: loss = 1.38137 (* 1 = 1.38137 loss)
I0410 14:04:03.622782 18353 sgd_solver.cpp:105] Iteration 4704, lr = 0.00393846
I0410 14:04:08.541153 18353 solver.cpp:218] Iteration 4716 (2.4399 iter/s, 4.91824s/12 iters), loss = 1.15609
I0410 14:04:08.541209 18353 solver.cpp:237]     Train net output #0: loss = 1.15609 (* 1 = 1.15609 loss)
I0410 14:04:08.541222 18353 sgd_solver.cpp:105] Iteration 4716, lr = 0.00392911
I0410 14:04:13.462571 18353 solver.cpp:218] Iteration 4728 (2.43842 iter/s, 4.92123s/12 iters), loss = 1.14382
I0410 14:04:13.462615 18353 solver.cpp:237]     Train net output #0: loss = 1.14382 (* 1 = 1.14382 loss)
I0410 14:04:13.462623 18353 sgd_solver.cpp:105] Iteration 4728, lr = 0.00391978
I0410 14:04:18.374457 18353 solver.cpp:218] Iteration 4740 (2.44315 iter/s, 4.9117s/12 iters), loss = 1.03227
I0410 14:04:18.374598 18353 solver.cpp:237]     Train net output #0: loss = 1.03227 (* 1 = 1.03227 loss)
I0410 14:04:18.374608 18353 sgd_solver.cpp:105] Iteration 4740, lr = 0.00391047
I0410 14:04:23.292039 18353 solver.cpp:218] Iteration 4752 (2.44036 iter/s, 4.91731s/12 iters), loss = 1.09029
I0410 14:04:23.292088 18353 solver.cpp:237]     Train net output #0: loss = 1.09029 (* 1 = 1.09029 loss)
I0410 14:04:23.292100 18353 sgd_solver.cpp:105] Iteration 4752, lr = 0.00390119
I0410 14:04:23.813587 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:04:28.201828 18353 solver.cpp:218] Iteration 4764 (2.44419 iter/s, 4.9096s/12 iters), loss = 1.05631
I0410 14:04:28.201884 18353 solver.cpp:237]     Train net output #0: loss = 1.05631 (* 1 = 1.05631 loss)
I0410 14:04:28.201896 18353 sgd_solver.cpp:105] Iteration 4764, lr = 0.00389193
I0410 14:04:33.077327 18353 solver.cpp:218] Iteration 4776 (2.46138 iter/s, 4.87531s/12 iters), loss = 0.932104
I0410 14:04:33.077379 18353 solver.cpp:237]     Train net output #0: loss = 0.932104 (* 1 = 0.932104 loss)
I0410 14:04:33.077392 18353 sgd_solver.cpp:105] Iteration 4776, lr = 0.00388269
I0410 14:04:37.994797 18353 solver.cpp:218] Iteration 4788 (2.44037 iter/s, 4.91728s/12 iters), loss = 1.08139
I0410 14:04:37.994853 18353 solver.cpp:237]     Train net output #0: loss = 1.08139 (* 1 = 1.08139 loss)
I0410 14:04:37.994865 18353 sgd_solver.cpp:105] Iteration 4788, lr = 0.00387347
I0410 14:04:39.996443 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4794.caffemodel
I0410 14:04:40.337741 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4794.solverstate
I0410 14:04:40.568343 18353 solver.cpp:330] Iteration 4794, Testing net (#0)
I0410 14:04:40.568374 18353 net.cpp:676] Ignoring source layer train-data
I0410 14:04:43.169656 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:04:45.168460 18353 solver.cpp:397]     Test net output #0: accuracy = 0.471814
I0410 14:04:45.168509 18353 solver.cpp:397]     Test net output #1: loss = 2.3401 (* 1 = 2.3401 loss)
I0410 14:04:46.931562 18353 solver.cpp:218] Iteration 4800 (1.34281 iter/s, 8.93647s/12 iters), loss = 1.10191
I0410 14:04:46.931609 18353 solver.cpp:237]     Train net output #0: loss = 1.10191 (* 1 = 1.10191 loss)
I0410 14:04:46.931620 18353 sgd_solver.cpp:105] Iteration 4800, lr = 0.00386427
I0410 14:04:51.918498 18353 solver.cpp:218] Iteration 4812 (2.40637 iter/s, 4.98675s/12 iters), loss = 1.08118
I0410 14:04:51.918601 18353 solver.cpp:237]     Train net output #0: loss = 1.08118 (* 1 = 1.08118 loss)
I0410 14:04:51.918613 18353 sgd_solver.cpp:105] Iteration 4812, lr = 0.0038551
I0410 14:04:56.863299 18353 solver.cpp:218] Iteration 4824 (2.42691 iter/s, 4.94456s/12 iters), loss = 1.20497
I0410 14:04:56.863353 18353 solver.cpp:237]     Train net output #0: loss = 1.20497 (* 1 = 1.20497 loss)
I0410 14:04:56.863365 18353 sgd_solver.cpp:105] Iteration 4824, lr = 0.00384594
I0410 14:05:01.915419 18353 solver.cpp:218] Iteration 4836 (2.37533 iter/s, 5.05193s/12 iters), loss = 0.941107
I0410 14:05:01.915475 18353 solver.cpp:237]     Train net output #0: loss = 0.941107 (* 1 = 0.941107 loss)
I0410 14:05:01.915488 18353 sgd_solver.cpp:105] Iteration 4836, lr = 0.00383681
I0410 14:05:02.296501 18353 blocking_queue.cpp:49] Waiting for data
I0410 14:05:06.918074 18353 solver.cpp:218] Iteration 4848 (2.39883 iter/s, 5.00244s/12 iters), loss = 1.17652
I0410 14:05:06.918131 18353 solver.cpp:237]     Train net output #0: loss = 1.17652 (* 1 = 1.17652 loss)
I0410 14:05:06.918146 18353 sgd_solver.cpp:105] Iteration 4848, lr = 0.0038277
I0410 14:05:09.515722 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:05:11.836706 18353 solver.cpp:218] Iteration 4860 (2.4398 iter/s, 4.91844s/12 iters), loss = 0.993077
I0410 14:05:11.836747 18353 solver.cpp:237]     Train net output #0: loss = 0.993077 (* 1 = 0.993077 loss)
I0410 14:05:11.836755 18353 sgd_solver.cpp:105] Iteration 4860, lr = 0.00381862
I0410 14:05:16.763666 18353 solver.cpp:218] Iteration 4872 (2.43567 iter/s, 4.92678s/12 iters), loss = 1.07611
I0410 14:05:16.763718 18353 solver.cpp:237]     Train net output #0: loss = 1.07611 (* 1 = 1.07611 loss)
I0410 14:05:16.763729 18353 sgd_solver.cpp:105] Iteration 4872, lr = 0.00380955
I0410 14:05:21.707432 18353 solver.cpp:218] Iteration 4884 (2.4274 iter/s, 4.94357s/12 iters), loss = 1.11437
I0410 14:05:21.707485 18353 solver.cpp:237]     Train net output #0: loss = 1.11437 (* 1 = 1.11437 loss)
I0410 14:05:21.707499 18353 sgd_solver.cpp:105] Iteration 4884, lr = 0.0038005
I0410 14:05:26.124266 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4896.caffemodel
I0410 14:05:26.588547 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4896.solverstate
I0410 14:05:26.834033 18353 solver.cpp:330] Iteration 4896, Testing net (#0)
I0410 14:05:26.834053 18353 net.cpp:676] Ignoring source layer train-data
I0410 14:05:29.303061 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:05:31.230383 18353 solver.cpp:397]     Test net output #0: accuracy = 0.492647
I0410 14:05:31.230418 18353 solver.cpp:397]     Test net output #1: loss = 2.18453 (* 1 = 2.18453 loss)
I0410 14:05:31.311601 18353 solver.cpp:218] Iteration 4896 (1.2495 iter/s, 9.60386s/12 iters), loss = 0.927349
I0410 14:05:31.311642 18353 solver.cpp:237]     Train net output #0: loss = 0.927349 (* 1 = 0.927349 loss)
I0410 14:05:31.311650 18353 sgd_solver.cpp:105] Iteration 4896, lr = 0.00379148
I0410 14:05:35.416991 18353 solver.cpp:218] Iteration 4908 (2.9231 iter/s, 4.10523s/12 iters), loss = 1.17862
I0410 14:05:35.417038 18353 solver.cpp:237]     Train net output #0: loss = 1.17862 (* 1 = 1.17862 loss)
I0410 14:05:35.417048 18353 sgd_solver.cpp:105] Iteration 4908, lr = 0.00378248
I0410 14:05:40.404382 18353 solver.cpp:218] Iteration 4920 (2.40616 iter/s, 4.9872s/12 iters), loss = 1.14615
I0410 14:05:40.404430 18353 solver.cpp:237]     Train net output #0: loss = 1.14615 (* 1 = 1.14615 loss)
I0410 14:05:40.404439 18353 sgd_solver.cpp:105] Iteration 4920, lr = 0.0037735
I0410 14:05:45.308162 18353 solver.cpp:218] Iteration 4932 (2.44719 iter/s, 4.90359s/12 iters), loss = 1.13513
I0410 14:05:45.308210 18353 solver.cpp:237]     Train net output #0: loss = 1.13513 (* 1 = 1.13513 loss)
I0410 14:05:45.308219 18353 sgd_solver.cpp:105] Iteration 4932, lr = 0.00376454
I0410 14:05:50.207789 18353 solver.cpp:218] Iteration 4944 (2.44926 iter/s, 4.89944s/12 iters), loss = 0.981502
I0410 14:05:50.207829 18353 solver.cpp:237]     Train net output #0: loss = 0.981502 (* 1 = 0.981502 loss)
I0410 14:05:50.207839 18353 sgd_solver.cpp:105] Iteration 4944, lr = 0.0037556
I0410 14:05:54.847867 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:05:55.039412 18353 solver.cpp:218] Iteration 4956 (2.48373 iter/s, 4.83144s/12 iters), loss = 0.846399
I0410 14:05:55.039458 18353 solver.cpp:237]     Train net output #0: loss = 0.846399 (* 1 = 0.846399 loss)
I0410 14:05:55.039467 18353 sgd_solver.cpp:105] Iteration 4956, lr = 0.00374669
I0410 14:06:00.061573 18353 solver.cpp:218] Iteration 4968 (2.3895 iter/s, 5.02197s/12 iters), loss = 1.08995
I0410 14:06:00.061650 18353 solver.cpp:237]     Train net output #0: loss = 1.08995 (* 1 = 1.08995 loss)
I0410 14:06:00.061662 18353 sgd_solver.cpp:105] Iteration 4968, lr = 0.00373779
I0410 14:06:04.944831 18353 solver.cpp:218] Iteration 4980 (2.45748 iter/s, 4.88305s/12 iters), loss = 0.930892
I0410 14:06:04.944873 18353 solver.cpp:237]     Train net output #0: loss = 0.930892 (* 1 = 0.930892 loss)
I0410 14:06:04.944883 18353 sgd_solver.cpp:105] Iteration 4980, lr = 0.00372892
I0410 14:06:09.757166 18353 solver.cpp:218] Iteration 4992 (2.49369 iter/s, 4.81215s/12 iters), loss = 1.16513
I0410 14:06:09.757223 18353 solver.cpp:237]     Train net output #0: loss = 1.16513 (* 1 = 1.16513 loss)
I0410 14:06:09.757236 18353 sgd_solver.cpp:105] Iteration 4992, lr = 0.00372006
I0410 14:06:11.759436 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4998.caffemodel
I0410 14:06:12.056474 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4998.solverstate
I0410 14:06:12.249815 18353 solver.cpp:330] Iteration 4998, Testing net (#0)
I0410 14:06:12.249840 18353 net.cpp:676] Ignoring source layer train-data
I0410 14:06:14.722052 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:06:16.682961 18353 solver.cpp:397]     Test net output #0: accuracy = 0.500613
I0410 14:06:16.683013 18353 solver.cpp:397]     Test net output #1: loss = 2.25058 (* 1 = 2.25058 loss)
I0410 14:06:18.546725 18353 solver.cpp:218] Iteration 5004 (1.3653 iter/s, 8.78926s/12 iters), loss = 0.969264
I0410 14:06:18.546778 18353 solver.cpp:237]     Train net output #0: loss = 0.969264 (* 1 = 0.969264 loss)
I0410 14:06:18.546792 18353 sgd_solver.cpp:105] Iteration 5004, lr = 0.00371123
I0410 14:06:23.490792 18353 solver.cpp:218] Iteration 5016 (2.42725 iter/s, 4.94387s/12 iters), loss = 0.743767
I0410 14:06:23.490849 18353 solver.cpp:237]     Train net output #0: loss = 0.743767 (* 1 = 0.743767 loss)
I0410 14:06:23.490861 18353 sgd_solver.cpp:105] Iteration 5016, lr = 0.00370242
I0410 14:06:28.436518 18353 solver.cpp:218] Iteration 5028 (2.42643 iter/s, 4.94553s/12 iters), loss = 1.0275
I0410 14:06:28.436573 18353 solver.cpp:237]     Train net output #0: loss = 1.0275 (* 1 = 1.0275 loss)
I0410 14:06:28.436585 18353 sgd_solver.cpp:105] Iteration 5028, lr = 0.00369363
I0410 14:06:33.353547 18353 solver.cpp:218] Iteration 5040 (2.44059 iter/s, 4.91684s/12 iters), loss = 1.05361
I0410 14:06:33.353711 18353 solver.cpp:237]     Train net output #0: loss = 1.05361 (* 1 = 1.05361 loss)
I0410 14:06:33.353726 18353 sgd_solver.cpp:105] Iteration 5040, lr = 0.00368486
I0410 14:06:38.280802 18353 solver.cpp:218] Iteration 5052 (2.43558 iter/s, 4.92695s/12 iters), loss = 1.08544
I0410 14:06:38.280855 18353 solver.cpp:237]     Train net output #0: loss = 1.08544 (* 1 = 1.08544 loss)
I0410 14:06:38.280869 18353 sgd_solver.cpp:105] Iteration 5052, lr = 0.00367611
I0410 14:06:40.171129 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:06:43.203887 18353 solver.cpp:218] Iteration 5064 (2.43759 iter/s, 4.92289s/12 iters), loss = 1.04984
I0410 14:06:43.203946 18353 solver.cpp:237]     Train net output #0: loss = 1.04984 (* 1 = 1.04984 loss)
I0410 14:06:43.203958 18353 sgd_solver.cpp:105] Iteration 5064, lr = 0.00366738
I0410 14:06:48.124760 18353 solver.cpp:218] Iteration 5076 (2.43869 iter/s, 4.92068s/12 iters), loss = 0.851784
I0410 14:06:48.124807 18353 solver.cpp:237]     Train net output #0: loss = 0.851784 (* 1 = 0.851784 loss)
I0410 14:06:48.124819 18353 sgd_solver.cpp:105] Iteration 5076, lr = 0.00365868
I0410 14:06:53.141297 18353 solver.cpp:218] Iteration 5088 (2.39218 iter/s, 5.01635s/12 iters), loss = 0.918238
I0410 14:06:53.141342 18353 solver.cpp:237]     Train net output #0: loss = 0.918238 (* 1 = 0.918238 loss)
I0410 14:06:53.141356 18353 sgd_solver.cpp:105] Iteration 5088, lr = 0.00364999
I0410 14:06:57.715163 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5100.caffemodel
I0410 14:06:58.016777 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5100.solverstate
I0410 14:06:58.209563 18353 solver.cpp:330] Iteration 5100, Testing net (#0)
I0410 14:06:58.209581 18353 net.cpp:676] Ignoring source layer train-data
I0410 14:07:00.555544 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:07:02.559027 18353 solver.cpp:397]     Test net output #0: accuracy = 0.476103
I0410 14:07:02.559065 18353 solver.cpp:397]     Test net output #1: loss = 2.295 (* 1 = 2.295 loss)
I0410 14:07:02.640404 18353 solver.cpp:218] Iteration 5100 (1.26332 iter/s, 9.4988s/12 iters), loss = 1.1357
I0410 14:07:02.640455 18353 solver.cpp:237]     Train net output #0: loss = 1.1357 (* 1 = 1.1357 loss)
I0410 14:07:02.640465 18353 sgd_solver.cpp:105] Iteration 5100, lr = 0.00364132
I0410 14:07:06.747450 18353 solver.cpp:218] Iteration 5112 (2.92192 iter/s, 4.10688s/12 iters), loss = 0.892774
I0410 14:07:06.747589 18353 solver.cpp:237]     Train net output #0: loss = 0.892774 (* 1 = 0.892774 loss)
I0410 14:07:06.747602 18353 sgd_solver.cpp:105] Iteration 5112, lr = 0.00363268
I0410 14:07:11.726480 18353 solver.cpp:218] Iteration 5124 (2.41024 iter/s, 4.97875s/12 iters), loss = 1.14238
I0410 14:07:11.726542 18353 solver.cpp:237]     Train net output #0: loss = 1.14238 (* 1 = 1.14238 loss)
I0410 14:07:11.726555 18353 sgd_solver.cpp:105] Iteration 5124, lr = 0.00362405
I0410 14:07:16.567629 18353 solver.cpp:218] Iteration 5136 (2.47885 iter/s, 4.84095s/12 iters), loss = 0.952066
I0410 14:07:16.567687 18353 solver.cpp:237]     Train net output #0: loss = 0.952066 (* 1 = 0.952066 loss)
I0410 14:07:16.567699 18353 sgd_solver.cpp:105] Iteration 5136, lr = 0.00361545
I0410 14:07:21.642412 18353 solver.cpp:218] Iteration 5148 (2.36473 iter/s, 5.07458s/12 iters), loss = 0.867987
I0410 14:07:21.642472 18353 solver.cpp:237]     Train net output #0: loss = 0.867987 (* 1 = 0.867987 loss)
I0410 14:07:21.642485 18353 sgd_solver.cpp:105] Iteration 5148, lr = 0.00360687
I0410 14:07:25.668248 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:07:26.589309 18353 solver.cpp:218] Iteration 5160 (2.42586 iter/s, 4.94669s/12 iters), loss = 0.906246
I0410 14:07:26.589354 18353 solver.cpp:237]     Train net output #0: loss = 0.906246 (* 1 = 0.906246 loss)
I0410 14:07:26.589365 18353 sgd_solver.cpp:105] Iteration 5160, lr = 0.0035983
I0410 14:07:31.521200 18353 solver.cpp:218] Iteration 5172 (2.43324 iter/s, 4.9317s/12 iters), loss = 1.0638
I0410 14:07:31.521260 18353 solver.cpp:237]     Train net output #0: loss = 1.0638 (* 1 = 1.0638 loss)
I0410 14:07:31.521272 18353 sgd_solver.cpp:105] Iteration 5172, lr = 0.00358976
I0410 14:07:36.415705 18353 solver.cpp:218] Iteration 5184 (2.45183 iter/s, 4.8943s/12 iters), loss = 1.0089
I0410 14:07:36.415762 18353 solver.cpp:237]     Train net output #0: loss = 1.0089 (* 1 = 1.0089 loss)
I0410 14:07:36.415776 18353 sgd_solver.cpp:105] Iteration 5184, lr = 0.00358124
I0410 14:07:41.302879 18353 solver.cpp:218] Iteration 5196 (2.45551 iter/s, 4.88698s/12 iters), loss = 1.16452
I0410 14:07:41.303005 18353 solver.cpp:237]     Train net output #0: loss = 1.16452 (* 1 = 1.16452 loss)
I0410 14:07:41.303020 18353 sgd_solver.cpp:105] Iteration 5196, lr = 0.00357273
I0410 14:07:43.296200 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5202.caffemodel
I0410 14:07:43.637859 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5202.solverstate
I0410 14:07:43.848836 18353 solver.cpp:330] Iteration 5202, Testing net (#0)
I0410 14:07:43.848866 18353 net.cpp:676] Ignoring source layer train-data
I0410 14:07:46.391675 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:07:48.435194 18353 solver.cpp:397]     Test net output #0: accuracy = 0.485294
I0410 14:07:48.435233 18353 solver.cpp:397]     Test net output #1: loss = 2.30226 (* 1 = 2.30226 loss)
I0410 14:07:50.272680 18353 solver.cpp:218] Iteration 5208 (1.33788 iter/s, 8.96943s/12 iters), loss = 0.730962
I0410 14:07:50.272733 18353 solver.cpp:237]     Train net output #0: loss = 0.730962 (* 1 = 0.730962 loss)
I0410 14:07:50.272745 18353 sgd_solver.cpp:105] Iteration 5208, lr = 0.00356425
I0410 14:07:55.372608 18353 solver.cpp:218] Iteration 5220 (2.35306 iter/s, 5.09973s/12 iters), loss = 0.739734
I0410 14:07:55.372665 18353 solver.cpp:237]     Train net output #0: loss = 0.739734 (* 1 = 0.739734 loss)
I0410 14:07:55.372678 18353 sgd_solver.cpp:105] Iteration 5220, lr = 0.00355579
I0410 14:08:00.289011 18353 solver.cpp:218] Iteration 5232 (2.44091 iter/s, 4.91621s/12 iters), loss = 0.91464
I0410 14:08:00.289067 18353 solver.cpp:237]     Train net output #0: loss = 0.91464 (* 1 = 0.91464 loss)
I0410 14:08:00.289079 18353 sgd_solver.cpp:105] Iteration 5232, lr = 0.00354735
I0410 14:08:05.112500 18353 solver.cpp:218] Iteration 5244 (2.48793 iter/s, 4.82329s/12 iters), loss = 0.874483
I0410 14:08:05.112555 18353 solver.cpp:237]     Train net output #0: loss = 0.874483 (* 1 = 0.874483 loss)
I0410 14:08:05.112566 18353 sgd_solver.cpp:105] Iteration 5244, lr = 0.00353892
I0410 14:08:10.047274 18353 solver.cpp:218] Iteration 5256 (2.43182 iter/s, 4.93458s/12 iters), loss = 0.807583
I0410 14:08:10.047319 18353 solver.cpp:237]     Train net output #0: loss = 0.807583 (* 1 = 0.807583 loss)
I0410 14:08:10.047329 18353 sgd_solver.cpp:105] Iteration 5256, lr = 0.00353052
I0410 14:08:11.281033 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:08:14.927904 18353 solver.cpp:218] Iteration 5268 (2.45879 iter/s, 4.88045s/12 iters), loss = 0.734387
I0410 14:08:14.928041 18353 solver.cpp:237]     Train net output #0: loss = 0.734387 (* 1 = 0.734387 loss)
I0410 14:08:14.928052 18353 sgd_solver.cpp:105] Iteration 5268, lr = 0.00352214
I0410 14:08:19.857867 18353 solver.cpp:218] Iteration 5280 (2.43423 iter/s, 4.92968s/12 iters), loss = 0.82683
I0410 14:08:19.857924 18353 solver.cpp:237]     Train net output #0: loss = 0.82683 (* 1 = 0.82683 loss)
I0410 14:08:19.857937 18353 sgd_solver.cpp:105] Iteration 5280, lr = 0.00351378
I0410 14:08:24.745258 18353 solver.cpp:218] Iteration 5292 (2.4554 iter/s, 4.88719s/12 iters), loss = 1.20893
I0410 14:08:24.745316 18353 solver.cpp:237]     Train net output #0: loss = 1.20893 (* 1 = 1.20893 loss)
I0410 14:08:24.745329 18353 sgd_solver.cpp:105] Iteration 5292, lr = 0.00350544
I0410 14:08:29.172103 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5304.caffemodel
I0410 14:08:30.766839 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5304.solverstate
I0410 14:08:31.373553 18353 solver.cpp:330] Iteration 5304, Testing net (#0)
I0410 14:08:31.373584 18353 net.cpp:676] Ignoring source layer train-data
I0410 14:08:33.766425 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:08:35.873558 18353 solver.cpp:397]     Test net output #0: accuracy = 0.492034
I0410 14:08:35.873610 18353 solver.cpp:397]     Test net output #1: loss = 2.33675 (* 1 = 2.33675 loss)
I0410 14:08:35.954913 18353 solver.cpp:218] Iteration 5304 (1.07054 iter/s, 11.2093s/12 iters), loss = 0.75875
I0410 14:08:35.954972 18353 solver.cpp:237]     Train net output #0: loss = 0.75875 (* 1 = 0.75875 loss)
I0410 14:08:35.954986 18353 sgd_solver.cpp:105] Iteration 5304, lr = 0.00349711
I0410 14:08:40.149448 18353 solver.cpp:218] Iteration 5316 (2.86099 iter/s, 4.19435s/12 iters), loss = 0.772529
I0410 14:08:40.149497 18353 solver.cpp:237]     Train net output #0: loss = 0.772529 (* 1 = 0.772529 loss)
I0410 14:08:40.149508 18353 sgd_solver.cpp:105] Iteration 5316, lr = 0.00348881
I0410 14:08:45.111250 18353 solver.cpp:218] Iteration 5328 (2.41857 iter/s, 4.96161s/12 iters), loss = 0.959453
I0410 14:08:45.111363 18353 solver.cpp:237]     Train net output #0: loss = 0.959453 (* 1 = 0.959453 loss)
I0410 14:08:45.111377 18353 sgd_solver.cpp:105] Iteration 5328, lr = 0.00348053
I0410 14:08:49.985132 18353 solver.cpp:218] Iteration 5340 (2.46223 iter/s, 4.87363s/12 iters), loss = 1.02819
I0410 14:08:49.985189 18353 solver.cpp:237]     Train net output #0: loss = 1.02819 (* 1 = 1.02819 loss)
I0410 14:08:49.985203 18353 sgd_solver.cpp:105] Iteration 5340, lr = 0.00347226
I0410 14:08:54.874246 18353 solver.cpp:218] Iteration 5352 (2.45453 iter/s, 4.88891s/12 iters), loss = 0.989113
I0410 14:08:54.874308 18353 solver.cpp:237]     Train net output #0: loss = 0.989113 (* 1 = 0.989113 loss)
I0410 14:08:54.874321 18353 sgd_solver.cpp:105] Iteration 5352, lr = 0.00346402
I0410 14:08:58.183200 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:08:59.716583 18353 solver.cpp:218] Iteration 5364 (2.47825 iter/s, 4.84214s/12 iters), loss = 0.729132
I0410 14:08:59.716634 18353 solver.cpp:237]     Train net output #0: loss = 0.729132 (* 1 = 0.729132 loss)
I0410 14:08:59.716646 18353 sgd_solver.cpp:105] Iteration 5364, lr = 0.0034558
I0410 14:09:04.547833 18353 solver.cpp:218] Iteration 5376 (2.48393 iter/s, 4.83106s/12 iters), loss = 0.942957
I0410 14:09:04.547892 18353 solver.cpp:237]     Train net output #0: loss = 0.942957 (* 1 = 0.942957 loss)
I0410 14:09:04.547904 18353 sgd_solver.cpp:105] Iteration 5376, lr = 0.00344759
I0410 14:09:09.354701 18353 solver.cpp:218] Iteration 5388 (2.49653 iter/s, 4.80667s/12 iters), loss = 0.729248
I0410 14:09:09.354769 18353 solver.cpp:237]     Train net output #0: loss = 0.729248 (* 1 = 0.729248 loss)
I0410 14:09:09.354781 18353 sgd_solver.cpp:105] Iteration 5388, lr = 0.00343941
I0410 14:09:14.189153 18353 solver.cpp:218] Iteration 5400 (2.48229 iter/s, 4.83425s/12 iters), loss = 0.942244
I0410 14:09:14.189218 18353 solver.cpp:237]     Train net output #0: loss = 0.942244 (* 1 = 0.942244 loss)
I0410 14:09:14.189229 18353 sgd_solver.cpp:105] Iteration 5400, lr = 0.00343124
I0410 14:09:16.159343 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5406.caffemodel
I0410 14:09:16.461465 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5406.solverstate
I0410 14:09:16.656494 18353 solver.cpp:330] Iteration 5406, Testing net (#0)
I0410 14:09:16.656522 18353 net.cpp:676] Ignoring source layer train-data
I0410 14:09:18.961336 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:09:21.084326 18353 solver.cpp:397]     Test net output #0: accuracy = 0.48652
I0410 14:09:21.084384 18353 solver.cpp:397]     Test net output #1: loss = 2.34564 (* 1 = 2.34564 loss)
I0410 14:09:22.980984 18353 solver.cpp:218] Iteration 5412 (1.36495 iter/s, 8.79153s/12 iters), loss = 0.814675
I0410 14:09:22.981040 18353 solver.cpp:237]     Train net output #0: loss = 0.814675 (* 1 = 0.814675 loss)
I0410 14:09:22.981051 18353 sgd_solver.cpp:105] Iteration 5412, lr = 0.00342309
I0410 14:09:27.884358 18353 solver.cpp:218] Iteration 5424 (2.44739 iter/s, 4.90318s/12 iters), loss = 0.747278
I0410 14:09:27.884421 18353 solver.cpp:237]     Train net output #0: loss = 0.747278 (* 1 = 0.747278 loss)
I0410 14:09:27.884435 18353 sgd_solver.cpp:105] Iteration 5424, lr = 0.00341497
I0410 14:09:32.762265 18353 solver.cpp:218] Iteration 5436 (2.46017 iter/s, 4.87771s/12 iters), loss = 0.853731
I0410 14:09:32.762310 18353 solver.cpp:237]     Train net output #0: loss = 0.853731 (* 1 = 0.853731 loss)
I0410 14:09:32.762320 18353 sgd_solver.cpp:105] Iteration 5436, lr = 0.00340686
I0410 14:09:37.678890 18353 solver.cpp:218] Iteration 5448 (2.44079 iter/s, 4.91644s/12 iters), loss = 0.733606
I0410 14:09:37.678942 18353 solver.cpp:237]     Train net output #0: loss = 0.733606 (* 1 = 0.733606 loss)
I0410 14:09:37.678954 18353 sgd_solver.cpp:105] Iteration 5448, lr = 0.00339877
I0410 14:09:42.592376 18353 solver.cpp:218] Iteration 5460 (2.44235 iter/s, 4.9133s/12 iters), loss = 0.880428
I0410 14:09:42.592434 18353 solver.cpp:237]     Train net output #0: loss = 0.880428 (* 1 = 0.880428 loss)
I0410 14:09:42.592447 18353 sgd_solver.cpp:105] Iteration 5460, lr = 0.0033907
I0410 14:09:43.135032 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:09:47.451612 18353 solver.cpp:218] Iteration 5472 (2.46962 iter/s, 4.85904s/12 iters), loss = 0.680865
I0410 14:09:47.451711 18353 solver.cpp:237]     Train net output #0: loss = 0.680865 (* 1 = 0.680865 loss)
I0410 14:09:47.451722 18353 sgd_solver.cpp:105] Iteration 5472, lr = 0.00338265
I0410 14:09:52.338194 18353 solver.cpp:218] Iteration 5484 (2.45582 iter/s, 4.88634s/12 iters), loss = 0.718375
I0410 14:09:52.338235 18353 solver.cpp:237]     Train net output #0: loss = 0.718375 (* 1 = 0.718375 loss)
I0410 14:09:52.338244 18353 sgd_solver.cpp:105] Iteration 5484, lr = 0.00337462
I0410 14:09:57.357069 18353 solver.cpp:218] Iteration 5496 (2.39106 iter/s, 5.01869s/12 iters), loss = 0.786509
I0410 14:09:57.357118 18353 solver.cpp:237]     Train net output #0: loss = 0.786509 (* 1 = 0.786509 loss)
I0410 14:09:57.357129 18353 sgd_solver.cpp:105] Iteration 5496, lr = 0.00336661
I0410 14:10:01.968523 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5508.caffemodel
I0410 14:10:02.594524 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5508.solverstate
I0410 14:10:02.802541 18353 solver.cpp:330] Iteration 5508, Testing net (#0)
I0410 14:10:02.802568 18353 net.cpp:676] Ignoring source layer train-data
I0410 14:10:05.044905 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:10:07.219151 18353 solver.cpp:397]     Test net output #0: accuracy = 0.496324
I0410 14:10:07.219180 18353 solver.cpp:397]     Test net output #1: loss = 2.32296 (* 1 = 2.32296 loss)
I0410 14:10:07.300184 18353 solver.cpp:218] Iteration 5508 (1.2069 iter/s, 9.9428s/12 iters), loss = 1.06439
I0410 14:10:07.300221 18353 solver.cpp:237]     Train net output #0: loss = 1.06439 (* 1 = 1.06439 loss)
I0410 14:10:07.300230 18353 sgd_solver.cpp:105] Iteration 5508, lr = 0.00335861
I0410 14:10:11.647392 18353 solver.cpp:218] Iteration 5520 (2.7605 iter/s, 4.34704s/12 iters), loss = 0.965831
I0410 14:10:11.647454 18353 solver.cpp:237]     Train net output #0: loss = 0.965831 (* 1 = 0.965831 loss)
I0410 14:10:11.647467 18353 sgd_solver.cpp:105] Iteration 5520, lr = 0.00335064
I0410 14:10:12.423095 18353 blocking_queue.cpp:49] Waiting for data
I0410 14:10:16.610991 18353 solver.cpp:218] Iteration 5532 (2.4177 iter/s, 4.9634s/12 iters), loss = 0.932608
I0410 14:10:16.611043 18353 solver.cpp:237]     Train net output #0: loss = 0.932608 (* 1 = 0.932608 loss)
I0410 14:10:16.611055 18353 sgd_solver.cpp:105] Iteration 5532, lr = 0.00334268
I0410 14:10:21.536157 18353 solver.cpp:218] Iteration 5544 (2.43656 iter/s, 4.92497s/12 iters), loss = 0.749615
I0410 14:10:21.536309 18353 solver.cpp:237]     Train net output #0: loss = 0.749615 (* 1 = 0.749615 loss)
I0410 14:10:21.536324 18353 sgd_solver.cpp:105] Iteration 5544, lr = 0.00333475
I0410 14:10:26.471607 18353 solver.cpp:218] Iteration 5556 (2.43153 iter/s, 4.93516s/12 iters), loss = 0.509865
I0410 14:10:26.471654 18353 solver.cpp:237]     Train net output #0: loss = 0.509865 (* 1 = 0.509865 loss)
I0410 14:10:26.471665 18353 sgd_solver.cpp:105] Iteration 5556, lr = 0.00332683
I0410 14:10:29.080044 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:10:31.348310 18353 solver.cpp:218] Iteration 5568 (2.46077 iter/s, 4.87651s/12 iters), loss = 0.734006
I0410 14:10:31.348371 18353 solver.cpp:237]     Train net output #0: loss = 0.734006 (* 1 = 0.734006 loss)
I0410 14:10:31.348383 18353 sgd_solver.cpp:105] Iteration 5568, lr = 0.00331893
I0410 14:10:36.311226 18353 solver.cpp:218] Iteration 5580 (2.41803 iter/s, 4.96272s/12 iters), loss = 0.653475
I0410 14:10:36.311272 18353 solver.cpp:237]     Train net output #0: loss = 0.653475 (* 1 = 0.653475 loss)
I0410 14:10:36.311282 18353 sgd_solver.cpp:105] Iteration 5580, lr = 0.00331105
I0410 14:10:41.227285 18353 solver.cpp:218] Iteration 5592 (2.44108 iter/s, 4.91587s/12 iters), loss = 0.881881
I0410 14:10:41.227345 18353 solver.cpp:237]     Train net output #0: loss = 0.881881 (* 1 = 0.881881 loss)
I0410 14:10:41.227357 18353 sgd_solver.cpp:105] Iteration 5592, lr = 0.00330319
I0410 14:10:46.200119 18353 solver.cpp:218] Iteration 5604 (2.41321 iter/s, 4.97263s/12 iters), loss = 0.874188
I0410 14:10:46.200178 18353 solver.cpp:237]     Train net output #0: loss = 0.874188 (* 1 = 0.874188 loss)
I0410 14:10:46.200191 18353 sgd_solver.cpp:105] Iteration 5604, lr = 0.00329535
I0410 14:10:48.233809 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5610.caffemodel
I0410 14:10:48.561676 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5610.solverstate
I0410 14:10:48.764101 18353 solver.cpp:330] Iteration 5610, Testing net (#0)
I0410 14:10:48.764130 18353 net.cpp:676] Ignoring source layer train-data
I0410 14:10:51.075510 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:10:53.332931 18353 solver.cpp:397]     Test net output #0: accuracy = 0.511642
I0410 14:10:53.333132 18353 solver.cpp:397]     Test net output #1: loss = 2.19515 (* 1 = 2.19515 loss)
I0410 14:10:55.276001 18353 solver.cpp:218] Iteration 5616 (1.32223 iter/s, 9.07557s/12 iters), loss = 0.788042
I0410 14:10:55.276055 18353 solver.cpp:237]     Train net output #0: loss = 0.788042 (* 1 = 0.788042 loss)
I0410 14:10:55.276067 18353 sgd_solver.cpp:105] Iteration 5616, lr = 0.00328752
I0410 14:11:00.304826 18353 solver.cpp:218] Iteration 5628 (2.38634 iter/s, 5.02862s/12 iters), loss = 0.831445
I0410 14:11:00.304879 18353 solver.cpp:237]     Train net output #0: loss = 0.831445 (* 1 = 0.831445 loss)
I0410 14:11:00.304889 18353 sgd_solver.cpp:105] Iteration 5628, lr = 0.00327972
I0410 14:11:05.329720 18353 solver.cpp:218] Iteration 5640 (2.38821 iter/s, 5.02469s/12 iters), loss = 0.738849
I0410 14:11:05.329777 18353 solver.cpp:237]     Train net output #0: loss = 0.738849 (* 1 = 0.738849 loss)
I0410 14:11:05.329788 18353 sgd_solver.cpp:105] Iteration 5640, lr = 0.00327193
I0410 14:11:10.273906 18353 solver.cpp:218] Iteration 5652 (2.42719 iter/s, 4.94398s/12 iters), loss = 0.735652
I0410 14:11:10.273980 18353 solver.cpp:237]     Train net output #0: loss = 0.735652 (* 1 = 0.735652 loss)
I0410 14:11:10.273993 18353 sgd_solver.cpp:105] Iteration 5652, lr = 0.00326416
I0410 14:11:15.030660 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:11:15.195209 18353 solver.cpp:218] Iteration 5664 (2.43847 iter/s, 4.92111s/12 iters), loss = 0.632614
I0410 14:11:15.195256 18353 solver.cpp:237]     Train net output #0: loss = 0.632614 (* 1 = 0.632614 loss)
I0410 14:11:15.195267 18353 sgd_solver.cpp:105] Iteration 5664, lr = 0.00325641
I0410 14:11:20.092293 18353 solver.cpp:218] Iteration 5676 (2.45054 iter/s, 4.89689s/12 iters), loss = 0.771162
I0410 14:11:20.092353 18353 solver.cpp:237]     Train net output #0: loss = 0.771162 (* 1 = 0.771162 loss)
I0410 14:11:20.092367 18353 sgd_solver.cpp:105] Iteration 5676, lr = 0.00324868
I0410 14:11:25.202016 18353 solver.cpp:218] Iteration 5688 (2.34856 iter/s, 5.10951s/12 iters), loss = 0.661734
I0410 14:11:25.202148 18353 solver.cpp:237]     Train net output #0: loss = 0.661734 (* 1 = 0.661734 loss)
I0410 14:11:25.202162 18353 sgd_solver.cpp:105] Iteration 5688, lr = 0.00324097
I0410 14:11:30.074534 18353 solver.cpp:218] Iteration 5700 (2.46293 iter/s, 4.87224s/12 iters), loss = 0.735425
I0410 14:11:30.074592 18353 solver.cpp:237]     Train net output #0: loss = 0.735425 (* 1 = 0.735425 loss)
I0410 14:11:30.074604 18353 sgd_solver.cpp:105] Iteration 5700, lr = 0.00323328
I0410 14:11:34.476070 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5712.caffemodel
I0410 14:11:34.763168 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5712.solverstate
I0410 14:11:35.399518 18353 solver.cpp:330] Iteration 5712, Testing net (#0)
I0410 14:11:35.399540 18353 net.cpp:676] Ignoring source layer train-data
I0410 14:11:37.544523 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:11:39.782631 18353 solver.cpp:397]     Test net output #0: accuracy = 0.509804
I0410 14:11:39.782683 18353 solver.cpp:397]     Test net output #1: loss = 2.27155 (* 1 = 2.27155 loss)
I0410 14:11:39.864259 18353 solver.cpp:218] Iteration 5712 (1.22582 iter/s, 9.78939s/12 iters), loss = 1.00892
I0410 14:11:39.864336 18353 solver.cpp:237]     Train net output #0: loss = 1.00892 (* 1 = 1.00892 loss)
I0410 14:11:39.864353 18353 sgd_solver.cpp:105] Iteration 5712, lr = 0.0032256
I0410 14:11:43.993985 18353 solver.cpp:218] Iteration 5724 (2.90591 iter/s, 4.12952s/12 iters), loss = 0.742137
I0410 14:11:43.994040 18353 solver.cpp:237]     Train net output #0: loss = 0.742137 (* 1 = 0.742137 loss)
I0410 14:11:43.994053 18353 sgd_solver.cpp:105] Iteration 5724, lr = 0.00321794
I0410 14:11:48.828318 18353 solver.cpp:218] Iteration 5736 (2.48235 iter/s, 4.83413s/12 iters), loss = 0.76919
I0410 14:11:48.828382 18353 solver.cpp:237]     Train net output #0: loss = 0.76919 (* 1 = 0.76919 loss)
I0410 14:11:48.828395 18353 sgd_solver.cpp:105] Iteration 5736, lr = 0.0032103
I0410 14:11:53.620748 18353 solver.cpp:218] Iteration 5748 (2.50406 iter/s, 4.79222s/12 iters), loss = 0.805564
I0410 14:11:53.620816 18353 solver.cpp:237]     Train net output #0: loss = 0.805564 (* 1 = 0.805564 loss)
I0410 14:11:53.620829 18353 sgd_solver.cpp:105] Iteration 5748, lr = 0.00320268
I0410 14:11:58.493387 18353 solver.cpp:218] Iteration 5760 (2.46284 iter/s, 4.87243s/12 iters), loss = 0.574332
I0410 14:11:58.493547 18353 solver.cpp:237]     Train net output #0: loss = 0.574332 (* 1 = 0.574332 loss)
I0410 14:11:58.493561 18353 sgd_solver.cpp:105] Iteration 5760, lr = 0.00319508
I0410 14:12:00.406102 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:12:03.317028 18353 solver.cpp:218] Iteration 5772 (2.4879 iter/s, 4.82334s/12 iters), loss = 0.794722
I0410 14:12:03.317083 18353 solver.cpp:237]     Train net output #0: loss = 0.794722 (* 1 = 0.794722 loss)
I0410 14:12:03.317096 18353 sgd_solver.cpp:105] Iteration 5772, lr = 0.00318749
I0410 14:12:08.159518 18353 solver.cpp:218] Iteration 5784 (2.47816 iter/s, 4.8423s/12 iters), loss = 0.692924
I0410 14:12:08.159572 18353 solver.cpp:237]     Train net output #0: loss = 0.692924 (* 1 = 0.692924 loss)
I0410 14:12:08.159585 18353 sgd_solver.cpp:105] Iteration 5784, lr = 0.00317992
I0410 14:12:13.007699 18353 solver.cpp:218] Iteration 5796 (2.47526 iter/s, 4.84798s/12 iters), loss = 0.784665
I0410 14:12:13.007762 18353 solver.cpp:237]     Train net output #0: loss = 0.784665 (* 1 = 0.784665 loss)
I0410 14:12:13.007776 18353 sgd_solver.cpp:105] Iteration 5796, lr = 0.00317237
I0410 14:12:17.806195 18353 solver.cpp:218] Iteration 5808 (2.50089 iter/s, 4.79829s/12 iters), loss = 0.750609
I0410 14:12:17.806258 18353 solver.cpp:237]     Train net output #0: loss = 0.750609 (* 1 = 0.750609 loss)
I0410 14:12:17.806270 18353 sgd_solver.cpp:105] Iteration 5808, lr = 0.00316484
I0410 14:12:19.774613 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5814.caffemodel
I0410 14:12:20.084256 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5814.solverstate
I0410 14:12:20.280352 18353 solver.cpp:330] Iteration 5814, Testing net (#0)
I0410 14:12:20.280380 18353 net.cpp:676] Ignoring source layer train-data
I0410 14:12:22.744614 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:12:25.025694 18353 solver.cpp:397]     Test net output #0: accuracy = 0.515319
I0410 14:12:25.025728 18353 solver.cpp:397]     Test net output #1: loss = 2.29007 (* 1 = 2.29007 loss)
I0410 14:12:26.971669 18353 solver.cpp:218] Iteration 5820 (1.30931 iter/s, 9.16516s/12 iters), loss = 0.643536
I0410 14:12:26.971724 18353 solver.cpp:237]     Train net output #0: loss = 0.643536 (* 1 = 0.643536 loss)
I0410 14:12:26.971737 18353 sgd_solver.cpp:105] Iteration 5820, lr = 0.00315733
I0410 14:12:31.996568 18353 solver.cpp:218] Iteration 5832 (2.3882 iter/s, 5.0247s/12 iters), loss = 0.590614
I0410 14:12:31.996675 18353 solver.cpp:237]     Train net output #0: loss = 0.590614 (* 1 = 0.590614 loss)
I0410 14:12:31.996685 18353 sgd_solver.cpp:105] Iteration 5832, lr = 0.00314983
I0410 14:12:36.852124 18353 solver.cpp:218] Iteration 5844 (2.47152 iter/s, 4.85531s/12 iters), loss = 0.744024
I0410 14:12:36.852166 18353 solver.cpp:237]     Train net output #0: loss = 0.744024 (* 1 = 0.744024 loss)
I0410 14:12:36.852176 18353 sgd_solver.cpp:105] Iteration 5844, lr = 0.00314235
I0410 14:12:41.739073 18353 solver.cpp:218] Iteration 5856 (2.45561 iter/s, 4.88677s/12 iters), loss = 0.828271
I0410 14:12:41.739114 18353 solver.cpp:237]     Train net output #0: loss = 0.828271 (* 1 = 0.828271 loss)
I0410 14:12:41.739123 18353 sgd_solver.cpp:105] Iteration 5856, lr = 0.00313489
I0410 14:12:45.889986 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:12:46.694523 18353 solver.cpp:218] Iteration 5868 (2.42167 iter/s, 4.95527s/12 iters), loss = 0.687506
I0410 14:12:46.694558 18353 solver.cpp:237]     Train net output #0: loss = 0.687506 (* 1 = 0.687506 loss)
I0410 14:12:46.694568 18353 sgd_solver.cpp:105] Iteration 5868, lr = 0.00312745
I0410 14:12:51.629735 18353 solver.cpp:218] Iteration 5880 (2.43159 iter/s, 4.93504s/12 iters), loss = 0.576345
I0410 14:12:51.629776 18353 solver.cpp:237]     Train net output #0: loss = 0.576345 (* 1 = 0.576345 loss)
I0410 14:12:51.629784 18353 sgd_solver.cpp:105] Iteration 5880, lr = 0.00312002
I0410 14:12:56.500676 18353 solver.cpp:218] Iteration 5892 (2.46369 iter/s, 4.87075s/12 iters), loss = 0.693972
I0410 14:12:56.500733 18353 solver.cpp:237]     Train net output #0: loss = 0.693972 (* 1 = 0.693972 loss)
I0410 14:12:56.500744 18353 sgd_solver.cpp:105] Iteration 5892, lr = 0.00311262
I0410 14:13:01.454926 18353 solver.cpp:218] Iteration 5904 (2.42226 iter/s, 4.95405s/12 iters), loss = 0.669614
I0410 14:13:01.454973 18353 solver.cpp:237]     Train net output #0: loss = 0.669614 (* 1 = 0.669614 loss)
I0410 14:13:01.454983 18353 sgd_solver.cpp:105] Iteration 5904, lr = 0.00310523
I0410 14:13:05.903183 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5916.caffemodel
I0410 14:13:06.221999 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5916.solverstate
I0410 14:13:06.429867 18353 solver.cpp:330] Iteration 5916, Testing net (#0)
I0410 14:13:06.429888 18353 net.cpp:676] Ignoring source layer train-data
I0410 14:13:08.565433 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:13:10.883311 18353 solver.cpp:397]     Test net output #0: accuracy = 0.502451
I0410 14:13:10.883361 18353 solver.cpp:397]     Test net output #1: loss = 2.28403 (* 1 = 2.28403 loss)
I0410 14:13:10.964967 18353 solver.cpp:218] Iteration 5916 (1.26187 iter/s, 9.50973s/12 iters), loss = 0.797538
I0410 14:13:10.965020 18353 solver.cpp:237]     Train net output #0: loss = 0.797538 (* 1 = 0.797538 loss)
I0410 14:13:10.965032 18353 sgd_solver.cpp:105] Iteration 5916, lr = 0.00309785
I0410 14:13:15.178560 18353 solver.cpp:218] Iteration 5928 (2.84805 iter/s, 4.21341s/12 iters), loss = 0.654663
I0410 14:13:15.178618 18353 solver.cpp:237]     Train net output #0: loss = 0.654663 (* 1 = 0.654663 loss)
I0410 14:13:15.178632 18353 sgd_solver.cpp:105] Iteration 5928, lr = 0.0030905
I0410 14:13:20.062857 18353 solver.cpp:218] Iteration 5940 (2.45695 iter/s, 4.88409s/12 iters), loss = 0.665314
I0410 14:13:20.062911 18353 solver.cpp:237]     Train net output #0: loss = 0.665314 (* 1 = 0.665314 loss)
I0410 14:13:20.062925 18353 sgd_solver.cpp:105] Iteration 5940, lr = 0.00308316
I0410 14:13:24.999192 18353 solver.cpp:218] Iteration 5952 (2.43105 iter/s, 4.93614s/12 iters), loss = 0.789209
I0410 14:13:24.999246 18353 solver.cpp:237]     Train net output #0: loss = 0.789209 (* 1 = 0.789209 loss)
I0410 14:13:24.999259 18353 sgd_solver.cpp:105] Iteration 5952, lr = 0.00307584
I0410 14:13:29.913111 18353 solver.cpp:218] Iteration 5964 (2.44214 iter/s, 4.91372s/12 iters), loss = 0.696207
I0410 14:13:29.913156 18353 solver.cpp:237]     Train net output #0: loss = 0.696207 (* 1 = 0.696207 loss)
I0410 14:13:29.913166 18353 sgd_solver.cpp:105] Iteration 5964, lr = 0.00306854
I0410 14:13:31.228168 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:13:34.888600 18353 solver.cpp:218] Iteration 5976 (2.41192 iter/s, 4.9753s/12 iters), loss = 0.762595
I0410 14:13:34.888646 18353 solver.cpp:237]     Train net output #0: loss = 0.762595 (* 1 = 0.762595 loss)
I0410 14:13:34.888656 18353 sgd_solver.cpp:105] Iteration 5976, lr = 0.00306125
I0410 14:13:39.729584 18353 solver.cpp:218] Iteration 5988 (2.47893 iter/s, 4.8408s/12 iters), loss = 0.543626
I0410 14:13:39.729683 18353 solver.cpp:237]     Train net output #0: loss = 0.543626 (* 1 = 0.543626 loss)
I0410 14:13:39.729694 18353 sgd_solver.cpp:105] Iteration 5988, lr = 0.00305398
I0410 14:13:44.644070 18353 solver.cpp:218] Iteration 6000 (2.44188 iter/s, 4.91424s/12 iters), loss = 0.736389
I0410 14:13:44.644115 18353 solver.cpp:237]     Train net output #0: loss = 0.736389 (* 1 = 0.736389 loss)
I0410 14:13:44.644125 18353 sgd_solver.cpp:105] Iteration 6000, lr = 0.00304673
I0410 14:13:49.726470 18353 solver.cpp:218] Iteration 6012 (2.36118 iter/s, 5.08221s/12 iters), loss = 0.817832
I0410 14:13:49.726511 18353 solver.cpp:237]     Train net output #0: loss = 0.817832 (* 1 = 0.817832 loss)
I0410 14:13:49.726521 18353 sgd_solver.cpp:105] Iteration 6012, lr = 0.0030395
I0410 14:13:51.759835 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6018.caffemodel
I0410 14:13:52.061887 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6018.solverstate
I0410 14:13:52.254604 18353 solver.cpp:330] Iteration 6018, Testing net (#0)
I0410 14:13:52.254628 18353 net.cpp:676] Ignoring source layer train-data
I0410 14:13:54.358570 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:13:56.894659 18353 solver.cpp:397]     Test net output #0: accuracy = 0.521446
I0410 14:13:56.894717 18353 solver.cpp:397]     Test net output #1: loss = 2.15105 (* 1 = 2.15105 loss)
I0410 14:13:58.818353 18353 solver.cpp:218] Iteration 6024 (1.3199 iter/s, 9.09159s/12 iters), loss = 0.710735
I0410 14:13:58.818408 18353 solver.cpp:237]     Train net output #0: loss = 0.710735 (* 1 = 0.710735 loss)
I0410 14:13:58.818419 18353 sgd_solver.cpp:105] Iteration 6024, lr = 0.00303228
I0410 14:14:03.704183 18353 solver.cpp:218] Iteration 6036 (2.45618 iter/s, 4.88563s/12 iters), loss = 0.912934
I0410 14:14:03.704241 18353 solver.cpp:237]     Train net output #0: loss = 0.912934 (* 1 = 0.912934 loss)
I0410 14:14:03.704252 18353 sgd_solver.cpp:105] Iteration 6036, lr = 0.00302508
I0410 14:14:08.662220 18353 solver.cpp:218] Iteration 6048 (2.42041 iter/s, 4.95784s/12 iters), loss = 0.7883
I0410 14:14:08.662276 18353 solver.cpp:237]     Train net output #0: loss = 0.7883 (* 1 = 0.7883 loss)
I0410 14:14:08.662287 18353 sgd_solver.cpp:105] Iteration 6048, lr = 0.0030179
I0410 14:14:13.515789 18353 solver.cpp:218] Iteration 6060 (2.47251 iter/s, 4.85337s/12 iters), loss = 0.636425
I0410 14:14:13.523874 18353 solver.cpp:237]     Train net output #0: loss = 0.636425 (* 1 = 0.636425 loss)
I0410 14:14:13.523890 18353 sgd_solver.cpp:105] Iteration 6060, lr = 0.00301074
I0410 14:14:16.935297 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:14:18.456315 18353 solver.cpp:218] Iteration 6072 (2.43294 iter/s, 4.9323s/12 iters), loss = 0.670976
I0410 14:14:18.456359 18353 solver.cpp:237]     Train net output #0: loss = 0.670976 (* 1 = 0.670976 loss)
I0410 14:14:18.456368 18353 sgd_solver.cpp:105] Iteration 6072, lr = 0.00300359
I0410 14:14:23.365787 18353 solver.cpp:218] Iteration 6084 (2.44435 iter/s, 4.90927s/12 iters), loss = 0.722059
I0410 14:14:23.365841 18353 solver.cpp:237]     Train net output #0: loss = 0.722059 (* 1 = 0.722059 loss)
I0410 14:14:23.365854 18353 sgd_solver.cpp:105] Iteration 6084, lr = 0.00299646
I0410 14:14:28.240967 18353 solver.cpp:218] Iteration 6096 (2.46155 iter/s, 4.87498s/12 iters), loss = 0.770837
I0410 14:14:28.241019 18353 solver.cpp:237]     Train net output #0: loss = 0.770837 (* 1 = 0.770837 loss)
I0410 14:14:28.241031 18353 sgd_solver.cpp:105] Iteration 6096, lr = 0.00298934
I0410 14:14:33.153525 18353 solver.cpp:218] Iteration 6108 (2.44282 iter/s, 4.91237s/12 iters), loss = 0.697178
I0410 14:14:33.153575 18353 solver.cpp:237]     Train net output #0: loss = 0.697178 (* 1 = 0.697178 loss)
I0410 14:14:33.153586 18353 sgd_solver.cpp:105] Iteration 6108, lr = 0.00298225
I0410 14:14:37.614692 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6120.caffemodel
I0410 14:14:39.098008 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6120.solverstate
I0410 14:14:39.305007 18353 solver.cpp:330] Iteration 6120, Testing net (#0)
I0410 14:14:39.305027 18353 net.cpp:676] Ignoring source layer train-data
I0410 14:14:41.429913 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:14:43.823109 18353 solver.cpp:397]     Test net output #0: accuracy = 0.51348
I0410 14:14:43.823321 18353 solver.cpp:397]     Test net output #1: loss = 2.22052 (* 1 = 2.22052 loss)
I0410 14:14:43.904706 18353 solver.cpp:218] Iteration 6120 (1.11619 iter/s, 10.7508s/12 iters), loss = 0.521173
I0410 14:14:43.904759 18353 solver.cpp:237]     Train net output #0: loss = 0.521173 (* 1 = 0.521173 loss)
I0410 14:14:43.904772 18353 sgd_solver.cpp:105] Iteration 6120, lr = 0.00297517
I0410 14:14:48.134862 18353 solver.cpp:218] Iteration 6132 (2.8369 iter/s, 4.22997s/12 iters), loss = 0.531146
I0410 14:14:48.134914 18353 solver.cpp:237]     Train net output #0: loss = 0.531146 (* 1 = 0.531146 loss)
I0410 14:14:48.134927 18353 sgd_solver.cpp:105] Iteration 6132, lr = 0.0029681
I0410 14:14:53.252141 18353 solver.cpp:218] Iteration 6144 (2.34509 iter/s, 5.11708s/12 iters), loss = 0.973454
I0410 14:14:53.252185 18353 solver.cpp:237]     Train net output #0: loss = 0.973454 (* 1 = 0.973454 loss)
I0410 14:14:53.252195 18353 sgd_solver.cpp:105] Iteration 6144, lr = 0.00296105
I0410 14:14:58.177613 18353 solver.cpp:218] Iteration 6156 (2.43641 iter/s, 4.92528s/12 iters), loss = 0.662244
I0410 14:14:58.177662 18353 solver.cpp:237]     Train net output #0: loss = 0.662244 (* 1 = 0.662244 loss)
I0410 14:14:58.177672 18353 sgd_solver.cpp:105] Iteration 6156, lr = 0.00295402
I0410 14:15:03.130434 18353 solver.cpp:218] Iteration 6168 (2.42296 iter/s, 4.95262s/12 iters), loss = 0.717742
I0410 14:15:03.130483 18353 solver.cpp:237]     Train net output #0: loss = 0.717742 (* 1 = 0.717742 loss)
I0410 14:15:03.130496 18353 sgd_solver.cpp:105] Iteration 6168, lr = 0.00294701
I0410 14:15:03.700232 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:15:08.033159 18353 solver.cpp:218] Iteration 6180 (2.44772 iter/s, 4.90253s/12 iters), loss = 0.627013
I0410 14:15:08.033202 18353 solver.cpp:237]     Train net output #0: loss = 0.627013 (* 1 = 0.627013 loss)
I0410 14:15:08.033212 18353 sgd_solver.cpp:105] Iteration 6180, lr = 0.00294001
I0410 14:15:12.938045 18353 solver.cpp:218] Iteration 6192 (2.44664 iter/s, 4.90469s/12 iters), loss = 0.455912
I0410 14:15:12.938097 18353 solver.cpp:237]     Train net output #0: loss = 0.455912 (* 1 = 0.455912 loss)
I0410 14:15:12.938109 18353 sgd_solver.cpp:105] Iteration 6192, lr = 0.00293303
I0410 14:15:17.844672 18353 solver.cpp:218] Iteration 6204 (2.44577 iter/s, 4.90643s/12 iters), loss = 0.567118
I0410 14:15:17.844766 18353 solver.cpp:237]     Train net output #0: loss = 0.567118 (* 1 = 0.567118 loss)
I0410 14:15:17.844776 18353 sgd_solver.cpp:105] Iteration 6204, lr = 0.00292607
I0410 14:15:22.728669 18353 solver.cpp:218] Iteration 6216 (2.45712 iter/s, 4.88376s/12 iters), loss = 0.606701
I0410 14:15:22.728718 18353 solver.cpp:237]     Train net output #0: loss = 0.606701 (* 1 = 0.606701 loss)
I0410 14:15:22.728729 18353 sgd_solver.cpp:105] Iteration 6216, lr = 0.00291912
I0410 14:15:24.728497 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6222.caffemodel
I0410 14:15:25.060364 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6222.solverstate
I0410 14:15:25.267180 18353 solver.cpp:330] Iteration 6222, Testing net (#0)
I0410 14:15:25.267204 18353 net.cpp:676] Ignoring source layer train-data
I0410 14:15:27.276801 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:15:28.193138 18353 blocking_queue.cpp:49] Waiting for data
I0410 14:15:29.745867 18353 solver.cpp:397]     Test net output #0: accuracy = 0.518995
I0410 14:15:29.745916 18353 solver.cpp:397]     Test net output #1: loss = 2.19724 (* 1 = 2.19724 loss)
I0410 14:15:31.503708 18353 solver.cpp:218] Iteration 6228 (1.36756 iter/s, 8.77474s/12 iters), loss = 0.683243
I0410 14:15:31.503755 18353 solver.cpp:237]     Train net output #0: loss = 0.683243 (* 1 = 0.683243 loss)
I0410 14:15:31.503765 18353 sgd_solver.cpp:105] Iteration 6228, lr = 0.00291219
I0410 14:15:36.444991 18353 solver.cpp:218] Iteration 6240 (2.42861 iter/s, 4.94109s/12 iters), loss = 0.650123
I0410 14:15:36.445051 18353 solver.cpp:237]     Train net output #0: loss = 0.650123 (* 1 = 0.650123 loss)
I0410 14:15:36.445065 18353 sgd_solver.cpp:105] Iteration 6240, lr = 0.00290528
I0410 14:15:41.386240 18353 solver.cpp:218] Iteration 6252 (2.42864 iter/s, 4.94105s/12 iters), loss = 0.597079
I0410 14:15:41.386287 18353 solver.cpp:237]     Train net output #0: loss = 0.597079 (* 1 = 0.597079 loss)
I0410 14:15:41.386298 18353 sgd_solver.cpp:105] Iteration 6252, lr = 0.00289838
I0410 14:15:46.334187 18353 solver.cpp:218] Iteration 6264 (2.42534 iter/s, 4.94775s/12 iters), loss = 0.648929
I0410 14:15:46.334239 18353 solver.cpp:237]     Train net output #0: loss = 0.648929 (* 1 = 0.648929 loss)
I0410 14:15:46.334251 18353 sgd_solver.cpp:105] Iteration 6264, lr = 0.0028915
I0410 14:15:49.062376 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:15:51.232539 18353 solver.cpp:218] Iteration 6276 (2.4499 iter/s, 4.89815s/12 iters), loss = 0.680992
I0410 14:15:51.232592 18353 solver.cpp:237]     Train net output #0: loss = 0.680992 (* 1 = 0.680992 loss)
I0410 14:15:51.232605 18353 sgd_solver.cpp:105] Iteration 6276, lr = 0.00288463
I0410 14:15:56.159600 18353 solver.cpp:218] Iteration 6288 (2.43563 iter/s, 4.92686s/12 iters), loss = 0.561973
I0410 14:15:56.159658 18353 solver.cpp:237]     Train net output #0: loss = 0.561973 (* 1 = 0.561973 loss)
I0410 14:15:56.159670 18353 sgd_solver.cpp:105] Iteration 6288, lr = 0.00287779
I0410 14:16:01.089377 18353 solver.cpp:218] Iteration 6300 (2.43429 iter/s, 4.92957s/12 iters), loss = 0.840369
I0410 14:16:01.089435 18353 solver.cpp:237]     Train net output #0: loss = 0.840369 (* 1 = 0.840369 loss)
I0410 14:16:01.089447 18353 sgd_solver.cpp:105] Iteration 6300, lr = 0.00287095
I0410 14:16:06.014642 18353 solver.cpp:218] Iteration 6312 (2.43652 iter/s, 4.92506s/12 iters), loss = 0.693389
I0410 14:16:06.014694 18353 solver.cpp:237]     Train net output #0: loss = 0.693389 (* 1 = 0.693389 loss)
I0410 14:16:06.014706 18353 sgd_solver.cpp:105] Iteration 6312, lr = 0.00286414
I0410 14:16:10.469365 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6324.caffemodel
I0410 14:16:10.789842 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6324.solverstate
I0410 14:16:11.054728 18353 solver.cpp:330] Iteration 6324, Testing net (#0)
I0410 14:16:11.054754 18353 net.cpp:676] Ignoring source layer train-data
I0410 14:16:13.128293 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:16:15.681551 18353 solver.cpp:397]     Test net output #0: accuracy = 0.507353
I0410 14:16:15.681622 18353 solver.cpp:397]     Test net output #1: loss = 2.29632 (* 1 = 2.29632 loss)
I0410 14:16:15.762956 18353 solver.cpp:218] Iteration 6324 (1.23102 iter/s, 9.74799s/12 iters), loss = 0.742045
I0410 14:16:15.763005 18353 solver.cpp:237]     Train net output #0: loss = 0.742045 (* 1 = 0.742045 loss)
I0410 14:16:15.763018 18353 sgd_solver.cpp:105] Iteration 6324, lr = 0.00285734
I0410 14:16:19.785337 18353 solver.cpp:218] Iteration 6336 (2.98343 iter/s, 4.02221s/12 iters), loss = 0.780067
I0410 14:16:19.785436 18353 solver.cpp:237]     Train net output #0: loss = 0.780067 (* 1 = 0.780067 loss)
I0410 14:16:19.785446 18353 sgd_solver.cpp:105] Iteration 6336, lr = 0.00285055
I0410 14:16:24.629740 18353 solver.cpp:218] Iteration 6348 (2.47721 iter/s, 4.84416s/12 iters), loss = 0.732231
I0410 14:16:24.629792 18353 solver.cpp:237]     Train net output #0: loss = 0.732231 (* 1 = 0.732231 loss)
I0410 14:16:24.629802 18353 sgd_solver.cpp:105] Iteration 6348, lr = 0.00284379
I0410 14:16:29.532035 18353 solver.cpp:218] Iteration 6360 (2.44793 iter/s, 4.9021s/12 iters), loss = 0.632658
I0410 14:16:29.532079 18353 solver.cpp:237]     Train net output #0: loss = 0.632658 (* 1 = 0.632658 loss)
I0410 14:16:29.532089 18353 sgd_solver.cpp:105] Iteration 6360, lr = 0.00283703
I0410 14:16:34.314391 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:16:34.451148 18353 solver.cpp:218] Iteration 6372 (2.43956 iter/s, 4.91892s/12 iters), loss = 0.526726
I0410 14:16:34.451198 18353 solver.cpp:237]     Train net output #0: loss = 0.526726 (* 1 = 0.526726 loss)
I0410 14:16:34.451210 18353 sgd_solver.cpp:105] Iteration 6372, lr = 0.0028303
I0410 14:16:39.296636 18353 solver.cpp:218] Iteration 6384 (2.47663 iter/s, 4.84529s/12 iters), loss = 0.688702
I0410 14:16:39.296686 18353 solver.cpp:237]     Train net output #0: loss = 0.688702 (* 1 = 0.688702 loss)
I0410 14:16:39.296698 18353 sgd_solver.cpp:105] Iteration 6384, lr = 0.00282358
I0410 14:16:44.156250 18353 solver.cpp:218] Iteration 6396 (2.46943 iter/s, 4.85942s/12 iters), loss = 0.566177
I0410 14:16:44.156293 18353 solver.cpp:237]     Train net output #0: loss = 0.566177 (* 1 = 0.566177 loss)
I0410 14:16:44.156302 18353 sgd_solver.cpp:105] Iteration 6396, lr = 0.00281687
I0410 14:16:48.989298 18353 solver.cpp:218] Iteration 6408 (2.483 iter/s, 4.83287s/12 iters), loss = 0.656679
I0410 14:16:48.989347 18353 solver.cpp:237]     Train net output #0: loss = 0.656679 (* 1 = 0.656679 loss)
I0410 14:16:48.989356 18353 sgd_solver.cpp:105] Iteration 6408, lr = 0.00281019
I0410 14:16:53.882079 18353 solver.cpp:218] Iteration 6420 (2.45269 iter/s, 4.89258s/12 iters), loss = 0.65559
I0410 14:16:53.882236 18353 solver.cpp:237]     Train net output #0: loss = 0.65559 (* 1 = 0.65559 loss)
I0410 14:16:53.882251 18353 sgd_solver.cpp:105] Iteration 6420, lr = 0.00280351
I0410 14:16:55.871907 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6426.caffemodel
I0410 14:16:56.181867 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6426.solverstate
I0410 14:16:56.381321 18353 solver.cpp:330] Iteration 6426, Testing net (#0)
I0410 14:16:56.381342 18353 net.cpp:676] Ignoring source layer train-data
I0410 14:16:58.540225 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:17:01.125926 18353 solver.cpp:397]     Test net output #0: accuracy = 0.511642
I0410 14:17:01.125977 18353 solver.cpp:397]     Test net output #1: loss = 2.31938 (* 1 = 2.31938 loss)
I0410 14:17:02.868880 18353 solver.cpp:218] Iteration 6432 (1.33535 iter/s, 8.9864s/12 iters), loss = 0.720779
I0410 14:17:02.868932 18353 solver.cpp:237]     Train net output #0: loss = 0.720779 (* 1 = 0.720779 loss)
I0410 14:17:02.868942 18353 sgd_solver.cpp:105] Iteration 6432, lr = 0.00279686
I0410 14:17:07.781014 18353 solver.cpp:218] Iteration 6444 (2.44303 iter/s, 4.91194s/12 iters), loss = 0.534056
I0410 14:17:07.781071 18353 solver.cpp:237]     Train net output #0: loss = 0.534056 (* 1 = 0.534056 loss)
I0410 14:17:07.781083 18353 sgd_solver.cpp:105] Iteration 6444, lr = 0.00279022
I0410 14:17:12.683377 18353 solver.cpp:218] Iteration 6456 (2.4479 iter/s, 4.90216s/12 iters), loss = 0.540262
I0410 14:17:12.683434 18353 solver.cpp:237]     Train net output #0: loss = 0.540262 (* 1 = 0.540262 loss)
I0410 14:17:12.683446 18353 sgd_solver.cpp:105] Iteration 6456, lr = 0.00278359
I0410 14:17:17.614414 18353 solver.cpp:218] Iteration 6468 (2.43366 iter/s, 4.93084s/12 iters), loss = 0.455039
I0410 14:17:17.614466 18353 solver.cpp:237]     Train net output #0: loss = 0.455039 (* 1 = 0.455039 loss)
I0410 14:17:17.614477 18353 sgd_solver.cpp:105] Iteration 6468, lr = 0.00277698
I0410 14:17:19.647624 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:17:22.593626 18353 solver.cpp:218] Iteration 6480 (2.41012 iter/s, 4.97901s/12 iters), loss = 0.627442
I0410 14:17:22.593681 18353 solver.cpp:237]     Train net output #0: loss = 0.627442 (* 1 = 0.627442 loss)
I0410 14:17:22.593693 18353 sgd_solver.cpp:105] Iteration 6480, lr = 0.00277039
I0410 14:17:27.485996 18353 solver.cpp:218] Iteration 6492 (2.45291 iter/s, 4.89216s/12 iters), loss = 0.527949
I0410 14:17:27.486090 18353 solver.cpp:237]     Train net output #0: loss = 0.527949 (* 1 = 0.527949 loss)
I0410 14:17:27.486105 18353 sgd_solver.cpp:105] Iteration 6492, lr = 0.00276381
I0410 14:17:32.394727 18353 solver.cpp:218] Iteration 6504 (2.44474 iter/s, 4.90849s/12 iters), loss = 0.748762
I0410 14:17:32.394780 18353 solver.cpp:237]     Train net output #0: loss = 0.748762 (* 1 = 0.748762 loss)
I0410 14:17:32.394793 18353 sgd_solver.cpp:105] Iteration 6504, lr = 0.00275725
I0410 14:17:37.289431 18353 solver.cpp:218] Iteration 6516 (2.45173 iter/s, 4.89451s/12 iters), loss = 0.590916
I0410 14:17:37.289479 18353 solver.cpp:237]     Train net output #0: loss = 0.590916 (* 1 = 0.590916 loss)
I0410 14:17:37.289489 18353 sgd_solver.cpp:105] Iteration 6516, lr = 0.00275071
I0410 14:17:41.789191 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6528.caffemodel
I0410 14:17:43.386485 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6528.solverstate
I0410 14:17:43.943349 18353 solver.cpp:330] Iteration 6528, Testing net (#0)
I0410 14:17:43.943379 18353 net.cpp:676] Ignoring source layer train-data
I0410 14:17:45.839660 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:17:48.389947 18353 solver.cpp:397]     Test net output #0: accuracy = 0.529412
I0410 14:17:48.390027 18353 solver.cpp:397]     Test net output #1: loss = 2.24723 (* 1 = 2.24723 loss)
I0410 14:17:48.471159 18353 solver.cpp:218] Iteration 6528 (1.07321 iter/s, 11.1814s/12 iters), loss = 0.615411
I0410 14:17:48.471215 18353 solver.cpp:237]     Train net output #0: loss = 0.615411 (* 1 = 0.615411 loss)
I0410 14:17:48.471226 18353 sgd_solver.cpp:105] Iteration 6528, lr = 0.00274418
I0410 14:17:52.590828 18353 solver.cpp:218] Iteration 6540 (2.91298 iter/s, 4.11949s/12 iters), loss = 0.500604
I0410 14:17:52.590883 18353 solver.cpp:237]     Train net output #0: loss = 0.500604 (* 1 = 0.500604 loss)
I0410 14:17:52.590893 18353 sgd_solver.cpp:105] Iteration 6540, lr = 0.00273766
I0410 14:17:57.550961 18353 solver.cpp:218] Iteration 6552 (2.41939 iter/s, 4.95993s/12 iters), loss = 0.59679
I0410 14:17:57.551095 18353 solver.cpp:237]     Train net output #0: loss = 0.59679 (* 1 = 0.59679 loss)
I0410 14:17:57.551107 18353 sgd_solver.cpp:105] Iteration 6552, lr = 0.00273116
I0410 14:18:02.536068 18353 solver.cpp:218] Iteration 6564 (2.4073 iter/s, 4.98483s/12 iters), loss = 0.498057
I0410 14:18:02.536120 18353 solver.cpp:237]     Train net output #0: loss = 0.498057 (* 1 = 0.498057 loss)
I0410 14:18:02.536132 18353 sgd_solver.cpp:105] Iteration 6564, lr = 0.00272468
I0410 14:18:06.680294 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:18:07.453735 18353 solver.cpp:218] Iteration 6576 (2.44028 iter/s, 4.91747s/12 iters), loss = 0.579873
I0410 14:18:07.453790 18353 solver.cpp:237]     Train net output #0: loss = 0.579873 (* 1 = 0.579873 loss)
I0410 14:18:07.453804 18353 sgd_solver.cpp:105] Iteration 6576, lr = 0.00271821
I0410 14:18:12.460707 18353 solver.cpp:218] Iteration 6588 (2.39675 iter/s, 5.00677s/12 iters), loss = 0.416741
I0410 14:18:12.460762 18353 solver.cpp:237]     Train net output #0: loss = 0.416741 (* 1 = 0.416741 loss)
I0410 14:18:12.460774 18353 sgd_solver.cpp:105] Iteration 6588, lr = 0.00271175
I0410 14:18:17.412199 18353 solver.cpp:218] Iteration 6600 (2.42361 iter/s, 4.95129s/12 iters), loss = 0.66588
I0410 14:18:17.412253 18353 solver.cpp:237]     Train net output #0: loss = 0.66588 (* 1 = 0.66588 loss)
I0410 14:18:17.412266 18353 sgd_solver.cpp:105] Iteration 6600, lr = 0.00270532
I0410 14:18:22.370187 18353 solver.cpp:218] Iteration 6612 (2.42044 iter/s, 4.95778s/12 iters), loss = 0.56734
I0410 14:18:22.370240 18353 solver.cpp:237]     Train net output #0: loss = 0.56734 (* 1 = 0.56734 loss)
I0410 14:18:22.370254 18353 sgd_solver.cpp:105] Iteration 6612, lr = 0.00269889
I0410 14:18:27.514252 18353 solver.cpp:218] Iteration 6624 (2.33288 iter/s, 5.14386s/12 iters), loss = 0.46341
I0410 14:18:27.514312 18353 solver.cpp:237]     Train net output #0: loss = 0.46341 (* 1 = 0.46341 loss)
I0410 14:18:27.514324 18353 sgd_solver.cpp:105] Iteration 6624, lr = 0.00269248
I0410 14:18:29.592909 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6630.caffemodel
I0410 14:18:30.175792 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6630.solverstate
I0410 14:18:30.395292 18353 solver.cpp:330] Iteration 6630, Testing net (#0)
I0410 14:18:30.395324 18353 net.cpp:676] Ignoring source layer train-data
I0410 14:18:32.304487 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:18:34.889278 18353 solver.cpp:397]     Test net output #0: accuracy = 0.541667
I0410 14:18:34.889333 18353 solver.cpp:397]     Test net output #1: loss = 2.19279 (* 1 = 2.19279 loss)
I0410 14:18:36.711316 18353 solver.cpp:218] Iteration 6636 (1.30481 iter/s, 9.19674s/12 iters), loss = 0.439095
I0410 14:18:36.711371 18353 solver.cpp:237]     Train net output #0: loss = 0.439095 (* 1 = 0.439095 loss)
I0410 14:18:36.711383 18353 sgd_solver.cpp:105] Iteration 6636, lr = 0.00268609
I0410 14:18:41.654682 18353 solver.cpp:218] Iteration 6648 (2.42759 iter/s, 4.94317s/12 iters), loss = 0.302347
I0410 14:18:41.654728 18353 solver.cpp:237]     Train net output #0: loss = 0.302347 (* 1 = 0.302347 loss)
I0410 14:18:41.654738 18353 sgd_solver.cpp:105] Iteration 6648, lr = 0.00267971
I0410 14:18:46.578032 18353 solver.cpp:218] Iteration 6660 (2.43746 iter/s, 4.92316s/12 iters), loss = 0.69557
I0410 14:18:46.578090 18353 solver.cpp:237]     Train net output #0: loss = 0.69557 (* 1 = 0.69557 loss)
I0410 14:18:46.578104 18353 sgd_solver.cpp:105] Iteration 6660, lr = 0.00267335
I0410 14:18:51.481019 18353 solver.cpp:218] Iteration 6672 (2.44759 iter/s, 4.90279s/12 iters), loss = 0.524651
I0410 14:18:51.481072 18353 solver.cpp:237]     Train net output #0: loss = 0.524651 (* 1 = 0.524651 loss)
I0410 14:18:51.481087 18353 sgd_solver.cpp:105] Iteration 6672, lr = 0.00266701
I0410 14:18:52.819705 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:18:56.575670 18353 solver.cpp:218] Iteration 6684 (2.3555 iter/s, 5.09445s/12 iters), loss = 0.376114
I0410 14:18:56.575721 18353 solver.cpp:237]     Train net output #0: loss = 0.376114 (* 1 = 0.376114 loss)
I0410 14:18:56.575733 18353 sgd_solver.cpp:105] Iteration 6684, lr = 0.00266067
I0410 14:19:01.498241 18353 solver.cpp:218] Iteration 6696 (2.43785 iter/s, 4.92238s/12 iters), loss = 0.654246
I0410 14:19:01.498327 18353 solver.cpp:237]     Train net output #0: loss = 0.654246 (* 1 = 0.654246 loss)
I0410 14:19:01.498337 18353 sgd_solver.cpp:105] Iteration 6696, lr = 0.00265436
I0410 14:19:06.413019 18353 solver.cpp:218] Iteration 6708 (2.44173 iter/s, 4.91455s/12 iters), loss = 0.413601
I0410 14:19:06.413077 18353 solver.cpp:237]     Train net output #0: loss = 0.413601 (* 1 = 0.413601 loss)
I0410 14:19:06.413090 18353 sgd_solver.cpp:105] Iteration 6708, lr = 0.00264805
I0410 14:19:11.297039 18353 solver.cpp:218] Iteration 6720 (2.4571 iter/s, 4.88381s/12 iters), loss = 0.636433
I0410 14:19:11.297098 18353 solver.cpp:237]     Train net output #0: loss = 0.636433 (* 1 = 0.636433 loss)
I0410 14:19:11.297112 18353 sgd_solver.cpp:105] Iteration 6720, lr = 0.00264177
I0410 14:19:16.254699 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6732.caffemodel
I0410 14:19:16.563588 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6732.solverstate
I0410 14:19:16.771816 18353 solver.cpp:330] Iteration 6732, Testing net (#0)
I0410 14:19:16.771845 18353 net.cpp:676] Ignoring source layer train-data
I0410 14:19:18.587491 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:19:21.315678 18353 solver.cpp:397]     Test net output #0: accuracy = 0.530025
I0410 14:19:21.315730 18353 solver.cpp:397]     Test net output #1: loss = 2.28978 (* 1 = 2.28978 loss)
I0410 14:19:21.397089 18353 solver.cpp:218] Iteration 6732 (1.18815 iter/s, 10.0997s/12 iters), loss = 0.561599
I0410 14:19:21.397140 18353 solver.cpp:237]     Train net output #0: loss = 0.561599 (* 1 = 0.561599 loss)
I0410 14:19:21.397153 18353 sgd_solver.cpp:105] Iteration 6732, lr = 0.0026355
I0410 14:19:25.674952 18353 solver.cpp:218] Iteration 6744 (2.80526 iter/s, 4.27768s/12 iters), loss = 0.467465
I0410 14:19:25.675011 18353 solver.cpp:237]     Train net output #0: loss = 0.467465 (* 1 = 0.467465 loss)
I0410 14:19:25.675024 18353 sgd_solver.cpp:105] Iteration 6744, lr = 0.00262924
I0410 14:19:30.553550 18353 solver.cpp:218] Iteration 6756 (2.45982 iter/s, 4.8784s/12 iters), loss = 0.532863
I0410 14:19:30.553599 18353 solver.cpp:237]     Train net output #0: loss = 0.532863 (* 1 = 0.532863 loss)
I0410 14:19:30.553609 18353 sgd_solver.cpp:105] Iteration 6756, lr = 0.002623
I0410 14:19:35.520421 18353 solver.cpp:218] Iteration 6768 (2.4161 iter/s, 4.96667s/12 iters), loss = 0.591368
I0410 14:19:35.520539 18353 solver.cpp:237]     Train net output #0: loss = 0.591368 (* 1 = 0.591368 loss)
I0410 14:19:35.520550 18353 sgd_solver.cpp:105] Iteration 6768, lr = 0.00261677
I0410 14:19:38.935684 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:19:40.417479 18353 solver.cpp:218] Iteration 6780 (2.45058 iter/s, 4.89679s/12 iters), loss = 0.666838
I0410 14:19:40.417536 18353 solver.cpp:237]     Train net output #0: loss = 0.666838 (* 1 = 0.666838 loss)
I0410 14:19:40.417548 18353 sgd_solver.cpp:105] Iteration 6780, lr = 0.00261056
I0410 14:19:45.285915 18353 solver.cpp:218] Iteration 6792 (2.46496 iter/s, 4.86823s/12 iters), loss = 0.286074
I0410 14:19:45.285995 18353 solver.cpp:237]     Train net output #0: loss = 0.286074 (* 1 = 0.286074 loss)
I0410 14:19:45.286010 18353 sgd_solver.cpp:105] Iteration 6792, lr = 0.00260436
I0410 14:19:50.122663 18353 solver.cpp:218] Iteration 6804 (2.48112 iter/s, 4.83653s/12 iters), loss = 0.694836
I0410 14:19:50.122706 18353 solver.cpp:237]     Train net output #0: loss = 0.694836 (* 1 = 0.694836 loss)
I0410 14:19:50.122716 18353 sgd_solver.cpp:105] Iteration 6804, lr = 0.00259817
I0410 14:19:54.941778 18353 solver.cpp:218] Iteration 6816 (2.49018 iter/s, 4.81892s/12 iters), loss = 0.556946
I0410 14:19:54.941838 18353 solver.cpp:237]     Train net output #0: loss = 0.556946 (* 1 = 0.556946 loss)
I0410 14:19:54.941851 18353 sgd_solver.cpp:105] Iteration 6816, lr = 0.00259201
I0410 14:19:59.877501 18353 solver.cpp:218] Iteration 6828 (2.43136 iter/s, 4.93552s/12 iters), loss = 0.412786
I0410 14:19:59.877549 18353 solver.cpp:237]     Train net output #0: loss = 0.412786 (* 1 = 0.412786 loss)
I0410 14:19:59.877560 18353 sgd_solver.cpp:105] Iteration 6828, lr = 0.00258585
I0410 14:20:01.874425 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6834.caffemodel
I0410 14:20:02.177280 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6834.solverstate
I0410 14:20:02.384155 18353 solver.cpp:330] Iteration 6834, Testing net (#0)
I0410 14:20:02.384182 18353 net.cpp:676] Ignoring source layer train-data
I0410 14:20:04.145292 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:20:06.805377 18353 solver.cpp:397]     Test net output #0: accuracy = 0.532475
I0410 14:20:06.805480 18353 solver.cpp:397]     Test net output #1: loss = 2.32753 (* 1 = 2.32753 loss)
I0410 14:20:08.716609 18353 solver.cpp:218] Iteration 6840 (1.35765 iter/s, 8.8388s/12 iters), loss = 0.485697
I0410 14:20:08.716667 18353 solver.cpp:237]     Train net output #0: loss = 0.485697 (* 1 = 0.485697 loss)
I0410 14:20:08.716681 18353 sgd_solver.cpp:105] Iteration 6840, lr = 0.00257971
I0410 14:20:13.577514 18353 solver.cpp:218] Iteration 6852 (2.46878 iter/s, 4.86071s/12 iters), loss = 0.577252
I0410 14:20:13.577558 18353 solver.cpp:237]     Train net output #0: loss = 0.577252 (* 1 = 0.577252 loss)
I0410 14:20:13.577567 18353 sgd_solver.cpp:105] Iteration 6852, lr = 0.00257359
I0410 14:20:18.499302 18353 solver.cpp:218] Iteration 6864 (2.43823 iter/s, 4.9216s/12 iters), loss = 0.433717
I0410 14:20:18.499351 18353 solver.cpp:237]     Train net output #0: loss = 0.433717 (* 1 = 0.433717 loss)
I0410 14:20:18.499361 18353 sgd_solver.cpp:105] Iteration 6864, lr = 0.00256748
I0410 14:20:23.405505 18353 solver.cpp:218] Iteration 6876 (2.44598 iter/s, 4.90601s/12 iters), loss = 0.581418
I0410 14:20:23.405550 18353 solver.cpp:237]     Train net output #0: loss = 0.581418 (* 1 = 0.581418 loss)
I0410 14:20:23.405560 18353 sgd_solver.cpp:105] Iteration 6876, lr = 0.00256138
I0410 14:20:24.019603 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:20:28.318297 18353 solver.cpp:218] Iteration 6888 (2.4427 iter/s, 4.9126s/12 iters), loss = 0.432214
I0410 14:20:28.318352 18353 solver.cpp:237]     Train net output #0: loss = 0.432214 (* 1 = 0.432214 loss)
I0410 14:20:28.318365 18353 sgd_solver.cpp:105] Iteration 6888, lr = 0.0025553
I0410 14:20:33.272722 18353 solver.cpp:218] Iteration 6900 (2.42217 iter/s, 4.95423s/12 iters), loss = 0.569574
I0410 14:20:33.272768 18353 solver.cpp:237]     Train net output #0: loss = 0.569574 (* 1 = 0.569574 loss)
I0410 14:20:33.272778 18353 sgd_solver.cpp:105] Iteration 6900, lr = 0.00254923
I0410 14:20:38.123504 18353 solver.cpp:218] Iteration 6912 (2.47392 iter/s, 4.8506s/12 iters), loss = 0.375758
I0410 14:20:38.125365 18353 solver.cpp:237]     Train net output #0: loss = 0.375758 (* 1 = 0.375758 loss)
I0410 14:20:38.125377 18353 sgd_solver.cpp:105] Iteration 6912, lr = 0.00254318
I0410 14:20:42.967500 18353 solver.cpp:218] Iteration 6924 (2.47832 iter/s, 4.84199s/12 iters), loss = 0.591047
I0410 14:20:42.967545 18353 solver.cpp:237]     Train net output #0: loss = 0.591047 (* 1 = 0.591047 loss)
I0410 14:20:42.967554 18353 sgd_solver.cpp:105] Iteration 6924, lr = 0.00253714
I0410 14:20:47.394068 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6936.caffemodel
I0410 14:20:47.681661 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6936.solverstate
I0410 14:20:47.875120 18353 solver.cpp:330] Iteration 6936, Testing net (#0)
I0410 14:20:47.875138 18353 net.cpp:676] Ignoring source layer train-data
I0410 14:20:48.092234 18353 blocking_queue.cpp:49] Waiting for data
I0410 14:20:49.506413 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:20:52.241219 18353 solver.cpp:397]     Test net output #0: accuracy = 0.535539
I0410 14:20:52.241263 18353 solver.cpp:397]     Test net output #1: loss = 2.26357 (* 1 = 2.26357 loss)
I0410 14:20:52.322482 18353 solver.cpp:218] Iteration 6936 (1.28278 iter/s, 9.35467s/12 iters), loss = 0.467544
I0410 14:20:52.322532 18353 solver.cpp:237]     Train net output #0: loss = 0.467544 (* 1 = 0.467544 loss)
I0410 14:20:52.322543 18353 sgd_solver.cpp:105] Iteration 6936, lr = 0.00253112
I0410 14:20:56.558121 18353 solver.cpp:218] Iteration 6948 (2.83322 iter/s, 4.23546s/12 iters), loss = 0.450545
I0410 14:20:56.558178 18353 solver.cpp:237]     Train net output #0: loss = 0.450545 (* 1 = 0.450545 loss)
I0410 14:20:56.558189 18353 sgd_solver.cpp:105] Iteration 6948, lr = 0.00252511
I0410 14:21:01.491282 18353 solver.cpp:218] Iteration 6960 (2.43262 iter/s, 4.93296s/12 iters), loss = 0.440145
I0410 14:21:01.491334 18353 solver.cpp:237]     Train net output #0: loss = 0.440145 (* 1 = 0.440145 loss)
I0410 14:21:01.491345 18353 sgd_solver.cpp:105] Iteration 6960, lr = 0.00251911
I0410 14:21:06.427083 18353 solver.cpp:218] Iteration 6972 (2.43132 iter/s, 4.9356s/12 iters), loss = 0.503401
I0410 14:21:06.427132 18353 solver.cpp:237]     Train net output #0: loss = 0.503401 (* 1 = 0.503401 loss)
I0410 14:21:06.427142 18353 sgd_solver.cpp:105] Iteration 6972, lr = 0.00251313
I0410 14:21:09.099288 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:21:11.304221 18353 solver.cpp:218] Iteration 6984 (2.46056 iter/s, 4.87694s/12 iters), loss = 0.470529
I0410 14:21:11.304270 18353 solver.cpp:237]     Train net output #0: loss = 0.470529 (* 1 = 0.470529 loss)
I0410 14:21:11.304280 18353 sgd_solver.cpp:105] Iteration 6984, lr = 0.00250717
I0410 14:21:16.266705 18353 solver.cpp:218] Iteration 6996 (2.41824 iter/s, 4.96229s/12 iters), loss = 0.344354
I0410 14:21:16.266757 18353 solver.cpp:237]     Train net output #0: loss = 0.344354 (* 1 = 0.344354 loss)
I0410 14:21:16.266770 18353 sgd_solver.cpp:105] Iteration 6996, lr = 0.00250121
I0410 14:21:21.193593 18353 solver.cpp:218] Iteration 7008 (2.43571 iter/s, 4.92669s/12 iters), loss = 0.693073
I0410 14:21:21.193634 18353 solver.cpp:237]     Train net output #0: loss = 0.693073 (* 1 = 0.693073 loss)
I0410 14:21:21.193642 18353 sgd_solver.cpp:105] Iteration 7008, lr = 0.00249528
I0410 14:21:26.087723 18353 solver.cpp:218] Iteration 7020 (2.45201 iter/s, 4.89394s/12 iters), loss = 0.336238
I0410 14:21:26.087782 18353 solver.cpp:237]     Train net output #0: loss = 0.336238 (* 1 = 0.336238 loss)
I0410 14:21:26.087795 18353 sgd_solver.cpp:105] Iteration 7020, lr = 0.00248935
I0410 14:21:31.005491 18353 solver.cpp:218] Iteration 7032 (2.44023 iter/s, 4.91756s/12 iters), loss = 0.317085
I0410 14:21:31.005546 18353 solver.cpp:237]     Train net output #0: loss = 0.317085 (* 1 = 0.317085 loss)
I0410 14:21:31.005560 18353 sgd_solver.cpp:105] Iteration 7032, lr = 0.00248344
I0410 14:21:32.996740 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7038.caffemodel
I0410 14:21:33.290553 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7038.solverstate
I0410 14:21:33.497277 18353 solver.cpp:330] Iteration 7038, Testing net (#0)
I0410 14:21:33.497295 18353 net.cpp:676] Ignoring source layer train-data
I0410 14:21:35.095932 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:21:37.837136 18353 solver.cpp:397]     Test net output #0: accuracy = 0.533701
I0410 14:21:37.837172 18353 solver.cpp:397]     Test net output #1: loss = 2.32735 (* 1 = 2.32735 loss)
I0410 14:21:39.716154 18353 solver.cpp:218] Iteration 7044 (1.37767 iter/s, 8.71036s/12 iters), loss = 0.323803
I0410 14:21:39.716277 18353 solver.cpp:237]     Train net output #0: loss = 0.323803 (* 1 = 0.323803 loss)
I0410 14:21:39.716287 18353 sgd_solver.cpp:105] Iteration 7044, lr = 0.00247755
I0410 14:21:44.618693 18353 solver.cpp:218] Iteration 7056 (2.44785 iter/s, 4.90227s/12 iters), loss = 0.554409
I0410 14:21:44.618736 18353 solver.cpp:237]     Train net output #0: loss = 0.554409 (* 1 = 0.554409 loss)
I0410 14:21:44.618746 18353 sgd_solver.cpp:105] Iteration 7056, lr = 0.00247166
I0410 14:21:49.696239 18353 solver.cpp:218] Iteration 7068 (2.36343 iter/s, 5.07736s/12 iters), loss = 0.497578
I0410 14:21:49.696280 18353 solver.cpp:237]     Train net output #0: loss = 0.497578 (* 1 = 0.497578 loss)
I0410 14:21:49.696290 18353 sgd_solver.cpp:105] Iteration 7068, lr = 0.0024658
I0410 14:21:54.492274 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:21:54.591624 18353 solver.cpp:218] Iteration 7080 (2.45138 iter/s, 4.8952s/12 iters), loss = 0.454659
I0410 14:21:54.591665 18353 solver.cpp:237]     Train net output #0: loss = 0.454659 (* 1 = 0.454659 loss)
I0410 14:21:54.591672 18353 sgd_solver.cpp:105] Iteration 7080, lr = 0.00245994
I0410 14:21:59.487835 18353 solver.cpp:218] Iteration 7092 (2.45097 iter/s, 4.89603s/12 iters), loss = 0.427035
I0410 14:21:59.487879 18353 solver.cpp:237]     Train net output #0: loss = 0.427035 (* 1 = 0.427035 loss)
I0410 14:21:59.487890 18353 sgd_solver.cpp:105] Iteration 7092, lr = 0.0024541
I0410 14:22:04.357770 18353 solver.cpp:218] Iteration 7104 (2.46419 iter/s, 4.86975s/12 iters), loss = 0.426499
I0410 14:22:04.357815 18353 solver.cpp:237]     Train net output #0: loss = 0.426499 (* 1 = 0.426499 loss)
I0410 14:22:04.357825 18353 sgd_solver.cpp:105] Iteration 7104, lr = 0.00244827
I0410 14:22:09.297428 18353 solver.cpp:218] Iteration 7116 (2.42941 iter/s, 4.93947s/12 iters), loss = 0.365494
I0410 14:22:09.297468 18353 solver.cpp:237]     Train net output #0: loss = 0.365494 (* 1 = 0.365494 loss)
I0410 14:22:09.297478 18353 sgd_solver.cpp:105] Iteration 7116, lr = 0.00244246
I0410 14:22:14.193352 18353 solver.cpp:218] Iteration 7128 (2.45111 iter/s, 4.89573s/12 iters), loss = 0.449697
I0410 14:22:14.193465 18353 solver.cpp:237]     Train net output #0: loss = 0.449697 (* 1 = 0.449697 loss)
I0410 14:22:14.193478 18353 sgd_solver.cpp:105] Iteration 7128, lr = 0.00243666
I0410 14:22:18.700829 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7140.caffemodel
I0410 14:22:18.993100 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7140.solverstate
I0410 14:22:19.196791 18353 solver.cpp:330] Iteration 7140, Testing net (#0)
I0410 14:22:19.196820 18353 net.cpp:676] Ignoring source layer train-data
I0410 14:22:20.848943 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:22:23.643405 18353 solver.cpp:397]     Test net output #0: accuracy = 0.531863
I0410 14:22:23.643450 18353 solver.cpp:397]     Test net output #1: loss = 2.38475 (* 1 = 2.38475 loss)
I0410 14:22:23.724684 18353 solver.cpp:218] Iteration 7140 (1.25906 iter/s, 9.53095s/12 iters), loss = 0.471034
I0410 14:22:23.724735 18353 solver.cpp:237]     Train net output #0: loss = 0.471034 (* 1 = 0.471034 loss)
I0410 14:22:23.724747 18353 sgd_solver.cpp:105] Iteration 7140, lr = 0.00243088
I0410 14:22:28.073588 18353 solver.cpp:218] Iteration 7152 (2.75943 iter/s, 4.34872s/12 iters), loss = 0.408697
I0410 14:22:28.073642 18353 solver.cpp:237]     Train net output #0: loss = 0.408697 (* 1 = 0.408697 loss)
I0410 14:22:28.073652 18353 sgd_solver.cpp:105] Iteration 7152, lr = 0.00242511
I0410 14:22:33.042829 18353 solver.cpp:218] Iteration 7164 (2.41496 iter/s, 4.96903s/12 iters), loss = 0.264834
I0410 14:22:33.042888 18353 solver.cpp:237]     Train net output #0: loss = 0.264834 (* 1 = 0.264834 loss)
I0410 14:22:33.042901 18353 sgd_solver.cpp:105] Iteration 7164, lr = 0.00241935
I0410 14:22:38.022423 18353 solver.cpp:218] Iteration 7176 (2.40994 iter/s, 4.97938s/12 iters), loss = 0.390787
I0410 14:22:38.022481 18353 solver.cpp:237]     Train net output #0: loss = 0.390787 (* 1 = 0.390787 loss)
I0410 14:22:38.022495 18353 sgd_solver.cpp:105] Iteration 7176, lr = 0.0024136
I0410 14:22:40.236450 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:22:43.145560 18353 solver.cpp:218] Iteration 7188 (2.34241 iter/s, 5.12293s/12 iters), loss = 0.462879
I0410 14:22:43.145609 18353 solver.cpp:237]     Train net output #0: loss = 0.462879 (* 1 = 0.462879 loss)
I0410 14:22:43.145623 18353 sgd_solver.cpp:105] Iteration 7188, lr = 0.00240787
I0410 14:22:48.017293 18353 solver.cpp:218] Iteration 7200 (2.46329 iter/s, 4.87154s/12 iters), loss = 0.411483
I0410 14:22:48.017419 18353 solver.cpp:237]     Train net output #0: loss = 0.411483 (* 1 = 0.411483 loss)
I0410 14:22:48.017429 18353 sgd_solver.cpp:105] Iteration 7200, lr = 0.00240216
I0410 14:22:52.927667 18353 solver.cpp:218] Iteration 7212 (2.44394 iter/s, 4.9101s/12 iters), loss = 0.225682
I0410 14:22:52.927721 18353 solver.cpp:237]     Train net output #0: loss = 0.225682 (* 1 = 0.225682 loss)
I0410 14:22:52.927732 18353 sgd_solver.cpp:105] Iteration 7212, lr = 0.00239645
I0410 14:22:57.987843 18353 solver.cpp:218] Iteration 7224 (2.37155 iter/s, 5.05998s/12 iters), loss = 0.410161
I0410 14:22:57.987891 18353 solver.cpp:237]     Train net output #0: loss = 0.410161 (* 1 = 0.410161 loss)
I0410 14:22:57.987901 18353 sgd_solver.cpp:105] Iteration 7224, lr = 0.00239076
I0410 14:23:02.962646 18353 solver.cpp:218] Iteration 7236 (2.41225 iter/s, 4.9746s/12 iters), loss = 0.439174
I0410 14:23:02.962702 18353 solver.cpp:237]     Train net output #0: loss = 0.439174 (* 1 = 0.439174 loss)
I0410 14:23:02.962713 18353 sgd_solver.cpp:105] Iteration 7236, lr = 0.00238509
I0410 14:23:05.011018 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7242.caffemodel
I0410 14:23:05.308598 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7242.solverstate
I0410 14:23:05.515314 18353 solver.cpp:330] Iteration 7242, Testing net (#0)
I0410 14:23:05.515347 18353 net.cpp:676] Ignoring source layer train-data
I0410 14:23:07.146154 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:23:09.997645 18353 solver.cpp:397]     Test net output #0: accuracy = 0.537377
I0410 14:23:09.997695 18353 solver.cpp:397]     Test net output #1: loss = 2.34597 (* 1 = 2.34597 loss)
I0410 14:23:11.767606 18353 solver.cpp:218] Iteration 7248 (1.36292 iter/s, 8.80466s/12 iters), loss = 0.446899
I0410 14:23:11.767663 18353 solver.cpp:237]     Train net output #0: loss = 0.446899 (* 1 = 0.446899 loss)
I0410 14:23:11.767675 18353 sgd_solver.cpp:105] Iteration 7248, lr = 0.00237942
I0410 14:23:16.694267 18353 solver.cpp:218] Iteration 7260 (2.43583 iter/s, 4.92646s/12 iters), loss = 0.288486
I0410 14:23:16.694325 18353 solver.cpp:237]     Train net output #0: loss = 0.288486 (* 1 = 0.288486 loss)
I0410 14:23:16.694339 18353 sgd_solver.cpp:105] Iteration 7260, lr = 0.00237378
I0410 14:23:21.641233 18353 solver.cpp:218] Iteration 7272 (2.42583 iter/s, 4.94676s/12 iters), loss = 0.366645
I0410 14:23:21.641387 18353 solver.cpp:237]     Train net output #0: loss = 0.366645 (* 1 = 0.366645 loss)
I0410 14:23:21.641403 18353 sgd_solver.cpp:105] Iteration 7272, lr = 0.00236814
I0410 14:23:25.799857 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:23:26.538076 18353 solver.cpp:218] Iteration 7284 (2.45071 iter/s, 4.89655s/12 iters), loss = 0.452616
I0410 14:23:26.538130 18353 solver.cpp:237]     Train net output #0: loss = 0.452616 (* 1 = 0.452616 loss)
I0410 14:23:26.538143 18353 sgd_solver.cpp:105] Iteration 7284, lr = 0.00236252
I0410 14:23:31.472009 18353 solver.cpp:218] Iteration 7296 (2.43223 iter/s, 4.93374s/12 iters), loss = 0.686861
I0410 14:23:31.472060 18353 solver.cpp:237]     Train net output #0: loss = 0.686861 (* 1 = 0.686861 loss)
I0410 14:23:31.472072 18353 sgd_solver.cpp:105] Iteration 7296, lr = 0.00235691
I0410 14:23:36.342562 18353 solver.cpp:218] Iteration 7308 (2.46388 iter/s, 4.87036s/12 iters), loss = 0.480239
I0410 14:23:36.342605 18353 solver.cpp:237]     Train net output #0: loss = 0.480239 (* 1 = 0.480239 loss)
I0410 14:23:36.342615 18353 sgd_solver.cpp:105] Iteration 7308, lr = 0.00235131
I0410 14:23:41.259428 18353 solver.cpp:218] Iteration 7320 (2.44067 iter/s, 4.91667s/12 iters), loss = 0.557563
I0410 14:23:41.259477 18353 solver.cpp:237]     Train net output #0: loss = 0.557563 (* 1 = 0.557563 loss)
I0410 14:23:41.259490 18353 sgd_solver.cpp:105] Iteration 7320, lr = 0.00234573
I0410 14:23:46.185106 18353 solver.cpp:218] Iteration 7332 (2.43631 iter/s, 4.92548s/12 iters), loss = 0.501118
I0410 14:23:46.185161 18353 solver.cpp:237]     Train net output #0: loss = 0.501118 (* 1 = 0.501118 loss)
I0410 14:23:46.185173 18353 sgd_solver.cpp:105] Iteration 7332, lr = 0.00234016
I0410 14:23:50.873056 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7344.caffemodel
I0410 14:23:51.431845 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7344.solverstate
I0410 14:23:52.242501 18353 solver.cpp:330] Iteration 7344, Testing net (#0)
I0410 14:23:52.242563 18353 net.cpp:676] Ignoring source layer train-data
I0410 14:23:53.731781 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:23:56.592808 18353 solver.cpp:397]     Test net output #0: accuracy = 0.548407
I0410 14:23:56.592857 18353 solver.cpp:397]     Test net output #1: loss = 2.28191 (* 1 = 2.28191 loss)
I0410 14:23:56.674088 18353 solver.cpp:218] Iteration 7344 (1.1441 iter/s, 10.4886s/12 iters), loss = 0.267234
I0410 14:23:56.674139 18353 solver.cpp:237]     Train net output #0: loss = 0.267234 (* 1 = 0.267234 loss)
I0410 14:23:56.674150 18353 sgd_solver.cpp:105] Iteration 7344, lr = 0.0023346
I0410 14:24:00.775830 18353 solver.cpp:218] Iteration 7356 (2.92571 iter/s, 4.10157s/12 iters), loss = 0.209648
I0410 14:24:00.775885 18353 solver.cpp:237]     Train net output #0: loss = 0.209648 (* 1 = 0.209648 loss)
I0410 14:24:00.775898 18353 sgd_solver.cpp:105] Iteration 7356, lr = 0.00232906
I0410 14:24:05.670747 18353 solver.cpp:218] Iteration 7368 (2.45162 iter/s, 4.89471s/12 iters), loss = 0.333602
I0410 14:24:05.670807 18353 solver.cpp:237]     Train net output #0: loss = 0.333602 (* 1 = 0.333602 loss)
I0410 14:24:05.670820 18353 sgd_solver.cpp:105] Iteration 7368, lr = 0.00232353
I0410 14:24:10.577785 18353 solver.cpp:218] Iteration 7380 (2.44557 iter/s, 4.90683s/12 iters), loss = 0.342976
I0410 14:24:10.577849 18353 solver.cpp:237]     Train net output #0: loss = 0.342976 (* 1 = 0.342976 loss)
I0410 14:24:10.577865 18353 sgd_solver.cpp:105] Iteration 7380, lr = 0.00231802
I0410 14:24:11.934064 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:24:15.491094 18353 solver.cpp:218] Iteration 7392 (2.44245 iter/s, 4.9131s/12 iters), loss = 0.519873
I0410 14:24:15.491148 18353 solver.cpp:237]     Train net output #0: loss = 0.519873 (* 1 = 0.519873 loss)
I0410 14:24:15.491159 18353 sgd_solver.cpp:105] Iteration 7392, lr = 0.00231251
I0410 14:24:20.379324 18353 solver.cpp:218] Iteration 7404 (2.45498 iter/s, 4.88803s/12 iters), loss = 0.239125
I0410 14:24:20.379379 18353 solver.cpp:237]     Train net output #0: loss = 0.239125 (* 1 = 0.239125 loss)
I0410 14:24:20.379390 18353 sgd_solver.cpp:105] Iteration 7404, lr = 0.00230702
I0410 14:24:25.317571 18353 solver.cpp:218] Iteration 7416 (2.43011 iter/s, 4.93805s/12 iters), loss = 0.27665
I0410 14:24:25.317716 18353 solver.cpp:237]     Train net output #0: loss = 0.27665 (* 1 = 0.27665 loss)
I0410 14:24:25.317729 18353 sgd_solver.cpp:105] Iteration 7416, lr = 0.00230154
I0410 14:24:30.211930 18353 solver.cpp:218] Iteration 7428 (2.45195 iter/s, 4.89407s/12 iters), loss = 0.274236
I0410 14:24:30.211982 18353 solver.cpp:237]     Train net output #0: loss = 0.274236 (* 1 = 0.274236 loss)
I0410 14:24:30.211993 18353 sgd_solver.cpp:105] Iteration 7428, lr = 0.00229608
I0410 14:24:35.138453 18353 solver.cpp:218] Iteration 7440 (2.43589 iter/s, 4.92632s/12 iters), loss = 0.453611
I0410 14:24:35.138509 18353 solver.cpp:237]     Train net output #0: loss = 0.453611 (* 1 = 0.453611 loss)
I0410 14:24:35.138520 18353 sgd_solver.cpp:105] Iteration 7440, lr = 0.00229063
I0410 14:24:37.114804 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7446.caffemodel
I0410 14:24:37.421856 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7446.solverstate
I0410 14:24:37.627766 18353 solver.cpp:330] Iteration 7446, Testing net (#0)
I0410 14:24:37.627800 18353 net.cpp:676] Ignoring source layer train-data
I0410 14:24:39.177572 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:24:42.263938 18353 solver.cpp:397]     Test net output #0: accuracy = 0.542892
I0410 14:24:42.263990 18353 solver.cpp:397]     Test net output #1: loss = 2.33987 (* 1 = 2.33987 loss)
I0410 14:24:44.111340 18353 solver.cpp:218] Iteration 7452 (1.33741 iter/s, 8.97258s/12 iters), loss = 0.437316
I0410 14:24:44.111397 18353 solver.cpp:237]     Train net output #0: loss = 0.437316 (* 1 = 0.437316 loss)
I0410 14:24:44.111409 18353 sgd_solver.cpp:105] Iteration 7452, lr = 0.00228519
I0410 14:24:49.080281 18353 solver.cpp:218] Iteration 7464 (2.41508 iter/s, 4.96879s/12 iters), loss = 0.357817
I0410 14:24:49.080339 18353 solver.cpp:237]     Train net output #0: loss = 0.357817 (* 1 = 0.357817 loss)
I0410 14:24:49.080353 18353 sgd_solver.cpp:105] Iteration 7464, lr = 0.00227976
I0410 14:24:53.983656 18353 solver.cpp:218] Iteration 7476 (2.44737 iter/s, 4.90323s/12 iters), loss = 0.460165
I0410 14:24:53.983711 18353 solver.cpp:237]     Train net output #0: loss = 0.460165 (* 1 = 0.460165 loss)
I0410 14:24:53.983726 18353 sgd_solver.cpp:105] Iteration 7476, lr = 0.00227435
I0410 14:24:57.473981 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:24:58.944938 18353 solver.cpp:218] Iteration 7488 (2.4188 iter/s, 4.96114s/12 iters), loss = 0.628991
I0410 14:24:58.944988 18353 solver.cpp:237]     Train net output #0: loss = 0.628991 (* 1 = 0.628991 loss)
I0410 14:24:58.944998 18353 sgd_solver.cpp:105] Iteration 7488, lr = 0.00226895
I0410 14:25:03.788949 18353 solver.cpp:218] Iteration 7500 (2.47735 iter/s, 4.84388s/12 iters), loss = 0.34601
I0410 14:25:03.788987 18353 solver.cpp:237]     Train net output #0: loss = 0.34601 (* 1 = 0.34601 loss)
I0410 14:25:03.788996 18353 sgd_solver.cpp:105] Iteration 7500, lr = 0.00226357
I0410 14:25:08.711354 18353 solver.cpp:218] Iteration 7512 (2.4379 iter/s, 4.92228s/12 iters), loss = 0.243745
I0410 14:25:08.711395 18353 solver.cpp:237]     Train net output #0: loss = 0.243745 (* 1 = 0.243745 loss)
I0410 14:25:08.711405 18353 sgd_solver.cpp:105] Iteration 7512, lr = 0.00225819
I0410 14:25:13.653805 18353 solver.cpp:218] Iteration 7524 (2.42801 iter/s, 4.94232s/12 iters), loss = 0.243297
I0410 14:25:13.653862 18353 solver.cpp:237]     Train net output #0: loss = 0.243297 (* 1 = 0.243297 loss)
I0410 14:25:13.653877 18353 sgd_solver.cpp:105] Iteration 7524, lr = 0.00225283
I0410 14:25:18.594547 18353 solver.cpp:218] Iteration 7536 (2.42886 iter/s, 4.9406s/12 iters), loss = 0.296403
I0410 14:25:18.594606 18353 solver.cpp:237]     Train net output #0: loss = 0.296403 (* 1 = 0.296403 loss)
I0410 14:25:18.594619 18353 sgd_solver.cpp:105] Iteration 7536, lr = 0.00224748
I0410 14:25:23.016207 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7548.caffemodel
I0410 14:25:23.342218 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7548.solverstate
I0410 14:25:23.548983 18353 solver.cpp:330] Iteration 7548, Testing net (#0)
I0410 14:25:23.549005 18353 net.cpp:676] Ignoring source layer train-data
I0410 14:25:24.938483 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:25:27.875013 18353 solver.cpp:397]     Test net output #0: accuracy = 0.551471
I0410 14:25:27.875126 18353 solver.cpp:397]     Test net output #1: loss = 2.39349 (* 1 = 2.39349 loss)
I0410 14:25:27.957713 18353 solver.cpp:218] Iteration 7548 (1.28165 iter/s, 9.36295s/12 iters), loss = 0.238834
I0410 14:25:27.957757 18353 solver.cpp:237]     Train net output #0: loss = 0.238834 (* 1 = 0.238834 loss)
I0410 14:25:27.957767 18353 sgd_solver.cpp:105] Iteration 7548, lr = 0.00224215
I0410 14:25:32.069738 18353 solver.cpp:218] Iteration 7560 (2.91836 iter/s, 4.1119s/12 iters), loss = 0.379549
I0410 14:25:32.069787 18353 solver.cpp:237]     Train net output #0: loss = 0.379549 (* 1 = 0.379549 loss)
I0410 14:25:32.069797 18353 sgd_solver.cpp:105] Iteration 7560, lr = 0.00223682
I0410 14:25:36.958432 18353 solver.cpp:218] Iteration 7572 (2.45471 iter/s, 4.88856s/12 iters), loss = 0.405818
I0410 14:25:36.958473 18353 solver.cpp:237]     Train net output #0: loss = 0.405818 (* 1 = 0.405818 loss)
I0410 14:25:36.958482 18353 sgd_solver.cpp:105] Iteration 7572, lr = 0.00223151
I0410 14:25:42.049321 18353 solver.cpp:218] Iteration 7584 (2.35721 iter/s, 5.09075s/12 iters), loss = 0.399859
I0410 14:25:42.049367 18353 solver.cpp:237]     Train net output #0: loss = 0.399859 (* 1 = 0.399859 loss)
I0410 14:25:42.049377 18353 sgd_solver.cpp:105] Iteration 7584, lr = 0.00222621
I0410 14:25:42.682785 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:25:46.988426 18353 solver.cpp:218] Iteration 7596 (2.42966 iter/s, 4.93897s/12 iters), loss = 0.320253
I0410 14:25:46.988482 18353 solver.cpp:237]     Train net output #0: loss = 0.320253 (* 1 = 0.320253 loss)
I0410 14:25:46.988495 18353 sgd_solver.cpp:105] Iteration 7596, lr = 0.00222093
I0410 14:25:51.906843 18353 solver.cpp:218] Iteration 7608 (2.43988 iter/s, 4.91828s/12 iters), loss = 0.213703
I0410 14:25:51.906881 18353 solver.cpp:237]     Train net output #0: loss = 0.213703 (* 1 = 0.213703 loss)
I0410 14:25:51.906891 18353 sgd_solver.cpp:105] Iteration 7608, lr = 0.00221565
I0410 14:25:56.984663 18353 solver.cpp:218] Iteration 7620 (2.36328 iter/s, 5.07769s/12 iters), loss = 0.363002
I0410 14:25:56.984716 18353 solver.cpp:237]     Train net output #0: loss = 0.363002 (* 1 = 0.363002 loss)
I0410 14:25:56.984730 18353 sgd_solver.cpp:105] Iteration 7620, lr = 0.00221039
I0410 14:25:57.759373 18353 blocking_queue.cpp:49] Waiting for data
I0410 14:26:01.951365 18353 solver.cpp:218] Iteration 7632 (2.41616 iter/s, 4.96656s/12 iters), loss = 0.482575
I0410 14:26:01.951457 18353 solver.cpp:237]     Train net output #0: loss = 0.482575 (* 1 = 0.482575 loss)
I0410 14:26:01.951472 18353 sgd_solver.cpp:105] Iteration 7632, lr = 0.00220515
I0410 14:26:06.885231 18353 solver.cpp:218] Iteration 7644 (2.43226 iter/s, 4.93369s/12 iters), loss = 0.273094
I0410 14:26:06.885277 18353 solver.cpp:237]     Train net output #0: loss = 0.273094 (* 1 = 0.273094 loss)
I0410 14:26:06.885286 18353 sgd_solver.cpp:105] Iteration 7644, lr = 0.00219991
I0410 14:26:08.881283 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7650.caffemodel
I0410 14:26:09.193208 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7650.solverstate
I0410 14:26:09.399282 18353 solver.cpp:330] Iteration 7650, Testing net (#0)
I0410 14:26:09.399308 18353 net.cpp:676] Ignoring source layer train-data
I0410 14:26:10.854840 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:26:13.855095 18353 solver.cpp:397]     Test net output #0: accuracy = 0.547794
I0410 14:26:13.855126 18353 solver.cpp:397]     Test net output #1: loss = 2.37706 (* 1 = 2.37706 loss)
I0410 14:26:15.805285 18353 solver.cpp:218] Iteration 7656 (1.34531 iter/s, 8.91985s/12 iters), loss = 0.408765
I0410 14:26:15.805335 18353 solver.cpp:237]     Train net output #0: loss = 0.408765 (* 1 = 0.408765 loss)
I0410 14:26:15.805344 18353 sgd_solver.cpp:105] Iteration 7656, lr = 0.00219469
I0410 14:26:20.728349 18353 solver.cpp:218] Iteration 7668 (2.43758 iter/s, 4.92292s/12 iters), loss = 0.288223
I0410 14:26:20.728397 18353 solver.cpp:237]     Train net output #0: loss = 0.288223 (* 1 = 0.288223 loss)
I0410 14:26:20.728406 18353 sgd_solver.cpp:105] Iteration 7668, lr = 0.00218948
I0410 14:26:25.663763 18353 solver.cpp:218] Iteration 7680 (2.43148 iter/s, 4.93526s/12 iters), loss = 0.425493
I0410 14:26:25.663812 18353 solver.cpp:237]     Train net output #0: loss = 0.425493 (* 1 = 0.425493 loss)
I0410 14:26:25.663822 18353 sgd_solver.cpp:105] Iteration 7680, lr = 0.00218428
I0410 14:26:28.425352 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:26:30.627898 18353 solver.cpp:218] Iteration 7692 (2.41741 iter/s, 4.96399s/12 iters), loss = 0.327503
I0410 14:26:30.627951 18353 solver.cpp:237]     Train net output #0: loss = 0.327503 (* 1 = 0.327503 loss)
I0410 14:26:30.627962 18353 sgd_solver.cpp:105] Iteration 7692, lr = 0.00217909
I0410 14:26:35.506058 18353 solver.cpp:218] Iteration 7704 (2.46002 iter/s, 4.87801s/12 iters), loss = 0.375226
I0410 14:26:35.506201 18353 solver.cpp:237]     Train net output #0: loss = 0.375226 (* 1 = 0.375226 loss)
I0410 14:26:35.506222 18353 sgd_solver.cpp:105] Iteration 7704, lr = 0.00217392
I0410 14:26:40.388186 18353 solver.cpp:218] Iteration 7716 (2.45806 iter/s, 4.88189s/12 iters), loss = 0.367266
I0410 14:26:40.388236 18353 solver.cpp:237]     Train net output #0: loss = 0.367266 (* 1 = 0.367266 loss)
I0410 14:26:40.388245 18353 sgd_solver.cpp:105] Iteration 7716, lr = 0.00216876
I0410 14:26:45.293215 18353 solver.cpp:218] Iteration 7728 (2.44654 iter/s, 4.90488s/12 iters), loss = 0.374394
I0410 14:26:45.293273 18353 solver.cpp:237]     Train net output #0: loss = 0.374394 (* 1 = 0.374394 loss)
I0410 14:26:45.293287 18353 sgd_solver.cpp:105] Iteration 7728, lr = 0.00216361
I0410 14:26:50.314680 18353 solver.cpp:218] Iteration 7740 (2.38982 iter/s, 5.02131s/12 iters), loss = 0.434383
I0410 14:26:50.314741 18353 solver.cpp:237]     Train net output #0: loss = 0.434383 (* 1 = 0.434383 loss)
I0410 14:26:50.314755 18353 sgd_solver.cpp:105] Iteration 7740, lr = 0.00215847
I0410 14:26:54.816440 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7752.caffemodel
I0410 14:26:55.114588 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7752.solverstate
I0410 14:26:55.315079 18353 solver.cpp:330] Iteration 7752, Testing net (#0)
I0410 14:26:55.315102 18353 net.cpp:676] Ignoring source layer train-data
I0410 14:26:56.720266 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:26:59.737787 18353 solver.cpp:397]     Test net output #0: accuracy = 0.548407
I0410 14:26:59.737835 18353 solver.cpp:397]     Test net output #1: loss = 2.40263 (* 1 = 2.40263 loss)
I0410 14:26:59.819334 18353 solver.cpp:218] Iteration 7752 (1.26257 iter/s, 9.50442s/12 iters), loss = 0.289652
I0410 14:26:59.819386 18353 solver.cpp:237]     Train net output #0: loss = 0.289652 (* 1 = 0.289652 loss)
I0410 14:26:59.819398 18353 sgd_solver.cpp:105] Iteration 7752, lr = 0.00215335
I0410 14:27:04.016626 18353 solver.cpp:218] Iteration 7764 (2.85908 iter/s, 4.19715s/12 iters), loss = 0.25654
I0410 14:27:04.016680 18353 solver.cpp:237]     Train net output #0: loss = 0.25654 (* 1 = 0.25654 loss)
I0410 14:27:04.016690 18353 sgd_solver.cpp:105] Iteration 7764, lr = 0.00214823
I0410 14:27:08.920397 18353 solver.cpp:218] Iteration 7776 (2.44718 iter/s, 4.90361s/12 iters), loss = 0.411403
I0410 14:27:08.920547 18353 solver.cpp:237]     Train net output #0: loss = 0.411403 (* 1 = 0.411403 loss)
I0410 14:27:08.920562 18353 sgd_solver.cpp:105] Iteration 7776, lr = 0.00214313
I0410 14:27:13.823060 18353 solver.cpp:218] Iteration 7788 (2.44777 iter/s, 4.90242s/12 iters), loss = 0.216058
I0410 14:27:13.823120 18353 solver.cpp:237]     Train net output #0: loss = 0.216058 (* 1 = 0.216058 loss)
I0410 14:27:13.823134 18353 sgd_solver.cpp:105] Iteration 7788, lr = 0.00213805
I0410 14:27:13.831174 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:27:18.984700 18353 solver.cpp:218] Iteration 7800 (2.32492 iter/s, 5.16148s/12 iters), loss = 0.367202
I0410 14:27:18.984750 18353 solver.cpp:237]     Train net output #0: loss = 0.367202 (* 1 = 0.367202 loss)
I0410 14:27:18.984762 18353 sgd_solver.cpp:105] Iteration 7800, lr = 0.00213297
I0410 14:27:23.937992 18353 solver.cpp:218] Iteration 7812 (2.42271 iter/s, 4.95312s/12 iters), loss = 0.403354
I0410 14:27:23.938052 18353 solver.cpp:237]     Train net output #0: loss = 0.403354 (* 1 = 0.403354 loss)
I0410 14:27:23.938067 18353 sgd_solver.cpp:105] Iteration 7812, lr = 0.00212791
I0410 14:27:28.816071 18353 solver.cpp:218] Iteration 7824 (2.46006 iter/s, 4.87792s/12 iters), loss = 0.284613
I0410 14:27:28.816126 18353 solver.cpp:237]     Train net output #0: loss = 0.284613 (* 1 = 0.284613 loss)
I0410 14:27:28.816138 18353 sgd_solver.cpp:105] Iteration 7824, lr = 0.00212285
I0410 14:27:33.652515 18353 solver.cpp:218] Iteration 7836 (2.48124 iter/s, 4.83629s/12 iters), loss = 0.217343
I0410 14:27:33.652575 18353 solver.cpp:237]     Train net output #0: loss = 0.217343 (* 1 = 0.217343 loss)
I0410 14:27:33.652588 18353 sgd_solver.cpp:105] Iteration 7836, lr = 0.00211781
I0410 14:27:38.575361 18353 solver.cpp:218] Iteration 7848 (2.4377 iter/s, 4.92268s/12 iters), loss = 0.567162
I0410 14:27:38.575413 18353 solver.cpp:237]     Train net output #0: loss = 0.567162 (* 1 = 0.567162 loss)
I0410 14:27:38.575425 18353 sgd_solver.cpp:105] Iteration 7848, lr = 0.00211279
I0410 14:27:40.544589 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7854.caffemodel
I0410 14:27:40.880473 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7854.solverstate
I0410 14:27:41.092520 18353 solver.cpp:330] Iteration 7854, Testing net (#0)
I0410 14:27:41.092545 18353 net.cpp:676] Ignoring source layer train-data
I0410 14:27:42.423786 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:27:45.489166 18353 solver.cpp:397]     Test net output #0: accuracy = 0.553309
I0410 14:27:45.489198 18353 solver.cpp:397]     Test net output #1: loss = 2.34634 (* 1 = 2.34634 loss)
I0410 14:27:47.322710 18353 solver.cpp:218] Iteration 7860 (1.37188 iter/s, 8.74713s/12 iters), loss = 0.353139
I0410 14:27:47.322762 18353 solver.cpp:237]     Train net output #0: loss = 0.353139 (* 1 = 0.353139 loss)
I0410 14:27:47.322774 18353 sgd_solver.cpp:105] Iteration 7860, lr = 0.00210777
I0410 14:27:52.169790 18353 solver.cpp:218] Iteration 7872 (2.4758 iter/s, 4.84692s/12 iters), loss = 0.374548
I0410 14:27:52.169848 18353 solver.cpp:237]     Train net output #0: loss = 0.374548 (* 1 = 0.374548 loss)
I0410 14:27:52.169860 18353 sgd_solver.cpp:105] Iteration 7872, lr = 0.00210277
I0410 14:27:57.051791 18353 solver.cpp:218] Iteration 7884 (2.45809 iter/s, 4.88184s/12 iters), loss = 0.307182
I0410 14:27:57.051848 18353 solver.cpp:237]     Train net output #0: loss = 0.307182 (* 1 = 0.307182 loss)
I0410 14:27:57.051862 18353 sgd_solver.cpp:105] Iteration 7884, lr = 0.00209777
I0410 14:27:59.149827 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:28:02.072031 18353 solver.cpp:218] Iteration 7896 (2.3904 iter/s, 5.02008s/12 iters), loss = 0.305245
I0410 14:28:02.072094 18353 solver.cpp:237]     Train net output #0: loss = 0.305245 (* 1 = 0.305245 loss)
I0410 14:28:02.072108 18353 sgd_solver.cpp:105] Iteration 7896, lr = 0.00209279
I0410 14:28:06.935024 18353 solver.cpp:218] Iteration 7908 (2.4677 iter/s, 4.86283s/12 iters), loss = 0.392762
I0410 14:28:06.935082 18353 solver.cpp:237]     Train net output #0: loss = 0.392762 (* 1 = 0.392762 loss)
I0410 14:28:06.935096 18353 sgd_solver.cpp:105] Iteration 7908, lr = 0.00208782
I0410 14:28:11.835238 18353 solver.cpp:218] Iteration 7920 (2.44895 iter/s, 4.90006s/12 iters), loss = 0.394749
I0410 14:28:11.835392 18353 solver.cpp:237]     Train net output #0: loss = 0.394749 (* 1 = 0.394749 loss)
I0410 14:28:11.835407 18353 sgd_solver.cpp:105] Iteration 7920, lr = 0.00208287
I0410 14:28:16.741601 18353 solver.cpp:218] Iteration 7932 (2.44593 iter/s, 4.90611s/12 iters), loss = 0.479549
I0410 14:28:16.741655 18353 solver.cpp:237]     Train net output #0: loss = 0.479549 (* 1 = 0.479549 loss)
I0410 14:28:16.741669 18353 sgd_solver.cpp:105] Iteration 7932, lr = 0.00207792
I0410 14:28:21.628052 18353 solver.cpp:218] Iteration 7944 (2.45585 iter/s, 4.88629s/12 iters), loss = 0.247805
I0410 14:28:21.628105 18353 solver.cpp:237]     Train net output #0: loss = 0.247805 (* 1 = 0.247805 loss)
I0410 14:28:21.628118 18353 sgd_solver.cpp:105] Iteration 7944, lr = 0.00207299
I0410 14:28:26.100972 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7956.caffemodel
I0410 14:28:26.411490 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7956.solverstate
I0410 14:28:26.607393 18353 solver.cpp:330] Iteration 7956, Testing net (#0)
I0410 14:28:26.607414 18353 net.cpp:676] Ignoring source layer train-data
I0410 14:28:27.944399 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:28:31.042133 18353 solver.cpp:397]     Test net output #0: accuracy = 0.54902
I0410 14:28:31.042179 18353 solver.cpp:397]     Test net output #1: loss = 2.37796 (* 1 = 2.37796 loss)
I0410 14:28:31.123400 18353 solver.cpp:218] Iteration 7956 (1.26381 iter/s, 9.49511s/12 iters), loss = 0.341276
I0410 14:28:31.123457 18353 solver.cpp:237]     Train net output #0: loss = 0.341276 (* 1 = 0.341276 loss)
I0410 14:28:31.123469 18353 sgd_solver.cpp:105] Iteration 7956, lr = 0.00206807
I0410 14:28:35.166353 18353 solver.cpp:218] Iteration 7968 (2.96823 iter/s, 4.04281s/12 iters), loss = 0.317345
I0410 14:28:35.166401 18353 solver.cpp:237]     Train net output #0: loss = 0.317345 (* 1 = 0.317345 loss)
I0410 14:28:35.166409 18353 sgd_solver.cpp:105] Iteration 7968, lr = 0.00206316
I0410 14:28:40.059130 18353 solver.cpp:218] Iteration 7980 (2.45267 iter/s, 4.89262s/12 iters), loss = 0.198083
I0410 14:28:40.059177 18353 solver.cpp:237]     Train net output #0: loss = 0.198083 (* 1 = 0.198083 loss)
I0410 14:28:40.059188 18353 sgd_solver.cpp:105] Iteration 7980, lr = 0.00205826
I0410 14:28:44.395006 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:28:45.108878 18353 solver.cpp:218] Iteration 7992 (2.37643 iter/s, 5.0496s/12 iters), loss = 0.291573
I0410 14:28:45.108922 18353 solver.cpp:237]     Train net output #0: loss = 0.291573 (* 1 = 0.291573 loss)
I0410 14:28:45.108932 18353 sgd_solver.cpp:105] Iteration 7992, lr = 0.00205337
I0410 14:28:50.052569 18353 solver.cpp:218] Iteration 8004 (2.42741 iter/s, 4.94354s/12 iters), loss = 0.341455
I0410 14:28:50.052623 18353 solver.cpp:237]     Train net output #0: loss = 0.341455 (* 1 = 0.341455 loss)
I0410 14:28:50.052635 18353 sgd_solver.cpp:105] Iteration 8004, lr = 0.0020485
I0410 14:28:55.008193 18353 solver.cpp:218] Iteration 8016 (2.42157 iter/s, 4.95546s/12 iters), loss = 0.389072
I0410 14:28:55.008241 18353 solver.cpp:237]     Train net output #0: loss = 0.389072 (* 1 = 0.389072 loss)
I0410 14:28:55.008251 18353 sgd_solver.cpp:105] Iteration 8016, lr = 0.00204363
I0410 14:28:59.923367 18353 solver.cpp:218] Iteration 8028 (2.4415 iter/s, 4.91501s/12 iters), loss = 0.377484
I0410 14:28:59.923426 18353 solver.cpp:237]     Train net output #0: loss = 0.377484 (* 1 = 0.377484 loss)
I0410 14:28:59.923439 18353 sgd_solver.cpp:105] Iteration 8028, lr = 0.00203878
I0410 14:29:04.853814 18353 solver.cpp:218] Iteration 8040 (2.43394 iter/s, 4.93028s/12 iters), loss = 0.380077
I0410 14:29:04.853865 18353 solver.cpp:237]     Train net output #0: loss = 0.380077 (* 1 = 0.380077 loss)
I0410 14:29:04.853878 18353 sgd_solver.cpp:105] Iteration 8040, lr = 0.00203394
I0410 14:29:09.737445 18353 solver.cpp:218] Iteration 8052 (2.45727 iter/s, 4.88348s/12 iters), loss = 0.420411
I0410 14:29:09.737501 18353 solver.cpp:237]     Train net output #0: loss = 0.420411 (* 1 = 0.420411 loss)
I0410 14:29:09.737514 18353 sgd_solver.cpp:105] Iteration 8052, lr = 0.00202911
I0410 14:29:11.765049 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8058.caffemodel
I0410 14:29:12.086783 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8058.solverstate
I0410 14:29:12.297161 18353 solver.cpp:330] Iteration 8058, Testing net (#0)
I0410 14:29:12.297191 18353 net.cpp:676] Ignoring source layer train-data
I0410 14:29:13.495993 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:29:16.641419 18353 solver.cpp:397]     Test net output #0: accuracy = 0.561275
I0410 14:29:16.641598 18353 solver.cpp:397]     Test net output #1: loss = 2.3362 (* 1 = 2.3362 loss)
I0410 14:29:18.501336 18353 solver.cpp:218] Iteration 8064 (1.36929 iter/s, 8.76366s/12 iters), loss = 0.236726
I0410 14:29:18.501389 18353 solver.cpp:237]     Train net output #0: loss = 0.236726 (* 1 = 0.236726 loss)
I0410 14:29:18.501400 18353 sgd_solver.cpp:105] Iteration 8064, lr = 0.00202429
I0410 14:29:23.442833 18353 solver.cpp:218] Iteration 8076 (2.42849 iter/s, 4.94134s/12 iters), loss = 0.291229
I0410 14:29:23.442883 18353 solver.cpp:237]     Train net output #0: loss = 0.291229 (* 1 = 0.291229 loss)
I0410 14:29:23.442895 18353 sgd_solver.cpp:105] Iteration 8076, lr = 0.00201949
I0410 14:29:28.426789 18353 solver.cpp:218] Iteration 8088 (2.4078 iter/s, 4.9838s/12 iters), loss = 0.232112
I0410 14:29:28.426843 18353 solver.cpp:237]     Train net output #0: loss = 0.232112 (* 1 = 0.232112 loss)
I0410 14:29:28.426858 18353 sgd_solver.cpp:105] Iteration 8088, lr = 0.00201469
I0410 14:29:29.789894 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:29:33.322602 18353 solver.cpp:218] Iteration 8100 (2.45116 iter/s, 4.89564s/12 iters), loss = 0.265101
I0410 14:29:33.322669 18353 solver.cpp:237]     Train net output #0: loss = 0.265101 (* 1 = 0.265101 loss)
I0410 14:29:33.322685 18353 sgd_solver.cpp:105] Iteration 8100, lr = 0.00200991
I0410 14:29:38.230742 18353 solver.cpp:218] Iteration 8112 (2.44501 iter/s, 4.90796s/12 iters), loss = 0.337756
I0410 14:29:38.230799 18353 solver.cpp:237]     Train net output #0: loss = 0.337756 (* 1 = 0.337756 loss)
I0410 14:29:38.230811 18353 sgd_solver.cpp:105] Iteration 8112, lr = 0.00200514
I0410 14:29:43.164695 18353 solver.cpp:218] Iteration 8124 (2.43221 iter/s, 4.93378s/12 iters), loss = 0.230637
I0410 14:29:43.164750 18353 solver.cpp:237]     Train net output #0: loss = 0.230637 (* 1 = 0.230637 loss)
I0410 14:29:43.164762 18353 sgd_solver.cpp:105] Iteration 8124, lr = 0.00200038
I0410 14:29:48.046355 18353 solver.cpp:218] Iteration 8136 (2.45826 iter/s, 4.88149s/12 iters), loss = 0.264065
I0410 14:29:48.046458 18353 solver.cpp:237]     Train net output #0: loss = 0.264065 (* 1 = 0.264065 loss)
I0410 14:29:48.046471 18353 sgd_solver.cpp:105] Iteration 8136, lr = 0.00199563
I0410 14:29:53.277913 18353 solver.cpp:218] Iteration 8148 (2.29387 iter/s, 5.23134s/12 iters), loss = 0.36992
I0410 14:29:53.277990 18353 solver.cpp:237]     Train net output #0: loss = 0.36992 (* 1 = 0.36992 loss)
I0410 14:29:53.278004 18353 sgd_solver.cpp:105] Iteration 8148, lr = 0.00199089
I0410 14:29:57.830231 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8160.caffemodel
I0410 14:29:58.127279 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8160.solverstate
I0410 14:29:58.332096 18353 solver.cpp:330] Iteration 8160, Testing net (#0)
I0410 14:29:58.332129 18353 net.cpp:676] Ignoring source layer train-data
I0410 14:29:59.629873 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:30:03.178757 18353 solver.cpp:397]     Test net output #0: accuracy = 0.54902
I0410 14:30:03.178804 18353 solver.cpp:397]     Test net output #1: loss = 2.39821 (* 1 = 2.39821 loss)
I0410 14:30:03.260154 18353 solver.cpp:218] Iteration 8160 (1.20217 iter/s, 9.98196s/12 iters), loss = 0.329622
I0410 14:30:03.260202 18353 solver.cpp:237]     Train net output #0: loss = 0.329622 (* 1 = 0.329622 loss)
I0410 14:30:03.260215 18353 sgd_solver.cpp:105] Iteration 8160, lr = 0.00198616
I0410 14:30:07.502768 18353 solver.cpp:218] Iteration 8172 (2.82855 iter/s, 4.24246s/12 iters), loss = 0.160831
I0410 14:30:07.502828 18353 solver.cpp:237]     Train net output #0: loss = 0.160831 (* 1 = 0.160831 loss)
I0410 14:30:07.502841 18353 sgd_solver.cpp:105] Iteration 8172, lr = 0.00198145
I0410 14:30:12.435073 18353 solver.cpp:218] Iteration 8184 (2.43303 iter/s, 4.93213s/12 iters), loss = 0.305543
I0410 14:30:12.435139 18353 solver.cpp:237]     Train net output #0: loss = 0.305543 (* 1 = 0.305543 loss)
I0410 14:30:12.435153 18353 sgd_solver.cpp:105] Iteration 8184, lr = 0.00197674
I0410 14:30:15.882968 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:30:17.310302 18353 solver.cpp:218] Iteration 8196 (2.46151 iter/s, 4.87505s/12 iters), loss = 0.209504
I0410 14:30:17.310350 18353 solver.cpp:237]     Train net output #0: loss = 0.209504 (* 1 = 0.209504 loss)
I0410 14:30:17.310361 18353 sgd_solver.cpp:105] Iteration 8196, lr = 0.00197205
I0410 14:30:22.243680 18353 solver.cpp:218] Iteration 8208 (2.43249 iter/s, 4.93322s/12 iters), loss = 0.383752
I0410 14:30:22.243818 18353 solver.cpp:237]     Train net output #0: loss = 0.383752 (* 1 = 0.383752 loss)
I0410 14:30:22.243830 18353 sgd_solver.cpp:105] Iteration 8208, lr = 0.00196737
I0410 14:30:27.290490 18353 solver.cpp:218] Iteration 8220 (2.37786 iter/s, 5.04656s/12 iters), loss = 0.305497
I0410 14:30:27.290542 18353 solver.cpp:237]     Train net output #0: loss = 0.305497 (* 1 = 0.305497 loss)
I0410 14:30:27.290555 18353 sgd_solver.cpp:105] Iteration 8220, lr = 0.0019627
I0410 14:30:32.269920 18353 solver.cpp:218] Iteration 8232 (2.41 iter/s, 4.97926s/12 iters), loss = 0.328397
I0410 14:30:32.269990 18353 solver.cpp:237]     Train net output #0: loss = 0.328397 (* 1 = 0.328397 loss)
I0410 14:30:32.270004 18353 sgd_solver.cpp:105] Iteration 8232, lr = 0.00195804
I0410 14:30:37.209275 18353 solver.cpp:218] Iteration 8244 (2.42955 iter/s, 4.93918s/12 iters), loss = 0.26712
I0410 14:30:37.209321 18353 solver.cpp:237]     Train net output #0: loss = 0.26712 (* 1 = 0.26712 loss)
I0410 14:30:37.209331 18353 sgd_solver.cpp:105] Iteration 8244, lr = 0.00195339
I0410 14:30:42.114804 18353 solver.cpp:218] Iteration 8256 (2.4463 iter/s, 4.90537s/12 iters), loss = 0.268471
I0410 14:30:42.114861 18353 solver.cpp:237]     Train net output #0: loss = 0.268471 (* 1 = 0.268471 loss)
I0410 14:30:42.114873 18353 sgd_solver.cpp:105] Iteration 8256, lr = 0.00194875
I0410 14:30:44.116461 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8262.caffemodel
I0410 14:30:44.412611 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8262.solverstate
I0410 14:30:44.616968 18353 solver.cpp:330] Iteration 8262, Testing net (#0)
I0410 14:30:44.617002 18353 net.cpp:676] Ignoring source layer train-data
I0410 14:30:45.817574 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:30:49.187706 18353 solver.cpp:397]     Test net output #0: accuracy = 0.567402
I0410 14:30:49.187757 18353 solver.cpp:397]     Test net output #1: loss = 2.3375 (* 1 = 2.3375 loss)
I0410 14:30:51.006175 18353 solver.cpp:218] Iteration 8268 (1.34966 iter/s, 8.89112s/12 iters), loss = 0.352603
I0410 14:30:51.006233 18353 solver.cpp:237]     Train net output #0: loss = 0.352603 (* 1 = 0.352603 loss)
I0410 14:30:51.006245 18353 sgd_solver.cpp:105] Iteration 8268, lr = 0.00194412
I0410 14:30:55.851364 18353 solver.cpp:218] Iteration 8280 (2.47677 iter/s, 4.84502s/12 iters), loss = 0.205603
I0410 14:30:55.851521 18353 solver.cpp:237]     Train net output #0: loss = 0.205603 (* 1 = 0.205603 loss)
I0410 14:30:55.851536 18353 sgd_solver.cpp:105] Iteration 8280, lr = 0.00193951
I0410 14:31:00.779754 18353 solver.cpp:218] Iteration 8292 (2.43501 iter/s, 4.92812s/12 iters), loss = 0.24311
I0410 14:31:00.779798 18353 solver.cpp:237]     Train net output #0: loss = 0.24311 (* 1 = 0.24311 loss)
I0410 14:31:00.779808 18353 sgd_solver.cpp:105] Iteration 8292, lr = 0.0019349
I0410 14:31:01.450857 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:31:05.809840 18353 solver.cpp:218] Iteration 8304 (2.38572 iter/s, 5.02992s/12 iters), loss = 0.199807
I0410 14:31:05.809886 18353 solver.cpp:237]     Train net output #0: loss = 0.199807 (* 1 = 0.199807 loss)
I0410 14:31:05.809896 18353 sgd_solver.cpp:105] Iteration 8304, lr = 0.00193031
I0410 14:31:06.986207 18353 blocking_queue.cpp:49] Waiting for data
I0410 14:31:10.771862 18353 solver.cpp:218] Iteration 8316 (2.41845 iter/s, 4.96186s/12 iters), loss = 0.328271
I0410 14:31:10.771909 18353 solver.cpp:237]     Train net output #0: loss = 0.328271 (* 1 = 0.328271 loss)
I0410 14:31:10.771919 18353 sgd_solver.cpp:105] Iteration 8316, lr = 0.00192573
I0410 14:31:15.886297 18353 solver.cpp:218] Iteration 8328 (2.34638 iter/s, 5.11426s/12 iters), loss = 0.201862
I0410 14:31:15.886358 18353 solver.cpp:237]     Train net output #0: loss = 0.201862 (* 1 = 0.201862 loss)
I0410 14:31:15.886370 18353 sgd_solver.cpp:105] Iteration 8328, lr = 0.00192115
I0410 14:31:20.858796 18353 solver.cpp:218] Iteration 8340 (2.41336 iter/s, 4.97232s/12 iters), loss = 0.336924
I0410 14:31:20.858850 18353 solver.cpp:237]     Train net output #0: loss = 0.336924 (* 1 = 0.336924 loss)
I0410 14:31:20.858860 18353 sgd_solver.cpp:105] Iteration 8340, lr = 0.00191659
I0410 14:31:25.750823 18353 solver.cpp:218] Iteration 8352 (2.45305 iter/s, 4.89186s/12 iters), loss = 0.217553
I0410 14:31:25.750865 18353 solver.cpp:237]     Train net output #0: loss = 0.217553 (* 1 = 0.217553 loss)
I0410 14:31:25.750874 18353 sgd_solver.cpp:105] Iteration 8352, lr = 0.00191204
I0410 14:31:30.185088 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8364.caffemodel
I0410 14:31:31.956107 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8364.solverstate
I0410 14:31:33.250620 18353 solver.cpp:330] Iteration 8364, Testing net (#0)
I0410 14:31:33.250650 18353 net.cpp:676] Ignoring source layer train-data
I0410 14:31:34.443787 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:31:37.702507 18353 solver.cpp:397]     Test net output #0: accuracy = 0.566789
I0410 14:31:37.702556 18353 solver.cpp:397]     Test net output #1: loss = 2.43773 (* 1 = 2.43773 loss)
I0410 14:31:37.784090 18353 solver.cpp:218] Iteration 8364 (0.997261 iter/s, 12.033s/12 iters), loss = 0.400595
I0410 14:31:37.784144 18353 solver.cpp:237]     Train net output #0: loss = 0.400595 (* 1 = 0.400595 loss)
I0410 14:31:37.784157 18353 sgd_solver.cpp:105] Iteration 8364, lr = 0.0019075
I0410 14:31:41.987370 18353 solver.cpp:218] Iteration 8376 (2.85502 iter/s, 4.20312s/12 iters), loss = 0.233826
I0410 14:31:41.987426 18353 solver.cpp:237]     Train net output #0: loss = 0.233826 (* 1 = 0.233826 loss)
I0410 14:31:41.987439 18353 sgd_solver.cpp:105] Iteration 8376, lr = 0.00190297
I0410 14:31:46.826704 18353 solver.cpp:218] Iteration 8388 (2.47977 iter/s, 4.83916s/12 iters), loss = 0.427233
I0410 14:31:46.826762 18353 solver.cpp:237]     Train net output #0: loss = 0.427233 (* 1 = 0.427233 loss)
I0410 14:31:46.826776 18353 sgd_solver.cpp:105] Iteration 8388, lr = 0.00189846
I0410 14:31:49.644484 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:31:51.754027 18353 solver.cpp:218] Iteration 8400 (2.43549 iter/s, 4.92715s/12 iters), loss = 0.279025
I0410 14:31:51.754079 18353 solver.cpp:237]     Train net output #0: loss = 0.279025 (* 1 = 0.279025 loss)
I0410 14:31:51.754091 18353 sgd_solver.cpp:105] Iteration 8400, lr = 0.00189395
I0410 14:31:56.610452 18353 solver.cpp:218] Iteration 8412 (2.47104 iter/s, 4.85626s/12 iters), loss = 0.195502
I0410 14:31:56.610507 18353 solver.cpp:237]     Train net output #0: loss = 0.195502 (* 1 = 0.195502 loss)
I0410 14:31:56.610520 18353 sgd_solver.cpp:105] Iteration 8412, lr = 0.00188945
I0410 14:32:01.440502 18353 solver.cpp:218] Iteration 8424 (2.48453 iter/s, 4.82988s/12 iters), loss = 0.371617
I0410 14:32:01.440661 18353 solver.cpp:237]     Train net output #0: loss = 0.371617 (* 1 = 0.371617 loss)
I0410 14:32:01.440676 18353 sgd_solver.cpp:105] Iteration 8424, lr = 0.00188497
I0410 14:32:06.267702 18353 solver.cpp:218] Iteration 8436 (2.48606 iter/s, 4.82692s/12 iters), loss = 0.224545
I0410 14:32:06.267756 18353 solver.cpp:237]     Train net output #0: loss = 0.224545 (* 1 = 0.224545 loss)
I0410 14:32:06.267767 18353 sgd_solver.cpp:105] Iteration 8436, lr = 0.00188049
I0410 14:32:11.231158 18353 solver.cpp:218] Iteration 8448 (2.41776 iter/s, 4.96328s/12 iters), loss = 0.257283
I0410 14:32:11.231209 18353 solver.cpp:237]     Train net output #0: loss = 0.257283 (* 1 = 0.257283 loss)
I0410 14:32:11.231220 18353 sgd_solver.cpp:105] Iteration 8448, lr = 0.00187603
I0410 14:32:16.124188 18353 solver.cpp:218] Iteration 8460 (2.45256 iter/s, 4.89286s/12 iters), loss = 0.216957
I0410 14:32:16.124240 18353 solver.cpp:237]     Train net output #0: loss = 0.216957 (* 1 = 0.216957 loss)
I0410 14:32:16.124253 18353 sgd_solver.cpp:105] Iteration 8460, lr = 0.00187157
I0410 14:32:18.117771 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8466.caffemodel
I0410 14:32:18.400900 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8466.solverstate
I0410 14:32:18.595048 18353 solver.cpp:330] Iteration 8466, Testing net (#0)
I0410 14:32:18.595082 18353 net.cpp:676] Ignoring source layer train-data
I0410 14:32:19.629070 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:32:23.023052 18353 solver.cpp:397]     Test net output #0: accuracy = 0.548407
I0410 14:32:23.023104 18353 solver.cpp:397]     Test net output #1: loss = 2.44418 (* 1 = 2.44418 loss)
I0410 14:32:24.961500 18353 solver.cpp:218] Iteration 8472 (1.35792 iter/s, 8.83706s/12 iters), loss = 0.164592
I0410 14:32:24.961555 18353 solver.cpp:237]     Train net output #0: loss = 0.164592 (* 1 = 0.164592 loss)
I0410 14:32:24.961567 18353 sgd_solver.cpp:105] Iteration 8472, lr = 0.00186713
I0410 14:32:29.898959 18353 solver.cpp:218] Iteration 8484 (2.43048 iter/s, 4.93729s/12 iters), loss = 0.319812
I0410 14:32:29.899014 18353 solver.cpp:237]     Train net output #0: loss = 0.319812 (* 1 = 0.319812 loss)
I0410 14:32:29.899027 18353 sgd_solver.cpp:105] Iteration 8484, lr = 0.0018627
I0410 14:32:34.841655 18353 solver.cpp:218] Iteration 8496 (2.42791 iter/s, 4.94252s/12 iters), loss = 0.248408
I0410 14:32:34.841776 18353 solver.cpp:237]     Train net output #0: loss = 0.248408 (* 1 = 0.248408 loss)
I0410 14:32:34.841792 18353 sgd_solver.cpp:105] Iteration 8496, lr = 0.00185827
I0410 14:32:34.880514 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:32:39.675909 18353 solver.cpp:218] Iteration 8508 (2.48241 iter/s, 4.83402s/12 iters), loss = 0.247901
I0410 14:32:39.675967 18353 solver.cpp:237]     Train net output #0: loss = 0.247901 (* 1 = 0.247901 loss)
I0410 14:32:39.675979 18353 sgd_solver.cpp:105] Iteration 8508, lr = 0.00185386
I0410 14:32:44.529790 18353 solver.cpp:218] Iteration 8520 (2.47234 iter/s, 4.8537s/12 iters), loss = 0.207359
I0410 14:32:44.529850 18353 solver.cpp:237]     Train net output #0: loss = 0.207359 (* 1 = 0.207359 loss)
I0410 14:32:44.529860 18353 sgd_solver.cpp:105] Iteration 8520, lr = 0.00184946
I0410 14:32:49.365579 18353 solver.cpp:218] Iteration 8532 (2.48159 iter/s, 4.83561s/12 iters), loss = 0.171135
I0410 14:32:49.365638 18353 solver.cpp:237]     Train net output #0: loss = 0.171135 (* 1 = 0.171135 loss)
I0410 14:32:49.365651 18353 sgd_solver.cpp:105] Iteration 8532, lr = 0.00184507
I0410 14:32:54.200006 18353 solver.cpp:218] Iteration 8544 (2.48229 iter/s, 4.83425s/12 iters), loss = 0.341448
I0410 14:32:54.200069 18353 solver.cpp:237]     Train net output #0: loss = 0.341448 (* 1 = 0.341448 loss)
I0410 14:32:54.200083 18353 sgd_solver.cpp:105] Iteration 8544, lr = 0.00184069
I0410 14:32:59.061676 18353 solver.cpp:218] Iteration 8556 (2.46838 iter/s, 4.86148s/12 iters), loss = 0.221535
I0410 14:32:59.061739 18353 solver.cpp:237]     Train net output #0: loss = 0.221535 (* 1 = 0.221535 loss)
I0410 14:32:59.061753 18353 sgd_solver.cpp:105] Iteration 8556, lr = 0.00183632
I0410 14:33:03.454402 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8568.caffemodel
I0410 14:33:03.785652 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8568.solverstate
I0410 14:33:03.993767 18353 solver.cpp:330] Iteration 8568, Testing net (#0)
I0410 14:33:03.993793 18353 net.cpp:676] Ignoring source layer train-data
I0410 14:33:05.122313 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:33:08.502822 18353 solver.cpp:397]     Test net output #0: accuracy = 0.549632
I0410 14:33:08.502873 18353 solver.cpp:397]     Test net output #1: loss = 2.41547 (* 1 = 2.41547 loss)
I0410 14:33:08.584102 18353 solver.cpp:218] Iteration 8568 (1.26022 iter/s, 9.52215s/12 iters), loss = 0.233711
I0410 14:33:08.584153 18353 solver.cpp:237]     Train net output #0: loss = 0.233711 (* 1 = 0.233711 loss)
I0410 14:33:08.584165 18353 sgd_solver.cpp:105] Iteration 8568, lr = 0.00183196
I0410 14:33:12.640694 18353 solver.cpp:218] Iteration 8580 (2.95826 iter/s, 4.05644s/12 iters), loss = 0.184957
I0410 14:33:12.640745 18353 solver.cpp:237]     Train net output #0: loss = 0.184957 (* 1 = 0.184957 loss)
I0410 14:33:12.640756 18353 sgd_solver.cpp:105] Iteration 8580, lr = 0.00182761
I0410 14:33:17.615176 18353 solver.cpp:218] Iteration 8592 (2.4124 iter/s, 4.97431s/12 iters), loss = 0.242305
I0410 14:33:17.615236 18353 solver.cpp:237]     Train net output #0: loss = 0.242305 (* 1 = 0.242305 loss)
I0410 14:33:17.615248 18353 sgd_solver.cpp:105] Iteration 8592, lr = 0.00182327
I0410 14:33:19.700482 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:33:22.469069 18353 solver.cpp:218] Iteration 8604 (2.47233 iter/s, 4.85371s/12 iters), loss = 0.234007
I0410 14:33:22.469128 18353 solver.cpp:237]     Train net output #0: loss = 0.234007 (* 1 = 0.234007 loss)
I0410 14:33:22.469142 18353 sgd_solver.cpp:105] Iteration 8604, lr = 0.00181894
I0410 14:33:27.547739 18353 solver.cpp:218] Iteration 8616 (2.36291 iter/s, 5.07849s/12 iters), loss = 0.218544
I0410 14:33:27.547793 18353 solver.cpp:237]     Train net output #0: loss = 0.218544 (* 1 = 0.218544 loss)
I0410 14:33:27.547804 18353 sgd_solver.cpp:105] Iteration 8616, lr = 0.00181462
I0410 14:33:32.396983 18353 solver.cpp:218] Iteration 8628 (2.4747 iter/s, 4.84907s/12 iters), loss = 0.252311
I0410 14:33:32.397045 18353 solver.cpp:237]     Train net output #0: loss = 0.252311 (* 1 = 0.252311 loss)
I0410 14:33:32.397059 18353 sgd_solver.cpp:105] Iteration 8628, lr = 0.00181031
I0410 14:33:37.202036 18353 solver.cpp:218] Iteration 8640 (2.49746 iter/s, 4.80488s/12 iters), loss = 0.25067
I0410 14:33:37.205322 18353 solver.cpp:237]     Train net output #0: loss = 0.25067 (* 1 = 0.25067 loss)
I0410 14:33:37.205332 18353 sgd_solver.cpp:105] Iteration 8640, lr = 0.00180602
I0410 14:33:42.204701 18353 solver.cpp:218] Iteration 8652 (2.40036 iter/s, 4.99926s/12 iters), loss = 0.133641
I0410 14:33:42.204747 18353 solver.cpp:237]     Train net output #0: loss = 0.133641 (* 1 = 0.133641 loss)
I0410 14:33:42.204756 18353 sgd_solver.cpp:105] Iteration 8652, lr = 0.00180173
I0410 14:33:47.089359 18353 solver.cpp:218] Iteration 8664 (2.45675 iter/s, 4.88449s/12 iters), loss = 0.349766
I0410 14:33:47.089406 18353 solver.cpp:237]     Train net output #0: loss = 0.349766 (* 1 = 0.349766 loss)
I0410 14:33:47.089416 18353 sgd_solver.cpp:105] Iteration 8664, lr = 0.00179745
I0410 14:33:49.351301 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8670.caffemodel
I0410 14:33:50.105013 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8670.solverstate
I0410 14:33:50.349575 18353 solver.cpp:330] Iteration 8670, Testing net (#0)
I0410 14:33:50.349603 18353 net.cpp:676] Ignoring source layer train-data
I0410 14:33:51.437636 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:33:55.029505 18353 solver.cpp:397]     Test net output #0: accuracy = 0.566789
I0410 14:33:55.029539 18353 solver.cpp:397]     Test net output #1: loss = 2.41216 (* 1 = 2.41216 loss)
I0410 14:33:56.869036 18353 solver.cpp:218] Iteration 8676 (1.22707 iter/s, 9.7794s/12 iters), loss = 0.134833
I0410 14:33:56.869084 18353 solver.cpp:237]     Train net output #0: loss = 0.134833 (* 1 = 0.134833 loss)
I0410 14:33:56.869094 18353 sgd_solver.cpp:105] Iteration 8676, lr = 0.00179318
I0410 14:34:01.846196 18353 solver.cpp:218] Iteration 8688 (2.4111 iter/s, 4.97699s/12 iters), loss = 0.206649
I0410 14:34:01.846252 18353 solver.cpp:237]     Train net output #0: loss = 0.206649 (* 1 = 0.206649 loss)
I0410 14:34:01.846266 18353 sgd_solver.cpp:105] Iteration 8688, lr = 0.00178893
I0410 14:34:06.110931 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:34:06.785404 18353 solver.cpp:218] Iteration 8700 (2.42963 iter/s, 4.93903s/12 iters), loss = 0.203242
I0410 14:34:06.785460 18353 solver.cpp:237]     Train net output #0: loss = 0.203242 (* 1 = 0.203242 loss)
I0410 14:34:06.785472 18353 sgd_solver.cpp:105] Iteration 8700, lr = 0.00178468
I0410 14:34:11.874472 18353 solver.cpp:218] Iteration 8712 (2.35808 iter/s, 5.08889s/12 iters), loss = 0.182803
I0410 14:34:11.874590 18353 solver.cpp:237]     Train net output #0: loss = 0.182803 (* 1 = 0.182803 loss)
I0410 14:34:11.874600 18353 sgd_solver.cpp:105] Iteration 8712, lr = 0.00178044
I0410 14:34:16.775470 18353 solver.cpp:218] Iteration 8724 (2.4486 iter/s, 4.90076s/12 iters), loss = 0.260401
I0410 14:34:16.775516 18353 solver.cpp:237]     Train net output #0: loss = 0.260401 (* 1 = 0.260401 loss)
I0410 14:34:16.775527 18353 sgd_solver.cpp:105] Iteration 8724, lr = 0.00177621
I0410 14:34:21.652289 18353 solver.cpp:218] Iteration 8736 (2.4607 iter/s, 4.87665s/12 iters), loss = 0.231608
I0410 14:34:21.652338 18353 solver.cpp:237]     Train net output #0: loss = 0.231608 (* 1 = 0.231608 loss)
I0410 14:34:21.652348 18353 sgd_solver.cpp:105] Iteration 8736, lr = 0.001772
I0410 14:34:26.598891 18353 solver.cpp:218] Iteration 8748 (2.42599 iter/s, 4.94643s/12 iters), loss = 0.335587
I0410 14:34:26.598935 18353 solver.cpp:237]     Train net output #0: loss = 0.335587 (* 1 = 0.335587 loss)
I0410 14:34:26.598944 18353 sgd_solver.cpp:105] Iteration 8748, lr = 0.00176779
I0410 14:34:31.556612 18353 solver.cpp:218] Iteration 8760 (2.42055 iter/s, 4.95755s/12 iters), loss = 0.151108
I0410 14:34:31.556668 18353 solver.cpp:237]     Train net output #0: loss = 0.151108 (* 1 = 0.151108 loss)
I0410 14:34:31.556680 18353 sgd_solver.cpp:105] Iteration 8760, lr = 0.00176359
I0410 14:34:36.030589 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8772.caffemodel
I0410 14:34:36.342351 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8772.solverstate
I0410 14:34:36.549528 18353 solver.cpp:330] Iteration 8772, Testing net (#0)
I0410 14:34:36.549561 18353 net.cpp:676] Ignoring source layer train-data
I0410 14:34:37.565647 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:34:40.989535 18353 solver.cpp:397]     Test net output #0: accuracy = 0.570466
I0410 14:34:40.989573 18353 solver.cpp:397]     Test net output #1: loss = 2.42441 (* 1 = 2.42441 loss)
I0410 14:34:41.070848 18353 solver.cpp:218] Iteration 8772 (1.2613 iter/s, 9.51396s/12 iters), loss = 0.277366
I0410 14:34:41.070906 18353 solver.cpp:237]     Train net output #0: loss = 0.277366 (* 1 = 0.277366 loss)
I0410 14:34:41.070919 18353 sgd_solver.cpp:105] Iteration 8772, lr = 0.00175941
I0410 14:34:45.314833 18353 solver.cpp:218] Iteration 8784 (2.82764 iter/s, 4.24382s/12 iters), loss = 0.151882
I0410 14:34:45.314981 18353 solver.cpp:237]     Train net output #0: loss = 0.151882 (* 1 = 0.151882 loss)
I0410 14:34:45.314992 18353 sgd_solver.cpp:105] Iteration 8784, lr = 0.00175523
I0410 14:34:50.140313 18353 solver.cpp:218] Iteration 8796 (2.48694 iter/s, 4.82521s/12 iters), loss = 0.201846
I0410 14:34:50.140368 18353 solver.cpp:237]     Train net output #0: loss = 0.201846 (* 1 = 0.201846 loss)
I0410 14:34:50.140383 18353 sgd_solver.cpp:105] Iteration 8796, lr = 0.00175106
I0410 14:34:51.541460 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:34:55.062031 18353 solver.cpp:218] Iteration 8808 (2.43826 iter/s, 4.92154s/12 iters), loss = 0.38256
I0410 14:34:55.062085 18353 solver.cpp:237]     Train net output #0: loss = 0.38256 (* 1 = 0.38256 loss)
I0410 14:34:55.062099 18353 sgd_solver.cpp:105] Iteration 8808, lr = 0.0017469
I0410 14:34:59.951509 18353 solver.cpp:218] Iteration 8820 (2.45434 iter/s, 4.8893s/12 iters), loss = 0.189713
I0410 14:34:59.951563 18353 solver.cpp:237]     Train net output #0: loss = 0.189713 (* 1 = 0.189713 loss)
I0410 14:34:59.951576 18353 sgd_solver.cpp:105] Iteration 8820, lr = 0.00174276
I0410 14:35:04.886657 18353 solver.cpp:218] Iteration 8832 (2.43163 iter/s, 4.93497s/12 iters), loss = 0.132332
I0410 14:35:04.886705 18353 solver.cpp:237]     Train net output #0: loss = 0.132332 (* 1 = 0.132332 loss)
I0410 14:35:04.886714 18353 sgd_solver.cpp:105] Iteration 8832, lr = 0.00173862
I0410 14:35:09.795842 18353 solver.cpp:218] Iteration 8844 (2.44448 iter/s, 4.90901s/12 iters), loss = 0.20236
I0410 14:35:09.795886 18353 solver.cpp:237]     Train net output #0: loss = 0.20236 (* 1 = 0.20236 loss)
I0410 14:35:09.795895 18353 sgd_solver.cpp:105] Iteration 8844, lr = 0.00173449
I0410 14:35:14.738719 18353 solver.cpp:218] Iteration 8856 (2.42782 iter/s, 4.94271s/12 iters), loss = 0.230461
I0410 14:35:14.738759 18353 solver.cpp:237]     Train net output #0: loss = 0.230461 (* 1 = 0.230461 loss)
I0410 14:35:14.738767 18353 sgd_solver.cpp:105] Iteration 8856, lr = 0.00173037
I0410 14:35:19.636201 18353 solver.cpp:218] Iteration 8868 (2.45032 iter/s, 4.89732s/12 iters), loss = 0.295618
I0410 14:35:19.636334 18353 solver.cpp:237]     Train net output #0: loss = 0.295618 (* 1 = 0.295618 loss)
I0410 14:35:19.636344 18353 sgd_solver.cpp:105] Iteration 8868, lr = 0.00172626
I0410 14:35:21.627316 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8874.caffemodel
I0410 14:35:21.928701 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8874.solverstate
I0410 14:35:22.144127 18353 solver.cpp:330] Iteration 8874, Testing net (#0)
I0410 14:35:22.144157 18353 net.cpp:676] Ignoring source layer train-data
I0410 14:35:23.103143 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:35:26.738929 18353 solver.cpp:397]     Test net output #0: accuracy = 0.5625
I0410 14:35:26.738973 18353 solver.cpp:397]     Test net output #1: loss = 2.42246 (* 1 = 2.42246 loss)
I0410 14:35:28.732332 18353 solver.cpp:218] Iteration 8880 (1.31929 iter/s, 9.09578s/12 iters), loss = 0.213152
I0410 14:35:28.732383 18353 solver.cpp:237]     Train net output #0: loss = 0.213152 (* 1 = 0.213152 loss)
I0410 14:35:28.732396 18353 sgd_solver.cpp:105] Iteration 8880, lr = 0.00172217
I0410 14:35:33.801215 18353 solver.cpp:218] Iteration 8892 (2.36747 iter/s, 5.06871s/12 iters), loss = 0.249368
I0410 14:35:33.801265 18353 solver.cpp:237]     Train net output #0: loss = 0.249368 (* 1 = 0.249368 loss)
I0410 14:35:33.801276 18353 sgd_solver.cpp:105] Iteration 8892, lr = 0.00171808
I0410 14:35:37.283007 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:35:38.701995 18353 solver.cpp:218] Iteration 8904 (2.44868 iter/s, 4.9006s/12 iters), loss = 0.150592
I0410 14:35:38.702051 18353 solver.cpp:237]     Train net output #0: loss = 0.150592 (* 1 = 0.150592 loss)
I0410 14:35:38.702064 18353 sgd_solver.cpp:105] Iteration 8904, lr = 0.001714
I0410 14:35:43.618988 18353 solver.cpp:218] Iteration 8916 (2.44061 iter/s, 4.91681s/12 iters), loss = 0.195952
I0410 14:35:43.619047 18353 solver.cpp:237]     Train net output #0: loss = 0.195952 (* 1 = 0.195952 loss)
I0410 14:35:43.619061 18353 sgd_solver.cpp:105] Iteration 8916, lr = 0.00170993
I0410 14:35:48.503921 18353 solver.cpp:218] Iteration 8928 (2.45662 iter/s, 4.88475s/12 iters), loss = 0.270088
I0410 14:35:48.503978 18353 solver.cpp:237]     Train net output #0: loss = 0.270088 (* 1 = 0.270088 loss)
I0410 14:35:48.503989 18353 sgd_solver.cpp:105] Iteration 8928, lr = 0.00170587
I0410 14:35:53.334822 18353 solver.cpp:218] Iteration 8940 (2.4841 iter/s, 4.83072s/12 iters), loss = 0.209815
I0410 14:35:53.334978 18353 solver.cpp:237]     Train net output #0: loss = 0.209815 (* 1 = 0.209815 loss)
I0410 14:35:53.334991 18353 sgd_solver.cpp:105] Iteration 8940, lr = 0.00170182
I0410 14:35:58.196499 18353 solver.cpp:218] Iteration 8952 (2.46842 iter/s, 4.8614s/12 iters), loss = 0.32383
I0410 14:35:58.196547 18353 solver.cpp:237]     Train net output #0: loss = 0.32383 (* 1 = 0.32383 loss)
I0410 14:35:58.196555 18353 sgd_solver.cpp:105] Iteration 8952, lr = 0.00169778
I0410 14:36:03.073809 18353 solver.cpp:218] Iteration 8964 (2.46046 iter/s, 4.87714s/12 iters), loss = 0.220448
I0410 14:36:03.073860 18353 solver.cpp:237]     Train net output #0: loss = 0.220448 (* 1 = 0.220448 loss)
I0410 14:36:03.073871 18353 sgd_solver.cpp:105] Iteration 8964, lr = 0.00169375
I0410 14:36:07.505633 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8976.caffemodel
I0410 14:36:07.848467 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8976.solverstate
I0410 14:36:08.052081 18353 solver.cpp:330] Iteration 8976, Testing net (#0)
I0410 14:36:08.052109 18353 net.cpp:676] Ignoring source layer train-data
I0410 14:36:09.015918 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:36:12.542117 18353 solver.cpp:397]     Test net output #0: accuracy = 0.560662
I0410 14:36:12.542150 18353 solver.cpp:397]     Test net output #1: loss = 2.48925 (* 1 = 2.48925 loss)
I0410 14:36:12.623476 18353 solver.cpp:218] Iteration 8976 (1.25663 iter/s, 9.54939s/12 iters), loss = 0.222873
I0410 14:36:12.623520 18353 solver.cpp:237]     Train net output #0: loss = 0.222873 (* 1 = 0.222873 loss)
I0410 14:36:12.623530 18353 sgd_solver.cpp:105] Iteration 8976, lr = 0.00168973
I0410 14:36:16.798837 18353 solver.cpp:218] Iteration 8988 (2.87412 iter/s, 4.1752s/12 iters), loss = 0.153636
I0410 14:36:16.798902 18353 solver.cpp:237]     Train net output #0: loss = 0.153636 (* 1 = 0.153636 loss)
I0410 14:36:16.798915 18353 sgd_solver.cpp:105] Iteration 8988, lr = 0.00168571
I0410 14:36:18.380266 18353 blocking_queue.cpp:49] Waiting for data
I0410 14:36:21.669431 18353 solver.cpp:218] Iteration 9000 (2.46386 iter/s, 4.87041s/12 iters), loss = 0.282873
I0410 14:36:21.669487 18353 solver.cpp:237]     Train net output #0: loss = 0.282873 (* 1 = 0.282873 loss)
I0410 14:36:21.669498 18353 sgd_solver.cpp:105] Iteration 9000, lr = 0.00168171
I0410 14:36:22.372877 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:36:26.637550 18353 solver.cpp:218] Iteration 9012 (2.41549 iter/s, 4.96794s/12 iters), loss = 0.222454
I0410 14:36:26.637639 18353 solver.cpp:237]     Train net output #0: loss = 0.222454 (* 1 = 0.222454 loss)
I0410 14:36:26.637652 18353 sgd_solver.cpp:105] Iteration 9012, lr = 0.00167772
I0410 14:36:31.522013 18353 solver.cpp:218] Iteration 9024 (2.45688 iter/s, 4.88425s/12 iters), loss = 0.141025
I0410 14:36:31.522076 18353 solver.cpp:237]     Train net output #0: loss = 0.141025 (* 1 = 0.141025 loss)
I0410 14:36:31.522089 18353 sgd_solver.cpp:105] Iteration 9024, lr = 0.00167374
I0410 14:36:36.440255 18353 solver.cpp:218] Iteration 9036 (2.43999 iter/s, 4.91805s/12 iters), loss = 0.160364
I0410 14:36:36.440317 18353 solver.cpp:237]     Train net output #0: loss = 0.160364 (* 1 = 0.160364 loss)
I0410 14:36:36.440330 18353 sgd_solver.cpp:105] Iteration 9036, lr = 0.00166976
I0410 14:36:41.350888 18353 solver.cpp:218] Iteration 9048 (2.44377 iter/s, 4.91044s/12 iters), loss = 0.324145
I0410 14:36:41.350944 18353 solver.cpp:237]     Train net output #0: loss = 0.324145 (* 1 = 0.324145 loss)
I0410 14:36:41.350955 18353 sgd_solver.cpp:105] Iteration 9048, lr = 0.0016658
I0410 14:36:46.374720 18353 solver.cpp:218] Iteration 9060 (2.3887 iter/s, 5.02364s/12 iters), loss = 0.227145
I0410 14:36:46.374774 18353 solver.cpp:237]     Train net output #0: loss = 0.227144 (* 1 = 0.227144 loss)
I0410 14:36:46.374786 18353 sgd_solver.cpp:105] Iteration 9060, lr = 0.00166184
I0410 14:36:51.193707 18353 solver.cpp:218] Iteration 9072 (2.49024 iter/s, 4.81881s/12 iters), loss = 0.17629
I0410 14:36:51.193750 18353 solver.cpp:237]     Train net output #0: loss = 0.17629 (* 1 = 0.17629 loss)
I0410 14:36:51.193758 18353 sgd_solver.cpp:105] Iteration 9072, lr = 0.0016579
I0410 14:36:53.189146 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9078.caffemodel
I0410 14:36:53.542078 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9078.solverstate
I0410 14:36:53.752290 18353 solver.cpp:330] Iteration 9078, Testing net (#0)
I0410 14:36:53.752313 18353 net.cpp:676] Ignoring source layer train-data
I0410 14:36:54.636091 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:36:58.179864 18353 solver.cpp:397]     Test net output #0: accuracy = 0.569853
I0410 14:36:58.180008 18353 solver.cpp:397]     Test net output #1: loss = 2.46042 (* 1 = 2.46042 loss)
I0410 14:37:00.108847 18353 solver.cpp:218] Iteration 9084 (1.34606 iter/s, 8.91488s/12 iters), loss = 0.226079
I0410 14:37:00.108893 18353 solver.cpp:237]     Train net output #0: loss = 0.226079 (* 1 = 0.226079 loss)
I0410 14:37:00.108902 18353 sgd_solver.cpp:105] Iteration 9084, lr = 0.00165396
I0410 14:37:04.978091 18353 solver.cpp:218] Iteration 9096 (2.46453 iter/s, 4.86907s/12 iters), loss = 0.404843
I0410 14:37:04.978137 18353 solver.cpp:237]     Train net output #0: loss = 0.404843 (* 1 = 0.404843 loss)
I0410 14:37:04.978145 18353 sgd_solver.cpp:105] Iteration 9096, lr = 0.00165003
I0410 14:37:07.814417 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:37:09.831142 18353 solver.cpp:218] Iteration 9108 (2.47276 iter/s, 4.85288s/12 iters), loss = 0.239005
I0410 14:37:09.831190 18353 solver.cpp:237]     Train net output #0: loss = 0.239005 (* 1 = 0.239005 loss)
I0410 14:37:09.831199 18353 sgd_solver.cpp:105] Iteration 9108, lr = 0.00164612
I0410 14:37:14.742961 18353 solver.cpp:218] Iteration 9120 (2.44317 iter/s, 4.91164s/12 iters), loss = 0.230034
I0410 14:37:14.743007 18353 solver.cpp:237]     Train net output #0: loss = 0.230034 (* 1 = 0.230034 loss)
I0410 14:37:14.743018 18353 sgd_solver.cpp:105] Iteration 9120, lr = 0.00164221
I0410 14:37:19.706104 18353 solver.cpp:218] Iteration 9132 (2.41791 iter/s, 4.96297s/12 iters), loss = 0.249215
I0410 14:37:19.706151 18353 solver.cpp:237]     Train net output #0: loss = 0.249215 (* 1 = 0.249215 loss)
I0410 14:37:19.706161 18353 sgd_solver.cpp:105] Iteration 9132, lr = 0.00163831
I0410 14:37:24.919384 18353 solver.cpp:218] Iteration 9144 (2.30189 iter/s, 5.2131s/12 iters), loss = 0.213244
I0410 14:37:24.919433 18353 solver.cpp:237]     Train net output #0: loss = 0.213244 (* 1 = 0.213244 loss)
I0410 14:37:24.919445 18353 sgd_solver.cpp:105] Iteration 9144, lr = 0.00163442
I0410 14:37:29.882719 18353 solver.cpp:218] Iteration 9156 (2.41782 iter/s, 4.96316s/12 iters), loss = 0.253894
I0410 14:37:29.882807 18353 solver.cpp:237]     Train net output #0: loss = 0.253894 (* 1 = 0.253894 loss)
I0410 14:37:29.882817 18353 sgd_solver.cpp:105] Iteration 9156, lr = 0.00163054
I0410 14:37:34.817373 18353 solver.cpp:218] Iteration 9168 (2.43189 iter/s, 4.93444s/12 iters), loss = 0.182433
I0410 14:37:34.817416 18353 solver.cpp:237]     Train net output #0: loss = 0.182433 (* 1 = 0.182433 loss)
I0410 14:37:34.817425 18353 sgd_solver.cpp:105] Iteration 9168, lr = 0.00162667
I0410 14:37:39.256000 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9180.caffemodel
I0410 14:37:39.564245 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9180.solverstate
I0410 14:37:39.771158 18353 solver.cpp:330] Iteration 9180, Testing net (#0)
I0410 14:37:39.771191 18353 net.cpp:676] Ignoring source layer train-data
I0410 14:37:40.581583 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:37:44.161528 18353 solver.cpp:397]     Test net output #0: accuracy = 0.567402
I0410 14:37:44.161577 18353 solver.cpp:397]     Test net output #1: loss = 2.43285 (* 1 = 2.43285 loss)
I0410 14:37:44.242144 18353 solver.cpp:218] Iteration 9180 (1.27328 iter/s, 9.42449s/12 iters), loss = 0.220207
I0410 14:37:44.242197 18353 solver.cpp:237]     Train net output #0: loss = 0.220207 (* 1 = 0.220207 loss)
I0410 14:37:44.242209 18353 sgd_solver.cpp:105] Iteration 9180, lr = 0.00162281
I0410 14:37:48.512907 18353 solver.cpp:218] Iteration 9192 (2.80991 iter/s, 4.2706s/12 iters), loss = 0.1856
I0410 14:37:48.512953 18353 solver.cpp:237]     Train net output #0: loss = 0.1856 (* 1 = 0.1856 loss)
I0410 14:37:48.512964 18353 sgd_solver.cpp:105] Iteration 9192, lr = 0.00161895
I0410 14:37:53.441063 18353 solver.cpp:218] Iteration 9204 (2.43507 iter/s, 4.92798s/12 iters), loss = 0.257251
I0410 14:37:53.441113 18353 solver.cpp:237]     Train net output #0: loss = 0.257251 (* 1 = 0.257251 loss)
I0410 14:37:53.441123 18353 sgd_solver.cpp:105] Iteration 9204, lr = 0.00161511
I0410 14:37:53.508723 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:37:58.380167 18353 solver.cpp:218] Iteration 9216 (2.42968 iter/s, 4.93893s/12 iters), loss = 0.096656
I0410 14:37:58.380210 18353 solver.cpp:237]     Train net output #0: loss = 0.096656 (* 1 = 0.096656 loss)
I0410 14:37:58.380220 18353 sgd_solver.cpp:105] Iteration 9216, lr = 0.00161128
I0410 14:38:03.278777 18353 solver.cpp:218] Iteration 9228 (2.44976 iter/s, 4.89844s/12 iters), loss = 0.220446
I0410 14:38:03.278895 18353 solver.cpp:237]     Train net output #0: loss = 0.220446 (* 1 = 0.220446 loss)
I0410 14:38:03.278906 18353 sgd_solver.cpp:105] Iteration 9228, lr = 0.00160745
I0410 14:38:08.211591 18353 solver.cpp:218] Iteration 9240 (2.43281 iter/s, 4.93258s/12 iters), loss = 0.21657
I0410 14:38:08.211628 18353 solver.cpp:237]     Train net output #0: loss = 0.21657 (* 1 = 0.21657 loss)
I0410 14:38:08.211637 18353 sgd_solver.cpp:105] Iteration 9240, lr = 0.00160363
I0410 14:38:13.197633 18353 solver.cpp:218] Iteration 9252 (2.4068 iter/s, 4.98587s/12 iters), loss = 0.224744
I0410 14:38:13.197687 18353 solver.cpp:237]     Train net output #0: loss = 0.224744 (* 1 = 0.224744 loss)
I0410 14:38:13.197700 18353 sgd_solver.cpp:105] Iteration 9252, lr = 0.00159983
I0410 14:38:18.079635 18353 solver.cpp:218] Iteration 9264 (2.4581 iter/s, 4.88182s/12 iters), loss = 0.257278
I0410 14:38:18.079694 18353 solver.cpp:237]     Train net output #0: loss = 0.257278 (* 1 = 0.257278 loss)
I0410 14:38:18.079707 18353 sgd_solver.cpp:105] Iteration 9264, lr = 0.00159603
I0410 14:38:22.955104 18353 solver.cpp:218] Iteration 9276 (2.4614 iter/s, 4.87528s/12 iters), loss = 0.195508
I0410 14:38:22.955164 18353 solver.cpp:237]     Train net output #0: loss = 0.195508 (* 1 = 0.195508 loss)
I0410 14:38:22.955178 18353 sgd_solver.cpp:105] Iteration 9276, lr = 0.00159224
I0410 14:38:24.929638 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9282.caffemodel
I0410 14:38:25.252574 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9282.solverstate
I0410 14:38:25.462672 18353 solver.cpp:330] Iteration 9282, Testing net (#0)
I0410 14:38:25.462703 18353 net.cpp:676] Ignoring source layer train-data
I0410 14:38:26.415925 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:38:30.467905 18353 solver.cpp:397]     Test net output #0: accuracy = 0.558824
I0410 14:38:30.467936 18353 solver.cpp:397]     Test net output #1: loss = 2.53961 (* 1 = 2.53961 loss)
I0410 14:38:32.272337 18353 solver.cpp:218] Iteration 9288 (1.28798 iter/s, 9.31694s/12 iters), loss = 0.250262
I0410 14:38:32.272387 18353 solver.cpp:237]     Train net output #0: loss = 0.250262 (* 1 = 0.250262 loss)
I0410 14:38:32.272397 18353 sgd_solver.cpp:105] Iteration 9288, lr = 0.00158846
I0410 14:38:37.449395 18353 solver.cpp:218] Iteration 9300 (2.318 iter/s, 5.17687s/12 iters), loss = 0.199941
I0410 14:38:37.450652 18353 solver.cpp:237]     Train net output #0: loss = 0.199941 (* 1 = 0.199941 loss)
I0410 14:38:37.450665 18353 sgd_solver.cpp:105] Iteration 9300, lr = 0.00158469
I0410 14:38:39.661242 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:38:42.416648 18353 solver.cpp:218] Iteration 9312 (2.4165 iter/s, 4.96587s/12 iters), loss = 0.312211
I0410 14:38:42.416707 18353 solver.cpp:237]     Train net output #0: loss = 0.312211 (* 1 = 0.312211 loss)
I0410 14:38:42.416719 18353 sgd_solver.cpp:105] Iteration 9312, lr = 0.00158092
I0410 14:38:47.331915 18353 solver.cpp:218] Iteration 9324 (2.44147 iter/s, 4.91508s/12 iters), loss = 0.185038
I0410 14:38:47.331976 18353 solver.cpp:237]     Train net output #0: loss = 0.185038 (* 1 = 0.185038 loss)
I0410 14:38:47.331990 18353 sgd_solver.cpp:105] Iteration 9324, lr = 0.00157717
I0410 14:38:52.210268 18353 solver.cpp:218] Iteration 9336 (2.45994 iter/s, 4.87816s/12 iters), loss = 0.169188
I0410 14:38:52.210327 18353 solver.cpp:237]     Train net output #0: loss = 0.169188 (* 1 = 0.169188 loss)
I0410 14:38:52.210340 18353 sgd_solver.cpp:105] Iteration 9336, lr = 0.00157343
I0410 14:38:57.089310 18353 solver.cpp:218] Iteration 9348 (2.45959 iter/s, 4.87885s/12 iters), loss = 0.207522
I0410 14:38:57.089371 18353 solver.cpp:237]     Train net output #0: loss = 0.207522 (* 1 = 0.207522 loss)
I0410 14:38:57.089385 18353 sgd_solver.cpp:105] Iteration 9348, lr = 0.00156969
I0410 14:39:02.031010 18353 solver.cpp:218] Iteration 9360 (2.42841 iter/s, 4.9415s/12 iters), loss = 0.142523
I0410 14:39:02.031075 18353 solver.cpp:237]     Train net output #0: loss = 0.142523 (* 1 = 0.142523 loss)
I0410 14:39:02.031087 18353 sgd_solver.cpp:105] Iteration 9360, lr = 0.00156596
I0410 14:39:06.909809 18353 solver.cpp:218] Iteration 9372 (2.45972 iter/s, 4.87861s/12 iters), loss = 0.205736
I0410 14:39:06.909860 18353 solver.cpp:237]     Train net output #0: loss = 0.205736 (* 1 = 0.205736 loss)
I0410 14:39:06.909873 18353 sgd_solver.cpp:105] Iteration 9372, lr = 0.00156225
I0410 14:39:11.439465 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9384.caffemodel
I0410 14:39:11.750174 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9384.solverstate
I0410 14:39:11.950016 18353 solver.cpp:330] Iteration 9384, Testing net (#0)
I0410 14:39:11.950034 18353 net.cpp:676] Ignoring source layer train-data
I0410 14:39:12.702491 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:39:16.507683 18353 solver.cpp:397]     Test net output #0: accuracy = 0.561275
I0410 14:39:16.507732 18353 solver.cpp:397]     Test net output #1: loss = 2.502 (* 1 = 2.502 loss)
I0410 14:39:16.588929 18353 solver.cpp:218] Iteration 9384 (1.23982 iter/s, 9.67883s/12 iters), loss = 0.194883
I0410 14:39:16.588992 18353 solver.cpp:237]     Train net output #0: loss = 0.194883 (* 1 = 0.194883 loss)
I0410 14:39:16.589006 18353 sgd_solver.cpp:105] Iteration 9384, lr = 0.00155854
I0410 14:39:20.694499 18353 solver.cpp:218] Iteration 9396 (2.92298 iter/s, 4.10539s/12 iters), loss = 0.26062
I0410 14:39:20.694545 18353 solver.cpp:237]     Train net output #0: loss = 0.26062 (* 1 = 0.26062 loss)
I0410 14:39:20.694555 18353 sgd_solver.cpp:105] Iteration 9396, lr = 0.00155484
I0410 14:39:24.914744 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:39:25.570451 18353 solver.cpp:218] Iteration 9408 (2.46115 iter/s, 4.87578s/12 iters), loss = 0.158863
I0410 14:39:25.570495 18353 solver.cpp:237]     Train net output #0: loss = 0.158863 (* 1 = 0.158863 loss)
I0410 14:39:25.570505 18353 sgd_solver.cpp:105] Iteration 9408, lr = 0.00155114
I0410 14:39:30.740375 18353 solver.cpp:218] Iteration 9420 (2.3212 iter/s, 5.16975s/12 iters), loss = 0.233335
I0410 14:39:30.740419 18353 solver.cpp:237]     Train net output #0: loss = 0.233335 (* 1 = 0.233335 loss)
I0410 14:39:30.740428 18353 sgd_solver.cpp:105] Iteration 9420, lr = 0.00154746
I0410 14:39:35.633522 18353 solver.cpp:218] Iteration 9432 (2.4525 iter/s, 4.89296s/12 iters), loss = 0.212536
I0410 14:39:35.633579 18353 solver.cpp:237]     Train net output #0: loss = 0.212536 (* 1 = 0.212536 loss)
I0410 14:39:35.633594 18353 sgd_solver.cpp:105] Iteration 9432, lr = 0.00154379
I0410 14:39:40.639696 18353 solver.cpp:218] Iteration 9444 (2.39713 iter/s, 5.00599s/12 iters), loss = 0.136083
I0410 14:39:40.639739 18353 solver.cpp:237]     Train net output #0: loss = 0.136083 (* 1 = 0.136083 loss)
I0410 14:39:40.639748 18353 sgd_solver.cpp:105] Iteration 9444, lr = 0.00154012
I0410 14:39:45.494421 18353 solver.cpp:218] Iteration 9456 (2.47191 iter/s, 4.85455s/12 iters), loss = 0.29401
I0410 14:39:45.494556 18353 solver.cpp:237]     Train net output #0: loss = 0.29401 (* 1 = 0.29401 loss)
I0410 14:39:45.494572 18353 sgd_solver.cpp:105] Iteration 9456, lr = 0.00153647
I0410 14:39:50.390158 18353 solver.cpp:218] Iteration 9468 (2.45124 iter/s, 4.89547s/12 iters), loss = 0.231563
I0410 14:39:50.390208 18353 solver.cpp:237]     Train net output #0: loss = 0.231563 (* 1 = 0.231563 loss)
I0410 14:39:50.390220 18353 sgd_solver.cpp:105] Iteration 9468, lr = 0.00153282
I0410 14:39:55.332906 18353 solver.cpp:218] Iteration 9480 (2.42789 iter/s, 4.94256s/12 iters), loss = 0.236992
I0410 14:39:55.332958 18353 solver.cpp:237]     Train net output #0: loss = 0.236992 (* 1 = 0.236992 loss)
I0410 14:39:55.332970 18353 sgd_solver.cpp:105] Iteration 9480, lr = 0.00152918
I0410 14:39:57.339954 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9486.caffemodel
I0410 14:39:57.645627 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9486.solverstate
I0410 14:39:57.857519 18353 solver.cpp:330] Iteration 9486, Testing net (#0)
I0410 14:39:57.857539 18353 net.cpp:676] Ignoring source layer train-data
I0410 14:39:58.527278 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:40:02.361070 18353 solver.cpp:397]     Test net output #0: accuracy = 0.575368
I0410 14:40:02.361124 18353 solver.cpp:397]     Test net output #1: loss = 2.46542 (* 1 = 2.46542 loss)
I0410 14:40:04.189177 18353 solver.cpp:218] Iteration 9492 (1.35501 iter/s, 8.85599s/12 iters), loss = 0.209818
I0410 14:40:04.189240 18353 solver.cpp:237]     Train net output #0: loss = 0.209818 (* 1 = 0.209818 loss)
I0410 14:40:04.189254 18353 sgd_solver.cpp:105] Iteration 9492, lr = 0.00152555
I0410 14:40:09.099334 18353 solver.cpp:218] Iteration 9504 (2.44401 iter/s, 4.90996s/12 iters), loss = 0.150755
I0410 14:40:09.099398 18353 solver.cpp:237]     Train net output #0: loss = 0.150755 (* 1 = 0.150755 loss)
I0410 14:40:09.099412 18353 sgd_solver.cpp:105] Iteration 9504, lr = 0.00152193
I0410 14:40:10.545635 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:40:14.011312 18353 solver.cpp:218] Iteration 9516 (2.4431 iter/s, 4.91179s/12 iters), loss = 0.303428
I0410 14:40:14.011361 18353 solver.cpp:237]     Train net output #0: loss = 0.303428 (* 1 = 0.303428 loss)
I0410 14:40:14.011370 18353 sgd_solver.cpp:105] Iteration 9516, lr = 0.00151831
I0410 14:40:18.888329 18353 solver.cpp:218] Iteration 9528 (2.46061 iter/s, 4.87683s/12 iters), loss = 0.216133
I0410 14:40:18.888453 18353 solver.cpp:237]     Train net output #0: loss = 0.216133 (* 1 = 0.216133 loss)
I0410 14:40:18.888468 18353 sgd_solver.cpp:105] Iteration 9528, lr = 0.00151471
I0410 14:40:23.739980 18353 solver.cpp:218] Iteration 9540 (2.47351 iter/s, 4.8514s/12 iters), loss = 0.115403
I0410 14:40:23.740038 18353 solver.cpp:237]     Train net output #0: loss = 0.115403 (* 1 = 0.115403 loss)
I0410 14:40:23.740052 18353 sgd_solver.cpp:105] Iteration 9540, lr = 0.00151111
I0410 14:40:28.627835 18353 solver.cpp:218] Iteration 9552 (2.45516 iter/s, 4.88767s/12 iters), loss = 0.0946793
I0410 14:40:28.627885 18353 solver.cpp:237]     Train net output #0: loss = 0.0946793 (* 1 = 0.0946793 loss)
I0410 14:40:28.627898 18353 sgd_solver.cpp:105] Iteration 9552, lr = 0.00150752
I0410 14:40:33.575628 18353 solver.cpp:218] Iteration 9564 (2.42541 iter/s, 4.94761s/12 iters), loss = 0.227629
I0410 14:40:33.575678 18353 solver.cpp:237]     Train net output #0: loss = 0.227629 (* 1 = 0.227629 loss)
I0410 14:40:33.575690 18353 sgd_solver.cpp:105] Iteration 9564, lr = 0.00150395
I0410 14:40:38.493116 18353 solver.cpp:218] Iteration 9576 (2.44036 iter/s, 4.91731s/12 iters), loss = 0.166091
I0410 14:40:38.493171 18353 solver.cpp:237]     Train net output #0: loss = 0.166091 (* 1 = 0.166091 loss)
I0410 14:40:38.493186 18353 sgd_solver.cpp:105] Iteration 9576, lr = 0.00150037
I0410 14:40:42.998380 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9588.caffemodel
I0410 14:40:43.549921 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9588.solverstate
I0410 14:40:43.956768 18353 solver.cpp:330] Iteration 9588, Testing net (#0)
I0410 14:40:43.956790 18353 net.cpp:676] Ignoring source layer train-data
I0410 14:40:44.646225 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:40:48.408604 18353 solver.cpp:397]     Test net output #0: accuracy = 0.567402
I0410 14:40:48.408655 18353 solver.cpp:397]     Test net output #1: loss = 2.52206 (* 1 = 2.52206 loss)
I0410 14:40:48.490751 18353 solver.cpp:218] Iteration 9588 (1.20032 iter/s, 9.99733s/12 iters), loss = 0.237975
I0410 14:40:48.490798 18353 solver.cpp:237]     Train net output #0: loss = 0.237975 (* 1 = 0.237975 loss)
I0410 14:40:48.490811 18353 sgd_solver.cpp:105] Iteration 9588, lr = 0.00149681
I0410 14:40:52.762014 18353 solver.cpp:218] Iteration 9600 (2.80958 iter/s, 4.2711s/12 iters), loss = 0.249677
I0410 14:40:52.762166 18353 solver.cpp:237]     Train net output #0: loss = 0.249677 (* 1 = 0.249677 loss)
I0410 14:40:52.762177 18353 sgd_solver.cpp:105] Iteration 9600, lr = 0.00149326
I0410 14:40:56.398937 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:40:57.769876 18353 solver.cpp:218] Iteration 9612 (2.39637 iter/s, 5.00758s/12 iters), loss = 0.147398
I0410 14:40:57.769922 18353 solver.cpp:237]     Train net output #0: loss = 0.147398 (* 1 = 0.147398 loss)
I0410 14:40:57.769932 18353 sgd_solver.cpp:105] Iteration 9612, lr = 0.00148971
I0410 14:41:02.708220 18353 solver.cpp:218] Iteration 9624 (2.43006 iter/s, 4.93816s/12 iters), loss = 0.243233
I0410 14:41:02.708272 18353 solver.cpp:237]     Train net output #0: loss = 0.243233 (* 1 = 0.243233 loss)
I0410 14:41:02.708282 18353 sgd_solver.cpp:105] Iteration 9624, lr = 0.00148618
I0410 14:41:07.602622 18353 solver.cpp:218] Iteration 9636 (2.45187 iter/s, 4.89422s/12 iters), loss = 0.158165
I0410 14:41:07.602680 18353 solver.cpp:237]     Train net output #0: loss = 0.158165 (* 1 = 0.158165 loss)
I0410 14:41:07.602694 18353 sgd_solver.cpp:105] Iteration 9636, lr = 0.00148265
I0410 14:41:12.509347 18353 solver.cpp:218] Iteration 9648 (2.44572 iter/s, 4.90653s/12 iters), loss = 0.123908
I0410 14:41:12.509409 18353 solver.cpp:237]     Train net output #0: loss = 0.123908 (* 1 = 0.123908 loss)
I0410 14:41:12.509423 18353 sgd_solver.cpp:105] Iteration 9648, lr = 0.00147913
I0410 14:41:17.444718 18353 solver.cpp:218] Iteration 9660 (2.43152 iter/s, 4.93518s/12 iters), loss = 0.309192
I0410 14:41:17.444767 18353 solver.cpp:237]     Train net output #0: loss = 0.309192 (* 1 = 0.309192 loss)
I0410 14:41:17.444777 18353 sgd_solver.cpp:105] Iteration 9660, lr = 0.00147562
I0410 14:41:22.437513 18353 solver.cpp:218] Iteration 9672 (2.40355 iter/s, 4.99261s/12 iters), loss = 0.239987
I0410 14:41:22.437564 18353 solver.cpp:237]     Train net output #0: loss = 0.239987 (* 1 = 0.239987 loss)
I0410 14:41:22.437575 18353 sgd_solver.cpp:105] Iteration 9672, lr = 0.00147211
I0410 14:41:27.348919 18353 solver.cpp:218] Iteration 9684 (2.44338 iter/s, 4.91122s/12 iters), loss = 0.214072
I0410 14:41:27.349099 18353 solver.cpp:237]     Train net output #0: loss = 0.214072 (* 1 = 0.214072 loss)
I0410 14:41:27.349114 18353 sgd_solver.cpp:105] Iteration 9684, lr = 0.00146862
I0410 14:41:29.415582 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9690.caffemodel
I0410 14:41:29.697649 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9690.solverstate
I0410 14:41:29.893741 18353 solver.cpp:330] Iteration 9690, Testing net (#0)
I0410 14:41:29.893774 18353 net.cpp:676] Ignoring source layer train-data
I0410 14:41:30.664129 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:41:33.499447 18353 blocking_queue.cpp:49] Waiting for data
I0410 14:41:34.874099 18353 solver.cpp:397]     Test net output #0: accuracy = 0.561275
I0410 14:41:34.874150 18353 solver.cpp:397]     Test net output #1: loss = 2.504 (* 1 = 2.504 loss)
I0410 14:41:36.772971 18353 solver.cpp:218] Iteration 9696 (1.27339 iter/s, 9.42364s/12 iters), loss = 0.184445
I0410 14:41:36.773020 18353 solver.cpp:237]     Train net output #0: loss = 0.184445 (* 1 = 0.184445 loss)
I0410 14:41:36.773031 18353 sgd_solver.cpp:105] Iteration 9696, lr = 0.00146513
I0410 14:41:41.762070 18353 solver.cpp:218] Iteration 9708 (2.40534 iter/s, 4.98891s/12 iters), loss = 0.182564
I0410 14:41:41.762142 18353 solver.cpp:237]     Train net output #0: loss = 0.182564 (* 1 = 0.182564 loss)
I0410 14:41:41.762156 18353 sgd_solver.cpp:105] Iteration 9708, lr = 0.00146165
I0410 14:41:42.490715 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:41:46.773536 18353 solver.cpp:218] Iteration 9720 (2.39461 iter/s, 5.01126s/12 iters), loss = 0.14053
I0410 14:41:46.773582 18353 solver.cpp:237]     Train net output #0: loss = 0.14053 (* 1 = 0.14053 loss)
I0410 14:41:46.773593 18353 sgd_solver.cpp:105] Iteration 9720, lr = 0.00145818
I0410 14:41:51.831698 18353 solver.cpp:218] Iteration 9732 (2.37249 iter/s, 5.05798s/12 iters), loss = 0.247079
I0410 14:41:51.831756 18353 solver.cpp:237]     Train net output #0: loss = 0.247079 (* 1 = 0.247079 loss)
I0410 14:41:51.831768 18353 sgd_solver.cpp:105] Iteration 9732, lr = 0.00145472
I0410 14:41:56.748929 18353 solver.cpp:218] Iteration 9744 (2.44049 iter/s, 4.91704s/12 iters), loss = 0.16689
I0410 14:41:56.748987 18353 solver.cpp:237]     Train net output #0: loss = 0.16689 (* 1 = 0.16689 loss)
I0410 14:41:56.748998 18353 sgd_solver.cpp:105] Iteration 9744, lr = 0.00145127
I0410 14:42:01.675180 18353 solver.cpp:218] Iteration 9756 (2.43603 iter/s, 4.92606s/12 iters), loss = 0.195629
I0410 14:42:01.675348 18353 solver.cpp:237]     Train net output #0: loss = 0.195629 (* 1 = 0.195629 loss)
I0410 14:42:01.675361 18353 sgd_solver.cpp:105] Iteration 9756, lr = 0.00144782
I0410 14:42:06.582463 18353 solver.cpp:218] Iteration 9768 (2.44549 iter/s, 4.90699s/12 iters), loss = 0.158777
I0410 14:42:06.582518 18353 solver.cpp:237]     Train net output #0: loss = 0.158777 (* 1 = 0.158777 loss)
I0410 14:42:06.582531 18353 sgd_solver.cpp:105] Iteration 9768, lr = 0.00144438
I0410 14:42:11.535090 18353 solver.cpp:218] Iteration 9780 (2.42305 iter/s, 4.95244s/12 iters), loss = 0.195333
I0410 14:42:11.535133 18353 solver.cpp:237]     Train net output #0: loss = 0.195334 (* 1 = 0.195334 loss)
I0410 14:42:11.535142 18353 sgd_solver.cpp:105] Iteration 9780, lr = 0.00144095
I0410 14:42:15.887224 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9792.caffemodel
I0410 14:42:16.218623 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9792.solverstate
I0410 14:42:16.416877 18353 solver.cpp:330] Iteration 9792, Testing net (#0)
I0410 14:42:16.416898 18353 net.cpp:676] Ignoring source layer train-data
I0410 14:42:17.031771 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:42:20.933519 18353 solver.cpp:397]     Test net output #0: accuracy = 0.579044
I0410 14:42:20.933555 18353 solver.cpp:397]     Test net output #1: loss = 2.37845 (* 1 = 2.37845 loss)
I0410 14:42:21.015034 18353 solver.cpp:218] Iteration 9792 (1.26587 iter/s, 9.47965s/12 iters), loss = 0.198765
I0410 14:42:21.015090 18353 solver.cpp:237]     Train net output #0: loss = 0.198765 (* 1 = 0.198765 loss)
I0410 14:42:21.015102 18353 sgd_solver.cpp:105] Iteration 9792, lr = 0.00143753
I0410 14:42:25.479322 18353 solver.cpp:218] Iteration 9804 (2.6881 iter/s, 4.46411s/12 iters), loss = 0.133107
I0410 14:42:25.479365 18353 solver.cpp:237]     Train net output #0: loss = 0.133108 (* 1 = 0.133108 loss)
I0410 14:42:25.479374 18353 sgd_solver.cpp:105] Iteration 9804, lr = 0.00143412
I0410 14:42:28.635205 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:42:30.593412 18353 solver.cpp:218] Iteration 9816 (2.34654 iter/s, 5.1139s/12 iters), loss = 0.337727
I0410 14:42:30.593467 18353 solver.cpp:237]     Train net output #0: loss = 0.337728 (* 1 = 0.337728 loss)
I0410 14:42:30.593482 18353 sgd_solver.cpp:105] Iteration 9816, lr = 0.00143072
I0410 14:42:35.510040 18353 solver.cpp:218] Iteration 9828 (2.44079 iter/s, 4.91644s/12 iters), loss = 0.245532
I0410 14:42:35.510167 18353 solver.cpp:237]     Train net output #0: loss = 0.245532 (* 1 = 0.245532 loss)
I0410 14:42:35.510177 18353 sgd_solver.cpp:105] Iteration 9828, lr = 0.00142732
I0410 14:42:40.390113 18353 solver.cpp:218] Iteration 9840 (2.45912 iter/s, 4.8798s/12 iters), loss = 0.126397
I0410 14:42:40.390188 18353 solver.cpp:237]     Train net output #0: loss = 0.126397 (* 1 = 0.126397 loss)
I0410 14:42:40.390202 18353 sgd_solver.cpp:105] Iteration 9840, lr = 0.00142393
I0410 14:42:45.275735 18353 solver.cpp:218] Iteration 9852 (2.45628 iter/s, 4.88543s/12 iters), loss = 0.185942
I0410 14:42:45.275786 18353 solver.cpp:237]     Train net output #0: loss = 0.185942 (* 1 = 0.185942 loss)
I0410 14:42:45.275799 18353 sgd_solver.cpp:105] Iteration 9852, lr = 0.00142055
I0410 14:42:50.214323 18353 solver.cpp:218] Iteration 9864 (2.42993 iter/s, 4.93841s/12 iters), loss = 0.158418
I0410 14:42:50.214365 18353 solver.cpp:237]     Train net output #0: loss = 0.158418 (* 1 = 0.158418 loss)
I0410 14:42:50.214373 18353 sgd_solver.cpp:105] Iteration 9864, lr = 0.00141718
I0410 14:42:55.104404 18353 solver.cpp:218] Iteration 9876 (2.45404 iter/s, 4.8899s/12 iters), loss = 0.255006
I0410 14:42:55.104452 18353 solver.cpp:237]     Train net output #0: loss = 0.255006 (* 1 = 0.255006 loss)
I0410 14:42:55.104462 18353 sgd_solver.cpp:105] Iteration 9876, lr = 0.00141381
I0410 14:43:00.034459 18353 solver.cpp:218] Iteration 9888 (2.43414 iter/s, 4.92987s/12 iters), loss = 0.186781
I0410 14:43:00.034512 18353 solver.cpp:237]     Train net output #0: loss = 0.186781 (* 1 = 0.186781 loss)
I0410 14:43:00.034523 18353 sgd_solver.cpp:105] Iteration 9888, lr = 0.00141045
I0410 14:43:02.052062 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9894.caffemodel
I0410 14:43:02.355147 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9894.solverstate
I0410 14:43:02.549393 18353 solver.cpp:330] Iteration 9894, Testing net (#0)
I0410 14:43:02.549412 18353 net.cpp:676] Ignoring source layer train-data
I0410 14:43:03.019343 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:43:06.852264 18353 solver.cpp:397]     Test net output #0: accuracy = 0.572917
I0410 14:43:06.852387 18353 solver.cpp:397]     Test net output #1: loss = 2.44568 (* 1 = 2.44568 loss)
I0410 14:43:08.824234 18353 solver.cpp:218] Iteration 9900 (1.36527 iter/s, 8.7895s/12 iters), loss = 0.151593
I0410 14:43:08.824286 18353 solver.cpp:237]     Train net output #0: loss = 0.151593 (* 1 = 0.151593 loss)
I0410 14:43:08.824298 18353 sgd_solver.cpp:105] Iteration 9900, lr = 0.00140711
I0410 14:43:13.762876 18353 solver.cpp:218] Iteration 9912 (2.42991 iter/s, 4.93846s/12 iters), loss = 0.151792
I0410 14:43:13.762931 18353 solver.cpp:237]     Train net output #0: loss = 0.151793 (* 1 = 0.151793 loss)
I0410 14:43:13.762944 18353 sgd_solver.cpp:105] Iteration 9912, lr = 0.00140377
I0410 14:43:13.872913 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:43:18.871026 18353 solver.cpp:218] Iteration 9924 (2.34928 iter/s, 5.10796s/12 iters), loss = 0.148947
I0410 14:43:18.871080 18353 solver.cpp:237]     Train net output #0: loss = 0.148947 (* 1 = 0.148947 loss)
I0410 14:43:18.871093 18353 sgd_solver.cpp:105] Iteration 9924, lr = 0.00140043
I0410 14:43:23.963198 18353 solver.cpp:218] Iteration 9936 (2.35665 iter/s, 5.09198s/12 iters), loss = 0.158432
I0410 14:43:23.963248 18353 solver.cpp:237]     Train net output #0: loss = 0.158432 (* 1 = 0.158432 loss)
I0410 14:43:23.963258 18353 sgd_solver.cpp:105] Iteration 9936, lr = 0.00139711
I0410 14:43:28.974037 18353 solver.cpp:218] Iteration 9948 (2.3949 iter/s, 5.01065s/12 iters), loss = 0.221105
I0410 14:43:28.974088 18353 solver.cpp:237]     Train net output #0: loss = 0.221105 (* 1 = 0.221105 loss)
I0410 14:43:28.974102 18353 sgd_solver.cpp:105] Iteration 9948, lr = 0.00139379
I0410 14:43:33.869300 18353 solver.cpp:218] Iteration 9960 (2.45144 iter/s, 4.89508s/12 iters), loss = 0.329547
I0410 14:43:33.869354 18353 solver.cpp:237]     Train net output #0: loss = 0.329547 (* 1 = 0.329547 loss)
I0410 14:43:33.869366 18353 sgd_solver.cpp:105] Iteration 9960, lr = 0.00139048
I0410 14:43:38.764585 18353 solver.cpp:218] Iteration 9972 (2.45144 iter/s, 4.89509s/12 iters), loss = 0.208258
I0410 14:43:38.764758 18353 solver.cpp:237]     Train net output #0: loss = 0.208258 (* 1 = 0.208258 loss)
I0410 14:43:38.764775 18353 sgd_solver.cpp:105] Iteration 9972, lr = 0.00138718
I0410 14:43:43.735884 18353 solver.cpp:218] Iteration 9984 (2.41401 iter/s, 4.97099s/12 iters), loss = 0.219894
I0410 14:43:43.735946 18353 solver.cpp:237]     Train net output #0: loss = 0.219894 (* 1 = 0.219894 loss)
I0410 14:43:43.735960 18353 sgd_solver.cpp:105] Iteration 9984, lr = 0.00138389
I0410 14:43:48.102035 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9996.caffemodel
I0410 14:43:48.554915 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9996.solverstate
I0410 14:43:49.502020 18353 solver.cpp:330] Iteration 9996, Testing net (#0)
I0410 14:43:49.502048 18353 net.cpp:676] Ignoring source layer train-data
I0410 14:43:50.019614 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:43:54.244901 18353 solver.cpp:397]     Test net output #0: accuracy = 0.585172
I0410 14:43:54.244952 18353 solver.cpp:397]     Test net output #1: loss = 2.45077 (* 1 = 2.45077 loss)
I0410 14:43:54.326189 18353 solver.cpp:218] Iteration 9996 (1.13315 iter/s, 10.59s/12 iters), loss = 0.14048
I0410 14:43:54.326243 18353 solver.cpp:237]     Train net output #0: loss = 0.14048 (* 1 = 0.14048 loss)
I0410 14:43:54.326256 18353 sgd_solver.cpp:105] Iteration 9996, lr = 0.0013806
I0410 14:43:58.552924 18353 solver.cpp:218] Iteration 10008 (2.83919 iter/s, 4.22656s/12 iters), loss = 0.24623
I0410 14:43:58.552978 18353 solver.cpp:237]     Train net output #0: loss = 0.24623 (* 1 = 0.24623 loss)
I0410 14:43:58.552991 18353 sgd_solver.cpp:105] Iteration 10008, lr = 0.00137732
I0410 14:44:00.771916 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:44:03.500396 18353 solver.cpp:218] Iteration 10020 (2.42557 iter/s, 4.94729s/12 iters), loss = 0.115294
I0410 14:44:03.500452 18353 solver.cpp:237]     Train net output #0: loss = 0.115294 (* 1 = 0.115294 loss)
I0410 14:44:03.500464 18353 sgd_solver.cpp:105] Iteration 10020, lr = 0.00137405
I0410 14:44:08.447297 18353 solver.cpp:218] Iteration 10032 (2.42585 iter/s, 4.94671s/12 iters), loss = 0.149437
I0410 14:44:08.447352 18353 solver.cpp:237]     Train net output #0: loss = 0.149437 (* 1 = 0.149437 loss)
I0410 14:44:08.447366 18353 sgd_solver.cpp:105] Iteration 10032, lr = 0.00137079
I0410 14:44:13.289423 18353 solver.cpp:218] Iteration 10044 (2.47835 iter/s, 4.84194s/12 iters), loss = 0.165375
I0410 14:44:13.289572 18353 solver.cpp:237]     Train net output #0: loss = 0.165375 (* 1 = 0.165375 loss)
I0410 14:44:13.289587 18353 sgd_solver.cpp:105] Iteration 10044, lr = 0.00136754
I0410 14:44:18.081051 18353 solver.cpp:218] Iteration 10056 (2.50451 iter/s, 4.79135s/12 iters), loss = 0.102136
I0410 14:44:18.081095 18353 solver.cpp:237]     Train net output #0: loss = 0.102136 (* 1 = 0.102136 loss)
I0410 14:44:18.081104 18353 sgd_solver.cpp:105] Iteration 10056, lr = 0.00136429
I0410 14:44:22.994966 18353 solver.cpp:218] Iteration 10068 (2.44213 iter/s, 4.91374s/12 iters), loss = 0.137657
I0410 14:44:22.995013 18353 solver.cpp:237]     Train net output #0: loss = 0.137658 (* 1 = 0.137658 loss)
I0410 14:44:22.995023 18353 sgd_solver.cpp:105] Iteration 10068, lr = 0.00136105
I0410 14:44:27.875278 18353 solver.cpp:218] Iteration 10080 (2.45895 iter/s, 4.88013s/12 iters), loss = 0.0803564
I0410 14:44:27.875335 18353 solver.cpp:237]     Train net output #0: loss = 0.0803565 (* 1 = 0.0803565 loss)
I0410 14:44:27.875349 18353 sgd_solver.cpp:105] Iteration 10080, lr = 0.00135782
I0410 14:44:32.799690 18353 solver.cpp:218] Iteration 10092 (2.43694 iter/s, 4.92422s/12 iters), loss = 0.204733
I0410 14:44:32.799747 18353 solver.cpp:237]     Train net output #0: loss = 0.204733 (* 1 = 0.204733 loss)
I0410 14:44:32.799762 18353 sgd_solver.cpp:105] Iteration 10092, lr = 0.0013546
I0410 14:44:34.807947 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10098.caffemodel
I0410 14:44:35.112897 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10098.solverstate
I0410 14:44:35.320705 18353 solver.cpp:330] Iteration 10098, Testing net (#0)
I0410 14:44:35.320735 18353 net.cpp:676] Ignoring source layer train-data
I0410 14:44:35.798521 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:44:39.769268 18353 solver.cpp:397]     Test net output #0: accuracy = 0.574755
I0410 14:44:39.769312 18353 solver.cpp:397]     Test net output #1: loss = 2.41586 (* 1 = 2.41586 loss)
I0410 14:44:41.557220 18353 solver.cpp:218] Iteration 10104 (1.37029 iter/s, 8.75724s/12 iters), loss = 0.0673311
I0410 14:44:41.557289 18353 solver.cpp:237]     Train net output #0: loss = 0.0673311 (* 1 = 0.0673311 loss)
I0410 14:44:41.557302 18353 sgd_solver.cpp:105] Iteration 10104, lr = 0.00135138
I0410 14:44:45.793998 18357 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:44:46.420656 18353 solver.cpp:218] Iteration 10116 (2.46749 iter/s, 4.86323s/12 iters), loss = 0.150756
I0410 14:44:46.420707 18353 solver.cpp:237]     Train net output #0: loss = 0.150756 (* 1 = 0.150756 loss)
I0410 14:44:46.420717 18353 sgd_solver.cpp:105] Iteration 10116, lr = 0.00134817
I0410 14:44:51.800137 18353 solver.cpp:218] Iteration 10128 (2.23078 iter/s, 5.37928s/12 iters), loss = 0.151478
I0410 14:44:51.800191 18353 solver.cpp:237]     Train net output #0: loss = 0.151478 (* 1 = 0.151478 loss)
I0410 14:44:51.800205 18353 sgd_solver.cpp:105] Iteration 10128, lr = 0.00134497
I0410 14:44:56.886482 18353 solver.cpp:218] Iteration 10140 (2.35935 iter/s, 5.08615s/12 iters), loss = 0.100837
I0410 14:44:56.886536 18353 solver.cpp:237]     Train net output #0: loss = 0.100837 (* 1 = 0.100837 loss)
I0410 14:44:56.886549 18353 sgd_solver.cpp:105] Iteration 10140, lr = 0.00134178
I0410 14:45:01.694110 18353 solver.cpp:218] Iteration 10152 (2.49613 iter/s, 4.80745s/12 iters), loss = 0.171949
I0410 14:45:01.694154 18353 solver.cpp:237]     Train net output #0: loss = 0.171949 (* 1 = 0.171949 loss)
I0410 14:45:01.694164 18353 sgd_solver.cpp:105] Iteration 10152, lr = 0.00133859
I0410 14:45:06.600464 18353 solver.cpp:218] Iteration 10164 (2.44589 iter/s, 4.90618s/12 iters), loss = 0.22351
I0410 14:45:06.600507 18353 solver.cpp:237]     Train net output #0: loss = 0.22351 (* 1 = 0.22351 loss)
I0410 14:45:06.600515 18353 sgd_solver.cpp:105] Iteration 10164, lr = 0.00133541
I0410 14:45:11.500793 18353 solver.cpp:218] Iteration 10176 (2.44891 iter/s, 4.90015s/12 iters), loss = 0.107566
I0410 14:45:11.500850 18353 solver.cpp:237]     Train net output #0: loss = 0.107566 (* 1 = 0.107566 loss)
I0410 14:45:11.500864 18353 sgd_solver.cpp:105] Iteration 10176, lr = 0.00133224
I0410 14:45:16.397441 18353 solver.cpp:218] Iteration 10188 (2.45075 iter/s, 4.89646s/12 iters), loss = 0.21583
I0410 14:45:16.397604 18353 solver.cpp:237]     Train net output #0: loss = 0.21583 (* 1 = 0.21583 loss)
I0410 14:45:16.397619 18353 sgd_solver.cpp:105] Iteration 10188, lr = 0.00132908
I0410 14:45:20.889103 18353 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10200.caffemodel
I0410 14:45:21.179427 18353 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10200.solverstate
I0410 14:45:21.416931 18353 solver.cpp:310] Iteration 10200, loss = 0.178516
I0410 14:45:21.416970 18353 solver.cpp:330] Iteration 10200, Testing net (#0)
I0410 14:45:21.416980 18353 net.cpp:676] Ignoring source layer train-data
I0410 14:45:21.945623 18358 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:45:25.985695 18353 solver.cpp:397]     Test net output #0: accuracy = 0.576593
I0410 14:45:25.985738 18353 solver.cpp:397]     Test net output #1: loss = 2.49071 (* 1 = 2.49071 loss)
I0410 14:45:25.985749 18353 solver.cpp:315] Optimization Done.
I0410 14:45:25.985756 18353 caffe.cpp:259] Optimization Done.