DIGITS-CNN/cars/architecture-investigations/fc/1-layer/512/caffe_output.log

I0409 22:51:01.582617  4596 upgrade_proto.cpp:1082] Attempting to upgrade input file specified using deprecated 'solver_type' field (enum)': /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210409-205409-a633/solver.prototxt
I0409 22:51:01.582784  4596 upgrade_proto.cpp:1089] Successfully upgraded file specified using deprecated 'solver_type' field (enum) to 'type' field (string).
W0409 22:51:01.582792  4596 upgrade_proto.cpp:1091] Note that future Caffe releases will only support 'type' field (string) for a solver's type.
I0409 22:51:01.582860  4596 caffe.cpp:218] Using GPUs 0
I0409 22:51:01.597378  4596 caffe.cpp:223] GPU 0: GeForce GTX 1080 Ti
I0409 22:51:01.859549  4596 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"
I0409 22:51:01.862200  4596 solver.cpp:87] Creating training net from net file: train_val.prototxt
I0409 22:51:01.863777  4596 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer val-data
I0409 22:51:01.863792  4596 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy
I0409 22:51:01.863924  4596 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: 512
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"
}
I0409 22:51:01.864007  4596 layer_factory.hpp:77] Creating layer train-data
I0409 22:51:01.870805  4596 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/train_db
I0409 22:51:01.871563  4596 net.cpp:84] Creating Layer train-data
I0409 22:51:01.871577  4596 net.cpp:380] train-data -> data
I0409 22:51:01.871598  4596 net.cpp:380] train-data -> label
I0409 22:51:01.871611  4596 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto
I0409 22:51:01.877714  4596 data_layer.cpp:45] output data size: 128,3,227,227
I0409 22:51:02.014798  4596 net.cpp:122] Setting up train-data
I0409 22:51:02.014823  4596 net.cpp:129] Top shape: 128 3 227 227 (19787136)
I0409 22:51:02.014829  4596 net.cpp:129] Top shape: 128 (128)
I0409 22:51:02.014833  4596 net.cpp:137] Memory required for data: 79149056
I0409 22:51:02.014843  4596 layer_factory.hpp:77] Creating layer conv1
I0409 22:51:02.014864  4596 net.cpp:84] Creating Layer conv1
I0409 22:51:02.014871  4596 net.cpp:406] conv1 <- data
I0409 22:51:02.014883  4596 net.cpp:380] conv1 -> conv1
I0409 22:51:02.639611  4596 net.cpp:122] Setting up conv1
I0409 22:51:02.639632  4596 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0409 22:51:02.639637  4596 net.cpp:137] Memory required for data: 227833856
I0409 22:51:02.639657  4596 layer_factory.hpp:77] Creating layer relu1
I0409 22:51:02.639668  4596 net.cpp:84] Creating Layer relu1
I0409 22:51:02.639673  4596 net.cpp:406] relu1 <- conv1
I0409 22:51:02.639679  4596 net.cpp:367] relu1 -> conv1 (in-place)
I0409 22:51:02.639966  4596 net.cpp:122] Setting up relu1
I0409 22:51:02.639974  4596 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0409 22:51:02.639978  4596 net.cpp:137] Memory required for data: 376518656
I0409 22:51:02.639982  4596 layer_factory.hpp:77] Creating layer norm1
I0409 22:51:02.639992  4596 net.cpp:84] Creating Layer norm1
I0409 22:51:02.639997  4596 net.cpp:406] norm1 <- conv1
I0409 22:51:02.640002  4596 net.cpp:380] norm1 -> norm1
I0409 22:51:02.644203  4596 net.cpp:122] Setting up norm1
I0409 22:51:02.644214  4596 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0409 22:51:02.644218  4596 net.cpp:137] Memory required for data: 525203456
I0409 22:51:02.644222  4596 layer_factory.hpp:77] Creating layer pool1
I0409 22:51:02.644232  4596 net.cpp:84] Creating Layer pool1
I0409 22:51:02.644235  4596 net.cpp:406] pool1 <- norm1
I0409 22:51:02.644241  4596 net.cpp:380] pool1 -> pool1
I0409 22:51:02.644299  4596 net.cpp:122] Setting up pool1
I0409 22:51:02.644307  4596 net.cpp:129] Top shape: 128 96 27 27 (8957952)
I0409 22:51:02.644310  4596 net.cpp:137] Memory required for data: 561035264
I0409 22:51:02.644315  4596 layer_factory.hpp:77] Creating layer conv2
I0409 22:51:02.644326  4596 net.cpp:84] Creating Layer conv2
I0409 22:51:02.644330  4596 net.cpp:406] conv2 <- pool1
I0409 22:51:02.644335  4596 net.cpp:380] conv2 -> conv2
I0409 22:51:02.651644  4596 net.cpp:122] Setting up conv2
I0409 22:51:02.651659  4596 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0409 22:51:02.651662  4596 net.cpp:137] Memory required for data: 656586752
I0409 22:51:02.651672  4596 layer_factory.hpp:77] Creating layer relu2
I0409 22:51:02.651682  4596 net.cpp:84] Creating Layer relu2
I0409 22:51:02.651686  4596 net.cpp:406] relu2 <- conv2
I0409 22:51:02.651692  4596 net.cpp:367] relu2 -> conv2 (in-place)
I0409 22:51:02.652179  4596 net.cpp:122] Setting up relu2
I0409 22:51:02.652189  4596 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0409 22:51:02.652194  4596 net.cpp:137] Memory required for data: 752138240
I0409 22:51:02.652197  4596 layer_factory.hpp:77] Creating layer norm2
I0409 22:51:02.652204  4596 net.cpp:84] Creating Layer norm2
I0409 22:51:02.652209  4596 net.cpp:406] norm2 <- conv2
I0409 22:51:02.652215  4596 net.cpp:380] norm2 -> norm2
I0409 22:51:02.652568  4596 net.cpp:122] Setting up norm2
I0409 22:51:02.652576  4596 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0409 22:51:02.652580  4596 net.cpp:137] Memory required for data: 847689728
I0409 22:51:02.652585  4596 layer_factory.hpp:77] Creating layer pool2
I0409 22:51:02.652593  4596 net.cpp:84] Creating Layer pool2
I0409 22:51:02.652598  4596 net.cpp:406] pool2 <- norm2
I0409 22:51:02.652603  4596 net.cpp:380] pool2 -> pool2
I0409 22:51:02.652632  4596 net.cpp:122] Setting up pool2
I0409 22:51:02.652639  4596 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0409 22:51:02.652643  4596 net.cpp:137] Memory required for data: 869840896
I0409 22:51:02.652647  4596 layer_factory.hpp:77] Creating layer conv3
I0409 22:51:02.652657  4596 net.cpp:84] Creating Layer conv3
I0409 22:51:02.652662  4596 net.cpp:406] conv3 <- pool2
I0409 22:51:02.652667  4596 net.cpp:380] conv3 -> conv3
I0409 22:51:02.662829  4596 net.cpp:122] Setting up conv3
I0409 22:51:02.662847  4596 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0409 22:51:02.662851  4596 net.cpp:137] Memory required for data: 903067648
I0409 22:51:02.662864  4596 layer_factory.hpp:77] Creating layer relu3
I0409 22:51:02.662873  4596 net.cpp:84] Creating Layer relu3
I0409 22:51:02.662878  4596 net.cpp:406] relu3 <- conv3
I0409 22:51:02.662885  4596 net.cpp:367] relu3 -> conv3 (in-place)
I0409 22:51:02.663414  4596 net.cpp:122] Setting up relu3
I0409 22:51:02.663424  4596 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0409 22:51:02.663429  4596 net.cpp:137] Memory required for data: 936294400
I0409 22:51:02.663432  4596 layer_factory.hpp:77] Creating layer conv4
I0409 22:51:02.663444  4596 net.cpp:84] Creating Layer conv4
I0409 22:51:02.663448  4596 net.cpp:406] conv4 <- conv3
I0409 22:51:02.663455  4596 net.cpp:380] conv4 -> conv4
I0409 22:51:02.674574  4596 net.cpp:122] Setting up conv4
I0409 22:51:02.674592  4596 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0409 22:51:02.674597  4596 net.cpp:137] Memory required for data: 969521152
I0409 22:51:02.674607  4596 layer_factory.hpp:77] Creating layer relu4
I0409 22:51:02.674616  4596 net.cpp:84] Creating Layer relu4
I0409 22:51:02.674621  4596 net.cpp:406] relu4 <- conv4
I0409 22:51:02.674628  4596 net.cpp:367] relu4 -> conv4 (in-place)
I0409 22:51:02.674968  4596 net.cpp:122] Setting up relu4
I0409 22:51:02.674978  4596 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0409 22:51:02.674980  4596 net.cpp:137] Memory required for data: 1002747904
I0409 22:51:02.674985  4596 layer_factory.hpp:77] Creating layer conv5
I0409 22:51:02.674996  4596 net.cpp:84] Creating Layer conv5
I0409 22:51:02.675000  4596 net.cpp:406] conv5 <- conv4
I0409 22:51:02.675025  4596 net.cpp:380] conv5 -> conv5
I0409 22:51:02.683524  4596 net.cpp:122] Setting up conv5
I0409 22:51:02.683540  4596 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0409 22:51:02.683544  4596 net.cpp:137] Memory required for data: 1024899072
I0409 22:51:02.683557  4596 layer_factory.hpp:77] Creating layer relu5
I0409 22:51:02.683566  4596 net.cpp:84] Creating Layer relu5
I0409 22:51:02.683570  4596 net.cpp:406] relu5 <- conv5
I0409 22:51:02.683576  4596 net.cpp:367] relu5 -> conv5 (in-place)
I0409 22:51:02.684154  4596 net.cpp:122] Setting up relu5
I0409 22:51:02.684165  4596 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0409 22:51:02.684170  4596 net.cpp:137] Memory required for data: 1047050240
I0409 22:51:02.684173  4596 layer_factory.hpp:77] Creating layer pool5
I0409 22:51:02.684180  4596 net.cpp:84] Creating Layer pool5
I0409 22:51:02.684185  4596 net.cpp:406] pool5 <- conv5
I0409 22:51:02.684190  4596 net.cpp:380] pool5 -> pool5
I0409 22:51:02.684227  4596 net.cpp:122] Setting up pool5
I0409 22:51:02.684233  4596 net.cpp:129] Top shape: 128 256 6 6 (1179648)
I0409 22:51:02.684237  4596 net.cpp:137] Memory required for data: 1051768832
I0409 22:51:02.684240  4596 layer_factory.hpp:77] Creating layer fc6
I0409 22:51:02.684250  4596 net.cpp:84] Creating Layer fc6
I0409 22:51:02.684254  4596 net.cpp:406] fc6 <- pool5
I0409 22:51:02.684259  4596 net.cpp:380] fc6 -> fc6
I0409 22:51:02.731528  4596 net.cpp:122] Setting up fc6
I0409 22:51:02.731549  4596 net.cpp:129] Top shape: 128 512 (65536)
I0409 22:51:02.731552  4596 net.cpp:137] Memory required for data: 1052030976
I0409 22:51:02.731562  4596 layer_factory.hpp:77] Creating layer relu6
I0409 22:51:02.731571  4596 net.cpp:84] Creating Layer relu6
I0409 22:51:02.731575  4596 net.cpp:406] relu6 <- fc6
I0409 22:51:02.731582  4596 net.cpp:367] relu6 -> fc6 (in-place)
I0409 22:51:02.732209  4596 net.cpp:122] Setting up relu6
I0409 22:51:02.732218  4596 net.cpp:129] Top shape: 128 512 (65536)
I0409 22:51:02.732223  4596 net.cpp:137] Memory required for data: 1052293120
I0409 22:51:02.732225  4596 layer_factory.hpp:77] Creating layer drop6
I0409 22:51:02.732234  4596 net.cpp:84] Creating Layer drop6
I0409 22:51:02.732236  4596 net.cpp:406] drop6 <- fc6
I0409 22:51:02.732244  4596 net.cpp:367] drop6 -> fc6 (in-place)
I0409 22:51:02.732271  4596 net.cpp:122] Setting up drop6
I0409 22:51:02.732276  4596 net.cpp:129] Top shape: 128 512 (65536)
I0409 22:51:02.732280  4596 net.cpp:137] Memory required for data: 1052555264
I0409 22:51:02.732283  4596 layer_factory.hpp:77] Creating layer fc8
I0409 22:51:02.732290  4596 net.cpp:84] Creating Layer fc8
I0409 22:51:02.732293  4596 net.cpp:406] fc8 <- fc6
I0409 22:51:02.732300  4596 net.cpp:380] fc8 -> fc8
I0409 22:51:02.733253  4596 net.cpp:122] Setting up fc8
I0409 22:51:02.733259  4596 net.cpp:129] Top shape: 128 196 (25088)
I0409 22:51:02.733263  4596 net.cpp:137] Memory required for data: 1052655616
I0409 22:51:02.733268  4596 layer_factory.hpp:77] Creating layer loss
I0409 22:51:02.733274  4596 net.cpp:84] Creating Layer loss
I0409 22:51:02.733278  4596 net.cpp:406] loss <- fc8
I0409 22:51:02.733281  4596 net.cpp:406] loss <- label
I0409 22:51:02.733290  4596 net.cpp:380] loss -> loss
I0409 22:51:02.733299  4596 layer_factory.hpp:77] Creating layer loss
I0409 22:51:02.733865  4596 net.cpp:122] Setting up loss
I0409 22:51:02.733875  4596 net.cpp:129] Top shape: (1)
I0409 22:51:02.733877  4596 net.cpp:132]     with loss weight 1
I0409 22:51:02.733894  4596 net.cpp:137] Memory required for data: 1052655620
I0409 22:51:02.733898  4596 net.cpp:198] loss needs backward computation.
I0409 22:51:02.733906  4596 net.cpp:198] fc8 needs backward computation.
I0409 22:51:02.733909  4596 net.cpp:198] drop6 needs backward computation.
I0409 22:51:02.733912  4596 net.cpp:198] relu6 needs backward computation.
I0409 22:51:02.733916  4596 net.cpp:198] fc6 needs backward computation.
I0409 22:51:02.733919  4596 net.cpp:198] pool5 needs backward computation.
I0409 22:51:02.733923  4596 net.cpp:198] relu5 needs backward computation.
I0409 22:51:02.733944  4596 net.cpp:198] conv5 needs backward computation.
I0409 22:51:02.733948  4596 net.cpp:198] relu4 needs backward computation.
I0409 22:51:02.733952  4596 net.cpp:198] conv4 needs backward computation.
I0409 22:51:02.733975  4596 net.cpp:198] relu3 needs backward computation.
I0409 22:51:02.733978  4596 net.cpp:198] conv3 needs backward computation.
I0409 22:51:02.733983  4596 net.cpp:198] pool2 needs backward computation.
I0409 22:51:02.733986  4596 net.cpp:198] norm2 needs backward computation.
I0409 22:51:02.733990  4596 net.cpp:198] relu2 needs backward computation.
I0409 22:51:02.733994  4596 net.cpp:198] conv2 needs backward computation.
I0409 22:51:02.733997  4596 net.cpp:198] pool1 needs backward computation.
I0409 22:51:02.734001  4596 net.cpp:198] norm1 needs backward computation.
I0409 22:51:02.734004  4596 net.cpp:198] relu1 needs backward computation.
I0409 22:51:02.734009  4596 net.cpp:198] conv1 needs backward computation.
I0409 22:51:02.734012  4596 net.cpp:200] train-data does not need backward computation.
I0409 22:51:02.734015  4596 net.cpp:242] This network produces output loss
I0409 22:51:02.734030  4596 net.cpp:255] Network initialization done.
I0409 22:51:03.121142  4596 solver.cpp:172] Creating test net (#0) specified by net file: train_val.prototxt
I0409 22:51:03.121227  4596 net.cpp:294] The NetState phase (1) differed from the phase (0) specified by a rule in layer train-data
I0409 22:51:03.121568  4596 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: 512
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"
}
I0409 22:51:03.121783  4596 layer_factory.hpp:77] Creating layer val-data
I0409 22:51:03.534797  4596 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/val_db
I0409 22:51:03.535799  4596 net.cpp:84] Creating Layer val-data
I0409 22:51:03.535833  4596 net.cpp:380] val-data -> data
I0409 22:51:03.535858  4596 net.cpp:380] val-data -> label
I0409 22:51:03.535876  4596 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto
I0409 22:51:03.593775  4596 data_layer.cpp:45] output data size: 32,3,227,227
I0409 22:51:03.640612  4596 net.cpp:122] Setting up val-data
I0409 22:51:03.640638  4596 net.cpp:129] Top shape: 32 3 227 227 (4946784)
I0409 22:51:03.640645  4596 net.cpp:129] Top shape: 32 (32)
I0409 22:51:03.640648  4596 net.cpp:137] Memory required for data: 19787264
I0409 22:51:03.640656  4596 layer_factory.hpp:77] Creating layer label_val-data_1_split
I0409 22:51:03.640671  4596 net.cpp:84] Creating Layer label_val-data_1_split
I0409 22:51:03.640676  4596 net.cpp:406] label_val-data_1_split <- label
I0409 22:51:03.640683  4596 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_0
I0409 22:51:03.640695  4596 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_1
I0409 22:51:03.640756  4596 net.cpp:122] Setting up label_val-data_1_split
I0409 22:51:03.640763  4596 net.cpp:129] Top shape: 32 (32)
I0409 22:51:03.640769  4596 net.cpp:129] Top shape: 32 (32)
I0409 22:51:03.640772  4596 net.cpp:137] Memory required for data: 19787520
I0409 22:51:03.640776  4596 layer_factory.hpp:77] Creating layer conv1
I0409 22:51:03.640790  4596 net.cpp:84] Creating Layer conv1
I0409 22:51:03.640794  4596 net.cpp:406] conv1 <- data
I0409 22:51:03.640801  4596 net.cpp:380] conv1 -> conv1
I0409 22:51:03.643462  4596 net.cpp:122] Setting up conv1
I0409 22:51:03.643476  4596 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0409 22:51:03.643481  4596 net.cpp:137] Memory required for data: 56958720
I0409 22:51:03.643492  4596 layer_factory.hpp:77] Creating layer relu1
I0409 22:51:03.643501  4596 net.cpp:84] Creating Layer relu1
I0409 22:51:03.643527  4596 net.cpp:406] relu1 <- conv1
I0409 22:51:03.643534  4596 net.cpp:367] relu1 -> conv1 (in-place)
I0409 22:51:03.644080  4596 net.cpp:122] Setting up relu1
I0409 22:51:03.644093  4596 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0409 22:51:03.644098  4596 net.cpp:137] Memory required for data: 94129920
I0409 22:51:03.644102  4596 layer_factory.hpp:77] Creating layer norm1
I0409 22:51:03.644112  4596 net.cpp:84] Creating Layer norm1
I0409 22:51:03.644116  4596 net.cpp:406] norm1 <- conv1
I0409 22:51:03.644124  4596 net.cpp:380] norm1 -> norm1
I0409 22:51:03.644510  4596 net.cpp:122] Setting up norm1
I0409 22:51:03.644521  4596 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0409 22:51:03.644526  4596 net.cpp:137] Memory required for data: 131301120
I0409 22:51:03.644529  4596 layer_factory.hpp:77] Creating layer pool1
I0409 22:51:03.644537  4596 net.cpp:84] Creating Layer pool1
I0409 22:51:03.644541  4596 net.cpp:406] pool1 <- norm1
I0409 22:51:03.644547  4596 net.cpp:380] pool1 -> pool1
I0409 22:51:03.644583  4596 net.cpp:122] Setting up pool1
I0409 22:51:03.644589  4596 net.cpp:129] Top shape: 32 96 27 27 (2239488)
I0409 22:51:03.644593  4596 net.cpp:137] Memory required for data: 140259072
I0409 22:51:03.644598  4596 layer_factory.hpp:77] Creating layer conv2
I0409 22:51:03.644606  4596 net.cpp:84] Creating Layer conv2
I0409 22:51:03.644611  4596 net.cpp:406] conv2 <- pool1
I0409 22:51:03.644618  4596 net.cpp:380] conv2 -> conv2
I0409 22:51:03.662106  4596 net.cpp:122] Setting up conv2
I0409 22:51:03.662128  4596 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0409 22:51:03.662133  4596 net.cpp:137] Memory required for data: 164146944
I0409 22:51:03.662147  4596 layer_factory.hpp:77] Creating layer relu2
I0409 22:51:03.662156  4596 net.cpp:84] Creating Layer relu2
I0409 22:51:03.662161  4596 net.cpp:406] relu2 <- conv2
I0409 22:51:03.662168  4596 net.cpp:367] relu2 -> conv2 (in-place)
I0409 22:51:03.662781  4596 net.cpp:122] Setting up relu2
I0409 22:51:03.662792  4596 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0409 22:51:03.662797  4596 net.cpp:137] Memory required for data: 188034816
I0409 22:51:03.662802  4596 layer_factory.hpp:77] Creating layer norm2
I0409 22:51:03.662813  4596 net.cpp:84] Creating Layer norm2
I0409 22:51:03.662818  4596 net.cpp:406] norm2 <- conv2
I0409 22:51:03.662827  4596 net.cpp:380] norm2 -> norm2
I0409 22:51:03.663453  4596 net.cpp:122] Setting up norm2
I0409 22:51:03.663465  4596 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0409 22:51:03.663468  4596 net.cpp:137] Memory required for data: 211922688
I0409 22:51:03.663473  4596 layer_factory.hpp:77] Creating layer pool2
I0409 22:51:03.663480  4596 net.cpp:84] Creating Layer pool2
I0409 22:51:03.663486  4596 net.cpp:406] pool2 <- norm2
I0409 22:51:03.663493  4596 net.cpp:380] pool2 -> pool2
I0409 22:51:03.663530  4596 net.cpp:122] Setting up pool2
I0409 22:51:03.663537  4596 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0409 22:51:03.663542  4596 net.cpp:137] Memory required for data: 217460480
I0409 22:51:03.663545  4596 layer_factory.hpp:77] Creating layer conv3
I0409 22:51:03.663556  4596 net.cpp:84] Creating Layer conv3
I0409 22:51:03.663560  4596 net.cpp:406] conv3 <- pool2
I0409 22:51:03.663566  4596 net.cpp:380] conv3 -> conv3
I0409 22:51:03.680588  4596 net.cpp:122] Setting up conv3
I0409 22:51:03.680608  4596 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0409 22:51:03.680611  4596 net.cpp:137] Memory required for data: 225767168
I0409 22:51:03.680625  4596 layer_factory.hpp:77] Creating layer relu3
I0409 22:51:03.680634  4596 net.cpp:84] Creating Layer relu3
I0409 22:51:03.680640  4596 net.cpp:406] relu3 <- conv3
I0409 22:51:03.680647  4596 net.cpp:367] relu3 -> conv3 (in-place)
I0409 22:51:03.682452  4596 net.cpp:122] Setting up relu3
I0409 22:51:03.682463  4596 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0409 22:51:03.682467  4596 net.cpp:137] Memory required for data: 234073856
I0409 22:51:03.682472  4596 layer_factory.hpp:77] Creating layer conv4
I0409 22:51:03.682502  4596 net.cpp:84] Creating Layer conv4
I0409 22:51:03.682507  4596 net.cpp:406] conv4 <- conv3
I0409 22:51:03.682515  4596 net.cpp:380] conv4 -> conv4
I0409 22:51:03.700093  4596 net.cpp:122] Setting up conv4
I0409 22:51:03.700111  4596 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0409 22:51:03.700115  4596 net.cpp:137] Memory required for data: 242380544
I0409 22:51:03.700124  4596 layer_factory.hpp:77] Creating layer relu4
I0409 22:51:03.700136  4596 net.cpp:84] Creating Layer relu4
I0409 22:51:03.700142  4596 net.cpp:406] relu4 <- conv4
I0409 22:51:03.700148  4596 net.cpp:367] relu4 -> conv4 (in-place)
I0409 22:51:03.700721  4596 net.cpp:122] Setting up relu4
I0409 22:51:03.700731  4596 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0409 22:51:03.700734  4596 net.cpp:137] Memory required for data: 250687232
I0409 22:51:03.700739  4596 layer_factory.hpp:77] Creating layer conv5
I0409 22:51:03.700750  4596 net.cpp:84] Creating Layer conv5
I0409 22:51:03.700755  4596 net.cpp:406] conv5 <- conv4
I0409 22:51:03.700763  4596 net.cpp:380] conv5 -> conv5
I0409 22:51:03.710196  4596 net.cpp:122] Setting up conv5
I0409 22:51:03.710212  4596 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0409 22:51:03.710217  4596 net.cpp:137] Memory required for data: 256225024
I0409 22:51:03.710229  4596 layer_factory.hpp:77] Creating layer relu5
I0409 22:51:03.710237  4596 net.cpp:84] Creating Layer relu5
I0409 22:51:03.710242  4596 net.cpp:406] relu5 <- conv5
I0409 22:51:03.710248  4596 net.cpp:367] relu5 -> conv5 (in-place)
I0409 22:51:03.710798  4596 net.cpp:122] Setting up relu5
I0409 22:51:03.710809  4596 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0409 22:51:03.710813  4596 net.cpp:137] Memory required for data: 261762816
I0409 22:51:03.710817  4596 layer_factory.hpp:77] Creating layer pool5
I0409 22:51:03.710829  4596 net.cpp:84] Creating Layer pool5
I0409 22:51:03.710834  4596 net.cpp:406] pool5 <- conv5
I0409 22:51:03.710839  4596 net.cpp:380] pool5 -> pool5
I0409 22:51:03.710881  4596 net.cpp:122] Setting up pool5
I0409 22:51:03.710888  4596 net.cpp:129] Top shape: 32 256 6 6 (294912)
I0409 22:51:03.710892  4596 net.cpp:137] Memory required for data: 262942464
I0409 22:51:03.710896  4596 layer_factory.hpp:77] Creating layer fc6
I0409 22:51:03.710902  4596 net.cpp:84] Creating Layer fc6
I0409 22:51:03.710906  4596 net.cpp:406] fc6 <- pool5
I0409 22:51:03.710916  4596 net.cpp:380] fc6 -> fc6
I0409 22:51:03.760459  4596 net.cpp:122] Setting up fc6
I0409 22:51:03.760483  4596 net.cpp:129] Top shape: 32 512 (16384)
I0409 22:51:03.760485  4596 net.cpp:137] Memory required for data: 263008000
I0409 22:51:03.760496  4596 layer_factory.hpp:77] Creating layer relu6
I0409 22:51:03.760504  4596 net.cpp:84] Creating Layer relu6
I0409 22:51:03.760509  4596 net.cpp:406] relu6 <- fc6
I0409 22:51:03.760516  4596 net.cpp:367] relu6 -> fc6 (in-place)
I0409 22:51:03.761193  4596 net.cpp:122] Setting up relu6
I0409 22:51:03.761201  4596 net.cpp:129] Top shape: 32 512 (16384)
I0409 22:51:03.761205  4596 net.cpp:137] Memory required for data: 263073536
I0409 22:51:03.761209  4596 layer_factory.hpp:77] Creating layer drop6
I0409 22:51:03.761216  4596 net.cpp:84] Creating Layer drop6
I0409 22:51:03.761220  4596 net.cpp:406] drop6 <- fc6
I0409 22:51:03.761226  4596 net.cpp:367] drop6 -> fc6 (in-place)
I0409 22:51:03.761252  4596 net.cpp:122] Setting up drop6
I0409 22:51:03.761260  4596 net.cpp:129] Top shape: 32 512 (16384)
I0409 22:51:03.761262  4596 net.cpp:137] Memory required for data: 263139072
I0409 22:51:03.761266  4596 layer_factory.hpp:77] Creating layer fc8
I0409 22:51:03.761273  4596 net.cpp:84] Creating Layer fc8
I0409 22:51:03.761277  4596 net.cpp:406] fc8 <- fc6
I0409 22:51:03.761286  4596 net.cpp:380] fc8 -> fc8
I0409 22:51:03.762336  4596 net.cpp:122] Setting up fc8
I0409 22:51:03.762342  4596 net.cpp:129] Top shape: 32 196 (6272)
I0409 22:51:03.762346  4596 net.cpp:137] Memory required for data: 263164160
I0409 22:51:03.762352  4596 layer_factory.hpp:77] Creating layer fc8_fc8_0_split
I0409 22:51:03.762359  4596 net.cpp:84] Creating Layer fc8_fc8_0_split
I0409 22:51:03.762382  4596 net.cpp:406] fc8_fc8_0_split <- fc8
I0409 22:51:03.762389  4596 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_0
I0409 22:51:03.762399  4596 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_1
I0409 22:51:03.762432  4596 net.cpp:122] Setting up fc8_fc8_0_split
I0409 22:51:03.762439  4596 net.cpp:129] Top shape: 32 196 (6272)
I0409 22:51:03.762442  4596 net.cpp:129] Top shape: 32 196 (6272)
I0409 22:51:03.762445  4596 net.cpp:137] Memory required for data: 263214336
I0409 22:51:03.762449  4596 layer_factory.hpp:77] Creating layer accuracy
I0409 22:51:03.762457  4596 net.cpp:84] Creating Layer accuracy
I0409 22:51:03.762461  4596 net.cpp:406] accuracy <- fc8_fc8_0_split_0
I0409 22:51:03.762466  4596 net.cpp:406] accuracy <- label_val-data_1_split_0
I0409 22:51:03.762471  4596 net.cpp:380] accuracy -> accuracy
I0409 22:51:03.762480  4596 net.cpp:122] Setting up accuracy
I0409 22:51:03.762485  4596 net.cpp:129] Top shape: (1)
I0409 22:51:03.762487  4596 net.cpp:137] Memory required for data: 263214340
I0409 22:51:03.762490  4596 layer_factory.hpp:77] Creating layer loss
I0409 22:51:03.762497  4596 net.cpp:84] Creating Layer loss
I0409 22:51:03.762501  4596 net.cpp:406] loss <- fc8_fc8_0_split_1
I0409 22:51:03.762506  4596 net.cpp:406] loss <- label_val-data_1_split_1
I0409 22:51:03.762511  4596 net.cpp:380] loss -> loss
I0409 22:51:03.762518  4596 layer_factory.hpp:77] Creating layer loss
I0409 22:51:03.763350  4596 net.cpp:122] Setting up loss
I0409 22:51:03.763360  4596 net.cpp:129] Top shape: (1)
I0409 22:51:03.763363  4596 net.cpp:132]     with loss weight 1
I0409 22:51:03.763375  4596 net.cpp:137] Memory required for data: 263214344
I0409 22:51:03.763378  4596 net.cpp:198] loss needs backward computation.
I0409 22:51:03.763383  4596 net.cpp:200] accuracy does not need backward computation.
I0409 22:51:03.763388  4596 net.cpp:198] fc8_fc8_0_split needs backward computation.
I0409 22:51:03.763391  4596 net.cpp:198] fc8 needs backward computation.
I0409 22:51:03.763396  4596 net.cpp:198] drop6 needs backward computation.
I0409 22:51:03.763399  4596 net.cpp:198] relu6 needs backward computation.
I0409 22:51:03.763402  4596 net.cpp:198] fc6 needs backward computation.
I0409 22:51:03.763406  4596 net.cpp:198] pool5 needs backward computation.
I0409 22:51:03.763411  4596 net.cpp:198] relu5 needs backward computation.
I0409 22:51:03.763413  4596 net.cpp:198] conv5 needs backward computation.
I0409 22:51:03.763417  4596 net.cpp:198] relu4 needs backward computation.
I0409 22:51:03.763422  4596 net.cpp:198] conv4 needs backward computation.
I0409 22:51:03.763425  4596 net.cpp:198] relu3 needs backward computation.
I0409 22:51:03.763429  4596 net.cpp:198] conv3 needs backward computation.
I0409 22:51:03.763433  4596 net.cpp:198] pool2 needs backward computation.
I0409 22:51:03.763438  4596 net.cpp:198] norm2 needs backward computation.
I0409 22:51:03.763442  4596 net.cpp:198] relu2 needs backward computation.
I0409 22:51:03.763445  4596 net.cpp:198] conv2 needs backward computation.
I0409 22:51:03.763449  4596 net.cpp:198] pool1 needs backward computation.
I0409 22:51:03.763454  4596 net.cpp:198] norm1 needs backward computation.
I0409 22:51:03.763458  4596 net.cpp:198] relu1 needs backward computation.
I0409 22:51:03.763463  4596 net.cpp:198] conv1 needs backward computation.
I0409 22:51:03.763466  4596 net.cpp:200] label_val-data_1_split does not need backward computation.
I0409 22:51:03.763470  4596 net.cpp:200] val-data does not need backward computation.
I0409 22:51:03.763473  4596 net.cpp:242] This network produces output accuracy
I0409 22:51:03.763478  4596 net.cpp:242] This network produces output loss
I0409 22:51:03.763494  4596 net.cpp:255] Network initialization done.
I0409 22:51:03.763584  4596 solver.cpp:56] Solver scaffolding done.
I0409 22:51:03.764000  4596 caffe.cpp:248] Starting Optimization
I0409 22:51:03.764009  4596 solver.cpp:272] Solving
I0409 22:51:03.764014  4596 solver.cpp:273] Learning Rate Policy: exp
I0409 22:51:03.768204  4596 solver.cpp:330] Iteration 0, Testing net (#0)
I0409 22:51:03.768224  4596 net.cpp:676] Ignoring source layer train-data
I0409 22:51:03.772044  4596 blocking_queue.cpp:49] Waiting for data
I0409 22:51:08.312523  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:51:08.356509  4596 solver.cpp:397]     Test net output #0: accuracy = 0.00796569
I0409 22:51:08.356559  4596 solver.cpp:397]     Test net output #1: loss = 5.27909 (* 1 = 5.27909 loss)
I0409 22:51:08.443028  4596 solver.cpp:218] Iteration 0 (-5.20556e-30 iter/s, 4.67886s/12 iters), loss = 5.2794
I0409 22:51:08.443075  4596 solver.cpp:237]     Train net output #0: loss = 5.2794 (* 1 = 5.2794 loss)
I0409 22:51:08.443102  4596 sgd_solver.cpp:105] Iteration 0, lr = 0.01
I0409 22:51:12.399868  4596 solver.cpp:218] Iteration 12 (3.03285 iter/s, 3.95667s/12 iters), loss = 5.27637
I0409 22:51:12.399920  4596 solver.cpp:237]     Train net output #0: loss = 5.27637 (* 1 = 5.27637 loss)
I0409 22:51:12.399932  4596 sgd_solver.cpp:105] Iteration 12, lr = 0.00997626
I0409 22:51:17.288866  4596 solver.cpp:218] Iteration 24 (2.45459 iter/s, 4.88881s/12 iters), loss = 5.2797
I0409 22:51:17.288924  4596 solver.cpp:237]     Train net output #0: loss = 5.2797 (* 1 = 5.2797 loss)
I0409 22:51:17.288938  4596 sgd_solver.cpp:105] Iteration 24, lr = 0.00995257
I0409 22:51:22.153409  4596 solver.cpp:218] Iteration 36 (2.46693 iter/s, 4.86435s/12 iters), loss = 5.27581
I0409 22:51:22.153462  4596 solver.cpp:237]     Train net output #0: loss = 5.27581 (* 1 = 5.27581 loss)
I0409 22:51:22.153473  4596 sgd_solver.cpp:105] Iteration 36, lr = 0.00992894
I0409 22:51:27.120139  4596 solver.cpp:218] Iteration 48 (2.41617 iter/s, 4.96654s/12 iters), loss = 5.28215
I0409 22:51:27.120189  4596 solver.cpp:237]     Train net output #0: loss = 5.28215 (* 1 = 5.28215 loss)
I0409 22:51:27.120201  4596 sgd_solver.cpp:105] Iteration 48, lr = 0.00990537
I0409 22:51:32.022330  4596 solver.cpp:218] Iteration 60 (2.44798 iter/s, 4.902s/12 iters), loss = 5.28331
I0409 22:51:32.022514  4596 solver.cpp:237]     Train net output #0: loss = 5.28331 (* 1 = 5.28331 loss)
I0409 22:51:32.022527  4596 sgd_solver.cpp:105] Iteration 60, lr = 0.00988185
I0409 22:51:36.903270  4596 solver.cpp:218] Iteration 72 (2.4587 iter/s, 4.88063s/12 iters), loss = 5.28427
I0409 22:51:36.903328  4596 solver.cpp:237]     Train net output #0: loss = 5.28427 (* 1 = 5.28427 loss)
I0409 22:51:36.903340  4596 sgd_solver.cpp:105] Iteration 72, lr = 0.00985839
I0409 22:51:41.767226  4596 solver.cpp:218] Iteration 84 (2.46722 iter/s, 4.86377s/12 iters), loss = 5.28098
I0409 22:51:41.767287  4596 solver.cpp:237]     Train net output #0: loss = 5.28098 (* 1 = 5.28098 loss)
I0409 22:51:41.767297  4596 sgd_solver.cpp:105] Iteration 84, lr = 0.00983498
I0409 22:51:46.950291  4596 solver.cpp:218] Iteration 96 (2.31532 iter/s, 5.18287s/12 iters), loss = 5.28838
I0409 22:51:46.950340  4596 solver.cpp:237]     Train net output #0: loss = 5.28838 (* 1 = 5.28838 loss)
I0409 22:51:46.950351  4596 sgd_solver.cpp:105] Iteration 96, lr = 0.00981163
I0409 22:51:48.671231  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:51:48.984671  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_102.caffemodel
I0409 22:51:49.418983  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_102.solverstate
I0409 22:51:49.708441  4596 solver.cpp:330] Iteration 102, Testing net (#0)
I0409 22:51:49.708464  4596 net.cpp:676] Ignoring source layer train-data
I0409 22:51:54.225001  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:51:54.305276  4596 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0409 22:51:54.305323  4596 solver.cpp:397]     Test net output #1: loss = 5.2779 (* 1 = 5.2779 loss)
I0409 22:51:56.144635  4596 solver.cpp:218] Iteration 108 (1.30519 iter/s, 9.19405s/12 iters), loss = 5.27863
I0409 22:51:56.144687  4596 solver.cpp:237]     Train net output #0: loss = 5.27863 (* 1 = 5.27863 loss)
I0409 22:51:56.144701  4596 sgd_solver.cpp:105] Iteration 108, lr = 0.00978834
I0409 22:52:01.120404  4596 solver.cpp:218] Iteration 120 (2.41178 iter/s, 4.97559s/12 iters), loss = 5.27123
I0409 22:52:01.120446  4596 solver.cpp:237]     Train net output #0: loss = 5.27123 (* 1 = 5.27123 loss)
I0409 22:52:01.120456  4596 sgd_solver.cpp:105] Iteration 120, lr = 0.0097651
I0409 22:52:06.077001  4596 solver.cpp:218] Iteration 132 (2.42111 iter/s, 4.95641s/12 iters), loss = 5.23356
I0409 22:52:06.077188  4596 solver.cpp:237]     Train net output #0: loss = 5.23356 (* 1 = 5.23356 loss)
I0409 22:52:06.077204  4596 sgd_solver.cpp:105] Iteration 132, lr = 0.00974192
I0409 22:52:10.965137  4596 solver.cpp:218] Iteration 144 (2.45508 iter/s, 4.88782s/12 iters), loss = 5.24654
I0409 22:52:10.965181  4596 solver.cpp:237]     Train net output #0: loss = 5.24654 (* 1 = 5.24654 loss)
I0409 22:52:10.965190  4596 sgd_solver.cpp:105] Iteration 144, lr = 0.00971879
I0409 22:52:15.910324  4596 solver.cpp:218] Iteration 156 (2.42669 iter/s, 4.94501s/12 iters), loss = 5.20866
I0409 22:52:15.910369  4596 solver.cpp:237]     Train net output #0: loss = 5.20866 (* 1 = 5.20866 loss)
I0409 22:52:15.910378  4596 sgd_solver.cpp:105] Iteration 156, lr = 0.00969571
I0409 22:52:21.116240  4596 solver.cpp:218] Iteration 168 (2.30515 iter/s, 5.20573s/12 iters), loss = 5.19931
I0409 22:52:21.116286  4596 solver.cpp:237]     Train net output #0: loss = 5.19931 (* 1 = 5.19931 loss)
I0409 22:52:21.116295  4596 sgd_solver.cpp:105] Iteration 168, lr = 0.00967269
I0409 22:52:26.069054  4596 solver.cpp:218] Iteration 180 (2.42295 iter/s, 4.95263s/12 iters), loss = 5.13785
I0409 22:52:26.069108  4596 solver.cpp:237]     Train net output #0: loss = 5.13785 (* 1 = 5.13785 loss)
I0409 22:52:26.069119  4596 sgd_solver.cpp:105] Iteration 180, lr = 0.00964973
I0409 22:52:30.995985  4596 solver.cpp:218] Iteration 192 (2.43568 iter/s, 4.92675s/12 iters), loss = 5.23957
I0409 22:52:30.996021  4596 solver.cpp:237]     Train net output #0: loss = 5.23957 (* 1 = 5.23957 loss)
I0409 22:52:30.996031  4596 sgd_solver.cpp:105] Iteration 192, lr = 0.00962682
I0409 22:52:34.831178  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:52:35.518887  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_204.caffemodel
I0409 22:52:37.685012  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_204.solverstate
I0409 22:52:39.170714  4596 solver.cpp:330] Iteration 204, Testing net (#0)
I0409 22:52:39.170743  4596 net.cpp:676] Ignoring source layer train-data
I0409 22:52:43.577030  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:52:43.698316  4596 solver.cpp:397]     Test net output #0: accuracy = 0.00796569
I0409 22:52:43.698364  4596 solver.cpp:397]     Test net output #1: loss = 5.17752 (* 1 = 5.17752 loss)
I0409 22:52:43.779958  4596 solver.cpp:218] Iteration 204 (0.938702 iter/s, 12.7836s/12 iters), loss = 5.10245
I0409 22:52:43.780019  4596 solver.cpp:237]     Train net output #0: loss = 5.10245 (* 1 = 5.10245 loss)
I0409 22:52:43.780032  4596 sgd_solver.cpp:105] Iteration 204, lr = 0.00960396
I0409 22:52:48.022634  4596 solver.cpp:218] Iteration 216 (2.82852 iter/s, 4.2425s/12 iters), loss = 5.17723
I0409 22:52:48.022692  4596 solver.cpp:237]     Train net output #0: loss = 5.17723 (* 1 = 5.17723 loss)
I0409 22:52:48.022704  4596 sgd_solver.cpp:105] Iteration 216, lr = 0.00958116
I0409 22:52:52.952288  4596 solver.cpp:218] Iteration 228 (2.43434 iter/s, 4.92946s/12 iters), loss = 5.17927
I0409 22:52:52.952342  4596 solver.cpp:237]     Train net output #0: loss = 5.17927 (* 1 = 5.17927 loss)
I0409 22:52:52.952353  4596 sgd_solver.cpp:105] Iteration 228, lr = 0.00955841
I0409 22:52:57.955971  4596 solver.cpp:218] Iteration 240 (2.39832 iter/s, 5.00349s/12 iters), loss = 5.18586
I0409 22:52:57.956025  4596 solver.cpp:237]     Train net output #0: loss = 5.18586 (* 1 = 5.18586 loss)
I0409 22:52:57.956038  4596 sgd_solver.cpp:105] Iteration 240, lr = 0.00953572
I0409 22:53:02.952136  4596 solver.cpp:218] Iteration 252 (2.40193 iter/s, 4.99598s/12 iters), loss = 5.09722
I0409 22:53:02.952181  4596 solver.cpp:237]     Train net output #0: loss = 5.09722 (* 1 = 5.09722 loss)
I0409 22:53:02.952190  4596 sgd_solver.cpp:105] Iteration 252, lr = 0.00951308
I0409 22:53:08.288318  4596 solver.cpp:218] Iteration 264 (2.24888 iter/s, 5.33598s/12 iters), loss = 5.19348
I0409 22:53:08.288478  4596 solver.cpp:237]     Train net output #0: loss = 5.19348 (* 1 = 5.19348 loss)
I0409 22:53:08.288491  4596 sgd_solver.cpp:105] Iteration 264, lr = 0.00949049
I0409 22:53:13.200784  4596 solver.cpp:218] Iteration 276 (2.44291 iter/s, 4.91217s/12 iters), loss = 5.19623
I0409 22:53:13.200834  4596 solver.cpp:237]     Train net output #0: loss = 5.19623 (* 1 = 5.19623 loss)
I0409 22:53:13.200845  4596 sgd_solver.cpp:105] Iteration 276, lr = 0.00946796
I0409 22:53:18.106283  4596 solver.cpp:218] Iteration 288 (2.44633 iter/s, 4.90531s/12 iters), loss = 5.00255
I0409 22:53:18.106330  4596 solver.cpp:237]     Train net output #0: loss = 5.00255 (* 1 = 5.00255 loss)
I0409 22:53:18.106340  4596 sgd_solver.cpp:105] Iteration 288, lr = 0.00944548
I0409 22:53:23.013758  4596 solver.cpp:218] Iteration 300 (2.44534 iter/s, 4.90729s/12 iters), loss = 5.1587
I0409 22:53:23.013803  4596 solver.cpp:237]     Train net output #0: loss = 5.1587 (* 1 = 5.1587 loss)
I0409 22:53:23.013813  4596 sgd_solver.cpp:105] Iteration 300, lr = 0.00942305
I0409 22:53:23.995183  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:53:25.046104  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_306.caffemodel
I0409 22:53:26.257836  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_306.solverstate
I0409 22:53:26.800287  4596 solver.cpp:330] Iteration 306, Testing net (#0)
I0409 22:53:26.800307  4596 net.cpp:676] Ignoring source layer train-data
I0409 22:53:31.184494  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:53:31.345526  4596 solver.cpp:397]     Test net output #0: accuracy = 0.0110294
I0409 22:53:31.345563  4596 solver.cpp:397]     Test net output #1: loss = 5.1229 (* 1 = 5.1229 loss)
I0409 22:53:33.201997  4596 solver.cpp:218] Iteration 312 (1.17787 iter/s, 10.1879s/12 iters), loss = 5.03696
I0409 22:53:33.202064  4596 solver.cpp:237]     Train net output #0: loss = 5.03696 (* 1 = 5.03696 loss)
I0409 22:53:33.202078  4596 sgd_solver.cpp:105] Iteration 312, lr = 0.00940068
I0409 22:53:38.125820  4596 solver.cpp:218] Iteration 324 (2.43723 iter/s, 4.92362s/12 iters), loss = 5.13344
I0409 22:53:38.125867  4596 solver.cpp:237]     Train net output #0: loss = 5.13344 (* 1 = 5.13344 loss)
I0409 22:53:38.125877  4596 sgd_solver.cpp:105] Iteration 324, lr = 0.00937836
I0409 22:53:43.063197  4596 solver.cpp:218] Iteration 336 (2.43053 iter/s, 4.93719s/12 iters), loss = 5.13843
I0409 22:53:43.063308  4596 solver.cpp:237]     Train net output #0: loss = 5.13843 (* 1 = 5.13843 loss)
I0409 22:53:43.063319  4596 sgd_solver.cpp:105] Iteration 336, lr = 0.0093561
I0409 22:53:47.982940  4596 solver.cpp:218] Iteration 348 (2.43927 iter/s, 4.9195s/12 iters), loss = 5.03958
I0409 22:53:47.982987  4596 solver.cpp:237]     Train net output #0: loss = 5.03958 (* 1 = 5.03958 loss)
I0409 22:53:47.982997  4596 sgd_solver.cpp:105] Iteration 348, lr = 0.00933388
I0409 22:53:53.370599  4596 solver.cpp:218] Iteration 360 (2.22739 iter/s, 5.38746s/12 iters), loss = 5.13826
I0409 22:53:53.370648  4596 solver.cpp:237]     Train net output #0: loss = 5.13826 (* 1 = 5.13826 loss)
I0409 22:53:53.370657  4596 sgd_solver.cpp:105] Iteration 360, lr = 0.00931172
I0409 22:53:58.247014  4596 solver.cpp:218] Iteration 372 (2.46092 iter/s, 4.87623s/12 iters), loss = 5.04286
I0409 22:53:58.247073  4596 solver.cpp:237]     Train net output #0: loss = 5.04286 (* 1 = 5.04286 loss)
I0409 22:53:58.247085  4596 sgd_solver.cpp:105] Iteration 372, lr = 0.00928961
I0409 22:54:03.200248  4596 solver.cpp:218] Iteration 384 (2.42276 iter/s, 4.95304s/12 iters), loss = 5.0703
I0409 22:54:03.200305  4596 solver.cpp:237]     Train net output #0: loss = 5.0703 (* 1 = 5.0703 loss)
I0409 22:54:03.200317  4596 sgd_solver.cpp:105] Iteration 384, lr = 0.00926756
I0409 22:54:08.367818  4596 solver.cpp:218] Iteration 396 (2.32226 iter/s, 5.16737s/12 iters), loss = 5.00585
I0409 22:54:08.367873  4596 solver.cpp:237]     Train net output #0: loss = 5.00585 (* 1 = 5.00585 loss)
I0409 22:54:08.367885  4596 sgd_solver.cpp:105] Iteration 396, lr = 0.00924556
I0409 22:54:11.570281  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:54:13.016825  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_408.caffemodel
I0409 22:54:13.992784  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_408.solverstate
I0409 22:54:14.832198  4596 solver.cpp:330] Iteration 408, Testing net (#0)
I0409 22:54:14.832226  4596 net.cpp:676] Ignoring source layer train-data
I0409 22:54:19.700619  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:54:19.959861  4596 solver.cpp:397]     Test net output #0: accuracy = 0.0183824
I0409 22:54:19.959895  4596 solver.cpp:397]     Test net output #1: loss = 5.05171 (* 1 = 5.05171 loss)
I0409 22:54:20.041445  4596 solver.cpp:218] Iteration 408 (1.02799 iter/s, 11.6733s/12 iters), loss = 5.14139
I0409 22:54:20.041492  4596 solver.cpp:237]     Train net output #0: loss = 5.14139 (* 1 = 5.14139 loss)
I0409 22:54:20.041501  4596 sgd_solver.cpp:105] Iteration 408, lr = 0.00922361
I0409 22:54:24.670742  4596 solver.cpp:218] Iteration 420 (2.59229 iter/s, 4.62911s/12 iters), loss = 5.09531
I0409 22:54:24.670807  4596 solver.cpp:237]     Train net output #0: loss = 5.09531 (* 1 = 5.09531 loss)
I0409 22:54:24.670820  4596 sgd_solver.cpp:105] Iteration 420, lr = 0.00920171
I0409 22:54:29.905439  4596 solver.cpp:218] Iteration 432 (2.29249 iter/s, 5.23449s/12 iters), loss = 5.00456
I0409 22:54:29.905496  4596 solver.cpp:237]     Train net output #0: loss = 5.00456 (* 1 = 5.00456 loss)
I0409 22:54:29.905508  4596 sgd_solver.cpp:105] Iteration 432, lr = 0.00917986
I0409 22:54:34.834100  4596 solver.cpp:218] Iteration 444 (2.43483 iter/s, 4.92847s/12 iters), loss = 5.02429
I0409 22:54:34.834146  4596 solver.cpp:237]     Train net output #0: loss = 5.02429 (* 1 = 5.02429 loss)
I0409 22:54:34.834153  4596 sgd_solver.cpp:105] Iteration 444, lr = 0.00915807
I0409 22:54:39.804008  4596 solver.cpp:218] Iteration 456 (2.41462 iter/s, 4.96972s/12 iters), loss = 5.06995
I0409 22:54:39.804064  4596 solver.cpp:237]     Train net output #0: loss = 5.06995 (* 1 = 5.06995 loss)
I0409 22:54:39.804075  4596 sgd_solver.cpp:105] Iteration 456, lr = 0.00913632
I0409 22:54:44.745942  4596 solver.cpp:218] Iteration 468 (2.42829 iter/s, 4.94174s/12 iters), loss = 5.05077
I0409 22:54:44.746088  4596 solver.cpp:237]     Train net output #0: loss = 5.05077 (* 1 = 5.05077 loss)
I0409 22:54:44.746099  4596 sgd_solver.cpp:105] Iteration 468, lr = 0.00911463
I0409 22:54:49.882246  4596 solver.cpp:218] Iteration 480 (2.33644 iter/s, 5.13601s/12 iters), loss = 4.99072
I0409 22:54:49.882303  4596 solver.cpp:237]     Train net output #0: loss = 4.99072 (* 1 = 4.99072 loss)
I0409 22:54:49.882316  4596 sgd_solver.cpp:105] Iteration 480, lr = 0.00909299
I0409 22:54:54.846105  4596 solver.cpp:218] Iteration 492 (2.41757 iter/s, 4.96367s/12 iters), loss = 5.01518
I0409 22:54:54.846156  4596 solver.cpp:237]     Train net output #0: loss = 5.01518 (* 1 = 5.01518 loss)
I0409 22:54:54.846165  4596 sgd_solver.cpp:105] Iteration 492, lr = 0.0090714
I0409 22:54:59.729887  4596 solver.cpp:218] Iteration 504 (2.45721 iter/s, 4.88359s/12 iters), loss = 5.09098
I0409 22:54:59.729945  4596 solver.cpp:237]     Train net output #0: loss = 5.09098 (* 1 = 5.09098 loss)
I0409 22:54:59.729977  4596 sgd_solver.cpp:105] Iteration 504, lr = 0.00904986
I0409 22:54:59.993188  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:55:01.941026  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_510.caffemodel
I0409 22:55:03.546294  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_510.solverstate
I0409 22:55:04.420549  4596 solver.cpp:330] Iteration 510, Testing net (#0)
I0409 22:55:04.420576  4596 net.cpp:676] Ignoring source layer train-data
I0409 22:55:08.789131  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:55:09.029266  4596 solver.cpp:397]     Test net output #0: accuracy = 0.0238971
I0409 22:55:09.029294  4596 solver.cpp:397]     Test net output #1: loss = 4.99599 (* 1 = 4.99599 loss)
I0409 22:55:10.902241  4596 solver.cpp:218] Iteration 516 (1.07411 iter/s, 11.172s/12 iters), loss = 4.9029
I0409 22:55:10.902302  4596 solver.cpp:237]     Train net output #0: loss = 4.9029 (* 1 = 4.9029 loss)
I0409 22:55:10.902313  4596 sgd_solver.cpp:105] Iteration 516, lr = 0.00902838
I0409 22:55:15.795442  4596 solver.cpp:218] Iteration 528 (2.45248 iter/s, 4.89301s/12 iters), loss = 4.97943
I0409 22:55:15.795593  4596 solver.cpp:237]     Train net output #0: loss = 4.97943 (* 1 = 4.97943 loss)
I0409 22:55:15.795604  4596 sgd_solver.cpp:105] Iteration 528, lr = 0.00900694
I0409 22:55:20.656400  4596 solver.cpp:218] Iteration 540 (2.4688 iter/s, 4.86067s/12 iters), loss = 4.98277
I0409 22:55:20.656466  4596 solver.cpp:237]     Train net output #0: loss = 4.98277 (* 1 = 4.98277 loss)
I0409 22:55:20.656476  4596 sgd_solver.cpp:105] Iteration 540, lr = 0.00898556
I0409 22:55:25.845741  4596 solver.cpp:218] Iteration 552 (2.31253 iter/s, 5.18912s/12 iters), loss = 4.99441
I0409 22:55:25.845818  4596 solver.cpp:237]     Train net output #0: loss = 4.99441 (* 1 = 4.99441 loss)
I0409 22:55:25.845835  4596 sgd_solver.cpp:105] Iteration 552, lr = 0.00896423
I0409 22:55:30.777333  4596 solver.cpp:218] Iteration 564 (2.4334 iter/s, 4.93138s/12 iters), loss = 4.91282
I0409 22:55:30.777397  4596 solver.cpp:237]     Train net output #0: loss = 4.91282 (* 1 = 4.91282 loss)
I0409 22:55:30.777410  4596 sgd_solver.cpp:105] Iteration 564, lr = 0.00894294
I0409 22:55:36.176966  4596 solver.cpp:218] Iteration 576 (2.22246 iter/s, 5.39943s/12 iters), loss = 4.93251
I0409 22:55:36.177011  4596 solver.cpp:237]     Train net output #0: loss = 4.93251 (* 1 = 4.93251 loss)
I0409 22:55:36.177017  4596 sgd_solver.cpp:105] Iteration 576, lr = 0.00892171
I0409 22:55:41.133994  4596 solver.cpp:218] Iteration 588 (2.4209 iter/s, 4.95684s/12 iters), loss = 4.75873
I0409 22:55:41.134049  4596 solver.cpp:237]     Train net output #0: loss = 4.75873 (* 1 = 4.75873 loss)
I0409 22:55:41.134057  4596 sgd_solver.cpp:105] Iteration 588, lr = 0.00890053
I0409 22:55:46.311023  4596 solver.cpp:218] Iteration 600 (2.31802 iter/s, 5.17683s/12 iters), loss = 4.96609
I0409 22:55:46.311121  4596 solver.cpp:237]     Train net output #0: loss = 4.96609 (* 1 = 4.96609 loss)
I0409 22:55:46.311131  4596 sgd_solver.cpp:105] Iteration 600, lr = 0.0088794
I0409 22:55:48.747702  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:55:50.990850  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_612.caffemodel
I0409 22:55:51.525285  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_612.solverstate
I0409 22:55:52.149014  4596 solver.cpp:330] Iteration 612, Testing net (#0)
I0409 22:55:52.149039  4596 net.cpp:676] Ignoring source layer train-data
I0409 22:55:56.357179  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:55:56.641062  4596 solver.cpp:397]     Test net output #0: accuracy = 0.0324755
I0409 22:55:56.641099  4596 solver.cpp:397]     Test net output #1: loss = 4.92816 (* 1 = 4.92816 loss)
I0409 22:55:56.722831  4596 solver.cpp:218] Iteration 612 (1.15258 iter/s, 10.4114s/12 iters), loss = 4.84092
I0409 22:55:56.722877  4596 solver.cpp:237]     Train net output #0: loss = 4.84092 (* 1 = 4.84092 loss)
I0409 22:55:56.722887  4596 sgd_solver.cpp:105] Iteration 612, lr = 0.00885831
I0409 22:56:00.854254  4596 solver.cpp:218] Iteration 624 (2.90468 iter/s, 4.13126s/12 iters), loss = 4.80235
I0409 22:56:00.854306  4596 solver.cpp:237]     Train net output #0: loss = 4.80235 (* 1 = 4.80235 loss)
I0409 22:56:00.854317  4596 sgd_solver.cpp:105] Iteration 624, lr = 0.00883728
I0409 22:56:05.895404  4596 solver.cpp:218] Iteration 636 (2.3805 iter/s, 5.04095s/12 iters), loss = 4.85135
I0409 22:56:05.895476  4596 solver.cpp:237]     Train net output #0: loss = 4.85135 (* 1 = 4.85135 loss)
I0409 22:56:05.895493  4596 sgd_solver.cpp:105] Iteration 636, lr = 0.0088163
I0409 22:56:10.910498  4596 solver.cpp:218] Iteration 648 (2.39288 iter/s, 5.01488s/12 iters), loss = 4.98769
I0409 22:56:10.910563  4596 solver.cpp:237]     Train net output #0: loss = 4.98769 (* 1 = 4.98769 loss)
I0409 22:56:10.910576  4596 sgd_solver.cpp:105] Iteration 648, lr = 0.00879537
I0409 22:56:16.099390  4596 solver.cpp:218] Iteration 660 (2.31273 iter/s, 5.18868s/12 iters), loss = 4.90198
I0409 22:56:16.099453  4596 solver.cpp:237]     Train net output #0: loss = 4.90198 (* 1 = 4.90198 loss)
I0409 22:56:16.099464  4596 sgd_solver.cpp:105] Iteration 660, lr = 0.00877449
I0409 22:56:21.136610  4596 solver.cpp:218] Iteration 672 (2.38236 iter/s, 5.03702s/12 iters), loss = 4.80367
I0409 22:56:21.136755  4596 solver.cpp:237]     Train net output #0: loss = 4.80367 (* 1 = 4.80367 loss)
I0409 22:56:21.136770  4596 sgd_solver.cpp:105] Iteration 672, lr = 0.00875366
I0409 22:56:25.204003  4596 blocking_queue.cpp:49] Waiting for data
I0409 22:56:26.068907  4596 solver.cpp:218] Iteration 684 (2.43308 iter/s, 4.93202s/12 iters), loss = 4.76905
I0409 22:56:26.068953  4596 solver.cpp:237]     Train net output #0: loss = 4.76905 (* 1 = 4.76905 loss)
I0409 22:56:26.068964  4596 sgd_solver.cpp:105] Iteration 684, lr = 0.00873287
I0409 22:56:31.018802  4596 solver.cpp:218] Iteration 696 (2.42438 iter/s, 4.94972s/12 iters), loss = 4.77579
I0409 22:56:31.018853  4596 solver.cpp:237]     Train net output #0: loss = 4.77579 (* 1 = 4.77579 loss)
I0409 22:56:31.018864  4596 sgd_solver.cpp:105] Iteration 696, lr = 0.00871214
I0409 22:56:35.559748  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:56:35.940093  4596 solver.cpp:218] Iteration 708 (2.43847 iter/s, 4.92111s/12 iters), loss = 5.00123
I0409 22:56:35.940140  4596 solver.cpp:237]     Train net output #0: loss = 5.00123 (* 1 = 5.00123 loss)
I0409 22:56:35.940150  4596 sgd_solver.cpp:105] Iteration 708, lr = 0.00869145
I0409 22:56:38.054471  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_714.caffemodel
I0409 22:56:38.618283  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_714.solverstate
I0409 22:56:38.930842  4596 solver.cpp:330] Iteration 714, Testing net (#0)
I0409 22:56:38.930873  4596 net.cpp:676] Ignoring source layer train-data
I0409 22:56:43.283191  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:56:43.603195  4596 solver.cpp:397]     Test net output #0: accuracy = 0.0306373
I0409 22:56:43.603235  4596 solver.cpp:397]     Test net output #1: loss = 4.91492 (* 1 = 4.91492 loss)
I0409 22:56:45.384424  4596 solver.cpp:218] Iteration 720 (1.27064 iter/s, 9.44405s/12 iters), loss = 4.96619
I0409 22:56:45.384466  4596 solver.cpp:237]     Train net output #0: loss = 4.96619 (* 1 = 4.96619 loss)
I0409 22:56:45.384474  4596 sgd_solver.cpp:105] Iteration 720, lr = 0.00867082
I0409 22:56:50.296017  4596 solver.cpp:218] Iteration 732 (2.44329 iter/s, 4.91142s/12 iters), loss = 4.71612
I0409 22:56:50.296061  4596 solver.cpp:237]     Train net output #0: loss = 4.71612 (* 1 = 4.71612 loss)
I0409 22:56:50.296069  4596 sgd_solver.cpp:105] Iteration 732, lr = 0.00865023
I0409 22:56:55.122298  4596 solver.cpp:218] Iteration 744 (2.48648 iter/s, 4.8261s/12 iters), loss = 4.79406
I0409 22:56:55.122449  4596 solver.cpp:237]     Train net output #0: loss = 4.79406 (* 1 = 4.79406 loss)
I0409 22:56:55.122465  4596 sgd_solver.cpp:105] Iteration 744, lr = 0.0086297
I0409 22:57:00.062695  4596 solver.cpp:218] Iteration 756 (2.42909 iter/s, 4.94013s/12 iters), loss = 4.82795
I0409 22:57:00.062733  4596 solver.cpp:237]     Train net output #0: loss = 4.82795 (* 1 = 4.82795 loss)
I0409 22:57:00.062742  4596 sgd_solver.cpp:105] Iteration 756, lr = 0.00860921
I0409 22:57:05.425470  4596 solver.cpp:218] Iteration 768 (2.23773 iter/s, 5.36259s/12 iters), loss = 4.84387
I0409 22:57:05.425523  4596 solver.cpp:237]     Train net output #0: loss = 4.84387 (* 1 = 4.84387 loss)
I0409 22:57:05.425534  4596 sgd_solver.cpp:105] Iteration 768, lr = 0.00858877
I0409 22:57:10.324040  4596 solver.cpp:218] Iteration 780 (2.44979 iter/s, 4.89838s/12 iters), loss = 4.79535
I0409 22:57:10.324095  4596 solver.cpp:237]     Train net output #0: loss = 4.79535 (* 1 = 4.79535 loss)
I0409 22:57:10.324105  4596 sgd_solver.cpp:105] Iteration 780, lr = 0.00856838
I0409 22:57:15.514564  4596 solver.cpp:218] Iteration 792 (2.31199 iter/s, 5.19033s/12 iters), loss = 4.58328
I0409 22:57:15.514617  4596 solver.cpp:237]     Train net output #0: loss = 4.58328 (* 1 = 4.58328 loss)
I0409 22:57:15.514629  4596 sgd_solver.cpp:105] Iteration 792, lr = 0.00854803
I0409 22:57:21.283833  4596 solver.cpp:218] Iteration 804 (2.08006 iter/s, 5.76906s/12 iters), loss = 4.76833
I0409 22:57:21.283883  4596 solver.cpp:237]     Train net output #0: loss = 4.76833 (* 1 = 4.76833 loss)
I0409 22:57:21.283892  4596 sgd_solver.cpp:105] Iteration 804, lr = 0.00852774
I0409 22:57:23.356035  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:57:26.087208  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_816.caffemodel
I0409 22:57:27.374855  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_816.solverstate
I0409 22:57:28.399085  4596 solver.cpp:330] Iteration 816, Testing net (#0)
I0409 22:57:28.399112  4596 net.cpp:676] Ignoring source layer train-data
I0409 22:57:32.522802  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:57:32.876250  4596 solver.cpp:397]     Test net output #0: accuracy = 0.0441176
I0409 22:57:32.876308  4596 solver.cpp:397]     Test net output #1: loss = 4.78774 (* 1 = 4.78774 loss)
I0409 22:57:32.957824  4596 solver.cpp:218] Iteration 816 (1.02796 iter/s, 11.6736s/12 iters), loss = 4.77299
I0409 22:57:32.957880  4596 solver.cpp:237]     Train net output #0: loss = 4.77299 (* 1 = 4.77299 loss)
I0409 22:57:32.957891  4596 sgd_solver.cpp:105] Iteration 816, lr = 0.00850749
I0409 22:57:37.157295  4596 solver.cpp:218] Iteration 828 (2.85762 iter/s, 4.1993s/12 iters), loss = 4.95904
I0409 22:57:37.157342  4596 solver.cpp:237]     Train net output #0: loss = 4.95904 (* 1 = 4.95904 loss)
I0409 22:57:37.157352  4596 sgd_solver.cpp:105] Iteration 828, lr = 0.00848729
I0409 22:57:42.077494  4596 solver.cpp:218] Iteration 840 (2.43902 iter/s, 4.92001s/12 iters), loss = 4.66602
I0409 22:57:42.077551  4596 solver.cpp:237]     Train net output #0: loss = 4.66602 (* 1 = 4.66602 loss)
I0409 22:57:42.077564  4596 sgd_solver.cpp:105] Iteration 840, lr = 0.00846714
I0409 22:57:46.980381  4596 solver.cpp:218] Iteration 852 (2.44764 iter/s, 4.90269s/12 iters), loss = 4.75991
I0409 22:57:46.980428  4596 solver.cpp:237]     Train net output #0: loss = 4.75991 (* 1 = 4.75991 loss)
I0409 22:57:46.980438  4596 sgd_solver.cpp:105] Iteration 852, lr = 0.00844704
I0409 22:57:51.973568  4596 solver.cpp:218] Iteration 864 (2.40337 iter/s, 4.993s/12 iters), loss = 4.62729
I0409 22:57:51.973624  4596 solver.cpp:237]     Train net output #0: loss = 4.62729 (* 1 = 4.62729 loss)
I0409 22:57:51.973635  4596 sgd_solver.cpp:105] Iteration 864, lr = 0.00842698
I0409 22:57:57.197926  4596 solver.cpp:218] Iteration 876 (2.29702 iter/s, 5.22416s/12 iters), loss = 4.67628
I0409 22:57:57.198052  4596 solver.cpp:237]     Train net output #0: loss = 4.67628 (* 1 = 4.67628 loss)
I0409 22:57:57.198061  4596 sgd_solver.cpp:105] Iteration 876, lr = 0.00840698
I0409 22:58:02.166707  4596 solver.cpp:218] Iteration 888 (2.41521 iter/s, 4.96852s/12 iters), loss = 4.70904
I0409 22:58:02.166761  4596 solver.cpp:237]     Train net output #0: loss = 4.70904 (* 1 = 4.70904 loss)
I0409 22:58:02.166774  4596 sgd_solver.cpp:105] Iteration 888, lr = 0.00838702
I0409 22:58:07.110142  4596 solver.cpp:218] Iteration 900 (2.42755 iter/s, 4.94325s/12 iters), loss = 4.76914
I0409 22:58:07.110188  4596 solver.cpp:237]     Train net output #0: loss = 4.76914 (* 1 = 4.76914 loss)
I0409 22:58:07.110195  4596 sgd_solver.cpp:105] Iteration 900, lr = 0.0083671
I0409 22:58:10.910614  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:58:11.987828  4596 solver.cpp:218] Iteration 912 (2.46027 iter/s, 4.8775s/12 iters), loss = 4.44497
I0409 22:58:11.987879  4596 solver.cpp:237]     Train net output #0: loss = 4.44497 (* 1 = 4.44497 loss)
I0409 22:58:11.987890  4596 sgd_solver.cpp:105] Iteration 912, lr = 0.00834724
I0409 22:58:14.080698  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_918.caffemodel
I0409 22:58:14.499037  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_918.solverstate
I0409 22:58:14.799139  4596 solver.cpp:330] Iteration 918, Testing net (#0)
I0409 22:58:14.799161  4596 net.cpp:676] Ignoring source layer train-data
I0409 22:58:19.269202  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:58:19.676784  4596 solver.cpp:397]     Test net output #0: accuracy = 0.0459559
I0409 22:58:19.676834  4596 solver.cpp:397]     Test net output #1: loss = 4.70383 (* 1 = 4.70383 loss)
I0409 22:58:21.405114  4596 solver.cpp:218] Iteration 924 (1.27429 iter/s, 9.41699s/12 iters), loss = 4.6836
I0409 22:58:21.405164  4596 solver.cpp:237]     Train net output #0: loss = 4.6836 (* 1 = 4.6836 loss)
I0409 22:58:21.405171  4596 sgd_solver.cpp:105] Iteration 924, lr = 0.00832742
I0409 22:58:26.420207  4596 solver.cpp:218] Iteration 936 (2.39287 iter/s, 5.01491s/12 iters), loss = 4.71763
I0409 22:58:26.420251  4596 solver.cpp:237]     Train net output #0: loss = 4.71763 (* 1 = 4.71763 loss)
I0409 22:58:26.420259  4596 sgd_solver.cpp:105] Iteration 936, lr = 0.00830765
I0409 22:58:31.547578  4596 solver.cpp:218] Iteration 948 (2.34047 iter/s, 5.12718s/12 iters), loss = 4.51741
I0409 22:58:31.547724  4596 solver.cpp:237]     Train net output #0: loss = 4.51741 (* 1 = 4.51741 loss)
I0409 22:58:31.547732  4596 sgd_solver.cpp:105] Iteration 948, lr = 0.00828793
I0409 22:58:36.588058  4596 solver.cpp:218] Iteration 960 (2.38086 iter/s, 5.0402s/12 iters), loss = 4.45163
I0409 22:58:36.588110  4596 solver.cpp:237]     Train net output #0: loss = 4.45163 (* 1 = 4.45163 loss)
I0409 22:58:36.588122  4596 sgd_solver.cpp:105] Iteration 960, lr = 0.00826825
I0409 22:58:41.621405  4596 solver.cpp:218] Iteration 972 (2.38419 iter/s, 5.03315s/12 iters), loss = 4.64952
I0409 22:58:41.621464  4596 solver.cpp:237]     Train net output #0: loss = 4.64952 (* 1 = 4.64952 loss)
I0409 22:58:41.621475  4596 sgd_solver.cpp:105] Iteration 972, lr = 0.00824862
I0409 22:58:46.638618  4596 solver.cpp:218] Iteration 984 (2.39186 iter/s, 5.01702s/12 iters), loss = 4.44733
I0409 22:58:46.638665  4596 solver.cpp:237]     Train net output #0: loss = 4.44733 (* 1 = 4.44733 loss)
I0409 22:58:46.638676  4596 sgd_solver.cpp:105] Iteration 984, lr = 0.00822903
I0409 22:58:51.604776  4596 solver.cpp:218] Iteration 996 (2.41644 iter/s, 4.96597s/12 iters), loss = 4.36958
I0409 22:58:51.604832  4596 solver.cpp:237]     Train net output #0: loss = 4.36958 (* 1 = 4.36958 loss)
I0409 22:58:51.604844  4596 sgd_solver.cpp:105] Iteration 996, lr = 0.0082095
I0409 22:58:56.581897  4596 solver.cpp:218] Iteration 1008 (2.41112 iter/s, 4.97693s/12 iters), loss = 4.62457
I0409 22:58:56.581943  4596 solver.cpp:237]     Train net output #0: loss = 4.62457 (* 1 = 4.62457 loss)
I0409 22:58:56.581974  4596 sgd_solver.cpp:105] Iteration 1008, lr = 0.00819001
I0409 22:58:57.611877  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:59:01.150763  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1020.caffemodel
I0409 22:59:02.025218  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1020.solverstate
I0409 22:59:02.692936  4596 solver.cpp:330] Iteration 1020, Testing net (#0)
I0409 22:59:02.692977  4596 net.cpp:676] Ignoring source layer train-data
I0409 22:59:07.073453  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:59:07.686934  4596 solver.cpp:397]     Test net output #0: accuracy = 0.0643382
I0409 22:59:07.686980  4596 solver.cpp:397]     Test net output #1: loss = 4.57899 (* 1 = 4.57899 loss)
I0409 22:59:07.768553  4596 solver.cpp:218] Iteration 1020 (1.07274 iter/s, 11.1863s/12 iters), loss = 4.42897
I0409 22:59:07.768611  4596 solver.cpp:237]     Train net output #0: loss = 4.42897 (* 1 = 4.42897 loss)
I0409 22:59:07.768622  4596 sgd_solver.cpp:105] Iteration 1020, lr = 0.00817056
I0409 22:59:12.236300  4596 solver.cpp:218] Iteration 1032 (2.68603 iter/s, 4.46757s/12 iters), loss = 4.49797
I0409 22:59:12.236347  4596 solver.cpp:237]     Train net output #0: loss = 4.49797 (* 1 = 4.49797 loss)
I0409 22:59:12.236357  4596 sgd_solver.cpp:105] Iteration 1032, lr = 0.00815116
I0409 22:59:17.292022  4596 solver.cpp:218] Iteration 1044 (2.37364 iter/s, 5.05553s/12 iters), loss = 4.47898
I0409 22:59:17.292078  4596 solver.cpp:237]     Train net output #0: loss = 4.47898 (* 1 = 4.47898 loss)
I0409 22:59:17.292088  4596 sgd_solver.cpp:105] Iteration 1044, lr = 0.00813181
I0409 22:59:22.241040  4596 solver.cpp:218] Iteration 1056 (2.42482 iter/s, 4.94883s/12 iters), loss = 4.49455
I0409 22:59:22.241089  4596 solver.cpp:237]     Train net output #0: loss = 4.49455 (* 1 = 4.49455 loss)
I0409 22:59:22.241098  4596 sgd_solver.cpp:105] Iteration 1056, lr = 0.0081125
I0409 22:59:27.248450  4596 solver.cpp:218] Iteration 1068 (2.39654 iter/s, 5.00722s/12 iters), loss = 4.34151
I0409 22:59:27.248497  4596 solver.cpp:237]     Train net output #0: loss = 4.34151 (* 1 = 4.34151 loss)
I0409 22:59:27.248507  4596 sgd_solver.cpp:105] Iteration 1068, lr = 0.00809324
I0409 22:59:32.119537  4596 solver.cpp:218] Iteration 1080 (2.46361 iter/s, 4.8709s/12 iters), loss = 4.39895
I0409 22:59:32.119652  4596 solver.cpp:237]     Train net output #0: loss = 4.39895 (* 1 = 4.39895 loss)
I0409 22:59:32.119663  4596 sgd_solver.cpp:105] Iteration 1080, lr = 0.00807403
I0409 22:59:37.025868  4596 solver.cpp:218] Iteration 1092 (2.44595 iter/s, 4.90608s/12 iters), loss = 4.43759
I0409 22:59:37.025925  4596 solver.cpp:237]     Train net output #0: loss = 4.43759 (* 1 = 4.43759 loss)
I0409 22:59:37.025936  4596 sgd_solver.cpp:105] Iteration 1092, lr = 0.00805486
I0409 22:59:41.963922  4596 solver.cpp:218] Iteration 1104 (2.4302 iter/s, 4.93786s/12 iters), loss = 4.47862
I0409 22:59:41.963985  4596 solver.cpp:237]     Train net output #0: loss = 4.47862 (* 1 = 4.47862 loss)
I0409 22:59:41.963999  4596 sgd_solver.cpp:105] Iteration 1104, lr = 0.00803573
I0409 22:59:45.055907  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:59:46.872270  4596 solver.cpp:218] Iteration 1116 (2.44491 iter/s, 4.90815s/12 iters), loss = 4.37947
I0409 22:59:46.872325  4596 solver.cpp:237]     Train net output #0: loss = 4.37947 (* 1 = 4.37947 loss)
I0409 22:59:46.872336  4596 sgd_solver.cpp:105] Iteration 1116, lr = 0.00801666
I0409 22:59:48.904343  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1122.caffemodel
I0409 22:59:54.028515  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1122.solverstate
I0409 22:59:54.993176  4596 solver.cpp:330] Iteration 1122, Testing net (#0)
I0409 22:59:54.993203  4596 net.cpp:676] Ignoring source layer train-data
I0409 22:59:59.016537  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:59:59.498554  4596 solver.cpp:397]     Test net output #0: accuracy = 0.0710784
I0409 22:59:59.498608  4596 solver.cpp:397]     Test net output #1: loss = 4.50505 (* 1 = 4.50505 loss)
I0409 23:00:01.380718  4596 solver.cpp:218] Iteration 1128 (0.827128 iter/s, 14.508s/12 iters), loss = 4.45943
I0409 23:00:01.380766  4596 solver.cpp:237]     Train net output #0: loss = 4.45943 (* 1 = 4.45943 loss)
I0409 23:00:01.380775  4596 sgd_solver.cpp:105] Iteration 1128, lr = 0.00799762
I0409 23:00:06.283237  4596 solver.cpp:218] Iteration 1140 (2.44781 iter/s, 4.90233s/12 iters), loss = 4.47415
I0409 23:00:06.283387  4596 solver.cpp:237]     Train net output #0: loss = 4.47415 (* 1 = 4.47415 loss)
I0409 23:00:06.283399  4596 sgd_solver.cpp:105] Iteration 1140, lr = 0.00797863
I0409 23:00:11.176352  4596 solver.cpp:218] Iteration 1152 (2.45257 iter/s, 4.89283s/12 iters), loss = 4.13435
I0409 23:00:11.176417  4596 solver.cpp:237]     Train net output #0: loss = 4.13435 (* 1 = 4.13435 loss)
I0409 23:00:11.176430  4596 sgd_solver.cpp:105] Iteration 1152, lr = 0.00795969
I0409 23:00:16.049634  4596 solver.cpp:218] Iteration 1164 (2.46251 iter/s, 4.87308s/12 iters), loss = 4.34512
I0409 23:00:16.049693  4596 solver.cpp:237]     Train net output #0: loss = 4.34512 (* 1 = 4.34512 loss)
I0409 23:00:16.049705  4596 sgd_solver.cpp:105] Iteration 1164, lr = 0.00794079
I0409 23:00:20.880745  4596 solver.cpp:218] Iteration 1176 (2.484 iter/s, 4.83092s/12 iters), loss = 4.20813
I0409 23:00:20.880800  4596 solver.cpp:237]     Train net output #0: loss = 4.20813 (* 1 = 4.20813 loss)
I0409 23:00:20.880810  4596 sgd_solver.cpp:105] Iteration 1176, lr = 0.00792194
I0409 23:00:25.708360  4596 solver.cpp:218] Iteration 1188 (2.48579 iter/s, 4.82743s/12 iters), loss = 4.23693
I0409 23:00:25.708421  4596 solver.cpp:237]     Train net output #0: loss = 4.23693 (* 1 = 4.23693 loss)
I0409 23:00:25.708434  4596 sgd_solver.cpp:105] Iteration 1188, lr = 0.00790313
I0409 23:00:30.624470  4596 solver.cpp:218] Iteration 1200 (2.44105 iter/s, 4.91592s/12 iters), loss = 4.20993
I0409 23:00:30.624518  4596 solver.cpp:237]     Train net output #0: loss = 4.20993 (* 1 = 4.20993 loss)
I0409 23:00:30.624527  4596 sgd_solver.cpp:105] Iteration 1200, lr = 0.00788437
I0409 23:00:35.723623  4596 solver.cpp:218] Iteration 1212 (2.35342 iter/s, 5.09896s/12 iters), loss = 4.24728
I0409 23:00:35.723675  4596 solver.cpp:237]     Train net output #0: loss = 4.24728 (* 1 = 4.24728 loss)
I0409 23:00:35.723686  4596 sgd_solver.cpp:105] Iteration 1212, lr = 0.00786565
I0409 23:00:36.010284  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:00:40.200898  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1224.caffemodel
I0409 23:00:40.838336  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1224.solverstate
I0409 23:00:41.251365  4596 solver.cpp:330] Iteration 1224, Testing net (#0)
I0409 23:00:41.251389  4596 net.cpp:676] Ignoring source layer train-data
I0409 23:00:45.115409  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:00:45.641656  4596 solver.cpp:397]     Test net output #0: accuracy = 0.0968137
I0409 23:00:45.641695  4596 solver.cpp:397]     Test net output #1: loss = 4.35107 (* 1 = 4.35107 loss)
I0409 23:00:45.723408  4596 solver.cpp:218] Iteration 1224 (1.20006 iter/s, 9.99947s/12 iters), loss = 4.24027
I0409 23:00:45.723459  4596 solver.cpp:237]     Train net output #0: loss = 4.24027 (* 1 = 4.24027 loss)
I0409 23:00:45.723469  4596 sgd_solver.cpp:105] Iteration 1224, lr = 0.00784697
I0409 23:00:49.938063  4596 solver.cpp:218] Iteration 1236 (2.84733 iter/s, 4.21448s/12 iters), loss = 4.47002
I0409 23:00:49.938130  4596 solver.cpp:237]     Train net output #0: loss = 4.47002 (* 1 = 4.47002 loss)
I0409 23:00:49.938141  4596 sgd_solver.cpp:105] Iteration 1236, lr = 0.00782834
I0409 23:00:54.771785  4596 solver.cpp:218] Iteration 1248 (2.48266 iter/s, 4.83353s/12 iters), loss = 4.11851
I0409 23:00:54.771840  4596 solver.cpp:237]     Train net output #0: loss = 4.11851 (* 1 = 4.11851 loss)
I0409 23:00:54.771852  4596 sgd_solver.cpp:105] Iteration 1248, lr = 0.00780976
I0409 23:00:59.795585  4596 solver.cpp:218] Iteration 1260 (2.38872 iter/s, 5.02361s/12 iters), loss = 4.21397
I0409 23:00:59.795632  4596 solver.cpp:237]     Train net output #0: loss = 4.21397 (* 1 = 4.21397 loss)
I0409 23:00:59.795645  4596 sgd_solver.cpp:105] Iteration 1260, lr = 0.00779122
I0409 23:01:04.969287  4596 solver.cpp:218] Iteration 1272 (2.31951 iter/s, 5.17351s/12 iters), loss = 4.135
I0409 23:01:04.969338  4596 solver.cpp:237]     Train net output #0: loss = 4.135 (* 1 = 4.135 loss)
I0409 23:01:04.969350  4596 sgd_solver.cpp:105] Iteration 1272, lr = 0.00777272
I0409 23:01:10.130009  4596 solver.cpp:218] Iteration 1284 (2.32534 iter/s, 5.16053s/12 iters), loss = 4.07104
I0409 23:01:10.130056  4596 solver.cpp:237]     Train net output #0: loss = 4.07104 (* 1 = 4.07104 loss)
I0409 23:01:10.130066  4596 sgd_solver.cpp:105] Iteration 1284, lr = 0.00775426
I0409 23:01:15.064640  4596 solver.cpp:218] Iteration 1296 (2.43188 iter/s, 4.93445s/12 iters), loss = 4.14229
I0409 23:01:15.064759  4596 solver.cpp:237]     Train net output #0: loss = 4.14229 (* 1 = 4.14229 loss)
I0409 23:01:15.064772  4596 sgd_solver.cpp:105] Iteration 1296, lr = 0.00773585
I0409 23:01:20.010041  4596 solver.cpp:218] Iteration 1308 (2.42662 iter/s, 4.94515s/12 iters), loss = 4.17218
I0409 23:01:20.010097  4596 solver.cpp:237]     Train net output #0: loss = 4.17218 (* 1 = 4.17218 loss)
I0409 23:01:20.010107  4596 sgd_solver.cpp:105] Iteration 1308, lr = 0.00771749
I0409 23:01:22.485977  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:01:24.897645  4596 solver.cpp:218] Iteration 1320 (2.45528 iter/s, 4.88742s/12 iters), loss = 3.8628
I0409 23:01:24.897692  4596 solver.cpp:237]     Train net output #0: loss = 3.8628 (* 1 = 3.8628 loss)
I0409 23:01:24.897703  4596 sgd_solver.cpp:105] Iteration 1320, lr = 0.00769916
I0409 23:01:26.903496  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1326.caffemodel
I0409 23:01:27.661366  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1326.solverstate
I0409 23:01:28.062966  4596 solver.cpp:330] Iteration 1326, Testing net (#0)
I0409 23:01:28.062992  4596 net.cpp:676] Ignoring source layer train-data
I0409 23:01:32.011433  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:01:32.565268  4596 solver.cpp:397]     Test net output #0: accuracy = 0.11152
I0409 23:01:32.565299  4596 solver.cpp:397]     Test net output #1: loss = 4.1294 (* 1 = 4.1294 loss)
I0409 23:01:34.361712  4596 solver.cpp:218] Iteration 1332 (1.26799 iter/s, 9.46377s/12 iters), loss = 4.06991
I0409 23:01:34.361776  4596 solver.cpp:237]     Train net output #0: loss = 4.06991 (* 1 = 4.06991 loss)
I0409 23:01:34.361788  4596 sgd_solver.cpp:105] Iteration 1332, lr = 0.00768088
I0409 23:01:39.227736  4596 solver.cpp:218] Iteration 1344 (2.46618 iter/s, 4.86582s/12 iters), loss = 3.95122
I0409 23:01:39.227792  4596 solver.cpp:237]     Train net output #0: loss = 3.95122 (* 1 = 3.95122 loss)
I0409 23:01:39.227802  4596 sgd_solver.cpp:105] Iteration 1344, lr = 0.00766265
I0409 23:01:44.167125  4596 solver.cpp:218] Iteration 1356 (2.42955 iter/s, 4.93919s/12 iters), loss = 4.067
I0409 23:01:44.167179  4596 solver.cpp:237]     Train net output #0: loss = 4.067 (* 1 = 4.067 loss)
I0409 23:01:44.167191  4596 sgd_solver.cpp:105] Iteration 1356, lr = 0.00764446
I0409 23:01:48.634608  4596 blocking_queue.cpp:49] Waiting for data
I0409 23:01:49.080499  4596 solver.cpp:218] Iteration 1368 (2.44241 iter/s, 4.91318s/12 iters), loss = 4.19173
I0409 23:01:49.080559  4596 solver.cpp:237]     Train net output #0: loss = 4.19173 (* 1 = 4.19173 loss)
I0409 23:01:49.080569  4596 sgd_solver.cpp:105] Iteration 1368, lr = 0.00762631
I0409 23:01:54.165072  4596 solver.cpp:218] Iteration 1380 (2.36017 iter/s, 5.08437s/12 iters), loss = 3.71858
I0409 23:01:54.165119  4596 solver.cpp:237]     Train net output #0: loss = 3.71858 (* 1 = 3.71858 loss)
I0409 23:01:54.165127  4596 sgd_solver.cpp:105] Iteration 1380, lr = 0.0076082
I0409 23:01:59.123805  4596 solver.cpp:218] Iteration 1392 (2.42006 iter/s, 4.95855s/12 iters), loss = 3.92163
I0409 23:01:59.123844  4596 solver.cpp:237]     Train net output #0: loss = 3.92163 (* 1 = 3.92163 loss)
I0409 23:01:59.123854  4596 sgd_solver.cpp:105] Iteration 1392, lr = 0.00759014
I0409 23:02:04.092936  4596 solver.cpp:218] Iteration 1404 (2.41499 iter/s, 4.96896s/12 iters), loss = 3.96667
I0409 23:02:04.092981  4596 solver.cpp:237]     Train net output #0: loss = 3.96667 (* 1 = 3.96667 loss)
I0409 23:02:04.092993  4596 sgd_solver.cpp:105] Iteration 1404, lr = 0.00757212
I0409 23:02:08.738248  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:02:09.086800  4596 solver.cpp:218] Iteration 1416 (2.40304 iter/s, 4.99368s/12 iters), loss = 3.87167
I0409 23:02:09.086858  4596 solver.cpp:237]     Train net output #0: loss = 3.87167 (* 1 = 3.87167 loss)
I0409 23:02:09.086869  4596 sgd_solver.cpp:105] Iteration 1416, lr = 0.00755414
I0409 23:02:13.596915  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1428.caffemodel
I0409 23:02:14.647367  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1428.solverstate
I0409 23:02:15.460160  4596 solver.cpp:330] Iteration 1428, Testing net (#0)
I0409 23:02:15.460192  4596 net.cpp:676] Ignoring source layer train-data
I0409 23:02:19.276404  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:02:19.866333  4596 solver.cpp:397]     Test net output #0: accuracy = 0.128064
I0409 23:02:19.866376  4596 solver.cpp:397]     Test net output #1: loss = 3.95352 (* 1 = 3.95352 loss)
I0409 23:02:19.947849  4596 solver.cpp:218] Iteration 1428 (1.1049 iter/s, 10.8607s/12 iters), loss = 3.8903
I0409 23:02:19.947894  4596 solver.cpp:237]     Train net output #0: loss = 3.8903 (* 1 = 3.8903 loss)
I0409 23:02:19.947904  4596 sgd_solver.cpp:105] Iteration 1428, lr = 0.0075362
I0409 23:02:24.138613  4596 solver.cpp:218] Iteration 1440 (2.86355 iter/s, 4.1906s/12 iters), loss = 3.7865
I0409 23:02:24.138661  4596 solver.cpp:237]     Train net output #0: loss = 3.7865 (* 1 = 3.7865 loss)
I0409 23:02:24.138671  4596 sgd_solver.cpp:105] Iteration 1440, lr = 0.00751831
I0409 23:02:29.056715  4596 solver.cpp:218] Iteration 1452 (2.44006 iter/s, 4.91792s/12 iters), loss = 4.05781
I0409 23:02:29.056759  4596 solver.cpp:237]     Train net output #0: loss = 4.05781 (* 1 = 4.05781 loss)
I0409 23:02:29.056768  4596 sgd_solver.cpp:105] Iteration 1452, lr = 0.00750046
I0409 23:02:33.979280  4596 solver.cpp:218] Iteration 1464 (2.43784 iter/s, 4.92239s/12 iters), loss = 3.7619
I0409 23:02:33.979331  4596 solver.cpp:237]     Train net output #0: loss = 3.7619 (* 1 = 3.7619 loss)
I0409 23:02:33.979343  4596 sgd_solver.cpp:105] Iteration 1464, lr = 0.00748265
I0409 23:02:38.860796  4596 solver.cpp:218] Iteration 1476 (2.45835 iter/s, 4.88133s/12 iters), loss = 3.72596
I0409 23:02:38.860849  4596 solver.cpp:237]     Train net output #0: loss = 3.72596 (* 1 = 3.72596 loss)
I0409 23:02:38.860862  4596 sgd_solver.cpp:105] Iteration 1476, lr = 0.00746489
I0409 23:02:43.750604  4596 solver.cpp:218] Iteration 1488 (2.45418 iter/s, 4.88962s/12 iters), loss = 3.77285
I0409 23:02:43.750660  4596 solver.cpp:237]     Train net output #0: loss = 3.77285 (* 1 = 3.77285 loss)
I0409 23:02:43.750674  4596 sgd_solver.cpp:105] Iteration 1488, lr = 0.00744716
I0409 23:02:48.657994  4596 solver.cpp:218] Iteration 1500 (2.4454 iter/s, 4.90718s/12 iters), loss = 3.55514
I0409 23:02:48.658048  4596 solver.cpp:237]     Train net output #0: loss = 3.55514 (* 1 = 3.55514 loss)
I0409 23:02:48.658061  4596 sgd_solver.cpp:105] Iteration 1500, lr = 0.00742948
I0409 23:02:53.592018  4596 solver.cpp:218] Iteration 1512 (2.43218 iter/s, 4.93384s/12 iters), loss = 3.74166
I0409 23:02:53.592156  4596 solver.cpp:237]     Train net output #0: loss = 3.74166 (* 1 = 3.74166 loss)
I0409 23:02:53.592170  4596 sgd_solver.cpp:105] Iteration 1512, lr = 0.00741184
I0409 23:02:55.381202  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:02:58.544239  4596 solver.cpp:218] Iteration 1524 (2.42329 iter/s, 4.95195s/12 iters), loss = 3.81533
I0409 23:02:58.544286  4596 solver.cpp:237]     Train net output #0: loss = 3.81533 (* 1 = 3.81533 loss)
I0409 23:02:58.544294  4596 sgd_solver.cpp:105] Iteration 1524, lr = 0.00739425
I0409 23:03:00.544544  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1530.caffemodel
I0409 23:03:00.955969  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1530.solverstate
I0409 23:03:01.246803  4596 solver.cpp:330] Iteration 1530, Testing net (#0)
I0409 23:03:01.246829  4596 net.cpp:676] Ignoring source layer train-data
I0409 23:03:05.130942  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:03:05.762850  4596 solver.cpp:397]     Test net output #0: accuracy = 0.137255
I0409 23:03:05.762902  4596 solver.cpp:397]     Test net output #1: loss = 3.98479 (* 1 = 3.98479 loss)
I0409 23:03:07.649879  4596 solver.cpp:218] Iteration 1536 (1.31791 iter/s, 9.10536s/12 iters), loss = 3.8736
I0409 23:03:07.649935  4596 solver.cpp:237]     Train net output #0: loss = 3.8736 (* 1 = 3.8736 loss)
I0409 23:03:07.649946  4596 sgd_solver.cpp:105] Iteration 1536, lr = 0.00737669
I0409 23:03:12.786809  4596 solver.cpp:218] Iteration 1548 (2.33611 iter/s, 5.13673s/12 iters), loss = 3.38909
I0409 23:03:12.786856  4596 solver.cpp:237]     Train net output #0: loss = 3.38909 (* 1 = 3.38909 loss)
I0409 23:03:12.786868  4596 sgd_solver.cpp:105] Iteration 1548, lr = 0.00735918
I0409 23:03:17.737597  4596 solver.cpp:218] Iteration 1560 (2.42395 iter/s, 4.9506s/12 iters), loss = 3.72676
I0409 23:03:17.737653  4596 solver.cpp:237]     Train net output #0: loss = 3.72676 (* 1 = 3.72676 loss)
I0409 23:03:17.737663  4596 sgd_solver.cpp:105] Iteration 1560, lr = 0.00734171
I0409 23:03:22.649767  4596 solver.cpp:218] Iteration 1572 (2.443 iter/s, 4.91199s/12 iters), loss = 3.80692
I0409 23:03:22.649812  4596 solver.cpp:237]     Train net output #0: loss = 3.80692 (* 1 = 3.80692 loss)
I0409 23:03:22.649823  4596 sgd_solver.cpp:105] Iteration 1572, lr = 0.00732427
I0409 23:03:27.569011  4596 solver.cpp:218] Iteration 1584 (2.43949 iter/s, 4.91907s/12 iters), loss = 3.74059
I0409 23:03:27.569155  4596 solver.cpp:237]     Train net output #0: loss = 3.74059 (* 1 = 3.74059 loss)
I0409 23:03:27.569169  4596 sgd_solver.cpp:105] Iteration 1584, lr = 0.00730688
I0409 23:03:32.448863  4596 solver.cpp:218] Iteration 1596 (2.45923 iter/s, 4.87958s/12 iters), loss = 3.6046
I0409 23:03:32.448915  4596 solver.cpp:237]     Train net output #0: loss = 3.6046 (* 1 = 3.6046 loss)
I0409 23:03:32.448930  4596 sgd_solver.cpp:105] Iteration 1596, lr = 0.00728954
I0409 23:03:37.334106  4596 solver.cpp:218] Iteration 1608 (2.45647 iter/s, 4.88506s/12 iters), loss = 3.61931
I0409 23:03:37.334162  4596 solver.cpp:237]     Train net output #0: loss = 3.61931 (* 1 = 3.61931 loss)
I0409 23:03:37.334175  4596 sgd_solver.cpp:105] Iteration 1608, lr = 0.00727223
I0409 23:03:41.085106  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:03:42.145262  4596 solver.cpp:218] Iteration 1620 (2.4943 iter/s, 4.81097s/12 iters), loss = 3.63507
I0409 23:03:42.145318  4596 solver.cpp:237]     Train net output #0: loss = 3.63507 (* 1 = 3.63507 loss)
I0409 23:03:42.145330  4596 sgd_solver.cpp:105] Iteration 1620, lr = 0.00725496
I0409 23:03:46.627666  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1632.caffemodel
I0409 23:03:47.083411  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1632.solverstate
I0409 23:03:47.389369  4596 solver.cpp:330] Iteration 1632, Testing net (#0)
I0409 23:03:47.389394  4596 net.cpp:676] Ignoring source layer train-data
I0409 23:03:51.352620  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:03:52.019539  4596 solver.cpp:397]     Test net output #0: accuracy = 0.15625
I0409 23:03:52.019578  4596 solver.cpp:397]     Test net output #1: loss = 3.78993 (* 1 = 3.78993 loss)
I0409 23:03:52.101092  4596 solver.cpp:218] Iteration 1632 (1.20536 iter/s, 9.95552s/12 iters), loss = 3.57413
I0409 23:03:52.101137  4596 solver.cpp:237]     Train net output #0: loss = 3.57413 (* 1 = 3.57413 loss)
I0409 23:03:52.101148  4596 sgd_solver.cpp:105] Iteration 1632, lr = 0.00723774
I0409 23:03:56.301425  4596 solver.cpp:218] Iteration 1644 (2.85703 iter/s, 4.20017s/12 iters), loss = 3.63538
I0409 23:03:56.301482  4596 solver.cpp:237]     Train net output #0: loss = 3.63538 (* 1 = 3.63538 loss)
I0409 23:03:56.301493  4596 sgd_solver.cpp:105] Iteration 1644, lr = 0.00722056
I0409 23:04:01.201771  4596 solver.cpp:218] Iteration 1656 (2.4489 iter/s, 4.90015s/12 iters), loss = 3.65216
I0409 23:04:01.201922  4596 solver.cpp:237]     Train net output #0: loss = 3.65216 (* 1 = 3.65216 loss)
I0409 23:04:01.201934  4596 sgd_solver.cpp:105] Iteration 1656, lr = 0.00720341
I0409 23:04:06.147184  4596 solver.cpp:218] Iteration 1668 (2.42663 iter/s, 4.94513s/12 iters), loss = 3.0746
I0409 23:04:06.147234  4596 solver.cpp:237]     Train net output #0: loss = 3.0746 (* 1 = 3.0746 loss)
I0409 23:04:06.147245  4596 sgd_solver.cpp:105] Iteration 1668, lr = 0.00718631
I0409 23:04:11.013433  4596 solver.cpp:218] Iteration 1680 (2.46606 iter/s, 4.86607s/12 iters), loss = 3.52874
I0409 23:04:11.013484  4596 solver.cpp:237]     Train net output #0: loss = 3.52874 (* 1 = 3.52874 loss)
I0409 23:04:11.013494  4596 sgd_solver.cpp:105] Iteration 1680, lr = 0.00716925
I0409 23:04:15.944489  4596 solver.cpp:218] Iteration 1692 (2.43365 iter/s, 4.93087s/12 iters), loss = 3.51309
I0409 23:04:15.944530  4596 solver.cpp:237]     Train net output #0: loss = 3.51309 (* 1 = 3.51309 loss)
I0409 23:04:15.944537  4596 sgd_solver.cpp:105] Iteration 1692, lr = 0.00715223
I0409 23:04:20.884155  4596 solver.cpp:218] Iteration 1704 (2.42941 iter/s, 4.93948s/12 iters), loss = 3.37603
I0409 23:04:20.884212  4596 solver.cpp:237]     Train net output #0: loss = 3.37603 (* 1 = 3.37603 loss)
I0409 23:04:20.884227  4596 sgd_solver.cpp:105] Iteration 1704, lr = 0.00713525
I0409 23:04:25.759599  4596 solver.cpp:218] Iteration 1716 (2.46141 iter/s, 4.87526s/12 iters), loss = 3.51079
I0409 23:04:25.759645  4596 solver.cpp:237]     Train net output #0: loss = 3.51079 (* 1 = 3.51079 loss)
I0409 23:04:25.759656  4596 sgd_solver.cpp:105] Iteration 1716, lr = 0.00711831
I0409 23:04:26.790455  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:04:30.688135  4596 solver.cpp:218] Iteration 1728 (2.43489 iter/s, 4.92836s/12 iters), loss = 3.37233
I0409 23:04:30.688174  4596 solver.cpp:237]     Train net output #0: loss = 3.37233 (* 1 = 3.37233 loss)
I0409 23:04:30.688184  4596 sgd_solver.cpp:105] Iteration 1728, lr = 0.00710141
I0409 23:04:32.679531  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1734.caffemodel
I0409 23:04:33.951906  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1734.solverstate
I0409 23:04:35.924266  4596 solver.cpp:330] Iteration 1734, Testing net (#0)
I0409 23:04:35.924304  4596 net.cpp:676] Ignoring source layer train-data
I0409 23:04:39.659063  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:04:40.443187  4596 solver.cpp:397]     Test net output #0: accuracy = 0.172181
I0409 23:04:40.443235  4596 solver.cpp:397]     Test net output #1: loss = 3.65364 (* 1 = 3.65364 loss)
I0409 23:04:42.318051  4596 solver.cpp:218] Iteration 1740 (1.03185 iter/s, 11.6296s/12 iters), loss = 3.2183
I0409 23:04:42.318110  4596 solver.cpp:237]     Train net output #0: loss = 3.2183 (* 1 = 3.2183 loss)
I0409 23:04:42.318122  4596 sgd_solver.cpp:105] Iteration 1740, lr = 0.00708455
I0409 23:04:47.243497  4596 solver.cpp:218] Iteration 1752 (2.43642 iter/s, 4.92526s/12 iters), loss = 3.33243
I0409 23:04:47.243544  4596 solver.cpp:237]     Train net output #0: loss = 3.33243 (* 1 = 3.33243 loss)
I0409 23:04:47.243557  4596 sgd_solver.cpp:105] Iteration 1752, lr = 0.00706773
I0409 23:04:52.152689  4596 solver.cpp:218] Iteration 1764 (2.44449 iter/s, 4.909s/12 iters), loss = 3.54959
I0409 23:04:52.152738  4596 solver.cpp:237]     Train net output #0: loss = 3.54959 (* 1 = 3.54959 loss)
I0409 23:04:52.152750  4596 sgd_solver.cpp:105] Iteration 1764, lr = 0.00705094
I0409 23:04:57.026039  4596 solver.cpp:218] Iteration 1776 (2.46246 iter/s, 4.87317s/12 iters), loss = 3.34304
I0409 23:04:57.026082  4596 solver.cpp:237]     Train net output #0: loss = 3.34304 (* 1 = 3.34304 loss)
I0409 23:04:57.026089  4596 sgd_solver.cpp:105] Iteration 1776, lr = 0.0070342
I0409 23:05:01.984633  4596 solver.cpp:218] Iteration 1788 (2.42013 iter/s, 4.95842s/12 iters), loss = 3.37001
I0409 23:05:01.984674  4596 solver.cpp:237]     Train net output #0: loss = 3.37001 (* 1 = 3.37001 loss)
I0409 23:05:01.984684  4596 sgd_solver.cpp:105] Iteration 1788, lr = 0.0070175
I0409 23:05:07.012769  4596 solver.cpp:218] Iteration 1800 (2.38666 iter/s, 5.02796s/12 iters), loss = 3.32622
I0409 23:05:07.012935  4596 solver.cpp:237]     Train net output #0: loss = 3.32622 (* 1 = 3.32622 loss)
I0409 23:05:07.012948  4596 sgd_solver.cpp:105] Iteration 1800, lr = 0.00700084
I0409 23:05:11.927654  4596 solver.cpp:218] Iteration 1812 (2.44171 iter/s, 4.91458s/12 iters), loss = 3.215
I0409 23:05:11.927706  4596 solver.cpp:237]     Train net output #0: loss = 3.215 (* 1 = 3.215 loss)
I0409 23:05:11.927716  4596 sgd_solver.cpp:105] Iteration 1812, lr = 0.00698422
I0409 23:05:15.053736  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:05:16.839638  4596 solver.cpp:218] Iteration 1824 (2.4431 iter/s, 4.9118s/12 iters), loss = 3.755
I0409 23:05:16.839694  4596 solver.cpp:237]     Train net output #0: loss = 3.755 (* 1 = 3.755 loss)
I0409 23:05:16.839704  4596 sgd_solver.cpp:105] Iteration 1824, lr = 0.00696764
I0409 23:05:21.371021  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1836.caffemodel
I0409 23:05:21.817216  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1836.solverstate
I0409 23:05:22.127115  4596 solver.cpp:330] Iteration 1836, Testing net (#0)
I0409 23:05:22.127136  4596 net.cpp:676] Ignoring source layer train-data
I0409 23:05:25.941534  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:05:26.859122  4596 solver.cpp:397]     Test net output #0: accuracy = 0.207721
I0409 23:05:26.859163  4596 solver.cpp:397]     Test net output #1: loss = 3.51678 (* 1 = 3.51678 loss)
I0409 23:05:26.940486  4596 solver.cpp:218] Iteration 1836 (1.18806 iter/s, 10.1005s/12 iters), loss = 3.18731
I0409 23:05:26.940537  4596 solver.cpp:237]     Train net output #0: loss = 3.18731 (* 1 = 3.18731 loss)
I0409 23:05:26.940547  4596 sgd_solver.cpp:105] Iteration 1836, lr = 0.0069511
I0409 23:05:31.087584  4596 solver.cpp:218] Iteration 1848 (2.89371 iter/s, 4.14693s/12 iters), loss = 3.20001
I0409 23:05:31.087637  4596 solver.cpp:237]     Train net output #0: loss = 3.20001 (* 1 = 3.20001 loss)
I0409 23:05:31.087649  4596 sgd_solver.cpp:105] Iteration 1848, lr = 0.00693459
I0409 23:05:36.002080  4596 solver.cpp:218] Iteration 1860 (2.44185 iter/s, 4.9143s/12 iters), loss = 3.34633
I0409 23:05:36.002137  4596 solver.cpp:237]     Train net output #0: loss = 3.34633 (* 1 = 3.34633 loss)
I0409 23:05:36.002151  4596 sgd_solver.cpp:105] Iteration 1860, lr = 0.00691813
I0409 23:05:40.910578  4596 solver.cpp:218] Iteration 1872 (2.44484 iter/s, 4.90831s/12 iters), loss = 3.04632
I0409 23:05:40.910693  4596 solver.cpp:237]     Train net output #0: loss = 3.04632 (* 1 = 3.04632 loss)
I0409 23:05:40.910706  4596 sgd_solver.cpp:105] Iteration 1872, lr = 0.0069017
I0409 23:05:45.858685  4596 solver.cpp:218] Iteration 1884 (2.42529 iter/s, 4.94785s/12 iters), loss = 3.23401
I0409 23:05:45.858732  4596 solver.cpp:237]     Train net output #0: loss = 3.23401 (* 1 = 3.23401 loss)
I0409 23:05:45.858741  4596 sgd_solver.cpp:105] Iteration 1884, lr = 0.00688532
I0409 23:05:50.774662  4596 solver.cpp:218] Iteration 1896 (2.44111 iter/s, 4.91579s/12 iters), loss = 3.23814
I0409 23:05:50.774718  4596 solver.cpp:237]     Train net output #0: loss = 3.23814 (* 1 = 3.23814 loss)
I0409 23:05:50.774731  4596 sgd_solver.cpp:105] Iteration 1896, lr = 0.00686897
I0409 23:05:55.751129  4596 solver.cpp:218] Iteration 1908 (2.41144 iter/s, 4.97627s/12 iters), loss = 3.46131
I0409 23:05:55.751181  4596 solver.cpp:237]     Train net output #0: loss = 3.46131 (* 1 = 3.46131 loss)
I0409 23:05:55.751192  4596 sgd_solver.cpp:105] Iteration 1908, lr = 0.00685266
I0409 23:06:00.719898  4596 solver.cpp:218] Iteration 1920 (2.41517 iter/s, 4.96859s/12 iters), loss = 3.10336
I0409 23:06:00.719944  4596 solver.cpp:237]     Train net output #0: loss = 3.10336 (* 1 = 3.10336 loss)
I0409 23:06:00.719951  4596 sgd_solver.cpp:105] Iteration 1920, lr = 0.00683639
I0409 23:06:01.035362  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:06:05.777235  4596 solver.cpp:218] Iteration 1932 (2.37288 iter/s, 5.05716s/12 iters), loss = 2.94803
I0409 23:06:05.777278  4596 solver.cpp:237]     Train net output #0: loss = 2.94803 (* 1 = 2.94803 loss)
I0409 23:06:05.777287  4596 sgd_solver.cpp:105] Iteration 1932, lr = 0.00682016
I0409 23:06:07.754920  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1938.caffemodel
I0409 23:06:08.165602  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1938.solverstate
I0409 23:06:08.457020  4596 solver.cpp:330] Iteration 1938, Testing net (#0)
I0409 23:06:08.457044  4596 net.cpp:676] Ignoring source layer train-data
I0409 23:06:12.097565  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:06:13.207187  4596 solver.cpp:397]     Test net output #0: accuracy = 0.208333
I0409 23:06:13.207237  4596 solver.cpp:397]     Test net output #1: loss = 3.43542 (* 1 = 3.43542 loss)
I0409 23:06:15.041363  4596 solver.cpp:218] Iteration 1944 (1.29536 iter/s, 9.26384s/12 iters), loss = 2.80943
I0409 23:06:15.041411  4596 solver.cpp:237]     Train net output #0: loss = 2.80943 (* 1 = 2.80943 loss)
I0409 23:06:15.041421  4596 sgd_solver.cpp:105] Iteration 1944, lr = 0.00680397
I0409 23:06:19.960264  4596 solver.cpp:218] Iteration 1956 (2.43966 iter/s, 4.91872s/12 iters), loss = 3.28098
I0409 23:06:19.960309  4596 solver.cpp:237]     Train net output #0: loss = 3.28098 (* 1 = 3.28098 loss)
I0409 23:06:19.960319  4596 sgd_solver.cpp:105] Iteration 1956, lr = 0.00678782
I0409 23:06:24.885192  4596 solver.cpp:218] Iteration 1968 (2.43667 iter/s, 4.92475s/12 iters), loss = 3.18545
I0409 23:06:24.885243  4596 solver.cpp:237]     Train net output #0: loss = 3.18545 (* 1 = 3.18545 loss)
I0409 23:06:24.885255  4596 sgd_solver.cpp:105] Iteration 1968, lr = 0.0067717
I0409 23:06:29.845443  4596 solver.cpp:218] Iteration 1980 (2.41932 iter/s, 4.96007s/12 iters), loss = 3.17171
I0409 23:06:29.845500  4596 solver.cpp:237]     Train net output #0: loss = 3.17171 (* 1 = 3.17171 loss)
I0409 23:06:29.845515  4596 sgd_solver.cpp:105] Iteration 1980, lr = 0.00675562
I0409 23:06:34.802668  4596 solver.cpp:218] Iteration 1992 (2.4208 iter/s, 4.95704s/12 iters), loss = 2.9192
I0409 23:06:34.802714  4596 solver.cpp:237]     Train net output #0: loss = 2.9192 (* 1 = 2.9192 loss)
I0409 23:06:34.802724  4596 sgd_solver.cpp:105] Iteration 1992, lr = 0.00673958
I0409 23:06:39.680857  4596 solver.cpp:218] Iteration 2004 (2.46002 iter/s, 4.87801s/12 iters), loss = 2.90331
I0409 23:06:39.680909  4596 solver.cpp:237]     Train net output #0: loss = 2.90331 (* 1 = 2.90331 loss)
I0409 23:06:39.680922  4596 sgd_solver.cpp:105] Iteration 2004, lr = 0.00672358
I0409 23:06:44.553915  4596 solver.cpp:218] Iteration 2016 (2.46261 iter/s, 4.87287s/12 iters), loss = 3.33249
I0409 23:06:44.554069  4596 solver.cpp:237]     Train net output #0: loss = 3.33249 (* 1 = 3.33249 loss)
I0409 23:06:44.554085  4596 sgd_solver.cpp:105] Iteration 2016, lr = 0.00670762
I0409 23:06:47.034859  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:06:49.518213  4596 solver.cpp:218] Iteration 2028 (2.4174 iter/s, 4.96401s/12 iters), loss = 3.05215
I0409 23:06:49.518270  4596 solver.cpp:237]     Train net output #0: loss = 3.05215 (* 1 = 3.05215 loss)
I0409 23:06:49.518281  4596 sgd_solver.cpp:105] Iteration 2028, lr = 0.00669169
I0409 23:06:54.056118  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2040.caffemodel
I0409 23:06:54.568975  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2040.solverstate
I0409 23:06:54.861865  4596 solver.cpp:330] Iteration 2040, Testing net (#0)
I0409 23:06:54.861887  4596 net.cpp:676] Ignoring source layer train-data
I0409 23:06:58.458676  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:06:59.282790  4596 solver.cpp:397]     Test net output #0: accuracy = 0.227941
I0409 23:06:59.282832  4596 solver.cpp:397]     Test net output #1: loss = 3.28245 (* 1 = 3.28245 loss)
I0409 23:06:59.364161  4596 solver.cpp:218] Iteration 2040 (1.21881 iter/s, 9.84563s/12 iters), loss = 2.90155
I0409 23:06:59.364212  4596 solver.cpp:237]     Train net output #0: loss = 2.90155 (* 1 = 2.90155 loss)
I0409 23:06:59.364223  4596 sgd_solver.cpp:105] Iteration 2040, lr = 0.00667581
I0409 23:07:03.473701  4596 solver.cpp:218] Iteration 2052 (2.92015 iter/s, 4.10937s/12 iters), loss = 3.09212
I0409 23:07:03.473750  4596 solver.cpp:237]     Train net output #0: loss = 3.09212 (* 1 = 3.09212 loss)
I0409 23:07:03.473762  4596 sgd_solver.cpp:105] Iteration 2052, lr = 0.00665996
I0409 23:07:03.474040  4596 blocking_queue.cpp:49] Waiting for data
I0409 23:07:08.387954  4596 solver.cpp:218] Iteration 2064 (2.44197 iter/s, 4.91407s/12 iters), loss = 3.07898
I0409 23:07:08.388000  4596 solver.cpp:237]     Train net output #0: loss = 3.07898 (* 1 = 3.07898 loss)
I0409 23:07:08.388010  4596 sgd_solver.cpp:105] Iteration 2064, lr = 0.00664414
I0409 23:07:13.251981  4596 solver.cpp:218] Iteration 2076 (2.46719 iter/s, 4.86384s/12 iters), loss = 3.02819
I0409 23:07:13.252024  4596 solver.cpp:237]     Train net output #0: loss = 3.02819 (* 1 = 3.02819 loss)
I0409 23:07:13.252034  4596 sgd_solver.cpp:105] Iteration 2076, lr = 0.00662837
I0409 23:07:18.204784  4596 solver.cpp:218] Iteration 2088 (2.42296 iter/s, 4.95262s/12 iters), loss = 3.05196
I0409 23:07:18.204913  4596 solver.cpp:237]     Train net output #0: loss = 3.05196 (* 1 = 3.05196 loss)
I0409 23:07:18.204923  4596 sgd_solver.cpp:105] Iteration 2088, lr = 0.00661263
I0409 23:07:23.102598  4596 solver.cpp:218] Iteration 2100 (2.45021 iter/s, 4.89755s/12 iters), loss = 2.92387
I0409 23:07:23.102658  4596 solver.cpp:237]     Train net output #0: loss = 2.92387 (* 1 = 2.92387 loss)
I0409 23:07:23.102670  4596 sgd_solver.cpp:105] Iteration 2100, lr = 0.00659693
I0409 23:07:28.325472  4596 solver.cpp:218] Iteration 2112 (2.29767 iter/s, 5.22267s/12 iters), loss = 2.92228
I0409 23:07:28.325521  4596 solver.cpp:237]     Train net output #0: loss = 2.92228 (* 1 = 2.92228 loss)
I0409 23:07:28.325532  4596 sgd_solver.cpp:105] Iteration 2112, lr = 0.00658127
I0409 23:07:33.045235  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:07:33.355657  4596 solver.cpp:218] Iteration 2124 (2.38569 iter/s, 5.03s/12 iters), loss = 2.7922
I0409 23:07:33.355708  4596 solver.cpp:237]     Train net output #0: loss = 2.7922 (* 1 = 2.7922 loss)
I0409 23:07:33.355720  4596 sgd_solver.cpp:105] Iteration 2124, lr = 0.00656564
I0409 23:07:38.245884  4596 solver.cpp:218] Iteration 2136 (2.45397 iter/s, 4.89004s/12 iters), loss = 2.78321
I0409 23:07:38.245936  4596 solver.cpp:237]     Train net output #0: loss = 2.78321 (* 1 = 2.78321 loss)
I0409 23:07:38.245947  4596 sgd_solver.cpp:105] Iteration 2136, lr = 0.00655006
I0409 23:07:40.255020  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2142.caffemodel
I0409 23:07:40.694213  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2142.solverstate
I0409 23:07:41.005797  4596 solver.cpp:330] Iteration 2142, Testing net (#0)
I0409 23:07:41.005829  4596 net.cpp:676] Ignoring source layer train-data
I0409 23:07:44.600878  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:07:45.461091  4596 solver.cpp:397]     Test net output #0: accuracy = 0.22549
I0409 23:07:45.461127  4596 solver.cpp:397]     Test net output #1: loss = 3.36317 (* 1 = 3.36317 loss)
I0409 23:07:47.367036  4596 solver.cpp:218] Iteration 2148 (1.31566 iter/s, 9.12087s/12 iters), loss = 2.94822
I0409 23:07:47.367075  4596 solver.cpp:237]     Train net output #0: loss = 2.94822 (* 1 = 2.94822 loss)
I0409 23:07:47.367085  4596 sgd_solver.cpp:105] Iteration 2148, lr = 0.00653451
I0409 23:07:52.200701  4596 solver.cpp:218] Iteration 2160 (2.48268 iter/s, 4.83349s/12 iters), loss = 3.02726
I0409 23:07:52.200819  4596 solver.cpp:237]     Train net output #0: loss = 3.02726 (* 1 = 3.02726 loss)
I0409 23:07:52.200831  4596 sgd_solver.cpp:105] Iteration 2160, lr = 0.00651899
I0409 23:07:57.082980  4596 solver.cpp:218] Iteration 2172 (2.45799 iter/s, 4.88203s/12 iters), loss = 2.96961
I0409 23:07:57.083029  4596 solver.cpp:237]     Train net output #0: loss = 2.96961 (* 1 = 2.96961 loss)
I0409 23:07:57.083041  4596 sgd_solver.cpp:105] Iteration 2172, lr = 0.00650351
I0409 23:08:02.063674  4596 solver.cpp:218] Iteration 2184 (2.40939 iter/s, 4.98051s/12 iters), loss = 3.11212
I0409 23:08:02.063730  4596 solver.cpp:237]     Train net output #0: loss = 3.11212 (* 1 = 3.11212 loss)
I0409 23:08:02.063742  4596 sgd_solver.cpp:105] Iteration 2184, lr = 0.00648807
I0409 23:08:07.002568  4596 solver.cpp:218] Iteration 2196 (2.42979 iter/s, 4.9387s/12 iters), loss = 2.79882
I0409 23:08:07.002616  4596 solver.cpp:237]     Train net output #0: loss = 2.79882 (* 1 = 2.79882 loss)
I0409 23:08:07.002627  4596 sgd_solver.cpp:105] Iteration 2196, lr = 0.00647267
I0409 23:08:12.089118  4596 solver.cpp:218] Iteration 2208 (2.35925 iter/s, 5.08636s/12 iters), loss = 2.87417
I0409 23:08:12.089169  4596 solver.cpp:237]     Train net output #0: loss = 2.87417 (* 1 = 2.87417 loss)
I0409 23:08:12.089179  4596 sgd_solver.cpp:105] Iteration 2208, lr = 0.0064573
I0409 23:08:16.985011  4596 solver.cpp:218] Iteration 2220 (2.45112 iter/s, 4.89571s/12 iters), loss = 2.59218
I0409 23:08:16.985050  4596 solver.cpp:237]     Train net output #0: loss = 2.59218 (* 1 = 2.59218 loss)
I0409 23:08:16.985059  4596 sgd_solver.cpp:105] Iteration 2220, lr = 0.00644197
I0409 23:08:18.754061  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:08:21.890354  4596 solver.cpp:218] Iteration 2232 (2.4464 iter/s, 4.90516s/12 iters), loss = 2.76954
I0409 23:08:21.890411  4596 solver.cpp:237]     Train net output #0: loss = 2.76954 (* 1 = 2.76954 loss)
I0409 23:08:21.890424  4596 sgd_solver.cpp:105] Iteration 2232, lr = 0.00642668
I0409 23:08:26.364353  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2244.caffemodel
I0409 23:08:27.246048  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2244.solverstate
I0409 23:08:28.636653  4596 solver.cpp:330] Iteration 2244, Testing net (#0)
I0409 23:08:28.636675  4596 net.cpp:676] Ignoring source layer train-data
I0409 23:08:32.297271  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:08:33.200896  4596 solver.cpp:397]     Test net output #0: accuracy = 0.231005
I0409 23:08:33.200946  4596 solver.cpp:397]     Test net output #1: loss = 3.31956 (* 1 = 3.31956 loss)
I0409 23:08:33.282366  4596 solver.cpp:218] Iteration 2244 (1.0534 iter/s, 11.3917s/12 iters), loss = 2.89839
I0409 23:08:33.282423  4596 solver.cpp:237]     Train net output #0: loss = 2.89839 (* 1 = 2.89839 loss)
I0409 23:08:33.282434  4596 sgd_solver.cpp:105] Iteration 2244, lr = 0.00641142
I0409 23:08:37.458142  4596 solver.cpp:218] Iteration 2256 (2.87384 iter/s, 4.1756s/12 iters), loss = 2.93538
I0409 23:08:37.458189  4596 solver.cpp:237]     Train net output #0: loss = 2.93538 (* 1 = 2.93538 loss)
I0409 23:08:37.458199  4596 sgd_solver.cpp:105] Iteration 2256, lr = 0.0063962
I0409 23:08:42.385913  4596 solver.cpp:218] Iteration 2268 (2.43527 iter/s, 4.92759s/12 iters), loss = 2.9916
I0409 23:08:42.385987  4596 solver.cpp:237]     Train net output #0: loss = 2.9916 (* 1 = 2.9916 loss)
I0409 23:08:42.385998  4596 sgd_solver.cpp:105] Iteration 2268, lr = 0.00638101
I0409 23:08:47.363211  4596 solver.cpp:218] Iteration 2280 (2.41105 iter/s, 4.97709s/12 iters), loss = 2.57906
I0409 23:08:47.363260  4596 solver.cpp:237]     Train net output #0: loss = 2.57906 (* 1 = 2.57906 loss)
I0409 23:08:47.363268  4596 sgd_solver.cpp:105] Iteration 2280, lr = 0.00636586
I0409 23:08:52.224902  4596 solver.cpp:218] Iteration 2292 (2.46837 iter/s, 4.86151s/12 iters), loss = 2.70175
I0409 23:08:52.224952  4596 solver.cpp:237]     Train net output #0: loss = 2.70175 (* 1 = 2.70175 loss)
I0409 23:08:52.224961  4596 sgd_solver.cpp:105] Iteration 2292, lr = 0.00635075
I0409 23:08:57.199857  4596 solver.cpp:218] Iteration 2304 (2.41217 iter/s, 4.97477s/12 iters), loss = 3.03186
I0409 23:08:57.201872  4596 solver.cpp:237]     Train net output #0: loss = 3.03186 (* 1 = 3.03186 loss)
I0409 23:08:57.201885  4596 sgd_solver.cpp:105] Iteration 2304, lr = 0.00633567
I0409 23:09:02.135421  4596 solver.cpp:218] Iteration 2316 (2.43239 iter/s, 4.93342s/12 iters), loss = 3.03821
I0409 23:09:02.135466  4596 solver.cpp:237]     Train net output #0: loss = 3.03821 (* 1 = 3.03821 loss)
I0409 23:09:02.135475  4596 sgd_solver.cpp:105] Iteration 2316, lr = 0.00632063
I0409 23:09:06.287048  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:09:07.325407  4596 solver.cpp:218] Iteration 2328 (2.31223 iter/s, 5.1898s/12 iters), loss = 2.59781
I0409 23:09:07.325467  4596 solver.cpp:237]     Train net output #0: loss = 2.59781 (* 1 = 2.59781 loss)
I0409 23:09:07.325480  4596 sgd_solver.cpp:105] Iteration 2328, lr = 0.00630562
I0409 23:09:12.575724  4596 solver.cpp:218] Iteration 2340 (2.28566 iter/s, 5.25012s/12 iters), loss = 2.7719
I0409 23:09:12.575773  4596 solver.cpp:237]     Train net output #0: loss = 2.7719 (* 1 = 2.7719 loss)
I0409 23:09:12.575783  4596 sgd_solver.cpp:105] Iteration 2340, lr = 0.00629065
I0409 23:09:14.818603  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2346.caffemodel
I0409 23:09:15.645655  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2346.solverstate
I0409 23:09:16.030752  4596 solver.cpp:330] Iteration 2346, Testing net (#0)
I0409 23:09:16.030782  4596 net.cpp:676] Ignoring source layer train-data
I0409 23:09:19.533246  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:09:20.529381  4596 solver.cpp:397]     Test net output #0: accuracy = 0.266544
I0409 23:09:20.529413  4596 solver.cpp:397]     Test net output #1: loss = 3.08252 (* 1 = 3.08252 loss)
I0409 23:09:22.395956  4596 solver.cpp:218] Iteration 2352 (1.222 iter/s, 9.81993s/12 iters), loss = 2.76183
I0409 23:09:22.396010  4596 solver.cpp:237]     Train net output #0: loss = 2.76183 (* 1 = 2.76183 loss)
I0409 23:09:22.396023  4596 sgd_solver.cpp:105] Iteration 2352, lr = 0.00627571
I0409 23:09:27.480113  4596 solver.cpp:218] Iteration 2364 (2.36036 iter/s, 5.08396s/12 iters), loss = 2.46669
I0409 23:09:27.480240  4596 solver.cpp:237]     Train net output #0: loss = 2.46669 (* 1 = 2.46669 loss)
I0409 23:09:27.480253  4596 sgd_solver.cpp:105] Iteration 2364, lr = 0.00626081
I0409 23:09:32.550806  4596 solver.cpp:218] Iteration 2376 (2.36667 iter/s, 5.07043s/12 iters), loss = 2.33604
I0409 23:09:32.550863  4596 solver.cpp:237]     Train net output #0: loss = 2.33604 (* 1 = 2.33604 loss)
I0409 23:09:32.550875  4596 sgd_solver.cpp:105] Iteration 2376, lr = 0.00624595
I0409 23:09:37.484233  4596 solver.cpp:218] Iteration 2388 (2.43248 iter/s, 4.93324s/12 iters), loss = 2.41549
I0409 23:09:37.484287  4596 solver.cpp:237]     Train net output #0: loss = 2.41549 (* 1 = 2.41549 loss)
I0409 23:09:37.484299  4596 sgd_solver.cpp:105] Iteration 2388, lr = 0.00623112
I0409 23:09:42.376255  4596 solver.cpp:218] Iteration 2400 (2.45307 iter/s, 4.89184s/12 iters), loss = 2.42962
I0409 23:09:42.376305  4596 solver.cpp:237]     Train net output #0: loss = 2.42962 (* 1 = 2.42962 loss)
I0409 23:09:42.376317  4596 sgd_solver.cpp:105] Iteration 2400, lr = 0.00621633
I0409 23:09:47.265595  4596 solver.cpp:218] Iteration 2412 (2.45441 iter/s, 4.88915s/12 iters), loss = 2.521
I0409 23:09:47.265642  4596 solver.cpp:237]     Train net output #0: loss = 2.521 (* 1 = 2.521 loss)
I0409 23:09:47.265651  4596 sgd_solver.cpp:105] Iteration 2412, lr = 0.00620157
I0409 23:09:52.174326  4596 solver.cpp:218] Iteration 2424 (2.44471 iter/s, 4.90855s/12 iters), loss = 2.66407
I0409 23:09:52.174376  4596 solver.cpp:237]     Train net output #0: loss = 2.66407 (* 1 = 2.66407 loss)
I0409 23:09:52.174387  4596 sgd_solver.cpp:105] Iteration 2424, lr = 0.00618684
I0409 23:09:53.257983  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:09:57.168059  4596 solver.cpp:218] Iteration 2436 (2.4031 iter/s, 4.99355s/12 iters), loss = 2.47165
I0409 23:09:57.168105  4596 solver.cpp:237]     Train net output #0: loss = 2.47165 (* 1 = 2.47165 loss)
I0409 23:09:57.168115  4596 sgd_solver.cpp:105] Iteration 2436, lr = 0.00617215
I0409 23:10:01.559199  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2448.caffemodel
I0409 23:10:01.996867  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2448.solverstate
I0409 23:10:02.291429  4596 solver.cpp:330] Iteration 2448, Testing net (#0)
I0409 23:10:02.291450  4596 net.cpp:676] Ignoring source layer train-data
I0409 23:10:05.638514  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:10:06.612138  4596 solver.cpp:397]     Test net output #0: accuracy = 0.275735
I0409 23:10:06.612180  4596 solver.cpp:397]     Test net output #1: loss = 3.11249 (* 1 = 3.11249 loss)
I0409 23:10:06.693769  4596 solver.cpp:218] Iteration 2448 (1.25979 iter/s, 9.52542s/12 iters), loss = 2.81123
I0409 23:10:06.693825  4596 solver.cpp:237]     Train net output #0: loss = 2.81123 (* 1 = 2.81123 loss)
I0409 23:10:06.693836  4596 sgd_solver.cpp:105] Iteration 2448, lr = 0.0061575
I0409 23:10:10.875602  4596 solver.cpp:218] Iteration 2460 (2.86968 iter/s, 4.18166s/12 iters), loss = 2.46404
I0409 23:10:10.875655  4596 solver.cpp:237]     Train net output #0: loss = 2.46404 (* 1 = 2.46404 loss)
I0409 23:10:10.875666  4596 sgd_solver.cpp:105] Iteration 2460, lr = 0.00614288
I0409 23:10:15.767329  4596 solver.cpp:218] Iteration 2472 (2.45321 iter/s, 4.89154s/12 iters), loss = 2.44735
I0409 23:10:15.767380  4596 solver.cpp:237]     Train net output #0: loss = 2.44735 (* 1 = 2.44735 loss)
I0409 23:10:15.767393  4596 sgd_solver.cpp:105] Iteration 2472, lr = 0.0061283
I0409 23:10:20.675318  4596 solver.cpp:218] Iteration 2484 (2.44509 iter/s, 4.9078s/12 iters), loss = 2.15476
I0409 23:10:20.675374  4596 solver.cpp:237]     Train net output #0: loss = 2.15476 (* 1 = 2.15476 loss)
I0409 23:10:20.675386  4596 sgd_solver.cpp:105] Iteration 2484, lr = 0.00611375
I0409 23:10:25.610402  4596 solver.cpp:218] Iteration 2496 (2.43166 iter/s, 4.9349s/12 iters), loss = 2.53342
I0409 23:10:25.610453  4596 solver.cpp:237]     Train net output #0: loss = 2.53342 (* 1 = 2.53342 loss)
I0409 23:10:25.610466  4596 sgd_solver.cpp:105] Iteration 2496, lr = 0.00609923
I0409 23:10:30.526613  4596 solver.cpp:218] Iteration 2508 (2.441 iter/s, 4.91603s/12 iters), loss = 2.608
I0409 23:10:30.526665  4596 solver.cpp:237]     Train net output #0: loss = 2.608 (* 1 = 2.608 loss)
I0409 23:10:30.526679  4596 sgd_solver.cpp:105] Iteration 2508, lr = 0.00608475
I0409 23:10:35.446599  4596 solver.cpp:218] Iteration 2520 (2.43912 iter/s, 4.9198s/12 iters), loss = 2.43295
I0409 23:10:35.448859  4596 solver.cpp:237]     Train net output #0: loss = 2.43295 (* 1 = 2.43295 loss)
I0409 23:10:35.448873  4596 sgd_solver.cpp:105] Iteration 2520, lr = 0.0060703
I0409 23:10:38.602769  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:10:40.355710  4596 solver.cpp:218] Iteration 2532 (2.44563 iter/s, 4.90672s/12 iters), loss = 2.74764
I0409 23:10:40.355756  4596 solver.cpp:237]     Train net output #0: loss = 2.74764 (* 1 = 2.74764 loss)
I0409 23:10:40.355765  4596 sgd_solver.cpp:105] Iteration 2532, lr = 0.00605589
I0409 23:10:45.273468  4596 solver.cpp:218] Iteration 2544 (2.44023 iter/s, 4.91757s/12 iters), loss = 2.37186
I0409 23:10:45.273525  4596 solver.cpp:237]     Train net output #0: loss = 2.37186 (* 1 = 2.37186 loss)
I0409 23:10:45.273536  4596 sgd_solver.cpp:105] Iteration 2544, lr = 0.00604151
I0409 23:10:47.248831  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2550.caffemodel
I0409 23:10:47.686789  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2550.solverstate
I0409 23:10:47.978147  4596 solver.cpp:330] Iteration 2550, Testing net (#0)
I0409 23:10:47.978168  4596 net.cpp:676] Ignoring source layer train-data
I0409 23:10:51.546487  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:10:52.564538  4596 solver.cpp:397]     Test net output #0: accuracy = 0.29473
I0409 23:10:52.564589  4596 solver.cpp:397]     Test net output #1: loss = 3.07782 (* 1 = 3.07782 loss)
I0409 23:10:54.526243  4596 solver.cpp:218] Iteration 2556 (1.29695 iter/s, 9.25248s/12 iters), loss = 2.19785
I0409 23:10:54.526280  4596 solver.cpp:237]     Train net output #0: loss = 2.19785 (* 1 = 2.19785 loss)
I0409 23:10:54.526289  4596 sgd_solver.cpp:105] Iteration 2556, lr = 0.00602717
I0409 23:10:59.508080  4596 solver.cpp:218] Iteration 2568 (2.40883 iter/s, 4.98166s/12 iters), loss = 2.3888
I0409 23:10:59.508124  4596 solver.cpp:237]     Train net output #0: loss = 2.3888 (* 1 = 2.3888 loss)
I0409 23:10:59.508133  4596 sgd_solver.cpp:105] Iteration 2568, lr = 0.00601286
I0409 23:11:04.496740  4596 solver.cpp:218] Iteration 2580 (2.40554 iter/s, 4.98848s/12 iters), loss = 2.21977
I0409 23:11:04.496791  4596 solver.cpp:237]     Train net output #0: loss = 2.21977 (* 1 = 2.21977 loss)
I0409 23:11:04.496803  4596 sgd_solver.cpp:105] Iteration 2580, lr = 0.00599858
I0409 23:11:09.448415  4596 solver.cpp:218] Iteration 2592 (2.42351 iter/s, 4.95149s/12 iters), loss = 2.28521
I0409 23:11:09.448578  4596 solver.cpp:237]     Train net output #0: loss = 2.28521 (* 1 = 2.28521 loss)
I0409 23:11:09.448593  4596 sgd_solver.cpp:105] Iteration 2592, lr = 0.00598434
I0409 23:11:14.331779  4596 solver.cpp:218] Iteration 2604 (2.45747 iter/s, 4.88307s/12 iters), loss = 2.54795
I0409 23:11:14.331825  4596 solver.cpp:237]     Train net output #0: loss = 2.54795 (* 1 = 2.54795 loss)
I0409 23:11:14.331835  4596 sgd_solver.cpp:105] Iteration 2604, lr = 0.00597013
I0409 23:11:19.257617  4596 solver.cpp:218] Iteration 2616 (2.43622 iter/s, 4.92565s/12 iters), loss = 2.4274
I0409 23:11:19.257673  4596 solver.cpp:237]     Train net output #0: loss = 2.4274 (* 1 = 2.4274 loss)
I0409 23:11:19.257684  4596 sgd_solver.cpp:105] Iteration 2616, lr = 0.00595596
I0409 23:11:24.226464  4596 solver.cpp:218] Iteration 2628 (2.41514 iter/s, 4.96865s/12 iters), loss = 2.21951
I0409 23:11:24.226522  4596 solver.cpp:237]     Train net output #0: loss = 2.21951 (* 1 = 2.21951 loss)
I0409 23:11:24.226534  4596 sgd_solver.cpp:105] Iteration 2628, lr = 0.00594182
I0409 23:11:24.663687  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:11:29.119503  4596 solver.cpp:218] Iteration 2640 (2.45256 iter/s, 4.89285s/12 iters), loss = 2.22648
I0409 23:11:29.119554  4596 solver.cpp:237]     Train net output #0: loss = 2.22648 (* 1 = 2.22648 loss)
I0409 23:11:29.119565  4596 sgd_solver.cpp:105] Iteration 2640, lr = 0.00592771
I0409 23:11:33.540053  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2652.caffemodel
I0409 23:11:34.425192  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2652.solverstate
I0409 23:11:35.111630  4596 solver.cpp:330] Iteration 2652, Testing net (#0)
I0409 23:11:35.111654  4596 net.cpp:676] Ignoring source layer train-data
I0409 23:11:38.534535  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:11:39.587666  4596 solver.cpp:397]     Test net output #0: accuracy = 0.30576
I0409 23:11:39.587774  4596 solver.cpp:397]     Test net output #1: loss = 3.00187 (* 1 = 3.00187 loss)
I0409 23:11:39.669194  4596 solver.cpp:218] Iteration 2652 (1.13751 iter/s, 10.5494s/12 iters), loss = 2.12548
I0409 23:11:39.669247  4596 solver.cpp:237]     Train net output #0: loss = 2.12548 (* 1 = 2.12548 loss)
I0409 23:11:39.669260  4596 sgd_solver.cpp:105] Iteration 2652, lr = 0.00591364
I0409 23:11:43.900149  4596 solver.cpp:218] Iteration 2664 (2.83636 iter/s, 4.23078s/12 iters), loss = 2.2519
I0409 23:11:43.900210  4596 solver.cpp:237]     Train net output #0: loss = 2.2519 (* 1 = 2.2519 loss)
I0409 23:11:43.900223  4596 sgd_solver.cpp:105] Iteration 2664, lr = 0.0058996
I0409 23:11:48.752673  4596 solver.cpp:218] Iteration 2676 (2.47304 iter/s, 4.85233s/12 iters), loss = 2.36627
I0409 23:11:48.752720  4596 solver.cpp:237]     Train net output #0: loss = 2.36627 (* 1 = 2.36627 loss)
I0409 23:11:48.752729  4596 sgd_solver.cpp:105] Iteration 2676, lr = 0.00588559
I0409 23:11:53.691510  4596 solver.cpp:218] Iteration 2688 (2.42981 iter/s, 4.93865s/12 iters), loss = 2.28481
I0409 23:11:53.691565  4596 solver.cpp:237]     Train net output #0: loss = 2.28481 (* 1 = 2.28481 loss)
I0409 23:11:53.691577  4596 sgd_solver.cpp:105] Iteration 2688, lr = 0.00587162
I0409 23:11:58.599293  4596 solver.cpp:218] Iteration 2700 (2.44519 iter/s, 4.90759s/12 iters), loss = 2.42255
I0409 23:11:58.599342  4596 solver.cpp:237]     Train net output #0: loss = 2.42255 (* 1 = 2.42255 loss)
I0409 23:11:58.599351  4596 sgd_solver.cpp:105] Iteration 2700, lr = 0.00585768
I0409 23:12:03.523382  4596 solver.cpp:218] Iteration 2712 (2.43709 iter/s, 4.9239s/12 iters), loss = 2.07055
I0409 23:12:03.523434  4596 solver.cpp:237]     Train net output #0: loss = 2.07055 (* 1 = 2.07055 loss)
I0409 23:12:03.523445  4596 sgd_solver.cpp:105] Iteration 2712, lr = 0.00584377
I0409 23:12:08.445094  4596 solver.cpp:218] Iteration 2724 (2.43827 iter/s, 4.92153s/12 iters), loss = 2.57682
I0409 23:12:08.445147  4596 solver.cpp:237]     Train net output #0: loss = 2.57682 (* 1 = 2.57682 loss)
I0409 23:12:08.445158  4596 sgd_solver.cpp:105] Iteration 2724, lr = 0.0058299
I0409 23:12:10.983716  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:12:13.361949  4596 solver.cpp:218] Iteration 2736 (2.44068 iter/s, 4.91667s/12 iters), loss = 2.20191
I0409 23:12:13.362013  4596 solver.cpp:237]     Train net output #0: loss = 2.20191 (* 1 = 2.20191 loss)
I0409 23:12:13.362025  4596 sgd_solver.cpp:105] Iteration 2736, lr = 0.00581605
I0409 23:12:18.291002  4596 solver.cpp:218] Iteration 2748 (2.43464 iter/s, 4.92885s/12 iters), loss = 2.37814
I0409 23:12:18.291047  4596 solver.cpp:237]     Train net output #0: loss = 2.37814 (* 1 = 2.37814 loss)
I0409 23:12:18.291056  4596 sgd_solver.cpp:105] Iteration 2748, lr = 0.00580225
I0409 23:12:20.291702  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2754.caffemodel
I0409 23:12:20.721031  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2754.solverstate
I0409 23:12:21.012683  4596 solver.cpp:330] Iteration 2754, Testing net (#0)
I0409 23:12:21.012710  4596 net.cpp:676] Ignoring source layer train-data
I0409 23:12:23.887418  4596 blocking_queue.cpp:49] Waiting for data
I0409 23:12:24.489081  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:12:25.589107  4596 solver.cpp:397]     Test net output #0: accuracy = 0.320466
I0409 23:12:25.589154  4596 solver.cpp:397]     Test net output #1: loss = 2.91776 (* 1 = 2.91776 loss)
I0409 23:12:27.455101  4596 solver.cpp:218] Iteration 2760 (1.3095 iter/s, 9.16382s/12 iters), loss = 2.29668
I0409 23:12:27.455147  4596 solver.cpp:237]     Train net output #0: loss = 2.29668 (* 1 = 2.29668 loss)
I0409 23:12:27.455158  4596 sgd_solver.cpp:105] Iteration 2760, lr = 0.00578847
I0409 23:12:32.386195  4596 solver.cpp:218] Iteration 2772 (2.43362 iter/s, 4.93092s/12 iters), loss = 2.12753
I0409 23:12:32.386234  4596 solver.cpp:237]     Train net output #0: loss = 2.12753 (* 1 = 2.12753 loss)
I0409 23:12:32.386242  4596 sgd_solver.cpp:105] Iteration 2772, lr = 0.00577473
I0409 23:12:37.292098  4596 solver.cpp:218] Iteration 2784 (2.44612 iter/s, 4.90573s/12 iters), loss = 2.08392
I0409 23:12:37.292142  4596 solver.cpp:237]     Train net output #0: loss = 2.08392 (* 1 = 2.08392 loss)
I0409 23:12:37.292153  4596 sgd_solver.cpp:105] Iteration 2784, lr = 0.00576102
I0409 23:12:42.172616  4596 solver.cpp:218] Iteration 2796 (2.45884 iter/s, 4.88034s/12 iters), loss = 2.02799
I0409 23:12:42.172686  4596 solver.cpp:237]     Train net output #0: loss = 2.02799 (* 1 = 2.02799 loss)
I0409 23:12:42.172698  4596 sgd_solver.cpp:105] Iteration 2796, lr = 0.00574734
I0409 23:12:47.134616  4596 solver.cpp:218] Iteration 2808 (2.41848 iter/s, 4.9618s/12 iters), loss = 1.82803
I0409 23:12:47.134661  4596 solver.cpp:237]     Train net output #0: loss = 1.82803 (* 1 = 1.82803 loss)
I0409 23:12:47.134670  4596 sgd_solver.cpp:105] Iteration 2808, lr = 0.00573369
I0409 23:12:52.089761  4596 solver.cpp:218] Iteration 2820 (2.42181 iter/s, 4.95496s/12 iters), loss = 2.07369
I0409 23:12:52.089805  4596 solver.cpp:237]     Train net output #0: loss = 2.07369 (* 1 = 2.07369 loss)
I0409 23:12:52.089815  4596 sgd_solver.cpp:105] Iteration 2820, lr = 0.00572008
I0409 23:12:56.729665  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:12:57.013617  4596 solver.cpp:218] Iteration 2832 (2.43721 iter/s, 4.92367s/12 iters), loss = 1.83693
I0409 23:12:57.013675  4596 solver.cpp:237]     Train net output #0: loss = 1.83693 (* 1 = 1.83693 loss)
I0409 23:12:57.013689  4596 sgd_solver.cpp:105] Iteration 2832, lr = 0.0057065
I0409 23:13:01.963171  4596 solver.cpp:218] Iteration 2844 (2.42455 iter/s, 4.94936s/12 iters), loss = 2.21354
I0409 23:13:01.963227  4596 solver.cpp:237]     Train net output #0: loss = 2.21354 (* 1 = 2.21354 loss)
I0409 23:13:01.963238  4596 sgd_solver.cpp:105] Iteration 2844, lr = 0.00569295
I0409 23:13:06.453444  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2856.caffemodel
I0409 23:13:06.903605  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2856.solverstate
I0409 23:13:07.212932  4596 solver.cpp:330] Iteration 2856, Testing net (#0)
I0409 23:13:07.212962  4596 net.cpp:676] Ignoring source layer train-data
I0409 23:13:10.690042  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:13:11.858465  4596 solver.cpp:397]     Test net output #0: accuracy = 0.335172
I0409 23:13:11.858515  4596 solver.cpp:397]     Test net output #1: loss = 2.84079 (* 1 = 2.84079 loss)
I0409 23:13:11.940348  4596 solver.cpp:218] Iteration 2856 (1.20278 iter/s, 9.97687s/12 iters), loss = 1.80561
I0409 23:13:11.940398  4596 solver.cpp:237]     Train net output #0: loss = 1.80561 (* 1 = 1.80561 loss)
I0409 23:13:11.940409  4596 sgd_solver.cpp:105] Iteration 2856, lr = 0.00567944
I0409 23:13:16.445446  4596 solver.cpp:218] Iteration 2868 (2.66376 iter/s, 4.50492s/12 iters), loss = 2.28466
I0409 23:13:16.448014  4596 solver.cpp:237]     Train net output #0: loss = 2.28466 (* 1 = 2.28466 loss)
I0409 23:13:16.448027  4596 sgd_solver.cpp:105] Iteration 2868, lr = 0.00566595
I0409 23:13:21.354004  4596 solver.cpp:218] Iteration 2880 (2.44605 iter/s, 4.90586s/12 iters), loss = 2.05608
I0409 23:13:21.354063  4596 solver.cpp:237]     Train net output #0: loss = 2.05608 (* 1 = 2.05608 loss)
I0409 23:13:21.354076  4596 sgd_solver.cpp:105] Iteration 2880, lr = 0.0056525
I0409 23:13:26.338973  4596 solver.cpp:218] Iteration 2892 (2.40733 iter/s, 4.98477s/12 iters), loss = 2.32182
I0409 23:13:26.339018  4596 solver.cpp:237]     Train net output #0: loss = 2.32182 (* 1 = 2.32182 loss)
I0409 23:13:26.339027  4596 sgd_solver.cpp:105] Iteration 2892, lr = 0.00563908
I0409 23:13:31.176578  4596 solver.cpp:218] Iteration 2904 (2.48066 iter/s, 4.83742s/12 iters), loss = 1.94898
I0409 23:13:31.176635  4596 solver.cpp:237]     Train net output #0: loss = 1.94898 (* 1 = 1.94898 loss)
I0409 23:13:31.176646  4596 sgd_solver.cpp:105] Iteration 2904, lr = 0.00562569
I0409 23:13:36.110324  4596 solver.cpp:218] Iteration 2916 (2.43232 iter/s, 4.93356s/12 iters), loss = 1.57775
I0409 23:13:36.110364  4596 solver.cpp:237]     Train net output #0: loss = 1.57775 (* 1 = 1.57775 loss)
I0409 23:13:36.110373  4596 sgd_solver.cpp:105] Iteration 2916, lr = 0.00561233
I0409 23:13:41.067493  4596 solver.cpp:218] Iteration 2928 (2.42082 iter/s, 4.95699s/12 iters), loss = 1.92647
I0409 23:13:41.067544  4596 solver.cpp:237]     Train net output #0: loss = 1.92647 (* 1 = 1.92647 loss)
I0409 23:13:41.067556  4596 sgd_solver.cpp:105] Iteration 2928, lr = 0.00559901
I0409 23:13:42.861476  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:13:45.932566  4596 solver.cpp:218] Iteration 2940 (2.46666 iter/s, 4.86488s/12 iters), loss = 1.70455
I0409 23:13:45.932624  4596 solver.cpp:237]     Train net output #0: loss = 1.70455 (* 1 = 1.70455 loss)
I0409 23:13:45.932637  4596 sgd_solver.cpp:105] Iteration 2940, lr = 0.00558572
I0409 23:13:50.769629  4596 solver.cpp:218] Iteration 2952 (2.48094 iter/s, 4.83688s/12 iters), loss = 2.0661
I0409 23:13:50.769737  4596 solver.cpp:237]     Train net output #0: loss = 2.0661 (* 1 = 2.0661 loss)
I0409 23:13:50.769745  4596 sgd_solver.cpp:105] Iteration 2952, lr = 0.00557245
I0409 23:13:52.792831  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2958.caffemodel
I0409 23:13:53.647711  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2958.solverstate
I0409 23:13:54.342135  4596 solver.cpp:330] Iteration 2958, Testing net (#0)
I0409 23:13:54.342164  4596 net.cpp:676] Ignoring source layer train-data
I0409 23:13:57.606375  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:13:58.781929  4596 solver.cpp:397]     Test net output #0: accuracy = 0.327819
I0409 23:13:58.782002  4596 solver.cpp:397]     Test net output #1: loss = 2.86721 (* 1 = 2.86721 loss)
I0409 23:14:00.682142  4596 solver.cpp:218] Iteration 2964 (1.21064 iter/s, 9.91215s/12 iters), loss = 1.90086
I0409 23:14:00.682209  4596 solver.cpp:237]     Train net output #0: loss = 1.90086 (* 1 = 1.90086 loss)
I0409 23:14:00.682221  4596 sgd_solver.cpp:105] Iteration 2964, lr = 0.00555922
I0409 23:14:05.593662  4596 solver.cpp:218] Iteration 2976 (2.44333 iter/s, 4.91133s/12 iters), loss = 2.01264
I0409 23:14:05.593715  4596 solver.cpp:237]     Train net output #0: loss = 2.01264 (* 1 = 2.01264 loss)
I0409 23:14:05.593729  4596 sgd_solver.cpp:105] Iteration 2976, lr = 0.00554603
I0409 23:14:10.638226  4596 solver.cpp:218] Iteration 2988 (2.37889 iter/s, 5.04438s/12 iters), loss = 1.7605
I0409 23:14:10.638269  4596 solver.cpp:237]     Train net output #0: loss = 1.7605 (* 1 = 1.7605 loss)
I0409 23:14:10.638278  4596 sgd_solver.cpp:105] Iteration 2988, lr = 0.00553286
I0409 23:14:15.807022  4596 solver.cpp:218] Iteration 3000 (2.32171 iter/s, 5.16861s/12 iters), loss = 1.92154
I0409 23:14:15.807075  4596 solver.cpp:237]     Train net output #0: loss = 1.92154 (* 1 = 1.92154 loss)
I0409 23:14:15.807087  4596 sgd_solver.cpp:105] Iteration 3000, lr = 0.00551972
I0409 23:14:20.802575  4596 solver.cpp:218] Iteration 3012 (2.40223 iter/s, 4.99536s/12 iters), loss = 1.92121
I0409 23:14:20.802702  4596 solver.cpp:237]     Train net output #0: loss = 1.92121 (* 1 = 1.92121 loss)
I0409 23:14:20.802713  4596 sgd_solver.cpp:105] Iteration 3012, lr = 0.00550662
I0409 23:14:25.801762  4596 solver.cpp:218] Iteration 3024 (2.40051 iter/s, 4.99893s/12 iters), loss = 2.04412
I0409 23:14:25.801810  4596 solver.cpp:237]     Train net output #0: loss = 2.04412 (* 1 = 2.04412 loss)
I0409 23:14:25.801818  4596 sgd_solver.cpp:105] Iteration 3024, lr = 0.00549354
I0409 23:14:29.743729  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:14:30.763413  4596 solver.cpp:218] Iteration 3036 (2.41864 iter/s, 4.96147s/12 iters), loss = 1.84202
I0409 23:14:30.763473  4596 solver.cpp:237]     Train net output #0: loss = 1.84202 (* 1 = 1.84202 loss)
I0409 23:14:30.763486  4596 sgd_solver.cpp:105] Iteration 3036, lr = 0.0054805
I0409 23:14:35.687121  4596 solver.cpp:218] Iteration 3048 (2.43728 iter/s, 4.92352s/12 iters), loss = 1.68771
I0409 23:14:35.687176  4596 solver.cpp:237]     Train net output #0: loss = 1.68771 (* 1 = 1.68771 loss)
I0409 23:14:35.687187  4596 sgd_solver.cpp:105] Iteration 3048, lr = 0.00546749
I0409 23:14:40.147346  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3060.caffemodel
I0409 23:14:40.581526  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3060.solverstate
I0409 23:14:40.903888  4596 solver.cpp:330] Iteration 3060, Testing net (#0)
I0409 23:14:40.903908  4596 net.cpp:676] Ignoring source layer train-data
I0409 23:14:44.070834  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:14:45.288854  4596 solver.cpp:397]     Test net output #0: accuracy = 0.338848
I0409 23:14:45.288902  4596 solver.cpp:397]     Test net output #1: loss = 2.81636 (* 1 = 2.81636 loss)
I0409 23:14:45.370599  4596 solver.cpp:218] Iteration 3060 (1.23926 iter/s, 9.68318s/12 iters), loss = 2.0019
I0409 23:14:45.370644  4596 solver.cpp:237]     Train net output #0: loss = 2.0019 (* 1 = 2.0019 loss)
I0409 23:14:45.370656  4596 sgd_solver.cpp:105] Iteration 3060, lr = 0.00545451
I0409 23:14:49.451419  4596 solver.cpp:218] Iteration 3072 (2.9407 iter/s, 4.08066s/12 iters), loss = 1.87325
I0409 23:14:49.451470  4596 solver.cpp:237]     Train net output #0: loss = 1.87325 (* 1 = 1.87325 loss)
I0409 23:14:49.451483  4596 sgd_solver.cpp:105] Iteration 3072, lr = 0.00544156
I0409 23:14:54.302736  4596 solver.cpp:218] Iteration 3084 (2.47365 iter/s, 4.85113s/12 iters), loss = 1.67305
I0409 23:14:54.302897  4596 solver.cpp:237]     Train net output #0: loss = 1.67305 (* 1 = 1.67305 loss)
I0409 23:14:54.302911  4596 sgd_solver.cpp:105] Iteration 3084, lr = 0.00542864
I0409 23:14:59.264016  4596 solver.cpp:218] Iteration 3096 (2.41887 iter/s, 4.96099s/12 iters), loss = 1.69045
I0409 23:14:59.264065  4596 solver.cpp:237]     Train net output #0: loss = 1.69045 (* 1 = 1.69045 loss)
I0409 23:14:59.264076  4596 sgd_solver.cpp:105] Iteration 3096, lr = 0.00541575
I0409 23:15:04.248095  4596 solver.cpp:218] Iteration 3108 (2.40775 iter/s, 4.9839s/12 iters), loss = 1.75045
I0409 23:15:04.248137  4596 solver.cpp:237]     Train net output #0: loss = 1.75045 (* 1 = 1.75045 loss)
I0409 23:15:04.248147  4596 sgd_solver.cpp:105] Iteration 3108, lr = 0.00540289
I0409 23:15:09.271569  4596 solver.cpp:218] Iteration 3120 (2.38887 iter/s, 5.02329s/12 iters), loss = 1.60802
I0409 23:15:09.271621  4596 solver.cpp:237]     Train net output #0: loss = 1.60802 (* 1 = 1.60802 loss)
I0409 23:15:09.271631  4596 sgd_solver.cpp:105] Iteration 3120, lr = 0.00539006
I0409 23:15:14.291887  4596 solver.cpp:218] Iteration 3132 (2.39038 iter/s, 5.02013s/12 iters), loss = 2.19696
I0409 23:15:14.291939  4596 solver.cpp:237]     Train net output #0: loss = 2.19696 (* 1 = 2.19696 loss)
I0409 23:15:14.291951  4596 sgd_solver.cpp:105] Iteration 3132, lr = 0.00537727
I0409 23:15:15.414739  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:15:19.315287  4596 solver.cpp:218] Iteration 3144 (2.38891 iter/s, 5.02322s/12 iters), loss = 1.73396
I0409 23:15:19.315328  4596 solver.cpp:237]     Train net output #0: loss = 1.73396 (* 1 = 1.73396 loss)
I0409 23:15:19.315337  4596 sgd_solver.cpp:105] Iteration 3144, lr = 0.0053645
I0409 23:15:24.265810  4596 solver.cpp:218] Iteration 3156 (2.42407 iter/s, 4.95035s/12 iters), loss = 1.96001
I0409 23:15:24.265857  4596 solver.cpp:237]     Train net output #0: loss = 1.96001 (* 1 = 1.96001 loss)
I0409 23:15:24.265868  4596 sgd_solver.cpp:105] Iteration 3156, lr = 0.00535176
I0409 23:15:26.243233  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3162.caffemodel
I0409 23:15:27.562294  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3162.solverstate
I0409 23:15:27.867986  4596 solver.cpp:330] Iteration 3162, Testing net (#0)
I0409 23:15:27.868016  4596 net.cpp:676] Ignoring source layer train-data
I0409 23:15:31.262039  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:15:32.587271  4596 solver.cpp:397]     Test net output #0: accuracy = 0.348652
I0409 23:15:32.587319  4596 solver.cpp:397]     Test net output #1: loss = 2.8156 (* 1 = 2.8156 loss)
I0409 23:15:34.333303  4596 solver.cpp:218] Iteration 3168 (1.19199 iter/s, 10.0672s/12 iters), loss = 1.57315
I0409 23:15:34.333362  4596 solver.cpp:237]     Train net output #0: loss = 1.57315 (* 1 = 1.57315 loss)
I0409 23:15:34.333376  4596 sgd_solver.cpp:105] Iteration 3168, lr = 0.00533906
I0409 23:15:39.199167  4596 solver.cpp:218] Iteration 3180 (2.46625 iter/s, 4.86568s/12 iters), loss = 1.79858
I0409 23:15:39.199213  4596 solver.cpp:237]     Train net output #0: loss = 1.79858 (* 1 = 1.79858 loss)
I0409 23:15:39.199223  4596 sgd_solver.cpp:105] Iteration 3180, lr = 0.00532638
I0409 23:15:44.243259  4596 solver.cpp:218] Iteration 3192 (2.37911 iter/s, 5.04391s/12 iters), loss = 1.96942
I0409 23:15:44.243310  4596 solver.cpp:237]     Train net output #0: loss = 1.96942 (* 1 = 1.96942 loss)
I0409 23:15:44.243322  4596 sgd_solver.cpp:105] Iteration 3192, lr = 0.00531374
I0409 23:15:49.186733  4596 solver.cpp:218] Iteration 3204 (2.42753 iter/s, 4.94329s/12 iters), loss = 1.92659
I0409 23:15:49.186779  4596 solver.cpp:237]     Train net output #0: loss = 1.92659 (* 1 = 1.92659 loss)
I0409 23:15:49.186789  4596 sgd_solver.cpp:105] Iteration 3204, lr = 0.00530112
I0409 23:15:54.152215  4596 solver.cpp:218] Iteration 3216 (2.41677 iter/s, 4.9653s/12 iters), loss = 1.65542
I0409 23:15:54.152273  4596 solver.cpp:237]     Train net output #0: loss = 1.65542 (* 1 = 1.65542 loss)
I0409 23:15:54.152287  4596 sgd_solver.cpp:105] Iteration 3216, lr = 0.00528853
I0409 23:15:59.102635  4596 solver.cpp:218] Iteration 3228 (2.42413 iter/s, 4.95023s/12 iters), loss = 1.85103
I0409 23:15:59.102799  4596 solver.cpp:237]     Train net output #0: loss = 1.85103 (* 1 = 1.85103 loss)
I0409 23:15:59.102814  4596 sgd_solver.cpp:105] Iteration 3228, lr = 0.00527598
I0409 23:16:02.333395  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:16:04.104902  4596 solver.cpp:218] Iteration 3240 (2.39905 iter/s, 5.00197s/12 iters), loss = 1.68138
I0409 23:16:04.104957  4596 solver.cpp:237]     Train net output #0: loss = 1.68138 (* 1 = 1.68138 loss)
I0409 23:16:04.104969  4596 sgd_solver.cpp:105] Iteration 3240, lr = 0.00526345
I0409 23:16:09.289252  4596 solver.cpp:218] Iteration 3252 (2.31474 iter/s, 5.18416s/12 iters), loss = 1.51864
I0409 23:16:09.289305  4596 solver.cpp:237]     Train net output #0: loss = 1.51864 (* 1 = 1.51864 loss)
I0409 23:16:09.289316  4596 sgd_solver.cpp:105] Iteration 3252, lr = 0.00525095
I0409 23:16:13.731356  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3264.caffemodel
I0409 23:16:14.231221  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3264.solverstate
I0409 23:16:14.872735  4596 solver.cpp:330] Iteration 3264, Testing net (#0)
I0409 23:16:14.872751  4596 net.cpp:676] Ignoring source layer train-data
I0409 23:16:18.361356  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:16:20.010358  4596 solver.cpp:397]     Test net output #0: accuracy = 0.346814
I0409 23:16:20.010387  4596 solver.cpp:397]     Test net output #1: loss = 2.90745 (* 1 = 2.90745 loss)
I0409 23:16:20.091950  4596 solver.cpp:218] Iteration 3264 (1.11087 iter/s, 10.8024s/12 iters), loss = 1.86367
I0409 23:16:20.091995  4596 solver.cpp:237]     Train net output #0: loss = 1.86367 (* 1 = 1.86367 loss)
I0409 23:16:20.092003  4596 sgd_solver.cpp:105] Iteration 3264, lr = 0.00523849
I0409 23:16:24.437034  4596 solver.cpp:218] Iteration 3276 (2.76185 iter/s, 4.34492s/12 iters), loss = 1.686
I0409 23:16:24.437085  4596 solver.cpp:237]     Train net output #0: loss = 1.686 (* 1 = 1.686 loss)
I0409 23:16:24.437095  4596 sgd_solver.cpp:105] Iteration 3276, lr = 0.00522605
I0409 23:16:29.373929  4596 solver.cpp:218] Iteration 3288 (2.43078 iter/s, 4.93669s/12 iters), loss = 1.85183
I0409 23:16:29.374130  4596 solver.cpp:237]     Train net output #0: loss = 1.85183 (* 1 = 1.85183 loss)
I0409 23:16:29.374148  4596 sgd_solver.cpp:105] Iteration 3288, lr = 0.00521364
I0409 23:16:34.224706  4596 solver.cpp:218] Iteration 3300 (2.47399 iter/s, 4.85046s/12 iters), loss = 1.79899
I0409 23:16:34.224750  4596 solver.cpp:237]     Train net output #0: loss = 1.79899 (* 1 = 1.79899 loss)
I0409 23:16:34.224758  4596 sgd_solver.cpp:105] Iteration 3300, lr = 0.00520126
I0409 23:16:39.163632  4596 solver.cpp:218] Iteration 3312 (2.42977 iter/s, 4.93874s/12 iters), loss = 1.79084
I0409 23:16:39.163684  4596 solver.cpp:237]     Train net output #0: loss = 1.79084 (* 1 = 1.79084 loss)
I0409 23:16:39.163695  4596 sgd_solver.cpp:105] Iteration 3312, lr = 0.00518892
I0409 23:16:44.221662  4596 solver.cpp:218] Iteration 3324 (2.37257 iter/s, 5.0578s/12 iters), loss = 1.72068
I0409 23:16:44.221715  4596 solver.cpp:237]     Train net output #0: loss = 1.72068 (* 1 = 1.72068 loss)
I0409 23:16:44.221729  4596 sgd_solver.cpp:105] Iteration 3324, lr = 0.0051766
I0409 23:16:49.231142  4596 solver.cpp:218] Iteration 3336 (2.39555 iter/s, 5.00929s/12 iters), loss = 1.58004
I0409 23:16:49.231197  4596 solver.cpp:237]     Train net output #0: loss = 1.58004 (* 1 = 1.58004 loss)
I0409 23:16:49.231207  4596 sgd_solver.cpp:105] Iteration 3336, lr = 0.00516431
I0409 23:16:49.680434  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:16:54.280530  4596 solver.cpp:218] Iteration 3348 (2.37662 iter/s, 5.04919s/12 iters), loss = 1.96293
I0409 23:16:54.280587  4596 solver.cpp:237]     Train net output #0: loss = 1.96293 (* 1 = 1.96293 loss)
I0409 23:16:54.280599  4596 sgd_solver.cpp:105] Iteration 3348, lr = 0.00515204
I0409 23:16:59.212208  4596 solver.cpp:218] Iteration 3360 (2.43335 iter/s, 4.93148s/12 iters), loss = 1.58447
I0409 23:16:59.212271  4596 solver.cpp:237]     Train net output #0: loss = 1.58447 (* 1 = 1.58447 loss)
I0409 23:16:59.212285  4596 sgd_solver.cpp:105] Iteration 3360, lr = 0.00513981
I0409 23:17:01.194108  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3366.caffemodel
I0409 23:17:02.032609  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3366.solverstate
I0409 23:17:02.723033  4596 solver.cpp:330] Iteration 3366, Testing net (#0)
I0409 23:17:02.723067  4596 net.cpp:676] Ignoring source layer train-data
I0409 23:17:05.945287  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:17:07.306342  4596 solver.cpp:397]     Test net output #0: accuracy = 0.355392
I0409 23:17:07.306391  4596 solver.cpp:397]     Test net output #1: loss = 2.81404 (* 1 = 2.81404 loss)
I0409 23:17:09.387187  4596 solver.cpp:218] Iteration 3372 (1.1794 iter/s, 10.1747s/12 iters), loss = 1.66272
I0409 23:17:09.387230  4596 solver.cpp:237]     Train net output #0: loss = 1.66272 (* 1 = 1.66272 loss)
I0409 23:17:09.387239  4596 sgd_solver.cpp:105] Iteration 3372, lr = 0.00512761
I0409 23:17:14.425506  4596 solver.cpp:218] Iteration 3384 (2.38183 iter/s, 5.03813s/12 iters), loss = 1.85236
I0409 23:17:14.425565  4596 solver.cpp:237]     Train net output #0: loss = 1.85236 (* 1 = 1.85236 loss)
I0409 23:17:14.425575  4596 sgd_solver.cpp:105] Iteration 3384, lr = 0.00511544
I0409 23:17:19.336120  4596 solver.cpp:218] Iteration 3396 (2.44378 iter/s, 4.91042s/12 iters), loss = 1.65516
I0409 23:17:19.336163  4596 solver.cpp:237]     Train net output #0: loss = 1.65516 (* 1 = 1.65516 loss)
I0409 23:17:19.336172  4596 sgd_solver.cpp:105] Iteration 3396, lr = 0.00510329
I0409 23:17:24.250185  4596 solver.cpp:218] Iteration 3408 (2.44206 iter/s, 4.91389s/12 iters), loss = 1.49442
I0409 23:17:24.250233  4596 solver.cpp:237]     Train net output #0: loss = 1.49442 (* 1 = 1.49442 loss)
I0409 23:17:24.250242  4596 sgd_solver.cpp:105] Iteration 3408, lr = 0.00509117
I0409 23:17:29.144007  4596 solver.cpp:218] Iteration 3420 (2.45216 iter/s, 4.89364s/12 iters), loss = 1.55489
I0409 23:17:29.144059  4596 solver.cpp:237]     Train net output #0: loss = 1.55489 (* 1 = 1.55489 loss)
I0409 23:17:29.144069  4596 sgd_solver.cpp:105] Iteration 3420, lr = 0.00507909
I0409 23:17:34.069589  4596 solver.cpp:218] Iteration 3432 (2.43635 iter/s, 4.92539s/12 iters), loss = 1.71019
I0409 23:17:34.069787  4596 solver.cpp:237]     Train net output #0: loss = 1.71019 (* 1 = 1.71019 loss)
I0409 23:17:34.069803  4596 sgd_solver.cpp:105] Iteration 3432, lr = 0.00506703
I0409 23:17:36.639626  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:17:38.997973  4596 solver.cpp:218] Iteration 3444 (2.43504 iter/s, 4.92805s/12 iters), loss = 1.62137
I0409 23:17:38.998019  4596 solver.cpp:237]     Train net output #0: loss = 1.62137 (* 1 = 1.62137 loss)
I0409 23:17:38.998028  4596 sgd_solver.cpp:105] Iteration 3444, lr = 0.005055
I0409 23:17:43.928539  4596 solver.cpp:218] Iteration 3456 (2.43389 iter/s, 4.93039s/12 iters), loss = 1.54011
I0409 23:17:43.928583  4596 solver.cpp:237]     Train net output #0: loss = 1.54011 (* 1 = 1.54011 loss)
I0409 23:17:43.928592  4596 sgd_solver.cpp:105] Iteration 3456, lr = 0.005043
I0409 23:17:48.408916  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3468.caffemodel
I0409 23:17:48.829866  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3468.solverstate
I0409 23:17:49.125946  4596 solver.cpp:330] Iteration 3468, Testing net (#0)
I0409 23:17:49.125983  4596 net.cpp:676] Ignoring source layer train-data
I0409 23:17:49.153000  4596 blocking_queue.cpp:49] Waiting for data
I0409 23:17:52.540473  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:17:53.916962  4596 solver.cpp:397]     Test net output #0: accuracy = 0.367034
I0409 23:17:53.917001  4596 solver.cpp:397]     Test net output #1: loss = 2.76389 (* 1 = 2.76389 loss)
I0409 23:17:53.998715  4596 solver.cpp:218] Iteration 3468 (1.19167 iter/s, 10.0699s/12 iters), loss = 1.20295
I0409 23:17:53.998773  4596 solver.cpp:237]     Train net output #0: loss = 1.20295 (* 1 = 1.20295 loss)
I0409 23:17:53.998785  4596 sgd_solver.cpp:105] Iteration 3468, lr = 0.00503102
I0409 23:17:58.277457  4596 solver.cpp:218] Iteration 3480 (2.80468 iter/s, 4.27856s/12 iters), loss = 1.34578
I0409 23:17:58.277505  4596 solver.cpp:237]     Train net output #0: loss = 1.34578 (* 1 = 1.34578 loss)
I0409 23:17:58.277514  4596 sgd_solver.cpp:105] Iteration 3480, lr = 0.00501908
I0409 23:18:03.153774  4596 solver.cpp:218] Iteration 3492 (2.46097 iter/s, 4.87613s/12 iters), loss = 1.38608
I0409 23:18:03.153816  4596 solver.cpp:237]     Train net output #0: loss = 1.38608 (* 1 = 1.38608 loss)
I0409 23:18:03.153825  4596 sgd_solver.cpp:105] Iteration 3492, lr = 0.00500716
I0409 23:18:08.077756  4596 solver.cpp:218] Iteration 3504 (2.43714 iter/s, 4.9238s/12 iters), loss = 1.77895
I0409 23:18:08.077934  4596 solver.cpp:237]     Train net output #0: loss = 1.77895 (* 1 = 1.77895 loss)
I0409 23:18:08.077947  4596 sgd_solver.cpp:105] Iteration 3504, lr = 0.00499527
I0409 23:18:12.982254  4596 solver.cpp:218] Iteration 3516 (2.44689 iter/s, 4.90419s/12 iters), loss = 1.17048
I0409 23:18:12.982312  4596 solver.cpp:237]     Train net output #0: loss = 1.17048 (* 1 = 1.17048 loss)
I0409 23:18:12.982324  4596 sgd_solver.cpp:105] Iteration 3516, lr = 0.00498341
I0409 23:18:17.892249  4596 solver.cpp:218] Iteration 3528 (2.44409 iter/s, 4.9098s/12 iters), loss = 1.62118
I0409 23:18:17.892307  4596 solver.cpp:237]     Train net output #0: loss = 1.62118 (* 1 = 1.62118 loss)
I0409 23:18:17.892319  4596 sgd_solver.cpp:105] Iteration 3528, lr = 0.00497158
I0409 23:18:22.527442  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:18:22.778045  4596 solver.cpp:218] Iteration 3540 (2.45619 iter/s, 4.88561s/12 iters), loss = 1.08932
I0409 23:18:22.778090  4596 solver.cpp:237]     Train net output #0: loss = 1.08932 (* 1 = 1.08932 loss)
I0409 23:18:22.778097  4596 sgd_solver.cpp:105] Iteration 3540, lr = 0.00495978
I0409 23:18:27.731151  4596 solver.cpp:218] Iteration 3552 (2.42281 iter/s, 4.95293s/12 iters), loss = 1.64602
I0409 23:18:27.731207  4596 solver.cpp:237]     Train net output #0: loss = 1.64602 (* 1 = 1.64602 loss)
I0409 23:18:27.731220  4596 sgd_solver.cpp:105] Iteration 3552, lr = 0.004948
I0409 23:18:32.601419  4596 solver.cpp:218] Iteration 3564 (2.46403 iter/s, 4.87008s/12 iters), loss = 1.81717
I0409 23:18:32.601480  4596 solver.cpp:237]     Train net output #0: loss = 1.81717 (* 1 = 1.81717 loss)
I0409 23:18:32.601492  4596 sgd_solver.cpp:105] Iteration 3564, lr = 0.00493626
I0409 23:18:34.621397  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3570.caffemodel
I0409 23:18:35.037984  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3570.solverstate
I0409 23:18:35.332453  4596 solver.cpp:330] Iteration 3570, Testing net (#0)
I0409 23:18:35.332469  4596 net.cpp:676] Ignoring source layer train-data
I0409 23:18:38.361388  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:18:39.815753  4596 solver.cpp:397]     Test net output #0: accuracy = 0.36826
I0409 23:18:39.815804  4596 solver.cpp:397]     Test net output #1: loss = 2.77355 (* 1 = 2.77355 loss)
I0409 23:18:41.609668  4596 solver.cpp:218] Iteration 3576 (1.33215 iter/s, 9.00796s/12 iters), loss = 1.48321
I0409 23:18:41.609710  4596 solver.cpp:237]     Train net output #0: loss = 1.48321 (* 1 = 1.48321 loss)
I0409 23:18:41.609720  4596 sgd_solver.cpp:105] Iteration 3576, lr = 0.00492454
I0409 23:18:46.648602  4596 solver.cpp:218] Iteration 3588 (2.38154 iter/s, 5.03876s/12 iters), loss = 1.34471
I0409 23:18:46.648649  4596 solver.cpp:237]     Train net output #0: loss = 1.34471 (* 1 = 1.34471 loss)
I0409 23:18:46.648658  4596 sgd_solver.cpp:105] Iteration 3588, lr = 0.00491284
I0409 23:18:51.734228  4596 solver.cpp:218] Iteration 3600 (2.35968 iter/s, 5.08544s/12 iters), loss = 1.4324
I0409 23:18:51.734272  4596 solver.cpp:237]     Train net output #0: loss = 1.4324 (* 1 = 1.4324 loss)
I0409 23:18:51.734282  4596 sgd_solver.cpp:105] Iteration 3600, lr = 0.00490118
I0409 23:18:56.743556  4596 solver.cpp:218] Iteration 3612 (2.39562 iter/s, 5.00914s/12 iters), loss = 1.27649
I0409 23:18:56.743603  4596 solver.cpp:237]     Train net output #0: loss = 1.27649 (* 1 = 1.27649 loss)
I0409 23:18:56.743613  4596 sgd_solver.cpp:105] Iteration 3612, lr = 0.00488954
I0409 23:19:01.709487  4596 solver.cpp:218] Iteration 3624 (2.41656 iter/s, 4.96574s/12 iters), loss = 1.52179
I0409 23:19:01.709547  4596 solver.cpp:237]     Train net output #0: loss = 1.52179 (* 1 = 1.52179 loss)
I0409 23:19:01.709559  4596 sgd_solver.cpp:105] Iteration 3624, lr = 0.00487793
I0409 23:19:06.797981  4596 solver.cpp:218] Iteration 3636 (2.35836 iter/s, 5.08828s/12 iters), loss = 1.42426
I0409 23:19:06.798039  4596 solver.cpp:237]     Train net output #0: loss = 1.42426 (* 1 = 1.42426 loss)
I0409 23:19:06.798050  4596 sgd_solver.cpp:105] Iteration 3636, lr = 0.00486635
I0409 23:19:08.645181  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:19:11.752502  4596 solver.cpp:218] Iteration 3648 (2.42213 iter/s, 4.95433s/12 iters), loss = 1.45193
I0409 23:19:11.752552  4596 solver.cpp:237]     Train net output #0: loss = 1.45193 (* 1 = 1.45193 loss)
I0409 23:19:11.752560  4596 sgd_solver.cpp:105] Iteration 3648, lr = 0.0048548
I0409 23:19:16.676266  4596 solver.cpp:218] Iteration 3660 (2.43725 iter/s, 4.92358s/12 iters), loss = 1.28396
I0409 23:19:16.676324  4596 solver.cpp:237]     Train net output #0: loss = 1.28396 (* 1 = 1.28396 loss)
I0409 23:19:16.676337  4596 sgd_solver.cpp:105] Iteration 3660, lr = 0.00484327
I0409 23:19:21.113595  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3672.caffemodel
I0409 23:19:24.204869  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3672.solverstate
I0409 23:19:25.520267  4596 solver.cpp:330] Iteration 3672, Testing net (#0)
I0409 23:19:25.520293  4596 net.cpp:676] Ignoring source layer train-data
I0409 23:19:28.565856  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:19:30.213618  4596 solver.cpp:397]     Test net output #0: accuracy = 0.368873
I0409 23:19:30.213671  4596 solver.cpp:397]     Test net output #1: loss = 2.82706 (* 1 = 2.82706 loss)
I0409 23:19:30.295397  4596 solver.cpp:218] Iteration 3672 (0.88114 iter/s, 13.6187s/12 iters), loss = 1.1545
I0409 23:19:30.295454  4596 solver.cpp:237]     Train net output #0: loss = 1.1545 (* 1 = 1.1545 loss)
I0409 23:19:30.295465  4596 sgd_solver.cpp:105] Iteration 3672, lr = 0.00483177
I0409 23:19:34.468261  4596 solver.cpp:218] Iteration 3684 (2.87584 iter/s, 4.17269s/12 iters), loss = 1.29417
I0409 23:19:34.468312  4596 solver.cpp:237]     Train net output #0: loss = 1.29417 (* 1 = 1.29417 loss)
I0409 23:19:34.468322  4596 sgd_solver.cpp:105] Iteration 3684, lr = 0.0048203
I0409 23:19:39.397975  4596 solver.cpp:218] Iteration 3696 (2.43432 iter/s, 4.92952s/12 iters), loss = 1.15414
I0409 23:19:39.398118  4596 solver.cpp:237]     Train net output #0: loss = 1.15414 (* 1 = 1.15414 loss)
I0409 23:19:39.398133  4596 sgd_solver.cpp:105] Iteration 3696, lr = 0.00480886
I0409 23:19:44.746845  4596 solver.cpp:218] Iteration 3708 (2.24358 iter/s, 5.34859s/12 iters), loss = 1.39149
I0409 23:19:44.746883  4596 solver.cpp:237]     Train net output #0: loss = 1.39149 (* 1 = 1.39149 loss)
I0409 23:19:44.746891  4596 sgd_solver.cpp:105] Iteration 3708, lr = 0.00479744
I0409 23:19:49.650135  4596 solver.cpp:218] Iteration 3720 (2.44742 iter/s, 4.90311s/12 iters), loss = 1.43735
I0409 23:19:49.650184  4596 solver.cpp:237]     Train net output #0: loss = 1.43735 (* 1 = 1.43735 loss)
I0409 23:19:49.650193  4596 sgd_solver.cpp:105] Iteration 3720, lr = 0.00478605
I0409 23:19:54.565176  4596 solver.cpp:218] Iteration 3732 (2.44158 iter/s, 4.91486s/12 iters), loss = 0.994028
I0409 23:19:54.565227  4596 solver.cpp:237]     Train net output #0: loss = 0.994028 (* 1 = 0.994028 loss)
I0409 23:19:54.565237  4596 sgd_solver.cpp:105] Iteration 3732, lr = 0.00477469
I0409 23:19:58.514132  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:19:59.491036  4596 solver.cpp:218] Iteration 3744 (2.43622 iter/s, 4.92567s/12 iters), loss = 1.26747
I0409 23:19:59.491098  4596 solver.cpp:237]     Train net output #0: loss = 1.26747 (* 1 = 1.26747 loss)
I0409 23:19:59.491112  4596 sgd_solver.cpp:105] Iteration 3744, lr = 0.00476335
I0409 23:20:04.420508  4596 solver.cpp:218] Iteration 3756 (2.43444 iter/s, 4.92927s/12 iters), loss = 1.38036
I0409 23:20:04.420563  4596 solver.cpp:237]     Train net output #0: loss = 1.38036 (* 1 = 1.38036 loss)
I0409 23:20:04.420576  4596 sgd_solver.cpp:105] Iteration 3756, lr = 0.00475204
I0409 23:20:09.335369  4596 solver.cpp:218] Iteration 3768 (2.44167 iter/s, 4.91467s/12 iters), loss = 1.39215
I0409 23:20:09.335415  4596 solver.cpp:237]     Train net output #0: loss = 1.39215 (* 1 = 1.39215 loss)
I0409 23:20:09.335424  4596 sgd_solver.cpp:105] Iteration 3768, lr = 0.00474076
I0409 23:20:11.340471  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3774.caffemodel
I0409 23:20:11.738749  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3774.solverstate
I0409 23:20:13.419661  4596 solver.cpp:330] Iteration 3774, Testing net (#0)
I0409 23:20:13.419682  4596 net.cpp:676] Ignoring source layer train-data
I0409 23:20:16.537163  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:20:18.027487  4596 solver.cpp:397]     Test net output #0: accuracy = 0.363971
I0409 23:20:18.027540  4596 solver.cpp:397]     Test net output #1: loss = 2.87235 (* 1 = 2.87235 loss)
I0409 23:20:19.976202  4596 solver.cpp:218] Iteration 3780 (1.12777 iter/s, 10.6405s/12 iters), loss = 1.47755
I0409 23:20:19.976264  4596 solver.cpp:237]     Train net output #0: loss = 1.47755 (* 1 = 1.47755 loss)
I0409 23:20:19.976276  4596 sgd_solver.cpp:105] Iteration 3780, lr = 0.00472951
I0409 23:20:25.065984  4596 solver.cpp:218] Iteration 3792 (2.35777 iter/s, 5.08956s/12 iters), loss = 1.08598
I0409 23:20:25.066037  4596 solver.cpp:237]     Train net output #0: loss = 1.08598 (* 1 = 1.08598 loss)
I0409 23:20:25.066048  4596 sgd_solver.cpp:105] Iteration 3792, lr = 0.00471828
I0409 23:20:30.031477  4596 solver.cpp:218] Iteration 3804 (2.41677 iter/s, 4.9653s/12 iters), loss = 1.2788
I0409 23:20:30.031523  4596 solver.cpp:237]     Train net output #0: loss = 1.2788 (* 1 = 1.2788 loss)
I0409 23:20:30.031533  4596 sgd_solver.cpp:105] Iteration 3804, lr = 0.00470707
I0409 23:20:35.528436  4596 solver.cpp:218] Iteration 3816 (2.1831 iter/s, 5.49676s/12 iters), loss = 1.08596
I0409 23:20:35.528482  4596 solver.cpp:237]     Train net output #0: loss = 1.08596 (* 1 = 1.08596 loss)
I0409 23:20:35.528491  4596 sgd_solver.cpp:105] Iteration 3816, lr = 0.0046959
I0409 23:20:40.433395  4596 solver.cpp:218] Iteration 3828 (2.4466 iter/s, 4.90477s/12 iters), loss = 1.28804
I0409 23:20:40.433460  4596 solver.cpp:237]     Train net output #0: loss = 1.28804 (* 1 = 1.28804 loss)
I0409 23:20:40.433473  4596 sgd_solver.cpp:105] Iteration 3828, lr = 0.00468475
I0409 23:20:45.298135  4596 solver.cpp:218] Iteration 3840 (2.46684 iter/s, 4.86453s/12 iters), loss = 1.23491
I0409 23:20:45.298306  4596 solver.cpp:237]     Train net output #0: loss = 1.23491 (* 1 = 1.23491 loss)
I0409 23:20:45.298318  4596 sgd_solver.cpp:105] Iteration 3840, lr = 0.00467363
I0409 23:20:46.408946  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:20:50.478952  4596 solver.cpp:218] Iteration 3852 (2.31637 iter/s, 5.18051s/12 iters), loss = 1.1307
I0409 23:20:50.478994  4596 solver.cpp:237]     Train net output #0: loss = 1.1307 (* 1 = 1.1307 loss)
I0409 23:20:50.479003  4596 sgd_solver.cpp:105] Iteration 3852, lr = 0.00466253
I0409 23:20:55.421329  4596 solver.cpp:218] Iteration 3864 (2.42807 iter/s, 4.9422s/12 iters), loss = 1.26031
I0409 23:20:55.421370  4596 solver.cpp:237]     Train net output #0: loss = 1.26031 (* 1 = 1.26031 loss)
I0409 23:20:55.421378  4596 sgd_solver.cpp:105] Iteration 3864, lr = 0.00465146
I0409 23:20:59.950932  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3876.caffemodel
I0409 23:21:00.384372  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3876.solverstate
I0409 23:21:00.916074  4596 solver.cpp:330] Iteration 3876, Testing net (#0)
I0409 23:21:00.916097  4596 net.cpp:676] Ignoring source layer train-data
I0409 23:21:03.914894  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:21:05.456071  4596 solver.cpp:397]     Test net output #0: accuracy = 0.363358
I0409 23:21:05.456110  4596 solver.cpp:397]     Test net output #1: loss = 2.88603 (* 1 = 2.88603 loss)
I0409 23:21:05.537626  4596 solver.cpp:218] Iteration 3876 (1.18624 iter/s, 10.116s/12 iters), loss = 1.14619
I0409 23:21:05.537678  4596 solver.cpp:237]     Train net output #0: loss = 1.14619 (* 1 = 1.14619 loss)
I0409 23:21:05.537689  4596 sgd_solver.cpp:105] Iteration 3876, lr = 0.00464042
I0409 23:21:09.868517  4596 solver.cpp:218] Iteration 3888 (2.77091 iter/s, 4.33071s/12 iters), loss = 1.18363
I0409 23:21:09.868577  4596 solver.cpp:237]     Train net output #0: loss = 1.18363 (* 1 = 1.18363 loss)
I0409 23:21:09.868588  4596 sgd_solver.cpp:105] Iteration 3888, lr = 0.0046294
I0409 23:21:14.801295  4596 solver.cpp:218] Iteration 3900 (2.4328 iter/s, 4.93258s/12 iters), loss = 0.848825
I0409 23:21:14.801345  4596 solver.cpp:237]     Train net output #0: loss = 0.848825 (* 1 = 0.848825 loss)
I0409 23:21:14.801355  4596 sgd_solver.cpp:105] Iteration 3900, lr = 0.00461841
I0409 23:21:19.751556  4596 solver.cpp:218] Iteration 3912 (2.42421 iter/s, 4.95007s/12 iters), loss = 1.29113
I0409 23:21:19.751668  4596 solver.cpp:237]     Train net output #0: loss = 1.29113 (* 1 = 1.29113 loss)
I0409 23:21:19.751678  4596 sgd_solver.cpp:105] Iteration 3912, lr = 0.00460744
I0409 23:21:24.759538  4596 solver.cpp:218] Iteration 3924 (2.39629 iter/s, 5.00774s/12 iters), loss = 1.19345
I0409 23:21:24.759582  4596 solver.cpp:237]     Train net output #0: loss = 1.19345 (* 1 = 1.19345 loss)
I0409 23:21:24.759589  4596 sgd_solver.cpp:105] Iteration 3924, lr = 0.0045965
I0409 23:21:29.617413  4596 solver.cpp:218] Iteration 3936 (2.47031 iter/s, 4.85769s/12 iters), loss = 1.25527
I0409 23:21:29.617475  4596 solver.cpp:237]     Train net output #0: loss = 1.25527 (* 1 = 1.25527 loss)
I0409 23:21:29.617488  4596 sgd_solver.cpp:105] Iteration 3936, lr = 0.00458559
I0409 23:21:33.059502  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:21:34.657115  4596 solver.cpp:218] Iteration 3948 (2.38119 iter/s, 5.0395s/12 iters), loss = 1.36489
I0409 23:21:34.657176  4596 solver.cpp:237]     Train net output #0: loss = 1.36489 (* 1 = 1.36489 loss)
I0409 23:21:34.657188  4596 sgd_solver.cpp:105] Iteration 3948, lr = 0.0045747
I0409 23:21:39.615478  4596 solver.cpp:218] Iteration 3960 (2.42025 iter/s, 4.95816s/12 iters), loss = 1.32624
I0409 23:21:39.615525  4596 solver.cpp:237]     Train net output #0: loss = 1.32624 (* 1 = 1.32624 loss)
I0409 23:21:39.615533  4596 sgd_solver.cpp:105] Iteration 3960, lr = 0.00456384
I0409 23:21:44.532387  4596 solver.cpp:218] Iteration 3972 (2.44065 iter/s, 4.91672s/12 iters), loss = 1.0115
I0409 23:21:44.532435  4596 solver.cpp:237]     Train net output #0: loss = 1.0115 (* 1 = 1.0115 loss)
I0409 23:21:44.532446  4596 sgd_solver.cpp:105] Iteration 3972, lr = 0.00455301
I0409 23:21:46.527076  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3978.caffemodel
I0409 23:21:46.957501  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3978.solverstate
I0409 23:21:47.438127  4596 solver.cpp:330] Iteration 3978, Testing net (#0)
I0409 23:21:47.438148  4596 net.cpp:676] Ignoring source layer train-data
I0409 23:21:50.632697  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:21:52.209468  4596 solver.cpp:397]     Test net output #0: accuracy = 0.403799
I0409 23:21:52.209520  4596 solver.cpp:397]     Test net output #1: loss = 2.80889 (* 1 = 2.80889 loss)
I0409 23:21:54.434806  4596 solver.cpp:218] Iteration 3984 (1.21186 iter/s, 9.90211s/12 iters), loss = 0.890881
I0409 23:21:54.434859  4596 solver.cpp:237]     Train net output #0: loss = 0.890881 (* 1 = 0.890881 loss)
I0409 23:21:54.434870  4596 sgd_solver.cpp:105] Iteration 3984, lr = 0.0045422
I0409 23:21:59.904521  4596 solver.cpp:218] Iteration 3996 (2.19398 iter/s, 5.46951s/12 iters), loss = 0.990241
I0409 23:21:59.904572  4596 solver.cpp:237]     Train net output #0: loss = 0.990241 (* 1 = 0.990241 loss)
I0409 23:21:59.904582  4596 sgd_solver.cpp:105] Iteration 3996, lr = 0.00453141
I0409 23:22:04.830055  4596 solver.cpp:218] Iteration 4008 (2.43638 iter/s, 4.92535s/12 iters), loss = 1.04793
I0409 23:22:04.830121  4596 solver.cpp:237]     Train net output #0: loss = 1.04793 (* 1 = 1.04793 loss)
I0409 23:22:04.830132  4596 sgd_solver.cpp:105] Iteration 4008, lr = 0.00452066
I0409 23:22:09.772562  4596 solver.cpp:218] Iteration 4020 (2.42801 iter/s, 4.94231s/12 iters), loss = 1.52396
I0409 23:22:09.772608  4596 solver.cpp:237]     Train net output #0: loss = 1.52396 (* 1 = 1.52396 loss)
I0409 23:22:09.772615  4596 sgd_solver.cpp:105] Iteration 4020, lr = 0.00450992
I0409 23:22:15.094836  4596 solver.cpp:218] Iteration 4032 (2.25476 iter/s, 5.32208s/12 iters), loss = 0.891917
I0409 23:22:15.094889  4596 solver.cpp:237]     Train net output #0: loss = 0.891917 (* 1 = 0.891917 loss)
I0409 23:22:15.094900  4596 sgd_solver.cpp:105] Iteration 4032, lr = 0.00449921
I0409 23:22:19.973398  4596 solver.cpp:218] Iteration 4044 (2.45983 iter/s, 4.87838s/12 iters), loss = 1.04617
I0409 23:22:19.973440  4596 solver.cpp:237]     Train net output #0: loss = 1.04617 (* 1 = 1.04617 loss)
I0409 23:22:19.973449  4596 sgd_solver.cpp:105] Iteration 4044, lr = 0.00448853
I0409 23:22:20.452195  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:22:24.877341  4596 solver.cpp:218] Iteration 4056 (2.4471 iter/s, 4.90376s/12 iters), loss = 1.29667
I0409 23:22:24.881127  4596 solver.cpp:237]     Train net output #0: loss = 1.29667 (* 1 = 1.29667 loss)
I0409 23:22:24.881140  4596 sgd_solver.cpp:105] Iteration 4056, lr = 0.00447788
I0409 23:22:29.732293  4596 solver.cpp:218] Iteration 4068 (2.4737 iter/s, 4.85104s/12 iters), loss = 1.49217
I0409 23:22:29.732350  4596 solver.cpp:237]     Train net output #0: loss = 1.49217 (* 1 = 1.49217 loss)
I0409 23:22:29.732362  4596 sgd_solver.cpp:105] Iteration 4068, lr = 0.00446724
I0409 23:22:34.137540  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4080.caffemodel
I0409 23:22:34.584251  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4080.solverstate
I0409 23:22:34.875877  4596 solver.cpp:330] Iteration 4080, Testing net (#0)
I0409 23:22:34.875900  4596 net.cpp:676] Ignoring source layer train-data
I0409 23:22:37.803470  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:22:39.443498  4596 solver.cpp:397]     Test net output #0: accuracy = 0.393995
I0409 23:22:39.443533  4596 solver.cpp:397]     Test net output #1: loss = 2.80175 (* 1 = 2.80175 loss)
I0409 23:22:39.524943  4596 solver.cpp:218] Iteration 4080 (1.22545 iter/s, 9.79234s/12 iters), loss = 1.10224
I0409 23:22:39.524987  4596 solver.cpp:237]     Train net output #0: loss = 1.10224 (* 1 = 1.10224 loss)
I0409 23:22:39.524996  4596 sgd_solver.cpp:105] Iteration 4080, lr = 0.00445664
I0409 23:22:43.708495  4596 solver.cpp:218] Iteration 4092 (2.86849 iter/s, 4.18338s/12 iters), loss = 1.19569
I0409 23:22:43.708554  4596 solver.cpp:237]     Train net output #0: loss = 1.19569 (* 1 = 1.19569 loss)
I0409 23:22:43.708564  4596 sgd_solver.cpp:105] Iteration 4092, lr = 0.00444606
I0409 23:22:48.664644  4596 solver.cpp:218] Iteration 4104 (2.42133 iter/s, 4.95595s/12 iters), loss = 0.883765
I0409 23:22:48.664700  4596 solver.cpp:237]     Train net output #0: loss = 0.883765 (* 1 = 0.883765 loss)
I0409 23:22:48.664711  4596 sgd_solver.cpp:105] Iteration 4104, lr = 0.0044355
I0409 23:22:53.600524  4596 solver.cpp:218] Iteration 4116 (2.43127 iter/s, 4.93569s/12 iters), loss = 1.21945
I0409 23:22:53.600584  4596 solver.cpp:237]     Train net output #0: loss = 1.21945 (* 1 = 1.21945 loss)
I0409 23:22:53.600595  4596 sgd_solver.cpp:105] Iteration 4116, lr = 0.00442497
I0409 23:22:58.498030  4596 solver.cpp:218] Iteration 4128 (2.45032 iter/s, 4.89731s/12 iters), loss = 0.975412
I0409 23:22:58.498176  4596 solver.cpp:237]     Train net output #0: loss = 0.975412 (* 1 = 0.975412 loss)
I0409 23:22:58.498188  4596 sgd_solver.cpp:105] Iteration 4128, lr = 0.00441447
I0409 23:23:03.598954  4596 solver.cpp:218] Iteration 4140 (2.35265 iter/s, 5.10064s/12 iters), loss = 1.55466
I0409 23:23:03.599012  4596 solver.cpp:237]     Train net output #0: loss = 1.55466 (* 1 = 1.55466 loss)
I0409 23:23:03.599025  4596 sgd_solver.cpp:105] Iteration 4140, lr = 0.00440398
I0409 23:23:06.373394  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:23:08.737749  4596 solver.cpp:218] Iteration 4152 (2.33527 iter/s, 5.1386s/12 iters), loss = 0.92074
I0409 23:23:08.737803  4596 solver.cpp:237]     Train net output #0: loss = 0.92074 (* 1 = 0.92074 loss)
I0409 23:23:08.737814  4596 sgd_solver.cpp:105] Iteration 4152, lr = 0.00439353
I0409 23:23:08.738077  4596 blocking_queue.cpp:49] Waiting for data
I0409 23:23:13.679484  4596 solver.cpp:218] Iteration 4164 (2.42839 iter/s, 4.94155s/12 iters), loss = 1.36579
I0409 23:23:13.679529  4596 solver.cpp:237]     Train net output #0: loss = 1.36579 (* 1 = 1.36579 loss)
I0409 23:23:13.679538  4596 sgd_solver.cpp:105] Iteration 4164, lr = 0.0043831
I0409 23:23:18.625566  4596 solver.cpp:218] Iteration 4176 (2.42625 iter/s, 4.9459s/12 iters), loss = 1.10888
I0409 23:23:18.625619  4596 solver.cpp:237]     Train net output #0: loss = 1.10888 (* 1 = 1.10888 loss)
I0409 23:23:18.625630  4596 sgd_solver.cpp:105] Iteration 4176, lr = 0.00437269
I0409 23:23:20.644058  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4182.caffemodel
I0409 23:23:21.052306  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4182.solverstate
I0409 23:23:21.342763  4596 solver.cpp:330] Iteration 4182, Testing net (#0)
I0409 23:23:21.342787  4596 net.cpp:676] Ignoring source layer train-data
I0409 23:23:24.110525  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:23:25.797389  4596 solver.cpp:397]     Test net output #0: accuracy = 0.403186
I0409 23:23:25.797437  4596 solver.cpp:397]     Test net output #1: loss = 2.77978 (* 1 = 2.77978 loss)
I0409 23:23:27.618503  4596 solver.cpp:218] Iteration 4188 (1.33442 iter/s, 8.99264s/12 iters), loss = 1.1465
I0409 23:23:27.618558  4596 solver.cpp:237]     Train net output #0: loss = 1.1465 (* 1 = 1.1465 loss)
I0409 23:23:27.618569  4596 sgd_solver.cpp:105] Iteration 4188, lr = 0.00436231
I0409 23:23:32.677918  4596 solver.cpp:218] Iteration 4200 (2.37191 iter/s, 5.05922s/12 iters), loss = 0.964553
I0409 23:23:32.678086  4596 solver.cpp:237]     Train net output #0: loss = 0.964553 (* 1 = 0.964553 loss)
I0409 23:23:32.678095  4596 sgd_solver.cpp:105] Iteration 4200, lr = 0.00435195
I0409 23:23:37.581856  4596 solver.cpp:218] Iteration 4212 (2.44716 iter/s, 4.90364s/12 iters), loss = 1.11987
I0409 23:23:37.581912  4596 solver.cpp:237]     Train net output #0: loss = 1.11987 (* 1 = 1.11987 loss)
I0409 23:23:37.581923  4596 sgd_solver.cpp:105] Iteration 4212, lr = 0.00434162
I0409 23:23:42.463105  4596 solver.cpp:218] Iteration 4224 (2.45848 iter/s, 4.88106s/12 iters), loss = 1.04959
I0409 23:23:42.463151  4596 solver.cpp:237]     Train net output #0: loss = 1.04959 (* 1 = 1.04959 loss)
I0409 23:23:42.463160  4596 sgd_solver.cpp:105] Iteration 4224, lr = 0.00433131
I0409 23:23:47.430584  4596 solver.cpp:218] Iteration 4236 (2.4158 iter/s, 4.9673s/12 iters), loss = 0.867238
I0409 23:23:47.430639  4596 solver.cpp:237]     Train net output #0: loss = 0.867238 (* 1 = 0.867238 loss)
I0409 23:23:47.430649  4596 sgd_solver.cpp:105] Iteration 4236, lr = 0.00432103
I0409 23:23:52.600541  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:23:52.834302  4596 solver.cpp:218] Iteration 4248 (2.22077 iter/s, 5.40352s/12 iters), loss = 0.955624
I0409 23:23:52.834348  4596 solver.cpp:237]     Train net output #0: loss = 0.955624 (* 1 = 0.955624 loss)
I0409 23:23:52.834357  4596 sgd_solver.cpp:105] Iteration 4248, lr = 0.00431077
I0409 23:23:58.138448  4596 solver.cpp:218] Iteration 4260 (2.26246 iter/s, 5.30395s/12 iters), loss = 1.38397
I0409 23:23:58.138495  4596 solver.cpp:237]     Train net output #0: loss = 1.38397 (* 1 = 1.38397 loss)
I0409 23:23:58.138504  4596 sgd_solver.cpp:105] Iteration 4260, lr = 0.00430053
I0409 23:24:03.202545  4596 solver.cpp:218] Iteration 4272 (2.36971 iter/s, 5.06391s/12 iters), loss = 1.06467
I0409 23:24:03.202651  4596 solver.cpp:237]     Train net output #0: loss = 1.06467 (* 1 = 1.06467 loss)
I0409 23:24:03.202661  4596 sgd_solver.cpp:105] Iteration 4272, lr = 0.00429032
I0409 23:24:07.623174  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4284.caffemodel
I0409 23:24:08.575444  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4284.solverstate
I0409 23:24:10.264971  4596 solver.cpp:330] Iteration 4284, Testing net (#0)
I0409 23:24:10.265002  4596 net.cpp:676] Ignoring source layer train-data
I0409 23:24:12.994202  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:24:14.766243  4596 solver.cpp:397]     Test net output #0: accuracy = 0.39951
I0409 23:24:14.766281  4596 solver.cpp:397]     Test net output #1: loss = 2.8253 (* 1 = 2.8253 loss)
I0409 23:24:14.847827  4596 solver.cpp:218] Iteration 4284 (1.0305 iter/s, 11.6449s/12 iters), loss = 0.955667
I0409 23:24:14.847882  4596 solver.cpp:237]     Train net output #0: loss = 0.955667 (* 1 = 0.955667 loss)
I0409 23:24:14.847892  4596 sgd_solver.cpp:105] Iteration 4284, lr = 0.00428014
I0409 23:24:19.141463  4596 solver.cpp:218] Iteration 4296 (2.79495 iter/s, 4.29346s/12 iters), loss = 1.16554
I0409 23:24:19.141525  4596 solver.cpp:237]     Train net output #0: loss = 1.16554 (* 1 = 1.16554 loss)
I0409 23:24:19.141536  4596 sgd_solver.cpp:105] Iteration 4296, lr = 0.00426998
I0409 23:24:24.029237  4596 solver.cpp:218] Iteration 4308 (2.4552 iter/s, 4.88758s/12 iters), loss = 0.891316
I0409 23:24:24.029299  4596 solver.cpp:237]     Train net output #0: loss = 0.891316 (* 1 = 0.891316 loss)
I0409 23:24:24.029310  4596 sgd_solver.cpp:105] Iteration 4308, lr = 0.00425984
I0409 23:24:28.988823  4596 solver.cpp:218] Iteration 4320 (2.41965 iter/s, 4.95939s/12 iters), loss = 1.13226
I0409 23:24:28.988873  4596 solver.cpp:237]     Train net output #0: loss = 1.13226 (* 1 = 1.13226 loss)
I0409 23:24:28.988881  4596 sgd_solver.cpp:105] Iteration 4320, lr = 0.00424972
I0409 23:24:34.116014  4596 solver.cpp:218] Iteration 4332 (2.34055 iter/s, 5.127s/12 iters), loss = 0.901951
I0409 23:24:34.126049  4596 solver.cpp:237]     Train net output #0: loss = 0.901951 (* 1 = 0.901951 loss)
I0409 23:24:34.126062  4596 sgd_solver.cpp:105] Iteration 4332, lr = 0.00423964
I0409 23:24:39.090451  4596 solver.cpp:218] Iteration 4344 (2.41727 iter/s, 4.96428s/12 iters), loss = 0.986387
I0409 23:24:39.090502  4596 solver.cpp:237]     Train net output #0: loss = 0.986387 (* 1 = 0.986387 loss)
I0409 23:24:39.090509  4596 sgd_solver.cpp:105] Iteration 4344, lr = 0.00422957
I0409 23:24:40.951958  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:24:44.017130  4596 solver.cpp:218] Iteration 4356 (2.43581 iter/s, 4.92649s/12 iters), loss = 1.11677
I0409 23:24:44.017179  4596 solver.cpp:237]     Train net output #0: loss = 1.11677 (* 1 = 1.11677 loss)
I0409 23:24:44.017189  4596 sgd_solver.cpp:105] Iteration 4356, lr = 0.00421953
I0409 23:24:49.216454  4596 solver.cpp:218] Iteration 4368 (2.30808 iter/s, 5.19913s/12 iters), loss = 1.03512
I0409 23:24:49.216514  4596 solver.cpp:237]     Train net output #0: loss = 1.03512 (* 1 = 1.03512 loss)
I0409 23:24:49.216526  4596 sgd_solver.cpp:105] Iteration 4368, lr = 0.00420951
I0409 23:24:54.099712  4596 solver.cpp:218] Iteration 4380 (2.45747 iter/s, 4.88306s/12 iters), loss = 0.959994
I0409 23:24:54.099772  4596 solver.cpp:237]     Train net output #0: loss = 0.959994 (* 1 = 0.959994 loss)
I0409 23:24:54.099784  4596 sgd_solver.cpp:105] Iteration 4380, lr = 0.00419952
I0409 23:24:56.069245  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4386.caffemodel
I0409 23:24:56.820145  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4386.solverstate
I0409 23:24:57.130551  4596 solver.cpp:330] Iteration 4386, Testing net (#0)
I0409 23:24:57.130576  4596 net.cpp:676] Ignoring source layer train-data
I0409 23:24:59.795222  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:25:01.584362  4596 solver.cpp:397]     Test net output #0: accuracy = 0.406863
I0409 23:25:01.584393  4596 solver.cpp:397]     Test net output #1: loss = 2.76549 (* 1 = 2.76549 loss)
I0409 23:25:03.445299  4596 solver.cpp:218] Iteration 4392 (1.28407 iter/s, 9.34529s/12 iters), loss = 0.893664
I0409 23:25:03.445348  4596 solver.cpp:237]     Train net output #0: loss = 0.893664 (* 1 = 0.893664 loss)
I0409 23:25:03.445359  4596 sgd_solver.cpp:105] Iteration 4392, lr = 0.00418954
I0409 23:25:08.420485  4596 solver.cpp:218] Iteration 4404 (2.41206 iter/s, 4.975s/12 iters), loss = 0.794704
I0409 23:25:08.420615  4596 solver.cpp:237]     Train net output #0: loss = 0.794704 (* 1 = 0.794704 loss)
I0409 23:25:08.420627  4596 sgd_solver.cpp:105] Iteration 4404, lr = 0.0041796
I0409 23:25:13.297871  4596 solver.cpp:218] Iteration 4416 (2.46047 iter/s, 4.87712s/12 iters), loss = 0.988842
I0409 23:25:13.297937  4596 solver.cpp:237]     Train net output #0: loss = 0.988842 (* 1 = 0.988842 loss)
I0409 23:25:13.297948  4596 sgd_solver.cpp:105] Iteration 4416, lr = 0.00416967
I0409 23:25:18.544986  4596 solver.cpp:218] Iteration 4428 (2.28706 iter/s, 5.2469s/12 iters), loss = 1.13684
I0409 23:25:18.545047  4596 solver.cpp:237]     Train net output #0: loss = 1.13684 (* 1 = 1.13684 loss)
I0409 23:25:18.545058  4596 sgd_solver.cpp:105] Iteration 4428, lr = 0.00415977
I0409 23:25:23.499846  4596 solver.cpp:218] Iteration 4440 (2.42196 iter/s, 4.95466s/12 iters), loss = 0.883608
I0409 23:25:23.499895  4596 solver.cpp:237]     Train net output #0: loss = 0.883608 (* 1 = 0.883608 loss)
I0409 23:25:23.499907  4596 sgd_solver.cpp:105] Iteration 4440, lr = 0.0041499
I0409 23:25:27.598497  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:25:28.566421  4596 solver.cpp:218] Iteration 4452 (2.36855 iter/s, 5.06638s/12 iters), loss = 0.824358
I0409 23:25:28.566474  4596 solver.cpp:237]     Train net output #0: loss = 0.824358 (* 1 = 0.824358 loss)
I0409 23:25:28.566485  4596 sgd_solver.cpp:105] Iteration 4452, lr = 0.00414005
I0409 23:25:33.652046  4596 solver.cpp:218] Iteration 4464 (2.35968 iter/s, 5.08544s/12 iters), loss = 0.996354
I0409 23:25:33.652091  4596 solver.cpp:237]     Train net output #0: loss = 0.996354 (* 1 = 0.996354 loss)
I0409 23:25:33.652101  4596 sgd_solver.cpp:105] Iteration 4464, lr = 0.00413022
I0409 23:25:38.579596  4596 solver.cpp:218] Iteration 4476 (2.43538 iter/s, 4.92737s/12 iters), loss = 0.748831
I0409 23:25:38.579759  4596 solver.cpp:237]     Train net output #0: loss = 0.748831 (* 1 = 0.748831 loss)
I0409 23:25:38.579773  4596 sgd_solver.cpp:105] Iteration 4476, lr = 0.00412041
I0409 23:25:43.047184  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4488.caffemodel
I0409 23:25:43.568058  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4488.solverstate
I0409 23:25:43.947587  4596 solver.cpp:330] Iteration 4488, Testing net (#0)
I0409 23:25:43.947615  4596 net.cpp:676] Ignoring source layer train-data
I0409 23:25:46.740265  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:25:48.622409  4596 solver.cpp:397]     Test net output #0: accuracy = 0.41973
I0409 23:25:48.622457  4596 solver.cpp:397]     Test net output #1: loss = 2.73256 (* 1 = 2.73256 loss)
I0409 23:25:48.703929  4596 solver.cpp:218] Iteration 4488 (1.18531 iter/s, 10.1239s/12 iters), loss = 0.824095
I0409 23:25:48.703985  4596 solver.cpp:237]     Train net output #0: loss = 0.824095 (* 1 = 0.824095 loss)
I0409 23:25:48.703996  4596 sgd_solver.cpp:105] Iteration 4488, lr = 0.00411063
I0409 23:25:53.026466  4596 solver.cpp:218] Iteration 4500 (2.77626 iter/s, 4.32236s/12 iters), loss = 0.988574
I0409 23:25:53.026512  4596 solver.cpp:237]     Train net output #0: loss = 0.988574 (* 1 = 0.988574 loss)
I0409 23:25:53.026520  4596 sgd_solver.cpp:105] Iteration 4500, lr = 0.00410087
I0409 23:25:57.908208  4596 solver.cpp:218] Iteration 4512 (2.45823 iter/s, 4.88156s/12 iters), loss = 0.878356
I0409 23:25:57.908257  4596 solver.cpp:237]     Train net output #0: loss = 0.878356 (* 1 = 0.878356 loss)
I0409 23:25:57.908265  4596 sgd_solver.cpp:105] Iteration 4512, lr = 0.00409113
I0409 23:26:02.946902  4596 solver.cpp:218] Iteration 4524 (2.38166 iter/s, 5.03851s/12 iters), loss = 1.10817
I0409 23:26:02.946952  4596 solver.cpp:237]     Train net output #0: loss = 1.10817 (* 1 = 1.10817 loss)
I0409 23:26:02.946964  4596 sgd_solver.cpp:105] Iteration 4524, lr = 0.00408142
I0409 23:26:07.919988  4596 solver.cpp:218] Iteration 4536 (2.41308 iter/s, 4.9729s/12 iters), loss = 1.00083
I0409 23:26:07.920039  4596 solver.cpp:237]     Train net output #0: loss = 1.00083 (* 1 = 1.00083 loss)
I0409 23:26:07.920049  4596 sgd_solver.cpp:105] Iteration 4536, lr = 0.00407173
I0409 23:26:12.842103  4596 solver.cpp:218] Iteration 4548 (2.43807 iter/s, 4.92192s/12 iters), loss = 1.00004
I0409 23:26:12.842243  4596 solver.cpp:237]     Train net output #0: loss = 1.00004 (* 1 = 1.00004 loss)
I0409 23:26:12.842257  4596 sgd_solver.cpp:105] Iteration 4548, lr = 0.00406206
I0409 23:26:14.083130  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:26:17.740089  4596 solver.cpp:218] Iteration 4560 (2.45012 iter/s, 4.89771s/12 iters), loss = 0.795642
I0409 23:26:17.740150  4596 solver.cpp:237]     Train net output #0: loss = 0.795642 (* 1 = 0.795642 loss)
I0409 23:26:17.740162  4596 sgd_solver.cpp:105] Iteration 4560, lr = 0.00405242
I0409 23:26:22.663830  4596 solver.cpp:218] Iteration 4572 (2.43727 iter/s, 4.92354s/12 iters), loss = 0.999205
I0409 23:26:22.663878  4596 solver.cpp:237]     Train net output #0: loss = 0.999205 (* 1 = 0.999205 loss)
I0409 23:26:22.663887  4596 sgd_solver.cpp:105] Iteration 4572, lr = 0.0040428
I0409 23:26:27.588104  4596 solver.cpp:218] Iteration 4584 (2.437 iter/s, 4.92409s/12 iters), loss = 0.832093
I0409 23:26:27.588166  4596 solver.cpp:237]     Train net output #0: loss = 0.832093 (* 1 = 0.832093 loss)
I0409 23:26:27.588181  4596 sgd_solver.cpp:105] Iteration 4584, lr = 0.0040332
I0409 23:26:29.868077  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4590.caffemodel
I0409 23:26:30.274897  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4590.solverstate
I0409 23:26:30.560536  4596 solver.cpp:330] Iteration 4590, Testing net (#0)
I0409 23:26:30.560555  4596 net.cpp:676] Ignoring source layer train-data
I0409 23:26:33.151145  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:26:35.094980  4596 solver.cpp:397]     Test net output #0: accuracy = 0.414828
I0409 23:26:35.095027  4596 solver.cpp:397]     Test net output #1: loss = 2.73566 (* 1 = 2.73566 loss)
I0409 23:26:36.825119  4596 solver.cpp:218] Iteration 4596 (1.29916 iter/s, 9.23671s/12 iters), loss = 1.04115
I0409 23:26:36.825177  4596 solver.cpp:237]     Train net output #0: loss = 1.04115 (* 1 = 1.04115 loss)
I0409 23:26:36.825189  4596 sgd_solver.cpp:105] Iteration 4596, lr = 0.00402362
I0409 23:26:41.757632  4596 solver.cpp:218] Iteration 4608 (2.43293 iter/s, 4.93232s/12 iters), loss = 0.968838
I0409 23:26:41.757694  4596 solver.cpp:237]     Train net output #0: loss = 0.968838 (* 1 = 0.968838 loss)
I0409 23:26:41.757705  4596 sgd_solver.cpp:105] Iteration 4608, lr = 0.00401407
I0409 23:26:46.618257  4596 solver.cpp:218] Iteration 4620 (2.46892 iter/s, 4.86043s/12 iters), loss = 0.61076
I0409 23:26:46.618386  4596 solver.cpp:237]     Train net output #0: loss = 0.61076 (* 1 = 0.61076 loss)
I0409 23:26:46.618396  4596 sgd_solver.cpp:105] Iteration 4620, lr = 0.00400454
I0409 23:26:51.494158  4596 solver.cpp:218] Iteration 4632 (2.46122 iter/s, 4.87564s/12 iters), loss = 0.816236
I0409 23:26:51.494207  4596 solver.cpp:237]     Train net output #0: loss = 0.816236 (* 1 = 0.816236 loss)
I0409 23:26:51.494217  4596 sgd_solver.cpp:105] Iteration 4632, lr = 0.00399503
I0409 23:26:56.451514  4596 solver.cpp:218] Iteration 4644 (2.42074 iter/s, 4.95717s/12 iters), loss = 1.08744
I0409 23:26:56.451572  4596 solver.cpp:237]     Train net output #0: loss = 1.08744 (* 1 = 1.08744 loss)
I0409 23:26:56.451583  4596 sgd_solver.cpp:105] Iteration 4644, lr = 0.00398555
I0409 23:26:59.805534  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:27:01.406038  4596 solver.cpp:218] Iteration 4656 (2.42212 iter/s, 4.95434s/12 iters), loss = 0.702899
I0409 23:27:01.406075  4596 solver.cpp:237]     Train net output #0: loss = 0.702899 (* 1 = 0.702899 loss)
I0409 23:27:01.406082  4596 sgd_solver.cpp:105] Iteration 4656, lr = 0.00397608
I0409 23:27:06.355515  4596 solver.cpp:218] Iteration 4668 (2.42458 iter/s, 4.9493s/12 iters), loss = 0.837338
I0409 23:27:06.355571  4596 solver.cpp:237]     Train net output #0: loss = 0.837338 (* 1 = 0.837338 loss)
I0409 23:27:06.355581  4596 sgd_solver.cpp:105] Iteration 4668, lr = 0.00396664
I0409 23:27:11.616207  4596 solver.cpp:218] Iteration 4680 (2.28116 iter/s, 5.26049s/12 iters), loss = 0.842944
I0409 23:27:11.616273  4596 solver.cpp:237]     Train net output #0: loss = 0.842944 (* 1 = 0.842944 loss)
I0409 23:27:11.616289  4596 sgd_solver.cpp:105] Iteration 4680, lr = 0.00395723
I0409 23:27:16.161095  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4692.caffemodel
I0409 23:27:16.696403  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4692.solverstate
I0409 23:27:17.010195  4596 solver.cpp:330] Iteration 4692, Testing net (#0)
I0409 23:27:17.010218  4596 net.cpp:676] Ignoring source layer train-data
I0409 23:27:19.683017  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:27:21.545547  4596 solver.cpp:397]     Test net output #0: accuracy = 0.442402
I0409 23:27:21.545594  4596 solver.cpp:397]     Test net output #1: loss = 2.72067 (* 1 = 2.72067 loss)
I0409 23:27:21.627491  4596 solver.cpp:218] Iteration 4692 (1.19869 iter/s, 10.011s/12 iters), loss = 0.830335
I0409 23:27:21.627539  4596 solver.cpp:237]     Train net output #0: loss = 0.830335 (* 1 = 0.830335 loss)
I0409 23:27:21.627550  4596 sgd_solver.cpp:105] Iteration 4692, lr = 0.00394783
I0409 23:27:25.781368  4596 solver.cpp:218] Iteration 4704 (2.88898 iter/s, 4.15371s/12 iters), loss = 0.890381
I0409 23:27:25.781422  4596 solver.cpp:237]     Train net output #0: loss = 0.890381 (* 1 = 0.890381 loss)
I0409 23:27:25.781433  4596 sgd_solver.cpp:105] Iteration 4704, lr = 0.00393846
I0409 23:27:30.663350  4596 solver.cpp:218] Iteration 4716 (2.45811 iter/s, 4.8818s/12 iters), loss = 0.785408
I0409 23:27:30.663398  4596 solver.cpp:237]     Train net output #0: loss = 0.785408 (* 1 = 0.785408 loss)
I0409 23:27:30.663410  4596 sgd_solver.cpp:105] Iteration 4716, lr = 0.00392911
I0409 23:27:36.063731  4596 solver.cpp:218] Iteration 4728 (2.22215 iter/s, 5.40019s/12 iters), loss = 0.81158
I0409 23:27:36.063776  4596 solver.cpp:237]     Train net output #0: loss = 0.81158 (* 1 = 0.81158 loss)
I0409 23:27:36.063784  4596 sgd_solver.cpp:105] Iteration 4728, lr = 0.00391978
I0409 23:27:41.109524  4596 solver.cpp:218] Iteration 4740 (2.37831 iter/s, 5.04559s/12 iters), loss = 0.767536
I0409 23:27:41.109597  4596 solver.cpp:237]     Train net output #0: loss = 0.767536 (* 1 = 0.767536 loss)
I0409 23:27:41.109613  4596 sgd_solver.cpp:105] Iteration 4740, lr = 0.00391047
I0409 23:27:45.990537  4596 solver.cpp:218] Iteration 4752 (2.45861 iter/s, 4.88081s/12 iters), loss = 1.1056
I0409 23:27:45.990579  4596 solver.cpp:237]     Train net output #0: loss = 1.1056 (* 1 = 1.1056 loss)
I0409 23:27:45.990588  4596 sgd_solver.cpp:105] Iteration 4752, lr = 0.00390119
I0409 23:27:46.527518  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:27:50.952131  4596 solver.cpp:218] Iteration 4764 (2.41867 iter/s, 4.96141s/12 iters), loss = 0.826953
I0409 23:27:50.954187  4596 solver.cpp:237]     Train net output #0: loss = 0.826953 (* 1 = 0.826953 loss)
I0409 23:27:50.954200  4596 sgd_solver.cpp:105] Iteration 4764, lr = 0.00389193
I0409 23:27:55.874948  4596 solver.cpp:218] Iteration 4776 (2.43871 iter/s, 4.92063s/12 iters), loss = 0.752398
I0409 23:27:55.875000  4596 solver.cpp:237]     Train net output #0: loss = 0.752398 (* 1 = 0.752398 loss)
I0409 23:27:55.875010  4596 sgd_solver.cpp:105] Iteration 4776, lr = 0.00388269
I0409 23:28:00.775465  4596 solver.cpp:218] Iteration 4788 (2.44882 iter/s, 4.90033s/12 iters), loss = 0.958528
I0409 23:28:00.775523  4596 solver.cpp:237]     Train net output #0: loss = 0.958528 (* 1 = 0.958528 loss)
I0409 23:28:00.775535  4596 sgd_solver.cpp:105] Iteration 4788, lr = 0.00387347
I0409 23:28:02.725898  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4794.caffemodel
I0409 23:28:03.178462  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4794.solverstate
I0409 23:28:03.494742  4596 solver.cpp:330] Iteration 4794, Testing net (#0)
I0409 23:28:03.494774  4596 net.cpp:676] Ignoring source layer train-data
I0409 23:28:06.046208  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:28:07.947885  4596 solver.cpp:397]     Test net output #0: accuracy = 0.433824
I0409 23:28:07.947933  4596 solver.cpp:397]     Test net output #1: loss = 2.80923 (* 1 = 2.80923 loss)
I0409 23:28:09.733403  4596 solver.cpp:218] Iteration 4800 (1.33964 iter/s, 8.95765s/12 iters), loss = 0.669222
I0409 23:28:09.733453  4596 solver.cpp:237]     Train net output #0: loss = 0.669222 (* 1 = 0.669222 loss)
I0409 23:28:09.733462  4596 sgd_solver.cpp:105] Iteration 4800, lr = 0.00386427
I0409 23:28:14.689849  4596 solver.cpp:218] Iteration 4812 (2.42118 iter/s, 4.95625s/12 iters), loss = 0.790007
I0409 23:28:14.689910  4596 solver.cpp:237]     Train net output #0: loss = 0.790007 (* 1 = 0.790007 loss)
I0409 23:28:14.689921  4596 sgd_solver.cpp:105] Iteration 4812, lr = 0.0038551
I0409 23:28:19.586010  4596 solver.cpp:218] Iteration 4824 (2.451 iter/s, 4.89596s/12 iters), loss = 0.66465
I0409 23:28:19.586071  4596 solver.cpp:237]     Train net output #0: loss = 0.66465 (* 1 = 0.66465 loss)
I0409 23:28:19.586083  4596 sgd_solver.cpp:105] Iteration 4824, lr = 0.00384594
I0409 23:28:24.463229  4596 solver.cpp:218] Iteration 4836 (2.46052 iter/s, 4.87702s/12 iters), loss = 0.628054
I0409 23:28:24.463362  4596 solver.cpp:237]     Train net output #0: loss = 0.628054 (* 1 = 0.628054 loss)
I0409 23:28:24.463374  4596 sgd_solver.cpp:105] Iteration 4836, lr = 0.00383681
I0409 23:28:24.816879  4596 blocking_queue.cpp:49] Waiting for data
I0409 23:28:29.424557  4596 solver.cpp:218] Iteration 4848 (2.41884 iter/s, 4.96106s/12 iters), loss = 0.675441
I0409 23:28:29.424615  4596 solver.cpp:237]     Train net output #0: loss = 0.675441 (* 1 = 0.675441 loss)
I0409 23:28:29.424628  4596 sgd_solver.cpp:105] Iteration 4848, lr = 0.0038277
I0409 23:28:32.041327  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:28:34.343569  4596 solver.cpp:218] Iteration 4860 (2.43961 iter/s, 4.91882s/12 iters), loss = 0.640569
I0409 23:28:34.343621  4596 solver.cpp:237]     Train net output #0: loss = 0.640569 (* 1 = 0.640569 loss)
I0409 23:28:34.343631  4596 sgd_solver.cpp:105] Iteration 4860, lr = 0.00381862
I0409 23:28:39.245373  4596 solver.cpp:218] Iteration 4872 (2.44817 iter/s, 4.90162s/12 iters), loss = 0.92722
I0409 23:28:39.245421  4596 solver.cpp:237]     Train net output #0: loss = 0.92722 (* 1 = 0.92722 loss)
I0409 23:28:39.245432  4596 sgd_solver.cpp:105] Iteration 4872, lr = 0.00380955
I0409 23:28:44.229984  4596 solver.cpp:218] Iteration 4884 (2.40751 iter/s, 4.98441s/12 iters), loss = 0.881682
I0409 23:28:44.230038  4596 solver.cpp:237]     Train net output #0: loss = 0.881682 (* 1 = 0.881682 loss)
I0409 23:28:44.230051  4596 sgd_solver.cpp:105] Iteration 4884, lr = 0.0038005
I0409 23:28:48.673323  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4896.caffemodel
I0409 23:28:49.424820  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4896.solverstate
I0409 23:28:49.722960  4596 solver.cpp:330] Iteration 4896, Testing net (#0)
I0409 23:28:49.722985  4596 net.cpp:676] Ignoring source layer train-data
I0409 23:28:52.224557  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:28:54.212713  4596 solver.cpp:397]     Test net output #0: accuracy = 0.451593
I0409 23:28:54.212751  4596 solver.cpp:397]     Test net output #1: loss = 2.68072 (* 1 = 2.68072 loss)
I0409 23:28:54.294013  4596 solver.cpp:218] Iteration 4896 (1.1924 iter/s, 10.0637s/12 iters), loss = 0.662266
I0409 23:28:54.294060  4596 solver.cpp:237]     Train net output #0: loss = 0.662266 (* 1 = 0.662266 loss)
I0409 23:28:54.294070  4596 sgd_solver.cpp:105] Iteration 4896, lr = 0.00379148
I0409 23:28:58.357285  4596 solver.cpp:218] Iteration 4908 (2.95341 iter/s, 4.06311s/12 iters), loss = 0.866329
I0409 23:28:58.357427  4596 solver.cpp:237]     Train net output #0: loss = 0.866329 (* 1 = 0.866329 loss)
I0409 23:28:58.357439  4596 sgd_solver.cpp:105] Iteration 4908, lr = 0.00378248
I0409 23:29:03.266295  4596 solver.cpp:218] Iteration 4920 (2.44462 iter/s, 4.90874s/12 iters), loss = 0.670892
I0409 23:29:03.266338  4596 solver.cpp:237]     Train net output #0: loss = 0.670892 (* 1 = 0.670892 loss)
I0409 23:29:03.266346  4596 sgd_solver.cpp:105] Iteration 4920, lr = 0.0037735
I0409 23:29:08.147523  4596 solver.cpp:218] Iteration 4932 (2.45849 iter/s, 4.88105s/12 iters), loss = 0.595614
I0409 23:29:08.147567  4596 solver.cpp:237]     Train net output #0: loss = 0.595614 (* 1 = 0.595614 loss)
I0409 23:29:08.147578  4596 sgd_solver.cpp:105] Iteration 4932, lr = 0.00376454
I0409 23:29:13.067586  4596 solver.cpp:218] Iteration 4944 (2.43908 iter/s, 4.91989s/12 iters), loss = 0.639458
I0409 23:29:13.067627  4596 solver.cpp:237]     Train net output #0: loss = 0.639458 (* 1 = 0.639458 loss)
I0409 23:29:13.067636  4596 sgd_solver.cpp:105] Iteration 4944, lr = 0.0037556
I0409 23:29:17.755911  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:29:17.951889  4596 solver.cpp:218] Iteration 4956 (2.45694 iter/s, 4.88413s/12 iters), loss = 0.717184
I0409 23:29:17.951933  4596 solver.cpp:237]     Train net output #0: loss = 0.717184 (* 1 = 0.717184 loss)
I0409 23:29:17.951946  4596 sgd_solver.cpp:105] Iteration 4956, lr = 0.00374669
I0409 23:29:23.156244  4596 solver.cpp:218] Iteration 4968 (2.30584 iter/s, 5.20417s/12 iters), loss = 0.648663
I0409 23:29:23.156286  4596 solver.cpp:237]     Train net output #0: loss = 0.648663 (* 1 = 0.648663 loss)
I0409 23:29:23.156296  4596 sgd_solver.cpp:105] Iteration 4968, lr = 0.00373779
I0409 23:29:28.010533  4596 solver.cpp:218] Iteration 4980 (2.47213 iter/s, 4.85411s/12 iters), loss = 0.679407
I0409 23:29:28.010581  4596 solver.cpp:237]     Train net output #0: loss = 0.679407 (* 1 = 0.679407 loss)
I0409 23:29:28.010591  4596 sgd_solver.cpp:105] Iteration 4980, lr = 0.00372892
I0409 23:29:32.908447  4596 solver.cpp:218] Iteration 4992 (2.45011 iter/s, 4.89773s/12 iters), loss = 0.754785
I0409 23:29:32.908568  4596 solver.cpp:237]     Train net output #0: loss = 0.754785 (* 1 = 0.754785 loss)
I0409 23:29:32.908581  4596 sgd_solver.cpp:105] Iteration 4992, lr = 0.00372006
I0409 23:29:34.907768  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4998.caffemodel
I0409 23:29:35.565310  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4998.solverstate
I0409 23:29:36.036923  4596 solver.cpp:330] Iteration 4998, Testing net (#0)
I0409 23:29:36.036950  4596 net.cpp:676] Ignoring source layer train-data
I0409 23:29:38.733743  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:29:40.771149  4596 solver.cpp:397]     Test net output #0: accuracy = 0.435662
I0409 23:29:40.771195  4596 solver.cpp:397]     Test net output #1: loss = 2.77714 (* 1 = 2.77714 loss)
I0409 23:29:42.671023  4596 solver.cpp:218] Iteration 5004 (1.22923 iter/s, 9.7622s/12 iters), loss = 0.797446
I0409 23:29:42.671083  4596 solver.cpp:237]     Train net output #0: loss = 0.797446 (* 1 = 0.797446 loss)
I0409 23:29:42.671095  4596 sgd_solver.cpp:105] Iteration 5004, lr = 0.00371123
I0409 23:29:47.727440  4596 solver.cpp:218] Iteration 5016 (2.37332 iter/s, 5.05622s/12 iters), loss = 0.648277
I0409 23:29:47.727494  4596 solver.cpp:237]     Train net output #0: loss = 0.648277 (* 1 = 0.648277 loss)
I0409 23:29:47.727506  4596 sgd_solver.cpp:105] Iteration 5016, lr = 0.00370242
I0409 23:29:52.682408  4596 solver.cpp:218] Iteration 5028 (2.4219 iter/s, 4.95478s/12 iters), loss = 0.728292
I0409 23:29:52.682456  4596 solver.cpp:237]     Train net output #0: loss = 0.728292 (* 1 = 0.728292 loss)
I0409 23:29:52.682464  4596 sgd_solver.cpp:105] Iteration 5028, lr = 0.00369363
I0409 23:29:57.646281  4596 solver.cpp:218] Iteration 5040 (2.41756 iter/s, 4.96369s/12 iters), loss = 0.775089
I0409 23:29:57.646327  4596 solver.cpp:237]     Train net output #0: loss = 0.775089 (* 1 = 0.775089 loss)
I0409 23:29:57.646337  4596 sgd_solver.cpp:105] Iteration 5040, lr = 0.00368486
I0409 23:30:02.573565  4596 solver.cpp:218] Iteration 5052 (2.43551 iter/s, 4.9271s/12 iters), loss = 0.70397
I0409 23:30:02.573623  4596 solver.cpp:237]     Train net output #0: loss = 0.70397 (* 1 = 0.70397 loss)
I0409 23:30:02.573637  4596 sgd_solver.cpp:105] Iteration 5052, lr = 0.00367611
I0409 23:30:04.497496  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:30:07.555752  4596 solver.cpp:218] Iteration 5064 (2.40868 iter/s, 4.98199s/12 iters), loss = 0.723808
I0409 23:30:07.555804  4596 solver.cpp:237]     Train net output #0: loss = 0.723808 (* 1 = 0.723808 loss)
I0409 23:30:07.555814  4596 sgd_solver.cpp:105] Iteration 5064, lr = 0.00366738
I0409 23:30:12.481742  4596 solver.cpp:218] Iteration 5076 (2.43615 iter/s, 4.9258s/12 iters), loss = 0.717409
I0409 23:30:12.481789  4596 solver.cpp:237]     Train net output #0: loss = 0.717409 (* 1 = 0.717409 loss)
I0409 23:30:12.481799  4596 sgd_solver.cpp:105] Iteration 5076, lr = 0.00365868
I0409 23:30:17.372902  4596 solver.cpp:218] Iteration 5088 (2.4535 iter/s, 4.89098s/12 iters), loss = 0.69447
I0409 23:30:17.372953  4596 solver.cpp:237]     Train net output #0: loss = 0.69447 (* 1 = 0.69447 loss)
I0409 23:30:17.372964  4596 sgd_solver.cpp:105] Iteration 5088, lr = 0.00364999
I0409 23:30:21.785198  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5100.caffemodel
I0409 23:30:22.550798  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5100.solverstate
I0409 23:30:22.856595  4596 solver.cpp:330] Iteration 5100, Testing net (#0)
I0409 23:30:22.856624  4596 net.cpp:676] Ignoring source layer train-data
I0409 23:30:25.296834  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:30:27.306164  4596 solver.cpp:397]     Test net output #0: accuracy = 0.443015
I0409 23:30:27.306212  4596 solver.cpp:397]     Test net output #1: loss = 2.75501 (* 1 = 2.75501 loss)
I0409 23:30:27.387828  4596 solver.cpp:218] Iteration 5100 (1.19825 iter/s, 10.0146s/12 iters), loss = 0.713758
I0409 23:30:27.387882  4596 solver.cpp:237]     Train net output #0: loss = 0.713758 (* 1 = 0.713758 loss)
I0409 23:30:27.387894  4596 sgd_solver.cpp:105] Iteration 5100, lr = 0.00364132
I0409 23:30:31.545032  4596 solver.cpp:218] Iteration 5112 (2.88667 iter/s, 4.15703s/12 iters), loss = 0.555094
I0409 23:30:31.545079  4596 solver.cpp:237]     Train net output #0: loss = 0.555094 (* 1 = 0.555094 loss)
I0409 23:30:31.545087  4596 sgd_solver.cpp:105] Iteration 5112, lr = 0.00363268
I0409 23:30:36.428128  4596 solver.cpp:218] Iteration 5124 (2.45755 iter/s, 4.88291s/12 iters), loss = 0.710947
I0409 23:30:36.428251  4596 solver.cpp:237]     Train net output #0: loss = 0.710947 (* 1 = 0.710947 loss)
I0409 23:30:36.428261  4596 sgd_solver.cpp:105] Iteration 5124, lr = 0.00362405
I0409 23:30:41.385162  4596 solver.cpp:218] Iteration 5136 (2.42093 iter/s, 4.95678s/12 iters), loss = 0.809491
I0409 23:30:41.385210  4596 solver.cpp:237]     Train net output #0: loss = 0.809491 (* 1 = 0.809491 loss)
I0409 23:30:41.385218  4596 sgd_solver.cpp:105] Iteration 5136, lr = 0.00361545
I0409 23:30:46.211040  4596 solver.cpp:218] Iteration 5148 (2.48669 iter/s, 4.82568s/12 iters), loss = 0.717554
I0409 23:30:46.211108  4596 solver.cpp:237]     Train net output #0: loss = 0.717554 (* 1 = 0.717554 loss)
I0409 23:30:46.211128  4596 sgd_solver.cpp:105] Iteration 5148, lr = 0.00360687
I0409 23:30:50.237702  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:30:51.240432  4596 solver.cpp:218] Iteration 5160 (2.38607 iter/s, 5.0292s/12 iters), loss = 0.964615
I0409 23:30:51.240473  4596 solver.cpp:237]     Train net output #0: loss = 0.964615 (* 1 = 0.964615 loss)
I0409 23:30:51.240483  4596 sgd_solver.cpp:105] Iteration 5160, lr = 0.0035983
I0409 23:30:56.422041  4596 solver.cpp:218] Iteration 5172 (2.31597 iter/s, 5.18142s/12 iters), loss = 0.519808
I0409 23:30:56.422098  4596 solver.cpp:237]     Train net output #0: loss = 0.519808 (* 1 = 0.519808 loss)
I0409 23:30:56.422109  4596 sgd_solver.cpp:105] Iteration 5172, lr = 0.00358976
I0409 23:31:01.328333  4596 solver.cpp:218] Iteration 5184 (2.44593 iter/s, 4.9061s/12 iters), loss = 0.647712
I0409 23:31:01.328387  4596 solver.cpp:237]     Train net output #0: loss = 0.647712 (* 1 = 0.647712 loss)
I0409 23:31:01.328400  4596 sgd_solver.cpp:105] Iteration 5184, lr = 0.00358124
I0409 23:31:06.309796  4596 solver.cpp:218] Iteration 5196 (2.40902 iter/s, 4.98127s/12 iters), loss = 0.779175
I0409 23:31:06.309855  4596 solver.cpp:237]     Train net output #0: loss = 0.779175 (* 1 = 0.779175 loss)
I0409 23:31:06.309868  4596 sgd_solver.cpp:105] Iteration 5196, lr = 0.00357273
I0409 23:31:08.311281  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5202.caffemodel
I0409 23:31:09.069187  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5202.solverstate
I0409 23:31:12.288201  4596 solver.cpp:330] Iteration 5202, Testing net (#0)
I0409 23:31:12.288234  4596 net.cpp:676] Ignoring source layer train-data
I0409 23:31:14.694669  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:31:16.737599  4596 solver.cpp:397]     Test net output #0: accuracy = 0.447304
I0409 23:31:16.737646  4596 solver.cpp:397]     Test net output #1: loss = 2.86909 (* 1 = 2.86909 loss)
I0409 23:31:18.578577  4596 solver.cpp:218] Iteration 5208 (0.978122 iter/s, 12.2684s/12 iters), loss = 0.701087
I0409 23:31:18.578636  4596 solver.cpp:237]     Train net output #0: loss = 0.701087 (* 1 = 0.701087 loss)
I0409 23:31:18.578649  4596 sgd_solver.cpp:105] Iteration 5208, lr = 0.00356425
I0409 23:31:23.505445  4596 solver.cpp:218] Iteration 5220 (2.43572 iter/s, 4.92667s/12 iters), loss = 0.672902
I0409 23:31:23.505497  4596 solver.cpp:237]     Train net output #0: loss = 0.672902 (* 1 = 0.672902 loss)
I0409 23:31:23.505508  4596 sgd_solver.cpp:105] Iteration 5220, lr = 0.00355579
I0409 23:31:28.494880  4596 solver.cpp:218] Iteration 5232 (2.40517 iter/s, 4.98925s/12 iters), loss = 0.582613
I0409 23:31:28.494938  4596 solver.cpp:237]     Train net output #0: loss = 0.582613 (* 1 = 0.582613 loss)
I0409 23:31:28.494951  4596 sgd_solver.cpp:105] Iteration 5232, lr = 0.00354735
I0409 23:31:33.365388  4596 solver.cpp:218] Iteration 5244 (2.46391 iter/s, 4.87032s/12 iters), loss = 0.522428
I0409 23:31:33.365443  4596 solver.cpp:237]     Train net output #0: loss = 0.522428 (* 1 = 0.522428 loss)
I0409 23:31:33.365456  4596 sgd_solver.cpp:105] Iteration 5244, lr = 0.00353892
I0409 23:31:38.309252  4596 solver.cpp:218] Iteration 5256 (2.42735 iter/s, 4.94367s/12 iters), loss = 0.582771
I0409 23:31:38.309309  4596 solver.cpp:237]     Train net output #0: loss = 0.582771 (* 1 = 0.582771 loss)
I0409 23:31:38.309321  4596 sgd_solver.cpp:105] Iteration 5256, lr = 0.00353052
I0409 23:31:39.590675  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:31:43.169353  4596 solver.cpp:218] Iteration 5268 (2.46918 iter/s, 4.85991s/12 iters), loss = 0.657382
I0409 23:31:43.169414  4596 solver.cpp:237]     Train net output #0: loss = 0.657382 (* 1 = 0.657382 loss)
I0409 23:31:43.169428  4596 sgd_solver.cpp:105] Iteration 5268, lr = 0.00352214
I0409 23:31:48.064855  4596 solver.cpp:218] Iteration 5280 (2.45133 iter/s, 4.89531s/12 iters), loss = 0.565872
I0409 23:31:48.064900  4596 solver.cpp:237]     Train net output #0: loss = 0.565872 (* 1 = 0.565872 loss)
I0409 23:31:48.064909  4596 sgd_solver.cpp:105] Iteration 5280, lr = 0.00351378
I0409 23:31:53.011809  4596 solver.cpp:218] Iteration 5292 (2.42583 iter/s, 4.94677s/12 iters), loss = 0.769615
I0409 23:31:53.011860  4596 solver.cpp:237]     Train net output #0: loss = 0.769615 (* 1 = 0.769615 loss)
I0409 23:31:53.011873  4596 sgd_solver.cpp:105] Iteration 5292, lr = 0.00350544
I0409 23:31:57.429740  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5304.caffemodel
I0409 23:31:58.038290  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5304.solverstate
I0409 23:31:58.605320  4596 solver.cpp:330] Iteration 5304, Testing net (#0)
I0409 23:31:58.605352  4596 net.cpp:676] Ignoring source layer train-data
I0409 23:32:00.947245  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:32:03.075573  4596 solver.cpp:397]     Test net output #0: accuracy = 0.439338
I0409 23:32:03.075616  4596 solver.cpp:397]     Test net output #1: loss = 2.86678 (* 1 = 2.86678 loss)
I0409 23:32:03.157100  4596 solver.cpp:218] Iteration 5304 (1.18285 iter/s, 10.145s/12 iters), loss = 0.732964
I0409 23:32:03.157150  4596 solver.cpp:237]     Train net output #0: loss = 0.732964 (* 1 = 0.732964 loss)
I0409 23:32:03.157160  4596 sgd_solver.cpp:105] Iteration 5304, lr = 0.00349711
I0409 23:32:07.350160  4596 solver.cpp:218] Iteration 5316 (2.86199 iter/s, 4.19289s/12 iters), loss = 0.631607
I0409 23:32:07.350222  4596 solver.cpp:237]     Train net output #0: loss = 0.631607 (* 1 = 0.631607 loss)
I0409 23:32:07.350234  4596 sgd_solver.cpp:105] Iteration 5316, lr = 0.00348881
I0409 23:32:12.294332  4596 solver.cpp:218] Iteration 5328 (2.4272 iter/s, 4.94398s/12 iters), loss = 0.630177
I0409 23:32:12.294438  4596 solver.cpp:237]     Train net output #0: loss = 0.630177 (* 1 = 0.630177 loss)
I0409 23:32:12.294448  4596 sgd_solver.cpp:105] Iteration 5328, lr = 0.00348053
I0409 23:32:17.332913  4596 solver.cpp:218] Iteration 5340 (2.38174 iter/s, 5.03834s/12 iters), loss = 0.576296
I0409 23:32:17.332960  4596 solver.cpp:237]     Train net output #0: loss = 0.576296 (* 1 = 0.576296 loss)
I0409 23:32:17.332970  4596 sgd_solver.cpp:105] Iteration 5340, lr = 0.00347226
I0409 23:32:22.237846  4596 solver.cpp:218] Iteration 5352 (2.44661 iter/s, 4.90475s/12 iters), loss = 0.648355
I0409 23:32:22.237892  4596 solver.cpp:237]     Train net output #0: loss = 0.648355 (* 1 = 0.648355 loss)
I0409 23:32:22.237903  4596 sgd_solver.cpp:105] Iteration 5352, lr = 0.00346402
I0409 23:32:25.567687  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:32:27.101728  4596 solver.cpp:218] Iteration 5364 (2.46726 iter/s, 4.8637s/12 iters), loss = 0.914493
I0409 23:32:27.101776  4596 solver.cpp:237]     Train net output #0: loss = 0.914493 (* 1 = 0.914493 loss)
I0409 23:32:27.101784  4596 sgd_solver.cpp:105] Iteration 5364, lr = 0.0034558
I0409 23:32:32.116058  4596 solver.cpp:218] Iteration 5376 (2.39323 iter/s, 5.01414s/12 iters), loss = 0.489849
I0409 23:32:32.116120  4596 solver.cpp:237]     Train net output #0: loss = 0.489849 (* 1 = 0.489849 loss)
I0409 23:32:32.116132  4596 sgd_solver.cpp:105] Iteration 5376, lr = 0.00344759
I0409 23:32:37.013020  4596 solver.cpp:218] Iteration 5388 (2.4506 iter/s, 4.89677s/12 iters), loss = 0.568871
I0409 23:32:37.013067  4596 solver.cpp:237]     Train net output #0: loss = 0.568871 (* 1 = 0.568871 loss)
I0409 23:32:37.013077  4596 sgd_solver.cpp:105] Iteration 5388, lr = 0.00343941
I0409 23:32:41.906596  4596 solver.cpp:218] Iteration 5400 (2.45229 iter/s, 4.89339s/12 iters), loss = 0.566669
I0409 23:32:41.906656  4596 solver.cpp:237]     Train net output #0: loss = 0.566669 (* 1 = 0.566669 loss)
I0409 23:32:41.906667  4596 sgd_solver.cpp:105] Iteration 5400, lr = 0.00343124
I0409 23:32:43.922348  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5406.caffemodel
I0409 23:32:44.692135  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5406.solverstate
I0409 23:32:45.002245  4596 solver.cpp:330] Iteration 5406, Testing net (#0)
I0409 23:32:45.002276  4596 net.cpp:676] Ignoring source layer train-data
I0409 23:32:47.387003  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:32:49.514355  4596 solver.cpp:397]     Test net output #0: accuracy = 0.445466
I0409 23:32:49.514405  4596 solver.cpp:397]     Test net output #1: loss = 2.82106 (* 1 = 2.82106 loss)
I0409 23:32:51.403924  4596 solver.cpp:218] Iteration 5412 (1.26355 iter/s, 9.49703s/12 iters), loss = 0.683606
I0409 23:32:51.403964  4596 solver.cpp:237]     Train net output #0: loss = 0.683606 (* 1 = 0.683606 loss)
I0409 23:32:51.403973  4596 sgd_solver.cpp:105] Iteration 5412, lr = 0.00342309
I0409 23:32:56.298275  4596 solver.cpp:218] Iteration 5424 (2.4519 iter/s, 4.89417s/12 iters), loss = 0.657094
I0409 23:32:56.298321  4596 solver.cpp:237]     Train net output #0: loss = 0.657094 (* 1 = 0.657094 loss)
I0409 23:32:56.298334  4596 sgd_solver.cpp:105] Iteration 5424, lr = 0.00341497
I0409 23:33:01.191457  4596 solver.cpp:218] Iteration 5436 (2.45248 iter/s, 4.893s/12 iters), loss = 0.61679
I0409 23:33:01.191498  4596 solver.cpp:237]     Train net output #0: loss = 0.61679 (* 1 = 0.61679 loss)
I0409 23:33:01.191506  4596 sgd_solver.cpp:105] Iteration 5436, lr = 0.00340686
I0409 23:33:06.249792  4596 solver.cpp:218] Iteration 5448 (2.37241 iter/s, 5.05816s/12 iters), loss = 0.546204
I0409 23:33:06.249845  4596 solver.cpp:237]     Train net output #0: loss = 0.546204 (* 1 = 0.546204 loss)
I0409 23:33:06.249856  4596 sgd_solver.cpp:105] Iteration 5448, lr = 0.00339877
I0409 23:33:11.237830  4596 solver.cpp:218] Iteration 5460 (2.40584 iter/s, 4.98785s/12 iters), loss = 0.627632
I0409 23:33:11.237874  4596 solver.cpp:237]     Train net output #0: loss = 0.627632 (* 1 = 0.627632 loss)
I0409 23:33:11.237882  4596 sgd_solver.cpp:105] Iteration 5460, lr = 0.0033907
I0409 23:33:11.778103  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:33:16.329931  4596 solver.cpp:218] Iteration 5472 (2.35668 iter/s, 5.09191s/12 iters), loss = 0.656691
I0409 23:33:16.330083  4596 solver.cpp:237]     Train net output #0: loss = 0.656691 (* 1 = 0.656691 loss)
I0409 23:33:16.330096  4596 sgd_solver.cpp:105] Iteration 5472, lr = 0.00338265
I0409 23:33:21.251272  4596 solver.cpp:218] Iteration 5484 (2.4385 iter/s, 4.92106s/12 iters), loss = 0.601238
I0409 23:33:21.251317  4596 solver.cpp:237]     Train net output #0: loss = 0.601238 (* 1 = 0.601238 loss)
I0409 23:33:21.251325  4596 sgd_solver.cpp:105] Iteration 5484, lr = 0.00337462
I0409 23:33:26.352867  4596 solver.cpp:218] Iteration 5496 (2.35229 iter/s, 5.10141s/12 iters), loss = 0.599687
I0409 23:33:26.352914  4596 solver.cpp:237]     Train net output #0: loss = 0.599687 (* 1 = 0.599687 loss)
I0409 23:33:26.352924  4596 sgd_solver.cpp:105] Iteration 5496, lr = 0.00336661
I0409 23:33:30.876093  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5508.caffemodel
I0409 23:33:31.298728  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5508.solverstate
I0409 23:33:31.614208  4596 solver.cpp:330] Iteration 5508, Testing net (#0)
I0409 23:33:31.614228  4596 net.cpp:676] Ignoring source layer train-data
I0409 23:33:33.754258  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:33:35.918294  4596 solver.cpp:397]     Test net output #0: accuracy = 0.466299
I0409 23:33:35.918345  4596 solver.cpp:397]     Test net output #1: loss = 2.82959 (* 1 = 2.82959 loss)
I0409 23:33:36.000011  4596 solver.cpp:218] Iteration 5508 (1.24393 iter/s, 9.64685s/12 iters), loss = 0.676283
I0409 23:33:36.000059  4596 solver.cpp:237]     Train net output #0: loss = 0.676283 (* 1 = 0.676283 loss)
I0409 23:33:36.000072  4596 sgd_solver.cpp:105] Iteration 5508, lr = 0.00335861
I0409 23:33:40.143754  4596 solver.cpp:218] Iteration 5520 (2.89605 iter/s, 4.14358s/12 iters), loss = 0.652976
I0409 23:33:40.143811  4596 solver.cpp:237]     Train net output #0: loss = 0.652976 (* 1 = 0.652976 loss)
I0409 23:33:40.143823  4596 sgd_solver.cpp:105] Iteration 5520, lr = 0.00335064
I0409 23:33:40.901495  4596 blocking_queue.cpp:49] Waiting for data
I0409 23:33:45.019857  4596 solver.cpp:218] Iteration 5532 (2.46108 iter/s, 4.87592s/12 iters), loss = 0.519512
I0409 23:33:45.019906  4596 solver.cpp:237]     Train net output #0: loss = 0.519512 (* 1 = 0.519512 loss)
I0409 23:33:45.019917  4596 sgd_solver.cpp:105] Iteration 5532, lr = 0.00334268
I0409 23:33:49.960435  4596 solver.cpp:218] Iteration 5544 (2.42896 iter/s, 4.94039s/12 iters), loss = 0.592826
I0409 23:33:49.960592  4596 solver.cpp:237]     Train net output #0: loss = 0.592826 (* 1 = 0.592826 loss)
I0409 23:33:49.960605  4596 sgd_solver.cpp:105] Iteration 5544, lr = 0.00333475
I0409 23:33:54.858583  4596 solver.cpp:218] Iteration 5556 (2.45005 iter/s, 4.89787s/12 iters), loss = 0.447672
I0409 23:33:54.858618  4596 solver.cpp:237]     Train net output #0: loss = 0.447672 (* 1 = 0.447672 loss)
I0409 23:33:54.858626  4596 sgd_solver.cpp:105] Iteration 5556, lr = 0.00332683
I0409 23:33:57.539041  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:33:59.809765  4596 solver.cpp:218] Iteration 5568 (2.42375 iter/s, 4.95101s/12 iters), loss = 0.448743
I0409 23:33:59.809815  4596 solver.cpp:237]     Train net output #0: loss = 0.448743 (* 1 = 0.448743 loss)
I0409 23:33:59.809828  4596 sgd_solver.cpp:105] Iteration 5568, lr = 0.00331893
I0409 23:34:04.729413  4596 solver.cpp:218] Iteration 5580 (2.43929 iter/s, 4.91947s/12 iters), loss = 0.611429
I0409 23:34:04.729455  4596 solver.cpp:237]     Train net output #0: loss = 0.611429 (* 1 = 0.611429 loss)
I0409 23:34:04.729465  4596 sgd_solver.cpp:105] Iteration 5580, lr = 0.00331105
I0409 23:34:09.623793  4596 solver.cpp:218] Iteration 5592 (2.45188 iter/s, 4.8942s/12 iters), loss = 0.527225
I0409 23:34:09.623843  4596 solver.cpp:237]     Train net output #0: loss = 0.527225 (* 1 = 0.527225 loss)
I0409 23:34:09.623855  4596 sgd_solver.cpp:105] Iteration 5592, lr = 0.00330319
I0409 23:34:14.553519  4596 solver.cpp:218] Iteration 5604 (2.4343 iter/s, 4.92954s/12 iters), loss = 0.496756
I0409 23:34:14.553566  4596 solver.cpp:237]     Train net output #0: loss = 0.496756 (* 1 = 0.496756 loss)
I0409 23:34:14.553575  4596 sgd_solver.cpp:105] Iteration 5604, lr = 0.00329535
I0409 23:34:16.543519  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5610.caffemodel
I0409 23:34:17.929664  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5610.solverstate
I0409 23:34:18.810737  4596 solver.cpp:330] Iteration 5610, Testing net (#0)
I0409 23:34:18.810755  4596 net.cpp:676] Ignoring source layer train-data
I0409 23:34:21.039297  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:34:23.287483  4596 solver.cpp:397]     Test net output #0: accuracy = 0.463235
I0409 23:34:23.287533  4596 solver.cpp:397]     Test net output #1: loss = 2.86945 (* 1 = 2.86945 loss)
I0409 23:34:25.070124  4596 solver.cpp:218] Iteration 5616 (1.14109 iter/s, 10.5163s/12 iters), loss = 0.503959
I0409 23:34:25.070173  4596 solver.cpp:237]     Train net output #0: loss = 0.503959 (* 1 = 0.503959 loss)
I0409 23:34:25.070181  4596 sgd_solver.cpp:105] Iteration 5616, lr = 0.00328752
I0409 23:34:30.013895  4596 solver.cpp:218] Iteration 5628 (2.42739 iter/s, 4.94358s/12 iters), loss = 0.51058
I0409 23:34:30.013943  4596 solver.cpp:237]     Train net output #0: loss = 0.51058 (* 1 = 0.51058 loss)
I0409 23:34:30.013952  4596 sgd_solver.cpp:105] Iteration 5628, lr = 0.00327972
I0409 23:34:35.018851  4596 solver.cpp:218] Iteration 5640 (2.39771 iter/s, 5.00477s/12 iters), loss = 0.558277
I0409 23:34:35.018900  4596 solver.cpp:237]     Train net output #0: loss = 0.558277 (* 1 = 0.558277 loss)
I0409 23:34:35.018910  4596 sgd_solver.cpp:105] Iteration 5640, lr = 0.00327193
I0409 23:34:39.932430  4596 solver.cpp:218] Iteration 5652 (2.44231 iter/s, 4.91339s/12 iters), loss = 0.3645
I0409 23:34:39.932479  4596 solver.cpp:237]     Train net output #0: loss = 0.3645 (* 1 = 0.3645 loss)
I0409 23:34:39.932490  4596 sgd_solver.cpp:105] Iteration 5652, lr = 0.00326416
I0409 23:34:44.673986  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:34:44.839076  4596 solver.cpp:218] Iteration 5664 (2.44575 iter/s, 4.90646s/12 iters), loss = 0.490427
I0409 23:34:44.839125  4596 solver.cpp:237]     Train net output #0: loss = 0.490427 (* 1 = 0.490427 loss)
I0409 23:34:44.839135  4596 sgd_solver.cpp:105] Iteration 5664, lr = 0.00325641
I0409 23:34:49.765633  4596 solver.cpp:218] Iteration 5676 (2.43587 iter/s, 4.92637s/12 iters), loss = 0.540868
I0409 23:34:49.765681  4596 solver.cpp:237]     Train net output #0: loss = 0.540868 (* 1 = 0.540868 loss)
I0409 23:34:49.765689  4596 sgd_solver.cpp:105] Iteration 5676, lr = 0.00324868
I0409 23:34:54.692965  4596 solver.cpp:218] Iteration 5688 (2.43548 iter/s, 4.92715s/12 iters), loss = 0.507478
I0409 23:34:54.693069  4596 solver.cpp:237]     Train net output #0: loss = 0.507478 (* 1 = 0.507478 loss)
I0409 23:34:54.693079  4596 sgd_solver.cpp:105] Iteration 5688, lr = 0.00324097
I0409 23:34:59.614735  4596 solver.cpp:218] Iteration 5700 (2.43827 iter/s, 4.92153s/12 iters), loss = 0.463803
I0409 23:34:59.614789  4596 solver.cpp:237]     Train net output #0: loss = 0.463803 (* 1 = 0.463803 loss)
I0409 23:34:59.614801  4596 sgd_solver.cpp:105] Iteration 5700, lr = 0.00323328
I0409 23:35:04.062968  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5712.caffemodel
I0409 23:35:06.955608  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5712.solverstate
I0409 23:35:07.411836  4596 solver.cpp:330] Iteration 5712, Testing net (#0)
I0409 23:35:07.411864  4596 net.cpp:676] Ignoring source layer train-data
I0409 23:35:09.566692  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:35:11.842433  4596 solver.cpp:397]     Test net output #0: accuracy = 0.459559
I0409 23:35:11.842483  4596 solver.cpp:397]     Test net output #1: loss = 2.82897 (* 1 = 2.82897 loss)
I0409 23:35:11.923933  4596 solver.cpp:218] Iteration 5712 (0.97491 iter/s, 12.3088s/12 iters), loss = 0.448367
I0409 23:35:11.923986  4596 solver.cpp:237]     Train net output #0: loss = 0.448367 (* 1 = 0.448367 loss)
I0409 23:35:11.923998  4596 sgd_solver.cpp:105] Iteration 5712, lr = 0.0032256
I0409 23:35:16.156507  4596 solver.cpp:218] Iteration 5724 (2.83527 iter/s, 4.2324s/12 iters), loss = 0.600169
I0409 23:35:16.156553  4596 solver.cpp:237]     Train net output #0: loss = 0.600169 (* 1 = 0.600169 loss)
I0409 23:35:16.156563  4596 sgd_solver.cpp:105] Iteration 5724, lr = 0.00321794
I0409 23:35:21.081841  4596 solver.cpp:218] Iteration 5736 (2.43647 iter/s, 4.92515s/12 iters), loss = 0.585043
I0409 23:35:21.081895  4596 solver.cpp:237]     Train net output #0: loss = 0.585043 (* 1 = 0.585043 loss)
I0409 23:35:21.081907  4596 sgd_solver.cpp:105] Iteration 5736, lr = 0.0032103
I0409 23:35:25.996933  4596 solver.cpp:218] Iteration 5748 (2.44155 iter/s, 4.9149s/12 iters), loss = 0.462177
I0409 23:35:26.001401  4596 solver.cpp:237]     Train net output #0: loss = 0.462177 (* 1 = 0.462177 loss)
I0409 23:35:26.001411  4596 sgd_solver.cpp:105] Iteration 5748, lr = 0.00320268
I0409 23:35:30.895253  4596 solver.cpp:218] Iteration 5760 (2.45212 iter/s, 4.89372s/12 iters), loss = 0.503526
I0409 23:35:30.895301  4596 solver.cpp:237]     Train net output #0: loss = 0.503526 (* 1 = 0.503526 loss)
I0409 23:35:30.895311  4596 sgd_solver.cpp:105] Iteration 5760, lr = 0.00319508
I0409 23:35:32.852815  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:35:35.871814  4596 solver.cpp:218] Iteration 5772 (2.4114 iter/s, 4.97637s/12 iters), loss = 0.530202
I0409 23:35:35.871868  4596 solver.cpp:237]     Train net output #0: loss = 0.530202 (* 1 = 0.530202 loss)
I0409 23:35:35.871879  4596 sgd_solver.cpp:105] Iteration 5772, lr = 0.00318749
I0409 23:35:40.756767  4596 solver.cpp:218] Iteration 5784 (2.45662 iter/s, 4.88477s/12 iters), loss = 0.42835
I0409 23:35:40.756815  4596 solver.cpp:237]     Train net output #0: loss = 0.42835 (* 1 = 0.42835 loss)
I0409 23:35:40.756824  4596 sgd_solver.cpp:105] Iteration 5784, lr = 0.00317992
I0409 23:35:45.638226  4596 solver.cpp:218] Iteration 5796 (2.45838 iter/s, 4.88127s/12 iters), loss = 0.623886
I0409 23:35:45.638275  4596 solver.cpp:237]     Train net output #0: loss = 0.623886 (* 1 = 0.623886 loss)
I0409 23:35:45.638284  4596 sgd_solver.cpp:105] Iteration 5796, lr = 0.00317237
I0409 23:35:50.575623  4596 solver.cpp:218] Iteration 5808 (2.43052 iter/s, 4.93721s/12 iters), loss = 0.529234
I0409 23:35:50.575677  4596 solver.cpp:237]     Train net output #0: loss = 0.529234 (* 1 = 0.529234 loss)
I0409 23:35:50.575690  4596 sgd_solver.cpp:105] Iteration 5808, lr = 0.00316484
I0409 23:35:52.586078  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5814.caffemodel
I0409 23:35:53.005445  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5814.solverstate
I0409 23:35:53.300566  4596 solver.cpp:330] Iteration 5814, Testing net (#0)
I0409 23:35:53.300592  4596 net.cpp:676] Ignoring source layer train-data
I0409 23:35:55.363788  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:35:57.654156  4596 solver.cpp:397]     Test net output #0: accuracy = 0.460784
I0409 23:35:57.654275  4596 solver.cpp:397]     Test net output #1: loss = 2.83939 (* 1 = 2.83939 loss)
I0409 23:35:59.541244  4596 solver.cpp:218] Iteration 5820 (1.33849 iter/s, 8.96534s/12 iters), loss = 0.510699
I0409 23:35:59.541288  4596 solver.cpp:237]     Train net output #0: loss = 0.510699 (* 1 = 0.510699 loss)
I0409 23:35:59.541298  4596 sgd_solver.cpp:105] Iteration 5820, lr = 0.00315733
I0409 23:36:04.472795  4596 solver.cpp:218] Iteration 5832 (2.4334 iter/s, 4.93137s/12 iters), loss = 0.757452
I0409 23:36:04.472846  4596 solver.cpp:237]     Train net output #0: loss = 0.757452 (* 1 = 0.757452 loss)
I0409 23:36:04.472856  4596 sgd_solver.cpp:105] Iteration 5832, lr = 0.00314983
I0409 23:36:09.457538  4596 solver.cpp:218] Iteration 5844 (2.40744 iter/s, 4.98456s/12 iters), loss = 0.269623
I0409 23:36:09.457587  4596 solver.cpp:237]     Train net output #0: loss = 0.269623 (* 1 = 0.269623 loss)
I0409 23:36:09.457599  4596 sgd_solver.cpp:105] Iteration 5844, lr = 0.00314235
I0409 23:36:14.335346  4596 solver.cpp:218] Iteration 5856 (2.46021 iter/s, 4.87763s/12 iters), loss = 0.656986
I0409 23:36:14.335391  4596 solver.cpp:237]     Train net output #0: loss = 0.656986 (* 1 = 0.656986 loss)
I0409 23:36:14.335402  4596 sgd_solver.cpp:105] Iteration 5856, lr = 0.00313489
I0409 23:36:18.466938  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:36:19.263456  4596 solver.cpp:218] Iteration 5868 (2.4351 iter/s, 4.92792s/12 iters), loss = 0.554096
I0409 23:36:19.263514  4596 solver.cpp:237]     Train net output #0: loss = 0.554096 (* 1 = 0.554096 loss)
I0409 23:36:19.263526  4596 sgd_solver.cpp:105] Iteration 5868, lr = 0.00312745
I0409 23:36:24.202905  4596 solver.cpp:218] Iteration 5880 (2.42951 iter/s, 4.93926s/12 iters), loss = 0.61477
I0409 23:36:24.202946  4596 solver.cpp:237]     Train net output #0: loss = 0.61477 (* 1 = 0.61477 loss)
I0409 23:36:24.202955  4596 sgd_solver.cpp:105] Iteration 5880, lr = 0.00312002
I0409 23:36:29.072144  4596 solver.cpp:218] Iteration 5892 (2.46454 iter/s, 4.86906s/12 iters), loss = 0.426186
I0409 23:36:29.072306  4596 solver.cpp:237]     Train net output #0: loss = 0.426186 (* 1 = 0.426186 loss)
I0409 23:36:29.072319  4596 sgd_solver.cpp:105] Iteration 5892, lr = 0.00311262
I0409 23:36:33.983610  4596 solver.cpp:218] Iteration 5904 (2.44341 iter/s, 4.91117s/12 iters), loss = 0.377951
I0409 23:36:33.983661  4596 solver.cpp:237]     Train net output #0: loss = 0.377951 (* 1 = 0.377951 loss)
I0409 23:36:33.983673  4596 sgd_solver.cpp:105] Iteration 5904, lr = 0.00310523
I0409 23:36:38.507709  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5916.caffemodel
I0409 23:36:38.940990  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5916.solverstate
I0409 23:36:39.246824  4596 solver.cpp:330] Iteration 5916, Testing net (#0)
I0409 23:36:39.246852  4596 net.cpp:676] Ignoring source layer train-data
I0409 23:36:42.110968  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:36:44.926488  4596 solver.cpp:397]     Test net output #0: accuracy = 0.452819
I0409 23:36:44.926537  4596 solver.cpp:397]     Test net output #1: loss = 2.87389 (* 1 = 2.87389 loss)
I0409 23:36:45.008060  4596 solver.cpp:218] Iteration 5916 (1.08852 iter/s, 11.0241s/12 iters), loss = 0.538345
I0409 23:36:45.008109  4596 solver.cpp:237]     Train net output #0: loss = 0.538345 (* 1 = 0.538345 loss)
I0409 23:36:45.008121  4596 sgd_solver.cpp:105] Iteration 5916, lr = 0.00309785
I0409 23:36:49.260543  4596 solver.cpp:218] Iteration 5928 (2.82199 iter/s, 4.25231s/12 iters), loss = 0.601767
I0409 23:36:49.260605  4596 solver.cpp:237]     Train net output #0: loss = 0.601767 (* 1 = 0.601767 loss)
I0409 23:36:49.260618  4596 sgd_solver.cpp:105] Iteration 5928, lr = 0.0030905
I0409 23:36:54.158987  4596 solver.cpp:218] Iteration 5940 (2.44986 iter/s, 4.89825s/12 iters), loss = 0.462197
I0409 23:36:54.159040  4596 solver.cpp:237]     Train net output #0: loss = 0.462197 (* 1 = 0.462197 loss)
I0409 23:36:54.159050  4596 sgd_solver.cpp:105] Iteration 5940, lr = 0.00308316
I0409 23:36:59.049026  4596 solver.cpp:218] Iteration 5952 (2.45406 iter/s, 4.88985s/12 iters), loss = 0.509019
I0409 23:36:59.049082  4596 solver.cpp:237]     Train net output #0: loss = 0.509019 (* 1 = 0.509019 loss)
I0409 23:36:59.049094  4596 sgd_solver.cpp:105] Iteration 5952, lr = 0.00307584
I0409 23:37:04.129523  4596 solver.cpp:218] Iteration 5964 (2.36206 iter/s, 5.08031s/12 iters), loss = 0.342484
I0409 23:37:04.129642  4596 solver.cpp:237]     Train net output #0: loss = 0.342484 (* 1 = 0.342484 loss)
I0409 23:37:04.129655  4596 sgd_solver.cpp:105] Iteration 5964, lr = 0.00306854
I0409 23:37:05.416615  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:37:09.052706  4596 solver.cpp:218] Iteration 5976 (2.43757 iter/s, 4.92294s/12 iters), loss = 0.44862
I0409 23:37:09.052752  4596 solver.cpp:237]     Train net output #0: loss = 0.44862 (* 1 = 0.44862 loss)
I0409 23:37:09.052762  4596 sgd_solver.cpp:105] Iteration 5976, lr = 0.00306125
I0409 23:37:13.951792  4596 solver.cpp:218] Iteration 5988 (2.44953 iter/s, 4.89891s/12 iters), loss = 0.389091
I0409 23:37:13.951845  4596 solver.cpp:237]     Train net output #0: loss = 0.389091 (* 1 = 0.389091 loss)
I0409 23:37:13.951858  4596 sgd_solver.cpp:105] Iteration 5988, lr = 0.00305398
I0409 23:37:18.837082  4596 solver.cpp:218] Iteration 6000 (2.45644 iter/s, 4.88511s/12 iters), loss = 0.406208
I0409 23:37:18.837132  4596 solver.cpp:237]     Train net output #0: loss = 0.406208 (* 1 = 0.406208 loss)
I0409 23:37:18.837143  4596 sgd_solver.cpp:105] Iteration 6000, lr = 0.00304673
I0409 23:37:23.715695  4596 solver.cpp:218] Iteration 6012 (2.45981 iter/s, 4.87843s/12 iters), loss = 0.389872
I0409 23:37:23.715757  4596 solver.cpp:237]     Train net output #0: loss = 0.389872 (* 1 = 0.389872 loss)
I0409 23:37:23.715771  4596 sgd_solver.cpp:105] Iteration 6012, lr = 0.0030395
I0409 23:37:25.688966  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6018.caffemodel
I0409 23:37:26.130525  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6018.solverstate
I0409 23:37:26.438026  4596 solver.cpp:330] Iteration 6018, Testing net (#0)
I0409 23:37:26.438051  4596 net.cpp:676] Ignoring source layer train-data
I0409 23:37:28.450728  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:37:30.866379  4596 solver.cpp:397]     Test net output #0: accuracy = 0.463235
I0409 23:37:30.866425  4596 solver.cpp:397]     Test net output #1: loss = 2.88955 (* 1 = 2.88955 loss)
I0409 23:37:32.679147  4596 solver.cpp:218] Iteration 6024 (1.33881 iter/s, 8.96316s/12 iters), loss = 0.646316
I0409 23:37:32.679200  4596 solver.cpp:237]     Train net output #0: loss = 0.646316 (* 1 = 0.646316 loss)
I0409 23:37:32.679211  4596 sgd_solver.cpp:105] Iteration 6024, lr = 0.00303228
I0409 23:37:37.508249  4596 solver.cpp:218] Iteration 6036 (2.48503 iter/s, 4.82892s/12 iters), loss = 0.32671
I0409 23:37:37.508389  4596 solver.cpp:237]     Train net output #0: loss = 0.32671 (* 1 = 0.32671 loss)
I0409 23:37:37.508400  4596 sgd_solver.cpp:105] Iteration 6036, lr = 0.00302508
I0409 23:37:42.422823  4596 solver.cpp:218] Iteration 6048 (2.44185 iter/s, 4.9143s/12 iters), loss = 0.574759
I0409 23:37:42.422878  4596 solver.cpp:237]     Train net output #0: loss = 0.574759 (* 1 = 0.574759 loss)
I0409 23:37:42.422890  4596 sgd_solver.cpp:105] Iteration 6048, lr = 0.0030179
I0409 23:37:47.308465  4596 solver.cpp:218] Iteration 6060 (2.45627 iter/s, 4.88546s/12 iters), loss = 0.459422
I0409 23:37:47.308512  4596 solver.cpp:237]     Train net output #0: loss = 0.459422 (* 1 = 0.459422 loss)
I0409 23:37:47.308524  4596 sgd_solver.cpp:105] Iteration 6060, lr = 0.00301074
I0409 23:37:50.740263  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:37:52.268486  4596 solver.cpp:218] Iteration 6072 (2.41943 iter/s, 4.95984s/12 iters), loss = 0.406859
I0409 23:37:52.268535  4596 solver.cpp:237]     Train net output #0: loss = 0.406859 (* 1 = 0.406859 loss)
I0409 23:37:52.268548  4596 sgd_solver.cpp:105] Iteration 6072, lr = 0.00300359
I0409 23:37:57.133879  4596 solver.cpp:218] Iteration 6084 (2.46649 iter/s, 4.86522s/12 iters), loss = 0.471534
I0409 23:37:57.133922  4596 solver.cpp:237]     Train net output #0: loss = 0.471534 (* 1 = 0.471534 loss)
I0409 23:37:57.133931  4596 sgd_solver.cpp:105] Iteration 6084, lr = 0.00299646
I0409 23:38:02.141839  4596 solver.cpp:218] Iteration 6096 (2.39627 iter/s, 5.00778s/12 iters), loss = 0.433182
I0409 23:38:02.141896  4596 solver.cpp:237]     Train net output #0: loss = 0.433182 (* 1 = 0.433182 loss)
I0409 23:38:02.141908  4596 sgd_solver.cpp:105] Iteration 6096, lr = 0.00298934
I0409 23:38:07.157860  4596 solver.cpp:218] Iteration 6108 (2.39243 iter/s, 5.01583s/12 iters), loss = 0.298089
I0409 23:38:07.157915  4596 solver.cpp:237]     Train net output #0: loss = 0.298089 (* 1 = 0.298089 loss)
I0409 23:38:07.157928  4596 sgd_solver.cpp:105] Iteration 6108, lr = 0.00298225
I0409 23:38:11.682838  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6120.caffemodel
I0409 23:38:12.557507  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6120.solverstate
I0409 23:38:12.928453  4596 solver.cpp:330] Iteration 6120, Testing net (#0)
I0409 23:38:12.928478  4596 net.cpp:676] Ignoring source layer train-data
I0409 23:38:14.929694  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:38:17.341759  4596 solver.cpp:397]     Test net output #0: accuracy = 0.471814
I0409 23:38:17.341809  4596 solver.cpp:397]     Test net output #1: loss = 2.88279 (* 1 = 2.88279 loss)
I0409 23:38:17.423391  4596 solver.cpp:218] Iteration 6120 (1.169 iter/s, 10.2652s/12 iters), loss = 0.303232
I0409 23:38:17.423446  4596 solver.cpp:237]     Train net output #0: loss = 0.303232 (* 1 = 0.303232 loss)
I0409 23:38:17.423458  4596 sgd_solver.cpp:105] Iteration 6120, lr = 0.00297517
I0409 23:38:21.713438  4596 solver.cpp:218] Iteration 6132 (2.79729 iter/s, 4.28987s/12 iters), loss = 0.541581
I0409 23:38:21.713500  4596 solver.cpp:237]     Train net output #0: loss = 0.541581 (* 1 = 0.541581 loss)
I0409 23:38:21.713513  4596 sgd_solver.cpp:105] Iteration 6132, lr = 0.0029681
I0409 23:38:26.662628  4596 solver.cpp:218] Iteration 6144 (2.42473 iter/s, 4.949s/12 iters), loss = 0.36417
I0409 23:38:26.662667  4596 solver.cpp:237]     Train net output #0: loss = 0.36417 (* 1 = 0.36417 loss)
I0409 23:38:26.662675  4596 sgd_solver.cpp:105] Iteration 6144, lr = 0.00296105
I0409 23:38:31.541039  4596 solver.cpp:218] Iteration 6156 (2.45991 iter/s, 4.87824s/12 iters), loss = 0.333418
I0409 23:38:31.541100  4596 solver.cpp:237]     Train net output #0: loss = 0.333418 (* 1 = 0.333418 loss)
I0409 23:38:31.541113  4596 sgd_solver.cpp:105] Iteration 6156, lr = 0.00295402
I0409 23:38:36.443836  4596 solver.cpp:218] Iteration 6168 (2.44768 iter/s, 4.90261s/12 iters), loss = 0.374258
I0409 23:38:36.443881  4596 solver.cpp:237]     Train net output #0: loss = 0.374258 (* 1 = 0.374258 loss)
I0409 23:38:36.443892  4596 sgd_solver.cpp:105] Iteration 6168, lr = 0.00294701
I0409 23:38:37.016448  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:38:41.361404  4596 solver.cpp:218] Iteration 6180 (2.44032 iter/s, 4.91739s/12 iters), loss = 0.429412
I0409 23:38:41.361459  4596 solver.cpp:237]     Train net output #0: loss = 0.429412 (* 1 = 0.429412 loss)
I0409 23:38:41.361469  4596 sgd_solver.cpp:105] Iteration 6180, lr = 0.00294001
I0409 23:38:46.210415  4596 solver.cpp:218] Iteration 6192 (2.47483 iter/s, 4.84881s/12 iters), loss = 0.54669
I0409 23:38:46.210531  4596 solver.cpp:237]     Train net output #0: loss = 0.54669 (* 1 = 0.54669 loss)
I0409 23:38:46.210542  4596 sgd_solver.cpp:105] Iteration 6192, lr = 0.00293303
I0409 23:38:51.067871  4596 solver.cpp:218] Iteration 6204 (2.47055 iter/s, 4.85721s/12 iters), loss = 0.505117
I0409 23:38:51.067917  4596 solver.cpp:237]     Train net output #0: loss = 0.505117 (* 1 = 0.505117 loss)
I0409 23:38:51.067926  4596 sgd_solver.cpp:105] Iteration 6204, lr = 0.00292607
I0409 23:38:56.087981  4596 solver.cpp:218] Iteration 6216 (2.39047 iter/s, 5.01993s/12 iters), loss = 0.398862
I0409 23:38:56.088037  4596 solver.cpp:237]     Train net output #0: loss = 0.398862 (* 1 = 0.398862 loss)
I0409 23:38:56.088049  4596 sgd_solver.cpp:105] Iteration 6216, lr = 0.00291912
I0409 23:38:58.103444  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6222.caffemodel
I0409 23:38:58.570405  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6222.solverstate
I0409 23:38:58.880749  4596 solver.cpp:330] Iteration 6222, Testing net (#0)
I0409 23:38:58.880780  4596 net.cpp:676] Ignoring source layer train-data
I0409 23:39:00.862267  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:39:01.822863  4596 blocking_queue.cpp:49] Waiting for data
I0409 23:39:03.364570  4596 solver.cpp:397]     Test net output #0: accuracy = 0.478554
I0409 23:39:03.364607  4596 solver.cpp:397]     Test net output #1: loss = 2.83921 (* 1 = 2.83921 loss)
I0409 23:39:05.211632  4596 solver.cpp:218] Iteration 6228 (1.3153 iter/s, 9.12336s/12 iters), loss = 0.590486
I0409 23:39:05.211695  4596 solver.cpp:237]     Train net output #0: loss = 0.590486 (* 1 = 0.590486 loss)
I0409 23:39:05.211709  4596 sgd_solver.cpp:105] Iteration 6228, lr = 0.00291219
I0409 23:39:10.095328  4596 solver.cpp:218] Iteration 6240 (2.45725 iter/s, 4.88351s/12 iters), loss = 0.370294
I0409 23:39:10.095381  4596 solver.cpp:237]     Train net output #0: loss = 0.370294 (* 1 = 0.370294 loss)
I0409 23:39:10.095391  4596 sgd_solver.cpp:105] Iteration 6240, lr = 0.00290528
I0409 23:39:14.991636  4596 solver.cpp:218] Iteration 6252 (2.45092 iter/s, 4.89612s/12 iters), loss = 0.371222
I0409 23:39:14.991689  4596 solver.cpp:237]     Train net output #0: loss = 0.371222 (* 1 = 0.371222 loss)
I0409 23:39:14.991701  4596 sgd_solver.cpp:105] Iteration 6252, lr = 0.00289838
I0409 23:39:19.880484  4596 solver.cpp:218] Iteration 6264 (2.45466 iter/s, 4.88866s/12 iters), loss = 0.338813
I0409 23:39:19.880640  4596 solver.cpp:237]     Train net output #0: loss = 0.338813 (* 1 = 0.338813 loss)
I0409 23:39:19.880653  4596 sgd_solver.cpp:105] Iteration 6264, lr = 0.0028915
I0409 23:39:22.757210  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:39:24.972760  4596 solver.cpp:218] Iteration 6276 (2.35664 iter/s, 5.09199s/12 iters), loss = 0.343967
I0409 23:39:24.972817  4596 solver.cpp:237]     Train net output #0: loss = 0.343967 (* 1 = 0.343967 loss)
I0409 23:39:24.972829  4596 sgd_solver.cpp:105] Iteration 6276, lr = 0.00288463
I0409 23:39:29.903523  4596 solver.cpp:218] Iteration 6288 (2.43379 iter/s, 4.93058s/12 iters), loss = 0.505573
I0409 23:39:29.903568  4596 solver.cpp:237]     Train net output #0: loss = 0.505573 (* 1 = 0.505573 loss)
I0409 23:39:29.903578  4596 sgd_solver.cpp:105] Iteration 6288, lr = 0.00287779
I0409 23:39:34.791095  4596 solver.cpp:218] Iteration 6300 (2.4553 iter/s, 4.88739s/12 iters), loss = 0.331276
I0409 23:39:34.791149  4596 solver.cpp:237]     Train net output #0: loss = 0.331276 (* 1 = 0.331276 loss)
I0409 23:39:34.791162  4596 sgd_solver.cpp:105] Iteration 6300, lr = 0.00287095
I0409 23:39:39.676826  4596 solver.cpp:218] Iteration 6312 (2.45623 iter/s, 4.88554s/12 iters), loss = 0.405477
I0409 23:39:39.676883  4596 solver.cpp:237]     Train net output #0: loss = 0.405477 (* 1 = 0.405477 loss)
I0409 23:39:39.676894  4596 sgd_solver.cpp:105] Iteration 6312, lr = 0.00286414
I0409 23:39:44.155122  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6324.caffemodel
I0409 23:39:44.610543  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6324.solverstate
I0409 23:39:44.917979  4596 solver.cpp:330] Iteration 6324, Testing net (#0)
I0409 23:39:44.918007  4596 net.cpp:676] Ignoring source layer train-data
I0409 23:39:46.892467  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:39:49.364953  4596 solver.cpp:397]     Test net output #0: accuracy = 0.469363
I0409 23:39:49.365002  4596 solver.cpp:397]     Test net output #1: loss = 2.88422 (* 1 = 2.88422 loss)
I0409 23:39:49.446668  4596 solver.cpp:218] Iteration 6324 (1.22831 iter/s, 9.76954s/12 iters), loss = 0.39268
I0409 23:39:49.446722  4596 solver.cpp:237]     Train net output #0: loss = 0.39268 (* 1 = 0.39268 loss)
I0409 23:39:49.446732  4596 sgd_solver.cpp:105] Iteration 6324, lr = 0.00285734
I0409 23:39:53.650588  4596 solver.cpp:218] Iteration 6336 (2.85459 iter/s, 4.20375s/12 iters), loss = 0.358516
I0409 23:39:53.650993  4596 solver.cpp:237]     Train net output #0: loss = 0.358516 (* 1 = 0.358516 loss)
I0409 23:39:53.651005  4596 sgd_solver.cpp:105] Iteration 6336, lr = 0.00285055
I0409 23:39:58.583066  4596 solver.cpp:218] Iteration 6348 (2.43312 iter/s, 4.93194s/12 iters), loss = 0.339199
I0409 23:39:58.583114  4596 solver.cpp:237]     Train net output #0: loss = 0.339199 (* 1 = 0.339199 loss)
I0409 23:39:58.583123  4596 sgd_solver.cpp:105] Iteration 6348, lr = 0.00284379
I0409 23:40:03.533943  4596 solver.cpp:218] Iteration 6360 (2.4239 iter/s, 4.9507s/12 iters), loss = 0.367961
I0409 23:40:03.534014  4596 solver.cpp:237]     Train net output #0: loss = 0.367961 (* 1 = 0.367961 loss)
I0409 23:40:03.534027  4596 sgd_solver.cpp:105] Iteration 6360, lr = 0.00283703
I0409 23:40:08.290115  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:40:08.426939  4596 solver.cpp:218] Iteration 6372 (2.45258 iter/s, 4.8928s/12 iters), loss = 0.243614
I0409 23:40:08.426987  4596 solver.cpp:237]     Train net output #0: loss = 0.243614 (* 1 = 0.243614 loss)
I0409 23:40:08.427000  4596 sgd_solver.cpp:105] Iteration 6372, lr = 0.0028303
I0409 23:40:13.321040  4596 solver.cpp:218] Iteration 6384 (2.45202 iter/s, 4.89392s/12 iters), loss = 0.421157
I0409 23:40:13.321091  4596 solver.cpp:237]     Train net output #0: loss = 0.421157 (* 1 = 0.421157 loss)
I0409 23:40:13.321105  4596 sgd_solver.cpp:105] Iteration 6384, lr = 0.00282358
I0409 23:40:18.373616  4596 solver.cpp:218] Iteration 6396 (2.37511 iter/s, 5.05239s/12 iters), loss = 0.334357
I0409 23:40:18.373659  4596 solver.cpp:237]     Train net output #0: loss = 0.334357 (* 1 = 0.334357 loss)
I0409 23:40:18.373667  4596 sgd_solver.cpp:105] Iteration 6396, lr = 0.00281687
I0409 23:40:23.604806  4596 solver.cpp:218] Iteration 6408 (2.29401 iter/s, 5.231s/12 iters), loss = 0.21356
I0409 23:40:23.604857  4596 solver.cpp:237]     Train net output #0: loss = 0.21356 (* 1 = 0.21356 loss)
I0409 23:40:23.604867  4596 sgd_solver.cpp:105] Iteration 6408, lr = 0.00281019
I0409 23:40:28.523793  4596 solver.cpp:218] Iteration 6420 (2.43962 iter/s, 4.91881s/12 iters), loss = 0.467233
I0409 23:40:28.523917  4596 solver.cpp:237]     Train net output #0: loss = 0.467233 (* 1 = 0.467233 loss)
I0409 23:40:28.523928  4596 sgd_solver.cpp:105] Iteration 6420, lr = 0.00280351
I0409 23:40:30.556082  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6426.caffemodel
I0409 23:40:30.996032  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6426.solverstate
I0409 23:40:31.303159  4596 solver.cpp:330] Iteration 6426, Testing net (#0)
I0409 23:40:31.303182  4596 net.cpp:676] Ignoring source layer train-data
I0409 23:40:33.230710  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:40:35.747321  4596 solver.cpp:397]     Test net output #0: accuracy = 0.466912
I0409 23:40:35.747354  4596 solver.cpp:397]     Test net output #1: loss = 3.0157 (* 1 = 3.0157 loss)
I0409 23:40:37.498718  4596 solver.cpp:218] Iteration 6432 (1.33711 iter/s, 8.97457s/12 iters), loss = 0.441043
I0409 23:40:37.498769  4596 solver.cpp:237]     Train net output #0: loss = 0.441043 (* 1 = 0.441043 loss)
I0409 23:40:37.498781  4596 sgd_solver.cpp:105] Iteration 6432, lr = 0.00279686
I0409 23:40:42.481217  4596 solver.cpp:218] Iteration 6444 (2.40852 iter/s, 4.98232s/12 iters), loss = 0.366827
I0409 23:40:42.481269  4596 solver.cpp:237]     Train net output #0: loss = 0.366827 (* 1 = 0.366827 loss)
I0409 23:40:42.481281  4596 sgd_solver.cpp:105] Iteration 6444, lr = 0.00279022
I0409 23:40:47.339892  4596 solver.cpp:218] Iteration 6456 (2.4699 iter/s, 4.85849s/12 iters), loss = 0.408097
I0409 23:40:47.339951  4596 solver.cpp:237]     Train net output #0: loss = 0.408097 (* 1 = 0.408097 loss)
I0409 23:40:47.339963  4596 sgd_solver.cpp:105] Iteration 6456, lr = 0.00278359
I0409 23:40:52.235275  4596 solver.cpp:218] Iteration 6468 (2.45138 iter/s, 4.89519s/12 iters), loss = 0.258255
I0409 23:40:52.235328  4596 solver.cpp:237]     Train net output #0: loss = 0.258255 (* 1 = 0.258255 loss)
I0409 23:40:52.235340  4596 sgd_solver.cpp:105] Iteration 6468, lr = 0.00277698
I0409 23:40:54.332036  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:40:57.363015  4596 solver.cpp:218] Iteration 6480 (2.3403 iter/s, 5.12755s/12 iters), loss = 0.435946
I0409 23:40:57.363068  4596 solver.cpp:237]     Train net output #0: loss = 0.435946 (* 1 = 0.435946 loss)
I0409 23:40:57.363080  4596 sgd_solver.cpp:105] Iteration 6480, lr = 0.00277039
I0409 23:41:02.298563  4596 solver.cpp:218] Iteration 6492 (2.43143 iter/s, 4.93536s/12 iters), loss = 0.19251
I0409 23:41:02.298719  4596 solver.cpp:237]     Train net output #0: loss = 0.19251 (* 1 = 0.19251 loss)
I0409 23:41:02.298733  4596 sgd_solver.cpp:105] Iteration 6492, lr = 0.00276381
I0409 23:41:07.418787  4596 solver.cpp:218] Iteration 6504 (2.34378 iter/s, 5.11994s/12 iters), loss = 0.381346
I0409 23:41:07.418850  4596 solver.cpp:237]     Train net output #0: loss = 0.381346 (* 1 = 0.381346 loss)
I0409 23:41:07.418864  4596 sgd_solver.cpp:105] Iteration 6504, lr = 0.00275725
I0409 23:41:12.287987  4596 solver.cpp:218] Iteration 6516 (2.46457 iter/s, 4.86901s/12 iters), loss = 0.401814
I0409 23:41:12.288036  4596 solver.cpp:237]     Train net output #0: loss = 0.401814 (* 1 = 0.401814 loss)
I0409 23:41:12.288048  4596 sgd_solver.cpp:105] Iteration 6516, lr = 0.00275071
I0409 23:41:16.787940  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6528.caffemodel
I0409 23:41:19.047086  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6528.solverstate
I0409 23:41:19.809916  4596 solver.cpp:330] Iteration 6528, Testing net (#0)
I0409 23:41:19.809942  4596 net.cpp:676] Ignoring source layer train-data
I0409 23:41:21.830310  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:41:24.459080  4596 solver.cpp:397]     Test net output #0: accuracy = 0.479779
I0409 23:41:24.459131  4596 solver.cpp:397]     Test net output #1: loss = 2.9107 (* 1 = 2.9107 loss)
I0409 23:41:24.540748  4596 solver.cpp:218] Iteration 6528 (0.9794 iter/s, 12.2524s/12 iters), loss = 0.320729
I0409 23:41:24.540802  4596 solver.cpp:237]     Train net output #0: loss = 0.320729 (* 1 = 0.320729 loss)
I0409 23:41:24.540813  4596 sgd_solver.cpp:105] Iteration 6528, lr = 0.00274418
I0409 23:41:28.715235  4596 solver.cpp:218] Iteration 6540 (2.87472 iter/s, 4.17432s/12 iters), loss = 0.377124
I0409 23:41:28.715288  4596 solver.cpp:237]     Train net output #0: loss = 0.377124 (* 1 = 0.377124 loss)
I0409 23:41:28.715299  4596 sgd_solver.cpp:105] Iteration 6540, lr = 0.00273766
I0409 23:41:33.642855  4596 solver.cpp:218] Iteration 6552 (2.43534 iter/s, 4.92744s/12 iters), loss = 0.304691
I0409 23:41:33.642959  4596 solver.cpp:237]     Train net output #0: loss = 0.304691 (* 1 = 0.304691 loss)
I0409 23:41:33.642971  4596 sgd_solver.cpp:105] Iteration 6552, lr = 0.00273116
I0409 23:41:38.544384  4596 solver.cpp:218] Iteration 6564 (2.44834 iter/s, 4.90129s/12 iters), loss = 0.252253
I0409 23:41:38.544441  4596 solver.cpp:237]     Train net output #0: loss = 0.252253 (* 1 = 0.252253 loss)
I0409 23:41:38.544453  4596 sgd_solver.cpp:105] Iteration 6564, lr = 0.00272468
I0409 23:41:42.739534  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:41:43.489033  4596 solver.cpp:218] Iteration 6576 (2.42696 iter/s, 4.94446s/12 iters), loss = 0.431883
I0409 23:41:43.489091  4596 solver.cpp:237]     Train net output #0: loss = 0.431883 (* 1 = 0.431883 loss)
I0409 23:41:43.489104  4596 sgd_solver.cpp:105] Iteration 6576, lr = 0.00271821
I0409 23:41:48.378265  4596 solver.cpp:218] Iteration 6588 (2.45447 iter/s, 4.88904s/12 iters), loss = 0.426658
I0409 23:41:48.378319  4596 solver.cpp:237]     Train net output #0: loss = 0.426658 (* 1 = 0.426658 loss)
I0409 23:41:48.378331  4596 sgd_solver.cpp:105] Iteration 6588, lr = 0.00271175
I0409 23:41:53.299299  4596 solver.cpp:218] Iteration 6600 (2.4386 iter/s, 4.92085s/12 iters), loss = 0.233019
I0409 23:41:53.299341  4596 solver.cpp:237]     Train net output #0: loss = 0.233019 (* 1 = 0.233019 loss)
I0409 23:41:53.299350  4596 sgd_solver.cpp:105] Iteration 6600, lr = 0.00270532
I0409 23:41:58.217727  4596 solver.cpp:218] Iteration 6612 (2.43989 iter/s, 4.91825s/12 iters), loss = 0.236356
I0409 23:41:58.217772  4596 solver.cpp:237]     Train net output #0: loss = 0.236356 (* 1 = 0.236356 loss)
I0409 23:41:58.217782  4596 sgd_solver.cpp:105] Iteration 6612, lr = 0.00269889
I0409 23:42:03.145709  4596 solver.cpp:218] Iteration 6624 (2.43516 iter/s, 4.92781s/12 iters), loss = 0.305591
I0409 23:42:03.145747  4596 solver.cpp:237]     Train net output #0: loss = 0.305591 (* 1 = 0.305591 loss)
I0409 23:42:03.145756  4596 sgd_solver.cpp:105] Iteration 6624, lr = 0.00269248
I0409 23:42:05.118268  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6630.caffemodel
I0409 23:42:05.540091  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6630.solverstate
I0409 23:42:06.335319  4596 solver.cpp:330] Iteration 6630, Testing net (#0)
I0409 23:42:06.335350  4596 net.cpp:676] Ignoring source layer train-data
I0409 23:42:08.073750  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:42:10.665989  4596 solver.cpp:397]     Test net output #0: accuracy = 0.469976
I0409 23:42:10.666043  4596 solver.cpp:397]     Test net output #1: loss = 2.93917 (* 1 = 2.93917 loss)
I0409 23:42:12.482841  4596 solver.cpp:218] Iteration 6636 (1.28523 iter/s, 9.33685s/12 iters), loss = 0.381103
I0409 23:42:12.482900  4596 solver.cpp:237]     Train net output #0: loss = 0.381103 (* 1 = 0.381103 loss)
I0409 23:42:12.482913  4596 sgd_solver.cpp:105] Iteration 6636, lr = 0.00268609
I0409 23:42:17.453173  4596 solver.cpp:218] Iteration 6648 (2.41442 iter/s, 4.97014s/12 iters), loss = 0.440024
I0409 23:42:17.453219  4596 solver.cpp:237]     Train net output #0: loss = 0.440024 (* 1 = 0.440024 loss)
I0409 23:42:17.453229  4596 sgd_solver.cpp:105] Iteration 6648, lr = 0.00267971
I0409 23:42:22.385208  4596 solver.cpp:218] Iteration 6660 (2.43316 iter/s, 4.93185s/12 iters), loss = 0.414387
I0409 23:42:22.385260  4596 solver.cpp:237]     Train net output #0: loss = 0.414387 (* 1 = 0.414387 loss)
I0409 23:42:22.385270  4596 sgd_solver.cpp:105] Iteration 6660, lr = 0.00267335
I0409 23:42:27.521617  4596 solver.cpp:218] Iteration 6672 (2.33635 iter/s, 5.13622s/12 iters), loss = 0.438344
I0409 23:42:27.521677  4596 solver.cpp:237]     Train net output #0: loss = 0.438344 (* 1 = 0.438344 loss)
I0409 23:42:27.521688  4596 sgd_solver.cpp:105] Iteration 6672, lr = 0.00266701
I0409 23:42:28.862538  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:42:32.412248  4596 solver.cpp:218] Iteration 6684 (2.45377 iter/s, 4.89044s/12 iters), loss = 0.245687
I0409 23:42:32.412299  4596 solver.cpp:237]     Train net output #0: loss = 0.245687 (* 1 = 0.245687 loss)
I0409 23:42:32.412310  4596 sgd_solver.cpp:105] Iteration 6684, lr = 0.00266067
I0409 23:42:37.306115  4596 solver.cpp:218] Iteration 6696 (2.45214 iter/s, 4.89369s/12 iters), loss = 0.393302
I0409 23:42:37.306234  4596 solver.cpp:237]     Train net output #0: loss = 0.393302 (* 1 = 0.393302 loss)
I0409 23:42:37.306247  4596 sgd_solver.cpp:105] Iteration 6696, lr = 0.00265436
I0409 23:42:42.224287  4596 solver.cpp:218] Iteration 6708 (2.44006 iter/s, 4.91792s/12 iters), loss = 0.384868
I0409 23:42:42.224339  4596 solver.cpp:237]     Train net output #0: loss = 0.384868 (* 1 = 0.384868 loss)
I0409 23:42:42.224352  4596 sgd_solver.cpp:105] Iteration 6708, lr = 0.00264805
I0409 23:42:47.161553  4596 solver.cpp:218] Iteration 6720 (2.43059 iter/s, 4.93708s/12 iters), loss = 0.262747
I0409 23:42:47.161612  4596 solver.cpp:237]     Train net output #0: loss = 0.262747 (* 1 = 0.262747 loss)
I0409 23:42:47.161625  4596 sgd_solver.cpp:105] Iteration 6720, lr = 0.00264177
I0409 23:42:51.770812  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6732.caffemodel
I0409 23:42:52.200922  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6732.solverstate
I0409 23:42:52.506561  4596 solver.cpp:330] Iteration 6732, Testing net (#0)
I0409 23:42:52.506588  4596 net.cpp:676] Ignoring source layer train-data
I0409 23:42:54.339150  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:42:57.051373  4596 solver.cpp:397]     Test net output #0: accuracy = 0.487132
I0409 23:42:57.051414  4596 solver.cpp:397]     Test net output #1: loss = 2.8814 (* 1 = 2.8814 loss)
I0409 23:42:57.132642  4596 solver.cpp:218] Iteration 6732 (1.20352 iter/s, 9.97078s/12 iters), loss = 0.469051
I0409 23:42:57.132702  4596 solver.cpp:237]     Train net output #0: loss = 0.469051 (* 1 = 0.469051 loss)
I0409 23:42:57.132716  4596 sgd_solver.cpp:105] Iteration 6732, lr = 0.0026355
I0409 23:43:01.318260  4596 solver.cpp:218] Iteration 6744 (2.86708 iter/s, 4.18544s/12 iters), loss = 0.228456
I0409 23:43:01.318315  4596 solver.cpp:237]     Train net output #0: loss = 0.228456 (* 1 = 0.228456 loss)
I0409 23:43:01.318325  4596 sgd_solver.cpp:105] Iteration 6744, lr = 0.00262924
I0409 23:43:06.200249  4596 solver.cpp:218] Iteration 6756 (2.45811 iter/s, 4.8818s/12 iters), loss = 0.348562
I0409 23:43:06.200304  4596 solver.cpp:237]     Train net output #0: loss = 0.348562 (* 1 = 0.348562 loss)
I0409 23:43:06.200314  4596 sgd_solver.cpp:105] Iteration 6756, lr = 0.002623
I0409 23:43:11.115505  4596 solver.cpp:218] Iteration 6768 (2.44147 iter/s, 4.91507s/12 iters), loss = 0.347795
I0409 23:43:11.115607  4596 solver.cpp:237]     Train net output #0: loss = 0.347795 (* 1 = 0.347795 loss)
I0409 23:43:11.115617  4596 sgd_solver.cpp:105] Iteration 6768, lr = 0.00261677
I0409 23:43:14.527999  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:43:15.999390  4596 solver.cpp:218] Iteration 6780 (2.45718 iter/s, 4.88365s/12 iters), loss = 0.49307
I0409 23:43:15.999433  4596 solver.cpp:237]     Train net output #0: loss = 0.49307 (* 1 = 0.49307 loss)
I0409 23:43:15.999441  4596 sgd_solver.cpp:105] Iteration 6780, lr = 0.00261056
I0409 23:43:20.947569  4596 solver.cpp:218] Iteration 6792 (2.42522 iter/s, 4.948s/12 iters), loss = 0.204844
I0409 23:43:20.947623  4596 solver.cpp:237]     Train net output #0: loss = 0.204844 (* 1 = 0.204844 loss)
I0409 23:43:20.947633  4596 sgd_solver.cpp:105] Iteration 6792, lr = 0.00260436
I0409 23:43:25.867307  4596 solver.cpp:218] Iteration 6804 (2.43925 iter/s, 4.91955s/12 iters), loss = 0.388391
I0409 23:43:25.867369  4596 solver.cpp:237]     Train net output #0: loss = 0.388391 (* 1 = 0.388391 loss)
I0409 23:43:25.867381  4596 sgd_solver.cpp:105] Iteration 6804, lr = 0.00259817
I0409 23:43:30.762032  4596 solver.cpp:218] Iteration 6816 (2.45172 iter/s, 4.89453s/12 iters), loss = 0.349995
I0409 23:43:30.762089  4596 solver.cpp:237]     Train net output #0: loss = 0.349995 (* 1 = 0.349995 loss)
I0409 23:43:30.762100  4596 sgd_solver.cpp:105] Iteration 6816, lr = 0.00259201
I0409 23:43:35.728318  4596 solver.cpp:218] Iteration 6828 (2.41638 iter/s, 4.9661s/12 iters), loss = 0.273776
I0409 23:43:35.728356  4596 solver.cpp:237]     Train net output #0: loss = 0.273776 (* 1 = 0.273776 loss)
I0409 23:43:35.728364  4596 sgd_solver.cpp:105] Iteration 6828, lr = 0.00258585
I0409 23:43:37.709007  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6834.caffemodel
I0409 23:43:38.150768  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6834.solverstate
I0409 23:43:38.466240  4596 solver.cpp:330] Iteration 6834, Testing net (#0)
I0409 23:43:38.466262  4596 net.cpp:676] Ignoring source layer train-data
I0409 23:43:40.622793  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:43:43.300230  4596 solver.cpp:397]     Test net output #0: accuracy = 0.484681
I0409 23:43:43.300336  4596 solver.cpp:397]     Test net output #1: loss = 2.91185 (* 1 = 2.91185 loss)
I0409 23:43:45.261629  4596 solver.cpp:218] Iteration 6840 (1.25878 iter/s, 9.53302s/12 iters), loss = 0.172781
I0409 23:43:45.261687  4596 solver.cpp:237]     Train net output #0: loss = 0.172781 (* 1 = 0.172781 loss)
I0409 23:43:45.261700  4596 sgd_solver.cpp:105] Iteration 6840, lr = 0.00257971
I0409 23:43:50.218078  4596 solver.cpp:218] Iteration 6852 (2.42118 iter/s, 4.95626s/12 iters), loss = 0.321077
I0409 23:43:50.218119  4596 solver.cpp:237]     Train net output #0: loss = 0.321077 (* 1 = 0.321077 loss)
I0409 23:43:50.218128  4596 sgd_solver.cpp:105] Iteration 6852, lr = 0.00257359
I0409 23:43:55.150902  4596 solver.cpp:218] Iteration 6864 (2.43277 iter/s, 4.93265s/12 iters), loss = 0.276141
I0409 23:43:55.150946  4596 solver.cpp:237]     Train net output #0: loss = 0.276141 (* 1 = 0.276141 loss)
I0409 23:43:55.150955  4596 sgd_solver.cpp:105] Iteration 6864, lr = 0.00256748
I0409 23:44:00.048303  4596 solver.cpp:218] Iteration 6876 (2.45037 iter/s, 4.89722s/12 iters), loss = 0.259232
I0409 23:44:00.048347  4596 solver.cpp:237]     Train net output #0: loss = 0.259232 (* 1 = 0.259232 loss)
I0409 23:44:00.048358  4596 sgd_solver.cpp:105] Iteration 6876, lr = 0.00256138
I0409 23:44:00.652873  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:44:04.950508  4596 solver.cpp:218] Iteration 6888 (2.44797 iter/s, 4.90202s/12 iters), loss = 0.255388
I0409 23:44:04.950558  4596 solver.cpp:237]     Train net output #0: loss = 0.255389 (* 1 = 0.255389 loss)
I0409 23:44:04.950569  4596 sgd_solver.cpp:105] Iteration 6888, lr = 0.0025553
I0409 23:44:09.886250  4596 solver.cpp:218] Iteration 6900 (2.43134 iter/s, 4.93555s/12 iters), loss = 0.290974
I0409 23:44:09.886297  4596 solver.cpp:237]     Train net output #0: loss = 0.290974 (* 1 = 0.290974 loss)
I0409 23:44:09.886308  4596 sgd_solver.cpp:105] Iteration 6900, lr = 0.00254923
I0409 23:44:14.817473  4596 solver.cpp:218] Iteration 6912 (2.43356 iter/s, 4.93104s/12 iters), loss = 0.377712
I0409 23:44:14.817586  4596 solver.cpp:237]     Train net output #0: loss = 0.377712 (* 1 = 0.377712 loss)
I0409 23:44:14.817595  4596 sgd_solver.cpp:105] Iteration 6912, lr = 0.00254318
I0409 23:44:19.745208  4596 solver.cpp:218] Iteration 6924 (2.43532 iter/s, 4.92749s/12 iters), loss = 0.258305
I0409 23:44:19.745258  4596 solver.cpp:237]     Train net output #0: loss = 0.258305 (* 1 = 0.258305 loss)
I0409 23:44:19.745270  4596 sgd_solver.cpp:105] Iteration 6924, lr = 0.00253714
I0409 23:44:24.244340  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6936.caffemodel
I0409 23:44:25.712102  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6936.solverstate
I0409 23:44:26.917878  4596 solver.cpp:330] Iteration 6936, Testing net (#0)
I0409 23:44:26.917909  4596 net.cpp:676] Ignoring source layer train-data
I0409 23:44:27.181253  4596 blocking_queue.cpp:49] Waiting for data
I0409 23:44:28.650399  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:44:31.377005  4596 solver.cpp:397]     Test net output #0: accuracy = 0.46875
I0409 23:44:31.377043  4596 solver.cpp:397]     Test net output #1: loss = 3.0148 (* 1 = 3.0148 loss)
I0409 23:44:31.458665  4596 solver.cpp:218] Iteration 6936 (1.02449 iter/s, 11.7131s/12 iters), loss = 0.366245
I0409 23:44:31.458729  4596 solver.cpp:237]     Train net output #0: loss = 0.366245 (* 1 = 0.366245 loss)
I0409 23:44:31.458742  4596 sgd_solver.cpp:105] Iteration 6936, lr = 0.00253112
I0409 23:44:35.501142  4596 solver.cpp:218] Iteration 6948 (2.96861 iter/s, 4.0423s/12 iters), loss = 0.434163
I0409 23:44:35.501205  4596 solver.cpp:237]     Train net output #0: loss = 0.434163 (* 1 = 0.434163 loss)
I0409 23:44:35.501217  4596 sgd_solver.cpp:105] Iteration 6948, lr = 0.00252511
I0409 23:44:40.350939  4596 solver.cpp:218] Iteration 6960 (2.47443 iter/s, 4.8496s/12 iters), loss = 0.179421
I0409 23:44:40.351001  4596 solver.cpp:237]     Train net output #0: loss = 0.179422 (* 1 = 0.179422 loss)
I0409 23:44:40.351013  4596 sgd_solver.cpp:105] Iteration 6960, lr = 0.00251911
I0409 23:44:45.219269  4596 solver.cpp:218] Iteration 6972 (2.46501 iter/s, 4.86814s/12 iters), loss = 0.329729
I0409 23:44:45.219403  4596 solver.cpp:237]     Train net output #0: loss = 0.329729 (* 1 = 0.329729 loss)
I0409 23:44:45.219414  4596 sgd_solver.cpp:105] Iteration 6972, lr = 0.00251313
I0409 23:44:47.845341  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:44:50.018725  4596 solver.cpp:218] Iteration 6984 (2.50042 iter/s, 4.79919s/12 iters), loss = 0.306984
I0409 23:44:50.018779  4596 solver.cpp:237]     Train net output #0: loss = 0.306984 (* 1 = 0.306984 loss)
I0409 23:44:50.018791  4596 sgd_solver.cpp:105] Iteration 6984, lr = 0.00250717
I0409 23:44:54.881481  4596 solver.cpp:218] Iteration 6996 (2.46783 iter/s, 4.86257s/12 iters), loss = 0.260825
I0409 23:44:54.881539  4596 solver.cpp:237]     Train net output #0: loss = 0.260825 (* 1 = 0.260825 loss)
I0409 23:44:54.881551  4596 sgd_solver.cpp:105] Iteration 6996, lr = 0.00250121
I0409 23:44:59.711632  4596 solver.cpp:218] Iteration 7008 (2.48449 iter/s, 4.82996s/12 iters), loss = 0.213245
I0409 23:44:59.711690  4596 solver.cpp:237]     Train net output #0: loss = 0.213245 (* 1 = 0.213245 loss)
I0409 23:44:59.711702  4596 sgd_solver.cpp:105] Iteration 7008, lr = 0.00249528
I0409 23:45:04.524250  4596 solver.cpp:218] Iteration 7020 (2.49354 iter/s, 4.81243s/12 iters), loss = 0.288599
I0409 23:45:04.524308  4596 solver.cpp:237]     Train net output #0: loss = 0.288599 (* 1 = 0.288599 loss)
I0409 23:45:04.524322  4596 sgd_solver.cpp:105] Iteration 7020, lr = 0.00248935
I0409 23:45:09.376211  4596 solver.cpp:218] Iteration 7032 (2.47332 iter/s, 4.85177s/12 iters), loss = 0.234315
I0409 23:45:09.376272  4596 solver.cpp:237]     Train net output #0: loss = 0.234315 (* 1 = 0.234315 loss)
I0409 23:45:09.376286  4596 sgd_solver.cpp:105] Iteration 7032, lr = 0.00248344
I0409 23:45:11.333356  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7038.caffemodel
I0409 23:45:12.070808  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7038.solverstate
I0409 23:45:12.606676  4596 solver.cpp:330] Iteration 7038, Testing net (#0)
I0409 23:45:12.606704  4596 net.cpp:676] Ignoring source layer train-data
I0409 23:45:14.587216  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:45:17.483855  4596 solver.cpp:397]     Test net output #0: accuracy = 0.487745
I0409 23:45:17.484045  4596 solver.cpp:397]     Test net output #1: loss = 2.99112 (* 1 = 2.99112 loss)
I0409 23:45:19.279233  4596 solver.cpp:218] Iteration 7044 (1.21179 iter/s, 9.90271s/12 iters), loss = 0.286336
I0409 23:45:19.279278  4596 solver.cpp:237]     Train net output #0: loss = 0.286336 (* 1 = 0.286336 loss)
I0409 23:45:19.279289  4596 sgd_solver.cpp:105] Iteration 7044, lr = 0.00247755
I0409 23:45:24.275915  4596 solver.cpp:218] Iteration 7056 (2.40168 iter/s, 4.9965s/12 iters), loss = 0.19807
I0409 23:45:24.275972  4596 solver.cpp:237]     Train net output #0: loss = 0.19807 (* 1 = 0.19807 loss)
I0409 23:45:24.275985  4596 sgd_solver.cpp:105] Iteration 7056, lr = 0.00247166
I0409 23:45:29.187083  4596 solver.cpp:218] Iteration 7068 (2.44351 iter/s, 4.91097s/12 iters), loss = 0.153741
I0409 23:45:29.187139  4596 solver.cpp:237]     Train net output #0: loss = 0.153741 (* 1 = 0.153741 loss)
I0409 23:45:29.187151  4596 sgd_solver.cpp:105] Iteration 7068, lr = 0.0024658
I0409 23:45:33.947686  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:45:34.055570  4596 solver.cpp:218] Iteration 7080 (2.46493 iter/s, 4.8683s/12 iters), loss = 0.228107
I0409 23:45:34.055616  4596 solver.cpp:237]     Train net output #0: loss = 0.228107 (* 1 = 0.228107 loss)
I0409 23:45:34.055625  4596 sgd_solver.cpp:105] Iteration 7080, lr = 0.00245994
I0409 23:45:38.955878  4596 solver.cpp:218] Iteration 7092 (2.44892 iter/s, 4.90013s/12 iters), loss = 0.285547
I0409 23:45:38.955930  4596 solver.cpp:237]     Train net output #0: loss = 0.285547 (* 1 = 0.285547 loss)
I0409 23:45:38.955941  4596 sgd_solver.cpp:105] Iteration 7092, lr = 0.0024541
I0409 23:45:44.044054  4596 solver.cpp:218] Iteration 7104 (2.3585 iter/s, 5.08797s/12 iters), loss = 0.269234
I0409 23:45:44.044108  4596 solver.cpp:237]     Train net output #0: loss = 0.269234 (* 1 = 0.269234 loss)
I0409 23:45:44.044119  4596 sgd_solver.cpp:105] Iteration 7104, lr = 0.00244827
I0409 23:45:49.195490  4596 solver.cpp:218] Iteration 7116 (2.32953 iter/s, 5.15124s/12 iters), loss = 0.25418
I0409 23:45:49.195588  4596 solver.cpp:237]     Train net output #0: loss = 0.25418 (* 1 = 0.25418 loss)
I0409 23:45:49.195597  4596 sgd_solver.cpp:105] Iteration 7116, lr = 0.00244246
I0409 23:45:54.167840  4596 solver.cpp:218] Iteration 7128 (2.41346 iter/s, 4.97212s/12 iters), loss = 0.257157
I0409 23:45:54.167891  4596 solver.cpp:237]     Train net output #0: loss = 0.257157 (* 1 = 0.257157 loss)
I0409 23:45:54.167904  4596 sgd_solver.cpp:105] Iteration 7128, lr = 0.00243666
I0409 23:45:58.594063  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7140.caffemodel
I0409 23:45:59.236994  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7140.solverstate
I0409 23:45:59.667255  4596 solver.cpp:330] Iteration 7140, Testing net (#0)
I0409 23:45:59.667273  4596 net.cpp:676] Ignoring source layer train-data
I0409 23:46:01.225725  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:46:04.015501  4596 solver.cpp:397]     Test net output #0: accuracy = 0.481618
I0409 23:46:04.015525  4596 solver.cpp:397]     Test net output #1: loss = 2.9304 (* 1 = 2.9304 loss)
I0409 23:46:04.096876  4596 solver.cpp:218] Iteration 7140 (1.20861 iter/s, 9.92873s/12 iters), loss = 0.28946
I0409 23:46:04.096921  4596 solver.cpp:237]     Train net output #0: loss = 0.28946 (* 1 = 0.28946 loss)
I0409 23:46:04.096930  4596 sgd_solver.cpp:105] Iteration 7140, lr = 0.00243088
I0409 23:46:08.231555  4596 solver.cpp:218] Iteration 7152 (2.90239 iter/s, 4.13452s/12 iters), loss = 0.36656
I0409 23:46:08.231600  4596 solver.cpp:237]     Train net output #0: loss = 0.36656 (* 1 = 0.36656 loss)
I0409 23:46:08.231611  4596 sgd_solver.cpp:105] Iteration 7152, lr = 0.00242511
I0409 23:46:13.246837  4596 solver.cpp:218] Iteration 7164 (2.39277 iter/s, 5.0151s/12 iters), loss = 0.295658
I0409 23:46:13.246876  4596 solver.cpp:237]     Train net output #0: loss = 0.295658 (* 1 = 0.295658 loss)
I0409 23:46:13.246886  4596 sgd_solver.cpp:105] Iteration 7164, lr = 0.00241935
I0409 23:46:18.136703  4596 solver.cpp:218] Iteration 7176 (2.45414 iter/s, 4.8897s/12 iters), loss = 0.287742
I0409 23:46:18.136759  4596 solver.cpp:237]     Train net output #0: loss = 0.287742 (* 1 = 0.287742 loss)
I0409 23:46:18.136771  4596 sgd_solver.cpp:105] Iteration 7176, lr = 0.0024136
I0409 23:46:20.190433  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:46:22.998420  4596 solver.cpp:218] Iteration 7188 (2.46836 iter/s, 4.86153s/12 iters), loss = 0.31862
I0409 23:46:22.998476  4596 solver.cpp:237]     Train net output #0: loss = 0.31862 (* 1 = 0.31862 loss)
I0409 23:46:22.998487  4596 sgd_solver.cpp:105] Iteration 7188, lr = 0.00240787
I0409 23:46:27.986497  4596 solver.cpp:218] Iteration 7200 (2.40583 iter/s, 4.98789s/12 iters), loss = 0.429117
I0409 23:46:27.986547  4596 solver.cpp:237]     Train net output #0: loss = 0.429117 (* 1 = 0.429117 loss)
I0409 23:46:27.986558  4596 sgd_solver.cpp:105] Iteration 7200, lr = 0.00240216
I0409 23:46:32.895224  4596 solver.cpp:218] Iteration 7212 (2.44472 iter/s, 4.90854s/12 iters), loss = 0.305963
I0409 23:46:32.895274  4596 solver.cpp:237]     Train net output #0: loss = 0.305963 (* 1 = 0.305963 loss)
I0409 23:46:32.895285  4596 sgd_solver.cpp:105] Iteration 7212, lr = 0.00239645
I0409 23:46:37.812041  4596 solver.cpp:218] Iteration 7224 (2.44069 iter/s, 4.91663s/12 iters), loss = 0.29528
I0409 23:46:37.812093  4596 solver.cpp:237]     Train net output #0: loss = 0.29528 (* 1 = 0.29528 loss)
I0409 23:46:37.812105  4596 sgd_solver.cpp:105] Iteration 7224, lr = 0.00239076
I0409 23:46:42.728955  4596 solver.cpp:218] Iteration 7236 (2.44065 iter/s, 4.91673s/12 iters), loss = 0.145657
I0409 23:46:42.729015  4596 solver.cpp:237]     Train net output #0: loss = 0.145657 (* 1 = 0.145657 loss)
I0409 23:46:42.729028  4596 sgd_solver.cpp:105] Iteration 7236, lr = 0.00238509
I0409 23:46:44.711666  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7242.caffemodel
I0409 23:46:45.623914  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7242.solverstate
I0409 23:46:46.990242  4596 solver.cpp:330] Iteration 7242, Testing net (#0)
I0409 23:46:46.990268  4596 net.cpp:676] Ignoring source layer train-data
I0409 23:46:48.598738  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:46:51.425599  4596 solver.cpp:397]     Test net output #0: accuracy = 0.480392
I0409 23:46:51.425763  4596 solver.cpp:397]     Test net output #1: loss = 2.86702 (* 1 = 2.86702 loss)
I0409 23:46:53.211130  4596 solver.cpp:218] Iteration 7248 (1.14484 iter/s, 10.4819s/12 iters), loss = 0.198383
I0409 23:46:53.211187  4596 solver.cpp:237]     Train net output #0: loss = 0.198383 (* 1 = 0.198383 loss)
I0409 23:46:53.211199  4596 sgd_solver.cpp:105] Iteration 7248, lr = 0.00237942
I0409 23:46:58.230690  4596 solver.cpp:218] Iteration 7260 (2.39074 iter/s, 5.01937s/12 iters), loss = 0.269436
I0409 23:46:58.230748  4596 solver.cpp:237]     Train net output #0: loss = 0.269436 (* 1 = 0.269436 loss)
I0409 23:46:58.230759  4596 sgd_solver.cpp:105] Iteration 7260, lr = 0.00237378
I0409 23:47:03.184729  4596 solver.cpp:218] Iteration 7272 (2.42236 iter/s, 4.95385s/12 iters), loss = 0.203579
I0409 23:47:03.184788  4596 solver.cpp:237]     Train net output #0: loss = 0.203579 (* 1 = 0.203579 loss)
I0409 23:47:03.184800  4596 sgd_solver.cpp:105] Iteration 7272, lr = 0.00236814
I0409 23:47:07.398751  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:47:08.136101  4596 solver.cpp:218] Iteration 7284 (2.42367 iter/s, 4.95118s/12 iters), loss = 0.326745
I0409 23:47:08.136157  4596 solver.cpp:237]     Train net output #0: loss = 0.326745 (* 1 = 0.326745 loss)
I0409 23:47:08.136169  4596 sgd_solver.cpp:105] Iteration 7284, lr = 0.00236252
I0409 23:47:13.347179  4596 solver.cpp:218] Iteration 7296 (2.30287 iter/s, 5.21089s/12 iters), loss = 0.271747
I0409 23:47:13.347234  4596 solver.cpp:237]     Train net output #0: loss = 0.271747 (* 1 = 0.271747 loss)
I0409 23:47:13.347247  4596 sgd_solver.cpp:105] Iteration 7296, lr = 0.00235691
I0409 23:47:18.276005  4596 solver.cpp:218] Iteration 7308 (2.43475 iter/s, 4.92864s/12 iters), loss = 0.337639
I0409 23:47:18.276053  4596 solver.cpp:237]     Train net output #0: loss = 0.337639 (* 1 = 0.337639 loss)
I0409 23:47:18.276065  4596 sgd_solver.cpp:105] Iteration 7308, lr = 0.00235131
I0409 23:47:23.184367  4596 solver.cpp:218] Iteration 7320 (2.4449 iter/s, 4.90818s/12 iters), loss = 0.211557
I0409 23:47:23.184490  4596 solver.cpp:237]     Train net output #0: loss = 0.211557 (* 1 = 0.211557 loss)
I0409 23:47:23.184502  4596 sgd_solver.cpp:105] Iteration 7320, lr = 0.00234573
I0409 23:47:28.159993  4596 solver.cpp:218] Iteration 7332 (2.41188 iter/s, 4.97537s/12 iters), loss = 0.443949
I0409 23:47:28.160048  4596 solver.cpp:237]     Train net output #0: loss = 0.443949 (* 1 = 0.443949 loss)
I0409 23:47:28.160060  4596 sgd_solver.cpp:105] Iteration 7332, lr = 0.00234016
I0409 23:47:32.584129  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7344.caffemodel
I0409 23:47:33.012768  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7344.solverstate
I0409 23:47:33.313256  4596 solver.cpp:330] Iteration 7344, Testing net (#0)
I0409 23:47:33.313287  4596 net.cpp:676] Ignoring source layer train-data
I0409 23:47:34.906864  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:47:37.909637  4596 solver.cpp:397]     Test net output #0: accuracy = 0.490196
I0409 23:47:37.909685  4596 solver.cpp:397]     Test net output #1: loss = 2.78411 (* 1 = 2.78411 loss)
I0409 23:47:37.991346  4596 solver.cpp:218] Iteration 7344 (1.22062 iter/s, 9.83105s/12 iters), loss = 0.296533
I0409 23:47:37.991397  4596 solver.cpp:237]     Train net output #0: loss = 0.296533 (* 1 = 0.296533 loss)
I0409 23:47:37.991410  4596 sgd_solver.cpp:105] Iteration 7344, lr = 0.0023346
I0409 23:47:42.197233  4596 solver.cpp:218] Iteration 7356 (2.85326 iter/s, 4.20572s/12 iters), loss = 0.272041
I0409 23:47:42.197293  4596 solver.cpp:237]     Train net output #0: loss = 0.272041 (* 1 = 0.272041 loss)
I0409 23:47:42.197304  4596 sgd_solver.cpp:105] Iteration 7356, lr = 0.00232906
I0409 23:47:47.169668  4596 solver.cpp:218] Iteration 7368 (2.4134 iter/s, 4.97224s/12 iters), loss = 0.254812
I0409 23:47:47.169724  4596 solver.cpp:237]     Train net output #0: loss = 0.254812 (* 1 = 0.254812 loss)
I0409 23:47:47.169737  4596 sgd_solver.cpp:105] Iteration 7368, lr = 0.00232353
I0409 23:47:52.107753  4596 solver.cpp:218] Iteration 7380 (2.43019 iter/s, 4.9379s/12 iters), loss = 0.215131
I0409 23:47:52.107807  4596 solver.cpp:237]     Train net output #0: loss = 0.215131 (* 1 = 0.215131 loss)
I0409 23:47:52.107820  4596 sgd_solver.cpp:105] Iteration 7380, lr = 0.00231802
I0409 23:47:53.462159  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:47:57.038343  4596 solver.cpp:218] Iteration 7392 (2.43388 iter/s, 4.9304s/12 iters), loss = 0.303838
I0409 23:47:57.038401  4596 solver.cpp:237]     Train net output #0: loss = 0.303838 (* 1 = 0.303838 loss)
I0409 23:47:57.038414  4596 sgd_solver.cpp:105] Iteration 7392, lr = 0.00231251
I0409 23:48:01.946709  4596 solver.cpp:218] Iteration 7404 (2.4449 iter/s, 4.90818s/12 iters), loss = 0.218385
I0409 23:48:01.946766  4596 solver.cpp:237]     Train net output #0: loss = 0.218385 (* 1 = 0.218385 loss)
I0409 23:48:01.946779  4596 sgd_solver.cpp:105] Iteration 7404, lr = 0.00230702
I0409 23:48:06.887599  4596 solver.cpp:218] Iteration 7416 (2.42881 iter/s, 4.9407s/12 iters), loss = 0.314675
I0409 23:48:06.887660  4596 solver.cpp:237]     Train net output #0: loss = 0.314675 (* 1 = 0.314675 loss)
I0409 23:48:06.887670  4596 sgd_solver.cpp:105] Iteration 7416, lr = 0.00230154
I0409 23:48:11.897188  4596 solver.cpp:218] Iteration 7428 (2.3955 iter/s, 5.00939s/12 iters), loss = 0.174608
I0409 23:48:11.897248  4596 solver.cpp:237]     Train net output #0: loss = 0.174608 (* 1 = 0.174608 loss)
I0409 23:48:11.897260  4596 sgd_solver.cpp:105] Iteration 7428, lr = 0.00229608
I0409 23:48:16.936316  4596 solver.cpp:218] Iteration 7440 (2.38146 iter/s, 5.03893s/12 iters), loss = 0.311734
I0409 23:48:16.936369  4596 solver.cpp:237]     Train net output #0: loss = 0.311734 (* 1 = 0.311734 loss)
I0409 23:48:16.936383  4596 sgd_solver.cpp:105] Iteration 7440, lr = 0.00229063
I0409 23:48:18.890302  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7446.caffemodel
I0409 23:48:20.669354  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7446.solverstate
I0409 23:48:20.978771  4596 solver.cpp:330] Iteration 7446, Testing net (#0)
I0409 23:48:20.978802  4596 net.cpp:676] Ignoring source layer train-data
I0409 23:48:22.517290  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:48:25.425787  4596 solver.cpp:397]     Test net output #0: accuracy = 0.472426
I0409 23:48:25.425920  4596 solver.cpp:397]     Test net output #1: loss = 2.90046 (* 1 = 2.90046 loss)
I0409 23:48:27.245848  4596 solver.cpp:218] Iteration 7452 (1.16401 iter/s, 10.3092s/12 iters), loss = 0.255186
I0409 23:48:27.245891  4596 solver.cpp:237]     Train net output #0: loss = 0.255186 (* 1 = 0.255186 loss)
I0409 23:48:27.245900  4596 sgd_solver.cpp:105] Iteration 7452, lr = 0.00228519
I0409 23:48:32.174549  4596 solver.cpp:218] Iteration 7464 (2.43481 iter/s, 4.92852s/12 iters), loss = 0.242409
I0409 23:48:32.174602  4596 solver.cpp:237]     Train net output #0: loss = 0.242409 (* 1 = 0.242409 loss)
I0409 23:48:32.174614  4596 sgd_solver.cpp:105] Iteration 7464, lr = 0.00227976
I0409 23:48:37.179615  4596 solver.cpp:218] Iteration 7476 (2.39766 iter/s, 5.00488s/12 iters), loss = 0.185807
I0409 23:48:37.179668  4596 solver.cpp:237]     Train net output #0: loss = 0.185807 (* 1 = 0.185807 loss)
I0409 23:48:37.179678  4596 sgd_solver.cpp:105] Iteration 7476, lr = 0.00227435
I0409 23:48:40.649935  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:48:42.114665  4596 solver.cpp:218] Iteration 7488 (2.43168 iter/s, 4.93486s/12 iters), loss = 0.341998
I0409 23:48:42.114725  4596 solver.cpp:237]     Train net output #0: loss = 0.341998 (* 1 = 0.341998 loss)
I0409 23:48:42.114737  4596 sgd_solver.cpp:105] Iteration 7488, lr = 0.00226895
I0409 23:48:47.019141  4596 solver.cpp:218] Iteration 7500 (2.44684 iter/s, 4.90428s/12 iters), loss = 0.177934
I0409 23:48:47.019186  4596 solver.cpp:237]     Train net output #0: loss = 0.177934 (* 1 = 0.177934 loss)
I0409 23:48:47.019196  4596 sgd_solver.cpp:105] Iteration 7500, lr = 0.00226357
I0409 23:48:51.952088  4596 solver.cpp:218] Iteration 7512 (2.43271 iter/s, 4.93277s/12 iters), loss = 0.22796
I0409 23:48:51.952140  4596 solver.cpp:237]     Train net output #0: loss = 0.22796 (* 1 = 0.22796 loss)
I0409 23:48:51.952152  4596 sgd_solver.cpp:105] Iteration 7512, lr = 0.00225819
I0409 23:48:56.808600  4596 solver.cpp:218] Iteration 7524 (2.47101 iter/s, 4.85632s/12 iters), loss = 0.370792
I0409 23:48:56.808760  4596 solver.cpp:237]     Train net output #0: loss = 0.370792 (* 1 = 0.370792 loss)
I0409 23:48:56.808774  4596 sgd_solver.cpp:105] Iteration 7524, lr = 0.00225283
I0409 23:49:01.913612  4596 solver.cpp:218] Iteration 7536 (2.35077 iter/s, 5.10472s/12 iters), loss = 0.289883
I0409 23:49:01.913661  4596 solver.cpp:237]     Train net output #0: loss = 0.289883 (* 1 = 0.289883 loss)
I0409 23:49:01.913671  4596 sgd_solver.cpp:105] Iteration 7536, lr = 0.00224748
I0409 23:49:06.460443  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7548.caffemodel
I0409 23:49:06.872948  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7548.solverstate
I0409 23:49:07.171746  4596 solver.cpp:330] Iteration 7548, Testing net (#0)
I0409 23:49:07.171772  4596 net.cpp:676] Ignoring source layer train-data
I0409 23:49:08.664557  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:49:11.659099  4596 solver.cpp:397]     Test net output #0: accuracy = 0.48652
I0409 23:49:11.659130  4596 solver.cpp:397]     Test net output #1: loss = 2.93444 (* 1 = 2.93444 loss)
I0409 23:49:11.740463  4596 solver.cpp:218] Iteration 7548 (1.22118 iter/s, 9.82655s/12 iters), loss = 0.194227
I0409 23:49:11.740501  4596 solver.cpp:237]     Train net output #0: loss = 0.194227 (* 1 = 0.194227 loss)
I0409 23:49:11.740510  4596 sgd_solver.cpp:105] Iteration 7548, lr = 0.00224215
I0409 23:49:15.929852  4596 solver.cpp:218] Iteration 7560 (2.86449 iter/s, 4.18922s/12 iters), loss = 0.250885
I0409 23:49:15.929929  4596 solver.cpp:237]     Train net output #0: loss = 0.250885 (* 1 = 0.250885 loss)
I0409 23:49:15.930034  4596 sgd_solver.cpp:105] Iteration 7560, lr = 0.00223682
I0409 23:49:20.848706  4596 solver.cpp:218] Iteration 7572 (2.43969 iter/s, 4.91865s/12 iters), loss = 0.298485
I0409 23:49:20.848754  4596 solver.cpp:237]     Train net output #0: loss = 0.298485 (* 1 = 0.298485 loss)
I0409 23:49:20.848763  4596 sgd_solver.cpp:105] Iteration 7572, lr = 0.00223151
I0409 23:49:25.766507  4596 solver.cpp:218] Iteration 7584 (2.4402 iter/s, 4.91762s/12 iters), loss = 0.296406
I0409 23:49:25.766554  4596 solver.cpp:237]     Train net output #0: loss = 0.296406 (* 1 = 0.296406 loss)
I0409 23:49:25.766563  4596 sgd_solver.cpp:105] Iteration 7584, lr = 0.00222621
I0409 23:49:26.407439  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:49:30.687822  4596 solver.cpp:218] Iteration 7596 (2.43846 iter/s, 4.92113s/12 iters), loss = 0.351479
I0409 23:49:30.687930  4596 solver.cpp:237]     Train net output #0: loss = 0.351479 (* 1 = 0.351479 loss)
I0409 23:49:30.687940  4596 sgd_solver.cpp:105] Iteration 7596, lr = 0.00222093
I0409 23:49:35.623514  4596 solver.cpp:218] Iteration 7608 (2.43139 iter/s, 4.93545s/12 iters), loss = 0.249437
I0409 23:49:35.623571  4596 solver.cpp:237]     Train net output #0: loss = 0.249437 (* 1 = 0.249437 loss)
I0409 23:49:35.623584  4596 sgd_solver.cpp:105] Iteration 7608, lr = 0.00221565
I0409 23:49:40.587235  4596 solver.cpp:218] Iteration 7620 (2.41763 iter/s, 4.96353s/12 iters), loss = 0.265329
I0409 23:49:40.587280  4596 solver.cpp:237]     Train net output #0: loss = 0.265329 (* 1 = 0.265329 loss)
I0409 23:49:40.587289  4596 sgd_solver.cpp:105] Iteration 7620, lr = 0.00221039
I0409 23:49:41.343837  4596 blocking_queue.cpp:49] Waiting for data
I0409 23:49:45.536584  4596 solver.cpp:218] Iteration 7632 (2.42465 iter/s, 4.94917s/12 iters), loss = 0.186662
I0409 23:49:45.536640  4596 solver.cpp:237]     Train net output #0: loss = 0.186662 (* 1 = 0.186662 loss)
I0409 23:49:45.536653  4596 sgd_solver.cpp:105] Iteration 7632, lr = 0.00220515
I0409 23:49:50.483497  4596 solver.cpp:218] Iteration 7644 (2.42585 iter/s, 4.94672s/12 iters), loss = 0.202084
I0409 23:49:50.483558  4596 solver.cpp:237]     Train net output #0: loss = 0.202084 (* 1 = 0.202084 loss)
I0409 23:49:50.483572  4596 sgd_solver.cpp:105] Iteration 7644, lr = 0.00219991
I0409 23:49:52.475332  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7650.caffemodel
I0409 23:49:52.896714  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7650.solverstate
I0409 23:49:53.185060  4596 solver.cpp:330] Iteration 7650, Testing net (#0)
I0409 23:49:53.185076  4596 net.cpp:676] Ignoring source layer train-data
I0409 23:49:54.667618  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:49:57.651280  4596 solver.cpp:397]     Test net output #0: accuracy = 0.496324
I0409 23:49:57.651311  4596 solver.cpp:397]     Test net output #1: loss = 2.9057 (* 1 = 2.9057 loss)
I0409 23:49:59.374275  4596 solver.cpp:218] Iteration 7656 (1.34976 iter/s, 8.8905s/12 iters), loss = 0.131805
I0409 23:49:59.374330  4596 solver.cpp:237]     Train net output #0: loss = 0.131805 (* 1 = 0.131805 loss)
I0409 23:49:59.374341  4596 sgd_solver.cpp:105] Iteration 7656, lr = 0.00219469
I0409 23:50:04.208416  4596 solver.cpp:218] Iteration 7668 (2.48244 iter/s, 4.83396s/12 iters), loss = 0.228653
I0409 23:50:04.210069  4596 solver.cpp:237]     Train net output #0: loss = 0.228653 (* 1 = 0.228653 loss)
I0409 23:50:04.210083  4596 sgd_solver.cpp:105] Iteration 7668, lr = 0.00218948
I0409 23:50:09.134907  4596 solver.cpp:218] Iteration 7680 (2.43669 iter/s, 4.92471s/12 iters), loss = 0.21533
I0409 23:50:09.134956  4596 solver.cpp:237]     Train net output #0: loss = 0.21533 (* 1 = 0.21533 loss)
I0409 23:50:09.134968  4596 sgd_solver.cpp:105] Iteration 7680, lr = 0.00218428
I0409 23:50:11.852768  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:50:14.033332  4596 solver.cpp:218] Iteration 7692 (2.44986 iter/s, 4.89824s/12 iters), loss = 0.197283
I0409 23:50:14.033375  4596 solver.cpp:237]     Train net output #0: loss = 0.197283 (* 1 = 0.197283 loss)
I0409 23:50:14.033383  4596 sgd_solver.cpp:105] Iteration 7692, lr = 0.00217909
I0409 23:50:18.946060  4596 solver.cpp:218] Iteration 7704 (2.44272 iter/s, 4.91255s/12 iters), loss = 0.219912
I0409 23:50:18.946108  4596 solver.cpp:237]     Train net output #0: loss = 0.219912 (* 1 = 0.219912 loss)
I0409 23:50:18.946117  4596 sgd_solver.cpp:105] Iteration 7704, lr = 0.00217392
I0409 23:50:23.929980  4596 solver.cpp:218] Iteration 7716 (2.40784 iter/s, 4.98372s/12 iters), loss = 0.234896
I0409 23:50:23.930027  4596 solver.cpp:237]     Train net output #0: loss = 0.234896 (* 1 = 0.234896 loss)
I0409 23:50:23.930037  4596 sgd_solver.cpp:105] Iteration 7716, lr = 0.00216876
I0409 23:50:28.798250  4596 solver.cpp:218] Iteration 7728 (2.46503 iter/s, 4.86809s/12 iters), loss = 0.243299
I0409 23:50:28.798306  4596 solver.cpp:237]     Train net output #0: loss = 0.243299 (* 1 = 0.243299 loss)
I0409 23:50:28.798318  4596 sgd_solver.cpp:105] Iteration 7728, lr = 0.00216361
I0409 23:50:33.706964  4596 solver.cpp:218] Iteration 7740 (2.44473 iter/s, 4.90852s/12 iters), loss = 0.26376
I0409 23:50:33.707018  4596 solver.cpp:237]     Train net output #0: loss = 0.26376 (* 1 = 0.26376 loss)
I0409 23:50:33.707031  4596 sgd_solver.cpp:105] Iteration 7740, lr = 0.00215847
I0409 23:50:38.154719  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7752.caffemodel
I0409 23:50:38.594858  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7752.solverstate
I0409 23:50:38.880285  4596 solver.cpp:330] Iteration 7752, Testing net (#0)
I0409 23:50:38.880304  4596 net.cpp:676] Ignoring source layer train-data
I0409 23:50:40.227412  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:50:43.427469  4596 solver.cpp:397]     Test net output #0: accuracy = 0.496936
I0409 23:50:43.427520  4596 solver.cpp:397]     Test net output #1: loss = 2.88657 (* 1 = 2.88657 loss)
I0409 23:50:43.509371  4596 solver.cpp:218] Iteration 7752 (1.22423 iter/s, 9.8021s/12 iters), loss = 0.366225
I0409 23:50:43.509446  4596 solver.cpp:237]     Train net output #0: loss = 0.366225 (* 1 = 0.366225 loss)
I0409 23:50:43.509464  4596 sgd_solver.cpp:105] Iteration 7752, lr = 0.00215335
I0409 23:50:47.622992  4596 solver.cpp:218] Iteration 7764 (2.91727 iter/s, 4.11344s/12 iters), loss = 0.238401
I0409 23:50:47.623047  4596 solver.cpp:237]     Train net output #0: loss = 0.238401 (* 1 = 0.238401 loss)
I0409 23:50:47.623059  4596 sgd_solver.cpp:105] Iteration 7764, lr = 0.00214823
I0409 23:50:52.587605  4596 solver.cpp:218] Iteration 7776 (2.4172 iter/s, 4.96443s/12 iters), loss = 0.241565
I0409 23:50:52.587647  4596 solver.cpp:237]     Train net output #0: loss = 0.241565 (* 1 = 0.241565 loss)
I0409 23:50:52.587656  4596 sgd_solver.cpp:105] Iteration 7776, lr = 0.00214313
I0409 23:50:57.458842  4596 solver.cpp:218] Iteration 7788 (2.46353 iter/s, 4.87106s/12 iters), loss = 0.193616
I0409 23:50:57.458887  4596 solver.cpp:237]     Train net output #0: loss = 0.193617 (* 1 = 0.193617 loss)
I0409 23:50:57.458896  4596 sgd_solver.cpp:105] Iteration 7788, lr = 0.00213805
I0409 23:50:57.466948  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:51:02.373402  4596 solver.cpp:218] Iteration 7800 (2.44181 iter/s, 4.91438s/12 iters), loss = 0.179761
I0409 23:51:02.373459  4596 solver.cpp:237]     Train net output #0: loss = 0.179761 (* 1 = 0.179761 loss)
I0409 23:51:02.373472  4596 sgd_solver.cpp:105] Iteration 7800, lr = 0.00213297
I0409 23:51:07.294730  4596 solver.cpp:218] Iteration 7812 (2.43847 iter/s, 4.92113s/12 iters), loss = 0.1572
I0409 23:51:07.294823  4596 solver.cpp:237]     Train net output #0: loss = 0.1572 (* 1 = 0.1572 loss)
I0409 23:51:07.294836  4596 sgd_solver.cpp:105] Iteration 7812, lr = 0.00212791
I0409 23:51:12.190578  4596 solver.cpp:218] Iteration 7824 (2.45116 iter/s, 4.89565s/12 iters), loss = 0.108655
I0409 23:51:12.190723  4596 solver.cpp:237]     Train net output #0: loss = 0.108655 (* 1 = 0.108655 loss)
I0409 23:51:12.190737  4596 sgd_solver.cpp:105] Iteration 7824, lr = 0.00212285
I0409 23:51:17.301381  4596 solver.cpp:218] Iteration 7836 (2.3481 iter/s, 5.11052s/12 iters), loss = 0.217771
I0409 23:51:17.301437  4596 solver.cpp:237]     Train net output #0: loss = 0.217771 (* 1 = 0.217771 loss)
I0409 23:51:17.301450  4596 sgd_solver.cpp:105] Iteration 7836, lr = 0.00211781
I0409 23:51:22.522224  4596 solver.cpp:218] Iteration 7848 (2.29857 iter/s, 5.22064s/12 iters), loss = 0.0937077
I0409 23:51:22.522282  4596 solver.cpp:237]     Train net output #0: loss = 0.0937078 (* 1 = 0.0937078 loss)
I0409 23:51:22.522295  4596 sgd_solver.cpp:105] Iteration 7848, lr = 0.00211279
I0409 23:51:24.646914  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7854.caffemodel
I0409 23:51:25.645169  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7854.solverstate
I0409 23:51:26.137593  4596 solver.cpp:330] Iteration 7854, Testing net (#0)
I0409 23:51:26.137630  4596 net.cpp:676] Ignoring source layer train-data
I0409 23:51:27.416072  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:51:30.540676  4596 solver.cpp:397]     Test net output #0: accuracy = 0.497549
I0409 23:51:30.540725  4596 solver.cpp:397]     Test net output #1: loss = 2.96855 (* 1 = 2.96855 loss)
I0409 23:51:32.330832  4596 solver.cpp:218] Iteration 7860 (1.22345 iter/s, 9.8083s/12 iters), loss = 0.265954
I0409 23:51:32.330871  4596 solver.cpp:237]     Train net output #0: loss = 0.265955 (* 1 = 0.265955 loss)
I0409 23:51:32.330880  4596 sgd_solver.cpp:105] Iteration 7860, lr = 0.00210777
I0409 23:51:37.187554  4596 solver.cpp:218] Iteration 7872 (2.47089 iter/s, 4.85655s/12 iters), loss = 0.12282
I0409 23:51:37.187611  4596 solver.cpp:237]     Train net output #0: loss = 0.12282 (* 1 = 0.12282 loss)
I0409 23:51:37.187624  4596 sgd_solver.cpp:105] Iteration 7872, lr = 0.00210277
I0409 23:51:42.099177  4596 solver.cpp:218] Iteration 7884 (2.44328 iter/s, 4.91143s/12 iters), loss = 0.173454
I0409 23:51:42.099220  4596 solver.cpp:237]     Train net output #0: loss = 0.173454 (* 1 = 0.173454 loss)
I0409 23:51:42.099229  4596 sgd_solver.cpp:105] Iteration 7884, lr = 0.00209777
I0409 23:51:44.186940  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:51:47.010931  4596 solver.cpp:218] Iteration 7896 (2.44321 iter/s, 4.91158s/12 iters), loss = 0.469565
I0409 23:51:47.010973  4596 solver.cpp:237]     Train net output #0: loss = 0.469565 (* 1 = 0.469565 loss)
I0409 23:51:47.010982  4596 sgd_solver.cpp:105] Iteration 7896, lr = 0.00209279
I0409 23:51:51.920104  4596 solver.cpp:218] Iteration 7908 (2.4445 iter/s, 4.90899s/12 iters), loss = 0.119495
I0409 23:51:51.920173  4596 solver.cpp:237]     Train net output #0: loss = 0.119495 (* 1 = 0.119495 loss)
I0409 23:51:51.920190  4596 sgd_solver.cpp:105] Iteration 7908, lr = 0.00208782
I0409 23:51:57.043982  4596 solver.cpp:218] Iteration 7920 (2.34207 iter/s, 5.12367s/12 iters), loss = 0.149745
I0409 23:51:57.044039  4596 solver.cpp:237]     Train net output #0: loss = 0.149745 (* 1 = 0.149745 loss)
I0409 23:51:57.044050  4596 sgd_solver.cpp:105] Iteration 7920, lr = 0.00208287
I0409 23:52:01.961843  4596 solver.cpp:218] Iteration 7932 (2.44018 iter/s, 4.91767s/12 iters), loss = 0.261083
I0409 23:52:01.961891  4596 solver.cpp:237]     Train net output #0: loss = 0.261083 (* 1 = 0.261083 loss)
I0409 23:52:01.961900  4596 sgd_solver.cpp:105] Iteration 7932, lr = 0.00207792
I0409 23:52:06.867012  4596 solver.cpp:218] Iteration 7944 (2.44649 iter/s, 4.90498s/12 iters), loss = 0.103179
I0409 23:52:06.867071  4596 solver.cpp:237]     Train net output #0: loss = 0.103179 (* 1 = 0.103179 loss)
I0409 23:52:06.867084  4596 sgd_solver.cpp:105] Iteration 7944, lr = 0.00207299
I0409 23:52:11.280441  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7956.caffemodel
I0409 23:52:11.738344  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7956.solverstate
I0409 23:52:12.073390  4596 solver.cpp:330] Iteration 7956, Testing net (#0)
I0409 23:52:12.073411  4596 net.cpp:676] Ignoring source layer train-data
I0409 23:52:13.414461  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:52:16.544842  4596 solver.cpp:397]     Test net output #0: accuracy = 0.495098
I0409 23:52:16.545388  4596 solver.cpp:397]     Test net output #1: loss = 2.94743 (* 1 = 2.94743 loss)
I0409 23:52:16.627226  4596 solver.cpp:218] Iteration 7956 (1.22952 iter/s, 9.7599s/12 iters), loss = 0.280508
I0409 23:52:16.627296  4596 solver.cpp:237]     Train net output #0: loss = 0.280508 (* 1 = 0.280508 loss)
I0409 23:52:16.627312  4596 sgd_solver.cpp:105] Iteration 7956, lr = 0.00206807
I0409 23:52:20.834234  4596 solver.cpp:218] Iteration 7968 (2.8525 iter/s, 4.20683s/12 iters), loss = 0.254045
I0409 23:52:20.834275  4596 solver.cpp:237]     Train net output #0: loss = 0.254045 (* 1 = 0.254045 loss)
I0409 23:52:20.834282  4596 sgd_solver.cpp:105] Iteration 7968, lr = 0.00206316
I0409 23:52:25.804476  4596 solver.cpp:218] Iteration 7980 (2.41446 iter/s, 4.97006s/12 iters), loss = 0.203232
I0409 23:52:25.804531  4596 solver.cpp:237]     Train net output #0: loss = 0.203232 (* 1 = 0.203232 loss)
I0409 23:52:25.804543  4596 sgd_solver.cpp:105] Iteration 7980, lr = 0.00205826
I0409 23:52:29.952234  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:52:30.653834  4596 solver.cpp:218] Iteration 7992 (2.47465 iter/s, 4.84916s/12 iters), loss = 0.370685
I0409 23:52:30.653894  4596 solver.cpp:237]     Train net output #0: loss = 0.370685 (* 1 = 0.370685 loss)
I0409 23:52:30.653908  4596 sgd_solver.cpp:105] Iteration 7992, lr = 0.00205337
I0409 23:52:35.566282  4596 solver.cpp:218] Iteration 8004 (2.44287 iter/s, 4.91225s/12 iters), loss = 0.0999623
I0409 23:52:35.566354  4596 solver.cpp:237]     Train net output #0: loss = 0.0999623 (* 1 = 0.0999623 loss)
I0409 23:52:35.566366  4596 sgd_solver.cpp:105] Iteration 8004, lr = 0.0020485
I0409 23:52:40.487713  4596 solver.cpp:218] Iteration 8016 (2.43842 iter/s, 4.92123s/12 iters), loss = 0.177836
I0409 23:52:40.487768  4596 solver.cpp:237]     Train net output #0: loss = 0.177836 (* 1 = 0.177836 loss)
I0409 23:52:40.487782  4596 sgd_solver.cpp:105] Iteration 8016, lr = 0.00204363
I0409 23:52:45.413846  4596 solver.cpp:218] Iteration 8028 (2.43608 iter/s, 4.92595s/12 iters), loss = 0.292454
I0409 23:52:45.413892  4596 solver.cpp:237]     Train net output #0: loss = 0.292454 (* 1 = 0.292454 loss)
I0409 23:52:45.413902  4596 sgd_solver.cpp:105] Iteration 8028, lr = 0.00203878
I0409 23:52:50.393512  4596 solver.cpp:218] Iteration 8040 (2.40989 iter/s, 4.97948s/12 iters), loss = 0.300982
I0409 23:52:50.393637  4596 solver.cpp:237]     Train net output #0: loss = 0.300982 (* 1 = 0.300982 loss)
I0409 23:52:50.393651  4596 sgd_solver.cpp:105] Iteration 8040, lr = 0.00203394
I0409 23:52:55.388456  4596 solver.cpp:218] Iteration 8052 (2.40255 iter/s, 4.99469s/12 iters), loss = 0.256412
I0409 23:52:55.388504  4596 solver.cpp:237]     Train net output #0: loss = 0.256412 (* 1 = 0.256412 loss)
I0409 23:52:55.388512  4596 sgd_solver.cpp:105] Iteration 8052, lr = 0.00202911
I0409 23:52:57.452227  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8058.caffemodel
I0409 23:52:57.994874  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8058.solverstate
I0409 23:52:58.545439  4596 solver.cpp:330] Iteration 8058, Testing net (#0)
I0409 23:52:58.545459  4596 net.cpp:676] Ignoring source layer train-data
I0409 23:52:59.859549  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:53:03.062440  4596 solver.cpp:397]     Test net output #0: accuracy = 0.492647
I0409 23:53:03.062484  4596 solver.cpp:397]     Test net output #1: loss = 2.90235 (* 1 = 2.90235 loss)
I0409 23:53:05.007899  4596 solver.cpp:218] Iteration 8064 (1.24751 iter/s, 9.61915s/12 iters), loss = 0.2286
I0409 23:53:05.007956  4596 solver.cpp:237]     Train net output #0: loss = 0.2286 (* 1 = 0.2286 loss)
I0409 23:53:05.007968  4596 sgd_solver.cpp:105] Iteration 8064, lr = 0.00202429
I0409 23:53:09.960239  4596 solver.cpp:218] Iteration 8076 (2.42319 iter/s, 4.95215s/12 iters), loss = 0.254225
I0409 23:53:09.960294  4596 solver.cpp:237]     Train net output #0: loss = 0.254225 (* 1 = 0.254225 loss)
I0409 23:53:09.960307  4596 sgd_solver.cpp:105] Iteration 8076, lr = 0.00201949
I0409 23:53:14.916947  4596 solver.cpp:218] Iteration 8088 (2.42105 iter/s, 4.95653s/12 iters), loss = 0.146577
I0409 23:53:14.916993  4596 solver.cpp:237]     Train net output #0: loss = 0.146577 (* 1 = 0.146577 loss)
I0409 23:53:14.917003  4596 sgd_solver.cpp:105] Iteration 8088, lr = 0.00201469
I0409 23:53:16.278627  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:53:19.786439  4596 solver.cpp:218] Iteration 8100 (2.46441 iter/s, 4.86931s/12 iters), loss = 0.178537
I0409 23:53:19.786495  4596 solver.cpp:237]     Train net output #0: loss = 0.178537 (* 1 = 0.178537 loss)
I0409 23:53:19.786509  4596 sgd_solver.cpp:105] Iteration 8100, lr = 0.00200991
I0409 23:53:24.748383  4596 solver.cpp:218] Iteration 8112 (2.4185 iter/s, 4.96176s/12 iters), loss = 0.13858
I0409 23:53:24.748478  4596 solver.cpp:237]     Train net output #0: loss = 0.13858 (* 1 = 0.13858 loss)
I0409 23:53:24.748489  4596 sgd_solver.cpp:105] Iteration 8112, lr = 0.00200514
I0409 23:53:29.635850  4596 solver.cpp:218] Iteration 8124 (2.45538 iter/s, 4.88724s/12 iters), loss = 0.239715
I0409 23:53:29.635905  4596 solver.cpp:237]     Train net output #0: loss = 0.239715 (* 1 = 0.239715 loss)
I0409 23:53:29.635917  4596 sgd_solver.cpp:105] Iteration 8124, lr = 0.00200038
I0409 23:53:34.553848  4596 solver.cpp:218] Iteration 8136 (2.44011 iter/s, 4.91781s/12 iters), loss = 0.178411
I0409 23:53:34.553901  4596 solver.cpp:237]     Train net output #0: loss = 0.178411 (* 1 = 0.178411 loss)
I0409 23:53:34.553915  4596 sgd_solver.cpp:105] Iteration 8136, lr = 0.00199563
I0409 23:53:39.493645  4596 solver.cpp:218] Iteration 8148 (2.42935 iter/s, 4.9396s/12 iters), loss = 0.203517
I0409 23:53:39.493714  4596 solver.cpp:237]     Train net output #0: loss = 0.203517 (* 1 = 0.203517 loss)
I0409 23:53:39.493728  4596 sgd_solver.cpp:105] Iteration 8148, lr = 0.00199089
I0409 23:53:43.943125  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8160.caffemodel
I0409 23:53:44.397245  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8160.solverstate
I0409 23:53:44.712455  4596 solver.cpp:330] Iteration 8160, Testing net (#0)
I0409 23:53:44.712482  4596 net.cpp:676] Ignoring source layer train-data
I0409 23:53:45.978967  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:53:49.157332  4596 solver.cpp:397]     Test net output #0: accuracy = 0.485907
I0409 23:53:49.157380  4596 solver.cpp:397]     Test net output #1: loss = 2.91473 (* 1 = 2.91473 loss)
I0409 23:53:49.238826  4596 solver.cpp:218] Iteration 8160 (1.23142 iter/s, 9.74486s/12 iters), loss = 0.238514
I0409 23:53:49.238898  4596 solver.cpp:237]     Train net output #0: loss = 0.238514 (* 1 = 0.238514 loss)
I0409 23:53:49.238914  4596 sgd_solver.cpp:105] Iteration 8160, lr = 0.00198616
I0409 23:53:53.441144  4596 solver.cpp:218] Iteration 8172 (2.85569 iter/s, 4.20213s/12 iters), loss = 0.134901
I0409 23:53:53.441190  4596 solver.cpp:237]     Train net output #0: loss = 0.134901 (* 1 = 0.134901 loss)
I0409 23:53:53.441200  4596 sgd_solver.cpp:105] Iteration 8172, lr = 0.00198145
I0409 23:53:58.369144  4596 solver.cpp:218] Iteration 8184 (2.43515 iter/s, 4.92782s/12 iters), loss = 0.248687
I0409 23:53:58.369303  4596 solver.cpp:237]     Train net output #0: loss = 0.248687 (* 1 = 0.248687 loss)
I0409 23:53:58.369318  4596 sgd_solver.cpp:105] Iteration 8184, lr = 0.00197674
I0409 23:54:01.832760  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:54:03.269574  4596 solver.cpp:218] Iteration 8196 (2.44891 iter/s, 4.90014s/12 iters), loss = 0.227666
I0409 23:54:03.269623  4596 solver.cpp:237]     Train net output #0: loss = 0.227666 (* 1 = 0.227666 loss)
I0409 23:54:03.269634  4596 sgd_solver.cpp:105] Iteration 8196, lr = 0.00197205
I0409 23:54:08.226310  4596 solver.cpp:218] Iteration 8208 (2.42104 iter/s, 4.95656s/12 iters), loss = 0.179367
I0409 23:54:08.226351  4596 solver.cpp:237]     Train net output #0: loss = 0.179367 (* 1 = 0.179367 loss)
I0409 23:54:08.226361  4596 sgd_solver.cpp:105] Iteration 8208, lr = 0.00196737
I0409 23:54:13.350075  4596 solver.cpp:218] Iteration 8220 (2.34211 iter/s, 5.12358s/12 iters), loss = 0.29867
I0409 23:54:13.350136  4596 solver.cpp:237]     Train net output #0: loss = 0.29867 (* 1 = 0.29867 loss)
I0409 23:54:13.350149  4596 sgd_solver.cpp:105] Iteration 8220, lr = 0.0019627
I0409 23:54:18.266479  4596 solver.cpp:218] Iteration 8232 (2.4409 iter/s, 4.91621s/12 iters), loss = 0.159887
I0409 23:54:18.266525  4596 solver.cpp:237]     Train net output #0: loss = 0.159887 (* 1 = 0.159887 loss)
I0409 23:54:18.266535  4596 sgd_solver.cpp:105] Iteration 8232, lr = 0.00195804
I0409 23:54:23.144913  4596 solver.cpp:218] Iteration 8244 (2.4599 iter/s, 4.87825s/12 iters), loss = 0.149041
I0409 23:54:23.144969  4596 solver.cpp:237]     Train net output #0: loss = 0.149041 (* 1 = 0.149041 loss)
I0409 23:54:23.144981  4596 sgd_solver.cpp:105] Iteration 8244, lr = 0.00195339
I0409 23:54:27.976764  4596 solver.cpp:218] Iteration 8256 (2.48362 iter/s, 4.83167s/12 iters), loss = 0.179832
I0409 23:54:27.976816  4596 solver.cpp:237]     Train net output #0: loss = 0.179832 (* 1 = 0.179832 loss)
I0409 23:54:27.976828  4596 sgd_solver.cpp:105] Iteration 8256, lr = 0.00194875
I0409 23:54:30.002912  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8262.caffemodel
I0409 23:54:30.435185  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8262.solverstate
I0409 23:54:30.728787  4596 solver.cpp:330] Iteration 8262, Testing net (#0)
I0409 23:54:30.728812  4596 net.cpp:676] Ignoring source layer train-data
I0409 23:54:32.156961  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:54:35.393172  4596 solver.cpp:397]     Test net output #0: accuracy = 0.515931
I0409 23:54:35.393201  4596 solver.cpp:397]     Test net output #1: loss = 2.89015 (* 1 = 2.89015 loss)
I0409 23:54:37.258692  4596 solver.cpp:218] Iteration 8268 (1.29287 iter/s, 9.28164s/12 iters), loss = 0.188059
I0409 23:54:37.258749  4596 solver.cpp:237]     Train net output #0: loss = 0.188059 (* 1 = 0.188059 loss)
I0409 23:54:37.258761  4596 sgd_solver.cpp:105] Iteration 8268, lr = 0.00194412
I0409 23:54:42.127972  4596 solver.cpp:218] Iteration 8280 (2.46452 iter/s, 4.86909s/12 iters), loss = 0.16031
I0409 23:54:42.128032  4596 solver.cpp:237]     Train net output #0: loss = 0.16031 (* 1 = 0.16031 loss)
I0409 23:54:42.128046  4596 sgd_solver.cpp:105] Iteration 8280, lr = 0.00193951
I0409 23:54:47.056996  4596 solver.cpp:218] Iteration 8292 (2.43465 iter/s, 4.92883s/12 iters), loss = 0.253621
I0409 23:54:47.057049  4596 solver.cpp:237]     Train net output #0: loss = 0.253621 (* 1 = 0.253621 loss)
I0409 23:54:47.057061  4596 sgd_solver.cpp:105] Iteration 8292, lr = 0.0019349
I0409 23:54:47.716055  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:54:51.959988  4596 solver.cpp:218] Iteration 8304 (2.44758 iter/s, 4.90281s/12 iters), loss = 0.284988
I0409 23:54:51.960049  4596 solver.cpp:237]     Train net output #0: loss = 0.284988 (* 1 = 0.284988 loss)
I0409 23:54:51.960062  4596 sgd_solver.cpp:105] Iteration 8304, lr = 0.00193031
I0409 23:54:53.120496  4596 blocking_queue.cpp:49] Waiting for data
I0409 23:54:56.839655  4596 solver.cpp:218] Iteration 8316 (2.45928 iter/s, 4.87948s/12 iters), loss = 0.174983
I0409 23:54:56.839713  4596 solver.cpp:237]     Train net output #0: loss = 0.174983 (* 1 = 0.174983 loss)
I0409 23:54:56.839725  4596 sgd_solver.cpp:105] Iteration 8316, lr = 0.00192573
I0409 23:55:01.788581  4596 solver.cpp:218] Iteration 8328 (2.42486 iter/s, 4.94873s/12 iters), loss = 0.282239
I0409 23:55:01.788733  4596 solver.cpp:237]     Train net output #0: loss = 0.282239 (* 1 = 0.282239 loss)
I0409 23:55:01.788748  4596 sgd_solver.cpp:105] Iteration 8328, lr = 0.00192115
I0409 23:55:06.671947  4596 solver.cpp:218] Iteration 8340 (2.45746 iter/s, 4.88308s/12 iters), loss = 0.204006
I0409 23:55:06.671999  4596 solver.cpp:237]     Train net output #0: loss = 0.204006 (* 1 = 0.204006 loss)
I0409 23:55:06.672011  4596 sgd_solver.cpp:105] Iteration 8340, lr = 0.00191659
I0409 23:55:11.629115  4596 solver.cpp:218] Iteration 8352 (2.42083 iter/s, 4.95698s/12 iters), loss = 0.239874
I0409 23:55:11.629171  4596 solver.cpp:237]     Train net output #0: loss = 0.239874 (* 1 = 0.239874 loss)
I0409 23:55:11.629184  4596 sgd_solver.cpp:105] Iteration 8352, lr = 0.00191204
I0409 23:55:16.162178  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8364.caffemodel
I0409 23:55:16.835608  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8364.solverstate
I0409 23:55:17.585088  4596 solver.cpp:330] Iteration 8364, Testing net (#0)
I0409 23:55:17.585115  4596 net.cpp:676] Ignoring source layer train-data
I0409 23:55:18.766252  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:55:22.028616  4596 solver.cpp:397]     Test net output #0: accuracy = 0.511642
I0409 23:55:22.028664  4596 solver.cpp:397]     Test net output #1: loss = 2.9091 (* 1 = 2.9091 loss)
I0409 23:55:22.110689  4596 solver.cpp:218] Iteration 8364 (1.1449 iter/s, 10.4813s/12 iters), loss = 0.205487
I0409 23:55:22.110733  4596 solver.cpp:237]     Train net output #0: loss = 0.205487 (* 1 = 0.205487 loss)
I0409 23:55:22.110743  4596 sgd_solver.cpp:105] Iteration 8364, lr = 0.0019075
I0409 23:55:26.126209  4596 solver.cpp:218] Iteration 8376 (2.98852 iter/s, 4.01536s/12 iters), loss = 0.231195
I0409 23:55:26.126260  4596 solver.cpp:237]     Train net output #0: loss = 0.231195 (* 1 = 0.231195 loss)
I0409 23:55:26.126272  4596 sgd_solver.cpp:105] Iteration 8376, lr = 0.00190297
I0409 23:55:31.047377  4596 solver.cpp:218] Iteration 8388 (2.43854 iter/s, 4.92098s/12 iters), loss = 0.284041
I0409 23:55:31.047430  4596 solver.cpp:237]     Train net output #0: loss = 0.284041 (* 1 = 0.284041 loss)
I0409 23:55:31.047443  4596 sgd_solver.cpp:105] Iteration 8388, lr = 0.00189846
I0409 23:55:33.827706  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:55:35.988301  4596 solver.cpp:218] Iteration 8400 (2.42879 iter/s, 4.94073s/12 iters), loss = 0.210318
I0409 23:55:35.988359  4596 solver.cpp:237]     Train net output #0: loss = 0.210318 (* 1 = 0.210318 loss)
I0409 23:55:35.988371  4596 sgd_solver.cpp:105] Iteration 8400, lr = 0.00189395
I0409 23:55:40.918200  4596 solver.cpp:218] Iteration 8412 (2.43422 iter/s, 4.9297s/12 iters), loss = 0.113503
I0409 23:55:40.918268  4596 solver.cpp:237]     Train net output #0: loss = 0.113503 (* 1 = 0.113503 loss)
I0409 23:55:40.918280  4596 sgd_solver.cpp:105] Iteration 8412, lr = 0.00188945
I0409 23:55:45.798005  4596 solver.cpp:218] Iteration 8424 (2.45922 iter/s, 4.8796s/12 iters), loss = 0.193524
I0409 23:55:45.798059  4596 solver.cpp:237]     Train net output #0: loss = 0.193524 (* 1 = 0.193524 loss)
I0409 23:55:45.798071  4596 sgd_solver.cpp:105] Iteration 8424, lr = 0.00188497
I0409 23:55:50.720821  4596 solver.cpp:218] Iteration 8436 (2.43772 iter/s, 4.92262s/12 iters), loss = 0.200612
I0409 23:55:50.720883  4596 solver.cpp:237]     Train net output #0: loss = 0.200612 (* 1 = 0.200612 loss)
I0409 23:55:50.720897  4596 sgd_solver.cpp:105] Iteration 8436, lr = 0.00188049
I0409 23:55:55.694989  4596 solver.cpp:218] Iteration 8448 (2.41256 iter/s, 4.97397s/12 iters), loss = 0.145415
I0409 23:55:55.695044  4596 solver.cpp:237]     Train net output #0: loss = 0.145415 (* 1 = 0.145415 loss)
I0409 23:55:55.695055  4596 sgd_solver.cpp:105] Iteration 8448, lr = 0.00187603
I0409 23:56:00.689445  4596 solver.cpp:218] Iteration 8460 (2.40276 iter/s, 4.99426s/12 iters), loss = 0.193838
I0409 23:56:00.689502  4596 solver.cpp:237]     Train net output #0: loss = 0.193838 (* 1 = 0.193838 loss)
I0409 23:56:00.689513  4596 sgd_solver.cpp:105] Iteration 8460, lr = 0.00187157
I0409 23:56:02.801482  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8466.caffemodel
I0409 23:56:03.212994  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8466.solverstate
I0409 23:56:03.535256  4596 solver.cpp:330] Iteration 8466, Testing net (#0)
I0409 23:56:03.535284  4596 net.cpp:676] Ignoring source layer train-data
I0409 23:56:04.553354  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:56:07.975010  4596 solver.cpp:397]     Test net output #0: accuracy = 0.504289
I0409 23:56:07.975059  4596 solver.cpp:397]     Test net output #1: loss = 2.94365 (* 1 = 2.94365 loss)
I0409 23:56:09.885695  4596 solver.cpp:218] Iteration 8472 (1.30492 iter/s, 9.19596s/12 iters), loss = 0.0906469
I0409 23:56:09.885749  4596 solver.cpp:237]     Train net output #0: loss = 0.090647 (* 1 = 0.090647 loss)
I0409 23:56:09.885761  4596 sgd_solver.cpp:105] Iteration 8472, lr = 0.00186713
I0409 23:56:14.864773  4596 solver.cpp:218] Iteration 8484 (2.41018 iter/s, 4.97889s/12 iters), loss = 0.241481
I0409 23:56:14.864830  4596 solver.cpp:237]     Train net output #0: loss = 0.241481 (* 1 = 0.241481 loss)
I0409 23:56:14.864842  4596 sgd_solver.cpp:105] Iteration 8484, lr = 0.0018627
I0409 23:56:19.790577  4596 solver.cpp:218] Iteration 8496 (2.43624 iter/s, 4.92561s/12 iters), loss = 0.171158
I0409 23:56:19.790627  4596 solver.cpp:237]     Train net output #0: loss = 0.171158 (* 1 = 0.171158 loss)
I0409 23:56:19.790637  4596 sgd_solver.cpp:105] Iteration 8496, lr = 0.00185827
I0409 23:56:19.839785  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:56:24.731305  4596 solver.cpp:218] Iteration 8508 (2.42888 iter/s, 4.94054s/12 iters), loss = 0.0943911
I0409 23:56:24.731357  4596 solver.cpp:237]     Train net output #0: loss = 0.0943911 (* 1 = 0.0943911 loss)
I0409 23:56:24.731369  4596 sgd_solver.cpp:105] Iteration 8508, lr = 0.00185386
I0409 23:56:29.850455  4596 solver.cpp:218] Iteration 8520 (2.34423 iter/s, 5.11896s/12 iters), loss = 0.164118
I0409 23:56:29.850507  4596 solver.cpp:237]     Train net output #0: loss = 0.164118 (* 1 = 0.164118 loss)
I0409 23:56:29.850520  4596 sgd_solver.cpp:105] Iteration 8520, lr = 0.00184946
I0409 23:56:34.761719  4596 solver.cpp:218] Iteration 8532 (2.44346 iter/s, 4.91107s/12 iters), loss = 0.204586
I0409 23:56:34.761893  4596 solver.cpp:237]     Train net output #0: loss = 0.204586 (* 1 = 0.204586 loss)
I0409 23:56:34.761909  4596 sgd_solver.cpp:105] Iteration 8532, lr = 0.00184507
I0409 23:56:39.679426  4596 solver.cpp:218] Iteration 8544 (2.44031 iter/s, 4.91741s/12 iters), loss = 0.172672
I0409 23:56:39.679474  4596 solver.cpp:237]     Train net output #0: loss = 0.172672 (* 1 = 0.172672 loss)
I0409 23:56:39.679484  4596 sgd_solver.cpp:105] Iteration 8544, lr = 0.00184069
I0409 23:56:44.609933  4596 solver.cpp:218] Iteration 8556 (2.43392 iter/s, 4.93032s/12 iters), loss = 0.178063
I0409 23:56:44.609997  4596 solver.cpp:237]     Train net output #0: loss = 0.178063 (* 1 = 0.178063 loss)
I0409 23:56:44.610005  4596 sgd_solver.cpp:105] Iteration 8556, lr = 0.00183632
I0409 23:56:49.344734  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8568.caffemodel
I0409 23:56:49.806360  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8568.solverstate
I0409 23:56:50.124302  4596 solver.cpp:330] Iteration 8568, Testing net (#0)
I0409 23:56:50.124330  4596 net.cpp:676] Ignoring source layer train-data
I0409 23:56:51.263572  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:56:54.762773  4596 solver.cpp:397]     Test net output #0: accuracy = 0.509804
I0409 23:56:54.762818  4596 solver.cpp:397]     Test net output #1: loss = 2.95883 (* 1 = 2.95883 loss)
I0409 23:56:54.844646  4596 solver.cpp:218] Iteration 8568 (1.17252 iter/s, 10.2344s/12 iters), loss = 0.0977178
I0409 23:56:54.844694  4596 solver.cpp:237]     Train net output #0: loss = 0.0977178 (* 1 = 0.0977178 loss)
I0409 23:56:54.844705  4596 sgd_solver.cpp:105] Iteration 8568, lr = 0.00183196
I0409 23:56:59.031085  4596 solver.cpp:218] Iteration 8580 (2.86651 iter/s, 4.18627s/12 iters), loss = 0.158705
I0409 23:56:59.031126  4596 solver.cpp:237]     Train net output #0: loss = 0.158705 (* 1 = 0.158705 loss)
I0409 23:56:59.031136  4596 sgd_solver.cpp:105] Iteration 8580, lr = 0.00182761
I0409 23:57:03.894582  4596 solver.cpp:218] Iteration 8592 (2.46745 iter/s, 4.86333s/12 iters), loss = 0.166706
I0409 23:57:03.894621  4596 solver.cpp:237]     Train net output #0: loss = 0.166706 (* 1 = 0.166706 loss)
I0409 23:57:03.894630  4596 sgd_solver.cpp:105] Iteration 8592, lr = 0.00182327
I0409 23:57:06.160912  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:57:08.974727  4596 solver.cpp:218] Iteration 8604 (2.36222 iter/s, 5.07996s/12 iters), loss = 0.210731
I0409 23:57:08.974787  4596 solver.cpp:237]     Train net output #0: loss = 0.210731 (* 1 = 0.210731 loss)
I0409 23:57:08.974798  4596 sgd_solver.cpp:105] Iteration 8604, lr = 0.00181894
I0409 23:57:13.855170  4596 solver.cpp:218] Iteration 8616 (2.45889 iter/s, 4.88025s/12 iters), loss = 0.169063
I0409 23:57:13.855216  4596 solver.cpp:237]     Train net output #0: loss = 0.169063 (* 1 = 0.169063 loss)
I0409 23:57:13.855224  4596 sgd_solver.cpp:105] Iteration 8616, lr = 0.00181462
I0409 23:57:18.798606  4596 solver.cpp:218] Iteration 8628 (2.42755 iter/s, 4.94325s/12 iters), loss = 0.169191
I0409 23:57:18.798651  4596 solver.cpp:237]     Train net output #0: loss = 0.169191 (* 1 = 0.169191 loss)
I0409 23:57:18.798661  4596 sgd_solver.cpp:105] Iteration 8628, lr = 0.00181031
I0409 23:57:23.666857  4596 solver.cpp:218] Iteration 8640 (2.46504 iter/s, 4.86807s/12 iters), loss = 0.203732
I0409 23:57:23.666904  4596 solver.cpp:237]     Train net output #0: loss = 0.203732 (* 1 = 0.203732 loss)
I0409 23:57:23.666914  4596 sgd_solver.cpp:105] Iteration 8640, lr = 0.00180602
I0409 23:57:28.615307  4596 solver.cpp:218] Iteration 8652 (2.42509 iter/s, 4.94827s/12 iters), loss = 0.161098
I0409 23:57:28.615348  4596 solver.cpp:237]     Train net output #0: loss = 0.161098 (* 1 = 0.161098 loss)
I0409 23:57:28.615357  4596 sgd_solver.cpp:105] Iteration 8652, lr = 0.00180173
I0409 23:57:33.517498  4596 solver.cpp:218] Iteration 8664 (2.44797 iter/s, 4.90201s/12 iters), loss = 0.205026
I0409 23:57:33.517549  4596 solver.cpp:237]     Train net output #0: loss = 0.205026 (* 1 = 0.205026 loss)
I0409 23:57:33.517557  4596 sgd_solver.cpp:105] Iteration 8664, lr = 0.00179745
I0409 23:57:35.639421  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8670.caffemodel
I0409 23:57:36.075101  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8670.solverstate
I0409 23:57:36.431989  4596 solver.cpp:330] Iteration 8670, Testing net (#0)
I0409 23:57:36.432077  4596 net.cpp:676] Ignoring source layer train-data
I0409 23:57:37.443588  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:57:40.818624  4596 solver.cpp:397]     Test net output #0: accuracy = 0.494485
I0409 23:57:40.818676  4596 solver.cpp:397]     Test net output #1: loss = 3.03007 (* 1 = 3.03007 loss)
I0409 23:57:42.713946  4596 solver.cpp:218] Iteration 8676 (1.30489 iter/s, 9.19616s/12 iters), loss = 0.235427
I0409 23:57:42.714011  4596 solver.cpp:237]     Train net output #0: loss = 0.235427 (* 1 = 0.235427 loss)
I0409 23:57:42.714022  4596 sgd_solver.cpp:105] Iteration 8676, lr = 0.00179318
I0409 23:57:47.701409  4596 solver.cpp:218] Iteration 8688 (2.40613 iter/s, 4.98726s/12 iters), loss = 0.141069
I0409 23:57:47.701468  4596 solver.cpp:237]     Train net output #0: loss = 0.141069 (* 1 = 0.141069 loss)
I0409 23:57:47.701481  4596 sgd_solver.cpp:105] Iteration 8688, lr = 0.00178893
I0409 23:57:51.962082  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:57:52.637797  4596 solver.cpp:218] Iteration 8700 (2.43102 iter/s, 4.9362s/12 iters), loss = 0.189061
I0409 23:57:52.637857  4596 solver.cpp:237]     Train net output #0: loss = 0.189061 (* 1 = 0.189061 loss)
I0409 23:57:52.637871  4596 sgd_solver.cpp:105] Iteration 8700, lr = 0.00178468
I0409 23:57:57.506150  4596 solver.cpp:218] Iteration 8712 (2.465 iter/s, 4.86816s/12 iters), loss = 0.0992251
I0409 23:57:57.506211  4596 solver.cpp:237]     Train net output #0: loss = 0.0992252 (* 1 = 0.0992252 loss)
I0409 23:57:57.506223  4596 sgd_solver.cpp:105] Iteration 8712, lr = 0.00178044
I0409 23:58:02.384034  4596 solver.cpp:218] Iteration 8724 (2.46018 iter/s, 4.87769s/12 iters), loss = 0.137932
I0409 23:58:02.384081  4596 solver.cpp:237]     Train net output #0: loss = 0.137932 (* 1 = 0.137932 loss)
I0409 23:58:02.384091  4596 sgd_solver.cpp:105] Iteration 8724, lr = 0.00177621
I0409 23:58:07.549823  4596 solver.cpp:218] Iteration 8736 (2.32306 iter/s, 5.1656s/12 iters), loss = 0.250504
I0409 23:58:07.549952  4596 solver.cpp:237]     Train net output #0: loss = 0.250504 (* 1 = 0.250504 loss)
I0409 23:58:07.549990  4596 sgd_solver.cpp:105] Iteration 8736, lr = 0.001772
I0409 23:58:12.471508  4596 solver.cpp:218] Iteration 8748 (2.43832 iter/s, 4.92143s/12 iters), loss = 0.203462
I0409 23:58:12.471562  4596 solver.cpp:237]     Train net output #0: loss = 0.203462 (* 1 = 0.203462 loss)
I0409 23:58:12.471575  4596 sgd_solver.cpp:105] Iteration 8748, lr = 0.00176779
I0409 23:58:17.379137  4596 solver.cpp:218] Iteration 8760 (2.44527 iter/s, 4.90744s/12 iters), loss = 0.0833634
I0409 23:58:17.379194  4596 solver.cpp:237]     Train net output #0: loss = 0.0833634 (* 1 = 0.0833634 loss)
I0409 23:58:17.379207  4596 sgd_solver.cpp:105] Iteration 8760, lr = 0.00176359
I0409 23:58:22.014812  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8772.caffemodel
I0409 23:58:23.072916  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8772.solverstate
I0409 23:58:23.430583  4596 solver.cpp:330] Iteration 8772, Testing net (#0)
I0409 23:58:23.430614  4596 net.cpp:676] Ignoring source layer train-data
I0409 23:58:24.403193  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:58:27.880048  4596 solver.cpp:397]     Test net output #0: accuracy = 0.520221
I0409 23:58:27.880089  4596 solver.cpp:397]     Test net output #1: loss = 2.97367 (* 1 = 2.97367 loss)
I0409 23:58:27.961560  4596 solver.cpp:218] Iteration 8772 (1.13399 iter/s, 10.5821s/12 iters), loss = 0.113036
I0409 23:58:27.961627  4596 solver.cpp:237]     Train net output #0: loss = 0.113036 (* 1 = 0.113036 loss)
I0409 23:58:27.961642  4596 sgd_solver.cpp:105] Iteration 8772, lr = 0.00175941
I0409 23:58:32.188454  4596 solver.cpp:218] Iteration 8784 (2.83909 iter/s, 4.22671s/12 iters), loss = 0.131171
I0409 23:58:32.188509  4596 solver.cpp:237]     Train net output #0: loss = 0.131171 (* 1 = 0.131171 loss)
I0409 23:58:32.188521  4596 sgd_solver.cpp:105] Iteration 8784, lr = 0.00175523
I0409 23:58:37.085980  4596 solver.cpp:218] Iteration 8796 (2.45031 iter/s, 4.89733s/12 iters), loss = 0.256408
I0409 23:58:37.086028  4596 solver.cpp:237]     Train net output #0: loss = 0.256408 (* 1 = 0.256408 loss)
I0409 23:58:37.086040  4596 sgd_solver.cpp:105] Iteration 8796, lr = 0.00175106
I0409 23:58:38.519173  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:58:41.964265  4596 solver.cpp:218] Iteration 8808 (2.45997 iter/s, 4.8781s/12 iters), loss = 0.225221
I0409 23:58:41.964320  4596 solver.cpp:237]     Train net output #0: loss = 0.225221 (* 1 = 0.225221 loss)
I0409 23:58:41.964334  4596 sgd_solver.cpp:105] Iteration 8808, lr = 0.0017469
I0409 23:58:46.889518  4596 solver.cpp:218] Iteration 8820 (2.43652 iter/s, 4.92507s/12 iters), loss = 0.137348
I0409 23:58:46.889564  4596 solver.cpp:237]     Train net output #0: loss = 0.137348 (* 1 = 0.137348 loss)
I0409 23:58:46.889573  4596 sgd_solver.cpp:105] Iteration 8820, lr = 0.00174276
I0409 23:58:51.981271  4596 solver.cpp:218] Iteration 8832 (2.35684 iter/s, 5.09156s/12 iters), loss = 0.152904
I0409 23:58:51.981328  4596 solver.cpp:237]     Train net output #0: loss = 0.152904 (* 1 = 0.152904 loss)
I0409 23:58:51.981338  4596 sgd_solver.cpp:105] Iteration 8832, lr = 0.00173862
I0409 23:58:56.865061  4596 solver.cpp:218] Iteration 8844 (2.45721 iter/s, 4.88359s/12 iters), loss = 0.0914955
I0409 23:58:56.865114  4596 solver.cpp:237]     Train net output #0: loss = 0.0914955 (* 1 = 0.0914955 loss)
I0409 23:58:56.865124  4596 sgd_solver.cpp:105] Iteration 8844, lr = 0.00173449
I0409 23:59:01.814711  4596 solver.cpp:218] Iteration 8856 (2.42451 iter/s, 4.94946s/12 iters), loss = 0.184419
I0409 23:59:01.814756  4596 solver.cpp:237]     Train net output #0: loss = 0.184419 (* 1 = 0.184419 loss)
I0409 23:59:01.814764  4596 sgd_solver.cpp:105] Iteration 8856, lr = 0.00173037
I0409 23:59:06.669189  4596 solver.cpp:218] Iteration 8868 (2.47203 iter/s, 4.8543s/12 iters), loss = 0.100535
I0409 23:59:06.669234  4596 solver.cpp:237]     Train net output #0: loss = 0.100535 (* 1 = 0.100535 loss)
I0409 23:59:06.669243  4596 sgd_solver.cpp:105] Iteration 8868, lr = 0.00172626
I0409 23:59:08.649394  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8874.caffemodel
I0409 23:59:10.081903  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8874.solverstate
I0409 23:59:10.936565  4596 solver.cpp:330] Iteration 8874, Testing net (#0)
I0409 23:59:10.936595  4596 net.cpp:676] Ignoring source layer train-data
I0409 23:59:11.928411  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:59:15.563124  4596 solver.cpp:397]     Test net output #0: accuracy = 0.515319
I0409 23:59:15.563154  4596 solver.cpp:397]     Test net output #1: loss = 2.97537 (* 1 = 2.97537 loss)
I0409 23:59:17.466526  4596 solver.cpp:218] Iteration 8880 (1.11142 iter/s, 10.797s/12 iters), loss = 0.198057
I0409 23:59:17.466580  4596 solver.cpp:237]     Train net output #0: loss = 0.198057 (* 1 = 0.198057 loss)
I0409 23:59:17.466593  4596 sgd_solver.cpp:105] Iteration 8880, lr = 0.00172217
I0409 23:59:22.402763  4596 solver.cpp:218] Iteration 8892 (2.43109 iter/s, 4.93605s/12 iters), loss = 0.22016
I0409 23:59:22.402823  4596 solver.cpp:237]     Train net output #0: loss = 0.22016 (* 1 = 0.22016 loss)
I0409 23:59:22.402835  4596 sgd_solver.cpp:105] Iteration 8892, lr = 0.00171808
I0409 23:59:26.072178  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:59:27.526160  4596 solver.cpp:218] Iteration 8904 (2.34229 iter/s, 5.1232s/12 iters), loss = 0.128743
I0409 23:59:27.526211  4596 solver.cpp:237]     Train net output #0: loss = 0.128743 (* 1 = 0.128743 loss)
I0409 23:59:27.526221  4596 sgd_solver.cpp:105] Iteration 8904, lr = 0.001714
I0409 23:59:32.368142  4596 solver.cpp:218] Iteration 8916 (2.47842 iter/s, 4.8418s/12 iters), loss = 0.203571
I0409 23:59:32.368191  4596 solver.cpp:237]     Train net output #0: loss = 0.203571 (* 1 = 0.203571 loss)
I0409 23:59:32.368203  4596 sgd_solver.cpp:105] Iteration 8916, lr = 0.00170993
I0409 23:59:37.227350  4596 solver.cpp:218] Iteration 8928 (2.46963 iter/s, 4.85903s/12 iters), loss = 0.123052
I0409 23:59:37.227401  4596 solver.cpp:237]     Train net output #0: loss = 0.123052 (* 1 = 0.123052 loss)
I0409 23:59:37.227412  4596 sgd_solver.cpp:105] Iteration 8928, lr = 0.00170587
I0409 23:59:42.150990  4596 solver.cpp:218] Iteration 8940 (2.43731 iter/s, 4.92346s/12 iters), loss = 0.133554
I0409 23:59:42.151088  4596 solver.cpp:237]     Train net output #0: loss = 0.133554 (* 1 = 0.133554 loss)
I0409 23:59:42.151098  4596 sgd_solver.cpp:105] Iteration 8940, lr = 0.00170182
I0409 23:59:47.096951  4596 solver.cpp:218] Iteration 8952 (2.42634 iter/s, 4.94573s/12 iters), loss = 0.182074
I0409 23:59:47.097002  4596 solver.cpp:237]     Train net output #0: loss = 0.182074 (* 1 = 0.182074 loss)
I0409 23:59:47.097012  4596 sgd_solver.cpp:105] Iteration 8952, lr = 0.00169778
I0409 23:59:51.998482  4596 solver.cpp:218] Iteration 8964 (2.44831 iter/s, 4.90135s/12 iters), loss = 0.177684
I0409 23:59:51.998534  4596 solver.cpp:237]     Train net output #0: loss = 0.177684 (* 1 = 0.177684 loss)
I0409 23:59:51.998546  4596 sgd_solver.cpp:105] Iteration 8964, lr = 0.00169375
I0409 23:59:56.478365  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8976.caffemodel
I0409 23:59:56.952706  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8976.solverstate
I0409 23:59:57.262162  4596 solver.cpp:330] Iteration 8976, Testing net (#0)
I0409 23:59:57.262192  4596 net.cpp:676] Ignoring source layer train-data
I0409 23:59:58.220243  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:00:01.717566  4596 solver.cpp:397]     Test net output #0: accuracy = 0.508578
I0410 00:00:01.717613  4596 solver.cpp:397]     Test net output #1: loss = 2.93868 (* 1 = 2.93868 loss)
I0410 00:00:01.799013  4596 solver.cpp:218] Iteration 8976 (1.22446 iter/s, 9.80022s/12 iters), loss = 0.105429
I0410 00:00:01.799069  4596 solver.cpp:237]     Train net output #0: loss = 0.105429 (* 1 = 0.105429 loss)
I0410 00:00:01.799080  4596 sgd_solver.cpp:105] Iteration 8976, lr = 0.00168973
I0410 00:00:05.854367  4596 solver.cpp:218] Iteration 8988 (2.95917 iter/s, 4.05518s/12 iters), loss = 0.234769
I0410 00:00:05.854419  4596 solver.cpp:237]     Train net output #0: loss = 0.234769 (* 1 = 0.234769 loss)
I0410 00:00:05.854430  4596 sgd_solver.cpp:105] Iteration 8988, lr = 0.00168571
I0410 00:00:07.462185  4596 blocking_queue.cpp:49] Waiting for data
I0410 00:00:10.849884  4596 solver.cpp:218] Iteration 9000 (2.40224 iter/s, 4.99533s/12 iters), loss = 0.0582469
I0410 00:00:10.849916  4596 solver.cpp:237]     Train net output #0: loss = 0.0582469 (* 1 = 0.0582469 loss)
I0410 00:00:10.849923  4596 sgd_solver.cpp:105] Iteration 9000, lr = 0.00168171
I0410 00:00:11.539530  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:00:15.760773  4596 solver.cpp:218] Iteration 9012 (2.44363 iter/s, 4.91072s/12 iters), loss = 0.189639
I0410 00:00:15.760864  4596 solver.cpp:237]     Train net output #0: loss = 0.189639 (* 1 = 0.189639 loss)
I0410 00:00:15.760874  4596 sgd_solver.cpp:105] Iteration 9012, lr = 0.00167772
I0410 00:00:20.673336  4596 solver.cpp:218] Iteration 9024 (2.44283 iter/s, 4.91234s/12 iters), loss = 0.125531
I0410 00:00:20.673377  4596 solver.cpp:237]     Train net output #0: loss = 0.125531 (* 1 = 0.125531 loss)
I0410 00:00:20.673386  4596 sgd_solver.cpp:105] Iteration 9024, lr = 0.00167374
I0410 00:00:25.572469  4596 solver.cpp:218] Iteration 9036 (2.4495 iter/s, 4.89896s/12 iters), loss = 0.13937
I0410 00:00:25.572506  4596 solver.cpp:237]     Train net output #0: loss = 0.13937 (* 1 = 0.13937 loss)
I0410 00:00:25.572515  4596 sgd_solver.cpp:105] Iteration 9036, lr = 0.00166976
I0410 00:00:30.444523  4596 solver.cpp:218] Iteration 9048 (2.46311 iter/s, 4.87188s/12 iters), loss = 0.100144
I0410 00:00:30.444579  4596 solver.cpp:237]     Train net output #0: loss = 0.100144 (* 1 = 0.100144 loss)
I0410 00:00:30.444592  4596 sgd_solver.cpp:105] Iteration 9048, lr = 0.0016658
I0410 00:00:35.388996  4596 solver.cpp:218] Iteration 9060 (2.42705 iter/s, 4.94428s/12 iters), loss = 0.0855557
I0410 00:00:35.389055  4596 solver.cpp:237]     Train net output #0: loss = 0.0855557 (* 1 = 0.0855557 loss)
I0410 00:00:35.389065  4596 sgd_solver.cpp:105] Iteration 9060, lr = 0.00166184
I0410 00:00:40.371124  4596 solver.cpp:218] Iteration 9072 (2.4087 iter/s, 4.98193s/12 iters), loss = 0.142363
I0410 00:00:40.371183  4596 solver.cpp:237]     Train net output #0: loss = 0.142363 (* 1 = 0.142363 loss)
I0410 00:00:40.371196  4596 sgd_solver.cpp:105] Iteration 9072, lr = 0.0016579
I0410 00:00:42.496578  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9078.caffemodel
I0410 00:00:42.974874  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9078.solverstate
I0410 00:00:43.266808  4596 solver.cpp:330] Iteration 9078, Testing net (#0)
I0410 00:00:43.266826  4596 net.cpp:676] Ignoring source layer train-data
I0410 00:00:44.164855  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:00:47.732758  4596 solver.cpp:397]     Test net output #0: accuracy = 0.518382
I0410 00:00:47.732861  4596 solver.cpp:397]     Test net output #1: loss = 2.93962 (* 1 = 2.93962 loss)
I0410 00:00:49.577149  4596 solver.cpp:218] Iteration 9084 (1.30354 iter/s, 9.20573s/12 iters), loss = 0.107106
I0410 00:00:49.577204  4596 solver.cpp:237]     Train net output #0: loss = 0.107106 (* 1 = 0.107106 loss)
I0410 00:00:49.577215  4596 sgd_solver.cpp:105] Iteration 9084, lr = 0.00165396
I0410 00:00:54.557430  4596 solver.cpp:218] Iteration 9096 (2.4096 iter/s, 4.98009s/12 iters), loss = 0.0927073
I0410 00:00:54.557474  4596 solver.cpp:237]     Train net output #0: loss = 0.0927073 (* 1 = 0.0927073 loss)
I0410 00:00:54.557483  4596 sgd_solver.cpp:105] Iteration 9096, lr = 0.00165003
I0410 00:00:57.484012  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:00:59.537672  4596 solver.cpp:218] Iteration 9108 (2.40961 iter/s, 4.98006s/12 iters), loss = 0.183333
I0410 00:00:59.537720  4596 solver.cpp:237]     Train net output #0: loss = 0.183333 (* 1 = 0.183333 loss)
I0410 00:00:59.537729  4596 sgd_solver.cpp:105] Iteration 9108, lr = 0.00164612
I0410 00:01:04.519834  4596 solver.cpp:218] Iteration 9120 (2.40869 iter/s, 4.98197s/12 iters), loss = 0.239333
I0410 00:01:04.519882  4596 solver.cpp:237]     Train net output #0: loss = 0.239333 (* 1 = 0.239333 loss)
I0410 00:01:04.519892  4596 sgd_solver.cpp:105] Iteration 9120, lr = 0.00164221
I0410 00:01:09.430461  4596 solver.cpp:218] Iteration 9132 (2.44377 iter/s, 4.91045s/12 iters), loss = 0.205037
I0410 00:01:09.430505  4596 solver.cpp:237]     Train net output #0: loss = 0.205037 (* 1 = 0.205037 loss)
I0410 00:01:09.430513  4596 sgd_solver.cpp:105] Iteration 9132, lr = 0.00163831
I0410 00:01:14.368631  4596 solver.cpp:218] Iteration 9144 (2.43014 iter/s, 4.93799s/12 iters), loss = 0.18539
I0410 00:01:14.368682  4596 solver.cpp:237]     Train net output #0: loss = 0.18539 (* 1 = 0.18539 loss)
I0410 00:01:14.368695  4596 sgd_solver.cpp:105] Iteration 9144, lr = 0.00163442
I0410 00:01:19.206419  4596 solver.cpp:218] Iteration 9156 (2.48057 iter/s, 4.8376s/12 iters), loss = 0.111362
I0410 00:01:19.206590  4596 solver.cpp:237]     Train net output #0: loss = 0.111362 (* 1 = 0.111362 loss)
I0410 00:01:19.206604  4596 sgd_solver.cpp:105] Iteration 9156, lr = 0.00163054
I0410 00:01:24.149487  4596 solver.cpp:218] Iteration 9168 (2.42779 iter/s, 4.94276s/12 iters), loss = 0.257018
I0410 00:01:24.149549  4596 solver.cpp:237]     Train net output #0: loss = 0.257018 (* 1 = 0.257018 loss)
I0410 00:01:24.149562  4596 sgd_solver.cpp:105] Iteration 9168, lr = 0.00162667
I0410 00:01:28.768481  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9180.caffemodel
I0410 00:01:29.900586  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9180.solverstate
I0410 00:01:31.714713  4596 solver.cpp:330] Iteration 9180, Testing net (#0)
I0410 00:01:31.714742  4596 net.cpp:676] Ignoring source layer train-data
I0410 00:01:32.588510  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:01:36.170260  4596 solver.cpp:397]     Test net output #0: accuracy = 0.525123
I0410 00:01:36.170310  4596 solver.cpp:397]     Test net output #1: loss = 2.93508 (* 1 = 2.93508 loss)
I0410 00:01:36.252338  4596 solver.cpp:218] Iteration 9180 (0.991532 iter/s, 12.1025s/12 iters), loss = 0.142765
I0410 00:01:36.252379  4596 solver.cpp:237]     Train net output #0: loss = 0.142765 (* 1 = 0.142765 loss)
I0410 00:01:36.252391  4596 sgd_solver.cpp:105] Iteration 9180, lr = 0.00162281
I0410 00:01:40.610147  4596 solver.cpp:218] Iteration 9192 (2.75378 iter/s, 4.35764s/12 iters), loss = 0.143792
I0410 00:01:40.610204  4596 solver.cpp:237]     Train net output #0: loss = 0.143792 (* 1 = 0.143792 loss)
I0410 00:01:40.610216  4596 sgd_solver.cpp:105] Iteration 9192, lr = 0.00161895
I0410 00:01:45.449177  4596 solver.cpp:218] Iteration 9204 (2.47993 iter/s, 4.83884s/12 iters), loss = 0.164089
I0410 00:01:45.449232  4596 solver.cpp:237]     Train net output #0: loss = 0.164089 (* 1 = 0.164089 loss)
I0410 00:01:45.449244  4596 sgd_solver.cpp:105] Iteration 9204, lr = 0.00161511
I0410 00:01:45.519014  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:01:50.403582  4596 solver.cpp:218] Iteration 9216 (2.42218 iter/s, 4.95421s/12 iters), loss = 0.0875546
I0410 00:01:50.403715  4596 solver.cpp:237]     Train net output #0: loss = 0.0875546 (* 1 = 0.0875546 loss)
I0410 00:01:50.403729  4596 sgd_solver.cpp:105] Iteration 9216, lr = 0.00161128
I0410 00:01:55.338536  4596 solver.cpp:218] Iteration 9228 (2.43176 iter/s, 4.93469s/12 iters), loss = 0.14179
I0410 00:01:55.338579  4596 solver.cpp:237]     Train net output #0: loss = 0.14179 (* 1 = 0.14179 loss)
I0410 00:01:55.338591  4596 sgd_solver.cpp:105] Iteration 9228, lr = 0.00160745
I0410 00:02:00.484529  4596 solver.cpp:218] Iteration 9240 (2.332 iter/s, 5.1458s/12 iters), loss = 0.123006
I0410 00:02:00.484592  4596 solver.cpp:237]     Train net output #0: loss = 0.123006 (* 1 = 0.123006 loss)
I0410 00:02:00.484607  4596 sgd_solver.cpp:105] Iteration 9240, lr = 0.00160363
I0410 00:02:05.548887  4596 solver.cpp:218] Iteration 9252 (2.36959 iter/s, 5.06416s/12 iters), loss = 0.0958764
I0410 00:02:05.548934  4596 solver.cpp:237]     Train net output #0: loss = 0.0958764 (* 1 = 0.0958764 loss)
I0410 00:02:05.548944  4596 sgd_solver.cpp:105] Iteration 9252, lr = 0.00159983
I0410 00:02:10.411754  4596 solver.cpp:218] Iteration 9264 (2.46778 iter/s, 4.86268s/12 iters), loss = 0.27008
I0410 00:02:10.411810  4596 solver.cpp:237]     Train net output #0: loss = 0.27008 (* 1 = 0.27008 loss)
I0410 00:02:10.411821  4596 sgd_solver.cpp:105] Iteration 9264, lr = 0.00159603
I0410 00:02:15.355232  4596 solver.cpp:218] Iteration 9276 (2.42754 iter/s, 4.94328s/12 iters), loss = 0.169911
I0410 00:02:15.355291  4596 solver.cpp:237]     Train net output #0: loss = 0.169911 (* 1 = 0.169911 loss)
I0410 00:02:15.355304  4596 sgd_solver.cpp:105] Iteration 9276, lr = 0.00159224
I0410 00:02:17.330020  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9282.caffemodel
I0410 00:02:19.570312  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9282.solverstate
I0410 00:02:19.913538  4596 solver.cpp:330] Iteration 9282, Testing net (#0)
I0410 00:02:19.913559  4596 net.cpp:676] Ignoring source layer train-data
I0410 00:02:20.742400  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:02:24.369405  4596 solver.cpp:397]     Test net output #0: accuracy = 0.517157
I0410 00:02:24.369470  4596 solver.cpp:397]     Test net output #1: loss = 2.98939 (* 1 = 2.98939 loss)
I0410 00:02:26.192217  4596 solver.cpp:218] Iteration 9288 (1.10735 iter/s, 10.8367s/12 iters), loss = 0.188706
I0410 00:02:26.192268  4596 solver.cpp:237]     Train net output #0: loss = 0.188706 (* 1 = 0.188706 loss)
I0410 00:02:26.192279  4596 sgd_solver.cpp:105] Iteration 9288, lr = 0.00158846
I0410 00:02:31.178388  4596 solver.cpp:218] Iteration 9300 (2.40674 iter/s, 4.98599s/12 iters), loss = 0.138668
I0410 00:02:31.178426  4596 solver.cpp:237]     Train net output #0: loss = 0.138668 (* 1 = 0.138668 loss)
I0410 00:02:31.178436  4596 sgd_solver.cpp:105] Iteration 9300, lr = 0.00158469
I0410 00:02:33.528445  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:02:36.274366  4596 solver.cpp:218] Iteration 9312 (2.35488 iter/s, 5.0958s/12 iters), loss = 0.0918236
I0410 00:02:36.274428  4596 solver.cpp:237]     Train net output #0: loss = 0.0918236 (* 1 = 0.0918236 loss)
I0410 00:02:36.274441  4596 sgd_solver.cpp:105] Iteration 9312, lr = 0.00158092
I0410 00:02:41.318315  4596 solver.cpp:218] Iteration 9324 (2.37918 iter/s, 5.04375s/12 iters), loss = 0.0554574
I0410 00:02:41.318369  4596 solver.cpp:237]     Train net output #0: loss = 0.0554574 (* 1 = 0.0554574 loss)
I0410 00:02:41.318383  4596 sgd_solver.cpp:105] Iteration 9324, lr = 0.00157717
I0410 00:02:46.248034  4596 solver.cpp:218] Iteration 9336 (2.43431 iter/s, 4.92953s/12 iters), loss = 0.13915
I0410 00:02:46.248083  4596 solver.cpp:237]     Train net output #0: loss = 0.13915 (* 1 = 0.13915 loss)
I0410 00:02:46.248095  4596 sgd_solver.cpp:105] Iteration 9336, lr = 0.00157343
I0410 00:02:51.185482  4596 solver.cpp:218] Iteration 9348 (2.4305 iter/s, 4.93726s/12 iters), loss = 0.117345
I0410 00:02:51.185572  4596 solver.cpp:237]     Train net output #0: loss = 0.117345 (* 1 = 0.117345 loss)
I0410 00:02:51.185585  4596 sgd_solver.cpp:105] Iteration 9348, lr = 0.00156969
I0410 00:02:56.451660  4596 solver.cpp:218] Iteration 9360 (2.27879 iter/s, 5.26594s/12 iters), loss = 0.183324
I0410 00:02:56.451712  4596 solver.cpp:237]     Train net output #0: loss = 0.183324 (* 1 = 0.183324 loss)
I0410 00:02:56.451725  4596 sgd_solver.cpp:105] Iteration 9360, lr = 0.00156596
I0410 00:03:01.438182  4596 solver.cpp:218] Iteration 9372 (2.40658 iter/s, 4.98633s/12 iters), loss = 0.110356
I0410 00:03:01.438251  4596 solver.cpp:237]     Train net output #0: loss = 0.110356 (* 1 = 0.110356 loss)
I0410 00:03:01.438263  4596 sgd_solver.cpp:105] Iteration 9372, lr = 0.00156225
I0410 00:03:05.911727  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9384.caffemodel
I0410 00:03:06.485693  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9384.solverstate
I0410 00:03:07.106364  4596 solver.cpp:330] Iteration 9384, Testing net (#0)
I0410 00:03:07.106391  4596 net.cpp:676] Ignoring source layer train-data
I0410 00:03:07.809353  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:03:11.558349  4596 solver.cpp:397]     Test net output #0: accuracy = 0.522672
I0410 00:03:11.558399  4596 solver.cpp:397]     Test net output #1: loss = 2.9998 (* 1 = 2.9998 loss)
I0410 00:03:11.640038  4596 solver.cpp:218] Iteration 9384 (1.1763 iter/s, 10.2015s/12 iters), loss = 0.141676
I0410 00:03:11.640100  4596 solver.cpp:237]     Train net output #0: loss = 0.141676 (* 1 = 0.141676 loss)
I0410 00:03:11.640115  4596 sgd_solver.cpp:105] Iteration 9384, lr = 0.00155854
I0410 00:03:15.862713  4596 solver.cpp:218] Iteration 9396 (2.84192 iter/s, 4.2225s/12 iters), loss = 0.0745708
I0410 00:03:15.862756  4596 solver.cpp:237]     Train net output #0: loss = 0.0745709 (* 1 = 0.0745709 loss)
I0410 00:03:15.862766  4596 sgd_solver.cpp:105] Iteration 9396, lr = 0.00155484
I0410 00:03:20.220921  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:03:20.870774  4596 solver.cpp:218] Iteration 9408 (2.39623 iter/s, 5.00788s/12 iters), loss = 0.14661
I0410 00:03:20.870828  4596 solver.cpp:237]     Train net output #0: loss = 0.14661 (* 1 = 0.14661 loss)
I0410 00:03:20.870839  4596 sgd_solver.cpp:105] Iteration 9408, lr = 0.00155114
I0410 00:03:25.860738  4596 solver.cpp:218] Iteration 9420 (2.40492 iter/s, 4.98977s/12 iters), loss = 0.186244
I0410 00:03:25.860862  4596 solver.cpp:237]     Train net output #0: loss = 0.186244 (* 1 = 0.186244 loss)
I0410 00:03:25.860872  4596 sgd_solver.cpp:105] Iteration 9420, lr = 0.00154746
I0410 00:03:30.901163  4596 solver.cpp:218] Iteration 9432 (2.38088 iter/s, 5.04016s/12 iters), loss = 0.0391637
I0410 00:03:30.901226  4596 solver.cpp:237]     Train net output #0: loss = 0.0391638 (* 1 = 0.0391638 loss)
I0410 00:03:30.901239  4596 sgd_solver.cpp:105] Iteration 9432, lr = 0.00154379
I0410 00:03:35.846732  4596 solver.cpp:218] Iteration 9444 (2.42651 iter/s, 4.94538s/12 iters), loss = 0.173002
I0410 00:03:35.846781  4596 solver.cpp:237]     Train net output #0: loss = 0.173002 (* 1 = 0.173002 loss)
I0410 00:03:35.846791  4596 sgd_solver.cpp:105] Iteration 9444, lr = 0.00154012
I0410 00:03:40.759606  4596 solver.cpp:218] Iteration 9456 (2.44265 iter/s, 4.91269s/12 iters), loss = 0.123617
I0410 00:03:40.759651  4596 solver.cpp:237]     Train net output #0: loss = 0.123617 (* 1 = 0.123617 loss)
I0410 00:03:40.759661  4596 sgd_solver.cpp:105] Iteration 9456, lr = 0.00153647
I0410 00:03:45.650764  4596 solver.cpp:218] Iteration 9468 (2.4535 iter/s, 4.89098s/12 iters), loss = 0.141938
I0410 00:03:45.650820  4596 solver.cpp:237]     Train net output #0: loss = 0.141938 (* 1 = 0.141938 loss)
I0410 00:03:45.650831  4596 sgd_solver.cpp:105] Iteration 9468, lr = 0.00153282
I0410 00:03:50.621563  4596 solver.cpp:218] Iteration 9480 (2.41419 iter/s, 4.97061s/12 iters), loss = 0.137489
I0410 00:03:50.621614  4596 solver.cpp:237]     Train net output #0: loss = 0.137489 (* 1 = 0.137489 loss)
I0410 00:03:50.621625  4596 sgd_solver.cpp:105] Iteration 9480, lr = 0.00152918
I0410 00:03:52.596915  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9486.caffemodel
I0410 00:03:53.508083  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9486.solverstate
I0410 00:03:53.870677  4596 solver.cpp:330] Iteration 9486, Testing net (#0)
I0410 00:03:53.870699  4596 net.cpp:676] Ignoring source layer train-data
I0410 00:03:54.624727  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:03:58.399762  4596 solver.cpp:397]     Test net output #0: accuracy = 0.523284
I0410 00:03:58.399839  4596 solver.cpp:397]     Test net output #1: loss = 2.97598 (* 1 = 2.97598 loss)
I0410 00:04:00.219805  4596 solver.cpp:218] Iteration 9492 (1.25027 iter/s, 9.59794s/12 iters), loss = 0.118858
I0410 00:04:00.219859  4596 solver.cpp:237]     Train net output #0: loss = 0.118858 (* 1 = 0.118858 loss)
I0410 00:04:00.219871  4596 sgd_solver.cpp:105] Iteration 9492, lr = 0.00152555
I0410 00:04:05.120380  4596 solver.cpp:218] Iteration 9504 (2.44878 iter/s, 4.90039s/12 iters), loss = 0.0706428
I0410 00:04:05.120430  4596 solver.cpp:237]     Train net output #0: loss = 0.0706429 (* 1 = 0.0706429 loss)
I0410 00:04:05.120441  4596 sgd_solver.cpp:105] Iteration 9504, lr = 0.00152193
I0410 00:04:06.564616  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:04:10.035292  4596 solver.cpp:218] Iteration 9516 (2.44164 iter/s, 4.91473s/12 iters), loss = 0.175881
I0410 00:04:10.035342  4596 solver.cpp:237]     Train net output #0: loss = 0.175881 (* 1 = 0.175881 loss)
I0410 00:04:10.035351  4596 sgd_solver.cpp:105] Iteration 9516, lr = 0.00151831
I0410 00:04:15.027241  4596 solver.cpp:218] Iteration 9528 (2.40396 iter/s, 4.99176s/12 iters), loss = 0.0819424
I0410 00:04:15.027295  4596 solver.cpp:237]     Train net output #0: loss = 0.0819425 (* 1 = 0.0819425 loss)
I0410 00:04:15.027305  4596 sgd_solver.cpp:105] Iteration 9528, lr = 0.00151471
I0410 00:04:20.081686  4596 solver.cpp:218] Iteration 9540 (2.37424 iter/s, 5.05425s/12 iters), loss = 0.0809286
I0410 00:04:20.081745  4596 solver.cpp:237]     Train net output #0: loss = 0.0809287 (* 1 = 0.0809287 loss)
I0410 00:04:20.081758  4596 sgd_solver.cpp:105] Iteration 9540, lr = 0.00151111
I0410 00:04:24.997867  4596 solver.cpp:218] Iteration 9552 (2.44102 iter/s, 4.91598s/12 iters), loss = 0.122494
I0410 00:04:24.997920  4596 solver.cpp:237]     Train net output #0: loss = 0.122494 (* 1 = 0.122494 loss)
I0410 00:04:24.997932  4596 sgd_solver.cpp:105] Iteration 9552, lr = 0.00150752
I0410 00:04:29.913076  4596 solver.cpp:218] Iteration 9564 (2.44149 iter/s, 4.91502s/12 iters), loss = 0.0897684
I0410 00:04:29.913205  4596 solver.cpp:237]     Train net output #0: loss = 0.0897684 (* 1 = 0.0897684 loss)
I0410 00:04:29.913220  4596 sgd_solver.cpp:105] Iteration 9564, lr = 0.00150395
I0410 00:04:34.812569  4596 solver.cpp:218] Iteration 9576 (2.44936 iter/s, 4.89924s/12 iters), loss = 0.0985809
I0410 00:04:34.812609  4596 solver.cpp:237]     Train net output #0: loss = 0.0985809 (* 1 = 0.0985809 loss)
I0410 00:04:34.812618  4596 sgd_solver.cpp:105] Iteration 9576, lr = 0.00150037
I0410 00:04:39.296761  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9588.caffemodel
I0410 00:04:40.000010  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9588.solverstate
I0410 00:04:40.568490  4596 solver.cpp:330] Iteration 9588, Testing net (#0)
I0410 00:04:40.568523  4596 net.cpp:676] Ignoring source layer train-data
I0410 00:04:41.258416  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:04:45.015537  4596 solver.cpp:397]     Test net output #0: accuracy = 0.528186
I0410 00:04:45.015584  4596 solver.cpp:397]     Test net output #1: loss = 2.96521 (* 1 = 2.96521 loss)
I0410 00:04:45.097229  4596 solver.cpp:218] Iteration 9588 (1.16682 iter/s, 10.2843s/12 iters), loss = 0.181848
I0410 00:04:45.097285  4596 solver.cpp:237]     Train net output #0: loss = 0.181848 (* 1 = 0.181848 loss)
I0410 00:04:45.097296  4596 sgd_solver.cpp:105] Iteration 9588, lr = 0.00149681
I0410 00:04:49.298453  4596 solver.cpp:218] Iteration 9600 (2.85643 iter/s, 4.20105s/12 iters), loss = 0.233325
I0410 00:04:49.298504  4596 solver.cpp:237]     Train net output #0: loss = 0.233325 (* 1 = 0.233325 loss)
I0410 00:04:49.298516  4596 sgd_solver.cpp:105] Iteration 9600, lr = 0.00149326
I0410 00:04:52.820660  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:04:54.180203  4596 solver.cpp:218] Iteration 9612 (2.45823 iter/s, 4.88157s/12 iters), loss = 0.0504531
I0410 00:04:54.180248  4596 solver.cpp:237]     Train net output #0: loss = 0.0504532 (* 1 = 0.0504532 loss)
I0410 00:04:54.180258  4596 sgd_solver.cpp:105] Iteration 9612, lr = 0.00148971
I0410 00:04:59.060353  4596 solver.cpp:218] Iteration 9624 (2.45903 iter/s, 4.87997s/12 iters), loss = 0.160116
I0410 00:04:59.060410  4596 solver.cpp:237]     Train net output #0: loss = 0.160116 (* 1 = 0.160116 loss)
I0410 00:04:59.060423  4596 sgd_solver.cpp:105] Iteration 9624, lr = 0.00148618
I0410 00:05:03.961163  4596 solver.cpp:218] Iteration 9636 (2.44867 iter/s, 4.90062s/12 iters), loss = 0.148177
I0410 00:05:03.961298  4596 solver.cpp:237]     Train net output #0: loss = 0.148177 (* 1 = 0.148177 loss)
I0410 00:05:03.961313  4596 sgd_solver.cpp:105] Iteration 9636, lr = 0.00148265
I0410 00:05:08.898509  4596 solver.cpp:218] Iteration 9648 (2.43059 iter/s, 4.93708s/12 iters), loss = 0.13467
I0410 00:05:08.898555  4596 solver.cpp:237]     Train net output #0: loss = 0.13467 (* 1 = 0.13467 loss)
I0410 00:05:08.898566  4596 sgd_solver.cpp:105] Iteration 9648, lr = 0.00147913
I0410 00:05:13.979435  4596 solver.cpp:218] Iteration 9660 (2.36186 iter/s, 5.08074s/12 iters), loss = 0.0893475
I0410 00:05:13.979487  4596 solver.cpp:237]     Train net output #0: loss = 0.0893476 (* 1 = 0.0893476 loss)
I0410 00:05:13.979499  4596 sgd_solver.cpp:105] Iteration 9660, lr = 0.00147562
I0410 00:05:18.917704  4596 solver.cpp:218] Iteration 9672 (2.43009 iter/s, 4.93808s/12 iters), loss = 0.114317
I0410 00:05:18.917768  4596 solver.cpp:237]     Train net output #0: loss = 0.114318 (* 1 = 0.114318 loss)
I0410 00:05:18.917781  4596 sgd_solver.cpp:105] Iteration 9672, lr = 0.00147211
I0410 00:05:23.764001  4596 solver.cpp:218] Iteration 9684 (2.47622 iter/s, 4.8461s/12 iters), loss = 0.0539471
I0410 00:05:23.764057  4596 solver.cpp:237]     Train net output #0: loss = 0.0539471 (* 1 = 0.0539471 loss)
I0410 00:05:23.764070  4596 sgd_solver.cpp:105] Iteration 9684, lr = 0.00146862
I0410 00:05:25.757691  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9690.caffemodel
I0410 00:05:26.754292  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9690.solverstate
I0410 00:05:27.529561  4596 solver.cpp:330] Iteration 9690, Testing net (#0)
I0410 00:05:27.529592  4596 net.cpp:676] Ignoring source layer train-data
I0410 00:05:28.249497  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:05:30.734676  4596 blocking_queue.cpp:49] Waiting for data
I0410 00:05:32.157018  4596 solver.cpp:397]     Test net output #0: accuracy = 0.522672
I0410 00:05:32.157069  4596 solver.cpp:397]     Test net output #1: loss = 2.97895 (* 1 = 2.97895 loss)
I0410 00:05:33.881618  4596 solver.cpp:218] Iteration 9696 (1.18609 iter/s, 10.1173s/12 iters), loss = 0.138187
I0410 00:05:33.881669  4596 solver.cpp:237]     Train net output #0: loss = 0.138187 (* 1 = 0.138187 loss)
I0410 00:05:33.881680  4596 sgd_solver.cpp:105] Iteration 9696, lr = 0.00146513
I0410 00:05:38.798439  4596 solver.cpp:218] Iteration 9708 (2.44069 iter/s, 4.91664s/12 iters), loss = 0.108637
I0410 00:05:38.798609  4596 solver.cpp:237]     Train net output #0: loss = 0.108637 (* 1 = 0.108637 loss)
I0410 00:05:38.798620  4596 sgd_solver.cpp:105] Iteration 9708, lr = 0.00146165
I0410 00:05:39.529711  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:05:43.640797  4596 solver.cpp:218] Iteration 9720 (2.47828 iter/s, 4.84206s/12 iters), loss = 0.0716573
I0410 00:05:43.640846  4596 solver.cpp:237]     Train net output #0: loss = 0.0716573 (* 1 = 0.0716573 loss)
I0410 00:05:43.640858  4596 sgd_solver.cpp:105] Iteration 9720, lr = 0.00145818
I0410 00:05:48.595960  4596 solver.cpp:218] Iteration 9732 (2.4218 iter/s, 4.95498s/12 iters), loss = 0.111842
I0410 00:05:48.596009  4596 solver.cpp:237]     Train net output #0: loss = 0.111842 (* 1 = 0.111842 loss)
I0410 00:05:48.596019  4596 sgd_solver.cpp:105] Iteration 9732, lr = 0.00145472
I0410 00:05:53.551436  4596 solver.cpp:218] Iteration 9744 (2.42165 iter/s, 4.95529s/12 iters), loss = 0.0813798
I0410 00:05:53.551481  4596 solver.cpp:237]     Train net output #0: loss = 0.0813798 (* 1 = 0.0813798 loss)
I0410 00:05:53.551489  4596 sgd_solver.cpp:105] Iteration 9744, lr = 0.00145127
I0410 00:05:58.456521  4596 solver.cpp:218] Iteration 9756 (2.44653 iter/s, 4.9049s/12 iters), loss = 0.221502
I0410 00:05:58.456568  4596 solver.cpp:237]     Train net output #0: loss = 0.221502 (* 1 = 0.221502 loss)
I0410 00:05:58.456578  4596 sgd_solver.cpp:105] Iteration 9756, lr = 0.00144782
I0410 00:06:03.337482  4596 solver.cpp:218] Iteration 9768 (2.45862 iter/s, 4.88078s/12 iters), loss = 0.149252
I0410 00:06:03.337529  4596 solver.cpp:237]     Train net output #0: loss = 0.149252 (* 1 = 0.149252 loss)
I0410 00:06:03.337539  4596 sgd_solver.cpp:105] Iteration 9768, lr = 0.00144438
I0410 00:06:08.262689  4596 solver.cpp:218] Iteration 9780 (2.43653 iter/s, 4.92503s/12 iters), loss = 0.188335
I0410 00:06:08.262732  4596 solver.cpp:237]     Train net output #0: loss = 0.188335 (* 1 = 0.188335 loss)
I0410 00:06:08.262739  4596 sgd_solver.cpp:105] Iteration 9780, lr = 0.00144095
I0410 00:06:12.719512  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9792.caffemodel
I0410 00:06:13.578258  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9792.solverstate
I0410 00:06:14.370564  4596 solver.cpp:330] Iteration 9792, Testing net (#0)
I0410 00:06:14.370584  4596 net.cpp:676] Ignoring source layer train-data
I0410 00:06:14.915022  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:06:18.772107  4596 solver.cpp:397]     Test net output #0: accuracy = 0.52451
I0410 00:06:18.772159  4596 solver.cpp:397]     Test net output #1: loss = 2.98458 (* 1 = 2.98458 loss)
I0410 00:06:18.853873  4596 solver.cpp:218] Iteration 9792 (1.13305 iter/s, 10.5909s/12 iters), loss = 0.104666
I0410 00:06:18.853927  4596 solver.cpp:237]     Train net output #0: loss = 0.104666 (* 1 = 0.104666 loss)
I0410 00:06:18.853940  4596 sgd_solver.cpp:105] Iteration 9792, lr = 0.00143753
I0410 00:06:23.054570  4596 solver.cpp:218] Iteration 9804 (2.85678 iter/s, 4.20053s/12 iters), loss = 0.0678238
I0410 00:06:23.054625  4596 solver.cpp:237]     Train net output #0: loss = 0.0678238 (* 1 = 0.0678238 loss)
I0410 00:06:23.054637  4596 sgd_solver.cpp:105] Iteration 9804, lr = 0.00143412
I0410 00:06:25.916369  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:06:27.887382  4596 solver.cpp:218] Iteration 9816 (2.48312 iter/s, 4.83263s/12 iters), loss = 0.142497
I0410 00:06:27.887434  4596 solver.cpp:237]     Train net output #0: loss = 0.142497 (* 1 = 0.142497 loss)
I0410 00:06:27.887446  4596 sgd_solver.cpp:105] Iteration 9816, lr = 0.00143072
I0410 00:06:32.795104  4596 solver.cpp:218] Iteration 9828 (2.44522 iter/s, 4.90754s/12 iters), loss = 0.11236
I0410 00:06:32.795161  4596 solver.cpp:237]     Train net output #0: loss = 0.11236 (* 1 = 0.11236 loss)
I0410 00:06:32.795173  4596 sgd_solver.cpp:105] Iteration 9828, lr = 0.00142732
I0410 00:06:37.735911  4596 solver.cpp:218] Iteration 9840 (2.42884 iter/s, 4.94062s/12 iters), loss = 0.206306
I0410 00:06:37.735965  4596 solver.cpp:237]     Train net output #0: loss = 0.206306 (* 1 = 0.206306 loss)
I0410 00:06:37.735975  4596 sgd_solver.cpp:105] Iteration 9840, lr = 0.00142393
I0410 00:06:42.813138  4596 solver.cpp:218] Iteration 9852 (2.36358 iter/s, 5.07703s/12 iters), loss = 0.107602
I0410 00:06:42.813266  4596 solver.cpp:237]     Train net output #0: loss = 0.107602 (* 1 = 0.107602 loss)
I0410 00:06:42.813279  4596 sgd_solver.cpp:105] Iteration 9852, lr = 0.00142055
I0410 00:06:47.738320  4596 solver.cpp:218] Iteration 9864 (2.43659 iter/s, 4.92492s/12 iters), loss = 0.053204
I0410 00:06:47.738374  4596 solver.cpp:237]     Train net output #0: loss = 0.053204 (* 1 = 0.053204 loss)
I0410 00:06:47.738387  4596 sgd_solver.cpp:105] Iteration 9864, lr = 0.00141718
I0410 00:06:52.684620  4596 solver.cpp:218] Iteration 9876 (2.42615 iter/s, 4.94611s/12 iters), loss = 0.145206
I0410 00:06:52.684679  4596 solver.cpp:237]     Train net output #0: loss = 0.145206 (* 1 = 0.145206 loss)
I0410 00:06:52.684691  4596 sgd_solver.cpp:105] Iteration 9876, lr = 0.00141381
I0410 00:06:57.599475  4596 solver.cpp:218] Iteration 9888 (2.44167 iter/s, 4.91466s/12 iters), loss = 0.120061
I0410 00:06:57.599537  4596 solver.cpp:237]     Train net output #0: loss = 0.120061 (* 1 = 0.120061 loss)
I0410 00:06:57.599550  4596 sgd_solver.cpp:105] Iteration 9888, lr = 0.00141045
I0410 00:06:59.597079  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9894.caffemodel
I0410 00:07:00.683552  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9894.solverstate
I0410 00:07:01.089184  4596 solver.cpp:330] Iteration 9894, Testing net (#0)
I0410 00:07:01.089212  4596 net.cpp:676] Ignoring source layer train-data
I0410 00:07:01.645939  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:07:05.631232  4596 solver.cpp:397]     Test net output #0: accuracy = 0.530025
I0410 00:07:05.631284  4596 solver.cpp:397]     Test net output #1: loss = 2.96727 (* 1 = 2.96727 loss)
I0410 00:07:07.432752  4596 solver.cpp:218] Iteration 9900 (1.22039 iter/s, 9.83296s/12 iters), loss = 0.184976
I0410 00:07:07.432812  4596 solver.cpp:237]     Train net output #0: loss = 0.184976 (* 1 = 0.184976 loss)
I0410 00:07:07.432824  4596 sgd_solver.cpp:105] Iteration 9900, lr = 0.00140711
I0410 00:07:12.280261  4596 solver.cpp:218] Iteration 9912 (2.4756 iter/s, 4.84732s/12 iters), loss = 0.108575
I0410 00:07:12.280310  4596 solver.cpp:237]     Train net output #0: loss = 0.108575 (* 1 = 0.108575 loss)
I0410 00:07:12.280321  4596 sgd_solver.cpp:105] Iteration 9912, lr = 0.00140377
I0410 00:07:12.378369  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:07:17.195019  4596 solver.cpp:218] Iteration 9924 (2.44172 iter/s, 4.91458s/12 iters), loss = 0.106127
I0410 00:07:17.195170  4596 solver.cpp:237]     Train net output #0: loss = 0.106127 (* 1 = 0.106127 loss)
I0410 00:07:17.195185  4596 sgd_solver.cpp:105] Iteration 9924, lr = 0.00140043
I0410 00:07:22.042603  4596 solver.cpp:218] Iteration 9936 (2.4756 iter/s, 4.8473s/12 iters), loss = 0.0992252
I0410 00:07:22.042654  4596 solver.cpp:237]     Train net output #0: loss = 0.0992252 (* 1 = 0.0992252 loss)
I0410 00:07:22.042665  4596 sgd_solver.cpp:105] Iteration 9936, lr = 0.00139711
I0410 00:07:26.930291  4596 solver.cpp:218] Iteration 9948 (2.45524 iter/s, 4.88751s/12 iters), loss = 0.13135
I0410 00:07:26.930346  4596 solver.cpp:237]     Train net output #0: loss = 0.13135 (* 1 = 0.13135 loss)
I0410 00:07:26.930356  4596 sgd_solver.cpp:105] Iteration 9948, lr = 0.00139379
I0410 00:07:31.823829  4596 solver.cpp:218] Iteration 9960 (2.45231 iter/s, 4.89335s/12 iters), loss = 0.175317
I0410 00:07:31.823889  4596 solver.cpp:237]     Train net output #0: loss = 0.175317 (* 1 = 0.175317 loss)
I0410 00:07:31.823900  4596 sgd_solver.cpp:105] Iteration 9960, lr = 0.00139048
I0410 00:07:36.679411  4596 solver.cpp:218] Iteration 9972 (2.47148 iter/s, 4.85539s/12 iters), loss = 0.148897
I0410 00:07:36.679468  4596 solver.cpp:237]     Train net output #0: loss = 0.148897 (* 1 = 0.148897 loss)
I0410 00:07:36.679481  4596 sgd_solver.cpp:105] Iteration 9972, lr = 0.00138718
I0410 00:07:41.624876  4596 solver.cpp:218] Iteration 9984 (2.42656 iter/s, 4.94528s/12 iters), loss = 0.153773
I0410 00:07:41.624923  4596 solver.cpp:237]     Train net output #0: loss = 0.153773 (* 1 = 0.153773 loss)
I0410 00:07:41.624933  4596 sgd_solver.cpp:105] Iteration 9984, lr = 0.00138389
I0410 00:07:46.073721  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9996.caffemodel
I0410 00:07:46.605587  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9996.solverstate
I0410 00:07:47.575354  4596 solver.cpp:330] Iteration 9996, Testing net (#0)
I0410 00:07:47.575423  4596 net.cpp:676] Ignoring source layer train-data
I0410 00:07:48.085201  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:07:52.259806  4596 solver.cpp:397]     Test net output #0: accuracy = 0.525735
I0410 00:07:52.259856  4596 solver.cpp:397]     Test net output #1: loss = 2.96769 (* 1 = 2.96769 loss)
I0410 00:07:52.341759  4596 solver.cpp:218] Iteration 9996 (1.11976 iter/s, 10.7166s/12 iters), loss = 0.135649
I0410 00:07:52.341814  4596 solver.cpp:237]     Train net output #0: loss = 0.135649 (* 1 = 0.135649 loss)
I0410 00:07:52.341826  4596 sgd_solver.cpp:105] Iteration 9996, lr = 0.0013806
I0410 00:07:56.583441  4596 solver.cpp:218] Iteration 10008 (2.82919 iter/s, 4.2415s/12 iters), loss = 0.178349
I0410 00:07:56.583495  4596 solver.cpp:237]     Train net output #0: loss = 0.178349 (* 1 = 0.178349 loss)
I0410 00:07:56.583508  4596 sgd_solver.cpp:105] Iteration 10008, lr = 0.00137732
I0410 00:07:58.964162  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:08:01.756858  4596 solver.cpp:218] Iteration 10020 (2.31964 iter/s, 5.17322s/12 iters), loss = 0.172866
I0410 00:08:01.756912  4596 solver.cpp:237]     Train net output #0: loss = 0.172866 (* 1 = 0.172866 loss)
I0410 00:08:01.756924  4596 sgd_solver.cpp:105] Iteration 10020, lr = 0.00137405
I0410 00:08:06.607079  4596 solver.cpp:218] Iteration 10032 (2.47421 iter/s, 4.85003s/12 iters), loss = 0.0593151
I0410 00:08:06.607141  4596 solver.cpp:237]     Train net output #0: loss = 0.0593151 (* 1 = 0.0593151 loss)
I0410 00:08:06.607154  4596 sgd_solver.cpp:105] Iteration 10032, lr = 0.00137079
I0410 00:08:11.525951  4596 solver.cpp:218] Iteration 10044 (2.43968 iter/s, 4.91868s/12 iters), loss = 0.061924
I0410 00:08:11.526022  4596 solver.cpp:237]     Train net output #0: loss = 0.061924 (* 1 = 0.061924 loss)
I0410 00:08:11.526036  4596 sgd_solver.cpp:105] Iteration 10044, lr = 0.00136754
I0410 00:08:16.382808  4596 solver.cpp:218] Iteration 10056 (2.47084 iter/s, 4.85665s/12 iters), loss = 0.123918
I0410 00:08:16.382870  4596 solver.cpp:237]     Train net output #0: loss = 0.123918 (* 1 = 0.123918 loss)
I0410 00:08:16.382884  4596 sgd_solver.cpp:105] Iteration 10056, lr = 0.00136429
I0410 00:08:21.334610  4596 solver.cpp:218] Iteration 10068 (2.42346 iter/s, 4.95161s/12 iters), loss = 0.0692149
I0410 00:08:21.334759  4596 solver.cpp:237]     Train net output #0: loss = 0.0692149 (* 1 = 0.0692149 loss)
I0410 00:08:21.334774  4596 sgd_solver.cpp:105] Iteration 10068, lr = 0.00136105
I0410 00:08:26.232378  4596 solver.cpp:218] Iteration 10080 (2.45024 iter/s, 4.89749s/12 iters), loss = 0.144064
I0410 00:08:26.232439  4596 solver.cpp:237]     Train net output #0: loss = 0.144064 (* 1 = 0.144064 loss)
I0410 00:08:26.232451  4596 sgd_solver.cpp:105] Iteration 10080, lr = 0.00135782
I0410 00:08:31.185149  4596 solver.cpp:218] Iteration 10092 (2.42298 iter/s, 4.95258s/12 iters), loss = 0.0815728
I0410 00:08:31.185194  4596 solver.cpp:237]     Train net output #0: loss = 0.0815728 (* 1 = 0.0815728 loss)
I0410 00:08:31.185204  4596 sgd_solver.cpp:105] Iteration 10092, lr = 0.0013546
I0410 00:08:33.211560  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10098.caffemodel
I0410 00:08:33.635579  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10098.solverstate
I0410 00:08:33.935530  4596 solver.cpp:330] Iteration 10098, Testing net (#0)
I0410 00:08:33.935559  4596 net.cpp:676] Ignoring source layer train-data
I0410 00:08:34.354117  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:08:38.350744  4596 solver.cpp:397]     Test net output #0: accuracy = 0.529412
I0410 00:08:38.350790  4596 solver.cpp:397]     Test net output #1: loss = 3.04375 (* 1 = 3.04375 loss)
I0410 00:08:40.245486  4596 solver.cpp:218] Iteration 10104 (1.3245 iter/s, 9.06004s/12 iters), loss = 0.0799353
I0410 00:08:40.245548  4596 solver.cpp:237]     Train net output #0: loss = 0.0799353 (* 1 = 0.0799353 loss)
I0410 00:08:40.245560  4596 sgd_solver.cpp:105] Iteration 10104, lr = 0.00135138
I0410 00:08:44.537943  4600 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:08:45.182238  4596 solver.cpp:218] Iteration 10116 (2.43084 iter/s, 4.93656s/12 iters), loss = 0.102954
I0410 00:08:45.182294  4596 solver.cpp:237]     Train net output #0: loss = 0.102954 (* 1 = 0.102954 loss)
I0410 00:08:45.182307  4596 sgd_solver.cpp:105] Iteration 10116, lr = 0.00134817
I0410 00:08:50.107379  4596 solver.cpp:218] Iteration 10128 (2.43657 iter/s, 4.92496s/12 iters), loss = 0.13533
I0410 00:08:50.107426  4596 solver.cpp:237]     Train net output #0: loss = 0.13533 (* 1 = 0.13533 loss)
I0410 00:08:50.107436  4596 sgd_solver.cpp:105] Iteration 10128, lr = 0.00134497
I0410 00:08:54.934514  4596 solver.cpp:218] Iteration 10140 (2.48604 iter/s, 4.82695s/12 iters), loss = 0.157722
I0410 00:08:54.934633  4596 solver.cpp:237]     Train net output #0: loss = 0.157722 (* 1 = 0.157722 loss)
I0410 00:08:54.934644  4596 sgd_solver.cpp:105] Iteration 10140, lr = 0.00134178
I0410 00:08:59.854799  4596 solver.cpp:218] Iteration 10152 (2.43901 iter/s, 4.92003s/12 iters), loss = 0.109904
I0410 00:08:59.854857  4596 solver.cpp:237]     Train net output #0: loss = 0.109904 (* 1 = 0.109904 loss)
I0410 00:08:59.854869  4596 sgd_solver.cpp:105] Iteration 10152, lr = 0.00133859
I0410 00:09:04.741179  4596 solver.cpp:218] Iteration 10164 (2.4559 iter/s, 4.88619s/12 iters), loss = 0.111918
I0410 00:09:04.741240  4596 solver.cpp:237]     Train net output #0: loss = 0.111918 (* 1 = 0.111918 loss)
I0410 00:09:04.741252  4596 sgd_solver.cpp:105] Iteration 10164, lr = 0.00133541
I0410 00:09:09.654803  4596 solver.cpp:218] Iteration 10176 (2.44229 iter/s, 4.91343s/12 iters), loss = 0.187862
I0410 00:09:09.654861  4596 solver.cpp:237]     Train net output #0: loss = 0.187862 (* 1 = 0.187862 loss)
I0410 00:09:09.654875  4596 sgd_solver.cpp:105] Iteration 10176, lr = 0.00133224
I0410 00:09:14.635638  4596 solver.cpp:218] Iteration 10188 (2.40933 iter/s, 4.98065s/12 iters), loss = 0.143465
I0410 00:09:14.635684  4596 solver.cpp:237]     Train net output #0: loss = 0.143465 (* 1 = 0.143465 loss)
I0410 00:09:14.635694  4596 sgd_solver.cpp:105] Iteration 10188, lr = 0.00132908
I0410 00:09:19.171694  4596 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10200.caffemodel
I0410 00:09:20.372944  4596 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10200.solverstate
I0410 00:09:21.014652  4596 solver.cpp:310] Iteration 10200, loss = 0.117031
I0410 00:09:21.014688  4596 solver.cpp:330] Iteration 10200, Testing net (#0)
I0410 00:09:21.014698  4596 net.cpp:676] Ignoring source layer train-data
I0410 00:09:21.454978  4602 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:09:25.481180  4596 solver.cpp:397]     Test net output #0: accuracy = 0.532475
I0410 00:09:25.481323  4596 solver.cpp:397]     Test net output #1: loss = 2.95214 (* 1 = 2.95214 loss)
I0410 00:09:25.481335  4596 solver.cpp:315] Optimization Done.
I0410 00:09:25.481343  4596 caffe.cpp:259] Optimization Done.