DIGITS-CNN/cars/architecture-investigations/conv/nonlinear/l4/2parts/caffe_output.log

I0412 12:56:05.765990  8032 upgrade_proto.cpp:1082] Attempting to upgrade input file specified using deprecated 'solver_type' field (enum)': /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210412-125604-40a6/solver.prototxt
I0412 12:56:05.766211  8032 upgrade_proto.cpp:1089] Successfully upgraded file specified using deprecated 'solver_type' field (enum) to 'type' field (string).
W0412 12:56:05.766219  8032 upgrade_proto.cpp:1091] Note that future Caffe releases will only support 'type' field (string) for a solver's type.
I0412 12:56:05.766310  8032 caffe.cpp:218] Using GPUs 1
I0412 12:56:05.795572  8032 caffe.cpp:223] GPU 1: GeForce GTX 1080 Ti
I0412 12:56:06.098641  8032 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: 1
net: "train_val.prototxt"
train_state {
level: 0
stage: ""
}
type: "SGD"
I0412 12:56:06.099412  8032 solver.cpp:87] Creating training net from net file: train_val.prototxt
I0412 12:56:06.100018  8032 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer val-data
I0412 12:56:06.100033  8032 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy
I0412 12:56:06.100180  8032 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: 192
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu4"
type: "ReLU"
bottom: "conv4"
top: "conv4"
}
layer {
name: "conv4.2"
type: "Convolution"
bottom: "conv4"
top: "conv4.2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 192
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu4.2"
type: "ReLU"
bottom: "conv4.2"
top: "conv4.2"
}
layer {
name: "conv5"
type: "Convolution"
bottom: "conv4.2"
top: "conv5"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu5"
type: "ReLU"
bottom: "conv5"
top: "conv5"
}
layer {
name: "pool5"
type: "Pooling"
bottom: "conv5"
top: "pool5"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "fc6"
type: "InnerProduct"
bottom: "pool5"
top: "fc6"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 4096
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu6"
type: "ReLU"
bottom: "fc6"
top: "fc6"
}
layer {
name: "drop6"
type: "Dropout"
bottom: "fc6"
top: "fc6"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc7"
type: "InnerProduct"
bottom: "fc6"
top: "fc7"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 4096
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu7"
type: "ReLU"
bottom: "fc7"
top: "fc7"
}
layer {
name: "drop7"
type: "Dropout"
bottom: "fc7"
top: "fc7"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc8"
type: "InnerProduct"
bottom: "fc7"
top: "fc8"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 196
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "loss"
type: "SoftmaxWithLoss"
bottom: "fc8"
bottom: "label"
top: "loss"
}
I0412 12:56:06.100272  8032 layer_factory.hpp:77] Creating layer train-data
I0412 12:56:06.102432  8032 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/train_db
I0412 12:56:06.102643  8032 net.cpp:84] Creating Layer train-data
I0412 12:56:06.102655  8032 net.cpp:380] train-data -> data
I0412 12:56:06.102680  8032 net.cpp:380] train-data -> label
I0412 12:56:06.102695  8032 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto
I0412 12:56:06.107628  8032 data_layer.cpp:45] output data size: 128,3,227,227
I0412 12:56:06.240501  8032 net.cpp:122] Setting up train-data
I0412 12:56:06.240526  8032 net.cpp:129] Top shape: 128 3 227 227 (19787136)
I0412 12:56:06.240532  8032 net.cpp:129] Top shape: 128 (128)
I0412 12:56:06.240536  8032 net.cpp:137] Memory required for data: 79149056
I0412 12:56:06.240547  8032 layer_factory.hpp:77] Creating layer conv1
I0412 12:56:06.240568  8032 net.cpp:84] Creating Layer conv1
I0412 12:56:06.240574  8032 net.cpp:406] conv1 <- data
I0412 12:56:06.240586  8032 net.cpp:380] conv1 -> conv1
I0412 12:56:06.821391  8032 net.cpp:122] Setting up conv1
I0412 12:56:06.821413  8032 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0412 12:56:06.821417  8032 net.cpp:137] Memory required for data: 227833856
I0412 12:56:06.821436  8032 layer_factory.hpp:77] Creating layer relu1
I0412 12:56:06.821447  8032 net.cpp:84] Creating Layer relu1
I0412 12:56:06.821471  8032 net.cpp:406] relu1 <- conv1
I0412 12:56:06.821478  8032 net.cpp:367] relu1 -> conv1 (in-place)
I0412 12:56:06.821768  8032 net.cpp:122] Setting up relu1
I0412 12:56:06.821776  8032 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0412 12:56:06.821780  8032 net.cpp:137] Memory required for data: 376518656
I0412 12:56:06.821784  8032 layer_factory.hpp:77] Creating layer norm1
I0412 12:56:06.821794  8032 net.cpp:84] Creating Layer norm1
I0412 12:56:06.821796  8032 net.cpp:406] norm1 <- conv1
I0412 12:56:06.821802  8032 net.cpp:380] norm1 -> norm1
I0412 12:56:06.822247  8032 net.cpp:122] Setting up norm1
I0412 12:56:06.822257  8032 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0412 12:56:06.822260  8032 net.cpp:137] Memory required for data: 525203456
I0412 12:56:06.822264  8032 layer_factory.hpp:77] Creating layer pool1
I0412 12:56:06.822273  8032 net.cpp:84] Creating Layer pool1
I0412 12:56:06.822276  8032 net.cpp:406] pool1 <- norm1
I0412 12:56:06.822281  8032 net.cpp:380] pool1 -> pool1
I0412 12:56:06.822317  8032 net.cpp:122] Setting up pool1
I0412 12:56:06.822324  8032 net.cpp:129] Top shape: 128 96 27 27 (8957952)
I0412 12:56:06.822326  8032 net.cpp:137] Memory required for data: 561035264
I0412 12:56:06.822329  8032 layer_factory.hpp:77] Creating layer conv2
I0412 12:56:06.822340  8032 net.cpp:84] Creating Layer conv2
I0412 12:56:06.822343  8032 net.cpp:406] conv2 <- pool1
I0412 12:56:06.822347  8032 net.cpp:380] conv2 -> conv2
I0412 12:56:06.831840  8032 net.cpp:122] Setting up conv2
I0412 12:56:06.831854  8032 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0412 12:56:06.831859  8032 net.cpp:137] Memory required for data: 656586752
I0412 12:56:06.831868  8032 layer_factory.hpp:77] Creating layer relu2
I0412 12:56:06.831876  8032 net.cpp:84] Creating Layer relu2
I0412 12:56:06.831879  8032 net.cpp:406] relu2 <- conv2
I0412 12:56:06.831887  8032 net.cpp:367] relu2 -> conv2 (in-place)
I0412 12:56:06.832381  8032 net.cpp:122] Setting up relu2
I0412 12:56:06.832389  8032 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0412 12:56:06.832393  8032 net.cpp:137] Memory required for data: 752138240
I0412 12:56:06.832397  8032 layer_factory.hpp:77] Creating layer norm2
I0412 12:56:06.832406  8032 net.cpp:84] Creating Layer norm2
I0412 12:56:06.832410  8032 net.cpp:406] norm2 <- conv2
I0412 12:56:06.832415  8032 net.cpp:380] norm2 -> norm2
I0412 12:56:06.832774  8032 net.cpp:122] Setting up norm2
I0412 12:56:06.832783  8032 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0412 12:56:06.832787  8032 net.cpp:137] Memory required for data: 847689728
I0412 12:56:06.832790  8032 layer_factory.hpp:77] Creating layer pool2
I0412 12:56:06.832799  8032 net.cpp:84] Creating Layer pool2
I0412 12:56:06.832803  8032 net.cpp:406] pool2 <- norm2
I0412 12:56:06.832808  8032 net.cpp:380] pool2 -> pool2
I0412 12:56:06.832839  8032 net.cpp:122] Setting up pool2
I0412 12:56:06.832844  8032 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0412 12:56:06.832846  8032 net.cpp:137] Memory required for data: 869840896
I0412 12:56:06.832850  8032 layer_factory.hpp:77] Creating layer conv3
I0412 12:56:06.832859  8032 net.cpp:84] Creating Layer conv3
I0412 12:56:06.832863  8032 net.cpp:406] conv3 <- pool2
I0412 12:56:06.832870  8032 net.cpp:380] conv3 -> conv3
I0412 12:56:06.842919  8032 net.cpp:122] Setting up conv3
I0412 12:56:06.842931  8032 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0412 12:56:06.842934  8032 net.cpp:137] Memory required for data: 903067648
I0412 12:56:06.842943  8032 layer_factory.hpp:77] Creating layer relu3
I0412 12:56:06.842950  8032 net.cpp:84] Creating Layer relu3
I0412 12:56:06.842955  8032 net.cpp:406] relu3 <- conv3
I0412 12:56:06.842960  8032 net.cpp:367] relu3 -> conv3 (in-place)
I0412 12:56:06.843444  8032 net.cpp:122] Setting up relu3
I0412 12:56:06.843456  8032 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0412 12:56:06.843459  8032 net.cpp:137] Memory required for data: 936294400
I0412 12:56:06.843463  8032 layer_factory.hpp:77] Creating layer conv4
I0412 12:56:06.843473  8032 net.cpp:84] Creating Layer conv4
I0412 12:56:06.843493  8032 net.cpp:406] conv4 <- conv3
I0412 12:56:06.843500  8032 net.cpp:380] conv4 -> conv4
I0412 12:56:06.850950  8032 net.cpp:122] Setting up conv4
I0412 12:56:06.850962  8032 net.cpp:129] Top shape: 128 192 13 13 (4153344)
I0412 12:56:06.850966  8032 net.cpp:137] Memory required for data: 952907776
I0412 12:56:06.850975  8032 layer_factory.hpp:77] Creating layer relu4
I0412 12:56:06.850982  8032 net.cpp:84] Creating Layer relu4
I0412 12:56:06.850986  8032 net.cpp:406] relu4 <- conv4
I0412 12:56:06.850993  8032 net.cpp:367] relu4 -> conv4 (in-place)
I0412 12:56:06.851327  8032 net.cpp:122] Setting up relu4
I0412 12:56:06.851337  8032 net.cpp:129] Top shape: 128 192 13 13 (4153344)
I0412 12:56:06.851341  8032 net.cpp:137] Memory required for data: 969521152
I0412 12:56:06.851344  8032 layer_factory.hpp:77] Creating layer conv4.2
I0412 12:56:06.851353  8032 net.cpp:84] Creating Layer conv4.2
I0412 12:56:06.851357  8032 net.cpp:406] conv4.2 <- conv4
I0412 12:56:06.851366  8032 net.cpp:380] conv4.2 -> conv4.2
I0412 12:56:06.861027  8032 net.cpp:122] Setting up conv4.2
I0412 12:56:06.861039  8032 net.cpp:129] Top shape: 128 192 13 13 (4153344)
I0412 12:56:06.861043  8032 net.cpp:137] Memory required for data: 986134528
I0412 12:56:06.861053  8032 layer_factory.hpp:77] Creating layer relu4.2
I0412 12:56:06.861061  8032 net.cpp:84] Creating Layer relu4.2
I0412 12:56:06.861065  8032 net.cpp:406] relu4.2 <- conv4.2
I0412 12:56:06.861070  8032 net.cpp:367] relu4.2 -> conv4.2 (in-place)
I0412 12:56:06.861552  8032 net.cpp:122] Setting up relu4.2
I0412 12:56:06.861562  8032 net.cpp:129] Top shape: 128 192 13 13 (4153344)
I0412 12:56:06.861567  8032 net.cpp:137] Memory required for data: 1002747904
I0412 12:56:06.861569  8032 layer_factory.hpp:77] Creating layer conv5
I0412 12:56:06.861579  8032 net.cpp:84] Creating Layer conv5
I0412 12:56:06.861584  8032 net.cpp:406] conv5 <- conv4.2
I0412 12:56:06.861591  8032 net.cpp:380] conv5 -> conv5
I0412 12:56:06.866694  8032 net.cpp:122] Setting up conv5
I0412 12:56:06.866705  8032 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0412 12:56:06.866709  8032 net.cpp:137] Memory required for data: 1024899072
I0412 12:56:06.866715  8032 layer_factory.hpp:77] Creating layer relu5
I0412 12:56:06.866724  8032 net.cpp:84] Creating Layer relu5
I0412 12:56:06.866727  8032 net.cpp:406] relu5 <- conv5
I0412 12:56:06.866734  8032 net.cpp:367] relu5 -> conv5 (in-place)
I0412 12:56:06.867223  8032 net.cpp:122] Setting up relu5
I0412 12:56:06.867233  8032 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0412 12:56:06.867235  8032 net.cpp:137] Memory required for data: 1047050240
I0412 12:56:06.867239  8032 layer_factory.hpp:77] Creating layer pool5
I0412 12:56:06.867246  8032 net.cpp:84] Creating Layer pool5
I0412 12:56:06.867249  8032 net.cpp:406] pool5 <- conv5
I0412 12:56:06.867256  8032 net.cpp:380] pool5 -> pool5
I0412 12:56:06.867292  8032 net.cpp:122] Setting up pool5
I0412 12:56:06.867298  8032 net.cpp:129] Top shape: 128 256 6 6 (1179648)
I0412 12:56:06.867301  8032 net.cpp:137] Memory required for data: 1051768832
I0412 12:56:06.867305  8032 layer_factory.hpp:77] Creating layer fc6
I0412 12:56:06.867312  8032 net.cpp:84] Creating Layer fc6
I0412 12:56:06.867316  8032 net.cpp:406] fc6 <- pool5
I0412 12:56:06.867321  8032 net.cpp:380] fc6 -> fc6
I0412 12:56:07.226191  8032 net.cpp:122] Setting up fc6
I0412 12:56:07.226214  8032 net.cpp:129] Top shape: 128 4096 (524288)
I0412 12:56:07.226218  8032 net.cpp:137] Memory required for data: 1053865984
I0412 12:56:07.226228  8032 layer_factory.hpp:77] Creating layer relu6
I0412 12:56:07.226238  8032 net.cpp:84] Creating Layer relu6
I0412 12:56:07.226241  8032 net.cpp:406] relu6 <- fc6
I0412 12:56:07.226248  8032 net.cpp:367] relu6 -> fc6 (in-place)
I0412 12:56:07.226642  8032 net.cpp:122] Setting up relu6
I0412 12:56:07.226650  8032 net.cpp:129] Top shape: 128 4096 (524288)
I0412 12:56:07.226655  8032 net.cpp:137] Memory required for data: 1055963136
I0412 12:56:07.226658  8032 layer_factory.hpp:77] Creating layer drop6
I0412 12:56:07.226686  8032 net.cpp:84] Creating Layer drop6
I0412 12:56:07.226689  8032 net.cpp:406] drop6 <- fc6
I0412 12:56:07.226696  8032 net.cpp:367] drop6 -> fc6 (in-place)
I0412 12:56:07.226722  8032 net.cpp:122] Setting up drop6
I0412 12:56:07.226729  8032 net.cpp:129] Top shape: 128 4096 (524288)
I0412 12:56:07.226733  8032 net.cpp:137] Memory required for data: 1058060288
I0412 12:56:07.226737  8032 layer_factory.hpp:77] Creating layer fc7
I0412 12:56:07.226744  8032 net.cpp:84] Creating Layer fc7
I0412 12:56:07.226748  8032 net.cpp:406] fc7 <- fc6
I0412 12:56:07.226755  8032 net.cpp:380] fc7 -> fc7
I0412 12:56:07.385761  8032 net.cpp:122] Setting up fc7
I0412 12:56:07.385784  8032 net.cpp:129] Top shape: 128 4096 (524288)
I0412 12:56:07.385788  8032 net.cpp:137] Memory required for data: 1060157440
I0412 12:56:07.385797  8032 layer_factory.hpp:77] Creating layer relu7
I0412 12:56:07.385807  8032 net.cpp:84] Creating Layer relu7
I0412 12:56:07.385812  8032 net.cpp:406] relu7 <- fc7
I0412 12:56:07.385819  8032 net.cpp:367] relu7 -> fc7 (in-place)
I0412 12:56:07.390307  8032 net.cpp:122] Setting up relu7
I0412 12:56:07.390321  8032 net.cpp:129] Top shape: 128 4096 (524288)
I0412 12:56:07.390323  8032 net.cpp:137] Memory required for data: 1062254592
I0412 12:56:07.390327  8032 layer_factory.hpp:77] Creating layer drop7
I0412 12:56:07.390336  8032 net.cpp:84] Creating Layer drop7
I0412 12:56:07.390339  8032 net.cpp:406] drop7 <- fc7
I0412 12:56:07.390345  8032 net.cpp:367] drop7 -> fc7 (in-place)
I0412 12:56:07.390372  8032 net.cpp:122] Setting up drop7
I0412 12:56:07.390377  8032 net.cpp:129] Top shape: 128 4096 (524288)
I0412 12:56:07.390380  8032 net.cpp:137] Memory required for data: 1064351744
I0412 12:56:07.390383  8032 layer_factory.hpp:77] Creating layer fc8
I0412 12:56:07.390390  8032 net.cpp:84] Creating Layer fc8
I0412 12:56:07.390393  8032 net.cpp:406] fc8 <- fc7
I0412 12:56:07.390406  8032 net.cpp:380] fc8 -> fc8
I0412 12:56:07.398075  8032 net.cpp:122] Setting up fc8
I0412 12:56:07.398088  8032 net.cpp:129] Top shape: 128 196 (25088)
I0412 12:56:07.398092  8032 net.cpp:137] Memory required for data: 1064452096
I0412 12:56:07.398102  8032 layer_factory.hpp:77] Creating layer loss
I0412 12:56:07.398113  8032 net.cpp:84] Creating Layer loss
I0412 12:56:07.398116  8032 net.cpp:406] loss <- fc8
I0412 12:56:07.398121  8032 net.cpp:406] loss <- label
I0412 12:56:07.398128  8032 net.cpp:380] loss -> loss
I0412 12:56:07.398139  8032 layer_factory.hpp:77] Creating layer loss
I0412 12:56:07.398749  8032 net.cpp:122] Setting up loss
I0412 12:56:07.398758  8032 net.cpp:129] Top shape: (1)
I0412 12:56:07.398761  8032 net.cpp:132]     with loss weight 1
I0412 12:56:07.398778  8032 net.cpp:137] Memory required for data: 1064452100
I0412 12:56:07.398782  8032 net.cpp:198] loss needs backward computation.
I0412 12:56:07.398789  8032 net.cpp:198] fc8 needs backward computation.
I0412 12:56:07.398792  8032 net.cpp:198] drop7 needs backward computation.
I0412 12:56:07.398795  8032 net.cpp:198] relu7 needs backward computation.
I0412 12:56:07.398798  8032 net.cpp:198] fc7 needs backward computation.
I0412 12:56:07.398802  8032 net.cpp:198] drop6 needs backward computation.
I0412 12:56:07.398805  8032 net.cpp:198] relu6 needs backward computation.
I0412 12:56:07.398809  8032 net.cpp:198] fc6 needs backward computation.
I0412 12:56:07.398813  8032 net.cpp:198] pool5 needs backward computation.
I0412 12:56:07.398816  8032 net.cpp:198] relu5 needs backward computation.
I0412 12:56:07.398819  8032 net.cpp:198] conv5 needs backward computation.
I0412 12:56:07.398823  8032 net.cpp:198] relu4.2 needs backward computation.
I0412 12:56:07.398826  8032 net.cpp:198] conv4.2 needs backward computation.
I0412 12:56:07.398831  8032 net.cpp:198] relu4 needs backward computation.
I0412 12:56:07.398834  8032 net.cpp:198] conv4 needs backward computation.
I0412 12:56:07.398838  8032 net.cpp:198] relu3 needs backward computation.
I0412 12:56:07.398841  8032 net.cpp:198] conv3 needs backward computation.
I0412 12:56:07.398844  8032 net.cpp:198] pool2 needs backward computation.
I0412 12:56:07.398867  8032 net.cpp:198] norm2 needs backward computation.
I0412 12:56:07.398870  8032 net.cpp:198] relu2 needs backward computation.
I0412 12:56:07.398874  8032 net.cpp:198] conv2 needs backward computation.
I0412 12:56:07.398877  8032 net.cpp:198] pool1 needs backward computation.
I0412 12:56:07.398881  8032 net.cpp:198] norm1 needs backward computation.
I0412 12:56:07.398885  8032 net.cpp:198] relu1 needs backward computation.
I0412 12:56:07.398887  8032 net.cpp:198] conv1 needs backward computation.
I0412 12:56:07.398891  8032 net.cpp:200] train-data does not need backward computation.
I0412 12:56:07.398895  8032 net.cpp:242] This network produces output loss
I0412 12:56:07.398910  8032 net.cpp:255] Network initialization done.
I0412 12:56:07.399443  8032 solver.cpp:172] Creating test net (#0) specified by net file: train_val.prototxt
I0412 12:56:07.399474  8032 net.cpp:294] The NetState phase (1) differed from the phase (0) specified by a rule in layer train-data
I0412 12:56:07.399623  8032 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: 192
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu4"
type: "ReLU"
bottom: "conv4"
top: "conv4"
}
layer {
name: "conv4.2"
type: "Convolution"
bottom: "conv4"
top: "conv4.2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 192
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu4.2"
type: "ReLU"
bottom: "conv4.2"
top: "conv4.2"
}
layer {
name: "conv5"
type: "Convolution"
bottom: "conv4.2"
top: "conv5"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu5"
type: "ReLU"
bottom: "conv5"
top: "conv5"
}
layer {
name: "pool5"
type: "Pooling"
bottom: "conv5"
top: "pool5"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "fc6"
type: "InnerProduct"
bottom: "pool5"
top: "fc6"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 4096
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu6"
type: "ReLU"
bottom: "fc6"
top: "fc6"
}
layer {
name: "drop6"
type: "Dropout"
bottom: "fc6"
top: "fc6"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc7"
type: "InnerProduct"
bottom: "fc6"
top: "fc7"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 4096
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu7"
type: "ReLU"
bottom: "fc7"
top: "fc7"
}
layer {
name: "drop7"
type: "Dropout"
bottom: "fc7"
top: "fc7"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc8"
type: "InnerProduct"
bottom: "fc7"
top: "fc8"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 196
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "accuracy"
type: "Accuracy"
bottom: "fc8"
bottom: "label"
top: "accuracy"
include {
phase: TEST
}
}
layer {
name: "loss"
type: "SoftmaxWithLoss"
bottom: "fc8"
bottom: "label"
top: "loss"
}
I0412 12:56:07.399720  8032 layer_factory.hpp:77] Creating layer val-data
I0412 12:56:07.401391  8032 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/val_db
I0412 12:56:07.401594  8032 net.cpp:84] Creating Layer val-data
I0412 12:56:07.401604  8032 net.cpp:380] val-data -> data
I0412 12:56:07.401612  8032 net.cpp:380] val-data -> label
I0412 12:56:07.401618  8032 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto
I0412 12:56:07.405503  8032 data_layer.cpp:45] output data size: 32,3,227,227
I0412 12:56:07.447587  8032 net.cpp:122] Setting up val-data
I0412 12:56:07.447609  8032 net.cpp:129] Top shape: 32 3 227 227 (4946784)
I0412 12:56:07.447613  8032 net.cpp:129] Top shape: 32 (32)
I0412 12:56:07.447618  8032 net.cpp:137] Memory required for data: 19787264
I0412 12:56:07.447623  8032 layer_factory.hpp:77] Creating layer label_val-data_1_split
I0412 12:56:07.447635  8032 net.cpp:84] Creating Layer label_val-data_1_split
I0412 12:56:07.447639  8032 net.cpp:406] label_val-data_1_split <- label
I0412 12:56:07.447646  8032 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_0
I0412 12:56:07.447655  8032 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_1
I0412 12:56:07.447705  8032 net.cpp:122] Setting up label_val-data_1_split
I0412 12:56:07.447710  8032 net.cpp:129] Top shape: 32 (32)
I0412 12:56:07.447715  8032 net.cpp:129] Top shape: 32 (32)
I0412 12:56:07.447736  8032 net.cpp:137] Memory required for data: 19787520
I0412 12:56:07.447739  8032 layer_factory.hpp:77] Creating layer conv1
I0412 12:56:07.447751  8032 net.cpp:84] Creating Layer conv1
I0412 12:56:07.447754  8032 net.cpp:406] conv1 <- data
I0412 12:56:07.447759  8032 net.cpp:380] conv1 -> conv1
I0412 12:56:07.449777  8032 net.cpp:122] Setting up conv1
I0412 12:56:07.449788  8032 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0412 12:56:07.449792  8032 net.cpp:137] Memory required for data: 56958720
I0412 12:56:07.449801  8032 layer_factory.hpp:77] Creating layer relu1
I0412 12:56:07.449808  8032 net.cpp:84] Creating Layer relu1
I0412 12:56:07.449812  8032 net.cpp:406] relu1 <- conv1
I0412 12:56:07.449817  8032 net.cpp:367] relu1 -> conv1 (in-place)
I0412 12:56:07.450273  8032 net.cpp:122] Setting up relu1
I0412 12:56:07.450282  8032 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0412 12:56:07.450285  8032 net.cpp:137] Memory required for data: 94129920
I0412 12:56:07.450289  8032 layer_factory.hpp:77] Creating layer norm1
I0412 12:56:07.450299  8032 net.cpp:84] Creating Layer norm1
I0412 12:56:07.450301  8032 net.cpp:406] norm1 <- conv1
I0412 12:56:07.450306  8032 net.cpp:380] norm1 -> norm1
I0412 12:56:07.450750  8032 net.cpp:122] Setting up norm1
I0412 12:56:07.450760  8032 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0412 12:56:07.450763  8032 net.cpp:137] Memory required for data: 131301120
I0412 12:56:07.450767  8032 layer_factory.hpp:77] Creating layer pool1
I0412 12:56:07.450774  8032 net.cpp:84] Creating Layer pool1
I0412 12:56:07.450778  8032 net.cpp:406] pool1 <- norm1
I0412 12:56:07.450783  8032 net.cpp:380] pool1 -> pool1
I0412 12:56:07.450811  8032 net.cpp:122] Setting up pool1
I0412 12:56:07.450816  8032 net.cpp:129] Top shape: 32 96 27 27 (2239488)
I0412 12:56:07.450819  8032 net.cpp:137] Memory required for data: 140259072
I0412 12:56:07.450824  8032 layer_factory.hpp:77] Creating layer conv2
I0412 12:56:07.450830  8032 net.cpp:84] Creating Layer conv2
I0412 12:56:07.450834  8032 net.cpp:406] conv2 <- pool1
I0412 12:56:07.450839  8032 net.cpp:380] conv2 -> conv2
I0412 12:56:07.461030  8032 net.cpp:122] Setting up conv2
I0412 12:56:07.461048  8032 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0412 12:56:07.461051  8032 net.cpp:137] Memory required for data: 164146944
I0412 12:56:07.461061  8032 layer_factory.hpp:77] Creating layer relu2
I0412 12:56:07.461069  8032 net.cpp:84] Creating Layer relu2
I0412 12:56:07.461073  8032 net.cpp:406] relu2 <- conv2
I0412 12:56:07.461081  8032 net.cpp:367] relu2 -> conv2 (in-place)
I0412 12:56:07.461439  8032 net.cpp:122] Setting up relu2
I0412 12:56:07.461447  8032 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0412 12:56:07.461450  8032 net.cpp:137] Memory required for data: 188034816
I0412 12:56:07.461454  8032 layer_factory.hpp:77] Creating layer norm2
I0412 12:56:07.461464  8032 net.cpp:84] Creating Layer norm2
I0412 12:56:07.461467  8032 net.cpp:406] norm2 <- conv2
I0412 12:56:07.461472  8032 net.cpp:380] norm2 -> norm2
I0412 12:56:07.462028  8032 net.cpp:122] Setting up norm2
I0412 12:56:07.462038  8032 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0412 12:56:07.462041  8032 net.cpp:137] Memory required for data: 211922688
I0412 12:56:07.462045  8032 layer_factory.hpp:77] Creating layer pool2
I0412 12:56:07.462052  8032 net.cpp:84] Creating Layer pool2
I0412 12:56:07.462055  8032 net.cpp:406] pool2 <- norm2
I0412 12:56:07.462062  8032 net.cpp:380] pool2 -> pool2
I0412 12:56:07.462093  8032 net.cpp:122] Setting up pool2
I0412 12:56:07.462103  8032 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0412 12:56:07.462106  8032 net.cpp:137] Memory required for data: 217460480
I0412 12:56:07.462110  8032 layer_factory.hpp:77] Creating layer conv3
I0412 12:56:07.462119  8032 net.cpp:84] Creating Layer conv3
I0412 12:56:07.462122  8032 net.cpp:406] conv3 <- pool2
I0412 12:56:07.462128  8032 net.cpp:380] conv3 -> conv3
I0412 12:56:07.480446  8032 net.cpp:122] Setting up conv3
I0412 12:56:07.480463  8032 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0412 12:56:07.480482  8032 net.cpp:137] Memory required for data: 225767168
I0412 12:56:07.480494  8032 layer_factory.hpp:77] Creating layer relu3
I0412 12:56:07.480504  8032 net.cpp:84] Creating Layer relu3
I0412 12:56:07.480509  8032 net.cpp:406] relu3 <- conv3
I0412 12:56:07.480515  8032 net.cpp:367] relu3 -> conv3 (in-place)
I0412 12:56:07.480880  8032 net.cpp:122] Setting up relu3
I0412 12:56:07.480890  8032 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0412 12:56:07.480892  8032 net.cpp:137] Memory required for data: 234073856
I0412 12:56:07.480895  8032 layer_factory.hpp:77] Creating layer conv4
I0412 12:56:07.480906  8032 net.cpp:84] Creating Layer conv4
I0412 12:56:07.480911  8032 net.cpp:406] conv4 <- conv3
I0412 12:56:07.480917  8032 net.cpp:380] conv4 -> conv4
I0412 12:56:07.491151  8032 net.cpp:122] Setting up conv4
I0412 12:56:07.491168  8032 net.cpp:129] Top shape: 32 192 13 13 (1038336)
I0412 12:56:07.491173  8032 net.cpp:137] Memory required for data: 238227200
I0412 12:56:07.491180  8032 layer_factory.hpp:77] Creating layer relu4
I0412 12:56:07.491189  8032 net.cpp:84] Creating Layer relu4
I0412 12:56:07.491194  8032 net.cpp:406] relu4 <- conv4
I0412 12:56:07.491200  8032 net.cpp:367] relu4 -> conv4 (in-place)
I0412 12:56:07.491691  8032 net.cpp:122] Setting up relu4
I0412 12:56:07.491701  8032 net.cpp:129] Top shape: 32 192 13 13 (1038336)
I0412 12:56:07.491704  8032 net.cpp:137] Memory required for data: 242380544
I0412 12:56:07.491708  8032 layer_factory.hpp:77] Creating layer conv4.2
I0412 12:56:07.491719  8032 net.cpp:84] Creating Layer conv4.2
I0412 12:56:07.491722  8032 net.cpp:406] conv4.2 <- conv4
I0412 12:56:07.491730  8032 net.cpp:380] conv4.2 -> conv4.2
I0412 12:56:07.497539  8032 net.cpp:122] Setting up conv4.2
I0412 12:56:07.497555  8032 net.cpp:129] Top shape: 32 192 13 13 (1038336)
I0412 12:56:07.497557  8032 net.cpp:137] Memory required for data: 246533888
I0412 12:56:07.497570  8032 layer_factory.hpp:77] Creating layer relu4.2
I0412 12:56:07.497577  8032 net.cpp:84] Creating Layer relu4.2
I0412 12:56:07.497581  8032 net.cpp:406] relu4.2 <- conv4.2
I0412 12:56:07.497588  8032 net.cpp:367] relu4.2 -> conv4.2 (in-place)
I0412 12:56:07.498102  8032 net.cpp:122] Setting up relu4.2
I0412 12:56:07.498112  8032 net.cpp:129] Top shape: 32 192 13 13 (1038336)
I0412 12:56:07.498116  8032 net.cpp:137] Memory required for data: 250687232
I0412 12:56:07.498119  8032 layer_factory.hpp:77] Creating layer conv5
I0412 12:56:07.498133  8032 net.cpp:84] Creating Layer conv5
I0412 12:56:07.498136  8032 net.cpp:406] conv5 <- conv4.2
I0412 12:56:07.498142  8032 net.cpp:380] conv5 -> conv5
I0412 12:56:07.503863  8032 net.cpp:122] Setting up conv5
I0412 12:56:07.503875  8032 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0412 12:56:07.503880  8032 net.cpp:137] Memory required for data: 256225024
I0412 12:56:07.503886  8032 layer_factory.hpp:77] Creating layer relu5
I0412 12:56:07.503892  8032 net.cpp:84] Creating Layer relu5
I0412 12:56:07.503896  8032 net.cpp:406] relu5 <- conv5
I0412 12:56:07.503902  8032 net.cpp:367] relu5 -> conv5 (in-place)
I0412 12:56:07.504398  8032 net.cpp:122] Setting up relu5
I0412 12:56:07.504408  8032 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0412 12:56:07.504412  8032 net.cpp:137] Memory required for data: 261762816
I0412 12:56:07.504415  8032 layer_factory.hpp:77] Creating layer pool5
I0412 12:56:07.504422  8032 net.cpp:84] Creating Layer pool5
I0412 12:56:07.504426  8032 net.cpp:406] pool5 <- conv5
I0412 12:56:07.504431  8032 net.cpp:380] pool5 -> pool5
I0412 12:56:07.504469  8032 net.cpp:122] Setting up pool5
I0412 12:56:07.504475  8032 net.cpp:129] Top shape: 32 256 6 6 (294912)
I0412 12:56:07.504478  8032 net.cpp:137] Memory required for data: 262942464
I0412 12:56:07.504482  8032 layer_factory.hpp:77] Creating layer fc6
I0412 12:56:07.504490  8032 net.cpp:84] Creating Layer fc6
I0412 12:56:07.504493  8032 net.cpp:406] fc6 <- pool5
I0412 12:56:07.504498  8032 net.cpp:380] fc6 -> fc6
I0412 12:56:07.865087  8032 net.cpp:122] Setting up fc6
I0412 12:56:07.865108  8032 net.cpp:129] Top shape: 32 4096 (131072)
I0412 12:56:07.865135  8032 net.cpp:137] Memory required for data: 263466752
I0412 12:56:07.865145  8032 layer_factory.hpp:77] Creating layer relu6
I0412 12:56:07.865154  8032 net.cpp:84] Creating Layer relu6
I0412 12:56:07.865159  8032 net.cpp:406] relu6 <- fc6
I0412 12:56:07.865166  8032 net.cpp:367] relu6 -> fc6 (in-place)
I0412 12:56:07.868643  8032 net.cpp:122] Setting up relu6
I0412 12:56:07.868654  8032 net.cpp:129] Top shape: 32 4096 (131072)
I0412 12:56:07.868659  8032 net.cpp:137] Memory required for data: 263991040
I0412 12:56:07.868662  8032 layer_factory.hpp:77] Creating layer drop6
I0412 12:56:07.868669  8032 net.cpp:84] Creating Layer drop6
I0412 12:56:07.868672  8032 net.cpp:406] drop6 <- fc6
I0412 12:56:07.868677  8032 net.cpp:367] drop6 -> fc6 (in-place)
I0412 12:56:07.868706  8032 net.cpp:122] Setting up drop6
I0412 12:56:07.868711  8032 net.cpp:129] Top shape: 32 4096 (131072)
I0412 12:56:07.868714  8032 net.cpp:137] Memory required for data: 264515328
I0412 12:56:07.868718  8032 layer_factory.hpp:77] Creating layer fc7
I0412 12:56:07.868726  8032 net.cpp:84] Creating Layer fc7
I0412 12:56:07.868728  8032 net.cpp:406] fc7 <- fc6
I0412 12:56:07.868733  8032 net.cpp:380] fc7 -> fc7
I0412 12:56:08.029983  8032 net.cpp:122] Setting up fc7
I0412 12:56:08.030006  8032 net.cpp:129] Top shape: 32 4096 (131072)
I0412 12:56:08.030010  8032 net.cpp:137] Memory required for data: 265039616
I0412 12:56:08.030019  8032 layer_factory.hpp:77] Creating layer relu7
I0412 12:56:08.030028  8032 net.cpp:84] Creating Layer relu7
I0412 12:56:08.030033  8032 net.cpp:406] relu7 <- fc7
I0412 12:56:08.030041  8032 net.cpp:367] relu7 -> fc7 (in-place)
I0412 12:56:08.030694  8032 net.cpp:122] Setting up relu7
I0412 12:56:08.030704  8032 net.cpp:129] Top shape: 32 4096 (131072)
I0412 12:56:08.030707  8032 net.cpp:137] Memory required for data: 265563904
I0412 12:56:08.030711  8032 layer_factory.hpp:77] Creating layer drop7
I0412 12:56:08.030719  8032 net.cpp:84] Creating Layer drop7
I0412 12:56:08.030722  8032 net.cpp:406] drop7 <- fc7
I0412 12:56:08.030728  8032 net.cpp:367] drop7 -> fc7 (in-place)
I0412 12:56:08.030753  8032 net.cpp:122] Setting up drop7
I0412 12:56:08.030759  8032 net.cpp:129] Top shape: 32 4096 (131072)
I0412 12:56:08.030762  8032 net.cpp:137] Memory required for data: 266088192
I0412 12:56:08.030766  8032 layer_factory.hpp:77] Creating layer fc8
I0412 12:56:08.030773  8032 net.cpp:84] Creating Layer fc8
I0412 12:56:08.030777  8032 net.cpp:406] fc8 <- fc7
I0412 12:56:08.030783  8032 net.cpp:380] fc8 -> fc8
I0412 12:56:08.038452  8032 net.cpp:122] Setting up fc8
I0412 12:56:08.038465  8032 net.cpp:129] Top shape: 32 196 (6272)
I0412 12:56:08.038467  8032 net.cpp:137] Memory required for data: 266113280
I0412 12:56:08.038480  8032 layer_factory.hpp:77] Creating layer fc8_fc8_0_split
I0412 12:56:08.038487  8032 net.cpp:84] Creating Layer fc8_fc8_0_split
I0412 12:56:08.038491  8032 net.cpp:406] fc8_fc8_0_split <- fc8
I0412 12:56:08.038498  8032 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_0
I0412 12:56:08.038506  8032 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_1
I0412 12:56:08.038538  8032 net.cpp:122] Setting up fc8_fc8_0_split
I0412 12:56:08.038544  8032 net.cpp:129] Top shape: 32 196 (6272)
I0412 12:56:08.038547  8032 net.cpp:129] Top shape: 32 196 (6272)
I0412 12:56:08.038550  8032 net.cpp:137] Memory required for data: 266163456
I0412 12:56:08.038554  8032 layer_factory.hpp:77] Creating layer accuracy
I0412 12:56:08.038561  8032 net.cpp:84] Creating Layer accuracy
I0412 12:56:08.038564  8032 net.cpp:406] accuracy <- fc8_fc8_0_split_0
I0412 12:56:08.038569  8032 net.cpp:406] accuracy <- label_val-data_1_split_0
I0412 12:56:08.038575  8032 net.cpp:380] accuracy -> accuracy
I0412 12:56:08.038583  8032 net.cpp:122] Setting up accuracy
I0412 12:56:08.038587  8032 net.cpp:129] Top shape: (1)
I0412 12:56:08.038589  8032 net.cpp:137] Memory required for data: 266163460
I0412 12:56:08.038594  8032 layer_factory.hpp:77] Creating layer loss
I0412 12:56:08.038599  8032 net.cpp:84] Creating Layer loss
I0412 12:56:08.038623  8032 net.cpp:406] loss <- fc8_fc8_0_split_1
I0412 12:56:08.038628  8032 net.cpp:406] loss <- label_val-data_1_split_1
I0412 12:56:08.038633  8032 net.cpp:380] loss -> loss
I0412 12:56:08.038641  8032 layer_factory.hpp:77] Creating layer loss
I0412 12:56:08.039081  8032 net.cpp:122] Setting up loss
I0412 12:56:08.039089  8032 net.cpp:129] Top shape: (1)
I0412 12:56:08.039093  8032 net.cpp:132]     with loss weight 1
I0412 12:56:08.039103  8032 net.cpp:137] Memory required for data: 266163464
I0412 12:56:08.039108  8032 net.cpp:198] loss needs backward computation.
I0412 12:56:08.039113  8032 net.cpp:200] accuracy does not need backward computation.
I0412 12:56:08.039117  8032 net.cpp:198] fc8_fc8_0_split needs backward computation.
I0412 12:56:08.039120  8032 net.cpp:198] fc8 needs backward computation.
I0412 12:56:08.039124  8032 net.cpp:198] drop7 needs backward computation.
I0412 12:56:08.039129  8032 net.cpp:198] relu7 needs backward computation.
I0412 12:56:08.039131  8032 net.cpp:198] fc7 needs backward computation.
I0412 12:56:08.039135  8032 net.cpp:198] drop6 needs backward computation.
I0412 12:56:08.039139  8032 net.cpp:198] relu6 needs backward computation.
I0412 12:56:08.039142  8032 net.cpp:198] fc6 needs backward computation.
I0412 12:56:08.039146  8032 net.cpp:198] pool5 needs backward computation.
I0412 12:56:08.039150  8032 net.cpp:198] relu5 needs backward computation.
I0412 12:56:08.039153  8032 net.cpp:198] conv5 needs backward computation.
I0412 12:56:08.039157  8032 net.cpp:198] relu4.2 needs backward computation.
I0412 12:56:08.039161  8032 net.cpp:198] conv4.2 needs backward computation.
I0412 12:56:08.039165  8032 net.cpp:198] relu4 needs backward computation.
I0412 12:56:08.039168  8032 net.cpp:198] conv4 needs backward computation.
I0412 12:56:08.039171  8032 net.cpp:198] relu3 needs backward computation.
I0412 12:56:08.039175  8032 net.cpp:198] conv3 needs backward computation.
I0412 12:56:08.039180  8032 net.cpp:198] pool2 needs backward computation.
I0412 12:56:08.039182  8032 net.cpp:198] norm2 needs backward computation.
I0412 12:56:08.039186  8032 net.cpp:198] relu2 needs backward computation.
I0412 12:56:08.039191  8032 net.cpp:198] conv2 needs backward computation.
I0412 12:56:08.039194  8032 net.cpp:198] pool1 needs backward computation.
I0412 12:56:08.039198  8032 net.cpp:198] norm1 needs backward computation.
I0412 12:56:08.039201  8032 net.cpp:198] relu1 needs backward computation.
I0412 12:56:08.039206  8032 net.cpp:198] conv1 needs backward computation.
I0412 12:56:08.039209  8032 net.cpp:200] label_val-data_1_split does not need backward computation.
I0412 12:56:08.039214  8032 net.cpp:200] val-data does not need backward computation.
I0412 12:56:08.039217  8032 net.cpp:242] This network produces output accuracy
I0412 12:56:08.039220  8032 net.cpp:242] This network produces output loss
I0412 12:56:08.039237  8032 net.cpp:255] Network initialization done.
I0412 12:56:08.039321  8032 solver.cpp:56] Solver scaffolding done.
I0412 12:56:08.039829  8032 caffe.cpp:248] Starting Optimization
I0412 12:56:08.039839  8032 solver.cpp:272] Solving
I0412 12:56:08.039842  8032 solver.cpp:273] Learning Rate Policy: exp
I0412 12:56:08.041286  8032 solver.cpp:330] Iteration 0, Testing net (#0)
I0412 12:56:08.041296  8032 net.cpp:676] Ignoring source layer train-data
I0412 12:56:08.127015  8032 blocking_queue.cpp:49] Waiting for data
I0412 12:56:12.307719  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 12:56:12.352257  8032 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 12:56:12.352303  8032 solver.cpp:397]     Test net output #1: loss = 5.2796 (* 1 = 5.2796 loss)
I0412 12:56:12.453359  8032 solver.cpp:218] Iteration 0 (-5.04467e-44 iter/s, 4.41333s/12 iters), loss = 5.28824
I0412 12:56:12.454871  8032 solver.cpp:237]     Train net output #0: loss = 5.28824 (* 1 = 5.28824 loss)
I0412 12:56:12.454892  8032 sgd_solver.cpp:105] Iteration 0, lr = 0.01
I0412 12:56:16.326056  8032 solver.cpp:218] Iteration 12 (3.09995 iter/s, 3.87103s/12 iters), loss = 5.29409
I0412 12:56:16.326139  8032 solver.cpp:237]     Train net output #0: loss = 5.29409 (* 1 = 5.29409 loss)
I0412 12:56:16.326150  8032 sgd_solver.cpp:105] Iteration 12, lr = 0.00997626
I0412 12:56:21.211719  8032 solver.cpp:218] Iteration 24 (2.45629 iter/s, 4.88542s/12 iters), loss = 5.28236
I0412 12:56:21.211767  8032 solver.cpp:237]     Train net output #0: loss = 5.28236 (* 1 = 5.28236 loss)
I0412 12:56:21.211776  8032 sgd_solver.cpp:105] Iteration 24, lr = 0.00995257
I0412 12:56:26.114434  8032 solver.cpp:218] Iteration 36 (2.44773 iter/s, 4.9025s/12 iters), loss = 5.29315
I0412 12:56:26.114482  8032 solver.cpp:237]     Train net output #0: loss = 5.29315 (* 1 = 5.29315 loss)
I0412 12:56:26.114493  8032 sgd_solver.cpp:105] Iteration 36, lr = 0.00992894
I0412 12:56:31.013247  8032 solver.cpp:218] Iteration 48 (2.44968 iter/s, 4.8986s/12 iters), loss = 5.31025
I0412 12:56:31.013288  8032 solver.cpp:237]     Train net output #0: loss = 5.31025 (* 1 = 5.31025 loss)
I0412 12:56:31.013298  8032 sgd_solver.cpp:105] Iteration 48, lr = 0.00990537
I0412 12:56:36.159621  8032 solver.cpp:218] Iteration 60 (2.33184 iter/s, 5.14615s/12 iters), loss = 5.29013
I0412 12:56:36.159802  8032 solver.cpp:237]     Train net output #0: loss = 5.29013 (* 1 = 5.29013 loss)
I0412 12:56:36.159816  8032 sgd_solver.cpp:105] Iteration 60, lr = 0.00988185
I0412 12:56:41.149011  8032 solver.cpp:218] Iteration 72 (2.40527 iter/s, 4.98904s/12 iters), loss = 5.30697
I0412 12:56:41.149056  8032 solver.cpp:237]     Train net output #0: loss = 5.30697 (* 1 = 5.30697 loss)
I0412 12:56:41.149068  8032 sgd_solver.cpp:105] Iteration 72, lr = 0.00985839
I0412 12:56:46.140774  8032 solver.cpp:218] Iteration 84 (2.40407 iter/s, 4.99154s/12 iters), loss = 5.29812
I0412 12:56:46.140818  8032 solver.cpp:237]     Train net output #0: loss = 5.29812 (* 1 = 5.29812 loss)
I0412 12:56:46.140827  8032 sgd_solver.cpp:105] Iteration 84, lr = 0.00983498
I0412 12:56:51.177175  8032 solver.cpp:218] Iteration 96 (2.38276 iter/s, 5.03618s/12 iters), loss = 5.30993
I0412 12:56:51.177230  8032 solver.cpp:237]     Train net output #0: loss = 5.30993 (* 1 = 5.30993 loss)
I0412 12:56:51.177243  8032 sgd_solver.cpp:105] Iteration 96, lr = 0.00981163
I0412 12:56:52.896311  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 12:56:53.239117  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_102.caffemodel
I0412 12:56:57.988775  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_102.solverstate
I0412 12:57:02.018167  8032 solver.cpp:330] Iteration 102, Testing net (#0)
I0412 12:57:02.018195  8032 net.cpp:676] Ignoring source layer train-data
I0412 12:57:06.583135  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 12:57:06.659936  8032 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 12:57:06.659987  8032 solver.cpp:397]     Test net output #1: loss = 5.29047 (* 1 = 5.29047 loss)
I0412 12:57:08.632896  8032 solver.cpp:218] Iteration 108 (0.687478 iter/s, 17.4551s/12 iters), loss = 5.31307
I0412 12:57:08.632953  8032 solver.cpp:237]     Train net output #0: loss = 5.31307 (* 1 = 5.31307 loss)
I0412 12:57:08.632966  8032 sgd_solver.cpp:105] Iteration 108, lr = 0.00978834
I0412 12:57:13.857015  8032 solver.cpp:218] Iteration 120 (2.29714 iter/s, 5.22389s/12 iters), loss = 5.27878
I0412 12:57:13.857061  8032 solver.cpp:237]     Train net output #0: loss = 5.27878 (* 1 = 5.27878 loss)
I0412 12:57:13.857071  8032 sgd_solver.cpp:105] Iteration 120, lr = 0.0097651
I0412 12:57:18.802996  8032 solver.cpp:218] Iteration 132 (2.42632 iter/s, 4.94577s/12 iters), loss = 5.24911
I0412 12:57:18.803035  8032 solver.cpp:237]     Train net output #0: loss = 5.24911 (* 1 = 5.24911 loss)
I0412 12:57:18.803043  8032 sgd_solver.cpp:105] Iteration 132, lr = 0.00974192
I0412 12:57:23.927465  8032 solver.cpp:218] Iteration 144 (2.34181 iter/s, 5.12425s/12 iters), loss = 5.32002
I0412 12:57:23.927511  8032 solver.cpp:237]     Train net output #0: loss = 5.32002 (* 1 = 5.32002 loss)
I0412 12:57:23.927520  8032 sgd_solver.cpp:105] Iteration 144, lr = 0.00971879
I0412 12:57:28.955991  8032 solver.cpp:218] Iteration 156 (2.38649 iter/s, 5.02831s/12 iters), loss = 5.26659
I0412 12:57:28.956032  8032 solver.cpp:237]     Train net output #0: loss = 5.26659 (* 1 = 5.26659 loss)
I0412 12:57:28.956041  8032 sgd_solver.cpp:105] Iteration 156, lr = 0.00969571
I0412 12:57:34.000895  8032 solver.cpp:218] Iteration 168 (2.37874 iter/s, 5.04468s/12 iters), loss = 5.27264
I0412 12:57:34.000941  8032 solver.cpp:237]     Train net output #0: loss = 5.27264 (* 1 = 5.27264 loss)
I0412 12:57:34.000952  8032 sgd_solver.cpp:105] Iteration 168, lr = 0.00967269
I0412 12:57:39.101243  8032 solver.cpp:218] Iteration 180 (2.35288 iter/s, 5.10012s/12 iters), loss = 5.2831
I0412 12:57:39.101411  8032 solver.cpp:237]     Train net output #0: loss = 5.2831 (* 1 = 5.2831 loss)
I0412 12:57:39.101425  8032 sgd_solver.cpp:105] Iteration 180, lr = 0.00964973
I0412 12:57:44.216595  8032 solver.cpp:218] Iteration 192 (2.34603 iter/s, 5.11501s/12 iters), loss = 5.29391
I0412 12:57:44.216645  8032 solver.cpp:237]     Train net output #0: loss = 5.29391 (* 1 = 5.29391 loss)
I0412 12:57:44.216656  8032 sgd_solver.cpp:105] Iteration 192, lr = 0.00962682
I0412 12:57:47.997571  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 12:57:48.682423  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_204.caffemodel
I0412 12:57:53.357458  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_204.solverstate
I0412 12:58:00.935190  8032 solver.cpp:330] Iteration 204, Testing net (#0)
I0412 12:58:00.935216  8032 net.cpp:676] Ignoring source layer train-data
I0412 12:58:05.305629  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 12:58:05.428984  8032 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 12:58:05.429018  8032 solver.cpp:397]     Test net output #1: loss = 5.28821 (* 1 = 5.28821 loss)
I0412 12:58:05.516860  8032 solver.cpp:218] Iteration 204 (0.563393 iter/s, 21.2995s/12 iters), loss = 5.27231
I0412 12:58:05.516906  8032 solver.cpp:237]     Train net output #0: loss = 5.27231 (* 1 = 5.27231 loss)
I0412 12:58:05.516916  8032 sgd_solver.cpp:105] Iteration 204, lr = 0.00960396
I0412 12:58:09.919549  8032 solver.cpp:218] Iteration 216 (2.72574 iter/s, 4.40248s/12 iters), loss = 5.28874
I0412 12:58:09.925128  8032 solver.cpp:237]     Train net output #0: loss = 5.28874 (* 1 = 5.28874 loss)
I0412 12:58:09.925148  8032 sgd_solver.cpp:105] Iteration 216, lr = 0.00958116
I0412 12:58:15.358954  8032 solver.cpp:218] Iteration 228 (2.20846 iter/s, 5.43366s/12 iters), loss = 5.28309
I0412 12:58:15.358994  8032 solver.cpp:237]     Train net output #0: loss = 5.28309 (* 1 = 5.28309 loss)
I0412 12:58:15.359002  8032 sgd_solver.cpp:105] Iteration 228, lr = 0.00955841
I0412 12:58:20.184244  8032 solver.cpp:218] Iteration 240 (2.487 iter/s, 4.82508s/12 iters), loss = 5.29764
I0412 12:58:20.184286  8032 solver.cpp:237]     Train net output #0: loss = 5.29764 (* 1 = 5.29764 loss)
I0412 12:58:20.184294  8032 sgd_solver.cpp:105] Iteration 240, lr = 0.00953572
I0412 12:58:25.611981  8032 solver.cpp:218] Iteration 252 (2.21096 iter/s, 5.42751s/12 iters), loss = 5.28075
I0412 12:58:25.612028  8032 solver.cpp:237]     Train net output #0: loss = 5.28075 (* 1 = 5.28075 loss)
I0412 12:58:25.612036  8032 sgd_solver.cpp:105] Iteration 252, lr = 0.00951308
I0412 12:58:30.691871  8032 solver.cpp:218] Iteration 264 (2.36236 iter/s, 5.07966s/12 iters), loss = 5.27737
I0412 12:58:30.691929  8032 solver.cpp:237]     Train net output #0: loss = 5.27737 (* 1 = 5.27737 loss)
I0412 12:58:30.691941  8032 sgd_solver.cpp:105] Iteration 264, lr = 0.00949049
I0412 12:58:35.761399  8032 solver.cpp:218] Iteration 276 (2.36719 iter/s, 5.06929s/12 iters), loss = 5.29669
I0412 12:58:35.761454  8032 solver.cpp:237]     Train net output #0: loss = 5.29669 (* 1 = 5.29669 loss)
I0412 12:58:35.761466  8032 sgd_solver.cpp:105] Iteration 276, lr = 0.00946796
I0412 12:58:40.730834  8032 solver.cpp:218] Iteration 288 (2.41487 iter/s, 4.96921s/12 iters), loss = 5.29286
I0412 12:58:40.733122  8032 solver.cpp:237]     Train net output #0: loss = 5.29286 (* 1 = 5.29286 loss)
I0412 12:58:40.733132  8032 sgd_solver.cpp:105] Iteration 288, lr = 0.00944548
I0412 12:58:45.870052  8032 solver.cpp:218] Iteration 300 (2.3361 iter/s, 5.13676s/12 iters), loss = 5.28804
I0412 12:58:45.870095  8032 solver.cpp:237]     Train net output #0: loss = 5.28804 (* 1 = 5.28804 loss)
I0412 12:58:45.870105  8032 sgd_solver.cpp:105] Iteration 300, lr = 0.00942305
I0412 12:58:46.958283  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 12:58:48.034027  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_306.caffemodel
I0412 12:58:50.988782  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_306.solverstate
I0412 12:58:53.290102  8032 solver.cpp:330] Iteration 306, Testing net (#0)
I0412 12:58:53.290123  8032 net.cpp:676] Ignoring source layer train-data
I0412 12:58:57.629873  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 12:58:57.787333  8032 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0412 12:58:57.787370  8032 solver.cpp:397]     Test net output #1: loss = 5.28416 (* 1 = 5.28416 loss)
I0412 12:58:59.707466  8032 solver.cpp:218] Iteration 312 (0.867245 iter/s, 13.8369s/12 iters), loss = 5.28155
I0412 12:58:59.707520  8032 solver.cpp:237]     Train net output #0: loss = 5.28155 (* 1 = 5.28155 loss)
I0412 12:58:59.707532  8032 sgd_solver.cpp:105] Iteration 312, lr = 0.00940068
I0412 12:59:04.715936  8032 solver.cpp:218] Iteration 324 (2.39605 iter/s, 5.00825s/12 iters), loss = 5.2578
I0412 12:59:04.715984  8032 solver.cpp:237]     Train net output #0: loss = 5.2578 (* 1 = 5.2578 loss)
I0412 12:59:04.715996  8032 sgd_solver.cpp:105] Iteration 324, lr = 0.00937836
I0412 12:59:09.689811  8032 solver.cpp:218] Iteration 336 (2.41271 iter/s, 4.97366s/12 iters), loss = 5.27012
I0412 12:59:09.689867  8032 solver.cpp:237]     Train net output #0: loss = 5.27012 (* 1 = 5.27012 loss)
I0412 12:59:09.689879  8032 sgd_solver.cpp:105] Iteration 336, lr = 0.0093561
I0412 12:59:14.694465  8032 solver.cpp:218] Iteration 348 (2.39787 iter/s, 5.00443s/12 iters), loss = 5.25753
I0412 12:59:14.696569  8032 solver.cpp:237]     Train net output #0: loss = 5.25753 (* 1 = 5.25753 loss)
I0412 12:59:14.696580  8032 sgd_solver.cpp:105] Iteration 348, lr = 0.00933388
I0412 12:59:19.901705  8032 solver.cpp:218] Iteration 360 (2.30549 iter/s, 5.20496s/12 iters), loss = 5.27633
I0412 12:59:19.901758  8032 solver.cpp:237]     Train net output #0: loss = 5.27633 (* 1 = 5.27633 loss)
I0412 12:59:19.901770  8032 sgd_solver.cpp:105] Iteration 360, lr = 0.00931172
I0412 12:59:24.934355  8032 solver.cpp:218] Iteration 372 (2.38454 iter/s, 5.03242s/12 iters), loss = 5.24685
I0412 12:59:24.934409  8032 solver.cpp:237]     Train net output #0: loss = 5.24685 (* 1 = 5.24685 loss)
I0412 12:59:24.934422  8032 sgd_solver.cpp:105] Iteration 372, lr = 0.00928961
I0412 12:59:29.959259  8032 solver.cpp:218] Iteration 384 (2.38821 iter/s, 5.02468s/12 iters), loss = 5.22751
I0412 12:59:29.959311  8032 solver.cpp:237]     Train net output #0: loss = 5.22751 (* 1 = 5.22751 loss)
I0412 12:59:29.959321  8032 sgd_solver.cpp:105] Iteration 384, lr = 0.00926756
I0412 12:59:35.032203  8032 solver.cpp:218] Iteration 396 (2.36559 iter/s, 5.07273s/12 iters), loss = 5.12188
I0412 12:59:35.032246  8032 solver.cpp:237]     Train net output #0: loss = 5.12188 (* 1 = 5.12188 loss)
I0412 12:59:35.032258  8032 sgd_solver.cpp:105] Iteration 396, lr = 0.00924556
I0412 12:59:38.093830  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 12:59:39.504258  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_408.caffemodel
I0412 12:59:43.371423  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_408.solverstate
I0412 12:59:47.249428  8032 solver.cpp:330] Iteration 408, Testing net (#0)
I0412 12:59:47.249552  8032 net.cpp:676] Ignoring source layer train-data
I0412 12:59:51.628481  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 12:59:51.833102  8032 solver.cpp:397]     Test net output #0: accuracy = 0.00735294
I0412 12:59:51.833135  8032 solver.cpp:397]     Test net output #1: loss = 5.19702 (* 1 = 5.19702 loss)
I0412 12:59:51.921051  8032 solver.cpp:218] Iteration 408 (0.710552 iter/s, 16.8883s/12 iters), loss = 5.25369
I0412 12:59:51.921097  8032 solver.cpp:237]     Train net output #0: loss = 5.25369 (* 1 = 5.25369 loss)
I0412 12:59:51.921106  8032 sgd_solver.cpp:105] Iteration 408, lr = 0.00922361
I0412 12:59:56.765784  8032 solver.cpp:218] Iteration 420 (2.47703 iter/s, 4.84452s/12 iters), loss = 5.27075
I0412 12:59:56.765832  8032 solver.cpp:237]     Train net output #0: loss = 5.27075 (* 1 = 5.27075 loss)
I0412 12:59:56.765843  8032 sgd_solver.cpp:105] Iteration 420, lr = 0.00920171
I0412 13:00:01.719800  8032 solver.cpp:218] Iteration 432 (2.42238 iter/s, 4.9538s/12 iters), loss = 5.18013
I0412 13:00:01.719853  8032 solver.cpp:237]     Train net output #0: loss = 5.18013 (* 1 = 5.18013 loss)
I0412 13:00:01.719866  8032 sgd_solver.cpp:105] Iteration 432, lr = 0.00917986
I0412 13:00:06.697278  8032 solver.cpp:218] Iteration 444 (2.41097 iter/s, 4.97726s/12 iters), loss = 5.1706
I0412 13:00:06.697327  8032 solver.cpp:237]     Train net output #0: loss = 5.1706 (* 1 = 5.1706 loss)
I0412 13:00:06.697340  8032 sgd_solver.cpp:105] Iteration 444, lr = 0.00915807
I0412 13:00:11.705672  8032 solver.cpp:218] Iteration 456 (2.39608 iter/s, 5.00817s/12 iters), loss = 5.22719
I0412 13:00:11.705729  8032 solver.cpp:237]     Train net output #0: loss = 5.22719 (* 1 = 5.22719 loss)
I0412 13:00:11.705742  8032 sgd_solver.cpp:105] Iteration 456, lr = 0.00913632
I0412 13:00:16.722728  8032 solver.cpp:218] Iteration 468 (2.39195 iter/s, 5.01684s/12 iters), loss = 5.17774
I0412 13:00:16.722766  8032 solver.cpp:237]     Train net output #0: loss = 5.17774 (* 1 = 5.17774 loss)
I0412 13:00:16.722775  8032 sgd_solver.cpp:105] Iteration 468, lr = 0.00911463
I0412 13:00:21.729199  8032 solver.cpp:218] Iteration 480 (2.397 iter/s, 5.00626s/12 iters), loss = 5.14215
I0412 13:00:21.729318  8032 solver.cpp:237]     Train net output #0: loss = 5.14215 (* 1 = 5.14215 loss)
I0412 13:00:21.729331  8032 sgd_solver.cpp:105] Iteration 480, lr = 0.00909299
I0412 13:00:26.685840  8032 solver.cpp:218] Iteration 492 (2.42113 iter/s, 4.95635s/12 iters), loss = 5.17009
I0412 13:00:26.685899  8032 solver.cpp:237]     Train net output #0: loss = 5.17009 (* 1 = 5.17009 loss)
I0412 13:00:26.685910  8032 sgd_solver.cpp:105] Iteration 492, lr = 0.0090714
I0412 13:00:31.663373  8032 solver.cpp:218] Iteration 504 (2.41094 iter/s, 4.9773s/12 iters), loss = 5.19825
I0412 13:00:31.663431  8032 solver.cpp:237]     Train net output #0: loss = 5.19825 (* 1 = 5.19825 loss)
I0412 13:00:31.663444  8032 sgd_solver.cpp:105] Iteration 504, lr = 0.00904986
I0412 13:00:31.933831  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:00:33.655474  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_510.caffemodel
I0412 13:00:39.373415  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_510.solverstate
I0412 13:00:43.557799  8032 solver.cpp:330] Iteration 510, Testing net (#0)
I0412 13:00:43.557826  8032 net.cpp:676] Ignoring source layer train-data
I0412 13:00:47.973470  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:00:48.210806  8032 solver.cpp:397]     Test net output #0: accuracy = 0.00857843
I0412 13:00:48.210857  8032 solver.cpp:397]     Test net output #1: loss = 5.15945 (* 1 = 5.15945 loss)
I0412 13:00:50.194761  8032 solver.cpp:218] Iteration 516 (0.647572 iter/s, 18.5307s/12 iters), loss = 5.14721
I0412 13:00:50.194818  8032 solver.cpp:237]     Train net output #0: loss = 5.14721 (* 1 = 5.14721 loss)
I0412 13:00:50.194829  8032 sgd_solver.cpp:105] Iteration 516, lr = 0.00902838
I0412 13:00:55.116175  8032 solver.cpp:218] Iteration 528 (2.43843 iter/s, 4.92119s/12 iters), loss = 5.19665
I0412 13:00:55.116582  8032 solver.cpp:237]     Train net output #0: loss = 5.19665 (* 1 = 5.19665 loss)
I0412 13:00:55.116595  8032 sgd_solver.cpp:105] Iteration 528, lr = 0.00900694
I0412 13:01:00.119969  8032 solver.cpp:218] Iteration 540 (2.39846 iter/s, 5.00322s/12 iters), loss = 5.16401
I0412 13:01:00.120015  8032 solver.cpp:237]     Train net output #0: loss = 5.16401 (* 1 = 5.16401 loss)
I0412 13:01:00.120024  8032 sgd_solver.cpp:105] Iteration 540, lr = 0.00898556
I0412 13:01:05.084097  8032 solver.cpp:218] Iteration 552 (2.41745 iter/s, 4.96392s/12 iters), loss = 5.12106
I0412 13:01:05.084143  8032 solver.cpp:237]     Train net output #0: loss = 5.12106 (* 1 = 5.12106 loss)
I0412 13:01:05.084152  8032 sgd_solver.cpp:105] Iteration 552, lr = 0.00896423
I0412 13:01:10.141256  8032 solver.cpp:218] Iteration 564 (2.37297 iter/s, 5.05694s/12 iters), loss = 5.17253
I0412 13:01:10.141307  8032 solver.cpp:237]     Train net output #0: loss = 5.17253 (* 1 = 5.17253 loss)
I0412 13:01:10.141319  8032 sgd_solver.cpp:105] Iteration 564, lr = 0.00894294
I0412 13:01:15.149981  8032 solver.cpp:218] Iteration 576 (2.39593 iter/s, 5.0085s/12 iters), loss = 5.09646
I0412 13:01:15.150020  8032 solver.cpp:237]     Train net output #0: loss = 5.09646 (* 1 = 5.09646 loss)
I0412 13:01:15.150030  8032 sgd_solver.cpp:105] Iteration 576, lr = 0.00892171
I0412 13:01:20.262459  8032 solver.cpp:218] Iteration 588 (2.3473 iter/s, 5.11227s/12 iters), loss = 5.0929
I0412 13:01:20.262508  8032 solver.cpp:237]     Train net output #0: loss = 5.0929 (* 1 = 5.0929 loss)
I0412 13:01:20.262517  8032 sgd_solver.cpp:105] Iteration 588, lr = 0.00890053
I0412 13:01:25.205271  8032 solver.cpp:218] Iteration 600 (2.42787 iter/s, 4.9426s/12 iters), loss = 5.15156
I0412 13:01:25.205379  8032 solver.cpp:237]     Train net output #0: loss = 5.15156 (* 1 = 5.15156 loss)
I0412 13:01:25.205390  8032 sgd_solver.cpp:105] Iteration 600, lr = 0.0088794
I0412 13:01:27.579768  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:01:29.696189  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_612.caffemodel
I0412 13:01:35.291451  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_612.solverstate
I0412 13:01:38.564833  8032 solver.cpp:330] Iteration 612, Testing net (#0)
I0412 13:01:38.564862  8032 net.cpp:676] Ignoring source layer train-data
I0412 13:01:42.871809  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:01:43.156450  8032 solver.cpp:397]     Test net output #0: accuracy = 0.00980392
I0412 13:01:43.156499  8032 solver.cpp:397]     Test net output #1: loss = 5.1112 (* 1 = 5.1112 loss)
I0412 13:01:43.244493  8032 solver.cpp:218] Iteration 612 (0.665242 iter/s, 18.0385s/12 iters), loss = 5.10494
I0412 13:01:43.244542  8032 solver.cpp:237]     Train net output #0: loss = 5.10494 (* 1 = 5.10494 loss)
I0412 13:01:43.244554  8032 sgd_solver.cpp:105] Iteration 612, lr = 0.00885831
I0412 13:01:47.524574  8032 solver.cpp:218] Iteration 624 (2.80381 iter/s, 4.27989s/12 iters), loss = 5.14054
I0412 13:01:47.524616  8032 solver.cpp:237]     Train net output #0: loss = 5.14054 (* 1 = 5.14054 loss)
I0412 13:01:47.524623  8032 sgd_solver.cpp:105] Iteration 624, lr = 0.00883728
I0412 13:01:52.483906  8032 solver.cpp:218] Iteration 636 (2.41978 iter/s, 4.95912s/12 iters), loss = 4.99235
I0412 13:01:52.483960  8032 solver.cpp:237]     Train net output #0: loss = 4.99235 (* 1 = 4.99235 loss)
I0412 13:01:52.483973  8032 sgd_solver.cpp:105] Iteration 636, lr = 0.0088163
I0412 13:01:57.470811  8032 solver.cpp:218] Iteration 648 (2.40641 iter/s, 4.98669s/12 iters), loss = 5.1953
I0412 13:01:57.470919  8032 solver.cpp:237]     Train net output #0: loss = 5.1953 (* 1 = 5.1953 loss)
I0412 13:01:57.470932  8032 sgd_solver.cpp:105] Iteration 648, lr = 0.00879537
I0412 13:02:02.467705  8032 solver.cpp:218] Iteration 660 (2.40163 iter/s, 4.99662s/12 iters), loss = 5.11587
I0412 13:02:02.467758  8032 solver.cpp:237]     Train net output #0: loss = 5.11587 (* 1 = 5.11587 loss)
I0412 13:02:02.467772  8032 sgd_solver.cpp:105] Iteration 660, lr = 0.00877449
I0412 13:02:07.387569  8032 solver.cpp:218] Iteration 672 (2.4392 iter/s, 4.91965s/12 iters), loss = 5.07346
I0412 13:02:07.387629  8032 solver.cpp:237]     Train net output #0: loss = 5.07346 (* 1 = 5.07346 loss)
I0412 13:02:07.387643  8032 sgd_solver.cpp:105] Iteration 672, lr = 0.00875366
I0412 13:02:12.310245  8032 solver.cpp:218] Iteration 684 (2.43781 iter/s, 4.92245s/12 iters), loss = 4.95721
I0412 13:02:12.310302  8032 solver.cpp:237]     Train net output #0: loss = 4.95721 (* 1 = 4.95721 loss)
I0412 13:02:12.310313  8032 sgd_solver.cpp:105] Iteration 684, lr = 0.00873287
I0412 13:02:13.083784  8032 blocking_queue.cpp:49] Waiting for data
I0412 13:02:17.408983  8032 solver.cpp:218] Iteration 696 (2.35363 iter/s, 5.09851s/12 iters), loss = 5.03226
I0412 13:02:17.409034  8032 solver.cpp:237]     Train net output #0: loss = 5.03226 (* 1 = 5.03226 loss)
I0412 13:02:17.409044  8032 sgd_solver.cpp:105] Iteration 696, lr = 0.00871214
I0412 13:02:22.455507  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:02:22.857208  8032 solver.cpp:218] Iteration 708 (2.20265 iter/s, 5.44799s/12 iters), loss = 5.15022
I0412 13:02:22.857264  8032 solver.cpp:237]     Train net output #0: loss = 5.15022 (* 1 = 5.15022 loss)
I0412 13:02:22.857276  8032 sgd_solver.cpp:105] Iteration 708, lr = 0.00869145
I0412 13:02:24.969043  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_714.caffemodel
I0412 13:02:33.207159  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_714.solverstate
I0412 13:02:36.043423  8032 solver.cpp:330] Iteration 714, Testing net (#0)
I0412 13:02:36.043452  8032 net.cpp:676] Ignoring source layer train-data
I0412 13:02:40.426287  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:02:40.745467  8032 solver.cpp:397]     Test net output #0: accuracy = 0.0128676
I0412 13:02:40.745517  8032 solver.cpp:397]     Test net output #1: loss = 5.07169 (* 1 = 5.07169 loss)
I0412 13:02:42.772181  8032 solver.cpp:218] Iteration 720 (0.602582 iter/s, 19.9143s/12 iters), loss = 5.15602
I0412 13:02:42.772224  8032 solver.cpp:237]     Train net output #0: loss = 5.15602 (* 1 = 5.15602 loss)
I0412 13:02:42.772233  8032 sgd_solver.cpp:105] Iteration 720, lr = 0.00867082
I0412 13:02:47.735023  8032 solver.cpp:218] Iteration 732 (2.41807 iter/s, 4.96263s/12 iters), loss = 4.96896
I0412 13:02:47.735074  8032 solver.cpp:237]     Train net output #0: loss = 4.96896 (* 1 = 4.96896 loss)
I0412 13:02:47.735086  8032 sgd_solver.cpp:105] Iteration 732, lr = 0.00865023
I0412 13:02:52.707341  8032 solver.cpp:218] Iteration 744 (2.41347 iter/s, 4.9721s/12 iters), loss = 5.01426
I0412 13:02:52.707392  8032 solver.cpp:237]     Train net output #0: loss = 5.01426 (* 1 = 5.01426 loss)
I0412 13:02:52.707404  8032 sgd_solver.cpp:105] Iteration 744, lr = 0.0086297
I0412 13:02:57.768354  8032 solver.cpp:218] Iteration 756 (2.37117 iter/s, 5.0608s/12 iters), loss = 5.07753
I0412 13:02:57.768393  8032 solver.cpp:237]     Train net output #0: loss = 5.07753 (* 1 = 5.07753 loss)
I0412 13:02:57.768402  8032 sgd_solver.cpp:105] Iteration 756, lr = 0.00860921
I0412 13:03:03.087517  8032 solver.cpp:218] Iteration 768 (2.25609 iter/s, 5.31895s/12 iters), loss = 5.14375
I0412 13:03:03.087560  8032 solver.cpp:237]     Train net output #0: loss = 5.14375 (* 1 = 5.14375 loss)
I0412 13:03:03.087569  8032 sgd_solver.cpp:105] Iteration 768, lr = 0.00858877
I0412 13:03:08.212044  8032 solver.cpp:218] Iteration 780 (2.34178 iter/s, 5.12431s/12 iters), loss = 5.0934
I0412 13:03:08.212193  8032 solver.cpp:237]     Train net output #0: loss = 5.0934 (* 1 = 5.0934 loss)
I0412 13:03:08.212208  8032 sgd_solver.cpp:105] Iteration 780, lr = 0.00856838
I0412 13:03:13.131500  8032 solver.cpp:218] Iteration 792 (2.43945 iter/s, 4.91915s/12 iters), loss = 4.95669
I0412 13:03:13.131558  8032 solver.cpp:237]     Train net output #0: loss = 4.95669 (* 1 = 4.95669 loss)
I0412 13:03:13.131570  8032 sgd_solver.cpp:105] Iteration 792, lr = 0.00854803
I0412 13:03:18.122802  8032 solver.cpp:218] Iteration 804 (2.40429 iter/s, 4.99108s/12 iters), loss = 5.08503
I0412 13:03:18.122843  8032 solver.cpp:237]     Train net output #0: loss = 5.08503 (* 1 = 5.08503 loss)
I0412 13:03:18.122850  8032 sgd_solver.cpp:105] Iteration 804, lr = 0.00852774
I0412 13:03:19.886520  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:03:22.688232  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_816.caffemodel
I0412 13:03:29.395213  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_816.solverstate
I0412 13:03:33.959380  8032 solver.cpp:330] Iteration 816, Testing net (#0)
I0412 13:03:33.959403  8032 net.cpp:676] Ignoring source layer train-data
I0412 13:03:38.211899  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:03:38.566380  8032 solver.cpp:397]     Test net output #0: accuracy = 0.0165441
I0412 13:03:38.566485  8032 solver.cpp:397]     Test net output #1: loss = 5.0328 (* 1 = 5.0328 loss)
I0412 13:03:38.651634  8032 solver.cpp:218] Iteration 816 (0.584563 iter/s, 20.5281s/12 iters), loss = 5.06284
I0412 13:03:38.651686  8032 solver.cpp:237]     Train net output #0: loss = 5.06284 (* 1 = 5.06284 loss)
I0412 13:03:38.651698  8032 sgd_solver.cpp:105] Iteration 816, lr = 0.00850749
I0412 13:03:43.005252  8032 solver.cpp:218] Iteration 828 (2.75645 iter/s, 4.35342s/12 iters), loss = 5.13101
I0412 13:03:43.005300  8032 solver.cpp:237]     Train net output #0: loss = 5.13101 (* 1 = 5.13101 loss)
I0412 13:03:43.005312  8032 sgd_solver.cpp:105] Iteration 828, lr = 0.00848729
I0412 13:03:48.189924  8032 solver.cpp:218] Iteration 840 (2.31461 iter/s, 5.18445s/12 iters), loss = 4.98066
I0412 13:03:48.189970  8032 solver.cpp:237]     Train net output #0: loss = 4.98066 (* 1 = 4.98066 loss)
I0412 13:03:48.189980  8032 sgd_solver.cpp:105] Iteration 840, lr = 0.00846714
I0412 13:03:53.258026  8032 solver.cpp:218] Iteration 852 (2.36788 iter/s, 5.06783s/12 iters), loss = 4.9437
I0412 13:03:53.258116  8032 solver.cpp:237]     Train net output #0: loss = 4.9437 (* 1 = 4.9437 loss)
I0412 13:03:53.258143  8032 sgd_solver.cpp:105] Iteration 852, lr = 0.00844704
I0412 13:03:58.358598  8032 solver.cpp:218] Iteration 864 (2.35279 iter/s, 5.10032s/12 iters), loss = 4.99605
I0412 13:03:58.358637  8032 solver.cpp:237]     Train net output #0: loss = 4.99605 (* 1 = 4.99605 loss)
I0412 13:03:58.358645  8032 sgd_solver.cpp:105] Iteration 864, lr = 0.00842698
I0412 13:04:03.293066  8032 solver.cpp:218] Iteration 876 (2.43197 iter/s, 4.93426s/12 iters), loss = 4.98043
I0412 13:04:03.293112  8032 solver.cpp:237]     Train net output #0: loss = 4.98043 (* 1 = 4.98043 loss)
I0412 13:04:03.293120  8032 sgd_solver.cpp:105] Iteration 876, lr = 0.00840698
I0412 13:04:08.372081  8032 solver.cpp:218] Iteration 888 (2.36276 iter/s, 5.0788s/12 iters), loss = 4.88912
I0412 13:04:08.372133  8032 solver.cpp:237]     Train net output #0: loss = 4.88912 (* 1 = 4.88912 loss)
I0412 13:04:08.372143  8032 sgd_solver.cpp:105] Iteration 888, lr = 0.00838702
I0412 13:04:13.630178  8032 solver.cpp:218] Iteration 900 (2.28229 iter/s, 5.25787s/12 iters), loss = 5.01801
I0412 13:04:13.630260  8032 solver.cpp:237]     Train net output #0: loss = 5.01801 (* 1 = 5.01801 loss)
I0412 13:04:13.630280  8032 sgd_solver.cpp:105] Iteration 900, lr = 0.0083671
I0412 13:04:17.794450  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:04:18.892385  8032 solver.cpp:218] Iteration 912 (2.28052 iter/s, 5.26195s/12 iters), loss = 4.94136
I0412 13:04:18.892428  8032 solver.cpp:237]     Train net output #0: loss = 4.94136 (* 1 = 4.94136 loss)
I0412 13:04:18.892439  8032 sgd_solver.cpp:105] Iteration 912, lr = 0.00834724
I0412 13:04:20.912603  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_918.caffemodel
I0412 13:04:28.013753  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_918.solverstate
I0412 13:04:32.088620  8032 solver.cpp:330] Iteration 918, Testing net (#0)
I0412 13:04:32.088647  8032 net.cpp:676] Ignoring source layer train-data
I0412 13:04:36.662223  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:04:37.062839  8032 solver.cpp:397]     Test net output #0: accuracy = 0.0220588
I0412 13:04:37.062876  8032 solver.cpp:397]     Test net output #1: loss = 4.99736 (* 1 = 4.99736 loss)
I0412 13:04:38.974347  8032 solver.cpp:218] Iteration 924 (0.597571 iter/s, 20.0813s/12 iters), loss = 5.03162
I0412 13:04:38.974393  8032 solver.cpp:237]     Train net output #0: loss = 5.03162 (* 1 = 5.03162 loss)
I0412 13:04:38.974401  8032 sgd_solver.cpp:105] Iteration 924, lr = 0.00832742
I0412 13:04:44.416997  8032 solver.cpp:218] Iteration 936 (2.2049 iter/s, 5.44242s/12 iters), loss = 5.01727
I0412 13:04:44.417111  8032 solver.cpp:237]     Train net output #0: loss = 5.01727 (* 1 = 5.01727 loss)
I0412 13:04:44.417124  8032 sgd_solver.cpp:105] Iteration 936, lr = 0.00830765
I0412 13:04:49.591997  8032 solver.cpp:218] Iteration 948 (2.31897 iter/s, 5.17472s/12 iters), loss = 4.95314
I0412 13:04:49.592047  8032 solver.cpp:237]     Train net output #0: loss = 4.95314 (* 1 = 4.95314 loss)
I0412 13:04:49.592058  8032 sgd_solver.cpp:105] Iteration 948, lr = 0.00828793
I0412 13:04:54.642838  8032 solver.cpp:218] Iteration 960 (2.37594 iter/s, 5.05062s/12 iters), loss = 4.89703
I0412 13:04:54.642884  8032 solver.cpp:237]     Train net output #0: loss = 4.89703 (* 1 = 4.89703 loss)
I0412 13:04:54.642895  8032 sgd_solver.cpp:105] Iteration 960, lr = 0.00826825
I0412 13:04:59.666731  8032 solver.cpp:218] Iteration 972 (2.38868 iter/s, 5.02369s/12 iters), loss = 5.00577
I0412 13:04:59.666774  8032 solver.cpp:237]     Train net output #0: loss = 5.00577 (* 1 = 5.00577 loss)
I0412 13:04:59.666785  8032 sgd_solver.cpp:105] Iteration 972, lr = 0.00824862
I0412 13:05:04.712164  8032 solver.cpp:218] Iteration 984 (2.37849 iter/s, 5.04522s/12 iters), loss = 4.91794
I0412 13:05:04.712214  8032 solver.cpp:237]     Train net output #0: loss = 4.91794 (* 1 = 4.91794 loss)
I0412 13:05:04.712225  8032 sgd_solver.cpp:105] Iteration 984, lr = 0.00822903
I0412 13:05:09.671134  8032 solver.cpp:218] Iteration 996 (2.41996 iter/s, 4.95875s/12 iters), loss = 4.79772
I0412 13:05:09.671181  8032 solver.cpp:237]     Train net output #0: loss = 4.79772 (* 1 = 4.79772 loss)
I0412 13:05:09.671190  8032 sgd_solver.cpp:105] Iteration 996, lr = 0.0082095
I0412 13:05:14.816756  8032 solver.cpp:218] Iteration 1008 (2.33218 iter/s, 5.1454s/12 iters), loss = 4.99861
I0412 13:05:14.816845  8032 solver.cpp:237]     Train net output #0: loss = 4.99861 (* 1 = 4.99861 loss)
I0412 13:05:14.816855  8032 sgd_solver.cpp:105] Iteration 1008, lr = 0.00819001
I0412 13:05:15.844970  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:05:19.401829  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1020.caffemodel
I0412 13:05:24.022734  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1020.solverstate
I0412 13:05:26.553241  8032 solver.cpp:330] Iteration 1020, Testing net (#0)
I0412 13:05:26.553263  8032 net.cpp:676] Ignoring source layer train-data
I0412 13:05:30.748991  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:05:31.190091  8032 solver.cpp:397]     Test net output #0: accuracy = 0.0208333
I0412 13:05:31.190129  8032 solver.cpp:397]     Test net output #1: loss = 4.95093 (* 1 = 4.95093 loss)
I0412 13:05:31.278234  8032 solver.cpp:218] Iteration 1020 (0.729002 iter/s, 16.4609s/12 iters), loss = 4.79213
I0412 13:05:31.278295  8032 solver.cpp:237]     Train net output #0: loss = 4.79213 (* 1 = 4.79213 loss)
I0412 13:05:31.278307  8032 sgd_solver.cpp:105] Iteration 1020, lr = 0.00817056
I0412 13:05:35.596974  8032 solver.cpp:218] Iteration 1032 (2.77872 iter/s, 4.31853s/12 iters), loss = 4.87935
I0412 13:05:35.597023  8032 solver.cpp:237]     Train net output #0: loss = 4.87935 (* 1 = 4.87935 loss)
I0412 13:05:35.597035  8032 sgd_solver.cpp:105] Iteration 1032, lr = 0.00815116
I0412 13:05:40.754048  8032 solver.cpp:218] Iteration 1044 (2.327 iter/s, 5.15685s/12 iters), loss = 4.94582
I0412 13:05:40.754108  8032 solver.cpp:237]     Train net output #0: loss = 4.94582 (* 1 = 4.94582 loss)
I0412 13:05:40.754122  8032 sgd_solver.cpp:105] Iteration 1044, lr = 0.00813181
I0412 13:05:45.788826  8032 solver.cpp:218] Iteration 1056 (2.38353 iter/s, 5.03455s/12 iters), loss = 4.84512
I0412 13:05:45.788944  8032 solver.cpp:237]     Train net output #0: loss = 4.84512 (* 1 = 4.84512 loss)
I0412 13:05:45.788954  8032 sgd_solver.cpp:105] Iteration 1056, lr = 0.0081125
I0412 13:05:50.896013  8032 solver.cpp:218] Iteration 1068 (2.34976 iter/s, 5.1069s/12 iters), loss = 4.94456
I0412 13:05:50.896064  8032 solver.cpp:237]     Train net output #0: loss = 4.94456 (* 1 = 4.94456 loss)
I0412 13:05:50.896076  8032 sgd_solver.cpp:105] Iteration 1068, lr = 0.00809324
I0412 13:05:56.013512  8032 solver.cpp:218] Iteration 1080 (2.345 iter/s, 5.11728s/12 iters), loss = 4.89893
I0412 13:05:56.013566  8032 solver.cpp:237]     Train net output #0: loss = 4.89893 (* 1 = 4.89893 loss)
I0412 13:05:56.013576  8032 sgd_solver.cpp:105] Iteration 1080, lr = 0.00807403
I0412 13:06:01.071319  8032 solver.cpp:218] Iteration 1092 (2.37267 iter/s, 5.05758s/12 iters), loss = 4.84712
I0412 13:06:01.071377  8032 solver.cpp:237]     Train net output #0: loss = 4.84712 (* 1 = 4.84712 loss)
I0412 13:06:01.071393  8032 sgd_solver.cpp:105] Iteration 1092, lr = 0.00805486
I0412 13:06:06.156479  8032 solver.cpp:218] Iteration 1104 (2.35991 iter/s, 5.08493s/12 iters), loss = 4.85184
I0412 13:06:06.156522  8032 solver.cpp:237]     Train net output #0: loss = 4.85184 (* 1 = 4.85184 loss)
I0412 13:06:06.156530  8032 sgd_solver.cpp:105] Iteration 1104, lr = 0.00803573
I0412 13:06:09.259141  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:06:11.131399  8032 solver.cpp:218] Iteration 1116 (2.4122 iter/s, 4.97471s/12 iters), loss = 4.92975
I0412 13:06:11.131458  8032 solver.cpp:237]     Train net output #0: loss = 4.92975 (* 1 = 4.92975 loss)
I0412 13:06:11.131470  8032 sgd_solver.cpp:105] Iteration 1116, lr = 0.00801666
I0412 13:06:13.164556  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1122.caffemodel
I0412 13:06:16.125387  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1122.solverstate
I0412 13:06:21.347937  8032 solver.cpp:330] Iteration 1122, Testing net (#0)
I0412 13:06:21.347965  8032 net.cpp:676] Ignoring source layer train-data
I0412 13:06:25.380223  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:06:25.867929  8032 solver.cpp:397]     Test net output #0: accuracy = 0.0300245
I0412 13:06:25.867983  8032 solver.cpp:397]     Test net output #1: loss = 4.88031 (* 1 = 4.88031 loss)
I0412 13:06:27.737499  8032 solver.cpp:218] Iteration 1128 (0.722651 iter/s, 16.6055s/12 iters), loss = 4.88932
I0412 13:06:27.737542  8032 solver.cpp:237]     Train net output #0: loss = 4.88932 (* 1 = 4.88932 loss)
I0412 13:06:27.737552  8032 sgd_solver.cpp:105] Iteration 1128, lr = 0.00799762
I0412 13:06:33.000514  8032 solver.cpp:218] Iteration 1140 (2.28016 iter/s, 5.26279s/12 iters), loss = 4.91539
I0412 13:06:33.000560  8032 solver.cpp:237]     Train net output #0: loss = 4.91539 (* 1 = 4.91539 loss)
I0412 13:06:33.000571  8032 sgd_solver.cpp:105] Iteration 1140, lr = 0.00797863
I0412 13:06:38.099587  8032 solver.cpp:218] Iteration 1152 (2.35347 iter/s, 5.09886s/12 iters), loss = 4.83856
I0412 13:06:38.099637  8032 solver.cpp:237]     Train net output #0: loss = 4.83856 (* 1 = 4.83856 loss)
I0412 13:06:38.099648  8032 sgd_solver.cpp:105] Iteration 1152, lr = 0.00795969
I0412 13:06:43.109547  8032 solver.cpp:218] Iteration 1164 (2.39533 iter/s, 5.00974s/12 iters), loss = 4.77672
I0412 13:06:43.109601  8032 solver.cpp:237]     Train net output #0: loss = 4.77672 (* 1 = 4.77672 loss)
I0412 13:06:43.109611  8032 sgd_solver.cpp:105] Iteration 1164, lr = 0.00794079
I0412 13:06:48.133512  8032 solver.cpp:218] Iteration 1176 (2.38866 iter/s, 5.02374s/12 iters), loss = 4.8013
I0412 13:06:48.133671  8032 solver.cpp:237]     Train net output #0: loss = 4.8013 (* 1 = 4.8013 loss)
I0412 13:06:48.133682  8032 sgd_solver.cpp:105] Iteration 1176, lr = 0.00792194
I0412 13:06:53.555794  8032 solver.cpp:218] Iteration 1188 (2.21323 iter/s, 5.42195s/12 iters), loss = 4.78486
I0412 13:06:53.555838  8032 solver.cpp:237]     Train net output #0: loss = 4.78486 (* 1 = 4.78486 loss)
I0412 13:06:53.555847  8032 sgd_solver.cpp:105] Iteration 1188, lr = 0.00790313
I0412 13:06:58.674710  8032 solver.cpp:218] Iteration 1200 (2.34435 iter/s, 5.11869s/12 iters), loss = 4.78735
I0412 13:06:58.674765  8032 solver.cpp:237]     Train net output #0: loss = 4.78735 (* 1 = 4.78735 loss)
I0412 13:06:58.674777  8032 sgd_solver.cpp:105] Iteration 1200, lr = 0.00788437
I0412 13:07:03.752833  8032 solver.cpp:218] Iteration 1212 (2.36318 iter/s, 5.0779s/12 iters), loss = 4.8711
I0412 13:07:03.752883  8032 solver.cpp:237]     Train net output #0: loss = 4.8711 (* 1 = 4.8711 loss)
I0412 13:07:03.752894  8032 sgd_solver.cpp:105] Iteration 1212, lr = 0.00786565
I0412 13:07:04.031002  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:07:08.412937  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1224.caffemodel
I0412 13:07:11.446923  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1224.solverstate
I0412 13:07:14.531919  8032 solver.cpp:330] Iteration 1224, Testing net (#0)
I0412 13:07:14.531940  8032 net.cpp:676] Ignoring source layer train-data
I0412 13:07:18.354262  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:07:18.865804  8032 solver.cpp:397]     Test net output #0: accuracy = 0.0349265
I0412 13:07:18.865870  8032 solver.cpp:397]     Test net output #1: loss = 4.81447 (* 1 = 4.81447 loss)
I0412 13:07:18.954345  8032 solver.cpp:218] Iteration 1224 (0.789423 iter/s, 15.201s/12 iters), loss = 4.77563
I0412 13:07:18.954404  8032 solver.cpp:237]     Train net output #0: loss = 4.77563 (* 1 = 4.77563 loss)
I0412 13:07:18.954418  8032 sgd_solver.cpp:105] Iteration 1224, lr = 0.00784697
I0412 13:07:23.172227  8032 solver.cpp:218] Iteration 1236 (2.84516 iter/s, 4.21768s/12 iters), loss = 4.90139
I0412 13:07:23.172269  8032 solver.cpp:237]     Train net output #0: loss = 4.90139 (* 1 = 4.90139 loss)
I0412 13:07:23.172278  8032 sgd_solver.cpp:105] Iteration 1236, lr = 0.00782834
I0412 13:07:28.303530  8032 solver.cpp:218] Iteration 1248 (2.33868 iter/s, 5.13109s/12 iters), loss = 4.65549
I0412 13:07:28.303578  8032 solver.cpp:237]     Train net output #0: loss = 4.65549 (* 1 = 4.65549 loss)
I0412 13:07:28.303591  8032 sgd_solver.cpp:105] Iteration 1248, lr = 0.00780976
I0412 13:07:33.517220  8032 solver.cpp:218] Iteration 1260 (2.30173 iter/s, 5.21347s/12 iters), loss = 4.71664
I0412 13:07:33.517262  8032 solver.cpp:237]     Train net output #0: loss = 4.71664 (* 1 = 4.71664 loss)
I0412 13:07:33.517272  8032 sgd_solver.cpp:105] Iteration 1260, lr = 0.00779122
I0412 13:07:38.852314  8032 solver.cpp:218] Iteration 1272 (2.24935 iter/s, 5.33487s/12 iters), loss = 4.74571
I0412 13:07:38.852365  8032 solver.cpp:237]     Train net output #0: loss = 4.74571 (* 1 = 4.74571 loss)
I0412 13:07:38.852377  8032 sgd_solver.cpp:105] Iteration 1272, lr = 0.00777272
I0412 13:07:44.348006  8032 solver.cpp:218] Iteration 1284 (2.18362 iter/s, 5.49546s/12 iters), loss = 4.77594
I0412 13:07:44.348062  8032 solver.cpp:237]     Train net output #0: loss = 4.77594 (* 1 = 4.77594 loss)
I0412 13:07:44.348073  8032 sgd_solver.cpp:105] Iteration 1284, lr = 0.00775426
I0412 13:07:49.555094  8032 solver.cpp:218] Iteration 1296 (2.30465 iter/s, 5.20685s/12 iters), loss = 4.51665
I0412 13:07:49.555214  8032 solver.cpp:237]     Train net output #0: loss = 4.51665 (* 1 = 4.51665 loss)
I0412 13:07:49.555227  8032 sgd_solver.cpp:105] Iteration 1296, lr = 0.00773585
I0412 13:07:55.040943  8032 solver.cpp:218] Iteration 1308 (2.18756 iter/s, 5.48555s/12 iters), loss = 4.60515
I0412 13:07:55.040992  8032 solver.cpp:237]     Train net output #0: loss = 4.60515 (* 1 = 4.60515 loss)
I0412 13:07:55.041002  8032 sgd_solver.cpp:105] Iteration 1308, lr = 0.00771749
I0412 13:07:57.746807  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:08:00.425029  8032 solver.cpp:218] Iteration 1320 (2.22888 iter/s, 5.38386s/12 iters), loss = 4.62997
I0412 13:08:00.425083  8032 solver.cpp:237]     Train net output #0: loss = 4.62997 (* 1 = 4.62997 loss)
I0412 13:08:00.425093  8032 sgd_solver.cpp:105] Iteration 1320, lr = 0.00769916
I0412 13:08:02.478305  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1326.caffemodel
I0412 13:08:05.532095  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1326.solverstate
I0412 13:08:08.985507  8032 solver.cpp:330] Iteration 1326, Testing net (#0)
I0412 13:08:08.985534  8032 net.cpp:676] Ignoring source layer train-data
I0412 13:08:13.143754  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:08:13.707940  8032 solver.cpp:397]     Test net output #0: accuracy = 0.0349265
I0412 13:08:13.707989  8032 solver.cpp:397]     Test net output #1: loss = 4.70052 (* 1 = 4.70052 loss)
I0412 13:08:15.640480  8032 solver.cpp:218] Iteration 1332 (0.7887 iter/s, 15.2149s/12 iters), loss = 4.56171
I0412 13:08:15.640534  8032 solver.cpp:237]     Train net output #0: loss = 4.56171 (* 1 = 4.56171 loss)
I0412 13:08:15.640547  8032 sgd_solver.cpp:105] Iteration 1332, lr = 0.00768088
I0412 13:08:20.556267  8032 solver.cpp:218] Iteration 1344 (2.44122 iter/s, 4.91557s/12 iters), loss = 4.5732
I0412 13:08:20.556387  8032 solver.cpp:237]     Train net output #0: loss = 4.5732 (* 1 = 4.5732 loss)
I0412 13:08:20.556401  8032 sgd_solver.cpp:105] Iteration 1344, lr = 0.00766265
I0412 13:08:25.601815  8032 solver.cpp:218] Iteration 1356 (2.37847 iter/s, 5.04527s/12 iters), loss = 4.69156
I0412 13:08:25.601857  8032 solver.cpp:237]     Train net output #0: loss = 4.69156 (* 1 = 4.69156 loss)
I0412 13:08:25.601866  8032 sgd_solver.cpp:105] Iteration 1356, lr = 0.00764446
I0412 13:08:30.584084  8032 solver.cpp:218] Iteration 1368 (2.40864 iter/s, 4.98206s/12 iters), loss = 4.54843
I0412 13:08:30.584132  8032 solver.cpp:237]     Train net output #0: loss = 4.54843 (* 1 = 4.54843 loss)
I0412 13:08:30.584143  8032 sgd_solver.cpp:105] Iteration 1368, lr = 0.00762631
I0412 13:08:31.781409  8032 blocking_queue.cpp:49] Waiting for data
I0412 13:08:35.609997  8032 solver.cpp:218] Iteration 1380 (2.38774 iter/s, 5.02567s/12 iters), loss = 4.46416
I0412 13:08:35.610045  8032 solver.cpp:237]     Train net output #0: loss = 4.46416 (* 1 = 4.46416 loss)
I0412 13:08:35.610054  8032 sgd_solver.cpp:105] Iteration 1380, lr = 0.0076082
I0412 13:08:40.857386  8032 solver.cpp:218] Iteration 1392 (2.28695 iter/s, 5.24716s/12 iters), loss = 4.55988
I0412 13:08:40.857442  8032 solver.cpp:237]     Train net output #0: loss = 4.55988 (* 1 = 4.55988 loss)
I0412 13:08:40.857455  8032 sgd_solver.cpp:105] Iteration 1392, lr = 0.00759014
I0412 13:08:45.969810  8032 solver.cpp:218] Iteration 1404 (2.34733 iter/s, 5.1122s/12 iters), loss = 4.52012
I0412 13:08:45.969851  8032 solver.cpp:237]     Train net output #0: loss = 4.52012 (* 1 = 4.52012 loss)
I0412 13:08:45.969861  8032 sgd_solver.cpp:105] Iteration 1404, lr = 0.00757212
I0412 13:08:50.688789  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:08:51.043490  8032 solver.cpp:218] Iteration 1416 (2.36525 iter/s, 5.07347s/12 iters), loss = 4.43863
I0412 13:08:51.043534  8032 solver.cpp:237]     Train net output #0: loss = 4.43863 (* 1 = 4.43863 loss)
I0412 13:08:51.043543  8032 sgd_solver.cpp:105] Iteration 1416, lr = 0.00755414
I0412 13:08:55.696169  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1428.caffemodel
I0412 13:08:58.714083  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1428.solverstate
I0412 13:09:01.818372  8032 solver.cpp:330] Iteration 1428, Testing net (#0)
I0412 13:09:01.818392  8032 net.cpp:676] Ignoring source layer train-data
I0412 13:09:05.737920  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:09:06.359019  8032 solver.cpp:397]     Test net output #0: accuracy = 0.0526961
I0412 13:09:06.359047  8032 solver.cpp:397]     Test net output #1: loss = 4.51223 (* 1 = 4.51223 loss)
I0412 13:09:06.444195  8032 solver.cpp:218] Iteration 1428 (0.779212 iter/s, 15.4002s/12 iters), loss = 4.47687
I0412 13:09:06.444234  8032 solver.cpp:237]     Train net output #0: loss = 4.47687 (* 1 = 4.47687 loss)
I0412 13:09:06.444244  8032 sgd_solver.cpp:105] Iteration 1428, lr = 0.0075362
I0412 13:09:10.698917  8032 solver.cpp:218] Iteration 1440 (2.82052 iter/s, 4.25454s/12 iters), loss = 4.30954
I0412 13:09:10.698966  8032 solver.cpp:237]     Train net output #0: loss = 4.30954 (* 1 = 4.30954 loss)
I0412 13:09:10.698977  8032 sgd_solver.cpp:105] Iteration 1440, lr = 0.00751831
I0412 13:09:15.723733  8032 solver.cpp:218] Iteration 1452 (2.38825 iter/s, 5.0246s/12 iters), loss = 4.78505
I0412 13:09:15.723784  8032 solver.cpp:237]     Train net output #0: loss = 4.78505 (* 1 = 4.78505 loss)
I0412 13:09:15.723796  8032 sgd_solver.cpp:105] Iteration 1452, lr = 0.00750046
I0412 13:09:20.728947  8032 solver.cpp:218] Iteration 1464 (2.3976 iter/s, 5.005s/12 iters), loss = 4.48388
I0412 13:09:20.729097  8032 solver.cpp:237]     Train net output #0: loss = 4.48388 (* 1 = 4.48388 loss)
I0412 13:09:20.729115  8032 sgd_solver.cpp:105] Iteration 1464, lr = 0.00748265
I0412 13:09:25.806028  8032 solver.cpp:218] Iteration 1476 (2.36371 iter/s, 5.07676s/12 iters), loss = 4.48415
I0412 13:09:25.806094  8032 solver.cpp:237]     Train net output #0: loss = 4.48415 (* 1 = 4.48415 loss)
I0412 13:09:25.806107  8032 sgd_solver.cpp:105] Iteration 1476, lr = 0.00746489
I0412 13:09:30.892637  8032 solver.cpp:218] Iteration 1488 (2.35924 iter/s, 5.08638s/12 iters), loss = 4.55361
I0412 13:09:30.892689  8032 solver.cpp:237]     Train net output #0: loss = 4.55361 (* 1 = 4.55361 loss)
I0412 13:09:30.892700  8032 sgd_solver.cpp:105] Iteration 1488, lr = 0.00744716
I0412 13:09:35.946929  8032 solver.cpp:218] Iteration 1500 (2.37432 iter/s, 5.05407s/12 iters), loss = 4.2888
I0412 13:09:35.946981  8032 solver.cpp:237]     Train net output #0: loss = 4.2888 (* 1 = 4.2888 loss)
I0412 13:09:35.946993  8032 sgd_solver.cpp:105] Iteration 1500, lr = 0.00742948
I0412 13:09:41.025033  8032 solver.cpp:218] Iteration 1512 (2.36319 iter/s, 5.07789s/12 iters), loss = 4.59271
I0412 13:09:41.025074  8032 solver.cpp:237]     Train net output #0: loss = 4.59271 (* 1 = 4.59271 loss)
I0412 13:09:41.025082  8032 sgd_solver.cpp:105] Iteration 1512, lr = 0.00741184
I0412 13:09:42.792960  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:09:46.044054  8032 solver.cpp:218] Iteration 1524 (2.39101 iter/s, 5.01881s/12 iters), loss = 4.53495
I0412 13:09:46.044098  8032 solver.cpp:237]     Train net output #0: loss = 4.53495 (* 1 = 4.53495 loss)
I0412 13:09:46.044106  8032 sgd_solver.cpp:105] Iteration 1524, lr = 0.00739425
I0412 13:09:48.110991  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1530.caffemodel
I0412 13:09:51.131624  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1530.solverstate
I0412 13:09:53.451434  8032 solver.cpp:330] Iteration 1530, Testing net (#0)
I0412 13:09:53.451462  8032 net.cpp:676] Ignoring source layer train-data
I0412 13:09:57.555150  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:09:58.276401  8032 solver.cpp:397]     Test net output #0: accuracy = 0.0569853
I0412 13:09:58.276435  8032 solver.cpp:397]     Test net output #1: loss = 4.54833 (* 1 = 4.54833 loss)
I0412 13:10:00.282917  8032 solver.cpp:218] Iteration 1536 (0.842794 iter/s, 14.2384s/12 iters), loss = 4.44751
I0412 13:10:00.282968  8032 solver.cpp:237]     Train net output #0: loss = 4.44751 (* 1 = 4.44751 loss)
I0412 13:10:00.282979  8032 sgd_solver.cpp:105] Iteration 1536, lr = 0.00737669
I0412 13:10:05.400028  8032 solver.cpp:218] Iteration 1548 (2.34518 iter/s, 5.11688s/12 iters), loss = 4.10244
I0412 13:10:05.400081  8032 solver.cpp:237]     Train net output #0: loss = 4.10244 (* 1 = 4.10244 loss)
I0412 13:10:05.400094  8032 sgd_solver.cpp:105] Iteration 1548, lr = 0.00735918
I0412 13:10:10.569984  8032 solver.cpp:218] Iteration 1560 (2.32121 iter/s, 5.16971s/12 iters), loss = 4.43521
I0412 13:10:10.570050  8032 solver.cpp:237]     Train net output #0: loss = 4.43521 (* 1 = 4.43521 loss)
I0412 13:10:10.570067  8032 sgd_solver.cpp:105] Iteration 1560, lr = 0.00734171
I0412 13:10:15.536999  8032 solver.cpp:218] Iteration 1572 (2.41605 iter/s, 4.96679s/12 iters), loss = 4.49348
I0412 13:10:15.537043  8032 solver.cpp:237]     Train net output #0: loss = 4.49348 (* 1 = 4.49348 loss)
I0412 13:10:15.537053  8032 sgd_solver.cpp:105] Iteration 1572, lr = 0.00732427
I0412 13:10:20.446377  8032 solver.cpp:218] Iteration 1584 (2.44441 iter/s, 4.90917s/12 iters), loss = 4.35628
I0412 13:10:20.446429  8032 solver.cpp:237]     Train net output #0: loss = 4.35628 (* 1 = 4.35628 loss)
I0412 13:10:20.446440  8032 sgd_solver.cpp:105] Iteration 1584, lr = 0.00730688
I0412 13:10:25.698385  8032 solver.cpp:218] Iteration 1596 (2.28494 iter/s, 5.25178s/12 iters), loss = 4.43161
I0412 13:10:25.698529  8032 solver.cpp:237]     Train net output #0: loss = 4.43161 (* 1 = 4.43161 loss)
I0412 13:10:25.698541  8032 sgd_solver.cpp:105] Iteration 1596, lr = 0.00728954
I0412 13:10:30.762184  8032 solver.cpp:218] Iteration 1608 (2.36991 iter/s, 5.06349s/12 iters), loss = 4.32174
I0412 13:10:30.762238  8032 solver.cpp:237]     Train net output #0: loss = 4.32174 (* 1 = 4.32174 loss)
I0412 13:10:30.762249  8032 sgd_solver.cpp:105] Iteration 1608, lr = 0.00727223
I0412 13:10:34.772616  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:10:35.872375  8032 solver.cpp:218] Iteration 1620 (2.34835 iter/s, 5.10996s/12 iters), loss = 4.23378
I0412 13:10:35.872428  8032 solver.cpp:237]     Train net output #0: loss = 4.23378 (* 1 = 4.23378 loss)
I0412 13:10:35.872440  8032 sgd_solver.cpp:105] Iteration 1620, lr = 0.00725496
I0412 13:10:40.427394  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1632.caffemodel
I0412 13:10:44.553946  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1632.solverstate
I0412 13:10:48.553676  8032 solver.cpp:330] Iteration 1632, Testing net (#0)
I0412 13:10:48.553704  8032 net.cpp:676] Ignoring source layer train-data
I0412 13:10:52.389621  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:10:53.060293  8032 solver.cpp:397]     Test net output #0: accuracy = 0.0661765
I0412 13:10:53.060346  8032 solver.cpp:397]     Test net output #1: loss = 4.33669 (* 1 = 4.33669 loss)
I0412 13:10:53.148450  8032 solver.cpp:218] Iteration 1632 (0.694626 iter/s, 17.2755s/12 iters), loss = 4.1539
I0412 13:10:53.148501  8032 solver.cpp:237]     Train net output #0: loss = 4.1539 (* 1 = 4.1539 loss)
I0412 13:10:53.148511  8032 sgd_solver.cpp:105] Iteration 1632, lr = 0.00723774
I0412 13:10:57.596278  8032 solver.cpp:218] Iteration 1644 (2.69807 iter/s, 4.44763s/12 iters), loss = 4.32828
I0412 13:10:57.596371  8032 solver.cpp:237]     Train net output #0: loss = 4.32828 (* 1 = 4.32828 loss)
I0412 13:10:57.596382  8032 sgd_solver.cpp:105] Iteration 1644, lr = 0.00722056
I0412 13:11:02.708993  8032 solver.cpp:218] Iteration 1656 (2.34721 iter/s, 5.11245s/12 iters), loss = 4.3165
I0412 13:11:02.709043  8032 solver.cpp:237]     Train net output #0: loss = 4.3165 (* 1 = 4.3165 loss)
I0412 13:11:02.709053  8032 sgd_solver.cpp:105] Iteration 1656, lr = 0.00720341
I0412 13:11:07.661096  8032 solver.cpp:218] Iteration 1668 (2.42332 iter/s, 4.95188s/12 iters), loss = 4.08486
I0412 13:11:07.661147  8032 solver.cpp:237]     Train net output #0: loss = 4.08486 (* 1 = 4.08486 loss)
I0412 13:11:07.661159  8032 sgd_solver.cpp:105] Iteration 1668, lr = 0.00718631
I0412 13:11:12.759428  8032 solver.cpp:218] Iteration 1680 (2.35381 iter/s, 5.09811s/12 iters), loss = 4.2563
I0412 13:11:12.759479  8032 solver.cpp:237]     Train net output #0: loss = 4.2563 (* 1 = 4.2563 loss)
I0412 13:11:12.759490  8032 sgd_solver.cpp:105] Iteration 1680, lr = 0.00716925
I0412 13:11:17.829852  8032 solver.cpp:218] Iteration 1692 (2.36677 iter/s, 5.0702s/12 iters), loss = 4.28217
I0412 13:11:17.829903  8032 solver.cpp:237]     Train net output #0: loss = 4.28217 (* 1 = 4.28217 loss)
I0412 13:11:17.829914  8032 sgd_solver.cpp:105] Iteration 1692, lr = 0.00715223
I0412 13:11:22.938131  8032 solver.cpp:218] Iteration 1704 (2.34923 iter/s, 5.10806s/12 iters), loss = 3.8998
I0412 13:11:22.938179  8032 solver.cpp:237]     Train net output #0: loss = 3.8998 (* 1 = 3.8998 loss)
I0412 13:11:22.938189  8032 sgd_solver.cpp:105] Iteration 1704, lr = 0.00713525
I0412 13:11:27.989006  8032 solver.cpp:218] Iteration 1716 (2.37593 iter/s, 5.05066s/12 iters), loss = 4.12206
I0412 13:11:27.989158  8032 solver.cpp:237]     Train net output #0: loss = 4.12206 (* 1 = 4.12206 loss)
I0412 13:11:27.989172  8032 sgd_solver.cpp:105] Iteration 1716, lr = 0.00711831
I0412 13:11:29.067955  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:11:33.094873  8032 solver.cpp:218] Iteration 1728 (2.35038 iter/s, 5.10555s/12 iters), loss = 4.12931
I0412 13:11:33.094928  8032 solver.cpp:237]     Train net output #0: loss = 4.12931 (* 1 = 4.12931 loss)
I0412 13:11:33.094940  8032 sgd_solver.cpp:105] Iteration 1728, lr = 0.00710141
I0412 13:11:35.185554  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1734.caffemodel
I0412 13:11:40.792161  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1734.solverstate
I0412 13:11:45.476166  8032 solver.cpp:330] Iteration 1734, Testing net (#0)
I0412 13:11:45.476194  8032 net.cpp:676] Ignoring source layer train-data
I0412 13:11:49.317521  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:11:50.025768  8032 solver.cpp:397]     Test net output #0: accuracy = 0.0759804
I0412 13:11:50.025808  8032 solver.cpp:397]     Test net output #1: loss = 4.33976 (* 1 = 4.33976 loss)
I0412 13:11:51.790113  8032 solver.cpp:218] Iteration 1740 (0.641897 iter/s, 18.6946s/12 iters), loss = 4.01946
I0412 13:11:51.790158  8032 solver.cpp:237]     Train net output #0: loss = 4.01946 (* 1 = 4.01946 loss)
I0412 13:11:51.790166  8032 sgd_solver.cpp:105] Iteration 1740, lr = 0.00708455
I0412 13:11:56.839299  8032 solver.cpp:218] Iteration 1752 (2.37672 iter/s, 5.04897s/12 iters), loss = 4.07616
I0412 13:11:56.839354  8032 solver.cpp:237]     Train net output #0: loss = 4.07616 (* 1 = 4.07616 loss)
I0412 13:11:56.839365  8032 sgd_solver.cpp:105] Iteration 1752, lr = 0.00706773
I0412 13:12:01.917241  8032 solver.cpp:218] Iteration 1764 (2.36327 iter/s, 5.07771s/12 iters), loss = 4.21458
I0412 13:12:01.917354  8032 solver.cpp:237]     Train net output #0: loss = 4.21458 (* 1 = 4.21458 loss)
I0412 13:12:01.917366  8032 sgd_solver.cpp:105] Iteration 1764, lr = 0.00705094
I0412 13:12:07.151803  8032 solver.cpp:218] Iteration 1776 (2.29258 iter/s, 5.23428s/12 iters), loss = 4.30918
I0412 13:12:07.151845  8032 solver.cpp:237]     Train net output #0: loss = 4.30918 (* 1 = 4.30918 loss)
I0412 13:12:07.151854  8032 sgd_solver.cpp:105] Iteration 1776, lr = 0.0070342
I0412 13:12:12.197059  8032 solver.cpp:218] Iteration 1788 (2.37857 iter/s, 5.04504s/12 iters), loss = 4.21914
I0412 13:12:12.197111  8032 solver.cpp:237]     Train net output #0: loss = 4.21914 (* 1 = 4.21914 loss)
I0412 13:12:12.197124  8032 sgd_solver.cpp:105] Iteration 1788, lr = 0.0070175
I0412 13:12:17.140013  8032 solver.cpp:218] Iteration 1800 (2.42781 iter/s, 4.94273s/12 iters), loss = 4.05374
I0412 13:12:17.140065  8032 solver.cpp:237]     Train net output #0: loss = 4.05374 (* 1 = 4.05374 loss)
I0412 13:12:17.140075  8032 sgd_solver.cpp:105] Iteration 1800, lr = 0.00700084
I0412 13:12:22.246701  8032 solver.cpp:218] Iteration 1812 (2.34996 iter/s, 5.10647s/12 iters), loss = 4.12332
I0412 13:12:22.246747  8032 solver.cpp:237]     Train net output #0: loss = 4.12332 (* 1 = 4.12332 loss)
I0412 13:12:22.246757  8032 sgd_solver.cpp:105] Iteration 1812, lr = 0.00698422
I0412 13:12:25.481926  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:12:27.361241  8032 solver.cpp:218] Iteration 1824 (2.34635 iter/s, 5.11432s/12 iters), loss = 4.1738
I0412 13:12:27.361287  8032 solver.cpp:237]     Train net output #0: loss = 4.1738 (* 1 = 4.1738 loss)
I0412 13:12:27.361296  8032 sgd_solver.cpp:105] Iteration 1824, lr = 0.00696764
I0412 13:12:32.019724  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1836.caffemodel
I0412 13:12:36.772804  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1836.solverstate
I0412 13:12:40.059373  8032 solver.cpp:330] Iteration 1836, Testing net (#0)
I0412 13:12:40.059401  8032 net.cpp:676] Ignoring source layer train-data
I0412 13:12:43.949417  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:12:44.696799  8032 solver.cpp:397]     Test net output #0: accuracy = 0.0778186
I0412 13:12:44.696837  8032 solver.cpp:397]     Test net output #1: loss = 4.27359 (* 1 = 4.27359 loss)
I0412 13:12:44.784814  8032 solver.cpp:218] Iteration 1836 (0.688746 iter/s, 17.423s/12 iters), loss = 4.2145
I0412 13:12:44.784862  8032 solver.cpp:237]     Train net output #0: loss = 4.2145 (* 1 = 4.2145 loss)
I0412 13:12:44.784869  8032 sgd_solver.cpp:105] Iteration 1836, lr = 0.0069511
I0412 13:12:48.996047  8032 solver.cpp:218] Iteration 1848 (2.84966 iter/s, 4.21103s/12 iters), loss = 4.21158
I0412 13:12:48.996102  8032 solver.cpp:237]     Train net output #0: loss = 4.21158 (* 1 = 4.21158 loss)
I0412 13:12:48.996114  8032 sgd_solver.cpp:105] Iteration 1848, lr = 0.00693459
I0412 13:12:54.066890  8032 solver.cpp:218] Iteration 1860 (2.36658 iter/s, 5.07062s/12 iters), loss = 4.13306
I0412 13:12:54.066946  8032 solver.cpp:237]     Train net output #0: loss = 4.13306 (* 1 = 4.13306 loss)
I0412 13:12:54.066958  8032 sgd_solver.cpp:105] Iteration 1860, lr = 0.00691813
I0412 13:12:59.283342  8032 solver.cpp:218] Iteration 1872 (2.30052 iter/s, 5.21622s/12 iters), loss = 4.13747
I0412 13:12:59.283393  8032 solver.cpp:237]     Train net output #0: loss = 4.13747 (* 1 = 4.13747 loss)
I0412 13:12:59.283404  8032 sgd_solver.cpp:105] Iteration 1872, lr = 0.0069017
I0412 13:13:04.360867  8032 solver.cpp:218] Iteration 1884 (2.36346 iter/s, 5.07731s/12 iters), loss = 4.11452
I0412 13:13:04.360949  8032 solver.cpp:237]     Train net output #0: loss = 4.11452 (* 1 = 4.11452 loss)
I0412 13:13:04.360960  8032 sgd_solver.cpp:105] Iteration 1884, lr = 0.00688532
I0412 13:13:09.478262  8032 solver.cpp:218] Iteration 1896 (2.34506 iter/s, 5.11714s/12 iters), loss = 4.08499
I0412 13:13:09.478315  8032 solver.cpp:237]     Train net output #0: loss = 4.08499 (* 1 = 4.08499 loss)
I0412 13:13:09.478327  8032 sgd_solver.cpp:105] Iteration 1896, lr = 0.00686897
I0412 13:13:14.457105  8032 solver.cpp:218] Iteration 1908 (2.4103 iter/s, 4.97862s/12 iters), loss = 4.13032
I0412 13:13:14.457145  8032 solver.cpp:237]     Train net output #0: loss = 4.13032 (* 1 = 4.13032 loss)
I0412 13:13:14.457154  8032 sgd_solver.cpp:105] Iteration 1908, lr = 0.00685266
I0412 13:13:19.676627  8032 solver.cpp:218] Iteration 1920 (2.29916 iter/s, 5.2193s/12 iters), loss = 3.86511
I0412 13:13:19.676685  8032 solver.cpp:237]     Train net output #0: loss = 3.86511 (* 1 = 3.86511 loss)
I0412 13:13:19.676697  8032 sgd_solver.cpp:105] Iteration 1920, lr = 0.00683639
I0412 13:13:20.014505  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:13:24.937402  8032 solver.cpp:218] Iteration 1932 (2.28113 iter/s, 5.26054s/12 iters), loss = 4.09307
I0412 13:13:24.937448  8032 solver.cpp:237]     Train net output #0: loss = 4.09307 (* 1 = 4.09307 loss)
I0412 13:13:24.937459  8032 sgd_solver.cpp:105] Iteration 1932, lr = 0.00682016
I0412 13:13:27.070981  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1938.caffemodel
I0412 13:13:34.625907  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1938.solverstate
I0412 13:13:45.391598  8032 solver.cpp:330] Iteration 1938, Testing net (#0)
I0412 13:13:45.391628  8032 net.cpp:676] Ignoring source layer train-data
I0412 13:13:49.172957  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:13:49.959863  8032 solver.cpp:397]     Test net output #0: accuracy = 0.109069
I0412 13:13:49.959897  8032 solver.cpp:397]     Test net output #1: loss = 4.0387 (* 1 = 4.0387 loss)
I0412 13:13:51.871666  8032 solver.cpp:218] Iteration 1944 (0.445544 iter/s, 26.9334s/12 iters), loss = 3.87122
I0412 13:13:51.871716  8032 solver.cpp:237]     Train net output #0: loss = 3.87122 (* 1 = 3.87122 loss)
I0412 13:13:51.871727  8032 sgd_solver.cpp:105] Iteration 1944, lr = 0.00680397
I0412 13:13:56.959084  8032 solver.cpp:218] Iteration 1956 (2.35886 iter/s, 5.0872s/12 iters), loss = 3.76589
I0412 13:13:56.959144  8032 solver.cpp:237]     Train net output #0: loss = 3.76589 (* 1 = 3.76589 loss)
I0412 13:13:56.959161  8032 sgd_solver.cpp:105] Iteration 1956, lr = 0.00678782
I0412 13:14:02.168911  8032 solver.cpp:218] Iteration 1968 (2.30344 iter/s, 5.20959s/12 iters), loss = 3.8276
I0412 13:14:02.168959  8032 solver.cpp:237]     Train net output #0: loss = 3.8276 (* 1 = 3.8276 loss)
I0412 13:14:02.168969  8032 sgd_solver.cpp:105] Iteration 1968, lr = 0.0067717
I0412 13:14:07.315536  8032 solver.cpp:218] Iteration 1980 (2.33172 iter/s, 5.14641s/12 iters), loss = 3.96816
I0412 13:14:07.315634  8032 solver.cpp:237]     Train net output #0: loss = 3.96816 (* 1 = 3.96816 loss)
I0412 13:14:07.315644  8032 sgd_solver.cpp:105] Iteration 1980, lr = 0.00675562
I0412 13:14:12.419891  8032 solver.cpp:218] Iteration 1992 (2.35106 iter/s, 5.10408s/12 iters), loss = 3.91976
I0412 13:14:12.419945  8032 solver.cpp:237]     Train net output #0: loss = 3.91976 (* 1 = 3.91976 loss)
I0412 13:14:12.419955  8032 sgd_solver.cpp:105] Iteration 1992, lr = 0.00673958
I0412 13:14:17.494855  8032 solver.cpp:218] Iteration 2004 (2.36465 iter/s, 5.07474s/12 iters), loss = 3.7267
I0412 13:14:17.494904  8032 solver.cpp:237]     Train net output #0: loss = 3.7267 (* 1 = 3.7267 loss)
I0412 13:14:17.494916  8032 sgd_solver.cpp:105] Iteration 2004, lr = 0.00672358
I0412 13:14:22.595520  8032 solver.cpp:218] Iteration 2016 (2.35274 iter/s, 5.10044s/12 iters), loss = 3.92854
I0412 13:14:22.595562  8032 solver.cpp:237]     Train net output #0: loss = 3.92854 (* 1 = 3.92854 loss)
I0412 13:14:22.595571  8032 sgd_solver.cpp:105] Iteration 2016, lr = 0.00670762
I0412 13:14:25.216336  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:14:27.764160  8032 solver.cpp:218] Iteration 2028 (2.32179 iter/s, 5.16842s/12 iters), loss = 3.81987
I0412 13:14:27.764210  8032 solver.cpp:237]     Train net output #0: loss = 3.81987 (* 1 = 3.81987 loss)
I0412 13:14:27.764221  8032 sgd_solver.cpp:105] Iteration 2028, lr = 0.00669169
I0412 13:14:32.333520  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2040.caffemodel
I0412 13:14:39.525389  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2040.solverstate
I0412 13:14:42.854260  8032 solver.cpp:330] Iteration 2040, Testing net (#0)
I0412 13:14:42.854288  8032 net.cpp:676] Ignoring source layer train-data
I0412 13:14:47.294237  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:14:48.159199  8032 solver.cpp:397]     Test net output #0: accuracy = 0.114583
I0412 13:14:48.159233  8032 solver.cpp:397]     Test net output #1: loss = 3.94061 (* 1 = 3.94061 loss)
I0412 13:14:48.247457  8032 solver.cpp:218] Iteration 2040 (0.585863 iter/s, 20.4826s/12 iters), loss = 3.50446
I0412 13:14:48.247498  8032 solver.cpp:237]     Train net output #0: loss = 3.50446 (* 1 = 3.50446 loss)
I0412 13:14:48.247507  8032 sgd_solver.cpp:105] Iteration 2040, lr = 0.00667581
I0412 13:14:52.325502  8032 solver.cpp:218] Iteration 2052 (2.94272 iter/s, 4.07786s/12 iters), loss = 3.90014
I0412 13:14:52.325554  8032 solver.cpp:237]     Train net output #0: loss = 3.90014 (* 1 = 3.90014 loss)
I0412 13:14:52.325567  8032 sgd_solver.cpp:105] Iteration 2052, lr = 0.00665996
I0412 13:14:53.927244  8032 blocking_queue.cpp:49] Waiting for data
I0412 13:14:57.528520  8032 solver.cpp:218] Iteration 2064 (2.30645 iter/s, 5.20279s/12 iters), loss = 3.75183
I0412 13:14:57.528564  8032 solver.cpp:237]     Train net output #0: loss = 3.75183 (* 1 = 3.75183 loss)
I0412 13:14:57.528573  8032 sgd_solver.cpp:105] Iteration 2064, lr = 0.00664414
I0412 13:15:02.777305  8032 solver.cpp:218] Iteration 2076 (2.28634 iter/s, 5.24856s/12 iters), loss = 3.78889
I0412 13:15:02.777362  8032 solver.cpp:237]     Train net output #0: loss = 3.78889 (* 1 = 3.78889 loss)
I0412 13:15:02.777374  8032 sgd_solver.cpp:105] Iteration 2076, lr = 0.00662837
I0412 13:15:07.870055  8032 solver.cpp:218] Iteration 2088 (2.35639 iter/s, 5.09253s/12 iters), loss = 3.81699
I0412 13:15:07.870100  8032 solver.cpp:237]     Train net output #0: loss = 3.81699 (* 1 = 3.81699 loss)
I0412 13:15:07.870111  8032 sgd_solver.cpp:105] Iteration 2088, lr = 0.00661263
I0412 13:15:12.878357  8032 solver.cpp:218] Iteration 2100 (2.39612 iter/s, 5.00809s/12 iters), loss = 3.86132
I0412 13:15:12.878470  8032 solver.cpp:237]     Train net output #0: loss = 3.86132 (* 1 = 3.86132 loss)
I0412 13:15:12.878479  8032 sgd_solver.cpp:105] Iteration 2100, lr = 0.00659693
I0412 13:15:17.987522  8032 solver.cpp:218] Iteration 2112 (2.34885 iter/s, 5.10888s/12 iters), loss = 3.78934
I0412 13:15:17.987562  8032 solver.cpp:237]     Train net output #0: loss = 3.78934 (* 1 = 3.78934 loss)
I0412 13:15:17.987571  8032 sgd_solver.cpp:105] Iteration 2112, lr = 0.00658127
I0412 13:15:22.896342  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:15:23.242063  8032 solver.cpp:218] Iteration 2124 (2.28384 iter/s, 5.25432s/12 iters), loss = 3.66652
I0412 13:15:23.242125  8032 solver.cpp:237]     Train net output #0: loss = 3.66652 (* 1 = 3.66652 loss)
I0412 13:15:23.242139  8032 sgd_solver.cpp:105] Iteration 2124, lr = 0.00656564
I0412 13:15:28.649684  8032 solver.cpp:218] Iteration 2136 (2.21919 iter/s, 5.40738s/12 iters), loss = 3.80894
I0412 13:15:28.649729  8032 solver.cpp:237]     Train net output #0: loss = 3.80894 (* 1 = 3.80894 loss)
I0412 13:15:28.649737  8032 sgd_solver.cpp:105] Iteration 2136, lr = 0.00655006
I0412 13:15:30.660553  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2142.caffemodel
I0412 13:15:39.759253  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2142.solverstate
I0412 13:15:42.108490  8032 solver.cpp:330] Iteration 2142, Testing net (#0)
I0412 13:15:42.108511  8032 net.cpp:676] Ignoring source layer train-data
I0412 13:15:45.888012  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:15:46.788237  8032 solver.cpp:397]     Test net output #0: accuracy = 0.122549
I0412 13:15:46.788267  8032 solver.cpp:397]     Test net output #1: loss = 3.89458 (* 1 = 3.89458 loss)
I0412 13:15:48.778371  8032 solver.cpp:218] Iteration 2148 (0.596184 iter/s, 20.128s/12 iters), loss = 3.59458
I0412 13:15:48.778426  8032 solver.cpp:237]     Train net output #0: loss = 3.59458 (* 1 = 3.59458 loss)
I0412 13:15:48.778436  8032 sgd_solver.cpp:105] Iteration 2148, lr = 0.00653451
I0412 13:15:53.799844  8032 solver.cpp:218] Iteration 2160 (2.38985 iter/s, 5.02124s/12 iters), loss = 3.97154
I0412 13:15:53.799897  8032 solver.cpp:237]     Train net output #0: loss = 3.97154 (* 1 = 3.97154 loss)
I0412 13:15:53.799909  8032 sgd_solver.cpp:105] Iteration 2160, lr = 0.00651899
I0412 13:15:59.023932  8032 solver.cpp:218] Iteration 2172 (2.29715 iter/s, 5.22386s/12 iters), loss = 3.8417
I0412 13:15:59.023969  8032 solver.cpp:237]     Train net output #0: loss = 3.8417 (* 1 = 3.8417 loss)
I0412 13:15:59.023978  8032 sgd_solver.cpp:105] Iteration 2172, lr = 0.00650351
I0412 13:16:04.429761  8032 solver.cpp:218] Iteration 2184 (2.21992 iter/s, 5.4056s/12 iters), loss = 3.74603
I0412 13:16:04.429818  8032 solver.cpp:237]     Train net output #0: loss = 3.74603 (* 1 = 3.74603 loss)
I0412 13:16:04.429829  8032 sgd_solver.cpp:105] Iteration 2184, lr = 0.00648807
I0412 13:16:09.455029  8032 solver.cpp:218] Iteration 2196 (2.38804 iter/s, 5.02504s/12 iters), loss = 3.86774
I0412 13:16:09.455071  8032 solver.cpp:237]     Train net output #0: loss = 3.86774 (* 1 = 3.86774 loss)
I0412 13:16:09.455080  8032 sgd_solver.cpp:105] Iteration 2196, lr = 0.00647267
I0412 13:16:14.516216  8032 solver.cpp:218] Iteration 2208 (2.37109 iter/s, 5.06097s/12 iters), loss = 3.44665
I0412 13:16:14.516260  8032 solver.cpp:237]     Train net output #0: loss = 3.44665 (* 1 = 3.44665 loss)
I0412 13:16:14.516269  8032 sgd_solver.cpp:105] Iteration 2208, lr = 0.0064573
I0412 13:16:19.616017  8032 solver.cpp:218] Iteration 2220 (2.35313 iter/s, 5.09959s/12 iters), loss = 3.50992
I0412 13:16:19.616124  8032 solver.cpp:237]     Train net output #0: loss = 3.50992 (* 1 = 3.50992 loss)
I0412 13:16:19.616133  8032 sgd_solver.cpp:105] Iteration 2220, lr = 0.00644197
I0412 13:16:21.464926  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:16:24.940572  8032 solver.cpp:218] Iteration 2232 (2.25383 iter/s, 5.32427s/12 iters), loss = 3.71471
I0412 13:16:24.940613  8032 solver.cpp:237]     Train net output #0: loss = 3.71471 (* 1 = 3.71471 loss)
I0412 13:16:24.940623  8032 sgd_solver.cpp:105] Iteration 2232, lr = 0.00642668
I0412 13:16:29.560240  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2244.caffemodel
I0412 13:16:38.386909  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2244.solverstate
I0412 13:16:41.138708  8032 solver.cpp:330] Iteration 2244, Testing net (#0)
I0412 13:16:41.138734  8032 net.cpp:676] Ignoring source layer train-data
I0412 13:16:44.676457  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:16:45.585651  8032 solver.cpp:397]     Test net output #0: accuracy = 0.141544
I0412 13:16:45.585698  8032 solver.cpp:397]     Test net output #1: loss = 3.81237 (* 1 = 3.81237 loss)
I0412 13:16:45.673840  8032 solver.cpp:218] Iteration 2244 (0.578799 iter/s, 20.7326s/12 iters), loss = 3.73727
I0412 13:16:45.673897  8032 solver.cpp:237]     Train net output #0: loss = 3.73727 (* 1 = 3.73727 loss)
I0412 13:16:45.673907  8032 sgd_solver.cpp:105] Iteration 2244, lr = 0.00641142
I0412 13:16:50.060482  8032 solver.cpp:218] Iteration 2256 (2.73571 iter/s, 4.38644s/12 iters), loss = 3.20893
I0412 13:16:50.060590  8032 solver.cpp:237]     Train net output #0: loss = 3.20893 (* 1 = 3.20893 loss)
I0412 13:16:50.060601  8032 sgd_solver.cpp:105] Iteration 2256, lr = 0.0063962
I0412 13:16:55.046785  8032 solver.cpp:218] Iteration 2268 (2.40672 iter/s, 4.98603s/12 iters), loss = 3.69013
I0412 13:16:55.046825  8032 solver.cpp:237]     Train net output #0: loss = 3.69013 (* 1 = 3.69013 loss)
I0412 13:16:55.046834  8032 sgd_solver.cpp:105] Iteration 2268, lr = 0.00638101
I0412 13:17:00.190827  8032 solver.cpp:218] Iteration 2280 (2.33289 iter/s, 5.14383s/12 iters), loss = 3.63568
I0412 13:17:00.190882  8032 solver.cpp:237]     Train net output #0: loss = 3.63568 (* 1 = 3.63568 loss)
I0412 13:17:00.190898  8032 sgd_solver.cpp:105] Iteration 2280, lr = 0.00636586
I0412 13:17:05.240708  8032 solver.cpp:218] Iteration 2292 (2.3764 iter/s, 5.04966s/12 iters), loss = 3.47965
I0412 13:17:05.240754  8032 solver.cpp:237]     Train net output #0: loss = 3.47965 (* 1 = 3.47965 loss)
I0412 13:17:05.240764  8032 sgd_solver.cpp:105] Iteration 2292, lr = 0.00635075
I0412 13:17:10.360602  8032 solver.cpp:218] Iteration 2304 (2.3439 iter/s, 5.11967s/12 iters), loss = 3.72957
I0412 13:17:10.360666  8032 solver.cpp:237]     Train net output #0: loss = 3.72957 (* 1 = 3.72957 loss)
I0412 13:17:10.360680  8032 sgd_solver.cpp:105] Iteration 2304, lr = 0.00633567
I0412 13:17:15.627494  8032 solver.cpp:218] Iteration 2316 (2.27849 iter/s, 5.26666s/12 iters), loss = 3.35113
I0412 13:17:15.627539  8032 solver.cpp:237]     Train net output #0: loss = 3.35113 (* 1 = 3.35113 loss)
I0412 13:17:15.627548  8032 sgd_solver.cpp:105] Iteration 2316, lr = 0.00632063
I0412 13:17:19.551343  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:17:20.631309  8032 solver.cpp:218] Iteration 2328 (2.39827 iter/s, 5.0036s/12 iters), loss = 3.45853
I0412 13:17:20.631426  8032 solver.cpp:237]     Train net output #0: loss = 3.45853 (* 1 = 3.45853 loss)
I0412 13:17:20.631436  8032 sgd_solver.cpp:105] Iteration 2328, lr = 0.00630562
I0412 13:17:25.623149  8032 solver.cpp:218] Iteration 2340 (2.40406 iter/s, 4.99156s/12 iters), loss = 3.40605
I0412 13:17:25.623191  8032 solver.cpp:237]     Train net output #0: loss = 3.40605 (* 1 = 3.40605 loss)
I0412 13:17:25.623200  8032 sgd_solver.cpp:105] Iteration 2340, lr = 0.00629065
I0412 13:17:27.733690  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2346.caffemodel
I0412 13:17:34.366202  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2346.solverstate
I0412 13:17:36.762137  8032 solver.cpp:330] Iteration 2346, Testing net (#0)
I0412 13:17:36.762162  8032 net.cpp:676] Ignoring source layer train-data
I0412 13:17:40.266531  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:17:41.210364  8032 solver.cpp:397]     Test net output #0: accuracy = 0.150735
I0412 13:17:41.210413  8032 solver.cpp:397]     Test net output #1: loss = 3.80982 (* 1 = 3.80982 loss)
I0412 13:17:43.169842  8032 solver.cpp:218] Iteration 2352 (0.683913 iter/s, 17.5461s/12 iters), loss = 3.54456
I0412 13:17:43.169910  8032 solver.cpp:237]     Train net output #0: loss = 3.54456 (* 1 = 3.54456 loss)
I0412 13:17:43.169926  8032 sgd_solver.cpp:105] Iteration 2352, lr = 0.00627571
I0412 13:17:48.246989  8032 solver.cpp:218] Iteration 2364 (2.36364 iter/s, 5.07691s/12 iters), loss = 3.32086
I0412 13:17:48.247036  8032 solver.cpp:237]     Train net output #0: loss = 3.32086 (* 1 = 3.32086 loss)
I0412 13:17:48.247045  8032 sgd_solver.cpp:105] Iteration 2364, lr = 0.00626081
I0412 13:17:53.280594  8032 solver.cpp:218] Iteration 2376 (2.38408 iter/s, 5.03339s/12 iters), loss = 3.31129
I0412 13:17:53.286059  8032 solver.cpp:237]     Train net output #0: loss = 3.31129 (* 1 = 3.31129 loss)
I0412 13:17:53.286079  8032 sgd_solver.cpp:105] Iteration 2376, lr = 0.00624595
I0412 13:17:58.310544  8032 solver.cpp:218] Iteration 2388 (2.38838 iter/s, 5.02433s/12 iters), loss = 3.50438
I0412 13:17:58.310591  8032 solver.cpp:237]     Train net output #0: loss = 3.50438 (* 1 = 3.50438 loss)
I0412 13:17:58.310602  8032 sgd_solver.cpp:105] Iteration 2388, lr = 0.00623112
I0412 13:18:03.442296  8032 solver.cpp:218] Iteration 2400 (2.33848 iter/s, 5.13154s/12 iters), loss = 3.18178
I0412 13:18:03.442338  8032 solver.cpp:237]     Train net output #0: loss = 3.18178 (* 1 = 3.18178 loss)
I0412 13:18:03.442348  8032 sgd_solver.cpp:105] Iteration 2400, lr = 0.00621633
I0412 13:18:08.503615  8032 solver.cpp:218] Iteration 2412 (2.37102 iter/s, 5.0611s/12 iters), loss = 3.12125
I0412 13:18:08.503659  8032 solver.cpp:237]     Train net output #0: loss = 3.12125 (* 1 = 3.12125 loss)
I0412 13:18:08.503669  8032 sgd_solver.cpp:105] Iteration 2412, lr = 0.00620157
I0412 13:18:13.466953  8032 solver.cpp:218] Iteration 2424 (2.41783 iter/s, 4.96313s/12 iters), loss = 3.28977
I0412 13:18:13.466996  8032 solver.cpp:237]     Train net output #0: loss = 3.28977 (* 1 = 3.28977 loss)
I0412 13:18:13.467005  8032 sgd_solver.cpp:105] Iteration 2424, lr = 0.00618684
I0412 13:18:14.537755  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:18:18.642717  8032 solver.cpp:218] Iteration 2436 (2.3186 iter/s, 5.17555s/12 iters), loss = 3.33779
I0412 13:18:18.642761  8032 solver.cpp:237]     Train net output #0: loss = 3.33779 (* 1 = 3.33779 loss)
I0412 13:18:18.642771  8032 sgd_solver.cpp:105] Iteration 2436, lr = 0.00617215
I0412 13:18:23.312038  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2448.caffemodel
I0412 13:18:29.868366  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2448.solverstate
I0412 13:18:35.054368  8032 solver.cpp:330] Iteration 2448, Testing net (#0)
I0412 13:18:35.054391  8032 net.cpp:676] Ignoring source layer train-data
I0412 13:18:38.455382  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:18:39.440073  8032 solver.cpp:397]     Test net output #0: accuracy = 0.166054
I0412 13:18:39.440112  8032 solver.cpp:397]     Test net output #1: loss = 3.7174 (* 1 = 3.7174 loss)
I0412 13:18:39.528406  8032 solver.cpp:218] Iteration 2448 (0.574576 iter/s, 20.885s/12 iters), loss = 3.46236
I0412 13:18:39.528465  8032 solver.cpp:237]     Train net output #0: loss = 3.46236 (* 1 = 3.46236 loss)
I0412 13:18:39.528481  8032 sgd_solver.cpp:105] Iteration 2448, lr = 0.0061575
I0412 13:18:43.597759  8032 solver.cpp:218] Iteration 2460 (2.94902 iter/s, 4.06915s/12 iters), loss = 3.28646
I0412 13:18:43.597817  8032 solver.cpp:237]     Train net output #0: loss = 3.28646 (* 1 = 3.28646 loss)
I0412 13:18:43.597833  8032 sgd_solver.cpp:105] Iteration 2460, lr = 0.00614288
I0412 13:18:48.715785  8032 solver.cpp:218] Iteration 2472 (2.34476 iter/s, 5.1178s/12 iters), loss = 3.35594
I0412 13:18:48.715832  8032 solver.cpp:237]     Train net output #0: loss = 3.35594 (* 1 = 3.35594 loss)
I0412 13:18:48.715843  8032 sgd_solver.cpp:105] Iteration 2472, lr = 0.0061283
I0412 13:18:53.830392  8032 solver.cpp:218] Iteration 2484 (2.34632 iter/s, 5.11438s/12 iters), loss = 3.39719
I0412 13:18:53.830533  8032 solver.cpp:237]     Train net output #0: loss = 3.39719 (* 1 = 3.39719 loss)
I0412 13:18:53.830547  8032 sgd_solver.cpp:105] Iteration 2484, lr = 0.00611375
I0412 13:18:58.761202  8032 solver.cpp:218] Iteration 2496 (2.43383 iter/s, 4.93051s/12 iters), loss = 3.12007
I0412 13:18:58.761255  8032 solver.cpp:237]     Train net output #0: loss = 3.12007 (* 1 = 3.12007 loss)
I0412 13:18:58.761267  8032 sgd_solver.cpp:105] Iteration 2496, lr = 0.00609923
I0412 13:19:04.206529  8032 solver.cpp:218] Iteration 2508 (2.20382 iter/s, 5.44509s/12 iters), loss = 3.22709
I0412 13:19:04.206581  8032 solver.cpp:237]     Train net output #0: loss = 3.22709 (* 1 = 3.22709 loss)
I0412 13:19:04.206593  8032 sgd_solver.cpp:105] Iteration 2508, lr = 0.00608475
I0412 13:19:09.711338  8032 solver.cpp:218] Iteration 2520 (2.18001 iter/s, 5.50457s/12 iters), loss = 3.18205
I0412 13:19:09.711391  8032 solver.cpp:237]     Train net output #0: loss = 3.18205 (* 1 = 3.18205 loss)
I0412 13:19:09.711403  8032 sgd_solver.cpp:105] Iteration 2520, lr = 0.0060703
I0412 13:19:13.251595  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:19:15.034955  8032 solver.cpp:218] Iteration 2532 (2.2542 iter/s, 5.32338s/12 iters), loss = 3.29079
I0412 13:19:15.035001  8032 solver.cpp:237]     Train net output #0: loss = 3.29079 (* 1 = 3.29079 loss)
I0412 13:19:15.035012  8032 sgd_solver.cpp:105] Iteration 2532, lr = 0.00605589
I0412 13:19:20.040621  8032 solver.cpp:218] Iteration 2544 (2.39739 iter/s, 5.00545s/12 iters), loss = 3.248
I0412 13:19:20.040669  8032 solver.cpp:237]     Train net output #0: loss = 3.248 (* 1 = 3.248 loss)
I0412 13:19:20.040681  8032 sgd_solver.cpp:105] Iteration 2544, lr = 0.00604151
I0412 13:19:22.215288  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2550.caffemodel
I0412 13:19:25.276964  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2550.solverstate
I0412 13:19:32.578279  8032 solver.cpp:330] Iteration 2550, Testing net (#0)
I0412 13:19:32.578310  8032 net.cpp:676] Ignoring source layer train-data
I0412 13:19:36.033779  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:19:37.072841  8032 solver.cpp:397]     Test net output #0: accuracy = 0.189951
I0412 13:19:37.072893  8032 solver.cpp:397]     Test net output #1: loss = 3.5856 (* 1 = 3.5856 loss)
I0412 13:19:38.932407  8032 solver.cpp:218] Iteration 2556 (0.635218 iter/s, 18.8911s/12 iters), loss = 3.5144
I0412 13:19:38.932457  8032 solver.cpp:237]     Train net output #0: loss = 3.5144 (* 1 = 3.5144 loss)
I0412 13:19:38.932468  8032 sgd_solver.cpp:105] Iteration 2556, lr = 0.00602717
I0412 13:19:44.140497  8032 solver.cpp:218] Iteration 2568 (2.30421 iter/s, 5.20787s/12 iters), loss = 3.09954
I0412 13:19:44.140537  8032 solver.cpp:237]     Train net output #0: loss = 3.09954 (* 1 = 3.09954 loss)
I0412 13:19:44.140545  8032 sgd_solver.cpp:105] Iteration 2568, lr = 0.00601286
I0412 13:19:49.274238  8032 solver.cpp:218] Iteration 2580 (2.33757 iter/s, 5.13353s/12 iters), loss = 3.19201
I0412 13:19:49.274286  8032 solver.cpp:237]     Train net output #0: loss = 3.19201 (* 1 = 3.19201 loss)
I0412 13:19:49.274296  8032 sgd_solver.cpp:105] Iteration 2580, lr = 0.00599858
I0412 13:19:54.543313  8032 solver.cpp:218] Iteration 2592 (2.27756 iter/s, 5.2688s/12 iters), loss = 3.42228
I0412 13:19:54.543360  8032 solver.cpp:237]     Train net output #0: loss = 3.42228 (* 1 = 3.42228 loss)
I0412 13:19:54.543370  8032 sgd_solver.cpp:105] Iteration 2592, lr = 0.00598434
I0412 13:19:59.633246  8032 solver.cpp:218] Iteration 2604 (2.3577 iter/s, 5.08971s/12 iters), loss = 3.39662
I0412 13:19:59.633399  8032 solver.cpp:237]     Train net output #0: loss = 3.39662 (* 1 = 3.39662 loss)
I0412 13:19:59.633414  8032 sgd_solver.cpp:105] Iteration 2604, lr = 0.00597013
I0412 13:20:04.848928  8032 solver.cpp:218] Iteration 2616 (2.3009 iter/s, 5.21535s/12 iters), loss = 3.12293
I0412 13:20:04.848989  8032 solver.cpp:237]     Train net output #0: loss = 3.12293 (* 1 = 3.12293 loss)
I0412 13:20:04.849002  8032 sgd_solver.cpp:105] Iteration 2616, lr = 0.00595596
I0412 13:20:09.820956  8032 solver.cpp:218] Iteration 2628 (2.41361 iter/s, 4.9718s/12 iters), loss = 2.68216
I0412 13:20:09.821013  8032 solver.cpp:237]     Train net output #0: loss = 2.68216 (* 1 = 2.68216 loss)
I0412 13:20:09.821025  8032 sgd_solver.cpp:105] Iteration 2628, lr = 0.00594182
I0412 13:20:10.219758  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:20:14.858312  8032 solver.cpp:218] Iteration 2640 (2.38231 iter/s, 5.03713s/12 iters), loss = 3.11465
I0412 13:20:14.858355  8032 solver.cpp:237]     Train net output #0: loss = 3.11465 (* 1 = 3.11465 loss)
I0412 13:20:14.858363  8032 sgd_solver.cpp:105] Iteration 2640, lr = 0.00592771
I0412 13:20:19.611515  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2652.caffemodel
I0412 13:20:22.636848  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2652.solverstate
I0412 13:20:27.070006  8032 solver.cpp:330] Iteration 2652, Testing net (#0)
I0412 13:20:27.070035  8032 net.cpp:676] Ignoring source layer train-data
I0412 13:20:30.397125  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:20:31.478415  8032 solver.cpp:397]     Test net output #0: accuracy = 0.177083
I0412 13:20:31.478449  8032 solver.cpp:397]     Test net output #1: loss = 3.60818 (* 1 = 3.60818 loss)
I0412 13:20:31.566562  8032 solver.cpp:218] Iteration 2652 (0.718233 iter/s, 16.7077s/12 iters), loss = 3.06118
I0412 13:20:31.566607  8032 solver.cpp:237]     Train net output #0: loss = 3.06118 (* 1 = 3.06118 loss)
I0412 13:20:31.566617  8032 sgd_solver.cpp:105] Iteration 2652, lr = 0.00591364
I0412 13:20:35.680183  8032 solver.cpp:218] Iteration 2664 (2.91727 iter/s, 4.11343s/12 iters), loss = 2.76293
I0412 13:20:35.680240  8032 solver.cpp:237]     Train net output #0: loss = 2.76293 (* 1 = 2.76293 loss)
I0412 13:20:35.680253  8032 sgd_solver.cpp:105] Iteration 2664, lr = 0.0058996
I0412 13:20:40.480311  8032 solver.cpp:218] Iteration 2676 (2.50005 iter/s, 4.79991s/12 iters), loss = 3.19928
I0412 13:20:40.480351  8032 solver.cpp:237]     Train net output #0: loss = 3.19928 (* 1 = 3.19928 loss)
I0412 13:20:40.480360  8032 sgd_solver.cpp:105] Iteration 2676, lr = 0.00588559
I0412 13:20:45.836870  8032 solver.cpp:218] Iteration 2688 (2.24034 iter/s, 5.35634s/12 iters), loss = 3.15388
I0412 13:20:45.836925  8032 solver.cpp:237]     Train net output #0: loss = 3.15388 (* 1 = 3.15388 loss)
I0412 13:20:45.836935  8032 sgd_solver.cpp:105] Iteration 2688, lr = 0.00587162
I0412 13:20:51.253749  8032 solver.cpp:218] Iteration 2700 (2.2154 iter/s, 5.41664s/12 iters), loss = 3.10841
I0412 13:20:51.253803  8032 solver.cpp:237]     Train net output #0: loss = 3.10841 (* 1 = 3.10841 loss)
I0412 13:20:51.253814  8032 sgd_solver.cpp:105] Iteration 2700, lr = 0.00585768
I0412 13:20:56.542186  8032 solver.cpp:218] Iteration 2712 (2.2692 iter/s, 5.2882s/12 iters), loss = 2.85167
I0412 13:20:56.542230  8032 solver.cpp:237]     Train net output #0: loss = 2.85167 (* 1 = 2.85167 loss)
I0412 13:20:56.542240  8032 sgd_solver.cpp:105] Iteration 2712, lr = 0.00584377
I0412 13:21:01.561090  8032 solver.cpp:218] Iteration 2724 (2.39106 iter/s, 5.01869s/12 iters), loss = 3.14679
I0412 13:21:01.561260  8032 solver.cpp:237]     Train net output #0: loss = 3.14679 (* 1 = 3.14679 loss)
I0412 13:21:01.561273  8032 sgd_solver.cpp:105] Iteration 2724, lr = 0.0058299
I0412 13:21:04.334072  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:21:07.007086  8032 solver.cpp:218] Iteration 2736 (2.2036 iter/s, 5.44564s/12 iters), loss = 2.7821
I0412 13:21:07.007136  8032 solver.cpp:237]     Train net output #0: loss = 2.7821 (* 1 = 2.7821 loss)
I0412 13:21:07.007148  8032 sgd_solver.cpp:105] Iteration 2736, lr = 0.00581605
I0412 13:21:12.352103  8032 solver.cpp:218] Iteration 2748 (2.24518 iter/s, 5.34479s/12 iters), loss = 2.81386
I0412 13:21:12.352146  8032 solver.cpp:237]     Train net output #0: loss = 2.81386 (* 1 = 2.81386 loss)
I0412 13:21:12.352154  8032 sgd_solver.cpp:105] Iteration 2748, lr = 0.00580225
I0412 13:21:14.417625  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2754.caffemodel
I0412 13:21:17.754271  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2754.solverstate
I0412 13:21:20.053488  8032 solver.cpp:330] Iteration 2754, Testing net (#0)
I0412 13:21:20.053512  8032 net.cpp:676] Ignoring source layer train-data
I0412 13:21:23.398612  8032 blocking_queue.cpp:49] Waiting for data
I0412 13:21:23.634470  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:21:24.868624  8032 solver.cpp:397]     Test net output #0: accuracy = 0.20527
I0412 13:21:24.868667  8032 solver.cpp:397]     Test net output #1: loss = 3.5195 (* 1 = 3.5195 loss)
I0412 13:21:26.875874  8032 solver.cpp:218] Iteration 2760 (0.82626 iter/s, 14.5233s/12 iters), loss = 2.98651
I0412 13:21:26.875916  8032 solver.cpp:237]     Train net output #0: loss = 2.98651 (* 1 = 2.98651 loss)
I0412 13:21:26.875926  8032 sgd_solver.cpp:105] Iteration 2760, lr = 0.00578847
I0412 13:21:32.116447  8032 solver.cpp:218] Iteration 2772 (2.28992 iter/s, 5.24035s/12 iters), loss = 2.99767
I0412 13:21:32.116565  8032 solver.cpp:237]     Train net output #0: loss = 2.99767 (* 1 = 2.99767 loss)
I0412 13:21:32.116578  8032 sgd_solver.cpp:105] Iteration 2772, lr = 0.00577473
I0412 13:21:37.051916  8032 solver.cpp:218] Iteration 2784 (2.43152 iter/s, 4.93519s/12 iters), loss = 3.07716
I0412 13:21:37.051965  8032 solver.cpp:237]     Train net output #0: loss = 3.07716 (* 1 = 3.07716 loss)
I0412 13:21:37.051975  8032 sgd_solver.cpp:105] Iteration 2784, lr = 0.00576102
I0412 13:21:41.910944  8032 solver.cpp:218] Iteration 2796 (2.46974 iter/s, 4.85882s/12 iters), loss = 3.05295
I0412 13:21:41.910988  8032 solver.cpp:237]     Train net output #0: loss = 3.05295 (* 1 = 3.05295 loss)
I0412 13:21:41.910996  8032 sgd_solver.cpp:105] Iteration 2796, lr = 0.00574734
I0412 13:21:47.055070  8032 solver.cpp:218] Iteration 2808 (2.33286 iter/s, 5.14391s/12 iters), loss = 3.03448
I0412 13:21:47.055122  8032 solver.cpp:237]     Train net output #0: loss = 3.03448 (* 1 = 3.03448 loss)
I0412 13:21:47.055135  8032 sgd_solver.cpp:105] Iteration 2808, lr = 0.00573369
I0412 13:21:52.357553  8032 solver.cpp:218] Iteration 2820 (2.26319 iter/s, 5.30225s/12 iters), loss = 2.8205
I0412 13:21:52.357596  8032 solver.cpp:237]     Train net output #0: loss = 2.8205 (* 1 = 2.8205 loss)
I0412 13:21:52.357606  8032 sgd_solver.cpp:105] Iteration 2820, lr = 0.00572008
I0412 13:21:57.219280  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:21:57.507098  8032 solver.cpp:218] Iteration 2832 (2.3304 iter/s, 5.14933s/12 iters), loss = 2.71223
I0412 13:21:57.507149  8032 solver.cpp:237]     Train net output #0: loss = 2.71223 (* 1 = 2.71223 loss)
I0412 13:21:57.507160  8032 sgd_solver.cpp:105] Iteration 2832, lr = 0.0057065
I0412 13:22:02.603966  8032 solver.cpp:218] Iteration 2844 (2.35449 iter/s, 5.09664s/12 iters), loss = 2.85606
I0412 13:22:02.606423  8032 solver.cpp:237]     Train net output #0: loss = 2.85606 (* 1 = 2.85606 loss)
I0412 13:22:02.606437  8032 sgd_solver.cpp:105] Iteration 2844, lr = 0.00569295
I0412 13:22:07.387336  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2856.caffemodel
I0412 13:22:10.417264  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2856.solverstate
I0412 13:22:12.720969  8032 solver.cpp:330] Iteration 2856, Testing net (#0)
I0412 13:22:12.720996  8032 net.cpp:676] Ignoring source layer train-data
I0412 13:22:16.135504  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:22:17.275112  8032 solver.cpp:397]     Test net output #0: accuracy = 0.208333
I0412 13:22:17.275154  8032 solver.cpp:397]     Test net output #1: loss = 3.44801 (* 1 = 3.44801 loss)
I0412 13:22:17.363310  8032 solver.cpp:218] Iteration 2856 (0.813206 iter/s, 14.7564s/12 iters), loss = 2.71009
I0412 13:22:17.363373  8032 solver.cpp:237]     Train net output #0: loss = 2.71009 (* 1 = 2.71009 loss)
I0412 13:22:17.363385  8032 sgd_solver.cpp:105] Iteration 2856, lr = 0.00567944
I0412 13:22:21.586905  8032 solver.cpp:218] Iteration 2868 (2.84132 iter/s, 4.22339s/12 iters), loss = 3.08069
I0412 13:22:21.586966  8032 solver.cpp:237]     Train net output #0: loss = 3.08069 (* 1 = 3.08069 loss)
I0412 13:22:21.586977  8032 sgd_solver.cpp:105] Iteration 2868, lr = 0.00566595
I0412 13:22:26.573609  8032 solver.cpp:218] Iteration 2880 (2.40651 iter/s, 4.98648s/12 iters), loss = 2.82909
I0412 13:22:26.573657  8032 solver.cpp:237]     Train net output #0: loss = 2.82909 (* 1 = 2.82909 loss)
I0412 13:22:26.573668  8032 sgd_solver.cpp:105] Iteration 2880, lr = 0.0056525
I0412 13:22:31.574929  8032 solver.cpp:218] Iteration 2892 (2.39947 iter/s, 5.0011s/12 iters), loss = 2.71815
I0412 13:22:31.574990  8032 solver.cpp:237]     Train net output #0: loss = 2.71815 (* 1 = 2.71815 loss)
I0412 13:22:31.575002  8032 sgd_solver.cpp:105] Iteration 2892, lr = 0.00563908
I0412 13:22:36.590140  8032 solver.cpp:218] Iteration 2904 (2.39283 iter/s, 5.01498s/12 iters), loss = 2.95686
I0412 13:22:36.590261  8032 solver.cpp:237]     Train net output #0: loss = 2.95686 (* 1 = 2.95686 loss)
I0412 13:22:36.590274  8032 sgd_solver.cpp:105] Iteration 2904, lr = 0.00562569
I0412 13:22:41.714926  8032 solver.cpp:218] Iteration 2916 (2.34169 iter/s, 5.1245s/12 iters), loss = 2.75387
I0412 13:22:41.714970  8032 solver.cpp:237]     Train net output #0: loss = 2.75387 (* 1 = 2.75387 loss)
I0412 13:22:41.714979  8032 sgd_solver.cpp:105] Iteration 2916, lr = 0.00561233
I0412 13:22:47.364241  8032 solver.cpp:218] Iteration 2928 (2.12424 iter/s, 5.64908s/12 iters), loss = 2.91014
I0412 13:22:47.364285  8032 solver.cpp:237]     Train net output #0: loss = 2.91014 (* 1 = 2.91014 loss)
I0412 13:22:47.364293  8032 sgd_solver.cpp:105] Iteration 2928, lr = 0.00559901
I0412 13:22:49.264147  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:22:52.367815  8032 solver.cpp:218] Iteration 2940 (2.39839 iter/s, 5.00336s/12 iters), loss = 2.98391
I0412 13:22:52.367862  8032 solver.cpp:237]     Train net output #0: loss = 2.98391 (* 1 = 2.98391 loss)
I0412 13:22:52.367872  8032 sgd_solver.cpp:105] Iteration 2940, lr = 0.00558572
I0412 13:22:57.497509  8032 solver.cpp:218] Iteration 2952 (2.33942 iter/s, 5.12948s/12 iters), loss = 2.78897
I0412 13:22:57.497557  8032 solver.cpp:237]     Train net output #0: loss = 2.78897 (* 1 = 2.78897 loss)
I0412 13:22:57.497570  8032 sgd_solver.cpp:105] Iteration 2952, lr = 0.00557245
I0412 13:22:59.535138  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2958.caffemodel
I0412 13:23:03.931502  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2958.solverstate
I0412 13:23:07.128304  8032 solver.cpp:330] Iteration 2958, Testing net (#0)
I0412 13:23:07.128412  8032 net.cpp:676] Ignoring source layer train-data
I0412 13:23:10.364400  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:23:11.554637  8032 solver.cpp:397]     Test net output #0: accuracy = 0.242647
I0412 13:23:11.554672  8032 solver.cpp:397]     Test net output #1: loss = 3.30152 (* 1 = 3.30152 loss)
I0412 13:23:13.580771  8032 solver.cpp:218] Iteration 2964 (0.746143 iter/s, 16.0827s/12 iters), loss = 2.43774
I0412 13:23:13.580826  8032 solver.cpp:237]     Train net output #0: loss = 2.43774 (* 1 = 2.43774 loss)
I0412 13:23:13.580837  8032 sgd_solver.cpp:105] Iteration 2964, lr = 0.00555922
I0412 13:23:19.012853  8032 solver.cpp:218] Iteration 2976 (2.20919 iter/s, 5.43185s/12 iters), loss = 2.67973
I0412 13:23:19.012897  8032 solver.cpp:237]     Train net output #0: loss = 2.67973 (* 1 = 2.67973 loss)
I0412 13:23:19.012909  8032 sgd_solver.cpp:105] Iteration 2976, lr = 0.00554603
I0412 13:23:24.409229  8032 solver.cpp:218] Iteration 2988 (2.22381 iter/s, 5.39615s/12 iters), loss = 2.82124
I0412 13:23:24.409277  8032 solver.cpp:237]     Train net output #0: loss = 2.82124 (* 1 = 2.82124 loss)
I0412 13:23:24.409288  8032 sgd_solver.cpp:105] Iteration 2988, lr = 0.00553286
I0412 13:23:29.603292  8032 solver.cpp:218] Iteration 3000 (2.31043 iter/s, 5.19384s/12 iters), loss = 2.68606
I0412 13:23:29.603344  8032 solver.cpp:237]     Train net output #0: loss = 2.68606 (* 1 = 2.68606 loss)
I0412 13:23:29.603355  8032 sgd_solver.cpp:105] Iteration 3000, lr = 0.00551972
I0412 13:23:34.979177  8032 solver.cpp:218] Iteration 3012 (2.23229 iter/s, 5.37565s/12 iters), loss = 2.89523
I0412 13:23:34.979235  8032 solver.cpp:237]     Train net output #0: loss = 2.89523 (* 1 = 2.89523 loss)
I0412 13:23:34.979246  8032 sgd_solver.cpp:105] Iteration 3012, lr = 0.00550662
I0412 13:23:40.361685  8032 solver.cpp:218] Iteration 3024 (2.22954 iter/s, 5.38227s/12 iters), loss = 2.9071
I0412 13:23:40.361789  8032 solver.cpp:237]     Train net output #0: loss = 2.9071 (* 1 = 2.9071 loss)
I0412 13:23:40.361802  8032 sgd_solver.cpp:105] Iteration 3024, lr = 0.00549354
I0412 13:23:44.297246  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:23:45.336997  8032 solver.cpp:218] Iteration 3036 (2.41204 iter/s, 4.97504s/12 iters), loss = 2.92618
I0412 13:23:45.337044  8032 solver.cpp:237]     Train net output #0: loss = 2.92618 (* 1 = 2.92618 loss)
I0412 13:23:45.337054  8032 sgd_solver.cpp:105] Iteration 3036, lr = 0.0054805
I0412 13:23:50.398754  8032 solver.cpp:218] Iteration 3048 (2.37082 iter/s, 5.06154s/12 iters), loss = 2.87351
I0412 13:23:50.398798  8032 solver.cpp:237]     Train net output #0: loss = 2.87351 (* 1 = 2.87351 loss)
I0412 13:23:50.398808  8032 sgd_solver.cpp:105] Iteration 3048, lr = 0.00546749
I0412 13:23:55.007449  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3060.caffemodel
I0412 13:24:01.825683  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3060.solverstate
I0412 13:24:05.682060  8032 solver.cpp:330] Iteration 3060, Testing net (#0)
I0412 13:24:05.682088  8032 net.cpp:676] Ignoring source layer train-data
I0412 13:24:08.889943  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:24:10.115051  8032 solver.cpp:397]     Test net output #0: accuracy = 0.25
I0412 13:24:10.115097  8032 solver.cpp:397]     Test net output #1: loss = 3.17958 (* 1 = 3.17958 loss)
I0412 13:24:10.203531  8032 solver.cpp:218] Iteration 3060 (0.605935 iter/s, 19.8041s/12 iters), loss = 2.59694
I0412 13:24:10.203568  8032 solver.cpp:237]     Train net output #0: loss = 2.59694 (* 1 = 2.59694 loss)
I0412 13:24:10.203578  8032 sgd_solver.cpp:105] Iteration 3060, lr = 0.00545451
I0412 13:24:14.759922  8032 solver.cpp:218] Iteration 3072 (2.63378 iter/s, 4.55619s/12 iters), loss = 2.52023
I0412 13:24:14.760140  8032 solver.cpp:237]     Train net output #0: loss = 2.52023 (* 1 = 2.52023 loss)
I0412 13:24:14.760164  8032 sgd_solver.cpp:105] Iteration 3072, lr = 0.00544156
I0412 13:24:20.105585  8032 solver.cpp:218] Iteration 3084 (2.24497 iter/s, 5.34528s/12 iters), loss = 2.64738
I0412 13:24:20.105636  8032 solver.cpp:237]     Train net output #0: loss = 2.64738 (* 1 = 2.64738 loss)
I0412 13:24:20.105648  8032 sgd_solver.cpp:105] Iteration 3084, lr = 0.00542864
I0412 13:24:25.330680  8032 solver.cpp:218] Iteration 3096 (2.29671 iter/s, 5.22487s/12 iters), loss = 2.78296
I0412 13:24:25.330721  8032 solver.cpp:237]     Train net output #0: loss = 2.78296 (* 1 = 2.78296 loss)
I0412 13:24:25.330730  8032 sgd_solver.cpp:105] Iteration 3096, lr = 0.00541575
I0412 13:24:30.847815  8032 solver.cpp:218] Iteration 3108 (2.17513 iter/s, 5.51691s/12 iters), loss = 2.35977
I0412 13:24:30.847854  8032 solver.cpp:237]     Train net output #0: loss = 2.35977 (* 1 = 2.35977 loss)
I0412 13:24:30.847863  8032 sgd_solver.cpp:105] Iteration 3108, lr = 0.00540289
I0412 13:24:36.229377  8032 solver.cpp:218] Iteration 3120 (2.22993 iter/s, 5.38134s/12 iters), loss = 2.36242
I0412 13:24:36.229434  8032 solver.cpp:237]     Train net output #0: loss = 2.36242 (* 1 = 2.36242 loss)
I0412 13:24:36.229446  8032 sgd_solver.cpp:105] Iteration 3120, lr = 0.00539006
I0412 13:24:41.312829  8032 solver.cpp:218] Iteration 3132 (2.36071 iter/s, 5.08323s/12 iters), loss = 2.69775
I0412 13:24:41.312877  8032 solver.cpp:237]     Train net output #0: loss = 2.69775 (* 1 = 2.69775 loss)
I0412 13:24:41.312889  8032 sgd_solver.cpp:105] Iteration 3132, lr = 0.00537727
I0412 13:24:42.429563  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:24:46.414422  8032 solver.cpp:218] Iteration 3144 (2.35231 iter/s, 5.10138s/12 iters), loss = 2.26129
I0412 13:24:46.414520  8032 solver.cpp:237]     Train net output #0: loss = 2.26129 (* 1 = 2.26129 loss)
I0412 13:24:46.414530  8032 sgd_solver.cpp:105] Iteration 3144, lr = 0.0053645
I0412 13:24:51.825270  8032 solver.cpp:218] Iteration 3156 (2.21788 iter/s, 5.41057s/12 iters), loss = 2.43423
I0412 13:24:51.825320  8032 solver.cpp:237]     Train net output #0: loss = 2.43423 (* 1 = 2.43423 loss)
I0412 13:24:51.825330  8032 sgd_solver.cpp:105] Iteration 3156, lr = 0.00535176
I0412 13:24:53.824945  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3162.caffemodel
I0412 13:25:00.814482  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3162.solverstate
I0412 13:25:04.918721  8032 solver.cpp:330] Iteration 3162, Testing net (#0)
I0412 13:25:04.918751  8032 net.cpp:676] Ignoring source layer train-data
I0412 13:25:08.073192  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:25:09.340181  8032 solver.cpp:397]     Test net output #0: accuracy = 0.254289
I0412 13:25:09.340219  8032 solver.cpp:397]     Test net output #1: loss = 3.21982 (* 1 = 3.21982 loss)
I0412 13:25:11.286756  8032 solver.cpp:218] Iteration 3168 (0.616624 iter/s, 19.4608s/12 iters), loss = 2.46443
I0412 13:25:11.286809  8032 solver.cpp:237]     Train net output #0: loss = 2.46443 (* 1 = 2.46443 loss)
I0412 13:25:11.286821  8032 sgd_solver.cpp:105] Iteration 3168, lr = 0.00533906
I0412 13:25:16.301386  8032 solver.cpp:218] Iteration 3180 (2.3931 iter/s, 5.01441s/12 iters), loss = 2.64677
I0412 13:25:16.301425  8032 solver.cpp:237]     Train net output #0: loss = 2.64677 (* 1 = 2.64677 loss)
I0412 13:25:16.301434  8032 sgd_solver.cpp:105] Iteration 3180, lr = 0.00532638
I0412 13:25:21.441015  8032 solver.cpp:218] Iteration 3192 (2.3349 iter/s, 5.13941s/12 iters), loss = 2.65569
I0412 13:25:21.441138  8032 solver.cpp:237]     Train net output #0: loss = 2.65569 (* 1 = 2.65569 loss)
I0412 13:25:21.441150  8032 sgd_solver.cpp:105] Iteration 3192, lr = 0.00531374
I0412 13:25:26.429311  8032 solver.cpp:218] Iteration 3204 (2.40577 iter/s, 4.988s/12 iters), loss = 2.2948
I0412 13:25:26.429369  8032 solver.cpp:237]     Train net output #0: loss = 2.2948 (* 1 = 2.2948 loss)
I0412 13:25:26.429383  8032 sgd_solver.cpp:105] Iteration 3204, lr = 0.00530112
I0412 13:25:31.434511  8032 solver.cpp:218] Iteration 3216 (2.39762 iter/s, 5.00497s/12 iters), loss = 2.37607
I0412 13:25:31.434574  8032 solver.cpp:237]     Train net output #0: loss = 2.37607 (* 1 = 2.37607 loss)
I0412 13:25:31.434589  8032 sgd_solver.cpp:105] Iteration 3216, lr = 0.00528853
I0412 13:25:36.506600  8032 solver.cpp:218] Iteration 3228 (2.366 iter/s, 5.07185s/12 iters), loss = 2.46055
I0412 13:25:36.506651  8032 solver.cpp:237]     Train net output #0: loss = 2.46055 (* 1 = 2.46055 loss)
I0412 13:25:36.506661  8032 sgd_solver.cpp:105] Iteration 3228, lr = 0.00527598
I0412 13:25:39.785765  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:25:41.529790  8032 solver.cpp:218] Iteration 3240 (2.38903 iter/s, 5.02296s/12 iters), loss = 2.94779
I0412 13:25:41.529845  8032 solver.cpp:237]     Train net output #0: loss = 2.94779 (* 1 = 2.94779 loss)
I0412 13:25:41.529858  8032 sgd_solver.cpp:105] Iteration 3240, lr = 0.00526345
I0412 13:25:46.475523  8032 solver.cpp:218] Iteration 3252 (2.42644 iter/s, 4.94552s/12 iters), loss = 2.41645
I0412 13:25:46.475565  8032 solver.cpp:237]     Train net output #0: loss = 2.41645 (* 1 = 2.41645 loss)
I0412 13:25:46.475574  8032 sgd_solver.cpp:105] Iteration 3252, lr = 0.00525095
I0412 13:25:50.970285  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3264.caffemodel
I0412 13:26:00.260248  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3264.solverstate
I0412 13:26:03.354216  8032 solver.cpp:330] Iteration 3264, Testing net (#0)
I0412 13:26:03.354243  8032 net.cpp:676] Ignoring source layer train-data
I0412 13:26:06.450623  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:26:07.756057  8032 solver.cpp:397]     Test net output #0: accuracy = 0.258578
I0412 13:26:07.756108  8032 solver.cpp:397]     Test net output #1: loss = 3.20451 (* 1 = 3.20451 loss)
I0412 13:26:07.844012  8032 solver.cpp:218] Iteration 3264 (0.561593 iter/s, 21.3678s/12 iters), loss = 2.49051
I0412 13:26:07.844061  8032 solver.cpp:237]     Train net output #0: loss = 2.49051 (* 1 = 2.49051 loss)
I0412 13:26:07.844072  8032 sgd_solver.cpp:105] Iteration 3264, lr = 0.00523849
I0412 13:26:12.270177  8032 solver.cpp:218] Iteration 3276 (2.71127 iter/s, 4.42597s/12 iters), loss = 2.62758
I0412 13:26:12.270223  8032 solver.cpp:237]     Train net output #0: loss = 2.62758 (* 1 = 2.62758 loss)
I0412 13:26:12.270236  8032 sgd_solver.cpp:105] Iteration 3276, lr = 0.00522605
I0412 13:26:17.447472  8032 solver.cpp:218] Iteration 3288 (2.31791 iter/s, 5.17707s/12 iters), loss = 2.48828
I0412 13:26:17.447599  8032 solver.cpp:237]     Train net output #0: loss = 2.48828 (* 1 = 2.48828 loss)
I0412 13:26:17.447611  8032 sgd_solver.cpp:105] Iteration 3288, lr = 0.00521364
I0412 13:26:22.724958  8032 solver.cpp:218] Iteration 3300 (2.27391 iter/s, 5.27726s/12 iters), loss = 2.71639
I0412 13:26:22.725003  8032 solver.cpp:237]     Train net output #0: loss = 2.71639 (* 1 = 2.71639 loss)
I0412 13:26:22.725011  8032 sgd_solver.cpp:105] Iteration 3300, lr = 0.00520126
I0412 13:26:27.769594  8032 solver.cpp:218] Iteration 3312 (2.37887 iter/s, 5.04442s/12 iters), loss = 2.31623
I0412 13:26:27.769646  8032 solver.cpp:237]     Train net output #0: loss = 2.31623 (* 1 = 2.31623 loss)
I0412 13:26:27.769659  8032 sgd_solver.cpp:105] Iteration 3312, lr = 0.00518892
I0412 13:26:32.833398  8032 solver.cpp:218] Iteration 3324 (2.36986 iter/s, 5.06358s/12 iters), loss = 2.05275
I0412 13:26:32.833487  8032 solver.cpp:237]     Train net output #0: loss = 2.05275 (* 1 = 2.05275 loss)
I0412 13:26:32.833498  8032 sgd_solver.cpp:105] Iteration 3324, lr = 0.0051766
I0412 13:26:37.936115  8032 solver.cpp:218] Iteration 3336 (2.35181 iter/s, 5.10245s/12 iters), loss = 2.06808
I0412 13:26:37.936161  8032 solver.cpp:237]     Train net output #0: loss = 2.06808 (* 1 = 2.06808 loss)
I0412 13:26:37.936172  8032 sgd_solver.cpp:105] Iteration 3336, lr = 0.00516431
I0412 13:26:38.397588  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:26:43.261438  8032 solver.cpp:218] Iteration 3348 (2.25348 iter/s, 5.32509s/12 iters), loss = 2.46995
I0412 13:26:43.261508  8032 solver.cpp:237]     Train net output #0: loss = 2.46995 (* 1 = 2.46995 loss)
I0412 13:26:43.261524  8032 sgd_solver.cpp:105] Iteration 3348, lr = 0.00515204
I0412 13:26:48.493291  8032 solver.cpp:218] Iteration 3360 (2.29375 iter/s, 5.2316s/12 iters), loss = 2.15003
I0412 13:26:48.493346  8032 solver.cpp:237]     Train net output #0: loss = 2.15003 (* 1 = 2.15003 loss)
I0412 13:26:48.493356  8032 sgd_solver.cpp:105] Iteration 3360, lr = 0.00513981
I0412 13:26:50.576817  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3366.caffemodel
I0412 13:26:55.791153  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3366.solverstate
I0412 13:26:58.108265  8032 solver.cpp:330] Iteration 3366, Testing net (#0)
I0412 13:26:58.108290  8032 net.cpp:676] Ignoring source layer train-data
I0412 13:27:01.184062  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:27:02.526392  8032 solver.cpp:397]     Test net output #0: accuracy = 0.258578
I0412 13:27:02.526450  8032 solver.cpp:397]     Test net output #1: loss = 3.18938 (* 1 = 3.18938 loss)
I0412 13:27:04.315197  8032 solver.cpp:218] Iteration 3372 (0.758469 iter/s, 15.8213s/12 iters), loss = 2.18724
I0412 13:27:04.315337  8032 solver.cpp:237]     Train net output #0: loss = 2.18724 (* 1 = 2.18724 loss)
I0412 13:27:04.315349  8032 sgd_solver.cpp:105] Iteration 3372, lr = 0.00512761
I0412 13:27:09.406942  8032 solver.cpp:218] Iteration 3384 (2.3569 iter/s, 5.09144s/12 iters), loss = 2.484
I0412 13:27:09.406985  8032 solver.cpp:237]     Train net output #0: loss = 2.484 (* 1 = 2.484 loss)
I0412 13:27:09.406993  8032 sgd_solver.cpp:105] Iteration 3384, lr = 0.00511544
I0412 13:27:14.397989  8032 solver.cpp:218] Iteration 3396 (2.40442 iter/s, 4.9908s/12 iters), loss = 2.52458
I0412 13:27:14.398033  8032 solver.cpp:237]     Train net output #0: loss = 2.52458 (* 1 = 2.52458 loss)
I0412 13:27:14.398042  8032 sgd_solver.cpp:105] Iteration 3396, lr = 0.00510329
I0412 13:27:19.295776  8032 solver.cpp:218] Iteration 3408 (2.45019 iter/s, 4.89758s/12 iters), loss = 2.34152
I0412 13:27:19.295816  8032 solver.cpp:237]     Train net output #0: loss = 2.34152 (* 1 = 2.34152 loss)
I0412 13:27:19.295826  8032 sgd_solver.cpp:105] Iteration 3408, lr = 0.00509117
I0412 13:27:24.352942  8032 solver.cpp:218] Iteration 3420 (2.37297 iter/s, 5.05695s/12 iters), loss = 2.16518
I0412 13:27:24.352984  8032 solver.cpp:237]     Train net output #0: loss = 2.16518 (* 1 = 2.16518 loss)
I0412 13:27:24.352993  8032 sgd_solver.cpp:105] Iteration 3420, lr = 0.00507909
I0412 13:27:29.325469  8032 solver.cpp:218] Iteration 3432 (2.41336 iter/s, 4.97231s/12 iters), loss = 2.17867
I0412 13:27:29.325515  8032 solver.cpp:237]     Train net output #0: loss = 2.17867 (* 1 = 2.17867 loss)
I0412 13:27:29.325526  8032 sgd_solver.cpp:105] Iteration 3432, lr = 0.00506703
I0412 13:27:31.876350  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:27:34.320155  8032 solver.cpp:218] Iteration 3444 (2.40266 iter/s, 4.99447s/12 iters), loss = 2.16156
I0412 13:27:34.320240  8032 solver.cpp:237]     Train net output #0: loss = 2.16156 (* 1 = 2.16156 loss)
I0412 13:27:34.320250  8032 sgd_solver.cpp:105] Iteration 3444, lr = 0.005055
I0412 13:27:39.396553  8032 solver.cpp:218] Iteration 3456 (2.364 iter/s, 5.07615s/12 iters), loss = 2.21358
I0412 13:27:39.396595  8032 solver.cpp:237]     Train net output #0: loss = 2.21358 (* 1 = 2.21358 loss)
I0412 13:27:39.396605  8032 sgd_solver.cpp:105] Iteration 3456, lr = 0.005043
I0412 13:27:44.324942  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3468.caffemodel
I0412 13:27:52.621742  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3468.solverstate
I0412 13:27:54.950513  8032 solver.cpp:330] Iteration 3468, Testing net (#0)
I0412 13:27:54.950541  8032 net.cpp:676] Ignoring source layer train-data
I0412 13:27:55.463038  8032 blocking_queue.cpp:49] Waiting for data
I0412 13:27:58.133271  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:27:59.519456  8032 solver.cpp:397]     Test net output #0: accuracy = 0.253064
I0412 13:27:59.519493  8032 solver.cpp:397]     Test net output #1: loss = 3.19809 (* 1 = 3.19809 loss)
I0412 13:27:59.607262  8032 solver.cpp:218] Iteration 3468 (0.593765 iter/s, 20.21s/12 iters), loss = 2.36247
I0412 13:27:59.607321  8032 solver.cpp:237]     Train net output #0: loss = 2.36247 (* 1 = 2.36247 loss)
I0412 13:27:59.607332  8032 sgd_solver.cpp:105] Iteration 3468, lr = 0.00503102
I0412 13:28:04.328624  8032 solver.cpp:218] Iteration 3480 (2.54176 iter/s, 4.72114s/12 iters), loss = 2.27544
I0412 13:28:04.328780  8032 solver.cpp:237]     Train net output #0: loss = 2.27544 (* 1 = 2.27544 loss)
I0412 13:28:04.328792  8032 sgd_solver.cpp:105] Iteration 3480, lr = 0.00501908
I0412 13:28:09.351884  8032 solver.cpp:218] Iteration 3492 (2.38904 iter/s, 5.02293s/12 iters), loss = 2.56693
I0412 13:28:09.351938  8032 solver.cpp:237]     Train net output #0: loss = 2.56693 (* 1 = 2.56693 loss)
I0412 13:28:09.351950  8032 sgd_solver.cpp:105] Iteration 3492, lr = 0.00500716
I0412 13:28:14.573729  8032 solver.cpp:218] Iteration 3504 (2.29814 iter/s, 5.22162s/12 iters), loss = 2.13096
I0412 13:28:14.573777  8032 solver.cpp:237]     Train net output #0: loss = 2.13096 (* 1 = 2.13096 loss)
I0412 13:28:14.573789  8032 sgd_solver.cpp:105] Iteration 3504, lr = 0.00499527
I0412 13:28:19.629456  8032 solver.cpp:218] Iteration 3516 (2.37365 iter/s, 5.0555s/12 iters), loss = 1.95403
I0412 13:28:19.629510  8032 solver.cpp:237]     Train net output #0: loss = 1.95403 (* 1 = 1.95403 loss)
I0412 13:28:19.629586  8032 sgd_solver.cpp:105] Iteration 3516, lr = 0.00498341
I0412 13:28:24.857836  8032 solver.cpp:218] Iteration 3528 (2.29527 iter/s, 5.22814s/12 iters), loss = 2.16614
I0412 13:28:24.857903  8032 solver.cpp:237]     Train net output #0: loss = 2.16614 (* 1 = 2.16614 loss)
I0412 13:28:24.857919  8032 sgd_solver.cpp:105] Iteration 3528, lr = 0.00497158
I0412 13:28:29.824622  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:28:30.106055  8032 solver.cpp:218] Iteration 3540 (2.2866 iter/s, 5.24797s/12 iters), loss = 1.71088
I0412 13:28:30.106104  8032 solver.cpp:237]     Train net output #0: loss = 1.71088 (* 1 = 1.71088 loss)
I0412 13:28:30.106115  8032 sgd_solver.cpp:105] Iteration 3540, lr = 0.00495978
I0412 13:28:35.299276  8032 solver.cpp:218] Iteration 3552 (2.3108 iter/s, 5.193s/12 iters), loss = 2.12728
I0412 13:28:35.309866  8032 solver.cpp:237]     Train net output #0: loss = 2.12728 (* 1 = 2.12728 loss)
I0412 13:28:35.309881  8032 sgd_solver.cpp:105] Iteration 3552, lr = 0.004948
I0412 13:28:40.372151  8032 solver.cpp:218] Iteration 3564 (2.37055 iter/s, 5.06212s/12 iters), loss = 2.06077
I0412 13:28:40.372198  8032 solver.cpp:237]     Train net output #0: loss = 2.06077 (* 1 = 2.06077 loss)
I0412 13:28:40.372210  8032 sgd_solver.cpp:105] Iteration 3564, lr = 0.00493626
I0412 13:28:42.433709  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3570.caffemodel
I0412 13:28:45.571302  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3570.solverstate
I0412 13:28:47.893352  8032 solver.cpp:330] Iteration 3570, Testing net (#0)
I0412 13:28:47.893379  8032 net.cpp:676] Ignoring source layer train-data
I0412 13:28:50.864727  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:28:52.317562  8032 solver.cpp:397]     Test net output #0: accuracy = 0.287377
I0412 13:28:52.317602  8032 solver.cpp:397]     Test net output #1: loss = 3.14161 (* 1 = 3.14161 loss)
I0412 13:28:54.288786  8032 solver.cpp:218] Iteration 3576 (0.862308 iter/s, 13.9161s/12 iters), loss = 2.21601
I0412 13:28:54.288831  8032 solver.cpp:237]     Train net output #0: loss = 2.21601 (* 1 = 2.21601 loss)
I0412 13:28:54.288841  8032 sgd_solver.cpp:105] Iteration 3576, lr = 0.00492454
I0412 13:28:59.391623  8032 solver.cpp:218] Iteration 3588 (2.35173 iter/s, 5.10262s/12 iters), loss = 2.19535
I0412 13:28:59.391670  8032 solver.cpp:237]     Train net output #0: loss = 2.19535 (* 1 = 2.19535 loss)
I0412 13:28:59.391682  8032 sgd_solver.cpp:105] Iteration 3588, lr = 0.00491284
I0412 13:29:04.538069  8032 solver.cpp:218] Iteration 3600 (2.33181 iter/s, 5.14622s/12 iters), loss = 2.20516
I0412 13:29:04.538125  8032 solver.cpp:237]     Train net output #0: loss = 2.20516 (* 1 = 2.20516 loss)
I0412 13:29:04.538137  8032 sgd_solver.cpp:105] Iteration 3600, lr = 0.00490118
I0412 13:29:09.723675  8032 solver.cpp:218] Iteration 3612 (2.3142 iter/s, 5.18538s/12 iters), loss = 2.28089
I0412 13:29:09.723820  8032 solver.cpp:237]     Train net output #0: loss = 2.28089 (* 1 = 2.28089 loss)
I0412 13:29:09.723834  8032 sgd_solver.cpp:105] Iteration 3612, lr = 0.00488954
I0412 13:29:14.868283  8032 solver.cpp:218] Iteration 3624 (2.33268 iter/s, 5.1443s/12 iters), loss = 2.28518
I0412 13:29:14.868325  8032 solver.cpp:237]     Train net output #0: loss = 2.28518 (* 1 = 2.28518 loss)
I0412 13:29:14.868335  8032 sgd_solver.cpp:105] Iteration 3624, lr = 0.00487793
I0412 13:29:20.127190  8032 solver.cpp:218] Iteration 3636 (2.28194 iter/s, 5.25867s/12 iters), loss = 1.94745
I0412 13:29:20.127239  8032 solver.cpp:237]     Train net output #0: loss = 1.94745 (* 1 = 1.94745 loss)
I0412 13:29:20.127250  8032 sgd_solver.cpp:105] Iteration 3636, lr = 0.00486635
I0412 13:29:22.043129  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:29:25.475184  8032 solver.cpp:218] Iteration 3648 (2.24393 iter/s, 5.34776s/12 iters), loss = 2.10033
I0412 13:29:25.475235  8032 solver.cpp:237]     Train net output #0: loss = 2.10033 (* 1 = 2.10033 loss)
I0412 13:29:25.475246  8032 sgd_solver.cpp:105] Iteration 3648, lr = 0.0048548
I0412 13:29:30.612711  8032 solver.cpp:218] Iteration 3660 (2.33586 iter/s, 5.1373s/12 iters), loss = 1.98879
I0412 13:29:30.612772  8032 solver.cpp:237]     Train net output #0: loss = 1.98879 (* 1 = 1.98879 loss)
I0412 13:29:30.612787  8032 sgd_solver.cpp:105] Iteration 3660, lr = 0.00484327
I0412 13:29:35.541438  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3672.caffemodel
I0412 13:29:40.949805  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3672.solverstate
I0412 13:29:44.904290  8032 solver.cpp:330] Iteration 3672, Testing net (#0)
I0412 13:29:44.904311  8032 net.cpp:676] Ignoring source layer train-data
I0412 13:29:47.927487  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:29:49.452090  8032 solver.cpp:397]     Test net output #0: accuracy = 0.302083
I0412 13:29:49.452127  8032 solver.cpp:397]     Test net output #1: loss = 3.01262 (* 1 = 3.01262 loss)
I0412 13:29:49.540505  8032 solver.cpp:218] Iteration 3672 (0.63401 iter/s, 18.9271s/12 iters), loss = 2.20981
I0412 13:29:49.540552  8032 solver.cpp:237]     Train net output #0: loss = 2.20981 (* 1 = 2.20981 loss)
I0412 13:29:49.540561  8032 sgd_solver.cpp:105] Iteration 3672, lr = 0.00483177
I0412 13:29:53.844338  8032 solver.cpp:218] Iteration 3684 (2.78834 iter/s, 4.30363s/12 iters), loss = 1.8899
I0412 13:29:53.844393  8032 solver.cpp:237]     Train net output #0: loss = 1.8899 (* 1 = 1.8899 loss)
I0412 13:29:53.844404  8032 sgd_solver.cpp:105] Iteration 3684, lr = 0.0048203
I0412 13:29:58.902436  8032 solver.cpp:218] Iteration 3696 (2.37254 iter/s, 5.05787s/12 iters), loss = 1.93972
I0412 13:29:58.902493  8032 solver.cpp:237]     Train net output #0: loss = 1.93972 (* 1 = 1.93972 loss)
I0412 13:29:58.902504  8032 sgd_solver.cpp:105] Iteration 3696, lr = 0.00480886
I0412 13:30:04.081220  8032 solver.cpp:218] Iteration 3708 (2.31725 iter/s, 5.17855s/12 iters), loss = 1.91732
I0412 13:30:04.081274  8032 solver.cpp:237]     Train net output #0: loss = 1.91732 (* 1 = 1.91732 loss)
I0412 13:30:04.081285  8032 sgd_solver.cpp:105] Iteration 3708, lr = 0.00479744
I0412 13:30:09.187534  8032 solver.cpp:218] Iteration 3720 (2.35014 iter/s, 5.10608s/12 iters), loss = 2.12419
I0412 13:30:09.187609  8032 solver.cpp:237]     Train net output #0: loss = 2.12419 (* 1 = 2.12419 loss)
I0412 13:30:09.187630  8032 sgd_solver.cpp:105] Iteration 3720, lr = 0.00478605
I0412 13:30:14.165787  8032 solver.cpp:218] Iteration 3732 (2.4106 iter/s, 4.97802s/12 iters), loss = 1.97178
I0412 13:30:14.165936  8032 solver.cpp:237]     Train net output #0: loss = 1.97178 (* 1 = 1.97178 loss)
I0412 13:30:14.165948  8032 sgd_solver.cpp:105] Iteration 3732, lr = 0.00477469
I0412 13:30:18.232698  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:30:19.265375  8032 solver.cpp:218] Iteration 3744 (2.35328 iter/s, 5.09927s/12 iters), loss = 1.87305
I0412 13:30:19.265426  8032 solver.cpp:237]     Train net output #0: loss = 1.87305 (* 1 = 1.87305 loss)
I0412 13:30:19.265439  8032 sgd_solver.cpp:105] Iteration 3744, lr = 0.00476335
I0412 13:30:24.426611  8032 solver.cpp:218] Iteration 3756 (2.32515 iter/s, 5.16096s/12 iters), loss = 1.98858
I0412 13:30:24.426652  8032 solver.cpp:237]     Train net output #0: loss = 1.98858 (* 1 = 1.98858 loss)
I0412 13:30:24.426661  8032 sgd_solver.cpp:105] Iteration 3756, lr = 0.00475204
I0412 13:30:29.675001  8032 solver.cpp:218] Iteration 3768 (2.28651 iter/s, 5.24817s/12 iters), loss = 1.93981
I0412 13:30:29.675053  8032 solver.cpp:237]     Train net output #0: loss = 1.93981 (* 1 = 1.93981 loss)
I0412 13:30:29.675066  8032 sgd_solver.cpp:105] Iteration 3768, lr = 0.00474076
I0412 13:30:31.733167  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3774.caffemodel
I0412 13:30:34.721577  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3774.solverstate
I0412 13:30:37.370419  8032 solver.cpp:330] Iteration 3774, Testing net (#0)
I0412 13:30:37.370450  8032 net.cpp:676] Ignoring source layer train-data
I0412 13:30:40.257333  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:30:41.751648  8032 solver.cpp:397]     Test net output #0: accuracy = 0.318015
I0412 13:30:41.751688  8032 solver.cpp:397]     Test net output #1: loss = 3.00299 (* 1 = 3.00299 loss)
I0412 13:30:43.518412  8032 solver.cpp:218] Iteration 3780 (0.86687 iter/s, 13.8429s/12 iters), loss = 2.15157
I0412 13:30:43.518466  8032 solver.cpp:237]     Train net output #0: loss = 2.15157 (* 1 = 2.15157 loss)
I0412 13:30:43.518477  8032 sgd_solver.cpp:105] Iteration 3780, lr = 0.00472951
I0412 13:30:48.904402  8032 solver.cpp:218] Iteration 3792 (2.2281 iter/s, 5.38575s/12 iters), loss = 1.88733
I0412 13:30:48.904516  8032 solver.cpp:237]     Train net output #0: loss = 1.88733 (* 1 = 1.88733 loss)
I0412 13:30:48.904531  8032 sgd_solver.cpp:105] Iteration 3792, lr = 0.00471828
I0412 13:30:53.842830  8032 solver.cpp:218] Iteration 3804 (2.43006 iter/s, 4.93815s/12 iters), loss = 1.83613
I0412 13:30:53.842880  8032 solver.cpp:237]     Train net output #0: loss = 1.83613 (* 1 = 1.83613 loss)
I0412 13:30:53.842891  8032 sgd_solver.cpp:105] Iteration 3804, lr = 0.00470707
I0412 13:30:58.787724  8032 solver.cpp:218] Iteration 3816 (2.42685 iter/s, 4.94468s/12 iters), loss = 2.04534
I0412 13:30:58.787775  8032 solver.cpp:237]     Train net output #0: loss = 2.04534 (* 1 = 2.04534 loss)
I0412 13:30:58.787786  8032 sgd_solver.cpp:105] Iteration 3816, lr = 0.0046959
I0412 13:31:03.771318  8032 solver.cpp:218] Iteration 3828 (2.40801 iter/s, 4.98337s/12 iters), loss = 1.41818
I0412 13:31:03.771376  8032 solver.cpp:237]     Train net output #0: loss = 1.41818 (* 1 = 1.41818 loss)
I0412 13:31:03.771389  8032 sgd_solver.cpp:105] Iteration 3828, lr = 0.00468475
I0412 13:31:08.848213  8032 solver.cpp:218] Iteration 3840 (2.36376 iter/s, 5.07667s/12 iters), loss = 1.86828
I0412 13:31:08.848260  8032 solver.cpp:237]     Train net output #0: loss = 1.86828 (* 1 = 1.86828 loss)
I0412 13:31:08.848269  8032 sgd_solver.cpp:105] Iteration 3840, lr = 0.00467363
I0412 13:31:09.970268  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:31:13.956677  8032 solver.cpp:218] Iteration 3852 (2.34914 iter/s, 5.10824s/12 iters), loss = 1.7255
I0412 13:31:13.956729  8032 solver.cpp:237]     Train net output #0: loss = 1.7255 (* 1 = 1.7255 loss)
I0412 13:31:13.956741  8032 sgd_solver.cpp:105] Iteration 3852, lr = 0.00466253
I0412 13:31:19.010761  8032 solver.cpp:218] Iteration 3864 (2.37442 iter/s, 5.05386s/12 iters), loss = 2.05781
I0412 13:31:19.010900  8032 solver.cpp:237]     Train net output #0: loss = 2.05781 (* 1 = 2.05781 loss)
I0412 13:31:19.010912  8032 sgd_solver.cpp:105] Iteration 3864, lr = 0.00465146
I0412 13:31:23.489605  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3876.caffemodel
I0412 13:31:26.478999  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3876.solverstate
I0412 13:31:28.821159  8032 solver.cpp:330] Iteration 3876, Testing net (#0)
I0412 13:31:28.821188  8032 net.cpp:676] Ignoring source layer train-data
I0412 13:31:31.819813  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:31:33.459597  8032 solver.cpp:397]     Test net output #0: accuracy = 0.318015
I0412 13:31:33.459633  8032 solver.cpp:397]     Test net output #1: loss = 2.89415 (* 1 = 2.89415 loss)
I0412 13:31:33.547762  8032 solver.cpp:218] Iteration 3876 (0.825514 iter/s, 14.5364s/12 iters), loss = 1.71869
I0412 13:31:33.547816  8032 solver.cpp:237]     Train net output #0: loss = 1.71869 (* 1 = 1.71869 loss)
I0412 13:31:33.547827  8032 sgd_solver.cpp:105] Iteration 3876, lr = 0.00464042
I0412 13:31:37.887472  8032 solver.cpp:218] Iteration 3888 (2.76529 iter/s, 4.3395s/12 iters), loss = 1.75712
I0412 13:31:37.887526  8032 solver.cpp:237]     Train net output #0: loss = 1.75712 (* 1 = 1.75712 loss)
I0412 13:31:37.887537  8032 sgd_solver.cpp:105] Iteration 3888, lr = 0.0046294
I0412 13:31:43.227696  8032 solver.cpp:218] Iteration 3900 (2.24719 iter/s, 5.33999s/12 iters), loss = 1.80088
I0412 13:31:43.227738  8032 solver.cpp:237]     Train net output #0: loss = 1.80088 (* 1 = 1.80088 loss)
I0412 13:31:43.227747  8032 sgd_solver.cpp:105] Iteration 3900, lr = 0.00461841
I0412 13:31:48.214398  8032 solver.cpp:218] Iteration 3912 (2.4065 iter/s, 4.98649s/12 iters), loss = 1.51045
I0412 13:31:48.214449  8032 solver.cpp:237]     Train net output #0: loss = 1.51045 (* 1 = 1.51045 loss)
I0412 13:31:48.214463  8032 sgd_solver.cpp:105] Iteration 3912, lr = 0.00460744
I0412 13:31:53.325690  8032 solver.cpp:218] Iteration 3924 (2.34784 iter/s, 5.11107s/12 iters), loss = 1.59292
I0412 13:31:53.325783  8032 solver.cpp:237]     Train net output #0: loss = 1.59292 (* 1 = 1.59292 loss)
I0412 13:31:53.325791  8032 sgd_solver.cpp:105] Iteration 3924, lr = 0.0045965
I0412 13:31:58.515640  8032 solver.cpp:218] Iteration 3936 (2.31228 iter/s, 5.18969s/12 iters), loss = 1.75948
I0412 13:31:58.515681  8032 solver.cpp:237]     Train net output #0: loss = 1.75948 (* 1 = 1.75948 loss)
I0412 13:31:58.515689  8032 sgd_solver.cpp:105] Iteration 3936, lr = 0.00458559
I0412 13:32:01.933020  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:32:03.582790  8032 solver.cpp:218] Iteration 3948 (2.36829 iter/s, 5.06694s/12 iters), loss = 1.61979
I0412 13:32:03.582844  8032 solver.cpp:237]     Train net output #0: loss = 1.61979 (* 1 = 1.61979 loss)
I0412 13:32:03.582855  8032 sgd_solver.cpp:105] Iteration 3948, lr = 0.0045747
I0412 13:32:08.667146  8032 solver.cpp:218] Iteration 3960 (2.36029 iter/s, 5.08413s/12 iters), loss = 1.76276
I0412 13:32:08.667199  8032 solver.cpp:237]     Train net output #0: loss = 1.76276 (* 1 = 1.76276 loss)
I0412 13:32:08.667210  8032 sgd_solver.cpp:105] Iteration 3960, lr = 0.00456384
I0412 13:32:14.146044  8032 solver.cpp:218] Iteration 3972 (2.19032 iter/s, 5.47866s/12 iters), loss = 1.95209
I0412 13:32:14.146095  8032 solver.cpp:237]     Train net output #0: loss = 1.95209 (* 1 = 1.95209 loss)
I0412 13:32:14.146106  8032 sgd_solver.cpp:105] Iteration 3972, lr = 0.00455301
I0412 13:32:16.204303  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3978.caffemodel
I0412 13:32:22.259281  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3978.solverstate
I0412 13:32:25.678236  8032 solver.cpp:330] Iteration 3978, Testing net (#0)
I0412 13:32:25.678347  8032 net.cpp:676] Ignoring source layer train-data
I0412 13:32:28.578388  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:32:30.302994  8032 solver.cpp:397]     Test net output #0: accuracy = 0.327206
I0412 13:32:30.303036  8032 solver.cpp:397]     Test net output #1: loss = 2.8424 (* 1 = 2.8424 loss)
I0412 13:32:32.284250  8032 solver.cpp:218] Iteration 3984 (0.66161 iter/s, 18.1376s/12 iters), loss = 1.78591
I0412 13:32:32.284302  8032 solver.cpp:237]     Train net output #0: loss = 1.78591 (* 1 = 1.78591 loss)
I0412 13:32:32.284313  8032 sgd_solver.cpp:105] Iteration 3984, lr = 0.0045422
I0412 13:32:37.244128  8032 solver.cpp:218] Iteration 3996 (2.41952 iter/s, 4.95966s/12 iters), loss = 2.23738
I0412 13:32:37.244171  8032 solver.cpp:237]     Train net output #0: loss = 2.23738 (* 1 = 2.23738 loss)
I0412 13:32:37.244181  8032 sgd_solver.cpp:105] Iteration 3996, lr = 0.00453141
I0412 13:32:42.282070  8032 solver.cpp:218] Iteration 4008 (2.38203 iter/s, 5.03773s/12 iters), loss = 1.68127
I0412 13:32:42.282116  8032 solver.cpp:237]     Train net output #0: loss = 1.68127 (* 1 = 1.68127 loss)
I0412 13:32:42.282124  8032 sgd_solver.cpp:105] Iteration 4008, lr = 0.00452066
I0412 13:32:47.321081  8032 solver.cpp:218] Iteration 4020 (2.38152 iter/s, 5.03879s/12 iters), loss = 1.74091
I0412 13:32:47.321130  8032 solver.cpp:237]     Train net output #0: loss = 1.74091 (* 1 = 1.74091 loss)
I0412 13:32:47.321141  8032 sgd_solver.cpp:105] Iteration 4020, lr = 0.00450992
I0412 13:32:52.389012  8032 solver.cpp:218] Iteration 4032 (2.36793 iter/s, 5.06771s/12 iters), loss = 1.57737
I0412 13:32:52.389060  8032 solver.cpp:237]     Train net output #0: loss = 1.57737 (* 1 = 1.57737 loss)
I0412 13:32:52.389070  8032 sgd_solver.cpp:105] Iteration 4032, lr = 0.00449921
I0412 13:32:57.503433  8032 solver.cpp:218] Iteration 4044 (2.34641 iter/s, 5.1142s/12 iters), loss = 1.36361
I0412 13:32:57.503543  8032 solver.cpp:237]     Train net output #0: loss = 1.36361 (* 1 = 1.36361 loss)
I0412 13:32:57.503553  8032 sgd_solver.cpp:105] Iteration 4044, lr = 0.00448853
I0412 13:32:58.014132  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:33:02.518082  8032 solver.cpp:218] Iteration 4056 (2.39312 iter/s, 5.01437s/12 iters), loss = 1.66356
I0412 13:33:02.518139  8032 solver.cpp:237]     Train net output #0: loss = 1.66356 (* 1 = 1.66356 loss)
I0412 13:33:02.518152  8032 sgd_solver.cpp:105] Iteration 4056, lr = 0.00447788
I0412 13:33:07.640844  8032 solver.cpp:218] Iteration 4068 (2.34259 iter/s, 5.12253s/12 iters), loss = 1.70373
I0412 13:33:07.640905  8032 solver.cpp:237]     Train net output #0: loss = 1.70373 (* 1 = 1.70373 loss)
I0412 13:33:07.640918  8032 sgd_solver.cpp:105] Iteration 4068, lr = 0.00446724
I0412 13:33:12.229765  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4080.caffemodel
I0412 13:33:23.172292  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4080.solverstate
I0412 13:33:29.027669  8032 solver.cpp:330] Iteration 4080, Testing net (#0)
I0412 13:33:29.027752  8032 net.cpp:676] Ignoring source layer train-data
I0412 13:33:31.887013  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:33:33.610028  8032 solver.cpp:397]     Test net output #0: accuracy = 0.348652
I0412 13:33:33.610064  8032 solver.cpp:397]     Test net output #1: loss = 2.81936 (* 1 = 2.81936 loss)
I0412 13:33:33.696910  8032 solver.cpp:218] Iteration 4080 (0.460561 iter/s, 26.0552s/12 iters), loss = 1.5362
I0412 13:33:33.696954  8032 solver.cpp:237]     Train net output #0: loss = 1.5362 (* 1 = 1.5362 loss)
I0412 13:33:33.696962  8032 sgd_solver.cpp:105] Iteration 4080, lr = 0.00445664
I0412 13:33:38.131103  8032 solver.cpp:218] Iteration 4092 (2.70637 iter/s, 4.43399s/12 iters), loss = 1.6698
I0412 13:33:38.131150  8032 solver.cpp:237]     Train net output #0: loss = 1.6698 (* 1 = 1.6698 loss)
I0412 13:33:38.131160  8032 sgd_solver.cpp:105] Iteration 4092, lr = 0.00444606
I0412 13:33:43.378279  8032 solver.cpp:218] Iteration 4104 (2.28704 iter/s, 5.24695s/12 iters), loss = 1.78751
I0412 13:33:43.378324  8032 solver.cpp:237]     Train net output #0: loss = 1.78751 (* 1 = 1.78751 loss)
I0412 13:33:43.378332  8032 sgd_solver.cpp:105] Iteration 4104, lr = 0.0044355
I0412 13:33:48.927528  8032 solver.cpp:218] Iteration 4116 (2.16254 iter/s, 5.54902s/12 iters), loss = 1.4328
I0412 13:33:48.927572  8032 solver.cpp:237]     Train net output #0: loss = 1.4328 (* 1 = 1.4328 loss)
I0412 13:33:48.927580  8032 sgd_solver.cpp:105] Iteration 4116, lr = 0.00442497
I0412 13:33:54.097182  8032 solver.cpp:218] Iteration 4128 (2.32134 iter/s, 5.16944s/12 iters), loss = 1.43512
I0412 13:33:54.097225  8032 solver.cpp:237]     Train net output #0: loss = 1.43512 (* 1 = 1.43512 loss)
I0412 13:33:54.097236  8032 sgd_solver.cpp:105] Iteration 4128, lr = 0.00441447
I0412 13:33:59.141227  8032 solver.cpp:218] Iteration 4140 (2.37914 iter/s, 5.04383s/12 iters), loss = 1.74731
I0412 13:33:59.141378  8032 solver.cpp:237]     Train net output #0: loss = 1.74731 (* 1 = 1.74731 loss)
I0412 13:33:59.141388  8032 sgd_solver.cpp:105] Iteration 4140, lr = 0.00440398
I0412 13:34:01.762553  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:34:04.183231  8032 solver.cpp:218] Iteration 4152 (2.38016 iter/s, 5.04169s/12 iters), loss = 1.33551
I0412 13:34:04.183281  8032 solver.cpp:237]     Train net output #0: loss = 1.33551 (* 1 = 1.33551 loss)
I0412 13:34:04.183293  8032 sgd_solver.cpp:105] Iteration 4152, lr = 0.00439353
I0412 13:34:05.776886  8032 blocking_queue.cpp:49] Waiting for data
I0412 13:34:09.176009  8032 solver.cpp:218] Iteration 4164 (2.40358 iter/s, 4.99256s/12 iters), loss = 1.69151
I0412 13:34:09.176051  8032 solver.cpp:237]     Train net output #0: loss = 1.69151 (* 1 = 1.69151 loss)
I0412 13:34:09.176062  8032 sgd_solver.cpp:105] Iteration 4164, lr = 0.0043831
I0412 13:34:14.256603  8032 solver.cpp:218] Iteration 4176 (2.36203 iter/s, 5.08038s/12 iters), loss = 1.4044
I0412 13:34:14.256654  8032 solver.cpp:237]     Train net output #0: loss = 1.4044 (* 1 = 1.4044 loss)
I0412 13:34:14.256665  8032 sgd_solver.cpp:105] Iteration 4176, lr = 0.00437269
I0412 13:34:16.301429  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4182.caffemodel
I0412 13:34:21.514183  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4182.solverstate
I0412 13:34:26.559130  8032 solver.cpp:330] Iteration 4182, Testing net (#0)
I0412 13:34:26.559156  8032 net.cpp:676] Ignoring source layer train-data
I0412 13:34:29.406685  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:34:31.130715  8032 solver.cpp:397]     Test net output #0: accuracy = 0.359069
I0412 13:34:31.130764  8032 solver.cpp:397]     Test net output #1: loss = 2.80121 (* 1 = 2.80121 loss)
I0412 13:34:33.129639  8032 solver.cpp:218] Iteration 4188 (0.63585 iter/s, 18.8724s/12 iters), loss = 1.28144
I0412 13:34:33.129685  8032 solver.cpp:237]     Train net output #0: loss = 1.28144 (* 1 = 1.28144 loss)
I0412 13:34:33.129694  8032 sgd_solver.cpp:105] Iteration 4188, lr = 0.00436231
I0412 13:34:38.451426  8032 solver.cpp:218] Iteration 4200 (2.25498 iter/s, 5.32156s/12 iters), loss = 1.56353
I0412 13:34:38.451476  8032 solver.cpp:237]     Train net output #0: loss = 1.56353 (* 1 = 1.56353 loss)
I0412 13:34:38.451489  8032 sgd_solver.cpp:105] Iteration 4200, lr = 0.00435195
I0412 13:34:43.826905  8032 solver.cpp:218] Iteration 4212 (2.23246 iter/s, 5.37525s/12 iters), loss = 1.47748
I0412 13:34:43.826949  8032 solver.cpp:237]     Train net output #0: loss = 1.47748 (* 1 = 1.47748 loss)
I0412 13:34:43.826958  8032 sgd_solver.cpp:105] Iteration 4212, lr = 0.00434162
I0412 13:34:49.281567  8032 solver.cpp:218] Iteration 4224 (2.20005 iter/s, 5.45443s/12 iters), loss = 1.39147
I0412 13:34:49.281620  8032 solver.cpp:237]     Train net output #0: loss = 1.39147 (* 1 = 1.39147 loss)
I0412 13:34:49.281631  8032 sgd_solver.cpp:105] Iteration 4224, lr = 0.00433131
I0412 13:34:54.505108  8032 solver.cpp:218] Iteration 4236 (2.29739 iter/s, 5.22331s/12 iters), loss = 1.40363
I0412 13:34:54.505151  8032 solver.cpp:237]     Train net output #0: loss = 1.40363 (* 1 = 1.40363 loss)
I0412 13:34:54.505160  8032 sgd_solver.cpp:105] Iteration 4236, lr = 0.00432103
I0412 13:34:59.274435  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:34:59.497658  8032 solver.cpp:218] Iteration 4248 (2.40368 iter/s, 4.99234s/12 iters), loss = 1.19281
I0412 13:34:59.497750  8032 solver.cpp:237]     Train net output #0: loss = 1.19281 (* 1 = 1.19281 loss)
I0412 13:34:59.497759  8032 sgd_solver.cpp:105] Iteration 4248, lr = 0.00431077
I0412 13:35:04.760799  8032 solver.cpp:218] Iteration 4260 (2.28012 iter/s, 5.26287s/12 iters), loss = 1.39465
I0412 13:35:04.760840  8032 solver.cpp:237]     Train net output #0: loss = 1.39465 (* 1 = 1.39465 loss)
I0412 13:35:04.760848  8032 sgd_solver.cpp:105] Iteration 4260, lr = 0.00430053
I0412 13:35:09.772249  8032 solver.cpp:218] Iteration 4272 (2.39462 iter/s, 5.01124s/12 iters), loss = 1.31776
I0412 13:35:09.772291  8032 solver.cpp:237]     Train net output #0: loss = 1.31776 (* 1 = 1.31776 loss)
I0412 13:35:09.772301  8032 sgd_solver.cpp:105] Iteration 4272, lr = 0.00429032
I0412 13:35:14.519520  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4284.caffemodel
I0412 13:35:17.585983  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4284.solverstate
I0412 13:35:21.767613  8032 solver.cpp:330] Iteration 4284, Testing net (#0)
I0412 13:35:21.767638  8032 net.cpp:676] Ignoring source layer train-data
I0412 13:35:24.671838  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:35:26.364713  8032 solver.cpp:397]     Test net output #0: accuracy = 0.354167
I0412 13:35:26.364763  8032 solver.cpp:397]     Test net output #1: loss = 2.8855 (* 1 = 2.8855 loss)
I0412 13:35:26.453001  8032 solver.cpp:218] Iteration 4284 (0.719417 iter/s, 16.6802s/12 iters), loss = 1.40769
I0412 13:35:26.453052  8032 solver.cpp:237]     Train net output #0: loss = 1.40769 (* 1 = 1.40769 loss)
I0412 13:35:26.453064  8032 sgd_solver.cpp:105] Iteration 4284, lr = 0.00428014
I0412 13:35:30.754139  8032 solver.cpp:218] Iteration 4296 (2.79009 iter/s, 4.30094s/12 iters), loss = 1.2725
I0412 13:35:30.754233  8032 solver.cpp:237]     Train net output #0: loss = 1.2725 (* 1 = 1.2725 loss)
I0412 13:35:30.754243  8032 sgd_solver.cpp:105] Iteration 4296, lr = 0.00426998
I0412 13:35:35.927744  8032 solver.cpp:218] Iteration 4308 (2.31959 iter/s, 5.17334s/12 iters), loss = 1.52061
I0412 13:35:35.927791  8032 solver.cpp:237]     Train net output #0: loss = 1.52061 (* 1 = 1.52061 loss)
I0412 13:35:35.927803  8032 sgd_solver.cpp:105] Iteration 4308, lr = 0.00425984
I0412 13:35:41.102775  8032 solver.cpp:218] Iteration 4320 (2.31893 iter/s, 5.17481s/12 iters), loss = 1.58977
I0412 13:35:41.102818  8032 solver.cpp:237]     Train net output #0: loss = 1.58977 (* 1 = 1.58977 loss)
I0412 13:35:41.102826  8032 sgd_solver.cpp:105] Iteration 4320, lr = 0.00424972
I0412 13:35:46.318461  8032 solver.cpp:218] Iteration 4332 (2.30085 iter/s, 5.21547s/12 iters), loss = 1.43684
I0412 13:35:46.318514  8032 solver.cpp:237]     Train net output #0: loss = 1.43684 (* 1 = 1.43684 loss)
I0412 13:35:46.318526  8032 sgd_solver.cpp:105] Iteration 4332, lr = 0.00423964
I0412 13:35:51.291862  8032 solver.cpp:218] Iteration 4344 (2.41294 iter/s, 4.97318s/12 iters), loss = 1.33633
I0412 13:35:51.291903  8032 solver.cpp:237]     Train net output #0: loss = 1.33633 (* 1 = 1.33633 loss)
I0412 13:35:51.291913  8032 sgd_solver.cpp:105] Iteration 4344, lr = 0.00422957
I0412 13:35:53.188058  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:35:56.328006  8032 solver.cpp:218] Iteration 4356 (2.38287 iter/s, 5.03593s/12 iters), loss = 1.28031
I0412 13:35:56.328052  8032 solver.cpp:237]     Train net output #0: loss = 1.28031 (* 1 = 1.28031 loss)
I0412 13:35:56.328063  8032 sgd_solver.cpp:105] Iteration 4356, lr = 0.00421953
I0412 13:36:01.452500  8032 solver.cpp:218] Iteration 4368 (2.3418 iter/s, 5.12427s/12 iters), loss = 1.47255
I0412 13:36:01.453646  8032 solver.cpp:237]     Train net output #0: loss = 1.47255 (* 1 = 1.47255 loss)
I0412 13:36:01.453657  8032 sgd_solver.cpp:105] Iteration 4368, lr = 0.00420951
I0412 13:36:06.701036  8032 solver.cpp:218] Iteration 4380 (2.28693 iter/s, 5.24721s/12 iters), loss = 1.20982
I0412 13:36:06.701092  8032 solver.cpp:237]     Train net output #0: loss = 1.20982 (* 1 = 1.20982 loss)
I0412 13:36:06.701104  8032 sgd_solver.cpp:105] Iteration 4380, lr = 0.00419952
I0412 13:36:08.700624  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4386.caffemodel
I0412 13:36:11.844907  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4386.solverstate
I0412 13:36:14.172137  8032 solver.cpp:330] Iteration 4386, Testing net (#0)
I0412 13:36:14.172164  8032 net.cpp:676] Ignoring source layer train-data
I0412 13:36:16.846309  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:36:18.788730  8032 solver.cpp:397]     Test net output #0: accuracy = 0.368873
I0412 13:36:18.788772  8032 solver.cpp:397]     Test net output #1: loss = 2.71814 (* 1 = 2.71814 loss)
I0412 13:36:20.938467  8032 solver.cpp:218] Iteration 4392 (0.842879 iter/s, 14.2369s/12 iters), loss = 1.39213
I0412 13:36:20.938514  8032 solver.cpp:237]     Train net output #0: loss = 1.39213 (* 1 = 1.39213 loss)
I0412 13:36:20.938524  8032 sgd_solver.cpp:105] Iteration 4392, lr = 0.00418954
I0412 13:36:26.060643  8032 solver.cpp:218] Iteration 4404 (2.34285 iter/s, 5.12196s/12 iters), loss = 1.21385
I0412 13:36:26.060688  8032 solver.cpp:237]     Train net output #0: loss = 1.21385 (* 1 = 1.21385 loss)
I0412 13:36:26.060698  8032 sgd_solver.cpp:105] Iteration 4404, lr = 0.0041796
I0412 13:36:31.154979  8032 solver.cpp:218] Iteration 4416 (2.35566 iter/s, 5.09412s/12 iters), loss = 1.21802
I0412 13:36:31.155030  8032 solver.cpp:237]     Train net output #0: loss = 1.21802 (* 1 = 1.21802 loss)
I0412 13:36:31.155040  8032 sgd_solver.cpp:105] Iteration 4416, lr = 0.00416967
I0412 13:36:36.549075  8032 solver.cpp:218] Iteration 4428 (2.22475 iter/s, 5.39386s/12 iters), loss = 1.11614
I0412 13:36:36.549165  8032 solver.cpp:237]     Train net output #0: loss = 1.11614 (* 1 = 1.11614 loss)
I0412 13:36:36.549175  8032 sgd_solver.cpp:105] Iteration 4428, lr = 0.00415977
I0412 13:36:41.468039  8032 solver.cpp:218] Iteration 4440 (2.43967 iter/s, 4.91871s/12 iters), loss = 1.03993
I0412 13:36:41.468098  8032 solver.cpp:237]     Train net output #0: loss = 1.03993 (* 1 = 1.03993 loss)
I0412 13:36:41.468111  8032 sgd_solver.cpp:105] Iteration 4440, lr = 0.0041499
I0412 13:36:45.682466  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:36:46.607625  8032 solver.cpp:218] Iteration 4452 (2.33493 iter/s, 5.13935s/12 iters), loss = 1.23011
I0412 13:36:46.607686  8032 solver.cpp:237]     Train net output #0: loss = 1.23011 (* 1 = 1.23011 loss)
I0412 13:36:46.607700  8032 sgd_solver.cpp:105] Iteration 4452, lr = 0.00414005
I0412 13:36:51.575727  8032 solver.cpp:218] Iteration 4464 (2.41552 iter/s, 4.96788s/12 iters), loss = 1.27818
I0412 13:36:51.575773  8032 solver.cpp:237]     Train net output #0: loss = 1.27818 (* 1 = 1.27818 loss)
I0412 13:36:51.575783  8032 sgd_solver.cpp:105] Iteration 4464, lr = 0.00413022
I0412 13:36:56.632668  8032 solver.cpp:218] Iteration 4476 (2.37308 iter/s, 5.05672s/12 iters), loss = 1.22317
I0412 13:36:56.632721  8032 solver.cpp:237]     Train net output #0: loss = 1.22317 (* 1 = 1.22317 loss)
I0412 13:36:56.632733  8032 sgd_solver.cpp:105] Iteration 4476, lr = 0.00412041
I0412 13:37:01.224519  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4488.caffemodel
I0412 13:37:05.846987  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4488.solverstate
I0412 13:37:08.161340  8032 solver.cpp:330] Iteration 4488, Testing net (#0)
I0412 13:37:08.161427  8032 net.cpp:676] Ignoring source layer train-data
I0412 13:37:10.835341  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:37:12.694166  8032 solver.cpp:397]     Test net output #0: accuracy = 0.381127
I0412 13:37:12.694197  8032 solver.cpp:397]     Test net output #1: loss = 2.73325 (* 1 = 2.73325 loss)
I0412 13:37:12.782550  8032 solver.cpp:218] Iteration 4488 (0.743066 iter/s, 16.1493s/12 iters), loss = 1.18425
I0412 13:37:12.782609  8032 solver.cpp:237]     Train net output #0: loss = 1.18425 (* 1 = 1.18425 loss)
I0412 13:37:12.782621  8032 sgd_solver.cpp:105] Iteration 4488, lr = 0.00411063
I0412 13:37:17.150144  8032 solver.cpp:218] Iteration 4500 (2.74764 iter/s, 4.36738s/12 iters), loss = 1.20375
I0412 13:37:17.150209  8032 solver.cpp:237]     Train net output #0: loss = 1.20375 (* 1 = 1.20375 loss)
I0412 13:37:17.150220  8032 sgd_solver.cpp:105] Iteration 4500, lr = 0.00410087
I0412 13:37:22.232952  8032 solver.cpp:218] Iteration 4512 (2.36101 iter/s, 5.08258s/12 iters), loss = 1.45414
I0412 13:37:22.232995  8032 solver.cpp:237]     Train net output #0: loss = 1.45414 (* 1 = 1.45414 loss)
I0412 13:37:22.233002  8032 sgd_solver.cpp:105] Iteration 4512, lr = 0.00409113
I0412 13:37:27.353688  8032 solver.cpp:218] Iteration 4524 (2.34351 iter/s, 5.12052s/12 iters), loss = 1.30552
I0412 13:37:27.353732  8032 solver.cpp:237]     Train net output #0: loss = 1.30552 (* 1 = 1.30552 loss)
I0412 13:37:27.353744  8032 sgd_solver.cpp:105] Iteration 4524, lr = 0.00408142
I0412 13:37:32.566196  8032 solver.cpp:218] Iteration 4536 (2.30225 iter/s, 5.21229s/12 iters), loss = 1.07833
I0412 13:37:32.566247  8032 solver.cpp:237]     Train net output #0: loss = 1.07833 (* 1 = 1.07833 loss)
I0412 13:37:32.566259  8032 sgd_solver.cpp:105] Iteration 4536, lr = 0.00407173
I0412 13:37:37.643452  8032 solver.cpp:218] Iteration 4548 (2.36358 iter/s, 5.07704s/12 iters), loss = 1.18095
I0412 13:37:37.643502  8032 solver.cpp:237]     Train net output #0: loss = 1.18095 (* 1 = 1.18095 loss)
I0412 13:37:37.643513  8032 sgd_solver.cpp:105] Iteration 4548, lr = 0.00406206
I0412 13:37:38.944834  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:37:42.919097  8032 solver.cpp:218] Iteration 4560 (2.2747 iter/s, 5.27542s/12 iters), loss = 1.11327
I0412 13:37:42.919142  8032 solver.cpp:237]     Train net output #0: loss = 1.11327 (* 1 = 1.11327 loss)
I0412 13:37:42.919152  8032 sgd_solver.cpp:105] Iteration 4560, lr = 0.00405242
I0412 13:37:47.940748  8032 solver.cpp:218] Iteration 4572 (2.38976 iter/s, 5.02143s/12 iters), loss = 1.14086
I0412 13:37:47.940801  8032 solver.cpp:237]     Train net output #0: loss = 1.14086 (* 1 = 1.14086 loss)
I0412 13:37:47.940812  8032 sgd_solver.cpp:105] Iteration 4572, lr = 0.0040428
I0412 13:37:53.042290  8032 solver.cpp:218] Iteration 4584 (2.35233 iter/s, 5.10132s/12 iters), loss = 1.20336
I0412 13:37:53.042338  8032 solver.cpp:237]     Train net output #0: loss = 1.20336 (* 1 = 1.20336 loss)
I0412 13:37:53.042348  8032 sgd_solver.cpp:105] Iteration 4584, lr = 0.0040332
I0412 13:37:55.253726  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4590.caffemodel
I0412 13:38:03.049695  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4590.solverstate
I0412 13:38:07.154008  8032 solver.cpp:330] Iteration 4590, Testing net (#0)
I0412 13:38:07.154034  8032 net.cpp:676] Ignoring source layer train-data
I0412 13:38:09.804822  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:38:11.622495  8032 solver.cpp:397]     Test net output #0: accuracy = 0.382966
I0412 13:38:11.622527  8032 solver.cpp:397]     Test net output #1: loss = 2.81489 (* 1 = 2.81489 loss)
I0412 13:38:13.717034  8032 solver.cpp:218] Iteration 4596 (0.580438 iter/s, 20.674s/12 iters), loss = 1.37189
I0412 13:38:13.717089  8032 solver.cpp:237]     Train net output #0: loss = 1.37189 (* 1 = 1.37189 loss)
I0412 13:38:13.717100  8032 sgd_solver.cpp:105] Iteration 4596, lr = 0.00402362
I0412 13:38:19.053887  8032 solver.cpp:218] Iteration 4608 (2.24861 iter/s, 5.33663s/12 iters), loss = 1.12304
I0412 13:38:19.053939  8032 solver.cpp:237]     Train net output #0: loss = 1.12304 (* 1 = 1.12304 loss)
I0412 13:38:19.053951  8032 sgd_solver.cpp:105] Iteration 4608, lr = 0.00401407
I0412 13:38:24.259908  8032 solver.cpp:218] Iteration 4620 (2.30512 iter/s, 5.2058s/12 iters), loss = 1.03584
I0412 13:38:24.259959  8032 solver.cpp:237]     Train net output #0: loss = 1.03584 (* 1 = 1.03584 loss)
I0412 13:38:24.259971  8032 sgd_solver.cpp:105] Iteration 4620, lr = 0.00400454
I0412 13:38:29.331020  8032 solver.cpp:218] Iteration 4632 (2.36644 iter/s, 5.0709s/12 iters), loss = 1.23911
I0412 13:38:29.331064  8032 solver.cpp:237]     Train net output #0: loss = 1.23911 (* 1 = 1.23911 loss)
I0412 13:38:29.331073  8032 sgd_solver.cpp:105] Iteration 4632, lr = 0.00399503
I0412 13:38:34.694839  8032 solver.cpp:218] Iteration 4644 (2.2373 iter/s, 5.36361s/12 iters), loss = 1.08047
I0412 13:38:34.694895  8032 solver.cpp:237]     Train net output #0: loss = 1.08047 (* 1 = 1.08047 loss)
I0412 13:38:34.694907  8032 sgd_solver.cpp:105] Iteration 4644, lr = 0.00398555
I0412 13:38:38.068982  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:38:39.634310  8032 solver.cpp:218] Iteration 4656 (2.42951 iter/s, 4.93926s/12 iters), loss = 0.947513
I0412 13:38:39.634361  8032 solver.cpp:237]     Train net output #0: loss = 0.947513 (* 1 = 0.947513 loss)
I0412 13:38:39.634371  8032 sgd_solver.cpp:105] Iteration 4656, lr = 0.00397608
I0412 13:38:44.756783  8032 solver.cpp:218] Iteration 4668 (2.34272 iter/s, 5.12226s/12 iters), loss = 1.1006
I0412 13:38:44.756880  8032 solver.cpp:237]     Train net output #0: loss = 1.1006 (* 1 = 1.1006 loss)
I0412 13:38:44.756889  8032 sgd_solver.cpp:105] Iteration 4668, lr = 0.00396664
I0412 13:38:49.896183  8032 solver.cpp:218] Iteration 4680 (2.33502 iter/s, 5.13914s/12 iters), loss = 1.18656
I0412 13:38:49.896227  8032 solver.cpp:237]     Train net output #0: loss = 1.18656 (* 1 = 1.18656 loss)
I0412 13:38:49.896237  8032 sgd_solver.cpp:105] Iteration 4680, lr = 0.00395723
I0412 13:38:54.414683  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4692.caffemodel
I0412 13:39:02.659288  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4692.solverstate
I0412 13:39:08.310109  8032 solver.cpp:330] Iteration 4692, Testing net (#0)
I0412 13:39:08.310138  8032 net.cpp:676] Ignoring source layer train-data
I0412 13:39:10.915787  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:39:12.855901  8032 solver.cpp:397]     Test net output #0: accuracy = 0.370711
I0412 13:39:12.855932  8032 solver.cpp:397]     Test net output #1: loss = 2.83234 (* 1 = 2.83234 loss)
I0412 13:39:12.945858  8032 solver.cpp:218] Iteration 4692 (0.520631 iter/s, 23.0489s/12 iters), loss = 1.0495
I0412 13:39:12.945925  8032 solver.cpp:237]     Train net output #0: loss = 1.0495 (* 1 = 1.0495 loss)
I0412 13:39:12.945935  8032 sgd_solver.cpp:105] Iteration 4692, lr = 0.00394783
I0412 13:39:17.138664  8032 solver.cpp:218] Iteration 4704 (2.86219 iter/s, 4.1926s/12 iters), loss = 1.18947
I0412 13:39:17.138731  8032 solver.cpp:237]     Train net output #0: loss = 1.18947 (* 1 = 1.18947 loss)
I0412 13:39:17.138741  8032 sgd_solver.cpp:105] Iteration 4704, lr = 0.00393846
I0412 13:39:22.226809  8032 solver.cpp:218] Iteration 4716 (2.35853 iter/s, 5.08791s/12 iters), loss = 1.2322
I0412 13:39:22.226859  8032 solver.cpp:237]     Train net output #0: loss = 1.2322 (* 1 = 1.2322 loss)
I0412 13:39:22.226871  8032 sgd_solver.cpp:105] Iteration 4716, lr = 0.00392911
I0412 13:39:27.334385  8032 solver.cpp:218] Iteration 4728 (2.34955 iter/s, 5.10736s/12 iters), loss = 1.16409
I0412 13:39:27.334441  8032 solver.cpp:237]     Train net output #0: loss = 1.16409 (* 1 = 1.16409 loss)
I0412 13:39:27.334452  8032 sgd_solver.cpp:105] Iteration 4728, lr = 0.00391978
I0412 13:39:32.638367  8032 solver.cpp:218] Iteration 4740 (2.26255 iter/s, 5.30375s/12 iters), loss = 1.02082
I0412 13:39:32.638412  8032 solver.cpp:237]     Train net output #0: loss = 1.02082 (* 1 = 1.02082 loss)
I0412 13:39:32.638422  8032 sgd_solver.cpp:105] Iteration 4740, lr = 0.00391047
I0412 13:39:37.849133  8032 solver.cpp:218] Iteration 4752 (2.30302 iter/s, 5.21055s/12 iters), loss = 1.13832
I0412 13:39:37.849177  8032 solver.cpp:237]     Train net output #0: loss = 1.13832 (* 1 = 1.13832 loss)
I0412 13:39:37.849187  8032 sgd_solver.cpp:105] Iteration 4752, lr = 0.00390119
I0412 13:39:38.426307  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:39:43.160854  8032 solver.cpp:218] Iteration 4764 (2.25925 iter/s, 5.31151s/12 iters), loss = 1.11062
I0412 13:39:43.160904  8032 solver.cpp:237]     Train net output #0: loss = 1.11062 (* 1 = 1.11062 loss)
I0412 13:39:43.160917  8032 sgd_solver.cpp:105] Iteration 4764, lr = 0.00389193
I0412 13:39:48.298523  8032 solver.cpp:218] Iteration 4776 (2.33579 iter/s, 5.13745s/12 iters), loss = 1.05929
I0412 13:39:48.298658  8032 solver.cpp:237]     Train net output #0: loss = 1.05929 (* 1 = 1.05929 loss)
I0412 13:39:48.298673  8032 sgd_solver.cpp:105] Iteration 4776, lr = 0.00388269
I0412 13:39:53.392828  8032 solver.cpp:218] Iteration 4788 (2.35571 iter/s, 5.09401s/12 iters), loss = 1.24923
I0412 13:39:53.392885  8032 solver.cpp:237]     Train net output #0: loss = 1.24923 (* 1 = 1.24923 loss)
I0412 13:39:53.392900  8032 sgd_solver.cpp:105] Iteration 4788, lr = 0.00387347
I0412 13:39:55.377089  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4794.caffemodel
I0412 13:40:14.115272  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4794.solverstate
I0412 13:40:21.679862  8032 solver.cpp:330] Iteration 4794, Testing net (#0)
I0412 13:40:21.679919  8032 net.cpp:676] Ignoring source layer train-data
I0412 13:40:24.239573  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:40:26.173588  8032 solver.cpp:397]     Test net output #0: accuracy = 0.373774
I0412 13:40:26.173648  8032 solver.cpp:397]     Test net output #1: loss = 2.88007 (* 1 = 2.88007 loss)
I0412 13:40:28.093173  8032 solver.cpp:218] Iteration 4800 (0.345829 iter/s, 34.6992s/12 iters), loss = 1.02722
I0412 13:40:28.093225  8032 solver.cpp:237]     Train net output #0: loss = 1.02722 (* 1 = 1.02722 loss)
I0412 13:40:28.093236  8032 sgd_solver.cpp:105] Iteration 4800, lr = 0.00386427
I0412 13:40:33.082669  8032 solver.cpp:218] Iteration 4812 (2.40516 iter/s, 4.98928s/12 iters), loss = 1.15693
I0412 13:40:33.082713  8032 solver.cpp:237]     Train net output #0: loss = 1.15693 (* 1 = 1.15693 loss)
I0412 13:40:33.082723  8032 sgd_solver.cpp:105] Iteration 4812, lr = 0.0038551
I0412 13:40:38.009384  8032 solver.cpp:218] Iteration 4824 (2.4358 iter/s, 4.92651s/12 iters), loss = 1.08398
I0412 13:40:38.009426  8032 solver.cpp:237]     Train net output #0: loss = 1.08398 (* 1 = 1.08398 loss)
I0412 13:40:38.009435  8032 sgd_solver.cpp:105] Iteration 4824, lr = 0.00384594
I0412 13:40:43.085341  8032 solver.cpp:218] Iteration 4836 (2.36419 iter/s, 5.07574s/12 iters), loss = 1.01156
I0412 13:40:43.085415  8032 solver.cpp:237]     Train net output #0: loss = 1.01156 (* 1 = 1.01156 loss)
I0412 13:40:43.085435  8032 sgd_solver.cpp:105] Iteration 4836, lr = 0.00383681
I0412 13:40:45.123968  8032 blocking_queue.cpp:49] Waiting for data
I0412 13:40:48.067261  8032 solver.cpp:218] Iteration 4848 (2.40882 iter/s, 4.98169s/12 iters), loss = 1.40005
I0412 13:40:48.067310  8032 solver.cpp:237]     Train net output #0: loss = 1.40005 (* 1 = 1.40005 loss)
I0412 13:40:48.067322  8032 sgd_solver.cpp:105] Iteration 4848, lr = 0.0038277
I0412 13:40:50.947402  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:40:53.321666  8032 solver.cpp:218] Iteration 4860 (2.28389 iter/s, 5.25418s/12 iters), loss = 1.05346
I0412 13:40:53.321774  8032 solver.cpp:237]     Train net output #0: loss = 1.05346 (* 1 = 1.05346 loss)
I0412 13:40:53.321785  8032 sgd_solver.cpp:105] Iteration 4860, lr = 0.00381862
I0412 13:40:58.860049  8032 solver.cpp:218] Iteration 4872 (2.16681 iter/s, 5.5381s/12 iters), loss = 0.939489
I0412 13:40:58.860090  8032 solver.cpp:237]     Train net output #0: loss = 0.939489 (* 1 = 0.939489 loss)
I0412 13:40:58.860098  8032 sgd_solver.cpp:105] Iteration 4872, lr = 0.00380955
I0412 13:41:03.963353  8032 solver.cpp:218] Iteration 4884 (2.35151 iter/s, 5.10309s/12 iters), loss = 0.883472
I0412 13:41:03.963405  8032 solver.cpp:237]     Train net output #0: loss = 0.883472 (* 1 = 0.883472 loss)
I0412 13:41:03.963416  8032 sgd_solver.cpp:105] Iteration 4884, lr = 0.0038005
I0412 13:41:08.937948  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4896.caffemodel
I0412 13:41:12.966732  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4896.solverstate
I0412 13:41:16.781152  8032 solver.cpp:330] Iteration 4896, Testing net (#0)
I0412 13:41:16.781177  8032 net.cpp:676] Ignoring source layer train-data
I0412 13:41:19.290397  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:41:21.273255  8032 solver.cpp:397]     Test net output #0: accuracy = 0.389093
I0412 13:41:21.273304  8032 solver.cpp:397]     Test net output #1: loss = 2.75266 (* 1 = 2.75266 loss)
I0412 13:41:21.361459  8032 solver.cpp:218] Iteration 4896 (0.689753 iter/s, 17.3975s/12 iters), loss = 1.18432
I0412 13:41:21.361510  8032 solver.cpp:237]     Train net output #0: loss = 1.18432 (* 1 = 1.18432 loss)
I0412 13:41:21.361521  8032 sgd_solver.cpp:105] Iteration 4896, lr = 0.00379148
I0412 13:41:25.709904  8032 solver.cpp:218] Iteration 4908 (2.75973 iter/s, 4.34825s/12 iters), loss = 1.34366
I0412 13:41:25.710063  8032 solver.cpp:237]     Train net output #0: loss = 1.34366 (* 1 = 1.34366 loss)
I0412 13:41:25.710075  8032 sgd_solver.cpp:105] Iteration 4908, lr = 0.00378248
I0412 13:41:30.744689  8032 solver.cpp:218] Iteration 4920 (2.38357 iter/s, 5.03446s/12 iters), loss = 0.981235
I0412 13:41:30.744740  8032 solver.cpp:237]     Train net output #0: loss = 0.981235 (* 1 = 0.981235 loss)
I0412 13:41:30.744750  8032 sgd_solver.cpp:105] Iteration 4920, lr = 0.0037735
I0412 13:41:35.741752  8032 solver.cpp:218] Iteration 4932 (2.40151 iter/s, 4.99685s/12 iters), loss = 0.876219
I0412 13:41:35.741816  8032 solver.cpp:237]     Train net output #0: loss = 0.876219 (* 1 = 0.876219 loss)
I0412 13:41:35.741828  8032 sgd_solver.cpp:105] Iteration 4932, lr = 0.00376454
I0412 13:41:40.670212  8032 solver.cpp:218] Iteration 4944 (2.43495 iter/s, 4.92824s/12 iters), loss = 0.885811
I0412 13:41:40.670261  8032 solver.cpp:237]     Train net output #0: loss = 0.885811 (* 1 = 0.885811 loss)
I0412 13:41:40.670270  8032 sgd_solver.cpp:105] Iteration 4944, lr = 0.0037556
I0412 13:41:45.835319  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:41:46.048576  8032 solver.cpp:218] Iteration 4956 (2.23125 iter/s, 5.37814s/12 iters), loss = 0.719517
I0412 13:41:46.048619  8032 solver.cpp:237]     Train net output #0: loss = 0.719517 (* 1 = 0.719517 loss)
I0412 13:41:46.048629  8032 sgd_solver.cpp:105] Iteration 4956, lr = 0.00374669
I0412 13:41:51.467344  8032 solver.cpp:218] Iteration 4968 (2.21462 iter/s, 5.41855s/12 iters), loss = 0.933844
I0412 13:41:51.467396  8032 solver.cpp:237]     Train net output #0: loss = 0.933844 (* 1 = 0.933844 loss)
I0412 13:41:51.467409  8032 sgd_solver.cpp:105] Iteration 4968, lr = 0.00373779
I0412 13:41:56.433430  8032 solver.cpp:218] Iteration 4980 (2.4165 iter/s, 4.96587s/12 iters), loss = 0.780992
I0412 13:41:56.433514  8032 solver.cpp:237]     Train net output #0: loss = 0.780992 (* 1 = 0.780992 loss)
I0412 13:41:56.433527  8032 sgd_solver.cpp:105] Iteration 4980, lr = 0.00372892
I0412 13:42:01.603855  8032 solver.cpp:218] Iteration 4992 (2.321 iter/s, 5.17018s/12 iters), loss = 0.997907
I0412 13:42:01.603904  8032 solver.cpp:237]     Train net output #0: loss = 0.997907 (* 1 = 0.997907 loss)
I0412 13:42:01.603915  8032 sgd_solver.cpp:105] Iteration 4992, lr = 0.00372006
I0412 13:42:03.672506  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4998.caffemodel
I0412 13:42:07.697068  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4998.solverstate
I0412 13:42:10.017866  8032 solver.cpp:330] Iteration 4998, Testing net (#0)
I0412 13:42:10.017894  8032 net.cpp:676] Ignoring source layer train-data
I0412 13:42:12.691977  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:42:14.734071  8032 solver.cpp:397]     Test net output #0: accuracy = 0.403799
I0412 13:42:14.734122  8032 solver.cpp:397]     Test net output #1: loss = 2.67105 (* 1 = 2.67105 loss)
I0412 13:42:16.688803  8032 solver.cpp:218] Iteration 5004 (0.795522 iter/s, 15.0844s/12 iters), loss = 0.865084
I0412 13:42:16.688853  8032 solver.cpp:237]     Train net output #0: loss = 0.865084 (* 1 = 0.865084 loss)
I0412 13:42:16.688864  8032 sgd_solver.cpp:105] Iteration 5004, lr = 0.00371123
I0412 13:42:21.702282  8032 solver.cpp:218] Iteration 5016 (2.39365 iter/s, 5.01327s/12 iters), loss = 0.98601
I0412 13:42:21.702330  8032 solver.cpp:237]     Train net output #0: loss = 0.98601 (* 1 = 0.98601 loss)
I0412 13:42:21.702340  8032 sgd_solver.cpp:105] Iteration 5016, lr = 0.00370242
I0412 13:42:26.854319  8032 solver.cpp:218] Iteration 5028 (2.32927 iter/s, 5.15182s/12 iters), loss = 0.890042
I0412 13:42:26.854462  8032 solver.cpp:237]     Train net output #0: loss = 0.890042 (* 1 = 0.890042 loss)
I0412 13:42:26.854480  8032 sgd_solver.cpp:105] Iteration 5028, lr = 0.00369363
I0412 13:42:32.208729  8032 solver.cpp:218] Iteration 5040 (2.24128 iter/s, 5.35409s/12 iters), loss = 0.915572
I0412 13:42:32.208777  8032 solver.cpp:237]     Train net output #0: loss = 0.915572 (* 1 = 0.915572 loss)
I0412 13:42:32.208786  8032 sgd_solver.cpp:105] Iteration 5040, lr = 0.00368486
I0412 13:42:37.366104  8032 solver.cpp:218] Iteration 5052 (2.32687 iter/s, 5.15715s/12 iters), loss = 0.810976
I0412 13:42:37.366166  8032 solver.cpp:237]     Train net output #0: loss = 0.810976 (* 1 = 0.810976 loss)
I0412 13:42:37.366179  8032 sgd_solver.cpp:105] Iteration 5052, lr = 0.00367611
I0412 13:42:39.232434  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:42:42.307766  8032 solver.cpp:218] Iteration 5064 (2.42844 iter/s, 4.94144s/12 iters), loss = 1.15219
I0412 13:42:42.307816  8032 solver.cpp:237]     Train net output #0: loss = 1.15219 (* 1 = 1.15219 loss)
I0412 13:42:42.307826  8032 sgd_solver.cpp:105] Iteration 5064, lr = 0.00366738
I0412 13:42:47.253183  8032 solver.cpp:218] Iteration 5076 (2.4266 iter/s, 4.9452s/12 iters), loss = 0.895168
I0412 13:42:47.253243  8032 solver.cpp:237]     Train net output #0: loss = 0.895168 (* 1 = 0.895168 loss)
I0412 13:42:47.253257  8032 sgd_solver.cpp:105] Iteration 5076, lr = 0.00365868
I0412 13:42:52.280231  8032 solver.cpp:218] Iteration 5088 (2.38719 iter/s, 5.02683s/12 iters), loss = 0.913304
I0412 13:42:52.280277  8032 solver.cpp:237]     Train net output #0: loss = 0.913304 (* 1 = 0.913304 loss)
I0412 13:42:52.280284  8032 sgd_solver.cpp:105] Iteration 5088, lr = 0.00364999
I0412 13:42:56.836668  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5100.caffemodel
I0412 13:43:00.780557  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5100.solverstate
I0412 13:43:03.862136  8032 solver.cpp:330] Iteration 5100, Testing net (#0)
I0412 13:43:03.862162  8032 net.cpp:676] Ignoring source layer train-data
I0412 13:43:06.393110  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:43:08.426542  8032 solver.cpp:397]     Test net output #0: accuracy = 0.421569
I0412 13:43:08.426604  8032 solver.cpp:397]     Test net output #1: loss = 2.6262 (* 1 = 2.6262 loss)
I0412 13:43:08.515038  8032 solver.cpp:218] Iteration 5100 (0.739177 iter/s, 16.2343s/12 iters), loss = 0.825493
I0412 13:43:08.515089  8032 solver.cpp:237]     Train net output #0: loss = 0.825493 (* 1 = 0.825493 loss)
I0412 13:43:08.515100  8032 sgd_solver.cpp:105] Iteration 5100, lr = 0.00364132
I0412 13:43:12.833489  8032 solver.cpp:218] Iteration 5112 (2.7789 iter/s, 4.31825s/12 iters), loss = 0.922046
I0412 13:43:12.833539  8032 solver.cpp:237]     Train net output #0: loss = 0.922046 (* 1 = 0.922046 loss)
I0412 13:43:12.833549  8032 sgd_solver.cpp:105] Iteration 5112, lr = 0.00363268
I0412 13:43:18.036237  8032 solver.cpp:218] Iteration 5124 (2.30657 iter/s, 5.20252s/12 iters), loss = 0.726298
I0412 13:43:18.036283  8032 solver.cpp:237]     Train net output #0: loss = 0.726298 (* 1 = 0.726298 loss)
I0412 13:43:18.036295  8032 sgd_solver.cpp:105] Iteration 5124, lr = 0.00362405
I0412 13:43:23.514303  8032 solver.cpp:218] Iteration 5136 (2.19065 iter/s, 5.47784s/12 iters), loss = 0.833272
I0412 13:43:23.514361  8032 solver.cpp:237]     Train net output #0: loss = 0.833272 (* 1 = 0.833272 loss)
I0412 13:43:23.514374  8032 sgd_solver.cpp:105] Iteration 5136, lr = 0.00361545
I0412 13:43:28.820200  8032 solver.cpp:218] Iteration 5148 (2.26173 iter/s, 5.30568s/12 iters), loss = 0.781012
I0412 13:43:28.820240  8032 solver.cpp:237]     Train net output #0: loss = 0.781012 (* 1 = 0.781012 loss)
I0412 13:43:28.820247  8032 sgd_solver.cpp:105] Iteration 5148, lr = 0.00360687
I0412 13:43:32.851608  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:43:33.777279  8032 solver.cpp:218] Iteration 5160 (2.42088 iter/s, 4.95687s/12 iters), loss = 0.860746
I0412 13:43:33.777333  8032 solver.cpp:237]     Train net output #0: loss = 0.860746 (* 1 = 0.860746 loss)
I0412 13:43:33.777345  8032 sgd_solver.cpp:105] Iteration 5160, lr = 0.0035983
I0412 13:43:38.927654  8032 solver.cpp:218] Iteration 5172 (2.33003 iter/s, 5.15015s/12 iters), loss = 0.858186
I0412 13:43:38.927700  8032 solver.cpp:237]     Train net output #0: loss = 0.858186 (* 1 = 0.858186 loss)
I0412 13:43:38.927709  8032 sgd_solver.cpp:105] Iteration 5172, lr = 0.00358976
I0412 13:43:44.094550  8032 solver.cpp:218] Iteration 5184 (2.32257 iter/s, 5.16668s/12 iters), loss = 0.656308
I0412 13:43:44.094592  8032 solver.cpp:237]     Train net output #0: loss = 0.656308 (* 1 = 0.656308 loss)
I0412 13:43:44.094601  8032 sgd_solver.cpp:105] Iteration 5184, lr = 0.00358124
I0412 13:43:49.252041  8032 solver.cpp:218] Iteration 5196 (2.32681 iter/s, 5.15728s/12 iters), loss = 0.857863
I0412 13:43:49.252096  8032 solver.cpp:237]     Train net output #0: loss = 0.857863 (* 1 = 0.857863 loss)
I0412 13:43:49.252108  8032 sgd_solver.cpp:105] Iteration 5196, lr = 0.00357273
I0412 13:43:51.491837  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5202.caffemodel
I0412 13:43:55.967739  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5202.solverstate
I0412 13:43:58.598088  8032 solver.cpp:330] Iteration 5202, Testing net (#0)
I0412 13:43:58.598119  8032 net.cpp:676] Ignoring source layer train-data
I0412 13:44:00.993778  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:44:03.085145  8032 solver.cpp:397]     Test net output #0: accuracy = 0.404412
I0412 13:44:03.085230  8032 solver.cpp:397]     Test net output #1: loss = 2.74904 (* 1 = 2.74904 loss)
I0412 13:44:04.773372  8032 solver.cpp:218] Iteration 5208 (0.773156 iter/s, 15.5208s/12 iters), loss = 0.916829
I0412 13:44:04.773427  8032 solver.cpp:237]     Train net output #0: loss = 0.916829 (* 1 = 0.916829 loss)
I0412 13:44:04.773439  8032 sgd_solver.cpp:105] Iteration 5208, lr = 0.00356425
I0412 13:44:09.808184  8032 solver.cpp:218] Iteration 5220 (2.38351 iter/s, 5.03459s/12 iters), loss = 0.77636
I0412 13:44:09.808241  8032 solver.cpp:237]     Train net output #0: loss = 0.77636 (* 1 = 0.77636 loss)
I0412 13:44:09.808254  8032 sgd_solver.cpp:105] Iteration 5220, lr = 0.00355579
I0412 13:44:14.884555  8032 solver.cpp:218] Iteration 5232 (2.36399 iter/s, 5.07615s/12 iters), loss = 0.63869
I0412 13:44:14.884594  8032 solver.cpp:237]     Train net output #0: loss = 0.63869 (* 1 = 0.63869 loss)
I0412 13:44:14.884603  8032 sgd_solver.cpp:105] Iteration 5232, lr = 0.00354735
I0412 13:44:20.239980  8032 solver.cpp:218] Iteration 5244 (2.24081 iter/s, 5.3552s/12 iters), loss = 0.911289
I0412 13:44:20.240044  8032 solver.cpp:237]     Train net output #0: loss = 0.911289 (* 1 = 0.911289 loss)
I0412 13:44:20.240061  8032 sgd_solver.cpp:105] Iteration 5244, lr = 0.00353892
I0412 13:44:25.299463  8032 solver.cpp:218] Iteration 5256 (2.37189 iter/s, 5.05925s/12 iters), loss = 0.801762
I0412 13:44:25.299512  8032 solver.cpp:237]     Train net output #0: loss = 0.801762 (* 1 = 0.801762 loss)
I0412 13:44:25.299523  8032 sgd_solver.cpp:105] Iteration 5256, lr = 0.00353052
I0412 13:44:26.498776  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:44:30.315153  8032 solver.cpp:218] Iteration 5268 (2.3926 iter/s, 5.01547s/12 iters), loss = 0.672972
I0412 13:44:30.315204  8032 solver.cpp:237]     Train net output #0: loss = 0.672972 (* 1 = 0.672972 loss)
I0412 13:44:30.315215  8032 sgd_solver.cpp:105] Iteration 5268, lr = 0.00352214
I0412 13:44:35.552431  8032 solver.cpp:218] Iteration 5280 (2.29136 iter/s, 5.23705s/12 iters), loss = 0.875995
I0412 13:44:35.552603  8032 solver.cpp:237]     Train net output #0: loss = 0.875995 (* 1 = 0.875995 loss)
I0412 13:44:35.552618  8032 sgd_solver.cpp:105] Iteration 5280, lr = 0.00351378
I0412 13:44:40.562786  8032 solver.cpp:218] Iteration 5292 (2.3952 iter/s, 5.01003s/12 iters), loss = 0.575709
I0412 13:44:40.562834  8032 solver.cpp:237]     Train net output #0: loss = 0.575709 (* 1 = 0.575709 loss)
I0412 13:44:40.562844  8032 sgd_solver.cpp:105] Iteration 5292, lr = 0.00350544
I0412 13:44:45.158404  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5304.caffemodel
I0412 13:44:50.055243  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5304.solverstate
I0412 13:44:55.090278  8032 solver.cpp:330] Iteration 5304, Testing net (#0)
I0412 13:44:55.090301  8032 net.cpp:676] Ignoring source layer train-data
I0412 13:44:57.358150  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:44:59.476338  8032 solver.cpp:397]     Test net output #0: accuracy = 0.414216
I0412 13:44:59.476387  8032 solver.cpp:397]     Test net output #1: loss = 2.67444 (* 1 = 2.67444 loss)
I0412 13:44:59.564391  8032 solver.cpp:218] Iteration 5304 (0.631546 iter/s, 19.001s/12 iters), loss = 0.743374
I0412 13:44:59.564445  8032 solver.cpp:237]     Train net output #0: loss = 0.743374 (* 1 = 0.743374 loss)
I0412 13:44:59.564455  8032 sgd_solver.cpp:105] Iteration 5304, lr = 0.00349711
I0412 13:45:03.770983  8032 solver.cpp:218] Iteration 5316 (2.8528 iter/s, 4.2064s/12 iters), loss = 0.76467
I0412 13:45:03.771028  8032 solver.cpp:237]     Train net output #0: loss = 0.76467 (* 1 = 0.76467 loss)
I0412 13:45:03.771036  8032 sgd_solver.cpp:105] Iteration 5316, lr = 0.00348881
I0412 13:45:08.742118  8032 solver.cpp:218] Iteration 5328 (2.41404 iter/s, 4.97093s/12 iters), loss = 1.03331
I0412 13:45:08.742240  8032 solver.cpp:237]     Train net output #0: loss = 1.03331 (* 1 = 1.03331 loss)
I0412 13:45:08.742250  8032 sgd_solver.cpp:105] Iteration 5328, lr = 0.00348053
I0412 13:45:14.041716  8032 solver.cpp:218] Iteration 5340 (2.26445 iter/s, 5.29931s/12 iters), loss = 0.931166
I0412 13:45:14.041760  8032 solver.cpp:237]     Train net output #0: loss = 0.931166 (* 1 = 0.931166 loss)
I0412 13:45:14.041772  8032 sgd_solver.cpp:105] Iteration 5340, lr = 0.00347226
I0412 13:45:18.995669  8032 solver.cpp:218] Iteration 5352 (2.42241 iter/s, 4.95374s/12 iters), loss = 0.785755
I0412 13:45:18.995721  8032 solver.cpp:237]     Train net output #0: loss = 0.785755 (* 1 = 0.785755 loss)
I0412 13:45:18.995733  8032 sgd_solver.cpp:105] Iteration 5352, lr = 0.00346402
I0412 13:45:22.554662  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:45:24.127854  8032 solver.cpp:218] Iteration 5364 (2.33829 iter/s, 5.13196s/12 iters), loss = 0.728403
I0412 13:45:24.127903  8032 solver.cpp:237]     Train net output #0: loss = 0.728403 (* 1 = 0.728403 loss)
I0412 13:45:24.127914  8032 sgd_solver.cpp:105] Iteration 5364, lr = 0.0034558
I0412 13:45:29.089787  8032 solver.cpp:218] Iteration 5376 (2.41851 iter/s, 4.96172s/12 iters), loss = 0.66427
I0412 13:45:29.089834  8032 solver.cpp:237]     Train net output #0: loss = 0.66427 (* 1 = 0.66427 loss)
I0412 13:45:29.089843  8032 sgd_solver.cpp:105] Iteration 5376, lr = 0.00344759
I0412 13:45:34.106849  8032 solver.cpp:218] Iteration 5388 (2.39194 iter/s, 5.01685s/12 iters), loss = 0.593727
I0412 13:45:34.106897  8032 solver.cpp:237]     Train net output #0: loss = 0.593727 (* 1 = 0.593727 loss)
I0412 13:45:34.106907  8032 sgd_solver.cpp:105] Iteration 5388, lr = 0.00343941
I0412 13:45:39.108140  8032 solver.cpp:218] Iteration 5400 (2.39948 iter/s, 5.00108s/12 iters), loss = 0.682391
I0412 13:45:39.108278  8032 solver.cpp:237]     Train net output #0: loss = 0.682391 (* 1 = 0.682391 loss)
I0412 13:45:39.108289  8032 sgd_solver.cpp:105] Iteration 5400, lr = 0.00343124
I0412 13:45:41.268620  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5406.caffemodel
I0412 13:45:49.314070  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5406.solverstate
I0412 13:45:53.843159  8032 solver.cpp:330] Iteration 5406, Testing net (#0)
I0412 13:45:53.843186  8032 net.cpp:676] Ignoring source layer train-data
I0412 13:45:56.210219  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:45:58.388993  8032 solver.cpp:397]     Test net output #0: accuracy = 0.416054
I0412 13:45:58.389042  8032 solver.cpp:397]     Test net output #1: loss = 2.62302 (* 1 = 2.62302 loss)
I0412 13:46:00.162226  8032 solver.cpp:218] Iteration 5412 (0.569982 iter/s, 21.0533s/12 iters), loss = 1.3395
I0412 13:46:00.162271  8032 solver.cpp:237]     Train net output #0: loss = 1.3395 (* 1 = 1.3395 loss)
I0412 13:46:00.162279  8032 sgd_solver.cpp:105] Iteration 5412, lr = 0.00342309
I0412 13:46:05.129937  8032 solver.cpp:218] Iteration 5424 (2.4157 iter/s, 4.9675s/12 iters), loss = 0.557344
I0412 13:46:05.129999  8032 solver.cpp:237]     Train net output #0: loss = 0.557344 (* 1 = 0.557344 loss)
I0412 13:46:05.130008  8032 sgd_solver.cpp:105] Iteration 5424, lr = 0.00341497
I0412 13:46:10.141052  8032 solver.cpp:218] Iteration 5436 (2.39478 iter/s, 5.01089s/12 iters), loss = 0.692448
I0412 13:46:10.141139  8032 solver.cpp:237]     Train net output #0: loss = 0.692448 (* 1 = 0.692448 loss)
I0412 13:46:10.141147  8032 sgd_solver.cpp:105] Iteration 5436, lr = 0.00340686
I0412 13:46:15.136883  8032 solver.cpp:218] Iteration 5448 (2.40213 iter/s, 4.99557s/12 iters), loss = 0.904668
I0412 13:46:15.136940  8032 solver.cpp:237]     Train net output #0: loss = 0.904668 (* 1 = 0.904668 loss)
I0412 13:46:15.136952  8032 sgd_solver.cpp:105] Iteration 5448, lr = 0.00339877
I0412 13:46:20.273288  8032 solver.cpp:218] Iteration 5460 (2.33637 iter/s, 5.13618s/12 iters), loss = 0.869146
I0412 13:46:20.273341  8032 solver.cpp:237]     Train net output #0: loss = 0.869146 (* 1 = 0.869146 loss)
I0412 13:46:20.273352  8032 sgd_solver.cpp:105] Iteration 5460, lr = 0.0033907
I0412 13:46:20.840313  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:46:25.372514  8032 solver.cpp:218] Iteration 5472 (2.3534 iter/s, 5.099s/12 iters), loss = 0.718199
I0412 13:46:25.372570  8032 solver.cpp:237]     Train net output #0: loss = 0.718199 (* 1 = 0.718199 loss)
I0412 13:46:25.372584  8032 sgd_solver.cpp:105] Iteration 5472, lr = 0.00338265
I0412 13:46:30.416038  8032 solver.cpp:218] Iteration 5484 (2.37939 iter/s, 5.04331s/12 iters), loss = 0.48533
I0412 13:46:30.416080  8032 solver.cpp:237]     Train net output #0: loss = 0.48533 (* 1 = 0.48533 loss)
I0412 13:46:30.416090  8032 sgd_solver.cpp:105] Iteration 5484, lr = 0.00337462
I0412 13:46:35.427918  8032 solver.cpp:218] Iteration 5496 (2.39441 iter/s, 5.01167s/12 iters), loss = 0.645921
I0412 13:46:35.427969  8032 solver.cpp:237]     Train net output #0: loss = 0.645921 (* 1 = 0.645921 loss)
I0412 13:46:35.427980  8032 sgd_solver.cpp:105] Iteration 5496, lr = 0.00336661
I0412 13:46:40.065325  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5508.caffemodel
I0412 13:46:43.141031  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5508.solverstate
I0412 13:46:47.478978  8032 solver.cpp:330] Iteration 5508, Testing net (#0)
I0412 13:46:47.478999  8032 net.cpp:676] Ignoring source layer train-data
I0412 13:46:49.779211  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:46:52.146391  8032 solver.cpp:397]     Test net output #0: accuracy = 0.440564
I0412 13:46:52.146437  8032 solver.cpp:397]     Test net output #1: loss = 2.62419 (* 1 = 2.62419 loss)
I0412 13:46:52.233650  8032 solver.cpp:218] Iteration 5508 (0.714066 iter/s, 16.8052s/12 iters), loss = 0.522163
I0412 13:46:52.233697  8032 solver.cpp:237]     Train net output #0: loss = 0.522163 (* 1 = 0.522163 loss)
I0412 13:46:52.233708  8032 sgd_solver.cpp:105] Iteration 5508, lr = 0.00335861
I0412 13:46:56.843268  8032 solver.cpp:218] Iteration 5520 (2.60337 iter/s, 4.60942s/12 iters), loss = 0.782961
I0412 13:46:56.843314  8032 solver.cpp:237]     Train net output #0: loss = 0.782961 (* 1 = 0.782961 loss)
I0412 13:46:56.843322  8032 sgd_solver.cpp:105] Iteration 5520, lr = 0.00335064
I0412 13:46:59.531950  8032 blocking_queue.cpp:49] Waiting for data
I0412 13:47:02.326272  8032 solver.cpp:218] Iteration 5532 (2.18867 iter/s, 5.48277s/12 iters), loss = 0.809523
I0412 13:47:02.326314  8032 solver.cpp:237]     Train net output #0: loss = 0.809523 (* 1 = 0.809523 loss)
I0412 13:47:02.326324  8032 sgd_solver.cpp:105] Iteration 5532, lr = 0.00334268
I0412 13:47:07.820181  8032 solver.cpp:218] Iteration 5544 (2.18433 iter/s, 5.49369s/12 iters), loss = 0.594185
I0412 13:47:07.820230  8032 solver.cpp:237]     Train net output #0: loss = 0.594185 (* 1 = 0.594185 loss)
I0412 13:47:07.820241  8032 sgd_solver.cpp:105] Iteration 5544, lr = 0.00333475
I0412 13:47:12.850100  8032 solver.cpp:218] Iteration 5556 (2.38583 iter/s, 5.0297s/12 iters), loss = 0.5927
I0412 13:47:12.850147  8032 solver.cpp:237]     Train net output #0: loss = 0.5927 (* 1 = 0.5927 loss)
I0412 13:47:12.850158  8032 sgd_solver.cpp:105] Iteration 5556, lr = 0.00332683
I0412 13:47:15.556497  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:47:17.850507  8032 solver.cpp:218] Iteration 5568 (2.39991 iter/s, 5.0002s/12 iters), loss = 0.532312
I0412 13:47:17.850553  8032 solver.cpp:237]     Train net output #0: loss = 0.532312 (* 1 = 0.532312 loss)
I0412 13:47:17.850562  8032 sgd_solver.cpp:105] Iteration 5568, lr = 0.00331893
I0412 13:47:22.901201  8032 solver.cpp:218] Iteration 5580 (2.37601 iter/s, 5.05048s/12 iters), loss = 0.81186
I0412 13:47:22.901257  8032 solver.cpp:237]     Train net output #0: loss = 0.81186 (* 1 = 0.81186 loss)
I0412 13:47:22.901269  8032 sgd_solver.cpp:105] Iteration 5580, lr = 0.00331105
I0412 13:47:27.865573  8032 solver.cpp:218] Iteration 5592 (2.41733 iter/s, 4.96416s/12 iters), loss = 0.68974
I0412 13:47:27.865620  8032 solver.cpp:237]     Train net output #0: loss = 0.68974 (* 1 = 0.68974 loss)
I0412 13:47:27.865631  8032 sgd_solver.cpp:105] Iteration 5592, lr = 0.00330319
I0412 13:47:32.994771  8032 solver.cpp:218] Iteration 5604 (2.33965 iter/s, 5.12898s/12 iters), loss = 0.827058
I0412 13:47:32.994823  8032 solver.cpp:237]     Train net output #0: loss = 0.827058 (* 1 = 0.827058 loss)
I0412 13:47:32.994835  8032 sgd_solver.cpp:105] Iteration 5604, lr = 0.00329535
I0412 13:47:35.097239  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5610.caffemodel
I0412 13:47:38.129706  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5610.solverstate
I0412 13:47:45.028861  8032 solver.cpp:330] Iteration 5610, Testing net (#0)
I0412 13:47:45.028888  8032 net.cpp:676] Ignoring source layer train-data
I0412 13:47:47.398684  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:47:49.821681  8032 solver.cpp:397]     Test net output #0: accuracy = 0.431985
I0412 13:47:49.821727  8032 solver.cpp:397]     Test net output #1: loss = 2.73961 (* 1 = 2.73961 loss)
I0412 13:47:51.765667  8032 solver.cpp:218] Iteration 5616 (0.639309 iter/s, 18.7703s/12 iters), loss = 0.573297
I0412 13:47:51.765725  8032 solver.cpp:237]     Train net output #0: loss = 0.573297 (* 1 = 0.573297 loss)
I0412 13:47:51.765738  8032 sgd_solver.cpp:105] Iteration 5616, lr = 0.00328752
I0412 13:47:57.052523  8032 solver.cpp:218] Iteration 5628 (2.26988 iter/s, 5.28662s/12 iters), loss = 0.654063
I0412 13:47:57.052577  8032 solver.cpp:237]     Train net output #0: loss = 0.654063 (* 1 = 0.654063 loss)
I0412 13:47:57.052589  8032 sgd_solver.cpp:105] Iteration 5628, lr = 0.00327972
I0412 13:48:02.105141  8032 solver.cpp:218] Iteration 5640 (2.37511 iter/s, 5.0524s/12 iters), loss = 0.681815
I0412 13:48:02.105185  8032 solver.cpp:237]     Train net output #0: loss = 0.681815 (* 1 = 0.681815 loss)
I0412 13:48:02.105193  8032 sgd_solver.cpp:105] Iteration 5640, lr = 0.00327193
I0412 13:48:07.123132  8032 solver.cpp:218] Iteration 5652 (2.39149 iter/s, 5.01779s/12 iters), loss = 0.511187
I0412 13:48:07.123170  8032 solver.cpp:237]     Train net output #0: loss = 0.511187 (* 1 = 0.511187 loss)
I0412 13:48:07.123178  8032 sgd_solver.cpp:105] Iteration 5652, lr = 0.00326416
I0412 13:48:12.092872  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:48:12.272274  8032 solver.cpp:218] Iteration 5664 (2.33058 iter/s, 5.14893s/12 iters), loss = 0.710445
I0412 13:48:12.272326  8032 solver.cpp:237]     Train net output #0: loss = 0.710445 (* 1 = 0.710445 loss)
I0412 13:48:12.272337  8032 sgd_solver.cpp:105] Iteration 5664, lr = 0.00325641
I0412 13:48:17.363587  8032 solver.cpp:218] Iteration 5676 (2.35706 iter/s, 5.09109s/12 iters), loss = 0.647534
I0412 13:48:17.363641  8032 solver.cpp:237]     Train net output #0: loss = 0.647534 (* 1 = 0.647534 loss)
I0412 13:48:17.363652  8032 sgd_solver.cpp:105] Iteration 5676, lr = 0.00324868
I0412 13:48:22.648279  8032 solver.cpp:218] Iteration 5688 (2.27081 iter/s, 5.28446s/12 iters), loss = 0.61363
I0412 13:48:22.648442  8032 solver.cpp:237]     Train net output #0: loss = 0.61363 (* 1 = 0.61363 loss)
I0412 13:48:22.648453  8032 sgd_solver.cpp:105] Iteration 5688, lr = 0.00324097
I0412 13:48:27.771713  8032 solver.cpp:218] Iteration 5700 (2.34233 iter/s, 5.1231s/12 iters), loss = 0.60316
I0412 13:48:27.771770  8032 solver.cpp:237]     Train net output #0: loss = 0.60316 (* 1 = 0.60316 loss)
I0412 13:48:27.771782  8032 sgd_solver.cpp:105] Iteration 5700, lr = 0.00323328
I0412 13:48:32.372822  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5712.caffemodel
I0412 13:48:36.993499  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5712.solverstate
I0412 13:48:40.780544  8032 solver.cpp:330] Iteration 5712, Testing net (#0)
I0412 13:48:40.780568  8032 net.cpp:676] Ignoring source layer train-data
I0412 13:48:43.002480  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:48:45.377094  8032 solver.cpp:397]     Test net output #0: accuracy = 0.433211
I0412 13:48:45.377135  8032 solver.cpp:397]     Test net output #1: loss = 2.64936 (* 1 = 2.64936 loss)
I0412 13:48:45.465062  8032 solver.cpp:218] Iteration 5712 (0.678244 iter/s, 17.6927s/12 iters), loss = 0.55574
I0412 13:48:45.465107  8032 solver.cpp:237]     Train net output #0: loss = 0.55574 (* 1 = 0.55574 loss)
I0412 13:48:45.465117  8032 sgd_solver.cpp:105] Iteration 5712, lr = 0.0032256
I0412 13:48:49.860154  8032 solver.cpp:218] Iteration 5724 (2.73044 iter/s, 4.39489s/12 iters), loss = 0.656344
I0412 13:48:49.860208  8032 solver.cpp:237]     Train net output #0: loss = 0.656344 (* 1 = 0.656344 loss)
I0412 13:48:49.860219  8032 sgd_solver.cpp:105] Iteration 5724, lr = 0.00321794
I0412 13:48:55.354441  8032 solver.cpp:218] Iteration 5736 (2.18418 iter/s, 5.49405s/12 iters), loss = 0.421302
I0412 13:48:55.354938  8032 solver.cpp:237]     Train net output #0: loss = 0.421302 (* 1 = 0.421302 loss)
I0412 13:48:55.354954  8032 sgd_solver.cpp:105] Iteration 5736, lr = 0.0032103
I0412 13:49:00.431428  8032 solver.cpp:218] Iteration 5748 (2.36391 iter/s, 5.07633s/12 iters), loss = 0.529667
I0412 13:49:00.431478  8032 solver.cpp:237]     Train net output #0: loss = 0.529667 (* 1 = 0.529667 loss)
I0412 13:49:00.431489  8032 sgd_solver.cpp:105] Iteration 5748, lr = 0.00320268
I0412 13:49:05.511390  8032 solver.cpp:218] Iteration 5760 (2.36232 iter/s, 5.07974s/12 iters), loss = 0.543959
I0412 13:49:05.511445  8032 solver.cpp:237]     Train net output #0: loss = 0.543959 (* 1 = 0.543959 loss)
I0412 13:49:05.511457  8032 sgd_solver.cpp:105] Iteration 5760, lr = 0.00319508
I0412 13:49:07.471925  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:49:10.538933  8032 solver.cpp:218] Iteration 5772 (2.38696 iter/s, 5.02732s/12 iters), loss = 0.531806
I0412 13:49:10.538991  8032 solver.cpp:237]     Train net output #0: loss = 0.531806 (* 1 = 0.531806 loss)
I0412 13:49:10.539002  8032 sgd_solver.cpp:105] Iteration 5772, lr = 0.00318749
I0412 13:49:15.618010  8032 solver.cpp:218] Iteration 5784 (2.36274 iter/s, 5.07885s/12 iters), loss = 0.448747
I0412 13:49:15.618059  8032 solver.cpp:237]     Train net output #0: loss = 0.448747 (* 1 = 0.448747 loss)
I0412 13:49:15.618069  8032 sgd_solver.cpp:105] Iteration 5784, lr = 0.00317992
I0412 13:49:20.794085  8032 solver.cpp:218] Iteration 5796 (2.31846 iter/s, 5.17585s/12 iters), loss = 0.583601
I0412 13:49:20.794135  8032 solver.cpp:237]     Train net output #0: loss = 0.583601 (* 1 = 0.583601 loss)
I0412 13:49:20.794147  8032 sgd_solver.cpp:105] Iteration 5796, lr = 0.00317237
I0412 13:49:26.026564  8032 solver.cpp:218] Iteration 5808 (2.29347 iter/s, 5.23224s/12 iters), loss = 0.610117
I0412 13:49:26.026723  8032 solver.cpp:237]     Train net output #0: loss = 0.610117 (* 1 = 0.610117 loss)
I0412 13:49:26.026736  8032 sgd_solver.cpp:105] Iteration 5808, lr = 0.00316484
I0412 13:49:28.094571  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5814.caffemodel
I0412 13:49:33.748107  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5814.solverstate
I0412 13:49:36.078294  8032 solver.cpp:330] Iteration 5814, Testing net (#0)
I0412 13:49:36.078320  8032 net.cpp:676] Ignoring source layer train-data
I0412 13:49:38.121317  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:49:40.537670  8032 solver.cpp:397]     Test net output #0: accuracy = 0.428922
I0412 13:49:40.537714  8032 solver.cpp:397]     Test net output #1: loss = 2.60024 (* 1 = 2.60024 loss)
I0412 13:49:42.537175  8032 solver.cpp:218] Iteration 5820 (0.726835 iter/s, 16.5099s/12 iters), loss = 0.44928
I0412 13:49:42.537228  8032 solver.cpp:237]     Train net output #0: loss = 0.44928 (* 1 = 0.44928 loss)
I0412 13:49:42.537240  8032 sgd_solver.cpp:105] Iteration 5820, lr = 0.00315733
I0412 13:49:47.828418  8032 solver.cpp:218] Iteration 5832 (2.26799 iter/s, 5.29102s/12 iters), loss = 0.378925
I0412 13:49:47.828464  8032 solver.cpp:237]     Train net output #0: loss = 0.378925 (* 1 = 0.378925 loss)
I0412 13:49:47.828474  8032 sgd_solver.cpp:105] Iteration 5832, lr = 0.00314983
I0412 13:49:52.956218  8032 solver.cpp:218] Iteration 5844 (2.34028 iter/s, 5.12758s/12 iters), loss = 0.540987
I0412 13:49:52.956257  8032 solver.cpp:237]     Train net output #0: loss = 0.540987 (* 1 = 0.540987 loss)
I0412 13:49:52.956265  8032 sgd_solver.cpp:105] Iteration 5844, lr = 0.00314235
I0412 13:49:58.314491  8032 solver.cpp:218] Iteration 5856 (2.23962 iter/s, 5.35806s/12 iters), loss = 0.405285
I0412 13:49:58.314560  8032 solver.cpp:237]     Train net output #0: loss = 0.405285 (* 1 = 0.405285 loss)
I0412 13:49:58.314570  8032 sgd_solver.cpp:105] Iteration 5856, lr = 0.00313489
I0412 13:50:02.556493  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:50:03.378208  8032 solver.cpp:218] Iteration 5868 (2.36991 iter/s, 5.06349s/12 iters), loss = 0.562451
I0412 13:50:03.378250  8032 solver.cpp:237]     Train net output #0: loss = 0.562451 (* 1 = 0.562451 loss)
I0412 13:50:03.378259  8032 sgd_solver.cpp:105] Iteration 5868, lr = 0.00312745
I0412 13:50:08.493690  8032 solver.cpp:218] Iteration 5880 (2.34592 iter/s, 5.11527s/12 iters), loss = 0.463132
I0412 13:50:08.493743  8032 solver.cpp:237]     Train net output #0: loss = 0.463132 (* 1 = 0.463132 loss)
I0412 13:50:08.493755  8032 sgd_solver.cpp:105] Iteration 5880, lr = 0.00312002
I0412 13:50:13.861925  8032 solver.cpp:218] Iteration 5892 (2.23547 iter/s, 5.368s/12 iters), loss = 0.63882
I0412 13:50:13.862000  8032 solver.cpp:237]     Train net output #0: loss = 0.63882 (* 1 = 0.63882 loss)
I0412 13:50:13.862012  8032 sgd_solver.cpp:105] Iteration 5892, lr = 0.00311262
I0412 13:50:18.825266  8032 solver.cpp:218] Iteration 5904 (2.41784 iter/s, 4.9631s/12 iters), loss = 0.614667
I0412 13:50:18.825317  8032 solver.cpp:237]     Train net output #0: loss = 0.614667 (* 1 = 0.614667 loss)
I0412 13:50:18.825330  8032 sgd_solver.cpp:105] Iteration 5904, lr = 0.00310523
I0412 13:50:23.399298  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5916.caffemodel
I0412 13:50:26.517170  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5916.solverstate
I0412 13:50:28.887768  8032 solver.cpp:330] Iteration 5916, Testing net (#0)
I0412 13:50:28.887872  8032 net.cpp:676] Ignoring source layer train-data
I0412 13:50:31.006510  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:50:33.392163  8032 solver.cpp:397]     Test net output #0: accuracy = 0.422181
I0412 13:50:33.392192  8032 solver.cpp:397]     Test net output #1: loss = 2.84669 (* 1 = 2.84669 loss)
I0412 13:50:33.480181  8032 solver.cpp:218] Iteration 5916 (0.818866 iter/s, 14.6544s/12 iters), loss = 0.512181
I0412 13:50:33.480229  8032 solver.cpp:237]     Train net output #0: loss = 0.512181 (* 1 = 0.512181 loss)
I0412 13:50:33.480240  8032 sgd_solver.cpp:105] Iteration 5916, lr = 0.00309785
I0412 13:50:37.809981  8032 solver.cpp:218] Iteration 5928 (2.77162 iter/s, 4.32959s/12 iters), loss = 0.520547
I0412 13:50:37.810026  8032 solver.cpp:237]     Train net output #0: loss = 0.520547 (* 1 = 0.520547 loss)
I0412 13:50:37.810035  8032 sgd_solver.cpp:105] Iteration 5928, lr = 0.0030905
I0412 13:50:42.913367  8032 solver.cpp:218] Iteration 5940 (2.35148 iter/s, 5.10317s/12 iters), loss = 0.417839
I0412 13:50:42.913414  8032 solver.cpp:237]     Train net output #0: loss = 0.417839 (* 1 = 0.417839 loss)
I0412 13:50:42.913422  8032 sgd_solver.cpp:105] Iteration 5940, lr = 0.00308316
I0412 13:50:48.050673  8032 solver.cpp:218] Iteration 5952 (2.33595 iter/s, 5.13709s/12 iters), loss = 0.512849
I0412 13:50:48.050720  8032 solver.cpp:237]     Train net output #0: loss = 0.512849 (* 1 = 0.512849 loss)
I0412 13:50:48.050730  8032 sgd_solver.cpp:105] Iteration 5952, lr = 0.00307584
I0412 13:50:53.101094  8032 solver.cpp:218] Iteration 5964 (2.37614 iter/s, 5.05021s/12 iters), loss = 0.498528
I0412 13:50:53.101145  8032 solver.cpp:237]     Train net output #0: loss = 0.498528 (* 1 = 0.498528 loss)
I0412 13:50:53.101157  8032 sgd_solver.cpp:105] Iteration 5964, lr = 0.00306854
I0412 13:50:54.458338  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:50:58.152333  8032 solver.cpp:218] Iteration 5976 (2.37576 iter/s, 5.05103s/12 iters), loss = 0.713401
I0412 13:50:58.152375  8032 solver.cpp:237]     Train net output #0: loss = 0.713401 (* 1 = 0.713401 loss)
I0412 13:50:58.152384  8032 sgd_solver.cpp:105] Iteration 5976, lr = 0.00306125
I0412 13:51:03.402113  8032 solver.cpp:218] Iteration 5988 (2.28591 iter/s, 5.24956s/12 iters), loss = 0.497363
I0412 13:51:03.402228  8032 solver.cpp:237]     Train net output #0: loss = 0.497363 (* 1 = 0.497363 loss)
I0412 13:51:03.402240  8032 sgd_solver.cpp:105] Iteration 5988, lr = 0.00305398
I0412 13:51:08.383919  8032 solver.cpp:218] Iteration 6000 (2.4089 iter/s, 4.98153s/12 iters), loss = 0.550455
I0412 13:51:08.383962  8032 solver.cpp:237]     Train net output #0: loss = 0.550455 (* 1 = 0.550455 loss)
I0412 13:51:08.383970  8032 sgd_solver.cpp:105] Iteration 6000, lr = 0.00304673
I0412 13:51:13.449205  8032 solver.cpp:218] Iteration 6012 (2.36916 iter/s, 5.06508s/12 iters), loss = 0.647086
I0412 13:51:13.449256  8032 solver.cpp:237]     Train net output #0: loss = 0.647086 (* 1 = 0.647086 loss)
I0412 13:51:13.449267  8032 sgd_solver.cpp:105] Iteration 6012, lr = 0.0030395
I0412 13:51:15.628448  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6018.caffemodel
I0412 13:51:21.882604  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6018.solverstate
I0412 13:51:24.193233  8032 solver.cpp:330] Iteration 6018, Testing net (#0)
I0412 13:51:24.193256  8032 net.cpp:676] Ignoring source layer train-data
I0412 13:51:26.499579  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:51:28.870918  8032 solver.cpp:397]     Test net output #0: accuracy = 0.440564
I0412 13:51:28.870968  8032 solver.cpp:397]     Test net output #1: loss = 2.88678 (* 1 = 2.88678 loss)
I0412 13:51:30.877542  8032 solver.cpp:218] Iteration 6024 (0.688557 iter/s, 17.4277s/12 iters), loss = 0.622547
I0412 13:51:30.877595  8032 solver.cpp:237]     Train net output #0: loss = 0.622547 (* 1 = 0.622547 loss)
I0412 13:51:30.877606  8032 sgd_solver.cpp:105] Iteration 6024, lr = 0.00303228
I0412 13:51:36.123050  8032 solver.cpp:218] Iteration 6036 (2.28777 iter/s, 5.24528s/12 iters), loss = 0.500513
I0412 13:51:36.123198  8032 solver.cpp:237]     Train net output #0: loss = 0.500513 (* 1 = 0.500513 loss)
I0412 13:51:36.123210  8032 sgd_solver.cpp:105] Iteration 6036, lr = 0.00302508
I0412 13:51:41.294409  8032 solver.cpp:218] Iteration 6048 (2.32061 iter/s, 5.17105s/12 iters), loss = 0.397394
I0412 13:51:41.294456  8032 solver.cpp:237]     Train net output #0: loss = 0.397394 (* 1 = 0.397394 loss)
I0412 13:51:41.294467  8032 sgd_solver.cpp:105] Iteration 6048, lr = 0.0030179
I0412 13:51:46.598621  8032 solver.cpp:218] Iteration 6060 (2.26245 iter/s, 5.30399s/12 iters), loss = 0.660294
I0412 13:51:46.598673  8032 solver.cpp:237]     Train net output #0: loss = 0.660294 (* 1 = 0.660294 loss)
I0412 13:51:46.598685  8032 sgd_solver.cpp:105] Iteration 6060, lr = 0.00301074
I0412 13:51:50.160364  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:51:51.725103  8032 solver.cpp:218] Iteration 6072 (2.34089 iter/s, 5.12626s/12 iters), loss = 0.439985
I0412 13:51:51.725157  8032 solver.cpp:237]     Train net output #0: loss = 0.439985 (* 1 = 0.439985 loss)
I0412 13:51:51.725167  8032 sgd_solver.cpp:105] Iteration 6072, lr = 0.00300359
I0412 13:51:56.825331  8032 solver.cpp:218] Iteration 6084 (2.35294 iter/s, 5.1s/12 iters), loss = 0.509449
I0412 13:51:56.825387  8032 solver.cpp:237]     Train net output #0: loss = 0.509449 (* 1 = 0.509449 loss)
I0412 13:51:56.825399  8032 sgd_solver.cpp:105] Iteration 6084, lr = 0.00299646
I0412 13:52:01.843922  8032 solver.cpp:218] Iteration 6096 (2.39122 iter/s, 5.01836s/12 iters), loss = 0.376423
I0412 13:52:01.843976  8032 solver.cpp:237]     Train net output #0: loss = 0.376423 (* 1 = 0.376423 loss)
I0412 13:52:01.843989  8032 sgd_solver.cpp:105] Iteration 6096, lr = 0.00298934
I0412 13:52:06.769502  8032 solver.cpp:218] Iteration 6108 (2.43637 iter/s, 4.92536s/12 iters), loss = 0.657602
I0412 13:52:06.769616  8032 solver.cpp:237]     Train net output #0: loss = 0.657602 (* 1 = 0.657602 loss)
I0412 13:52:06.769629  8032 sgd_solver.cpp:105] Iteration 6108, lr = 0.00298225
I0412 13:52:11.397292  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6120.caffemodel
I0412 13:52:16.735256  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6120.solverstate
I0412 13:52:19.685899  8032 solver.cpp:330] Iteration 6120, Testing net (#0)
I0412 13:52:19.685920  8032 net.cpp:676] Ignoring source layer train-data
I0412 13:52:21.732754  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:52:24.166296  8032 solver.cpp:397]     Test net output #0: accuracy = 0.43076
I0412 13:52:24.166344  8032 solver.cpp:397]     Test net output #1: loss = 2.80628 (* 1 = 2.80628 loss)
I0412 13:52:24.254560  8032 solver.cpp:218] Iteration 6120 (0.686326 iter/s, 17.4844s/12 iters), loss = 0.376542
I0412 13:52:24.254611  8032 solver.cpp:237]     Train net output #0: loss = 0.376542 (* 1 = 0.376542 loss)
I0412 13:52:24.254621  8032 sgd_solver.cpp:105] Iteration 6120, lr = 0.00297517
I0412 13:52:28.490804  8032 solver.cpp:218] Iteration 6132 (2.83283 iter/s, 4.23605s/12 iters), loss = 0.476296
I0412 13:52:28.490856  8032 solver.cpp:237]     Train net output #0: loss = 0.476296 (* 1 = 0.476296 loss)
I0412 13:52:28.490869  8032 sgd_solver.cpp:105] Iteration 6132, lr = 0.0029681
I0412 13:52:33.534430  8032 solver.cpp:218] Iteration 6144 (2.37934 iter/s, 5.04341s/12 iters), loss = 0.541534
I0412 13:52:33.534485  8032 solver.cpp:237]     Train net output #0: loss = 0.541534 (* 1 = 0.541534 loss)
I0412 13:52:33.534497  8032 sgd_solver.cpp:105] Iteration 6144, lr = 0.00296105
I0412 13:52:38.533335  8032 solver.cpp:218] Iteration 6156 (2.40063 iter/s, 4.99868s/12 iters), loss = 0.294288
I0412 13:52:38.533476  8032 solver.cpp:237]     Train net output #0: loss = 0.294288 (* 1 = 0.294288 loss)
I0412 13:52:38.533488  8032 sgd_solver.cpp:105] Iteration 6156, lr = 0.00295402
I0412 13:52:43.507555  8032 solver.cpp:218] Iteration 6168 (2.41259 iter/s, 4.97391s/12 iters), loss = 0.47401
I0412 13:52:43.507606  8032 solver.cpp:237]     Train net output #0: loss = 0.47401 (* 1 = 0.47401 loss)
I0412 13:52:43.507617  8032 sgd_solver.cpp:105] Iteration 6168, lr = 0.00294701
I0412 13:52:44.097697  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:52:48.657369  8032 solver.cpp:218] Iteration 6180 (2.33028 iter/s, 5.14959s/12 iters), loss = 0.514648
I0412 13:52:48.657424  8032 solver.cpp:237]     Train net output #0: loss = 0.514648 (* 1 = 0.514648 loss)
I0412 13:52:48.657438  8032 sgd_solver.cpp:105] Iteration 6180, lr = 0.00294001
I0412 13:52:53.741407  8032 solver.cpp:218] Iteration 6192 (2.36043 iter/s, 5.08382s/12 iters), loss = 0.642243
I0412 13:52:53.741459  8032 solver.cpp:237]     Train net output #0: loss = 0.642243 (* 1 = 0.642243 loss)
I0412 13:52:53.741470  8032 sgd_solver.cpp:105] Iteration 6192, lr = 0.00293303
I0412 13:52:58.822930  8032 solver.cpp:218] Iteration 6204 (2.3616 iter/s, 5.0813s/12 iters), loss = 0.399468
I0412 13:52:58.822986  8032 solver.cpp:237]     Train net output #0: loss = 0.399468 (* 1 = 0.399468 loss)
I0412 13:52:58.822999  8032 sgd_solver.cpp:105] Iteration 6204, lr = 0.00292607
I0412 13:53:03.930892  8032 solver.cpp:218] Iteration 6216 (2.34938 iter/s, 5.10774s/12 iters), loss = 0.500883
I0412 13:53:03.930936  8032 solver.cpp:237]     Train net output #0: loss = 0.500883 (* 1 = 0.500883 loss)
I0412 13:53:03.930945  8032 sgd_solver.cpp:105] Iteration 6216, lr = 0.00291912
I0412 13:53:05.967878  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6222.caffemodel
I0412 13:53:12.552314  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6222.solverstate
I0412 13:53:15.009722  8032 solver.cpp:330] Iteration 6222, Testing net (#0)
I0412 13:53:15.009748  8032 net.cpp:676] Ignoring source layer train-data
I0412 13:53:17.202740  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:53:18.614686  8032 blocking_queue.cpp:49] Waiting for data
I0412 13:53:19.932268  8032 solver.cpp:397]     Test net output #0: accuracy = 0.433211
I0412 13:53:19.932313  8032 solver.cpp:397]     Test net output #1: loss = 2.86917 (* 1 = 2.86917 loss)
I0412 13:53:21.831997  8032 solver.cpp:218] Iteration 6228 (0.670372 iter/s, 17.9005s/12 iters), loss = 0.407367
I0412 13:53:21.832054  8032 solver.cpp:237]     Train net output #0: loss = 0.407367 (* 1 = 0.407367 loss)
I0412 13:53:21.832065  8032 sgd_solver.cpp:105] Iteration 6228, lr = 0.00291219
I0412 13:53:26.868752  8032 solver.cpp:218] Iteration 6240 (2.38259 iter/s, 5.03653s/12 iters), loss = 0.529889
I0412 13:53:26.868811  8032 solver.cpp:237]     Train net output #0: loss = 0.529889 (* 1 = 0.529889 loss)
I0412 13:53:26.868824  8032 sgd_solver.cpp:105] Iteration 6240, lr = 0.00290528
I0412 13:53:31.849694  8032 solver.cpp:218] Iteration 6252 (2.40929 iter/s, 4.98071s/12 iters), loss = 0.254053
I0412 13:53:31.849761  8032 solver.cpp:237]     Train net output #0: loss = 0.254053 (* 1 = 0.254053 loss)
I0412 13:53:31.849773  8032 sgd_solver.cpp:105] Iteration 6252, lr = 0.00289838
I0412 13:53:36.888202  8032 solver.cpp:218] Iteration 6264 (2.38177 iter/s, 5.03827s/12 iters), loss = 0.351192
I0412 13:53:36.888252  8032 solver.cpp:237]     Train net output #0: loss = 0.351192 (* 1 = 0.351192 loss)
I0412 13:53:36.888263  8032 sgd_solver.cpp:105] Iteration 6264, lr = 0.0028915
I0412 13:53:39.647927  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:53:41.972805  8032 solver.cpp:218] Iteration 6276 (2.36017 iter/s, 5.08439s/12 iters), loss = 0.456425
I0412 13:53:41.972856  8032 solver.cpp:237]     Train net output #0: loss = 0.456425 (* 1 = 0.456425 loss)
I0412 13:53:41.972867  8032 sgd_solver.cpp:105] Iteration 6276, lr = 0.00288463
I0412 13:53:47.489675  8032 solver.cpp:218] Iteration 6288 (2.17524 iter/s, 5.51664s/12 iters), loss = 0.526326
I0412 13:53:47.489815  8032 solver.cpp:237]     Train net output #0: loss = 0.526326 (* 1 = 0.526326 loss)
I0412 13:53:47.489827  8032 sgd_solver.cpp:105] Iteration 6288, lr = 0.00287779
I0412 13:53:52.887301  8032 solver.cpp:218] Iteration 6300 (2.22333 iter/s, 5.39731s/12 iters), loss = 0.378523
I0412 13:53:52.887342  8032 solver.cpp:237]     Train net output #0: loss = 0.378523 (* 1 = 0.378523 loss)
I0412 13:53:52.887353  8032 sgd_solver.cpp:105] Iteration 6300, lr = 0.00287095
I0412 13:53:58.057461  8032 solver.cpp:218] Iteration 6312 (2.32111 iter/s, 5.16994s/12 iters), loss = 0.478188
I0412 13:53:58.057503  8032 solver.cpp:237]     Train net output #0: loss = 0.478188 (* 1 = 0.478188 loss)
I0412 13:53:58.057512  8032 sgd_solver.cpp:105] Iteration 6312, lr = 0.00286414
I0412 13:54:02.611198  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6324.caffemodel
I0412 13:54:07.848058  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6324.solverstate
I0412 13:54:10.830294  8032 solver.cpp:330] Iteration 6324, Testing net (#0)
I0412 13:54:10.830322  8032 net.cpp:676] Ignoring source layer train-data
I0412 13:54:12.817545  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:54:15.602856  8032 solver.cpp:397]     Test net output #0: accuracy = 0.417279
I0412 13:54:15.602890  8032 solver.cpp:397]     Test net output #1: loss = 3.01747 (* 1 = 3.01747 loss)
I0412 13:54:15.690865  8032 solver.cpp:218] Iteration 6324 (0.680549 iter/s, 17.6328s/12 iters), loss = 0.336868
I0412 13:54:15.690910  8032 solver.cpp:237]     Train net output #0: loss = 0.336868 (* 1 = 0.336868 loss)
I0412 13:54:15.690918  8032 sgd_solver.cpp:105] Iteration 6324, lr = 0.00285734
I0412 13:54:20.306118  8032 solver.cpp:218] Iteration 6336 (2.60019 iter/s, 4.61505s/12 iters), loss = 0.308992
I0412 13:54:20.313988  8032 solver.cpp:237]     Train net output #0: loss = 0.308992 (* 1 = 0.308992 loss)
I0412 13:54:20.314002  8032 sgd_solver.cpp:105] Iteration 6336, lr = 0.00285055
I0412 13:54:25.524377  8032 solver.cpp:218] Iteration 6348 (2.30315 iter/s, 5.21024s/12 iters), loss = 0.218251
I0412 13:54:25.524435  8032 solver.cpp:237]     Train net output #0: loss = 0.218251 (* 1 = 0.218251 loss)
I0412 13:54:25.524446  8032 sgd_solver.cpp:105] Iteration 6348, lr = 0.00284379
I0412 13:54:30.742241  8032 solver.cpp:218] Iteration 6360 (2.29989 iter/s, 5.21763s/12 iters), loss = 0.500516
I0412 13:54:30.742292  8032 solver.cpp:237]     Train net output #0: loss = 0.500516 (* 1 = 0.500516 loss)
I0412 13:54:30.742303  8032 sgd_solver.cpp:105] Iteration 6360, lr = 0.00283703
I0412 13:54:35.790961  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:54:35.929992  8032 solver.cpp:218] Iteration 6372 (2.31324 iter/s, 5.18753s/12 iters), loss = 0.443318
I0412 13:54:35.930040  8032 solver.cpp:237]     Train net output #0: loss = 0.443318 (* 1 = 0.443318 loss)
I0412 13:54:35.930052  8032 sgd_solver.cpp:105] Iteration 6372, lr = 0.0028303
I0412 13:54:40.930819  8032 solver.cpp:218] Iteration 6384 (2.3997 iter/s, 5.00062s/12 iters), loss = 0.349584
I0412 13:54:40.930866  8032 solver.cpp:237]     Train net output #0: loss = 0.349584 (* 1 = 0.349584 loss)
I0412 13:54:40.930876  8032 sgd_solver.cpp:105] Iteration 6384, lr = 0.00282358
I0412 13:54:45.802330  8032 solver.cpp:218] Iteration 6396 (2.46341 iter/s, 4.8713s/12 iters), loss = 0.416847
I0412 13:54:45.802387  8032 solver.cpp:237]     Train net output #0: loss = 0.416847 (* 1 = 0.416847 loss)
I0412 13:54:45.802399  8032 sgd_solver.cpp:105] Iteration 6396, lr = 0.00281687
I0412 13:54:50.787135  8032 solver.cpp:218] Iteration 6408 (2.40742 iter/s, 4.98458s/12 iters), loss = 0.453566
I0412 13:54:50.787235  8032 solver.cpp:237]     Train net output #0: loss = 0.453566 (* 1 = 0.453566 loss)
I0412 13:54:50.787245  8032 sgd_solver.cpp:105] Iteration 6408, lr = 0.00281019
I0412 13:54:56.014158  8032 solver.cpp:218] Iteration 6420 (2.29588 iter/s, 5.22675s/12 iters), loss = 0.463255
I0412 13:54:56.014214  8032 solver.cpp:237]     Train net output #0: loss = 0.463255 (* 1 = 0.463255 loss)
I0412 13:54:56.014226  8032 sgd_solver.cpp:105] Iteration 6420, lr = 0.00280351
I0412 13:54:58.044258  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6426.caffemodel
I0412 13:55:03.153703  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6426.solverstate
I0412 13:55:07.456389  8032 solver.cpp:330] Iteration 6426, Testing net (#0)
I0412 13:55:07.456414  8032 net.cpp:676] Ignoring source layer train-data
I0412 13:55:09.536643  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:55:12.069182  8032 solver.cpp:397]     Test net output #0: accuracy = 0.439951
I0412 13:55:12.069218  8032 solver.cpp:397]     Test net output #1: loss = 2.80703 (* 1 = 2.80703 loss)
I0412 13:55:13.883836  8032 solver.cpp:218] Iteration 6432 (0.671552 iter/s, 17.8691s/12 iters), loss = 0.415725
I0412 13:55:13.883893  8032 solver.cpp:237]     Train net output #0: loss = 0.415725 (* 1 = 0.415725 loss)
I0412 13:55:13.883904  8032 sgd_solver.cpp:105] Iteration 6432, lr = 0.00279686
I0412 13:55:18.931015  8032 solver.cpp:218] Iteration 6444 (2.37767 iter/s, 5.04696s/12 iters), loss = 0.461105
I0412 13:55:18.931068  8032 solver.cpp:237]     Train net output #0: loss = 0.461105 (* 1 = 0.461105 loss)
I0412 13:55:18.931082  8032 sgd_solver.cpp:105] Iteration 6444, lr = 0.00279022
I0412 13:55:23.852954  8032 solver.cpp:218] Iteration 6456 (2.43817 iter/s, 4.92172s/12 iters), loss = 0.331421
I0412 13:55:23.853089  8032 solver.cpp:237]     Train net output #0: loss = 0.331421 (* 1 = 0.331421 loss)
I0412 13:55:23.853101  8032 sgd_solver.cpp:105] Iteration 6456, lr = 0.00278359
I0412 13:55:29.162762  8032 solver.cpp:218] Iteration 6468 (2.2601 iter/s, 5.3095s/12 iters), loss = 0.373152
I0412 13:55:29.162813  8032 solver.cpp:237]     Train net output #0: loss = 0.373152 (* 1 = 0.373152 loss)
I0412 13:55:29.162824  8032 sgd_solver.cpp:105] Iteration 6468, lr = 0.00277698
I0412 13:55:31.262974  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:55:34.409433  8032 solver.cpp:218] Iteration 6480 (2.28726 iter/s, 5.24645s/12 iters), loss = 0.504205
I0412 13:55:34.409487  8032 solver.cpp:237]     Train net output #0: loss = 0.504205 (* 1 = 0.504205 loss)
I0412 13:55:34.409499  8032 sgd_solver.cpp:105] Iteration 6480, lr = 0.00277039
I0412 13:55:39.649250  8032 solver.cpp:218] Iteration 6492 (2.29025 iter/s, 5.23959s/12 iters), loss = 0.210692
I0412 13:55:39.649298  8032 solver.cpp:237]     Train net output #0: loss = 0.210692 (* 1 = 0.210692 loss)
I0412 13:55:39.649308  8032 sgd_solver.cpp:105] Iteration 6492, lr = 0.00276381
I0412 13:55:44.712345  8032 solver.cpp:218] Iteration 6504 (2.37019 iter/s, 5.06287s/12 iters), loss = 0.363648
I0412 13:55:44.712401  8032 solver.cpp:237]     Train net output #0: loss = 0.363648 (* 1 = 0.363648 loss)
I0412 13:55:44.712414  8032 sgd_solver.cpp:105] Iteration 6504, lr = 0.00275725
I0412 13:55:49.705776  8032 solver.cpp:218] Iteration 6516 (2.40326 iter/s, 4.99321s/12 iters), loss = 0.304427
I0412 13:55:49.705821  8032 solver.cpp:237]     Train net output #0: loss = 0.304427 (* 1 = 0.304427 loss)
I0412 13:55:49.705830  8032 sgd_solver.cpp:105] Iteration 6516, lr = 0.00275071
I0412 13:55:54.262748  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6528.caffemodel
I0412 13:55:57.298190  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6528.solverstate
I0412 13:56:04.947592  8032 solver.cpp:330] Iteration 6528, Testing net (#0)
I0412 13:56:04.947616  8032 net.cpp:676] Ignoring source layer train-data
I0412 13:56:06.774150  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:56:09.559496  8032 solver.cpp:397]     Test net output #0: accuracy = 0.434436
I0412 13:56:09.559536  8032 solver.cpp:397]     Test net output #1: loss = 2.91791 (* 1 = 2.91791 loss)
I0412 13:56:09.647703  8032 solver.cpp:218] Iteration 6528 (0.601767 iter/s, 19.9413s/12 iters), loss = 0.341807
I0412 13:56:09.647748  8032 solver.cpp:237]     Train net output #0: loss = 0.341807 (* 1 = 0.341807 loss)
I0412 13:56:09.647758  8032 sgd_solver.cpp:105] Iteration 6528, lr = 0.00274418
I0412 13:56:14.120914  8032 solver.cpp:218] Iteration 6540 (2.68275 iter/s, 4.47302s/12 iters), loss = 0.631898
I0412 13:56:14.120957  8032 solver.cpp:237]     Train net output #0: loss = 0.631898 (* 1 = 0.631898 loss)
I0412 13:56:14.120965  8032 sgd_solver.cpp:105] Iteration 6540, lr = 0.00273766
I0412 13:56:19.402801  8032 solver.cpp:218] Iteration 6552 (2.27201 iter/s, 5.28167s/12 iters), loss = 0.300827
I0412 13:56:19.402842  8032 solver.cpp:237]     Train net output #0: loss = 0.300827 (* 1 = 0.300827 loss)
I0412 13:56:19.402853  8032 sgd_solver.cpp:105] Iteration 6552, lr = 0.00273116
I0412 13:56:24.464674  8032 solver.cpp:218] Iteration 6564 (2.37076 iter/s, 5.06166s/12 iters), loss = 0.418869
I0412 13:56:24.464762  8032 solver.cpp:237]     Train net output #0: loss = 0.418869 (* 1 = 0.418869 loss)
I0412 13:56:24.464772  8032 sgd_solver.cpp:105] Iteration 6564, lr = 0.00272468
I0412 13:56:28.820263  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:56:29.563828  8032 solver.cpp:218] Iteration 6576 (2.35345 iter/s, 5.09889s/12 iters), loss = 0.467589
I0412 13:56:29.563884  8032 solver.cpp:237]     Train net output #0: loss = 0.467589 (* 1 = 0.467589 loss)
I0412 13:56:29.563894  8032 sgd_solver.cpp:105] Iteration 6576, lr = 0.00271821
I0412 13:56:34.551934  8032 solver.cpp:218] Iteration 6588 (2.40583 iter/s, 4.98788s/12 iters), loss = 0.294446
I0412 13:56:34.551983  8032 solver.cpp:237]     Train net output #0: loss = 0.294446 (* 1 = 0.294446 loss)
I0412 13:56:34.551995  8032 sgd_solver.cpp:105] Iteration 6588, lr = 0.00271175
I0412 13:56:39.529819  8032 solver.cpp:218] Iteration 6600 (2.41077 iter/s, 4.97767s/12 iters), loss = 0.324539
I0412 13:56:39.529872  8032 solver.cpp:237]     Train net output #0: loss = 0.324539 (* 1 = 0.324539 loss)
I0412 13:56:39.529886  8032 sgd_solver.cpp:105] Iteration 6600, lr = 0.00270532
I0412 13:56:44.698285  8032 solver.cpp:218] Iteration 6612 (2.32187 iter/s, 5.16824s/12 iters), loss = 0.276562
I0412 13:56:44.698345  8032 solver.cpp:237]     Train net output #0: loss = 0.276562 (* 1 = 0.276562 loss)
I0412 13:56:44.698359  8032 sgd_solver.cpp:105] Iteration 6612, lr = 0.00269889
I0412 13:56:49.849684  8032 solver.cpp:218] Iteration 6624 (2.32957 iter/s, 5.15117s/12 iters), loss = 0.439997
I0412 13:56:49.849735  8032 solver.cpp:237]     Train net output #0: loss = 0.439997 (* 1 = 0.439997 loss)
I0412 13:56:49.849746  8032 sgd_solver.cpp:105] Iteration 6624, lr = 0.00269248
I0412 13:56:51.856118  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6630.caffemodel
I0412 13:56:55.060600  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6630.solverstate
I0412 13:56:58.043846  8032 solver.cpp:330] Iteration 6630, Testing net (#0)
I0412 13:56:58.043874  8032 net.cpp:676] Ignoring source layer train-data
I0412 13:56:59.917044  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:57:02.516090  8032 solver.cpp:397]     Test net output #0: accuracy = 0.435049
I0412 13:57:02.516139  8032 solver.cpp:397]     Test net output #1: loss = 2.9312 (* 1 = 2.9312 loss)
I0412 13:57:04.271543  8032 solver.cpp:218] Iteration 6636 (0.832099 iter/s, 14.4214s/12 iters), loss = 0.455764
I0412 13:57:04.271595  8032 solver.cpp:237]     Train net output #0: loss = 0.455764 (* 1 = 0.455764 loss)
I0412 13:57:04.271606  8032 sgd_solver.cpp:105] Iteration 6636, lr = 0.00268609
I0412 13:57:09.291935  8032 solver.cpp:218] Iteration 6648 (2.39035 iter/s, 5.02018s/12 iters), loss = 0.399027
I0412 13:57:09.291988  8032 solver.cpp:237]     Train net output #0: loss = 0.399027 (* 1 = 0.399027 loss)
I0412 13:57:09.292001  8032 sgd_solver.cpp:105] Iteration 6648, lr = 0.00267971
I0412 13:57:14.510215  8032 solver.cpp:218] Iteration 6660 (2.29971 iter/s, 5.21805s/12 iters), loss = 0.438967
I0412 13:57:14.510257  8032 solver.cpp:237]     Train net output #0: loss = 0.438967 (* 1 = 0.438967 loss)
I0412 13:57:14.510267  8032 sgd_solver.cpp:105] Iteration 6660, lr = 0.00267335
I0412 13:57:19.722311  8032 solver.cpp:218] Iteration 6672 (2.30243 iter/s, 5.21188s/12 iters), loss = 0.299424
I0412 13:57:19.722353  8032 solver.cpp:237]     Train net output #0: loss = 0.299424 (* 1 = 0.299424 loss)
I0412 13:57:19.722362  8032 sgd_solver.cpp:105] Iteration 6672, lr = 0.00266701
I0412 13:57:21.099048  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:57:24.773298  8032 solver.cpp:218] Iteration 6684 (2.37588 iter/s, 5.05077s/12 iters), loss = 0.256409
I0412 13:57:24.773352  8032 solver.cpp:237]     Train net output #0: loss = 0.256409 (* 1 = 0.256409 loss)
I0412 13:57:24.773365  8032 sgd_solver.cpp:105] Iteration 6684, lr = 0.00266067
I0412 13:57:29.777770  8032 solver.cpp:218] Iteration 6696 (2.39796 iter/s, 5.00425s/12 iters), loss = 0.238205
I0412 13:57:29.777896  8032 solver.cpp:237]     Train net output #0: loss = 0.238205 (* 1 = 0.238205 loss)
I0412 13:57:29.777907  8032 sgd_solver.cpp:105] Iteration 6696, lr = 0.00265436
I0412 13:57:34.779984  8032 solver.cpp:218] Iteration 6708 (2.39908 iter/s, 5.00192s/12 iters), loss = 0.251261
I0412 13:57:34.780045  8032 solver.cpp:237]     Train net output #0: loss = 0.251261 (* 1 = 0.251261 loss)
I0412 13:57:34.780056  8032 sgd_solver.cpp:105] Iteration 6708, lr = 0.00264805
I0412 13:57:39.738256  8032 solver.cpp:218] Iteration 6720 (2.42031 iter/s, 4.95805s/12 iters), loss = 0.338754
I0412 13:57:39.738310  8032 solver.cpp:237]     Train net output #0: loss = 0.338754 (* 1 = 0.338754 loss)
I0412 13:57:39.738323  8032 sgd_solver.cpp:105] Iteration 6720, lr = 0.00264177
I0412 13:57:44.239369  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6732.caffemodel
I0412 13:57:47.332506  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6732.solverstate
I0412 13:57:49.749706  8032 solver.cpp:330] Iteration 6732, Testing net (#0)
I0412 13:57:49.749734  8032 net.cpp:676] Ignoring source layer train-data
I0412 13:57:51.570267  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:57:54.212991  8032 solver.cpp:397]     Test net output #0: accuracy = 0.441789
I0412 13:57:54.213035  8032 solver.cpp:397]     Test net output #1: loss = 2.81463 (* 1 = 2.81463 loss)
I0412 13:57:54.301416  8032 solver.cpp:218] Iteration 6732 (0.824026 iter/s, 14.5627s/12 iters), loss = 0.284281
I0412 13:57:54.301463  8032 solver.cpp:237]     Train net output #0: loss = 0.284281 (* 1 = 0.284281 loss)
I0412 13:57:54.301474  8032 sgd_solver.cpp:105] Iteration 6732, lr = 0.0026355
I0412 13:57:58.570649  8032 solver.cpp:218] Iteration 6744 (2.81094 iter/s, 4.26904s/12 iters), loss = 0.29632
I0412 13:57:58.570703  8032 solver.cpp:237]     Train net output #0: loss = 0.29632 (* 1 = 0.29632 loss)
I0412 13:57:58.570719  8032 sgd_solver.cpp:105] Iteration 6744, lr = 0.00262924
I0412 13:58:03.890914  8032 solver.cpp:218] Iteration 6756 (2.25563 iter/s, 5.32003s/12 iters), loss = 0.431126
I0412 13:58:03.891038  8032 solver.cpp:237]     Train net output #0: loss = 0.431126 (* 1 = 0.431126 loss)
I0412 13:58:03.891052  8032 sgd_solver.cpp:105] Iteration 6756, lr = 0.002623
I0412 13:58:08.868131  8032 solver.cpp:218] Iteration 6768 (2.41113 iter/s, 4.97693s/12 iters), loss = 0.313282
I0412 13:58:08.868186  8032 solver.cpp:237]     Train net output #0: loss = 0.313282 (* 1 = 0.313282 loss)
I0412 13:58:08.868198  8032 sgd_solver.cpp:105] Iteration 6768, lr = 0.00261677
I0412 13:58:12.343590  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:58:13.804024  8032 solver.cpp:218] Iteration 6780 (2.43128 iter/s, 4.93567s/12 iters), loss = 0.251515
I0412 13:58:13.804083  8032 solver.cpp:237]     Train net output #0: loss = 0.251515 (* 1 = 0.251515 loss)
I0412 13:58:13.804095  8032 sgd_solver.cpp:105] Iteration 6780, lr = 0.00261056
I0412 13:58:18.780373  8032 solver.cpp:218] Iteration 6792 (2.41152 iter/s, 4.97612s/12 iters), loss = 0.186644
I0412 13:58:18.780427  8032 solver.cpp:237]     Train net output #0: loss = 0.186644 (* 1 = 0.186644 loss)
I0412 13:58:18.780438  8032 sgd_solver.cpp:105] Iteration 6792, lr = 0.00260436
I0412 13:58:23.789939  8032 solver.cpp:218] Iteration 6804 (2.39552 iter/s, 5.00934s/12 iters), loss = 0.337346
I0412 13:58:23.790014  8032 solver.cpp:237]     Train net output #0: loss = 0.337346 (* 1 = 0.337346 loss)
I0412 13:58:23.790030  8032 sgd_solver.cpp:105] Iteration 6804, lr = 0.00259817
I0412 13:58:28.760258  8032 solver.cpp:218] Iteration 6816 (2.41445 iter/s, 4.97008s/12 iters), loss = 0.273544
I0412 13:58:28.760305  8032 solver.cpp:237]     Train net output #0: loss = 0.273544 (* 1 = 0.273544 loss)
I0412 13:58:28.760314  8032 sgd_solver.cpp:105] Iteration 6816, lr = 0.00259201
I0412 13:58:33.900004  8032 solver.cpp:218] Iteration 6828 (2.33485 iter/s, 5.13953s/12 iters), loss = 0.403876
I0412 13:58:33.900156  8032 solver.cpp:237]     Train net output #0: loss = 0.403876 (* 1 = 0.403876 loss)
I0412 13:58:33.900171  8032 sgd_solver.cpp:105] Iteration 6828, lr = 0.00258585
I0412 13:58:36.006599  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6834.caffemodel
I0412 13:58:39.018476  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6834.solverstate
I0412 13:58:41.808609  8032 solver.cpp:330] Iteration 6834, Testing net (#0)
I0412 13:58:41.808630  8032 net.cpp:676] Ignoring source layer train-data
I0412 13:58:43.584983  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:58:46.386941  8032 solver.cpp:397]     Test net output #0: accuracy = 0.443015
I0412 13:58:46.386977  8032 solver.cpp:397]     Test net output #1: loss = 2.97814 (* 1 = 2.97814 loss)
I0412 13:58:48.154498  8032 solver.cpp:218] Iteration 6840 (0.841875 iter/s, 14.2539s/12 iters), loss = 0.292757
I0412 13:58:48.154553  8032 solver.cpp:237]     Train net output #0: loss = 0.292757 (* 1 = 0.292757 loss)
I0412 13:58:48.154567  8032 sgd_solver.cpp:105] Iteration 6840, lr = 0.00257971
I0412 13:58:53.519086  8032 solver.cpp:218] Iteration 6852 (2.23699 iter/s, 5.36436s/12 iters), loss = 0.557449
I0412 13:58:53.519126  8032 solver.cpp:237]     Train net output #0: loss = 0.557449 (* 1 = 0.557449 loss)
I0412 13:58:53.519137  8032 sgd_solver.cpp:105] Iteration 6852, lr = 0.00257359
I0412 13:58:58.532845  8032 solver.cpp:218] Iteration 6864 (2.39351 iter/s, 5.01355s/12 iters), loss = 0.44598
I0412 13:58:58.532903  8032 solver.cpp:237]     Train net output #0: loss = 0.44598 (* 1 = 0.44598 loss)
I0412 13:58:58.532915  8032 sgd_solver.cpp:105] Iteration 6864, lr = 0.00256748
I0412 13:59:03.542910  8032 solver.cpp:218] Iteration 6876 (2.39529 iter/s, 5.00984s/12 iters), loss = 0.228476
I0412 13:59:03.542969  8032 solver.cpp:237]     Train net output #0: loss = 0.228476 (* 1 = 0.228476 loss)
I0412 13:59:03.542981  8032 sgd_solver.cpp:105] Iteration 6876, lr = 0.00256138
I0412 13:59:04.153592  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:59:08.513852  8032 solver.cpp:218] Iteration 6888 (2.41414 iter/s, 4.97072s/12 iters), loss = 0.371772
I0412 13:59:08.513911  8032 solver.cpp:237]     Train net output #0: loss = 0.371772 (* 1 = 0.371772 loss)
I0412 13:59:08.513922  8032 sgd_solver.cpp:105] Iteration 6888, lr = 0.0025553
I0412 13:59:13.606772  8032 solver.cpp:218] Iteration 6900 (2.35632 iter/s, 5.09269s/12 iters), loss = 0.317392
I0412 13:59:13.606825  8032 solver.cpp:237]     Train net output #0: loss = 0.317392 (* 1 = 0.317392 loss)
I0412 13:59:13.606838  8032 sgd_solver.cpp:105] Iteration 6900, lr = 0.00254923
I0412 13:59:18.828462  8032 solver.cpp:218] Iteration 6912 (2.29821 iter/s, 5.22146s/12 iters), loss = 0.220369
I0412 13:59:18.828517  8032 solver.cpp:237]     Train net output #0: loss = 0.220369 (* 1 = 0.220369 loss)
I0412 13:59:18.828529  8032 sgd_solver.cpp:105] Iteration 6912, lr = 0.00254318
I0412 13:59:23.877607  8032 solver.cpp:218] Iteration 6924 (2.37674 iter/s, 5.04892s/12 iters), loss = 0.406273
I0412 13:59:23.877647  8032 solver.cpp:237]     Train net output #0: loss = 0.406273 (* 1 = 0.406273 loss)
I0412 13:59:23.877656  8032 sgd_solver.cpp:105] Iteration 6924, lr = 0.00253714
I0412 13:59:28.731458  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6936.caffemodel
I0412 13:59:31.781582  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6936.solverstate
I0412 13:59:34.490080  8032 solver.cpp:330] Iteration 6936, Testing net (#0)
I0412 13:59:34.490155  8032 net.cpp:676] Ignoring source layer train-data
I0412 13:59:35.153352  8032 blocking_queue.cpp:49] Waiting for data
I0412 13:59:36.370586  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:59:39.236524  8032 solver.cpp:397]     Test net output #0: accuracy = 0.445466
I0412 13:59:39.236575  8032 solver.cpp:397]     Test net output #1: loss = 2.81872 (* 1 = 2.81872 loss)
I0412 13:59:39.324672  8032 solver.cpp:218] Iteration 6936 (0.776873 iter/s, 15.4465s/12 iters), loss = 0.411323
I0412 13:59:39.324720  8032 solver.cpp:237]     Train net output #0: loss = 0.411323 (* 1 = 0.411323 loss)
I0412 13:59:39.324731  8032 sgd_solver.cpp:105] Iteration 6936, lr = 0.00253112
I0412 13:59:43.605046  8032 solver.cpp:218] Iteration 6948 (2.80362 iter/s, 4.28018s/12 iters), loss = 0.228508
I0412 13:59:43.605096  8032 solver.cpp:237]     Train net output #0: loss = 0.228508 (* 1 = 0.228508 loss)
I0412 13:59:43.605109  8032 sgd_solver.cpp:105] Iteration 6948, lr = 0.00252511
I0412 13:59:48.634836  8032 solver.cpp:218] Iteration 6960 (2.38589 iter/s, 5.02957s/12 iters), loss = 0.390699
I0412 13:59:48.634891  8032 solver.cpp:237]     Train net output #0: loss = 0.390699 (* 1 = 0.390699 loss)
I0412 13:59:48.634904  8032 sgd_solver.cpp:105] Iteration 6960, lr = 0.00251911
I0412 13:59:53.805622  8032 solver.cpp:218] Iteration 6972 (2.32083 iter/s, 5.17056s/12 iters), loss = 0.413845
I0412 13:59:53.805675  8032 solver.cpp:237]     Train net output #0: loss = 0.413845 (* 1 = 0.413845 loss)
I0412 13:59:53.805685  8032 sgd_solver.cpp:105] Iteration 6972, lr = 0.00251313
I0412 13:59:56.608757  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 13:59:58.920202  8032 solver.cpp:218] Iteration 6984 (2.34634 iter/s, 5.11435s/12 iters), loss = 0.273179
I0412 13:59:58.920255  8032 solver.cpp:237]     Train net output #0: loss = 0.273179 (* 1 = 0.273179 loss)
I0412 13:59:58.920267  8032 sgd_solver.cpp:105] Iteration 6984, lr = 0.00250717
I0412 14:00:04.152949  8032 solver.cpp:218] Iteration 6996 (2.29335 iter/s, 5.23252s/12 iters), loss = 0.371186
I0412 14:00:04.152990  8032 solver.cpp:237]     Train net output #0: loss = 0.371186 (* 1 = 0.371186 loss)
I0412 14:00:04.153000  8032 sgd_solver.cpp:105] Iteration 6996, lr = 0.00250121
I0412 14:00:09.196990  8032 solver.cpp:218] Iteration 7008 (2.37914 iter/s, 5.04383s/12 iters), loss = 0.303791
I0412 14:00:09.197088  8032 solver.cpp:237]     Train net output #0: loss = 0.303791 (* 1 = 0.303791 loss)
I0412 14:00:09.197098  8032 sgd_solver.cpp:105] Iteration 7008, lr = 0.00249528
I0412 14:00:14.456082  8032 solver.cpp:218] Iteration 7020 (2.28188 iter/s, 5.25882s/12 iters), loss = 0.331984
I0412 14:00:14.456130  8032 solver.cpp:237]     Train net output #0: loss = 0.331984 (* 1 = 0.331984 loss)
I0412 14:00:14.456141  8032 sgd_solver.cpp:105] Iteration 7020, lr = 0.00248935
I0412 14:00:19.936193  8032 solver.cpp:218] Iteration 7032 (2.18983 iter/s, 5.47988s/12 iters), loss = 0.453428
I0412 14:00:19.936239  8032 solver.cpp:237]     Train net output #0: loss = 0.453428 (* 1 = 0.453428 loss)
I0412 14:00:19.936249  8032 sgd_solver.cpp:105] Iteration 7032, lr = 0.00248344
I0412 14:00:22.061321  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7038.caffemodel
I0412 14:00:25.080865  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7038.solverstate
I0412 14:00:27.452158  8032 solver.cpp:330] Iteration 7038, Testing net (#0)
I0412 14:00:27.452183  8032 net.cpp:676] Ignoring source layer train-data
I0412 14:00:29.057000  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:00:31.919149  8032 solver.cpp:397]     Test net output #0: accuracy = 0.436274
I0412 14:00:31.919183  8032 solver.cpp:397]     Test net output #1: loss = 2.87042 (* 1 = 2.87042 loss)
I0412 14:00:33.851397  8032 solver.cpp:218] Iteration 7044 (0.862396 iter/s, 13.9147s/12 iters), loss = 0.443134
I0412 14:00:33.851436  8032 solver.cpp:237]     Train net output #0: loss = 0.443134 (* 1 = 0.443134 loss)
I0412 14:00:33.851446  8032 sgd_solver.cpp:105] Iteration 7044, lr = 0.00247755
I0412 14:00:39.201053  8032 solver.cpp:218] Iteration 7056 (2.24323 iter/s, 5.34944s/12 iters), loss = 0.242311
I0412 14:00:39.201287  8032 solver.cpp:237]     Train net output #0: loss = 0.242311 (* 1 = 0.242311 loss)
I0412 14:00:39.201308  8032 sgd_solver.cpp:105] Iteration 7056, lr = 0.00247166
I0412 14:00:44.416700  8032 solver.cpp:218] Iteration 7068 (2.30094 iter/s, 5.21525s/12 iters), loss = 0.404997
I0412 14:00:44.416746  8032 solver.cpp:237]     Train net output #0: loss = 0.404997 (* 1 = 0.404997 loss)
I0412 14:00:44.416755  8032 sgd_solver.cpp:105] Iteration 7068, lr = 0.0024658
I0412 14:00:49.423949  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:00:49.543160  8032 solver.cpp:218] Iteration 7080 (2.3409 iter/s, 5.12623s/12 iters), loss = 0.227999
I0412 14:00:49.543216  8032 solver.cpp:237]     Train net output #0: loss = 0.227999 (* 1 = 0.227999 loss)
I0412 14:00:49.543227  8032 sgd_solver.cpp:105] Iteration 7080, lr = 0.00245994
I0412 14:00:54.655086  8032 solver.cpp:218] Iteration 7092 (2.34756 iter/s, 5.1117s/12 iters), loss = 0.194628
I0412 14:00:54.655138  8032 solver.cpp:237]     Train net output #0: loss = 0.194628 (* 1 = 0.194628 loss)
I0412 14:00:54.655148  8032 sgd_solver.cpp:105] Iteration 7092, lr = 0.0024541
I0412 14:00:59.710202  8032 solver.cpp:218] Iteration 7104 (2.37394 iter/s, 5.05489s/12 iters), loss = 0.252814
I0412 14:00:59.710250  8032 solver.cpp:237]     Train net output #0: loss = 0.252814 (* 1 = 0.252814 loss)
I0412 14:00:59.710263  8032 sgd_solver.cpp:105] Iteration 7104, lr = 0.00244827
I0412 14:01:04.625763  8032 solver.cpp:218] Iteration 7116 (2.44133 iter/s, 4.91534s/12 iters), loss = 0.197578
I0412 14:01:04.625813  8032 solver.cpp:237]     Train net output #0: loss = 0.197578 (* 1 = 0.197578 loss)
I0412 14:01:04.625823  8032 sgd_solver.cpp:105] Iteration 7116, lr = 0.00244246
I0412 14:01:09.746362  8032 solver.cpp:218] Iteration 7128 (2.34358 iter/s, 5.12037s/12 iters), loss = 0.26795
I0412 14:01:09.746515  8032 solver.cpp:237]     Train net output #0: loss = 0.26795 (* 1 = 0.26795 loss)
I0412 14:01:09.746531  8032 sgd_solver.cpp:105] Iteration 7128, lr = 0.00243666
I0412 14:01:14.343806  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7140.caffemodel
I0412 14:01:19.427340  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7140.solverstate
I0412 14:01:24.979177  8032 solver.cpp:330] Iteration 7140, Testing net (#0)
I0412 14:01:24.979203  8032 net.cpp:676] Ignoring source layer train-data
I0412 14:01:26.608595  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:01:29.519480  8032 solver.cpp:397]     Test net output #0: accuracy = 0.441176
I0412 14:01:29.519522  8032 solver.cpp:397]     Test net output #1: loss = 2.85641 (* 1 = 2.85641 loss)
I0412 14:01:29.607587  8032 solver.cpp:218] Iteration 7140 (0.604216 iter/s, 19.8605s/12 iters), loss = 0.236447
I0412 14:01:29.607633  8032 solver.cpp:237]     Train net output #0: loss = 0.236447 (* 1 = 0.236447 loss)
I0412 14:01:29.607642  8032 sgd_solver.cpp:105] Iteration 7140, lr = 0.00243088
I0412 14:01:33.888553  8032 solver.cpp:218] Iteration 7152 (2.80323 iter/s, 4.28077s/12 iters), loss = 0.504069
I0412 14:01:33.888602  8032 solver.cpp:237]     Train net output #0: loss = 0.504069 (* 1 = 0.504069 loss)
I0412 14:01:33.888613  8032 sgd_solver.cpp:105] Iteration 7152, lr = 0.00242511
I0412 14:01:38.943480  8032 solver.cpp:218] Iteration 7164 (2.37402 iter/s, 5.05471s/12 iters), loss = 0.224628
I0412 14:01:38.943516  8032 solver.cpp:237]     Train net output #0: loss = 0.224628 (* 1 = 0.224628 loss)
I0412 14:01:38.943526  8032 sgd_solver.cpp:105] Iteration 7164, lr = 0.00241935
I0412 14:01:43.988164  8032 solver.cpp:218] Iteration 7176 (2.37884 iter/s, 5.04448s/12 iters), loss = 0.269392
I0412 14:01:43.988284  8032 solver.cpp:237]     Train net output #0: loss = 0.269392 (* 1 = 0.269392 loss)
I0412 14:01:43.988294  8032 sgd_solver.cpp:105] Iteration 7176, lr = 0.0024136
I0412 14:01:46.166419  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:01:49.293481  8032 solver.cpp:218] Iteration 7188 (2.26201 iter/s, 5.30502s/12 iters), loss = 0.262146
I0412 14:01:49.293524  8032 solver.cpp:237]     Train net output #0: loss = 0.262146 (* 1 = 0.262146 loss)
I0412 14:01:49.293532  8032 sgd_solver.cpp:105] Iteration 7188, lr = 0.00240787
I0412 14:01:54.442689  8032 solver.cpp:218] Iteration 7200 (2.33055 iter/s, 5.149s/12 iters), loss = 0.162426
I0412 14:01:54.442729  8032 solver.cpp:237]     Train net output #0: loss = 0.162426 (* 1 = 0.162426 loss)
I0412 14:01:54.442737  8032 sgd_solver.cpp:105] Iteration 7200, lr = 0.00240216
I0412 14:01:59.467082  8032 solver.cpp:218] Iteration 7212 (2.38845 iter/s, 5.02419s/12 iters), loss = 0.202825
I0412 14:01:59.467125  8032 solver.cpp:237]     Train net output #0: loss = 0.202825 (* 1 = 0.202825 loss)
I0412 14:01:59.467133  8032 sgd_solver.cpp:105] Iteration 7212, lr = 0.00239645
I0412 14:02:04.551131  8032 solver.cpp:218] Iteration 7224 (2.36042 iter/s, 5.08384s/12 iters), loss = 0.185158
I0412 14:02:04.551187  8032 solver.cpp:237]     Train net output #0: loss = 0.185158 (* 1 = 0.185158 loss)
I0412 14:02:04.551196  8032 sgd_solver.cpp:105] Iteration 7224, lr = 0.00239076
I0412 14:02:09.532742  8032 solver.cpp:218] Iteration 7236 (2.40897 iter/s, 4.98139s/12 iters), loss = 0.356488
I0412 14:02:09.532796  8032 solver.cpp:237]     Train net output #0: loss = 0.356488 (* 1 = 0.356488 loss)
I0412 14:02:09.532809  8032 sgd_solver.cpp:105] Iteration 7236, lr = 0.00238509
I0412 14:02:11.545753  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7242.caffemodel
I0412 14:02:14.569444  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7242.solverstate
I0412 14:02:16.877785  8032 solver.cpp:330] Iteration 7242, Testing net (#0)
I0412 14:02:16.877810  8032 net.cpp:676] Ignoring source layer train-data
I0412 14:02:18.455138  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:02:21.504401  8032 solver.cpp:397]     Test net output #0: accuracy = 0.449142
I0412 14:02:21.504433  8032 solver.cpp:397]     Test net output #1: loss = 2.92778 (* 1 = 2.92778 loss)
I0412 14:02:23.420966  8032 solver.cpp:218] Iteration 7248 (0.864072 iter/s, 13.8877s/12 iters), loss = 0.255681
I0412 14:02:23.421020  8032 solver.cpp:237]     Train net output #0: loss = 0.255681 (* 1 = 0.255681 loss)
I0412 14:02:23.421032  8032 sgd_solver.cpp:105] Iteration 7248, lr = 0.00237942
I0412 14:02:28.446974  8032 solver.cpp:218] Iteration 7260 (2.38769 iter/s, 5.02579s/12 iters), loss = 0.188124
I0412 14:02:28.447023  8032 solver.cpp:237]     Train net output #0: loss = 0.188124 (* 1 = 0.188124 loss)
I0412 14:02:28.447036  8032 sgd_solver.cpp:105] Iteration 7260, lr = 0.00237378
I0412 14:02:33.459568  8032 solver.cpp:218] Iteration 7272 (2.39408 iter/s, 5.01237s/12 iters), loss = 0.226231
I0412 14:02:33.459625  8032 solver.cpp:237]     Train net output #0: loss = 0.226231 (* 1 = 0.226231 loss)
I0412 14:02:33.459638  8032 sgd_solver.cpp:105] Iteration 7272, lr = 0.00236814
I0412 14:02:37.758395  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:02:38.540586  8032 solver.cpp:218] Iteration 7284 (2.36184 iter/s, 5.08079s/12 iters), loss = 0.229455
I0412 14:02:38.540634  8032 solver.cpp:237]     Train net output #0: loss = 0.229455 (* 1 = 0.229455 loss)
I0412 14:02:38.540643  8032 sgd_solver.cpp:105] Iteration 7284, lr = 0.00236252
I0412 14:02:43.553495  8032 solver.cpp:218] Iteration 7296 (2.39392 iter/s, 5.01269s/12 iters), loss = 0.213416
I0412 14:02:43.553547  8032 solver.cpp:237]     Train net output #0: loss = 0.213416 (* 1 = 0.213416 loss)
I0412 14:02:43.553560  8032 sgd_solver.cpp:105] Iteration 7296, lr = 0.00235691
I0412 14:02:48.735203  8032 solver.cpp:218] Iteration 7308 (2.31594 iter/s, 5.18148s/12 iters), loss = 0.324042
I0412 14:02:48.735373  8032 solver.cpp:237]     Train net output #0: loss = 0.324042 (* 1 = 0.324042 loss)
I0412 14:02:48.735391  8032 sgd_solver.cpp:105] Iteration 7308, lr = 0.00235131
I0412 14:02:53.759999  8032 solver.cpp:218] Iteration 7320 (2.38832 iter/s, 5.02446s/12 iters), loss = 0.283758
I0412 14:02:53.760047  8032 solver.cpp:237]     Train net output #0: loss = 0.283758 (* 1 = 0.283758 loss)
I0412 14:02:53.760056  8032 sgd_solver.cpp:105] Iteration 7320, lr = 0.00234573
I0412 14:02:58.758688  8032 solver.cpp:218] Iteration 7332 (2.40073 iter/s, 4.99847s/12 iters), loss = 0.18419
I0412 14:02:58.758744  8032 solver.cpp:237]     Train net output #0: loss = 0.184189 (* 1 = 0.184189 loss)
I0412 14:02:58.758754  8032 sgd_solver.cpp:105] Iteration 7332, lr = 0.00234016
I0412 14:03:03.383672  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7344.caffemodel
I0412 14:03:06.409783  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7344.solverstate
I0412 14:03:09.923105  8032 solver.cpp:330] Iteration 7344, Testing net (#0)
I0412 14:03:09.923127  8032 net.cpp:676] Ignoring source layer train-data
I0412 14:03:11.543838  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:03:14.730908  8032 solver.cpp:397]     Test net output #0: accuracy = 0.461397
I0412 14:03:14.730960  8032 solver.cpp:397]     Test net output #1: loss = 2.73637 (* 1 = 2.73637 loss)
I0412 14:03:14.818936  8032 solver.cpp:218] Iteration 7344 (0.747213 iter/s, 16.0597s/12 iters), loss = 0.213858
I0412 14:03:14.818992  8032 solver.cpp:237]     Train net output #0: loss = 0.213858 (* 1 = 0.213858 loss)
I0412 14:03:14.819003  8032 sgd_solver.cpp:105] Iteration 7344, lr = 0.0023346
I0412 14:03:19.123839  8032 solver.cpp:218] Iteration 7356 (2.78766 iter/s, 4.30469s/12 iters), loss = 0.358784
I0412 14:03:19.123971  8032 solver.cpp:237]     Train net output #0: loss = 0.358784 (* 1 = 0.358784 loss)
I0412 14:03:19.123988  8032 sgd_solver.cpp:105] Iteration 7356, lr = 0.00232906
I0412 14:03:24.153620  8032 solver.cpp:218] Iteration 7368 (2.38593 iter/s, 5.02949s/12 iters), loss = 0.253697
I0412 14:03:24.153673  8032 solver.cpp:237]     Train net output #0: loss = 0.253697 (* 1 = 0.253697 loss)
I0412 14:03:24.153685  8032 sgd_solver.cpp:105] Iteration 7368, lr = 0.00232353
I0412 14:03:29.682229  8032 solver.cpp:218] Iteration 7380 (2.17062 iter/s, 5.52837s/12 iters), loss = 0.219497
I0412 14:03:29.682281  8032 solver.cpp:237]     Train net output #0: loss = 0.219497 (* 1 = 0.219497 loss)
I0412 14:03:29.682292  8032 sgd_solver.cpp:105] Iteration 7380, lr = 0.00231802
I0412 14:03:31.182440  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:03:34.842306  8032 solver.cpp:218] Iteration 7392 (2.32565 iter/s, 5.15985s/12 iters), loss = 0.252485
I0412 14:03:34.842360  8032 solver.cpp:237]     Train net output #0: loss = 0.252485 (* 1 = 0.252485 loss)
I0412 14:03:34.842371  8032 sgd_solver.cpp:105] Iteration 7392, lr = 0.00231251
I0412 14:03:39.950302  8032 solver.cpp:218] Iteration 7404 (2.34936 iter/s, 5.10777s/12 iters), loss = 0.198375
I0412 14:03:39.950353  8032 solver.cpp:237]     Train net output #0: loss = 0.198375 (* 1 = 0.198375 loss)
I0412 14:03:39.950366  8032 sgd_solver.cpp:105] Iteration 7404, lr = 0.00230702
I0412 14:03:44.993361  8032 solver.cpp:218] Iteration 7416 (2.37961 iter/s, 5.04284s/12 iters), loss = 0.303306
I0412 14:03:44.993414  8032 solver.cpp:237]     Train net output #0: loss = 0.303306 (* 1 = 0.303306 loss)
I0412 14:03:44.993427  8032 sgd_solver.cpp:105] Iteration 7416, lr = 0.00230154
I0412 14:03:50.047683  8032 solver.cpp:218] Iteration 7428 (2.37431 iter/s, 5.0541s/12 iters), loss = 0.292293
I0412 14:03:50.047852  8032 solver.cpp:237]     Train net output #0: loss = 0.292293 (* 1 = 0.292293 loss)
I0412 14:03:50.047866  8032 sgd_solver.cpp:105] Iteration 7428, lr = 0.00229608
I0412 14:03:55.090705  8032 solver.cpp:218] Iteration 7440 (2.37968 iter/s, 5.04269s/12 iters), loss = 0.271521
I0412 14:03:55.090754  8032 solver.cpp:237]     Train net output #0: loss = 0.271521 (* 1 = 0.271521 loss)
I0412 14:03:55.090762  8032 sgd_solver.cpp:105] Iteration 7440, lr = 0.00229063
I0412 14:03:57.280154  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7446.caffemodel
I0412 14:04:03.114225  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7446.solverstate
I0412 14:04:06.556777  8032 solver.cpp:330] Iteration 7446, Testing net (#0)
I0412 14:04:06.556803  8032 net.cpp:676] Ignoring source layer train-data
I0412 14:04:08.097859  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:04:11.092566  8032 solver.cpp:397]     Test net output #0: accuracy = 0.469363
I0412 14:04:11.092619  8032 solver.cpp:397]     Test net output #1: loss = 2.83266 (* 1 = 2.83266 loss)
I0412 14:04:12.806751  8032 solver.cpp:218] Iteration 7452 (0.677375 iter/s, 17.7154s/12 iters), loss = 0.161587
I0412 14:04:12.806797  8032 solver.cpp:237]     Train net output #0: loss = 0.161587 (* 1 = 0.161587 loss)
I0412 14:04:12.806805  8032 sgd_solver.cpp:105] Iteration 7452, lr = 0.00228519
I0412 14:04:17.935276  8032 solver.cpp:218] Iteration 7464 (2.33995 iter/s, 5.12831s/12 iters), loss = 0.275799
I0412 14:04:17.935328  8032 solver.cpp:237]     Train net output #0: loss = 0.275799 (* 1 = 0.275799 loss)
I0412 14:04:17.935341  8032 sgd_solver.cpp:105] Iteration 7464, lr = 0.00227976
I0412 14:04:23.006351  8032 solver.cpp:218] Iteration 7476 (2.36647 iter/s, 5.07085s/12 iters), loss = 0.282928
I0412 14:04:23.006472  8032 solver.cpp:237]     Train net output #0: loss = 0.282928 (* 1 = 0.282928 loss)
I0412 14:04:23.006486  8032 sgd_solver.cpp:105] Iteration 7476, lr = 0.00227435
I0412 14:04:26.632899  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:04:28.316737  8032 solver.cpp:218] Iteration 7488 (2.25985 iter/s, 5.31008s/12 iters), loss = 0.17362
I0412 14:04:28.316797  8032 solver.cpp:237]     Train net output #0: loss = 0.17362 (* 1 = 0.17362 loss)
I0412 14:04:28.316812  8032 sgd_solver.cpp:105] Iteration 7488, lr = 0.00226895
I0412 14:04:33.466142  8032 solver.cpp:218] Iteration 7500 (2.33047 iter/s, 5.14918s/12 iters), loss = 0.176578
I0412 14:04:33.466197  8032 solver.cpp:237]     Train net output #0: loss = 0.176577 (* 1 = 0.176577 loss)
I0412 14:04:33.466210  8032 sgd_solver.cpp:105] Iteration 7500, lr = 0.00226357
I0412 14:04:38.475544  8032 solver.cpp:218] Iteration 7512 (2.3956 iter/s, 5.00919s/12 iters), loss = 0.184295
I0412 14:04:38.475585  8032 solver.cpp:237]     Train net output #0: loss = 0.184295 (* 1 = 0.184295 loss)
I0412 14:04:38.475594  8032 sgd_solver.cpp:105] Iteration 7512, lr = 0.00225819
I0412 14:04:43.691431  8032 solver.cpp:218] Iteration 7524 (2.30076 iter/s, 5.21567s/12 iters), loss = 0.225193
I0412 14:04:43.691483  8032 solver.cpp:237]     Train net output #0: loss = 0.225193 (* 1 = 0.225193 loss)
I0412 14:04:43.691494  8032 sgd_solver.cpp:105] Iteration 7524, lr = 0.00225283
I0412 14:04:49.096335  8032 solver.cpp:218] Iteration 7536 (2.2203 iter/s, 5.40468s/12 iters), loss = 0.261028
I0412 14:04:49.096381  8032 solver.cpp:237]     Train net output #0: loss = 0.261028 (* 1 = 0.261028 loss)
I0412 14:04:49.096391  8032 sgd_solver.cpp:105] Iteration 7536, lr = 0.00224748
I0412 14:04:53.765789  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7548.caffemodel
I0412 14:04:57.426378  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7548.solverstate
I0412 14:04:59.746975  8032 solver.cpp:330] Iteration 7548, Testing net (#0)
I0412 14:04:59.747001  8032 net.cpp:676] Ignoring source layer train-data
I0412 14:05:01.231344  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:05:04.289599  8032 solver.cpp:397]     Test net output #0: accuracy = 0.454657
I0412 14:05:04.289644  8032 solver.cpp:397]     Test net output #1: loss = 2.88026 (* 1 = 2.88026 loss)
I0412 14:05:04.377665  8032 solver.cpp:218] Iteration 7548 (0.785299 iter/s, 15.2808s/12 iters), loss = 0.140151
I0412 14:05:04.377720  8032 solver.cpp:237]     Train net output #0: loss = 0.140151 (* 1 = 0.140151 loss)
I0412 14:05:04.377730  8032 sgd_solver.cpp:105] Iteration 7548, lr = 0.00224215
I0412 14:05:08.578974  8032 solver.cpp:218] Iteration 7560 (2.85639 iter/s, 4.20111s/12 iters), loss = 0.180089
I0412 14:05:08.579023  8032 solver.cpp:237]     Train net output #0: loss = 0.180089 (* 1 = 0.180089 loss)
I0412 14:05:08.579033  8032 sgd_solver.cpp:105] Iteration 7560, lr = 0.00223682
I0412 14:05:13.646122  8032 solver.cpp:218] Iteration 7572 (2.3683 iter/s, 5.06693s/12 iters), loss = 0.241175
I0412 14:05:13.646168  8032 solver.cpp:237]     Train net output #0: loss = 0.241175 (* 1 = 0.241175 loss)
I0412 14:05:13.646178  8032 sgd_solver.cpp:105] Iteration 7572, lr = 0.00223151
I0412 14:05:18.578081  8032 solver.cpp:218] Iteration 7584 (2.43322 iter/s, 4.93174s/12 iters), loss = 0.258537
I0412 14:05:18.578133  8032 solver.cpp:237]     Train net output #0: loss = 0.258537 (* 1 = 0.258537 loss)
I0412 14:05:18.578145  8032 sgd_solver.cpp:105] Iteration 7584, lr = 0.00222621
I0412 14:05:19.249852  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:05:23.594805  8032 solver.cpp:218] Iteration 7596 (2.3921 iter/s, 5.0165s/12 iters), loss = 0.26083
I0412 14:05:23.594856  8032 solver.cpp:237]     Train net output #0: loss = 0.26083 (* 1 = 0.26083 loss)
I0412 14:05:23.594866  8032 sgd_solver.cpp:105] Iteration 7596, lr = 0.00222093
I0412 14:05:28.901907  8032 solver.cpp:218] Iteration 7608 (2.26122 iter/s, 5.30687s/12 iters), loss = 0.274976
I0412 14:05:28.902009  8032 solver.cpp:237]     Train net output #0: loss = 0.274976 (* 1 = 0.274976 loss)
I0412 14:05:28.902019  8032 sgd_solver.cpp:105] Iteration 7608, lr = 0.00221565
I0412 14:05:34.110123  8032 solver.cpp:218] Iteration 7620 (2.30417 iter/s, 5.20794s/12 iters), loss = 0.197683
I0412 14:05:34.110177  8032 solver.cpp:237]     Train net output #0: loss = 0.197683 (* 1 = 0.197683 loss)
I0412 14:05:34.110189  8032 sgd_solver.cpp:105] Iteration 7620, lr = 0.00221039
I0412 14:05:36.574645  8032 blocking_queue.cpp:49] Waiting for data
I0412 14:05:39.126736  8032 solver.cpp:218] Iteration 7632 (2.39216 iter/s, 5.01639s/12 iters), loss = 0.259485
I0412 14:05:39.126782  8032 solver.cpp:237]     Train net output #0: loss = 0.259485 (* 1 = 0.259485 loss)
I0412 14:05:39.126792  8032 sgd_solver.cpp:105] Iteration 7632, lr = 0.00220515
I0412 14:05:44.313254  8032 solver.cpp:218] Iteration 7644 (2.31379 iter/s, 5.1863s/12 iters), loss = 0.127251
I0412 14:05:44.313306  8032 solver.cpp:237]     Train net output #0: loss = 0.127251 (* 1 = 0.127251 loss)
I0412 14:05:44.313318  8032 sgd_solver.cpp:105] Iteration 7644, lr = 0.00219991
I0412 14:05:46.261854  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7650.caffemodel
I0412 14:05:49.295347  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7650.solverstate
I0412 14:05:51.686808  8032 solver.cpp:330] Iteration 7650, Testing net (#0)
I0412 14:05:51.686837  8032 net.cpp:676] Ignoring source layer train-data
I0412 14:05:53.210916  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:05:56.294008  8032 solver.cpp:397]     Test net output #0: accuracy = 0.458946
I0412 14:05:56.294059  8032 solver.cpp:397]     Test net output #1: loss = 2.93984 (* 1 = 2.93984 loss)
I0412 14:05:58.096519  8032 solver.cpp:218] Iteration 7656 (0.870652 iter/s, 13.7828s/12 iters), loss = 0.225326
I0412 14:05:58.096576  8032 solver.cpp:237]     Train net output #0: loss = 0.225326 (* 1 = 0.225326 loss)
I0412 14:05:58.096588  8032 sgd_solver.cpp:105] Iteration 7656, lr = 0.00219469
I0412 14:06:03.244622  8032 solver.cpp:218] Iteration 7668 (2.33106 iter/s, 5.14787s/12 iters), loss = 0.170401
I0412 14:06:03.244789  8032 solver.cpp:237]     Train net output #0: loss = 0.170401 (* 1 = 0.170401 loss)
I0412 14:06:03.244802  8032 sgd_solver.cpp:105] Iteration 7668, lr = 0.00218948
I0412 14:06:08.483572  8032 solver.cpp:218] Iteration 7680 (2.29068 iter/s, 5.23862s/12 iters), loss = 0.238901
I0412 14:06:08.483618  8032 solver.cpp:237]     Train net output #0: loss = 0.2389 (* 1 = 0.2389 loss)
I0412 14:06:08.483629  8032 sgd_solver.cpp:105] Iteration 7680, lr = 0.00218428
I0412 14:06:11.407886  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:06:13.649914  8032 solver.cpp:218] Iteration 7692 (2.32283 iter/s, 5.16612s/12 iters), loss = 0.28223
I0412 14:06:13.649992  8032 solver.cpp:237]     Train net output #0: loss = 0.28223 (* 1 = 0.28223 loss)
I0412 14:06:13.650007  8032 sgd_solver.cpp:105] Iteration 7692, lr = 0.00217909
I0412 14:06:18.598502  8032 solver.cpp:218] Iteration 7704 (2.42505 iter/s, 4.94835s/12 iters), loss = 0.141608
I0412 14:06:18.598560  8032 solver.cpp:237]     Train net output #0: loss = 0.141608 (* 1 = 0.141608 loss)
I0412 14:06:18.598577  8032 sgd_solver.cpp:105] Iteration 7704, lr = 0.00217392
I0412 14:06:23.693646  8032 solver.cpp:218] Iteration 7716 (2.35529 iter/s, 5.09492s/12 iters), loss = 0.275006
I0412 14:06:23.693703  8032 solver.cpp:237]     Train net output #0: loss = 0.275006 (* 1 = 0.275006 loss)
I0412 14:06:23.693717  8032 sgd_solver.cpp:105] Iteration 7716, lr = 0.00216876
I0412 14:06:28.768637  8032 solver.cpp:218] Iteration 7728 (2.36464 iter/s, 5.07477s/12 iters), loss = 0.25366
I0412 14:06:28.768682  8032 solver.cpp:237]     Train net output #0: loss = 0.25366 (* 1 = 0.25366 loss)
I0412 14:06:28.768692  8032 sgd_solver.cpp:105] Iteration 7728, lr = 0.00216361
I0412 14:06:33.813612  8032 solver.cpp:218] Iteration 7740 (2.37871 iter/s, 5.04476s/12 iters), loss = 0.180848
I0412 14:06:33.813678  8032 solver.cpp:237]     Train net output #0: loss = 0.180848 (* 1 = 0.180848 loss)
I0412 14:06:33.813688  8032 sgd_solver.cpp:105] Iteration 7740, lr = 0.00215847
I0412 14:06:38.361493  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7752.caffemodel
I0412 14:06:44.740015  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7752.solverstate
I0412 14:06:48.456902  8032 solver.cpp:330] Iteration 7752, Testing net (#0)
I0412 14:06:48.456923  8032 net.cpp:676] Ignoring source layer train-data
I0412 14:06:49.786533  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:06:52.964210  8032 solver.cpp:397]     Test net output #0: accuracy = 0.46875
I0412 14:06:52.964260  8032 solver.cpp:397]     Test net output #1: loss = 2.86414 (* 1 = 2.86414 loss)
I0412 14:06:53.052345  8032 solver.cpp:218] Iteration 7752 (0.623764 iter/s, 19.238s/12 iters), loss = 0.206609
I0412 14:06:53.052410  8032 solver.cpp:237]     Train net output #0: loss = 0.206609 (* 1 = 0.206609 loss)
I0412 14:06:53.052423  8032 sgd_solver.cpp:105] Iteration 7752, lr = 0.00215335
I0412 14:06:57.334020  8032 solver.cpp:218] Iteration 7764 (2.80278 iter/s, 4.28146s/12 iters), loss = 0.214502
I0412 14:06:57.334076  8032 solver.cpp:237]     Train net output #0: loss = 0.214502 (* 1 = 0.214502 loss)
I0412 14:06:57.334087  8032 sgd_solver.cpp:105] Iteration 7764, lr = 0.00214823
I0412 14:07:02.597443  8032 solver.cpp:218] Iteration 7776 (2.27999 iter/s, 5.26319s/12 iters), loss = 0.316924
I0412 14:07:02.597487  8032 solver.cpp:237]     Train net output #0: loss = 0.316924 (* 1 = 0.316924 loss)
I0412 14:07:02.597496  8032 sgd_solver.cpp:105] Iteration 7776, lr = 0.00214313
I0412 14:07:07.482023  8032 solver.cpp:218] Iteration 7788 (2.45682 iter/s, 4.88437s/12 iters), loss = 0.195496
I0412 14:07:07.482161  8032 solver.cpp:237]     Train net output #0: loss = 0.195496 (* 1 = 0.195496 loss)
I0412 14:07:07.482172  8032 sgd_solver.cpp:105] Iteration 7788, lr = 0.00213805
I0412 14:07:07.490131  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:07:12.476722  8032 solver.cpp:218] Iteration 7800 (2.40269 iter/s, 4.99439s/12 iters), loss = 0.305726
I0412 14:07:12.476773  8032 solver.cpp:237]     Train net output #0: loss = 0.305726 (* 1 = 0.305726 loss)
I0412 14:07:12.476784  8032 sgd_solver.cpp:105] Iteration 7800, lr = 0.00213297
I0412 14:07:17.594897  8032 solver.cpp:218] Iteration 7812 (2.34469 iter/s, 5.11795s/12 iters), loss = 0.174593
I0412 14:07:17.594939  8032 solver.cpp:237]     Train net output #0: loss = 0.174593 (* 1 = 0.174593 loss)
I0412 14:07:17.594949  8032 sgd_solver.cpp:105] Iteration 7812, lr = 0.00212791
I0412 14:07:22.653676  8032 solver.cpp:218] Iteration 7824 (2.37222 iter/s, 5.05856s/12 iters), loss = 0.167955
I0412 14:07:22.653721  8032 solver.cpp:237]     Train net output #0: loss = 0.167955 (* 1 = 0.167955 loss)
I0412 14:07:22.653730  8032 sgd_solver.cpp:105] Iteration 7824, lr = 0.00212285
I0412 14:07:28.152202  8032 solver.cpp:218] Iteration 7836 (2.18249 iter/s, 5.4983s/12 iters), loss = 0.293593
I0412 14:07:28.152253  8032 solver.cpp:237]     Train net output #0: loss = 0.293593 (* 1 = 0.293593 loss)
I0412 14:07:28.152266  8032 sgd_solver.cpp:105] Iteration 7836, lr = 0.00211781
I0412 14:07:33.407768  8032 solver.cpp:218] Iteration 7848 (2.28339 iter/s, 5.25534s/12 iters), loss = 0.286235
I0412 14:07:33.407814  8032 solver.cpp:237]     Train net output #0: loss = 0.286235 (* 1 = 0.286235 loss)
I0412 14:07:33.407824  8032 sgd_solver.cpp:105] Iteration 7848, lr = 0.00211279
I0412 14:07:35.555192  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7854.caffemodel
I0412 14:07:38.588213  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7854.solverstate
I0412 14:07:40.898609  8032 solver.cpp:330] Iteration 7854, Testing net (#0)
I0412 14:07:40.898633  8032 net.cpp:676] Ignoring source layer train-data
I0412 14:07:42.274704  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:07:45.426096  8032 solver.cpp:397]     Test net output #0: accuracy = 0.463848
I0412 14:07:45.426138  8032 solver.cpp:397]     Test net output #1: loss = 2.83978 (* 1 = 2.83978 loss)
I0412 14:07:47.336447  8032 solver.cpp:218] Iteration 7860 (0.861562 iter/s, 13.9282s/12 iters), loss = 0.159391
I0412 14:07:47.336494  8032 solver.cpp:237]     Train net output #0: loss = 0.159391 (* 1 = 0.159391 loss)
I0412 14:07:47.336504  8032 sgd_solver.cpp:105] Iteration 7860, lr = 0.00210777
I0412 14:07:52.450091  8032 solver.cpp:218] Iteration 7872 (2.34676 iter/s, 5.11343s/12 iters), loss = 0.157973
I0412 14:07:52.450136  8032 solver.cpp:237]     Train net output #0: loss = 0.157973 (* 1 = 0.157973 loss)
I0412 14:07:52.450145  8032 sgd_solver.cpp:105] Iteration 7872, lr = 0.00210277
I0412 14:07:57.620254  8032 solver.cpp:218] Iteration 7884 (2.32111 iter/s, 5.16995s/12 iters), loss = 0.203298
I0412 14:07:57.620298  8032 solver.cpp:237]     Train net output #0: loss = 0.203298 (* 1 = 0.203298 loss)
I0412 14:07:57.620309  8032 sgd_solver.cpp:105] Iteration 7884, lr = 0.00209777
I0412 14:07:59.777581  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:08:02.712586  8032 solver.cpp:218] Iteration 7896 (2.35659 iter/s, 5.09211s/12 iters), loss = 0.160718
I0412 14:08:02.712641  8032 solver.cpp:237]     Train net output #0: loss = 0.160718 (* 1 = 0.160718 loss)
I0412 14:08:02.712652  8032 sgd_solver.cpp:105] Iteration 7896, lr = 0.00209279
I0412 14:08:07.968820  8032 solver.cpp:218] Iteration 7908 (2.2831 iter/s, 5.25601s/12 iters), loss = 0.116061
I0412 14:08:07.968860  8032 solver.cpp:237]     Train net output #0: loss = 0.116061 (* 1 = 0.116061 loss)
I0412 14:08:07.968869  8032 sgd_solver.cpp:105] Iteration 7908, lr = 0.00208782
I0412 14:08:13.090914  8032 solver.cpp:218] Iteration 7920 (2.34289 iter/s, 5.12188s/12 iters), loss = 0.188641
I0412 14:08:13.091055  8032 solver.cpp:237]     Train net output #0: loss = 0.188641 (* 1 = 0.188641 loss)
I0412 14:08:13.091066  8032 sgd_solver.cpp:105] Iteration 7920, lr = 0.00208287
I0412 14:08:18.011970  8032 solver.cpp:218] Iteration 7932 (2.43865 iter/s, 4.92075s/12 iters), loss = 0.137276
I0412 14:08:18.012029  8032 solver.cpp:237]     Train net output #0: loss = 0.137276 (* 1 = 0.137276 loss)
I0412 14:08:18.012042  8032 sgd_solver.cpp:105] Iteration 7932, lr = 0.00207792
I0412 14:08:23.094835  8032 solver.cpp:218] Iteration 7944 (2.36098 iter/s, 5.08264s/12 iters), loss = 0.256588
I0412 14:08:23.094882  8032 solver.cpp:237]     Train net output #0: loss = 0.256588 (* 1 = 0.256588 loss)
I0412 14:08:23.094893  8032 sgd_solver.cpp:105] Iteration 7944, lr = 0.00207299
I0412 14:08:27.871206  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7956.caffemodel
I0412 14:08:30.935521  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7956.solverstate
I0412 14:08:33.239027  8032 solver.cpp:330] Iteration 7956, Testing net (#0)
I0412 14:08:33.239051  8032 net.cpp:676] Ignoring source layer train-data
I0412 14:08:34.593734  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:08:37.799301  8032 solver.cpp:397]     Test net output #0: accuracy = 0.449142
I0412 14:08:37.799350  8032 solver.cpp:397]     Test net output #1: loss = 2.97616 (* 1 = 2.97616 loss)
I0412 14:08:37.887215  8032 solver.cpp:218] Iteration 7956 (0.811257 iter/s, 14.7919s/12 iters), loss = 0.239625
I0412 14:08:37.887269  8032 solver.cpp:237]     Train net output #0: loss = 0.239625 (* 1 = 0.239625 loss)
I0412 14:08:37.887280  8032 sgd_solver.cpp:105] Iteration 7956, lr = 0.00206807
I0412 14:08:42.490303  8032 solver.cpp:218] Iteration 7968 (2.60707 iter/s, 4.60288s/12 iters), loss = 0.19718
I0412 14:08:42.490355  8032 solver.cpp:237]     Train net output #0: loss = 0.19718 (* 1 = 0.19718 loss)
I0412 14:08:42.490366  8032 sgd_solver.cpp:105] Iteration 7968, lr = 0.00206316
I0412 14:08:47.738673  8032 solver.cpp:218] Iteration 7980 (2.28652 iter/s, 5.24814s/12 iters), loss = 0.0965604
I0412 14:08:47.738785  8032 solver.cpp:237]     Train net output #0: loss = 0.0965604 (* 1 = 0.0965604 loss)
I0412 14:08:47.738796  8032 sgd_solver.cpp:105] Iteration 7980, lr = 0.00205826
I0412 14:08:52.099296  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:08:52.864431  8032 solver.cpp:218] Iteration 7992 (2.34124 iter/s, 5.12548s/12 iters), loss = 0.281828
I0412 14:08:52.864480  8032 solver.cpp:237]     Train net output #0: loss = 0.281828 (* 1 = 0.281828 loss)
I0412 14:08:52.864490  8032 sgd_solver.cpp:105] Iteration 7992, lr = 0.00205337
I0412 14:08:57.971927  8032 solver.cpp:218] Iteration 8004 (2.34959 iter/s, 5.10727s/12 iters), loss = 0.264524
I0412 14:08:57.971978  8032 solver.cpp:237]     Train net output #0: loss = 0.264524 (* 1 = 0.264524 loss)
I0412 14:08:57.971990  8032 sgd_solver.cpp:105] Iteration 8004, lr = 0.0020485
I0412 14:09:03.136418  8032 solver.cpp:218] Iteration 8016 (2.32366 iter/s, 5.16427s/12 iters), loss = 0.139937
I0412 14:09:03.136473  8032 solver.cpp:237]     Train net output #0: loss = 0.139937 (* 1 = 0.139937 loss)
I0412 14:09:03.136487  8032 sgd_solver.cpp:105] Iteration 8016, lr = 0.00204363
I0412 14:09:08.233850  8032 solver.cpp:218] Iteration 8028 (2.35423 iter/s, 5.09721s/12 iters), loss = 0.180661
I0412 14:09:08.233902  8032 solver.cpp:237]     Train net output #0: loss = 0.180661 (* 1 = 0.180661 loss)
I0412 14:09:08.233914  8032 sgd_solver.cpp:105] Iteration 8028, lr = 0.00203878
I0412 14:09:13.215462  8032 solver.cpp:218] Iteration 8040 (2.40896 iter/s, 4.98139s/12 iters), loss = 0.151138
I0412 14:09:13.215518  8032 solver.cpp:237]     Train net output #0: loss = 0.151138 (* 1 = 0.151138 loss)
I0412 14:09:13.215531  8032 sgd_solver.cpp:105] Iteration 8040, lr = 0.00203394
I0412 14:09:18.186529  8032 solver.cpp:218] Iteration 8052 (2.41408 iter/s, 4.97084s/12 iters), loss = 0.28343
I0412 14:09:18.186694  8032 solver.cpp:237]     Train net output #0: loss = 0.28343 (* 1 = 0.28343 loss)
I0412 14:09:18.186707  8032 sgd_solver.cpp:105] Iteration 8052, lr = 0.00202911
I0412 14:09:20.360914  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8058.caffemodel
I0412 14:09:23.331171  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8058.solverstate
I0412 14:09:25.642532  8032 solver.cpp:330] Iteration 8058, Testing net (#0)
I0412 14:09:25.642555  8032 net.cpp:676] Ignoring source layer train-data
I0412 14:09:27.011929  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:09:30.329058  8032 solver.cpp:397]     Test net output #0: accuracy = 0.466299
I0412 14:09:30.329113  8032 solver.cpp:397]     Test net output #1: loss = 2.95173 (* 1 = 2.95173 loss)
I0412 14:09:32.430063  8032 solver.cpp:218] Iteration 8064 (0.842524 iter/s, 14.2429s/12 iters), loss = 0.204879
I0412 14:09:32.430105  8032 solver.cpp:237]     Train net output #0: loss = 0.204879 (* 1 = 0.204879 loss)
I0412 14:09:32.430115  8032 sgd_solver.cpp:105] Iteration 8064, lr = 0.00202429
I0412 14:09:37.527789  8032 solver.cpp:218] Iteration 8076 (2.35409 iter/s, 5.09751s/12 iters), loss = 0.0826255
I0412 14:09:37.527837  8032 solver.cpp:237]     Train net output #0: loss = 0.0826255 (* 1 = 0.0826255 loss)
I0412 14:09:37.527846  8032 sgd_solver.cpp:105] Iteration 8076, lr = 0.00201949
I0412 14:09:42.683218  8032 solver.cpp:218] Iteration 8088 (2.32774 iter/s, 5.15521s/12 iters), loss = 0.135866
I0412 14:09:42.683274  8032 solver.cpp:237]     Train net output #0: loss = 0.135866 (* 1 = 0.135866 loss)
I0412 14:09:42.683285  8032 sgd_solver.cpp:105] Iteration 8088, lr = 0.00201469
I0412 14:09:44.136484  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:09:47.862841  8032 solver.cpp:218] Iteration 8100 (2.31687 iter/s, 5.1794s/12 iters), loss = 0.141433
I0412 14:09:47.862898  8032 solver.cpp:237]     Train net output #0: loss = 0.141433 (* 1 = 0.141433 loss)
I0412 14:09:47.862911  8032 sgd_solver.cpp:105] Iteration 8100, lr = 0.00200991
I0412 14:09:52.976263  8032 solver.cpp:218] Iteration 8112 (2.34687 iter/s, 5.11319s/12 iters), loss = 0.096346
I0412 14:09:52.976366  8032 solver.cpp:237]     Train net output #0: loss = 0.096346 (* 1 = 0.096346 loss)
I0412 14:09:52.976378  8032 sgd_solver.cpp:105] Iteration 8112, lr = 0.00200514
I0412 14:09:58.083633  8032 solver.cpp:218] Iteration 8124 (2.34967 iter/s, 5.1071s/12 iters), loss = 0.150701
I0412 14:09:58.083684  8032 solver.cpp:237]     Train net output #0: loss = 0.150701 (* 1 = 0.150701 loss)
I0412 14:09:58.083696  8032 sgd_solver.cpp:105] Iteration 8124, lr = 0.00200038
I0412 14:10:03.300330  8032 solver.cpp:218] Iteration 8136 (2.3004 iter/s, 5.21648s/12 iters), loss = 0.196319
I0412 14:10:03.300374  8032 solver.cpp:237]     Train net output #0: loss = 0.196319 (* 1 = 0.196319 loss)
I0412 14:10:03.300382  8032 sgd_solver.cpp:105] Iteration 8136, lr = 0.00199563
I0412 14:10:08.384039  8032 solver.cpp:218] Iteration 8148 (2.36058 iter/s, 5.08349s/12 iters), loss = 0.228065
I0412 14:10:08.384084  8032 solver.cpp:237]     Train net output #0: loss = 0.228065 (* 1 = 0.228065 loss)
I0412 14:10:08.384093  8032 sgd_solver.cpp:105] Iteration 8148, lr = 0.00199089
I0412 14:10:13.086756  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8160.caffemodel
I0412 14:10:16.078676  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8160.solverstate
I0412 14:10:18.428611  8032 solver.cpp:330] Iteration 8160, Testing net (#0)
I0412 14:10:18.428635  8032 net.cpp:676] Ignoring source layer train-data
I0412 14:10:19.580848  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:10:22.811370  8032 solver.cpp:397]     Test net output #0: accuracy = 0.448529
I0412 14:10:22.811417  8032 solver.cpp:397]     Test net output #1: loss = 3.00113 (* 1 = 3.00113 loss)
I0412 14:10:22.899788  8032 solver.cpp:218] Iteration 8160 (0.826717 iter/s, 14.5152s/12 iters), loss = 0.270993
I0412 14:10:22.899834  8032 solver.cpp:237]     Train net output #0: loss = 0.270993 (* 1 = 0.270993 loss)
I0412 14:10:22.899845  8032 sgd_solver.cpp:105] Iteration 8160, lr = 0.00198616
I0412 14:10:27.230034  8032 solver.cpp:218] Iteration 8172 (2.77133 iter/s, 4.33005s/12 iters), loss = 0.302502
I0412 14:10:27.230129  8032 solver.cpp:237]     Train net output #0: loss = 0.302502 (* 1 = 0.302502 loss)
I0412 14:10:27.230139  8032 sgd_solver.cpp:105] Iteration 8172, lr = 0.00198145
I0412 14:10:32.749480  8032 solver.cpp:218] Iteration 8184 (2.17424 iter/s, 5.51917s/12 iters), loss = 0.118737
I0412 14:10:32.749532  8032 solver.cpp:237]     Train net output #0: loss = 0.118737 (* 1 = 0.118737 loss)
I0412 14:10:32.749545  8032 sgd_solver.cpp:105] Iteration 8184, lr = 0.00197674
I0412 14:10:36.741478  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:10:38.233832  8032 solver.cpp:218] Iteration 8196 (2.18814 iter/s, 5.48412s/12 iters), loss = 0.116199
I0412 14:10:38.233886  8032 solver.cpp:237]     Train net output #0: loss = 0.116199 (* 1 = 0.116199 loss)
I0412 14:10:38.233898  8032 sgd_solver.cpp:105] Iteration 8196, lr = 0.00197205
I0412 14:10:43.231570  8032 solver.cpp:218] Iteration 8208 (2.40119 iter/s, 4.99752s/12 iters), loss = 0.102011
I0412 14:10:43.231621  8032 solver.cpp:237]     Train net output #0: loss = 0.102011 (* 1 = 0.102011 loss)
I0412 14:10:43.231631  8032 sgd_solver.cpp:105] Iteration 8208, lr = 0.00196737
I0412 14:10:48.264079  8032 solver.cpp:218] Iteration 8220 (2.3846 iter/s, 5.0323s/12 iters), loss = 0.118814
I0412 14:10:48.264120  8032 solver.cpp:237]     Train net output #0: loss = 0.118814 (* 1 = 0.118814 loss)
I0412 14:10:48.264129  8032 sgd_solver.cpp:105] Iteration 8220, lr = 0.0019627
I0412 14:10:53.425756  8032 solver.cpp:218] Iteration 8232 (2.32492 iter/s, 5.16146s/12 iters), loss = 0.128648
I0412 14:10:53.425808  8032 solver.cpp:237]     Train net output #0: loss = 0.128648 (* 1 = 0.128648 loss)
I0412 14:10:53.425820  8032 sgd_solver.cpp:105] Iteration 8232, lr = 0.00195804
I0412 14:10:58.677127  8032 solver.cpp:218] Iteration 8244 (2.28522 iter/s, 5.25115s/12 iters), loss = 0.128725
I0412 14:10:58.681403  8032 solver.cpp:237]     Train net output #0: loss = 0.128725 (* 1 = 0.128725 loss)
I0412 14:10:58.681414  8032 sgd_solver.cpp:105] Iteration 8244, lr = 0.00195339
I0412 14:11:04.491374  8032 solver.cpp:218] Iteration 8256 (2.06548 iter/s, 5.80978s/12 iters), loss = 0.25891
I0412 14:11:04.491427  8032 solver.cpp:237]     Train net output #0: loss = 0.25891 (* 1 = 0.25891 loss)
I0412 14:11:04.491441  8032 sgd_solver.cpp:105] Iteration 8256, lr = 0.00194875
I0412 14:11:06.618922  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8262.caffemodel
I0412 14:11:09.602039  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8262.solverstate
I0412 14:11:11.916296  8032 solver.cpp:330] Iteration 8262, Testing net (#0)
I0412 14:11:11.916321  8032 net.cpp:676] Ignoring source layer train-data
I0412 14:11:13.125110  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:11:16.603543  8032 solver.cpp:397]     Test net output #0: accuracy = 0.466299
I0412 14:11:16.603580  8032 solver.cpp:397]     Test net output #1: loss = 2.89326 (* 1 = 2.89326 loss)
I0412 14:11:18.578652  8032 solver.cpp:218] Iteration 8268 (0.851862 iter/s, 14.0868s/12 iters), loss = 0.182444
I0412 14:11:18.578704  8032 solver.cpp:237]     Train net output #0: loss = 0.182444 (* 1 = 0.182444 loss)
I0412 14:11:18.578716  8032 sgd_solver.cpp:105] Iteration 8268, lr = 0.00194412
I0412 14:11:23.699245  8032 solver.cpp:218] Iteration 8280 (2.34358 iter/s, 5.12037s/12 iters), loss = 0.102648
I0412 14:11:23.699301  8032 solver.cpp:237]     Train net output #0: loss = 0.102648 (* 1 = 0.102648 loss)
I0412 14:11:23.699311  8032 sgd_solver.cpp:105] Iteration 8280, lr = 0.00193951
I0412 14:11:28.642827  8032 solver.cpp:218] Iteration 8292 (2.4275 iter/s, 4.94336s/12 iters), loss = 0.168712
I0412 14:11:28.642879  8032 solver.cpp:237]     Train net output #0: loss = 0.168712 (* 1 = 0.168712 loss)
I0412 14:11:28.642891  8032 sgd_solver.cpp:105] Iteration 8292, lr = 0.0019349
I0412 14:11:29.280347  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:11:33.612938  8032 solver.cpp:218] Iteration 8304 (2.41454 iter/s, 4.9699s/12 iters), loss = 0.191821
I0412 14:11:33.612980  8032 solver.cpp:237]     Train net output #0: loss = 0.191821 (* 1 = 0.191821 loss)
I0412 14:11:33.612989  8032 sgd_solver.cpp:105] Iteration 8304, lr = 0.00193031
I0412 14:11:36.523901  8032 blocking_queue.cpp:49] Waiting for data
I0412 14:11:38.787997  8032 solver.cpp:218] Iteration 8316 (2.31891 iter/s, 5.17484s/12 iters), loss = 0.126075
I0412 14:11:38.788049  8032 solver.cpp:237]     Train net output #0: loss = 0.126075 (* 1 = 0.126075 loss)
I0412 14:11:38.788062  8032 sgd_solver.cpp:105] Iteration 8316, lr = 0.00192573
I0412 14:11:44.446148  8032 solver.cpp:218] Iteration 8328 (2.12092 iter/s, 5.65791s/12 iters), loss = 0.1586
I0412 14:11:44.446205  8032 solver.cpp:237]     Train net output #0: loss = 0.1586 (* 1 = 0.1586 loss)
I0412 14:11:44.446219  8032 sgd_solver.cpp:105] Iteration 8328, lr = 0.00192115
I0412 14:11:49.647011  8032 solver.cpp:218] Iteration 8340 (2.30741 iter/s, 5.20064s/12 iters), loss = 0.21948
I0412 14:11:49.647051  8032 solver.cpp:237]     Train net output #0: loss = 0.21948 (* 1 = 0.21948 loss)
I0412 14:11:49.647059  8032 sgd_solver.cpp:105] Iteration 8340, lr = 0.00191659
I0412 14:11:54.875363  8032 solver.cpp:218] Iteration 8352 (2.29527 iter/s, 5.22814s/12 iters), loss = 0.239365
I0412 14:11:54.875407  8032 solver.cpp:237]     Train net output #0: loss = 0.239365 (* 1 = 0.239365 loss)
I0412 14:11:54.875416  8032 sgd_solver.cpp:105] Iteration 8352, lr = 0.00191204
I0412 14:11:59.626332  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8364.caffemodel
I0412 14:12:05.814157  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8364.solverstate
I0412 14:12:08.381436  8032 solver.cpp:330] Iteration 8364, Testing net (#0)
I0412 14:12:08.381464  8032 net.cpp:676] Ignoring source layer train-data
I0412 14:12:09.636186  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:12:12.985415  8032 solver.cpp:397]     Test net output #0: accuracy = 0.458333
I0412 14:12:12.985466  8032 solver.cpp:397]     Test net output #1: loss = 2.92445 (* 1 = 2.92445 loss)
I0412 14:12:13.073873  8032 solver.cpp:218] Iteration 8364 (0.659417 iter/s, 18.1979s/12 iters), loss = 0.0994249
I0412 14:12:13.073942  8032 solver.cpp:237]     Train net output #0: loss = 0.0994248 (* 1 = 0.0994248 loss)
I0412 14:12:13.073982  8032 sgd_solver.cpp:105] Iteration 8364, lr = 0.0019075
I0412 14:12:17.329398  8032 solver.cpp:218] Iteration 8376 (2.82 iter/s, 4.25531s/12 iters), loss = 0.1964
I0412 14:12:17.329454  8032 solver.cpp:237]     Train net output #0: loss = 0.1964 (* 1 = 0.1964 loss)
I0412 14:12:17.329468  8032 sgd_solver.cpp:105] Iteration 8376, lr = 0.00190297
I0412 14:12:22.359167  8032 solver.cpp:218] Iteration 8388 (2.38588 iter/s, 5.02959s/12 iters), loss = 0.129255
I0412 14:12:22.359210  8032 solver.cpp:237]     Train net output #0: loss = 0.129255 (* 1 = 0.129255 loss)
I0412 14:12:22.359220  8032 sgd_solver.cpp:105] Iteration 8388, lr = 0.00189846
I0412 14:12:25.269191  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:12:27.490798  8032 solver.cpp:218] Iteration 8400 (2.33851 iter/s, 5.13147s/12 iters), loss = 0.161738
I0412 14:12:27.490854  8032 solver.cpp:237]     Train net output #0: loss = 0.161738 (* 1 = 0.161738 loss)
I0412 14:12:27.490865  8032 sgd_solver.cpp:105] Iteration 8400, lr = 0.00189395
I0412 14:12:32.671485  8032 solver.cpp:218] Iteration 8412 (2.31637 iter/s, 5.18051s/12 iters), loss = 0.135826
I0412 14:12:32.682055  8032 solver.cpp:237]     Train net output #0: loss = 0.135826 (* 1 = 0.135826 loss)
I0412 14:12:32.682072  8032 sgd_solver.cpp:105] Iteration 8412, lr = 0.00188945
I0412 14:12:37.648283  8032 solver.cpp:218] Iteration 8424 (2.41637 iter/s, 4.96612s/12 iters), loss = 0.188408
I0412 14:12:37.648332  8032 solver.cpp:237]     Train net output #0: loss = 0.188408 (* 1 = 0.188408 loss)
I0412 14:12:37.648344  8032 sgd_solver.cpp:105] Iteration 8424, lr = 0.00188497
I0412 14:12:42.878099  8032 solver.cpp:218] Iteration 8436 (2.29461 iter/s, 5.22965s/12 iters), loss = 0.157586
I0412 14:12:42.878144  8032 solver.cpp:237]     Train net output #0: loss = 0.157585 (* 1 = 0.157585 loss)
I0412 14:12:42.878154  8032 sgd_solver.cpp:105] Iteration 8436, lr = 0.00188049
I0412 14:12:48.112253  8032 solver.cpp:218] Iteration 8448 (2.29271 iter/s, 5.23398s/12 iters), loss = 0.094879
I0412 14:12:48.112309  8032 solver.cpp:237]     Train net output #0: loss = 0.094879 (* 1 = 0.094879 loss)
I0412 14:12:48.112321  8032 sgd_solver.cpp:105] Iteration 8448, lr = 0.00187603
I0412 14:12:53.392211  8032 solver.cpp:218] Iteration 8460 (2.27282 iter/s, 5.27978s/12 iters), loss = 0.105675
I0412 14:12:53.392256  8032 solver.cpp:237]     Train net output #0: loss = 0.105675 (* 1 = 0.105675 loss)
I0412 14:12:53.392264  8032 sgd_solver.cpp:105] Iteration 8460, lr = 0.00187157
I0412 14:12:55.442150  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8466.caffemodel
I0412 14:12:59.588295  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8466.solverstate
I0412 14:13:02.533221  8032 solver.cpp:330] Iteration 8466, Testing net (#0)
I0412 14:13:02.533246  8032 net.cpp:676] Ignoring source layer train-data
I0412 14:13:03.768676  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:13:07.103238  8032 solver.cpp:397]     Test net output #0: accuracy = 0.466912
I0412 14:13:07.103274  8032 solver.cpp:397]     Test net output #1: loss = 2.98202 (* 1 = 2.98202 loss)
I0412 14:13:09.031396  8032 solver.cpp:218] Iteration 8472 (0.767322 iter/s, 15.6388s/12 iters), loss = 0.106802
I0412 14:13:09.031459  8032 solver.cpp:237]     Train net output #0: loss = 0.106802 (* 1 = 0.106802 loss)
I0412 14:13:09.031476  8032 sgd_solver.cpp:105] Iteration 8472, lr = 0.00186713
I0412 14:13:14.219106  8032 solver.cpp:218] Iteration 8484 (2.31324 iter/s, 5.18752s/12 iters), loss = 0.166199
I0412 14:13:14.219163  8032 solver.cpp:237]     Train net output #0: loss = 0.166199 (* 1 = 0.166199 loss)
I0412 14:13:14.219175  8032 sgd_solver.cpp:105] Iteration 8484, lr = 0.0018627
I0412 14:13:19.491176  8032 solver.cpp:218] Iteration 8496 (2.27622 iter/s, 5.27189s/12 iters), loss = 0.188608
I0412 14:13:19.491219  8032 solver.cpp:237]     Train net output #0: loss = 0.188607 (* 1 = 0.188607 loss)
I0412 14:13:19.491230  8032 sgd_solver.cpp:105] Iteration 8496, lr = 0.00185827
I0412 14:13:19.502792  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:13:24.362501  8032 solver.cpp:218] Iteration 8508 (2.46348 iter/s, 4.87116s/12 iters), loss = 0.175845
I0412 14:13:24.362555  8032 solver.cpp:237]     Train net output #0: loss = 0.175844 (* 1 = 0.175844 loss)
I0412 14:13:24.362566  8032 sgd_solver.cpp:105] Iteration 8508, lr = 0.00185386
I0412 14:13:29.518333  8032 solver.cpp:218] Iteration 8520 (2.32754 iter/s, 5.15565s/12 iters), loss = 0.211023
I0412 14:13:29.518385  8032 solver.cpp:237]     Train net output #0: loss = 0.211023 (* 1 = 0.211023 loss)
I0412 14:13:29.518396  8032 sgd_solver.cpp:105] Iteration 8520, lr = 0.00184946
I0412 14:13:34.620967  8032 solver.cpp:218] Iteration 8532 (2.35181 iter/s, 5.10246s/12 iters), loss = 0.117492
I0412 14:13:34.621085  8032 solver.cpp:237]     Train net output #0: loss = 0.117492 (* 1 = 0.117492 loss)
I0412 14:13:34.621099  8032 sgd_solver.cpp:105] Iteration 8532, lr = 0.00184507
I0412 14:13:39.493652  8032 solver.cpp:218] Iteration 8544 (2.46282 iter/s, 4.87245s/12 iters), loss = 0.16915
I0412 14:13:39.493695  8032 solver.cpp:237]     Train net output #0: loss = 0.16915 (* 1 = 0.16915 loss)
I0412 14:13:39.493703  8032 sgd_solver.cpp:105] Iteration 8544, lr = 0.00184069
I0412 14:13:44.646688  8032 solver.cpp:218] Iteration 8556 (2.3288 iter/s, 5.15287s/12 iters), loss = 0.2243
I0412 14:13:44.646734  8032 solver.cpp:237]     Train net output #0: loss = 0.2243 (* 1 = 0.2243 loss)
I0412 14:13:44.646744  8032 sgd_solver.cpp:105] Iteration 8556, lr = 0.00183632
I0412 14:13:49.378983  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8568.caffemodel
I0412 14:13:54.086135  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8568.solverstate
I0412 14:13:58.524423  8032 solver.cpp:330] Iteration 8568, Testing net (#0)
I0412 14:13:58.524451  8032 net.cpp:676] Ignoring source layer train-data
I0412 14:13:59.685636  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:14:03.236188  8032 solver.cpp:397]     Test net output #0: accuracy = 0.474265
I0412 14:14:03.236238  8032 solver.cpp:397]     Test net output #1: loss = 2.90721 (* 1 = 2.90721 loss)
I0412 14:14:03.324769  8032 solver.cpp:218] Iteration 8568 (0.64248 iter/s, 18.6776s/12 iters), loss = 0.0890709
I0412 14:14:03.324823  8032 solver.cpp:237]     Train net output #0: loss = 0.0890709 (* 1 = 0.0890709 loss)
I0412 14:14:03.324834  8032 sgd_solver.cpp:105] Iteration 8568, lr = 0.00183196
I0412 14:14:07.805928  8032 solver.cpp:218] Iteration 8580 (2.67798 iter/s, 4.48099s/12 iters), loss = 0.148383
I0412 14:14:07.806097  8032 solver.cpp:237]     Train net output #0: loss = 0.148383 (* 1 = 0.148383 loss)
I0412 14:14:07.806108  8032 sgd_solver.cpp:105] Iteration 8580, lr = 0.00182761
I0412 14:14:13.278187  8032 solver.cpp:218] Iteration 8592 (2.193 iter/s, 5.47196s/12 iters), loss = 0.188333
I0412 14:14:13.278241  8032 solver.cpp:237]     Train net output #0: loss = 0.188333 (* 1 = 0.188333 loss)
I0412 14:14:13.278254  8032 sgd_solver.cpp:105] Iteration 8592, lr = 0.00182327
I0412 14:14:15.696604  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:14:18.796615  8032 solver.cpp:218] Iteration 8604 (2.17461 iter/s, 5.51823s/12 iters), loss = 0.0660966
I0412 14:14:18.796679  8032 solver.cpp:237]     Train net output #0: loss = 0.0660966 (* 1 = 0.0660966 loss)
I0412 14:14:18.796694  8032 sgd_solver.cpp:105] Iteration 8604, lr = 0.00181894
I0412 14:14:24.251595  8032 solver.cpp:218] Iteration 8616 (2.1999 iter/s, 5.45479s/12 iters), loss = 0.0565305
I0412 14:14:24.251643  8032 solver.cpp:237]     Train net output #0: loss = 0.0565305 (* 1 = 0.0565305 loss)
I0412 14:14:24.251653  8032 sgd_solver.cpp:105] Iteration 8616, lr = 0.00181462
I0412 14:14:29.808848  8032 solver.cpp:218] Iteration 8628 (2.15941 iter/s, 5.55706s/12 iters), loss = 0.198804
I0412 14:14:29.808904  8032 solver.cpp:237]     Train net output #0: loss = 0.198804 (* 1 = 0.198804 loss)
I0412 14:14:29.808917  8032 sgd_solver.cpp:105] Iteration 8628, lr = 0.00181031
I0412 14:14:34.968487  8032 solver.cpp:218] Iteration 8640 (2.32583 iter/s, 5.15945s/12 iters), loss = 0.139953
I0412 14:14:34.968561  8032 solver.cpp:237]     Train net output #0: loss = 0.139953 (* 1 = 0.139953 loss)
I0412 14:14:34.968583  8032 sgd_solver.cpp:105] Iteration 8640, lr = 0.00180602
I0412 14:14:40.173409  8032 solver.cpp:218] Iteration 8652 (2.3056 iter/s, 5.20472s/12 iters), loss = 0.165303
I0412 14:14:40.173892  8032 solver.cpp:237]     Train net output #0: loss = 0.165303 (* 1 = 0.165303 loss)
I0412 14:14:40.173908  8032 sgd_solver.cpp:105] Iteration 8652, lr = 0.00180173
I0412 14:14:45.408201  8032 solver.cpp:218] Iteration 8664 (2.29262 iter/s, 5.23418s/12 iters), loss = 0.217175
I0412 14:14:45.408252  8032 solver.cpp:237]     Train net output #0: loss = 0.217175 (* 1 = 0.217175 loss)
I0412 14:14:45.408264  8032 sgd_solver.cpp:105] Iteration 8664, lr = 0.00179745
I0412 14:14:47.466298  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8670.caffemodel
I0412 14:14:52.601738  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8670.solverstate
I0412 14:14:57.792028  8032 solver.cpp:330] Iteration 8670, Testing net (#0)
I0412 14:14:57.792055  8032 net.cpp:676] Ignoring source layer train-data
I0412 14:14:58.810819  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:15:02.313160  8032 solver.cpp:397]     Test net output #0: accuracy = 0.473652
I0412 14:15:02.313202  8032 solver.cpp:397]     Test net output #1: loss = 2.89066 (* 1 = 2.89066 loss)
I0412 14:15:04.418573  8032 solver.cpp:218] Iteration 8676 (0.631251 iter/s, 19.0099s/12 iters), loss = 0.160737
I0412 14:15:04.418635  8032 solver.cpp:237]     Train net output #0: loss = 0.160737 (* 1 = 0.160737 loss)
I0412 14:15:04.418648  8032 sgd_solver.cpp:105] Iteration 8676, lr = 0.00179318
I0412 14:15:09.437140  8032 solver.cpp:218] Iteration 8688 (2.39121 iter/s, 5.01838s/12 iters), loss = 0.129835
I0412 14:15:09.437196  8032 solver.cpp:237]     Train net output #0: loss = 0.129835 (* 1 = 0.129835 loss)
I0412 14:15:09.437207  8032 sgd_solver.cpp:105] Iteration 8688, lr = 0.00178893
I0412 14:15:13.829344  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:15:14.571853  8032 solver.cpp:218] Iteration 8700 (2.33712 iter/s, 5.13453s/12 iters), loss = 0.0952455
I0412 14:15:14.571909  8032 solver.cpp:237]     Train net output #0: loss = 0.0952455 (* 1 = 0.0952455 loss)
I0412 14:15:14.571920  8032 sgd_solver.cpp:105] Iteration 8700, lr = 0.00178468
I0412 14:15:19.748450  8032 solver.cpp:218] Iteration 8712 (2.31821 iter/s, 5.17641s/12 iters), loss = 0.11294
I0412 14:15:19.748497  8032 solver.cpp:237]     Train net output #0: loss = 0.11294 (* 1 = 0.11294 loss)
I0412 14:15:19.748509  8032 sgd_solver.cpp:105] Iteration 8712, lr = 0.00178044
I0412 14:15:24.872051  8032 solver.cpp:218] Iteration 8724 (2.34219 iter/s, 5.12342s/12 iters), loss = 0.0655398
I0412 14:15:24.872107  8032 solver.cpp:237]     Train net output #0: loss = 0.0655398 (* 1 = 0.0655398 loss)
I0412 14:15:24.872117  8032 sgd_solver.cpp:105] Iteration 8724, lr = 0.00177621
I0412 14:15:30.249601  8032 solver.cpp:218] Iteration 8736 (2.23158 iter/s, 5.37736s/12 iters), loss = 0.204817
I0412 14:15:30.249652  8032 solver.cpp:237]     Train net output #0: loss = 0.204817 (* 1 = 0.204817 loss)
I0412 14:15:30.249663  8032 sgd_solver.cpp:105] Iteration 8736, lr = 0.001772
I0412 14:15:35.755538  8032 solver.cpp:218] Iteration 8748 (2.17954 iter/s, 5.50574s/12 iters), loss = 0.106152
I0412 14:15:35.755587  8032 solver.cpp:237]     Train net output #0: loss = 0.106152 (* 1 = 0.106152 loss)
I0412 14:15:35.755596  8032 sgd_solver.cpp:105] Iteration 8748, lr = 0.00176779
I0412 14:15:41.161546  8032 solver.cpp:218] Iteration 8760 (2.21983 iter/s, 5.40582s/12 iters), loss = 0.110275
I0412 14:15:41.161600  8032 solver.cpp:237]     Train net output #0: loss = 0.110275 (* 1 = 0.110275 loss)
I0412 14:15:41.161612  8032 sgd_solver.cpp:105] Iteration 8760, lr = 0.00176359
I0412 14:15:45.958107  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8772.caffemodel
I0412 14:15:49.365583  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8772.solverstate
I0412 14:15:53.038798  8032 solver.cpp:330] Iteration 8772, Testing net (#0)
I0412 14:15:53.038826  8032 net.cpp:676] Ignoring source layer train-data
I0412 14:15:54.026794  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:15:57.479122  8032 solver.cpp:397]     Test net output #0: accuracy = 0.466912
I0412 14:15:57.479164  8032 solver.cpp:397]     Test net output #1: loss = 2.96563 (* 1 = 2.96563 loss)
I0412 14:15:57.567765  8032 solver.cpp:218] Iteration 8772 (0.73145 iter/s, 16.4058s/12 iters), loss = 0.188505
I0412 14:15:57.567834  8032 solver.cpp:237]     Train net output #0: loss = 0.188505 (* 1 = 0.188505 loss)
I0412 14:15:57.567849  8032 sgd_solver.cpp:105] Iteration 8772, lr = 0.00175941
I0412 14:16:01.854912  8032 solver.cpp:218] Iteration 8784 (2.79918 iter/s, 4.28697s/12 iters), loss = 0.233927
I0412 14:16:01.854959  8032 solver.cpp:237]     Train net output #0: loss = 0.233927 (* 1 = 0.233927 loss)
I0412 14:16:01.854967  8032 sgd_solver.cpp:105] Iteration 8784, lr = 0.00175523
I0412 14:16:07.158852  8032 solver.cpp:218] Iteration 8796 (2.26255 iter/s, 5.30376s/12 iters), loss = 0.170884
I0412 14:16:07.158900  8032 solver.cpp:237]     Train net output #0: loss = 0.170884 (* 1 = 0.170884 loss)
I0412 14:16:07.158908  8032 sgd_solver.cpp:105] Iteration 8796, lr = 0.00175106
I0412 14:16:08.647789  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:16:12.541723  8032 solver.cpp:218] Iteration 8808 (2.22937 iter/s, 5.38268s/12 iters), loss = 0.109316
I0412 14:16:12.541765  8032 solver.cpp:237]     Train net output #0: loss = 0.109316 (* 1 = 0.109316 loss)
I0412 14:16:12.541774  8032 sgd_solver.cpp:105] Iteration 8808, lr = 0.0017469
I0412 14:16:17.640730  8032 solver.cpp:218] Iteration 8820 (2.35348 iter/s, 5.09883s/12 iters), loss = 0.13025
I0412 14:16:17.640902  8032 solver.cpp:237]     Train net output #0: loss = 0.13025 (* 1 = 0.13025 loss)
I0412 14:16:17.640916  8032 sgd_solver.cpp:105] Iteration 8820, lr = 0.00174276
I0412 14:16:22.586339  8032 solver.cpp:218] Iteration 8832 (2.42654 iter/s, 4.94531s/12 iters), loss = 0.0707032
I0412 14:16:22.586387  8032 solver.cpp:237]     Train net output #0: loss = 0.0707032 (* 1 = 0.0707032 loss)
I0412 14:16:22.586400  8032 sgd_solver.cpp:105] Iteration 8832, lr = 0.00173862
I0412 14:16:27.442807  8032 solver.cpp:218] Iteration 8844 (2.47102 iter/s, 4.85629s/12 iters), loss = 0.138285
I0412 14:16:27.442864  8032 solver.cpp:237]     Train net output #0: loss = 0.138285 (* 1 = 0.138285 loss)
I0412 14:16:27.442876  8032 sgd_solver.cpp:105] Iteration 8844, lr = 0.00173449
I0412 14:16:32.718210  8032 solver.cpp:218] Iteration 8856 (2.27479 iter/s, 5.27521s/12 iters), loss = 0.0911897
I0412 14:16:32.718266  8032 solver.cpp:237]     Train net output #0: loss = 0.0911897 (* 1 = 0.0911897 loss)
I0412 14:16:32.718279  8032 sgd_solver.cpp:105] Iteration 8856, lr = 0.00173037
I0412 14:16:37.729157  8032 solver.cpp:218] Iteration 8868 (2.39485 iter/s, 5.01076s/12 iters), loss = 0.0682498
I0412 14:16:37.729218  8032 solver.cpp:237]     Train net output #0: loss = 0.0682498 (* 1 = 0.0682498 loss)
I0412 14:16:37.729230  8032 sgd_solver.cpp:105] Iteration 8868, lr = 0.00172626
I0412 14:16:39.757081  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8874.caffemodel
I0412 14:16:42.856169  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8874.solverstate
I0412 14:16:45.175671  8032 solver.cpp:330] Iteration 8874, Testing net (#0)
I0412 14:16:45.175698  8032 net.cpp:676] Ignoring source layer train-data
I0412 14:16:46.114815  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:16:49.664372  8032 solver.cpp:397]     Test net output #0: accuracy = 0.479167
I0412 14:16:49.664489  8032 solver.cpp:397]     Test net output #1: loss = 2.9533 (* 1 = 2.9533 loss)
I0412 14:16:51.710521  8032 solver.cpp:218] Iteration 8880 (0.85831 iter/s, 13.981s/12 iters), loss = 0.180451
I0412 14:16:51.710575  8032 solver.cpp:237]     Train net output #0: loss = 0.180451 (* 1 = 0.180451 loss)
I0412 14:16:51.710590  8032 sgd_solver.cpp:105] Iteration 8880, lr = 0.00172217
I0412 14:16:57.039144  8032 solver.cpp:218] Iteration 8892 (2.25207 iter/s, 5.32842s/12 iters), loss = 0.133444
I0412 14:16:57.039192  8032 solver.cpp:237]     Train net output #0: loss = 0.133444 (* 1 = 0.133444 loss)
I0412 14:16:57.039202  8032 sgd_solver.cpp:105] Iteration 8892, lr = 0.00171808
I0412 14:17:00.632334  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:17:02.152292  8032 solver.cpp:218] Iteration 8904 (2.34697 iter/s, 5.11297s/12 iters), loss = 0.0248051
I0412 14:17:02.152335  8032 solver.cpp:237]     Train net output #0: loss = 0.0248051 (* 1 = 0.0248051 loss)
I0412 14:17:02.152348  8032 sgd_solver.cpp:105] Iteration 8904, lr = 0.001714
I0412 14:17:07.560132  8032 solver.cpp:218] Iteration 8916 (2.21908 iter/s, 5.40765s/12 iters), loss = 0.128694
I0412 14:17:07.560195  8032 solver.cpp:237]     Train net output #0: loss = 0.128694 (* 1 = 0.128694 loss)
I0412 14:17:07.560211  8032 sgd_solver.cpp:105] Iteration 8916, lr = 0.00170993
I0412 14:17:12.529254  8032 solver.cpp:218] Iteration 8928 (2.41501 iter/s, 4.96893s/12 iters), loss = 0.190176
I0412 14:17:12.529297  8032 solver.cpp:237]     Train net output #0: loss = 0.190176 (* 1 = 0.190176 loss)
I0412 14:17:12.529305  8032 sgd_solver.cpp:105] Iteration 8928, lr = 0.00170587
I0412 14:17:17.616299  8032 solver.cpp:218] Iteration 8940 (2.35902 iter/s, 5.08686s/12 iters), loss = 0.142237
I0412 14:17:17.616358  8032 solver.cpp:237]     Train net output #0: loss = 0.142237 (* 1 = 0.142237 loss)
I0412 14:17:17.616376  8032 sgd_solver.cpp:105] Iteration 8940, lr = 0.00170182
I0412 14:17:23.001731  8032 solver.cpp:218] Iteration 8952 (2.22832 iter/s, 5.38523s/12 iters), loss = 0.0964781
I0412 14:17:23.001884  8032 solver.cpp:237]     Train net output #0: loss = 0.0964781 (* 1 = 0.0964781 loss)
I0412 14:17:23.001899  8032 sgd_solver.cpp:105] Iteration 8952, lr = 0.00169778
I0412 14:17:28.231853  8032 solver.cpp:218] Iteration 8964 (2.29453 iter/s, 5.22984s/12 iters), loss = 0.113249
I0412 14:17:28.231899  8032 solver.cpp:237]     Train net output #0: loss = 0.113249 (* 1 = 0.113249 loss)
I0412 14:17:28.231907  8032 sgd_solver.cpp:105] Iteration 8964, lr = 0.00169375
I0412 14:17:32.844199  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8976.caffemodel
I0412 14:17:36.054044  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8976.solverstate
I0412 14:17:38.419034  8032 solver.cpp:330] Iteration 8976, Testing net (#0)
I0412 14:17:38.419060  8032 net.cpp:676] Ignoring source layer train-data
I0412 14:17:39.370043  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:17:42.901741  8032 solver.cpp:397]     Test net output #0: accuracy = 0.476103
I0412 14:17:42.901784  8032 solver.cpp:397]     Test net output #1: loss = 2.91971 (* 1 = 2.91971 loss)
I0412 14:17:42.990197  8032 solver.cpp:218] Iteration 8976 (0.813122 iter/s, 14.7579s/12 iters), loss = 0.0253515
I0412 14:17:42.990254  8032 solver.cpp:237]     Train net output #0: loss = 0.0253515 (* 1 = 0.0253515 loss)
I0412 14:17:42.990267  8032 sgd_solver.cpp:105] Iteration 8976, lr = 0.00168973
I0412 14:17:47.696908  8032 solver.cpp:218] Iteration 8988 (2.54966 iter/s, 4.70652s/12 iters), loss = 0.0518003
I0412 14:17:47.696964  8032 solver.cpp:237]     Train net output #0: loss = 0.0518003 (* 1 = 0.0518003 loss)
I0412 14:17:47.696976  8032 sgd_solver.cpp:105] Iteration 8988, lr = 0.00168571
I0412 14:17:51.030730  8032 blocking_queue.cpp:49] Waiting for data
I0412 14:17:52.811408  8032 solver.cpp:218] Iteration 9000 (2.34636 iter/s, 5.11431s/12 iters), loss = 0.269431
I0412 14:17:52.811457  8032 solver.cpp:237]     Train net output #0: loss = 0.269431 (* 1 = 0.269431 loss)
I0412 14:17:52.811468  8032 sgd_solver.cpp:105] Iteration 9000, lr = 0.00168171
I0412 14:17:53.473449  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:17:57.613404  8032 solver.cpp:218] Iteration 9012 (2.49905 iter/s, 4.80182s/12 iters), loss = 0.0645164
I0412 14:17:57.613451  8032 solver.cpp:237]     Train net output #0: loss = 0.0645165 (* 1 = 0.0645165 loss)
I0412 14:17:57.613462  8032 sgd_solver.cpp:105] Iteration 9012, lr = 0.00167772
I0412 14:18:02.960711  8032 solver.cpp:218] Iteration 9024 (2.2442 iter/s, 5.34712s/12 iters), loss = 0.0714488
I0412 14:18:02.960757  8032 solver.cpp:237]     Train net output #0: loss = 0.0714489 (* 1 = 0.0714489 loss)
I0412 14:18:02.960767  8032 sgd_solver.cpp:105] Iteration 9024, lr = 0.00167374
I0412 14:18:08.678411  8032 solver.cpp:218] Iteration 9036 (2.09882 iter/s, 5.7175s/12 iters), loss = 0.0341797
I0412 14:18:08.678465  8032 solver.cpp:237]     Train net output #0: loss = 0.0341798 (* 1 = 0.0341798 loss)
I0412 14:18:08.678476  8032 sgd_solver.cpp:105] Iteration 9036, lr = 0.00166976
I0412 14:18:13.675083  8032 solver.cpp:218] Iteration 9048 (2.40169 iter/s, 4.99649s/12 iters), loss = 0.154288
I0412 14:18:13.675130  8032 solver.cpp:237]     Train net output #0: loss = 0.154288 (* 1 = 0.154288 loss)
I0412 14:18:13.675140  8032 sgd_solver.cpp:105] Iteration 9048, lr = 0.0016658
I0412 14:18:18.863066  8032 solver.cpp:218] Iteration 9060 (2.31312 iter/s, 5.18779s/12 iters), loss = 0.0404
I0412 14:18:18.863128  8032 solver.cpp:237]     Train net output #0: loss = 0.0404001 (* 1 = 0.0404001 loss)
I0412 14:18:18.863142  8032 sgd_solver.cpp:105] Iteration 9060, lr = 0.00166184
I0412 14:18:24.363513  8032 solver.cpp:218] Iteration 9072 (2.18172 iter/s, 5.50024s/12 iters), loss = 0.163352
I0412 14:18:24.363668  8032 solver.cpp:237]     Train net output #0: loss = 0.163352 (* 1 = 0.163352 loss)
I0412 14:18:24.363682  8032 sgd_solver.cpp:105] Iteration 9072, lr = 0.0016579
I0412 14:18:26.459237  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9078.caffemodel
I0412 14:18:29.923486  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9078.solverstate
I0412 14:18:32.199476  8032 solver.cpp:330] Iteration 9078, Testing net (#0)
I0412 14:18:32.199501  8032 net.cpp:676] Ignoring source layer train-data
I0412 14:18:33.111455  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:18:36.758924  8032 solver.cpp:397]     Test net output #0: accuracy = 0.483456
I0412 14:18:36.758961  8032 solver.cpp:397]     Test net output #1: loss = 2.97421 (* 1 = 2.97421 loss)
I0412 14:18:38.765723  8032 solver.cpp:218] Iteration 9084 (0.833235 iter/s, 14.4017s/12 iters), loss = 0.164739
I0412 14:18:38.765777  8032 solver.cpp:237]     Train net output #0: loss = 0.164739 (* 1 = 0.164739 loss)
I0412 14:18:38.765789  8032 sgd_solver.cpp:105] Iteration 9084, lr = 0.00165396
I0412 14:18:43.900225  8032 solver.cpp:218] Iteration 9096 (2.33722 iter/s, 5.13431s/12 iters), loss = 0.131306
I0412 14:18:43.900271  8032 solver.cpp:237]     Train net output #0: loss = 0.131306 (* 1 = 0.131306 loss)
I0412 14:18:43.900280  8032 sgd_solver.cpp:105] Iteration 9096, lr = 0.00165003
I0412 14:18:46.970414  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:18:49.068632  8032 solver.cpp:218] Iteration 9108 (2.32188 iter/s, 5.16822s/12 iters), loss = 0.0836777
I0412 14:18:49.068684  8032 solver.cpp:237]     Train net output #0: loss = 0.0836777 (* 1 = 0.0836777 loss)
I0412 14:18:49.068696  8032 sgd_solver.cpp:105] Iteration 9108, lr = 0.00164612
I0412 14:18:54.196276  8032 solver.cpp:218] Iteration 9120 (2.34034 iter/s, 5.12745s/12 iters), loss = 0.135569
I0412 14:18:54.196328  8032 solver.cpp:237]     Train net output #0: loss = 0.135569 (* 1 = 0.135569 loss)
I0412 14:18:54.196339  8032 sgd_solver.cpp:105] Iteration 9120, lr = 0.00164221
I0412 14:18:59.378994  8032 solver.cpp:218] Iteration 9132 (2.31547 iter/s, 5.18252s/12 iters), loss = 0.112213
I0412 14:18:59.379112  8032 solver.cpp:237]     Train net output #0: loss = 0.112213 (* 1 = 0.112213 loss)
I0412 14:18:59.379124  8032 sgd_solver.cpp:105] Iteration 9132, lr = 0.00163831
I0412 14:19:04.671983  8032 solver.cpp:218] Iteration 9144 (2.26726 iter/s, 5.29273s/12 iters), loss = 0.0985844
I0412 14:19:04.672034  8032 solver.cpp:237]     Train net output #0: loss = 0.0985844 (* 1 = 0.0985844 loss)
I0412 14:19:04.672044  8032 sgd_solver.cpp:105] Iteration 9144, lr = 0.00163442
I0412 14:19:10.294775  8032 solver.cpp:218] Iteration 9156 (2.13425 iter/s, 5.6226s/12 iters), loss = 0.0960109
I0412 14:19:10.294816  8032 solver.cpp:237]     Train net output #0: loss = 0.0960109 (* 1 = 0.0960109 loss)
I0412 14:19:10.294824  8032 sgd_solver.cpp:105] Iteration 9156, lr = 0.00163054
I0412 14:19:15.464419  8032 solver.cpp:218] Iteration 9168 (2.32133 iter/s, 5.16946s/12 iters), loss = 0.0445728
I0412 14:19:15.464463  8032 solver.cpp:237]     Train net output #0: loss = 0.0445728 (* 1 = 0.0445728 loss)
I0412 14:19:15.464473  8032 sgd_solver.cpp:105] Iteration 9168, lr = 0.00162667
I0412 14:19:20.419634  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9180.caffemodel
I0412 14:19:23.405599  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9180.solverstate
I0412 14:19:25.778075  8032 solver.cpp:330] Iteration 9180, Testing net (#0)
I0412 14:19:25.778098  8032 net.cpp:676] Ignoring source layer train-data
I0412 14:19:26.576834  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:19:30.439774  8032 solver.cpp:397]     Test net output #0: accuracy = 0.473652
I0412 14:19:30.439934  8032 solver.cpp:397]     Test net output #1: loss = 3.01549 (* 1 = 3.01549 loss)
I0412 14:19:30.528579  8032 solver.cpp:218] Iteration 9180 (0.796615 iter/s, 15.0637s/12 iters), loss = 0.0607797
I0412 14:19:30.528630  8032 solver.cpp:237]     Train net output #0: loss = 0.0607797 (* 1 = 0.0607797 loss)
I0412 14:19:30.528641  8032 sgd_solver.cpp:105] Iteration 9180, lr = 0.00162281
I0412 14:19:34.915321  8032 solver.cpp:218] Iteration 9192 (2.73563 iter/s, 4.38656s/12 iters), loss = 0.148058
I0412 14:19:34.915380  8032 solver.cpp:237]     Train net output #0: loss = 0.148058 (* 1 = 0.148058 loss)
I0412 14:19:34.915392  8032 sgd_solver.cpp:105] Iteration 9192, lr = 0.00161895
I0412 14:19:40.008697  8032 solver.cpp:218] Iteration 9204 (2.35609 iter/s, 5.09318s/12 iters), loss = 0.174324
I0412 14:19:40.008752  8032 solver.cpp:237]     Train net output #0: loss = 0.174324 (* 1 = 0.174324 loss)
I0412 14:19:40.008765  8032 sgd_solver.cpp:105] Iteration 9204, lr = 0.00161511
I0412 14:19:40.091717  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:19:45.058917  8032 solver.cpp:218] Iteration 9216 (2.37623 iter/s, 5.05002s/12 iters), loss = 0.121579
I0412 14:19:45.058972  8032 solver.cpp:237]     Train net output #0: loss = 0.121579 (* 1 = 0.121579 loss)
I0412 14:19:45.058984  8032 sgd_solver.cpp:105] Iteration 9216, lr = 0.00161128
I0412 14:19:50.134557  8032 solver.cpp:218] Iteration 9228 (2.36432 iter/s, 5.07545s/12 iters), loss = 0.061577
I0412 14:19:50.134598  8032 solver.cpp:237]     Train net output #0: loss = 0.061577 (* 1 = 0.061577 loss)
I0412 14:19:50.134605  8032 sgd_solver.cpp:105] Iteration 9228, lr = 0.00160745
I0412 14:19:55.155894  8032 solver.cpp:218] Iteration 9240 (2.38989 iter/s, 5.02116s/12 iters), loss = 0.119659
I0412 14:19:55.155934  8032 solver.cpp:237]     Train net output #0: loss = 0.119659 (* 1 = 0.119659 loss)
I0412 14:19:55.155943  8032 sgd_solver.cpp:105] Iteration 9240, lr = 0.00160363
I0412 14:20:00.424996  8032 solver.cpp:218] Iteration 9252 (2.27751 iter/s, 5.26891s/12 iters), loss = 0.135695
I0412 14:20:00.425050  8032 solver.cpp:237]     Train net output #0: loss = 0.135695 (* 1 = 0.135695 loss)
I0412 14:20:00.425063  8032 sgd_solver.cpp:105] Iteration 9252, lr = 0.00159983
I0412 14:20:05.776206  8032 solver.cpp:218] Iteration 9264 (2.24257 iter/s, 5.351s/12 iters), loss = 0.0724511
I0412 14:20:05.776322  8032 solver.cpp:237]     Train net output #0: loss = 0.0724511 (* 1 = 0.0724511 loss)
I0412 14:20:05.776335  8032 sgd_solver.cpp:105] Iteration 9264, lr = 0.00159603
I0412 14:20:10.873718  8032 solver.cpp:218] Iteration 9276 (2.35421 iter/s, 5.09725s/12 iters), loss = 0.0976801
I0412 14:20:10.873770  8032 solver.cpp:237]     Train net output #0: loss = 0.0976801 (* 1 = 0.0976801 loss)
I0412 14:20:10.873780  8032 sgd_solver.cpp:105] Iteration 9276, lr = 0.00159224
I0412 14:20:12.990300  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9282.caffemodel
I0412 14:20:16.028350  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9282.solverstate
I0412 14:20:18.351660  8032 solver.cpp:330] Iteration 9282, Testing net (#0)
I0412 14:20:18.351686  8032 net.cpp:676] Ignoring source layer train-data
I0412 14:20:19.149487  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:20:22.932770  8032 solver.cpp:397]     Test net output #0: accuracy = 0.466299
I0412 14:20:22.932811  8032 solver.cpp:397]     Test net output #1: loss = 3.00211 (* 1 = 3.00211 loss)
I0412 14:20:24.913323  8032 solver.cpp:218] Iteration 9288 (0.85475 iter/s, 14.0392s/12 iters), loss = 0.0847554
I0412 14:20:24.913370  8032 solver.cpp:237]     Train net output #0: loss = 0.0847554 (* 1 = 0.0847554 loss)
I0412 14:20:24.913380  8032 sgd_solver.cpp:105] Iteration 9288, lr = 0.00158846
I0412 14:20:30.033509  8032 solver.cpp:218] Iteration 9300 (2.34375 iter/s, 5.11999s/12 iters), loss = 0.0293582
I0412 14:20:30.033569  8032 solver.cpp:237]     Train net output #0: loss = 0.0293582 (* 1 = 0.0293582 loss)
I0412 14:20:30.033583  8032 sgd_solver.cpp:105] Iteration 9300, lr = 0.00158469
I0412 14:20:32.345096  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:20:35.191722  8032 solver.cpp:218] Iteration 9312 (2.32648 iter/s, 5.15801s/12 iters), loss = 0.0791316
I0412 14:20:35.191771  8032 solver.cpp:237]     Train net output #0: loss = 0.0791316 (* 1 = 0.0791316 loss)
I0412 14:20:35.191781  8032 sgd_solver.cpp:105] Iteration 9312, lr = 0.00158092
I0412 14:20:40.641611  8032 solver.cpp:218] Iteration 9324 (2.20196 iter/s, 5.44969s/12 iters), loss = 0.0548814
I0412 14:20:40.641727  8032 solver.cpp:237]     Train net output #0: loss = 0.0548814 (* 1 = 0.0548814 loss)
I0412 14:20:40.641736  8032 sgd_solver.cpp:105] Iteration 9324, lr = 0.00157717
I0412 14:20:45.855505  8032 solver.cpp:218] Iteration 9336 (2.30166 iter/s, 5.21363s/12 iters), loss = 0.0464497
I0412 14:20:45.855549  8032 solver.cpp:237]     Train net output #0: loss = 0.0464497 (* 1 = 0.0464497 loss)
I0412 14:20:45.855558  8032 sgd_solver.cpp:105] Iteration 9336, lr = 0.00157343
I0412 14:20:51.446015  8032 solver.cpp:218] Iteration 9348 (2.14657 iter/s, 5.59032s/12 iters), loss = 0.114463
I0412 14:20:51.446058  8032 solver.cpp:237]     Train net output #0: loss = 0.114463 (* 1 = 0.114463 loss)
I0412 14:20:51.446069  8032 sgd_solver.cpp:105] Iteration 9348, lr = 0.00156969
I0412 14:20:56.558940  8032 solver.cpp:218] Iteration 9360 (2.34708 iter/s, 5.11274s/12 iters), loss = 0.0697213
I0412 14:20:56.558982  8032 solver.cpp:237]     Train net output #0: loss = 0.0697213 (* 1 = 0.0697213 loss)
I0412 14:20:56.558991  8032 sgd_solver.cpp:105] Iteration 9360, lr = 0.00156596
I0412 14:21:01.622268  8032 solver.cpp:218] Iteration 9372 (2.37007 iter/s, 5.06314s/12 iters), loss = 0.0667925
I0412 14:21:01.622314  8032 solver.cpp:237]     Train net output #0: loss = 0.0667925 (* 1 = 0.0667925 loss)
I0412 14:21:01.622323  8032 sgd_solver.cpp:105] Iteration 9372, lr = 0.00156225
I0412 14:21:06.173506  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9384.caffemodel
I0412 14:21:09.249609  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9384.solverstate
I0412 14:21:11.617535  8032 solver.cpp:330] Iteration 9384, Testing net (#0)
I0412 14:21:11.617609  8032 net.cpp:676] Ignoring source layer train-data
I0412 14:21:12.403970  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:21:16.074429  8032 solver.cpp:397]     Test net output #0: accuracy = 0.478554
I0412 14:21:16.074478  8032 solver.cpp:397]     Test net output #1: loss = 3.01758 (* 1 = 3.01758 loss)
I0412 14:21:16.163761  8032 solver.cpp:218] Iteration 9384 (0.825249 iter/s, 14.5411s/12 iters), loss = 0.0796176
I0412 14:21:16.163825  8032 solver.cpp:237]     Train net output #0: loss = 0.0796176 (* 1 = 0.0796176 loss)
I0412 14:21:16.163836  8032 sgd_solver.cpp:105] Iteration 9384, lr = 0.00155854
I0412 14:21:20.737800  8032 solver.cpp:218] Iteration 9396 (2.62361 iter/s, 4.57384s/12 iters), loss = 0.0961561
I0412 14:21:20.737867  8032 solver.cpp:237]     Train net output #0: loss = 0.0961561 (* 1 = 0.0961561 loss)
I0412 14:21:20.737886  8032 sgd_solver.cpp:105] Iteration 9396, lr = 0.00155484
I0412 14:21:25.227699  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:21:25.893719  8032 solver.cpp:218] Iteration 9408 (2.32752 iter/s, 5.15571s/12 iters), loss = 0.134061
I0412 14:21:25.893796  8032 solver.cpp:237]     Train net output #0: loss = 0.134061 (* 1 = 0.134061 loss)
I0412 14:21:25.893810  8032 sgd_solver.cpp:105] Iteration 9408, lr = 0.00155114
I0412 14:21:31.030999  8032 solver.cpp:218] Iteration 9420 (2.33597 iter/s, 5.13706s/12 iters), loss = 0.109167
I0412 14:21:31.031050  8032 solver.cpp:237]     Train net output #0: loss = 0.109167 (* 1 = 0.109167 loss)
I0412 14:21:31.031061  8032 sgd_solver.cpp:105] Iteration 9420, lr = 0.00154746
I0412 14:21:36.200948  8032 solver.cpp:218] Iteration 9432 (2.3212 iter/s, 5.16975s/12 iters), loss = 0.174548
I0412 14:21:36.201000  8032 solver.cpp:237]     Train net output #0: loss = 0.174548 (* 1 = 0.174548 loss)
I0412 14:21:36.201012  8032 sgd_solver.cpp:105] Iteration 9432, lr = 0.00154379
I0412 14:21:41.351135  8032 solver.cpp:218] Iteration 9444 (2.3301 iter/s, 5.14999s/12 iters), loss = 0.0597359
I0412 14:21:41.351188  8032 solver.cpp:237]     Train net output #0: loss = 0.0597359 (* 1 = 0.0597359 loss)
I0412 14:21:41.351200  8032 sgd_solver.cpp:105] Iteration 9444, lr = 0.00154012
I0412 14:21:46.621006  8032 solver.cpp:218] Iteration 9456 (2.27718 iter/s, 5.26967s/12 iters), loss = 0.104595
I0412 14:21:46.621124  8032 solver.cpp:237]     Train net output #0: loss = 0.104595 (* 1 = 0.104595 loss)
I0412 14:21:46.621134  8032 sgd_solver.cpp:105] Iteration 9456, lr = 0.00153647
I0412 14:21:51.645800  8032 solver.cpp:218] Iteration 9468 (2.38828 iter/s, 5.02454s/12 iters), loss = 0.0721537
I0412 14:21:51.645840  8032 solver.cpp:237]     Train net output #0: loss = 0.0721536 (* 1 = 0.0721536 loss)
I0412 14:21:51.645849  8032 sgd_solver.cpp:105] Iteration 9468, lr = 0.00153282
I0412 14:21:56.877791  8032 solver.cpp:218] Iteration 9480 (2.29367 iter/s, 5.2318s/12 iters), loss = 0.0597184
I0412 14:21:56.877848  8032 solver.cpp:237]     Train net output #0: loss = 0.0597184 (* 1 = 0.0597184 loss)
I0412 14:21:56.877861  8032 sgd_solver.cpp:105] Iteration 9480, lr = 0.00152918
I0412 14:21:59.029103  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9486.caffemodel
I0412 14:22:01.954408  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9486.solverstate
I0412 14:22:04.249675  8032 solver.cpp:330] Iteration 9486, Testing net (#0)
I0412 14:22:04.249701  8032 net.cpp:676] Ignoring source layer train-data
I0412 14:22:05.007411  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:22:08.865906  8032 solver.cpp:397]     Test net output #0: accuracy = 0.477328
I0412 14:22:08.865949  8032 solver.cpp:397]     Test net output #1: loss = 2.98022 (* 1 = 2.98022 loss)
I0412 14:22:10.922524  8032 solver.cpp:218] Iteration 9492 (0.854439 iter/s, 14.0443s/12 iters), loss = 0.123621
I0412 14:22:10.922577  8032 solver.cpp:237]     Train net output #0: loss = 0.123621 (* 1 = 0.123621 loss)
I0412 14:22:10.922590  8032 sgd_solver.cpp:105] Iteration 9492, lr = 0.00152555
I0412 14:22:16.303386  8032 solver.cpp:218] Iteration 9504 (2.23021 iter/s, 5.38066s/12 iters), loss = 0.0422153
I0412 14:22:16.303431  8032 solver.cpp:237]     Train net output #0: loss = 0.0422153 (* 1 = 0.0422153 loss)
I0412 14:22:16.303440  8032 sgd_solver.cpp:105] Iteration 9504, lr = 0.00152193
I0412 14:22:17.881726  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:22:21.678236  8032 solver.cpp:218] Iteration 9516 (2.2327 iter/s, 5.37465s/12 iters), loss = 0.13533
I0412 14:22:21.678273  8032 solver.cpp:237]     Train net output #0: loss = 0.13533 (* 1 = 0.13533 loss)
I0412 14:22:21.678282  8032 sgd_solver.cpp:105] Iteration 9516, lr = 0.00151831
I0412 14:22:26.774379  8032 solver.cpp:218] Iteration 9528 (2.35481 iter/s, 5.09596s/12 iters), loss = 0.0500655
I0412 14:22:26.774425  8032 solver.cpp:237]     Train net output #0: loss = 0.0500655 (* 1 = 0.0500655 loss)
I0412 14:22:26.774433  8032 sgd_solver.cpp:105] Iteration 9528, lr = 0.00151471
I0412 14:22:31.992512  8032 solver.cpp:218] Iteration 9540 (2.29976 iter/s, 5.21794s/12 iters), loss = 0.0946619
I0412 14:22:31.992569  8032 solver.cpp:237]     Train net output #0: loss = 0.0946619 (* 1 = 0.0946619 loss)
I0412 14:22:31.992584  8032 sgd_solver.cpp:105] Iteration 9540, lr = 0.00151111
I0412 14:22:37.056393  8032 solver.cpp:218] Iteration 9552 (2.36982 iter/s, 5.06367s/12 iters), loss = 0.0438274
I0412 14:22:37.056447  8032 solver.cpp:237]     Train net output #0: loss = 0.0438274 (* 1 = 0.0438274 loss)
I0412 14:22:37.056458  8032 sgd_solver.cpp:105] Iteration 9552, lr = 0.00150752
I0412 14:22:42.067278  8032 solver.cpp:218] Iteration 9564 (2.39488 iter/s, 5.01068s/12 iters), loss = 0.0484788
I0412 14:22:42.067332  8032 solver.cpp:237]     Train net output #0: loss = 0.0484788 (* 1 = 0.0484788 loss)
I0412 14:22:42.067343  8032 sgd_solver.cpp:105] Iteration 9564, lr = 0.00150395
I0412 14:22:47.175320  8032 solver.cpp:218] Iteration 9576 (2.34933 iter/s, 5.10784s/12 iters), loss = 0.0581066
I0412 14:22:47.175372  8032 solver.cpp:237]     Train net output #0: loss = 0.0581065 (* 1 = 0.0581065 loss)
I0412 14:22:47.175382  8032 sgd_solver.cpp:105] Iteration 9576, lr = 0.00150037
I0412 14:22:51.750952  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9588.caffemodel
I0412 14:22:54.788075  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9588.solverstate
I0412 14:22:57.101752  8032 solver.cpp:330] Iteration 9588, Testing net (#0)
I0412 14:22:57.101779  8032 net.cpp:676] Ignoring source layer train-data
I0412 14:22:57.848184  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:23:01.694289  8032 solver.cpp:397]     Test net output #0: accuracy = 0.48652
I0412 14:23:01.694337  8032 solver.cpp:397]     Test net output #1: loss = 2.98046 (* 1 = 2.98046 loss)
I0412 14:23:01.782825  8032 solver.cpp:218] Iteration 9588 (0.821521 iter/s, 14.6071s/12 iters), loss = 0.115572
I0412 14:23:01.782872  8032 solver.cpp:237]     Train net output #0: loss = 0.115572 (* 1 = 0.115572 loss)
I0412 14:23:01.782882  8032 sgd_solver.cpp:105] Iteration 9588, lr = 0.00149681
I0412 14:23:06.782405  8032 solver.cpp:218] Iteration 9600 (2.40029 iter/s, 4.99939s/12 iters), loss = 0.120474
I0412 14:23:06.782449  8032 solver.cpp:237]     Train net output #0: loss = 0.120474 (* 1 = 0.120474 loss)
I0412 14:23:06.782457  8032 sgd_solver.cpp:105] Iteration 9600, lr = 0.00149326
I0412 14:23:10.641324  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:23:12.177336  8032 solver.cpp:218] Iteration 9612 (2.22439 iter/s, 5.39473s/12 iters), loss = 0.0878812
I0412 14:23:12.177387  8032 solver.cpp:237]     Train net output #0: loss = 0.0878812 (* 1 = 0.0878812 loss)
I0412 14:23:12.177398  8032 sgd_solver.cpp:105] Iteration 9612, lr = 0.00148971
I0412 14:23:17.622433  8032 solver.cpp:218] Iteration 9624 (2.2039 iter/s, 5.44489s/12 iters), loss = 0.0611142
I0412 14:23:17.622484  8032 solver.cpp:237]     Train net output #0: loss = 0.0611142 (* 1 = 0.0611142 loss)
I0412 14:23:17.622495  8032 sgd_solver.cpp:105] Iteration 9624, lr = 0.00148618
I0412 14:23:22.667330  8032 solver.cpp:218] Iteration 9636 (2.37873 iter/s, 5.0447s/12 iters), loss = 0.0564873
I0412 14:23:22.667409  8032 solver.cpp:237]     Train net output #0: loss = 0.0564873 (* 1 = 0.0564873 loss)
I0412 14:23:22.667423  8032 sgd_solver.cpp:105] Iteration 9636, lr = 0.00148265
I0412 14:23:27.751112  8032 solver.cpp:218] Iteration 9648 (2.36055 iter/s, 5.08355s/12 iters), loss = 0.102642
I0412 14:23:27.751159  8032 solver.cpp:237]     Train net output #0: loss = 0.102642 (* 1 = 0.102642 loss)
I0412 14:23:27.751170  8032 sgd_solver.cpp:105] Iteration 9648, lr = 0.00147913
I0412 14:23:32.868710  8032 solver.cpp:218] Iteration 9660 (2.34494 iter/s, 5.1174s/12 iters), loss = 0.0993326
I0412 14:23:32.868769  8032 solver.cpp:237]     Train net output #0: loss = 0.0993326 (* 1 = 0.0993326 loss)
I0412 14:23:32.868786  8032 sgd_solver.cpp:105] Iteration 9660, lr = 0.00147562
I0412 14:23:37.985744  8032 solver.cpp:218] Iteration 9672 (2.3452 iter/s, 5.11683s/12 iters), loss = 0.0485323
I0412 14:23:37.985792  8032 solver.cpp:237]     Train net output #0: loss = 0.0485322 (* 1 = 0.0485322 loss)
I0412 14:23:37.985801  8032 sgd_solver.cpp:105] Iteration 9672, lr = 0.00147211
I0412 14:23:43.220562  8032 solver.cpp:218] Iteration 9684 (2.29243 iter/s, 5.23462s/12 iters), loss = 0.102614
I0412 14:23:43.220605  8032 solver.cpp:237]     Train net output #0: loss = 0.102614 (* 1 = 0.102614 loss)
I0412 14:23:43.220613  8032 sgd_solver.cpp:105] Iteration 9684, lr = 0.00146862
I0412 14:23:45.253438  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9690.caffemodel
I0412 14:23:48.876560  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9690.solverstate
I0412 14:23:51.246268  8032 solver.cpp:330] Iteration 9690, Testing net (#0)
I0412 14:23:51.246295  8032 net.cpp:676] Ignoring source layer train-data
I0412 14:23:51.862074  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:23:54.958139  8032 blocking_queue.cpp:49] Waiting for data
I0412 14:23:55.952543  8032 solver.cpp:397]     Test net output #0: accuracy = 0.493873
I0412 14:23:55.952581  8032 solver.cpp:397]     Test net output #1: loss = 2.95954 (* 1 = 2.95954 loss)
I0412 14:23:57.875437  8032 solver.cpp:218] Iteration 9696 (0.818865 iter/s, 14.6544s/12 iters), loss = 0.158326
I0412 14:23:57.875483  8032 solver.cpp:237]     Train net output #0: loss = 0.158326 (* 1 = 0.158326 loss)
I0412 14:23:57.875491  8032 sgd_solver.cpp:105] Iteration 9696, lr = 0.00146513
I0412 14:24:02.948668  8032 solver.cpp:218] Iteration 9708 (2.36545 iter/s, 5.07303s/12 iters), loss = 0.0827165
I0412 14:24:02.948734  8032 solver.cpp:237]     Train net output #0: loss = 0.0827165 (* 1 = 0.0827165 loss)
I0412 14:24:02.948746  8032 sgd_solver.cpp:105] Iteration 9708, lr = 0.00146165
I0412 14:24:03.677026  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:24:07.899219  8032 solver.cpp:218] Iteration 9720 (2.42408 iter/s, 4.95034s/12 iters), loss = 0.0958916
I0412 14:24:07.899276  8032 solver.cpp:237]     Train net output #0: loss = 0.0958916 (* 1 = 0.0958916 loss)
I0412 14:24:07.899288  8032 sgd_solver.cpp:105] Iteration 9720, lr = 0.00145818
I0412 14:24:12.921582  8032 solver.cpp:218] Iteration 9732 (2.38941 iter/s, 5.02216s/12 iters), loss = 0.036704
I0412 14:24:12.921643  8032 solver.cpp:237]     Train net output #0: loss = 0.036704 (* 1 = 0.036704 loss)
I0412 14:24:12.921654  8032 sgd_solver.cpp:105] Iteration 9732, lr = 0.00145472
I0412 14:24:17.901343  8032 solver.cpp:218] Iteration 9744 (2.40986 iter/s, 4.97955s/12 iters), loss = 0.14867
I0412 14:24:17.901408  8032 solver.cpp:237]     Train net output #0: loss = 0.14867 (* 1 = 0.14867 loss)
I0412 14:24:17.901420  8032 sgd_solver.cpp:105] Iteration 9744, lr = 0.00145127
I0412 14:24:22.822016  8032 solver.cpp:218] Iteration 9756 (2.43879 iter/s, 4.92046s/12 iters), loss = 0.250834
I0412 14:24:22.822070  8032 solver.cpp:237]     Train net output #0: loss = 0.250834 (* 1 = 0.250834 loss)
I0412 14:24:22.822083  8032 sgd_solver.cpp:105] Iteration 9756, lr = 0.00144782
I0412 14:24:27.757845  8032 solver.cpp:218] Iteration 9768 (2.4313 iter/s, 4.93563s/12 iters), loss = 0.0531197
I0412 14:24:27.757933  8032 solver.cpp:237]     Train net output #0: loss = 0.0531197 (* 1 = 0.0531197 loss)
I0412 14:24:27.757946  8032 sgd_solver.cpp:105] Iteration 9768, lr = 0.00144438
I0412 14:24:32.767982  8032 solver.cpp:218] Iteration 9780 (2.39526 iter/s, 5.0099s/12 iters), loss = 0.0831798
I0412 14:24:32.768035  8032 solver.cpp:237]     Train net output #0: loss = 0.0831798 (* 1 = 0.0831798 loss)
I0412 14:24:32.768047  8032 sgd_solver.cpp:105] Iteration 9780, lr = 0.00144095
I0412 14:24:37.308048  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9792.caffemodel
I0412 14:24:41.428753  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9792.solverstate
I0412 14:24:45.067683  8032 solver.cpp:330] Iteration 9792, Testing net (#0)
I0412 14:24:45.067711  8032 net.cpp:676] Ignoring source layer train-data
I0412 14:24:45.620959  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:24:49.525378  8032 solver.cpp:397]     Test net output #0: accuracy = 0.485294
I0412 14:24:49.525432  8032 solver.cpp:397]     Test net output #1: loss = 2.99133 (* 1 = 2.99133 loss)
I0412 14:24:49.613996  8032 solver.cpp:218] Iteration 9792 (0.712357 iter/s, 16.8455s/12 iters), loss = 0.126416
I0412 14:24:49.614049  8032 solver.cpp:237]     Train net output #0: loss = 0.126416 (* 1 = 0.126416 loss)
I0412 14:24:49.614060  8032 sgd_solver.cpp:105] Iteration 9792, lr = 0.00143753
I0412 14:24:53.779126  8032 solver.cpp:218] Iteration 9804 (2.88119 iter/s, 4.16495s/12 iters), loss = 0.0271288
I0412 14:24:53.779191  8032 solver.cpp:237]     Train net output #0: loss = 0.0271287 (* 1 = 0.0271287 loss)
I0412 14:24:53.779206  8032 sgd_solver.cpp:105] Iteration 9804, lr = 0.00143412
I0412 14:24:56.777307  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:24:58.801709  8032 solver.cpp:218] Iteration 9816 (2.38931 iter/s, 5.02237s/12 iters), loss = 0.0441181
I0412 14:24:58.801862  8032 solver.cpp:237]     Train net output #0: loss = 0.044118 (* 1 = 0.044118 loss)
I0412 14:24:58.801873  8032 sgd_solver.cpp:105] Iteration 9816, lr = 0.00143072
I0412 14:25:03.821761  8032 solver.cpp:218] Iteration 9828 (2.39056 iter/s, 5.01975s/12 iters), loss = 0.031117
I0412 14:25:03.821818  8032 solver.cpp:237]     Train net output #0: loss = 0.0311169 (* 1 = 0.0311169 loss)
I0412 14:25:03.821830  8032 sgd_solver.cpp:105] Iteration 9828, lr = 0.00142732
I0412 14:25:09.134682  8032 solver.cpp:218] Iteration 9840 (2.25874 iter/s, 5.3127s/12 iters), loss = 0.0692747
I0412 14:25:09.134732  8032 solver.cpp:237]     Train net output #0: loss = 0.0692747 (* 1 = 0.0692747 loss)
I0412 14:25:09.134743  8032 sgd_solver.cpp:105] Iteration 9840, lr = 0.00142393
I0412 14:25:14.257208  8032 solver.cpp:218] Iteration 9852 (2.34269 iter/s, 5.12232s/12 iters), loss = 0.187117
I0412 14:25:14.257264  8032 solver.cpp:237]     Train net output #0: loss = 0.187117 (* 1 = 0.187117 loss)
I0412 14:25:14.257277  8032 sgd_solver.cpp:105] Iteration 9852, lr = 0.00142055
I0412 14:25:19.341421  8032 solver.cpp:218] Iteration 9864 (2.36034 iter/s, 5.08401s/12 iters), loss = 0.0485055
I0412 14:25:19.341468  8032 solver.cpp:237]     Train net output #0: loss = 0.0485054 (* 1 = 0.0485054 loss)
I0412 14:25:19.341480  8032 sgd_solver.cpp:105] Iteration 9864, lr = 0.00141718
I0412 14:25:24.276262  8032 solver.cpp:218] Iteration 9876 (2.43179 iter/s, 4.93464s/12 iters), loss = 0.116283
I0412 14:25:24.276324  8032 solver.cpp:237]     Train net output #0: loss = 0.116283 (* 1 = 0.116283 loss)
I0412 14:25:24.276340  8032 sgd_solver.cpp:105] Iteration 9876, lr = 0.00141381
I0412 14:25:29.376780  8032 solver.cpp:218] Iteration 9888 (2.3528 iter/s, 5.10031s/12 iters), loss = 0.0913596
I0412 14:25:29.376864  8032 solver.cpp:237]     Train net output #0: loss = 0.0913595 (* 1 = 0.0913595 loss)
I0412 14:25:29.376873  8032 sgd_solver.cpp:105] Iteration 9888, lr = 0.00141045
I0412 14:25:31.456961  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9894.caffemodel
I0412 14:25:34.452499  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9894.solverstate
I0412 14:25:36.719099  8032 solver.cpp:330] Iteration 9894, Testing net (#0)
I0412 14:25:36.719121  8032 net.cpp:676] Ignoring source layer train-data
I0412 14:25:37.347957  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:25:41.472829  8032 solver.cpp:397]     Test net output #0: accuracy = 0.484681
I0412 14:25:41.472880  8032 solver.cpp:397]     Test net output #1: loss = 2.94409 (* 1 = 2.94409 loss)
I0412 14:25:43.223009  8032 solver.cpp:218] Iteration 9900 (0.866692 iter/s, 13.8458s/12 iters), loss = 0.21525
I0412 14:25:43.223064  8032 solver.cpp:237]     Train net output #0: loss = 0.21525 (* 1 = 0.21525 loss)
I0412 14:25:43.223076  8032 sgd_solver.cpp:105] Iteration 9900, lr = 0.00140711
I0412 14:25:48.256497  8032 solver.cpp:218] Iteration 9912 (2.38413 iter/s, 5.03328s/12 iters), loss = 0.127196
I0412 14:25:48.256541  8032 solver.cpp:237]     Train net output #0: loss = 0.127196 (* 1 = 0.127196 loss)
I0412 14:25:48.256549  8032 sgd_solver.cpp:105] Iteration 9912, lr = 0.00140377
I0412 14:25:48.354678  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:25:53.324400  8032 solver.cpp:218] Iteration 9924 (2.36794 iter/s, 5.0677s/12 iters), loss = 0.0472139
I0412 14:25:53.324458  8032 solver.cpp:237]     Train net output #0: loss = 0.0472139 (* 1 = 0.0472139 loss)
I0412 14:25:53.324471  8032 sgd_solver.cpp:105] Iteration 9924, lr = 0.00140043
I0412 14:25:58.269395  8032 solver.cpp:218] Iteration 9936 (2.42679 iter/s, 4.94479s/12 iters), loss = 0.0585075
I0412 14:25:58.269439  8032 solver.cpp:237]     Train net output #0: loss = 0.0585074 (* 1 = 0.0585074 loss)
I0412 14:25:58.269450  8032 sgd_solver.cpp:105] Iteration 9936, lr = 0.00139711
I0412 14:26:03.260586  8032 solver.cpp:218] Iteration 9948 (2.40433 iter/s, 4.991s/12 iters), loss = 0.136667
I0412 14:26:03.260741  8032 solver.cpp:237]     Train net output #0: loss = 0.136667 (* 1 = 0.136667 loss)
I0412 14:26:03.260754  8032 sgd_solver.cpp:105] Iteration 9948, lr = 0.00139379
I0412 14:26:08.544381  8032 solver.cpp:218] Iteration 9960 (2.27123 iter/s, 5.28348s/12 iters), loss = 0.114557
I0412 14:26:08.544433  8032 solver.cpp:237]     Train net output #0: loss = 0.114557 (* 1 = 0.114557 loss)
I0412 14:26:08.544445  8032 sgd_solver.cpp:105] Iteration 9960, lr = 0.00139048
I0412 14:26:13.818668  8032 solver.cpp:218] Iteration 9972 (2.27528 iter/s, 5.27408s/12 iters), loss = 0.0365471
I0412 14:26:13.818722  8032 solver.cpp:237]     Train net output #0: loss = 0.0365471 (* 1 = 0.0365471 loss)
I0412 14:26:13.818732  8032 sgd_solver.cpp:105] Iteration 9972, lr = 0.00138718
I0412 14:26:19.103731  8032 solver.cpp:218] Iteration 9984 (2.27064 iter/s, 5.28485s/12 iters), loss = 0.086662
I0412 14:26:19.103775  8032 solver.cpp:237]     Train net output #0: loss = 0.086662 (* 1 = 0.086662 loss)
I0412 14:26:19.103785  8032 sgd_solver.cpp:105] Iteration 9984, lr = 0.00138389
I0412 14:26:23.912631  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9996.caffemodel
I0412 14:26:26.937786  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9996.solverstate
I0412 14:26:29.323062  8032 solver.cpp:330] Iteration 9996, Testing net (#0)
I0412 14:26:29.323089  8032 net.cpp:676] Ignoring source layer train-data
I0412 14:26:29.835678  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:26:33.959442  8032 solver.cpp:397]     Test net output #0: accuracy = 0.5
I0412 14:26:33.959506  8032 solver.cpp:397]     Test net output #1: loss = 3.00734 (* 1 = 3.00734 loss)
I0412 14:26:34.047878  8032 solver.cpp:218] Iteration 9996 (0.803015 iter/s, 14.9437s/12 iters), loss = 0.136005
I0412 14:26:34.047928  8032 solver.cpp:237]     Train net output #0: loss = 0.136004 (* 1 = 0.136004 loss)
I0412 14:26:34.047940  8032 sgd_solver.cpp:105] Iteration 9996, lr = 0.0013806
I0412 14:26:38.523937  8032 solver.cpp:218] Iteration 10008 (2.68104 iter/s, 4.47587s/12 iters), loss = 0.0475579
I0412 14:26:38.523989  8032 solver.cpp:237]     Train net output #0: loss = 0.0475579 (* 1 = 0.0475579 loss)
I0412 14:26:38.524000  8032 sgd_solver.cpp:105] Iteration 10008, lr = 0.00137732
I0412 14:26:40.944492  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:26:43.705631  8032 solver.cpp:218] Iteration 10020 (2.31594 iter/s, 5.18148s/12 iters), loss = 0.0423334
I0412 14:26:43.705686  8032 solver.cpp:237]     Train net output #0: loss = 0.0423334 (* 1 = 0.0423334 loss)
I0412 14:26:43.705698  8032 sgd_solver.cpp:105] Iteration 10020, lr = 0.00137405
I0412 14:26:48.873785  8032 solver.cpp:218] Iteration 10032 (2.32201 iter/s, 5.16794s/12 iters), loss = 0.0707297
I0412 14:26:48.873836  8032 solver.cpp:237]     Train net output #0: loss = 0.0707297 (* 1 = 0.0707297 loss)
I0412 14:26:48.873847  8032 sgd_solver.cpp:105] Iteration 10032, lr = 0.00137079
I0412 14:26:54.506557  8032 solver.cpp:218] Iteration 10044 (2.13047 iter/s, 5.63255s/12 iters), loss = 0.0377946
I0412 14:26:54.506605  8032 solver.cpp:237]     Train net output #0: loss = 0.0377946 (* 1 = 0.0377946 loss)
I0412 14:26:54.506615  8032 sgd_solver.cpp:105] Iteration 10044, lr = 0.00136754
I0412 14:26:59.569088  8032 solver.cpp:218] Iteration 10056 (2.37045 iter/s, 5.06232s/12 iters), loss = 0.0826398
I0412 14:26:59.569141  8032 solver.cpp:237]     Train net output #0: loss = 0.0826398 (* 1 = 0.0826398 loss)
I0412 14:26:59.569154  8032 sgd_solver.cpp:105] Iteration 10056, lr = 0.00136429
I0412 14:27:04.685366  8032 solver.cpp:218] Iteration 10068 (2.34555 iter/s, 5.11607s/12 iters), loss = 0.0869745
I0412 14:27:04.685483  8032 solver.cpp:237]     Train net output #0: loss = 0.0869745 (* 1 = 0.0869745 loss)
I0412 14:27:04.685493  8032 sgd_solver.cpp:105] Iteration 10068, lr = 0.00136105
I0412 14:27:10.108362  8032 solver.cpp:218] Iteration 10080 (2.21292 iter/s, 5.42271s/12 iters), loss = 0.049313
I0412 14:27:10.108417  8032 solver.cpp:237]     Train net output #0: loss = 0.049313 (* 1 = 0.049313 loss)
I0412 14:27:10.108428  8032 sgd_solver.cpp:105] Iteration 10080, lr = 0.00135782
I0412 14:27:15.573760  8032 solver.cpp:218] Iteration 10092 (2.19572 iter/s, 5.46518s/12 iters), loss = 0.0792774
I0412 14:27:15.573813  8032 solver.cpp:237]     Train net output #0: loss = 0.0792774 (* 1 = 0.0792774 loss)
I0412 14:27:15.573825  8032 sgd_solver.cpp:105] Iteration 10092, lr = 0.0013546
I0412 14:27:17.851361  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10098.caffemodel
I0412 14:27:23.722730  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10098.solverstate
I0412 14:27:27.610383  8032 solver.cpp:330] Iteration 10098, Testing net (#0)
I0412 14:27:27.610414  8032 net.cpp:676] Ignoring source layer train-data
I0412 14:27:28.093248  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:27:32.231264  8032 solver.cpp:397]     Test net output #0: accuracy = 0.49326
I0412 14:27:32.231298  8032 solver.cpp:397]     Test net output #1: loss = 2.93962 (* 1 = 2.93962 loss)
I0412 14:27:34.268633  8032 solver.cpp:218] Iteration 10104 (0.641907 iter/s, 18.6943s/12 iters), loss = 0.0912263
I0412 14:27:34.268688  8032 solver.cpp:237]     Train net output #0: loss = 0.0912263 (* 1 = 0.0912263 loss)
I0412 14:27:34.268699  8032 sgd_solver.cpp:105] Iteration 10104, lr = 0.00135138
I0412 14:27:38.636579  8036 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:27:39.279255  8032 solver.cpp:218] Iteration 10116 (2.39501 iter/s, 5.01041s/12 iters), loss = 0.211717
I0412 14:27:39.279297  8032 solver.cpp:237]     Train net output #0: loss = 0.211717 (* 1 = 0.211717 loss)
I0412 14:27:39.279306  8032 sgd_solver.cpp:105] Iteration 10116, lr = 0.00134817
I0412 14:27:44.434705  8032 solver.cpp:218] Iteration 10128 (2.32773 iter/s, 5.15524s/12 iters), loss = 0.186586
I0412 14:27:44.434756  8032 solver.cpp:237]     Train net output #0: loss = 0.186586 (* 1 = 0.186586 loss)
I0412 14:27:44.434767  8032 sgd_solver.cpp:105] Iteration 10128, lr = 0.00134497
I0412 14:27:49.662498  8032 solver.cpp:218] Iteration 10140 (2.29551 iter/s, 5.22759s/12 iters), loss = 0.0665451
I0412 14:27:49.662537  8032 solver.cpp:237]     Train net output #0: loss = 0.0665451 (* 1 = 0.0665451 loss)
I0412 14:27:49.662546  8032 sgd_solver.cpp:105] Iteration 10140, lr = 0.00134178
I0412 14:27:54.853602  8032 solver.cpp:218] Iteration 10152 (2.31174 iter/s, 5.1909s/12 iters), loss = 0.0725212
I0412 14:27:54.853644  8032 solver.cpp:237]     Train net output #0: loss = 0.0725212 (* 1 = 0.0725212 loss)
I0412 14:27:54.853652  8032 sgd_solver.cpp:105] Iteration 10152, lr = 0.00133859
I0412 14:28:00.181365  8032 solver.cpp:218] Iteration 10164 (2.25244 iter/s, 5.32755s/12 iters), loss = 0.0217107
I0412 14:28:00.181419  8032 solver.cpp:237]     Train net output #0: loss = 0.0217107 (* 1 = 0.0217107 loss)
I0412 14:28:00.181434  8032 sgd_solver.cpp:105] Iteration 10164, lr = 0.00133541
I0412 14:28:05.566229  8032 solver.cpp:218] Iteration 10176 (2.22856 iter/s, 5.38464s/12 iters), loss = 0.0435951
I0412 14:28:05.566274  8032 solver.cpp:237]     Train net output #0: loss = 0.0435951 (* 1 = 0.0435951 loss)
I0412 14:28:05.566282  8032 sgd_solver.cpp:105] Iteration 10176, lr = 0.00133224
I0412 14:28:10.735625  8032 solver.cpp:218] Iteration 10188 (2.32145 iter/s, 5.16919s/12 iters), loss = 0.0166095
I0412 14:28:10.735787  8032 solver.cpp:237]     Train net output #0: loss = 0.0166095 (* 1 = 0.0166095 loss)
I0412 14:28:10.735800  8032 sgd_solver.cpp:105] Iteration 10188, lr = 0.00132908
I0412 14:28:15.495311  8032 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10200.caffemodel
I0412 14:28:18.498514  8032 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10200.solverstate
I0412 14:28:20.846794  8032 solver.cpp:310] Iteration 10200, loss = 0.0776016
I0412 14:28:20.846822  8032 solver.cpp:330] Iteration 10200, Testing net (#0)
I0412 14:28:20.846827  8032 net.cpp:676] Ignoring source layer train-data
I0412 14:28:21.330977  8037 data_layer.cpp:73] Restarting data prefetching from start.
I0412 14:28:25.585819  8032 solver.cpp:397]     Test net output #0: accuracy = 0.497549
I0412 14:28:25.585871  8032 solver.cpp:397]     Test net output #1: loss = 3.06837 (* 1 = 3.06837 loss)
I0412 14:28:25.585884  8032 solver.cpp:315] Optimization Done.
I0412 14:28:25.585891  8032 caffe.cpp:259] Optimization Done.